December 2024 arXiv papers — page 112

Dev Shah

Touch is one of the most intuitive ways for humans to interact with the world, and as we advance toward a ubiquitous computing environment where technology seamlessly integrates into daily life, natural interaction methods are essential. This paper introduces UbiTouch, a system leveraging thermal imaging to detect touch interactions on arbitrary surfaces. By

Xiaoming Wang

In many non-cooperative settings, agents often possess useful information that provide an advantage over their opponent(s), but acting on such information too frequently can lead to detection. I develop a simple framework to analyze such a trade-off and characterize the optimal way in which to act on information.

Harlan Campbell, Nicholas Latimer, Jeroen P Jansen, Shannon Cope

Randomized controlled trials (RCTs) in oncology often allow control group participants to crossover to experimental treatments, a practice that, while often ethically necessary, complicates the accurate estimation of long-term treatment effects. When crossover rates are high or sample sizes are limited, commonly used methods for crossover adjustment (such as

Leonardo Patimo

We give a new construction of Lascoux-Sch\"uetzenberger's charge statistic in type A which is motivated by the geometric Satake equivalence. We obtain a new formula for the charge statistic in terms of modified crystal operators and an independent proof of this formula which does not rely on tableaux combinatorics.

Grigory Kolesov

In the laser processing of glass, a ~50-1000 $\mu$m-thick layer of glass is heated to a high temperature by the laser beam. Due to the shallow depth of this hot layer, the infrared emission and absorption spectra may deviate from the black-body spectra and can be influenced by the vibrational structure of the material. Real-time time-dependent density functi

Haowu Duan, Alex Kovner, Michael Lublinsky

We continue exploring the Born-Oppenheimer renormalization group generating evolution in frequency of physical observables. In this paper we study the evolution of the total cross section for dilute-dilute scattering retaining only eikonal emissions. We derive and analyze the analog of the BFKL equation in this framework. The frequency evolution has a very s

Siyang Hu, Nick Wulbusch, Alexey Chernov, Tamara Bechtold

Depending on the frequency range of interest, finite element-based modeling of acoustic problems leads to dynamical systems with very high dimensional state spaces. As these models can mostly be described with second order linear dynamical system with sparse matrices, mathematical model order reduction provides an interesting possibility to speed up the simu

Mohammad Reza Samsami, Mats Leon Richter, Juan Rodriguez, Megh Thakkar

Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses. This paper investigates this interplay by analyzing how models of varying capacities within the same family handle intentionally misleading in-context information. Our experiments demonstrate that larger models exhibit hi

K. Z. Arellano-Córdova, F. Cullen, A. C. Carnall, D. Scholte

We present a spectroscopic analysis of two star-forming galaxies at $z\simeq5$ observed with JWST/NIRSpec as part of the EXCELS survey. The detection of the CIII]~$\lambda\lambda$1906,09, [OII] $\lambda\lambda$3726,29, [OIII] $\lambda\lambda$4363,5007, and [NII] $\lambda$6584 emission lines enables an investigation of the $\mathrm{C/O}$, $\mathrm{N/O}$, and

Maria Gillespie, Joseph Pappe, Kyle Salois

We investigate the problem of when a chromatic quasisymmetric function (CQF) $X_G(x;q)$ of a graph $G$ is in fact symmetric. We first prove the remarkable fact that if a product of two quasisymmetric functions $f$ and $g$ in countably infinitely many variables is symmetric, then in fact $f$ and $g$ must be symmetric. This allows the problem to be reduced to

Nadeera Meghapathirana, Oshada Rathnayake, Thisali S Rathnayake, Roshan Godaliyadda

The study of biomechanics during locomotion provides valuable insights into the effects of varying conditions on specific movement patterns. This research focuses on examining the influence of different shoe parameters on walking biomechanics, aiming to understand their impact on gait patterns. To achieve this, various methodologies are explored to estimate

Zhirui Liang, Qi Li, Anqi Liu, Yury Dvorkin

This paper presents an end-to-end framework for calibrating wind power forecast models to minimize operational costs in two-stage power markets, where the first stage involves a distributionally robust optimal power flow (DR-OPF) model. Unlike traditional methods that adjust forecast parameters and uncertainty quantification (UQ) separately, this framework j

Ahmed Mohamed Hussain, Nada Abughanam, Panos Papadimitratos

The deployment of the Internet of Things (IoT) in smart cities and critical infrastructure has enhanced connectivity and real-time data exchange but introduced significant security challenges. While effective, cryptography can often be resource-intensive for small-footprint resource-constrained (i.e., IoT) devices. Radio Frequency Fingerprinting (RFF) offers

