March 2023 arXiv papers — page 93

Ning Qi, Peng Li, Lin Cheng, Ziyi Zhang

Energy storage (ES) and virtual energy storage (VES) are key components to realizing power system decarbonization. Although ES and VES have been proven to deliver various types of grid services, little work has so far provided a systematical framework for quantifying their adequacy contribution and credible capacity value while incorporating human and market

Wei Jiang, Hans D. Schotten

Reconfigurable intelligent surface (RIS) has recently drawn intensive attention due to its potential of simultaneously realizing high spectral and energy efficiency in a sustainable way. This paper focuses on the design of efficient transmission methods to maximize the uplink sum throughput in a RIS-aided multi-user multi-input multi-output (MU-MIMO) system.

Liang He, Zherong Pan, Dinesh Manocha

We present a lightweight, decentralized algorithm for navigating multiple nonholonomic agents through challenging environments with narrow passages. Our key idea is to allow agents to yield to each other in large open areas instead of narrow passages, to increase the success rate of conventional decentralized algorithms. At pre-processing time, our method co

Daniel Berwick-Evans

We construct deformation invariants of $2|1$-dimensional Euclidean field theories valued in a cohomology theory approximating topological modular forms. This implies several results anticipated by Stolz and Teichner and gives the first torsion invariants of field theories valued in $\pi_*{\rm TMF}$. The framework leads to a version of the elliptic Euler clas

D. Tourret, J. Klemm-Toole, A. Eres Castellanos, B. Rodgers

Understanding rapid solidification behavior at velocities relevant to additive manufacturing (AM) is critical to controlling microstructure selection. Although in-situ visualization of solidification dynamics is now possible, systematic studies under AM conditions with microstructural outcomes compared to solidification theory remain lacking. Here we measure

Zhongxiang Sun

Large language models (LLMs) have transformed many fields, including natural language processing, computer vision, and reinforcement learning. These models have also made a significant impact in the field of law, where they are being increasingly utilized to automate various legal tasks, such as legal judgement prediction, legal document analysis, and legal

Nicolás E. Díaz Ferreyra, Gautam Kishore Shahi, Catherine Tony, Stefan Stieglitz

During the outbreak of the COVID-19 pandemic, many people shared their symptoms across Online Social Networks (OSNs) like Twitter, hoping for others' advice or moral support. Prior studies have shown that those who disclose health-related information across OSNs often tend to regret it and delete their publications afterwards. Hence, deleted posts containing

J. Morgan, P. I. McCauley, A. Waszewski, R. Ekers

We have shown previously that the Murchison Widefield Array (MWA), can detect hundreds of Interplanetary Scintillation (IPS) sources simultaneously across a field of view $\sim30^\circ$ in extent. To test if we can use this capability to track heliospheric structures, we undertook a search of 88 hours of MWA IPS data, and identified an observation likely to

Sonia Boscolo, J. M. Dudley, Christophe Finot

We deploy a supervised machine-learning model based on a neural network to predict the temporal and spectral reshaping of a simple sinusoidal modulation into a pulse train having a comb structure in the frequency domain, which occurs upon nonlinear propagation in an optical fibre. Both normal and anomalous second-order dispersion regimes of the fibre are stu

Dominique Langevin

Viscoelastic materials containing bubbles or drops are encountered in numerous application fields, and are presently the object of much interest. The motion of bubbles and drops in these matrices can be significantly different than in Newtonian fluids. This review is restricted to the case of motion in quiescent fluids (or small Reynolds number) and of small

Krzysztof A. Meissner, Hermann Nicolai

Some time ago it was suggested that dark matter may consist in part of an extremely dilute gas of supermassive gravitinos with fractional charge 2$e$/3 \cite{MeissnerNicolai2019}. This scheme makes the definite (and falsifiable) prediction that massive gravitinos are the {\em only} new fermionic degrees of freedom beyond the known three generations of quarks

Anne Costille, A. Caillat, C. Rossin, S. Pascal

ESA EUCLID mission will be launched in 2020 to understand the nature of the dark energy responsible of the accelerated expansion of the Universe and to map the geometry of the dark matter. The map will investigate the distanceredshift relationship and the evolution of cosmic structures thanks to two instruments: the NISP and the VIS. The NISP (Near Infrared

Xiaofeng Mai, Bo Zhang, M. J. Reid, L. Moscadelli

We report a trigonometric parallax measurement of 22 GHz water masers in the massive star-forming region G034.43+0.24 as part of the Bar and Spiral Structure Legacy (BeSSeL) Survey using the Very Long Baseline Array. The parallax is 0.330$\pm$50.018 mas, corresponding to a distance of $3.03^{+0.17}_{-0.16}$ kpc. This locates G034.43+0.24 near the inner edge

