December 2024 arXiv papers — page 58

Jos Wigchert, Savio Sciancalepore, Gabriele Oligeri

Detecting spoofing attacks to Low-Earth-Orbit (LEO) satellite systems is a cornerstone to assessing the authenticity of the received information and guaranteeing robust service delivery in several application domains. The solutions available today for spoofing detection either rely on additional communication systems, receivers, and antennas, or require mobi

Indranil Maiti, Mikołaj Lasota

Typically practical realizations of discrete-variable quantum key distribution (QKD) protocols, based on exchanging single-photon signals between the trusted parties, can provide its users with only very low key generation rates. One of the potential solutions for this problem, that can be adapted for the case of entanglement-based QKD schemes using broadban

Laurent Deniau

The paper will provide an overview of the capabilities of the Methodical Accelerator Design Next Generation (MAD-NG) tool. MAD-NG is a standalone, all-in-one, multi-platform tool well-suited for linear and nonlinear optics design and optimization, and has already been used in large-scale studies such as HiLumi-LHC or FCC-ee. It embeds LuaJIT, an extremely fa

S. Geiskopf, J. Igarashi, G. Malinowski, J. -X. Lin

Single pulse All Optical Helicity-Independent Switching is demonstrated in an in-plane magnetized magnetic tunnel junction. A toggle switching of the 2nm thick Co40Fe40B20 soft layer could be achieved by exchange coupling the Co40Fe40B20 with a 10nm thick Co85Gd15 layer monitored by measuring the Tunnel magneto resistance of the device. The use of in plane m

Juliet Cooke, Robert Laugwitz

We give presentations, in terms of the generators and relations, for the reflection equation algebras of type $GL_n$ and $SL_n$, i.e., the covariantized algebras of the dual Hopf algebras of the small quantum groups of $\mathfrak{gl}_n$ and $\mathfrak{sl}_n$. Our presentations display these algebras as quotients of the infinite-dimensional reflection equatio

Felix Tempel, Daniel Groos, Espen Alexander F. Ihlen, Lars Adde

Explaining machine learning (ML) models using eXplainable AI (XAI) techniques has become essential to make them more transparent and trustworthy. This is especially important in high-stakes domains like healthcare, where understanding model decisions is critical to ensure ethical, sound, and trustworthy outcome predictions. However, users are often confused

Chen Liu, Zheng Sun, Xiangxiong Zhang

In this paper, we develop bound-preserving techniques for the Runge--Kutta (RK) discontinuous Galerkin (DG) method with compact stencils (cRKDG method) for hyperbolic conservation laws. The cRKDG method was recently introduced in [Q. Chen, Z. Sun, and Y. Xing, SIAM J. Sci. Comput., 46: A1327--A1351, 2024]. It enhances the compactness of the standard RKDG met

Miguel O. Blom, Kristian F. D. Rietveld, Rob V. van Nieuwpoort

Important memory-bound kernels, such as linear algebra, convolutions, and stencils, rely on SIMD instructions as well as optimizations targeting improved vectorized data traversal and data re-use to attain satisfactory performance. On on temporary CPU architectures, the hardware prefetcher is of key importance for efficient utilization of the memory hierarch

Mads A. Jørgensen, Devashish Pandey, Ehsan Amooghorban, Sanshui Xiao

We study the collective photon decay of multiple quantum emitters embedded in a thin high-index dielectric layer such as hexagonal boron nitride (hBN), with and without a metal substrate. We first explore the significant role that guided modes including surface plasmon modes play in the collective decay of identical singlephoton emitters (super- and subradia

Tristan Pace, Gordan Zitkovic

We consider a sequence of Hawkes processes whose excitation measures may depend on the generation, and study its scaling limits in the near-unstable limiting regime. The limiting random measures, characterized via a nonlinear convolutional equation, form a family parameterized by a pair consisting of a locally finite measure and a geometrically infinitely di

Muthukumar G, Jyosna Philip

Remaining Useful Life (RUL) of a component or a system is defined as the length from the current time to the end of the useful life. Accurate RUL estimation plays a crucial role in Predictive Maintenance applications. Traditional regression methods, both linear and non-linear, have struggled to achieve high accuracy in this domain. While Convolutional Neural

Jordi Valero, Josep Ginebra

The maxima and the minima of a randomly stopped sample of a random variable, $X$, together with two newly defined random variables that make $X$ into the maxima or minima of a randomly stopped sample of them, can be used to define statistical model transformation mechanisms. These transformations can be used to define models for extreme value data that are n

