Scaling issues in the design and implementation of the Tenet RCAP2 signaling protocol

合集下载

computer simulation of liquid 分子模拟

I NSTITUTE OF P HYSICS P UBLISHING R EPORTS ON P ROGRESS IN P HYSICS Rep.Prog.Phys.68(2005)2665–2700doi:10.1088/0034-4885/68/11/R04Computer simulation of liquid crystalsC M Care andD J CleaverMaterials and Engineering Research Institute,Shefﬁeld Hallam University,Howard Street, Shefﬁeld,S11WB,UKE-mail:c.m.care@ and d.j.cleaver@Received5August2002,inﬁnal form8August2005Published12September2005Online at /RoPP/68/2665AbstractA review is presented of molecular and mesoscopic computer simulations of liquid crystalline systems.Molecular simulation approaches applied to such systems are described,and the key ﬁndings for bulk phase behaviour are reported.Following this,recently developed lattice Boltzmann approaches to the mesoscale modelling of nemato-dynamics are reviewed.This paper concludes with a discussion of possible areas for future development in thisﬁeld. (Someﬁgures in this article are in colour only in the electronic version)0034-4885/05/112665+36$90.00©2005IOP Publishing Ltd Printed in the UK26652666C M Care and D J CleaverContentsPage 1.Introduction26671.1.The role of computer simulation in liquid crystal research26672.Materials and phases26693.Molecular simulations of liquid crystals26713.1.Molecular simulation techniques26713.2.All-atom simulations26733.3.Generic models—their bases,uses and limitations2674ttice models.26743.3.2.Off-lattice generic models.2675Hard particle models.2676 Soft particle models.2678 4.Mesoscopic simulations26824.1.Macroscopic equations for nemato-dynamics26834.1.1.Constant order parameter:Ericksen–Leslie–Parodi formalism.26844.1.2.Variable order parameter:Beris–Edwards formalism.26844.1.3.Variable order parameter:Qian–Sheng formalism26864.2.The LB method for liquid crystals26874.2.1.The problem.26874.2.2.LB scheme for the ELP formalism.26884.2.3.LB scheme for Beris–Edwards formalism.26884.2.4.LB scheme for Qian–Sheng formalism.26904.2.5.Applications of the LB method.26925.Conclusions and future directions2692Acknowledgments2693 Appendix.The LB method for isotropicﬂuids2693 References2696Computer simulation of liquid crystals2667 1.IntroductionIn this paper,we review molecular and mesoscopic computer simulations of liquid crystalline (LC)systems.Owing to their ability to form LC mesophases,the molecules of LC materials are often called mesogens.Following a scene setting introduction and a brief description of the key points of LC behaviour,weﬁrst review the application of molecular simulation approaches to these mesogenic systems;we only consider bulk behaviour and do not report work on conﬁned or inhomogeneous systems.This section is largely broken down by model-type,rather than area of application,and concentrates on the core characteristics of the various models and the results obtained.In contrast,in section4,we give relatively detailed descriptions of a series of recently developed lattice Boltzmann(LB)approaches to LC modelling and nemato-dynamics—following a period of relatively rapid development,a unifying review of this area is particularly timely.Finally,in section5,we identify a number of key unresolved issues and suggest areas in which future developments are likely to make most impact.Note that we do not review results obtained using conventional solvers for the continuum partial differential equations of LC behaviour.Whilst it might legitimately be argued that the mesoscopic technique considered in this review,LB,is simply an alternative method of solving the macroscopic equations of motion for the LCs,this particular method is perhaps best thought of as lying on the boundary between macroscopic and molecular methods.Additionally,it is straightforward to adapt the LB method to include additional physics(e.g.the moving interfaces found in LC colloids);as such,a clear distinction can be drawn between the LB approaches reviewed in section4and conventional continuum solvers.Previous reviews of LC simulation include the general overviews by Allen and Wilson(1989)and Crain and Komolkin(1999)and a number of other works which concentrate on speciﬁc classes of model.For hard particle models,the papers by Frenkel(1987) and Allen(1993)offer accessible alternatives to the all-encompassing Allen et al(1993). Zannoni’s(1979)very early review of lattice models of LCs has now been signiﬁcantly updated by Pasini et al(2000a)in a NATO ARW proceedings(Pasini et al2000b)which contains some other useful overview material,while Wilson(1999)has summarized work performed using all-atom models.Finally,there are two accessible accounts of work performed using generic models—Rull’s(1995)summary of the studies that enabled initial characterization of the Gay–Berne mesogen and the more recent overview by Zannoni(2001)which cogently illustrates subsequent developments and diversiﬁcations.1.1.The role of computer simulation in liquid crystal researchComputer simulation is simply one of the tools available for investigating mesogenic behaviour. It is,however,a relative newcomer compared with the many experimental and theoretical approaches available,and its role is often complementary;there is little to be gained from simulating a system which is already well characterized by more established routes.That said, appropriately focused computer simulation studies can yield a unique insight into molecular ordering and phase behaviour and so inform the development of new experiments or theories. Most obviously,molecular simulations can provide systematic structure property information, through which links can be established between molecular properties and macroscopic behaviour.Alternatively,simulation can be used to test the validity of various theoretical assumptions.For example,by applying both theory and simulation to the same underlying model,uncertainties regarding the treatment of many-body effects in the former can be quantiﬁed by the latter.One of the strengths of simulation in the context of LCs arises in situations for which there are(spatial or temporal)gradients in key quantities such as the order tensor or the2668C M Care and D J Cleaver composition proﬁle.Such gradients are often difﬁcult to resolve experimentally and are only accessible to relatively coarse-grained theories.In comparison,computer simulation of‘gradient regions’can often be achieved at the same computational cost as that needed to treat‘uniform regions’;in these contexts,therefore,simulation is becoming the lead complementary technique for improved understanding of behaviours which are not fully accessible to experimental investigation.At continuum length scales,LCs are characterized by a large number of experimentally observable parameters:viscosities,determined by the Leslie coefﬁcients;orientational elasticity,controlled by the Frank constants;substrate–LC orientational coupling,governed by anchoring coefﬁcients and surface viscosities.Given a full set of these parameters,mesoscale simulations are now able to incorporate much of this complex behaviour into models of real devices.Thus,subject to the usual provisos concerning continuum models,these approaches are now starting to gain the status of design and optimization tools for various LC device applications.Before presenting the details of this review,weﬁrst ask the rather fundamental question—why perform computer simulations of LCs?LCs are fascinating systems to study because, like much of soft-condensed matter,their behaviour is characterized by the interplay of several very different effects which operate over a wide range of time-and length-scales.These effects range from changes in intra-molecular conﬁgurations,through molecular librations to many-body properties such as massﬂow modes and net orientational order and ultimately to the fully equilibrated directorﬁeld observed at the continuum time-and length-scales. The extent of the associated time-and length-scale spectra dictate that no single computer simulation model will ever be able to give a full‘atom-to-device’description for even the simplest mesogen.Moreover,since these different phenomena are,in general,highly coupled (e.g.intramolecular conﬁgurations are inﬂuenced,in part,by the local orientational order), they represent a multi-level feedback system rather than a simple linear chain of independent links.Thus,addressing each phenomenon with its own model and simply collating the outputs of a series of stand-alone studies will again fail to achieve a full description.In partial recognition of this,most of the methods and models currently used to simulate LCs seek to explore only a subset of the spectrum of behaviours present in a real system.In some cases, this pragmatic approach is entirely appropriate:for certain bulk switching applications,for example,a continuum description can prove perfectly adequate(explaining the popularity of Ericksen–Leslie theory),and the molecular basis of LCs can be neglected.Alternatively,a well deﬁned generic model approach can provide the cleanest route to establishing relationships between molecular characteristics and bulk properties such as the Frank constants or the Leslie coefﬁcients.Returning to the original question,then,we can reply that appropriately focused simulation studies have certainly provided a sound understanding of many of the processes underlying bulk mesogenic behaviour and the operation of some simple switching devices.The role of computer simulations in studying LCs is relatively well established;as noted above and shown in more detail in the following sections,successful approaches have now been developed for many of the regions in the atom-to-device spectrum.As such,several of the fundamental problems in thisﬁeld are now essentially solved.Given these achievements,it is now appropriate to raise the supplementary question—why continue to perform computer simulations of LCs?There is little of note to be gained by simply reﬁning existing approaches and exploiting Moore’s law to incrementally enlarge the scope of, say,three-dimensional bulk simulations of generic LC models of conventional thermotropic behaviour.Many of the outstanding challenges in LC science and engineering call,instead, for either predictive modelling,needed to make simulation an effective design tool for device engineers,or simulation methodologies capable of describing‘butterﬂy’s wings’problems,inComputer simulation of liquid crystals2669(a)(b)(c)Figure1.(a)Isotropic,(b)nematic and(c)smectic phases(conﬁgurations obtained fromsimulations performed using the Gay–Berne mesogen).which processes acting at molecular length-scales induce responses at the macroscale.Whilst addressing these classes of problem may require some development of new models,a more pressing need comes from the lack of adequate hybrid methodologies,i.e.two-way interfaces between existing classes of model.Indeed,the maturity of theﬁeld of LC simulation and the problems still posed to it mark it out as an ideal test-bed for moving established(but largely independent)models on to another level through the development of novel integrated simulation methodologies.As indicated above,the remainder of this paper is arranged as follows.In the next section, we give a very brief introduction to theﬁeld of LCs.We then review molecular simulations of LC behaviour,and mesoscopic LB approaches to nemato-dynamics,before concluding with suggestions for possible future developments.2.Materials and phasesThe LC phases are states of matter that exist between the isotropic liquid and crystalline solid forms in which the molecules have orientational order but no,or possibly partial,positional order.Particles which are able to form LC phases are called mesogenic;hence the term mesogen is used to refer to a molecule that forms a mesophase or LC phase.Typically,mesophases have some material properties associated with the isotropic liquid(ability toﬂow,inability to resist a shear)and others more commonly found in true crystals(long range orientational and,in some cases,positional order,anisotropic optical properties,ability to transmit a torque).The term LC actually encompasses several different phases,the most common of which are nematic and smectic;these are described at length in the classic texts dedicated to LCs(Chandrasekhar 1992,deGennes and Prost1993,Kumar2001)and in a recently published collection of some of the key early research papers(Sluckin et al2004).Most mesogens are either calamitic(rod shaped)or discotic(disc like);a sufﬁcient(though not necessary)requirement for a substance to form a mesophase is a strong anisotropy in its molecular shape.Typically,calamitic mesogens contain an aromatic rigid core,formed from, e.g.1,4-phenyl or cyclohexyl groups,linked to one or moreﬂexible alkyl chain(s).In families of LCs,the variants with short alkyl chains tend to be nematogens(mesogens that form nematic phases),while those with longer alkyl chains are smectogens(mesogens that form smectic phases).The nematic phase is the simplest LC phase and is characterized by long range orientational order but no long range translational order.In the nematic phase(ﬁgure1(b)),correlations in molecular positions are essentially the same as those found in an isotropicﬂuid,but the molecular axes point,on average,along a common direction,the directorˆn.In the usual case2670C M Care and D J Cleaver(a)(b)Figure2.Molecules of4-pentyl-4 -cyanobiphenyl(5CB)and4-octyl-4 -cyanobiphenyl(8CB)molecules.of a nematic phase with a zero polar moment,the symmetry properties of the phase remain unchanged upon inversion of the director.If chiral molecules are used(or a chiral dopant is introduced),a cholesteric or chiral nematic phase can be obtained.The difference between this and the standard nematic phase is that in the former,the director twists as a function of position,but with a pitch which is much larger than molecular dimensions.The smectic phases are characterized by long range translational order,in one or two dimensions,as well as long range orientational order.Thus,in smectic phases(ﬁgure1(c)), in addition to having a director,the molecules are arranged in layers.Depending on the angle between the director and the layer normal,and details of any in-plane positional ordering, numerous different smectic phases have been hypothecated.In practice,however,in computer simulation studies it is often impossible to distinguish these phases from one another or from the underlying crystalline solid phase.The stability of LC phases can often be enhanced by increasing the length and polarizability of the molecule or by the addition of,e.g.a terminal cyano group to induce polar interactions between the molecule teral substituents can also inﬂuence molecular packing.For example,incorporating aﬂuoro group at the side of the rigid core can enhance the molecular polarizability but disrupt molecular packing,leading to a shift in the nematic–isotropic(N–I) transition.Creating a lateral dipole in this way can also promote formation of tilted smectic phases and,in the case of chiral phases,give rise to ferroelectricity.Further details regarding the effects of various molecular features on nematic behaviour can be found in Dunmur et al (2001).The classic example of a room-temperature mesogen is the n-cyanobiphenyl or n CB family shown inﬁgure2.Here the rigid core is made of a meta biphenyl unit;at one end of this core is theﬂexible tail,an alkyl chain of n carbons(C n H2n+1),while at the other is the polar cyano head group.The inﬂuence of the alkyl chain length is apparent from a comparison of the phase sequences for5CB and8CB.5CB:crystal23◦C→nematic35◦C→isotropic,8CB:crystal21◦C→smectic A32.5◦C→nematic40◦C→isotropic.For molecules such as HHTT(ﬁgure3),in which one of the molecular axes is signiﬁcantly shorter than the other two,the alternative family of discotic phases can arise.Discotic mesogens typically have a core composed of aromatic rings connected in an approximately circular arrangement from which alkyl chains extend radially.In the discotic nematic phases, the director is the average orientation of the short molecular axes.As with the smectic phases, several types of columnar discotic arrangement have been suggested,the characterization relating to column–column correlations and the relationship between orientational and positional symmetry axes.Again,though,distinguishing between these different columnar phases is generally beyond the capabilities of current simulation models.In addition to these classic thermotropic calamitic and discotic systems,several other forms of LC behaviour are known.For example,there are numerous experimentally established systems—such as LC polymers and lyotropic LCs—which involve some level of mesogenic behaviour.Also,recent years have seen growing interest in the design and examination ofComputer simulation of liquid crystals2671Figure3.Molecular representation of the HHTT molecule(2,3,6,7,11-hexahexylthiotriphenylene). alternative classes of mesogenic molecule(see,e.g.Tschierske(2001)).Thus,various families of‘bent’(or‘banana-shaped’)and‘tapered’(or‘pear-shaped’)molecules have recently been synthesized with the aim of inducing exotic behaviour such as biaxial(Acharya et al2004, Madsen et al2004),and ferroelectric nematic phases and enhancedﬂexoelectricity.Finally,before closing this section,it is relevant to note that virtually all of the mesogenic materials used in practical applications are multi-component formulations,typically comprising a dozen or more molecule types.Broadly speaking,the prevalence of multi-component systems is explained by the relative ease with which they can be used to relocate phase transition points and selectively modify material properties.The issue of formulation has only recently become accessible to simulation studies,however.3.Molecular simulations of liquid crystals3.1.Molecular simulation techniquesThe burgeoningﬁeld of molecular simulation underpins all of the work described in this section,and so a brief summary of its key components is appropriate.Due to obvious space constraints,a full overview is not possible,and we strongly recommend the standard texts (Allen and Tildesley1986,Rapaport1995,Frenkel and Smit2002)to the interested reader. Here,we restrict ourselves to a brief discussion of the approaches adopted,with the intention of illustrating what is and what is not available on the molecular simulator’s palette.Just as each experimental technique is restricted to a certain time-and length-scale window, so different simulation approaches are able to probe different sets of observables.As such,the choice of appropriate model type(s)and simulation technique(s)is crucial in any project:this is driven by the scientiﬁc problem of interest and tempered by knowledge/understanding of the limitations imposed by,e.g.the computational resources available and the range of applicability of the different models considered.Any molecular simulation has,at its heart,an interaction potential which represents,to some level of approximation,the microscopic energetics that deﬁne the simulated system.As we shall see in the following subsections,a range of interaction potentials have been developed for simulating LC behaviour;these are all classical(i.e.they take no direct account of quantum mechanical effects)and based on one-and two-body interactions plus,in some cases,some higher order intramolecular terms.The sum of these contributions is then taken to give the total potential energy of the system.In order to deﬁne the system fully,it is also necessary to impose an appropriate thermodynamic ensemble.2672C M Care and D J Cleaver Once an interaction potential and the associated thermodynamic conditions have been decided upon,the task for the simulator is commonly to evolve the system conﬁguration from its starting point to its equilibrium state.Once equilibrated,the objective becomes generating a series of representative particle conﬁgurations from which appropriate system observables can be measured and averaged.If,as is often the case,only static equilibrium properties are required,a broad range of techniques can be used to perform these equilibration and production stages.By far the most common of these are molecular dynamics(MD)and Monte Carlo(MC) methods.In an MD simulation,the net force and torque acting on each interaction site are used to determine the consequent accelerations.By recursively integrating through the effects of these accelerations on the particle velocities and displacements,essentially by applying Newton’s laws of motion over short but discretized time intervals,the micro-mechanical evolution of the many-body system can be tracked within an acceptable degree of accuracy.Since it mimics the way in which a real system evolves,an MD simulation can be used to calculate dynamic properties(such as diffusion coefﬁcients)as well as static equilibrium observables.Its strict adherence to microscopic dynamics means,however,that in some situations(e.g.bulk phase separation)MD does not offer the most efﬁcient route to the equilibrium state;in such situations, MC methods often prove preferable.In an MC simulation,the microscopic processes(e.g.the particle moves)through which the simulated system evolves are limited only by the simulator’s imagination—in principle, any type of move may be attempted,though some will prove more effective than others.For example,in the case of phase separation raised above,particle identity-swap moves can be considered.Despite this free rein in terms of the attempted moves made,adherence to the laws of statistical mechanics is ultimately ensured through the rules by which these moves are either accepted or rejected.Essentially,these rules are imposed such that,once the system has equilibrated,there is a direct relationship between run-averaged observable measurements and the static properties of interest.Thus,while MC simulations routinely use random numbers in the generation of new conﬁgurations,the statistical mechanical framework within which these random moves are set ensures that any averages calculated are equivalent to those that would have been obtained using another equilibrium simulation method(such as MD).Most molecular simulations of LCs,be they MD or MC,involve the translation and/or rotation of interaction sites,processes that are well described by the formalism of rigid-body mechanics(Goldstein et al2002).The mechanical scheme adopted depends on the symmetry andﬂexibility of the model used but can,in some cases,require the use of quaternions(Allen and Tildesley1986)rather than the conventional direction cosine description.Additionally,the director constraint approach introduced by Sarman(1996)can prove a useful tool when using MD simulations to investigate long length-scale phenomena. Most LC simulations involve either bulk systems requiring three-dimensional periodic boundary conditions(PBCs)or some combination of PBCs and conﬁning walls(in one or more direction),the latter usually being imposed as a static forceﬁeld.While cubic simulation boxes are adequate for isotropic and nematicﬂuids,it has been shown(Dom´ınguez et al2002) that the pressure tensor can become anisotropic at the onset of smectic order unless the box length ratios are allowed to vary.Calculation of the key orientational observables—the nematic order parameter and director—is commonly based on the order tensor methodology described in the appendix to Eppenga and Frenkel(1984),although an alternative method,based on long range orientational correlations(see Zannoni(1979)),is useful in some situations.For systems in which small numbers of particles are available for order parameter calculations(e.g.when calculating order parameter proﬁles in conﬁned systems)the systematic overestimation inherent in theseComputer simulation of liquid crystals2673 standard methods can become problematic.In such situations,it can prove beneﬁcial to compensate directly for this systematic effect(Wall and Cleaver1997)or calculate run-averages of orientational order with respect to some box-ﬁxed axis(e.g.the substrate normal). Procedures have also been established for the measurement of higher rank order parameters (Zannoni2000)and phase biaxiality(Allen1990).The onset of smectic order is signalled by mid-range features in the radial distribution function g(r).The extent and in-plane structure of untilted smectic phases can be seen more clearly by resolving this function parallel and perpendicular to the director;smectic A and smectic B phases can be distinguished by the non-zero bond-orientational order found in the latter(Halperin and Nelson1998).For tilted smectics,where the director is of little use when determining the layer normal,alternative schemes have been developed for projecting out the in-plane and out-of-plane components of g(r)(de Miguel et al1991b,Withers et al2000) and determining the direction of tilt.3.2.All-atom simulationsConventionally,molecular simulation is dominated by models based on psuedo-atomistic representations of the molecules found,experimentally,to display the relevant type of behaviour.In theﬁeld of LC phase behaviour,however,all-atom models do not dominate: instead,the various generic models described in section3.3are far more prevalent.While all-atom simulations of mesogenic molecules wereﬁrst performed some15years ago,relatively little progress has been made since that time in terms of using such simulations to inform mesogenic phase behaviour.This appears somewhat surprising,given the conclusion drawn from Wilson and Allen’s(1991)early simulations on all-atom systems,that1ns is sufﬁcient for establishing nematic order.However,as evidenced in the more recent review (Wilson1999),this time-scale has proved to be a serious underestimate.Indeed,a recent(and impressive)foray by the Bologna group into all-atom modelling of aminocinnamate systems, which employed run-lengths of over50ns,concluded that order parameter stability could only be considered reliable when no signiﬁcant drift was observed for10ns(Berardi et al 2004).Sadly,this casts doubt on the thermodynamic stability of many of the previous all-atom simulations of bulk LC behaviour.In addition to this considerable issue of the time-scales required for establishing nematic stability in all-atom models,recent evidence suggests that a non-trivial system-size threshold also needs to be exceeded before a qualitative temperature dependence of orientational observables(particularly the nematic order parameter)can be achieved. Thus,while Berardi et al(2004)were able to establish nematic stability in their very long runs,they failed to observe increasing nematic order with decrease in temperature in their simulations of98-molecule systems.However,increasing system size to hundreds of molecules(i.e.thousands of atomic interaction sites)has been shown by recent studies,e.g. (McDonald and Hanna2004,Cheung et al2004),to yield a qualitatively correct temperature dependence of the order parameter.Even here,though,a long-lived dependence on the choice of initial conditions can prove signiﬁcant(McDonald2002).Now that these issues are recognized,it is to be hoped that more progress will start to be made in thisﬁeld through application of parallel MD approaches along with,e.g.multiple timestep methods and efﬁcient treatments of long range interactions(Glaser2000).Due to the uncertainties associated with the simulations performed to date with all-atom models,it is not appropriate to draw too many conclusions regarding the various model parametrizations employed.In the main,these have been based on parameter sets derived for liquid-state simulation(e.g.Amber),both with and without various electrostatic contributions.。

