November 2024 arXiv papers — page 17

Roozbeh Yousefzadeh, Xuenan Cao, Azim Ospanov

Using AI to write formal proofs for mathematical problems is a challenging task that has seen some advancements in recent years. Automated systems such as Lean can verify the correctness of proofs written in formal language, yet writing the proofs in formal language can be challenging for humans and machines. The miniF2F benchmark has 20 IMO problems in its

Zijian He, Kang Wang, Tian Fang, Lei Su

With the rapid development of global industrial production, the demand for reliability in power equipment has been continuously increasing. Ensuring the stability of power system operations requires accurate methods to detect potential faults in power equipment, thereby guaranteeing the normal supply of electrical energy. In this article, the performance of

Chao Sun, Jielei Ni, Chuntao Li, Jintian Lin

Thin-film lithium niobate (TFLN) based optical microresonators offer large nonlinear coefficient d_33 and high light-wave confinement, allowing highly efficient second-order optical nonlinear frequency conversion. Here, we achieved ultra-efficiency second harmonic generation (SHG) from high-Q polygon modes by maximizing the utilization of the highest nonline

Guofeng Zhang, Xingyu Guo, Enke Wang, Hongxi Xing

We implement a variational quantum algorithm to investigate the chiral condensate in a 1+1 dimensional SU(2) non-Abelian gauge theory. The algorithm is evaluated using a proposed Monte Carlo sampling method, which allows the extension to large qubit systems. The obtained results through quantum simulations on classical and actual quantum hardware are in good

Zhi-He Hao, Zhen-Xuan He, Jovan Maksimovic, Tomas Katkus

Near infrared emission in silicon carbide is relevant for quantum technology specifically single photon emission and spin qubits for integrated quantum photonics, quantum communication and quantum sensing. In this paper we study the fluorescence emission of direct femtosecond laser written array of color centres in silicon carbide followed by thermal anneali

Muhammad Saeed, Arash Khalatbarisoltani, Zhongwei Deng, Wenxue Liu

The reliability, lower computational complexity, and ease of implementation of control observers make them one of the most promising methods for the state estimation of Li-ion batteries (LIBs) in commercial applications. To pave their way, this study performs a comprehensive and systematic evaluation of four main categories of control observer-based methods

Jiacheng Wang, Zhedong Zheng, Wei Xu, Ping Liu

Given a single image of a target object, image-to-3D generation aims to reconstruct its texture and geometric shape. Recent methods often utilize intermediate media, such as multi-view images or videos, to bridge the gap between input image and the 3D target, thereby guiding the generation of both shape and texture. However, inconsistencies in the generated

Xunzhou Chen, Tao-Chung Ching, Di Li, Carl Heiles

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has been fully operational since 11 January 2020. We present a comprehensive analysis of the beam structure for each of the 19 feed horns on FAST's L-band receiver across the Stokes I, Q, U, and V parameters. Using an on-the-fly mapping pattern, we conducted simultaneous sky mapping using all 1

Chatchuea Kimchaiwong, Jeremie Houssineau, Adam M. Johansen

The problem of incorporating information from observations received serially in time is widespread in the field of uncertainty quantification. Within a probabilistic framework, such problems can be addressed using standard filtering techniques. However, in many real-world problems, some (or all) of the uncertainty is epistemic, arising from a lack of knowled

Michael Giudici, Luke Morgan, Cheryl E. Praeger

For an element $x$ of a finite group $T$, the $\mathrm{Aut}(T)$-class of $x$ is the set $\{ x^\sigma\mid \sigma\in \mathrm{Aut}(T)\}$. We prove that the order $|T|$ of a finite nonabelian simple group $T$ is bounded above by a function of the parameter $m(T)$, where $m(T)$ is the maximum, over all primes $p$, of the number of $\mathrm{Aut}(T)$-classes of ele

H. Keith Edwards, Michael R. Peterson, Francis Cristobal

This paper examines two senior capstone experiences developed as augmented reality tables over the past two years. Both projects were public facing efforts that required working implementations. The first project was deployed at an astronomy center and focused on interactions between land use and ecological aspects of Hawaii Island while the second project f

Chinmayee Mishra, Thomas Busch, Thomás Fogarty

We propose shortcut to adiabaticity protocols for Bose-Einstein condensates trapped in generalized anisotropic harmonic traps in three dimensions. These protocols enable high-fidelity tuning of trap geometries on time scales much faster than those required for adiabatic processes and are robust across a wide range of interaction strengths, from weakly intera

Dacheng Liao, Mengshi Qi, Liang Liu, Huadong Ma

In current open real-world autonomous driving scenarios, challenges such as sensor failure and extreme weather conditions hinder the generalization of most autonomous driving perception models to these unseen domain due to the domain shifts between the test and training data. As the parameter scale of autonomous driving perception models grows, traditional t

Shinya Kanemura, Mariko Kikuchi, Kei Yagyu

We discuss a new renormalization scheme for mixing angles in extended Higgs sectors for the coming era of the Higgs precise measurements at future lepton colliders. We focus on the two Higgs doublet models (2HDMs) with a softly-broken $Z_2$ symmetry as a simple and important example, in which two mixing angles $\alpha$ and $\beta$ appear in the Higgs sector.

