Isospin-Dependence of $pi^-pi^+$ Ratio and Density-Dependence of Nuclear Symmetry Energy

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corenlp 三元组原理

corenlp 三元组原理

corenlp 三元组原理English: The principle of extracting triples in CoreNLP involves identifying the subject, predicate, and object in a sentence to generate structured information. CoreNLP uses natural language processing techniques to parse the sentence and identify the grammatical structure, including parts of speech and dependencies between words. Once the sentence is parsed, CoreNLP applies patterns and rules to identify the entities and their relationships, which form the triples. For example, in the sentence "Bob bought a car", CoreNLP would identify "Bob" as the subject, "bought" as the predicate, and "car" as the object. These triples can then be used for various tasks such as knowledge graph construction, semantic parsing, and question answering.中文翻译: 在CoreNLP中提取三元组的原理涉及识别句子中的主语、谓语和宾语,以生成结构化信息。

关于人工智能思考的英语作文

关于人工智能思考的英语作文

关于人工智能思考的英语作文英文回答:When we contemplate the intriguing realm of artificial intelligence (AI), a fundamental question arises: can AI think? This profound inquiry has captivated the minds of philosophers, scientists, and futurists alike, generating a rich tapestry of perspectives.One school of thought posits that AI can achieve true thought by emulating the intricate workings of the human brain. This approach, known as symbolic AI, seeks to encode human knowledge and reasoning processes into computational models. By simulating the cognitive functions of the mind, proponents argue, AI can unlock the ability to think, reason, and solve problems akin to humans.A contrasting perspective, known as connectionism, eschews symbolic representations and instead focuses on the interconnectedness of neurons and the emergence ofintelligent behavior from complex networks. This approach, inspired by biological neural systems, posits that thought and consciousness arise from the collective activity of vast numbers of nodes and connections within an artificial neural network.Yet another framework, termed embodied AI, emphasizes the role of physical interaction and embodiment in shaping thought. This perspective contends that intelligence is inextricably linked to the body and its experiences in the real world. By grounding AI systems in physical environments, proponents argue, we can foster a more naturalistic and intuitive form of thought.Beyond these overarching approaches, ongoing research in natural language processing (NLP) and machine learning (ML) is contributing to the development of AI systems that can engage in sophisticated dialogue, understand complex texts, and make predictions based on vast data sets. These advancements are gradually expanding the cognitive capabilities of AI, bringing us closer to the possibility of artificial thought.However, it is essential to recognize the limitations of current AI systems. While they may excel at performing specific tasks, they still lack the comprehensive understanding, self-awareness, and creativity that characterize human thought. The development of truly thinking machines remains a distant horizon, requiring significant breakthroughs in our understanding of consciousness, cognition, and embodiment.中文回答:人工智能是否能够思考?人工智能领域的核心问题之一就是人工智能是否能够思考。

byconity 源码编译

byconity 源码编译

Byconity 源码编译一、引言源码编译是将人类可读的程序代码转换为机器可执行的二进制代码的过程,是软件开发的重要环节。

Byconity 作为一种广泛应用的开源软件,其源码编译过程对于深入理解其功能和性能优化具有重要意义。

本文将详细探讨Byconity 的源码编译过程及其重要性。

二、 Byconity 源码编译的重要性Byconity 源码编译的重要性主要体现在以下几个方面:1.性能优化:通过源码编译,可以生成更加高效的机器代码,从而提高程序的运行速度。

2.安全性增强:源码编译可以隐藏原始代码,增强软件的安全性,防止代码被轻易篡改或窃取。

3.跨平台兼容性:通过调整编译参数,可以在不同操作系统和硬件平台上编译出适应特定环境的二进制文件。

4.定制化功能实现:通过修改源代码和编译选项,可以实现定制化的功能和模块。

三、 Byconity 源码编译的工作原理Byconity 源码编译的过程涉及多个环节,每个环节都有其特定的作用和工作原理。

下面将详细介绍 Byconity 源码编译的工作原理:1.词法分析:源码编译的第一步是将源代码分解成一个个的记号(token),这一过程称为词法分析。

词法分析器将源代码按照语法规则拆分成记号,为后续的语法分析做准备。

2.语法分析:语法分析是源码编译的第二步,其主要任务是将记号组合成语法结构,如表达式、语句、控制流等。

在这个过程中,编译器会检查源代码是否符合语法规则,并构建一棵语法树(parse tree)来表示程序的语法结构。

3.语义分析:语义分析阶段主要进行类型检查、符号表管理以及语义检查等工作。

编译器会检查语法树中的语义是否符合语言规范,并进行相应的类型检查和语义分析。

4.中间代码生成:在语义分析之后,编译器会生成中间代码。

中间代码是一种抽象的代码表示,介于源代码和目标代码之间。

常见的中间代码形式包括三地址码、抽象语法树(AST)等。

5.优化:编译器在生成中间代码之后会进行一系列的优化操作,以提高生成代码的性能。

德国工业4.0原版

德国工业4.0原版
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Intense research activities in universities and other research institutions Drastically increasing number of publications in recent years Large amount of funding by the German government
Model predictive control (MPC)
Modern, optimization-based control technique Successful applications in many industrial fields Can handle hard constraints on states and inputs Optimization of some performance criterion Applicable to nonlinear, MIMO systems
A system is strictly dissipative on a set W ⊆ Z with respect to the supply rate s if there exists a storage function λ such that for all (x , u ) ∈ W it holds that λ(f (x , u )) − λ(x ) ≤ s (x , u ) − ρ(x ) with ρ > 0.
k =0 x (k |t + 1) x (t + 1) state x input u t+1 u (k |t + 1) k =N
Basic MPC scheme

自动模型检测-模型检测工具SPIN安装使用

自动模型检测-模型检测工具SPIN安装使用

计算机研究生开放课程《自动模型检测—模型检测工具SPIN安装使用》美国GeneChiu基金资助1 SPIN 概述1.1 SPIN的历史背景SPIN(Simple Promela Interpreter)是适合于并行系统,尤其是协议一致性的辅助分析检测工具,由贝尔实验室的形式化方法与验证小组于1980年开始开发的pan就是现在SPIN的前身。

1989年SPIN的0版本推出主要用于检测一系列的ω-regular属性。

1995年偏序简约和线性时序逻辑转换的引入使得SPIN的功能进一步扩大。

2001年推出的SPIN4.0版本支持C代码的植入,应用的灵活性进一步增强。

在随后2003年推出的SPIN4.1版本加入了深度优先搜索算法,更是使得SPIN的发展上了一个新台阶[/spin/Doc/course/]。

NASA 使用SPIN检测早在1996年火星探测者所存在的错误,结果发现一些错误是可以在发射之前就可以被改正的。

SPIN从此就被用来检测土星火箭控制软件和一些应用与外层空间的程序。

Lucent公司也发现了SPIN 的优点,PathStar Access Server是受益于Holzmann(SPIN开发者)的工作的第一个Lucent 产品,Holzmann用SPIN 检测了5ESS Switch的新版本代码,这个软件现在用于Lucent的灵活性部分来改善软件测试的过程。

SPIN良好的算法设计和非凡的检测能力得到了ACM(Association for Computing Machinery)(世界最早的专业计算机协会)的认可,在2001年授予SPIN的开发者Holzmann享有声望的软件系统奖[获奖名单http:// /awards/ssaward.html](Software Systems Award)(其它获得该奖的还有Unix,TCP/IP,Tcl/Tk,Java,WWW等)。

Holzmann由此成为继Ken Thompson and Dennis Ritchie(UNIX的开发者)和John M. Chambers(S系统的开发者)之后又一个获得此项殊荣的贝尔人。

恩格列净联合二甲双胍治疗2型糖尿病的效果评价

恩格列净联合二甲双胍治疗2型糖尿病的效果评价

恩格列净联合二甲双胍治疗2型糖尿病的效果评价林智化厦门大学附属第一医院同安院区(厦门市第三医院)内分泌科,福建厦门361100[摘要]目的分析恩格列净联合二甲双胍治疗2型糖尿病的效果。

方法选取2021年3月—2022年4月厦门大学附属第一医院同安院区(厦门市第三医院)收治的98例2型糖尿病患者为研究对象,按照随机数表法分为观察组和对照组,每组49例。

两组均予以二甲双胍治疗,同时观察组加行恩格列净治疗。

比较两组血糖水平、血脂水平、胰岛β细胞功能、不良反应发生率以及临床疗效。

结果治疗6个月后,与对照组相比,观察组血糖水平、血脂水平均较低,胰岛β细胞功能的改善情况较好,差异有统计学意义(P<0.05)。

观察组不良反应发生率8.16%与对照组6.12%对比,差异无统计学意义(P>0.05)。

观察组治疗总有效率95.92%较对照组83.67%更高,差异有统计学意义(P<0.05)。

结论应用恩格列净联合二甲双胍治疗,效果突出,可有效调节糖脂代谢,纠正胰岛β细胞功能。

[关键词] 恩格列净;二甲双胍;2型糖尿病;糖脂代谢;胰岛β细胞功能[中图分类号] R446.1 [文献标识码] A [文章编号] 1672-4062(2023)08(a)-0087-04 Efficacy Evaluation of Empagliflozin Combined with Metformin in the Treatment of Type 2 Diabetes MellitusLIN ZhihuaDepartment of Endocrinology, Tong'an District of the First Affiliated Hospital of Xiamen University (Xiamen Third Hospital), Xiamen, Fujian Province, 361100 China[Abstract] Objective To analyze the effect of Empagliflozin combined with metformin in the treatment of type 2 dia⁃betes mellitus. Methods From March 2021 to April 2022, 98 patients with type 2 diabetes treated in Tong 'an District the First Affiliated Hospital of Xiamen University (Xiamen Third Hospital) were selected as the research objects. Ac⁃cording to the random number table method, they were divided into observation group and control group, 49 cases in each group. Both groups were treated with metformin, while the observation group was treated with empagliflozin. The blood glucose level, blood lipid level, islet β cell function, incidence of adverse reactions and clinical efficacy were compared between the two groups. Results After 6 months of treatment, compared with the control group, the blood glucose level and blood lipid level in the observation group were lower, and the improvement of islet β cell function was better, the difference was statistically significant (P<0.05). There was no statistically significant difference in the incidence of adverse reactions between the observation group (8.16%) and the control group (6.12%) (P>0.05). The to⁃tal effective rate of treatment in the observation group was 95.92% higher than that in the control group (83.67%), and the difference was statistically significant (P<0.05). Conclusion The treatment of Empagliflozin combined with metfor⁃min has a remarkable effect, which can effectively regulate the metabolism of glucose and lipid and correct the func⁃tion of islet beta cells.[Key words] Empagliflozin; Metformin; Type 2 diabetes; Glycolipid metabolism; Islet beta cell function2型糖尿病是最为常见的糖尿病类型,由于多发于成年,故又称成人发病型糖尿病,疾病早期症状不典型,随着疾病进展,患者可出现多饮、多食、多尿、消瘦或短期内体质量减轻等典型症状[1]。

intriguing properties of neural networks 精读

intriguing properties of neural networks 精读

intriguing properties of neural networks 精读Intriguing Properties of Neural NetworksIntroduction:Neural networks are a type of machine learning model inspired by the human brain's functioning. They are composed of interconnected nodes known as neurons that work together to process and analyze complex data. Neural networks have gained immense popularity due to their ability to learn, adapt, and make accurate predictions. In this article, we will delve into some of the intriguing properties of neural networks and explore how they contribute to their success in various fields.1. Non-linearity:One of the key properties of neural networks is their ability to model nonlinear relationships in data. Traditional linear models assume a linear relationship between input variables and the output. However, neural networks introduce non-linear activation functions that allow them to capture complex patterns and correlations. This property enables neural networks to excel in tasks such as image recognition, natural language processing, and voice recognition.2. Parallel Processing:Neural networks possess the remarkable ability to perform parallel processing. Unlike traditional algorithms that follow a sequential execution path, neural networks operate by simultaneously processing multiple inputs in parallel. This parallel architecture allows for faster and efficientcomputations, making neural networks suitable for handling large-scale datasets and real-time applications.3. Distributed Representation:Neural networks utilize distributed representation to process and store information. In traditional computing systems, data is stored in a centralized manner. However, neural networks distribute information across interconnected neurons, enabling efficient storage, retrieval, and association of knowledge. This distributed representation enhances their ability to learn complex patterns and generalize from limited training examples.4. Adaptability:Neural networks exhibit a high degree of adaptability, enabling them to adjust their internal parameters and optimize their performance based on changing input. Through a process called backpropagation, neural networks continuously learn from the errors they make during training. This iterative learning process allows them to adapt to new data and improve their accuracy over time. The adaptability of neural networks makes them robust to noise, varying input patterns, and changing environments.5. Feature Extraction:Neural networks are adept at automatically extracting relevant features from raw data. In traditional machine learning approaches, feature engineering is often a time-consuming and manual process. However, neural networks can learn to identify important features directly from the input data. This property eliminates the need for human intervention and enables neuralnetworks to handle complex, high-dimensional data without prior knowledge or domain expertise.6. Capacity for Representation:Neural networks possess an impressive capacity for representation, making them capable of modeling intricate relationships in data. Deep neural networks, in particular, with multiple layers, can learn hierarchies of features, capturing both low-level and high-level representations. This property allows neural networks to excel in tasks such as image recognition, where they can learn to detect complex shapes, textures, and objects.Conclusion:The intriguing properties of neural networks, such as non-linearity, parallel processing, distributed representation, adaptability, feature extraction, and capacity for representation, contribute to their exceptional performance in various domains. These properties enable neural networks to tackle complex problems, make accurate predictions, and learn from diverse datasets. As researchers continue to explore and enhance the capabilities of neural networks, we can expect these models to revolutionize fields such as healthcare, finance, and autonomous systems.。

