March 2025 arXiv papers — page 145

Dickson Akuoko Sarpong, Adam Kamrath, Rohit Bhusal, Hongzhi Guo

Near Field Communication (NFC) is widely used in security applications such as door access systems and ID cards. However, clone attacks can replicate digital information, enabling unauthorized access. RF fingerprinting offers a robust defense by extracting unique physical-layer features from NFC cards that cannot be cloned. While RF fingerprinting has been e

Diego Gomez-Zara, Carolina Perez-Arredondo, Denis Parra

This study examines the communication mechanisms that shape the formation of digitally-enabled mobilization networks. Informed by the logic of connective action, we postulate that the emergence of networks enabled by organizations and individuals is differentiated by network and framing mechanisms. From a case comparison within two mobilization networks -- o

Riku Takahashi, Ryugo Morita, Fuma Kimishima, Kosuke Iwama

Existing deep facial animation coding techniques efficiently compress talking head videos by applying deep generative models. Instead of compressing the entire video sequence, these methods focus on compressing only the keyframe and the keypoints of non-keyframes (target frames). The target frames are then reconstructed by utilizing a single keyframe, and th

Daniel F. Villarraga, Ricardo A. Daziano

We introduce a novel model architecture that incorporates network effects into discrete choice problems, achieving higher predictive performance than standard discrete choice models while offering greater interpretability than general-purpose flexible model classes. Econometric discrete choice models aid in studying individual decision-making, where agents s

Patrick Aigner, Luigi Bellafronte, Emanuele Gendy, Dominik Haslehner

We present a systematic study of one-loop quantum corrections in scalar effective field theories from a geometric viewpoint, emphasizing the role of field-space curvature and its renormalisation. By treating the scalar fields as coordinates on a Riemannian manifold, we exploit field redefinition invariance to maintain manifest coordinate independence of phys

Shucheta Shegufta, Michael Zaiser

A particular failure mode of highly porous brittle materials consists in the propagation of cracks under uniaxial compressive loads. Such 'anticracks' have been observed in a range of materials, from snow and porous sandstone to brittle foams. Here we present a computational model for the formation and propagation of anticrack-type failure in porous material

Daniel Alvarez-Gavela, Tim Large, Abigail Ward

For a Weinstein manifold, we compare and contrast the properties of admitting an arboreal skeleton and admitting Maslov data. Both properties are implied by the existence of a polarization, for which a basic obstruction is that the odd Chern classes are 2-torsion. In a similar spirit we establish cohomological obstructions to the existence of arboreal skelet

N. V. Bakholdin, V. A. Chernienko, S. A. Bakhurin

Farrow filters allow to develop universal oversamplers for fractional delay correction and sampling rate conversion. The use of cubic Lagrangian interpolation in Farrow filter does not allow to provide processing bandwidth more than 0.4 of sampling frequency, and also does not guarantee smoothness of impulse response of the interpolator. In this paper, we pr

William L. Tong, Cengiz Pehlevan

Equality reasoning is ubiquitous and purely abstract: sameness or difference may be evaluated no matter the nature of the underlying objects. As a result, same-different (SD) tasks have been extensively studied as a starting point for understanding abstract reasoning in humans and across animal species. With the rise of neural networks that exhibit striking

Arman Zharmagambetov, Chuan Guo, Ivan Evtimov, Maya Pavlova

Autonomous AI agents that can follow instructions and perform complex multi-step tasks have tremendous potential to boost human productivity. However, to perform many of these tasks, the agents need access to personal information from their users, raising the question of whether they are capable of using it appropriately. In this work, we introduce a new ben

Vasileios Chousionis, Sean Li, Lingxiao Zhang

We study singular integral operators induced by Calder\'on-Zygmund kernels in any step-$2$ Carnot group $\mathbb{G}$. We show that if such an operator satisfies some natural cancellation conditions then it is $L^2$ bounded on all intrinsic graphs of $C^{1,\alpha}$ functions over vertical hyperplanes that do not have rapid growth at $\infty$. In particular, t

A. Archer, P. Bangale, J. T. Bartkoske, W. Benbow

Observations of GeV gamma-ray emission from the well-studied mixed-morphology supernova remnant (SNR) W44 by Fermi-LAT and AGILE imply that it is a site of significant cosmic ray acceleration. The spectral energy distribution (SED) derived from the GeV data suggest that the gamma-ray emission likely originates from the decay of neutral pions generated by cos

Hamad Yahya, Matteo Nerini, Bruno Clerckx, Merouane Debbah

This work develops a physically consistent model for stacked intelligent metasurfaces (SIM) using multiport network theory and transfer scattering parameters (T-parameters). Unlike the scattering parameters (S-parameters) model, the developed T-parameters model is simpler and more tractable. Moreover, the T-parameters constraints for lossless reciprocal reco

