December 2024 arXiv papers — page 127

Gaoxiang Cong, Jiadong Pan, Liang Li, Yuankai Qi

Given a piece of text, a video clip, and a reference audio, the movie dubbing task aims to generate speech that aligns with the video while cloning the desired voice. The existing methods have two primary deficiencies: (1) They struggle to simultaneously hold audio-visual sync and achieve clear pronunciation; (2) They lack the capacity to express user-define

Rakhymzhan Kazbek, Yogi Erlangga, Yerlan Amanbek, Dongming Wei

Computational efficiency is essential for enhancing the accuracy and practicality of pricing complex financial derivatives. In this paper, we discuss Isogeometric Analysis (IGA) for valuing financial derivatives, modeled by two nonlinear Black-Scholes PDEs: the Leland model for European call with transaction costs and the AFV model for convertible bonds with

Leo S. I. Lam, Hai-Yao Deng, Wei-Bing Zhang, Udoka Nwankwo

The physics of glass has been a significant topic of interest for decades. Dynamical facilitation is widely believed to be an important characteristic of glassy dynamics, but the precise mechanism is still under debate. We propose a lattice model of glass called the facilitated random walk (FRW). Each particle performs continuous time random walk in the pres

Tianshi Zheng, Weihan Li, Jiaxin Bai, Weiqi Wang

Retrieval-Augmented Generation (RAG) systems show remarkable potential as question answering tools in the K-12 Education domain, where knowledge is typically queried within the restricted scope of authoritative textbooks. However, discrepancies between these textbooks and the parametric knowledge inherent in Large Language Models (LLMs) can undermine the eff

Javad M Alizadeh, Jay S Patel, Gabriel Tajeu, Yuzhou Chen

Over 30 million Americans are affected by Type II diabetes (T2D), a treatable condition with significant health risks. This study aims to develop and validate predictive models using machine learning (ML) techniques to estimate emergency department (ED) visits among patients with T2D. Data for these patients was obtained from the HealthShare Exchange (HSX),

Daniel A. Williams, Airlie Chapman, Chris Manzie

Inspired by the increased cooperation between humans and autonomous systems, we present a new hybrid systems framework capturing the interconnected dynamics underlying these interactions. The framework accommodates models arising from both the autonomous systems and cognitive psychology literature in order to represent key elements such as human trust in the

Xin He, Jingwen Xie, Aohua Zhang, Weiwei Jiang

The potential of Wi-Fi backscatter communications systems is immense, yet challenges such as signal instability and energy constraints impose performance limits. This paper introduces FlexScatter, a Wi-Fi backscatter system using a designed scheduling strategy based on excitation prediction and rateless coding to enhance system performance. Initially, a Wi-F

Chengdi Ma

The multilevel Schwarz preconditioner is one of the most popular parallel preconditioners for enhancing convergence and improving parallel efficiency. However, its parallel implementation on arbitrary unstructured triangular/tetrahedral meshes remains challenging. The challenges mainly arise from the inability to ensure that mesh hierarchies are nested, whic

Dimitrios Sikeridis, Dennis Ramdass, Pranay Pareek

Recently, the number of off-the-shelf Large Language Models (LLMs) has exploded with many open-source options. This creates a diverse landscape regarding both serving options (e.g., inference on local hardware vs remote LLM APIs) and model heterogeneous expertise. However, it is hard for the user to efficiently optimize considering operational cost (pricing

Márton Marits

We define the cover number of a graph $G$ by a graph class $\mathcal P$ as the minimum number of graphs of class $\mathcal P$ required to cover the edge set of $G$. Taking inspiration from a paper by Harary, Hsu and Miller, we find an exact formula for the cover number by the graph classes $\{ G \mid \chi(G) \leq f(\omega(G))\}$ for an arbitrary non-decreasi

Zirun Guo, Xize Cheng, Yangyang Wu, Tao Jin

Efficient transfer learning methods such as adapter-based methods have shown great success in unimodal models and vision-language models. However, existing methods have two main challenges in fine-tuning multimodal models. Firstly, they are designed for vision-language tasks and fail to extend to situations where there are more than two modalities. Secondly,

Hongzhi Pan, Shengliang Wu, Lingyun Wang, Yujun Zhu

To address the challenges of robust data transmission over complex time-varying channels, this paper introduces channel learning and enhanced adaptive reconstruction (CLEAR) strategy for semantic communications. CLEAR integrates deep joint source-channel coding (DeepJSCC) with an adaptive diffusion denoising model (ADDM) to form a unique framework. It levera

