February 2024 arXiv papers — page 24

Benjamin Campillo Aveleira, Aude Gehrmann-De Ridder, Christian T Preuss

Jet production from hadronic Higgs decays at future lepton colliders will have significantly different phenomenological implications than jet production via off-shell photon and $Z$-boson decays, owing to the fact that Higgs bosons decay to both pairs of quarks and gluons. We compute observables involving flavoured jets in hadronic Higgs decays to three part

Felix Truger, Johanna Barzen, Frank Leymann, Julian Obst

The Variational Quantum Eigensolver (VQE) is a Variational Quantum Algorithm (VQA) to determine the ground state of quantum-mechanical systems. As a VQA, it makes use of a classical computer to optimize parameter values for its quantum circuit. However, each iteration of the VQE requires a multitude of measurements, and the optimization is subject to obstruc

Seongbo Jang, Seonghyeon Lee, Hwanjo Yu

As language models are often deployed as chatbot assistants, it becomes a virtue for models to engage in conversations in a user's first language. While these models are trained on a wide range of languages, a comprehensive evaluation of their proficiency in low-resource languages such as Korean has been lacking. In this work, we introduce KoDialogBench, a b

Shuchen Xue, Zhaoqiang Liu, Fei Chen, Shifeng Zhang

Diffusion probabilistic models (DPMs) have shown remarkable performance in high-resolution image synthesis, but their sampling efficiency is still to be desired due to the typically large number of sampling steps. Recent advancements in high-order numerical ODE solvers for DPMs have enabled the generation of high-quality images with much fewer sampling steps

Wenhan Cao, Wei Pan

Integral reinforcement learning (IntRL) demands the precise computation of the utility function's integral at its policy evaluation (PEV) stage. This is achieved through quadrature rules, which are weighted sums of utility functions evaluated from state samples obtained in discrete time. Our research reveals a critical yet underexplored phenomenon: the choic

Ruixuan Liu, Zhengfei Yu

This paper introduces a quasi-Bayesian method that integrates frequentist nonparametric estimation with Bayesian inference in a two-stage process. Applied to an endogenous discrete choice model, the approach first uses kernel or sieve estimators to estimate the control function nonparametrically, followed by Bayesian methods to estimate the structural parame

Antoine Detaille

We consider the strong density problem in the Sobolev space $ W^{s,p}(Q^{m};\mathscr{N}) $ of maps with values into a compact Riemannian manifold $ \mathscr{N} $. It is known, from the seminal work of Bethuel, that such maps may always be strongly approximated by $ \mathscr{N} $-valued maps that are smooth outside of a finite union of $ (m -\lfloor sp \rfloo

Matteo Bastico, Etienne Decencière, Laurent Corté, Yannick Tillier

Point cloud matching, a crucial technique in computer vision, medical and robotics fields, is primarily concerned with finding correspondences between pairs of point clouds or voxels. In some practical scenarios, emphasizing local differences is crucial for accurately identifying a correct match, thereby enhancing the overall robustness and reliability of th

Michael Toker, Oren Mishali, Ophir Münz-Manor, Benny Kimelfeld

There is a large volume of late antique and medieval Hebrew texts. They represent a crucial linguistic and cultural bridge between Biblical and modern Hebrew. Poetry is prominent in these texts and one of its main haracteristics is the frequent use of metaphor. Distinguishing figurative and literal language use is a major task for scholars of the Humanities,

Guodong Sun, Yuting Peng, Le Cheng, Mengya Xu

The precise segmentation of ore images is critical to the successful execution of the beneficiation process. Due to the homogeneous appearance of the ores, which leads to low contrast and unclear boundaries, accurate segmentation becomes challenging, and recognition becomes problematic. This paper proposes a lightweight framework based on Multi-Layer Percept

Angelo A. Casulli, Daniel Kressner, Leonardo Robol

The aim of this work is to develop a fast algorithm for approximating the matrix function $f(A)$ of a square matrix $A$ that is symmetric and has hierarchically semiseparable (HSS) structure. Appearing in a wide variety of applications, often in the context of discretized (fractional) differential and integral operators, HSS matrices have a number of attract

Jan Philipp Drennhaus, Anthuan Ferino-Pérez, Florian Matz, Thomas-C. Jagau

We perform ab initio simulations of the total and partial Auger decay widths of 1s^-1, 2s^-1, and 2p^-1 ionized hydrogen sulfide and 2s^-1 ionized argon with non-Hermitian quantum chemistry. We use coupled cluster theory with single and double substitutions (CCSD) and equation of motion CCSD (EOM-CCSD) and discuss the novel application of (equation of motion