Aaron Welch, Mariam Kiran

As quantum networking continues to grow in importance, its study is of interest to an ever wider community and at an increasing scale. However, the development of its physical infrastructure remains burdensome, and services providing third party access are not enough to meet demand. A variety of simulation frameworks provide a method for testing aspects of s

Anmol Dwivedi, Ali Tajer

This paper introduces a data-driven graphical framework for the real-time search of risky cascading fault chains (FCs) in power-grids, crucial for enhancing grid resiliency in the face of climate change. As extreme weather events driven by climate change increase, identifying risky FCs becomes crucial for mitigating cascading failures and ensuring grid stabi

Luyang Fang, Ehsan Latif, Haoran Lu, Yifan Zhou

Automatic scoring of student responses enhances efficiency in education, but deploying a separate neural network for each task increases storage demands, maintenance efforts, and redundant computations. To address these challenges, this paper introduces the Gromov-Wasserstein Scoring Model Merging (GW-SMM) method, which merges models based on feature distrib

Ian Harry, Charlie Hoy

Current template-based gravitational-wave searches for compact binary mergers neglect the general relativistic phenomenon of spin-induced orbital precession. Owing to their asymmetric masses, gravitational-waves from neutron star-black hole (NSBH) binaries are prime candidates for displaying strong imprints of spin-precession. Current searches may therefore

Jakub Lang, Michał Przybytek, Michał Lesiuk

We propose a method of estimating the uncertainty of a result obtained through extrapolation to the complete basis set limit. The method is based on an ensemble of random walks which simulate all possible extrapolation outcomes that could have been obtained if results from larger basis sets had been available. The results assembled from a large collection of

Behrang Forghani, Vadim Kaimanovich

We show that the product or convex combination of two Markov operators with equivalent stationary measures need not have a stationary measure from the same measure class. More specifically, we exhibit examples of a hitherto undescribed phenomenon: maximal entropy random walks for which the resulting compound random walks no longer have maximal entropy. The u

Amin Aboubrahim, Pran Nath

The Standard Model of cosmology, $\Lambda$CDM, while enormously successful, is currently unable to account for several cosmological anomalies the most prominent of which are in the measurements of the Hubble parameter and $S_8$. Additionally, the inclusion of the cosmological constant is theoretically unappealing. This has lead to extensions of the model suc

Anna T. Bui, Stephen J. Cox

Electromechanics in fluids describes the response of the number density to electric fields, and thus provides a powerful means by which to control the behavior of liquids. While continuum approaches have proven successful in describing electromechanical phenomena in macroscopic bodies, their use is questionable when relevant length scales become comparable t

Luis Scoccola, Uzu Lim, Heather A. Harrington

Classical unsupervised learning methods like clustering and linear dimensionality reduction parametrize large-scale geometry when it is discrete or linear, while more modern methods from manifold learning find low dimensional representation or infer local geometry by constructing a graph on the input data. More recently, topological data analysis popularized

Gustavo O. de Carvalho, Fábio P. Machado

We study the frog model on \( \mathbb{Z} \) with geometric lifetimes, introducing a random survival parameter. Active and inactive particles are placed at the vertices of \( \mathbb{Z} \). The lifetime of each active particle follows a geometric random variable with parameter \( 1-p \), where \( p \) is randomly sampled from a distribution \( \pi \). Each ac

Ehsan Latif, Xiaoming Zhai

Data privacy remains a critical concern in educational research, requiring strict adherence to ethical standards and regulatory protocols. While traditional approaches rely on anonymization and centralized data collection, they often expose raw student data to security vulnerabilities and impose substantial logistical overhead. In this study, we propose a fe

Marcelo Bagnulo, Angel Hernando-Veciana, Efthymios Smyrniotis

Market fragmentation across multiple Automated Market Makers (AMMs) creates inefficiencies such as costly arbitrage, unnecessarily high slippage and delayed incorporation of new information into prices. These inefficiencies raise trading costs, reduce liquidity provider profits, and degrade overall market efficiency. To address these issues, we propose a mod

Dimitri Bytchenkoff, Michael Speckbacher, Peter Balazs

In this paper, we investigate whether the tensor product of two frames, each individually localised with respect to a spectral matrix algebra, is also localised with respect to a suitably chosen tensor product algebra. We provide a partial answer by constructing an involutive Banach algebra of rank-four tensors that is built from two solid spectral matrix al

