April 2023 arXiv papers — page 5

Masataka Kuwamura, Takashi Teramoto, Hideo Ikeda

Mass-conserving reaction-diffusion systems with bistable nonlinearity are useful models for studying cell polarity formation, which is a key process in cell division and differentiation. We rigorously show the existence and stability of stationary solutions with a single internal transition layer in such reaction-diffusion systems under general assumptions b

Vitor S. Barroso, Cameron R. D. Bunney, Silke Weinfurtner

Analogue gravity offers an approach for testing the universality and robustness of quantum field theories in curved spacetimes and validating them using down-to-earth, laboratory-based experiments. Fluid interfaces are a promising framework for creating these gravity simulators and have successfully replicated phenomena such as Hawking radiation and black ho

Vignesh V Menon, Jingwen Zhu, Prajit T Rajendran, Hadi Amirpour

In video streaming applications, a fixed set of bitrate-resolution pairs (known as a bitrate ladder) is typically used during the entire streaming session. However, an optimized bitrate ladder per scene may result in (i) decreased storage or delivery costs or/and (ii) increased Quality of Experience. This paper introduces a Just Noticeable Difference (JND)-a

Hannes Reichert, Manuel Hetzel, Steven Schreck, Konrad Doll

In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. This addresses the issue of sensor-dependent object representation in projection-based sensors, such as LiDAR, which can lead to distorted physical and geometric properties due to variations in sensor resolution

Mohsen Jenadeleh, Johannes Zagermann, Harald Reiterer, Ulf-Dietrich Reips

In image quality assessment, a collective visual quality score for an image or video is obtained from the individual ratings of many subjects. One commonly used format for these experiments is the two-alternative forced choice method. Two stimuli with the same content but differing visual quality are presented sequentially or side-by-side. Subjects are asked

Ali Tahaei, Giulio Biroli, Misaki Ozawa, Marko Popović

We provide a theoretical description of dynamical heterogeneities in glass-forming liquids, based on the premise that relaxation occurs via local rearrangements coupled by elasticity. In our framework, the growth of the dynamical correlation length $\xi$ and of the correlation volume $\chi_4$ are controlled by a zero-temperature fixed point. We connect this

Vasilis Chasiotis, Dimitris Karlis

In the big data era researchers face a series of problems. Even standard approaches/methodologies, like linear regression, can be difficult or problematic with huge volumes of data. Traditional approaches for regression in big datasets may suffer due to the large sample size, since they involve inverting huge data matrices or even because the data cannot fit

Alexander Koplenig

In a recent paper published in the Journal of Language Evolution, Kauhanen, Einhaus & Walkden (https://doi.org/10.1093/jole/lzad005, KEW) challenge the results presented in one of my papers (Koplenig, Royal Society Open Science, 6, 181274 (2019), https://doi.org/10.1098/rsos.181274), in which I tried to show through a series of statistical analyses that larg

Mei Yang, Gao Qiu, Yong Wu, Junyong Liu

The increasing scale of alternating current and direct current (AC/DC) hybrid systems necessitates a faster power flow analysis tool than ever. This letter thus proposes a specific physics-guided graph neural network (PG-GNN). The tailored graph modelling of AC and DC grids is firstly advanced to enhance the topology adaptability of the PG-GNN. To eschew unr

Aga Shahee, Kyongjun Yoo, B. Koteswararao, N. V. Ter-Oganessian

Cu$_3$TeO$_6$, a three-dimensional antiferromagnet forming a unique spin-web lattice of spin-1/2 Cu2+ ions below the Neel temperature T$_N$ = 62 K, has recently been found to exhibit topological Dirac or nodal magnon dispersion. In this study, we report the discovery of the linear magnetoelectric (ME) effects in Cu$_3$TeO$_6$ below TN. Our pyroelectric curre

Giulio Foggi Rota, Alessandro Monti, Marco E. Rosti, Maurizio Quadrio

We show that the energy required by a turbulent flow to displace a given amount of fluid through a straight duct in a given time interval can be reduced by modulating in time the pumping power. The control strategy is hybrid: it is passive, as it requires neither a control system nor control energy, but it manipulates how pumping energy is delivered to the s

Yuhao Zhong, Anirban Bhattacharya, Satish Bukkapatnam

We propose EBLIME to explain black-box machine learning models and obtain the distribution of feature importance using Bayesian ridge regression models. We provide mathematical expressions of the Bayesian framework and theoretical outcomes including the significance of ridge parameter. Case studies were conducted on benchmark datasets and a real-world indust

