November 2024 arXiv papers — page 27

Marcie Mun, Emily Wisnioski, Katherine E. Harborne, Claudia D. P. Lagos

We investigate the internal and external mechanisms that regulate and quench star formation (SF) in galaxies at $z \sim 0.3$ using MAGPI observations and the EAGLE, Magneticum, and IllustrisTNG cosmological simulations. Using SimSpin to generate mock observations of simulated galaxies, we match detection/resolution limits in star formation rates and stellar

P. Cristofari

Understanding the transition from Galactic to extragalactic cosmic rays (CRs) is essential to make sense of the Local cosmic ray spectrum. Several models have been proposed to account for this transition in the 0.1 - 10 $\times 10^{18}$ eV range. For instance: ankle models, where the change from a steep Galactic component to a hard extragalactic spectrum occ

Tyler J. Smith, Theresa Kline, Adrienne Kline

GeneralizIT is a Python package designed to streamline the application of Generalizability Theory (G-Theory) in research and practice. G-Theory extends classical test theory by estimating multiple sources of error variance, providing a more flexible and detailed approach to reliability assessment. Despite its advantages, G-Theory's complexity can present a s

Chunmei Wang, Shangyou Zhang

This paper presents a weak Galerkin (WG) finite element method for linear elasticity on general polygonal and polyhedral meshes, free from convexity constraints, by leveraging bubble functions as central analytical tools. The proposed method eliminates the need for stabilizers commonly used in traditional WG methods, resulting in a simplified formulation. Th

E. S. Martsen, P. S. Barry, B. A. Benson, K. R. Dibert

SPT-3G+ is the next-generation camera for the South Pole Telescope (SPT). SPT is designed to measure the cosmic microwave background (CMB) and the mm/sub-mm sky. The planned focal plane consists of 34,000 microwave kinetic inductance detectors (MKIDs), divided among three observing bands centered at 220, 285, and 345 GHz. Each readout line is designed to mea

Xin Zhang, Frank Göhmann, Andreas Klümper, Vladislav Popkov

We calculate the values of observables in chiral eigenstates of the XX quantum spin chain that were introduced in previous work and compare the form of the result with the respective expressions obtained in the more familiar eigenbasis of states with fixed magnetization in the $z$ direction. We carry out the diagonalization of the XY spin chain in the chiral

Haniyeh Ehsani Oskouie, Christina Chance, Claire Huang, Margaret Capetz

Content moderation and toxicity classification represent critical tasks with significant social implications. However, studies have shown that major classification models exhibit tendencies to magnify or reduce biases and potentially overlook or disadvantage certain marginalized groups within their classification processes. Researchers suggest that the posit

Lara Pudwell

We consider a 2-player permutation game inspired by the celebrated Erd\H{o}s-Szekeres Theorem. The game depends on two positive integer parameters $a$ and $b$ and we determine the winner and give a winning strategy when $a \geq b$ and $b \in \left\{2,3,4,5\right\}$.

Su Yang, Gino Biondini, Christopher Chong, Panayotis G. Kevrekidis

In this paper we focus on a discrete physical model describing granular crystals, whose equations of motion can be described by a system of differential difference equations (DDEs). After revisiting earlier continuum approximations, we propose a regularized continuum model variant to approximate the discrete granular crystal model through a suitable partial

David Favero, Mykola Sapronov

We consider a finite collection of line bundles $\Phi$ introduced by Bondal on a smooth, projective toric variety $X$. For any coherent sheaf $F$ on $X$, we construct minimal resolutions of $F$ by line bundles in $\Phi$, up to twist, with length bounded by the dimension of $X$ and provide explicit formulae for their Betti numbers. For a toric subvariety $Y \

Rui Li, Sergey Polevoy, Vladimir Tuz, Oleh Yermakov

The polarization degree of freedom is an inherent feature of plane waves propagating in an isotropic homogeneous medium. The miniaturization of optical systems leads to the high localization of electromagnetic waves, but also to the loss of polarization control, namely, breaking TE-TM polarization degeneracy. In this work, we discover the near-field polariza

Yasamin Masoumi Sefidkhani, Alberto de la Torre, Gregory A. Fiete

The dynamical phase transition of a system with two coexisting competing order parameters is studied using the time-dependent-Ginzburg-Landau framework. The dynamics are induced by parameters capturing the physics of driving the system with an ultrafast laser pulse. A remarkable enhancement of the order parameter with a smaller mean-field value following the

