December 2024 arXiv papers — page 87

Ricardo Euler, Pedro Maristany de las Casas, Ralf Borndörfer

The logic-constrained shortest path problem (LCSPP) combines a one-to-one shortest path problem with satisfiability constraints imposed on the routing graph. This setting arises in flight planning, where air traffic control (ATC) authorities are enforcing a set of traffic flow restrictions (TFRs) on aircraft routes in order to increase safety and throughput.

Holger Kammeyer, Steffen Kionke, Ralf Köhl

We show that the covolume of an irreducible lattice in a higher rank semisimple Lie group with the congruence subgroup property is determined by the profinite completion. Without relying on CSP, we additionally show that volume is a profinite invariant of octonionic hyperbolic congruence manifolds.

Ewan O'Sullivan, P. N. Appleton, B. A. Joshi, L. Lanz

We present Chandra and XMM-Newton X-ray observations of the compact group HCG 57, and optical integral field spectroscopy of the interacting galaxy pair HCG 57A/D. These two spiral galaxies recently suffered an off-axis collision with HCG 57D passing through the disk of A. We find evidence of a gas bridge linking the galaxies, containing ~10^8 Msol of hot, ~

Kun Huang, Shi Pu, Angelia Nedić

Consider $n$ agents connected over a network collaborating to minimize the average of their local cost functions combined with a common nonsmooth function. This paper introduces a unified algorithmic framework for solving such a problem through distributed stochastic proximal gradient methods, leveraging the normal map update scheme. Within this framework, w

Mátyás Vincze, Laura Ferrarotti, Leonardo Lucio Custode, Bruno Lepri

Continuous control tasks often involve high-dimensional, dynamic, and non-linear environments. State-of-the-art performance in these tasks is achieved through complex closed-box policies that are effective, but suffer from an inherent opacity. Interpretable policies, while generally underperforming compared to their closed-box counterparts, advantageously fa

Chang-Ki Moon, Matthias Koenig, Ranjini Sircar, Julian F. Butscher

Electrochemiluminescence (ECL) produces light through electrochemical reactions and has shown promise for various analytic applications in biomedicine. However, the use of ECL devices (ECLDs) as light sources has been limited due to insufficient light output and low operational stability. In this study, we present a high-power pulsed operation strategy for E

Juan P. Aguilera, Anton Freund, Andreas Weiermann

One of the most important principles of J.-Y. Girard's $\Pi^1_2$-logic is induction on dilators. In particular, Girard used this principle to construct his famous functor $\Lambda$. He claimed that the totality of $\Lambda$ is equivalent to the set existence axiom of $\Pi^1_1$-comprehension from reverse mathematics. While Girard provided a plausible descript

Weiguo Pian, Shijian Deng, Shentong Mo, Mingrui Liu

In this paper, we introduce Modality-Inconsistent Continual Learning (MICL), a new continual learning scenario for Multimodal Large Language Models (MLLMs) that involves tasks with inconsistent modalities (image, audio, or video) and varying task types (captioning or question-answering). Unlike existing vision-only or modality-incremental settings, MICL comb

Joshua Groen, Simone Di Valerio, Imtiaz Karim, Davide Villa

5G and beyond cellular systems embrace the disaggregation of Radio Access Network (RAN) components, exemplified by the evolution of the fronthaul (FH) connection between cellular baseband and radio unit equipment. Crucially, synchronization over the FH is pivotal for reliable 5G services. In recent years, there has been a push to move these links to an Ether

Matteo Fontana, Federico Scali, Sergio Luigi Cacciatori

In this work we investigate some non-Newtonian effects in exact solutions of the Einstein equations, which describe stationary and axisymmetric configurations of self-gravitating dust. A distinctive feature of these solutions is the potential presence of conical singularities along the rotation axis, manifesting as angular deficits. While such singularities

Luca Savant Aira, Gabriele Facciolo, Thibaud Ehret

Recently, Gaussian splatting has emerged as a strong alternative to NeRF, demonstrating impressive 3D modeling capabilities while requiring only a fraction of the training and rendering time. In this paper, we show how the standard Gaussian splatting framework can be adapted for remote sensing, retaining its high efficiency. This enables us to achieve state-

Maximilian Degner, Raffaele Soloperto, Melanie N. Zeilinger, John Lygeros

We consider the problem of optimizing the economic performance of nonlinear constrained systems subject to uncertain time-varying parameters and bounded disturbances. In particular, we propose an adaptive economic model predictive control (MPC) framework that: (i) directly minimizes transient economic costs, (ii) addresses parametric uncertainty through onli

