July 2023 arXiv papers — page 8

Yuchen Guo, Shuo Yang

Understanding quantum systems is of significant importance for assessing the performance of quantum hardware and software, as well as exploring quantum control and quantum sensing. An efficient representation of quantum states enables realizing quantum state tomography with minimal measurements. In this study, we propose an alternative approach to state tomo

Shaoshuai Chu, Alexander Kurganov, Ruixiao Xin

We introduce second-order low-dissipation (LD) path-conservative central-upwind (PCCU) schemes for the one- (1-D) and two-dimensional (2-D) multifluid systems, whose components are assumed to be immiscible and separated by material interfaces. The proposed LD PCCU schemes are derived within the flux globalization based PCCU framework and they employ the LD c

Ang Li, Yubo Wang, Lei Fan, Jiming Peng

In this paper, we study the strategic investment problem of battery energy storage systems (BESSs) in the wholesale electricity market from the perspective of BESSs owners. Large-scale BESSs planning without considering the possible wholesale market price change may result in possible locational marginal price (LMP) changes. To overcome such limits, we propo

Dashuang Ye, Cuihua Du, Jianrong Shi, Jun Ma

Based on the \textit{Gaia} DR3 RR Lyrae catalog, we use two methods to fit the density profiles with an improved broken power law, and find that there are two break radii coinciding with the two apocenter pile-ups of high-eccentricity Gaia-Sausage-Enceladus (GSE) merger. Also, there is a break caused by the Sagittarius (Sgr) stream. Combining the positions o

Jiahao Li, Zongxin Yang, Xiaohan Wang, Jianxin Ma

In this study, we focus on the problem of 3D human mesh recovery from a single image under obscured conditions. Most state-of-the-art methods aim to improve 2D alignment technologies, such as spatial averaging and 2D joint sampling. However, they tend to neglect the crucial aspect of 3D alignment by improving 3D representations. Furthermore, recent methods s

Jin Chen, Zheng Liu, Xu Huang, Chenwang Wu

The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model parameters, the capability of large language models has been dramatically improved, leading to human-like performances in understanding, language synthesizing, and common-sense reasoning, etc. Such a major leap

Hao Lin, Ke Wu, Jie Li, Jun Li

Distributed learning is commonly used for training deep learning models, especially large models. In distributed learning, manual parallelism (MP) methods demand considerable human effort and have limited flexibility. Hence, automatic parallelism (AP) methods have recently been proposed for automating the parallel strategy optimization process. Existing AP m

Carsten Uhlig, Steffen Uhlig

In this paper, we propose a test procedure based on the LASSO methodology to test the global null hypothesis of no dependence between a response variable and $p$ predictors, where $n$ observations with $n < p$ are available. The proposed procedure is similar to the F-test for a linear model, which evaluates significance based on the ratio of explained to une

J. H. Peterson, M. Prado Rodriguez, K. Hanson

IceCube DeepCore is an extension of the IceCube Neutrino Observatory designed to measure GeV scale atmospheric neutrino interactions for the purpose of neutrino oscillation studies. Distinguishing muon neutrinos from other flavors and reconstructing inelasticity are especially difficult tasks at GeV scale energies in IceCube DeepCore due to sparse instrument

SeungHeon Doh, Keunwoo Choi, Jongpil Lee, Juhan Nam

Automatic music captioning, which generates natural language descriptions for given music tracks, holds significant potential for enhancing the understanding and organization of large volumes of musical data. Despite its importance, researchers face challenges due to the costly and time-consuming collection process of existing music-language datasets, which

Junchen Zhu, Huan Yang, Wenjing Wang, Huiguo He

Videos for mobile devices become the most popular access to share and acquire information recently. For the convenience of users' creation, in this paper, we present a system, namely MobileVidFactory, to automatically generate vertical mobile videos where users only need to give simple texts mainly. Our system consists of two parts: basic and customized gene

Jungjun Choi, Hyukjun Kwon, Yuan Liao

This paper studies the inferential theory for estimating low-rank matrices. It also provides an inference method for the average treatment effect as an application. We show that the least square estimation of eigenvectors following the nuclear norm penalization attains the asymptotic normality. The key contribution of our method is that it does not require s

Soyeon Caren Han, Yihao Ding, Siwen Luo, Josiah Poon

Document understanding and information extraction include different tasks to understand a document and extract valuable information automatically. Recently, there has been a rising demand for developing document understanding among different domains, including business, law, and medicine, to boost the efficiency of work that is associated with a large number

