May 2024 arXiv papers — page 9

Eliyar Asgarieh, Kapil Thadani, Neil O'Hare

News articles typically mention numerous entities, a large fraction of which are tangential to the story. Detecting the salience of entities in articles is thus important to applications such as news search, analysis and summarization. In this work, we explore new approaches for efficient and effective salient entity detection by fine-tuning pretrained trans

Alexander S. Tikhomirov, Danil A. Vassiliev

In this paper we investigate the moduli spaces of semistable coherent sheaves of rank two on the projective space $\mathbb{P}^3$ and the following rational Fano manifolds of the main series - the three-dimensional quadric $X_2$, the intersection of two 4-dimensional quadrics $X_4$ and the Fano manifold $X_5$ of degree 5. For the quadric $X_2$, the boundednes

Selim Kuzucu, Kemal Oksuz, Jonathan Sadeghi, Puneet K. Dokania

Reliable usage of object detectors require them to be calibrated -- a crucial problem that requires careful attention. Recent approaches towards this involve (1) designing new loss functions to obtain calibrated detectors by training them from scratch, and (2) post-hoc Temperature Scaling (TS) that learns to scale the likelihood of a trained detector to outp

Fausto Vega, Zachary Manchester, Martin Lo, Ricardo Restrepo

We present an algorithm to perform fuel-optimal stationkeeping for spacecraft in unstable halo orbits with additional constraints to ensure safety in the event of a control failure. We formulate a convex trajectory-optimization problem to generate impulsive spacecraft maneuvers to loosely track a halo orbit using a receding-horizon controller. Our solution a

J. Hunter Priniski, Bryce Linford, Sai Krishna, Fred Morstatter

While narratives have shaped cognition and cultures for centuries, digital media and online social networks have introduced new narrative phenomena. With increased narrative agency, networked groups of individuals can directly contribute and steer narratives that center our collective discussions of politics, science, and morality. We report the results of a

Ian Covert, Wenlong Ji, Tatsunori Hashimoto, James Zou

Recent works have shown that machine learning models improve at a predictable rate with the total amount of training data, leading to scaling laws that describe the relationship between error and dataset size. These scaling laws can help design a model's training dataset, but they typically take an aggregate view of the data by only considering the dataset's

Mohannad Alhanahnah, Yazan Boshmaf

In the era of Large Language Models (LLMs) with their advanced capabilities, a unique opportunity arises to develop LLM-based digital assistant tools that can support software developers by facilitating comprehensive reasoning about software dependencies and open-source libraries before importing them. This reasoning process is daunting, mandating multiple s

Fatemeh Azari, Anne M. Robertson, Lori A. Birder

This study employs micro-computed tomography (micro-CT) to unravel the geometrical intricacies of the rat urinary bladder wall during various states of ex-vivo filling, contrasting markedly with the commonly held idealizations of uniform bladder geometry. Through precise 3D reconstructions at resolutions between 10-20 micrometers, the research meticulously d

F. Criado-Aldeanueva, N. Odishelidze, J. M. Sanchez, M. Khachidze

This paper offers a number of examples showing that in the case of two independent variables the uniform ellipticity of a linear system of differential equations with partial derivatives of the second order, which fulfills condition (3), do not always cause the normal solvability of formulated exterior elliptic problems in the sense of Noether. Nevertheless,

Jorge F. Silva, Victor Faraggi, Camilo Ramirez, Alvaro Egana

We present new results to model and understand the role of encoder-decoder design in machine learning (ML) from an information-theoretic angle. We use two main information concepts, information sufficiency (IS) and mutual information loss (MIL), to represent predictive structures in machine learning. Our first main result provides a functional expression tha

Zhiyi Li, Yunbei Xu, Ruohan Zhan

The Robust Satisficing (RS) model is an emerging approach to robust optimization, offering streamlined procedures and robust generalization across various applications. However, the statistical theory of RS remains unexplored in the literature. This paper fills in the gap by comprehensively analyzing the theoretical properties of the RS model. Notably, the R

Daniel Olshansky, Ramiro Rodriguez Colmeiro, Bowen Li

POKT Network's decentralized Remote Procedure Call (RPC) infrastructure, surpassing 740 billion requests since launching on MainNet in 2020, is well-positioned to extend into providing AI inference services with minimal design or implementation modifications. This litepaper illustrates how the network's open-source and permissionless design aligns incentives

Chiara Pozzi, Mauro Pontani, Alessandro Beolchi, Elena Fantino

The Gateway will represent a primary space system useful for the Artemis program, Earth-Moon transportation, and deep space exploration. It is expected to serve as a staging location on the way to the lunar surface. This study focuses on low-thrust transfer dynamics, from the Near-Rectilinear Halo Orbit traveled by Gateway to a specified Low-altitude Lunar O

