March 2025 arXiv papers — page 117

Runjia Li, Victor B. Talisa, Chung-Chou H. Chang

Motivated by conflicting conclusions regarding hydrocortisone's treatment effect on ICU patients with vasopressor-dependent septic shock, we developed a novel instrumental variable (IV) estimator to assess the average treatment effect (ATE) in time-to-event data. In real-world data, IV methods are widely used for estimating causal treatment effects in the pr

Runyu Zhang, Arvind Raghunathan, Jeff Shamma, Na Li

Tools from control and dynamical systems have proven valuable for analyzing and developing optimization methods. In this paper, we establish rigorous theoretical foundations for using feedback linearization (FL) -- a well-established nonlinear control technique -- to solve constrained optimization problems. For equality-constrained optimization, we establish

Roland Schwan, Daniel Kuhn, Colin N. Jones

This paper presents an efficient structure-exploiting algorithm for multistage optimization problems. The proposed method extends existing approaches by supporting full coupling between stages and global decision variables in the cost, as well as equality and inequality constraints. The algorithm is implemented as a new backend in the PIQP solver and leverag

Imran Kabir, Md Alimoor Reza, Syed Billah

Large multimodal models (LMMs) are increasingly integrated into autonomous driving systems for user interaction. However, their limitations in fine-grained spatial reasoning pose challenges for system interpretability and user trust. We introduce Logic-RAG, a novel Retrieval-Augmented Generation (RAG) framework that improves LMMs' spatial understanding in dr

Arthur Corrêa, Cristóvão Silva, Liming Xu, Alexandra Brintrup

This paper introduces TuneNSearch, a hybrid transfer learning and local search approach for addressing diverse variants of the vehicle routing problem (VRP). Our method uses reinforcement learning to generate high-quality solutions, which are subsequently refined by an efficient local search procedure. To ensure broad adaptability across VRP variants, TuneNS

Purnaprajna Bangere, Jayan Mukherjee

In arXiv:2409.03960, we introduced an approach to the question of extendability of projective varieties via degeneration to ribbons. In this article we build on these methods to give a new proof of optimal results on the extendability of general non-prime $K3$ surfaces, classification of non-prime Fano threefolds and Mukai varieties and the irreducibility of

Tiziano Guadagnino, Benedikt Mersch, Saurabh Gupta, Ignacio Vizzo

Robust and accurate localization and mapping of an environment using laser scanners, so-called LiDAR SLAM, is essential to many robotic applications. Early 3D LiDAR SLAM methods often exploited additional information from IMU or GNSS sensors to enhance localization accuracy and mitigate drift. Later, advanced systems further improved the estimation at the co

Gordon I. Ogilvie

We develop several aspects of the theory of gaseous astrophysical discs in which the gravity of the disc makes a significant contribution to its structure and dynamics. We show how the internal gravitational potential can be expanded in powers of the aspect ratio of the disc (or of a structure within it) and separated into near and far contributions. We anal

Govind M Chari, Behçet Açikmeşe

Second-order cone programs (SOCPs) with quadratic objective functions are common in optimal control and other fields. Most SOCP solvers which use interior-point methods are designed for linear objectives and convert quadratic objectives into linear ones via slack variables and extra constraints, despite the computational advantages of handling quadratic obje

Md Santo Ali, Sapnil Sarker Bipro, Mohammod Abdul Motin, Sumaiya Kabir

Mental stress poses a significant public health concern due to its detrimental effects on physical and mental well-being, necessitating the development of continuous stress monitoring tools for wearable devices. Blood volume pulse (BVP) sensors, readily available in many smartwatches, offer a convenient and cost-effective solution for stress monitoring. This

Matthew Stover

A Fuchsian group $\Gamma$ has a modular embedding if its adjoint trace field is a totally real number field and every unbounded Galois conjugate $\Gamma^\sigma$ comes equipped with a holomorphic (or conjugate holomorphic) map ${\phi^\sigma : \mathbb{B}^1 \to \mathbb{B}^1}$ intertwining the actions of $\Gamma$ and $\Gamma^\sigma$ on the Poincar\'e disk $\math

Callum Duffy, Marcin Jastrzebski, Stefano Vergani, Leigh H. Whitehead

We present a new approach to separate track-like and shower-like topologies in liquid argon time projection chamber (LArTPC) experiments for neutrino physics using quantum machine learning. Effective reconstruction of neutrino events in LArTPCs requires accurate and granular information about the energy deposited in the detector. These energy deposits can be