Majid Behzadpour, Bengie L. Ortiz, Ebrahim Azizi, Kai Wu

Precise breast cancer classification on histopathological images has the potential to greatly improve the diagnosis and patient outcome in oncology. The data imbalance problem largely stems from the inherent imbalance within medical image datasets, where certain tumor subtypes may appear much less frequently. This constitutes a considerable limitation in bia

Marco Colussi, Sergio Mascetti, Jose Dolz, Christian Desrosiers

The remarkable progress in deep learning (DL) showcases outstanding results in various computer vision tasks. However, adaptation to real-time variations in data distributions remains an important challenge. Test-Time Training (TTT) was proposed as an effective solution to this issue, which increases the generalization ability of trained models by adding an

Schuyler G. Wolff, Jason Wang, Karl Stapelfeldt, Vanessa P. Bailey

The Coronagraphic Instrument onboard the Nancy Grace Roman Space Telescope is an important stepping stone towards the characterization of habitable, rocky exoplanets. In a technology demonstration phase conducted during the first 18 months of the mission (expected to launch in late 2026), novel starlight suppression technology may enable direct imaging of a

Andreas Karatzas, Dimitrios Stamoulis, Iraklis Anagnostopoulos

Modern edge data centers simultaneously handle multiple Deep Neural Networks (DNNs), leading to significant challenges in workload management. Thus, current management systems must leverage the architectural heterogeneity of new embedded systems to efficiently handle multi-DNN workloads. This paper introduces RankMap, a priority-aware manager specifically de

Shuhua Yu, Ding Zhou, Cong Xie, An Xu

Pre-training Transformer models is resource-intensive, and recent studies have shown that sign momentum is an efficient technique for training large-scale deep learning models, particularly Transformers. However, its application in distributed training remains underexplored. This paper investigates a novel communication-efficient distributed sign momentum me

Pawel Jucha, Mariola Klusek-Gawenda, Antoni Szczurek, Michal Ciemala

The ultraperipheral collisions are the source of various interesting phenomena based on photon-induced reactions. We calculate cross sections for single and any number of n, p, $\alpha$, $\gamma$-rays in ultraperipheral heavy-ion collision for LHC energies. We analyze the production of a given number of neutrons relevant for a recent ALICE experiment, for $\

Jinrui Yang, Qing Liu, Yijun Li, Soo Ye Kim

Recent advancements in large generative models, particularly diffusion-based methods, have significantly enhanced the capabilities of image editing. However, achieving precise control over image composition tasks remains a challenge. Layered representations, which allow for independent editing of image components, are essential for user-driven content creati

Jeovane Honorio Alves, Radu State, Cinthia Obladen de Almendra Freitas, Jean Paul Barddal

In an era of information overload, manually annotating the vast and growing corpus of documents and scholarly papers is increasingly impractical. Automated keyphrase extraction addresses this challenge by identifying representative terms within texts. However, most existing methods focus on short documents (up to 512 tokens), leaving a gap in processing long

H. Jahani, F. Zamio

Clustering algorithms are pivotal in data analysis, enabling the organization of data into meaningful groups. However, individual clustering methods often exhibit inherent limitations and biases, preventing the development of a universal solution applicable to diverse datasets. To address these challenges, we introduce a robust clustering framework that inte

Ahmad Ahmad, Mehdi Kermanshah, Kevin Leahy, Zachary Serlin

In this paper, we tackle the challenging problem of delayed rewards in reinforcement learning (RL). While Proximal Policy Optimization (PPO) has emerged as a leading Policy Gradient method, its performance can degrade under delayed rewards. We introduce two key enhancements to PPO: a hybrid policy architecture that combines an offline policy (trained on expe

Guglielmo Fucci, Jonathan Stanfill

This work focuses on the analysis of the spectral $\zeta$-function associated with a Schr\"{o}dinger operator endowed with a P\"oschl--Teller potential. We construct the spectral $\zeta$-function using a contour integral representation and, for particular self-adjoint extensions, we perform its analytic continuation to a larger region of the complex plane. W

Sven Serneels

A method is introduced to perform simultaneous sparse dimension reduction on two blocks of variables. Beyond dimension reduction, it also yields an estimator for multivariate regression with the capability to intrinsically deselect uninformative variables in both independent and dependent blocks. An algorithm is provided that leads to a straightforward imple

Paolo Villani, Daniel Andrés-Arcones, Jörg F. Unger, Martin Weiser

Posterior sampling by Monte Carlo methods provides a more comprehensive solution approach to inverse problems than computing point estimates such as the maximum posterior using optimization methods, at the expense of usually requiring many more evaluations of the forward model. Replacing computationally expensive forward models by fast surrogate models is an

