Hasil untuk "Computer software"

G. Stecher, K. Tamura, Sudhir Kumar

The Molecular Evolutionary Genetics Analysis (MEGA) software enables comparative analysis of molecular sequences in phylogenetics and evolutionary medicine. Here, we introduce the macOS version of the MEGA software. This new version eliminates the need for virtualization and emulation programs previously required to use MEGA on Apple computers. MEGA for macOS utilizes memory and computing resources efficiently for conducting evolutionary analyses on Apple computers. It has a native Cocoa graphical user interface that is programmed to provide a consistent user experience across macOS, Windows, and Linux. MEGA for macOS is available from www.megasoftware.net free of charge.

Marcello Benedetti, Erika Lloyd, Stefan H. Sack et al.

Hybrid quantum–classical systems make it possible to utilize existing quantum computers to their fullest extent. Within this framework, parameterized quantum circuits can be regarded as machine learning models with remarkable expressive power. This Review presents the components of these models and discusses their application to a variety of data-driven tasks, such as supervised learning and generative modeling. With an increasing number of experimental demonstrations carried out on actual quantum hardware and with software being actively developed, this rapidly growing field is poised to have a broad spectrum of real-world applications.

Julia Dressel, Hany Farid

Should we trust computers to make life-altering decisions in the criminal justice system? Algorithms for predicting recidivism are commonly used to assess a criminal defendant’s likelihood of committing a crime. These predictions are used in pretrial, parole, and sentencing decisions. Proponents of these systems argue that big data and advanced machine learning make these analyses more accurate and less biased than humans. We show, however, that the widely used commercial risk assessment software COMPAS is no more accurate or fair than predictions made by people with little or no criminal justice expertise. In addition, despite COMPAS’s collection of 137 features, the same accuracy can be achieved with a simple linear classifier with only two features.

B. Guo, Juanjuan Zhu

Preface Thi s textbook is about signals and systems, a discipline rooted in the intellectual tradition of electrical engineering (EE). This tradition, however, has evolved in unexpected ways. EE has lost its tight coupling with the " electrical. " Electricity provides the impetus, the potential, but not the body of the subject. How else could microelectromechanical systems (MEMS) become so important in EE? Is this not mechanical engineering? Or signal processing? Is this not mathe-matics? Or digital networking? Is this not computer science? How is it that control system techniques are profitably applied to aeronautical systems, structural mechanics , electrical systems, and options pricing? This book approaches signals and systems from a computational point of view. It is intended for students interested in the modern, highly digital problems of electrical engineering, computer science, and computer engineering. In particular , the approach is applicable to problems in computer networking, wireless communication systems, embedded control, audio and video signal processing, and, of course, circuits. A more traditional introduction to signals and systems would be biased toward the latter application, circuits. It would focus almost exclusively on linear time-invariant systems, and would develop continuous-time models first, with discrete-time models then treated as an advanced topic. The discipline, after all, grew out of the context of circuit analysis. But it has changed. Even pure EE xiii xiv Preface graduates are more likely to write software than to push electrons, and yet we still recognize them as electrical engineers. The approach in this book benefits students by showing from the start that the methods of signals and systems are applicable to software systems, and most interestingly, to systems that mix computers with physical devices such as circuits, mechanical control systems, and physical media. Such systems have become pervasive, and profoundly affect our daily lives. The shift away from circuits implies some changes in the way the methodology of signals and systems is presented. While it is still true that a voltage that varies over time is a signal, so is a packet sequence on a network. This text defines signals to cover both. While it is still true that an RLC circuit is a system, so is a computer program for decoding Internet audio. This text defines systems to cover both. While for some systems the state is still captured adequately by variables in a differential equation, for many it is now the values in …

Nees Jan van Eck, L. Waltman

Clustering scientific publications in an important problem in bibliometric research. We demonstrate how two software tools, CitNetExplorer and VOSviewer, can be used to cluster publications and to analyze the resulting clustering solutions. CitNetExplorer is used to cluster a large set of publications in the field of astronomy and astrophysics. The publications are clustered based on direct citation relations. CitNetExplorer and VOSviewer are used together to analyze the resulting clustering solutions. Both tools use visualizations to support the analysis of the clustering solutions, with CitNetExplorer focusing on the analysis at the level of individual publications and VOSviewer focusing on the analysis at an aggregate level. The demonstration provided in this paper shows how a clustering of publications can be created and analyzed using freely available software tools. Using the approach presented in this paper, bibliometricians are able to carry out sophisticated cluster analyses without the need to have a deep knowledge of clustering techniques and without requiring advanced computer skills.

M. Perțea, Daehwan Kim, G. Pertea et al.

J. Schindelin, C. Rueden, M. Hiner et al.

S. Pronk, Szilárd Páll, R. Schulz et al.

Edward A. Lee, S. Seshia

Yang Liao, G. Smyth, Wei Shi

MOTIVATION Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. RESULTS We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. AVAILABILITY AND IMPLEMENTATION featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

C. Trapnell, Adam Roberts, L. Goff et al.

Mark A. Larkin, G. Blackshields, N. P. Brown et al.

Manuel G. CIaros, G. Heijne

Fred D. Davis, R. Bagozzi, P. R. Warshaw

E. Lindahl, B. Hess, D. van der Spoel

B. Myers, Scott E. Hudson, R. Pausch

M. Tomita, Kenta Hashimoto, Koichi Takahashi et al.

M. Lill, Matthew L. Danielson

Wendy H. Chun

Vehbi Celik, E. Yeşilyurt