March 2025 arXiv papers — page 211

Ziang Zhou, Tianyuan Jin, Jieming Shi, Qing Li

Large Language Models (LLMs) exhibit impressive performance across diverse domains but often suffer from overconfidence, limiting their reliability in critical applications. We propose SteerConf, a novel framework that systematically steers LLMs' confidence scores to improve their calibration and reliability. SteerConf introduces three key components: (1) a

Hong Guan, Lei Yu, Lixi Zhou, Li Xiong

With the growing adoption of privacy-preserving machine learning algorithms, such as Differentially Private Stochastic Gradient Descent (DP-SGD), training or fine-tuning models on private datasets has become increasingly prevalent. This shift has led to the need for models offering varying privacy guarantees and utility levels to satisfy diverse user require

Jingfei Huang, Alexandros Haridis

Recent advancements in multimodal Generative AI have the potential to democratize specialized architectural tasks, such as interpreting technical drawings and creating 3D CAD models, which traditionally require expert knowledge. This paper presents a comparative evaluation of two systems: GPT-4o and Claude 3.5, in the task of architectural 3D synthesis. We c

Luke Vaughan, Mohammed Rakib, Shivang Patel, Flera Rizatdinova

The Large Hadron Collider, LHC, collides bunches of protons resulting in multiple interactions that occur practically simultaneously. This creates a pileup effect that distorts physics measurements due to the products of pileup collisions. In order to improve the discovery potential of the LHC, it is necessary to mitigate the effect of pileup interactions on

Emma Ceccherini, Ian Gallagher, Andrew Jones, Daniel Lawson

Stability for dynamic network embeddings ensures that nodes behaving the same at different times receive the same embedding, allowing comparison of nodes in the network across time. We present attributed unfolded adjacency spectral embedding (AUASE), a stable unsupervised representation learning framework for dynamic networks in which nodes are attributed wi

Tuan Q. Do, W. F. Kao

Inspired by the five-dimensional Kaluza-Klein theory, we would like to study the dimensional reduction issue of six-dimensional Kaluza-Klein extension in this paper. In particular, we will examine two possible approaches of dimensional reduction from six-dimensional spacetimes to four-dimensional ones. The first one is a direct dimensional reduction, i.e., f

Jingyu Liu, Xiaoting Wang, Xiaozhe Wang

Assessing the risk of low-probability high-impact transient instability (TI) events is crucial for ensuring robust and stable power system operation under high uncertainty. However, direct Monte Carlo (DMC) simulation for rare TI event sampling is computationally intensive. This paper proposes a subset simulation-based method for efficient small TI probabili

Manuel Gadella, Luis P. Lara

We provide of a method to integrate first order non-linear systems of differential equations with variable coefficients. It determines approximate solutions given initial or boundary conditions or even for Sturm-Liouville problems. This method is a mixture between an iterative process, a la Picard, plus a segmentary integration, which gives explicit approxim

Francesco Aprile, Stefano Giusto, Rodolfo Russo

We consider heavy-heavy-light-light (HHLL) correlators in AdS/CFT, focussing on the D1D5 CFT$_2$ and the ${\cal N}= 4$ super Yang-Mills theory. Out of the lightest $1/2$-BPS operator in the spectrum, $O$, we construct a particular heavy operator $O_H$ given by a coherent superposition of multi-particle operators $O^n$, and study the HHLL correlator. When $n$

Belinda Z. Li, Zifan Carl Guo, Jacob Andreas

Transformer language models (LMs) exhibit behaviors -- from storytelling to code generation -- that seem to require tracking the unobserved state of an evolving world. How do they do this? We study state tracking in LMs trained or fine-tuned to compose permutations (i.e., to compute the order of a set of objects after a sequence of swaps). Despite the simple

Luis Marquez-Carpintero, Sergio Suescun-Ferrandiz, Monica Pina-Navarro, Miguel Cazorla

The monitoring and prediction of in-class student activities is of paramount importance for the comprehension of engagement and the enhancement of pedagogical efficacy. The accurate detection of these activities enables educators to modify their lessons in real time, thereby reducing negative emotional states and enhancing the overall learning experience. To

Joseph J. Webb, Richard Beanland, Rudolf A. Römer

We show how generative machine learning can be used for the rapid computation of strongly dynamical electron diffraction directly from crystal structures, specifically in large-angle convergent-beam electron diffraction (LACBED) patterns. We find that a conditional generative adversarial network can learn the connection between the projected potential from a