Laura Di Gregorio, Walter Lacarbonara

We investigate the nonlinear equations governing wave propagation across a metamaterial consisting of a cellular periodic structure hosting resonators with linear and cubic springs. The resulting system of two coupled equations with cubic nonlinearity is Hamiltonian, with the origin being an elliptic equilibrium characterized by two distinct linear frequenci

Francesco Calcagno, Luca Serfilippi, Giorgio Franceschelli, Marco Garavelli

The inverse design of molecules has challenged chemists for decades. In the past years, machine learning and artificial intelligence have emerged as new tools to generate molecules tailoring desired properties, but with the limit of relying on models that are pretrained on large datasets. Here, we present a data-free generative model based on reinforcement l

Tsu-Jui Fu, Yusu Qian, Chen Chen, Wenze Hu

Text-to-Image (T2I) diffusion models have shown impressive results in generating visually compelling images following user prompts. Building on this, various methods further fine-tune the pre-trained T2I model for specific tasks. However, this requires separate model architectures, training designs, and multiple parameter sets to handle different tasks. In t

Yoo Yeon Sung, Hannah Kim, Dan Zhang

AI practitioners increasingly use large language model (LLM) agents in compound AI systems to solve complex reasoning tasks, these agent executions often fail to meet human standards, leading to errors that compromise the system's overall performance. Addressing these failures through human intervention is challenging due to the agents' opaque reasoning proc

Denis Sedov, Mathias S. Scheurer

Motivated by experiments on rhombohedral tetralayer graphene showing signs of superconductivity emerging from a valley-polarized normal state, we here analyze theoretically how scanning tunneling spectroscopy can be used to probe the superconducting order parameter of the system. To describe different pairing scenarios on equal footing, we develop a microsco

Zhe Wang, Aladine Chetouani, Rachid Jennane

Generally, X-ray, as an inexpensive and popular medical imaging technique, is widely chosen by medical practitioners. With the development of medical technology, Magnetic Resonance Imaging (MRI), an advanced medical imaging technique, has already become a supplementary diagnostic option for the diagnosis of KOA. We propose in this paper a deep-learning-based

Hao Mark Chen, Shell Xu Hu, Wayne Luk, Timothy Hospedales

Model merging has emerged as a promising approach for multi-task learning (MTL), offering a data-efficient alternative to conventional fine-tuning. However, with the rapid development of the open-source AI ecosystem and the increasing availability of fine-tuned foundation models, existing model merging methods face two key limitations: (i) They are primarily

Andreas Teller, Uta Pigorsch, Christian Pigorsch

Forecasting the volatility of financial assets is essential for various financial applications. This paper addresses the challenging task of forecasting the volatility of financial assets with limited historical data, such as new issues or spin-offs, by proposing a multi-source transfer learning approach. Specifically, we exploit complementary source data of

Aravind Muraleedharan, Abhishek K. Gupta

Molecular communication (MC) offers a groundbreaking approach to communication inspired by biological signaling. It is particularly suited for environments where traditional electromagnetic methods fail, such as fluid mediums or within the human body. This study focuses on addressing a major challenge in MC systems: inter symbol interference (ISI), which ari

Shiekh Zia Uddin, Sahil Pontula, Jiaxin Liu, Shutao Xu

To reach the next frontier in multimode nonlinear optics, it is crucial to better understand the classical and quantum phenomena of systems with many interacting degrees of freedom -- both how they emerge and how they can be tailored to emerging applications, from multimode quantum light generation to optical computing. Soliton fission and Raman scattering c

Dmitry Kovalev

Optimization with matrix gradient orthogonalization has recently demonstrated impressive results in the training of deep neural networks (Jordan et al., 2024; Liu et al., 2025). In this paper, we provide a theoretical analysis of this approach. In particular, we show that the orthogonalized gradient method can be seen as a first-order trust-region optimizati

Folkmar Bornemann

We continue our work [arXiv:2403.07628] on asymptotic expansions at the soft edge for the classical $n$-dimensional Gaussian and Laguerre random matrix ensembles. By revisiting the construction of the associated skew-orthogonal polynomials in terms of wave functions, we obtain concise expressions for the level densities that are well suited for proving asymp

Ping-Yen Shen, Ryan J. Caverly

Solar sails enable propellant-free space missions by utilizing solar radiation pressure as thrust. However, disturbance torques act on the solar sail and effective attitude control leads to the continuous accumulation of reaction wheel angular momentum, necessitating an efficient momentum management strategy to prevent saturation. This paper presents a novel