Siao-Hao Guo

The level set flow of a mean-convex closed hypersurface is stable off singularities, in the sense that the level set flow of the perturbed hypersurface would be close in the smooth topology to the original flow wherever the latter is regular. To study the behavior near singularities, we further assume that the initial hypersurface is two-convex and that the

Lianrui Mu, Xingze Zhou, Wenjie Zheng, Jiangnan Ye

Creating realistic pose-guided image-to-video character animations while preserving facial identity remains challenging, especially in complex and dynamic scenarios such as dancing, where precise identity consistency is crucial. Existing methods frequently encounter difficulties maintaining facial coherence due to misalignments between facial landmarks extra

Suhwan Cho, Seoung Wug Oh, Sangyoun Lee, Joon-Young Lee

Video inpainting (VI) is a challenging task that requires effective propagation of observable content across frames while simultaneously generating new content not present in the original video. In this study, we propose a robust and practical VI framework that leverages a large generative model for reference generation in combination with an advanced pixel

Wenlong Shi, Yang Jiao, Salvatore Torquato

Rigorous theories connecting physical properties of a heterogeneous material to its microstructure offer a promising avenue to guide the computational material design and optimization. We present here an efficient Fourier-space based computational framework and employ a variety of analytical ${\tilde \chi}_{_V}({k})$ functions that satisfy all known necessar

Yifan Zhang, Junhui Hou

Cross-modal contrastive distillation has recently been explored for learning effective 3D representations. However, existing methods focus primarily on modality-shared features, neglecting the modality-specific features during the pre-training process, which leads to suboptimal representations. In this paper, we theoretically analyze the limitations of curre

Ruiwen Zhou, Wenyue Hua, Liangming Pan, Sitao Cheng

This paper introduces RuleArena, a novel and challenging benchmark designed to evaluate the ability of large language models (LLMs) to follow complex, real-world rules in reasoning. Covering three practical domains -- airline baggage fees, NBA transactions, and tax regulations -- RuleArena assesses LLMs' proficiency in handling intricate natural language ins

Yujin An, Daniel Mitchell, John Lathrop, David Flynn

Brain-computer interfaces (BCI) have the potential to provide transformative control in prosthetics, assistive technologies (wheelchairs), robotics, and human-computer interfaces. While Motor Imagery (MI) offers an intuitive approach to BCI control, its practical implementation is often limited by the requirement for expensive devices, extensive training dat

Xiaochuan Lin, Xiangyong Chen

Query-focused summarization over multi-table data is a challenging yet critical task for extracting precise and relevant information from structured data. Existing methods often rely on complex preprocessing steps and struggle to generalize across domains or handle the logical reasoning required for multi-table queries. In this paper, we propose QueryTableSu

Tianyang Wang, Ziqian Bi, Yichao Zhang, Ming Liu

Deep learning has transformed AI applications but faces critical security challenges, including adversarial attacks, data poisoning, model theft, and privacy leakage. This survey examines these vulnerabilities, detailing their mechanisms and impact on model integrity and confidentiality. Practical implementations, including adversarial examples, label flippi

Finn M. Stokes, Waseem Kamleh, Derek B. Leinweber, Benjamin J. Owen

Lattice QCD calculations of the $2s$ radial excitation of the nucleon place the state at an energy of approximately 1.9 GeV, raising the possibility that it is associated with the $N1/2^+(1880)$ and $N1/2^+(1710)$ resonances through mixing with two-particle meson-baryon states. The discovery of the $N1/2^+(1880)$ resonance in pion photoproduction but not in

José Luis Flores, Jónatan Herrera, Didier A. Solis

In this work we establish a version of the Bartnik Splitting Conjecture in the context of Lorentzian length spaces. In precise terms, we show that under an appropriate timelike completeness condition, a globally hyperbolic Lorentzian length space of the form $\Sigma\times \mathbb{R}$ with $\Sigma$ compact splits as a metric Lorentzian product, provided it ha

Gourab Kumar Sar, Sheida Ansarinasab, Fahimeh Nazarimehr, Farnaz Ghassemi

Swarmalators are entities that combine the swarming behavior of particles with the oscillatory dynamics of coupled phase oscillators and represent a novel and rich area of study within the field of complex systems. Unlike traditional models that treat spatial movement and phase synchronization separately, swarmalators exhibit a unique coupling between their

Zihan Ji, Xuetao Tian, Ye Liu

The scarcity of high-quality large-scale labeled datasets poses a huge challenge for employing deep learning models in video deception detection. To address this issue, inspired by the psychological theory on the relation between deception and expressions, we propose a novel method called AFFAKT in this paper, which enhances the classification performance by

