Summary of Description Logic

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A Brief History of Linguistics An Overview summary

A Brief History of Linguistics: An Overview1. IntroductionLinguistics in a broad sense boasts a history as long as the history of writing. Serious discussions of the origin of language in the ancient times have been recorded in both the West and East.Linguistics, like other science, builds on the past. A study of the history of linguistics acquaints us with not only the great advances made in the scientific investigation of language in the past decades, but also the continuity of linguistic theory from the earliest times to the present day.2. Linguistics in Ancient Times2.1 Indian LinguisticsIt is in the fourth century BC that the great Indian grammarian Panini produced his famous Sanskrit grammar, called Eight Books. Linguistics in India must have been seriously under way well before the middle of the first millennium BC.The Indian linguistic scholarship focused on three aspects: general linguistic theory and semantics, phonetics and phonology, and grammatical description. The main focus of Indian linguistics is not on theory but on observation, and the questions they raised are closely related to literary study and philosophy. The semantic relationship between a sentence and the words that make up the sentence was also under discussion.2.2 Chinese LinguisticsThe earliest writing system was witnessed in China in the 14th century BC, called Jiaguwen. There was no such ting as philology or grammar then, and the only practice was Xungu, critical interpretations of ancient texts. Interpretations of ancient texts inevitably involved issues concerning grammar. For its systematicity and clarity, Er Ya is considered as a very important pioneer work in Chines Xungu. Ancient China enjoys special fame in her theories on the origins of language.Dictionaries were produced in China from the second century AD onward. In the Qin and Han dynasties, studies of language focused on both compiling classical texts (philology) and interpreting the meaning of words (Xungu).2.3 Greek LinguisticsAncient Greece is considered as the cradle of Western civilization, and work on language started there in about the 5th century BC. Ancient Greek linguistics was philosophical, more interested in problems on the relationship between language and the natural world, language and human thought, language and logical form.There were several famous philosophers in ancient Greece. They were Socrates, Plato, Aristotle, and later Stoic philosophers. The three aspects of linguistic study among early Greek scholars were etymology, phonetics and grammar. The ancient Greekshave done outstanding work in many fields, and their achievement in linguistics is one of many that are especially memorable in terms of grammatical theory and grammatical description.2.4 Roman LinguisticsThe Romans borrowed their writing system from the Greeks. And their writing was called Cynillic writing. In the west half of the empire, Latin became the language of administration, business, law, learning, and social achievement.Roman linguistics was largely the application of Greek thought, Greek controversies on language, and Greek categories to the Latin language. It is through theses Latin grammarians that the accepted grammatical description of the language was brought to completion and handed on to philosophy of language in the Middle Ages.3. Linguistics in the Middle AgesThe Middle Ages refers to the period of European history starting from the fall of the Roman Empire in the year 476 and the beginning of the Renaissance in the fifteenth century. Latin remained the language of learning. This ensured the language a high place, and linguistic studies in the early years of the Middle Ages were largely represented by students in Latin grammar.Around 1000, Aelfric wrote Latin Grammar and Colloquium. As his grammar was one of the known grammars especially directed at English-speaking learners, it was taken as setting the seal on several centuries of Latin-inspired English grammar.The second part of the Middle Ages was characterized by scholastic philosophy, in which linguistics had an important place and a considerable amount of linguistic work was carried on. Linguistics works had been almost wholly pedagogical. One such grammar is the Doctrinale of Alexander of Villedieu. One of the most striking examples of practical work in the second part was the First Grammatical Treaties. Scholasticism is the synthesis of Aristotelian philosophy and Christian revelation in European thought. It sought to resole the conflict of faith and reason and of nominalism and realism. Nominalism refers to the doctrine that are no universal essences in reality and that abstract concepts are mere names.In the Middle Ages, Latin remained the only really necessary scholar's language, despite the later increase in men's knowledge of Greek and some study of Arabic and Hebrew.In the early decades of the thirteenth century, Petrus Hispanus produced his Summulae Logicales, a summary of logic. The theoretical basis of his arguments is that sensation, memory, imagination, judgement and reasoning, etc. , are attributed to various faculties in human beings.4. Linguistics in the RenaissanceThe Renaissance is traditionally regarded as the birth of the modern wold and the beginning of modern history. The study of Greek and Latin grammars continued, and the further refinements and the developments that carried it from the Medieval Periodto modern teaching practice in the classical languages were a proper object of specialist study.4.1 Hebrew and ArabicIts biblical status gave Hebrew a place alongside Latin and Greek. A number of Hebrew grammars were written in Europe, in particular Reuchlin's On Fundamental Rules of Hebrew.Arabic linguistic studies were concerned with the Koran, the sacred book of Islam. The school of Basra laid stress on the strict regularity and the systemic nature of language. Arabic grammatical scholarship reached its culmination at the end of the eight century in the grammar of Sibawaih of Basra. The Arabic achievement in this branch of linguistics was far more successful in terms of descriptive accuracy than that of the Greeks and the Romans.4.2 European LanguagesThe Renaissance itself saw the publication of many of the first grammars of the European languages. These new grammars of modern languages paid great attention to the relations between spelling and pronunciation.Persian Ramus, a precursor of modern structuralism, stressed the need in the ancient languages to follow the observed usage of the classical authors, and in the modern languages the observed usage of native speakers. His grammatical descriptions and classifications relied on the relations between actual word forms.4.3 The Port-Royal GrammariansThe rationalist movement make itself feel in the production of philosophical grammars, especially associated with the French Port-Royal schools. Port-Royal scholars were the writers of the universal grammars. On the basis of this general grammar, Port-Royal scholars took the nine classical word classes, but re-divided them semantically. With the first six relating to the objects of our thought and the last three to the form or manner of our thought.In making a genuine attempt to write a general grammar, the Port-Royal grammarians drew examples from Latin, Greek, Hebrew, and other European languages, seeking to refer to alleged universal characteristics of language that underlie them all. In many ways their ideas are similar to Chomsky's modern theory.4.4 Empiricism and the Reworking of English GrammarsThe centerpiece of empiricism is the thesis that all human knowledge is derived externally from the sense in impressions and the operations of the mind upon them in abstraction and generalization.The most radical proposal of the age was the invention of a new language for the advancement for learning and commerce throughout the civilized world.One aspect of English empiricism in linguistic studies during these two centuries was the beginning of systematic phonetic description of the sounds of the English language, and of the formal analysis of English grammar. English grammars, in a sense, are the products of reworking by pioneers of the English language studies. Since then, grammars of English have continued to be written from this period up to the present day, gradually remodeling the Latin tradition in the interest of formal correspondence with the actual patterns and paradigms of English.5. Linguistics in the 18th Century5.1 The Discovery of SanskritIn this year, Sir William Jones established beyond doubt the historical kinship of Sanskrit with Latin, Greek and the Germanic languages. The discovery of Sanskrit by Western scholars was one of the principal factors in the development of comparative philology in the 19th century.5.2 Philosophy and Linguistic InquiriesIn the 18th century, speculation was turning towards historical questions, though in a rather general way. Attempts at seriously thought-out explanations of the origin and development of human language united philosophers of both the empiricist and the rationalist camps of the 18th century.Halfway through the century, Condillac and Rousseau discussed the origin and early development of human speech. Their conceptions of the genesis of language were very similar.Herder asserted that language and thought are inseparable, and that language is the tool, the content, and the form of human thinking. A prominent representative of the universal philosophical theory of grammar in English during 18th century was James Harris. He held that words are related to what they designate by convention and that language is "a system of articulate voices signified by compact". He saw the intricate connection between human society and human speech.In the 18th century, thinkers in different countries and with diverse backgrounds were drawn towards the study of the history of language. This happened on the eve of the 19th century when the history of language made unprecedented advances, which owed much to the work done in the 18th century.6. ConclusionFrom the earliest period in the human history to more recent times, language studies developed along a route characterized by incremental in depth. However, as language is such a complicated phenomenon and each inquiry has its limitations, we are still far from having exhausted all aspects of language. It is safe to say, though, that these inquiries have certainly paved the way for unprecedented linguistic developments in the time that followed.。

大二英语听力教程Unit2

listening material
Recognizing the use of transitional words
Pay attention to transitional words such as "however," "there before," "in addition," etc., which indicates relationships between ideas and help you follow
02
Listening skills
Predicting answers
Predicting answers before listening
Use the title, subtitles, or any given information to predict the content and type of answers This can help you focus on the important information during the listening process
To develop students' listening comprehension and critical thinking skills by analyzing real life English language samples from different media sources
Summary word
Analyze the logic of a long conversation
Detailed description
Students need to listen to a lengthy conversation and analyze the logical structure, topic switching, and detailed information of the conversation.