Anjali Dharmik

SARS-CoV-2, the causative agent of COVID-19, remains a global health concern due to its high transmissibility and evolving variants. Although vaccination efforts and therapeutic advancements have mitigated disease severity, emerging mutations continue to challenge diagnostics and containment strategies. As of mid-February 2025, global test positivity has ris

Ran Zhou, Jianru Ding, Chenfeng Gao, Wanli Qian

Driven by the vision of everyday haptics, the HCI community is advocating for "design touch first" and investigating "how to touch well." However, a gap remains between the exploratory nature of haptic design and technical reproducibility. We present Shape-Kit, a hybrid design toolkit embodying our "crafting haptics" metaphor, where hand touch is transduced

Yagil Elias, Tom P. Humbert, Lauren Olson, Emitzá Guzmán

Software has the potential to improve lives. Yet, unethical and uninformed software practices are at the root of an increasing number of ethical concerns. Despite its pervasiveness, few research has analyzed end-users perspectives on the ethical issues of the software they use. We address this gap, and investigate end-user's ethical concerns in software thro

Dave Smith

We study the small amplitude linearization of the Korteweg de Vries equation on the line with a local defect scattering waves represented by a metric graph domain adjoined at one point. For a representative collection of examples, we derive explicit solution formulae expressed as contour integrals and obtain existence and unicity results for piecewise absolu

Federico Ricciuti

Many studies suggest that LLMs have left wing leans. The article extends previous analysis of US presidential elections considering several virtual elections in multiple European countries. The analysis considers multiple LLMs and the results confirm the extent of the leaning. Furthermore, the results show that the leaning is not uniform between countries. S

Philipp D. Siedler, Ian Gemp

In complex multi-agent environments, achieving efficient learning and desirable behaviours is a significant challenge for Multi-Agent Reinforcement Learning (MARL) systems. This work explores the potential of combining MARL with Large Language Model (LLM)-mediated interventions to guide agents toward more desirable behaviours. Specifically, we investigate ho

Ayoub Ammar Boudjelal, Rania Yasmine Bir, Huseyin Arslan

The emergence of 6G wireless networks demands solutions that seamlessly integrate communication and sensing. This letter proposes a novel waveform design for joint sensing and communication (JSAC) systems, combining single-carrier interleaved frequency division multiplexing (SC-IFDM), a 5G communication candidate signal, with frequency modulated continuous w

Heye Huang, Zheng Li, Hao Cheng, Haoran Wang

Ensuring safe interactions between autonomous vehicles (AVs) and human drivers in mixed traffic systems remains a major challenge, particularly in complex, high-risk scenarios. This paper presents a cognition-decision framework that integrates individual variability and commonalities in driver behavior to quantify risk cognition and model dynamic decision-ma

M. Kerr, S. Johnston, C. J. Clark, F. Camilo

We discovered four millisecond pulsars (MSPs) in searches of 80 $\gamma$-ray sources conducted from 2015 to 2017 with the Murriyang radio telescope of the Parkes Observatory. We provide an overview of the survey and focus on the results of a follow-up pulsar timing campaign. Using Fermi Large Area Telescope data, we have detected $\gamma$-ray pulsations from

Amin Banayeeanzade, Mohammad Rostami

Continual learning is crucial for creating AI agents that can learn and improve themselves autonomously. A primary challenge in continual learning is to learn new tasks without losing previously learned knowledge. Current continual learning methods primarily focus on enabling a neural network with mechanisms that mitigate forgetting effects. Inspired by the

Elliot H. Young, Peter Bühlmann

We develop Clustered Random Forests, a random forests algorithm for clustered data, arising from independent groups that exhibit within-cluster dependence. The leaf-wise predictions for each decision tree making up clustered random forests takes the form of a weighted least squares estimator, which leverage correlations between observations for improved pred

Mohammad Al-Jarrah, Bamdad Hosseini, Amirhossein Taghvaei

In this paper, we present the amortized optimal transport filter (A-OTF) designed to mitigate the computational burden associated with the real-time training of optimal transport filters (OTFs). OTFs can perform accurate non-Gaussian Bayesian updates in the filtering procedure, but they require training at every time step, which makes them expensive. The pro

Dana Fisman, Elina Sudit

Roughly speaking, a system is said to be robust if it can resist disturbances and still function correctly. For instance, if the requirement is that the temperature remains in an allowed range $[l,h]$, then a system that remains in a range $[l',h']\subset[l,h]$ is more robust than one that reaches $l$ and $h$ from time to time. In this example the initial sp

