April 2024 arXiv papers — page 61

Anupama R Itagi, Rakhee Kallimani, Krishna Pai, Sridhar Iyer

Efficient and reliable energy systems are key to progress of society. High performance batteries are essential for widely used technologies like Electric Vehicles (EVs) and portable electronics. Additionally, an effective Battery Management System (BMS) is crucial to oversee vital parameters of battery. However, BMS can experience cell imbalance due to charg

Xiao-Wei Bai, Feng Feng, Chang-Man Gan, Yingsheng Huang

Within the framework of nonrelativistic QCD (NRQCD), we calculate the fragmentation function for a charm quark into an $S$-wave fully-charmed tetraquark, denoted as $T_{4c}$. The charm-to-$T_{4c}$ fragmentation function is expressed as a sum of products of the perturbatively calculable short-distance coefficients and the nonperturbative long-distance matrix

Sang-Yoon Kim, Woochang Lim

We are concerned about action selection in the basal ganglia (BG). We quantitatively analyze functions of direct pathway (DP) and indirect pathway (IP) for action selection in a spiking neural network with 3 competing channels. For such quantitative analysis, in each channel, we obtain the competition degree ${\cal C}_d$, given by the ratio of strength of DP

Bertrand Chauvineau, Hoang Ky Nguyen

We derive the complete expression for the Brans Class I exterior spacetime explicitly in terms of the energy and pressures profiles of a stationary spherisymmetric gravity source. This novel and generic expression is achieved in a parsimonious manner, requiring only a subset of the Brans-Dicke field equation and the scalar equation. For distant orbiting test

Sandeep Kumar, Aravinda Prasad, Sreenivas Subramoney

Memory accounts for 33 - 50% of the total cost of ownership (TCO) in modern data centers. We propose a novel solution to tame memory TCO through the novel creation and judicious management of multiple software-defined compressed memory tiers. As opposed to the state-of-the-art solutions that employ a 2-Tier solution, a single compressed tier along with DRAM,

Qingyang Wu, Ying Xu, Tingsong Xiao, Yunze Xiao

Large Language Models (LLMs) have emerged as potent tools for advancing the United Nations' Sustainable Development Goals (SDGs). However, the attitudinal disparities between LLMs and humans towards these goals can pose significant challenges. This study conducts a comprehensive review and analysis of the existing literature on the attitudes of LLMs towards

Zhihao Chen, Yiyuan Ge

Underwater Image Enhancement (UIE) techniques aim to address the problem of underwater image degradation due to light absorption and scattering. In recent years, both Convolution Neural Network (CNN)-based and Transformer-based methods have been widely explored. In addition, combining CNN and Transformer can effectively combine global and local information f

Suraka Bhattacharjee, Koushik Mandal, Supurna Sinha

The study of decoherence plays a key role in our understanding of the transition from the quantum to the classical world. Typically, one considers a system coupled to an external bath which forms a model for an open quantum system. While most of the studies pertain to a position coupling between the system and the environment, some involve a momentum couplin

Javier Rando, Francesco Croce, Kryštof Mitka, Stepan Shabalin

Large language models are aligned to be safe, preventing users from generating harmful content like misinformation or instructions for illegal activities. However, previous work has shown that the alignment process is vulnerable to poisoning attacks. Adversaries can manipulate the safety training data to inject backdoors that act like a universal sudo comman

Kirill A. Rivkin

We propose a mathematical apparatus which converts magnetostatic (Walker) equations into a wave equation by introducing a specific definition of the magnetic refractive index. Its value can be manipulated by adjusting spatial distribution of the saturation magnetization or the bias magnetic field. We demonstrate that the latter can be accomplished efficientl

Alexander Shlapunov, Alexander Polkovnikov, Victor Mironov

We propose a new technique to generate reasonable systems of partial differential equations (PDE) that could be potential candidates for depicting models in natural sciences related to quasi-linear equations. Such systems appear within typical constructions of the Homological Algebra as complexes of differential operators describing compatibility conditions

Zhicheng Ding, Panfeng Li, Qikai Yang, Siyang Li

This paper presents a novel contribution to the field of regional style transfer. Existing methods often suffer from the drawback of applying style homogeneously across the entire image, leading to stylistic inconsistencies or foreground object twisted when applied to image with foreground elements such as person figures. To address this limitation, we propo

