October 2022 arXiv papers — page 103

Abinash Deka, Subaveerapandiyan A

The present study aim is to know the information professionals-library professionals knowledge sharing behaviours and attitudes among the institutes. This study investigated six countries' library professionals: Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka. The study discussed knowledge sharing behaviour, technological equipment used for knowled

Jeong Ryeol Choi

An exact invariant operator of time-dependent coupled oscillators is derived using the Liouville-von Neumann equation. The unitary relation between this invariant and the invariant of two uncoupled simple harmonic oscillators is represented. If we consider the fact that quantum solutions of the simple harmonic oscillator is well-known, this unitary relation

Madhuri Kumari, Subaveerapandiyan A

Predatory journals that pretended to resemble refereed journals but are used for money-making purposes. Predatory publishers produce less quality scientific and research papers; it is a severe academic threat in scientific publications. Researchers are ensuring the quality of the journal and peer-reviewing process before submitting the manuscript. This paper

Subaveerapandiyan A, Priyanka Sinha

The study attempted to understand the University students' digital reading habits and their related skills. It also has a view of students' preferred sources of reading, whether physical or digital resources. For this study, we conducted a survey study with students and research scholars of the Central University of Tamil Nadu, India. The instrument was a st

Mesut Şahin, Oğuz Yayla

We obtain certain algebraic invariants relevant to study codes on subgroups of weighted projective tori inside an $n$-dimensional weighted projective space. As application, we compute all the main parameters of generalized toric codes on these subgroups of tori lying inside a weighted projective plane of the form $\Pp(1,1,a)$.

Subaveerapandiyan A, Anuradha Maurya

Purpose - The study's main objective was to find out the possibility of a paperless library and society with particular reference to Top 60 Universities from QS world University ranking 2021 and their library professionals. ICT knowledge and skills of these LIS professionals and evaluated their digital literacy skills was another aim of this study. Design/me

Lijun Bo, Wenyuan Wang, Kaixin Yan

In this paper we consider the De Finetti's optimal dividend and capital injection problem under a Markov additive model. We assume that the surplus process before dividends and capital injections follows a spectrally positive Markov additive process. Dividend payments are made only at the jump times of an independent Poisson process. Capitals are required to

Shinji Yamada, Satoshi Kamiya, Kazuhiro Hotta

Anomaly detection is an important problem in computer vision; however, the scarcity of anomalous samples makes this task difficult. Thus, recent anomaly detection methods have used only normal images with no abnormal areas for training. In this work, a powerful anomaly detection method is proposed based on student-teacher feature pyramid matching (STPM), whi

Songyang Gao, Shihan Dou, Qi Zhang, Xuanjing Huang

Dataset bias has attracted increasing attention recently for its detrimental effect on the generalization ability of fine-tuned models. The current mainstream solution is designing an additional shallow model to pre-identify biased instances. However, such two-stage methods scale up the computational complexity of training process and obstruct valid feature

Emily R. Bartusiak, Edward J. Delp

Speech synthesis methods can create realistic-sounding speech, which may be used for fraud, spoofing, and misinformation campaigns. Forensic methods that detect synthesized speech are important for protection against such attacks. Forensic attribution methods provide even more information about the nature of synthesized speech signals because they identify t

Mane Sunita D, Subaveerapandiyan A

This study investigated e-resources use, storage, the preferred format for reading, and difficulties faced while accessing e-resources. Electronic resources are playing a crucial role all over the world, and they are increasing widely in all age groups of the academic community. The main aim of the law academics' role is to know the effective use of electron

Agnese Barbensi, Iris H. R. Yoon, Christian Degnbol Madsen, Deborah O. Ajayi

Scientific data has been growing in both size and complexity across the modern physical, engineering, life and social sciences. Spatial structure, for example, is a hallmark of many of the most important real-world complex systems, but its analysis is fraught with statistical challenges. Topological data analysis can provide a powerful computational window o

Abhay Shukla, Paheli Bhattacharya, Soham Poddar, Rajdeep Mukherjee

Summarization of legal case judgement documents is a challenging problem in Legal NLP. However, not much analyses exist on how different families of summarization models (e.g., extractive vs. abstractive) perform when applied to legal case documents. This question is particularly important since many recent transformer-based abstractive summarization models

Chenxi Gu, Chengsong Huang, Xiaoqing Zheng, Kai-Wei Chang

Large pre-trained language models (PLMs) have proven to be a crucial component of modern natural language processing systems. PLMs typically need to be fine-tuned on task-specific downstream datasets, which makes it hard to claim the ownership of PLMs and protect the developer's intellectual property due to the catastrophic forgetting phenomenon. We show tha

