December 2023 arXiv papers — page 82

Yadi Cao, Yidong Zhao, Minchen Li, Yin Yang

The Material Point Method (MPM) is a hybrid Eulerian Lagrangian simulation technique for solid mechanics with significant deformation. Structured background grids are commonly employed in the standard MPM, but they may give rise to several accuracy problems in handling complex geometries. When using (2D) unstructured triangular or (3D) tetrahedral background

Mu-Kun Lee, Rubén M. Otxoa, Masahito Mochizuki

We theoretically discover possible emergence of reentrant Walker breakdowns for current-driven domain walls in layered antiferromagnets in striking contrast to the unique Walker breakdown in ferromagnets. We reveal that the Lorentz contraction of domain-wall width in antiferromagnets gives rise to nonlinear current-dependence of the wall velocity and the pre

Jiaqi Liu, Jian Lou, Zhan Qin, Kui Ren

We study the problem of $(\epsilon,\delta)$-certified machine unlearning for minimax models. Most of the existing works focus on unlearning from standard statistical learning models that have a single variable and their unlearning steps hinge on the direct Hessian-based conventional Newton update. We develop a new $(\epsilon,\delta)$-certified machine unlear

Karthik H J, Sarga P K, Swastibrata Bhattacharyya

Search for one dimensional (1D) van der Waals materials has become an urgent need to meet the demand as building blocks for high performance, miniaturized, lightweight device applications. Polyyne, a 1D atomic chain of carbon is the thinnest and strongest allotrope of carbon, showing promising applications in new generation low dimensional devices due to the

Pilar Ruiz-Lapuente, Jonay I. González Hernández

Here we present an approach to the measurement of extragalactic distances using twin SNe Ia, taken from the early down to the nebular phase.The approach is purely empirical, although we can give a theoretical background on why the method is reliable. By studying those twins in galaxies where peculiar velocities are relatively unimportant, we can tackle the H

Denis Chetverikov, Jinyong Hahn, Zhipeng Liao, Shuyang Sheng

We propose logit-based IV and augmented logit-based IV estimators that serve as alternatives to the traditionally used 2SLS estimator in the model where both the endogenous treatment variable and the corresponding instrument are binary. Our novel estimators are as easy to compute as the 2SLS estimator but have an advantage over the 2SLS estimator in terms of

Raviteja Anantha, Bortik Bandyopadhyay, Anirudh Kashi, Sayantan Mahinder

Large language models (LLMs) are increasingly employed for complex multi-step planning tasks, where the tool retrieval (TR) step is crucial for achieving successful outcomes. Two prevalent approaches for TR are single-step retrieval, which utilizes the complete query, and sequential retrieval using task decomposition (TD), where a full query is segmented int

David J. Aldous, F. Thomas Bruss

We give elementary examples within a framework for studying decisions under uncertainty where probabilities are only roughly known. The framework, in gambling terms, is that the size of a bet is proportional to the gambler's perceived advantage based on their perceived probability, and their accuracy in estimating true probabilities is measured by mean squar

Yuchen Li, Laura Balzano, Deanna Needell, Hanbaek Lyu

Block majorization-minimization (BMM) is a simple iterative algorithm for nonconvex optimization that sequentially minimizes a majorizing surrogate of the objective function in each block coordinate while the other block coordinates are held fixed. We consider a family of BMM algorithms for minimizing smooth nonconvex objectives, where each parameter block i

Yu-An Liu, Ruqing Zhang, Mingkun Zhang, Wei Chen

Neural ranking models (NRMs) have shown great success in information retrieval (IR). But their predictions can easily be manipulated using adversarial examples, which are crafted by adding imperceptible perturbations to legitimate documents. This vulnerability raises significant concerns about their reliability and hinders the widespread deployment of NRMs.

Daniel Harnack, Christoph Lüth, Lukas Gross, Shivesh Kumar

Generating physical movement behaviours from their symbolic description is a long-standing challenge in artificial intelligence (AI) and robotics, requiring insights into numerical optimization methods as well as into formalizations from symbolic AI and reasoning. In this paper, a novel approach to finding a reward function from a symbolic description is pro

Kushal Patel, Palki Gakkhar, Korak Biswas, S Sagar Maurya

This article presents spectroscopy results of the $5s5p{\;^3}P_0 \rightarrow 5s5d{\;^3}D_1$ transition in all isotopes of laser cooled Sr atoms and the utility of this transition for repumping application. By employing the $5s5p{\;^{3} P_{0}} \rightarrow 5s5d{\;^3}D_1 $ (483 nm) transition in combination with the excitation of $5s5p{\;^3}P_2 \rightarrow 5s6s

