November 2024 arXiv papers — page 38

Antonios Antoniadis, Marek Eliáš, Adam Polak, Moritz Venzin

We initiate a systematic study of utilizing predictions to improve over approximation guarantees of classic algorithms, without increasing the running time. We propose a systematic method for a wide class of optimization problems that ask to select a feasible subset of input items of minimal (or maximal) total weight. This gives simple (near-)linear time alg

Andre Kassis, Urs Hengartner, Yaoliang Yu

Diffusion-based purification (DBP) has become a cornerstone defense against adversarial examples (AEs), regarded as robust due to its use of diffusion models (DMs) that project AEs onto the natural data manifold. We refute this core claim, theoretically proving that gradient-based attacks effectively target the DM rather than the classifier, causing DBP's ou

Steven D. Bass

We discuss the role of gluon dynamics in $\eta'$ physics and in nucleon resonances where excitations of gluonic potentials may also be important. Interesting phenomenology includes a possible narrow near threshold resonance in $\eta'$ photoproduction and whether the parity doublets observed in the higher mass nucleon resonance spectrum might be hinting at a

Aaron L. Putterman, Vadim Zaripov

In this work, we present a generalization of the linear operator family of codes that captures more codes that achieve list decoding capacity. Linear operator (LO) codes were introduced by Bhandari, Harsha, Kumar, and Sudan [BHKS24] as a way to capture capacity-achieving codes. In their framework, a code is specified by a collection of linear operators that

Xinhua Wu, Yanchao Wang, Ekin Ugurel, Cynthia Chen

Today, GPS-equipped mobile devices are ubiquitous, and they generate Location-Based Service (LBS) data, which has become a critical resource for understanding human mobility. However, inherent limitations in LBS datasets, primarily characterized by discontinuity and sparsity, may introduce significant biases in representing individual movement patterns. This

Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi

Assessment and evaluation have long been critical challenges in artificial intelligence (AI) and natural language processing (NLP). Traditional methods, usually matching-based or small model-based, often fall short in open-ended and dynamic scenarios. Recent advancements in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm, where LLMs are le

Zachary T. Hilliard, Mohammad Farazmand

Shape-morphing solutions (also known as evolutional deep neural networks, reduced-order nonlinear solutions, and neural Galerkin schemes) are a new class of methods for approximating the solution of time-dependent partial differential equations (PDEs). Here, we introduce a sequential data assimilation method for incorporating observational data in a shape-mo

Caleb Miller, J. Michael Roney

Consideration is being given to upgrading the SuperKEKB electron-positron collider with the introduction of electron-beam polarization. This would enable a unique precision electroweak physics program that opens new ways to search for physics beyond the Standard Model. The upgrade would enable Belle II to make a number of high precision measurements, one of

Fabian Hinder, Valerie Vaquet, Barbara Hammer

Concept drift refers to the change of data distributions over time. While drift poses a challenge for learning models, requiring their continual adaption, it is also relevant in system monitoring to detect malfunctions, system failures, and unexpected behavior. In the latter case, the robust and reliable detection of drifts is imperative. This work studies t

Jeremy M. Wachter, Ken D. Olum, Jose J. Blanco-Pillado

We derive a general procedure for calculating the gravitational wave background (GWB) from cosmic string loops whose typical shape evolves over time, as in gravitational backreaction. Using the results of a large-scale study of numerical gravitational backreaction on Nambu-Goto cosmic string loops, we construct GWBs of backreacted cosmic strings for a range

Antonio Lerario, Andrea Rosana

Motivated by the concept of Euclidean Distance Degree, which measures the complexity of finding the nearest point to an algebraic set in Euclidean space, we introduce the notion of Grassmann Distance Complexity (GDC). This concept quantifies the complexity of solving the nearest point problem for subanalytic sets in the Grassmannian, using the intrinsic Riem

Anouar Boumeftah, Olfa Ben Yahia, Jean-François Frigon, Gregory Falco

This paper introduces a scenario where a maneuverable satellite in geostationary orbit (GEO) conducts on-orbit attacks, targeting communication between a GEO satellite and a ground station, with the ability to switch between stationary and time-variant jamming modes. We propose a machine learning-based detection approach, employing the random forest algorith

Klinsmann Agyei, Pouria Sarhadi, Wasif Naeem

In the field of autonomous surface vehicles (ASVs), devising decision-making and obstacle avoidance solutions that address maritime COLREGs (Collision Regulations), primarily defined for human operators, has long been a pressing challenge. Recent advancements in explainable Artificial Intelligence (AI) and machine learning have shown promise in enabling huma

