October 2024 arXiv papers — page 46

Jeremiah Ridge, Oiwi Parker Jones

Machine learning techniques have enabled researchers to leverage neuroimaging data to decode speech from brain activity, with some amazing recent successes achieved by applications built using invasive devices. However, research requiring surgical implants has a number of practical limitations. Non-invasive neuroimaging techniques provide an alternative but

Benne W. Holwerda, Clayton Robertson, Kyle Cook, Kevin A. Pimbblet

Galaxy Zoo is an online project to classify morphological features in extra-galactic imaging surveys with public voting. In this paper, we compare the classifications made for two different surveys, the Dark Energy Spectroscopic Instrument (DESI) imaging survey and a part of the Kilo-Degree Survey (KiDS), in the equatorial fields of the Galaxy And Mass Assem

Nikhil Karthik, Rajamani Narayanan, Ray Romero

The quantum numbers of monopoles in $R^3$ in the presence of massless fermions have been analyzed using a uniform flux background in $S^2\times R$ coupled to fermions. An analogous study in $T^2\times R$ is performed by studying the discrete symmetries of the Dirac Hamiltonian in the presence of a static uniform field on $T^2$ with a total flux of $Q$ in the

W. Peter Maksym, Ioannis Liodakis, M. Lynne Saade, Dawoon E. Kim

X-ray polarization is a unique new probe of the particle acceleration in astrophysical jets made possible through the Imaging X-ray Polarimetry Explorer. Here we report on the first dense X-ray polarization monitoring campaign on the blazar Mrk 421. Our observations were accompanied by an even denser radio and optical polarization campaign. We find significa

Weiqin Chen, Santiago Paternain

Pretrained foundation models have exhibited extraordinary in-context learning performance, allowing zero-shot generalization to new tasks not encountered during pretraining. In the case of reinforcement learning (RL), in-context RL (ICRL) emerges when pretraining FMs on decision-making problems in an autoregressive-supervised manner. Nevertheless, current st

Chris Connell, Yuping Ruan, Shi Wang

We show that for any closed Riemannian manifold with dimension at least two and with nonpositive curvature, if it admits an isolated, closed totally geodesic submanifold of codimension one, then its simplicial volume is positive. As a direct corollary of this, for any nonpositively curved analytic manifold with dimension at least three, if its universal cove

E. Zhixuan Zeng, Yuhao Chen, Alexander Wong

Recent advances in image generation have made diffusion models powerful tools for creating high-quality images. However, their iterative denoising process makes understanding and interpreting their semantic latent spaces more challenging than other generative models, such as GANs. Recent methods have attempted to address this issue by identifying semanticall

Margaret L. Berrens, Marcos F. Calegari Andrade, John T. Fourkas, Tuan Anh Pham

Understanding the molecular-level structure and dynamics of ice surfaces is crucial for deciphering several chemical, physical, and atmospheric processes. Vibrational sum-frequency generation (SFG) spectroscopy is the most prominent tool for probing the molecular-level structure of the air--ice interface as it is a surface-specific technique, but the molecul

Joao Rulff, Giancarlo Pereira, Maryam Hosseini, Marcos Lage

Over the past decades, improvements in data collection hardware coupled with novel artificial intelligence algorithms have made it possible for researchers to understand urban environments at an unprecedented scale. From local interactions between actors to city-wide infrastructural problems, this new data-driven approach enables a more informed and trustwor

Yujin H. Kim, Xaver Kriechbaum

In this article, we consider the multiplicative chaos measure associated to the log-correlated random Fourier series, or random wave model, with i.i.d. coefficients taken from a general class of distributions. This measure was shown to be non-degenerate when the inverse temperature is subcritical by Junnila (Int. Math. Res. Not. 2020 (2020), no. 19, 6169-619

Yinhan He, Wendy Zheng, Yaochen Zhu, Jing Ma

Graph Neural Networks (GNNs) have been widely deployed in various real-world applications. However, most GNNs are black-box models that lack explanations. One strategy to explain GNNs is through counterfactual explanation, which aims to find minimum perturbations on input graphs that change the GNN predictions. Existing works on GNN counterfactual explanatio

Jerry P. Sun, Xavier Siemens, Dustin R. Madison

In this work we derive two computationally efficient frequentist detection statistics that can be used in searches for gravitational-wave bursts with memory in pulsar timing data. By maximizing the likelihood ratio in two different ways we construct a coherent statistic and an incoherent statistic, which are analogs of the $\pazocal{F}_e$ and $\pazocal{F}_p$

Adonai Schlup Sant'Anna

Equations are ubiquitous in most mathematical activities. Nevertheless, in this paper it is shown how to do standard mathematics without any equation at all. More than that, it is proven there is a foundational framework for standard mathematics where equations cannot be even written, in the sense they are not formulas. The proof of those claims is very simp