Xiaoqin Zhang, Zhenni Yu, Li Zhao, Deng-Ping Fan

We rethink the segment anything model (SAM) and propose a novel multiprompt network called COMPrompter for camouflaged object detection (COD). SAM has zero-shot generalization ability beyond other models and can provide an ideal framework for COD. Our network aims to enhance the single prompt strategy in SAM to a multiprompt strategy. To achieve this, we pro

Dirceu Bagio, G. A. García, O. Márquez

We give an explicit presentation of a family of finite-dimensional pointed Hopf algebras over an algebraically closed field of characteristic zero that constitute all liftings of Nichols algebras of diagonal Cartan type $B_{3}$ over a finite abelian group.

Xinyu Shi, Yixin Tao, Shih-Chi Lin

The accurate prediction of B-cell epitopes is critical for guiding vaccine development against infectious diseases, including SARS and COVID-19. This study explores the use of a deep neural network (DNN) model to predict B-cell epitopes for SARS-CoVandSARS-CoV-2,leveraging a dataset that incorporates essential protein and peptide features. Traditional sequen

Xiaopeng Wang, Chengyi Tu, Shuhao Chen, Sicheng Wang

As the global population and the per capita demand for resource intensive diets continues to grow, the corresponding increase in food demand challenges the global food system, enhancing its reliance on trade. Most previous research typically constructed either unweighted networks or weighted solely by tonnage to represent food trade, and focused on bilateral

Ram Zaveri, Shivang Patel, Yu Gu, Gianfranco Doretto

Efficient visual trackers overfit to their training distributions and lack generalization abilities, resulting in them performing well on their respective in-distribution (ID) test sets and not as well on out-of-distribution (OOD) sequences, imposing limitations to their deployment in-the-wild under constrained resources. We introduce SiamABC, a highly effic

Ting Xiaoyang, Minfeng Zhang, Saimin Chen Zhang

In edge computing, emerging network slicing and computation offloading can support Edge Service Providers (ESPs) better handling diverse distributions of user requests, to improve Quality-of-Service (QoS) and resource efficiency. However, fluctuating traffic and heterogeneous resources seriously hinder their broader application in multi-edge systems. Existin

Xiang-Dong Shi, Sheng-Bang Qian, Lin-Jia Li

Pulsating stars in eclipsing binaries are very important to understand stellar interior structures through astroseismology because their absolute parameters such as the masses and radii can be determined in high precision based on photometric and spectroscopic data. The high-precision and continuously time-series photometric data of the Transiting Exoplanet

Yang Li, Xiangyang Wu, Zhikang Shuai, Junbin Fang

Due to the black-box nature of inverters and the wide variation range of operating points, it is challenging to on-line predict and adaptively enhance the stability of inverter-based systems. To solve this problem, this paper provides a feasible self-adaptive active damping method to eliminate potential small-signal instability of systems with black-box inve

Ziyi Xu

We investigate the $W^{1,p}$ estimates of the Neumann problem for the Schr\"odinger equation $-\Delta u+ V u={\rm div}(f)$ in the region above a convex graph. For any $p>2$, we obtain a sufficient condition for the $W^{1,p}$ solvability. As a result, we obtain sharp $W^{1,p}$ estimate $$\|\nabla u\|_{L^p(\Omega)}+\|V^\frac{1}{2}u\|_{L^p(\Omega)}\leq C\|f\|_{

Taixia Shi, Fangzheng Zhang, Yang Chen

Integrating functions such as radar and communication into a single system is of great significance for the miniaturization and functional integration of future electronic warfare and 6G systems. Here, we show a dual-functional microwave photonic system for concurrent radar and secure communication. The scheme utilizes microwave photonic frequency multiplyin

Daojun Liang, Haixia Zhang, Jing Wang, Dongfeng Yuan

In this paper, we find that existing online forecasting methods have the following issues: 1) They do not consider the update frequency of streaming data and directly use labels (future signals) to update the model, leading to information leakage. 2) Eliminating information leakage can exacerbate concept drift and online parameter updates can damage predicti

