April 2024 arXiv papers — page 32

Alexander J. Dittmann

Nested sampling is a promising tool for Bayesian statistical analysis because it simultaneously performs parameter estimation and facilitates model comparison. MultiNest is one of the most popular nested sampling implementations, and has been applied to a wide variety of problems in the physical sciences. However, MultiNest results, like those of any samplin

Yu-Peng Wang, Jie Ren, Chen Fang

We show that 1D lattice models exhibit superdiffusive transport in the presence of random "nodal impurities" in the absence of interaction. Here a nodal impurity is defined as a localized state, the wave function of which has zeros (nodes) in momentum space. The dynamics exponent $z$, a defining quantity for transport behaviors, is computed to establish this

Avinash Chaturvedi, Stephanie Tonnesen, Greg L. Bryan, Gergö Popping

The presence of dense, neutral hydrogen clouds in the hot, diffuse intra-group and intra-cluster medium is an important clue to the physical processes controlling the survival of cold gas and sheds light on cosmological baryon flows in massive halos. Advances in numerical modeling and observational surveys means that theory and observational comparisons are

Vladimir Kazakov, Zechuan Zheng

We introduce a comprehensive framework for analyzing finite $N$ lattice Yang-Mills theory and finite $N$ matrix models. Utilizing this framework, we examine the bootstrap approach to SU(2) Lattice Yang-Mills Theory in 2,3 and 4 dimensions. The SU(2) Makeenko-Migdal loop equations on the lattice are linear and closed exclusively on single-trace Wilson loops.

Andrew King

Observations of high-redshift quasars frequently promote suggestions of large black hole masses, whose presence so early in cosmic time is not easily explicable. I consider the parallel with ultraluminous X-ray sources (ULXs) -- now known to be stellar-mass black hole (and neutron star) binaries apparently radiating far above their Eddington luminosities $L_

Jaime Spencer, Fabio Tosi, Matteo Poggi, Ripudaman Singh Arora

This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised.

Roberto Bonezzi, Felipe Diaz-Jaramillo, Olaf Hohm

We apply an algebraic double copy construction of gravity from gauge theory to three-dimensional (3D) Chern-Simons theory. The kinematic algebra ${\cal K}$ is the 3D de Rham complex of forms equipped, for a choice of metric, with a graded Lie algebra that is equivalent to the Schouten-Nijenhuis bracket on polyvector fields. The double copied gravity is defin

Ye Fang, Zeyi Sun, Tong Wu, Jiaqi Wang

Physically realistic materials are pivotal in augmenting the realism of 3D assets across various applications and lighting conditions. However, existing 3D assets and generative models often lack authentic material properties. Manual assignment of materials using graphic software is a tedious and time-consuming task. In this paper, we exploit advancements in

Charig Yang, Weidi Xie, Andrew Zisserman

Our objective is to discover and localize monotonic temporal changes in a sequence of images. To achieve this, we exploit a simple proxy task of ordering a shuffled image sequence, with `time' serving as a supervisory signal, since only changes that are monotonic with time can give rise to the correct ordering. We also introduce a transformer-based model for

Paul Hill, Philipp Lunt, Johannes Reiter, Maciej Galka

We present an optical aberration correction technique for ultracold quantum gas experiments which directly utilizes the quantum gas as a wavefront sensor. The direct use of the quantum gas enables correcting aberrations that are otherwise impractical to measure, e.g. introduced by vacuum windows. We report a root-mean-square precision and accuracy of 0.01{\l

Purnanand Elango, Dayou Luo, Abhinav G. Kamath, Samet Uzun

We present successive convexification, a real-time-capable solution method for nonconvex trajectory optimization, with continuous-time constraint satisfaction and guaranteed convergence, that only requires first-order information. The proposed framework combines several key methods to solve a large class of nonlinear optimal control problems: (i) exterior pe

Weiqi Li, Shijie Zhao, Bin Chen, Xinhua Cheng

With the advent of virtual reality technology, omnidirectional image (ODI) rescaling techniques are increasingly embraced for reducing transmitted and stored file sizes while preserving high image quality. Despite this progress, current ODI rescaling methods predominantly focus on enhancing the quality of images in equirectangular projection (ERP) format, wh

Xuanyu Zhang, Youmin Xu, Runyi Li, Jiwen Yu

AI-generated video has revolutionized short video production, filmmaking, and personalized media, making video local editing an essential tool. However, this progress also blurs the line between reality and fiction, posing challenges in multimedia forensics. To solve this urgent issue, V2A-Mark is proposed to address the limitations of current video tamperin

