March 2025 arXiv papers — page 109

Maximilian Beck, Korbinian Pöppel, Phillip Lippe, Richard Kurle

Recent breakthroughs in solving reasoning, math and coding problems with Large Language Models (LLMs) have been enabled by investing substantial computation budgets at inference time. Therefore, inference speed is one of the most critical properties of LLM architectures, and there is a growing need for LLMs that are efficient and fast at inference. Recently,

Terese T. Hansen, Ian U. Roederer, Shivani P. Shah, Rana Ezzeddine

Context. Over the past few years, the $R$-Process Alliance (RPA) has successfully carried out a search for stars that are highly enhanced in elements produced via the rapid neutron-capture ($r$-) process. In particular, the RPA has identified a number of relatively bright, highly $r$-process-enhanced ($r$-II) stars, suitable for observations with the Hubble

Di Luo, Timothy Zaklama, Liang Fu

The emergence of moir\'e materials, such as twisted transition-metal dichalcogenides (TMDs), has created a fertile ground for discovering novel quantum phases of matter. However, solving many-electron problems in moir\'e systems presents significant challenges due to strong electron correlation and strong moir\'e band mixing. Recent advancements in neural qu

Alexandra Hammerberg, Samuel Grunblatt, Patricia Kramer

By 2050, a quarter of the US population will be over the age of 65 with greater than a 40% risk of developing life-altering neuromusculoskeletal pathologies. The potential of wearables, such as Apple AirPods and hearing aids, to provide personalized preventative and predictive health monitoring outside of the clinic is nascent, but large quantities of open-e

Xinyu Lian, Zichao Yu, Ruiming Liang, Yitong Wang

Large-scale articulated objects with high quality are desperately needed for multiple tasks related to embodied AI. Most existing methods for creating articulated objects are either data-driven or simulation based, which are limited by the scale and quality of the training data or the fidelity and heavy labour of the simulation. In this paper, we propose Inf

Alisa Liu, Jonathan Hayase, Valentin Hofmann, Sewoong Oh

The assumption across nearly all language model (LM) tokenization schemes is that tokens should be subwords, i.e., contained within word boundaries. While providing a seemingly reasonable inductive bias, is this common practice limiting the potential of modern LMs? Whitespace is not a reliable delimiter of meaning, as evidenced by multi-word expressions (e.g

Shengling Qin, Hai Wu, Hongyang Du, Kaibin Huang

The emergence of distributed Mixture-of-Experts (DMoE) systems, which deploy expert models at edge nodes, offers a pathway to achieving connected intelligence in sixth-generation (6G) mobile networks and edge artificial intelligence (AI). However, current DMoE systems lack an effective expert selection algorithm to address the simultaneous task-expert releva

M. M. Akbar, C. P. Brewer, S. M. Modumudi

We revisit the one-parameter generalization of the C-metric derived by Ernst, which solves the vacuum Einstein equations. Resolving conflicting claims in the literature, we determine the correct value of the parameter that ensures the regularity of the metric on the axis. This "regularized C-metric" describes a pair of accelerating black holes without the li

Andreas Fritsch

Business Process Management (BPM) has the potential to help companies manage and reduce their activities' negative social and environmental impacts. However, so far, only limited capabilities for analysing the sustainability impacts of processes have been integrated into established BPM methods and tools. One of the main challenges of existing Sustainable BP

Ripan Kumar Kundu, Matthew Denton, Genova Mongalo, Prasad Calyam

The synergy between virtual reality (VR) and artificial intelligence (AI), specifically deep learning (DL)-based cybersickness detection models, has ushered in unprecedented advancements in immersive experiences by automatically detecting cybersickness severity and adaptively various mitigation techniques, offering a smooth and comfortable VR experience. Whi

Shijie Fang, Wenchang Gao, Shivam Goel, Christopher Thierauf

Learning to manipulate objects efficiently, particularly those involving sustained contact (e.g., pushing, sliding) and articulated parts (e.g., drawers, doors), presents significant challenges. Traditional methods, such as robot-centric reinforcement learning (RL), imitation learning, and hybrid techniques, require massive training and often struggle to gen

Alexander Steinhoff, Frank Jahnke, Matthias Florian

Marrying the predictive power of ab initio calculations with many-body effects remains a challenging task in two-dimensional (2d) materials, where efficient carrier-carrier interaction challenges established approximation schemes. In particular, understanding exciton-phonon interaction from first principles is a field of growing interest. Here, we present a

