December 2020 arXiv papers — page 49

Mikkel Abrahamsen, Jacob Holm, Eva Rotenberg, Christian Wulff-Nilsen

We consider the following game played in the Euclidean plane: There is any countable set of unit speed lions and one fast man who can run with speed $1+\varepsilon$ for some value $\varepsilon>0$. Can the man survive? We answer the question in the affirmative for any $\varepsilon>0$.

Bhaskar Bagchi, Sunanda Bagchi

The concept of orthogonality through the block factor (OTB), defined in Bagchi (2010), is extended here to orthogonality through a set (say S) of other factors. We discuss the impact of such an orthogonality on the precision of the estimates as well as on the inference procedure. Concentrating on the case when $S$ is of size two, we construct a series of pla

Vamshi M. Katukuri, Nikolay A. Bogdanov, Oskar Weser, Jeroen van den Brink

The large antiferromagnetic exchange coupling in the parent high-$T_{\rm c}$ cuprate superconductors is believed to play a crucial role in pairing the superconducting carriers. The recent observation of superconductivity in hole-doped infinite-layer (IL-) NdNiO$_2$ brings to the fore the relevance of magnetic coupling in high-$T_{\rm c}$ superconductors, par

Daofeng Li, Kaihe Deng, Ming Zhao, Sihai Zhang

The power of big data and machine learning has been drastically demonstrated in many fields during the past twenty years which somehow leads to the vague even false understanding that the huge amount of precious human knowledge accumulated to date no longer seems to matter. In this paper, we are pioneering to propose the knowledge-driven machine learning(KDM

Zhen Lin, Lianying Miao, Shuguang Guo

For every real $0\leq \alpha \leq 1$, Nikiforov defined the $A_{\alpha}$-matrix of a graph $G$ as $A_{\alpha}(G)=\alpha D(G)+(1-\alpha)A(G)$, where $A(G)$ and $D(G)$ are the adjacency matrix and the degree diagonal matrix of a graph $G$, respectively. The eigenvalues of $A_{\alpha}(G)$ are called the $A_{\alpha}$-eigenvalues of $G$. Let $S_k(A_{\alpha}(G))$

Selman Akbulut, Eylem Zeliha Yildiz

Here, we prove that $0-$shake slice knots are slice.

Pengyong Li, Jun Wang, Yixuan Qiao, Hao Chen

How to produce expressive molecular representations is a fundamental challenge in AI-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and have poor generalization capability. Here, we proposed a novel Mole

Xiong Cai, Zhiyong Wu, Kuo Zhong, Bin Su

By using deep learning approaches, Speech Emotion Recog-nition (SER) on a single domain has achieved many excellentresults. However, cross-domain SER is still a challenging taskdue to the distribution shift between source and target domains.In this work, we propose a Domain Adversarial Neural Net-work (DANN) based approach to mitigate this distribution shift

Neelam Saikia

Let $p$ be an odd prime and $\mathbb{F}_p$ be the finite field with $p$ elements. McCarthy \cite{mccarthy-pacific} initiated a study of hypergeometric functions in the $p$-adic setting. This function can be understood as $p$-adic analogue of Gauss' hypergeometric function, and also some kind of extension of Greene's hypergeometric function over $\mathbb{F}_p

Amin Javari, Mehrab Norouzitallab, Mahdi Jalili

Popularity of social networks has rapidly increased over the past few years, and daily lives interrupt without their proper functioning. Social networking platform provide multiple interaction types between individuals, such as creating and joining groups, sending and receiving messages, sharing interests and creating friendship relationships. This paper add

Daoud Burghal, Ashwin T. Ravi, Varun Rao, Abdullah A. Alghafis

The last few decades have witnessed a growing interest in location-based services. Using localization systems based on Radio Frequency (RF) signals has proven its efficacy for both indoor and outdoor applications. However, challenges remain with respect to both complexity and accuracy of such systems. Machine Learning (ML) is one of the most promising method

