December 2024 arXiv papers — page 64

Tairan Fu, Raquel Ferrando, Javier Conde, Carlos Arriaga

Large Language Models (LLMs) have achieved unprecedented performance on many complex tasks, being able, for example, to answer questions on almost any topic. However, they struggle with other simple tasks, such as counting the occurrences of letters in a word, as illustrated by the inability of many LLMs to count the number of "r" letters in "strawberry". Se

Longfei He, Ursula Ludacka, Payel Chatterjee, Matthias Hartl

We present our study of (0001) oriented Mn$_3$Sn (c-Mn$_3$Sn) thin films synthesized directly on an MgO (111) substrate via molecular beam epitaxy. We identify a growth window where Mn$_3$Sn growth can be controlled through slight adjustments of the Mn flux, achieving either $\mu$m$^2$-sized high crystalline-quality islands or an almost completely continuous

Francisco Durán, Matias Martinez, Patricia Lago, Silverio Martínez-Fernández

The rapid growth of language models, particularly in code generation, requires substantial computational resources, raising concerns about energy consumption and environmental impact. Optimizing language models inference resource utilization is crucial, and Small Language Models (SLMs) offer a promising solution to reduce resource demands. Our goal is to ana

David Stewart, Joern Putschke

Jets clustered from heavy ion collision measurements combine a dense background of particles with those actually resulting from a hard partonic scattering. The background contribution to jet transverse momentum ($p_{T}$) may be corrected by subtracting the collision average background; however, the background inhomogeneity limits the resolution of this corre

Maniraj Sai Adapa, Marco Zullich, Matias Valdenegro-Toro

Deep Learning-based image super-resolution (SR) has been gaining traction with the aid of Generative Adversarial Networks. Models like SRGAN and ESRGAN are constantly ranked between the best image SR tools. However, they lack principled ways for estimating predictive uncertainty. In the present work, we enhance these models using Monte Carlo-Dropout and Deep

Vahid Zehtab, David B. Lindell, Marcus A. Brubaker, Michael S. Brown

3D color lookup tables (LUTs) enable precise color manipulation by mapping input RGB values to specific output RGB values. 3D LUTs are instrumental in various applications, including video editing, in-camera processing, photographic filters, computer graphics, and color processing for displays. While an individual LUT does not incur a high memory overhead, s

Mohammed Alyaseen, Nikolay Atanasov, Jorge Cortes

This paper studies the design of controllers for discontinuous dynamics that ensure the safety of non-smooth sets. The safe set is represented by arbitrarily nested unions and intersections of 0-superlevel sets of differentiable functions. We show that any optimization-based controller that satisfies only the point-wise active safety constraints is generally

Daniel Adu Worae, Athar Sheikh, Spyridon Mastorakis

The rapid expansion of Internet of Things (IoT) ecosystems has introduced growing complexities in device management and network security. To address these challenges, we present a unified framework that combines context-driven large language models (LLMs) for IoT administrative tasks with a fine-tuned anomaly detection module for network traffic analysis. Th

Jean-Camille Birget, Gili Golan, Alexander Olshanskii, Mikhail Volkov

This memorial article for Mark Sapir provides a brief overview of his life and career. Among his many contributions we highlight two of his most celebrated achievements: his groundbreaking solutions to Burnside-type problems for semigroups and his innovative construction of S-machines. Additionally, reflections from his colleagues and friends offer a heartfe

Priyanka Chakraborty, Arnab Sarkar, Randall Smith, Gary J. Ferland

We report the detection of the [O III] auroral line in 42 galaxies within the redshift range of $3 < z < 10$. These galaxies were selected from publicly available JWST data releases, including the JADES and PRIMALsurveys, and observed using both the low-resolution PRISM/CLEAR configuration and medium-resolution gratings. The measured electron temperatures in

Paolo Bova, Alessandro Di Stefano, The Anh Han

We present a quantitative model for tracking dangerous AI capabilities over time. Our goal is to help the policy and research community visualise how dangerous capability testing can give us an early warning about approaching AI risks. We first use the model to provide a novel introduction to dangerous capability testing and how this testing can directly inf

L. O. Souza, R. A. Dumer, M. Godoy

In this study, we explored the magnetic coupling in a multilayer system consisting of thin layers separated by a distance $d$. We have employed Monte Carlo (MC) simulations to calculate the thermodynamic quantities such as the magnetization per spin $m_{L}^{\mu}$, magnetic susceptibility $\chi_{L}^{\mu}$, and the reduced fourth-order Binder cumulant $U_{L}^{

