February 2025 arXiv papers — page 3

Kulin Shah, Alkis Kalavasis, Adam R. Klivans, Giannis Daras

There is strong empirical evidence that the state-of-the-art diffusion modeling paradigm leads to models that memorize the training set, especially when the training set is small. Prior methods to mitigate the memorization problem often lead to a decrease in image quality. Is it possible to obtain strong and creative generative models, i.e., models that achi

Qinghua Lei, Didier Sornette

Forecasting volcanic eruptions remains a formidable challenge due to the inherent complexity and variability of volcanic processes. A key source of uncertainty arises from the sporadic nature of volcanic unrest, which is often characterised by intermittent phases of quiescent deceleration and sudden acceleration, rather than a consistent, predictable progres

Yuan Bian, Grace Y. Yi, Wenqing He

Boosting has emerged as a useful machine learning technique over the past three decades, attracting increased attention. Most advancements in this area, however, have primarily focused on numerical implementation procedures, often lacking rigorous theoretical justifications. Moreover, these approaches are generally designed for datasets with fully observed d

Umadini Ranasinghe, Abigail L. Stressinger, Guangpeng Xu, Yasmin Sarhan

Overcoming the strong chlorophyll background poses a significant challenge for measuring and optimizing plant growth. This research investigates the novel application of specialized quantum light emitters introduced into intact leaves of tobacco (Nicotiana tabacum), a well-characterized model plant system for studies of plant health and productivity. Leaves

Roman Klypa, Alberto Bietti, Sergei Grudinin

Designing RNA molecules that interact with specific proteins is a critical challenge in experimental and computational biology. Existing computational approaches require a substantial amount of previously known interacting RNA sequences for each specific protein or a detailed knowledge of RNA structure, restricting their utility in practice. To address this

Vishvesh Kumar, Berikbol T. Torebek

This paper explores the critical behavior of the semilinear heat equation $u_t+\mathcal{L}_{a, b}u=|u|^p+f(x)$, considering both the presence and absence of a forcing term $f(x).$ The mixed local-nonlocal operator $\mathcal{L}_{a, b}=-a\Delta+b(-\Delta)^s,\,a,\,b \in \mathbb{R}_+,$ incorporates both local and nonlocal Laplacians. We determine the Fujita-type

Xi Tang, Jihao Qiu, Lingxi Xie, Yunjie Tian

Multimodal large language models (MLLMs) have enabled open-world visual understanding by injecting visual input as extra tokens into large language models (LLMs) as contexts. However, when the visual input changes from a single image to a long video, the above paradigm encounters difficulty because the vast amount of video tokens has significantly exceeded t

Daebeom Choi

In this work, we study a family of vector bundles on the moduli space of curves constructed from representations of $\text{Vir}_{2k+1,2}$, a family of vertex operator algebras derived from the Virasoro Lie algebra. Using the relationship between rank and degree, we characterize their asymptotic behavior, demonstrating that their first Chern classes are nef o

Andrea Montanari, Pierfrancesco Urbani

Understanding the inductive bias and generalization properties of large overparametrized machine learning models requires to characterize the dynamics of the training algorithm. We study the learning dynamics of large two-layer neural networks via dynamical mean field theory, a well established technique of non-equilibrium statistical physics. We show that,

Alexander Scarlatos, Yusong Wu, Ian Simon, Adam Roberts

Recent advances in generative artificial intelligence (AI) have created models capable of high-quality musical content generation. However, little consideration is given to how to use these models for real-time or cooperative jamming musical applications because of crucial required features: low latency, the ability to communicate planned actions, and the ab

Lucio Anderlini, Matteo Barbetti, Giulio Bianchini, Diego Ciangottini

Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly in terms of provisioning and orchestrating access to hardware accelerators for development, testing, and production. The INFN-funded project

Rachel Wicks, Kartik Ravisankar, Xinchen Yang, Philipp Koehn

Model ensembling is a technique to combine the predicted distributions of two or more models, often leading to improved robustness and performance. For ensembling in text generation, the next token's probability distribution is derived from a weighted sum of the distributions of each individual model. This requires the underlying models to share the same sub

Nita Mulliqi, Anders Blilie, Xiaoyi Ji, Kelvin Szolnoky

The role of artificial intelligence (AI) in pathology has evolved from aiding diagnostics to uncovering predictive morphological patterns in whole slide images (WSIs). Recently, foundation models (FMs) leveraging self-supervised pre-training have been widely advocated as a universal solution for diverse downstream tasks. However, open questions remain about

