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In the railway domain, an interlocking is the system ensuring safe train traffic inside a station by controlling its active elements such as the signals or points. Modern interlockings are configured using particular data, called…

Software Engineering · Computer Science 2017-08-07 Quentin Cappart , Christophe Limbree , Pierre Schaus , Jean Quilbeuf , Louis-Marie Traonouez , Axel Legay

The foundations of mathematics have long been considered settled by the Zermelo-Fraenkel-Choice axioms. But set theory abounds in models with different truths and even classical questions such as the measurability of projective sets can…

Logic · Mathematics 2026-05-06 David Mumford , Sy-David Friedman

Stochastic branching processes are a classical model for describing random trees, which have applications in numerous fields including biology, physics, and natural language processing. In particular, they have recently been proposed to…

Logic in Computer Science · Computer Science 2012-06-07 Taolue Chen , Klaus Dräger , Stefan Kiefer

Here I will review the theoretical results that have been obtained for spin glasses. I will concentrate my attention on the predictions of the mean field approach in three dimensional systems and on its numerical and experimental…

Disordered Systems and Neural Networks · Physics 2009-11-13 Giorgio Parisi

Scanning Electron Microscopy (SEM) is indispensable in modern materials science, enabling high-resolution imaging across a wide range of structural, chemical, and functional investigations. However, SEM imaging remains constrained by…

You may develop a potential prediction model, but how can I trust your model that it will benefit my software?. Using a software defect prediction (SDP) model as a tool, we address this fundamental problem in machine learning research. This…

Software Engineering · Computer Science 2023-01-16 Umamaheswara Sharma B , Ravichandra Sadam

The simulations of the light scalar mesons on the lattice are presented at the introductory level. The methods for determining the scalar meson masses are described. The problems related to some of these methods are presented and their…

High Energy Physics - Phenomenology · Physics 2007-05-23 Sasa Prelovsek

Model checking procedures are considered based on the use of the Dirichlet process and relative belief. This combination is seen to lead to some unique advantages for this problem. In particular, it avoids double use of the data and…

Methodology · Statistics 2016-06-28 Luai Al-Labadi , Michael Evans

This paper establishes fundamental results for statistical inference of diagnostic classification models (DCM). The results are developed at a high level of generality, applicable to essentially all diagnostic classification models. In…

Statistics Theory · Mathematics 2025-01-08 Guanhua Fang , Jingchen Liu , Zhiliang Ying

In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a spotlight to the growing prevalence of stochastic computer…

We propose a model-based approach to the model checking problem for recursive schemes. Since simply typed lambda calculus with the fixpoint operator, lambda-Y-calculus, is equivalent to schemes, we propose the use of a model of…

Logic in Computer Science · Computer Science 2017-01-11 Sylvain Salvati , Igor Walukiewicz

Organizations that develop software have recognized that software process models are particularly useful for maintaining a high standard of quality. In the last decade, simulations of software processes were used in several settings and…

Software Engineering · Computer Science 2014-02-24 Holger Neu , Thomas Hanne , Jürgen Münch , Stefan Nickel , Andreas Wirsen

Spatial documentation is exponentially increasing given the availability of Big IoT Data, enabled by the devices miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence…

Methodology · Statistics 2020-10-01 Francisco Louzada , Diego C. Nascimento , Osafu Augustine Egbon

We present a formal model developed to reason about topologies created by sensor ranges. This model is used to formalise the topological aspects of an existing counting algorithm to estimate the number of targets in the area covered by the…

Logic in Computer Science · Computer Science 2018-02-07 Sven Linker , Michele Sevegnani

Adoption of machine learning models in healthcare requires end users' trust in the system. Models that provide additional supportive evidence for their predictions promise to facilitate adoption. We define consistent evidence to be both…

Computer Vision and Pattern Recognition · Computer Science 2021-11-16 Peiqi Wang , Ruizhi Liao , Daniel Moyer , Seth Berkowitz , Steven Horng , Polina Golland

Symmetry tests provide an important probe for the structure of elementary particle interactions and for the validity of the standard model. However, it is pointed out that in the interpretation of such experiments one must keep in mind that…

High Energy Physics - Phenomenology · Physics 2011-04-15 Barry R. Holstein

The statistical model of hadronization succeeds in reproducing particle abundances and transverse momentum spectra in high energy collisions of elementary particles as well as of heavy ions. Despite its apparent success, the interpretation…

High Energy Physics - Phenomenology · Physics 2009-11-10 F. Becattini

This is a support document which describes the properties of the cable and parameters of the formulas for the statistical powerline channel modeling. The cable parameters help the reader build powerline channel according to the transmission…

Information Theory · Computer Science 2012-03-16 Bo Tan , John Thompson

Determining whether a nonlinear multi-input system is differentially flat remains challenging. One way to obtain computationally tractable sufficient conditions is to give complete characterizations of flat normal forms. We introduce a…

Dynamical Systems · Mathematics 2025-11-17 Georg Hartl , Conrad Gstöttner , Markus Schöberl

Structural causal models (SCMs) are widely used in various disciplines to represent causal relationships among variables in complex systems. Unfortunately, the underlying causal structure is often unknown, and estimating it from data…

Machine Learning · Computer Science 2024-01-17 Tianyu Chen , Kevin Bello , Bryon Aragam , Pradeep Ravikumar
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