March 2025 arXiv papers — page 190

Yao Meng, Sean P. Cornelius, Yang-Yu Liu, Aming Li

Cooperation underlies many aspects of the evolution of human and animal societies, where cooperators produce social goods to benefit others. Explaining the emergence of cooperation among selfish individuals has become a major research interest in evolutionary dynamics. Previous studies typically use complex networks to capture the interactions between indivi

Shima Gholam-Mirzaei, Aleksey Korobenko, Nida Haram, David N. Purschke

High-harmonic generation (HHG) in solids has typically been explored in transparent dielectrics and semiconductors. Metals have long been dismissed due to their strong reflectivity at infrared wavelengths. Here, we demonstrate HHG from silver - a noble metal - using few-cycle near-infrared laser pulses at near-normal incidence. Our results show that sub-cycl

Paola Savarese, Sarvesh Bansal, Maria Gorizia Ammendola, Lorenzo Amato

The evolution of a closed quantum system is described by a unitary operator generated by a Hermitian Hamiltonian. However, when certain degrees of freedom are coupled to an environment, the relevant dynamics can be captured by non-unitary evolution operators, arising from non-Hermitian Hamiltonians. Here we introduce a photonic platform that implements non-u

Pavel Surynek, Vojtěch Bubník, Lukáš Matěna, Petr Kubiš

We address the problem of object arrangement and scheduling for sequential 3D printing. Unlike the standard 3D printing, where all objects are printed slice by slice at once, in sequential 3D printing, objects are completed one after other. In the sequential case, it is necessary to ensure that the moving parts of the printer do not collide with previously p

Reshabh K Sharma, Jonathan De Halleux, Shraddha Barke, Dan Grossman

Large language models (LLMs) are being used in many applications and prompts for these models are integrated into software applications as code-like artifacts. These prompts behave much like traditional software in that they take inputs, generate outputs, and perform some specific function. However, prompts differ from traditional code in many ways and requi

Jinlu Li, Yanghai Yu, Neng Zhu

In this paper, we consider the Cauchy problem for the 3D Euler equations with the Coriolis force in the whole space. We first establish the local-in-time existence and uniqueness of solution to this system in $B^s_{p,r}(\R^3)$. Then we prove that the Cauchy problem is ill-posed in two different sense: (1) the solution of this system is not uniformly continuo

Mingtao Xia, Qijing Shen, Philip Maini, Eamonn Gaffney

In this work, we propose and analyze a new local time-decoupled squared Wasserstein-2 method for reconstructing the distribution of unknown parameters in dynamical systems. Specifically, we show that a stochastic neural network model, which can be effectively trained by minimizing our proposed local time-decoupled squared Wasserstein-2 loss function, is an e

Shwai He, Weilin Cai, Jiayi Huang, Ang Li

The Mixture of Experts (MoE) is an effective architecture for scaling large language models by leveraging sparse expert activation to balance performance and efficiency. However, under expert parallelism, MoE suffers from inference inefficiencies due to imbalanced token-to-expert assignment, where underloaded experts complete computations early but must wait

Nitheesha Nakka, Omer F. Yalcin, Bruce A. Desmarais, Sarah Rajtmajer

Statistical topic modeling is widely used in political science to study text. Researchers examine documents of varying lengths, from tweets to speeches. There is ongoing debate on how document length affects the interpretability of topic models. We investigate the effects of aggregating short documents into larger ones based on natural units that partition t

Qingxuan Jia, Guoqin Tang, Zeyuan Huang, Zixuan Hao

Vision-Language Models (VLMs) demonstrate remarkable potential in robotic manipulation, yet challenges persist in executing complex fine manipulation tasks with high speed and precision. While excelling at high-level planning, existing VLM methods struggle to guide robots through precise sequences of fine motor actions. To address this limitation, we introdu

Haosen Zhang, Jiahao Huang, Yinzhe Wu, Congren Dai

Magnetic Resonance Imaging (MRI) is crucial for clinical diagnostics but is hindered by prolonged scan times. Current deep learning models enhance MRI reconstruction but are often memory-intensive and unsuitable for resource-limited systems. This paper introduces a lightweight MRI reconstruction model leveraging Kronecker-Parameterized Hypercomplex Neural Ne

Songsong Li, Shu Liu, Liming Ma, Yunqi Wan

Despite of tremendous research on decoding Reed-Solomon (RS) and algebraic geometry (AG) codes under the random and adversary substitution error models, few studies have explored these codes under the burst substitution error model. Burst errors are prevalent in many communication channels, such as wireless networks, magnetic recording systems, and flash mem

