December 2024 arXiv papers — page 148

Sergio H. Ramirez, Boris T. Gaensicke, Detlev Koester, Marina Lafarga

WD\,0141--675 was reported as the first astrometrically detected white dwarf planet host candidate as part of the third data release from \textit{Gaia}, just to be later retracted via a news item on the \textit{Gaia} web site$^1$. We present time-resolved, high-resolution optical ESPRESSO spectroscopy of \obj. A radial velocity analysis of the \Ion{Ca}~K abs

J. Michael Shull, Rachel M. Curran, Michael W. Topping

The B2 star $\epsilon$ CMa, at parallax distance $d = 124\pm2$~pc, dominates the H I photoionization of the local interstellar cloud (LIC). At its closer parallax distance compared to previous estimates, $\epsilon$ CMa has a 0.9 mag fainter absolute magnitude $M_V =-3.97\pm0.04$. We combine measurements of distance with the integrated flux $f = (41.5\pm3.3)

Elena I. Titova, Mikhail A. Kashchenko, Andrey V. Miakonkikh, Alexander D. Morozov

Electrically induced $p-n$ junctions in graphene bilayer (GBL) have shown superior performance for detection of sub-THz radiation at cryogenic temperatures, especially upon electrical induction of the band gap $E_g$. Still, the upper limits of responsivity and noise equivalent power (NEP) at very large $E_g$ remained unknown. Here, we study the cryogenic per

Ahmet Fatih Tabak, Islam S. M. Khalil

One of the motivations for the development of wirelessly guided untethered magnetic devices (UMDs), such as microrobots and nanorobots, is the continuous demand to manipulate, sort, and assemble micro-objects with high level of accuracy and dexterity. UMDs can function as microgrippers or manipulators and move micro-objects with or without direct contact. In

Kushagra Aggarwal, Alberto Rolandi, Yikai Yang, Joseph Hickie

The conversion of thermal energy into work is usually more efficient in the slow-driving regime, where the power output is vanishingly small. Efficient work extraction for fast driving protocols remains an outstanding challenge at the nanoscale, where fluctuations play a significant role. In this Letter, we use a quantum-dot Szilard engine to extract work fr

Leo Radzihovsky, Emil Pellett

Motivated to elucidate the nature of quantum phases and their criticality when entangled with a correlated quantum bath, we study interacting bosons on a "comb lattice" -- a one-dimensional backbone (system) coupled at its sites to otherwise independent one-dimensional "teeth" chains (bath). We map out the corresponding phase diagram, detailing the nature of

Raphael Baer-Way, Poonam Chandra, Maryam Modjaz, Sahana Kumar

While the subclass of interacting supernovae with narrow hydrogen emission lines (SNe IIn) consists of some of the longest-lasting and brightest SNe ever discovered, their progenitors are still not well understood. Investigating SNe IIn as they emit across the electromagnetic spectrum is the most robust way to understand the progenitor evolution before the e

Jannik Mielke, Shrobana Ghosh, Angela Borchers, Frank Ohme

Precession in black-hole binaries is caused by a misalignment between the total spin and the orbital angular momentum. The gravitational-wave emission of such systems is anisotropic, which leads to an asymmetry in the $\pm m$ multipoles when decomposed into a spherical harmonic basis. This asymmetric emission can impart a kick to the merger remnant black hol

Christoph Chiaffrino, Noah Hassan, Olaf Hohm

We present, for the harmonic oscillator and the spin-$\frac{1}{2}$ system, an alternative formulation of quantum mechanics that is `off-shell': it is based on classical off-shell configurations and thus similar to the path integral. The core elements are Batalin-Vilkovisky (BV) algebras and factorization algebras, following a program by Costello and Gwilliam

Hong Tang, Alan Champneys, David Simpson

A boundary equilibrium bifurcation (BEB) in a hybrid dynamical system occurs when a regular equilibrium collides with a switching surface in phase space. This causes a transition to a pseudo-equilibrium embedded within the switching surface, but limit cycles (LCs) and other invariant sets can also be created and the nature of these is not well understood for

Zhongzhou Chen, Tong Wan

This study examines the feasibility and potential advantages of using large language models, in particular GPT-4o, to perform partial credit grading of large numbers of student written responses to introductory level physics problems. Students were instructed to write down verbal explanations of their reasoning process when solving one conceptual and two num

Adam Glos, Özlem Salehi

Efficient and effective compilation of quantum circuits remains an important aspect of executing quantum programs. In this paper, we propose a generic compilation framework particularly suitable for limited connectivity, that extends many of the known techniques for Pauli network synthesis. Our compilation method, built on the introduced Clifford Executive R

Oscar A. Testa, Efrain R. Fonseca C., Germán Montejano, Oscar Dieste

Context: Ubiquitous devices and pervasive environments are in permanent interaction in people's daily lives. In today's hyper-connected environments, it is necessary for these devices to interact with each other, transparently to the users. The problem is analyzed from the different perspectives that compose it: SOA, service composition, interaction, and the

Balt C. van Rees

Consider a conformally covariant four-point function of identical scalar operators with a discrete spectrum, a twist gap, and compatible with the unitarity conditions. We give a mathematical proof confirming that the spectrum and OPE coefficients at large spin and fixed twist always become that of a generalized free field theory.

