Simulated division with approximate factoring for the multiple recursive generator with both unrestricted multiplier and non-mersenne prime modulus

AbstractThis paper focuses on devising a general and efficient way of generating random numbers for the multiple recursive generator with both unrestricted multiplier and non-Mersenne prime modulus. We propose a new algorithm that embeds the technique of approximate factoring into the simulated division method. The proposed new algorithm improves the decomposition method in terms of both the suitability for various word-sizes of the computers and the efficiency characteristics, such as the number of arithmetic operations required and the computational time. Empirical simulations are conducted to compare and evaluate the computational time of this algorithm with the decomposition method for various computers

First-principles molecular structure search with a genetic algorithm

The identification of low-energy conformers for a given molecule is a fundamental problem in computational chemistry and cheminformatics. We assess here a conformer search that employs a genetic algorithm for sampling the low-energy segment of the conformation space of molecules. The algorithm is designed to work with first-principles methods, facilitated by the incorporation of local optimization and blacklisting conformers to prevent repeated evaluations of very similar solutions. The aim of the search is not only to find the global minimum, but to predict all conformers within an energy window above the global minimum. The performance of the search strategy is: (i) evaluated for a reference data set extracted from a database with amino acid dipeptide conformers obtained by an extensive combined force field and first-principles search and (ii) compared to the performance of a systematic search and a random conformer generator for the example of a drug-like ligand with 43 atoms, 8 rotatable bonds and 1 cis/trans bond

Design, Search and Implementation of Improved Large Order Multiple Recursive Generators and Matrix Congruential Generators

Large order, maximum period multiple recursive generators (MRGs) with few nonzero terms (e.g., DX-k-s generators) have become popular in the area of computer simulation. They are efficient, portable, have a long period, and have the nice property of high-dimensional equi-distribution. The latter two properties become more advantageous as k increases. The performance on the spectral test, a theoretical test that provides some measure of uniformity in dimensions beyond the MRG\u27s order k, could be improved by choosing multipliers that yield a better spectral test value. We propose a new method to compute the spectral test which is simple, intuitive, and efficient for some special classes of large order MRGs. Using this procedure, we list \u27\u27better\u27\u27 FMRG-k and DX-k-s generators with respect to performance on the spectral test. Even so, MRGs with few nonzero terms do not perform as well with respect to the spectral test as MRGs with many nonzero terms. However, MRGs with many nonzero terms can be inefficient or lack a feasible parallelization method, i.e., a method of producing substreams of (pseudo) random numbers that appear independent. To implement these MRGs efficiently and in parallel, we can use an equivalent recursion from another type of generator, the matrix congruential generator (MCG), a k-dimensional generalization of a first order linear recursion where the multipliers are embedded in a k by k matrix. When MRGs are used to construct MCGs and the recursion of the MCG is implemented k at a time for a k-dimensional vector sequence, then the MCG mimics k copies of a MRG in parallel with different starting seeds. Therefore, we propose a method for efficiently finding MRGs with many nonzero terms from an MRG with few nonzero terms and then give an efficient and parallel MCG implementation of these MRGs with many nonzero terms. This method works best for moderate order k. For large order MRGs with many nonzero terms, we propose a special class called DW-k. This special class has a characteristic polynomial that yields many nonzero terms and corresponds to an efficient and parallel MCG implementation

Learning How to Count: A High Multiplicity Search for the LHC

We introduce a search technique that is sensitive to a broad class of signals with large final state multiplicities. Events are clustered into large radius jets and jet substructure techniques are used to count the number of subjets within each jet. The search consists of a cut on the total number of subjets in the event as well as the summed jet mass and missing energy. Two different techniques for counting subjets are described and expected sensitivities are presented for eight benchmark signals. These signals exhibit diverse phenomenology, including 2-step cascade decays, direct three body decays, and multi-top final states. We find improved sensitivity to these signals as compared to previous high multiplicity searches as well as a reduced reliance on missing energy requirements. One benefit of this approach is that it allows for natural data driven estimates of the QCD background.Comment: 36 pages, 12 Figures, 5 Tables; journal versio

Les Houches 2013: Physics at TeV Colliders: Standard Model Working Group Report

This Report summarizes the proceedings of the 2013 Les Houches workshop on Physics at TeV Colliders. Session 1 dealt primarily with (1) the techniques for calculating standard model multi-leg NLO and NNLO QCD and NLO EW cross sections and (2) the comparison of those cross sections with LHC data from Run 1, and projections for future measurements in Run 2.Comment: Proceedings of the Standard Model Working Group of the 2013 Les Houches Workshop, Physics at TeV Colliders, Les houches 3-21 June 2013. 200 page

Nearest-Neighbor Queries in Customizable Contraction Hierarchies and Applications

Customizable contraction hierarchies are one of the most popular route planning frameworks in practice, due to their simplicity and versatility. In this work, we present a novel algorithm for finding k-nearest neighbors in customizable contraction hierarchies by systematically exploring the associated separator decomposition tree. Compared to previous bucket-based approaches, our algorithm requires much less target-dependent preprocessing effort. Moreover, we use our novel approach in two concrete applications. The first application are online k-closest point-of-interest queries, where the points of interest are only revealed at query time. We achieve query times of about 25 milliseconds on a continental road network, which is fast enough for interactive systems. The second application is travel demand generation. We show how to accelerate a recently introduced travel demand generator by a factor of more than 50 using our novel nearest-neighbor algorithm

An operational definition of quark and gluon jets

While "quark" and "gluon" jets are often treated as separate, well-defined objects in both theoretical and experimental contexts, no precise, practical, and hadron-level definition of jet flavor presently exists. To remedy this issue, we develop and advocate for a data-driven, operational definition of quark and gluon jets that is readily applicable at colliders. Rather than specifying a per-jet flavor label, we aggregately define quark and gluon jets at the distribution level in terms of measured hadronic cross sections. Intuitively, quark and gluon jets emerge as the two maximally separable categories within two jet samples in data. Benefiting from recent work on data-driven classifiers and topic modeling for jets, we show that the practical tools needed to implement our definition already exist for experimental applications. As an informative example, we demonstrate the power of our operational definition using Z+jet and dijet samples, illustrating that pure quark and gluon distributions and fractions can be successfully extracted in a fully well-defined manner.Comment: 38 pages, 10 figures, 1 table; v2: updated to match JHEP versio

(Machine) Learning to Do More with Less

Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data. In this paper, we compare the standard "fully supervised" approach (that relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training. This so-called "weakly supervised" technique has access to less information than the fully supervised method and yet is still able to yield impressive discriminating power. In addition, weak supervision seems particularly well suited to particle physics since quantum mechanics is incompatible with the notion of mapping an individual event onto any single Feynman diagram. We examine the technique in detail -- both analytically and numerically -- with a focus on the robustness to issues of mischaracterizing the training samples. Weakly supervised networks turn out to be remarkably insensitive to systematic mismodeling. Furthermore, we demonstrate that the event level outputs for weakly versus fully supervised networks are probing different kinematics, even though the numerical quality metrics are essentially identical. This implies that it should be possible to improve the overall classification ability by combining the output from the two types of networks. For concreteness, we apply this technology to a signature of beyond the Standard Model physics to demonstrate that all these impressive features continue to hold in a scenario of relevance to the LHC.Comment: 32 pages, 12 figures. Example code is provided at https://github.com/bostdiek/PublicWeaklySupervised . v3: Version published in JHEP, discussion adde