To order Numerical Recipes books,diskettes, or CDROMs visit website http://www.nr.com or call It may seem perverse to use a computer, that most precise and deterministic of all machines conceived by the human mind, to produce “random” numbers. More than perverse, it may seem to be a conceptual impossibility. Any program, after all, will produce output that is entirely predictable, hence not truly “random.”
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article An FPGA Implementation of a Parallelized MT19937 Uniform Random Number Generator
Chapter 4 presents the assorted topics (and more on pointers). The main contents of this chapter include all of the following: Makefiles, file I/O, command line arguments, random numbers, re-covering pointers, memory and functions, and homework.
Lecture 10: Key distribution for symmetric key cryptography and generating random numbers. The goals of this chapter are: Why might we need key distribution centers? Master key vs. session key, hierarchical and decentralized key distributions, generating pseudorandom numbers.
In the previous section, we learned how to generate random deviates with a uniform probability distribution, so that the probability of generating a number between x and x + dx, denoted p(x)dx, is given by p(x)dx = dx 0
Conditional Random Fields (CRFs) have been applied with considerable success to a number of natural language processing tasks. However, these tasks have mostly involved very small label sets. When deployed on tasks with larger label sets, the requirements for computational resources mean that training becomes intractable. This paper describes a method for training CRFs on such tasks, using error correcting output codes (ECOC). A number of CRFs are independently trained on the separate binary labelling tasks of distinguishing between a subset of the labels and its complement. ...
They are not very random for that purpose; see Knuth . Examples of acceptable uses of these random bits are: (i) multiplying a signal randomly by ±1 at a rapid “chip rate,” so as to spread its spectrum uniformly (but recoverably) across some desired bandpass
psdes(&lword,&irword); itemp=jflone | (jflmsk & irword); ++(*idum); return (*(float *)&itemp)-1.0; }
“Pseudo-DES” encode the words. Mask to a ﬂoating number between 1 and 2. Subtraction moves range to 0. to 1.
This paper presents techniques to apply semi-CRFs to Named Entity Recognition tasks with a tractable computational cost. Our framework can handle an NER task that has long named entities and many labels which increase the computational cost. To reduce the computational cost, we propose two techniques: the ﬁrst is the use of feature forests, which enables us to pack feature-equivalent states, and the second is the introduction of a ﬁltering process which signiﬁcantly reduces the number of candidate states. ...
Frequency distribution models tuned to words and other linguistic events can predict the number of distinct types and their frequency distribution in samples of arbitrary sizes. We conduct, for the ﬁrst time, a rigorous evaluation of these models based on cross-validation and separation of training and test data. Our experiments reveal that the prediction accuracy of the models is marred by serious overﬁtting problems, due to violations of the random sampling assumption in corpus data. We then propose a simple pre-processing method to alleviate such non-randomness problems. ...
In this paper, we explore the power of randomized algorithm to address the challenge of working with very large amounts of data. We apply these algorithms to generate noun similarity lists from 70 million pages. We reduce the running time from quadratic to practically linear in the number of elements to be computed.
In this paper, we present an automated, quantitative, knowledge-poor method to evaluate the randomness of a collection of documents (corpus), with respect to a number of biased partitions. The method is based on the comparison of the word frequency distribution of the target corpus to word frequency distributions from corpora built in deliberately biased ways. We apply the method to the task of building a corpus via queries to Google.
Chapter 3 - Numerical data. After you have read and studied this chapter, you should be able to: Select proper types for numerical data; write arithmetic expressions in Java; evaluate arithmetic expressions using the precedence rules; describe how the memory allocation works for objects and primitive data values; write mathematical expressions, using methods in the Math class; generate pseudo random numbers.
This lecture is an overview of the fundamental tools and techniques for numeric computation. For some, numerics are everything. For many, numerics are occasionally essential. Here, we present the basic problems of size, precision, truncation, and error handling in standard mathematical functions. We present multidimensional matrices and the standard library complex numbers.
Dynamic HTML isn’t a single piece of technology that you can point to and say,
“This is DHTML.” The term is a descriptor that encompasses all of the technologies
that combine to make a web page dynamic: the technologies that let you
create new elements without refreshing the page, change the color of those elements,
and make them expand, contract, and zoom around the screen.
DHTML uses HTML, the DOM, and CSS in combination with a client-side
There are many DSP applications that are used in our daily lives, some of which have been introduced in previous chapters. DSP algorithms, such as random number generation, tone generation and detection, echo cancellation, channel equalization, noise reduction, speech and image coding, and many others can be found in a variety of communication systems. In this chapter, we will introduce some selected DSP applications in communications that played an important role in the realization of the systems.
Monte Carlo methods are ubiquitous in applications in the finance and
insurance industry. They are often the only accessible tool for financial engineers
and actuaries when it comes to complicated price or risk computations,
in particular for those that are based on many underlyings. However, as they
tend to be slow, it is very important to have a big tool box for speeding them
up or – equivalently – for increasing their accuracy. Further, recent years have
seen a lot of developments in Monte Carlo methods with a high potential for
success in applications.
Basic principles underlying the transactions of financial markets are tied to
probability and statistics. Accordingly it is natural that books devoted to
mathematical finance are dominated by stochastic methods. Only in recent
years, spurred by the enormous economical success of financial derivatives,
a need for sophisticated computational technology has developed. For example,
to price an American put, quantitative analysts have asked for the
numerical solution of a free-boundary partial differential equation.