Weiqiang Jin, Hongyang Du, Shixiang Tang, Biao Zhao

With the rapid development of artificial intelligence, intelligent decision-making techniques have gradually surpassed human levels in various human-machine competitions, especially in complex multi-agent cooperative task scenarios. Multi-agent cooperative decision-making involves multiple agents working together to complete established tasks and achieve spe

Gustavo Correia, Victor Alves, Paulo Novais

Artificial Intelligence (AI) is revolutionizing emergency medicine by enhancing diagnostic processes and improving patient outcomes. This article provides a review of the current applications of AI in emergency imaging studies, focusing on the last five years of advancements. AI technologies, particularly machine learning and deep learning, are pivotal in in

Kevin Vora, Yu Zhang

In this paper, we propose a new solution to reward adaptation (RA) in reinforcement learning, where the agent adapts to a target reward function based on one or more existing source behaviors learned a priori under the same domain dynamics but different reward functions. While learning the target behavior from scratch is possible, it is often inefficient giv

Dengyun Peng, Yuhang Zhou, Qiguang Chen, Jinhao Liu

Large Language Models (LLMs) have achieved remarkable success across diverse tasks, largely driven by well-designed prompts. However, crafting and selecting such prompts often requires considerable human effort, significantly limiting its scalability. To mitigate this, recent studies have explored automated prompt optimization as a promising solution. Despit

Alexander B. Ivanov, Sian Nie

We essentially complete a program initiated by Boyarchenko--Weinstein to give a full description of the cohomology of deep level Deligne--Lusztig varieties for elliptic tori, with coefficients in arbitrary non-defining characteristics. We give several applications of our results: we show that the $\phi$-weight part of the cohomology is very often concentrate

Margarita A. Guerrero, Braghadeesh Lakshminarayanan, Cristian R. Rojas

Estimating the size of the modeling error is crucial for robust control. Over the years, numerous metrics have been developed to quantify the model error in a control relevant manner. One of the most important such metrics is the structured singular value, as it leads to necessary and sufficient conditions for ensuring stability and robustness in feedback co

Endre Boros, Vladimir Gurvich, Kazuhisa Makino

We consider finite $n$-person deterministic graphical games and study the existence of pure stationary Nash-equilibrium in such games. We assume that all infinite plays are equivalent and form a unique outcome, while each terminal position is a separate outcome. It is known that for $n=2$ such a game always has a Nash equilibrium, while that may not be true

Gabriel Bathie, Guillaume Lagarde

Efficiently computing accurate representations of high-dimensional data is essential for data analysis and unsupervised learning. Dendrograms, also known as ultrametrics, are widely used representations that preserve hierarchical relationships within the data. However, popular methods for computing them, such as linkage algorithms, suffer from quadratic time

Zhong-Yuan Sun, You-Yu Li, Shuai Y. F. Liu

As the spin alignment of a vector meson is predicted to be correlated with its spectral properties, we study the spectral properties of the $\phi$ meson using two microscopic Lagrangians based either on chiral effective field theory or the quark-meson model. We calculate the self-energies and spectral functions of the $\phi$ meson for these two Lagrangians a

Robin Strässer, Manuel Schaller, Julian Berberich, Karl Worthmann

We derive novel deterministic bounds on the approximation error of data-based bilinear surrogate models for unknown nonlinear systems. The surrogate models are constructed using kernel-based extended dynamic mode decomposition to approximate the Koopman operator in a reproducing kernel Hilbert space. Unlike previous methods that require restrictive assumptio

Parameshwar R. Pasnoori, Patrick Azaria, Ari Mizel

We propose a superconducting quantum circuit whose low-energy degrees of freedom are described by the sine-Gordon (SG) quantum field theory. For suitably chosen parameters, the circuit hosts a symmetry protected topological (SPT) phase protected by a discrete $\mathbb{Z}_2$ symmetry. The ground state of the system is twofold degenerate and exhibits local spo

Robin A. Lange, Anna Gibson, Milo Z. Trujillo, Brooke Foucault Welles

Invisible labor is an intrinsic part of the modern workplace, and includes labor that is undervalued or unrecognized such as creating collaborative atmospheres. Open source software (OSS) is software that is viewable, editable and shareable by anyone with internet access. Contributors are mostly volunteers, who participate for personal edification and becaus

Cheoljoon Jeong, Xubo Yue, Seokhyun Chung

Many failure mechanisms of machinery are closely related to the behavior of condition monitoring (CM) signals. To achieve a cost-effective preventive maintenance strategy, accurate remaining useful life (RUL) prediction based on the signals is of paramount importance. However, the CM signals are often recorded at different factories and production lines, wit