Xiaofei Zhu, Liang Li, Weidong Liu, Xin Luo

Sequential recommender systems (SRSs) aim to suggest next item for a user based on her historical interaction sequences. Recently, many research efforts have been devoted to attenuate the influence of noisy items in sequences by either assigning them with lower attention weights or discarding them directly. The major limitation of these methods is that the f

Wenwen Li, Murad Ozaydin

In this paper, we study pointwise finite-dimensional (p.f.d.) $2$-parameter persistence modules where each module admits a finite convex isotopy subdivision. We show that a p.f.d. $2$-parameter persistence module $M$ (with a finite convex isotopy subdivision) is isomorphic to a $2$-parameter persistence module $N$ where the restriction of $N$ to each chamber

Yuan Da Liao, Menghan Song, Jiarui Zhao, Zi Yang Meng

The subleading corner logarithmic corrections in entanglement entropy (EE) are crucial for revealing universal characteristics of the quantum critical points (QCPs), but they are challenging to detect. Motivated by recent developments in the stable computation of EE in (2+1)D quantum many-body systems, we have developed a new method for directly measuring th

Zhangjie Peng, Zecheng Lu, Xue Liu, Cunhua Pan

This letter considers an active reconfigurable intelligent surface (RIS)-aided multi-user uplink massive multipleinput multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). The letter derives the closedform approximate expression for the sum achievable rate (AR), where the maximum ratio combination (MRC) processing and low-re

Haoyi Qiu, Wenbo Hu, Zi-Yi Dou, Nanyun Peng

Large Vision-Language Models (LVLMs) suffer from hallucination issues, wherein the models generate plausible-sounding but factually incorrect outputs, undermining their reliability. A comprehensive quantitative evaluation is necessary to identify and understand the extent of hallucinations in these models. However, existing benchmarks are often limited in sc

Yunfei Li, Yuezun Li, Baoyuan Wu, Junyu Dong

Sequential DeepFake detection is an emerging task that predicts the manipulation sequence in order. Existing methods typically formulate it as an image-to-sequence problem, employing conventional Transformer architectures. However, these methods lack dedicated design and consequently result in limited performance. As such, this paper describes a new Transfor

Hanzhe Li, Jiaran Zhou, Yuezun Li, Baoyuan Wu

Generating synthetic fake faces, known as pseudo-fake faces, is an effective way to improve the generalization of DeepFake detection. Existing methods typically generate these faces by blending real or fake faces in spatial domain. While these methods have shown promise, they overlook the simulation of frequency distribution in pseudo-fake faces, limiting th

Tetsu Toyoda

Andoni, Naor and Neiman (2018) established a family of quadratic metric inequalities that hold true in every CAT(0) space. As stated in their paper, this family seems to include all previously used quadratic metric inequalities that hold true in every CAT(0) space. We prove that there exists a metric space that satisfies all inequalities in this family but d

Galina Levitina, Alexandr Usachev

In the present paper we suggest a construction of symmetric functionals on a large class of symmetric spaces over a semifinite von Neumann algebra. This approach establishes a bijection between the symmetric functionals on symmetric spaces and shift-invariant functionals on the space of bounded sequences. It allows to obtain a bijection between the classes o

Gordon Getty, Nikita Tkachenko

Net profit is sometimes found from data for net operating surplus. We propose a way to find it from data for consumption, pay and market-value capital, and concomitantly to reveal the factor shares in consumption.

Atom Scott, Ikuma Uchida, Ning Ding, Rikuhei Umemoto

Multi-object tracking (MOT) is a critical and challenging task in computer vision, particularly in situations involving objects with similar appearances but diverse movements, as seen in team sports. Current methods, largely reliant on object detection and appearance, often fail to track targets in such complex scenarios accurately. This limitation is furthe

James W. Gardner, Tuvia Gefen, Simon A. Haine, Joseph J. Hope

Although measuring the deterministic waveform of a weak classical force is a well-studied problem, estimating a random waveform, such as the spectral density of a stochastic signal field, is much less well-understood despite it being a widespread task at the frontier of experimental physics. State-of-the-art precision sensors of random forces must account fo

Zhongqi Wang, Bingnan Wang, Maosheng Xiang

The Plug-and-Play (PnP) algorithm is popular for inverse image problem-solving. However, this algorithm lacks theoretical analysis of its convergence with more advanced plug-in denoisers. We demonstrate that discrete PnP iteration can be described by a continuous stochastic differential equation (SDE). We can also achieve this transformation through Markov p

