March 2025 arXiv papers — page 73

Peter Fratrič, Nils Holzenberger, David Restrepo Amariles

The legislative process is the backbone of a state built on solid institutions. Yet, due to the complexity of laws -- particularly tax law -- policies may lead to inequality and social tensions. In this study, we introduce a novel prototype system designed to address the issues of tax loopholes and tax avoidance. Our hybrid solution integrates a natural lang

André Barreto, Vincent Dumoulin, Yiran Mao, Mark Rowland

Reinforcement learning from human feedback usually models preferences using a reward function that does not distinguish between people. We argue that this is unlikely to be a good design choice in contexts with high potential for disagreement, like in the training of large language models. We formalise and analyse the problem of learning a reward model that

Darius Erös, Michael Kunzinger, Argam Ohanyan, Alessio Vardabasso

Sectional curvature bounds are of central importance in the study of Riemannian manifolds, both in smooth differential geometry and in the generalized synthetic setting of Alexandrov spaces. Riemannian metrics along with metric spaces of bounded sectional curvature enjoy a variety of, oftentimes rigid, geometric properties. The purpose of this article is to

Reem Gody, Mohamed Abdelghaffar, Mohammed Jabreel, Ahmed Tawfik

Large language models (LLMs) have showcased remarkable capabilities in conversational AI, enabling open-domain responses in chat-bots, as well as advanced processing of conversations like summarization, intent classification, and insights generation. However, these models are resource-intensive, demanding substantial memory and computational power. To addres

M. Bonamente, D. Zimmerman, Y. Chen

A novel model of systematic errors for the regression of Poisson data is applied to hypothesis testing of nested model components with the introduction of a generalization of the $\Delta C$ statistic that applies in the presence of systematic errors. This paper shows that the null-hypothesis parent distribution of this $\Delta C_{sys}$ statistic can be obtai

Alexandr Grebennikov, Letícia Mattos, Tibor Szabó

For $t \in \mathbb{N}$, we say that a colouring of $E(K_n)$ is $\textit{almost}$ $t$-$\textit{Gallai}$ if no two rainbow $t$-cliques share an edge. Motivated by a lemma of Berkowitz on bounding the modulus of the characteristic function of clique counts in random graphs, we study the maximum number $\tau_t(n)$ of rainbow $t$-cliques in an almost $t$-Gallai c

Vasileios Titopoulos, George Alexakis, Kosmas Alexandridis, Chrysostomos Nicopoulos

The deployment of Machine Learning (ML) applications at the edge on resource-constrained devices has accentuated the need for efficient ML processing on low-cost processors. While traditional CPUs provide programming flexibility, their general-purpose architecture often lacks the throughput required for complex ML models. The augmentation of a RISC-V process

Yuxuan Zhu, Antony Kellermann, Dylan Bowman, Philip Li

Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are l

Francisco Tellez, Enrique Torres-Giese

In this paper, we introduce a shape descriptor that we call "interior function". This is a Topological Data Analysis (TDA) based descriptor that refines previous descriptors for image analysis. Using this concept, we define subcomplex lacunarity, a new index that quantifies geometric characteristics of necrosis in tumors such as conglomeration. Building on t

Andrew Fiori, Hiva Gheisari

We generalize the work of Erdos-Pomerance and Fiori-Shallue on counting Frobenius pseudoprimes from the cases of degree one and two respectively to arbitrary degree. More specifically we provide formulas for counting the number of false witnesses for a number $n$ with respect to Grantham's Frobenius primality test. We also provide conditional assymptotic low

Andrew Silva, Pradyumna Tambwekar, Mariah Schrum, Matthew Gombolay

As robots and digital assistants are deployed in the real world, these agents must be able to communicate their decision-making criteria to build trust, improve human-robot teaming, and enable collaboration. While the field of explainable artificial intelligence (xAI) has made great strides to enable such communication, these advances often assume that one x

Do-kyum Kim, Han Zhao, Huiji Gao, Liwei He

Airbnb is an online marketplace that connects hosts and guests to unique stays and experiences. When guests stay at homes booked on Airbnb, there are a small fraction of stays that lead to support needed from Airbnb's Customer Support (CS), which may cause inconvenience to guests and hosts and require Airbnb resources to resolve. In this work, we show that i

Anton Leontyev, Takashi Yamauchi

Impulsive individuals exhibit abnormal reward processing (heightened preference for immediate rewards, i.e., impulsive choice, IC) and a penchant for maladaptive action (the inability to inhibit inappropriate actions, i.e., impulsive action, IA). Both impulsive choice and impulsive action are strongly influenced by emotions (emotional impulsivity); yet how e