Peter Barkley, Robert L. Bassett

We present a novel application of a recently-proposed matrix-parametrized proximal splitting method to sensor network localization, the problem of estimating the locations of a set of sensors using only noisy pairwise distance information between the sensors. The decentralized computation required by our approach respects the communication structure between

Farhad Rezazadeh, Amir Ashtari Gargari, Sandra Lagen, Houbing Song

The move toward open Sixth-Generation (6G) networks necessitates a novel approach to full-stack simulation environments for evaluating complex technology developments before prototyping and real-world implementation. This paper introduces an innovative approach\footnote{A lightweight, mock version of the code is available on GitHub at that combines a multi-a

Alexander Y. Ku, Declan Campbell, Xuechunzi Bai, Jiayi Geng

Modern artificial intelligence systems, such as large language models, are increasingly powerful but also increasingly hard to understand. Recognizing this problem as analogous to the historical difficulties in understanding the human mind, we argue that methods developed in cognitive science can be useful for understanding large language models. We propose

Qi Zhang, Xiuyuan Chen, Ziyi He, Kun Wang

T2 hyperintensities in spinal cord MR images are crucial biomarkers for conditions such as degenerative cervical myelopathy. However, current clinical diagnoses primarily rely on manual evaluation. Deep learning methods have shown promise in lesion detection, but most supervised approaches are heavily dependent on large, annotated datasets. Unsupervised anom

James Burgess, Jeffrey J Nirschl, Laura Bravo-Sánchez, Alejandro Lozano

Scientific research demands sophisticated reasoning over multimodal data, a challenge especially prevalent in biology. Despite recent advances in multimodal large language models (MLLMs) for AI-assisted research, existing multimodal reasoning benchmarks only target up to college-level difficulty, while research-level benchmarks emphasize lower-level percepti

Jean-Lou De Carufel, Anil Maheshwari, Saeed Odak, Bodhayan Roy

The \emph{interestingness score} of a directed path $\Pi = e_1, e_2, e_3, \dots, e_\ell$ in an edge-weighted directed graph $G$ is defined as $\texttt{score}(\Pi) := \sum_{i=1}^\ell w(e_i) \cdot \log{(i+1)}$, where $w(e_i)$ is the weight of the edge $e_i$. We consider two optimization problems that arise in the analysis of Mapper graphs, which is a powerful

Panjun Feng, Chao-Yang Tan, Miao Gao, Xun-Wang Yan

Quantum spin Hall effect is usually realized in two-dimensional materials with time-reversal symmetry, but whether it can be realized without symmetry protection remains unexplored. Here, we propose type-II quantum spin Hall insulator with quantized spin Hall conductivity, whose edge states with opposite chirality and polarization, distributed in different B

Eder Baron-Prada, Alberto Padoan, Adolfo Anta, Florian Dörfler

This paper proposes a frequency-wise approach for stability analysis of multi-input, multi-output (MIMO) Linear Time-Invariant (LTI) feedback systems through Scaled Relative Graphs (SRGs). Unlike traditional methods, such as the Generalized Nyquist Criterion (GNC), which relies on a coupled analysis that requires the multiplication of models, our approach en

Angelo Felice Lopez, Debaditya Raychaudhury

We give a lower bound on the Ulrich complexity of hypersurfaces of dimension $n \ge 6$. The bound improves the result in [BES] for $n \le 78$.

Erik Hoel

Complex systems can be described at myriad different scales, and their causal workings often have multiscale structure (e.g., a computer can be described at the microscale of its hardware circuitry, the mesoscale of its machine code, and the macroscale of its operating system). While scientists study and model systems across the full hierarchy of their scale

Jan Kyzioł, Andrzej Okniński

The dynamics of nonlinear oscillators are investigated. We study the formation of $1:2$ resonance in nonlinear periodically forced oscillators due to period doubling of the primary $1:1$ resonance, or born independently. We compute the amplitude-frequency implicit function, the steady-state asymptotic solution, for the effective equation approximating couple

Aenne Benjes, Kamillo Ferry, Benjamin Schröter

Cosmological polytopes of graphs are a geometric tool in physics to study wavefunctions for cosmological models whose Feynman diagram is given by the graph. After their recent introduction by Arkani-Hamed, Benincasa and Postnikov the focus of interest shifted towards their mathematical properties, e.g., their face structure and triangulations. Juhnke, Solus

Luke Rickard, Alessandro Abate, Kostas Margellos

We consider the problem of verifying safety for continuous-time dynamical systems. Developing upon recent advancements in data-driven verification, we use only a finite number of sampled trajectories to learn a barrier certificate, namely a function which verifies safety. We train a safety-informed neural network to act as this certificate, with an appropria