Ashwani Pandey, Chen Hu, Jian-Min Wang, Bozena Czerny

In our previous study, we identified a shift in the synchrotron peak frequency of the blazar B2 1308$+$326 from 10$^{12.9}$ Hz to 10$^{14.8}$ Hz during a flare, suggesting it could be a changing-look blazar (CLB). In this work, we investigate the CL behaviour of B2 1308+326 by analysing a newly acquired optical spectrum and comparing it with an archival spec

Madison L. Lytle, Efstathios G. Charalampidis, Dionyssios Mantzavinos, Jesus Cuevas-Maraver

In this work, we investigate the formation of time-periodic solutions with a non-zero background that emulate rogue waves, known as Kuzentsov-Ma (KM) breathers, in physically relevant lattice nonlinear dynamical systems. Starting from the completely integrable Ablowitz-Ladik (AL) model, we demonstrate that the evolution of KM initial data is proximal to that

M. Spieker, S. Almaraz-Calderon

Since its foundation in the 1960s, the John D. Fox Superconducting Linear Accelerator Laboratory at Florida State University (FSU) pursued research at the forefront of nuclear science. In this contribution, we present recent highlights from nuclear structure and reaction studies conducted at the John D. Fox Superconducting Linear Accelerator Laboratory, also

Francesco Giovanni Celiberto, Gabriele Gatto

We study the semi-inclusive hadroproduction of doubly bottomed tetraquarks ($X_{b\bar{b}q\bar{q}}$) as well as fully bottomed ones ($T_{4b}$), to which we collectively refer as "bottomoniumlike" states. We rely upon the variable-flavor number-scheme fragmentation at leading power, where a single parton perturbatively splits into the corresponding Fock state,

Giovanni Baccichet, Fabio Palmese, Alessandro E. C. Redondi, Matteo Cesana

Probe Requests are Wi-Fi management frames periodically sent by devices during network discovery. Tracking Probe Requests over time offers insights into movement patterns, traffic flows, and behavior trends, which are keys in applications such as urban planning, human mobility analysis, and retail analytics. To protect user privacy, techniques such as MAC ad

Juan C. Herrera, Laura L. Sandoval, Piyush Kumar, Sanjay S. Kumar

Temperature gradients developed at ultra-high temperatures create a challenge for temperature measurements that are required for material processing. At ultra-high temperatures, the components of the system can react and change phases depending on their thermodynamic stability. These reactions change the system's physical properties, such as thermal conducti

Daniel Alabi, Eugene Wu

The emerging discipline of Computational Science is concerned with using computers to simulate or solve scientific problems. These problems span the natural, political, and social sciences. The discipline has exploded over the past decade due to the emergence of larger amounts of observational data and large-scale simulations that were previously unavailable

Brandon Gower-Winter, Georg Krempl, Sergey Dragomiretskiy, Tineke Jelsma

Concept Drift has been extensively studied within the context of Stream Learning. However, it is often assumed that the deployed model's predictions play no role in the concept drift the system experiences. Closer inspection reveals that this is not always the case. Automated trading might be prone to self-fulfilling feedback loops. Likewise, malicious entit

Naomi Giertych, Jonathan P Williams, Sujit Ghosh

Astronomers often deal with data where the covariates and the dependent variable are measured with heteroscedastic non-Gaussian error. For instance, while TESS and Kepler datasets provide a wealth of information, addressing the challenges of measurement errors and systematic biases is critical for extracting reliable scientific insights and improving machine

Siddhant Ray, Rui Pan, Zhuohan Gu, Kuntai Du

RAG (Retrieval Augmented Generation) allows LLMs (large language models) to generate better responses with external knowledge, but using more external knowledge often improves generation quality at the expense of response delay. Prior work either reduces the response delay (through better scheduling of RAG queries) or strives to maximize quality (which invol

Minsu Park, Masafumi Udagawa, Eun-Gook Moon

A quantum spin liquid hosts massive quantum entanglement whose identification is one of the most significant problems in physics. Yet, its detection is known to be notoriously difficult because of featureless properties without a symmetry order parameter. Here, we demonstrate dynamic signatures of a quantum spin liquid state by investigating Kitaev's spin mo

Byunghyun Lee, Hwanjin Kim, David J. Love, James V. Krogmeier

Integrated sensing and communications (ISAC) is a key enabler of new applications, such as precision agriculture, extended reality (XR), and digital twins, for 6G wireless systems. However, the implementation of ISAC technology is very challenging due to practical constraints such as high complexity. In this paper, we introduce a novel ISAC waveform design s

