March 2024 arXiv papers — page 20

Zhizhang Wu, Renaud Raquépas, Jack Xin, Zhiwen Zhang

We develop an interacting particle method (IPM) for computing the large deviation rate function of entropy production for diffusion processes, with emphasis on the vanishing-noise limit and high dimensions. The crucial ingredient to obtain the rate function is the computation of the principal eigenvalue $\lambda$ of elliptic, non-self-adjoint operators. We s

Jiazheng Dou, Shamik Ghosh, Larissa Santos, Wen Zhao

The correlations between T, E modes and B modes in cosmic microwave background (CMB) radiation, which are expected to vanish under parity symmetry, have become a sensitive probe of the new physics beyond the standard model. In this paper, we forecast the estimation of TB and EB cross power spectra using NILC and cILC on AliCPT-1 simulations together with Pla

Sishuo Chen, Lei Li, Shuhuai Ren, Rundong Gao

Video paragraph captioning (VPC) involves generating detailed narratives for long videos, utilizing supportive modalities such as speech and event boundaries. However, the existing models are constrained by the assumption of constant availability of a single auxiliary modality, which is impractical given the diversity and unpredictable nature of real-world s

Shengjun Zhang, Xin Fei, Yueqi Duan

Point clouds captured by different sensors such as RGB-D cameras and LiDAR possess non-negligible domain gaps. Most existing methods design different network architectures and train separately on point clouds from various sensors. Typically, point-based methods achieve outstanding performances on even-distributed dense point clouds from RGB-D cameras, while

Ming Yan, Joey Tianyi Zhou, Ivor W. Tsang

Stance detection is the view towards a specific target by a given context (\textit{e.g.} tweets, commercial reviews). Target-related knowledge is often needed to assist stance detection models in understanding the target well and making detection correctly. However, prevailing works for knowledge-infused stance detection predominantly incorporate target know

Dongpeng Han, Chaolu Temuer

Various traditional numerical methods for solving initial value problems of differential equations often produce local solutions near the initial value point, despite the problems having larger interval solutions. Even current popular neural network algorithms or deep learning methods cannot guarantee yielding large interval solutions for these problems. In

Thomas Deppisch, Nils Meyer-Kahlen, Sebastià V. Amengual Garí

Smart glasses are increasingly recognized as a key medium for augmented reality, offering a hands-free platform with integrated microphones and non-ear-occluding loudspeakers to seamlessly mix virtual sound sources into the real-world acoustic scene. To convincingly integrate virtual sound sources, the room acoustic rendering of the virtual sources must matc

Hengran Zhang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke

Retrieval-augmented generation (RAG) is considered to be a promising approach to alleviate the hallucination issue of large language models (LLMs), and it has received widespread attention from researchers recently. Due to the limitation in the semantic understanding of retrieval models, the success of RAG heavily lies on the ability of LLMs to identify pass

João Luís Rosa

In this work, we apply the formalism of dynamical systems to analyze the viability of the $\Lambda$CDM model in a generalized form of the hybrid metric-Palatini gravity theory written in terms of its dynamically equivalent scalar-tensor representation. Adopting a matter distribution composed of two relativistic fluids described by the equations of state of r

Ryong-Gyu Lee, Yong-Hoon Kim

The self-consistent field (SCF) generation of the three-dimensional (3D) electron density distribution ($\rho$) represents a fundamental aspect of density functional theory (DFT) and related first-principles calculations, and how one can shorten or bypass the SCF loop represents a critical question from both practical and fundamental standpoints. Herein, a m

Weihao Jiang, Zhaozhi Xie, Yuxiang Lu, Longjie Qi

Mask-guided matting networks have achieved significant improvements and have shown great potential in practical applications in recent years. However, simply learning matting representation from synthetic and lack-of-real-world-diversity matting data, these approaches tend to overfit low-level details in wrong regions, lack generalization to objects with com

Chuan He, Cong Kevin Xu, Ute Lisenfeld, Y Sophia Dai

We present a study of star-forming galaxies (SFGs) with pseudobulges (bulges with S\'ersic index $\rm n < 2$) in a local close major-merger galaxy pair sample (H-KPAIR). With data from new aperture photometries in the optical and near-infrared bands (aperture size of 7\;kpc) and from the literature, we find that the mean Age of central stellar populations in

Peter Fettke, Wolfgang Reisig

Process science is a highly interdisciplinary field of research. Despite numerous proposals, process science lacks an adequate understanding of the core concepts of the field, including notions such as process, event, and system. A more systematic framework to cope with process science is mandatory. We suggest such a framework using an example. The framework

