March 2025 arXiv papers — page 157

Ryan Wong, Necati Cihan Camgoz, Richard Bowden

Sign language representation learning presents unique challenges due to the complex spatio-temporal nature of signs and the scarcity of labeled datasets. Existing methods often rely either on models pre-trained on general visual tasks, that lack sign-specific features, or use complex multimodal and multi-branch architectures. To bridge this gap, we introduce

Avijit Chowdhury, Gargi Sen, Sayan Chakrabarti, Santabrata Das

The environment surrounding a black hole or black hole binaries is generally expected to play an important role in understanding various astrophysical phenomena around them. In this paper, we study relativistic, low angular momentum, inviscid, and advective hot accretion flow onto a galactic supermassive black hole dressed with a cold dark matter halo. Focus

Martin Magnuson, Per Eklund, Craig Polley

The class of two-dimensional carbides and nitrides known as MXenes exhibit remarkable electronic properties. Tailoring these properties, however, requires an in-depth understanding of the band structure and Fermi-surface topology. Surface oxidation of MXenes has previously hampered the characterization of their Fermi surface, which is crucial for understandi

Prahllad Deb, Victor Vinnikov

The classical Cowen-Douglas class of (commuting tuples of) operators possessing an open set of (joint) eigenvalues of finite constant multiplicity was introduced by Cowen and Douglas, generalizing the backward shifts. Their unitary equivalence classes are determined by the equivalence classes of certain hermitian holomorphic vector bundles associated with th

Tong Wei, Yijun Yang, Junliang Xing, Yuanchun Shi

Reinforcement learning with verifiable outcome rewards (RLVR) has effectively scaled up chain-of-thought (CoT) reasoning in large language models (LLMs). Yet, its efficacy in training vision-language model (VLM) agents for goal-directed action reasoning in visual environments is less established. This work investigates this problem through extensive experime

Matthew Niichel, Stylianos Chatzidakis

One of the persisting challenges associated with the peaceful use of the atom is monitoring the subsequent radiation. For example, nuclear power plants need environmental monitoring to ensure public trust. While this may be conducted by the frequent monitoring of low-cost thermoluminescent dosimeters placed in concentric rings around the plant, there have be

Siqi Fan, Xuezhi Fang, Xingrun Xing, Peng Han

Due to the large number of parameters, the inference phase of Large Language Models (LLMs) is resource-intensive. Unlike traditional model compression, which needs retraining, recent dynamic computation methods show that not all components are required for inference, enabling a training-free pipeline. In this paper, we focus on the dynamic depth of LLM gener

J. Varga, A. Matter, F. Millour, G. Weigelt

T CrA is a Herbig Ae-type young star in a complex circumstellar environment; it includes a circumstellar disk, accretion streamers, jets, and outflows. It has long been suspected to be a binary. However, until now, there has been no direct detection of a companion. Here we present new VLTI/MATISSE L- and N-band observations of T CrA taken between 2023 May an

Lahcen El Bourkhissi, Ion Necoara

In this paper, we consider nonconvex optimization problems with nonlinear equality constraints. We assume that the objective function and the functional constraints are locally smooth. To solve this problem, we introduce a linearized $\ell_q$ penalty based method, where $q \in (1,2]$ is the parameter defining the norm used in the construction of the penalty

Marilena Crupi, Antonino Ficarra

We classify all graphs $G$ satisfying the property that all matching powers $I(G)^{[k]}$ of the edge ideal $I(G)$ are bi-Cohen-Macaulay for $1\le k\le\nu(G)$, where $\nu(G)$ is the maximum size of a matching of $G$.

Murchana Khusroo, Ankita Thapa

Solitons are predominantly observed in near-earth plasmas as well as planetary magnetospheres; however, their existence in the solar corona remains largely unexplored, despite theoretical investigations. This study aims to address this gap by examining the presence and dynamics of solitons in the solar corona, particularly in the context of coronal heating.

S. Khardazi, D. Mezzane, M. Amjoud, N. Novak

Lead-free perovskite ceramics Ba(1-x)LaxTi0.89Sn0.11O3 with x= 0, 0.015, 0.025, and 0.035 (BLTSnx) were synthesized by solid-state reaction method. The enhanced energy storage properties were studied across the ferroelectric relaxor conversion. The pure perovskite structure of all prepared samples was confirmed by X-ray diffraction (XRD) analysis and the Ram

Deepesh Bhamre, Aman Gupta, Anuradha Misra, Siddhesh Padval

We study transverse single spin asymmetries (TSSAs) in back-to-back photon plus jet production in the scattering of unpolarized beams of protons off a transversely polarized proton target as probes of Gluon Sivers Function (GSF). We provide estimates within the region where the imbalance $\vec{q}_{\perp} \equiv \vec{p}_{\gamma\perp} + \vec{p}_{J\perp}$ betwe

