May 2023 arXiv papers — page 171

Maram Hasanain, Ahmed Oumar El-Shangiti, Rabindra Nath Nandi, Preslav Nakov

Misinformation spreading in mainstream and social media has been misleading users in different ways. Manual detection and verification efforts by journalists and fact-checkers can no longer cope with the great scale and quick spread of misleading information. This motivated research and industry efforts to develop systems for analyzing and verifying news spr

Aurélien Drezet

In this chapter we discuss the Einstein Podolsky Rosen theorem and its strong relation with Bell's theorem. The central role played by the concept of beable introduced by Bell is emphasized. In particular we stress that beables involved in EPR and Bell theorems are not limited to hidden supplementary variables (e.g., like in the de Broglie-Bohm (dBB) pilot-w

Heitor P. Casagrande, Bo Xing, Marcello Dalmonte, Alex Rodriguez

We study the Hamiltonian dynamics of a many-body quantum system subjected to periodic projective measurements which leads to probabilistic cellular automata dynamics. Given a sequence of measured values, we characterize their dynamics by performing a principal component analysis. The number of principal components required for an almost complete description

Hanlin Zhang, Jiani Huang, Ziyang Li, Mayur Naik

Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. In this work, we tackle this challenge through the lens of symbolic programming. We propose DSR-LM, a Differentiable Symbolic Reasoning framework where pre-trained LMs govern the perception of factual knowledge, and a symboli

Pengcheng Tang

Let $\mu$ be a finite positive Borel measure on the interval $[0, 1)$ and $f(z)=\sum_{n=0}^{\infty}a_{n}z^{n} \in H(\mathbb{D})$. The Ces\`aro-like operator is defined by $$ \mathcal {C}_{\mu} (f)(z)=\sum^\infty_{n=0}\left(\mu_n\sum^n_{k=0}a_k\right)z^n, \ z\in \mathbb{D}, $$ where, for $n\geq 0$, $\mu_n$ denotes the $n$-th moment of the measure $\mu$, that

Sai Anirudh Karre, Neeraj Mathur, Y. Raghu Reddy

New hires (novice or experienced) usually undergo an onboarding program for a specific period to get acquainted with the processes of the hiring organization to reach expected programming productivity levels. This paper presents a programming productivity framework developed as an outcome of a three-year-long industry study with small to medium-scale organiz

Zeyan Li, Junjie Chen, Yihao Chen, Chengyang Luo

Localizing root causes for multi-dimensional data is critical to ensure online service systems' reliability. When a fault occurs, only the measure values within specific attribute combinations are abnormal. Such attribute combinations are substantial clues to the underlying root causes and thus are called root causes of multidimensional data. This paper prop

Yu Gao, Xiaochuan Pan, Chong Chen

This work investigates conditions for quantitative image reconstruction in multispectral computed tomography (MSCT), which remains a topic of active research. In MSCT, one seeks to obtain from data the spatial distribution of linear attenuation coefficient, referred to as a virtual monochromatic image (VMI), at a given X-ray energy, within the subject imaged

Carlos Mejuto-Zaera, Michele Fabrizio

The theoretical description of strongly correlated materials relies on the ability to simultaneously capture, on equal footing, the different competing energy scales. Unfortunately, existing approaches are either typically extremely computationally demanding, making systematic screenings of correlated materials challenging or are limited to a subset of obser

Jian Guan, Youde Liu, Qiaoxi Zhu, Tieran Zheng

Although deep learning is the mainstream method in unsupervised anomalous sound detection, Gaussian Mixture Model (GMM) with statistical audio frequency representation as input can achieve comparable results with much lower model complexity and fewer parameters. Existing statistical frequency representations, e.g, the log-Mel spectrogram's average or maximum

Yuzhong Zhao, Weijia Wu, Zhuang Li, Jiahong Li

Current video text spotting methods can achieve preferable performance, powered with sufficient labeled training data. However, labeling data manually is time-consuming and labor-intensive. To overcome this, using low-cost synthetic data is a promising alternative. This paper introduces a novel video text synthesis technique called FlowText, which utilizes o

Zihao Cheng, Anthony Wachs

We present a physics-informed neural network (PINN) model to predict the hydrodynamic force and torque fluctuations in a random array of stationary bidisperse spheres. The PINN model is formulated based on two hypotheses: (i) pairwise interaction assumption that approximates the total force/torque exerted on a target sphere by linear superposition of individ

