November 2024 arXiv papers — page 132

Lewis McCallum, Kenneth Wood, Robert Benjamin, Dhanesh Krishnarao

We extend our time-dependent hydrogen ionization simulations of diffuse ionized gas to include metals important for collisional cooling and diagnostic emission lines. The combination of heating from supernovae and time-dependent collisional and photoionization from midplane OB stars produces emission line intensities (and emission line ratios) that follow th

Alexandra Butoi, Ghazal Khalighinejad, Anej Svete, Josef Valvoda

Characterizing the computational power of neural network architectures in terms of formal language theory remains a crucial line of research, as it describes lower and upper bounds on the reasoning capabilities of modern AI. However, when empirically testing these bounds, existing work often leaves a discrepancy between experiments and the formal claims they

Ulrich Schmid, Stephan Felber, Hugo Rincon-Galeana

We provide a complete characterization of the solvability/impossibility of deterministic stabilizing consensus in any computing model with benign process and communication faults using point-set topology. Relying on the topologies for infinite executions introduced by Nowak, Schmid and Winkler (JACM, 2024) for terminating consensus, we prove that semi-open d

R. Ayoub, M. Oulghelou, P. J Schmid

Model reduction is a key technology for large-scale physical systems in science and engineering, as it brings behavior expressed in many degrees of freedom to a more manageable size that subsequently allows control, optimization, and analysis with multi-query algorithms. We introduce an enhanced regression technique tailored to uncover quadratic parametric r

Teng Zhang

Let \(F(z) = \prod_{k=1}^{n}(z - z_k)\) be a monic complex polynomial of degree \(n\) whose zeros satisfy \(\max\limits_{1 \le k \le n} |z_k| \le 1\). Paw{\l}owski [Trans. Amer. Math. Soc. 350(11) (1998)] considered the radius \(\gamma_n\) of the smallest disk, centered at the centroid \(\frac{1}{n}\sum_{k=1}^n z_k\), containing at least one critical point o

Fernando Peralta Castro

Cryptography, derived from Greek meaning hidden writing, uses mathematical techniques to secure information by converting it into an unreadable format. While cryptography as a science began around 100 years ago, its roots trace back to ancient civilizations like Mesopotamia and Egypt. Over time, cryptography evolved from basic methods to complex systems invo

Yuming Feng, Chuye Hong, Yaru Niu, Shiqi Liu

Recently, quadrupedal locomotion has achieved significant success, but their manipulation capabilities, particularly in handling large objects, remain limited, restricting their usefulness in demanding real-world applications such as search and rescue, construction, industrial automation, and room organization. This paper tackles the task of obstacle-aware,

Zakaria Derbazi

Consider a discrete-time optimal selection problem where one observes a sequence of independent Bernoulli trials and receives a nonnegative reward upon stopping on a success. The aim is to find a single-choice strategy that maximises the expected payoff. These Bernoulli stopping problems are characterised by two key properties: (i) a recurrence relation conn

Martin Robert, Simon Brodeur, Francois Ferland

Quadrupedal locomotion is a complex, open-ended problem vital to expanding autonomous vehicle reach. Traditional reinforcement learning approaches often fall short due to training instability and sample inefficiency. We propose a novel method leveraging multi-objective evolutionary algorithms as an automatic curriculum learning mechanism, which we named Mult

Matteo Lapucci, Davide Pucci

In this work, we address unconstrained finite-sum optimization problems, with particular focus on instances originating in large scale deep learning scenarios. Our main interest lies in the exploration of the relationship between recent line search approaches for stochastic optimization in the overparametrized regime and momentum directions. First, we point

Wei Wang, Ji Xu, Qi-An Zhang, Shuai Zhao

The heavy meson light-cone distribution amplitude (LCDA), as defined in full QCD, plays a key role in the collinear factorization for exclusive heavy meson production and in lattice computations of the LCDA within heavy-quark effective theory (HQET). In addition to its dependence on the renormalization scale, the QCD LCDA also evolves with the heavy quark ma

Maykel Marquez-Mijares, German Rojas-Lorenzo, Paulo E. Ibanez-Almaguer, Jesus Robayo-Soneira

This paper reports on a comprehensive study of an ultra-relativistic positron beam deflection by an oriented quasi-mosaic crystal. The analysis was carried out for the positron energy of 530 MeV incident on the quasi-mosaic bent Si(111) crystal. This particular case was chosen because it has recently been studied experimentally at the Mainz Microtron MAMI. T