Sofia Z. Sheikh, Grayce C. Brown, Jackson MacTaggart, Thomas Nguyen

A pulsar's scintillation bandwidth is inversely proportional to the scattering delay, making accurate measurements of scintillation bandwidth critical to characterize unmitigated delays in efforts to measure low-frequency gravitational waves with pulsar timing arrays. In this pilot work, we searched for a subset of known pulsars within $\sim$97% of the data

Hongni Jin, Kenneth M. Merz

A key step in interpreting gas-phase ion mobility coupled with mass spectrometry (IM-MS) data for unknown structure prediction involves identifying the most favorable protonated structure. In the gas phase, the site of protonation is determined using proton affinity (PA) measurements. Currently, mass spectrometry and ab initio computation methods are widely

Pedro Alves, Bruno Pereira Cipriano

The impact of Large Language Models (LLMs) like GPT-3, GPT-4, and Bard in computer science (CS) education is expected to be profound. Students now have the power to generate code solutions for a wide array of programming assignments. For first-year students, this may be particularly problematic since the foundational skills are still in development and an ov

M. A. Fasihi, R. Jafarzadeh Bahrbeig, B. Mojaveri, R. Haji Mohammadzadeh

We revisit the adiabatic charging of a three-level QBs, using the adiabatic quantum master equation formalism. We restrict ourselves to the weak-coupling regime with an Ohmic thermal bath and investigate the effects of relaxation and dephasing on the charging process. We analyze the dependence of the stored energy, ergotropy as well as efficiency of QB on th

Ander Artola, Sarah E. I. Bosman, Prakash Gaikwad, Frederick B. Davies

We aim to construct a machine-learning approach that allows for a pixel-by-pixel reconstruction of the intergalactic medium (IGM) density field for various warm dark matter (WDM) models using the Lyman-alpha forest. With this regression machinery, we constrain the mass of a potential WDM particle from observed Lyman-alpha sightlines directly from the density

José Nicolás Orce

The resembling behaviour of giant dipole resonances built on ground and excited states supports the validity of the Brink-Axel hypothesis and assigns giant dipole resonances as spectroscopic probes -- or ``nuclear thermometers'' -- to explore the cooling of the kilonova ejecta in neutron-star mergers down to the production of heavy elements beyond iron throu

H. Netzel, R. I. Anderson, G. Viviani

Line splitting in spectral lines is observed in various types of stars due to phenomena such as shocks, spectroscopic binaries, magnetic fields, spots, and non-radial modes. In pulsating stars, line splitting is often attributed to pulsation-induced shocks. However, this is rarely observed in classical Cepheids, with only a few reports, including X Sagittari

Soorena Salari, Hassan Rivaz, Yiming Xiao

Automated detection of anatomical landmarks plays a crucial role in many diagnostic and surgical applications. Progresses in deep learning (DL) methods have resulted in significant performance enhancement in tasks related to anatomical landmark detection. While current research focuses on accurately localizing these landmarks in medical scans, the importance

Yilin Lu, Chongwei Chen, Yuxin Chen, Kexin Huang

Graph Neural Networks (GNNs) have achieved significant success across various applications. However, their complex structures and inner workings can be challenging for non-AI experts to understand. To address this issue, this study presents \name{}, an educational visualization tool for interactive learning of GNNs. GNN 101 introduces a set of animated visua

Loïc Herviou, Anthony Rey, Frédéric Mila

The Affleck-Kennedy-Lieb-Tasaki (AKLT) point of the bilinear-biquadratic spin-1 chain is a cornerstone example of a disorder point where short-range correlations become incommensurate, and correlation lengths and momenta are non-analytic. While the presence of singularities appears to be generic for AKLT points, we show that for a family of SU(N) models, the

Mariam Rakka, Jinhao Li, Guohao Dai, Ahmed Eltawil

Recent research efforts focus on reducing the computational and memory overheads of Large Language Models (LLMs) to make them feasible on resource-constrained devices. Despite advancements in compression techniques, non-linear operators like Softmax and Layernorm remain bottlenecks due to their sensitivity to quantization. We propose SoftmAP, a software-hard

Leontine Alkema, Herbert Susmann, Evan Ray

Statistical models are used to produce estimates of demographic and global health indicators in populations with limited data. Such models integrate multiple data sources to produce estimates and forecasts with uncertainty based on model assumptions. Model assumptions can be divided into assumptions that describe latent trends in the indicator of interest ve

Pu Wang, Hugo Van hamme

End-to-end transformer-based automatic speech recognition (ASR) systems often capture multiple speech traits in their learned representations that are highly entangled, leading to a lack of interpretability. In this study, we propose the explainable Disentangled-Transformer, which disentangles the internal representations into sub-embeddings with explicit co

