July 2023 arXiv papers — page 64

Helmut Waldl, Werner G. Müller, Paula Camelia Trandafir

In this paper, we further investigate the problem of selecting a set of design points for universal kriging, which is a widely used technique for spatial data analysis. Our goal is to select the design points in order to make simultaneous predictions of the random variable of interest at a finite number of unsampled locations with maximum precision. Specific

Vinit Sinha

Let $V(1)$ be the natural representation of $U(\mathfrak{sl}_2).$ The multiplicities of $V(k)$ in $V(1)^{\otimes N}$ have multiple interpretations in combinatorics. In this paper, we investigate one such combinatorial interpretation of their multiplicities. Furthermore, we extend our analysis to the two-dimensional representation $T(1)$ of the restricted qua

The Pierre Auger Collaboration, A. Abdul Halim, P. Abreu, M. Aglietta

A search for time-directional coincidences of ultra-high-energy (UHE) photons above 10 EeV with gravitational wave (GW) events from the LIGO/Virgo runs O1 to O3 is conducted with the Pierre Auger Observatory. Due to the distinctive properties of photon interactions and to the background expected from hadronic showers, a subset of the most interesting GW even

Zixi Chen, Xuyang Ren, Matteo Bernabei, Vanessa Mainardi

Soft robots have been leveraged in considerable areas like surgery, rehabilitation, and bionics due to their softness, flexibility, and safety. However, it is challenging to produce two same soft robots even with the same mold and manufacturing process owing to the complexity of soft materials. Meanwhile, widespread usage of a system requires the ability to

ALICE Collaboration

The pseudorapidity dependence of elliptic ($v_2$), triangular ($v_3$), and quadrangular ($v_4$) flow coefficients of charged particles measured in Pb-Pb collisions at a centre-of-mass energy per nucleon pair of $\sqrt{s_{\rm NN}}=5.02$ TeV and in Xe-Xe collisions at $\sqrt{s_{\rm NN}}=5.44$ TeV with ALICE at the LHC are presented. The measurements are perfor

Li Qiao, Anwen Liao, Zhuoran Li, Hua Wang

This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna su

David Gruner, Sydney A. Barnes, Kenneth A. Janes

Gyrochronology enables the derivation of ages of late-type main sequence stars based on their rotation periods and a mass proxy, such as color. It has been explored in open clusters, but a connection to field stars has yet to be successfully established. We explore the rotation rates of wide binaries, representing enlightening intermediaries between clusters

David Al-Attar, Frank Syvret, Ophelia Crawford, Jerry X. Mitrovica

Reciprocity theorems are established for the elastic sea level fingerprint problem including rotational feedbacks. In their simplest form, these results show that the sea level change at a location x due to melting a unit point mass of ice at x' is equal to the sea level change at x' due to melting a unit point mass of ice at x. This identity holds irrespect

Changhong Wang, Gaël Richard, Brian McFee

Deep neural network models have become the dominant approach to a large variety of tasks within music information retrieval (MIR). These models generally require large amounts of (annotated) training data to achieve high accuracy. Because not all applications in MIR have sufficient quantities of training data, it is becoming increasingly common to transfer m

V. I. Yukalov

This review article is the second part of the project ``Selected Topics of Social Physics". The first part has been devoted to equilibrium systems. The present part considers nonequilibrium systems. The style of the paper combines the features of a tutorial and a review, which, from one side, makes it easy to read for nonspecialists aiming at grasping the ba

Kevin McAreavey, Weiru Liu

Miller recently proposed a definition of contrastive (counterfactual) explanations based on the well-known Halpern-Pearl (HP) definitions of causes and (non-contrastive) explanations. Crucially, the Miller definition was based on the original HP definition of explanations, but this has since been modified by Halpern; presumably because the original yields co

Nazma Husain, Yash Bhargava, Akash Garg, Sneha Prakash Mudambi

4U 1543-47 underwent its brightest outburst in 2021 after two decades of inactivity. During its decay phase, AstroSat conducted nine observations of the source spanning from July $1^{st}$ to September $26^{th}$, 2021. The first three observations were performed with an offset of 40 arcmin with AstroSat/LAXPC, while the remaining six were on-axis observations

Nanna Aamand, Dani Kaufman

The study of this paper is Wilson line operators in 3-dimensional Chern-Simons theory on a manifold with boundaries. We prove to leading order through a direct calculation of Feynman integrals that the merging of parallel Wilson lines reproduces the coproduct on the quantum group $U_h(\mathfrak g)$. We outline a connection of this theory with the moduli spac

