May 2024 arXiv papers — page 29

Nayan Myerson-Jain, Xiao-Chuan Wu, Cenke Xu

Bulk topology and criticality can both lead to nontrivial boundary effects. Topological orders are often characterized by their robust edge states, while bulk critical points can have different boundary scalings governed by boundary conditions. The interplay between these two different boundary effects is an intriguing problem. The boundary of the deconfined

Joshua Berger, Gray Putnam

We study the sensitivity of fixed target experiments to hadronically-coupled axion like particles (ALPs) produced in kaon decays, with a particular emphasis on current and upcoming short-baseline neutrino experiments. We demonstrate that below the kaon decay mass threshold ($m_a < m_K - m_\pi$) kaon decay is the dominant production mechanism for ALPs at neut

Kwangmin Oh, Jongsuk Hong, C. Y. Hui, Sangin Kim

Dynamical interactions in globular clusters (GCs) significantly impact the formation and evolution of binary sources, including cataclysmic variables (CVs). This study investigates the connection between dynamical states of GCs and X-ray luminosity ($L_{x}$) distributions of CV populations through both simulations and actual observations. Utilizing a Monte C

Julian Heeck, Dima Watkins

Baryon number violation is our most sensitive probe of physics beyond the Standard Model, especially through the study of nucleon decays. Angular momentum conservation requires a lepton in the final state of such decays, kinematically restricted to electrons, muons, or neutrinos. We show that operators involving taus, which are at first sight too heavy to pl

Nora Reinić, Daniel Jaschke, Darvin Wanisch, Pietro Silvi

As one of the most prominent platforms for analog quantum simulators, Rydberg atom arrays are a promising tool for exploring quantum phases and transitions. While the ground state properties of one-dimensional Rydberg systems are already thoroughly examined, we extend the analysis towards the finite-temperature scenario. For this purpose, we develop a tensor

Yuguang Chen, Tucker Jones, Ryan L. Sanders, Dario Fadda

Accurate chemical compositions of star-forming regions are a critical diagnostic tool to characterize the star formation history and gas flows which regulate galaxy formation. However, the abundance discrepancy factor (ADF) between measurements from the "direct" optical electron temperature ($T_e$) method and from the recombination lines (RL) represents $\si

Zhenjiu Wang, Paul McClarty, Dobromila Dankova, Andreas Honecker

The quantum magnet SrCu$_2($BO$_3)_2$ and its remarkably accurate theoretical description, the spin-$1/2$ Shastry-Sutherland model, host a variety of intriguing phenomena such as a dimer ground state with a nearly flat band of triplon excitations, a series of magnetization plateaux, and a possible pressure-induced deconfined quantum critical point. One open

Bo Zhao, Ralph E. Pudritz, Rachel Pillsworth, Hector Robinson

There is now abundant observational evidence that star formation is a highly dynamical process that connects filament hierarchies and supernova feedback from galaxy scale kpc filaments and superbubbles, to giant molecular clouds (GMCs) on 100 pc scales and star clusters (1 pc). Here we present galactic multi-scale MHD simulations that track the formation of

Joan Bernabeu, Alberto Cortijo

Magnetic Catalysis is a known proposal for inducing dynamical axionic gapped phases by means of external magnetic fields from a Weyl or Dirac semimetal phase. At finite Fermi level, the phase transition is of first order type and the magnetic field needs to reach a critical value for the transition to take place. Using the theory of bubble nucleation, we pre

Yohei Ema, Robert McGehee, Maxim Pospelov, Anupam Ray

WIMP-type dark matter may have additional interactions that break baryon number, leading to induced nucleon decays which are subject to direct experimental constraints from proton decay experiments. In this work, we analyze the possibility of continuous baryon destruction, deriving strong limits from the dark matter accumulating inside old neutron stars, as

Shehu AbdusSalam, Steve Abel, Miguel Crispim Romao

We propose symbolic regression as a powerful tool for studying Beyond the Standard Model physics. As a benchmark model, we consider the so-called Constrained Minimal Supersymmetric Standard Model, which has a four-dimensional parameter space defined at the GUT scale. We provide a set of analytical expressions that reproduce three low-energy observables of in

Meredith A. Stone, Stacey Alberts, George H. Rieke, Andrew J. Bunker

Galaxy number counts probe the evolution of galaxies over cosmic time, and serve as a valuable comparison point to theoretical models of galaxy formation. We present new galaxy number counts in eight photometric bands between 5 and 25 $\mu$m from the Systematic Mid-infrared Instrument Legacy Extragalactic Survey (SMILES) and the JWST Advanced Deep Extragalac