Jack Borthwick, Maël Chantreau, Yannick Herfray

The goal of this paper is to provide a definition for a notion of extended boundary at time and space-like infinity which, following Figueroa-O'Farril--Have--Prohazka--Salzer, we refer to as Ti and Spi. This definition applies to asymptotically flat spacetime in the sense of Ashtekar--Romano and we wish to demonstrate, by example, its pertinence in a number

Fabio Amadio, Clemente Donoso, Dionis Totsila, Raphael Lorenzo

This paper describes the Inria team's integrated robotics system used in the 1st euROBIN coopetition, during which service robots performed voice-activated household tasks in a kitchen setting. The team developed a modified TIAGo++ platform that leverages a whole-body control stack for autonomous and teleoperated modes, and an LLM-based pipeline for instruct

Maximillian Chen, Ruoxi Sun, Sercan Ö. Arık

Conversational assistants are increasingly popular across diverse real-world applications, highlighting the need for advanced multimodal speech modeling. Speech, as a natural mode of communication, encodes rich user-specific characteristics such as speaking rate and pitch, making it critical for effective interaction. Our work introduces a data-centric custo

Marco Cicalese, Dario Reggiani, Francesco Solombrino

We analyze the discrete-to-continuum limit of a frustrated ferromagnetic/anti-ferromagnetic $\mathbb{S}^2$-valued spin system on the lattice $\lambda_n\mathbb{Z}^2$ as $\lambda_n\to 0$. For $\mathbb{S}^2$ spin systems close to the Landau-Lifschitz point (where the helimagnetic/ferromagnetic transition occurs), it is well established that for chirality transi

Lynn Greschner, Roman Klinger

Arguments evoke emotions, influencing the effect of the argument itself. Not only the emotional intensity but also the category influence the argument's effects, for instance, the willingness to adapt stances. While binary emotionality has been studied in arguments, there is no work on discrete emotion categories (e.g., "Anger") in such data. To fill this ga

Thomas G. Seidel, Julien Javaloyes, Svetlana V. Gurevich

We study the dynamics of multipulse solutions in mode-locked lasers in presence of time-delayed feedback stemming, e.g., from reflections upon optical elements, and carrier dynamics. We demonstrate that the dynamics of such a high dimensional problem can be successfully described by some effective equations of motion for the pulses' phases and positions. Ana

Myles Foley, Sergio Maffeis

REST APIs have become key components of web services. However, they often contain logic flaws resulting in server side errors or security vulnerabilities. HTTP requests are used as test cases to find and mitigate such issues. Existing methods to modify requests, including those using deep learning, suffer from limited performance and precision, relying on un

Jianfeng Yang, Haotian Pi, Zixuan Deng, Hongshuang Guo

Natural organisms can convert environmental stimuli into sensory feedback to regulate their body and realize active adaptivity. However, realizing such a feedback-regulation mechanism in synthetic material systems remains a grand challenge. It is believed that achieving complex feedback mechanisms in responsive materials will pave the way toward autonomous,

Dario Di Domenico, Nicolò Boccardo, Andrea Marinelli, Michele Canepa

Noninvasive human-machine interfaces such as surface electromyography (sEMG) have long been employed for controlling robotic prostheses. However, classical controllers are limited to few degrees of freedom (DoF). More recently, machine learning methods have been proposed to learn personalized controllers from user data. While promising, they often suffer fro

S. Clesse, V. Dandoy, S. Verma

We consider the possibility that the stochastic gravitational wave (GW) background suggested by Pulsar Timing Array (PTA) datasets is sourced by Primordial Black Holes (PBHs). Specifically, we perform a Bayesian search in the International PTA Data Release 2 (IPTA DR2) for a combined GW background arising from scalar perturbations and unresolved PBH mergers,

Pablo Destic, Nuno Hultberg, Michał Szachniewicz

We study the variation of heights of cycles in flat families over number fields or, more generally, globally valued fields. To a finite type scheme over a GVF we associate a locally compact Hausdorff space which we refer to as its GVF analytification. For a flat projective family, we prove that the height of fibres is a continuous function on the GVF analyti

Junyu Meng

We consider a prime Fano 6-fold $Y$ of index 3, which is a fine quiver moduli space and a blow down of $\mathrm{Hilb}^3(\mathds{P}^2)$. We calculate the quantum cohomology ring of $Y$ and obtain Quantum Chevalley formulas for the Schubert type subvarieties. The famous Dubrovin's Conjecture relating the quantum cohomology and the derived category is verified