Tianchen Zhang, Gururaj Saileshwar, David Lie

This paper demonstrates a new side-channel that enables an adversary to extract sensitive information about inference inputs in large language models (LLMs) based on the number of output tokens in the LLM response. We construct attacks using this side-channel in two common LLM tasks: recovering the target language in machine translation tasks and recovering

Tomoya Kitamura, Yuki Saito, Hiroshi Asai, Kouhei Ohnishi

The current methods to generate robot actions for automation in significantly different environments have limitations. This paper proposes a new method that matches the impedance of two prerecorded action data with the current environmental impedance to generate highly adaptable actions. This method recalculates the command values for the position and force

Ze Gong, Akshat Kumar, Pradeep Varakantham

Offline safe reinforcement learning (RL) has emerged as a promising approach for learning safe behaviors without engaging in risky online interactions with the environment. Most existing methods in offline safe RL rely on cost constraints at each time step (derived from global cost constraints) and this can result in either overly conservative policies or vi

V. M. Gorkavenko, A. I. Yakimenko, A. O. Zaporozhchenko, E. V. Gorbar

In models of dark matter composed of feebly interacting ultralight bosons in the state of Bose-Einstein condensate, the dynamical friction force acting on circularly moving globular clusters modelled as Plummer spheres is determined. Analytic expressions for both radial and tangential components of the dynamical friction force are given. We reveal that the d

Hemant Kumawat, Saibal Mukhopadhyay

Reinforcement learning (RL) can be formulated as a sequence modeling problem, where models predict future actions based on historical state-action-reward sequences. Current approaches typically require long trajectory sequences to model the environment in offline RL settings. However, these models tend to over-rely on memorizing long-term representations, wh

Yingfan Wang, Yiyang Sun, Haiyang Huang, Cynthia Rudin

Dimension reduction (DR) algorithms have proven to be extremely useful for gaining insight into large-scale high-dimensional datasets, particularly finding clusters in transcriptomic data. The initial phase of these DR methods often involves converting the original high-dimensional data into a graph. In this graph, each edge represents the similarity or diss

Uri Gabor

The finitary isomorphism theorem, due to Keane and Smorodinsky, raised the natural question of how "finite" the isomorphism can be, in terms of moments of the coding radius. More precisely, for which values does there exist an isomorphism between any two i.i.d. processes of equal entropy, with coding radii exhibiting finite t-moments? [3, 4]. Parry [13] and

Leonardo Biliotti, Alessandro Minuzzo

Let $M_i$, for $i=1,2$, be a K\"ahler manifold, and let $G$ be a Lie group acting on $M_i$ by K\"ahler isometries. Suppose that the action admits a momentum map $\mu_i$ and let $N_i:=\mu_i^{-1}(0)$ be a regular level set. When the action of $G$ on $N_i$ is proper and free, the Meyer--Marsden--Weinstein quotient $P_i:=N_i/G$ is a K\"ahler manifold and $\pi_i:

Bhupendra Acharya, Thorsten Holz

In the recent past, so-called pig-butchering scams are on the rise. This term is based on a translation of the Chinese term "Sha Zhu Pan", where scammers refer to victims as "pig" which are to be "fattened up before slaughter" so that scammer can siphon off as much monetary value as possible. In this type of scam, attackers perform social engineering tricks

Hao Shen, Scott A. Smith, Rongchan Zhu

In this paper, we prove the convergence of the discrete Makeenko-Migdal equations for the Yang-Mills model on $(\varepsilon \mathbf{Z})^{2}$ to their continuum counterparts on the plane, in an appropriate sense. The key step in the proof is identifying the limits of the contributions from deformations as the area derivatives of the Wilson loop expectations.

Julian A. Steele, Patrick J. Strohbeen, Carla Verdi, Ardeshir Baktash

Doping-induced superconductivity in group IV elements may enable quantum functionalities in material systems accessible with well-established semiconductor technologies. Non-equilibrium hyperdoping of group III atoms into C, Si, or Ge can yield superconductivity; however, its origin is obscured by structural disorder and dopant clustering. Here, we report th

Lu Hao, Yuhua Sun

We study the equivalence between the $L^p$-parabolicity, the $L^q$-Liouville property of positive super-harmonic functions, and the existence of nonharmonic positive solutions to the following elliptic differential system \begin{equation*} \left\{ \begin{array}{lr} -\Delta u\geq 0, \Delta(|\Delta u|^{p-2}\Delta u)\geq 0, \end{array} \right. \end{equation*} o