M. Gazdzicki, D. Kikola, I. Pidhurskyi, L. Tinti

Heavy-ion collisions are a unique tool for studying properties of strong interactions at high energy densities. In particular, the momentum correlations of charm and bottom hadrons have been considered for testing heavy quark thermalisation in the dense matter produced by the collisions. In this respect, two effects have been considered: the decrease of the

Christian Corda

In 2014, in a famous paper Hawking strongly criticized the firewall paradox by claiming that it violates the equivalence principle and breaks the CPT invariance of quantum gravity. He proposed that the final result of the gravitational collapse should not be an event horizon, but an apparent horizon instead. On the other hand, Hawking did not give a mechanis

P. Sai, V. V. Korotyeyev, M. Dub, M. Słowikowski

We present an extensive study of resonant two-dimensional (2D) plasmon excitations in grating-gated quantum well heterostructures, which enable an electrical control of periodic charge carrier density profile. Our study combines theoretical and experimental investigations of nanometer-scale AlGaN/GaN grating-gate structures and reveals that all terahertz (TH

Shahroz Khan, Kosa Goucher-Lambert, Konstantinos Kostas, Panagiotis Kaklis

In this work, we introduce ShipHullGAN, a generic parametric modeller built using deep convolutional generative adversarial networks (GANs) for the versatile representation and generation of ship hulls. At a high level, the new model intends to address the current conservatism in the parametric ship design paradigm, where parametric modellers can only handle

B. M. Mese, T. Ergin

Recently the Galactic and Extra-galactic All-sky Murchison Widefield Array survey has published 27 new candidate radio supernova remnants (SNRs) which are located within the longitude ranges of 345{\deg} < l < 60{\deg} and 180{\deg} < l < 240{\deg}. To search for the gamma-ray counterparts of these candidate radio SNRs, we analyzed 14 years of {\it Fermi}-LA

Abdul Karim Gizzini, Marwa Chafii

Doubly-selective channel estimation represents a key element in ensuring communication reliability in wireless systems. Due to the impact of multi-path propagation and Doppler interference in dynamic environments, doubly-selective channel estimation becomes challenging. Conventional channel estimation schemes encounter performance degradation in high mobilit

Tianchen Xu, Yuan Chen, Donglin Zeng, Yuanjia Wang

Digital technologies (e.g., mobile phones) can be used to obtain objective, frequent, and real-world digital phenotypes from individuals. However, modeling these data poses substantial challenges since observational data are subject to confounding and various sources of variabilities. For example, signals on patients' underlying health status and treatment e

Elwin Huaman, David Lindemann, Valeria Caruso, Jorge Luis Huaman

Over the last decade, the Web has increasingly become a space of language and knowledge representation. However, it is only true for well-spread languages and well-established communities, while minority communities and their resources received less attention. In this paper, we propose QICHWABASE to support the harmonization process of the Quechua language a

Trevor Standley, Ruohan Gao, Dawn Chen, Jiajun Wu

We present EMMa, an Extensible, Multimodal dataset of Amazon product listings that contains rich Material annotations. It contains more than 2.8 million objects, each with image(s), listing text, mass, price, product ratings, and position in Amazon's product-category taxonomy. We also design a comprehensive taxonomy of 182 physical materials (e.g., Plastic $

Fuyu Dong, Changbom Park, Sungwook E. Hong, Juhan Kim

The apparent shape of galaxy clustering depends on the adopted cosmology used to convert observed redshift to comoving distance, the $r(z)$ relation, as it changes the line elements along and across the line of sight differently. The Alcock-Paczy\'nski (AP) test exploits this property to constrain the expansion history of the universe. We present an extensiv

Petr Prucha

Companies that use robotic process automation very often deal with problems maintaining the bots in their RPA portfolio. Current key performance indicators do not track the behavior of RPA bots or processes. For better maintainability of RPA bots, it is crucial to easily identify problematic behavior in RPA bots. Therefore, we propose a strategy that tracks

Maciej Wielgosz, Antonio M. López, Muhammad Naveed Riaz

We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0.9.13). We provide use cases for pedestrian detection, autoencoding, pose estimation, and pose lifting. We also showcase baseline results. For more information, visit https://project-arcane.eu/.