Vincenzo Cirigliano, Wouter Dekens, Jordy de Vries, Stefano Gandolfi

The accuracy of $V_{ud}$ determinations from superallowed $\beta$ decays critically hinges on control over radiative corrections. Recently, substantial progress has been made on the single-nucleon, universal corrections, while nucleus-dependent effects, typically parameterized by a quantity $\delta_\text{NS}$, are much less well constrained. Here, we lay out

Talia M. O'Shea, Josh Borrow, Stephanie O'Neil, Mark Vogelsberger

The splashback radius is one popular method of constraining the size of galaxy clusters, often measured through the logarithmic derivative of the galaxy number density profile. However, measuring the splashback radius through the galaxy number density has consistently produced smaller values of the splashback radius than those inferred from the underlying gr

Thomas Basile, Chrysoula Markou

We propose a covariant method of constructing entire trajectories of physical states in superstring theory in the critical dimension. It is inspired by a recently developed covariant technology of excavating bosonic string trajectories, that is facilitated by the observation that the Virasoro constraints can be written as linear combinations of lowering oper

Hugo Schérer, Katelin Schutz

In an isotropic background comprised of free charges, the transverse and longitudinal modes of the photon acquire large corrections to their dispersion relations, described by the in-medium photon self-energy. Previous work has developed simple approximations that describe the propagation of on-shell photons in plasmas of varying temperatures and densities.

Stefano Antonini, Luis Gabriel C. Bariuan

We construct 4D flat Big Bang-Big Crunch cosmologies and Anti-de Sitter (AdS) planar eternally traversable wormholes using braneworlds embedded in asymptotically AdS${}_5$ spacetimes. The background geometries are the AdS${}_5$ magnetic black brane and the magnetically charged AdS${}_5$ soliton, respectively. The two setups arise from different analytic cont

Vincenzo Cirigliano, Wouter Dekens, Jordy de Vries, Stefano Gandolfi

Radiative corrections are essential for an accurate determination of $V_{ud}$ from superallowed $\beta$ decays. In view of recent progress in the single-nucleon sector, the uncertainty is dominated by the theoretical description of nucleus-dependent effects, limiting the precision that can currently be achieved for $V_{ud}$. In this work, we provide a detail

Eric R. Moseley, Romain Teyssier

We investigate the gas-grain relative drift velocity distributions of charged astrophysical dust grains in MHD turbulence. We do this using a range of MHD-PIC simulations spanning different plasma-$\beta$, sonic/Alfv\'en Mach number, and with grains of varying size and charge-to-mass ratio. We find that the root-mean-square drift velocity is a strong functio

Jakob M. Helton, George H. Rieke, Stacey Alberts, Zihao Wu

The James Webb Space Telescope (JWST) has spectroscopically confirmed numerous galaxies at $z > 10$. While weak rest-ultraviolet emission lines have only been seen in a handful of sources, the stronger rest-optical emission lines are highly diagnostic and accessible at mid-infrared wavelengths with the Mid-Infrared Instrument (MIRI) of JWST. We report the ph

Łukasz Borchmann

We perform a missing, reproducible evaluation of all publicly available GPT-4 family models concerning the Document Understanding field, where it is frequently required to comprehend text spacial arrangement and visual clues in addition to textual semantics. Benchmark results indicate that though it is hard to achieve satisfactory results with text-only mode

Eliahu Horwitz, Asaf Shul, Yedid Hoshen

The number of models shared online has recently skyrocketed, with over one million public models available on Hugging Face. Sharing models allows other users to build on existing models, using them as initialization for fine-tuning, improving accuracy, and saving compute and energy. However, it also raises important intellectual property issues, as fine-tuni

Tanmay Inamdar

We prove that for every colouring of pairs of reals with finitely-many colours, there is a set homeomorphic to the rationals which takes no more than two colours. This was conjectured by Galvin in 1970, and a colouring of Sierpi{\'n}ski from 1933 witnesses that the number of colours cannot be reduced to one. Previously in 1985 Shelah had shown that a stronge

Yixiang Yao, Joseph Cecil, Praveen Angyan, Neil Bahroos

Patient datasets contain confidential information which is protected by laws and regulations such as HIPAA and GDPR. Ensuring comprehensive patient information necessitates privacy-preserving entity resolution (PPER), which identifies identical patient entities across multiple databases from different healthcare organizations while maintaining data privacy.