Yanxia Zhang, Francine Chen, Shabnam Hakimi, Totte Harinen

Understanding consumer preferences is essential to product design and predicting market response to these new products. Choice-based conjoint analysis is widely used to model user preferences using their choices in surveys. However, traditional conjoint estimation techniques assume simple linear models. This assumption may lead to limited predictability and

Pushkar Shukla, Aditya Chinchure, Emily Diana, Alexander Tolbert

The biases exhibited by Text-to-Image (TTI) models are often treated as if they are independent, but in reality, they may be deeply interrelated. Addressing bias along one dimension, such as ethnicity or age, can inadvertently influence another dimension, like gender, either mitigating or exacerbating existing disparities. Understanding these interdependenci

Süleyman Kerimov, Pengyu Qian, Mingwei Yang, Sophie H. Yu

We study a centralized discrete-time dynamic two-way matching model with finitely many agent types. Agents arrive stochastically over time and join their type-dedicated queues waiting to be matched. We focus on availability-based policies that make matching decisions based solely on agent availability across types (i.e., whether queues are empty or not), rat

Kristian D. Barajas, Wesley C. Campbell

We present a multi-timescale Quantum Averaging Theory (QAT), a unitarity-preserving generalized Floquet framework for analytically modeling periodically and almost-periodically driven quantum systems across multiple timescales. By integrating the Magnus expansion with the method of averaging on multiple scales, QAT captures the effects of both far-detuned an

Bhabani Sankar Tripathy, Nilmani Mathur, M. Padmanath

We present a lattice QCD investigation of isoscalar tetraquark systems involving bottom quarks with explicit flavor content $bb\bar{u}\bar{d}$ and $bs\bar{u}\bar{d}$. In the doubly bottom sector, the study focuses on axialvector $J^P=1^+$ quantum numbers, whereas in the $bs\bar{u}\bar{d}$ channel both axial vector $J^P=1^+$ and scalar $J^P=0^+$ quantum numbe

Francesco Spezzati, Eleonora Vanzan, Alvise Raccanelli, Zvonimir Vlah

The next generation of galaxy surveys will provide highly precise measurements of galaxy clustering, therefore requiring a corresponding accuracy. Current approaches, which rely on approximations and idealized assumptions, may fall short in capturing the level of detail required for high-precision observations. In order to increase the modeling accuracy, rec

Weizheng Wang, Ike Obi, Byung-Cheol Min

Recent advances in robotics and large language models (LLMs) have sparked growing interest in human-robot collaboration and embodied intelligence. To enable the broader deployment of robots in human-populated environments, socially-aware robot navigation (SAN) has become a key research area. While deep reinforcement learning approaches that integrate human-r

Gabriel Gomez, Jose F. Rodriguez

Previous studies have identified potential instabilities in self-interacting vector theories associated with the breakdown of the well-posedness of the initial-value problem. However, these conclusions are restricted to Abelian vector fields, leaving room to explore alternative setups, such as non-Abelian vector fields with internal symmetries. Building on t

Carlos Payá, Ramón Aguado, Pablo San-Jose, Elsa Prada

We perform microscopic numerical simulations of the Josephson effect through short junctions between two full-shell hybrid nanowires, comprised of a semiconductor core fully wrapped by a thin superconductor shell, both in the trivial and topological regimes. We explore the behavior of the current-phase relation and the critical current $I_c$ as a function of

Guangyi Liu, Suzan Iloglu, Michael Caldara, Joseph W. Durham

In Amazon robotic warehouses, the destination-to-chute mapping problem is crucial for efficient package sorting. Often, however, this problem is complicated by uncertain and dynamic package induction rates, which can lead to increased package recirculation. To tackle this challenge, we introduce a Distributionally Robust Multi-Agent Reinforcement Learning (D

Joseph L. Greene, Adrish Kar, Ignacio Galindo, Elijah Quiles

Event, or neuromorphic cameras, offer a novel encoding of natural scenes by asynchronously reporting significant changes in brightness, known as events, with improved dynamic range, temporal resolution and lower data bandwidth when compared to conventional cameras. However, their adoption in domain-specific research tasks is hindered in part by limited comme

V. M. Sampaio

This thesis investigates the evolution of galaxies in diverse environments, utilizing Sloan Digital Sky Survey (SDSS) data to explore the impact of environmental richness on central and satellite galaxies across stellar mass ranges, compared to isolated systems. The sample is limited to 0.03 < z < 0.1 and apparent magnitudes brighter than 17.78, ensuring spe

Charles L. Samuels

A 2009 article of Allcock and Vaaler explored the $\mathbb Q$-vector space $\mathcal G := \overline{\mathbb Q}^\times/{\overline{\mathbb Q}^\times_{\mathrm{tors}}}$, showing how to represent it as part of a function space on the places of $\overline{\mathbb Q}$. We establish a representation theorem for the $\mathbb R$-vector space of $\mathbb Q$-linear maps