Yannick Eich, Christian Fabian, Kai Cui, Heinz Koeppl

Mean field games (MFGs) tractably model behavior in large agent populations. The literature on learning MFG equilibria typically focuses on finding Nash equilibria (NE), which assume perfectly rational agents and are hence implausible in many realistic situations. To overcome these limitations, we incorporate bounded rationality into MFGs by leveraging the w

Myeongsoo Kim, Tyler Stennett, Saurabh Sinha, Alessandro Orso

As modern web services increasingly rely on REST APIs, their thorough testing has become crucial. Furthermore, the advent of REST API documentation languages, such as the OpenAPI Specification, has led to the emergence of many black-box REST API testing tools. However, these tools often focus on individual test elements in isolation (e.g., APIs, parameters,

Jannik Franzen, Claudia Winklmayr, Vanessa E. Guarino, Christoph Karg

Uncertainty Quantification (UQ) is crucial for reliable image segmentation. Yet, while the field sees continual development of novel methods, a lack of agreed-upon benchmarks limits their systematic comparison and evaluation: Current UQ methods are typically tested either on overly simplistic toy datasets or on complex real-world datasets that do not allow t

Hana Bezalel, Dotan Ankri, Ruojin Cai, Hadar Averbuch-Elor

We present a technique and benchmark dataset for estimating the relative 3D orientation between a pair of Internet images captured in an extreme setting, where the images have limited or non-overlapping field of views. Prior work targeting extreme rotation estimation assume constrained 3D environments and emulate perspective images by cropping regions from p

Harvey Weinberger, Paolo Comaron, Marzena H. Szymańska

We show that the multicomponent Kardar-Parisi-Zhang equation describes the low-energy theory for phase fluctuations in a $\mathbb{Z}_{2}$ degenerate non-equilibrium driven-dissipative condensate with global $U(1)\times U(1)$ symmetry. Using dynamical renormalisation group in spatial dimension $d=1$, we demonstrate that coupled stochastic complex Ginsburg-Lan

Yauhen Yakimenka, Chung-Wei Weng, Hsuan-Yin Lin, Eirik Rosnes

We consider the problem of collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. In particular, we provide a method based on hypothesis testing coupled with differential privacy and data variance estimation. Two privacy

Chao Min, Pixin Fang

We further study the orthogonal polynomials with respect to the generalized Airy weight based on the work of Clarkson and Jordaan [{\em J. Phys. A: Math. Theor.} {\bf 54} ({2021}) {185202}]. We prove the ladder operator equations and associated compatibility conditions for orthogonal polynomials with respect to a general Laguerre-type weight of the form $w(x

Avi Kaufman, James Corona, Zane Ozzello, Blake Senseman

Using the bitstring probabilities of ground states of bipartitioned ladders of Rydberg atoms, we calculate the mutual information, which is a lower bound on the corresponding bipartite von Neumann quantum entanglement entropy $S^{vN}_A$. We show that in many cases these lower bounds can be improved by removing the bitstrings with a probability lower than som

Doriane Olewicki, Leuson Da Silva, Suhaib Mujahid, Arezou Amini

We conduct a large-scale empirical user study in a live setup to evaluate the acceptance of LLM-generated comments and their impact on the review process. This user study was performed in two organizations, Mozilla (which has its codebase available as open source) and Ubisoft (fully closed-source). Inside their usual review environment, participants were giv

Xizhi Liu, Sijie Ren, Jian Wang

The celebrated Andr\'{a}sfai--Erd\H{o}s--S\'{o}s Theorem from 1974 shows that every $n$-vertex triangle-free graph with minimum degree greater than $2n/5$ must be bipartite. We establish a positive codegree extension of this result for the $r$-uniform generalized triangle $\mathrm{T}_{r} = \left\{\{1,\ldots, r-1,r\}, \{1,\ldots, r-1,r+1\},\{r,r+1, \ldots, 2r

Alaric Hartsock, Luiz Manella Pereira, Glenn Fink

Threat hunting analyzes large, noisy, high-dimensional data to find sparse adversarial behavior. We believe adversarial activities, however they are disguised, are extremely difficult to completely obscure in high dimensional space. In this paper, we employ these latent features of cyber data to find anomalies via a prototype tool called Cyber Log Embeddings

Federico Mason, Federico Chiariotti, Pietro Talli, Andrea Zanella

Goal-oriented communication is a new paradigm that considers the meaning of transmitted information to optimize communication. One possible application is the remote monitoring of a process under communication costs: scheduling updates based on goal-oriented considerations can significantly reduce transmission frequency while maintaining high-quality trackin