WhatisLogic（什么是逻辑）

LogicThis article is about reasoning and its study. For other uses, see Logic (disambiguation).Logic (from the Ancient Greek: λογική, logike)[1] is the use and study of valid reasoning.[2][3] The study of logic features most prominently in the subjects of philosophy, mathematics, and computer science.Logic was studied in several ancient civilizations, includingIndia,[4] China,[5] Persia and Greece. In the West, logic was established as a formal discipline by Aristotle, who gave it a fundamental place in philosophy. The study of logic was part of the classical trivium, which also included grammar and rhetoric. Logic was further extended by Al-Farabi who categorized it into two separate groups (idea and proof). Later, Avicenna revived the study of logic and developed relationship between temporalis and the implication. In the East, logic was developed by Buddhists and Jains.Logic is often divided into three parts: inductive reasoning, abductive reasoning, and deductive reasoning.The study of logic“Upon this first, and in one sense this sole, rule of reason, that in order to learn you must desire to learn, and in so desiring not be satisfied with what you alreadyincline to think, there follows one corollary which itself deserves to be inscribed upon every wall of the city of philosophy: Do not block the way of inquiry.”—Charles Sanders Peirce, "First Rule of Logic" The concept of logical form is central to logic, it being held that the validity of an argument is determined by its logical form, not by its content. Traditional Aristotelian syllogistic logic and modern symbolic logic are examples of formal logics.∙Informal logic is the study of natural language arguments. The study of fallacies is an especially important branch of informal logic. The dialogues of Plato[6] are goodexamples of informal logic.∙Formal logic is the study of inference with purely formal content. An inference possessesa purely formal content if it can be expressed as a particular application of a whollyabstract rule, that is, a rule that is not about any particular thing or property. The worksof Aristotle contain the earliest known formal study of logic. Modern formal logic followsand expands on Aristotle.[7] In many definitions of logic, logical inference and inferencewith purely formal content are the same. This does not render the notion of informallogic vacuous, because no formal logic captures all of the nuances of natural language.∙Symbolic logic is the study of symbolic abstractions that capture the formal features of logical inference.[8][9] Symbolic logic is often divided into two branches: propositionallogic and predicate logic.∙Mathematical logic is an extension of symbolic logic into other areas, in particular to the study of model theory, proof theory, set theory, and recursion theory.Logical formMain article: Logical formLogic is generally considered formal when it analyzes and represents the form of any valid argument type. The form of an argument is displayed by representing its sentences in the formal grammar and symbolism of a logical language to make its content usable in formal inference. If one considers the notion of form too philosophically loaded, one could say that formalizing simply means translating English sentences into the language of logic.This is called showing the logical form of the argument. It is necessary because indicative sentences of ordinary language show a considerable variety of form and complexity that makes their use in inference impractical. It requires, first, ignoring those grammatical features irrelevant to logic (such as gender and declension, if the argument is in Latin), replacing conjunctions irrelevant to logic (such as "but") with logical conjunctions like "and" and replacing ambiguous, or alternative logical expressions ("any", "every", etc.) with expressions of a standard type (such as "all", or the universalquantifier ∀).Second, certain parts of the sentence must be replaced with schematic letters. Thus, for example, the expression "all As are Bs" shows the logical form common to the sentences "all men are mortals", "all cats are carnivores", "all Greeks are philosophers", and so on.That the concept of form is fundamental to logic was already recognized in ancient times. Aristotle uses variable letters to represent valid inferences in Prior Analytics, leading JanŁukasiewicz to say that the introduction of variables was "one of Aristotle's greatest inventions".[10] According to the followers of Aristotle (such as Ammonius), only the logical principles stated inschematic terms belong to logic, not those given in concrete terms. The concrete terms "man", "mortal", etc., are analogous to the substitution values of the schematic placeholders A, B, C, which were called the "matter" (Greek hyle) of the inference.The fundamental difference between modern formal logic and traditional, or Aristotelian logic, lies in their differing analysis of the logical form of the sentences they treat.∙In the traditional view, the form of the sentence consists of (1) a subject (e.g., "man") plus a sign of quantity ("all" or "some" or "no"); (2) the copula, which is of the form "is"or "is not"; (3) a predicate (e.g., "mortal"). Thus: all men are mortal. The logicalconstants such as "all", "no" and so on, plus sentential connectives such as "and" and"or" were called "syncategorematic" terms (from the Greek kategorei – to predicate,and syn – together with). This is a fixed scheme, where each judgment has an identifiedquantity and copula, determining the logical form of the sentence.∙According to the modern view, the fundamental form of a simple sentence is given by a recursive schema, involving logical connectives, such as a quantifier with its boundvariable, which are joined by juxtaposition to other sentences, which in turn may havelogical structure.∙The modern view is more complex, since a single judgement of Aristotle's system involves two or more logical connectives. For example, the sentence "All men aremortal" involves, in term logic, two non-logical terms "is a man" (here M) and "is mortal"(here D): the sentence is given by the judgement A(M,D). In predicate logic, the sentenceinvolves the same two non-logical concepts, here analyzed as and , and thesentence is given by , involving the logical connectives foruniversal quantification and implication.∙But equally, the modern view is more powerful. Medieval logicians recognized the problem of multiple generality, where Aristotelian logic is unable to satisfactorily render such sentences as "Some guys have all the luck", because both quantities "all" and"some" may be relevant in an inference, but the fixed scheme that Aristotle used allowsonly one to govern the inference. Just as linguists recognize recursive structure innatural languages, it appears that logic needs recursive structure.Deductive and inductive reasoning, and abductive inferenceDeductive reasoning concerns what follows necessarily from given premises (if a, then b). However, inductive reasoning—the process of deriving a reliable generalization from observations—has sometimes been included in the study of logic. Similarly, it is important to distinguish deductive validity and inductive validity (called "cogency"). An inference is deductively valid if and only if there isno possible situation in which all the premises are true but the conclusion false. An inductive argument can be neither valid nor invalid; its premises give only some degree of probability, but not certainty, to its conclusion.The notion of deductive validity can be rigorously stated for systems of formal logic in terms of the well-understood notions of semantics. Inductive validity on the other hand requires us to define a reliable generalization of some set of observations. The task of providingthis definition may be approached in various ways, some less formal than others; some of these definitions may use mathematical models of probability. For the most part this discussion of logic deals only with deductive logic.Abduction[11] is a form of logical inference that goes from observation to a hypothesis that accounts for the reliable data (observation) and seeks to explain relevant evidence. The American philosopher Charles Sanders Peirce (1839–1914) first introduced the term as "guessing".[12] Peirce said that to abduce a hypothetical explanation from an observed surprising circumstance is to surmise that may be true because then would be a matter of course.[13] Thus, to abduce from involves determining that is sufficient (or nearly sufficient), but not necessary, for .Consistency, validity, soundness, and completenessAmong the important properties that logical systems can have:∙Consistency, which means that no theorem of the system contradicts another.[14]∙Validity, which means that the system's rules of proof never allow a false inference from true premises. A logical system has the property of soundness when the logical systemhas the property of validity and uses only premises that prove true (or, in the case ofaxioms, are true by definition).[14]∙Completeness, of a logical system, which means that if a formula is true, it can be proven (if it is true, it is a theorem of the system).∙Soundness, the term soundness has multiple separate meanings, which creates a bit of confusion throughout the literature. Most commonly, soundness refers to logicalsystems, which means that if some formula can be proven in a system, then it is true inthe relevant model/structure (if A is a theorem, it is true). This is the converse ofcompleteness. A distinct, peripheral use of soundness refers to arguments, which means that the premises of a valid argument are true in the actual world.Some logical systems do not have all four properties. As an example, Kurt Gödel's incompleteness theorems show that sufficiently complexformal systems of arithmetic cannot be consistent and complete;[9] however, first-order predicate logics not extended by specific axioms to be arithmetic formal systems with equality can be complete and consistent.[15]Rival conceptions of logicMain article: Definitions of logicLogic arose (see below) from a concern with correctness of argumentation. Modern logicians usually wish to ensure that logic studies just those arguments that arise from appropriately general forms of inference. For example, Thomas Hofweber writes in the Stanford Encyclopedia of Philosophy that logic "does not, however, cover good reasoning as a whole. That is the job of the theory of rationality. Rather it deals with inferences whose validity can be traced back to the formal features of the representations that are involved in that inference, be they linguistic, mental, or other representations".[16]By contrast, Immanuel Kant argued that logic should be conceived as the science of judgement, an idea taken up in Gottlob Frege's logical and philosophical work. But Frege's work is ambiguous in the sense that it is both concerned with the "laws of thought" as well as with the "laws of truth", i.e. it both treats logic in the context of a theory of the mind, and treats logic as the study of abstract formal structures.HistoryMain article: History of logicAristotle, 384–322 BCE.In Europe, logic was first developed by Aristotle.[17] Aristotelian logic became widely accepted in science and mathematics and remained in wide use in the West until the early 19th century.[18] Aristotle's system of logic was responsible for the introduction of hypothetical syllogism,[19] temporal modal logic,[20][21] and inductive logic,[22] as well as influential terms such as terms, predicables, syllogisms and propositions. In Europe during the later medieval period, major efforts were made to show that Aristotle's ideas were compatible with Christian faith. During the High Middle Ages, logic became a main focus of philosophers, who would engage in critical logical analyses of philosophical arguments, often using variations of the methodology of scholasticism. In 1323, William of Ockham's influential Summa Logicae was released. By the 18th century, the structured approach to arguments had degenerated and fallen out of favour, as depicted in Holberg's satirical play Erasmus Montanus.The Chinese logical philosopher Gongsun Long (c. 325–250 BCE) proposed the paradox "One and one cannot become two, since neither becomes two."[23] In China, the tradition of scholarly investigation into logic, however, was repressed by the Qin dynasty following the legalist philosophy of Han Feizi.In India, innovations in the scholastic school, called Nyaya, continued from ancient times into the early 18th century with the Navya-Nyaya school. By the 16th century, it developed theories resembling modern logic, such as Gottlob Frege's "distinction between sense and reference of proper names" and his "definition of number", as well as the theory of "restrictive conditions for universals" anticipating some of the developments in modern set theory.[24] Since 1824, Indian logic attracted the attention of many Western scholars, and has had an influence on important 19th-century logicians such as Charles Babbage, Augustus De Morgan, and George Boole.[25] In the20th century, Western philosophers like Stanislaw Schayer and Klaus Glashoff have explored Indian logic more extensively.The syllogistic logic developed by Aristotle predominated in the West until the mid-19th century, when interest in the foundations of mathematics stimulated the development of symbolic logic (now called mathematical logic). In 1854, George Boole published An Investigation of the Laws of Thought on Which are Founded the Mathematical Theories of Logic and Probabilities, introducing symbolic logic and the principles of what is now known as Boolean logic. In 1879, Gottlob Frege published Begriffsschrift, which inaugurated modern logic with the invention of quantifier notation. From 1910 to 1913, Alfred North Whitehead and Bertrand Russell published Principia Mathematica[8] onthe foundations of mathematics, attempting to derive mathematical truths from axioms and inference rules in symbolic logic. In 1931,Gödel raised serious problems with the foundationalist program and logic ceased to focus on such issues.The development of logic since Frege, Russell, and Wittgenstein had a profound influence on the practice of philosophy and the perceived nature of philosophical problems (see Analytic philosophy), and Philosophy of mathematics. Logic, especially sentential logic, is implemented in computer logic circuits and is fundamental to computer science. Logic is commonly taught by university philosophy departments, often as a compulsory discipline.Types of logicSyllogistic logicMain article: Aristotelian logicThe Organon was Aristotle's body of work on logic, with the Prior Analytics constituting the first explicit work in formal logic, introducing the syllogistic.[26] The parts of syllogistic logic, also known by the name term logic, are the analysis of the judgements into propositions consisting of two terms that are related by one of a fixed number of relations, and the expression of inferences by means of syllogisms that consist of two propositions sharing a common term as premise, and a conclusion that is a proposition involving the two unrelated terms from the premises.Aristotle's work was regarded in classical times and from medieval times in Europe and the Middle East as the very picture of a fully worked out system. However, it was not alone: the Stoics proposed a system of propositional logic that was studied by medieval logicians. Also, the problem of multiple generality was recognized in medieval times. Nonetheless, problems with syllogistic logic were not seen as being in need of revolutionary solutions.Today, some academics claim that Aristotle's system is generally seen as having little more than historical value (though there is some current interest in extending term logics), regarded as made obsolete by the advent of propositional logic and the predicate calculus. Others use Aristotle in argumentation theory to help develop andcritically question argumentation schemes that are used in artificial intelligence and legal arguments.Propositional logic (sentential logic)Main article: Propositional calculusA propositional calculus or logic (also a sentential calculus) is a formal system in which formulae representing propositions can be formed by combining atomic propositions using logical connectives, and in which a system of formal proof rules establishes certain formulae as "theorems".Predicate logicMain article: Predicate logicPredicate logic is the generic term for symbolic formal systems such as first-order logic, second-order logic, many-sorted logic, and infinitary logic.Predicate logic provides an account of quantifiers general enough to express a wide set of arguments occurring in natural language. Aristotelian syllogistic logic specifies a small number of forms that the relevant part of the involved judgements may take. Predicatelogic allows sentences to be analysed into subject and argument in several additional ways—allowing predicate logic to solve the problem of multiple generality that had perplexed medieval logicians.The development of predicate logic is usually attributed to Gottlob Frege, who is also credited as one of the founders of analytical philosophy, but the formulation of predicate logic most often used today is the first-order logic presented in Principles of Mathematical Logic by David Hilbert and Wilhelm Ackermann in 1928. The analytical generality of predicate logic allowed theformalization of mathematics, drove the investigation of set theory, and allowed the development of Alfred Tarski's approach to model theory. It provides the foundation of modern mathematical logic.Frege's original system of predicate logic was second-order, rather than first-order. Second-order logic is most prominently defended (against the criticism of Willard Van Orman Quine and others) by George Boolos and Stewart Shapiro.Modal logicMain article: Modal logicIn languages, modality deals with the phenomenon that sub-parts of a sentence may have their semantics modified by special verbs or modal particles. For example, "We go to the games" can be modified to give "We should go to the games", and "We can go to the games" and perhaps "We will go to the games". More abstractly, we might say that modality affects the circumstances in which we take an assertion to be satisfied.Aristotle's logic is in large parts concerned with the theory of non-modalized logic. Although, there are passages in his work, such as the famous sea-battle argument in De Interpretatione § 9, that are now seen as anticipations of modal logic and its connection with potentiality and time, the earliest formal system of modal logic was developed by Avicenna, whom ultimately developed a theory of "temporally modalized" syllogistic.[27]While the study of necessity and possibility remained important to philosophers, little logical innovation happened until the landmark investigations of Clarence Irving Lewis in 1918, who formulated a family of rival axiomatizations of the alethic modalities. His work unleashed a torrent of new work on the topic, expanding the kinds of modality treated to include deontic logic and epistemic logic. The seminal work of Arthur Prior applied the same formal language totreat temporal logic and paved the way for the marriage of the two subjects. Saul Kripke discovered (contemporaneously with rivals) his theory of frame semantics, which revolutionized the formal technology available to modal logicians and gave a new graph-theoretic way of looking at modality that has driven many applications in computational linguistics and computer science, such as dynamic logic.Informal reasoningMain article: Informal logicThe motivation for the study of logic in ancient times was clear: it is so that one may learn to distinguish good from bad arguments, and so become more effective in argument and oratory, and perhaps also to become a better person. Half of the works of Aristotle's Organontreat inference as it occurs in an informal setting, side by sidewith the development of the syllogistic, and in the Aristotelian school, these informal works on logic were seen as complementary to Aristotle's treatment of rhetoric.This ancient motivation is still alive, although it no longer takes centre stage in the picture of logic; typically dialectical logic forms the heart of a course in critical thinking, a compulsory course at many universities.Argumentation theory is the study and research of informal logic, fallacies, and critical questions as they relate to every day and practical situations. Specific types of dialogue can be analyzed and questioned to reveal premises, conclusions, and fallacies. Argumentation theory is now applied in artificial intelligence and law.Mathematical logicMain article: Mathematical logicMathematical logic really refers to two distinct areas of research: the first is the application of the techniques of formal logic to mathematics and mathematical reasoning, and the second, in the other direction, the application of mathematical techniques to the representation and analysis of formal logic.[28]The earliest use of mathematics and geometry in relation to logic and philosophy goes back to the ancient Greeks such as Euclid, Plato, and Aristotle.[29] Many other ancient and medieval philosophers applied mathematical ideas and methods to their philosophical claims.[30]One of the boldest attempts to apply logic to mathematics was undoubtedly the logicism pioneered by philosopher-logicians such as Gottlob Frege and Bertrand Russell: the idea was that mathematical theories were logical tautologies, and the programme was to show this by means to a reduction of mathematics to logic.[8] The various attempts to carry this out met with a series of failures, from the crippling of Frege's project in his Grundgesetze by Russell's paradox, to the defeat of Hilbert's program by Gödel's incompleteness theorems.Both the statement of Hilbert's program and its refutation by Gödel depended upon their work establishing the second area of mathematical logic, the application of mathematics to logic in the form of proof theory.[31] Despite the negative nature of the incompleteness theorems,Gödel's completeness theorem, a result in model theory and another application of mathematics to logic, can be understood as showing how close logicism came to being true: every rigorously defined mathematical theory can be exactly captured by a first-order logical theory; Frege's proof calculus is enough to describe the whole of mathematics, though not equivalent to it. Thus we see how complementary the two areas of mathematical logic have been.[citation needed]If proof theory and model theory have been the foundation of mathematical logic, they have been but two of the four pillars of the subject. Set theory originated in the study of the infinite by Georg Cantor, and it has been the source of many of the most challenging and important issues in mathematical logic, from Cantor's theorem, through the status of the Axiom of Choice and the question of the independence of the continuum hypothesis, to the modern debate on large cardinal axioms.Recursion theory captures the idea of computation in logical and arithmetic terms; its most classical achievements are the undecidability of the Entscheidungsproblem by Alan Turing, and his presentation of the Church–Turing thesis.[32] Today recursion theory is mostly concerned with the more refined problem of complexity classes—when is a problem efficiently solvable?—and the classification of degrees of unsolvability.[33]Philosophical logicMain article: Philosophical logicPhilosophical logic deals with formal descriptions of ordinary, non-specialist ("natural") language. Most philosophers assume that the bulk of everyday reasoning can be captured in logic if a method or methods to translate ordinary language into that logic can be found. Philosophical logic is essentially a continuation of the traditional discipline called "logic" before the invention of mathematical logic. Philosophical logic has a much greater concern with the connection between natural language and logic. As a result, philosophical logicians have contributed a great deal to the development of non-standard logics (e.g. free logics, tense logics) as well as various extensions of classical logic (e.g. modal logics) and non-standard semantics for such logics (e.g. Kripke's supervaluationism in the semantics of logic).Logic and the philosophy of language are closely related. Philosophy of language has to do with the study of how our language engages and interacts with our thinking. Logic has an immediate impact on other areas of study. Studying logic and the relationship between logic and ordinary speech can help a person better structure his own arguments and critique the arguments of others. Many popular arguments arefilled with errors because so many people are untrained in logic and unaware of how to formulate an argument correctly.Computational logicMain article: Logic in computer scienceLogic cut to the heart of computer science as it emerged as a discipline: Alan Turing's work on the Entscheidungsproblem followed from Kurt Gödel's work on the incompleteness theorems. The notion of the general purpose computer that came from this work was of fundamental importance to the designers of the computer machinery in the 1940s.In the 1950s and 1960s, researchers predicted that when human knowledge could be expressed using logic with mathematical notation, it would be possible to create a machine that reasons, or artificial intelligence. This was more difficult than expected because of the complexity of human reasoning. In logic programming, a program consists of a set of axioms and rules. Logic programming systems such as Prolog compute the consequences of the axioms and rules in order to answer a query.Today, logic is extensively applied in the fields of Artificial Intelligence, and Computer Science, and these fields provide a rich source of problems in formal and informal logic. Argumentation theory is one good example of how logic is being applied to artificial intelligence. The ACM Computing Classification System in particular regards:∙Section F.3 on Logics and meanings of programs and F.4 on Mathematical logic and formal languages as part of the theory of computer science: this work covers formalsemantics of programming languages, as well as work of formal methods such as Hoarelogic;∙Boolean logic as fundamental to computer hardware: particularly, the system's sectionB.2 on Arithmetic and logic structures, relating to operatives AND, NOT, and OR;。