Francisco Bento Lustosa, Milko Estrada, Marcony S. Cunha, Celio R. Muniz

This study examines the gravitational and thermodynamic properties of static, spherically symmetric black holes within cosmic voids -- vast underdense regions of the universe. By deriving a novel solution based on a universal density profile for voids, we analyze its spacetime structure, which reveals two horizons: One of the black hole and the other related

Andreas Waldis, Vagrant Gautam, Anne Lauscher, Dietrich Klakow

We introduce aligned probing, a novel interpretability framework that aligns the behavior of language models (LMs), based on their outputs, and their internal representations (internals). Using this framework, we examine over 20 OLMo, Llama, and Mistral models, bridging behavioral and internal perspectives for toxicity for the first time. Our results show th

Yanjia Huang, Renjie Li, Zhengzhong Tu

We present PANDORA, a novel diffusion-based policy learning framework designed specifically for dexterous robotic piano performance. Our approach employs a conditional U-Net architecture enhanced with FiLM-based global conditioning, which iteratively denoises noisy action sequences into smooth, high-dimensional trajectories. To achieve precise key execution

Cheng-Hsi Hsiao, Ellen Rathje, Krishna Kumar

This study proposes an autoencoder approach to extract latent features from cone penetration test profiles to evaluate the potential of incorporating CPT data in an AI model. We employ autoencoders to compress 200 CPT profiles of soil behavior type index (Ic) and normalized cone resistance (qc1Ncs) into ten latent features while preserving critical informati

Wenya Luo, Hua Li, Zhidong Bai, Zhijun Liu

Quadratic discriminant analysis (QDA) is a widely used method for classification problems, particularly preferable over Linear Discriminant Analysis (LDA) for heterogeneous data. However, QDA loses its effectiveness in high-dimensional settings, where the data dimension and sample size tend to infinity. To address this issue, we propose a novel QDA method ut

Antonio Falcó, Daniela Falcó--Pomares, Hermann G. Matthies

In this work, we develop a novel mathematical framework for universal digital quantum computation using algebraic probability theory. We rigorously define quantum circuits as finite sequences of elementary quantum gates and establish their role in implementing unitary transformations. A key result demonstrates that every unitary matrix in \(\mathrm{U}(N)\) c

Mariano Caruso, Guillermo Rus, Juan Melchor

The Westervelt equation describes the propagation of pressure waves in continuous nonlinear and, eventually, diffusive media. The classical framework of this equation corresponds to fluid dynamics theory. This work seeks to connect this equation with the theory of deformations, considering the propagation of mechanical waves in nonlinear and loss-energy medi

Beatrice Brown-Mulry, Rohan Satya Isaac, Sang Hyup Lee, Ambika Seth

While research has established the potential of AI models for mammography to improve breast cancer screening outcomes, there have not been any detailed subgroup evaluations performed to assess the strengths and weaknesses of commercial models for digital breast tomosynthesis (DBT) imaging. This study presents a granular evaluation of the Lunit INSIGHT DBT mo

Mahaveer Prasad, S. Harshini Tekur, Bijay Kumar Agarwalla, Manas Kulkarni

Chaotic behavior or lack thereof in non-Hermitian systems is often diagnosed via spectral analysis of associated complex eigenvalues. Very recently, singular values of the associated non-Hermitian systems have been proposed as an effective measure to study dissipative quantum chaos. Motivated by the rich properties of non-Hermitian power-law banded random ma

Nex C. X. Stuhlmüller, René van Roij, Marjolein Dijkstra

Conical microfluidic channels filled with electrolytes exhibit volatile memristive behavior, offering a promising platform for energy-efficient, neuromorphic computing. Here, we integrate these iontronic channels as additional nonlinear elements in nonlinear Shinriki-inspired oscillators and demonstrate that they exhibit alternating chaotic and non-chaotic d

Qing Zhou, Junyu Gao, Qi Wang

The rapid growth of dataset scales has been a key driver in advancing deep learning research. However, as dataset scale increases, the training process becomes increasingly inefficient due to the presence of low-value samples, including excessive redundant samples, overly challenging samples, and inefficient easy samples that contribute little to model impro

Izumi Hachisu, Mariko Kato

The classical nova V392 Per 2018 is characterized by a very fast optical decline, long binary orbital period of 3.23 days, detection of GeV gamma rays, and almost identical decay trends of $B$, $V$, and $I_{\rm C}$ light curves. The last feature is unique because most novae develop strong emission lines in the nebular phase and these lines contribute especia

