March 2025 arXiv papers — page 15

Deeksha Arun, Kagan Ozturk, Kevin W. Bowyer, Patrick Flynn

Ear recognition has emerged as a promising biometric modality due to the relative stability in appearance during adulthood. Although Vision Transformers (ViTs) have been widely used in image recognition tasks, their efficiency in ear recognition has been hampered by a lack of attention to overlapping patches, which is crucial for capturing intricate ear feat

Weisheng Jin, Maojia Song, Tej Deep Pala, Yew Ken Chia

As large language models (LLMs) tackle increasingly complex tasks and longer documents, their computational and memory costs during inference become a major bottleneck. To address this, we propose PromptDistill, a novel, training-free method that improves inference efficiency while preserving generation quality. PromptDistill identifies and retains the most

Shuguang Li, Zhenxin Wen, Jing Wei

This paper studies the bicriteria problem of scheduling $n$ jobs on a serial-batch machine to minimize makespan and maximum cost simultaneously. A serial-batch machine can process up to $b$ jobs as a batch, where $b$ is known as the batch capacity. When a new batch starts, a constant setup time is required for the machine. Within each batch, the jobs are pro

Hongjie Dong, Seick Kim, Boyan Sirakov

We establish, for the first time, a Zaremba-Hopf-Oleinik type boundary point lemma for uniformly elliptic partial differential equations in double divergence form, also known as stationary Fokker-Planck-Kolmogorov equations. As an application, we derive sharp two-sided estimates for the Green's function associated with second-order elliptic equations in non-

Haonan Chen, Jiaming Xu, Lily Sheng, Tianchen Ji

When performing tasks like laundry, humans naturally coordinate both hands to manipulate objects and anticipate how their actions will change the state of the clothes. However, achieving such coordination in robotics remains challenging due to the need to model object movement, predict future states, and generate precise bimanual actions. In this work, we ad

Chaoqi Liu, Yunzhu Li, Kris Hauser

Predictive models can be particularly helpful for robots to effectively manipulate terrains in construction sites and extraterrestrial surfaces. However, terrain state representations become extremely high-dimensional especially to capture fine-resolution details and when depth is unknown or unbounded. This paper introduces a learning-based approach for terr

Sainan Zhang, Shaoyan Guo, Melvyn Sim, Huifu Xu

In this paper, we propose a modified polyhedral method to elicit a decision maker's (DM's) nonlinear univariate utility function, which does not rely on explicit information about the shape structure, Lipschitz modulus, and the inflection point of the utility. The method is inspired by Toubia et al. (2004) for elicitation of the linear multi-variate utility

Claire Levaillant

We present a multi-image quantum encryption/decryption scheme based on blocks of bit planes and images. We provide a quantum circuit for the quantum baker map.

Shuo Liu, Wei Xiao, Calin A. Belta

In safety-critical control systems, ensuring both system safety and smooth control input is essential for practical deployment. Existing Control Barrier Function (CBF) frameworks, especially High-Order CBFs (HOCBFs), effectively enforce safety constraints, but also raise concerns about the smoothness of the resulting control inputs. While smoothness typicall

Shihao Cheng, Jinlu Zhang, Yue Liu, Zhigang Tu

Human action recognition in low-light environments is crucial for various real-world applications. However, the existing approaches overlook the full utilization of brightness information throughout the training phase, leading to suboptimal performance. To address this limitation, we propose OwlSight, a biomimetic-inspired framework with whole-stage illumina

Björn Möller, Lucas Görnhardt, Tim Fingscheidt

Transformer architectures prominently lead single-image super-resolution (SISR) benchmarks, reconstructing high-resolution (HR) images from their low-resolution (LR) counterparts. Their strong representative power, however, comes with a higher demand for training data compared to convolutional neural networks (CNNs). For many real-world SR applications, the

Mahshid Iraniparast, Nishant Kumar, Igor Sokolov

Optical-based nanothermometry represents a transformative approach for precise temperature measurements at the nanoscale, which finds versatile applications across biology, medicine, and electronics. The assembly of ratiometric fluorescent 40 nm nanoparticles designed to serve as individual nanothermometers is introduced here. These nanoparticles exhibit unp

Steven W. Ellingson

A common problem in justice applications is localization of a user of a cellular network using a call detail record (CDR), which typically reveals only the base station and sector to which the user was connected. This precludes precise estimation of location. Instead, one is limited to estimating a region of plausible locations (RPL) using static information

