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.
Spreadsheets provide one of the most easily learned routes to scientific computing. This book uses Excel®, the most powerful spreadsheet available, to explore and solve problems in general and chemical data analysis. It follows the usual sequence of college textbooks in analytical chemistry: statistics, chemical equilibria, pH calculations, titrations, and instrumental methods such as chromatography, spectrometry, and
The solutions as presented generally just provide a guidance to solving the problems, rather than step by step manipulation, and leave much to the students to work out for themselves, of whom much is demanded of the basic knowledge in physics. Thus the series would provide an invaluable complement to the textbooks. The present volume for Mechanics which consists of three parts Newtonian Mechanics, Analytical Mechanics, and Special Relativity contains 410 problems.
Solutions to IRODOV’S problems in General Physics, available in two volumes, are meant for those dedicated physics students who face the challenge of solving numerical problems, particularly JEE (Mains & Advanced). The two volumes provide the complete solutions for each of the 1878 problems in I.E. IRODOV’s problems in General Physics.
Prolog is considered difficult by students. Usually, by the time they learn Prolog, which is most likely to happen
in preparation for a course in Artificial Intelligence (AI) or Expert Systems, they will have studied imperative
programming and/or the object oriented paradigm. Unfortunately, this prior experience is not always conducive
to learning Prolog. Even though there is a good provision of traditional Prolog textbooks (for example ),
students still find it hard to write solutions in Prolog to problems of any notable complexity.
Solutions to I.E. Irodov's problems in General Physics, available in two volumes, are meant for those dedicated physics students who face the challenge of solving numerical problems, particularly JEE (Main & Advanced) aspirants. The two volumes provide the complete solutions for each of the 1878 problems in I.E. Irodov's problems in General Physics. The solutions presented in this book are crisp, and guaranteed to make you think beyond the box. This book is exactly what you need to establish a strong foundation for discovering the beauty of physics and cracking any entrance exam in India.
We prove that if f (x) = n−1 ak xk is a polynomial with no cyclotomic k=0 factors whose coeﬃcients satisfy ak ≡ 1 mod 2 for 0 ≤ k 1 + log 3 , 2n
resolving a conjecture of Schinzel and Zassenhaus  for this class of polynomials. More generally, we solve the problems of Lehmer and Schinzel and Zassenhaus for the class of polynomials
This book is designed to explain to dental students the processes of diagnosis and treatment planning, through consideration of clinical cases and problems associated with aspects of all dental specialties. It presents a series of case histories from all the major areas of dentistry, and uses a question-and-answer format to guide readers through the process of examination, differential diagnosis, investigations, diagnosis and treatment. It prepares readers for the wide variety of problems likely to be encountered in clinical practice.
The present volume deals with various practical problems in economics,
as a volume published a year earlier dealt with the broader economic
principles of value and distribution. To the student beginning
economics and to the general reader the study of principles is likely
to appear more difficult than does that of concrete questions. In
fact, the difficulty of the latter, tho less obvious, is equally
Two trends are evident in the recent evolution of the field of information extraction: a preference for simple, often corpus-driven techniques over linguistically sophisticated ones; and a broadening of the central problem definition to include many non-traditional text domains. This development calls for information extraction systems which are as retctrgetable and general as possible. Here, we describe SRV, a learning architecture for information extraction which is designed for maximum generality and flexibility. ...
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. ...
Ristad (1986a) examines the computational complexity of two components of the G P S G formal system (metarules and the feature system) and shows how each of these systems can lead to computational intractability. Rlstad also proves that the universal recognition problem for G P S G s is E X P - P O L Y hard, and intractable.2 In another words, the fastest recognition algorithm for G P S G s can take more than exponential time. These results m a y appear surprising, given GPSG's weak context-fres generative power. ...
The offline parsable grammars apparently have enough formal power to describe human language, yet the parsing problem for these grammars is solvable. Unfortunately they exclude grammars that use x-bar theory - and these grammars have strong linguistic justification. We define a more general class of unification grammars, which admits x-bar grammars while preserving the desirable properties of offline parsable grammars. Consider a unification grammar based on term unification.
Generalized Vector Space Models (GVSM) extend the standard Vector Space Model (VSM) by embedding additional types of information, besides terms, in the representation of documents. An interesting type of information that can be used in such models is semantic information from word thesauri like WordNet. Previous attempts to construct GVSM reported contradicting results. The most challenging problem is to incorporate the semantic information in a theoretically sound and rigorous manner and to modify the standard interpretation of the VSM.
(BQ) Ebook Problems in mathematical analysis of problems and exercises in mathematical anal ysis covers the maximum requirements of general courses in higher mathematics for higher technical schools. it contains over 3,000 problems sequentially arranged in chapters i to x covering all branches of higher mathematics (with the exception of ana lytical geometry) given in college courses.
Large corpora of parsed sentences with semantic role labels (e.g. PropBank) provide training data for use in the creation of high-performance automatic semantic role labeling systems. Despite the size of these corpora, individual verbs (or rolesets) often have only a handful of instances in these corpora, and only a fraction of English verbs have even a single annotation. In this paper, we describe an approach for dealing with this sparse data problem, enabling accurate semantic role labeling for novel verbs (rolesets) with only a single training example. ...
Recent text and speech processing applications such as speech mining raise new and more general problems related to the construction of language models. We present and describe in detail several new and efﬁcient algorithms to address these more general problems and report experimental results demonstrating their usefulness.
We describe a speedup for training conditional maximum entropy models. The algorithm is a simple variation on Generalized Iterative Scaling, but converges roughly an order of magnitude faster, depending on the number of constraints, and the way speed is measured. Rather than attempting to train all model parameters simultaneously, the algorithm trains them sequentially. The algorithm is easy to implement, typically uses only slightly more memory, and will lead to improvements for most maximum entropy problems. ...
In natural language processing, ambiguity resolution is a central issue, and can be regarded as a preference assignment problem. In this paper, a Generalized Probabilistic Semantic Model (GPSM) is proposed for preference computation. An effective semantic tagging procedure is proposed for tagging semantic features. A semantic score function is derived based on a score function, which integrates lexical, syntactic and semantic preference under a uniform formulation. The semantic score measure shows substantial improvement in structural disambiguation over a syntax-based approach. ...