March 2025 arXiv papers — page 222

Eduardo Abi Jaber, Donatien Hainaut, Edouard Motte

We introduce the Volterra Stein-Stein model with stochastic interest rates, where both volatility and interest rates are driven by correlated Gaussian Volterra processes. This framework unifies various well-known Markovian and non-Markovian models while preserving analytical tractability for pricing and hedging financial derivatives. We derive explicit formu

Antoni Bigata, Michał Stypułkowski, Rodrigo Mira, Stella Bounareli

Current audio-driven facial animation methods achieve impressive results for short videos but suffer from error accumulation and identity drift when extended to longer durations. Existing methods attempt to mitigate this through external spatial control, increasing long-term consistency but compromising the naturalness of motion. We propose KeyFace, a novel

Chenxi Wang, Tianle Gu, Zhongyu Wei, Lang Gao

Human readers can efficiently comprehend scrambled words, a phenomenon known as Typoglycemia, primarily by relying on word form; if word form alone is insufficient, they further utilize contextual cues for interpretation. While advanced large language models (LLMs) exhibit similar abilities, the underlying mechanisms remain unclear. To investigate this, we c

Jintao Zhang, Guoliang Li, Jinyang Su

Retrieval-augmented generation (RAG) has demonstrated significant proficiency in conducting question-answering (QA) tasks within a specified corpus. Nonetheless, numerous failure instances of RAG in QA still exist. These failures are not solely attributable to the limitations of Large Language Models (LLMs); instead, they predominantly arise from the retriev

Rémi Robin, Pierre Rouchon, Lev-Arcady Sellem

We examine the time discretization of Lindblad master equations in infinite-dimensional Hilbert spaces. Our study is motivated by the fact that, with unbounded Lindbladian, projecting the evolution onto a finite-dimensional subspace using a Galerkin approximation inherently introduces stiffness, leading to a Courant--Friedrichs--Lewy type condition for expli

Hernando Gonzalez, Carlos Julio Arizmendi, Beatriz F. Giraldo

Spontaneous breathing trials (SBTs) represent a pivotal phase in the weaning process of mechanically ventilated patients. The objective of these trials is to assess patients readiness to resume independent breathing, thereby facilitating timely weaning and reducing the duration of mechanical ventilation (MV). Nevertheless, accurately predicting the success o

Weicong Qin, Yi Xu, Weijie Yu, Chenglei Shen

Personalized product search aims to retrieve and rank items that match users' preferences and search intent. Despite their effectiveness, existing approaches typically assume that users' query fully captures their real motivation. However, our analysis of a real-world e-commerce platform reveals that users often engage in relevant consultations before search

I. M. Braver, Kh. L. Garb, P. Sh. Friedberg

We formulate the dispersion equation for H modes of a circular waveguide with a perfectly conducting strip of zero thickness placed symmetrically in its diametral plane. The wave number of the lowest mode is calculated with high accuracy for various strip widths. Simple asymptotic formulas are derived for the two extreme cases in which the width of the strip

Xinsheng Wang, Mingqi Jiang, Ziyang Ma, Ziyu Zhang

Recent advancements in large language models (LLMs) have driven significant progress in zero-shot text-to-speech (TTS) synthesis. However, existing foundation models rely on multi-stage processing or complex architectures for predicting multiple codebooks, limiting efficiency and integration flexibility. To overcome these challenges, we introduce Spark-TTS,

Ryosuke Akashi, Mihira Sogal, Kieron Burke

Density functional theory has become the world's favorite electronic structure method, and is routinely applied to both materials and molecules. Here, we review recent attempts to use modern machine-learning to improve density functional approximations. Many different researchers have tried many different approaches, but some common themes and lessons have e

Curtis Grant, Aukosh Jagannath, Justin Ko

We develop a pseudo-likelihood theory for rank one matrix estimation problems in the high dimensional limit. We prove a variational principle for the limiting pseudo-maximum likelihood which also characterizes the performance of the corresponding pseudo-maximum likelihood estimator. We show that this variational principle is universal and depends only on fou

Jakob Robnik, Reuben Cohn-Gordon, Uroš Seljak

Sampling from high dimensional distributions is a computational bottleneck in many scientific applications. Hamiltonian Monte Carlo (HMC), and in particular the No-U-Turn Sampler (NUTS), are widely used, yet they struggle on problems with a very large number of parameters or a complicated geometry. Microcanonical Langevin Monte Carlo (MCLMC) has been recentl