Anton A. Lunyov, Mark M. Malamud

The paper is concerned with the stability property under perturbation $Q\to\widetilde Q$ of different spectral characteristics of a BVP associated in $L^2([0,1];\Bbb C^2)$ with the following $2\times2$ Dirac type equation $$L_U(Q)y=-iB^{-1}y'+Q(x)y=\lambda y,\quad B={\rm diag}(b_1,b_2),\quad b_1<0<b_2,\quad y={\rm col}(y_1,y_2),\quad(1)$$ with a potential ma

Álvaro G. López, Rustam Ali, Laxmikanta Mandi, Prasanta Chatterjee

We consider a hydrodynamic model of a quantum dusty plasma. We prove mathematically that the resulting dust ion acoustic plasma waves present the property of being conservative on average. Furthermore, we test this property numerically, confirming its validity. Using standard techniques from the study of dynamical systems, as for example the Lyapunov charact

Ke Shi, Zhengyuan Liu, Nancy F. Chen

Document-level discourse parsing, in accordance with the Rhetorical Structure Theory (RST), remains notoriously challenging. Challenges include the deep structure of document-level discourse trees, the requirement of subtle semantic judgments, and the lack of large-scale training corpora. To address such challenges, we propose to exploit robust representatio

Xiang Zhang, Shamik Sarkar, Arupjyoti Bhuyan, Sneha Kumar Kasera

This paper studies the problem of distributed beam scheduling for 5G millimeter-Wave (mm-Wave) cellular networks where base stations (BSs) belonging to different operators share the same spectrum without centralized coordination among them. Our goal is to design efficient distributed scheduling algorithms to maximize the network utility, which is a function

Jian-Ling Li, Bo Peng, Cheng-Jin Jin, Hui Li

The Five-hundred-meter Aperture Spherical radio Telescope (FAST), the largest single dish radio telescope in the world, has implemented an innovative technology for its huge reflector, which changes the shape of the primary reflector from spherical to that of a paraboloid of 300 m aperture. Here we explore how the current FAST sensitivity can potentially be

Amit Berman, Sarit Buzaglo, Avner Dor, Yaron Shany

We consider the repair problem for Reed--Solomon (RS) codes, evaluated on an $\mathbb{F}_q$-linear subspace $U\subseteq\mathbb{F}_{q^m}$ of dimension $d$, where $q$ is a prime power, $m$ is a positive integer, and $\mathbb{F}_q$ is the Galois field of size $q$. For the case of $q\geq 3$, we show the existence of a linear repair scheme for the RS code of leng

Dániel Gerbner, Balázs Patkós, Zsolt Tuza, Máté Vizer

For a graph $F$, we say that another graph $G$ is $F$-saturated, if $G$ is $F$-free and adding any edge to $G$ would create a copy of $F$. We study for a given graph $F$ and integer $n$ whether there exists a regular $n$-vertex $F$-saturated graph, and if it does, what is the smallest number of edges of such a graph. We mainly focus on the case when $F$ is a

Gary Gindler

A formal theory of meaning (the process of knowledge accumulation) as multiplicative chaos is proposed. The epistemological process is understood as the process of subjective extraction of some knowledge from the incoming information. The concepts of nonsense are introduced as a meaning that has a minimum value equal to one and the level of intelligence as a

Ishani Mondal, Sukannya Purkayastha, Sudeshna Sarkar, Pawan Goyal

Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB). This task is of great importance in the medical domain. It can also be used for merging different medical and clinical ontologies. In this paper, we center around the problem of disease l

Pingchuan Ma, Shuai Wang

Text-to-SQL is a task to generate SQL queries from human utterances. However, due to the variation of natural language, two semantically equivalent utterances may appear differently in the lexical level. Likewise, user preferences (e.g., the choice of normal forms) can lead to dramatic changes in table structures when expressing conceptually identical schema

Zhen Lin, Lianying Miao, Guanglong Yu, Han Sheng

Let $A(G)$ and $D(G)$ be the adjacency matrix and the degree diagonal matrix of a graph $G$, respectively. Then $L(G)=D(G)-A(G)$ is called Laplacian matrix of the graph $G$. Let $G$ be a graph with $n$ vertices and $m$ edges. Then the $LI$-matrix of $G$ are defined as $LI(G)=L(G)-\frac{2m}{n}I_n$, where $I_n$ is the identity matrix. In this paper, we are int

