October 2024 arXiv papers — page 32

He Jiang, Yutong Wang, Rishi Veerapaneni, Tanishq Duhan

Lifelong Multi-Agent Path Finding (LMAPF) repeatedly finds collision-free paths for multiple agents that are continually assigned new goals when they reach current ones. Recently, this field has embraced learning-based methods, which reactively generate single-step actions based on individual local observations. However, it is still challenging for them to m

Bowen Zhao, Tianhao Cheng, Yuejie Zhang, Ying Cheng

Multimodal Question Answering (MMQA) is crucial as it enables comprehensive understanding and accurate responses by integrating insights from diverse data representations such as tables, charts, and text. Most existing researches in MMQA only focus on two modalities such as image-text QA, table-text QA and chart-text QA, and there remains a notable scarcity

Adam Hutchings, Eric Yarnot, Xinpeng Li, Qiang Guan

The variational quantum eigensolver (VQE), a type of variational quantum algorithm, is a hybrid quantum-classical algorithm to find the lowest-energy eigenstate of a particular Hamiltonian. We investigate ways to optimize the VQE solving process on multiple instances of the same problem, by observing the process on one instance of the problem to inform initi

Wanhua Li, Zibin Meng, Jiawei Zhou, Donglai Wei

Social relation reasoning aims to identify relation categories such as friends, spouses, and colleagues from images. While current methods adopt the paradigm of training a dedicated network end-to-end using labeled image data, they are limited in terms of generalizability and interpretability. To address these issues, we first present a simple yet well-craft

Guochao Sun, Adam Lidz, Tzu-Ching Chang, Jordan Mirocha

The timeline of cosmic reionization remains uncertain despite sustained efforts to study how the ionizing output of early galaxies shaped the intergalactic medium (IGM). Using the semi-numerical code LIMFAST, we investigate the prospects for timing the reionization process by cross-correlating the 21 cm signal with the cosmic near-infrared background (NIRB)

Guochao Sun, Julian B. Muñoz, Jordan Mirocha, Claude-André Faucher-Giguère

The observed prevalence of galaxies exhibiting bursty star formation histories (SFHs) at $z\gtrsim6$ has created new challenges and opportunities for understanding their formation pathways. The degenerate effects of the efficiency and burstiness of star formation on the observed UV luminosity function are separable by galaxy clustering. However, quantifying

Nisarga Paul, Philip J. D. Crowley

Sub-dimensional localization, also known as directional localization, arises when 2d electrons are subject to a periodic potential and incommensurate magnetic field which cause them to become exponentially localized along one crystal axis, while remaining extended in the orthogonal direction. We establish that sub-dimensional localization is robust to the pr

Juan Ignacio Ibañez, Aayush Ladda, Paolo Tasca, Logan Aldred

The environmental impact of Bitcoin mining has become a significant concern, prompting several governments to consider or implement bans on cryptocurrency mining. However, these well-intentioned policies may lead to unintended consequences, notably the redirection of mining activities to regions with higher carbon intensities. This study aims to quantify the

Henrik Madsen, Gudmund Grov, Federico Mancini, Magnus Baksaas

Unmanned vehicles able to conduct advanced operations without human intervention are being developed at a fast pace for many purposes. Not surprisingly, they are also expected to significantly change how military operations can be conducted. To leverage the potential of this new technology in a physically and logically contested environment, security risks a

Michael Przystupa, Gauthier Gidel, Matthew E. Taylor, Martin Jagersand

As robots become more common for both able-bodied individuals and those living with a disability, it is increasingly important that lay people be able to drive multi-degree-of-freedom platforms with low-dimensional controllers. One approach is to use state-conditioned action mapping methods to learn mappings between low-dimensional controllers and high DOF m

Arpan Dasgupta, Gagan Jain, Arun Suggala, Karthikeyan Shanmugam

Mobile health (mHealth) programs face a critical challenge in optimizing the timing of automated health information calls to beneficiaries. This challenge has been formulated as a collaborative multi-armed bandit problem, requiring online learning of a low-rank reward matrix. Existing solutions often rely on heuristic combinations of offline matrix completio

Vincenzo Cirigliano, Wouter Dekens, Emanuele Mereghetti, Oleksandr Tomalak

We discuss the hadronic structure-dependent radiative corrections to the axial-vector coupling that controls single-nucleon weak charged-current processes -- commonly denoted by $g_A$. We match the Standard Model at the GeV scale onto chiral perturbation theory at next-to-leading order in the one-nucleon sector, in the presence of electromagnetic and weak in