Zicong He, Boxuan Zhang, Lu Cheng

Large language models (LLMs) are known to hallucinate, a phenomenon often linked to creativity. While previous research has primarily explored this connection through theoretical or qualitative lenses, our work takes a quantitative approach to systematically examine the relationship between hallucination and creativity in LLMs. Given the complex nature of cr

Ekkehard Glimm, Lillian Yau

The comparison of different medical treatments from observational studies or across different clinical studies is often biased by confounding factors such as systematic differences in patient demographics or in the inclusion criteria for the trials. Propensity score matching is a popular method to adjust for such confounding. It compares weighted averages of

Amin Honarmandi Shandiz

Molecular subtyping of breast cancer is crucial for personalized treatment and prognosis. Traditional classification approaches rely on either histopathological images or gene expression profiling, limiting their predictive power. In this study, we propose a deep multimodal learning framework that integrates histopathological images and gene expression data

Mithilesh Nayak, Julian Thoenniss, Michael Sonner, Dmitry A. Abanin

We implement the recently developed influence functional matrix product states approach as impurity solver in equilibrium and nonequilibrium dynamical mean field theory (DMFT) calculations of the single-band Hubbard model. The method yields numerically exact descriptions of metallic states without sharp spectral features, at a moderate numerical cost. System

Brandon Hardy, Judith Zimmermann, Vincent Lechner, Mia Bonini

Magnetic resonance imaging (MRI) can estimate three-dimensional (3D) time-resolved relative pressure fields using 4D-flow MRI, thereby providing rich pressure field information. Clinical alternatives include catheterization and Doppler echocardiography, which only provide one-dimensional pressure drops. The accuracy of one-dimensional pressure drops derived

Yuzhe Gu, Wenwei Zhang, Chengqi Lyu, Dahua Lin

Large language models (LLMs) exhibit hallucinations (i.e., unfaithful or nonsensical information) when serving as AI assistants in various domains. Since hallucinations always come with truthful content in the LLM responses, previous factuality alignment methods that conduct response-level preference learning inevitably introduced noises during training. The

Md Tashfiq Bin Kashem, Sadia Anjum Esha

Lead-free all-inorganic halide perovskite solar cells (PSCs) have emerged as a promising alternative to toxic lead-based solar cells and organic solar cells, which have limited stability. This work explores such a PSC with sodium tin chloride (NaSnCl$_3$) as the absorber, due to its significant potential for optoelectronic applications. To investigate this p

Vaibhav Singh, Paul Janson, Paria Mehrbod, Adam Ibrahim

The ever-growing availability of unlabeled data presents both opportunities and challenges for training artificial intelligence systems. While self-supervised learning (SSL) has emerged as a powerful paradigm for extracting meaningful representations from vast amounts of unlabeled data, existing methods still struggle to adapt to the non-stationary, non-IID

Maicol A. Ochoa, Keyi Liu, Piotr Różański, Michał Zieliński

Donor-based quantum devices in silicon are attractive platforms for universal quantum computing and analog quantum simulations. The nearly-atomic precision in dopant placement promises great control over the quantum properties of these devices. We present atomistic calculations and a detailed analysis of many-electron states in a single phosphorus atom and s

Carla Binucci, Fabrizio Montecchiani, Daniel Perz, Alessandra Tappini

Given a point set $\mathcal{P}$ and a plane perfect matching $\mathcal{M}$ on $\mathcal{P}$, a flip is an operation that replaces two edges of $\mathcal{M}$ such that another plane perfect matching on $\mathcal{P}$ is obtained. Given two plane perfect matchings on $\mathcal{P}$, we show that it is NP-hard to minimize the number of flips that are needed to tr

Theodore Zhao, Sid Kiblawi, Naoto Usuyama, Ho Hin Lee

Detecting and segmenting small objects, such as lung nodules and tumor lesions, remains a critical challenge in image analysis. These objects often occupy less than 0.1% of an image, making traditional transformer architectures inefficient and prone to performance degradation due to redundant attention computations on irrelevant regions. Existing sparse atte

Alexander Petrov, Leonid Skripnikov

The g-factors for $J = 1$, $F=3/2$, $|M_F|=3/2$ hyperfine levels of the ground electronic state $^3\Delta_1$ of the $^{232}$ThF$^+$ cation are calculated as functions of the external electric field. These calculations are necessary for the analysis of systematic effects in the experiment aimed at searching for the electron electric dipole moment.