Michael Krumdick, Charles Lovering, Varshini Reddy, Seth Ebner

Reliable evaluation of large language models (LLMs) is critical as their deployment rapidly expands, particularly in high-stakes domains such as business and finance. The LLM-as-a-Judge framework, which uses prompted LLMs to evaluate response quality, is appealing due to its scalability, low cost, and strong correlations with human stylistic preferences. How

Yosuke Yamagishi, Tomohiro Kikuchi, Shouhei Hanaoka, Takeharu Yoshikawa

Japanese language models for medical text classification face challenges with complex vocabulary and linguistic structures in radiology reports. This study compared three Japanese models--BERT Base, JMedRoBERTa, and ModernBERT--for multi-label classification of 18 chest CT findings. Using the CT-RATE-JPN dataset, all models were fine-tuned under identical co

S. H. Oh, Sangwoo Kim, Y. M. Cho

It is well known that, making the Abelian projection of Einstein's theory one can obtain the restricted gravity which is simpler than Einstein's theory but describes the core dynamics of Einstein's gravity. In this paper we present the Lagrangian formalism of the restricted gravity which makes the restricted gravity a self consistent field theory by itself,

Niels J. Gommesen

Recent studies reveal widespread concern and increasing lack of understanding about how personal data is collected, shared, and used online without consent. This issue is compounded by limited options available for digital citizens to understand, control and manage their data flows across platforms, underscoring the need to explore how this lack of trust and

Jianshu Zhou, Junda Huang, Boyuan Liang, Xiang Zhang

Robotic manipulators, traditionally designed with classical joint-link articulated structures, excel in industrial applications but face challenges in human-centered and general-purpose tasks requiring greater dexterity and adaptability. To address these challenges, we propose the Prismatic-Bending Transformable (PBT) Joint, a novel, scissors-inspired mechan

Sujith Ranasinghe, Denis Leahy

This document provides a user guide for reducing UVIT data using CCDLAB. While CCDLAB offers a straightforward data reduction work-flow, users may encounter certain challenges that require additional guidance. This guide provides instructions by addressing common issues related to key processing steps, including WCS solutions and VIS drift tracking.

Collin Cherubim, Robin Wordsworth, Dan Bower, Paolo Sossi

We present a population-level view of volatile gas species (H$_2$, He, H$_2$O, O$_2$, CO, CO$_2$, CH$_4$) distribution during the sub-Neptune to rocky planet transition, revealing in detail the dynamic nature of small planet atmospheric compositions. Our novel model couples the atmospheric escape model $\texttt{IsoFATE}$ with the magma ocean-atmosphere equil

Masayuki Yamanaka, Takahiro Nagayama, Tsukiha Horikiri

We report the multi-band photometric observations of the Type IIb supernova (SN) 2024iss with ultra-violet (UV), optical, and near-infrared (NIR) wavelengths starting one day after the explosion. The UV and optical light curves show the first peak two days after the explosion date. Following a first peak, a secondary maximum is observed in the optical and NI

Siavash Monfared, Aleksandra Ardaševa, Amin Doostmohammadi

The development of complex multicellular organisms from a single parent cell is a highly orchestrated process that cells conduct collectively without central guidance, creating intricate dynamic patterns essential for development and regeneration. Despite significant advances in imaging spatiotemporal dynamics of cell collectives and mechanical characterizat

Shigeo Hakkaku, Yasunari Suzuki, Yuuki Tokunaga, Suguru Endo

Quantum dynamics simulation via Hamilton simulation algorithms is one of the most crucial applications in the quantum computing field. While this task has been relatively considered the target in the fault-tolerance era, the experiment for demonstrating utility by an IBM team simulates the dynamics of an Ising-type quantum system with the Trotter-based Hamil

Di Xu, Hengjie Liu, Xin Miao, Daniel O'Connor

The scanning time for a fully sampled MRI can be undesirably lengthy. Compressed sensing has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is computationally complex and difficult to generalize on new cases. Image-domain-based deep learning methods (e.g., convolutional neural networks) emerged as a

Erica Sawczynec, Kyle F. Kaplan, Gregory N. Mace, Jae-Joon Lee

The Immersion GRating INfrared Spectrometer (IGRINS) is a compact, high-resolution (R~45,000) near-infrared spectrograph spanning 1.45 to 2.45 um in a single exposure. We introduce the Raw and Reduced IGRINS Spectral Archive (RRISA), which provides public data access for all non-proprietary IGRINS data taken at McDonald Observatory's Harlan J. Smith Tele

Dingkun Liu, Siyang Li, Ziwei Wang, Wei Li

A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. However, decoding EEG signals across different headsets remains a significant challenge due to differences in the number and locations of the electrodes. To address this challenge, we propose a spat