Yang Liu, Shi Shu, Ying Yang

The Poisson-Nernst-Planck (PNP) equations are one of the most effective model for describing electrostatic interactions and diffusion processes in ion solution systems, and have been widely used in the numerical simulations of biological ion channels, semiconductor devices, and nanopore systems. Due to the characteristics of strong coupling, convection domin

Yehjin Shin, Jeongwhan Choi, Hyowon Wi, Noseong Park

Sequential recommendation (SR) models based on Transformers have achieved remarkable successes. The self-attention mechanism of Transformers for computer vision and natural language processing suffers from the oversmoothing problem, i.e., hidden representations becoming similar to tokens. In the SR domain, we, for the first time, show that the same problem o

Lei Wang, Jieming Bian, Jie Xu

Federated learning (FL) with noisy labels poses a significant challenge. Existing methods designed for handling noisy labels in centralized learning tend to lose their effectiveness in the FL setting, mainly due to the small dataset size and the heterogeneity of client data. While some attempts have been made to tackle FL with noisy labels, they primarily fo

Pascal Passigan, Kidus Yohannes, Joshua Pereira

The wayward quality of continuous prompts stresses the importance of their interpretability as unexpected and unpredictable behaviors appear following training, especially in the context of large language models automating people-sensitive tasks such as resume screening. In this paper we present a novel method of constructing continuous prompts via discrete

Hang Cheung, Ho Man Tai, Jinniao Qiu

This paper is devoted to solving a class of second order Hamilton-Jacobi-Bellman (HJB) equations in the Wasserstein space, associated with mean field control problems involving common noise. The well-posedness of viscosity solutions to the HJB equation under a new notion is established under general assumptions on the coefficients. Our approach adopts the sm

Fuheng Zhao, Jiayue Chen, Lawrence Lim, Ishtiyaque Ahmad

Judging the equivalence between two SQL queries is a fundamental problem with many practical applications in data management and SQL generation (i.e., evaluating the quality of generated SQL queries in text-to-SQL task). While the research community has reasoned about SQL equivalence for decades, it poses considerable difficulties and no complete solutions e

Decheng Liu, Xu Luo, Chunlei Peng, Nannan Wang

This paper studies the problem of zero-shot sketch-based image retrieval (ZS-SBIR), which aims to use sketches from unseen categories as queries to match the images of the same category. Due to the large cross-modality discrepancy, ZS-SBIR is still a challenging task and mimics realistic zero-shot scenarios. The key is to leverage transferable knowledge from

Mark E. Ritchie, Christopher P. Kempes

Metabolic scaling is one of the most important patterns in biology. Theory explaining the 3/4-power size-scaling of biological metabolic rate does not predict the non-linear scaling observed for smaller life forms. Here we present a new model for cells $<10^{-8}$ m$^{3}$ that maximizes power from the reaction-displacement dynamics of enzyme-catalyzed reactio

Yug Dedhia, Anjali Singh, Vaibhav Singh Tomar, Nimmi Rangaswamy

AI is about learning algorithms and huge amounts of data and are drivers of economic growth -- what does this mean for the field of development studies? Can we re-orient to twin AI studies and development theory and practice to generate how development challenges are identified and researched? To do this a good grasp is needed of AI internal mechanisms and o

Yuan Yu, Zuojian Qin, Siwei Chen, Shi Shu

In this paper, we propose a novel two-relaxation-time regularized lattice Boltzmann (TRT-RLB) model for simulating weakly compressible isothermal flows. A free relaxation parameter, $\tau_{s,2}$, is employed to relax the regularized non-equilibrium third-order terms. Chapman-Enskog analysis reveals that our model can accurately recover the Navier-Stokes equa

Rui Yang, Wenrui Dai, Huajun She, Yiping P. Du

Building comprehensive brain connectomes has proved of fundamental importance in resting-state fMRI (rs-fMRI) analysis. Based on the foundation of brain network, spatial-temporal-based graph convolutional networks have dramatically improved the performance of deep learning methods in rs-fMRI time series classification. However, existing works either pre-defi

Yuan Yu, Zuojian Qin, Haizhuan Yuan, Shi Shu

In this paper, a new two-relaxation-time regularized (TRT-R) lattice Boltzmann (LB) model for convection-diffusion equation (CDE) with variable coefficients is proposed. Within this framework, we first derive a TRT-R collision operator by constructing a new regularized procedure through the high-order Hermite expansion of non-equilibrium. Then a first-order