Tobias Lenz, Sil Linskens, Phil Pützstück

We show that the $\infty$-category of normed algebras in genuine $G$-spectra, as introduced by Bachmann-Hoyois, is modelled by strictly commutative algebras in $G$-symmetric spectra for any finite group $G$. We moreover provide an analogous description of Schwede's ultra-commutative global ring spectra in higher categorical terms. Using these new description

Henri Guenancia, Ursula Hamenstädt

Given any integer $n\geq 2$, we construct a compact K\"ahler-Einstein manifold of dimension n of negative sectional curvature which is not covered by the ball.

Yu Jiang

The Terwilliger algebras of association schemes over an arbitrary field $\mathbb{F}$ were briefly called the Terwilliger $\mathbb{F}$-algebras of association schemes in [9]. In this paper, the Terwilliger $\mathbb{F}$-algebras of direct products of group divisible association schemes are studied. The centers, the semisimplicity, the Jacobson radicals and the

Ting-Ji Huang, Xu-Yang Chen, Han-Jia Ye

Unlike traditional time-series forecasting methods that require extensive in-task data for training, zero-shot forecasting can directly predict future values given a target time series without additional training data. Current zero-shot approaches primarily rely on pre-trained generalized models, with their performance often depending on the variety and rele

Afsana Ahsan Jeny, Masum Shah Junayed, Md Robel Mia, Md Baharul Islam

Facial acne is a common disease, especially among adolescents, negatively affecting both physically and psychologically. Classifying acne is vital to providing the appropriate treatment. Traditional visual inspection or expert scanning is time-consuming and difficult to differentiate acne types. This paper introduces an automated expert system for acne recog

Michal Nazarczuk, Karla Stepanova, Jan Kristof Behrens, Matej Hoffmann

Current embodied reasoning agents struggle to plan for long-horizon tasks that require to physically interact with the world to obtain the necessary information (e.g. 'sort the objects from lightest to heaviest'). The improvement of the capabilities of such an agent is highly dependent on the availability of relevant training environments. In order to facili

Raula Gaikovina Kula, Christoph Treude

The rise of AI-driven coding assistants signals a fundamental shift in how software is built. While AI coding assistants have been integrated into existing Integrated Development Environments (IDEs), their full potential remains largely untapped. A key challenge is that these AI assistants can suffer from hallucinations, leading developers down decision path

Songming Zhang, Xue Zhang, Tong Zhang, Bojie Hu

In modern large language models (LLMs), LLM alignment is of crucial importance and is typically achieved through methods such as reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO). However, in most existing methods for LLM alignment, all tokens in the response are optimized using a sparse, response-level reward or pref

Kevin McKee, Eric Alt, Andrew Grebenisan, Mick van Gelderen

Exploration algorithms for reinforcement learning typically replace or augment the reward function with an additional ``intrinsic'' reward that trains the agent to seek previously unseen states of the environment. Here, we consider an exploration algorithm that exploits meta-learning, or learning to learn, such that the agent learns to maximize its explorati

Melvin Hochster, Yongwei Yao

We prove a form of generic local duality that generalizes a result of Karen E. Smith. Specifically, let $R$ be a Noetherian ring, let $P$ be a prime ideal of $R$ of height $h$, let $A:=R/P$, and $W$ be a subset of $R$ that maps onto $A\setminus \{0\}$. Suppose that $R_P$ is Cohen-Macaulay, and that $\omega$ is a finitely generated $R$-module such that $\omeg

Ralf Kotulla, Marsha Wolf, Matt Bershady

We describe the basic instrument detrending software for the NIRWALS spectrograph on the SALT telescope. Its basic purpose is to process multiple non-destructive reads of increasing exposure time into a final image reflecting the observed source intensity as expressed in counts (or electrons) per second, including its uncertainty. The output products of this

Miroslav Hopjan, Lev Vidmar

We investigate critical transport and the dynamical exponent through the spreading of an initially localized particle in quadratic Hamiltonians with short-range hopping in lattice dimension $d_l$. We consider critical dynamics that emerges when the Thouless time, i.e., the saturation time of the mean-squared displacement, approaches the typical Heisenberg ti

Manuel Morales Alvarado

The Standard Model (SM) is one of the most successful theories ever conceived, representing a triumph of human intellect and collaboration. The SM provides a very good description of visible matter, with the proton being a central part of this understanding. The structure of the proton can be parametrised in terms of parton distribution functions (PDFs), ess