Burak Boyacıoğlu, Floris van Breugel

Given a set of measurements, observability characterizes the distinguishability of a system's initial state, whereas constructability focuses on the final state in a trajectory. In the presence of process and/or measurement noise, the Fisher information matrices with respect to the initial and final states$\unicode{x2013}$equivalent to the stochastic observa

Silvia Terragni, Hoang Cuong, Joachim Daiber, Pallavi Gudipati

Large Language Models (LLMs) have demonstrated potential as effective search relevance evaluators. However, there is a lack of comprehensive guidance on which models consistently perform optimally across various contexts or within specific use cases. In this paper, we assess several LLMs and Multimodal Language Models (MLLMs) in terms of their alignment with

Dev Rishi Verma, Vibhor Saxena, Dhruv Sharma, Arpan Gupta

In this work for Capsule Vision Challenge 2024, we addressed the challenge of multiclass anomaly classification in video capsule Endoscopy (VCE)[1] with a variety of deep learning models, ranging from custom CNNs to advanced transformer architectures. The purpose is to correctly classify diverse gastrointestinal disorders, which is critical for increasing di

Angel Da Silva, Santiago Fernández, Braian Vidal, Hiago Sodre

This research focused on utilizing ROS2 and Gazebo for simulating the TurtleBot3 robot, with the aim of exploring autonomous navigation capabilities. While the study did not achieve full autonomous navigation, it successfully established the connection between ROS2 and Gazebo and enabled manual simulation of the robot's movements. The primary objective was t

Duncan Neill, David Tsang, Christian Drischler, Jeremy W. Holt

Chiral effective field theory ($\chi$EFT) has proved to be a powerful microscopic framework for predicting the properties of neutron-rich nuclear matter with quantified theoretical uncertainties up to about twice the nuclear saturation density. Tests of $\chi$EFT predictions are typically performed at low densities using nuclear experiments, with neutron sta

Elzon C. Bezerra Júnior, João Vitor da Silva, Thialita M. Nascimento, Ginaldo S. Sá

In this work, we establish sharp and improved regularity estimates for viscosity solutions of Hardy-H\'{e}non-type equations with possibly singular weights and strong absorption governed by the $\infty$-Laplacian $$ \Delta_{\infty} u(x) = |x|^{\alpha}u_+^m(x) \quad \text{in} \quad B_1, $$ under suitable assumptions on the data. In this setting, we derive an

Robert Carlson

The Fast Fourier Transform is extended to functions on finite graphs whose edges are identified with intervals of finite length. Spectral and pseudospectral methods are developed to solve a wide variety of time dependent partial differential equations on domains which are modeled as networks of one dimensional segments joined at nodes.

Vishal Jayswal, Sergei M. Kopeikin

We consider the orbital motion of a test particle in the gravitational field of a massive body (that might be a black hole) with mass $m$ placed on the expanding cosmological manifold described by the McVittie metric. We introduce the local coordinates attached to the massive body to eliminate nonphysical, coordinates-dependent effects associated with Hubble

Ian Melbourne, Jens Rademacher, Bob Rink, Sergey Zelik

In this paper, we propose a general mechanism for the existence of quasicrystals in spatially extended systems (partial differential equations with Euclidean symmetry). We argue that the existence of quasicrystals with higher order rotational symmetry, icosahedral symmetry, etc, is a natural and universal consequence of spontaneous symmetry breaking, bypassi

Ahmed Luqman, Hassan Jaleel

We propose a game-theoretic framework to model and optimize user engagement in cooperative activities over social networks. While traditional diffusion models suggest that individuals are only influenced by their neighbors, empirical evidence shows that diffusion alone does not fully explain network evolution, and non-diffusion factors play a significant rol

Philipe Dias, Aristeidis Tsaris, Jordan Bowman, Abhishek Potnis

While the pretraining of Foundation Models (FMs) for remote sensing (RS) imagery is on the rise, models remain restricted to a few hundred million parameters. Scaling models to billions of parameters has been shown to yield unprecedented benefits including emergent abilities, but requires data scaling and computing resources typically not available outside i

Tianyue H. Zhang, Lucas Maes, Alan Milligan, Alexia Jolicoeur-Martineau

Despite its widespread adoption, Adam's advantage over Stochastic Gradient Descent (SGD) lacks a comprehensive theoretical explanation. This paper investigates Adam's sensitivity to rotations of the parameter space. We observe that Adam's performance in training transformers degrades under random rotations of the parameter space, indicating a crucial sensiti