Yimeng Zhang, Alexander Cloninger, Bo Li, Xiaochuan Tian

We develop a data-driven machine learning approach to identifying parameters with steady-state solutions, locating such solutions, and determining their linear stability for systems of ordinary differential equations and dynamical systems with parameters. Our approach first constructs target functions for these tasks, then designs a parameter-solution neural

Yitian Liu, Zhouhui Lian

Few-shot font generation, especially for Chinese calligraphy fonts, is a challenging and ongoing problem. With the help of prior knowledge that is mainly based on glyph consistency assumptions, some recently proposed methods can synthesize high-quality Chinese glyph images. However, glyphs in calligraphy font styles often do not meet these assumptions. To ad

Uriel A. Aceves Rodriguez, Filipe Guimarães, Samir Lounis

The ability to simulate realistically the electronic structure of superconducting materials is important to understand and predict various properties emerging in both the superconducting topological and spintronics realms. We introduce a tight-binding implementation of the Bogoliubov-de Gennes method, parameterized from density functional theory, which we ut

Danish Gufran, Saideep Tiku, Sudeep Pasricha

Smartphone-based indoor localization has emerged as a cost-effective and accurate solution to localize mobile and IoT devices indoors. However, the challenges of device heterogeneity and temporal variations have hindered its widespread adoption and accuracy. Towards jointly addressing these challenges comprehensively, we propose STELLAR, a novel framework im

Akihiro Ishibashi, Kengo Maeda, Takashi Okamura

We show that $3$-dimensional AdS spacetime can be semiclassically unstable due to strongly interacting quantum field effects. In our previous paper, we have pointed out the possibility of such an instability of AdS$_3$ by inspecting linear perturbations of the (covering space of) static BTZ black hole with AdS${}_4$ gravity dual in the context of holographic

Rui Yang, Wenrui Dai, Chenglin Li, Junni Zou

Single-cell RNA sequencing (scRNA-seq) technology provides high-throughput gene expression data to study the cellular heterogeneity and dynamics of complex organisms. Graph neural networks (GNNs) have been widely used for automatic cell type classification, which is a fundamental problem to solve in scRNA-seq analysis. However, existing methods do not suffic

Yuxin Chen, Yifan Yin, Julian Brown, Kevin Wang

Ultrasound (US) imaging is a vital adjunct to mammography in breast cancer screening and diagnosis, but its reliance on hand-held transducers often lacks repeatability and heavily depends on sonographers' skills. Integrating US systems from different vendors further complicates clinical standards and workflows. This research introduces a co-robotic US platfo

Hyewon Jeong, Nassim Oufattole, Matthew Mcdermott, Aparna Balagopalan

In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event. For example, quantifying the risk of adverse outcomes after an acute cardiovascular event helps healthcare providers identify those patients at the highest risk of poor outcomes; i.e., patients who benefit from invasive therap

Shulei Ji, Xinyu Yang

Generating music with emotion is an important task in automatic music generation, in which emotion is evoked through a variety of musical elements (such as pitch and duration) that change over time and collaborate with each other. However, prior research on deep learning-based emotional music generation has rarely explored the contribution of different music

Isabelle Tingzon, Nuala Margaret Cowan, Pierre Chrzanowski

Comprehensive information on housing stock is crucial for climate adaptation initiatives aiming to reduce the adverse impacts of climate-extreme hazards in high-risk regions like the Caribbean. In this study, we propose a workflow for rapidly generating critical baseline housing stock data using very high-resolution drone images and deep learning techniques.

Zhaoxi Mu, Xinyu Yang, Sining Sun, Qing Yang

Speech signals are inherently complex as they encompass both global acoustic characteristics and local semantic information. However, in the task of target speech extraction, certain elements of global and local semantic information in the reference speech, which are irrelevant to speaker identity, can lead to speaker confusion within the speech extraction n

Yiqian Chen, Peng Wang, Haitang Yang

It has been reported that the photon ring structure in black hole images produces strong and universal interferometric signatures on long interferometric baselines, holding promise for measuring black hole parameters and testing general relativity. This paper investigates the interferometric signatures of black holes with one or two photon spheres, specifica