Sergii V. Akkelin, Dirk H. Rischke

We compute the expectation value of the energy-momentum tensor of a real scalar field in an approximation which accounts for spacetime gradients of the hydrodynamical variables in local thermodynamical equilibrium. We show that the energy-momentum tensor receives corrections with respect to the standard local-equilibrium result. Notably, the relation between

Sara Mustafi, Rongsheng Cai, Sam Sullivan-Allsop, Matthew Smith

Convergent beam electron diffraction (CBED) on two-dimensional materials allows simultaneous recording of the real-space image (tens of nanometers in size) and diffraction pattern of the same sample in one single-shot intensity measurement. In this study, we employ time-series CBED to visualize single Au nanoparticles deposited on graphene. The real-space im

Carlos Arizmendi, Jorge Pinto, Alejandro Arboleda, Hernando González

According to the World Health Organization (WHO), pneumonia is a disease that causes a significant number of deaths each year. In response to this issue, the development of a decision support system for the classification of patients into those without pneumonia and those with viral or bacterial pneumonia is proposed. This is achieved by implementing transfe

Mohammod N. I. Suvon, Shuo Zhou, Prasun C. Tripathi, Wenrui Fan

Recent advancements in early assessment of pulmonary hypertension (PH) primarily focus on applying machine learning methods to centralized diagnostic modalities, such as 12-lead electrocardiogram (12L-ECG). Despite their potential, these approaches fall short in decentralized clinical settings, e.g., point-of-care and general practice, where handheld 6-lead

Minoo Hosseinzadeh, Hana Khamfroush

With a recent trend of using Large Language Models (LLMs) for different applications within smart cities, there is a need for pushing these models toward the edge of network while still preserving their performance. Edge Computing (EC) as a physically closer computing resource to the end users can help to reduce the communication delay for serving end users'

Vatsal Srivastava

The aim of this paper is to investigate an attempt to build a binary classification algorithm using principles of geometry such as vectors, planes, and vector algebra. The basic idea behind the proposed algorithm is that a hyperplane can be used to completely separate a given set of data points mapped to n dimensional space, if the given data points are line

Nandi Schoots, Mattia Jacopo Villani, Niels uit de Bos

Kolmogorov-Arnold Networks are a new family of neural network architectures which holds promise for overcoming the curse of dimensionality and has interpretability benefits (arXiv:2404.19756). In this paper, we explore the connection between Kolmogorov Arnold Networks (KANs) with piecewise linear (univariate real) functions and ReLU networks. We provide comp

Yongchao Chen, Yilun Hao, Yang Zhang, Chuchu Fan

Recent works have shown great potentials of Large Language Models (LLMs) in robot task and motion planning (TAMP). Current LLM approaches generate text- or code-based reasoning chains with sub-goals and action plans. However, they do not fully leverage LLMs' symbolic computing and code generation capabilities. Many robot TAMP tasks involve complex optimizati

Jiankai Tang, Xin Liu, Daniel McDuff, Zhang Jiang

Blood oxygen saturation (SpO2) is a crucial vital sign routinely monitored in medical settings. Traditional methods require dedicated contact sensors, limiting accessibility and comfort. This study presents a deep learning framework for contactless SpO2 measurement using an off-the-shelf camera, addressing challenges related to lighting variations and skin t

A. V. Dodin, K. A. Postnov, A. M. Cherepashchuk, A. M. Tatarnikov

We report on the discovery of rare emergence (31 nights from 360 nights of observations) of narrow absorption features in hydrogen and helium lines in stationary SS433 spectra with velocities ranging from $-650$ to $-1900$ km/s. The components arise independently of the appearance of P-Cygni line profiles which are frequently observed in the SS433 stationary

Da-Jian Zhang, D. M. Tong

Efficiently estimating the quantum Fisher information (QFI) is pivotal in quantum information science but remains an outstanding challenge for large systems due to its high nonlinearity. In this Letter, we tackle this long-standing challenge by integrating the Krylov subspace method--a celebrated tool from applied mathematics--into the framework of shadow to

Kun Li, Tianhua Zhang, Yunxiang Li, Hongyin Luo

Improving context faithfulness in large language models is essential for developing trustworthy retrieval augmented generation systems and mitigating hallucinations, especially in long-form question answering (LFQA) tasks or scenarios involving knowledge conflicts. Existing methods either intervene LLMs only at inference without addressing their inherent lim

Zhanxin Hao, Jianxiao Jiang, Jifan Yu, Zhiyuan Liu

Integrating Large Language Models (LLMs) into educational practice enables personalized learning by accommodating diverse learner behaviors. This study explored diverse learner profiles within a multi-agent, LLM-empowered learning environment. Data was collected from 312 undergraduate students at a university in China as they participated in a six-module cou