Kan Kitamura

We characterize the simplicity of Pimsner algebras for non-proper C*-correspondences. With the aid of this criterion, we give a systematic strategy to produce outer actions of unitary tensor categories on Kirchberg algebras. In particular, every countable unitary tensor category admits an outer action on the Cuntz algebra $\mathcal{O}_2$. We also study the r

Lianghui Zhu, Zilong Huang, Bencheng Liao, Jun Hao Liew

Diffusion models with large-scale pre-training have achieved significant success in the field of visual content generation, particularly exemplified by Diffusion Transformers (DiT). However, DiT models have faced challenges with quadratic complexity efficiency, especially when handling long sequences. In this paper, we aim to incorporate the sub-quadratic mo

Khen Cohen, Noam Levi, Yaron Oz

We derive closed-form expressions for the Bayes optimal decision boundaries in binary classification of high dimensional overlapping Gaussian mixture model (GMM) data, and show how they depend on the eigenstructure of the class covariances, for particularly interesting structured data. We empirically demonstrate, through experiments on synthetic GMMs inspire

Shizun Wang, Xingyi Yang, Qiuhong Shen, Zhenxiang Jiang

Recovering 4D world from monocular video is a crucial yet challenging task. Conventional methods usually rely on the assumptions of multi-view videos, known camera parameters, or static scenes. In this paper, we relax all these constraints and tackle a highly ambitious but practical task: With only one monocular video without camera parameters, we aim to rec

Bencheng Liao, Xinggang Wang, Lianghui Zhu, Qian Zhang

Recently, linear complexity sequence modeling networks have achieved modeling capabilities similar to Vision Transformers on a variety of computer vision tasks, while using fewer FLOPs and less memory. However, their advantage in terms of actual runtime speed is not significant. To address this issue, we introduce Gated Linear Attention (GLA) for vision, lev

Qihang Zhang, Yinghao Xu, Chaoyang Wang, Hsin-Ying Lee

Scene image editing is crucial for entertainment, photography, and advertising design. Existing methods solely focus on either 2D individual object or 3D global scene editing. This results in a lack of a unified approach to effectively control and manipulate scenes at the 3D level with different levels of granularity. In this work, we propose 3DitScene, a no

Gal Birenbaum, Ramandeep Gill, Omer Bromberg, Paz Beniamini

Gamma-ray bursts (GRBs) are powered by ultra-relativistic jets. The launching sites of these jets are surrounded by dense media, which the jets must cross before they can accelerate and release the high energy emission. Interaction with the medium leads to the formation of a mildly relativistic sheath around the jet resulting in an angular structures in the

Jens Kammerer, Kellen Lawson, Marshall D. Perrin, Isabel Rebollido

We present the first JWST/NIRCam observations of the directly-imaged gas giant exoplanet $\beta$ Pic b. Observations in six filters using NIRCam's round coronagraphic masks provide a high signal-to-noise detection of $\beta$ Pic b and the archetypal debris disk around $\beta$ Pic over a wavelength range of $\sim$1.7-5 $\mu$m. This paper focuses on the detect

Vittal Srinivasan, Nak-seung Patrick Hyun

In nature, different species of smaller animals produce ultra-fast movements to aid in their locomotion or protect themselves against predators. These ultra-fast impulsive motions are possible, as often times, there exist a small latch in the organism that could hold the potential energy of the system, and once released, generate an impulsive motion. These t

Goran S. Djordjevic, Neven Bilić, Dragoljub D. Dimitrijevic, Milan Milosevic

We investigate in detail the attractor behavior of some inflationary models based on braneworld dynamics under the constant-roll condition. We describe the dynamics of the models, assuming that the second slow-roll parameter remains constant during inflation. We show that the dynamics of the considered models have the property of a cosmological attractor.