Quan Yu, Yu-Hong Dai, Minru Bai

Low rank tensor representation (LRTR) methods are very useful for hyperspectral anomaly detection (HAD). To overcome the limitations that they often overlook spectral anomaly and rely on large-scale matrix singular value decomposition, we first apply non-negative matrix factorization (NMF) to alleviate spectral dimensionality redundancy and extract spectral

Farshid Keivanian, Raymond Chiong, Zongwen Fan

Predicting body fat can provide medical practitioners and users with essential information for preventing and diagnosing heart diseases. Hybrid machine learning models offer better performance than simple regression analysis methods by selecting relevant body measurements and capturing complex nonlinear relationships among selected features in modelling body

Melkamu Abay Mersha, Mesay Gemeda Yigezu, Hassan Shakil, Ali K. AlShami

The increasing complexity of LLMs presents significant challenges to their transparency and interpretability, necessitating the use of eXplainable AI (XAI) techniques to enhance trustworthiness and usability. This study introduces a comprehensive evaluation framework with four novel metrics for assessing the effectiveness of five XAI techniques across five L

Preetam Prabhu Srikar Dammu, Himanshu Naidu, Chirag Shah

As question answering (QA) systems advance alongside the rapid evolution of foundation models, the need for robust, adaptable, and large-scale evaluation benchmarks becomes increasingly critical. Traditional QA benchmarks are often static and publicly available, making them susceptible to data contamination and memorization by large language models (LLMs). C

Noe Kuhn

As a unified theory of sentient behaviour, active inference is formally intertwined with multiple normative theories of optimal behaviour. Specifically, we address what we call the subsumption thesis: The claim that expected utility from economics, as an account of agency, is subsumed by active inference. To investigate this claim, we present multiple exampl

Mostafa A. Mostafa, Mohamed K. Mohamed, Radwa W. Ezzat

The automotive industry is increasingly reliant on software to manage complex vehicle functionalities, making efficient and secure firmware updates essential. Traditional firmware update methods, requiring physical connections through On-Board Diagnostics (OBD) ports, are inconvenient, costly, and time-consuming. Firmware Over-the-Air (FOTA) technology offer

Grace Proebsting, Adam Poliak

We test whether NLP datasets created with Large Language Models (LLMs) contain annotation artifacts and social biases like NLP datasets elicited from crowd-source workers. We recreate a portion of the Stanford Natural Language Inference corpus using GPT-4, Llama-2 70b for Chat, and Mistral 7b Instruct. We train hypothesis-only classifiers to determine whethe

Chang Yu, Wenxin Du, Zeshun Zong, Alejandro Castro

We present a novel convex formulation that weakly couples the Material Point Method (MPM) with rigid body dynamics through frictional contact, optimized for efficient GPU parallelization. Our approach features an asynchronous time-splitting scheme to integrate MPM and rigid body dynamics under different time step sizes. We develop a globally convergent quasi

Rúben Barreiro, Walter O. Krawec, Paulo Mateus, Nikola Paunković

We propose a semi-quantum conference key agreement (SQCKA) protocol that leverages on GHZ states. We provide a comprehensive security analysis for our protocol that does not rely on a trusted mediator party. We present information-theoretic security proof, addressing collective attacks within the asymptotic limit of infinitely many rounds. This assumption is

Rey Guadarrama, Sergei Gleyzer, Mariia Baidachna, Kyoungchul Kong

Quantum computing has the potential to offer significant advantages over classical computing, making it a promising avenue for exploring alternative methods in High Energy Physics (HEP) simulations. This work presents the implementation of a Quantum Generative Adversarial Network (qGAN) to simultaneously generate gluon-initiated jet images for both ECAL and

Sanjay Bachhar, M. Baenitz, John Wilkinson, A. V. Mahajan

We report our investigations on Ag3LiIr1.4Ru0.6O6, which results from the Ru substitution in the Kitaev quantum spin liquid candidate Ag3LiIr2O6. It crystallizes in the monoclinic C2/m space group like its parent compound, Ag3LiIr2O6. Our susceptibility measurements reveal an effective moment = 2.6 muB, which is higher than the moments of the parent compound

Beyazit Yalcinkaya, Niklas Lauffer, Marcell Vazquez-Chanlatte, Sanjit A. Seshia

Automata-conditioned reinforcement learning (RL) has given promising results for learning multi-task policies capable of performing temporally extended objectives given at runtime, done by pretraining and freezing automata embeddings prior to training the downstream policy. However, no theoretical guarantees were given. This work provides a theoretical frame