Soorena Salari, Arash Harirpoush, Hassan Rivaz, Yiming Xiao

Anatomical landmark detection in medical images is essential for various clinical and research applications, including disease diagnosis and surgical planning. However, manual landmark annotation is time-consuming and requires significant expertise. Existing deep learning (DL) methods often require large amounts of well-annotated data, which are costly to ac

Jinyung Hong, Yearim Kim, Keun Hee Park, Sangyu Han

Inner interpretability is a promising field aiming to uncover the internal mechanisms of AI systems through scalable, automated methods. While significant research has been conducted on large language models, limited attention has been paid to applying inner interpretability to large-scale image tasks, focusing primarily on architectural and functional level

Xin Chen, Jessica Martinez, Xuecheng Shao, Marc Riera

We present a reformulation of QM/MM as a fully quantum mechanical theory of interacting subsystems, all treated at the level of density functional theory (DFT). For the MM subsystem, which lacks orbitals, we assign an ad hoc electron density and apply orbital-free DFT functionals to describe its quantum properties. The interaction between the QM and MM subsy

Leontine Alkema, Herbert Susmann, Evan Ray, Shauna Mooney

The Family Planning Estimation Tool (FPET) is used in low- and middle-income countries to produce estimates and short-term forecasts of family planning indicators, such as modern contraceptive use and unmet need for contraceptives. Estimates are obtained via a Bayesian statistical model that is fitted to country-specific data from surveys and service statist

Peter Seokhee Seong

The Torres formula, which relates the Alexander polynomial of a link to the Alexander polyomial of its sublinks, admits a generalization to the twisted setting due to Morifuji. This paper uses twisted Reidemeister torsion to obtain a second proof of Morifuji's result that is closer in appearance to Torres' original formula.

Mohammad Ali S. Afshar, Jafar Sadeghi

The integration of non-commutative geometry and Gauss-Bonnet corrections in an action and the study of their black hole responses can provide highly intriguing insights. Our primary motivation for this study is to understand the interplay of these two parameters on the geodesics of spacetime, including photon spheres and time-like orbits. In this study, we f

D. V. Khomitsky, M. V. Bastrakova, D. S. Pashin

Coupled spin evolution and tunneling together with the relaxation and decoherence effects are studied for the double quantum dot formed in a semiconductor nanowire and driven by the periodic electric field. Such system represents a model of the spin and charge qubits interacting via the strong spin-orbit coupling. It is found that at certain regimes the comb

Ajesh Jose, Benjamin Perez-Estay, Shira Omer Bendori, Avigdor Eldar

Bacterial swarming is a complex phenomenon in which thousands of self-propelled rod-shaped cells move coherently on surfaces, providing an excellent example of active matter. However, bacterial swarming is different from most studied examples of active systems because single isolated cells do not move, while clusters do. The biophysical aspects underlying th

Dionisio Alves-Neto, Vera Lucia Tomazella, Adriano Suzuki, Danilo Alvares

Regression models have a substantial impact on interpretation of treatments, genetic characteristics and other potential risk factors in survival analysis. In many applications, the description of censoring and survival curve reveals the presence of cure fraction on data, which leads to alternative modeling. The most common approach to introduce covariates u

Hoje Chun, Hao Tang, Bin Xing, Rafael Gomez-Bombarelli

Processes slow compared to atomic vibrations pose significant challenges in atomistic simulations, particularly for phenomena such as diffusive relaxations and phase transitions, where repeated crossings and the shear number of thermally activated transitions make direct numerical simulations impossible. We present a computational framework that captures ato

Christopher Holder, Anthony Bagnall

Time series data has become increasingly prevalent across numerous domains, driving a growing demand for time series machine learning techniques. Among these, time series clustering (TSCL) stands out as one of the most popular machine learning tasks. TSCL serves as a powerful exploratory analysis tool and is also employed as a preprocessing step or subroutin

Hanqi Jiang, Yi Pan, Junhao Chen, Zhengliang Liu

Oracle bone script (OBS), as China's earliest mature writing system, present significant challenges in automatic recognition due to their complex pictographic structures and divergence from modern Chinese characters. We introduce OracleSage, a novel cross-modal framework that integrates hierarchical visual understanding with graph-based semantic reasoning. S

M. Misiurewicz, S. Štimac

We study the zero entropy locus for the Lozi maps. We first define a region $R$ in the parameter space and prove that for the parameters in $R$, the Lozi maps have the topological entropy zero. $R$ is contained in a larger region where every Lozi map has a unique period-two orbit, and that orbit is attracting. It is easy to see that the zero entropy locus ca