Marek Biskup, Haiyu Huang

Given a square box $\Lambda_n\subseteq\mathbb Z^2$ of side length $L^n$ with $L,n>1$, we study hierarchical random fields $\{\phi_x\colon x\in\Lambda_n\}$ with law proportional to ${\rm e}^{\frac12\beta(\phi,\Delta_n\phi)}\prod_{x\in\Lambda_n}\nu({\rm d}\phi_x)$, where $\beta>0$ is the inverse temperature, $\Delta_n$ is a hierarchical Laplacian on $\Lambda_n

Berikbol T. Torebek

The paper is devoted to the study of critical cases of the nonlinear Schr\"{o}dinger (NLS) equation with source and gradient terms, subsequently providing answers to some open questions posed by Alotaibi et al in [Z. Angew. Math. Phys., 73 (2022), 1-17]. The main results state that in critical cases, the problem under consideration does not have any global-i

Moumita Patra

In this paper we present the Kaluza-Klein mass spectrum of 0-form and 1-form fields that appear in AdS$_4\times B_7$ compactification of $d=11$ supergravity, where, $B_7=U(1)\setminus U(3)/ U(1)$. This theory belongs to the class of gravity duals to three dimensional quiver Chern-Simons matter theories as demonstrated by D.L Jafferis and A. Tomasiello in \ci

Yin Tang, Bing Li

We introduce a unified, flexible, and easy-to-implement framework of sufficient dimension reduction that can accommodate both linear and nonlinear dimension reduction, and both the conditional distribution and the conditional mean as the targets of estimation. This unified framework is achieved by a specially structured neural network -- the Belted and Ensem

Thomas Kabelitz, Waseem Kamleh, Derek Leinweber

The magnetic polarisabilities of octet baryons are calculated close to the physical quark-mass point using the background field method in lattice QCD. This first calculation draws on the identification and elimination of exceptional configurations that have hindered previous attempts. The origin of the exceptional configuration problem lies in the use of a W

Hayato Kanno, Shinichiro Akiyama, Kotaro Murakami, Shinji Takeda

We use the Grassmann tensor renormalization group method to investigate the $N_f=2$ Schwinger model with the staggered fermions in the presence of a $2\pi$ periodic $\theta$ term in a broad range of mass. The method allows us to deal with the massive staggered fermions straightforwardly and to study the $\theta$ dependence of the free energy and topological

Stephen P. Martin

A higgsino could be some or all of the dark matter, with a mass bounded from above by about 1.1 TeV assuming a thermal freezeout density, and from below by collider searches. Direct detection experiments imply purity constraints on a dark matter higgsino, limiting the mixing with the electroweak gauginos. Using the new strong limits available as of the end o

Dongliang Cai, Liang Zhang, Borui Chen, Haibin Kan

Decentralized data sovereignty and secure data exchange are regarded as foundational pillars of the new era. Attribute-based encryption (ABE) is a promising solution that enables fine-grained access control in data sharing. Recently, Hohenberger et al. (Eurocrypt 2023) introduced registered ABE (RABE) to eliminate trusted authority and gain decentralization.

Huiping Lin, Ruixuan Deng, Chris Z. Yao, Zhengfeng Ji

Quantum network research at both the software stack and hardware implementation level has become an exciting area of quantum information science. Although demonstrations of small-scale quantum networks have emerged in the past decade, quantum communication and computation hardware remain scarce resources today. As a result, the evaluation and validation of q

Mateo Alejandro Rojas, Rafael Carranza

Cross-lingual in-context learning (XICL) has emerged as a transformative paradigm for leveraging large language models (LLMs) to tackle multilingual tasks, especially for low-resource languages. However, existing approaches often rely on external retrievers or task-specific fine-tuning, limiting their scalability and generalizability. In this paper, we propo

Hippolyte Charvin, Nicola Catenacci Volpi, Daniel Polani

Extraction of structure, in particular of group symmetries, is increasingly crucial to understanding and building intelligent models. In particular, some information-theoretic models of parsimonious learning have been argued to induce invariance extraction. Here, we formalise these arguments from a group-theoretic perspective. We then extend them to the stud

Tarique Anwar, Giulia Tagliabue

Harnessing natural evaporation offers a sustainable and untapped pathway for next-generation energy technologies. Here, we present a unified physical and experimental framework for evaporation-driven hydrovoltaic (EDHV) systems that decouples and systematically controls the key interfacial processes underlying electricity generation from ambient heat and sun