Le Yue, Tram Thi Minh Tran, Xinyan Yu, Marius Hoggenmueller

External Human-Machine Interfaces (eHMIs) are critical for seamless interactions between autonomous vehicles (AVs) and pedestrians in shared spaces. However, they often struggle to adapt to these environments, where pedestrian movement is fluid and right-of-way is ambiguous. To address these challenges, we propose PaveFlow, an eHMI that projects the AV's int

John C. Flournoy, Carol S. Lee, Maggie Wu, Catherine M. Hicks

Understanding factors that influence software development velocity is crucial for engineering teams and organizations, yet empirical evidence at scale remains limited. A more robust understanding of the dynamics of cycle time may help practitioners avoid pitfalls in relying on velocity measures while evaluating software work. We analyze cycle time, a widely-

Si Chen, Reid Metoyer, Khiem Le, Adam Acunin

Instructors play a pivotal role in integrating AI into education, yet their adoption of AI-powered tools remains inconsistent. Despite this, limited research explores how to design AI tools that support broader instructor adoption. This study applies a human-centered design approach, incorporating qualitative methods, to investigate the design of interactive

Andreas Gastel, Katarzyna Mazowiecka, Michał Miśkiewicz

We derive the sharp vectorial Kato inequality for $p$-harmonic mappings. Surprisingly, the optimal constant differs from the one obtained for scalar valued $p$-harmonic functions by Chang, Chen, and Wei. As an application we demonstrate how this inequality can be used in the study of regularity of $p$-harmonic maps. Furthermore, in the case of $p$-harmonic m

Mohsen Fayyaz, Ali Modarressi, Hinrich Schuetze, Nanyun Peng

Dense retrieval models are commonly used in Information Retrieval (IR) applications, such as Retrieval-Augmented Generation (RAG). Since they often serve as the first step in these systems, their robustness is critical to avoid downstream failures. In this work, we repurpose a relation extraction dataset (e.g., Re-DocRED) to design controlled experiments tha

Dušan Denčić, Hranislav Stanković, Mihailo Krstić, Ivan Damnjanović

Here, we study the $q$-numerical radius of rank-one operators on a Hilbert space $\mathcal{H}$. More precisely, for $q \in [0,1]$ and $a, b \in \mathcal{H}$, we establish the formula \[ \omega_q(a \otimes b) = \frac{1}{2}\left(\|a\|\|b\| + q|\langle a, b \rangle| + \sqrt{1-q^2}\sqrt{\|a\|^2\|b\|^2 - |\langle a, b \rangle|^2}\right), \] which represents a gen

Yuyou Zhang, Yihang Yao, Shiqi Liu, Yaru Niu

When operating at their full capacity, quadrupedal robots can produce loud footstep noise, which can be disruptive in human-centered environments like homes, offices, and hospitals. As a result, balancing locomotion performance with noise constraints is crucial for the successful real-world deployment of quadrupedal robots. However, achieving adaptive noise

Jan Greve

Ewens-Pitman's partition structure arises as a system of sampling consistent probability distributions on set partitions induced by the Pitman-Yor process. It is widely used in statistical applications, particularly in species sampling models in Bayesian nonparametrics. Drawing references from the area of representation theory of the infinite symmetric group

Martijn Bastiaan, Christiaan Baaij, Martin Izzard, Felix Klein

This paper presents the first hardware implementation of bittide, a decentralized clock synchronization mechanism for achieving logical synchrony in distributed systems. We detail the design and implementation of an 8-node bittide network using off-the-shelf FPGA boards and adjustable clock sources. Through experiments with various network topologies, includ

Simon Stastny, Guido Burkard

Semiconductor-based spin qubits embedded into a superconducting microwave cavity constitute a fast-progressing and promising platform for realizing fast and fault-tolerant qubit control with long-range two-qubit coupling. The flopping-mode spin qubit consists of a single electron in a double quantum dot; it combines a charge qubit with a spin qubit. With its

Qasim Bin Saeed, Ijaz Ahmed

The increasing prevalence of mental health disorders, such as depression, anxiety, and bipolar disorder, calls for immediate need in developing tools for early detection and intervention. Social media platforms, like Reddit, represent a rich source of user-generated content, reflecting emotional and behavioral patterns. In this work, we propose a multi-modal

Yanxi Chen, Mohammad Farazi, Zhangsihao Yang, Yonghui Fan

Alzheimer's disease (AD) is a major neurodegenerative condition that affects millions around the world. As one of the main biomarkers in the AD diagnosis procedure, brain amyloid positivity is typically identified by positron emission tomography (PET), which is costly and invasive. Brain structural magnetic resonance imaging (sMRI) may provide a safer and mo