Amr Gomaa, Bilal Mahdy

Integration of human feedback plays a key role in improving the learning capabilities of intelligent systems. This comparative study delves into the performance, robustness, and limitations of imitation learning compared to traditional reinforcement learning methods within these systems. Recognizing the value of human-in-the-loop feedback, we investigate the

Sanket Chirame, Abhinav Prem, Sarang Gopalakrishnan, Fiona J. Burnell

An important open question for the current generation of highly controllable quantum devices is understanding which phases can be realized as stable steady-states under local quantum dynamics. In this work, we show how robust steady-state phases with both Abelian and non-Abelian mixed-state topological order can be stabilized, in two spatial dimensions (2d),

Cecilia Sgalletta, Michela Mapelli, Lumen Boco, Filippo Santoliquido

As the number of gravitational-wave detections grows, the merger rate of binary black holes (BBHs) can help us to constrain their formation, the properties of their progenitors, and their birth environment. Here, we aim to address the impact of the metal-dependent star formation rate (SFR) on the BBH merger rate. To this end, we have developed a fully data-d

M. G. Lodge, H. R. Wakeford, Z. M. Leinhardt

Correctly modelling the absorptive properties of dust and haze particles is of great importance for determining the abundance of solid matter within protoplanetary disks and planetary atmospheres. Rigorous analyses such as the discrete dipole approximation (DDA) can be used to obtain accurate absorption cross-sections, but these require significant computing

Jakob den Brok, María J. Jiménez-Donaire, Adam Leroy, Eva Schinnerer

Over the past decade, several millimeter interferometer programs have mapped the nearby star-forming galaxy M51 at a spatial resolution of ${\le}170$ pc. This study combines observations from three major programs: the PdBI Arcsecond Whirlpool Survey (PAWS), the SMA M51 large program (SMA-PAWS), and the Surveying the Whirlpool at Arcseconds with NOEMA (SWAN).

Luis Briceño-Arias, Sergio López-Rivera, Emilio Vilches

In this paper, we provide different splitting methods for solving distributionally robust optimization problems in cases where the uncertainties are described by discrete distributions. The first method involves computing the proximity operator of the supremum function that appears in the optimization problem. The second method solves an equivalent monotone

Alexey Milekhin, Sara Murciano

Measurements in many-body quantum systems can generate non-trivial phenomena, such as preparation of long-range entangled states, dynamical phase transitions, or measurement-altered criticality. Here, we introduce a new measurement scheme that produces an ensemble of mixed states in a subsystem, obtained by measuring a local Hermitian observable on part of i

Alicia Middleton, Steve K. Choi, Samantha Walker, Jason Austermann

Prime-Cam, one of the primary instruments for the Fred Young Submillimeter Telescope (FYST) developed by the CCAT Collaboration, will house up to seven instrument modules, with the first operating at 280 GHz. Each module will include three arrays of superconducting microwave kinetic inductance detectors (KIDs). The first KID array fabricated for the 280 GHz

Yu-Tse Lee

We propose a geometric formulation of effective field theories via nonlinear supersymmetry. Non-supersymmetric particles are embedded in constrained superfields governed by a nonlinear sigma model, and operators are collected into potentials on the target space. The use of chiral superfields standardizes the treatment of flavor across scalars and fermions, a

Matthias Fabry, Pablo Marchant, Norbert Langer, Hugues Sana

Among massive stars, binary interaction is the rule rather than the exception. The closest binaries, those with periods of less than about 10 days, undergo mass transfer during core-hydrogen burning, with many of them experiencing a nuclear-timescale contact phase. Current binary population synthesis models predict the mass-ratio distribution of contact bina

Vishal Singh, Mark M. Wilde

Secret-key distillation from quantum states and channels is a central task of interest in quantum information theory, as it facilitates private communication over a quantum network. Here, we study the task of secret-key distillation from bipartite states and point-to-point quantum channels using local operations and one-way classical communication (one-way L

Yui Hayashi, Tatsuhiro Misumi, Yuya Tanizaki

We study ${\mathcal N} = 1$ $SU(N)$ super Yang-Mills (SYM) theory on $\mathbb{R}^2\times (S^1)_3\times (S^1)_4$ with the 't Hooft twist. The theory becomes weakly coupled if the length $L_4$ of $(S^1)_4$ is sufficiently small, $NL_4\Lambda\ll 1$. We explore the nonperturbative dynamics at the weak-coupling regime by changing the size of $L_3$ and uncover how

N. Castro, P. M. Weilbacher, M. M. Roth, P. A. Crowther

Melnick 34 (Mk 34) is one of the most massive binary systems known and is one of the brightest X-ray point sources in the 30 Doradus region. We investigated the impact of this massive system on the surrounding interstellar medium (ISM) using the optical spectroscopic capabilities of the narrow-field mode (NFM) of the Multi-Unit Spectroscopic Explorer (MUSE).

