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Festival Multisyn Voices for the 2007 Blizzard Challenge

Festival Multisyn Voices for the2007Blizzard Challenge Korin Richmond,Volker Strom,Robert Clark,Junichi Yamagishi and Sue Fitt Centre for Speech Technology ResearchUniversity of Edinburgh,Edinburgh,United Kingdom(korin|vstrom|robert|jyamagis|sue)@AbstractThis paper describes selected aspects of the Festival Mul-tisyn entry to the Blizzard Challenge2007.We provide an overview of the process of building the three required voices from the speech data provided.This paper focuses on new fea-tures of Multisyn which are currently under development and which have been employed in the system used for this Bliz-zard Challenge.These differences are the application of a more ﬂexible phonetic lattice representation during forced alignment labelling and the use of a pitch accent target cost component. Finally,we also examine aspects of the speech data provided for this year’s Blizzard Challenge and raise certain issues for discussion concerning the aim of comparing voices made with differing subsets of the data provided.1.IntroductionMultisyn is a waveform synthesis module which has recently been added to the Festival speech synthesis system[1].It pro-vides aﬂexible,general implementation of unit selection and a set of associated voice building tools.Strong emphasis is placed onﬂexibility as a research tool on one hand,and a high level of automation using default settings during“standard”voice build-ing on the other.This paper accompanies the Festival Multisyn entry to the Blizzard Challenge2007.Similar to the Blizzard Challenges of the previous two years([2,3]),the2007Blizzard Challenge required entrants to build three voices from the speech data pro-vided by speaker“EM001”,then submit a set of synthesised test sentences for evaluation.Theﬁrst voice,labelled voice“A”, used the entire voice database.Two smaller voices,“B”and“C”used subsections of the database.V oice“B”used the set of sen-tences from the ARCTIC database[4]which were recorded by the EM001speaker.For voice“C”,entrants were invited to per-form their own text selection on the voice database prompts to select a subset of sentences no larger than the ARCTIC data set in terms of total duration of speech in seconds.V oices“B”and “C”are intended as a means to compare different text selection algorithms,as well as to evaluate the performance of synthesis systems when using more limited amounts of speech data.Multisyn and the process of building voices for Multisyn is described in detail in[1].In addition,entrants to the Blizzard Challenge this year have been asked to provide a separate sys-tem description in the form of a template questionnaire.For the reader’s convenience this paper will provide a brief overview of Multisyn and the voices built.To limit redundancy,however,we will not repeat all details comprehensively.Instead,we aim to focus here on areas where the use of Multisyn differs from[1]. Those signiﬁcant differences are two-fold.First,we will intro-duce a new technique we have been developing to help in forced alignment labelling.Next,we describe a target cost component which uses a simple pitch accent prediction model.Finally,we will discuss our experience of building voice“C”,and highlight some issues we believe may complicate comparison of entrants’voices“B”and“C”.2.Multisyn voice buildingWe use our own Unisyn lexicon and phone set[5],so only used the prompts and associated waveﬁles from the distributed data, performing all other processing for voice building from scratch. Theﬁrst step of voice building involved some brief examina-tion of the text prompts toﬁnd missing words and to add some of them to our lexicon,ﬁx gross text normalisation problems and so on.Next,we used an automatic script to reduce the du-ration of any single silence found in a waveﬁle to a maximum of50msec.From this point,the process for building Multisyn voices“A”,“B”and“C”described in the remainder of this sec-tion was repeated separately for the relevant utterance subset for each voice.We used HTK tools in a scripted process to perform forced alignment using frames of12MFCCs plus log energy(utter-ance based energy normalisation switched off)computed with a10msec window and2msec frame shift.The process be-gan with single mixture monophone models with three emitting states,trained from a“ﬂat start”.Initial labelling used a single phone sequence predicted by the Festival Multisyn front end. However,as the process progressed with further iterations of reestimation,realignment,mixing up,adding a short pause tee model,and so on,we switched to using a phone lattice for align-ment described in Section3.Once labelling was completed,we used it to perform a waveform power factor normalisation of all waveforms in the database.This process looks at the energy in the vowels of each utterance to compute a single factor to scale its waveform.The power normalised waveforms were then used throughout the remainder of the voice building process,which began with repeating the whole labelling process.Once the labelling had been completed,it was used to build utterance structures1,which are used as part of the internal rep-resentation within aﬁnal Multisyn voice.At this stage,the text prompts were run through a simple pitch accent prediction model(see Section4),and this information stored in the utter-ance structures.Additional information was also added to the utterance structures at this stage;for example,phones with a duration more than2standard deviations from the mean were ﬂagged.Such information could be used later at unit selection time in the target cost function.In addition to labelling and linguistic information stored in utteranceﬁles,Multisyn requires join cost coefﬁcients and RELP synthesis parameters.To create the synthesis parameters, weﬁrst performed pitchmarking using a custom script which makes use of Entropic’s epochs,get resid,get f0 and refcof programs.We then used the sig2fv and sigfilter programs from the Edinburgh Speech Tools for lpc analysis and residual signal generation respectively.The 1a data structure deﬁned in the Edinburgh Speech Tools libraryMultisyn join cost uses three equally weighted components: spectral,f0and log energy.The spectral and log energy join cost coefﬁcients were taken from the MFCCﬁles calculated by HTK’s HCopy used for labelling.The f0contours were pro-vided by the ESPS program get f0.All three of these feature streams were globally normalised and saved in the appropriate voice data structure.During unit selection,Multisyn does not use any acoustic prosodic targets in terms of pitch or duration.Instead,the target cost is a weighted normalised sum of a series of components which consider the following:lexical stress,syllable position, word position,phrase position,part of speech,left and right phonetic context,“bad duration”and“bad f0”.As mentioned above,“bad duration”is aﬂag which is set on a phone within a voice database utterance during voice building and suggests a segment should not be used.Similarly,the“bad f0”target cost component looks at a candidate unit’s f0at concatenation points,considering voicing status rather than a speciﬁc target f0 value.We have also used an additional target cost component for the presence or absence of a pitch accent on a vowel.This is described further in Section4.Finally,we stress that during concatenation of the best can-didate unit sequence,Multisyn does not currently employ any signal processing apart from a simple overlap-add windowing at unit boundaries.No prosodic modiﬁcation of candidate units is attempted and no spectral,amplitude or f0interpolation is performed across concatenation boundaries.3.Finite state phonetic lattice labelling For all three voices for this Blizzard Challenge we employed a forced alignment system we have been developing which makes use of aﬁnite state representation of the predicted phonetic real-isation of the recorded prompts.The advantage of theﬁnite state phonetic representation is that it makes it possible to elegantly encode and process a wide variety pronunciation variation dur-ing labelling of speech data.In the following two sections we ﬁrst give a general introduction to how our phonetic lattice la-belling works,and then give some more speciﬁc details of how the system was applied to building voices for this Blizzard Chal-lenge.3.1.General implementationIf we consider how forced alignment is standardly performed using HTK,for example,the user is required to provide,among other things,a pronunciation lexicon and word level transcrip-tion.The pronunciation lexicon contains a mapping between a given word and a corresponding sequence of phone model labels.During forced alignment,the HTK recognition engine loads the word level transcription and expands this into a recog-nition network,or“lattice”,of phone models using the pronun-ciation dictionary.This lattice is then used to align against the sequence of acoustic parameter vectors.The predominant way to include pronunciation variation within this system is to use multiple entries in the lexicon for the same word.This approach generally suits speech recognition,but in the case of labelling for building a unit selection voice,we could perhaps proﬁt from moreﬂpleteﬂexibility is achieved if we compose the phone lattice directly and pass that to the recognition engine.To build the phone lattice for a given prompt sentence,we ﬁrst lookup each word in the lexicon and convert the phone string to a simpleﬁnite state structure.When a word is not found in the lexicon,we use the CART letter-to-sound rules the ﬁnal festival voice would use to generate a phone string.Where multiple pronunciations for a word are found,we can combine these into a singleﬁnite state representation using the union op-eration.Theﬁnite state machines for the separate words are then concatenated in sequence to give a single representation of the sentence.The topﬁnite state acceptor(FSA)in Figure 1gives a simpliﬁed example of the result of this process for a phrase fragment“...wider economic...”.At this stage,there is little advantage over the standard HTK method,which would internally arrive at the same result.How-ever,once we have a predicted phonetic realisation for a record-ing prompt in aﬁnite state form,it is then straightforward to process this representation further in an elegant and robust way. This is useful to help perform simple tasks,such as splitting stops and affricates into separate symbols for their stop and release parts during forced alignment(done to identify a suit-able concatenation point).More signiﬁcantly,though,we can also robustly apply more complex context dependent postlex-ical rules,for example optional“r”epenthesis intervocalically across word boundaries for certain British English accents.This is indicated in the bottom FSA of Figure1.This may be conveniently achieved by writing rules in the form of context dependent regular expressions.It is then possi-ble to automatically compile these rules into an equivalentﬁnite state transducer which can operate on the input lattice which resulted from lexical lookup(e.g.top FSA in Figure1).Sev-eral variations of compilation methods have been previously described to convert a system of handwritten context dependent mapping rules into an equivalent FST machine to perform the transduction,e.g.[6,7,8].Note that the use of context depen-dent modiﬁcations is moreﬂexible and powerful than the stan-dard HTK methods.For example,a standard way to implement optional“r”epenthesis pronunciation variation using a pronun-ciation lexicon alone would be to include multiple entries for “wider”,one of which contains the additional“r”.However,this introduces a number of problems.The most signiﬁcant problem is the absence of any mechanism to disallow“r”epenthesis in environments where a vowel does not follow.The phonetic lattice alignment code has been implemented as a set of python modules which underlyingly use and extend the MIT Finite State Transducer Toolkit[9].We use CSTR’s Unisyn lexicon[5]to build voices and within the running syn-thesis system.For forced alignment,we use scripts which un-derlying make use of the HTK speech recognition library[10]. Finally,we are planning to make this labelling system publicly available once it reaches a more mature state of development.3.2.Application to EM001voiceSpeaker EM001exhibits a rather careful and deliberate ap-proach to pronunciation during the recordings and uses a rel-atively slow rate of speech.This in fact tends to limit the ap-plicability and usefulness of postlexical rules for the Blizzard Challenge voices somewhat.Postlexical rules are more use-fully applied to the processes of moreﬂuent and rapid connected speech.Thus,in building the three voices for the2007Bliz-zard Challenge,the sole postlexical rule we used was a“tap”rule.Under this rule,alveolar stops in an intervocalic cross word environment could undergo optional transformation to a tap.Speciﬁcally,the left phonetic context for this rule com-prised the set of vowels together with/r,l,n/(central and lateral approximants and alveolar nasal stop),while the right context contained just the set of vowels.4.Pitch accent predictionIn this year’s system,we have experimented with a simple pitch accent target cost function component.To use pitch accent pre-diction in the voices built for the Blizzard Challenge required three changes.First,we ran a pitch accent predictor on the textFigure1:Toy exampleﬁnite state phonetic lattices for the phrase fragment“wider economic”:a)after lexical lookup,the lattice encodes multiple pronunciation variants for“economic”b)after additional“r”insertion postlexical rule,the input lattice(top)is modiﬁed to allow optional insertion of“r”(instead of short pause“sp”).prompts andﬂagged words with a predicted accent as such in the voice data structures.Next,at synthesis time,our front end linguistic processor was modiﬁed to run the accent predictor on the input sentence to be synthesised,and words with a predicted accent were similarlyﬂagged.Finally,an additional target cost component compared the values of the pitch accentﬂag for the word associated with each target vowel and returned a suitable cost depending on whether they match or not.The method for pitch accent prediction we used here is very simple.It is centred on a look-up table of probabilities that a word will be accented,or“accent ratios”,along the lines of the approach described in[11].The accent predictor simply looks up a word in this list.If the word is found and its probability for being accented is less than the threshold of0.28,it is not accented.Otherwise it will receive an accent.These accent ratios are based on the BU Radio Corpus and six Switchboard dialogues.The list contains157words with an accent ratio of less than0.282.The pitch accent target cost component has recently been evaluated in a large scale listening test and was found to be beneﬁcial[12].5.Voice“C”and text selection Entrants to the2007Blizzard Challenge were encouraged to enter a third voice with a voice database size equal to that of the ARCTIC subset,but with a freely selected subset of utterances. The purpose of this voice is to probe the performance of each team’s text selection process,as well as to provide some insight into the suitability of the ARCTIC data set itself.5.1.Text selection processOrdinarily,when designing a prompt set for recording a unit selection voice database,we would seek to avoid longer sen-tences.They are generally harder to read,which means they are more taxing on the speaker and are more likely to slow down the recording process.In this case,however,since the sentences had been recorded already,we decided to relax this constraint.In a simple greedy text selection process,sentences were chosen in an iterative way.First,the diphones present in the EM001text prompts were subcategorised to include certain contextual features.The features we included were lexical stress,pitch accent and proximity to word boundary.Syllable boundary information was not used in the speciﬁcation of di-phone subtypes.Next,sentences were ranked according to the number of context dependent diphones contained.The top ranking sen-tence was selected,then the ranking of the remaining sentences was recomputed to reﬂect the diphones now present in the sub-set of selected sentences.Sentences were selected one at a time in this way until the total time of the selected subset reached the 2using the accent ratio table in this way is essentially equivalent to using an(incomplete)list of English function words.count of diphone type in full EM001 setcountofcountsofmissingdiphonetypesFigure2:Histogram of counts of unique context dependent di-phone types present in the full EM001set which are missing from the selected subset used to build for voice“C”.prescribed threshold.This resulted in a subset comprising431 utterances,with a total duration of2908.75seconds.Our deﬁnition of context dependent diphones implied a to-tal of6,199distinct diphones with context in the entire EM001 corpus.Our selected subset for voice“C”contained4,660of these,which meant1,539were missing.Figure2shows a his-togram of the missing diphone types in terms of their counts in the full EM001data set.We see that the large majority of the missing diphone types only occur1–5times in the full EM001 dataset.For example,773of the diphone types which are miss-ing from the selected subset only occur once in the full EM001 set,while only one diphone type which is missing occurred as many as26times in the full data set.5.2.Evaluation problemsAlthough it is certainly interesting to compare different text se-lection algorithms against the ARCTIC sentence set,we suggest the way it has been performed this year could potentially con-fuse this comparison.Theﬁrst issue to which we would like to draw attention concerns the consistency of the recorded speech material throughout the database.The second issue concerns the question of how far the full EM001data set satisﬁes the se-lection criteria used by arbitrary text selection algorithms.5.2.1.Consistency of recorded utterancesFigures3–5show plots of MFCC parameter means from the EM001database taken in alphabeticalﬁle ordering.To produceEMOO1 File (alphabetical sorting)m e a n f o r 9t h M F C C c h a n n e lFigure 3:Mean value for 9th MFCC channel for each ﬁle of the EM001voice database.EMOO1 File (alphabetical sorting)m e a n f o r 7t h M F C C c h a n n e lFigure 4:Mean value for 7th MFCC channel for each ﬁle of the EM001voice database.EMOO1 File (alphabetical sorting)m e a n f o r 11t h M F C C c h a n n e lFigure 5:Mean value for 11th MFCC channel for each ﬁle of the EM001voice database.these plots we have taken all ﬁles in the EM001data set in al-phabetical ordering (along the x-axis)and calculated the mean MFCC parameters 3for each ﬁle.In calculating these means,we have omitted the silence at the beginning and end of ﬁles us-ing the labelling provided by the force alignment we conducted during voice building.A single selected dimension of this mean vector is then plotted in each of the Figures 3–5.From these ﬁgures,we notice that there seem to be three distinct sections of the database,which correspond to the “ARC-TIC”,“BTEC”and “NEWS”ﬁle labels as indicated in the plots.Within each of these blocks,the MFCC mean varies randomly,but apparently uniformly so.Between these three sections,however,we observe marked differences.For example,com-pare the distributions of per-ﬁle means of the 9th (Fig.3)and 7th (Fig.4)MFCC parameters within the “NEWS”section with those from the other two sections of the database.We naturally expect the MFCC means to vary “randomly”from ﬁle to ﬁle according to the phonetic content of the utter-ance contained.However,an obvious trend such as that exhib-ited in these plots suggests the inﬂuence of something more than phonetic variation alone.Speciﬁcally,we suspect this situation has arisen due to the signiﬁcant difﬁculty of ensuring consis-tency throughout the many days necessary to record a speech corpus of this size.We have observed similar effects of incon-sistency within other databases,both those we have recorded at CSTR,as well as other commercially recorded databases.Recording a speech corpus over time allows the introduction of variability,with potential sources ranging from the acous-tic recording environment (e.g.microphone placement relative to speaker)to the quality of the speaker’s own voice,which of course can vary over a very short space of time [13].In addi-tion,even the genre and nature of the prompts themselves can inﬂuence a speaker’s reading style and voice characteristics.Note that although we do not see any trends within each of the three sections of the EM001data set,and that they appear relatively homogeneous,this does not imply that these subsec-tions are free of the same variability and inconsistency.These plots have been produced by taking the ﬁles in alphabetical,and hence numerical,order.But it is not necessarily the case that the ﬁles were recorded in this order.In fact,it is likely the ﬁle order-ing within the subsections has been randomised which has the effect of disguising inconsistency within the three sections.The inconsistency between the sections is evident purely because the genre identity tag has maintained three distinct groups.Therefore,despite the probable randomisation of ﬁle order within sections,we infer from the patterns evident in Figures 3–5that the speech data corresponding to the ARCTIC prompt set was recorded all together,and constitutes a reasonably con-sistent “block”of data.Meanwhile,the rest of the data seems to have been recorded at different times.This introduces in-consistency throughout the database,which a selection algo-rithm based entirely upon text features will not take account of.This means that unless it is explicitly and effectively dealt with by the synthesis system which uses the voice data,both at voice building time (ing cepstral mean normalisation dur-ing forced alignment)and at synthesis time,voice “C”stands a high chance of being disadvantaged by selecting data indis-criminately from inconsistent subsections of the database.The forced alignment labelling may suffer because of the increased variance of the speech data.Unit selection may suffer because the spectral component of the join cost may result in a nonuni-form probability of making joins across sections of the database,compared with the those joins within a single section.This has the effect of “partitioning”the voice database.3extractedusing HTK’s HCopy as part of our force alignment pro-cessing,and also subsequently used in the Multisyn join costThe Multisyn voice building process currently takes ac-count of amplitude inconsistency,and attempts waveform power normalisation on a per-utterance basis.However,other sources of inconsistency,most notably spectral inconsistency are not currently addressed.This means that Multisyn voice “C”is potentially affected by database inconsistency,which in-troduces uncertainty and confusion in any comparison between voices“B”and“C”.Within the subset of431sentences we se-lected to build voice“C”,261came from the“NEWS”section, 169came from the“BTEC”section,and the remaining36came from the“ARCTIC”section.This issue of inconsistency can potentially affect the com-parison between the“C”voices from different entrants.For example,according to our automatic phonetic transcriptions of the EM001sentence set,the minimum number of phones con-tained in a single sentence within the“NEWS”section is52. Meanwhile,the“BTEC”section contains1,374sentences with less than52phones.Although we have not done so here,it is not unreasonable for a text selection strategy to favour short sentences,in which case a large majority may be selected from the“BTEC”section.This would result in avoiding the large discontinuity we observe in Figures3and4and could poten-tially confer an advantage which is in fact unrelated to the text selection algorithm per se.The problem has the potential,however,to introduce most confusion into the comparison between entrants’voices“B”and “C”,as there is most likely to be a bias in favour of the ARCTIC subset,which seems to have been recorded as a single block. We suggest there are at least two ways of avoiding this bias in future challenges.One way would be to provide a database without the inconsistency we observe here,for example through post-processing.This is likely to be rather difﬁcult to realise, and our own previous attempts have failed toﬁnd a satisfactory solution,although[14]reported some success.A second,sim-pler way would be to record the set of ARCTIC sentences ran-domly throughout the recording of a future Blizzard Challenge corpus.5.2.2.Selection criteria coverageThe second problem inherent in attempting to compare text se-lection processes in this way arises from differing selection cri-teria.It is usual to choose text selection criteria(i.e.which di-phone context features to consider)which complement the syn-thesis system’s target cost function.Hence the criteria may vary between systems.The set of ARCTIC sentences was selected from a very large amount of text,and so the possibility for the algorithm to reach its optimal subset in terms of the selection criteria it used is maximised.In contrast,the text selection required for voice “C”was performed on a far smaller set of sentences.Although, admittedly,it is likely to be phonetically much richer than if the same number of sentences had been selected randomly from a large corpus,it is possible that the initial set of sentences does not contain a sufﬁcient variety of material to satisfy the selec-tion criteria of arbitrary text selection systems.This again may tend to accord an inherent advantage to voice“B”.6.ConclusionWe have introduced two new features of the Multisyn unit selec-tion system.We have also raised issues for discussion concern-ing the comparison of voices built with differing subsets of the provided data.Finally,we note that,as in previous years,par-ticipating in this Blizzard Challenge has proved both interesting and useful.7.AcknowledgmentsKorin Richmond is currently supported by EPSRC grant EP/E027741/1.Many thanks to Lee Hetherington for making the MITFST toolkit available under a BSD-style license,and for other technical guidance.Thanks to A.Nenkova for process-ing the Blizzard text prompts for pitch accent prediction.8.References[1]R.A.J.Clark,K.Richmond,and S.King,“Multisyn:Open-domain unit selection for the Festival speech syn-thesis system,”Speech Communication,vol.49,no.4,pp.317–330,2007.[2]R.Clark,K.Richmond,V.Strom,and S.King,“Multisyn voice for the Blizzard Challenge2006,”in Proc.Blizzard Challenge Workshop(Inter-speech Satellite),Pittsburgh,USA,Sept.2006, (/blizzard/blizzard2006.html).[3]R.A.Clark,K.Richmond,and S.King,“Multisyn voicesfrom ARCTIC data for the Blizzard challenge,”in Proc.Interspeech2005,Sept.2005.[4]J.Kominek and A.Black,“The CMU ARCTIC speechdatabases,”in5th ISCA Speech Synthesis Workshop,Pitts-burgh,PA,2004,pp.223–224.[5]S.Fitt and S.Isard,“Synthesis of regional English usinga keyword lexicon,”in Proc.Eurospeech’99,vol.2,Bu-dapest,1999,pp.823–826.[6]M.Mohri and R.Sproat,“An efﬁcient compiler forweighted rewrite rules,”in Proc.34th annual meeting of Association for Computational Linguistics,1996,pp.231–238.[7]R.Kaplan and M.Kay,“Regular models of phonologicalrule systems,”Computational Linguistics,vol.20,no.3, pp.331–378,Sep1994.[8]L.Karttunen,“The replace operator,”in Proc.33th an-nual meeting of Association for Computational Linguis-tics,1995,pp.16–23.[9]L.Hetherington,“The MITﬁnite-state transducer toolkitfor speech and language processing,”in Proc.ICSLP, 2004.[10]S.Young,G.Evermann,D.Kershaw,G.Moore,J.Odell,D.Ollason,D.Povey,V.Valtchev,and P.Woodland,TheHTK Book(for HTK version3.2),Cambridge University Engineering Department,2002.[11]J.Brenier,A.Nenkova,A.Kothari,L.Whitton,D.Beaver,and D.Jurafsky,“The(non)utility of linguistic features for predicting prominence on spontaneous speech,”in IEEE/ACL2006Workshop on Spoken Language Technol-ogy,2006.[12]V.Strom,A.Nenkova,R.Clark,Y.Vazquez-Alvarez,J.Brenier,S.King,and D.Jurafsky,“Modelling promi-nence and emphasis improves unit-selection synthesis,”in Proc.Interspeech,Antwerp,2007.[13]H.Kawai and M.Tsuzaki,“Study on time-dependentvoice quality variation in a large-scale single speaker speech corpus used for speech synthesis,”in Proc.IEEE Workshop on Speech Synthesis,2002,pp.15–18. [14]Y.Stylianou,“Assessment and correction of voice qualityvariabilities in large speech databases for concatentative speech synthesis,”in Proc.ICASSP-99,Phoenix,Arizona, Mar.1999,pp.377–380.。