Principles of Plasma Discharges and Materials Processing9

Principles of Plasma Discharges and Materials Processing9

CHAPTER8MOLECULAR COLLISIONS8.1INTRODUCTIONBasic concepts of gas-phase collisions were introduced in Chapter3,where we described only those processes needed to model the simplest noble gas discharges: electron–atom ionization,excitation,and elastic scattering;and ion–atom elastic scattering and resonant charge transfer.In this chapter we introduce other collisional processes that are central to the description of chemically reactive discharges.These include the dissociation of molecules,the generation and destruction of negative ions,and gas-phase chemical reactions.Whereas the cross sections have been measured reasonably well for the noble gases,with measurements in reasonable agreement with theory,this is not the case for collisions in molecular gases.Hundreds of potentially significant collisional reactions must be examined in simple diatomic gas discharges such as oxygen.For feedstocks such as CF4/O2,SiH4/O2,etc.,the complexity can be overwhelming.Furthermore,even when the significant processes have been identified,most of the cross sections have been neither measured nor calculated. Hence,one must often rely on estimates based on semiempirical or semiclassical methods,or on measurements made on molecules analogous to those of interest. As might be expected,data are most readily available for simple diatomic and polyatomic gases.Principles of Plasma Discharges and Materials Processing,by M.A.Lieberman and A.J.Lichtenberg. ISBN0-471-72001-1Copyright#2005John Wiley&Sons,Inc.235236MOLECULAR COLLISIONS8.2MOLECULAR STRUCTUREThe energy levels for the electronic states of a single atom were described in Chapter3.The energy levels of molecules are more complicated for two reasons. First,molecules have additional vibrational and rotational degrees of freedom due to the motions of their nuclei,with corresponding quantized energies E v and E J. Second,the energy E e of each electronic state depends on the instantaneous con-figuration of the nuclei.For a diatomic molecule,E e depends on a single coordinate R,the spacing between the two nuclei.Since the nuclear motions are slow compared to the electronic motions,the electronic state can be determined for anyfixed spacing.We can therefore represent each quantized electronic level for a frozen set of nuclear positions as a graph of E e versus R,as shown in Figure8.1.For a mole-cule to be stable,the ground(minimum energy)electronic state must have a minimum at some value R1corresponding to the mean intermolecular separation (curve1).In this case,energy must be supplied in order to separate the atoms (R!1).An excited electronic state can either have a minimum( R2for curve2) or not(curve3).Note that R2and R1do not generally coincide.As for atoms, excited states may be short lived(unstable to electric dipole radiation)or may be metastable.Various electronic levels may tend to the same energy in the unbound (R!1)limit. Array FIGURE8.1.Potential energy curves for the electronic states of a diatomic molecule.For diatomic molecules,the electronic states are specifiedfirst by the component (in units of hÀ)L of the total orbital angular momentum along the internuclear axis, with the symbols S,P,D,and F corresponding to L¼0,+1,+2,and+3,in analogy with atomic nomenclature.All but the S states are doubly degenerate in L.For S states,þandÀsuperscripts are often used to denote whether the wave function is symmetric or antisymmetric with respect to reflection at any plane through the internuclear axis.The total electron spin angular momentum S (in units of hÀ)is also specified,with the multiplicity2Sþ1written as a prefixed superscript,as for atomic states.Finally,for homonuclear molecules(H2,N2,O2, etc.)the subscripts g or u are written to denote whether the wave function is sym-metric or antisymmetric with respect to interchange of the nuclei.In this notation, the ground states of H2and N2are both singlets,1Sþg,and that of O2is a triplet,3SÀg .For polyatomic molecules,the electronic energy levels depend on more thanone nuclear coordinate,so Figure8.1must be generalized.Furthermore,since there is generally no axis of symmetry,the states cannot be characterized by the quantum number L,and other naming conventions are used.Such states are often specified empirically through characterization of measured optical emission spectra.Typical spacings of low-lying electronic energy levels range from a few to tens of volts,as for atoms.Vibrational and Rotational MotionsUnfreezing the nuclear vibrational and rotational motions leads to additional quan-tized structure on smaller energy scales,as illustrated in Figure8.2.The simplest (harmonic oscillator)model for the vibration of diatomic molecules leads to equally spaced quantized,nondegenerate energy levelse E v¼hÀv vib vþ1 2(8:2:1)where v¼0,1,2,...is the vibrational quantum number and v vib is the linearized vibration frequency.Fitting a quadratic functione E v¼12k vib(RÀ R)2(8:2:2)near the minimum of a stable energy level curve such as those shown in Figure8.1, we can estimatev vib%k vibm Rmol1=2(8:2:3)where k vib is the“spring constant”and m Rmol is the reduced mass of the AB molecule.The spacing hÀv vib between vibrational energy levels for a low-lying8.2MOLECULAR STRUCTURE237stable electronic state is typically a few tenths of a volt.Hence for molecules in equi-librium at room temperature (0.026V),only the v ¼0level is significantly popula-ted.However,collisional processes can excite strongly nonequilibrium vibrational energy levels.We indicate by the short horizontal line segments in Figure 8.1a few of the vibrational energy levels for the stable electronic states.The length of each segment gives the range of classically allowed vibrational motions.Note that even the ground state (v ¼0)has a finite width D R 1as shown,because from(8.2.1),the v ¼0state has a nonzero vibrational energy 1h Àv vib .The actual separ-ation D R about Rfor the ground state has a Gaussian distribution,and tends toward a distribution peaked at the classical turning points for the vibrational motion as v !1.The vibrational motion becomes anharmonic and the level spa-cings tend to zero as the unbound vibrational energy is approached (E v !D E 1).FIGURE 8.2.Vibrational and rotational levels of two electronic states A and B of a molecule;the three double arrows indicate examples of transitions in the pure rotation spectrum,the rotation–vibration spectrum,and the electronic spectrum (after Herzberg,1971).238MOLECULAR COLLISIONSFor E v.D E1,the vibrational states form a continuum,corresponding to unbound classical motion of the nuclei(breakup of the molecule).For a polyatomic molecule there are many degrees of freedom for vibrational motion,leading to a very compli-cated structure for the vibrational levels.The simplest(dumbbell)model for the rotation of diatomic molecules leads to the nonuniform quantized energy levelse E J¼hÀ22I molJ(Jþ1)(8:2:4)where I mol¼m Rmol R2is the moment of inertia and J¼0,1,2,...is the rotational quantum number.The levels are degenerate,with2Jþ1states for the J th level. The spacing between rotational levels increases with J(see Figure8.2).The spacing between the lowest(J¼0to J¼1)levels typically corresponds to an energy of0.001–0.01V;hence,many low-lying levels are populated in thermal equilibrium at room temperature.Optical EmissionAn excited molecular state can decay to a lower energy state by emission of a photon or by breakup of the molecule.As shown in Figure8.2,the radiation can be emitted by a transition between electronic levels,between vibrational levels of the same electronic state,or between rotational levels of the same electronic and vibrational state;the radiation typically lies within the optical,infrared,or microwave frequency range,respectively.Electric dipole radiation is the strongest mechanism for photon emission,having typical transition times of t rad 10À9s,as obtained in (3.4.13).The selection rules for electric dipole radiation areDL¼0,+1(8:2:5a)D S¼0(8:2:5b) In addition,for transitions between S states the only allowed transitions areSþÀ!Sþand SÀÀ!SÀ(8:2:6) and for homonuclear molecules,the only allowed transitions aregÀ!u and uÀ!g(8:2:7) Hence homonuclear diatomic molecules do not have a pure vibrational or rotational spectrum.Radiative transitions between electronic levels having many different vibrational and rotational initial andfinal states give rise to a structure of emission and absorption bands within which a set of closely spaced frequencies appear.These give rise to characteristic molecular emission and absorption bands when observed8.2MOLECULAR STRUCTURE239using low-resolution optical spectrometers.As for atoms,metastable molecular states having no electric dipole transitions to lower levels also exist.These have life-times much exceeding10À6s;they can give rise to weak optical band structures due to magnetic dipole or electric quadrupole radiation.Electric dipole radiation between vibrational levels of the same electronic state is permitted for molecules having permanent dipole moments.In the harmonic oscillator approximation,the selection rule is D v¼+1;weaker transitions D v¼+2,+3,...are permitted for anharmonic vibrational motion.The preceding description of molecular structure applies to molecules having arbi-trary electronic charge.This includes neutral molecules AB,positive molecular ions ABþ,AB2þ,etc.and negative molecular ions ABÀ.The potential energy curves for the various electronic states,regardless of molecular charge,are commonly plotted on the same diagram.Figures8.3and8.4give these for some important electronic statesof HÀ2,H2,and Hþ2,and of OÀ2,O2,and Oþ2,respectively.Examples of both attractive(having a potential energy minimum)and repulsive(having no minimum)states can be seen.The vibrational levels are labeled with the quantum number v for the attrac-tive levels.The ground states of both Hþ2and Oþ2are attractive;hence these molecular ions are stable against autodissociation(ABþ!AþBþor AþþB).Similarly,the ground states of H2and O2are attractive and lie below those of Hþ2and Oþ2;hence they are stable against autodissociation and autoionization(AB!ABþþe).For some molecules,for example,diatomic argon,the ABþion is stable but the AB neutral is not stable.For all molecules,the AB ground state lies below the ABþground state and is stable against autoionization.Excited states can be attractive or repulsive.A few of the attractive states may be metastable;some examples are the 3P u state of H2and the1D g,1Sþgand3D u states of O2.Negative IonsRecall from Section7.2that many neutral atoms have a positive electron affinity E aff;that is,the reactionAþeÀ!AÀis exothermic with energy E aff(in volts).If E aff is negative,then AÀis unstable to autodetachment,AÀ!Aþe.A similar phenomenon is found for negative molecular ions.A stable ABÀion exists if its ground(lowest energy)state has a potential minimum that lies below the ground state of AB.This is generally true only for strongly electronegative gases having large electron affinities,such as O2 (E aff%1:463V for O atoms)and the halogens(E aff.3V for the atoms).For example,Figure8.4shows that the2P g ground state of OÀ2is stable,with E aff% 0:43V for O2.For weakly electronegative or for electropositive gases,the minimum of the ground state of ABÀgenerally lies above the ground state of AB,and ABÀis unstable to autodetachment.An example is hydrogen,which is weakly electronegative(E aff%0:754V for H atoms).Figure8.3shows that the2Sþu ground state of HÀ2is unstable,although the HÀion itself is stable.In an elec-tropositive gas such as N2(E aff.0),both NÀ2and NÀare unstable. 240MOLECULAR COLLISIONS8.3ELECTRON COLLISIONS WITH MOLECULESThe interaction time for the collision of a typical (1–10V)electron with a molecule is short,t c 2a 0=v e 10À16–10À15s,compared to the typical time for a molecule to vibrate,t vib 10À14–10À13s.Hence for electron collisional excitation of a mole-cule to an excited electronic state,the new vibrational (and rotational)state canbeFIGURE 8.3.Potential energy curves for H À2,H 2,and H þ2.(From Jeffery I.Steinfeld,Molecules and Radiation:An Introduction to Modern Molecular Spectroscopy ,2d ed.#MIT Press,1985.)8.3ELECTRON COLLISIONS WITH MOLECULES 241FIGURE 8.4.Potential energy curves for O À2,O 2,and O þ2.(From Jeffery I.Steinfeld,Molecules and Radiation:An Introduction to Modern Molecular Spectroscopy ,2d ed.#MIT Press,1985.)242MOLECULAR COLLISIONS8.3ELECTRON COLLISIONS WITH MOLECULES243 determined by freezing the nuclear motions during the collision.This is known as the Franck–Condon principle and is illustrated in Figure8.1by the vertical line a,showing the collisional excitation atfixed R to a high quantum number bound vibrational state and by the vertical line b,showing excitation atfixed R to a vibra-tionally unbound state,in which breakup of the molecule is energetically permitted. Since the typical transition time for electric dipole radiation(t rad 10À9–10À8s)is long compared to the dissociation( vibrational)time t diss,excitation to an excited state will generally lead to dissociation when it is energetically permitted.Finally, we note that the time between collisions t c)t rad in typical low-pressure processing discharges.Summarizing the ordering of timescales for electron–molecule collisions,we havet at t c(t vib t diss(t rad(t cDissociationElectron impact dissociation,eþABÀ!AþBþeof feedstock gases plays a central role in the chemistry of low-pressure reactive discharges.The variety of possible dissociation processes is illustrated in Figure8.5.In collisions a or a0,the v¼0ground state of AB is excited to a repulsive state of AB.The required threshold energy E thr is E a for collision a and E a0for Array FIGURE8.5.Illustrating the variety of dissociation processes for electron collisions with molecules.collision a0,and it leads to an energy after dissociation lying between E aÀE diss and E a0ÀE diss that is shared among the dissociation products(here,A and B). Typically,E aÀE diss few volts;consequently,hot neutral fragments are typically generated by dissociation processes.If these hot fragments hit the substrate surface, they can profoundly affect the process chemistry.In collision b,the ground state AB is excited to an attractive state of AB at an energy E b that exceeds the binding energy E diss of the AB molecule,resulting in dissociation of AB with frag-ment energy E bÀE diss.In collision b0,the excitation energy E b0¼E diss,and the fragments have low energies;hence this process creates fragments having energies ranging from essentially thermal energies up to E bÀE diss few volts.In collision c,the AB atom is excited to the bound excited state ABÃ(labeled5),which sub-sequently radiates to the unbound AB state(labeled3),which then dissociates.The threshold energy required is large,and the fragments are hot.Collision c can also lead to dissociation of an excited state by a radiationless transfer from state5to state4near the point where the two states cross:ABÃðboundÞÀ!ABÃðunboundÞÀ!AþBÃThe fragments can be both hot and in excited states.We discuss such radiationless electronic transitions in the next section.This phenomenon is known as predisso-ciation.Finally,a collision(not labeled in thefigure)to state4can lead to dis-sociation of ABÃ,again resulting in hot excited fragments.The process of electron impact excitation of a molecule is similar to that of an atom,and,consequently,the cross sections have a similar form.A simple classical estimate of the dissociation cross section for a level having excitation energy U1can be found by requiring that an incident electron having energy W transfer an energy W L lying between U1and U2to a valence electron.Here,U2is the energy of the next higher level.Then integrating the differential cross section d s[given in(3.4.20)and repeated here],d s¼pe24021Wd W LW2L(3:4:20)over W L,we obtains diss¼0W,U1pe24pe021W1U1À1WU1,W,U2pe24021W1U1À1U2W.U28>>>>>><>>>>>>:(8:3:1)244MOLECULAR COLLISIONSLetting U2ÀU1(U1and introducing voltage units W¼e E,U1¼e E1and U2¼e E2,we haves diss¼0E,E1s0EÀE11E1,E,E2s0E2ÀE1EE.E28>>>><>>>>:(8:3:2)wheres0¼pe4pe0E12(8:3:3)We see that the dissociation cross section rises linearly from the threshold energy E thr%E1to a maximum value s0(E2ÀE1)=E thr at E2and then falls off as1=E. Actually,E1and E2can depend on the nuclear separation R.In this case,(8.3.2) should be averaged over the range of R s corresponding to the ground-state vibrational energy,leading to a broadened dependence of the average cross section on energy E.The maximum cross section is typically of order10À15cm2. Typical rate constants for a single dissociation process with E thr&T e have an Arrhenius formK diss/K diss0expÀE thr T e(8:3:4)where K diss0 10À7cm3=s.However,in some cases E thr.T e.For excitation to an attractive state,an appropriate average over the fraction of the ground-state vibration that leads to dissociation must be taken.Dissociative IonizationIn addition to normal ionization,eþABÀ!ABþþ2eelectron–molecule collisions can lead to dissociative ionizationeþABÀ!AþBþþ2eThese processes,common for polyatomic molecules,are illustrated in Figure8.6.In collision a having threshold energy E iz,the molecular ion ABþis formed.Collisionsb andc occur at higher threshold energies E diz and result in dissociative ionization,8.3ELECTRON COLLISIONS WITH MOLECULES245leading to the formation of fast,positively charged ions and neutrals.These cross sections have a similar form to the Thompson ionization cross section for atoms.Dissociative RecombinationThe electron collision,e þAB þÀ!A þB Ãillustrated as d and d 0in Figure 8.6,destroys an electron–ion pair and leads to the production of fast excited neutral fragments.Since the electron is captured,it is not available to carry away a part of the reaction energy.Consequently,the collision cross section has a resonant character,falling to very low values for E ,E d and E .E d 0.However,a large number of excited states A Ãand B Ãhaving increasing principal quantum numbers n and energies can be among the reaction products.Consequently,the rate constants can be large,of order 10À7–10À6cm 3=s.Dissocia-tive recombination to the ground states of A and B cannot occur because the potential energy curve for AB þis always greater than the potential energycurveFIGURE 8.6.Illustration of dissociative ionization and dissociative recombination for electron collisions with molecules.246MOLECULAR COLLISIONSfor the repulsive state of AB.Two-body recombination for atomic ions or for mol-ecular ions that do not subsequently dissociate can only occur with emission of a photon:eþAþÀ!Aþh n:As shown in Section9.2,the rate constants are typically three tofive orders of magnitude lower than for dissociative recombination.Example of HydrogenThe example of H2illustrates some of the inelastic electron collision phenomena we have discussed.In order of increasing electron impact energy,at a threshold energy of 8:8V,there is excitation to the repulsive3Sþu state followed by dissociation into two fast H fragments carrying 2:2V/atom.At11.5V,the1Sþu bound state is excited,with subsequent electric dipole radiation in the ultraviolet region to the1Sþg ground state.At11.8V,there is excitation to the3Sþg bound state,followedby electric dipole radiation to the3Sþu repulsive state,followed by dissociation with 2:2V/atom.At12.6V,the1P u bound state is excited,with UV emission tothe ground state.At15.4V,the2Sþg ground state of Hþ2is excited,leading to the pro-duction of Hþ2ions.At28V,excitation of the repulsive2Sþu state of Hþ2leads to thedissociative ionization of H2,with 5V each for the H and Hþfragments.Dissociative Electron AttachmentThe processes,eþABÀ!AþBÀproduce negative ion fragments as well as neutrals.They are important in discharges containing atoms having positive electron affinities,not only because of the pro-duction of negative ions,but because the threshold energy for production of negative ion fragments is usually lower than for pure dissociation processes.A variety of pro-cesses are possible,as shown in Figure8.7.Since the impacting electron is captured and is not available to carry excess collision energy away,dissociative attachment is a resonant process that is important only within a narrow energy range.The maximum cross sections are generally much smaller than the hard-sphere cross section of the molecule.Attachment generally proceeds by collisional excitation from the ground AB state to a repulsive ABÀstate,which subsequently either auto-detaches or dissociates.The attachment cross section is determined by the balance between these processes.For most molecules,the dissociation energy E diss of AB is greater than the electron affinity E affB of B,leading to the potential energy curves shown in Figure8.7a.In this case,the cross section is large only for impact energies lying between a minimum value E thr,for collision a,and a maximum value E0thr for8.3ELECTRON COLLISIONS WITH MOLECULES247FIGURE 8.7.Illustration of a variety of electron attachment processes for electron collisions with molecules:(a )capture into a repulsive state;(b )capture into an attractive state;(c )capture of slow electrons into a repulsive state;(d )polar dissociation.248MOLECULAR COLLISIONScollision a 0.The fragments are hot,having energies lying between minimum and maximum values E min ¼E thr þE affB ÀE diss and E max ¼E 0thr þE af fB ÀE diss .Since the AB Àstate lies above the AB state for R ,R x ,autodetachment can occur as the mol-ecules begin to separate:AB À!AB þe.Hence the cross section for production of negative ions can be much smaller than that for excitation of the AB Àrepulsive state.As a crude estimate,for the same energy,the autodetachment rate is ffiffiffiffiffiffiffiffiffiffiffiffiffiM R =m p 100times the dissociation rate of the repulsive AB Àmolecule,where M R is the reduced mass.Hence only one out of 100excitations lead to dissociative attachment.Excitation to the AB Àbound state can also lead to dissociative attachment,as shown in Figure 8.7b .Here the cross section is significant only for E thr ,E ,E 0thr ,but the fragments can have low energies,with a minimum energy of zero and a maximum energy of E 0thr þE affB ÀE diss .Collision b,e þAB À!AB ÀÃdoes not lead to production of AB Àions because energy and momentum are not gen-erally conserved when two bodies collide elastically to form one body (see Problem3.12).Hence the excited AB ÀÃion separates,AB ÀÃÀ!e þABunless vibrational radiation or collision with a third body carries off the excess energy.These processes are both slow in low-pressure discharges (see Section 9.2).At high pressures (say,atmospheric),three-body attachment to form AB Àcan be very important.For a few molecules,such as some halogens,the electron affinity of the atom exceeds the dissociation energy of the neutral molecule,leading to the potential energy curves shown in Figure 8.7c .In this case the range of electron impact ener-gies E for excitation of the AB Àrepulsive state includes E ¼0.Consequently,there is no threshold energy,and very slow electrons can produce dissociative attachment,resulting in hot neutral and negative ion fragments.The range of R s over which auto-detachment can occur is small;hence the maximum cross sections for dissociative attachment can be as high as 10À16cm 2.A simple classical estimate of electron capture can be made using the differential scattering cross section for energy loss (3.4.20),in a manner similar to that done for dissociation.For electron capture to an energy level E 1that is unstable to autode-tachment,and with the additional constraint for capture that the incident electron energy lie within E 1and E 2¼E 1þD E ,where D E is a small energy difference characteristic of the dissociative attachment timescale,we obtain,in place of (8.3.2),s att¼0E ,E 1s 0E ÀE 1E 1E 1,E ,E 20E .E 28>><>>:(8:3:5)8.3ELECTRON COLLISIONS WITH MOLECULES 249wheres 0%p m M R 1=2e 4pe 0E 1 2(8:3:6)The factor of (m =M R )1=2roughly gives the fraction of excited states that do not auto-detach.We see that the dissociative attachment cross section rises linearly at E 1to a maximum value s 0D E =E 1and then falls abruptly to zero.As for dissociation,E 1can depend strongly on the nuclear separation R ,and (8.3.5)must be averaged over the range of E 1s corresponding to the ground state vibrational motion;e.g.,from E thr to E 0thr in Figure 8.7a .Because generally D E (E 0thr ÀE thr ,we can write (8.3.5)in the forms att %p m M R 1=2e 4pe 0 2(D E )22E 1d (E ÀE 1)(8:3:7)where d is the Dirac delta ing (8.3.7),the average over the vibrational motion can be performed,leading to a cross section that is strongly peaked lying between E thr and E 0thr .We leave the details of the calculation to a problem.Polar DissociationThe process,e þAB À!A þþB Àþeproduces negative ions without electron capture.As shown in Figure 8.7d ,the process proceeds by excitation of a polar state A þand B Àof AB Ãthat has a separ-ated atom limit of A þand B À.Hence at large R ,this state lies above the A þB ground state by the difference between the ionization potential of A and the electron affinity of B.The polar state is weakly bound at large R by the Coulomb attraction force,but is repulsive at small R .The maximum cross section and the dependence of the cross section on electron impact energy are similar to that of pure dissociation.The threshold energy E thr for polar dissociation is generally large.The measured cross section for negative ion production by electron impact in O 2is shown in Figure 8.8.The sharp peak at 6.5V is due to dissociative attachment.The variation of the cross section with energy is typical of a resonant capture process.The maximum cross section of 10À18cm 2is quite low because autode-tachment from the repulsive O À2state is strong,inhibiting dissociative attachment.The second gradual maximum near 35V is due to polar dissociation;the variation of the cross section with energy is typical of a nonresonant process.250MOLECULAR COLLISIONS。