Utsab Dey, Soumitra Nandi

This study investigates the decay modes of the $B_c$ meson, focusing on semileptonic and nonleptonic decay into S and P wave charmonia. The primary objective is to extract the shape parameter of the $B_{c}$ meson distribution amplitude through a data-driven approach, utilizing the lattice results on $B_{c}\rightarrow\eta_{c},J/\psi$ semileptonic form factors

Germán Molpeceres, Joan Enrique-Romero, Atsuki Ishibashi, Yasuhiro Oba

Carbon dioxide (CO$_2$) is one of the most important interstellar molecules. While it is considered that it forms on the surface of interstellar dust grains, the exact contribution of different chemical mechanisms is still poorly constrained. Traditionally it is deemed that the CO + OH reaction occurring on top of ices is the main reaction path for its forma

Yuyan Chen, Nico Lang, B. Christian Schmidt, Aditya Jain

Global biodiversity is declining at an unprecedented rate, yet little information is known about most species and how their populations are changing. Indeed, some 90% of Earth's species are estimated to be completely unknown. Machine learning has recently emerged as a promising tool to facilitate long-term, large-scale biodiversity monitoring, including algo

Eliza Cohn, Ning Qi, Upmanu Lall, Bolun Xu

This paper presents a long-term dispatch framework for coupled hydropower and floating photovoltaic systems. We introduce a temporal decomposition algorithm based on partial Lagrangian relaxation to address long-term water contract constraints. We derive a real-time, non-anticipatory dispatch policy based on water contract pricing. Our framework is evaluated

Benno Harling, Zamin Mamiyev, Christoph Tegenkamp, Martin Wenderoth

The dynamics of Sn intercalation of the buffer layer on SiC was investigated with a frozen-in diffusion front using Kelvin Probe Force Microscopy. The technique allows to laterally distinguish between intercalated regions and the pristine buffer layer. Comparing topography features with the surface potential on the mesoscopic scale confirms that surface step

Petr Sychev, Andrey Goncharov, Daniil Vyazhev, Edvard Khalafyan

Uncertainty estimation is crucial for evaluating Large Language Models (LLMs), particularly in high-stakes domains where incorrect answers result in significant consequences. Numerous approaches consider this problem, while focusing on a specific type of uncertainty, ignoring others. We investigate what estimates, specifically token-wise entropy and model-as

Miika Tuominen

We establish Rezk completion functors for $\Theta_n$-spaces with respect to each and all of the completeness conditions. As a consequence, we obtain a characterization completeness of Segal $\Theta_n$-spaces as locality with respect to higher-dimensional Dwyer-Kan equivalences.

Honglin Fu, Yebo Feng, Cong Wu, Jiahua Xu

Masterminds are entities organizing, coordinating, and orchestrating cryptocurrency pump-and-dump schemes, a form of trade-based manipulation undermining market integrity and causing financial losses for unwitting investors. Previous research detects pump-and-dump activities in the market, predicts the target cryptocurrency, and examines investors and \ac{os

Sonam Phuntsho

Chiral semiconductors such as elemental tellurium (Te) exhibit unconventional spin textures and large charge-to-spin conversion efficiencies, yet the influence of introducing elements on these properties remains underexplored. Here, we address this gap by investigating how substituting Te with lighter (S, Se) or heavier (Sb) elements systematically modifies

Ana Puente-Santamaria, Josselyn N. Molina-Basurto, Eva Gerardin, Francisco Ortega

This study explores the hydrogen bond-mediated association between chitosan (CHI) and alkyl polyglucoside (APG), a bio-based surfactant, in acidic conditions with varying ionic strengths. Unlike conventional polyelectrolyte-surfactant interactions that depend on electrostatic forces, the association in this system relies purely on non-ionic interactions. Usi

Mufan Qiu, Xinyu Hu, Fengwei Zhan, Sukwon Yun

Foundation models for single-cell RNA sequencing (scRNA-seq) have shown promising capabilities in capturing gene expression patterns. However, current approaches face critical limitations: they ignore biological prior knowledge encoded in gene regulatory relationships and fail to leverage multi-omics signals that could provide complementary regulatory insigh

Carlos Manuel Rivera-Aguilar, Andrei Mursa, Clément Carlé, Jean-Michel Friedt

We present a coherent-population trapping (CPT) microcell atomic clock using symmetric auto-balanced Ramsey (SABR) spectroscopy. The pulsed SABR sequence is applied through direct current-based power modulation of the vertical-cavity surface-emitting laser, eliminating the need for an external optical shutter and enabling compatibility with fully-integrated