Lipeng Gu, Xuefeng Yan, Weiming Wang, Honghua Chen

The fusion of camera- and LiDAR-based detections offers a promising solution to mitigate tracking failures in 3D multi-object tracking (MOT). However, existing methods predominantly exploit camera detections to correct tracking failures caused by potential LiDAR detection problems, neglecting the reciprocal benefit of refining camera detections using LiDAR d

Paul Égré, Ellie Ripley

This paper investigates logical consequence defined in terms of probability distributions, for a classical propositional language using a standard notion of probability. We examine three distinct probabilistic consequence notions, which we call material consequence, preservation consequence, and symmetric consequence. While material consequence is fully clas

Cassiano A. Daniel

A quantizable and manifestly $\text{PSU}(1,1|2) \times \text{PSU}(1,1|2)$-invariant action for the superstring in $\rm AdS_3 \times S^3 \times T^4$ with mixed NS-NS and R-R self-dual three-form flux is constructed, which is the analogue of the $\rm AdS_5 \times S^ 5$ pure spinor action for $\rm AdS_3 \times S^3$. The model is then quantized and proven to be

Chaijun Xu, Xingdi Wei, Yu Zhang, Kaiwei Li

Graph databases are getting more and more attention in the highly interconnected data domain, and the demand for efficient querying of big data is increasing. We noticed that there are duplicate patterns in graph database queries, and the results of these patterns can be stored as materialized views first, which can speed up the query rate. So we propose mat

David Corwin, Sa'ar Zehavi

Kantor's Thesis was the first step in unifying the Chabauty-Kim and Lawrence-Venkatesh methods via relative completion. In this work, we refine Kantor's approach by addressing its limitations, achieving the first unification where a dimension inequality between local and global Galois cohomology implies Diophantine finiteness for curves. This results in a ne

Minzhe Tan, Xinlin Fan, Jian He, Yi Hou

This paper introduces a high-performance artificial intelligence operating system tailored for low-altitude aviation, designed to address key challenges such as real-time task execution, computational efficiency, and seamless modular collaboration. Built on a powerful hardware platform and leveraging the UNIX architecture, the system implements a distributed

Mohamadou Sall, M. Anwar Hasan

In 2022, a prominent supersingular isogeny-based cryptographic scheme, namely SIDH, was compromised by a key recovery attack. However, this attack does not undermine the isogeny path problem, which remains central to the security of isogeny-based cryptography. Following the attacks by Castryck and Decru, as well as Maino and Martindale, Robert gave a mature

Gagan Bansal, Jennifer Wortman Vaughan, Saleema Amershi, Eric Horvitz

Remarkable advancements in modern generative foundation models have enabled the development of sophisticated and highly capable autonomous agents that can observe their environment, invoke tools, and communicate with other agents to solve problems. Although such agents can communicate with users through natural language, their complexity and wide-ranging fai

Hongyu Chen, Xi Wu, Jiali Yang, Peizhe Tang

A Terahertz (THz) laser with strong strength could excite more than one phonons and induce a transient lattice distortion termed as nonlinear phononics. This process allows dynamic control of various physical properties, including topological properties. Here, using first-principles calculations and dynamical simulations, we demonstrate that THz laser excita

Sara Davies, Peter Gill, Daniel Horsley

For positive integers $s,t,m$ and $n$, the Zarankiewicz number $z(m,n;s,t)$ is the maximum number of edges in a subgraph of $K_{m,n}$ that has no complete bipartite subgraph containing $s$ vertices in the part of size $m$ and $t$ vertices in the part of size $n$. The best general upper bound on Zarankiewicz numbers is a bound due to Roman that can be viewed

Benjamin Jones, Guo-Wei Wei

Khovanov homology has been the subject of much study in knot theory and low dimensional topology since 2000. This work introduces a Khovanov Laplacian and a Khovanov Dirac to study knot and link diagrams. The harmonic spectrum of the Khovanov Laplacian or the Khovanov Dirac retains the topological invariants of Khovanov homology, while their non-harmonic spe

Surjyasish Mitra, A-Reum Kim, Boxin Zhao, Sushanta K. Mitra

On perfectly vertical surfaces, rolling is conventionally deemed impossible without external torque. While various species like geckos and spiders exhibit vertical locomotion, they cannot achieve rolling; instead, they fall. In this study, we demonstrate the spontaneous rolling of an elastic polyacrylamide sphere on an elastic polydimethylsiloxane (PDMS) sub