patran错误日志及解决方法

patran错误日志及解决方法

2. USER WARNING MESSAGE 4124 (IFS3P)
THE SPCADD OR MPCADD UNION CONSISTS OF A SINGLE SET 在图中用了 RB3 的 MPC,其中 dependent node (ux,uy,uz), independent(ux,uy,uz,rx,ry,rz),有可 能是这里的问题。不过这个倒不影响计算结果。
^^^ RUN TERMINATED DUE TO EXCESSIVE PIVOT RATIOS IN MATRIX KLL. ^^^ USER ACTION: CONSTRAIN MECHANISMS WITH SPCI OR SUPORTI ENTRIES OR SPECIFY PARAM,BAILOUT,-1 TO CONTINUE THE RUN WITH MECHANISMS. 以前也遇到这种情况,这次遇到后,又在一节点加载了位移约束,就解决了。看来这 种错误主要是由于约束不够,线性方程组无解造成的。 还有可能是没有 equivalence,This should solve your problem or reduce the number of failed ratios.有一次就遇到此情况。
7. 常用材料定义对比表
MAT1 MAT2 MAT3 MAT8 MAT9
isotropic anisotropic(2) orthotropic(3) orthotropic(2) anisotropic(3) • Use a FORCE entry if you want to define a static, concentrated force at a grid point by 一个点的力 specifying a vector. • Use a FORCE1 entry if the direction is determined by a vector connecting two grid points. 两个点的力 • Use a FORCE2 entry if the direction is specified by the cross product of two such vectors. 以上的乘积

ios_pointinside_用法_示例及概述说明

ios_pointinside_用法_示例及概述说明

ios pointinside 用法示例及概述说明1. 引言1.1 概述本篇长文将详细介绍iOS开发中的pointInside方法的用法示例及概述说明。

在iOS开发中,pointInside方法是一个十分常用且重要的方法之一。

通过理解和熟练运用这个方法,开发者可以更加灵活地响应用户操作,并实现各种交互效果。

1.2 文章结构本文分为五个部分:引言、正文、iOS pointInside 用法示例、结论和参考资料。

引言部分将对文章内容进行简要介绍,正文部分将深入探讨相关知识点,iOS pointInside 用法示例部分将演示该方法在实际开发中的应用场景,并给出具体代码示例,最后结论部分将总结全文内容并评价pointInside方法的重要性和应用前景。

1.3 目的本篇长文旨在帮助读者深入了解iOS开发中pointInside方法的使用方式和相关知识点。

通过阅读本文,读者可以对pointInside方法有一个全面而系统的认识,并能够灵活运用到自己的项目中,提升程序的可交互性和用户体验。

同时,本文也将对pointInside方法进行评价和展望,探讨其在未来iOS开发中的可能应用领域。

2. 正文在iOS开发中,`pointInside`是一个非常有用的方法。

它主要用于判断一个点是否在特定视图的边界内部。

此方法可以用于各种情况,如触摸事件处理、点击事件响应等。

本节将详细介绍`pointInside`方法的使用和示例。

首先,我们来了解一下`pointInside`方法的定义:该方法是UIView类中的一个实例方法,用于判断给定的点是否在视图当前的坐标系内。

其定义如下:```- (BOOL)pointInside:(CGPoint)point withEvent:(UIEvent *)event```该方法接受两个参数:第一个参数是一个CGPoint类型的点,表示需要检查的点;第二个参数是UIEvent类型的事件对象,表示与操作相关联的事件。