Xiaochuan Zhang, Mengran Li, Ye Wang, Haojun Fei

Attribute graphs are ubiquitous in multimedia applications, and graph representation learning (GRL) has been successful in analyzing attribute graph data. However, incomplete graph data and missing node attributes can have a negative impact on media knowledge discovery. Existing methods for handling attribute missing graph have limited assumptions or fail to

Sonia Sousa, Jose Cravino, Paulo Martins

The Internet revolution in 1990, followed by the data-driven and information revolution, has transformed the world as we know it. Nowadays, what seam to be 10 to 20 years ago, a science fiction idea (i.e., machines dominating the world) is seen as possible. This revolution also brought a need for new regulatory practices where user trust and artificial Intel

Jiaojiao Chen, Xiao-Gang Fan, Wei Xiong, Dong Wang

Cavity optomechanics, a promising platform to investigate macroscopic quantum effects, has been widely used to study nonreciprocal entanglement with Sagnec effect. Here we propose an alternative way to realize nonreciprocal entanglemment among magnons, photons, and phonons in a hybrid cavity-magnon optomechanics, where magnon Kerr effect is used. We show tha

Zhihao Wen, Yuan Fang

Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification, with few or no labeled samples, poses a serious concern for supervised learning. Meanwhile, many text data a

Wenjie Zhou, Jinniu Hu, Ying Zhang, Hong Shen

It is of great interest to understand the equation of state (EOS) of the neutron star (NS), whose core includes highly dense matter. However, there are large uncertainties in the theoretical predictions for the EOS of NS. It is useful to develop a new framework, which is flexible enough to consider the systematic error in theoretical predictions and to use t

Gilles Dowek

Peter Andrews has proposed, in 1971, the problem of finding an analog of the Skolem theorem for Simple Type Theory. A first idea lead to a naive rule that worked only for Simple Type Theory with the axiom of choice and the general case has only been solved, more than ten years later, by Dale Miller. More recently, we have proposed with Th{\'e}r{\`e}se Hardin

Ching-Feng Kung, Kao-Yueh Kuo, Ching-Yi Lai

In this paper, we investigate the use of quaternary reliability statistics for ordered statistics decoding (OSD) of quantum codes. OSD can be used to improve the performance of belief propagation (BP) decoding when it fails to correct the error syndrome. We propose an approach that leverages quaternary reliability information and the hard-decision history ou

Irene Li, Aosong Feng, Dragomir Radev, Rex Ying

Encoding long sequences in Natural Language Processing (NLP) is a challenging problem. Though recent pretraining language models achieve satisfying performances in many NLP tasks, they are still restricted by a pre-defined maximum length, making them challenging to be extended to longer sequences. So some recent works utilize hierarchies to model long sequen

Devika Tharakkal, Anvar Shukurov, Frederick A. Gent, Graeme R. Sarson

We model the Parker instability in vertically stratified isothermal gas using non-ideal MHD three-dimensional simulations. Rotation, especially differential, more strongly and diversely affects the nonlinear state than the linear stage (where we confirm the most important conclusions of analytical models), and stronger than any linear analyses predict. Stead

Nibedita Behera, Ashwina Kumar, Ebenezer Rajadurai T, Sai Nitish

Graphs model several real-world phenomena. With the growth of unstructured and semi-structured data, parallelization of graph algorithms is inevitable. Unfortunately, due to inherent irregularity of computation, memory access, and communication, graph algorithms are traditionally challenging to parallelize. To tame this challenge, several libraries, framewor

Michael Horodynski, Tobias Reiter, Matthias Kühmayer, Stefan Rotter

Moving objects with optical or acoustical waves is a topic both of fundamental interest and of importance for a range of practical applications. One particularly intriguing example is the tractor beam, which pulls an object toward the wave's source, in opposition to the wave's momentum. In this study, we introduce a protocol that enables the identification o

Jin Li, Yang Gao, Ju Dai, Shuai Li

High-accuracy, high-efficiency physics-based fluid-solid interaction is essential for reality modeling and computer animation in online games or real-time Virtual Reality (VR) systems. However, the large-scale simulation of incompressible fluid and its interaction with the surrounding solid environment is either time-consuming or suffering from the reduced t

Haiyun Yang

Recently, Chinese Spell Checking(CSC), a task to detect erroneous characters in a sentence and correct them, has attracted extensive interest because of its wide applications in various NLP tasks. Most of the existing methods have utilized BERT to extract semantic information for CSC task. However, these methods directly take sentences with only a few errors