Quantum Mechanics

Quantum MechanicsQuantum mechanics is a branch of physics that deals with the behavior of matter and energy at a microscopic level. It is a fundamental theory that explains how the universe works at its most basic level. Quantum mechanics is a complex and fascinating field that has revolutionized our understanding of the universe. In this essay, I will explore the basics of quantum mechanics, its implications, and the challenges it presents.At the heart of quantum mechanics is the concept of the wave-particle duality. This means that particles, such as electrons and photons, can behave as both waves and particles. This is a fundamental departure from classical physics, which assumes that particles are always particles and waves are always waves. The wave-particle duality is a key aspect of quantum mechanics and is essential to understanding its many applications.One of the most famous applications of quantum mechanics is in the field of quantum computing. Quantum computers use the properties of quantum mechanics to perform calculations that are impossible for classical computers. This is because quantum computers can perform multiple calculations simultaneously, whereas classical computers can only perform one calculation at a time. Quantum computers have the potential to revolutionize fields such as cryptography, drug discovery, and artificial intelligence.Another important aspect of quantum mechanics is quantum entanglement. This is a phenomenon where two particles become entangled and share a quantum state. When this happens, any change to one particle will instantly affect the other particle, no matter how far apart they are. This has important implications for the field of quantum communication, where information can be transmitted using entangled particles. Quantum entanglement also has implications for the nature of reality, as it challenges our classical understanding of causality and locality.Despite its many applications, quantum mechanics presents many challenges. One of the biggest challenges is the measurement problem. In quantum mechanics, particles exist in a state of superposition, where they can exist in multiple states simultaneously. However, when a measurement is taken, the particle collapses into a single state. This presents a paradox, as it is unclear whatcauses the collapse of the wave function. This has led to many interpretations of quantum mechanics, such as the Copenhagen interpretation, the many-worlds interpretation, and the pilot wave theory.Another challenge presented by quantum mechanics is the problem of decoherence. Decoherence is the process by which a quantum system interacts with its environment, causing it to lose its quantum properties. This makes it difficult to maintain a quantum state for any length of time, which is a major obstacle for the development of practical quantum technologies.In conclusion, quantum mechanics is a fascinating and complex field that has revolutionized our understanding of the universe. It is a fundamental theory that has many applications, from quantum computing to quantum communication. However,it also presents many challenges, such as the measurement problem and the problem of decoherence. Despite these challenges, quantum mechanics is a field that is constantly evolving, and it will continue to shape our understanding of the universe for many years to come.。

TW 117设计学中英对照术语

Chinese 1次以上接触率二维动画三维动画 6-3-5方法 80/20法则抽象造形抽象形状抽象重点色彩亲和性；易接近；可触及性折迭夹页 {平面设计} 业务代表声学模型行动；开拍动作游戏行动模式行动计划行动程序行动研究行动理论动作转场促动式广告实线具体化；实现具体化行动广告页曝光适应策略；调适策略附加价值加法雕塑加成价值函数广告印象累积冒险游戏广告广告代理商广告活动广告干扰广告设计广告公司广告策略美学机能美即好用效应美学亲和图后像；残像辅助回忆演算方法对齐；调整替选解决方案；替选解答替选阶段替选方案；其他选项扩大透视法类似色；相似色；模拟色模拟式问题模拟模型
模拟；模拟法分析辅助角色取镜角度动画人体计测学应用程序外观；外貌；表象应用艺术原型；原形建筑设计建筑艺术；艺术作品艺术指导；艺术导演；艺术总监器物分析；文物分析人工智能；人工智能旁白组装图；组合图装配时间助理创意总监联想方法联想理论非对称平衡非对称互补大气透视法；空气透视法吸引力属性表属性模拟观众；阅听众阅听众累计阅听众组成阅听众流量阅听分众音响；声音视听觉扩增实境授权自动优化前卫；前卫风格化身背景背景故事平衡频带；乐队带宽；带宽横幅{网络广告} 条形码基本概念批次行为行为识别行为模型；行为模式行为对映行为科学标竿
Hale Waihona Puke cognition cognitive dissonance cohesive strength collaborate design collaborative design collage collateral material color associations color chart color contrast color correction color filter color forecasting color harmony color intensity color management color marketing color marketing group color match color scale color scheme color sensation color separation color system color temperature color value color wheel combinatorial explosion comic comic supplement command line commercial commercial audience commercial film {=CF} commercial impression commercial pool commercial time common language communication communication design communication skill community community relations company policy compatibility compatible solution competing product analysis competitor analysis complementary color completeness complexity component composition comprehensive designing compression

人工智能英语作文

Artificial Intelligence AI has become an integral part of modern technology, transforming various aspects of our lives.From personal assistants like Siri and Alexa to autonomous vehicles and advanced healthcare diagnostics,AI is reshaping the future.The Evolution of AI:The concept of AI dates back to the mid20th century when computer scientists began to explore the possibility of creating machines that could think and learn.Over the decades, AI has evolved from simple rulebased systems to complex neural networks capable of processing vast amounts of data and making decisions.Applications of AI:1.Healthcare:AI is used in diagnosing diseases,predicting patient outcomes,and even in robotic surgery,where precision is crucial.2.Finance:AI algorithms are employed in fraud detection,risk management,and automated trading systems.3.Transportation:Selfdriving cars and drones are examples of AIs impact on transportation,promising safer and more efficient travel.4.Customer Service:AI chatbots provide24/7customer support,answering queries and resolving issues swiftly.cation:Adaptive learning platforms powered by AI personalize education to meet the needs of individual students.Challenges of AI:Despite its benefits,AI also presents challenges such as:Ethical Concerns:Issues like data privacy and the potential for AI to make biased decisions are significant.Job Displacement:The automation of tasks by AI could lead to job losses in certain sectors.Security Risks:AI systems can be vulnerable to hacking and misuse.The Future of AI:The future of AI is promising but requires careful management.It is essential to develop AI responsibly,ensuring it benefits humanity while addressing the ethical and societal implications.The collaboration between governments,businesses,and academia will be crucial in shaping AIs role in the future.Conclusion:Artificial Intelligence is not just a technological advancement it is a catalyst for a new era of innovation.As we move forward,it is imperative to educate ourselves about AI,engage in thoughtful discussions about its implications,and work towards a future where AI is a force for good,enhancing our lives without compromising our values.。

天津市测绘院成功中标国家会展中心工程“多测合一”综合测绘项目

城市勘测2019年4月夯工程（150T·m）实际的加固深度约为5m。

现将夯击能E=150T·m代入回归方程：H=－4ˑ10－5E2+0．0334E－0．0346计算得到加固深度H=4．1m。

由于该公式是根据单次夯击能数值分析所得，而实际工程中需经过多次夯击，因此根据回归方程计算的结果较实际工程要稍小，说明数值模拟的结果是基本准确的。

6结论本文主要通过数值模拟分析方法对填土地基强夯有效加固深度进行了研究，并与实际工程进行了比较，得到了如下结论：（1）以数值分析理论为基础，运用ANSYS/LS－DYNA软件模拟强夯精度较高，能得出地基中各点的动反应数据，具有较高的可靠性，对强夯设计施工具有参考价值和指导意义。

（2）针对不同夯击能，同一深度处的竖向位移随着夯击能的增大而增大，并且在4m深度内，土体中竖向位移急剧变化，说明4m以内土体受强夯影响较明显。

（3）强夯有效加固深度的回归方程为：H=－4ˑ10－5E2+0．0334E－0．0346（4）本文结合工程实例，综合分析了强夯前后地基土的动探击数和压缩模量的变化，得出实际工程的有效加固深度，并与数值模拟得到的加固深度进行对比，根据回归方程计算的结果较实际工程要稍小，说明数值模拟的结果是基本准确的。