Vikas Arora, Sukanya Pal, Luminita Harnagea, D. V. S. Muthu

The spontaneous condensation of excitons in the excitonic insulating phase has been reported in Ta$_2$NiSe$_5$ below 325 K. In this context, we present the temperature-dependent optical pump optical probe spectroscopy of Ta$_2$NiSe$_5$, with a focus on coherent phonon dynamics. In addition to the fast relaxation process involving excitonic recombination, we

Chaitanya Joshi, Jinming Yang, Sergeja Slapnicar, Ryan K L Ko

Protecting against cyber-threats is vital for every organization and can be done by investing in cybersecurity controls and purchasing cyber insurance. However, these are interlinked since insurance premiums could be reduced by investing more in cybersecurity controls. The expected utility theory and the prospect theory are two alternative theories explainin

Nathan Duignan, Naoki Sato

Classical Hamiltonian mechanics, characterized by a single conserved Hamiltonian (energy) and symplectic geometry, `hides' other invariants into symmetries of the Hamiltonian or into the kernel of the Poisson tensor. Nambu mechanics aims to generalize classical Hamiltonian mechanics to ideal dynamical systems bearing two Hamiltonians, but its connection to a

Rainer Liebhart, Mansoor Shafi, Harsh Tataria, Gajan Shivanandan

Mobile communications have been undergoing a generational change every ten years. While 5G network deployments are maturing, significant efforts are being made to standardize 6G, which is expected to be commercially introduced by 2030. This paper provides unique perspectives on the 6G network (radio and core) architecture(s) from the anticipated 6G use cases

Fumiya Kakizawa, Satoshi Terasaki, Hiroshi Shinaoka

We propose a physics-informed neural network (PINN) model to efficiently predict the self-energy of Anderson impurity models (AIMs) based on the Lehmann representation. As an example, we apply the PINN model to a single-orbital AIM (SAIM) for a noninteracting electron bath with a semicircular density of states. Trained across a wide range of onsite Coulomb i

Francisco Estrada, Miguel A. Altamirano del Carmen, Oscar Calderon-Bustamante, W. J. Wouter Botzen

The financial sector is increasingly concerned with the physical risks of climate change, but economic and financial impact representations are still developing, particularly for chronic risks. Mexico's Central Bank conducted a comprehensive assessment using a suite of global models to evaluate both physical and transition risks. We present the analysis conc

Fabian Offert, Ranjodh Singh Dhaliwal

We outline some common methodological issues in the field of critical AI studies, including a tendency to overestimate the explanatory power of individual samples (the benchmark casuistry), a dependency on theoretical frameworks derived from earlier conceptualizations of computation (the black box casuistry), and a preoccupation with a cause-and-effect model

Benjamin Lou, Hudson A. Loughlin, Nergis Mavalvala

Entanglement often increases quantum measurement schemes' sensitivity. However, we find that in precision measurements with zero-mean Gaussian states, such as squeezed states, entanglement between different paths degrades measurement sensitivity. We prove an inverse relationship between entanglement entropy and sensitivity for measurements of single-mode pha

Jianyou Wang, Weili Cao, Longtian Bao, Youze Zheng

Systems that answer questions by reviewing the scientific literature are becoming increasingly feasible. To draw reliable conclusions, these systems should take into account the quality of available evidence from different studies, placing more weight on studies that use a valid methodology. We present a benchmark for measuring the methodological strength of

Yonghe Lu, Yanrong Yang, Terry Zhang

We study the relationship between model complexity and out-of-sample performance in the context of mean-variance portfolio optimization. Representing model complexity by the number of assets, we find that the performance of low-dimensional models initially improves with complexity but then declines due to overfitting. As model complexity becomes sufficiently

Raghuvansh R. Saxena, Noah G. Singer, Madhu Sudan, Santhoshini Velusamy

We explore the use of local algorithms in the design of streaming algorithms for the Maximum Directed Cut problem. Specifically, building on the local algorithm of Buchbinder et al. (FOCS'12) and Censor-Hillel et al. (ALGOSENSORS'17), we develop streaming algorithms for both adversarially and randomly ordered streams that approximate the value of maximum dir

Chao Lei, Perry T. Mahon, Allan H. MacDonald

We propose twisted bilayers of certain group IV and IVB trigonal transition metal dichalcogenides (TMDs) MX$_{2}$ (M$=$Zr, Hf, Sn and X$=$S, Se) as moir\'{e} materials. In monolayer form these TMDs have conduction band minima near the three inequivalent Brillouin zone $M$ points and negligible spin-orbit coupling, implying six flavors of low-energy conductio