Timothy L. Molloy

We introduce a class of partially observed Markov decision processes (POMDPs) with costs that can depend on both the value and (future) uncertainty associated with the initial state. These Initial-State Cost POMDPs (ISC-POMDPs) enable the specification of objectives relative to a priori unknown initial states, which is useful in applications such as robot na

Benjamin Thérien, Charles-Étienne Joseph, Zain Sarwar, Ashwinee Panda

Sparsely-activated Mixture of Experts (MoE) transformers are promising architectures for foundation models. Compared to dense transformers that require the same amount of floating-point operations (FLOPs) per forward pass, MoEs benefit from improved sample efficiency at training time and achieve much stronger performance. Many closed-source and open-source f

Mohammad Amin Esabat, Saeed Jaamei, Fatemeh Asadi, Ahmad Reza Kohansal

This research examines the aerodynamic performance of wavy (corrugated) airfoils, focusing specifically on analyzing the impact of two angles of attack: the airfoil's angle of attack and the tail's angle of attack (beta). Simulations were conducted using the W1011 airfoil at a Reynolds number of 200,000, considering attack angles of 0, 2, 5, and 8 degrees fo

Vikram Ravindranath, Yiqiu Han, Xiao Chen

Decoherence is ubiquitous, and poses a significant impediment to the observation of quantum phenomena, such as the measurement-induced entanglement phase transition (MIPT). In this work, we study entanglement transitions in quantum circuits on trees, subject to both noise and measurements. We uncover a rich phase diagram that describes the ability of a tree

Dayi Dong, Albert Xu, Geordan Gutow, Howie Choset

Robotic search and rescue, exploration, and inspection require trajectory planning across a variety of domains. A popular approach to trajectory planning for these types of missions is ergodic search, which biases a trajectory to spend time in parts of the exploration domain that are believed to contain more information. Most prior work on ergodic search has

Hannes Stark, Bowen Jing, Tomas Geffner, Jason Yim

We develop ProtComposer to generate protein structures conditioned on spatial protein layouts that are specified via a set of 3D ellipsoids capturing substructure shapes and semantics. At inference time, we condition on ellipsoids that are hand-constructed, extracted from existing proteins, or from a statistical model, with each option unlocking new capabili

Yordan P. Raykov, Hengrui Luo, Justin D. Strait, Wasiur R. KhudaBukhsh

We propose causal effect estimators based on empirical Fr\'{e}chet means and operator-valued kernels, tailored to functional data spaces. These methods address the challenges of high-dimensionality, sequential ordering, and model complexity while preserving robustness to treatment misspecification. Using structural assumptions, we obtain compact representati

Cheng Lee, Hsi Lee

Credit risk assessment is a crucial aspect of financial decision-making, enabling institutions to predict the likelihood of default and make informed lending decisions. Two prominent methodologies in credit risk modeling are logistic regression and survival analysis. Logistic regression is widely used in scorecard development due to its simplicity, interpret

Arash J. Khabbazi, Elias N. Pergantis, Levi D. Reyes Premer, Panagiotis Papageorgiou

A large body of simulation research suggests that model predictive control (MPC) and reinforcement learning (RL) for heating, ventilation, and air-conditioning (HVAC) in residential and commercial buildings could reduce energy costs, pollutant emissions, and strain on power grids. Despite this potential, neither MPC nor RL has seen widespread industry adopti

Yuyou Zhang, Miao Li, William Han, Yihang Yao

Large Language Models (LLMs) are vulnerable to jailbreak attacks that exploit weaknesses in traditional safety alignment, which often relies on rigid refusal heuristics or representation engineering to block harmful outputs. While they are effective for direct adversarial attacks, they fall short of broader safety challenges requiring nuanced, context-aware

Siyu Ma, Wenxin Du, Chang Yu, Ying Jiang

Grasping is fundamental to robotic manipulation, and recent advances in large-scale grasping datasets have provided essential training data and evaluation benchmarks, accelerating the development of learning-based methods for robust object grasping. However, most existing datasets exclude deformable bodies due to the lack of scalable, robust simulation pipel

Fangfang Zhang, Zilong Chen, Chen Cui, Zhihua Yang

The lack of suitable vacuum ultraviolet (VUV) nonlinear optical (NLO) crystals has hindered the development of compact, high-power VUV sources via second harmonic generation (SHG). Here, we report on the development of the fluorooxoborate crystal NH4B4O6F (ABF) as a promising material for VUV light generation. For the first time, devices with specific phase-

Soubhik Mondal, Ksenia B. Bravaya

The complex absorbing potential (CAP) technique is one of the commonly used Non-Hermitian quantum mechanics approaches for characterizing electronic resonances. CAP combined with various electronic structure methods has shown promising results in quantifying the energies and widths of electronic resonances in molecular systems. While CAP-based methods can be