Dražen Glavan

Photon propagators for power-law inflation are constructed in two one-parameter families of noncovariant gauges, in an arbitrary number of spacetime dimensions. In both gauges photon propagators take relatively simple forms expressed in terms of scalar propagators and their derivatives. These are considerably simpler compared to their general covariant gauge

Xu Wu, Lesego E. Moloko, Pavel M. Bokov, Gregory K. Delipei

Machine learning (ML) has been leveraged to tackle a diverse range of tasks in almost all branches of nuclear engineering. Many of the successes in ML applications can be attributed to the recent performance breakthroughs in deep learning, the growing availability of computational power, data, and easy-to-use ML libraries. However, these empirical successes

Alvaro Ortiz, Tomasa Rodrigo, David Sarasa, Sirenia Vazquez

Using a panel data local projections model and controlling for firm characteristics, procurement bid attributes, and macroeconomic conditions, the study estimates the dynamic effects of procurement awards on new lending, a more precise measure than the change in the stock of credit. The analysis further examines heterogeneity in credit responses based on fir

Oluwadamilola Fasina

We extend Bony's celebrated work on paraproducts to continous and multiscale \emph{tensor} paraproducts. For $A \in \mathcal{C}^2(\mathbb{R})$ and $f \in \Lambda_{\alpha}([0,1]^2, d_d(x,y)^{\alpha} \times d'_d(x',y')^{\alpha})$, we construct an approximation, $\tilde{A}_{(N,N')}(f)$ to $A(f)$, replacing the operator $T: f \to A(f)$ with the continous tensor

Ritik Batra, Narjes Pourjafarian, Samantha Chang, Margaret Tsai

Recently, there has been a surge of interest in sustainable energy sources, particularly for wearable computing. Triboelectric nanogenerators (TENGs) have shown promise in converting human motion into electric power. Textile-based TENGs, valued for their flexibility and breathability, offer an ideal form factor for wearables. However, uptake in maker communi

Rui Cao

Online misinformation detection is an important issue and methods are proposed to detect and curb misinformation in various forms. However, previous studies are conducted in an offline manner. We claim a realistic misinformation detection setting that has not been studied yet is online misinformation detection in live streaming videos (MDLS). In the proposal

Leonardo Rosa Amado, Adriano Vogel, Dalvan Griebler, Gabriel Paludo Licks

Data pipeline frameworks provide abstractions for implementing sequences of data-intensive transformation operators, automating the deployment and execution of such transformations in a cluster. Deploying a data pipeline, however, requires computing resources to be allocated in a data center, ideally minimizing the overhead for communicating data and executi

Mohammed Ababneh, Kartick Kolachala, Roopa Vishwanathan

Payment channel networks (PCNs) are a promising solution to address blockchain scalability and throughput challenges, However, the security of PCNs and their vulnerability to attacks are not sufficiently studied. In this paper, we introduce SCOOP, a framework that includes two novel congestion attacks on PCNs. These attacks consider the minimum transferable

A. Zubankov, S. Afanasiev, M. Golubeva, F. Guber

The time-of-flight Highly Granular Neutron Detector (HGND) with a multilayer longitudinal structure of interleaved absorber and scintillator plates, high transverse granularity and a time resolution of about 150 ps is currently under development. The detector is designed to identify neutrons produced in nucleus-nucleus collisions and measure neutron kinetic

Vrushank Ahire, Kunal Shah, Mudasir Nazir Khan, Nikhil Pakhale

Dynamic emotion recognition in the wild remains challenging due to the transient nature of emotional expressions and temporal misalignment of multi-modal cues. Traditional approaches predict valence and arousal and often overlook the inherent correlation between these two dimensions. The proposed Multi-modal Attention for Valence-Arousal Emotion Network (MAV

Dian Chen, Han Jun Yoon, Zelin Wan, Nithin Alluru

Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust calibration, and adaptive teaming. This survey presents a comprehensive taxonomy of HMT, analyzing theoretical models, including reinforcement learning, instance-based learning, and in

Khayrul Islam, Ryan F. Forelli, Jianzhong Han, Deven Bhadane

Precise cell classification is essential in biomedical diagnostics and therapeutic monitoring, particularly for identifying diverse cell types involved in various diseases. Traditional cell classification methods such as flow cytometry depend on molecular labeling which is often costly, time-intensive, and can alter cell integrity. To overcome these limitati

