January 2022 arXiv papers — page 104

Lars Ødegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, Paal Engelstad

With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. Deep learning (DL) models can learn complex patterns in the data and have found wide success in predicting wake losses and expected power production. This paper proposes a modular f

Martin W. Hess, Gianluigi Rozza

This work investigates the use of sparse polynomial interpolation as a model order reduction method for the incompressible Navier-Stokes equations. Numerical results are presented underscoring the validity of sparse polynomial approximations and comparing with established reduced basis techniques. Two numerical models serve to access the accuracy of the redu

Jakob Lindner

In this paper we continue the perturbative analysis of the quartic Kontsevich model. We investigate meromorphic functions $\Omega^{(0)}_m$ with $m=1,2$, that obey blobbed topological recursion. We calculate their expansions and check their equivalence to sums of ribbon graph weights, which are obtained with common methods of perturbation theory in QFT, up to

Adrien Bourgoin, Christophe Le Poncin-Lafitte, Stéphane Mathis, Marie-Christine Angonin

White dwarfs (WDs) and neutron stars (NSs) are among the most magnetized astrophysical objects in the universe, with magnetic fields that can reach up to $10^9\,\mathrm{G}$ for WDs and up to $10^{15}\,\mathrm{G}$ for NSs. The galaxy is expected to be populated with approximately one hundred million of double WD and millions of NS-WD binaries. Throughout the

Muhammad Sakib Khan Inan, Istiakur Rahman

Landslides have been a regular occurrence and an alarming threat to human life and property in the era of anthropogenic global warming. An early prediction of landslide susceptibility using a data-driven approach is a demand of time. In this study, we explored the eloquent features that best describe landslide susceptibility with state-of-the-art machine lea

Paul Lafourcade, Jean-Bernard Maillet, Nicolas Bruzy, Christophe Denoual

The equation of state of the triclinic compound 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) as well as its second-order isothermal elastic tensor were computed through classical molecular dynamics simulations under various temperature and pressure conditions. Hydrostatic pressures similar to previous diamond anvil cell experiments were imposed within the ran

Guodu Chen, Chuyu Zhou

Let $(X, \Delta)$ be a projective log canonical Calabi-Yau pair and $L$ an ample $\mathbb{Q}$-line bundle on $X$, we show that there is a correspondence between lc places of $(X, \Delta)$ and weakly special test configurations of $(X, \Delta;L)$.

Nicola Locatelli, Gabriele Ponti, Stefano Bianchi

Collecting and analysing X-ray photons over either spatial or temporal scales encompassing varying optical depth values requires knowledge about the optical depth distribution. In the case of sufficiently broad optical depth distribution, assuming a single column density value leads to a misleading interpretation of the source emission properties, nominally

Shobha Sundar Ram, Gourab Ghatak

Recently joint radar communication (JRC) systems have gained considerable interest for several applications such as vehicular communications, indoor localization and activity recognition, covert military communications, and satellite-based remote sensing. In these frameworks, bistatic/passive radar deployments with directional beams explore the angular searc

Gordon Hoi, Ammar Fathin Sabili, Frank Stephan

The Maximum Induced Matching problem asks to find the maximum $k$ such that, given a graph $G=(V,E)$, can we find a subset of vertices $S$ of size $k$ for which every vertices $v$ in the induced graph $G[S]$ has exactly degree $1$. In this paper, we design an exact algorithm running in $O(1.2630^n)$ time and polynomial space to solve the Maximum Induced Matc

Sishu Shankar Muni, Hammed Olawale Fatoyinbo, Indranil Ghosh

We consider the dynamical effects of electromagnetic flux on the discrete Chialvo neuron. It is shown that the model can exhibit rich dynamical behaviors such as multistability, firing patterns, antimonotonicity, closed invariant curves, various routes to chaos, fingered chaotic attractors. The system enters chaos via period-doubling cascades, reverse period

Kwan-Lok Li

We report here a new result extracted from the Fermi Large Area Telescope observation of the classical nova ASASSN-16ma that exhibits coherent gamma-ray pulsations at 544.84(7) seconds during its outburst in 2016. Considering the number of independent trials, the significance of the evidence is 4.0 sigma, equivalent to a false alarm probability of 5.9e-5. Th