Aleksandr Nesterov, Andrey Sakhovskiy, Ivan Sviridov, Airat Valiev

This study investigates the feasibility of automating clinical coding in Russian, a language with limited biomedical resources. We present a new dataset for ICD coding, which includes diagnosis fields from electronic health records (EHRs) annotated with over 10,000 entities and more than 1,500 unique ICD codes. This dataset serves as a benchmark for several

Leon Lang, Patrick Forré

As AI systems advance beyond human capabilities, scalable oversight becomes critical: how can we supervise AI that exceeds our abilities? A key challenge is that human evaluators may form incorrect beliefs about AI behavior in complex tasks, leading to unreliable feedback and poor value inference. To address this, we propose modeling evaluators' beliefs to i

L. V. Tambovtseva, A. A. Djupvik, V. P. Grinin, H. Weber

UX Orionis stars are the most active young stars; they undergo sporadic fadings of 2 - 4 magnitudes in the V-band, due to variable circumstellar extinction caused by a nearly edge-on star-disc system. The long-lasting monitoring of a number of stars of this type with the Nordic Optical Telescope from 2019 to 2024 has given a rich collection of material of hi

Boxiao Yu, Savas Ozdemir, Jiong Wu, Yizhou Chen

Low-dose Positron Emission Tomography (PET) imaging presents a significant challenge due to increased noise and reduced image quality, which can compromise its diagnostic accuracy and clinical utility. Denoising diffusion probabilistic models (DDPMs) have demonstrated promising performance for PET image denoising. However, existing DDPM-based methods typical

Peilin Chen, Xiaoxuan Yang

Processing-in-memory (PIM) is a promising computing paradigm to tackle the "memory wall" challenge. However, PIM system-level benefits over traditional von Neumann architecture can be reduced when the memory array cannot fully store all the neural network (NN) weights. The NN size is increasing while the PIM design size cannot scale up accordingly due to are

Robin A. Heinonen, Luca Biferale, Antonio Celani, Massimo Vergassola

The problem of tracking the source of a passive scalar in a turbulent flow is relevant to flying insect behavior and several other applications. Extensive previous work has shown that certain Bayesian strategies, such as "infotaxis," can be very effective for this difficult "olfactory search" problem. More recently, a quasi-optimal Bayesian strategy was comp

Yuheng Ji, Huajie Tan, Jiayu Shi, Xiaoshuai Hao

Recent advancements in Multimodal Large Language Models (MLLMs) have shown remarkable capabilities across various multimodal contexts. However, their application in robotic scenarios, particularly for long-horizon manipulation tasks, reveals significant limitations. These limitations arise from the current MLLMs lacking three essential robotic brain capabili

Aleksandr Kovalev, Anna Makarova, Petr Chizhov, Matvey Antonov

We present a system for decoding hand movements using surface EMG signals. The interface provides real-time (25 Hz) reconstruction of finger joint angles across 20 degrees of freedom, designed for upper limb amputees. Our offline analysis shows 0.8 correlation between predicted and actual hand movements. The system functions as an integrated pipeline with th

Federico Librino, Giorgio Quer

The increasing traffic demand in cellular networks has recently led to the investigation of new strategies to save precious resources like spectrum and energy. Direct device-to-device (D2D) communication becomes a promising solution if the two terminals are located in close proximity. In this case, the D2D communications should coexist with cellular transmis

Hans van Haren

A correction by a reduction factor O(100) is proposed for previously calculated turbulence values on unresolved convection-overturns induced by general geothermal heating in the deep Western Mediterranean. The correction includes modified application of reordering method for calculating turbulence values in convection turbulence with and without stratificati

Peijun Zhang, Yan Gu, Okyay Altay, Chuanzeng Zhang

In this study, we propose a novel approach, termed boundary integrated neural networks (BINNs), for analyzing in-plane crack problems within the framework of linear elastic fracture mechanics. The proposed approach integrates artificial neural networks (ANNs) with classical boundary integral equations (BIEs), enabling an efficient and accurate evaluation of

Aram Karakhanyan, Takis Konstantopoulos, Matthew Lorig, Evgenii Samutichev

We introduce a class of short-rate models that exhibit a ``higher for longer'' phenomenon. Specifically, the short-rate is modeled as a general time-homogeneous one-factor Markov diffusion on a finite interval. The lower endpoint is assumed to be regular, exit or natural according to boundary classification while the upper endpoint is assumed to be regular w

Maninder Singh Dhanauta

We prove that the virtual cactus group has a finite index subgroup that is the fundamental group of a compact special cube complex.