Jakob Harzer, Jochem De Schutter, Moritz Diehl

In order to solve continuous-time optimal control problems, direct methods transcribe the infinite-dimensional problem to a nonlinear program (NLP) using numerical integration methods. In cases where the integration error can be manipulated by the chosen control trajectory, the transcription might produce spurious local NLP solutions as a by-product. While o

Haocheng Ren, Muzhe Wu, Gregory Croisdale, Anhong Guo

Learning to solve a Rubik's Cube requires the learners to repeatedly practice a skill component, e.g., identifying a misplaced square and putting it back. However, for 3D physical tasks such as this, generating sufficient repeated practice opportunities for learners can be challenging, in part because it is difficult for novices to reconfigure the physical o

Yosuke Kubota

We provide a mathematical realization of a conjecture by Kitaev, on the basis of the operator-algebraic formulation of infinite quantum spin systems. Our main results are threefold. First, we construct an $\Omega$-spectrum $\mathit{IP}_*$ whose homotopy groups are isomorphic to the smooth homotopy group of invertible gapped quantum systems on Euclidean space

Anthony Lamelas, Harrison Muchnic

This study explores the impact of scaling semantic categories on the image classification performance of vision transformers (ViTs). In this specific case, the CLIP server provided by Jina AI is used for experimentation. The research hypothesizes that as the number of ground truth and artificially introduced semantically equivalent categories increases, the

Myisha A. Chowdhury, Qiugang Lu

Accurate state of temperature (SOT) estimation for batteries is crucial for regulating their temperature within a desired range to ensure safe operation and optimal performance. The existing measurement-based methods often generate noisy signals and cannot scale up for large-scale battery packs. The electrochemical model-based methods, on the contrary, offer

Alessio Spagnoletti, Jean Prost, Andrés Almansa, Nicolas Papadakis

Text-to-image latent diffusion models (LDMs) have recently emerged as powerful generative models with great potential for solving inverse problems in imaging. However, leveraging such models in a Plug & Play (PnP), zero-shot manner remains challenging because it requires identifying a suitable text prompt for the unknown image of interest. Also, existing tex

Shiran Dudy, Thulasi Tholeti, Resmi Ramachandranpillai, Muhammad Ali

Recent advancements in Large Language Models (LLMs) have made them a popular information-seeking tool among end users. However, the statistical training methods for LLMs have raised concerns about their representation of under-represented topics, potentially leading to biases that could influence real-world decisions and opportunities. These biases could hav

Rashid Mushkani, Hugo Berard, Shin Koseki

Urban assessments often compress diverse needs into single scores, which can obscure minority perspectives. We present a community-centered study in Montreal (n=35; wheelchair users, seniors, LGBTQIA2+ residents, and immigrants). Participants rated 20 streets (accessibility, inclusivity, aesthetics, practicality) and ranked 7 images on 12 interview-elicited

Danila Makarov, Dmitry Makarov, Kirill Kozyrev, Noam Libeskind

The total mass of a galaxy group, such as the Milky Way (MW) and the Andromeda Galaxy (M 31), is typically determined from the kinematics of satellites within their virial zones. Bahcall and Tremaine (1981) proposed the $v^2r$ estimator as an alternative to the virial theorem. In this work, we extend their approach by incorporating the three-dimensional spat

Adam McCabe, Matthew H. Chequers

Organizations generate vast amounts of interconnected content across various platforms. While language models enable sophisticated reasoning for use in business applications, retrieving and contextualizing information from organizational memory remains challenging. We explore this challenge through the lens of entropy, proposing a measure of entity entropy t

Sven Otto, Luis Winter

We propose a function-on-function linear regression model for time-dependent curve data that is consistently estimated by imposing factor structures on the regressors. An integral operator based on cross-covariances identifies two components for each functional regressor: a predictive low-dimensional component, along with associated factors that are guarante

Seungwoo Lee, Mouad Ramil, Insuk Seo

Consider the underdamped Langevin process $(q(t),p(t))_{t\geq0}$ in $\R^d\times\R^d$. We derive the low-temperature asymptotic of its mean-transition time between basins of attraction for a double-well potential. This asymptotic is called Eyring-Kramers law and often relies in the literature on Potential theory tools which are ill-defined for hypoelliptic pr

Yitian Shi, Di Wen, Guanqi Chen, Edgar Welte

We propose VISO-Grasp, a novel vision-language-informed system designed to systematically address visibility constraints for grasping in severely occluded environments. By leveraging Foundation Models (FMs) for spatial reasoning and active view planning, our framework constructs and updates an instance-centric representation of spatial relationships, enhanci