Minh Nguyen, Batuhan K. Karaman, Heejong Kim, Alan Q. Wang

Deep learning models benefit from rich (e.g., multi-modal) input features. However, multimodal models might be challenging to deploy, because some inputs may be missing at inference. Current popular solutions include marginalization, imputation, and training multiple models. Marginalization achieves calibrated predictions, but it is computationally expensive

Seamus Somerstep, Ya'acov Ritov, Yuekai Sun

In many prediction problems, the predictive model affects the distribution of the prediction target. This phenomenon is known as performativity and is often caused by the behavior of individuals with vested interests in the outcome of the predictive model. Although performativity is generally problematic because it manifests as distribution shifts, we develo

Maya Anderson, Guy Amit, Abigail Goldsteen

Retrieval Augmented Generation (RAG) systems have shown great promise in natural language processing. However, their reliance on data stored in a retrieval database, which may contain proprietary or sensitive information, introduces new privacy concerns. Specifically, an attacker may be able to infer whether a certain text passage appears in the retrieval da

Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin, Hesham Mostafa

One fundamental challenge in graph machine learning is generalizing to new graphs. Many existing methods following the inductive setup can generalize to test graphs with new structures, but assuming the feature and label spaces remain the same as the training ones. This paper introduces a fully-inductive setup, where models should perform inference on arbitr

Joseph Levine, Benjamin Godfrey, J. Anthony Tyson, S. Mani Tripathi

We report new limits on the kinetic mixing strength of the dark photon spanning the mass range 0.21 -- 1.24 $\mu$eV corresponding to a frequency span of 50 -- 300 MHz. The Dark E-Field Radio experiment is a wide-band search for dark photon dark matter. In this paper we detail changes in calibration and upgrades since our proof-of-concept pilot run. Our detec

Qi Zhang, Guohua Geng, Longquan Yan, Pengbo Zhou

Diffusion models and multi-scale features are essential components in semantic segmentation tasks that deal with remote-sensing images. They contribute to improved segmentation boundaries and offer significant contextual information. U-net-like architectures are frequently employed in diffusion models for segmentation tasks. These architectural designs inclu

M. Carrasco, N. M. Santos, E. Contreras

We study the spontaneous scalarization of charged black holes in Einstein's gravity minimally coupled to power--Maxwell electrodynamics which, in turn, is non--minimally coupled to a real scalar field. We point out the existence of a specific power for which the scalarized solution is well-behaved, and entropically preferred in comparison to the scalar-free

Dipkamal Bhusal, Md Tanvirul Alam, Le Nguyen, Ashim Mahara

Large Language Models (LLMs) have demonstrated potential in cybersecurity applications but have also caused lower confidence due to problems like hallucinations and a lack of truthfulness. Existing benchmarks provide general evaluations but do not sufficiently address the practical and applied aspects of LLM performance in cybersecurity-specific tasks. To ad

Rory Bowens, Michael R. Meyer, Taylor L. Tobin, Eric Viges

New longwave HgCdTe detectors are critical to upcoming plans for ground-based infrared astronomy. These detectors, with fast-readouts and deep well-depths, will be key components of extremely large telescope instruments and therefore must be well understood prior to deployment. We analyze one such HgCdTe detector, a Teledyne Imaging Sensors GeoSnap, at the U

Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan

Sharpness-Aware Minimization (SAM) has emerged as a promising alternative optimizer to stochastic gradient descent (SGD). The originally-proposed motivation behind SAM was to bias neural networks towards flatter minima that are believed to generalize better. However, recent studies have shown conflicting evidence on the relationship between flatness and gene

Pierre-Henri Chavanis

We consider an isothermal self-gravitating system surrounding a central body. This model can represent a galaxy or a globular cluster harboring a central black hole. It can also represent a gaseous atmosphere surrounding a protoplanet. In three dimensions, the Boltzmann-Poisson equation must be solved numerically in order to obtain the density profile of the

M. Anda, E. Fuenmayor, L. Leal, E. Contreras

In this work, we undertake a perturbative analysis of the topological non-Abelian Chern-Simons-Wong model with the aim to explicitly construct the second-order on-shell action. The resulting action is a topological quantity depending solely on closed curves, so it correspond to an analytical expression of a link invariant. Additionally, we construct an Abeli

Ivan A. Chernyshev

In extreme environments such as core-collapse supernovae, neutron-star mergers, and the early Universe, neutrinos are dense enough that their self-interactions significantly affect, if not dominate, their flavor dynamics. In order to develop techniques for characterizing the resulting quantum entanglement, I present the results of simulations of Dirac neutri