B. Szányi, A. Yagüe López, A. I. Karakas, M. Lugaro

We study the impact of nuclear input related to weak-decay rates and neutron-capture reactions on predictions for the $s$ process in AGB stars. We provide the first database of surface abundances and stellar yields of the isotopes heavier than iron from the $Monash$ models. We run nucleosynthesis calculations with the $Monash$ post-processing code for 7 stel

Nayandeep Deka Baruah, Abhishek Sarma

We study the behavior of the signs of the coefficients of certain infinite products involving the Rogers-Ramanujan continued fraction. For example, if $$\sum_{n=0}^{\infty}A(n)q^{n}:= \dfrac{(q^2;q^5)_\infty^5(q^3;q^5)_\infty^5}{(q;q^5)_\infty^5(q^4;q^5)_\infty^5},$$then $A(5n+1)>0$, $A(5n+2)>0$, $A(5n+3)>0$, and $A(5n+4)<0$. We also find a few congruences s

Jianfu Zhang, Yujie Gao, Jiahui Zhan, Wentao Wang

In this work, we introduce a novel high-fidelity 3D head reconstruction method from a single portrait image, regardless of perspective, expression, or accessories. Despite significant efforts in adapting 2D generative models for novel view synthesis and 3D optimization, most methods struggle to produce high-quality 3D portraits. The lack of crucial informati

David Vallmanya Poch, Yorick Estievenart, Elnura Zhalieva, Sukanya Patra

Accurate dose calculations in proton therapy rely on high-quality CT images. While planning CTs (pCTs) serve as a reference for dosimetric planning, Cone Beam CT (CBCT) is used throughout Adaptive Radiotherapy (ART) to generate sCTs for improved dose calculations. Despite its lower cost and reduced radiation exposure advantages, CBCT suffers from severe arte

Gemma Cheng, H. R. A. Jones, R. L. Smart, Federico Marocco

Near-infrared spectra from the IRTF/SpeX and Blanco/ARCoIRIS telescope/instrument combinations are used for spectroscopic classification, to measure radial velocities and for the inference of astrophysical properties of 51 Gaia-selected nearby ultracool dwarfs. In this sample, 44 are newly classified in the near infrared. All but one of the UCDs are within 1

Edson Luiz Ursini, Elaine Cristina Catapani Poletti, Loreno Menezes da Silveira, José Roberto Emiliano Leite

The double hypothesis test (DHT) is a test that allows controlling Type I (producer) and Type II (consumer) errors. It is possible to say whether the batch has a defect rate, p, between 1.5 and 2%, or between 2 and 5%, or between 5 and 10%, and so on, until finding a required value for this probability. Using the two probabilities side by side, the Type I er

Chong Liu, Le Li, Shao-Chun Chen, Xiankun Yao

We report the results of experimental studies of recurrent spectral dynamics of the two component Akhmediev breathers (ABs) in a single mode optical fibre. We also provide the theoretical analysis and numerical simulations of the ABs based on the two component Manakov equations that confirm the experimental data. In particular, we observed spectral asymmetry

Zhuoyuan Li, Jiahao Lu, Jiacheng Deng, Hanzhi Chang

The open vocabulary capability of 3D models is increasingly valued, as traditional methods with models trained with fixed categories fail to recognize unseen objects in complex dynamic 3D scenes. In this paper, we propose a simple yet effective approach, SAS, to integrate the open vocabulary capability of multiple 2D models and migrate it to 3D domain. Speci

Yihang Chen, Mengyao Li, Qianyi Wu, Weiyao Lin

3D Gaussian Splatting (3DGS) achieves impressive rendering fidelity and speed for novel view synthesis. However, its substantial data size poses a significant challenge for practical applications. While many compression techniques have been proposed, they fail to efficiently utilize existing bitstreams in on-demand applications due to their lack of progressi

Da-Wei Zhou, Kai-Wen Li, Jingyi Ning, Han-Jia Ye

Class-Incremental Learning (CIL) enables learning systems to continuously adapt to evolving data streams. With the advancement of pre-training, leveraging pre-trained vision-language models (e.g., CLIP) offers a promising starting point for CIL. However, CLIP makes decisions by matching visual embeddings to class names, overlooking the rich contextual inform

Sergey Alyaev, Kristian Fossum, Hibat Errahmen Djecta, Jan Tveranger

The real-time process of directional changes while drilling, known as geosteering, is crucial for hydrocarbon extraction and emerging directional drilling applications such as geothermal energy, civil infrastructure, and CO2 storage. The geo-energy industry seeks an automatic geosteering workflow that continually updates the subsurface uncertainties and capt