参考文献［1］张利洁，聂文波，刘贵应等．强夯效果浅析［J］．土工基础，2002，16（1）：24 27．［2］左名麒．震动波与强夯法机理［J］．岩土工程学报，1998，8（3）：55 62．［3］龚晓南．土塑性力学［M］．杭州：浙江大学出版社，1997．［4］何君毅，林样都．工程结构非线性问题的数值解法［M］．北京：国防工业出版社，1994．［5］吴铭炳，王钟琦．强夯机理的数值分析［J］．工程勘察，1989（3）：1 5．［6］王铁宏，新编全国重大工程项目地基处理工程实录［M］．北京：中国建筑工业出版社，2005：32．Numerical Simulation Study on EffectiveＲeinforcement Depthof Dynamic Compaction of Filled FoundationLuo Wei（Changsha Planning and Design SurveyＲesearch Institute，Changsha410007，China）Abstract：This paper conduct numerical simulation of effective reinforcement depth in the filled foundation after dy-namic compaction which is based in the use of ANSYS/LS－DYNA simulation software，concluded the curve of vertical displacement with depth in soils after dynamic compaction，and summarizes the distribution characteristics and variation law。

bdingsuli单词 -回复

bdingsuli单词-回复"Bdingsuli" refers to a term or concept that is not widely known or commonly used. In this case, the topic of the article will be the importance of exploring and embracing bdingsuli concepts in order to foster creativity, innovation, and personal growth.Title: Embracing Bdingsuli: Unleashing Creative Potential and Personal GrowthIntroduction: (150 words)In a world dominated by well-known, mainstream ideas, concepts, and words, it is essential to delve into the realm of bdingsuli - the uncharted territory of innovative ideas and unheard-of terminologies. By exploring and embracing bdingsuli, individuals can unlock their creative potential, spark innovation, and foster personal growth. In this article, we will take a step-by-step journey to understand the significance of bdingsuli concepts and how they can positively impact different aspects of our lives.1. Defining Bdingsuli: (200 words)To understand the concept of bdingsuli, we need to start by defining it. Bdingsuli refers to ideas, words, or terminologies thatare not widely known or commonly used. These concepts often challenge conventional wisdom, disrupt established norms, and pave the way for new ways of thinking. Bdingsuli can emerge from various sources, including different cultures, disciplines, and areas of expertise. By exploring bdingsuli, individuals open the door to fresh perspectives and unexplored possibilities.2. The Role of Bdingsuli in Creativity: (400 words)Creativity thrives on novel ideas and unconventional thinking. Bdingsuli concepts act as catalysts for innovation by providing new angles, perspectives, and approaches. When individuals encounter bdingsuli ideas, their minds are stimulated and forced to think beyond established boundaries. This cognitive leap encourages the generation of creative solutions, sparking a cascade of new ideas. Embracing bdingsuli nurtures a creative mindset, enabling individuals to break free from the mundane and conventional.3. Bdingsuli as a Tool for Innovation: (400 words)Innovation is fueled by the exploration of uncharted territories, thinking outside the box, and embracing unusual concepts. Bdingsuli serves as a potent tool for innovation by uncovering hidden gems, unconventional approaches, and unexploredknowledge. Organizations that encourage bdingsuli thinking in their team members create a fertile ground for disruptive ideas and breakthrough innovations. Bdingsuli concepts often challenge the status quo, pushing individuals to question the usual ways of doing things and find novel solutions to problems.4. Personal Growth through Bdingsuli: (400 words)Embracing bdingsuli can also lead to personal growth andself-discovery. By diving into unfamiliar concepts and terminologies, individuals broaden their horizons and expand their intellectual capacity. Bdingsuli prompts individuals to step outside their comfort zones and embrace intellectual discomfort, encouraging continuous learning and personal development. The exposure to new ideas and perspectives helps individuals gain a deeper understanding of themselves and the world around them.Conclusion: (150 words)In a society where conformity and well-known ideas often take center stage, there is immense value in exploring and embracing bdingsuli concepts. These previously unheard-of ideas can ignite creativity, inspire innovation, and foster personal growth. Bydelving into the uncharted territories of bdingsuli, individuals can break free from the confines of conventional thinking and open themselves up to new possibilities. The journey towards embracing bdingsuli may be challenging, requiring curiosity,open-mindedness, and a willingness to step outside one's comfort zone. However, the rewards of cultivating a bdingsuli mindset are immense, offering the potential for transformative ideas and personal growth that can shape not only individuals but also communities and societies at large.。