Piotr Tarasiewicz, Sultan Kenjeyev, Ilana Sebag, Shehab Alshehabi

The recent emergence of deep learning methods has enabled the research community to achieve state-of-the art results in several domains including natural language processing. However, the current robocall system remains unstable and inaccurate: text generator and chat-bots can be tedious and misunderstand human-like dialogue. In this work, we study the perfo

Frank Siebers, Robin Bebon, Ashreya Jayaram, Thomas Speck

Active fluids composed of constituents that are constantly driven away from thermal equilibrium can support spontaneous currents and can be engineered to have unconventional transport properties. Here we report the emergence of (meta-)stable traveling bands in computer simulations of aligning circle swimmers. These bands are different from polar flocks and w

Michal Bujak, Rafal Kucharski

Ride-pooling remains a promising emerging mode with a potential to contribute towards urban sustainability and emission reductions. Recent studies revealed complexity and diversity among travellers' ride-pooling aptitudes. So far, ride-poling analyses assumed homogeneity and/or determinism of ride-pooling travellers. This, as we demonstrate, leads to a false

Wenyu Liao, Yiqing Shi, Yujia Hu, Wei Quan

This study examines the relationship between Yelp reviews and food types, investigating how ratings, sentiments, and topics vary across different types of food. Specifically, we analyze how ratings and sentiments of reviews vary across food types, cluster food types based on ratings and sentiments, infer review topics using machine learning models, and compa

Saddek Bensalem, Chih-Hong Cheng, Wei Huang, Xiaowei Huang

Machine learning has made remarkable advancements, but confidently utilising learning-enabled components in safety-critical domains still poses challenges. Among the challenges, it is known that a rigorous, yet practical, way of achieving safety guarantees is one of the most prominent. In this paper, we first discuss the engineering and research challenges a

Aparajita Dasgupta, Vishvesh Kumar, Lalit Mohan, Shyam Swarup Mondal

In this article, we introduce and study $M$-elliptic pseudo-differential operators in the framework of non-harmonic analysis of boundary value problems on a manifold $\Omega$ with boundary $\partial \Omega$, introduced by Ruzhansky and Tokmagambetov ( Int. Math. Res. Not. IMRN, (12), 3548-3615, 2016) in terms of a model operator $\mathfrak{L}$. More precisel

Jianpeng Zhang, Xianghua Ye, Jianfeng Zhang, Yuxing Tang

Lung cancer is a leading cause of death worldwide and early screening is critical for improving survival outcomes. In clinical practice, the contextual structure of nodules and the accumulated experience of radiologists are the two core elements related to the accuracy of identification of benign and malignant nodules. Contextual information provides compreh

Yunlong Li, Adam Davis, Sajan Easo, Keith Evans

We present the investigation of the use of Opticks, a GPU-accelerated optical photon interface with the LHCb detector simulation, to improve computation time of optical photon propagation. The hybrid workflow, combining the particle simulation package Geant4 and Opticks, offloads optical photon propagation to GPUs, thereby accelerating the overall simulation

Peter J. Bussey

An overview is presented of the ALFA forward detector system in the ATLAS detector at the LHC, CERN. Details of the construction are given, with summaries of the resulting analysis.

Wei Cong, Yang Cong, Jiahua Dong, Gan Sun

Incremental semantic segmentation aims to continually learn the segmentation of new coming classes without accessing the training data of previously learned classes. However, most current methods fail to address catastrophic forgetting and background shift since they 1) treat all previous classes equally without considering different forgetting paces caused

Michael J. Schlosser, Nian Hong Zhou

Motivated by recent work of George Andrews and Mircea Merca on the expansion of the quotient of the truncation of Euler's pentagonal number series by the complete series, we provide similar expansion results for averages involving truncations of selected, more general, basic hypergeometric series. In particular, our expansions include new results for average

Farhang Loran, Ali Mostafazadeh

For the scattering of plane electromagnetic waves by a general possibly anisotropic stationary linear medium in three dimensions, we give a condition on the permittivity and permeability tensors of the medium under which the first Born approximation yields the exact expression for the scattered wave whenever the incident wavenumber $k$ does not exceed a pre-