E. V. Arbuzova, A. D. Dolgov, A. A. Nikitenko

Multidimensional modification of gravity with a smaller mass scale of the gravitational interaction is considered. Stable by assumption dark matter particles could decay via interactions with virtual black holes. The decay rates of such processes are estimated. It is shown that with the proper fixation of the parameters the decays of these ultra-massive part

Ivo Batkovic, Ankit Gupta, Mario Zanon, Paolo Falcone

The full deployment of autonomous driving systems on a worldwide scale requires that the self-driving vehicle be operated in a provably safe manner, i.e., the vehicle must be able to avoid collisions in any possible traffic situation. In this paper, we propose a framework based on Model Predictive Control (MPC) that endows the self-driving vehicle with the n

Lan Zhang, Xiang-Xiang Xue, Chengqun Yang, Feilu Wang

We present a catalog of distances for 19544 K giants drawn from LAMOST DR8. Most of them are located in the halo of the Milky Way up to ~120~kpc. There are 15% K giants without SDSS photometry, for which we supplements with Pan-STARRS1 (PS1) photometry calibrated to SDSS photometric system. The possible contamination of the red clumps/horizontal branch are r

Jun Li, Wenyi Zhang

We consider a joint sampling and compression system for timely status updates. Samples are taken, quantized and encoded into binary sequences, which are sent to the destination. We formulate an optimization problem to jointly design sampler, quantizer and encoder, minimizing the age of information (AoI) on the basis of satisfying a mean-squared error (MSE) d

Martin Möller, Scott Mullane

We provide a complete classification of Teichm\"uller curves occurring in hyperelliptic components of the meromorphic strata of differentials. Using a non-existence criterion based on how Teichm\"uller curves intersect the boundary of the moduli space we derive a contradiction to the algebraicity of any candidate outside of Hurwitz covers of strata with proj

Yali Zheng, Chen Wu, Peizheng Cai, Zhiqiang Zhong

Photoplethysmogram (PPG) signals are easily contaminated by motion artifacts in real-world settings, despite their widespread use in Internet-of-Things (IoT) based wearable and smart health devices for cardiovascular health monitoring. This study proposed a lightweight deep neural network, called Tiny-PPG, for accurate and real-time PPG artifact segmentation

Dorna Abdolazimi, Kasper Lindberg, Shayan Oveis Gharan

Given a matroid $M=(E,{\cal I})$, and a total ordering over the elements $E$, a broken circuit is a circuit where the smallest element is removed and an NBC independent set is an independent set in ${\cal I}$ with no broken circuit. The set of NBC independent sets of any matroid $M$ define a simplicial complex called the broken circuit complex which has been

Sonia Sousa, Jose Cravino, Paulo Martins, David Lamas

The rationale of this work is based on the current user trust discourse of Artificial Intelligence (AI). We aim to produce novel HCI approaches that use trust as a facilitator for the uptake (or appropriation) of current technologies. We propose a framework (HCTFrame) to guide non-experts to unlock the full potential of user trust in AI design. Results deriv

Shih-Yu Chang

This is Part II of our work about random tensor inequalities and tail bounds for bivariate random tensor means. After reviewing basic facts about random tensors, we first consider tail bounds with more general connection functions. Then, a general Lie-Trotter formula for tensors is derived and this formula is applied to establish tail bounds for bivariate ra

Igor Frolov, Albert Schwarz

In quantum mechanics, one can express the evolution operator and other quantities in terms of functional integrals. The main goal of this paper is to prove corresponding results in the geometric approach to quantum theory. We apply these results to the formalism of L-functionals.

Haochen Liu, Zhiyu Huang, Chen Lv

Forecasting the scalable future states of surrounding traffic participants in complex traffic scenarios is a critical capability for autonomous vehicles, as it enables safe and feasible decision-making. Recent successes in learning-based prediction and planning have introduced two primary challenges: generating accurate joint predictions for the environment

Menghua Wu, Hao Zhu, Linjia Huang, Yiyu Zhuang

Synthesizing high-quality 3D face models from natural language descriptions is very valuable for many applications, including avatar creation, virtual reality, and telepresence. However, little research ever tapped into this task. We argue the major obstacle lies in 1) the lack of high-quality 3D face data with descriptive text annotation, and 2) the complex

Shih-Yu Chang

In this work, we apply the concept about operator connection to consider bivariate random tensor means. We first extend classical Markov and Chebyshev inequalities from a random variable to a random tensor by establishing Markov inequality for tensors and Chebyshev inequality for tensors. These inequalities are applied to establish tail bounds for bivariate