Tao Hou, Salman Parsa, Bei Wang

The persistence barcode is a topological descriptor of data that plays a fundamental role in topological data analysis. Given a filtration of data, the persistence barcode tracks the evolution of its homology groups. In this paper, we introduce a new type of barcode, called the harmonic chain barcode, which tracks the evolution of harmonic chains. In additio

Andrés Burgos-Caminal, Brener R. C. Vale, André F. V. Fonseca, Elisa P. P. Collet

CuInS2 quantum dots have been studied in a broad range of applications, but despite this, the fine details of their charge carrier dynamics remain a subject of intense debate. Two of the most relevant points of discussion are the hole dynamics and the influence of Cu:In synthesis stoichiometry on them. It has been proposed that Cu-deficiency leads to the for

Rachel Scrandis, Cosmin Deaconu

The long-duration balloon platform for radio detection of energetic neutrinos, pioneered by ANITA, affords large instantaneous effective areas but has limited livetime. Conversely, tethered balloons, traditionally used for radio detection of non-science objectives (such as electronic surveillance), allow for much longer livetimes, albeit at significantly low

Kadek Hendrawan Palgunadi, Alice-Agnes Gabriel, Dmitry Igor Garagash, Thomas Ulrich

Fault zones exhibit geometrical complexity and are often surrounded by multiscale fracture networks within their damage zones, influencing rupture dynamics and near-field ground motions. We investigate the ground-motion characteristics of cascading ruptures across damage zone fracture networks of moderate-sized earthquakes using high-resolution 3D dynamic ru

Niko Moritz, Ruiming Xie, Yashesh Gaur, Ke Li

We propose the joint speech translation and recognition (JSTAR) model that leverages the fast-slow cascaded encoder architecture for simultaneous end-to-end automatic speech recognition (ASR) and speech translation (ST). The model is transducer-based and uses a multi-objective training strategy that optimizes both ASR and ST objectives simultaneously. This a

J. A. Vila

Since protein mutations are the main driving force of evolution at the molecular level, a proper analysis of them (and the factors controlling them) will enable us to find a response to several crucial queries in evolutionary biology. Among them, we highlight the following: At the molecular level, what factors determine whether protein evolution is repeatabl

Giorgio Comitini, Fabio Siringo

The screened massive expansion of full QCD is used in conjunction with a model for infrared quark masses to compute the Landau-gauge gluon propagator at finite temperature and baryonic density. Analytic expressions up to a one-dimensional momentum integral are provided for the propagator, and its behavior is studied at zero Matsubara frequency with respect t

CMS Collaboration

Measurements of light-by-light scattering (LbL, $\gamma\gamma$ $\to$ $\gamma\gamma$) and the Breit-Wheeler process (BW, $\gamma\gamma$ $\to$ $\mathrm{e^+e^-}$) are reported in ultraperipheral PbPb collisions at a centre-of-mass energy per nucleon pair of 5.02 TeV. The data sample, corresponding to an integrated luminosity of 1.7 nb$^{-1}$, was collected by t

Shadi Sartipi, Mie Andersen, Natalie Hauglund, Celia Kjaerby

Efficiently identifying sleep stages is crucial for unraveling the intricacies of sleep in both preclinical and clinical research. The labor-intensive nature of manual sleep scoring, demanding substantial expertise, has prompted a surge of interest in automated alternatives. Sleep studies in mice play a significant role in understanding sleep patterns and di

Swapnil Gandhi, Christos Kozyrakis

As large language models scale, training them requires thousands of GPUs over extended durations--making frequent failures an inevitable reality. While checkpointing remains the primary fault-tolerance mechanism, existing methods fall short when applied to Mixture-of-Experts (MoE) models. Due to their substantially larger training state, MoE models exacerbat

Edoardo Allegrini, Edoardo Di Paolo, Marinella Petrocchi, Angelo Spognardi

Social media platforms continue to struggle with the growing presence of social bots-automated accounts that can influence public opinion and facilitate the spread of disinformation. Over time, these social bots have advanced significantly, making them increasingly difficult to distinguish from genuine users. Recently, new groups of bots have emerged, utiliz

Li Hua Yu

We analyze the evolution of a particle wave function when it propagates through free space in the longitudinal z-direction from a thin entrance slit to a detector behind a thin exit slit parallel to the horizontal y-axis. We consider an extra aperture slit between the two slits to probe the evolution of the wave function and close the aperture slit starting

Lianxia Li, Cole Gruninger, Jae H. Lee, Boyce E. Griffith

In the class of immersed boundary (IB) methods, the choice of the delta function plays a crucial role in transferring information between fluid and solid domains. Most prior work has used isotropic kernels that do not preserve the divergence-free condition of the velocity field, leading to loss of incompressibility of the solid when interpolating velocity to