Feiyang Xiao, Jian Guan, Haiyan Lan, Qiaoxi Zhu

Automated audio captioning aims to describe audio data with captions using natural language. Existing methods often employ an encoder-decoder structure, where the attention-based decoder (e.g., Transformer decoder) is widely used and achieves state-of-the-art performance. Although this method effectively captures global information within audio data via the

DRHBc Mass Table Collaboration, Kaiyuan Zhang, Myung-Ki Cheoun, Yong-Beom Choi

Ground-state properties of even-even nuclei with $8\le Z\le120$ from the proton drip line to the neutron drip line have been investigated using the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with the density functional PC-PK1. With the effects of deformation and continuum included simultaneously, 2583 even-even nuclei are predicted

Hung Tuan Nguyen, Cuong Tuan Nguyen, Haruki Oka, Tsunenori Ishioka

This paper presents an experiment of automatically scoring handwritten descriptive answers in the trial tests for the new Japanese university entrance examination, which were made for about 120,000 examinees in 2017 and 2018. There are about 400,000 answers with more than 20 million characters. Although all answers have been scored by human examiners, handwr

Liwei Yuan, Hideaki Ishii

In this paper, we study the problem of resilient consensus for a multi-agent network where some of the nodes might be adversarial, attempting to prevent consensus by transmitting faulty values. Our approach is based on that of the so-called weighted mean subsequence reduced (W-MSR) algorithm with a special emphasis on its use in agents capable to communicate

Mei-Ling Cai, Zhang-HangJian Chen, Sai-Ping Li, Xiong Xiong

In this paper, we propose a new dynamical model to study the two-stage volatility evolution of stock market index after extreme events, and find that the volatility after extreme events follows a stretched exponential decay in the initial stage and becomes a power law decay at later times by using high frequency minute data. Empirical study of the evolutiona

M. Usman Maqbool Bhutta, Yuxiang Sun, Darwin Lau, Ming Liu

Deep learning-based image retrieval techniques for the loop closure detection demonstrate satisfactory performance. However, it is still challenging to achieve high-level performance based on previously trained models in different geographical regions. This paper addresses the problem of their deployment with simultaneous localization and mapping (SLAM) syst

Ethan Grooby, Chiranjibi Sitaula, Davood Fattahi, Reza Sameni

Stethoscope-recorded chest sounds provide the opportunity for remote cardio-respiratory health monitoring of neonates. However, reliable monitoring requires high-quality heart and lung sounds. This paper presents novel Non-negative Matrix Factorisation (NMF) and Non-negative Matrix Co-Factorisation (NMCF) methods for neonatal chest sound separation. To asses

Marcos V. Conde, Steven McDonagh, Matteo Maggioni, Aleš Leonardis

Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP). Computational photography tasks such as image denoising and colour constancy are commonly performed in the RAW domain, in part due to the inherent hardware design, but also due to the appealing simplicity of noise statistics that result from the dire

Yao Ge, P. C. Ching

This paper investigates a distant proactive eavesdropping system in cooperative cognitive radio (CR) networks. Specifically, an amplify-and-forward (AF) full-duplex (FD) secondary transmitter assists to relay the received signal from suspicious users to legitimate monitor for wireless information surveillance. In return, the secondary transmitter is granted

Matthias Hoeft, Kamlesh Rajpurohit, Denis Wittor, Gabriella di Gennaro

Radio relics are extended radio emission features which trace shock waves in the periphery of galaxy clusters originating from cluster mergers. Some radio relics show a highly polarised emission, which make relics an excellent probe for the magnetisation of the intra-cluster medium. The origin of the relic polarisation is still debated. It could be a result

Jānis Bajārs, Juan F. R. Archilla

We study nonlinear excitations propagating in a hexagonal layer which is a model for the cation layer of silicates. We consider their properties in the frequency-momentum or $\omega-k$ representation, extending the theory on pterobreathers in their moving frame for the first time to two dimensions. It can also be easily extended to three dimensions. Exact tr

Hsi-Hung Lu, Chung-An Shen, Mohammed E. Fouda, Ahmed M. Eltawil

Independent component analysis (ICA) has been used in many applications, including self-interference cancellation for in-band full-duplex wireless systems and anomaly detection in industrial internet of things. This paper presents a high-throughput and highly efficient configurable preprocessing accelerator for the ICA algorithm. The proposed ICA accelerator

Haifeng Wang, Yufeng Zhang, Binlu Feng

We construct a new class of N-dimensional Lie algebras and apply them to integrable systems. In this paper, we obtain a nonisospectral KdV integrable hierarchy by introducing a nonisospectral spectral problem. Then, a coupled nonisospectral KdV hierarchy is deduced by means of the corresponding higher-dimensional loop algebra. It follows that the K symmetrie