Dongwei Xiao, Zhibo Liu, Shuai Wang

A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch i

Anna Sanfilippo, Ian Moore, Francesco Ballarin, Traian Iliescu

In this paper, we propose a novel ROM stabilization strategy for under-resolved convection-dominated flows, the approximate deconvolution Leray ROM (ADL-ROM). The new ADL-ROM introduces AD as a new means to increase the accuracy of the classical Leray ROM (L-ROM) without degrading its numerical stability. We also introduce two new AD ROM strategies: the Tikh

Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu

Recent text-to-image diffusion models have demonstrated an astonishing capacity to generate high-quality images. However, researchers mainly studied the way of synthesizing images with only text prompts. While some works have explored using other modalities as conditions, considerable paired data, e.g., box/mask-image pairs, and fine-tuning time are required

Jaewon Yun, Yongjeong Oh, Yo-Seb Jeon, H. Vincent Poor

In this paper, a communication-efficient federated learning (FL) framework is proposed for improving the convergence rate of FL under a limited uplink capacity. The central idea of the proposed framework is to transmit the values and positions of the top-$S$ entries of a local model update for uplink transmission. A lossless encoding technique is considered

Ephrem Afele Retta, Richard Sutcliffe, Jabar Mahmood, Michael Abebe Berwo

In a conventional Speech emotion recognition (SER) task, a classifier for a given language is trained on a pre-existing dataset for that same language. However, where training data for a language does not exist, data from other languages can be used instead. We experiment with cross-lingual and multilingual SER, working with Amharic, English, German and URDU

Zongmin Liu, Jirui Wang, Jie Li, Zufeng Li

To better care for the elderly and disabled, it is essential for service robots to have an effective fusion method of object detection and grasp estimation. However, limited research has been observed on the combination of object detection and grasp estimation. To overcome this technical difficulty, a novel integrated method of detection-grasping for specifi

Xilei Zhu, Huiyu Duan, Yuqin Cao, Yuxin Zhu

Omnidirectional videos (ODVs) play an increasingly important role in the application fields of medical, education, advertising, tourism, etc. Assessing the quality of ODVs is significant for service-providers to improve the user's Quality of Experience (QoE). However, most existing quality assessment studies for ODVs only focus on the visual distortions of v

Ilana Sebag, Samuel Cohen, Marc Peter Deisenroth

Imitation learning (IL) seeks to teach agents specific tasks through expert demonstrations. One of the key approaches to IL is to define a distance between agent and expert and to find an agent policy that minimizes that distance. Optimal transport methods have been widely used in imitation learning as they provide ways to measure meaningful distances betwee

Andy Tanjaroon Ly, Benjamin Cohen-Stead, Sohan Malkaruge Costa, Steven Johnston

Theoretical studies suggest that Su-Schrieffer-Heeger-like electron-phonon ($e$-ph) interactions can mediate high-temperature bipolaronic superconductivity that is robust against repulsive electron-electron interactions. Here we present a comparative analysis of the pairing and competing charge/bond correlations in the two-dimensional Holstein and optical Su

Pritam Ganguly, Tapendu Rana, Jayanta Sarkar

Our aim in this article is to study the weighted boundedness of the centered Hardy-Littlewood maximal operator in Harmonic $NA$ groups. Following Ombrosi et al. \cite{ORR}, we define a suitable notion of $A_p$ weights, and for such weights, we prove the weighted $L^p$-boundedness of the maximal operator. Furthermore, as an endpoint case, we prove a variant o

Yongjeong Oh, Jaeho Lee, Christopher G. Brinton, Yo-Seb Jeon

This paper proposes a novel communication-efficient split learning (SL) framework, named SplitFC, which reduces the communication overhead required for transmitting intermediate feature and gradient vectors during the SL training process. The key idea of SplitFC is to leverage different dispersion degrees exhibited in the columns of the matrices. SplitFC inc

Sanjar Shaymatov, Bobomurat Ahmedov, Mariafelicia De Laurentis, Mubasher Jamil

In this paper, we find the higher order expansion parameters $\alpha$ and $\lambda$ of spherically symmetric parametrized Rezzolla--Zhidenko (PRZ) spacetime by using its functions of the radial coordinate. We subject the parameters of this spacetime to classical tests including weak gravitational field effects in Solar System, observations of the S2 star loc

Hanchen Yang, Wengen Li, Shuyu Wang, Hui Li

With the rapid amassing of spatial-temporal (ST) ocean data, many spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, including climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated but with unique characteristics, e.g., diverse regionalit

Yiyuan Zhang, Kaixiong Gong, Kaipeng Zhang, Hongsheng Li

Multimodal learning aims to build models that can process and relate information from multiple modalities. Despite years of development in this field, it still remains challenging to design a unified network for processing various modalities ($\textit{e.g.}$ natural language, 2D images, 3D point clouds, audio, video, time series, tabular data) due to the inh