E. K. Karkaryan, K. V. Kiselev, V. F. Obraztsov, I. V. Surkov

The OKA Collaboration has obtained a new upper limit on the $K^+ \to \pi^0\pi^0\pi^0 e^+ \nu$ decay probability which is 65 times lower than the one currently listed by PDG. However it is still about $10^{4}$ times worse than the theoretical prediction. It was suggested that production of pionium $A_{2\pi}$ in the final state of the semileptonic $K^+$ decay

Eilyan Bitar

We consider decision-making problems involving the optimization of linear objective functions with uncertain coefficients. The probability distribution of the coefficients--which are assumed to be stochastic in nature--is unknown to the decision maker but is assumed to lie within a given ambiguity set, defined as a type-1 Wasserstein ball centered at a given

Juan Calles, Jacky H. T. Yip, Gabriella Contardo, Jorge Noreña

Building upon [2308.02636], we investigate the constraining power of persistent homology on cosmological parameters and primordial non-Gaussianity in a likelihood-free inference pipeline utilizing machine learning. We evaluate the ability of Persistence Images (PIs) to infer parameters, comparing them to the combined Power Spectrum and Bispectrum (PS/BS). We

Ahmet Yasin Aytar, Kemal Kilic, Kamer Kaya

In the rapidly evolving field of data science, efficiently navigating the expansive body of academic literature is crucial for informed decision-making and innovation. This paper presents an enhanced Retrieval-Augmented Generation (RAG) application, an artificial intelligence (AI)-based system designed to assist data scientists in accessing precise and conte

S. Peil, W. Tobias, J. Whalen, B. Hemingway

While optical clock technology has advanced rapidly in recent years, incorporating the technology into operational timescales has progressed more slowly. The highest accuracy frequency standards for groundbreaking measurements do not easily translate to critical timing where continuous, uninterrupted operation over many months and years is required. For exam

Matteo Bettini, Ryan Kortvelesy, Amanda Prorok

Many of the world's most pressing issues, such as climate change and global peace, require complex collective problem-solving skills. Recent studies indicate that diversity in individuals' behaviors is key to developing such skills and increasing collective performance. Yet behavioral diversity in collective artificial learning is understudied, with today's

Mirhan Ürkmez, Carsten Kallesøe, Jan Dimon Bendtsen, John Leth

The paper introduces a procedure for determining an approximation of the optimal amount of photovoltaics (PVs) for powering water distribution networks (WDNs) through grid-connected PVs. The procedure aims to find the PV amount minimizing the total expected cost of the WDN over the lifespan of the PVs. The approach follows an iterative process, starting with

Canyi Chen, Yinqiu He, Huixia J. Wang, Gongjun Xu

Mediation analytics help examine if and how an intermediate variable mediates the influence of an exposure variable on an outcome of interest. Quantiles, rather than the mean, of an outcome are scientifically relevant to the comparison among specific subgroups in practical studies. Albeit some empirical studies available in the literature, there lacks a thor

Zhuowen Shen, Yuan Liu, Zhang Chen, Zhong Li

Gaussian splatting has achieved impressive improvements for both novel-view synthesis and surface reconstruction from multi-view images. However, current methods still struggle to reconstruct high-quality surfaces from only sparse view input images using Gaussian splatting. In this paper, we propose a novel method called SolidGS to address this problem. We o

Daniel Amaro, Shokoufe Faraji

This paper studies the impact of vacuum polarization on light propagation in the background of a distorted, deformed compact object. Focusing on a spacetime containing two quadrupole parameters associated with the central object and external fields, we explore how these parameters influence observable effects as dynamical degrees of freedom. In this setup, w

Kai Gao

This thesis provides an in-depth structural analysis and efficient algorithmic solutions for tabletop object rearrangement with overhand grasps (TORO), a foundational task in advancing intelligent robotic manipulation. Rearranging multiple objects in a confined workspace presents two primary challenges: sequencing actions to minimize pick-and-place operation

Ahis Shrestha, Eleftherios Kirkinis, Monica Olvera de la Cruz

We develop a hydrodynamic description of self-generated electrolyte flow in capillaries whose bounding walls feature both non-uniform distributions of charge and non-uniform active ionic fluxes. The hydrodynamic velocity arising in such a system has components that are forbidden by symmetry in the absence of charge and fluxes. However, when these two boundar

Austin Stone, Hagen Soltau, Robert Geirhos, Xi Yi

Visual imagery does not consist of solitary objects, but instead reflects the composition of a multitude of fluid concepts. While there have been great advances in visual representation learning, such advances have focused on building better representations for a small number of discrete objects bereft of an understanding of how these objects are interacting