【转】Isoform

【转】Isoform

【转】Isoform expre...Exon-centric DEDSGseq summary:This programs uses gapped alignments to the genome to generate differential splicing for groups of technical and biological replicates in two treatments. You can't compare just two samples, two samples per group is the minimum.It generates a ranking of differentially spliced genes using their negative binomial statistic which focuses of difference in expression. The NB statistic is provided per gene and per exon. A threshold used in the paper is NB > 5. The program doesn't support reconstruction of isoforms or quantification of specific isoforms, which apparently is computationally harder.I found it easy to get it to run using the example data provided and the instructions. You need to run a preparation step on the gene annotation. Starting from BAM files, you also need to run two preparation steps on each library, first to convert it to BED, and then to get the counts.While the paper clearly says that transcript annotation information is not necessary for the algorithm, you do need to provide a gene annotation file in refFlat format, which the output is based on.The developers are unresponsive so no help is at hand if you get stuck.DEXseq summaryThis is similar to DSGseq and Diffsplice insofar as the isoform reconstruction and quantification are skipped and differential exon expression is carried out. Whereas the other two tools say that they don't need an annotation for their statistics, this program is based on only annotated exons, and uses the supplied transcript annotation in the form of a GFF file.It also needs at least two replicates per group.I found the usage of this program extremely tedious (as a matlab person). To install it you need to also install numpy and HTSeq. For preparing the data (similarly to DSGseq) you need to do a preparation step on the annotations, and another preparation step for every sample separately which collects the counts (both using python scripts). Then you switch to R, where you need to prepare something called an ExonCountSet object. To do this you need to first make a data.frame R object with the files that come out of the counting step. Yo also need to define a bunch of parameters in the R console. Then you can finally run the analysis. Despite the long instructional PDF, all this is not especially clear, and it's a rather tedious process compared to the others I've tried so far. In the end, I ran makeCompleteDEUAnalysis, and printed out a table of the results. I tried to plot some graphics too, but couldn't because "semi-transparency is not supported on this device". However, there's an extremely useful function that creates a browsable HTML directory with the graphics for all the significant genes. If anyone wants a copy of the workflow I used, send me a message, trying to figure it out might take weeks, but after you get the hang of it, this program is really useful.DiffSplice summaryThis is a similar approach for exon-centric differential expression to DEXseq and DSGseq (no attempt to reconstruct or quantify specific isoforms). Also supports groups of treatments, minimum 2 samples per group. The SAM inputs and various rather detailed parameters are supplied in two config files. I found this very convenient. In the data config file you can specify treatment group ID, individual IDs, and sample IDs, which determine how the shuffling in their permuation test is done. It was unclear to me what the sample IDs are (as opposed to the individual ID).DiffSplice prefers alignments that come from TopHat or MapSplice because it looks for the XS (strand) tag which BWA doesn't create. There's no need to do a separate preparation step on the alignments. However, if you want you can separate the three steps of the analysis using parameters for selective re-running. This program is user friendly and the doc page makes sense.On the downside, when the program has bad inputs or stops in the middle there's no errors or warnings - it just completes in an unreasonably short time and you get no results.Diffsplice appears to be sensitive to rare deviations from the SAM spec, because while I'm able to successfully run it on mini datasets, the whole datasets are crashing it. I ran Picard's FixMateInformation and ValidateSamFile tools to see if they will make my data acceptable (mates are fine, and sam files are valid! woot), but no dice. It definitely isn't due to the presence of unaligned reads.SplicingCompass summary:SplicingCompass would be included together with DEXseq, DiffSplice, and DSGseq, insofar as it's an exon-centric differential expression tool. However, unlike DEXseq and DSGseq, it provides novel junctions as well. Unlike DiffSplice, it does use an annotation. The annotation + novel detection feature of this program is pretty attractive.This is an R package, though as far as i can tell, it's not included in bioconductor. Personally I find it tedious to type lines upon lines of commands into R, and would much prefer to supply a configuration file and run one or a few things on the command line. Alas. Here, at least the instructions are complete, step by step, and on a "for dummies" level. Great.This tool is based on genome alignments. You basically have to run Tophat, because the inputs are both bam files and junction.bed files which Tophat provides. A downside is that you basically have to use the GTF annotation that they provide which is based on UCSC ccds genes. If you want to use ensembl or something else, you meed to email the developer for an untested procedure that might get you a useable annotation at the end (directly using an ensembl GTF doesn't work).Another problem is that I got no significant exons at the end of the analysis:>sc=initSigGenesFromResults(sc,adjusted=TRUE,threshold=0.1)Error in order(df[, pValName]) : argument 1 is not a vectorI'm still unsure as to whether this is due to some mistake or because this tool is extremely conservative.Transcriptome based reconstruction and quantificationeXpress summary:This program can take a BAM file in a stream, or a complete SAM or BAM file.It produces a set of isoforms and a quantification of said isoforms. There is no built in differential expression function (yet) so they recommend inputting the rounded effective counts that eXpress produces into EdgeR or DEGSeq. No novel junctions or isoforms are assembled.I used bowtie2 for the alignments to the transcriptome. Once you have those, using eXpress is extremely simple and fun. There's also a cloud version available on Galaxy, though running from the command line is so simple in this case I don't see any advantage to that. Definite favorite!SailFish summary:This program is unique insofar as it isn't based on read alignment to the genome or the transcriptome. It is based on k-mer alignment, which is based on a k-merized reference transcriptome. It is extremely fast. The first, indexing step took about 20 minutes. This step only needs to be run once per reference transcriptome for a certain k-mer size. The second, quant step took from 15 minutes to 1.5 hours depending on the library. The input for the quant step is fastq's as opposed to bam files. No novel junctions or isoforms are assembled.Like eXpress, there is no built in differential expression function. I used the counts from the non-bias-corrected (quant.sf) output file as inputs for DESeq and got reasonable results.The method is published on arXiv, and has been discussed in Lior Pachter's blog. According to the website the manuscript has been submitted for publication. The program is quite user friendly.RSEM +EBSeq summary:This also generates isoforms and quantifies them. It also needs to be followed by an external cont-based DE tool - they recommend EBSeq, which is actually included in the latest RSEM release, and can be run from the command line easily.RSEM can't tolerate any gaps in your transcriptome alignment, including the indels bowtie2 supports. Hence, you either need to align ahead of time with bowtie and input a SAM/BAM, or use the bowtie that's built into the RSEM call and input a fsta/fastq. For me this was unfortunate because we don't keep fastq files on hand (only illumina qseq files) which bowtie doesn't take as inputs. However, it does work! I successfully followed the instructions to execute EBSeq, which is conveniently included as an RSEM function, and gives intelligible results. Together, this workflow is complete.An advantage of RSEM is that it supplies expression relative to the whole transcriptome (RPKM, TPM) and, if supplied with a transcript-to-gene mapping, it also supplies relative expression of transcripts within genes (PSI). ie. transcript A comprises 70% of the expression of gene X, transcript B comprises 20 %, etc. MISO is the only other transcript-based program, as far as I know, that provides this useful information.BitSeq summary:This, like DEXSeq, is an R bioconductor package. I found the manual a lot easier to understand than DEXSeq.They recalculate the probability of each alignment, come up with a set of isoforms, quantify them, and also provide a DE function. In this way, it is the most complete tool I've tried so far, since all the other tools have assumed, skipped, or left out at least one of these stages. Also, BitSeq automatically generates results files, which is useful for people that don't know R. One annoying thing is that (as far as I know) you have to use sam files.For running BitSeq I used the same bowtie2 alignments to the transcriptome as for eXpress. You need to run the function getExpression on each sample separately. Then you make a list of the result objects in each treatment group and run the function getDE on those.Genome based reconstruction and quantificationiReckon summary:iReckon generates isoforms and quantifies them. However, this is based on gapped alignment to the genome (unlike eXpress, RSEM and BitSeq which are based on alignments to the transcriptome). It doesn't have a built in DE function, so each sample is run separately.This tool is a little curious because it requires both a gapped alignment to the genome, and the unaligned reads in fastq or fasta format with a reference genome. Since it requires a BWA executable, it's doing some re-alignment. iReckon claims to generate novel isoforms with low false positives by taking into consideration a whole slew of biological and technical biases.One irritating thing in getting the program running is that you need to re-format your refgene annotation file using an esoteric indexing tool from the Savant genome browser package. If you happen to use IGV, this is a bit tedious. Apparently, this will change in the next version. Also, iReckon takes up an enormous amount of memory and scratch space. For a library with 350 million reads, you would need about 800 G scratch space. Apparently everything (run time, RAM, and space) is linear to the number of reads, so this program would be a alright for a subset of the library or for lower coverage libraries.Cufflinks + cuffdiff2 summary:This pipeline, like iReckon, is based on gapped alignment to the genome. It requires the XS tag, so if you're not using tophat to align your RNA, you need to add that tag. I also found out that our gapped aligner introduces some pesky 0M and 0N's in the cigars, since cufflinks doesn't tolerate these. But with these matters sorted out, it's pretty easy to use.I like the versatility. You can run cufflinks for transcriptome reconstruction and isoform quantification in a variety of different modes. For example, with annotations and novel transcript discovery, with annotations and no novel discovery, with no annotations, and with annotations to be ignored in the output. For differential expression, cuffdiff 2 can be run with the results of the transcript quantification from cufflinks to include novel transcripts, or, it can be run directly from the alignment bam files with an annotation. Unlike the exon-based approaches, you don't need to have more than one library in each treatment group, (ie. you can do pairwise comparisons) though if you do it's better to keep them separate than to merge them. The problem here is that the results of cuffdiff are so numerous that it's not easy to figure out what you need in the end. Also, not all the files include the gene/transcript names so you need to do a fair bit of command line munging. There's also cummeRbund, which is a visualization package in R that so far seems to work ok.。