Nikolay V. Kolotinskiy, Victor K. Kornev

A highly effective method of the device linearity calculation on the stage of the device development is worked out and reported. The method allows expressing the linearity in terms of the achievable spurious-free dynamic range (SFDR) of the created devices, in particular superconductive electronic devices, and therefore can be easy correlated with the obtain

Nan van Geloven, Ruth H Keogh, Wouter van Amsterdam, Giovanni Cinà

Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who have already received the treatment the prediction model is meant to inform. Special attention to the causal role of those earlier treatments is required when interpreting the res

Zhen Zhang, Wen-Ming Yan, Jian-Ping Yuan, Na Wang

We report on the results of a search for radio pulsars in five supernova remnants (SNRs) with FAST. The observations were made using the 19-beam receiver in the Snapshot mode. The integration time for each pointing is 10 min. We discovered a new pulsar PSR J1845$-$0306 which has a spin period of 983.6 ms and a dispersion measure of 444.6$\pm$2.0 cm$^{-3}$ pc

Zicheng Zhang, Ruobing Zheng, Ziwen Liu, Congying Han

Recent works in implicit representations, such as Neural Radiance Fields (NeRF), have advanced the generation of realistic and animatable head avatars from video sequences. These implicit methods are still confronted by visual artifacts and jitters, since the lack of explicit geometric constraints poses a fundamental challenge in accurately modeling complex

Munan Li, Xianshi Su, Runze Ma, Tongbang Jiang

Dynamic graphs are extensively employed for detecting anomalous behavior in nodes within the Internet of Things (IoT). Graph generative models are often used to address the issue of imbalanced node categories in dynamic graphs. Nevertheless, the constraints it faces include the monotonicity of adjacency relationships, the difficulty in constructing multi-dim

Yhonatan Gayer, Vladimir Tourbabin, Zamir Ben-Hur, Jacob Donley

In the rapidly evolving fields of virtual and augmented reality, accurate spatial audio capture and reproduction are essential. For these applications, Ambisonics has emerged as a standard format. However, existing methods for encoding Ambisonics signals from arbitrary microphone arrays face challenges, such as errors due to the irregular array configuration

Noureddine Igbida, José Miguel Urbano

We study a granular model for congested crowd motion and pedestrian flow. Our approach is based on an approximation through a Hele-Shaw type equation involving a degenerate operator of $p$-Laplacian type and a linear drift, for which we prove existence and uniqueness using nonlinear semigroup methods and the doubling variables technique. Our main result show

Lian Fu, Ryoichi Ishikawa, Yoshihiro Sato, Takeshi Oishi

The ability to estimate joint parameters is essential for various applications in robotics and computer vision. In this paper, we propose CAPT: category-level articulation estimation from a point cloud using Transformer. CAPT uses an end-to-end transformer-based architecture for joint parameter and state estimation of articulated objects from a single point

Konark Jain, Nick Firoozye, Jonathan Kochems, Philip Treleaven

Limit Order Books (LOBs) serve as a mechanism for buyers and sellers to interact with each other in the financial markets. Modelling and simulating LOBs is quite often necessary for calibrating and fine-tuning the automated trading strategies developed in algorithmic trading research. The recent AI revolution and availability of faster and cheaper compute po

Xinyu Lu, Bowen Yu, Yaojie Lu, Hongyu Lin

The alignment problem in Large Language Models (LLMs) involves adapting them to the broad spectrum of human values. This requirement challenges existing alignment methods due to diversity of preferences and regulatory standards. This paper introduces a novel alignment paradigm, priority rule following, which defines rules as the primary control mechanism in

Sk. Safique Ahmad, Pinki Khatun

This paper proposes a new parameterized enhanced shift-splitting (PESS) preconditioner to solve the three-by-three block saddle point problem (SPP). Additionally, we introduce a local PESS (LPESS) preconditioner by relaxing the PESS preconditioner. Necessary and sufficient criteria are established for the convergence of the proposed PESS iterative process fo

Mei-Wei Hu, Zhuo-Yan Fang, Hou-Jian Duan, Mou Yang

The phenomenon of nonlinear transport has attracted tremendous interest within the condensed matter community. We present a theoretical framework for nonlinear transport based on the nonequilibrium retarded Green's function, and examine the impact of disorder on nonlinear magnetotransport in Weyl semimetals (WSMs). It is demonstrated that bilinear magnetocon