R. A. Konoplya, D. Ovchinnikov, B. Ahmedov

The Bardeen black hole holds historical significance as the first model of a regular black hole. Recently, there have been proposed interpretations of the Bardeen spacetime as quantum corrections to the Schwarzschild solution. Our study focuses on investigating the quasinormal modes and Hawking radiation of the Bardeen black hole. We have observed that previ

Ben Hambly, Aldaïr Petronilia, Christoph Reisinger, Stefan Rigger

We consider a family of McKean--Vlasov equations arising as the large particle limit of a system of interacting particles on the positive half-line with common noise and feedback. Such systems are motivated by structural models for systemic risk with contagion. This contagious interaction is such that when a particle hits zero, the impact is to move all the

Yuki Amari, Muneto Nitta

Chiral magnets with the Dzyaloshinskii-Moriya (DM) interaction have received quite an intensive focus in condensed matter physics because of the presence of a chiral soliton lattice (CSL), an array of magnetic domain walls and anti-domain walls, and magnetic skyrmions. In this paper, we realize chiral magnets in type-IIA/B string theory by using the Hanany-W

Meixuan He, Yuqing Liang, Jinlan Liu, Dongpo Xu

Adam is a commonly used stochastic optimization algorithm in machine learning. However, its convergence is still not fully understood, especially in the non-convex setting. This paper focuses on exploring hyperparameter settings for the convergence of vanilla Adam and tackling the challenges of non-ergodic convergence related to practical application. The pr

Yafang Zheng, Lei Lin, Shuangtao Li, Yuxuan Yuan

Existing neural models are demonstrated to struggle with compositional generalization (CG), i.e., the ability to systematically generalize to unseen compositions of seen components. A key reason for failure on CG is that the syntactic and semantic representations of sequences in both the uppermost layer of the encoder and decoder are entangled. However, prev

Pietro Curone, Leonardo Testi, Enrique Macias, Marco Tazzari

Protoplanetary disks emit radiation across a broad range of wavelengths, requiring a multiwavelength approach to fully understand their physical mechanisms and how they form planets. Observations at sub-millimeter to centimeter wavelengths can provide insights into the thermal emission from dust, free-free emission from ionized gas, and possible gyro-synchro

Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis Patras

In this paper, we present our method for neural face reenactment, called HyperReenact, that aims to generate realistic talking head images of a source identity, driven by a target facial pose. Existing state-of-the-art face reenactment methods train controllable generative models that learn to synthesize realistic facial images, yet producing reenacted faces

N. P. Ikonnikova, M. A. Burlak, A. V. Dodin, A. A. Belinski

The observations and comprehensive study of intermediate initial mass stars at the late stages of evolution, and after the asymptotic giant branch (AGB) in particular, are of crucial importance to identify the common properties for the stars of given group and to reveal binaries among them. This work aims to investigate photometric and spectral peculiarities

Audrique Vertessen, Robin C. Verstraten, Cristiane Morais Smith

Quantum diffusion is a major topic in condensed-matter physics, and the Caldeira-Leggett model has been one of the most successful approaches to study this phenomenon. Here, we generalize this model by coupling the bath to the system through a Weyl fractional derivative. The Weyl fractional Langevin equation is then derived without imposing a non-Ohmic macro

Mateusz P. Mrozowski, Richard J. Murchie, John Jeffers, Jonathan D. Pritchard

In this paper we demonstrate operation of a quantum-enhanced lidar based on a continuously pumped photon pair source combined with simple detection in regimes with over 5 orders of magnitude separation between signal and background levels and target reflectivity down to -52 dB. We characterise the performance of our detector using a log-likelihood analysis f

Patricia Widjojo, Christoph Treude

Compiler error messages serve as an initial resource for programmers dealing with compilation errors. However, previous studies indicate that they often lack sufficient targeted information to resolve code issues. Consequently, programmers typically rely on their own research to fix errors. Historically, Stack Overflow has been the primary resource for such

João Santos, Triet Tran, Oliver Rippel

Few-shot anomaly detection (AD) is an emerging sub-field of general AD, and tries to distinguish between normal and anomalous data using only few selected samples. While newly proposed few-shot AD methods do compare against pre-existing algorithms developed for the full-shot domain as baselines, they do not dedicatedly optimize them for the few-shot setting.