Deokkeun An, Timothy C. Beers, Young Sun Lee, Thomas Masseron

We continue our series of papers on phase-space distributions of stars in the Milky Way based on photometrically derived metallicities and Gaia astrometry, with a focus on the halo-disk interface in the local volume. To exploit various photometric databases, we develop a method of empirically calibrating synthetic stellar spectra based on a comparison with o

Kazuma Kobayashi, Dinesh Kumar, Matthew Bonney, Souvik Chakraborty

To understand the potential of intelligent confirmatory tools, the U.S. Nuclear Regulatory Committee (NRC) initiated a future-focused research project to assess the regulatory viability of machine learning (ML) and artificial intelligence (AI)-driven Digital Twins (DTs) for nuclear power applications. Advanced accident tolerant fuel (ATF) is one of the prior

Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo

Vision Transformers (ViTs) have recently achieved competitive performance in broad vision tasks. Unfortunately, on popular threat models, naturally trained ViTs are shown to provide no more adversarial robustness than convolutional neural networks (CNNs). Adversarial training is still required for ViTs to defend against such adversarial attacks. In this pape

Hongbing Shang, Qixiu Yang, Chuang Sun, Xuefeng Chen

Aero-engine is the core component of aircraft and other spacecraft. The high-speed rotating blades provide power by sucking in air and fully combusting, and various defects will inevitably occur, threatening the operation safety of aero-engine. Therefore, regular inspections are essential for such a complex system. However, existing traditional technology wh

Bashar Alhafni, Ossama Obeid, Nizar Habash

We introduce the User-Aware Arabic Gender Rewriter, a user-centric web-based system for Arabic gender rewriting in contexts involving two users. The system takes either Arabic or English sentences as input, and provides users with the ability to specify their desired first and/or second person target genders. The system outputs gender rewritten alternatives

R. Mohanasubha, M. Senthilvelan

In this paper, we present a method of deriving extended Prelle-Singer method's quantifiers from Darboux Polynomials for third-order nonlinear ordinary differential equations. By knowing the Darboux polynomials and its cofactors, we extract the extended Prelle-Singer method's quantities without evaluating the Prelle-Singer method's determining equations. We c

Ziyang Tang, Yiheng Duan, Stephanie Zhang, Lihong Li

Randomized experiments (a.k.a. A/B tests) are a powerful tool for estimating treatment effects, to inform decisions making in business, healthcare and other applications. In many problems, the treatment has a lasting effect that evolves over time. A limitation with randomized experiments is that they do not easily extend to measure long-term effects, since r

Ganesh Jawahar, Subhabrata Mukherjee, Xiaodong Liu, Young Jin Kim

Mixture-of-Expert (MoE) models have obtained state-of-the-art performance in Neural Machine Translation (NMT) tasks. Existing works in MoE mostly consider a homogeneous design where the same number of experts of the same size are placed uniformly throughout the network. Furthermore, existing MoE works do not consider computational constraints (e.g., FLOPs, l

Xin Lyu, Weihao Zhu

In the Element Distinctness problem, one is given an array $a_1,\dots, a_n$ of integers from $[poly(n)]$ and is tasked to decide if $\{a_i\}$ are mutually distinct. Beame, Clifford and Machmouchi (FOCS 2013) gave a low-space algorithm for this problem running in space $S(n)$ and time $T(n)$ where $T(n) \le \widetilde{O}(n^{3/2}/S(n)^{1/2})$, assuming a rando

Dillon Lohr, Samantha Aziz, Lee Friedman, Oleg V Komogortsev

We present GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking (ET) dataset collected at 250 Hz with an ET-enabled virtual-reality (VR) headset. GazeBaseVR comprises 5,020 binocular recordings from a diverse population of 407 college-aged participants. Participants were recorded up to six times each over a 26-month period, each time performing a

Qi Lyu, Xiao Fu

Unsupervised mixture learning (UML) aims at identifying linearly or nonlinearly mixed latent components in a blind manner. UML is known to be challenging: Even learning linear mixtures requires highly nontrivial analytical tools, e.g., independent component analysis or nonnegative matrix factorization. In this work, the post-nonlinear (PNL) mixture model --