Consensus and Cooperation in Networked Multi-Agent Systems

Consensus and Cooperation in Networked Multi-Agent Systems

Consensus and Cooperation in Networked Multi-Agent SystemsAlgorithms that provide rapid agreement and teamwork between all participants allow effective task performance by self-organizing networked systems.By Reza Olfati-Saber,Member IEEE,J.Alex Fax,and Richard M.Murray,Fellow IEEEABSTRACT|This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow,robustness to changes in network topology due to link/node failures,time-delays,and performance guarantees. An overview of basic concepts of information consensus in networks and methods of convergence and performance analysis for the algorithms are provided.Our analysis frame-work is based on tools from matrix theory,algebraic graph theory,and control theory.We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators,flocking,formation control,fast consensus in small-world networks,Markov processes and gossip-based algo-rithms,load balancing in networks,rendezvous in space, distributed sensor fusion in sensor networks,and belief propagation.We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms.A brief introduction is provided on networked systems with nonlocal information flow that are considerably faster than distributed systems with lattice-type nearest neighbor interactions.Simu-lation results are presented that demonstrate the role of small-world effects on the speed of consensus algorithms and cooperative control of multivehicle formations.KEYWORDS|Consensus algorithms;cooperative control; flocking;graph Laplacians;information fusion;multi-agent systems;networked control systems;synchronization of cou-pled oscillators I.INTRODUCTIONConsensus problems have a long history in computer science and form the foundation of the field of distributed computing[1].Formal study of consensus problems in groups of experts originated in management science and statistics in1960s(see DeGroot[2]and references therein). The ideas of statistical consensus theory by DeGroot re-appeared two decades later in aggregation of information with uncertainty obtained from multiple sensors1[3]and medical experts[4].Distributed computation over networks has a tradition in systems and control theory starting with the pioneering work of Borkar and Varaiya[5]and Tsitsiklis[6]and Tsitsiklis,Bertsekas,and Athans[7]on asynchronous asymptotic agreement problem for distributed decision-making systems and parallel computing[8].In networks of agents(or dynamic systems),B con-sensus[means to reach an agreement regarding a certain quantity of interest that depends on the state of all agents.A B consensus algorithm[(or protocol)is an interaction rule that specifies the information exchange between an agent and all of its neighbors on the network.2 The theoretical framework for posing and solving consensus problems for networked dynamic systems was introduced by Olfati-Saber and Murray in[9]and[10] building on the earlier work of Fax and Murray[11],[12]. The study of the alignment problem involving reaching an agreement V without computing any objective functions V appeared in the work of Jadbabaie et al.[13].Further theoretical extensions of this work were presented in[14] and[15]with a look toward treatment of directed infor-mation flow in networks as shown in Fig.1(a).Manuscript received August8,2005;revised September7,2006.This work was supported in part by the Army Research Office(ARO)under Grant W911NF-04-1-0316. R.Olfati-Saber is with Dartmouth College,Thayer School of Engineering,Hanover,NH03755USA(e-mail:olfati@).J.A.Fax is with Northrop Grumman Corp.,Woodland Hills,CA91367USA(e-mail:alex.fax@).R.M.Murray is with the California Institute of Technology,Control and Dynamical Systems,Pasadena,CA91125USA(e-mail:murray@).Digital Object Identifier:10.1109/JPROC.2006.8872931This is known as sensor fusion and is an important application of modern consensus algorithms that will be discussed later.2The term B nearest neighbors[is more commonly used in physics than B neighbors[when applied to particle/spin interactions over a lattice (e.g.,Ising model).Vol.95,No.1,January2007|Proceedings of the IEEE2150018-9219/$25.00Ó2007IEEEThe common motivation behind the work in [5],[6],and [10]is the rich history of consensus protocols in com-puter science [1],whereas Jadbabaie et al.[13]attempted to provide a formal analysis of emergence of alignment in the simplified model of flocking by Vicsek et al.[16].The setup in [10]was originally created with the vision of de-signing agent-based amorphous computers [17],[18]for collaborative information processing in ter,[10]was used in development of flocking algorithms with guaranteed convergence and the capability to deal with obstacles and adversarial agents [19].Graph Laplacians and their spectral properties [20]–[23]are important graph-related matrices that play a crucial role in convergence analysis of consensus and alignment algo-rithms.Graph Laplacians are an important point of focus of this paper.It is worth mentioning that the second smallest eigenvalue of graph Laplacians called algebraic connectivity quantifies the speed of convergence of consensus algo-rithms.The notion of algebraic connectivity of graphs has appeared in a variety of other areas including low-density parity-check codes (LDPC)in information theory and com-munications [24],Ramanujan graphs [25]in number theory and quantum chaos,and combinatorial optimization prob-lems such as the max-cut problem [21].More recently,there has been a tremendous surge of interest V among researchers from various disciplines of engineering and science V in problems related to multia-gent networked systems with close ties to consensus prob-lems.This includes subjects such as consensus [26]–[32],collective behavior of flocks and swarms [19],[33]–[37],sensor fusion [38]–[40],random networks [41],[42],syn-chronization of coupled oscillators [42]–[46],algebraic connectivity 3of complex networks [47]–[49],asynchro-nous distributed algorithms [30],[50],formation control for multirobot systems [51]–[59],optimization-based co-operative control [60]–[63],dynamic graphs [64]–[67],complexity of coordinated tasks [68]–[71],and consensus-based belief propagation in Bayesian networks [72],[73].A detailed discussion of selected applications will be pre-sented shortly.In this paper,we focus on the work described in five key papers V namely,Jadbabaie,Lin,and Morse [13],Olfati-Saber and Murray [10],Fax and Murray [12],Moreau [14],and Ren and Beard [15]V that have been instrumental in paving the way for more recent advances in study of self-organizing networked systems ,or swarms .These networked systems are comprised of locally interacting mobile/static agents equipped with dedicated sensing,computing,and communication devices.As a result,we now have a better understanding of complex phenomena such as flocking [19],or design of novel information fusion algorithms for sensor networks that are robust to node and link failures [38],[72]–[76].Gossip-based algorithms such as the push-sum protocol [77]are important alternatives in computer science to Laplacian-based consensus algorithms in this paper.Markov processes establish an interesting connection between the information propagation speed in these two categories of algorithms proposed by computer scientists and control theorists [78].The contribution of this paper is to present a cohesive overview of the key results on theory and applications of consensus problems in networked systems in a unified framework.This includes basic notions in information consensus and control theoretic methods for convergence and performance analysis of consensus protocols that heavily rely on matrix theory and spectral graph theory.A byproduct of this framework is to demonstrate that seem-ingly different consensus algorithms in the literature [10],[12]–[15]are closely related.Applications of consensus problems in areas of interest to researchers in computer science,physics,biology,mathematics,robotics,and con-trol theory are discussed in this introduction.A.Consensus in NetworksThe interaction topology of a network of agents is rep-resented using a directed graph G ¼ðV ;E Þwith the set of nodes V ¼f 1;2;...;n g and edges E V ÂV .TheFig.1.Two equivalent forms of consensus algorithms:(a)a networkof integrator agents in which agent i receives the state x j of its neighbor,agent j ,if there is a link ði ;j Þconnecting the two nodes;and (b)the block diagram for a network of interconnecteddynamic systems all with identical transfer functions P ðs Þ¼1=s .The collective networked system has a diagonal transfer function and is a multiple-input multiple-output (MIMO)linear system.3To be defined in Section II-A.Olfati-Saber et al.:Consensus and Cooperation in Networked Multi-Agent Systems216Proceedings of the IEEE |Vol.95,No.1,January 2007neighbors of agent i are denoted by N i ¼f j 2V :ði ;j Þ2E g .According to [10],a simple consensus algorithm to reach an agreement regarding the state of n integrator agents with dynamics _x i ¼u i can be expressed as an n th-order linear system on a graph_x i ðt Þ¼X j 2N ix j ðt ÞÀx i ðt ÞÀÁþb i ðt Þ;x i ð0Þ¼z i2R ;b i ðt Þ¼0:(1)The collective dynamics of the group of agents following protocol (1)can be written as_x ¼ÀLx(2)where L ¼½l ij is the graph Laplacian of the network and itselements are defined as follows:l ij ¼À1;j 2N i j N i j ;j ¼i :&(3)Here,j N i j denotes the number of neighbors of node i (or out-degree of node i ).Fig.1shows two equivalent forms of the consensus algorithm in (1)and (2)for agents with a scalar state.The role of the input bias b in Fig.1(b)is defined later.According to the definition of graph Laplacian in (3),all row-sums of L are zero because of P j l ij ¼0.Therefore,L always has a zero eigenvalue 1¼0.This zero eigenvalues corresponds to the eigenvector 1¼ð1;...;1ÞT because 1belongs to the null-space of L ðL 1¼0Þ.In other words,an equilibrium of system (2)is a state in the form x üð ;...; ÞT ¼ 1where all nodes agree.Based on ana-lytical tools from algebraic graph theory [23],we later show that x Ãis a unique equilibrium of (2)(up to a constant multiplicative factor)for connected graphs.One can show that for a connected network,the equilibrium x üð ;...; ÞT is globally exponentially stable.Moreover,the consensus value is ¼1=n P i z i that is equal to the average of the initial values.This im-plies that irrespective of the initial value of the state of each agent,all agents reach an asymptotic consensus regarding the value of the function f ðz Þ¼1=n P i z i .While the calculation of f ðz Þis simple for small net-works,its implications for very large networks is more interesting.For example,if a network has n ¼106nodes and each node can only talk to log 10ðn Þ¼6neighbors,finding the average value of the initial conditions of the nodes is more complicated.The role of protocol (1)is to provide a systematic consensus mechanism in such a largenetwork to compute the average.There are a variety of functions that can be computed in a similar fashion using synchronous or asynchronous distributed algorithms (see [10],[28],[30],[73],and [76]).B.The f -Consensus Problem and Meaning of CooperationTo understand the role of cooperation in performing coordinated tasks,we need to distinguish between un-constrained and constrained consensus problems.An unconstrained consensus problem is simply the alignment problem in which it suffices that the state of all agents asymptotically be the same.In contrast,in distributed computation of a function f ðz Þ,the state of all agents has to asymptotically become equal to f ðz Þ,meaning that the consensus problem is constrained.We refer to this con-strained consensus problem as the f -consensus problem .Solving the f -consensus problem is a cooperative task and requires willing participation of all the agents.To demonstrate this fact,suppose a single agent decides not to cooperate with the rest of the agents and keep its state unchanged.Then,the overall task cannot be performed despite the fact that the rest of the agents reach an agree-ment.Furthermore,there could be scenarios in which multiple agents that form a coalition do not cooperate with the rest and removal of this coalition of agents and their links might render the network disconnected.In a dis-connected network,it is impossible for all nodes to reach an agreement (unless all nodes initially agree which is a trivial case).From the above discussion,cooperation can be infor-mally interpreted as B giving consent to providing one’s state and following a common protocol that serves the group objective.[One might think that solving the alignment problem is not a cooperative task.The justification is that if a single agent (called a leader)leaves its value unchanged,all others will asymptotically agree with the leader according to the consensus protocol and an alignment is reached.However,if there are multiple leaders where two of whom are in disagreement,then no consensus can be asymptot-ically reached.Therefore,alignment is in general a coop-erative task as well.Formal analysis of the behavior of systems that involve more than one type of agent is more complicated,partic-ularly,in presence of adversarial agents in noncooperative games [79],[80].The focus of this paper is on cooperative multi-agent systems.C.Iterative Consensus and Markov ChainsIn Section II,we show how an iterative consensus algorithm that corresponds to the discrete-time version of system (1)is a Markov chainðk þ1Þ¼ ðk ÞP(4)Olfati-Saber et al.:Consensus and Cooperation in Networked Multi-Agent SystemsVol.95,No.1,January 2007|Proceedings of the IEEE217with P ¼I À L and a small 90.Here,the i th element of the row vector ðk Þdenotes the probability of being in state i at iteration k .It turns out that for any arbitrary graph G with Laplacian L and a sufficiently small ,the matrix P satisfies the property Pj p ij ¼1with p ij !0;8i ;j .Hence,P is a valid transition probability matrix for the Markov chain in (4).The reason matrix theory [81]is so widely used in analysis of consensus algorithms [10],[12]–[15],[64]is primarily due to the structure of P in (4)and its connection to graphs.4There are interesting connections between this Markov chain and the speed of information diffusion in gossip-based averaging algorithms [77],[78].One of the early applications of consensus problems was dynamic load balancing [82]for parallel processors with the same structure as system (4).To this date,load balancing in networks proves to be an active area of research in computer science.D.ApplicationsMany seemingly different problems that involve inter-connection of dynamic systems in various areas of science and engineering happen to be closely related to consensus problems for multi-agent systems.In this section,we pro-vide an account of the existing connections.1)Synchronization of Coupled Oscillators:The problem of synchronization of coupled oscillators has attracted numer-ous scientists from diverse fields including physics,biology,neuroscience,and mathematics [83]–[86].This is partly due to the emergence of synchronous oscillations in coupled neural oscillators.Let us consider the generalized Kuramoto model of coupled oscillators on a graph with dynamics_i ¼ Xj 2N isin ð j À i Þþ!i (5)where i and !i are the phase and frequency of the i thoscillator.This model is the natural nonlinear extension of the consensus algorithm in (1)and its linearization around the aligned state 1¼...¼ n is identical to system (2)plus a nonzero input bias b i ¼ð!i À"!Þ= with "!¼1=n P i !i after a change of variables x i ¼ð i À"!t Þ= .In [43],Sepulchre et al.show that if is sufficiently large,then for a network with all-to-all links,synchroni-zation to the aligned state is globally achieved for all ini-tial states.Recently,synchronization of networked oscillators under variable time-delays was studied in [45].We believe that the use of convergence analysis methods that utilize the spectral properties of graph Laplacians willshed light on performance and convergence analysis of self-synchrony in oscillator networks [42].2)Flocking Theory:Flocks of mobile agents equipped with sensing and communication devices can serve as mobile sensor networks for massive distributed sensing in an environment [87].A theoretical framework for design and analysis of flocking algorithms for mobile agents with obstacle-avoidance capabilities is developed by Olfati-Saber [19].The role of consensus algorithms in particle-based flocking is for an agent to achieve velocity matching with respect to its neighbors.In [19],it is demonstrated that flocks are networks of dynamic systems with a dynamic topology.This topology is a proximity graph that depends on the state of all agents and is determined locally for each agent,i.e.,the topology of flocks is a state-dependent graph.The notion of state-dependent graphs was introduced by Mesbahi [64]in a context that is independent of flocking.3)Fast Consensus in Small-Worlds:In recent years,network design problems for achieving faster consensus algorithms has attracted considerable attention from a number of researchers.In Xiao and Boyd [88],design of the weights of a network is considered and solved using semi-definite convex programming.This leads to a slight increase in algebraic connectivity of a network that is a measure of speed of convergence of consensus algorithms.An alternative approach is to keep the weights fixed and design the topology of the network to achieve a relatively high algebraic connectivity.A randomized algorithm for network design is proposed by Olfati-Saber [47]based on random rewiring idea of Watts and Strogatz [89]that led to creation of their celebrated small-world model .The random rewiring of existing links of a network gives rise to considerably faster consensus algorithms.This is due to multiple orders of magnitude increase in algebraic connectivity of the network in comparison to a lattice-type nearest-neighbort graph.4)Rendezvous in Space:Another common form of consensus problems is rendezvous in space [90],[91].This is equivalent to reaching a consensus in position by a num-ber of agents with an interaction topology that is position induced (i.e.,a proximity graph).We refer the reader to [92]and references therein for a detailed discussion.This type of rendezvous is an unconstrained consensus problem that becomes challenging under variations in the network topology.Flocking is somewhat more challenging than rendezvous in space because it requires both interagent and agent-to-obstacle collision avoidance.5)Distributed Sensor Fusion in Sensor Networks:The most recent application of consensus problems is distrib-uted sensor fusion in sensor networks.This is done by posing various distributed averaging problems require to4In honor of the pioneering contributions of Oscar Perron (1907)to the theory of nonnegative matrices,were refer to P as the Perron Matrix of graph G (See Section II-C for details).Olfati-Saber et al.:Consensus and Cooperation in Networked Multi-Agent Systems218Proceedings of the IEEE |Vol.95,No.1,January 2007implement a Kalman filter [38],[39],approximate Kalman filter [74],or linear least-squares estimator [75]as average-consensus problems .Novel low-pass and high-pass consensus filters are also developed that dynamically calculate the average of their inputs in sensor networks [39],[93].6)Distributed Formation Control:Multivehicle systems are an important category of networked systems due to their commercial and military applications.There are two broad approaches to distributed formation control:i)rep-resentation of formations as rigid structures [53],[94]and the use of gradient-based controls obtained from their structural potentials [52]and ii)representation of form-ations using the vectors of relative positions of neighboring vehicles and the use of consensus-based controllers with input bias.We discuss the later approach here.A theoretical framework for design and analysis of distributed controllers for multivehicle formations of type ii)was developed by Fax and Murray [12].Moving in formation is a cooperative task and requires consent and collaboration of every agent in the formation.In [12],graph Laplacians and matrix theory were extensively used which makes one wonder whether relative-position-based formation control is a consensus problem.The answer is yes.To see this,consider a network of self-interested agents whose individual desire is to minimize their local cost U i ðx Þ¼Pj 2N i k x j Àx i Àr ij k 2via a distributed algorithm (x i is the position of vehicle i with dynamics _x i ¼u i and r ij is a desired intervehicle relative-position vector).Instead,if the agents use gradient-descent algorithm on the collective cost P n i ¼1U i ðx Þusing the following protocol:_x i ¼Xj 2N iðx j Àx i Àr ij Þ¼Xj 2N iðx j Àx i Þþb i (6)with input bias b i ¼Pj 2N i r ji [see Fig.1(b)],the objective of every agent will be achieved.This is the same as the consensus algorithm in (1)up to the nonzero bias terms b i .This nonzero bias plays no role in stability analysis of sys-tem (6).Thus,distributed formation control for integrator agents is a consensus problem.The main contribution of the work by Fax and Murray is to extend this scenario to the case where all agents are multiinput multioutput linear systems _x i ¼Ax i þBu i .Stability analysis of relative-position-based formation control for multivehicle systems is extensively covered in Section IV.E.OutlineThe outline of the paper is as follows.Basic concepts and theoretical results in information consensus are presented in Section II.Convergence and performance analysis of consensus on networks with switching topology are given in Section III.A theoretical framework for cooperative control of formations of networked multi-vehicle systems is provided in Section IV.Some simulationresults related to consensus in complex networks including small-worlds are presented in Section V.Finally,some concluding remarks are stated in Section VI.RMATION CONSENSUSConsider a network of decision-making agents with dynamics _x i ¼u i interested in reaching a consensus via local communication with their neighbors on a graph G ¼ðV ;E Þ.By reaching a consensus,we mean asymptot-ically converging to a one-dimensional agreement space characterized by the following equation:x 1¼x 2¼...¼x n :This agreement space can be expressed as x ¼ 1where 1¼ð1;...;1ÞT and 2R is the collective decision of the group of agents.Let A ¼½a ij be the adjacency matrix of graph G .The set of neighbors of a agent i is N i and defined byN i ¼f j 2V :a ij ¼0g ;V ¼f 1;...;n g :Agent i communicates with agent j if j is a neighbor of i (or a ij ¼0).The set of all nodes and their neighbors defines the edge set of the graph as E ¼fði ;j Þ2V ÂV :a ij ¼0g .A dynamic graph G ðt Þ¼ðV ;E ðt ÞÞis a graph in which the set of edges E ðt Þand the adjacency matrix A ðt Þare time-varying.Clearly,the set of neighbors N i ðt Þof every agent in a dynamic graph is a time-varying set as well.Dynamic graphs are useful for describing the network topology of mobile sensor networks and flocks [19].It is shown in [10]that the linear system_x i ðt Þ¼Xj 2N ia ij x j ðt ÞÀx i ðt ÞÀÁ(7)is a distributed consensus algorithm ,i.e.,guarantees con-vergence to a collective decision via local interagent interactions.Assuming that the graph is undirected (a ij ¼a ji for all i ;j ),it follows that the sum of the state of all nodes is an invariant quantity,or P i _xi ¼0.In particular,applying this condition twice at times t ¼0and t ¼1gives the following result¼1n Xix i ð0Þ:In other words,if a consensus is asymptotically reached,then necessarily the collective decision is equal to theOlfati-Saber et al.:Consensus and Cooperation in Networked Multi-Agent SystemsVol.95,No.1,January 2007|Proceedings of the IEEE219average of the initial state of all nodes.A consensus algo-rithm with this specific invariance property is called an average-consensus algorithm [9]and has broad applications in distributed computing on networks (e.g.,sensor fusion in sensor networks).The dynamics of system (7)can be expressed in a compact form as_x ¼ÀLx(8)where L is known as the graph Laplacian of G .The graph Laplacian is defined asL ¼D ÀA(9)where D ¼diag ðd 1;...;d n Þis the degree matrix of G with elements d i ¼Pj ¼i a ij and zero off-diagonal elements.By definition,L has a right eigenvector of 1associated with the zero eigenvalue 5because of the identity L 1¼0.For the case of undirected graphs,graph Laplacian satisfies the following sum-of-squares (SOS)property:x T Lx ¼12Xði ;j Þ2Ea ij ðx j Àx i Þ2:(10)By defining a quadratic disagreement function as’ðx Þ¼12x T Lx(11)it becomes apparent that algorithm (7)is the same as_x ¼Àr ’ðx Þor the gradient-descent algorithm.This algorithm globallyasymptotically converges to the agreement space provided that two conditions hold:1)L is a positive semidefinite matrix;2)the only equilibrium of (7)is 1for some .Both of these conditions hold for a connected graph and follow from the SOS property of graph Laplacian in (10).Therefore,an average-consensus is asymptotically reached for all initial states.This fact is summarized in the following lemma.Lemma 1:Let G be a connected undirected graph.Then,the algorithm in (7)asymptotically solves an average-consensus problem for all initial states.A.Algebraic Connectivity and Spectral Propertiesof GraphsSpectral properties of Laplacian matrix are instrumen-tal in analysis of convergence of the class of linear consensus algorithms in (7).According to Gershgorin theorem [81],all eigenvalues of L in the complex plane are located in a closed disk centered at Áþ0j with a radius of Á¼max i d i ,i.e.,the maximum degree of a graph.For undirected graphs,L is a symmetric matrix with real eigenvalues and,therefore,the set of eigenvalues of L can be ordered sequentially in an ascending order as0¼ 1 2 ÁÁÁ n 2Á:(12)The zero eigenvalue is known as the trivial eigenvalue of L .For a connected graph G , 290(i.e.,the zero eigenvalue is isolated).The second smallest eigenvalue of Laplacian 2is called algebraic connectivity of a graph [20].Algebraic connectivity of the network topology is a measure of performance/speed of consensus algorithms [10].Example 1:Fig.2shows two examples of networks of integrator agents with different topologies.Both graphs are undirected and have 0–1weights.Every node of the graph in Fig.2(a)is connected to its 4nearest neighbors on a ring.The other graph is a proximity graph of points that are distributed uniformly at random in a square.Every node is connected to all of its spatial neighbors within a closed ball of radius r 90.Here are the important degree information and Laplacian eigenvalues of these graphsa Þ 1¼0; 2¼0:48; n ¼6:24;Á¼4b Þ 1¼0; 2¼0:25; n ¼9:37;Á¼8:(13)In both cases, i G 2Áfor all i .B.Convergence Analysis for Directed Networks The convergence analysis of the consensus algorithm in (7)is equivalent to proving that the agreement space characterized by x ¼ 1; 2R is an asymptotically stable equilibrium of system (7).The stability properties of system (7)is completely determined by the location of the Laplacian eigenvalues of the network.The eigenvalues of the adjacency matrix are irrelevant to the stability analysis of system (7),unless the network is k -regular (all of its nodes have the same degree k ).The following lemma combines a well-known rank property of graph Laplacians with Gershgorin theorem to provide spectral characterization of Laplacian of a fixed directed network G .Before stating the lemma,we need to define the notion of strong connectivity of graphs.A graph5These properties were discussed earlier in the introduction for graphs with 0–1weights.Olfati-Saber et al.:Consensus and Cooperation in Networked Multi-Agent Systems220Proceedings of the IEEE |Vol.95,No.1,January 2007。