Ahmad Mousavi, George Michailidis

Mean-reverting portfolios with volatility and sparsity constraints are of prime interest to practitioners in finance since they are both profitable and well-diversified, while also managing risk and minimizing transaction costs. Three main measures that serve as statistical proxies to capture the mean-reversion property are predictability, portmanteau criter

Jay Jorgenson, Anders Karlsson, Lejla Smajlović

Let $X_m$ denote the discrete circle with $m$ vertices. For $x,y\in X_{m}$ and complex $s$, let $G_{X_m,\chi_{\beta}}(x,y;s)$ be the resolvent kernel associated to the combinatorial Laplacian which acts on the space of functions on $X_{m}$ that are twisted by a character $\chi_{\beta}$. We will compute $G_{X_m,\chi_{\beta}}(x,y;s)$ in two different ways. Fir

Zhenxiang Xiao, Yuzhong Chen, Lu Zhang, Junjie Yao

Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks. In this paper, we focus on adapting prompt design based on instruction tuning into a visual transformer model for image classification which we called Instruction-ViT.

Benjamin Joseph, Gregoire Loeper, Jan Obloj

We develop a non-parametric, semimartingale optimal transport, calibration methodology for local volatility models with stochastic interest rate. The method finds a fully calibrated model which is the closest, in a way that can be defined by a general cost function, to a given reference model. We establish a general duality result which allows to solve the p

Ying Sun, Hengshu Zhu, Hui Xiong

The outbreak of the COVID-19 pandemic has had an unprecedented impact on China's labour market, and has largely changed the structure of labour supply and demand in different regions. It becomes critical for policy makers to understand the emerging dynamics of the post-pandemic labour market and provide the right policies for supporting the sustainable devel

Jacek Wesołowski, Agnieszka Zięba

Quadratic harnesses are time-inhomogeneous Markov polynomial processes with linear conditional expectations and quadratic conditional variances with respect to the past-future filtrations. Typically they are determined by five numerical constants hidden in the form of conditional variances. In this paper we derive infinitesimal generators of such processes,

Ali H. Chamseddine, Ola Malaeb, Sara Najem

We study numerically the curvature tensor in a three-dimensional discrete space. Starting from the continuous metric of a three-sphere, we transformed it into a discrete space using three integers $n_1, n_2$, and $n_3$. The numerical results are compared with the expected values in the continuous limit. We show that as the number of cells in the lattice incr

Shayan Gharib, Minh Tran, Diep Luong, Konstantinos Drossos

Sound event detection systems are widely used in various applications such as surveillance and environmental monitoring where data is automatically collected, processed, and sent to a cloud for sound recognition. However, this process may inadvertently reveal sensitive information about users or their surroundings, hence raising privacy concerns. In this stu

Emmanuele Battista, Harold C. Steinacker

Recently, solutions of the Ishibashi, Kawai, Kitazawa and Tsuchiya matrix theory have been found, which can be interpreted as 3+1-dimensional quantum geometries describing an effective Friedmann-Lema\^{i}tre-Robertson-Walker cosmology with a big bounce. In this paper, we examine the propagation of a scalar field in an open Friedmann-Lema\^{i}tre-Robertson-Wa

Zachary Izzo, Ruishan Liu, James Zou

Medical studies frequently require to extract the relationship between each covariate and the outcome with statistical confidence measures. To do this, simple parametric models are frequently used (e.g. coefficients of linear regression) but usually fitted on the whole dataset. However, it is common that the covariates may not have a uniform effect over the

Yesheng Zhang, Xu Zhao

Feature matching is a crucial technique in computer vision. A unified perspective for this task is to treat it as a searching problem, aiming at an efficient search strategy to narrow the search space to point matches between images. One of the key aspects of search strategy is the search space, which in current approaches is not carefully defined, resulting

Carlo Sinigaglia, Francesco Braghin, Mattia Serra

Objective Eulerian Coherent Structures (OECSs) and instantaneous Lyapunov exponents (iLEs) govern short-term material transport in fluid flows as Lagrangian Coherent Structures and the Finite-Time Lyapunov Exponent do over longer times. Attracting OECSs and iLEs reveal short-time attractors and are computable from the Eulerian rate-of-strain tensor. Here we

Verena Häberle, Linbin Huang, Xiuqiang He, Eduardo Prieto-Araujo

A fast and accurate grid impedance measurement of three-phase power systems is crucial for online assessment of power system stability and adaptive control of grid-connected converters. Existing grid impedance measurement approaches typically rely on pointwise sinusoidal injections or sequential wideband perturbations to identify a nonparametric grid impedan