Daniel J. Reardon, Robert Main, Stella Koch Ocker, Ryan M. Shannon

The ionized interstellar medium contains au-scale (and below) structures that scatter radio waves from pulsars, resulting in scintillation. Power spectral analysis of scintillation often shows parabolic arcs, with curvatures that encode the locations and kinematics of the pulsar, Earth, and interstellar plasma. Here we report the discovery of 25 distinct pla

Derek Churchill, Emily Z. Zhang, Hae-Young Kee

The exactly solvable spin-1/2 Kitaev model on a honeycomb lattice has drawn significant interest, as it offers a pathway to realizing the long-sought after quantum spin liquid. Building upon the Kitaev model, Yao and Lee introduced another exactly solvable model on an unusual star lattice featuring non-abelian spinons. The additional pseudospin degrees of fr

Shang-Shun Zhang, Cristian D. Batista, Gábor B. Halász

Recent experimental work indicates that the Kitaev spin liquid may be realizable close to zero magnetic field in exfoliated $\alpha$-RuCl$_3$ flakes, thus providing a more versatile setting for studying non-Abelian anyons. Here, we propose a robust nanoscale signature of the Kitaev spin liquid that results from its edge states and manifests in the low-energy

E. Dalla Bontà, B. M. Peterson, C. J. Grier, M. Berton

The goal of this project is to construct an estimator for the masses of supermassive black holes in active galactic nuclei (AGNs) based on the broad Halpha emission line. We make use of published reverberation mapping data. We remeasure all Halpha time lags from the original data as we find that often the reverberation measurements are improved by detrending

Shuo Liu, Zhengzhi Wu, Shi-Xin Zhang, Hong Yao

Spacetime supersymmetry (SUSY) that interchanges fermions and bosons is of great theoretical importance but has not yet been revealed experimentally in particle physics. It has also been desired to explore quantum-mechanical SUSY in microscopic lattice models. Inspired by the recent experiments of Floquet engineering of Rydberg atom arrays, we propose to sim

Nicolás Bernal, Yong Xu

In this work, we revisit the generation of stochastic gravitational waves (GWs) from interactions in the thermal plasma. We extend the existing literature by incorporating the reheating phase into the thermal history. Our results show that the amplitude of the GW spectrum can be significantly enhanced because the temperature during reheating can be much high

Wen-Feng Duan, Syuhei Iguro, Xin-Qiang Li, Ryoutaro Watanabe

The semi-leptonic $b \to c l \nu$ processes are receiving a lot of attention, as the lepton flavor universality violation has been hinted by the measured ratios $R_{D^{(*)}} = \Gamma(B \to D^{(*)} \tau\nu)/\Gamma(B \to D^{(*)} \ell\nu)$ for $\ell = e,\mu$. Recently, it has also been pointed out that the baryonic counterpart, $R_{\Lambda_c} = \Gamma (\Lambda_

Rodolfo Capdevilla, Francesco Garosi, David Marzocca, Bernd Stechauner

Collinear emission of $W$ bosons off a high-energy muon induces a large muon-neutrino component among the Parton Distribution Functions (PDFs) of a muon. In this paper we study the phenomenology related to the $\nu_\mu$ PDF at future high-energy muon colliders. We examine total rates and differential distributions of the $e \bar{\nu}_e$ and $W \gamma$ produc

Saburo Howard, Ravit Helled, Simon Müller

Revealing the internal composition and structure of giant planets is fundamental for understanding planetary formation. However, the bulk composition can only be inferred through interior models. As a result, advancements in modelling aspects are essential to better characterise the interiors of giant planets. We investigate the effects of model assumptions

Régis Cartier, Carlos Contreras, Maximilian Stritzinger, Mario Hamuy

We present optical and infrared observations from maximum light until around +800 days of supernova (SN) 2022jli, a peculiar stripped-envelope (SE) SN showing two maxima, each one with a peak luminosity of about $3 \times 10^{42}$ erg s$^{-1}$, separated by 50 days. The second maximum is followed by unprecedented periodic undulations with a period of $P \sim

Maxim Dzero

I formulate a theory of the inverse Faraday effect in impure two-dimensional metallic system with lifted spin degeneracy induced by Rashba spin-orbit coupling. Using the formalism of non-equilibrium quantum field theory, the static contributions to the current density up to the second order in powers of external electromagnetic field are evaluated. For circu

Andre Maeder, Frederic Courbin

We first briefly review the adventure of scale invariance in physics, from Galileo Galilei, Weyl, Einstein, and Feynman to the revival by Dirac (1973) and Canuto et al. (1977). We then gather concrete observational evidence that scale-invariant effects are present and measurable in astronomical objects spanning a vast range of masses (0.5 M$_{\odot} <$ M $<