Avinash Anand, Kritarth Prasad, Ujjwal Goel, Mohit Gupta

Citation text plays a pivotal role in elucidating the connection between scientific documents, demanding an in-depth comprehension of the cited paper. Constructing citations is often time-consuming, requiring researchers to delve into extensive literature and grapple with articulating relevant content. To address this challenge, the field of citation text ge

Lior Shamir

The distribution of the spin directions of galaxies has been a question in the past decade, with numerous Earth-based and space-based experiments showing that the distribution is not necessarily random. These experiments were based on different statistical methods, one of them was a simple and empirically verified open source $\chi^2$ method. Patel & Desmond

Zhangjing Yang, Dun Liu, Wensheng Cheng, Jinqiao Wang

Labeling pixel-wise object masks in videos is a resource-intensive and laborious process. Box-supervised Video Instance Segmentation (VIS) methods have emerged as a viable solution to mitigate the labor-intensive annotation process. . In practical applications, the two-step approach is not only more flexible but also exhibits a higher recognition accuracy. I

Hao Wang, Qingshan Xu, Hongyuan Chen, Rui Ma

Recent techniques on implicit geometry representation learning and neural rendering have shown promising results for 3D clothed human reconstruction from sparse video inputs. However, it is still challenging to reconstruct detailed surface geometry and even more difficult to synthesize photorealistic novel views with animated human poses. In this work, we in

Jessica Dai

What is agency, and why does it matter? In this work, we draw from the political science and philosophy literature and give two competing visions of what it means to be an (ethical) agent. The first view, which we term mechanistic, is commonly--and implicitly--assumed in AI research, yet it is a fundamentally limited means to understand the ethical character

Huan Bao, Kaimin Wei, Yongdong Wu, Jin Qian

A Model Inversion (MI) attack based on Generative Adversarial Networks (GAN) aims to recover the private training data from complex deep learning models by searching codes in the latent space. However, they merely search a deterministic latent space such that the found latent code is usually suboptimal. In addition, the existing distributional MI schemes ass

Yanbiao Ma, Licheng Jiao, Fang Liu, Lingling Li

Building fair deep neural networks (DNNs) is a crucial step towards achieving trustworthy artificial intelligence. Delving into deeper factors that affect the fairness of DNNs is paramount and serves as the foundation for mitigating model biases. However, current methods are limited in accurately predicting DNN biases, relying solely on the number of trainin

Jiyeon Kim, Donggeon Lee, Dongwoo Lee, Xin Li

Recently, machine-learning approaches have accelerated computational materials design and the search for advanced solid electrolytes. However, the predictors are currently limited to static structural parameters, which may not fully account for the dynamic nature of ionic transport. In this study, we meticulously curated features considering dynamic properti

Biao-Peng Li, Zhi-Fu Gao

The inclination angle $\chi$ between magnetic and rotation axes of pulsars is an important parameter in pulsar physics. The changes in the inclination angle of a pulsar would lead to observable effects, such as changes in the pulse beam width and braking index of the star. In this paper, we perform a short review on the evolution of pulsar's magnetic inclina

Alex Hiro Mayo, Darius-Alexandru Deaconu, Hidetoshi Masuda, Yoichi Nii

Chiral magnetic textures give rise to unconventional magnetotransport phenomena such as the topological Hall effect and nonreciprocal electronic transport. While the correspondence between real-space magnetic topology/symmetry and such transport phenomena has been well established, a microscopic understanding based on the spin-dependent band structure in mom

Ajinkya Deshpande, Anmol Agarwal, Shashank Shet, Arun Iyer

LLMs have demonstrated significant potential in code generation tasks, achieving promising results at the function or statement level across various benchmarks. However, the complexities associated with creating code artifacts like classes, particularly within the context of real-world software repositories, remain underexplored. Prior research treats class-

Sunit Bhattacharya, Ondřej Bojar

Multilingualism in Large Language Models (LLMs) is an yet under-explored area. In this paper, we conduct an in-depth analysis of the multilingual capabilities of a family of a Large Language Model, examining its architecture, activation patterns, and processing mechanisms across languages. We introduce novel metrics to probe the model's multilingual behaviou

Haolin Yang, Chaoqiang Zhao, Lu Sheng, Yang Tang

Nighttime self-supervised monocular depth estimation has received increasing attention in recent years. However, using night images for self-supervision is unreliable because the photometric consistency assumption is usually violated in the videos taken under complex lighting conditions. Even with domain adaptation or photometric loss repair, performance is