Qi Zhang, Yiming Zhang, Haobo Wang, Junbo Zhao

In the current landscape of large language models (LLMs), the process of instruction tuning serves as an essential step. Considering the high computing power overhead, data-efficient instruction tuning was proposed to reduce the training data size in this process, aiming at selecting high-quality instructional data. Nevertheless, we argue that most current d

Miguel A. Porras

We report a mathematical error and a misinterpretation in arXiv:2103.03263v4 [Phys. Rev. Lett. 127, 193901 (2021)] that has led to a debate about the nature of the transverse orbital angular momentum (OAM) of spatiotemporal optical vortices (STOVs). The transverse OAM of STOVs evaluated theoretically in that Letter is actually only the intrinsic contribution

Michael Ruzhansky, Serikbol Shaimardan, Kanat Tulenov

In this paper, we study H\"ormander type Fourier multiplier theorem and the Nikolskii inequality on quantum tori. On the way to obtain these results, we also prove some classical inequalities such as Paley type, Hausdorff-Young-Paley, Hardy-Littlewood, and Logarithmic Sobolev inequalities on quantum tori. As applications we establish embedding theorems betwe

Wenlian Li, Tian-Qi Huang, Donglian Xu, Huihai He

The Cygnus region, which contains massive molecular and atomic clouds and young stars, is a promising Galactic neutrino source candidate. Cosmic rays transport in the region can produce neutrinos and $\gamma$-rays. Recently, the Large High Altitude Air Shower Observatory (LHAASO) detected an ultrahigh-energy $\gamma$-ray bubble (Cygnus Bubble) in this region

Yancong Lin, Holger Caesar

Scene flow characterizes the 3D motion between two LiDAR scans captured by an autonomous vehicle at nearby timesteps. Prevalent methods consider scene flow as point-wise unconstrained flow vectors that can be learned by either large-scale training beforehand or time-consuming optimization at inference. However, these methods do not take into account that obj

François Hublet, Alexander Kvamme, Srđan Krstić

While Privacy by Design (PbD) is prescribed by modern privacy regulations such as the EU's GDPR, achieving PbD in real software systems is a notoriously difficult task. One emerging technique to realize PbD is Runtime enforcement (RE), in which an enforcer, loaded with a specification of a system's privacy requirements, observes the actions performed by the

Le Cheng, Peican Zhu, Keke Tang, Chao Gao

Source detection in graphs has demonstrated robust efficacy in the domain of rumor source identification. Although recent solutions have enhanced performance by leveraging deep neural networks, they often require complete user data. In this paper, we address a more challenging task, rumor source detection with incomplete user data, and propose a novel framew

Shun-Jia Huang, En-Kun Li, Jian-dong Zhang, Xian Chen

The cosmic distance duality relation (CDDR), expressed as DL(z) = (1 + z)2DA(z), plays an important role in modern cosmology. In this paper, we propose a new method of testing CDDR using strongly lensed gravitational wave (SLGW) signals. Under the geometric optics approximation, we calculate the gravitational lens effects of two lens models, the point mass a

Fabrice Mottez

Electrospheres are environments with the same origin as pulsars; a highly magnetized rotating neutron star. In pulsars, a cascade of electron-positron pair creation enriches the plasma. The plasma surrounding an electrosphere consists only of particles that have escaped from the neutron star's surface. Electrospheres with a magnetic axis aligned with the rot

Yuming Qin, Huite Jiang

In this paper, we study the long-time dynamics of 3D non-autonomous Navier-Stokes-Voigt(NSV) equations with delay. Inspired by [36], we use the contractive function method to prove the pullback D-asymptotical compactness and existence of the pullback attractors. Furthermore, we verify the regularity of pullback attractors by the method in [14, 43, 47] and th

Rahul Sundar, Didier Lucor, Sunetra Sarkar

Recently immersed boundary method-inspired physics-informed neural networks (PINNs) including the moving boundary-enabled PINNs (MB-PINNs) have shown the ability to accurately reconstruct velocity and recover pressure as a hidden variable for unsteady flow past moving bodies. Considering flow past a plunging foil, MB-PINNs were trained with global physics lo

Haojun Jiang, Jiawei Sun, Jie Li, Chentao Wu

Graph representation learning (GRL) makes considerable progress recently, which encodes graphs with topological structures into low-dimensional embeddings. Meanwhile, the time-consuming and costly process of annotating graph labels manually prompts the growth of self-supervised learning (SSL) techniques. As a dominant approach of SSL, Contrastive learning (C