Timothy Cohen, Xiaochuan Lu, Zhengkang Zhang

We elaborate on a recently proposed geometric framework for scalar effective field theories. Starting from the action, a metric can be identified that enables the construction of geometric quantities on the associated functional manifold. These objects transform covariantly under general field redefinitions that relate different operator bases, including tho

Russell L. Graf, Andrew Wetzel, Jeremy Bailin, Matthew E. Orr

Within the Milky Way (MW), younger stellar populations exhibit steeper (more negative) metallicity radial gradients; the origin of this trend remains debated. The FIRE-2 cosmological simulations of MW-mass galaxies show the same trend as the MW, which in FIRE-2 arises because the metallicity gradient of the interstellar medium (ISM), and thus of stars at bir

Nikolay Bobev, Pieter Bomans, Fridrik Freyr Gautason

We study the maximally supersymmetric Yang-Mills theory on $S^d$ using supersymmetric localisation and holography. We argue that the analytic continuation in dimension to $d=1$ yields a Euclidean version of the BMN matrix quantum mechanics. This system can be analysed at large $N$ using supersymmetric localisation and leads to explicit results for the free e

Minjung Park, Charlie Conroy, Benjamin D. Johnson, Joel Leja

We present new stellar population models, $\alpha$-MC, self-consistently taking into account non-solar $\rm [\alpha/Fe]$ abundances for both isochrones and stellar spectra. The $\alpha$-MC models are based on $\alpha$-enhanced MIST isochrones and C3K spectral libraries, which are publicly available in FSPS. Our new models cover a wide range of ages ($\rm \lo

Aseem Paranjape, Ravi K. Sheth

We consider approximating the linearly evolved 2-point correlation function (2pcf) of dark matter $\xi_{\rm lin}(r;\boldsymbol{\theta})$ in a cosmological model with parameters $\boldsymbol{\theta}$ as the linear combination $\xi_{\rm lin}(r;\boldsymbol{\theta})\approx\sum_i\,b_i(r)\,w_i(\boldsymbol{\theta})$, where the functions $\mathcal{B}=\{b_i(r)\}$ for

Milan Kornjača, Rebecca Flint

We theoretically explore how light-driven optical phonons can be used to drive magnetic exchange interactions into interesting physical regimes by developing a general theory of spin-phonon pumping in magnetic insulators with non-equilibrium optical phonon distributions, focusing on the diabatic regime where phonon frequencies are much larger than the magnet

Dean Johnstone, Shanya Mishra, Zhaoxuan Zhu, Laurent Sanchez-Palencia

A twist between two systems offers the possibility to drastically change the underlying physical properties. To that end, we study the bandstructure of twisted moir\'e potentials in detail. At sets of commensurate twisting angles, the low energy single-particle spectrum of a twisted moir\'e potential will form into distinct bands and gaps. To a first approxi

Visal Sok, Adam Muzzin, Pascale Jablonka, Vivian Yun Yan Tan

Despite the ubiquity of clumpy star-forming galaxies at high-redshift, the origin of clumps are still largely unconstrained due to the limited observations that can validate the mechanisms for clump formation. We postulate that if clumps form due to the accretion of metal-poor gas that leads to violent disk instability, clumpy galaxies should have lower gas-

Abhishek Banerjee, Manuel A. Buen-Abad

We propose a relaxation mechanism for the initial misalignment angle of the pre-inflationary QCD axion with a large decay constant. The proposal addresses the challenges posed to the axion dark matter scenario by an overabundance of axions overclosing the Universe, as well as by isocurvature constraints. Many state-of-the-art experiments are searching for QC

O. Grace Telford, Karin M. Sandstrom, Kristen B. W. McQuinn, Simon C. O. Glover

The James Webb Space Telescope (JWST) has revealed unexpectedly rapid galaxy assembly in the early universe, in tension with models of star and galaxy formation. In the gas conditions typical of early galaxies, particularly their low abundances of heavy elements (metals) and dust, the star-formation process is poorly understood. Some models predict that star

Edward D. Ramirez, Yitian Sun, Matthew R. Buckley, Siddharth Mishra-Sharma

Descriptions of the Galactic Center using Fermi gamma-ray data have so far modeled the Galactic Center Excess (GCE) as a template with fixed spatial morphology or as a linear combination of such templates. Although these templates are informed by various physical expectations, the morphology of the excess is a priori unknown. For the first time, we describe