Yanlin Qu, Jose Blanchet, Peter Glynn

Convergence rate analysis for general state-space Markov chains is fundamentally important in areas such as Markov chain Monte Carlo and algorithmic analysis (for computing explicit convergence bounds). This problem, however, is notoriously difficult because traditional analytical methods often do not generate practically useful convergence bounds for realis

Hyo Jin Do, Rachel Ostrand, Justin D. Weisz, Casey Dugan

While humans increasingly rely on large language models (LLMs), they are susceptible to generating inaccurate or false information, also known as "hallucinations". Technical advancements have been made in algorithms that detect hallucinated content by assessing the factuality of the model's responses and attributing sections of those responses to specific so

Rohit Konda, Jordan Prescott, Vikas Chandan, Jesse Crossno

The widespread use of industrial refrigeration systems across various sectors contribute significantly to global energy consumption, highlighting substantial opportunities for energy conservation through intelligent control design. As such, this work focuses on control algorithm design in industrial refrigeration that minimize operational costs and provide e

Manibrata Sen

A core-collapse supernova (SN) releases almost all of its energy in the form of neutrinos, which provide a unique opportunity to probe the working machinery of a SN. These sites are prone to neutrino-neutrino refractive effects, which can lead to fascinating collective flavour oscillations among neutrinos. This causes rapid neutrino flavour conversions deep

Khiem Le, Nhan Luong-Ha, Manh Nguyen-Duc, Danh Le-Phuoc

Federated Learning (FL) is a promising paradigm that offers significant advancements in privacy-preserving, decentralized machine learning by enabling collaborative training of models across distributed devices without centralizing data. However, the practical deployment of FL systems faces a significant bottleneck: the communication overhead caused by frequ

Yinan Wang, M. Giselle Fernández-Godino, Nipun Gunawardena, Donald D. Lucas

Industrial accidents, chemical spills, and structural fires can release large amounts of harmful materials that disperse into urban atmospheres and impact populated areas. Computer models are typically used to predict the transport of toxic plumes by solving fluid dynamical equations. However, these models can be computationally expensive due to the need for

Georgios Tsoumplekas, Ilias Siniosoglou, Vasileios Argyriou, Ioannis D. Moscholios

The increased availability of medical data has significantly impacted healthcare by enabling the application of machine / deep learning approaches in various instances. However, medical datasets are usually small and scattered across multiple providers, suffer from high class-imbalance, and are subject to stringent data privacy constraints. In this paper, th

Hao Liu, Xiaotian You, Raymond Chi-Wing Wong

Given a large dataset of many tuples, it is hard for users to pick out their preferred tuples. Thus, the preference query problem, which is to find the most preferred tuples from a dataset, is widely discussed in the database area. In this problem, a utility function is given by the user to evaluate to what extent the user prefers a tuple. However, consideri

G. Christopher Hruska, Genevieve S. Walsh

In this article, we prove a version of Martin and Skora's conjecture that convergence groups on the $2$-sphere are covered by Kleinian groups. Given a relatively hyperbolic group pair $(G,\mathcal{P})$ with planar boundary and no Sierpinski carpet or cut points in its boundary, and with $G$ one ended and virtually having no $2$-torsion, we show that $G$ is v

T. Contreras, B. Palmeiro, H. Almazán, A. Para

The NEXT-White detector, a high-pressure gaseous xenon time projection chamber, demonstrated the excellence of this technology for future neutrinoless double beta decay searches using photomultiplier tubes (PMTs) to measure energy and silicon photomultipliers (SiPMs) to extract topology information. This analysis uses $^{83m}\text{Kr}$ data from the NEXT-Whi

Rohit Konda, Rahul Chandan, Jason Marden

Examining the behavior of multi-agent systems is vitally important to many emerging distributed applications - game theory has emerged as a powerful tool set in which to do so. The main approach of game-theoretic techniques is to model agents as players in a game, and predict the emergent behavior through the relevant Nash equilibrium. The virtue from this v

Alissa A. Valentine, Alexander W. Charney, Isotta Landi

As interest in implementing artificial intelligence (AI) in medical systems grows, discussion continues on how to evaluate the fairness of these systems, or the disparities they may perpetuate. Socioeconomic status (SES) is commonly included in machine learning models to control for health inequities, with the underlying assumption that increased SES is asso

Remco van der Hofstad, Pim van der Hoorn, Céline Kerriou, Neeladri Maitra

We identify the upper large deviation probability for the number of edges in scale-free geometric random graph models as the space volume goes to infinity. Our result covers the models of scale-free percolation, the Boolean model with heavy-tailed radius distribution, and the age-dependent random connection model. In all these cases the mechanism behind the