Uddipan Banik, Amitava Bhattacharjee

Collisionless self-gravitating systems, e.g., cold dark matter halos, harbor universal density profiles despite the intricate non-linear physics of hierarchical structure formation, the origin of which has been a persistent mystery. To solve this problem, we develop a self-consistent quasilinear theory (QLT) in action-angle space for the collisionless relaxa

Fanny Dupont, Marianne Marcoux, Nigel Hussey, Marie Auger-Méthé

Hidden Markov models (HMMs) are a versatile statistical framework commonly used in ecology to characterize behavioural patterns from animal movement data. In HMMs, the observed data depend on a finite number of underlying hidden states, generally interpreted as the animal's unobserved behaviour. The number of states is a crucial parameter, controlling the tr

Yadong Wu, Ce Wang, Juan Yao, Hui Zhai

The rapid development of quantum technology demands efficient characterization of complex quantum many-body states. However, full quantum state tomography requires an exponential number of measurements in system size, preventing its practical use in large-scale quantum devices. A major recent breakthrough in this direction, called classical shadow tomography

A. Wild, R. R. Hartmann, E. Mariani, M. E. Portnoi

We show that a planar array of bipolar waveguides in graphene can be used to engineer gapped and tilted two-dimensional Dirac cones within the electronic band structure. The presence of these gapped and tilted Dirac cones is demonstrated through a superlattice tight-binding model and verified using a transfer matrix calculation. By varying the applied gate v

M. Vieweg, K. P. Schmidt

We introduce the XY checkerboard toric code. It represents a generalization of the $\mathbb{Z}_2$ toric code with two types of star operators with $x$ and $y$ flavor and two anisotropic star sublattices forming a checkerboard lattice. The quantum phase diagram is deduced exactly by a duality transformation to two copies of self-dual Xu-Moore models, which bu

Letian Chen, Nina Moorman, Matthew Gombolay

Reinforcement learning (RL) has demonstrated compelling performance in robotic tasks, but its success often hinges on the design of complex, ad hoc reward functions. Researchers have explored how Large Language Models (LLMs) could enable non-expert users to specify reward functions more easily. However, LLMs struggle to balance the importance of different fe

Junyang Chen, Jinshan Pan, Jiangxin Dong

Faithful image super-resolution (SR) not only needs to recover images that appear realistic, similar to image generation tasks, but also requires that the restored images maintain fidelity and structural consistency with the input. To this end, we propose a simple and effective method, named FaithDiff, to fully harness the impressive power of latent diffusio

Jingdong Zhang, Hanrong Ye, Xin Li, Wenping Wang

In recent years, simultaneous learning of multiple dense prediction tasks with partially annotated label data has emerged as an important research area. Previous works primarily focus on leveraging cross-task relations or conducting adversarial training for extra regularization, which achieve promising performance improvements, while still suffering from the

Maxwell A. Xu, Jaya Narain, Gregory Darnell, Haraldur Hallgrimsson

We present RelCon, a novel self-supervised Relative Contrastive learning approach for training a motion foundation model from wearable accelerometry sensors. First, a learnable distance measure is trained to capture motif similarity and domain-specific semantic information such as rotation invariance. Then, the learned distance provides a measurement of sema

Jonas Klüter, Macy J. Huston, Abigail Aronica, Samson A. Johnson

We present SynthPop, a new open source, modular population synthesis Galactic modeling software to simulate catalogs of Milky Way stars along any sightline outward from the Sun. Motivated by a lack flexibility in existing Galactic models, SynthPop is coded entirely in python, can be run standalone or as an imported module, and is configured by json files tha

Jiawang Nie, Zheng Qu, Xindong Tang, Linghao Zhang

This paper studies the hierarchy of sparse matrix Moment-SOS relaxations for solving sparse polynomial optimization problems with matrix constraints. First, we prove a sufficient and necessary condition for the sparse hierarchy to be tight. Second, we discuss how to detect the tightness and extract minimizers. Third, for the convex case, we show that the hie

Md. Firoz Pervez, Hongrui Zhang, Yen-Lin Huang, Lucas Caretta

Bismuth ferrite (BiFeO3) is one of the rare materials that exhibits multiferroic properties already at room-temperature. Therefore, it offers tremendous potential for future technological applications, such as memory and logic. However, a weak magnetoelectric coupling together with the presence of a noncollinear cycloidal spin order restricts various practic

John P. Bartolotta, Brian Estey, Michael Foss-Feig, David Hayes

Laser cooling methods for trapped ions are most commonly studied at low energies, i.e., in the Lamb-Dicke regime. However, ions in experiments are often excited to higher energies for which the Lamb-Dicke approximation breaks down. Here we construct a non-perturbative, semiclassical method for predicting the energy-dependent cooling dynamics of trapped-ion c