Yi Rong, Yingchi Mao, Yinqiu Liu, Ling Chen

Traffic speed prediction is significant for intelligent navigation and congestion alleviation. However, making accurate predictions is challenging due to three factors: 1) traffic diffusion, i.e., the spatial and temporal causality existing between the traffic conditions of multiple neighboring roads, 2) the poor interpretability of traffic data with complic

Eunho Lee, Minwoo Jung, Ayoung Kim

Robust 3D object detection is a core challenge for autonomous mobile systems in field robotics. To tackle this issue, many researchers have demonstrated improvements in 3D object detection performance in datasets. However, real-world urban scenarios with unstructured and dynamic situations can still lead to numerous false positives, posing a challenge for ro

Junjie Lu, Zhongyi Guo, David H. Rehkopf

This study presents an approach to analyze health disparities in Sexual and Gender Minority (SGM) populations, with a focus on the role of social support levels as an example to allow causal interpretations of regression models. We advocate for precisely defining the exposure variable and incorporating mediators into analyses, to address the limitations of c

M. S. Zobaer, N. Lotfi, C. M. Domenico, C. Hoffman

Recently discovered constituents of the brain waves -- the oscillons -- provide high-resolution representation of the extracellular field dynamics. Here we study the most robust, highest-amplitude oscillons that manifest in actively behaving rats and generally correspond to the traditional theta-waves. We show that the resemblances between theta-oscillons an

Jinghua Zhang, Maosheng Xiang, Jie Yu, Jian Ge

Accurately characterizing intrinsic stellar photometric noise induced by stellar astrophysics, such as stellar activity, granulation, and oscillations, is of crucial importance for detecting transiting exoplanets. In this study, we investigate the relation between the intrinsic stellar photometric noise, as quantified by the Kepler rrmsCDPP measurement, and

Huiyi Kang, Guo Chen, Chengzi Jiang, Enric Palle

The spectral signatures of optical absorbers, when combined with those of infrared molecules, play a critical role in constraining the cloud properties of exoplanet atmospheres. We aim to use optical transmission spectroscopy to confirm the tentative color signature previously observed by multiband photometry in the atmosphere of hot Jupiter HAT-P-55b. We ob

Juncheng Yang, Zuchao Li, Shuai Xie, Wei Yu

Domain generalization faces challenges due to the distribution shift between training and testing sets, and the presence of unseen target domains. Common solutions include domain alignment, meta-learning, data augmentation, or ensemble learning, all of which rely on domain labels or domain adversarial techniques. In this paper, we propose a Dual-Stream Separ

Mingjie Ma, Zhihuan Yu, Yichao Ma, Guohui Li

Visual Commonsense Reasoning (VCR) is a cognitive task, challenging models to answer visual questions requiring human commonsense, and to provide rationales explaining why the answers are correct. With emergence of Large Language Models (LLMs), it is natural and imperative to explore their applicability to VCR. However, VCR task demands more external knowled

Tetsuro Morimura, Mitsuki Sakamoto, Yuu Jinnai, Kenshi Abe

Reinforcement learning from human feedback (RLHF) plays a crucial role in aligning language models with human preferences. While the significance of dataset quality is generally recognized, explicit investigations into its impact within the RLHF framework, to our knowledge, have been limited. This paper addresses the issue of text quality within the preferen

Jun Wu, Mingnan Ding, Xiangjun Xing

We study stochastic thermodynamics of over-damped Brownian motion in a flowing fluid. Unlike some previous works, we treat the effects of the flow field as a non-conservational driving force acting on the Brownian particle. This allows us to apply the theoretical formalism developed in a recent work for general non-conservative Langevin dynamics. We define h

Enmao Diao, Qi Le, Suya Wu, Xinran Wang

A primary function of back-propagation is to compute both the gradient of hidden representations and parameters for optimization with gradient descent. Training large models requires high computational costs due to their vast parameter sizes. While Parameter-Efficient Fine-Tuning (PEFT) methods aim to train smaller auxiliary models to save computational spac

Yusuke Tsukamoto

In our previous study (Tsukamoto {\it et al.} 2023), we investigated formation and early evolution of protoplanetary disks with 3D non-ideal magnetohydrodynamics simulations considering dust growth, and found that the modified equations of the conventional steady accretion disk model which consider the magnetic braking, { dust growth} and ambipolar diffusion

Gang Ma, Hui Wei

Over the years, scene understanding has attracted a growing interest in computer vision, providing the semantic and physical scene information necessary for robots to complete some particular tasks autonomously. In 3D scenes, rich spatial geometric and topological information are often ignored by RGB-based approaches for scene understanding. In this study, w