Daniel B. Cooney

Two mechanisms that have been used to study the evolution of cooperative behavior are altruistic punishment, in which cooperative individuals pay additional costs to punish defection, and multilevel selection, in which competition between groups can help to counteract individual-level incentives to cheat. Boyd, Gintis, Bowles, and Richerson have used simulat

Nicklas Hansen, Jyothir S, Vlad Sobal, Yann LeCun

Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visual observations further exacerbates this difficulty. In this work, we explore highly data-driven approaches to visual whole-body humanoid control based on reinforcement learning, w

Juan I. Rodriguez, Ulises A. Vergara-Beltran

We introduce a machine learning (ML) supervised model function that is inspired by the variational principle of physics. This ML hypothesis evolutionary method, termed ML-Omega, allows us to go from data to differential equation(s) underlying the physical (chemical, engineering, etc.) phenomena the data are derived from. The fundamental equations of physics

Jingwei Xu, Yikai Wang, Yiqun Zhao, Yanwei Fu

Unveiling an empty street from crowded observations captured by in-car cameras is crucial for autonomous driving. However, removing all temporarily static objects, such as stopped vehicles and standing pedestrians, presents a significant challenge. Unlike object-centric 3D inpainting, which relies on thorough observation in a small scene, street scene cases

Yuhui Zhang, Alyssa Unell, Xiaohan Wang, Dhruba Ghosh

Image classification is one of the most fundamental capabilities of machine vision intelligence. In this work, we revisit the image classification task using visually-grounded language models (VLMs) such as GPT-4V and LLaVA. We find that existing proprietary and public VLMs, despite often using CLIP as a vision encoder and having many more parameters, signif

Jialin Dong, Bahare Fatemi, Bryan Perozzi, Lin F. Yang

Retrieval Augmented Generation (RAG) has greatly improved the performance of Large Language Model (LLM) responses by grounding generation with context from existing documents. These systems work well when documents are clearly relevant to a question context. But what about when a document has partial information, or less obvious connections to the context? A

Jaydip Sen, Hetvi Waghela, Sneha Rakshit

This paper explores using a deep learning Long Short-Term Memory (LSTM) model for accurate stock price prediction and its implications for portfolio design. Despite the efficient market hypothesis suggesting that predicting stock prices is impossible, recent research has shown the potential of advanced algorithms and predictive models. The study builds upon

Hanh T. D. Pham, Daniel K. Sewell

Homophily and social influence are two key concepts of social network analysis. Distinguishing between these phenomena is difficult, and approaches to disambiguate the two have been primarily limited to longitudinal data analyses. In this study, we provide sufficient conditions for valid estimation of social influence through cross-sectional data, leading to

Arnab Auddy, Dong Xia, Ming Yuan

Large amount of multidimensional data represented by multiway arrays or tensors are prevalent in modern applications across various fields such as chemometrics, genomics, physics, psychology, and signal processing. The structural complexity of such data provides vast new opportunities for modeling and analysis, but efficiently extracting information content

Nugzar Shavlakadze, Nana Odishelidze, Francisco Criado-Aldeanueva

A piecewise-homogeneous elastic orthotropic plate, reinforced with a finite patch of the wedgeshaped, which meets the interface at a right angle and is loaded with tangential and normal forces is considered. By using methods of the theory of analytic functions, the problem is reduced to the system of singular integro-differential equations (SIDE) with fixed

Mahrokh Najaf, Gregory Ongie

Implicit neural representations (INRs) have emerged as a powerful tool for solving inverse problems in computer vision and computational imaging. INRs represent images as continuous domain functions realized by a neural network taking spatial coordinates as inputs. However, unlike traditional pixel representations, little is known about the sample complexity

Doron Shafrir

We show that every product of f.g.\ submonoids of a group $G$ is a section of a f.g.\ submonoid of $G{\times}H_5(\mathbb{Z})$, where $H_5(\mathbb{Z})$ is a Heisenberg group. This gives us a converse of a reduction of Bodart, and a new simple proof of the existence of a submonoid of a nilpotent group of class 2 with undecidable membership problem.

Peter Bierhorst

This paper examines networks of $n$ measuring parties sharing $m$ nonsignaling resources that can be locally wired together: that is, each party follows a scheme to measure the resources in a cascaded fashion with inputs to later resources possibly depending on outputs of earlier-measured ones. A specific framework is provided for studying probability distri

Andrew Balch

Algorithms are unavoidable in our social lives, yet often perpetuate social injustices. The popular means of addressing this is through algorithmic reformism: fine-tuning algorithms themselves to be more fair, accountable, and transparent. However, reformism fails to curtail algorithmic injustice because it ignores the power structure surrounding algorithms.