Toru Lin, Yu Zhang, Qiyang Li, Haozhi Qi

Aiming to replicate human-like dexterity, perceptual experiences, and motion patterns, we explore learning from human demonstrations using a bimanual system with multifingered hands and visuotactile data. Two significant challenges exist: the lack of an affordable and accessible teleoperation system suitable for a dual-arm setup with multifingered hands, and

Itamar J. Allali, Daniel Aloni, Nils Schöneberg

Models of stepped dark radiation have recently been found to have an important impact on the anisotropies of the cosmic microwave background, aiding in easing the Hubble tension. In this work, we study models with a sector of dark radiation with a step in its abundance, which thermalizes after big bang nucleosynthesis by mixing with the standard model neutri

Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye

In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements: (1) Strong Vision Encoder: we explored a continuous learning strategy for the large-scale vision foundation mod

Olivia Wiles, Chuhan Zhang, Isabela Albuquerque, Ivana Kajić

While text-to-image (T2I) generative models have become ubiquitous, they do not necessarily generate images that align with a given prompt. While previous work has evaluated T2I alignment by proposing metrics, benchmarks, and templates for collecting human judgements, the quality of these components is not systematically measured. Human-rated prompt sets are

Yongqi Li, Xinyu Lin, Wenjie Wang, Fuli Feng

With the information explosion on the Web, search and recommendation are foundational infrastructures to satisfying users' information needs. As the two sides of the same coin, both revolve around the same core research problem, matching queries with documents or users with items. In the recent few decades, search and recommendation have experienced synchron

Peter K. Morse, Paul J. Steinhardt, Salvatore Torquato

In previous work [Phys. Rev. X 5, 021020 (2015)], it was shown that stealthy hyperuniform systems can be regarded as hard spheres in Fourier-space in the sense that the the structure factor is exactly zero in a spherical region around the origin in analogy with the pair-correlation function of real-space hard spheres. In this work, we exploit this correspond

Oliver Hahn, Nikita Araslanov, Simone Schaub-Meyer, Stefan Roth

Unsupervised semantic segmentation aims to automatically partition images into semantically meaningful regions by identifying global semantic categories within an image corpus without any form of annotation. Building upon recent advances in self-supervised representation learning, we focus on how to leverage these large pre-trained models for the downstream

Harman Singh, Nitish Gupta, Shikhar Bharadwaj, Dinesh Tewari

As large language models (LLMs) see increasing adoption across the globe, it is imperative for LLMs to be representative of the linguistic diversity of the world. India is a linguistically diverse country of 1.4 Billion people. To facilitate research on multilingual LLM evaluation, we release IndicGenBench - the largest benchmark for evaluating LLMs on user-

Md Riajul Haque, Suvashis Maity, Debaprasad Maity, Yann Mambrini

We compute the relic abundance of dark matter in the presence of Primordial Black Holes (PBHs) beyond the semiclassical approximation. We take into account the quantum corrections due to the memory burden effect, which is assumed to suppress the black hole evaporation rate by the inverse power of its own entropy. Such quantum effect significantly enhances th

Zeynep Özdemir, Hacer Yalim Keles, Ömer Özgür Tanrıöver

Building accurate models for rare skin diseases remains challenging due to the lack of sufficient labeled data and the inherently long-tailed distribution of available samples. These issues are further complicated by inconsistencies in how datasets are collected and their varying objectives. To address these challenges, we compare three learning strategies:

Joshua D. Lothringer, Yifan Zhou, Daniel Apai, Xianyu Tan

We present an atmospheric retrieval analysis of HST/WFC3/G141 spectroscopic phase curve observations of two brown dwarfs, WD-0137B and EPIC-2122B, in ultra-short period orbits around white dwarf hosts. These systems are analogous to hot and ultra-hot Jupiter systems, enabling a unique and high-precision comparison to exoplanet systems. We use the PETRA retri

Xinning Hui, Yuanchao Xu, Zhishan Guo, Xipeng Shen

Recent years have witnessed increasing interest in machine learning inferences on serverless computing for its auto-scaling and cost effective properties. Existing serverless computing, however, lacks effective job scheduling methods to handle the schedule space dramatically expanded by GPU sharing, task batching, and inter-task relations. Prior solutions ha

Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng

While many contemporary large language models (LLMs) can process lengthy input, they still struggle to fully utilize information within the long context, known as the lost-in-the-middle challenge. We hypothesize that it stems from insufficient explicit supervision during the long-context training, which fails to emphasize that any position in a long context

