Mathematics for Computer Science


Book Description

This book covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions.




Ant Colony Optimization


Book Description

An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.




Why the Garden Club Couldn't Save Youngstown


Book Description

In this book, Sean Safford compares the recent history of Allentown, Pennsylvania, with that of Youngstown, Ohio. Allentown has seen a noticeable rebound over the course of the past twenty years. Facing a collapse of its steel-making firms, its economy has reinvented itself by transforming existing companies, building an entrepreneurial sector, and attracting inward investment. Youngstown was similar to Allentown in its industrial history, the composition of its labor force, and other important variables, and yet instead of adapting in the face of acute economic crisis, it fell into a mean race to the bottom.Challenging various theoretical perspectives on regional socioeconomic change, Why the Garden Club Couldn’t Save Youngstown argues that the structure of social networks among the cities’ economic, political, and civic leaders account for the divergent trajectories of post-industrial regions. It offers a probing historical explanation for the decline, fall, and unlikely rejuvenation of the Rust Belt. Emphasizing the power of social networks to shape action, determine access to and control over information and resources, define the contexts in which problems are viewed, and enable collective action in the face of externally generated crises, this book points toward present-day policy prescriptions for the ongoing plight of mature industrial regions in the U.S. and abroad.




The Atomic Nucleus


Book Description




The Cross-Entropy Method


Book Description

Rubinstein is the pioneer of the well-known score function and cross-entropy methods. Accessible to a broad audience of engineers, computer scientists, mathematicians, statisticians and in general anyone, theorist and practitioner, who is interested in smart simulation, fast optimization, learning algorithms, and image processing.




Resisting Reduction


Book Description

Provocative, hopeful essays imagine a future that is not reduced to algorithms. When Joi Ito published an essay, "Resisting Reduction: A Manifesto," about human flourishing in an age of machine intelligence, his argument against industrial optimizations in the pursuit of growth and for the importance of natural complexity and resilience received such an impassioned response that he invited writers to develop full-length essays continuing the conversation. Resisting Reduction is the result: Ito's manifesto and nine equally provocative responses, all imagining a future that is not limited by a worldview defined by algorithm. Rather than await our inevitable domination by machines, Ito and his respondents argue, we should work toward a future of interconnected complex systems. Ito blames Silicon Valley's "groupthink" and "cult of technology" for claiming that narrow technical solutions can resolve the world's complex problems. More computing power does not make us more "intelligent," he tells us, only more computationally powerful. In their responses, the other writers offer persuasive and compelling variations on Ito's argument. Among other things, they call for a "Human+AI Centaur" as the best way to augment intelligence; draw on indigenous epistemology to argue for an extended "circle of relationships" that includes the nonhuman and robotic; debunk the myth of the lone pioneer and propose instead a model of adaptive interconnectivity; cast "Snow White" as a tale of AI featuring a "smart mirror"; point out the "cisnormativity" of security protocol algorithms; and consider the limits of moral mathematics. Contributors Noelani Arista, Nicky Case, Sasha Costanza-Chock, Vafa Ghazavi, Kat Holmes, Joi Ito, Suzanne aka Kite, Cathryn Klusmeier, Jason Edward Lewis, Molly McCue, Archer Pechawis, Jaclyn Sawyer, Gary Zhexi Zhang, Snoweria Zhang




Thinking about Nuclear Weapons


Book Description

En studie vedr. kernevåbens betydning og indflydelse på sikkerhedspolitik og magtbalance




A Short History of Biological Warfare


Book Description

This publication gives a history of biological warfare (BW) from the prehistoric period through the present, with a section on the future of BW. The publication relies on works by historians who used primary sources dealing with BW. In-depth definitions of biological agents, biological weapons, and biological warfare (BW) are included, as well as an appendix of further reading on the subject. Related items: Arms & Weapons publications can be found here: https://bookstore.gpo.gov/catalog/arms-weapons Hazardous Materials (HAZMAT & CBRNE) publications can be found here: https://bookstore.gpo.gov/catalog/hazardous-materials-hazmat-cbrne




Algorithms for Decision Making


Book Description

A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.




It's Complicated


Book Description

Surveys the online social habits of American teens and analyzes the role technology and social media plays in their lives, examining common misconceptions about such topics as identity, privacy, danger, and bullying.