Weijie Zhou, Manli Tao, Chaoyang Zhao, Honghui Dong

In recent years, lightweight large language models (LLMs) have garnered significant attention in the robotics field due to their low computational resource requirements and suitability for edge deployment. However, in task planning -- particularly for complex tasks that involve dynamic semantic logic reasoning -- lightweight LLMs have underperformed. To addr

Qing Jiang, Lin Wu, Zhaoyang Zeng, Tianhe Ren

Humans are undoubtedly the most important participants in computer vision, and the ability to detect any individual given a natural language description, a task we define as referring to any person, holds substantial practical value. However, we find that existing models generally fail to achieve real-world usability, and current benchmarks are limited by th

Xian Gao, Jiacheng Ruan, Zongyun Zhang, Jingsheng Gao

Academic paper review is a critical yet time-consuming task within the research community. With the increasing volume of academic publications, automating the review process has become a significant challenge. The primary issue lies in generating comprehensive, accurate, and reasoning-consistent review comments that align with human reviewers' judgments. In

Fan Wu, Sijun Dong, Xiaoliang Meng

Change detection is a crucial and widely applied task in remote sensing, aimed at identifying and analyzing changes occurring in the same geographical area over time. Due to variability in acquisition conditions, bi-temporal remote sensing images often exhibit significant differences in image style. Even with the powerful generalization capabilities of DNNs,

Xing Wang, An Zhang, Cheng Zhang

We establish new Strichartz estimates for orthonormal systems on compact Riemannian manifolds in the case of wave, Klein-Gordon and fractional Schr\"odinger equations. Our results generalize the classical (single-function) Strichartz estimates on compact manifolds by Kapitanski, Burq-G\'erard-Tzvetkov, Dinh, and extend the Euclidean orthonormal version by Fr

Michal Lewandowski, Bernhard Heinzl, Raphael Pisoni, Bernhard A. Moser

Recent findings suggest that consecutive layers of neural networks with the ReLU activation function \emph{fold} the input space during the learning process. While many works hint at this phenomenon, an approach to quantify the folding was only recently proposed by means of a space folding measure based on Hamming distance in the ReLU activation space. We ge

Mustapha Bounoua, Giulio Franzese, Pietro Michiardi

Multimodal data is a precious asset enabling a variety of downstream tasks in machine learning. However, real-world data collected across different modalities is often not paired, which is a significant challenge to learn a joint distribution. A prominent approach to address the modality coupling problem is Minimum Entropy Coupling (MEC), which seeks to mini

Micah Chrisman

Gordon and Litherland's paper $\textit{On the Signature of a link}$ introduced a bilinear form that simultaneously unifies both the quadratic forms of Trotter and Goeritz. This remarkable pairing of combinatorics and topology has had widespread application in low-dimensional topology. In this expository note, we give a picture proof (via Kirby diagrams) of t

Amir Džambić, Anitha Thillaisundaram

Using structural properties of groups of small order, we establish the non-existence of varieties isogenous to a higher product of dimension $n$ greater than 3 with fixed topological Euler number $(-2)^n$ and trivial first Betti number.

Tarakanta Nayak, Soumen Pal, Pooja Phogat

For a rational function $R$, let $N_R(z)=z-\frac{R(z)}{R'(z)}.$ Any such $N_R$ is referred to as a Newton map. We determine all the rational functions $R$ for which $N_R$ has exactly two attracting fixed points, one of which is an exceptional point. Further, if all the repelling fixed points of any such Newton map are with multiplier $2$, or the multiplier o

Yuncheng Guo, Xiaodong Gu

Large-scale pre-trained Vision-Language Models (VLMs) have become essential for transfer learning across diverse tasks. However, adapting these models with limited few-shot data often leads to overfitting, diminishing their performance on new tasks. To tackle this issue, we propose a novel Multi-Modal Representation Learning (MMRL) framework that introduces

Henry Senior, Luca Rossi, Gregory Slabaugh, Shanxin Yuan

It has been a longstanding goal within image captioning to move beyond a dependence on object detection. We investigate using superpixels coupled with Vision Language Models (VLMs) to bridge the gap between detector-based captioning architectures and those that solely pretrain on large datasets. Our novel superpixel approach ensures that the model receives o

Han Cao, Lingwei Wei, Wei Zhou, Songlin Hu

The rapid development of social platforms exacerbates the dissemination of misinformation, which stimulates the research in fact verification. Recent studies tend to leverage semantic features to solve this problem as a single-hop task. However, the process of verifying a claim requires several pieces of evidence with complicated inner logic and relations to