Elizabeth A. Handorf, J. Robert Beck, Daniel M. Geynisman

Many patients with advanced cancers undergo multiple lines of treatment. We develop methods for estimating quality-adjusted outcomes and cost-effectiveness of therapy sequences, informed by patient-level longitudinal data from Electronic Health Records (EHRs). We develop microsimulation models with a discrete-time health-state transition framework and propos

Christopher J. McDevitt, Jonathan Arnaud, Xian-Zhu Tang

This work extends the adjoint-deep learning framework for runaway electron (RE) evolution developed in Ref. [C. McDevitt et al., A physics-constrained deep learning treatment of runaway electron dynamics, Submitted to Physics of Plasmas (2024)] to account for large-angle collisions. By incorporating large-angle collisions the framework allows the avalanche o

Philippa Cowderoy

Can we use the flow of information to understand type systems? I present two familiar type systems in pursuit of an `Information Aware' style, using information effects to reveal data flow and help in implementing them. I also calculate a general, scoped, constraint-based representation of typechecking problems from the typing rules.

Franz R. Sattler, Jan M. Pawlowski

We introduce DiFfRG (Discretisation Framework for functional Renormalisation Group flows), a comprehensive computational C++ framework for solving functional Renormalisation Group flows in very general truncation schemes. Its central features are threefold: Firstly, the use of Finite Element Methods (FEM) for efficient, easy to set up and quantitatively reli

Lena Engström, Pascal Simon, Andrej Mesaros

Many systems are topologically trivial in the bulk, but still have non-trivial wavefunctions locally in the Brillouin zone. For example, in a small-gap Dirac material the Berry curvature is strongly peaked, but cancels over the full Brillouin zone, while in semimetals and in nodal superconductors there may be a lower-dimensional winding topology associated t

Hugo Math, Rainer Lienhart, Robin Schön

In this paper, we draw an analogy between processing natural languages and processing multivariate event streams from vehicles in order to predict $\textit{when}$ and $\textit{what}$ error pattern is most likely to occur in the future for a given car. Our approach leverages the temporal dynamics and contextual relationships of our event data from a fleet of

Mohamed Osman, Tamer Nadeem

LoRa technology, crucial for low-power wide-area networks, faces significant performance degradation at extremely low signal-to-noise ratios (SNRs). We present LoRaFlow, a novel approach using rectified flow to reconstruct high-quality LoRa signals in challenging noise conditions. Unlike existing neural-enhanced methods focused on classification, LoRaFlow re

Eleni Papafilippou, Lucia Baldauf, Guillaume Charras, Alexandre Kabla

Epithelial tissues are continuously exposed to cyclic stretch. Physiological stretching has been found to regulate soft tissue function at the molecular, cellular, and tissue scales, allowing tissues to preserve their homeostasis and adapt to challenges. In contrast, dysregulated or pathological stretching can induce damage and tissue fragilisation. Many mec

Alessandro Bressan, Kendall Gale Shepherd

Stars are unique bodies of the Universe where self-gravity compress matter to such high temperature and density that several nuclear fusion reactions ignite, providing enough feedback against further compression for a time that can be even larger than the age of the universe. The main property of a star is its mass because it determines its structure, evolut

Jack Keeler, Alberto Alberello, Ben Humphries, Emilian Parau

The higher-order nonlinear Schrodinger equation (Dysthe's equation in the context of water-waves) models the time evolution of the slowly modulated amplitude of a wave-packet in dispersive partial differential equations (PDE). These systems, of which water-waves are a canonical example, require the presence of a small-valued ordering parameter so that a mult

Zhenyuan Xiao, Huanran Hu, Guili Xu, Junwei He

The increasing prevalence of compact UAVs has introduced significant risks to public safety, while traditional drone detection systems are often bulky and costly. To address these challenges, we present TAME, the Temporal Audio-based Mamba for Enhanced Drone Trajectory Estimation and Classification. This innovative anti-UAV detection model leverages a parall

Thai-Hoang Pham, Yuanlong Wang, Changchang Yin, Xueru Zhang

Domain adaptation (DA) tackles the issue of distribution shift by learning a model from a source domain that generalizes to a target domain. However, most existing DA methods are designed for scenarios where the source and target domain data lie within the same feature space, which limits their applicability in real-world situations. Recently, heterogeneous

Pedro C. S. Costa, Mauro E. S. Morales, Dong An, Yuval R. Sanders

Many recent investigations conclude, based on asymptotic complexity analyses, that quantum computers could accelerate combinatorial optimization (CO) tasks relative to a purely classical computer. However, asymptotic analysis alone cannot support a credible claim of quantum advantage. Here, we highlight the challenges involved in benchmarking quantum and cla