Daniel Fernández-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato

Bayesian optimization (BO) methods based on information theory have obtained state-of-the-art results in several tasks. These techniques heavily rely on the Kullback-Leibler (KL) divergence to compute the acquisition function. In this work, we introduce a novel information-based class of acquisition functions for BO called Alpha Entropy Search (AES). AES is

Egor Sevriugov, Nikita Dragunov, Anton Razzhigaev, Andrey Kuznetsov

Non-autoregressive (NAR) language models offer notable efficiency in text generation by circumventing the sequential bottleneck of autoregressive decoding. However, accurately modeling dependencies in discrete sequences remains challenging in this paradigm. In this work, we advance the field of NAR generation by applying conditional flow matching (CFM) metho

Aaron Wheeler, Jeffrey D. Varner

This work presents a generative pre-trained transformer (GPT) designed for modeling financial time series. The GPT functions as an order generation engine within a discrete event simulator, enabling realistic replication of limit order book dynamics. Our model leverages recent advancements in large language models to produce long sequences of order messages

Hao-Ning Wu

Given a set of scattered points on a regular or irregular 2D polygon, we aim to employ them as quadrature points to construct a quadrature rule that establishes Marcinkiewicz--Zygmund inequalities on this polygon. The quadrature construction is aided by Bernstein--B\'{e}zier polynomials. For this purpose, we first propose a quadrature rule on triangles with

Gabriel Zarzoso, Eduardo Roque, Francisco Montero-Chacón, Javier Segurado

An FFT based method is proposed to simulate chemo-mechanical problems at the microscale including fracture, specially suited to predict crack formation during the intercalation process in batteries. The method involves three fields fully coupled, concentration, deformation gradient and damage. The mechanical problem is set in a finite strain framework and so

Johan Medrano, Karl J. Friston, Peter Zeidman

This paper introduces a novel approach for modelling time-varying connectivity in neuroimaging data, focusing on the slow fluctuations in synaptic efficacy that mediate neuronal dynamics. Building on the framework of Dynamic Causal Modelling (DCM), we propose a method that incorporates temporal basis functions into neural models, allowing for the explicit re

Alessandro Forte, Thomas Gawne, Oliver S. Humphries, Thomas Campbell

Spectroscopic techniques are essential for studying material properties, but the small cross-sections of some methods may result in low signal-to-noise ratios (SNRs) in the collected spectra. In this article we present methods, based on combining Bragg spectroscopy with photon counting and deconvolution algorithms, which increase the SNRs, making the spectra

Tomasz Krajewski, Marek Lewicki, Ignacy Nałęcz, Mateusz Zych

We investigate the hydrodynamic solutions for expanding bubbles in cosmological first-order phase transitions going beyond local thermal equilibrium approximation. Under the assumption of a tangenosidal field profile, we supplement the matching conditions with the entropy produced due to the interaction of the bubble wall with ambient plasma. This allows us

Zhiheng Xi, Dingwen Yang, Jixuan Huang, Jiafu Tang

Training large language models (LLMs) to spend more time thinking and reflection before responding is crucial for effectively solving complex reasoning tasks in fields such as science, coding, and mathematics. However, the effectiveness of mechanisms like self-reflection and self-correction depends on the model's capacity to accurately assess its own perform

Ken Deng, Yuan-Chen Guo, Jingxiang Sun, Zi-Xin Zou

Modern 3D generation methods can rapidly create shapes from sparse or single views, but their outputs often lack geometric detail due to computational constraints. We present DetailGen3D, a generative approach specifically designed to enhance these generated 3D shapes. Our key insight is to model the coarse-to-fine transformation directly through data-depend

Daya Ram Gaur, Barun Gorain, Shaswati Patra, Rishi Ranjan Singh

We study approximation algorithms for the forest cover and bounded forest cover problems. A probabilistic $2+\epsilon$ approximation algorithm for the forest cover problem is given using the method of dual fitting. A deterministic algorithm with a 2-approximation ratio that rounds the optimal solution to a linear program is given next. The 2-approximation fo

Allison M. McCarthy, Johanna M. Vos, Philip S. Muirhead, Beth A. Biller

Isolated planetary-mass objects share their mass range with planets but do not orbit a star. They lack the necessary mass to support fusion in their cores and thermally radiate their heat from formation as they cool, primarily at infrared wavelengths. Many isolated planetary-mass objects show variations in their infrared brightness consistent with non-unifor