Marine Prunier, Julie Hlavacek-Larrondo, Annalisa Pillepich, Katrin Lehle

Active galactic nuclei (AGN) feedback from supermassive black holes (SMBHs) at the centers of galaxy clusters plays a key role in regulating star formation and shaping the intracluster medium (ICM), often manifesting through prominent X-ray cavities embedded in the cluster's hot atmosphere. Here we show that X-ray cavities arise naturally due to AGN feedback

Elliot Y. Davies, Vasily Belokurov, Andrey Kravtsov, Stephanie Monty

The stellar halo of the Milky Way comprises an abundance of chemical signatures from accretion events and \textit{in-situ} evolution, that form an interweaving tapestry in kinematic space. To untangle this, we consider the mixtures of chemical information, in a given region of integral of motion space, as a variant of the blind source separation problem usin

Elise Lei, Paul Mollière

For modeling the spectra of exoplanets one must know their atmospheric composition. This is necessary because the abundance of molecules, atoms, ions and condensates is needed to construct the total cross-section for the interaction between electro-magnetic radiation and matter. In addition, when solving for the temperature structure of an atmosphere the so-

Andrew R. Frey

Thus far, the literature regarding holographic complexity almost entirely focuses on the context of $(d+1)$-dimensional anti-de Sitter spacetime rather than the full higher-dimensional gauge/gravity duality in string or M theory. We provide a framework to study holographic complexity in the full duality, explaining the relation of complexity functionals in t

Manuel Benavent-Lledo, David Mulero-Pérez, David Ortiz-Perez, Jose Garcia-Rodriguez

We propose a novel approach to improve action recognition by exploiting the hierarchical organization of actions and by incorporating contextualized textual information, including location and previous actions, to reflect the action's temporal context. To achieve this, we introduce a transformer architecture tailored for action recognition that employs both

Carlos Alberto da Silva Junior, Roberto Yuji Tanaka, Luiz Carlos Farias da Silva, Angelo Passaro

The Travelling Salesman Problem - TSP is one of the most explored problems in the scientific literature to solve real problems regarding the economy, transportation, and logistics, to cite a few cases. Adapting TSP to solve different problems has originated several variants of the optimization problem with more complex objectives and different restrictions.

Jie An, De Wang, Pengsheng Guo, Jiebo Luo

Recent work studying the generalization of diffusion models with UNet-based denoisers reveals inductive biases that can be expressed via geometry-adaptive harmonic bases. However, in practice, more recent denoising networks are often based on transformers, e.g., the diffusion transformer (DiT). This raises the question: do transformer-based denoising network

Yaniv Nikankin, Anja Reusch, Aaron Mueller, Yonatan Belinkov

Do large language models (LLMs) solve reasoning tasks by learning robust generalizable algorithms, or do they memorize training data? To investigate this question, we use arithmetic reasoning as a representative task. Using causal analysis, we identify a subset of the model (a circuit) that explains most of the model's behavior for basic arithmetic logic and

Shih-Yang Liu, Maksim Khadkevich, Nai Chit Fung, Charbel Sakr

While post-training compression techniques effectively reduce the memory footprint, latency, and power consumption of Large Language Models (LLMs), they often result in noticeable accuracy degradation and remain limited by hardware and kernel constraints that restrict supported compression formats - ultimately reducing flexibility across a wide range of depl

Samuel Talkington, Rahul Gupta, Richard Asiamah, Paprapee Buason

Despite their wide-scale deployment and ability to make accurate high-frequency voltage measurements, communication network limitations have largely precluded the use of smart meters for real-time monitoring purposes in electric distribution systems. Although smart meter communication networks have limited bandwidth available per meter, they also have the ab

Xize Cheng, Siqi Zheng, Zehan Wang, Minghui Fang

The scaling up has brought tremendous success in the fields of vision and language in recent years. When it comes to audio, however, researchers encounter a major challenge in scaling up the training data, as most natural audio contains diverse interfering signals. To address this limitation, we introduce Omni-modal Sound Separation (OmniSep), a novel framew

Wonjun Lee, Hyukjoon Kwon, Gil Young Cho

Quantum chaos is central to understanding quantum dynamics and is crucial for generating random quantum states, a key resource for quantum information tasks. In this work, we introduce a new class of quantum many-body dynamics, termed pseudochaotic dynamics. Although distinct from chaotic dynamics, out-of-time-ordered correlators, the key indicators of quant

Reinhard Wiesmayr, Darja Nonaca, Chris Dick, Christoph Studer

Rate-matching of low-density parity-check (LDPC) codes enables a single code description to support a wide range of code lengths and rates. In 5G NR, rate matching is accomplished by extending (lifting) a base code to a desired target length and by puncturing (not transmitting) certain code bits. LDPC codes and rate matching are typically designed for the as