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Example-based metonymy recognition for proper nouns

Example-Based Metonymy Recognition for Proper NounsYves PeirsmanQuantitative Lexicology and Variational LinguisticsUniversity of Leuven,Belgiumyves.peirsman@arts.kuleuven.beAbstractMetonymy recognition is generally ap-proached with complex algorithms thatrely heavily on the manual annotation oftraining and test data.This paper will re-lieve this complexity in two ways.First,it will show that the results of the cur-rent learning algorithms can be replicatedby the‘lazy’algorithm of Memory-BasedLearning.This approach simply stores alltraining instances to its memory and clas-siﬁes a test instance by comparing it to alltraining examples.Second,this paper willargue that the number of labelled trainingexamples that is currently used in the lit-erature can be reduced drastically.Thisﬁnding can help relieve the knowledge ac-quisition bottleneck in metonymy recog-nition,and allow the algorithms to be ap-plied on a wider scale.1IntroductionMetonymy is aﬁgure of speech that uses“one en-tity to refer to another that is related to it”(Lakoff and Johnson,1980,p.35).In example(1),for in-stance,China and Taiwan stand for the govern-ments of the respective countries:(1)China has always threatened to use forceif Taiwan declared independence.(BNC) Metonymy resolution is the task of automatically recognizing these words and determining their ref-erent.It is therefore generally split up into two phases:metonymy recognition and metonymy in-terpretation(Fass,1997).The earliest approaches to metonymy recogni-tion identify a word as metonymical when it vio-lates selectional restrictions(Pustejovsky,1995).Indeed,in example(1),China and Taiwan both violate the restriction that threaten and declare require an animate subject,and thus have to be interpreted metonymically.However,it is clear that many metonymies escape this characteriza-tion.Nixon in example(2)does not violate the se-lectional restrictions of the verb to bomb,and yet, it metonymically refers to the army under Nixon’s command.(2)Nixon bombed Hanoi.This example shows that metonymy recognition should not be based on rigid rules,but rather on statistical information about the semantic and grammatical context in which the target word oc-curs.This statistical dependency between the read-ing of a word and its grammatical and seman-tic context was investigated by Markert and Nis-sim(2002a)and Nissim and Markert(2003; 2005).The key to their approach was the in-sight that metonymy recognition is basically a sub-problem of Word Sense Disambiguation(WSD). Possibly metonymical words are polysemous,and they generally belong to one of a number of pre-deﬁned metonymical categories.Hence,like WSD, metonymy recognition boils down to the auto-matic assignment of a sense label to a polysemous word.This insight thus implied that all machine learning approaches to WSD can also be applied to metonymy recognition.There are,however,two differences between metonymy recognition and WSD.First,theo-retically speaking,the set of possible readings of a metonymical word is open-ended(Nunberg, 1978).In practice,however,metonymies tend to stick to a small number of patterns,and their la-bels can thus be deﬁned a priori.Second,classic 71WSD algorithms take training instances of one par-ticular word as their input and then disambiguate test instances of the same word.By contrast,since all words of the same semantic class may undergo the same metonymical shifts,metonymy recogni-tion systems can be built for an entire semantic class instead of one particular word(Markert and Nissim,2002a).To this goal,Markert and Nissim extracted from the BNC a corpus of possibly metonymical words from two categories:country names (Markert and Nissim,2002b)and organization names(Nissim and Markert,2005).All these words were annotated with a semantic label —either literal or the metonymical cate-gory they belonged to.For the country names, Markert and Nissim distinguished between place-for-people,place-for-event and place-for-product.For the organi-zation names,the most frequent metonymies are organization-for-members and organization-for-product.In addition, Markert and Nissim used a label mixed for examples that had two readings,and othermet for examples that did not belong to any of the pre-deﬁned metonymical patterns.For both categories,the results were promis-ing.The best algorithms returned an accuracy of 87%for the countries and of76%for the orga-nizations.Grammatical features,which gave the function of a possibly metonymical word and its head,proved indispensable for the accurate recog-nition of metonymies,but led to extremely low recall values,due to data sparseness.Therefore Nissim and Markert(2003)developed an algo-rithm that also relied on semantic information,and tested it on the mixed country data.This algo-rithm used Dekang Lin’s(1998)thesaurus of se-mantically similar words in order to search the training data for instances whose head was sim-ilar,and not just identical,to the test instances. Nissim and Markert(2003)showed that a combi-nation of semantic and grammatical information gave the most promising results(87%). However,Nissim and Markert’s(2003)ap-proach has two major disadvantages.Theﬁrst of these is its complexity:the best-performing al-gorithm requires smoothing,backing-off to gram-matical roles,iterative searches through clusters of semantically similar words,etc.In section2,I will therefore investigate if a metonymy recognition al-gorithm needs to be that computationally demand-ing.In particular,I will try and replicate Nissim and Markert’s results with the‘lazy’algorithm of Memory-Based Learning.The second disadvantage of Nissim and Mark-ert’s(2003)algorithms is their supervised nature. Because they rely so heavily on the manual an-notation of training and test data,an extension of the classiﬁers to more metonymical patterns is ex-tremely problematic.Yet,such an extension is es-sential for many tasks throughout theﬁeld of Nat-ural Language Processing,particularly Machine Translation.This knowledge acquisition bottle-neck is a well-known problem in NLP,and many approaches have been developed to address it.One of these is active learning,or sample selection,a strategy that makes it possible to selectively an-notate those examples that are most helpful to the classiﬁer.It has previously been applied to NLP tasks such as parsing(Hwa,2002;Osborne and Baldridge,2004)and Word Sense Disambiguation (Fujii et al.,1998).In section3,I will introduce active learning into theﬁeld of metonymy recog-nition.2Example-based metonymy recognition As I have argued,Nissim and Markert’s(2003) approach to metonymy recognition is quite com-plex.I therefore wanted to see if this complexity can be dispensed with,and if it can be replaced with the much more simple algorithm of Memory-Based Learning.The advantages of Memory-Based Learning(MBL),which is implemented in the T i MBL classiﬁer(Daelemans et al.,2004)1,are twofold.First,it is based on a plausible psycho-logical hypothesis of human learning.It holds that people interpret new examples of a phenom-enon by comparing them to“stored representa-tions of earlier experiences”(Daelemans et al., 2004,p.19).This contrasts to many other classi-ﬁcation algorithms,such as Naive Bayes,whose psychological validity is an object of heavy de-bate.Second,as a result of this learning hypothe-sis,an MBL classiﬁer such as T i MBL eschews the formulation of complex rules or the computation of probabilities during its training phase.Instead it stores all training vectors to its memory,together with their labels.In the test phase,it computes the distance between the test vector and all these train-ing vectors,and simply returns the most frequentlabel of the most similar training examples.One of the most important challenges inMemory-Based Learning is adapting the algorithmto one’s data.This includesﬁnding a represen-tative seed set as well as determining the rightdistance measures.For my purposes,however, T i MBL’s default settings proved more than satis-factory.T i MBL implements the IB1and IB2algo-rithms that were presented in Aha et al.(1991),butadds a broad choice of distance measures.Its de-fault implementation of the IB1algorithm,whichis called IB1-IG in full(Daelemans and Van denBosch,1992),proved most successful in my ex-periments.It computes the distance between twovectors X and Y by adding up the weighted dis-tancesδbetween their corresponding feature val-ues x i and y i:∆(X,Y)=ni=1w iδ(x i,y i)(3)The most important element in this equation is theweight that is given to each feature.In IB1-IG,features are weighted by their Gain Ratio(equa-tion4),the division of the feature’s InformationGain by its split rmation Gain,the nu-merator in equation(4),“measures how much in-formation it[feature i]contributes to our knowl-edge of the correct class label[...]by comput-ing the difference in uncertainty(i.e.entropy)be-tween the situations without and with knowledgeof the value of that feature”(Daelemans et al.,2004,p.20).In order not“to overestimate the rel-evance of features with large numbers of values”(Daelemans et al.,2004,p.21),this InformationGain is then divided by the split info,the entropyof the feature values(equation5).In the followingequations,C is the set of class labels,H(C)is theentropy of that set,and V i is the set of values forfeature i.w i=H(C)− v∈V i P(v)×H(C|v)2This data is publicly available and can be downloadedfrom /mnissim/mascara.73P F86.6%49.5%N&M81.4%62.7%Table1:Results for the mixed country data.T i MBL:my T i MBL resultsN&M:Nissim and Markert’s(2003)results simple learning phase,T i MBL is able to replicate the results from Nissim and Markert(2003;2005). As table1shows,accuracy for the mixed coun-try data is almost identical to Nissim and Mark-ert’sﬁgure,and precision,recall and F-score for the metonymical class lie only slightly lower.3 T i MBL’s results for the Hungary data were simi-lar,and equally comparable to Markert and Nis-sim’s(Katja Markert,personal communication). Note,moreover,that these results were reached with grammatical information only,whereas Nis-sim and Markert’s(2003)algorithm relied on se-mantics as well.Next,table2indicates that T i MBL’s accuracy for the mixed organization data lies about1.5%be-low Nissim and Markert’s(2005)ﬁgure.This re-sult should be treated with caution,however.First, Nissim and Markert’s available organization data had not yet been annotated for grammatical fea-tures,and my annotation may slightly differ from theirs.Second,Nissim and Markert used several feature vectors for instances with more than one grammatical role andﬁltered all mixed instances from the training set.A test instance was treated as mixed only when its several feature vectors were classiﬁed differently.My experiments,in contrast, were similar to those for the location data,in that each instance corresponded to one vector.Hence, the slightly lower performance of T i MBL is prob-ably due to differences between the two experi-ments.Theseﬁrst experiments thus demonstrate that Memory-Based Learning can give state-of-the-art performance in metonymy recognition.In this re-spect,it is important to stress that the results for the country data were reached without any se-mantic information,whereas Nissim and Mark-ert’s(2003)algorithm used Dekang Lin’s(1998) clusters of semantically similar words in order to deal with data sparseness.This fact,togetherAcc RT i MBL78.65%65.10%76.0%—Figure1:Accuracy learning curves for the mixed country data with and without semantic informa-tion.in more detail.4Asﬁgure1indicates,with re-spect to overall accuracy,semantic features have a negative inﬂuence:the learning curve with both features climbs much more slowly than that with only grammatical features.Hence,contrary to my expectations,grammatical features seem to allow a better generalization from a limited number of training instances.With respect to the F-score on the metonymical category inﬁgure2,the differ-ences are much less outspoken.Both features give similar learning curves,but semantic features lead to a higherﬁnal F-score.In particular,the use of semantic features results in a lower precisionﬁg-ure,but a higher recall score.Semantic features thus cause the classiﬁer to slightly overgeneralize from the metonymic training examples.There are two possible reasons for this inabil-ity of semantic information to improve the clas-siﬁer’s performance.First,WordNet’s synsets do not always map well to one of our semantic la-bels:many are rather broad and allow for several readings of the target word,while others are too speciﬁc to make generalization possible.Second, there is the predominance of prepositional phrases in our data.With their closed set of heads,the number of examples that beneﬁts from semantic information about its head is actually rather small. Nevertheless,myﬁrst round of experiments has indicated that Memory-Based Learning is a sim-ple but robust approach to metonymy recogni-tion.It is able to replace current approaches that need smoothing or iterative searches through a the-saurus,with a simple,distance-based algorithm.Figure3:Accuracy learning curves for the coun-try data with random and maximum-distance se-lection of training examples.over all possible labels.The algorithm then picks those instances with the lowest conﬁdence,since these will contain valuable information about the training set(and hopefully also the test set)that is still unknown to the system.One problem with Memory-Based Learning al-gorithms is that they do not directly output prob-abilities.Since they are example-based,they can only give the distances between the unlabelled in-stance and all labelled training instances.Never-theless,these distances can be used as a measure of certainty,too:we can assume that the system is most certain about the classiﬁcation of test in-stances that lie very close to one or more of its training instances,and less certain about those that are further away.Therefore the selection function that minimizes the probability of the most likely label can intuitively be replaced by one that max-imizes the distance from the labelled training in-stances.However,ﬁgure3shows that for the mixed country instances,this function is not an option. Both learning curves give the results of an algo-rithm that starts withﬁfty random instances,and then iteratively adds ten new training instances to this initial seed set.The algorithm behind the solid curve chooses these instances randomly,whereas the one behind the dotted line selects those that are most distant from the labelled training exam-ples.In theﬁrst half of the learning process,both functions are equally successful;in the second the distance-based function performs better,but only slightly so.There are two reasons for this bad initial per-formance of the active learning function.First,it is not able to distinguish between informativeandFigure4:Accuracy learning curves for the coun-try data with random and maximum/minimum-distance selection of training examples. unusual training instances.This is because a large distance from the seed set simply means that the particular instance’s feature values are relatively unknown.This does not necessarily imply that the instance is informative to the classiﬁer,how-ever.After all,it may be so unusual and so badly representative of the training(and test)set that the algorithm had better exclude it—something that is impossible on the basis of distances only.This bias towards outliers is a well-known disadvantage of many simple active learning algorithms.A sec-ond type of bias is due to the fact that the data has been annotated with a few features only.More par-ticularly,the present algorithm will keep adding instances whose head is not yet represented in the training set.This entails that it will put off adding instances whose function is pp,simply because other functions(subj,gen,...)have a wider variety in heads.Again,the result is a labelled set that is not very representative of the entire training set.There are,however,a few easy ways to increase the number of prototypical examples in the train-ing set.In a second run of experiments,I used an active learning function that added not only those instances that were most distant from the labelled training set,but also those that were closest to it. After a few test runs,I decided to add six distant and four close instances on each iteration.Figure4 shows that such a function is indeed fairly success-ful.Because it builds a labelled training set that is more representative of the test set,this algorithm clearly reduces the number of annotated instances that is needed to reach a given performance.Despite its success,this function is obviously not yet a sophisticated way of selecting good train-76Figure5:Accuracy learning curves for the organi-zation data with random and distance-based(AL) selection of training examples with a random seed set.ing examples.The selection of the initial seed set in particular can be improved upon:ideally,this seed set should take into account the overall dis-tribution of the training examples.Currently,the seeds are chosen randomly.Thisﬂaw in the al-gorithm becomes clear if it is applied to another data set:ﬁgure5shows that it does not outper-form random selection on the organization data, for instance.As I suggested,the selection of prototypical or representative instances as seeds can be used to make the present algorithm more robust.Again,it is possible to use distance measures to do this:be-fore the selection of seed instances,the algorithm can calculate for each unlabelled instance its dis-tance from each of the other unlabelled instances. In this way,it can build a prototypical seed set by selecting those instances with the smallest dis-tance on average.Figure6indicates that such an algorithm indeed outperforms random sample se-lection on the mixed organization data.For the calculation of the initial distances,each feature re-ceived the same weight.The algorithm then se-lected50random samples from the‘most proto-typical’half of the training set.5The other settings were the same as above.With the present small number of features,how-ever,such a prototypical seed set is not yet always as advantageous as it could be.A few experiments indicated that it did not lead to better performance on the mixed country data,for instance.However, as soon as a wider variety of features is taken into account(as with the organization data),the advan-pling can help choose those instances that are most helpful to the classiﬁer.A few distance-based al-gorithms were able to drastically reduce the num-ber of training instances that is needed for a given accuracy,both for the country and the organization names.If current metonymy recognition algorithms are to be used in a system that can recognize all pos-sible metonymical patterns across a broad variety of semantic classes,it is crucial that the required number of labelled training examples be reduced. This paper has taken theﬁrst steps along this path and has set out some interesting questions for fu-ture research.This research should include the investigation of new features that can make clas-siﬁers more robust and allow us to measure their conﬁdence more reliably.This conﬁdence mea-surement can then also be used in semi-supervised learning algorithms,for instance,where the clas-siﬁer itself labels the majority of training exam-ples.Only with techniques such as selective sam-pling and semi-supervised learning can the knowl-edge acquisition bottleneck in metonymy recogni-tion be addressed.AcknowledgementsI would like to thank Mirella Lapata,Dirk Geer-aerts and Dirk Speelman for their feedback on this project.I am also very grateful to Katja Markert and Malvina Nissim for their helpful information about their research.ReferencesD.W.Aha, D.Kibler,and M.K.Albert.1991.Instance-based learning algorithms.Machine Learning,6:37–66.W.Daelemans and A.Van den Bosch.1992.Generali-sation performance of backpropagation learning on a syllabiﬁcation task.In M.F.J.Drossaers and A.Ni-jholt,editors,Proceedings of TWLT3:Connection-ism and Natural Language Processing,pages27–37, Enschede,The Netherlands.W.Daelemans,J.Zavrel,K.Van der Sloot,andA.Van den Bosch.2004.TiMBL:Tilburg Memory-Based Learner.Technical report,Induction of Linguistic Knowledge,Computational Linguistics, Tilburg University.D.Fass.1997.Processing Metaphor and Metonymy.Stanford,CA:Ablex.A.Fujii,K.Inui,T.Tokunaga,and H.Tanaka.1998.Selective sampling for example-based wordsense putational Linguistics, 24(4):573–597.R.Hwa.2002.Sample selection for statistical parsing.Computational Linguistics,30(3):253–276.koff and M.Johnson.1980.Metaphors We LiveBy.London:The University of Chicago Press.D.Lin.1998.An information-theoretic definition ofsimilarity.In Proceedings of the International Con-ference on Machine Learning,Madison,USA.K.Markert and M.Nissim.2002a.Metonymy res-olution as a classification task.In Proceedings of the Conference on Empirical Methods in Natural Language Processing(EMNLP2002),Philadelphia, USA.K.Markert and M.Nissim.2002b.Towards a cor-pus annotated for metonymies:the case of location names.In Proceedings of the Third International Conference on Language Resources and Evaluation (LREC2002),Las Palmas,Spain.M.Nissim and K.Markert.2003.Syntactic features and word similarity for supervised metonymy res-olution.In Proceedings of the41st Annual Meet-ing of the Association for Computational Linguistics (ACL-03),Sapporo,Japan.M.Nissim and K.Markert.2005.Learning to buy a Renault and talk to BMW:A supervised approach to conventional metonymy.In H.Bunt,editor,Pro-ceedings of the6th International Workshop on Com-putational Semantics,Tilburg,The Netherlands. 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机器学习与人工智能领域中常用的英语词汇