K. Walczyk, G. Krasucki, K. Olkowska-Pucko, Z. Chen

Layered materials have attracted significant interest because of their unique properties. Van der Waals heterostructures based on transition-metal dichalcogenides have been extensively studied because of potential optoelectronic applications. We investigate the optical response of a light-emitting tunneling structure based on a WSe\textsubscript{2} monolayer

Zichong Meng, Yiming Xie, Xiaogang Peng, Zeyu Han

Since 2023, Vector Quantization (VQ)-based discrete generation methods have rapidly dominated human motion generation, primarily surpassing diffusion-based continuous generation methods in standard performance metrics. However, VQ-based methods have inherent limitations. Representing continuous motion data as limited discrete tokens leads to inevitable infor

Connor Douglas, Foster Provost, Arun Sundararajan

Algorithmic agents are used in a variety of competitive decision-making settings, including pricing contexts that range from online retail to residential home rental. We study the emergence of algorithmic collusion when competing agents employ multi-armed bandit algorithms and competition is modeled as a repeated Prisoner's Dilemma game. Notably, agents in o

Vagelis Plevris, Afaq Ahmad

This study extends the use of symbolic computation in Matrix Structural Analysis (MSA) to plane (2D) trusses, building on previous work that focused on continuous beams. An open-source MATLAB program, hosted on GitHub, was developed to perform symbolic analysis of 2D trusses under point loads for any configuration. Using MATLAB's Symbolic Math Toolbox, the p

Giorgio Cipolloni, László Erdős, Yuanyuan Xu

We consider the standard overlap $\mathcal{O}_{ij}: =\langle \mathbf{r}_j, \mathbf{r}_i\rangle\langle \mathbf{l}_j, \mathbf{l}_i\rangle$ of any bi-orthogonal family of left and right eigenvectors of a large random matrix $X$ with centred i.i.d. entries and we prove that it decays as an inverse second power of the distance between the corresponding eigenvalue

Jacob Prinz, Henry Blanchette, Leonidas Lampropoulos

Structure editors operate directly on a program's syntactic tree structure. At first glance, this allows for the exciting possibility that such an editor could enforce correctness properties: programs could be well-formed and sometimes even well-typed by construction. Unfortunately, traditional approaches to structure editing that attempt to rigidly enforce

L. Massacrier

Quarkonium production in high-energy hadronic collisions is a useful tool to investigate fundamental aspects of Quantum Chromodynamics, from the proton and nucleus structure to deconfinement and the properties of the Quark Gluon Plasma (QGP). In these proceedings, emphasis is made on few recent quarkonium results from the RHIC and LHC colliders in proton-pro

Jerick Shi, Burton Hollifield

Predicting the movement of the stock market and other assets has been valuable over the past few decades. Knowing how the value of a certain sector market may move in the future provides much information for investors, as they use that information to develop strategies to maximize profit or minimize risk. However, market data are quite noisy, and it is chall

Marzia Binta Nizam, Marian Zlateva, James Davis

Medical image segmentation is crucial for diagnosis and treatment planning. Traditional CNN-based models, like U-Net, have shown promising results but struggle to capture long-range dependencies and global context. To address these limitations, we propose a transformer-based architecture that jointly applies Channel Attention and Pyramid Attention mechanisms

Yinqiu Feng, Aoran Shen, Jiacheng Hu, Yingbin Liang

This paper presents an innovative approach to enhancing few-shot learning by integrating data augmentation with model fine-tuning in a framework designed to tackle the challenges posed by small-sample data. Recognizing the critical limitations of traditional machine learning models that require large datasets-especially in fields such as drug discovery, targ

Mohammad Ramadan, Mihai Anitescu

Many traditional robust control approaches assume linearity of the system and independence between the system state-input and the parameters of its approximant (possibly lower-order) model. This assumption implies that the application of robust control design to the underlying system introduces no distributional shifts in the parameters of its approximant mo

Jonas Glombitza, Martin Schneider, Franziska Leitl, Stefan Funk

With their wide field of view and high duty cycle, water-Cherenkov-based observatories are integral to studying the very high-energy gamma-ray sky. For gamma-ray observations, precise event reconstruction and highly effective background rejection are crucial and have been continuously improving in recent years. In this work, we investigate the application of

Kevin Batz, Benjamin Lucien Kaminski, Christoph Matheja, Tobias Winkler

Markov decision processes (MDPs) with rewards are a widespread and well-studied model for systems that make both probabilistic and nondeterministic choices. A fundamental result about MDPs is that their minimal and maximal expected rewards satisfy Bellmann's optimality equations. For various classes of MDPs - notably finite-state MDPs, positive bounded model

Ángel del Río, Marco Vergani

We introduce and study some families of groups whose irreducible characters take values on quadratic extensions of the rationals. We focus mostly on a generalization of inverse semi-rational groups, which we call uniformly semi-rational groups. Moreover, we associate to every finite group two invariants, called rationality and semi-rationality of the group.