Matthew Chan, Nathaniel Snyder, Marcus Lucas, Luis Garcia

Although organizations are continuously making concerted efforts to harden their systems against network attacks by air-gapping critical systems, attackers continuously adapt and uncover covert channels to exfiltrate data from air-gapped systems. For instance, attackers have demonstrated the feasibility of exfiltrating data from a computer sitting in a Farad

Massimo Cairo, Shahbaz Khan, Romeo Rizzi, Sebastian Schmidt

In a strongly connected graph $G = (V,E)$, a cut arc (also called strong bridge) is an arc $e \in E$ whose removal makes the graph no longer strongly connected. Equivalently, there exist $u,v \in V$, such that all $u$-$v$ walks contain $e$. Cut arcs are a fundamental graph-theoretic notion, with countless applications, especially in reachability problems. In

Teng Man, Zaohui Zhang, Herbert E. Huppert, Sergio A. Galindo-Torres

The behavior of granular column collapses is associated with the dynamics of geohazards, such as debris flows, landslides, and pyroclastic flows, yet its underlying physics is still not well understood. In this paper, we explore granular column collapses using the spheropolyhedral discrete element method (DEM), where the system contains two types of particle

Tian Qin, Ruixia Zhong, Weizheng Cao, Shiwei Shen

Here we use low-temperature and variable-temperature scanning tunneling microscopy to study the pnictide superconductor, Ba$_{1-x}$Sr$_x$Ni$_2$As$_2$. In the low-temperature phase (triclinic phase) of BaNi$_2$As$_2$, we observe the unidirectional charge density wave (CDW) with $Q$ = 1/3 on both the Ba and NiAs surfaces. On the NiAs surface of the triclinic B

Scott Mutchnik

We initiate the study of a generalization of Kim-independence, Conant-independence, based on the "strong Kim-dividing" of Kaplan, Ramsey and Shelah. We introduce an axiom on stationary independence relations essentially generalizing the "freedom" axiom in some of the free amalgamation theories of Conant, and show that this axiom provides the correct setting

Eiichi Oishi, Yasuhiro Fujii, Akitoshi Koreeda

We report anomalous circularly polarized Raman spectra of phonons in right- and left-handed quartzes. The phonon branches splitting from the E-mode at a finite wavenumber were found to be chiral with mutually opposite angular momenta. Our analysis reveals the Raman selection rules for chiral phonons. We also find that the conservation of angular momentum sho

Dhritimalya Roy, Ayanendu Dutta, Subenoy Chakraborty

The present work investigates the interrelation between the validity (or violation) of the cosmic no-hair conjecture and the existence (or non-existence) of wormholes, both in Einstein's Gravity and in modified gravity theories. It is found that the existence of wormholes implies a violation of the cosmic no-hair conjecture, and the validity of the cosmic no

Scott Mutchnik

We exhibit a connection between geometric stability theory and the classification of unstable structures at the level of simplicity and the $\mathrm{NSOP}_{1}$-$\mathrm{SOP}_{3}$ gap. Particularly, we introduce generic expansions $T^{R}$ of a theory $T$ associated with a definable relation $R$ of $T$, which can consist of adding a new unary predicate or a ne

Kosuke Nishida, Naoki Yoshinaga, Kyosuke Nishida

Although named entity recognition (NER) helps us to extract domain-specific entities from text (e.g., artists in the music domain), it is costly to create a large amount of training data or a structured knowledge base to perform accurate NER in the target domain. Here, we propose self-adaptive NER, which retrieves external knowledge from unstructured text to

Praveen Ravirathinam, Rahul Ghosh, Ke Wang, Keyang Xuan

Creating separable representations via representation learning and clustering is critical in analyzing large unstructured datasets with only a few labels. Separable representations can lead to supervised models with better classification capabilities and additionally aid in generating new labeled samples. Most unsupervised and semisupervised methods to analy

Richa Verma, Dinesh Kumar, Kazuma Kobayashi, Syed Alam

Robust optimization is a method for optimization under uncertainties in engineering systems and designs for applications ranging from aeronautics to nuclear. In a robust design process, parameter variability (or uncertainty) is incorporated into the engineering systems' optimization process to assure the systems' quality and reliability. This chapter focuses

Joydip Saha, Indranath Sengupta, Pranjal Srivastava

In this paper, we give the necessary and sufficient conditions for the Cohen-Macaulayness of the associated graded ring of a simplicial affine semigroups using Gr\"{o}bner basis. We generalize the concept of homogeneous numerical semigroup for the simplicial affine semigroup and show that the Betti numbers of the corresponding semigroup ring matches with the