Nijesh Upreti, Jessica Ciupa, Vaishak Belle

A computational ethics framework is essential for AI and autonomous systems operating in complex, real-world environments. Existing approaches often lack the adaptability needed to integrate ethical principles into dynamic and ambiguous contexts, limiting their effectiveness across diverse scenarios. To address these challenges, we outline the necessary ingr

Bruno Machado Pacheco, Pedro Marcolin Antunes, Eduardo Camponogara, Laio Oriel Seman

This paper introduces a novel algorithm for Mixed-Integer Nonlinear Programming (MINLP) problems with multilinear interpolations of look-up tables. These problems arise when objective or constraints contain black-box functions only known at a finite set of evaluations on a predefined grid. We derive a piecewise-linear relaxation for the multilinear constrain

Vijayalaxmi Methuku, Praveen Kumar Myakala

The rapid advancement of generative AI has enabled the creation of pre-mortem digital twins, AI-driven replicas that mimic the behavior, personality, and knowledge of living individuals. These digital doppelgangers serve various functions, including enhancing productivity, enabling creative collaboration, and preserving personal legacies. However, their deve

Diana Barseghyan, Swanhild Bernstein, Baruch Schneider, Martha Lina Zimmermann

For a two-dimensional curved waveguide, it is well known that the spectrum of the Dirichlet Laplacian is unstable. Any perturbation of the straight strip produces eigenvalues below the essential spectrum. In this paper, a magnetic field is added. We explicitly prove that the spectrum of the magnetic Laplacian is stable under small but non-local deformations

Sebastian Schulz, Dennis Willsch, Kristel Michielsen

We present Learning-Driven Annealing (LDA), a framework that links individual quantum annealing evolutions into a global solution strategy to mitigate hardware constraints such as short annealing times and integrated control errors. Unlike other iterative methods, LDA does not tune the annealing procedure (e.g. annealing time or annealing schedule), but inst

Haoran Zhang, Yong Liu, Yunzhong Qiu, Haixuan Liu

Time series analysis is crucial in diverse scenarios. Beyond forecasting, considerable real-world tasks are categorized into classification, imputation, and anomaly detection, underscoring different capabilities termed time series understanding in this paper. While GPT-style models have been positioned as foundation models for time series forecasting, the BE

Alberto Mario Ceballos-Arroyo, Jisoo Kim, Chu-Hsuan Lin, Lei Qin

Intracranial aneurysms are a major cause of morbidity and mortality worldwide, and detecting them manually is a complex, time-consuming task. Albeit automated solutions are desirable, the limited availability of training data makes it difficult to develop such solutions using typical supervised learning frameworks. In this work, we propose a novel pre-traini

Barbara Kaltenbacher, William Rundell

In this paper we study the simultaneous reconstruction of two coefficients in a reaction-subdiffusion equation, namely a nonlinearity and a space dependent factor. The fact that these are coupled in a multiplicative matter makes the reconstruction particularly challenging. Several situations of overposed data are considered: boundary observations over a time

Maria Koshkina, James H. Elder

In team sports analytics, long-term player tracking remains a challenging task due to player appearance similarity, occlusion, and dynamic motion patterns. Accurately re-identifying players and reconnecting tracklets after extended absences from the field of view or prolonged occlusions is crucial for robust analysis. We introduce SportsSUSHI, a hierarchical

Sebastian Schaper, Matthias Duwe, Ursula Wurstbauer

The unambiguous and universal determination of optical constants such as complex refractive indices (n, k) and thickness (d) in one measurement is typically confined for films with a thickness of more than 10~nm that hampers its application for ultra-thin films such as two-dimensional materials. We demonstrate that the commonly accepted limit of n, k, d coup

Emile Anand, Jan van den Brand, Rose McCarty

We consider the problem of preprocessing an $n\times n$ matrix $\mathbf{M}$, and supporting queries that, for any vector $v$, returns the matrix-vector product $\mathbf{M} v$. This problem has been extensively studied in both theory and practice: on one side, practitioners have developed algorithms that are highly efficient in practice, whereas on the other

Xiaomin Li, Zhou Yu, Ziji Zhang, Yingying Zhuang

Large language models (LLMs) have demonstrated remarkable performance across diverse tasks by encoding vast amounts of factual knowledge. However, they are still prone to hallucinations, generating incorrect or misleading information, often accompanied by high uncertainty. Existing methods for hallucination detection primarily focus on quantifying internal u

