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Kernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of…

Machine Learning · Statistics 2019-04-18 Yu Nishiyama , Motonobu Kanagawa , Arthur Gretton , Kenji Fukumizu

Midline shift (MLS) is a well-established factor used for outcome prediction in traumatic brain injury, stroke and brain tumors. The importance of automatic estimation of MLS was recently highlighted by ACR Data Science Institute. In this…

Compositional data, which is data consisting of fractions or probabilities, is common in many fields including ecology, economics, physical science and political science. If these data would otherwise be normally distributed, their spread…

Methodology · Statistics 2022-07-26 Matthew P. Adams

The main result of the paper gives criteria for extendibility of sesquilinear form-valued mappings defined on symmetric subsets of *-semigroups to positive definite ones. By specifying this we obtain new solutions of: * the truncated…

Functional Analysis · Mathematics 2009-07-01 D. Cichoń , J. Stochel , F. H. Szafraniec

Recent developments in understanding the influence of the nucleus on deep-inelastic structure functions, the EMC effect, are reviewed. A new data base which expresses ratios of structure functions in terms of the Bjorken variable…

Nuclear Theory · Physics 2013-06-20 O. Hen , D. W. Higinbotham , G. A. Miller , E. Piasetzky , L. B. Weinstein

We investigate the impact of momentum-dependent relaxation time approximation in the Boltzmann equation within the Bjorken flow framework by analyzing the moments of the single-particle distribution function. The moment equations, which…

Nuclear Theory · Physics 2025-10-23 Reghukrishnan Gangadharan , Sukanya Mitra , Victor Roy

Markov State Models (MSM) are widely used to elucidate dynamic properties of molecular systems from unbiased Molecular Dynamics (MD). However, the implementation of reweighting schemes for MSMs to analyze biased simulations, for example…

Chemical Physics · Physics 2020-11-26 Stefanie Kieninger , Luca Donati , Bettina G. Keller

Scale-independent energy-momentum squared gravity (EMSG) allows different gravitational couplings for different types of sources and has been proven to have interesting implications in cosmology. In this paper, the Big Bang Nucleosynthesis…

Cosmology and Nongalactic Astrophysics · Physics 2025-02-26 Dukjae Jang , Mayukh R. Gangopadhyay , Myung-Ki Cheoun , Toshitaka Kajino , M. Sami

Extracting automatically the complex set of features composing real high-dimensional data is crucial for achieving high performance in machine--learning tasks. Restricted Boltzmann Machines (RBM) are empirically known to be efficient for…

Data Analysis, Statistics and Probability · Physics 2017-04-05 Jérôme Tubiana , Rémi Monasson

We discuss the possible target mass corrections in the QCD analysis of nucleon's spin-dependent structure functions measured in the polarized deep-inelastic leptoproduction. The target mass correction for the QCD Bjorken sum rule is…

High Energy Physics - Phenomenology · Physics 2016-09-01 Hiroyuki KAWAMURA , Tsuneo UEMATSU

The weighted ensemble (WE) simulation strategy provides unbiased sampling of non-equilibrium processes, such as molecular folding or binding, but the extraction of rate constants relies on characterizing steady state behavior.…

Statistical Mechanics · Physics 2020-10-02 Jeremy Copperman , Daniel Zuckerman

Modern compression methods can summarize a target distribution $\mathbb{P}$ more succinctly than i.i.d. sampling but require access to a low-bias input sequence like a Markov chain converging quickly to $\mathbb{P}$. We introduce a new…

Machine Learning · Statistics 2024-08-02 Lingxiao Li , Raaz Dwivedi , Lester Mackey

Robins 1997 introduced marginal structural models (MSMs), a general class of counterfactual models for the joint effects of time-varying treatment regimes in complex longitudinal studies subject to time-varying confounding. In his work,…

Methodology · Statistics 2020-07-27 Haben Michael , Yifan Cui , Scott Lorch , Eric Tchetgen Tchetgen

A highly anticipated use of quantum computers is the simulation of complex quantum systems including molecules and other many-body systems. One promising method involves directly applying a linear combination of unitaries (LCU) to…

Quantum Physics · Physics 2022-02-02 Richard Meister , Simon C. Benjamin , Earl T. Campbell

For many complex systems the interaction of different scales is among the most interesting and challenging features. It seems not very successful to extract the physical properties in different scale regimes by the existing approaches, such…

Fluid Dynamics · Physics 2015-05-14 L. P. Wang , Y. X. Huang

When data are collected subject to a detection limit, observations below the detection limit may be considered censored. In addition, the domain of such observations may be restricted; for example, values may be required to be non-negative.…

Applications · Statistics 2020-06-30 Justin R. Williams , Hyung-Woo Kim , Catherine M. Crespi

Microscopic models, which embody the simplicity and significance of a dynamical symmetry approach to nuclear structure, are reviewed. They can reveal striking features of atomic nuclei when a symmetry dominates and solutions in domains that…

Nuclear Theory · Physics 2017-08-23 J. P. Draayer , K. D. Sviratcheva , T. Dytrych , C. Bahri , K. Drumev , J. P. Vary

A method used previously to calculate the masses of the vector mesons is extended to the calculation of the nucleon resonances. The method is based on the choice of integration kernels which eliminate the unknown parts of the spectrum. We…

High Energy Physics - Phenomenology · Physics 2023-10-03 Nasrallah F. Nasrallah , Karl Schilcher

Tethered particle motion experiments are versatile single-molecule techniques enabling one to address in vitro the molecular properties of DNA and its interactions with various partners involved in genetic regulations. These techniques…

Biological Physics · Physics 2019-09-05 Manoel Manghi , Nicolas Destainville , Annaël Brunet

We use Big Bang Nucleosynthesis (BBN) data in order to impose constraints on higher-order modified gravity, and in particular on: (i) $f(G)$ Gauss-Bonnet gravity, and $f(P)$ cubic gravities, arising respectively through the use of the…

General Relativity and Quantum Cosmology · Physics 2022-04-11 Petros Asimakis , Spyros Basilakos , Nick E. Mavromatos , Emmanuel N. Saridakis