Recent advances in compression of 3D meshes

Recent Advances in Compression of3D Meshes3 3.1Triangle MeshesThe triangle is the basic geometric primitive for standard graphics render-ing hardware and for many simulation algorithms.This partially explains why much of the work in the area of mesh compression prior to2000has been concerned with triangle meshes only.The Edgebreaker coder[52]gives a worst-case bound on the connectivity compression bit rate of4bits per ver-tex.Besides the popular Edgebreaker and its derivatives[39,17,59,30],two techniques transform the connectivity of a triangle mesh into a sequence of valence codes[62,28],hence can automatically beneﬁt from the low statistical dispersion around the average valency of6when using entropy encoding.This is achieved either through a deterministic conquest[62]or by a sequence of half edge collapses[28].In[62],Touma and Gotsman proposed the conquest approach and compress the connectivity down to less than0.2bit per ver-tex(b/v)for very regular meshes,and between2and3.5b/v otherwise,in practice.The so-called conquest consists of conquering the edges of succes-sive pivot vertices in an orientation-consistent manner and generating valence codes for traversed vertices.Three additional codes:dummy,merge and split are required in order to encode boundaries,handles and conquest incidents respectively.The dummy code occurs each time a boundary is encountered during the conquest;the number of merge codes is equal to the genus of the mesh being encoded.The split code frequency is linked mainly to the mesh irregularity.Intuitively,if one looks at the coding process as a conquest along a spiraling vertex tree,the split codes thus indicate the presence of its branching nodes.The Mesh Collapse Compression scheme by Isenburg and Snoeyink[28]performs a sequence of edge contractions until a single vertex remains in order to obtain bit rates of1to4b/v.For a complete survey of these approaches,we refer the reader to[14].One interesting variant on the Edgebreaker technique is the Cut-Border Machine(CBM)of Gumhold and Strasser[18].The main diﬀerence is that the CBM encodes the split values as a parameter like the valence based schemes. This makes an upper bound on the resulting code more diﬃcult to establish (although there is a bound of5b/v in[17]),but on the other hand allows for single pass coding and decoding.This diﬀerence is signiﬁcant for coding massive data sets.3.2Non-triangle MeshesCompared with triangle meshes,little work has been dedicated to the harder problem of connectivity coding of2-manifold graphs with arbitrary face de-grees and vertex valences.There are a signiﬁcant number of non-triangular meshes in use,in particular those carefully designed,e.g.,the high-quality3D models of the Viewpoint library[65]contain a surprisingly small proportion of triangles.Likewise,few triangles are generated by tessellation routines in 4Pierre Alliez and Craig Gotsmanexisting modeling softwares.The dominant element in these meshes is thequadrilateral,but pentagons,hexagons and higher degree faces are also com-mon.The performance of compression algorithms is traditionally measured inbits per vertex(b/v)or bits per edge(b/e).Some early attempts to codegeneral graphs[63,34],which are the connectivity component of a geometricmesh,led to rates of around9b/v.These methods are based on building inter-locking spanning trees for vertices and faces.Chuang et al.[7]later describeda more compact code using canonical ordering and multiple parentheses.Theystate that any simple3-connected planar graph can be encoded using at most1.5log2(3)E+32.377bits per edge.Li and Kuo[48]proposed a so-called“dual”approach that traverses the edges of the dual mesh3and outputs avariable length sequence of symbols based on the type of a visited edge.Theﬁnal sequence is then coded using a context based entropy coder.Isenburg andSnoeyink coded the connectivity of polygon meshes along with their proper-ties in a method called Face Fixer[29].This algorithm is gate-based,thegate designating an oriented edge incident to a facet that is about to be tra-versed.A complete traversal of the mesh is organized through successive gatelabeling along an active boundary loop.As in[62,52],both the encoder anddecoder need a stack of boundary loops.Seven distinct labels F n,R,L,S,E,H n and M i,k,l are used in order to describe the way toﬁx faces or holes to-gether while traversing the current active gate.King et al.[40],Kronrod andGotsman[42]and Lee et al.[46]also generalized existing methods to quad,ar-bitrary polygon and hybrid triangle-quad meshes respectively.However,noneof these polygon mesh coders come close to the bit rates of any of the best,specialized coders[62,3]when applied to the special case of a triangle mesh.At the intuitive level,given that a polygon mesh with the same number ofvertices contains fewer edges than a triangle mesh,it should be possible toencode it with fewer bits.These observations motivated the design of a betterapproach to code the connectivity of polygonal meshes.The Degree/Valence ApproachSince the Touma-Gotsman(TG)valence coder[62]is generally considered tohave the best performance,it seems natural to try to generalize it to arbitrarypolygon meshes.This was done independently by Khodakovsky et al.[35]andIsenburg[23].The generalization relies on the key concept of duality.Consideran arbitrary2-manifold triangle graph M.Its dual graph˜M,in which faces are represented as dual vertices and vertices become dual faces(see Fig.2),should have the same connectivity information since dualization neither addsnor removes information.The valences of˜M are now all equal to3,while the face degrees take on the same values as the vertex valences of M.Since a 3See Fig.2for an illustration of a dual mesh.Recent Advances in Compression of 3D Meshes 7is equivalent to the one of constructive enumeration [41].Poulalhon and Scha-eﬀer [51]have described a provably optimal coder for connectivity of meshes homeomorphic to a sphere,using a bijection between a triangulation and a Schnyder tree decomposition (i.e.a constructive enumeration of the connec-tivity graph).Although eﬀective bounds are obtained,the code lengths do not adapt to the mesh regularity (every mesh consumes the same number of bits,whatever the distribution of valences).An objective of theoretical interest is to add ﬂexibility to these methods in order to beneﬁt from mesh regular-ity.Another obvious extension is to obtain similar results for high genus and non-triangular graphs.3.4Geometry CompressionAlthough the geometry data is often given in precise ﬂoating point represen-tation for representing vertex positions,some applications may tolerate the reduction of this precision in order to achieve higher compression rates.The reduction of the precision involves quantization .The resulting values are then typically compressed by entropy coding after prediction relying on some data smoothness assumptions.Quantization.The early works usually quantize uniformly the vertex positions for each coordinate separately in Cartesian space [10,61,62],and a more so-phisticated vector quantization has also been proposed by Lee and Ko [45].Karni and Gotsman [33]have also demonstrated the relevance of applying quantization in the space of spectral coeﬃcients (see [14]for more details on this approach).In their elegant work,Sorkine et al.[57]address the issue of reducing the visual eﬀect of quantization errors.Building on the fact that the human visual system is more sensitive to normal than to geometric distortion,they propose to apply quantization not in the coordinate space as usual,but rather in a transformed coordinate space obtained by applying a so-called “k-anchor invertible Laplacian transformation”over the original vertex coor-dinates.This concentrates the quantization error at the low-frequency end of the spectrum,thus preserving the ﬁne normal variations over the surface,even after aggressive quantization (see Fig.3).To avoid signiﬁcant low-frequency errors,a set of anchor vertex positions are also selected to “nail down”the geometry at a select number of vertex locations.Prediction.The early work employed simple delta coding [10]or linear predic-tion along a vertex ordering dictated by the coding of the connectivity [61,62].The approach proposed by Lee et al.[46]consists of quantizing in the angle space after prediction.By applying diﬀerent levels of precision while quan-tizing the dihedral or the internal angles between or inside each facet,this method achieves better visual appearance by allocating more precision to the dihedral angles since they are more related to the geometry and nor-mals.Inspired by the TG parallelogram prediction scheme [62],Isenburg and8Pierre Alliez and Craig GotsmanFig.3.The delta-coordinates quantization to 5bits/coordinate (left)introduces low-frequency errors to the geometry,whereas Cartesian coordinates quantization to 11bits/coordinate (right)introduces noticeable high-frequency errors.The up-per rows shows the quantized model and the bottom ﬁgures use color to visualize corresponding quantization errors.Data courtesy O.Sorkine.Alliez [25]complete the techniques described in [23,35]by generalizing it to polygon mesh geometry compression.The polygon information dictates where to apply the parallelogram rule used to predict the vertex positions.Since polygons tend to be fairly planar and fairly convex,it is more appropri-ate to predict within polygons rather than across them.Intuitively,this idea avoids poor predictions resulting from a crease angle between polygons.Despite the eﬀectiveness of the published predictive geometry schemes,they are not optimal because the mesh traversal is dictated by the connec-tivity scheme.Since this traversal order is independent of the geometry,and prediction from one polygon to the next is performed along this,it cannot be expected to do the best job possible.A ﬁrst approach to improve the pre-diction was the prediction trees [43],where the geometry drives the traversal instead of the connectivity as before.This is based on the solution of an opti-mization problem.In some case it results in an decrease of up to 50%in the geometry code entropy,in particular in meshes with signiﬁcant creases and corners,such as CAD models.Cohen-Or et al.[9]suggest a multi-way predic-Recent Advances in Compression of3D Meshes9 tion technique,where each vertex position is predicted from all its neighboring vertices,as opposed to the one-way parallelogram prediction.An extreme ap-proach to prediction is the feature discovery approach by Shikhare et al.[56], which removes the redundancy by detecting similar geometric patterns.How-ever,this technique works well only for a certain class of models and involves expensive matching computations.3.5Optimality of Spectral CodingKarni and Gotsman[33]showed that the eigenvectors of the Laplacian ma-trix derived from the mesh connectivity graph may be used to transform code the three Cartesian geometry n-vectors(x,y,z).The eigenvectors are ranked according to their respective eigenvalues,which are analogous to the notion of frequency in Fourier analysis.Smaller eigenvalues correspond to lower fre-quencies.Karni and Gotsman showed empirically that when projected on these basis vectors,the resulting projection coeﬃcients decrease rapidly as the frequency increases.Hence,similarly to traditional transform coding,a good approximation to the geometry vectors may be obtained by using just a linear combination of a small number of basis vectors.The code for the geom-etry is then just this small number of coeﬃcients(quantized appropriately). While this method seems to work quite well,and intuitively it seems that the Laplacian is a linear operator which captures well the smoothness of the mesh geometry relative to the mesh neighbor structure,there was no proof that this is the optimal basis for this purpose.The only indication that this might be the case is that in the case of a regular mesh,the eigenvectors of the Laplacian are the well-known2D Fourier basis,which is known to be optimal for common classes of signals[20].Ben-Chen and Gotsman[5]have imposed a very natural probability dis-tribution on the class of meshes with a given connectivity,and then used principal component analysis(also known as the Karhunen-Loeve transform) to derive the optimal basis for that class.A series of statistical derivations then shows that this basis is identical to the eigenvectors of the symmetric Lapla-cian of the given connectivity(the sparse matrix whose diagonal is the vertex valence sequence,a negative unit entry for an edge,and zero otherwise).While this is a very interesting result,it remains theoretical,since computation of the Laplacian eigenvectors is still considered too expensive to be practical.3.6Coding Massive Data SetsDue to their size and complexity,massive datasets[47]require dedicated algo-rithms since existing mesh compression are eﬀective only if the representation of the mesh connectivity and geometry is small enough toﬁt“in-core”.For large polygonal models that do notﬁt into main memory,Ho et al.[21]pro-pose cutting meshes into smaller pieces that can be encoded in-core.They 10Pierre Alliez and Craig Gotsmanprocess each piece separately,coding the connectivity using the Edgebreaker coder,and the vertex positions using the TG parallelogram linear predictor. Additional information required to stitch the pieces back together after decod-ing is also recorded,leading to bit-rates25%higher than the in-core version of the same compression algorithm.A recent out-of-core technique introduced by Isenburg and Gumhold[27]makes several improvements upon[21]by(i) avoiding the need to explicitly break the model into several pieces,(ii)decod-ing the entire model in a single pass without any restarts,and(iii)streaming the entire mesh through main memory with a small memory foot-print.The core technique underlying this compression method consists of building a new external memory data structure–the out-of-core mesh–in several stages,all of them being restricted to clusters and active traversal fronts whichﬁt in-core. The latter traversal order,consisting of a reordering of the mesh primitives,is computed in order to minimize the number of memory cache misses,similar in spirit to the notion of a“rendering sequence”[6]developed for improving performance of modern graphics cards,but at a much larger scale.The re-sulting compressed mesh format can stream very large meshes through the main memory by providing the compressor transparent access to a so-called processing sequence that represents a mesh as aﬁxed,yet seamless interleaved ordering of indexed triangles and vertices.At any point in time,the remaining part of the mesh data is kept on disk.3.7Remeshing for Single-rate Geometry CompressionThe majority of mesh coders adapt to the regularity and the uniformity of the meshes(with the noticeable exception of[12]that adapts to the non-uniformity).Therefore,if the application allows lossy compression,it is pru-dent to exploit the existing degrees of freedom in the meshing process to transform the input into a mesh with high regularity and uniformity.Recent work produces either(i)piecewise regular meshes by using the subdivision paradigm,or(ii)highly regular remeshing by local mesh adaptation,or(iii) perfectly regular remeshing by surface cutting and global parameterization.Szymczak et al.[60]ﬁrst split the mesh into relativelyﬂat patches with smooth boundaries.Six axis-aligned vectors(so-called deﬁning vectors)ﬁrst determine some reference directions.From these vectors a partition of the mesh is built with a set of patches whose normals do not deviate more than a prescribed threshold.An approximation of the geodesic distance using Di-jkstra’s algorithm is then used in combination with a variant of the farthest point Voronoi diagram to smooth the patch boundaries.Each patch is then resampled by mutual tessellation over a regular hexagonal grid,and all the original vertices,but the boundary ones,are removed by half edge collapses (see Fig.4).The connectivity of the resulting mesh is encoded using a ver-sion of Edgebreaker optimized for regular meshes,and vertex positions are compressed using diﬀerential coding and separation of tangential and normalRecent Advances in Compression of 3D Meshes 11components.Fig.4.Piecewise regular remeshing (data courtesy A.Szymczak).Attene et al.[4]tile the input mesh using isosceles “triangloids”.From each boundary edge of the tiling process,they compute a circle centered on the edge mid-point and lying on the bisecting plane between the two edge vertices.The location where the circle pierces the original mesh designates the tip vertex of the newly created triangloid tile.The original mesh is this way wrapped,the regions already discovered being identiﬁed as the triangles lying inside the regions bounded by geodesic paths between the three vertices of the new tile.Connectivity of the new mesh is compressed by Edgebreaker,while geometry is compressed by entropy coding one dihedral angle per ver-tex,after quantization.Surazhsky and Gotsman [58]generate a triangle mesh with user-controlled sample distribution and high regularity through a series of atomic Euler oper-ators and vertex relocations applied locally.A density function is ﬁrst speciﬁed by the user as a function of the curvature onto the original mesh.This mesh is kept for later reference to the original surface geometry,and the mesh adapta-tion process starts on a second mesh,initialized to a copy of the original mesh.The vertex density approaches the prescribed ideal density by local decima-tion or reﬁnement.A new area-based smoothing technique is then performed12Pierre Alliez and Craig Gotsmanto isotropically repartition the density function among the mesh vertices.A novel component of the remeshing scheme is a surprisingly eﬃcient algorithm to improve the mesh regularity.The high level of regularity is obtained by performing a series of local edge-ﬂip operations as well as some edge-collapses and edge-splits.The vertices are ﬁrst classiﬁed as black,regular or white ac-cording to their valence deﬁcit or excess (respectively <6,=6and >6).The edges are then classiﬁed as regular,long,short,or drifting according to their vertex colors (regular if both vertices are regular,long if both are white,short if both are black and drifting if bi-colored).Long edges are reﬁned by edge-split,and short edges are removed by edge-collapse until only drifting edges remain.The drifting edges have the nice property that they can migrate through regular regions of the mesh by edge-ﬂips without changing the repar-tition of the vertex valences.Improving the mesh regularity thus amounts to applying a sequence of drifting-edge migrations until they meet irregular ver-tices,and then have a chance to generate short or long edges whose removal becomes trivial.As a result the models are better compressed using the TG coder which beneﬁts from the regularity in mesh connectivity and geometry.Fig.5.Highly regular remeshing (data courtesy V.Surazhsky and C.Gotsman).Gu et al.[15]proposed a technique for completely regular remeshing of sur-face meshes using a rectangular grid.Surfaces of arbitrary genus must be cut to reduce them to a surface which is homeomorphic to a disc,then parameter-ized by minimizing a geometric-stretch measure [53],and ﬁnally represented as a so-called geometry image that stores the geometry,the normals and any attributes required for visualization purposes.Such a regular grid structure is compact and drastically simpliﬁes the rendering pipeline since all cache indirections found in usual irregular mesh rendering are eliminated.Besides its appealing properties for eﬃcient rendering,the regular structure allowsRecent Advances in Compression of 3D Meshes 13direct application of “pixel-based”image-compression methods.The authors apply wavelet-based coding techniques and compress separately the topolog-ical sideband due to the cutting.After decoding,the topological sideband is used to fuse the cut and ensure a proper welding of the surface throughout the cuts.Despite its obvious importance for eﬃcient rendering,this technique reveals a few drawbacks due to the inevitable surface cutting:each geometry image has to be homeomorphic to a disk,therefore closed or genus >0models have to be cut along a cut graph to extract either a polygonal schema [15]or an atlas [54].Finding a “smart”cut graph (i.e.minimizing a notion of dis-tortion)is a delicate issue and introduces a set of artiﬁcial boundary curves,associated pairwise.These boundaries are later sampled as a set of curves (i.e.1-manifolds)and therefore generate a visually displeasing seam tree.Another drawback comes from the fact that the triangle or the quad primitives of the newly generated meshes have neither orientation nor shape consistent with approximation theory,which makes this representation not fully optimized for eﬃcient geometry compression as reﬂected in the rate-distortion tradeoﬀ.Fig.6.Geometry image (data courtesy D.X.Gu).3.8Comparison and DiscussionA recent trend in mesh connectivity compression is generalization from trian-gle meshes to general polygon meshes,with arbitrary genus and boundaries.Adapting to the regularity of the mesh,i.e.the dispersion in the distribution of valences or degrees,usually reﬂects in the coding schemes.Semi-regularity being a common property of “real-world”meshes,this is a very convenient feature.On the theoretical side,the bit-rates achievable by degree/valence connec-tivity coders have been shown to approach the Tutte entropy lower bound.Because of some remaining “split”symbols,whose number has not been14Pierre Alliez and Craig Gotsmanbounded,some additional work has to be done in order to design truly optimal polygon mesh coders which also adapt to regularity.In particular,the con-nectivity coder of Poulalhon and Schaeﬀer [51]for triangle meshes oﬀers some promise for extension to polygonal models.As for volume meshes,although some recent work has demonstrated a generalization of the valence coder to hexahedral meshes [24],nothing has been proven concerning the optimality of this approach.Most of the previous work has studied the coding of geometry as dictated by the connectivity code,the vertex positions being predicted in an order dictated by the connectivity coder.This happens even though the geometry component dominates the code sizes,so the result will tend to be subopti-mal.One attempt to change this was to make the coding of the geometry cooperate with the coding of the connectivity,using prediction trees [43]or multi-way prediction techniques [9].Other work [57]compresses the geometry globally,showing that applying quantization in the space of Laplacian trans-formed coeﬃcients,instead of in the usual space of Cartesian coordinates,is very useful.In a way,the latter is an extension of the multi-way approach since it amounts to predicting each vertex as the barycenter of its neighbors.More recent work [5]aims to ﬁnd an optimal basis best suited to decorrelate the geometric signal.Isenburg et al.provide an on-line implementation of the degree/valence coder for bench marking purposes [26].Isenburg also demonstrates an ASCII-based compression format for web applications able to achieve bit-rates within 1to 2percent of those of the binary benchmark code [31].In order to beneﬁt most from the adaptation of a coding scheme to regu-larity or uniformity in the input mesh,recent work advocates highly (or even completely)regular remeshing without distorting the geometry too much.In particular,the geometry images [15]technique demonstrates the eﬃciency of modern image compression techniques when applied to geometry which has been remeshed in a completely regular manner.A more recent trend takes the remeshing paradigm further,with the design of eﬃcient meshes for approximation of surfaces [1].This leads to anisotropic polygon meshes,that “look like”carefully designed meshes.The eﬃciency of such a scheme is expressed in terms of error per number of geometric primitives.The question that now naturally arises is whether the remeshing process should be inﬂuenced by the mesh compression scheme used,namely,should it remesh in a manner that suits the coder best.Since rapid progress in the direction of eﬃcient surface meshing is emerging,it seems that it will certainly motivate new approaches for dedicated single-rate mesh compression schemes.Recent Advances in Compression of 3D Meshes 154Progressive CompressionProgressive compression of 3D meshes uses the notion of reﬁnement:the origi-nal mesh is transformed into a sequence (or a hierarchy)of reﬁnements applied to a simple,coarse mesh.During decoding the connectivity and the geome-try are reconstructed incrementally from this stream.The main advantage of progressive compression is that it provides access to intermediate states of the object during its transmission through the network (see Fig.7).The challenge then consists of rebuilding a least distorted object at all points in time during the transmission (i.e.optimization of rate-distortion tradeoﬀ).Fig.7.Intermediate stages during the decoding of a mesh using a single-rate (top)or a progressive technique (bottom).4.1General TechniquesWe call lossless the methods that restore the original mesh connectivity and geometry once the transmission is complete.This is even though intermediate stages are obviously lossy.These techniques mostly proceed by decimating the mesh while recording the (minimally redundant)information required to reverse this process.The basic ingredients behind most of progressive mesh compression techniques are (i)the choice of an atomic mesh decimation oper-ator,(ii)the choice of a geometric distance metric to determine the elements to be decimated,and (iii)an eﬃcient coding of the information required to reverse the decimation process (i.e.to reﬁne the mesh).At the intuitive level,one has to encode for the decoder both the location of the reﬁnement (“where”16Pierre Alliez and Craig Gotsmanto reﬁne)and the parameters to perform the reﬁnement itself (“how”to re-ﬁne).The progressive mesh technique introduced by Hoppe [22]transforms a triangle surface mesh into a stream of reﬁnements.During encoding the input mesh undergoes a sequence of edge collapses ,reversed during decoding as a sequence of vertex splits .The symbols generated provide the explicit location of each vertex being split and a designation of two edges incident to this ver-tex.This is a very ﬂexible,but rather expensive code.In order to reduce the bit consumption due to the explicit vertex location,several researchers have proposed to specify these locations implicitly,using independent sets deﬁned on the mesh.This approach improves the compression ratios,at the price of additional constraints during decimation (the decimation sequence cannot be arbitrary).Pajarola and Rossignac [49]group some edge collapses into a series of independent sets,each of them corresponding to a level of detail.The location of each vertex to decimate is done by a 2-coloring of the mesh vertices,leading to 1bit per vertex,for each set.Experimental results show an amortized cost of 3bits per vertex for vertex location for all sets,plus the cost of local reﬁnements inverting the edge collapses,leading to 7.2bits per vertex.Cohen-or et al.[8]deﬁne an alternation of 4-and 2-coloring over the triangles in order to locate an independent set of vertices to decimate.A local,deterministic retriangulation then ﬁlls the holes generated by vertex removal at no cost,leading to 6bits per vertex.Observing the local change of repartition of valences when removing a ver-tex,Alliez and Desbrun [2]improved the previous approaches by generating an alternation of independent sets composed of patches centered onto the ver-tices to be removed.Each independent set thus corresponds to one decimation pass.The even decimation passes remove valence ≤6vertices,while the odd ones remove only valence 3vertices.Such a selection of valences reduces the dispersion of valences during decimation,the latter dispersion being further reduced by a deterministic patch retriangulation designed to generate valence-3vertices,later removed by odd decimation passes.This way the decimation is coordinated with the coding,and for “progressively regular”meshes the dec-imation generates a regular inverse √3-subdivision,and coding one valence per vertex is suﬃcient to rebuild the connectivity.For more general meshes some additional symbols are necessary.The latter approach can be seen as a progressive version of the TG coder [62].Using the edge collapse as the atomic mesh decimation operator,Karni et al.[32]build a sequence of edge collapses arranged along a so called “vertex sequence”that traverses all the mesh vertices.The mesh traversal is optimized so that the number of jumps between two non-incident vertices is minimized.The decoding process is this way provided with an access to the mesh triangles optimized for eﬃcient rendering using the modern vertex buﬀpression。

设计师遇到的问题和解决方法英文作文

设计师遇到的问题和解决方法英文作文In the dynamic and competitive world of design, professionals often encounter various challenges thatrequire innovative thinking and problem-solving skills. These challenges range from understanding client requirements, managing time effectively, staying up-to-date with design trends, to executing creative ideas effectively. In this article, we will explore some common problems faced by designers and discuss potential solutions to overcome these obstacles.One of the most significant challenges designers faceis understanding and interpreting client requirements. Clients often have a vague vision of what they want, which can lead to confusion and misunderstandings. To addressthis issue, designers should actively engage with clients, seeking clarification and feedback throughout the design process. It is also essential to create wireframes or mockups early in the process to visualize the designconcept and gain client approval.Another challenge is managing time effectively. Designers often have multiple projects runningsimultaneously, each with its own unique deadlines and requirements. To ensure timely delivery, designers need to prioritize tasks, plan their work schedule meticulously,and allocate sufficient time for revisions and feedback. Using project management tools like Trello or Asana canhelp keep track of tasks and deadlines more efficiently.Staying up-to-date with design trends is also crucialfor designers. The design industry is constantly evolving, and staying relevant requires continuous learning and exploration. Designers should subscribe to design blogs, follow influential designers on social media, and attend design conferences and workshops to stay informed about the latest trends and techniques.Executing creative ideas effectively is another challenge that designers often face. Bringing a unique concept to life requires skill, patience, and perseverance. Designers should experiment with different design tools and techniques to find the most suitable medium for their ideas. They should also be open to criticism and feedback, usingit as an opportunity to improve their designs.In conclusion, designers face various challenges intheir professional lives. By actively engaging with clients, managing time effectively, staying up-to-date with design trends, and executing creative ideas effectively, designers can overcome these obstacles and create impactful and innovative designs.**设计师在创意过程中遇到的问题及解决方案**在充满活力和竞争的设计领域，设计师们常常会遇到各种挑战，需要运用创新思维和解决问题的能力。