Shufan Wang, Guojun Xiong, Jian Li

Restless multi-armed bandits (RMAB) have been widely used to model sequential decision making problems with constraints. The decision maker (DM) aims to maximize the expected total reward over an infinite horizon under an "instantaneous activation constraint" that at most B arms can be activated at any decision epoch, where the state of each arm evolves stoc

Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang

Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may compromise model performance. To address this challenge, we introduce \textsc{Nuggets}, a novel and efficient methodology that leverages one-shot learning to discern and select high-qu

Xinyu Chen, Jiannan Tian, Ian Beaver, Cynthia Freeman

While both the database and high-performance computing (HPC) communities utilize lossless compression methods to minimize floating-point data size, a disconnect persists between them. Each community designs and assesses methods in a domain-specific manner, making it unclear if HPC compression techniques can benefit database applications or vice versa. With t

Mingfei Han, Linjie Yang, Xiaojun Chang, Lina Yao

A short clip of video may contain progression of multiple events and an interesting story line. A human need to capture both the event in every shot and associate them together to understand the story behind it. In this work, we present a new multi-shot video understanding benchmark Shot2Story with detailed shot-level captions, comprehensive video summaries

Dexter Neo, Tsuhan Chen

We present a soft benchmark for calibrating facial expression recognition (FER). While prior works have focused on identifying affective states, we find that FER models are uncalibrated. This is particularly true when out-of-distribution (OOD) shifts further exacerbate the ambiguity of facial expressions. While most OOD benchmarks provide hard labels, we arg

Conghan Yue, Zhengwei Peng, Junlong Ma, Shiyan Du

Diffusion models exhibit powerful generative capabilities enabling noise mapping to data via reverse stochastic differential equations. However, in image restoration, the focus is on the mapping relationship from low-quality to high-quality images. Regarding this issue, we introduce the Generalized Ornstein-Uhlenbeck Bridge (GOUB) model. By leveraging the na

Akash Ghosh, Arkadeep Acharya, Raghav Jain, Sriparna Saha

In the era of modern healthcare, swiftly generating medical question summaries is crucial for informed and timely patient care. Despite the increasing complexity and volume of medical data, existing studies have focused solely on text-based summarization, neglecting the integration of visual information. Recognizing the untapped potential of combining textua

Aditya Pawar, Yingheng Li, Zewei Mo, Yanan Guo

Quantum computing has recently emerged as a promising computing paradigm for many application domains. However, the size of quantum circuits that can be run with high fidelity is constrained by the limited quantity and quality of physical qubits. Recently proposed schemes, such as wire cutting and qubit reuse, mitigate the problem but produce sub-optimal res

Mia Mohammad Imran, Preetha Chatterjee, Kostadin Damevski

Use of figurative language, such as metaphors and idioms, is common in our daily-life communications, and it can also be found in Software Engineering (SE) channels, such as comments on GitHub. Automatically interpreting figurative language is a challenging task, even with modern Large Language Models (LLMs), as it often involves subtle nuances. This is part

Qian Deng, Ru-Hui Ni, Qi Li, Xian-Hui Zhong

In this work, we study the charmonium spectrum within an unquenched quark model including coupled-channel effects. In couple-channel calculations, we include all of the opened charmed meson channels with the once-subtracted method, meanwhile adopt a suppressed factor to soften the hard vertices given by the $^3P_0$ model in the high momentum region. We obtai

Sawyer Robertson, Dhruv Kohli, Gal Mishne, Alexander Cloninger

We propose a model of optimal parallel transport between vector fields on a connection graph, which consists of a weighted graph along with a map from its edges to an orthogonal group. Inspired by the well-known equivalence of 1-Wasserstein distance and minimum cost flows on standard graphs, we consider two versions of this problem: a minimum norm vector-val

Sai Varun Vadlamudi, Sasoun Krikorian, Benjamin Skarnes

Currently, there are over 14 billion IoT devices [7], and with many devices come many protocols, the main ones being MQTT and CoAP. We are interested in connecting the many diverse IoT devices to the cloud. To do so, we use the middleware architecture proposed by article [8] in which a device, called the middleware, acts as the middleman between the various

Yixin Song, Zeyu Mi, Haotong Xie, Haibo Chen

This paper introduces PowerInfer, a high-speed Large Language Model (LLM) inference engine on a personal computer (PC) equipped with a single consumer-grade GPU. The key principle underlying the design of PowerInfer is exploiting the high locality inherent in LLM inference, characterized by a power-law distribution in neuron activation. This distribution ind