Omkar Bait, Daniel Schaerer, Yuri I. Izotov, Biny Sebastian

Recent optical surveys have identified a rare population of low-$z$ ($z \sim 0.01 - 0.06$) extreme star-forming galaxies (xSFGs) characterized by very low metallicity, strong emission lines, extremely high specific star-formation rate, low stellar mass, and strong Ly~$\alpha$ emission. Their global properties resemble recently discovered $z > 6$ reionization

Kristian Benda, Jerzy Matyjasek

We discuss new recurrence-based methods for calculating the complex frequencies of the quasinormal modes of black holes. These methods are based on the Frobenius series solutions of the differential equation describing the linearized radial perturbations. Within the general method, we propose two approaches: the first involves calculating the series coeffici

Courtney B. Watson, Elizabeth L. Blanton, Emmet Golden-Marx, Matthew L. N. Ashby

We present new Hubble Space Telescope/Wide Field Camera 3 G141 grism observations for COBRA1411+3415, originally identified as a high-redshift cluster candidate in the Clusters Occupied by Bent Radio AGN (COBRA) survey using radio, infrared, and optical data. We spectroscopically identify seven cluster members within a 0.5 Mpc radius with grism redshifts in

Jorge Martin Camalich, Robert Ziegler

The dark sector offers a compelling theoretical framework for addressing the nature of dark matter while potentially solving other fundamental problems in physics. This review focuses on light dark flavored sector models, which are those where the flavor structure of the interactions with the standard model is non-trivial and distinguish among different ferm

Matteo Paltenghi, Michael Pradel

Quantum computing platforms are susceptible to quantum-specific bugs (e.g., incorrect ordering of qubits or incorrect implementation of quantum abstractions), which are difficult to detect and require specialized expertise. The field faces challenges due to a fragmented landscape of platforms and rapid development cycles that often prioritize features over t

Dan Abramovich

I begin by explaining to non-specialists why resolution of singularities in characteristic 0 works. Then I go into some ideas telling how it actually works. I finish with a brief discussion of related results on foliations. I report on work with Andr\'e Belotto da Silva, Michael Temkin, and Jaros{\l}aw W{\l}odarczyk; any claim to originality is joint with th

Pegah Azizi, Rahul Dev Kundu, Weichen Li, Kai Sun

Topological states of matter, first discovered in quantum systems, have opened new avenues for wave manipulation beyond the quantum realm. In elastic media, realizing these topological effects requires identifying lattices that support the corresponding topological bands. However, among the vast number of theoretically predicted topological states, only a sm

Calum Hughes

We extend the model structure on the category $\mathbf{Cat}(\mathcal{E})$ of internal categories studied by Everaert, Kieboom and Van der Linden to an algebraic model structure. Moreover, we show that it restricts to the category of internal groupoids. We show that in this case, the algebraic weak factorisation system that consists of the algebraic trivial c

Breno L. Giacchini, Ivan Kolář

We study exact static spherically symmetric vacuum solutions in generic six-derivative gravity (i.e., without assuming specific relations between the coupling constants). Using modified Schwarzschild coordinates, we systematically classify solutions through Frobenius expansions, determining their number of free parameters and confirming previously known case

P. Capuzzi, D. M. Jezek

We study the dynamics of vortices in a Bose-Einstein condensate within a rotating four-site lattice which can be effectively described by a multimode model. Such a vortex dynamics develops along the low-density paths that separate the sites, and it is ruled by the phase differences between them. Hence, by appropriately selecting the initial conditions for on

Wonbong Jang, Philippe Weinzaepfel, Vincent Leroy, Lourdes Agapito

We present Pow3r, a novel large 3D vision regression model that is highly versatile in the input modalities it accepts. Unlike previous feed-forward models that lack any mechanism to exploit known camera or scene priors at test time, Pow3r incorporates any combination of auxiliary information such as intrinsics, relative pose, dense or sparse depth, alongsid

Jon-Ivar Skullerud, Gert Aarts, Chris Allton, M. Naeem Anwar

We investigate spectral features of bottomonium at high temperature, in particular the thermal mass shift and width of ground state S-wave and P-wave state. We employ and compare a range of methods for determining these features from lattice NRQCD correlators, including direct correlator analyses (multi-exponential fits and moments of spectral functions), li

Rebecca Kohlhaas, Johannes Hommel, Felix Weinhardt, Holger Class

The usability of enzymatically induced calcium carbonate precipitation (EICP) as a method for altering porous-media properties, soil stabilization, or biocementation depends on our ability to predict the spatial distribution of the precipitated calcium carbonate in porous media. While current REV-scale models are able to reproduce the main features of labora