Taiyi Wang, Jianheng Liu, Bryan Lee, Zhihao Wu

In many practical applications, decision-making processes must balance the costs of acquiring information with the benefits it provides. Traditional control systems often assume full observability, an unrealistic assumption when observations are expensive. We tackle the challenge of simultaneously learning observation and control strategies in such cost-sens

Nikolai Leopold

We study the spinless Pauli-Fierz Hamiltonian in a semiclassical mean-field limit of many fermions. For appropriate initial conditions, we prove, in the trace norm topology of reduced density matrices, that the many-body quantum state converges to a tensor product of a semiclassically structured Slater determinant and a coherent photon state. These evolve ac

Sambatriniaina H. A. Rajohnson, Renée C. Kraan-Korteweg, Bradley S. Frank, Hao Chen

We conducted the MeerKAT Vela Supercluster survey, named Vela$-$HI, to bridge the gap between the Vela SARAO MeerKAT Galactic Plane Survey (Vela$-$SMGPS, $-2^{\circ} \leq b \leq 1^{\circ}$), and optical and near-infrared spectroscopic observations of the Vela Supercluster (hereafter Vela$-$OPT/NIR) at $|b| \gtrsim 7^{\circ}$. Covering coordinates from $263^{

Ryota Akagi

In this paper, we consider mutations of skew-symmetrizable matrices of rank 3. Every skew-symmetrizable matrix corresponds to a weighted quiver, and we study the conditions when this quiver is always cyclic after applying mutations. In this study, the Markov constant has an essential meaning. It has already appeared in some previous works for skew-symmetric

Gen Ye, Jun-Qian Jiang, Alessandra Silvestri

We propose a new model-independent reconstruction method for the matter power spectrum based on its time dependence and a combination of observations from different redshifts. The method builds on a perturbative expansion in terms of the linear growth function, with each coefficient in the expansion being a free function of scale, to be reconstructed from th

Luc Cornet

Thylakoid membranes are the site of oxygenic photosynthesis, one of the most important biochemical processes on earth. The ancestral state of these membranes is represented today in Gloeobacterales, where they are lacking and photosynthesis instead takes place in the cytoplasmic membrane. The evolutionary transition from this ancestral state to the modern th

Jiyao Zhang, Yue Hu, A. Lazarian

Magnetic fields play a crucial role in various astrophysical processes within the intracluster medium, including heat conduction, cosmic ray acceleration, and the generation of synchrotron radiation. However, measuring magnetic field strength is typically challenging due to the limited availability of Faraday Rotation Measure sources. To address the challeng

Adam Heins, Angela P. Schoellig

We consider the nonprehensile object transportation task known as the waiter's problem - in which a robot must move an object on a tray from one location to another - when the transported object has uncertain inertial parameters. In contrast to existing approaches that completely ignore uncertainty in the inertia matrix or which only consider small parameter

Philippe Heim, Rayna Dimitrova

Infinite-state reactive synthesis has attracted significant attention in recent years, which has led to the emergence of novel symbolic techniques for solving infinite-state games. Temporal logics featuring variables over infinite domains offer an expressive high-level specification language for infinite-state reactive systems. Currently, the only way to tra

Erin Carson, Yuxin Ma

The block classical Gram--Schmidt (BCGS) algorithm and its reorthogonalized variant are widely-used methods for computing the economic QR factorization of block columns $X$ due to their lower communication cost compared to other approaches such as modified Gram--Schmidt and Householder QR. To further reduce communication, i.e., synchronization, there has bee

Yichen He, Yuan Lin, Jianchao Wu, Hanchong Zhang

Existing large vision-language models (LVLMs) are largely limited to processing short, seconds-long videos and struggle with generating coherent descriptions for extended video spanning minutes or more. Long video description introduces new challenges, such as consistent character identification and plot-level descriptions incorporating both visual and audio

Kristijan Armeni, Marko Pranjić, Senja Pollak

To predict upcoming text, language models must in some cases retrieve in-context information verbatim. In this report, we investigated how the ability of language models to retrieve arbitrary in-context nouns developed during training (across time) and as language models trained on the same dataset increase in size (across scale). We then asked whether learn

Chengyu Yang, Chengjun Liu

Approximately 16 million Americans suffer from rosacea according to the National Rosacea Society. To increase rosacea awareness, automatic rosacea detection methods using deep learning and explainable statistical approaches are presented in this paper. The deep learning method applies the ResNet-18 for rosacea detection, and the statistical approaches utiliz