Haoyue Bai

Deep learning has been demonstrated with tremendous success in recent years. Despite so, its performance in practice often degenerates drastically when encountering out-of-distribution (OoD) data, i.e. training and test data are sampled from different distributions. In this thesis, we study ways toward robust OoD generalization for deep learning, i.e., its p

Gustavo Avila, Ayaki Sunaga, Stanislav Komorovsky, Edit Matyus

Rovibrational energies, wave functions, and Raman transition moments are reported for the lowest-energy states of the H$_3^+$ molecular ion including the magnetic couplings of the proton spins and molecular rotation in the presence of a weak external magnetic field. The rovibrational-hyperfine-Zeeman Hamiltonian matrix is constructed and diagonalized using t

Radhika Bhuckory, Bhaskar Krishnamachari

We are interested in studying how heterogeneous agents can learn to communicate and cooperate with each other without being explicitly pre-programmed to do so. Motivated by this goal, we present and analyze a distributed solution to a two-player signaler-responder game which is defined as follows. The signaler agent has a random, exogenous need and can choos

Elana Kalashnikov

In this paper, we show that there is a finite SAGBI basis of the coordinate ring of a Kronecker quiver moduli space, indexed by primitive semi-standard tableaux pairs. This induces a toric degeneration of the Kronecker moduli space to a normal toric variety, a generalization of the toric degeneration of the Grassmannian to the Gelfand--Cetlin polytope constr

Pier Domenico Lamberti, Dirk Pauly, Michele Zaccaron

We study eigenvalue problems for the de Rham complex on varying three dimensional domains. Our analysis includes the Helmholtz equation as well as the Maxwell system with mixed boundary conditions and non-constant coefficients. We provide Hadamard-type formulas for the shape derivatives under weak regularity assumptions on the domain and its perturbations. O

David Galvin, Phillip Marmorino

An $n$-vertex, $d$-regular graph can have at most $2^{n/2+o_d(n)}$ independent sets. In this paper we address what happens with this upper bound when we impose the further condition that the graph has independence number at most $\alpha$. We give upper and lower bounds that in many cases are close to each other. In particular, for each $0 < c_{\rm ind} \leq

J. Joe Payne, James Zhu, Nathan J. Kong, Aaron M. Johnson

In this paper we present Hybrid iterative Linear Quadratic Estimation (HiLQE), an optimization based offline state estimation algorithm for hybrid dynamical systems. We utilize the saltation matrix, a first order approximation of the variational update through an event driven hybrid transition, to calculate gradient information through hybrid events in the b

Laura Colmenarejo, Arun Ram

This paper develops the theory of Macdonald-Koornwinder polynomials in parallel analogy with the work done for the $GL_n$ case in [CR22]. In the context of the type $CC_n$ affine root system the Macdonald polynomials of other root systems of classical type are specializations of the Koornwinder polynomials. We derive $c$-function formulas for symmetrizers an

Ronan Legin, Maximiliano Isi, Kaze W. K. Wong, Yashar Hezaveh

Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled on a case-by-case basis, e.g., through bespoke procedures to ``clean'' non-Gaussian noise transients (glitches), as was famously the case for the GW170817 neutron-star binary. Altho

Pengfei Hu, Chang Lu, Fei Wang, Yue Ning

Despite the growing use of Electronic Health Records (EHR) for AI-assisted diagnosis prediction, most data-driven models struggle to incorporate clinically meaningful medical knowledge. They often rely on limited ontologies, lacking structured reasoning capabilities and comprehensive coverage. This raises an important research question: Will medical knowledg

José Antonio Siqueira de Cerqueira, Mamia Agbese, Rebekah Rousi, Nannan Xi

AI-based systems, including Large Language Models (LLMs), impact millions by supporting diverse tasks but face issues like misinformation, bias, and misuse. AI ethics is crucial as new technologies and concerns emerge, but objective, practical guidance remains debated. This study explores the extent to which trustworthiness-enhancing techniques in LLMs can s

Santosh Srinivasaiah, Sai Kumar Nekkanti, Rohith Reddy Nedhunuri

Navigating indoor environments presents significant challenges for visually impaired individuals due to complex layouts and the absence of GPS signals. This paper introduces a novel system that provides turn-by-turn navigation inside buildings using only a smartphone equipped with a camera, leveraging multimodal models, deep learning algorithms, and large la

Parisa Ahmadi Ghomroudi, Michele Scaltritti, Bianca Monachesi, Peera Wongupparaj

In daily interactions, emotions are frequently conveyed and triggered through verbal exchanges. Sometimes, we must modulate our emotional reactions to align with societal norms. Among the emotional words, taboo words represent a specific category that has been poorly studied. One intriguing question is whether these word categories can be predicted from EEG