Constantinos Skordis, David M. J. Vokrouhlicky

The Aether Scalar Tensor (AeST) theory is an extension of general relativity(GR) successful at reproducing galactic rotational curves, gravitational lensing, linear large scale structure and cosmic microwave background power spectrum observations. We solve the most general static spherically symmetric vacuum equations in the strong-field regime of AeST and f

Sukanta Mukherjee, Enrico Skoruppa, Holger Merlitz, Jens-Uwe Sommer

Epigenetic inheritance during cell division is essential for preserving cell identity by stabilizing the overall chromatin organisation. Heterochromatin,the condensed and transcriptionally silent fraction of chromatin,is marked by specific epigenetic modifications that are diluted during each cell division. Here we build a physical model,based on the formati

Zhiqiang Tang, Zihan Zhong, Tong He, Gerald Friedland

This paper studies the best practices for automatic machine learning (AutoML). While previous AutoML efforts have predominantly focused on unimodal data, the multimodal aspect remains under-explored. Our study delves into classification and regression problems involving flexible combinations of image, text, and tabular data. We curate a benchmark comprising

Christopher W. Adair, Oliver K. Johnson

As microstructure property models improve, additional information from crystallographic degrees of freedom and grain boundary networks (GBNs) can be included in microstructure design problems. However, the high dimensional nature of including this information precludes the use of many common optimization approaches and requires less efficient methods to gene

Berke Levent Cesur, Ayse Humeyra Dur Karasayar, Pinar Bulutay, Nilgun Kapucuoglu

Cell detection and segmentation are integral parts of automated systems in digital pathology. Encoder-decoder networks have emerged as a promising solution for these tasks. However, training of these networks has typically required full boundary annotations of cells, which are labor-intensive and difficult to obtain on a large scale. However, in many applica

Taylor Martin, Rachel Meyers

Mosaic knots, first introduced in 2008 by Lomanoco and Kauffman, have become a useful tool for studying combinatorial invariants of knots and links. In 2020, by considering knot mosaics on $n \times n$ polygons with boundary edge identification, Ganzell and Henrich extended the study of mosaic knots to include virtual knots - knots embedded in thickened surf

Svetlana Makarova, Junyu Meng

We consider the fine quiver moduli space of representations of the 3-Kronecker quiver of dimension vector $(2,3)$, which is a blow down of the Hilbert scheme of 3 points on $\mathds{P}^2$. A short description of its geometry and Chow ring is given. Then we exhibit an exceptional sequence for the derived category by understanding a $\mathds{P}^1$-bundle over

Zeeshan Nisar, Friedrich Feuerhake, Thomas Lampert

Semantic segmentation under domain shift remains a fundamental challenge in computer vision, particularly when labelled training data is scarce. This challenge is particularly exemplified in histopathology image analysis, where the same tissue structures must be segmented across images captured under different imaging conditions (stains), each representing a

Sharlin Utke, Jeremie Houssineau, Giovanni Montana

This paper explores the impact of relational state abstraction on sample efficiency and performance in collaborative Multi-Agent Reinforcement Learning. The proposed abstraction is based on spatial relationships in environments where direct communication between agents is not allowed, leveraging the ubiquity of spatial reasoning in real-world multi-agent sce

Selcuk Sözeri, Nihad Abuawwad, Amal Aldarawsheh, Samir Lounis

We conduct a comprehensive density functional theory (DFT) study to explore the intricate magnetic properties of frustrated Mn monolayer on the Ag(111) surface. Spin-polarized scanning tunneling microscopy demonstrates that a N\'eel magnetic state characterizes such an interface, which contradicts systematic ab-initio predictions made in the last two decades

Lavanya Gupta, Saket Sharma, Yiyun Zhao

Long-context large language models (LC LLMs) promise to increase reliability of LLMs in real-world tasks requiring processing and understanding of long input documents. However, this ability of LC LLMs to reliably utilize their growing context windows remains under investigation. In this work, we evaluate the performance of state-of-the-art GPT-4 suite of LC

Zhongyuan Zhao, Jake Perazzone, Gunjan Verma, Kevin Chan

A significant challenge for computation offloading in wireless multi-hop networks is the complex interaction among traffic flows in the presence of interference. Existing approaches often ignore these key effects and/or rely on outdated queueing and channel state information. To fill these gaps, we reformulate joint offloading and routing as a routing proble