Tommaso Tenna

A general high-order fully explicit scheme based on projective integration methods is here presented to solve systems of degenerate parabolic equations in general dimensions. The method is based on a BGK approximation of the advection-diffusion equation, where we introduce projective integration method as time integrator to deal with the stiff relaxation ter

Lin Jiao, Han Pu, Jun-Hong An

As a crucial resource in the field of quantum metrology, spin squeezing can facilitate highly precise measurements that surpass the limitations imposed by classical physics. However, the quantum advantage of spin squeezing is significantly compromised by decoherence, thus impeding its practical implementation. Here, by investigating the influence of local di

Chengxi Zhang, Justin Phillips, Inder Monga, Erhan Saglamyurek

Atom-cavity systems offer unique advantages for building large-scale distributed quantum computers by providing strong atom-photon coupling while allowing for high-fidelity local operations of atomic qubits. However, in prevalent schemes where the photonic state is encoded in polarization, cavity birefringence introduces an energy splitting of the cavity eig

Lin Li, Pengcheng Xie, Li Zhang

Unconstrained optimization problems become more common in scientific computing and engineering applications with the rapid development of artificial intelligence, and numerical methods for solving them more quickly and efficiently have been getting more attention and research. Moreover, an efficient method to minimize all kinds of objective functions is urge

Jovan Mikić

For $0\leq k \leq n$, the number $C(n,k)$ represents the number of all lattice paths in the plane from the point $(0,0)$ to the point $(n,k)$, using steps $(1,0)$ and $(0,1)$, that never rise above the main diagonal $y=x$. The Fuss-Catalan number of order three $C^{(3)}_n$ represents the number of all lattice paths in the plane from the point $(0,0)$ to the

Benyamin Tabarsi, Heidi Reichert, Sam Gilson, Ally Limke

Large language models (LLMs) are rapidly reshaping software development, but their impact across the software development lifecycle is underexplored. Existing work focuses on isolated activities such as code generation or testing, leaving open questions about how LLMs affect developers, processes, products, and the software ecosystem. We address this gap thr

Renjie Li, Weiguang Cui, Ang Liu, Huiyuan Wang

We investigate the physical properties and detectability of warm-hot intergalactic medium (WHIM) gas with temperatures in the range $10^5<T<10^7$K around galaxy clusters using simulated galaxy clusters from The Three Hundred project. In simulations with different input physics (GIZMO-SIMBA and Gadget-X), we consistently find that the median gas temperature d

Bryan Li, Jiaming Luo, Eleftheria Briakou, Colin Cherry

While large language models (LLMs) have been increasingly adopted for machine translation (MT), their performance for specialist domains such as medicine and law remains an open challenge. Prior work has shown that LLMs can be domain-adapted at test-time by retrieving targeted few-shot demonstrations or terminologies for inclusion in the prompt. Meanwhile, f

Divakar Vashisth, Rohan Sharma, Tejas Ganesh Iyer, Tapan Mukerji

Seismic inversion-including post-stack, pre-stack, and full waveform inversion is compute and memory-intensive. Recently, several approaches, including physics-informed machine learning, have been developed to address some of these limitations. Motivated by the potential of quantum computing, we report on our attempt to map one such classical physics-informe

Shimiao Liu, Alexander Lerch

A fitting soundtrack can help a video better convey its content and provide a better immersive experience. This paper introduces a novel approach utilizing self-supervised learning and contrastive learning to automatically recommend audio for video content, thereby eliminating the need for manual labeling. We use a dual-branch cross-modal embedding model tha

Emil Toftegaard Gæde, Ivor van der Hoog, Eva Rotenberg, Tord Stordalen

Indexing data is a fundamental problem in computer science. Recently, various papers apply machine learning to this problem. For a fixed integer $\varepsilon$, a \emph{learned index} is a function $h : \mathcal{U} \rightarrow [0, n]$ where $\forall q \in \mathcal{U}$, $h(q) \in [\text{rank}(q) - \varepsilon, \text{rank}(q) + \varepsilon]$. These works use ma

Carlos Esparza

We show that smooth polarized Fano fibrations have no nontrivial finite covers. Using results by Sun-Zhang and Wylie, it follows that shrinking K\"ahler-Ricci solitons are simply-connected.