Shuqi Lu, Zhifeng Gao, Di He, Linfeng Zhang

Recent developments in deep learning have made remarkable progress in speeding up the prediction of quantum chemical (QC) properties by removing the need for expensive electronic structure calculations like density functional theory. However, previous methods learned from 1D SMILES sequences or 2D molecular graphs failed to achieve high accuracy as QC proper

Shushan Arakelyan, Rocktim Jyoti Das, Yi Mao, Xiang Ren

We systematically study how three large language models with code capabilities - CodeT5, Codex, and ChatGPT - generalize to out-of-domain data. We consider two fundamental applications - code summarization, and code generation. We split data into domains following its natural boundaries - by an organization, by a project, and by a module within the software

Loïc Thomassey, Raphaël Lachièze-Rey

We study the almost periods of the eigenmodes of flat planar manifolds in the high energy limit. We prove in particular that the Gaussian Arithmetic Random Waves replicate almost identically at a scale at most ${\ell}$n := n -- 1 2 exp (Nn), where Nn is the number of ways n can be written as a sum of two squares. It provides a qualitative interpretation of t

M. K. Panda, S. K. Rajput

The onset of light-induced bioconvection via linear stability theory is investigated qualitatively for a suspension of phototactic algae. The forward scattering algal suspension is uniformly illuminated by both diffuse and oblique collimated flux. An unstable mode of disturbance at bioconvective instability transits from the stationary (overstable) to overst

Isabelle Rivals, Cédric Sautier, Guillaume Cognon, Vincent Cuzuel

The issue of distinguishing between the same-source and different-source hypotheses based on various types of traces is a generic problem in forensic science. This problem is often tackled with Bayesian approaches, which are able to provide a likelihood ratio that quantifies the relative strengths of evidence supporting each of the two competing hypotheses.

Gilyoung Cheong, Myungjun Yu

Given a prime $p$, let $P(t)$ be a non-constant monic polynomial in $t$ over the ring $\mathbb{Z}_{p}$ of $p$-adic integers. Let $X_{n}$ be an $n \times n$ random matrix over $\mathbb{Z}_{p}$ with independent entries that lie in any residue class modulo $p$ with probability at most $1 - \epsilon$ for a fixed real number $0 < \epsilon < 1$. We prove that as $

Wan Liu, Yuqian Chen, Chuyang Ye, Nikos Makris

Neuroimaging measures of the brain's white matter connections can enable the prediction of non-imaging phenotypes, such as demographic and cognitive measures. Existing works have investigated traditional microstructure and connectivity measures from diffusion MRI tractography, without considering the shape of the connections reconstructed by tractography. In

Georges Gagneré

My research-creation process coincides with the encounter with the ''digital paradigm'' and the attempt to incorporate it into the foundation of my scenic writing. I propose in this paper to give an account from a director point of view of how I realized my shows between 2000 and 2007 and which researches influenced the process. I will formulate some remarks

Timothy M. Chan

We present a (combinatorial) algorithm with running time close to $O(n^d)$ for computing the minimum directed $L_\infty$ Hausdorff distance between two sets of $n$ points under translations in any constant dimension $d$. This substantially improves the best previous time bound near $O(n^{5d/4})$ by Chew, Dor, Efrat, and Kedem from more than twenty years ago.

Lian-Peng Zhao

Single-center two-electron integration is an important core technology in ab initio calculation of atomic and molecular structures. Therefore, this paper reviews and optimizes the method of Zhao et al., and draws a conclusion: Because this method is an accurate calculation without truncation error, it is superior to Slater-Condon integration method.

Hermès Bélusca-Maïto, Amon Ilakovac, Paul Kühler, Marija Mađor-Božinović

This review provides a detailed introduction to chiral gauge theories, renormalization theory, and the application of dimensional regularization with the non-anticommuting BMHV scheme for $\gamma_5$. One goal is to show how chiral gauge theories can be renormalized despite the spurious breaking of gauge invariance and how to obtain the required symmetry-rest

Lingting Zhu, Xian Liu, Xuanyu Liu, Rui Qian

Animating virtual avatars to make co-speech gestures facilitates various applications in human-machine interaction. The existing methods mainly rely on generative adversarial networks (GANs), which typically suffer from notorious mode collapse and unstable training, thus making it difficult to learn accurate audio-gesture joint distributions. In this work, w