N. R. Landin, L. T. S. Mendes, L. P. R. Vaz, S. H. P. Alencar

The impact of disk-locking on the stellar properties related to magnetic activity from the theoretical point of view is investigated. We use the ATON stellar evolution code to calculate theoretical values of convective turnover times ($\tau_{\rm c}$) and Rossby numbers ($Ro$, the ratio between rotation periods and $\tau_{\rm c}$) for pre-main sequence (pre-M

Dominik Eckert, Ludwig Ritschl, Christopher Syben, Christian Hümmer

Radiologists have preferred visual impressions or 'styles' of X-ray images that are manually adjusted to their needs to support their diagnostic performance. In this work, we propose an automatic and interpretable X-ray style transfer by introducing a trainable version of the Local Laplacian Filter (LLF). From the shape of the LLF's optimized remap function,

Niclas Luick

While induction is considered a key mechanism for in-context learning in LLMs, understanding its precise circuit decomposition beyond toy models remains elusive. Here, we study the emergence of induction behavior within LLMs by probing their response to weak single-token perturbations of the residual stream. We find that LLMs exhibit a robust, universal regi

Qian Sun, Hanpeng Wu, Xi Sheryl Zhang

The pretraining and fine-tuning approach has become the leading technique for various NLP applications. However, recent studies reveal that fine-tuning data, due to their sensitive nature, domain-specific characteristics, and identifiability, pose significant privacy concerns. To help develop more privacy-resilient fine-tuning models, we introduce a novel ac

Manling Hu, Manqi Xu, Dunnan Liu

While wind and solar power contribute to sustainability, their intermittent nature poses challenges when integrated into the grid. To mitigate these issues, renewable energy can be combined with coal fired power and hydropower sources to stabilize the energy system, with battery storage serving as a backup source to smooth the total output. This study develo

R. Seoane Souto, V. V. Baran, M. Nitsch, L. Maffi

Majorana modes can be engineered in arrays where quantum dots (QDs) are coupled via grounded superconductors, effectively realizing an artificial Kitaev chain. Minimal Kitaev chains, composed by two QDs, can host fully-localized Majorana modes at discrete points in parameter space, known as Majorana sweet spots. Here, we extend previous works by theoreticall

Arpita Ghosh, MD Muhtasim Fuad, Seemanta Bhattacharjee

The incorporation of quantum ansatz with machine learning classification models demonstrates the ability to extract patterns from data for classification tasks. However, taking advantage of the enhanced computational power of quantum machine learning necessitates dealing with various constraints. In this paper, we focus on constraints like finding suitable d

Kristina Komander, Gunnar K. Pálsson, Sotirios A. Droulias, Theofanis Tsakiris

Nanoscaling interstitial metal hydrides offers opportunities for hydrogenation applications by enhancing kinetics, increasing surface area, and allowing for tunable properties. The introduction of interfaces impacts hydrogen absorption properties and distribution heterogeneously, making it however challenging to examine the multiple concurrent mechanisms, es

Elia Cunegatti, Leonardo Lucio Custode, Giovanni Iacca

Network pruning focuses on algorithms that aim to reduce a given model's computational cost by removing a subset of its parameters while having minimal impact on performance. Throughout the last decade, the most widely used pruning paradigm has been pruning and re-training, which nowadays is inconvenient due to the vast amount of pre-trained models, which ar

Katarzyna Siudzińska

We use operators from generalized equiangular measurements to construct positive maps. Their positivity follows from the inequality for indices of coincidence corresponding to few equiangular tight frames. These maps give rise to entanglement witnesses, which include as special cases many important classes considered in the literature. Additionally, we intro

Maxime Breden, Hugo Chu, Jeroen S. W. Lamb, Martin Rasmussen

We develop a powerful and general method to provide rigorous and accurate upper and lower bounds for Lyapunov exponents of stochastic flows. Our approach is based on computer-assisted tools, the adjoint method and established results on the ergodicity of diffusion processes. We do not require any structural assumptions on the stochastic system and work under

Thomas Dave, William J. Torres Bobadilla

We analytically calculate one- and two-loop helicity amplitudes in massless QED, by adopting a four-dimensional tensor decomposition. We draw our attention to four-fermion and Compton scattering processes to higher orders in the dimensional regulator, as required for theoretical predictions at N$^3$LO. We organise loop amplitudes by proposing an efficient al

Hannes Tröpgen, Robert Schöne, Thomas Ilsche, Daniel Hackenberg

The SPEC Power benchmark offers valuable insights into the energy efficiency of server systems, allowing comparisons across various hardware and software configurations. Benchmark results are publicly available for hundreds of systems from different vendors, published since 2007. We leverage this data to perform an analysis of trends in x86 server systems, f