R软件Logic回归介绍

Package‘LogicReg’January12,2010Version1.4.9Date2010-01-11Title Logic RegressionAuthor Charles Kooperberg<clk@>and Ingo Ruczinski<ingo@> Maintainer Charles Kooperberg<clk@>Depends survivalDescription Routines for Logic RegressionLicense GPL(>=2)Repository CRANDate/Publication2010-01-1211:17:05R topics documented:cumhaz (2)eval.logreg (3)frame.logreg (4)logreg (6)logreg.anneal.control (15)logreg.mc.control (19)logreg.myown (20)logreg.saveﬁt1 (23)logreg.testdat (24)logreg.tree.control (25)logregmodel (26)logregtree (27)plot.logreg (29)plot.logregmodel (31)plot.logregtree (33)predict.logreg (34)12cumhazprint.logreg (36)print.logregmodel (37)print.logregtree (39)Index41 cumhaz Cumulative hazard transformationDescriptionTransforms survival times using the cumulative hazard function.Usagecumhaz(y,d)Argumentsy vector of nonnegative survival timesd vector of censoring indicators,should be the same length as y.If d is missingthe data is assumed to be uncensored.ValueA vector of transformed survival times.NoteThe primary use of doing a cumulative hazard transformation is that after such a transformation, exponential survival models yield results that are often very much comparable to proportional haz-ards models.In our implementation of Logic Regression,however,exponential survival models run much faster than proportional hazards models when there are no continuous separate covariates. Author(s)Ingo Ruczinski<ingo@>and Charles Kooperberg<clk@>.ReferencesRuczinski I,Kooperberg C,LeBlanc ML(2003).Logic Regression,Journal of Computational and Graphical Statistics,12,475-511.See Alsologregeval.logreg3Examplesdata(logreg.testdat)##this is not survival data,but it shows the functionalityyy<-cumhaz(exp(logreg.testdat[,1]),logreg.testdat[,2])#then we would use#logreg(resp=yy,cens=logreg.testdat[,2],type=5,...#insted of#logreg(resp=logreg.testdat[,1],cens=logreg.testdat[,2],type=4,...eval.logreg Evaluate a Logic Regression treeDescriptionThis function evaluates a logic tree,typically a part of an object generated by logreg.Usageeval.logreg(ltree,data)Argumentsltree an object of class logregmodel or an object of class logregtree.Typi-cally this object will be part of the result of an object of class logreg,generatedwith select=1(single modelﬁt),select=2(multiple modelﬁt),orselect=6(greedy stepwiseﬁt).data a data frame on which the logic tree is to be evaluated.data should be binary, and have the same number of columns as the bin component of the originallogregﬁt.ValueA binary vector with length equal to the number of rows of data;a1corresponds to cases forwhich ltree was TRUE and a0corresponds to cases for which ltree was FALSE if ltree was an object of class logregtree or the trees component of such an object.Otherwise a matrix with one column for each tree in ltree.Author(s)Ingo Ruczinski<ingo@>and Charles Kooperberg<clk@> ReferencesRuczinski I,Kooperberg C,LeBlanc ML(2003).Logic Regression,Journal of Computational and Graphical Statistics,12,475-511.Ruczinski I,Kooperberg C,LeBlanc ML(2002).Logic Regression-methods and software.Pro-ceedings of the MSRI workshop on Nonlinear Estimation and Classiﬁcation(Eds:D.Denison,M.Hansen,C.Holmes,B.Mallick,B.Yu),Springer:New York,333-344.See Alsologreg,logregtree,logregmodel,frame.logreg,logreg.testdatExamplesdata(logreg.savefit1)#myanneal<-logreg.anneal.control(start=-1,end=-4,iter=25000,update=1000) #logreg.savefit1<-logreg(resp=logreg.testdat[,1],bin=logreg.testdat[,2:21], #type=2,select=1,ntrees=2,anneal.control=myanneal)tree1<-eval.logreg(logreg.savefit1$model$trees[[1]],logreg.savefit1$binary)tree2<-eval.logreg(logreg.savefit1$model$trees[[2]],logreg.savefit1$binary)alltrees<-eval.logreg(logreg.savefit1$model,logreg.savefit1$binary)frame.logreg Constructs a data frame for one or more Logic Regression modelsDescriptionEvaluates all components of one or more Logic Regression modelsﬁtted by a single call to logreg.Usageframe.logreg(fit,msz,ntr,newbin,newresp,newsep,newcens,newweight) Argumentsfit object of class logreg,that resulted from applying the function logreg withselect=1(single modelﬁt),select=2(multiple modelﬁt),or select=6(greedy stepwiseﬁt).msz if frame.logreg is executed on an object of class logreg,that resultedfrom applying the function logreg with select=2(multiple modelﬁt)or select=6(greedy stepwiseﬁt)all logic trees for allﬁtted models arereturned.To restrict the model size and the number of trees to some models,specify msz and ntr(for select=2)or just msz(for select=6).ntr see msz.newbin binary predictors to evaluate the logic trees at.If newbin is omitted,the origi-nal(training)data is used.newresp the response.If newbin is omitted,the original(training)response is used.Ifnewbin is speciﬁed and newresp is omitted,the resulting data frame will nothave a response column.newsep separate(linear)predictors.If newbin is omitted,the original(training)pre-dictors are used,even if newsep is speciﬁed.newweight case weights.If newbin is omitted,the original(training)weights are used.Ifnewbin is speciﬁed and newweight is omitted,the weights are taken to be1.newcens censoring indicator.For proportional hazards models and exponential survivalmodels only.If newbin is omitted,the original(training)censoring indica-tors are used.If newbin is speciﬁed and newcens is omitted,the censoringindicators are taken to be1.DetailsThis function calls eval.logreg.ValueA data frame.Theﬁrst column is the response,later columns are weights,censoring indicator,separate predictors(all of which are only provided if they are relevant)and all logic trees.Columnnames should be transparent.Author(s)Ingo Ruczinski<ingo@>and Charles Kooperberg<clk@>ReferencesRuczinski I,Kooperberg C,LeBlanc ML(2003).Logic Regression,Journal of Computational andGraphical Statistics,12,475-511.Ruczinski I,Kooperberg C,LeBlanc ML(2002).Logic Regression-methods and software.Pro-ceedings of the MSRI workshop on Nonlinear Estimation and Classiﬁcation(Eds:D.Denison,M.Hansen,C.Holmes,B.Mallick,B.Yu),Springer:New York,333-344.See Alsologreg,eval.logreg,predict.logreg,logreg.testdatExamplesdata(logreg.savefit1,logreg.savefit2,logreg.savefit6)##fit a single mode#myanneal<-logreg.anneal.control(start=-1,end=-4,iter=25000,update=1000) #logreg.savefit1<-logreg(resp=logreg.testdat[,1],bin=logreg.testdat[,2:21],#type=2,select=1,ntrees=2,anneal.control=myanneal)frame1<-frame.logreg(logreg.savefit1)##a complete sequence#myanneal2<-logreg.anneal.control(start=-1,end=-4,iter=25000,update=0)#logreg.savefit2<-logreg(select=2,ntrees=c(1,2),nleaves=c(1,7),#oldfit=logreg.savefit1,anneal.control=myanneal2)frame2<-frame.logreg(logreg.savefit2)##a greedy sequence#logreg.savefit6<-logreg(select=6,ntrees=2,nleaves=c(1,12),oldfit=logreg.savefi frame6<-frame.logreg(logreg.savefit6,msz=3:5)#restrict the sizelogreg Logic RegressionDescriptionFit one or a series of Logic Regression models,carry out cross-validation or permutation tests forsuch models,orﬁt Monte Carlo Logic Regression models.Logic regression is a(generalized)regression methodology that is primarily applied when most ofthe covariates in the data to be analyzed are binary.The goal of logic regression is toﬁnd predictorsthat are Boolean(logical)combinations of the original predictors.Currently the Logic Regressionmethodology has scoring functions for linear regression(residual sum of squares),logistic regres-sion(deviance),classiﬁcation(misclassiﬁcation),proportional hazards models(partial likelihood),and exponential survival models(log-likelihood).A feature of the Logic Regression methodologyis that it is easily possible to extend the method to write ones own scoring function if you have adifferent scoring function.logreg.myown contains information on how to do so.Usagelogreg(resp,bin,sep,wgt,cens,type,select,ntrees,nleaves, penalty,seed,kfold,nrep,oldfit,anneal.control,tree.control,mc.control)Argumentsresp vector with the response variables.Let n1be the length of this column.bin matrix or data frame with binary data.Let n2be the number of columns of thisobject.bin should have n1rows.sep(optional)matrix or data frame that isﬁtted additively in the logic regressionmodel.sep should have n1rows.When exponential survival models(type=5)are used,the additive predictors have to be binary.When logistic regres-sion models(type=3)are used logreg is much faster when all additivepredictors are binary.wgt(optional)vector of length n1with case weights;default is rep(1,n1).cens(optional)an indicator variable with censoring indicators if type equals4(pro-portional hazards model)or5(exponential survival model);default is rep(1,n1).type type of model to beﬁt:(1)classiﬁcation,(2)regression,(3)logistic regres-sion,(4)proportional hazards model(Cox regression),(5)exponential survivalmodel,or(0)your own scoring function.If type=0,the code needs to berecompiled,uncompiled type=0results in a constant score of0,which maybe useful to generate a sample from the prior when select=7(Monte CarloLogic Regression).select type of model selection to be carried out:(1)ﬁt a single model,(2)ﬁt multiplemodels,(3)cross-validation,(4)null-model permutation test,(5)conditionalpermutation test,(6)a greedy stepwise algorithm,or(7)Monte Carlo LogicRegression(using MCMC).See details below.ntrees number of logic trees to beﬁt.A single number if you select toﬁt a singlemodel(select=1),carry out the null-model permutation test(select=4),carry out greedy stepwise selection(select=6)or,select using MCMC(select=7),or a range(e.g.c(ntreeslow,ntreeshigh))for anyof the other selection options.In our applications,we usually ended up withmodels having between one and four trees.In general,ﬁtting one and two treesin the initial exploratory analysis is a good idea.nleaves maximum number of leaves to beﬁt in all trees combined.A single number ifyou select toﬁt a single model(select=1)carry out the null-model per-mutation test(select=4),carry out greedy stepwise selection(select=6)or,select using MCMC(select=7),or a range(e.g.c(nleaveslow,nleaveshigh))for any of the other selection options.If select is1,4,6,or7,the default is-1,which is expanded to become ntrees*tree.control$treesize.penalty specifying the penalty parameter allows you to penalize the score of larger mod-els.The penalty takes the form penalty times the number of leaves in themodel.(For some score functions,we compute average scores:the penaltyis naturally adjusted.)Thus penalty=2is somewhat comparable to AIC.Note,however,that there is no relation between the size of the model and thenumber of parameters(dimension)of the model,as is usual the case for AIClike penalties.penalty is only relevant when select=1.seed a seed for the random number generator.The random seed is taken to be abs(seed).\For the cross-validation version,if seed<0the sequence of the cases is notpermuted for division in training-test sets.This is useful if you already per-muted the sequence,and wish to compare results with other approaches,or ifthere is a relation between the sequence of the cases,for example for a matchedcase-control study.kfold the number of groups the cases are randomly assigned to.In turn,the modelis trained on(kfold-1)of those groups,and scored on the group left out.Common choices are kfold=5and kfold=10.Only relevant for cross-validation(select=3).nrep the number of runs on permuted data for each model size.We recommendﬁrstrunning this program with a small number of repetitions(e.g.10or25)beforesending off a big job.Only relevant for the null-model test(select=4)orthe permutation test(select=5).oldfit object of class logreg,typically the result of a previous call to logreg.Alloptions that are not speciﬁed default to the value used in oldfit.For thepermutation test(select=5)an oldfit object obtained with select=2(ﬁt multiple models)is mandatory,as the best models of each size need to bein place.anneal.controlsimulated annealing parameters-best set using the function logreg.anneal.control. tree.control several secondary parameters-best set using the function logreg.tree.control.mc.control Markov chain Monte Carlo parameters-best set using the function logreg.mc.control.DetailsLogic Regression is an adaptive regression methodology that attempts to construct predictors as Boolean combinations of binary covariates.In most regression problems a model is developed that only relates the main effects(the predictors or transformations thereof)to the response.Although interactions between predictors are consid-ered sometimes as well,those interactions are usually kept simple(two-to three-way interactions at most).But often,especially when all predictors are binary,the interaction between many predictors is what causes the differences in response.This issue often arises in the analysis of SNP microarray data or in data mining problems.Given a set of binary predictors X,we try to create new,better predictors for the response by considering combinations of those binary predictors.For example,if the response is binary as well(which is not required in general),we attempt toﬁnd decision rules such as“if X1,X2,X3and X4are true”,or“X5or X6but not X7are true”,then the response is more likely to be in class0.In other words,we try toﬁnd Boolean statements involving the binary predictors that enhance the prediction for the response.In more speciﬁc terms:Let X1,...,Xk be binary predictors,and let Y be a response variable.We try toﬁt regression models of the form g(E[Y])=b0+b1L1+...