Rahul Mehta, Vasudeva Varma

Named Entity Recognition(NER) is a task of recognizing entities at a token level in a sentence. This paper focuses on solving NER tasks in a multilingual setting for complex named entities. Our team, LLM-RM participated in the recently organized SemEval 2023 task, Task 2: MultiCoNER II,Multilingual Complex Named Entity Recognition. We approach the problem by

Kuicai Dong, Aixin Sun, Jung-Jae Kim, Xiaoli Li

Open Information Extraction (OIE) aims to extract relational tuples from open-domain sentences. Existing OIE systems split a sentence into tokens and recognize token spans as tuple relations and arguments. We instead propose Sentence as Chunk sequence (SaC) and recognize chunk spans as tuple relations and arguments. We argue that SaC has better quantitative

Ravi Kumar, Aryya Ghosh, Nayana Vaval

A molecular cluster's inner valence ionized state undergoes autoionization, which is nonlocal by nature. In a molecular system, when the inner valence's ionization potential (IP) is higher than the double ionization energy (DIP), it is energetically favorable for the initially ionized system to emit a secondary electron and reach a final state which is lower

Hongmei Hu, Stephan Ewert, Birger Kollmeier, Deborah Vickers

The objective was to determine the effect of pulse rate on the sensitivity to use interaural-time-difference (ITD) cues and to explore the mechanisms behind rate-dependent degradation in ITD perception in bilateral cochlear implant (CI) listeners using CI simulations and electroencephalogram (EEG) measures. To eliminate the impact of CI stimulation artifacts

Weixiang Zhao, Yanyan Zhao, Shilong Wang, Bing Qin

Emotion Support Conversation (ESC) is an emerging and challenging task with the goal of reducing the emotional distress of people. Previous attempts fail to maintain smooth transitions between utterances in ESC because they ignore to grasp the fine-grained transition information at each dialogue turn. To solve this problem, we propose to take into account tu

Paweł Wachel, Krzysztof Kowalczyk, Cristian R. Rojas

We study the problem of diffusion-based network learning of a nonlinear phenomenon, $m$, from local agents' measurements collected in a noisy environment. For a decentralized network and information spreading merely between directly neighboring nodes, we propose a non-parametric learning algorithm, that avoids raw data exchange and requires only mild \textit

Wuji Zhang, Shuyue Wang, Chunfeng Wu, Gangcheng Wang

The Dicke model is a fundamental model in quantum optics, which describes the interaction between quantum cavity field and a large ensemble of two-level atoms. In this work, we propose an efficient charging quantum battery achieved by considering an extension Dicke model with dipole-dipole interaction and an external driving field. We focus on the influence

Souradeep Bhattacharya

Andromeda (M 31) is the nearest giant spiral galaxy to our Milky Way, and over the past few decades, has been dubbed the most massive member of the Local Group. I explore the evolution of the measured mass of M 31 over the past ~80 years, reviewing the different observational and modelling techniques that have developed over time to measure its mass. I discu

Yuchen Shi, Zheqi Zhu, Pingyi Fan, Khaled B. Letaief

Federated Learning (FL) is a promising distributed learning mechanism which still faces two major challenges, namely privacy breaches and system efficiency. In this work, we reconceptualize the FL system from the perspective of network information theory, and formulate an original FL communication framework, FedNC, which is inspired by Network Coding (NC). T

Jordan W. Suchow, Lea Burton, Vahid Ashrafimoghari

We consider the problem of how a platform designer, owner, or operator can improve the design and operation of a digital platform by leveraging a computational cognitive model that represents users's folk theories about a platform as a sociotechnical system. We do so in the context of Reddit, a social media platform whose owners and administrators make exten

Flor Aguilar, Gabriela Araujo-Pardo, Leah Bermann

An \emph{$(3,m;g)$ semicubic graph} is a graph in which all vertices have degrees either $3$ or $m$ and fixed girth $g$. In this paper, we construct families of semicubic graphs of even girth and small order using two different techniques. The first technique generalizes a previous construction which glues cubic cages of girth $g$ together at remote vertices

Jacob Merson, Mark S. Shephard

This paper presents efforts to improve the hierarchical parallelism of a two scale simulation code. Two methods to improve the GPU parallel performance were developed and compared. The first used the NVIDIA Multi-Process Service and the second moved the entire sub-problem loop into a single kernel using Kokkos hierarchical parallelism and a PackedView data s