Sizheng Ma, Mark A. Scheel, Jordan Moxon, Kyle C. Nelli

Cauchy-characteristic matching (CCM) is a numerical-relativity technique that solves Einstein's equations on an effectively infinite computational domain, thereby eliminating systematic errors associated with artificial boundary conditions. Whether CCM can robustly handle fully nonlinear, dynamical spacetimes, such as binary black hole (BBH) mergers, has rem

Michael D Albrow

We present algorithms and software for constructing high-precision difference images to detect and measure transients, such as microlensing events, in crowded stellar fields using the Nancy Grace Roman Space Telescope. Our method generates difference images by subtracting an over-sampled reference, with iterative masking to address outlier pixels. We also pr

Jake Chuharski, Elias Rojas Collins, Mark Meringolo

We present a novel approach to generating mathematical conjectures using Large Language Models (LLMs). Focusing on the solubilizer, a relatively recent construct in group theory, we demonstrate how LLMs such as ChatGPT, Gemini, and Claude can be leveraged to generate conjectures. These conjectures are pruned by allowing the LLMs to generate counterexamples.

Elena Tomasetti, Michele Moresco, Carmela Lardo, Frédéric Courbin

Determining reliable ages for old stellar objects at different redshifts offers a powerful means to constrain cosmology without relying on a specific cosmological model: this is known as the cosmic clocks method. Globular clusters (GCs), long recognised as hosts of the Universe's oldest stars, have served as the archetypical cosmic clocks. However, their age

Chi-Ming Chang, Yiming Chen, Bik Soon Sia, Zhenbin Yang

We study the fortuity phenomenon in supersymmetric Sachdev-Ye-Kitaev (SYK) models. For generic choices of couplings, all the BPS states in the $\mathcal{N}=2$ SUSY SYK model are fortuitous. The SYK models reveal an intimate connection between fortuity and the Schwarzian description of supersymmetric black holes, reflected in a sharp feature of $R$-charge con

Emmanuel Chávez Nambo, Alberto Diez-Tejedor, Edgar Preciado-Govea, Armando A. Roque

In this paper we follow an effective theory approach to study the nonrelativistic limit of a selfgravitating and selfinteracting massive vector field. Our effective theory is characterized by three parameters: the field's mass $m_0$ and the selfinteraction constants $\lambda_n$ and $\lambda_s$. For definiteness, we focus on a systematic study of the equilibr

Jeongrak Son, Ray Ganardi, Shintaro Minagawa, Francesco Buscemi

Catalysis refers to the possibility of enabling otherwise inaccessible quantum state transitions by supplying an auxiliary system, provided that the auxiliary is returned to its initial state at the end of the protocol. We show that previous studies on catalysis are largely impractical, because even small errors in the system's initial state can irreversibly

Andrea Solfanelli, Francesco Petocchi, Nicolò Defenu

The Sachdev-Ye-Kitaev (SYK) model, has emerged as a powerful tool for exploring the quantum nature of black holes, particularly their residual entropy at zero temperature. In this work, we investigate the role of long-range interactions in a chain of SYK dots with power-law decaying couplings and demonstrate how these interactions can lead to finite residual

Andrei Antipov, Wladimir E. Banda-Barragán, Yuval Birnboim, Christoph Federrath

We investigate the interaction between a shock-driven hot wind and a cold multi-cloud layer, for conditions commonly found in interstellar and circumgalactic gas. We present a method for identifying distinct clouds using a Friends-of-Friends algorithm. This approach unveils novel detailed information about individual clouds and their collective behaviour. By

Robert J. Harris, Jonathan Crass, Marshall C. Johnson, Andrew Bechter

Diffraction-limited radial velocity instruments offer a pathway towards improved precision and stability, and the exploration of new parameter spaces at high spatial and spectral resolution. However, achieving the necessary performance requires careful instrument design and considerable on-sky testing. We describe the design and construction of ``Little iLoc

Sara Vitali, Alvaro Rojas-Arriagada, Paula Jofré, Federico Sestito

The Sagittarius dwarf spheroidal galaxy (Sgr dSph) is a satellite orbiting the Milky Way that has experienced multiple stripping events due to tidal interactions with our Galaxy. Its accretion history led to a distinct stellar overdensity, the remnant of the core of the progenitor. We present a complete chemical analysis of 111 giant stars in the core of Sgr