Wenqing Zhao, Antai Xie, Yuchi Wu, Xinlei Yi

This paper studies the distributed generalized Nash equilibrium seeking problem for aggregative games with coupling constraints, where each player optimizes its strategy depending on its local cost function and the estimated strategy aggregation. The information transmission in distributed networks may go beyond bandwidth capacity and eventuate communication

F. Giarrè, I. A. Meer, M. Masoudi, M. Ozger

Multi-connectivity (MC) for aerial users via a set of ground access points offers the potential for highly reliable communication. Within an open radio access network (O-RAN) architecture, edge clouds (ECs) enable MC with low latency for users within their coverage area. However, ensuring seamless service continuity for transitional users-those moving betwee

Hanyi Zhang, Kaizhong Deng, Zhaoyang Jacopo Hu, Baoru Huang

Radioguided surgery, such as sentinel lymph node biopsy, relies on the precise localization of radioactive targets by non-imaging gamma/beta detectors. Manual radioactive target detection based on visual display or audible indication of gamma level is highly dependent on the ability of the surgeon to track and interpret the spatial information. This paper pr

Konstantin Haubner, Federico Lelli, Enrico Di Teodoro, Francis Duey

The systemic velocity or redshift of galaxies is a convenient tool to calculate their distances in the absence of primary methods, but the uncertainties on these flow distances may be substantial due to galaxy peculiar motions. Here, we derived a simple and easily applicable method to assign uncertainties to flow distances from four different methodologies,

Run-Wu Zhang, Chaoxi Cui, Yang Wang, Jingyi Duan

Due to spin-orbit coupling (SOC), crucial for the quantum spin Hall (QSH) effect, a quantized spin-Hall conductivity has not yet been reported in QSH insulators and other realistic materials. Here, we tackle this challenge by predicting robust quantized spin-Hall conductivity in monolayer Fe$_2$Te$_2$O. The underlying physics originates from the unrecognized

Rui Pacheco, Mehmood Ur Rehman

We classify primitive minimal immersions of constant curvature from the two-sphere $S^2$ into the low-dimensional flag manifolds $F_{2,1,1}$ and $F_{2,2,1}$.

Chengcheng Yan, Jiawei Xu, Qingsong Wang, Zheng Peng

The stochastic gradient descent (SGD) algorithm has achieved remarkable success in training deep learning models. However, it has several limitations, including susceptibility to vanishing gradients, sensitivity to input data, and a lack of robust theoretical guarantees. In recent years, alternating minimization (AM) methods have emerged as a promising alter

Zhou Gang

We study the continuity of magnetization at the phase transition of the ferromagnetic XY model in the three-dimensional square lattice with the nearest neighborhood interaction. We assume that, at the critical temperature, with probability 1, for every edge in the infinite directed graph generated by the random path representation, finitely many edges exist

Yajie Yuan, Alexander Y. Chen, Martin Luepker

Black holes can launch powerful jets through the Blandford-Znajek process. This relies on enough plasma in the jet funnel to conduct the necessary current. However, in some low luminosity active galactic nuclei, the plasma supply near the jet base may be an issue. It has been proposed that spark gaps -- local regions with unscreened electric field -- can for

Leon Bettscheider, Andreas Zeller

Generating effective test inputs for a software system requires that these inputs be valid, as they will otherwise be rejected without reaching actual functionality. In the absence of a specification for the input language, common test generation techniques rely on sample inputs, which are abstracted into matching grammars and/or evolved guided by test cover

Nadine Suzan Cetin, Michal Pavelka, Emil Varga

A standard description of superfluid helium-4 is based on the concept of two components (superfluid and normal), which leads to the so called two-fluid models. However, as there are no two kinds of atoms in helium-4, the two components can not be separated. Superfluid helium-4 is not a mixture of two components, being rather a single fluid with two motions.

Fengyi Zhang, Xiangyu Sun, Huitong Yang, Zheng Zhang

Self-supervised 3D occupancy prediction offers a promising solution for understanding complex driving scenes without requiring costly 3D annotations. However, training dense occupancy decoders to capture fine-grained geometry and semantics can demand hundreds of GPU hours, and once trained, such models struggle to adapt to varying voxel resolutions or novel

Shengpeng Xiao, Yuanfang Guo, Heqi Peng, Hui Miao

With the rapid development of image synthesis techniques, AI-generated images have become increasingly realistic, which heightens the potential risk associated with their misuse and creates a growing need for reliable detection. However, the growing diversity of generative models makes it increasingly difficult for detectors to generalize to images produced

