Books on the topic 'Linear generative model'

The textbook covers the basics of classical numerical methods of computational mathematics used for solving linear and nonlinear equations and systems; interpolation and approximation of functions; numerical integration and differentiation; solutions of ordinary differential equations by methods of one-dimensional and multidimensional optimization. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is intended for students of higher educational institutions studying in the discipline "Numerical methods".

The textbook presents the basics of the classical theory of automatic control, based on mathematical models of real systems, given in the form of systems of linear differential equations with constant coefficients. Methods based on Laplace and Fourier transforms, stability, controllability, and observability theory, as well as directed graph theory and linear algebra are used. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students of higher educational institutions studying in the areas of training and specialties 15.00.00 "Mechanical Engineering", 27.00.00 "Management in technical systems".

The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

Physical and mathematical modeling is widely used in scientific research. This is due to the fact that field experiments on real construction sites are often impossible to organize for various reasons. The material included in the textbook is a summary of the authors ' experience in the field of system analysis. In the first section, the regularities of physical modeling of the functioning of objects based on the similarity and dimension theorems are considered. The second section presents modern models and methods for choosing optimal solutions: linear, nonlinear, stochastic, and statistical. The third section deals with experimental methods of system optimization based on the theory of experimental planning. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students of higher educational institutions studying in the direction of training 08.04.01 "Construction", and graduate students of higher educational institutions. It will be useful for specialists in the field of mathematical methods for the study of complex systems and their applications.

The selection intensity, the mean change in a trait within a generation expressed in phenotypic standard deviations, provides an important metric for comparing the strength of selection over designs. Further, under truncation selection (only individuals above some threshold leave offspring), the selection intensity is a function of the fraction saved, and hence the breeder's equation is often expressed in terms of the selection intensity. An important special case of truncation selection is a threshold trait, wherein an individual only expresses a particular phenotype when its underlying liability value exceeds some threshold. This chapter examines selection on such traits, and generalizes this binary-trait setting (with binomial residuals) to other classes of discrete traits, wherein some underling linear model (generating the threshold) is this transformed via a generalized linear mixed model into an observed trait value.

We analyze models that combine precautionary saving and liquidity constraints to provide a unified, more realistic treatment of intertemporal decisions. We start off with a simple three-period model to illustrate how the expectation of future borrowing constraints can induce precautionary saving even in scenarios in which marginal utility is linear. A more general model that allows liquidity constraints and precautionary saving to interact fully is the buffer stock model, of which there are two versions. One, developed by Deaton (1991), emphasizes the possibility that a prudent and impatient consumer may face credit constraints. The other, by Carroll (1997), features the same type of consumer but allows for the possibility of income falling to zero and so generating a natural borrowing constraint.

Exponential random graph models (ERGMs) provide conceptually elegant recipes for generating soft-constrained random graphs. This chapter begins by explaining the theory and describing how to properly specify an ERGM, including demonstrating Lagrange’s method to derive the values of the model parameters that correspond to the desired constraints. Three ERGMs, all with constraints depending linearly on the adjacency matrix, are solved exactly: the targeted total number of links, targeted individual node degrees and targeted number of two-way links in a directed graph. However, when the controlled features become more complicated, ERGMs have a tendency to produce graphs in extreme phases (very dense or very sparse). The two-star model and the Strauss model are worked through in detail using advanced techniques from statistical mechanics in order to analyze the phase transitions. The chapter closes with a discussion of the strengths and weaknesses of ERGMs as null models.

Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes. Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation. Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.

This article examines conceptual and structural issues related to supersymmetry. It first provides an overview of generating functions before discussing supermathematics, with a focus on Grassmann or anticommuting variables, vectors and matrices, groups and symmetric spaces, and derivatives and integrals. It then considers various applications of supersymmetry to random matrices, such as the representation of the ensemble average and the Hubbard–Stratonovich transformation, along with its generalization and superbosonization. It also describes matrix δ functions and an alternative representation as well as important and technically challenging problems that supersymmetry addresses beyond the invariant and factorizing ensembles. The article concludes with an analysis of the supersymmetric non-linear σ model, Brownian motion in superspace, circular ensembles and the Colour-Flavour-Transformation.

Social scientific efforts to understand the political and economic forces generating precarious employment have been mired in uncertainty. In this context, the Doellgast–Lillie–Pulignano (D–L–P) model represents an important step forward in both theoretical and empirical terms. This concluding chapter scrutinizes the authors’ theoretical model and assesses the present volume’s empirical applications of it. Building on the strengths of the D–L–P model, the chapter identifies several lines of analysis that can fruitfully extend our understanding of the dynamics of precarization, whether at the micro-, meso-, or macro-social levels of analysis. Especially needed are studies that explore the dynamics of organizational fields as these shape employer strategy and state policy towards employment. Such analysis will hopefully shed light on the perils and possibilities that workers’ organizations face as they struggle to cope with the demands of neoliberal capitalism.