Ahmad Biniaz, Anil Maheshwari, Michiel Smid

Let $M$ be a perfect matching on a set of points in the plane where every edge is a line segment between two points. We say that $M$ is globally maximum if it is a maximum-length matching on all points. We say that $M$ is $k$-local maximum if for any subset $M'=\{a_1b_1,\dots,a_kb_k\}$ of $k$ edges of $M$ it holds that $M'$ is a maximum-length matching on po

Yingkai Li, Argyris Oikonomou

We study a single-agent contracting environment where the agent has misspecified beliefs about the outcome distributions for each chosen action. First, we show that for a myopic Bayesian learning agent with only two possible actions, the empirical frequency of the chosen actions converges to a Berk-Nash equilibrium. However, through a constructed example, we

Anoop Kumar, Madan Mohan Tito Ayyalasomayajula, Dheerendra Panwar, Yeshwanth Vasa

With a particular focus on Scipy's minimize function the eclipse mapping method is thoroughly researched and implemented utilizing Python and essential libraries. Many optimization techniques are used, including Sequential Least Squares Programming (SLSQP), Nelder-Mead, and Conjugate Gradient (CG). However, for the purpose of examining photometric light curv

Jacob C. Thompson, Elizabeth Winstanley

We study the back-reaction of a quantum scalar field on anti-de Sitter (AdS) space-time. The renormalized expectation value of the stress-energy tensor operator for a massless, conformally-coupled quantum scalar field on global AdS space-time in four space-time dimensions acts as a source term on the right-hand-side of the Einstein equations for the quantum-

Qianqi Yan, Xuehai He, Xiang Yue, Xin Eric Wang

Large Multimodal Models (LMMs) have shown remarkable progress in medical Visual Question Answering (Med-VQA), achieving high accuracy on existing benchmarks. However, their reliability under robust evaluation is questionable. This study reveals that when subjected to simple probing evaluation, state-of-the-art models perform worse than random guessing on med

Levy Chaves, Eduardo Valle, Alceu Bissoto, Sandra Avila

In the evolving landscape of deep learning, selecting the best pre-trained models from a growing number of choices is a challenge. Transferability scorers propose alleviating this scenario, but their recent proliferation, ironically, poses the challenge of their own assessment. In this work, we propose both robust benchmark guidelines for transferability sco

Simon A. Lee, Trevor Brokowski, Jeffrey N. Chiang

The rapid emergence of antibiotic-resistant bacteria is recognized as a global healthcare crisis, undermining the efficacy of life-saving antibiotics. This crisis is driven by the improper and overuse of antibiotics, which escalates bacterial resistance. In response, this study explores the use of clinical decision support systems, enhanced through the integ

Danilo Alvares, Jessica K. Barrett, François Mercier, Spyros Roumpanis

Predicting cancer-associated clinical events is challenging in oncology. In Multiple Myeloma (MM), a cancer of plasma cells, disease progression is determined by changes in biomarkers, such as serum concentration of the paraprotein secreted by plasma cells (M-protein). Therefore, the time-dependent behaviour of M-protein and the transition across lines of th

Jerzy Szulga

``Orderly divergence'' deals with limit theorems for weighted stochastic Gamma integrals of otherwise nonintegrable functions. Although for monotonic functions this category usually coincides with the classical notion of weighted limit theorems for sums of i.i.d. random variables but there are exceptions and the lack of monotonicity reveals new aspects that

Hao Liu, Xiaotian You, Raymond Chi-Wing Wong

Quantum computing is a popular topic in computer science, which has recently attracted many studies in various areas such as machine learning and network. However, the topic of quantum data structures seems neglected. There is an open problem in the database area: Can we improve existing data structures by quantum techniques? Consider a dataset of key-record

Rohit Konda, Rahul Chandan, David Grimsman, Jason R. Marden

Deriving competitive, distributed solutions to multi-agent problems is crucial for many developing application domains; Game theory has emerged as a useful framework to design such algorithms. However, much of the attention within this framework is on the study of equilibrium behavior, whereas transient behavior is often ignored. Therefore, in this paper we

Kelly Ramsay, Jairo Diaz-Rodriguez

Despite the potential of differentially private data visualization to harmonize data analysis and privacy, research in this area remains underdeveloped. Boxplots are a widely popular visualization used for summarizing a dataset and for comparison of multiple datasets. Consequentially, we introduce a differentially private boxplot. We evaluate its effectivene