Di Wang, Jinhui Xu

We study the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) with heavy-tailed data. Specifically, we focus on the $\ell_1$-norm linear regression in the $\epsilon$-DP model. While most of the previous work focuses on the case where the loss function is Lipschitz, here we only need to assume the variates has bounded moments. Firstly

G. Kacarevic, I. Bozovic Jelisavcic, N. Vukasinovic, I. Smiljanic

In this paper we address the potential of a 3 TeV centre-of-mass energy Compact Linear Collider (CLIC) to measure the branching fraction of the Higgs boson decay to two photons, BR($H\rightarrow\gamma\gamma$). Since photons are massless, the Higgs boson coupling to photons is realized through higher order processes involving heavy particles either from the S

Yangyang Wu, Jun Wang, Xiaoye Miao, Wenjia Wang

Data imputation has been extensively explored to solve the missing data problem. The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications. In this paper, we propose an effective scalable imputation system named SCIS to significantly speed up the training of the differentiable

Qinglin Yang, Yetong Zhao, Huawei Huang, Zehui Xiong

Metaverse as the latest buzzword has attracted great attention from both industry and academia. Metaverse seamlessly integrates the real world with the virtual world and allows avatars to carry out rich activities including creation, display, entertainment, social networking, and trading. Thus, it is promising to build an exciting digital world and to transf

Shashwat Bhattacharya, Mahendra K Verma, Arnab Bhattacharya

In this paper, we develop a multivariate regression model and a neural network model to predict the Reynolds number (Re) and Nusselt number in turbulent thermal convection. We compare their predictions with those of earlier models of convection: Grossmann-Lohse~[Phys. Rev. Lett. \textbf{86}, 3316 (2001)], revised Grossmann-Lohse~[Phys. Fluids \textbf{33}, 01

Guowei Cui, Xiaoping Chen

One of the challenges of task planning is to find out what causes the planning failure and how to handle the failure intelligently. This paper shows how to achieve this. The idea is inspired by the connected graph: each verticle represents a set of compatible \textit{states}, and each edge represents an \textit{action}. For any given initial states and goals

Zhi Qi

In this paper, we prove the following asymptotic formula for the spectral cubic moment of central $L$-values: $$ \sum_{t_f \leqslant T} \frac {2 L \big( \tfrac 1 2 , f \big)^3} {L(1, \mathrm{Sym}^2 f)} + \frac {2} {\pi} \int_{0}^{T} \frac {\left| \zeta \big(\tfrac 1 2 + it \big) \right|^{6} } { | \zeta (1 + 2 it ) |^2 } \mathrm{d} t = T^2 P_3 (\log T) + O (T

Ming Zhang, Zhenning Guo, Xiaoyu Mi, Zheng Li

The requirement of high space-time resolution and brightness is a great challenge for imaging atomic motion and making molecular movies. Important breakthroughs in ultrabright tabletop laser, x-ray and electron sources have enabled the direct imaging of evolving molecular structures in chemical processes. And recent experimental advances in preparing ultrafa

Qing Wang, Jia Zhu, Hongji Shu, Kwame Omono Asamoah

In natural language processing, extreme multi-label text classification is an emerging but essential task. The problem of extreme multi-label text classification (XMTC) is to recall some of the most relevant labels for a text from an extremely large label set. Large-scale pre-trained models have brought a new trend to this problem. Though the large-scale pre

Hung-Yu Jian, Lihwai Lin, Bau-Ching Hsieh, Kai-Yang Lin

We utilize the Hyper Suprime-Cam (HSC) Wide Survey to explore the properties of galaxies located in the voids identified from the Baryon Oscillation Spectroscopic Survey (BOSS) up to z~0.7. The HSC reaches i~25, allowing us to characterize the void galaxies down to 10$^{9.2}$ solar mass. We find that the revised void galaxy densities, when including faint ga

Yuyang Wu, Wei Gao

As a fundamental data format representing spatial information, depth map is widely used in signal processing and computer vision fields. Massive amount of high precision depth maps are produced with the rapid development of equipment like laser scanner or LiDAR. Therefore, it is urgent to explore a new compression method with better compression ratio for hig