Thibaut Delcroix, Simon Jubert

In K-stability, the delta invariant of a Fano variety encodes the existence of K\"ahler-Einstein metrics. We introduce a weighted analytic delta invariant, and a reduced version, that characterize the existence of weighted solitons. We further prove a sufficient condition of existence of weighted cscK metrics in terms of this invariant. We elucidate the rela

Boris Sbarufatti, Renato Falomo, Aldo Treves

We present UV and X-ray observations, obtained with the Swift Observatory, of Gaia BH3 a binary system containing a 33 solar masses black hole discovered through Gaia astrometry. The system is well detected in all UV and optical filters (1700-6500 {\AA}). We compare our results with the modeling of the non collapsed component using synthetic stellar librarie

Sushanth Varada, Christian Spånslätt, Matteo Acciai

Two-particle interferometry is an important tool for extracting the exchange statistics of quantum particles. We theoretically investigate the prospects of such interferometry to probe the statistics of point-like anyonic excitations injected in a Hong-Ou-Mandel (HOM) setup based on a quantum point contact device in the fractional quantum Hall regime. We com

Emile Breton

We prove that for any global solution to the Vlasov-Maxwell system arising from compactly supported data, and such that the electromagnetic field decays fast enough, the distribution function exhibits a modified scattering dynamic. In particular, our result applies to every small data solution constructed by Glassey-Strauss.

Elahe Delavari, John Moore, Junho Hong, Jaerock Kwon

This paper presents a novel Perceptual Motor Learning (PML) framework integrated with Active Inference (AIF) to enhance lateral control in Highly Automated Vehicles (HAVs). PML, inspired by human motor learning, emphasizes the seamless integration of perception and action, enabling efficient decision-making in dynamic environments. Traditional autonomous dri

Justin N. Kreikemeyer, Miłosz Jankowski, Pia Wilsdorf, Adelinde M. Uhrmacher

Formal languages are an integral part of modeling and simulation. They allow the distillation of knowledge into concise simulation models amenable to automatic execution, interpretation, and analysis. However, the arguably most humanly accessible means of expressing models is through natural language, which is not easily interpretable by computers. Here, we

Michela Esposito, Stefano Borgani, Veronica Strazzullo, Maurilio Pannella

The study of protoclusters at cosmic noon is essential to understand the impact on galaxies of the environment and of the transformational processes occurring in this epoch. This work tests the predictions of the DIANOGA cosmological hydrodynamical simulations of cluster progenitors at z=2.2, comparing them with observations, and investigates the environment

Xiao Liu, Zirui Wu, Jiayi Li, Zhicheng Shao

Longitudinal network data are essential for analyzing political, economic, and social systems and processes. In political science, these datasets are often generated through human annotation or supervised machine learning applied to evolving corpora. However, as semantic contexts shift over time, inferring dynamic interaction types on emerging issues among a

Bogdan Ćmiel, Teresa Ledwina

This paper reconsiders the problem of testing the equality of two unspecified continuous distributions. The framework, which we propose, allows for readable and insightful data visualisation and helps to understand and quantify how two groups of data differ. We consider a useful weighted rank empirical process on (0,1) and utilise a grid-based approach, base

Siya Qi, Rui Cao, Yulan He, Zheng Yuan

With the rapid development of large language models (LLMs), LLM-as-a-judge has emerged as a widely adopted approach for text quality evaluation, including hallucination evaluation. While previous studies have focused exclusively on single-context evaluation (e.g., discourse faithfulness or world factuality), real-world hallucinations typically involve mixed

Arvin Hosseinzadeh, Ladan Khoshnevisan, Mohammad Pirani, Shojaeddin Chenouri

In continual time series analysis using neural networks, catastrophic forgetting (CF) of previously learned models when training on new data domains has always been a significant challenge. This problem is especially challenging in vehicle estimation and control, where new information is sequentially introduced to the model. Unfortunately, existing work on c

Linyue Hu, Luchu Li, Cunhua Pan, Hong Ren

This paper investigates a two-timescale uplink transmission framework for a fluid antenna-enabled multiuser multi-input multi-output system (MIMO-FAS). Antenna positions are optimized based on statistical channel state information (CSI), while beamforming vectors at the base station (BS) adapt to instantaneous CSI. Under a Rician fading channel with imperfec