Zhisheng Tang, Mayank Kejriwal

In recent years, transformer-based language representation models (LRMs) have achieved state-of-the-art results on difficult natural language understanding problems, such as question answering and text summarization. As these models are integrated into real-world applications, evaluating their ability to make rational decisions is an important research agend

Yizhou Zhang, Defu Cao, Yan Liu

Recent years have witnessed the rise of misinformation campaigns that spread specific narratives on social media to manipulate public opinions on different areas, such as politics and healthcare. Consequently, an effective and efficient automatic methodology to estimate the influence of the misinformation on user beliefs and activities is needed. However, ex

Indranil Biswas, Manish Kumar, A. J. Parameswaran

Take an irreducible smooth projective curve $X$ defined over an algebraically closed field of characteristic zero, and fix finitely many distinct point $D\, =\, \{x_1,\, \cdots,\, x_n\}$ of it; for each point $x\, \in\, D$ fix a positive integer $N_x$. Take a nonconstant map $f\, :\, Y\, \longrightarrow \, X$ from an irreducible smooth projective curve. We c

Tomohiro Harada, Takahisa Igata, Hiromi Saida, Yohsuke Takamori

We study the periapsis shift of a quasi-circular orbit in general static spherically symmetric spacetimes. We derive two formulae in full order with respect to the gravitational field, one in terms of the gravitational mass $m$ and the Einstein tensor and the other in terms of the orbital angular velocity and the Einstein tensor. These formulae reproduce the

M. G. Cowling, M. Ganji, A. Ottazzi, G. Schmalz

We show that a connected, simply connected nilpotent Lie group with an integrable left-invariant complex structure on a generating and suitably complemented subbundle of the tangent bundle admits a CR embedding in complex space as the edge of a wedge in a complex domain defined by polynomials.

Yongkai Liu, Jiawei Hu, Wei Dong

Planning coverage path for multiple robots in a decentralized way enhances robustness to coverage tasks handling uncertain malfunctions. To achieve high efficiency in a distributed manner for each single robot, a comprehensive understanding of both the complicated environments and cooperative agents intent is crucial. Unfortunately, existing works commonly c

Yuhua Zhu, Zachary Izzo, Lexing Ying

This paper revisits the bandit problem in the Bayesian setting. The Bayesian approach formulates the bandit problem as an optimization problem, and the goal is to find the optimal policy which minimizes the Bayesian regret. One of the main challenges facing the Bayesian approach is that computation of the optimal policy is often intractable, especially when

Yu Liu, Xiao Tong, V. N. Ivanovski, Zhixiang Hu

Charge density waves (CDWs) with superconductivity, competing Fermi surface instabilities and collective orders, have captured much interest in two-dimensional van der Waals (vdW) materials. Understanding of CDW suppression mechanism, its connection to emerging superconducting state and electronic correlations provides opportunities for engineering the elect

Zhen Huan, Matthew B. Young

In this paper we construct twisted Real quasi-elliptic cohomology as the twisted KR-theory of loop groupoids. The theory systematically incorporates loop rotation and reflection. After establishing basic properties of the theory, we construct twisted elliptic Pontryagin characters and, without twists, Real analogues of the string power operation of quasi-ell

Sungkyung Kang

We construct an example of a cork that remains exotic after taking a connected sum with $S^2 \times S^2$. Combined with a work of Akbulut-Ruberman, this implies the existence of an exotic pair of contractible 4-manifolds which remains absolutely exotic after taking a connected sum with $S^2 \times S^2$.

Connor Malone, Stephen Hausler, Tobias Fischer, Michael Milford

One recent promising approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques using methods such as SRAL and multi-process fusion. These approaches come with a substantial practical limitation: they require all potential VPR methods to be brute-force run before they are s

Naoya Takahashi, Mayank Kumar, Singh, Yuki Mitsufuji

Recent progress in deep generative models has improved the quality of neural vocoders in speech domain. However, generating a high-quality singing voice remains challenging due to a wider variety of musical expressions in pitch, loudness, and pronunciations. In this work, we propose a hierarchical diffusion model for singing voice neural vocoders. The propos

Yu Liu, M. M. Bordelon, A. Weiland, P. F. S. Rosa

We report a detailed study of the magnetic, transport, and thermodynamic properties of Ce$_2$Te$_5$ single crystals, a layered $f$-electron van der Waals magnet. Four consecutive transitions at $\sim$ 5.2, 2.1, 0.9, and 0.4 K were observed in the $ac$-plane electrical resistivity $\rho$(T), which were further confirmed in specific heat $C_\textrm{p}$(T) meas