Zihan Guan, Mengxuan Hu, Zhongliang Zhou, Jielu Zhang

Recently, the Segment Anything Model (SAM) has gained significant attention as an image segmentation foundation model due to its strong performance on various downstream tasks. However, it has been found that SAM does not always perform satisfactorily when faced with challenging downstream tasks. This has led downstream users to demand a customized SAM model

Huy Nguyen, TrungTin Nguyen, Nhat Ho

Understanding the parameter estimation of softmax gating Gaussian mixture of experts has remained a long-standing open problem in the literature. It is mainly due to three fundamental theoretical challenges associated with the softmax gating function: (i) the identifiability only up to the translation of parameters; (ii) the intrinsic interaction via partial

Anagha P, Selvakumar R

For the study of functional aspects of the brain network. This paper is a study on the hypergraph representation, based on the functional regions of the brain network. A new parameter that can measure how many multifunctioning regions each function contains and thereby the correlation of other functions with each function.

Jiawei Liu, Zi Xiong, Yi Jiang, Yongqiang Ma

Fine-tuning pre-trained language models (PLMs), e.g., SciBERT, generally requires large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining the fine-tune data for scientific NLP task is still challenging and expensive. Inspired by recent advancement in prompt learning, in this

Gokul Yenduri, Rajesh Kaluri, Dharmendra Singh Rajput, Kuruva Lakshmanna

The development of new technologies and their expanding use in a wide range of educational environments are driving the transformation of higher education. Assistive technologies are a subset of cutting-edge technology that can help students learn more effectively and make education accessible to everyone. Assistive technology can enhance, maintain, or impro

Pei Xu, Xiumin Shang, Victor Zordan, Ioannis Karamouzas

We present a deep learning method for composite and task-driven motion control for physically simulated characters. In contrast to existing data-driven approaches using reinforcement learning that imitate full-body motions, we learn decoupled motions for specific body parts from multiple reference motions simultaneously and directly by leveraging the use of

Reina Ishikawa

In the present paper, we give proofs of the existence of a 3-design in the extended ternary quadratic residue code of length 14 and the extended quaternary quadratic residue code of length 18.

Ali G. Sheikh, Casey J. Pellizzari, Sherman J. Kisner, Gregery T. Buzzard

Directed energy applications require the estimation of digital-holographic (DH) phase errors due to atmospheric turbulence in order to accurately focus the outgoing beam. These phase error estimates must be computed with very low latency to keep pace with changing atmospheric parameters, which requires that phase errors be estimated in a single shot of DH da

Katsushi Ito, Mingshuo Zhu

We study the WKB analysis of the solutions to the linear problem for a modified affine Toda field equation, which is equivalent to the higher-order ordinary differential equation (ODE) studied in the ODE/IM correspondence. After gauge transformation, we diagonalize the flat connection of the linear problem to reduce the latter to a set of independent first-o

Midya Parto, Christian Leefmans, James Williams, Alireza Marandi

Sensors are indispensable tools of modern life that are ubiquitously used in diverse settings ranging from smartphones and autonomous vehicles to the healthcare industry and space technology. By interfacing multiple sensors that collectively interact with the signal to be measured, one can go beyond the signal-to-noise ratios (SNR) than those attainable by t

Weijie Ren, Wenjia Xie, Ye Zhang, Hang Yu

Modern shock-capturing schemes often suffer from numerical shock anomalies if the flow field contains strong shocks, which may limit their further application in hypersonic flow computations. In the current study, we devote our efforts to exploring the primary numerical characteristics and the underlying mechanism of shock instability for second-order finite

Wenwen Chen, Bing Wang, Mingqing Zhai

The signless Laplacian spectral radius of a graph $G$, denoted by $q(G)$, is the largest eigenvalue of its signless Laplacian matrix. In this paper, we investigate extremal signless Laplacian spectral radius for graphs without short cycles or long cycles. Let $\mathcal{G}(m,g)$ be the family of graphs on $m$ edges with girth $g$ and $\mathcal{H}(m,c)$ be the

Daomin Cao, Guodong Wang, Bijun Zuo

Rossby-Haurwitz (RH) waves are important explicit solutions of the incompressible Euler equation on a two-dimensional rotating sphere. In this paper, we prove the orbital stability of degree-2 RH waves, which confirms a conjecture proposed by A. Constantin and P. Germain in [Arch. Ration. Mech. Anal. 245, 587-644, 2022]. The proofs are based on a variational