Michele Leonardo Bianchi, Dario Ruzzi, Anatoli Segura

We use granular regulatory data on euro interest rate swap trades between January 2021 and June 2023 to assess whether derivative positions of Italian banks can offset losses on their debt securities holdings should interest rates rise unexpectedly. At the aggregate level of the banking system, we find that a 100-basis-point upward shift of the yield curve i

Johannes M. Schumacher

In a variety of applications, the problem comes up of describing the principal part of the inverse of a holomorphic operator at an eigenvalue in terms of left and right root functions associated to the eigenvalue. Such a description was given by Keldysh in 1951. His theorem, the proof of which was published only in 1971, is a fundamental result in the local

Klaus Werner, Nicole Reindl, Max Pritzkuleit, Stephan Geier

We have detected three new hydrogen-deficient (H < 0.001 mass fraction) pre-white dwarfs (WDs) with helium-dominated atmospheres. The first object is a relatively cool PG1159 star (effective temperature Teff = 72,000 K) that has the lowest surface gravity of any PG1159 star known (log g = 4.8). It is a PG1159 star in the earliest pre-WD phase. The second obj

Bojian Yin, Federico Corradi

Recurrent Neural Networks (RNNs) are widely used for sequential processing but face fundamental limitations with continual inference due to state saturation, requiring disruptive hidden state resets. However, reset-based methods impose synchronization requirements with input boundaries and increase computational costs at inference. To address this, we propos

Alec R. Cheney, Borui Chen, Tim Thomay

Leveraging thermal losses as a useful consequence of surface plasmons in metal nanostructures has gained traction in recent years. This thermalization of hot electrons also induces a resistance change to an applied bias current, which we use to realize an all electronic readout of surface plasmons. The interplay of the plasmonic k-vector dependence and the a

Debshikha Banerjee, Jinu Thomas, Alberto Nocera, Steven Johnston

Su-Schrieffer-Heeger (SSH)-like electron-phonon (e-ph) interactions can drive the formation of light (bi)polarons and several novel states of matter. It is, therefore, prudent to develop experimental protocols for identifying such couplings in real materials and quantifying their strength. Here, we investigate how resonant inelastic x-ray scattering (RIXS) p

Huanqi Yang, Mingda Han, Xinyue Li, Di Duan

Millimeter-wave (mmWave) radar-based gesture recognition is gaining attention as a key technology to enable intuitive human-machine interaction. Nevertheless, the significant challenge lies in obtaining large-scale, high-quality mmWave gesture datasets. To tackle this problem, we present iRadar, a novel cross-modal gesture recognition framework that employs

Bowen Dong, Zitong Huang, Guanglei Yang, Lei Zhang

Open-world (OW) recognition and detection models show strong zero- and few-shot adaptation abilities, inspiring their use as initializations in continual learning methods to improve performance. Despite promising results on seen classes, such OW abilities on unseen classes are largely degenerated due to catastrophic forgetting. To tackle this challenge, we p

Patrick Haller, Jonas Golde, Alan Akbik

This paper explores the potential of recurrent neural networks (RNNs) and other subquadratic architectures as competitive alternatives to transformer-based models in low-resource language modeling scenarios. We utilize HGRN2 (Qin et al., 2024), a recently proposed RNN-based architecture, and comparatively evaluate its effectiveness against transformer-based

Jingyuan Qi, Zian Jia, Minqian Liu, Wangzhi Zhan

The discovery of novel mechanical metamaterials, whose properties are dominated by their engineered structures rather than chemical composition, is a knowledge-intensive and resource-demanding process. To accelerate the design of novel metamaterials, we present MetaScientist, a human-in-the-loop system that integrates advanced AI capabilities with expert ove

Zeguan Wu, Pouya Sampourmahani, Mohammadhossein Mohammadisiahroudi, Tamás Terlaky

Quantum computing has the potential to speed up some optimization methods. One can use quantum computers to solve linear systems via Quantum Linear System Algorithms (QLSAs). QLSAs can be used as a subroutine for algorithms that require solving linear systems, such as the dual logarithmic barrier method (DLBM) for solving linear optimization (LO) problems. I

J. Amira Geuther, Kasra Asnaashari, Jeremy O. Richardson

In this work, we describe various improved implementations of the mapping approach to surface hopping (MASH) for simulating nonadiabatic dynamics. These include time-reversible and piecewise-continuous integrators, which is only formally possible because of the deterministic nature of the underlying MASH equations of motion. The new algorithms allow for the