Hakim El Massari, Noreddine Gherabi, Sajida Mhammedi, Hamza Ghandi

Cardiovascular disease is one of the chronic diseases that is on the rise. The complications occur when cardiovascular disease is not discovered early and correctly diagnosed at the right time. Various machine learning approaches, including ontology-based Machine Learning techniques, have lately played an essential role in medical science by building an auto

Haibo Jin, Andy Zhou, Joe D. Menke, Haohan Wang

Large Language Models (LLMs) are typically harmless but remain vulnerable to carefully crafted prompts known as ``jailbreaks'', which can bypass protective measures and induce harmful behavior. Recent advancements in LLMs have incorporated moderation guardrails that can filter outputs, which trigger processing errors for certain malicious questions. Existing

Bastien Arcelin, Nicolas Chaverou

Creating realistic or stylized facial and lip sync animation is a tedious task. It requires lot of time and skills to sync the lips with audio and convey the right emotion to the character's face. To allow animators to spend more time on the artistic and creative part of the animation, we present Audio2Rig: a new deep learning based tool leveraging previousl

Vincent Cheung, Ramona Vogt

We calculate the production and polarization of direct $J/\psi$ in the improved color evaporation model in $e+p$ photoproduction. We present the production as functions of transverse momentun, mass of the hadronic final state, and inelasticity. We also present the polarization parameters $\lambda_\vartheta$, $\lambda_{\varphi}$, and $\lambda_{\vartheta \varp

Yifei Jiao, Bin Cheng, Yukun Huang, Erik Asphaug

Among the nearly 30,000 known near-Earth asteroids (NEAs), only tens of them possess Earth co-orbital characteristics with semi-major axes $\sim$1 au. In particular, 469219 Kamo`oalewa (2016 HO3), upcoming target of China's Tianwen-2 asteroid sampling mission, exhibits a meta-stable 1:1 mean-motion resonance with Earth. Intriguingly, recent ground-based obse

Hongyu Gong, Bandhav Veluri

Expressive speech-to-speech translation (S2ST) is a key research topic in seamless communication, which focuses on the preservation of semantics and speaker vocal style in translated speech. Early works synthesized speaker style aligned speech in order to directly learn the mapping from speech to target speech spectrogram. Without reliance on style aligned d

B. Carreres, D. Rosselli, J. E. Bautista, F. Feinstein

SNe Ia are used to determine the distance-redshift relation and build the Hubble diagram. Neglecting their host-galaxy peculiar velocities (PVs) may bias the measurement of cosmological parameters. The smaller the redshift, the larger the effect is. We use realistic simulations of SNe Ia observed by the Zwicky Transient Facility (ZTF) to investigate the effe

Renato M. S. Farias, Thiago O. Maciel, Giancarlo Camilo, Ruge Lin

We present an exact $n$-qubit computational-basis amplitude encoder of real- or complex-valued data vectors of $d=\binom{n}{k}$ components into a subspace of fixed Hamming weight $k$. This represents a polynomial space compression of degree $k$. The circuit is optimal in that it expresses an arbitrary data vector using only $d-1$ (controlled) Reconfigurable

Thorsten Buss, Frank Gaede, Gregor Kasieczka, Claudius Krause

In the quest to build generative surrogate models as computationally efficient alternatives to rule-based simulations, the quality of the generated samples remains a crucial frontier. So far, normalizing flows have been among the models with the best fidelity. However, as the latent space in such models is required to have the same dimensionality as the data

I. Colazzo, J. Okniński, A. Van Antwerpen

A complete classification of all finite bijective set-theoretic solutions $(S,s)$ to the Pentagon Equation is obtained. First, it is shown that every such solution determines a semigroup structure on the set $S$ that is the direct product $E\times G$ of a semigroup of left zeros $E$ and a group $G$. Next, we prove that this leads to a decomposition of the se

Chengwei Wei, Kee Kiat Koo, Amir Tavanaei, Karim Bouyarmane

Large Language Models (LLMs) have facilitated structured data generation, with applications in domains like tabular data, document databases, product catalogs, etc. However, concerns persist about generation veracity due to incorrect references or hallucinations, necessitating the incorporation of some form of model confidence for mitigation. Existing confid

Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis

We study person-level differentially private (DP) mean estimation in the case where each person holds multiple samples. DP here requires the usual notion of distributional stability when $\textit{all}$ of a person's datapoints can be modified. Informally, if $n$ people each have $m$ samples from an unknown $d$-dimensional distribution with bounded $k$-th mom

Yurui Chang, Bochuan Cao, Yujia Wang, Jinghui Chen

Large Language Models (LLMs) have demonstrated impressive performances in complex text generation tasks. However, the contribution of the input prompt to the generated content still remains obscure to humans, underscoring the necessity of understanding the causality between input and output pairs. Existing works for providing prompt-specific explanation ofte