Shun Ohkubo

One of the phenomena peculiar in the theory of $p$-adic differential equations is that solutions $f$ of $p$-adic differential equations defined on open discs may satisfy growth conditions at the boundaries. This phenomenon is first studied by Dwork, who proves the fundamental theorem asserting that if a $p$-adic differential equation defined on an open unit

Shahriyar Zaman Ridoy, Md. Shazzad Hossain Shaon, Alfredo Cuzzocrea, Mst Shapna Akter

Automated detection of software vulnerabilities is critical for enhancing security, yet existing methods often struggle with the complexity and diversity of modern codebases. In this paper, we introduce EnStack, a novel ensemble stacking framework that enhances vulnerability detection using natural language processing (NLP) techniques. Our approach synergize

Callum Duffy, Smit Chaudhary, Gergana V. Velikova

This study introduces growth-based training strategies that incrementally increase parameterized quantum circuit (PQC) depth during training, mitigating overfitting and managing model complexity dynamically. We develop three distinct methods: Block Growth, Sequential Feature Map Growth, and Interleave Feature Map Growth, which add reuploader blocks to PQCs a

Denis S. Krotov, Ferruh Özbudak, Vladimir N. Potapov

We characterize mixed-level orthogonal arrays in terms of algebraic designs in a special multigraph. We prove a mixed-level analog of the Bierbrauer-Friedman (BF) bound for pure-level orthogonal arrays and show that arrays attaining it are radius-1 completely regular codes (equivalently, intriguing sets, equitable 2-partitions, perfect 2-colorings) in the co

Sepehr Gourabi, Masoud Lotfalipour, Reza Khosrowabadi, Reza Jafari

Working memory plays a crucial role in various aspects of human life. Therefore, it has been an area of interest in different research studies, especially neuroscience. The neuroscientists investigating working memory have primarily emphasized the brain's functional modularity. At the same time, a holistic perspective is still required to investigate the bra

Noam Rotstein, Gal Yona, Daniel Silver, Roy Velich

Recent advances in image editing, driven by image diffusion models, have shown remarkable progress. However, significant challenges remain, as these models often struggle to follow complex edit instructions accurately and frequently compromise fidelity by altering key elements of the original image. Simultaneously, video generation has made remarkable stride

Tianfu Qi, Jun Wang

The channel polarization behavior of polar codes under noise with memory is investigated. By introducing a genie-aided channel model, we first show that the polarized subchannels still converge to extremal channels under the standard polar coding framework. More importantly, we explicitly quantify the gap between the mutual information achieved by ignoring m

Ben Jacobson-Bell, Steve Croft, Carmen Choza, Alex Andersson

The search for radio technosignatures is an anomaly detection problem: Candidate signals represent needles of interest in the proverbial haystack of radio-frequency interference (RFI). Current search frameworks find an enormity of false-positive signals, especially in large surveys, requiring manual follow-up to a sometimes prohibitive degree. Unsupervised l

P. Popesso, A. Biviano, I. Marini, K. Dolag

By using eROSITA data in the eFEDS area, we provide a measure of the hot gas fraction vs. halo mass relation over the largest halo mass range, from Milky Way-sized halos to massive clusters, and to the largest radii ever probed so far in local systems. To cope with the incompleteness and selection biases of the X-ray selection, we apply the stacking techniqu

Erfan Aasi, Phat Nguyen, Shiva Sreeram, Guy Rosman

The deployment of autonomous vehicles controlled by machine learning techniques requires extensive testing in diverse real-world environments, robust handling of edge cases and out-of-distribution scenarios, and comprehensive safety validation to ensure that these systems can navigate safely and effectively under unpredictable conditions. Addressing Out-Of-D

Manish Jha

This paper demonstrates that hedge funds tend to design their activist campaigns to align with the preferences and ideologies of institutions holding large stakes in the target company. I estimate these preferences by analyzing the institutions' previous proxy voting behavior. The results reveal that activists benefit from this approach. Campaigns with a str