Koshvendra Singh, Devendra K. Ojha, Joe P. Ninan, Saurabh Sharma

LDN1415-IRS, a low-mass young stellar object (YSO) went into an outburst between 2001 and 2006, illuminating a surrounding nebula, LDN1415-Neb. LDN1415-Neb was found to have brightened by I=3.77 mag by April 2006. The optical light curve covering $\sim$ 15.5 years, starting from October 2006 to January 2022, is presented in this study. The initial optical sp

Weixing Chen, Yang Liu, Ce Wang, Jiarui Zhu

Radiology Report Generation (RRG) is essential for computer-aided diagnosis and medication guidance, which can relieve the heavy burden of radiologists by automatically generating the corresponding radiology reports according to the given radiology image. However, generating accurate lesion descriptions remains challenging due to spurious correlations from v

Athanasios Bakopoulos, Theodoros Nakas

In the framework of a simple gravitational theory that contains a scalar field minimally coupled to gravity, we investigate the emergence of analytic black-hole solutions with non-trivial scalar hair of secondary type. Although it is possible for one to obtain asymptotically (A)dS solutions using our setup, in the context of the present work, we are solely i

Cuong V. Nguyen, Khiem H. Le, Anh M. Tran, Quang H. Pham

Transfer learning plays an essential role in Deep Learning, which can remarkably improve the performance of the target domain, whose training data is not sufficient. Our work explores beyond the common practice of transfer learning with a single pre-trained model. We focus on the task of Vietnamese sentiment classification and propose LIFA, a framework to le

Shukang Yin, Shiwei Wu, Tong Xu, Shifeng Liu

Automatic Micro-Expression (ME) spotting in long videos is a crucial step in ME analysis but also a challenging task due to the short duration and low intensity of MEs. When solving this problem, previous works generally lack in considering the structures of human faces and the correspondence between expressions and relevant facial muscles. To address this i

Lucianna Kiffer, Joachim Neu, Srivatsan Sridhar, Aviv Zohar

For Nakamoto's longest-chain consensus protocol, whose proof-of-work (PoW) and proof-of-stake (PoS) variants power major blockchains such as Bitcoin and Cardano, we revisit the classic problem of the security-performance tradeoff: Given a network of nodes with finite communication- and computation-resources, against what fraction of adversary power is Nakamo

YuPeng Huang, Hong Zhang, Siyuan Jiang, Dajiong Yue

Virtual screening, including molecular docking, plays an essential role in drug discovery. Many traditional and machine-learning based methods are available to fulfil the docking task. The traditional docking methods are normally extensively time-consuming, and their performance in blind docking remains to be improved. Although the runtime of docking based o

Jiaming Liang, Meiqin Liu, Chao Yao, Chunyu Lin

Variable-rate mechanism has improved the flexibility and efficiency of learning-based image compression that trains multiple models for different rate-distortion tradeoffs. One of the most common approaches for variable-rate is to channel-wisely or spatial-uniformly scale the internal features. However, the diversity of spatial importance is instructive for

Kewal Singh Rana, Raveena Gupta, Debattam Sarkar, Niraj Kumar Singh

Large unit cell copper-chalcogenide based minerals with high crystalline anharmonicity have a potential for thermoelectric applications owing to their inherent poor lattice thermal conductivity. Here, the softening of copper-selenium bonding and hence crystal framework plays an important role in superionic conduction and thermal conductivity. We have studied

Linjie Zhao

We consider stationary fluctuations for the multi-species zero range process with long jumps in one dimension, where the underlying transition probability kernel is $p(x) = c_+ |x|^{-1-\alpha}$ if $x > 0$ and $= c_-|x|^{-1-\alpha}$ if $x < 0$. Above, $c_{\pm} \geq 0, \alpha > 0$ are parameters. We prove that for $0 < \alpha < 3/2$, the density fluctuation fi

Mehmet Tanriver, Atila Poro, Ahmet Bulut, Ahmet Keskin

The first multiband photometric solutions of the short-period V Gru eclipsing binary from the southern hemisphere is presented in this study. Light curves of the system were observed through BVI filters at the Congarinni Observatory in Australia for 15 nights. In addition to the new ground-based data, we also used the TESS observations in two sectors. We ana

Huicheng Guo, Henglei Du, Chengpu Liu

Finite-difference time-domain (FDTD) is an effective algorithm for resolving Maxwell equations directly in time domain. Although FDTD has obtained sufficient development, there still exists some improvement space for it, such as ultra-wide-band response and frequency-dependent nonlinearity. In order to resolve these troubles, a modified version of FDTD calle