Recent investigations have demonstrated the psychological features (e.g. cognitive, affective, and social) of task-unrelated thoughts, as well as their underlying neural correlates in spontaneous activity, which cover various networks and regions, including the default-mode and central executive networks. Despite impressive progress in recent research, the mechanisms by means of which the brain’s spontaneous activity generates and constitutes thoughts remain unclear. This chapter suggests that the spatiotemporal structure of the brain’s spontaneous activity can integrate both content- and process-based approaches to task-unrelated or spontaneous thought—this amounts to what is described as the “spatiotemporal theory of task-unrelated thought” (STTT). Based on various lines of empirical evidence, the STTT postulates two main spatiotemporal mechanisms, spatiotemporal integration and extension. The STTT provides a novel brain-based spatiotemporal theory of task-unrelated thought that focuses on the brain’s spontaneous activity, including its spatiotemporal structure, which allows integrating content- and process-based approaches.

This chapter examines short-term (a few generations) selection response in the mean of a trait. Traditionally, such experiments are analyzed using least-squares (LS) approaches. While ordinary LS (OLS) is often used, genetic drift causes the residual to be both correlated and heteroscedastic, resulting in the sampling variances given by OLS being too small. This chapter details the appropriate general LS (GLS) approaches to properly account for this residual error structure. It also reviews some of the common features observed in short-term selection experiments and examines experimental designs, such as the use of a control population versus a divergence-selection approach. It concludes by discussing another linear model used mainly by plant breeders, generation-means analysis (GMA), wherein remnant seed for several generations of response are crossed and then grown in a common garden. Such an analysis can provide insight into the genetic nature of any response.

This interview was conducted by e-mail in December 2013.JEFF MENNE:The party line on you has been that you were too much an artist to be a good businessperson. But it seems history will tell a different story—that is, that you have been responsible for creating different business models perhaps more than any other filmmaker in your generation. Do you think your artistic sense has opposed your business sense, or has it complemented it?...

Over roughly the last decade, there has been a notable rise in new research on historical German syntax in a generative perspective. This volume presents a state-of-the-art survey of this thriving new line of research by leading scholars in the field, combining it with new insights into the syntax of historical German. It is the first comprehensive and concise generative historical syntax of German covering numerous central aspects of clause structure and word order, tracing them throughout various historical stages. Each chapter combines a solid empirical basis and valid descriptive generalizations with reference also to the more traditional topological model of the German clause with a detailed discussion of theoretical analyses couched in the generative framework. The volume is divided into three parts according to the main parts of the clause: the left periphery dealing with verbal placement and the filling of the prefield (verb second, verb first, verb third orders) as well as adverbial connectives; the middle field including discussion of pronominal syntax, order of full NPs and the history of negation; and the right periphery with chapters on basic word order (OV/VO), prosodic and information-structural factors, and the verbal complex including the development of periphrastic verb forms and the phenomena of IPP (infinitivus pro participio) and ACI (accusativus cum infinitivo). This book thus provides a convenient overview of current research on the major issues concerning historical German clause structure both for scholars interested in more traditional description and for those interested in formal accounts of diachronic syntax.

Throughout her life, Hannah Arendt was deeply and energetically engaged with Greek and Roman ideas, which she saw as sparking new, vital thinking about modern problems. The contemporary artist Tino Sehgal creates artworks out of dialogue that encourage participants to develop new lines of thought and conversation, in keen awareness of their evolving spatial and temporal relationship with one another. This chapter argues that Arendt and Sehgal build a new model for what we now typically call ‘classical reception’—a method of creative, purposive conservation that remembers Greek and Roman texts in their historical specificity even as it focuses on generating novel thought in and for the present time. In their view, to think is to think with—another person, another text, another culture—a treatment of thought as dialogue and, as I argue, an ethics of reading.

Works Righteousness is the first full-length study of the place of practice in ethical theory. It is a critique of the idealism of dominant approaches, an analysis of alternative models in which practice plays a more significant role, and an argument for taking practice seriously both in broad questions about ethical theory and in concrete case studies. The book’s main argument is that what people actually do should be central to ethical theory. Rather than assuming that pre-established moral ideas guide action, ethicists should acknowledge and explore the ways that practices generate values and the mutual shaping between ideas and actions. This argument challenges dominant philosophical and religious theories that assume that ideas are what really matter. Works Righteousness analyses the place of practice in these traditions, showing the links between their emphasis on internal states and simple, linear relationships between ideas, actions, and results. These themes are challenged by alternative models such as pragmatism, Marxism, and religious pacifism, which give practice a larger role and in the process highlight important themes such as the way social structures condition moral ideas and actions, the dangers of thinking about moral problems as polarized dilemmas, and the complex mutual shaping of ideas and actions. A practice-focused approach sheds new light on concrete case studies, underlining the value of attention to people’s concrete experiences and relationships in efforts to analyse and address contemporary problems such as hate speech, euthanasia, and climate change.