机器学习与人工智能领域中常用的英语词汇1.General Concepts (基础概念)•Artificial Intelligence (AI) - 人工智能1)Artificial Intelligence (AI) - 人工智能2)Machine Learning (ML) - 机器学习3)Deep Learning (DL) - 深度学习4)Neural Network - 神经网络5)Natural Language Processing (NLP) - 自然语言处理6)Computer Vision - 计算机视觉7)Robotics - 机器人技术8)Speech Recognition - 语音识别9)Expert Systems - 专家系统10)Knowledge Representation - 知识表示11)Pattern Recognition - 模式识别12)Cognitive Computing - 认知计算13)Autonomous Systems - 自主系统14)Human-Machine Interaction - 人机交互15)Intelligent Agents - 智能代理16)Machine Translation - 机器翻译17)Swarm Intelligence - 群体智能18)Genetic Algorithms - 遗传算法19)Fuzzy Logic - 模糊逻辑20)Reinforcement Learning - 强化学习•Machine Learning (ML) - 机器学习1)Machine Learning (ML) - 机器学习2)Artificial Neural Network - 人工神经网络3)Deep Learning - 深度学习4)Supervised Learning - 有监督学习5)Unsupervised Learning - 无监督学习6)Reinforcement Learning - 强化学习7)Semi-Supervised Learning - 半监督学习8)Training Data - 训练数据9)Test Data - 测试数据10)Validation Data - 验证数据11)Feature - 特征12)Label - 标签13)Model - 模型14)Algorithm - 算法15)Regression - 回归16)Classification - 分类17)Clustering - 聚类18)Dimensionality Reduction - 降维19)Overfitting - 过拟合20)Underfitting - 欠拟合•Deep Learning (DL) - 深度学习1)Deep Learning - 深度学习2)Neural Network - 神经网络3)Artificial Neural Network (ANN) - 人工神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Autoencoder - 自编码器9)Generative Adversarial Network (GAN) - 生成对抗网络10)Transfer Learning - 迁移学习11)Pre-trained Model - 预训练模型12)Fine-tuning - 微调13)Feature Extraction - 特征提取14)Activation Function - 激活函数15)Loss Function - 损失函数16)Gradient Descent - 梯度下降17)Backpropagation - 反向传播18)Epoch - 训练周期19)Batch Size - 批量大小20)Dropout - 丢弃法•Neural Network - 神经网络1)Neural Network - 神经网络2)Artificial Neural Network (ANN) - 人工神经网络3)Deep Neural Network (DNN) - 深度神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Feedforward Neural Network - 前馈神经网络9)Multi-layer Perceptron (MLP) - 多层感知器10)Radial Basis Function Network (RBFN) - 径向基函数网络11)Hopfield Network - 霍普菲尔德网络12)Boltzmann Machine - 玻尔兹曼机13)Autoencoder - 自编码器14)Spiking Neural Network (SNN) - 脉冲神经网络15)Self-organizing Map (SOM) - 自组织映射16)Restricted Boltzmann Machine (RBM) - 受限玻尔兹曼机17)Hebbian Learning - 海比安学习18)Competitive Learning - 竞争学习19)Neuroevolutionary - 神经进化20)Neuron - 神经元•Algorithm - 算法1)Algorithm - 算法2)Supervised Learning Algorithm - 有监督学习算法3)Unsupervised Learning Algorithm - 无监督学习算法4)Reinforcement Learning Algorithm - 强化学习算法5)Classification Algorithm - 分类算法6)Regression Algorithm - 回归算法7)Clustering Algorithm - 聚类算法8)Dimensionality Reduction Algorithm - 降维算法9)Decision Tree Algorithm - 决策树算法10)Random Forest Algorithm - 随机森林算法11)Support Vector Machine (SVM) Algorithm - 支持向量机算法12)K-Nearest Neighbors (KNN) Algorithm - K近邻算法13)Naive Bayes Algorithm - 朴素贝叶斯算法14)Gradient Descent Algorithm - 梯度下降算法15)Genetic Algorithm - 遗传算法16)Neural Network Algorithm - 神经网络算法17)Deep Learning Algorithm - 深度学习算法18)Ensemble Learning Algorithm - 集成学习算法19)Reinforcement Learning Algorithm - 强化学习算法20)Metaheuristic Algorithm - 元启发式算法•Model - 模型1)Model - 模型2)Machine Learning Model - 机器学习模型3)Artificial Intelligence Model - 人工智能模型4)Predictive Model - 预测模型5)Classification Model - 分类模型6)Regression Model - 回归模型7)Generative Model - 生成模型8)Discriminative Model - 判别模型9)Probabilistic Model - 概率模型10)Statistical Model - 统计模型11)Neural Network Model - 神经网络模型12)Deep Learning Model - 深度学习模型13)Ensemble Model - 集成模型14)Reinforcement Learning Model - 强化学习模型15)Support Vector Machine (SVM) Model - 支持向量机模型16)Decision Tree Model - 决策树模型17)Random Forest Model - 随机森林模型18)Naive Bayes Model - 朴素贝叶斯模型19)Autoencoder Model - 自编码器模型20)Convolutional Neural Network (CNN) Model - 卷积神经网络模型•Dataset - 数据集1)Dataset - 数据集2)Training Dataset - 训练数据集3)Test Dataset - 测试数据集4)Validation Dataset - 验证数据集5)Balanced Dataset - 平衡数据集6)Imbalanced Dataset - 不平衡数据集7)Synthetic Dataset - 合成数据集8)Benchmark Dataset - 基准数据集9)Open Dataset - 开放数据集10)Labeled Dataset - 标记数据集11)Unlabeled Dataset - 未标记数据集12)Semi-Supervised Dataset - 半监督数据集13)Multiclass Dataset - 多分类数据集14)Feature Set - 特征集15)Data Augmentation - 数据增强16)Data Preprocessing - 数据预处理17)Missing Data - 缺失数据18)Outlier Detection - 异常值检测19)Data Imputation - 数据插补20)Metadata - 元数据•Training - 训练1)Training - 训练2)Training Data - 训练数据3)Training Phase - 训练阶段4)Training Set - 训练集5)Training Examples - 训练样本6)Training Instance - 训练实例7)Training Algorithm - 训练算法8)Training Model - 训练模型9)Training Process - 训练过程10)Training Loss - 训练损失11)Training Epoch - 训练周期12)Training Batch - 训练批次13)Online Training - 在线训练14)Offline Training - 离线训练15)Continuous Training - 连续训练16)Transfer Learning - 迁移学习17)Fine-Tuning - 微调18)Curriculum Learning - 课程学习19)Self-Supervised Learning - 自监督学习20)Active Learning - 主动学习•Testing - 测试1)Testing - 测试2)Test Data - 测试数据3)Test Set - 测试集4)Test Examples - 测试样本5)Test Instance - 测试实例6)Test Phase - 测试阶段7)Test Accuracy - 测试准确率8)Test Loss - 测试损失9)Test Error - 测试错误10)Test Metrics - 测试指标11)Test Suite - 测试套件12)Test Case - 测试用例13)Test Coverage - 测试覆盖率14)Cross-Validation - 交叉验证15)Holdout Validation - 留出验证16)K-Fold Cross-Validation - K折交叉验证17)Stratified Cross-Validation - 分层交叉验证18)Test Driven Development (TDD) - 测试驱动开发19)A/B Testing - A/B 测试20)Model Evaluation - 模型评估•Validation - 验证1)Validation - 验证2)Validation Data - 验证数据3)Validation Set - 验证集4)Validation Examples - 验证样本5)Validation Instance - 验证实例6)Validation Phase - 验证阶段7)Validation Accuracy - 验证准确率8)Validation Loss - 验证损失9)Validation Error - 验证错误10)Validation Metrics - 验证指标11)Cross-Validation - 交叉验证12)Holdout Validation - 留出验证13)K-Fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation - 留一法交叉验证16)Validation Curve - 验证曲线17)Hyperparameter Validation - 超参数验证18)Model Validation - 模型验证19)Early Stopping - 提前停止20)Validation Strategy - 验证策略•Supervised Learning - 有监督学习1)Supervised Learning - 有监督学习2)Label - 标签3)Feature - 特征4)Target - 目标5)Training Labels - 训练标签6)Training Features - 训练特征7)Training Targets - 训练目标8)Training Examples - 训练样本9)Training Instance - 训练实例10)Regression - 回归11)Classification - 分类12)Predictor - 预测器13)Regression Model - 回归模型14)Classifier - 分类器15)Decision Tree - 决策树16)Support Vector Machine (SVM) - 支持向量机17)Neural Network - 神经网络18)Feature Engineering - 特征工程19)Model Evaluation - 模型评估20)Overfitting - 过拟合21)Underfitting - 欠拟合22)Bias-Variance Tradeoff - 偏差-方差权衡•Unsupervised Learning - 无监督学习1)Unsupervised Learning - 无监督学习2)Clustering - 聚类3)Dimensionality Reduction - 降维4)Anomaly Detection - 异常检测5)Association Rule Learning - 关联规则学习6)Feature Extraction - 特征提取7)Feature Selection - 特征选择8)K-Means - K均值9)Hierarchical Clustering - 层次聚类10)Density-Based Clustering - 基于密度的聚类11)Principal Component Analysis (PCA) - 主成分分析12)Independent Component Analysis (ICA) - 独立成分分析13)T-distributed Stochastic Neighbor Embedding (t-SNE) - t分布随机邻居嵌入14)Gaussian Mixture Model (GMM) - 高斯混合模型15)Self-Organizing Maps (SOM) - 自组织映射16)Autoencoder - 自动编码器17)Latent Variable - 潜变量18)Data Preprocessing - 数据预处理19)Outlier Detection - 异常值检测20)Clustering Algorithm - 聚类算法•Reinforcement Learning - 强化学习1)Reinforcement Learning - 强化学习2)Agent - 代理3)Environment - 环境4)State - 状态5)Action - 动作6)Reward - 奖励7)Policy - 策略8)Value Function - 值函数9)Q-Learning - Q学习10)Deep Q-Network (DQN) - 深度Q网络11)Policy Gradient - 策略梯度12)Actor-Critic - 演员-评论家13)Exploration - 探索14)Exploitation - 开发15)Temporal Difference (TD) - 时间差分16)Markov Decision Process (MDP) - 马尔可夫决策过程17)State-Action-Reward-State-Action (SARSA) - 状态-动作-奖励-状态-动作18)Policy Iteration - 策略迭代19)Value Iteration - 值迭代20)Monte Carlo Methods - 蒙特卡洛方法•Semi-Supervised Learning - 半监督学习1)Semi-Supervised Learning - 半监督学习2)Labeled Data - 有标签数据3)Unlabeled Data - 无标签数据4)Label Propagation - 标签传播5)Self-Training - 自训练6)Co-Training - 协同训练7)Transudative Learning - 传导学习8)Inductive Learning - 归纳学习9)Manifold Regularization - 流形正则化10)Graph-based Methods - 基于图的方法11)Cluster Assumption - 聚类假设12)Low-Density Separation - 低密度分离13)Semi-Supervised Support Vector Machines (S3VM) - 半监督支持向量机14)Expectation-Maximization (EM) - 期望最大化15)Co-EM - 协同期望最大化16)Entropy-Regularized EM - 熵正则化EM17)Mean Teacher - 平均教师18)Virtual Adversarial Training - 虚拟对抗训练19)Tri-training - 三重训练20)Mix Match - 混合匹配•Feature - 特征1)Feature - 特征2)Feature Engineering - 特征工程3)Feature Extraction - 特征提取4)Feature Selection - 特征选择5)Input Features - 输入特征6)Output Features - 输出特征7)Feature Vector - 特征向量8)Feature Space - 特征空间9)Feature Representation - 特征表示10)Feature Transformation - 特征转换11)Feature Importance - 特征重要性12)Feature Scaling - 特征缩放13)Feature Normalization - 特征归一化14)Feature Encoding - 特征编码15)Feature Fusion - 特征融合16)Feature Dimensionality Reduction - 特征维度减少17)Continuous Feature - 连续特征18)Categorical Feature - 分类特征19)Nominal Feature - 名义特征20)Ordinal Feature - 有序特征•Label - 标签1)Label - 标签2)Labeling - 标注3)Ground Truth - 地面真值4)Class Label - 类别标签5)Target Variable - 目标变量6)Labeling Scheme - 标注方案7)Multi-class Labeling - 多类别标注8)Binary Labeling - 二分类标注9)Label Noise - 标签噪声10)Labeling Error - 标注错误11)Label Propagation - 标签传播12)Unlabeled Data - 无标签数据13)Labeled Data - 有标签数据14)Semi-supervised Learning - 半监督学习15)Active Learning - 主动学习16)Weakly Supervised Learning - 弱监督学习17)Noisy Label Learning - 噪声标签学习18)Self-training - 自训练19)Crowdsourcing Labeling - 众包标注20)Label Smoothing - 标签平滑化•Prediction - 预测1)Prediction - 预测2)Forecasting - 预测3)Regression - 回归4)Classification - 分类5)Time Series Prediction - 时间序列预测6)Forecast Accuracy - 预测准确性7)Predictive Modeling - 预测建模8)Predictive Analytics - 预测分析9)Forecasting Method - 预测方法10)Predictive Performance - 预测性能11)Predictive Power - 预测能力12)Prediction Error - 预测误差13)Prediction Interval - 预测区间14)Prediction Model - 预测模型15)Predictive Uncertainty - 预测不确定性16)Forecast Horizon - 预测时间跨度17)Predictive Maintenance - 预测性维护18)Predictive Policing - 预测式警务19)Predictive Healthcare - 预测性医疗20)Predictive Maintenance - 预测性维护•Classification - 分类1)Classification - 分类2)Classifier - 分类器3)Class - 类别4)Classify - 对数据进行分类5)Class Label - 类别标签6)Binary Classification - 二元分类7)Multiclass Classification - 多类分类8)Class Probability - 类别概率9)Decision Boundary - 决策边界10)Decision Tree - 决策树11)Support Vector Machine (SVM) - 支持向量机12)K-Nearest Neighbors (KNN) - K最近邻算法13)Naive Bayes - 朴素贝叶斯14)Logistic Regression - 逻辑回归15)Random Forest - 随机森林16)Neural Network - 神经网络17)SoftMax Function - SoftMax函数18)One-vs-All (One-vs-Rest) - 一对多(一对剩余)19)Ensemble Learning - 集成学习20)Confusion Matrix - 混淆矩阵•Regression - 回归1)Regression Analysis - 回归分析2)Linear Regression - 线性回归3)Multiple Regression - 多元回归4)Polynomial Regression - 多项式回归5)Logistic Regression - 逻辑回归6)Ridge Regression - 岭回归7)Lasso Regression - Lasso回归8)Elastic Net Regression - 弹性网络回归9)Regression Coefficients - 回归系数10)Residuals - 残差11)Ordinary Least Squares (OLS) - 普通最小二乘法12)Ridge Regression Coefficient - 岭回归系数13)Lasso Regression Coefficient - Lasso回归系数14)Elastic Net Regression Coefficient - 弹性网络回归系数15)Regression Line - 回归线16)Prediction Error - 预测误差17)Regression Model - 回归模型18)Nonlinear Regression - 非线性回归19)Generalized Linear Models (GLM) - 广义线性模型20)Coefficient of Determination (R-squared) - 决定系数21)F-test - F检验22)Homoscedasticity - 同方差性23)Heteroscedasticity - 异方差性24)Autocorrelation - 自相关25)Multicollinearity - 多重共线性26)Outliers - 异常值27)Cross-validation - 交叉验证28)Feature Selection - 特征选择29)Feature Engineering - 特征工程30)Regularization - 正则化2.Neural Networks and Deep Learning (神经网络与深度学习)•Convolutional Neural Network (CNN) - 卷积神经网络1)Convolutional Neural Network (CNN) - 卷积神经网络2)Convolution Layer - 卷积层3)Feature Map - 特征图4)Convolution Operation - 卷积操作5)Stride - 步幅6)Padding - 填充7)Pooling Layer - 池化层8)Max Pooling - 最大池化9)Average Pooling - 平均池化10)Fully Connected Layer - 全连接层11)Activation Function - 激活函数12)Rectified Linear Unit (ReLU) - 线性修正单元13)Dropout - 随机失活14)Batch Normalization - 批量归一化15)Transfer Learning - 迁移学习16)Fine-Tuning - 微调17)Image Classification - 图像分类18)Object Detection - 物体检测19)Semantic Segmentation - 语义分割20)Instance Segmentation - 实例分割21)Generative Adversarial Network (GAN) - 生成对抗网络22)Image Generation - 图像生成23)Style Transfer - 风格迁移24)Convolutional Autoencoder - 卷积自编码器25)Recurrent Neural Network (RNN) - 循环神经网络•Recurrent Neural Network (RNN) - 循环神经网络1)Recurrent Neural Network (RNN) - 循环神经网络2)Long Short-Term Memory (LSTM) - 长短期记忆网络3)Gated Recurrent Unit (GRU) - 门控循环单元4)Sequence Modeling - 序列建模5)Time Series Prediction - 时间序列预测6)Natural Language Processing (NLP) - 自然语言处理7)Text Generation - 文本生成8)Sentiment Analysis - 情感分析9)Named Entity Recognition (NER) - 命名实体识别10)Part-of-Speech Tagging (POS Tagging) - 词性标注11)Sequence-to-Sequence (Seq2Seq) - 序列到序列12)Attention Mechanism - 注意力机制13)Encoder-Decoder Architecture - 编码器-解码器架构14)Bidirectional RNN - 双向循环神经网络15)Teacher Forcing - 强制教师法16)Backpropagation Through Time (BPTT) - 通过时间的反向传播17)Vanishing Gradient Problem - 梯度消失问题18)Exploding Gradient Problem - 梯度爆炸问题19)Language Modeling - 语言建模20)Speech