Sebastian Engelke, Jevgenijs Ivanovs, Jakob D. Thøstesen

Conditional independence and graphical models are crucial concepts for sparsity and statistical modeling in higher dimensions. For L\'evy processes, a widely applied class of stochastic processes, these notions have not been studied. By the L\'evy-It\^o decomposition, a multivariate L\'evy process can be decomposed into the sum of a Brownian motion part and

Rafael L. Delgado, Raquel Gómez-Ambrosio, Javier Martínez-Martín, Alexandre Salas-Bernárdez

In these proceedings we present the main results of [arXiv:2311.04280 [hep-ph]], where we explore the phenomenological implications of multi-Higgs boson production through longitudinal vector boson scattering within the framework of Effective Field Theories (EFTs). We derive compact expressions for effective tree-level amplitudes involving up to four final-s

Hanwen Huang, Peng Zeng

We consider the classification problem of a high-dimensional mixture of two Gaussians with general covariance matrices. Using the replica method from statistical physics, we investigate the asymptotic behavior of a general class of regularized convex classifiers in the high-dimensional limit, where both the sample size $n$ and the dimension $p$ approach infi

Paata Ivanisvili, Roman Vershynin, Xinyuan Xie

We investigate the best constant $J(n,d)$ such that Jackson's inequality \[ \inf_{\mathrm{deg}(g) \leq d} \|f - g\|_{\infty} \leq J(n,d) \, s(f), \] holds for all functions $f$ on the hypercube $\{0,1\}^n$, where $s(f)$ denotes the sensitivity of $f$. We show that the quantity $J(n, 0.499n)$ is bounded below by an absolute positive constant, independent of $

Kayvan Kousha, Mike Thelwall

Academics and departments are sometimes judged by how their research has benefitted society. For example, the UK Research Excellence Framework (REF) assesses Impact Case Studies (ICS), which are five-page evidence-based claims of societal impacts. This study investigates whether ChatGPT can evaluate societal impact claims and therefore potentially support ex

Viktoriia Ermolina, Igor Danilkin, Marc Vanderhaeghen

We present an analysis of the $e^+ e^- \to \gamma^* \to J/\psi \, \pi \, \pi \, (K \bar{K})$ and $e^+ e^- \to \gamma^* \to h_c \, \pi \, \pi$ processes employing the recently proposed Dalitz-plot decomposition approach, which is based on the helicity formalism for three-body decays. For the above reactions, we validate the factorization of the overall rotati

M. A. García-Blázquez, J. J. Palacios

Excitons, namely neutral excitations in a system of electrons arising from the electron-hole interaction, are often essential to explain optical measurements in materials. They are governed by the Bethe-Salpeter equation, which can be cast into a matrix form that is formally analogous to the one for electrons at the mean-field level. However, constructing th

Nagarajan Ganapathy, Podakanti Satyajith Chary, Teja Venkata Ramana Kumar Pithani, Pavan Kavati

This Paper presents an advanced approach for fine-tuning BiomedCLIP PubMedBERT, a multimodal model, to classify abnormalities in Video Capsule Endoscopy (VCE) frames, aiming to enhance diagnostic efficiency in gastrointestinal healthcare. By integrating the PubMedBERT language model with a Vision Transformer (ViT) to process endoscopic images, our method cat

Bhumi Bhusal, Pia Panravi Sanpitak, Jasmine Vu, Fuchang Jiang

Purpose: Low-field MRI has been assumed to be implant-friendly based on limited studies. However, RF-induced heating due to an implant is a complex resonance phenomenon, highly dependent on the implant's configurations and the applied RF frequencies. This study aims to evaluate the RF heating of DBS implants during MRI at low-field strengths compared to high

Martin Heßler, Oliver Kamps

The Greenland Ice Sheet may be nearing a tipping point, transitioning to permanent melting. This article analyses two melt rate time series using the Bayesian Langevin estimation (BLE), providing further evidence for destabilizing melt dynamics, along with new insights from the method's nonlinear parameterisation. Comparing the results for Western Central Gr

Fábio Botler, Luiz Moreira, João Pedro de Souza

Given graphs $G$ and $H$, we say that $G$ is $H$-$good$ if the Ramsey number $R(G,H)$ equals the trivial lower bound $(|G| - 1)(\chi(H) - 1) + \sigma(H)$, where $\chi(H)$ denotes the usual chromatic number of $H$, and $\sigma(H)$ denotes the minimum size of a color class in a $\chi(H)$-coloring of $H$. Pokrovskiy and Sudakov [Ramsey goodness of paths. Journa