Benyamin Jamialahmadi, Parsa Kavehzadeh, Mehdi Rezagholizadeh, Parsa Farinneya

Deploying large language models (LLMs) in real-world applications is often hindered by strict computational and latency constraints. While dynamic inference offers the flexibility to adjust model behavior based on varying resource budgets, existing methods are frequently limited by hardware inefficiencies or performance degradation. In this paper, we introdu

Benjamin Moseley, Helia Niaparast, Karan Singh

Global minimum cut is a fundamental combinatorial optimization problem with wide-ranging applications. Often in practice, these problems are solved repeatedly on families of similar or related instances. However, the de facto algorithmic approach is to solve each instance of the problem from scratch discarding information from prior instances. In this paper,

Alexander Cowtan, Zhiyang He, Dominic J. Williamson, Theodore J. Yoder

Quantum code surgery is a flexible and low overhead technique for performing logical measurements on quantum error-correcting codes, which generalises lattice surgery. In this work, we present a code surgery scheme, applicable to any qubit stabiliser low-density parity check (LDPC) code, that fault-tolerantly measures many logical Pauli operators in parallel

Said Khaireddine, Redouane Assad, Mohammed El Falaki, Rachid Ahl Lamara

The magnetic properties of the double perovskite oxide $Sr_{2}$FeMo$O_{6}$ are analyzed using a mixed-spin Ising model with spins $\left( \frac{1}{2},\frac{5}{2}\right) $ in the presence of a random crystal field $\Delta$ and exchange interactions $ J $ on a three-dimensional (3D) cubic lattice. The study employs both the Mean-Field Approximation (MFA) based

Riccardo Falcone, Claudio Conti

The emission of light pulses is expected to generate gravitational waves, opening the possibility of controlling gravity in an Earthed laboratory. However, measuring the optically-driven spacetime deformations is challenging due to the inherently weak interaction. We explore the possibility to achieve a detectable gravitational effect from light emission by

Adrien Busnot Laurent, Oscar Cosserat

We exhibit a new pre-Lie algebra in the framework of symplectic groupoids and, in turn, introduce a pre-Lie formalism of Butcher trees for the approximation of Hamilton-Jacobi solutions on any symplectic groupoid $\mathcal{G} \rightrightarrows M.$ The impact of this new algebraic approach is twofold. On the geometric side, it yields algebraic operations to a

Trevor J. Weiss, Noah J. Downing, Marc H. Pinsonneault, Joel C. Zinn

The Galactic Bulge Time Domain Survey (GBTDS) of the Roman Space Telescope will take high cadence data of the Galactic bulge. We investigate the asteroseismic potential of this survey for red giants. We simulate the detectability of global asteroseismic frequencies, $\nu_{\mathrm{max}}$ and $\Delta\nu$, by modify ing Kepler data to match nominal GBTDS observ

Elena Wittemyer, Ananya Rao, Ian Abraham, Howie Choset

In this work, we consider the problem of multi-agent informative path planning (IPP) for robots whose sensor visibility continuously changes as a consequence of a time-varying natural phenomenon. We leverage ergodic trajectory optimization (ETO), which generates paths such that the amount of time an agent spends in an area is proportional to the expected inf

Paul J. Krassnig, Dieter P. Gruber

Automatic visual inspection using machine learning plays a key role in achieving zero-defect policies in industry. Research on anomaly detection is constrained by the availability of datasets that capture complex defect appearances and imperfect imaging conditions, which are typical of production processes. Recent benchmarks indicate that most publicly avail

Xuheng Cai, Erica Zhang

Egyptian hieroglyphs are found on numerous ancient Egyptian artifacts, but it is common that they are blurry or even missing due to erosion. Existing efforts to restore blurry hieroglyphs adopt computer vision techniques such as CNNs and model hieroglyph recovery as an image classification task, which suffers from two major limitations: (i) They cannot handl

Richa Namballa, Giovana Morais, Magdalena Fuentes

Musical source separation (MSS) has recently seen a big breakthrough in separating instruments from a mixture in the context of Western music, but research on non-Western instruments is still limited due to a lack of data. In this demo, we use an existing dataset of Brazilian sama percussion to create artificial mixtures for training a U-Net model to separat

Laura Zheng, Hamidreza Yaghoubi Araghi, Tony Wu, Sandeep Thalapanane

Trajectory forecasting has become a popular deep learning task due to its relevance for scenario simulation for autonomous driving. Specifically, trajectory forecasting predicts the trajectory of a short-horizon future for specific human drivers in a particular traffic scenario. Robust and accurate future predictions can enable autonomous driving planners to

Nacira Agram, Bernt Øksendal, Frank Proske, Olena Tymoshenko

This paper studies a nonzero-sum stochastic differential game in the context of shared spatial-domain pollution control. The pollution dynamics are governed by a stochastic partial differential equation (SPDE) driven by a Brownian sheet, capturing the stochastic nature of environmental fluctuations. Two players, representing different regions, aim to minimiz