Kwangjun Ahn, Gagik Magakyan, Ashok Cutkosky

This work investigates the effectiveness of schedule-free methods, developed by A. Defazio et al. (NeurIPS 2024), in nonconvex optimization settings, inspired by their remarkable empirical success in training neural networks. Specifically, we show that schedule-free SGD achieves optimal iteration complexity for nonsmooth, nonconvex optimization problems. Our

Giovanni Montani, Nakia Carlevaro, Maria G. Dainotti

We discuss an evolutionary dark energy model, based on the presence of non-equilibrium effects on the dark energy constituents, which are described via a bulk viscosity contribution. We implement the proposed dynamics by the analysis of the 40-bins Type Ia Supernovae (SNe) Pantheon sample data, in order to outline the existence of a running Hubble constant w

ALICE Collaboration

Differential two-particle normalized cumulants ($R_2$) and transverse momentum correlations ($P_2$) are measured as a function of the relative pseudorapidity and azimuthal angle difference $( \Delta \eta, \Delta \varphi )$ of charged particle pairs in minimum bias pp collisions at $\sqrt{\textit{s}}$ = 13 TeV. The measurements use charged hadrons in the pseu

ALICE Collaboration

The first measurements of proton emission accompanied by neutron emission in the electromagnetic dissociation (EMD) of $^{208}$Pb nuclei in the ALICE experiment at the LHC are presented. The EMD protons and neutrons emitted at very forward rapidities are detected by the proton and neutron Zero Degree Calorimeters of the ALICE experiment. The emission cross s

Ruyin Wan, Qian Zhang, George Em Karniadakis

Spiking neural networks (SNNs) represent a promising approach in machine learning, combining the hierarchical learning capabilities of deep neural networks with the energy efficiency of spike-based computations. Traditional end-to-end training of SNNs is often based on back-propagation, where weight updates are derived from gradients computed through the cha

Simon Jones, Sabine Hauert

Building a distributed spatial awareness within a swarm of locally sensing and communicating robots enables new swarm algorithms. We use local observations by robots of each other and Gaussian Belief Propagation message passing combined with continuous swarm movement to build a global and distributed swarm-centric frame of reference. With low bandwidth and c

Pavel V. Kuptsov, Nataliya V. Stankevich

This paper examines the reconstruction of a family of dynamical systems with neuromorphic behavior using a single scalar time series. A model of a physiological neuron based on the Hodgkin-Huxley formalism is considered. Single time series of one of its variables is shown to be enough to train a neural network that can operate as a discrete time dynamical sy

Stevan Pilipovic, Dragana Risteski, Dimitris Scarpalezos, Milica Zigic

Compiling essential results for non-quasianalytic ultradistribution spaces and Colombeau versions of generalized ultradistribution algebras, we analyze strong $B$- and strong $R$-association of a generalized ultradistribution $[(f_\varepsilon)]$. The strong association of $[(f_\varepsilon)]$ to a Komatsu-type ultradistribution $T$, with additional assumption

Shadman Tajwar Shahid, Shah Md. Ahasan Siddique, Md. Mahidul Alam

This article addresses the challenge of UAV survey coverage path planning for areas that are complex concave polygons, containing exclusion zones or obstacles. While standard drone path planners typically generate coverage paths for simple convex polygons, this study proposes a method to manage more intricate regions, including boundary splits, merges, and i

Brian Nelson, Hussein Moradi, Behrouz Farhang-Boroujeny

The idea of ultra-wideband (UWB) communications for short ranges (up to a few tens of meters) has been around for nearly three decades. However, despite significant efforts by the industry, UWB deployment has not yet reached its predicted potential. This article, thus, seeks to rectify this situation by providing a practical examination of UWB interference c

Zoltán M. Balogh, Gergely Kiss, Tamás Titkos, Dániel Virosztek

We study $p$-Wasserstein spaces over the branching spaces $\mathbb{R}^2$ and $[-1,1]^2$ equipped with the maximum norm metric. We show that these spaces are isometrically rigid for all $p\geq1,$ meaning that all isometries of these spaces are induced by isometries of the underlying space via the push-forward operation. This is in contrast to the case of the

Shabnam Ghasemirad, Christoph Sprenger, Si Liu, Luca Multazzu

Modern web services crucially rely on high-performance distributed databases, where concurrent transactions are isolated from each other using concurrency control protocols. Relaxed isolation levels, which permit more complex concurrent behaviors than strong levels like serializability, are used in practice for higher performance and availability. In this pa