Andrei-Marius Avram, Marian Lupaşcu, Dumitru-Clementin Cercel, Ionuţ Mironică

This paper presents UniBERT, a compact multilingual language model that uses an innovative training framework that integrates three components: masked language modeling, adversarial training, and knowledge distillation. Pre-trained on a meticulously curated Wikipedia corpus spanning 107 languages, UniBERT is designed to reduce the computational demands of la

Fengxing Zhu

In this paper we obtain the critical probability $p_c(Q_{k,n},r)$ for bootstrap percolation with the infection threshold $r=\frac{N}{2}$ on the generalized $n$-dimensional hypercube $Q_{k,n}$ with vertex set $V(Q_{k,n})=\{0,1\}^n$ and edges connecting the pairs at Hamming distance $1,2,\dots,k$, where $k\ge 2$ and $N=\sum_{i=1}^k\binom{n}{i}$. More precisely

Federico Buseghin, Nicola Garofalo

We establish new intrinsic Strichartz estimates for solutions of the Cauchy problem for a class of possibly degenerate Schr\"odinger equations with a real drift.

Yaoting Wang, Shengqiong Wu, Yuecheng Zhang, Shuicheng Yan

By extending the advantage of chain-of-thought (CoT) reasoning in human-like step-by-step processes to multimodal contexts, multimodal CoT (MCoT) reasoning has recently garnered significant research attention, especially in the integration with multimodal large language models (MLLMs). Existing MCoT studies design various methodologies and innovative reasoni

Margaret Hawton

Based on the physical interpretation of the photon continuity equation derived in [M. Hawton, Phys. Rev. A 109, 062221 (2024) ] the standard Lagrangian is second quantized to obtain a Lorentz and gauge invariant theory of single photons. The scalar potential is not independently second quantized so all modes have positive definite norm. The continuity equati

Graham J. Norris, Kieran Dalton, Dante Colao Zanuz, Alexander Rommens

Three-dimensional integration technologies such as flip-chip bonding are a key prerequisite to realize large-scale superconducting quantum processors. Modular architectures, in which circuit elements are spread over multiple chips, can further improve scalability and performance by enabling the integration of elements with different substrates or fabrication

Kunyang Sun, Dorian Bagni, Joseph M. Cavanagh, Yingze Wang

Generative machine learning models for exploring chemical space have shown immense promise, but many molecules they generate are too difficult to synthesize, making them impractical for further investigation or development. In this work, we present a novel approach by fine-tuning Meta's Llama3 Large Language Models (LLMs) to create SynLlama, which generates

Basab Jha, Firoj Paudel

The application of on-device language models (ODLMs) on resource-constrained edge devices is a multi-dimensional problem that strikes a fine balance between computational effectiveness, memory, power usage, and linguistic capacity across heterogeneous tasks. This holistic study conducts a thorough investigation of the trade-offs between domain-specific optim

Ting Bai, Anni Li, Gehui Xu, Christos G. Cassandras

This paper presents a dynamic routing guidance system that optimizes route recommendations for individual vehicles in an emerging transportation system while enhancing travelers' trip equity. We develop a framework to quantify trip quality and equity in dynamic travel environments, providing new insights into how routing guidance influences equity in road tr

Tao Feng, Yihang Sun, Jiaxuan You

The powerful capabilities of Large Language Models (LLMs) have led to their growing use in evaluating human-generated content, particularly in evaluating research ideas within academic settings. Existing solutions primarily rely on prompt-based LLM methods or fine-tuned lightweight language models for idea evaluation. However, these methods are often unstabl

Hranislav Stanković

In this paper, we present new characterizations of normal and positive operators in terms of their powers. Among other things, we show that if $T^2$ is normal, $\mathcal{W}(T^{2k+1})$ lies on one side of a line passing through the origin (possibly including some points on the line) for some $k\in\mathbb{N}$, and $\mathrm{asc\,}(T)= 1$ (or $\mathrm{dsc\,}(T)=

Zhuoqi, Liu, Sean D. Johnson, Jennifer I-Hsiu Li

Gas flows between galaxies and the CGM play a crucial role in galaxy evolution. When ionized by a quasar, these gas flows can be directly traced as giant nebulae. We present a study of a giant nebula around a radio-loud quasar, 3C$\,$57 at $z\approx0.672$. Observations from MUSE reveal that the nebula is elongated with a major axis of $70 \, \rm kpc$ and a m