Qi Zhao, Shijie Wang, Ce Zhang, Changcheng Fu

Can we better anticipate an actor's future actions (e.g. mix eggs) by knowing what commonly happens after his/her current action (e.g. crack eggs)? What if we also know the longer-term goal of the actor (e.g. making egg fried rice)? The long-term action anticipation (LTA) task aims to predict an actor's future behavior from video observations in the form of

Russell A. Brown

NMR pulse sequences that are modifications of the HSQC experiment are proposed to observe ${}^{13}\textrm{C}$-coupled relaxation in AX, AX$_2$, and AX$_3$ spin systems. ${}^{13}\textrm{CH}$ and ${}^{13}{\textrm{CH}}_2$ moieties are discussed as exemplary AX and AX$_2$ spin systems. The pulse sequences may be used to produce 1D or 2D proton NMR spectra.

Omid Mozafar, Colin Denniston

In recent decades, computer experiments have led to an accurate and fundamental understanding of atomic and molecular mechanisms in fluids, such as different kinds of relaxation processes toward steady physical states. In this paper, we investigate how exactly the configuration of initial states in a molecular-dynamics simulation can affect the rates of deca

Yanteng Zhanga, Xiaohai He, Yi Hao Chan, Qizhi Teng

In recent years, deep learning models have been applied to neuroimaging data for early diagnosis of Alzheimer's disease (AD). Structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) images provide structural and functional information about the brain, respectively. Combining these features leads to improved performance than using

Jian-hao Kang, Zhun Gou, Nan-jing Huang

This paper considers consumption and portfolio optimization problems with recursive preferences in both infinite and finite time regions. Specially, the financial market consists of a risk-free asset and a risky asset that follows a general stochastic volatility process. By using Bellman's dynamic programming principle, the Hamilton-Jacobi-Bellman (HJB) equa

Paul Denny, Juho Leinonen, James Prather, Andrew Luxton-Reilly

With their remarkable ability to generate code, large language models (LLMs) are a transformative technology for computing education practice. They have created an urgent need for educators to rethink pedagogical approaches and teaching strategies for newly emerging skill sets. Traditional approaches to learning programming have focused on frequent and repea

Jing-Xiao Liao, Sheng-Lai Wei, Chen-Long Xie, Tieyong Zeng

Deep learning has achieved remarkable success in the field of bearing fault diagnosis. However, this success comes with larger models and more complex computations, which cannot be transferred into industrial fields requiring models to be of high speed, strong portability, and low power consumption. In this paper, we propose a lightweight and deployable mode

Rebecca Lin, Marten H. van Kerkwijk

We analyzed four epochs of beamformed EVN data of the Crab Pulsar at 1658.49 MHz. With the high sensitivity resulting from resolving out the Crab Nebula, we are able to detect even the faint high-frequency components in the folded profile. We also detect a total of 65951 giant pulses, which we use to investigate the rates, fluence, phase, and arrival time di

Qiufan Ji, Lin Wang, Cong Shi, Shengshan Hu

Deep Neural Networks (DNNs) for 3D point cloud recognition are vulnerable to adversarial examples, threatening their practical deployment. Despite the many research endeavors have been made to tackle this issue in recent years, the diversity of adversarial examples on 3D point clouds makes them more challenging to defend against than those on 2D images. For

Subhankar Ghosh, Yuanjie Shi, Taha Belkhouja, Yan Yan

Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, CP produces a prediction set of candidate labels with a user-specified coverage (i.e., true class label is contained with high probability). Almost all the existing work on CP assum

Yutong Bu, Andrew Melatos

An individual's opinions about media bias derive from their own independent assessment of media outputs combined with peer pressure from networked political allies and opponents. Here we generalize previous idealized, probabilistic models of the perception formation process, based on a network of Bayesian learners inferring the bias of a coin, by introducing

Dai Hai Nguyen, Tetsuya Sakurai

We consider a general optimization problem of minimizing a composite objective functional defined over a class of probability distributions. The objective is composed of two functionals: one is assumed to possess the variational representation and the other is expressed in terms of the expectation operator of a possibly nonsmooth convex regularizer function.