Dariush Kari, Andrew C. Singer

In this paper, we propose a method to adapt a pre-trained deep-learning-based model for underwater acoustic localization to a new environment. We use unsupervised domain adaptation to improve the generalization performance of the model, i.e., using an unsupervised loss, fine-tune the pre-trained network parameters without access to any labels of the target e

Daiki Yamaguchi, Aki Kitaori, Naoto Nagaosa, Yoshinori Tokura

A multiferroic state with both electric polarization ($P$) and magnetization ($M$) shows the inherently strong $P$-$M$ coupling, when $P$ is induced by cycloidal (N\'eel-wall like) spin modulation. The sign of $P$ is determined by clockwise or counterclockwise rotation of spin, termed the spin helicity. Such a multiferroic state is not limited to magnetic in

Dariush Kari, Yongjie Zhuang, Andrew C. Singer

In this paper, we study the underwater acoustic localization in the presence of environmental mismatch. Especially, we exploit a pre-trained neural network for the acoustic wave propagation in a gradient-based optimization framework to estimate the source location. To alleviate the effect of mismatch between the training data and the test data, we simultaneo

Damba Lkhagvasuren, Purevdorj Tuvaandorj

The overwhelming majority of homeless individuals are jobless, despite many expressing a willingness to work. While this strong individual-level link between homelessness and unemployment is well-documented, the broader impact of labor market dynamics on homelessness remains largely unexplored. To fill this gap, this paper investigates the impact of local la

Dariush Kari, Hari Vishnu, Andrew C. Singer

Adapting pre-trained deep learning models to new and unknown environments remains a major challenge in underwater acoustic localization. We show that although the performance of pre-trained models suffers from mismatch between the training and test data, they generally exhibit a higher uncertainty in environments where there is more mismatch. Additionally, i

Jurek Eisinger, Ward Gauderis, Lin de Huybrecht, Geraint A. Wiggins

The Categorical Compositional Distributional (DisCoCat) framework models meaning in natural language using the mathematical framework of quantum theory, expressed as formal diagrams. DisCoCat diagrams can be associated with tensor networks and quantum circuits. DisCoCat diagrams have been connected to density matrices in various contexts in Quantum Natural L

Vishnu Pulloor Kuttanikkad, Rajesh Narayanan, Thomas Vojta

We study the superfluid-insulator quantum phase transition of interacting bosons by means of large-scale Monte Carlo simulations in the presence of both topological and generic quenched disorders. Recent work has demonstrated that the amplitude mode at this transition broadens and localizes in the presence of dilution disorder, whereas it remains a well-defi

Yuhao Deng, Shasha Han, Xiao-Hua Zhou

In randomized controlled trials (RCTs) that focus on time-to-event outcomes, intercurrent events can arise in two ways: as semi-competing events, which modify the hazard of the primary outcome events, or as competing events, which make the definition of the primary outcome events unclear. Although five strategies have been proposed in the ICH E9 (R1) addendu

Mohammadmahdi Honarmand, Onur Cezmi Mutlu, Parnian Azizian, Saimourya Surabhi

Robust facial expression recognition in unconstrained, "in-the-wild" environments remains challenging due to significant domain shifts between training and testing distributions. Test-time adaptation (TTA) offers a promising solution by adapting pre-trained models during inference without requiring labeled test data. However, existing TTA approaches typicall

Lucas O'Brien, Forest Kobayashi, Young-Heon Kim

For a fixed, compactly supported probability measure $\mu$ on the $d$-dimensional space $\mathbb{R}^d$, we consider the problem of minimizing the $p^{\mathrm{th}}$-power average distance functional over all compact, connected $\Sigma \subseteq \mathbb{R}^d$ with Hausdorff 1-measure $\mathcal{H}^1(\Sigma) \leq l$. This problem, known as the average distance p

Mingjia He, Yannik Werner, Andrea Censi, Emilio Frazzoli

Transportation network design often involves multiple stakeholders with diverse priorities. We consider a system with a hierarchical multi-agent structure, featuring self-optimized subnetwork operators at the lower level and a central organization at the upper level. Independent regional planning can lead to inefficiencies due to the lack of coordination, hi

Lihui Liu, Zihao Wang, Dawei Zhou, Ruijie Wang

Knowledge graphs (KGs) are ubiquitous and widely used in various applications. However, most real-world knowledge graphs are incomplete, which significantly degrades their performance on downstream tasks. Additionally, the relationships in real-world knowledge graphs often follow a long-tail distribution, meaning that most relations are represented by only a

