November 2022 arXiv papers — page 40

Huisheng Mao, Baozheng Zhang, Hua Xu, Ziqi Yuan

Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among researchers. For Multimodal Sentiment Analysis (MSA), there is also a debate on whether multimodal models are more effective against noisy features than unimodal ones. Stressing o

Maria Spichkova, Johan van Zyl

In this report we analysed a possibility of using computer vision techniques for automated reading of utility meters. In our study, we focused on two computer vision techniques: an open-source solution Tensorflow Object Detection (Tensorflow) and a commercial solution Anyline. This report extends our previous publication: We start with presentation of a stru

Adrien Chaigneau, Denis S. Grebenkov

In this paper, we investigate the influence of target anisotropy on two characteristics of diffusion-controlled reactions: harmonic measure density and mean first-passage time. First, we compute the volume-averaged harmonic measure density on prolate and oblate spheroidal targets inside a confining domain in three dimensions. This allows us to quantify the u

V. Madhurima, Ram Ramaswamy, Deepa Chari, Vandana Nanal

When academic institutions in India closed abruptly in March 2020 due to the COVID-19 pandemic, formal education moved online. This transition had a very uneven impact given the significant digital divide between rural and urban India and the unequal distribution of digital resources in different institutions. Access to resources varied substantially by indi

Fan Yang, Shigeyuki Odashima, Shoichi Masui, Shan Jiang

This is our second-place solution for CVPR 2022 SoccerNet Tracking Challenge. Our method mainly includes two steps: online short-term tracking using our Cascaded Buffer-IoU (C-BIoU) Tracker, and, offline long-term tracking using appearance feature and hierarchical clustering. At each step, online tracking yielded HOTA scores near 90, and offline tracking fur

Alain Valette

This is a semi-survey paper, where we start by advertising Tits' synthetic construction from \cite{Tits}, of the hyperbolic plane $H^2(Cay)$ over the Cayley numbers $Cay$, and of its automorphism group which is the exceptional simple Lie group $G=F_{4(-20)}$. Let $G=KAN$ be the Iwasawa decomposition. Our contributions are: a) Writing down explicitly the acti

Yihui Huang, Zi Wang, Xinlin Zhang, Jian Cao

Undersampling can accelerate the signal acquisition but at the cost of bringing in artifacts. Removing these artifacts is a fundamental problem in signal processing and this task is also called signal reconstruction. Through modeling signals as the superimposed exponential functions, deep learning has achieved fast and high-fidelity signal reconstruction by

Satyaki Mazumder, Sayantan Banerjee, Sourabh Bhattacharya

The solutions of Hamiltonian equations are known to describe the underlying phase space of a mechanical system. In this article, we propose a novel spatio-temporal model using a strategic modification of the Hamiltonian equations, incorporating appropriate stochasticity via Gaussian processes. The resultant spatio-temporal process, continuously varying with

Ken-ichi Nakao, Kazumasa Okabayashi, Tomohiro Harada

The gravastar is an exotic compact object proposed as a final product of gravitational collapse of a massive object in order to resolve problems associated with black holes. It is enclosed by a thin crust and the inside of it is occupied by the positive cosmological constant. Recently, the present authors studied quantum particle creation through spherically

H. Sana, O. H. Ramírez-Agudelo, V. Hénault-Brunet, L. Mahy

Two main scenarios have been proposed for origin of massive runaway stars -- dynamical ejection or release from a binary at the first core collapse -- but their relative contribution remains debated. Using two large spectroscopic campaigns towards massive stars in 30 Doradus, we aim to provide observational constraints on the properties of the O-type runaway

Manelo Anona

Using a almost product structure defined by a spray, we give a necessary and sufficient condition, for a linear connection with vanishing torsion to be Riemannian and, for the semi-simplicity of Lie algebra of projectable vector fields which commute with a spray. We show the equivalence of the semi-simplicity of a finite dimensional Lie algebra to the coinci

Lei Wang, Hongyu Yang, Yi Lin, Suwan Yin

Increasing traffic demands, higher levels of automation, and communication enhancements provide novel design opportunities for future air traffic controllers (ATCs). This article presents a novel deep reinforcement learning (DRL) controller to aid conflict resolution for autonomous free flight. Although DRL has achieved important advancements in this field,