Alexandra Ramôa

This thesis explores adaptive inference as a tool to characterize quantum systems using experimental data, with applications in sensing, calibration, control, and metrology. I propose and test algorithms for learning Hamiltonian and Kraus operators using Bayesian experimental design and advanced Monte Carlo techniques, including Sequential and Hamiltonian Mo

Kirsten Endresen, Aniruddh Murali, Birte Geerds, Daniel J. G. Pearce

The alignment of fibers and cells in living tissues affect their mechanical properties and functionality. In this context, one can draw an analogy between tissues and nematic liquid crystal elastomers. We explore this analogy by growing fibroblasts on 2D-patterned substrates and observing the contraction of cell sheets upon detachment from the substrates. Wh

Zhaofang Qian, Abolfazl Sharifi, Tucker Carroll, Ser-Nam Lim

Video generation has achieved impressive quality, but it still suffers from artifacts such as temporal inconsistency and violation of physical laws. Leveraging 3D scenes can fundamentally resolve these issues by providing precise control over scene entities. To facilitate the easy generation of diverse photorealistic scenes, we propose Scene Copilot, a frame

Allan M. de Souza, Filipe Maciel, Joahannes B. D. da Costa, Luiz F. Bittencourt

Federated Learning (FL) is a distributed approach to collaboratively training machine learning models. FL requires a high level of communication between the devices and a central server, thus imposing several challenges, including communication bottlenecks and network scalability. This article introduces ACSP-FL (https://github.com/AllanMSouza/ACSP-FL), a so

Ximing Xing, Qian Yu, Chuang Wang, Haitao Zhou

Recently, text-guided scalable vector graphics (SVG) synthesis has demonstrated significant potential in domains such as iconography and sketching. However, SVGs generated from existing Text-to-SVG methods often lack editability and exhibit deficiencies in visual quality and diversity. In this paper, we propose a novel text-guided vector graphics synthesis m

Meghan Plumridge, Rasmus Maråk, Chiara Ceccobello, Pablo Gómez

Segmentation of Earth observation (EO) satellite data is critical for natural hazard analysis and disaster response. However, processing EO data at ground stations introduces delays due to data transmission bottlenecks and communication windows. Using segmentation models capable of near-real-time data analysis onboard satellites can therefore improve respons

Atefeh Zakeri, S. Mohammad Razavizadeh

This paper investigates the physical layer security of a Reconfigurable Intelligent Surface (RIS)-aided wireless network in the presence of full-duplex active eavesdropping. In this scenario, the RIS cooperates with the Base Station (BS) to transfer information to the intended user while an active attacker attempts to intercept the information through a wire

Laurent Baulieu, Tom Wetzstein, Siye Wu

We provide a proof of the BRST Noether 1.5th theorem, conjectured in [JHEP 10 (2024) 055], for a broad class of rank-1 BV theories including supergravity and 2-form gauge theories. The theorem asserts that the BRST Noether current of any BRST invariant gauge fixed Lagrangian decomposes on-shell into a sum of a BRST-exact term and a corner term that defines N

Deepak Kumar, Hiranmaya Mishra

Charge-conjugation and parity violation in strong interaction for cold dense quark matter is studied with axions of quantum chromodynamic within the three flavor Nambu--Jona-Lasinio model that includes the coupling of axions to quarks. We first calculate the effective potential for axions at finite baryon density and zero temperature including the effects of

Denis Denisov, Will FitzGerald, Vitali Wachtel

The Airy line ensemble is a random collection of continuous ordered paths that plays an important role within random matrix theory and the Kardar-Parisi-Zhang universality class. The aim of this paper is to prove a universality property of the Airy line ensemble. We study growing numbers of i.i.d. continuous-time random walks which are then conditioned to st

Aman Sinha, Payam Nikdel, Supratik Paul, Shimon Whiteson

Ensuring the safety of autonomous vehicles (AVs) requires both accurate estimation of their performance and efficient discovery of potential failure cases. This paper introduces Bayesian adaptive multifidelity sampling (BAMS), which leverages the power of adaptive Bayesian sampling to achieve efficient discovery while simultaneously estimating the rate of ad

Valentin Gutev

The classical McShane-Whitney extension theorem for Lipschitz functions is refined by showing that for a closed subset of the domain, it remains valid for any interval of the real line. This result is also extended to the setting of locally (pointwise) Lipschitz functions. In contrast to Lipschitz and pointwise Lipschitz extensions, the construction of local