Amjad Sohail, Hazrat Ali, Khalid Naseer, Rizwan Ahmed

The term "light dragging" describes how the trajectory of light changes as it travels through a moving medium. This phenomenon facilitates the precise detection of incredibly slow speeds of light, which is widely used in quantum gate operations, state transfer, and quantum memory implementations, etc. To the best of our knowledge, this is the first time we h

Haoan Feng, Diana Aldana, Tiago Novello, Leila De Floriani

Sinusoidal neural networks (SIRENs) are powerful implicit neural representations (INRs) for low-dimensional signals in vision and graphics. By encoding input coordinates with sinusoidal functions, they enable high-frequency image and surface reconstruction. However, training SIRENs is often unstable and highly sensitive to frequency initialization: small fre

Yongle Yuan, Kevin W. Bowyer

This study presents the first automated classifier designed to determine whether a pair of iris images originates from monozygotic individuals, addressing a previously untackled problem in biometric recognition. In Daugman-style iris recognition, the textures of the left and right irises of the same person are traditionally considered as being as different a

Vincent Liu, Ehsan Latif, Xiaoming Zhai

This study systematically reviews the transformative role of Tutoring Systems, encompassing Intelligent Tutoring Systems (ITS) and Robot Tutoring Systems (RTS), in addressing global educational challenges through advanced technologies. As many students struggle with proficiency in core academic areas, Tutoring Systems emerge as promising solutions to bridge

Timo Eichhorn, Gianluca Fuwa, Christian Hoelbling, Lukas Varnhorst

When approaching the continuum limit in lattice QCD or other theories in a setup with topological sectors, conventional update algorithms experience a particularly severe form of critical slowing down that is caused by high action barriers between the sectors. The qualitative scaling behavior of this critical slowing down appears to be universal across diffe

Luca Scimeca, Siddarth Venkatraman, Moksh Jain, Minsu Kim

This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (RL) objective that can asymptotically solve Bayesian inverse problems optimally. We extend the original work by using RTB to train conditional diffusion model posteriors from pretrained unconditional priors for challengin

Jelena Katić, Darko Milinković

It is known that Morse-Smale diffeomorphisms have the shadowing property; however, the question of whether $C(f)$ also has the shadowing property when $f$ is Morse-Smale remains open and has been resolved only in a few specific cases~\cite{AB}. We prove that if $f:M\to M$ is a time-one-map of Morse gradient flow, the induced map $C(f):C(M)\to C(M)$ on the hy

Chloe P. Wilkins, David I. Pontin, Anthony R. Yeates, Spiro K. Antiochos

The Sun's open-closed flux boundary (OCB) separates closed and open magnetic field lines, and is the site for interchange magnetic reconnection processes thought to be linked to the origin of the slow solar wind (SSW). We analyze the global magnetic field structure and OCB from 2010 December to 2019 December using three coronal magnetic field models: a poten

Timothy Laurence, Joshua Harris, Leo Loman, Amy Douglas

Foodborne gastrointestinal (GI) illness is a common cause of ill health in the UK. However, many cases do not interact with the healthcare system, posing significant challenges for traditional surveillance methods. The growth of publicly available online restaurant reviews and advancements in large language models (LLMs) present potential opportunities to ex

CMS Collaboration

Measurements are presented of the W and Z boson production cross sections in proton-proton collisions at a center-of-mass energy of 13.6 TeV. Data collected in 2022 and corresponding to an integrated luminosity of 5.01 fb$^{-1}$ with one or two identified muons in the final state are analyzed. The results for the products of total inclusive cross sections an

Evelyne S. Knight, Carlos Alexov Matos, Amira Sefidi, Matthew P. Young

The newform Dedekind sum $S_{\chi_1, \chi_2}$ associated to a pair of primitive Dirichlet characters $\chi_1$, $\chi_2$ of respective conductors $q_1$, $q_2$, is a group homomorphism from $\Gamma_1(q_1 q_2)$ into the number field $F_{\chi_1, \chi_2}$ generated by the values of the characters. It is a basic question to identify the image of this map, which is

Renato Calleja, Alex Haro, Pedro Porras

In this paper, we prove a KAM theorem in a-posteriori format, using the parameterization method to look invariant tori in non-autonomous Hamiltonian systems with $n$ degrees of freedom that depend periodically or quasi-periodically (QP) on time, with $\ell$ external frequencies. Such a system is described by a Hamiltonian function in the $2n$-dimensional pha