Yiyuan Yang, Guodong Long, Tao Shen, Jing Jiang

Recently, foundation models, particularly large language models (LLMs), have demonstrated an impressive ability to adapt to various tasks by fine-tuning diverse instruction data. Notably, federated foundation models (FedFM) emerge as a privacy preservation method to fine-tune models collaboratively under federated learning (FL) settings by leveraging many di

Giordano Mattoni, Kazumi Fukushima, Shingo Yonezawa, Fumihiko Nakamura

An appealing direction to change the properties of strongly correlated materials is to induce nonequilibrium steady states by the application of a direct current. While access to these novel states is of high scientific interest, Joule heating due to current flow often constitutes a hurdle to identify nonthermal effects. The biggest challenge usually resides

Mingzhou Xu

In this paper, the complete moment convergence for the partial sums of moving average processes $\{X_n=\sum_{i=-\infty}^{\infty}a_iY_{i+n},n\ge 1\}$ is proved under some proper conditions, where $\{Y_i,-\infty<i<\infty\}$ is a doubly sequence of identically distributed, negatively dependent random variables under sub-linear expectations and $\{a_i,-\infty<i<

Zu-Xing Yang, Xiao-Hua Fan, Zhi-Pan Li, Shunji Nishimura

Employing the isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model, the 1 GeV/nucleon deformed uranium-uranium ultra-central collisions are simulated. Based on sensitive observables, mean square collective flow and pion meson multiplicity, the impacts of high-momentum tails caused by short-range correlations and the symmetry energy in high-density r

L. Benussi, S. Bianco, R. Campagnola, M. Caponero

The High Luminosity LHC phase includes an upgrade to the muon stations for the CMS Experiment. CMS trigger and muon identification performance will be crucial, and it is, therefore, necessary to install new GEM stations to extend acceptance in the high-{\eta} region. An explanation of the quality control test and an update on the status of production will be

Yuya Fujita, Shinji Watanabe, Xuankai Chang, Takashi Maekaku

Non-autoregressive (NAR) models for automatic speech recognition (ASR) aim to achieve high accuracy and fast inference by simplifying the autoregressive (AR) generation process of conventional models. Connectionist temporal classification (CTC) is one of the key techniques used in NAR ASR models. In this paper, we propose a new model combining CTC and a late

Hongbo Lan, Yanrong Li, Shixuan Li, Xin Yi

Vascular activities offer valuable signatures for psychological monitoring applications. We present CogniDot, an affordable, miniature skin sensor placed on the temporal area on the head that senses cognitive loads with a single-pixel color sensor. With its energy-efficient design, bio-compatible adhesive, and compact size (22mm diameter, 8.5mm thickness), i

Hemanth Saratchandran, Sameera Ramasinghe, Simon Lucey

In the realm of computer vision, Neural Fields have gained prominence as a contemporary tool harnessing neural networks for signal representation. Despite the remarkable progress in adapting these networks to solve a variety of problems, the field still lacks a comprehensive theoretical framework. This article aims to address this gap by delving into the int

Wenzhi Ruan, Rony Keppens, Limei Yan, Patrick Antolin

The hour-long, gradual phase of solar flares is well-observed across the electromagnetic spectrum, demonstrating many multi-phase aspects, where cold condensations form within the heated post-flare system, but a complete three-dimensional (3D) model is lacking. Using a state-of-the-art 3D magnetohydrodynamic simulation, we identify the key role played by the

Peng Tang, Tobias Lasser

In this study, we introduce a multi-modal approach that efficiently integrates multi-scale clinical and dermoscopy features within a single network, thereby substantially reducing model parameters. The proposed method includes three novel fusion schemes. Firstly, unlike current methods that usually employ two individual models for for clinical and dermoscopy

Nesrine Bannour, Christophe Servan, Aurélie Névéol, Xavier Tannier

Background: Transformer-based language models have shown strong performance on many Natural LanguageProcessing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest because they can be adaptedto different languages and sub-domains through training or fine-tuning on specific corpora while remaining lighterthan modern Large Language Models (LLM

Olivier Fercoq

Primal-dual algorithms for the resolution of convex-concave saddle point problems usually come with one or several step size parameters. Within the range where convergence is guaranteed, choosing well the step size can make the difference between a slow or a fast algorithm. A usual way to adaptively set step sizes is to ensure that there is a fair balance be