Nhat Phuong Anh Vu, Abhishek Saroha, Or Litany, Daniel Cremers

Current 3D stylization techniques primarily focus on static scenes, while our world is inherently dynamic, filled with moving objects and changing environments. Existing style transfer methods primarily target appearance -- such as color and texture transformation -- but often neglect the geometric characteristics of the style image, which are crucial for ac

Pouya Shaeri, Saud AlKhaled, Ariane Middel

Outdoor thermal comfort is a critical determinant of urban livability, particularly in hot desert climates where extreme heat poses challenges to public health, energy consumption, and urban planning. Mean Radiant Temperature ($T_{mrt}$) is a key parameter for evaluating outdoor thermal comfort, especially in urban environments where radiation dynamics signi

Weijie Zhou, Manli Tao, Chaoyang Zhao, Haiyun Guo

Understanding the environment and a robot's physical reachability is crucial for task execution. While state-of-the-art vision-language models (VLMs) excel in environmental perception, they often generate inaccurate or impractical responses in embodied visual reasoning tasks due to a lack of understanding of robotic physical reachability. To address this iss

Soumyajyoti Biswas, Bikas K. Chakrabarti, Asim Ghosh, Sourav Ghosh

We study the inequality of citations received for different publications of various researchers and Nobel laureates in Physics, Chemistry, Medicine and Economics using Google Scholar data from 2012 to 2024. Citation distributions are found to be highly unequal, with even greater disparity among Nobel laureates. Measures of inequality, such as the Gini and Ko

Nicholas Mohammad, Nicola Bezzo

Motion planning failures during autonomous navigation often occur when safety constraints are either too conservative, leading to deadlocks, or too liberal, resulting in collisions. To improve robustness, a robot must dynamically adapt its safety constraints to ensure it reaches its goal while balancing safety and performance measures. To this end, we propos

Han-Wei Kung, Tuomas Varanka, Terence Sim, Nicu Sebe

Privacy concerns around ever increasing number of cameras are increasing in today's digital age. Although existing anonymization methods are able to obscure identity information, they often struggle to preserve the utility of the images. In this work, we introduce a training-free method for face anonymization that preserves key non-identity-related attribute

Manuel Schlenkrich, Jean-François Cordeau, Sophie N. Parragh

We investigate multi-stage demand uncertainty for the multi-item multi-echelon capacitated lot sizing problem with setup carry-over. Considering a multi-stage decision framework helps to quantify the benefits of being able to adapt decisions to newly available information. The drawback is that multi-stage stochastic optimization approaches lead to very chall

I. Villar Rodriguez, Y. Gul, C. P. Dempsey, J. T. Dong

In this letter, we report the magneto-electronic properties of high mobility InAs quantum point contacts grown on InP substrates. The 1D conductance reaches a maximum value of 17 plateaus, quantized in units of 2e^2/h, where e is the fundamental unit of charge and h is Planck's constant. The in-plane effective g-factor was estimated to be -10.9 +/- 1.5 for s

Johannes Droschl

In this paper we study two classes of $\ell$-modular standard modules of the general linear group. The first class is obtained by reducing existing standard modules over $\overline{\mathbb{Q}}_\ell$ to $\overline{\mathbb{F}}_\ell$ with respect to their natural integral structure. The second class is obtained by studying the generic extension map of the cycli

Bryan W Roberts

We show that heat defines a gauge connection on a line bundle over work configurations. Vanishing curvature is equivalent to the local existence of entropy and temperature functions such that heat can be expressed as $TdS$. A conjecture of Jauch, that entropy and temperature arise from a conservation law, is shown to follow as a special case. Global equilibr

Tim Steinke, Martin Büchner, Niclas Vödisch, Abhinav Valada

Mapping and scene representation are fundamental to reliable planning and navigation in mobile robots. While purely geometric maps using voxel grids allow for general navigation, obtaining up-to-date spatial and semantically rich representations that scale to dynamic large-scale environments remains challenging. In this work, we present CURB-OSG, an open-voc

Bin Yang, Yuxuan Liang, Chenjuan Guo, Christian S. Jensen

Time series data captures properties that change over time. Such data occurs widely, ranging from the scientific and medical domains to the industrial and environmental domains. When the properties in time series exhibit spatial variations, we often call the data spatio-temporal. As part of the continued digitalization of processes throughout society, increa

Hao Jiang, Yixing Xu, Pradeep Varakantham

The Ride-Pool Matching Problem (RMP) is central to on-demand ride-pooling services, where vehicles must be matched with multiple requests while adhering to service constraints such as pickup delays, detour limits, and vehicle capacity. Most existing RMP solutions assume passengers are picked up and dropped off at their original locations, neglecting the pote