Anya Shchetkina

Targeting and personalization policies can be used to improve outcomes beyond the uniform policy that assigns the best performing treatment in an A/B test to everyone. Personalization relies on the presence of heterogeneity of treatment effects, yet, as we show in this paper, heterogeneity alone is not sufficient for personalization to be successful. We deve

Harshavardhan Battula, Jiacheng Liu, Jaideep Srivastava

To evaluate a multi-representational framework in which large language model (LLM)-generated expert summaries of intensive care unit (ICU) notes are fused with physiology for in-hospital mortality (IHM) prediction, and to determine how much of the resulting gain is non-redundant with the notes themselves. Using MIMIC-III (19,211 first ICU stays, 12.83% morta

Anders Emil Vrålstad, Peter Fosodeder, Karin Ulrike Deibele, Siri Ann Nyrnes

The purpose of this work is to demonstrate a robust and clinically validated method for correcting sound speed aberrations in medical ultrasound. We propose a correction method that calculates focusing delays directly from the observed two-way distributed average sound speed. The method beamforms multiple coherence images and selects the sound speed that max

Maithili Kulkarni, Mark Stamp

Android malware detection based on machine learning (ML) and deep learning (DL) models is widely used for mobile device security. Such models offer benefits in terms of detection accuracy and efficiency, but it is often difficult to understand how such learning models make decisions. As a result, these popular malware detection strategies are generally treat

Wenhao Zhao, Qiran Zou, Rushi Shah, Dianbo Liu

Vector quantization is a technique in machine learning that discretizes continuous representations into a set of discrete vectors. It is widely employed in tokenizing data representations for large language models, diffusion models, and other generative models. Despite its prevalence, the characteristics and behaviors of vector quantization in generative mod

Weimin Wu, Maojiang Su, Jerry Yao-Chieh Hu, Zhao Song

We investigate the transformer's capability to simulate the training process of deep models via in-context learning (ICL), i.e., in-context deep learning. Our key contribution is providing a positive example of using a transformer to train a deep neural network by gradient descent in an implicit fashion via ICL. Specifically, we provide an explicit construct

Feodor F. Dragan, Guillaume Ducoffe, Michel Habib, Laurent Viennot

A ($\lambda,\mu$)-bow metric was defined in (Dragan & Ducoffe, 2023) as a far reaching generalization of an $\alpha_i$-metric (which is equivalent to a ($0,i$)-bow metric). A graph $G=(V,E)$ is said to satisfy ($\lambda,\mu$)-bow metric if for every four vertices $u,v,w,x$ of $G$ the following holds: if two shortest paths $P(u,w)$ and $P(v,x)$ share a common

Cesar A. Ipanaque Zapata, Josué A. Aguirre Enciso, Wilman Francisco Cuba Ramos

We present the notion of hom-complexity, $\text{C}(G;H)$, for two graphs $G$ and $H$, along with basic results for this numerical invariant. This invariant $\text{C}(G;H)$ is a number that measures the \aspas{complexity} of the question: when is there a homomorphism $G\to H$? More precisely, $\text{C}(G;H)$ is the least positive integer $k$ such that there a

P. Popesso, I. Marini, K. Dolag, G. Lamer

Hydrodynamical simulation predictions are often compared with observational data without fully accounting for systematics and biases specific to observational techniques. Using the magnetohydrodynamical simulation Magneticum, we generate mock eROSITA eRASS:4 data, combined with GAMA-like spectroscopic surveys and optically selected galaxy catalogs from the s

Shiquan Ren

In this paper, we consider hypergraphs whose vertices are distinct points moving smoothly on a Riemannian manifold M. We take these hypergraphs as graded submanifolds of configuration spaces. We construct double complexes of differential forms on configuration spaces. Then we construct double complexes of differential forms on hypergraphs which are sub-doubl

Olivier Melançon, Manuel Serrano, Marc Feeley

Dynamic and polymorphic languages attach information, such as types, to run time objects, and therefore adapt the memory layout of values to include space for this information. This makes it difficult to efficiently implement IEEE754 floating-point numbers as this format does not leave an easily accessible space to store type information. The three main floa

Hanzhi Jiang, Márk Mezei, Julio Virrueta

The time evolution of entanglement entropy in generic chaotic many-body systems has an effective description in terms of a minimal membrane, characterised by a tension function. For 2d CFTs, a degenerate tension function reproduces several results regarding the dynamics of the entropy; this stands in contrast to higher dimensions where the tension is non-deg