Jie Yang, Yong Zeng, Shi Jin, Chao-Kai Wen

Achieving high-rate communication with accurate localization and wireless environment sensing has emerged as an important trend of beyond-fifth and sixth generation cellular systems. Extension of the antenna array to an extremely large scale is a potential technology for achieving such goals. However, the super massive operating antennas significantly increa

Minh Q. Tran, Ngoc Q. Ly

This study develops a robot mobility policy based on deep reinforcement learning. Since traditional methods of conventional robotic navigation depend on accurate map reproduction as well as require high-end sensors, learning-based methods are positive trends, especially deep reinforcement learning. The problem is modeled in the form of a Markov Decision Proc

Wei Yao, Shen Chen, Jiamin Cui, Yaolin Lou

Speaker verification aims to verify whether an input speech corresponds to the claimed speaker, and conventionally, this kind of system is deployed based on single-stream scenario, wherein the feature extractor operates in full frequency range. In this paper, we hypothesize that machine can learn enough knowledge to do classification task when listening to p

Ivan Pavić, Hrvoje Pandžić, Tomislav Capuder

Shift of the power system generation from the fossil to the variable renewable sources prompted the system operators to search for new sources of flexibility, that is, new reserve providers. With the introduction of electric vehicles, smart charging emerged as one of the relevant solutions. However, electric vehicle aggregators face the uncertainty of reserv

Deng Cai, Yizhe Zhang, Yichen Huang, Wai Lam

We propose the task of narrative incoherence detection as a new arena for inter-sentential semantic understanding: Given a multi-sentence narrative, decide whether there exist any semantic discrepancies in the narrative flow. Specifically, we focus on the missing sentence and discordant sentence detection. Despite its simple setup, this task is challenging a

Parada T. P. Hutauruk, Dong Woo Kang, Jongkuk Kim, Hiroshi Okada

We study a successful model to explain the muon anomalous magnetic moment originating from Yukawa-type interactions {in a supersymmetric theory}. Thanks to a modular $A_4$ flavor symmetry, any lepton flavor violations that spoil the model are forbidden. We also investigate a predictive radiative seesaw model including a dark matter (DM) candidate. At first,

Yogesh Kumar, Sukumar Srikant, Debasish Chatterjee, Masaaki Nagahara

This article treats optimal sparse control problems with multiple constraints defined at intermediate points of the time domain. For such problems with intermediate constraints, we first establish a new Pontryagin maximum principle that provides first order necessary conditions for optimality in such problems. Then we announce and employ a new numerical algo

Ishani Mondal

Traditional biomedical version of embeddings obtained from pre-trained language models have recently shown state-of-the-art results for relation extraction (RE) tasks in the medical domain. In this paper, we explore how to incorporate domain knowledge, available in the form of molecular structure of drugs, for predicting Drug-Drug Interaction from textual co

Isaac Godfried, Kriti Mahajan, Maggie Wang, Kevin Li

Flooding results in 8 billion dollars of damage annually in the US and causes the most deaths of any weather related event. Due to climate change scientists expect more heavy precipitation events in the future. However, no current datasets exist that contain both hourly precipitation and river flow data. We introduce a novel hourly river flow and precipitati

Xavier Porte, Anas Skalli, Nasibeh Haghighi, Stephan Reitzenstein

Neural networks are one of the disruptive computing concepts of our time. However, they fundamentally differ from classical, algorithmic computing in a number of fundamental aspects. These differences result in equally fundamental, severe and relevant challenges for neural network computing using current computing substrates. Neural networks urge for paralle

Na Li, Yun Zhang, C. -C. Jay Kuo

Machine learning techniques provide a chance to explore the coding performance potential of transform. In this work, we propose an explainable transform based intra video coding to improve the coding efficiency. Firstly, we model machine learning based transform design as an optimization problem of maximizing the energy compaction or decorrelation capability