Matteo Bergonzoni, Sven Jandura, Guido Pupillo

Ultracold polar molecules in optical lattices or tweezer arrays offer a promising platform for quantum information processing and simulation, thanks to their rich internal structure and long-range dipolar interactions. Recent experimental advances now allow precise control over individual molecules, enabling two-qubit gates based on the iSWAP gate. A key cha

Armen Jerbashian, Joel E. Restrepo

This paper describes the known results on the projection from the most general holomorphic spaces $A^p_\omega$, which depend on a functional parameter $\omega$ and are over the unit disc, upper half-plane and the finite complex plane, to the classical Hardy spaces $H^p.$ The paper can be considered as a survey in the mentioned topic. A new result on the proj

Daniil Filienko, Mahek Nizar, Javier Roberti, Denise Galdamez

Tuberculosis (TB) is the leading cause of death from an infectious disease globally, with the highest burden in low- and middle-income countries. In these regions, limited healthcare access and high patient-to-provider ratios impede effective patient support, communication, and treatment completion. To bridge this gap, we propose integrating a specialized La

Hyoshin Kim, Sujit K. Ghosh, Emily C. Hector

Some evidence suggests that people with autism spectrum disorder exhibit patterns of brain functional dysconnectivity relative to their typically developing peers, but specific findings have yet to be replicated. To facilitate this replication goal with data from the Autism Brain Imaging Data Exchange (ABIDE), we propose a flexible and interpretable model fo

Daniele Oriti

We summarize basic features of quantum gravity states and processes, common to a number of related quantum gravity formalisms, and sharing a purely combinatorial and algebraic language, and a discrete geometric interpretation. We emphasize how, in this context, entanglement is a seed of topological and geometric properties, and how a pre-geometric, discrete

Kai Töpfer, Jingchun Wang, Shimoni Patel, Markus Meuwly

Generating energy functions for heterogeneous systems suitable for quantitative and predictive atomistic simulations is a challenging undertaking. The present work combines a cluster-based approach with electronic structure calculations at the density functional theory level and machine learning-based energy functions for a spectroscopic reporter for eutecti

Mathieu Roget, Giuseppe Di Molfetta

A discrete time quantum walk is known to be the single-particle sector of a quantum cellular automaton. For a long time, these models have interested the community for their nice properties such as locality or translation invariance. This work introduces a model of distributed computation for arbitrary graphs inspired by quantum cellular automata. As a by-pr

Hao Ge, Junda Feng, Qi Huang, Fangcheng Fu

Scaling long-context ability is essential for Large Language Models (LLMs). To amortize the memory consumption across multiple devices in long-context training, inter-data partitioning (a.k.a. Data Parallelism) and intra-data partitioning (a.k.a. Context Parallelism) are commonly used. Current training frameworks predominantly treat the two techniques as ort

Maksim Parfenov, Igor Burmistrov

The spin and integer quantum Hall effects are two cousins of topological phase transitions in two-dimensional electronic systems. Their close relationship makes it possible to transform spin to integer quantum Hall effect in two-dimensional topological superconductors by continuous increase in a symmetry breaking Zeeman magnetic field. We study peculiarities

Kevin McKee

Training of deep reinforcement learning agents is slowed considerably by the presence of input dimensions that do not usefully condition the reward function. Existing modules such as layer normalization can be trained with weight decay to act as a form of selective attention, i.e. an input mask, that shrinks the scale of unnecessary inputs, which in turn acc

Omer Goldman, Uri Shaham, Dan Malkin, Sivan Eiger

To achieve equitable performance across languages, large language models (LLMs) must be able to abstract knowledge beyond the language in which it was learnt. However, the current literature lacks reliable ways to measure LLMs' capability of such cross-lingual knowledge transfer. To that end, we present ECLeKTic, a multilingual closed-book QA dataset that Ev

Marc Gillioz

A new bootstrap equation in 2-dimensional conformal field theory is derived starting from the momentum-space representation of the correlation functions. Since Wightman functions are not crossing-symmetric, the analyticity properties of the commutator are leveraged instead to obtain a relation between two distinct operator product expansions. The procedure r