Recognition - 语音识别•Long Short-Term Memory (LSTM) - 长短期记忆网络1)Long Short-Term Memory (LSTM) - 长短期记忆网络2)Cell State - 细胞状态3)Hidden State - 隐藏状态4)Forget Gate - 遗忘门5)Input Gate - 输入门6)Output Gate - 输出门7)Peephole Connections - 窥视孔连接8)Gated Recurrent Unit (GRU) - 门控循环单元9)Vanishing Gradient Problem - 梯度消失问题10)Exploding Gradient Problem - 梯度爆炸问题11)Sequence Modeling - 序列建模12)Time Series Prediction - 时间序列预测13)Natural Language Processing (NLP) - 自然语言处理14)Text Generation - 文本生成15)Sentiment Analysis - 情感分析16)Named Entity Recognition (NER) - 命名实体识别17)Part-of-Speech Tagging (POS Tagging) - 词性标注18)Attention Mechanism - 注意力机制19)Encoder-Decoder Architecture - 编码器-解码器架构20)Bidirectional LSTM - 双向长短期记忆网络•Attention Mechanism - 注意力机制1)Attention Mechanism - 注意力机制2)Self-Attention - 自注意力3)Multi-Head Attention - 多头注意力4)Transformer - 变换器5)Query - 查询6)Key - 键7)Value - 值8)Query-Value Attention - 查询-值注意力9)Dot-Product Attention - 点积注意力10)Scaled Dot-Product Attention - 缩放点积注意力11)Additive Attention - 加性注意力12)Context Vector - 上下文向量13)Attention Score - 注意力分数14)SoftMax Function - SoftMax函数15)Attention Weight - 注意力权重16)Global Attention - 全局注意力17)Local Attention - 局部注意力18)Positional Encoding - 位置编码19)Encoder-Decoder Attention - 编码器-解码器注意力20)Cross-Modal Attention - 跨模态注意力•Generative Adversarial Network (GAN) - 生成对抗网络1)Generative Adversarial Network (GAN) - 生成对抗网络2)Generator - 生成器3)Discriminator - 判别器4)Adversarial Training - 对抗训练5)Minimax Game - 极小极大博弈6)Nash Equilibrium - 纳什均衡7)Mode Collapse - 模式崩溃8)Training Stability - 训练稳定性9)Loss Function - 损失函数10)Discriminative Loss - 判别损失11)Generative Loss - 生成损失12)Wasserstein GAN (WGAN) - Wasserstein GAN（WGAN）13)Deep Convolutional GAN (DCGAN) - 深度卷积生成对抗网络（DCGAN）14)Conditional GAN (c GAN) - 条件生成对抗网络（c GAN）15)Style GAN - 风格生成对抗网络16)Cycle GAN - 循环生成对抗网络17)Progressive Growing GAN (PGGAN) - 渐进式增长生成对抗网络（PGGAN）18)Self-Attention GAN (SAGAN) - 自注意力生成对抗网络（SAGAN）19)Big GAN - 大规模生成对抗网络20)Adversarial Examples - 对抗样本•Encoder-Decoder - 编码器-解码器1)Encoder-Decoder Architecture - 编码器-解码器架构2)Encoder - 编码器3)Decoder - 解码器4)Sequence-to-Sequence Model (Seq2Seq) - 序列到序列模型5)State Vector - 状态向量6)Context Vector - 上下文向量7)Hidden State - 隐藏状态8)Attention Mechanism - 注意力机制9)Teacher Forcing - 强制教师法10)Beam Search - 束搜索11)Recurrent Neural Network (RNN) - 循环神经网络12)Long Short-Term Memory (LSTM) - 长短期记忆网络13)Gated Recurrent Unit (GRU) - 门控循环单元14)Bidirectional Encoder - 双向编码器15)Greedy Decoding - 贪婪解码16)Masking - 遮盖17)Dropout - 随机失活18)Embedding Layer - 嵌入层19)Cross-Entropy Loss - 交叉熵损失20)Tokenization - 令牌化•Transfer Learning - 迁移学习1)Transfer Learning - 迁移学习2)Source Domain - 源领域3)Target Domain - 目标领域4)Fine-Tuning - 微调5)Domain Adaptation - 领域自适应6)Pre-Trained Model - 预训练模型7)Feature Extraction - 特征提取8)Knowledge Transfer - 知识迁移9)Unsupervised Domain Adaptation - 无监督领域自适应10)Semi-Supervised Domain Adaptation - 半监督领域自适应11)Multi-Task Learning - 多任务学习12)Data Augmentation - 数据增强13)Task Transfer - 任务迁移14)Model Agnostic Meta-Learning (MAML) - 与模型无关的元学习（MAML）15)One-Shot Learning - 单样本学习16)Zero-Shot Learning - 零样本学习17)Few-Shot Learning - 少样本学习18)Knowledge Distillation - 知识蒸馏19)Representation Learning - 表征学习20)Adversarial Transfer Learning - 对抗迁移学习•Pre-trained Models - 预训练模型1)Pre-trained Model - 预训练模型2)Transfer Learning - 迁移学习3)Fine-Tuning - 微调4)Knowledge Transfer - 知识迁移5)Domain Adaptation - 领域自适应6)Feature Extraction - 特征提取7)Representation Learning - 表征学习8)Language Model - 语言模型9)Bidirectional Encoder Representations from Transformers (BERT) - 双向编码器结构转换器10)Generative Pre-trained Transformer (GPT) - 生成式预训练转换器11)Transformer-based Models - 基于转换器的模型12)Masked Language Model (MLM) - 掩蔽语言模型13)Cloze Task - 填空任务14)Tokenization - 令牌化15)Word Embeddings - 词嵌入16)Sentence Embeddings - 句子嵌入17)Contextual Embeddings - 上下文嵌入18)Self-Supervised Learning - 自监督学习19)Large-Scale Pre-trained Models - 大规模预训练模型•Loss Function - 损失函数1)Loss Function - 损失函数2)Mean Squared Error (MSE) - 均方误差3)Mean Absolute Error (MAE) - 平均绝对误差4)Cross-Entropy Loss - 交叉熵损失5)Binary Cross-Entropy Loss - 二元交叉熵损失6)Categorical Cross-Entropy Loss - 分类交叉熵损失7)Hinge Loss - 合页损失8)Huber Loss - Huber损失9)Wasserstein Distance - Wasserstein距离10)Triplet Loss - 三元组损失11)Contrastive Loss - 对比损失12)Dice Loss - Dice损失13)Focal Loss - 焦点损失14)GAN Loss - GAN损失15)Adversarial Loss - 对抗损失16)L1 Loss - L1损失17)L2 Loss - L2损失18)Huber Loss - Huber损失19)Quantile Loss - 分位数损失•Activation Function - 激活函数1)Activation Function - 激活函数2)Sigmoid Function - Sigmoid函数3)Hyperbolic Tangent Function (Tanh) - 双曲正切函数4)Rectified Linear Unit (Re LU) - 矩形线性单元5)Parametric Re LU (P Re LU) - 参数化Re LU6)Exponential Linear Unit (ELU) - 指数线性单元7)Swish Function - Swish函数8)Softplus Function - Soft plus函数9)Softmax Function - SoftMax函数10)Hard Tanh Function - 硬双曲正切函数11)Softsign Function - Softsign函数12)GELU (Gaussian Error Linear Unit) - GELU（高斯误差线性单元）13)Mish Function - Mish函数14)CELU (Continuous Exponential Linear Unit) - CELU（连续指数线性单元）15)Bent Identity Function - 弯曲恒等函数16)Gaussian Error Linear Units (GELUs) - 高斯误差线性单元17)Adaptive Piecewise Linear (APL) - 自适应分段线性函数18)Radial Basis Function (RBF) - 径向基函数•Backpropagation - 反向传播1)Backpropagation - 反向传播2)Gradient Descent - 梯度下降3)Partial Derivative - 偏导数4)Chain Rule - 链式法则5)Forward Pass - 前向传播6)Backward Pass - 反向传播7)Computational Graph - 计算图8)Neural Network - 神经网络9)Loss Function - 损失函数10)Gradient Calculation - 梯度计算11)Weight Update - 权重更新12)Activation Function - 激活函数13)Optimizer - 优化器14)Learning Rate - 学习率15)Mini-Batch Gradient Descent - 小批量梯度下降16)Stochastic Gradient Descent (SGD) - 随机梯度下降17)Batch Gradient Descent - 批量梯度下降18)Momentum - 动量19)Adam Optimizer - Adam优化器20)Learning Rate Decay - 学习率衰减•Gradient Descent - 梯度下降1)Gradient Descent - 梯度下降2)Stochastic Gradient Descent (SGD) - 随机梯度下降3)Mini-Batch Gradient Descent - 小批量梯度下降4)Batch Gradient Descent - 批量梯度下降5)Learning Rate - 学习率6)Momentum - 动量7)Adaptive Moment Estimation (Adam) - 自适应矩估计8)RMSprop - 均方根传播9)Learning Rate Schedule - 学习率调度10)Convergence - 收敛11)Divergence - 发散12)Adagrad - 自适应学习速率方法13)Adadelta - 自适应增量学习率方法14)Adamax - 自适应矩估计的扩展版本15)Nadam - Nesterov Accelerated Adaptive Moment Estimation16)Learning Rate Decay - 学习率衰减17)Step Size - 步长18)Conjugate Gradient Descent - 共轭梯度下降19)Line Search - 线搜索20)Newton's Method - 牛顿法•Learning Rate - 学习率1)Learning Rate - 学习率2)Adaptive Learning Rate - 自适应学习率3)Learning Rate Decay - 学习率衰减4)Initial Learning Rate - 初始学习率5)Step Size - 步长6)Momentum - 动量7)Exponential Decay - 指数衰减8)Annealing - 退火9)Cyclical Learning Rate - 循环学习率10)Learning Rate Schedule - 学习率调度11)Warm-up - 预热12)Learning Rate Policy - 学习率策略13)Learning Rate Annealing - 学习率退火14)Cosine Annealing - 余弦退火15)Gradient Clipping - 梯度裁剪16)Adapting Learning Rate - 适应学习率17)Learning Rate Multiplier - 学习率倍增器18)Learning Rate Reduction - 学习率降低19)Learning Rate Update - 学习率更新20)Scheduled Learning Rate - 定期学习率•Batch Size - 批量大小1)Batch Size - 批量大小2)Mini-Batch - 小批量3)Batch Gradient Descent - 批量梯度下降4)Stochastic Gradient Descent (SGD) - 随机梯度下降5)Mini-Batch Gradient Descent - 小批量梯度下降6)Online Learning - 在线学习7)Full-Batch - 全批量8)Data Batch - 数据批次9)Training Batch - 训练批次10)Batch Normalization - 批量归一化11)Batch-wise Optimization - 批量优化12)Batch Processing - 批量处理13)Batch Sampling - 批量采样14)Adaptive Batch Size - 自适应批量大小15)Batch Splitting - 批量分割16)Dynamic Batch Size - 动态批量大小17)Fixed Batch Size - 固定批量大小18)Batch-wise Inference - 批量推理19)Batch-wise Training - 批量训练20)Batch Shuffling - 批量洗牌•Epoch - 训练周期1)Training Epoch - 训练周期2)Epoch Size - 周期大小3)Early Stopping - 提前停止4)Validation Set - 验证集5)Training Set - 训练集6)Test Set - 测试集7)Overfitting - 过拟合8)Underfitting - 欠拟合9)Model Evaluation - 模型评估10)Model Selection - 模型选择11)Hyperparameter Tuning - 超参数调优12)Cross-Validation - 交叉验证13)K-fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation (LOOCV) - 留一法交叉验证16)Grid Search - 网格搜索17)Random Search - 随机搜索18)Model Complexity - 模型复杂度19)Learning Curve - 学习曲线20)Convergence - 收敛3.Machine Learning Techniques and Algorithms (机器学习技术与算法)•Decision Tree - 决策树1)Decision Tree - 决策树2)Node - 节点3)Root Node - 根节点4)Leaf Node - 叶节点5)Internal Node - 内部节点6)Splitting Criterion - 分裂准则7)Gini Impurity - 基尼不纯度8)Entropy - 熵9)Information Gain - 信息增益10)Gain Ratio - 增益率11)Pruning - 剪枝12)Recursive Partitioning - 递归分割13)CART (Classification and Regression Trees) - 分类回归树14)ID3 (Iterative Dichotomiser 3) - 迭代二叉树315)C4.5 (successor of ID3) - C4.5（ID3的后继者）16)C5.0 (successor of C4.5) - C5.0（C4.5的后继者）17)Split Point - 分裂点18)Decision Boundary - 决策边界19)Pruned Tree - 剪枝后的树20)Decision Tree Ensemble - 决策树集成•Random Forest - 随机森林1)Random Forest - 随机森林2)Ensemble Learning - 集成学习3)Bootstrap Sampling - 自助采样4)Bagging (Bootstrap Aggregating) - 装袋法5)Out-of-Bag (OOB) Error - 袋外误差6)Feature Subset - 特征子集7)Decision Tree - 决策树8)Base Estimator - 基础估计器9)Tree Depth - 树深度10)Randomization - 随机化11)Majority Voting - 多数投票12)Feature Importance - 特征重要性13)OOB Score - 袋外得分14)Forest Size - 森林大小15)Max Features - 最大特征数16)Min Samples Split - 最小分裂样本数17)Min Samples Leaf - 最小叶节点样本数18)Gini Impurity - 基尼不纯度19)Entropy - 熵20)Variable Importance - 变量重要性•Support Vector Machine (SVM) - 支持向量机1)Support Vector Machine (SVM) - 支持向量机2)Hyperplane - 超平面3)Kernel Trick - 核技巧4)Kernel Function - 核函数5)Margin - 间隔6)Support Vectors - 支持向量7)Decision Boundary - 决策边界8)Maximum Margin Classifier - 最大间隔分类器9)Soft Margin Classifier - 软间隔分类器10) C Parameter - C参数11)Radial Basis Function (RBF) Kernel - 径向基函数核12)Polynomial Kernel - 多项式核13)Linear Kernel - 线性核14)Quadratic Kernel - 二次核15)Gaussian Kernel - 高斯核16)Regularization - 正则化17)Dual Problem - 对偶问题18)Primal Problem - 原始问题19)Kernelized SVM - 核化支持向量机20)Multiclass SVM - 多类支持向量机•K-Nearest Neighbors (KNN) - K-最近邻1)K-Nearest Neighbors (KNN) - K-最近邻2)Nearest Neighbor - 最近邻3)Distance Metric - 距离度量4)Euclidean Distance - 欧氏距离5)Manhattan Distance - 曼哈顿距离6)Minkowski Distance - 闵可夫斯基距离7)Cosine Similarity - 余弦相似度8)K Value - K值9)Majority Voting - 多数投票10)Weighted KNN - 加权KNN11)Radius Neighbors - 半径邻居12)Ball Tree - 球树13)KD Tree - KD树14)Locality-Sensitive Hashing (LSH) - 局部敏感哈希15)Curse of Dimensionality - 维度灾难16)Class Label - 类标签17)Training Set - 训练集18)Test Set - 测试集19)Validation Set - 验证集20)Cross-Validation - 交叉验证•Naive Bayes - 朴素贝叶斯1)Naive Bayes - 朴素贝叶斯2)Bayes' Theorem - 贝叶斯定理3)Prior Probability - 先验概率4)Posterior Probability - 后验概率5)Likelihood - 似然6)Class Conditional Probability - 类条件概率7)Feature Independence Assumption - 特征独立假设8)Multinomial Naive Bayes - 多项式朴素贝叶斯9)Gaussian Naive Bayes - 高斯朴素贝叶斯10)Bernoulli Naive Bayes - 伯努利朴素贝叶斯11)Laplace Smoothing - 拉普拉斯平滑12)Add-One Smoothing - 加一平滑13)Maximum A Posteriori (MAP) - 最大后验概率14)Maximum Likelihood Estimation (MLE) - 最大似然估计15)Classification - 分类16)Feature Vectors - 特征向量17)Training Set - 训练集18)Test Set - 测试集19)Class Label - 类标签20)Confusion Matrix - 混淆矩阵•Clustering - 聚类1)Clustering - 聚类2)Centroid - 质心3)Cluster Analysis - 聚类分析4)Partitioning Clustering - 划分式聚类5)Hierarchical Clustering - 层次聚类6)Density-Based Clustering - 基于密度的聚类7)K-Means Clustering - K均值聚类8)K-Medoids Clustering - K中心点聚类9)DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - 基于密度的空间聚类算法10)Agglomerative Clustering - 聚合式聚类11)Dendrogram - 系统树图12)Silhouette Score - 轮廓系数13)Elbow Method - 肘部法则14)Clustering Validation - 聚类验证15)Intra-cluster Distance - 类内距离16)Inter-cluster Distance - 类间距离17)Cluster Cohesion - 类内连贯性18)Cluster Separation - 类间分离度19)Cluster Assignment - 聚类分配20)Cluster Label - 聚类标签•K-Means - K-均值1)K-Means - K-均值2)Centroid - 质心3)Cluster - 聚类4)Cluster Center - 聚类中心5)Cluster Assignment - 聚类分配6)Cluster Analysis - 聚类分析7)K Value - K值8)Elbow Method - 肘部法则9)Inertia - 惯性10)Silhouette Score - 轮廓系数11)Convergence - 收敛12)Initialization - 初始化13)Euclidean Distance - 欧氏距离14)Manhattan Distance - 曼哈顿距离15)Distance Metric - 距离度量16)Cluster Radius - 聚类半径17)Within-Cluster Variation - 类内变异18)Cluster Quality - 聚类质量19)Clustering Algorithm - 聚类算法20)Clustering Validation - 聚类验证•Dimensionality Reduction - 降维1)Dimensionality Reduction - 降维2)Feature Extraction - 特征提取3)Feature Selection - 特征选择4)Principal Component Analysis (PCA) - 主成分分析5)Singular Value Decomposition (SVD) - 奇异值分解6)Linear Discriminant Analysis (LDA) - 线性判别分析7)t-Distributed Stochastic Neighbor Embedding (t-SNE) - t-分布随机邻域嵌入8)Autoencoder - 自编码器9)Manifold Learning - 流形学习10)Locally Linear Embedding (LLE) - 局部线性嵌入11)Isomap - 等度量映射12)Uniform Manifold Approximation and Projection (UMAP) - 均匀流形逼近与投影13)Kernel PCA - 核主成分分析14)Non-negative Matrix Factorization (NMF) - 非负矩阵分解15)Independent Component Analysis (ICA) - 独立成分分析16)Variational Autoencoder (VAE) - 变分自编码器17)Sparse Coding - 稀疏编码18)Random Projection - 随机投影19)Neighborhood Preserving Embedding (NPE) - 保持邻域结构的嵌入20)Curvilinear Component Analysis (CCA) - 曲线成分分析•Principal Component Analysis (PCA) - 主成分分析1)Principal Component Analysis (PCA) - 主成分分析2)Eigenvector - 特征向量3)Eigenvalue - 特征值4)Covariance Matrix - 协方差矩阵。