Peihao Chen, Dongyu Ji, Kunyang Lin, Runhao Zeng

We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. The instructions often contain descriptions of objects in the environment. To achieve accurate and efficient navigation, it is critical to build a map that accurately represents both spatial location

Peihao Chen, Dongyu Ji, Kunyang Lin, Weiwen Hu

Getting robots to navigate to multiple objects autonomously is essential yet difficult in robot applications. One of the key challenges is how to explore environments efficiently with camera sensors only. Existing navigation methods mainly focus on fixed cameras and few attempts have been made to navigate with active cameras. As a result, the agent may take

Zhipeng Sun

The Fermi surface topology in the two-dimensional Hubbard model is particularly relevant for the high-temperature superconductors, whereas its theoretical research encounters with the difficulty of the analytical continuation problem. To this end, we proposed the concept of the momentum-dependent compressibility, defined as the variation of the momentum dist

Dasom Ahn, Sangwon Kim, Hyunsu Hong, Byoung Chul Ko

In action recognition, although the combination of spatio-temporal videos and skeleton features can improve the recognition performance, a separate model and balancing feature representation for cross-modal data are required. To solve these problems, we propose Spatio-TemporAl cRoss (STAR)-transformer, which can effectively represent two cross-modal features

Jianfei Li, Han Feng, Ding-Xuan Zhou

Deep learning based on deep neural networks has been very successful in many practical applications, but it lacks enough theoretical understanding due to the network architectures and structures. In this paper we establish some analysis for linear feature extraction by a deep multi-channel convolutional neural networks (CNNs), which demonstrates the power of

Giannis Fikioris, Éva Tardos

We study the liquid welfare in sequential first-price auctions with budget-limited buyers. We focus on first-price auctions, which are increasingly commonly used in many settings, and consider liquid welfare, a natural and well-studied generalization of social welfare for buyers with budgets. We use a behavioral model for the buyers, assuming a learning styl

Bashir Elbousefi, William Schupbach, Kannan N. Premnath, Samuel W. J. Welch

Self-rewetting fluids (SRFs), such as aqueous solutions of long-chain alcohols, exhibit anomalous quadratic dependence of surface tension on temperature having a minimum and with a positive gradient. When compared to the normal fluids (NFs), the SRFs can be associated with significantly modified interfacial dynamics, which have recently been exploited to enh

Tiantian Chen, Siwen Yan, Jianxiong Guo, Weili Wu

Aiming at selecting a small subset of nodes with maximum influence on networks, the Influence Maximization (IM) problem has been extensively studied. Since it is #P-hard to compute the influence spread given a seed set, the state-of-the-art methods, including heuristic and approximation algorithms, faced with great difficulties such as theoretical guarantee,

Jinchuan Tian, Brian Yan, Jianwei Yu, Chao Weng

Sequence-to-Sequence (seq2seq) tasks transcribe the input sequence to a target sequence. The Connectionist Temporal Classification (CTC) criterion is widely used in multiple seq2seq tasks. Besides predicting the target sequence, a side product of CTC is to predict the alignment, which is the most probable input-long sequence that specifies a hard aligning re

Jianqiang Zhang, Ze Chen, Yuhong Yang, Wangli Xu

Interaction selection for linear regression models with both continuous and categorical predictors is useful in many fields of modern science, yet very challenging when the number of predictors is relatively large. Existing interaction selection methods focus on finding one optimal model. While attractive properties such as consistency and oracle property ha

Ye Liu, Chen Shen, Zhaojian Wang

Due to possible emergency faults and frequency regulation reserve shortage in the multi-infeed hybrid AC-DC (MIDC) system, the emergency frequency control (EFC) with LCC-HVDC systems participating is important for system frequency stability. Nevertheless, the existing decentralized EFC strategies cannot guarantee the transient stability constraints of lines

Alexander Barg, Alexey Glazyrin, Wei-Jiun Kao, Ching-Yi Lai

We address the maximum size of binary codes and binary constant weight codes with few distances. Previous works established a number of bounds for these quantities as well as the exact values for a range of small code lengths. As our main results, we determine the exact size of maximal binary codes with two distances for all lengths $n\ge 6$ as well as the e

Kazuma Kobayashi, Dinesh Kumar, Matthew Bonney, Syed Alam

The application of digital twin (DT) technology to the nuclear field is one of the challenges in the future development of nuclear energy. Possible applications of DT technology in the nuclear field are expected to be very wide: operate commercial nuclear reactors, monitor spent fuel storage and disposal facilities, and develop new nuclear systems. As U.S. N