现象学implications

现象学implications英文回答：Phenomenology is a philosophical approach that focuses on the study of human experiences and the way we perceive and interpret the world around us. It emphasizes the importance of subjective experiences and aims to understand the essence of these experiences through careful observation and reflection. In other words, it seeks to uncover the underlying structures and meanings that shape our understanding of reality.One of the implications of phenomenology is that it challenges the traditional dualistic view of subject and object. Instead of seeing the subject as separate from the object, phenomenology emphasizes the interconnectedness between the two. It recognizes that our experiences are always embedded in a specific context and influenced by our own preconceptions and biases. For example, when we see a beautiful sunset, our perception of its beauty is notsolely determined by the objective qualities of the sunset itself, but also by our personal preferences and cultural background.Another implication of phenomenology is its emphasis on the importance of lived experiences. It recognizes that our experiences are not just passive observations, but active engagements with the world. Phenomenology encourages us to pay attention to the details of our experiences and to reflect on the significance they hold for us. For instance, when we taste a delicious meal, phenomenology encourages us to explore the sensory qualities of the food, such as its texture, flavor, and aroma, as well as the emotions and memories it evokes.Furthermore, phenomenology highlights the role of intentionality in our experiences. Intentionality refers to the directedness of our consciousness towards objects or phenomena. It suggests that our experiences are always directed towards something, whether it is an external object, an idea, or an emotion. For example, when we listen to music, our consciousness is directed towards the soundsand rhythms, and we interpret and make meaning out of them based on our own personal understanding and preferences.In addition, phenomenology recognizes the importance of the body in shaping our experiences. It acknowledges that our bodily sensations and movements play a crucial role in how we perceive and interact with the world. For instance, when we dance, our bodily movements and sensations become an integral part of the experience, and they contribute to our understanding and interpretation of the dance.Overall, phenomenology has several implications for our understanding of human experiences. It challenges the traditional dualistic view, emphasizes the importance of lived experiences, highlights intentionality, and recognizes the role of the body. By exploring these implications, we can gain a deeper understanding of the complexity and richness of human experiences.中文回答：现象学是一种哲学方法，专注于研究人类的经验以及我们对周围世界的感知和解释方式。

implications的语料

implications的语料Implications refer to the potential consequences or effects of a particular action, event, or decision. It involves analyzing the various outcomes and impacts that may arise as a result. In this article, we will explore the implications of different scenarios to gain a better understanding of their significance.When considering the implications of a decision, it is important to assess both the immediate and long-term effects. Often, there are unintended consequences that can arise and have far-reaching impacts. Understanding these implications is crucial for making informed choices and avoiding negative outcomes.One significant implication of technological advancements is the potential for job displacement. As automation and artificial intelligence continue to progress, there is a growing concern that many traditional jobs may become obsolete. While this may lead to increased efficiency and productivity, it also raises questions about income inequality and the retraining needs of the workforce.Another implication is the impact of climate change on our environment and society. Rising global temperatures, melting ice caps, and extreme weather events have wide-ranging consequences. These include the displacement of communities, increased strain on resources, and threats to biodiversity. Addressing these implications requires global cooperation and proactive measures to mitigate and adapt to the changing climate.Economic decisions also have significant implications. For example, a trade policy that restricts imports can protect domestic industries but may lead to higher prices for consumers. On the other hand, opening up trade can result in lower prices and a greater variety of goods but may also lead to job losses in certain sectors. Understanding the trade-offs and potential implications of economic policies is essential for policymakers and stakeholders.In the field of healthcare, ethical implications are frequently considered. Medical advancements, such as genetic engineering and stem cell research, raise questions aboutthe boundaries of science and the potential consequences of altering the human genome. Balancing the potential benefits with ethical considerations is crucial to ensure responsible and safe medical progress.Social media and digital communication have revolutionized the way we connect and share information. However, there are implications associated with these platforms as well. Privacy concerns, fake news, and the spread of misinformation are all critical issues that need to be addressed. Understanding the implications of our digital actions is necessary to protect individual rights and promote a healthy online ecosystem.In conclusion, implications play a significant role in decision-making and understanding the potential effects and consequences of various actions. Whether it is in the realm of technology, climate change, economics, healthcare, or social media, analyzing the implications allows us to make more informed choices and take proactive measures. By considering the long-term impacts, we can strive for a more sustainable and equitable future.。

【工程学科英语(整合第二稿)】参考答案

Unit OneTask 1⑩④⑧③⑥⑦②⑤①⑨Task 2① be consistent with他说，未来的改革必须符合自由贸易和开放投资的原则。

② specialize in启动成本较低，因为每个企业都可以只专门从事一个很窄的领域。

③ d erive from以上这些能力都源自一种叫机器学习的东西，它在许多现代人工智能应用中都处于核心地位。

④ A range of创业公司和成熟品牌推出的一系列穿戴式产品让人们欢欣鼓舞,跃跃欲试。

⑤ date back to置身硅谷的我们时常淹没在各种"新新"方式之中，我们常常忘记了，我们只是在重新发现一些可追溯至涉及商业根本的朴素教训。

Task 3T F F T FTask 4The most common viewThe principle task of engineering: To take into account the customers ‘ needs and to find the appropriate technical means to accommodate these needs.Commonly accepted claims:Technology tries to find appropriate means for given ends or desires;Technology is applied science;Technology is the aggregate of all technological artifacts;Technology is the total of all actions and institutions required to create artefacts or products and the total of all actions which make use of these artefacts or products.The author’s opinion: it is a viewpoint with flaws.Arguments: It must of course be taken for granted that the given simplified view of engineers with regard to technology has taken a turn within the last few decades. Observable changes: In many technical universities, the inter‐disciplinary courses arealready inherent parts of the curriculum.Task 5① 工程师对于自己的职业行为最常见的观点是：他们是通过应用科学结论来计划、开发、设计和推出技术产品的。

2024年研究生考试考研英语(一201)试题与参考答案

2024年研究生考试考研英语(一201)自测试题与参考答案一、完型填空（10分）Passage:Many people today believe that the world is becoming more and more competitive. This is particularly true in the fields of education and employment. The pressure to succeed in these areas has never been greater, and people are feeling the stress more than ever before.One of the reasons for this increased pressure is the rapid technological advancements we have seen in recent years. These advancements have led to a greater demand for skilled workers. Consequently, young people feel that they need to continuously upgrade their knowledge and abilities in order to stay competitive.In the realm of education, the competition starts from a very young age. Toddlers are sent to special schools to develop their language and cognitive skills. Children in primary school are enrolled in extra-curricular activities to enhance their extracurricular abilities. And in high school, students are expected to excel in their academic studies and participate in various competitions to showcase their talents.Besides education, the job market is also highly competitive. With the onsetof the digital age, many traditional jobs have been replaced by technology. This has led to a scarcity of certain kinds of jobs, making them even more sought after. As a result, candidates for these positions must possess not only knowledge but also certain soft skills, such as teamwork, problem-solving, and communication.Even in the field of sports, competition is intense. Athletes from all over the world compete at the highest level, pushing themselves to their limits. The desire to win and recognition often drives them to train harder and longer than ever before.Questions:While the pressure to succeed in education and employment is increasing, many argue that the advancements in technology have also created opportunities for personal and career growth. Pick the most appropriate word or phrase for each of the following blanks:1.The pressure to succeed in these areas has_______________never been greater.A) barelyB) certainlyC) perhapsD) rarely2.These advancements have_______________to a greater demand for skilled workers.A) ledB) resultedC) contributedD) impacted3.Toddlers are sent to special schools to_______________their language and cognitive skills.A) cultivateB) enhanceC) inhibitD) damage4.In primary school, children are enrolled in extra-curricular activities to_______________their extracurricular abilities.A) exploitB) refineC) diminishD) thwart5.And in high school, students are expected to_______________in their academic studies.A) relayB) augmentC) thriveD) wane6.This has led to a scarcity of certain kinds of jobs,which_______________them even more sought after.A) rendersB) signifiesC) ensuresD) manifests7.Candidates for these positions must possess not only knowledge but also certain_______________skills.A) fundamentalB) creativeC) tenderD) diverse8.Even in the field of sports, competition is _______________.A) uniformB) incrementalC) intenseD) adverse9.Athletes from all over the world compete at the highestlevel,_______________themselves to their limits.A) pushingB) pullingC) draggingD) resisting10.The desire to win and recognition often_______________them to trainharder and longer.A) inducementsB) motivesC) obstaclesD) pressuresAnswers:1.A) barely2.A) led3.A) cultivate4.B) enhance5.C) thrive6.A) renders7.A) fundamental8.C) intense9.A) pushing10.D) pressures二、传统阅读理解（本部分有4大题，每大题10分，共40分）First QuestionPassage:In recent years, the concept of resilience has gained significant traction across various sectors, including education, business, and mental health.Resilience, often defined as the capacity to recover quickly from difficulties, is now seen as a critical skill that can be developed and nurtured over time. The ability to bounce back after setbacks or failures is not just a personal asset but also a professional one, particularly in today’s rapidly changing world.Educators have begun to incorporate resilience-building activities into their curricula, recognizing that academic success is not solely dependent on intelligence or hard work. Instead, it is increasingly acknowledged that emotional intelligence, adaptability, and the willingness to take risks play crucial roles in achieving long-term goals. For instance, students who are taught to view failure as a learning opportunity rather than a personal shortcoming are more likely to persist through challenges and ultimately succeed.In the business world, resilience is equally important. Companies that can adapt to market changes and overcome obstacles tend to outperform those that cannot. Leaders who demonstrate resilience inspire confidence in their teams and foster a culture of perseverance and innovation. Moreover, resilient organizations are better equipped to manage crises, such as economic downturns or unexpected disruptions, by leveraging their agility and flexibility.Mental health professionals also emphasize the importance of resilience. They argue that building resilience can help individuals cope with stress, anxiety, and depression. Techniques such as mindfulness, positive thinking, andsocial support are effective tools in developing this trait. By cultivating these practices, individuals can improve their mental well-being and lead more fulfilling lives.Despite the growing recognition of resilience, there are still challenges in its implementation. For example, some critics argue that the emphasis on resilience may overlook systemic issues that contribute to adversity. Others point out that not everyone has equal access to resources that promote resilience, such as quality education or supportive communities. Therefore, while resilience is a valuable trait, it is essential to address broader societal factors that affect individuals’ ability to thrive.Questions:1、According to the passage, what is the primary definition of resilience?•A) The ability to avoid difficulties.•B) The capacity to recover quickly from difficulties.•C) The willingness to take risks.•D) The skill to adapt to market changes.•Answer: B2、How do educators incorporate resilience into their teaching?•A) By focusing solely on intelligence and hard work.•B) By discouraging students from taking risks.•C) By teaching students to view failure as a learning opportunity.•D) By emphasizing the importance of avoiding challenges.•Answer: C3、What advantage do resilient companies have in the business world?•A) They are less likely to face market changes.•B) They tend to outperform less adaptable companies.•C) They avoid taking any risks.•D) They rely solely on traditional methods.•Answer: B4、Which of the following is NOT mentioned as a technique for building resilience in mental health?•A) Mindfulness.•B) Positive thinking.•C) Social support.•D) Physical exercise.•Answer: D5、What challenge is mentioned regarding the implementation of resilience?•A) The concept of resilience is too new to be understood.•B) There is a lack of interest in developing resilience.•C) Some people may not have equal access to resources that promote resilience.•D) Resilience is only beneficial for personal, not professional, development.•Answer: CSecond QuestionPassage:The traditional view of the relationship between women and technology has been one of conflict and resistance. Historically, women have been underrepresented in the fields of science, technology, engineering, and mathematics (STEM). This underrepresentation can be attributed to various factors, including societal biases, stereotypes, and discrimination. However, recent studies and initiatives have highlighted the significant contributions women have made to technological advancements, challenging the notion that women are naturally less capable or interested in technology.In the late 19th century, Ada Lovelace, an English mathematician, is often cited as the first computer programmer for her insights into Charles Babbage’s early mechanical general-purpose computer, the Analytical Engine. Lovelace not only programmed the machine but also foresaw its potential for future applications, including what could be considered modern computing. Her detailed notes on the Analytical Engine are considered the first algorithm written for a machine.During the 20th century, women like Grace Hopper continued to make groundbreaking contributions. As a naval reserve officer in the U.S. Navy, Hopper developed the first compiler to translate code written in English into machine language, which helped to simplify programming. She also coined the term “debugging,” coined from the removal of a moth that was jamming an earlycomputer. Her contributions were significant, paving the way for modern programming languages.In more recent times, women like propName (a pseudonym to protect her privacy) have been challenging gender biases and stereotypes within tech companies. PropName, a software engineer, has shared her experiences and insights on how to create more inclusive workplace cultures. Through interviews, articles, and public speaking engagements, PropName has advocated for equal opportunities and supported initiatives that aim to increase female representation in tech.Despite these advances, challenges remain. Intersectional factors such as race, socioeconomic status, and personal identity continue to influence the experiences of women in technology. For instance, women of color often face additional barriers due to systemic inequalities and lack of role models. Nonetheless, the narrative is shifting as more women come forward with their stories and the tech industry begins to recognize the importance of diversity and inclusion.1、Who is Ada Lovelace considered to be in the history of computing?1、Ada Lovelace is considered the first computer programmer.2、What is Grace Hopper known for contributing to the tech industry?2、Grace Hopper is known for developing the first compiler and coining the term “debugging.”3、What is the pseudonym of the software engineer who advocated for equal opportunities and supported diversity initiatives?3、The pseudonym of the software engineer is propName.4、What additional barriers do women of color face in the tech industry, according to the passage?4、Women of color face additional barriers due to systemic inequalities anda lack of role models.5、What is the significance of the changing narrative in the tech industry according to the passage?5、The significance of the changing narrative is that the tech industry is recognizing the importance of diversity and inclusion.第三题For this part, you will read a passage. After reading the passage, you must complete the table below with the information given in the passage. Some of the information may be given in the passage; other information you will have to write in your own words.P了个G is an entertainment company based in Los Angeles. It specializes in pop musiccontracts and record producing. The company was founded in 1964 by Terry Melcher, who wanted to create a recording contract that would give artists the opportunity to keep more of their earnings and retain better control over their music. Over the years, P了个G has become one of the most successful entertainment companies, working with some of the biggest pop stars in the world.The company’s business model is centered on its contracts. These contrac ts are designed to help artists achieve financial success while giving them asignificant share of the profits from their music. The contracts also provide artistic freedom for the artists, allowing them to have creative control over their work.1、What is the main focus of P了个G’s company?A. Book publishingB. Film productionC. Pop music contracts and record producingD. Fashion design2、Who founded P了个G?A. Barry MelcerB. Terry MelcherC. Bob MelcerD. Jim Melcer3、What is one of the key benefits of the contracts offered by P了个G?A. Higher salaryB. Creative controlC. Exclusive merchandise sales rightsD. More opportunities for international exposure4、Why was P了个G founded?A. To give artists the opportunity to keep more of their earnings and retain better control over their musicB. To specialize in book publishingC. To produce filmsD. To design clothing5、How has P了个G become successful?A. By working with independent book publishersB. By producing high-quality filmsC. By specializing in pop music contracts and record producingD. By designing trendy fashionAnswers:1、C2、B3、B4、A5、C第四题Read the following passage and answer the questions that follow.In recent years, the rise of social media has had a significant impact on the way we communicate and share information. Platforms like Facebook, Twitter, and Instagram have become integral parts of our daily lives, allowing us to connect with friends and family across the globe, share our thoughts and experiences, and even influence public opinion. However, this shift in communication has also raised concerns about the impact on traditional reading habits.The decline in reading traditional books and newspapers has been a topic of discussion among educators and researchers. Many argue that the ease of accessing information online has led to a decrease in deep reading and critical thinking skills. While online content is often concise and easy to digest, it lacks the depth and complexity that printed materials provide. This has raised questions about the future of literacy and the importance of reading for personal and intellectual development.One study conducted by researchers at the University of California, Irvine, found that students who spent more time on social media were less likely to engage in deep reading activities. The researchers noted that the constant stream of information and the need to keep up with the latest posts created a sense of urgency and distraction that hindered their ability to focus on longer, more complex texts. Moreover, the study suggested that the superficial nature of much online content contributed to a decline in overall literacy skills.Despite these concerns, some argue that social media can also be a valuable tool for promoting reading. Platforms like Goodreads and Book Riot have gained popularity, allowing book lovers to share recommendations, discuss favorite titles, and even organize virtual book clubs. These communities have the potential to inspire individuals to pick up a book and delve into a new story or topic.1、What is the main topic of the passage?A) The benefits of social mediaB) The decline of traditional reading habitsC) The impact of social media on educationD) The rise of online communities2、According to the passage, what has been a concern regarding the rise of social media?A) The increase in online communitiesB) The decline in reading traditional books and newspapersC) The decrease in critical thinking skillsD) The rise in book sales3、What study mentioned in the passage found about students using social media?A) They spent more time on deep reading activities.B) They were more likely to engage in critical thinking.C) They were less likely to engage in deep reading activities.D) They preferred online content over printed materials.4、How does the passage suggest social media can be a valuable tool for promoting reading?A) By providing concise and easy-to-digest information.B) By encouraging superficial reading habits.C) By allowing book lovers to share recommendations and discuss titles.D) By creating a sense of urgency and distraction.5、What is the overall tone of the passage regarding the impact of socialmedia on reading?A) NegativeB) PositiveC) NeutralD) AmbiguousAnswers:1、B) The decline of traditional reading habits2、B) The decline in reading traditional books and newspapers3、C) They were less likely to engage in deep reading activities.4、C) By allowing book lovers to share recommendations and discuss titles.5、D) Ambiguous三、阅读理解新题型（10分）PassageArtificial Intelligence: A Path to Future Innovation and ChallengesArtificial intelligence (AI) has been a key buzzword in recent years. With the rapid advancement in machine learning algorithms and the increasing availability of big data, AI is transforming nearly every industry and field. AI systems can now perform tasks that were once thought to require human intelligence, such as natural language processing, image recognition, and decision-making. These capabilities are largely due to the development of deep learning neural networks, which enable AI to learn from vast datasets and improveover time.However, as AI continues to grow, it also raises significant ethical and societal concerns. For example, AI could be used to discriminate against certain groups, leading to unfair hiring practices or biased decision-making. Privacy concerns are another major issue, as AI may collect and analyze large amounts of personal data without proper oversight. As AI becomes more integrated into our daily lives, it is crucial for society to address these challenges through a combination of technological advances and policy measures.In this changing landscape, the role of researchers and policymakers is more important than ever. Academics and experts need to continue developing AI technologies that are robust and fair, while policymakers must ensure that AI is used ethically and for the betterment of society.Questions1.What is the primary reason AI is transforming nearly every industry and field?A. The rapid advancement in machine learning algorithms.B. The decreasing cost of big data storage.C. The development of new types of computer processors.D. The improvement in user interface and interaction design.Answer: A. The rapid advancement in machine learning algorithms.2.Which of the following is NOT mentioned as a concern related to the use of AI?A. Discrimination against certain groups.B. Privacy concerns.C. Job displacement.D. Unfair hiring practices.Answer: C. Job displacement. (Not explicitly mentioned in the passage.)3.What capability has AI demonstrated in recent years?A. Predicting stock market trends.B. Performing tasks requiring human intelligence, such as natural language processing.C. Designing new molecular compounds.D. Creating complex artworks.Answer: B. Performing tasks requiring human intelligence, such as natural language processing.4.What is the role of policymakers in addressing the challenges posed by the integration of AI into society?A. To ensure ethical use of AI.B. To develop AI technologies.C. To collect and analyze personal data.D. To promote the use of AI in industries.Answer: A. To ensure ethical use of AI.5.What is the significance of the role of researchers and experts in this changing landscape?A. To address technological challenges.B. To develop robust and fair AI technologies.C. To control the distribution of AI tools.D. To manage AI-related privacy concerns.Answer: B. To develop robust and fair AI technologies.This passage and the associated questions are designed to test the examinee’s comprehension and analytical skills regarding the topic of artificial intelligence, including its benefits, challenges, and the roles of various stakeholders.四、翻译（本大题有5小题，每小题2分，共10分）第一题中文：Translate the following passage into English.随着互联网的普及，人们获取信息的渠道日益多样化。