Karthik Reddy Lyathakula, Fuh-Gwo Yuan

The remaining useful life (RUL) estimation is an important metric that helps in condition-based maintenance. Damage data obtained from the diagnostics techniques are often noisy and the RUL estimated from the data is less reliable. Estimating the probabilistic RUL by quantifying the uncertainty in the predictive model parameters using the noisy data increase

Valentin Blomer, Morten S. Risager, Igor E. Shparlinski

We investigate the distribution of modular inverses modulo positive integers $c$ in a large interval. We provide upper and lower bounds for their box, ball and isotropic discrepancy, thereby exhibiting some deviations from random point sets. The analysis is based, among other things, on a new bound for a triple sum of Kloosterman sums.

Guliuxin Jin, D. O. Oriekhov, Lukas Johannes Splitthoff, Eliska Greplova

Topological phases of matter have been widely studied for their robustness against impurities and disorder. The broad applicability of topological materials relies on the reliable transition from idealized, mathematically perfect models to finite, real-world implementations. In this paper, we explore the effects of finite size and disorders on topological pr

Johannes Christmann, Petr Ivashkov, Mattia Chiurco, Guglielmo Mazzola

We perform a comprehensive analysis of the quantum-enhanced Monte Carlo method [Nature, 619, 282-287 (2023)], aimed at identifying the optimal working point of the algorithm. We observe an optimal mixing Hamiltonian strength and analyze the scaling of the total evolution time with the size of the system. We also explore extensions of the circuit, including t

Xinhao Liu, Jintong Li, Yicheng Jiang, Niranjan Sujay

Navigating dynamic urban environments presents significant challenges for embodied agents, requiring advanced spatial reasoning and adherence to common-sense norms. Despite progress, existing visual navigation methods struggle in map-free or off-street settings, limiting the deployment of autonomous agents like last-mile delivery robots. To overcome these ob

Dolev Bashi, Andrei Tokovinin

Compact hierarchical triple (CHT) systems, where a tertiary component orbits an inner binary, provide critical insights into stellar formation and evolution. Despite their importance, the detection of such systems, especially compact ones, remains challenging due to the complexity of their orbital dynamics and the limitations of traditional observational met

Sofie Castro Holbæk, Mark H. Fischer

In the $\textit{A}$V$_{3}$Sb$_{5}$ ($\textit{A}$ $=$ K, Rb, Cs) kagome materials, superconductivity coexists with a charge density wave (CDW), constituting a new platform to study the interplay of these two orders. Despite extensive research, the symmetry of the superconducting order parameter remains disputed, with experiments seemingly supporting different

Tianliang Yan, Yubao Liu, Leonid Prokhorov, Jiri Smetana

The Schr\"odinger-Newton equation, a theoretical framework connecting quantum mechanics with classical gravity, predicts that gravity may induce measurable deviations in low-frequency mechanical systems-an intriguing hypothesis at the frontier of fundamental physics. In this study, we developed and operated an advanced optomechanical platform to investigate

Thais de Lima Silva, Lucas Borges, Leandro Aolita

Estimating quantum partition functions is a critical task in a variety of fields. However, the problem is classically intractable in general due to the exponential scaling of the Hamiltonian dimension $N$ in the number of particles. This paper introduces a quantum algorithm for estimating the partition function $Z_\beta$ of a generic Hamiltonian $H$ up to mu

Sudarshan Rajagopalan, Vishal M. Patel

All-weather image restoration (AWIR) is crucial for reliable autonomous navigation under adverse weather conditions. AWIR models are trained to address a specific set of weather conditions such as fog, rain, and snow. But this causes them to often struggle with out-of-distribution (OoD) samples or unseen degradations which limits their effectiveness for real

E. Uzeirbegovic, G. Martin, S. Kaviraj, R. A. Jackson

We introduce a new concept -- termed "planarity" -- which aims to quantify planar structure in galaxy satellite systems without recourse to the number or thickness of planes. We use positions and velocities from the Gaia EDR3 to measure planarity in Milky Way (MW) satellites and the extent to which planes within the MW system are kinematically supported. We

Juan F. Pulido, José L. Ramírez, Andrés R. Vindas-Meléndez

We study a new class of polyominoes, called $p$-Fibonacci polyominoes, defined using $p$-Fibonacci words. We enumerate these polyominoes by applying generating functions to capture geometric parameters such as area, semi-perimeter, and the number of inner points. Additionally, we establish bijections between Fibonacci polyominoes, binary Fibonacci words, and