Carianna Herrera, Benoit Carry, Anthony Lagain, Dmitrii E. Vavilov

Context. Airless planetary objects have their surfaces covered by craters, and these can be used to study the characteristics of asteroid populations. Planetary surfaces present binary craters that are associated with the synchronous impact of binary asteroids. Aims. We identify binary craters on asteroids (1) Ceres and (4) Vesta, and aim to characterize the

Fu-Yun Wang, Zhaoyang Huang, Alexander William Bergman, Dazhong Shen

Consistency Models (CMs) have made significant progress in accelerating the generation of diffusion models. However, their application to high-resolution, text-conditioned image generation in the latent space remains unsatisfactory. In this paper, we identify three key flaws in the current design of Latent Consistency Models (LCMs). We investigate the reason

Jaehong Yoon, Shoubin Yu, Mohit Bansal

Recent video generative models primarily rely on carefully written text prompts for specific tasks, like inpainting or style editing. They require labor-intensive textual descriptions for input videos, hindering their flexibility to adapt personal/raw videos to user specifications. This paper proposes RACCooN, a versatile and user-friendly video-to-paragraph

Jiawei Ma, Yulei Niu, Shiyuan Huang, Guangxing Han

Language has been useful in extending the vision encoder to data from diverse distributions without empirical discovery in training domains. However, as the image description is mostly at coarse-grained level and ignores visual details, the resulted embeddings are still ineffective in overcoming complexity of domains at inference time. We present a self-supe

Luca Pezzè, Augusto Smerzi

We study the multiparameter sensitivity bounds of a sensor made by an array of $d$ spatially-distributed Mach-Zehnder interferometers (MZIs). A generic single non-classical state is mixed with $d-1$ vacuums to create a $d$-modes entangled state, each mode entering one input port of a MZI, while a coherent state enters its second port. We show that local meas

H. Hawley-Herrera, E. Alfonso-Pita, E. Behnke, M. Bressler

The Scintillating Bubble Chamber (SBC) collaboration purchased 32 Hamamatsu VUV4 silicon photomultipliers (SiPMs) for use in SBC-LAr10, a bubble chamber containing 10~kg of liquid argon. A dark-count characterization technique, which avoids the use of a single-photon source, was used at two temperatures to measure the VUV4 SiPMs breakdown voltage ($V_{\text{

Zhangyu Wang, Krzysztof Janowicz, Gengchen Mai, Ivan Majic

Intuitively, there is a relation between measures of spatial dependence and information theoretical measures of entropy. For instance, we can provide an intuition of why spatial data is special by stating that, on average, spatial data samples contain less than expected information. Similarly, spatial data, e.g., remotely sensed imagery, that is easy to comp

Leonid Bunimovich, Athulya Ram

The paper continues the study of the phenomenon of local immunodeficiency (LI) in viral cross-immunoreactivity networks, with a focus on the roles and interactions between altruistic and persistent viral variants. As always, only the state of stable (i.e. observable) LI is analysed. First, we show that a single altruistic viral variant has an upper limit for

Erik Thordsen, Erich Schubert

Many algorithms require discriminative boundaries, such as separating hyperplanes or hyperballs, or are specifically designed to work on spherical data. By applying inversive geometry, we show that the two discriminative boundaries can be used interchangeably, and that general Euclidean data can be transformed into spherical data, whenever a change in point

Ethan Shen, Alan Fan, Sarah M. Pratt, Jae Sung Park

Many applications today provide users with multiple auto-complete drafts as they type, including GitHub's code completion, Gmail's smart compose, and Apple's messaging auto-suggestions. Under the hood, language models support this by running an autoregressive inference pass to provide a draft. Consequently, providing $k$ drafts to the user requires running a

Haoze He, Daniel Kressner

We present and analyze a simple numerical method that diagonalizes a complex normal matrix A by diagonalizing the Hermitian matrix obtained from a random linear combination of the Hermitian and skew-Hermitian parts of A.

Denis Nesterov

Kim, Kresch and Oh defined unramified Gromov-Witten invariants. For a threefold, Pandharipande conjectured that they are equal to Gopakumar-Vafa invariants (BPS invariants) in the case of Fano classes and primitive Calabi-Yau classes. We prove the conjecture using a wall-crossing technique. This provides an algebro-geometric construction of Gopakumar-Vafa in

Samuel Blitz, Shahn Majid

Understanding the microscopic behavior of spacetime is critical for developing a theory of quantum gravity and perhaps solving the cosmological constant problem. In this context, it has been proposed that the quantity of interest is the quantum uncertainty in the Ricci scalar and here we investigate this for a discrete baby quantum gravity model based a sing

Xiang-Long Peng, Mozhdeh Fathidoost, Binbin Lin, Yangyiwei Yang

Machine learning techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of microstructure-informed materials modeling. This work provides a comprehensive review of the current machine learning-assisted and data-driven advancements in this field, including mic