Kristjan Greenewald, Yuancheng Yu, Hao Wang, Kai Xu

Training generative models with differential privacy (DP) typically involves injecting noise into gradient updates or adapting the discriminator's training procedure. As a result, such approaches often struggle with hyper-parameter tuning and convergence. We consider the slicing privacy mechanism that injects noise into random low-dimensional projections of

Saketh Ram Kasibatla, Arpan Agarwal, Yuriy Brun, Sorin Lerner

Formal verification using proof assistants, such as Coq, is an effective way of improving software quality, but requires significant effort and expertise. Machine learning can automatically synthesize proofs, but such tools are able to prove only a fraction of desired software properties. We introduce Cobblestone, a divide-and-conquer approach for proof synt

Alexandra Kozyreva, Andrea Caputo, Petr Baklanov, Alexey Mironov

Abridged: The fortunate proximity of the SN2023ixf allowed astronomers to follow its evolution from almost the moment of the collapse of the progenitor's core. SN2023ixf can be explained as an explosion of a massive star with an energy of 0.7e51 erg, however with a greatly reduced envelope mass, probably because of binary interaction. In our radiative-transf

Enrico M. Brehm, Ingo Runkel

In this paper we continue to investigate the lattice models obtained from 2d CFTs via the construction introduced in [arXiv:2112.01563]. On the side of the 2d CFT we consider the cloaking boundary condition relative to a fixed fusion category F of topological line defects. The resulting lattice model realises the topological symmetry F exactly. We compute th

Tanqiu Jiang, Zian Wang, Jiacheng Liang, Changjiang Li

Jailbreak attacks circumvent LLMs' built-in safeguards by concealing harmful queries within jailbreak prompts. While existing defenses primarily focus on mitigating the effects of jailbreak prompts, they often prove inadequate as jailbreak prompts can take arbitrary, adaptive forms. This paper presents RobustKV, a novel defense that adopts a fundamentally di

Xin Wang, Yifan Zhang, Xiaojing Zhang, Longhui Yu

Pharmaceutical patents play a vital role in biochemical industries, especially in drug discovery, providing researchers with unique early access to data, experimental results, and research insights. With the advancement of machine learning, patent analysis has evolved from manual labor to tasks assisted by automatic tools. However, there still lacks an unifi

Richard Hind, Kyler Siegel

We summarize some of the main ideas and results around symplectic field theory, from its early inception up to recent and ongoing developments.

Opeyemi Osakuade, Simon King

Discrete representations of speech, obtained from Self-Supervised Learning (SSL) foundation models, are widely used, especially where there are limited data for the downstream task, such as for a low-resource language. Typically, discretization of speech into a sequence of symbols is achieved by unsupervised clustering of the latents from an SSL model. Our s

Tobias G. Oliver, Eric G. Blackman, John A. Tarduno, Michael A. Calkins

Seismic and geodynamic studies indicate that the boundary between the Earth's liquid outer core and solid mantle is not spherical, but is likely characterized by topography in the form of inverted mountains and valleys that have typical amplitudes of several kilometers. One of the dynamical consequences of these deformations is that turbulent flow in the cor

Xiyue Peng, Hengquan Guo, Jiawei Zhang, Dongqing Zou

Balancing helpfulness and safety (harmlessness) is a critical challenge in aligning large language models (LLMs). Current approaches often decouple these two objectives, training separate preference models for helpfulness and safety, while framing safety as a constraint within a constrained Markov Decision Process (CMDP) framework. This paper identifies a po

Raphael Sarfati

Identifying firefly flashes from other bright features in nature images is complicated. I provide a training dataset and trained neural networks for reliable flash classification. The training set consists of thousands of cropped images (patches) extracted by manual labeling from video recordings of fireflies in their natural habitat. The trained network app

Hadi Daneshmand

Despite their empirical success, the internal mechanism by which transformer models align tokens during language processing remains poorly understood. This paper provides a mechanistic and theoretical explanation of token alignment in LLMs. We first present empirical evidences showing that, in machine translation, attention weights progressively align transl

Yigal Reiss, Moshe Schechter

Structural two level systems (TLSs) ubiquitous in amorphous solids are dramatically sensitive to thermal cycling to about $20$K and then back to low temperature, a process upon which the excitation energy of most TLSs is significantly changed. Using Monte Carlo simulations we demonstrate that this phenomenon is not contained within the standard tunneling mod

Tafari Clarke-James

It was shown by Claus Scheiderer prior to 1994 that real closed spaces have \'{e}tale cohomology. Following Scheiderer, study of real closed spaces fell out of fashion and o-minimal geometry became the focus for those at the intersection of model theory and geometry. I decided to breathe new life into the theory of real closed rings and spaces, as studied by