Anindya Guria, Banibrata Mukhopadhyay

Understanding nonlinear properties in accreting systems, particularly for black holes, from observation is illuminating as they are expected to be general relativistic magnetohydrodynamic flows that are nonlinear. Two features associated with nonlinear systems, used commonly, are chaos, which is deterministic, and random, which is stochastic. The differentia

Elia de Sabbata, Nadav Drukker, Andreas Stergiou

This note introduces a novel paradigm for conformal defects with continuously adjustable dimensions. Just as the standard $\varepsilon$ expansion interpolates between integer spacetime dimensions, a new parameter, $\delta$, is used to interpolate between different integer-dimensional defects. The ensuing framework is explored in detail for defects of dimensi

Roman Parzer, Laura Vana-Gür, Peter Filzmoser

Package spar for R builds ensembles of predictive generalized linear models with high-dimensional predictors. It employs an algorithm utilizing variable screening and random projection tools to efficiently handle the computational challenges associated with large sets of predictors. The package is designed with a strong focus on extensibility. Screening and

Indranil Halder

We develop an analytically tractable single-step diffusion model based on a linear denoiser and present an explicit formula for the Kullback-Leibler divergence between the generated and sampling distribution, taken to be isotropic Gaussian, showing the effect of finite diffusion time and noise scale. Our study further reveals that the monotonic fall phase of

Marc Aragonès Fontboté, David Mateos, Guillem Pérez Martín, Wilke van der Schee

We investigate cosmic censorship in anti-de Sitter space in holographic models in which the ground state is described by a good singularity. These include supersymmetric truncations of string/M-theory, for which a positive-energy theorem holds. At the boundary, our solutions describe a boost-invariant fluid in which the temperature decreases monotonically wi

Mokhtar Hassaine, David Kubiznak, Aravindhan Srinivasan

We propose a novel ansatz, where the full black hole geometry is written as a linear in mass perturbation of the associated extremal black hole base. Contrary to its "standard" version, the corresponding "extremal Kerr-Schild form" is no longer restricted to special algebraic type spacetimes, and is applicable to numerous black hole solutions with matter, su

Nithyanandan Thyagarajan

Two major areas of modern radio astronomy, namely, explosive astrophysical transient phenomena and observations of cosmological structures, are driving the design of aperture arrays towards large numbers of low-cost elements consisting of multiple spatial scales spanning the dimensions of individual elements, the size of stations (groupings of individual ele

Yonghee Kim, Makoto Oka, Kei Suzuki

Chiral effective theory of light diquarks is revisited. We construct an effective Lagrangian based on the linear representation of three-flavor chiral symmetry. Here, we focus on the effect of a chiral and $U(1)_A$ symmetric term originated from an eight-point quark interaction. From this model, we obtain the mass formulas of scalar, pseudoscalar, vector, an

Rahel Baumgartner, Pietro Pelliconi, Soumik Bandyopadhyay, Francesca Orsi

The Sachdev--Ye--Kitaev (SYK) model is a paradigm for extreme quantum chaos, non-Fermi-liquid behavior, and holographic matter. Yet, the dense random all-to-all interactions that characterize it are an extreme challenge for realistic laboratory realizations. Here, we propose a general scheme for densifying the coupling distribution of random disorder Hamilto

Aaron B. Pearlman

Several thousand fast radio burst (FRB) sources have been discovered using the Canadian Hydrogen Intensity Mapping Experiment (CHIME) radio telescope, as part of the CHIME/FRB project. Currently, CHIME/FRB is able to localize most FRBs to a limiting precision of several arcminutes, which can be improved to subarcminute precision for some FRB sources through

Sergei Alexandrov, Khalil Bendriss

Generating functions $h_r(τ)$ of D4-D2-D0 BPS indices, appearing in Calabi-Yau compactifications of type IIA string theory and identical to rank 0 Donaldson-Thomas invariants, are known to be higher depth mock modular forms satisfying a specific modular anomaly equation, with depth determined by the D4-brane charge $r$. We develop a method to solve the anoma

Ziyang Chen, Prem Seetharaman, Bryan Russell, Oriol Nieto

Generating sound effects for videos often requires creating artistic sound effects that diverge significantly from real-life sources and flexible control in the sound design. To address this problem, we introduce MultiFoley, a model designed for video-guided sound generation that supports multimodal conditioning through text, audio, and video. Given a silent

Shuyuan Tu, Zhen Xing, Xintong Han, Zhi-Qi Cheng

Current diffusion models for human image animation struggle to ensure identity (ID) consistency. This paper presents StableAnimator, the first end-to-end ID-preserving video diffusion framework, which synthesizes high-quality videos without any post-processing, conditioned on a reference image and a sequence of poses. Building upon a video diffusion model, S