Pyeongjae Park, Qianli Ma, G. Sala, S. Calder

We report on the crystal field level splitting and magnetic ground state of the Jeff = 1/2 square lattice antiferromagnets YbBi2ClO4 and YbBi2IO4 using powder inelastic neutron scattering (INS) and neutron diffraction measurements. Both compounds exhibit a well-isolated $\Gamma_{7}$ doublet ground state under a tetragonal crystal field environment, confirmin

Rihab Ben Belgacem, Mohamed Majdoub

The purpose of this work is to analyze the well-posedness and blow-up behavior of solutions to the nonlocal semilinear parabolic equation with a forcing term: \[ \partial_t u - \Delta u = \|u(t)\|_{q}^\alpha |u|^p + t^{\varrho} \mathbf{w}(x) \quad \text{in} \quad \mathbb{R}^N \times (0, \infty), \] where $N \geq 1$, $p, q \geq 1$, $\alpha \geq 0$, $\varrho >

Jacky Hao Jiang, Jerry Cai, Anastasios Kyrillidis

Soccer analysis tools emphasize metrics such as expected goals, leading to an overrepresentation of attacking players' contributions and overlooking players who facilitate ball control and link attacks. Examples include Rodri from Manchester City and Palhinha who just transferred to Bayern Munich. To address this bias, we aim to identify players with pivotal

Zhiyuan Wang, Katharine E. Daniel, Laura E. Barnes, Philip I. Chow

Cancer survivors face unique emotional challenges that impact their quality of life. Mobile diary entries provide a promising method for tracking emotional states, improving self-awareness, and promoting well-being outcome. This paper aims to, through mobile diaries, understand cancer survivors' emotional states and key variables related to just-in-time inte

Qian Ma, Ziping Ye

Adversarial examples are a major problem for machine learning models, leading to a continuous search for effective defenses. One promising direction is to leverage model explanations to better understand and defend against these attacks. We looked at AmI, a method proposed by a NeurIPS 2018 spotlight paper that uses model explanations to detect adversarial e

Tim Cunningham, Pier-Emmanuel Tremblay, Mairi O'Brien, Evan B. Bauer

We present a population synthesis model which addresses the different mass distributions of the metal-polluted and non-metal-polluted hydrogen-atmosphere white dwarfs identified in volume-limited samples. Specifically, metal-pollution has been observed to be rare in white dwarfs more massive than $\approx$0.7 $M_{\odot}$. Our population synthesis model invok

Zhiyuan Zhang, Dongdong Chen, Jing Liao

We present I2V3D, a novel framework for animating static images into dynamic videos with precise 3D control, leveraging the strengths of both 3D geometry guidance and advanced generative models. Our approach combines the precision of a computer graphics pipeline, enabling accurate control over elements such as camera movement, object rotation, and character

Jorge Morales, Jing Ming, Maria Alessandra Papa, Heinz-Bernd Eggenstein

We conduct two searches for continuous, nearly monochromatic gravitational waves originating from the central compact objects in the supernova remnants Cassiopeia A and Vela Jr. using public LIGO data. The search for Cassiopeia A targets signal frequencies between 20 Hz and 400 Hz; the Vela Jr. search between 400 Hz and 1700 Hz, and both investigate the broa

Sara Rajaee, Kumar Pratik, Gabriele Cesa, Arash Behboodi

The most promising recent methods for AI reasoning require applying variants of reinforcement learning (RL) either on rolled out trajectories from the LLMs, even for the step-wise rewards, or large quantities of human-annotated trajectory data. The reliance on the rolled-out trajectory renders the compute cost and time prohibitively high. In particular, the

Harris K. Armeniakos, Petros S. Bithas, Sotiris A. Tegos, Athanasios G. Kanatas

One of the most promising technologies for next-generation wireless networks is integrated communication and sensing (ISAC). It is considered a key enabler for applications that require both enhanced communication and accurate sensing capabilities. Examples of such applications include smart environments, augmented and virtual reality, or the internet of thi

Lennart Duvenbeck, Cedric Riethmüller, Christian Rohde

Restarted GMRES is a robust and widely used iterative solver for linear systems. The control of the restart parameter is a key task to accelerate convergence and to prevent the well-known stagnation phenomenon. We focus on the Proportional-Derivative GMRES (PD-GMRES), which has been derived using control-theoretic ideas in [Cuevas N\'u\~nez, Schaerer, and Bh

Zhaohan Xi

Knowledge graph reasoning (KGR), which answers complex, logical queries over large knowledge graphs (KGs), represents an important artificial intelligence task with a range of applications. Many KGs require extensive domain expertise and engineering effort to build and are hence considered proprietary within organizations and enterprises. Yet, spurred by the