Bhagyarathi Sahoo, Captain R. Singh, Raghunath Sahoo

The recent observation of global spin polarization of $\Lambda$ ($\bar{\Lambda}$) hyperons and the spin alignment of $\phi$ and $K^{*0}$ vector mesons create remarkable interest in investigating the particle polarization in the relativistic fluid produced in heavy-ion collisions at GeV/TeV energies. Among other sources of spin polarization phenomena, the Deb

Arun Kumar A, Alistair Shilton, Sunil Gupta, Santu Rana

Experimental (design) optimization is a key driver in designing and discovering new products and processes. Bayesian Optimization (BO) is an effective tool for optimizing expensive and black-box experimental design processes. While Bayesian optimization is a principled data-driven approach to experimental optimization, it learns everything from scratch and c

Reza Javan, Mehrzad Mohammadi, Mohammad Beheshti-Atashgah, Mohammad Reza Aref

In recent years, the healthcare sector's transition to digital platforms has intensified concerns over data security, privacy, and scalability. Blockchain technology offers a decentralized, secure, and immutable solution to these challenges. This paper presents a scalable, multi-layered blockchain architecture for secure Electronic Health Record (EHR) sharin

Sho Kubota, Kiyoto Yoshino

We completely characterize circulant graphs with valency up to $4$ that admit perfect state transfer. Those of valency $3$ do not admit it. On the other hand, circulant graphs with valency $4$ admit perfect state transfer only in two infinite families: one discovered by Zhan and another new family, while no others do. The main tools for deriving these result

Hiroyuki Ochiai

Tauchi provides an example illustrating the action of a real algebraic subgroup $H$ of $GL(2n, \mathbb{R})$ with finitely many orbits on $\mathbb{R}^{2n}$, while the dimension of the space of relative $H$-invariant distributions on $\mathbb{R}^{2n}$ is infinite. We offer a perspective on this example from the viewpoint of D-modules, where we explicitly deter

Wei Xiang, Haoteng Yin, He Wang, Xiaogang Jin

Pedestrian trajectory prediction is the key technology in many applications for providing insights into human behavior and anticipating human future motions. Most existing empirical models are explicitly formulated by observed human behaviors using explicable mathematical terms with a deterministic nature, while recent work has focused on developing hybrid m

Jing Tian, Sandi Klavžar

Let $X$ be a vertex subset of a graph $G$. Then $u, v\in V(G)$ are $X$-positionable if $V(P)\cap X \subseteq \{u,v\}$ holds for any shortest $u,v$-path $P$. If each two vertices from $X$ are $X$-positionable, then $X$ is a general position set. The general position number of $G$ is the cardinality of a largest general position set of $G$ and has been already

Rahul Sundar, Dipanjan Majumdar, Chhote Lal Shah, Sunetra Sarkar

High-fidelity simulations of unsteady fluid flow are now possible with advancements in high-performance computing hardware and software frameworks. Since computational fluid dynamics (CFD) computations are dominated by linear algebraic routines, they can be significantly accelerated through massive parallelization on graphics processing units (GPUs). Thus, G

Hrant Khachatrian, Rafayel Mkrtchyan, Theofanis P. Raptis

Conventional methods for outdoor environment reconstruction rely predominantly on vision-based techniques like photogrammetry and LiDAR, facing limitations such as constrained coverage, susceptibility to environmental conditions, and high computational and energy demands. These challenges are particularly pronounced in applications like augmented reality nav

Hideyuki Mizuno, Kuniyasu Saitoh, Yusuke Hara, Atsushi Ikeda

Recent research has made significant progress in understanding the non-phonon vibrational states present in amorphous materials. It has been established that their vibrational density of states follows non-Debye scaling laws. Here, we show that the non-Debye scaling laws play a crucial role in determining material properties of a broad range of amorphous sol

Jiaxi Hu, Jingtong Gao, Xiangyu Zhao, Yuehong Hu

The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research. In the initial stages of multimodal sequential recommendation models, the mainstream paradigm was ID-dominant recommendations, wherein multimodal information was fused as side information. However, due to their limitations in

Qin Zhang, Hao Ge, Xiaojun Chen, Meng Fang

Unsupervised question answering is a promising yet challenging task, which alleviates the burden of building large-scale annotated data in a new domain. It motivates us to study the unsupervised multiple-choice question answering (MCQA) problem. In this paper, we propose a novel framework designed to generate synthetic MCQA data barely based on contexts from

Shaoguang Zhang, Haoran Li, Yufei Zhang

Aerodynamic simulations were carried out in the study presented in this paper focusing on the stall performance of the High-Lift Common Research Model obtained from the fourth AIAA High-Lift Prediction Workshop. Various turbulence models of Reynolds-average Navier-Stokes simulations are analyzed. A modified version of the transitional k-(v^2 )-{\omega} model