A Comprehensive Survey of Multiagent Reinforcement Learning

156
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 38, NO. 2, MARCH 2008
A Comprehensive Survey of Multiagent ReinfoN
A
MULTIAGENT system [1] can be deﬁned as a group of autonomous, interacting entities sharing a common environment, which they perceive with sensors and upon which they act with actuators [2]. Multiagent systems are ﬁnding applications in a wide variety of domains including robotic teams, distributed control, resource management, collaborative decision support systems, data mining, etc. [3], [4]. They may arise as the most natural way of looking at the system, or may provide an alternative perspective on systems that are originally regarded as centralized. For instance, in robotic teams, the control authority is naturally distributed among the robots [4]. In resource management, while resources can be managed by a central authority, identifying each resource with an agent may provide a helpful, distributed perspective on the system [5].

FPGA可编程逻辑器件芯片XCKU085-2FLVA1517I中文规格书

Zynq® UltraScale+ MPSoC devices provide 64-bit processor scalability while combining real-time control with soft and hard engines for graphics, video, waveform, and packet processing. Integrating an Arm®-based system for advanced analytics and on-chip programmable logic for task acceleration creates unlimited possibilities for applications including 5G Wireless, next generation ADAS, and Industrial Internet-of-Things.This user guide describes the UltraScale architecture GTH transceivers and is part of the UltraScale architecture documentation suite available.FeaturesThe GTH transceivers in the UltraScale architecture are power-efficient transceivers, supporting line rates from 500Mb/s to 16.375Gb/s. The GTH transceiver is highly configurable and tightly integrated with the programmable logic resources of the UltraScale architecture. Table1-1 summarizes the features by functional group that support a wide variety of applications.Table 1-1:GTH Transceiver FeaturesGroup FeaturePCS2-byte and 4-byte internal datapath to support different line rate requirements 8B/10B encoding and decoding64B/66B and 64B/67B support128B/130B encoding and decoding for PCI Express® Gen3Comma detection and byte and word alignmentPRBS generator and checkerTX phase FIFORX elastic FIFO for clock correction and channel bondingBuffer bypass support for fixed latencyProgrammable logic interface100 Gb attachment unit interface (CAUI) supportNative multi-lane support for buffer bypassTX phase interpolator PPM controller for external voltage-controlled crystal oscillator(VCXO) replacementRefer to Figure2-11, page45 for the description of the channel clocking architecture, which provides clocks to the RX and TX clock dividers.UltraScale FPGAs Transceivers WizardThe UltraScale FPGAs Transceivers Wizard (hereinafter called the Wizard) is the preferred tool to generate a wrapper to instantiate the GTHE3_COMMON and GTHE3_CHANNEL primitives in UltraScale FPGAs and GTHE4_COMMON and GTHE4_CHANNEL primitives in UltraScale+ FPGAs. The Wizard is located in the IP catalog under the IO Interfaces category.Design Suite (PG182) [Ref4].GTHE3/4_CHANNEL AttributesThe GTHE3/4_CHANNEL primitive has attributes intended only for simulation, and they have no impact on synthesis. Table1-3 lists the simulation-only attributes of theGTHE3/4_CHANNEL primitive. The names of these attributes start with SIM_.Table 1-3:GTHE3/4_CHANNEL Simulation-Only AttributesAttribute Type DescriptionSIM_MODE String This attribute selects the simulation mode. The default forthis attribute is FAST.SIM_RESET_SPEEDUP String If the SIM_RESET_SPEEDUP attribute is set to TRUE(default), an approximated reset sequence is used tospeed up the reset time for simulations, where faster resettimes and faster simulation times are desirable. If theSIM_RESET_SPEEDUP attribute is set to FALSE, the modelemulates hardware reset behavior in detail.SIM_RESET_SPEEDUP must be set to FALSE when the TX orRX buffer bypass features are used.SIM_RECEIVER_DETECT_PASS Boolean UltraScale FPGAs only:SIM_RECEIVER_DETECT_PASS is a string TRUE/FALSEattribute to determine if a receiver detect operationshould indicate a pass or fail in simulation.SIM_TX_EIDLE_DRIVE_LEVEL String UltraScale FPGAs only:SIM_TX_EIDLE_DRIVE_LEVEL can be set to 0, 1, X, or Z toallow for simulation of electrical idle and receiver detectoperations using an external pull-up resistor. The defaultfor this attribute is 0.SIM_VERSION Integer UltraScale FPGAs only:This attribute selects the simulation version to matchdifferent revisions of silicon. The default for this attributeis 2.SIM_DEVICE String UltraScale+ FPGAs only:This attribute selects the simulation version to matchdifferent revisions of silicon. The default for this attributeis ULTRASCALE_PLUS.Chapter 2:Shared Features Port Dir Clock Domain DescriptionCEB In N/A This is the active-Low asynchronous clock enable signal forthe clock buffer. Setting this signal High powers down theclock buffer.I In N/A Recovered clock input. Connect to the output portRXRECCLKOUT of one of the four GTHE3/4_CHANNEL in thesame Quad.O Out N/A Reference clock output ports that get mapped toGTREFCLK0P and GTREFCLK1P.OB Out N/A Reference clock output ports that get mapped toGTREFCLK0N and GTREFCLK1N.Attribute Type DescriptionREFCLK_EN_TX_PATH1-bit Binary Reserved. This attribute must always be set to 1'b1. REFCLK_ICNTL_TX5-bit Binary Reserved. Use the recommended value from the Wizard.。

efficient_multi-scale_attention_module_概述及解释说明

efficient multi-scale attention module 概述及解释说明1. 引言1.1 概述本篇文章将介绍“efficient multi-scale attention module（高效多尺度注意力模块）”的概念和解释。

该模块是一种用于计算机视觉领域的新型技术，旨在提升在多尺度场景下的特征提取和表征能力。

本文将详细阐述该模块的定义、应用场景以及优势。

1.2 文章结构本文共分为五个部分：引言、efficient multi-scale attention module概述、解释说明efficient multi-scale attention module的关键要点、其他相关研究工作概述和比较分析以及结论。

通过这样的结构，读者能够全面了解并深入探索efficient multi-scale attention module的概念和其在计算机视觉领域中的重要性。

1.3 目的本文旨在向读者介绍efficient multi-scale attention module的基本原理、设计思路以及其在实践中所展现出来的优越性。

通过对其关键要点进行详细解释和说明，希望读者能够对该模块有更加清晰全面的理解，并认识到其在计算机视觉领域中所具有的广泛应用前景和重要意义。

此外，通过与其他相关研究工作的比较分析，读者将能够更好地理解efficient multi-scale attention module与传统注意力机制以及现有多尺度模型之间的差异与优势所在。

通过对未来发展方向和应用领域进行展望，并回顾整篇文章的主要内容，我们希望本文能够为读者提供一个全面深入了解efficient multi-scale attention module的参考，并为相关领域研究提供有益启示。

2. efficient multi-scale attention module 概述:2.1 多尺度注意力机制简介:在计算机视觉和深度学习领域，多尺度注意力机制被广泛应用于图像和视频处理任务中。

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.。

基于聚类与自适应ALGBM_的预测模型研究

第 22卷第 3期2023年 3月Vol.22 No.3Mar.2023软件导刊Software Guide基于聚类与自适应ALGBM的预测模型研究廖雪超1，2，马亚文1，2（1.武汉科技大学计算机科学与技术学院；2.智能信息处理与实时工业系统重点实验室，湖北武汉 430065）摘要：建筑能耗预测在建筑能源管理、节能和故障诊断等方面发挥着重要作用，而建筑能耗数据之间存在非线性和离群值点，导致能耗预测精度降低。

为解决以上问题，提出基于特征提取、聚类和改进LGBM的MRGALnet建筑能耗预测模型。

首先通过MI+RFE二次特征选择算法筛选出对建筑能耗影响最大的特征子集，然后利用GMM高斯混合模型算法将能耗特性相似的建筑进行归类，并采用LGBM模型对每个聚类的能耗数据进行预测，进一步设计自适应损失函数以改进LGBM的预测性能。

通过对比实验可知，MI+RFE特征选择算法能有效去除冗余特征，GMM聚类方法则能对原始数据进行合理的聚类划分，而ALGBM模型可根据不同聚类的能耗数据自适应地确定损失函数超参数，以提高模型预测性能，综合以上算法的MRGALnet模型能够进一步提升预测精度和收敛速度。

关键词：建筑能耗预测；特征选择；聚类；轻量级梯度提升机；自适应损失函数DOI：10.11907/rjdk.222471开放科学（资源服务）标识码（OSID）：中图分类号：TP183 文献标识码：A文章编号：1672-7800（2023）003-0010-08Research on Predictive Model Based on Clustering and Adaptive ALGBMLIAO Xue-chao1，2， MA Ya-wen1，2（1.College of Computer Science and Technology， Wuhan University of Science and Technology；2.Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems， Wuhan 430065， China）Abstract：Building energy consumption prediction plays an important role in building energy management， energy conservation and fault di⁃agnosis. However， there are nonlinear and outlier points among building energy consumption data， which leads to the decrease of energy con⁃sumption prediction accuracy. To solve the above problems， the MRGALnet building energy consumption prediction model based on feature ex⁃traction， clustering and improved LGBM is proposed. Firstly， the subsets of features that have the greatest impact on building energy consump⁃tion are selected through MI+RFE secondary feature selection algorithm. Secondly， building data with similar energy consumption characteris⁃tics are grouped by Gaussian mixture clustering algorithm. Thirdly， energy consumption data for each cluster are predicted by LGBM. Furter more， an adaptive loss function is designed to improve the prediction performance of LGBM. Through comparative experimental analysis， it can be seen that MI+RFE feature selection algorithm can effectively remove redundant features， GMM can reasonably cluster the original da⁃ta， and ALGBM model can adaptively determine the hyperparameters of the loss function according to the energy consumption data of different clustering， so as to improve the model prediction performance. The MRGALnet model combined with the above algorithms is optimal in terms of prediction accuracy and convergence speed. The MRGALnet model integrating the above algorithms can further improve the prediction accu⁃racy and convergence speed.Key Words：building energy consumption prediction； feature selection； clustering； light gradient boosting machine； adaptive loss function0 引言随着时代的发展，近年来能源消耗量持续增长，能源问题已成为一个全球性问题。

efficientformer 解析

《Efficientformer 解析》一、导论在当前人工智能领域，以Transformer为代表的模型架构备受瞩目，然而，随着问题规模的不断扩大，传统的Transformer模型在计算资源、时间成本方面存在较大的缺陷。

为了解决这一问题，近期出现了一种新的模型架构——Efficientformer，本文将对Efficientformer进行深入解析，帮助读者全面了解这一新兴模型。

二、Efficientformer的基本原理Efficientformer是一种旨在提高Transformer模型计算效率的模型架构。

它借鉴了传统Transformer模型的自注意力机制，并在此基础上进行了优化和改进。

Efficientformer的关键在于引入了轻量化的注意力机制，采用了一系列有效的参数共享和剪枝策略，从而在保证模型性能的前提下，大幅减少了模型参数量和计算复杂度，提高了模型的计算效率。