Marie Siew, Haoran Zhang, Jong-Ik Park, Yuezhou Liu

Federated learning (FL) enables collaborative learning across multiple clients. In most FL work, all clients train a single learning task. However, the recent proliferation of FL applications may increasingly require multiple FL tasks to be trained simultaneously, sharing clients' computing and communication resources, which we call Multiple-Model Federated

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using data samples with an integrated luminosity of $4.5~\text{fb}^{-1}$ collected by the BESIII detector at center-of-mass energies ranging from 4.66 to 4.95 GeV, we study the processes of $e^+e^-\to\omega X(3872)$ and $e^+e^-\to\gamma X(3872)$. With the $e^+e^-\to\omega X(3872)$ process, the branching fraction ratio $R\equiv\frac{\mathcal{B}(X(3872)\to\gam

Remi Cocou Avohou

Gross, Mansour, and Tucker introduced the partial-duality polynomial of a ribbon graph [Distributions, European J. Combin. 86, 1--20, 2020], the generating function enumerating partial duals by the Euler genus. Chmutov and Vignes-Tourneret wondered if this polynomial and its conjectured properties would hold for general delta-matroids, which are combinatoria

Chengxi Han, Chen Wu, Meiqi Hu, Jiepan Li

A high-precision feature extraction model is crucial for change detection (CD). In the past, many deep learning-based supervised CD methods learned to recognize change feature patterns from a large number of labelled bi-temporal images, whereas labelling bi-temporal remote sensing images is very expensive and often time-consuming; therefore, we propose a coa

Bocheng Zhu, Volker Springel

In this study, we investigate the impact of local stellar radiation in cosmological zoom simulations of the formation of Milky Way-sized galaxies. We include the radiation field as an additional feedback component that is computed alongside gravity with a tree code in an optically thin approximation. We resimulate the initial conditions of five Milk Way-like

Tian Lan, Ziyue Li, Junpeng Lin, Zhishuai Li

Directed Acyclic Graphical (DAG) models efficiently formulate causal relationships in complex systems. Traditional DAGs assume nodes to be scalar variables, characterizing complex systems under a facile and oversimplified form. This paper considers that nodes can be multivariate functional data and thus proposes a multivariate functional DAG (MultiFun-DAG).

Sven Buder, Luka Mijnarends, Tobias Buck

Exploring the marks left by galactic accretion in the Milky Way helps us understand how our Galaxy was formed. However, finding and studying accreted stars and the galaxies they came from has been challenging. This study uses a simulation from the NIHAO project, which now includes a wider range of chemical compositions, to find better ways to spot these accr

Danshu Sheng, Dehui Wang

In this paper, we propose a computationally valid and theoretically justified methods, the likelihood ratio scan method (LRSM), for estimating multiple change-points in a piecewise stationary generalized conditional integer-valued autoregressive process. LRSM with the usual window parameter $h$ is more satisfied to be used in long-time series with few and ev

Dengchun Li, Yingzi Ma, Naizheng Wang, Zhengmao Ye

Fine-tuning Large Language Models (LLMs) is a common practice to adapt pre-trained models for specific applications. While methods like LoRA have effectively addressed GPU memory constraints during fine-tuning, their performance often falls short, especially in multi-task scenarios. In contrast, Mixture-of-Expert (MoE) models, such as Mixtral 8x7B, demonstra

Deng Wang

We demonstrate that the constraints on the evolution of dark energy implemented by the DESI collaboration may be insufficient or incomplete using their own BAO data. Using large enough prior ranges for the present-day equation of state of dark energy $\omega_0$ and amplitude of dark energy evolution $\omega_a$, we obtain the complete $1\,\sigma$ and $2\,\sig

Dinh Hoa Nguyen, Kaname Matsue

This research studies a non-convex geometric optimization problem arising from the field of optical wireless power transfer. In the considered optimization problem, the cost function is a sum of negatively and fractionally powered distances from given points arbitrarily located in a plane to another point belonging to a different plane. Therefore, it is a st

Rajiv Sambharya, Bartolomeo Stellato

We introduce a data-driven approach to analyze the performance of continuous optimization algorithms using generalization guarantees from statistical learning theory. We study classical and learned optimizers to solve families of parametric optimization problems. We build generalization guarantees for classical optimizers, using a sample convergence bound, a