Canlin Zhang, Xiuwen Liu

Link prediction is a crucial research area in knowledge graphs, with many downstream applications. In many real-world scenarios, inductive link prediction is required, where predictions have to be made among unseen entities. Embedding-based models usually need fine-tuning on new entity embeddings, and hence are difficult to be directly applied to inductive l

Zhi-Gang Wang

We take the scalar, pseudoscalar, axialvector, vector and tensor diquarks as the basic building blocks to construct the four-quark currents with implicit P-waves, and investigate the hidden-charm-hidden-strange tetraquark states with the $J^{PC}=1^{--}$ and $1^{-+}$ via the QCD sum rules in a comprehensive and consistent way, and revisit the assignments of t

Yash Bhargava, Gulab Chand Dewangan, G. C. Anupama, U. S. Kamath

Nova Her 2021 or V1674 Her was one of the fastest novae to be observed so far. We report here the results from our timing and spectral studies of the source observed at multiple epochs with AstroSat. We report the detection of a periodicity in the source in soft X-rays at a period of 501.4--501.5 s which was detected with high significance after the peak of

Weijie Zheng, Benjamin Doerr

This paper conducts the first rigorous runtime analysis of the SMS-EMOA for many-objective optimization. To this aim, we first propose a many-objective counterpart of the bi-objective OJZJ benchmark. We prove that SMS-EMOA computes the full Pareto front of this benchmark in an expected number of $O(\mu M n^k)$ iterations, where $n$ denotes the problem size (

Doseok Jang, Larry Yan, Lucas Spangher, Costas Spanos

Reinforcement learning (RL) is a powerful tool for optimal control that has found great success in Atari games, the game of Go, robotic control, and building optimization. RL is also very brittle; agents often overfit to their training environment and fail to generalize to new settings. Unsupervised environment design (UED) has been proposed as a solution to

Yuke Li, Victor Steinberg

We have recently discovered stochastic resonance (SR) in chaotic inertia-less viscoelastic channel flow. SR appears just above a pure elastic instability at a critical Weissenberg number, $Wi_c=150$, of a transition regime. In this lower sub-region up to $Wi\sim 300$, only the streamwise velocity, $u$, exhibits a chaotic spectrum, $E_u$, while the spanwise v

Yi Xiao, Lei Bai, Wei Xue, Kang Chen

Weather forecasting is a crucial yet highly challenging task. With the maturity of Artificial Intelligence (AI), the emergence of data-driven weather forecasting models has opened up a new paradigm for the development of weather forecasting systems. Despite the significant successes that have been achieved (e.g., surpassing advanced traditional physical mode

Jialin Wang, Jianhua Zhang, Yuxiang Zhang, Yutong Sun

The digital twin channel (DTC) is crucial for 6G wireless autonomous networks as it replicates the wireless channel fading states in 6G air interface transmissions. It is well known that the physical environment influences channels. A key task for accurately twinning channels in complex 6G scenarios is establishing precise relationships between the environme

Jia Yu, Yuchen Zhou, Xiao Wang, Ananth Dodabalapur

Charge transport in amorphous semiconductors is considerably more complicated than process in crystalline materials due to abundant localized states. In addition to device-scale characterization, spatially resolved measurements are important to unveil electronic properties. Here, we report gigahertz conductivity mapping in amorphous indium gallium zinc oxide

Erik J. Gustafson, Henry Lamm, Felicity Lovelace

We construct a primitive gate set for the digital quantum simulation of the 48-element binary octahedral ($\mathbb{BO}$) group. This nonabelian discrete group better approximates $SU(2)$ lattice gauge theory than previous work on the binary tetrahedral group at the cost of one additional qubit -- for a total of six -- per gauge link. The necessary primitives

Enesio Marinho, Cesar E. P. Villegas, Pedro Venezuela, Alexandre R. Rocha

Transition metal dichalcogenides (TMDCs) have garnered significant interest in optoelectronics, owing to their scalability and thickness-dependent electrical and optical properties. In particular, thin films of TMDCs could be used in photovoltaic devices. In this work, we employ $ab$ $initio$ many-body perturbation theory within $G_0W_0$-BSE approach to accu

Wenqi Zhu, Coralia Cartis

High-order tensor methods for solving both convex and nonconvex optimization problems have generated significant research interest, leading to algorithms with optimal global rates of convergence and local rates that are faster than Newton's method. On each iteration, these methods require the unconstrained local minimization of a (potentially nonconvex) mult