Keisuke Uemura, Yoshito Otake, Masaki Takao, Mazen Soufi

Purpose: To apply a convolutional neural network (CNN) to develop a system that segments intensity calibration phantom regions in computed tomography (CT) images, and to test the system in a large cohort to evaluate its robustness. Methods: A total of 1040 cases (520 cases each from two institutions), in which an intensity calibration phantom (B-MAS200, Kyot

Sungwon Park, Sungwon Han, Sundong Kim, Danu Kim

Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current research proposes an innovative model RUC that is inspired by robust learning. RUC's novelty is at utilizing pseudo-labels of existing image clust

Weiqiang Zhu, Kailai Xu, Eric Darve, Biondo Biondi

Full-waveform inversion (FWI) is an accurate imaging approach for modeling velocity structure by minimizing the misfit between recorded and predicted seismic waveforms. However, the strong non-linearity of FWI resulting from fitting oscillatory waveforms can trap the optimization in local minima. We propose a neural-network-based full waveform inversion meth

Jiaqi Gu, Chenghao Feng, Zheng Zhao, Zhoufeng Ying

Optical neural networks (ONNs) have demonstrated record-breaking potential in high-performance neuromorphic computing due to their ultra-high execution speed and low energy consumption. However, current learning protocols fail to provide scalable and efficient solutions to photonic circuit optimization in practical applications. In this work, we propose a no

Li Zhang, Yan Ge, Haiping Lu

Graph Neural Networks (GNNs) are widely used in graph representation learning. However, most GNN methods are designed for either homogeneous or heterogeneous graphs. In this paper, we propose a new model, Hop-Hop Relation-aware Graph Neural Network (HHR-GNN), to unify representation learning for these two types of graphs. HHR-GNN learns a personalized recept

H. B. Lee, G. C. Kim, Young Jin Shon, Dongjin Kim

We have studied flux-pinning effects of MgB$_2$ superconductor by doping (Fe, Ti) particles of which radius is 163 nm on average. 5 wt.\% (Fe, Ti) doped MgB$_2$ among the specimens showed the best field dependence of magnetization and 25 wt.\% one did the worst at 5 K . The difference of field dependence of magnetization of the two increased as temperature i

Tejas Vaidhya, Ayush Kaushal

Supervised models trained to predict properties from representations have been achieving high accuracy on a variety of tasks. For instance, the BERT family seems to work exceptionally well on the downstream task from NER tagging to the range of other linguistic tasks. But the vocabulary used in the medical field contains a lot of different tokens used only i

Dilshat Salikhov, Giancarlo Succi, Alexander Tormasov

Agile methodologies are used for improving productivity and quality of development originally created for small teams. However , now they are expanding to larger organizations, for which "scaled up" approaches have been proposed. This study presents the preliminary outcomes from a survey on the effects of the Scaled Agile Framework (SAFe), which is considere

Fan Yang, Fei Zhou

Time reversal invariant (TRI) topological superfluids (TSFs) and topological superconductors (TSCs) are robust symmetry protected gapped topological states. In this article, we study the evolution of these topological states in the presence of time reversal symmetry breaking (TRB) fields and/or sufficiently large TRI fields. Physically, one of the realizatio

Ishani Mondal

Off-the-shelf biomedical embeddings obtained from the recently released various pre-trained language models (such as BERT, XLNET) have demonstrated state-of-the-art results (in terms of accuracy) for the various natural language understanding tasks (NLU) in the biomedical domain. Relation Classification (RC) falls into one of the most critical tasks. In this

Piotr Bizoń, Maciej Dunajski, Michał Kahl, Michał Kowalczyk

In an attempt to understand the soliton resolution conjecture, we consider the Sine-Gordon equation on a spherically symmetric wormhole spacetime. We show that within each topological sector (indexed by a positive integer degree $n$) there exists a unique linearly stable soliton, which we call the $n$-kink. We give numerical evidence that the $n$-kink is a g

Alessandro Achille, Aditya Golatkar, Avinash Ravichandran, Marzia Polito

Classifiers that are linear in their parameters, and trained by optimizing a convex loss function, have predictable behavior with respect to changes in the training data, initial conditions, and optimization. Such desirable properties are absent in deep neural networks (DNNs), typically trained by non-linear fine-tuning of a pre-trained model. Previous attem