Nicolas Gutehrlé, Iana Atanassova

The digitisation campaigns carried out by libraries and archives in recent years have facilitated access to documents in their collections. However, exploring and exploiting these documents remain difficult tasks due to the sheer quantity of documents available for consultation. In this article, we show how the semantic annotation of the textual content of s

Seongah Jeong, Jinkyu Kang, Osvaldo Simeone, Shlomo Shamai

Perceptive mobile networks implement sensing and communication by reusing existing cellular infrastructure. Cell-free multiple-input multiple-output, thanks to the cooperation among distributed access points, supports the deployment of multistatic radar sensing, while providing high spectral efficiency for data communication services. To this end, the distri

A. M. Kamchatnov

We consider motion of a "magnetic'' soliton in two-component condensates along a non-uniform and time-dependent backgrounds in framework of the Hamiltonian mechanics. Our approach is based on generalization of Stokes' remark that soliton's velocity is related with its inverse half-width by the dispersion law for linear waves continued to the region of comple

Yusuke Yanagisawa, Shin-ichi Sasa

We investigate phase coexistence in a weakly stochastic reaction-diffusion system without assuming a continuum description. Concretely, for $(2N+1)$ diffusion-coupled vessels in which a chemical reaction exhibiting bistability occurs, we derive a condition for the phase coexistence in the limit $N \to \infty$. We then find that the phase coexistence conditio

Martin Durand, Fanny Pascual

We study the collective schedules problem, which consists in computing a one machine schedule of a set of tasks, knowing that a set of individuals (also called voters) have preferences regarding the order of the execution of the tasks. Our aim is to return a consensus schedule. We consider the setting in which all tasks have the same length -- such a schedul

Jeffrey Näf, Erwan Scornet, Julie Josse

Missing values pose a persistent challenge in modern data science. Consequently, there is an ever-growing number of publications introducing new imputation methods in various fields. The present paper attempts to take a step back and provide a more systematic analysis. Starting from an in-depth discussion of the Missing at Random (MAR) condition for nonparam

Dimitrios S. Karachalios, Hossam S. Abbas

In this paper, we present efficient solutions for the nonlinear program (NLP) associated with nonlinear model predictive control (NMPC) by leveraging the linear parameter-varying (LPV) embedding of nonlinear models and sequential quadratic programming (SQP). The corresponding quadratic program (QP) subproblem is systematically constructed and efficiently upd

Martin Durand, Fanny Pascual

The aim of this paper is to introduce models and algorithms for the Participatory Budgeting problem when projects can interact with each other. In this problem, the objective is to select a set of projects that fits in a given budget. Voters express their preferences over the projects and the goal is then to find a consensus set of projects that does not exc

Yiyu Wang, Hao Luo, Jungang Xu, Yingfei Sun

Supervised image captioning approaches have made great progress, but it is challenging to collect high-quality human-annotated image-text data. Recently, large-scale vision and language models (e.g., CLIP) and large-scale generative language models (e.g., GPT-2) have shown strong performances in various tasks, which also provide some new solutions for image

Havi Murad, Nirit Agay, Rachel Dankner

We propose a procedure for imputing missing values of time-dependent covariates in a survival model using fully conditional specification. Specifically, we focus on imputing missing values of a longitudinal marker in joint modeling of the marker and time-to-event data, but the procedure can be easily applied to a time-varying covariate survival model as well

Xue Yang, Wenkai Bai, Chen Jiao, Wu-Ming Liu

We examine the properties of atomic current in a superfluid oscillating circuit consisting of a mesoscopic channel that connects two reservoirs of a Bose-Einstein condensate. We investigate the presence of a critical current in the channel and examine how the amplitude of the oscillations in the number imbalance between the two reservoirs varies with system

Pavel Ondračka, Pauline Kümmerl, Marcus Hans, Stanislav Mráz

We investigate the effect of selected N and Ti point defects in $B$1 TiN on N 1s and Ti\,2p$_{3/2}$ binding energies (BE) by experiments and ab initio calculations. X-ray photoelectron spectroscopy (XPS) measurements of TiN$_x$ films with 1.18 $\le x \le$ 1.37 reveal additional N 1s spectral components at lower binding energies. Ab initio calculations predic

Yaqi Xie, Anjali Rawal, Yujing Cen, Dixuan Zhao

As advanced modern systems like deep neural networks (DNNs) and generative AI continue to enhance their capabilities in producing convincing and realistic content, the need to distinguish between user-generated and machine generated content is becoming increasingly evident. In this research, we undertake a comparative evaluation of eight traditional machine-