David A. Towers

This paper is concerned with generalising the results for Lie $CT$-algebras to Leibniz algebras. In some cases our results give a generalisation even for the case of a Lie algebra. Results on $A$-algebras are used to show every Leibniz $CT$-algebra over an algebraically closed field of characteristic different from 2,3 is solvable or is isomorphic to $sl_2(F

Nicolò Oreste Pinciroli Vago, Francesca Forbicini, Piero Fraternali

Non-neural Machine Learning (ML) and Deep Learning (DL) models are often used to predict system failures in the context of industrial maintenance. However, only a few researches jointly assess the effect of varying the amount of past data used to make a prediction and the extension in the future of the forecast. This study evaluates the impact of the size of

Giorgio Saracco

We review some geometric criteria and prove a refined version, that yield existence of capillary surfaces in tubes $\Omega\times \mathbb{R}$ in a gravity free environment, in the case of physical interest, that is, for bounded, open, and simply connected $\Omega \subset \mathbb{R}^2$. These criteria rely on suitable weak one-sided bounds on the curvature of

Léo Mangeolle, Lucile Savary, Leon Balents

We systematically derive the quantum kinetic equation in full phase space for any quadratic hamiltonian of bosonic fields, including in the absence of translational invariance. This enables the treatment of boundaries, inhomogeneous systems and states with non-trivial textures, such as skyrmions in the context of magnetic bosons. We relate the evolution of t

Jose M. Pereira, Daniel Tezze, Iris Niehues, Yaiza Asensio

When doped into a certain range of charge carrier concentrations, MoS2 departs from its pristine semiconducting character to become a strongly correlated material characterized by exotic phenomena such as charge density waves or superconductivity. However, the required doping levels are typically achieved using ionic-liquid gating or air-sensitive alkali-ion

Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome

We study the data selection problem, whose aim is to select a small representative subset of data that can be used to efficiently train a machine learning model. We present a new data selection approach based on $k$-means clustering and sensitivity sampling. Assuming access to an embedding representation of the data with respect to which the model loss is H\

Yohei Nakayama, Shoichi Toyabe

We demonstrate asymmetric enzyme kinetics of a biomolecular motor F1-ATPase between synthesis and hydrolysis of adenosine triphosphate (ATP). Our experiments show that ATP hydrolysis follows Michaelis-Menten kinetics, but ATP synthesis, which is an F1-ATPase's primary biological role, deviates from it. Specifically, the synthesis rate is sustained even at lo

Noriharu Watanabe, Norio Narita, Yasunori Hori

TOI-1518b, a hot Jupiter around a late A-type star, is one of the few planetary systems that transit the edge of the stellar surface (the impact parameter $b\sim0.9 $) among hot Jupiters around hot stars (Cabot et al. 2021). The high rotation speed of the host star ($\sim85$ km s$^{-1}$) and the nearly polar orbit of the planet ($\sim 120$ deg) may cause a n

Mohammad Hossein Keshavarz, Guodong Zhou

This paper is devoted to study the relationship between two important notions in ring theory, category theory, and representation theory of Artin algebras; namely, Gabriel topologies and higher Auslander(-Gorenstein) algebras. It is well-known that the class of all torsionless modules over a higher Auslander(-Gorenstein) algebra is a torsion-free class of a

Alex Teachey, Garvit Agarwal

The search for exomoons in time-domain photometric data has to-date generally consisted of fitting transit models that are comprised of a planet hosting a single moon. This simple model has its advantages, but it may not be particularly representative, as most of the major moons in our Solar System are found in multi-moon satellite systems. It is critical th

Junsu Kim, Hoseong Cho, Jihyeon Kim, Yihalem Yimolal Tiruneh

In the field of class incremental learning (CIL), generative replay has become increasingly prominent as a method to mitigate the catastrophic forgetting, alongside the continuous improvements in generative models. However, its application in class incremental object detection (CIOD) has been significantly limited, primarily due to the complexities of scenes

Timothy M. Chan, Qizheng He, Jie Xue

Let $X$ be a set of points in $\mathbb{R}^2$ and $\mathcal{O}$ be a set of geometric objects in $\mathbb{R}^2$, where $|X| + |\mathcal{O}| = n$. We study the problem of computing a minimum subset $\mathcal{O}^* \subseteq \mathcal{O}$ that encloses all points in $X$. Here a point $x \in X$ is enclosed by $\mathcal{O}^*$ if it lies in a bounded connected compo

Takashi Yamazoe

We summarize the current knowledge on the three limit notions: ultrafilter-limits, closed-ultrafilter-limits and FAM-limits. Also, we consider the possibility to perform an iteration which has all the three limits and clarify the problem we face.