Abhishek Banerjee, Subhajit Das, Surjeet Kour

We use categorification of monoid actions to study algebraic geometry over symmetric monoidal categories. This brings together the relative algebraic geometry over symmetric monoidal categories developed by To\"{e}n and Vaqui\'{e}, along with the theory of actegories over monoidal categories. We obtain schemes over a datum $(\mathcal C,\mathcal M)$, where $(

Zhongyang Zhang, Jinhe Wen, Zixi Chen, Dara Arbab

Frames Per Second (FPS) significantly affects the gaming experience. Providing players with accurate FPS estimates prior to purchase benefits both players and game developers. However, we have a limited understanding of how to predict a game's FPS performance on a specific device. In this paper, we first conduct a comprehensive analysis of a wide range of fa

Lulu Zhao, Weihao Zeng, Xiaofeng Shi, Hua Zhou

Recently, both closed-source LLMs and open-source communities have made significant strides, outperforming humans in various general domains. However, their performance in specific professional domains such as medicine, especially within the open-source community, remains suboptimal due to the complexity of medical knowledge. In this paper, we propose CareBo

Xuehai He, Shuohang Wang, Jianwei Yang, Xiaoxia Wu

Recent advancements in diffusion models have shown great promise in producing high-quality video content. However, efficiently training video diffusion models capable of integrating directional guidance and controllable motion intensity remains a challenging and under-explored area. To tackle these challenges, this paper introduces Mojito, a diffusion model

Yifeng Yao, Zichen Liu, Zhenyu Cui, Yuxin Peng

Pre-trained Vision Mamba (Vim) models have demonstrated exceptional performance across various computer vision tasks in a computationally efficient manner, attributed to their unique design of selective state space models. To further extend their applicability to diverse downstream vision tasks, Vim models can be adapted using the efficient fine-tuning techn

Lulu Zhao, Weihao Zeng, Xiaofeng Shi, Hua Zhou

Recently, LoRA has emerged as a crucial technique for fine-tuning large pre-trained models, yet its performance in multi-task learning scenarios often falls short. In contrast, the MoE architecture presents a natural solution to this issue. However, it introduces challenges such as mutual interference of data across multiple domains and knowledge forgetting

Gyeo-Re Han, Artem Goncharov, Merve Eryilmaz, Shun Ye

Democratizing biomarker testing at the point-of-care requires innovations that match laboratory-grade sensitivity and precision in an accessible format. Here, we demonstrate high-sensitivity detection of cardiac troponin I (cTnI) through innovations in chemiluminescence-based sensing, imaging, and deep learning-driven analysis. This chemiluminescence vertica

Tornike Karchkhadze, Keren Shao, Shlomo Dubnov

This work presents a novel method for composing and improvising music inspired by Cornelius Cardew's Treatise, using AI to bridge graphic notation and musical expression. By leveraging OpenAI's ChatGPT to interpret the abstract visual elements of Treatise, we convert these graphical images into descriptive textual prompts. These prompts are then input into M

Jiaqi Liu, Changhua Yang

In this paper we compute the higher order long time asymptotics of the defocussing nonlinear Schr\"odinger equation using the $\overline{\partial}$-nonlinear steepest descent method. We assume initial condition in weighted Sobolev space with finite order of regularity and decay.

Rajes Ghosh, Akash K Mishra, Sudipta Sarkar

The uniqueness and rigidity theorems assert that the asymptotically flat, vacuum, stationary rotating black hole solution in general relativity must be the Kerr solution, exhibiting novel symmetries such as axisymmetry and circularity. In our analysis of post-merger ringdown signal from coalescing black hole binary systems, we identify potential observationa

Xichen Ye, Yifan Wu, Weizhong Zhang, Xiaoqiang Li

Previous research has shown that constraining the gradient of loss function with respect to model-predicted probabilities can enhance the model robustness against noisy labels. These methods typically specify a fixed optimal threshold for gradient clipping through validation data to obtain the desired robustness against noise. However, this common practice o

Kart-Leong Lim

Deep clustering is an emerging topic in deep learning where traditional clustering is performed in deep learning feature space. However, clustering and deep learning are often mutually exclusive. In the autoencoder based deep clustering, the challenge is how to jointly optimize both clustering and dimension reduction together, so that the weights in the hidd

Yunshuai Zhou, Junbo Qiao, Jincheng Liao, Wei Li

Knowledge distillation (KD) is a valuable yet challenging approach that enhances a compact student network by learning from a high-performance but cumbersome teacher model. However, previous KD methods for image restoration overlook the state of the student during the distillation, adopting a fixed solution space that limits the capability of KD. Additionall