Sebastian Yepez Rodriguez, Marshall A. Campbell, Jinyu Liu, Luis A. Jauregui

Excitons, or bound electron-hole pairs, play a crucial role in the optical response of monolayer, 2H-phase transition-metal dichalcogenides (TMDs). They hold significant promise for the development of novel quantum opto-electronic devices due to their large binding energies and strong spin-orbit coupling. Among the monolayer TMDs, MoTe2 stands out because of

Xianchao Wu, Lan Zhang

The problem of obtaining the lower bounds on the restriction of Laplacian eigenfunctions to hypersurfaces inside a compact Riemannian manifold $(M,g)$ is challenging and has been attempted by many authors \cite{BR, GRS, Jun, ET}. This paper aims to show that if $(M,g)$ is assumed to be a negatively curved surface then one can get the corresponding restricted

Dali Zangurashvili

Effective codescent morphisms of $n$-quasigroups and of $n$-loops are characterized. To this end, it is proved that, for any $n\geq 1$, every codescent morphism of $n$-quasigroups (resp. $n$-loops) is effective. This statement generalizes our earlier results on qusigroups and loops. Moreover, it is shown that the elements of the amalgamated free products of

William Gilpin

Living systems operate far from equilibrium, yet few general frameworks provide global bounds on biological transients. In high-dimensional biological networks like ecosystems, long transients arise from the separate timescales of interactions within versus among subcommunities. Here, we use tools from computational complexity theory to frame equilibration i

Suhang Fan, Wei Xu, Renjie Xie, Shi Jin

Channel state information (CSI) feedback is critical for achieving the promised advantages of enhancing spectral and energy efficiencies in massive multiple-input multiple-output (MIMO) wireless communication systems. Deep learning (DL)-based methods have been proven effective in reducing the required signaling overhead for CSI feedback. In practical dual-po

Ray Chew, Stamen Dolaptchiev, Maja-Sophie Wedel, Ulrich Achatz

The representation of subgrid-scale orography is a challenge in the physical parameterization of orographic gravity-wave sources in weather forecasting. A significant hurdle is encoding as much physical information with as simple a representation as possible. Other issues include scale awareness, i.e., the orographic representation has to change according to

Adithya Kulkarni, Oliver Eulenstein, Qi Li

Dependency parsing is an essential task in NLP, and the quality of dependency parsers is crucial for many downstream tasks. Parsers' quality often varies depending on the domain and the language involved. Therefore, it is essential to combat the issue of varying quality to achieve stable performance. In various NLP tasks, aggregation methods are used for pos

Yutaro Mino, Shigeyuki Ishida, Junichiro Kato, Shungo Nakagawa

We grew (Hg,Re)Ba$_2$Ca$_2$Cu$_3$O$_{8+\delta}$ ((Hg,Re)1223) single crystals with good reproducibility via the single-step flux method using monoxides as raw materials. A double-sealing method using a thick-walled quartz tube and a stainless-steel container was adopted for explosion protection. The maximum crystal size was approximately 1 mm x 1 mm in the a

Wen-Shuo Chao, Zhi Zheng, Hengshu Zhu, Hao Liu

Large Language Models (LLMs) demonstrate robust capabilities across various fields, leading to a paradigm shift in LLM-enhanced Recommender System (RS). Research to date focuses on point-wise and pair-wise recommendation paradigms, which are inefficient for LLM-based recommenders due to high computational costs. However, existing list-wise approaches also fa

Maaz Salman, Javad Bolboli, Wan-Young Chung

Underwater environment is substantially less explored territory as compared to earth surface due to lack of robust underwater communication infrastructure. For Internet of Underwater things connectivity, underwater wireless optical communication can play a vital role, compared to conventional radio frequency communication, due to longer range, high data rate

Zhaozhong Chen, Ultan Daly, Aleksandr Boldin, Lenny Hirsch

Atmospheric interaction with light has been an area of fascination for many researchers over the last century. Environmental conditions, such as temperature and wind speed, heavily influence the complex and rapidly varying optical distortions propagating optical fields experience. The continuous random phase fluctuations commonly make deciphering the exact o

Ji Liu, Chunlu Chen, Yu Li, Lin Sun

While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distributed computing with cryptographic techniques, decentralized technologies introduce a novel computing paradigm. Blockchain ensures secure, t