Kunjun Li, Cheng-Yen Yang, Hsiang-Wei Huang, Jenq-Neng Hwang

This report introduces ReID-SAM, a novel model developed for the SkiTB Challenge that addresses the complexities of tracking skier appearance. Our approach integrates the SAMURAI tracker with a person re-identification (Re-ID) module and advanced post-processing techniques to enhance accuracy in challenging skiing scenarios. We employ an OSNet-based Re-ID mo

Arash Ardakani, Kevin He, John Wawrzynek

In this work, we introduce a novel GPU-accelerated circuit satisfiability (CircuitSAT) sampling technique for sequential circuits. This work is motivated by the requirement in constrained random verification (CRV) to generate input stimuli to validate the functionality of digital hardware circuits. A major challenge in CRV is generating inputs for sequential

Amitayush Jha Thakur, Maximilian Thees, Franck Fortuna, Emmanouil Frantzeskakis

We report the measurement, using angle-resolved photoemission spectroscopy, of the metallic electronic structure of the hole-doped thermoelectric oxide CuRh$_{0.9}$Mg$_{0.1}$O$_2$. The material is found to have a ``pudding mold'' type band structure, with a nearly flat band edge located near the Fermi level, which is thought to be the origin of the thermoele

Sidney Wong, Benjamin Adams, Jonathan Dunn

This chapter explores the efficacy of using social media data to examine changing linguistic behaviour of a place. We focus our investigation on Aotearoa New Zealand where official statistics from the census is the only source of language use data. We use published census data as the ground truth and the social media sub-corpus from the Corpus of Global Lang

The IDEA Study Group

A detector concept, named IDEA, optimized for the physics and running conditions at the FCC-ee is presented. After discussing the expected running conditions and the main physics drivers, a detailed description of the individual sub-detectors is given. These include: a very light tracking system with a powerful vertex detector inside a large drift chamber su

Gert Heckman

This article has a twofold purpose. On the one hand I would like to draw attention to some nice exercises on the Kepler laws, due to Otto Laporte from 1970. Our discussion here has a more geometric flavour than the original analytic approach of Laporte. On the other hand it serves as an addendum to a paper of mine from 1998 on the quantum integrability of th

Yaping Mao, Aaron Robertson, Jian Wang, Chenxu Yang

Schur's Theorem states that, for any $r \in \mathbb{Z}^+$, there exists a minimum integer $S(r)$ such that every $r$-coloring of $\{1,2,\dots,S(r)\}$ admits a monochromatic solution to $x+y=z$. Recently, Budden determined the related Gallai-Schur numbers; that is, he determined the minimum integer $GS(r)$ such that every $r$-coloring of $\{1,2,\dots,GS(r)\}$

Kirsten N. Morehouse, Siddharth Swaroop, Weiwei Pan

The proliferation of LLM bias probes introduces three significant challenges: (1) we lack principled criteria for choosing appropriate probes, (2) we lack a system for reconciling conflicting results across probes, and (3) we lack formal frameworks for reasoning about when (and why) probe results will generalize to real user behavior. We address these challe

Daniel Larby, Joshua Kershaw, Matthew Allen, Fulvio Forni

Robotic assistance allows surgeries to be reliably and accurately executed while still under direct supervision of the surgeon, combining the strengths of robotic technology with the surgeon's expertise. This paper describes a robotic system designed to assist in surgical procedures by implementing a virtual drill guide. The system integrates virtual-fixture

Nick Bryan-Kinns, Shuoyang Jasper Zheng, Francisco Castro, Makayla Lewis

Explainable AI (XAI) is concerned with how to make AI models more understandable to people. To date these explanations have predominantly been technocentric - mechanistic or productivity oriented. This paper introduces the Explainable AI for the Arts (XAIxArts) manifesto to provoke new ways of thinking about explainability and AI beyond technocentric discour

Xinyu Shi, Yinghou Wang, Ryan Rossi, Jian Zhao

Expressing design intent using natural language prompts requires designers to verbalize the ambiguous visual details concisely, which can be challenging or even impossible. To address this, we introduce Brickify, a visual-centric interaction paradigm -- expressing design intent through direct manipulation on design tokens. Brickify extracts visual elements (

Longyu Qing, Jorge Viñals

A Self Consistent Field Theory description of equilibrium, but non uniform, configurations adopted by semi flexible liquid crystal molecules is presented. Two cases are considered, isotropic-nematic phase boundaries, and topological defects in the nematic phase (disclinations). Nematogens are modeled by worm-like chains, with microscopic interaction potentia

