Random numbers are the nuts and bolts of simulation. Typically, all the randomness required by the model is simulated by a random number generator whose output is assumed to be a sequence of independent and identically distributed (IID) U(0, 1) random variables (i.e., continuous random variables distributed uniformly over the interval

We present a simple dictionary with worst case constant lookup time, equaling the theoretical performance of the classic dynamic perfect hashing scheme of Dietzfelbinger et al. (Dynamic perfect hashing: Upper and lower bounds. SIAM J. Comput., 23(4):738–761, 1994). The space usage is similar to that of binary search trees, i.e., three words per key on average. Besides being conceptually much simpler than previous dynamic dictionaries with worst case constant lookup time, our data structure is interesting in that it does not use perfect hashing, but rather a variant of open addressing where keys can be moved back in their probe sequences. An implementation inspired by our algorithm, but using weaker hash functions, is found to be quite practical. It is competitive with the best known dictionaries having an average case (but no nontrivial worst case) guarantee. Key Words: data structures, dictionaries, information retrieval, searching, hashing, experiments * Partially supported by the Future and Emerging Technologies programme of the EU

This document describes the software library TestU01, implemented in the ANSI C language, and offering a collection of utilities for the (empirical) statistical testing of uniform random number generators (RNG). The library implements several types of generators in generic form, as well as many specific generators proposed in the literature or found in widely-used software. It provides general implementations of the classical statistical tests for random number generators, as well as several others proposed in the literature, and some original ones. These tests can be applied to the generators predefined in the library and to user-defined generators. Specific tests suites for either sequences of uniform random numbers in [0, 1] or bit sequences are also available. Basic tools for plotting vectors of points produced by generators are provided as well. Additional software permits one to perform systematic studies of the interaction between a specific test and the structure of the point sets produced by a given family of RNGs. That is, for a given kind of test and a given class of RNGs, to determine how large should be the sample size of the test, as a function of the generator’s period length, before the generator starts to fail the test systematically.

Bloom filtering is an important technique for space efficient storage of a conservative approximation of a set S. The set stored may have up to some specified number of false positive members, but all elements of S are included. In this paper we consider lossy dictionaries that are also allowed to have false negatives, i.e., leave out elements of S. The aim is to maximize the weight of included keys within a given space constraint. This relaxation allows a very fast and simple data structure making almost optimal use of memory. Being more time efficient than Bloom filters, we believe our data structure to be well suited for replacing Bloom filters in some applications. Also, the fact that our data structure supports information associated to keys paves the way for new uses, as illustrated by an application in lossy image compression.

this paper, we study the structure and point out weaknesses of these types of generators, both theoretically and empirically. In Section 2, we discuss their lattice structures and provide bounds on their best possible performance in the spectral test, showing that they cannot perform well, especially when a is small. In Section 3, we apply well-known empirical statistical tests to selected instances of these generators. They fail the tests decisively. Improvements and alternatives are pointed out in Section 4

The National Software Reference Library (NSRL) provides a repository of known software, file profiles, and file signatures for use by law enforcement and other organizations involved with computer forensic investigations. The NSRL is comprised of three major elements: 1. A physical library of commercial software packages. 2. A database of information about each file within each software package. 3. A smaller database of the most widely used information that is updated and released quarterly. This database is called the NSRL Reference Data Set (RDS) and is NIST Special Database #28 [18]. During a forensic investigation, hundreds of thousands of files may be encountered. The NSRL is used to identify known files. This can reduce the amount of time spent examining a computer. Matches for common operating systems and applications do not need to be searched, either manually or electronically, for evidence. Additionally, the NSRL is used to determine which software applications are present on a system. This may suggest how the computer was being used and provide information on how and

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Random number generators are critically important for computational statistics. Simulation methods are becoming ever more common for estimation; Monte Carlo Markov Chain is but one approach. Also, simulation methods such as the Bootstrap have long been used in inference and are becoming a standard part of a rigorous analysis. As random number