Tianyi Liu, Zhaorui Tan, Kaizhu Huang, Haochuan Jiang

Medical image segmentation presents the challenge of segmenting various-size targets, demanding the model to effectively capture both local and global information. Despite recent efforts using CNNs and ViTs to predict annotations of different scales, these approaches often struggle to effectively balance the detection of targets across varying sizes. Simply

Igor P. Ivanov, Duanyang Zhao

CP4 3HDM is a three-Higgs-doublet model based on a $CP$ symmetry of order 4 (CP4). It is the minimal model incorporating CP4 without leading to accidental symmetries or running into immediate conflict with experiment. Imposing CP4 on the lagrangian induces remarkably tight connections between the scalar and Yukawa sectors, including the unavoidable tree-leve

Gokul Krishnan S, Charalambos Konstantinou

This paper presents a DC microgrid testbed setup that consists of various Distributed Energy Resources (DERs) including solar Photovoltaics (PV), supercapacitors for voltage regulation, and Battery Energy Storage Systems (BESS). The DC microgrid accommodates both non-flexible and flexible loads which can be dynamically adjusted based on PV power availability

Kentaro Ohno, Tatsuhiko Shirai, Nozomu Togawa

Combinatorial optimization has wide applications from industry to natural science. Ising machines bring an emerging computing paradigm for efficiently solving a combinatorial optimization problem by searching a ground state of a given Ising model. Current cutting-edge Ising machines achieve fast sampling of near-optimal solutions of the max-cut problem. Howe

Louie Søs Meyer, Johanne Engel Aaen, Anitamalina Regitse Tranberg, Peter Kun

This Research through Design paper explores how object detection may be applied to a large digital art museum collection to facilitate new ways of encountering and experiencing art. We present the design and evaluation of an interactive application called SMKExplore, which allows users to explore a museum's digital collection of paintings by browsing through

Anil Khachi, Shikha Awasthi, Lalit Kumar, O. S. K. S Sastri

An algorithm$^{\ref{Fig1}}$ has been developed with the purpose of obtaining inverse potentials, where the Riccati-type non-linear differential equation, also called phase equation, has been kept in tandem with the Variational Monte Carlo method. The optimization of Gaussian function parameters is achieved such that the experimental phase shifts are reproduc

Dylan Callaghan, Bernd Fischer

Datasets such as Defects4J and BugsInPy that contain bugs from real-world software projects are necessary for a realistic evaluation of automated debugging tools. However these datasets largely identify only a single bug in each entry, while real-world software projects (including those used in Defects4J and BugsInPy) typically contain multiple bugs at the s

Julian Gerber, Jeremy Hare, John A. Tomsick, Benjamin M. Coughenour

IGR J18434-0508 is a Galactic Intermediate Polar (IP) type Cataclysmic Variable (CV) previously classified through optical spectroscopy. The source is already known to have a hard Chandra spectrum. In this paper, we have used follow-up XMM-Newton and NuSTAR observations to measure the white dwarf (WD) mass and spin period. We measure a spin period of P = 304

Hongyi Sheng

On a compact manifold with boundary, the map consisting of the scalar curvature in the interior and the mean curvature on the boundary is a local surjection at generic metrics. Moreover, this result may be localized to compact subdomains in an arbitrary Riemannian manifold with boundary. The non-generic case (also called non-generic domains) corresponds to s

Qi Xu, Yi Lin, Yunfei Tan, Jianzhao Geng

Magnetic levitation based on the flux pinning nature of type II superconductors has the merit of self-stability, making it appealing for applications such as high speed bearings, maglev trains, space generators, etc. However, such levitation systems physically rely on the superconductor pre-capturing magnetic flux (i.e. field cooling process) before establis

Yexin Wu, Zhuosheng Zhang, Hai Zhao

Large language models have manifested remarkable capabilities by leveraging chain-of-thought (CoT) reasoning techniques to solve intricate questions through step-by-step reasoning chains. Despite its success, the efficacy of such reasoning is inherently contingent upon the quality of CoT. However, flawless CoT reasoning cannot be guaranteed due to the presen

Kazuki Morimoto

Lapid and Mao conjectured Ichino-Ikeda type formula of Whittaker periods for any quasi-split reductive groups and metaplectic groups. In this paper, we prove this formula for any irreducible cuspidal globally generic automorphic representation of quasi-split unitary groups.