Yukinari Sumino

As a straightforward application of the recently calculated two-loop heavy quarkonium Hamiltonian, we evaluate the two-loop $O(\epsilon)$ term of the $1/(mr^2)$ heavy quarkonium potential. Compared to a previous calculation we find a small difference in the coefficient of the maximally non-abelian color factor $C_F C_A^2 $. We further examine this coefficien

Pedro Pereira, Paulo Mendes, João Vitorino, Eva Maia

Artificial Intelligence (AI) has emerged in popularity recently, recording great progress in various industries. However, the environmental impact of AI is a growing concern, in terms of the energy consumption and carbon footprint of Machine Learning (ML) and Deep Learning (DL) models, making essential investigate Green AI, an attempt to reduce the climate i

Shadman Tajwar Shahid, Shah Md. Ahasan Siddique, Md. Humayun Kabir Bhuiyan

This paper presents an open-loop articulated 6-degree-of-freedom (DoF) robotic system for three-dimensional (3D) scanning of objects by contact-based method. A digitizer probe was used to detect contact with the object. Inverse kinematics (IK) was used to determine the joint angles of the robot corresponding to the probe position and orientation, and straigh

Long Meng, Heinz Siedentop

We study atomic ground state energies for neutral atoms as the nuclear charge $Z$ is large in the no-pair formalism. We show that for a large class of projections defining the underlying Dirac sea -- covering not only the physical reasonable cases but also ``weird'' ones -- the corresponding no-pair ground state energy does not exceed the one of the Furry en

José Omar Ledesma-Martin, Edgar Galindez-Ruales, Sachin Krishnia, Felix Fuhrmann

In magnetic systems, angular momentum is carried by spin and orbital degrees of freedom. Nonlocal devices, comprising heavy-metal nanowires on magnetic insulators like yttrium iron garnet (YIG), enable angular momentum transport via magnons. These magnons are polarized by spin accumulation at the interface through the spin Hall effect (SHE) and detected via

Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Jussi Tohka, Vanessa Gómez-Verdejo

We present BALDUR, a novel Bayesian algorithm designed to deal with multi-modal datasets and small sample sizes in high-dimensional settings while providing explainable solutions. To do so, the proposed model combines within a common latent space the different data views to extract the relevant information to solve the classification task and prune out the i

Xianzhe Fan, Qing Xiao, Xuhui Zhou, Yuran Su

AI companions are designed to foster emotionally engaging interactions, yet users often encounter conflicts that feel frustrating or hurtful, such as discriminatory statements and controlling behavior. This paper examines how users negotiate such harmful conflicts with AI companions and what emotional and practical burdens are created when mitigation is push

Martin T. Brolly

Stochastic parameterisations deployed in models of the Earth system frequently invoke locality assumptions such as Markovianity or spatial locality. This work highlights the impact of such assumptions on predictive performance. Both in terms of short-term forecasting and the representation of long-term statistics, we find locality assumptions to be detriment

Bernardo Cabral, Tiago Fonseca, Clarisse Sousa, Luis Lino Ferreira

Electric vehicles (EVs) and renewable energy sources (RES) are vital components of sustainable energy systems, yet their uncoordinated integration can pose substantial challenges to grid stability, such as unmanaged peak loads and energy balance issues. Vehicle-to-Grid (V2G), offer a promising solution to address these challenges by enabling bidirectional en

Minah Lee, Uday Kamal, Saibal Mukhopadhyay

This paper proposes a novel problem: vision-based perception to learn and predict the collective dynamics of multi-agent systems, specifically focusing on interaction strength and convergence time. Multi-agent systems are defined as collections of more than ten interacting agents that exhibit complex group behaviors. Unlike prior studies that assume knowledg

Alejandro Leonardo García Navarro, Nataliia Koneva, Alfonso Sánchez-Macián, José Alberto Hernández

In social sciences, researchers often face challenges when conducting large-scale experiments, particularly due to the simulations' complexity and the lack of technical expertise required to develop such frameworks. Agent-Based Modeling (ABM) is a computational approach that simulates agents' actions and interactions to evaluate how their behaviors influence

Xiaodong Wu, Minhao Wang, Yichen Liu, Xiaoming Shi

As Large Language Models (LLMs) evolve in natural language processing (NLP), their ability to stably follow instructions in long-context inputs has become critical for real-world applications. However, existing benchmarks seldom focus on instruction-following in long-context scenarios or stability on different inputs. To bridge this gap, we introduce LIFBenc

Tao Ren, Qiongxiu Li

Backdoor attacks pose significant challenges to the security of machine learning models, particularly for overparameterized models like deep neural networks. In this paper, we propose ProP (Propagation Perturbation), a novel and scalable backdoor detection method that leverages statistical output distributions to identify backdoored models and their target c

Teagan A. Clarke, Paul D. Lasky, Eric Thrane

Neutron star - black hole (NSBH) mergers that undergo tidal disruption may launch jets that could power a gamma-ray burst. We use a population of simulated NSBH systems to measure jet parameters from the gravitational waves emitted by these systems. The conditions during the tidal disruption and merger phase required to power a gamma-ray burst are uncertain.