Alexandr Andoni, Jarosław Błasiok, Arnold Filtser

We introduce and study the communication complexity of computing the inner product of two vectors, where the input is restricted w.r.t. a norm $N$ on the space $\mathbb{R}^n$. Here, Alice and Bob hold two vectors $v,u$ such that $\|v\|_N\le 1$ and $\|u\|_{N^*}\le 1$, where $N^*$ is the dual norm. They want to compute their inner product $\langle v,u \rangle$

Kotaro Murakami, Daiki Suenaga, Kei Iida, Etsuko Itou

We investigate hadron spectra in 2-color QCD using lattice simulation with $N_{f}=2$ at low temperature and finite density in which there appears not only the hadronic phase but also the superfluid phase. We first calculate the pion and rho meson spectrum, which is well-known from previous works. The spectral ordering of these mesons flips around the quark c

WonJun Moon, Hyun Seok Seong, Jae-Pil Heo

A dramatic increase in real-world video volume with extremely diverse and emerging topics naturally forms a long-tailed video distribution in terms of their categories, and it spotlights the need for Video Long-Tailed Recognition (VLTR). In this work, we summarize the challenges in VLTR and explore how to overcome them. The challenges are: (1) it is impracti

Zhiwei Ding, Xuezhe Ren, Erwan David, Melissa Vo

Visual search is a ubiquitous challenge in natural vision, including daily tasks such as finding a friend in a crowd or searching for a car in a parking lot. Human rely heavily on relevant target features to perform goal-directed visual search. Meanwhile, context is of critical importance for locating a target object in complex scenes as it helps narrow down

Haoran Luo, Haihong E, Yuhao Yang, Gengxian Zhou

Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs). Currently, most approaches are limited to queries among binary relational facts and pay less attention to n-ary facts (n>=2) containing more than two entities, which are more prevalent in the real world. Moreover, previous CQA methods can only ma

S. C. Park

In this short note, we give a self-contained derivation of the formula for the $2$-point full-plane Ising spin correlation function under massive scaling limit in terms of a third Painlev\'e transcendant. This formula, first derived in a celebrated work of Wu, McCoy, Tracy, and Barouch, was subsequently reformulated in terms of the theory of isomonodromic de

Daisuke Kurisu, Yasumasa Matsuda

This paper develops a general asymptotic theory of local polynomial (LP) regression for spatial data observed at irregularly spaced locations in a sampling region $R_n \subset \mathbb{R}^d$. We adopt a stochastic sampling design that can generate irregularly spaced sampling sites in a flexible manner including both pure increasing and mixed increasing domain

Jiahang Zhang, Lilang Lin, Jiaying Liu

Contrastive learning has been proven beneficial for self-supervised skeleton-based action recognition. Most contrastive learning methods utilize carefully designed augmentations to generate different movement patterns of skeletons for the same semantics. However, it is still a pending issue to apply strong augmentations, which distort the images/skeletons' s

Otabek Nazarov, Mohammad Yaqub, Karthik Nandakumar

Chest X-ray is one of the most popular medical imaging modalities due to its accessibility and effectiveness. However, there is a chronic shortage of well-trained radiologists who can interpret these images and diagnose the patient's condition. Therefore, automated radiology report generation can be a very helpful tool in clinical practice. A typical report

Jordon Kho, Winston Koh, Jian Cheng Wong, Pao-Hsiung Chiu

Reaction-diffusion (Turing) systems are fundamental to the formation of spatial patterns in nature and engineering. These systems are governed by a set of non-linear partial differential equations containing parameters that determine the rate of constituent diffusion and reaction. Critically, these parameters, such as diffusion coefficient, heavily influence

Thomas H. Geisser, Baptiste Morin

Let $K$ be a finite extension of the $p$-adic numbers $\mathbb Q_p$ with ring of integers $\mathcal O_K$, $\mathcal X$ a regular scheme, proper, flat, and geometrically irreducible over $\mathcal O_K$ of dimension $d$, and $\mathcal X_K$ its generic fiber. We show, under some assumptions on $\mathcal X_K$, that there is a reciprocity isomorphism of locally c

Probir Mondal

Bioinformatics, which is now a well known field of study, originated in the context of biological sequence analysis. Recently graphical representation takes place for the research on DNA sequence. Research in biological sequence is mainly based on the function and its structure. Bioinformatics finds wide range of applications specifically in the domain of mo

Oleg Kiselyov, Tomoaki Kobayashi, Aggelos Biboudis, Nick Palladinos

We present the stream processing library that achieves the highest performance of existing OCaml streaming libraries, attaining the speed and memory efficiency of hand-written state machines. It supports finite and infinite streams with the familiar declarative interface, of any combination of map, filter, take(while), drop(while), zip, flatmap combinators a