Mary Hall, Cosmin Oancea, Anne C. Elster, Ari Rasch

Scheduling languages express to a compiler a sequence of optimizations to apply. Compilers that support a scheduling language interface allow exploration of compiler optimizations, i.e., exploratory compilers. While scheduling languages have become a common feature of tools for expert users, the proliferation of these languages without unifying common featur

Randy Lemons, Jack Hirschman, Hao Zhang, Charles Durfee

Lightwave pulse shaping in the picosecond regime has remained unaddressed because it resides beyond the limits of state-of-the-art techniques, either due to its inherently narrow spectral content or fundamental speed limitations in electronic devices. The so-called picosecond shaping gap hampers progress in all areas correlated with time-modulated light-matt

Shikhar Srivastava, Md Yousuf Harun, Robik Shrestha, Christopher Kanan

Generative large language models (LLMs) exhibit impressive capabilities, which can be further augmented by integrating a pre-trained vision model into the original LLM to create a multimodal LLM (MLLM). However, this integration often significantly decreases performance on natural language understanding and generation tasks, compared to the original LLM. Thi

Riadh Azzaz, Valentin Hurel, Patrice Menard, Mohammad Jahazi

The scrap-based electric arc furnace process is expected to capture a significant share of the steel market in the future due to its potential for reducing environmental impacts through steel recycling. However, managing impurities, particularly phosphorus, remains a challenge. This study aims to develop a machine learning model to estimate the steel phospho

John Gkountouras, Matthias Lindemann, Phillip Lippe, Efstratios Gavves

Large Language Models (LLMs) have recently shown great promise in planning and reasoning applications. These tasks demand robust systems, which arguably require a causal understanding of the environment. While LLMs can acquire and reflect common sense causal knowledge from their pretraining data, this information is often incomplete, incorrect, or inapplicab

Benjamin Newman, Yoonjoo Lee, Aakanksha Naik, Pao Siangliulue

When conducting literature reviews, scientists often create literature review tables - tables whose rows are publications and whose columns constitute a schema, a set of aspects used to compare and contrast the papers. Can we automatically generate these tables using language models (LMs)? In this work, we introduce a framework that leverages LMs to perform

Anirudha Powadi, Talukder Zaki Jubery, Michael C. Tross, James C. Schnable

This study introduces a compositional autoencoder (CAE) framework designed to disentangle the complex interplay between genotypic and environmental factors in high-dimensional phenotype data to improve trait prediction in plant breeding and genetics programs. Traditional predictive methods, which use compact representations of high-dimensional data through h

Viacheslav Kuzmin, Wilfrid Somogyi, Ekaterina Pankovets, Alexey Melnikov

In the current quantum computing paradigm, significant focus is placed on the reduction or mitigation of quantum decoherence. When designing new quantum processing units, the general objective is to reduce the amount of noise qubits are subject to, and in algorithm design, a large effort is underway to provide scalable error correction or mitigation techniqu

Mohamed Salim Aissi, Clement Romac, Thomas Carta, Sylvain Lamprier

Reinforcement learning (RL) is a promising approach for aligning large language models (LLMs) knowledge with sequential decision-making tasks. However, few studies have thoroughly investigated the impact on LLM agents capabilities of fine-tuning them with RL in a specific environment. In this paper, we propose a novel framework to analyze the sensitivity of

Avik Kar, Rahul Singh

We study infinite-horizon average-reward reinforcement learning (RL) for Lipschitz MDPs, a broad class that subsumes several important classes such as linear and RKHS MDPs, function approximation frameworks, and develop an adaptive algorithm $\text{ZoRL}$ with regret bounded as $\mathcal{O}\big(T^{1 - d_{\text{eff.}}^{-1}}\big)$, where $d_{\text{eff.}}= 2d_\

M. Loewe, R. Cadiz, R. Zamora

In the frame of the linear sigma model and working in the weak field approximation, we discuss the role played by an external electric field on the behavior of $\pi$-$\pi$ scattering lengths. For this purpose, we have considered all relevant one-loop diagrams in the $s$, $t$, and $u$ channels where we have Schwinger propagators for charged pions. An importan

Mohamed Seif, Yuqi Nie, Andrea J. Goldsmith, H. Vincent Poor

Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for sensing and computer vision. This approach typically involves a three-stage process: a) data acquisition through sensing, b) feature extraction, and c) feature encoding for transmission. However,

Daniel R Häupl, Clare R Higgins, Danielle Pizzey, Jack D Briscoe

Laser spectroscopy of hot atomic vapours has been studied extensively. Theoretical models that predict the absolute value of the electric susceptibility are crucial for optimising the design of photonic devices that use hot vapours, and for extracting parameters, such as external fields, when these devices are used as sensors. To date, most of the models dev