Jessica Hullman, Yifan Wu, Dawei Xie, Ziyang Guo

Methods to quantify uncertainty in predictions from arbitrary models are in demand in high-stakes domains like medicine and finance. Conformal prediction has emerged as a popular method for producing a set of predictions with specified average coverage, in place of a single prediction and confidence value. However, the value of conformal prediction sets to a

William Earl Boebert

This paper is a tutorial on the proven but currently under-appreciated security mechanisms associated with "tagged" or "descriptor" architectures. The tutorial shows how the principles behind such architectures can be applied to mitigate or eliminate vulnerabilities. The tutorial incorporates systems engineering practices by presenting the mechanisms in an i

Mir Imtiaz Mostafiz, Imtiaz Karim, Elisa Bertino

Graph Neural Networks (GNNs) are widely used and deployed for graph-based prediction tasks. However, as good as GNNs are for learning graph data, they also come with the risk of privacy leakage. For instance, an attacker can run carefully crafted queries on the GNNs and, from the responses, can infer the existence of an edge between a pair of nodes. This att

Marzieh Soltani, Rozita Dara, Zvonimir Poljak, Caroline Dubé

Avian Influenza Virus (AIV) poses significant threats to the poultry industry, humans, domestic animals, and wildlife health worldwide. Monitoring this infectious disease is important for rapid and effective response to potential outbreaks. Conventional avian influenza surveillance systems have exhibited limitations in providing timely alerts for potential o

India Bhalla-Ladd, Izzy Ginnett, Tim M. P. Tait

We explore leptogenesis during a cosmological epoch during which the electroweak $SU(2)_L$ force is confined. During weak confinement, there is only one conserved non-anomalous global charge, $r$, which is a linear combination of lepton-number, baryon-number, and hypercharge. The inclusion of heavy Majorana neutrinos leads to an $r$-charge and $CP$-violating

Max Simchowitz, Daniel Pfrommer, Ali Jadbabaie

We study the problem of imitating an expert demonstrator in a discrete-time, continuous state-and-action control system. We show that, even if the dynamics satisfy a control-theoretic property called exponential stability (i.e. the effects of perturbations decay exponentially quickly), and the expert is smooth and deterministic, any smooth, deterministic imi

Manish Nagaraj, Deepak Ravikumar, Efstathia Soufleri, Kaushik Roy

Deep learning models achieve state-of-the-art performance across domains but face scalability challenges in real-time or resource-constrained scenarios. To address this, we propose Loss Trajectory Correlation (LTC), a novel metric for coreset selection that identifies critical training samples driving generalization. $LTC$ quantifies the alignment between tr

Ryan Milton, Vinicius Mikuni, Trevin Lee, Miguel Arratia

Machine learning has enabled differential cross section measurements that are not discretized. Going beyond the traditional histogram-based paradigm, these unbinned unfolding methods are rapidly being integrated into experimental workflows. In order to enable widespread adaptation and standardization, we develop methods, benchmarks, and software for unbinned

Alberto Caron, Vasilios Mavroudis, Chris Hicks

Real-world decision-making problems are often marked by complex, uncertain dynamics that can shift or break under changing conditions. Traditional Model-Based Reinforcement Learning (MBRL) approaches learn predictive models of environment dynamics from queried trajectories and then use these models to simulate rollouts for policy optimization. However, such

S. Ertl, S. H. Suyu, S. Schuldt, G. Granata

The strongly lensed Supernova (SN) Encore at a redshift of $z = 1.949$, discovered behind the galaxy cluster MACS J0138$-$2155 at $z=0.336$, provides a rare opportunity for time-delay cosmography and studies of the SN host galaxy, where previously another SN, called SN Requiem, had appeared. To enable these studies, we combine new James Webb Space Telescope

Tairan Xu, Leyang Xue, Zhan Lu, Adrian Jackson

This paper presents MoE-Gen, a high-throughput MoE inference system optimized for single-GPU execution. Existing inference systems rely on model-based or continuous batching strategies, originally designed for interactive inference, which result in excessively small batches for MoE's key modules-attention and expert modules-leading to poor throughput. To add

Cian C. Reeves, Scott K. Cushing, Vojtech Vlcek

Understanding how to control changes in electronic structure and related dynamical renormalizations by external driving fields is the key for understanding ultrafast spectroscopy and applications in electronics. Here we focus on the band-gap's modulation by external electric fields and uncover the effect of band dispersion on the gap renormalization. We empl

S. Novell-Masot, H. Gil-Marín, L. Verde, J. Aguilar

We present the first joint analysis of the power spectrum and bispectrum using the Data Release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI), focusing on Luminous Red Galaxies (LRGs) and quasars (QSOs) across a redshift range of $0.4\leq z\leq2.1$. By combining the two- and three-point statistics, we are able to partially break the degeneracy b