Soroush Omranpour, Guillaume Rabusseau, Reihaneh Rabbany

In this paper, we tackle the challenge of predicting stock movements in financial markets by introducing Higher Order Transformers, a novel architecture designed for processing multivariate time-series data. We extend the self-attention mechanism and the transformer architecture to a higher order, effectively capturing complex market dynamics across time and

Viviana Gomez, Maria Jose Saenz, Johanns Canaval, Liliana Cuadrado

Bismuth Telluride (Bi$_2$Te$_3$) is a widely studied topological insulator, recognized for its unique surface states, low electronic bandgap, and low thermal conductivity. In this study, we characterize exfoliated Bi$_2$Te$_3$ dispersions produced via solvothermal intercalation, where ferromagnetism was measured at room temperature. DFT simulations show that

Martins Samuel Dogo

Mis- and disinformation, commonly collectively called fake news, continue to menace society. Perhaps, the impact of this age-old problem is presently most plain in politics and healthcare. However, fake news is affecting an increasing number of domains. It takes many different forms and continues to shapeshift as technology advances. Though it arguably most

Mohamed Debbagh, Shangpeng Sun, Mark Lefsrud

Accurate predictions and representations of plant growth patterns in simulated and controlled environments are important for addressing various challenges in plant phenomics research. This review explores various works on state-of-the-art predictive pattern recognition techniques, focusing on the spatiotemporal modeling of plant traits and the integration of

Jinnan Guo, Kapil Vaswani, Andrew Paverd, Peter Pietzuch

In federated learning (FL), data providers jointly train a machine learning model without sharing their training data. This makes it challenging to provide verifiable claims about the trained FL model, e.g., related to the employed training data, any data sanitization, or the correct training algorithm-a malicious data provider can simply deviate from the co

Kelsey Doerksen, Casper Fibaek, Rochelle Schneider, Do-Hyung Kim

Internet connectivity in schools is critical to provide students with the digital literary skills necessary to compete in modern economies. In order for governments to effectively implement digital infrastructure development in schools, accurate internet connectivity information is required. However, traditional survey-based methods can exceed the financial

Luca Rossetto, Klaus Schoeffmann, Cathal Gurrin, Jakub Lokoč

This report presents the results of the 13th Video Browser Showdown, held at the 2024 International Conference on Multimedia Modeling on the 29th of January 2024 in Amsterdam, the Netherlands.

Gevin von Witte, Konstantin Tamarov, Neva Sahin, Aaron Himmler

Hyperpolarized nanoparticles (NPs) offer high polarization levels with room temperature relaxation times exceeding half an hour. In this work, we demonstrate that the achievable hyperpolarization enhancement and relaxation (decay) time at room temperature are largely independent of the particle size contrary to previous assumptions. This is explained through

April Yang, Jordan Tab, Parth Shah, Paul Kotchavong

The increasing reliance on large language models (LLMs) for diverse applications necessitates a thorough understanding of their robustness to adversarial perturbations and out-of-distribution (OOD) inputs. In this study, we investigate the correlation between adversarial robustness and OOD robustness in LLMs, addressing a critical gap in robustness evaluatio

Akash Yadav

In this paper, we prove that there exist Whittaker and Schwartz functions such that the local Flicker integrals are non-vanishing for all complex values of $s$, and the local Bump-Friedberg integrals are non-vanishing for all complex pairs $(s_1,s_2)$. As a corollary, we determine the potential locations of poles for their corresponding partial $L$-functions

Yufan Zhou, Ruiyi Zhang, Jiuxiang Gu, Nanxuan Zhao

We present SUGAR, a zero-shot method for subject-driven video customization. Given an input image, SUGAR is capable of generating videos for the subject contained in the image and aligning the generation with arbitrary visual attributes such as style and motion specified by user-input text. Unlike previous methods, which require test-time fine-tuning or fail

Filip Blašković, Tim O. F. Conrad, Stefan Klus, Nataša Djurdjevac Conrad

The evolution of many dynamical systems that describe relationships or interactions between objects can be effectively modeled by temporal networks, which are typically represented as a sequence of static network snapshots. In this paper, we introduce a novel random walk-based approach that can identify clusters of time-snapshots in which network community s

Kyoung Yeon Kim

The Schrodinger equation is incomplete, inherently unable to explain the collapse of the wavefunction caused by measurement; a fundamental issue known as the quantum measurement problem. Quantum mechanics is generally constrained by the uncertainty principle and, therefore, cannot interpret definite observations without uncertainty. Here, we resolve this eni