Peter Boldog, Gergely Röst

In this study, we present a stochastic simulation model designed to explicitly incorporate cell cycle length, overcoming limitations associated with classical compartmental models. Our approach employs a delay mechanism to represent the cell cycle, allowing the use of arbitrary distributions for cell cycle lengths. We demonstrate the feasibility of our model

Jake Levinson, Haggai Liu

The ordinary and $S_n$-equivariant fundamental groups of the moduli space $\overline{M_{0,n+1}}(\mathbb{R})$ of real $(n+1)$-marked stable curves of genus $0$ are known as \emph{cactus groups} $J_n$ and have applications both in geometry and the representation theory of Lie algebras. In this paper, we compute the ordinary and $S_n$-equivariant fundamental gr

Lior Gishboliner, Stefan Glock, Amedeo Sgueglia

In this paper, we initiate the study of discrepancy questions for combinatorial designs. Specifically, we show that, for every fixed $r\ge 3$ and $n\equiv 1,3 \pmod{6}$, any $r$-colouring of the triples on $[n]$ admits a Steiner triple system of order $n$ with discrepancy $\Omega(n^2)$. This is not true for $r=2$, but we are able to asymptotically characteri

S. Saei, N. Tajik

Infrastructure networks are increasingly vulnerable to natural hazards and design flaws, making resilience assessment essential. This paper presents a scenario-based framework to evaluate network vulnerability by combining local measures and topological analysis, assessing each node's role in maintaining network integrity during disruptions. The framework id

Shih-Han Chan

Security threats like prompt injection attacks pose significant risks to applications that integrate Large Language Models (LLMs), potentially leading to unauthorized actions such as API misuse. Unlike previous approaches that aim to detect these attacks on a best-effort basis, this paper introduces a novel method that appends an Encrypted Prompt to each use

Mahtab Jamali, Paul Davidsson, Reza Khoshkangini, Martin Georg Ljungqvist

Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and effectiveness of object detectors. For example, where recognizing an isolated object might be challenging, context information can improve co

Dan-Virgil Voiculescu

This is an update on the quasicentral modulus, an invariant for an n-tuple of Hilbert space operators and a rearrangement invariant norm, that plays a key-role in sharp multivariable generalizations of the classical Weyl-von Neumann-Kuroda and Kato-Rosenblum theorems of perturbation theory. There are also connections with self-similar measures on certain fra

Daniel Dilley, Jerry Chang, Jeffrey Larson, Eric Chitambar

The geometric measure of entanglement (GME) quantifies how close a multi-partite quantum state is to the set of separable states under the Hilbert-Schmidt inner product. The GME can be non-multiplicative, meaning that the closest product state to two states is entangled across subsystems. In this work, we explore the GME in two families of states: those that

Shashwat Sharan, Judith Gonzalez Sorribes, Patrick Sprenger, Mark A. Hoefer

An experimental and theoretical study of sonic horizons emerging from the dam-break problem in a Bose-Einstein condensate confined in an anisotropic harmonic trap is presented. Measurements, analysis, and numerics reveal the formation of a sonic horizon that undergoes acceleration due to harmonic confinement. The superfluid is characterized using a robust me

Khalid M. Saqr

The symbolic architecture of non-ordinary consciousness remains largely unmapped in cognitive science and artificial intelligence. While conventional models prioritize rational coherence, altered states such as those induced by psychedelics reveal distinct symbolic regimes characterized by recursive metaphor, ego dissolution, and semantic destabilization. We

Özkan Canay, Ümit Kocabıçak

In web analytics, cloud-based solutions have limitations in data ownership and privacy, whereas client-side user tracking tools face challenges such as data accuracy and a lack of server-side metrics. This paper presents the Combined Analytics and Web Application Log (CAWAL) framework as an alternative model and an on-premises framework, offering web analyti

Megan A. Brown, Shubham Atreja, Libby Hemphill, Patrick Y. Wu

Researchers have proposed the use of generative large language models (LLMs) to label data for research and applied settings. This literature emphasizes the improved performance of these models relative to other natural language models, noting that generative LLMs typically outperform other models and even humans across several metrics. Previous literature h

Dominik Macko, Aashish Anantha Ramakrishnan, Jason Samuel Lucas, Robert Moro

Increased sophistication of large language models (LLMs) and the consequent quality of generated multilingual text raises concerns about potential disinformation misuse. While humans struggle to distinguish LLM-generated content from human-written texts, the scholarly debate about their impact remains divided. Some argue that heightened fears are overblown d

Nam Anh Dinh, Itai Lang, Hyunwoo Kim, Oded Stein

We present Geometry in Style, a new method for identity-preserving mesh stylization. Existing techniques either adhere to the original shape through overly restrictive deformations such as bump maps or significantly modify the input shape using expressive deformations that may introduce artifacts or alter the identity of the source shape. In contrast, we rep