Katherine Cure, Diego R Barneche, Martial Depczynski, Rebecca Fisher

First Nations Australians have a cultural obligation to look after land and sea Country, and Indigenous-partnered science is beginning to drive socially inclusive initiatives in conservation. The Australian Institute of Marine Science has partnered with Indigenous communities in systematically collecting monitoring data to understand the natural variability

Cheng Zhang, Chang Liu, Yindi Jing, Minjie Ding

Interleaved training has been studied for single-user and multi-user massive MIMO downlink with either fully-digital or hybrid beamforming. However, the impact of channel correlation on its average training overhead is rarely addressed. In this paper, we explore the channel correlation to improve the interleaved training for single-user massive MIMO downlink

Xian Zhang, Wenfei Yu, Casey Law, Di Li

Among more than 800 known fast radio bursts (FRBs), only two, namely FRB 20121102A and FRB 20190520B, are confirmed to be associated with a persistent radio sources (PRS). Here we report evidence of apparent temporal variability in the PRS associated with the bursting FRB 20190520B based on the Karl G. Jansky Very Large Array (VLA) observations taken in 2020

Karlijn Kruiswijk, Bennett Brinson, Rachel Procter-Murphy, Jessie Thwaites

GRB 221009A is the brightest Gamma Ray Burst (GRB) ever observed. The observed extremely high flux of high and very-high-energy photons provide a unique opportunity to probe the predicted neutrino counterpart to the electromagnetic emission. We have used a variety of methods to search for neutrinos in coincidence with the GRB over several time windows during

Chan Park, Eric Tchetgen Tchetgen

Synthetic control methods are widely used to estimate the treatment effect on a single treated unit in time-series settings. A common approach to estimate synthetic control weights is to regress the treated unit's pre-treatment outcome and covariates' time series measurements on those of untreated units via ordinary least squares. However, this approach can

Ananthan Nambiar, Chao Pan, Vishal Rana, Mahdi Cheraghchi

Pathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide. Efficient and timely testing plays a critical role in disease control and transmission prevention. Group testing is a well-established method for reducing the number of tests needed t

Navid Hasanzadeh, Shahrokh Valaee, Hojjat Salehinejad

Hypertension is commonly referred to as the "silent killer", since it can lead to severe health complications without any visible symptoms. Early detection of hypertension is crucial in preventing significant health issues. Although some studies suggest a relationship between blood pressure and certain vital signals, such as Photoplethysmogram (PPG), reliabl

Hoang Tien Nguyen, Dae-Hyun Choi

Operational constraint violations may occur when deep reinforcement learning (DRL) agents interact with real-world active distribution systems to learn their optimal policies during training. This letter presents a universal distributionally robust safety filter (DRSF) using which any DRL agent can reduce the constraint violations of distribution systems sig

Ruoxuan Zhen

Orientation recognition and standardization play a crucial role in the effectiveness of medical image processing tasks. Deep learning-based methods have proven highly advantageous in orientation recognition and prediction tasks. In this paper, we address the challenge of imaging orientation in cardiac MRI and present a method that employs deep neural network

N. Guerra-Varas, M. Merello, L. Bronfman, N. Duronea

(Abridged) High-mass star formation is far less understood than low-mass star formation. It entails molecular outflows, which disturb the protostellar clump. Studying these outflows and the shocked gas they cause is key for a better understanding of this process. This study aims to characterise the behaviour of molecular outflows in the most massive protoste

Francesco Grotto, Eliseo Luongo, Marco Romito

We consider a system of N point vortices in a bounded domain with null total circulation, whose statistics are given by the Canonical Gibbs Ensemble at inverse temperature $β\geq 0$. We prove that the space-time fluctuation field around the (constant) Mean Field limit satisfies when $N\to\infty$ a generalized version of 2-dimensional Euler dynamics preservin

Hamed Amini, Andreea Minca, Oscar Peralta

This paper presents a novel model for bivariate stochastic fluid processes that incorporate a ruin-dependent behavioral switch. Unlike typical models that assume a shared underlying process, our model allows each process to operate independently until a ruin event in one triggers a change in the other. We develop a mathematical framework for our model, explo

Kaki Ryan, Matthew Gregoire, Cynthia Sturton

We present SEIF, a methodology that combines static analysis with symbolic execution to verify and explicate information flow paths in a hardware design. SEIF begins with a statically built model of the information flow through a design and uses guided symbolic execution to recognize and eliminate non-flows with high precision or to find corresponding paths