Alan Kogut, Eric Switzer, Dale Fixsen, Nabila Aghanim

The Primordial Inflation Explorer (PIXIE) is an Explorer-class mission concept to measure the energy spectrum and linear polarization of the cosmic microwave background (CMB). A single cryogenic Fourier transform spectrometer compares the sky to an external blackbody calibration target, measuring the Stokes I, Q, U parameters to levels ~200 Jy/sr in each 2.6

Zhong-Qiu Wang, Anurag Kumar, Shinji Watanabe

While far-field multi-talker mixtures are recorded, each speaker can wear a close-talk microphone so that close-talk mixtures can be recorded at the same time. Although each close-talk mixture has a high signal-to-noise ratio (SNR) of the wearer, it has a very limited range of applications, as it also contains significant cross-talk speech by other speakers

Po-Shen Hsin, Ryohei Kobayashi, Carolyn Zhang

We investigate fractionalization of non-invertible symmetry in (2+1)D topological orders. We focus on coset non-invertible symmetries obtained by gauging non-normal subgroups of invertible $0$-form symmetries. These symmetries can arise as global symmetries in quantum spin liquids, given by the quotient of the projective symmetry group by a non-normal subgro

Jiaxing Qiu, Douglas E. Lake, Pavel Chernyavskiy, Teague R. Henry

For prediction models developed on clustered data that do not account for cluster heterogeneity in model parameterization, it is crucial to use cluster-based validation to assess model generalizability on unseen clusters. This paper introduces a clustered estimator of the Network Information Criterion (NICc) to approximate leave-one-cluster-out deviance for

Rafael Andersson Lipcsey

Rapid advances in AI have incited extensive inquiry into its effects on productivity and labor, potentially profound in both positive and negative ways. Often neglected, however, is comprehension of how AI technologies diffuse across and within economies. Developing nations, in particular, face substantial labor market impacts from either swift AI adoption o

Simone Albanesi, Alireza Rashti, Francesco Zappa, Rossella Gamba

We study initially unbound systems of two black holes using numerical relativity (NR) simulations performed with GR-Athena++. We focus on regions of the parameter space close to the transition from scatterings to dynamical captures, considering equal mass and spin-aligned configurations, as well as unequal mass and nonspinning ones. The numerical results are

Ujjwala Anantheswaran, Himanshu Gupta, Kevin Scaria, Shreyas Verma

Large Language Models (LLMs) excel at various tasks, including solving math word problems (MWPs), but struggle with real-world problems containing irrelevant information. To address this, we propose a prompting framework that generates adversarial variants of MWPs by adding irrelevant variables. We introduce a dataset, PROBLEMATHIC, containing both adversari

Tirtha Vinchurkar, Janghoon Ock, Amir Barati Farimani

The increasing popularity of machine learning (ML) in catalysis has spurred interest in leveraging these techniques to enhance catalyst design. Our study aims to bridge the gap between physics-based studies and data-driven methodologies by integrating ML techniques with eXplainable AI (XAI). Specifically, we employ two XAI techniques: Post-hoc XAI analysis a

Ayanah L. Cason, Nicole M. Lloyd-Ronning, Roseanne M. Cheng

We use COSMIC, a galaxy population synthesis code, to investigate how metallicity affects the rate of formation of massive stars with a closely orbiting compact object companion, the suggested progenitors of radio loud long gamma-ray bursts. We present the evolution time of these systems at different metallicities, and how the formation rates of these system

Francesco Fournier-Facio, Nicolas Monod, Sam Nariman, Alexander Kupers

We prove that the groups of orientation-preserving homeomorphisms and diffeomorphisms of $\mathbb{R}^n$ are boundedly acyclic, in all regularities. This is the first full computation of the bounded cohomology of a transformation group that is not compactly supported, and it implies that many characteristic classes of flat $\mathbb{R}^n$- and $S^n$-bundles ar

Yueyang Wang, Hongxia Xue, Xiong Wang, Kenji Watanabe

We report on the observation of Coulomb drag between graphene-hexagonal boron nitride (hBN) moir\'{e} heterostructure with a moir\'{e} wavelength of $\sim$14 nm and an intrinsic graphene with a lattice constant of $\sim$0.25 nm. By tuning carrier densities of each graphene layer independently, we find that the charge carriers in moir\'{e} mini-bands, i.e., n