Giorgos Kotsovolis

We give a complete list of the points in the spectrum $$\mathcal{Z}=\{\inf_{(x,y)\in\Lambda,xy\neq0}{\left\vert xy\right\vert},\,\text{$\Lambda$ is a unimodular rational lattice of $\mathbb{R}^2$}\}$$ above $\frac{1}{3}.$ We further show that the set of limit points of $\mathcal{Z}$ with values larger than $\frac{1}{3},$ is equal to the set $\{\frac{2m}{\sqr

Egor I. Kiselev, Yiming Pan

In this work, we explore the dynamics of time varying photonic media with an optical Kerr nonlinearity and an associated phase transition. The interplay between a periodically modulated permittivity and the nonlinearity induces a continuous transition of electromagnetic waves to a state with broken spatial and time translation symmetries. This transition giv

M. Žerjal, E. L. Martín, A. Pérez-Garrido

Sigma Orionis is an open cluster in the nearest giant star formation site - Orion. Its youth (3-5 Myr), low reddening, and relative vicinity make it an important benchmark cluster to study stellar and substellar formation and evolution. Young star-forming sites are complex and hierarchical. Precision astrometry from Gaia DR3 enables the exploration of their

Christian P. N. Tanner, Vivian R. K. Wall, Joshua Portner, Ahhyun Jeong

Bottom-up assembly of nanocrystals (NCs) into ordered arrays, or superlattices (SLs), is a promising route to design materials with new functionalities, but the degree of control over assembly into functional structures remains challenging. Using electrostatics, rather than density, to tune the interactions between semiconductor NCs, we watch self-assembly p

Sophie Kadan

This paper provides an overview of supersymmetry (SUSY) and the ongoing efforts to detect SUSY particles at the Large Hadron Collider (LHC). SUSY proposes corresponding "sparticles" for each Standard Model particle, with the potential to resolve the hierarchy problem, identify dark matter, and unify forces at high energy scales. We explore the key observable

Tianhui Zhang, Bei Peng, Danushka Bollegala

Generative Commonsense Reasoning (GCR) requires a model to reason about a situation using commonsense knowledge, while generating coherent sentences. Although the quality of the generated sentences is crucial, the diversity of the generation is equally important because it reflects the model's ability to use a range of commonsense knowledge facts. Large Lang

Christian N. Mayemba, D'Jeff K. Nkashama, Jean Marie Tshimula, Maximilien V. Dialufuma

This paper provides a comprehensive survey of recent advancements in leveraging machine learning techniques, particularly Transformer models, for predicting human mobility patterns during epidemics. Understanding how people move during epidemics is essential for modeling the spread of diseases and devising effective response strategies. Forecasting populatio

Frank J. Qu, Kristen M. Surrao, Boris Bolliet, J. Colin Hill

The early dark energy (EDE) extension to $\Lambda$CDM has been proposed as a candidate scenario to resolve the "Hubble tension". We present new constraints on the EDE model by incorporating new data from the Dark Energy Spectroscopic Instrument (DESI) Baryon Acoustic Oscillation (BAO) survey and CMB lensing measurements from the Atacama Cosmology Telescope (

Gahyeon Kim, Sohee Kim, Seokju Lee

Recent advances in large pre-trained vision-language models have demonstrated remarkable performance on zero-shot downstream tasks. Building upon this, recent studies, such as CoOp and CoCoOp, have proposed the use of prompt learning, where context within a prompt is replaced with learnable vectors, leading to significant improvements over manually crafted p

Yui Hayashi, Yuya Tanizaki, Hiromasa Watanabe

We study the phase diagrams of the bifundamental QCD (QCD(BF)) of different ranks, which is the $4$d $SU(N_1) \times SU(N_2)$ gauge theory coupled with a bifundamental Dirac fermion. After discussing the anomaly constraints on possible vacuum structures, we apply a novel semiclassical approach on $\mathbb{R}^2\times T^2$ with the baryon-'t Hooft flux to obta

Remi Delaunay, Ruisi Zhang, Filipe C. Pedrosa, Navid Feizi

Ultrasound imaging is a cost-effective and radiation-free modality for visualizing anatomical structures in real-time, making it ideal for guiding surgical interventions. However, its limited field-of-view, speckle noise, and imaging artifacts make it difficult to interpret the images for inexperienced users. In this paper, we propose a new 2D ultrasound to

Zoe Himwich, Shalin Parekh

We show that the directed landscape is a black noise in the sense of Tsirelson and Vershik. As a corollary, we show that for any microscopic system in which the height profile converges in law to the directed landscape, the driving noise is asymptotically independent of the height profile. This decoupling result provides one answer to the question of what ha