Asmeret Naugle, Saeed Langarudi, Timothy Clancy

A clear definition of system dynamics modeling can provide shared understanding and clarify the impact of the field. We introduce a set of characteristics that define quantitative system dynamics, selected to capture core philosophy, describe theoretical and practical principles, and apply to historical work but be flexible enough to remain relevant as the f

Collin Bleak, Luke Elliott, James Hyde

Let $\mathfrak{C}$ be some Cantor space. We study groups of homeomorphisms of $\mathfrak{C}$ which are vigorous, or, which are flawless, where we introduce both of these terms here. We say a group $G\leq \operatorname{Homeo}(\mathfrak{C})$ is $vigorous$ if for any clopen set $A$ and proper clopen subsets $B$ and $C$ of $A$ there is $γ\in G$ in the pointwise-

Ronan Keane, H. Oliver Gao

Lane changing dynamics are an important part of traffic microsimulation and are vital for modeling weaving sections and merge bottlenecks. However, there is often much more emphasis placed on car following and gap acceptance models, whereas lane changing dynamics such as tactical, cooperation, and relaxation models receive comparatively little attention. Thi

Eoin Ó Colgáin, Saeed Pourojaghi, M. M. Sheikh-Jabbari, Darragh Sherwin

Hubble tension is a problem in one-dimensional (1D) posteriors, since local $H_0$ determinations are only sensitive to a single parameter. Projected 1D posteriors for $\Lambda$CDM cosmological parameters become more non-Gaussian with increasing effective redshift when the model is fitted to redshift-binned data in the late Universe. We explain mathematically

Devin White, Mingkang Wu, Ellen Novoseller, Vernon J. Lawhern

This paper develops a novel rating-based reinforcement learning approach that uses human ratings to obtain human guidance in reinforcement learning. Different from the existing preference-based and ranking-based reinforcement learning paradigms, based on human relative preferences over sample pairs, the proposed rating-based reinforcement learning approach i

Boxuan Tian, Wenzhe Yan, Zhibo Hou, Guo-Yong Xiang

Reducing the average resource consumption is the central quest in discriminating non-orthogonal quantum states for a fixed admissible error rate $\varepsilon$. The globally optimal fixed local projective measurement (GOFL) for this task is found to be different from that for previous minimum-error discrimination tasks [PRL 118, 030502 (2017)]. To achieve the

Bryden Cais, Douglas Ulmer

Let $Y\to X$ be an unramified Galois cover of curves over a perfect field $k$ of characteristic $p>0$ with $\mathrm{Gal}(Y/X)\cong\mathbb{Z}/p\mathbb{Z}$, and let $J_X$ and $J_Y$ be the Jacobians of $X$ and $Y$ respectively. We consider the $p$-torsion subgroup schemes $J_X[p]$ and $J_Y[p]$, analyze the Galois-module structure of $J_Y[p]$, and find restricti

Vlad Stirbu, Majid Haghparast, Muhammad Waseem, Niraj Dayama

The emergence of quantum computing has introduced a revolutionary paradigm capable of transforming numerous scientific and industrial sectors. Nevertheless, realizing the practical utilization of quantum software in real-world applications presents significant challenges. Factors such as variations in hardware implementations, the intricacy of quantum algori

Katja-Sophia Csizi, Markus Reiher

Structure and function in nanoscale atomistic assemblies are tightly coupled, and every atom with its specific position and even every electron will have a decisive effect on the electronic structure, and hence, on the molecular properties. Molecular simulations of nanoscopic atomistic structures therefore require accurately resolved three-dimensional input

Amit Anand, Jack Davis, Shohini Ghose

The correspondence principle plays an important role in understanding the emergence of classical chaos from an underlying quantum mechanics. Here we present an infinite family of quantum dynamics that never resembles the analogous classical chaotic dynamics irrespective of dimension. These take the form of stroboscopic unitary evolutions in the quantum kicke

Boyang Li, Bingyu Shen, Qing Lu, Taeho Jung

The continuous thriving of the Blockchain society motivates research in novel designs of schemes supporting cryptocurrencies. Previously multiple Proof-of-Deep-Learning(PoDL) consensuses have been proposed to replace hashing with useful work such as deep learning model training tasks. The energy will be more efficiently used while maintaining the ledger. How

M. I. Jimenez, R. Tojeiro

Li, Ma and Wang have provided in [\emph{Deformations of hypersurfaces preserving the M\"obius metric and a reduction theorem}, Adv. Math. 256 (2014), 156--205] a partial classification of the so-called Moebius deformable hypersurfaces, that is, the umbilic-free Euclidean hypersurfaces $f\colon M^n\to \mathbb{R}^{n+1}$ that admit non-trivial deformations pres