三、Efficientformer的技术特点1. 轻量化的注意力机制Efficientformer在设计自注意力机制时，采用了一些轻量级的技术，如低秩注意力、深度可分离卷积等，有效降低了原始Transformer模型中复杂的注意力计算，提高了模型的计算效率。

2. 参数共享和剪枝Efficientformer通过合理的参数共享和剪枝策略，减少了模型的参数量和计算复杂度，在不影响模型性能的前提下，显著提高了模型的计算效率。

3. 网络架构优化Efficientformer在网络架构设计上，充分考虑了模型的计算效率和性能，通过设计精妙的网络结构，使得模型在保持高精度的大幅提高了计算效率。

四、Efficientformer的应用前景作为一种新兴的模型架构，Efficientformer在自然语言处理、计算机视觉、语音识别等领域都具有广泛的应用前景。

在大规模数据集上，Efficientformer能够显著降低模型训练时的时间成本，并且在保持模型精度的大幅提高了模型的计算效率，因此受到了业界的高度关注。

英语作文手写模板范文

英语作文手写模板范文英文回答：Topic: The Role of Technology in Education: Empoweringor Enervating?Introduction:Technology has become an indispensable part of our lives, influencing every aspect of our existence. Education, in particular, has witnessed a profound transformation with the advent of digital tools and resources. However, the integration of technology into education has sparked a debate: Is it empowering or enervating?Empowering Effects:Enhanced Accessibility: Technology bridges physicaland temporal barriers, making education accessible to a wider range of learners, including those in remote areas orwith disabilities.Personalized Learning: Digital platforms enable educators to tailor instruction to individual student needs, creating personalized learning experiences that foster academic growth.Interactive Content: Technology provides interactive content, such as simulations, videos, and games, which engage students and make learning more enjoyable.Collaboration and Communication: Technologyfacilitates collaboration among students and teachers, fostering a sense of community and promoting effective communication.Enervating Effects:Digital Distraction: The constant availability of technology can be distracting, leading students to lose focus on academic tasks.Equity Concerns: Access to and proficiency withdigital tools vary widely, creating an opportunity gap between students with and without access to technology.Reduced Social Interaction: Excessive use of technology can reduce face-to-face interaction, which is essential for social and emotional development.Erosion of Critical Thinking Skills: Reliance on technology for information retrieval may diminish students' ability to think critically and develop independent problem-solving skills.Balancing Empowerment and Enervation:To harness the benefits of technology while mitigating its potential drawbacks, a balanced approach is crucial:Integrate Technology Meaningfully: Use technology to enhance student learning, rather than merely as a distraction.Foster Digital Literacy: Provide students with instruction and support in digital literacy to bridge the equity gap.Encourage Human Interaction: Create opportunities for students to interact with teachers and peers face-to-face, fostering social and emotional development.Cultivate Critical Thinking: Encourage students to critically evaluate information obtained through digital sources and develop problem-solving skills.Conclusion:The integration of technology into education has the potential to both empower and enervate students. By understanding the empowering and enervating effects of technology, educators and policymakers can strike a balance that maximizes its benefits while minimizing its negative consequences. Ultimately, technology should be used as a tool to enhance student learning, rather than a replacement for traditional educational practices.中文回答：科技在教育中的作用，赋能还是萎靡？引言：科技已成为我们生活中不可或缺的一部分，影响着我们存在的方方面面。

stable diffusion 面试题目

stable diffusion 面试题目问题1：文本生成的几大预训练任务？GPT（Generative Pre-trained Transformer）系列：包括GPT、GPT-2、GPT-3等。

这些模型使用Transformer架构进行预训练，在大规模语料上学习语言模型，能够生成连贯、具有语义的文本。

BART（Bidirectional and Auto-Regressive Transformer）：BART是一种基于Transformer的生成式预训练模型。

它通过自回归解码器实现文本生成，通过自编码器预训练目标来重构输入文本，能够生成流畅、连贯的文本。

T5（Text-to-Text Transfer Transformer）：T5是一种通用的文本生成模型，使用了编码器-解码器结构。

它将不同的自然语言处理（NLP）任务转换为文本到文本的转换任务，可用于机器翻译、摘要生成、问题回答等多个NLP任务。

XLNet：XLNet是一种基于Transformer架构的预训练模型，采用了自回归和自编码器的组合方式进行训练。

它在语言建模任务上引入了全局的上下文信息，能够生成更加准确和连贯的文本。

UniLM（Unified Language Model）：UniLM是一种多任务学习的预训练模型，将不同的自然语言处理任务转化为统一的生成式任务。

它可以用于文本摘要、问答系统、机器翻译等多个任务。

问题2：多模态中常见的SOTA模型有哪些？Vision Transformer (ViT): 将自注意力机制引入计算机视觉领域，通过将图像划分为图像补丁并应用Transformer模型，实现了在图像分类和目标检测等任务上的出色表现。

CLIP (Contrastive Language-Image Pretraining): 结合了图像和文本的对比学习，通过训练一个模型，使其能够根据图像和文本之间的相互关系进行推理，实现了图像与文本之间的联合理解和表示学习。

IBM Cognos Transformer V11.0 用户指南说明书

Dimensional Modeling Workflow................................................................................................................. 1 Analyzing Your Requirements and Source Data.................................................................................... 1 Preprocessing Your ...................................................................................................................... 2 Building a Prototype............................................................................................................................... 4 Refining Your Model............................................................................................................................... 5 Diagnose and Resolve Any Design Problems........................................................................................ 6

TLK2711中文资料

D Receiver Differential Input Thresholds
200 mV Minimum
D Low Power: < 500 mW D 3 V Tolerance on Parallel Data Input Signals D 16-Bit Parallel TTL Compatible Data D D D D
Please be aware that an important notice concerning availability, standard warranty, and use in critical applications of Texas Instruments semiconductor products and disclaimers thereto appears at the end of this data sheet. MicroStar Junior and PowerPAD are trademarks of Texas Instruments.
元器件交易网
TLK2711 1.6 TO 2.7 GBPS TRANSCEIVER
SLLS501 – SEPTEMBER 2001
D 1.6 to 2.7 Gigabits Per Second (Gbps) D D D D D D D
Serializer/Deserializer Hot-Plug Protection High-Performance 80-Pin BGA Microstar Junior Package (GQE) 2.5-V Power Supply for Low Power Operation Programmable Preemphasis Levels on Serial Output Interfaces to Backplane, Copper Cables, or Optical Converters On-Chip 8-bit/10-bit Encoding/Decoding, Comma Detect On-Chip PLL Provides Clock Synthesis From Low-Speed Reference

Microwave Theory and Technique Electronic Measurements Fundamentals of Electronics Circuit