Davide Tornotti, Michele Fumagalli, Matteo Fossati, Fabrizio Arrigoni Battaia

We detect Ly$\alpha$ emission from a ~5 Mpc-long (comoving) portion of the cosmic web hosting an overdensity ($\delta \approx 25$) of 19 Ly$\alpha$ emitters (LAEs) at $z\approx 4$ within the MUSE Ultra Deep Field (MUDF), reaching an average surface brightness (SB) of $5\times 10^{-20}~\rm{erg~s^{-1}~ cm^{-2}~arcsec^{-2}}$. This large-scale structure has an a

Fengwu Sun, Feige Wang, Jinyi Yang, Jaclyn B. Champagne

We present a stringent measurement of the dust-obscured star-formation rate density (SFRD) at $z=4-6$ from the ASPIRE JWST Cycle-1 medium and ALMA Cycle-9 large program. We obtained JWST/NIRCam grism spectroscopy and ALMA 1.2-mm continuum map along 25 independent quasar sightlines, covering a total survey area of $\sim$35 arcmin$^2$ where we search for dusty

Ygal Y. Klein, Boaz Katz

The very long-term evolution of the hierarchical restricted three-body problem with a slightly aligned precessing quadrupole potential is investigated analytically and solved for both rotating and librating Kozai-Lidov cycles (KLCs) with high eccentricities. We describe the finding of a striking similarity between librating and rotating KLCs for some range o

Roger de Belsunce, Shi-Fan Chen, Mikhail M. Ivanov, Corentin Ravoux

We present precision measurements of the bias parameters of the one-loop power spectrum model of the Lyman-alpha (Lya) forest, derived within the effective field theory of large-scale structure (EFT). We fit our model to the three-dimensional flux power spectrum measured from the ACCEL2 hydrodynamic simulations. The EFT model fits the data with an accuracy o

Sera Cremonini, Brian McPeak, Mohammad Moezzi, Muthusamy Rajaguru

Effective field theories are constrained by the requirement that their constituents never move superluminally on non-trivial backgrounds. In this paper, we study time delays experienced by photons propagating on charged shockwave backgrounds in five dimensions. In the absence of gravity -- where the shockwaves are electric fields sourced by boosted charges -

David Jonas Moser, Lukas Janssen

A central concept in the theory of phase transitions beyond the Landau-Ginzburg-Wilson paradigm is fractionalization: the formation of new quasiparticles that interact via emergent gauge fields. This concept has been extensively explored in the context of continuous quantum phase transitions between distinct orders that break different symmetries. We propose

Indra Kumar Banerjee, Francesco Rescigno, Alberto Salvio

We study in detail the production of primordial black holes (PBHs), as well as their mass and initial spin, due to the phase transitions corresponding to radiative symmetry breaking (RSB) and featuring a large supercooling. The latter property allows us to use a model-independent approach. In this context, we demonstrate that the decay rate of the false vacu

Simone T. Gordon, Britton D. Smith, Sadegh Khochfar, Ricarda S. Beckmann

Observations of supermassive black holes at high redshift challenge our understanding of the evolution of the first generation of black holes (BHs) in proto-galactic environments. One possibility is that they grow much more rapidly than current estimates of feedback and accretion efficiency permit. Following our previous analysis of super-Eddington accretion

Marina De Amicis, Hannes Rüter, Gregorio Carullo, Simone Albanesi

We uncover late-time gravitational-wave tails in fully nonlinear 3+1 dimensional numerical relativity simulations of merging black holes, using the highly accurate SpEC code. We achieve this result by exploiting the strong magnification of late-time tails due to binary eccentricity, recently observed in perturbative evolutions, and showcase here the tail pre

Mahlet Shiferaw, Nickolas Kokron, Risa H. Wechsler

Our ability to extract cosmological information from galaxy surveys is limited by uncertainties in the galaxy-dark matter halo relationship for a given galaxy population, which are governed by the intricacies of galaxy formation. To quantify these uncertainties, we examine quenched and star-forming galaxies using two distinct approaches to modeling galaxy fo

Y. Homayouni, Yuanzhe Jiang, W. N. Brandt, C. J. Grier

Multi-year observations from the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) project have significantly increased the number of quasars with reliable reverberation-mapping lag measurements. We statistically analyze target properties, light-curve characteristics, and survey design choices to identify factors crucial for successful and efficient R