Alexis Metz--Donnadieu

Consider the boundary $\partial \mathbb D$ of the Brownian disk $\mathbb D$ as a metric space by endowing it with the (restriction of the) metric of $\mathbb D$. We show that the uniform measure on $\partial \mathbb D$ coincides with the Hausdorff measure associated with the gauge function $h(s)=\kappa s^2\log\log(1/s)$ for some deterministic constant $\kapp

Fabio Tanania

The main goal of this paper is to study relative versions of the category of modules over the isotropic motivic Brown-Peterson spectrum, with a particular emphasis on their cellular subcategories. Using techniques developed by Levine, we equip these categories with motivic $t$-structures, whose hearts are Tannakian categories over ${\mathbb F}_2$. This allow

Juan Camilo López Carreño

A two-level system (2LS) is the most fundamental building block of matter. Its response to classical light is well known, as it converts pulses of coherent light into antibunched emission. However, recent theoretical proposals have predicted that it is advantageous to illuminate two-level systems with Quantum Light; i.e., the light emitted from a quantum sys

Mohamed Ould El Hadj

With the aim of testing massive gravity in the context of black hole physics, we investigate the gravitational radiation emitted by a massive particle plunging into a Schwarzschild black hole from slightly below the innermost stable circular orbit. To do so, we first construct the quasinormal and quasibound resonance spectra of the spin-2 massive field for o

Chan Hee Song, Valts Blukis, Jonathan Tremblay, Stephen Tyree

Spatial understanding is a crucial capability that enables robots to perceive their surroundings, reason about their environment, and interact with it meaningfully. In modern robotics, these capabilities are increasingly provided by vision-language models. However, these models face significant challenges in spatial reasoning tasks, as their training data ar

Salvador Esquivel, Hendrik Weber

We show a priori bounds for the dynamic fractional $\Phi^4$ model on $\mathbb{T}^3$ in the full subcritical regime using the framework of Hairer's regularity structures theory. Assuming the model bounds our estimates imply global existence of solutions and existence of an invariant measure. We extend the method developed for the usual heat operator by Chandr

Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu

Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth. In this paper, we propose a path-specific path loss prediction method that uses convolutional neural networks to autom

Yuyang Hu, Albert Peng, Weijie Gan, Ulugbek S. Kamilov

Diffusion bridges (DB) have emerged as a promising alternative to diffusion models for imaging inverse problems, achieving faster sampling by directly bridging low- and high-quality image distributions. While incorporating measurement consistency has been shown to improve performance, existing DB methods fail to maintain this consistency in blind inverse pro

Aurélie Astoul, Adrian J. Barker

Stars and gaseous planets are magnetised objects but the influence of magnetic fields on their tidal responses and dissipation rates has not been well explored. We present the first exploratory nonlinear magnetohydrodynamic (MHD) simulations of tidally-excited waves in incompressible convective envelopes harbouring an initial dipolar magnetic field. Simulati

Johann Coraux, Nicodème Rougier, Benjamin Canals, Nicolas Rougemaille

The square ice is a canonical example of a Coulomb phase in two dimensions: Its ground state is extensively degenerate and satisfies a local constraint on the spin arrangement (the so-called ice rule). In this paper, we use a loop flip algorithm to explore the properties of this ground state that we analyze not in terms of a spin texture, but rather in terms

Muhammad Burhan Hafez, Kerim Erekmen

Central to the development of universal learning systems is the ability to solve multiple tasks without retraining from scratch when new data arrives. This is crucial because each task requires significant training time. Addressing the problem of continual learning necessitates various methods due to the complexity of the problem space. This problem space in

Georg Hess, Carl Lindström, Maryam Fatemi, Christoffer Petersson

Ensuring the safety of autonomous robots, such as self-driving vehicles, requires extensive testing across diverse driving scenarios. Simulation is a key ingredient for conducting such testing in a cost-effective and scalable way. Neural rendering methods have gained popularity, as they can build simulation environments from collected logs in a data-driven m

Bahman Rostami-Tabar, Travis Greene, Galit Shmueli, Rob J. Hyndman

Organizations worldwide that rely on data-driven approaches regularly employ forecasting methods to enhance their planning and decision-making processes. While extensive research has examined the harms associated with traditional machine learning applications, relatively little attention has been given to the ethical implications of time series forecasting.