Muratcan Ayik, Elif Tugce Ceran, Elif Uysal

We consider a network with multiple sources and a base station that send time-sensitive information to remote clients. The Age of Incorrect Information (AoII) captures the freshness of the informative pieces of status update packets at the destinations. We derive the closed-form Whittle Index formulation for a push-based multi-user network over unreliable ch

Sanjay Chandrasekaran, Vishnu Varadan, Siva Vignesh Krishnan, Florian Dörfler

Distributed sensor networks often include a multitude of sensors, each measuring parts of a process state space or observing the operations of a system. Communication of measurements between the sensor nodes and estimator(s) cannot realistically be considered delay-free due to communication errors and transmission latency in the channels. We propose a novel

Nadeem Malibari, Iyad Katib, Rashid Mehmood

Applications of Reinforcement Learning in the Finance Technology (Fintech) have acquired a lot of admiration lately. Undoubtedly Reinforcement Learning, through its vast competence and proficiency, has aided remarkable results in the field of Fintech. The objective of this systematic survey is to perform an exploratory study on a correlation between reinforc

Ayse Altay, Abdurrahman Gumus

Circulatory system abnormalities might be an indicator of diseases or tissue damage. Early detection of vascular abnormalities might have an important role during treatment and also raise the patient's awarenes. Current detection methods for vascular imaging are high-cost, invasive, and mostly radiation-based. In this study, a low-cost and portable microcomp

Peng Lin, Shaowei Cai, Mengchuan Zou, Jinkun Lin

Integer linear programming (ILP) models a wide range of practical combinatorial optimization problems and significantly impacts industry and management sectors. This work proposes new characterizations of ILP with the concept of boundary solutions. Motivated by the new characterizations, we develop a new local search algorithm Local-ILP, which is efficient f

Sebastian Zimper, Arnold Ngapasare, Malcolm Hillebrand, Matthaios Katsanikas

We investigate the ability of simple diagnostics based on Lagrangian descriptor (LD) computations of initially nearby orbits to detect chaos in conservative dynamical systems with phase space dimensionality higher than two. In particular, we consider the recently introduced methods of the difference ($D_L^n$) and the ratio ($R_L^n$) of the LDs of neighboring

Pascal Moyal, Ana Busic, Jean Mairesse

We consider a stochastic matching model with a general compatibility graph, as introduced in \cite{MaiMoy16}. We prove that most common matching policies (including FCFM, priorities and random) satisfy a particular sub-additive property, which we exploit to show in many cases, the coupling-from-the-past to the steady state, using a backwards scheme {\em \`a

Xiaoyu Chen, Jingcheng Liu, Yitong Yin

We characterize the uniqueness condition in the hardcore model for bipartite graphs with degree bounds only on one side, and provide a nearly linear time sampling algorithm that works up to the uniqueness threshold. We show that the uniqueness threshold for bipartite graph has almost the same form of the tree uniqueness threshold for general graphs, except w

Andrei Moroianu, Mihaela Pilca

We show that the Gauduchon metric $g_0$ of a compact locally conformally product manifold $(M,c,D)$ of dimension greater than $2$ is adapted, in the sense that the Lee form of $D$ with respect to $g_0$ vanishes on the $D$-flat distribution of $M$. We also characterize adapted metrics as critical points of a natural functional defined on the conformal class.

Itamar Cohen, Paolo Giaccone, Carla Fabiana Chiasserini

In the edge-cloud continuum, datacenters provide microservices (MSs) to mobile users, with each MS having specific latency constraints and computational requirements. Deploying such a variety of MSs matching their requirements with the available computing resources is challenging. In addition, time-critical MSs may have to be migrated as the users move, to k

Nahomi Kan, Kiyoshi Shiraishi

We consider the strong-coupling phase in a model of $O(N)$ spin-2 field theory in de Sitter spacetime and the effective mass of spin-2 fields therein. In the strong-coupling phase, the Higuchi bound limits the mass parameter in the theory. The analysis using the large $N$ approximation finds the critical value of the mass parameter with numerical calculation

David Alonso del Barrio, Daniel Gatica-Perez

This paper examines how the European press dealt with the no-vax reactions against the Covid-19 vaccine and the dis- and misinformation associated with this movement. Using a curated dataset of 1786 articles from 19 European newspapers on the anti-vaccine movement over a period of 22 months in 2020-2021, we used Natural Language Processing techniques includi