Jingbo Chen, Enrico Ballini, Stefano Micheletti

This study employs Deep Reinforcement Learning (DRL) for active flow control in a turbulent flow field of high Reynolds numbers at $Re=274000$. That is, an agent is trained to obtain a control strategy that can reduce the drag of a cylinder while also minimizing the oscillations of the lift. Probes are placed only around the surface of the cylinder, and a Pr

Stefan Kooij, Daniel T. A. Jordan, Cees J. M. van Rijn, Neil Ribe

The breakup of a capillary jet into drops is believed to be governed by initial disturbances on the surface of the jet that grow exponentially. The disturbances are often assumed to be due to external sources of noise, to turbulence, or to imperfections of the nozzle. However, even in conditions where external perturbations are minimal, the jet's length cann

Qijiong Liu, Lu Fan, Xiao-Ming Wu

We present Legommenders, a unique library designed for content-based recommendation that enables the joint training of content encoders alongside behavior and interaction modules, thereby facilitating the seamless integration of content understanding directly into the recommendation pipeline. Legommenders allows researchers to effortlessly create and analyze

Melanie Joan Weitz

The Pierre Auger Observatory has detected downward terrestrial gamma-ray flashes (TGFs) with its Surface Detector. A key to understanding this high-energy radiation in thunderstorms is to combine such measurements with measurements of lightning processes in their earliest stages. With eleven modified Auger Engineering Radio Array (AERA) stations we can build

Martin Friesen, Stefan Gerhold, Kristof Wiedermann

We study small-time central limit theorems for stochastic Volterra integral equations with H\"older continuous coefficients and general locally square integrable Volterra kernels. We prove the convergence of the finite-dimensional distributions, a functional CLT, and limit theorems for smooth transformations of the process, which covers a large class of Volt

Julia K. Varga, Sergey Ovchinnikov, Ora Schueler-Furman

One of the main advantages of deep learning models of protein structure, such as Alphafold2, is their ability to accurately estimate the confidence of a generated structural model, which allows us to focus on highly confident predictions.The ipTM score provides a confidence estimate of interchain contacts in protein-protein interactions. However, interaction

Djalil Chafaï, Max Fathi

We consider overdamped Langevin diffusions in Euclidean space, with curvature equal to the spectral gap. This includes the Ornstein-Uhlenbeck process as well as non-Gaussian and non-product extensions with convex interaction, such as the Dyson process from random matrix theory. We show that a cutoff phenomenon or abrupt convergence to equilibrium occurs in h

T. Stetz, H. Mayr, V. Werner, N. Pietralla

The $M1$ transition strengths between excited $2^+$ states of the neutron-rich, radioactive nuclide $^{132}$Te have been studied through direct lifetime measurements using the Doppler-shift attenuation method in a two-neutron transfer reaction on a $^{130}$Te target. An unambiguous identification of the lowest-lying mixed-symmetry $2^+$ state has been achiev

Ben Bouwen, James Gabe

We provide a new proof of the Kirchberg--Phillips theorem by adapting the framework laid out by Carri\'on--Gabe--Schafhauser--Tikuisis--White for classifying separable simple unital nuclear stably finite $\mathcal Z$-stable $C^\ast$-algebras satisfying the UCT. Not only does this give a unified approach to classifying stably finite and purely infinite $C^\as

Simon Langer, Jessica Ritter, Rickmer Braren, Daniel Rueckert

Modern deep learning-based clinical imaging workflows rely on accurate labels of the examined anatomical region. Knowing the anatomical region is required to select applicable downstream models and to effectively generate cohorts of high quality data for future medical and machine learning research efforts. However, this information may not be available in e

Bradley Scott, Clarisse de Vries, Aiden Durrant, Nir Oren

In this study, we investigated whether transfer learning from macaque monkeys could improve human pose estimation. Current state-of-the-art pose estimation techniques, often employing deep neural networks, can match human annotation in non-clinical datasets. However, they underperform in novel situations, limiting their generalisability to clinical populatio

M. Cristina Volpe

Core-collapse supernovae constitute a unique laboratory for particle physics and astrophysics. They are powerful neutrino sources of all flavors, emitting essentially all the gravitational binding energy through neutrinos, at the end of their life. I will highlight how crucial is the observation of the next core-collapse supernova and of the diffuse supernov

Naira Grigoryan, Piotr Chudzinski

The paradigmatic state of a 1D collective metal, the Tomonaga-Luttinger liquid (TLL), offers us an exact analytic solution for a strongly interacting quantum system not only for infinite systems at zero temperature but also at finite temperature and with a boundary. Potentially, these results are of high relevance for technology as they could lay the foundat