Gonçalo R. A. Faria, Sweta Agrawal, António Farinhas, Ricardo Rei

An important challenge in machine translation (MT) is to generate high-quality and diverse translations. Prior work has shown that the estimated likelihood from the MT model correlates poorly with translation quality. In contrast, quality evaluation metrics (such as COMET or BLEURT) exhibit high correlations with human judgments, which has motivated their us

Zhangyu Wang, Gengchen Mai, Krzysztof Janowicz, Ni Lao

A wide range of (multivariate) temporal (1D) and spatial (2D) data analysis tasks, such as grouping vehicle sensor trajectories, can be formulated as clustering with given metric constraints. Existing metric-constrained clustering algorithms overlook the rich correlation between feature similarity and metric distance, i.e., metric autocorrelation. The model-

Sebastian Schwalbe, Wanja Timm Schulze, Kai Trepte, Susi Lehtola

The Perdew-Zunger (PZ) self-interaction correction (SIC) is an established tool to correct unphysical behavior in density functional approximations. Yet, PZ-SIC is well-known to sometimes break molecular symmetries. An example of this is the benzene molecule, for which PZ-SIC predicts a symmetry-broken electron density and molecular geometry, since the metho

Daniel Gómez Ferro, Maysam Yabandeh

The support for transactions is an essential part of a database management system (DBMS). Without this support, the developers are burdened with ensuring atomic execution of a transaction despite failures as well as concurrent accesses to the database by other transactions. Ideally, a transactional system provides serializability, which means that the outcom

Alexander Hägele, Elie Bakouch, Atli Kosson, Loubna Ben Allal

Scale has become a main ingredient in obtaining strong machine learning models. As a result, understanding a model's scaling properties is key to effectively designing both the right training setup as well as future generations of architectures. In this work, we argue that scale and training research has been needlessly complex due to reliance on the cosine

Alka Yadav, Sourish Das, Anirban Chakraborti

In this article, we review the interdisciplinary techniques (borrowed from physics, mathematics, statistics, machine-learning, etc.) and methodological framework that we have used to understand climate systems, which serve as examples of "complex systems". We believe that this would offer valuable insights to comprehend the complexity of climate variability

Xiao Ren, Gang Tian

We prove the global well-posedness and scattering for the 3D incompressible Euler-Coriolis system with sufficiently small, regular and suitably localized initial data. Equivalently, we obtain the asymptotic stability for "rigid body" rotational solutions to the pure Euler equations. This extends the recent work of Guo, Pausader and Widmayer to the general no

C. Arusha, Indranil Biswas

Let $X$ be a smooth irreducible complex projective curve of genus $g\,\geq\, 2$, and let $D\,=\,x_1+\dots+x_r$ be a reduced effective divisor on $X$. Denote by $U_{\alpha}(L)$ the moduli space of stable parabolic vector bundles on $X$ of rank $n$, determinant $L$ of degree $d$ with flag type $\{\{k^i_j\}_{j=1}^{m_i}\}_{i=1}^r$. Assume that the greatest commo

Christian Sattler, David Wärn

In homotopy type theory, a natural number type is freely generated by an element and an endomorphism. Similarly, an integer type is freely generated by an element and an automorphism. Using only dependent sums, identity types, extensional dependent products, and a type of two elements with large elimination, we construct a natural number type from an integer

Ioanna Gogou, Dimitrios Koutsomitropoulos

Convolutional Neural Networks (CNN) are commonly used for the problem of object detection thanks to their increased accuracy. Nevertheless, the performance of CNN-based detection models is ambiguous when detection speed is considered. To the best of our knowledge, there has not been sufficient evaluation of the available methods in terms of the speed/accurac

Yizhi Wang, Minjia Chen, Chunhui Yao, Jie Ma

Photonic neural networks (PNNs) are fast in-propagation and high bandwidth paradigms that aim to popularize reproducible NN acceleration with higher efficiency and lower cost. However, the training of PNN is known to be challenging, where the device-to-device and system-to-system variations create imperfect knowledge of the PNN. Despite backpropagation (BP)-

Yixiao Zhang, Yukara Ikemiya, Woosung Choi, Naoki Murata

Recent advances in text-to-music editing, which employ text queries to modify music (e.g.\ by changing its style or adjusting instrumental components), present unique challenges and opportunities for AI-assisted music creation. Previous approaches in this domain have been constrained by the necessity to train specific editing models from scratch, which is bo