Claire Heffernan, Kirsten Koehler, Drew R. Gentner, Roger D. Peng

Low-cost air pollution sensor networks are increasingly being deployed globally, supplementing sparse regulatory monitoring with localized air quality data. In some areas, like Baltimore, Maryland, there are only few regulatory (reference) devices but multiple low-cost networks. While there are many available methods to calibrate data from each network indiv

Alfredo González-Calvin, Lía García-Pérez, Juan Jiménez

This paper presents a guidance algorithm for solving the problem of following parametric paths, as well as a curvature-varying speed setpoint for land-based car-type wheeled mobile robots (WMRs). The guidance algorithm relies on Singularity-Free Guiding Vector Fields SF-GVF. This novel GVF approach expands the desired robot path and the Guiding vector field

Duncan Dauvergne, Lingfu Zhang

We show that the directed landscape is the unique coupling of the KPZ fixed point from all initial conditions satisfying three natural properties: independent increments, monotonicity, and shift commutativity. Equivalently, we show that the directed landscape is the unique directed metric on $\mathbb R^2$ with independent increments and KPZ fixed point margi

Jie Hu, Anton Dzhamay, Yang Chen

We study the dependence of recurrence coefficients in the three-term recurrence relation for orthogonal polynomials with a certain deformation of the $q$-Laguerre weight on the degree parameter $n$. We show that this dependence is described by a discrete Painlev\'e equation on the family of $A_{5}^{(1)}$ Sakai surfaces, but this equation is different from th

Lucas Rosenblatt, Bill Howe, Julia Stoyanovich

Data privacy is a core tenet of responsible computing, and in the United States, differential privacy (DP) is the dominant technical operationalization of privacy-preserving data analysis. With this study, we qualitatively examine one class of DP mechanisms: private data synthesizers. To that end, we conducted semi-structured interviews with data experts: ac

Amal Alphonse, Gerd Wachsmuth

We consider a framework for approximating the obstacle problem through a penalty approach by nonlinear PDEs. By using tools from capacity theory, we show that derivatives of the solution maps of the penalised problems converge in the weak operator topology to an element of the strong-weak Bouligand subdifferential. We are able to treat smooth penalty terms a

Wei Gong, Yaru Li

This work introduces a new approach to the Epileptic Spasms (ESES) detection based on the EEG signals using Vision Transformers (ViT). Classic ESES detection approaches have usually been performed with manual processing or conventional algorithms, suffering from poor sample sizes, single-channel-based analyses, and low generalization abilities. In contrast,

Akaki Rusetsky

We give a brief survey of the theory of hadronic atoms, which represent important sources of information for studying hadron interactions at very low energy. It will be namely demonstrated that a systematic expansion of the observables of hadronic atoms (the energy levels and the decay width) in terms of the fine-structure constant can be obtained, using the

Karan Wanchoo, Xiaoye Zuo, Hannah Gonzalez, Soham Dan

We present NAVCON, a large-scale annotated Vision-Language Navigation (VLN) corpus built on top of two popular datasets (R2R and RxR). The paper introduces four core, cognitively motivated and linguistically grounded, navigation concepts and an algorithm for generating large-scale silver annotations of naturally occurring linguistic realizations of these con

Gianira N. Alfarano, Eimear Byrne, Andrew Fulcher

We introduce the notion of the free product of $q$-matroids, which is the $q$-analogue of the free product of matroids. We study the properties of this noncommutative binary operation, making an extensive use of the theory of cyclic flats. We show that the free product of two $q$-matroids $M_1$ and $M_2$ is maximal with respect to the weak order on $q$-matro

Daniel Patnaude, Kathryn Weil, Robert Fesen, Dan Milisavljevic

When the ejecta of supernovae interact with the progenitor star's circumstellar environment, a strong shock is driven back into the ejecta, causing the material to become bright optically and in X-rays. Most notably, as the shock traverses the H-rich envelope, it begins to interact with metal rich material. Thus, continued monitoring of bright and nearby sup

Lennert De Smet, Gabriele Venturato, Luc De Raedt, Giuseppe Marra

Sequential problems are ubiquitous in AI, such as in reinforcement learning or natural language processing. State-of-the-art deep sequential models, like transformers, excel in these settings but fail to guarantee the satisfaction of constraints necessary for trustworthy deployment. In contrast, neurosymbolic AI (NeSy) provides a sound formalism to enforce c