Orin Levy, Noam Touitou, Aviv Rosenberg

Online paging is a fundamental problem in the field of online algorithms, in which one maintains a cache of $k$ slots as requests for fetching pages arrive online. In the weighted variant of this problem, each page has its own fetching cost; a substantial line of work on this problem culminated in an (optimal) $O(\log k)$-competitive randomized algorithm, du

Jeremy Bernstein, Laker Newhouse

An old idea in optimization theory says that since the gradient is a dual vector it may not be subtracted from the weights without first being mapped to the primal space where the weights reside. We take this idea seriously in this paper and construct such a duality map for general neural networks. Our map, which we call modular dualization, forms a unifying

Hanyu Wang, Saksham Suri, Yixuan Ren, Hao Chen

We present LARP, a novel video tokenizer designed to overcome limitations in current video tokenization methods for autoregressive (AR) generative models. Unlike traditional patchwise tokenizers that directly encode local visual patches into discrete tokens, LARP introduces a holistic tokenization scheme that gathers information from the visual content using

Yuqi Gu, Zhongyuan Lyu, Kaizheng Wang

We propose a general transfer learning framework for clustering given a main dataset and an auxiliary one about the same subjects. The two datasets may reflect similar but different latent grouping structures of the subjects. We propose an adaptive transfer clustering (ATC) algorithm that automatically leverages the commonality in the presence of unknown dis

Changwoo Lee, Soo Min Kwon, Qing Qu, Hun-Seok Kim

Large-scale foundation models have demonstrated exceptional performance in language and vision tasks. However, the numerous dense matrix-vector operations involved in these large networks pose significant computational challenges during inference. To address these challenges, we introduce the Block-Level Adaptive STructured (BLAST) matrix, designed to learn

Mohamed R. Elshamy, Mehdi Elahi, Ahmad Patooghy, Abdel-Hameed A. Badawy

Fine-grained power estimation in multicore Systems on Chips (SoCs) is crucial for efficient thermal management. BPI (Blind Power Identification) is a recent approach that determines the power consumption of different cores and the thermal model of the chip using only thermal sensor measurements and total power consumption. BPI relies on steady-state thermal

Yuri V. Kovchegov, Brandon Manley

We calculate the elastic production of dijets from electron collisions with a longitudinally polarized proton target at small values of the Bjorken $x$ variable. Building on the pioneering proposals of \cite{Hatta:2016aoc,Bhattacharya:2022vvo, Bhattacharya:2023hbq, Bhattacharya:2024sck} for measuring the quark and gluon orbital angular momentum (OAM) distrib

Han Bao, Yue Huang, Yanbo Wang, Jiayi Ye

Large Vision-Language Models (LVLMs) have become essential for advancing the integration of visual and linguistic information. However, the evaluation of LVLMs presents significant challenges as the evaluation benchmark always demands lots of human cost for its construction, and remains static, lacking flexibility once constructed. Even though automatic eval

Zhendong Wang, Zhaoshuo Li, Ajay Mandlekar, Zhenjia Xu

Diffusion models, praised for their success in generative tasks, are increasingly being applied to robotics, demonstrating exceptional performance in behavior cloning. However, their slow generation process stemming from iterative denoising steps poses a challenge for real-time applications in resource-constrained robotics setups and dynamically changing env

Jan Witowski, Ken G. Zeng, Joseph Cappadona, Jailan Elayoubi

Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. However, current tools including genomic assays lack the accuracy required for optimal clinical decision-making. We developed a novel artificial intelligence (AI)-based approach that integrates digital pathology images with clinical data, providing a more robus

Cheryl S. C. Lau, Ian A. Bonnell

Galactic-scale simulations rely on sub-grid models to provide prescriptions for the coupling between supernova (SN) feedback and the interstellar medium (ISM). Many of these models are computed in 1-D to allow for an efficient way to account for the variability of properties of their local environment. However, small-scale simulations revealed that the relea

Lukas Edman, Lisa Bylinina, Faeze Ghorbanpour, Alexander Fraser

This paper describes a linguistically-motivated approach to the 2024 edition of the BabyLM Challenge (Warstadt et al. 2023). Rather than pursuing a first language learning (L1) paradigm, we approach the challenge from a second language (L2) learning perspective. In L2 learning, there is a stronger focus on learning explicit linguistic information, such as gr

Francesco D'Eramo, Alessandro Lenoci

We investigate the cosmological consequences of axion interactions with standard model fermions accurately and precisely. Our analysis is entirely based on a phase space framework that allows us to keep track of the axion distribution in momentum space across the entire expansion history. First, we consider flavor-diagonal couplings to charged leptons and qu