Nikolaos Vasilikopoulos, Nikos Kolotouros, Aggeliki Tsoli, Antonis Argyros

Reconstructing 3D human pose and shape from monocular videos is a well-studied but challenging problem. Common challenges include occlusions, the inherent ambiguities in the 2D to 3D mapping and the computational complexity of video processing. Existing methods ignore the ambiguities of the reconstruction and provide a single deterministic estimate for the 3

Ryuki Kido, Takiko Sasaki, Shu Takamatsu, Hiroyuki Takamura

We are interested in the so-called "combined effect" of two different kinds of nonlinear terms for semilinear wave equations in one space dimension. Recently, the first result with the same formulation as in the higher dimensional case has been obtained if and only if the total integral of the initial speed is zero, namely Huygens' principle holds. In this p

Shihang Lu, Fan Liu, Yunxin Li, Kecheng Zhang

It is anticipated that integrated sensing and communications (ISAC) would be one of the key enablers of next-generation wireless networks (such as beyond 5G (B5G) and 6G) for supporting a variety of emerging applications. In this paper, we provide a comprehensive review of the recent advances in ISAC systems, with a particular focus on their foundations, sys

Hassan Wasfi, Richard Stone

Using English words as passwords have been a popular topic in the last few years. The following article discusses a study to compare self-selection of the system-generated words for recognition and self-generated words for recall for nouns and mixture words. The results revealed no significant difference between recognition and recall of password nouns. The

Jiuzhao Hua

We present canonical forms for all indecomposable pairs $(A,B)$ of commuting nilpotent matrices over an arbitrary field under simultaneous similarity, where $A$ is the direct sum of two Jordan blocks with distinct sizes. We also provide the transformation matrix $X$ such that $(A, X^{-1}BX)$ is in its canonical form.

Ragesh Jaiswal, Amit Kumar

Constrained clustering problems generalize classical clustering formulations, e.g., $k$-median, $k$-means, by imposing additional constraints on the feasibility of clustering. There has been significant recent progress in obtaining approximation algorithms for these problems, both in the metric and the Euclidean settings. However, the outlier version of thes

Mehdi Arfaoui, Natalia Zawadzka, Sabrine Ayari, Zhaolong Chen

To fully explore exciton-based applications and improve their performance, it is essential to understand the exciton behavior in anisotropic materials. Here, we investigate the optical properties of anisotropic excitons in GeS encapsulated by h-BN, using different approaches that combine polarization- and temperature-dependent photoluminescence (PL) measurem

Subham Sabud, Preetam Kumar

OFDM-IM NOMA is a newly created flexible scheme for future generation communication systems. For the downlink OFDM-IM NOMA system, a low-complexity "rotated constellation based log likelihood ratio (LLR) detector" has been proposed in this work. This detector is able to significantly reduce the complexity by employing the rotating constellation-based concept

Vuong D. Nguyen, Nguyen Kim Duyen, Nguyen Minh Hai, Bui Khuong Duy

Portfolio selection involves optimizing simultaneously financial goals such as risk, return and Sharpe ratio. This problem holds considerable importance in economics. However, little has been studied related to the nonconvexity of the objectives. This paper proposes a novel generalized approach to solve the challenging Portfolio Selection problem in an intui

Md. Ahsan Habib, Md. Motaleb Hossen Manik

Blockchain denial of service (BDoS) and selfish mining are the two most crucial attacks on blockchain technology. A classical DoS attack targets the computer network to limit, restrict, or stop accessing the system of authorized users which is ineffective against renowned cryptocurrencies like Bitcoin, Ethereum, etc. Unlike the conventional DoS, the BDoS aff

Jing-Hang Fu, Sen Jia, Xing-Yu Zhou, Yu-Jie Zhang

The bound state of a $\tau^+\tau^-$ pair by the electromagnetic force is the heaviest and smallest QED atom. Since the discovery of the two lightest QED atoms more than 60 years ago, no evidence for the third one has been found. We demonstrate that the $J_\tau$ ($\tau^+\tau^-$ atom with $J^{PC}=1^{--}$) resonance signal can be observed with a significance la

Peng Fan, Dongyue Guo, JianWei Zhang, Bo Yang

Automatic speech recognition (ASR) technique is becoming increasingly popular to improve the efficiency and safety of air traffic control (ATC) operations. However, the conversation between ATC controllers and pilots using multilingual speech brings a great challenge to building high-accuracy ASR systems. In this work, we present a two-stage multilingual ASR