Vijay Balasubramanian, Ben Craps, Juan Hernandez, Mikhail Khramtsov

We construct and count the microstates of out-of-equilibrium eternal AdS black holes in which a shell carrying an order one fraction of the black hole mass is emitted from the past horizon and re-absorbed at the future horizon. Our microstates have semiclassical interpretations in terms of matter propagating behind the horizon. We show that they span a Hilbe

Gonzalo Alonso-Álvarez, David Curtin

We present a novel way of probing non-gravitational dark matter interactions: dark astronomy, which leverages the dark radiation emitted by dissipative dark sectors. If the mediator of the dark matter self interactions is a dark photon with a small mass that kinetically mixes with the visible photon, the dark radiation flux becomes accessible to underground

R. Canbay, T. Ak, S. Bilir, F. Soydugan

Using high-precision astrometric data from Gaia DR3 and updated systemic velocities from the literature, kinematical properties of cataclysmic variables (CVs) were investigated. By constraining the data according to the total space velocity error and Galactic population class, a reliable sample of data was obtained. Non-magnetic CVs located in the thin disk

Alberto M. Clavero, Robin Brüser, Vicent Mateu, Maximilian Stahlhofen

We compute the inclusive jet function for boosted heavy quarks to $\mathcal{O}(\alpha_s^3)$. The jet function is defined and calculated in the framework of boosted Heavy-Quark Effective Theory (bHQET). It describes the effect of radiation collimated in narrow jets arising from energetic heavy quarks on observables probing the jet invariant mass $M$ in the re

Basil M. Smitham, Andrew A. Houck

In this work, we introduce new methods for the quantization, decomposition, and extraction (from electromagnetic simulations) of lumped-element circuit models for superconducting quantum devices. Our flux-charge symmetric procedures center on the network matrix, which encodes the connectivity of a circuit's inductive loops and capacitive nodes. First, we use

Tomáš Šoltinský, Girish Kulkarni, Shriharsh P. Tendulkar, James S. Bolton

The 21-cm forest signal is a promising probe of the Epoch of Reionization complementary to other 21-cm line observables and Ly$\alpha$ forest signal. Prospects of detecting it have significantly improved in the last decade thanks to the discovery of more than 30 radio-loud quasars at these redshifts, upgrades to telescope facilities, and the notion that neut

Vincent Tao Hu, Björn Ommer

In generative models, two paradigms have gained attraction in various applications: next-set prediction-based Masked Generative Models and next-noise prediction-based Non-Autoregressive Models, e.g., Diffusion Models. In this work, we propose using discrete-state models to connect them and explore their scalability in the vision domain. First, we conduct a s

M. Hamza Mughal, Rishabh Dabral, Merel C. J. Scholman, Vera Demberg

Non-verbal communication often comprises of semantically rich gestures that help convey the meaning of an utterance. Producing such semantic co-speech gestures has been a major challenge for the existing neural systems that can generate rhythmic beat gestures, but struggle to produce semantically meaningful gestures. Therefore, we present RAG-Gesture, a diff

Ruihan Gao, Kangle Deng, Gengshan Yang, Wenzhen Yuan

3D generation methods have shown visually compelling results powered by diffusion image priors. However, they often fail to produce realistic geometric details, resulting in overly smooth surfaces or geometric details inaccurately baked in albedo maps. To address this, we introduce a new method that incorporates touch as an additional modality to improve the

Mara Levy, Siddhant Haldar, Lerrel Pinto, Abhinav Shirivastava

Developing generalizable robot policies that can robustly handle varied environmental conditions and object instances remains a fundamental challenge in robot learning. While considerable efforts have focused on collecting large robot datasets and developing policy architectures to learn from such data, naively learning from visual inputs often results in br

Thales Silva, Pablo J. Bilbao, Luis O. Silva

We show that circularly polarized lasers create plasmas with long-lasting ring-shaped weakly relativistic momentum distributions which, in the presence of an ambient magnetic field, are prone to the electron cyclotron maser instability. Theoretical results and particle-in-cell simulations show that current laser technology can effectively induce field ionize

Zhefei Gong, Pengxiang Ding, Shangke Lyu, Siteng Huang

In robotic visuomotor policy learning, diffusion-based models have achieved significant success in improving the accuracy of action trajectory generation compared to traditional autoregressive models. However, they suffer from inefficiency due to multiple denoising steps and limited flexibility from complex constraints. In this paper, we introduce Coarse-to-

Zhaorun Chen, Francesco Pinto, Minzhou Pan, Bo Li

With the rise of generative AI and rapid growth of high-quality video generation, video guardrails have become more crucial than ever to ensure safety and security across platforms. Current video guardrails, however, are either overly simplistic, relying on pure classification models trained on simple policies with limited unsafe categories, which lack detai