Kamiel Janssens, Guillaume Boileau, Nelson Christensen, Nick van Remortel

We report correlations in underground seismic measurements with horizontal separations of several hundreds of meters to a few kilometers in the frequency range 0.01Hz to 40Hz. These seismic correlations could threaten science goals of planned interferometric gravitational-wave detectors such as the Einstein Telescope as well as atom interferometers such as M

Zehui Chen, Qiuchen Wang, Zhenyu Li, Jiaming Liu

In this report, we present our solution to the multi-task robustness track of the 1st Visual Continual Learning (VCL) Challenge at ICCV 2023 Workshop. We propose a vanilla framework named UniNet that seamlessly combines various visual perception algorithms into a multi-task model. Specifically, we choose DETR3D, Mask2Former, and BinsFormer for 3D object dete

Chenxiang Ma, Jibin Wu, Chenyang Si, Kay Chen Tan

Deep neural networks are typically trained using global error signals that backpropagate (BP) end-to-end, which is not only biologically implausible but also suffers from the update locking problem and requires huge memory consumption. Local learning, which updates each layer independently with a gradient-isolated auxiliary network, offers a promising altern

André Ferreira, Naida Solak, Jianning Li, Philipp Dammann

Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are used to ma

Yaofo Chen, Shuaicheng Niu, Yaowei Wang, Shoukai Xu

The conventional deep learning paradigm often involves training a deep model on a server and then deploying the model or its distilled ones to resource-limited edge devices. Usually, the models shall remain fixed once deployed (at least for some period) due to the potential high cost of model adaptation for both the server and edge sides. However, in many re

Jinyu Zhao, Shu Cai, Yiwen Chen, Genda Gu

What factors fundamentally determine the value of superconducting transition temperature (Tc) in high temperature superconductors has been the subject of intense debate. Following the establishment of an empirical law known as Homes'law, there is a growing consensus in the community that the Tc value of the cuprate superconductors is closely linked to its su

Yoshihiro Tanabe, Yoshinori Yonekura

Results of the long-term monitoring observations of the 6.7 GHz Class II methanol masers associated with the four high-mass star-forming regions by Hitachi 32-m radio telescope are presented. We detected periodic flux variability in G06.795-0.257, G10.472+0.027, G12.209-0.102, and G13.657-0.599 with the periods of 968, 1624, 1272, and 1266 d, respectively, a

M. J. F. Versteeg, Y. Angarita, A. M. Magalhães, M. Haverkorn

Starlight polarimetry, when combined with accurate distance measurements, allows for exploration of the three-dimensional structure of local magnetic fields in great detail. We present optical polarimetric observations of stars in and close to the Southern Coalsack, taken from the Interstellar Polarization Survey (IPS). Located in five fields of view approxi

Francesca Forbicini, Nicolò Oreste Pinciroli Vago, Piero Fraternali

In both industrial and residential contexts, compressor-based machines, such as refrigerators, HVAC systems, heat pumps and chillers, are essential to fulfil production and consumers' needs. The diffusion of sensors and IoT connectivity supports the development of monitoring systems that can detect and predict faults, identify behavioural shifts and forecast

N. M. Jiménez Cruz, Ameek Malhotra, Gianmassimo Tasinato, Ivonne Zavala

Recent Pulsar Timing Array (PTA) collaborations show strong evidence for a stochastic gravitational wave background (SGWB) with the characteristic Hellings-Downs inter-pulsar correlations. The signal may stem from supermassive black hole binary mergers, or early universe phenomena. The former is expected to be strongly anisotropic while primordial background

Huy Quoc To, Ming Liu, Guangyan Huang, Hung-Nghiep Tran

Summarization for scientific text has shown significant benefits both for the research community and human society. Given the fact that the nature of scientific text is distinctive and the input of the multi-document summarization task is substantially long, the task requires sufficient embedding generation and text truncation without losing important inform

Mizuki Fukasawa, Tomokazu Fukuda, Takuya Akashi

High-throughput screening using cell images is an efficient method for screening new candidates for pharmaceutical drugs. To complete the screening process, it is essential to have an efficient process for analyzing cell images. This paper presents a new method for efficiently tracking cells and quantitatively detecting the signal ratio between cytoplasm and