Abhiroop Bhattacharya, Nandinee Haq

Accurate energy price forecasting is crucial for participants in day-ahead energy markets, as it significantly influences their decision-making processes. While machine learning-based approaches have shown promise in enhancing these forecasts, they often remain confined to the specific markets on which they are trained, thereby limiting their adaptability to

Arthur Fernandes, Daniel Panario, Lucas Reis

For each positive integer $n$, let $\mathbb F_{q^n}$ be the unique $n$-degree extension of the finite field $\mathbb F_q$ with $q$ elements, where $q$ is a prime power. It is known that for arbitrary $q$ and $n$, there exists an element $\beta\in \mathbb F_{q^n}$ such that its Galois conjugates $\beta, \beta^q, \ldots, \beta^{q^{n-1}}$ form a basis for $\mat

Lucas Napolitano, Adam D. Myers, Vicky Fawcett, Jessica Aguilar

In this paper, we study how absorption-line systems affect the spectra and redshifts of quasars (QSOs), using catalogs of Mg II absorbers from the early data release (EDR) and first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). We determine the reddening effect of an absorption system by fitting an un-reddened template spectrum to a

Matheus A. S. Pessôa, Rebecca Brosseau, Benjamin J. Dringoli, Armin Yazdani

Over the last five years, the McGill University Office of Science Education (OSE) has partnered with faculty members from the Department of Physics to form an education research group with the aim of charting the progression of student conceptual understanding of uncertainties across their undergraduate degree. The research conducted by this group seeks to p

Kyle Chadee, Wayne Goodridge, Koffka Khan

Wi-Fi Protected Access 3 (WPA3) is the accepted standard for next generation wireless security. WPA3 comes with exciting new features that allows for increased security of Wi-Fi networks. One such feature is the Simultaneous Authentication of Equals (SAE) which is a protocol whereby passphrases are hashed using a Password Authenticated Key Exchange with keys

Meghana Karri, Amit Soni Arya, Koushik Biswas, Nicol`o Gennaro

This work proposes a novel framework, Uncertainty-Guided Cross Attention Ensemble Mean Teacher (UG-CEMT), for achieving state-of-the-art performance in semi-supervised medical image segmentation. UG-CEMT leverages the strengths of co-training and knowledge distillation by combining a Cross-attention Ensemble Mean Teacher framework (CEMT) inspired by Vision T

Erik van den Eshof, Jorn van Kampen, Mauro Salazar

This paper presents an energy and thermal management system for electric race cars, where we tune a lift-off-throttle signal for the driver in real-time to respect energy budgets and thermal constraints. First, we compute the globally optimal state trajectories in a real-time capable solving time, optimizing a 47-kilometer horizon in 2.5 seconds. Next, for s

Samy Brahimi, Dibya Prakash Rai, Samir Lounis

The magnetic properties of bulk RuO$_2$ remain a subject of active debate, despite its pivotal role in the emergence of altermagnetism. The latter is a novel paradigm in magnetic phases, characterized by the absence of net magnetization due to anti-parallel alignment of magnetic moments, yet displaying finite spin-splitting in the electronic band structure.

Kecheng Lu, Lihang Zhu, Yunhai Wang, Qiong Zeng

Transparency is commonly utilized in visualizations to overlay color-coded histograms or sets, thereby facilitating the visual comparison of categorical data. However, these charts often suffer from significant overlap between objects, resulting in substantial color interactions. Existing color blending models struggle in these scenarios, frequently leading

Jann Weinand, Tristan Pelser, Max Kleinebrahm, Detlef Stolten

Land use is a critical factor in the siting of renewable energy facilities and is often scrutinized due to perceived conflicts with other land demands. Meanwhile, substantial areas are devoted to activities such as golf, which are accessible to only a select few and have a significant land and environmental footprint. Our study shows that in countries such a

Joanne Boisson, Zara Siddique, Hsuvas Borkakoty, Dimosthenis Antypas

Extracting metaphors and analogies from free text requires high-level reasoning abilities such as abstraction and language understanding. Our study focuses on the extraction of the concepts that form metaphoric analogies in literary texts. To this end, we construct a novel dataset in this domain with the help of domain experts. We compare the out-of-the-box

Mathieu Demarne, Miso Cilimdzic, Tom Falkowski, Timothy Johnson

We present ARCAS (Automated Root Cause Analysis System), a diagnostic platform based on a Domain Specific Language (DSL) built for fast diagnostic implementation and low learning curve. Arcas is composed of a constellation of automated troubleshooting guides (Auto-TSGs) that can execute in parallel to detect issues using product telemetry and apply mitigatio