M. Yokoyama, H. Nishigaki, S. Ogawa, S. Nita

Electronic nematicity, a consequence of rotational symmetry breaking, is an emergent phenomenon in various new materials. In order to fully utilize the functions of these materials, ability of tuning them through a knob, the nematic director, is desired. Here we report a successful manipulation of the nematic director, the vector order-parameter (d-vector),

Dana Ben Porath, Eliahu Cohen

The Leggett-Garg Inequality (LGI) constrains, under certain fundamental assumptions, the correlations between measurements of a quantity Q at different times. Here we analyze the LGI, and propose similar but somewhat more elaborate inequalities, employing a technique that utilizes the mathematical properties of correlation matrices, which was recently propos

Kangfeng Ye, Simon Foster, Jim Woodcock

RoboChart is a core notation in the RoboStar framework. It is a timed and probabilistic domain-specific and state machine-based language for robotics. RoboChart supports shared variables and communication across entities in its component model. It has formal denotational semantics given in CSP. The semantic technique of Interaction Trees (ITrees) represents

Huanran Chen, Yichi Zhang, Yinpeng Dong, Xiao Yang

It is widely recognized that deep learning models lack robustness to adversarial examples. An intriguing property of adversarial examples is that they can transfer across different models, which enables black-box attacks without any knowledge of the victim model. An effective strategy to improve the transferability is attacking an ensemble of models. However

Vanya Bannihatti Kumar, Shanbo Cheng, Ningxin Peng, Yuchen Zhang

Aiming to improve the Automatic Speech Recognition (ASR) outputs with a post-processing step, ASR error correction (EC) techniques have been widely developed due to their efficiency in using parallel text data. Previous works mainly focus on using text or/ and speech data, which hinders the performance gain when not only text and speech information, but othe

Luis B. Morales, Dursun A. Bulutoglu

We prove the equivalence of two-symbol supersaturated designs (SSDs) with $N$ (even) rows, $m$ columns, $s_{\rm max} = 4t +i$, where $i\in\{0,2\}$, $t \in \mathbb{Z}^{\geq 0}$ and resolvable incomplete block designs (RIBDs) whose any two blocks intersect in at most $(N+4t+i)/4$ points. Using this equivalence, we formulate the search for two-symbol E($s^2$)-o

Ayesha Heena, Nagashettappa Biradar, Najmuddin M. Maroof, Surbhi Bhatia

The popularity of Artificial intelligence and machine learning have prompted researchers to use it in the recent researches. The proposed method uses K-Nearest Neighbor (KNN) algorithm for segmentation of medical images, extracting of image features for analysis by classifying the data based on the neural networks. Classification of the images in medical ima

Jakub J. Dylag, Victor Suarez, James Wald, Aneesha Amodini Uvara

A study was conducted to prove AI software could be used to translate and generate illustrations without any human intervention. This was done with the purpose of showing and distributing it to the external customer, Pratham Books. The project aligns with the company's vision by leveraging the generalisation and scalability of Machine Learning algorithms, of

V. A. Dorodnitsyn, R. V. Kozlov, S. V. Meleshko

A Lagrangian formalism for variational second-order delay ordinary differential equations (DODEs) is developed. The Noether operator identity for a DODE is established, which relates the invariance of a Lagrangian function with the appropriate variational equations and the conserved quantities. The identity is used to formulate Noether-type theorems that giv

Shirui Huang, Keyan Wang, Huan Liu, Jun Chen

Despite the remarkable achievement of recent underwater image restoration techniques, the lack of labeled data has become a major hurdle for further progress. In this work, we propose a mean-teacher based Semi-supervised Underwater Image Restoration (Semi-UIR) framework to incorporate the unlabeled data into network training. However, the naive mean-teacher

Xinyang Liu, Dongsheng Wang, Bowei Fang, Miaoge Li

For downstream applications of vision-language pre-trained models, there has been significant interest in constructing effective prompts. Existing works on prompt engineering, which either require laborious manual designs or optimize the prompt tuning as a point estimation problem, may fail to describe diverse characteristics of categories and limit their ap

Roberto Martinez-Maldonado, Vanessa Echeverria, Gloria Fernandez-Nieto, Lixiang Yan

Multimodal Learning Analytics (MMLA) innovations make use of rapidly evolving sensing and artificial intelligence algorithms to collect rich data about learning activities that unfold in physical learning spaces. The analysis of these data is opening exciting new avenues for both studying and supporting learning. Yet, practical and logistical challenges comm