Milad Pooladsanj, Ketan Savla, Petros A. Ioannou

Urban Air Mobility (UAM) offers a solution to current traffic congestion by using electric Vertical Takeoff and Landing (eVTOL) vehicles to provide on-demand air mobility in urban areas. Effective traffic management is crucial for efficient operation of UAM systems, especially for high-demand scenarios. In this paper, we present a centralized framework for c

Atma Anand, Jonathan Carroll-Nellenback, Eric G. Blackman, John A. Tarduno

Asteroid (16) Psyche is a metal-rich body that might record an ancient coherent magnetization if some relict crust or mantle is preserved. Herein, we use magnetohydrodynamic simulations to predict (16) Psyche's field topology for several distinct pathways, (i) an early solar wind-induced magnetization imparted after a larger body was impacted, forming the pr

Iosif Bena, Nicholas P. Warner

We review the 20-year history of the Microstate Geometry Programme and the essential role that supergravity has played, and will continue to play, in the description of black-hole microstructure.

Kun Chu, Xufeng Zhao, Cornelius Weber, Stefan Wermter

Bimanual robotic manipulation provides significant versatility, but also presents an inherent challenge due to the complexity involved in the spatial and temporal coordination between two hands. Existing works predominantly focus on attaining human-level manipulation skills for robotic hands, yet little attention has been paid to task planning on long-horizo

Meisam Jamshidi Seikavandi, Jostein Fimland, Maria Jung Barrett, Paolo Burelli

Facial mimicry - the automatic, unconscious imitation of others' expressions - is vital for emotional understanding. This study investigates how mimicry differs across emotions using Face Action Units from videos and participants' responses. Dynamic Time Warping quantified the temporal alignment between participants' and stimuli's facial expressions, reveali

Carlo Burigana, Tiziana Trombetti, Matteo Bonato, Adélie Gorce

Cosmic background radiation, both diffuse and discrete in nature, produced at different cosmic epochs before and after recombination, provides key information on the evolution of cosmic structures. We discuss the main classes of sources that contribute to the extragalactic background light from radio to sub-millimetre wavelenghs and the currently open questi

C. Gallart, E. Fernández-Alvar, S. Cassisi, T. Ruiz-Lara

The derivation of precise stellar ages is considered the current major challenge to reconstruct the chronology of the Milky Way. Color-magnitude diagram (CMD)-fitting offers a robust alternative to individual age determinations via the derivation of dynamically evolved star formation histories (deSFH) and age-metallicity distributions (Gallart et al. 2024).

Gustavo. E. Massaccesi, Ofelia. B. Oña, Pablo Capuzzi, Juan I. Melo

The N-representability problem consists in determining whether, for a given p-body matrix, there exists at least one N-body density matrix from which the p-body matrix can be obtained by contraction, that is, if the given matrix is a p-body reduced density matrix (p-RDM). The knowledge of all necessary and sufficient conditions for a p-body matrix to be N-re

John Naulty, Eason Chen, Joy Wang, George Digkas

As software systems grow increasingly complex, ensuring security during development poses significant challenges. Traditional manual code audits are often expensive, time-intensive, and ill-suited for fast-paced workflows, while automated tools frequently suffer from high false-positive rates, limiting their reliability. To address these issues, we introduce

Shahn Majid, Francisco Simão

We study gauge theory with finite group $G$ on a graph $X$ using noncommutative differential geometry and Hopf algebra methods with $G$-valued holonomies replaced by gauge fields valued in a `finite group Lie algebra' subset of the group algebra $\mathbb{C} G$ corresponding to the complete graph differential structure on $G$. We show that this richer theory

Yashas Annadani, Syrine Belakaria, Stefano Ermon, Stefan Bauer

Offline multi-objective optimization aims to identify Pareto-optimal solutions given a dataset of designs and their objective values. In this work, we propose a preference-guided diffusion model that generates Pareto-optimal designs by leveraging a classifier-based guidance mechanism. Our guidance classifier is a preference model trained to predict the proba

Arthur Amorim, Max Taylor, Trevor Kann, Gary T. Leavens

Unmanned aerial vehicles (UAVs) depend on untrusted software components to automate dangerous or critical missions, making them a desirable target for attacks. Some work has been done to prevent an attacker who has either compromised a ground control station or parts of a UAV's software from sabotaging the vehicle, but not both. We present an architecture ru