Hongyu Lin, Mohan Ren, Paolo Barucca, Tomaso Aste

Discovering causal relationships in time series data is central in many scientific areas, ranging from economics to climate science. Granger causality is a powerful tool for causality detection. However, its original formulation is limited by its linear form and only recently nonlinear machine-learning generalizations have been introduced. This study contrib

Rini Jasmine Gladstone, Hadi Meidani

Physics-based deep learning frameworks have shown to be effective in accurately modeling the dynamics of complex physical systems with generalization capability across problem inputs. Data-driven networks like GNN, Neural Operators have proved to be very effective in generalizing the model across unseen domain and resolutions. But one of the most critical is

Xiao Hu, Jaehan Bae, Zhaohuan Zhu, Lile Wang

Magnetized winds and photoevaporative winds are critical in shaping protoplanetary disk evolution. Using 2D axisymmetric (magneto-)hydrodynamic simulations with Athena++ implementing fully coupled thermochemistry, we investigate the signatures of the two winds in CO and [C~I] ALMA observations, and examine the potential to distinguish the origins. Our simula

Aloïs Castellano, Romuald Béjaud, Pauline Richard, Olivier Nadeau

The acceleration of material property calculations while maintaining ab initio accuracy (1 meV/atom) is one of the major challenges in computational physics. In this paper, we introduce a Python package enhancing the computation of (finite temperature) material properties at the ab initio level using machine learning interatomic potentials (MLIP). The Machin

Amit Kumar, Jaison Jose, Archit Jain, Siddharth Kulkarni

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cos

Philippe Gimenez, Francesc Planas-Vilanova

Let $A$ be a noetherian ring, $I$ an ideal of $A$ and $N\subset M$ finitely generated $A$-modules. The relation type of $I$ with respect to $M$, denoted by ${\bf rt}\,(I;M)$, is the maximal degree in a minimal generating set of relations of the Rees module ${\bf R}(I;M)=\oplus_{n\geq 0}I^nM$. It is a well-known invariant that gives a first measure of the com

Mateo Frausto-Avila, José Pablo Manriquez-Amavizca, Ana Karen Susana Rocha-Robledo, Mario A. Quiroz-Juarez

In this paper, we present a powerful, compact electrocardiogram (ECG) classification algorithm for cardiac arrhythmia diagnosis that addresses the current reliance on deep learning and convolutional neural networks (CNNs) in ECG analysis. This work aims to reduce the demand for deep learning, which often requires extensive computational resources and large l

Zichao Zhang, Melda Yuksel, Gokhan M. Guvensen, Halim Yanikomeroglu

Faster-than-Nyquist (FTN) signaling is a non-orthogonal transmission technique offering a promising solution for future generations of communications. This paper studies the capacity of FTN signaling in multiple-input multiple-output (MIMO) channels for high acceleration factors. In our previous study [1], we found the capacity for MIMO FTN channels if the a

Bang An, Xun Zhou, Amin Vahedian, Nick Street

Traffic accident forecasting is an important task for intelligent transportation management and emergency response systems. However, this problem is challenging due to the spatial heterogeneity of the environment. Existing data-driven methods mostly focus on studying homogeneous areas with limited size (e.g. a single urban area such as New York City) and fai

Temple He, Veronika E. Hubeny, Massimiliano Rota

We compute the set of all extreme rays of the 6-party subadditivity cone that are compatible with strong subadditivity. In total, we identify 208 new (genuine 6-party) orbits, 52 of which violate at least one known holographic entropy inequality. For the remaining 156 orbits, which do not violate any such inequalities, we construct holographic graph models f

Andrew Bell, Julia Stoyanovich

Concerns about the risks and harms posed by artificial intelligence (AI) have resulted in significant study into algorithmic transparency, giving rise to a sub-field known as Explainable AI (XAI). Unfortunately, despite a decade of development in XAI, an existential challenge remains: progress in research has not been fully translated into the actual impleme

Medhanie Estiphanos, Ezio Iacocca

Dissipative exchange flows (DEFs) are large-amplitude boundary value solutions of ferromagnetic channels. In their low-injection limit, DEFs reduce to spin superfluids. However, in the strong injection limit, nonlinearities dominate close to the injection site and a soliton is formed; this solution has been termed a contact soliton dissipative exchange flow

Jonathan Schmidt, Luca Schmidt, Felix Strnad, Nicole Ludwig

Local climate information is crucial for impact assessment and decision-making, yet coarse global climate simulations cannot capture small-scale phenomena. Current statistical downscaling methods infer these phenomena as temporally decoupled spatial patches. However, to preserve physical properties, estimating spatio-temporally coherent high-resolution weath