+bn Ln,where Lj is a Boolean expression of the predictors X,such as Lj=[(X2or X4c)and X7].The above framework includes many forms of regression,such as linear regression(g(E[Y])=E[Y])and logistic regression(g(E[Y])=log(E[Y]/(1-E[Y]))).For every model type,we deﬁne a score function that reﬂects the“quality”of the model under consideration.For example,for linear regression the score could be the residual sum of squares and for logistic regres-sion the score could be the deviance.We try toﬁnd the Boolean expressions in the regression model that minimize the scoring function associated with this model type,estimating the parameters bj si-multaneously with the Boolean expressions Lj.In general,any type of model can be considered,as long as a scoring function can be deﬁned.For example,we also implemented the Cox proportional hazards model,using the partial likelihood as the score.Since the number of possible Logic Models we can construct for a given set of predictors is huge,we have to rely on some search algorithms to help usﬁnd the best scoring models.We deﬁne the move set by a set of standard operations such as splitting and pruning the tree(similar to the terminology introduced by Breiman et al(1984)for CART).We investigated two types of algorithms:a greedy and a simulated annealing algorithm.While the greedy algorithm is very fast,it does not always ﬁnd a good scoring model.The simulated annealing algorithm usually does,but computationally it is more expensive.Since we have to be certain toﬁnd good scoring models,we usually carry out simulated annealing for our case studies.However,as usual,the best scoring model generally over-ﬁts the data,and methods to separate signal and noise are needed.To assess the over-ﬁtting of large models,we offer the option toﬁt a model of a speciﬁc size.For the model selection itself we developed and implemented permutation tests and tests using cross-validation.If sufﬁcient data is available,an analysis using a training and a test set can also be carried out.These tests are rather complicated,so we will not go into detail here and refer you to Ruczinski I,Kooperberg C,LeBlanc ML(2003),cited below.There are two alternatives to the simulated annealing algorithm.One is a stepwise greedy selection of models.This is when setting select=6,and yields a sequence of models from size1througha maximum size.At each time among all the models that are one larger than the current model thebest model is selected,yielding a sequence of models of different ually these models are not the best possible,and,if the simulated annealing chain is long enough,you should expect that the models selected using select=2are better.The second alternative is to run a Markov Chain Monte Carlo(MCMC)algorithm.This is what is done in Monte Carlo Logic Regression.The algorithm used is a reversible jump MCMC algorithm,due to Green(1995).Other than the length of the Markov chain,the only parameter that needs to be set is a parameter for the geometric prior on model size.Other than in many MCMC problems,the goal in Monte Carlo Logic Regression is not to yield one single best predicting model,but rather to provide summaries of all models.These are exactly the elements that are shown above as the output when select=7.MONITORINGThe helpﬁle for logreg.anneal.control,contains more information on how to monitor the simulated annealing optimization for logreg.Here is some general information.Find the best scoring model of any size(select=1)During the iterations the following information is printed out:log-temp current score best score acc/rej/sing current parameters-1.000 1.494 1.4940(0)00 2.88-1.990.00-1.120 1.150 1.043655(54)22071 3.630.15-1.82-1.240 1.226 1.043555(49)31680 3.830.05-1.71 ...-2.3200.9880.980147(36)75958 3.00-2.111.11-2.4400.9820.98025(31)88460 2.89-2.121.24-2.5600.9880.97935(61)85051 3.00-2.111.11 ...-3.7600.9640.9642(22)96115 2.57-2.151.55-3.8800.9640.9640(17)96122 2.57-2.151.55-4.0000.9640.9640(13)97017 2.57-2.151.55log-temp:logarithm(base10)of the temperature at the last iteration before the print out.current score:the score after the last iterations.best score:the single lowest score seen at any iteration.acc:the number of proposed moves that were accepted since the last print out for which the model changed,within parenthesis,the number of those that were identical in score to the move before acceptance.rej:the number of proposed moves that gave numerically acceptable results,but were rejected by the simulated annealing algorithm since the last print out.sing:the number of proposed moves that were rejected because they gave numerically unacceptable re-sults,for example because they yielded a singular system.current parameters:the values of the coefﬁcients(ﬁrst for the intercept,then for the linear(separate)components,then for the logic trees).This information can be used to judge the convergence of the simulated annealing algorithm,as described in the helpﬁle of logreg.anneal.control.Typically we want(i)the number of acceptances to be high in the beginning,(ii)the number of acceptances with different scores to below at the end,and(iii)the number of iterations when the fraction of acceptances is moderate to be as large as possible.Find the best scoring models for various sizes(select=2)During the iterations the same information as forﬁnd the best scoring model of any size,for each size model considered.Carry out cross-validation for model selection(select=3)Information about the simulated annealing as described above can be printed out.Otherwise,during the cross-validation process information likeStep5of10[2trees;4leaves]The CV score is 1.127 1.120 1.052 1.122Theﬁrst of the four scores is the training-set score on the current validation sample,the second score is the average of all the training-set scores that have been processed for this model size,the third is the test-set score on the current validation sample,and the fourth score is the average of all the test-set scores that have been processed for this model size.Typically we would prefer the model with the lowest test-set score average over all cross-validation steps.Carry out a permutation test to check for signal in the data(select=4)Information about the simulated annealing as described above can be printed out.Otherwise,ﬁrst the score of the model of size0(typically onlyﬁtting an intercept)and the score of the best model are printed out.Then during the permutation lines likePermutation number21out of50has score 1.47777are printed.Each score is the result ofﬁtting a logic tree model,on data where the response has been permuted.Typically we would believe that there is signal in the data if most permutations have worse(higher)scores than the best model,but we may believe that there is substantial over-ﬁtting going on if these permutation scores are much better(lower)than the score of the model of size0. Carry out a permutation test for model selection(select=5)To be able to run this option,an object of class logreg that was run with(select=2)needsto be in rmation about the simulated annealing as described above can be printed out. Otherwise,lines likePermutation number8out of25has score 1.00767model size3with 2tree(s)are printed.We can compare these scores to the tree of the same size and the best tree.If the scores are about the same as the one for the best tree,we think that the“true”model size may be the one that is being tested,while if the scores are much worse,the true model size may be larger.The comparison with the model of the same size suggests us again how much over-ﬁtting may be going on.plot.logreg generates informative histograms.Greedy stepwise selection of Logic Regression models(select=6)The scores of the best greedy models of each size are printed.Monte Carlo Logic Regression(select=7)A status line is printed every so many iterations.This information is probably not very useful,other than that it helps youﬁgure out how far the code is.PARAMETERSAs Logic Regression is written in Fortran77some parameters had to be hard coded in.The default values of these parameters aremaximum sample size:20000maximum number of columns in the inputﬁle:1000maximum number of leaves in a logic tree:128maximum number of logic trees:5maximum number of separate parameters:50maximum number of total parameters(separate+trees):55If these parameters are not large enough(an error message will let you know this),you need to reset them in slogic.f and recompile.In particular,the statements deﬁning these parameters arePARAMETER(LGCn1MAX=20000)PARAMETER(LGCn2MAX=1000)PARAMETER(LGCnknMAX=128)PARAMETER(LGCntrMAX=5)PARAMETER(LGCnsepMAX=50)PARAMETER(LGCbetaMAX=55)The unfortunate thing is that you will have to change these parameter deﬁnitions several times in the code.So search until you have found them all.ValueAn object of the class logreg.This contains virtually all input parameters,and in additionIf select=1:an object of class logregmodel:the Logic Regression model.This model contains a list of ntrees objects of class logregtree.If select=2or select=6:nmodels:the number of modelsﬁtted.allscores:a matrix with3columns,containing the scores of all models.Column1contains the score,column2the number of leaves and column3the number of trees.alltrees:a list with nmodels objects of class logregmodel.If select=3:cvscores:a matrix with the results of cross validation.The train.ave and test.ave columns for train and test contain running averages of the scores for individual validation sets.As such these scores are of most interest for the rows where k=kfold.If select=4:nullscore:score of the null-model.bestscore:score of the best model.randscores:scores of the permutations;vector of length nrep.If select=5:bestscore:score of the best model.randscores:scores of the permutations;each column corresponds to one model size.If select=7:size:a matrix with two columns,indicating which size models wereﬁt how often.single:a vector with as many elements as there are binary predictors.single[i]shows how often predictor i is in any of the MCMC models.Note that when a predictor is twice in the same model,it is only counted once,thus,in particular,sum(size[,1]*size[,2]will typically be slightly larger than sum(single).double:square matrix with as size the number of binary predictors.double[i,j]shows how often predictors i and j are in the same tree of the same MCMC model if i>j,if i<=j double[i,j]equals zero.Note that for models with several logic trees two predictors can both be in the model but not be in the same tree.triple:square3D array with as size the number of binary predictors.See double,but here triple[i,j,k]shows how often three predictors are jointly in one logic tree.In addition,theﬁle slogiclisting.tmp in the current working directory can be created.This ﬁle contains a compact listing of all models visited.Column1:proportional to the log posterior probability of the model;column2:score(log-likelihood);column3and4:how often was this model visited(this column is here twice for historical reasons),column5through4+maximum number of leaves:summary of theﬁrst tree,if there are two trees,column5+maximum number of leaves through4+twice the maximum number of leaves contains the second tree,and so on.In this compact notation,leaves are in the same sequence as the rows in a logregtree object;a zero means that the leave is empty,a1000means an“and”and a2000an“or”,any other positive number means a predictor and a negative number means“not”that predictor.The mc.control element output can be used to surppress the creation of double,triple, and/or slogiclisting.tmp.There doesn’t seem to be too much use in surppressing double.Surpressing triple speeds up computations a bit(in particular on machines with limited memory when there are many binary predictors),and reduces the size of both the code and the object, surppressing slogiclisting.tmp saves the creation of a possibly very largeﬁle,which can slow down the code considerably.See logreg.mc.control for details.Author(s)Ingo Ruczinski<ingo@>and Charles Kooperberg<clk@>. ReferencesRuczinski I,Kooperberg C,LeBlanc ML(2003).Logic Regression,Journal of Computational and Graphical Statistics,12,475-511.Ruczinski I,Kooperberg C,LeBlanc ML(2002).Logic Regression-methods and software.Pro-ceedings of the MSRI workshop on Nonlinear Estimation and Classiﬁcation(Eds:D.Denison,M.Hansen,C.Holmes,B.Mallick,B.Yu),Springer:New York,333-344.Kooperberg C,Ruczinski I,LeBlanc ML,Hsu L(2001).Sequence Analysis using Logic Regres-sion,Genetic Epidemiology,21,S626-S631.Kooperberg C,Ruczinki I(2005).Identifying interacting SNPs using Monte Carlo Logic Regres-sion,Genetic Epidemiology,in press.Selected chapters from the dissertation of Ingo Ruczinski,available from http://kooperberg./logic/documents/ingophd-logic.pdfSee Alsoeval.logreg,frame.logreg,plot.logreg,print.logreg,predict.logreg,logregtree,plot.logregtree,print.logregtree,logregmodel,plot.logregtree,print.logregtree,logreg.myown,logreg.anneal.control,logreg.tree.control,logreg.mc.control,logreg.testdatExamplesdata(logreg.savefit1,logreg.savefit2,logreg.savefit3,logreg.savefit4,logreg.savefit5,logreg.savefit6,logreg.savefit7,logreg.testdat)myanneal<-logreg.anneal.control(start=-1,end=-4,iter=2500,update=100)#in practie we would use25000iterations or more-the use of2500is only#to have the examples run fast##Not run:myanneal<-logreg.anneal.control(start=-1,end=-4,iter=25000,update=1 fit1<-logreg(resp=logreg.testdat[,1],bin=logreg.testdat[,2:21],type=2,select=1,ntrees=2,anneal.control=myanneal)#the best score should be in the0.97-0.98rangeplot(fit1)#you'll probably see X1-X4as well as a few noise predictors#use logreg.savefit1for the results with25000iterationsplot(logreg.savefit1)print(logreg.savefit1)z<-predict(logreg.savefit1)plot(z,logreg.testdat[,1]-z,xlab="fitted values",ylab="residuals")#there are some streaks,thanks to the very discrete predictions##a bit less outputmyanneal2<-logreg.anneal.control(start=-1,end=-4,iter=2500,update=0)#in practie we would use25000iterations or more-the use of2500is only#to have the examples run fast##Not run:myanneal2<-logreg.anneal.control(start=-1,end=-4,iter=25000,update= ##fit multiple modelsfit2<-logreg(resp=logreg.testdat[,1],bin=logreg.testdat[,2:21],type=2,select=2,ntrees=c(1,2),nleaves=c(1,7),anneal.control=myanneal2) #equivalentfit2<-logreg(select=2,ntrees=c(1,2),nleaves=c(1,7),oldfit=fit1,anneal.control=myanneal2)plot(fit2)#use logreg.savefit2for the results with25000iterationsplot(logreg.savefit2)print(logreg.savefit2)#After an initial steep decline,the scores only get slightly better#for models with more than four leaves and two trees.##cross validationfit3<-logreg(resp=logreg.testdat[,1],bin=logreg.testdat[,2:21],type=2,。