Zhuoguang Chen, Kenan Li, Xiuyu Yang, Tao Jiang

Comprehensive and consistent dynamic scene understanding from camera input is essential for advanced autonomous systems. Traditional camera-based perception tasks like 3D object tracking and semantic occupancy prediction lack either spatial comprehensiveness or temporal consistency. In this work, we introduce a brand-new task, Camera-based 4D Panoptic Occupa

Kaizhong Deng, Christopher J. Peters, George P. Mylonas, Daniel S. Elson

Diffuse Reflectance Spectroscopy (DRS) is a well-established optical technique for tissue composition assessment which has been clinically evaluated for tumour detection to ensure the complete removal of cancerous tissue. While point-wise assessment has many potential applications, incorporating automated large-area scanning would enable holistic tissue samp

Maximilien F. Debbas, Takehito Suzuki, Danielle R. Yahne, Joseph G. Checkelsky

We report the synthesis of single crystals of Ce$_2$SnS$_5$ through a two-stage chemical vapor transport method. The Ce$_2$SnS$_5$ system is a member of the orthorhombic $Pbam$ (No. 55) space group and realizes a distorted trigonal tricapped prism (TTP) crystal field around each cerium site. We characterized the sample through orientation-dependent magnetiza

20k Collaboration, F. Acerbi, P. Adhikari, P. Agnes

The DarkSide-20k dark matter experiment, currently under construction at LNGS, features a dual-phase time projection chamber (TPC) with a ~50 t argon target from an underground well. At this scale, it is crucial to optimise the argon flow pattern for efficient target purification and for fast distribution of internal gaseous calibration sources with lifetime

Oana Balmau, Anne-Marie Kermarrec, Rafael Pires, André Loureiro Espírito Santo

Mixture of experts (MoE) models achieve state-of-the-art results in language modeling but suffer from inefficient hardware utilization due to imbalanced token routing and communication overhead. While prior work has focused on optimizing MoE training and decoder architectures, inference for encoder-based MoE models in a multi-GPU with expert parallelism sett

Samar Chebbi, Oussama Habachi, Jean-Pierre Cances, Vahid Meghdadi

With the evolution of 5G networks, optimizing resource allocation has become crucial to meeting the increasing demand for massive connectivity and high throughput. Combining Non-Orthogonal Multiple Access (NOMA) and massive Multi-Input Multi-Output (MIMO) enhances spectral efficiency, power efficiency, and device connectivity. However, deploying MIMO-NOMA in

Joanna Bisch, Antti Hannukainen

This work deals with approximate solution of generalized eigenvalue problem with coefficient matrix that is an affine function of d-parameters. The coefficient matrix is assumed to be symmetric positive definite and spectrally equivalent to an average matrix for all parameters in a given set. We develop a Ritz method for rapidly approximating the eigenvalues

Ali Hassaan Mughal

Modern software applications demand efficient and reliable testing methodologies to ensure robust user interface functionality. This paper introduces an autonomous reinforcement learning (RL) agent integrated within a Behavior-Driven Development (BDD) framework to enhance UI testing. By leveraging the adaptive decision-making capabilities of RL, the proposed

Junyoung Kim, Madhulika Balakumar, Kenneth Ross

Data visualization of aggregation queries is one of the most common ways of doing data exploration and data science as it can help identify correlations and patterns in the data. We propose DIVAN, a system that automatically normalizes the one-dimensional axes by frequency to generate large numbers of two-dimensional visualizations. DIVAN normalizes the inpu

Aleksandra Pac, Gavin M. Macauley, Jamie R. Massey, Aleksandr Kurenkov

Artificial spin systems, sometimes referred to as artificial spin ices, are arrays of coupled nanoscale magnets that order according to the lattice geometry, nanomagnet shape and magnetic anisotropy. Here we characterize a family of artificial spin systems that are formed by placing arrays of out-of-plane nanomagnets on the vertices of the Archimedean lattic

Jingyi Zhao, Haoxiang Yang, Yang Liu

The transition to electric vehicles (EVs) is critical to achieving sustainable transportation, but challenges such as limited driving range and insufficient charging infrastructure have hindered the widespread adoption of EVs, especially in time-sensitive logistics such as medical transportation. This paper presents a new model to break through this barrier

Jianian Zhu, Hang Wu, Haojie Wang, Yinghui Li

Multi-modal Large Language Models (MLLMs) serving systems commonly employ KV-cache compression to reduce memory footprint. However, existing compression methods introduce significant processing overhead and queuing delays, particularly in concurrent serving scenarios. We present \texttt{FastCache}, a novel serving framework that effectively addresses these c