T. Chaumont-Frelet

We propose a novel a posteriori error estimator for the N\'ed\'elec finite element discretization of time-harmonic Maxwell's equations. After the approximation of the electric field is computed, we propose a fully localized algorithm to reconstruct approximations to the electric displacement and the magnetic field, with such approximations respectively fulfi

JuHyeon Lee, Elahe Abdiha, Boris G. Sartakov, Gerard Meijer

Controlling the internal quantum states of chiral molecules for a selected enantiomer has a wide range of fundamental applications. Using tailored microwave fields, a chosen rotational state can be enriched for a selected enantiomer, even starting from a racemic mixture. This enables rapid switching between samples of different enantiomers in a given state,

Alicia Durrer, Philippe C. Cattin, Julia Wolleb

This paper is a contribution to the "BraTS 2023 Local Synthesis of Healthy Brain Tissue via Inpainting Challenge". The task of this challenge is to transform tumor tissue into healthy tissue in brain magnetic resonance (MR) images. This idea originates from the problem that MR images can be evaluated using automatic processing tools, however, many of these t

Sonja Hyrynsalmi, Ella Peltonen, Fanny Vainionpää, Sami Hyrynsalmi

In the extant literature, there has been discussion on the drivers and motivations of minorities to enter the software industry. For example, universities have invested in more diverse imagery for years to attract a more diverse pool of students. However, in our research, we consider whether we understand why students choose their current major and how they

Jianhao Sun, Tao Ying, Richard T. Scalettar, Rubem Mondaini

The interplay of spin and motional degrees of freedom forms a key element in explaining stripe formation accompanied by sublattice reversal of local antiferromagnetic ordering in interacting fermionic models. A long-standing question aims to relate pairing to stripe formation, intending to discern the applicability of simple models that observe this phenomen

Mingxu Tao, Quzhe Huang, Kun Xu, Liwei Chen

The advancement of Multimodal Large Language Models (MLLMs) has greatly accelerated the development of applications in understanding integrated texts and images. Recent works leverage image-caption datasets to train MLLMs, achieving state-of-the-art performance on image-to-text tasks. However, there are few studies exploring which layers of MLLMs make the mo

Timothée Nicolas

This paper investigates the physics of plasma separation in a two species rotating collisional Ohkawa filter, when the source of rotation is an orbital angular momentum carrying wave. The electric field is treated self-consistently with ion and electron radial motion. The injection of angular momentum causes radial currents leading to charge penetration and

Rifki Afina Putri, Faiz Ghifari Haznitrama, Dea Adhista, Alice Oh

Large Language Models (LLMs) are increasingly being used to generate synthetic data for training and evaluating models. However, it is unclear whether they can generate a good quality of question answering (QA) dataset that incorporates knowledge and cultural nuance embedded in a language, especially for low-resource languages. In this study, we investigate

David Cui, Giulio Malavolta, Arthur Mehta, Anand Natarajan

Nonlocal games are a foundational tool for understanding entanglement and constructing quantum protocols in settings with multiple spatially separated quantum devices. In this work, we continue the study initiated by Kalai et al. (STOC '23) of compiled nonlocal games, played between a classical verifier and a single cryptographically limited quantum device.

Linshan Wu, Jiaxin Zhuang, Hao Chen

Self-Supervised Learning (SSL) has demonstrated promising results in 3D medical image analysis. However, the lack of high-level semantics in pre-training still heavily hinders the performance of downstream tasks. We observe that 3D medical images contain relatively consistent contextual position information, i.e., consistent geometric relations between diffe

Runze Li, Yufei Zhang, Haixin Chen

Mesh-agnostic models have advantages in terms of processing unstructured spatial data and incorporating partial differential equations. Recently, they have been widely studied for constructing physics-informed neural networks, but they need to be trained on a case-by-case basis and require large training times. On the other hand, fast prediction and design t

Yang Liu, Xiaomin Yu, Gongyu Zhang, Zhen Zhu

"A data scientist is tasked with developing a low-cost surgical VQA system for a 2-month workshop. Due to data sensitivity, she collects 50 hours of surgical video from a hospital, requiring two months for privacy approvals. Privacy restrictions prevent uploading data to platforms like ChatGPT, so she assembles one annotator and a medical expert to manually

Seeun Park, Hee-Seok Oh, Yaeji Lim

This study proposes a novel method for forecasting a scalar variable based on high-dimensional predictors that is applicable to various data distributions. In the literature, one of the popular approaches for forecasting with many predictors is to use factor models. However, these traditional methods are ineffective when the data exhibit non-Gaussian charact