Ran Zhuo, Yingshu Lü

In this paper, we consider the following nonlinear system involving the fractional Laplacian \begin{equation} \left\{\begin{array}{ll} (-\Delta)^{s} u (x)= f(u,\,v), \\ (-\Delta)^{s} v (x)= g(u,\,v), \end{array} \right. (1) \end{equation} in two different types of domains, one is bounded, and the other is unbounded, where $0<s<1$. To investigate the qualitat

Valery E. Lobanov, Nikita M. Kondratiev, Artem E. Shitikov, Olga V. Borovkova

The stability of platicons in hot cavities with normal group velocity at the interplay of Kerr and thermal nonlinearities was addressed numerically. The stability analysis was performed for different ranges of pump amplitude, thermal nonlinearity coefficient and thermal relaxation time. It was revealed that for the positive thermal effect, the high-energy wi

Kazuo Habiro, Mai Katada

Borel's stability and vanishing theorem gives the stable cohomology of $\mathrm{GL}(n,\mathbb{Z})$ with coefficients in algebraic $\mathrm{GL}(n,\mathbb{Z})$-representations. By combining the Borel theorem with the Hochschild-Serre spectral sequence, we compute the twisted first cohomology of the automorphism group $\mathrm{Aut}(F_n)$ of the free group $F_n$

M. Imbrišak, K. Nomura

The framework of nuclear energy density functionals has been employed to describe nuclear structure phenomena for a wide range of nuclei. Recently, statistical properties of a given nuclear model, such as parameter confidence intervals and correlations, have received much attention, particularly when one tries to fit complex models. We apply information-theo

Dmitriy Stolyarov

We study limiting trace inequalities in the style of Maz'ya and Meyers--Ziemer for Sobolev martingales. We develop the Bellman function approach to such estimates, which allows to provide sufficient and almost necessary conditions on the martingale space and the martingale transform under which the trace inequalities hold true

Kong Yuan Ho, Chin Seng Lim, Matthena A. Kattar, Bharathi Boppana

Traffic emissions are known to contribute significantly to air pollution around the world, especially in heavily urbanized cities such as Singapore. It has been previously shown that the particulate pollution along major roadways exhibit strong correlation with increased traffic during peak hours, and that reductions in traffic emissions can lead to better h

Badih Ghazi, Junfeng He, Kai Kohlhoff, Ravi Kumar

We consider the task of producing heatmaps from users' aggregated data while protecting their privacy. We give a differentially private (DP) algorithm for this task and demonstrate its advantages over previous algorithms on real-world datasets. Our core algorithmic primitive is a DP procedure that takes in a set of distributions and produces an output that i

Daisuke Yamauchi, Shoya Ishimaru, Takahiko Matsubara, Tomo Takahashi

We investigate the skewness of galaxy number density fluctuations as a possible probe to test gravity theories. We find that the specific linear combination of the skewness parameters corresponds to the coefficients of the second-order kernels of the density contrast, which can be regarded as the consistency relation and used as a test of general relativity

Zhuo-Xu Cui, Qingyong Zhu, Jing Cheng, Dong Liang

Recently, deep unfolding methods that guide the design of deep neural networks (DNNs) through iterative algorithms have received increasing attention in the field of inverse problems. Unlike general end-to-end DNNs, unfolding methods have better interpretability and performance. However, to our knowledge, their accuracy and stability in solving inverse probl

Naoki Kitazawa

We present new explicit decompositions of manifolds via so-called fold maps into lower dimensional spaces. Fold maps form a nice class of so-called generic maps, generalizing Morse functions naturally. To understand the topologies and the differentibale structures of manifolds globally, decomposing manifolds are important and this presents interesting topics

Laura Baudis, Jeter Hall, Kevin T. Lesko, John L. Orrell

The decade since Snowmass 2013 has seen extraordinary progress of high energy physics research performed--or planned for--at underground facilities. Drs. T. Kajita and A.B. McDonald were awarded the 2015 Nobel Prize in Physics for the discovery of neutrino oscillation, which show that neutrinos have mass. The U.S. has embarked on the development of the world

Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli

Diffusion models have found widespread adoption in various areas. However, their sampling process is slow because it requires hundreds to thousands of network evaluations to emulate a continuous process defined by differential equations. In this work, we use neural operators, an efficient method to solve the probability flow differential equations, to accele