Jina Yoon, Shreya Sathyanarayanan, Franziska Roesner, Amy X. Zhang

Responding to misinformation online can be an exhausting and thankless task. It takes time and energy to write effective content, puts users at risk of online harassment, and strains personal relationships. Despite these challenges, there are people who voluntarily respond to misinformation online, and some have established communities on platforms such as R

Nicolas Kubista, Danielle Braun, Giovanni Parmigiani

Reliable tools and software for penetrance (age-specific risk among those who carry a genetic variant) estimation are critical to improving clinical decision making and risk assessment for hereditary syndromes. We introduce penetrance, an open-source R package available on CRAN, to estimate age-specific penetrance using family-history pedigree data. The pack

Theodoros Adamantopoulos, Dongwook Go, Peter M. Oppeneer, Yuriy Mokrousov

Understanding the coherent interplay of light with the magnetization in metals has been a long-standing problem in ultrafast magnetism. While it is known that when laser light acts on a metal it can induce magnetization via the process known as the inverse Faraday effect (IFE), the most basic ingredients of this phenomenon are still largely unexplored. In pa

Liu Yang, Fabian Paischer, Kaveh Hassani, Jiacheng Li

Sequential dense retrieval models utilize advanced sequence learning techniques to compute item and user representations, which are then used to rank relevant items for a user through inner product computation between the user and all item representations. However, this approach requires storing a unique representation for each item, resulting in significant

S. Rosswog, N. Sarin, E. Nakar, P. Diener

The ejection of neutron-rich matter is one of the most important consequences of a neutron star merger. While the bulk of the matter is ejected at fast, but non-relativistic velocities ($\sim0.2c$), a small amount of mildly relativistic dynamic ejecta have been seen in a number of numerical simulations. Such ejecta can have far reaching observational consequ

TEXONO Collaboration, S. Karmakar, M. K. Singh, V. Sharma

Neutrino nucleus elastic scattering ({\nu}Ael) with reactor neutrinos is an interaction under full quantum-mechanical coherence. It has not yet been experimentally observed. We present new results on the studies of {\nu}Ael cross section with an electro-cooled p-type point-contact germanium detector at the Kuo-Sheng Reactor Neutrino laboratory. A total of (2

Alexander Spangher, Kung-Hsiang Huang, Hyundong Cho, Jonathan May

As events progress, news articles often update with new information: if we are not cautious, we risk propagating outdated facts. In this work, we hypothesize that linguistic features indicate factual fluidity, and that we can predict which facts in a news article will update using solely the text of a news article (i.e. not external resources like search eng

Shuangqi Li, Hieu Le, Jingyi Xu, Mathieu Salzmann

Text-to-image diffusion models have demonstrated remarkable capability in generating realistic images from arbitrary text prompts. However, they often produce inconsistent results for compositional prompts such as "two dogs" or "a penguin on the right of a bowl". Understanding these inconsistencies is crucial for reliable image generation. In this paper, we

Ishan Bansal, Joseph Cheriyan, Sanjeev Khanna, Miles Simmons

We present improved approximation algorithms for some problems in the related areas of Capacitated Network Design and Flexible Graph Connectivity. In the Cap-$k$-ECSS problem, we are given a graph $G=(V,E)$ whose edges have non-negative costs and positive integer capacities, and the goal is to find a minimum-cost edge-set $F$ such that every non-trivial cut

Jiaman Li, C. Karen Liu, Jiajun Wu

Estimating 3D motion from 2D observations is a long-standing research challenge. Prior work typically requires training on datasets containing ground truth 3D motions, limiting their applicability to activities well-represented in existing motion capture data. This dependency particularly hinders generalization to out-of-distribution scenarios or subjects wh

Peter Kulits, Michael J. Black, Silvia Zuffi

The idea of 3D reconstruction as scene understanding is foundational in computer vision. Reconstructing 3D scenes from 2D visual observations requires strong priors to disambiguate structure. Much work has been focused on the anthropocentric, which, characterized by smooth surfaces, coherent normals, and regular edges, allows for the integration of strong ge

Daniel Martin Xavier, Ludovic Chamoin, Jawher Jerray, Laurent Fribourg

The early stopping strategy consists in stopping the training process of a neural network (NN) on a set $S$ of input data before training error is minimal. The advantage is that the NN then retains good generalization properties, i.e. it gives good predictions on data outside $S$, and a good estimate of the statistical error (``population loss'') is obtained