Herman Chau

The higher Bruhat orders $\mathcal{B}(n,k)$ were introduced by Manin-Schechtman to study discriminantal hyperplane arrangements and subsequently studied by Ziegler, who connected $\mathcal{B}(n,k)$ to oriented matroids. In this paper, we consider the enumeration of $\mathcal{B}(n,k)$ and improve upon Balko's asymptotic lower and upper bounds on $|\mathcal{B}

Marek Miltner, Jakub Zíka, Daniel Vašata, Artem Bryksa

This study addresses the challenge of predicting electric vehicle (EV) charging profiles in urban locations with limited data. Utilizing a neural network architecture, we aim to uncover latent charging profiles influenced by spatio-temporal factors. Our model focuses on peak power demand and daily load shapes, providing insights into charging behavior. Our r

Kyle Dunlap, Nathaniel Hamilton, Kerianne L. Hobbs

As the number of spacecraft in orbit continues to increase, it is becoming more challenging for human operators to manage each mission. As a result, autonomous control methods are needed to reduce this burden on operators. One method of autonomous control is Reinforcement Learning (RL), which has proven to have great success across a variety of complex tasks

Shambhavi Krishna, Aishwarya Sahoo

Collecting high-quality preference datasets for reinforcement learning from human feedback (RLHF) is resource-intensive and challenging. As a result, researchers often train reward models on extensive offline datasets which aggregate diverse generation sources and scoring/alignment policies. We hypothesize that this aggregation has an averaging effect on rew

Martina Cádiz-Leyton, Guillermo Cabrera-Vives, Pavlos Protopapas, Daniel Moreno-Cartagena

Classifying variable stars is key for understanding stellar evolution and galactic dynamics. With the demands of large astronomical surveys, machine learning models, especially attention-based neural networks, have become the state-of-the-art. While achieving high accuracy is crucial, enhancing model interpretability and uncertainty estimation is equally imp

Maite Fernández-Unzueta

Given a Banach space $X$ and $d\in \mathbb{N}$, we construct a metric space $\mathbb{V}_X^d$ with the property that every $d$-homogeneous polynomial defined on $X$ factors through a Lipschitz map on it. We prove that the metric on $\mathbb{V}_X^d$ is independent (up to a constant) of the norm of the tensor space in which it is embedded. We apply this fact to

Janet Zhong, Heming Wang, Alexander N Poddubny, Shanhui Fan

We numerically verify and analytically prove a winding number invariant that correctly predicts the number of edge states in one-dimensional, nearest-neighbor (between unit cells), two-band models with any complex couplings and open boundaries. Our winding number uses analytical continuation of the wave-vector into the complex plane and involves two special

Rahul Harsha Cheppally, Ajay Sharda

Crop row detection enables autonomous robots to navigate in gps denied environments. Vision based strategies often struggle in the environments due to gaps, curved crop rows and require post-processing steps. Furthermore, labeling crop rows in under the canopy environments accurately is very difficult due to occlusions. This study introduces RowDetr, an effi

Minhyeok Lee

The increasing reliance on digital platforms shapes how individuals understand the world, as recommendation systems direct users toward content "similar" to their existing preferences. While this process simplifies information retrieval, there is concern that it may foster insular communities, so-called echo chambers, reinforcing existing viewpoints and limi

Changan Chen, Juze Zhang, Shrinidhi K. Lakshmikanth, Yusu Fang

Human communication is inherently multimodal, involving a combination of verbal and non-verbal cues such as speech, facial expressions, and body gestures. Modeling these behaviors is essential for understanding human interaction and for creating virtual characters that can communicate naturally in applications like games, films, and virtual reality. However,

Tejaswi K. C., Taeyoung Lee

Mean field games and controls involve guiding the behavior of large populations of interacting agents, where each individual's influence on the group is negligible but collectively impacts overall dynamics. Hybrid systems integrate continuous dynamics with discrete transitions, effectively modeling the complex interplay between continuous flows and instantan

Mara Sherlin D. Talento, Sarbojit Roy, Tania Reyes Vallejo, Leena A Ibrahim

This work is inspired by the problem of characterizing a dependence measure between two cortical regions of the brain where each region contains multiple signal recordings from several neurons or channels (e.g., inhibitory and excitatory neurons). The goal is to identify differences in the structure of brain functional connectivity between known brain states