Alexander Bernal, J. Alberto Casas, Juan Falceto

For general bipartite mixed states, a sufficient and necessary mathematical condition for certifying entanglement and/or (Bell) non-locality remains unknown. In this paper, we examine this question for a broad and physically relevant class of bipartite systems, specifically those possessing an additive observable with a definite value. Such systems include,

Thrassos K. Oikonomou, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, George K. Karagiannidis

We present a novel cross-band modulation framework that combines 3D modulation in the RF domain with intensity modulation and direct detection in the optical domain, the first such integration to enhance communication reliability. By harnessing cross-band diversity, the framework optimizes symbol mapping across RF and optical links, significantly boosting mu

R. G. de Almeida, J. J. Arenzon, F. Corberi, W. G. Dantas

We investigate the coarsening dynamics of a simplified version of the persistent voter model in which an agent can become a zealot -- i.e. resistent to change opinion -- at each step, based on interactions with its nearest neighbors. We show that such a model captures the main features of the original, non-Markovian, persistent voter model. We derive the gov

Georg Loho, Matthew Maat, Mateusz Skomra

We introduce the concept of a \emph{cycle pattern} for directed graphs as functions from the set of cycles to the set $\{-,0,+\}$. The key example for such a pattern is derived from a weight function, giving rise to the sign of the total weight of the edges for each cycle. Hence, cycle patterns describe a fundamental structure of a weighted digraph, and they

A. Challen Hyman, Chloe Ramsay, Tiffanie A. Cross, Beverly Sauls

Effective management of recreational fisheries requires accurate forecasting of future harvests and real-time monitoring of ongoing harvests. Traditional methods that rely on historical catch data to predict short-term harvests can be unreliable, particularly if changes in management regulations alter angler behavior. In contrast, statistical modeling approa

Tianle Duan, Fengxiu Zhang, Qingchun Li

Extreme heat and hurricane-induced blackouts could occur simultaneously in the summer, posing great challenges to community health and well-being. Cooling centers serve as a key adaptation strategy to alleviate heat stress, especially among heat-vulnerable populations. This study leverages mobility data to examine how affected communities utilize cooling cen

Pracheta Singha, Sergiu Busuioc, Victor E. Ambrus, Maxim N. Chernodub

We study the effect of rotation on the confining and chiral properties of QCD using the Polyakov-enhanced linear sigma model coupled to quarks. Working in the homogeneous approximation, we obtain the phase diagram at finite temperature, baryon density and angular frequency, taking into account the causality constraint enforced by the spectral boundary condit

Sven Klaassen, Jan Rabenseifner, Jannis Kueck, Philipp Bach

The partitioning of data for estimation and calibration critically impacts the performance of propensity score based estimators like inverse probability weighting (IPW) and double/debiased machine learning (DML) frameworks. We extend recent advances in calibration techniques for propensity score estimation, improving the robustness of propensity scores in ch

Ali Rabeh, Adarsh Krishnamurthy, Baskar Ganapathysubramanian

Accurate modeling of fluid dynamics around complex geometries is critical for applications such as aerodynamic optimization and biomedical device design. While advancements in numerical methods and high-performance computing have improved simulation capabilities, the computational cost of high-fidelity 3D flow simulations remains a significant challenge. Sci

Xianghan Meng, Zhiyuan Huang, Wei He, Xianbiao Qi

Subspace clustering is a classical unsupervised learning task, built on a basic assumption that high-dimensional data can be approximated by a union of subspaces (UoS). Nevertheless, the real-world data are often deviating from the UoS assumption. To address this challenge, state-of-the-art deep subspace clustering algorithms attempt to jointly learn UoS rep

Mingyang Song, Mao Zheng, Zheng Li, Wenjie Yang

Improving training efficiency continues to be one of the primary challenges in large-scale Reinforcement Learning (RL). In this paper, we investigate how context length and the complexity of training data influence the RL scaling training process of R1-distilled reasoning models, e.g., DeepSeek-R1-Distill-Qwen-1.5B. Our experimental results reveal that: (1)

Minsu Kim, Jiayao Gu, Ye Yuan, Taeyoung Yun

Offline optimization is a fundamental challenge in science and engineering, where the goal is to optimize black-box functions using only offline datasets. This setting is particularly relevant when querying the objective function is prohibitively expensive or infeasible, with applications spanning protein engineering, material discovery, neural architecture

Louis Y. Kim, Michelle Karker, Victoria Valledor, Seiyoung C. Lee

Recent advances in open-vocabulary object detection models will enable Automatic Target Recognition systems to be sustainable and repurposed by non-technical end-users for a variety of applications or missions. New, and potentially nuanced, classes can be defined with natural language text descriptions in the field, immediately before runtime, without needin