Yu-Xin Zhang, Jie Gui, Baosheng Yu, Xiaofeng Cong

Point cloud registration involves determining a rigid transformation to align a source point cloud with a target point cloud. This alignment is fundamental in applications such as autonomous driving, robotics, and medical imaging, where precise spatial correspondence is essential. Deep learning has greatly advanced point cloud registration by providing robus

Anish S. Narkar, Jan J. Michalak, Candace E. Peacock, Brendan David-John

The use of ML models to predict a user's cognitive state from behavioral data has been studied for various applications which includes predicting the intent to perform selections in VR. We developed a novel technique that uses gaze-based intent models to adapt dwell-time thresholds to aid gaze-only selection. A dataset of users performing selection in arithm

S. Vignesh Raja, A. Govindarajan, M. Lakshmanan

In this work, we observe that the $\mathcal{PT}$-symmetric fiber Bragg gratings (PTFBGs) with saturable nonlinearity (SNL) exhibit ramp-like, mixed, optical multistability (OM) in the broken regime. The interplay between nonlinearity and detuning parameter plays a central role in transforming the characteristics of the hysteresis curves and facilitates the r

Anish S. Narkar, Brendan David-John

Video-based eye trackers capture the iris biometric and enable authentication to secure user identity. However, biometric authentication is susceptible to spoofing another user's identity through physical or digital manipulation. The current standard to identify physical spoofing attacks on eye-tracking sensors uses liveness detection. Liveness detection cla

Patryk Sofia Lykawka, Takashi Ito

The Jupiter-Saturn chaotic excitation (JSCE) scenario proposes that the protoplanetary disk was dynamically excited and depleted beyond ~1-1.5 au in a few Myr, offering a new and plausible explanation for several observed properties of the inner solar system. Here, we expanded our previous work by conducting a comprehensive analysis of 37 optimal terrestrial

Danshu Sheng, Chang Liu, Yao Kang

The first-order binomial autoregressive (BAR(1)) model is the most frequently used tool to analyze the bounded count time series. The BAR(1) model is stationary and assumes process parameters to remain constant throughout the time period, which may be incompatible with the non-stationary real data, which indicates piecewise stationary characteristic. To bett

Yosuke Mizuno, Luciano Rezzolla

Recent years have seen a significant progress in the development of general relativistic codes for the numerical solution of the equations of magnetohydrodynamics in spacetimes with high and dynamical curvature. These codes are valuable tools to explore the large-scale plasma dynamics such as that takes place when two neutron stars collide or when matter acc

Hao Lyu, Yongping Zhang, Thomas Busch

We study Thouless pumping and arresting of gap solitons in a two-component Bose gas loaded into an optical superlattice. We show that, depending on the atomic interactions and chemical potentials, the two solitons can be simultaneously pumped or trapped, but we also identify regimes where one soliton is pumped and the other arrested. These behaviors can be u

Anirban Chatterjee, Soham Dan, Bhaswar B. Bhattacharya

Exchangeable random graphs, which include some of the most widely studied network models, have emerged as the mainstay of statistical network analysis in recent years. Graphons, which are the central objects in graph limit theory, provide a natural way to sample exchangeable random graphs. It is well known that network moments (motif/subgraph counts) identif

Stine S. Johansen, Yanto Browning, Anthony Brumpton, Jared Donovan

Current research in robotic sounds generally focuses on either masking the consequential sound produced by the robot or on sonifying data about the robot to create a synthetic robot sound. We propose to capture, modify, and utilise rather than mask the sounds that robots are already producing. In short, this approach relies on capturing a robot's sounds, pro

Jinglu Song, Qiang Lu, Bozhou Tian, Jingwen Zhang

Symbolic regression (SR) is the task of discovering a symbolic expression that fits a given data set from the space of mathematical expressions. Despite the abundance of research surrounding the SR problem, there's a scarcity of works that confirm its NP-hard nature. Therefore, this paper introduces the concept of a symbol graph as a comprehensive representa

Supreeth Narasimhaswamy, Huy Anh Nguyen, Lihan Huang, Minh Hoai

We address the challenging task of identifying, segmenting, and tracking hand-held objects, which is crucial for applications such as human action segmentation and performance evaluation. This task is particularly challenging due to heavy occlusion, rapid motion, and the transitory nature of objects being hand-held, where an object may be held, released, and

Jiayue Zhang, Ken Seng Tan, Tony S. Wirjanto, Lysa Porth

This paper extends the application of ESG score assessment methodologies from large corporations to individual farmers' production, within the context of climate change. Our proposal involves the integration of crucial agricultural sustainability variables into conventional personal credit evaluation frameworks, culminating in the formulation of a holistic s