T. Shang, J. Meng, X. Y. Zhu, H. Zhang

We report a study of NiBi$_3$ single crystals by means of electrical-resistivity-, magnetization-, and muon-spin rotation and relaxation ($\mu$SR) measurements. As a single crystal, NiBi$_3$ adopts a needle-like shape and exhibits bulk superconductivity with $T_c \approx 4.1$ K. By applying magnetic fields parallel and perpendicular to the $b$-axis of NiBi$_

Ajian Liu, Zichang Tan, Zitong Yu, Chenxu Zhao

The availability of handy multi-modal (i.e., RGB-D) sensors has brought about a surge of face anti-spoofing research. However, the current multi-modal face presentation attack detection (PAD) has two defects: (1) The framework based on multi-modal fusion requires providing modalities consistent with the training input, which seriously limits the deployment s

Nishant Balepur, Jie Huang, Kevin Chen-Chuan Chang

Expository documents are vital resources for conveying complex information to readers. Despite their usefulness, writing expository text by hand is a challenging process that requires careful content planning, obtaining facts from multiple sources, and the ability to clearly synthesize these facts. To ease these burdens, we propose the task of expository tex

Thomas E. Foster, Henry Fetsch, Nathaniel J. Fisch

If the electrons in a plasma are suddenly heated, the resulting change in Debye shielding causes the ion kinetic energy to quickly increase. For the first time, this correlation heating, which is much faster than collisional energy exchange, is rigorously derived for a moderately coupled, electron-ion plasma. The electron-ion mass ratio is taken to be the sm

Kequan Zhou, Guangyi Zhang, Yunlong Cai, Qiyu Hu

Although existing semantic communication systems have achieved great success, they have not considered that the channel is time-varying wherein deep fading occurs occasionally. Moreover, the importance of each semantic feature differs from each other. Consequently, the important features may be affected by channel fading and corrupted, resulting in performan

Hans Thisanke, Chamli Deshan, Kavindu Chamith, Sachith Seneviratne

Semantic segmentation has a broad range of applications in a variety of domains including land coverage analysis, autonomous driving, and medical image analysis. Convolutional neural networks (CNN) and Vision Transformers (ViTs) provide the architecture models for semantic segmentation. Even though ViTs have proven success in image classification, they canno

Salish Maharjan, Prashant Tiwari, Rui Cheng, Zhaoyu Wang

Stochastic controllers are perceived as a promising solution for techno-economic operation of distribution networks having higher generation uncertainties at large penetration of renewables. These controllers are supported by forecasters capable of predicting generation uncertainty by means of lower/upper bounds rather than by probability density function (P

Shidong Pan, Dawen Zhang, Mark Staples, Zhenchang Xing

Privacy regulations protect and promote the privacy of individuals by requiring mobile apps to provide a privacy policy that explains what personal information is collected and how these apps process this information. However, developers often do not have sufficient legal knowledge to create such privacy policies. Online Automated Privacy Policy Generators (

Alexander Herzog, Kanishka Rao, Karol Hausman, Yao Lu

We describe a system for deep reinforcement learning of robotic manipulation skills applied to a large-scale real-world task: sorting recyclables and trash in office buildings. Real-world deployment of deep RL policies requires not only effective training algorithms, but the ability to bootstrap real-world training and enable broad generalization. To this en

Hongyu Sun, Zachary E. Ross, Weiqiang Zhu, Kamyar Azizzadenesheli

Seismic wave arrival time measurements form the basis for numerous downstream applications. State-of-the-art approaches for phase picking use deep neural networks to annotate seismograms at each station independently, yet human experts annotate seismic data by examining the whole network jointly. Here, we introduce a general-purpose network-wide phase pickin

Ruochen Zhao, Xingxuan Li, Shafiq Joty, Chengwei Qin

As large language models (LLMs) have become the norm in NLP, demonstrating good performance in generation and reasoning tasks, one of its most fatal disadvantages is the lack of factual correctness. Generating unfactual texts not only leads to lower performances but also degrades the trust and validity of their applications. Chain-of-Thought (CoT) prompting

Hanxi Fang, Song Gao, Feng Zhang

Tourists often go to multiple tourism destinations in one trip. The volume of tourism flow between tourism destinations, also referred to as ITF (Inter-Destination Tourism Flow) in this paper, is commonly used for tourism management on tasks like the classification of destinations' roles and visitation pattern mining. However, the ITF is hard to get due to t