Swati Rajwal

In recent years, the intersection of Natural Language Processing (NLP) and public health has opened innovative pathways for investigating various domains, including chronic pain in textual datasets. Despite the promise of NLP in chronic pain, the literature is dispersed across various disciplines, and there is a need to consolidate existing knowledge, identi

Diarmuid Crowley, Mark Grant

We provide the first documented examples of immersions of closed oriented manifolds which are not homologous to embeddings, thus answering a question posed by Zhenhua Liu. In these examples we show that for any representing self-transverse immersion the double points must represent a non-trivial homology class in the source manifold. We also provide examples

Abdullah Al Rahat, Hemanth Venkateswara

This paper proposes a dataset augmentation method by fine-tuning pre-trained diffusion models. Generating images using a pre-trained diffusion model with textual conditioning often results in domain discrepancy between real data and generated images. We propose a fine-tuning approach where we adapt the diffusion model by conditioning it with real images and

M. A. García-Márquez, H. M. Moya-Cessa, I. Ramos-Prieto, F. Soto-Eguibar

We present the dynamics of a single harmonically trapped ion interacting with a laser, considering a linear combination of two eigenstates of the system as the initial state. The conditions on the physical parameters that allow for the evolution of the system are discussed. We are able to obtain analytical results even though no approximations are realized a

Howard Baer, Vernon Barger, Jessica Bolich, Kairui Zhang

Hidden sector SUSY breaking where charged hidden sector fields obtain SUSY breaking vevs once seemed common in dynamical SUSY breaking (DSB). In such a case, scalars can obtain large masses but gauginos and A-terms gain loop-suppressed anomaly-mediated contributions which may be smaller by factors of 1/16\pi^2 ~1/160. This situation leads to models such as P

I. V. Vovchenko, A. A. Zyablovsky, A. A. Pukhov, E. S. Andrianov

Non-equilibrium quantum thermodynamics is an intensively developing field with many existing applications. We study the dynamics of temperatures and chemical potentials of fermionic reservoirs coupled to an open quantum system. We show that heat transfer from the coldest reservoir to the hottest one is allowed by the Clausius inequality and results in transi

Theodore Maranets, Yan Wang

This work investigates the impact of device length on thermal conductivity in periodic and aperiodic superlattices (SLs). While it is well known that thermal conductivity in aperiodic SLs exhibits a weaker dependence on device length compared to periodic SLs, existing literature attributes this behavior to the scattering of coherent phonons by aperiodically

Bang An, Xun Zhou, Zirui Zhou, Ronilo Ragodos

The problem of forecasting spatiotemporal events such as crimes and accidents is crucial to public safety and city management. Besides accuracy, interpretability is also a key requirement for spatiotemporal forecasting models to justify the decisions. Interpretation of the spatiotemporal forecasting mechanism is, however, challenging due to the complexity of

Liam Seymour, Basar Kutukcu, Sabur Baidya

Generative AI like the Large Language Models (LLMs) has become more available for the general consumer in recent years. Publicly available services, e.g., ChatGPT, perform token generation on networked cloud server hardware, effectively removing the hardware entry cost for end users. However, the reliance on network access for these services, privacy and sec

Gage Martin, Ina Petkova, Zachary Winkeler

We provide a partial answer to a question of Ekholm, Honda, and K\'alm\'an about the relationship between Khovanov homology and decomposable Lagrangian cobordisms. We also utilize previously defined filtered invariants to give obstructions to decomposable Lagrangian cobordisms from Khovanov homology.

Ruodu Wang, Qinyu Wu

We obtain a full characterization of consistency with respect to higher-order stochastic dominance within the rank-dependent utility model. Different from the results in the literature, we do not assume any condition on the utility functions and the probability weighting functions, such as differentiability or continuity. It turns out that the level of gener

Pratham Singla, Ayush Singh, Adesh Gupta, Shivank Garg

Urban planning faces a critical challenge in balancing city-wide infrastructure needs with localized demographic preferences, particularly in rapidly developing regions. Although existing approaches typically focus on top-down optimization or bottom-up community planning, only some frameworks successfully integrate both perspectives. Our methodology employs

Amal Yousseef, Shalaka Satam, Banafsheh Saber Latibari, Jesus Pacheco

Autonomous vehicles (AVs) are poised to revolutionize modern transportation, offering enhanced safety, efficiency, and convenience. However, the increasing complexity and connectivity of AV systems introduce significant cybersecurity challenges. This paper provides a comprehensive survey of AV security with a focus on threat modeling frameworks, including ST