描述逻辑~

3 描述逻辑的研究进展
◆ 描述逻辑的基础研究
研究描述逻辑的构造算子、表示和推理的基本问题，如可满足性、包含检测、一致性、可判定性等。一般都在最基本的ALC的基础上在扩展一些构造算子，如数量约束、逆关系、特征函数、关系的复合等。 TBox和Abox上的推理问题、包含检测算法等。 Schmidt-Schaub 和 Smolka首先建立了基于描述逻辑 ALC的Tableau算法，该算法能在多项式时间内判断描述逻辑ALC概念的可满足性问题。
computer equipment
包含与可满足性的关系
C D iff C D是不可满足的。 C T D iff C D关于T是不可满足的。 C 关于T是一致的 iff C T A A D
高级人工智能
第二章人工智能逻辑
第二部分
史忠植
中国科学院计算技术研究所
描述逻辑
Description Logics
主要内容
什么是描述逻辑？什么是描述逻辑？ ◆ 为什么用描述逻辑？为什么用描述逻辑？ ◆ 描述逻辑的研究进展 ◆ 描述逻辑的体系结构 ◆ 描述逻辑的构造算子 ◆ 描述逻辑的推理问题 ◆ 我们的工作
◆ C关于关于Tbox T是协调的吗？是协调的吗？关于是协调的吗
即检测是否有T的模型 I 使得 C ≠ ？
◆知识库知识库<T, A>是协调的吗？是协调的吗？是协调的吗
即检测是否有<T, A>的模型 (解释) I ？
概念可满足性( 2) 概念可满足性(Satisfiablity) )
另外，有两个类似于FOL中的全集(true)和空集(false)的算子
top Bottom T ⊥ △I Male Male Man Man

研究生科技英语阅读翻译

英⽂写作翻译频道为⼤家整理的研究⽣科技英语阅读翻译，供⼤家参考:)Group: Number 1 Members: Yu Zhehua Yang Jing Rong Wei Wangxiaoqiao Li Ni Liu Qian2011-12-231.What is it that makes mathematics mathematics?Mathematics n. 数学，数学运算，数学应⽤译：是什么使数学成为数学？是什么使数学成为数学？成为数学2.What are the precise characteristics that make mathematics into a discipline that is so central to every advanced civilization, especially our own?Precise adj. 精确的，准确的译：到底是数学的哪些特性使得数学成为对每⼀种⾼等⽂明，数学成为对每⼀种⾼等⽂明，尤其是对我们现在的⽂明如此重要的学科？3.Many explanations have been attempted.对此，译：（对此，）我们已经试着做出了⼀些解释。

解释。

4.One of these sees mathematics as the ultimate in rational expression; in fact, the expression “the light of reason” could be used to refer to mathematics.Ultimate In fact Refer to n.终极，顶点事实上，实际上把…归因（于），认为…起源（于）译：有⼀种解释认为数学是⼀种终极的理性表达⽅式；⽽实际上，极的理性表达⽅式；⽽实际上，我们可以⽤“ 理性之光”们可以⽤ “ 理性之光 ” 这个说法来形容数学。

大学英语四级选词填空技巧

How can I improve my vocabulary for this test? Building vocabulary is essential for this type of question Read books, newspapers, and other materials to expand your vocabulary Use flashcards or an app to help you learn new words
Tips for Filling in the Blank for College English Test Band 4
• Overview of word choice fill in the blank question types
• Vocabulary accumulation and application • Sentence structure analysis • Context reasoning and inference • Practical exercises and analysis
Transitional relationship
When the situation or fact described in a sentence is opposite to what is expected, use transitional words (such as "but", "although", etc.) to express this relationship.
02
What is the best strategy for answering these questions? Read the presence or paragraph carefully to understand the context Advisor the meaning and function of the missing word, and choose the option that best fits both

英语的四种文体的区别

Also, more people eat chicken than ducks, so they will bring more at the market. Not only that, chicken manure makes good fertilizer for plants around the farm. You will just have to admit it: all things considered, chickens are better than ducks to make a pleasant life on the farm!
Forms of composition ( summary): (1) Narration: (2) Argumentation: (4) Description:
order, diction
time, place, character, event point, supporting facts (details)

Example: Life on a Farm (Description)
The still morning calm is broken by the rooster’s crow. Shortly after that all the creatures on the farm are hungrily awake. The birds are singing delightfully, flying from tree to tree. The little chicks hurry around with their mother hen looking for food.
原创力文档是网络服务平台方若您的权利被侵害侵权客服qq

英语科技文写作-Title

Precision of Language
Use precision language and avoid jargon to ensure clarity and accessibility
Evidence Based Content
Cite relevant research, data, and experiments to support arguments and conclusions
It is important to avoid using emotional language or descriptive objectives that may affect the reader's understanding of the information Instad, the focus should be on improving objective facts and data
Implicitations
Discuss the broker implications of the research or ideas presented
Acknowledgements
Recognize the contributions of others and any resources used in the research or writing process
Inconsistent voice and tone
Voice and tone should be consistent throughout the text Solutions: Determine the voice and tone early in the writing process and stick to it. Use appropriate language for the target audience

Summary+of+Knowledge+Points+in+Third+Grade+English

Summary words
Taking care of pets
If there are pets at home, students should learn to take care of them and talk to them in English This can help improve their language skills and develop their sense of responsibility
Summary of Knowledge
Points in Third Grade
English
汇报人：
汇报时间：2024-01-08
目录
• Vocabulary learning • Grammar knowledge • Daily conversations • Reading comprehension • Writing Practice • Learning strategy
Numeric vocabulary
Summary words
Master vocabulary related to numbers, understand basic concepts and expressions of numbers.
Detailed description
Students need to master common numerical vocabulary, such as one, two, three, four, etc., understand the basic concepts and expressions of numbers, and be able to perform simple mathematical calculations and express the concept of quantity.

数字系统设计实验报告

数字系统设计实验报告1. 引言数字系统设计是计算机科学与工程中的重要领域之一。

本实验旨在通过设计一个基本的数字系统，深入理解数字系统的原理和设计过程。

本文将按照以下步骤详细介绍实验的设计和实施。

2. 实验目标本实验旨在设计一个简单的数字系统，包括输入、处理和输出三个模块。

具体目标如下： - 设计一个输入模块，用于接收用户的输入数据。

- 设计一个处理模块，对输入数据进行特定的处理。

- 设计一个输出模块，将处理结果展示给用户。

3. 实验设计3.1 输入模块设计输入模块主要用于接收用户的输入数据，并将其传递给处理模块进行处理。

在本实验中，我们选择使用键盘作为输入设备。

具体设计步骤如下： 1. 初始化输入设备，确保能够正确接收用户输入。

2. 设计输入缓冲区，用于存储用户输入的数据。

3. 实现输入函数，将用户输入的数据存储到输入缓冲区中。

3.2 处理模块设计处理模块是数字系统的核心部分，负责对输入数据进行特定的处理。

在本实验中，我们选择设计一个简单的加法器作为处理模块。

具体设计步骤如下： 1. 定义输入数据的格式和表示方法。

2. 实现加法器的逻辑电路，可以通过使用逻辑门和触发器等基本组件来完成。

3. 设计加法器的控制电路，用于控制加法器的运算过程。

4. 验证加法器的正确性，可以通过给定一些输入数据进行测试。

3.3 输出模块设计输出模块用于将处理结果展示给用户。

在本实验中，我们选择使用显示器作为输出设备。

具体设计步骤如下： 1. 初始化输出设备，确保能够正确显示处理结果。

2. 设计输出缓冲区，用于存储待显示的数据。

3. 实现输出函数，将输出数据从输出缓冲区中传输到显示器上。

4. 实验实施4.1 输入模块实施根据3.1节中的设计步骤，我们首先初始化输入设备，然后设计输入缓冲区，并实现相应的输入函数。

4.2 处理模块实施根据3.2节中的设计步骤，我们定义输入数据的格式和表示方法，然后实现加法器的逻辑电路和控制电路。

语言学试卷汇总考试

Translate the following terms from English into Chinese.把下列术语翻译成中文1.duality of structure _________结构的二元性_______________2.General Linguistics ________普通语言学________________3.voiceless consonant _________清辅音_______________plementary distribution ________互补分布________________5.free morpheme ________自由词素________________6.immediate constituent ________直接成份________________ponential Analysis ________成份分析________________8.American Structuralism ________美国结构主义________________9.zero morph _________零语子_______________10.structural ambiguity _________结构歧义_______________11.productivity _________多产性 ______________12.linguistic competence __________语言能力______________13.manner of articulation _________发音方法_______________14.intonation language _________语调语言_______________15.allophone __________音位变体______________16.inflectional morpheme _________曲折语素_______________17.phrase marker __________短语标记______________18.denotation __________指示______________19.Systemic-Functional Grammar __________系统功能语法______________20.bound morpheme __________粘着语素______________21.cultural transmission __________文化传播______________22.Descriptive Linguistics __________描写语言学______________23.derivational morpheme ___________派生词素_____________24.consonant ___________辅音_____________25.tone language ___________声调语言_____________26.empty morph ___________虚语子_____________27.syntax ___________语法_____________plementary antonym ___________互补反义词_____________29.mode of discourse ____________话语方式____________30.free variation _______自有变异_________________31.displacement __________取代______________32.paradigmatic relation __________集合体关系______________33.voiced consonant __________浊辅音______________34.minimal pair __________极小对______________35.phoneme __________音位______________36.lexical ambiguity __________词法的歧义性______________37.connotation __________内涵______________nguage acquisition device __________语言习得机制______________39.constituency __________选区______________40.alien __________相异______________41.design feature __________设计特点______________42.Theoretical Linguistics __________理论语言学______________43.diphthong __________双元音______________44.contrastive distribution __________对立分布______________45.translation-loan __________借译词______________46.ultimate constituent __________主要成分______________47.relational opposite __________关系对立词______________48.genre __________类型______________49.dependency __________从属______________50.denizen __________居民______________51.arbitrariness __________任意______________52.linguistic performance ___________语言行为_____________53.vowel ___________元音_____________54.free variation ___________自由变异_____________55.derivational morpheme _________派生词素_______________56.surface structure __________表层结构______________57.mode of discourse ___________话语方式_____________58.gradable antonym __________分级反义词______________59.Innateness Hypothesis __________天赋假说______________plementary antonym __________互补反义词______________61.interchangeability _______可交换性_________________62.syntagmatic relation ________结构关系________________63.pure vowel _________纯元音_______________64.intonation language _________语调语言_______________65.bound morpheme __________粘着语素______________66.linguistic competence __________语言能力______________67.deep structure __________深层结构______________68.semantic field __________语义场_____________69.context of situation ___________情境语境_____________70.manner of articulation ___________发音方法_____________71.discreteness ___________组件_____________72.Applied Linguistics _________应用语言学_______________73.immediate constituent __________直接成分______________74.place of articulation __________发音部位______________75.phoneme ___________音位；音素_____________76.zero morph _________零语子_______________77.structural ambiguity __________结构歧义______________78.hyponymy __________上下位关系______________79.tenor of discourse ___________语旨_____________ponential Analysis __________成分分析______________Answer the following questions.回答一下问题1.What are the differences between grammatical competence and pragmaticcompetence2.What is the difference between free morphemes and bound morphemes Illustrateit with examples.3.What are the three syntactic relations Illustrate them with examples.4.What does it mean to say that language is arbitrary Illustrate it with examples.5.What is the difference between tone languages and intonation languagesIllustrate it with examples.6.Explain the differences between inflectional affixes and derivational affixesin terms of both function and position.7.What does it mean to say that language is a system Illustrate it with examples.8.What is the difference between an empty morph and a zero morph Illustrate itwith examples.9.What are the differences between surface structure and deep structureIllustrate them with examples.10.What does it mean to say that language is symbolic Illustrate it with examples.11.What is a morpheme Illustrate the relationship between morphemes; morphs; andallomorphs with examples.12.What are the three general types of antonyms And how do they differ from eachother13.What are the three sub-branches of phonetics How do they differ from each other14.What does Semantic Filed Theory mainly propose Illustrate it with examples.15.What is the difference between segmental features and supra-segmental featuresWhat are the supra-segmental features in English16.What are the design features of languages17.How does denotation differ from connotation Illustrate their difference withexamples.18.Why do we say “abso lute synonyms are rare or even non-existent” Illustrateit with examples.19.What does it mean to say that language is dual-structured20.What does compounding mean Illustrate with examples the differences betweenhyphenated compounds; solid compounds and open compounds21.What are the essential factors for determining sentence meaning Illustrate themwith examples.22.How does a diachronic description of a language differ from a synchronicdescription of a language Illustrate their difference with examples.23.What are the three conditions for forming a minimal pair Illustrate it withexamples.24.What does clipping mean in morphology Illustrate with examples the differencebetween back clipping; front clipping; front and back clipping; and phrase clipping.Practical work.Write the symbol that corresponds to each of the following phonetic descriptions：写对应于每一个下面语音描述的符号Example: a voiceless velar plosive k1) a voiced bilabial plosive __ b ____2) a voiceless labiodental fricative __ f ____3) a voiced bilabial nasal __ m ____4) a high front unrounded vowel __ i ___5) a voiced bilabial glide __w____6) a voiceless dental fricative __θ____7) a voiced labiodental nasal __ ____8) a mid central unroudned vowel ___ : ___9) a voiced velar plosive __ /g/ ____10) a voiceless alveolar fricative _θ:_____11) a voiced alveolar liquid __ ____12) a mid back rounded vowel __ : ____13) a voiced dental fricative __ e ____14) a voiceless alveo-palatal affricative __ / /____15) a voiced post-alveolar liquid __ / / ____16) a high back rounded vowel __ u ____17) a voiceless bilabial plosive ___p___18) a voiced alveolar fricative __s____19) a voiceless post-alveolar affricate __ t ____20) a low front unrounded vowel __a____21) a voiceless alveo-palatal fricative __t____22) a voiced post-alveolar affricate __z____23) a voiceless palatal plosive __c____24) a mid front unrounded vowel _ε___25) a voiceless alveolar plosive __ t ____26) a voiced alveolar nasal ______27) a voiced palatal glide ______28) a low central unrounded vowel __Λ____29) a voiced alveo-palatal fricative __x____30) a voiceless dental fricative __θ____31) a voiced alveo-palatal affricate ______32) a low back rounded vowel ______Divide the following words into separate morphemes by placing a “+” between each morpheme and the next:Example: bookshelf = book + shelf1)manly = man+ly2)encourage = en+cour+age3)placement = place+ment4)agreement = agree+ment5)affixes =6)footprint = foot+print7)underestimation= under+estimation8)disapproval = dis+approval9)gentleman = gentle+man10)entertainment = enter+tain+ment11)entitle = en+title12)reread =re+read13)unfit = un+fit14)waterbed = water+bed15)disorderly = dis+order+ly16)unsuccessful = un+success+ful17)structural = structural18)sweeten = sweet+en19)marker = mark+er20)decided = decid+ed21)exciting = excit+ing22)greenhouse = green+house23)disgraceful = dis+grace+ful24)enlargement = en+large+ment25)informed =inform+ed26)amazing = amaz+ing27)advanced =advance+ed28)enrich =en+rich29)deafen =deaf+en30)undergo = un+dergo31)irregularly = ir+regular+ly32)decoded = decod+ed33)incorrect = in+correct34)undo = un+do35)weekly = week+ly36)functional =func+tion+al37)illiterate = ill+iterate38)sleepwalk = sleep+walk39)unmanly = un+man+ly40)befriended = be+friend+ed41)disobey = dis+oney42)rewrite = re+write43)yearly = year+ly44)troublesome = trouble+some45)talented = talent+ed46)lookout = look+out47)boyishness = boy+ish+ness48)disappearance = dis+appear+ance49)supervise = super+vise50)costly = cost+ly51)inspiring = inspire+ing52)prescription =pre+scrip+tion53)threaten = threat+en54)overlook = over+look55)undesirable = un+desir+able56)irreplaceable = ir+re+place+able57)eatable = eat+able58)amusement = amuse+ment59)monthly = month+ly60)generalize =generalize61)logical =logic+al62)grandfather = grand+father63)incorruptible = in+corrupt+ible64)reenactment =re+enact+mentMatch the names of linguistic figures in column A with the schools or theories or works of linguistics in column B:Column A Column B1)Saussure d a. Systemic-Functional Grammar2)Halliday a b. The London Schoolc.Transformational-Generative3)Firth bGrammar4)Chomsky c d. The Founder of Structuralism系统功能语法Systemic-functional Grammar由英国语言学家韩礼德Halliday伦敦学派是以长期在伦敦大学的东方与非洲研究学院教授语音学与语言学并于1944 年成为英国第一任语言学教授弗斯Firth为首的语言学派换-生成语法Transformational-generative grammar;简称TG是美国语言学家Column A Column B5)Chomsky a. American Structuralismc6)Bloomfieldab. Relational Grammar7)Lamb b c. Syntactic Structures8)Perlmutter and Postald d. Stratificational GrammarColumn A Column B9)Chomsky b a. The Prague School10)Mathesius ab. Aspects of The Theory ofSyntax11)Malinowski d c. Case Grammar12)Fillmore cd. Coral Gardens and TheirMagicColumn A Column B13)Chomsky b a. American Structuralism14)Firth d b. The Minimalist Program15)Bloomfield ac. The Distinctive FeatureTheory16)Jakobson cd. The Founder of theLondon SchoolColumn A Column B17)Chomsky c a. Montague Grammar18)Hjelmslev db. Lexical-FunctionalGrammarc. The Innateness19)Montague aHypothesis20)Brasnan & Kapland. The Copenhagen SchoolbColumn A Column B21)Chomsky b a. The Copenhagen Schoolb. Language Acquisition22)Jakobson dDevice23)Mathesius c c. Communicative Dynamismd. The Distinctive Feature24)Hjelmslev aTheoryColumn A Column Ba. The Founder of the25)Chomsky dLondon Schoolb. The Founder of26)Halliday cStructuralismc. Systemic-Functional27)Firth aGrammard. The Extended Standard28)Saussure bTheoryColumn A Column Ba. Coral Gardens and Their29)Chomsky bMagic30)Hjelmslev d b. The Classical Theoryc. The Distinctive Feature31)Jakobson cTheoryd. The Copenhagen School32)Malinowski aDraw the deep structure phrase markers for the following two sentences:1)John is attending the class.2)Mary could have seen the film.3)Mary is chasing the dog.4)John could have read the book.5) Tom is eating an orange.6) Nancy could have done her homework.7)Johnson is reading a book.8)David could have finished his homework.9)David is singing a song.10)Tim could have told the truth.11)Tim is playing the piano.12)Johnson could have stolen the wallet.13)Nancy is playing the badminton.14)Mary could have seen the poster.15)George is doing his homework.16)David could have read the novel.。