Muktabh Mayank Srivastava

Retail product or packaged grocery goods images need to classified in various computer vision applications like self checkout stores, supply chain automation and retail execution evaluation. Previous works explore ways to finetune deep models for this purpose. But because of the fact that finetuning a large model or even linear layer for a pretrained backbon

Yongtao Liu, Kevin Roccapriore, Marti Checa, Sai Mani Valleti

Microscopy, in particular scanning probe and electron microscopy, has been pivotal in improving our understanding of structure-function relationships at the nanoscale and is by now ubiquitous in most research characterization labs and facilities. However, traditional microscopy operations are still limited largely by a human-centric click-and-go paradigm uti

Valentina Gallardo, Barbara Arce, Francisco Muñoz, Rodolfo San Martín

There is great interest in the study of topological insulator-based heterostructures due to expected emerging phenomena. However, a challenge of topological insulator (TI) research is the contribution of the bulk conduction to the TI surface states. Both strain engineering and thickness control routes, which have been proposed to compensate for bulk doping,

Lidia Aceto, Pietro Antonio Grassi

The aim of this paper is to give a systematic mathematical interpretation of the diffusion problem on which Graph Neural Networks (GNNs) models are based. The starting point of our approach is a dissipative functional leading to dynamical equations which allows us to study the symmetries of the model. We discuss the conserved charges and provide a charge-pre

Tom Gannon, Harold Williams

We show that the algebra $D_\hbar(SL_n/U)$ of differential operators on the base affine space of $SL_n$ is the quantized Coulomb branch of a certain 3d $\mathcal{N} = 4$ quiver gauge theory. In the semiclassical limit this proves a conjecture of Dancer-Hanany-Kirwan about the universal hyperk\"ahler implosion of $SL_n$. We also formulate and prove a generali

Jeffrey Marshall, Dvir Kafri

Quantum error correcting codes typically do not account for quantum state transitions - leakage - out of the computational subspace. Since these errors can last for multiple detection rounds they can significantly contribute to logical errors. It is therefore important to understand how to numerically model them efficiently. Fully quantum simulations of leak

Lorenzo Steccanella, Anders Jonsson

This paper presents a state representation for reward-free Markov decision processes. The idea is to learn, in a self-supervised manner, an embedding space where distances between pairs of embedded states correspond to the minimum number of actions needed to transition between them. Unlike previous methods, our approach incorporates an asymmetric norm parame

Ray Doll

In an era where both commercial and private sectors place a premium on the longevity of digital data storage, the imperative to bolster resilience of digital information while simultaneously curbing costs and reducing failure rates becomes paramount. This study delves into the unique attributes of optical encoding methodologies, which are poised to offer end

Woojin Cho, Seunghyeon Cho, Hyundong Jin, Jinsung Jeon

Neural ordinary differential equations (NODEs), one of the most influential works of the differential equation-based deep learning, are to continuously generalize residual networks and opened a new field. They are currently utilized for various downstream tasks, e.g., image classification, time series classification, image generation, etc. Its key part is ho

Rui Jin, Yong Liao, Pengyuan Zhou

Completely Automated Public Turing Test To Tell Computers and Humans Apart (CAPTCHA) is a type of challenge-response test widely used in authentication systems. A well-known challenge it faces is the CAPTCHA farm, where workers are hired to solve CAPTCHAs manually. In this work, we propose to tackle this challenge from a novel perspective, converting CAPTCHA

S. Carlip, Weixuan Hu

In the covariant canonical approach to classical physics, each point in phase space represents an entire classical trajectory. Initial data at a fixed time serve as coordinates for this ``timeless'' phase space, and time evolution can be viewed as a coordinate change. We argue for a similar view in quantum theory. As in the Heisenberg picture, the wave funct

Kang Lin, Reinhard Heckel

Deep learning based methods for image reconstruction are state-of-the-art for a variety of imaging tasks. However, neural networks often perform worse if the training data differs significantly from the data they are applied to. For example, a model trained for accelerated magnetic resonance imaging (MRI) on one scanner performs worse on another scanner. In

Ryan DeWolfe, Jeffery L. Andrews

The Adjusted Rand Index (ARI) is a widely used method for comparing hard clusterings, but requires a choice of random model that is often left implicit. Several recent works have extended the Rand Index to fuzzy clusterings, but the assumptions of the most common random model is difficult to justify in fuzzy settings. We propose a single framework for comput

Amaury Trujillo, Tiziano Fagni, Stefano Cresci

Since September 2023, the Digital Services Act (DSA) obliges large online platforms to submit detailed data on each moderation action they take within the European Union (EU) to the DSA Transparency Database. From its inception, this centralized database has sparked scholarly interest as an unprecedented and potentially unique trove of data on real-world onl