Wenlei Zheng, Zhan Li, Bingkai Xiu, Bingliang Zhao

The coupling disturbance between the manipulator and the unmanned aerial vehicle (UAV) deteriorates the control performance of system. To get high performance of the aerial manipulator, a robust fractional order fast terminal sliding mode control (FOFTSMC) strategy based on mutable inertia parameters is proposed in this paper. First, the dynamics of aerial m

Yunqiu Han, Yizuo Chen, Adnan Darwiche

We study the computational complexity of counterfactual reasoning in relation to the complexity of associational and interventional reasoning on structural causal models (SCMs). We show that counterfactual reasoning is no harder than associational or interventional reasoning on fully specified SCMs in the context of two computational frameworks. The first fr

Xiaomin Wu, Yi Liao, Jinxin Zhong, Qing Xi

We use LAMMPS to randomly pack hard spheres to simulate the heat exchanger, where the hard spheres represent sintered metal particles in the heat exchanger. We simulated the heat exchanger under different sphere radii and different packing fractions of the metal particle and researched pore space. To improve the performance of the heat exchanger, we adopted

Yifei Ming, Ziyang Cai, Jiuxiang Gu, Yiyou Sun

Recognizing out-of-distribution (OOD) samples is critical for machine learning systems deployed in the open world. The vast majority of OOD detection methods are driven by a single modality (e.g., either vision or language), leaving the rich information in multi-modal representations untapped. Inspired by the recent success of vision-language pre-training, t

Corey Brooke

We describe the Fano scheme of lines on a general cubic threefold containing a plane over a field $k$ of characteristic different from 2. Then, we use the Fano scheme to characterize rationality for such cubic threefolds over nonclosed fields and to construct a Lagrangian fibration from the Fano variety of lines on a cubic fourfold containing a plane explici

Zhuoyuan Yao, Shuo Ren, Sanyuan Chen, Ziyang Ma

Self-supervised speech pre-training empowers the model with the contextual structure inherent in the speech signal while self-supervised text pre-training empowers the model with linguistic information. Both of them are beneficial for downstream speech tasks such as ASR. However, the distinct pre-training objectives make it challenging to jointly optimize th

Jiarui Sun, Xinzhong Er, Oleg Yu. Tsupko

Galactic microlensing has been widely used to study the star and planet. The stellar wind plays an important role in the formation, environment and habitability of the planet. In this work we study a binary microlensing system including the stellar wind, i.e. a star with plasma environment plus a planet. Plasma surrounding the main lens causes chromatic defl

Nitin Gupta, Santosh Kumar Chaudhary, Pradeep Kumar Sahu

In this paper, we define general weighted cumulative residual extropy (GWCRJ) and general weighted negative cumulative extropy (GWNCJ). We obtain its simple estimators for complete and right censored data. We obtain some results on GWCREJ and GWNCJ. We establish its connection to reliability theory and coherent systems. We also propose empirical estimators o

Xue Liu, Juan Zou, Xiawu Zheng, Cheng Li

Magnetic Resonance Imaging (MRI) has become an important technique in the clinic for the visualization, detection, and diagnosis of various diseases. However, one bottleneck limitation of MRI is the relatively slow data acquisition process. Fast MRI based on k-space undersampling and high-quality image reconstruction has been widely utilized, and many deep l

George A. Sutherland, James P. Pidgeon, Harrison Ka Hin Lee, Matthew S. Proctor

Singlet exciton fission is the spin-allowed generation of two triplet electronic excited states from a singlet state. Intramolecular singlet fission has been suggested to occur on individual carotenoid molecules within protein complexes, provided the conjugated backbone is twisted out-of-plane. However, this hypothesis has only been forwarded in protein comp

Junghyun Lee, Keigo Arai, Huiliang Zhang, Mark J. H. Ku

Chern numbers are gaining traction as they characterize topological phases in various physical systems. However, the resilience of the system topology to external perturbations makes it challenging to experimentally investigate transitions between different phases. In this study, we demonstrate the transitions of Chern number from 0 to 3, synthesized in an e

Yatai Ji, Rongcheng Tu, Jie Jiang, Weijie Kong

Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks have been proposed for VLP to further promote cross-modal in

Sunhyeon Kwon, Hwayong Choi, Sungsoo Park

The Bilevel Optimization Problem is a hierarchical optimization problem with two agents, a leader and a follower. The leader make their own decisions first, and the followers make the best choices accordingly. The leader knows the information of the followers, and the goal of the problem is to find the optimal solution by considering the reactions of the fol

Mikio Fujiwara, Hiroki Hashimoto, Kazuaki Doi, Mamiko Kujiraoka

We developed a high-performance one-stop system for large-scale genome data analysis with secure secondary use of the data by the data owner and multiple users with different levels of data access control.