Patrick Franco, Fernando Roig, Othon C. Winter, Rafael Sfair

The origin of Mercury still remains poorly understood compared to the other rocky planets of the Solar System. One of the most relevant constraints that any formation model has to fulfill refers to its internal structure, with a predominant iron core covered by a thin silicate layer. This led to the idea that it could be the product of a mantle stripping cau

Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer

Non-ergodic convergence of learning dynamics in games is widely studied recently because of its importance in both theory and practice. Recent work (Cai et al., 2024) showed that a broad class of learning dynamics, including Optimistic Multiplicative Weights Update (OMWU), can exhibit arbitrarily slow last-iterate convergence even in simple $2 \times 2$ matr

Yujin Oh, Robert Seifert, Yihan Cao, Christoph Clement

In oncology, Positron Emission Tomography-Computed Tomography (PET/CT) is widely used in cancer diagnosis, staging, and treatment monitoring, as it combines anatomical details from CT with functional metabolic activity and molecular marker expression information from PET. However, existing artificial intelligence-driven PET/CT analyses rely predominantly on

Matteo Spanio, Massimiliano Zampini, Antonio Rodà, Franco Pierucci

In recent decades, neuroscientific and psychological research has traced direct relationships between taste and auditory perceptions. This article explores multimodal generative models capable of converting taste information into music, building on this foundational research. We provide a brief review of the state of the art in this field, highlighting key f

Walter Bridges, Kathrin Bringmann, Caner Nazaroglu

In this paper we investigate how a typical, large-dimensional representation looks for a complex Lie algebra. In particular, we study the family $\mathfrak{sl}_{r+1}(\mathbb{C})$ of Lie algebras for $r \geq 2$ and derive asymptotic probability distributions for the multiplicity of small irreducible representations, as well as the largest dimension, the large

Carter E. Wade, Sunny Phan, Katherine Coffin, Gabe Paynter

The Heusler compound Co2MnGa is a topological semimetal with intriguing electronic and magnetic properties, making it a promising candidate for spintronic applications. This study systematically investigates the effects of substrate temperature and RF sputtering power on the structure, morphology, and anomalous Hall effect (AHE) in Co2MnGa thin films. Using

Timothy D Barfoot

Matrix Lie groups provide a language for describing motion in such fields as robotics, computer vision, and graphics. When using these tools, we are often faced with turning infinite-series expressions into more compact finite series (e.g., the Euler-Rodrigues formula), which can sometimes be onerous. In this paper, we identify some useful integral forms in

Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan

While score-based generative models are the model of choice across diverse domains, there are limited tools available for controlling inference-time behavior in a principled manner, e.g. for composing multiple pretrained models. Existing classifier-free guidance methods use a simple heuristic to mix conditional and unconditional scores to approximately sampl

Persi Diaconis, Michael Howes

This paper gives new, efficient algorithms for approximate uniform sampling of contingency tables and integer partitions. The algorithms use the Burnside process, a general algorithm for sampling a uniform orbit of a finite group acting on a finite set. We show that a technique called `lumping' can be used to derive efficient implementations of the Burnside

Raula Gaikovina Kula, Brittany Anne Reid, Christoph Treude

The widespread adoption of open source libraries and frameworks can be attributed to their licensing. Open Source Software Licenses (OSS licenses) ensure that software can be sold or distributed as part of aggregate programs from various sources without requiring a royalty or fee. The quality of such code rivals that of commercial software, with open source

Salomé A. Sepúveda Fontaine, José M. Amigó

Since its origin in the thermodynamics of the 19th century, the concept of entropy has also permeated other fields of physics and mathematics, such as Classical and Quantum Statistical Mechanics, Information Theory, Probability Theory, Ergodic Theory and the Theory of Dynamical Systems. Specifically, we are referring to the classical entropies: the Boltzmann

Sojeong Yun, Youn-kyung Lim

The advancement of Vision-Language Model (VLM) camera sensors, which enable autonomous understanding of household situations without user intervention, has the potential to completely transform the DIY smart home building experience. Will this simplify or complicate the DIY smart home process? Additionally, what features do users want to create using these s

Qingchen Zhang, Shijun Cheng, Wei Chen, Weijian Mao

Full waveform inversion (FWI) plays an important role in velocity modeling due to its high-resolution advantages. However, its highly non-linear characteristic leads to numerous local minimums, which is known as the cycle-skipping problem. Therefore, effectively addressing the cycle-skipping issue is crucial to the success of FWI. Well-log data contain rich