Egor Kashkarov, Egor Chistov, Ivan Molodetskikh, Dmitriy Vatolin

Perceptual losses play an important role in constructing deep-neural-network-based methods by increasing the naturalness and realism of processed images and videos. Use of perceptual losses is often limited to LPIPS, a fullreference method. Even though deep no-reference image-qualityassessment methods are excellent at predicting human judgment, little resear

Alberto Faraggi, Cristobal Moreno

We construct the probe D5-brane solution in $AdS_5\times S^5$ dual to the $\frac{1}{4}$-BPS latitude Wilson loop in $\mathcal{N}=4$ super Yang-Mills theory in the $k$-antisymmetric representation of $SU(N)$. The solution is exact in the latitude parameter $\theta_0$ and correctly reproduces the $\frac{1}{2}$-BPS limit. We compute the string charge $k$ and th

Lingkai Kong, Molei Tao

Explicit, momentum-based dynamics that optimize functions defined on Lie groups can be constructed via variational optimization and momentum trivialization. Structure preserving time discretizations can then turn this dynamics into optimization algorithms. This article investigates two types of discretization, Lie Heavy-Ball, which is a known splitting schem

John F. Wu, Alina Hyk, Kiera McCormick, Christine Ye

Large Language Models (LLMs) are shifting how scientific research is done. It is imperative to understand how researchers interact with these models and how scientific sub-communities like astronomy might benefit from them. However, there is currently no standard for evaluating the use of LLMs in astronomy. Therefore, we present the experimental design for a

Daniel B. Thomas, Theodore Anton, Timothy Clifton, Philip Bull

The Parameterised Post-Newtonian (PPN) approach is the default framework for performing precision tests of gravity in nearby astrophysical systems. In recent works we have extended this approach for cosmological applications, and in this paper we use observations of the anisotropies in the Cosmic Microwave Background to constrain the time variation of the PP

Astrid Eichhorn, Arthur Hebecker, Jan M. Pawlowski, Johannes Walcher

The ``Swampland Program'' aims to discriminate consistent-looking effective field theories that do not admit a UV completion in quantum gravity from those that do. While most often developed under the umbrella of string theory, several swampland criteria have been explored also in other contexts, especially asymptotically safe gravity. A comparison between d

Benjamin Csizi, Luca Tortorelli, Małgorzata Siudek, Daniel Gruen

Narrowband galaxy surveys have recently gained interest as a promising method to achieve the necessary accuracy on the photometric redshift estimate of individual galaxies for stage-IV cosmological surveys. One key advantage is the ability to provide higher spectral resolution information about galaxies that should allow a more accurate and precise estimatio

Mohamed Hibat-Allah, Ejaaz Merali, Giacomo Torlai, Roger G Melko

Rydberg atom array experiments have demonstrated the ability to act as powerful quantum simulators, preparing strongly-correlated phases of matter which are challenging to study for conventional computer simulations. A key direction has been the implementation of interactions on frustrated geometries, in an effort to prepare exotic many-body states such as s

Stefan Schuldt, Raoul Cañameras, Irham T. Andika, Satadru Bag

We performed a systematic search for strong gravitational lenses using Hyper Suprime-Cam (HSC) data, focusing on galaxy-scale lenses combined with an environment analysis resulting in the identification of lensing clusters. To identify these lens candidates, we exploited our neural network (NN) from HOLISMOKES VI. During our visual grading, we also simultane

Enrico Di Benedetto, Alejandro Gonzalez-Tudela, Francesco Ciccarello

Flat bands (FBs) are energy bands with zero group velocity, which in electronic systems were shown to favor strongly correlated phenomena. Indeed, a FB can be spanned with a basis of strictly localized states, the so called "compact localized states" (CLSs), which are yet generally non-orthogonal. Here, we study emergent dipole-dipole interactions between em

Jiyue Huang, Chi Hong, Lydia Y. Chen, Stefanie Roos

Diffusion models are becoming defector generative models, which generate exceptionally high-resolution image data. Training effective diffusion models require massive real data, which is privately owned by distributed parties. Each data party can collaboratively train diffusion models in a federated learning manner by sharing gradients instead of the raw dat

Indrajit Sau, Debasish Banerjee, Arnab Sen

We address the fate of many-body localization (MBL) of mid-spectrum eigenstates of a matter-free $U(1)$ quantum-link gauge theory Hamiltonian with random couplings on ladder geometries. Apart from level spacing distribution indicators like disorder-averaged mean level spacing, we also consider an intensive estimator $\mathcal{D} \in [0,1/4]$, which acts as a

Alexander Baur, Hans Peter Nilles, Saul Ramos-Sanchez, Andreas Trautner

Only four $\mathbb{T}^2/\mathbb{Z}_K$ orbifold building blocks are admissible in heterotic string compactifications. We investigate the flavor properties of all of these building blocks. In each case, we identify the traditional and modular flavor symmetries, and determine the corresponding representations and (fractional) modular weights of the available ma