Jeff Beck, Maxwell J. D. Ramstead

The free energy principle (FEP), along with the associated constructs of Markov blankets and ontological potentials, have recently been presented as the core components of a generalized modeling method capable of mathematically describing arbitrary objects that persist in random dynamical systems; that is, a mathematical theory of ``every'' ``thing''. Here,

Nijesh Upreti, Vaishak Belle

Abstraction is essential for reducing the complexity of systems across diverse fields, yet designing effective abstraction methodology for probabilistic models is inherently challenging due to stochastic behaviors and uncertainties. Current approaches often distill detailed probabilistic data into higher-level summaries to support tractable and interpretable

Kishore R. Sathia, Marco G. Giometto

Theoretical analyses of the hurricane boundary layer have traditionally relied on slab models, which provide a limited description of wind profiles. Literature on height-resolving methods is typically based on linear analyses, which may fall short of capturing the full sensitivity of the solution to variations in input parameters. This work proposes an appro

Ariel Caticha

Entropic Dynamics (ED) provides a framework that allows the reconstruction of the quantum formalism by insisting on ontological and epistemic clarity and adopting entropic methods and information geometry. Our present goal is to extend the ED framework to account for spin. The result is a realist {\psi}-epistemic model in which the ontology consists of a par

Jianhao Huang, Zixuan Wang, Jason D. Lee

Chain of Thought (CoT) prompting has been shown to significantly improve the performance of large language models (LLMs), particularly in arithmetic and reasoning tasks, by instructing the model to produce intermediate reasoning steps. Despite the remarkable empirical success of CoT and its theoretical advantages in enhancing expressivity, the mechanisms und

Zhihua Chang, Naihuan Jing, Ming Liu, Haitao Ma

We establish a parabolic presentation of the extended Yangian $\X(\mathfrak{g}_{N})$ associated with the Lie algebras $\mathfrak{g}_{N}$ of type $B$ and $C$, parameterized by a symmetric composition $\nu$ of $N$. By formulating a block matrix version of the RTT presentation of $\X(\mathfrak{g}_{N})$, we systematically derive the generators and relations thro

Daniel Corrales, David Ríos Insua, Marino J. González

Background and Objective. With minor differences, most national colorectal cancer (CRC) screening programs in Europe consist of one-size-fits-all aged-based strategies. This paper provides a decision analysis-based approach to personalized CRC screening, supporting decisions concerning whether and which screening method to consider and/or whether a colonosco

Ghulam Mujtaba, Sunder Ali Khowaja, Kapal Dev

Social media has become integral to minors' daily lives and is used for various purposes, such as making friends, exploring shared interests, and engaging in educational activities. However, the increase in screen time has also led to heightened challenges, including cyberbullying, online grooming, and exploitations posed by malicious actors. Traditional con

Omar Alnaseri, Yassine Himeur

In coherent optical orthogonal frequency-division multiplexing (CO-OFDM) fiber communications, a novel end-to-end learning framework to mitigate Laser Phase Noise (LPN) impairments is proposed in this paper. Inspired by Autoencoder (AE) principles, the proposed approach trains a model to learn robust symbol sequences capable of combat LPN, even from low-cost

Pedro Gimenes, Zeyu Cao, Jeffrey Wong, Yiren Zhao

Recent research has shown that LLM performance on reasoning tasks can be enhanced by scaling test-time compute. One promising approach, particularly with decomposable problems, involves arranging intermediate solutions as a graph on which transformations are performed to explore the solution space. However, prior works rely on pre-determined, task-specific t

Théo Cheynel, Thomas Rossi, Baptiste Bellot-Gurlet, Damien Rohmer

Preserving semantics, in particular in terms of contacts, is a key challenge when retargeting motion between characters of different morphologies. Our solution relies on a low-dimensional embedding of the character's mesh, based on rigged key vertices that are automatically transferred from the source to the target. Motion descriptors are extracted from the

Songrun He, Linying Lv, Asaf Manela, Jimmy Wu

Large language models are increasingly used in social sciences, but their training data can introduce lookahead bias and training leakage. A good chronologically consistent language model requires efficient use of training data to maintain accuracy despite time-restricted data. Here, we overcome this challenge by training a suite of chronologically consisten

Gian Paolo Leonardi, Giacomo Vianello

Given an axially-symmetric, $(n+1)$-dimensional convex cone $\Omega\subset \mathbb{R}^{n+1}$, we study the stability of the free-boundary minimal surface $\Sigma$ obtained by intersecting $\Omega$ with a $n$-plane that contains the axis of $\Omega$. In the case $n=2$, $\Sigma$ is always unstable, as a special case of the vertex-skipping property that we rece