Nicolas Dufour, David Picard, Vicky Kalogeiton, Loic Landrieu

Global visual geolocation predicts where an image was captured on Earth. Since images vary in how precisely they can be localized, this task inherently involves a significant degree of ambiguity. However, existing approaches are deterministic and overlook this aspect. In this paper, we aim to close the gap between traditional geolocalization and modern gener

Yanbo Xu, Jayanth Srinivasa, Gaowen Liu, Shubham Tulsiani

Score distillation of 2D diffusion models has proven to be a powerful mechanism to guide 3D optimization, for example enabling text-based 3D generation or single-view reconstruction. A common limitation of existing score distillation formulations, however, is that the outputs of the (mode-seeking) optimization are limited in diversity despite the underlying

Guanxing Lu, Tengbo Yu, Haoyuan Deng, Season Si Chen

Performing general language-conditioned bimanual manipulation tasks is of great importance for many applications ranging from household service to industrial assembly. However, collecting bimanual manipulation data is expensive due to the high-dimensional action space, which poses challenges for conventional methods to handle general bimanual manipulation ta

Rayff de Souza, Jamerson G. Rodrigues, Clarissa Siqueira, Felipe B. M. dos Santos

A compelling way to address the inflationary period is via the warm inflation scenario, where the interaction of the inflaton field with other degrees of freedom affects its dynamics in such a way that slow-roll inflation is maintained by dissipative effects in a thermal bath. In this context, if a dark matter particle is coupled to the bath due to non-renor

Xin Fei, Wenzhao Zheng, Yueqi Duan, Wei Zhan

Realtime 4D reconstruction for dynamic scenes remains a crucial challenge for autonomous driving perception. Most existing methods rely on depth estimation through self-supervision or multi-modality sensor fusion. In this paper, we propose Driv3R, a DUSt3R-based framework that directly regresses per-frame point maps from multi-view image sequences. To achiev

G. Fadini, S. Coros

We present a novel approach to quantifying and optimizing stability in robotic systems based on the Lyapunov exponents addressing an open challenge in the field of robot analysis, design, and optimization. Our method leverages differentiable simulation over extended time horizons. The proposed metric offers several properties, including a natural extension t

Yi-Lun Lee, Yi-Hsuan Tsai, Wei-Chen Chiu

While large vision-language models (LVLMs) have shown impressive capabilities in generating plausible responses correlated with input visual contents, they still suffer from hallucinations, where the generated text inaccurately reflects visual contents. To address this, recent approaches apply contrastive decoding to calibrate the model's response via contra

XuDong Wang, Xingyi Zhou, Alireza Fathi, Trevor Darrell

We present Visual Lexicon, a novel visual language that encodes rich image information into the text space of vocabulary tokens while retaining intricate visual details that are often challenging to convey in natural language. Unlike traditional methods that prioritize either high-level semantics (e.g., CLIP) or pixel-level reconstruction (e.g., VAE), ViLex

Thorsten Prüstel, Martin Meier-Schellersheim

T cell receptor signaling must operate reliably under tight time constraints. While assuming quite different mechanisms, two prominent models of T cell receptor activation, kinetic segregation and kinetic proofreading, both introduce a distinct time scale. However, a clear understanding of whether and how those characteristic times give rise to a consistent

Alexander Drewitz, Alexis Prévost, Pierre-François Rodriguez

We study the volume of the critical clusters for the percolation of the level sets of the Gaussian free field on metric graphs. On $\mathbb{Z}^d$ below the upper-critical dimension $d=6$, we show that the largest such cluster in a box of side length $r$ has volume of order $r^{\frac{d+2}{2}}$, as conjectured by Werner in arXiv:2002.11487. This is in contrast

Meera Hahn, Wenjun Zeng, Nithish Kannen, Rich Galt

User prompts for generative AI models are often underspecified, leading to a misalignment between the user intent and models' understanding. As a result, users commonly have to painstakingly refine their prompts. We study this alignment problem in text-to-image (T2I) generation and propose a prototype for proactive T2I agents equipped with an interface to (1

Viktor Rudnev, Gereon Fox, Mohamed Elgharib, Christian Theobalt

Volumetric reconstruction of dynamic scenes is an important problem in computer vision. It is especially challenging in poor lighting and with fast motion. This is partly due to limitations of RGB cameras: To capture frames under low lighting, the exposure time needs to be increased, which leads to more motion blur. In contrast, event cameras, which record c

Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li

Large language models (LLMs) are typically constrained to reason in the language space, where they express the reasoning process through a chain-of-thought (CoT) to solve complex problems. However, the language space may not always be optimal for reasoning. Most word tokens primarily ensure textual coherence and are not essential for reasoning, while some cr