Grant C. Weldon, Smadar Naoz, Bradley M. S. Hansen

Triple body systems are prevalent in nature, from planetary to stellar to supermassive black hole scales. In a hierarchical triple system, oscillations of the inner orbit's eccentricity and inclination can be induced on secular timescales. Over many cycles, the octupole-level terms in the secular equations of motion can drive the system to extremely high ecc

Yicheng Zhang, Yuan Le, David S. Weiss, Marcos Rigol

We study the quantum evolution of one-dimensional Bose gases immediately after several variants of high-energy quenches, both theoretically and experimentally. Using the advantages conveyed by the relative simplicity of these nearly integrable many-body systems, we are able to differentiate the behaviors of two distinct but often temporally overlapping proce

Nihat Sadik Deger, Ángel J. Murcia, Dmitri P. Sorokin

In six dimensions there exists a unique one-parameter family of non-linear conformal electrodynamics for a chiral 2-form gauge field which includes (in a free-field limit) the linear chiral 2-form theory and is related, by dimensional reduction, to the four-dimensional ModMax electrodynamics. In this work, we present the first exact solutions of this theory

Tim Zimmermann, James Alvey, David J. E. Marsh, Malcolm Fairbairn

It is widely established that a lower bound on the dark matter particle mass, $m$, can be obtained by demanding that the de Broglie wavelength in a given galaxy must be smaller than the virial radius of the galaxy, leading to $m\gtrsim 10^{-22}\text{ eV}$ when applied to typical dwarf galaxies. This lower limit has never been derived precisely or rigorously.

Arnab Chakraborty, Amitabha Lahiri

The interaction of fermion spin with spacetime can be non-universal, leading to a new interaction beyond the Standard Model, independent of gravitation. Fermions generate spacetime torsion, which can be integrated out in favor of a four-fermion interaction in a torsion-free background. This is a current-current interaction which involves all fermions and gen

Joshua Davies, Kay Schönwald, Matthias Steinhauser, Marco Vitti

We compute three-loop corrections to the process $gg\to HH$ originating from one-particle reducible diagrams. This requires the computation of two-loop corrections to the gluon-gluon-Higgs vertex with an off-shell gluon. We describe in detail our approach to obtain semi-analytic results for the vertex form factors and present results for the two form factors

Christophe Grojean, Guilherme Guedes, Jasper Roosmale Nepveu, Gabriel M. Salla

We investigate the renormalization of the radiative decays of the Higgs to two gauge bosons in the Standard Model Effective Field Theory at mass dimension eight. Given that these are loop-level processes, their one-loop renormalization can be phenomenologically important when triggered by operators generated through the tree-level exchange of heavy particles

Andrea Ferrara

We developed a model for the star formation history (SFH) of super-early galaxies and applied it to GS-z14-0, the most distant galaxy known, located at $z=14.32$ (294 million years after the Big Bang). The SFH, starting at $z=26.7$, is complex. Initially ($z>18$), the galaxy experiences feedback-regulated phases that are bursty, relatively faint (reaching $M

Nickolas Kokron, José Luis Bernal, Jo Dunkley

We investigate the potential of CO rotational lines at redshifts $z\sim 0-6$ being an appreciable source of extragalactic foreground anisotropies in the cosmic microwave background. Motivated by previous investigations, we specifically focus on the frequency bands and small scales probed by ground-based surveys. Using an empirical parameterization for the re

Honglin Zhu

We introduce graphical error-correcting codes, a new notion of error-correcting codes on $[q]^n$, where a code is a set of proper $q$-colorings of some fixed $n$-vertex graph $G$. We then say that a set of $M$ proper $q$-colorings of $G$ form a $(G, M, d)$ code if any pair of colorings in the set have Hamming distance at least $d$. This directly generalizes

Alexander J. Dittmann

In many cases accretion proceeds from disks onto planets, stars, white dwarfs, and neutron stars via a boundary layer, a region of intense shear where gas transitions from a near-Keplerian speed to that of the surface. These regions are \textit{not} susceptible to the common magnetorotational and Kelvin-Helmholtz instabilities, and instead global modes gener

Anatoly Dymarsky, Alfred Shapere

We outline a general derivation of holographic duality between "TQFT gravity" - the path integral of a 3d TQFT summed over different topologies - and an ensemble of boundary 2d CFTs. The key idea is to place the boundary ensemble on a Riemann surface of very high genus, where the duality trivializes. The duality relation at finite genus is then obtained by g