Yifan Yang, Kai Zhen, Bhavana Ganesh, Aram Galstyan

Large Language Models (LLMs) pruning seeks to remove unimportant weights for inference speedup with minimal accuracy impact. However, existing methods often suffer from accuracy degradation without full-model sparsity-aware fine-tuning. This paper presents Wanda++, a novel pruning framework that outperforms the state-of-the-art methods by utilizing decoder-b

Khan Shaikhul Hadi, Naveed Ul Mustafa, Mark Heinrich, Yan Solihin

Compute Express Link (CXL) switch allows memory extension via PCIe physical layer to address increasing demand for larger memory capacities in data centers. However, CXL attached memory introduces 170ns to 400ns memory latency. This becomes a significant performance bottleneck for applications that host data in persistent memory as all updates, after travers

Mingchen Li, Heng Fan, Song Fu, Junhua Ding

Prompt privacy is crucial, especially when using online large language models (LLMs), due to the sensitive information often contained within prompts. While LLMs can enhance prompt privacy through text rewriting, existing methods primarily focus on document-level rewriting, neglecting the rich, multi-granular representations of text. This limitation restrict

Rebecca Umbach, Nicola Henry, Gemma Beard

Image-based sexual abuse (IBSA) refers to the nonconsensual creating, taking, or sharing of intimate images, including threats to share intimate images. Despite the significant harms of IBSA, there is limited data on its prevalence and how it affects different identity or demographic groups. This study examines prevalence of, impacts from, and responses to I

Zouhir Benrahla, Tristan Saide, Louis Burnaz, Emilie Verneuil

The sliding motion of aqueous droplets on hydrohobic surfaces leads to charge separation at the trailing edge, with implications from triple-line friction to hydrovoltaic energy generation. Charges deposited on the solid surface have been attributed to ions or electrons ripped off from the liquid drop. However, the dynamics and exact physicochemical nature o

Fengqin Zhou

We present novel algorithmic techniques to efficiently verify the Kruskal rank of matrices that arise in sparse linear regression, tensor decomposition, and latent variable models. Our unified framework combines randomized hashing techniques with dynamic programming strategies, and is applicable in various settings, including binary fields, general finite fi

Yue Lyu, Pengcheng An, Yage Xiao, Zibo Selena Zhang

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects how children communicate and relate to other people and the world around them. Emerging studies have shown that neurofeedback training (NFT) games are an effective and playful intervention to enhance social and attentional capabilities for autistic children. However, NFT is primaril

Boris Malashenko, Ivan Jarsky, Valeria Efimova

In recent years, rapid advances in computer vision have significantly improved the processing and generation of raster images. However, vector graphics, which is essential in digital design, due to its scalability and ease of editing, have been relatively understudied. Traditional vectorization techniques, which are often used in vector generation, suffer fr

Souvik Kundu, Anahita Bhiwandiwalla, Sungduk Yu, Phillip Howard

Despite recent efforts in understanding the compression impact on large language models (LLMs) in terms of their downstream task performance and trustworthiness on relatively simpler uni-modal benchmarks (for example, question answering, common sense reasoning), their detailed study on multi-modal Large Vision-Language Models (LVLMs) is yet to be unveiled. T

Jifan Zhang, Fangxin Wang, Zihe Song, Philip S. Yu

Stream networks, a unique class of spatiotemporal graphs, exhibit complex directional flow constraints and evolving dependencies, making uncertainty quantification a critical yet challenging task. Traditional conformal prediction methods struggle in this setting due to the need for joint predictions across multiple interdependent locations and the intricate

Doron Serebro, Tammy Riklin-Raviv

Medical imaging datasets often vary due to differences in acquisition protocols, patient demographics, and imaging devices. These variations in data distribution, known as domain shift, present a significant challenge in adapting imaging analysis models for practical healthcare applications. Most current domain adaptation (DA) approaches aim either to align

B. V. Voitsekhovskii, B. B. Voitsekhovskii

The results of an experiment with a generator of a stream of charged drops are reported. The glow of subjects placed in the stream is observed. The volume of the glowing region reaches 20 cm$^3$ at a current less than 20 $\mu$A through the object. Ideas are expressed concerning the connection between St$.$Elmo's fire and the observed glow.

Jake McNaughton, A. H. Abbas, Ivan S. Maksymov

The mathematical complexity and high dimensionality of neural networks slow both training and deployment, demanding heavy computational resources. This has driven the search for alternative architectures built from novel components, including new activation functions. Taking a different approach from state-of-the-art neural and neuromorphic computational sys