Chen Li, Zeyi Liu, Limin Wang, Minyue Li

Fault diagnosis is a crucial area of research in industry. Industrial processes exhibit diverse operating conditions, where data often have non-Gaussian, multi-mode, and center-drift characteristics. Data-driven approaches are currently the main focus in the field, but continuous fault classification and parameter updates of fault classifiers pose challenges

Yuhang Liu, Sohee Kwon, George J. de Coster, Roger K. Lake

Two-dimensional chromium ditelluride (CrTe2) is a promising ferromagnetic layered material that exhibits long-range ferromagnetic ordering in the monolayer limit. The formation energies of the different possible structural phases (1T, 1H, 2H) calculated from density functional theory (DFT) show that the 1T phase is the ground state, and the energetic transit

Brandon T. Shapiro, David I. Spivak

We define the monoidal category $(Poly_E,y,\triangleleft)$ of polynomials under composition in any category $E$ with finite limits, including both cartesian and vertical morphisms of polynomials, and generalize to this setting the Dirichlet tensor product of polynomials $\otimes$, duoidality of $\otimes$ and $\triangleleft$, closure of $\otimes$, and coclosu

Sun Hanyu

Resistive random-access memory (RRAM) is a promising candidate for next-generation memory devices due to its high speed, low power consumption, and excellent scalability. Metal oxides are commonly used as the oxide layer in RRAM devices due to their high dielectric constant and stability. However, to further improve the performance of RRAM devices, recent re

Keqi Wang, Wei Xie, Sarah W. Harcum

The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize underlying mechanisms of iPSC

T. Tony Cai, Ran Chen, Yuancheng Zhu

Optimal estimation and inference for both the minimizer and minimum of a convex regression function under the white noise and nonparametric regression models are studied in a nonasymptotic local minimax framework, where the performance of a procedure is evaluated at individual functions. Fully adaptive and computationally efficient algorithms are proposed an

Kai Xu, Ziwei Yu, Xin Wang, Michael Bi Mi

In video super-resolution, it is common to use a frame-wise alignment to support the propagation of information over time. The role of alignment is well-studied for low-level enhancement in video, but existing works overlook a critical step -- resampling. We show through extensive experiments that for alignment to be effective, the resampling should preserve

Hanyu Sun, Xiao Huang, Wei Ma

To provide real-time parking information, existing studies focus on predicting parking availability, which seems an indirect approach to saving drivers' cruising time. In this paper, we first time propose an on-street parking recommendation (OPR) task to directly recommend a parking space for a driver. To this end, a learn-to-rank (LTR) based OPR model calle

Hongyu Sun, Yongcai Wang, Peng Wang, Xudong Cai

This paper presents ViewFormer, a simple yet effective model for multi-view 3d shape recognition and retrieval. We systematically investigate the existing methods for aggregating multi-view information and propose a novel ``view set" perspective, which minimizes the relation assumption about the views and releases the representation flexibility. We devise an

Yin-Tao Zou, Chengxiang Ding

We study the universal dynamical relaxation behaviors of a quantum XY chain following a quench, paying special attention to the case that the prequenched Hamiltonian, or the postquenched Hamiltonian, or both of them are at critical points of equilibrium quantum phase transitions. In such ``critical quench", we find very interesting real-time dynamical scalin

Juntao Sun, Shuai Yao, Jian Zhang

In this paper, we investigate a class of planar Schr\"{o}dinger-Poisson systems with critical exponential growth. We establish conditions for the local well-posedness of the Cauchy problem in the energy space, which seems innovative as it was not discussed at all in any previous results. By introducing some new ideas and relaxing some of the classical growth

Xiang He, Naizhen Zhang

This paper is a continuation of our study of degenerations of Grassmannians in our last paper, called linked Grassmannians, constructed using convex lattice configurations in Bruhat-Tits buildings. We describe the geometry and topology of linked Grassmannians associated to a larger class of lattice configurations, generalizing the results in the last paper.

Dao-Quan Sun

We study the extended thermodynamics of the hyperbolic black hole with scalar hair and obtain the extended holographic R\'{e}nyi entropy of holographic conformal field theories with scalar hair. We analyze the behaviors of the extended holographic R\'{e}nyi entropy in terms of holographic calculations. Moreover, we generalize the capacity of entanglement fro

Krzysztof Choromanski

We introduce in this paper the mechanism of graph random features (GRFs). GRFs can be used to construct unbiased randomized estimators of several important kernels defined on graphs' nodes, in particular the regularized Laplacian kernel. As regular RFs for non-graph kernels, they provide means to scale up kernel methods defined on graphs to larger networks.