Jacob R. Taylor, Sankar Das Sarma

1D superconductor-semiconductor nanowires are the leading candidates for topological quantum computation due to their ability to host non-Abelian Majorana zero modes (MZMs). However, the standard methods for identifying MZMs are often inadequate, particularly in the presence of disorder, where many properties considered to be heralds of MZMs are often genera

Antoine Guédon, Tomoki Ichikawa, Kohei Yamashita, Ko Nishino

We present a novel appearance model that simultaneously realizes explicit high-quality 3D surface mesh recovery and photorealistic novel view synthesis from sparse view samples. Our key idea is to model the underlying scene geometry Mesh as an Atlas of Charts which we render with 2D Gaussian surfels (MAtCha Gaussians). MAtCha distills high-frequency scene su

Frederik Benirschke, Benjamin Dozier, John Rached

We consider subvarieties $N$ of $\mathcal{M}_{g,n}$, the moduli space of genus $g$ Riemann surfaces with $n$ marked points, that are totally geodesic with respect to the Teichm\"uller metric. The Deligne-Mumford boundary of $\mathcal{M}_{g,n}$ decomposes into strata, each of which is essentially a product of lower complexity moduli spaces -- in such spaces t

A. J. Iovino, S. Matarrese, G. Perna, A. Ricciardone

Gravitational waves sourced by amplified scalar perturbations are a common prediction across a wide range of cosmological models. These scalar curvature fluctuations are inherently nonlinear and typically non-Gaussian. We argue that the effects of non-Gaussianity may not always be adequately captured by an expansion around a Gaussian field, expressed through

Sophia R. Flury, Karla Z. Arellano-Córdova, Edward C. Moran, Alaina Einsig

The new era of galaxy evolution studies hearkened in by JWST has led to the discovery of z > 5 galaxies exhibiting excess nitrogen with log(N/O)~1 dex or more than expected from log(N/O) vs 12+log(O/H) trends in the local Universe. A variety of novel enrichment pathways have been presented to explain the apparent nitrogen excess, invoking a wide range of pro

Kristian Blom, Dmitrii E. Makarov, Aljaž Godec

We investigate the transient and steady-state dynamics of the Bennati-Dragulescu-Yakovenko money game in the presence of probabilistic cheaters, who can misrepresent their financial status by claiming to have no money. We derive the steady-state wealth distribution per player analytically, and show how the presence of hidden cheaters can be inferred from the

Wei-Hsiang Yu, Yen-Yu Lin, Ming-Hsuan Yang, Yi-Hsuan Tsai

Recent advances in vision-language models (VLMs) have made significant progress in downstream tasks that require quantitative concepts such as facial age estimation and image quality assessment, enabling VLMs to explore applications like image ranking and retrieval. However, existing studies typically focus on the reasoning based on a single image and heavil

Shuqing Li, Chenran Zhang, Cuiyun Gao, Michael R. Lyu

The rapid advancement of Extended Reality (XR, encompassing AR, MR, and VR) and spatial computing technologies forms a foundational layer for the emerging Metaverse, enabling innovative applications across healthcare, education, manufacturing, and entertainment. However, research in this area is often limited by the lack of large, representative, and highqua

Daniel Beaulieu, Milan Kornjaca, Zoran Krunic, Michael Stivaktakis

Machine learning has been increasingly utilized in the field of biomedical research to accelerate the drug discovery process. In recent years, the emergence of quantum computing has been followed by extensive exploration of quantum machine learning algorithms. Quantum variational machine learning algorithms are currently the most prevalent but face issues wi

Joy Krishan Das, Saikat Mondal, Chanchal K. Roy

Large language models (LLMs) like ChatGPT have shown the potential to assist developers with coding and debugging tasks. However, their role in collaborative issue resolution is underexplored. In this study, we analyzed 1,152 Developer-ChatGPT conversations across 1,012 issues in GitHub to examine the diverse usage of ChatGPT and reliance on its generated co

L. S. Brito, J. F. Jesus, A. A. Escobal, S. H. Pereira

In the present work we analyze two different models of interaction between dark energy and dark matter, also known as vacuum decay models or $\Lambda(t)$CDM models. In both models, when the $H_0$ parameter is constrained by the Planck distance priors, its value is compatible with a higher value of $H_0$, in agreement with SH0ES data, while simultaneously red

Randy Bartels, Olivier Pinaud, Maxine Varughese

This work is concerned with optical imaging in strongly diffusive environments. We consider a typical setting in optical coherence tomography where a sample is probed by a collection of wavefields produced by a laser and propagating through a microscope. We operate in a scenario where the illuminations are in a speckle regime, namely fully randomized. This o

Shawn Ong, Stephanie Ma, Dexter Kozen

We introduce a version of probabilistic Kleene algebra with angelic nondeterminism and a corresponding class of automata. Our approach implements semantics via distributions over multisets in order to overcome theoretical barriers arising from the lack of a distributive law between the powerset and Giry monads. We produce a full Kleene theorem and a coalgebr