L. Peng, M. Naritsuka, S. Akutagawa, S. Suetsugu

We report an {\it in-situ} scanning tunneling microscopy study of atomically thin films of CeCoIn$_5$, a $d$-wave heavy-fermion superconductor. Both hybridization and superconducting gaps are observed even in monolayer CeCoIn$_5$, providing direct evidence of superconductivity of heavy quasiparticles mediated by purely two-dimensional bosonic excitations. In

Jazhiel Chacón, Isidro Gómez-Vargas, Ricardo Menchaca Méndez, José Alberto Vázquez

The properties of the matter density field in the initial conditions have a decisive impact on the features of the large-scale structure of the Universe as observed today. These need to be studied via $N$-body simulations, which are imperative to analyze high density collapsed regions into dark matter halos. In this paper, we train Machine Learning algorithm

Hitoshi Matsuyama, Nobuo Kawaguchi, Brian Y. Lim

AI-driven Action Quality Assessment (AQA) of sports videos can mimic Olympic judges to help score performances as a second opinion or for training. However, these AI methods are uninterpretable and do not justify their scores, which is important for algorithmic accountability. Indeed, to account for their decisions, instead of scoring subjectively, sports ju

Nadir Hajouji

We describe and compare algorithms for computing supersingular isogeny graphs. Along the way, we obtain a formula for the trace of the adjacency matrix of a general supersingular isogeny graph, and we prove a conjecture recently posed by Nakaya.

Yudi Dai, Yitai Lin, Xiping Lin, Chenglu Wen

We present SLOPER4D, a novel scene-aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human-scene interaction in the wild. Employing a head-mounted device integrated with a LiDAR and camera, we record 12 human subjects' activities over 10 diverse urban scenes from an egocentric view. Fra

Amitesh Kumar Singam

In the field of video processing, advancements in video compression at various temporal and spatial resolutions which are needed in our research to quantify estimation of video quality whereabouts within spatial and temporal domain itself. It was necessary in our research to study the impacts of related video coding conditions upon perceptual quality due to

Masod Sadipour, Ali N. Azadani

Purpose: Bioprosthetic Heart Valves (BHVs) are currently in widespread use with promising outcomes. Computational modeling provides a framework for quantitatively describing BHVs in the preclinical phase. To obtain reliable solutions in computational modeling, it is essential to consider accurate leaflet properties such as mechanical properties and density.

Qiusi Zhan, Sha Li, Kathryn Conger, Martha Palmer

The progress of event extraction research has been hindered by the absence of wide-coverage, large-scale datasets. To make event extraction systems more accessible, we build a general-purpose event detection dataset GLEN, which covers 205K event mentions with 3,465 different types, making it more than 20x larger in ontology than today's largest event dataset

Ian Porada, Alexandra Olteanu, Kaheer Suleman, Adam Trischler

It is increasingly common to evaluate the same coreference resolution (CR) model on multiple datasets. Do these multi-dataset evaluations allow us to draw meaningful conclusions about model generalization? Or, do they rather reflect the idiosyncrasies of a particular experimental setup (e.g., the specific datasets used)? To study this, we view evaluation thr

Daniel Berwick-Evans

Extending ideas of Atiyah--Bott--Shapiro and Quillen, we construct a model for differential $\rm KO$-theory whose cocycles are families of Clifford modules with superconnection. The model is built to accommodate an analytic pushforward for bundles of spin manifolds, affording a differential refinement of Atiyah and Singer's families index.

Eiji Inoue

We study non-archimedean $\mu$-entropy for toric variety as a further exploration of $\mu$K-stability. We show the existence of optimizer of toric non-archimedean $\mu^\lambda$-entropy for $\lambda \in \mathbb{R}$ and the uniqueness for $\lambda \le 0$. For the proof of existence, we establish a Rellich type compactness result for convex functions on simple

David Keating, Matthew Nicoletti

In this article we define a generalization of the domino shuffling algorithm for tilings of the Aztec diamond to the interacting $k$-tilings recently introduced by S. Corteel, A. Gitlin, and the first author. We describe the algorithm both in terms of dynamics on a system of colored particles and as operations on the dominos themselves.