Linhao Huang, Jing Yu

Recent training-free layout-to-image diffusion models have demonstrated remarkable performance in generating high-quality images with controllable layouts. These models follow a one-stage framework: Encouraging the model to focus the attention map of each concept on its corresponding region by defining attention map-based losses. However, these models still

Elmiloud Chil, Khansa Weslati

The purpose of the present paper is to prove the Nakano theorem for orthogonally additive polynomials in Riesz spaces

Alan Lew, Eran Nevo, Yuval Peled, Orit E. Raz

We define a generic rigidity matroid for $k$-volumes of a simplicial complex in $\mathbb{R}^d$, and prove that for $2\leq k \leq d-1$ it has the same rank as the classical generic $d$-rigidity matroid on the same vertex set (namely, the case $k=1$). This is in contrast with the $k=d$ case, previously studied by Lubetzky and Peled, which presents a different

Lukas Silvester Barth, Hannaneh Fahimi, Parvaneh Joharinad, Jürgen Jost

Many machine learning algorithms try to visualize high dimensional metric data in 2D in such a way that the essential geometric and topological features of the data are highlighted. In this paper, we introduce a framework for aggregating dissimilarity functions that arise from locally adjusting a metric through density-aware normalization, as employed in the

Pradeep Dubey, Siddhartha Sahi

In a country with many elections, it may prove economically expedient to hold multiple elections simultaneously on a common polling date. We show that in a polarized society, in which each voter has a preferred party, an increase in the simultaneity of polling will increase the likelihood of a single-party sweep, namely, it will become more likely that a sin

Daniel Lemire

Modern processors have instructions to process 16 bytes or more at once. These instructions are called SIMD, for single instruction, multiple data. Recent advances have leveraged SIMD instructions to accelerate parsing of common Internet formats such as JSON and base64. During HTML parsing, they quickly identify specific characters with a strategy called vec

Yohann Cabon, Lucas Stoffl, Leonid Antsfeld, Gabriela Csurka

DUSt3R introduced a novel paradigm in geometric computer vision by proposing a model that can provide dense and unconstrained Stereo 3D Reconstruction of arbitrary image collections with no prior information about camera calibration nor viewpoint poses. Under the hood, however, DUSt3R processes image pairs, regressing local 3D reconstructions that need to be

Thang Do, Arnulf Jentzen, Adrian Riekert

Despite the omnipresent use of stochastic gradient descent (SGD) optimization methods in the training of deep neural networks (DNNs), it remains, in basically all practically relevant scenarios, a fundamental open problem to provide a rigorous theoretical explanation for the success (and the limitations) of SGD optimization methods in deep learning. In parti

Yehonatan Bitton, Elad Bitton, Shai Nisan

Large language models (LLMs) have distinct and consistent stylistic fingerprints, even when prompted to write in different writing styles. Detecting these fingerprints is important for many reasons, among them protecting intellectual property, ensuring transparency regarding AI-generated content, and preventing the misuse of AI technologies. In this paper, w

Arturs Kanepajs, Aditi Basu, Sankalpa Ghose, Constance Li

As machine learning systems become increasingly embedded in society, their impact on human and nonhuman life continues to escalate. Technical evaluations have addressed a variety of potential harms from large language models (LLMs) towards humans and the environment, but there is little empirical work regarding harms towards nonhuman animals. Following the g

Youngbin Choi, Seunghyuk Cho, Minjong Lee, MoonJeong Park

Personalizing large language models (LLMs) is important for aligning outputs with diverse user preferences, yet existing methods struggle with flexibility and generalization. We propose CoPL (Collaborative Preference Learning), a graph-based collaborative filtering framework that models user-response relationships to enhance preference estimation, particular

Susanne Dandl, Torsten Hothorn

Although treatment effects can be estimated from observed outcome distributions obtained from proper randomization in clinical trials, covariate adjustment is recommended to increase precision. For important treatment effects, such as odds or hazard ratios, conditioning on covariates in binary logistic or proportional hazards models changes the interpretatio

Pinghui Huang, Xue-Ning Bai

The interaction between gas and dust in protoplanetary disks (PPDs) plays a crucial role in setting the stage of planet formation. In particular, the streaming instability (SI) is well recognized as the mechanism for planetesimal formation out of this interaction. The outer region of PPDs is likely subject to the vertical shear instability (VSI), representin

Ziyu Wang, Tao Xue, Jingyuan Li, Haibin Zhang

Object detection in sonar images is crucial for underwater robotics applications including autonomous navigation and resource exploration. However, complex noise patterns inherent in sonar imagery, particularly speckle, reverberation, and non-Gaussian noise, significantly degrade detection accuracy. While denoising techniques have achieved remarkable success