Han Yan, Owen Benton, Roderich Moessner, Andriy H. Nevidomskyy

Classical spin liquids (CSL) lack long-range magnetic order and are characterized by an extensive ground state degeneracy. We propose a classification scheme of CSLs based on the structure of the flat bands of their Hamiltonians. Depending on absence or presence of the gap from the flat band, the CSL are classified as algebraic or fragile topological, respec

Bin Du, Kun Qian, Christian Claudel, Dengfeng Sun

This paper proposes to leverage the emerging~learning techniques and devise a multi-agent online source {seeking} algorithm under unknown environment. Of particular significance in our problem setups are: i) the underlying environment is not only unknown, but dynamically changing and also perturbed by two types of non-stochastic disturbances; and ii) a group

W. Barrera, E. Montiel, J. P. Navarrete

We consider discrete subgroups of the group of orientation preserving isometries of the $m$-dimensional hyperbolic space, whose limit set is a $(m-1)$-dimensional real sphere, acting on the $n$-dimensional complex projective space for $n\geq m$, via an embedding from the group of orientation preserving isometries of the $m$-dimensional hyperbolic space to th

Steve Hanneke, Samory Kpotufe, Yasaman Mahdaviyeh

Theoretical studies on transfer learning or domain adaptation have so far focused on situations with a known hypothesis class or model; however in practice, some amount of model selection is usually involved, often appearing under the umbrella term of hyperparameter-tuning: for example, one may think of the problem of tuning for the right neural network arch

Shiming Zhang, Zhen Lu, Yue Yang

We model the boundary-layer flashback (BLF) of CH$_4$/H$_2$/air swirling flames via large-eddy simulations with the flame-surface-density method (LES-FSD), in particular, at high pressures. A local displacement speed model tabulating the stretched flame speed is employed to account for the thermo-diffusive effects, flame surface curvature, and heat loss in L

Zhongshu Zhao, Hui Peng, Qilong Zhai, Qian Zhang

In this paper, we develop an arbitrary-order locking-free enriched Galerkin method for the linear elasticity problem using the stress-displacement formulation in both two and three dimensions. The method is based on the mixed discontinuous Galerkin method in [30], but with a different stress approximation space that enriches the arbitrary order continuous Ga

Hao Liang, Kevin Ni, Guha Balakrishnan

Recent research demonstrates that deep learning models are capable of precisely extracting bio-information (e.g. race, gender and age) from patients' Chest X-Rays (CXRs). In this paper, we further show that deep learning models are also surprisingly accurate at recognition, i.e., distinguishing CXRs belonging to the same patient from those belonging to diffe

Svetlana Poznanović, Maria Rodriguez Hertz, Solomon Valore-Caplan, David Wichmann

Motivated by the properties of the descent polynomials, which enumerate permutations of $S_n$ with a fixed descent set, we define descent polynomials for labeled rooted trees. We give recursive and explicit formulas for these polynomials and show when known properties of the descent polynomials carry over to the setting of trees.

Hao Liang, Kevin Ni, Guha Balakrishnan

Recent work demonstrates that images from various chest X-ray datasets contain visual features that are strongly correlated with protected demographic attributes like race and gender. This finding raises issues of fairness, since some of these factors may be used by downstream algorithms for clinical predictions. In this work, we propose a framework, using g

Damir Kinzebulatov

We survey and refine recent results on weak and strong well-posedness of stochastic differential equations with singular drift satisfying some minimal assumptions.

A. E. Sharbaugh, L. Jones, A. N. Villano

The $^3$He(n,p) process is excellent for neutron detection between thermal and $\sim$4\,MeV because of the high cross section and near-complete energy transfer from the neutron to the proton. This process is typically used in gaseous forms with ionization readout detectors. Here we study the response of a liquid $^3$He neutron detector with a scintillation r

Eugene Dumitrescu

We briefly discuss the current state, and future computational implications, of quantum type theory.