Jiaheng Lu, Yiwen Zhang, Hasan Al Maruf, Minseo Park

Memory tiering has received wide adoption in recent years as an effective solution to address the increasing memory demands of memory-intensive workloads. However, existing tiered memory systems often fail to meet service-level objectives (SLOs) when multiple applications share the system because they lack Quality-of-Service (QoS) support. Consequently, appl

Yunhui Liu, Qizhuo Xie, Jinwei Shi, Jiaxu Shen

Heterogeneous Text-Attributed Graphs (HTAGs), where different types of entities are not only associated with texts but also connected by diverse relationships, have gained widespread popularity and application across various domains. However, current research on text-attributed graph learning predominantly focuses on homogeneous graphs, which feature a singl

Abdollah Jabbari, Y A Joarder, Benjamin Teyssier, Carol Fung

QUIC protocol is primarily designed to optimize web performance and security. However, previous research has pointed out that it is vulnerable to handshake flooding attacks. Attackers can send excessive volume of handshaking requests to exhaust the CPU resource of the server, through utilizing the large CPU amplification factor occurred during the handshake

Ken Kikuchi

Consider a renormalization group flow preserving a pre-modular fusion category $\mathcal S_1$. If it flows to a rational conformal field theory, the surviving symmetry $\mathcal S_1$ flows to a pre-modular fusion category $\mathcal S_2$ with monoidal functor $F:\mathcal S_1\to\mathcal S_2$. By clarifying mathematical (especially category theoretical) meaning

P. C. Lopez-Custodio

The need for statistical models of orientations arises in many applications in engineering and computer science. Orientational data appear as sets of angles, unit vectors, rotation matrices or quaternions. In the field of directional statistics, a lot of advances have been made in modelling such types of data. However, only a few of these tools are used in e

Kart-Leong Lim

Deep clustering is a recent deep learning technique which combines deep learning with traditional unsupervised clustering. At the heart of deep clustering is a loss function which penalizes samples for being an outlier from their ground truth cluster centers in the latent space. The probabilistic variant of deep clustering reformulates the loss using KL dive

Ion Grama, Hui Xiao

Consider a random walk $S_n=\sum_{i=1}^n X_i$ with independent and identically distributed real-valued increments with zero mean, finite variance and moment of order $2 + \delta$ for some $\delta>0$. For any starting point $x\in \mathbb R$, let $\tau_x = \inf \left\{ k\geq 1: x+S_{k} < 0 \right\}$ denote the first time when the random walk $x+S_n$ exits the

Shota Nakagawa, Yuichiro Nakai, Junxuan Xu, Yufei Zhang

We propose a novel paradigm for the QCD axion with high-quality Peccei-Quinn (PQ) symmetry on the basis of electric-magnetic duality in the conformal window of a supersymmetric gauge theory. PQ breaking fields, that contain the QCD axion, emerge in the magnetic theory and possess a large anomalous dimension, which leads to not only generation of an intermedi

Theodore J. Morin, Mingxiao Li, Federico Camponeschi, Hou Xiong

Heterogeneous integration of GaAs-based lasers with frequency doubling waveguides presents a clear path to scalable coherent sources in the so-called green gap, yet frequency doubling systems have so far relied on separately manufactured lasers to deliver enough power for second harmonic generation. In this work, we propose a photonic integrated circuit (PIC

Qiwei Li, Jiahuan Zhou

Recently, prompt tuning methods for pre-trained models have demonstrated promising performance in Class Incremental Learning (CIL). These methods typically involve learning task-specific prompts and predicting the task ID to select the appropriate prompts for inference. However, inaccurate task ID predictions can cause severe inconsistencies between the prom

Michael John Plank, Pubudu Senanayake, Richard Lyon

Background. The excess mortality rate in Aotearoa New Zealand during the Covid-19 pandemic is frequently estimated to be among the lowest in the world. However, to facilitate international comparisons, many of the methods that have been used to estimate excess mortality do not use age-stratified data on deaths and population size, which may compromise their

Yong Hu, Jianshi Yan

We prove that for all nonsingular projective 3-folds of general type with third plurigenus $P_3 \geq 2$, the pluricanonical map $\varphi_m$ is birational onto its image for all $m \geq 14$, which is optimal.