Chenji Fu

Let F be a non-archimedean local field with residue characteristic p. Let l be a prime number different from p. Let G be a connected reductive group which is split, semi-simple, and simply connected. On the one hand, we describe the category of quasi-coherent sheaves on the connected component of the stack of L-parameters over Z_l-bar containing a tame, regu

E. E. Sheldahl, G. B. Taylor, S. E. Tremblay, W. Peters

Compact symmetric objects (CSOs) are a unique class of jetted active galactic nuclei (AGN) defined by sub-kpc radio emission, showing radio structure on both sides of the central engine. CSOs tend to exhibit little to no relativistic beaming, thereby allowing us to determine their physical characteristics, such as the magnetic field strength and particle ene

Reza Esfandiarpoor, George Zerveas, Ruochen Zhang, Macton Mgonzo

Although synthetic data has changed various aspects of information retrieval (IR) pipelines, the main training paradigm remains: contrastive learning with binary relevance labels, where one positive document is compared against several negatives using the InfoNCE loss. This objective treats all documents that are not explicitly annotated as relevant on an eq

Léo Ducas, Lynn Engelberts, Johanna Loyer

At CRYPTO 2015, Kirchner and Fouque claimed that a carefully tuned variant of the Blum-Kalai-Wasserman (BKW) algorithm (JACM 2003) should solve the Learning with Errors problem (LWE) in slightly subexponential time for modulus $q=\mathrm{poly}(n)$ and narrow error distribution, when given enough LWE samples. Taking a modular view, one may regard BKW as a com

Anna Schwarz, Jens Keim, Christian Rohde, Andrea Beck

In this paper, a shock capturing for high-order entropy stable discontinuous Galerkin spectral element methods on moving meshes is proposed using Gauss--Lobatto nodes. The shock capturing is achieved via the convex blending of the high-order scheme with a low-order finite volume subcell operator. The free-stream and convergence properties of the hybrid schem

Ismaël Zighed, Nicolas Thome, Patrick Gallinari, Taraneh Sayadi

Reduced order models (ROMs) play a critical role in fluid mechanics by providing low-cost predictions, making them an attractive tool for engineering applications. However, for ROMs to be widely applicable, they must not only generalise well across different regimes, but also provide a measure of confidence in their predictions. While recent data-driven appr

Konstantin Y. Bliokh

Waves of various types carry momentum, which is associated with their propagation direction, i.e., the phase gradient. The circulation of the wave momentum density gives rise to orbital angular momentum (AM). Additionally, for waves described by vector fields, local rotation of the wavefield produces spin AM (or simply, spin). These dynamical wave properties

Alessio Borgi, Luca Maiano, Irene Amerini

We introduce Z-SASLM, a Zero-Shot Style-Aligned SLI (Spherical Linear Interpolation) Blending Latent Manipulation pipeline that overcomes the limitations of current multi-style blending methods. Conventional approaches rely on linear blending, assuming a flat latent space leading to suboptimal results when integrating multiple reference styles. In contrast,

Martin J. Savage

Simulating the dynamics of non-equilibrium matter under extreme conditions lies beyond the capabilities of classical computation alone. Remarkable advances in quantum information science and technology are profoundly changing how we understand and explore fundamental quantum many-body systems, and have brought us to the point of simulating essential aspects

Tales Panoutsos, Rodrygo L. T. Santos, Flavio Figueiredo

In this paper, we introduce Symmetric Low-Rank Adapters, an optimized variant of LoRA with even fewer weights. This method utilizes Low-Rank Symmetric Weight Matrices to learn downstream tasks more efficiently. Traditional LoRA accumulates fine-tuning weights with the original pre-trained weights via a Singular Value Decomposition (SVD) like approach, i.e.,

Deepak Sah, Manoranjan P. Singh

The quantum vacuum becomes unstable under an external field, leading to spontaneous particle-antiparticle pair creation. In canonical quantization, the time-dependent particle number, defined via Bogoliubov transformations lacks physical meaning until the external field vanishes. To address this, we explore dynamical quantities that remain well-defined at bo

Hangzhan Jin, Mohammad Hamdaqa

Unlike code generation, which involves creating code from scratch, code completion focuses on integrating new lines or blocks of code into an existing codebase. This process requires a deep understanding of the surrounding context, such as variable scope, object models, API calls, and database relations, to produce accurate results. These complex contextual