Alexander Sietsema, Zerrin Vural, James Chapman, Yotam Yaniv

Non-negative matrix factorization (NMF) and non-negative tensor factorization (NTF) decompose non-negative high-dimensional data into non-negative low-rank components. NMF and NTF methods are popular for their intrinsic interpretability and effectiveness on large-scale data. Recent work developed Stratified-NMF, which applies NMF to regimes where data may co

Ritvik Singh, Arthur Allshire, Ankur Handa, Nathan Ratliff

One of the most important, yet challenging, skills for a dexterous robot is grasping a diverse range of objects. Much of the prior work has been limited by speed, generality, or reliance on depth maps and object poses. In this paper, we introduce DextrAH-RGB, a system that can perform dexterous arm-hand grasping end-to-end from RGB image input. We train a pr

Luis C. N. Santos

In this paper, we investigate a generalization of Kiselev black holes by introducing a varying equation of state parameter for the anisotropic fluid surrounding the black hole. We extend this model by allowing $w$ in the expression $p_t(r)/\rho(r) = (3w + 1)/2$ to vary as a function of the radial coordinate, and derive new solutions to the Einstein field equ

Haibin Wu, Naoyuki Kanda, Sefik Emre Eskimez, Jinyu Li

Neural audio codecs (NACs) have garnered significant attention as key technologies for audio compression as well as audio representation for speech language models. While mainstream NAC models are predominantly convolution-based, the performance of NACs with a purely transformer-based, and convolution-free architecture remains unexplored. This paper introduc

Giuseppe Gaeta

Conditional symmetries were introduced by Levi and Winternitz in their 1989 seminal paper to deal with nonlinear PDEs. Here we discuss their application in the framework of ODEs, and more specifically Dynamical Systems; it turns out they are closely related to two established -- albeit maybe less widely known -- concepts, i.e. orbital symmetries and configur

Luc Jaulin

In this paper, we give a control approach to follow a trajectory for a Dubins car controlling the heading independently. The difficulty is that the Dubins car should have a heading corresponding to the argument of the vector speed of the vehicle. This non holonomic constraint can be relaxed thanks to a specific control which uses Lie brackets. We will show s

Shuangqing Wang, George A. Sutherland, James P. Pidgeon, David J. K. Swainsbury

Singlet fission (SF), the spin-allowed conversion of one singlet exciton into two triplet excitons, offers a promising strategy for enhancing the efficiency of photovoltaic devices. However, realising this potential necessitates materials capable of ultrafast (sub-picosecond) SF and the generation of long-lived (> microsecond) triplet excitons, a synthetic c

Jingyun Qian, Georg Hahn

We are interested in computing an approximation of the maximum flow in large (brain) connectivity networks. The maximum flow in such networks is of interest in order to better understand the routing of information in the human brain. However, the runtime of $O(|V||E|^2)$ for the classic Edmonds-Karp algorithm renders computations of the maximum flow on netwo

Abdelkader Belhenniche, Roman Chertovskih

This article provides a new approach on how to enhance data storage and retrieval in the Query By Image Content Systems (QBIC) by introducing the ${\rm NEM}_{\sigma}$ distance measure, satisfying the relaxed triangle inequality. By leveraging the concept of extended $b$-metric spaces, we address complex distance relationships, thereby improving the accuracy

Reetam Majumder, Shiqi Fang, A. Sankarasubramanian, Emily C. Hector

Global Climate Models (GCMs) are numerical models that simulate complex physical processes within the Earth's climate system and are essential for understanding and predicting climate change. However, GCMs suffer from systemic biases due to simplifications made to the underlying physical processes. GCM output therefore needs to be bias corrected before it ca

Luwen Huang, Lav R. Varshney, Karen E. Willcox

Verifying the correctness of a digital twin provides a formal guarantee that the digital twin operates as intended. Digital twin verification is challenging due to the presence of uncertainties in the virtual representation, the physical environment, and the bidirectional flow of information between physical and virtual. A further challenge is that a digital

Haomin Zhuang, Yihua Zhang, Kehan Guo, Jinghan Jia

Recent advancements in LLMs unlearning have shown remarkable success in removing unwanted data-model influences while preserving the model's utility for legitimate knowledge. Despite these strides, sparse Mixture-of-Experts (MoE) LLMs--a key subset of the LLM family--have remained unexplored in the context of unlearning. As MoE LLMs are celebrated for their

Maryam Khalid, Fadeel Sher Khan, John Broussard, Arko Barman

Early diagnosis and discovery of therapeutic drug targets are crucial objectives for effective management of Alzheimer's Disease (AD). Current approaches for AD diagnosis and treatment planning are based on radiological imaging and largely inaccessible for population-level screening due to prohibitive costs and limited availability. Recently, blood tests hav