Cristian F. Coletti, Manuel González-Navarrete, Víctor Hugo Vázquez Guevara

We discuss a complementary asymptotic analysis of the so called minimal random walk. More precisely, we present a version of the almost sure central limit theorem as well as a generalization of the recently proposed quadratic strong laws. In addition, alternative demonstrations of the functional limit theorems will be supplied based on a P\'olya urn scheme i

Leonie Vollmar, Rick Bebon, Julia Schimpf, Bastian Flietel

Single-molecule experiments provide insight into the motion (conformational dynamics) of individual protein molecules. Usually, a well-defined but coarse-grained intramolecular coordinate is measured and subsequently analysed with the help of Hidden Markov Models (HMMs) to deduce the kinetics of protein conformational changes. Such approaches rely on the ass

Henry von Wahl, L. Ridgway Scott

We consider a test problem for Navier-Stokes solvers based on the flow around a cylinder that exhibits chaotic behavior, to examine the behavior of various numerical methods. We choose a range of Reynolds numbers for which the flow is time-dependent but can be characterized as essentially two-dimensional. The problem requires accurate resolution of chaotic d

Julien Carron, Antony Lewis

We describe a general expansion of spherical (full-sky) bispectra into a set of orthogonal modes. For squeezed shapes, the basis separates physically-distinct signals and is dominated by the lowest moments. In terms of reduced bispectra, we identify a set of discrete polynomials that are pairwise orthogonal with respect to the relevant Wigner 3j symbol, and

Viktoriia Borovik, Timothy Duff, Elima Shehu

We introduce a detection algorithm for SAGBI basis in polynomial rings, analogous to a Gr\"obner basis detection algorithm previously proposed by Gritzmann and Sturmfels. We also present two accompanying software packages named SagbiGbDetection for Macaulay2 and Julia. Both packages allow the user to find one or more term orders for which a set of input poly

Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov

With the increasing computational costs associated with deep learning, automated hyperparameter optimization methods, strongly relying on black-box Bayesian optimization (BO), face limitations. Freeze-thaw BO offers a promising grey-box alternative, strategically allocating scarce resources incrementally to different configurations. However, the frequent sur

Mengqing Liu, Kailiang Wu

Numerically simulating magnetohydrodynamics (MHD) poses notable challenges, including the suppression of spurious oscillations near discontinuities (e.g., shocks) and preservation of essential physical structures (e.g., the divergence-free constraint of magnetic field and the positivity of density and pressure). This paper develops structure-preserving oscil

Jonathan Lifflander, Philippe P. Pebay, Nicole L. Slattengren, Pierre L. Pebay

While load balancing in distributed-memory computing has been well-studied, we present an innovative approach to this problem: a unified, reduced-order model that combines three key components to describe "work" in a distributed system: computation, communication, and memory. Our model enables an optimizer to explore complex tradeoffs in task placement, such

Chujie Zheng, Ziqi Wang, Heng Ji, Minlie Huang

Given the high computational cost of preference alignment training of large language models (LLMs), exploring efficient methods to reduce the training overhead remains an important and compelling research problem. Motivated by the observation that alignment training typically involves only small parameter changes without injecting new knowledge into models,

Patrice Koehl

Transportation matrices are $m\times n$ non-negative matrices whose row sums and row columns are equal to, or dominated above with given integral vectors $R$ and $C$. Those matrices belong to a convex polytope whose extreme points have been previously characterized. In this article, a more general set of non-negative transportation matrices is considered, wh

Bohao Li, Yuying Ge, Yi Chen, Yixiao Ge

Comprehending text-rich visual content is paramount for the practical application of Multimodal Large Language Models (MLLMs), since text-rich scenarios are ubiquitous in the real world, which are characterized by the presence of extensive texts embedded within images. Recently, the advent of MLLMs with impressive versatility has raised the bar for what we c

Haizhou Shi, Zihao Xu, Hengyi Wang, Weiyi Qin

The recent success of large language models (LLMs) trained on static, pre-collected, general datasets has sparked numerous research directions and applications. One such direction addresses the non-trivial challenge of integrating pre-trained LLMs into dynamic data distributions, task structures, and user preferences. Pre-trained LLMs, when tailored for spec

Muhittin Evren Aydin, Adela Mihai, Cihan Özgür

In this paper we study rectifying submanifolds of a Riemannian manifold endowed with an anti-torqued vector field. For this, we first determine a necessary and sufficient condition for the ambient space to admit such a vector field. Then we characterize submanifolds for which an anti-torqued vector field is always assumed to be tangent or normal. A similar c