Guanlin Li, Guowen Xu, Tianwei Zhang

In this paper, we consider the instance segmentation task on a long-tailed dataset, which contains label noise, i.e., some of the annotations are incorrect. There are two main reasons making this case realistic. First, datasets collected from real world usually obey a long-tailed distribution. Second, for instance segmentation datasets, as there are many ins

Nick Fagan, Jorge Hermo González, Travis Gagie

We describe how, given a text $T [1..n]$ and a positive constant $\epsilon$, we can build a simple $O (z \log n)$-space index, where $z$ is the number of phrases in the LZ77 parse of $T$, such that later, given a pattern $P [1..m]$, in $O (m \log \log z + \mathrm{polylog} (m + z))$ time and with high probability we can find a substring of $P$ that occurs in

Ryota Katsube, Masanao Ozawa, Masahiro Hotta

It is important to improve the accuracy of quantum measurements and operations both in engineering and fundamental physics. It is known, however, that the achievable accuracy of measurements and unitary operations are generally limited by conservation laws according to the Wigner-Araki-Yanase theorem (WAY theorem) and its generalizations. Although many resea

Keisuke Okumura

We propose a novel complete algorithm for multi-agent pathfinding (MAPF) called lazy constraints addition search for MAPF (LaCAM). MAPF is a problem of finding collision-free paths for multiple agents on graphs and is the foundation of multi-robot coordination. LaCAM uses a two-level search to find solutions quickly, even with hundreds of agents or more. At

Ritajit Majumdar, Christopher J. Wood

We investigate an error mitigated tomographic approach to the quantum circuit cutting problem in the presence of gate and measurement noise. We explore two tomography specific error mitigation techniques; readout error mitigated conditional fragment tomography, which uses knowledge of readout errors on all cut and conditional qubit measurements in the tomogr

Ji Liu, Juncheng Jia, Beichen Ma, Chendi Zhou

Recent years have witnessed a large amount of decentralized data in various (edge) devices of end-users, while the decentralized data aggregation remains complicated for machine learning jobs because of regulations and laws. As a practical approach to handling decentralized data, Federated Learning (FL) enables collaborative global machine learning model tra

Chanyoung Park, Jae Pyoung Kim, Won Joon Yun, Soohyun Park

Quantum machine learning (QML) has received a lot of attention according to its light training parameter numbers and speeds; and the advances of QML lead to active research on quantum multi-agent reinforcement learning (QMARL). Existing classical multi-agent reinforcement learning (MARL) features non-stationarity and uncertain properties. Therefore, this pap

Yangtianze Tao, John Doe

Multi-agent systems in which secondary agents with conflicting agendas also alter their methods need opponent modeling. In this study, we simulate the main agent's and secondary agents' tactics using Double Deep Q-Networks (DDQN) with a prioritized experience replay mechanism. Then, under the opponent modeling setup, a Mixture-of-Experts architecture is used

Ziwei Yu, Linlin Yang, You Xie, Ping Chen

We propose a novel framework for 3D hand shape reconstruction and hand-object grasp optimization from a single RGB image. The representation of hand-object contact regions is critical for accurate reconstructions. Instead of approximating the contact regions with sparse points, as in previous works, we propose a dense representation in the form of a UV coord

Shujuan Liu, Shaowei Cui, Chaofan Zhang, Yinghao Cai

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the

Man Yiu Tsang, Karmel S. Shehadeh

We propose a new framework that unifies different fairness measures into a general, parameterized class of convex fairness measures suitable for optimization contexts. First, we propose a new class of order-based fairness measures, discuss their properties, and derive an axiomatic characterization for such measures. Then, we introduce the class of convex fai

Tao Zhou, Yan-Li Lee, Qian Li, Duanbing Chen

Violations of laws and regulations about food safety, production safety, quality standard and environmental protection, or negative consequences from loan, guarantee and pledge contracts, may result in operating and credit risks of firms. The above illegal or trust-breaking activities are collectively called discreditable activities, and firms with discredit

Jimena Rodriguez, Janice Lee, Bradley Whitmore, David Thilker

The earliest stages of star formation occur enshrouded in dust and are not observable in the optical. Here we leverage the extraordinary new high-resolution infrared imaging from JWST to begin the study of dust-embedded star clusters in nearby galaxies throughout the local volume. We present a technique for identifying dust-embedded clusters in NGC 7496 (18.