Shuvendu Roy, Franklin Ogidi, Ali Etemad, Elham Dolatabadi

Multimodal representation learning has demonstrated remarkable potential in enabling models to process and integrate diverse data modalities, such as text and images, for improved understanding and performance. While the medical domain can benefit significantly from this paradigm, the scarcity of paired multimodal data and reliance on proprietary or pretrain

Yingxue Xu, Fengtao Zhou, Chenyu Zhao, Yihui Wang

The integration of multimodal data including pathology images and gene profiles is widely applied in precise survival prediction. Despite recent advances in multimodal survival models, collecting complete modalities for multimodal fusion still poses a significant challenge, hindering their application in clinical settings. Current approaches tackling incompl

Dmitrii Pavlov, Kristian Ranestad

In this article we study adjoint hypersurfaces of geometric objects obtained by intersecting simple polytopes with few facets in $\mathbb{P}^5$ with the Grassmannian $\mathrm{Gr}(2,4)$. These generalize the positive Grassmannian, which is the intersection of $\mathrm{Gr}(2,4)$ with the simplex. We show that if the resulting object has five facets, it is a po

Daniele Lamberto, Alberto Mercurio, Omar Di Stefano, Vincenzo Savona

The quantum Rabi model (QRM) is a cornerstone in the study of light-matter interactions within cavity and circuit quantum electrodynamics (QED). It effectively captures the dynamics of a two-level system coupled to a single-mode resonator, serving as a foundation for understanding quantum optical phenomena in a great variety of systems. However, this model m

Hamidreza Mirkhani, Behzad Khamidehi, Ehsan Ahmadi, Mohammed Elmahgiubi

In this paper, we introduce Context-Aware Priority Sampling (CAPS), a novel method designed to enhance data efficiency in learning-based autonomous driving systems. CAPS addresses the challenge of imbalanced datasets in imitation learning by leveraging Vector Quantized Variational Autoencoders (VQ-VAEs). In this way, we can get structured and interpretable d

Cheng-Cheng Yu, Yu-Hao Deng, Ming-Cheng Chen, Chao-Yang Lu

Qubit leakage and loss, particularly Rydberg-induced decay during two-qubit gates, pose significant challenges to fault-tolerant quantum computing with neutral atom arrays, as they propagate to correlated errors and degrade code distance. Here, we present a hardware-efficient scheme for addressing Rydberg decay using the SWAP-Leakage Reduction Circuit (SWAP-

G. Bossard, K. S. Stelle

We review the development of understanding for the problem of ultraviolet divergences in supergravity. This history proceeds from initial constructions of counterterms invariant under the relevant degrees of local supersymmetry, through a deeper understanding of non-renormalisation theorems for the relevant degrees of ``off-shell'' linearly realisable supers

Dianyi Yang, Yu Gao, Xihan Wang, Yufeng Yue

Recent advancements in 3D Gaussian Splatting have significantly improved the efficiency and quality of dense semantic SLAM. However, previous methods are generally constrained by limited-category pre-trained classifiers and implicit semantic representation, which hinder their performance in open-set scenarios and restrict 3D object-level scene understanding.

Zhendong Wang, Jianmin Bao, Shuyang Gu, Dong Chen

In this paper, we present DesignDiffusion, a simple yet effective framework for the novel task of synthesizing design images from textual descriptions. A primary challenge lies in generating accurate and style-consistent textual and visual content. Existing works in a related task of visual text generation often focus on generating text within given specific

Allen Zhang

We define the notion of a partial action on a generalized Boolean algebra and associate to every such system and commutative unital ring $R$ an $R$-algebra. We prove that every strongly $E^{\ast}$-unitary inverse semigroup has an associated partial action on the generalized Boolean algebra of compact open sets of tight filters in the meet semilattice of idem

Jiayu Wan, Liu Liu

In this paper, we construct an asymptotic-preserving neural networks (APNNs) [21] for the linearized Boltzmann equation in the acoustic scaling and with uncertain parameters. Utilizing the micro-macro decomposition, we design the loss function based on the stochastic-Galerkin system conducted from the micro-macro equations. Rigorous analysis is provided to s

Wenjie Wu, Yongcheng Jing, Yingjie Wang, Wenbin Hu

Recent large language model (LLM) reasoning, despite its success, suffers from limited domain knowledge, susceptibility to hallucinations, and constrained reasoning depth, particularly in small-scale models deployed in resource-constrained environments. This paper presents the first investigation into integrating step-wise knowledge graph retrieval with step