Manpreet Kaur, Soumya Chatterjee, T. Nandi

The complex physics of inner shell ionization of target atoms by heavy ion impact has remained only partially solved for decades. Recently, agreement between theory and experiment has been achieved by considering inner shell ionization of target atoms due to projectile electron capture in addition to direct Coulomb ionization including multiple ionization ef

Estela Suarez, Jorge Amaya, Martin Frank, Oliver Freyermuth

The growing energy demands of HPC systems have made energy efficiency a critical concern for system developers and operators. However, HPC users are generally less aware of how these energy concerns influence the design, deployment, and operation of supercomputers even though they experience the consequences. This paper examines the implications of HPC's ene

Nicola Lorenzoni, Thibaut Lacroix, James Lim, Dario Tamascelli

The presence of quantum effects in photosynthetic excitation energy transfer has been intensely debated over the past decade. Nonlinear spectroscopy cannot unambiguously distinguish coherent electronic dynamics from underdamped vibrational motion, and rigorous numerical simulations of realistic microscopic models have been intractable. Experimental studies s

Yuka Hashizume, Li Li, Atsushi Miyashita, Tomoki Toda

A flexible recommendation and retrieval system requires music similarity in terms of multiple partial elements of musical pieces to allow users to select the element they want to focus on. A method for music similarity learning using multiple networks with individual instrumental signals is effective but faces the problem that using each clean instrumental s

Stanislav Alexeyev, Oleg Zenin, Artem Baiderin

Using an improved version of the Newman-Janis algorithm, we obtain metrics of rotating black holes for a set of extended gravity theories that extend general relativity in different ways: the Horndeski model, the bumblebee model, the Gauss-Bonnet scalar gravity, the loop quantum gravity, the conformal gravity, and $f(Q)$ (symmetric teleparallel gravity STEGR

Gaifan Zhang, Yi Zhou, Danushka Bollegala

The meaning conveyed by a sentence often depends on the context in which it appears. Despite the progress of sentence embedding methods, it remains unclear as how to best modify a sentence embedding conditioned on its context. To address this problem, we propose Condition-Aware Sentence Embeddings (CASE), an efficient and accurate method to create an embeddi

Carlos Rafael Tordoya Taquichiri, Hans Dermot Doran, Pablo Ghiglino, Mandar Harshe

The reliance on radiation-hardened hardware, essential for domains requiring high-dependability such as space, nuclear energy and medical applications, severely restricts the choice of components available for modern AI-intensive tasks, particularly for real-time AI-based classifications. To address this challenge, we propose leveraging the High Performance

Michael L. Nastac, Robert J. Ewart, James Juno, Michael Barnes

The thermal fluctuation spectrum of the electric field arising due to particle noise in a quiescent Vlasov-Poisson plasma was derived in the 1960s. Here, we derive the universal fluctuation spectrum of the electric field, at Debye and sub-Debye scales, for a turbulent Vlasov-Poisson plasma. This spectrum arises from what is likely to be the final cascade - a

Robert Fraser

We consider measures supported on sets of irrational numbers possessing many consecutive partial quotients satisfying a condition based on the previous partial quotients. We show that under mild assumptions, such sets will always support measures whose Fourier transform decays to zero.

Maria Pilligua, Danna Xue, Javier Vazquez-Corral

Decomposing a video into a layer-based representation is crucial for easy video editing for the creative industries, as it enables independent editing of specific layers. Existing video-layer decomposition models rely on implicit neural representations (INRs) trained independently for each video, making the process time-consuming when applied to new videos.

Muhammad Ahmed Mohsin, Muhammad Jazib, Zeeshan Alam, Muhmmad Farhan Khan

In the evolving landscape of 6G networks, semantic communications are poised to revolutionize data transmission by prioritizing the transmission of semantic meaning over raw data accuracy. This paper presents a Vision Transformer (ViT)-based semantic communication framework that has been deliberately designed to achieve high semantic similarity during image

Benjamin Biaggi, Jan Draisma, Sarah Eggleston

We study the subrank of real order-three tensors and give a lower bound to the subrank of a real tensor given its complex subrank. Using similar arguments to those used by Bernardi-Blekherman-Ottaviani, we show that all subranks between the minimal typical subrank and the maximal typical subrank, which equals the generic subrank, are also typical. We then st