Bing-Qiu Chen, Hai-Bo Yuan, Bo-Wen Huang

The u band magnitude is vital for determining stellar parameters and investigating specific astronomical objects. However, flux calibration in the u band for stars in the Galactic disk presents significant challenges. In this study, we introduce a comprehensive re-calibration of $u$-band photometric magnitudes of the VPHAS+ Data Release 4 (DR4), employing th

Lei He, Leheng Li, Wenchao Sun, Zeyu Han

Neural Radiance Field (NeRF) has garnered significant attention from both academia and industry due to its intrinsic advantages, particularly its implicit representation and novel view synthesis capabilities. With the rapid advancements in deep learning, a multitude of methods have emerged to explore the potential applications of NeRF in the domain of Autono

Yujin Han, Difan Zou

Standard empirical risk minimization (ERM) models may prioritize learning spurious correlations between spurious features and true labels, leading to poor accuracy on groups where these correlations do not hold. Mitigating this issue often requires expensive spurious attribute (group) labels or relies on trained ERM models to infer group labels when group in

Jonathan L. Feng, Jinmian Li, Xufei Liao, Jian Ni

Quirks are generic predictions of strongly-coupled dark sectors. For weak-scale masses and a broad range of confining scales in the dark sector, quirks can be discovered only at the energy frontier, but quirk--anti-quirk pairs are produced with unusual signatures at low $p_T$, making them difficult to detect at the large LHC detectors. We determine the prosp

Maxim Enis, Mark Hopkins

We show that Claude 3 Opus, a large language model (LLM) released by Anthropic in March 2024, exhibits stronger machine translation competence than other LLMs. Though we find evidence of data contamination with Claude on FLORES-200, we curate new benchmarks that corroborate the effectiveness of Claude for low-resource machine translation into English. We fin

Qikai Yang, Panfeng Li, Xinhe Xu, Zhicheng Ding

In the ever-evolving landscape of social network advertising, the volume and accuracy of data play a critical role in the performance of predictive models. However, the development of robust predictive algorithms is often hampered by the limited size and potential bias present in real-world datasets. This study presents and explores a generative augmentation

Dilong Zhou, Rafael Guiraldello, Felipe Pereira

Multiscale mixed methods based on non-overlapping domain decompositions can efficiently handle the solution of significant subsurface flow problems in very heterogeneous formations of interest to the industry, especially when implemented on multi-core supercomputers. Efficiency in obtaining numerical solutions is dictated by the choice of interface spaces th

Pablo S. Cornaglia, Leandro M. Chinellato, Cristian D. Batista

We investigate the ground state magnetic configurations of a Fibonacci chain of classical spins with nearest-neighbor ferromagnetic and monoaxial Dzyaloshinskii-Moriya exchange interactions. Our analysis reveals a diverse array of magnetic textures induced by an external magnetic field perpendicular to the Dzyaloshinskii-Moriya vector. These textures exhibit

Muxin Liu, Lile Wang, Xiaoting Fu, Luis C. Ho

Stars with outflows impinging on ambient gas experience accelerations due to the gravitational feedback from the interaction morphology between the outflow and the ambient gas. Such ``negative dynamical friction'' (NDF), in contrast to the conventional ``dynamical friction'' (DF), is studied for its impact on the dynamics of open clusters (OCs) immersed in a

Jooeun Kim, Jinri Kim, Kwangeun Yeo, Eungi Kim

Cold-start item recommendation is a long-standing challenge in recommendation systems. A common remedy is to use a content-based approach, but rich information from raw contents in various forms has not been fully utilized. In this paper, we propose a domain/data-agnostic item representation learning framework for cold-start recommendations, naturally equipp

Safa C. Medin, Gengyan Li, Ruofei Du, Stephan Garbin

3D rendering of dynamic face captures is a challenging problem, and it demands improvements on several fronts$\unicode{x2014}$photorealism, efficiency, compatibility, and configurability. We present a novel representation that enables high-quality volumetric rendering of an actor's dynamic facial performances with minimal compute and memory footprint. It run

Gustavo de Paula Ramos

Consider the Hartree-type equation in $\mathbb{R}^3$ with a delta potential formally described by $$ i \partial_t \psi = - \Delta_x \psi + \alpha \delta_0 \psi - (I_\beta \ast |\psi|^p) |\psi|^{p - 2} \psi $$ where $\alpha \in \mathbb{R}$; $0 < \beta < 3$ and we want to solve for $\psi \colon \mathbb{R}^3 \times \mathbb{R} \to \mathbb{C}$. By means of a Poho

Junwu Tu

In this note we record a comparison theorem on the B-model variation of semi-infinite Hodge structures. This result is considered a folklore theorem by experts in the field. We only take this opportunity to write it down. Our motivation is to apply it in the study of B-model categorical enumerative invariants.

