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The success of neural networks builds to a large extent on their ability to create internal knowledge representations from real-world high-dimensional data, such as images, sound, or text. Approaches to extract and present these…

Artificial Intelligence · Computer Science 2023-01-03 Lars Holmberg , Paul Davidsson , Per Linde

In this paper we give a method, based on the characteristic function of a set, to solve some difficult problems of set theory in undergraduate research.

General Mathematics · Mathematics 2007-07-23 Mihaly Bencze , Florentin Smarandache

I describe various aspects of the construction of a dual standard model including how it may be possible to obtain the charge spectrum, the family structure and spin of the known matter particles. I summarize the encouraging features of the…

High Energy Physics - Phenomenology · Physics 2007-05-23 Tanmay Vachaspati

This paper shows how the theory of nonlinear adaptive observers can be effectively used in the design of internal models for nonlinear output regulation. The theory substantially enhances the existing results in the context of {\em…

Optimization and Control · Mathematics 2007-05-23 Francesco Delli Priscoli , Lorenzo Marconi , Alberto Isidori

The first-order model theory of modules has been studied for decades. More recently, the model theoretic study of nonelementary classes of modules--especially Abstract Elementary Classes of modules--has produced interesting results. This…

Logic · Mathematics 2025-07-21 Will Boney

Literature involving preferences of artificial agents or human beings often assume their preferences can be represented using a complete transitive binary relation. Much has been written however on different models of preferences. We review…

Artificial Intelligence · Computer Science 2018-01-17 Olivier Cailloux , Sébastien Destercke

In high dimensional settings, density estimation algorithms rely crucially on their inductive bias. Despite recent empirical success, the inductive bias of deep generative models is not well understood. In this paper we propose a framework…

Machine Learning · Computer Science 2018-11-09 Shengjia Zhao , Hongyu Ren , Arianna Yuan , Jiaming Song , Noah Goodman , Stefano Ermon

A short review of recent renormalization group analyses of the self-consistence of the Standard Model is presented.

High Energy Physics - Phenomenology · Physics 2014-12-16 Fred Jegerlehner , Mikhail Yu. Kalmykov , Bernd A. Kniehl

We shall consider some common models in linear thermo-elasticity within a common structural framework. Due to the flexibility of the structural perspective we will obtain well-posedness results for a large class of generalized models…

Mathematical Physics · Physics 2016-10-27 Santwana Mukhopadhyay , Rainer Picard , Sascha Trostorff , Marcus Waurick

The principal innovative idea in this paper is to transform the original complex nonlinear modeling problem into a combination of linear problem and very simple nonlinear problems. The key step is the generalized linearization of nonlinear…

Computational Engineering, Finance, and Science · Computer Science 2024-09-21 W. Chen

The basic assumptions and the general results of our bag model for nuclei are presented in detail. Nuclei are considered in a unified integration of the mean field theory and the MIT bag model

General Physics · Physics 2007-05-23 Nguyen Tuan Anh

The character theory of finite groups has numerous basic questions that are often already quite involved: enumerating of irreducible characters, their character formulas, point-wise product decompositions, and restriction/induction between…

Representation Theory · Mathematics 2018-10-03 Farid Aliniaeifard , Nathaniel Thiem

We know anything because we learn about it, there is anything we ever share about it, but now a lot of media that can represent how it happened as infrastructure of the knowledge sharing. This paper aims to introduce a model for…

Social and Information Networks · Computer Science 2013-01-08 Sufianto Mahfudz , Mahyuddin K. M. Nasution , Sawaluddin Nasution

In the present paper, we try to propose a self-similar network theory for the basic understanding. By extending the natural languages to a kind of so called idealy sufficient language, we can proceed a few steps to the investigation of the…

Artificial Intelligence · Computer Science 2018-02-02 Tong Chern

Meta-analysis based on only a few studies remains a challenging problem, as an accurate estimate of the between-study variance is apparently needed, but hard to attain, within this setting. Here we offer a new approach, based on the…

Methodology · Statistics 2024-04-30 Joyce Cahoon , Ryan Martin

I present a short review of models for transverse-momentum distributions and transversity, with a particular attention on general features common to many models. I compare some model results with experimental extractions. I discuss the…

High Energy Physics - Phenomenology · Physics 2022-03-02 Alessandro Bacchetta

This note documents the specification of normal forms in cubical type theory. The definition is already present in the proof of normalization for cubical type theory, but we present it in a more traditional style explicitly for reference.

Logic in Computer Science · Computer Science 2026-05-19 Xu Huang

This is a survey paper of Shelah's pcf theory. In this part the theory is developed up to the pcf theorem.

Logic · Mathematics 2016-09-06 Menachem Kojman

The aim of this review is to present an overview over available models and approaches to non-commutative gauge theory. Our main focus thereby is on gauge models formulated on flat Groenewold-Moyal spaces and renormalizability, but we will…

High Energy Physics - Theory · Physics 2015-03-14 Daniel N. Blaschke , Erwin Kronberger , Rene I. P. Sedmik , Michael Wohlgenannt

This paper describes a universal model for paraphrasing that transforms according to defined criteria. We showed that by using different criteria we could construct different kinds of paraphrasing systems including one for answering…

Computation and Language · Computer Science 2007-05-23 Masaki Murata , Hitoshi Isahara