Debajyoti De, Dipramit Majumdar, Sudipa Mondal

Let $E$ be an elliptic curve with $j$-invariant $0$ or $1728$ and let $\widetilde{E}$ be a $k^{th}$ twist of $E$. We show that for any prime $p$ of good reduction of $\widetilde{E}$, a degree $k$ relative $p$-class group and the root number of $\widetilde{E}$ determines the dimension of the $p$-Selmer group of $\widetilde{E}$. As a consequence, we construct

Augustin Godinot, Erwan Le Merrer, Camilla Penzo, François Taïani

The deployment of machine learning models in operational contexts represents a significant investment for any organisation. Consequently, the risk of these models being misappropriated by competitors needs to be addressed. In recent years, numerous proposals have been put forth to detect instances of model stealing. However, these proposals operate under imp

Nico Föge, Lena Schmid, Marc Ditzhaus, Markus Pauly

Random Forests have become a widely used tool in machine learning since their introduction in 2001, known for their strong performance in classification and regression tasks. One key feature of Random Forests is the Random Forest Permutation Importance Measure (RFPIM), an internal, non-parametric measure of variable importance. While widely used, theoretical

Yuzheng Cai, Zhenyue Guo, Yiwen Pei, Wanrui Bian

Recent advancements in large language models (LLMs) have shown impressive versatility across various tasks. To eliminate their hallucinations, retrieval-augmented generation (RAG) has emerged as a powerful approach, leveraging external knowledge sources like knowledge graphs (KGs). In this paper, we study the task of KG-driven RAG and propose a novel Similar

Luigi Bellomarini, Livia Blasi, Markus Nissl, Emanuel Sallinger

In the wake of the recent resurgence of the Datalog language of databases, together with its extensions for ontological reasoning settings, this work aims to bridge the gap between the theoretical studies of DatalogMTL (Datalog extended with metric temporal logic) and the development of production-ready reasoning systems. In particular, we lay out the functi

Shuting Wang, Jiejun Tan, Zhicheng Dou, Ji-Rong Wen

As a typical and practical application of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) techniques have gained extensive attention, particularly in vertical domains where LLMs may lack domain-specific knowledge. In this paper, we introduce an omnidirectional and automatic RAG benchmark, OmniEval, in the financial domain. Our benchmark is

Shijun Zheng, Weiquan Liu, Yu Guo, Yu Zang

Autonomous vehicles (AVs) rely on LiDAR sensors for environmental perception and decision-making in driving scenarios. However, ensuring the safety and reliability of AVs in complex environments remains a pressing challenge. To address this issue, we introduce a real-world dataset (ROLiD) comprising LiDAR-scanned point clouds of two random objects: water mis

Manwen Liu, Huimin Ji, Wenzheng Cheng, Le Zhang

The 3D silicon sensor has demonstrated excellent performances (signal collection, detection efficiency, power consumption, etc.) comparable or even better with respect to the traditional planar sensor of the ATLAS Detector at the Large Hadron Collider (LHC), especially after the high irradiation fluence, mainly due to the shorter drift length of the generate

Seung-Yeal Ha, Tommaso Ruggeri, Qinghua Xiao

We study quantitative estimates for the flocking and uniform-time classical limit to the relativistic Cucker-Smale (in short RCS) model introduced in \cite{Ha-Kim-Ruggeri-ARMA-2020}. Different from previous works, we do not neglect the relativistic effect on the presence of the pressure in momentum equation. For the RCS model, we provide a quantitative estim

R. Gauld, U. Haisch, J. Weiss

We derive constraints on dimension-six light-quark dipole operators within the Standard Model (SM) effective field theory, based on measurements of $Z$ production at SLC and LEP, as well as $Z$+jet production at the LHC. Our new constraints exclude the parameter space that could potentially explain the observed discrepancy between theoretical predictions and

Gavin Kader, Dongwoo Lee

As large language models (LLMs) have demonstrated strong reasoning abilities in structured tasks (e.g., coding and mathematics), we explore whether these abilities extend to strategic multi-agent environments. We investigate strategic reasoning capabilities -- the process of choosing an optimal course of action by predicting and adapting to others' actions -

Leah Turner, Madalin Guta, Gerardo Adesso

Which quantum phenomena are advantageous for information processing tasks? By classifying quantum states as resourceful versus non-resourceful, or free, the mathematical formalism of quantum resource theories helps to address such questions. For the task of discriminating channels applied to a probe state, it has been shown that under certain conditions -- n

Shizuka Akahori, Shotaro Teruya, Pragyan Shrestha, Yuichi Yoshii

Ultrasound imaging of the medial elbow is crucial for the early diagnosis of Ulnar Collateral Ligament (UCL) injuries. Specifically, measuring the elbow joint space in ultrasound images is used to assess the valgus instability of the elbow caused by UCL injuries. To automate this measurement, a model trained on a precisely annotated dataset is necessary; how