Mengyao Lyu, Yan Li, Huasong Zhong, Wenhao Yang

The hypothesis that pretrained large language models (LLMs) necessitate only minimal supervision during the fine-tuning (SFT) stage (Zhou et al., 2024) has been substantiated by recent advancements in data curation and selection research. However, their stability and generalizability are compromised due to the vulnerability to experimental setups and validat

Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez

Kemeny's constant quantifies the expected time for a random walk to reach a randomly chosen vertex, providing insight into the global behavior of a Markov chain. We present a novel eigenvector-based formula for computing Kemeny's constant. Moreover, we analyze the impact of network structure on Kemeny's constant. In particular, we use various spectral techni

D. Bazeia, M. A. Marques, R. Menezes

We study generalized scalar field models coupled to impurities in Minkowski spacetime with arbitrary dimensions. The investigation concerns a class of models that depends explicitly on the spacetime coordinates and also, it reveals the presence of a second-order tensor that can have null divergence if a first-order equation and a constraint are satisfied. We

Ha Quang Trung, Qianhui Xu, Bo Yang

Parity conservation dictates that when fusing pairs of Moore-Read (MR) quasiholes, such that each pair of charge-$e/4$ anyon forms a charge-$e/2$ anyon, the parity of the numbers of $1$-anyon and $\psi$-anyon must be conserved within a given system. This idea is illustrated here using the Jack polynomial formalism, which also provides a basis to numerically

Péter E. Frenkel, Milán Mosonyi, Péter Vrana, Mihály Weiner

The optimal error exponents of binary composite i.i.d. state discrimination are trivially bounded by the worst-case pairwise exponents of discriminating individual elements of the sets representing the two hypotheses, and in the finite-dimensional classical case, these bounds in fact give exact single-copy expressions for the error exponents. In contrast, in

Steven Finch

The nonlinear recurrences we consider here include simple continued fractions for the Golden & Silver means and a parametric family of cubics in connection with Abel's functional equation.

Ye Wang, Ziheng Wang, Boshen Xu, Yang Du

Temporal Video Grounding (TVG), the task of locating specific video segments based on language queries, is a core challenge in long-form video understanding. While recent Large Vision-Language Models (LVLMs) have shown early promise in tackling TVG through supervised fine-tuning (SFT), their abilities to generalize remain limited. To address this, we propose

Donghao Ouyang, Israel Michael Sigal

In this paper, we study the evolution of Markovian open quantum systems, whose dynamics are governed by the von Neumann-Lindblad equations. Our goal is to prove the return-to-equilibrium property for systems of infinite degrees of freedom under quantum detailed balance condition.

Ziyu Liu, Daniel Hestroffer, Josselin Desmars, Pedro David

Context. Binary asteroids are present in all populations of the Solar System, from near-Earth to trans-Neptunian regions. As is true for the small Solar System bodies (SSSBs), binary asteroids generally offer valuable insights into the formation of the Solar System, as well as its collisions and dynamic evolution. In particular, the binaries provide fundamen

Spencer Schutz, Charlott Vallon, Ben Recht, Francesco Borrelli

Invariant sets define regions of the state space where system constraints are always satisfied. The majority of numerical techniques for computing invariant sets have been developed for discrete-time systems with a fixed sampling time. Understanding how invariant sets change with sampling time is critical for designing adaptive-sampling control schemes that

Shahana Aziz, André H. A. Malavazi, Pedro R. Dieguez

In this proceeding, we revisit the discussion presented in Ref. [Commun Phys 7, 373 (2024)], which examines the behavior of a quantum switch involving two arbitrary quantum operations when the control is exposed to environmental effects. Our study extends this analysis by focusing on the evolution of entanglement in the target system within the quantum switc

Limeng Liu, Guannan Wang, Sandra E. Safo

Inflammatory Bowel Disease (IBD), including Crohn's Disease (CD) and Ulcerative Colitis (UC), presents significant public health challenges due to its complex etiology. Motivated by the IBD study of the Integrative Human Microbiome Project, our objective is to identify microbial pathways that distinguish between CD, UC and non-IBD over time. Most current res

Xulin Fan, Heting Gao, Ziyi Chen, Peng Chang

Talking head synthesis, also known as speech-to-lip synthesis, reconstructs the facial motions that align with the given audio tracks. The synthesized videos are evaluated on mainly two aspects, lip-speech synchronization and image fidelity. Recent studies demonstrate that GAN-based and diffusion-based models achieve state-of-the-art (SOTA) performance on th