Keyu Duan, Zirui Liu, Peihao Wang, Wenqing Zheng

Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs). Due to the nature of evolving graph structures into the training process, vanilla GNNs usually fail to scale up, limited by the GPU memory space. Up to now, though numerous scalable GNN architectures have been proposed, we still lack a comprehensive survey and f

Yuqiang Xie, Yue Hu, Yunpeng Li, Guanqun Bi

Controllable story generation is a challenging task in the field of NLP, which has attracted increasing research interest in recent years. However, most existing works generate a whole story conditioned on the appointed keywords or emotions, ignoring the psychological changes of the protagonist. Inspired by psychology theories, we introduce global psychologi

Satoshi Inoue, Keiji Hayashi, Takahiro Miyoshi

We present a newly developed data-driven magnetohydrodynamics (MHD) simulation code under a zero-beta approximation based on a method proposed by Hayashi et al. 2018 and 2019. Although many data-driven MHD simulations have been developed and conducted, there are not many studies on how accurately those simulations can reproduce the phenomena observed in the

Peter W. MacDonald, Elizaveta Levina, Ji Zhu

Network data are often sampled with auxiliary information or collected through the observation of a complex system over time, leading to multiple network snapshots indexed by a continuous variable. Many methods in statistical network analysis are traditionally designed for a single network, and can be applied to an aggregated network in this setting, but tha

Jin Ye, Haoyu Wang, Ziyan Huang, Zhongying Deng

Tumor lesion segmentation is one of the most important tasks in medical image analysis. In clinical practice, Fluorodeoxyglucose Positron-Emission Tomography~(FDG-PET) is a widely used technique to identify and quantify metabolically active tumors. However, since FDG-PET scans only provide metabolic information, healthy tissue or benign disease with irregula

Hyeonsu Lee, Chankyu Choi

Scene text removal (STR), a task of erasing text from natural scene images, has recently attracted attention as an important component of editing text or concealing private information such as ID, telephone, and license plate numbers. While there are a variety of different methods for STR actively being researched, it is difficult to evaluate superiority bec

Zequn Liu, Kefei Duan, Junwei Yang, Hanwen Xu

Heterogeneous Information Network (HIN) is essential to study complicated networks containing multiple edge types and node types. Meta-path, a sequence of node types and edge types, is the core technique to embed HINs. Since manually curating meta-paths is time-consuming, there is a pressing need to develop automated meta-path generation approaches. Existing

Matthew Allen, John Raisbeck, Hakho Lee

Several low-bandwidth distributable black-box optimization algorithms in the family of finite differences such as Evolution Strategies have recently been shown to perform nearly as well as tailored Reinforcement Learning methods in some Reinforcement Learning domains. One shortcoming of these black-box methods is that they must collect information about the

Wei Zhang, Yuxi Hu, Bolong Tan, Xiaohai Shi

The high tracking overhead, the amount of up-front effort required to selecting the trace points, and the lack of effective data analysis model are the significant barriers to the adoption of intra-component tracking for fault diagnosis today. This paper introduces a novel method for fault diagnosis by combining adaptive function level dynamic tracking, targ

Sishuo Chen, Xiaohan Bi, Rundong Gao, Xu Sun

Detecting out-of-distribution (OOD) instances is significant for the safe deployment of NLP models. Among recent textual OOD detection works based on pretrained language models (PLMs), distance-based methods have shown superior performance. However, they estimate sample distance scores in the last-layer CLS embedding space and thus do not make full use of li

Xiao Ma, Bingyi Kang, Zhongwen Xu, Min Lin

The major challenge of offline RL is the distribution shift that appears when out-of-distribution actions are queried, which makes the policy improvement direction biased by extrapolation errors. Most existing methods address this problem by penalizing the policy or value for deviating from the behavior policy during policy improvement or evaluation. In this

Xiaoxiao Zhao, Carly Pellow, David E. Goertz

Focused ultrasound stimulated microbubbles have been shown to be capable of inducing blood flow shutdown and necrosis in a range of tissue types in an approach termed antivascular ultrasound or mechanical ablation. In oncology, this approach has demonstrated tumor growth inhibition, and profound synergistic antitumor effects when combined with traditional pl