Jialin Yue, Tianyuan Yao, Ruining Deng, Siqi Lu

Artificial intelligence (AI) has demonstrated significant success in automating the detection of glomeruli, the key functional units of the kidney, from whole slide images (WSIs) in kidney pathology. However, existing open-source tools are often distributed as source code or Docker containers, requiring advanced programming skills that hinder accessibility f

Ery Arias-Castro, Elizabeth Coda, Wanli Qiao

We present a method for graph clustering that is analogous to gradient ascent methods previously proposed for clustering points in space. The algorithm, which can be viewed as a max-degree hill-climbing procedure on the graph, iteratively moves each node to a neighboring node of highest degree. We show that, when applied to a random geometric graph whose nod

Yicheng Zeng, Yuhao Huang, Xiaobin Xiong

State-of-the-art model-based control designs have been shown to be successful in realizing dynamic locomotion behaviors for robotic systems. The precision of the realized behaviors in terms of locomotion performance via fly, hopping, or walking has not yet been well investigated, despite the fact that the difference between the robot model and physical hardw

Yifei Song, Jalal Jalali, Yanyu Qin, Mostafa Darabi

This paper focuses on enhancing the energy efficiency (EE) of a cooperative network that features a miniature unmanned aerial vehicle (UAV) operating at terahertz (THz) frequencies and equipped with holographic surfaces to improve network performance. Unlike traditional reconfigurable intelligent surfaces (RIS), which serve as passive relays for signal refle

Vasileios G. Ataloglou, George V. Eleftheriades

Metasurface anomalous reflectors can enhance future wireless communication systems due to their ability to redirect electromagnetic waves and establish metasurface-assisted communication links. However, metasurface reflectors typically suffer from chromatic aberrations with the reflected angle shifting as the frequency varies. Herein, we design achromatic me

Akram Yoosoofsah

Predicting extubation failure in intensive care is challenging due to complex data and the severe consequences of inaccurate predictions. Machine learning shows promise in improving clinical decision-making but often fails to account for temporal patient trajectories and model interpretability, highlighting the need for innovative solutions. This study aimed

Ram Zaveri

Catastrophic forgetting has been the leading issue in the domain of lifelong learning in artificial systems. Current artificial systems are reasonably good at learning domains they have seen before; however, as soon as they encounter something new, they either go through a significant performance deterioration or if you try to teach them the new distribution

LHC Collaboration

We report the observation of neutrino interactions without final state muons at the LHC, with a significance of 6.4$ \sigma$. A data set of proton-proton collisions at $\sqrt{s}= 13.6$ TeV collected by SND@LHC in 2022 and 2023 is used, corresponding to an integrated luminosity of 68.6 fb$^{-1}$. Neutrino interactions without a reconstructed muon are selected

Barak A. Pearlmutter, Jeffrey Mark Siskind

We show that, under certain circumstances, it is possible to automatically compute Jacobian-inverse-vector and Jacobian-inverse-transpose-vector products about as efficiently as Jacobian-vector and Jacobian-transpose-vector products. The key insight is to notice that the Jacobian corresponding to the use of one basis function is of a form whose sparsity is i

Yimeng Wang, Jihang Zhu, G. William Burg, Anand Swain

We report on a double moir\'e system consisting of four graphene layers, where the top and bottom pairs form small-twist-angle bilayer graphene, and the middle interface has a large rotational mismatch. This system shows clear signatures of two sets of spatially separated flat bands associated with the top and bottom twisted bilayer graphene (TBG) subsystems

Melika Pooyan, Hadeel Awwad, Eloy García, Robert Martí

Integrating 2D mammography with 3D magnetic resonance imaging (MRI) is crucial for improving breast cancer diagnosis and treatment planning. However, this integration is challenging due to differences in imaging modalities and the need for precise tissue segmentation and alignment. This paper addresses these challenges by enhancing biomechanical breast model

Alex Nguyen, Gautam Reddy

Transformers exhibit in-context learning (ICL): the ability to use novel information presented in the context without additional weight updates. Recent work shows that ICL emerges when models are trained on a sufficiently diverse set of tasks and the transition from memorization to generalization is sharp with increasing task diversity. One interpretation is

Neha Nanda, Manpreet Singh

In this paper, we define generalized braid theories in alignment with the language of Fenn and Bartholomew for knot theories, and compute a generating set for the pure generalized braid theories. Using this, we prove that every oriented normal generalized knot is the closure of a quasitoric normal generalized braid. Further, we prove that the set of quasitor