Avani Dave Nilanjan Banerjee Chintan Patel

Modern society is getting accustomed to the Internet of Things (IoT) and Cyber-Physical Systems (CPS) for a variety of applications that involves security-critical user data and information transfers. In the lower end of the spectrum, these devices are resource-constrained with no attack protection. They become a soft target for malicious code modification a

Xinyi Wu, Lu Chen, Jianan Li, Megan Briggeman

The strongly correlated two-dimensional electron liquid within SmTiO$_3$/SrTiO$_3$/SmTiO$_3$ quantum well structures exhibits a pseudogap phase when the quantum well width is sufficiently narrow. Using low-temperature transport and optical experiments that drive the quantum-well system out of equilibrium, we find evidence of mobile, long-lived, negatively-ch

Jag Mohan Singh Sushma Venkatesh Raghavendra Ramachandra

Face Recognition System (FRS) are shown to be vulnerable to morphed images of newborns. Detecting morphing attacks stemming from face images of newborn is important to avoid unwanted consequences, both for security and society. In this paper, we present a new reference-based/Differential Morphing Attack Detection (MAD) method to detect newborn morphing image

Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy

All biological and artificial agents must learn and make decisions given limits on their ability to process information. As such, a general theory of adaptive behavior should be able to account for the complex interactions between an agent's learning history, decisions, and capacity constraints. Recent work in computer science has begun to clarify the princi

Yangyang Zhao, Zhenyu Wang, Mehdi Dastani, Shihan Wang

Training a dialogue policy using deep reinforcement learning requires a lot of exploration of the environment. The amount of wasted invalid exploration makes their learning inefficient. In this paper, we find and define an important reason for the invalid exploration: dead-ends. When a conversation enters a dead-end state, regardless of the actions taken aft

Aaron Trowbridge, Aditya Bhardwaj, Kevin He, David I. Schuster

We present an adaptation of direct collocation -- a trajectory optimization method commonly used in robotics and aerospace applications -- to quantum optimal control (QOC); we refer to this method as Pade Integrator COllocation (PICO). This approach supports general nonlinear constraints on the states and controls, takes advantage of state-of-the-art large-s

Ryotatsu Yanagimoto, Rajveer Nehra, Edwin Ng, Alireza Marandi

We propose a scheme to realize cubic quantum nondemolition (QND) Hamiltonian with optical parametric interactions. We show that strongly squeezed fundamental and second harmonic fields propagating in a $\chi^{(2)}$ nonlinear medium effectively evolve under a cubic QND Hamiltonian. We highlight the versatility offered by such Hamiltonian for engineering non-G

Xingyu Zhu, Xin Wang, Jonathan Freer, Hyung Jin Chang

Clothes grasping and unfolding is a core step in robotic-assisted dressing. Most existing works leverage depth images of clothes to train a deep learning-based model to recognize suitable grasping points. These methods often utilize physics engines to synthesize depth images to reduce the cost of real labeled data collection. However, the natural domain gap

Jiucai Wang, Zheng-Xin Liu

Motivated by the possible triple-$\bf Q$ classical order in the Kitaev candidate material Na$_2$Co$_2$TeO$_6$, we investigate microscopic models that may stabilize the triple-$\bf Q$ order by studying an extended Kitaev honeycomb model with ring-exchange interactions (namely, the $K$-$\Gamma$-$\Gamma'$-$J_{\rm R}$ model) using the variational Monte Carlo met

Tianqi Cui, Tom S. Bertalan, Nelson Ndahiro, Pratik Khare

Fed-batch culture is an established operation mode for the production of biologics using mammalian cell cultures. Quantitative modeling integrates both kinetics for some key reaction steps and optimization-driven metabolic flux allocation, using flux balance analysis; this is known to lead to certain mathematical inconsistencies. Here, we propose a physicall

Liqiang Jing, Xuemeng Song, Xuming Lin, Zhongzhou Zhao

Existing data-to-text generation efforts mainly focus on generating a coherent text from non-linguistic input data, such as tables and attribute-value pairs, but overlook that different application scenarios may require texts of different styles. Inspired by this, we define a new task, namely stylized data-to-text generation, whose aim is to generate coheren

Qinghui Sun, Sharon Xuesong Wang, Andrew W. Mann, Benjamin M. Tofflemire

Young planets (< 1 Gyr) are helpful for studying the physical processes occurring at the early stage of planet evolution. TOI-251 b is a recently discovered sub-Neptune orbiting a young G dwarf, which has an imprecise age estimation of 40-320 Myr. We select TOI-251 sibling candidates based on kinematics and spatial proximity to TOI-251, and further use the c

Dan Barbasch, Kayue Daniel Wong

In this paper, we classify all unitary representations with non-zero Dirac cohomology for complex Lie group of Type E8. This completes the classification of Dirac series for all complex simple Lie groups.