Paolo Frazzetto, Muhammad Uzair Ul Haq, Flavia Fabris, Alessandro Sperduti

The recruitment process is undergoing a significant transformation with the increasing use of machine learning and natural language processing techniques. While previous studies have focused on automating candidate selection, the role of multiple vacancies in this process remains understudied. This paper addresses this gap by proposing a novel pipeline that

Adam Karvonen

Sparse autoencoders (SAEs) are widely used for interpreting language model activations. A key evaluation metric is the increase in cross-entropy loss between the original model logits and the reconstructed model logits when replacing model activations with SAE reconstructions. Typically, SAEs are trained solely on mean squared error (MSE) when reconstructing

K. Kornoelje, L. E. Bleem, E. S. Rykoff, T. M. C. Abbott

We present a catalog of 500 galaxy cluster candidates in the SPT-Deep field: a 100 deg$^2$ field that combines data from the SPT-3G and SPTpol surveys to reach noise levels of 3.0, 2.2, and 9.0 $\mu$K-arcmin at 95, 150, and 220 GHz, respectively. This is comparable to noise levels expected for the wide field survey of CMB-S4, a next-generation CMB experiment

Vineet R Shenoy, Suhas Lohit, Hassan Mansour, Rama Chellappa

Camera-based monitoring of vital signs, also known as imaging photoplethysmography (iPPG), has seen applications in driver-monitoring, perfusion assessment in surgical settings, affective computing, and more. iPPG involves sensing the underlying cardiac pulse from video of the skin and estimating vital signs such as the heart rate or a full pulse waveform. S

Jorge Caravantes, Gema M. Diaz-Toca, Mario Fioravanti, Laureano Gonzalez-Vega

Efficient methods to determine the relative position of two conics are of great interest for applications in robotics, computer animation, CAGD, computational physics, and other areas. We present a method to obtain the relative position of a parabola or a hyperbola, and a coplanar ellipse, directly from the coefficients of their implicit equations, even if t

Hiromu Taketsugu, Takeru Oba, Takahiro Maeda, Shohei Nobuhara

Humans can predict future human trajectories even from momentary observations by using human pose-related cues. However, previous Human Trajectory Prediction (HTP) methods leverage the pose cues implicitly, resulting in implausible predictions. To address this, we propose Locomotion Embodiment, a framework that explicitly evaluates the physical plausibility

Jameel-Un Nabi, Muhammad Riaz, Tuncay Bayram, Muhammad Azaz

Recent studies \cite{1,2} predicted the sensitivity of the Gamow-Teller (GT) strength distributions to nuclear deformation in neutron-deficient Hg isotopes. Motivated by this work, we investigate nuclear ground-state properties and GT strength distributions for neutron-deficient Hg isotopes ($^{177\hbox{-}193}$Hg). The nuclear deformation ($\beta(E2)$) value

Yue Yu, Xinyue Hu, Sivaraman Rajaganapathy, Jingna Feng

Backgrounds: Artificial intelligence (AI) is transforming healthcare, yet translating AI models from theoretical frameworks to real-world clinical applications remains challenging. The Mayo Clinic Platform (MCP) was established to address these challenges by providing a scalable ecosystem that integrates real-world multiple modalities data from multiple inst

Disha Hegde, Jon Cockayne

Solving multiple parametrised related systems is an essential component of many numerical tasks, and learning from the already solved systems will make this process faster. In this work, we propose a novel probabilistic linear solver over the parameter space. This leverages information from the solved linear systems in a regression setting to provide an effi

Mateusz Basiak, Marcin Bienkowski, Martin Böhm, Marek Chrobak

We consider the List Update problem where the cost of each swap is assumed to be 1. This is in contrast to the ``standard'' model, in which an algorithm is allowed to swap the requested item with previous items for free. We construct an online algorithm Full-Or-Partial-Move (FPM), whose competitive ratio is at most $3.3904$, improving over the previous best

Zahra Jalali-Mola, Ortwin Hess

We demonstrate the emergence and control of Floquet states and topological bound states in the continuum (TBICs) in a two-dimensional colored quantum random walk (cQRW) on a square lattice. By introducing three internal degrees of freedom-termed "colors"-and leveraging SU(3) group representations, we realize dispersive TBICs and intrinsic Floquet dynamics wi

Shuang Guo, Friedhelm Hamann, Guillermo Gallego

Event cameras rely on motion to obtain information about scene appearance. This means that appearance and motion are inherently linked: either both are present and recorded in the event data, or neither is captured. Previous works treat the recovery of these two visual quantities as separate tasks, which does not fit with the above-mentioned nature of event