Terence Lehmann, Nikita Dimidziev, Thomas L. Howarth, Michael Gauding

This study investigates the characteristics of nitrogen oxide (NO) formation in two-dimensional (2D) laminar premixed ammonia/hydrogen/air flames and the impact of thermodiffusively driven intrinsic flame instabilities (IFIs). To this end, a set of three highly resolved direct numerical simulations (DNS) at lean ambient conditions and varying hydrogen fracti

Wan Ju Kang, Eunki Kim, Na Min An, Sangryul Kim

Often, the needs and visual abilities differ between the annotator group and the end user group. Generating detailed diagram descriptions for blind and low-vision (BLV) users is one such challenging domain. Sighted annotators could describe visuals with ease, but existing studies have shown that direct generations by them are costly, bias-prone, and somewhat

H. L. Dao

In this work, we explored and experimented with new forms of parameterized quantum circuits to be used as variational ansatzes for solving the bosonic and supersymmetric $SU(2)$ matrix models at different couplings using the Variational Quantum Eigensolver (VQE) algorithm. Working with IBM Qiskit quantum computing platform, we show that two types of quantum

Eder Baron-Prada, Adolfo Anta, Alberto Padoan, Florian Dörfler

We introduce a novel approach to feedback stability analysis for linear time-invariant (LTI) systems, overcoming the limitations of the sectoriality assumption in the small phase theorem. While phase analysis for single-input single-output (SISO) systems is well-established, multi-input multi-output (MIMO) systems lack a comprehensive phase analysis until re

Ricardo N. Ferreira, Cláudia Soares

Constrained Online Convex Optimization (COCO) can be seen as a generalization of the standard Online Convex Optimization (OCO) framework. At each round, a cost function and constraint function are revealed after a learner chooses an action. The goal is to minimize both the regret and cumulative constraint violation (CCV) against an adaptive adversary. We sho

Chuanjing Zhang, Shiyu Zhang, Xi Zhang

In this paper, we obtain the generalized Bogomolov inequality for reflexive Higgs sheaves defined on the regular locus of compact K\"ahler klt spaces. As an application, we establish the Miyaoka-Yau inequality for all minimal K\"ahler klt spaces. Apart from providing a self-contained formulation and investigation of Higgs sheaves on complex normal spaces, th

Juan S. Salcedo-Gallo, Michiel Burgelman, Vincent P. Flynn, Alexander S. Carney

Achieving and controlling non-reciprocity in engineered photonic structures is of fundamental interest in science and engineering. Here, we introduce a tunable, non-Hermitian, nonlinear microwave dimer designed to precisely implement phase-non-reciprocal hopping dynamics between two spatially separated cavities at room temperature. Our system incorporates si

Andriniaina Narindra Rasoanaivo, Fanomezantsoa Arlivah Andriantsarafara

We investigate the soft decomposition of tree-level gluon amplitudes with split-helicity configurations. First, we show how any split-helicity amplitude can be fully fixed from inverse soft limit using BCFW calculation. We show how the double and triple soft theorems can manifest beyond perturbative calculations through split-helicity decomposition. Next, we

Filip Elvander, Isabel Haasler

Crowd dynamics and many large biological systems can be described as populations of agents or particles, which can only be observed on aggregate population level. Identifying the dynamics of agents is crucial for understanding these large systems. However, the population of agents is typically not homogeneous, and thus the aggregate observations consist of t

Juan Sebastián Cañas, Camila Parra-Guevara, Manuela Montoya-Castrillón, Julieta M Ramírez-Mejía

The rise of artificial intelligence (AI) and the aggravating biodiversity crisis have resulted in a research area where AI-based computational methods are being developed to act as allies in conservation, and the sustainable use and management of natural resources. While important general guidelines have been established globally regarding the opportunities

Hai-Long Sun, Zhun Sun, Houwen Peng, Han-Jia Ye

Recent advancements in Large Language Models (LLMs) have demonstrated enhanced reasoning capabilities, evolving from Chain-of-Thought (CoT) prompting to advanced, product-oriented solutions like OpenAI o1. During our re-implementation of this model, we noticed that in multimodal tasks requiring visual input (e.g., geometry problems), Multimodal LLMs (MLLMs)

Yueyue Xu, Panpan Zhou, Lin Wang, Xiaoming Hu

This paper investigates the intrinsic formation problem of a multi-agent system using an exogenous system. The problem is formulated as an intrinsic infinite time-horizon linear quadratic optimal control problem, namely, no formation error information is incorporated in the performance index. Convergence to the formation is achieved by utilizing an exogenous

