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Related papers: Backbone decomposition of multitype superprocesses

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Model compression techniques reduce the computational load and memory consumption of deep neural networks. After the compression operation, e.g. parameter pruning, the model is normally fine-tuned on the original training dataset to recover…

Computer Vision and Pattern Recognition · Computer Science 2023-06-23 Adrian Holzbock , Achyut Hegde , Klaus Dietmayer , Vasileios Belagiannis

This paper investigates the problem of decomposition with respect to outputs for Boolean control networks (BCNs). First, with the linear expression of BCNs and the matrix semi-tensor product, some algebraic equivalent conditions for the…

Optimization and Control · Mathematics 2014-07-09 Yunlei Zou , Jiandong Zhu

Tensor decompositions are promising tools for big data analytics as they bring multiple modes and aspects of data to a unified framework, which allows us to discover complex internal structures and correlations of data. Unfortunately most…

Numerical Analysis · Computer Science 2014-12-30 Guoxu Zhou , Andrzej Cichocki , Shengli Xie

We discuss relaxation in bosonic and fermionic many-particle systems. For integrable systems, the time evolution can cause a dephasing effect, leading for finite subsystems to certain steady states. We give an explicit derivation of those…

Statistical Mechanics · Physics 2008-04-09 Thomas Barthel , Ulrich Schollwöck

Lossy compression introduces complex compression artifacts, particularly blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restore sharpened…

Computer Vision and Pattern Recognition · Computer Science 2016-08-10 Ke Yu , Chao Dong , Chen Change Loy , Xiaoou Tang

A multitype Dawson-Watanabe process is conditioned, in subcritical and critical cases, on non-extinction in the remote future. On every finite time interval, its distribution is absolutely continuous with respect to the law of the…

Probability · Mathematics 2011-12-05 Nicolas Champagnat , Sylvie Roelly

This study presents a theoretical model for a self-replicating mechanical system inspired by biological processes within living cells and supported by computer simulations. The model decomposes self-replication into core components, each of…

Other Quantitative Biology · Quantitative Biology 2025-02-18 Ralph P. Lano

Logic-based Benders decomposition (LBBD) is a substantial generalization of classical Benders decomposition that, in principle, allows the subproblem to be any optimization problem rather than specifically a linear or nonlinear programming…

Optimization and Control · Mathematics 2019-10-29 J. N. Hooker

A continuous-state polynomial branching process is constructed as the pathwise unique solution of a stochastic integral equation with absorbing boundary condition. The extinction and explosion probabilities and the mean extinction and…

Probability · Mathematics 2018-10-09 Pei-Sen Li

In this paper we suggest that, under suitable conditions, supervised learning can provide the basis to formulate at the microscopic level quantitative questions on the phenotype structure of multicellular organisms. The problem of…

Molecular Networks · Quantitative Biology 2012-06-14 D. Napoletani , E. Petricoin , D. C. Struppa

We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling of spatiotemporal systems with transformers. In MPP, rather than training one model on a specific physical…

A large number of complex systems find a natural abstraction in the form of weighted networks whose nodes represent the elements of the system and the weighted edges identify the presence of an interaction and its relative strength. In…

Physics and Society · Physics 2009-04-23 M. Angeles Serrano , Marian Boguna , Alessandro Vespignani

An intense activity is nowadays devoted to the definition of models capturing the properties of complex networks. Among the most promising approaches, it has been proposed to model these graphs via their clique incidence bipartite graphs.…

Discrete Mathematics · Computer Science 2021-03-09 Matthieu Latapy , Thi Ha Duong Phan , Christophe Crespelle , Thanh Qui Nguyen

The superposition of two independent point processes can be described by multiplication of their probability generating functionals (p.g.fl.s). The inverse operation, which can be viewed as a deconvolution, is defined by dividing the…

Probability · Mathematics 2012-02-07 Daniel Edward Clark

Conditioning a multitype Galton-Watson process to stay alive into the indefinite future leads to what is known as its associated $Q$-process. We show that the same holds true if the process is conditioned to reach a positive threshold or a…

Probability · Mathematics 2016-03-09 Sophie Pénisson

This paper considers the relative frequencies of distinct types of individuals in multitype branching processes. We prove that the frequencies are asymptotically multivariate normal when the initial number of ancestors is large and the time…

Probability · Mathematics 2009-03-02 Andrei Y. Yakovlev , Nikolay M. Yanev

Skeletons of branching processes are defined as trees of lineages characterized by an appropriate signature of future reproduction success. In the supercritical case a natural choice is to look for the lineages that survive forever. In the…

Probability · Mathematics 2013-04-02 Serik Sagitov , Maria C. Serra

Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate or assist in the retrosynthesis analysis,…

Quantitative Methods · Quantitative Biology 2020-11-06 Chaochao Yan , Qianggang Ding , Peilin Zhao , Shuangjia Zheng , Jinyu Yang , Yang Yu , Junzhou Huang

This paper introduces the Hawkes skeleton and the Hawkes graph. These objects summarize the branching structure of a multivariate Hawkes point process in a compact, yet meaningful way. We demonstrate how graph-theoretic vocabulary…

Methodology · Statistics 2017-06-14 Paul Embrechts , Matthias Kirchner

Multivariate functions emerge naturally in a wide variety of data-driven models. Popular choices are expressions in the form of basis expansions or neural networks. While highly effective, the resulting functions tend to be hard to…

Machine Learning · Statistics 2022-06-15 Jan Decuyper , Koen Tiels , Siep Weiland , Mark C. Runacres , Johan Schoukens
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