Marcin Muszyński, Igor Antoniazzi, Bruno Camargo

We demonstrate optical reflectivity and Raman responses of graphite microstructures as a function of light polarization when the incident light is applied perpendicular to the material's stacking direction (c-axis). For this, we employed novel graphite nanoribbons with edges polished through ion-beam etching. In this unique configuration, a strong polarizati

Mladen Pavicic

Quantum contextuality supports quantum computation and communication. One of its main vehicles is hypergraphs. The most elaborated are the Kochen-Specker ones, but there is also another class of contextual sets that are not of this kind. Their representation has been mostly operator-based and limited to special constructs in three- to six-dim spaces, a notab

Semere Kiros Bitew, Johannes Deleu, Chris Develder, Thomas Demeester

Large Language Models (LLMs) such as ChatGPT have demonstrated remarkable performance across various tasks and have garnered significant attention from both researchers and practitioners. However, in an educational context, we still observe a performance gap in generating distractors -- i.e., plausible yet incorrect answers -- with LLMs for multiple-choice q

Haoyang Yu, Yutong Sun, Yulin Liu, Luyao Zhang

Historically, gold and silver have played distinct roles in traditional monetary systems. While gold has primarily been revered as a superior store of value, prompting individuals to hoard it, silver has commonly been used as a medium of exchange. As the financial world evolves, the emergence of cryptocurrencies has introduced a new paradigm of value and exc

Germán Stefanich

We prove that if $R$ is a G-ring then every fully dualizable $R$-linear cocomplete category is equivalent to a twist by a $\mathbb{G}_m$-gerbe of the category of modules over a finite \'etale $R$-algebra. We also show that this holds more generally over an arbitrary commutative ring under an additional compact generation hypothesis. We include variants of th

Kai-Cheng Yang, Filippo Menczer

Large language models (LLMs) exhibit impressive capabilities in generating realistic text across diverse subjects. Concerns have been raised that they could be utilized to produce fake content with a deceptive intention, although evidence thus far remains anecdotal. This paper presents a case study about a Twitter botnet that appears to employ ChatGPT to gen

Jungin E. Kim, Yan Wang

The searching efficiency of the quantum approximate optimization algorithm is dependent on both the classical and quantum sides of the algorithm. Recently a quantum approximate Bayesian optimization algorithm (QABOA) that includes two mixers was developed, where surrogate-based Bayesian optimization is applied to improve the sampling efficiency of the classi

Marco Discacciati, Ben J. Evans, Matteo Giacomini

A non-intrusive proper generalized decomposition (PGD) strategy, coupled with an overlapping domain decomposition (DD) method, is proposed to efficiently construct surrogate models of parametric linear elliptic problems. A parametric multi-domain formulation is presented, with local subproblems featuring arbitrary Dirichlet interface conditions represented t

Eric Sun, Jinyu Li, Jian Xue, Yifan Gong

In end-to-end automatic speech recognition system, one of the difficulties for language expansion is the limited paired speech and text training data. In this paper, we propose a novel method to generate augmented samples with unpaired speech feature segments and text data for model pre-training, which has the advantage of low cost without using additional s

Ashish Hooda, Neal Mangaokar, Ryan Feng, Kassem Fawaz

Adversarial examples threaten the integrity of machine learning systems with alarming success rates even under constrained black-box conditions. Stateful defenses have emerged as an effective countermeasure, detecting potential attacks by maintaining a buffer of recent queries and detecting new queries that are too similar. However, these defenses fundamenta

Robert Lipshitz, Peter Ozsváth, Dylan Thurston

Bordered Floer homology is an invariant for 3-manifolds with boundary, defined by the authors in 2008. It extends the Heegaard Floer homology of closed 3-manifolds, defined in earlier work of Zolt\'an Szab\'o and the second author. In addition to its conceptual interest, bordered Floer homology also provides powerful computational tools. This survey outlines

Miho Kasai

In this paper we investigate solutions to a linear Hamilton-Jacobi equations in the Wasserstein space of probability vectors on a finite simply connected graph. We prove that there exists a solution under the assumption that the initial value function $u_0:\mathcal{P}(G)\to\mathbb{R}$ is Fr\'echet continuously differentiable.