Henrique Ennes, Clément Maria

Quantum invariants in low dimensional topology offer a wide variety of valuable invariants of knots and 3-manifolds, presented by explicit formulas that are readily computable. Their computational complexity has been actively studied and is tightly connected to topological quantum computing. In this article, we prove that for any 3-manifold quantum invariant

Anna Pandolfi, Ignacio Romero, Michael Ortiz

We formulate a finite-particle method of mass transport that accounts for general mixed boundary conditions. The particle method couples a geometrically-exact treatment of advection; Wasserstein gradient-flow dynamics; and a Kullback-Leibler representation of the entropy. General boundary conditions are enforced by introducing an adsorption/depletion layer a

Nathan Godey, Alessio Devoto, Yu Zhao, Simone Scardapane

Autoregressive language models rely on a Key-Value (KV) Cache, which avoids re-computing past hidden states during generation, making it faster. As model sizes and context lengths grow, the KV Cache becomes a significant memory bottleneck, which calls for compression methods that limit its size during generation. In this paper, we discover surprising propert

Chung-Yang Wang, Steven M. Anlage

Trapped vortices in superconductors introduce residual resistance in superconducting radio-frequency (SRF) cavities and disrupt the operation of superconducting quantum and digital electronic circuits. Understanding the detailed dynamics of trapped vortices under oscillating magnetic fields is essential for advancing these technologies. We have developed a n

Eric Anderson, Heonjoon Park, Kaijie Yang, Jiaqi Cai

Magnetoelectric effects and their coupling to light helicity are important for both fundamental science and applications in sensing, communication, and data storage. Traditional approaches require complex device architectures, involving separate spin-injection, ferromagnetic, and optically active layers. Recently, the emergence of 2D semiconductor moir\'e su

V. Zoryansky, P. Zinoviev, Yu. Semerenko

Optical properties of fullerite C$_{60}$ single crystals saturated with hydrogen and nitrogen molecules were studied in the temperature range from 20 K to 230 K using the spectral-luminescent method. Saturation was carried out under a pressure of 30 atm at various temperatures from 470 K to 720 K. At saturation temperatures above 520 K for hydrogen and 690 K

Wei Chen, Kelin Li, Dongmyoung Lee, Xiaoshuai Chen

Physical manipulation of garments is often crucial when performing fabric-related tasks, such as hanging garments. However, due to the deformable nature of fabrics, these operations remain a significant challenge for robots in household, healthcare, and industrial environments. In this paper, we propose GraphGarment, a novel approach that models garment dyna

Liming Liu, Zixuan Zhang, Simon Du, Tuo Zhao

Recent advances in deep learning optimization have unveiled two intriguing phenomena under large learning rates: Edge of Stability (EoS) and Progressive Sharpening (PS), challenging classical Gradient Descent (GD) analyses. Current research approaches, using either generalist frameworks or minimalist examples, face significant limitations in explaining these

Oisín Hamilton, Jonathan Demaeyer, Michel Crucifix, Stéphane Vannitsem

Unstable Periodic Orbits (UPOs) were used to identify regimes, and transitions between regimes, in a reduced-order coupled atmosphere-land spectral model. In this paper we describe how the chaotic attractor of this model was clustered using the numerically derived set of UPOs. Using continuation software, the origin of these clusters were also investigated.

Tanuj Karia, Giacomo Lastrucci, Artur M. Schweidtmann

To address the challenge of tractability for optimizing mathematical models in science and engineering, surrogate models are often employed. Recently, a new class of machine learning models named Kolmogorov Arnold Networks (KANs) have been proposed. It was reported that KANs can approximate a given input/output relationship with a high level of accuracy, req

Ankit Singh, Vinay Vaibhav, Alessio Zaccone

Viscosity, a fundamental transport and rheological property of liquids, quantifies the resistance to relative motion between molecular layers and plays a critical role in understanding material behavior. Conventional methods, such as the Green-Kubo (GK) approach, rely on time integration of correlation functions, which becomes computationally intensive near

Junhao Zhang, Jie Hou, Jie Lou, Yan Chen

Geometric frustration in quantum spin systems can lead to exotic ground states. In this study, we investigate the $\mathrm{SU}(3)$ spin model on the checkerboard lattice to explore the effects of frustration arising from its point-connected $(N+1)$-site local structure. We employ density matrix renormalization group (DMRG) and exact diagonalization (ED) tech

