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Bayesian networks are a class of popular graphical models that encode causal and conditional independence relations among variables by directed acyclic graphs (DAGs). We propose a novel structure learning method, annealing on regularized…

Machine Learning · Statistics 2020-05-04 Qiaoling Ye , Arash A. Amini , Qing Zhou

Finding connected components in a graph is a fundamental problem in graph analysis. In this work, we present a novel minimum-mapping based Contour algorithm to efficiently solve the connectivity problem. We prove that the Contour algorithm…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-11-08 Zhihui Du , Oliver Alvarado Rodriguez , Fuhuan Li , Mohammad Dindoost , David A. Bader

For a set system $(V,{\mathcal C}\subseteq 2^V)$, we call a subset $C\in{\mathcal C}$ a component. A nonempty subset $Y\subseteq C$ is a minimal removable set (MRS) of $C$ if $C\setminus Y\in{\mathcal C}$ and no proper nonempty subset…

Data Structures and Algorithms · Computer Science 2023-02-14 Takumi Tada , Kazuya Haraguchi

Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between variables in multivariate time series data, thus ignoring…

Machine Learning · Computer Science 2025-08-11 Falih Gozi Febrinanto , Kristen Moore , Chandra Thapa , Mujie Liu , Vidya Saikrishna , Jiangang Ma , Feng Xia

Lagrangian descriptors (LDs) based on the arc length of orbits previously demonstrated their utility in delineating structures governing the dynamics. Recently, a chaos indicator based on the second derivatives of the LDs, referred to as…

Chaotic Dynamics · Physics 2025-02-05 Alexandru Căliman , Jérôme Daquin , Anne-Sophie Libert

In complex multivariate systems, interactions among variables are defined by dependency structures, often encoded as directed acyclic graphs ($\text{DAGs}$). However, dependency structures can vary across subjects, and ignoring this…

Machine Learning · Statistics 2026-05-20 Honglin Du , Muxuan Liang , Xiang Zhong

Making accurate inferences about data is a key task in science and mathematics. Here we study the problem of \emph{retrodiction}, inferring past values of a series, in the context of chaotic dynamical systems. Specifically, we are…

Dynamical Systems · Mathematics 2025-11-06 Kamal Dingle , Boumediene Hamzi , Marcus Hutter , Houman Owhadi

Various Monte Carlo programs, developed either by small groups or widely available, have been used to calculate the effects of decays of radioactive chains, from the original parent nucleus to the final stable isotopes. These chains include…

Nuclear Experiment · Physics 2015-05-27 Kareem Kazkaz , Nick Walsh

We introduce an algorithm to locate contours of functions that are expensive to evaluate. The problem of locating contours arises in many applications, including classification, constrained optimization, and performance analysis of…

Machine Learning · Statistics 2018-12-20 Alexandre N. Marques , Remi R. Lam , Karen E. Willcox

A set $X$ of vertices of an acyclic digraph $D$ is convex if $X\neq \emptyset$ and there is no directed path between vertices of $X$ which contains a vertex not in $X$. A set $X$ is connected if $X\neq \emptyset$ and the underlying…

Discrete Mathematics · Computer Science 2007-12-18 P. Balister , S. Gerke , G. Gutin , A. Johnstone , J. Reddington , E. Scott , A. Soleimanfallah , A. Yeo

Graph based clustering is one of the major clustering methods. Most of it work in three separate steps: similarity graph construction, clustering label relaxing and label discretization with k-means. Such common practice has three…

Machine Learning · Computer Science 2019-04-26 Yudong Han , Lei Zhu , Zhiyong Cheng , Jingjing Li , Xiaobai Liu

We conduct a topological-numerical analysis of global dynamics in a discrete-time two-gene Andrecut-Kauffman model. This model describes gene expression regulation through nonlinear interactions. We use rigorous numerical methods to…

There has been a growing interest in causal learning in recent years. Commonly used representations of causal structures, including Bayesian networks and structural equation models (SEM), take the form of directed acyclic graphs (DAGs). We…

Machine Learning · Computer Science 2025-11-20 Pavel Rytir , Ales Wodecki , Jakub Marecek

We examine the complexity of the online Dictionary Matching with One Gap Problem (DMOG) which is the following. Preprocess a dictionary $D$ of $d$ patterns, where each pattern contains a special gap symbol that can match any string, so that…

Data Structures and Algorithms · Computer Science 2015-07-13 Amihood Amir , Tsvi Kopelowitz , Avivit Levy , Seth Pettie , Ely Porat , B. Riva Shalom

The modern data compression is mainly based on two approaches to entropy coding: Huffman (HC) and arithmetic/range coding (AC). The former is much faster, but approximates probabilities with powers of 2, usually leading to relatively low…

Information Theory · Computer Science 2014-01-07 Jarek Duda

Living systems are subject to the arrow of time; from birth, they undergo complex transformations (self-organization) in a constant battle for survival, but inevitably ageing and disease trap them to death. Can ageing be understood and…

Other Quantitative Biology · Quantitative Biology 2020-08-21 Tamàs Fülöp , Mathieu Desroches , Fernando Antônio Nóbrega Santos , Serafim Rodrigues

Computation fundamentally separates time from space: nondeterministic search is exponential in time but polynomially simulable in space (Savitch's Theorem). We propose that the brain physically instantiates a biological variant of this…

Neurons and Cognition · Quantitative Biology 2025-12-02 Xin Li

Many real-world phenomena exhibit strong hierarchical structure. Consequently, in many real-world directed social networks vertices do not play equal role. Instead, vertices form a hierarchy such that the edges appear mainly from upper…

Data Structures and Algorithms · Computer Science 2019-02-06 Nikolaj Tatti

The guessing number of a directed graph (digraph), equivalent to the entropy of that digraph, was introduced as a direct criterion on the solvability of a network coding instance. This paper makes two contributions on the guessing number.…

Information Theory · Computer Science 2015-03-17 Maximilien Gadouleau , Soren Riis

Compiling graphical models has recently been under intense investigation, especially for probabilistic modeling and processing. We present here a novel data structure for compiling weighted graphical models (in particular, probabilistic…

Artificial Intelligence · Computer Science 2012-06-26 Robert Mateescu , Rina Dechter