Alessandro Ignesti, Gianfranco Brunetti, Marco Gullieuszik, Nina Akerman

The microphysics of the intracluster medium (ICM) in galaxy clusters is still poorly understood. Observational evidence suggests that the effective viscosity is suppressed by plasma instabilities that reduce the mean free path of particles. Measuring the effective viscosity of the ICM is crucial to understanding the processes that govern its physics on small

Uri Bader, Tsachik Gelander, Arie Levit

We establish a general spectral gap theorem for actions of products of groups which may replace Kazhdan's property (T) in various situations. As a main application, we prove that a confined subgroup of an irreducible lattice in a higher rank semisimple Lie group is of finite index. This significantly strengthens the classical normal subgroup theorem of Margu

Yuanchu Liang, Edward Kim, Wil Thomason, Zachary Kingston

Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalability of POMDP solvers, long-horizon POMDPs (e.g., $\geq15$ steps) remain difficult to solve. This paper proposes a new approximate online POMDP solver, called Reference-Based Online

Orhan Erdem, Kristi Hassett, Feyzullah Egriboyun

We evaluate the reliability of two chatbots, ChatGPT (4o and o1-preview versions), and Gemini Advanced, in providing references on financial literature and employing novel methodologies. Alongside the conventional binary approach commonly used in the literature, we developed a nonbinary approach and a recency measure to assess how hallucination rates vary wi

Grigorii Dakhno, Dmitry Gribanov, Nikita Kasianov, Anastasiia Kats

In our work, we consider the problem of computing a vector $x \in Z^n$ of minimum $\|\cdot\|_p$-norm such that $a^\top x \not= a_0$, for any vector $(a,a_0)$ from a given subset of $Z^n$ of size $m$. In other words, we search for a vector of minimum norm that avoids a given finite set of hyperplanes, which is natural to call as the $\textit{Hyperplanes Avoid

Luis A. Anchordoqui, Ignatios Antoniadis, Dieter Lust, Karem Peñaló Castillo

We reexamine a dynamical dark matter model with Kaluza-Klein (KK) towers of gravitons and neutrinos fitting together in the dark dimension. We show that even though gravitational decays of neutrino KK towers have little impact in cosmology the weak decay channel could have significant cosmological effects. Taking conservative upper bounds on the dark matter

Karl Sigfrid, Ellinor Fackle-Fornius, Frank Miller

An intelligent tutoring system (ITS) aims to provide instructions and exercises tailored to the ability of a student. To do this, the ITS needs to estimate the ability based on student input. Rather than including frequent full-scale tests to update our ability estimate, we want to base estimates on the outcomes of practice exercises that are part of the lea

Linlin Huang, Mamiya Kawaguchi, Yadikaer Maitiniyazi, Shinya Matsuzaki

We perform a functional renormalization group analysis of a four-fermion model with $CP$ and $P$ violation to explore the nonperturbative infrared dynamics of quantum chromodynamics (QCD) within the Wilsonian renormalization group framework, particularly in the context of spontaneous $CP$-violation models. Our analysis of the fixed-point structure reveals th

Bruno Sicardy, Felipe Braga-Ribas, Marc W. Buie, José Luis Ortiz

Stellar occultations provide a powerful tool to explore objects of the outer solar system. The Gaia mission now provides milli-arcsec accuracy on the predictions of these events and makes possible observations that were previously unthinkable. Occultations return kilometric accuracies on the three-dimensional shape of bodies irrespective of their geocentric

Haohan Weng, Zibo Zhao, Biwen Lei, Xianghui Yang

We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces. BPT compresses mesh sequences by employing block-wise indexing and patch aggregation, reducing their length by approximately 75\% compared to the original sequences. This compression milestone unlock

Shohini Bhattacharya, Yoshitaka Hatta, Jakob Schoenleber

We discuss the nonlocal generalization of the QCD chiral anomaly along the light-cone and derive relations between twist-two, twist-three and twist-four generalized parton distributions (GPDs) mediated by the anomaly. We further establish the connection to the `anomaly pole' in the GPD $\tilde{E}$ recently identified in the perturbative calculation of the Co