python关联规则算法源代码

python关联规则算法源代码

Python关联规则算法源代码简介关联规则是数据挖掘中一种重要的技术,用于发现数据集中不同项之间的关联关系。

它可以帮助我们了解物品之间的相关性,从而做出相应的决策。

Python提供了多种用于实现关联规则算法的库和工具,本文将介绍几种常用的关联规则算法的源代码实现。

一、Apriori算法Apriori算法是关联规则中最经典和最常用的算法之一,它通过挖掘频繁项集来发现关联规则。

Apriori算法的基本思想是利用候选项集的频繁性质减少搜索空间,通过迭代生成候选项集并剪枝,最终找到频繁项集。

1. 导入所需库import itertools2. 定义函数:生成候选项集def generate_candidates(data, k):candidates = set()for itemset in data:for item in itemset:candidates.add(frozenset([item]))return candidates3. 定义函数:计算候选项集的支持度def get_support_count(dataset, candidates, min_support):support_counts = {}for candidate in candidates:for transaction in dataset:if candidate.issubset(transaction):support_counts[candidate] = support_counts.get(candidate, 0) +1support_counts = {candidate: count for candidate, count in support_counts.items() if count >= min_support}return support_counts4. 定义函数:生成频繁项集def generate_frequent_itemsets(dataset, k, min_support):candidates = generate_candidates(dataset, k)support_counts = get_support_count(dataset, candidates, min_support)frequent_itemsets = support_counts.keys()return frequent_itemsets5. 定义函数:生成关联规则def generate_association_rules(frequent_itemsets, min_confidence):rules = []for itemset in frequent_itemsets:if len(itemset) > 1:for i in range(1, len(itemset)):for subset in binations(itemset, i):antecedent = set(subset)consequent = itemset - antecedentconfidence = support_counts[itemset] / support_counts[ante cedent]if confidence >= min_confidence:rules.append((antecedent, consequent, confidence)) return rules二、FP-Growth算法FP-Growth算法是另一种常用的关联规则算法,它通过构建FP树来挖掘频繁项集。

The $pipi$ Final State Interaction in $Ktopipi$, $ppto pppipi$ and Related Processes

The $pipi$ Final State Interaction in $Ktopipi$, $ppto pppipi$ and Related Processes