Amanuel Tamirat Getachew

Quantum machine learning, though in its initial stage, has demonstrated its potential to speed up some of the costly machine learning calculations when compared to the existing classical approaches. Among the challenging subroutines, computing distance between with the large and high-dimensional data sets by the classical k-medians clustering algorithm is on

Shoma Ishida, Satoshi Ono

This paper proposes a black-box adversarial attack method to automatic speech recognition systems. Some studies have attempted to attack neural networks for speech recognition; however, these methods did not consider the robustness of generated adversarial examples against timing lag with a target speech. The proposed method in this paper adopts Evolutionary

Jiale Zhang, Danni Wei, Feng Zhang, Xi Chen

As machine learning becomes increasingly important in engineering and science, it is inevitable that machine learning techniques will be applied to the investigation of materials, and in particular the structural phase transitions common in ferroelectric materials. Here, we build and train an artificial neural network to accurately predict the energy change

Hung-Yu Yeh

We present a notion of $\Delta$-stability and stability filtration in arbitrary categories which is equivalent to the existence of Harder-Narasimhan (HN) sequences on objects. Indeed it is equivalent to the existence of a zero morphism, a partial order on objects, and a collection of some universal sequences. In additive categories embedded in an ambient tri

Kungang Zhang, Daniel W. Apley, Wei Chen

Microstructures are critical to the physical properties of materials. Stochastic microstructures are commonly observed in many kinds of materials and traditional descriptor-based image analysis of them can be challenging. In this paper, we introduce a powerful and versatile score-based framework for analyzing nonstationarity in stochastic materials microstru

Chao Yang, Su Feng, Dongsheng Li, Huawei Shen

Visual Question Answering (VQA) is a challenging multimodal task to answer questions about an image. Many works concentrate on how to reduce language bias which makes models answer questions ignoring visual content and language context. However, reducing language bias also weakens the ability of VQA models to learn context prior. To address this issue, we pr

Yu-Yang Li, Kai Li, Yuan Liu

V0842 Cep is a W UMa-type binary star that has been neglected since its discovery. We analysed the VR$_c$I$_c$ light curves, obtained by the 1 m telescope at the Weihai Observatory of Shandong University, using the Wilson-Devinney code. V0842 Cep was found to be a shallow contact binary system (f=8.7$\%$) with a mass ratio of 2.281. Because its orbital incli

Peter Bierhorst

Many three-party correlations, including some that are commonly described as genuinely tripartite nonlocal, can be simulated by a network of underlying subsystems that display only bipartite nonsignaling nonlocal behavior. Quantum mechanics predicts three-party correlations that admit no such simulation, suggesting there are versions of nonlocality in nature

Sarthak J. Shetty, Debasish Ghose

During floods, reaching survivors in the shortest possible time is a priority for rescue teams. Given their ability to explore difficult terrain in short spans of time, Unmanned Aerial Vehicles (UAVs) have become an increasingly valuable aid to search and rescue operations. Traditionally, UAVs utilize exhaustive lawnmower exploration patterns to locate stran

Po-Hsun Tseng, Hsi-Yu Schive, Tzihong Chiueh

We present a new special relativistic hydrodynamics (SRHD) code capable of handling coexisting ultra-relativistically hot and non-relativistically cold gases. We achieve this by designing a new algorithm for conversion between primitive and conserved variables in the SRHD solver, which incorporates a realistic ideal-gas equation of state covering both the re

Chou Kao, Yu-Yu Liu, Jack Xin

The Arnold-Beltrami-Childress (ABC) flow and the Kolmogorov flow are three dimensional periodic divergence free velocity fields that exhibit chaotic streamlines. We are interested in front speed enhancement in G-equation of turbulent combustion by large intensity ABC and Kolmogorov flows. We give a quantitative construction of the ballistic orbits of ABC and

Zhengmin Lai, You Peng, Shiyu Yang, Xuemin Lin

Graph plays a vital role in representing entities and their relationships in a variety of fields, such as e-commerce networks, social networks and biological networks. Given two vertices s and t, one of the fundamental problems in graph databases is to investigate the relationships between s and t. A well-studied problem in such area is k-hop constrained s-t