Vladimir A. Zyuzin

In this paper we theoretically describe a distinct class of two-dimensional N\'{e}el ordered metallic antiferromagnets on a honeycomb-like lattice in which the two sublattices are connected only by a combination of time-reversal and mirror symmetry operations. As a result of this symmetry, conducting fermions have antiferromagnetic spin-splitting consistent

Tongguan Wang, Junkai Li, Guixin Su, Yongcheng Zhang

Sarcasm typically conveys emotions of contempt or criticism by expressing a meaning that is contrary to the speaker's true intent. Accurate detection of sarcasm aids in identifying and filtering undesirable information on the Internet, thereby reducing malicious defamation and rumor-mongering. Nonetheless, the task of automatic sarcasm detection remains high

Athulya Sundaresan Geetha

This work explores the YOLOv6 object detection model in depth, concentrating on its design framework, optimization techniques, and detection capabilities. YOLOv6's core elements consist of the EfficientRep Backbone for robust feature extraction and the Rep-PAN Neck for seamless feature aggregation, ensuring high-performance object detection. Evaluated on the

V. Jacquier, W. M. Ruszel, C. Spitoni

In the present manuscript we address and solve for the first time a nonlocal discrete isoperimetric problem. We consider indeed a generalization of the classical perimeter, what we call a nonlocal bi-axial discrete perimeter, where, not only the external boundary of a polyomino $\mathcal{P}$ contributes to the perimeter, but all internal and external compone

Qingyuan Fan, Yi Cui, Yanbin Li, Julian A. Vigil

Mixed-halide lead perovskites, with photoexcited charge-carrier properties suitable for high-efficiency photovoltaics, hold significant promise for high-efficiency tandem solar cells. However, phase segregation under illumination, where an iodide-rich phase forms carrier trap states, remains a barrier to applications. This study employs plunge-freeze cryogen

Hongyu Shen, Zhizhen Zhao

Despite empirical risk minimization (ERM) is widely applied in the machine learning community, its performance is limited on data with spurious correlation or subpopulation that is introduced by hidden attributes. Existing literature proposed techniques to maximize group-balanced or worst-group accuracy when such correlation presents, yet, at the cost of low

Christina G. Taylor, Jesse Chan

High-order entropy stable summation-by-parts (SBP) schemes are a class of robust and accurate numerical methods for hyperbolic conservation laws that are numerically stable at arbitrary order without the need for artificial stabilization. While SBP schemes are well-established on simplicial and tensor-product elements, they have not been extended to cut mesh

Roberto Bramati, Matteo Dalla Riva, Paolo Luzzini, Paolo Musolino

In this paper, we review the construction of periodic fundamental solutions and periodic layer potentials for various differential operators. Specifically, we focus on the Laplace equation, the Helmholtz equation, the Lam\'e system, and the heat equation. We then describe how these layer potentials can be applied to analyze domain perturbation problems. In p

Carles Roch i Carceller, Jef Pauwels, Stefano Pironio, Armin Tavakoli

We study correlations in the prepare-and-measure scenario when quantum communication is constrained by photon-number statistics. Such constraints are natural and practical control parameters for semi-device-independent certification in optical platforms. To analyse these scenarios, we show how semidefinite programming relaxations for non-commutative polynomi

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $(2712.4\pm14.3)\times10^{6}$ $\psi(3686)$ events collected with the BESIII detector at the BEPCII collider, the decay $\eta_c\to\gamma\gamma$ in $J/\psi\to\gamma\eta_c$ is observed. We determine the product branching fraction $\mathcal{B}(J/\psi\to\gamma\eta_c)\times\mathcal{B}(\eta_c\to\gamma\gamma)=(5.23\pm0.26_{\rm{stat.}}\pm0.30_{\rm{syst.}})\time

Umer Butt, Stalin Varanasi, Günter Neumann

As the Information Retrieval (IR) field increasingly recognizes the importance of inclusivity, addressing the needs of low-resource languages remains a significant challenge. This paper introduces the first large-scale Urdu IR dataset, created by translating the MS MARCO dataset through machine translation. We establish baseline results through zero-shot lea

Emily Yu, Đorđe Žikelić, Thomas A. Henzinger

Learning-based methods provide a promising approach to solving highly non-linear control tasks that are often challenging for classical control methods. To ensure the satisfaction of a safety property, learning-based methods jointly learn a control policy together with a certificate function for the property. Popular examples include barrier functions for sa