critical design

critical designCritical design is a concept that challenges the traditional notion of design and prompts us to question the role and impact of different technologies and systems on society. It seeks to provoke thought and critical dialogue rather than simply producing aesthetically pleasing products. In this article, we will explore some key principles and examples of critical design to understand its relevance in today's world.One of the primary goals of critical design is to prompt reflection and critique. It encourages designers to create thought-provoking objects, installations, or experiences that challenge our assumptions and stimulate conversation about complex social, cultural, and environmental issues. This approach challenges the boundaries of traditional design and pushes the boundaries of creativity and problem-solving.Critical design often uses irony, satire, humor, or absurdity to engage viewers and disrupt their usual thought processes. By presenting familiar objects or scenarios in unfamiliar or exaggerated ways, critical design creates a sense of cognitive dissonance that encourages people to think critically about the underlying assumptions and values embedded in our society.For example, the "Juicero" is a famous critical design project that satirizes the notion of convenience and excess in the technology industry. The Juicero was marketed as a high-tech juicing machine that could produce fresh juice from pre-packaged, expensive juice packs. However, it was later revealed that the packs could be easily squeezed by hand, rendering the machine unnecessary. This projecthighlights the absurdity of technological solutions to everyday problems and questions our consumption habits.Another example is the "Gardening Gloves" project by the Design Interactions studio at the Royal College of Art. These gloves are designed to simulate the experience of gardening for people who live in urban environments with limited access to outdoor spaces. Users wear the gloves and navigate through a virtual reality garden, allowing them to experience the sights, sounds, and feel of gardening despite their physical constraints. This project challenges the traditional notion of gardening and explores alternative ways to connect with nature in urban settings.Critical design is also concerned with designing for social change. By creating objects or experiences that confront societal norms and challenge power structures, critical designers aim to inspire dialogue and promote alternative possibilities. For example, the "Peequal" urinal is a critical design project that questions gender norms in public restrooms. The urinal is designed to be used in a standing or sitting position, challenging the binary distinction between male and female restrooms and acknowledging the diverse needs and experiences of people using public facilities.In conclusion, critical design offers a valuable perspective on the role of design in society. By using irony, humor, and disruptive techniques, critical designers encourage us to question the status quo and engage in meaningful dialogue about social, cultural, and environmental issues. Through its thought-provoking projects, critical design challenges our assumptions and sparks innovative thinking for a better future.。

博士生发一篇information fusion

博士生发一篇information fusion Information Fusion: Enhancing Decision-Making through the Integration of Data and KnowledgeIntroduction:Information fusion, also known as data fusion or knowledge fusion, is a rapidly evolving field in the realm of decision-making. It involves the integration and analysis of data and knowledge from various sources to generate meaningful and accurate information. In this article, we will delve into the concept of information fusion, explore its key components, discuss its application in different domains, and highlight its significance in enhancingdecision-making processes.1. What is Information Fusion?Information fusion is the process of combining data and knowledge from multiple sources to provide a comprehensive and accurate representation of reality. The goal is to overcome the limitations inherent in individual sources and derive improved insights and predictions. By assimilating diverse information,information fusion enhances situational awareness, reduces uncertainty, and enables intelligent decision-making.2. Key Components of Information Fusion:a. Data Sources: Information fusion relies on various data sources, which can include sensors, databases, social media feeds, and expert opinions. These sources provide different types of data, such as text, images, audio, and numerical measurements.b. Data Processing: Once data is collected, it needs to be processed to extract relevant features and patterns. This step involves data cleaning, transformation, normalization, and aggregation to ensure compatibility and consistency.c. Information Extraction: Extracting relevant information is a crucial step in information fusion. This includes identifying and capturing the crucial aspects of the data, filtering out noise, and transforming data into knowledge.d. Knowledge Representation: The extracted information needs to be represented in a meaningful way for integration and analysis.Common methods include ontologies, semantic networks, and knowledge graphs.e. Fusion Algorithms: To integrate the information from various sources, fusion algorithms are employed. These algorithms can be rule-based, model-based, or data-driven, and they combine multiple pieces of information to generate a unified and coherent representation.f. Decision-Making Processes: The ultimate goal of information fusion is to enhance decision-making. This requires the fusion of information with domain knowledge and decision models to generate insights, predictions, and recommendations.3. Applications of Information Fusion:a. Defense and Security: Information fusion plays a critical role in defense and security applications, where it improves intelligence analysis, surveillance, threat detection, and situational awareness. By integrating information from multiple sources, such as radars, satellites, drones, and human intelligence, it enables effective decision-making in complex and dynamic situations.b. Health Monitoring: In healthcare, information fusion is used to monitor patient health, combine data from different medical devices, and provide real-time decision support to medical professionals. By fusing data from wearables, electronic medical records, and physiological sensors, it enables early detection of health anomalies and improves patient care.c. Smart Cities: Information fusion offers enormous potential for the development of smart cities. By integrating data from multiple urban systems, such as transportation, energy, and public safety, it enables efficient resource allocation, traffic management, and emergency response. This improves the overall quality of life for citizens.d. Financial Markets: In the financial sector, information fusion helps in the analysis of large-scale and diverse datasets. By integrating data from various sources, such as stock exchanges, news feeds, and social media mentions, it enables better prediction of market trends, risk assessment, and investmentdecision-making.4. Significance of Information Fusion:a. Enhanced Decision-Making: Information fusion enables decision-makers to obtain comprehensive and accurate information, reducing uncertainty and improving the quality of decisions.b. Improved Situational Awareness: By integrating data from multiple sources, information fusion enhances situational awareness, enabling timely and informed responses to dynamic and complex situations.c. Risk Reduction: By combining information from diverse sources, information fusion improves risk assessment capabilities, enabling proactive and preventive measures.d. Resource Optimization: Information fusion facilitates the efficient utilization of resources by providing a holistic view of the environment and enabling optimization of resource allocation.Conclusion:In conclusion, information fusion is a powerful approach to enhance decision-making by integrating data and knowledge from multiple sources. Its key components, including data sources, processing, extraction, knowledge representation, fusion algorithms, and decision-making processes, together create a comprehensive framework for generating meaningful insights. By applying information fusion in various domains, such as defense, healthcare, smart cities, and financial markets, we can maximize the potential of diverse information sources to achieve improved outcomes.。

一套练习题用英语怎么说

A Set of Practice ExercisesI. Vocabulary and GrammarA. Choose the correct answer.1. She _______ to the party last night because she was feeling sick.d) came2. If I _______ you, I would take that job offer.a) amb) werec) bed) have beenB. Fill in the blanks with the appropriate form of the word given in parentheses.1. The _______ (beauty) of the landscape is breathtaking.2. He is _______ (confidence) that he will pass the exam.II. Reading ComprehensionA. Read the following passage and answer the questions.Passage:John decided to go on a diet to lose weight. He started reducing his sugar intake and exercising regularly. After a month, he noticed significant changes in his body. His friends and family were impressed his determination.1. Why did John go on a diet?2. What changes did John make to his lifestyle?3. How did John's friends and family react to his transformation?B. Read the following poem and answer the questions.Poem:The sun sets in the west,Bringing an end to the day's quest.Stars begin to twinkle and glow,In the night sky, they begin to show.1. What natural phenomenon is described in the poem?2. What happens to the stars in the evening?III. WritingA. Rewrite the following sentences using the passive voice.1. The teacher gave the students a homework assignment.2. They built a new shopping mall in the city center.B. Write a short paragraph (5070 words) about your favorite holiday destination.IV. ListeningA. Listen to the following conversation and answer the questions.Conversation:Speaker A: Hey, did you watch the game last night?Speaker B: Yes, it was a thrilling match. The winning team played really well.1. What are the speakers discussing?2. How did Speaker B feel about the game?V. SpeakingA. Roleplay a conversation between a tourist and a tour guide at a famous landmark.Tourist: Excuse me, could you tell me more about this historical site?Tour Guide: __________B. Give a short speech (12 minutes) on the topic "The Importance of Learning a Second Language."Continuation of Practice ExercisesVI. MathematicsA. Arithmetic1. Calculate the sum of 243 and 576.2. Subtract 589 from 1,200.3. Multiply 35 42.4. Divide 1,050 15.B. Algebra1. Solve for x: 3x + 7 = 222. Simplify the expression: (4x 3y) + (2x + 5y)3. Expand and simplify: (2x 5)(x + 4)VII. ScienceA. Biology1. Name the process which plants convert sunlight into energy.2. List the three main parts of a cell.B. Physics1. Define the law of conservation of energy.2. What is the formula for calculating acceleration?VIII. GeographyA. World Geography1. Identify the largest continent land area.2. Name the countries that border the Mediterranean Sea.B. Physical Geography1. What is the primary cause of tides?2. Describe the characteristics of a desert climate.IX. HistoryA. Ancient History1. Who was the first emperor of Rome?2. What was the main purpose of the Great Wall of China?B. Modern History1. When did World War II end?2. Name one of the four presidents featured on Mount Rushmore.X. Arts and LiteratureA. Art1. Who painted the Mona Lisa?2. What is the difference between a fresco and an oil painting?B. Literature1. In which Shakespeare play does the character Hamlet appear?2. Who is the author of "Pride and Prejudice"?XI. TechnologyA. Computer Science1. What is the primary function of an operating system?B. Engineering1. What is the process of designing and building structures called?2. List one advantage of renewable energy sources over nonrenewable ones.XII. Logic and ReasoningA. Logical Reasoning1. If all cats are mammals and all mammals are animals, then all cats are what?2. Choose the logical conclusion: All students in this class are smart. John is a student in this class. Therefore, John is _______.B. Deductive Reasoning1. Every person who attends the conference gets a free gift. Sarah received a free gift. What can you deduce about Sarah?2. If it is raining, the ground is wet. The ground is wet. What does this imply about the weather?答案I. Vocabulary and GrammarA. Choose the correct answer.2. b) wereB. Fill in the blanks with the appropriate form of the word given in parentheses.1. beauty2. confidentII. Reading ComprehensionA. Read the following passage and answer the questions.1. To lose weight.2. Reducing his sugar intake and exercising regularly.3. They were impressed his determination.B. Read the following poem and answer the questions.1. The setting of the sun.2. They begin to twinkle and glow.III. WritingA. Rewrite the following sentences using the passive voice.1. The homework assignment was given to the students the teacher.2. A new shopping mall was built in the city center.B. Write a short paragraph (5070 words) about your favorite holiday destination.(Answer will vary based on personal preference.)IV. ListeningA. Listen to the following conversation and answer the questions.1. The speakers are discussing a game.2. Speaker B felt that the game was thrilling.V. SpeakingA. Roleplay a conversation between a tourist and a tour guide at a famous landmark.Tour Guide: Of course! This historical site dates back to the 15th century. It was originally built as a fortress to protect the city from invaders.B. Give a short speech (12 minutes) on the topic "The Importance of Learning a Second Language."(Answer will vary based on personal speech.)VI. MathematicsA. Arithmetic1. 8192. 6113. 1,4704. 70B. Algebra1. x = 52. 6x + 2y3. 2x^2 + 3x 20VII. ScienceA. Biology1. Photosynthesis2. Nucleus, cytoplasm, and cell membraneB. Physics1. Energy cannot be created or destroyed, only transformed from one form to another.2. Acceleration = change in velocity / timeVIII. GeographyA. World Geography1. Asia2. Spain, France, Italy, Greece, Turkey, Egypt, Lia, Tunisia, Algeria, MoroccoB. Physical Geography1. The gravitational pull of the moon and the sun.2. Low precipitation, high evaporation rates, and extreme temperature fluctuations.IX. HistoryA. Ancient History1. Augustus2. Defense against invasionsB. Modern History1. September 2, 19452. George Washington, Thomas Jefferson, Theodore Roosevelt, Abraham LincolnX. Arts and LiteratureA. Art1. Leonardo da Vinci2. A fresco is painted on wet plaster, while an oil painting is painted on canvas or another surface.B. Literature1. "Hamlet"2. Jane AustenXI. TechnologyA. Computer Science2. HTML, CSS, JavaScriptB. Engineering1. Civil engineering2. Renewable energy sources are inexhaustible and have a lower environmental impact.XII. Logic and ReasoningA. Logical Reasoning1. Animals2. SmartB. Deductive Reasoning1. Sarah attended the conference.2. It is raining.。