David Urbanik

We prove asymptotic estimates for the growth in the degree of the Hodge locus in terms of arithmetic properties of the integral vectors that define it. Our methods are general and apply to most variations of Hodge structures for which the Hodge locus is dense. As applications we give asymptotic formulas controlling the degrees of Noether-Lefschetz loci assoc

Kwok-Kun Kwong, Yong Wei

In this paper, we derive new sharp weighted Alexandrov-Fenchel and Minkowski inequalities for smooth, closed hypersurfaces under various convexity assumptions in Euclidean, spherical, and hyperbolic spaces. These inequalities extend classical results by incorporating weights given by convex, non-decreasing positive functions, which are otherwise arbitrary. O

Liyang He, Yuren Zhang, Rui Li, Zhenya Huang

Deep supervised hashing is essential for efficient storage and search in large-scale image retrieval. Traditional deep supervised hashing models generate single-length hash codes, but this creates a trade-off between efficiency and effectiveness for different code lengths. To find the optimal length for a task, multiple models must be trained, increasing tim

Yixin Cheng, Rui Guan, Tongguang Li, Mladen Raković

While the capacity to self-regulate has been found to be crucial for secondary school students, prior studies often rely on self-report surveys and think-aloud protocols that present notable limitations in capturing self-regulated learning (SRL) processes. This study advances the understanding of SRL in secondary education by using trace data to examine SRL

Pusen Dong, Tianchen Zhu, Yue Qiu, Haoyi Zhou

Safe reinforcement learning (RL) requires the agent to finish a given task while obeying specific constraints. Giving constraints in natural language form has great potential for practical scenarios due to its flexible transfer capability and accessibility. Previous safe RL methods with natural language constraints typically need to design cost functions man

Huanhuan Li, Zongchao Li, Zhengpan Wang

Leavitt inverse semigroups of directed finite graphs are related to Leavitt graph algebras of (directed) graphs. Leavitt path algebras of graphs have the natural $\mathbb Z$-grading via the length of paths in graphs. We consider the $\mathbb Z$-grading on Leavitt inverse semigroups. For connected finite graphs having vertices out-degree at most $1$, we give

Jianwei Cui, Yu Gu, Shihao Chen, Jie Zhang

Singing Voice Synthesis (SVS) aims to generate singing voices of high fidelity and expressiveness. Conventional SVS systems usually utilize an acoustic model to transform a music score into acoustic features, followed by a vocoder to reconstruct the singing voice. It was recently shown that end-to-end modeling is effective in the fields of SVS and Text to Sp

Alexandra Seceleanu

These lecture notes were prepared for the Lefschetz Preparatory School, a graduate summer course held in Krakow, May 6-10, 2024. They present the story of the algebraic Lefschetz properties from their origin in algebraic geometry to some recent developments in commutative algebra. The common thread of the notes is a bias towards topics surrounding the algebr

Minsu Kim, Evan L. Ray, Nicholas G. Reich

Ensemble forecasts often outperform forecasts from individual standalone models, and have been used to support decision-making and policy planning in various fields. As collaborative forecasting efforts to create effective ensembles grow, so does interest in understanding individual models' relative importance in the ensemble. To this end, we propose two pra

Zhongrui Chen, Isaac Grosof, Benjamin Berg

Modern cloud computing workloads are composed of multiresource jobs that require a variety of computational resources in order to run, such as CPU cores, memory, disk space, or hardware accelerators. A single cloud server can typically run many multiresource jobs in parallel, but only if the server has sufficient resources to satisfy the demands of every job

Qiang Fei

We consider the existence problem of the following Singular Toda system on a compact Riemann surface $(\Sigma, g)$ without boundary \begin{equation*} \begin{cases} -\Delta_gu_1=2\overline{\rho}_1\Big({\frac{h_1e^{u_1}}{\int_{\Sigma}h_1e^{u_1}dV_g}}-1\Big)-\rho_2\Big({\frac{h_2e^{u_2}}{\int_{\Sigma}h_2e^{u_2}dV_g}}-1\Big)-4\pi\alpha_1(\delta_0-1), -\Delta_gu_

Wenxuan Zhang, Peng Hu

The rapid increase of space assets represented by small satellites in low Earth orbit can enable ubiquitous digital services for everyone. However, due to the dynamic space environment, numerous space objects, complex atmospheric conditions, and unexpected events can easily introduce adverse conditions affecting space safety, operations, and sustainability o

Ali Mollaahmadi Dehaghi, Reza Razavi, Mohammad Moshirpour

In this paper, we introduce DiQP; a novel Transformer-Diffusion model for restoring 8K video quality degraded by codec compression. To the best of our knowledge, our model is the first to consider restoring the artifacts introduced by various codecs (AV1, HEVC) by Denoising Diffusion without considering additional noise. This approach allows us to model the