Leena Barshilia, Rajiuddin Sk, Prasanta K. Panigrahi, Avinash Khare

Explicit realization of quantum droplets, even and odd Schr\"{o}dinger cat states is demonstrated in an atom-molecular Bose-Einstein condensate in the presence of interconversion and Kerr non-linear interactions. The crucial roles of both the $\chi^2$-type nonlinearity and chemical potential in the formation of these macroscopic quantum states are shown, whe

Pushpita Das, Tuomo Salmi, Jordy Davelaar, Oliver Porth

The pulsed X-ray emission from the neutron star surface acts as a window to study the state of matter in the neutron star interior. For accreting millisecond pulsars, the surface X-ray emission is generated from the `hotspots' formed due to the magnetically channeled accretion flow hitting the stellar surface. The emission from these hotspots is modulated by

Carolin M. Schuster, Maria-Alexandra Dinisor, Shashwat Ghatiwala, Georg Groh

Large language models (LLMs) are the foundation of the current successes of artificial intelligence (AI), however, they are unavoidably biased. To effectively communicate the risks and encourage mitigation efforts these models need adequate and intuitive descriptions of their discriminatory properties, appropriate for all audiences of AI. We suggest bias pro

Johann Coraux, Nicolas Rougemaille

The square ice is a two-dimensional spin liquid hosting a Coulomb phase physics. When constrained under specific boundary conditions, the so-called domain-wall boundary conditions, a phase separation occurs that leads to the formation of a spin liquid confined within a disk surrounded by magnetically ordered regions. Here, we numerically characterize the gro

Jerry Yao-Chieh Hu, Wei-Po Wang, Ammar Gilani, Chenyang Li

We investigate the statistical and computational limits of prompt tuning for transformer-based foundation models. Our key contributions are prompt tuning on \emph{single-head} transformers with only a \emph{single} self-attention layer: (i) is universal, and (ii) supports efficient (even almost-linear time) algorithms under the Strong Exponential Time Hypoth

Lukas Weimar, Bastian Stumpf, Jeanette Hussong, Ilia V. Roisman

The impact of a liquid drop with high Reynolds and Weber numbers on a wet solid surface typically results in the emergence, rising, and expansion of a corona-like thin jet. This phenomenon is explained by the propagation of a kinematic discontinuity within the wall film. Conventional theories suggest that the corona-forming liquid jet comprises material from

Steven Song, Anirudh Subramanyam, Irene Madejski, Robert L. Grossman

In the current paradigm of image captioning, deep learning models are trained to generate text from image embeddings of latent features. We challenge the assumption that fine-tuning of large, bespoke models is required to improve model generation accuracy. Here we propose Label Boosted Retrieval Augmented Generation (LaB-RAG), a small-model-based approach to

Oleksandr Diatlyk, Zimo Sun, Yifan Wang

We study an $O(N)$ invariant surface defect in the Wilson-Fisher conformal field theory (CFT) in $d=4-\epsilon$ dimensions. This defect is defined by mass deformation on a two-dimensional surface that generates localized disorder and is conjectured to factorize into a pair of ordinary boundary conditions in $d=3$. We determine defect CFT data associated with

Nils Bruch, Tobias Binninger, Jun Huang, Michael Eikerling

A classical coulombic correlation functional in one-loop (1L) and local-density-approximation (LDA) is derived for electrolyte solutions, starting from a first-principles many-body partition function. The 1L-LDA functional captures correlations between electrolyte ions and solvent dipoles, such as screening and solvation, that are ignored by conventional mea

Arushi Singh, Vikram Mahamiya, Alok Shukla

Two-dimensional silicon carbide stands out among 2D materials, primarily due to its notable band gap, unlike its carbon-based counterparts. However, the binary nature and non-layered structure of bulk SiC present challenges in fabricating its 2D counterpart. Recent advancements in technology have led to the successful synthesis of atomically thin, large-scal

Luca Di Persio, Matteo Garbelli, Luca M. Giordano

In a day-ahead market, energy buyers and sellers submit their bids for a particular future time, including the amount of energy they wish to buy or sell and the price they are prepared to pay or receive. However, the dynamic for forming the Market Clearing Price (MCP) dictated by the bidding mechanism is frequently overlooked in the literature on energy mark

Oliver Jacobsen, Haya Schulmann, Niklas Vogel, Michael Waidner

In this work, we present a novel severe buffer-overflow vulnerability in the RPKI validator Fort, that allows an attacker to achieve Remote Code Execution (RCE) on the machine running the software. We discuss the unique impact of this RCE on networks that use RPKI, illustrating that RCE vulnerabilities are especially severe in the context of RPKI. The design