Jinbing Chen, Dmitry E. Pelinovsky

The Benjamin-Ono (BO) equation describes long internal waves of small amplitude in deep fluids. Compared to its counterpart for shallow fluids, the Korteweg-de Vries (KdV) equation, the BO equation admits exact solutions for the traveling periodic and solitary waves as well as their interactions expressed in elementary (trigonometric and polynomial) function

Phan Thanh An, Nguyen Thi Le

An autonomous robot with a limited vision range finds a path to the goal in an unknown environment in 2D avoiding polygonal obstacles. In the process of discovering the environmental map, the robot has to return to some positions marked previously, the regions where the robot traverses to return are defined as sequences of bundles of line segments. This pape

K. Kade, K. K. Knudsen, A. Bewketu Belete, C. Yang

The extent of the effect of active galactic nuclei (AGN) on their host galaxies at high-redshift is not apparent and studying this effect in the distant universe is a difficult process as the mechanisms of tracing AGN activity can often be inaccurately associated with intense star formation and vice versa. Our aim is to better understand the processes govern

Craig Jacobik

Asset owner identification is an important first step for any information security organization, allowing organizations the ability to identify and detect data breaches and losses, vulnerabilities, possible attack surfaces, and define effective countermeasures. Using existing asset ownership data, the research utilized an assortment of machine learning algor

Sheen An Goh, Manoj Gulati, Ambuj Varshney

Voice plays an important role in our lives by facilitating communication, conveying emotions, and indicating health. Therefore, tracking vocal interactions can provide valuable insight into many aspects of our lives. This paper presents our ongoing efforts to design a new vocal tracking system we call VoCopilot. VoCopilot is an end-to-end system centered aro

Li Niu, Yan Hong, Junyan Cao, Liqing Zhang

Painterly image harmonization aims to harmonize a photographic foreground object on the painterly background. Different from previous auto-encoder based harmonization networks, we develop a progressive multi-stage harmonization network, which harmonizes the composite foreground from low-level styles (e.g., color, simple texture) to high-level styles (e.g., c

Li Niu, Junyan Cao, Yan Hong, Liqing Zhang

Given a composite image with photographic object and painterly background, painterly image harmonization targets at stylizing the composite object to be compatible with the background. Despite the competitive performance of existing painterly harmonization works, they did not fully leverage the painterly objects in artistic paintings. In this work, we explor

Gustavo E. Romero

I present a brief review of the history of the Instituto Argentino de Radioastronom\'ia, a description of its current facilities and projects, and a view of his prospects for the future.

Hongsong Feng, Sean Cottrell, Yuta Hozumi, Guo-Wei Wei

Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology, offering unparalleled insights into the intricate landscape of cellular diversity and gene expression dynamics. The analysis of scRNA-seq data poses challenges attributed to both sparsity and the extensive number of genes implicated. An increasing number of computational tools

Shangshang Zheng, He Bai, Yizhe Zhang, Yi Su

Large Language Models (LLMs) might hallucinate facts, while curated Knowledge Graph (KGs) are typically factually reliable especially with domain-specific knowledge. Measuring the alignment between KGs and LLMs can effectively probe the factualness and identify the knowledge blind spots of LLMs. However, verifying the LLMs over extensive KGs can be expensive

Simon Dirckx, Karl Meerbergen, Daan Huybrechs

In this article a fast and parallelizable algorithm for rational approximation is presented. The method, called (P)QR-AAA, is a (parallel) set-valued variant of the AAA algorithm for scalar functions. It builds on the set-valued AAA framework introduced by Lietaert, Meerbergen, P{\'e}rez and Vandereycken, accelerating it by using an approximate orthogonal ba

Xiaofan Zhou

The target of Electronic Health Record (EHR) coding is to find the diagnostic codes according to the EHRs. In previous research, researchers have preferred to do multi-classification on the EHR coding task; most of them encode the EHR first and then process it to get the probability of each code based on the EHR representation. However, the question of compl

John Martin, Hanspeter Schaub

Scientific machine learning and the advent of the Physics-Informed Neural Network (PINN) have shown high potential in their ability to solve complex differential equations. One example is the use of PINNs to solve the gravity field modeling problem -- learning convenient representations of the gravitational potential from position and acceleration data. Thes