Katsiaryna Bahamazava

Urbanization and technological advancements are reshaping urban mobility, presenting both challenges and opportunities. This paper investigates how Artificial Intelligence (AI)-driven technologies can impact traffic congestion dynamics and explores their potential to enhance transportation systems' efficiency. Specifically, we assess the role of AI innovatio

Mariem Abdellatif, Peter Kuching, Barbara Rüdiger, Irene Ventura

The aim of this article is to write the $p$-Wasserstein metric $W_p$ with the $p$-norm, $p\in [1,\infty)$, on $\R^d$ in terms of copula. In particular for the case of one-dimensional distributions, we get that the copula employed to get the optimal coupling of the Wasserstein distances is the comotonicity copula. We obtain the equivalent result also for $d$-

Samir Canning, Hannah Larson, Sam Payne, Thomas Willwacher

We prove that the number of curves of a fixed genus g over finite fields is a polynomial function of the size of the field if and only if g is at most 8. Furthermore, we determine for each positive genus g the smallest n such that the moduli space of curves of genus g with n marked points does not have polynomial point count. A key ingredient in the proofs,

Hyunwook Kim, Revathy Venkataramanan, Amit Sheth

Diet plays a crucial role in managing chronic conditions and overall well-being. As people become more selective about their food choices, finding recipes that meet dietary needs is important. Ingredient substitution is key to adapting recipes for dietary restrictions, allergies, and availability constraints. However, identifying suitable substitutions is ch

Irina Babayan, Hazhir Aliahmadi, Greg van Anders

The broad range of neural network training techniques that invoke optimization but rely on ad hoc modification for validity suggests that optimization-based training is misguided. Shortcomings of optimization-based training are brought to particularly strong relief by the problem of overfitting, where naive optimization produces spurious outcomes. The broad

Eoin Caffrey, Tian Carey, Luke Doolan, Anthony Dawson

Many printed electronic applications require strain-independent electrical properties to ensure deformation-independent performance. Thus, developing printed, flexible devices using 2D and other nanomaterials will require an understanding of the effect of strain on the electrical properties of nano-networks. Here we introduce novel AC electrical techniques t

Christina W. Lindberg, Claire E. Murray, Petia Yanchulova Merica-Jones, Caroline Bot

By analyzing the spectral energy distributions (SEDs) of resolved stars in nearby galaxies, we can constrain their stellar properties and line-of-sight dust extinction. From the Scylla survey, we obtain ultraviolet to near-infrared photometry from Wide Field Camera 3 onboard the {\it Hubble Space Telescope} for more than 1.5 million stars in the SMC and LMC.

Christopher Eckner, Silvia Manconi, Francesca Calore

More than a decade ago, the Large Area Telescope aboard the Fermi Gamma-ray Space Telescope unveiled the existence of two gigantic gamma-ray lobes known as the Fermi bubbles. While their origin is still unknown, various studies identified intricate spectral and morphological structures within the bubbles. One peculiar region, the cocoon, has recently been as

James H. Matthews, Knox S. Long, Christian Knigge, Stuart A. Sim

Outflows are critical components of many astrophysical systems, including accreting compact binaries and active galactic nuclei (AGN). These outflows can significantly affect a system's evolution and alter its observational appearance by reprocessing the radiation produced by the central engine. Sirocco (Simulating Ionization and Radiation in Outflows Create

Sangjun Cha, M. James Jee, Sungwook E. Hong, Sangnam Park

Traditional weak-lensing mass reconstruction techniques suffer from various artifacts, including noise amplification and the mass-sheet degeneracy. In Hong et al. (2021), we demonstrated that many of these pitfalls of traditional mass reconstruction can be mitigated using a deep learning approach based on a convolutional neural network (CNN). In this paper,

Saquib Hassan, Gaurang Ramakant Kane, John March-Russell, Georges Obied

We study the dynamics and interactions of the solitonic domain walls that occur in realistic axion electrodynamics models including the Chern-Simons interaction, $a\epsilon_{\mu\nu\lambda\sigma}F^{\mu\nu} F^{\lambda\sigma}$, between an axion $a(x)$ of mass $m_a$, and a massless U(1) gauge field, e.g. EM, interacting with strength $\alpha=e^2/4\pi$ with charg

Ian G. McCarthy, Alexandra Amon, Joop Schaye, Emmanuel Schaan

Energetic feedback processes associated with accreting supermassive black holes can expel gas from massive haloes and significantly alter various measures of clustering on ~Mpc scales, potentially biasing the values of cosmological parameters inferred from analyses of large-scale structure (LSS) if not modelled accurately. Here we use the state-of-the-art FL