Amer Goel, Aida Maraj, Alvaro Ribot

Given a tree $T$, its path polytope is the convex hull of the edge indicator vectors for the paths between any two distinct leaves in $T$. These polytopes arise naturally in polyhedral geometry and applications, such as phylogenetics, tropical geometry, and algebraic statistics. We provide a minimal halfspace representation of these polytopes. The constructi

Luke Lozenski, Michael T. McCann, Brendt Wohlberg

This work presents a new method for online selection of multiple penalty parameters for the alternating direction method of multipliers (ADMM) algorithm applied to optimization problems with multiple constraints or functionals with block matrix components. ADMM is widely used for solving constrained optimization problems in a variety of fields, including sig

Otto Brookes, Maksim Kukushkin, Majid Mirmehdi, Colleen Stephens

Computer vision analysis of camera trap video footage is essential for wildlife conservation, as captured behaviours offer some of the earliest indicators of changes in population health. Recently, several high-impact animal behaviour datasets and methods have been introduced to encourage their use; however, the role of behaviour-correlated background inform

Jaime Angulo Pava, Andrés Gerardo Pérez Yépez

This work aims to study some dynamical aspects of the nonlinear logarithmic Schr\"odinger equation (NLS-log) on a tadpole graph, namely, a graph consisting of a circle with a half-line attached at a single vertex. By considering Neumann-Kirchhoff boundary conditions at the junction we show the existence and the orbital stability of standing wave solutions wi

Chiara Emonti, Roberto Fontana

We analyze a subclass of Ising models in the context of credit risk, focusing on Dandelion models when the correlations $\rho$ between the central node and each non-central node are negative. We establish the possible range of values for $\rho$ and derive an explicit formula linking the correlation between any pair of non-central nodes to $\rho$. The paper c

Qader Dorosti

Autonomous self-triggering for radio detection of extensive air showers remains a long-standing challenge, particularly in environments dominated by strong and variable radio-frequency interference. Current radio arrays usually rely on external particle-detector triggers: while this lowers thresholds for vertical showers, it excludes very inclined events, wh

Lars Rohwedder, Leander Schnaars

We provide an algorithm giving a $\frac{140}{41}$($<3.415$)-approximation for Coflow Scheduling and a $4.36$-approximation for Coflow Scheduling with release dates. This improves upon the best known $4$- and respectively $5$-approximations and addresses an open question posed by Agarwal, Rajakrishnan, Narayan, Agarwal, Shmoys, and Vahdat [Aga+18], Fukunaga [

Pedro Gimenes, Yiren Zhao, George Constantinides

Graph Neural Networks (GNNs) have recently gained attention due to their performance on non-Euclidean data. The use of custom hardware architectures proves particularly beneficial for GNNs due to their irregular memory access patterns, resulting from the sparse structure of graphs. However, existing FPGA accelerators are limited by their double buffering mec

Xiangyu Zhao, Yichao Wang, Bo Chen, Jingtong Gao

In today's digital landscape, Deep Recommender Systems (DRS) play a crucial role in navigating and customizing online content for individual preferences. However, conventional methods, which mainly depend on single recommendation task, scenario, data modality and user behavior, are increasingly seen as insufficient due to their inability to accurately reflec

Zihan Huang, Xinyu Shi, Zecheng Hao, Tong Bu

Spiking neural networks (SNNs) show great potential due to their energy efficiency, fast processing capabilities, and robustness. There are two main approaches to constructing SNNs. Direct training methods require much memory, while conversion methods offer a simpler and more efficient option. However, current conversion methods mainly focus on converting co

Dimitri Faure

We consider time-dependent singular stochastic partial differential equations on the three-dimensional torus. These equations are only well-posed after one adds renormalization terms. In order to construct a well-defined notion of solution, one should put the equation in a more general setting. In this article, we consider the paradigm of paracontrolled dist

Huiping Huang, Alireza Pourafzal, Hui Chen, Musa Furkan Keskin

In this paper, we consider near-field localization and sensing with an extremely large aperture array under partial blockage of array antennas, where spherical wavefront and spatial non-stationarity are accounted for. We propose an Ising model to characterize the clustered sparsity feature of the blockage pattern, develop an algorithm based on alternating op