Dom Huh, Prasant Mohapatra

Multi-agent systems (MAS) are widely prevalent and crucially important in numerous real-world applications, where multiple agents must make decisions to achieve their objectives in a shared environment. Despite their ubiquity, the development of intelligent decision-making agents in MAS poses several open challenges to their effective implementation. This su

Prasuna Bandi, Nicolas de Saxcé

Given a non-increasing function $\psi\colon\mathbb{N}\to\mathbb{R}^+$ such that $s^{\frac{n+1}{n}}\psi(s)$ tends to zero as $s$ goes to infinity, we show that the set of points in $\mathbb{R}^n$ that are exactly $\psi$-approximable is non-empty, and we compute its Hausdorff dimension. For $n\geq 2$, this answers questions of Jarn\'{i}k and of Beresnevich, Di

Sunil Jaiswal, Jean-Paul Blaizot, Rajeev S. Bhalerao, Zenan Chen

We present an alternative approach to deriving second-order non-conformal hydrodynamics from the relativistic Boltzmann equation. We demonstrate how constitutive relations for shear and bulk stresses can be transformed into dynamical evolution equations, resulting in Israel-Stewart-like (ISL) hydrodynamics. To understand the far-from-equilibrium applicabilit

Dirk Groeneveld, Anas Awadalla, Iz Beltagy, Akshita Bhagia

The success of large language models has shifted the evaluation paradigms in natural language processing (NLP). The community's interest has drifted towards comparing NLP models across many tasks, domains, and datasets, often at an extreme scale. This imposes new engineering challenges: efforts in constructing datasets and models have been fragmented, and th

Zhong-Xuan Mao, Xiao-Yue Du, Jing-Feng Tian

As an efficient mathematical tool, monotonicity rules play an extremely crucial role in the real analysis field. In this paper, we explore some monotonicity rules for quotient of Delta, Nabla and Diamond-Alpha integrals with variable upper limits and parameters on time scales, respectively. Moreover, we consider the monotonicity rules for quotient of the pro

Kun Yuan, Manasi Kattel, Joel L. Lavanchy, Nassir Navab

Modern operating room is becoming increasingly complex, requiring innovative intra-operative support systems. While the focus of surgical data science has largely been on video analysis, integrating surgical computer vision with language capabilities is emerging as a necessity. Our work aims to advance Visual Question Answering (VQA) in the surgical context

Weixing Zhang, Jörg Holtmann

We developed a textual concrete syntax and a textual editor that supports it for the domain-specific language EAST-ADL, which we named EATXT. This document is a technical report that describes potential advanced features that could be added to EATXT that have not yet been implemented. The purpose of this report is to share our understanding of the relevant t

Yusuke Mikura, Vincenzo Naso, Roberto Percacci

We consider antisymmetric Metric-Affine Theories of Gravity with a Lagrangian containing the most general terms up to dimension four and search for theories that are ghost- and tachyon-free when expanded around flat space. We find new examples that propagate only the graviton and one other massive degree of freedom of spin zero, one or two. These models requ

William V. Dixon

We have analyzed archival spectra of the hot UV-bright star ZNG 1 in the globular cluster M5 (NGC 5904) obtained with the Far Ultraviolet Spectroscopic Explorer (FUSE) and the Space Telescope Imaging Spectrograph (STIS). From these data, we derive an effective temperature $T_{\rm eff} = 43{,}000 \pm 1400$ K, a surface gravity $\log g = 4.47 \pm 0.08$, a rota

Purvi Goel, Kuan-Chieh Wang, C. Karen Liu, Kayvon Fatahalian

Text-to-motion diffusion models can generate realistic animations from text prompts, but do not support fine-grained motion editing controls. In this paper, we present a method for using natural language to iteratively specify local edits to existing character animations, a task that is common in most computer animation workflows. Our key idea is to represen

Marina Blanton, Michael T. Goodrich, Chen Yuan

Motivated by the importance of floating-point computations, we study the problem of securely and accurately summing many floating-point numbers. Prior work has focused on security absent accuracy or accuracy absent security, whereas our approach achieves both of them. Specifically, we show how to implement floating-point superaccumulators using secure multi-

Yuchun Liu, Benjamin Planche, Meng Zheng, Zhongpai Gao

Deep implicit functions (DIFs) have emerged as a potent and articulate means of representing 3D shapes. However, methods modeling object categories or non-rigid entities have mainly focused on single-object scenarios. In this work, we propose MODIF, a multi-object deep implicit function that jointly learns the deformation fields and instance-specific latent