a r X i v :h e p -p h /9604310v 1 12 A p r 1996PSI-PR-96-13The ππFinal State Interaction in K →ππ,pp →ppππand Related ProcessesM.P.Locher,V.E.Markushin and H.Q.Zheng Paul Scherrer Institute,5232Villigen PSI,SwitzerlandApril 10,1996AbstractFinal state interactions in the S –wave ππsystem (I=0,2)are re-examined on the basis of the Omn`e s-Muskhelishvili equation and the coupled chan-nel formalism.The contributions to the pion scalar form factor from ρand f 2(1270)exchange in the t –channel and from the f 0(980)s –channel resonance are separately evaluated and the role of the nontrivial polyno-mial in the Omn`e s function in a coupled channel situation is elucidated.Applications are made to K →ππand pp →ppππ.It is found that the contribution from the f 0resonance to the form-factor is strongly reduced by a nearby zero.1IntroductionFinal state interaction (FSI)in the ππsystem plays an important role for many production reactions and meson decays.A case of long-standing interest is the ∆I =1/2rule in K →ππdecays.The experimental ratio of the decay amplitudes A I with isospin I =0,2is [1]A 0(K →ππ)general features of FSI’s which are relevant to other reactions involving pions or other hadrons.Several methods for the evaluation of FSI have been used in the literature. In one approach rescattering diagrams are evaluated directly.At low energies this has been done by applying chiral perturbation theory(CHPT)[3,4].The relevant application in our context is the calculation of the scalar form-factor of the pion in next to leading chiral order at low energies[5,6].To extend the calculations to s∼1GeV2s-channel resonances and the coupling to the K¯K channel must be included.As a general tool the dispersive method based on the Omn`e s-Muskhelishvili(OM)equation[7,8]has turned out to be very efficient. It exploits analyticity and unitarity in order to connect the production or decay amplitude(or its form-factor)with the amplitude of elasticππscattering.To solve the OM equation we shall take the scattering phases either from phase shift analysis or from a theoretical model.We shall choose a model which satisfies the requirements of unitarity and analyticity,and hence the OM equation auto-matically.The model with parametersfitted to the experimental constraints is described in Sec.3.For the K→ππdecay it was realized a long time ago that the non-perturbative long-distance effects must be included.An enhancement of about a factor of2 in the I=0amplitude was estimated to result from the broadσ(J P C=0++) meson[9].For K→ππthe attraction in the I=0channel must be combined with the repulsion in the I=2channel which favours the∆I=1/2rule.The analysis was done in CHPT to one loop in[6,10].Rescattering in simple poten-tial model was evaluated in[11,12]without regard to the energy dependence of the form-factor.An extensive study of the FSI effects in the S-waveππsystem in production reactions and J/ψandψ′decays was conducted in[13,14,15,16]. Unitarity and analyticity of the production amplitudes was taken into account in a self-consistent way.It was noticed,in particular,that a narrow resonance(f0in the present notation)in theππscattering phaseδI=0J=0(s)corresponds to ashoulder in theππeffective mass distribution in the reaction pp→ppππ[14,15]. The occurrence of a shoulder rather than a peak results from an interplay of the resonant pole and a nearby zero.We shall discuss this feature in detail in Sec.3. Resonance phenomena in theππS-wave were emphasized in[17]where the f0 resonance was discussed within a single-resonance model for the decay of a light higgs boson.The prediction of a drastic enhancement due to the f0resonance is in striking contrast with thefindings for the pp→ppππreaction in[15].An analysis of theππfinal state interaction in the framework of the coupled channel OM equation was performed in[5]for the decay of a light higgs boson decay H→ππ.In this evaluation the f0resonance also produced significant effects far below the K¯K threshold.The dynamics of the I=0S-waveππinteraction is characterized by several overlapping resonances[15,16,18],narrow and broad.In the present paper we shall analyze the relative importance of the dynamical mechanisms inππscattering for the calculation of the form-factors occurring in meson decays and in the pion pair production in pp scattering.In Sec.2we prepare the ground with an evaluation of the OM equation for a restricted energy range(the cut-offused excludes the f0resonance).With respect to the pion dynamics we shall mainly use the picture of[19]which combines theρand f2exchanges in the t-channel with the f0resonance in the s-channel.The phases of the I=0,2 S-wave scattering are reproduced quite accurately in this model.To understand the role of the f0resonance for the calculation of the form-factor in the I=0 channel we shall introduce a coupled channel ansatz in Sec.3.Thefinal state interaction effects in the K→ππdecay are evaluated and the conclusions are presented in Sec.4.2Form-factors from the Omn`e s–Muskhelishvili equationThe OM equation[7,8]connects the form-factor F(s)with the elasticfinal state scattering phaseδ(s).For a single channel problem the OM equation isF−1(s)=1+ss′(s−s′)ds′(2)where a once-subtracted form has been used.The general solution of(2)has the formF(s)=P(s)exp s s′(s′−s)ds′ (3) as long asδ(s)→const,|F(s)|Born term for the ρ-exchange isT (s,t )I =0BA =2Gs −um 2ρ−u(5)T (s,t )I =2BA=−1s −4m 2πln1+s −4m 2π1−iρ(s )K IS (s )(8)whereK I S (s )=T IBA −S (s )(9)and ρ(s )=(1−4m 2π/s )1/2.The coupling constant g ρππis determined from the ρmeson decay width in the crossed I =1channel after K -matrix unitarization [19].Thecorrespondingvalue is g ρππ=6.04which is close to the result obtained from the KSFR relation [25],g ρππ=m ρ/√3s −2s −4m 2πln(1+s −4m 2πs −M 2r +ig 1ρ1(s )+ig 2ρ2(s )(12)where ρ1(s )=1−4m 2K /s ,and the resonance parametersare M r =0.9535GeV,g 1=0.1108GeV 2,g 2=0.4229GeV 2,respectively.The scattering phase in the meson exchange model is a good description of the data for s <1.4GeV 2,see Fig.1b.s 1/2(GeV)δ00 (d e g )90180270360s 1/2(GeV)δ00 (d e g )60120180240300360Figure 1:The ππS -wave scattering phase δ00vs.√πΛ24m 2πδ(s ′)6 r 2s s.(16)s 1/2(GeV)F 00.511.52s 1/2(GeV)Figure 2:The pion scalar form-factor F I =0(s )vs.√s ≈0.5GeV.On the other hand the form-factor corresponding to the phase inFig.2a displays a more prominent cusp due to a larger value of a 00=0.51m −1πand decreases above the ππthreshold.For√(M 2r−s )(17)corresponding to the resonance amplitudegk(s)T res(s)=s−4m2π/2,leads toF res(s)=exp s s′(s′−s)ds′ =(19)M2r+gmπ=(22)k2+µ2whereγis the coupling constant(dimension[γ]=[k]3/2)andµcharacterizes the range of interaction.The T-matrix satisfies the Lippmann-Schwinger equationT(E)=V|b b|s 1/2(GeV)|F |123Figure 3:The pion scalar form-factor F I =0(s )vs.√k 2µ(k +iµ)2.(26)In our model the form-factor F (k )describing the final state interaction is equal to the scattering wave function at zero distance according to standard results from scattering theory [27]:F (k )= r =0|k (+) = r =0|k + r =0|G 0(E )T (E )|k(27)=1+γ2Z (k )ξ(k )2m−E r −γ2D (k )(28)withZ(k)=−2im(30)B(E)A(E)=E−E r−γ2m(k2+µ2)(31)B(E)=E−E r−γ2m(k2+µ2)2.(32)In the limit of weak coupling the resonance in the scattering channel is directly connected to the bound state in the continuum which has an energy shift ∆E r and a width Γr :∆E r =γ2m (k 2−µ2)(k 2r+µ2)2(34)where E r =k 2r /2m .The form-factor in the vicinity of the resonance has the formF (k )=E −E zk rΓr (36)If |E z −E r −∆E r |>Γr ,the resonance produces a pronounced peak followed by a dip in the energy dependence of the form-factor.In case |E z −E r −∆E r |<Γr the energy dependence coming from the pole is damped completely by the zero in the nominator,and only a dip is visible in the form-factor.Notice that the zero is of dynamical nature and disappears for vanishing channel coupling:F →1as γ→0.Since A (E )is a real symmetric function of momentum k ,it does not contribute to the elastic scattering amplitude.The solution of the OM equation without a polynomialfactor reflects only the resonance pole in formula (35)as shown in Fig.4,dashed line.By including the factor (E −E z )one getsF (E )=F (0)(E z −E )π∞δ(E ′)1A careful analysis of the OM equation for the model considered shows that there is an extra factor (k 2+ν2)/(k 2+µ2)resulting from the singularities in the upper halfplane of complex momentum k :a pole at k =iµand a nearby zero at k =iν.For our example this factor is close to 1in the region of the resonance.2In the literature the first category is often called normal resonance and the second one molecular or bootstrap resonance ,see e.g.[15]and references therein.It must be emphasized that in the WW model considered,the resonance-dip structure occurs only in processes where the particles in the scattering channel are produced at small distance due to some extraneous interaction which can be treated perturbatively,so that the momentum dependence of the production amplitude is entirely determined by the form factor F(k)given by Eq.(28)(this is relevant for the K→ππdecay).This situation must be distinguished from a situation where the original bound state|b is produced as a resonance with amplitude C and then decays into the scattering channel.The corresponding amplitude with rescattering included isT b(k)=Cγξ(k)2m−E r−γ2D(k)(38)which has a purely resonant behavior,there is no nearby zero.Studying the energy dependence of the data in the vicinity of the resonance one can determine whether this situation is realized for the process in question.3.2Application to the f0resonance and constraint frompp→ppππTo evaluate the role of the f0resonance for K→ππdecay we use the S-matrix in Breit-Wigner formfitted to data[19],see Eq.(12).As we demonstrated in Sec.3.1,the polynomial in the solution of the OM equation is expected to have a zero at s=s z close to the resonance:P(s)=1−ss)of pion pairs produced in the reaction pp→ppππ[28],which can be expressed by[14]dσM3|F(M2)|2(40) Including the polynomial(39)into the calculation of the form-factor F(s)(f0 plusρand f2exchange)we obtain s z=1.0GeV2for the position of the zero, see Fig.5.Thefit shown for the mass distribution dσ/dM also contains a factor (1+0.25s)in the polynomial and an overall normalization constant.The position of the zero however,is determined very precisely from nearby data alone.The corresponding scalar form-factor will be discussed in sec.4.Note that the factor containing the zero can be incorporated into a formal solution of the OM equation,if a physically equivalent discontinuous scattering phase is introduced¯δ(s)=δ(s)−πθ(s−sz).(41)11M (GeV)d σ/d M 0369121500.30.60.9 1.2Figure 5:The effective mass distribution of pion pairs in pp →ppππvs.M =√2,3the I=0S-wavefinal state interaction in the preceeding sections.The solid line in Fig.3shows the net result for the model combining theρand f2exchange withthe f0resonance.The resonance in the form-factor is protected by the zero at s z=1GeV2as determined from the pion pair production data.At the kaon mass the I=0enhancement factor is F(m2K)=1.62,a result which is similar to thevalues obtained in the literature quoted above.Fromρexchange alone we obtain F(m2K)=1.38,ρand f2give F(m2K)=1.57while the enhancement from the f0 resonance alone is F(m2K)=1.03.For the complete form-factor the reduction ofthe f0contribution induced by the protective zero is of course crucial.The effects of the zero and the resonance largely cancel and only a very small contribution tothe form-factor far away from the pole(zero)survives.For example,at s=0the pion scalar radius is: r2s =0.52fm2when only consideringρand f2exchanges. When including the resonance protected by the zero we have r2s =0.58fm2. We see that the inclusion of the resonance does improve the result on the scalar radius but avoids too large an effect.Our full result is very close to the valueobtained in[6]where r2s is determined from chiral perturbation theory.Without the zero we would have obtained a rather large value r2s =0.81fm2.In order to complete the evaluation of the the overall∆I=1/2enhancementfactor the contribution of the I=2channel must be evaluated as well.Due to the signature of the crossing matrix the contribution fromρexchange is repulsive in the I=2channel,see(6).On the other hand f2exchange does not change sign relative to the I=0channel leading to destructive interference between ρand f2for the isotensor.The solid line in Fig.6a shows the unitarized sum ofρand f2exchange.Also shown isρexchange modified by a vertex form factor with monopole rangeΛρ=1.5GeV(dashed line)which is a good effectiveparametrization of the data.The phases at higher energies are not known,but√fortunately the form-factor ats 1/2 (GeV)δ20 (d e g )-40-30-20-100s 1/2 (GeV)|F |0.60.70.80.91Figure 6:The ππI =2S -wave scattering phase δI =20vs.√[9]A.I.Veinstein,V.I.Zakharov and M.A.Shifman,Sov.Phys.JETP45(1977)670.[10]A.A.Belkov,G.Bohm,D.Ebert and nyov,Phys.Lett.B220(1989)459.[11]G.E.Brown,J.W.Durso,M.B.Johnson and J.Speth,Phys.Lett.B238(1990)20.[12]N.Isgur,K.Maltman,J.Weinstein and T.Barnes,Phys.Rev.Lett.64(1990)161.[13]D.Morgan and M.R.Pennington,Phys.Rev.D12(1975)1283.[14]D.Morgan and M.R.Pennington,Phys.Lett.137B(1984)411.[15]K.L.Au,D.Morgan and M.R.Pennington,Phys.Rev.D35(1987)1633.[16]D.Morgan and M.R.Pennington,Phys.Rev.D48(1993)1185.[17]S.Raby and G.B.West,Phys.Rev.D38(1988)3488.[18]N.A.T¨o rnqvist,Z.Phys.C68(1995)647.[19]B.S.Zou and D.V.Bugg,Phys.Rev.D50(1994)591.[20]J.J.Sakurai,Ann.Phys.(N.Y.)11(1960)1.[21]J.L.Basdevant and J.Zinn–Justin,Phys.Rev.D3(1971)1865;D.Iagol-nitzer,J.Justin and J.B.Zuber,Nucl.Phys.B60(1973)233.[22]G.Grayer et al.,Nucl.Phys.B75(1974)189.[23]W.Ochs,Ph.D.thesis,Munich Univ.,1974.[24]L.Rosselet et al.,Phys.Rev.D15(1977)574.[25]K.Kawarabayashi and M.Suzuki,Phys.Rev.Lett.16(1966)255;Riazuddinand Fayyazuddin,Phys.Rev.147(1966)1071.[26]R.H.Dalitz and S.F.Tuan,Ann.Phys.(N.Y.)10(1960)307.[27]J.R.Taylor,Scattering Theory,John Wiley&Sons,New York,1972.[28]T.Akesson et al.Nucl.Phys.B264(1986)154.[29]J.Prukop et al.Phys.Rev.D10(1974)2055;W.Hoogland et al.Nucl.Phys.B126(1977)109.15。

SPRINT决策树方法中I/O分析及优化研究

SPRINT决策树方法中I/O分析及优化研究

决策树方法为对象 , 通过写 IO优化 、 IO优化 f 读 f 和磁盘数据存储 优化三个方 面来研究 S RN P IT算
法 的 IO优 化 。研 究 对 于 提 高 S RN f P IT方 法 的性 能 以及 推广 到其它 数据挖 掘算 法 中具有 实际 意义 。
列表进行排序 , 如果采用快速排序 , 则它的计算复 杂性 为 D( lg ) non 。
间。读优化可使 S R N P I T方法省去一次读操作 , 写优化 可以使 S R N P I T方法在交替层省去一次写操作 , 盘文件搜索优化可 磁 使 SRN P IT方 法的磁盘文件 搜索时问复杂性只和决 策树 的节点个 数相关。这三种方 法可 单独使用 , 也可结合起来使 用。
关键词 S RN 决策树 P IT
L ni m
() 4
+s ef P :t )+s efPi C V ) i o( iIi z j: d i o( i ) z %
() 1 数据准备阶段 的 IO分析 f
数 据准 备 阶 段 主要 是从 训 练 数 据集 中读 取 数
据, 为每个属性生成一个包含属性值 、 和记录号 类
的属 性列 表 , 对 连续 型 属 性 的属 性 列 表 进 行 排 并
向大规模数据集的决策树方法。但这种优势 是通
过 增加 大量磁 盘 IO读 写 时 间 和搜 索 磁 盘数 据 时 f 间来获得 的 。正 是 因 为这 个 特 点 ,P IT方 法 在 SRN
c . J i 3l 都需 要所 有训 练数 据 驻 留 内存 并 且 45 d 2 J
, ,
为方便 以下 叙述 , 先做 如下 假设 : n一 练 数 据集 的 记 录 数 。m 一 训 练数 据集 训

熵权topsis法 耦合度

熵权topsis法 耦合度

熵权topsis法耦合度英文回答:Entropy Weight TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) is a multi-criteria decision-making method that combines the entropy weight method and the TOPSIS method. The entropy weight method is used to determine the weights of the criteria, while the TOPSIS method is used to rank the alternatives.The entropy weight method is based on the concept of entropy, which is a measure of the uncertainty or randomness of a system. The higher the entropy, the more uncertain the system is. In the context of multi-criteria decision-making, the entropy of a criterion is a measure of how much information the criterion provides about the alternatives. The more information a criterion provides, the lower its entropy.The TOPSIS method is based on the concept of distance.The distance between an alternative and the ideal solution is calculated, as well as the distance between the alternative and the negative ideal solution. Thealternative with the smallest distance to the idealsolution and the largest distance to the negative ideal solution is the best alternative.The entropy weight TOPSIS method combines the advantages of the entropy weight method and the TOPSIS method. The entropy weight method provides a more objective way to determine the weights of the criteria, while the TOPSIS method provides a more robust way to rank the alternatives.The entropy weight TOPSIS method has been successfully applied to a variety of multi-criteria decision-making problems, including:Site selection for a new factory.Product design selection.Supplier selection.Investment decision-making.中文回答:熵权TOPSIS法(基于理想解相似度排序技术)是一种多准则决策方法,它结合了熵权法和TOPSIS法。

dependencytack原理

dependencytack原理

dependencytack原理
Dependency tracking 是指在计算机科学中,跟踪程序中各个部分之间的依赖关系,以便在其中一个部分发生变化时能够自动地重新计算受影响的部分。

这种技术通常用于构建自动化构建系统、编译器、数据流分析和许多其他领域。

在软件开发中,dependency tracking 的原理是通过分析代码中各个模块之间的依赖关系,当某个模块发生变化时,系统能够自动识别出受影响的其他模块,并进行相应的更新或重新计算。

这有助于确保软件系统的稳定性和一致性,同时提高开发效率。

在编译器中,dependency tracking 用于跟踪源代码文件之间的依赖关系,以便在某个文件发生变化时,能够自动重新编译受影响的文件,而不是重新编译整个项目。

这种技术可以大大加快编译过程,特别是在大型项目中。

在数据流分析中,dependency tracking 被用来确定数据流图中各个节点之间的依赖关系,以便在输入数据发生变化时,能够自动地重新计算受影响的节点。

这对于实时数据分析和优化算法非常重要。

总的来说,dependency tracking 的原理是通过分析和记录程
序中各个部分之间的依赖关系,以便在其中一个部分发生变化时能
够自动地重新计算受影响的部分,从而确保系统的稳定性和一致性,提高开发和计算效率。

partial_dependence原理

partial_dependence原理

partial_dependence原理
偏依赖性(partial dependence)是一种用于分析特征变量对机器学习模型预测结果的影响程度的方法。

偏依赖性可以帮助我们理解特征变量如何影响模型的输出,并揭示特征变量与其他变量之间的相互作用关系。

偏依赖性的原理基于这样的假设:在其他特征变量的取值固定的情况下,某个特征变量的取值如何影响模型的输出。

为了计算偏依赖性,我们可以按以下步骤进行:
1. 首先,我们选择一个特征变量,并指定一个取值范围;
2. 接下来,我们通过固定其他特征变量的取值,生成一系列新的数据集,在这些数据集中只有目标特征变量的取值变化;
3. 然后,我们使用这些新的数据集来重新预测目标变量,并记录模型的输出;
4. 最后,我们将目标变量的预测输出与特征变量的取值进行可视化,以了解其之间的关系。

通过偏依赖性分析,我们可以得出一些有关模型和特征变量之间的重要结论,例如:
- 特征变量的线性或非线性关系:偏依赖性可以告诉我们特征变量对模型输出的影响方式是线性的还是非线性的;
- 特征变量之间的相互作用:偏依赖性可以揭示特征变量之间的相互作用关系,即一个特征变量在其他特征变量取值不同时对模型输出的影响;
- 特征变量的重要性:通过比较不同特征变量的偏依赖性,我
们可以评估它们对模型预测的重要程度。