Ivan Srba, Olesya Razuvayevskaya, João A. Leite, Robert Moro

In the age of social media and generative AI, the ability to automatically assess the credibility of online content has become increasingly critical, complementing traditional approaches to false information detection. Credibility assessment relies on aggregating diverse credibility signals - small units of information, such as content subjectivity, bias, or

Jiajie Zhang, Zhongni Hou, Xin Lv, Shulin Cao

Though significant advancements have been achieved in developing long-context large language models (LLMs), the compromised quality of LLM-synthesized data for supervised fine-tuning (SFT) often affects the long-context performance of SFT models and leads to inherent limitations. In principle, reinforcement learning (RL) with appropriate reward signals can f

Evelia Rosa García Barroso, Patrick Popescu-Pampu

Let $(f,g): (S,s) \to (\mathbb{C}^2, 0)$ be a finite morphism from a germ of normal complex analytic surface to the germ of $\mathbb{C}^2$ at the origin. We show that the affine algebraic curve in $\mathbb{C}^2$ defined by the initial Newton polynomial of a defining series of the discriminant germ of $(f,g)$ depends up to toric automorphisms only on the germ

Manav Vora, Ilan Shomorony, Melkior Ornik

We study capacity- and budget-constrained multi-agent MDPs (CB-MA-MDPs), a class that captures many maintenance and scheduling tasks in which each agent can irreversibly fail and a planner must decide (i) when to apply a restorative action and (ii) which subset of agents to treat in parallel. The global budget limits the total number of restorations, while t

Ji Ma

As Large Language Model (LLM)-based agents increasingly engage with human society, how well do we understand their prosocial behaviors? We (1) investigate how LLM agents' prosocial behaviors can be induced by different personas and benchmarked against human behaviors; and (2) introduce a social science approach to evaluate LLM agents' decision-making. We exp

Aliakbar Daemi, Mike Miller Eismeier, Tye Lidman

The cosmetic surgery conjecture predicts that for a non-trivial knot in the three-sphere, performing two different Dehn surgeries results in distinct oriented three-manifolds. Hanselman reduced the problem to $\pm 2$ or $\pm 1/n$ surgeries being the only possible cosmetic surgeries. We remove the case of $\pm 1/n$-surgeries using the Chern-Simons filtration

Alina Sabyr, J. Colin Hill, Zoltan Haiman

The thermal Sunyaev-Zel'dovich effect (tSZ) is a sensitive probe of cosmology, as it traces the abundance of galaxy clusters and groups in the late-time Universe. Upcoming cosmic microwave background experiments such as the Simons Observatory (SO) and CMB-S4 will provide low-noise and high-resolution component-separated tSZ maps covering a large sky fraction

Sahan Liyanaarachchi, Sennur Ulukus, Nail Akar

Use of multi-path network topologies has become a prominent technique to assert timeliness in terms of age of information (AoI) and to improve resilience to link disruptions in communication systems. However, establishing multiple dedicated communication links among network nodes is a costly endeavor. Therefore, quite often, these secondary communication lin

Cengiz Aydin, Alexander Batkhin

In the framework of the spatial circular Hill three-body problem we illustrate the application of symplectic invariants to analyze the network structure of symmetric periodic orbit families. The extensive collection of families within this problem constitutes a complex network, fundamentally comprising the so-called basic families of periodic solutions, incl

Carles Casacuberta, Javier J. Gutiérrez

Homotopical localizations with respect to (possibly proper) classes of maps are known to exist assuming the validity of a large-cardinal axiom from set theory called Vop\v{e}nka's principle. In this article, we prove that each of the following statements is equivalent to an axiom of lower consistency strength than Vop\v{e}nka's principle, known as weak Vop\v

Sukannya Bhattacharya, Giulia Borghetto, Ameek Malhotra, Susha Parameswaran

We use a suite of the most recent cosmological observations to test models of dynamical dark energy motivated by quantum gravity. Specifically, we focus on hilltop quintessence scenarios, able to satisfy theoretical constraints from quantum gravity. We discuss their realisation based on axions, their supersymmetric partners, and Higgs-like string constructio

Nour Jedidi, Yung-Sung Chuang, Leslie Shing, James Glass

Building effective dense retrieval systems remains difficult when relevance supervision is not available. Recent work has looked to overcome this challenge by using a Large Language Model (LLM) to generate hypothetical documents that can be used to find the closest real document. However, this approach relies solely on the LLM to have domain-specific knowled

I. Andrade, M. A. Marques, R. Menezes

In this work we investigate the presence of scalar field models supporting kink solutions with logarithmic tails, which we call super long-range structures. We first consider models with a single real scalar field and associate the long-range profile to the orders of vanishing derivatives of the potential at its minima. We then present a model whose derivati