Bruno F. F. Gonçalves, Isabel S. Labouriau, Alexandre A. P. Rodrigues

We describe the fast-slow dynamics of two FitzHugh--Nagumo equations coupled symmetrically through the slow equations. We use symmetry arguments to find a non-empty open set of parameter values for which the two equations synchronise, and another set with antisynchrony -- where the solution of one equation is minus the solution of the other. By combining the

Dan Halperin, Niklas Eisl

Autonomous driving is a safety-critical application, and it is therefore a top priority that the accompanying assistance systems are able to provide precise information about the surrounding environment of the vehicle. Tasks such as 3D Object Detection deliver an insufficiently detailed understanding of the surrounding scene because they only predict a bound

Mohamed Amin Loualidi, Mohamed Miskaoui, Salah Nasri

Addressing the fermion flavor structures using modular invariance is a challenging task in the framework of quark-lepton unification. Building on recent applications of modular symmetry in non-supersymmetric models, we propose the first renormalizable $SU(5)$ grand unified theory incorporating level 3 nonholomorphic modular symmetry, $\Gamma_3 \simeq A_4$. T

Thayer Alshaabi, Daniel E. Milkie, Gaoxiang Liu, Cyna Shirazinejad

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. While wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement, and slow when serially mapping spatially varying aberrations across large fields

Harshit

Large Language Model (LLM) development has become increasingly centralized, limiting participation to well-resourced organizations. This paper introduces MoECollab, a novel framework leveraging Mixture of Experts (MoE) architecture to enable distributed, collaborative LLM development. By decomposing monolithic models into specialized expert modules coordinat

Xianglin Liu, Kai Yang, Yongxiang Liu, Fanli Zhou

The computational cost of traditional first-principles method quickly becomes prohibitively expensive as the number of atoms increases. This challenge is further amplified by the need to evaluate finite-temperature properties with Monte Carlo (MC) simulations, which is inherently challenging to parallelize due to sequential Markov chain updates. Here, we int

Haoran Feng, Zehuan Huang, Lin Li, Hairong Lv

Personalized image generation aims to produce images of user-specified concepts while enabling flexible editing. Recent training-free approaches, while exhibit higher computational efficiency than training-based methods, struggle with identity preservation, applicability, and compatibility with diffusion transformers (DiTs). In this paper, we uncover the unt

Wupeng Wang, Zexu Pan, Jingru Lin, Shuai Wang

Speech separation seeks to isolate individual speech signals from a multi-talk speech mixture. Despite much progress, a system well-trained on synthetic data often experiences performance degradation on out-of-domain data, such as real-world speech mixtures. To address this, we introduce a novel context-aware, two-stage training scheme for speech separation

Xiaoyu Han, Shengping Zhang, Qinglin Liu, Zonglin Li

Existing image-based virtual try-on methods directly transfer specific clothing to a human image without utilizing clothing attributes to refine the transferred clothing geometry and textures, which causes incomplete and blurred clothing appearances. In addition, these methods usually mask the limb textures of the input for the clothing-agnostic person repre

Ki-Nam Hong, Marwa Shahine, Seok-Bae Yun

We consider the existence of steady rarefied flows of polyatomic gas between two parallel condensed phases, where evaporation and condensation processes occur. To this end, we study the existence problem of stationary solutions in a one-dimensional slab for the polyatomic Boltzmann equation, which takes into account the effect of internal energy in the colli

Yael Hillman, Amir Michaelis, Hagai B. Perets

We investigate helium accumulation on carbon-oxygen (CO) white dwarfs (WDs), exploring a broad parameter space of initial WD masses ($0.65$--$1.0M_{\odot}$) and helium accretion rates ($10^{-10}$--$10^{-4}M_{\odot}\text{yr}^{-1}$). Our simulations, which were allowed to run for up to the order of a Gyr, reveal distinct regimes determined by the given accreti

Damian Rössler, Stefan Schröer

We coin the term \emph{$T$-trivial varieties} to denote smooth proper schemes over ground fields $k$ whose tangent sheaf is free. Over the complex numbers, this are precisely the abelian varieties. However, Igusa observed that in characteristic $p\leq 3$ certain bielliptic surfaces are $T$-trivial. We show that $T$-trivial varieties $X$ separably dominated b

Giovanni Franzese, Max Spahn, Jens Kober, Cosimo Della Santina

To increase the reliability of collaborative robots in performing daily tasks, we require them to be accurate and not only repeatable. However, having a calibrated kinematics model is regrettably a luxury, as available calibration tools are usually more expensive than the robots themselves. With this work, we aim to contribute to the democratization of cobot