Brittany Anne Reid, Raula Gaikovina Kula

Much of the success of modern software development can be attributed to code reuse. The ability to reuse existing functionality via third-party dependencies has enabled massive gains in productivity, but for a long time the dominant philosophy has been to 'reuse as much as possible, without thought for what is being depended upon', creating fragile dependenc

Vladimir Vovk

This paper introduces inductive randomness predictors, which form a proper superset of inductive conformal predictors but have the same principal property of validity under the assumption of randomness (i.e., of IID data). It turns out that every non-trivial inductive conformal predictor is strictly dominated by an inductive randomness predictor, although th

Guy Bresler, Alina Harbuzova

In this work, we show the first average-case reduction transforming the sparse Spiked Covariance Model into the sparse Spiked Wigner Model and as a consequence obtain the first computational equivalence result between two well-studied high-dimensional statistics models. Our approach leverages a new perturbation equivariance property for Gram-Schmidt orthogon

Andrea Cappelli, Riccardo Villa

We discuss two methods for relating bosonic and fermionic relativistic field theories in 2+1 dimensions, the $Z_2^f$ gauging and the flux attachment. The first is primarily a correspondence between topological theories. It amounts to summing over fermionic spin structures, as is familiar in two-dimensional conformal theories. Its inverse map, fermionization,

Alicia Russell-Gilbert, Sudip Mittal, Shahram Rahimi, Maria Seale

Anomaly detection in complex industrial environments poses unique challenges, particularly in contexts characterized by data sparsity and evolving operational conditions. Predictive maintenance (PdM) in such settings demands methodologies that are adaptive, transferable, and capable of integrating domain-specific knowledge. In this paper, we present RAAD-LLM

Weihang Wang, Duolin Sun, Jielei Zhang, Longwen Gao

Few-shot Font Generation (FFG) aims to create new font libraries using limited reference glyphs, with crucial applications in digital accessibility and equity for low-resource languages, especially in multilingual artificial intelligence systems. Although existing methods have shown promising performance, transitioning to unseen characters in low-resource la

Syamantak Kumar, Purnamrita Sarkar, Kevin Tian, Yusong Zhu

Posterior sampling with the spike-and-slab prior [MB88], a popular multimodal distribution used to model uncertainty in variable selection, is considered the theoretical gold standard method for Bayesian sparse linear regression [CPS09, Roc18]. However, designing provable algorithms for performing this sampling task is notoriously challenging. Existing poste

Nathan Drenkow, Mathias Unberath

Image quality plays an important role in the performance of deep neural networks (DNNs) that have been widely shown to exhibit sensitivity to changes in imaging conditions. Conventional image quality assessment (IQA) seeks to measure and align quality relative to human perceptual judgments, but we often need a metric that is not only sensitive to imaging con

Andrew Wilkinson, Michael A. Morgan, Michael Wilkinson

If a body of inviscid fluid is disturbed, it will typically eject a jet of fluid. If the effects of gravity and surface tension are negligible, these jets travel in straight lines, with the tips approaching a constant velocity. It has been observed that these jets can have a broad base, tapering progressively toward the tip, but the mathematical form of thei

Osama Abuzaid, Eveliina Peltola

We prove large deviation principles (LDPs) for full chordal, radial, and multichordal SLE(0+) curves parameterized by capacity. The rate function is given by the appropriate variant of the Loewner energy. There are two key novelties in the present work. First, we strengthen the topology in the known chordal LDPs into the topology of full parameterized curves

Lisa Blum Moyse, Ahmed El Hady

Social foraging is a widespread form of animal foraging in which groups of individuals coordinate their decisions to exploit resources in the environment. Animals show a variety of social structures from egalitarian to hierarchical. In this study, we examine how different forms of social hierarchy shape foraging decisions. We developed a mechanistic analytic

Justin Salez, Pierre Youssef

The chain rule lies at the heart of the powerful Gamma calculus for Markov diffusions on manifolds, providing remarkable connections between several fundamental notions such as Bakry-\'Emery curvature, entropy decay, and hypercontractivity. For Markov chains on finite state spaces, approximate versions of this chain rule have recently been put forward, with

Ada Altieri

The study of ecological systems is gaining momentum in modern scientific research, driven by an abundance of empirical data and advancements in bioengineering techniques. However, a full understanding of their dynamical and thermodynamical properties, also in light of the ongoing biodiversity crisis, remains a formidable endeavor. From a theoretical standpoi