Pau Rodriguez, Michal Klein, Eleonora Gualdoni, Valentino Maiorca

The growing use of generative models in daily life calls for efficient mechanisms to control their generation, to e.g., produce safe content or provide users with tools to explore style changes. Ideally, such mechanisms should require low volume of unpaired data (i.e., without explicit preference), and should be cheap, both at train and inference time, while

Genshiro Kitagawa

The information criterion AIC has been used successfully in many areas of statistical modeling, and since it is derived based on the Taylor expansion of the log-likelihood function and the asymptotic distribution of the maximum likelihood estimator, it is not directly justified for likelihood functions that include non-differentiable points such as the Lapla

Qingyun Zeng

Let ${\mathcal F}\subset TM$ be a regular foliation. We construct an $A_\infty$ integration functor from globally bounded finite-rank leafwise cohesive modules with locally constant fiber-cohomology rank to global plot-smooth infinity-local systems with the same regularity. The target retains the global higher-transport objects and sheafifies their raw Block

Liwei Ji, Roland Haas, Yosef Zlochower, Steven R Brandt

Adaptive Mesh Refinement (AMR) with subcycling in time enables different grid levels to advance using their own time steps, ensuring finer grids employ smaller steps for accuracy while coarser grids take larger steps to improve computational efficiency. We present the development, validation, and performance analysis of a subcycling in time algorithm impleme

Anthony Bonato, Juan Chavez Palan, Adam Szava

A novel network-based approach is introduced to analyze banking systems, focusing on two main themes: identifying influential nodes within global banking networks using Bank for International Settlements data and developing an algorithm to detect suspicious transactions for anti-money laundering. Leveraging the concept of adversarial networks, we examine Ban

Jason Becker, Chris Wendler, Peter Baylies, Robert West

Instead of performing text-conditioned denoising in the image domain, latent diffusion models (LDMs) operate in latent space of a variational autoencoder (VAE), enabling more efficient processing at reduced computational costs. However, while the diffusion process has moved to the latent space, the contrastive language-image pre-training (CLIP) models, as us

Zhangming Chan, Xiuying Chen, Yongliang Wang, Juntao Li

Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information. However, little attention has been paid to this problem. To bridge this gap, we propose a model named Fidelity-oriented Product Description Generator (FPDG). FPDG takes the

J. Bernier, S. Blanes, F. Casas, A. Escorihuela-Tomàs

The new class of alternating-conjugate splitting methods is presented and analyzed. They are obtained by concatenating a given composition involving complex coefficients with the same composition but with the complex conjugate coefficients. We show that schemes of this type exhibit a good long time behavior when applied to linear unitary and linear Hamiltoni

Hsin-Ling Hsu, Ping-Sheng Lin, Jing-Di Lin, Jengnan Tzeng

Hybrid Retrieval systems, combining Sparse and Dense Retrieval methods, struggle with Traditional Chinese non-narrative documents due to their complex formatting, rich vocabulary, and the insufficient understanding of Chinese synonyms by common embedding models. Previous approaches inadequately address the dual needs of these systems, focusing mainly on gene

Rick Fritschek, Rafael F. Schaefer

Early neural channel coding approaches leveraged dense neural networks with one-hot encodings to design adaptive encoder-decoder pairs, improving block error rate (BLER) and automating the design process. However, these methods struggled with scalability as the size of message sets and block lengths increased. TurboAE addressed this challenge by focusing on

He-Yu Lin, Rong-Qiang He, Yibin Guo, Zhong-Yi Lu

This paper introduces a hybrid approach combining Green's function Monte Carlo (GFMC) method with projected entangled pair state (PEPS) ansatz. This hybrid method regards PEPS as a trial state and a guiding wave function in GFMC. By leveraging PEPS's proficiency in capturing quantum state entanglement and GFMC's efficient parallel architecture, the hybrid me

Amy Rouillard, Matt Lourens, Francesco Petruccione

We present a computational method to automatically design the n-qubit realisations of quantum algorithms. Our approach leverages a domain-specific language (DSL) that enables the construction of quantum circuits via modular building blocks, making it well-suited for evolutionary search. In this DSL quantum circuits are abstracted beyond the usual gate-sequen

Shu-Yu Hsu

Let $n\ge 3$, $0<m<\frac{n-2}{n}$, $\gamma>0$ and $\eta>0$. Suppose either (i) $\alpha\ne 0$ and $\beta=0$ or (ii) $\alpha\in\mathbb{R}$ and $\beta\ne 0$ holds. We will study the elliptic equation $\Delta (f^m/m)+\alpha f+\beta x\cdot\nabla f=0$, $f>0$, in $\mathbb{R}^n\setminus\{0\}$ with $\underset{\substack{r\to 0}}{\lim}\,r^{\gamma}f(r)=\eta$. This equat