Claas Beger, Carl-Leander Henneking

Large Language Models provide significant new opportunities for the generation of high-quality written works. However, their employment in the research community is inhibited by their tendency to hallucinate invalid sources and lack of direct access to a knowledge base of relevant scientific articles. In this work, we present Citegeist: An application pipeli

Hansung Kim, Eric Yongkeun Choi, Eunhyek Joa, Hotae Lee

Urban driving with connected and automated vehicles (CAVs) offers potential for energy savings, yet most eco-driving strategies focus solely on longitudinal speed control within a single lane. This neglects the significant impact of lateral decisions, such as lane changes, on overall energy efficiency, especially in environments with traffic signals and hete

Yolanda Lozano, Niall T. Macpherson, Achilleas Passias

We consider a previously constructed class of massive Type IIA AdS$_2\times$S$^7\times I$ solutions with OSp$(8|2)$ symmetry, as well as OSp$(6|2)$-symmetric ones, by replacing the S$^7$ with the orbifold S$^7/\mathbb{Z}_k$. In both cases we construct global solutions for which the interval $I$ is bounded between physical singularities, by allowing D8-branes

Kushal Agrawal, Romi Banerjee

The intersection of generative AI and art is a fascinating area that brings both exciting opportunities and significant challenges, especially when it comes to identifying synthetic artworks. This study takes a unique approach by examining diffusion-based generative models in the context of Indian art, specifically focusing on the distinctive style of Jamini

Emanuelly Silva, Miguel A. Sabogal, Mateus Scherer, Rafael C. Nunes

In its second data release (DR2), the Dark Energy Spectroscopic Instrument (DESI) publicly released measurements of Baryon Acoustic Oscillations (BAO) from over 13.1 million galaxies and 1.6 million quasars, covering the redshift range $0.295 \leq z \leq 2.330$. In this work, we investigate the impact of this new dataset on dark sector interaction models, wh

Yiqian Wu, Yujie Liu, Yi Yin, Muhan Zeng

Testing-based fault localization has been a research focus in software engineering in the past decades. It localizes faulty program elements based on a set of passing and failing test executions. Since whether a fault could be triggered and detected by a test is related to program semantics, it is crucial to model program semantics in fault localization appr

L. Moriconi, G. Saisse

Extensive optical measurements of canonical turbulent pipe flows have revealed the existence of structural boundary states (SBSs) -- near-wall low-speed streaks strongly correlated with pairs of counter-rotating quasi-streamwise vortices. In this study, we investigate the number fluctuations of these structures within the framework of statistical mechanics.

V. I. Zhaba, Yu. A. Lashko, V. S. Vasilevsky

The nature and structure of high-energy resonance states in $^{8}$Be, located just below and above the $p+^{7}$Li threshold, are investigated in detail. A microscopic many-cluster and many-channel model is employed to study the formation of these resonances. This model includes three distinct three-cluster configurations: $^{4}$He+$^{3}$H+$p$, $^{4}$He+$^{3}

Rolando Rubilar-Torrealba, Lisandro Fermin, Soledad Torres

We propose to model the records of the maximum Drawdown in capital markets by means a Piecewise Deterministic Markov Process (PDMP). We derive statistical results such as the mean and variance that describes the sequence of maximum Drawdown records. In addition, we developed a simulation study and techniques for estimating the parameters governing the stocha

Marc-Antoine Lavoie, Anas Mahmoud, Steven L. Waslander

The current state-of-the-art methods in domain adaptive object detection (DAOD) use Mean Teacher self-labelling, where a teacher model, directly derived as an exponential moving average of the student model, is used to generate labels on the target domain which are then used to improve both models in a positive loop. This couples learning and generating labe

Sanjoy Chowdhury, Hanan Gani, Nishit Anand, Sayan Nag

Recent advancements in reasoning optimization have greatly enhanced the performance of large language models (LLMs). However, existing work fails to address the complexities of audio-visual scenarios, underscoring the need for further research. In this paper, we introduce AURELIA, a novel actor-critic based audio-visual (AV) reasoning framework that distills

Satyavrat Wagle, Anindya Bijoy Das, David J. Love, Christopher G. Brinton

Augmenting federated learning (FL) with device-to-device (D2D) communications can help improve convergence speed and reduce model bias through local information exchange. However, data privacy concerns, trust constraints between devices, and unreliable wireless channels each pose challenges in finding an effective yet resource efficient D2D graph structure.