Corey Lammie, Julian Büchel, Athanasios Vasilopoulos, Manuel Le Gallo

A key challenge for Deep Neural Network (DNN) algorithms is their vulnerability to adversarial attacks. Inherently non-deterministic compute substrates, such as those based on Analog In-Memory Computing (AIMC), have been speculated to provide significant adversarial robustness when performing DNN inference. In this paper, we experimentally validate this conj

Ao Liu, Jing Chen, Ruiying Du, Cong Wu

The rapid expansion of Internet of Things (IoT) has resulted in vast, heterogeneous graphs that capture complex interactions among devices, sensors, and systems. Efficient analysis of these graphs is critical for deriving insights in IoT scenarios such as smart cities, industrial IoT, and intelligent transportation systems. However, the scale and diversity o

Ziwei Liu, Liang Zhang, Qian Li, Jianghua Wu

Retrieval-augmented generation (RAG) has shown impressive capability in providing reliable answer predictions and addressing hallucination problems. A typical RAG implementation uses powerful retrieval models to extract external information and large language models (LLMs) to generate answers. In contrast, recent LLM-based retrieval has gained attention for

Gareth Cabourn Davies, Ian Harry, Michael J. Williams, Diganta Bandopadhyay

We demonstrate an end-to-end technique for observing and characterizing massive black hole binary signals before they merge with the LISA space-based gravitational-wave observatory. Our method uses a zero-latency whitening filter, originally designed for rapidly observing compact binary mergers in ground-based observatories, to be able to observe signals wit

Zhiqiang Liu, Yin Hua, Mingyang Chen, Yichi Zhang

Real-world knowledge graphs (KGs) contain not only standard triple-based facts, but also more complex, heterogeneous types of facts, such as hyper-relational facts with auxiliary key-value pairs, temporal facts with additional timestamps, and nested facts that imply relationships between facts. These richer forms of representation have attracted significant

Steven Goldenberg, Kawser Ahammed, Adam Carpenter, Jiang Li

Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation level

Saikat Panja, Anupam Singh

The study of word maps on groups has been of deep interest in recent years. This survey focuses on the case of power maps on groups; $viz.$ the map $x\mapsto x^M$ for a group $G$, and an integer $M\geq 2$. Here, we accumulate various results on the subject and pose some questions.

Ting Zhang, Mingsheng Wang, Xilong Xu, Ying Dai

Layertronics that manifests layer Hall effect is typically considered to intrinsically possess binary physics. Using symmetry arguments and a low-energy kp model, we show that the layer physics in layertronics can be engineered into quaternary mode, giving rise to the concept of quadri-layertronics. The mechanism correlates to the interplay between out-of-pl

Ahan Bhatt, Ishaan Mehta, Pravin Patidar

Satellite clock bias prediction plays a crucial role in enhancing the accuracy of satellite navigation systems. In this paper, we propose an approach utilizing Long Short-Term Memory (LSTM) networks to predict satellite clock bias. We gather data from the PRN 8 satellite of the Galileo and preprocess it to obtain a single difference sequence, crucial for nor

N. F. Bouché, M. Wendt, J. Zabl, M. Cherrey

We present the design, rationale, properties and catalogs of the MusE Gas FLOw and Wind survey (MEGAFLOW), a survey of the cool gaseous halos of $z\sim1$ galaxies using low-ionization MgII absorption systems. The survey consists of 22 quasar fields selected from the Sloan Digital Sky Survey (SDSS) having multiple ($\geq3$) strong MgII absorption lines over t

Rico Zöllner, Burkhard Kämpfer

A core-corona decomposition of compact (neutron) star models is compared to recent NICER data of masses and radii. It is in particular interesting to capture the outlier XTE~J1814-338. Instead of integrating the TOV equations from the center to surface, we follow here another pathway by accommodating all uncertainties of the equation(s) of state (EoS) at sup

Marco Franceschini

This thesis addresses the use of Cooperative Intelligent Transport Systems (CITS) to improve road safety and efficiency by enabling vehicle-to-vehicle communication, highlighting the importance of secure and accurate data exchange. To ensure safety, the thesis proposes a Machine Learning-based Misbehavior Detection System (MDS) using Long Short-Term Memory (

Natasha Grech, Md Farhad Hossain, Omar Alam

Software workplaces are increasingly recognized as key spaces for professional development, where developers encounter various challenges in their roles, which they often discuss in online forums. This paper analyzes 47,368 posts on the Workplace StackExchange site, aggregating developer insights and applying topic modeling techniques. Through manual analysi