Timothée David--Cléris, Guillaume Laibe, Yona Lapeyre

We present Shamrock, a performance portable framework developed in C++17 with the SYCL programming standard, tailored for numerical astrophysics on Exascale architectures. The core of Shamrock is an accelerated parallel tree with negligible construction time, whose efficiency is based on binary algebra. The Smoothed Particle Hydrodynamics algorithm of the Ph

Yuanmin Huang, Mi Zhang, Zhaoxiang Wang, Wenxuan Li

Time series classification (TSC) is a cornerstone of modern web applications, powering tasks such as financial data analysis, network traffic monitoring, and user behavior analysis. In recent years, deep neural networks (DNNs) have greatly enhanced the performance of TSC models in these critical domains. However, DNNs are vulnerable to backdoor attacks, wher

Rebekah L. Rogers, John P. Wares, Jeffrey T. Garner

Nearly neutral theory predicts that evolutionary processes will differ in small populations compared to large populations, a key point of concern for endangered species. The nearly-neutral threshold, the span of neutral variation, and the adaptive potential from new mutations all differ depending on N_e. To determine how genomes respond in small populations,

Sangjin Lee, Youngseok Kim, Seung-Woo Lee

Understanding the dynamics of quantum systems is crucial in many areas of physics, but simulating many-body systems presents significant challenges due to the large Hilbert space to navigate and the exponential growth of computational overhead. Quantum computers offer a promising platform to overcome these challenges, particularly for simulating the time evo

Shuai A. Chen, Roderich Moessner, Tai Kai Ng

We study the interplay between quantum geometry, interactions, and external fields in complex band systems. When Wannier obstructions preclude a description based solely on atomic-like orbitals, this complicates the prediction of electromagnetic responses particularly in the presence of disorder and interactions. In this work, we introduce a generalized Peie

E. Bortolan, J. Bruce, A. P. Milone, E. Vesperini

Globular Clusters (GCs) are known to host distinct stellar populations, characterized by different chemical compositions. Despite extensive research, the origin of these populations remains elusive. According to many formation scenarios, the second population (2P) originated within a compact and denser region embedded in a more extended first population (1P)

Ping Zhang, Zheda Mai, Quang-Huy Nguyen, Wei-Lun Chao

Semi-supervised learning (SSL) leverages abundant unlabeled data alongside limited labeled data to enhance learning. As vision foundation models (VFMs) increasingly serve as the backbone of vision applications, it remains unclear how SSL interacts with these pre-trained models. To address this gap, we develop new SSL benchmark datasets where frozen VFMs unde

Mounica Mahankali, Fang Xie, Yuan Fang, Lei Chen

Flat bands, when located close to the Fermi energy, can considerably enhance the influence of electron correlations on the low energy physics in kagome and other frustrated-lattice metals. A major challenge in describing the interaction effects in such bulk materials is that the flat band is often intermixed with a large number of other bands. Here we show t

Aayush Vijayvargia, Ezra Day-Roberts, Antia S. Botana, Onur Erten

Building on recent advancements in altermagnetism, we develop a highly-frustrated magnetic model with Kitaev-like interactions that integrates key aspects of both quantum spin liquids and altermagnets. While the ground state is a gapless quantum spin liquid, our analysis indicates that an altermagnetic local order emerges upon the introduction of additional

Jean C. Souza, Moshe Haim, Ambikesh Gupta, Mounica Mahankali

Strong correlations promote singular properties such as strange metallicity, which shows considerable commonality across quantum materials platforms. Understanding the mechanism for such emerging universality is an outstanding challenge, given that the underlying degrees of freedom can be complex and varied. Progress may be made in flat band systems, especia

Clément Virally

We study four-point correlators in superconformal theories in various dimensions. We develop an efficient method to solve the superconformal Ward identities in Mellin space. For 4d $\mathcal{N}=4$ SYM and the 6d $\mathcal{N}=(2,0)$ theory, our method reproduces the known solutions. As novel applications of this method, we also derive solutions in 3d $\mathca

Yukei S. Murakami, Adam G. Riess, Henry C. Ferguson, Alexei V. Filippenko

In the most precise distance ladder determination of $H_0$, the observed near-infrared (NIR) fluxes of Cepheids are corrected for dust, assuming that the extinction law in large, star-forming spiral hosts of Type Ia supernovae (SN Ia) is similar to the Milky Way's average value of $R_V \approx 3.1$. Intriguingly, studies of SNe Ia often point to lower values