Bing Luo, Wenli Xiao, Shiqiang Wang, Jianwei Huang

Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communication bandwidth is limited. Recent works on the convergence analysis of FL have focused on unbiased client sampling, e.g., sampling uniformly at random, which suffers from slow wall-cl

Si Chen, Feiyang Kang, Ning Yu, Ruoxi Jia

Fact tracing seeks to identify specific training examples that serve as the knowledge source for a given query. Existing approaches to fact tracing rely on assessing the similarity between each training sample and the query along a certain dimension, such as lexical similarity, gradient, or embedding space. However, these methods fall short of effectively di

Parnashree Ghosh, Ananya Pal

In this paper, over a field of positive characteristic we exhibit an infinite family of counter examples to the Zariski Cancellation Problem in higher dimensions ($\geqslant 3$) which are pairwise non-isomorphic and also non-isomorphic to the existing family of counter examples, demonstrated by Gupta in \cite{adv}.

Furu Zhang, Chenxi Ding, Jianhui Zhou, Yugui Yao

We find that the Berry curvature splits the edge plasmons propagating along the opposite directions in quantum anomalous Hall insulators even with vanishing Chern number. When the bulk is insulating, only one unidirectional edge plasmon mode survives whose direction can be changed by external fields. The unidirectional edge plasmon in the long-wavelength lim

Carles Barril, Àngel Calsina, Odo Diekmann, József Z. Farkas

We consider a population organised hierarchically with respect to size in such a way that the growth rate of each individual depends only on the presence of larger individuals. As a concrete example one might think of a forest, in which the incidence of light on a tree (and hence how fast it grows) is affected by shading of taller trees. The model is formula

Nari Johnson, Sanika Moharana, Christina N. Harrington, Nazanin Andalibi

As more algorithmic systems have come under scrutiny for their potential to inflict societal harms, an increasing number of organizations that hold power over harmful algorithms have chosen (or were required under the law) to abandon them. While social movements and calls to abandon harmful algorithms have emerged across application domains, little academic

Ying Li, Yang Shen, Linqiang Xu, Shiqi Liu

Sub-1-nm gate length $MoS_2$ transistors have been experimentally fabricated, but their device performance limit remains elusive. Herein, we explore the performance limits of the sub-1-nm gate length monolayer (ML) $MoS_2$ transistors through ab initio quantum transport simulations. Our simulation results demonstrate that, through appropriate doping and diel

Zachary L. Stevens-Hough, Matthew J. Davis, Lewis A. Williamson

In the easy-plane phase, a ferromagnetic spin-1 Bose-Einstein condensate is magnetized in a plane transverse to the applied Zeeman field. This phase supports polar-core spin vortices (PCVs), which consist of phase windings of transverse magnetization. Here we show that spin-changing collisions cause a PCV to accelerate down density gradients in an inhomogene

Kazuo Makishima, Nagomi Uchida, Teruaki Enoto

Four X-ray data sets of the Soft Gamma Repeater SGR 1806-20, taken with the Gas Imaging Spectrometer (GIS) onboad ASCA, were analyzed. Three of them were acquired over 1993 October 9-20, whereas the last one in 1995 October. Epoch-folding analysis of the 2.8-12 keV signals confirmed the $\sim 7.6$ s pulses in these data, which Kouveliotou et al. (1998) repor

Jensen Hwa, Qingyu Zhao, Aditya Lahiri, Adnan Masood

Conditional independence (CI) constraints are critical for defining and evaluating fairness in machine learning, as well as for learning unconfounded or causal representations. Traditional methods for ensuring fairness either blindly learn invariant features with respect to a protected variable (e.g., race when classifying sex from face images) or enforce CI

Huihui An, Zaili Yan

In this paper, we mainly study left invariant pseudo-Riemannian Ricci-parallel metrics on connected Lie groups which are not Einstein. Following a result of Boubel and B\'{e}rard Bergery, there are two typical types of such metrics, which are characterized by the minimal polynomial of the Ricci operator. Namely, its form is either $(X-\alpha)(X-\bar{\alpha})