Abdul Haddi Amjad, Shaoor Munir, Zubair Shafiq, Muhammad Ali Gulzar

Modern websites extensively rely on JavaScript to implement both functionality and tracking. Existing privacy enhancing content blocking tools struggle against mixed scripts, which simultaneously implement both functionality and tracking, because blocking the script would break functionality and not blocking it would allow tracking. We propose Not.js, a fine

Naim Shandi, Jason M. Merlo, Jeffrey A. Nanzer

We demonstrate a wireless, decentralized time-alignment method for distributed antenna arrays and distributed wireless networks that achieves picosecond-level synchronization. Distributed antenna arrays consist of spatially separated antennas that coordinate their functionality at the wavelength level to achieve coherent operations such as distributed beamfo

Dominic LaBella, Valeriia Abramova, Mehdi Astaraki, Andre Ferreira

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aimed to advance automated segmentation algorithms using the largest known multi-institutional dataset of 750 radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external b

Matthew Lefebvre, Jonathan Colen, Nikolas Claussen, Fridtjof Brauns

Morphogenesis is the process whereby the body of an organism develops its target shape. The morphogen BMP is known to play a conserved role across bilaterian organisms in determining the dorsoventral (DV) axis. Yet, how BMP governs the spatio-temporal dynamics of cytoskeletal proteins driving morphogenetic flow remains an open question. Here, we use machine

Julian Kappler

We present a perturbation approach to calculate the short-time propagator, or transition density, of the one-dimensional Fokker-Planck equation, to in principle arbitrary order in the time increment. Our approach preserves probability exactly and allows us to evaluate expectation values of analytical observables to in principle arbitrary accuracy; to showcas

Pengxiang Li, Lu Yin, Xiaowei Gao, Shiwei Liu

The rapid advancements in Large Language Models (LLMs) have revolutionized various natural language processing tasks. However, the substantial size of LLMs presents significant challenges in training or fine-tuning. While parameter-efficient approaches such as low-rank adaptation (LoRA) have gained popularity, they often compromise performance compared to fu

Tijana Zrnic

We introduce PPBoot: a bootstrap-based method for prediction-powered inference. PPBoot is applicable to arbitrary estimation problems and is very simple to implement, essentially only requiring one application of the bootstrap. Through a series of examples, we demonstrate that PPBoot often performs nearly identically to (and sometimes better than) the earlie

George Ma, Yifei Wang, Derek Lim, Stefanie Jegelka

In many applications, we desire neural networks to exhibit invariance or equivariance to certain groups due to symmetries inherent in the data. Recently, frame-averaging methods emerged to be a unified framework for attaining symmetries efficiently by averaging over input-dependent subsets of the group, i.e., frames. What we currently lack is a principled un

Anthony Sarah, Sharath Nittur Sridhar, Maciej Szankin, Sairam Sundaresan

The abilities of modern large language models (LLMs) in solving natural language processing, complex reasoning, sentiment analysis and other tasks have been extraordinary which has prompted their extensive adoption. Unfortunately, these abilities come with very high memory and computational costs which precludes the use of LLMs on most hardware platforms. To

Dongjie Chen, Kartik Patwari, Zhengfeng Lai, Xiaoguang Zhu

Existing SFDA methods struggle to fully use pre-trained knowledge and often rely on a single model's predictions or handcrafted prompts, limiting robustness under domain shift. Multimodal Large Language Models (MLLMs) offer a promising alternative: they encode rich visual-semantic knowledge and generalize well without task-specific tuning. However, their use

Phakphum Artkaew

Commonsense reasoning is one of the important aspect of natural language understanding, with several benchmarks developed to evaluate it. However, only a few of these benchmarks are available in languages other than English. Developing parallel benchmarks facilitates cross-lingual evaluation, enabling a better understanding of different languages. This resea

Daniel Hickey, Matheus Schmitz, Daniel M. T. Fessler, Paul E. Smaldino

Counterspeech has gained attention as a strategy to reduce hate speech on social media. Although previous studies suggest that counterspeech can reduce hate speech, little is known about its effects on participation in online hate communities. Relatedly, we lack an understanding about the degree of hostility in counterspeech. Hostile counterspeech may increa

Xiang Li, Zebang Shen, Liang Zhang, Niao He

Continuous-time approximation of Stochastic Gradient Descent (SGD) is a crucial tool to study its escaping behaviors from stationary points. However, existing stochastic differential equation (SDE) models fail to fully capture these behaviors, even for simple quadratic objectives. Built on a novel stochastic backward error analysis framework, we derive the H