（LAND）逻辑和logicOR（LOR）逻辑或logicanalyzer逻辑l..

本文档来自酷兔英语文档中心logic 逻辑logic AND (LAND) 逻辑和logic OR (LOR) 逻辑或logic analyzer 逻辑分析仪logic array block (LAB) 逻辑阵列区块logic cell array (LCA) 逻辑单元阵列logic description 逻辑描述logic design 逻辑设计logic emulation 逻辑仿真logic ground 逻辑地logic inhibit/enable 逻辑禁止/允许logic level 逻辑电平logic node 逻辑节点logic slice 逻辑片logic synthesis 逻辑合成logic, Boolean 布尔逻辑logic, backplane transceiver (BTL) 基架收发器逻辑logic, bipolar 双极逻辑logic, buried 隐敝式逻辑logic, clocked sequential 时钟式序列逻辑logic, combinational 混合式逻辑logic, complementary 互补逻辑logic, complementary transistor (CTL) 互补晶体管逻辑logic, control 控制逻辑logic, current mode (CML) 电流模式逻辑logic, diode-transistor (DTL) 二极管晶体管逻辑logic, direct-coupled transistor (DCTL) 直接耦合晶体管逻辑logic, discrete 胶合逻辑logic, emitter-coupled (ECL) 射极耦合逻辑logic, emitter-coupled transistor (ECTL) 射极耦合晶体管逻辑logic, fuzzy 模糊逻辑logic, generic array (GAL) 通用阵列逻辑logic, glue 胶合逻辑logic, hardwired 固定线路逻辑logic, hardwired control 固定线路控制逻辑logic, high threshold (HTL) 高临限逻辑logic, interrupt 中断逻辑logic, multiple array programmable (MAPL) 多重阵列可编程逻辑logic, negative 负逻辑logic, positive 正逻辑logic, positive emitter-coupled (PECL) 正射极耦合逻辑logic, programmable array (PAL) 可编程阵列逻辑logic, resistor-capacitor-transistor (RCTL) 电阻电容晶体管逻辑logic, resistor-transistor (RTL) 电阻晶体管逻辑logic, standard discrete 标准离散逻辑元件logic, state 状态逻辑logic, testing 测试逻辑logic, transistor-transistor (TTL) 晶体管晶体管逻辑logic, tristate 三态逻辑logic, unclocked sequential 非时钟式序列逻辑logic-level shifter 逻辑水平位移器logical architecture 逻辑架构logical block address (LBA) 逻辑区块地址logical channel 逻辑信道logical circuit 逻辑电路logical diagram 逻辑图logical interface 逻辑接口logical link control (LLC) 逻辑链路控制logical page 逻辑分页logical subnet 逻辑子网logical sum 逻辑和logical unit (LU) 逻辑单元，逻辑部件long haul 长程网络long-reach 长到达longitudinal balance 纵向平衡longitudinal redundancy check (LRC) 纵向冗余码检测longitudinal vibration 纵振动longitudinal wave 纵波look-ahead buffer 先行缓冲器look-ahead read 读取先行look-up table (LUT) 搜寻列表lookaside buffer 旁视缓冲器loop 环路loop antenna 环形天线loop back 回环loop compensation amplifier 环路补偿放大器loop gain 环路增益loop response 环路响应loop start signaling 环路启动信令loop, closed 闭合环路；闭环loop, digital adapter for subscriber (DASL) 用户环路数字配接器loop, digital phase-locked (DPLL) 数字锁相环路loop, electrical ground 电气接地环路loop, feedback 反馈环路loop, ground 接地环路loop, hysteresis 磁滞环路loop, input 输入环路loop, local 区域性环路loop, null 零位环路loop, open 开放环路；开环loop, output 输出环路loop, phase-locked (PLL) 锁相环路loop-back test 回环测试loop-compensation amplifier 环路补偿放大器loop-gain error 环路增益误差loos 损耗loos factor 损耗因数loos of lock (LOL) 失锁loos of power (LOP) 功率损失loos of signal 信号损耗loos of synchronization 失步,同步丢失loos, conduction 低传导损耗loos, crossover 交接损耗；交越损耗loos, dielectric 介电质损耗loos, hysteresis 磁滞损耗loos, insertion 插入损耗loos, power 功率损耗loos, return 回送损耗loos, shunt 分流损耗loos, switch 交换损耗loos, switch ing 开关损耗loose tube cable 松套电缆loosely coupled 松弛耦合lossless compression 非损耗式压缩lossless transmission 无损耗式传输lossy compression 损耗式压缩lossy medium 损耗式媒介lost call probability 呼损概率lost calls cleared 呼损[记录]清除lost calls held 呼损[记录]保持loudness 响度loudspeaker 扬声器low frequency (LF) 低频放大器low order 低值位low probability of intercept (LPI) 低拦截概率low switch ing transient 低转换瞬变low temperature cofired ceramic (LTCC) 低温烧结陶瓷，低温共烧陶瓷low-end 低档low-frequency amplifier 低频放大器low-frequency bypass 低频旁路low-jitter clock 低抖动时钟low-level format (LLF) 低阶格式化low-noise block converter (LNB) 低噪声块转换器low-pass filter 低通滤波器low-power television (LPTV) 低功耗电视，低功率电视low-pressure chemical vapor deposition (LPCVD) 低压化学汽相沉积low-voltage technology (LVT) 低电压技术lower bit 低位lower limit 下限lower-triangular matrix 下三角形矩阵lowpass 低通lowpass filter (LPF) 低通滤波器lubricant 润滑剂lug 套管lumen (lm) 流明luminaire 光源，发光体luminance 亮度luminance bandwidth 亮度频宽luminance sample 亮度取样luminance, cross 亮度互串luminescent, electro- (EL) 场致发光luminosity 光度luminous body 发光体luminous efficiency 发光效率luminous energy 光能luminous flux 光通量luminous intensity 光度lumped 总集的lumped capacitive load 总集电容负载lux (lx) 勒克司关键字:IT专业英语词典生词表:diagram [dai gr m] n.图解，图表四级词汇vibration [vai brei n] n.颤动；振动；摇动四级词汇antenna [n ten] n.触角；天线六级词汇subscriber [s b skraib] n.捐款人；预约者四级词汇transmission [tr nz mi n, tr ns-] n.传送；播送；发射六级词汇frequency [fri:kw nsi] n.频繁；周率六级词汇intercept [,int sept] vt.拦截；截获；窃听六级词汇transient [tr nzi nt, tr n nt] a.短暂的；无常的六级词汇filter [filt] n.滤器 v.过滤，渗入四级词汇酷兔英语服务列表酷兔背单词酷兔练听力酷兔动画英语酷兔英语游戏酷兔在线英语词典酷兔英语资料下载酷兔英语学习软件学习方案特色课程。

大学英语四级考试写作必备模板

Flexible use of templates
Do not overly rely on templates, but rather use them flexibly according to different writing tasks and requirements based on understanding templates.
Pay attention to language expression
Accurate expression of language
In the writing process, attention should be paid to the accuracy and clarity of language expression to avoid grammar errors, spelling errors, and other problems.
1. Missing or redundant vocabulary: for example, forgetting to add - ing after a verb or forgetting to use the third person singular form.
3. Incorrect tense: For example, using the present tense when describing past events.
Pay attention to language style
Based on different writing purposes and reader groups, it is important to choose an appropriate language style to better attract readers and convey information.