Jingzhou Chen, Peng Wang, Jian Liu, Yuntao Qian

Hierarchical multi-granularity classification (HMC) assigns hierarchical multi-granularity labels to each object and focuses on encoding the label hierarchy, e.g., ["Albatross", "Laysan Albatross"] from coarse-to-fine levels. However, the definition of what is fine-grained is subjective, and the image quality may affect the identification. Thus, samples coul

Muhammad Sajeer P

Philosophy has nurtured fundamental science by asking the right questions. This scientific growth has fuelled research in various domains and introduced diverse disciplines. Nanotechnology is an interdisciplinary domain with numerous applications ranging from medical diagnostics and food technology to electronics and psychology. Exploring nanotechnology's ph

Amit Samadder, Arnab Chattopadhyay, Anurag Sau, Sabyasachi Bhattacharya

In natural ecosystems, species can be characterized by the nonlinear density-dependent self-regulation of their growth profile. Species of many taxa show a substantial density-dependent reduction for low population size. Nevertheless, many show the opposite trend; density regulation is minimal for small populations and increases significantly when the popula

Yijie Mao, Onur Dizdar, Bruno Clerckx, Robert Schober

Rate-splitting multiple access (RSMA) has emerged as a novel, general, and powerful framework for the design and optimization of non-orthogonal transmission, multiple access (MA), and interference management strategies for future wireless networks. Through information and communication theoretic analysis, RSMA has been shown to be optimal (from a Degrees-of-

Matteo Seclì, Massimo Capone, Marco Schirò

We study a driven-dissipative Bose-Hubbard model in presence of two-particle losses and an incoherent single-particle drive on each lattice site, leading to a finite-density stationary state. Using dynamical mean-field theory (DMFT) and an impurity solver based on exact diagonalization of the associated Lindbladian, we investigate the regime of strong two-pa

Takaharu Otsuka, Noritaka Shimizu, Yusuke Tsunoda

The ground-state properties of neutron-rich exotic Na and Mg isotopes with even numbers of neutrons, N, are studied up to driplines. The shell-model calculations with an ab initio effective nucleon-nucleon interaction reported in [Tsunoda, Otsuka, Takayanagi et al., Nature 587, 66 (2020)] are extended to observables such as magnetic dipole and electric quadr

Harry Nguyen, Stone Yun, Hisham Mohammad

This work aims to reproduce results from the CVPR 2020 paper by Gidaris et al. Self-supervised learning (SSL) is used to learn feature representations of an image using an unlabeled dataset. This work proposes to use bag-of-words (BoW) deep feature descriptors as a self-supervised learning target to learn robust, deep representations. BowNet is trained to re

Matthew J. O'Rourke, Garnet Kin-Lic Chan

We report on the ground state phase diagram of interacting Rydberg atoms in the unfrustrated square lattice array. Using new tensor network algorithms, we scale to large systems in two dimensions while including all long-range interactions, revealing the phases in the bulk and their analogs in accessible finite arrays. We find a greatly altered phase diagram

Zhuo Xu, Yue Wang, Lu Bai, Lixin Cui

Event extraction, the technology that aims to automatically get the structural information from documents, has attracted more and more attention in many fields. Most existing works discuss this issue with the token-level multi-label classification framework by distinguishing the tokens as different roles while ignoring the writing styles of documents. The wr

Guangdong Xue, Qin Chang, Jian Wang, Kai Zhang

A major limitation of fuzzy or neuro-fuzzy systems is their failure to deal with high-dimensional datasets. This happens primarily due to the use of T-norm, particularly, product or minimum (or a softer version of it). Thus, there are hardly any work dealing with datasets with dimensions more than hundred or so. Here, we propose a neuro-fuzzy framework that

Huansheng Ning, Zhangfeng Pi, Wenxi Wang, Fadi Farha

Human beings have been affected by disasters from the beginning of life, bringing them many sad memories. In the long struggle against disaster, people have devised a variety of methods to train relevant participants in disaster relief capabilities. However, many traditional training methods, such as disaster exercises may not provide effective training to m

Lei Li, Fuping Wu, Sihan Wang, Xinzhe Luo

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac

Sanjana Gunna, Rohit Saluja, C. V. Jawahar

Scene-text recognition is remarkably better in Latin languages than the non-Latin languages due to several factors like multiple fonts, simplistic vocabulary statistics, updated data generation tools, and writing systems. This paper examines the possible reasons for low accuracy by comparing English datasets with non-Latin languages. We compare various featu