Jinu Thomas, Debshikha Banerjee, Alberto Nocera, Steven Johnston

An emerging application of resonant inelastic x-ray scattering (RIXS) is the study of lattice excitations and electron-phonon ($e$-ph) interactions in quantum materials. Despite the growing importance of this area of research, the community lacks a complete understanding of how the RIXS process excites the lattice and how these excitations encode information

Diana Carbajal, José Luis Romero

Integrate-and-fire is a resource efficient time-encoding mechanism that summarizes into a signed spike train those time intervals where a signal's charge exceeds a certain threshold. We analyze the IF encoder in terms of a very general notion of approximate bandwidth, which is shared by most commonly-used signal models. This complements results on exact enco

Hui Zhang

We propose a practical tool for evaluating and comparing the accuracy of FDMs for the Helmholtz equation. The tool based on Fourier analysis makes it easy to find wavenumber explicit order of convergence, and can be used for rigorous proof. It fills in the gap between the dispersion analysis and the actual error with source term.

Gabriele Dessena, Alessandro Pontillo, Dmitry I. Ignatyev, James F. Whidborne

In general, there is a mismatch between a finite element model {(FEM)} of a structure and its real behaviour. In aeronautics, this mismatch must be small because {FEM}s are a fundamental part of the development of an aircraft and of increasing importance with the trend to more flexible wings in modern designs. Iterative finite element model updating can be c

Yihong Zhou, Yuxin Xia, Hanbin Yang, Thomas Morstyn

Many real-world decision-making problems have uncertain parameters in constraints. Wasserstein distributionally robust joint chance constraints (WDRJCC) offer a promising solution by explicitly guaranteeing the probability of the simultaneous constraint satisfaction. However, WDRJCC are computationally demanding, and practical applications often require more

Nicholas Earl, K. Decker French, Enrico Ramirez-Ruiz, Katie Auchettl

We present a detailed analysis of AT 2020nov, a tidal disruption event (TDE) in the center of its host galaxy, located at a redshift of $z = 0.083$. AT 2020nov exhibits unique features, including double-peaked Balmer emission lines, a broad UV/optical flare, and a peak log luminosity in the extreme ultra-violet (EUV) estimated at $\sim$$45.66^{+0.10}_{-0.33}

Antonios Gasteratos, Stavros N. Moutsis, Konstantinos A. Tsintotas, Yiannis Aloimonos

Visual-based human action recognition can be found in various application fields, e.g., surveillance systems, sports analytics, medical assistive technologies, or human-robot interaction frameworks, and it concerns the identification and classification of individuals' activities within a video. Since actions typically occur over a sequence of consecutive ima

Bálint Seli, Krisztián Vida, Katalin Oláh, Anna Görgei

Stellar flares are abundant in space photometric light curves. As they are now available in large enough numbers, the statistical study of their overall temporal morphology is timely. We use light curves from the Transiting Exoplanet Survey Satellite (TESS) to study the shapes of stellar flares beyond a simple parameterization by duration and amplitude, and

Andrea Gabrielli, Diego Garlaschelli, Subodh P. Patil, M. Ángeles Serrano

The renormalization group (RG) is a powerful theoretical framework developed to consistently transform the description of configurations of systems with many degrees of freedom, along with the associated model parameters and coupling constants, across different levels of resolution. It also provides a way to identify critical points of phase transitions and

Chuan He, Zhanwang Deng

Conic optimization plays a crucial role in many machine learning (ML) problems. However, practical algorithms for conic constrained ML problems with large datasets are often limited to specific use cases, as stochastic algorithms for general conic optimization remain underdeveloped. To fill this gap, we introduce a stochastic interior-point method (SIPM) fra

Leonard Deuschle, Jiang Cao, Alexandros Nikolaos Ziogas, Anders Winka

The continuous scaling of metal-oxide-semiconductor field-effect transistors (MOSFETs) has led to device geometries where charged carriers are increasingly confined to ever smaller channel cross sections. This development is associated with reduced screening of long-range Coulomb interactions. To accurately predict the behavior of such ultra-scaled devices,

Giuseppe Cuono, Subhadeep Bandyopadhyay, Andrea Droghetti, Silvia Picozzi

We employ first-principles calculations to investigate the ferroelectric properties and the bulk photovoltaic effect (BPVE) of antimony sulfur iodide (SbSI). The BPVE enables direct sunlight-to-electricity conversion in homogeneous materials and, in ferroelectric compounds, can be tuned via an electric field controlling the polarization. However, most ferroe

Wei Ju, Zhengyang Mao, Siyu Yi, Yifang Qin

This paper studies the problem of class-imbalanced graph classification, which aims at effectively classifying the graph categories in scenarios with imbalanced class distributions. While graph neural networks (GNNs) have achieved remarkable success, their modeling ability on imbalanced graph-structured data remains suboptimal, which typically leads to predi