Adish Singla

Generative AI and large language models have the potential to drastically improve the landscape of computing education by automatically generating personalized feedback and content. Recent works have studied the capabilities of these models for different programming education scenarios; however, these works considered only text-based programming, in particul

Luis M. A Bettencourt, Nicholas Marchio

Sustainable development is an imperative worldwide but metrics and data on poverty and quality of life have remained too coarse and abstract to characterize challenges adequately and guide practical progress. Nowhere is this challenge greater than in Africa where we still know relatively little about the systematic spatial details and scope of development. H

Stanley Gudder

Our basic concept is the set $\mathcal{E}(H)$ of effects on a finite dimensional complex Hilbert space $H$. If $a,b\in\mathcal{E}(H)$, we define the sequential product $a[\mathcal{I}]b$ of $a$ then $b$. The sequential product depends on the operation $\mathcal{I}$ used to measure $a$. We begin by studying the properties of this sequential product. It is obse

Philipp Bitzenbauer, Tom Teußner, Joaquin Veith, Christoph Kulgemeyer

This mixed-methods study examines how pre-service teachers select instructional videos on YouTube for physics teaching. It focuses on the role of surface features that YouTube provides (e.g., likes, views, thumbnails) and the comments underneath the videos in the decision-making process using videos on quantum physics topics as an example. The study consists

Amit Seal Ami, Kevin Moran, Denys Poshyvanyk, Adwait Nadkarni

The demand for automated security analysis techniques, such as static analysis based security testing (SAST) tools continues to increase. To develop SASTs that are effectively leveraged by developers for finding vulnerabilities, researchers and tool designers must understand how developers perceive, select, and use SASTs, what they expect from the tools, whe

Jazmin Vidal, Pablo Riera, Luciana Ferrer

In recent years, self-supervised learning (SSL) models have produced promising results in a variety of speech-processing tasks, especially in contexts of data scarcity. In this paper, we study the use of SSL models for the task of mispronunciation detection for second language learners. We compare two downstream approaches: 1) training the model for phone re

Marcos Bonich, Daniel Carando, Martín Mazzitelli

We study $\ell^r$-valued extensions of linear operators defined on Lebesgue spaces with variable exponent. Under some natural (and usual) conditions on the exponents, we characterize $1\leq r\leq \infty$ such that every bounded linear operator $T\colon L^{q(\cdot)}(\Omega_2, \mu)\to L^{p(\cdot)}(\Omega_1, \nu)$ has a bounded $\ell^r$-valued extension. We con

Ajay Bati, Spencer H. Bryngelson

The rise of neural network-based machine learning ushered in high-level libraries, including TensorFlow and PyTorch, to support their functionality. Computational fluid dynamics (CFD) researchers have benefited from this trend and produced powerful neural networks that promise shorter simulation times. For example, multilayer perceptrons (MLPs) and Long Shor

R. James Cotton, J. D. Peiffer, Kunal Shah, Allison DeLillo

Markerless motion capture (MMC) is revolutionizing gait analysis in clinical settings by making it more accessible, raising the question of how to extract the most clinically meaningful information from gait data. In multiple fields ranging from image processing to natural language processing, self-supervised learning (SSL) from large amounts of unannotated

G. A. Bobkov, V. M. Gordeeva, A. M. Bobkov, I. V. Bobkova

One of the most famous proximity effects at ferromagnet/superconductor (F/S) interfaces is partial conversion of singlet superconductivity to triplet pairing correlations. Due to the presence of macroscopic exchange field in the ferromagnet the Cooper pairs penetrating into the ferromagnet from the superconductor acquire a finite momentum there. The finite-m

Sinead Lyle

We consider the core blocks corresponding to the Hecke algebras of type B over a field of arbitrary characteristic. To each core block B, we associate two non-negative integers which determine the indexing of the Specht modules and simple modules in the block, the weight of the block, the multicharge of the algebra (up to a shift) and the block decomposition

Xuyang Chen, Ashvin N. Iyer, Zixing Wang, Ahmed H. Qureshi

The robot exploration task has been widely studied with applications spanning from novel environment mapping to item delivery. For some time-critical tasks, such as rescue catastrophes, the agent is required to explore as efficiently as possible. Recently, Visit Frequency-based map representation achieved great success in such scenarios by discouraging repet