Parsa H. S. Panahi, Amir H. Jalilvand, Abolfazl Diyanat

The Internet service provider industry is currently experiencing intense competition as companies strive to provide top-notch services to their customers. Providers are introducing cutting-edge technologies to enhance service quality, understanding that their survival depends on the level of service they offer. However, evaluating service quality is a comple

Andrea Rivezzi

The universal Drinfeld-Yetter algebra is an associative algebra whose co-Hochschild cohomology controls the existence of quantization functors of Lie bialgebras, such as the renowned one due to Etingof and Kazhdan. It was initially introduced by Enriquez and later re-interpreted by Appel and Toledano Laredo as an algebra of endomorphisms in the colored PROP

Alexandros Konstantinou

We investigate the order of the Tate--Shafarevich group of abelian varieties modulo rational squares. Our main result shows that every square-free natural number appears as the non square-free part of the Tate--Shafarevich group of some abelian variety, thereby validating a conjecture of W. Stein.

Pascal Halffmann, Steve Lenk, Michael Trebing

In the rapidly advancing domain of quantum optimization, the confluence of quantum algorithms such as Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) with robust optimization methodologies presents a cutting-edge frontier. Although it seems natural to apply quantum algorithms when facing uncertainty, this has barely been appr

Xie-Hang Yu, J. Ignacio Cirac, Pavel Kos, Georgios Styliaris

Efficient characterization of higher dimensional many-body physical states presents significant challenges. In this paper, we propose a new class of Project Entangled Pair State (PEPS) that incorporates two isometric conditions. This new class facilitates the efficient calculation of general local observables and certain two-point correlation functions, whic

Per Berglund, Tim Gräfnitz, Michael Lathwood

Under mirror symmetry a non-Fano variety $X$ corresponds to an instanton corrected Hori-Vafa potential $W$. The classical period of $W$ equals the regularized quantum period of $X$, which is a generating function for descendant Gromov-Witten invariants. These periods define closed mirror maps relating complex with symplectic parameters and open mirror maps r

Karthik Gopinath, Xiaoling Hu, Malte Hoffmann, Oula Puonti

In human neuroimaging studies, atlas registration enables mapping MRI scans to a common coordinate frame, which is necessary to aggregate data from multiple subjects. Machine learning registration methods have achieved excellent speed and accuracy but lack interpretability and flexibility at test time (since their deformation model is fixed). More recently,

Jan Kochanowski, Alvaro M. Alhambra, Angela Capel, Cambyse Rouzé

Quantum systems typically reach thermal equilibrium rather quickly when coupled to a thermal environment. The usual way of bounding the speed of this process is by estimating the spectral gap of the dissipative generator. However the gap, by itself, does not always yield a reasonable estimate for the thermalization time in many-body systems: without further

Olli Järviniemi, Evan Hubinger

We study the tendency of AI systems to deceive by constructing a realistic simulation setting of a company AI assistant. The simulated company employees provide tasks for the assistant to complete, these tasks spanning writing assistance, information retrieval and programming. We then introduce situations where the model might be inclined to behave deceptive

Tongzhou Mu, Minghua Liu, Hao Su

The success of many RL techniques heavily relies on human-engineered dense rewards, which typically demand substantial domain expertise and extensive trial and error. In our work, we propose DrS (Dense reward learning from Stages), a novel approach for learning reusable dense rewards for multi-stage tasks in a data-driven manner. By leveraging the stage stru

Alberto Bombardelli, Laura Bozzelli, César Sánchez, Stefano Tonetta

We introduce and investigate a powerful hyper logical framework in the linear-time setting, we call generalized HyperLTL with stuttering and contexts (GHyperLTL_SC for short). GHyperLTL_SC unifies known asynchronous extensions of HyperLTL and the well-known extension KLTL of LTL with knowledge modalities under both the synchronous and asynchronous perfect re

Jianxiang Zang, Hui Liu

Representation-based Siamese networks have risen to popularity in lightweight text matching due to their low deployment and inference costs. While word-level attention mechanisms have been implemented within Siamese networks to improve performance, we propose Feature Attention (FA), a novel downstream block designed to enrich the modeling of dependencies amo

Matthew Speers, David Randell, Jonathan Angus Tawn, Philip Jonathan

Extreme value analysis (EVA) uses data to estimate long-term extreme environmental conditions for variables such as significant wave height and period, for the design of marine structures. Together with models for the short-term evolution of the ocean environment and for wave-structure interaction, EVA provides a basis for full probabilistic design analysis.