Thomas Pouplin, Katarzyna Kobalczyk, Hao Sun, Mihaela van der Schaar

Developing autonomous agents capable of performing complex, multi-step decision-making tasks specified in natural language remains a significant challenge, particularly in realistic settings where labeled data is scarce and real-time experimentation is impractical. Existing reinforcement learning (RL) approaches often struggle to generalize to unseen goals a

Howard Zhang, Yuval Alaluf, Sizhuo Ma, Achuta Kadambi

Face image restoration aims to enhance degraded facial images while addressing challenges such as diverse degradation types, real-time processing demands, and, most crucially, the preservation of identity-specific features. Existing methods often struggle with slow processing times and suboptimal restoration, especially under severe degradation, failing to a

Changhua Bao, Fei Wang, Haoyuan Zhong, Shaohua Zhou

Symmetry breaking plays an important role in fields of physics, ranging from particle physics to condensed matter physics. In solid-state materials, phase transitions are deeply linked to the underlying symmetry breakings, resulting in a rich variety of emergent phases. Such symmetry breakings are often induced by controlling the chemical composition and tem

Changhua Bao, Michael Schüler, Teng Xiao, Fei Wang

Strong light-matter interaction provides opportunities for tailoring the physical properties of quantum materials on the ultrafast timescale by forming photon-dressed electronic states, i.e., Floquet-Bloch states. While the light field can in principle imprint its symmetry properties onto the photon-dressed electronic states, so far, how to experimentally de

Isabella P. Carucci, José L. Bernal, Steven Cunnington, Mario G. Santos

Removing contaminants is a delicate, yet crucial step in neutral hydrogen (HI) intensity mapping and often considered the technique's greatest challenge. Here, we address this challenge by analysing HI intensity maps of about $100$ deg$^2$ at redshift $z\approx0.4$ collected by the MeerKAT radio telescope, an SKA Observatory (SKAO) precursor, with a combined

Ronan Herry, Dominique Malicet, Guillaume Poly

We characterize the limiting distributions of random variables of the form $P_n\left( (X_i)_{i \ge 1} \right)$, where: (i) $(P_n)_{n \ge 1}$ is a sequence of multivariate polynomials, each potentially involving countably many variables; (ii) there exists a constant $D \ge 1$ such that for all $n \ge 1$, the degree of $P_n$ is bounded above by $D$; (iii) $(X_

Neel Jain, Aditya Shrivastava, Chenyang Zhu, Daben Liu

A key component of building safe and reliable language models is enabling the models to appropriately refuse to follow certain instructions or answer certain questions. We may want models to output refusal messages for various categories of user queries, for example, ill-posed questions, instructions for committing illegal acts, or queries which require info

Beka Modrekiladze, Ira Z. Rothstein, Jordan Wilson-Gerow

We present a world-line effective field theory of compact objects moving relativistically through a viscous fluid. The theory is valid when velocity gradients are small compared to the inverse size of the object. Working within the EFT eliminates the need to solve a boundary value problem by turning all interactions between the fluid and the object into a so

Reshmi Biswas, Alexander Quaas

This article is concerned with the nonexistence of positive solutions for nonlinear fractional elliptic inequalities in exterior domains of $\mathbb R^n$, $n\geq1$. We would like to highlight the fact that our results are new with very weak assumption on the nonlinear term appearing in the inequalities. The results are also novel in this generality even if t

Adhiraj Ghosh, Sebastian Dziadzio, Ameya Prabhu, Vishaal Udandarao

Traditional fixed test sets fall short in evaluating open-ended capabilities of foundation models. To address this, we propose ONEBench(OpeN-Ended Benchmarking), a new testing paradigm that consolidates individual evaluation datasets into a unified, ever-expanding sample pool. ONEBench allows users to generate custom, open-ended evaluation benchmarks from th

Phattharaporn Singkanipa, Zihan Xia, Daniel A. Lidar

In the absence of fault tolerant quantum error correction for analog, Hamiltonian quantum computation, error suppression via energy penalties is an effective alternative. We construct families of distance-$2$ stabilizer subsystem codes we call ``trapezoid codes'', that are tailored for energy-penalty schemes. We identify a family of codes achieving the maxim

Harish Thangaraj, Diya Katariya, Eshaan Joshi, Sangeetha N

Pediatric brain tumors, particularly gliomas, represent a significant cause of cancer related mortality in children with complex infiltrative growth patterns that complicate treatment. Early, accurate segmentation of these tumors in neuroimaging data is crucial for effective diagnosis and intervention planning. This study presents a novel 3D UNet architectur