Julia Romberg, Christopher Schröder, Julius Gonsior, Katrin Tomanek

Supervised learning relies on data annotation which usually is time-consuming and therefore expensive. A longstanding strategy to reduce annotation costs is active learning, an iterative process, in which a human annotates only data instances deemed informative by a model. Research in active learning has made considerable progress, especially with the rise o

Shoham Jacoby, Rotem Arnon-Friedman, Serge Rosenblum

High-fidelity entanglement shared between distant quantum systems is an essential resource for quantum communication and computation. Entanglement distillation addresses this need by converting multiple noisy Bell pairs into fewer higher-fidelity pairs, using only local quantum operations and classical communication. However, this approach typically requires

Miguel Crispim Romao, Djuna Croon, Daniel Godines

We introduce a novel approach to detecting microlensing events and other transients in light curves, utilising the isolation forest (iForest) algorithm for anomaly detection. Focusing on the Legacy Survey of Space and Time by the Vera C. Rubin Observatory, we show that an iForest trained on signal-less light curves can efficiently identify microlensing event

Christopher Monaghan, Pierre-Alexis Roy, Björn Benneke, Ian J. M. Crossfield

The full range of conditions under which rocky planets can host atmospheres remains poorly understood, especially in the regime of close-in orbits around late-type stars. One way to assess the presence of atmospheres on rocky exoplanets is to measure their dayside emission as they are eclipsed by their host stars. Here, we present Spitzer observations of the

Antoine Dumont, Nadine Neumayer, Anil C. Seth, Torsten Böker

The point-spread function of the integral-field unit (IFU) mode of the JWST's NIRSpec is heavily under-sampled, creating resampling noise seen as low-frequency sinusoidal-like artifacts, or "wiggles". These artifacts in the data are not corrected in the JWST data pipeline, and significantly impact the science that can be achieved at a single-pixel level. We

David Riegler, Jannis Seufert, Ronny Thomale, Peter Wölfle

We determine the free energy of strongly correlated electron systems in the example of the Hubbard model by calculating the contribution of spin and charge fluctuations to the Gutzwiller approximation mean field result. We employ the slave boson formulation of Kotliar and Ruckenstein in its spin-rotation invariant form in the usual continuous time approximat

Anjishnu Bose, Kathleen Hart, Ruairidh Sutcliffe, Arun Paramekanti

Recent work has shown that the triangular lattice spin-$1/2$ $J_1$-$J_2$ Heisenberg and XXZ antiferromagnets may exhibit coplanar or supersolid orders proximate to a gapless Dirac spin liquid phase. We explore a distinct $SU(2N)\!\!\times\!\!SU(M)$ fermionic parton approach, complemented by variational Monte Carlo calculations for the spin-$1/2$ model, to st

Ana Acebron, Pietro Bergamini, Piero Rosati, Paolo Tozzi

We present a new parametric strong lensing analysis of the galaxy cluster MACS J0138.0-2155 at z = 0.336, the first known to show two multiply-imaged supernova (SN) siblings, SN Requiem and SN Encore at z= 1.949. We exploit HST and JWST multiband imaging in synergy with new MUSE spectroscopy to develop an improved lens mass model. We include 84 cluster membe

Philip Taranto, Simon Milz, Mio Murao, Marco Túlio Quintino

An operational description of quantum phenomena concerns developing models that describe experimentally observed behaviour. $\textit{Higher-order quantum operations}\unicode{x2014}$quantum operations that transform quantum operations$\unicode{x2014}$are fundamental to modern quantum theory, extending beyond basic state preparations, evolutions, and measureme

B. Andrei Bernevig, Yves H. Kwan

We present a model for interacting electrons in a continuum band structure that resembles a ``trashcan'', with a flat bottom of radius $k_b$ beyond which the dispersion increases rapidly with velocity $v$. The form factors of the Bloch wavefunctions can be well-approximated by the Girvin-MacDonald-Platzman algebra, which encodes the uniform Berry curvature.

Daniel Hey, Yaguang Li, Joel Ong

The stellar companions in the binary black hole systems Gaia BH2 and BH3, both of which are $\alpha$-enhanced red giant branch stars, are expected to show normal modes with the characteristic signature of convectively-driven solar-like oscillations. We investigate this using photometry from the TESS mission and find such a signal for Gaia BH2. For Gaia BH2,

Liangtao Peng, Christophe De Beule, Yiyang Lai, Du Li

Using a self-consistent Hartree-Fock theory, we show that the recently observed ferromagnetism in twisted bilayer WSe$_2$ [Nat. Commun. 16, 1959 (2025)] can be understood as a Stoner-like instability of interaction-renormalized moir\'e bands. We quantitatively reproduce the observed Lifshitz transition as function of hole filling and applied electric field t