Anxiang Ge, Nepomuk Ritz, Elias Walter, Santiago Aguirre

A major challenge in the field of correlated electrons is the computation of dynamical correlation functions. For comparisons with experiment, one is interested in their real-frequency dependence. This is difficult to compute, as imaginary-frequency data from the Matsubara formalism require analytic continuation, a numerically ill-posed problem. Here, we app

Zijiao Yang, Arjun Majumdar, Stefan Lee

To be successful, Vision-and-Language Navigation (VLN) agents must be able to ground instructions to actions based on their surroundings. In this work, we develop a methodology to study agent behavior on a skill-specific basis -- examining how well existing agents ground instructions about stopping, turning, and moving towards specified objects or rooms. Our

James H. Hepworth, Amit Kumar Mishra

Sea-Ice drift affects various global processes including the air-sea-ice energy system, numerical ocean modelling, and maritime activity in the polar regions. Drift has been investigated via various technologies ranging from satellite based systems to ship or ice-borne processes. This paper analyses the dynamics of sea-drift in the Arctic over 2019-2021 by F

Lucas Gnecco Heredia, Benjamin Negrevergne, Yann Chevaleyre

Mixtures of classifiers (a.k.a. randomized ensembles) have been proposed as a way to improve robustness against adversarial attacks. However, it has been shown that existing attacks are not well suited for this kind of classifiers. In this paper, we discuss the problem of attacking a mixture in a principled way and introduce two desirable properties of attac

Ondrej Bohdal, Da Li, Timothy Hospedales

Source-free domain adaptation has become popular because of its practical usefulness and no need to access source data. However, the adaptation process still takes a considerable amount of time and is predominantly based on optimization that relies on back-propagation. In this work we present a simple feed-forward approach that challenges the need for back-p

G. A. Ummarino, A. Bianconi

The temperature dependence of the two superconducting gaps in pressurised H_3S at 155 GPa with a critical temperature of 203 K has been determined by data analysis of the experimental curve of the upper critical magnetic field as a function of temperature in the framework of the two bands s-wave Eliashberg theory, with two different phonon mediated intra-ban

Richard J. Murchie, Jonathan D. Pritchard, John Jeffers

In a noisy environment with weak single levels, quantum illumination can outperform classical illumination in determining the presence and range of a target object even in the limit of sub-optimal measurements based on non-simultaneous, phase-insensitive coincidence counts. Motivated by realistic experimental protocols, we present a theoretical framework for

Jianan Liu, Qiuchi Zhao, Weiyi Xiong, Tao Huang

The 4D Millimeter wave (mmWave) radar is a promising technology for vehicle sensing due to its cost-effectiveness and operability in adverse weather conditions. However, the adoption of this technology has been hindered by sparsity and noise issues in radar point cloud data. This paper introduces spatial multi-representation fusion (SMURF), a novel approach

Paz Albares, Pilar G. Estévez, Alejandro González-Parra, Paula del Olmo

This article addresses the study of the complex version of the modified Korteweg-de Vries equation using two different approaches. Firstly, the singular manifold method is applied in order to obtain the associated spectral problem, binary Darboux transformations and $\tau$-functions. The second part concerns the identification of the classical Lie symmetries

Yuhang Lu, Qi Jiang, Runnan Chen, Yuenan Hou

Zero-shot point cloud segmentation aims to make deep models capable of recognizing novel objects in point cloud that are unseen in the training phase. Recent trends favor the pipeline which transfers knowledge from seen classes with labels to unseen classes without labels. They typically align visual features with semantic features obtained from word embeddi

Ahmad Rostami, Dhruvin Patel, Madhusudan Giyyarpuram, Finn Pedersen

5G non-public networks (NPNs) play a key role in enabling critical Industrial Internet of Things (IoT) applications in various vertical industries. Among other features, 5G NPNs enable novel operation models, where the roles and responsibilities for setting up and operating the network can be distributed among several stakeholders, i.e., among the public mob

Maxim Bonnaerens, Joni Dambre

Vision transformers have demonstrated remarkable success in a wide range of computer vision tasks over the last years. However, their high computational costs remain a significant barrier to their practical deployment. In particular, the complexity of transformer models is quadratic with respect to the number of input tokens. Therefore techniques that reduce

Jishnu Ray Chowdhury, Cornelia Caragea

Beam Tree Recursive Neural Network (BT-RvNN) was recently proposed as a simple extension of Gumbel Tree RvNN and it was shown to achieve state-of-the-art length generalization performance in ListOps while maintaining comparable performance on other tasks. However, although not the worst in its kind, BT-RvNN can be still exorbitantly expensive in memory usage