Andrei-Robert Alexandrescu, Razvan-Gabriel Petec, Alexandru Manole, Laura-Silvia Diosan

Deep Learning became an ubiquitous paradigm due to its extraordinary effectiveness and applicability in numerous domains. However, the approach suffers from the high demand of data required to achieve the potential of this type of model. An ever-increasing sub-field of Artificial Intelligence, Image Synthesis, aims to address this limitation through the desi

Ahmed Gaber Abdelmagid, Zhuoran Qiao, Boudewijn Coenegracht, Gaon Yu

Narrowband infrared organic photodetectors are in great demand for sensing, imaging, and spectroscopy applications. However, most existing strategies for narrowband detection depend on spectral filtering either through saturable absorption, which requires active layers exceeding 500 nm, restricting the choice of materials for producing high-quality films, or

Simone Azeglio, Olivier Marre, Peter Neri, Ulisse Ferrari

We propose a novel approach to image classification inspired by complex nonlinear biological visual processing, whereby classical convolutional neural networks (CNNs) are equipped with learnable higher-order convolutions. Our model incorporates a Volterra-like expansion of the convolution operator, capturing multiplicative interactions akin to those observed

D. Dey, Jeet Amrit Pattnaik, R. N. Panda, M. Bhuyan

We systematically investigate $f-$mode oscillations ($\ell$ = 2) in quarkyonic neutron stars with dark matter, employing the Cowling approximation within the framework of linearized general relativity. The relativistic mean-field approach is used to compute various macroscopic properties of neutron stars. The analysis focuses on three key free parameters in

Takuro Fujii, Satoru Katsumata

Recently some studies have highlighted the potential of Large Language Models (LLMs) as effective generators of supervised training data, offering advantages such as enhanced inference efficiency and reduced costs associated with data collection. However, these studies have predominantly focused on English language tasks. In this paper, we address the fundam

Mathav Murugan

Given a strongly local Dirichlet form on a metric measure space that satisfies Gaussian heat kernel bounds, we show that the martingale dimension of the associated diffusion process coincides with Cheeger's analytic dimension of the underlying metric measure space. More precisely, we show that the pointwise version of the martingale dimension introduced by H

Christopher M. Irwin, Kenta Hotokezaka

Despite two decades since the discovery of low-luminosity gamma-ray bursts, their origin remains poorly understood. In events such as GRB 060218, shock breakout from a progenitor with an extended ($10^{13}$ - $10^{14}$ cm), low-mass (0.01 - 0.1 M$_\odot$) envelope provides one possible interpretation for the smooth prompt X-ray emission lasting $\sim 1000$ s

Ali Devran Kara, Serdar Yuksel

In this review/tutorial article, we present recent progress on optimal control of partially observed Markov Decision Processes (POMDPs). We first present regularity and continuity conditions for POMDPs and their belief-MDP reductions, where these constitute weak Feller and Wasserstein regularity and controlled filter stability. These are then utilized to arr

Christopher M. Irwin, Kenta Hotokezaka

The spectrum of the first supernova light (i.e., the shock breakout and early cooling emission) is an important diagnostic for the state of the progenitor star just before explosion. We consider a streamlined model describing the emergent shock breakout spectrum, which enables a straightforward classification of the possible observed spectra during the early

Christopher M. Irwin, Kenta Hotokezaka

The first light that escapes from a supernova explosion is the shock breakout emission, which produces a bright flash of UV or X-ray radiation. Standard theory predicts that the shock breakout spectrum will be a blackbody if the gas and radiation are in thermal equilibrium, or a Comptonized free-free spectrum if not. Using recent results for the post-breakou

Kartik Patel, Junbo Zhang, John Kimionis, Lefteris Kampianakis

Backscatter radio is a promising technology for low-cost and low-power Internet-of-Things (IoT) networks. The conventional monostatic backscatter radio is constrained by its limited communication range, which restricts its utility in wide-area applications. An alternative bi-static backscatter radio architecture, characterized by a dis-aggregated illuminator

Benjamin Grimmer, Kevin Shu, Alex L. Wang

The study of convex optimization has historically been concerned with worst-case convergence rates. The development of the Optimized Gradient Method (OGM), due to \citet{drori2012PerformanceOF,Kim2016optimal}, marked a major milestone in this study, as OGM achieves the optimal worst-case convergence rate among all first-order methods for unconstrained smooth

Ariel Goodwin, Adrian S. Lewis, Genaro López-Acedo, Adriana Nicolae

As a foundation for optimization, convexity is useful beyond the classical settings of Euclidean and Hilbert space. The broader arena of nonpositively curved metric spaces, which includes manifolds like hyperbolic space, as well as metric trees and more general CAT(0) cubical complexes, supports primal tools like proximal operations for geodesically convex f