Ankita Joshi, Yi Hong

Deep learning based methods provide efficient solutions to medical image registration, including the challenging problem of diffeomorphic image registration. However, most methods register normal image pairs, facing difficulty handling those with missing correspondences, e.g., in the presence of pathology like tumors. We desire an efficient solution to joint

Krishna Shende, Arvind, Kavita Dorai

In this work, we experimentally demonstrate the implementation of a recently proposed robust and state-independent heat-bath algorithmic cooling (HBAC) method [1] on an NMR quantum processor. While HBAC methods improve the purity of a quantum system via iterative unitary entropy compression, they are difficult to implement experimentally since they use sort

Dongyue Li, Tina Eliassi-Rad, Hongyang R. Zhang

Suppose there is a spreading process such as an infectious disease propagating on a graph. How would we reduce the number of affected nodes in the spreading process? This question appears in recent studies about implementing mobility interventions on mobility networks (Chang et al. (2021)). A practical algorithm to reduce infections on unweighted graphs is t

Li-Chin Chen, Jung-Nien Lai, Hung-En Lin, Hsien-Te Chen

Low back pain (LBP) and sciatica may require surgical therapy when they are symptomatic of severe pain. However, there is no effective measures to evaluate the surgical outcomes in advance. This work combined elements of Eastern medicine and machine learning, and developed a preoperative assessment tool to predict the prognosis of lumbar spinal surgery in LB

Kien T. Pham, Duc M. Nguyen, Duy V. Tran, Vi D. Ao

We have developed a mathematical model that captures stress-induced mutagenesis, a fundamental aspect of pathogenic and neoplastic evolutionary dynamics, on the fitness landscape with multiple relevant genetic traits as a high-dimensional Euclidean space. In this framework, stress-induced mutagenesis manifests as a heterogeneous diffusion process. We show ho

Xinyue Huo, Lingxi Xie, Wengang Zhou, Houqiang Li

We study unsupervised domain adaptation (UDA) for semantic segmentation. Currently, a popular UDA framework lies in self-training which endows the model with two-fold abilities: (i) learning reliable semantics from the labeled images in the source domain, and (ii) adapting to the target domain via generating pseudo labels on the unlabeled images. We find tha

The GAMBIT Collaboration, Viktor Ananyev, Csaba Balázs, Ankit Beniwal

Using the GAMBIT global fitting framework, we constrain the MSSM with an eV-scale gravitino as the lightest supersymmetric particle, and the six electroweakinos (neutralinos and charginos) as the only other light new states. We combine 15 ATLAS and 12 CMS searches at 13\,TeV, along with a large collection of ATLAS and CMS measurements of Standard Model signa

Rayan El Helou, Ali Menati, Le Xie

Demand flexibility plays a pivotal role in modern power systems with high penetration of variable energy resources. In recent years, one of the fastest-growing flexible energy demands has been proof-of-work-based cryptocurrency mining facilities. Due to their competitive ramping capabilities and demonstrated flexibility, such fast-responding loads are capabl

Andrew P. Lawrence, Morten E. Nielsen, Bengt Fornberg

Subsampling of node sets is useful in contexts such as multilevel methods, computer graphics, and machine learning. On uniform grid-based node sets, the process of subsampling is simple. However, on node sets with high density variation, the process of coarsening a node set through node elimination is more interesting. A novel method for the subsampling of v

Mengxin Zheng, Jiaqi Xue, Zihao Wang, Xun Chen

Self-supervised learning (SSL) is a prevalent approach for encoding data representations. Using a pre-trained SSL image encoder and subsequently training a downstream classifier, impressive performance can be achieved on various tasks with very little labeled data. The growing adoption of SSL has led to an increase in security research on SSL encoders and as

Sathya Rengaswami, Mat Langford

We construct $O(1)\times O(n)$-invariant ancient ``pancake'' solutions to a large and natural class of fully nonlinear curvature flows. We then establish that these are the unique $O(n)$-invariant ancient solutions to the corresponding flow which sweep out a slab by carrying out a fine asymptotic analysis for this class. This extends the main results of \cit

Zachary D King, Han Yu, Thomas Vaessen, Iniz Myin-Germeys

As mobile health (mHealth) studies become increasingly productive due to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. The reliance on subjective responses for health constructs poses challenges, especially in populations with lower receptivity rates.

L. K. Duchaniya, Kanika Gandhi, B. Mishra

In this paper, we have performed the dynamical system analysis of $f(T)$ gravity cosmological models at both background and perturbation levels. We have presented three models pertaining to three distinct functional forms of $f(T)$. The first form is that of the logarithmic form of the torsion scalar $T$, the second one is in the power law form, and the thir

Tong Wu, Hao Wang, Zhongshen Zeng, Wei Wang

Recently, there has been a surge in the use of generated data to enhance the performance of downstream models, largely due to the advancements in pre-trained language models. However, most prevailing methods trained generative and discriminative models in isolation, which left them unable to adapt to changes in each other. These approaches lead to generative