解压产品设计理念英语翻译

解压产品设计理念英语翻译Unveiling the Product Design Philosophy。

Product design is the process of creating a product that meets the needs and desires of consumers while also being functional, aesthetically pleasing, and cost-effective. It involves a combination of creativity, technical knowledge, and an understanding of consumer behavior. The design philosophy behind a product is crucial in determining its success in the market.The first principle of product design philosophy is to focus on the user. A product should be designed with the end-user in mind, taking into consideration their needs, preferences, and behaviors. By understanding the target audience, designers can create products that are intuitive and easy to use, providing a seamless experience for the consumer.Another important aspect of product design philosophy is innovation. Designers should strive to create products that are unique and offer something new to the market. This could be in the form of a new feature, a different approach to solving a problem, or a fresh aesthetic that sets the product apart from its competitors. Innovation is essential for staying ahead in a competitive market and capturing the attention of consumers.Sustainability is also a key component of product design philosophy. With increasing concern for the environment, consumers are looking for products that are eco-friendly and have a minimal impact on the planet. Designers should consider the lifecycle of the product, from raw material sourcing to end-of-life disposal, and strive to minimize its environmental footprint.Aesthetics play a significant role in product design philosophy as well. A well-designed product should not only be functional but also visually appealing. The appearance of a product can influence consumer perception and purchasing decisions. Aesthetic design should be in harmony with the product's functionality, creating a cohesive and attractive overall experience for the consumer.Lastly, product design philosophy should also consider the manufacturing process and cost-effectiveness. A well-designed product should be feasible to produce at a reasonable cost without compromising on quality. Designers should work closely with engineers and manufacturers to ensure that the product can be efficiently and economically produced.In conclusion, product design philosophy encompasses a range of principles that guide the creation of successful products. By focusing on the user, innovation, sustainability, aesthetics, and cost-effectiveness, designers can create products that resonate with consumers and stand out in the market. A strong design philosophy is the foundation for creating products that meet the needs of consumers and drive business success.。

Contact of single asperities with varying adhesion

a r X i v :c o n d -m a t /0606588v 1 [c o n d -m a t .m t r l -s c i ] 22 J u n 2006Contact of Single Asperities with Varying Adhesion:ComparingContinuum Mechanics to Atomistic SimulationsBinquan Luan and Mark O.Robbins Department of Physics and Astronomy,The Johns Hopkins University,3400N.Charles Street,Baltimore,Maryland 21218(Dated:March 22,2006)Abstract Atomistic simulations are used to test the equations of continuum contact mechanics in nanome-ter scale contacts.Nominally spherical tips,made by bending crystals or cutting crystalline or amorphous solids,are pressed into a ﬂat,elastic substrate.The normal displacement,contact radius,stress distribution,friction and lateral stiﬀness are examined as a function of load and adhesion.The atomic scale roughness present on any tip made of discrete atoms is shown to have profound eﬀects on the results.Contact areas,local stresses,and the work of adhesion change by factors of two to four,and the friction and lateral stiﬀness vary by orders of magnitude.The microscopic factors responsible for these changes are discussed.The results are also used to test methods for analyzing experimental data with continuum theory to determine information,such as contact area,that can not be measured directly in nanometer scale contacts.Even when the data appear to be ﬁt by continuum theory,extracted quantities can diﬀer substantially from their true values.PACS numbers:81.40.Pq 68.35.Np 62.20.Dc 68.37.PsI.INTRODUCTIONThere has been rapidly growing interest in the behavior of materials at nanometer scales [1].One motivation is to construct ever smaller machines [2],and a second is to improve material properties by controlling their structure at nanometer scales [3].For example,decreasingcrystallite size may increase yield strength by suppressing dislocation plasticity,and material properties may be altered near free interfaces or grain boundaries.To make progress,this research area requires experimental tools for characterizing nanoscale prop-erties.Theoretical models are also needed both to interpret experiments and to allow new ideas to be evaluated.One common approach for measuring local properties is to press tips with characteristic radii of 10to 1000nm into surfaces using an atomic force microscope (AFM)or nanoindenter[4,5,6,7,8,9,10,11,12,13,14,15].Mechanical properties are then extracted from the measured forces and displacements using classic results from continuum mechanics [16].A potential problem with this approach is that continuum theories make two key assumptions that must fail as the size of contacting regions approaches atomic dimensions.One is to replace the atomic structure in the bulk of the solid bodies by a continuous medium with internal stresses determined by a continuously varying strain ﬁeld.The second is to model interfaces by continuous,diﬀerentiable surface heights with interactions depending only on the surface separation.Most authors go further and approximate the contacting bodies by smooth spheres.In a recent paper [17],we analyzed the limits of continuum mechanics in describing nanometer scale contacts between mental probes.As in studies of other bulk could be described by continuum atomic diameters.However,the atomic structure of surfaces had profound consequences for much larger contacts.In particular,atomic-scale changes in the conﬁguration of atoms on nominally cylindrical or spherical surfaces produced factor of two changes in the width of the contacting region and the stress needed to produce plastic yield,and order of magnitude changes in friction and stiﬀness.In this paper we brieﬂy revisit non-adhesive contacts with an emphasis on the role of surface roughness.We then extend our atomistic studies to the more common case of通常 AFM 的针尖直径在10nm~1000nm 之间。

UnidimensionalScaling（一维缩放）

J N F R O M A ’S D E S K UNIDIMENSIONAL SCALINGJAN DE LEEUWAbstract.This is an entry for The Encyclopedia of Statistics in BehavioralScience,to be published by Wiley in 2005.Unidimensional scaling is the special one-dimensional case of multidimen-sional scaling [5].It is often discussed separately,because the unidimen-sional case is quite different from the general multidimensional case.It is ap-plied in situations where we have a strong reason to believe there is only one interesting underlying dimension,such as time,ability,or preference.And unidimensional scaling techniques are very different from multidimensional scaling techniques,because they use very different algorithms to minimize their loss functions.The classical form of unidimensional scaling starts with a symmetric and non-negative matrix ={δi j }of dissimilarities and another symmetric and non-negative matrix W ={w i j }of weights .Both W and have a zero diagonal.Unidimensional scaling ﬁnds coordinates x i for n points on the Date :March 27,2004.Key words and phrases.ﬁtting distances,multidimensional scaling.1J N F R O M A ’S D E S K 2JAN DE LEEUWline such thatσ(x )=12n i =1nj =1w i j (δi j −|x i −x j |)2is minimized.Those n coordinates in x deﬁne the scale we were looking for.To analyze this unidimensional scaling problem in more detail,let us start with the situation in which we know the order of the x i ,and we are just looking for their scale values.Now |x i −x j |=s i j (x )(x i −x j ),where s i j (x )=sign (x i −x j ).If the order of the x i is known,then the s i j (x )are known numbers,equal to either −1or +1or 0,and thus our problem becomes minimization ofσ(x )=12n i =1n j =1w i j (δi j −s i j (x i −x j ))2over all x such that s i j (x i −x j )≥0.Assume,without loss of generality,that the weighted sum of squares of the dissimilarities is one.By expanding the sum of squares we see thatσ(x )=1−t t +(x −t ) V (x −t ).Here V is the matrix with off-diagonal elements v i j =−w i j and diagonal elements v ii =n j =1w i j .Also,t =V +r ,where r is the vector with elements r i = n j =1w i j δi j s i j ,and V +is the generalized inverse of V .If all the off-diagonal weights are equal we simply have t =r /n .J N F R O M A ’S D E S K UNIDIMENSIONAL SCALING 3Thus the unidimensional scaling problem,with a known scale order,requires us to minimize (x −t ) V (x −t )over all x satisfying the order restrictions.This is a monotone regression problem [2],which can be solved quickly and uniquely by simple quadratic programming methods.Now for some geometry.The vectors x satisfying the same set of order constraints form a polyhedral convex cone K in R n .Think of K as an ice cream cone with its apex at the origin,except for the fact that the ice cream cone is not round,but instead bounded by a ﬁnite number hyperplanes.Since there are n !different possible orderings of x ,there are n !cones,all with their apex at the origin.The interior of the cone consists of the vectors without ties,intersections of different cones are vectors with at least one tie.Obviously the union of the n !cones is all of R n .Thus the unidimensional scaling problem can be solved by solving n !mono-tone regression problems,one for each of the n !cones [3].The problem hasa solution which is at the same time very simple and prohibitively compli-cated.The simplicity comes from the n !subproblems,which are easy to solve,and the complications come from the fact that there are simply too many different subproblems.Enumeration of all possible orders is imprac-tical for n >10,although using combinatorial programming techniquesmakes it possible to ﬁnd solutions for n as large as 20[7].J N F R O M A ’S D E S K 4JAN DE LEEUWActually,the subproblems are even simpler than we suggested above.The geometry tells us that we solve the subproblem for cone K by ﬁnding the closest vector to t in the cone,or,in other words,by projecting t on the cone.There are three possibilities.Either t is in the interior of its cone,or on the boundary of its cone,or outside its cone.In the ﬁrst two cases t is equal to its projection,in the third case the projection is on the boundary.The general result in [1]tells us that the loss function σcannot have a local minimum at a point in which there are ties,and thus local minima can only occur in the interior of the cones.This means that we can only have a local minimum if t is in the interior of its cone,and it also means that we actually never have to compute the monotone regression.We just have to verify if t is in the interior,if it is not then σdoes not have a local minimum in this cone.There have been many proposals to solve the combinatorial optimization problem of moving through the n !until the global optimum of σhas been found.A recent review is [8].We illustrate the method with a simple example,using the vegetable paired comparison data from [6,page 160].Paired comparison data are usually given in a matrix P of proportions,indicating how many times stimulus i is preferred over stimulus j .P has 0.5on the diagonal,while corresponding el-ements p i j and p ji on both sides of the diagonal add up to 1.0.We transformJ N F R O M A ’S D E S K UNIDIMENSIONAL SCALING 5the proportions to dissimilarities by using using the probit transformation z i j = −1(p i j )and then deﬁning δi j =|z i j |.There are 9vegetables in the experiment,and we evaluate all 9!=362880permutations.Of these cones 14354or 4%have a local minimum in their interior.This may be a small percentage,but the fact that σhas 14354isolated local minima indicates how complicated the unidimensional scaling problem is.The global minimum is obtained for the order given in Guilford’s book,which is Turnips <Cabbage <Beets <Asparagus <Carrots <Spinach <String Beans <Peas <Corn.Since there are no weights in this example,the optimal unidimensional scal-ing values are the row averages of the matrix with elements s i j (x )δi j .Except for a single sign change of the smallest element (the Carrots and Spinach comparison),this matrix is identical to the probit matrix Z .And because the Thurstone Case V scale values are the row averages of Z ,they are virtually identical to the unidimensional scaling solution in this case.The second example is quite different.It has weights and incomplete infor-mation.We take it from a paper by Fisher [4],in which he studies crossover percentages of eight genes on the sex chromosome of Drosophila willistoni .He takes the crossover percentage as a measure of distance,and supposes the number n i j of crossovers in N i j observations is binomial.Although thereare eight genes,and thus82 =28possible dissimilarities,there are only 15pairs that are actually observed.Thus 13of the off-diagonal weightsJ N F R O M A ’SD E S K 6JAN DE LEEUWare zero,and the other weights are set to the inverses of the standard errors of the proportions.We investigate all 8!=40320permutations,and we ﬁnd 78local minima.The solution given by Fisher,computed by solving linearized likelihood equations,has Reduced <Scute <Peach <Beaded <Rough <Triple <Deformed <Rimmed.This order corresponds with a local minimum of σequal to 40.16.The global minimum is obtained for the permutation that interchanges Reduced and Scute,with value 35.88.In Figure 1we see the scales for the two local minima,one corresponding with Fisher’s order and the other one with the optimal order.−0.04−0.020.000.020.04−0.04−0.020.000.020.04fisher u d s reducedscute peachbeadedroughtriple deformed rimmed Figure 1.Genes on ChromosomeJ N F R O M A ’S D E S K UNIDIMENSIONAL SCALING 7In this entry we have only discussed least squares metric unidimensional scaling.The ﬁrst obvious generalizations are to replace the least squares loss function,for example by the least absolute value or l 1loss function.The second generalization is to look at nonmetric unidimensional scaling.These generalizations have not been studied in much detail,but in both we can continue to use the basic geometry we have discussed.The combinatorial nature of the problem remains intact.References[1]J.De Leeuw.Differentiability of Kruskal’s Stress at a Local Minimum.Psychometrika ,49:111–113,1984.[2]J.De Leeuw.Monotone Regression.In B.Everitt and D.Howell,editors,The Encyclopedia of Statistics in Behavioral Science .Wiley,2005.[3]J.De Leeuw and W.J.Heiser.Convergence of Correction Matrix Algo-rithms for Multidimensional Scaling.In J.C.Lingoes,editor,Geometric representations of relational data ,pages 735–752.Mathesis Press,Ann Arbor,Michigan,1977.[4]R.A.Fisher.The Systematic Location of Genes by Means of Crossover Observations.American Naturalist ,56:406–411,1922.[5]P.Groenen.Multidimensional scaling.In B.Everitt and D.Howell,editors,The Encyclopedia of Statistics in Behavioral Science .Wiley,J N F R O M A ’S D E S K 8JAN DE LEEUW2005.[6]J.P.Guilford.Psychometric Methods .McGrawHill,second edition,1954.[7]L.J.Hubert and P.Arabie.Unidimensional Scaling and Combinatorial Optimization.In J.De Leeuw,W.Heiser,J.Meulman,and F.Critchley,editors,Multidimensional data analysis ,Leiden,The Netherlands,1986.DSWO-Press.[8]L.J.Hubert,P.Arabie,and J.J.Meulman.Linear Unidimensional Scalingin the L 2-Norm:Basic Optimization Methods Using MATLAB.Journal of Classiﬁcation ,19:303–328,2002.Department of Statistics,University of California,Los Angeles,CA 90095-1554E-mail address ,Jan de Leeuw:*****************.eduURL ,Jan de Leeuw:。