Peng Zhao, Yingshan Zhang, Guangming Xue, Yirong Jin

Besides striving to assemble more and more qubits in a single monolithic quantum device, taking a modular design strategy may mitigate numerous engineering challenges for achieving large-scalable quantum processors with superconducting qubits. Nevertheless, a major challenge in the modular quantum device is how to realize high-fidelity entanglement operation

Andrea Schirato, Andrea Toma, Remo Proietti Zaccaria, Alessandro Alabastri

Optical metasurfaces have come into the spotlight as a promising platform for light manipulation at the nanoscale, including ultrafast all-optical control via excitation with femtosecond laser pulses. Recently, dichroic metasurfaces have been exploited to modulate the polarization state of light with unprecedented speed. Here, we theoretically predict and ex

Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang

There has been a surge of interest in developing robust estimators for models with heavy-tailed and bounded variance data in statistics and machine learning, while few works impose unbounded variance. This paper proposes two type of robust estimators, the ridge log-truncated M-estimator and the elastic net log-truncated M-estimator. The first estimator is ap

Daning Bi, Xiao Han, Adam Nie, Yanrong Yang

High-dimensional autocovariance matrices play an important role in dimension reduction for high-dimensional time series. In this article, we establish the central limit theorem (CLT) for spiked eigenvalues of high-dimensional sample autocovariance matrices, which are developed under general conditions. The spiked eigenvalues are allowed to go to infinity in

Sanjana Gunna, Rohit Saluja, C. V. Jawahar

Scene text recognition in low-resource Indian languages is challenging because of complexities like multiple scripts, fonts, text size, and orientations. In this work, we investigate the power of transfer learning for all the layers of deep scene text recognition networks from English to two common Indian languages. We perform experiments on the conventional

Mohadese Movahednia, Hamid Karimi, Shahram Jadid

In this study, a cooperative game model is presented to schedule the day-ahead operation of multi-microgrid (MMG) systems. In the proposed model, microgrids are scheduled to achieve a global optimum for the cost of the multi-microgrid system. The minimum cost is achieved by transactions of microgrids with each other. Also, price-based demand response is impl

Tao Chen, Yiran Liu, Haoyu Jiang, Ruirui Li

Accurately segmenting roads is challenging due to substantial intra-class variations, indistinct inter-class distinctions, and occlusions caused by shadows, trees, and buildings. To address these challenges, attention to important texture details and perception of global geometric contextual information are essential. Recent research has shown that CNN-Trans

José Cantarero, Ángel R. Jiménez

In this article we introduce the space of configurations of commuting elements in a topological group and show that it satisfies rational homological stability for the sequences of unitary, special unitary and symplectic groups. We also prove that it satisfies cohomological rational representation stability with respect to the number of elements in the tuple

Irtiza Hasan, Shengcai Liao, Jinpeng Li, Saad Ullah Akram

Pedestrian detection is the cornerstone of many vision based applications, starting from object tracking to video surveillance and more recently, autonomous driving. With the rapid development of deep learning in object detection, pedestrian detection has achieved very good performance in traditional single-dataset training and evaluation setting. However, i

Ruofan Liang, Bingsheng He, Shengen Yan, Peng Sun

Multi-tenant machine learning services have become emerging data-intensive workloads in data centers with heavy usage of GPU resources. Due to the large scale, many tuning parameters and heavy resource usage, it is usually impractical to evaluate and benchmark those machine learning services on real clusters. In this demonstration, we present AnalySIM, a clu

Reihane Boghrati, Jonah Berger, Grant Packard

Why do some things succeed in the marketplace of ideas? While some argue that content drives success, others suggest that style, or the way ideas are presented, also plays an important role. To provide a stringent test of style's importance, we examine it in a context where content should be paramount: academic research. While scientists often see writing as

Reihane Boghrati, Jonah Berger

Cultural items like songs have an important impact in creating and reinforcing stereotypes, biases, and discrimination. But the actual nature of such items is often less transparent. Take songs, for example. Are lyrics biased against women? And how have any such biases changed over time? Natural language processing of a quarter of a million songs over 50 yea

Jacob F Wycoff, Sam Dillavou, Menachem Stern, Andrea J Liu

In a neuron network, synapses update individually using local information, allowing for entirely decentralized learning. In contrast, elements in an artificial neural network (ANN) are typically updated simultaneously using a central processor. Here we investigate the feasibility and effect of desynchronous learning in a recently introduced decentralized, ph