Shijun Li, Hilaf Hasson, Jing Hu, Joydeep Ghosh

Multi-objective learning endeavors to concurrently optimize multiple objectives using a single model, aiming to achieve high and balanced performance across diverse objectives. However, this often entails a more complex optimization problem, particularly when navigating potential conflicts between objectives, leading to solutions with higher memory requireme

W. Q. Chen, K. J. Li, J. C. Xu

Long-term evolution characteristics of the solar transition region have been unclear. In this study, daily images of the solar full disk derived from the observations by the Solar Dynamics Observatory/Atmospheric Imaging Assembly at 304 A wavelength from 2011 January 1 to 2022 December 31 are used to investigate long-term evolution of the solar transition re

Zhixiang Wang, Xudong Li, Yizhai Zhang, Fan Zhang

Event cameras, as bio-inspired sensors, are asynchronously triggered with high-temporal resolution compared to intensity cameras. Recent work has focused on fusing the event measurements with inertial measurements to enable ego-motion estimation in high-speed and HDR environments. However, existing methods predominantly rely on IMU preintegration designed ma

Boxun Liu, Shijian Gao, Xuanyu Liu, Xiang Cheng

Channel prediction permits to acquire channel state information (CSI) without signaling overhead. However, almost all existing channel prediction methods necessitate the deployment of a dedicated model to accommodate a specific configuration. Leveraging the powerful modeling and multi-task learning capabilities of foundation models, we propose the first spac

Shengchao Chen, Guodong Long, Jing Jiang, Chengqi Zhang

Training a general-purpose time series foundation models with robust generalization capabilities across diverse applications from scratch is still an open challenge. Efforts are primarily focused on fusing cross-domain time series datasets to extract shared subsequences as tokens for training models on Transformer architecture. However, due to significant st

Marah Abdin, Jyoti Aneja, Harkirat Behl, Sébastien Bubeck

We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models, where pre-training is based primarily on organic data sources such as web content or code, phi-4 strategically incorporates synthetic data throughout the training process. While previous models in th

Zhengyang Zhang, Chengyuan Wu, Xianfei Zhang, Zhanwen Han

Blue Large-Amplitude Pulsators (BLAPs) represent a recently identified class of pulsating stars distinguished by their short pulsation periods ($2 - 60$ minutes) and asymmetric light curves. This study investigated the evolutionary channel of HD 133729 which is the first confirmed BLAP in a binary system. Using the binary evolution code MESA, we explored var

Zhonggen Li, Xiangyu Ke, Yifan Zhu, Yunjun Gao

Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental operation in graph computing and analytics. However, the irregularity of real-world graphs poses significant challenges to achieving efficient SpMM operation for graph data on GPUs. Recently, significant advancements in GPU computing power and the introduction of new efficient computing cores within

Ting Xiao, Lei Shi, Peng Liu, Zhe Wang

Automatic Radiology Report Generation (RRG) is an important topic for alleviating the substantial workload of radiologists. Existing RRG approaches rely on supervised regression based on different architectures or additional knowledge injection,while the generated report may not align optimally with radiologists' preferences. Especially, since the preference

Ting He, Kory Kreimeyer, Mimi Najjar, Jonathan Spiker

The delivery of appropriate targeted therapies to cancer patients requires the complete analysis of the molecular profiling of tumors and the patient's clinical characteristics in the context of existing knowledge and recent findings described in biomedical literature and several other sources. We evaluated the potential contributions of specific natural lan

Luo Yan, Junchi Liu, Yu-Feng Ding, Jiafang Wu

M\textbf{\textit{O}}enes, as emerging MXenes-like materials, also have wide structural spaces and various chemical and physical properties. Using first-principles and high-throughput calculations, we have built an online library (\url{https://moenes.online}) for M\textbf{\textit{O}}enes family materials from basic summaries, mechanical, phonon and electron a

Wei He, Yanqin Zhang, Yukai Shang, Mohammad Masoud Namazi

ZIP loads (the parallel combination of constant impedance loads, constant current loads and constant power loads) exist widely in power system. In order to stabilize buck converter based DC distributed system with ZIP load, an adaptive energy shaping controller (AESC) is devised in this paper. Firstly, based on the assumption that lumped disturbances are kno