Hans G. Feichtinger, Anupam Gumber

The main result of this paper is a far reaching generalization of the completeness result given by V.~Katsnelson in a recent paper [35]. Instead of just using a collection of dilated Gaussians it is shown that the key steps of an earlier paper [27] by the authors, combined with the use of Tauberian conditions (i.e. the non-vanishing of the Fourier transform)

Qingqing Gu, Haihu Liu, Lei Wu

A deep understanding of two-phase displacement in porous media with permeability contrast is essential for the design and optimisation of enhanced oil recovery processes. In this paper, we investigate the forced imbibition behaviour in two dual-permeability geometries that are of equal permeability contrast. First, a mathematical model is developed for the i

Rayhan Ahmed, Heechang Lim

This paper describes a study of the generation of a plughole vortex and its consequences in a drainpipe during drainage of water from a stationary rectangular tank. The critical and minimum depths of water above the inlet of the drainpipe, where a surface dip starts to develop for drainpipes of various diameters, were examined parametrically. This study expl

Pritam Ranjan, M. Harshvardhan

Gaussian process (GP) based statistical surrogates are popular, inexpensive substitutes for emulating the outputs of expensive computer models that simulate real-world phenomena or complex systems. Here, we discuss the evolution of dynamic GP model - a computationally efficient statistical surrogate for a computer simulator with time series outputs. The main

N. Hizawa, K. Hagino, K. Yoshida

We discuss an extension of the generator coordinate method (GCM) by taking simultaneously a collective coordinate and its conjugate momentum as generator coordinates. To this end, we follow the idea of the dynamical GCM (DGCM) proposed by Goeke and Reinhard. We first show that the DGCM method can be regarded as an extension of the double projection method fo

M. Harshvardhan, Pritam Ranjan

Over the last two decades, the science has come a long way from relying on only physical experiments and observations to experimentation using computer simulators. This chapter focusses on the modelling and analysis of data arising from computer simulators. It turns out that traditional statistical metamodels are often not very useful for analyzing such data

Kengo Matsumoto

We will study several subgroups of continuous full groups of one-sided topological Markov shifts from the view points of cohomology groups of full group actions on the shift spaces. We also study continuous orbit equivalence and strongly continuous orbit equivalence in terms of these subgroups of the continuous full groups and the cohomology groups.

Masatoshi Suzuki

We present a method to construct a chain of reproducing kernel Hilbert spaces controlled by a first-order system of differential equations from a given unimodular function satisfying several conditions. One of the applications of that method is a conditional but richly general solution to the inverse problem of recovering the structure Hamiltonian from a giv

L. J. Sun, Q. Yang, X. Chen, Z. X. Chen

As the commercial use of 5G technologies has grown more prevalent, smart vehicles have become an efficient platform for delivering a wide array of services directly to customers. The vehicular crowdsourcing service (VCS), for example, can provide immediate and timely feedback to the user regarding real-time transportation information. However, different sour

V L Gulko, A A Mescheryakov

There are investigated amplitude-phase method of the moving object bearing when there are used orthogonal linear polarized radio signals illuminated simultaneously from two horizontally spaced points with known co-ordinates. The bearing is measured onboard the moving object with two canal UHF system utilizing the amplitude-phase processing of the signals rec

Na Zhu, Xufeng Zhang, Xu Han, Chang-Ling Zou

Single-mode high-index-contrast waveguides have been ubiquitously exploited in optical, microwave, and phononic structures for achieving enhanced wave-matter interactions. Although micro-scale optomechanical and electro-optical devices have been widely studied, optomagnonic devices remain a grand challenge at the microscale. Here, we introduce a planar optom

Peng-Cheng Qiu, De-Liang Yao

The chiral effective meson-baryon Lagrangian for the description of interactions between the doubly charmed baryons and Goldstone bosons is constructed up to the order of $q^{4}$. The numbers of linearly independent invariant monomials of $\mathcal{O}(q^2)$, $\mathcal{O}(q^3)$ and $\mathcal{O}(q^4)$ are 8, 32 and 218, in order. The obtained Lagrangian can be