Geeho Kim, Jinkyu Kim, Bohyung Han

Federated learning often suffers from slow and unstable convergence due to the heterogeneous characteristics of participating client datasets. Such a tendency is aggravated when the client participation ratio is low since the information collected from the clients has large variations. To address this challenge, we propose a simple but effective federated le

Tiantian Zhang, T. Yilmaz, E. Vescovo, H. X. Li

Topological semimetals are a frontier of quantum materials. In multi-band electronic systems, topological band-crossings can form closed curves, known as nodal lines. In the presence of spin-orbit coupling and/or symmetry-breaking operations, topological nodal lines can break into Dirac/Weyl nodes and give rise to novel transport properties, such as the chir

Jinnavat Sanalohit, Tatpong Katanyukul

Thai Finger Spelling (TFS) sign recognition could benefit a community of hearing-difficulty people in bridging to a major hearing population. With a relatively large number of alphabets, TFS employs multiple signing schemes. Two schemes of more common signing -- static and dynamic single-hand signing, widely used in other sign languages -- have been addresse

Lingzhi Gao, Zhenyuan Zhang, Chao Wu

While existing federated learning approaches primarily focus on aggregating local models to construct a global model, in realistic settings, some clients may be reluctant to share their private models due to the inclusion of privacy-sensitive information. Knowledge distillation, which can extract model knowledge without accessing model parameters, is well-su

Èric Pairet, Simone Spanò, Nikita Mankovskii, Paolo Pellegrino

The Technology Innovation Institute in Abu Dhabi, United Arab Emirates, has recently finished the production and testing of a new unmanned surface vehicle, called Nukhada, specifically designed for autonomous survey, inspection, and support to underwater operations. This manuscript describes the main characteristics of the Nukhada USV, as well as some of the

Rabigul Tuniyaz, Gulshadam Yunus

The so called generalized down-up algebras are revisited from a viewpoint of Gr\"obner basis theory. Particularly it is shown explicitly that generalized down-up algebras are solvable polynomial algebras (provided $\lambda\omega\ne 0$), and by means of homogeneous Gr\"obner defining relations, the associated graded structures of generalized down-up algebras,

Xiaohu You, Chuan Zhang, Bin Sheng, Yongming Huang

To fully support vertical industries, 5G and its corresponding channel coding are expected to meet requirements of different applications. However, for applications of 5G and beyond 5G (B5G) such as URLLC, the transmission latency is required to be much shorter than that in eMBB. Therefore, the resulting channel code length reduces drastically. In this case,

Jérôme Buzzi, Sylvain Crovisier, Omri Sarig

Recently, Burguet proved a strong form of Viana's conjecture on physical measures, in the special case of $C^\infty$ surface diffeomorphisms. We give another proof, based on our analysis of entropy and Lyapunov exponents in [BCS].

Yue Wang, Siqi He

Some genes can promote or repress their own expressions, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types

Mamdouh Alenezi

In today's era of innovation of technological progression, digitalisation has not only transformed individual lives but also has a prominent influence on business activities. The world is surviving in a global yet complex technological progression that not only changes the lives of civilians but is also transforming the public, private, and academic spheres

Minsoo Kim, Joonwoo Ahn, Jaeheung Park

Rapidly-exploring random tree (RRT) has been applied for autonomous parking due to quickly solving high-dimensional motion planning and easily reflecting constraints. However, planning time increases by the low probability of extending toward narrow parking spots without collisions. To reduce the planning time, the target tree algorithm was proposed, substit

Mohadese Movahednia, Amin Kargarian

Power system components, particularly electrical substations, may be severely damaged due to flooding, resulting in prolonged power outages and resilience degradation. This problem is more severe in low-elevated regions such as Louisiana. Protective operational actions such as placing tiger dams around substations before flooding can reduce substation vulner

Yuxuan Liu, Qian Chen, Xiao-Xiong Zeng, HongBao Zhang

Taking into account that the real quantum materials are engineered generically at a finite chemical potential, we investigate the Einstein ring structure for the lensed response of the complex scalar field as a probe wave on the charged AdS black hole in the context of AdS/CFT. On the one hand, we find that the resulting Einstein ring radius has no variation

Chong-Bin Chen, Jiro Soda

An inflationary scenario is expected to be embedded into an ultraviolet (UV) complete theory such as string theory. The effect of UV complete theories may appear as nontrivial kinetic terms in the low energy effective field theory, which provides a nontrivial geometry in field space. In this paper, we study the effect of the geometry of multi-form-field spac