Lewis Hammond, Sam Adam-Day

We consider the problem of how a trusted, but computationally bounded agent (a 'verifier') can learn to interact with one or more powerful but untrusted agents ('provers') in order to solve a given task. More specifically, we study the case in which agents are represented using neural networks and refer to solutions of this problem as neural interactive proo

Kuntao Xiao, Xiongfei Wang, Pengfei Teng, Yi Sun

The analysis of interictal epileptiform discharges (IEDs) in magnetoencephalography (MEG) or electroencephalogram (EEG) recordings represents a critical component in the diagnosis of epilepsy. However, manual analysis of these IEDs, which appear as epileptic spikes, from the large amount of MEG/EEG data is labor intensive and requires high expertise. Althoug

Kwangryeol Park, Seulki Lee

We propose SMMF (Square-Matricized Momentum Factorization), a memory-efficient optimizer that reduces the memory requirement of the widely used adaptive learning rate optimizers, such as Adam, by up to 96%. SMMF enables flexible and efficient factorization of an arbitrary rank (shape) of the first and second momentum tensors during optimization, based on the

Peter N. Loxley

Optimal control and sequential decision making are widely used in many complex tasks. Optimal control over a sequence of natural images is a first step towards understanding the role of vision in control. Here, we formalize this problem as a reinforcement learning task, and derive general conditions under which an image includes enough information to impleme

Alexandre Nicolle

In this article, we apply the derived Morita theory of dg-categories to show how to extend the domain of validity of many identities relating Morita invariants from associative dg-algebras toward non-commutative scheme. Doing so, we obtain that the dg-category of associative algebras can be used to test the exactness of any sequence and the commutativity of

Siu Wun Cheung, Youngsoo Choi, Seung Whan Chung, Jean-Luc Fattebert

Large-scale eigenvalue problems arise in various fields of science and engineering and demand computationally efficient solutions. In this study, we investigate the subspace approximation for parametric linear eigenvalue problems, aiming to mitigate the computational burden associated with high-fidelity systems. We provide general error estimates under non-s

XENON Collaboration, E. Aprile, J. Aalbers, K. Abe

Characterizing low-energy, keV-range nuclear recoils near the detector threshold is one of the major challenges for large direct dark matter detectors. To that end, we have successfully used a Yttrium-Beryllium photoneutron source that emits 152 keV neutrons for the calibration of the light and charge yields of the XENONnT experiment for the first time. Afte

Mengxian Li, Qi Wang, Yongjun Xu

The rapid advancement of multi-agent reinforcement learning (MARL) has given rise to diverse training paradigms to learn the policies of each agent in the multi-agent system. The paradigms of decentralized training and execution (DTDE) and centralized training with decentralized execution (CTDE) have been proposed and widely applied. However, as the number o

Junhyuck Kim, Jongho Park, Jaewoong Cho, Dimitris Papailiopoulos

We introduce Lexico, a novel KV cache compression method that leverages sparse coding with a universal dictionary. Our key finding is that key-value cache in modern LLMs can be accurately approximated using sparse linear combination from a small, input-agnostic dictionary of ~4k atoms, enabling efficient compression across different input prompts, tasks and

Yu-Yang Songsheng, Jian-Min Wang, Yuan Cao, XueFei Chen

The growing ``Hubble tension'' has prompted the need for precise measurements of cosmological distances. This paper demonstrates a purely geometric approach for determining the distance to extragalactic binaries through a joint analysis of spectroastrometry (SA), radial velocity (RV), and light curve (LC) observations. A parameterized model for the binary sy

Maximiliano Bernal, Guidobeth Saez, Tomás P. Espinoza, Roberto E. Troncoso

Magnetic-ferroic ordering and magnetic-toroidal moments are essential concepts in molecular electronics and magnetics. The magnetic toroidal moment is critical in understanding new electronic states and their possible uses. This paper discusses the notion of toroidicity waves. In particular, we present a one-dimensional model of interconnected toroidicity le

Tatsuro Kawakami, Jakub Witaszek

We introduce the concept of higher $F$-injectivity, a generalisation of $F$-injectivity. We prove that an isolated singularity over a field of characteristic zero is $k$-Du Bois if it is $k$-$F$-injective after reductions modulo infinitely many primes $p$. Under the ordinarity conjecture, we also establish the converse. As an application, we study Frobenius

Liping Li, Jiangang Ying

The primary aim of this article is to investigate the domination relationship between two $L^2$-semigroups using probabilistic methods. According to Ouhabaz's domination criterion, the domination of semigroups can be transformed into relationships involving the corresponding Dirichlet forms. Our principal result establishes the equivalence between the domina