Jin Liu, Bo Wang, Chuanming Wang, Huiyuan Fu

Exposure correction aims to enhance visual data suffering from improper exposures, which can greatly improve satisfactory visual effects. However, previous methods mainly focus on the image modality, and the video counterpart is less explored in the literature. Directly applying prior image-based methods to videos results in temporal incoherence with low vis

Yuanhang Yang, Shiyi Qi, Wenchao Gu, Chaozheng Wang

Sparse models, including sparse Mixture-of-Experts (MoE) models, have emerged as an effective approach for scaling Transformer models. However, they often suffer from computational inefficiency since a significant number of parameters are unnecessarily involved in computations via multiplying values by zero or low activation values. To address this issue, we

Bernardo Ameneyro, Rebekah Herrman, George Siopsis, Vasileios Maroulas

Topological Data Analysis methods can be useful for classification and clustering tasks in many different fields as they can provide two dimensional persistence diagrams that summarize important information about the shape of potentially complex and high dimensional data sets. The space of persistence diagrams can be endowed with various metrics such as the

Xinyu Chen, Yuanqi Xie, Achraf Cohen, Shusen Pu

This paper presents a new methodology for generating continuous statistical distributions, integrating the exponentiated odds ratio within the framework of survival analysis. This new method enhances the flexibility and adaptability of distribution models to effectively address the complexities inherent in contemporary datasets. The core of this advancement

Zhenhui Ding, Mohammad Hossein Keshavarz, Guodong Zhou

It is well known that for Auslander algebras, the category of all (finitely generated) projective modules is an abelian category and this property of abelianness characterizes Auslander algebras by Tachikawa theorem in 1974. Let $n$ be a positive integer. In this paper, by using torsion theoretic methods, we show that $ n $-Auslander algebras can be characte

Weijing Tao, Biwen Lei, Kunhao Liu, Shijian Lu

Text-to-Avatar generation has recently made significant strides due to advancements in diffusion models. However, most existing work remains constrained by limited diversity, producing avatars with subtle differences in appearance for a given text prompt. We design DivAvatar, a novel framework that generates diverse avatars, empowering 3D creatives with a mu

Mark E. Turiansky, Chris G. Van de Walle

The coherence times of state-of-the-art superconducting qubits are limited by bulk dielectric loss, yet the microscopic mechanism leading to this loss is unclear. Here we propose that the experimentally observed loss can be attributed to the presence of charged defects that enable the absorption of electromagnetic radiation by the emission of acoustic phonon

Christoph Hunkenschröder, Kim-Manuel Klein, Martin Koutecký, Alexandra Lassota

We study fundamental block-structured integer programs called tree-fold and multi-stage IPs. Tree-fold IPs admit a constraint matrix with independent blocks linked together by few constraints in a recursive pattern; and transposing their constraint matrix yields multi-stage IPs. The state-of-the-art algorithms to solve these IPs have an exponential gap in th

Gabriele Serussi, Tamir Shor, Tom Hirshberg, Chaim Baskin

Multi-rotor aerial autonomous vehicles (MAVs) primarily rely on vision for navigation purposes. However, visual localization and odometry techniques suffer from poor performance in low or direct sunlight, a limited field of view, and vulnerability to occlusions. Acoustic sensing can serve as a complementary or even alternative modality for vision in many sit

Joanna Kujawa, Samer Al Gharabli, Anthony Szymczyk, Artur P. Terzyk

This article aims to: i) review the current membrane-based methods in REEs separation focusing on non-liquid membranes (imprinted, polymer inclusion, nanocomposite, metal-/covalent organic framework membranes), ii) present the considerations of the essential scientific and technical issues, e.g. extraction performances, separation efficiency, REEs transport

Jingwei Zhang, Cheuk Ting Li, Farzan Farnia

The massive developments of generative model frameworks require principled methods for the evaluation of a model's novelty compared to a reference dataset. While the literature has extensively studied the evaluation of the quality, diversity, and generalizability of generative models, the assessment of a model's novelty compared to a reference model has not

François Fages

Constraint logic programming emerged in the late 80's as a highly declarative class of programming languages based on first-order logic and theories with decidable constraint languages, thereby subsuming Prolog restricted to equality constraints over the Herbrand's term domain. This approach has proven extremely successfull in solving combinatorial problems

Zhaoyang Wang, Dongyang Li, Mingyang Zhang, Hao Luo

Existing hyperspectral image (HSI) super-resolution (SR) methods struggle to effectively capture the complex spectral-spatial relationships and low-level details, while diffusion models represent a promising generative model known for their exceptional performance in modeling complex relations and learning high and low-level visual features. The direct appli