Andrew J. Larkoski

Despite their inextricable quantum mechanical nature, events at a high energy particle collider experiment typically have very few unambiguous quantum signatures, due the type of data and the manner in which they are collected. We present a general analysis of one feature of quantum mechanics, interference between two orthogonal states on Hilbert space, proj

Lianghao Xia, Chao Huang, Yong Xu, Huance Xu

As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such as multi-layer perceptron, auto-encoder and graph neural networks. However, the majori

Jianqing Guo, Bingqing Cheng, Limei Xu, Enge Wang

Liquid-liquid phase transition of hydrogen is at the center of hydrogen phase diagram as a promising route towards emergent properties such as the Wigner-Huntington metallization, superconductivity, and superfluidity. Here we report a study on the liquid-liquid phase transition of hydrogen using the state-of-the-art diffusion quantum Monte Carlo and density

Shoffan Saifullah, Andiko Putro Suryotomo

This study aims to identify chicken eggs fertility using the support vector machine (SVM) classifier method. The classification basis used the first-order statistical (FOS) parameters as feature extraction in the identification process. This research was developed based on the process's identification process, which is still manual (conventional). Although c

Gangshan Jing, He Bai, Jemin George, Aranya Chakrabortty

Existing distributed cooperative multi-agent reinforcement learning (MARL) frameworks usually assume undirected coordination graphs and communication graphs while estimating a global reward via consensus algorithms for policy evaluation. Such a framework may induce expensive communication costs and exhibit poor scalability due to requirement of global consen

Hao Xie, Linfeng Zhang, Lei Wang

The quasiparticle effective mass $m^\ast$ of interacting electrons is a fundamental quantity in the Fermi liquid theory. However, the precise value of the effective mass of uniform electron gas is still elusive after decades of research. The newly developed neural canonical transformation approach [Xie et al., J. Mach. Learn. 1, (2022)] offers a principled w

Hua Feng, Philip Kaaret

Astronomical observations in the X-ray band are subject to atmospheric attenuation and have to be performed in the space. CubeSats offer a cost effective means for space-based X-ray astrophysics but allow only limited mass and volume. In this article, we describe two successful CubeSat-based missions, HaloSat and PolarLight, both sensitive in the keV energy

Shammi Tahura, Zhen Pan, Huan Yang

It has been suggested that there is possibly a class of stellar-mass black holes (BHs) residing near (distance $\le 10^3 M$) the galactic center massive black hole, Sgr A*. Possible formation scenarios include the mass segregation of massive stellar-mass black holes and/or the disk migration if there was an active accretion flow near Sgr A* within $\mathcal{

Derek Weber, Lucia Falzon, Lewis Mitchell, Mehwish Nasim

During Australia's unprecedented bushfires in 2019-2020, misinformation blaming arson resurfaced on Twitter using #ArsonEmergency. The extent to which bots were responsible for disseminating and amplifying this misinformation has received scrutiny in the media and academic research. Here we study Twitter communities spreading this misinformation during the p

B. -Q. Chen, H. -L. Guo, J. Gao, M. Yang

We present high-resolution maps of the dust reddening in the Magellanic Clouds (MCs). The maps cover the Large and Small Magellanic Cloud (LMC and SMC) area and have a spatial angular resolution between $\sim$ 26 arcsec and 55 arcmin. Based on the data from the optical and near-infrared (IR) photometric surveys, including the Gaia Survey, the SkyMapper South

Omar Rodríguez-Tzompantzi

Exotic General Massive Gravity is the next-to-simplest gravitational theory fulfilling the so-called third-way consistency, the simplest being Minimal Massive Gravity. We investigate the canonical structure of the first-order formulation of Exotic General Massive Gravity. By using the Dirac Hamiltonian formalism, we systematically discover the complete set o

Zubiao Xiao, Zhengyu Yin

We study the topological complexities of relative entropy zero extensions acted by countableinfinite amenable groups. Firstly, for a given Folner sequence $\{F_n\}_{n=0}^\infty$, we define respectively the relative entropy dimensions and the dimensions of the relative entropy generating sets to characterize the sub-exponential growth of the relative topologi