Sarah J. Hamilton, Peter Muller, Ville Kolehmainen, Jussi Toivanen

The first partial boundary data complex geometrical optics based methods for electrical impedance tomography in three dimensions are developed, and tested, on simulated and experimental data. The methods provide good localization of targets for both absolute and time-difference imaging, when large portions of the domain are inaccessible for measurement. As m

Tejaswi K. C., Maneesha Wickramasuriya, Silvere Bonnabel, Axel Barrau

This paper develops a geometric framework for invariant filtering of relative dynamics on Lie groups. We first revisit the notion of state trajectory independence, under which the estimation error evolves autonomously, and derive new equivalent conditions by decomposing the system vector field into left-invariant, intrinsic, and right-invariant components. B

Yerlan Myrzakulov, M. Koussour, A. Caliskan, E. Güdekli

In this paper, we investigate the freezing quintessence scenario in late-time cosmic expansion using a non-linear $f(R, L_m)$ gravity model, $f(R,L_m)=\frac{R}{2}+L_m^\alpha$, where $\alpha$ is a free parameter. We consider a solution for this model using an appropriate parametrization of the scale factor, and then the model is constrained by observational d

Tejaswi K. C., William Clark, Taeyoung Lee

Stochastic hybrid systems are dynamic systems that undergo both random continuous-time flows and random discrete jumps. Depending on how randomness is introduced into the continuous dynamics, discrete transitions, or both, stochastic hybrid systems exhibit distinct characteristics. This paper investigates the role of uncertainties in the interplay between co

Daniel Wines, Kamal Choudhary

In this work, we introduce CHIPS-FF (Computational High-Performance Infrastructure for Predictive Simulation-based Force Fields), a universal, open-source benchmarking platform for machine learning force fields (MLFFs). This platform provides robust evaluation beyond conventional metrics such as energy, focusing on complex properties including elastic consta

Jose Cuaran, Kulbir Singh Ahluwalia, Kendall Koe, Naveen Kumar Uppalapati

Semantic maps are fundamental for robotics tasks such as navigation and manipulation. They also enable yield prediction and phenotyping in agricultural settings. In this paper, we introduce an efficient and scalable approach for active semantic mapping in horticultural environments, employing a mobile robot manipulator equipped with an RGB-D camera. Our meth

Nolwenn Bernard, Hideaki Joko, Faegheh Hasibi, Krisztian Balog

We introduce CRS Arena, a research platform for scalable benchmarking of Conversational Recommender Systems (CRS) based on human feedback. The platform displays pairwise battles between anonymous conversational recommender systems, where users interact with the systems one after the other before declaring either a winner or a draw. CRS Arena collects convers

Ana Ozaki, Roberto Confalonieri, Ricardo Guimarães, Anders Imenes

Decision trees are a popular machine learning method, known for their inherent explainability. In Explainable AI, decision trees can be used as surrogate models for complex black box AI models or as approximations of parts of such models. A key challenge of this approach is determining how accurately the extracted decision tree represents the original model

Albert Cheu, Debanuj Nayak

Many algorithms have been developed to estimate probability distributions subject to differential privacy (DP): such an algorithm takes as input independent samples from a distribution and estimates the density function in a way that is insensitive to any one sample. A recent line of work, initiated by Raskhodnikova et al. (Neurips '21), explores a weaker ob

Joshua Adrian Cahyono, Jeremy Nathan Jusuf

This project aims to create an automated image captioning system that generates natural language descriptions for input images by integrating techniques from computer vision and natural language processing. We employ various different techniques, ranging from CNN-RNN to the more advanced transformer-based techniques. Training is carried out on image datasets

Tobias Braun, Mark Rothermel, Marcus Rohrbach, Anna Rohrbach

The proliferation of disinformation demands reliable and scalable fact-checking solutions. We present Dynamic Evidence-based FAct-checking with Multimodal Experts (DEFAME), a modular, zero-shot MLLM pipeline for open-domain, text-image claim verification. DEFAME operates in a six-stage process, dynamically selecting the tools and search depth to extract and

Maria Victoria Carro, Francisca Gauna Selasco, Denise Alejandra Mester, Margarita Gonzales

Causal learning is the cognitive process of developing the capability of making causal inferences based on available information, often guided by normative principles. This process is prone to errors and biases, such as the illusion of causality, in which people perceive a causal relationship between two variables despite lacking supporting evidence. This co

N. Jannsen, A. Tkachenko, P. Royer, J. De Ridder

With ESA's PLATO space mission set for launch in December 2026, a new photometric legacy and a future of new scientific discoveries await. In this work we investigate PLATO's potential for observing pulsating stars across the Hertzsprung-Russell diagram as part of the PLATO Complimentary Science program (PLATO-CS). Specifically, a PLATO mock asteroseismic ca