Weidong Li, Mengxiao Zhang, Libo Zhang, Jiamou Liu

In the digital age, data is a valuable commodity, and data marketplaces offer lucrative opportunities for data owners to monetize their private data. However, data privacy is a significant concern, and differential privacy has become a popular solution to address this issue. Private data trading systems (PDQS) facilitate the trade of private data by determin

E. E. Perepelkin, B. I. Sadovnikov, N. G. Inozemtseva, E. V. Burlakov

The Moyal equation describes the evolution of the Wigner function of a quantum system in the phase space. The right-hand side of the equation contains an infinite series with coefficients proportional to powers of the Planck constant. There is an interpretation of the Moyal equation as a quantum analogue of the classical Liouville equation. Indeed, if one us

Daniel Freund, Samuel B. Hopkins

We investigate practical algorithms to find or disprove the existence of small subsets of a dataset which, when removed, reverse the sign of a coefficient in an ordinary least squares regression involving that dataset. We empirically study the performance of well-established algorithmic techniques for this task -- mixed integer quadratically constrained opti

Karen Sanchez, Carlos Hinojosa, Kevin Arias, Henry Arguello

Data augmentation is classically used to improve the overall performance of deep learning models. It is, however, challenging in the case of medical applications, and in particular for multiparametric datasets. For example, traditional geometric transformations used in several applications to generate synthetic images can modify in a non-realistic manner the

Serafim Teknowijoyo, Armen Gulian

We report on magnetron deposition and superconducting properties of a novel superconducting material: rhenium-lutetium films on sapphire substrates. Different compositions of Re$_{x}$Lu binary are explored from $x\approx 3.8$ to close to pure Re stoichiometry. The highest critical temperature, up to $T_{c}\approx $ 6.95 K, is obtained for $x\approx 10.5$. De

Devin Jean, Suk Seo

An open-locating-dominating set of a graph models a detection system for a facility with a possible "intruder" or a multiprocessor network with a possible malfunctioning processor. A "sensor" or "detector" is assumed to be installed at a subset of vertices where it can detect an intruder or a malfunctioning processor in their neighborhood, but not at itself.

Junqing Xu, Yuan Ping

Spin relaxation, dephasing and diffusion are at the heart of spin-based information technology. Accurate theoretical approaches to simulate spin lifetimes ($\tau_s$), determining how fast the spin polarization and phase information will be lost, are important to the understandings of underlying mechanism of these spin processes, and invaluable to search for

Arghya Das, Mustansir Barma

The Takayasu aggregation model is a paradigmatic model of aggregation with mass injection, known to exhibit a power law distribution of mass over a range which grows in time. Working in one dimension we find that the mass profile in addition shows distinctive {\it dynamic condensates} which collectively hold a substantial portion of the mass (approximately $

Wenqing Wang, Kaifeng Gao, Yawei Luo, Tao Jiang

Video-based scene graph generation (VidSGG) is an approach that aims to represent video content in a dynamic graph by identifying visual entities and their relationships. Due to the inherently biased distribution and missing annotations in the training data, current VidSGG methods have been found to perform poorly on less-represented predicates. In this pape

S. D. Odintsov, V. K. Oikonomou, I. Giannakoudi, F. P. Fronimos

We review recent trends on inflationary dynamics in the context of viable modified gravity theories. After providing a general overview of the inflationary paradigm emphasizing on what problems of hot Big Bang theory inflation solves, and a somewhat introductory presentation of single field inflationary theories with minimal and non-minimal couplings, we rev

Samik Basu, Vasant Honavar, Ganesh Ram Santhanam, Jia Tao

Many decision-making scenarios, e.g., public policy, healthcare, business, and disaster response, require accommodating the preferences of multiple stakeholders. We offer the first formal treatment of reasoning with multi-stakeholder qualitative preferences in a setting where stakeholders express their preferences in a qualitative preference language, e.g.,

Victor V. Batyrev

The Fine interior $F(P)$ of a $d$-dimensional lattice polytope $P \subset {\Bbb R}^d$ is the set of all points $y \in P$ having integral distance at least $1$ to any integral supporting hyperplane of $P$. We call a lattice polytope $F$-hollow if its Fine interior is empty. The main theorem claims that up to unimodular equivalence in each dimension $d$ there

Deng Wang

Whether the first law of black hole mechanics is correct is an important question in black holes physics. Subjected to current limited gravitational wave events, we propose its weaker version that permits a relatively large perturbation to a black hole system and implement a simple test with the first event GW150914. Confronting the strain data with the theo