RESEARCH INTERESTSPower Electronics, Switching-Mode PWM and Resonant DC/DC Power Converters, DC/AC Inverters, Resonant Rectifiers, RF Tuned Power Transistor Amplifiers and Oscillators, Power Management, Magnetic Devices, Semi-conductor Device Modeling, Power Integrated Circuits, Electronic Ballasts, Lighting Systems, Modeling and Con-trols of Power Converters, Sensors, Electronic Circuits, Integrated Circuits, Energy Harvesting, and CAD.EDUCATION1966-1971 Department of Electronics, Technical University of Warsaw, Warsaw, Poland1972-1973 Post Graduate Study in Engineering Education, Technical University of Warsaw, Warsaw, PolandDEGREES1971M.S.Thesis: "Gunn diode oscillator for X-band with varactor tuning"Advisors: Professor Adam Smolinski and Professor Janusz DobrowolskiDepartment of Electronics, Technical University of Warsaw, Warsaw, Poland 1978Ph.D.Dissertation: "High-efficiency tuned power transistor amplifier"Advisor: Professor Jan EbertDepartment of Electronics, Technical University of Warsaw, Warsaw, Poland 1984D. Sci.Dissertation: "High-efficiency tuned power amplifiers, frequency multipliers, and oscillators," Warsaw Technical University Publisher, pp. 1-143, Warsaw 1984Department of Electronics, Technical University of Warsaw, Warsaw, PolandPROFESSIONAL ACADEMIC EXPERIENCE1972-1978 Instructor, Department of Electronics, Technical University of Warsaw, Warsaw, Poland1978-1984 Assistant Professor, Department of Electronics, Technical University of Warsaw, Warsaw, PolandCourses taught High-Frequency High-Power TechniquesRadio TransmittersElectromagnetic Field TheoryMicrowave Theory and TechniqueElectronic MeasurementsFundamentals of ElectronicsCircuit TheoryElectronic Circuits and SystemsRadio Transmitters LaboratoryRadio Receivers LaboratoryElectronics LaboratoryRadio Electronics Laboratory, Chair, 1978-1984Electronic Apparatus Laboratory, Chair, 1978-1984.MARIAN K. KAZIMIERCZUKProfessor of Electrical EngineeringWright State UniversityDayton, OH 45435Phone: 937 775-5059 Fax: 937-775-3936 mkazim@1984-1985 Visiting Professor, Department of Electrical Engineering, Virginia Polytechnic Institute and State Uni-versity, Blacksburg, VA 24061Courses taught EE3101 Electromagnetic FieldsEE3201 Electronics IEE3202 Electronics IIEE4201 Electronic Circuits and Systems I1985-1990 Assistant Professor, Department of Electrical Engineering, Wright State University, Dayton, OH 45435 1990-1994 Associate Professor, Department of Electrical Engineering, Wright State University, Dayton, OH 45435 1994-pres Professor, Department of Electrical Engineering, Wright State University, Dayton, OH 45435 Courses taught EE 331/531 Electronic DevicesEE 431/631 Electronic CircuitsEE 434/634 Electronic Circuits LaboratoryEE 444/644 Linear Integrated CircuitsEE 449/649 Pulse and Digital CircuitsEE 499/699 Special Problems in EngineeringEE 499 Design Industrial ClinicEE 741 Power Semiconductor DevicesEE 742 Power Electronics IIEE 743 Power Electronics IIIEGR 891 Ph.D. SeminarADVISING11 Ph.D. students81 M.S. students6 post-doctoral positions3 sabbatical positionsPROFESSIONAL NON-ACADEMIC EXPERIENCE1984 Design Automation, Inc., 809 Massachusetts Avenue, Lexington, MA 02173, (617) 862-8998 Project Engineer responsible for designing high-efficiency switching-mode dc/dc converters1991 Wright-Patterson AFB, Wright Laboratory, Dayton, OH, Summer Faculty Fellowship1995 Wright-Patterson AFB, Wright Laboratory, Dayton, OH, Summer Faculty Fellowship1996 Wright-Patterson AFB, Wright Laboratory, Dayton, OH, Summer Faculty Fellowship PROFESSIONAL MEMBERSHIPSIEEE, Fellow 2005-presentIEEE, Senior Member 1991-2004Power Electronics Society 1991-presentCircuit and Systems Society 1991-presentIndustrial Electronics Society 1991-presentAerospace and Electronic Systems Society 1991-presentIndustry Applications Society 1991-presentTau Beta Pi 1992-presentElectrical Manufacturing and Coil Winding Association 1991-presentAWARDS1977 President of the Technical University of Warsaw1978 President of the Technical University of Warsaw1979 President of the Technical University of Warsaw1980 President of the Technical University of Warsaw1981 Minister of Science, University Education, and Technology1982 Minister of Science, University Education, and Technology1983 Polish Academy of Sciences1984 President of the Technical University of Warsaw1985 Minister of Science, University Education, and Technology1990 Harrel V. Noble Award, IEEE Dayton Section1991 Excellence in Research Award, College of Engineering and Computer Science, Wright State University 1991 Presidential Award for Faculty Excellence in Research, Wright State University1993 Excellence in Teaching Award, College of Engineering and Computer Science, Wright State University 1993 Nominated for the Presidential Teaching Excellence Award, Wright State University1994 Nominated for the Presidential Teaching Excellence Award, Wright State University1994 Electrical Manufacturing and Coil Winding for outstanding contribution1995 Award for Outstanding Professional Achievement, the Affiliate Societies Council of the Engineering and Sci-ence Foundation of Dayton1995 Outstanding Faculty Member, College of Engineering and Computer Science, Wright State University1995 Presidential Award, Outstanding Faculty Member, Wright State University1996 Brage Golding Distinguished Professor of Research Award, Wright State University1997 Excellence in Professional Service Award, College of Engineering and Computer Science, Wright State Uni-versity1997 Nominated for the Presidential Professional Service Award, Wright State University2000 Excellence in Teaching Award, College of Engineering and Computer Science, Wright State University 2000 Nominated for the Presidential Teaching Award, Wright State University2002 Excellence in Professional Service Award, College of Engineering and Computer Science, Wright State Uni-versity2002 Nominated for the Presidential Professional Service Award, Wright State University2003 Excellence in Research Award, College of Engineering and Computer Science, Wright State University 2004 Board of Trustees’ Award for Faculty Excellence, Wright State University2005 Nominated for the Excellence in Teaching Award, College of Engineering and Computer Science, Wright State University2006 Nominated for Robert J. Kegerreis Distinguished Professor of Teaching by CECS2007 Nominated for Robert J. Kegerreis Distinguished Professor of Teaching by CECS2007 Nominated for the Excellence in Teaching Award, College of Engineering and Computer Science, Wright State UniversityPUBLICATIONSBooks1. M. K. Kazimierczuk and D. Czarkowski, "Resonant Power Converters," John Wiley & Sons, New York, NY, pp.1-481, 1995 (The text book is intended for graduate courses and practicing engineers).2. M. K. Kazimierczuk and D. Czarkowski, "Solutions for Resonant Power Converters," John Wiley & Sons, NewYork, NY, pp. 1-80, 1995.3. A. Aminian and M. K. Kazimierczuk, “Electronic Devices: A Design Approach,” Prentice Hall, Upper SaddleRiver, NJ, pp. 1-810, 2004 (The text book is intended for undergraduate courses, 3 quarters or 2 semesters). 4. M. K. Kazimierczuk and A. Aminian, “Laboratory Manual to Accompany Electronic Devices: A Design Ap-proach,” Prentice Hall, Upper Saddle River, NJ, pp. 1-149, 2004 (The book is intended for undergraduate courses).5. M. K. Kazimierczuk and A. Aminian, “Instructor’s Solutions Manual to Accompany Electronic Devices: A De-sign Approach,” Prentice Hall, Upper Saddle River, NJ, pp. 1-543, 2004.6. M. K. Kazimierczuk, “Pulse-width DC-DC Power Converters,” John Wiley & Sons, New York, NY, 2008, pp. 1-968 (in press). (The book is intended for graduate students and practicing engineers).7. M. K. Kazimierczuk, “Solutions Manual for Pulse-width WM DC-DC Power Converters,” John Wiley & Sons,New York, NY, 2008 (in press).Journal Articles1. M. K. Kazimierczuk and J. M. Modzelewski, "Drive-transformerless Class-D voltage switching tuned poweramplifier," Proceedings of the IEEE, Vol. 68, pp. 740-741, June 1980.2. M. K. Kazimierczuk, "Class E tuned power amplifier with shunt inductor," IEEE Journal of Solid-State Circuits,Vol. SC-16, pp. 2-7, February 1981.3. J. Ebert and M. K. Kazimierczuk, "Class E high-efficiency tuned power oscillator," IEEE Journal of Solid-StateCircuits, Vol. SC-16, pp. 62-66, April 1981.4. M. K. Kazimierczuk, "A new approach to the design of tuned power oscillators," IEEE Transactions on Circuitsand Systems, Vol. CAS-29, pp. 261-267, April 1982.5. J. Ebert and M. K. Kazimierczuk, "High-efficiency RF power transistor amplifier," Bull. Polon. Sci., Ser. Sci.Tech., Vol. 25, No. 2, pp. 135-138, 1977.6. J. Ebert and M. K. Kazimierczuk, "Applying the Class E concept to the RF power generator," Bull. Acad.Polon. Sci., Ser. Sci. Tech., Vol. 29, No. 1-2, pp. 79-87, 1981.7. M. K. Kazimierczuk, "Effects of the collector current fall time on the Class E tuned power amplifier," IEEEJournal of Solid-State Circuits, Vol. SC-18, pp. 181-193, April 1983.8. M. K. Kazimierczuk, "Exact analysis of Class E tuned power amplifier with only one inductor and one capacitorin load network," IEEE Journal of Solid-State Circuits, Vol. SC-18, pp. 214-221, April 1983.9. M. K. Kazimierczuk, "Parallel operation of power transistors in switching amplifiers," Proceedings of the IEEE,Vol. 71, pp. 1456-1457, December 1983.10. M. K. Kazimierczuk, "Charge-control analysis of Class E tuned power amplifier with only one inductor and onecapacitor in load network," IEEE Transactions on Electronic Devices, Vol. ED-31, pp. 366-373, March 1984.11. M. K. Kazimierczuk, "Accurate measurements of lifetime of excess base stored charge at high collector cur-rents," IEEE Transactions on Electronic Devices, Vol. ED-31, pp. 374-378, March 1984.12. M. K. Kazimierczuk, "Collector amplitude modulation of Class E tuned power amplifier," IEEE Transactions onCircuits and Systems, Vol. CAS-31, pp. 543-549, June 1984.13. M. K. Kazimierczuk and N. O. Sokal, "Cause of instability of power amplifier with parallel-connected powertransistors," IEEE Journal of Solid-State Circuits, Vol. SC-19, pp. 541-542, August 1984.14. M. K. Kazimierczuk, "Class E tuned power amplifier with nonsinusoidal output voltage," IEEE Journal of SolidState Circuits, Vol. SC-21, pp. 575-581, August 1986.15. M. K. Kazimierczuk, "Generalization of conditions for 100-percent efficiency and nonzero output power inpower amplifiers and frequency multipliers," IEEE Transactions on Circuits and Systems, Vol. CAS-33, pp.805-807, August 1986.16. M. K. Kazimierczuk and K. Puczko, "Exact analysis of Class E tuned power amplifier at any Q and switch dutycycle," IEEE Transactions on Circuits and Systems, Vol. CAS-34, pp. 149-159, February 1987.17. M. K. Kazimierczuk, "High-speed driver for switching power MOSFETs," IEEE Transactions on Circuits andSystems, Vol. CAS-35, pp. 254-256, February 1988.18. M. K. Kazimierczuk, "Design-oriented analysis of boost zero-voltage-switching resonant dc/dc converter,"IEEE Transactions on Power Electronics, Vol. PE-3, pp. 126-136, April 1988.19. M. K. Kazimierczuk, "Steady-state analysis of a buck zero-current-switching resonant dc/dc converter," IEEETransactions on Power Electronics, Vol. PE-3, pp. 286-296, July 1988.20. M. K. Kazimierczuk, "A network theorem dual to Miller's theorem," IEEE Transactions on Education, Vol. E-31,pp. 265-269, November 1988.21. M. K. Kazimierczuk and X. T. Bui, "Class E dc/dc converters with an inductive impedance inverter," IEEETransactions on Power Electronics, Vol. PE-4, pp. 124-135, January 1989.22. J. Jozwik and M. K. Kazimierczuk, "Dual sepic PWM switching-mode dc/dc power converter," IEEE Transac-tions on Industrial Electronics, Vol. IE-36, pp. 64-70, February 1989.23. M. K. Kazimierczuk and W. A. Tabisz, "Class C-E high-efficiency tuned power amplifier," IEEE Transactionson Circuits and Systems, Vol. CAS-36, pp. 421-428, March 1989.24. M. K. Kazimierczuk and W. D. Morse, "State-plane analysis of zero-voltage-switching resonant dc/dc convert-ers," IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-25, pp. 232-239, March 1989.25. M. K. Kazimierczuk and W. D. Morse, "State-plane analysis of zero-current-switching resonant dc/dc powerconverters," IEEE Transactions on Power Electronics, Vol. PE-4, pp. 265-271, April 1989.26. M. K. Kazimierczuk, "Analysis of buck/boost zero-current-switching resonant dc/dc converter," IEE Proceed-ings, Part B, Electric Power Applications, Vol. 136, pp. 127-135, May 1989.27. M. K. Kazimierczuk and J. Jozwik, "Optimal topologies of resonant dc/dc converters," IEEE Transactions onAerospace and Electronic Systems, Vol. AES-25, pp. 362-372, May 1989.28. M. K. Kazimierczuk and J. Jozwik, "Class E zero-voltage-switching rectifier with a series capacitor," IEEETransactions on Circuits and Systems, Vol. CAS-36, pp. 926-928, June 1989.29. M. K. Kazimierczuk and K. Puczko, "Power-output capability of Class E amplifier at any loaded Q and switchduty cycle," IEEE Transactions on Circuits and Systems, Vol. CAS-36, pp. 1142-1143, August 1989.30. M. K. Kazimierczuk and X. T. Bui, "Class E dc/dc converters with a capacitive impedance inverter," IEEETransactions on Industrial Electronics, Vol. IE-36, pp. 425-433, August 1989.31. M. K. Kazimierczuk, "Analysis and design of buck/boost zero-voltage-switching resonant dc/dc converter," IEEProceedings, Pt. G, Circuits, Devices, and Systems, Vol. 136, pp. 157-166, August 1989.32. M. K. Kazimierczuk and K. Puczko, "Class E tuned power amplifier with an antiparallel diode or a series diodeat switch, with any loaded Q and switch duty cycle," IEEE Transactions on Circuits and Systems, Vol. CAS-36, pp. 1201- 209, September 1989.33. M. K. Kazimierczuk and J. Jozwik, "DC/DC converter with Class E zero-voltage-switching inverter and Class Ezero-current-switching rectifier," IEEE Transactions on Circuits and Systems, Vol. CAS-36, pp. 1485-1488, November 1989.34. M. K. Kazimierczuk and J. Jozwik, "Resonant dc/dc converter with Class-E inverter and Class-E rectifier,"IEEE Transactions on Industrial Electronics, Vol. IE-36, pp. 568-578, November 1989.35. M. K. Kazimierczuk, "Class E low dv D/dt rectifier," IEE Proceedings, Pt. B, D Electric Power Applications, Vol.136, pp. 257-262, November 1989.36. M. K. Kazimierczuk and J. Jozwik, "Class E2 narrow-band resonant dc/dc converters," IEEE Transactions onInstrumentation and Measurement, Vol. IM-38, pp. 1064-1068, December 1989.37. M. K. Kazimierczuk and J. Jozwik, "Class E zero-voltage-switching and zero-current-switching rectifiers," IEEETransactions on Circuits and Systems, Vol. CAS-37, pp. 436-444, March 1990.38. M. K. Kazimierczuk and X. T. Bui, "Class-E amplifier with an inductive impedance inverter," IEEE Transactionson Industrial Electronics, Vol. IE-37, pp. 160-166, April 1990.39. J. Jozwik and M. K. Kazimierczuk, "Analysis and design of Class-E2 dc/dc converter," IEEE Transactions onIndustrial Electronics, Vol. IE-37, pp. 173-183, April 1990.40. M. K. Kazimierczuk, "Analysis of Class E zero-voltage-switching rectifier," IEEE Transactions on Circuits andSystems, Vol. CAS-37, pp. 747-755, June 1990.41. M. K. Kazimierczuk and J. Jozwik, "Class E2 resonant dc/dc power converter," IEE Proceedings, Pt. G, Cir-cuits, Devices and Systems, Vol. 137, pp. 193-196, June 1990.42. M. K. Kazimierczuk and J. Jozwik, "Analysis and design of Class E zero-current-switching rectifier," IEEETransactions on Circuits and Systems, Vol. CAS-37, pp. 1000-1009, August 1990.43. M. K. Kazimierczuk and K. Puczko, "Class E low dv/dt synchronous rectifier with controlled duty ratio and out-put voltage," IEEE Transactions on Circuits and Systems, Vol. CAS-38, pp. 1165-1172, October 1991.44. M. K. Kazimierczuk, "Class D current-driven rectifiers for resonant dc/dc converter applications," IEEE Trans-actions on Industrial Electronics, Vol. IE-38, pp. 344-354, October 1991.45. M. K. Kazimierczuk, "Class D voltage-switching MOSFET power amplifier," IEE Proceedings, Part B, ElectricPower Applications, Vol. 138, pp. 285-296, November 1991.46. M. K. Kazimierczuk, W. Szaraniec, and S. Wang, "Analysis and design of parallel resonant converter at highQ L," IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-28, pp. 35-50, January 1992.47. M. K. Kazimierczuk and S. Wang, "Frequency-domain analysis of series resonant converter for continuousconduction mode," IEEE Transactions on Power Electronics, Vol. PE-6, pp. 270-279, April 1992.48. M. K. Kazimierczuk and W. Szaraniec, "Analysis of Class E rectifier with a series capacitor," IEE Proceedings,Part G, Circuits, Devices and Systems, Vol. 139, pp. 269-276, June 1992.49. M. K. Kazimierczuk, "Synthesis of phase-modulated dc/ac inverters and dc/dc converters," IEE Proceedings,Pt. B, Electric Power Applications, Vol. 139, pp. 387-394, July 1992.50. A. Ivascu, M. K. Kazimierczuk, and S. Birca-Galateanu, "Class E resonant low dv/dt rectifier," IEEE Transac-tions on Circuits and Systems, Vol. CAS-39, pp. 604-613, August 1992.51. D. Czarkowski and M. K. Kazimierczuk, "Linear circuits models of PWM flyback and buck/boost converters,"IEEE Transactions on Circuits and Systems, Vol. CAS-39, pp. 688-693, August 1992.52. M. K. Kazimierczuk and W. Szaraniec, "Class D zero-voltage switching inverter with only one shunt capacitor,"IEE Proceedings, Part B, Electric Power Applications, Vol. 139, pp. 449-456, September 1992.53. D. Czarkowski and M. K. Kazimierczuk, "Static- and dynamic-circuit models of PWM buck-derived dc-dc con-verters," IEE Proceedings, Part G, Circuits, Devices and Systems, Vol. 139, pp. 669-679, December 1992. 54. M. K. Kazimierczuk, N. Thirunarayan, and S. Wang, "Analysis of series-parallel resonant converter," IEEETransactions on Aerospace and Electronic Systems, Vol. AES-29, pp. 88-99, January 1993.55. M. K. Kazimierczuk and W. Szaraniec, "Analysis of Class E low di/dt rectifier with a series inductor," IEEETransactions Aerospace and Electronic Systems, Vol. AES-29, pp. 278-287, January 1993.56. M. Mikolajewski and M. K. Kazimierczuk, "Zero-voltage-ripple rectifiers and dc/dc resonant converters," IEEETransactions on Power Electronics, Vol. PE-6, pp. 12-17, January 1993.57. A. Reatti, M. K. Kazimierczuk, and R. Redl, "Class E full-wave low dv/dt rectifier," IEEE Transactions on Cir-cuits and Systems, Vol. CAS-40, pp. 73-85, February 1993.58. M. K. Kazimierczuk and N. Thirunarayan, "Class D voltage-switching inverter with tapped resonant inductor,"IEE Proceedings, Pt. B., Electric Power Applications, Vol. 140, pp. 177-185, May 1993.59. D. Czarkowski and M. K. Kazimierczuk, "Single-capacitor phase-controlled series resonant converter," IEEETransactions on Circuits and Systems, Vol. CAS-40, pp. 383-391, June 1993.60. M. K. Kazimierczuk and W. Szaraniec, "Class D-E resonant dc/dc converter," IEEE Transactions on Aero-space and Electronics Systems, Vol. AES-29, pp. 963-976, July 1993.61. D. Czarkowski and M. K. Kazimierczuk, "Energy-conservation approach to modeling PWM dc-dc converters,"IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-29, pp. 1059-1063, July 1993.62. D. Czarkowski and M. K. Kazimierczuk, "Phase-controlled series-parallel resonant converter," IEEE Transac-tions on Power Electronics, Vol. PE-8, pp. 309-319, July 1993.63. M. K. Kazimierczuk and M. K. Jutty, "Phase-modulated series-parallel resonant converter with series load,"IEE Proceedings, Pt. B, Electric Power Applications, Vol. 140, pp. 297-306, September 1993.64. M. K. Kazimierczuk and W. Szaraniec, "Electronic ballast for fluorescent lamps," IEEE Transactions on PowerElectronics, Vol. PE-8, pp. 386-395, October 1993.65. A. Ivascu, M. K. Kazimierczuk, and S. Birca-Galateanu, "Class E resonant low di/dt rectifier," IEE Proceedings,Part G, Circuits, Devices and Systems, Vol. 140, pp. 417-423, December 1993.66. M. K. Kazimierczuk, D. Czarkowski, and N. Thirunarayan, "A new phase-controlled parallel resonant con-verter," IEEE Transactions on Industrial Electronics, Vol. IE-40, pp. 542-552, December 1993.67. D. Czarkowski and M. K. Kazimierczuk, "Application of state feedback with integral control to pulse-widthmodulated push-pull dc-dc converter," IEE Proceedings, Part D, Control Theory and Applications, Vol. 141, pp. 99-103, March 1994.68. M. K. Kazimierczuk, B. Tomescu, and A. Ivascu, "Class E resonant rectifier with a series capacitor," IEEETransactions on Circuits and Systems, Vol. 41, pp. 885-890, December 1994.69. M. K. Kazimierczuk and R. Cravens II, "Closed-loop characteristics of voltage-mode-controlled PWM boost dc-dc converter with an integral-lead controller," Journal of Circuits, Systems and Computers, Vol. 4, No. 4, pp.429-458, December 1994.70. M. K. Kazimierczuk and N. Thirunarayan, "Dynamic performance of MCTs under inductive load conditions,"Journal of Circuits, Systems and Computers, Vol. 4, No. 4, pp. 471-485, December 1994.71. M. K. Kazimierczuk and M. Jutty, "Fixed-frequency phase-controlled full-bridge resonant converter with a se-ries load," IEEE Transactions on Power Electronics, Vol. PE-10, pp. 9-18, January 1995.72. D. Czarkowski, L. R. Pujara, and M. K. Kazimierczuk, "Robust stability of state-feedback control of PWM dc-dcpush-pull converter," IEEE Transactions on Industrial Electronics, Vol. IE-41, pp. 108-111, February 1995.73. M. K. Kazimierczuk and A. Abdulkarim, "Current-source converter with parallel-resonant circuit," IEEE Trans-actions on Industrial Electronics, Vol. IE-42, pp. 199-208, April 1995.74. D. Czarkowski and M. K. Kazimierczuk, "Static characteristics of MOS-controlled thyristors - Analysis, simula-tion and experimental results," Journal of Circuits, Systems and Computers, Vol. 5, No. 1, pp. 65-80, March 1995.75. M. K. Kazimierczuk and R. Cravens II, "Open and closed-loop dc and small-signal characteristics of PWMbuck-boost converter for CCM," Journal of Circuits, Systems and Computers, Vol. 5, No. 3, pp. 261-303, Sep-tember 1995.76. M. K. Kazimierczuk, N. Thirunarayan, B. T. Nguyen, and J. A. Weimer, "Experimental static and dynamiccharacteristics of MOS-controlled thyristors for resistive loads," Journal of Circuits, Systems and Computers, Vol. 5, No. 3, pp. 393-410, September 1995.77. R. E. Siferd, R. C. Cravens II, and M. K. Kazimierczuk, "CMOS PWM control circuit with programmable deadtime," Journal of Circuits, Systems and Computers, Vol. 5, No. 3, pp. 429-441, September 1995.78. M. K. Kazimierczuk, "Reverse recovery of power pn junction diodes," Journal of Circuits, Systems and Com-puters, Vol. 5, No. 4, pp. 589-606, December 1995.79. M. Bartoli, N. Neferi, A. Reatti, and M. K. Kazimierczuk, "Modeling winding losses in high-frequency powerinductors," Journal of Circuits, Systems and Converters, Vol. 5, No. 4, pp. 607-626, December 1995.80. M. K. Kazimierczuk and R. C. Cravens II, "Experimental results for the small-signal study of the PWM boostDC-DC converter with an integral-lead controller," Journal of Circuits, Systems and Computers, Vol. 5, No. 4, pp. 747-755, December 1995.81. M. K. Kazimierczuk and R. S. Geise, "Single-loop current-mode control of a PWM boost dc-to-dc converterwith a non-symmetric phase control," Journal of Circuits, Systems and Computers, Vol. 5, No. 4, pp. 699-734, December 1995.82. M. K. Kazimierczuk and R. C. Cravens II, "Current-source parallel-resonant dc/ac inverter with transformer,"IEEE Transactions on Power Electronics, Vol. PE-11, pp. 275-284, March 1996.83. M. K. Kazimierczuk, M. J. Mescher, and R. P. Prenger, "Class D current-driven center-topped transformercontrollable synchronous rectifier," IEEE Transactions on Circuits and Systems, Part I, Vol. 43, pp. 670-680, August 1996.84. M. K. Kazimierczuk and A. Massarini, "Feedforward control of dc-dc PWM boost converter," IEEE Transac-tions on Circuits and Systems, Part I, Vol. 44, pp. 143-148, February 1997.85. M. K. Kazimierczuk and C. Wu, "Frequency-controlled series-resonant converter with center-topped synchro-nous rectifier," IEEE Transactions of Aerospace and Electronic Systems, Vol. 33, No. 3, pp. 939-947, July 1997.86. A. Massarini and M. K. Kazimierczuk, "Self-capacitance of inductors," IEEE Transactions on Power Electron-ics, Vol. 12, pp. 671-676, July 1997.87. A. Massarini, U. Reggiani, and M. K. Kazimierczuk, “Analysis of networks with ideal switches by state equa-tions,” IEEE Transactions on Circuits and Systems, Part I, Vol. 44, No. 8, pp. 692-697, August 1997.88. D. Czarkowski and M. K. Kazimierczuk, “ZVS Class D series resonant inverter − Time state space simulationand experimental results,” IEEE Transactions on Circuits and Systems, Part I, Vol. 45, No. 11, pp. 1141-1147, November 1998.89. M. K. Kazimierczuk, G. Sancineto, U. Reggiani, and A. Massarini, “Small-signal high-frequency model of ferriteinductors,” IEEE Transactions on Magnetics, Vol. 35, pp. 4185-4191, September 1999.90. G. Grandi, M. K. Kazimierczuk, A. Massarini, and U. Reggiani, “Stray capacitance of single layer solenoid air-core inductors,” IEEE Transactions on Industry Applications, Vol. 35, pp. 1162-1168, September/October 1999.91. M. K. Kazimierczuk and L. A. Starman, “Dynamic performance of PWM dc-dc boost converter with input volt-age feedforward control,” IEEE Transactions on Circuits and Systems, Part I, Vol. 46, No. 12, pp. 1473-1481, December 1999.92. A. J. Frazier and M. K. Kazimierczuk, “DC-AC power inversion using sigma-delta modulation,” IEEE Transac-tions on Circuits and Systems, Part I, Vol. 46, No.1, pp. 79-82, January 2000.93. M. K. Kazimierczuk and A. J. Edstrom, “Open-loop peak voltage feedforward control of a PWM buck con-verter,” IEEE Transactions on Circuits and Systems, Part I, Vol. 47, pp. 740-746, May 2000.94. M. K. Kazimierczuk, “Transfer function of current modulator in PWM converters with current-mode control,”IEEE Transactions on Circuits and Systems, Part I, Vol. 47, No. 9, pp. 1407-1412, September 2000.95. W. Pietrenko, W. Janke, and M. K. Kazimierczuk, “Application of semianalytical recursive convolution algo-rithms for large-signal time-domain simulation of switch-mode power converters,” IEEE Transactions on Cir-cuits and Systems, Part I, Vol. 48, No. 10, pp. 1246-1252, October 2001.96. A. Reatti and M. K. Kazimierczuk, “Comparison of various methods for calculating the ac resistance of induc-tors,” IEEE Transactions on Magnetics, Vol. 37, No. 3, pp. 1512-1518, May 2002.97. A. Reatti and M. K. Kazimierczuk, “Small-signal model of PWM converters for discontinuous conduction modeand its application for boost converter,” IEEE Transactions on Circuits and Systems, Part I, Fundamental The-ory and Applications, Vol. 50, No. 1, pp. 65-73, January 2003.98. B. Bryant and M. K. Kazimierczuk, “Effect of a current sensing resistor on required MOSFET size,” IEEETransactions on Circuits and Systems, Part I, Fundamental Theory and Applications, Vol. 50, pp. 708-711, May 2003.99. T. Suetsugu and M. K. Kazimierczuk, “ZVS condition predicting sensor for the Class E amplifier,” IEEE Trans-actions on Circuits and Systems, Part I, Fundamental Theory and Applications, Vol. 50, NO. 6, pp. 763-769, June 2003.100. T. Suetsugu and M. K. Kazimierczuk, “Comparison of Class E amplifier with nonlinear and linear shunt capaci-ties,” IEEE Transactions on Circuits and Systems, Part I, Fundamental Theory and Applications, Vol. 50, pp.1089-1097, August 2003.101. T. Suetsugu and M. K. Kazimierczuk, “Voltage clamped Class E amplifier with Zener diode,” IEEE Transac-tions on Circuits and Systems, Part I, Fundamental Theory and Applications, Vol. 50, No. 10, pp. 1347-1349, October 2003.102. G. Grandi, M. K. Kazimierczuk, A. Massarini, M. Reggiani, and G. Sancineto, “Model of laminated iron-core inductors,” IEEE Transactions on Magnetics, Vol. 40, No. 4, pp. 1839-1845, July 2004.103. T. Suetsugu and M. K. Kazimierczuk, “Analysis and design of Class E amplifier with shunt capacitance com-posed of nonlinear and linear capacitances,” IEEE Transactions on Circuits and Systems, Part I: Regular Pa-pers, Vol. 51, No. 7, pp. 1261-1268, July 2004.104. D. Kessler and M. K. Kazimierczuk, “Power losses and efficiency of Class E power amplifier at any duty cycle,”IEEE Transactions on Circuits and Systems, Part I: Regular Papers, Vol. 51, No. 9, pp. 1675-1689, September 2004.105. T. Suetsugu and M. K. Kazimierczuk, “Design procedure of lossless voltage-clamped Class E amplifier with transformer and diode,” IEEE Transactions on Power Electronics, Vol. 20, No. 1, pp. 56-64, January 2005. 106. M. K. Kazimierczuk, V. G. Krizhanovski, J. V. Rassokhina, and D. V. Chernov, “Class-E MOSFET tuned power oscillator design procedure,” IEEE Transactions on Circuits and Systems, Part I: Regular Papers, Vol. 52, No.6, pp. 1138-1147, June 2005.107. R. Kleismit, G. Kozlowski, R. Bigger, I. Maartense, M. K. Kazimierczuk, and D. B. Mast, “Characterization of local dielectric properties of superconductor YBa2Cu3O7-8using evanescent microwave microscopy,” IEEE Transactions on Applied Superconductivity, Vol. 15, No. 2, pp. 2915-2918, June 2005.108. R. A. Kleismit, M. ElAshry, G. Kozlowski, M. S. Amer, M. K. Kazimierczuk, and R. R. Bigger, “Local dielectric and strain measurements in YBa2Cu3O7-8 thin films by evanescent microscopy and Raman spectroscopy,” Su-perconductor Science and Technology, Vol. 18, pp. 1197-1203, July 2005.109. B. Bryant and M. K. Kazimierczuk, “Open-loop power-stage transfer functions relevant to current-mode control of boost PWM converter operating in CCM,” IEEE Transactions on Circuits and Systems, Part I: Regular Pa-pers, Vol. 52, No. 10, pp. 2158-2164, October 2005.110. B. Bryant and M. K. Kazimierczuk, “Modeling the closed-current loop of PWM DC-DC converters with peak current-mode control converter operating in CCM,” IEEE Transactions on Circuits and Systems, Part I: Regu-lar Papers, Vol. 52, No. 11, pp. 2404-2412, November 2005.111. B. Bryant and M. K. Kazimierczuk, “Voltage loop of boost PWM DC-DC converters with peak current-mode control,” IEEE Transactions on Circuits and Systems, Part I, Regular Papers, Vol. 53, No.1, pp. 99-105, Janu-ary 2006.。