Fangneng Zhan, Changgong Zhang, Yingchen Yu, Yuan Chang

Illumination estimation from a single image is critical in 3D rendering and it has been investigated extensively in the computer vision and computer graphic research community. On the other hand, existing works estimate illumination by either regressing light parameters or generating illumination maps that are often hard to optimize or tend to produce inaccu

Gadadhar Misra, Paramita Pramanick, Kalyan B. Sinha

For a commuting $d$- tuple of operators $\boldsymbol T$ defined on a complex separable Hilbert space $\mathcal H$, let $\big [ \!\!\big [ \boldsymbol T^*, \boldsymbol T \big ]\!\!\big ]$ be the $d\times d$ block operator $\big (\!\!\big (\big [ T_j^* , T_i\big ]\big )\!\!\big )$ of the commutators $[T^*_j , T_i] := T^*_j T_i - T_iT_j^*$. We define the determ

Huazhang Li, Yaotian Wang, Guofen Yan, Yinge Sun

The human brain is a directional network system of brain regions involving directional connectivity. Seizures are a directional network phenomenon as abnormal neuronal activities start from a seizure onset zone (SOZ) and propagate to otherwise healthy regions. To localize the SOZ of an epileptic patient, clinicians use iEEG to record the patient's intracrani

Yifei Yang, Shibing Xiang, Ruixiang Zhang

Autoencoder and its variants have been widely applicated in anomaly detection.The previous work memory-augmented deep autoencoder proposed memorizing normality to detect anomaly, however it neglects the feature discrepancy between different resolution scales, therefore we introduce multi-scale memories to record scale-specific features and multi-scale attent

Yuan-Pin Chang, Shan-Jung Wu, Min-Sian Lin, Che-Yu Chiang

The heterogeneous oxidation reaction of single aqueous ascorbic acid (AH$_2$) aerosol particles with gas-phase ozone was investigated in this study utilizing aerosol optical tweezers with Raman spectroscopy. The measured liquid-phase bimolecular rate coefficients of the AH$_2$ + O$_3$ reaction exhibit a significant pH dependence, and the corresponding values

Natalia D. Denesyuk, D. Thirumalai

Experiments and theories have shown that when steric interactions between crowding particles and proteins are dominant, which give rise to Asakura-Oosawa depletion forces, then the stabilities of the proteins increase compared to the infinite dilution case. We show using theoretical arguments that the crowder volume fraction ($\Phi_C$) dependent increase in

Takashi Ichikawa

We introduce a notion of generalized modular functors with Hilbert spaces of infinite dimension in general, and show that a generalized modular functor with data of conformal dimensions determines uniquely wave functions as its flat sections. Furthermore, we study an example of generalized modular functors derived from the Liouville conformal field theory. I

Md. Zubair, MD. Asif Iqbal, Avijeet Shil, Enamul Haque

COVID-19 hits the world like a storm by arising pandemic situations for most of the countries around the world. The whole world is trying to overcome this pandemic situation. A better health care quality may help a country to tackle the pandemic. Making clusters of countries with similar types of health care quality provides an insight into the quality of he

Annie Preston, Kwan-Liu Ma

Environmental sensors provide crucial data for understanding our surroundings. For example, air quality maps based on sensor readings help users make decisions to mitigate the effects of pollution on their health. Standard maps show readings from individual sensors or colored contours indicating estimated pollution levels. However, showing a single estimate

J. Socorro, S. Pérez-Payán, Rafael Hernández, Abraham Espinoza-García

In this work, first, we study a flat Friedmann-Robertson-Walker Universe with two scalar fields but only one potential term, which can be thought as a simple quintessence plus a K-essence model. Employing the Hamiltonian formalism we are able to obtain the classical and quantum solutions. The second model studied, is also a flat Friedmann-Robertson-Walker Un