Navnil Choudhury, Chaithanya Naik Mude, Sanjay Das, Preetham Chandra Tikkireddi

As quantum computing rapidly advances, its near-term applications are becoming increasingly evident. However, the high cost and under-utilization of quantum resources are prompting a shift from single-user to multi-user access models. In a multi-tenant environment, where multiple users share one quantum computer, protecting user confidentiality becomes cruci

Lukas Neumann, Maria J. Jimenez-Donaire, Adam K. Leroy, Frank Bigiel

Dense, cold gas is the key ingredient for star formation. Over the last two decades, HCN(1-0) emission has been utilised as the most accessible dense gas tracer to study external galaxies. We present new measurements tracing the relationship between dense gas tracers, bulk molecular gas tracers, and star formation in the ALMA ALMOND survey, the largest sampl

Kai-Siang Chen, Gelo Noel M. Tabia, Chung-Yun Hsieh, Yu-Chun Yin

As a striking manifestation of quantum entanglement, nonlocality has long played a pivotal role in shaping our understanding of the quantum world. When considering a Bell test involving three parties, we may even find a remarkable situation where the nonlocality in two bipartite subsystems {\em forces} the remaining bipartite subsystem to exhibit nonlocality

Oz Amram, Luca Anzalone, Joschka Birk, Darius A. Faroughy

Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrates how data collected by the CMS experiment at the Large Hadron Collider can be useful in pre-training foundation models for HEP. Specifically, we introduce the AspenOpenJets datas

Chris Hull, Ulf Lindström, Maxwell L. Velásquez Cotini Hutt

The free graviton theory given by linearising Einstein's theory has a dual formulation in terms of a dual graviton field. The dual graviton theory has two gauge invariances giving rise to two conserved charges, while the ADM charges of the graviton theory become magnetic charges for the dual graviton theory. These charges can be ill-defined in topologically

Tymoteusz Tula, Jorge Quintanilla, Gunnar Möller

Recently, a direct connection between static structure factors and quantum ground states for two-spin interaction Hamiltonians was proven. This suggests the possibility of quantum state tomography from neutron scattering. Here, we investigate the associated fitness landscape numerically. We find a linear relationship between the mean square distances of the

Diganta Bandopadhyay, Christopher J. Moore

This paper reports the first search for stellar-origin binary black holes within the LISA Data Challenges (LDC). The search algorithm and the \Yorsh{} LDC datasets, both previously described elsewhere, are only summarized briefly; the primary focus here is to present the results of applying the search to the challenge of data. The search employs a hierarchic

Azaz Ahmad, Gargee Sharma

Chiral anomaly (CA), a hallmark of Weyl fermions, has emerged as a cornerstone of condensed matter physics following the discovery of Weyl semimetals. While the anomaly in pseudospin-1/2 (Weyl) systems is well-established, its extension to higher-pseudospin fermions remains a frontier with critical implications for transport phenomena in materials with multi

Ludo Fraser-Taliente, John Wheater

We show that the conformal data of a range of large-$N$ CFTs, the melonic CFTs, are specified by constrained extremization of the universal part of the sphere free energy $F=-\log Z_{S^d}$, called $\tilde{F}$. This family includes the generalized SYK models, the vector models (O$(N)$, Gross-Neveu, etc.), and the tensor field theories. The known $F$ and $a$-m

Rohit Mukherjee, Haoyu Guo, Debanjan Chowdhury

Periodically driven Floquet quantum many-body systems have revealed new insights into the rich interplay of thermalization, and growth of entanglement. The phenomenology of dynamical freezing, whereby a translationally invariant many-body system exhibits emergent conservation laws and a slow growth of entanglement entropy at certain fixed ratios of a drive a

Vincenzo Cirigliano, Wouter Dekens, Sebastián Urrutia Quiroga

We study neutrinoless double-beta decay in extensions of the Standard Model that include $n$ right-handed neutrino singlets, with masses $m_s$ below the GeV scale. Generalizing recently developed matching methods, we determine the $m_s$ dependence of the short-range $nn \to pp$ couplings that appear to leading order in the chiral effective field theory descr

Paul Romatschke

In this work, I consider N-component scalar quantum field theory in two dimensions interacting with an upside-down quartic potential. Working in the large N limit, the model can be solved non-perturbatively using the saddle-point method for sufficiently strong negative coupling. At high temperature, the O(N) model dimensionally reduces to ${\cal PT}$-symmetr