Jens-Joris Decorte, Severine Verlinden, Jeroen Van Hautte, Johannes Deleu

Online job ads serve as a valuable source of information for skill requirements, playing a crucial role in labor market analysis and e-recruitment processes. Since such ads are typically formatted in free text, natural language processing (NLP) technologies are required to automatically process them. We specifically focus on the task of detecting skills (men

Indrajit Debnath, Amar Kumar Banerjee

In this paper we have studied on $\mathcal{I}$-density function using the notion of $\mathcal{I}$-density, introduced by Banerjee and Debnath \cite{banerjee 4} where $\mathcal{I}$ is an ideal of subsets of the set of natural numbers. We have explored certain properties of $\mathcal{I}$-density function and induced a topology using this function in the space

Fan Lu, Yan Xu, Guang Chen, Hongsheng Li

Neural Radiance Fields (NeRFs) have achieved great success in the past few years. However, most current methods still require intensive resources due to ray marching-based rendering. To construct urban-level radiance fields efficiently, we design Deformable Neural Mesh Primitive~(DNMP), and propose to parameterize the entire scene with such primitives. The D

Xifu Liu, Dongdong Liu, Yaping Shi

In this paper, we focus on the perturbation analysis of the largest C-eigenvalue of the piezoelectric-type tensor which has concrete physical meaning which determines the highest piezoelectric coupling constant. Three perturbation bounds are presented, theoretical analysis and numerical examples show that the third perturbation bound has high accuracy when t

Fabio Calefato, Luigi Quaranta, Filippo Lanubile, Marcos Kalinowski

Background. Due to the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) for building software applications, companies are struggling to recruit employees with a deep understanding of such technologies. In this scenario, AutoML is soaring as a promising solution to fill the AI/ML skills gap since it promises to automate the buildi

Junfei Zhang

Music recommendation systems have emerged as a vital component to enhance user experience and satisfaction for the music streaming services, which dominates music consumption. The key challenge in improving these recommender systems lies in comprehending the complexity of music data, specifically for the underpinning music genre classification. The limitatio

C. Adami, K. Parra Ramos, J. T. Harry, M. P. Ulmer

Within the framework of investigating the link between central super massive black holes in the core of galaxies and the galaxies themselves, we detected a variable X-ray source in the center of CGCG 077-102 NED02, member of the CGCG 077-102 galaxy pair within the Abell 2063 galaxy cluster. Our goal was then to combine X-ray and optical data to demonstrate t

Alexey S. Zhevlakov, Dmitry V. Kirpichnikov, Valery E. Lyubovitskij

We study possible impact of dark photons on lepton flavor phenomenology. We derive the constraints on non-diagonal dark photon couplings with leptons by analyzing corresponding contributions to lepton anomalous magnetic moments, rare lepton decays and the prospects of fixed-target experiments aiming for search for light dark matter based on missing energy/mo

Sarthak Johari, Gowri Namratha Meedinti, Radhakrishnan Delhibabu, Deepak Joshi

One of the most important study areas in affective computing is emotion identification using EEG data. In this study, the Gated Recurrent Unit (GRU) algorithm, which is a type of Recurrent Neural Networks (RNNs), is tested to see if it can use EEG signals to predict emotional states. Our publicly accessible dataset consists of resting neutral data as well as

Mustansir Barma

Fluctuation-dominated phase ordering refers to a steady state in which the magnitude of long-range order varies strongly owing to fluctuations, and to the associated coarsening phenomena during the approach to steady state. Strong fluctuations can lead to a number of interesting phenomena, including a cusp singularity in the scaled correlation function, impl

Chandrodoy Chattopadhyay, Ulrich Heinz, Thomas Schaefer

Using the recently developed ``Maximum Entropy'' (or ``least biased'') distribution function to truncate the moment hierarchy arising from kinetic theory, we formulate a far-from-equilibrium macroscopic theory that provides the possibility of describing both free-streaming and hydrodynamic regimes of heavy-ion collisions within a single framework. Unlike tra

Ankur Sikarwar, Mengmi Zhang

Working memory (WM), a fundamental cognitive process facilitating the temporary storage, integration, manipulation, and retrieval of information, plays a vital role in reasoning and decision-making tasks. Robust benchmark datasets that capture the multifaceted nature of WM are crucial for the effective development and evaluation of AI WM models. Here, we int