基于机器学习的光纤窃听检测方法

基于机器学习的光纤窃听检测方法陈孝莲1，秦奕1，张杰2，李亚杰2，宋浩鲲2，张会彬2（1. 国网江苏省电力有限公司无锡供电分公司，江苏无锡 214000；2. 北京邮电大学信息光子与光通信研究院，北京 100876）摘要：光纤窃听是信息安全的重大隐患之一，但其隐蔽性较高的特点导致筛查困难。

针对通信网络中面临的光纤窃听问题，提出了基于机器学习的光纤窃听检测方法。

首先基于窃听对传输物理层的影响，设计了7个维度的特征向量提取方法；其次通过实验，模拟窃听并收集特征向量，利用两种机器学习算法进行分类检测和模型优化。

实验证明，神经网络分类算法的性能优于K近邻分类算法，其在10%分光窃听中可以实现98.1%的窃听识别率。

关键词：窃听检测；光纤窃听；机器学习；神经网络中图分类号：TP393文献标识码：Adoi: 10.11959/j.issn.1000−0801.2020299Optical fiber eavesdropping detection methodbased on machine learningCHEN Xiaolian1, QIN Yi1, ZHANG Jie2, LI Yajie2, SONG Haokun2, ZHANG Huibin21. Wuxi Power Supply Company, State Grid JiangSu Electric Power Co., Ltd., Wuxi 214000, China2. State Key Laboratory of Information Photonics and Optical Communications,Beijing University of Posts and Telecommunications, Beijing 100876, China Abstract: Optical fiber eavesdropping is one of the major hidden dangers of power grid information security, but de-tection is difficult due to its high concealment. Aiming at the eavesdropping problems faced by communication net-works, an optical fiber eavesdropping detection method based on machine learning was proposed. Firstly, sev-en-dimensions feature vector extraction method was designed based on the influence of eavesdropping on the physi-cal layer of transmission. Then eavesdropping was simulated and experimental feature vectors were collected. Finally, two machine learning algorithms were used for classification detection and model optimization. Experiments show that the performance of the neural network classification is better than the K-nearest neighbor classification, and it can achieve 98.1% eavesdropping recognition rate in 10% splitting ratio eavesdropping.Key words: eavesdropping detection, fiber eavesdropping, machine learning, neural network收稿日期：2020−04−14；修回日期：2020−09−07基金项目：江苏省电力有限公司科技项目（No.J2019124）Foundation Item: Science and Technology Project of Jiangsu Electric Power Co., Ltd. (No.J2019124)研究与开发·62·1 引言随着信息技术的发展，网络安全形势日益严峻。

generative ai exists because of the transformer文章

generative ai exists because of the transformer文章Generative AI Exists Because of the TransformerIntroductionArtificial Intelligence (AI) has made significant advancements in recent years, particularly in the field of natural language processing and generative models. These models have become more sophisticated, thanks to the invention of a powerful neural network architecture known as the Transformer. This revolutionary model has transformed the way AI generates human-like text, enabling applications such as language translation, chatbots, and even creative writing. In this article, we will explore the origins of the Transformer and how it has paved the way for generative AI.The Birth of the TransformerThe Transformer model was introduced in a groundbreaking paper in 2017 by Vaswani et al. at Google. At the time, recurrent neural networks (RNNs), such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), were the go-to choice for sequence modeling tasks. However, RNNs had several limitations in processing long-range dependencies and parallelization.The Transformer model addressed these limitations by introducing a novel self-attention mechanism. This self-attention mechanism allowed the model to weigh different words in a sequence, capturing both local and global dependencies more effectively. It also enabled parallel processing of words in the sequence, leading to faster training and inference times. This breakthrough opened upnew possibilities for sequence modeling tasks.The Power of AttentionThe self-attention mechanism lies at the heart of the Transformer model's success. Attention allows the model to focus on different parts of the input sequence, assigning different levels of importance to each word. This mechanism helps the model understand the relationships between words, making it more capable of generating coherent and contextually accurate text.The attention mechanism works by creating three matrices: the Query matrix, the Key matrix, and the Value matrix. These matrices are derived from the input sequence and are used to compute the attention scores between the words. The attention scores determine the level of importance given to each word when generating the output sequence.The flexibility and power of the attention mechanism have made the Transformer model excel in various tasks, such as machine translation, text summarization, and question answering. By attending to different parts of the input sequence, the model can capture both local and global dependencies, leading to more accurate and contextually relevant responses.Transformer-Based Generative AIThe Transformer model's attention mechanism has not only revolutionized sequence modeling tasks but has also paved the way for generative AI. Generative AI refers to the ability of AI systemsto create new, human-like content, such as text, images, or even music. With the Transformer's attention mechanism, AI can generate coherent and contextually accurate text, making it ideal for creative writing, conversational agents, and content creation.One of the most popular applications of generative AI powered by the Transformer is language translation. Traditionally, statistical machine translation models relied on static alignment models and phrase-based translation approaches. However, the Transformer's attention mechanism allows the model to attend to different parts of the input and output sequences, capturing more nuanced relationships between the languages. This has significantly improved translation accuracy and fluency, enabling real-time translation services.Another area where the Transformer has made an impact is in the development of chatbots and conversational agents. By training the model on large amounts of dialogue data, it can generate human-like responses to user queries. The attention mechanism enables the model to understand the context and fine-grained nuances in the conversation, resulting in more natural and interactive dialogue systems.Even in creative writing, the Transformer has demonstrated its potential. By training the model on a vast corpus of literature, it can generate original stories, dialogues, and poems. While the generated content may not match the creativity and depth of human-authored works, it showcases the potential of AI as a creative tool.ConclusionThe Transformer model has undoubtedly revolutionized the field of generative AI. By introducing the attention mechanism, it has made AI systems more capable of processing long-range dependencies and capturing nuanced relationships between words. This breakthrough has paved the way for applications such as language translation, chatbots, and creative writing.However, generative AI still faces challenges, such as the generation of biased or inappropriate content and maintaining ethical standards. As researchers push the boundaries of generative AI, it is essential to ensure responsible development and deployment of these models. With the power of the Transformer and the continuous efforts in research and development, generative AI has the potential to transform various industries and enhance human creativity.。

efficientnet-b1到efficientnet-b7的参数的中文描述

efficientnet-b1到efficientnet-b7的参数的中文描述EfficientNet-b1：EfficientNet-b1是一种效率高的模型，它使用了参数化精度改进技术，使得参数更少，达到相同的精度。

它拥有8个卷积层，1个池化层和5个全连接层。

这种参数改进技术使它可以拥有更少的参数，同时保持性能良好。

EfficientNet-b2：EfficientNet-b2是一种效率高的模型，它使用了参数化精度改进技术，使得参数更少，达到相同的精度。

它拥有10个卷积层，2个池化层和5个全连接层。

这种参数改进技术使它可以拥有更少的参数，同时保持性能良好。

EfficientNet-b3：EfficientNet-b3是一种高效的模型，它使用了参数化精度改进技术，使得参数更少，达到相同的精度。

它拥有12个卷积层，2个池化层和5个全连接层。

这种参数改进技术使它可以拥有更少的参数，同时保持性能良好。

EfficientNet-b4：EfficientNet-b4是一种高效的模型，它使用了参数化精度改进技术，使得参数更少，达到相同的精度。

它拥有14个卷积层，2个池化层和4个全连接层。

这种参数改进技术使它可以拥有更少的参数，同时保持性能良好。

EfficientNet-b5：EfficientNet-b5是一种高效的模型，它使用了参数化精度改进技术，使得参数更少，达到相同的精度。

它拥有16个卷积层，2个池化层和4个全连接层。

这种参数改进技术使它可以拥有更少的参数，同时保持性能良好。

EfficientNet-b6：EfficientNet-b6是一种高效的模型，它使用了参数化精度改进技术，使得参数更少，达到相同的精度。

它拥有18个卷积层，3个池化层和4个全连接层。

这种参数改进技术使它可以拥有更少的参数，同时保持性能良好。

EfficientNet-b7：EfficientNet-b7是一种高效的模型，它使用了参数化精度改进技术，使得参数更少，达到相同的精度。

understanding the behaviour of contrastive loss

Understanding the Behaviour of Contrastive LossContrastive loss is a widely used loss function in deep learning, particularly in tasks that involve learning representations of data. It aims to maximize the similarity between similar pairs of samples and minimize the similarity between dissimilar pairs. In this article, we will delve into the details of contrastive loss, its mathematical formulation, and its applications.Introduction to Contrastive LossContrastive loss is a type of pairwise loss function that measures the similarity or dissimilarity between two samples. It is commonly used in tasks such as image retrieval, face recognition, and text matching. The goal is to learn a representation space where similar samples are mapped closer together while dissimilar samples are pushed apart.The idea behind contrastive loss can be traced back to Siamese networks, which were first introduced by Bromley et al. in 1994. Siamese networks consist of two identical subnetworks with shared weights, taking two input samples and producing their respective embeddings. Contrastive loss is then applied to these embeddings to optimize the network.Mathematical FormulationLet’s define contrastive loss mathematically. Given a pair of input samples (x1, x2) and their corresponding embeddings (f(x1), f(x2)), contrastive loss can be expressed as:L = (1 - y) * D^2 + y * max(0, m - D)^2,where L represents the contrastive loss value, y is a binary label indicating whether the pair is similar (y=0) or dissimilar (y=1), D is the Euclidean distance between the embeddings, and m is a margin hyperparameter that controls how far apart dissimilar pairs should be.The first term in the equation penalizes similar pairs by minimizing their distance D^2. The second term penalizes dissimilar pairs only if their distance D is smaller than the margin m. This formulationencourages the network to learn embeddings that are close for similar pairs and far apart for dissimilar pairs.Behaviour of Contrastive LossTo understand the behaviour of contrastive loss, let’s consider two scenarios: when the margin m is small and when it is large.Small Margin (m < D)When the margin is small, contrastive loss focuses on pushing dissimilar pairs apart only if their distance D is smaller than the margin. This means that dissimilar pairs with a large distance are not penalized significantly. As a result, the network may not be able to effectively distinguish between different classes or categories, leading to poor performance.Large Margin (m > D)On the other hand, when the margin is large, contrastive loss penalizes dissimilar pairs even if their distance D is larger than the margin. This can result in pushing dissimilar pairs too far apart, making it difficult for the network to generalize well on unseen data. In such cases, overfitting may occur, where the model performs well on training data but poorly on test data.Therefore, choosing an appropriate margin value is crucial in achieving good performance with contrastive loss. It should be selected based on the specific task and dataset at hand.Applications of Contrastive LossContrastive loss has been successfully applied in various domains and tasks. Here are some notable applications:1.Image Retrieval: By learning similarity measures between imagesusing contrastive loss, we can build systems that retrieve similar images given a query image.2.Face Recognition: Contrastive loss can help in learningdiscriminative face embeddings that are close for similar facesand far apart for different faces.3.Text Matching: By embedding text samples into a common space usingcontrastive loss, we can measure their semantic similarity andperform tasks such as duplicate detection or question answering. 4.Unsupervised Learning: Contrastive loss can be used as a self-supervised learning objective, where the network learns todifferentiate between different augmentations of the same input.These are just a few examples, and contrastive loss can be applied to various other domains and tasks where learning similarity ordissimilarity between samples is important.ConclusionContrastive loss is a powerful tool in deep learning for learning representations that capture similarity or dissimilarity between samples. By optimizing the embeddings using contrastive loss, we can enhance performance in tasks such as image retrieval, face recognition, and text matching. However, choosing an appropriate margin value is crucial to avoid underfitting or overfitting. With its versatility and effectiveness, contrastive loss continues to be an active area of research in the field of deep learning.。

课文参考译文 (1)-信息科学与电子工程专业英语(第2版)-吴雅婷-清华大学出版社

Unit 1 电子学：模拟和数字Unit 1-1第一部分：理想运算放大器和实际限制为了讨论运算放大器的理想参数，我们必须首先定义一些指标项，然后对这些指标项讲述我们所认为的理想值。

第一眼看运算放大器的性能指标表，感觉好像列出了大量的数值，有些是陌生的单位，有些是相关的，经常使那些对运放不熟悉的人感到迷惑。

如果没有对每一项性能指标有一个真正的评价，设计人员必将失败。

目标是能够依据公布的数据设计电路，并确认构建的样机将具有预计的功能。

对于线性电路而言，它们与现在的复杂逻辑电路结构相比看起来较为简单，（因而在设计中）太容易忽视具体的性能参数了，而这些参数可极大地削弱预期性能。

现在让我们来看一个简单但很引人注意的例子。

考虑对于一个在50kHz频率上电压增益为10的放大器驱动10k 负载时的要求。

选择一个普通的带有内部频率补偿的低价运放，它在闭环增益为10时具有所要求的带宽，并且看起来满足了价格要求。

器件连接后，发现有正确地增益。

但是它只能产生几伏的电压变化范围，然而数据却清楚地显示输出应该能驱动达到电源电压范围以内2到3伏。

设计人员忽视了最大输出电压变化范围是受频率严格限制的，而且最大低频输出变化范围大约在10 kHz受到限制。

当然，事实上这个信息也在数据表上，但是它的实用性并没有受到重视。

这种问题经常发生在那些缺乏经验的设计人员身上。

所以这个例子的寓意十分明显：在开始设计之前总要花上必要的时间来描写全部的工作要求。

关注性能指标的详情总是有益的。

建议下面列出的具体的性能指标应该考虑：1. 在温度，时间和供给电压下的闭环增益的精确性和稳定性2. 电源要求，电源和负载阻抗，功率消耗3. 输入误差电压和偏置电流，输入输出电阻，随着时间和温度的漂移4. 频率响应，相位偏移，输出变化范围，瞬态响应，电压转换速率，频率稳定性，电容性负载驱动，过载恢复5. 线性，失真和噪声6. 输入，输出或电源保护要求，输入电压范围，共模抑制7. 外部补偿调整要求不是所有的指标项都是有关的，但要记住最初就考虑它们会更好，而不要被迫返工。