Peiyang Jia, Chengwei Liu, Hongyu Sun, Chengyi Sun

Currently, little is known about the structure of the Cargo ecosystem and the potential for vulnerability propagation. Many empirical studies generalize third-party dependency governance strategies from a single software ecosystem to other ecosystems but ignore the differences in the technical structures of different software ecosystems, making it difficult

Mingfu Xue, Xin Wang, Yinghao Wu, Shifeng Ni

Intellectual property (IP) protection for Deep Neural Networks (DNNs) has raised serious concerns in recent years. Most existing works embed watermarks in the DNN model for IP protection, which need to modify the model and lack of interpretability. In this paper, for the first time, we propose an interpretable intellectual property protection method for DNN

Zhipeng Shen, Shiyu Zhou, Jianglong Yu

Computational guidance is an emerging and accelerating trend in aerospace guidance and control. Combining machine learning and convex optimization, this paper presents a real-time computational guidance method for the 6-degrees-of-freedom powered landing guidance problem. The powered landing guidance problem is formulated as an optimal control problem, which

Louis Breton, Cristhian Montoya, Pedro González-Casanova, Jesús López Estrada

The problem of identifying an obstruction into a fluid duct has several applications, one of them, for example in medicine the presence of Stenosis in coronary vessels is a life threatening disease. In this paper, we formulate a continuous setting and study from a numerical perspective the inverse problem of identifying an obstruction contained in a 2D duct

Yao Liu, Hendrik Linz, Min Fang, Thomas Henning

The total amount of dust grains in protoplanetary disks is one of the key properties that characterize the potential for planet formation. With (sub-)millimeter flux measurements, literature studies usually derive the dust mass using an analytic form under the assumption of optically thin emission, which may lead to substantial underestimation. In this work,

Vincent MacKay, Mark Lai, Peter Shmerko, Dallas Wulf

We have developed, built, and tested a new feed design for interferometric radio telescopes with "large-$N$, small-$D$" designs. Those arrays require low-cost and low-complexity feeds for mass production on reasonable timescales and budgets, and also require those feeds to be compact to minimize obstruction of the dishes, along with having ultra wide bands o

Christopher Eldred, Werner Bauer

TRiSK-type numerical schemes are widely used in both atmospheric and oceanic dynamical cores, due to their discrete analogues of important properties such as energy conservation and steady geostrophic modes. In this work, we show that these numerical methods are best understood as a discrete exterior calculus (DEC) scheme applied to a Hamiltonian formulation

Magda Amiridi, Gregory Darnell, Sean Jewell

Increased use of sensor signals from wearable devices as rich sources of physiological data has sparked growing interest in developing health monitoring systems to identify changes in an individual's health profile. Indeed, machine learning models for sensor signals have enabled a diverse range of healthcare related applications including early detection of

Xiaojian Ma, Silong Yong, Zilong Zheng, Qing Li

We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (SQA3D). Given a scene context (e.g., 3D scan), SQA3D requires the tested agent to first understand its situation (position, orientation, etc.) in the 3D scene as described by text, then reason about its surrounding environment and answer a que

Yutian Wang, Yuan-Hua Ni, Zengqiang Chen, Ji-Feng Zhang

This paper aims to build a probabilistic framework for Howard's policy iteration algorithm using the language of forward-backward stochastic differential equations (FBSDEs). As opposed to conventional formulations based on partial differential equations, our FBSDE-based formulation can be easily implemented by optimizing criteria over sample data, and is the

Liam Hebert, Raheleh Makki, Shubhanshu Mishra, Hamidreza Saghir

Entity Linking (EL) is the gateway into Knowledge Bases. Recent advances in EL utilize dense retrieval approaches for Candidate Generation, which addresses some of the shortcomings of the Lookup based approach of matching NER mentions against pre-computed dictionaries. In this work, we show that in the domain of Tweets, such methods suffer as users often inc

Himanshu Gupta, Neeraj Varshney, Swaroop Mishra, Kuntal Kumar Pal

In current NLP research, large-scale language models and their abilities are widely being discussed. Some recent works have also found notable failures of these models. Often these failure examples involve complex reasoning abilities. This work focuses on a simple commonsense ability, reasoning about when an action (or its effect) is feasible. To this end, w

Suresh Singh, Thanh Le, Ha Tran

This paper examines the performance of a 2x2 Line of Sight (LoS) Multiple Input Multiple Output (MIMO) channel at three terahertz frequencies-340 GHz, 410 Ghz, and 460 GHz. While theoretical models predict very high channel capacities, we observe lower capacity which is explained by asymmetric transmit-to-receive signal strengths as well as due to signal att