Shmuel Gilbaum, Evgeni Grishin, Nicholas C. Stone, Ilya Mandel

Stellar-mass black holes (BHs) embedded in active galactic nuclei (AGN) may be major sources of astrophysical gravitational waves (GWs), contributing both to the observed LIGO-Virgo-KAGRA population of binary BH mergers and to future populations of LISA-band extreme mass ratio inspirals (EMRIs). The ability of these BHs to pair up into binaries, inspiral, an

Pascal Baßler, Markus Heinrich, Martin Kliesch

Implementing the time evolution under a desired target Hamiltonian is critical for various applications in quantum science. Due to the exponential increase in the number of parameters with system size and experimental imperfections, this task can be challenging in quantum many-body settings. We introduce an efficient and robust scheme to engineer arbitrary l

Konstantin Springmann, Michael Stadlbauer, Stefan Stelzl, Andreas Weiler

We identify a new production channel for QCD axions in supernova environments that contributes to axion emissivity for all models solving the strong CP problem. This channel arises at tree-level from a shift-symmetry-breaking operator constructed at next-to-leading order in Chiral Perturbation Theory. In scenarios where model-dependent derivative couplings t

Bret D. Lehmer, Erik B. Monson, Rafael T. Eufrasio, Amirnezam Amiri

We present a new empirical framework modeling the metallicity and star-formation history (SFH) dependence of X-ray luminous ($L > 10^{36}$ ergs s$^{-1}$) point-source population luminosity functions (XLFs) in normal galaxies. We expect the X-ray point-source populations are dominated by X-ray binaries (XRBs), with contributions from supernova remnants near t

Eric Rohr, Annalisa Pillepich, Dylan Nelson, Mohammadreza Ayromlou

The intracluster medium (ICM) today is comprised largely of hot gas with clouds of cooler gas of unknown origin and lifespan. We analyze the evolution of cool gas (temperatures $\lesssim10^{4.5}$ K) in the ICM of 352 galaxy clusters from the TNG-Cluster simulations, with present-day mass $\sim10^{14.3-15.4}\,{\rm M_\odot}$. We follow the main progenitors of

George Stoica, Pratik Ramesh, Boglarka Ecsedi, Leshem Choshen

Recent model merging methods demonstrate that the parameters of fully-finetuned models specializing in distinct tasks can be combined into one model capable of solving all tasks without retraining. Yet, this success does not transfer well when merging LoRA finetuned models. We study this phenomenon and observe that the weights of LoRA finetuned models showca

Jayita Lahiri, Dipankar Pradhan, Abhik Sarkar

The dynamics and detection possibility of a pseudo-FIMP (pFIMP) dark matter (DM) in the presence of a thermal DM have been studied in different contexts. The pFIMP phenomenology largely depends on the WIMP-like partner DM, as pFIMP interacts with the standard model (SM) particles only via the partner DM loop. Introducing a lepton portal interaction, which co

Zilong Wang, Nan Chen, Luna K. Qiu, Ling Yue

Global aging calls for scalable and engaging cognitive interventions. Computerized cognitive training (CCT) is a promising non-pharmacological approach, yet many unsupervised programs rely on rigid, hand-authored puzzles that are difficult to personalize and can hinder adherence. Large language models (LLMs) offer more natural interaction, but their open-end

Yucheng Zhou, Zhi Rao, Jun Wan, Jianbing Shen

Large Vision-Language Models (LVLMs) excel in cross-model tasks but experience performance declines in long-context reasoning due to overreliance on textual information and reduced visual dependency. In this study, we empirically analyze LVLMs in long-context reasoning, revealing that increased context length leads to a higher dependence on language at the e

Yinhao Wu, Yi-Xian Chen

Accretion of protoplanetary discs (PPDs) could be driven by MHD disc winds rather than turbulent viscosity. With a dynamical prescription for angular momentum transport induced by disc winds, we perform 2D simulations of PPDs to systematically investigate the rate and direction of planet migration in a windy disc. We find that the the strength of disc winds

Xiang Zhang, Juntai Cao, Chenyu You

Transformers, the backbone of modern large language models (LLMs), face inherent architectural limitations that impede their reasoning capabilities. Unlike recurrent networks, Transformers lack recurrent connections, confining them to constant-depth computation. This restriction places them in the complexity class TC$^0$, making them theoretically incapable

Shishir Dholakia, Simon J. Murphy, Chelsea X. Huang, Alexander Venner

We present a catalog of binary companions to $\delta$ Scuti stars, detected through phase modulations of their pulsations in TESS data. Pulsation timing has provided orbits for hundreds of pulsating stars in binaries from space-based photometry. We have applied this technique to $\delta$ Sct stars observed in the first four years of TESS Mission photometry.