Kiera Cassar, Zhen Wang, Xiaoxuan Chu, Elke-Caroline Aschenauer

It has been found that the gluon density inside the proton grows rapidly at small momentum fractions. Quantum Chromodynamics (QCD) predicts that this growth can be regulated by nonlinear effects, ultimately leading to gluon saturation. Within the color glass condensate framework, nonlinear QCD effects are predicted to suppress and broaden back-to-back angula

Andreas Katsanikakis, Nikolaos Bekiaris-Liberis

The key challenges in design of predictor-based control laws for switched systems with arbitrary switching and long input delay are the potential unavailability of the future values of the switching signal (at current time) and the fact that dwell time may be arbitrary. In the present paper, we resolve these challenges developing a new predictor-based contro

Daria Gazizova, Rayan Farid, B. D. E. McNiven, I. Assi

We present a general representation for solving problems in many-body perturbation theory. By projecting the single-particle Green's function to an auxiliary space we show how one can convert an arbitrary Feynman graph to a universal kernel representation. Once constructed, the computation kernel contains no problem specific information yet contains all expl

Alexandru Aleman, Alex Bergman

In this paper we provide a far-reaching generalization of the existent results about invariant subspaces of the differentiation operator $D=\frac{\partial}{\partial t}$ on $C^\infty(0,1)$ and the Volterra operator $Vf(t)=\int_0^tf(s)ds$, on $L^2(0,1)$. We use an abstract approach to study invariant subspaces of pairs $D,V$ with $DV=I$, where $V$ is compact a

Gisya Abdi, Marlena Gryl, Andrzej Sławek, Ewelina Kowalewska

This study investigates the impacts of structure and composition on the optical and electronic properties of a series of pyridinium-based bismuth iodide complexes. Organic substrates with various functional groups, such as 4-aminopyridine (4-Ampy), 4-methylpyridine (4-Mepy), 4-dimethyaminopyridine (4-Dmapy), and 4-pyridinecarbonitrile (4-CNpy) with different

Sandeep Kumar Acharya, Paz Beniamini

Fast radio bursts (FRBs) are increasingly being used for cosmological applications such as measuring the Hubble constant and baryon abundance. The increasing number of localized FRBs and precise measurement of dispersion measure (DM) make them a suitable probe for such an approach. We use a sample of 110 localized FRBs as well as a small sub-sample of 24 FRB

Qiang Li, Chao-Hsi Chang, Xin Tong, Xiao-Ze Tan

By combining the effective Lagrangian and Bethe-Salpeter framework, we studied the mass spectra, wave functions, and strong decay widths of the two pentaquark states $P_\psi^N(4440)^+$ and $P_\psi^N(4457)^+$ reported by LHCb in 2019. Taking into account both the mass ordering and the decay widths, our results favor the interpretation of $P_\psi^N(4440)^+$ an

Andrew Cheek, Luca Visinelli, Hong-Yi Zhang

The origin of neutrino masses remains unknown to date. One popular idea involves interactions between neutrinos and ultralight dark matter, described as fields or particles with masses $m_\phi \ll 10\,\mathrm{eV}$. Due to the large phase-space number density, this type of dark matter exists in coherent states and can be effectively described by an oscillatin

Rüdiger Ehlers

In this paper, we introduce rerailing automata for $\omega$-regular languages. They generalize both deterministic parity (DPW) and minimized history-deterministic co-B\"uchi automata (with transition based acceptance, HdTbcBW) while combining their favorable properties. In particular, rerailing automata can represent arbitrary $\omega$-regular languages whil

Shankar Gangisetty, Abdul Wasi, Shyam Nandan Rai, C. V. Jawahar

The recent surge in the vehicle market has led to an alarming increase in road accidents. This underscores the critical importance of enhancing road safety measures, particularly for vulnerable road users like motorcyclists. Hence, we introduce the rider intention prediction (RIP) competition that aims to address challenges in rider safety by proactively pre

Qi Wang, Zhipeng Zhang, Baao Xie, Xin Jin

Training visual reinforcement learning (RL) in practical scenarios presents a significant challenge, $\textit{i.e.,}$ RL agents suffer from low sample efficiency in environments with variations. While various approaches have attempted to alleviate this issue by disentangled representation learning, these methods usually start learning from scratch without pr

Yang Wu, Dongfanghao Zhu, Yunhan Wang, Xing Rong

The energy level degeneracies, also known as exceptional points (EPs), are crucial for comprehending emerging phenomena in materials and enabling innovative functionalities for devices. Since EPs were proposed over half a century age, only two types of EPs have been experimentally discovered, revealing intriguing phases of materials such as Dirac and Weyl se