STAR Collaboration, M. I. Abdulhamid, B. E. Aboona, J. Adam

The polarization of $\Lambda$ and $\bar{\Lambda}$ hyperons along the beam direction has been measured relative to the second and third harmonic event planes in isobar Ru+Ru and Zr+Zr collisions at $\sqrt{s_{NN}}$ = 200 GeV. This is the first experimental evidence of the hyperon polarization by the triangular flow originating from the initial density fluctuat

Hugo Riggs, Shahid Tufail, Mohd Tariq, Arif Sarwat

Power systems engineers are actively developing larger power plants out of photovoltaics imposing some major challenges which include its intermittent power generation and its poor dispatchability. The issue is that PV is a variable generation source unless additional planning and system additions for mitigation of generation intermittencies. One underlying

Haokun Li, Bican Xia, Tianqi Zhao

Satisfiability Modulo the Theory of Nonlinear Real Arithmetic, SMT(NRA) for short, concerns the satisfiability of polynomial formulas, which are quantifier-free Boolean combinations of polynomial equations and inequalities with integer coefficients and real variables. In this paper, we propose a local search algorithm for a special subclass of SMT(NRA), wher

Litao Hu, Huaijin Chen, Jan P. Allebach

An image processing unit (IPU), or image signal processor (ISP) for high dynamic range (HDR) imaging usually consists of demosaicing, white balancing, lens shading correction, color correction, denoising, and tone-mapping. Besides noise from the imaging sensors, almost every step in the ISP introduces or amplifies noise in different ways, and denoising opera

Lokesh Chandra Das, Myounggyu Won

Discretionary lane-change is one of the critical challenges for autonomous vehicle (AV) design due to its significant impact on traffic efficiency. Existing intelligent lane-change solutions have primarily focused on optimizing the performance of the ego-vehicle, thereby suffering from limited generalization performance. Recent research has seen an increased

S. Ole Warnaar

The $\mathrm{A}_2$ Bailey chain of Andrews, Schilling and the author is extended to a four-parameter $\mathrm{A}_2$ Bailey tree. As main application of this tree, we prove the Kanade-Russell conjecture for a three-parameter family of Rogers-Ramanujan-type identities related to the principal characters of the affine Lie algebra $\mathrm{A}_2^{(1)}$. Combined

Jong-Ik Park, Sihoon Seong, JunKyu Lee, Cheol-Ho Hong

Tabular data from IIoT devices are typically analyzed using decision tree-based machine learning techniques, which struggle with high-dimensional and numeric data. To overcome these limitations, techniques converting tabular data into images have been developed, leveraging the strengths of image-based deep learning approaches such as Convolutional Neural Net

Nathaniel W. Rollings, Kent O'Sullivan, Sakshum Kulshrestha

Existing question-answering research focuses on unanswerable questions in the context of always providing an answer when a system can\dots but what about cases where a system {\bf should not} answer a question. This can either be to protect sensitive users or sensitive information. Many models expose sensitive information under interrogation by an adversaria

Rachid Kharoubi, Abdallah Mkhadri, Karim Oualkacha

The support vector machines (SVM) is a powerful classifier used for binary classification to improve the prediction accuracy. However, the non-differentiability of the SVM hinge loss function can lead to computational difficulties in high dimensional settings. To overcome this problem, we rely on Bernstein polynomial and propose a new smoothed version of the

Haeyong Kang, Chang D. Yoo, Yongcheon Na

An algorithm based on a deep probabilistic architecture referred to as a tree-structured sum-product network (t-SPN) is considered for cell classification. The t-SPN is constructed such that the unnormalized probability is represented as conditional probabilities of a subset of most similar cell classes. The constructed t-SPN architecture is learned by maxim

Bipul Neupane, Jagannath Aryal, Abbas Rajabifard

Urban buildings are extracted from high-resolution Earth observation (EO) images using semantic segmentation networks like U-Net and its successors. Each re-iteration aims to improve performance by employing a denser skip connection mechanism that harnesses multi-scale features for accurate object mapping. However, denser connections increase network paramet

Hasin Rehana, Muhammad Ibrahim, Md. Haider Ali

Agriculture plays an important role in the food and economy of Bangladesh. The rapid growth of population over the years also has increased the demand for food production. One of the major reasons behind low crop production is numerous bacteria, virus and fungal plant diseases. Early detection of plant diseases and proper usage of pesticides and fertilizers

Ankita Sontakke, Kanika Kalra, Manasi Patwardhan, Lovekesh Vig

Generation of pseudo-code descriptions of legacy source code for software maintenance is a manually intensive task. Recent encoder-decoder language models have shown promise for automating pseudo-code generation for high resource programming languages such as C++, but are heavily reliant on the availability of a large code-pseudocode corpus. Soliciting such