Xin Xia, Zonglin Meng, Xu Han, Hanzhao Li

This paper presents an automated driving system (ADS) data acquisition and processing platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected automated vehicle (CAV) cooperative perception. This platform presents a holistic pipeline from the raw advanced sensory data collection to data processing, which can process the s

Bosheng Yan, Chang-Tsun Li, Xuequan Lu

Deep learning has enabled realistic face manipulation (i.e., deepfake), which poses significant concerns over the integrity of the media in circulation. Most existing deep learning techniques for deepfake detection can achieve promising performance in the intra-dataset evaluation setting (i.e., training and testing on the same dataset), but are unable to per

Derek B. Leinweber, Anthony W. Thomas

QCD interactions for equal-mass fermion flavors are flavor blind. This fact is often used to state that disconnected sea-quark loop contributions are equal for $u$ and $d$ quarks in the mass symmetric case and therefore these disconnected sea-quark loop contributions cannot contribute to the well-known $\bar d - \bar u$ asymmetry in the proton. Instead, it i

C. J. Bradly, A. L. Owczarek

In order to study long chain polymers many lattice models accommodate a pulling force applied to a particular part of the chain, often a free endpoint. This is in addition to well-studied features such as energetic interaction between the lattice polymer and a surface. However, the critical behaviour of the pulling force alone is less well studied, such as c

Derek Leinweber, James Biddle, Waseem Kamleh, Adam Virgili

The non-trivial ground-state vacuum fields of QCD form the foundation of matter. Here we examine the centre vortices identified within the ground-state fields of lattice QCD. We aim to understand the manner in which dynamical fermions in the QCD vacuum alter the centre-vortex structure. Using modern visualisation techniques, the centre-vortex structure of pu

Xiaoshu Zeng, Roger Ghanem

The present work addresses the issue of accurate stochastic approximations in high-dimensional parametric space using tools from uncertainty quantification (UQ). The basis adaptation method and its accelerated algorithm in polynomial chaos expansions (PCE) were recently proposed to construct low-dimensional approximations adapted to specific quantities of in

Subhabrata Majumdar, Ganesh Subramaniam

We investigate the detection of botnet command and control (C2) hosts in massive IP traffic using machine learning methods. To this end, we use NetFlow data -- the industry standard for monitoring of IP traffic -- and ML models using two sets of features: conventional NetFlow variables and distributional features based on NetFlow variables. In addition to us

Sangeeta Yadav, Sashikumaar Ganesan

An artificial intelligence-augmented Streamline Upwind/Petrov-Galerkin finite element scheme (AiStab-FEM) is proposed for solving singularly perturbed partial differential equations. In particular, an artificial neural network framework is proposed to predict optimal values for the stabilization parameter. The neural network is trained by minimizing a physic

Yichen Tong

For a closed connected oriented manifold $M$ of dimension $2n$, it was proved by M\o ller and Raussen that the components of the mapping space from $M$ to $S^{2n}$ have exactly two different rational homotopy types. However, since this result was proved by the algebraic models for the components, it is unclear whether other homotopy invariants distinguish th

Mingxue Xu, Xiang-Yang Li

It is a growing direction to utilize unintended memorization in ML models to benefit real-world applications, with recent efforts like user auditing, dataset ownership inference and forgotten data measurement. Standing on the point of ML model development, we introduce a process named data origin inference, to assist ML developers in locating missed or fault

Arindam Bhattacharjee, Muktajyoti Saha

We attempt to understand the CFT$_1$ structure underlying (2+1)D gravity in flat spacetime via dimensional reduction. We observe that under superrotation, the hyperbolic (and dS$_2$) slices of flat spacetime transform to asymptotically (A)dS$_2$ slices. We consider a wedge region bounded by two such surfaces as End-of-the-World branes and employ Wedge hologr

Dai Zhuang, Ke Han

Vehicle-based mobile sensing (a.k.a drive-by sensing) is an important means of surveying urban environment by leveraging the mobility of public or private transport vehicles. Buses, for their extensive spatial coverage and reliable operations, have received much attention in drive-by sensing. Existing studies have focused on the assignment of sensors to a se