Jun Myeong Choi, Annie Wang, Pieter Peers, Anand Bhattad

Image-based relighting of indoor rooms creates an immersive virtual understanding of the space, which is useful for interior design, virtual staging, and real estate. Relighting indoor rooms from a single image is especially challenging due to complex illumination interactions between multiple lights and cluttered objects featuring a large variety in geometr

Hui-Huang Chen, Zi-Jun Tang

In this paper, we consider the time evolution of entanglement asymmetry of the black hole radiation in the Hayden-Preskill thought experiment. We assume the black hole is initially in a mixed state since it is entangled with the early radiation. Alice throws a diary maximally entangled with a reference system into the black hole. After the black hole has abs

Jiaxin Wen, Vivek Hebbar, Caleb Larson, Aryan Bhatt

As large language models (LLMs) become increasingly capable, it is prudent to assess whether safety measures remain effective even if LLMs intentionally try to bypass them. Previous work introduced control evaluations, an adversarial framework for testing deployment strategies of untrusted models (i.e., models which might be trying to bypass safety measures)

Xinhao Fan, Shreesh P Mysore

A cornerstone of our understanding of both biological and artificial neural networks is that they store information in the strengths of synaptic connections among the neurons. However, in contrast to the well-established theory for quantifying information encoded by the firing activity of neural networks, there does not exist a framework for quantifying info

Xu Ouyang, Tao Ge, Thomas Hartvigsen, Zhisong Zhang

We reveal that low-bit quantization favors undertrained large language models (LLMs) by observing that models with larger sizes or fewer training tokens experience less quantization-induced degradation (QiD) when applying low-bit quantization, whereas smaller models with extensive training tokens suffer significant QiD. To gain deeper insights into this tren

Akshita Gupta, Tatiana Likhomanenko, Karren Dai Yang, Richard He Bai

Unified decoder-only transformers have shown promise for multimodal generation, yet the mechanisms by which they synchronize modalities with heterogeneous sampling rates remain underexplored. We investigate these mechanisms through video-text-to-speech (VTTS) synthesis-a controlled task requiring fine-grained temporal alignment between sparse text, video, an

L. Peña-Moñino, M. Pérez-Torres, D. Kansabanik, G. Blázquez-Calero

We search for radio emission from star-planet interactions in the M-dwarf system GJ~486, which hosts an Earth-like planet. We observed the GJ~486 system with the upgraded Giant Metrewave Radio Telescope (uGMRT) from 550 to 750 MHz in nine different epochs, between October 2021 and February 2022, covering almost all orbital phases of GJ~486 b from different o

Mingkai Xia, Junhan Zhang, Ningshan Wang, Gabriel Antoniak

Marine mammal biomechanics research has focused on straight-line swimming at consistent speeds, resulting in a lack of knowledge about how animals select movement strategies to balance cost vs performance during tasks like cornering. In this work we examine performance, maneuverability and cost tradeoffs for bottlenose dolphins (Tursiops truncatus) during pr

Sudarshan Rajagopalan, Nithin Gopalakrishnan Nair, Jay N. Paranjape, Vishal M. Patel

Deep learning-based models for All-In-One Image Restoration (AIOR) have achieved significant advancements in recent years. However, their practical applicability is limited by poor generalization to samples outside the training distribution. This limitation arises primarily from insufficient diversity in degradation variations and scenes within existing data

Yuhang Han, Xuyang Liu, Zihan Zhang, Pengxiang Ding

The quadratic complexity of Multimodal Large Language Models (MLLMs) with respect to context length poses significant computational and memory challenges, hindering their real-world deployment. In the paper, we devise a ''filter-correlate-compress'' framework to accelerate the MLLM by systematically optimizing multimodal context length during prefilling. The

Yash Akhauri, Safeen Huda, Mohamed S. Abdelfattah

When predicting the next token in a sequence, vanilla transformers compute attention over all previous tokens, resulting in quadratic scaling of compute with sequence length. State-space models compress the entire sequence of tokens into a fixed-dimensional representation to improve efficiency, while other architectures achieve sub-quadratic complexity via l

Nima Sedaghat, Tanawan Chatchadanoraset, Colin Orion Chandler, Ashish Mahabal

Motivated by the scarcity of proper labels in an astrophysical application, we have developed a novel technique, called Selfish Evolution, which allows for the detection and correction of corrupted labels in a weakly supervised fashion. Unlike methods based on early stopping, we let the model train on the noisy dataset. Only then do we intervene and allow th