A. Mironov, A. Morozov, A. Popolitov

Within the context of wavefunctions of integrable many-body systems, rational multivariable Baker-Akhiezer (BA) functions were introduced by O. Chalykh, M. Feigin and A. Veselov and, in the case of the trigonometric Ruijsenaars-Schneider system, can be associated with a reduction of the Macdonald symmetric polynomials at $t=q^{-m}$ with integer partition lab

Shiming Wang, Liyao Xiang, Bowei Cheng, Zhe Ji

A surge in data-driven applications enhances everyday life but also raises serious concerns about private information leakage. Hence many privacy auditing tools are emerging for checking if the data sanitization performed meets the privacy standard of the data owner. Blackbox auditing for differential privacy is particularly gaining popularity for its effect

Nati Daniel, May Nathan, Eden Azeroual, Yael Fisher

Incorporating artificial intelligence (AI) into digital pathology offers promising prospects for automating and enhancing tasks such as image analysis and diagnostic processes. However, the diversity of tissue samples and the necessity for meticulous image labeling often result in biased datasets, constraining the applicability of algorithms trained on them.

Daniele Lamberto, Gabriele Orlando, Salvatore Savasta

The occurrence of a second-order quantum phase transition in the Dicke model is a well-established feature. On the contrary, a comprehensive understanding of the corresponding open system, particularly in the proximity of the critical point, remains elusive. When approaching the critical point, the system inevitably enters first the system-bath ultrastrong c

L. P. Chitta, Z. Huang, R. D'Amicis, D. Calchetti

Solar wind, classified by its bulk speed and the Alfv\'enic nature of its fluctuations, generates the heliosphere. The elusive physical processes responsible for the generation of the different types of this wind are a topic of active debate. Recent observations reveal intermittent jets, with kinetic energy in the picoflare range, emerging from dark areas of

Songning Lai, Yu Huang, Jiayu Yang, Gaoxiang Huang

The increasing complexity of AI models, especially in deep learning, has raised concerns about transparency and accountability, particularly in high-stakes applications like medical diagnostics, where opaque models can undermine trust. Explainable Artificial Intelligence (XAI) aims to address these issues by providing clear, interpretable models. Among XAI t

Smruti Suresh, Michael Angelo Carvajal, Nathaniel Hanson, Ethan Holand

The inspection of confined critical infrastructure such as attics or crawlspaces is challenging for human operators due to insufficient task space, limited visibility, and the presence of hazardous materials. This paper introduces a prototype of PARIS (Precision Application Robot for Inaccessible Spaces): a use-inspired teleoperated mobile robot manipulator

Maher A. Dayeh, Michael J. Starkey, Heather A. Elliott, Raphael Attie

Solar energetic particles (SEPs) associated with shocks driven by fast coronal mass ejections (CMEs) or shocks developed by corotating interaction regions (CIRs) often extend to high energies, and are thus key elements of space weather. The PUNCH mission, set to be launched in 2025, is equipped with photometric that enables 3D tracking of solar wind structur

Neeraj Dhanraj Bokde

The Jaya R package offers a robust and versatile implementation of the parameter-free Jaya optimization algorithm, suitable for solving both single-objective and multi-objective optimization problems. By integrating advanced features such as constraint handling, adaptive population management, Pareto front tracking for multi-objective trade-offs, and paralle

Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana, Noor Ahsan

Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource languages, all while effectively integrating corresponding visual cues. In pursuit of culturally diverse globa

Andreas Athenodorou, Sergei Dubovsky, Conghuan Luo, Michael Teper

The quest to develop an effective string theory capable of describing the confining flux tube has been a longstanding objective within the theoretical physics community. Recent lattice results indicate that the low-lying spectrum of the flux tube in both three and four dimensions can be partially described by the Nambu-Goto string with minor deviations. Howe

Hongzhi Zang, Yulun Zhang, He Jiang, Zhe Chen

We study the problem of optimizing a guidance policy capable of dynamically guiding the agents for lifelong Multi-Agent Path Finding based on real-time traffic patterns. Multi-Agent Path Finding (MAPF) focuses on moving multiple agents from their starts to goals without collisions. Its lifelong variant, LMAPF, continuously assigns new goals to agents. In thi

Qin Tan, You Wu, Lang Liu

We investigate whether the Pre-Big Bang (PBB) scenario from string cosmology can explain the stochastic gravitational wave background signal reported in the NANOGrav 15-year dataset. Using Bayesian analysis techniques, we constrain the key parameters of the PBB model by comparing its theoretical predictions with the observed data. Our analysis yields $\beta