Bin Ji

Large Language Models (LLMs, e.g., ChatGPT) have shown impressive zero- and few-shot capabilities in Named Entity Recognition (NER). However, these models can only be accessed via online APIs, which may cause data leak and non-reproducible problems. In this paper, we propose VicunaNER, a zero/few-shot NER framework based on the newly released open-source LLM

Mohammad Saeid Anwar, Emon Dey, Maloy Kumar Devnath, Indrajeet Ghosh

Gathering knowledge about surroundings and generating situational awareness for IoT devices is of utmost importance for systems developed for smart urban and uncontested environments. For example, a large-area surveillance system is typically equipped with multi-modal sensors such as cameras and LIDARs and is required to execute deep learning algorithms for

Takeru Tanaka, Hideaki Hata, Bodin Chinthanet, Raula Gaikovina Kula

Docker allows for the packaging of applications and dependencies, and its instructions are described in Dockerfiles. Nowadays, version pinning is recommended to avoid unexpected changes in the latest version of a package. However, version pinning in Dockerfiles is not yet fully realized (only 17k of the 141k Dockerfiles we analyzed), because of the difficult

Lingling Fan, Kai Wang, Heming Wang, Avik Dutt

Convolution is an essential operation in signal and image processing and consumes most of the computing power in convolutional neural networks. Photonic convolution has the promise of addressing computational bottlenecks and outperforming electronic implementations. Performing photonic convolution in the synthetic frequency dimension, which harnesses the dyn

Jinseok Bae, Jungdam Won, Donggeun Lim, Cheol-Hui Min

We present a method to animate a character incorporating multiple part-wise motion priors (PMP). While previous works allow creating realistic articulated motions from reference data, the range of motion is largely limited by the available samples. Especially for the interaction-rich scenarios, it is impractical to attempt acquiring every possible interactin

Miguel A. Cardona, Diego Alejandro Mejía

This paper is intended to survey the basics of localization and anti-localization cardinals on the reals, and its interplay with notions and cardinal characteristics related to measure and category.

Sobhan Moosavi, Rajiv Ramnath

Driving risk prediction has been a topic of much research over the past few decades to minimize driving risk and increase safety. The use of demographic information in risk prediction is a traditional solution with applications in insurance planning, however, it is difficult to capture true driving behavior via such coarse-grained factors. Therefor, the use

Zhong-Feng Sun, Jin-Chuan Zhou, Yun-Bin Zhao

Linear inverse problems arise in diverse engineering fields especially in signal and image reconstruction. The development of computational methods for linear inverse problems with sparsity is one of the recent trends in this field. The so-called optimal $k$-thresholding is a newly introduced method for sparse optimization and linear inverse problems. Compar

Kiran A Wani, Haritma Gaur, M K Patil

High synchrotron energy peaked blazar 1ES 1959+650 is studied with Swift and XMM-Newton satellite in total 127 observations during the period June 2018-December 2020. We extensively studied its flux and spectral variability on intra-day and long-term timescales. Discrete correlation function analysis between soft and hard X-ray bands indicates soft as well a

Yuhong Xu, Xinyao Zhao

In complex financial networks, systemically important nodes usually play crucial roles. Asset price forecasting is important for describing the evolution of a financial network. Naturally, the question arises as to whether node centrality impacts the effectiveness of price forecasting. To explore this, we examine networks composed of major global assets and

Ji-Yang Zhou, Qiang Li, Zhi-He Hao, Wu-Xi Lin

Optically addressable spin defects in silicon carbide (SiC) have emerged as attractable platforms for various quantum technologies. However, the low photon count rate significantly limits their applications. We strongly enhanced the brightness by 7 times and spin-control strength by 14 times of single divacancy defects in 4H-SiC membranes using surface plasm

Samuel Stein, Sara Sussman, Teague Tomesh, Charles Guinn

Noisy Intermediate-Scale Quantum Computing (NISQ) has dominated headlines in recent years, with the longer-term vision of Fault-Tolerant Quantum Computation (FTQC) offering significant potential albeit at currently intractable resource costs and quantum error correction (QEC) overheads. For problems of interest, FTQC will require millions of physical qubits