Jacopo Azzola, Oleksii Matsedonskyi, Andreas Weiler

The Standard Model extended by a real scalar singlet $S$ with an approximate $\mathbb{Z}_2$ symmetry offers a minimal framework for realizing electroweak baryogenesis (EWBG) during a first-order electroweak phase transition. In this work, we explore a novel mechanism where spontaneous $\mathbb{Z}_2$ breaking enables EWBG via domain walls separating two disti

Yushu Wu, Zhixing Zhang, Yanyu Li, Yanwu Xu

We have witnessed the unprecedented success of diffusion-based video generation over the past year. Recently proposed models from the community have wielded the power to generate cinematic and high-resolution videos with smooth motions from arbitrary input prompts. However, as a supertask of image generation, video generation models require more computation

Sicheng Zuo, Wenzhao Zheng, Yuanhui Huang, Jie Zhou

3D occupancy prediction is important for autonomous driving due to its comprehensive perception of the surroundings. To incorporate sequential inputs, most existing methods fuse representations from previous frames to infer the current 3D occupancy. However, they fail to consider the continuity of driving scenarios and ignore the strong prior provided by the

Runtao Liu, I Chieh Chen, Jindong Gu, Jipeng Zhang

Text-to-image (T2I) models are widespread, but their limited safety guardrails expose end users to harmful content and potentially allow for model misuse. Current safety measures are typically limited to text-based filtering or concept removal strategies, able to remove just a few concepts from the model's generative capabilities. In this work, we introduce

Muhammad Uzair Khattak, Shahina Kunhimon, Muzammal Naseer, Salman Khan

Vision-Language Models (VLMs) trained via contrastive learning have achieved notable success in natural image tasks. However, their application in the medical domain remains limited due to the scarcity of openly accessible, large-scale medical image-text datasets. Existing medical VLMs either train on closed-source proprietary or relatively small open-source

Wenzhao Zheng, Junjie Wu, Yao Zheng, Sicheng Zuo

Vision-based autonomous driving shows great potential due to its satisfactory performance and low costs. Most existing methods adopt dense representations (e.g., bird's eye view) or sparse representations (e.g., instance boxes) for decision-making, which suffer from the trade-off between comprehensiveness and efficiency. This paper explores a Gaussian-centri

Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis

We investigate some previously unexplored (or underexplored) computational aspects of total variation (TV) distance. First, we give a simple deterministic polynomial-time algorithm for checking equivalence between mixtures of product distributions, over arbitrary alphabets. This corresponds to a special case, whereby the TV distance between the two distribut

Coleman Haley, Sharon Goldwater, Edoardo Ponti

We propose a grounded approach to meaning in language typology. We treat data from perceptual modalities, such as images, as a language-agnostic representation of meaning. Hence, we can quantify the function--form relationship between images and captions across languages. Inspired by information theory, we define "groundedness", an empirical measure of conte

Dalia Hernandez, Soham Biswas

We present the first example where length scale for the growth of ordered regions and the correlation length for the two point correlations of persistent sites scale differently with time. We do so by studying a global spin exchange dynamics in one dimension where a selected spin interacts with its two nearest domains. We found domain growth exponent $z=2.47

Konstantinos Dimopoulos, Anish Ghoshal, Theodoros Papanikolaou

Considering inflationary magnetogenesis induced by time-dependent kinetic and axial couplings of a massless Abelian vector boson field breaking the conformal invariance we show in this article that, surprisingly, the spectral shape of the large-scale primordial magnetic field power spectrum is insensitive to the post-inflationary history, namely the barotrop

Marek Lewicki, Piotr Toczek, Ville Vaskonen

We study realistic models predicting primordial black hole (PBH) formation from density fluctuations generated in a first-order phase transition. We show that the second-order correction in the expansion of the bubble nucleation rate is necessary for accurate predictions and quantify its impact on the abundance of PBHs and gravitational waves (GWs). We find

Marcela Carena, Ying-Ying Li, Tong Ou, Hersh Singh

Motivated by the out-of-equilibrium dynamics during an early-universe first-order phase transition, we perform real-time simulations of fermion-bubble scattering in $1+1$ dimensions. This nonequilibrium process can generate a charge-conjugation $\mathsf{C}$ asymmetry outside the bubble wall, induced by the complex fermion mass profile. The resulting $\mathsf

Saskia Demulder, Dieter Lust, Thomas Raml

The Swampland Distance Conjecture postulates the emergence of an infinite tower of massless states when approaching infinite-distance points in moduli space. However, most string backgrounds are supported by fluxes, and therefore depart from the purely geometric paradigm. This fact requires an extension of the Swampland conjectures to scalar field spaces wit