Xiang Wei, Ziqing Zhu, Linghua Zhu, Ze Hu

Unit commitment (UC) optimizes the start-up and shutdown schedules of generating units to meet load demand while minimizing costs. However, the increasing integration of renewable energy introduces uncertainties for real-time scheduling. Existing solutions face limitations both in modeling and algorithmic design. At the modeling level, they fail to incorpora

Santiago T. Adams, S. R. Kingan

A non-planar graph is almost-planar if either deleting or contracting any edge makes it planar. A graph with $n$ vertices is pancyclic if it contains a cycle of every length from $3$ to $n$, and it is Hamiltonian if it contains a cycle of length $n$. A Hamiltonian path is a path of length $n$ and a graph with a Hamiltonian path between every pair of vertices

Shuxu Huffman, Si Chen, Kelly Avery Mack, Haotian Su

Generative AI tools, particularly those utilizing large language models (LLMs), are increasingly used in everyday contexts. While these tools enhance productivity and accessibility, little is known about how Deaf and Hard of Hearing (DHH) individuals engage with them or the challenges they face when using them. This paper presents a mixed-method study explor

Xuan Yao

We prove a generalization of Gromov's conjecture on scalar curvature rigidity of convex polytopes to arbitrary convex Riemannian polytope type domains via harmonic spinors on convex domians with boundary condition constructed by Brendle. In particular, we prove a rigidity results on comparison of scalar curvature and scaled mean curvature on the boundary for

Zhantao Yang, Han Zhang, Fangyi Chen, Anudeepsekhar Bolimera

Knowledge Graph (KG) is playing an increasingly important role in various AI systems. For e-commerce, an efficient and low-cost automated knowledge graph construction method is the foundation of enabling various successful downstream applications. In this paper, we propose a novel method for constructing structured product knowledge graphs from raw product i

Weizhe Chen, Zhicheng Zhang, Guanlin Liu, Renjie Zheng

Since the release of ChatGPT, large language models (LLMs) have demonstrated remarkable capabilities across various domains. A key challenge in developing these general capabilities is efficiently sourcing diverse, high-quality data. This becomes especially critical in reasoning-related tasks with sandbox checkers, such as math or code, where the goal is to

Patrick Fiorenza, Marco Zignale, Marco Camalleri, Laura Scalia

In this work, the effects of the duration of the post deposition annealing (PDA) in nitric oxide (NO) on the properties of SiO2/4H-SiC interfaces in n-channel lateral MOSFETs are investigated, with a special focus on the modifications of the energy profile of near-interface-oxide traps (NIOTs). For this purpose, the electrical characteristics of lateral MOSF

Shiqing Wei, Prashanth Krishnamurthy, Farshad Khorrami

Extracting dynamic models from data is of enormous importance in understanding the properties of unknown systems. In this work, we employ Lipschitz neural networks, a class of neural networks with a prescribed upper bound on their Lipschitz constant, to address the problem of data-efficient nonlinear system identification. Under the (fairly weak) assumption

Minkai Xu, Tomas Geffner, Karsten Kreis, Weili Nie

Despite remarkable progress in autoregressive language models, alternative generative paradigms beyond left-to-right generation are still being actively explored. Discrete diffusion models, with the capacity for parallel generation, have recently emerged as a promising alternative. Unfortunately, these models still underperform the autoregressive counterpart

Christian J. Steinmetz, Shubhr Singh, Marco Comunità, Ilias Ibnyahya

Audio production style transfer is the task of processing an input to impart stylistic elements from a reference recording. Existing approaches often train a neural network to estimate control parameters for a set of audio effects. However, these approaches are limited in that they can only control a fixed set of effects, where the effects must be differenti

Umme Faria Moon, MD Ahsan Habib Rasel, Md. Musfique Anwar

The use of social media platforms has been gradually increasing and fake news spreading is becoming an alarming issue nowadays. The spreading of fake news means disseminating false, confusing, and spurious information which hurts families, communities etc. As a result, this issue has to be resolved sooner so that we can limit the spread of fake news in the v

Yen-Lin Chen

We show that there are well-defined maps on sutured ECH induced by contact 2-handle attachments and that the sutured ECH contact class is functorial under such maps.

Florian Vincent, Waïss Azizian, Franck Iutzeler, Jérôme Malick

We present skwdro, a Python library for training robust machine learning models. The library is based on distributionally robust optimization using Wasserstein distances, popular in optimal transport and machine learnings. The goal of the library is to make the training of robust models easier for a wide audience by proposing a wrapper for PyTorch modules, e