总之,偏依赖性分析是一种有用的工具,可帮助我们更好地理解特征变量对机器学习模型预测结果的影响,并揭示特征变量之间的相互作用关系。

openempi 计算公式

openempi 计算公式

openempi 计算公式OpenEMPI是一个开源的用于实现实体匹配和身份解析的软件平台。

它提供了一种计算公式,用于计算两个实体之间的匹配程度。

本文将介绍OpenEMPI的计算公式以及其在实体匹配和身份解析中的应用。

OpenEMPI的计算公式基于实体匹配的原理,通过比较两个实体的属性值,计算它们之间的相似度。

这个相似度可以用于判断两个实体是否代表同一个现实世界的个体。

在实体匹配中,常常需要处理一些常见的问题,比如姓名拼音的不一致、地址的缩写、电话号码的格式不同等。

OpenEMPI的计算公式可以很好地解决这些问题,提高实体匹配的准确性和效率。

OpenEMPI的计算公式主要包括以下几个方面:1. 字符串匹配:在实体匹配中,常常需要比较两个字符串的相似度。

OpenEMPI提供了一些常用的字符串匹配算法,比如编辑距离、Jaro-Winkler距离等。

这些算法可以根据字符串的相似程度计算出一个相似度分值。

2. 属性权重:在实体匹配中,不同的属性可能具有不同的重要性。

OpenEMPI的计算公式允许为每个属性指定一个权重,以反映其在匹配过程中的重要性。

这样可以根据属性的权重计算出一个综合的匹配分值。

3. 阈值设置:在实体匹配中,可以根据实际需求设定一个阈值,用于判断两个实体是否匹配。

如果两个实体的匹配分值超过了设定的阈值,就认为它们代表同一个个体。

OpenEMPI的计算公式可以根据阈值来进行匹配判断。

除了实体匹配,OpenEMPI的计算公式还可以用于身份解析。

身份解析是指将一个实体的多个属性值解析出来,比如将一个姓名解析成姓和名、将一个地址解析成省、市和区等。

OpenEMPI的计算公式可以根据实体的属性值进行解析,提取出其中的关键信息。

OpenEMPI的计算公式是实现实体匹配和身份解析的重要工具。

它可以根据实体的属性值计算出一个匹配分值,用于判断两个实体是否代表同一个个体。

同时,它也可以用于身份解析,将一个实体的属性值解析出来。

junit零基础入门

junit零基础入门

JUnit 小结
1
使用非常简单
2
能指出代码中存在的问题
3 可以作为代码更准确的文档
4 在持续集成过程中起重要作用
单元测试特点
1 小步前进——简单 2 外部依赖多——困难 3 测试用例多——麻烦 4 有维护成本 ——讨厌
开发单元测试代码的原则
单元测试代码
被测代码宜于测试 1、用接口分离外 部依赖 (依赖的接 口以参数形式传入
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testxxx
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Eclipse“Ru
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Run
JUnit 3.x 示例
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bar
继承了
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a r X i v :n u c l -t h /0211037v 1 12 N o v 2002Phys.Rev.C in press.Isospin-Dependence of π−/π+Ratio and Density-Dependence ofNuclear Symmetry EnergyBao-An Li ∗Department of Chemistry and Physics P.O.Box 419,Arkansas State University State University,Arkansas 72467-0419,USAThe π−/π+ratio is examined as a function of isospin asymmetry and beam energy for central collisions induced by neutron-rich nuclei within an isospin-dependent hadronic transport model.The π−/π+ratio is found to increase with the isospin asymmetry,but decrease with the beam energy of the reaction.For neutron-rich systems,such as,124Sn +124Sn and132Sn +124Sn ,the sensitivityof π−/π+ratio to nuclear symmetry energy is almost beam energy independent.PACS numbers:25.70.-z,25.75.Ld.,24.10.LxNuclear reactions using rare isotopes has opened up several new frontiers in nuclear sciences[1,2,3].In particular,heavy rare isotopes currently available at several radioactive beam facilities in the world and the more energetic ones to be available at the planned Rare Isotope Accelerator(RIA)provide a unique opportunity to explore novel properties of dense neutron-rich matter that was not in reach in terrestrial laboratories before.This exploration will reveal crucial information about the equation of state(EOS)of neutron-rich matter[4,5,6,7,8].Within the parabolic approximation(see e.g.,[9]),the EOS of neutron-rich matter of isospin asymmetryδ≡(ρn−ρp)/(ρn+ρp)can be written ase(ρ,δ)=e(ρ,0)+E sym(ρ)δ2+O(δ4)(1)where e(ρ,0)is the energy per nucleon in isospin symmetric nuclear matter.The density-dependent nuclear symmetry energy E sym(ρ),especially at high densities,is very uncertain and has been a subject of extensive research with various microscopic and/or phenomeno-logical models over the last few decades.Besides being important for studying the structure of radioactive nuclei near neutron and proton driplines,the density dependence of nuclear symmetry energy has many profound consequences for several key issues in astrophysics [10,11,12,13,14].It is therefore an important theoretical challenge to make reliable predic-tions on which experimental observables can be used as sensitive probes of the E sym(ρ),thus constraining the isospin dependence of the nuclear EOS.Within a hadronic transport model it was recently shown that theπ−/π+ratio is a particularly useful probe for the high density behavior of the E sym(ρ)[15].For central collisions of132Sn+124Sn a significant sensitivity of theπ−/π+to the variation of E sym(ρ)was found.How does this sensitivity depend on the isospin asymmetry of the reaction system?In this Brief Report,we investigate this question.An answer to it is not only interesting in its own right but also useful for planning future experiments[16].It is well known that theπ−/π+ratio in heavy-ion collisions depends strongly on the isospin asymmetry of the reaction system,see,e.g.,[17,18,19,20].It is also qualitatively easy to understand why this dependence can be used to extract crucial information about the EOSof neutron-rich matter.On one hand,within the∆resonance model for pion production fromfirst-chance independent nucleon-nucleon collisions[20],the primordialπ−/π+ratio is (5N2+NZ)/(5Z2+NZ)≈(N/Z)2.It is thus a direct measure of the isospin asymmetry (N/Z)dense of the dense matter in the participant region of heavy-ion collisions.It was shown that the(N/Z)dense is uniquely determined by the high density behaviour of the nuclear symmetry energy[15].Therefore,theπ−/π+ratio can be used to probe sensitively the EOS of neutron-rich matter.On the other hand,within the statistical model for pion production [21],theπ−/π+ratio is proportional to exp[(µn−µp)/T],where T is the temperature,µn andµp are the chemical potentials of neutrons and protons,respectively.At modestly high temperatures(T≥4MeV),the difference in the neutron and proton chemical potentials can be written as[23]µn−µp=V n asy−V p asy−V Coulomb+T lnρn m b m(λ3Tthe average N/Z ratio of the participant region is the same as that of the initial reaction system,we concentrate on central collisions at an impact parameter of1fm.To explore the isospin-dependence of the nuclear EOS,two phenomenological equations of states with the same symmetric part e(ρ,0)corresponding to a compressibility K0=210MeV but different forms for the symmetry energy E sym(ρ)are used.Above the normal nuclear matter density ρ0,the density dependence of E sym(ρ)is very controversial.Theoretical results with various models and interactions can be classified into two approximately equally large groups,i.e., a group where the E sym(ρ)rises monotonously and one in which it begins to fall above about2ρ0,see e.g.,[13,24,25].As in ref.[15],we use the following two representative parameterizationsE a sym(ρ)≡E sym(ρ0)u(3) andE b sym(ρ)≡E sym(ρ0)u·3−u3∆03∆+.(5)This ratio naturally becomes thefinalπ−/π+ratio at the freeze-out after all∆resonances have decayed.In the early stage of the reaction,the(π−/π+)like ratio is rather high for the neutron richer systems because of the large numbers of neutron-neutron scatterings near the surfaces where the neutron skins of the colliding nuclei overlap.It saturates after about 25fm/c for all three systems.The higherπ−/π+ratio with the symmetry energy E b sym(ρ) reflects directly the higher n/p ratio reached in the high density region.To further examine the system dependence,we plot the saturatedfinalπ−/π+ratio as a function(N/Z)system in the left window of Fig.2.As references,we also plotted the(N/Z)and(N/Z)2.First of all,it is seen that theπ−/π+ratio falls far below thefirst-chance∆resonance model prediction(N/Z)2.This is because of the pion reabsorptions and rescatterings(π+N↔∆and N+∆↔N+N)which reduce the sensitivity of theπ−/π+ratio to the(N/Z)system. Moreover,what is more important for theπ−/π+ratio is the local,changing n/p ratio during particularly the compression phase of the reaction.Secondly,the effect of the symmetry energy on theπ−/π+ratio is seen to increase slightly as one goes from112Sn+112Sn to 124Sn+124Sn.As one goes further to the132Sn+124Sn system,the effect remains at about 15%.Thus,as far as theπ−/π+probe of the symmetry energy is concerned,neutron-rich stable beams,such as124Sn,seem to be sufficient.It is thus tempting to suggest a re-analysis of existingπ−/π+data from heavy-ion collisions at GSI energies.The beam energy dependence of theπ−/π+ratio for the reaction of132Sn+124Sn is shown in the right window of Fig.2.While theπ−/π+ratio decreases with the increasing beam energy,its sensitivity to the symmetry energy remains about the same.Similar results are found also for the other two reaction systems.The decreasingπ−/π+ratio is mainly because of the increasingly important contributions of pions from second-chance nucleon-nucleon collisions as the beam energy increases.If afirst chance nucleon-nucleon collision converts a neutron to a proton by producing aπ−,subsequent collisions of the still energetic proton can convert itself back to a neutron by producing aπ+.Eventually,at very high energies the sequential multiple nucleon-nucleon collisions will lead to aπ−/π+≈1.In summary,within an isospin-dependent hadronic transport model using two repre-sentative density-dependent symmetry energy functions predicted by many-body theories, it is shown that theπ−/π+ratio increases with the isospin asymmetry,but decrease with the beam energy of the reaction.For neutron-rich systems,such as,124Sn+124Sn and 132Sn+124Sn,the sensitivity ofπ−/π+ratio to nuclear symmetry energy is almost beam energy independent.The author would like to thank W.G.Lynch for suggesting me to conduct this research and useful discussions.This work was supported in part by the National Science Foundation Grant No.PHY-0088934and Arkansas Science and Technology Authority Grant No.00-B-14.REFERENCES[1]A special volume of Nucl.Phys.A693(2001),Ed.I.Tanihata.[2]Isospin Physics in Heavy-Ion Collisions at Intermediate Energies,Eds.Bao-An Li andW.Udo Schr¨o der,ISBN1-56072-888-4,Nova Science Publishers,Inc(2001,New York).[3]B.A.Li,C.M.Ko and W.Bauer,topical review,Int.Jou.Mod.Phys.E7,147(1998).[4]S.J.Yennello et al.,Nucl.Phys.A681,317c(2001).[5]H.S.Xu et al,Phys.Rev.Lett.85,716(2000).[6]M.B.Tsang et al.,Phys.Rev.Lett.86,5023(2001).[7]B.A.Li,Phys.Rev.Lett.85,4221(2000).[8]M.Di Toro,V.Baran,M.Colonna,S.Maccarone,M.Zielinska-Pfabe and H.H.Wolter,Nucl.Phys.A681,426c(2001).[9]R.B.Wiringa,V.Fiks and A.Fabrocini,Phys.Rev.C38,1010(1988).[10]M.Kutschera,Phys.Lett.B340,1(1994);Z.Phys.A348,263(1994);Acta Phys.Polon.B29,25(1998).[11]ttimer and M.Prakash,Astrophys J.550,426(2001).[12]M.Prakash et al.,Phys.Rep.280,1(1997).[13]I.Bombaci,“Equation of State for Dense Isospin Asymmetric Nuclear Matter for Astro-physical Applications”,Chapter2,page35of the book in ref.[2].[14]C.J.Horowitz and J.Piekarewicz,Phys.Rev.Lett.86,5647(2001);C.J.Horowitz et al.,Phys.Rev.C63,025501(2001).[15]B.A.Li,Phys.Rev.Lett.88,192701(2002);Nucl.Phys.A708,365(2002).[16]W.G.Lynch,private communications.[17]W.Benenson et al.,Phys.Rev.Lett.43,683(1979).[18]S.Nagamiya et al.,Phys.Rev.C24,971(1981).[19]J.Harris et al.,Phys.Lett.B153,377(1985).[20]R.Stock,Phys.Rep.,135,259(1986).[21]G.F.Bertsch,Nature283,280(1980).[22]B.A.Li and W.Bauer,Phys.Rev.C44,450,(1991);B.A.Li,W.Bauer and G.F.Bertsch,ibid,C44,2095(1991).[23]H.R.Jaqaman,A.Z.Mekjian and L.Zamick,Phys.Rev.C27,2782(1983);ibid,C29,2067(1984);H.R.Jaqaman,Phys.Rev.C39,169(1988).[24]J.R.Stone,P.D.Stevenson,ler and M.R.Strayer,Phys.Rev.C65,064312(2002).[25]J.Margueron,J.Navarro,N.Van Giai and W.Jing,nucl-th/0110026;J.Margueron,J.Navarro,N.Van Giai,Phys.Rev.C66,014303(2002).[26]B.D.Serot and J.D.Walecka,Adv.Nucl.Phys.16,1(1986).FIGURES1.11.21.31.41.51.61.11.21.31.41.5(n /p )ρ/ρ>1102030t (fm/c)1.11.21.31.41.5 1.51.71.92.11.51.71.92.1(π−/π+)l i k e01020301.31.51.71.92.1112Sn+112SnE/A=400 MeV E bsymE asymE bsymE asym124Sn+124Sn132Sn+124Snb=1 fmFIG.1.Left panels:average neutron/proton ratio in the whole space with densities higher than the normal nuclear matter density as a function of time.Right panels:the (pi −/π+)like ratio as a function of time for the three reactions.The solid (dashed)lines are the results using thesymmetry energy E a sym (ρ)(E b sym (ρ)).00.51 1.52E beam /A (GeV)1.21.31.4 1.5 1.6(N/Z)system1.21.41.61.822.22.4π−/π+FIG.2.The (pi −/π+)ratio as a function of the isospin asymmetry (left window)and beam energy (right window)of the reaction system。

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