Bernhard Haeupler, Yaowei Long, Thatchaphol Saranurak, Shengzhe Wang

We show the existence of length-constrained expander decomposition in directed graphs and undirected vertex-capacitated graphs. Previously, its existence was shown only in undirected edge-capacitated graphs [Haeupler-R\"acke-Ghaffari, STOC 2022; Haeupler-Hershkowitz-Tan, FOCS 2024]. Along the way, we prove the multi-commodity maxflow-mincut theorems for leng

N. K. Dulaev, D. A. Telnov, R. V. Popov, V. M. Shabaev

The effect of electron-electron interaction on positron emission in supercritical collisions of highly charged ions is studied within the monopole approximation using the time-dependent density functional theory and the time-dependent Hartree-Fock-Slater methods. Positron production probabilities and energy spectra are calculated for U-U, U-Cm, and Cm-Cm col

Vishnu Vardhan Baligodugula, Fathi Amsaad

This paper presents a comprehensive comparative analysis of prominent clustering algorithms K-means, DBSCAN, and Spectral Clustering on high-dimensional datasets. We introduce a novel evaluation framework that assesses clustering performance across multiple dimensionality reduction techniques (PCA, t-SNE, and UMAP) using diverse quantitative metrics. Experim

Mahrad Almotahari

Cooperative speech is purposive. From the speaker's perspective, one crucial purpose is the transmission of knowledge. Cooperative speakers care about getting things right for their conversational partners. This attitude is a kind of respect. Cooperative speech is an ideal form of communication because participants have respect for each other. And having res

Vincent Gbouna Zakka, Zhuangzhuang Dai, Luis J. Manso

The growing ageing population and their preference to maintain independence by living in their own homes require proactive strategies to ensure safety and support. Ambient Assisted Living (AAL) technologies have emerged to facilitate ageing in place by offering continuous monitoring and assistance within the home. Within AAL technologies, action recognition

Diana Bolanos, Mohammadmehdi Ataei, Daniele Grandi, Kosa Goucher-Lambert

Product recalls provide valuable insights into potential risks and hazards within the engineering design process, yet their full potential remains underutilized. In this study, we curate data from the United States Consumer Product Safety Commission (CPSC) recalls database to develop a multimodal dataset, RECALL-MM, that informs data-driven risk assessment u

Max Gupta, Sunayana Rane, R. Thomas McCoy, Thomas L. Griffiths

While convolutional neural networks (CNNs) have come to match and exceed human performance in many settings, the tasks these models optimize for are largely constrained to the level of individual objects, such as classification and captioning. Humans remain vastly superior to CNNs in visual tasks involving relations, including the ability to identify two obj

Sepideh Mosaferi, Abolfazl Safikhani, Peiliang Bai

This paper addresses the problem of detecting change points in the spectral density of time series, motivated by EEG analysis of seizure patients. Seizures disrupt coherence and functional connectivity, necessitating precise detection. Departing from traditional parametric approaches, we utilize the Wold decomposition, representing general time series as aut

Joannis Alexopoulos

We systematically find conditions which yield locally uniform convergence in the Fourier inversion formula in one and higher dimensions. We apply the gained knowledge to the complex inversion formula of the Laplace transform to extend known results for Banach space-valued functions and, specifically, for C_0-semigroups.

Karan Vombatkere, Evimaria Terzi, Theodoros Lappas

The team formation problem assumes a set of experts and a task, where each expert has a set of skills and the task requires some skills. The objective is to find a set of experts that maximizes coverage of the required skills while simultaneously minimizing the costs associated with the experts. Different definitions of cost have traditionally led to distinc

Ricardo Castillo, Ricardo Freire, Miguel Loayza

We are concerned with the existence of global and blow-up solutions for the nonlinear parabolic problem described by the Hardy-H\'enon equation $u_t - \Delta_{\mathbb{H}} u = |\cdot|_{\mathbb{H}}^{\gamma} u^p \mbox{ in } \mathbb{H}^N \times (0,T),$ where $\mathbb{H}^N$ is the $N$-dimensional Heisenberg group, and the singular term $|\cdot|_{\mathbb{H}}^{\gam

Lorenzo Ciardo

The largest known gap between quantum and classical chromatic number of graphs, obtained via quantum protocols for colouring Hadamard graphs based on the Deutsch--Jozsa algorithm and the quantum Fourier transform, is exponential. We put forth a quantum pseudo-telepathy version of Khot's $d$-to-$1$ Games Conjecture and prove that, conditional to its validity,

Demian Banakh, Lorenzo Ciardo, Marcin Kozik, Jan Tułowiecki

We prove that any perfect quantum strategy for the two-prover game encoding a constraint satisfaction problem (CSP) can be simulated via a perfect classical strategy with an extra classical communication channel, whose size depends only on $(i)$ the size of the shared quantum system used in the quantum strategy, and $(ii)$ structural parameters of the CSP te

Jianfang Chen, Kai Zhang, Aoran Gan, Shiwei Tong

Knowledge Graph Completion (KGC) aims to infer missing information in Knowledge Graphs (KGs) to address their inherent incompleteness. Traditional structure-based KGC methods, while effective, face significant computational demands and scalability challenges due to the need for dense embedding learning and scoring all entities in the KG for each prediction.