Shufan Wang, Guojun Xiong, Shichen Zhang, Huacheng Zeng

We study the data packet transmission problem (mmDPT) in dense cell-free millimeter wave (mmWave) networks, i.e., users sending data packet requests to access points (APs) via uplinks and APs transmitting requested data packets to users via downlinks. Our objective is to minimize the average delay in the system due to APs' limited service capacity and unreli

Heng Lin, Jinghui Pi, Yunyao Qi, Wei Qin

A unique phenomenon in non-Hermitian systems is the non-Hermitian skin effect (NHSE), namely the boundary localization of continuous-spectrum eigenstates. However, studies on the NHSE in systems without translational invariance are still limited. Here, we unveil a new class of NHSE, dubbed the imaginary-Stark skin effect (ISSE), in a one-dimensional lossy la

Xu Ji, Jianyi Zhang, Ziyin Zhou, Zhangchi Zhao

Ensuring the resilience of Large Language Models (LLMs) against malicious exploitation is paramount, with recent focus on mitigating offensive responses. Yet, the understanding of cant or dark jargon remains unexplored. This paper introduces a domain-specific Cant dataset and CantCounter evaluation framework, employing Fine-Tuning, Co-Tuning, Data-Diffusion,

David Rivas-Villar, Álvaro S. Hervella, José Rouco, Jorge Novo

Self-supervised contrastive learning has emerged as one of the most successful deep learning paradigms. In this regard, it has seen extensive use in image registration and, more recently, in the particular field of medical image registration. In this work, we propose to test and extend and improve a state-of-the-art framework for color fundus image registrat

Damir Bečirević, Svjetlana Fajfer, Nejc Košnik, Lovre Pavičić

Motivated by the long-standing discrepancy in lepton flavor universality ratios $R_D$ and $R_{D^{\ast}}$ we assess the status of scalar leptoquark states $R_2$, $\widetilde R_2$ and $S_1$ which can in principle provide a desired enhancement of $\mathcal{B}(B\to D^{(\ast )}\tau \nu)$ in a minimal setup with two Yukawa couplings only. We consider unavoidable l

Jiehui Huang, Xiao Dong, Wenhui Song, Zheng Chong

Diffusion-based technologies have made significant strides, particularly in personalized and customized facialgeneration. However, existing methods face challenges in achieving high-fidelity and detailed identity (ID)consistency, primarily due to insufficient fine-grained control over facial areas and the lack of a comprehensive strategy for ID preservation

Zheng-Yuan Yue, Zheng-Tao Xu, Shuo Yang, Zheng-Cheng Gu

The physical nature of pseudogap phase is one of the most important and intriguing problems towards understanding the key mechanism of high temperature superconductivity in cuprates. Theoretically, the square-lattice $t$-$J$ model is widely believed to be the simplest toy model that captures the essential physics of cuprate superconductors. We employ the Gra

Giulia Chiari, Martina Conte, Marcello Delitala

Tumor cell migration within the microenvironment is a crucial aspect for cancer progression and, in this context, hypoxia has a significant role. An inadequate oxygen supply acts as an environmental stressor inducing migratory bias and phenotypic changes. In this paper, we propose a novel multi-scale mathematical model to analyze the pivotal role of Snail pr

Abhinaba Bala, Ashok Urlana, Rahul Mishra, Parameswari Krishnamurthy

Obtaining sufficient information in one's mother tongue is crucial for satisfying the information needs of the users. While high-resource languages have abundant online resources, the situation is less than ideal for very low-resource languages. Moreover, the insufficient reporting of vital national and international events continues to be a worry, especiall

Philip Koopman, William Widen

Existing definitions and associated conceptual frameworks for computer-based system safety should be revisited in light of real-world experiences from deploying autonomous vehicles. Current terminology used by industry safety standards emphasizes mitigation of risk from specifically identified hazards, and carries assumptions based on human-supervised vehicl

Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell

While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) applications, including the fine-tuning of generative models. Unfortunately, PPO requires multiple heuristics to enable stable convergence (e.g. value networks, clipping), and is notorious fo

Runzhe Zhan, Xinyi Yang, Derek F. Wong, Lidia S. Chao

While supervised fine-tuning (SFT) has been a straightforward approach for tailoring the output of foundation large language model (LLM) to specific preferences, concerns have been raised about the depth of this alignment, with some critiques suggesting it is merely "superficial". We critically examine this hypothesis within the scope of cross-lingual genera