新编简明英语语言学教程何兆熊第四章笔记和习题

Chapter 4SyntaxWhat is syntax?----a branch of linguistics that studies how words are combined to form sentences and the rules that govern theformation of sentences.The term syntax is from the ancient Greek word syntaxis, which literally means “ arrangement ” or out together ”.Traditionally, it refers to the branch of grammar dealing with the ways in which words, with orwithout appropriate inflections, are arranged to show connections of meaning within the sentence.Syntax is a branch of linguistics that analyzes the structure of sentences.What is a sentence?Syntax is the analysis of sentence structure. A sentence is a sequence of words arranged in acertain order in accordance with grammatical rules.A sequence can be either well-formed or ill-formed. Native speakers of a language know intuitively whatstrings of words are grammatical and what are ungrammatical.Knowledge of sentence structureStructural ambiguityStructural ambiguity is one or more string(s) of words has/have more than one meaning. Forexample, the sentenceTom said he would come yesterdaycan be interpreted in different ways.Word orderDifferent arrangements of the same words have different meanings. For example, with the wordsTom, loveand Mary, we may sayTom loves Mary or Mary loves Tom.Grammatical relationsNative speakers know what element relates to what other element directly or indirectly. Forexample, in The boats are not big enough and We don’ t have enough boats, the word enough isrelated to different words in the two sentences.RecursionThe same rule can be used repeatedly to create infinite sentences. For example,I know that you arehappy. He knows that I know that you are happy. She knows that he knows that I know that you arehappy.Sentence relatednessSentences may be structurally variant but semantically related.Syntactic categoriesA syntactic category is a class of words or phrases that can substitute for one another withoutloss of grammaticality. For example, consider the following sentences:The child found the knife.A policeman found the knife.The man who just left herefound the knife.He found the knife.All the italicized parts belong to the same syntactic category called noun phrase (NP). Thenoun phrases in these sentences function as subjectThe. knife, also a noun phrase, functions asobject.Traditional grammarIn traditional grammar, a sentence is considered a sequence of words which are classified into parts of speech.Sentences are analyzed in terms of grammatical functions of words: subjects, objects, verbs(predicates), predicatives, ⋯Structural grammarStructural grammar arose out of an attempt to deviate from traditional grammar. It deals with the inter-relationships of different grammatical units. In the concern of structural grammar, words are not just independent grammatical units, but are inter-related to one another.Transformational-generative (TG) grammar1Adequacy of observationAdequacy of descriptionAdequacy of explanationWriting a TG grammar means working out two sets of rules – phrase structure rules andtransformation rules –which are followed by speakers of the language.TG grammar must account for all and only grammatical sentences.TG grammar accounts for the mental process of our speaking.Systematic-functional grammarBackground and the goal of systemic-functional grammar M.A. K. HallidayLanguage is a system of meaning potential and a network of meaning as choices.Meaning determines form, not vice versa. Meaning is realized through forms.The goal of systemic-functional grammar is to see how function and meaning are realizedthrough forms.The three meta-functions of languageIdeational functionInterpersonal functionTextual functionCategoriesCategory refers to a group of linguistic items which fulfill the same or similar functions in a particular language such as a sentence, a noun phrase or a verb. The most central categories to the syntactic study are the word-level categories (traditionally, parts of speech)Word-level categoriesMajor lexical categories: N, V, Adj, Prep.Minor Lexical categories: Det, Deg, Qual, Auxi, Conj.The criteria on which categories are determinedMeaningInflectionDistributionNote: The most reliable criterion of determining a word ’ s category is its distribution.Phrase categories and their structuresPhrase categories----the syntactic units that are built around a certain word category are called phrase categories, such as NP(N), VP(V), AP(A), PP(P).The structure: specifier + head + complementHead---- the word around which a phrase is formedSpecifier---- the words on the left side of the headsComplement---- the words on the right side of the headsPhrase structure rulesThe grammatical mechanism that regulates the arrangement of elements that make up a phrase is called aphrase structure rule, such as:NP (Det) + N +(PP) ⋯⋯ e.g. those people, the fish on the plate, pretty girls.VP (Qual) + V + (NP) ⋯⋯ e.g. always play games, finish assignments.AP (Deg) + A + (PP) ⋯⋯ very handsome, very pessimistic, familiar with, very close toPP (Deg) + P + (NP) ⋯⋯ on the shelf, in the boat, quite near the station.The XP ruleNote: The phrase structure rules can be summed up as XP rule shown in the diagram, in which X stands for N, V, A or P.Coordination ruleCoordination structures-----the structures that are formed by joining two or more elements of the same type with the help of a conjunction such as and, or, etc.----Coordination has four important properties:no limit on the number of coordinated categories before the conjunction;a category at any level can be coordinated; the categories must be ofthe same type;the category type of the coordinate phrase is identical to the category type of the elements being conjoined.Phrase elementsSpecifierHeadComplementSpecifiers----Semantically, specifiers make more precise the meaning of the head; syntactically, they typicallymark a phrase boundary. Specifiers can be determiners as in NP, qulifiers as in VP and degree words as in AP.Complements---- Complements themselves can be a phrase, they provide information about entities and locations whose existence is implied by the meaning of the head, e.g. a story about a sentimental girl; There can be no complement, one complement, or more than one complement in a phrase, e.g. appear, break, put ⋯;asentencelike- construction may also function as a complement such as in I believed that she“ was innocent. I doubt if she will come. They are keen for you to show up. ”That/if /for are complementizers, the clauses introduced by complementizers are complement clause.Modifiers---- Modifiers specify optionally expressible properties of heads.Sentences (the S rule)S NP VPS NP infl VPMany linguists believe that sentences, like other phrases, also have their own heads. Infl is an abstract category inflection (dubbed ‘ Infl ’ ) as their heads, which indicatest ensehesentenceandagreement. ’Infl realized by a tense labelInfl realized by an auxiliaryTransformationsAuxiliary movement (inversion)Do insertionDeep structure & surface structureWh-movementMove α and constraints on transformationsAuxiliary movement (inversion)Inversion Move Infl to the left of the subject NP.Inversion (revised) Move Infl to C.Auxiliary movement (inversion)Do insertionDo insertion---- Insert interrogative do into an empty Infl position.Deep structure & surface structure3John is easy to please.John is eager to please.Structurally similar sentencesmight be very different in their meanings, for they have quite different deep structures.Consider one more sentence: Flying planes can be dangerous.It can mean either that if you fly planes you are engaged in a dangerous activity or Planes that areflying are dangerous.Deep structure----formed by the XP rule in accordance with the head’s ub-categorization properties; it contains all the units and relationships that are necessary for interpreting the meaning of the sentence.Surface structure----corresponding to the final syntactic form of the sentence which results from appropriate transformations; it is that of the sentence as it is pronounced or written.D-structure and S-structureTwo levels of syntactic representation of a sentence structure:One that exists before movement takes placeThe other that occurs after movement takes placeFormal linguistic exploration:D-structure: phrase structure rules + lexiconSentence at the level of D-structureThe application of syntactic movement rules transforms a sentence fromD-structure level to S-structure levelTransformational-generative line of analysisThe organization of the syntactic componentWh-movementConsider the derivation of the following sentences:What languages can you speak?What can you talk about?These sentences may originate as:You can speak what languages.You can talk about what.Wh-movement---- Move a wh phrase to the beginning of the sentence.What language can you speak ?What can you talk about ?Wh-movement---- Move a wh phrase to the specifier position under CP. (Revised)Move α and constraints on transformationsInversion can move an auxiliary from the Infl to the nearest C position, but not to a more distant C position. No element may be removed from a coordinate structure.Chapter 4： SyntaxI. Decide whether each of the following statements is True or False:1.Syntax is a subfied of linguistics that studies the sentence structure of language, including thecombination of morphemes into words.2.Grammatical sentences are formed following a set of syntactic rules.3.Sentences are composed of sequence of words arranged in a simple linear order, with one adding ontoanother following a simple arithmetic logic.4.Universally found in the grammars of all human languages, syntactic rules that comprise the system of internalized linguistic knowledge of a language speaker are known as linguistic competence.5. The syntactic rules of any language are finite in number, but there is no limit to the number ofsentences native speakers of that language are able to produce and comprehend.6.In a complex sentence, the two clauses hold unequal status, one subordinating the other.7.Constituents that can be substituted for one another without loss of grammaticality belong to the same syntactic category.8.Minor lexical categories are open because these categories are not fixed and new members are allowed for.9.In English syntactic analysis, four phrasal categories are commonly recognized and discussed, namely, noun phrase, verb phrase, infinitive phrase, and auxiliary phrase.10.In English the subject usually precedes the verb and the direct object usually follows the verb.11.What is actually internalized in the mind of a native speaker is a complete list of words and phrasesrather than grammatical knowledge.12.A noun phrase must contain a noun, but other elements are optional.13.It is believed that phrase structure rules, with the insertion of the lexicon, generate sentences at the level of D-structure.14.WH-movement is obligatory in English which changes a sentence from affirmative to interrogative.II. Fill in each of the following blanks with one word which begins with the letter given:15. A s________ sentence consists of a single clause which contains a subject and a predicate and stands alone as its own sentence.16. A s______ is a structurally independent unit that usually comprises a number of words to form a complete statement, question or command.17.A s______ may be a noun or a noun phrase in a sentence that usually precedes the predicate.18.The part of a sentence which comprises a finite verb or a verb phrase and which says something about the subject is grammatically called p_________.19.A c_________ sentence contains two, or more, clauses, one of which is incorporated into the other.20.In the complex sentence, the incorporated or subordinate clause is normally called an e_______ clause.21.Major lexical categories are o___ categories in the sense that new words are constantly added.22.XP can be written as (specifier) X (complement), X is called the_____.23.In a tree diagram, _____is the root of tree.24.The information about a word’s complement is included in the head and termed________.III.There are four given choices for each statement below. Mark the choice that can best complete the statement:25. A sentence is considered ____ when it does not conform to the grammatical knowledge in the mind ofnative speakers.A. rightB. wrongC. grammaticalD. ungrammatical26. A __________ in the embedded clause refers to the introductory word that introduces the embedded clause.A. coordinatorB. particleC. prepositionD. subordinator27. Phrase structure rules have ____ properties.A. recursiveB. grammaticalC. socialD. functional28. Phrase structure rules allow us to better understand_____________.A.how words and phrases form sentences.B.what constitutes the grammaticality of strings of wordsC.how people produce and recognize possible sentencesD.All of the above.29. Syntactic movement is dictated by rules traditionally called ________.A.transformational rulesB.generative rulesC.phrase structure rulesD.x-bar theory30. The theory of case condition accounts for the fact that __________.A.noun phrases appear only in subject and object positions.B.noun phrases can be used to modify another noun phraseC.noun phrase can be used in adverbial positionsD.noun phrase can be moved to any place if necessary.31. The sentence structure is ________.A. only linearB. Only hierarchicalC. complexD. both linear and hierarchical32. The syntactic rules of any language are ____ in number.A. largeB. smallC. finiteD. infinite33. The ________ rules are the rules that group words and phrases to form grammatical sentences.A. lexicalB. morphologicalC. linguisticD. combinational34._______ rules may change the syntactic representation of a sentence.A. GenerativeB. TransformationalC. X-barD. Phrase structureIV. Define the following terms:35.syntax 36. Sentence 37. coordinate sentence 38. syntactic categories39.grammatical relations 40. linguistic competence 41. transformational rules42.D-structureV. Answer the following questions:43.What are the basic components of a sentence?44.What are the major types of sentences? Illustrate them with examples.45.Are the elements in a sentence linearly structured? Why?46.What are the advantages of using tree diagrams in the analysis of sentence structures?47.What is NP movement. Illustrate it with examples.VI. Given examples for word classes by using the words in the following sentence.Her dog always sleeps under the old tree.VII. Construct a sentence that has the following sentences.S Det, A, N, V, P, Det, NVIII. For each of the following sentences, supply three distinct surface structure sentences which may be regarded as derived from them:a.I told him to stop the car.b.He took his coat off.IX. Draw tree diagrams for each of the following entences.1.Mary advised John to see the dentist.2.Mary promised John to see the dentist.3. A clever magician fooled the audience.4.The tower on the hill collapsed in the wind.5.They knew that the senator would win the election.6.The mouse ran up the rock.7.The mouse ate up the cheese.8.John gave Mary the book.9.John gave the book to Mary.10.John went to the supermarket.11.The man who came to see me last night is my brother.12.The candle on the desk blows in the wind.13.She passed him the hammer and saw through the window. (2 tree diagrams)14.The boy saw the girl in the car. (2 tree diagrams)15.Flying planes can be dangerous. (2 tree diagrams)16.Old men and women were more careful. (2 tree diagrams)17.The man in the room helps me every day.18.John is easy to please.19.John is eager to please.Suggested answers to supplementary exercisesIV. Define the following terms:35.syntax: Syntax is a subfield of linguistics. It studies the sentence structure of language. It consistsof a set of abstract rules that allow words to be combined with other words to form grammatical sentences.36.Sentence: A sentence is a structurally independent unit that usually comprises a number of words to form a complete statement, question or command. Normally, a sentence consists of at least a subject and a predicate which contains a finite verb or a verb phrase.37.coordinate sentence: A coordinate sentence contains two clauses joined by a linking word called coordinating conjunction, such as "and", "but", "or".38.syntactic categories: Apart from sentences and clauses, a syntactic category usually refers to a word(calleda lexical category) or a phrase ( called a phrasal category) that performs a particular grammatical function.39.grammatical relations: The structural and logical functional relations of constituents are called grammatical relations. The grammatical relations of a sentence concern the way each noun phrase in the sentence relates to the verb. In many cases, grammatical relations in fact refer to who does what to whom .40.linguistic competence: Universally found in the grammars of all human languages, syntactic rules comprise the system of internalized linguistic knowledge of a language speaker known as linguistic competence.41.Transformational rules: Transformational rules are the rules that transform one sentence type intoanother type.42.D-structure: D- structure is the level of syntactic representation that exists before movement takesplace. Phrase structure rules, with the insertion of the lexicon, generate sentences at the level of D-structure.V. Answer the following questions:43. What are the basic components of a sentence?Normally, a sentence consists of at least a subject and its predicate which contains a finite verb or a verb phrase.44. What are the major types of sentences? Illustrate them with examples.Traditionally, there are three major types of sentences. They are simple sentence, coordinate( compound) sentence, and complex sentence. A simple sentence consists of a single clause which contains a subject and a predicate and stands alone as its own sentence, for example:John reads extensively.A coordinate sentence contains two clauses joined by a linking word that is called coordinating conjunction, such as "and", "but", "or". For example:John is reading a linguistic book, and Mary is preparingfor her history exam.A complex sentence contains two, or more, clauses, one of which is incorporated into the other. The two clauses in a complex sentence do not have equal status, one is subordinate to the other. For example:Before John gave her a lecture, Mary showed no interest in linguistics.45. Are the elements in a sentence linearly structured? Why?No. Language is both linearly and hierarchically structured. When a sentence is uttered or written down, the words of the sentence are produced one after another in a sequence. A closer examination of a sentence shows that a sentence is not composed of sequence of words arranged in a simple linear order with one adding onto another following a simple arithmetic logic. In fact, sentences are also hierarchically structured. They are orga-nized by grouping together words of the same syntactic category, such as noun phrase (NP) or verb phrase (VP), as can be seen from the following tree diagram:SNP VPDet N Vt NPThe boy likes the music.746. What are the advantages of using tree diagrams in the analysis of sentence structures?The tree diagram can not only reveal a linear order, but also a hierarchical structure that groups words into structural constituents. It can, in addition, show the syntactic category of each structural constituent, thus it is believed to most truthfully illustrate the constituent relationship among linguistic elements.47. What is NP movement. Illustrate it with examples.NP movement involves the movement of a noun phrase. NP-movement occurs when, for example, a sentence changes from the active voice to the passive voice:(A) The man beat the child.(B). The child was beaten by the man.B is the result of the movement of the noun phrases "the man" and "the child" from their original positions in(A)to new positions. That is, "the man" is postposed to the right and "the child" is preposed to the left.Not all instances of NP-movement, however, are related to changing a sentence from the active voice to the passive voice. For example:(C)It seems they are quite fit for the job.(D)They seem quite fit for the job.These sentences are identical in meaning, but different in their superficial syntactic representations. Itis believed that they have the same underlying structure, but (27b) is the result of an NP movement.8。

decidability一个逻辑系统的一个理论

China 2009 1
课程时间表Schedule
China 2009
2
讲座3：描述逻辑的可判定性与复杂性 Lecture 3: The Decidability and the Complexity of Description Logics
• • • • •
China 2009
为什么可判定性是重要的？描述逻辑的可判定性描述逻辑的tableau算法计算复杂性理论导论描述逻辑的复杂性
China 2009
7
一些逻辑系统的判定性
• 命题逻辑是可判定的
Propositional logic is decidable
• 一般来说，一阶谓词逻辑是不可判定的。 First-order logic is not decidable in general; in particular, the set of logical validities in any signature that includes equality and at least one other predicate with two or more arguments is not decidable. • 二阶逻辑也是不可判定的
14
描述逻辑的推理算法
• Tableau algorithms used to test satisfiability (consistency) • Try to build a tree-like model of the input concept C • Decompose C syntactically
China 2009
6
逻辑系统和可判定性 Logics and decidability