Liang Liu, Boshen Zhang, Jiangning Zhang, Wuhao Zhang

Scale variation across object instances remains a key challenge in object detection task. Despite the remarkable progress made by modern detection models, this challenge is particularly evident in the semi-supervised case. While existing semi-supervised object detection methods rely on strict conditions to filter high-quality pseudo labels from network predi

Jonathan King, Chenghao Wan, Tae Joon Park, Sanket Despande

We demonstrate an electrically controlled metal-VO2 metasurface for the mid-wave infrared that simultaneously functions as a tunable optical switch, an optical limiter with a tunable limiting threshold, and a nonlinear optical isolator with a tunable operating range. The tunability is achieved via Joule heating through the metal comprising the metasurface, r

Ki-Ahm Lee, Se-Chan Lee, Hyungsung Yun

We establish the interior $C^{1,\alpha}$-estimate for viscosity solutions of degenerate/singular fully nonlinear parabolic equations $$u_t = |Du|^{\gamma}F(D^2u) + f.$$ For this purpose, we prove the well-posedness of the regularized Dirichlet problem \begin{equation*} \left\{ \begin{aligned} u_t&=(1+|Du|^2)^{\gamma/2}F(D^2u) &&\text{in $Q_1$} \newline u&=\v

Shuhan Qi, Shuhao Zhang, Qiang Wang, Jiajia Zhang

Value-decomposition methods, which reduce the difficulty of a multi-agent system by decomposing the joint state-action space into local observation-action spaces, have become popular in cooperative multi-agent reinforcement learning (MARL). However, value-decomposition methods still have the problems of tremendous sample consumption for training and lack of

Hyun Joon Park, Seok Woo Yang, Jin Sob Kim, Wooseok Shin

Voice Conversion (VC) must be achieved while maintaining the content of the source speech and representing the characteristics of the target speaker. The existing methods do not simultaneously satisfy the above two aspects of VC, and their conversion outputs suffer from a trade-off problem between maintaining source contents and target characteristics. In th

Gary An, Chase Cockrell

The use of synthetic data is recognized as a crucial step in the development of neural network-based Artificial Intelligence (AI) systems. While the methods for generating synthetic data for AI applications in other domains have a role in certain biomedical AI systems, primarily related to image processing, there is a critical gap in the generation of time s

Tuan N. Tang, Kwonyoung Kim, Kwanghoon Sohn

Temporal Action Localization (TAL) is a challenging task in video understanding that aims to identify and localize actions within a video sequence. Recent studies have emphasized the importance of applying long-term temporal context modeling (TCM) blocks to the extracted video clip features such as employing complex self-attention mechanisms. In this paper,

Haruya Ishikawa, Yoshimitsu Aoki

With the increase in demands for service robots and automated inspection, agents need to localize in its surrounding environment to achieve more natural communication with humans by shared contexts. In this work, we propose a novel but straightforward task of precise target view localization for look around agents called the FindView task. This task imitates

Parth Mehta, Kumar Appaiah, Rajbabu Velmurugan

A new spatial IIR beamformer based direction-of-arrival (DoA) estimation method is proposed in this paper. We propose a retransmission based spatial feedback method for an array of transmit and receive antennas that improves the performance parameters of a beamformer, viz. half-power beamwidth (HPBW), side-lobe suppression, and directivity. Through quantitat

Wan-Shun Du, Weipeng Chen, Yangbo Zhou, Tengfei Zhou

In the absence of time-reversal invariance, metals without inversion symmetry may exhibit nonreciprocal charge transport -- a magnetochiral anisotropy that manifests as unequal electrical resistance for opposite current flow directions. If superconductivity also sets in, the charge transmission may become dissipationless in one direction while remaining diss

Minjong Lee, Dongwoo Kim

We question the current evaluation practice on diffusion-based purification methods. Diffusion-based purification methods aim to remove adversarial effects from an input data point at test time. The approach gains increasing attention as an alternative to adversarial training due to the disentangling between training and testing. Well-known white-box attacks

Christopher Broyles, Zack Rehfuss, Hasan Siddiquee, Jiahui Althena Zhu

Spin triplet superconductor UTe$_{2}$ is widely believed to host a quasi-two-dimensional Fermi surface, revealed by first principal calculations, photoemission and quantum oscillation measurements. An outstanding question still remains as to the existence of a three-dimensional Fermi surface pocket, which is crucial for our understanding of the exotic superc