Complex problems

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  • Parallel Processing & Distributed Systems: Lecture 3 - Abstract Machine Models includes Parallel Computer Models, Abstract Machine Models, Time Complexity Problem, Conflicts Resolution Schemes, PRAM Algorithm, Parallel Reduction.

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  • In this chapter, we will address the following questions: How does information technology help solve certain complex problems? How do expert systems support managers? What are the limitations of expert systems? What functions do machines perform better than humans? How do computers solve pattern recognition problems? Can computers understand speech?

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  • 101 Short Cuts in Math Anyone Can Do will unlock the secrets of the art of calculation. It will increase your power of computation and thereby enable you to get more out of the mathematic you now know. You will soon be amazed at your ability to solve once complex problems quickly.

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  • This text will strengthen a student's ability to apply the laws of physics to practical situations and problems that yield more easily to intuitive insight than to complex mathematics. These problems, chosen almost exclusively from classical (non-quantum) physics, are posed in accessible nontechnical language and require the student to select the right framework in which to analyze the situation. The book will be invaluable to undergraduates preparing for "general physics" papers. Some physics professors will even find the more difficult questions challenging.

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  • ASP.NET MVC 4 Recipes is a practical guide for developers creating modern web applications, cutting through the complexities of ASP.NET, jQuery, Knockout.js and HTML 5 to provide straightforward solutions to common web development problems using proven methods based on best practices. The problem-solution approach gets you in, out, and back to work quickly while deepening your understanding of the underlying platform and how to develop with it.

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  • This book is inspired by boredom and fascination: boredom with the usual presentation of data structures and algorithms, and fascination with complex systems. The problem with data structures is that they are often taught without a motivating context; the problem with complexity science is that it is usually not taught at all. In 2005 I developed a new class at Olin College where students read about topics in complexity, implement experiments in Python, and learn about algorithms and data structures. ...

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  • [ Team LiB ] Recipe 7.2 Binding Complex Data to Web Forms Controls Problem You want to bind multiple columns in multiple records to an ASP.NET control. Solution Set the control's advanced properties (see Table 7-1) before calling DataBind( ).

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  • This book presents state of the art contributes to Simulated Annealing (SA) that is a well-known probabilistic meta-heuristic. It is used to solve discrete and continuous optimization problems. The significant advantage of SA over other solution methods has made it a practical solution method for solving complex optimization problems. Book is consisted of 13 chapters, classified in single and multiple objectives applications and it provides the reader with the knowledge of SA and several applications.

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  • Dominance links were introduced in grammars to model long distance scrambling phenomena, motivating the definition of multiset-valued linear indexed grammars (MLIGs) by Rambow (1994b), and inspiring quite a few recent formalisms. It turns out that MLIGs have since been rediscovered and reused in a variety of contexts, and that the complexity of their emptiness problem has become the key to several open questions in computer science.

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  • We consider the problem of answering complex questions that require inferencing and synthesizing information from multiple documents and can be seen as a kind of topicoriented, informative multi-document summarization. The stochastic, graph-based method for computing the relative importance of textual units (i.e. sentences) is very successful in generic summarization.

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  • In this paper, we analyze the impact of different automatic annotation methods on the performance of supervised approaches to the complex question answering problem (defined in the DUC-2007 main task). Huge amount of annotated or labeled data is a prerequisite for supervised training. The task of labeling can be accomplished either by humans or by computer programs. When humans are employed, the whole process becomes time consuming and expensive.

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  • Our skin may just feel like a mere shield that protects us from the world outside. But, the fact is, it’s more than just the “mask” that keeps your insides in. It is a very unique and remarkable complex organ that reflects our general health. Thus, it is worth protecting from the outside and inside forces. It is commonly said that for a young, good looking skin, we must provide it with essential nutrients and protect it from external damage. Thanks to some pros out there that making this aim possible is not at all difficult to make. ...

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  • Morphological analysis must take into account the spelling-change processes of a language as well as its possible configurations of stems, affixes, and inflectional markings. The computational difficultyof the task can be clarified by investigating specific models of morphological processing. The use of finite-state machinery in the "twolevel" model by K i m m o Koskenniemi gives it the appearance of computational efficiency, but closer examination shows the model does not guarantee efficient processing.

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  • We study the problem of finding the best headdriven parsing strategy for Linear ContextFree Rewriting System productions. A headdriven strategy must begin with a specified righthand-side nonterminal (the head) and add the remaining nonterminals one at a time in any order. We show that it is NP-hard to find the best head-driven strategy in terms of either the time or space complexity of parsing.

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  • Many phrase alignment models operate over the combinatorial space of bijective phrase alignments. We prove that finding an optimal alignment in this space is NP-hard, while computing alignment expectations is #P-hard. On the other hand, we show that the problem of finding an optimal alignment can be cast as an integer linear program, which provides a simple, declarative approach to Viterbi inference for phrase alignment models that is empirically quite efficient.

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  • A challenging problem for spoken dialog systems is the design of utterance generation modules that are fast, flexible and general, yet produce high quality output in particular domains. A promising approach is trainable generation, which uses general-purpose linguistic knowledge automatically adapted to the application domain. This paper presents a trainable sentence planner for the MATCH dialog system.

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  • Drawing appropriate defeasible inferences has been proven to be one of the most pervasive puzzles of natural language processing and a recurrent problem in pragmatics. This paper provides a theoretical framework, called stratified logic, that can accommodate defeasible pragmatic inferences. The framework yields an algorithm that computes the conversational, conventional, scalar, clausal, and normal state implicatures; and the presuppositions that are associated with utterances. The algorithm applies equally to simple and complex utterances and sequences of utterances. ...

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  • One problem for the generation of natural language text is determining when to use a sequence of simple sentences and when a single complex one is more appropriate. In this paper, we show how focus of attention is one factor that influences this decision and describe its implementation in a system that generates explanations for a student advisor expert system.

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  • In this paper we study a set of problems that are of considerable importance to Statistical Machine Translation (SMT) but which have not been addressed satisfactorily by the SMT research community. Over the last decade, a variety of SMT algorithms have been built and empirically tested whereas little is known about the computational complexity of some of the fundamental problems of SMT. Our work aims at providing useful insights into the the computational complexity of those problems.

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  • When you’re faced with a problem to solve (and frankly, who isn’t these days?), the basic strategy usually taken by we computer people is called “divide and conquer.” It goes like this:Reducing complex problems down to the level of twiddling the states of a few billion bits is what we do all day. But “divide and conquer” is not the only possible strategy. We can also take a more generalist approach:

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