Vaibhav Sharma, Kaden R. A. Hazzard

Quantum simulation of lattice gauge theories is an important avenue to gain insights into both particle physics phenomena and constrained quantum many-body dynamics. There is a growing interest in probing analogs of high energy collision phenomena in lattice gauge theories that can be implemented on current quantum simulators. Motivated by this, we character

Marcus Becker, Maarten J. van den Broek, Dries Allaerts, Jan-Willem van Wingerden

Wind farm flow control has been a key research focus in recent years, driven by the idea that a collectively operating wind farm can outperform individually controlled turbines. Control strategies are predominantly applied in an open-loop manner, where the current flow conditions are used to look up precomputed steady-state set points. Closed-loop control ap

Paul N Zivich, Bonnie E Shook-Sa, Stephen R Cole, Eric T Lofgren

Introduction: Accounting for missing data by imputing or weighting conditional on covariates relies on the variable with missingness being observed at least some of the time for all unique covariate values. This requirement is referred to as positivity and positivity violations can result in bias. Here, we review a novel approach to addressing positivity vio

Hidetsugu Ikegami

In the scheme of chemonuclear reaction, bulk of itinerant s-electrons revealing the thermodynamical liquid activity in metallic systems undergo contact interaction with atomic nuclei and therein nucleons, inducing contagiously thermodynamical liquid activity among the bulk of nuclei under the irreversible action of Nature towards the chemical potential minim

Sjoerd Hermes

Ordinal user-provided ratings across multiple items are frequently encountered in both scientific and commercial applications. Whilst recommender systems are known to do well on these type of data from a predictive point of view, their typical reliance on large sample sizes and frequent lack of interpretability and uncertainty quantification limits their app

Mithun Ravisankar, Roberto Zenit

We study the effects of polymer additives on pseudoturbulence induced by a swarm of bubbles rising in a quiescent fluid. We find that, beyond a critical polymer concentration, the energy spectra of velocity fluctuations in bubble-induced turbulence decay more steeply with respect to the wavenumber $k$. This new scaling is significantly steeper than the class

Jaekyeom Kim, Sungryull Sohn, Gerrard Jeongwon Jo, Jihoon Choi

This paper argues that a dataset's legal risk cannot be accurately assessed by its license terms alone; instead, tracking dataset redistribution and its full lifecycle is essential. However, this process is too complex for legal experts to handle manually at scale. Tracking dataset provenance, verifying redistribution rights, and assessing evolving legal ris

Jie Wu, Haoling Li, Xin Zhang, Xiao Liu

Preference learning extends the performance of Code LLMs beyond traditional supervised fine-tuning by leveraging relative quality comparisons. In existing approaches, a set of n candidate solutions is evaluated based on test case success rates, with the candidate demonstrating a higher pass rate being labeled as positive and its counterpart with a lower pass

Alexander Sauter, A. Oguz Kislal, Giuseppe Durisi, Gianluigi Liva

We analyze the trade-off between the undetected error probability (i.e., the probability that the channel decoder outputs an erroneous message without detecting the error) and the total error probability in the short blocklength regime. We address the problem by developing two new finite blocklength achievability bounds, which we use to benchmark the perform

Rajesh P. Narayanan, R. Kelley Pace

We develop a formal economic framework to analyze whether neural scaling laws in artificial intelligence will activate Jevons' Paradox in labor markets, potentially leading to increased AI adoption and human labor substitution. By using a time-varying elasticity of substitution (VES) approach, we establish analytical conditions under which AI systems transit

Yepeng Huang, Xiaorui Su, Varun Ullanat, Intae Moon

Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations, reducing late-stage clinical failures, and accelerating the development of precision therapies. Current AI models rely on structural or target-based features but fail to incorporate the multimodal data necessary for accurate, clinically relev

Xavier Cadet, Simona Boboila, Edward Koh, Peter Chin

Cyber resilience is the ability of a system to recover from an attack with minimal impact on system operations. However, characterizing a network's resilience under a cyber attack is challenging, as there are no formal definitions of resilience applicable to diverse network topologies and attack patterns. In this work, we propose a quantifiable formulation o

S. Mor, F. Boschini, E. Razzoli, M. Zonno

The coupling between quasiparticles and bosonic excitations rules the energy transfer pathways in condensed matter systems. The possibility of inferring the strength of specific coupling channels from their characteristic time scales measured in nonequilibrium experiments is still an open question. Here, we investigate MgB$_2$, in which conventional supercon