Aden Haussmann

Multilingual Natural Language Generation (NLG) is challenging due to the lack of training data for low-resource languages. However, some low-resource languages have up to tens of millions of speakers globally, making it important to improve NLG tools for them. Table-to-Text NLG is an excellent measure of models' reasoning abilities but is very challenging in

Kevin Aguyar Brix, Julian Gonzales, Jeremy B. Hume, Xin Li

We develop a new approach to non-Hausdorff \'etale groupoids and their algebras based on Timmermann's construction of Hausdorff covers. As an application, we completely characterise when singular ideals vanish in Steinberg algebras over arbitrary rings. We also completely characterise when $C^*$-algebraic singular ideals have trivial intersection with the no

Svitlana Mayboroda, David N Spergel

Mesoscale structures can often be described as fractional dimensional across a wide range of scales. We consider a $\gamma$ dimensional measure embedded in an $N$ dimensional space and discuss how to determine its dimension, both in $N$ dimensions and projected into $D$ dimensions. It is a highly non-trivial problem to decode the original geometry from lower

E Kongkui Berinyuy, C. Tchodimou, P. Djorwe, A. -H. Abdel-Aty

We propose a scheme for enhancing bipartite quantum entanglement in a double-cavity molecular optomechanical (McOM) system incorporating an intracavity optical parametric amplifier (OPA). Utilizing a set of linearized quantum Langevin equations and numerical simulations, we investigate the impact of the OPA on both optical-vibration and vibration-vibration e

Pengyu Chen, Sicheng Wang, Cuizhen Wang, Senrong Wang

Precise detection of rooftops from historical aerial imagery is essential for analyzing long-term urban development and human settlement patterns. Nonetheless, black-and-white analog photographs present considerable challenges for modern object detection frameworks due to their limited spatial resolution, absence of color information, and archival degradatio

Jintao Cheng, Bohuan Xue, Shiyang Chen, Qiuchi Xiang

Currently, visual odometry and LIDAR odometry are performing well in pose estimation in some typical environments, but they still cannot recover the localization state at high speed or reduce accumulated drifts. In order to solve these problems, we propose a novel LIDAR-based localization framework, which achieves high accuracy and provides robust localizati

Ling Xiao

In this paper, we prove an optimal isoperimetric inequality for spacelike, compact, star-shaped, and $2$-convex hypersurfaces in de Sitter space.

J. R. Lane, C. Guria, J. Höller, T. D. Montalvo

Despite their apparent simplicity, coupled oscillators exhibit surprisingly complex phenomena. Two notable examples are Berry phase (a geometric or topological aspect of the oscillators' memory) and non-Hermiticity (the often counterintuitive impact of dissipation), both of which possess rich mathematical structures. Here, we demonstrate that combining Berry

Gabriel Nivasch, Oz Rubinstein

The game of i-Mark is an impartial combinatorial game introduced by Sopena (2016). The game is parametrized by two sets of positive integers $S$, $D$, where $\min D\ge 2$. From position $n\ge 0$ one can move to any position $n-s$, $s\in S$, as long as $n-s\ge 0$, as well as to any position $n/d$, $d\in D$, as long as $n>0$ and $d$ divides $n$. The game ends

Tomasz Radozycki

In this work, the paraxial version of Maxwell equations is derived with the use of two Riemann-Silberstein vectors. Exact solutions of these equations are then obtained representing the paraxial electromagnetic fields. These fields satisfy the full Maxwell equations up to the order of $(\lambda/w_0)^2$. The solutions contain some additional terms, which turn

Joel Spruck, Ling Xiao

In this paper, we prove that a closed minimally immersed hypersurface $M^4\subset\mathbb S^5$ with constant $S:=\sum\limits_{i=1}^4\lambda_i^2$ and $A_3:=\sum\limits_{i=1}^4\lambda_i^3$ whose scalar curvature $R_M$ is nonnegative must be isoparametric. Moreover, $S$ can only be $0, 4,$ and $12.$ That is $M^4$ is either an equatorial $4$-sphere, a clifford to