James Casey, Jessica Forsyth, Timothy Waite, Simon Cotter

Combining microstructural mechanical models with experimental data enhances our understanding of the mechanics of soft tissue, such as tendons. In previous work, a Bayesian framework was used to infer constitutive parameters from uniaxial stress-strain experiments on horse tendons, specifically the superficial digital flexor tendon (SDFT) and common digital

Ruijie Chen, Qi Mao, Zhengxue Cheng

Recent advances in Artificial Intelligence Generated Content (AIGC) have garnered significant interest, accompanied by an increasing need to transmit and compress the vast number of AI-generated images (AIGIs). However, there is a noticeable deficiency in research focused on compression methods for AIGIs. To address this critical gap, we introduce a scalable

Vivek Kumar, Eirini Ntoutsi, Pushpraj Singh Rajawat, Giacomo Medda

Large language models (LLMs) have shown promising capabilities in healthcare analysis but face several challenges like hallucinations, parroting, and bias manifestation. These challenges are exacerbated in complex, sensitive, and low-resource domains. Therefore, in this work we introduce IC-AnnoMI, an expert-annotated motivational interviewing (MI) dataset b

Christopher J. McDevitt, Jonathan Arnaud, Xian-Zhu Tang

An adjoint formulation leveraging a physics-informed neural network (PINN) is employed to advance the density moment of a runaway electron (RE) distribution forward in time. A distinguishing feature of this approach is that once the adjoint problem is solved, its solution can be used to project the RE density forward in time for an arbitrary initial momentum

Filippo Stocco, Maria Artigues-Lleixa, Andrea Hunklinger, Michele Garibbo

Protein engineering can optimize molecules for biotechnology and therapeutics, but navigating the high-dimensional sequence landscape remains challenging. Protein language models (pLMs) have shown to to generate functional proteins far from natural sequences, yet their outputs tend to reflect prevalent properties in training data, limiting discovery of rare

Laura Inno, Margherita Scuderi, Ivano Bertini, Marco Fulle

Among solar system objects, comets coming from the Oort Cloud are an elusive population, intrinsically rare and difficult to detect. Nonetheless, as the more pristine objects we can observe, they encapsulate critical cues on the formation of planetary systems and are the focus of many scientific investigations and science missions. The Legacy Survey of Space

Peter C. Bruns, Ales Cieply

We generalize the chirally motivated $\pi\Sigma - \bar{K}N$ coupled channels model to the cubic finite volume and use it to calculate the stationary energy spectrum that appears in a nice agreement with the spectrum obtained in the lattice QCD simulations by the BaSc collaboration. Several other comparisons with the BaSc results are made, in particular relat

Neil MacFarlane, Firooz Aflatouni

Optical modulators in the visible regime have far-reaching applications from biophotonics to quantum science. Implementations of such optical phase modulators on a complementary metal-oxide-semiconductor (CMOS) compatible platform have been mainly limited to utilization of the thermo-optic effect, liquid crystal technology, as well as piezo-optomechanical ef

Rubén Arjona, Savvas Nesseris, Sachiko Kuroyanagi

Pulsar timing array (PTA) experiments have recently provided strong evidence for the signal of the stochastic gravitational wave background (SGWB) in the nHz-frequency band. These experiments have shown a statistical preference for the Hellings-Downs (HD) correlation between pulsars, which is widely regarded as a definitive signature of the SGWB. Using the N

Wenhao Sun, Benlei Cui, Xue-Mei Dong, Jingqun Tang

Recently, diffusion models have emerged as promising newcomers in the field of generative models, shining brightly in image generation. However, when employed for object removal tasks, they still encounter issues such as generating random artifacts and the incapacity to repaint foreground object areas with appropriate content after removal. To tackle these p

Gergely Endrődi, Tamás G. Kovács, Gergely Markó, Laurin Pannullo

We study spontaneous symmetry breaking in quantum field theories with fermionic order parameters and construct, for the first time in the literature, the constraint effective potential for it. The Grassmann-valued constraint we encounter is handled using its large-volume expansion, corresponding to a saddle-point approximation. We test the method in the chir

The IceCube Collaboration

The nature of dark matter remains unresolved in fundamental physics. Weakly Interacting Massive Particles (WIMPs), which could explain the nature of dark matter, can be captured by celestial bodies like the Sun or Earth, leading to enhanced self-annihilation into Standard Model particles including neutrinos detectable by neutrino telescopes such as the IceCu