Dmitriy Sholokhov, Saran Shaju, Ke Li, Simon B. Jäger

We investigate properties of the lasing action observed on the 1S0--3P1 intercombination transition of ytterbium atoms that are laser-cooled and -trapped inside a high-finesse cavity. The dressing of the atomic states on the 1S0--1P1 transition by the magneto-optical trap (MOT) laser light allows the coupled atom-cavity system to lase, via a two-photon trans

Fynn Petersen-Frey, Chris Biemann

Extracting who says what to whom is a crucial part in analyzing human communication in today's abundance of data such as online news articles. Yet, the lack of annotated data for this task in German news articles severely limits the quality and usability of possible systems. To remedy this, we present a new, freely available, creative-commons-licensed datase

Wojciech J. Jankowski, Daniel Bennett, Aneesh Agarwal, Gaurav Chaudhary

Two-dimensional materials are a highly tunable platform for studying the momentum space topology of the electronic wavefunctions and real space topology in terms of skyrmions, merons, and vortices of an order parameter. Such textures for electronic polarization can exist in moir\'e heterostructures. A quantum-mechanical definition of local polarization textu

Liang Ji, Kalyanasundaram Seshadri

A novel method for analyzing counterflow diffusion flames, inspired by Zurada's sensitivity approach for neural networks, is proposed to identify critical species influencing the heat release rate in combustion. By further analyzing concentration changes of selected key species and radicals, this method reveals complex interactions among them across regions

Flavio Bento de Oliveira, Gabriel Elyas Gama Araujo, Andreia Luisa da Rosa

Here, we present a systematic first-principles study of hydrogen adsorption on pristine and Janus MX2 and MSSe monolayers (M = Ni, Pd, Pt; X = S, Se), combining density-functional theory (DFT) calculations with finite-temperature ab initio molecular dynamics simulations (AIMD). Orbital-resolved electronic-structure analysis reveals that hydrogen binding stre

J. García Galofre, M. Pérez Millán, A. Galarza Rial, R. Laubenbacher

Boolean networks can be viewed as functions on the set of binary strings of a given length, described via logical rules. They were introduced as dynamic models into biology, in particular as logical models of intracellular regulatory networks involving genes, proteins, and metabolites. Since genes can have several modes of action depending on their expressio

Vitor Cerqueira, Moisés Santos, Luis Roque, Yassine Baghoussi

Deep learning approaches are increasingly used to tackle forecasting tasks involving datasets with multiple univariate time series. A key factor in the successful application of these methods is a large enough training sample size, which is not always available. Synthetic data generation techniques can be applied in these scenarios to augment the dataset. Da

G. -Q. Hai, L. Cândido, B. G. A. Brito, Y. Liu

Electronic correlation is a complex many-body effect and the correlation energy depends on the specific electronic structure and spatial distribution of electrons in each atom and molecule. Although the total correlation energy in an atom can be decomposed into different components such as inter-orbital and intra-orbital pair-correlation energies (PCE), it i

Alan Chávez, Nelson Aragonés, Manuel Pinto, Ulices Zavaleta

In the present work, we prove that, if $A(\cdot)$ is a compact almost automorphic matrix and the system $$x'(t) = A(t)x(t)\, ,$$ possesses an exponential dichotomy with Green function $G(\cdot, \cdot)$, then its associated system $$y'(t) = B(t)y(t)\, ,$$ where $B(\cdot) \in H(A)$ (the hull of $A(\cdot)$) also possesses an exponential dichotomy. Moreover, the

Sebastián E. Deghi, Raúl A. Bustos-Marún

Due to technological needs, nanoscale heat management, energy conversion and quantum thermodynamics have become key areas of research, putting heat pumps and nanomotors center stage. The treatment of these particular systems often requires the use of adiabatic expansions in terms of the frequency of the external driving or the velocity of some classical degr

Gilles Bonnet, Anna Gusakova

In this article we obtain concentration inequalities for Poisson $U$-statistics $F_m(f,\eta)$ of order $m\ge 1$ with kernels $f$ under general assumptions on $f$ and the intensity measure $\gamma \Lambda$ of underlying Poisson point process $\eta$. The main result are new concentration bounds of the form \[ \mathbb{P}(|F_m ( f , \eta) -\mathbb{E} F_m ( f , \

Christopher Cornwell, Nathan McNew

In 1977 Diaconis and Graham proved two inequalities relating different measures of disarray in permutations, and asked for a characterization of those permutations for which equality holds in one of these inequalities. Such a characterization was first given in 2013. Recently, another characterization was given by Woo, using a topological link in $\mathbb R^