Silja Pohjolainen

Two similar-looking, two-part interplanetary type II burst events from 2003 and 2012 are reported and analysed. The 2012 event was observed from three different viewing angles, enabling comparisons between the spacecraft data. In these two events, a diffuse wide-band type II radio burst was followed by a type II burst that showed emission at the fundamental

Stefan Hohenegger, Mikolaj Myszkowski, Mattia Damia Paciarini, Francesco Sannino

We develop an effective metric description of 2+1 dimensional black holes describing deviations from the classical Ba\~nados-Teitelboim-Zanelli (BTZ) black hole. The latter is a classical 2+1 dimensional rotating black hole with constant negative curvature. The effective metric is constrained by imposing the black hole symmetries and asymptotic classical beh

Enzo Cognéville, Thomas Deschatre, Xavier Warin

In this paper, we propose a complete modelling framework to value several batteries in the electricity intraday market at the trading session scale. The model consists of a stochastic model for the 24 mid-prices (one price per delivery hour) combined with a deterministic model for the liquidity costs (representing the cost of going deeper in the order book).

Valerio Bertone, Michael Fucilla, Cédric Mezrag

We compute the one-loop correction to the forward matrix element of an off-light-cone bi-local quark correlator characterised by a space-like separation $z^2$ in the presence of heavy quarks with mass $m$. This calculation allows us to extract the one-loop matching kernel, necessary to connect quasi and pseudo-distributions to collinear parton distribution f

Ruize Shi, Hong Huang, Wei Zhou, Kehan Yin

The rapid development of large language models (LLMs) has transformed many industries, including healthcare. In practice, hospitals and patients increasingly seek LLM-based systems capable of interpreting personal health records and providing healthcare guidance. However, existing approaches mainly rely on general medical knowledge and often fail to account

Simon Vary, David Martínez-Rubio, Patrick Rebeschini

We study first-order algorithms that are uniformly stable for empirical risk minimization (ERM) problems that are convex and smooth with respect to $p$-norms, $p \geq 1$. We propose a black-box reduction method that, by employing properties of uniformly convex regularizers, turns an optimization algorithm for H\"older smooth convex losses into a uniformly st

Q. C. Zhao, L. Tao, S. Sergey Tsygankov, A. Alexander Mushtukov

We present a detailed time-resolved and phase-resolved polarimetric analysis of the transient X-ray pulsar RX J0440.9+4431/LS V +44 17, using data from the Imaging X-ray Polarimetry Explorer (IXPE) during the 2023 giant outburst. We conducted a time-resolved analysis by dividing the data into several intervals for each observation. This analysis reveals a co

Lorenzo Gnoatto, Thomas Molier, Jelte J. Lamberts, Artim L. Bassant

For magnonics and spintronics applications, the spin polarization ($P$) of a transport current and the magnetic damping ($\alpha$) play a crucial role, e.g. for magnetization dynamics and magnetization switching applications. In particular, $P$ in a glassy (amorphous) 3d transition ferromagnet such as CoFeB and $\alpha$ are both strongly affected by $s-d$ sc

Enrica Floris, Andreas Höring

We show that a divisor in a rational homogenous variety with split normal sequence is the preimage of a hyperplane section in either the projective space or a quadric.

R. A. Dumer, M. Godoy

In this work, we employed Monte Carlo simulations to study the Ising, $XY$, and Heisenberg models on a simple cubic lattice, where the system models evolve toward the steady state under the influence of competition between one- and two-spin flip dynamics. With probability $q$, the system is in contact with a thermal reservoir at temperature $T$ and evolves t

Giuliano Boava, Gilles G. de Castro, Daniel Gonçalves, Daniel W. van Wyk

We introduce partial group algebras with relations in a purely algebraic framework. Given a group and a set of relations, we define an algebraic partial action and prove that the resulting partial skew group ring is isomorphic to the associated partial group algebra with relations. Under suitable conditions - which always holds if the base ring is a field -

Yongtang Shi, Jianhua Tu, Ziyuan Wang

A maximal independent set in a graph $G$ is an independent set that cannot be extended to a larger independent set by adding any vertex from $G$. This paper investigates the problem of determining the maximum number of maximal independent sets in terms of the matching number of a graph. We establish the maximum number of maximal independent sets for general

Wei-jie Fu, Jan M. Pawlowski, Robert D. Pisarski, Fabian Rennecke

Dense QCD matter may exhibit crystalline phases. Their existence is reflected in a moat regime, where mesonic correlations feature spatial modulations. We study the realtime properties of pions at finite temperature and density in QCD in order to elucidate the nature of this regime. We show that the moat regime arises from particle-hole-like fluctuations nea