B. Lehnert, S. S. Nagorny, M. Thiesse, F. Ferella

Gadolinium is widely used in multiple low-background experiments, making its isotopes accessible for rare decay searches both in-situ and through radiopurity screening data. This study presents an improved search for rare alpha and double-beta decay modes in $^{152}$Gd, $^{154}$Gd, and $^{160}$Gd isotopes using ultra-low background HPGe detectors at the Boul

Mohsen Asgharzadeh

We study certain properties of modules over 1-dimensional local integral domains. First, we examine the order of the conductor ideal and its expected relationship with multiplicity. Next, we investigate the reflexivity of certain colength-two ideals. Finally, we consider the freeness problem of the absolute integral closure of a DVR, and connect this to the

Sueda Taner, Ziyi Wang, Christoph Studer

We introduce a novel class of regularization functions, called Cauchy-Schwarz (CS) regularizers, which can be designed to induce a wide range of properties in solution vectors of optimization problems. To demonstrate the versatility of CS regularizers, we derive regularization functions that promote discrete-valued vectors, eigenvectors of a given matrix, an

Stef Verpoest, Frank Schroeder, Alexander Novikov, Alan Coleman

We present the design and science case for a new array of radio antennas to be located at the Pierre Auger Observatory. Six stations of three SKALA antennas each will be deployed around a single water-Cherenkov surface detector triggering the radio readout. The planned antenna layout will allow for the detection of cosmic rays above a few tens of PeV and rea

K. Panchagnula, D. Graf, K. R. Bryenton, D. P. Tew

A recent study by Panchagnula et al. [J. Chem. Phys. 161, 054308 (2024)] illustrated the non-concordance of a variety of electronic structure methods at describing the symmetric double-well potential expected along the anisotropic direction of the endofullerene Ne@C$_{70}$. In this article we present new correlated-wavefunction data from coupled cluster theo

Guoming Zhang

We solve the Kato square root problem for parabolic operators of arbitrary order $2m$ whose coefficients are allowed to depend on both space and time in a merely measurable way and possess boundedness and ellipticity controlled by a Muckenhoupt $A_{2}-$weight. Notably, the proof applies to the weighted Kato problem within an elliptic framework.

Stephen Wechsler, James W. Shearer, Katrin Erk

The evolution of grammatical systems of syntactic and semantic composition is modeled here with a novel application of reinforcement learning theory. To test the functionalist thesis that speakers' expressive purposes shape their language, we include within the model a probability distribution over different messages that could be expressed in a given contex

Arnesh Batra, Arush Gumber, Anushk Kumar

The increasing prevalence of lumbar spinal canal stenosis has resulted in a surge of MRI (Magnetic Resonance Imaging), leading to labor-intensive interpretation and significant inter-reader variability, even among expert radiologists. This paper introduces a novel and efficient deep-learning framework that fully automates the grading of lumbar spinal canal s

Luyi Qiu, Tristan Till, Xiaobao Guo, Adams Wai-Kin Kong

Scribble annotations significantly reduce the cost and labor required for dense labeling in large medical datasets with complex anatomical structures. However, current scribble-supervised learning methods are limited in their ability to effectively propagate sparse annotation labels to dense segmentation masks and accurately segment object boundaries. To add

Tianchi Ren, Haibo Hu, Jiacheng Zuo, Xinhong Chen

With the acceleration of urbanization, modern urban traffic systems are becoming increasingly complex, leading to frequent traffic anomalies. These anomalies encompass not only common traffic jams but also more challenging issues such as phantom traffic jams, intersection deadlocks, and accident liability analysis, which severely impact traffic flow, vehicul

Jacopo Joy Colombini, Filippo Bonchi, Francesco Giannini, Fosca Giannotti

This paper introduces a rigorous mathematical framework for neural network explainability, and more broadly for the explainability of equivariant operators called Group Equivariant Operators (GEOs) based on Group Equivariant Non-Expansive Operators (GENEOs) transformations. The central concept involves quantifying the distance between GEOs by measuring the n

Joongi Shin, Anna Polyanskaya, Andrés Lucero, Antti Oulasvirta

Problem reframing is a designerly activity wherein alternative perspectives are created to recast what a stated design problem is about. Generating alternative problem frames is challenging because it requires devising novel and useful perspectives that fit the given problem context. Large language models (LLMs) could assist this activity via their generativ

Henrik Nolte, Michèle Finck, Kristof Meding

Does GPT know you? The answer depends on your level of public recognition; however, if your information was available on a website, the answer could be yes. Most Large Language Models (LLMs) memorize training data to some extent. Thus, even when an LLM memorizes only a small amount of personal data, it typically falls within the scope of data protection laws