Md Rahat Shahriar Zawad, Peter Washington

With the universal adoption of machine learning in healthcare, the potential for the automation of societal biases to further exacerbate health disparities poses a significant risk. We explore algorithmic fairness from the perspective of feature selection. Traditional feature selection methods identify features for better decision making by removing resource

Tianhao Zhou, Haipeng Li, Ziyi Wang, Ao Luo

Image stitching from different captures often results in non-rectangular boundaries, which is often considered unappealing. To solve non-rectangular boundaries, current solutions involve cropping, which discards image content, inpainting, which can introduce unrelated content, or warping, which can distort non-linear features and introduce artifacts. To over

Cameron Gordon, Lachlan Ewen MacDonald, Hemanth Saratchandran, Simon Lucey

Deep implicit functions have been found to be an effective tool for efficiently encoding all manner of natural signals. Their attractiveness stems from their ability to compactly represent signals with little to no offline training data. Instead, they leverage the implicit bias of deep networks to decouple hidden redundancies within the signal. In this paper

Lalit Pathak, Amit Reza, Anand S. Sengupta

Several theoretical waveform models have been developed over the years to capture the gravitational wave emission from the dynamical evolution of compact binary systems of neutron stars and black holes. As ground-based detectors improve their sensitivity at low frequencies, the real-time computation of these waveforms can become computationally expensive, ex

Nhu Vo, Dat Quoc Nguyen, Dung D. Le, Massimo Piccardi

Machine translation for Vietnamese-English in the medical domain is still an under-explored research area. In this paper, we introduce MedEV -- a high-quality Vietnamese-English parallel dataset constructed specifically for the medical domain, comprising approximately 360K sentence pairs. We conduct extensive experiments comparing Google Translate, ChatGPT (

Yutong Chen, Yifan Zhan, Zhihang Zhong, Wei Wang

Neural rendering techniques have significantly advanced 3D human body modeling. However, previous approaches often overlook dynamics induced by factors such as motion inertia, leading to challenges in scenarios like abrupt stops after rotation, where the pose remains static while the appearance changes. This limitation arises from reliance on a single pose a

Ryan Park, Rafael Rafailov, Stefano Ermon, Chelsea Finn

Reinforcement Learning from Human Feedback (RLHF) has been a crucial component in the recent success of Large Language Models. However, RLHF is know to exploit biases in human preferences, such as verbosity. A well-formatted and eloquent answer is often more highly rated by users, even when it is less helpful and objective. A number of approaches have been d

Wufei Ma, Jiahao Li, Bin Li, Yan Lu

Deep learning-based video compression is a challenging task, and many previous state-of-the-art learning-based video codecs use optical flows to exploit the temporal correlation between successive frames and then compress the residual error. Although these two-stage models are end-to-end optimized, the epistemic uncertainty in the motion estimation and the a

Matthias Allard, Mario Kieburg

Exploiting the explicit bijection between the density of singular values and the density of eigenvalues for bi-unitarily invariant complex random matrix ensembles of finite matrix size, we aim at finding the induced probability measure on $j$ eigenvalues and $k$ singular values that we coin $j,k$-point correlation measure. We find an expression for the $1,k$

Nur Rahimah Sakinah Abdul Salam, Jesni Shamsul Shaari, Stefano Mancini

Making use of the Quantum Network formalism of \textit{Phys. Rev. A,} \textbf{82} (2010) 062305, we present the case for quantum networks with finite outcomes, more specifically one which could distinguish only between specific unitary operators in a given basis for operators. Despite its simplicity, we proceed to build a network derived from the optimal str

Cheong-Eung Ahn, Wonjun Lee, Kunihiro Yananose, Youngwook Kim

Utilizing the realistic continuum description of twisted bilayer MoTe2 and many-body exact diagonalization calculation, we establish that the second moir\'e band of twisted bilayer MoTe2, at a small twist angle of approximately 2{\deg}, serves as an optimal platform for achieving the long-sought non-Abelian fractional quantum anomalous Hall states without th

Chinmaya Andukuri, Jan-Philipp Fränken, Tobias Gerstenberg, Noah D. Goodman

When prompting language models to complete a task, users often leave important aspects unsaid. While asking questions could resolve this ambiguity (GATE; Li et al., 2023), models often struggle to ask good questions. We explore a language model's ability to self-improve (STaR; Zelikman et al., 2022) by rewarding the model for generating useful questions-a si

Haoyu Dong, Tram Thi Minh Tran, Rutger Verstegen, Silvia Cazacu

Human-Machine Interfaces (HMIs) for automated vehicles (AVs) are typically divided into two categories: internal HMIs for interactions within the vehicle, and external HMIs for communication with other road users. In this work, we examine the prospects of bridging these two seemingly distinct domains. Through a participatory workshop with automotive user int