Ramin Hassannejad, Fatimah Shojai, Kazuharu Bamba

We study black holes with linear equation of state within the framework of asymptotically safe gravity. This study extends previous work on gravitational collapse in asymptotically safe gravity (that has been done for a dust fluid) by considering into account the pressure of stellar matter. We derive modified field equations containing the running gravitatio

Rusiru Gambheera, Cristian D. Popescu

We consider a finite, abelian, CM extension $H/F$ of a totally real number field $F$, and construct a $\mathbb{Z}_p[[G(H_\infty/F)]]-$module $\nabla_S^T(H_\infty)_p$, where $p>2$ is a prime and $H_\infty$ is the cyclotomic $\Bbb Z_p$-extension of $H$. This is the Iwasawa theoretic analogue of a module introduced by Ritter and Weiss in \cite{Ritter-Weiss} and

Eftychia Makri, Georgios Palaiokrassas, Sarah Bouraga, Antigoni Polychroniadou

Cryptocurrencies have transformed financial markets with their innovative blockchain technology and volatile price movements, presenting both challenges and opportunities for predictive analytics. Ethereum, being one of the leading cryptocurrencies, has experienced significant market fluctuations, making its price prediction an attractive yet complex problem

Maximilien Bernard, Jean-Philippe Bouchaud, Pierre Le Doussal

We study the competition between random multiplicative growth and redistribution/migration in the mean-field limit, when the number of sites is very large but finite. We find that for static random growth rates, migration should be strong enough to prevent localisation, i.e. extreme concentration on the fastest growing site. In the presence of an additional

Roberta Favata, Nicolas Baù, Antimo Marrazzo

Two-dimensional topological insulators are characterized by an insulating bulk and conductive edge states protected by the nontrivial topology of the bulk electronic structure. They remain robust against moderate disorder until Anderson localization occurs and destroys the topological phase. Interestingly, disorder can also induce a topological phase - known

Faizan A. Khattak, Fazal-E-Asim, Stephan Weiss, Andre L. F. de Almeida

The Khatri-Rao product is extensively used in array processing, tensor decomposition, and multi-way data analysis. Many applications require a least-squares (LS) Khatri-Rao factorization. In broadband sensor array problems, polynomial matrices effectively model frequency-dependent behaviors, necessitating extensions of conventional linear algebra techniques.

Vishnu Vardhan Baligodugula, Fathi Amsaad

This paper presents a comparative analysis of distributed training strategies for large-scale neural networks, focusing on data parallelism, model parallelism, and hybrid approaches. We evaluate these strategies on image classification tasks using the CIFAR-100 dataset, measuring training time, convergence rate, and model accuracy. Our experimental results d

Kevin Geyer Harrison

Removing carbon dioxide from the atmosphere may slow climate change and ocean acidification. My approach converts atmospheric carbon dioxide into graphite (CD2G). The net profit for this conversion is ~$381/ton CO2 removed from the atmosphere. At the gigaton scale, CD2G factories will increase the affordability and availability of graphite. Since graphite ca

Shota Hirose, Kazuki Kotoyori, Kasidis Arunruangsirilert, Fangzheng Lin

Transmission latency significantly affects users' quality of experience in real-time interaction and actuation. As latency is principally inevitable, video prediction can be utilized to mitigate the latency and ultimately enable zero-latency transmission. However, most of the existing video prediction methods are computationally expensive and impractical for

Bogdan C. Dumitru

We show that for any two distinct words $ s_1, s_2 $ over an arbitrary alphabets, there exists a deterministic finite automaton with $ O(\log^2 n) $ states that accepts $ s_1 $ and rejects $ s_2 $. This improves the previous upper bound of $O(n^{1/3}\log^7 n)$

Lai Wei, Rongzhi Dong, Nihang Fu, Sadman Sadeed Omee

Crystal structure prediction remains a major challenge in materials science, directly impacting the discovery and development of next-generation materials. We introduce TCSP 2.0, a substantial evolution of our template-based crystal structure prediction framework that advances predictive capabilities through synergistic integration of several key techniques

Anant Godbole, Lybitina Koene, Grant Shirley

The McCarty Conjecture states that any McCarty Matrix (an $n\times n$ matrix $A$ with positive integer entries and each of the $2n$ row and column sums equal to $n$), can be additively decomposed into two other matrices, $B$ and $C$, such that $B$ has row and column sumsets both equal to $\{1, 2,... n\}$, and $C$ has row and column sumsets both equal to $\{0