Zhuolin Li, Bogdan Raiţă

We prove that minimizers of variational problems on open sets $\Omega \subset \mathbb{R}^n$ $$ \mbox{minimize}\quad \mathcal E(v)=\int_\Omega f(v(x))\mathrm{d} x\quad\text{for } \mathscr{A} v=0, $$ are partially continuous provided that the integrands $f$ are strongly $\mathscr{A}$-quasiconvex in a suitable sense. We consider $p$-growth problems with $1<p<\i

Guido De Marchi, Giovanna Giardino, Katia Biazzo, Nino Panagia

Previous Hubble Space Telescope (HST) observations of the star-forming cluster NGC 346 in the Small Magellanic Cloud (SMC) had revealed a large population of pre-main sequence (PMS) candidates, characterised by Halpha excess emission in their photometry. However, without access to spectroscopy, the nature of these objects remained unclear. Using the NIRSpec

Samuel Price, Zhaoxi Cao, Ian McCue

Functional gradients have recently experienced an explosion in activity due to advances in manufacturing, where compositions can now be spatially varied on-the-fly during fabrication. In addition, modern computational thermodynamics has reached sufficient maturity -- with respect to property databases and the availability of commercial software -- that gradi

Orr Zohar, Xiaohan Wang, Yann Dubois, Nikhil Mehta

Despite the rapid integration of video perception capabilities into Large Multimodal Models (LMMs), the underlying mechanisms driving their video understanding remain poorly understood. Consequently, many design decisions in this domain are made without proper justification or analysis. The high computational cost of training and evaluating such models, coup

Lyne Moser, Joost Nuiten

In this paper, we construct a model structure for $(\infty,1)$-categories on the category of simplicial spaces, whose fibrant objects are the Segal spaces. In particular, we show that it is Quillen equivalent to the models of $(\infty,1)$-categories given by complete Segal spaces and Segal categories. We furthermore prove that this model structure has desira

Joachim König, Hanson Smith, Zack Wolske

Let $K$ be a number field with ring of integers $\mathcal{O}_K$, and let $f(x)\in\mathcal{O}_K[x]$ be a monic, irreducible polynomial. We establish necessary and sufficient conditions in terms of the critical points of $f(x)$ for the iterates of $f(x)$ to be monogenic polynomials. More generally, we give necessary and sufficient conditions for the backwards

Christian Janos Lebeda, Lukas Retschmeier

We consider the problem of releasing a sparse histogram under $(\varepsilon, \delta)$-differential privacy. The stability histogram independently adds noise from a Laplace or Gaussian distribution to the non-zero entries and removes those noisy counts below a threshold. Thereby, the introduction of new non-zero values between neighboring histograms is only r

Xuhao Wan, Subhasish Mandal, Yuzheng Guo, Kristjan Haule

Altermagnets (AM) are a novel class of magnetic materials with zero net magnetization but broken time-reversal symmetry and spin-split bands exceeding the spin-orbit coupling scale, offering unique control of individual spin-channel and high charge-spin conversion efficiency for spintronic applications. Still, only a few metallic altermagnets have been ident

T. J. Davidge

Photographic spectra of Plaskett's Star (PS; HR2420, HD47129, V640 Mon) that were recorded at the Dominion Astrophysical Observatory (DAO) have been digitized with a flatbed scanner. Many of the spectra were recorded during campaigns in 1922 and 1937, and sample wavelengths between 0.39 and 0.50um. Spectra of poor quality are identified. Mean spectra near or

Jean Kossaifi, Nikola Kovachki, Zongyi Li, David Pitt

We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimensional Euclidean spaces. They can be trained and inferenced on input and output functions given at various discretizations, satisfying a discretization convergence properties. Part

Francesco Villani, Igor Maljkovic, Dario Lazzaro, Angelo Sotgiu

Deep Neural Networks are vulnerable to adversarial examples, i.e., carefully crafted input samples that can cause models to make incorrect predictions with high confidence. To mitigate these vulnerabilities, adversarial training and detection-based defenses have been proposed to strengthen models in advance. However, most of these approaches focus on a singl

Manjunath Omana Kuttan, Kai Zhou, Jan Steinheimer, Horst Stoecker

A novel point cloud diffusion model for relativistic heavy-ion collisions, capable of ultra-fast generation of complete, event-by-event collision output, is introduced. When trained on UrQMD cascade simulations, the model generates realistic collision event output containing 26 distinct hadron species, as a list of particle momentum vectors along with their