Bernardo Williams, Hanlin Yu, Hoang Phuc Hau Luu, Georgios Arvanitidis

Traditional Markov Chain Monte Carlo sampling methods often struggle with sharp curvatures, intricate geometries, and multimodal distributions. Slice sampling can resolve local exploration inefficiency issues, and Riemannian geometries help with sharp curvatures. Recent extensions enable slice sampling on Riemannian manifolds, but they are restricted to case

Oliver Krumpek, Ole Kroeger, Sebastian Mohr

This paper presents the design and evaluation of a physical support structure for the OptiTrack X22 tracking systems, constructed from carbon fiber-reinforced polymer (CFRP) and Invar steel. These materials were chosen for their low thermal expansion, ensuring geometric stability and rigidity necessary for accurate spatial measurements. The support system is

Ray Garner, J. Christopher Mihos, F. Fabián Rosales-Ortega

A satellite galaxy of the nearby spiral M101, NGC 5474 has a prominent bulge offset from the kinematic center of the underlying star-forming disk that has gained attention in recent years. Recent studies have proposed that this putative offset bulge is not a classical bulge within the plane of the disk but instead a dwarf companion galaxy along the line-of-s

Baiting Luo, Ava Pettet, Aron Laszka, Abhishek Dubey

Sequential decision-making in high-dimensional continuous action spaces, particularly in stochastic environments, faces significant computational challenges. We explore this challenge in the traditional offline RL setting, where an agent must learn how to make decisions based on data collected through a stochastic behavior policy. We present Latent Macro Act

Jiafeng Xiong, Ahmad Zareie, Rizos Sakellariou

Temporal networks have gained significant prominence in the past decade for modelling dynamic interactions within complex systems. A key challenge in this domain is Temporal Link Prediction (TLP), which aims to forecast future connections by analysing historical network structures across various applications including social network analysis. While existing

Martina Finocchiaro, Ronja Stern, Abraham George Smith, Jens Petersen

High-resolution colon segmentation is crucial for clinical and research applications, such as digital twins and personalized medicine. However, the leading open-source abdominal segmentation tool, TotalSegmentator, struggles with accuracy for the colon, which has a complex and variable shape, requiring time-intensive labeling. Here, we present the first full

Mano Grunwald, Claudia E. Brunner

Understanding the re-energization of wind turbine wakes is crucial for the design and control of wind farms. Close to the rotor, this process is determined by the dynamics of the tip vortices. Here, we experimentally investigate the downstream evolution of the tip vortices for different inflow conditions. The experiments were performed in the Variable Densit

Muhammed Yusuf Satici, David L. Roberts

In human-in-the-loop reinforcement learning or environments where calculating a reward is expensive, the costly rewards can make learning efficiency challenging to achieve. The cost of obtaining feedback from humans or calculating expensive rewards means algorithms receiving feedback at every step of long training sessions may be infeasible, which may limit

Anca Preda, Goran Senjanović, Michael Zantedeschi

We revisit a minimal renormalisable $SO(10)$ grand unified theory, with the Higgs representation $45_{\rm H}$, $126_{\rm H}$ and complex $10_{\rm H}$, responsible for the unification, intermediate and the weak scale symmetry breaking, respectively. We perform the study of unification constraints and find that it allows for the Left-Right symmetric scale to b

Mads-Peter Verner Christiansen, Bjørk Hammer

Foundation models of interatomic potentials, so called universal potentials, may require fine-tuning or residual corrections when applied to specific subclasses of materials. In the present work, we demonstrate how such augmentation can be accomplished via $\Delta$-learning based on the representation already embedded in the universal potentials. The $\Delta

Daniela Molas, Daniel Campos

Dispersal is often used by living beings to gather information from conspecifics, integrating it with personal experience to guide decision-making. This mechanism has only recently been studied experimentally, facilitated by advancements in tracking animal groups over extended periods. Such studies enable the analysis of the adaptive dynamics underlying sequ

Naomi Andrew, Sebastian Hensel, Sam Hughes, Richard D. Wade

We survey a number of constructions and open problems related to the handlebody group, with a focus on recent trends in geometric group theory, (co)homological properties, and its relationship to outer automorphism groups of free groups. We also briefly describe how the \emph{cheap $\alpha$-rebuilding property} of Abert, Bergeron, Fraczyk, and Gaboriau can b

Carolyn Abbott, Stefanie Zbinden

We use small-cancellation techniques to construct a Morse local-to-global group G with an infinite-order Morse element that is not loxodromic in any action of G on a hyperbolic space. In particular, the element cannot be WPD.