Niklas Baumgarten, Sebastian Krumscheid, Christian Wieners

We present a novel variant of the multi-level Monte Carlo method that effectively utilizes a reserved computational budget on a high-performance computing system to minimize the mean squared error. Our approach combines concepts of the continuation multi-level Monte Carlo method with dynamic programming techniques following Bellman's optimality principle, an

Ada Canet, Ana I. Gómez De Castro

Forming planets around young, fast-rotating solar-like stars are exposed to an intense X-ray/extreme ultraviolet radiation field and strongly magnetized stellar winds, as a consequence of the high magnetic activity of these stars. Under these conditions, Earth-like exoplanets may experience a rapid loss of their primordial hydrogen atmospheres, resulting in

Sagar Biswas, Nibedita Padhi, Kamal L Panigrahi

We study pulsating strings both on a single stack of NS5-branes and two orthogonal stacks of NS5-branes (the so called I-brane) by using the Polyakov form of the fundamental string action. For the I-brane background, by using a symmetry that decouples the two spheres from the flat geometry, we study pulsating solutions of the string when it pulsates on both

Xue-Chao Feng, Ke-Wei Wei

The mass and decay of the $s\bar{s}$ member of the $1^{3}F_{4}$ meson nonet are investigated in the framework of the Regge phenomenology and the $^{3}P_{0}$ model. We propose, based on the results, that the assignment of the $s\bar{s}$ member of the $1^{3}F_{4}$ meson nonet will require additional testing in the future. Our results also provide information f

Anindya Mondal, Sauradip Nag, Joaquin M Prada, Xiatian Zhu

Existing action recognition methods are typically actor-specific due to the intrinsic topological and apparent differences among the actors. This requires actor-specific pose estimation (e.g., humans vs. animals), leading to cumbersome model design complexity and high maintenance costs. Moreover, they often focus on learning the visual modality alone and sin

Pallavi Saraf, Carlos Allende Prieto, Thirupathi Sivarani, Avrajit Bandyopadhyay

There has been a concerted effort in recent years to identify the astrophysical sites of the $r$-process that can operate early in the Galaxy. The discovery of many $r$-process-enhanced (RPE) stars (especially by the $R$-process Alliance collaboration) has significantly accelerated this effort. However, only limited data exist on the detailed elemental abund

Matteo Mezzadri, Alessandro Chiesa, Luca Lepori, Stefano Carretta

We discuss stabilizer quantum-error correction codes implemented in a single multi-level qudit to avoid resource escalation typical of multi-qubit codes. These codes can be customized to the specific physical errors on the qudit, effectively suppressing them. We demonstrate a Fault-Tolerant implementation on molecular spin qudits, showcasing nearly exponenti

Matthew Collins, Armando Martino

We study generalisations of Chiswell's Theorem that $0$-hyperbolic Lyndon length functions on groups always arise as based length functions of the the group acting isometrically on a tree. We produce counter-examples to show that this Theorem fails if one replaces $0$-hyperbolicity with $\delta$-hyperbolicity. We then propose a set of axioms for the length f

Xin Xu, Xuechun Wang, Shuming Yu, Chenhui Wang

Chemical activation of the intrinsically inert basal planes of transition metal dichalcogenides (TMDs) is crucial for developing high-efficiency electrocatalysts for energy technology applications. Here we report the discovery of an efficient TMD-based topological catalyst for hydrogen evolution reaction (HER), containing high-density single-atom reactive ce

Hiroshi Fujiwara, David Omogbhe, Kamran Sadiq, Alexandru Tamasan

We introduce an analytic method which stably reconstructs both components of a (sufficiently) smooth, real valued, vector field compactly supported in the plane from knowledge of its Doppler transform and its first moment Doppler transform. The method of proof is constructive. Numerical inversion results indicate robustness of the method.

Weidong Chen, Xiaofen Xing, Peihao Chen, Xiangmin Xu

This paper presents a paradigm that adapts general large-scale pretrained models (PTMs) to speech emotion recognition task. Although PTMs shed new light on artificial general intelligence, they are constructed with general tasks in mind, and thus, their efficacy for specific tasks can be further improved. Additionally, employing PTMs in practical application

Fares Essebei, Gianmarco Giovannardi, Simone Verzellesi

In this paper we study Monge solutions to stationary Hamilton-Jacobi equations associated to discontinuous Hamiltonians in the framework of Carnot groups. After showing the equivalence between Monge and viscosity solutions in the continuous setting, we prove existence and uniqueness for the Dirichlet problem, together with a comparison principle and a stabil