Wenhua Cao, Yupeng Li, Xiaodong Zhang, Falk Poenisch

We developed a novel method of creating intensity modulated proton arc therapy (IMPAT) plans that uses computing resources efficiently and may offer a dosimetric benefit for patients with ependymoma or similar tumor geometries. Our IMPAT planning method consists of a geometry-based energy selection step with major scanning spot contributions as inputs comput

Hannah Klawa

A domain $R$ is \emph{perinormal} if every going-down overring is flat and a perinormal domain $R$ is \emph{globally perinormal} if every flat overring is a localization of $R$ [Epstein-Shapiro 2016]. I show that global perinormality is preserved in a pullback construction which encompasses a classical $D+M$ construction. In doing so, a result is given for t

Matthew Crimson, Justin M. Kennedy, Daniel E. Quevedo

We study the problem of remote state estimation in the presence of a passive eavesdropper, under the challenging network environment of no packet receipt acknowledgments. A remote legitimate user estimates the state of a linear plant from the state information received from a sensor via an insecure and unreliable network. The transmission from the sensor may

Manoj Pandey, Dipendra Hamal, Bijaya Basnet, Bhim Kafle

Solvent engineering offers fine control over the photovoltaic efficiency, film morphology, and crystallization quality of perovskite films and also enables to optimize light transmittance and absorbance in solar cell applications. In the present work, the band gap and reflectance were reduced through solvent engineering. We found that perovskite thin films p

Xin Yang, Michael Bi Mi, Yuan Yuan, Xin Wang

Most existing domain adaptation (DA) methods align the features based on the domain feature distributions and ignore aspects related to fog, background and target objects, rendering suboptimal performance. In our DA framework, we retain the depth and background information during the domain feature alignment. A consistency loss between the generated depth an

Teddy Koker, Keegan Quigley, Will Spaeth, Nathan C. Frey

Recent work has shown the potential of graph neural networks to efficiently predict material properties, enabling high-throughput screening of materials. Training these models, however, often requires large quantities of labelled data, obtained via costly methods such as ab initio calculations or experimental evaluation. By leveraging a series of material-sp

Minhua Zhou, Minfeng Gu, Mai Liao, Muhammad S. Anjum

To understand the X-ray emission of active galactic nuclei (AGNs), we explored the optical-to-X-ray variation correlation of a radio-loud quasar (RLQ) SDSS J121426.52+140258.9 (hereafter J1214+1402) with multi-epoch observations of Swift and XMM-Newton telescopes. With the historical multi-band data, we found that the infrared to X-ray flux of RLQ J1214+1402

Jing Zhou, Tingting Shi, Xia-Ji Liu, Hui Hu

We study the crossover from the Bardeen-Cooper-Shrieffer (BCS) regime to the Bose-Einstein-condensation (BEC) regime in a quasi-two-dimensional quantum gas of ultracold fermionic atoms. Using an effective two-dimensional Hamiltonian with renormalized interactions between atoms and dressed molecules within a Gaussian pair fluctuation theory, we investigate ho

Elisabetta Nocerino, Ola K. Forslund, Hiroya Sakurai, Akinori Hoshikawa

The mixed valence Cr compound NaCr$_2$O$_4$, synthesized using a high-pressure technique, offers a unique playground for investigating unconventional physical properties in condensed matter. In the present study, muon spin rotation/relaxation ($\mu^+$SR) and high-resolution neutron powder diffraction (NPD) measurements were carried out to clarify the true ma

Juhi Jang, Junha Kim

We consider an initial boundary value problem of the multi-dimensional Boussinesq equations in the absence of thermal diffusion with velocity damping or velocity diffusion under the stress free boundary condition in horizontally periodic strip domain. We prove the global-in-time existence of classical solutions in high order Sobolev spaces satisfying high or

Harsh Mehta, Walid Krichene, Abhradeep Thakurta, Alexey Kurakin

Leveraging transfer learning has recently been shown to be an effective strategy for training large models with Differential Privacy (DP). Moreover, somewhat surprisingly, recent works have found that privately training just the last layer of a pre-trained model provides the best utility with DP. While past studies largely rely on algorithms like DP-SGD for

Yuki Hirayama, Sinya Takamaeda-Yamazaki

Bayesian neural networks (BNNs) have been an important framework in the study of uncertainty quantification. Deterministic variational inference, one of the inference methods, utilizes moment propagation to compute the predictive distributions and objective functions. Unfortunately, deriving the moments requires computationally expensive Taylor expansion in

