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Subgraph matching is a core task in graph analytics, widely used in domains such as biology, finance, and social networks. Existing top-k diversified methods typically focus on maximizing vertex coverage, but often return results in the…

Databases · Computer Science 2025-11-25 Liuyi Chen , Yuchen Hu , Zhengyi Yang , Xu Zhou , Wenjie Zhang , Kenli Li

How might one test the hypothesis that networks were sampled from the same distribution? Here, we compare two statistical tests that use subgraph counts to address this question. The first uses the empirical subgraph densities themselves as…

The recently developed "Data Set Diagonalization" method (DSD) is applied to measure compatibility of the data sets that are used to determine parton distribution functions (PDFs). Discrepancies among the experiments are found to be…

High Energy Physics - Phenomenology · Physics 2010-04-22 Jon Pumplin

Recent detection of high-redshift, massive clusters through Sunyaev-Zel'dovich observations has opened up a new way to test cosmological models. It is known that detection of a single supermassive cluster at a very high redshift can rule…

Cosmology and Nongalactic Astrophysics · Physics 2011-09-22 Arman Shafieloo , George F. Smoot

In this monograph, we prove an asymptotic approximation for integrals of probability densities over sets in finite dimensional euclidean space, which are far away from the origin (asymptotic sets). We use this approximation to investigate…

Probability · Mathematics 2009-09-29 Philippe Barbe

Downsampling images and labels, often necessitated by limited resources or to expedite network training, leads to the loss of small objects and thin boundaries. This undermines the segmentation network's capacity to interpret images…

Image and Video Processing · Electrical Eng. & Systems 2024-10-27 Shahzad Ali , Yu Rim Lee , Soo Young Park , Won Young Tak , Soon Ki Jung

The use of summary statistics beyond the two-point correlation function to analyze the non-Gaussian clustering on small scales is an active field of research in cosmology. In this paper, we explore a set of new summary statistics -- the…

Cosmology and Nongalactic Astrophysics · Physics 2021-02-03 Arka Banerjee , Tom Abel

We construct 2-query, quasi-linear size probabilistically checkable proofs (PCPs) with arbitrarily small constant soundness, improving upon Dinur's 2-query quasi-linear size PCPs with soundness $1-\Omega(1)$. As an immediate corollary, we…

Computational Complexity · Computer Science 2024-11-08 Mitali Bafna , Dor Minzer , Nikhil Vyas

We describe a new parameterized family of symmetric error-correcting codes with low-density parity-check matrices (LDPC). Our codes can be described in two seemingly different ways. First, in relation to Reed-Muller codes: our codes are…

Information Theory · Computer Science 2023-08-31 Irit Dinur , Siqi Liu , Rachel Yun Zhang

We introduce generalized spatially coupled parallel concatenated codes (GSC-PCCs), a class of spatially coupled turbo-like codes obtained by coupling parallel concatenated codes (PCCs) with a fraction of information bits repeated before the…

Information Theory · Computer Science 2021-05-04 Min Qiu , Xiaowei Wu , Jinhong Yuan , Alexandre Graell i Amat

We present a geometric version of Quickest Change Detection (QCD) and Quickest Hub Discovery (QHD) tests in correlation structures that allows us to include and combine new information with distance metrics. The topic falls within the scope…

Statistical Finance · Quantitative Finance 2023-09-07 Alejandro Rodriguez Dominguez , David Stynes

This paper introduces several depths for random sets with possibly non-convex realisations, proposes ways to estimate the depths based on the samples and compares them with existing ones. The depths are further applied for the comparison…

Methodology · Statistics 2024-02-06 Vesna Gotovac Đogaš

Multivariate interaction between two or more classes (or species) has important consequences in many fields and causes multivariate clustering patterns such as segregation or association. The spatial segregation occurs when members of a…

Methodology · Statistics 2008-05-13 Elvan Ceyhan

When scholars suspect units are dependent on each other within clusters but independent of each other across clusters, they employ cluster-robust standard errors (CRSEs). Nevertheless, what to cluster over is sometimes unknown. For…

Methodology · Statistics 2025-11-12 Kentaro Fukumoto

The dependence of galaxy clustering on local density provides an effective method for extracting non-Gaussian information from galaxy surveys. The two-point correlation function (2PCF) provides a complete statistical description of a…

In recent years, there has been increasing attention to the spatial dimensions of residential segregation, such as the spatial arrangement of segregated neighborhoods and the geographic scale or relative size of segregated areas. However,…

Physics and Society · Physics 2017-08-28 Elizabeth Roberto

Inference based on the penalized density ratio model is proposed and studied. The model under consideration is specified by assuming that the log--likelihood function of two unknown densities is of some parametric form. The model has been…

Statistics Theory · Mathematics 2008-07-17 Konstantinos Fokianos

Linear nested codes, where two or more sub-codes are nested in a global code, have been proposed as candidates for reliable multi-terminal communication. In this paper, we consider nested array-based spatially coupled low-density…

Information Theory · Computer Science 2021-02-23 Salman Habib , David G. M. Mitchell , Joerg Kliewer

Redshift-space clustering distortions provide one of the most powerful probes to test the gravity theory on the largest cosmological scales. We perform a systematic validation study of the state-of-the-art statistical methods currently used…

Cosmology and Nongalactic Astrophysics · Physics 2020-03-27 Jorge Enrique García-Farieta , Federico Marulli , Lauro Moscardini , Alfonso Veropalumbo , Rigoberto A. Casas-Miranda

Many real-life data sets can be analyzed using Linear Mixed Models (LMMs). Since these are ordinarily based on normality assumptions, under small deviations from the model the inference can be highly unstable when the associated parameters…

Methodology · Statistics 2024-02-06 Giovanni Saraceno , Abhik Ghosh , Ayanendranath Basu , Claudio Agostinelli