Jie Mei, Chenyu Lin, Yu Qiu, Yaonan Wang

Lung cancer is a leading cause of cancer-related deaths globally. PET-CT is crucial for imaging lung tumors, providing essential metabolic and anatomical information, while it faces challenges such as poor image quality, motion artifacts, and complex tumor morphology. Deep learning-based models are expected to address these problems, however, existing small-

Nicolas Lanchier, Max Mercer, Hyunsik Yun

This paper is concerned with a natural variant of the contact process modeling the spread of knowledge on the integer lattice. Each site is characterized by its knowledge, measured by a real number ranging from 0 = ignorant to 1 = omniscient. Neighbors interact at rate $\lambda$, which results in both neighbors attempting to teach each other a fraction $\mu$

Arianna Dragoni, Alessandro Margara

Today, data guides the decision-making process of most companies. Effectively analyzing and manipulating data at scale to extract and exploit relevant knowledge is a challenging task, due to data characteristics such as its size, the rate at which it changes, and the heterogeneity of formats. To address this challenge, software architects resort to build com

L. Origlia, F. R. Ferraro, C. Fanelli, B. Lanzoni

We address the chemical link between Terzan 5 (hereafter Ter5) and the Bulge, as probed by the observed distributions of [$\alpha$/Fe] abundance ratios with varying [Fe/H] and by suitable statistical tests to evaluate their significance. We also present a comprehensive review of the kinematic and evolutionary properties of Ter5, based on all the available ob

Silvia Cazacu, Stien Poncelet, Emma Feijtraij, Andrew Vande Moere

The environmental comfort in offices is traditionally captured by surveying an entire workforce simultaneously, which yet fails to capture the situatedness of the different personal experiences. To address this limitation, we developed the EnviroMapper Toolkit, a data physicalisation toolkit that allows individual office workers to record their personal expe

Jennifer Elder, Pamela E. Harris, Lybitina Koene, Ilana Lavene

We introduce a generalization of parking functions in which cars are limited in their movement backwards and forwards by two nonnegative integer parameters $k$ and $\ell$, respectively. In this setting, there are $n$ spots on a one-way street and $m$ cars attempting to park in those spots, and $1\leq m\leq n$. We let $\alpha=(a_1,a_2,\ldots,a_m)\in[n]^m$ den

Pierre Perrault

We present new and improved non-asymptotic deviation bounds for Dirichlet processes (DPs), formulated using the Kullback-Leibler (KL) divergence, which is known for its optimal characterization of the asymptotic behavior of DPs. Our method involves incorporating a controlled perturbation within the KL bound, effectively shifting the base distribution of the

ATLAS Collaboration

A search for a pseudoscalar $a$ produced in association with a top-quark pair, or in association with a single top quark plus a $W$ boson, with the pseudoscalar decaying into $b$-quarks ($a\rightarrow b\bar{b}$), is performed using the full Run 2 data sample using a dileptonic decay mode signature. The search covers pseudoscalar boson masses between 12-100 G

Lukas Fuchs, Kerstin Wein, Jens Friedland, Orkun Furat

Catalysis, particularly heterogeneous catalysis, is crucial in the chemical industry and energy storage. Approximately 80% of all chemical products produced by heterogeneous catalysis are produced by solid catalysts, which are essential for the synthesizing of ammonia, methanol, and hydrocarbons. Despite extensive use, challenges in catalyst development rema

Hyunwoo Lee, Young Woong Park

This study proposes a mathematical programming-based algorithm for the integrated selection of variable subsets and bandwidth estimation in geographically weighted regression, a local regression method that allows the kernel bandwidth and regression coefficients to vary across study areas. Unlike standard approaches in the literature, in which bandwidth and

Hilal Asi, John C. Duchi, Kunal Talwar

We investigate differentially private estimators for individual parameters within larger parametric models. While generic private estimators exist, the estimators we provide repose on new local notions of estimand stability, and these notions allow procedures that provide private certificates of their own stability. By leveraging these private certificates,

Jameel-Un Nabi, Asim Ullah, Muhammad Tahir

We investigate the effect of pairing correlations on the computed Gamow-Teller (GT) strength distributions and corresponding $\beta$-decay half-lives. The calculations are performed for a total of 47 sd-shell nuclei, for $20 < A < 30$, employing the pn-QRPA model. Our calculations use three different values of pairing gaps computed using three different empi

Jan Benáček, Axel Jessner, Martin Pohl, Tatiana Rievajová

It is crucial to know the polarization properties of coherent radio waves that escape from pulsar polar caps to calculate the radiative transfer through the magnetosphere and to predict observable radio properties. We describe pair cascades in the pulsar polar cap, and we determine for the first time the Stokes parameters of the escaping radio waves from fir