Hisashi Kotegawa, Toshiaki Uga, Hideki Tou, Eiichi Matsuoka

Cubic CeZn shows a structural phase transition under pressure, and it modifies the ground state from an antiferromagnetic (AFM) state to a ferromagnetic (FM) state. To investigate how the FM state terminates at a quantum phase transition, we measured the electrical resistivity under pressure for a single crystal CeZn. The transition temperature into the FM s

Florian Behr, Georg Dolzmann, Klaus Hackl, Ghina Jezdan

A variational model of pressure-dependent plasticity employing a time-incremental setting is introduced. A novel formulation of the dissipation potential allows one to construct the condensed energy in a variationally consistent manner. For a one-dimensional model problem, an explicit expression for the quasiconvex envelope can be found which turns out to be

Shukui Chen, Kirill Serkh, James Bremer

The Levin method is a well-known technique for evaluating oscillatory integrals, which operates by solving a certain ordinary differential equation in order to construct an antiderivative of the integrand. It was long believed that this approach suffers from "low-frequency breakdown," meaning that the accuracy of the calculated value of the integral deterior

Keiko I. Nagao, Satoshi Higashino, Tatsuhiro Naka, Kentaro Miuchi

Dark matter with MeV scale mass is difficult to detect with standard direct search detectors. However, they can be searched for by considering the up-scattering of kinetic energies by cosmic rays. Because the dark matter density is higher in the central region of the Galaxy, the up-scattered dark matter will arrive at Earth from the direction of the Galactic

Yang You, Wenhao He, Jin Liu, Hongkai Xiong

Object pose estimation constitutes a critical area within the domain of 3D vision. While contemporary state-of-the-art methods that leverage real-world pose annotations have demonstrated commendable performance, the procurement of such real training data incurs substantial costs. This paper focuses on a specific setting wherein only 3D CAD models are utilize

Murray Elder, Adam Piggott, Kane Townsend

We call a graph $k$-geodetic, for some $k\geq 1$, if it is connected and between any two vertices there are at most $k$ geodesics. It is shown that any hyperbolic group with a $k$-geodetic Cayley graph is virtually-free. Furthermore, in such a group the centraliser of any infinite order element is an infinite cyclic group. These results were known previously

Zhiqiang Huang, Xiao-Kan Guo

We study some aspects of the Leggett-Garg inequalities by using the operator-state formalism for multitime processes. The process tensor in its Choi-state form, which we call process state, is employed to investigate the Leggett-Garg inequalities and their violations. We find the sufficient conditions on process states for the Leggett-Garg inequalities to ho

Yufan Zhou, Bingchen Liu, Yizhe Zhu, Xiao Yang

We present Corgi, a novel method for text-to-image generation. Corgi is based on our proposed shifted diffusion model, which achieves better image embedding generation from input text. Unlike the baseline diffusion model used in DALL-E 2, our method seamlessly encodes prior knowledge of the pre-trained CLIP model in its diffusion process by designing a new i

Bo Rao, Liu Yang, Suhan Zhong, Guangming Zhou

This paper proposes a robust approximation method for solving chance constrained optimization (CCO) of polynomials. Assume the CCO is defined with an individual chance constraint that is affine in the decision variables. We construct a robust approximation by replacing the chance constraint with a robust constraint over an uncertainty set. When the objective

Juno Nam, YounJoon Jung

We present a method to explore the free energy landscapes of chemical reactions with post-transition-state bifurcations using an enhanced sampling method based on well-tempered metadynamics. Obviating the need for computationally expensive DFT-level ab initio molecular dynamics simulations, we obtain accurate energetics by utilizing a free energy perturbatio

Renkun Kuang, Weicheng Zang, Shude Mao, Jiyuan Zhang

Up to date, only 13 firmly established triple microlensing events have been discovered, so the occurrence rates of microlensing two-planet systems and planets in binary systems are still uncertain. With the upcoming space-based microlensing surveys, hundreds of triple microlensing events will be detected. To provide clues for future observations and statisti

Yang You, Zhuochen Miao, Kai Xiong, Weiming Wang

This paper presents a general one-shot object localization algorithm called OneLoc. Current one-shot object localization or detection methods either rely on a slow exhaustive feature matching process or lack the ability to generalize to novel objects. In contrast, our proposed OneLoc algorithm efficiently finds the object center and bounding box size by a sp