Jun Yang

We describe the local-global compatibility of local Plancherel measures and the Tamagawa measure under the Jacquet-Langlands correspondence. We apply the notion of densities of modules over a discrete group, which generalizes the dimensions over a discrete group. We prove that the global Jacquet-Langlands correspondence preserves the densities over principal

Wan-Hsuan Lin, Jason Cong

Quantum Layout Synthesis (QLS) plays a crucial role in optimizing quantum circuit execution on physical quantum devices. As we enter the era where quantum computers have hundreds of qubits, we are faced with scalability issues using optimal approaches and degrading heuristic methods' performance due to the lack of global optimization. To this end, we introdu

Semon Rezchikov

We extend the Cohen-Jones-Segal construction of stable homotopy types associated to flow categories of Morse-Smale functions $f$ to the setting where $f$ is equivariant under a finite group action and is Morse but no longer Morse-Smale. This setting occurs universally, as equivariant Morse functions can rarely be perturbed to nearby equivariant Morse-Smale f

Eshaan Agarwal, Joykirat Singh, Vivek Dani, Raghav Magazine

Large language models (LLMs) have transformed AI across diverse domains, with prompting being central to their success in guiding model outputs. However, manual prompt engineering is both labor-intensive and domain-specific, necessitating the need for automated solutions. We introduce PromptWizard, a novel, fully automated framework for discrete prompt optim

Maria Correia de Verdier, Rachit Saluja, Louis Gagnon, Dominic LaBella

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic

Tanvir Kaur, Ashish Saxena, Partha Sarathi Mandal, Kaushik Mondal

A black hole is considered to be a dangerous node present in a graph that disposes of any resources that enter that node. Therefore, it is essential to find such a node in the graph. Let a group of agents be present on a graph $G$. The Black Hole Search (BHS) problem aims for at least one agent to survive and terminate after {finding} the black hole. This pr

V. M Maslov, V. G. Pronyaev, N. A. Tetereva, K. I. Zolotarev

The diverse measured data base of n+243Am was evaluated using a statistical theory and genera-lized least squares codes. Consistent description of total, capture and fission measured data provides an important constraint for the inelastic scattering cross section. Important constraints for the measured capture cross section in the 0.15-300 keV energy range c

Francesco Cagnetta, Matthieu Wyart

How much data is required to learn the structure of a language via next-token prediction? We study this question for synthetic datasets generated via a Probabilistic Context-Free Grammar (PCFG) -- a tree-like generative model that captures many of the hierarchical structures found in natural languages. We determine token-token correlations analytically in ou

Magdy Morshed, Arianna Rizzieri, Clément Leloup, Josquin Errard

High fidelity separation of astrophysical foreground contributions from the cosmic microwave background (CMB) signal has been recognized as one of the main challenges of modern CMB data analysis, and one which needs to be addressed in a robust way to ensure that the next generation of CMB polarization experiments lives up to its promise. In this work we cons

Riku Masui, Keisuke Totsuka

It is a challenging problem to correctly characterize the symmetry-protected topological (SPT) phases in open quantum systems. As the measurement-based quantum computation (MBQC) utilizes non-trivial edge states of the SPT phases as the logical qubit, its computational power is closely tied to the non-trivial topological nature of the phases. In this paper,

Erin Carson, Ieva Daužickaitė

Various approaches to iterative refinement (IR) for least-squares problems have been proposed in the literature and it may not be clear which approach is suitable for a given problem. We consider three approaches to IR for least-squares problems when two precisions are used and review their theoretical guarantees, known shortcomings and when the method can b

Josefine Enkner, Lorenzo Graziotto, Dalin Boriçi, Felice Appugliese

The magnetotransport of a high-mobility two-dimensional electron gas coupled to a hovering split-ring resonator with controllable distance is studied in the quantum Hall regime. The measurements reveal an enhancement by more than a factor 2 of the quantum Hall energy gaps at the fractional filling factors 4/3, 5/3, and 7/5, alongside a concurrent reduction i

Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán

Scaling hyperparameter optimisation to very large datasets remains an open problem in the Gaussian process community. This paper focuses on iterative methods, which use linear system solvers, like conjugate gradients, alternating projections or stochastic gradient descent, to construct an estimate of the marginal likelihood gradient. We discuss three key imp