Huan Yang, Renji Zhang, Mingzhe Huang, Weijun Wang

Recent advances in long-text understanding have pushed the context length of large language models (LLMs) up to one million tokens. It boosts LLMs's accuracy and reasoning capacity but causes exorbitant computational costs and unsatisfactory Time to First Token (TTFT). KV cache reuse, which reuses the exact same KV cache of prefixes and templates or shares s

Bob Krekelberg, Alison Hsiang-Hsuan Liu, Fu-Hong Liu, Prudence W. H. Wong

We study online scheduling to minimize total completion time with explorable uncertainty on single and multiple machines. Each job comes with an upper limit of its processing time, which could be potentially reduced by testing the job, which also takes time. The objective is to schedule all jobs with minimum total completion time. The challenge lies in decid

Zhongwen Xu, Xianliang Wang, Siyi Li, Tao Yu

We present PORTAL, a novel framework for developing artificial intelligence agents capable of playing thousands of 3D video games through language-guided policy generation. By transforming decision-making problems into language modeling tasks, our approach leverages large language models (LLMs) to generate behavior trees represented in domain-specific langua

William H. Reinhardt, Marc Z. Miskin

Field-based reactive control provides a minimalist, decentralized route to guiding robots that lack onboard computation. Such schemes are well suited to resource-limited machines like microrobots, yet implementation artifacts, limited behaviors, and the frequent lack of formal guarantees blunt adoption. Here, we address these challenges with a new geometric

Laura Girometti, Jean-François Aujol, Antoine Guennec, Yann Traonmilin

In this work, we propose a parameter-free and efficient method to tackle the structure-texture image decomposition problem. In particular, we present a neural network LPR-NET based on the unrolling of the Low Patch Rank model. On the one hand, this allows us to automatically learn parameters from data, and on the other hand to be computationally faster while

Laura N. R. do Amaral, Evgenya L. Shkolnik, R. O. Parke Loyd, Sarah Peacock

The X-rays and Extreme Ultraviolet (XUV) emission from M stars can drive the atmospheric escape on planets orbiting them. M stars are also known for their frequent emission of stellar flares, which will increase the high-energy flux received by their orbiting planets. To understand how stellar flares impact the primordial atmospheres of planets orbiting youn

Ewan R. S. Wallace, Nathan C. Frey, Joshua A. Rackers

Ligand strain energy, the energy difference between the bound and unbound conformations of a ligand, is an important component of structure-based small molecule drug design. A large majority of observed ligands in protein-small molecule co-crystal structures bind in low-strain conformations, making strain energy a useful filter for structure-based drug desig

Xinmin Wang, Peipei Wang, Jian Lyu, Zhuang Xu

As a fundamental physical phenomenon, achieving and controlling a large anomalous Hall effect (AHE) is crucial for advancing the understanding of topological physics and for developing applied technologies in spintronics. The recently discovered topological Kagome metal $A$V$_3$Sb$_5$ ($A =$ K, Rb, Cs)exhibits a significant AHE along with charge density wave

Aliakbar Daemi, Christopher Scaduto

The odd character variety of a Riemann surface is a moduli space of SO(3) representations of the fundamental group which can be interpreted as the moduli space of stable holomorphic rank 2 bundles of odd degree and fixed determinant. This is a symplectic manifold, and there is a homomorphism from a finite extension of the mapping class group of the surface t

Otger Ballester, Oscar Blanch, Joan Boix, Paolo G. Calisse

This paper presents the technical design of the pathfinder Barcelona Raman LIDAR (pBRL) for the northern site of the Cherenkov Telescope Array Observatory (CTAO-N) located at the Roque de los Muchachos Observatory (ORM). The pBRL is developed for continuous atmospheric characterization, essential for correcting high-energy gamma-ray observations captured by

Aldo Rodriguez-Puebla, Vladimir Avila-Reese, Joel R. Primack, Carlo Cannarozzo

The cumulative number density matching approach equates number densities between adjacent redshifts to derive empirical galaxy evolution tracks from the observed galaxy stellar mass function. However, it is well known that this approach overlooks scatter in mass assembly histories and merger effects, with previous studies relying on model-based corrections,

Jiaming Kang, Keyan Chen, Zhengxia Zou, Zhenwei Shi

Remote sensing novel view synthesis (NVS) offers significant potential for 3D interpretation of remote sensing scenes, with important applications in urban planning and environmental monitoring. However, remote sensing scenes frequently lack sufficient multi-view images due to acquisition constraints. While existing NVS methods tend to overfit when processin