Aleksandr Podkopaev, Aaditya Ramdas

We study the problems of sequential nonparametric two-sample and independence testing. Sequential tests process data online and allow using observed data to decide whether to stop and reject the null hypothesis or to collect more data, while maintaining type I error control. We build upon the principle of (nonparametric) testing by betting, where a gambler p

A. Reshetnyak, P. Moshin

We deduce a non-linear commutator higher-spin (HS) symmetry algebra which encodes unitary irreducible representations of the AdS group -- subject to a Young tableaux $Y(s_1,\ldots ,s_k)$ with $k\geq 2$ rows -- in a $d$-dimensional anti-de-Sitter space. Auxiliary representations for a deformed non-linear HS symmetry algebra in terms of a generalized Verma mod

Samiul Based Shuvo, Syed Samiul Alam, Syeda Umme Ayman, Arbil Chakma

Cardiovascular diseases (CVDs) can be effectively treated when detected early, reducing mortality rates significantly. Traditionally, phonocardiogram (PCG) signals have been utilized for detecting cardiovascular disease due to their cost-effectiveness and simplicity. Nevertheless, various environmental and physiological noises frequently affect the PCG signa

Xuan Kien Phung, Sylvie Hamel

Kemeny's rule is one of the most studied and well-known voting schemes with various important applications in computational social choice and biology. Recently, Kemeny's rule was generalized via a set-wise approach by Gilbert et. al. This paradigm presents interesting advantages in comparison with Kemeny's rule since not only pairwise comparisons but also th

Namkyeong Lee, Dongmin Hyun, Gyoung S. Na, Sungwon Kim

Molecular relational learning, whose goal is to learn the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications. Recently, graph neural networks have recently shown great success in molecular relational learning by modeling a molecule as a graph structure, and considering atom-level

Feng Ji, See Hian Lee, Hanyang Meng, Kai Zhao

In node classification using graph neural networks (GNNs), a typical model generates logits for different class labels at each node. A softmax layer often outputs a label prediction based on the largest logit. We demonstrate that it is possible to infer hidden graph structural information from the dataset using these logits. We introduce the key notion of la

Sophia Fuhui Lin, Joshua Viszlai, Kaitlin N. Smith, Gokul Subramanian Ravi

Fabrication errors pose a significant challenge in scaling up solid-state quantum devices to the sizes required for fault-tolerant (FT) quantum applications. To mitigate the resource overhead caused by fabrication errors, we combine two approaches: (1) leveraging the flexibility of a modular architecture, (2) adapting the procedure of quantum error correctio

Siyi Yang, Robert Calderbank

Spatially-coupled (SC) codes is a class of convolutional LDPC codes that has been well investigated in classical coding theory thanks to their high performance and compatibility with low-latency decoders. We describe toric codes as quantum counterparts of classical two-dimensional spatially-coupled (2D-SC) codes, and introduce spatially-coupled quantum LDPC

Taha Khamis, Hamam Mokayed

The purpose of this study is to investigate the development process for Artificial inelegance (AI) and machine learning (ML) applications in order to provide the best support environment. The main stages of ML are problem understanding, data management, model building, model deployment and maintenance. This project focuses on investigating the data managemen

Christina Chaccour, Walid Saad, Merouane Debbah, H. Vincent Poor

In this paper a novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems. The proposed framework consists of three main components. First, a tensor decomposition framework is proposed to extract unique sensing parameters for XR users an

Keivan G. Stassun, Guillermo Torres, Marina Kounkel, Benjamin M. Tofflemire

We report the discovery of 2M0056-08 as an equal-mass eclipsing binary (EB), comprising two red straggler stars (RSSs) with an orbital period of 33.9 d. Both stars have masses of 1.419 Msun, identical to within 0.2%. Both stars appear to be in the early red-giant phase of evolution; however, they are far displaced to cooler temperatures and lower luminositie

Wen-Rong Sun, Jin-Hua Li, Lei Liu, P. G. Kevrekidis

The instabilities of the nontrivial phase elliptic solutions in a repulsive Bose-Einstein condensate (BEC) with a periodic potential are investigated. Based on the defocusing nonlinear Schr\"{o}dinger (NLS) equation with an elliptic function potential, the well-known modulational instability (MI), the more recently identified high-frequency instability, and

Emmanuel Martinez, Roman Jacome, Alejandra Hernandez-Rojas, Henry Arguello

Deep learning methods are state-of-the-art for spectral image (SI) computational tasks. However, these methods are constrained in their performance since available datasets are limited due to the highly expensive and long acquisition time. Usually, data augmentation techniques are employed to mitigate the lack of data. Surpassing classical augmentation metho