Markus Borg

In the software industry, the drive to add new features often overshadows the need to improve existing code. Large Language Models (LLMs) offer a new approach to improving codebases at an unprecedented scale through AI-assisted refactoring. However, LLMs come with inherent risks such as braking changes and the introduction of security vulnerabilities. We adv

Andrew H. Zhang, Alex He-Mo, Richard Fei Yin, Chunlin Li

Study Objectives: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Evanston, IL), a non-intrusive dual-module wireless wearable system measuring chest electrocardiography (ECG), triaxial accelerometry, and chest temperature, and finger photoplethysmography and finger temperature. Methods: We obtain

Kashif Mehmood, Katina Kralevska, Danilo Gligoroski

IBN is an emerging network management paradigm that allows automated closed-loop control and management of network devices and services. Closed-loop control requires security primitives to avoid intrusive human impact on network policies, posing a serious security challenge. This paper addresses this critical problem by securing the management plane in IBN s

Robin Scholle, Laura Classen

We perform unrestricted Hartree-Fock calculations on the 2D Hubbard model on a honeycomb and bilayer honeycomb lattice at both zero and finite temperatures. Finite size real space calculations are supplemented with RPA calculations in the thermodynamic limit. Our motivation comes from high doping levels achieved in graphene and Bernal bilayer graphene by int

J. Abdallah, M. N. Agaras, A. Ahmad, G. Arabidze

This paper presents a study of the radiation hardness of the hadronic Tile Calorimeter of the ATLAS experiment in the LHC Run 2. Both the plastic scintillators constituting the detector active media and the wavelength-shifting optical fibres collecting the scintillation light into the photodetector readout are elements susceptible to radiation damage. The de

Petr Zeman, Michaela Červená, Jiří Houška, Stanislav Haviar

The oxidation behavior of W-Zr thin-film metallic glasses (TFMGs) with 32, 48 and 61 at.% Zr, prepared by dc magnetron co-sputtering, was comprehensively studied after annealing in synthetic air. The study focuses on the effect of the annealing temperature (up to 600{\deg}C) on the oxidation process, oxygen saturation, structure evolution, and their subseque

Hsin-Lun Li

The multilayer garbage disposal game is an evolution of the garbage disposal game. Each layer represents a social relationship within a system of finitely many individuals and finitely many layers. An agent can redistribute their garbage and offload it onto their social neighbors in each layer at each time step. We study the game from a mathematical perspect

Isaac Y. Miranda-Valdez, Aaro Niinistö, Tero Mäkinen, Juha Lejon

Mathematical modeling is a powerful tool in rheology, and we present pyRheo, an open-source package for Python designed to streamline the analysis of creep, stress relaxation, oscillation, and rotation tests. pyRheo contains a comprehensive selection of viscoelastic models, including fractional order approaches. It integrates model selection and fitting feat

Sriram Sankaranarayanan, V. Shubha Vatsalya

We primarily consider bilevel programs where the lower level is a convex quadratic minimization problem under integer constraints. We show that it is $\Sigma_2^p$-hard to decide if the optimal objective for the leader is lesser than a given value. Following that, we consider a natural algorithm for bilevel programs that is used as a heuristic in practice. Us

Gautier Evennou, Antoine Chaffin, Vivien Chappelier, Ewa Kijak

The rise of the generative models quality during the past years enabled the generation of edited variations of images at an important scale. To counter the harmful effects of such technology, the Image Difference Captioning (IDC) task aims to describe the differences between two images. While this task is successfully handled for simple 3D rendered images, i

Tiago S. Amancio, Ricardo A. Mosna, Ronaldo S. S. Vieira

We analyze the orbital dynamics of spherical test bodies in ``black hole surrounded by dark matter halo'' spherically symmetric spacetimes. When the test body pulsates periodically (such as a variable star), altering its quadrupole tensor, Melnikov's method shows that its orbital dynamics presents homoclinic chaos near the corresponding unstable circular orb

Patrizio Bifulco, Joachim Kerner, Christian Rose

In the recent literature, various authors have studied spectral comparison results for Schrödinger operators with discrete spectrum in different settings including Euclidean domains and quantum graphs. In this note we derive such spectral comparison results in a rather general framework for general and possibly infinite discrete graphs. Along the way, we est

P. S. Koliogiannis, E. Yuksel, N. Paar

Parity-violating electron scattering experiments on $\rm ^{48}Ca$ (CREX) and $\rm ^{208}Pb$ (PREX-2) offer valuable insight into the isovector properties of finite nuclei, providing constraints for the density dependence of the nuclear equation of state, which is crucial for understanding astrophysical phenomena. In this work, we establish functional depende

Davide Addona, Luca Lorenzi, Marianna Porfido

We provide pointwise upper bounds for the transition kernels of semigroups associated with a class of systems of nondegenerate elliptic partial differential equations with unbounded coefficients with possibly unbounded diffusion coefficients, which may vary equation by equation.