Chaos in short-range spin glasses

a rXiv:c ond-ma t/93765v129J ul1993Chaos in short-range spin glasses F.Ritort 1),2)February 1,20081)Dipartimento di Fisica,Universit`a di Roma II,”Tor Vergata”,Via della Ricerca Scientiﬁca,Roma 00133,Italy 2)Departament de F´ısica Fonamental,Universitat de Barcelona,Diagonal 647,08028Barcelona,Spain Short title:Chaos in spin glasses PACS.75.24M–Numerical simulation studies.PACS.75.50L–Spin glasses.Abstract The nature of static chaos in spin glasses is studied.For the prob-lem of chaos with magnetic ﬁeld,scaling relations in the case of the SKmodel and short-range models are presented.Our results also suggest that if there is de Almeida-Thouless line then it is similar to that of mean-ﬁeld theory for d =4and close to the h =0axis for d =3.We estimate the lower critical dimension to be in the range 2.7−2.9.Nu-merical studies at d =4show that there is chaos against temperature changes and the correlation length diverges like ξ∼(T −T ∗)−1.The nature of the spin-glass phase is poorly understood in short-range spin glasses[1].One very interesting topic is the static chaos problem.By this we mean how the free energy landscape is modiﬁed when a small perturbation is applied to the system.Most generally one is interested in the problem of chaos when a small magneticﬁeld is applied or when the temperature of the system is slightly changed.The interest of this problem is twofold.On the one hand,it is important to discover what is the nature of the spin-glass phase in short-range models.The study of chaos can give interesting predictions regarding this question.On the other hand,it is relevant for the understanding of some recent cycling temperature experiments in spin glasses[2,3].Under the hypothesis that in the dynamical experiment one is probing some kind of equilibrium states[4]it is of the utmost importance to understand the eﬀect of changing the temperature on the free energy landscape.In this letter we shall present results on the problem of chaos in a magnetic ﬁeld and will see how scaling arguments may be used within the spin-glass phase.From these arguments one can predict the nature of the spin-glass phase inﬁnite dimensions.Also in the case when the temperature is changed we present some results which show that inﬁnite dimensions the system is more chaotic to temperature changes than in mean-ﬁeld theory.The study of chaos with a magneticﬁeld was adressed by I.Kondor in case of mean-ﬁeld theory[5].Let us suppose two copies of the same system (i.e.with the same realization of bonds):one at zero magneticﬁeld,the other one atﬁnite magneticﬁeld h(in the general case one could study diﬀerent non-zero magneticﬁelds but for simplicity we will focus on the particular case in which one of the magneticﬁelds is zero.)The hamiltonian can be written as:H[σ,τ]=− i<j J ijσiσj− i<j J ijτiτj−h iτi(1)1One deﬁnes the order parameter1q=σiσjτiτj (3) where(4)(p2+1−λ(q)λ(Q))3withλ(q)=β(1−q max+ q max q dq x(q))(5) whereβis the inverse of the temperature.The same expression applies in the case ofλ(Q).Here q(x)and Q(x)are the order parameter functions for the spin glass at zero and hﬁeld respectively.It was found that the correlation lengthξdiverges like(1−λ(Q min))−13.This givesξ∼h−2It can be shown that it is also given byχ=N(1−λ(Q))53.In the Gaussian approximation this gives G(x)∼12(δ+1)δ.From these results one getsξ∼h−dδ)(8) This gives the usual hyperscaling relationsβ(δ+1)=dνandδ=d+2−η.Hyperscaling relationsgiveη=0and d=6which is the upper critical dimension.In the spin-glass phase,the derivation is slightly diﬀerent because Q ab=0. To obtain the singular part of the free energy one has to substract from h2 a<b Q ab that part corresponding to zero magneticﬁeld.In mean-ﬁeld3theory this is given by h2( Q max Q min Q(x)dx− q max0q(x)dx)which is proportional to h2Q min x min with x min equal to theﬁrst breakpoint of the function Q(x). Because Q min∼x min∼h25f(N h103.We have performed Monte Carlo numerical simulations of the SK model in order to test the prediction eq.(9).The results for diﬀerent magnetic ﬁelds ranging from0.2up to1.0at T=0.6are shown inﬁgure1for several sizes(up to N=2016).There is agreement with the prediction eq.(9)and χnl versus theﬁeld h does not change its behaviour when crossing the de Almeida-Thouless line(h AT∼0.45for T=0.6).Now we present the results of our simulations and our predictions for short-range models.We expect that scaling is satisﬁed in the spin-glass phase belowﬁve dimensions.In general,one has:χnl=Lλf(L d h2Q min x min)(10) The critical point is a particular case of eq.(10).Since there is not replica symmetry breaking one has x min∼1and Q min=q∼h2equilibrium in a reasonable time.Our results inﬁgure2ﬁt well a scaling law χnl∼L3.25f(h L1.45).This givesλ≃3.25,ν≃0.69andγ=λν≃2.24.We should draw attention to the fact the value found forλis close to that found in mean-ﬁeld theory eq.(9)putting N=L4.We have also studied the3d±J Ising spin glass which has a transition close to T=1.2[10,11].Simulations in the critical temperature give ex-ponents in eq.(8)in agreement with those already known.We have also performed simulations for small sizes at T=0.8in the spin-glass phase.Our results are compatible withλ∼2.4,ν∼0.7andγ=λν≃1.9are a good From our results at d=3,4it seems thatλ=4d3 approximation to the exponents at least for d≤5.Now we can adress the question of the existence of a phase transition line inﬁnite magneticﬁeld in the4d Ising spin glass(the so called AT line [12]).From a theoretical point of view the problem remains unsolved[15]. Recent numerical studies suggest that this line really exists[13,8,14].If this is the case and there is also mean-ﬁeld behaviour in the spin-glass phase at zero magneticﬁeld[16,17]then it is natural to suppose that(as happens in mean-ﬁeld theory)Q min∼x min.This is a very plausible hypothesis which agrees with the fact that q(x)∼x for small x,or that P(0)=(dxδ(δ=3in the mean-ﬁeld case)we obtain, from eq.(10)the resultξ∼h−2(2+δ)βδδ∼τβor h∼τ2∼5ξθwhere q isﬁnite then this gives the thermal exponent introduced in droplet models[21,22,23].The exponentθvanishes for d=d l where d l is the lower critical dimension.We have obtainedθ=1,0.55,0.05in d=5,4,3 respectively.Extrapolating toθ=0we estimate d l=2.7−2.9which is in agreement with perturbative calculations in the spin-glass phase[24]but higher than the value reported in[25].We have also investigated the problem of chaos against temperature changes.The outline of the ideas follow that presented above in the case of a magneticﬁeld.Now one couples two copies of the system at diﬀerent temperatures.In mean-ﬁeld theory the problem has not yet been fully solved and chaos could be marginal[5,26].Inﬁnite dimensions a interesting be-haviour is expected in low dimensions[25].Our numerical results for the SK model indicate that,if there is chaos,it is very small(details will be presented elsewhere).We have performed simulations in the4d Ising spin glass.Figure 4shows the non-linear susceptibility deﬁned in eq.(6)using eq.(2)which is the overlap obtained by coupling two identical copies of the system at diﬀer-ent temperatures.Our results are consistent with a correlation length which diverges like(T−T∗)−1where T∗is the reference temperature of one of the two copies.This results are in agreement with perturbative calculations in the range of dimensionalities6<d<8[27].Summarizing,in the case of chaos with a magneticﬁeld weﬁnd that there is scaling behaviour in the spin-glass phase in mean-ﬁeld theory,the main result being eq.(7).Short-range systems also satisfy scaling relations from which we can extract the exponents associated to the correlation length. We derive that if there is an AT line then in d=4it is similar to that of mean-ﬁeld theory and in d=3it is very close to the h=0axis and more diﬃcult to see using numerical simulations.The lower critical dimension is also predicted to be in the range d l∼2.7−2.9.We also reported some results of chaos in temperature which show that short-range models are more chaotic than the SK model and the correlation length diverges like(T−T∗)−1.6Acknowledgements I gartefully acknowledge very stimulating conversa-tions with G.Parisi,A.J.Bray,M.A.Moore and S.Franz.This work has been supported by the European Community(Contract B/SC1*/915198).7References[1]K.Binder and A.P.Young,Rev.Mod.Phys.58(1986)801[2]Ph.Refregier,E.Vincent,J.Hamman and M.Ocio,J.Physique(France)48(1987)1533[3]L.Sandlund,P.Svendlindh,P.Granberg and P.Norblad,J.Appl.Phys.64(1988)5616[4]J.Hamman,M.Ledermann,M.Ocio,R.Orbach and E.Vincent,Phys-ica A185(1992)278[5]I.Kondor,J.Phys.A:Math.Gen.22(1989)L163[6]G.Parisi,J.Phys.A:Math.Gen.13(1980)1887[7]D.Sherrington and S.Kirkpatrick,Phys.Rev.B17(1978)4384[8]D.Badoni,J.C.Ciria,G.Parisi,J.Pech,F.Ritort and J.J.Ruiz,Europhys.Lett.21(1993)495[9]R.R.P.Singh and S.Chakravarty,Phys.Rev.Lett.57(1986)245[10]R.N.Bhatt and A.P.Young,Phys.Rev.B37(1988)5606[11]A.T.Ogielsky,Phys.Rev.B32(1985)7384[12]J.R.de Almeida and D.J.Thouless,J.Phys.A11(1978)983[13]E.R.Grannan and R.E.Hetzel,Phys.Rev.Lett.67(1992)907[14]J.C.Ciria,G.Parisi,F.Ritort and J.J.Ruiz,to appear in J.PhysiqueI(France)[15]A.J.Bray and S.A.Roberts,J.Phys.C13(1980)5405[16]G.Parisi and F.Ritort,submitted to J.Phys.A:Math.Gen.8[17]J.C.Ciria,G.Parisi and F.Ritort,submitted to J.Phys.A:Math.Gen.[18]J.D.Reger,R.N.Bhatt and A.P.Young,Phys.Rev.Lett.16(1990)1859[19]S.Caracciolo,G.Parisi,S.Patarnello and N.Sourlas,Europhys.Lett.11(1990)783[20]N.Kawashima,N.Ito and M.Suzuki(to be published in J.Phys.Soc.Jpn.)[21]A.J.Bray and M.A.Moore,Phys.Rev.Lett.58(1987)57[22]G.J.M.Koper and H.J.Hilhorst,J.Physique(France)49(1988)429[23]D.S.Fisher and D.A.Huse,Phys.Rev.B38(1988)386[24]C.De Dominicis,I.Kondor and T.Temesvari,Preprint SphT/92/079.Invited paper at the”International Conference on Transition Metals”, Darmstadt,2-24July1992.[25]M.Niﬂe and H.J.Hilhorst,Phys.Rev.Lett.68(1992)2992[26]S.Franz,in preparation[27]I.Kondor and A.Vegso,received preprint9FIGURE CAPTIONFig.1Chaos with magneticﬁeld in the SK model at T=0.6.Field values range from h=0.2up to h=1.0for the smaller sizes and up to h=0.4 for the largest ones.The number of samples range from200for N=32down to25for N=2016.Fig.2Chaos with magneticﬁeld in the4d Ising spin glass at T=1.5. Magneticﬁeld values range from h=0.1up to h=1..The number of samples is approximately100for all lattice sizes.Fig.3Chaos with temperature changes in the4d Ising spin glass.The reference temperature of one copy is T∗=1.5.Temperature values of the other copy range from1.6up to2.2.The number of samples is approximately 100for all lattice sizes.10。

Multidisciplinary Design Optimization

Multidisciplinary Design Optimization Multidisciplinary Design Optimization (MDO) is a complex and challenging process that involves integrating various engineering disciplines to achieve the best possible design solution. This approach considers the interactions between different components and subsystems of a system, aiming to optimize the overall performance while meeting multiple conflicting objectives. MDO has gained significant attention in recent years due to its potential to improve the efficiency, reliability, and cost-effectiveness of engineering systems. However,it also presents several challenges and requires a multidimensional perspective to be effectively implemented. From an engineering perspective, MDO offers a systematic framework for addressing the inherent complexity of modern engineering systems. By considering the interactions between different disciplines such as structural, thermal, fluid dynamics, and control systems, MDO enables engineers to develop more integrated and optimized designs. This holistic approach can lead to significant improvements in performance, weight, cost, and other key metrics. For example, in the aerospace industry, MDO has been used to design more fuel-efficient aircraft by optimizing the aerodynamic shape, structural layout, and propulsion system in a coordinated manner. However, the implementation of MDO is not without its challenges. One of the primary obstacles is the need for effective collaboration and communication between experts from different disciplines. Each discipline may have its own specialized tools, models, and optimization algorithms, making it difficult to integrate them into a unified framework. Furthermore, the conflicting objectives and constraints of different disciplines can lead to trade-offs and compromises that are not easily resolved. This requires a careful balance between the competing requirements to achieve a satisfactory solution. Moreover, the computational cost of MDO can be substantial, especially when dealing with complex engineering systems and high-fidelity models. The optimization process often involves running numerous simulations and analyses, which can be time-consuming and resource-intensive. This necessitates the use of advanced computational tools and techniques, as well as efficient algorithms for solving large-scale optimization problems. Additionally, the uncertainty and variabilityin the input parameters and models can further complicate the optimization process,requiring robust and reliable methods for handling these uncertainties. From a business perspective, MDO has the potential to provide a competitive advantage by enabling the development of innovative and high-performance products. By optimizing the design of engineering systems, companies can reduce development time, minimize costs, and improve the overall quality and reliability of their products. This can lead to increased customer satisfaction and market share, as well as enhanced profitability and sustainability. However, the initial investment in MDO capabilities and the training of personnel can be significant, requiring a long-term strategic commitment from the organization. Furthermore, theintegration of MDO into the product development process may require changes in the organizational structure and workflow. This can pose challenges in terms of resistance to change, cultural barriers, and the need for cross-functional collaboration. Effective leadership, communication, and change management are essential for successfully implementing MDO within an organization. Additionally, the intellectual property and data management issues associated with MDO, such as sharing proprietary information and protecting sensitive data, need to becarefully addressed to ensure confidentiality and security. From a societal perspective, MDO has the potential to contribute to sustainable development by promoting the efficient use of resources and the reduction of environmental impacts. By optimizing the design of engineering systems, MDO can help minimize energy consumption, emissions, and waste generation, contributing to a more sustainable and eco-friendly future. For example, in the automotive industry, MDO has been used to develop more fuel-efficient and low-emission vehicles, addressing the global challenges of climate change and air pollution. However, the adoption of MDO also raises ethical and social responsibility considerations. The potential misuse of MDO for military purposes, surveillance, or other controversial applications poses ethical dilemmas that need to be carefully considered. Additionally, the accessibility and affordability of MDO tools and technologies can raise equity and inclusivity concerns, as not all individuals and communities may have equal access to the benefits of MDO. It is essential to ensure that the deployment of MDO is aligned with ethical principles, social values, and regulatory frameworks to promote the common good and minimize potential risks andnegative impacts. In conclusion, Multidisciplinary Design Optimization offers significant opportunities for improving the efficiency, reliability, and sustainability of engineering systems. However, its implementation requires a multidimensional perspective that takes into account engineering, business, and societal considerations. By addressing the technical challenges, organizational barriers, and ethical implications, MDO can contribute to the development of innovative and high-performance products that benefit individuals, organizations, and the environment. Embracing a holistic and responsible approach to MDO can lead to a more prosperous and harmonious future for all stakeholders.。