Tess J. van Leeuwen, Wioletta M. Ruszel

Real abstract Wiener spaces (AWS) were originally defined by Gross using measurable norms, as a generalisation of the theory of advanced integral calculus in infinite dimensions as introduced by Cameron and Martin. In this paper we present a rigorous, complete and self-contained general framework for $\mathbb{K}$-AWS, where $\mathbb{K} \in \{\mathbb{R},\math

Dan Abramovich, Michael Temkin, Jarosław Włodarczyk

Theorem 1.2.6 of [ATW20] provides a relatively functorial logarithmic principalization of ideals on relative logarithmic orbifolds $X\to B$ in characteristic 0, relying on a delicate monomialization theorem for Kummer ideals. The paper [AdSTW25] provides a parallel avenue through weighted blowings up. In this paper we show that, if $X\to B$ is proper, monomi

Shashikant Verma, Harish Katti, Soumyaratna Debnath, Yamuna Swamy

We introduce STEP, a novel framework utilizing Transformer-based discriminative model prediction for simultaneous tracking and estimation of pose across diverse animal species and humans. We are inspired by the fact that the human brain exploits spatiotemporal continuity and performs concurrent localization and pose estimation despite the specialization of b

Andre Merzky, Mikhail Titov, Matteo Turilli, Ozgur Kilic

Hybrid workflows combining traditional HPC and novel ML methodologies are transforming scientific computing. This paper presents the architecture and implementation of a scalable runtime system that extends RADICAL-Pilot with service-based execution to support AI-out-HPC workflows. Our runtime system enables distributed ML capabilities, efficient resource ma

Ying Jiao, Luc De Raedt, Giuseppe Marra

Large language models have been used to translate natural language questions to SQL queries. Without hard constraints on syntax and database schema, they occasionally produce invalid queries that are not executable. These failures limit the usage of these systems in real-life scenarios. We propose a neurosymbolic framework that imposes SQL syntax and schema

Dan Abramovich, André belotto da Silva, Ming Hao Quek, Michael Temkin

In characteristic zero, we construct logarithmic resolution of singularities, with simple normal crossings exceptional divisor, using weighted blow-ups.

Xinyu Jessica Wang, Christine Lee, Bilge Mutlu

With the increasing prevalence of online learning, adapting education to diverse learner needs remains a persistent challenge. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), promise powerful tools and capabilities to enhance personalized learning in online educational environments. In this work, we explore how

Martín G. Richarte, Júnior D. Toniato

We explore scalarized Einstein-Gauss-Bonnet theories within the context of the Parameterized Post-Newtonian formalism, which serves as a robust framework for examining modifications to General Relativity that exhibit scalarization. This approach enables us to impose a variety of constraints on the parameter space, particularly focusing on the PPN parameters

Emilio N. M. Cirillo, Nicklas Jävergård, Rainey Lyons, A. Muntean

Film formation from solvent evaporation in polymer ternary solutions is relevant for several technological applications, such as the fabrication of organic solar cells. The performance of the final device will strongly depend on the internal morphology of the obtained film, which, in turn, is affected by the processing conditions. We are interested in modeli

Trung Chau, Nursel Erey, Aryaman Maithani

Every monomial ideal $I$ has a Scarf complex, which is a subcomplex of its minimal free resolution. We say that $I$ is Scarf if its Scarf complex is also its minimal free resolution. In this paper, we fully characterize all pairs $(G,n)$ of a graph $G$ and an integer $n$ such that the squarefree power $I(G)^{[n]}$ or the symbolic power $I(G)^{(n)}$ of the ed

Akito Igarashi, Yutaka Hori

Living organisms maintain stable functioning amid environmental fluctuations through homeostasis, a property that preserves a system's behavior despite changes in environmental conditions. To elucidate homeostasis in stochastic biochemical reactions, theoretical tools for assessing population-level invariance under parameter perturbations are crucial. In thi

Sang Truong, Yuheng Tu, Percy Liang, Bo Li

Comprehensive evaluations of language models (LM) during both development and deployment phases are necessary because these models possess numerous capabilities (e.g., mathematical reasoning, legal support, or medical diagnostic) as well as safety risks (e.g., racial bias, toxicity, or misinformation). The average score across a wide range of benchmarks prov

Takahisa Igata

We investigate the effects of extended mass and spheroidal deformation on the periapsis shift of quasi-circular orbits inside a gravitating mass distribution in the Newtonian framework. The analysis is restricted to orbits confined to the reflection-symmetric plane of the spheroidal configuration. Focusing on the internal gravitational potential of a spheroi