Lars Kröger, Cristian Cioflan, Victor Kartsch, Luca Benini

RISC-V-based architectures are paving the way for efficient On-Device Learning (ODL) in smart edge devices. When applied across multiple nodes, ODL enables the creation of intelligent sensor networks that preserve data privacy. However, developing ODL-capable, battery-operated embedded platforms presents significant challenges due to constrained computationa

Val G. Rousseau

When faced with overwhelming evidence supporting the reality of time dilation, confirmed in particular by the Hafele-Keating experiment, some anti-relativists reluctantly concede that time dilation applies to light clocks. However, they argue that the Theory of Relativity remains flawed, claiming that time dilation applies to light clocks only, not to massiv

Michael J Bommarito, Daniel Martin Katz, Jillian Bommarito

We present the KL3M tokenizers, a family of specialized tokenizers for legal, financial, and governmental text. Despite established work on tokenization, specialized tokenizers for professional domains remain understudied. Our paper offers two main contributions to this area. First, we introduce domain-specific BPE tokenizers for legal, financial, and govern

Alex Lynham, Geoff Goodell

Decentralization is understood both by professionals in the blockchain industry and general users as a core design goal of permissionless ledgers. However, its meaning is far from universally agreed, and often it is easier to get opinions on what it is not, rather than what it is. In this paper, we solicit definitions of 'decentralization' and 'decentralizat

Taiga Miyachi, Ryo Namba, Hidetoshi Omiya, Naritaka Oshita

We investigate black hole quasinormal modes using the exact WKB method. We perform an analytic continuation from the horizon to infinity along the positive real axis of the radial coordinate and impose appropriate boundary conditions at these asymptotic positions. We clarify the role of previously overlooked logarithmic spirals of Stokes curves and branch cu

Jyothi Rikhab Chand, Mathews Jacob

Current end-to-end (E2E) and plug-and-play (PnP) image reconstruction algorithms approximate the maximum a posteriori (MAP) estimate but cannot offer sampling from the posterior distribution, like diffusion models. By contrast, it is challenging for diffusion models to be trained in an E2E fashion. This paper introduces a Deep End-to-End Posterior ENergy (DE

Dorit Aharonov, Ori Alberton, Itai Arad, Yosi Atia

Quantum error mitigation (EM) is a family of hybrid quantum-classical methods for eliminating or reducing the effect of noise and decoherence on quantum algorithms run on quantum hardware, without applying quantum error correction (EC). While EM has many benefits compared to EC, specifically that it requires no (or little) qubit overhead, this benefit comes

Fakeha Farooq, Jameel-Un Nabi, Ramoona Shehzadi

In this study, we have reported key nuclear properties of weak $\beta$-decay processes on Yttrium isotopes for the mass number range $A = 101 - 108$. This mass region is important in astrophysical $r$-process abundances. Our study might be helpful in $r$-process simulations. We have computed charge-changing strength distributions, $\beta$-decay half-lives, $

Md. Ahsan Habib

Blockchain technology is rapidly evolving, with scalability remaining one of its most significant challenges. While various solutions have been proposed and continue to be developed, it is essential to consider the blockchain trilemma -- balancing scalability, security, and decentralization -- when designing new approaches. One promising solution is the zero

Pawan Kumar Srivastava, Vedanki Khandelwal, Ramesh Reddy, Kartick Tarafder

Here, we report on controlling strain in graphene by trapping molecules at the graphene-substrate interface, leveraging molecular dipole moments. Spectroscopic and transport measurements show that strain correlates with the dipole moments of trapped molecules, with a dipole range of 1.5 D to 4.9 D resulting in a 50-fold increase in strain and a substantial r

Aladin Djuhera, Swanand Ravindra Kadhe, Farhan Ahmed, Syed Zawad

Fine-tuning large language models (LLMs) is a common practice to adapt generalist models to specialized domains. However, recent studies show that fine-tuning can erode safety alignment, causing LLMs to respond to harmful or unethical prompts. Many methods to realign safety have been proposed, but often introduce custom algorithms that are difficult to imple

Devavrat Tomar, Guillaume Vray, Dwarikanath Mahapatra, Sudipta Roy

In this paper, we address the challenge of few-shot classification in histopathology whole slide images (WSIs) by utilizing foundational vision-language models (VLMs) and slide-level prompt learning. Given the gigapixel scale of WSIs, conventional multiple instance learning (MIL) methods rely on aggregation functions to derive slide-level (bag-level) predict