Nora Bauer, Kübra Yeter-Aydeniz, George Siopsis

We present a hardware-efficient optimization scheme for quantum chemistry calculations, utilizing the Sampled Quantum Diagonalization (SQD) method. Our algorithm, optimized SQD (SQDOpt), combines the classical Davidson method technique with added multi-basis measurements to optimize a quantum Ansatz on hardware using a fixed number of measurements per optimi

Geethika Santhosh, Rakhi R, Koshy George, Smitha Subramanian

Interactions play a significant role in the formation and evolution of galaxies in the Universe. The galaxy systems, NGC 7252 and NGC 5291 are two nearby interacting systems that are hosting Tidal Dwarf Galaxies (TDGs) and star-forming knots. The present work aims (a) To determine the attenuation-corrected star formation rate (SFR) of the interacting system

Xinru Lin, Luyang Li

Due to the implement of guardrails by developers, Large language models (LLMs) have demonstrated exceptional performance in explicit bias tests. However, bias in LLMs may occur not only explicitly, but also implicitly, much like humans who consciously strive for impartiality yet still harbor implicit bias. The unconscious and automatic nature of implicit bia

Nikolai G. Khlebtsov, Sergey V. Zarkov

From a structural point of view, plasmonic nanoparticles are always at least two-layer structures with a dielectric layer of stabilizing, targeting, fluorescent, Raman, or other functional molecules. To optimize the optical properties of such bioconjugates, one needs efficient analytical models based on simple physical ideas and with reasonable accuracy comp

Christian Cella, Matteo Bruce Robin, Marco Faroni, Andrea Maria Zanchettin

This paper addresses the optimization of human-robot collaborative work-cells before their physical deployment. Most of the times, such environments are designed based on the experience of the system integrators, often leading to sub-optimal solutions. Accurate simulators of the robotic cell, accounting for the presence of the human as well, are available to

Francesco Panelli, Doaa Almhaithawi, Tania Cerquitelli, Alessandro Bellini

In this paper, we propose a new theoretical approach to Explainable AI. Following the Scientific Method, this approach consists in formulating on the basis of empirical evidence, a mathematical model to explain and predict the behaviors of Neural Networks. We apply the method to a case study created in a controlled environment, which we call Prime Convolutio

Sergio Demian Lerner, Martin Jonas, Ariel Futoransky

The BitVM and BitVMX protocols have long relied on inefficient one-time signature (OTS) schemes like Lamport and Winternitz for signing program inputs. These schemes exhibit significant storage overheads, hindering their practical application. This paper introduces ESSPI, an optimized method leveraging ECDSA/Schnorr signatures to sign the BitVMX program inpu

Felix Schwebel, Sebastian Meynen, Manuel García-Herranz

This thesis presents a novel framework for analysing the societal impacts of armed conflict by applying principles from engineering and material science. Building on the idea of a "social fabric", it recasts communities as plates with properties, such as resilience and vulnerability, analogous to material parameters like thickness or elasticity. Conflict eve

Juan Cruz Viotti, Michael J. Mior

JSON Schemas provide useful guardrails for developers of Web APIs to guarantee that the semi-structured JSON input provided by clients matches a predefined structure. This is important both to ensure the correctness of the data received as input and also to avoid potential security issues from processing input that is not correctly validated. However, this v

Dingdong Wang, Jin Xu, Ruihang Chu, Zhifang Guo

Recent advancements in speech large language models (SpeechLLMs) have attracted considerable attention. Nonetheless, current methods exhibit suboptimal performance in adhering to speech instructions. Notably, the intelligence of models significantly diminishes when processing speech-form input as compared to direct text-form input. Prior work has attempted t

Noam Zilberstein, Daniele Gorla, Alexandra Silva

We develop a denotational model for probabilistic and concurrent imperative programs, a class of programs with standard control flow via conditionals and while-loops, as well as probabilistic actions and parallel composition. Whereas semantics for concurrent or randomized programs in isolation is well studied, their combination has not been thoroughly explor

Ru Ito, Supatta Viriyavisuthisakul, Kazuhiko Kawamoto, Hiroshi Kera

Most super-resolution (SR) models struggle with real-world low-resolution (LR) images. This issue arises because the degradation characteristics in the synthetic datasets differ from those in real-world LR images. Since SR models are trained on pairs of high-resolution (HR) and LR images generated by downsampling, they are optimized for simple degradation. H