W. Jacob Ogden, Micah Warren

We show the existence of a properly immersed translating solution to curve diffusion flow in the plane. Curve diffusion flow is a higher order version of curve shortening flow, namely \[ \left( \frac{dX}{dt}\right) ^{\perp}=-\kappa_{ss}N. \]

Alexander Burgman

The violation of baryon number is an essential ingredient for baryogenesis - the preferential creation of matter over antimatter - needed to account for the observed baryon asymmetry in the Universe. However, such a process has yet to be experimentally observed. The HIBEAM/NNBAR program is a proposed two-stage experiment at the European Spallation Source to

Jiaozi Wang, Merlin Füllgraf, Jochen Gemmer

We study the equilibration times $T_\text{eq}$ of local observables in quantum chaotic systems by considering their auto-correlation functions. Based on the recursion method, we suggest a scheme to estimate $T_\text{eq}$ from the corresponding Lanczos coefficients that is expected to hold in the thermodynamic limit. We numerically find that if the observable

Ruijie Meng, Gregory J. Duck, Abhik Roychoudhury

Greybox fuzzing is one of the most popular methods for detecting software vulnerabilities, which conducts a biased random search within the program input space. To enhance its effectiveness in achieving deep coverage of program behaviors, greybox fuzzing is often combined with concolic execution, which performs a path-sensitive search over the domain of prog

Jade Touresse, Etienne Pariat, Clara Froment, Valentin Aslanyan

Parker Solar Probe's (PSP) discovery of the prevalence of switchbacks (SBs), localised magnetic deflections in the nascent solar wind, has sparked interest in uncovering their origins. A prominent theory suggests these SBs originate in the lower corona through magnetic reconnection processes, closely linked to solar jet phenomena. Jets are impulsive events,

Paula Tuzón, Antoni Salvà Salvà, Juan Fernández-Gracia

This paper presents a methodological approach based on the use of complex networks to analyze the structure and content of curricula. We analyze the concept network built from the final year of a particular high school Physics curriculum, as well as that of Mathematics. We examine the most central nodes in each case, the community structures (coherent units

Andrew J. Blumberg, Michael A. Mandell

This paper studies the foundations of the geometric fixed point functor in multiplicative equivariant stable homotopy theory. We introduce a new class of equivariant orthogonal spectra called generalized orbit desuspension spectra and analyze their homotopical behavior with respect to the geometric fixed point functor and especially the interaction with smas

Timothy Bennett, Michael C. Bowdoin, Haley Broadus, Daniel Hodgins

Suppose $G$ is a graph and $L$ is a list assignment for $G$. A request of $L$ is a function $r$ with nonempty domain $D\subseteq V(G)$ such that $r(v) \in L(v)$ for each $v \in D$. The triple $(G,L,r)$ is $\epsilon$-satisfiable if there exists a proper $L$-coloring $f$ of $G$ such that $f(v) = r(v)$ for at least $\epsilon|D|$ vertices in $D$. We say $G$ is $

Elie Bretin, Antonin Chambolle, Simon Masnou

We investigate a new phase field model for representing non-oriented interfaces, approximating their area and simulating their area-minimizing flow. Our contribution is related to the approach proposed in arXiv:2105.09627 that involves ad hoc neural networks. We show here that, instead of neural networks, similar results can be obtained using a more standard

Andrea Moglia, Elia Clement Nastasio, Luca Mainardi, Pietro Cerveri

Problem: Pancreas radiological imaging is challenging due to the small size, blurred boundaries, and variability of shape and position of the organ among patients. Goal: In this work we present MiniGPT-Pancreas, a Multimodal Large Language Model (MLLM), as an interactive chatbot to support clinicians in pancreas cancer diagnosis by integrating visual and tex

Zhenghao Gao, Shengjie Xu, Meixi Chen, Fangyao Zhao

Contemporary adversarial attack methods face significant limitations in cross-model transferability and practical applicability. We present Watertox, an elegant adversarial attack framework achieving remarkable effectiveness through architectural diversity and precision-controlled perturbations. Our two-stage Fast Gradient Sign Method combines uniform baseli