Cedric Schuhmann, Benjamin Köhler, Anton J. Heckens, Thomas Guhr

Traders on financial markets generate non-Markovian effects in various ways, particularly through their competition with one another which can be interpreted as a game between different (types of) traders. To quantify the market mechanisms, we empirically analyze self-response functions for pairs of different stocks and the corresponding trade sign correlato

William Michael Laprade, Jesper Cairo Westergaard, Svend Christensen, Mads Nielsen

Spectral imaging data acquired via multispectral and hyperspectral cameras can have hundreds of channels, where each channel records the reflectance at a specific wavelength and bandwidth. Time and resource constraints limit our ability to collect large spectral datasets, making it difficult to build and train predictive models from scratch. In the RGB domai

Enrico Lipparini, Thomas Hader, Ahmed Irfan, Stéphane Graham-Lengrand

The Model Constructing Satisfiability (MCSat) approach to the SMT problem extends the ideas of CDCL from the SAT level to the theory level. Like SAT, its search is driven by incrementally constructing a model by assigning concrete values to theory variables and performing theory-level reasoning to learn lemmas when conflicts arise. Therefore, the selection o

Michael M. Bilevich, Dan Halperin

Subdivision methods such as quadtrees, octrees, and higher-dimensional orthrees are standard practice in different domains of computer science. We can use these methods to represent given geometries, such as curves, meshes, or surfaces. This representation is achieved by splitting some bounding voxel recursively while further splitting only sub-voxels that i

Arne Rubehn, Christoph Rzymski, Luca Ciucci, Kellen Parker van Dam

Numeral systems across the world's languages vary in fascinating ways, both regarding their synchronic structure and the diachronic processes that determined how they evolved in their current shape. For a proper comparison of numeral systems across different languages, however, it is important to code them in a standardized form that allows for the compariso

Alexandru Dimca

To each multiple point $p$ in a line arrangement $ \mathcal A$ in the complex projective plane we associate a local derivation $\tilde D_p \in D_0( \mathcal A)$. We show first that these derivations span the graded module of derivations $D_0( \mathcal A)$ in all degrees $\geq d -3$, where $d$ is the number of lines in $ \mathcal A$, see Theorem 1.4 and Theor

David Hartmann, Amin Oueslati, Dimitri Staufer, Lena Pohlmann

Commercial content moderation APIs are marketed as scalable solutions to combat online hate speech. However, the reliance on these APIs risks both silencing legitimate speech, called over-moderation, and failing to protect online platforms from harmful speech, known as under-moderation. To assess such risks, this paper introduces a framework for auditing bla

Eliya Habba, Ofir Arviv, Itay Itzhak, Yotam Perlitz

Recent work found that LLMs are sensitive to a wide range of arbitrary prompt dimensions, including the type of delimiters, answer enumerators, instruction wording, and more. This throws into question popular single-prompt evaluation practices. We present DOVE (Dataset Of Variation Evaluation) a large-scale dataset containing prompt perturbations of various

Lucas Broux, Felix Otto, Rhys Steele

We obtain (small-parameter) well-posedness for the (space-time periodic) $\Phi^4$ equation in the full subcritical regime in the context of regularity structures based on multi-indices. As opposed to Hairer's more extrinsic tree-based setting, due to the intrinsic description encoded by multi-indices, it is not possible to obtain a solution theory via the st

Giannis Papagiannopoulos, Orlando Luongo, Genly Leon, Andronikos Paliathanasis

The impact of topological terms that modify the Hilbert-Einstein action is here explored by virtue of a further $f(G)$ contribution. In particular, we investigate the phase-space stability and critical points of an equivalent scalar field representation that makes use of a massive field, whose potential is function of the topological correction. To do so, we

Tong Ge, Yashu Liu, Jieping Ye, Tianyi Li

Modern front-end (FE) development, especially when leveraging the unique features of frameworks like React and Vue, presents distinctive challenges. These include managing modular architectures, ensuring synchronization between data and visual outputs for declarative rendering, and adapting reusable components to various scenarios. Such complexities make it

Guang Lin, Changhong Mou, Jiahao Zhang

We introduce evolutionary Kolmogorov-Arnold Networks (EvoKAN), a novel framework for solving complex partial differential equations (PDEs). EvoKAN builds on Kolmogorov-Arnold Networks (KANs), where activation functions are spline based and trainable on each edge, offering localized flexibility across multiple scales. Rather than retraining the network repeat