Shirley Anugrah Hayati, Kyumin Park, Dheeraj Rajagopal, Lyle Ungar

Large pre-trained language models have achieved impressive results on various style classification tasks, but they often learn spurious domain-specific words to make predictions (Hayati et al., 2021). While human explanation highlights stylistic tokens as important features for this task, we observe that model explanations often do not align with them. To ta

Zhaofeng Wu, William Merrill, Hao Peng, Iz Beltagy

Many current NLP systems are built from language models trained to optimize unsupervised objectives on large amounts of raw text. Under what conditions might such a procedure acquire meaning? Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations (i.e., languages with strong trans

Ashkan Kazemi, Artem Abzaliev, Naihao Deng, Rui Hou

We propose a novel system to help fact-checkers formulate search queries for known misinformation claims and effectively search across multiple social media platforms. We introduce an adaptable rewriting strategy, where editing actions for queries containing claims (e.g., swap a word with its synonym; change verb tense into present simple) are automatically

Aliaksei Petsiuk, Harnoor Singh, Himanshu Dadhwal, Joshua M. Pearce

The application of computer vision and machine learning methods in the field of additive manufacturing (AM) for semantic segmentation of the structural elements of 3-D printed products will improve real-time failure analysis systems and can potentially reduce the number of defects by enabling in situ corrections. This work demonstrates the possibilities of u

Akash Nagaraj, Bishesh Sinha, Mukund Sood, Yash Mathur

Web applications are distributed applications, they are programs that run on more than one computer and communicate through a network or server. This very distributed nature of web applications, combined with the scale and sheer complexity of modern software systems complicate manual security auditing, while also creating a huge attack surface of potential h

Nan Wang, Qifan Wang, Yi-Chia Wang, Maziar Sanjabi

As language models become increasingly integrated into our digital lives, Personalized Text Generation (PTG) has emerged as a pivotal component with a wide range of applications. However, the bias inherent in user written text, often used for PTG model training, can inadvertently associate different levels of linguistic quality with users' protected attribut

Akash Nagaraj, Mukund Sood, Vivek Kapoor, Yash Mathur

During the investigation of criminal activity when evidence is available, the issue at hand is determining the credibility of the video and ascertaining that the video is real. Today, one way to authenticate the footage is to identify the camera that was used to capture the image or video in question. While a very common way to do this is by using image meta

Kui Liu, Meijie Lu, Xianchang Meng

For any integers $k\geq 2$, $q\geq 1$ and any finite set $\mathcal{A}=\{{\boldsymbol{\alpha}}_1,\cdots,{\boldsymbol{\alpha}}_q\}$, where ${ \boldsymbol{\alpha}_t}=(\alpha_{t,1},\cdots,\alpha_{t,k})~(1\leq t\leq q)$ with $0<\alpha_{t,1},\cdots,\alpha_{t,k}<1$ and $\alpha_{t,1}+\cdots+\alpha_{t,k}=1$, this paper concerns the visibility of lattice points in the

Xinyu Guan, Han Sun, Ningzhong Liu, Huiyu Zhou

Source-Free Domain Adaptation (SFDA) aims to solve the domain adaptation problem by transferring the knowledge learned from a pre-trained source model to an unseen target domain. Most existing methods assign pseudo-labels to the target data by generating feature prototypes. However, due to the discrepancy in the data distribution between the source domain an

Ye-Ryoung Lee, Dong-Young Kim, Yonghyeon Jo, Moonseok Kim

Imaging an object embedded within a scattering medium requires the correction of complex sample-induced wave distortions. Existing approaches have been designed to resolve them by optimizing signal waves recorded in each 2D image. Here, we present a volumetric image reconstruction framework that merges two fundamental degrees of freedom, the wavelength and p

S. Rasoul Etesami

We consider the non-metric data placement problem and develop distributed algorithms for computing or approximating its optimal integral solution. We first show that the non-metric data placement problem is inapproximable up to a logarithmic factor. We then provide a game-theoretic decomposition of the objective function and show that natural Glauber dynamic

G. Domínguez-Guzmán, M. Rodríguez, J. García-Rojas, C. Esteban

We use very deep spectra obtained with the Ultraviolet-Visual Echelle Spectrograph at the Very Large Telescope to derive physical conditions and chemical abundances of four H II regions of the Large Magellanic Cloud (LMC) and four H II regions of the Small Magellanic Cloud (SMC). The observations cover the spectral range 3100-10400 \A with a spectral resolut