Xiaoman Zhang, Chaoyi Wu, Ziheng Zhao, Jiayu Lei

Developing generalist foundation model has recently attracted tremendous attention among researchers in the field of AI for Medicine (AI4Medicine). A pivotal insight in developing these models is their reliance on dataset scaling, which emphasizes the requirements on developing open-source medical image datasets that incorporate diverse supervision signals a

Rahul Sahay, Ruben Verresen

Measurements and feedback have emerged as powerful resources for creating many-body quantum states. However, a detailed understanding has been restricted to fixed-point representatives of phases of matter. Here, we go beyond this and characterize the patterns of many-body entanglement that can be deterministically created from measurement. Focusing on one sp

Sai Kumar Dwivedi, Yu Sun, Priyanka Patel, Yao Feng

We address the problem of regressing 3D human pose and shape from a single image, with a focus on 3D accuracy. The current best methods leverage large datasets of 3D pseudo-ground-truth (p-GT) and 2D keypoints, leading to robust performance. With such methods, we observe a paradoxical decline in 3D pose accuracy with increasing 2D accuracy. This is caused by

Chi-Fang Chen, Adam Bouland, Fernando G. S. L. Brandão, Jordan Docter

In this work we give an efficient construction of unitary $k$-designs using $\tilde{O}(k\cdot poly(n))$ quantum gates, as well as an efficient construction of a parallel-secure pseudorandom unitary (PRU). Both results are obtained by giving an efficient quantum algorithm that lifts random permutations over $S(N)$ to random unitaries over $U(N)$ for $N=2^n$.

Patrick Sprenger, Christopher Chong, Emmanuel Okyere, Michael Herrmann

The Riemann problem for the discrete conservation law $2 \dot{u}_n + u^2_{n+1} - u^2_{n-1} = 0$ is classified using Whitham modulation theory, a quasi-continuum approximation, and numerical simulations. A surprisingly elaborate set of solutions to this simple discrete regularization of the inviscid Burgers' equation is obtained. In addition to discrete analo

Franco Herrera, Sergei Trofimchuk

We continue to study (see arXiv:2401.08618, https://doi.org/10.48550/arXiv.2401.08618) a renewal equation $\phi(t)=\frak F\phi_t$ proposed in [C. Barril et al., J. Math. Biology, https://doi.org/10.1007/s00285-024-02084-x] to model trees growth. This time we are considering the case when the per capita reproduction rate $\beta(x)$ is a non-monotone (unimodal

Junting Dong, Qi Fang, Zehuan Huang, Xudong Xu

This paper addresses the task of 3D clothed human generation from textural descriptions. Previous works usually encode the human body and clothes as a holistic model and generate the whole model in a single-stage optimization, which makes them struggle for clothing editing and meanwhile lose fine-grained control over the whole generation process. To solve th

Michael L. Palumbo, Steven H. Saar, Raphaëlle D. Haywood

Stellar photospheric inhomogeneities are a significant source of noise which currently precludes the discovery of Earth-mass planets orbiting Sun-like stars with the radial-velocity (RV) method. To complement several previous studies which have used ground- and spaced-based facilities to characterize the RV of the Sun, we here characterize the center-to-limb

Chenyang Wang, Yun Yang

This work introduces a new method for selecting the number of components in finite mixture models (FMMs) using variational Bayes, inspired by the large-sample properties of the Evidence Lower Bound (ELBO) derived from mean-field (MF) variational approximation. Specifically, we establish matching upper and lower bounds for the ELBO without assuming conjugate

Jing Ouyang, Chengyu Cui, Kean Ming Tan, Gongjun Xu

Latent variable models are popularly used to measure latent factors (e.g., abilities and personalities) from large-scale assessment data. Beyond understanding these latent factors, the covariate effect on responses controlling for latent factors is also of great scientific interest and has wide applications, such as evaluating the fairness of educational tes

Pasquale Caporaso, Giuseppe Bianchi, Francesco Quaglia

Modern malware poses a severe threat to cybersecurity, continually evolving in sophistication. To combat this threat, researchers and security professionals continuously explore advanced techniques for malware detection and analysis. Dynamic analysis, a prevalent approach, offers advantages over static analysis by enabling observation of runtime behavior and

Chanho Park, Mingjie Chen, Thomas Hain

Word error rate (WER) is a metric used to evaluate the quality of transcriptions produced by Automatic Speech Recognition (ASR) systems. In many applications, it is of interest to estimate WER given a pair of a speech utterance and a transcript. Previous work on WER estimation focused on building models that are trained with a specific ASR system in mind (re