Xiangsen Qin

In this paper, we consider the curvature strict positivity of direct image bundles (vector bundles) associated to a strictly pseudoconvex family of bounded domains.The main result is that the curvature of the direct image bundle associated to a strictly pseudoconvex family of bounded domains is strictly positive in the sense of Nakano even if the curvature o

S. J. Ben Yoo, Luis El-Srouji, Suman Datta, Shimeng Yu

The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match. For decades, reverse engineering the brain has been one of the top priorities of science and technology research. Despite numerous efforts, conventional electronics-based methods have failed to match the scalability, en

L. M. Platt, D. Baillie, P. B. Blakie

We examine the low-energy excitations of a dilute supersolid state of matter with a one-dimensional crystal structure. A hydrodynamic description is developed based on a Lagrangian, incorporating generalized elastic parameters derived from ground state calculations. The predictions of the hydrodynamic theory are validated against solutions of the Bogoliubov-

Chenshuang Zhang, Chaoning Zhang, Kang Zhang, Axi Niu

There is a growing concern about applying batch normalization (BN) in adversarial training (AT), especially when the model is trained on both adversarial samples and clean samples (termed Hybrid-AT). With the assumption that adversarial and clean samples are from two different domains, a common practice in prior works is to adopt Dual BN, where BN and BN are

Jinghan Huang, Nanguang Chen, Anqi Qiu

This study, we introduce a novel Topological Cycle Graph Attention Network (CycGAT), designed to delineate a functional backbone within brain functional graph--key pathways essential for signal transmissio--from non-essential, redundant connections that form cycles around this core structure. We first introduce a cycle incidence matrix that establishes an in

Saeed Behzadi, Amir Bagheri, Abbas Rabiee

Due to the increasing occurrence of natural disasters, importance of maintaining sustainable energy for cities and society is felt more than ever. On the other hand, power loss reduction is a challenging issue of active distribution networks (ADNs). In this paper, a new convex optimization model is proposed with two objective functions including energy loss

Mehrdad Ghadiri, Yin Tat Lee, Swati Padmanabhan, William Swartworth

We consider the communication complexity of some fundamental convex optimization problems in the point-to-point (coordinator) and blackboard communication models. We strengthen known bounds for approximately solving linear regression, $p$-norm regression (for $1\leq p\leq 2$), linear programming, minimizing the sum of finitely many convex nonsmooth functions

Alexander Sherman

Let $(\mathfrak{g},\mathfrak{k})$ be a supersymmetric pair arising from a finite-dimensional, symmetrizable Kac-Moody superalgebra $\mathfrak{g}$. An important branching problem is to determine the finite-dimensional highest-weight $\mathfrak{g}$-modules which admit a $\mathfrak{k}$-coinvariant, and thus appear as functions in a corresponding supersymmetric

Seyeon Kim, Siyoon Jin, Jihye Park, Kihong Kim

Conventional GAN-based models for talking head generation often suffer from limited quality and unstable training. Recent approaches based on diffusion models aimed to address these limitations and improve fidelity. However, they still face challenges, including extensive sampling times and difficulties in maintaining temporal consistency due to the high sto

Ahmad Ghasemi, Hossein Pishro-Nik

The surge in demand for efficient radio resource management has necessitated the development of sophisticated yet compact neural network architectures. In this paper, we introduce a novel approach to Graph Neural Networks (GNNs) tailored for radio resource management by presenting a new architecture: the Low Rank Message Passing Graph Neural Network (LR-MPGN

Manu Narayanan, Noëmi Aepli

We present the first parallel dataset for English-Tulu translation. Tulu, classified within the South Dravidian linguistic family branch, is predominantly spoken by approximately 2.5 million individuals in southwestern India. Our dataset is constructed by integrating human translations into the multilingual machine translation resource FLORES-200. Furthermor

Shubham Kumar, Sarmistha Sarkar, Biman Bagchi

A universal dynamical crossover temperature, Tcr, in glassy liquids, associated with the {\alpha}-\b{eta} bifurcation temperature, TB, has been observed in dielectric spectroscopy and other experiments. Tcr lies significantly above the glass transition temperature. Here, we introduce a new class of glass-forming liquids, binary mixtures of prolate and oblate

Huanpeng Chu, Wei Wu, Chengjie Zang, Kun Yuan

Diffusion models have revolutionized image synthesis, setting new benchmarks in quality and creativity. However, their widespread adoption is hindered by the intensive computation required during the iterative denoising process. Post-training quantization (PTQ) presents a solution to accelerate sampling, aibeit at the expense of sample quality, extremely in