Grigorii Veviurko, Wendelin Böhmer, Mathijs de Weerdt

Predict and optimize is an increasingly popular decision-making paradigm that employs machine learning to predict unknown parameters of optimization problems. Instead of minimizing the prediction error of the parameters, it trains predictive models using task performance as a loss function. The key challenge to train such models is the computation of the Jac

V A Kandappan, Vaishnavi Gujjula, Sivaram Ambikasaran

This article introduces HODLR3D, a class of hierarchical matrices arising out of $N$-body problems in three dimensions. HODLR3D relies on the fact that certain off-diagonal matrix sub-blocks arising out of the $N$-body problems in three dimensions are numerically low-rank. For the Laplace kernel in $3$D, which is widely encountered, we prove that all the off

Wei-Yang Liu, Edward Shuryak, Ismail Zahed

We use the QCD instanton vacuum model to discuss the emergence of the light scalar and vector mesons on the light front. We take into account both the instanton and anti-instanton single and molecular interactions on the light quarks, in the form of non-local effective interactions. Although the molecular induced interactions are suppressed by a power of the

Maria Teresa Filigheddu, Manuele Leonelli, Gherardo Varando, Miguel Ángel Gómez-Bermejo

Machine learning models are increasingly used in the medical domain to study the association between risk factors and diseases to support practitioners in predicting health outcomes. In this paper, we showcase the use of machine-learned staged tree models for investigating complex asymmetric dependence structures in health data. Staged trees are a specific c

Ramón G. Plaza, José M. Valdovinos

This paper studies the one dimensional Navier-Stokes-Fourier-Korteweg system of equations describing the evolution of a heat-conducting compressible fluid that exhibits viscosity and capillarity. The main goal of the present analysis is to examine the dissipative structure of the system and to prove the global existence and the asymptotic decay of perturbati

Tiezhu Sun, Weiguo Pian, Nadia Daoudi, Kevin Allix

Transfomer-based models have significantly advanced natural language processing, in particular the performance in text classification tasks. Nevertheless, these models face challenges in processing large files, primarily due to their input constraints, which are generally restricted to hundreds or thousands of tokens. Attempts to address this issue in existi

Sara Wade, Vanda Inacio, Sonia Petrone

For exchangeable data, mixture models are an extremely useful tool for density estimation due to their attractive balance between smoothness and flexibility. When additional covariate information is present, mixture models can be extended for flexible regression by modeling the mixture parameters, namely the weights and atoms, as functions of the covariates.

Aleksey Romanov, Oleg Lashinin, Marina Ananyeva, Sergey Kolesnikov

In this paper we study the next basket recommendation problem. Recent methods use different approaches to achieve better performance. However, many of them do not use information about the time of prediction and time intervals between baskets. To fill this gap, we propose a novel method, Time-Aware Item-based Weighting (TAIW), which takes timestamps and inte

Shipu Debnath

The use of technology in healthcare has become increasingly popular in recent years, with the potential to improve how healthcare is delivered, patient outcomes, and cost-effectiveness. This review paper provides an overview of how technology has been used in healthcare, particularly in cities and for personalized medicine. The paper discusses different ways

A. Mishra, G. Mamatsashvili, F. Stefani

The quest for an unambiguous detection of magnetorotational instability (MRI) in experiments is still ongoing despite recent promising results. To conclusively identify MRI in the laboratory, a large cylindrical Taylor-Couette experiment with liquid sodium is under construction within the DRESDYN project. In this paper, we investigate the linear and nonlinea

Arup Biswas, Anupam Kundu, Arnab Pal

Classical first passage under resetting is a paradigm in the search process. Despite its multitude of applications across interdisciplinary sciences, experimental realizations of such resetting processes posit practical challenges in calibrating these zero time irreversible transitions. Here, we consider a strategy in which resetting is performed using finit

Antonio Pasini

In this paper we propose a definition of regularity suited for polar spaces of infinite rank and we investigate to which extent properties of regular polar spaces of finite rank can be generalized to polar spaces of infinite rank.

Sayantani Lahiri, Claus Laemmerzahl

We consider a non-self-gravitating geometrically thick torus described by Weyssenhoff ideal spin fluid in a black hole spacetime. The Weyssenhof spin fluid shares the same symmetries of the background geometry, i,e. stationarity and axisymmetry and further describes circular orbital motion in the black hole spacetime. We further assume that assume the alignm