Qing Li, Jiahui Geng, Derui Zhu, Fengyu Cai

Unlearning methods for vision-language models (VLMs) have primarily adapted techniques from large language models (LLMs), relying on weight updates that demand extensive annotated forget sets. Moreover, these methods perform unlearning at a coarse granularity, often leading to excessive forgetting and reduced model utility. To address this issue, we introduc

Biagio Buonaura, Giuseppe Giuliani

Traditionally, Electromagnetism is taught following the chronological development of the matter. The final product of this path is a presentation of Electromagnetism realized by adding one layer over another with the risk of transferring concepts and formulae from Electrostatics to Electrodynamics. In this paper, we suggest a new approach based on the idea t

Tanuja Kistwal, Krishan Kanhaiya, Adrian Buchmann, Chen Ma

Quantum friction describes the transfer of energy and momentum from electronically excited states in a material to a surrounding solvent. Here, we show that near-infrared (NIR) fluorescent single-walled carbon nanotubes (SWCNTs) exhibit quantum friction in water. The diffusion constants of functionalized SWCNTs in aqueous solution decrease linearly by around

Emilio Cartoni, Gianluca Cioccolini, Gianluca Baldassarre

Open-Ended Learning (OEL) autonomous robots can acquire new skills and knowledge through direct interaction with their environment, relying on mechanisms such as intrinsic motivations and self-generated goals to guide learning processes. OEL robots are highly relevant for applications as they can autonomously leverage acquired knowledge to perform tasks bene

Ismar Volic, Leah Valentiner

We initiate the study of simple games from the point of view of combinatorial topology. The starting premise is that the losing coalitions of a simple game can be identified with a simplicial complex. Various topological constructions and results from the theory of simplicial complexes then carry over to the setting of simple games. Examples are cone, join,

Sreeram Rajesh, Alban Sauret

The presence of non-Brownian spherical particles dispersed in a liquid modifies the impact and spreading dynamics of a drop on a hydrophilic substrate. This difference in spreading dynamics is attributed to the increase in the suspension's viscosity caused by the particles. Similarly, anisotropic non-Brownian particles, such as fibers, also increase the bulk

Zhiwei He, Zhaopeng Tu, Xing Wang, Xingyu Chen

Low-rank adaptation (LoRA) has been prominently employed for parameter-efficient fine-tuning of large language models (LLMs). However, the limited expressive capacity of LoRA, stemming from the low-rank constraint, has been recognized as a bottleneck, particularly in rigorous tasks like code generation and mathematical reasoning. To address this limitation,

Dipesh Tamboli, Souradip Chakraborty, Aditya Malusare, Biplab Banerjee

Diffusion models have achieved remarkable progress in text-to-image generation, yet aligning them with human preference remains challenging due to the presence of multiple, sometimes conflicting, evaluation metrics (e.g., semantic consistency, aesthetics, and human preference scores). Existing alignment methods typically optimize for a single metric or rely

Amrita Sain, Poonam Choudhary, Bheemsehan Gurjar, Chandan Mondal

We calculate the gluon gravitational form factors (GFFs) of the proton using a light-front spectator model based on soft-wall AdS/QCD, where the active parton is a gluon. The model parameters are determined by fitting the unpolarized gluon distribution function to the NNPDF3.0nlo dataset. Subsequently, we predict the polarized gluon distribution, finding con

Yi Teng, Orazio Scarlatella, Shiyu Zhou, Armin Rahmani

Quantum coherence is a crucial resource in achieving quantum advantage over classical information processing, and more generally developing new quantum technologies. While its effects are observable in current quantum platforms, there are no standardized tools for systematically measuring and quantifying multi-qubit coherence across different gate-based quan

Francesco Girlanda, Denys Rozumnyi, Marc Pollefeys, Martin R. Oswald

We present Deblur-SLAM, a robust RGB SLAM pipeline designed to recover sharp reconstructions from motion-blurred inputs. The proposed method bridges the strengths of both frame-to-frame and frame-to-model approaches to model sub-frame camera trajectories that lead to high-fidelity reconstructions in motion-blurred settings. Moreover, our pipeline incorporate

Rafał Filipów, Małgorzata Kowalczuk, Adam Kwela

For each countable ordinal $\alpha$, we introduce an ideal $conv_\alpha$ and use it to characterize the class of all compact countable spaces which are homeomorphic to the space $\omega^{\alpha}\cdot n+1$ with the order topology. The characterization is expressed in terms of finding a convergent subsequence defined on a set not belonging to $conv_\alpha$.