Tansel Yucelen, Selahattin Burak Sarsilmaz, Emre Yildirim

Both fixed-gain control and adaptive learning architectures aim to mitigate the effects of uncertainties. In particular, fixed-gain control offers more predictable closed-loop system behavior but requires the knowledge of uncertainty bounds. In contrast, while adaptive learning does not necessarily require such knowledge, it often results in less predictable

Nozomi Nakatsuyama, Masatomo Takahashi

A Bertrand (respectively, Mannheim) curve is a space curve whose principal normal line is the same as the principal normal (respectively, bi-normal) line of another curve. By definition, another curve is a parallel curve with respect to the direction of the principal normal vector. In this paper, we consider the other cases, that is, a space curve whose tang

Saurav Jha, Dong Gong, Lina Yao

Continual learning (CL) aims to help deep neural networks learn new knowledge while retaining what has been learned. Owing to their powerful generalizability, pre-trained vision-language models such as Contrastive Language-Image Pre-training (CLIP) have lately gained traction as practical CL candidates. However, the domain mismatch between the pre-training a

Akihiro Ishibashi, Yoshinori Matsuo, Akane Tanaka

We consider the quantum focusing conjecture (QFC) for two-dimensional evaporating black holes. The QFC is closely related to the behavior of the generalized entropy -- the sum of the area entropy for a given co-dimension two surface and the entanglement entropy for quantum fields outside the area. In the context of the black hole evaporation, the entanglemen

Xiaodong Chen, Yuxuan Hu, Jing Zhang, Yanling Wang

This paper introduces LLM-Streamline, a pioneer work on layer pruning for large language models (LLMs). It is based on the observation that different layers have varying impacts on hidden states, enabling the identification of less important layers to be pruned.LLM-Streamline comprises two parts: layer pruning, which removes consecutive layers with the lowes

Yihong Du, Wenjie Ni, Linfei Shi

This is a continuation of our work \cite{dns-part1} to investigate the long-time dynamics of a two species competition model of Lotka-Volterra type with nonlocal diffusions, where the territory (represented by the real line $\R$) of a native species with density $v(t,x)$, is invaded by a competitor with density $u(t,x)$, via two fronts, $x=g(t)$ on the left

Da-Chao Deng, Hui-Chun Wu

An efficient plasma compression scheme by azimuthally-polarized (AP) light is proposed. An AP light possesses a donut-like intensity pattern, enabling it to compress and accelerate ions toward the optical axis across a wide range of parameters. When the light intensity reaches the relativistic regime of $10^{18}$ $\mathrm{W}/\mathrm{cm}^{2}$, and the plasma

Minje Kim, In-soo Kim, Junil Choi

Limited capacity of fronthaul links in a cell-free massive multiple-input multiple-output (MIMO) system can cause quantization errors at a central processing unit (CPU) during data transmission, complicating the centralized rate optimization problem. Addressing this challenge, we propose a harmony search (HS)-based algorithm that renders the combinatorial no

Yihong Du, Wenjie Ni, Linfei Shi

In this work, we investigate the long-time dynamics of a two species competition model of Lotka-Volterra type with nonlocal diffusions. One of the species, with density $v(t,x)$, is assumed to be a native in the environment (represented by the real line $\R$), while the other species, with density $u(t,x)$, is an invading species which invades the territory

Ameer Taweel, Burcu Yıldız, Alptekin Küpçü

The application of game theory in cybersecurity enables strategic analysis, adversarial modeling, and optimal decision-making to address security threats' complex and dynamic nature. Previous studies by Abraham et al. and Bi\c{c}er et al. presented various definitions of equilibria to examine the security aspects of games involving multiple parties. Nonethel

Kuniyuki Takahashi, Shimpei Masuda, Tadahiro Taniguchi

Ensuring stable object placement is crucial to prevent objects from toppling over, breaking, or causing spills. When an object makes initial contact to a surface, and some force is exerted, the moment of rotation caused by the instability of the object's placing can cause the object to rotate in a certain direction (henceforth referred to as direction of cor

Jianqiang Wan, Sibo Song, Wenwen Yu, Yuliang Liu

Recently, visually-situated text parsing (VsTP) has experienced notable advancements, driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs) capable of processing document-based questions. Various methods have been proposed to address the challenging problem of VsTP. However, due to t