Gaétan Leclerc

Let $\Gamma$ be a (convex-)cocompact group of isometries of the hyperbolic space $\mathbb{H}^d$, let $M := \mathbb{H}^d/\Gamma$ be the associated hyperbolic manifold, and consider a real valued potential $F$ on its unit tangent bundle $T^1 M$. Under a natural regularity condition on $F$, we prove that the associated $(\Gamma,F)$-Patterson-Sullivan densities

Haojie Hou, Yan-Xia Ren, Renming Song

Let $Z_t^{(0,\infty)}$ be the point process formed by the positions of all particles alive at time $t$ in a branching Brownian motion with drift and killed upon reaching 0. We study the asymptotic expansions of $Z_t^{(0,\infty)}(A)$ for $A= (a,b)$ and $A=(a,\infty)$ under the assumption that $\sum_{k=1}^\infty k(\log k)^{1+\lambda} p_k <\infty$ for large $\l

Ziyang Jiang, Peng Lin, Tianlei Wang

One-class classification (OCC) aims to train a classifier only with the target class data and attracts great attention for its strong applicability in real-world application. Despite a lot of advances have been made in OCC, it still lacks the effective OCC loss functions for deep learning. In this paper, a novel logarithmic barrier function based OCC loss (L

Josef Diblik, Marek Galewski, Igor Kossowski, Dumitru Motreanu

We investigate the existence of generalized solutions to coercive competing system driven by the (p,q) -Laplacian with unbounded perturbation corresponding to the leading term in the differential operator and with convection depending on the gradient. Some abstract principle leading to the existence of generalized solutions is also derived basing on the Gale

Advait Sarkar

Artificial Intelligence (AI), and in particular generative models, are transformative tools for knowledge work. They problematise notions of creativity, originality, plagiarism, the attribution of credit, and copyright ownership. Critics of generative models emphasise the reliance on large amounts of training data, and view the output of these models as no m

Manuel Klädtke, Moritz Schulze Darup

We introduce the notion of implicit predictors, which characterize the input-(state)-output prediction behavior underlying a predictive control scheme, even if it is not explicitly enforced as an equality constraint (as in traditional model or subspace predictive control). To demonstrate this concept, we derive and analyze implicit predictors for some basic

Ryosuke Ueda, Koh Takeuchi, Hisashi Kashima

The aggregation of multiple opinions plays a crucial role in decision-making, such as in hiring and loan review, and in labeling data for supervised learning. Although majority voting and existing opinion aggregation models are effective for simple tasks, they are inappropriate for tasks without objectively true labels in which disagreements may occur. In pa

Raphael Pruckner, Jakob Reiffenstein, Harald Woracek

The solutions of an indeterminate Hamburger moment problem can be parameterised using the Nevanlinna matrix of the problem. The entries of this matrix are entire functions of minimal exponential type, and any growth less than that can occur. An indeterminate moment problem can be considered as a canonical system in limit circle case by rewriting the three-te

Yingpeng Du, Di Luo, Rui Yan, Hongzhi Liu

Recommending suitable jobs to users is a critical task in online recruitment platforms, as it can enhance users' satisfaction and the platforms' profitability. While existing job recommendation methods encounter challenges such as the low quality of users' resumes, which hampers their accuracy and practical effectiveness. With the rapid development of large

Lei Ding, Claire V. Colin, Virginie Simonet, Chris Stock

In metal-organic-framework (MOF) perovskites, both magnetic and ferroelectric orderings can be readily realized by compounding spin and charge degrees of freedom. The hydrogen bonds that bridge the magnetic framework and organic molecules have long been thought of as a key in generating multiferroic properties. However, the underlying physical mechanisms rem

Md Abdul Kadir, Hasan Md Tusfiqur Alam, Daniel Sonntag

Active learning algorithms have become increasingly popular for training models with limited data. However, selecting data for annotation remains a challenging problem due to the limited information available on unseen data. To address this issue, we propose EdgeAL, which utilizes the edge information of unseen images as {\it a priori} information for measur

A. Roper Pol, A. Neronov, C. Caprini, T. Boyer

We study two possible cosmological consequences of a first-order phase transition in the temperature range of 1 GeV to $10^3$ TeV: the generation of a stochastic gravitational wave background (SGWB) within the sensitivity of the Laser Interferometer Space Antenna (LISA) and, simultaneously, primordial magnetic fields that would evolve through the Universe's