Marián Boguñá, M. Ángeles Serrano

Directed networks are essential for representing complex systems, capturing the asymmetry of interactions in fields such as neuroscience, transportation, and social networks. Directionality reveals how influence, information, or resources flow within a network, fundamentally shaping the behavior of dynamical processes and distinguishing directed networks fro

Yuhang He, Yash Jain, Xubo Liu, Andrew Markham

Despite significant advancements in Text-to-Audio (TTA) generation models achieving high-fidelity audio with fine-grained context understanding, they struggle to model the relations between audio events described in the input text. However, previous TTA methods have not systematically explored audio event relation modeling, nor have they proposed frameworks

Guang Yang, Yu Zhou, Xiangyu Zhang, Wei Cheng

The extensive application of Large Language Models (LLMs) in generative coding tasks has raised concerns due to their high computational demands and energy consumption. Unlike previous structural pruning methods designed for classification models that deal with lowdimensional classification logits, generative Code LLMs produce high-dimensional token logit se

Gianmario Voria, Rebecca Di Matteo, Giammaria Giordano, Gemma Catolino

As machine learning (ML) systems are increasingly adopted across industries, addressing fairness and bias has become essential. While many solutions focus on ethical challenges in ML, recent studies highlight that data itself is a major source of bias. Pre-processing techniques, which mitigate bias before training, are effective but may impact model performa

Edmund Heng, Anthony M. Licata

We describe spaces of Bridgeland stability conditions on certain triangulated categories associated to Coxeter systems. These categories are defined algebraically using the category of modules for zigzag algebras associated to Coxeter systems, which we construct as distinguished (quadratic, graded) algebra objects in fusion categories. The resulting stabilit

Zhongyuan Yu, Daniel Zeidler, Krishnan Chandran, Lars Engeln

Mixed reality (MR) environments provide great value in displaying 3D virtual content. Systems facilitating co-located multiuser MR (Co-MUMR) experiences allow multiple users to co-present in a shared immersive virtual environment with natural locomotion. They can be used to support a broad spectrum of applications such as immersive presentations, public exhi

Haotian Li, Arno Siebes, Siamak Mehrkanoon

Nowcasting, the short-term prediction of weather, is essential for making timely and weather-dependent decisions. Specifically, precipitation nowcasting aims to predict precipitation at a local level within a 6-hour time frame. This task can be framed as a spatial-temporal sequence forecasting problem, where deep learning methods have been particularly effec

L. Niggli, J. H. Strik, Z. Liu, A. Bergman

Spin glasses are magnetic materials exhibiting numerous magnetization patterns, that randomly vary both in real space and in time. To date, it is still not well understood what the nature of these spatiotemporal dynamics is, namely if they are completely random or if there are links between given time and length scales. Here we show the ubiquitous behavior o

Benoît Blossier, Jochen Heitger, Jan Neuendorf, Teseo San José

The extraction of decay parameters using lattice techniques is a computationally expensive task, requiring several volumes and group irreps to relate the spectrum on a lattice simulation to the infinite volume scattering. In this project we employ an alternative method based on a narrow-width approximation to extract the hadronic mixing $<\bar{D}D|\psi(3770)

Juan Sebastián Numpaque-Roa, Florent Schaffhauser

These notes grew out of a mini-course given by the second-named author at Casa Matem\'atica Oaxaca in the Fall of 2022. Their purpose is to provide an exposition, directed at graduate students, of the basic properties of complex analytic group bundles and torsors under them, including the flat case.

Koshiro Murai

We show that very general noncommutative projective planes do not admit phantom categories.

Dominik Szombathy, Angelo Valli, Cătălin Paşcu Moca, János Asbóth

We study the emergence of complexity in deep random $N$-qubit $T$-gate doped Clifford circuits, as reflected in their spectral properties and in magic generation, characterized by the stabilizer R\'enyi entropy distribution and the non-stabilizing power of the circuit. For pure (undoped) Clifford circuits, a unique periodic orbit structure in the space of Pa

P. Baglioni, L. Giambagli, A. Vezzani, R. Burioni

Finite-width one hidden layer networks with multiple neurons in the readout layer display non-trivial output-output correlations that vanish in the lazy-training infinite-width limit. In this manuscript we leverage recent progress in the proportional limit of Bayesian deep learning (that is the limit where the size of the training set $P$ and the width of th