Guillaume Couairon, Renu Singh, Anastase Charantonis, Christian Lessig

Weather forecasting plays a vital role in today's society, from agriculture and logistics to predicting the output of renewable energies, and preparing for extreme weather events. Deep learning weather forecasting models trained with the next state prediction objective on ERA5 have shown great success compared to numerical global circulation models. However,

Danielle Cox, M. E. Messinger, Kerry Ojakian

Graph burning models the spread of information or contagion in a graph. At each time step, two events occur: neighbours of already burned vertices become burned, and a new vertex is chosen to be burned. The big conjecture is known as the {\it burning number conjecture}: for any connected graph on $n$ vertices, all $n$ vertices can be burned after at most $\l

Maria Tsampazi, Tommaso Melodia

Reconfigurable Intelligent Surfaces (RISs) are a promising technique for enhancing the performance of Next Generation (NextG) wireless communication systems in terms of both spectral and energy efficiency, as well as resource utilization. However, current RIS research has primarily focused on theoretical modeling and Physical (PHY) layer considerations only.

Uri Stern, Tomer Yaacoby, Daphna Weinshall

The infrequent occurrence of overfitting in deep neural networks is perplexing: contrary to theoretical expectations, increasing model size often enhances performance in practice. But what if overfitting does occur, though restricted to specific sub-regions of the data space? In this work, we propose a novel score that captures the forgetting rate of deep mo

Tommy D. Beltran, Raul J. Villao, Luis E. Chuquimarca, Boris X. Vintimilla

The present study focuses on detecting the degree of deformity in fruits such as apples, mangoes, and strawberries during the process of inspecting their external quality, employing Single-Input and Multi-Input architectures based on convolutional neural network (CNN) models using sets of real and synthetic images. The datasets are segmented using the Segmen

Ziv Yaniv, Ifeanyichukwu U. Anidi, Leanne Arakkal, Armando J. Arroyo-Mejías

The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunofluorescence imaging methods. The design of the Knowledge-Base is modeled after efforts in the open-source software community and includes three facets: a development platform (GitHub), static website, and service f

Ambra Van Liedekerke, Blazhe Gjorgiev, Jonas Savelsberg, Xin Wen

The energy transition is reshaping electricity systems, bringing new challenges, and emphasizing the need for strategic planning. Energy policies play a crucial role in guiding this transition. However, assessing their impacts often requires robust modeling involving multiple models and going beyond a single country's scope, analyzing international interacti

Salvatore La Cagnina, Cornelius Grunwald, Timo Janßen, Kevin Kröninger

We present a study on using Markov Chain Monte Carlo (MCMC) techniques to explore the high-dimensional and multi-modal phase space of scattering events at high-energy particle colliders. To this end, we combine the BAT.jl package that provides implementations of a variety of MCMC algorithms with the Sherpa event generator framework. We discuss technical aspe

Nicolas Hayer, Hans Hasse, Fabian Jirasek

Predicting thermodynamic properties of mixtures is a cornerstone of chemical engineering, yet conventional group-contribution (GC) methods like modified UNIFAC (Dortmund) remain limited by incomplete tables of pair-interaction parameters. To address this, we present modified UNIFAC 2.0, a hybrid model that integrates a matrix completion method from machine l

Fatiha Ait Kbir, Jérémy Bourgoin, Rémy Decoupes, Marie Gradeler

The Land Matrix initiative (https://landmatrix.org) and its global observatory aim to provide reliable data on large-scale land acquisitions to inform debates and actions in sectors such as agriculture, extraction, or energy in low- and middle-income countries. Although these data are recognized in the academic world, they remain underutilized in public poli

Christian Quirouette, Risavarshni Thevakumaran, Kyosuke Adachi, Catherine A. A. Beauchemin

Endpoint dilution (TCID50) assays cannot count the number of infectious virions (IVs), and instead are limited to counting the number of Specific INfections caused by the sample (SIN). The latter depends not only on whether virions are infectious, but also on the cells and the experimental conditions under which they interact. These interactions are random a

Tom Jaeger, MinSik Kwon, Max Keller, Rouven Maier

Accurately estimating the performance of quantum hardware is crucial for comparing different platforms and predicting the performance and feasibility of quantum algorithms and applications. In this paper, we tackle the problem of benchmarking a quantum register based on the NV center in diamond operating at room temperature. We define the connectivity map as

Elisabeth Gaar, Dunja Pucher

The stability number of a graph, defined as the cardinality of the largest set of pairwise non-adjacent vertices, is NP-hard to compute. The exact subgraph hierarchy (ESH) provides a sequence of increasingly tighter upper bounds on the stability number, starting with the Lov\'asz theta function at the first level and including all exact subgraph constraints