气象科技英语翻译练习参考译

Terminology Consistency
The translation should use consistent and standard meteorological terminology to ensure clarity and precision
Readability
The translation should be easy to read and understand, following the style and language level of the target audience
01
03
Consulting Dictionaries and Resources: Consult dictionaries and other resources for more accurate translations and explanations.
04
Using contextual understanding to infer the meaning of a sentence.
Using Passive Voice Markers
Using passive voice markers such as "bei", "shou", etc.
Translation Techniques for Complex Sentences
Identifying the Main Idea: Identifying the main meaning of complex sentences in order to better
Technical
Metrological technology English is a technical language, with specific vocabulary and grammar used to describe meteorological concepts, phenomena, and equipment

logic的英文作文

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plc外文翻译

1 Bit Logic Instructions1.1 Overview of Bit Logic Instructions1.1.1 DescriptionBit logic instructions work with two digits, 1 and 0. These two digits form the base of a number system called the binary system. The two digits 1 and 0 are called binary digits or bits. In the world of contacts and coils, a 1 indicates activated or energized, and a 0 indicates not activated or not energized.The bit logic instructions interpret signal states of 1 and 0 and combine them according to Boolean logic. These combinations produce a result of 1 or 0 that is called the “result of logic operation” (RLO).The logic operations that are triggered by the bit logic instructions perform a variety of functions.There are bit logic instructions to perform the following functions:---| |--- Normally Open Contact (Address)---| / |--- Normally Closed Contact (Address)---(SAVE) Save RLO into BR MemoryXOR Bit Exclusive OR---( ) Output Coil---( # )--- Midline Output---|NOT|--- Invert Power FlowThe following instructions react to an RLO of 1:页脚内容1---( S ) Set Coil---( R ) Reset CoilSR Set-Reset Flip FlopRS Reset-Set Flip FlopOther instructions react to a positive or negative edge transition to perform the following functions: ---(N)--- Negative RLO Edge Detection---(P)--- Positive RLO Edge DetectionNEG Address Negative Edge DetectionPOS Address Positive Edge DetectionImmediate ReadImmediate Write1.2 ---| |--- Normally Open Contact (Address)1.2.1 Symbol<address>---| |---Parameter Data Type Memory Area Description页脚内容2<address>BOOL I, Q, M, L, D, T, C Checked bit 1.2.2 Description---| |---(Normally Open Contact) is closed when the bit value stored at the specified <address>is equal to "1". When the contact is closed, ladder rail power flows across the contact and the result of logic operation (RLO) = "1". Otherwise, if the signal state at the specified <address>is "0", the contact is open. When the contact is open, power does not flow across the contact and the result of logic operation (RLO) = "0".When used in series, ---| |--- is linked to the RLO bit by AND logic. When used in parallel, it is linked to the RLO by OR logic.1.2.3 Status wordBR CC1CC0OV OS OR STA RLO/FC writes:-----x x x11.2.4 Example页脚内容3Power flows if one of the following conditions exists:The signal state is "1" at inputs I0.0 and I0.1 Or the signal state is "1" at input I0.2.1.3 ---| / |--- Normally Closed Contact (Address)1.3.1 Symbol<address>---| / |---Parameter Data Type Memory Area Description<address>BOOL I, Q, M, L, D, T, C Checked bit1.3.2 Description---| / |--- (Normally Closed Contact) is closed when the bit value stored at the specified <address>is equal to "0". When the contact is closed, ladder rail power flows across the contact and the result of logic operation (RLO) = "1". Otherwise, if the signal state at the specified <address> is "1", the contact is opened. When the contact is opened, power does not flow across the contact and the result of logic operation (RLO) = "0".When used in series, ---| / |--- is linked to the RLO bit by AND logic. When used in parallel, it is linked to the RLO by OR logic.页脚内容41.3.3 Status wordBR CC1CC0OV OS OR STA RLO/FC writes:-----x x x11.3.4 ExamplePower flows if one of the following conditions exists:The signal state is "1" at inputs I0.0 and I0.1 Or the signal state is "1" at input I0.21.4 XOR Bit Exclusive ORFor the XOR function, a network of normally open and normally closed contacts must be created as shown below.1.4.1 Symbols页脚内容5Parameter Data Type Memory Area Description <address1>BOOL I, Q, M, L, D, T, C Scanned bit <address2>BOOL I, Q, M, L, D, T, C Scanned bit1.4.2 DescriptionXOR(Bit Exclusive OR) creates an RLO of "1" if the signal state of the two specified bits is different.1.4.3 ExampleThe output Q4.0 is "1" if (I0.0 = "0" AND I0.1 = "1") OR (I0.0 = "1" AND I0.1 = "0").1.5 --|NOT|-- Invert Power Flow1.5.1 Symbol---|NOT|---1.5.2 Description---|NOT|---(Invert Power Flow) negates the RLO bit.页脚内容61.5.3 Status wordBR CC1CC0OV OS OR STA RLO/FC writes:------1x-1.5.4 ExampleThe signal state of output Q4.0 is "0" if one of the following conditions exists:The signal state is "1" at input I0.0 Or the signal state is "1" at inputs I0.1 and I0.2.1.6 ---( ) Output Coil1.6.1 Symbol<address>---( )Parameter Data Type Memory Area Description<address>BOOL I, Q, M, L, D Assigned bit页脚内容71.6.2 Description---( ) (Output Coil) works like a coil in a relay logic diagram. If there is power flow to the coil (RLO = 1), the bit at location <address>is set to "1". If there is no power flow to the coil (RLO = 0), the bit at location <address>is set to "0". An output coil can only be placed at the right end of a ladder rung. Multiple output elements (max.16) are possible (see example). A negated output can be created by using the ---|NOT|--- (invert power flow) element.1.6.3 MCR (Master Control Relay) dependencyMCR dependency is activated only if an output coil is placed inside an active MCR zone. Within an activated MCR zone, if the MCR is on and there is power flow to an output coil , the addressed bit is set to the current status of power flow. If the MCR is off, a logic "0" is written to the specified address regardless of power flow status.1.6.4 Status wordBR CC1CC0OV OS OR STA RLO/FC writes:-----0x-01.6.5 Example页脚内容8The signal state of output Q4.0 is "1" if one of the following conditions exists:The signal state is "1" at inputs I0.0 AND I0.1 OR the signal state is "0" at input I0.2.The signal state of output Q4.1 is "1" if one of the following conditions exists:The signal state is "1" at inputs I0.0 AND I0.1 OR the signal state is "0" at input I0.2 AND "1" at input I0.3If the example rungs are within an activated MCR zone:When MCR is on, Q4.0 and Q4.1 are set according to power flow status as described above.When MCR is off (=0), Q4.0 and Q4.1 are reset to 0 regardless of power flow.1.7 ---( # )--- Midline Output1.7.1 Symbol<address>--( # )---Parameter Data Type Memory Area Description<address>BOOL I, Q, M, *L, D Assigned bit* An L area address can only be used if it is declared TEMP in the variable declaration table of a logic block (FC, FB, OB).1.7.2 Description页脚内容9---( # )--- (Midline Output) is an intermediate assigning element which saves the RLO bit (power flow status) to a specified <address>. The midline output element saves the logical result of the preceding branch elements. In series with other contacts, ---( # )--- is inserted like a contact. A ---( # )--- element may never be connected to the power rail or directly after a branch connection or at the end of a branch. A negated ---( # )--- can be created by using the ---|NOT|--- (invert power flow) element.1.7.3 MCR (Master Control Relay) dependencyMCR dependency is activated only if a midline output coil is placed inside an active MCR zone. Within an activated MCR zone, if the MCR is on and there is power flow to a midline output coil; the addressed bit is set to the current status of power flow. If the MCR is off, a logic "0" is written to the specified address regardless of power flow status.1.7.4 Status wordBR CC1CC0OV OS OR STA RLO/FC writes:-----0x-11.7.5 ExampleM 0.0 has the RLO:页脚内容10M 1.1 has the RLO:M 2.2 has the RLO of the entire bit logic combination.1.8 ---( R ) Reset Coil1.8.1 Symbol<address>---( R )Parameter Data Type Memory Area Description<address>BOOL I, Q, M, L, D, T, C Reset bit1.8.2 Description---( R )(Reset Coil) is executed only if the RLO of the preceding instructions is "1" (power flows to the coil). If power flows to the coil (RLO is "1"), the specified <address>of the element is reset to "0". A RLO of "0" (no power flow to the coil) has no effect and the state of the element's specified address remains unchanged. The <address>may also be a timer (T no.) whose timer value is reset to "0" or a counter (C no.) whose counter value is reset to "0".1.8.3 MCR (Master Control Relay) dependencyMCR dependency is activated only if a reset coil is placed inside an active MCR zone. Within an activated MCR zone, if页脚内容11the MCR is on and there is power flow to a reset coil; the addressed bit is reset to the "0" state. If the MCR is off, the current state of the element's specified address remains unchanged regardless of power flow status.1.8.4 Status wordBR CC1CC0OV OS OR STA RLO/FC writes:-----0x-01.8.5 ExampleNetwork 1Network 2Network 3The signal state of output Q4.0 is reset to "0" if one of the following conditions exists:The signal state is "1" at inputs I0.0 and I0.1 Or the signal state is "0" at input I0.2.页脚内容12If the RLO is "0", the signal state of output Q4.0 remains unchanged.The signal state of timer T1 is only reset if:the signal state is "1" at input I0.3.The signal state of counter C1 is only reset if:the signal state is "1" at input I0.4.If the example rungs are within an activated MCR zone:When MCR is on, Q4.0, T1, and C1 are reset as described above.When MCR is off, Q4.0, T1, and C1 are left unchanged regardless of RLO state (power flow status).1 位逻辑指令1.1 位逻辑指令概述1.1.1 描述位逻辑指令使用两个数字，1和0。

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Abstract
Description logic is a formal language of knowledge representation and is the first-order logic which can determine subset. Description logic, as an effective mechanism for knowledge representation, is widely applied to many areas of computer science. This paper is about the application research and present situation of description logic, it provides a system expounds about description logic in definition, basic syntax and semantics, the structure of the system, the status in semantic Web and so on. Keywords: Descriptiห้องสมุดไป่ตู้n Logic; Tbox; Abox; Knowledge Representation
2.1 The Development of DLs
Expressed in literature[2] is the description language expression ability and the contradiction between the complexity of reasoning, this major contribution was completed by Ron Brachman and Hector Levesque. The paradox is that the representation of a description language ability can not too strong, because it may cause the undecidable reasoning; description language, on the other hand, the representation of a language can't be too weak, or not enough to say knowledge in the field of application .This contradiction become an important subject of DLs study, based on the research progress of the problem, the development of DLs can be divided into the following four stages[3]: The first stage: the main concern for the implementation of some system, such as KLONE[4], K - REP[5], the BACK[6] and LOOM[7]. The disadvantage of these algorithms is that they are completed only for weak expressing ability DLs, for strong expressing ability DLs, can't detect the existing instances of inclusion or relationship. The second stage: development and implement the algorithm of the system based on table, such as Kris, Crack,
1 INTRODUCTION
Description logic (named DLs for short) is a formalized language of knowledge representation[1], suitable for knowledge in concept and the concept hierarchy. DLs unified logic base on such as Frames, Semantic Networks, Semantic Data Models and so on, presented to us a formalized, based on the logical semantics. The basic building blocks of DLs are concept, role and individual. The feature of DLs is from a lot of structural descriptors to simple concepts, to build more complex concepts. The center of DLs is reasoning, which can explicitly contain knowledge derived from the knowledge base of implied knowledge, DLs main reasoning classification, satisfiability problem detection, containment relationship and instance test. The Knowledge Base of DLs usually contains two parts: Tbox and Abox. Among them, Tbox is a set of collection contains the assertion about concepts and relationship, we can deduce general attributes of concepts and relationships from the Tbox; Abox is a set of instances of assertion about individuals, instance assert that an individual is a concept and a relationship between two individuals. Four basic components of DLs: the tectonic operator set which is used for concept and structure of relational expression; the axiom types allowed in Tbox; the assertion type allowed in Abox; the reasoning mechanism in Tbox and Abox reasoning.
2.2 The Main Method of Knowledge Representation
The knowledge representation is developed in the 1970s, and divided into two categories. The first category: knowledge representation method based on logic: representation language usually takes the variation of first order predicate logic, reasoning is equivalent to prove logic inference. Expression ability of firstorder logic must be restrained, otherwise it may damage the knowledge structure, lead to ineffective reasoning results. The second category: knowledge representation method not based on logic: usually is based on the use of graphical interface, knowledge in some special data structure to represent, reasoning can be simply completed by processing structure which is a special process. Semantic networks and frames are very important in knowledge representation methods of the two methods. However. But due to semantic networks and frames are the lack of precise semantics, it is not satisfactory in real system performance, so the frames based on first-order logic, given the semantics, which makes the knowledge representation had taken an important step forward again.
2 OVERVIEW OF DESCRIPTION LOGIC
This section arranged to introduce the development of DLs, introduces the main method of knowledge representation, the syntax and semantics of the DLs.