Wen He, Qiang Hu, Chaowei Jiang, Jiong Qiu

In order to bridge the gap between heliospheric and solar observations of coronal mass ejections (CMEs), one of the key steps is to improve the understanding of their corresponding magnetic structures like the magnetic flux ropes (MFRs). But it remains a challenge to confirm the existence of a coherent MFR before or upon the CME eruption on the Sun and to qu

Ruobing Zheng, Yuming Wang, Xiaolei Li, Chuanbing Wang

To better understand the physical processes associated with Jovian decametric (DAM) radio emissions, we present the statistical study of DAMs and inferred characteristics of DAM sources based on multi-view observation from Wind and STEREO spacecraft. The distribution of the apparent rotation speed of DAMs derived from multiple spacecraft suggests that the ro

Arjun Akula, Song-Chun Zhu

An explainable AI (XAI) model aims to provide transparency (in the form of justification, explanation, etc) for its predictions or actions made by it. Recently, there has been a lot of focus on building XAI models, especially to provide explanations for understanding and interpreting the predictions made by deep learning models. At UCLA, we propose a generic

Kush Singhal

In this paper, we study the Mellin transform of the weight 2 level $N$ polar harmonic Maass form $H_{N,z}^*(\tau)$, and analyze this (generalized) $L$-function as $\mathrm{Im}(z)\to \infty$. On the way, we also calculate the Fourier expansion of $H_{N,z}^*(\tau)$ at arbitrary cusps of $\Gamma_0(N)$, and we give a functional equation and factorization into lo

Lanqing Guo, Renjie Wan, Wenhan Yang, Alex Kot

Images captured in the low-light condition suffer from low visibility and various imaging artifacts, e.g., real noise. Existing supervised enlightening algorithms require a large set of pixel-aligned training image pairs, which are hard to prepare in practice. Though weakly-supervised or unsupervised methods can alleviate such challenges without using paired

Chun Yang

In this work, we aim to consider the application of contrastive learning in the scenario of the recommendation system adequately, making it more suitable for recommendation task. We propose a learning paradigm called supervised contrastive learning(SCL) to support the graph convolutional neural network. Specifically, we will calculate the similarity between

Michael Hutchings

The ECH capacities are a sequence of numerical invariants of symplectic four-manifolds which give (sometimes sharp) obstructions to symplectic embeddings. These capacities are defined using embedded contact homology, and establishing their basic properties currently requires Seiberg-Witten theory. In this note we define a new sequence of symplectic capacitie

Meixin Cheng, Shazhou Zhong, Nicolas Rivas, Tina Dekker

We report on time-resolved broadband transient reflectivity (tr-bb-TR) measurements performed on a bulk single crystal of T$_d$-MoTe$_2$ as a function of the incident pump fluence (F). Tr-bb-TR data unveil photoinduced electronic changes progressing on the sub-picosecond timescale as well as the dynamics of the coherent low-frequency $^1A_1$ interlayer shear

Ashwin Jacob, Diptapriyo Majumdar, Venkatesh Raman

Let $\Pi$ be a hereditary graph class. The problem of deletion to $\Pi$, takes as input a graph $G$ and asks for a minimum number (or a fixed integer $k$) of vertices to be deleted from $G$ so that the resulting graph belongs to $\Pi$. This is a well-studied problem in paradigms including approximation and parameterized complexity. Recently, the study of a n

Yi Zheng

The attention mechanism is widely used in deep learning because of its excellent performance in neural networks without introducing additional information. However, in unsupervised person re-identification, the attention module represented by multi-headed self-attention suffers from attention spreading in the condition of non-ground truth. To solve this prob

Jesse Gell-Redman, Sean Gomes, Andrew Hassell

We study the time-dependent Schr\"odinger operator $P = D_t + \Delta_g + V$ acting on functions defined on $\mathbb{R}^{n+1}$, where, using coordinates $z \in \mathbb{R}^n$ and $t \in \mathbb{R}$, $D_t$ denotes $-i \partial_t$, $\Delta_g$ is the positive Laplacian with respect to a time dependent family of non-trapping metrics $g_{ij}(z, t) dz^i dz^j$ on $\m

Dongjie Chen, Sen-ching Samson Cheung, Chen-Nee Chuah, Sally Ozonoff

To protect sensitive data in training a Generative Adversarial Network (GAN), the standard approach is to use differentially private (DP) stochastic gradient descent method in which controlled noise is added to the gradients. The quality of the output synthetic samples can be adversely affected and the training of the network may not even converge in the pre