The central aim of this book is to explore the ways in which social categories—especially gender, but also categories like race and religion—interact with and contribute to social solutions to problems of coordination and resource division. In particular, this book uses formal frameworks—game theory and evolutionary game theory—to explore the cultural evolution of conventions that piggyback on seemingly irrelevant factors like gender and race. As I argue, these frameworks elucidate a variety of topics. In particular, these frameworks help show how inequity can emerge from simple processes of cultural change. In groups with gender and racial categories, the process of learning conventions of coordination and resource division is such that under a wide array of situations some groups will tend to get more and others less. One theme that runs throughout the book is that surprisingly minimal conditions are needed to robustly produce phenomena related to inequity that we usually think of as psychologically complex. It takes very little to generate a situation in which social categories (like gender) are almost guaranteed to emerge. The preconditions under which models move toward outcomes that look like discrimination are, again, very minimal. Once inequity emerges in these models, it takes very little for it to persist indefinitely. Thus, we need to think of inequity as part of an ever-evolving process. It is not something we can expect to fix and be done with. Along these lines, the picture I present is ultimately one where those concerned with social justice must remain vigilant against the dynamic forces that push toward inequity.

The article provides an overview of ‘Theory of Mind’ (ToM) research, guided by two classifications. The first covers four competing approaches to mentalizing such as the theory-theory, modularity theory, rationality theory, and simulation theory. The second classification is the first-person/third-person contrast. Jerry Fodor claimed that commonsense psychology is so good at helping predict behavior that it is practically invisible. It works well because the intentional states it posits genuinely exist and possess the properties generally associated with them. The modularity model has two principal components. First, whereas the child-scientist approach claims that mentalizing utilizes domain-general cognitive equipment, the modularity approach posits one or more domain-specific modules, which use proprietary representations and computations for the mental domain. Second, the modularity approach holds that these modules are innate cognitive structures, which mature or come on line at preprogrammed stages and are not acquired through learning. The investigators concluded that autism impairs a domain-specific capacity dedicated to mentalizing. Gordon, Jane Heal, and Alvin Goldman explained simulation theory in such a way that mind readers simulate a target by trying to create similar mental states of their own as proxies or surrogates of those of the target. These initial pretend states are fed into the mind reader's own cognitive mechanisms to generate additional states, some of which are then imputed to the target.

For more than 2000 years Greek philosophers have thought about the puzzling introspectively assessed dichotomy between our physical bodies and our seemingly non-physical minds. How is it that we can think highly abstract thoughts, seemingly fully detached from actual, physical reality? Despite the obvious interactions between mind and body (we get tired, we are hungry, we stay up late despite being tired, etc.), until today it remains puzzling how our mind controls our body, and vice versa, how our body shapes our mind. Despite a big movement towards embodied cognitive science over the last 20 years or so, introductory books with a functional and computational perspective on how human thought and language capabilities may actually have come about – and are coming about over and over again – are missing. This book fills that gap. Starting with a historical background on traditional cognitive science and resulting fundamental challenges that have not been resolved, embodied cognitive science is introduced and its implications for how human minds have come and continue to come into being are detailed. In particular, the book shows that evolution has produced biological bodies that provide “morphologically intelligent” structures, which foster the development of suitable behavioral and cognitive capabilities. While these capabilities can be modified and optimized given positive and negative reward as feedback, to reach abstract cognitive capabilities, evolution has furthermore produced particular anticipatory control-oriented mechanisms, which cause the development of particular types of predictive encodings, modularizations, and abstractions. Coupled with an embodied motivational system, versatile, goal-directed, self-motivated behavior, learning becomes possible. These lines of thought are introduced and detailed from interdisciplinary, evolutionary, ontogenetic, reinforcement learning, and anticipatory predictive encoding perspectives in the first part of the book. A short excursus then provides an introduction to neuroscience, including general knowledge about brain anatomy, and basic neural and brain functionality, as well as the main research methodologies. With reference to this knowledge, the subsequent chapters then focus on how the human brain manages to develop abstract thought and language. Sensory systems, motor systems, and their predictive, control-oriented interactions are detailed from a functional and computational perspective. Bayesian information processing is introduced along these lines as are generative models. Moreover, it is shown how particular modularizations can develop. When control and attention come into play, these structures develop also dependent on the available motor capabilities. Vice versa, the development of more versatile motor capabilities depends on structural development. Event-oriented abstractions enable conceptualizations and behavioral compositions, paving the path towards abstract thought and language. Also evolutionary drives towards social interactions play a crucial role. Based on the developing sensorimotor- and socially-grounded structures, the human mind becomes language ready. The development of language in each human child then further facilitates the self-motivated generation of abstract, compositional, highly flexible thought about the present, past, and future, as well as about others. In conclusion, the book gives an overview over how the human mind comes into being – sketching out a developmental pathway towards the mastery of abstract and reflective thought, while detailing the critical body and neural functionalities, and computational mechanisms, which enable this development.