Botong Wu, Sijie Ren, Jing Li, Xinwei Sun

Forecasting Parapapillary atrophy (PPA), i.e., a symptom related to most irreversible eye diseases, provides an alarm for implementing an intervention to slow down the disease progression at early stage. A key question for this forecast is: how to fully utilize the historical data (e.g., retinal image) up to the current stage for future disease prediction? I

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Based on 586 $\rm{pb^{-1}}$ of $e^+e^-$ annihilation data collected at a center-of-mass energy of $\sqrt{s}=4.6~\rm{GeV}$ with the BESIII detector at the BEPCII collider, the absolute branching fraction of $\Lambda_c^+ \to p K^0_{\mathrm{S}}\eta$ decays is measured for the first time to be $\mathcal{B}(\Lambda_c^+ \to p K^0_{\mathrm{S}}\eta) = (0.414 \pm 0.0

Jean Li, Jeremiah D. Deng, Divya Adhia, Dirk de Ridder

Effective analysis of EEG signals for potential clinical applications remains a challenging task. So far, the analysis and conditioning of EEG have largely remained sex-neutral. This paper employs a machine learning approach to explore the evidence of sex effects on EEG signals, and confirms the generality of these effects by achieving successful sex predict

Ignatius William Primaatmaja, Asaph Ho, Valerio Scarani

Retrieving classical information encoded in optical modes is at the heart of many quantum information processing tasks, especially in the field of quantum communication and sensing. Yet, despite its importance, the fundamental limits of optical mode discrimination have been studied only in few specific examples. Here we present a toolbox to find the optimal

Yiwen Wu, Mengfan Fu, Huazhi Lun, Lilin Yi

We propose a degenerated hierarchical look-up table (DH-LUT) scheme to compensate component nonlinearities. For probabilistically shaped 64-QAM signals, it achieves up to 2-dB SNR improvement, while the size of table is only 8.59% compared to the conventional LUT method.

Au-Chen Lee, D. Baillie, P. B. Blakie

We describe and benchmark a method to accurately calculate the quantum droplet states that can be produced from a dipolar Bose-Einstein condensate. Our approach also allows us to consider vortex states, where the atoms circulate around the long-axis of the filament shaped droplet. We apply our approach to determine a phase diagram showing where self-bound dr

Naveed Naimipour, Shahin Khobahi, Mojtaba Soltanalian

Exploring the idea of phase retrieval has been intriguing researchers for decades, due to its appearance in a wide range of applications. The task of a phase retrieval algorithm is typically to recover a signal from linear phaseless measurements. In this paper, we approach the problem by proposing a hybrid model-based data-driven deep architecture, referred

Jie Qin, Jiemin Fang, Qian Zhang, Wenyu Liu

Data augmentation is a powerful technique to increase the diversity of data, which can effectively improve the generalization ability of neural networks in image recognition tasks. Recent data mixing based augmentation strategies have achieved great success. Especially, CutMix uses a simple but effective method to improve the classifiers by randomly cropping

Wei Liu, Huazhen Lin, Jin Liu, Shurong Zheng

This paper proposes a general two directional simultaneous inference (TOSI) framework for high-dimensional models with a manifest variable or latent variable structure, for example, high-dimensional mean models, high-dimensional sparse regression models, and high-dimensional latent factors models. TOSI performs simultaneous inference on a set of parameters f

Yongkang Liu, Shi Feng, Daling Wang, Kaisong Song

We investigate response selection for multi-turn conversation in retrieval-based chatbots. Existing studies pay more attention to the matching between utterances and responses by calculating the matching score based on learned features, leading to insufficient model reasoning ability. In this paper, we propose a graph-reasoning network (GRN) to address the p

Ninh Pham

We propose a novel dimensionality reduction method for maximum inner product search (MIPS), named CEOs, based on the theory of concomitants of extreme order statistics. Utilizing the asymptotic behavior of these concomitants, we show that a few dimensions associated with the extreme values of the query signature are enough to estimate inner products. Since C

Yi Ding, Fuyuan Tan, Zhen Qin, Mingsheng Cao

The need for medical image encryption is increasingly pronounced, for example to safeguard the privacy of the patients' medical imaging data. In this paper, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical image

Qian Xiao, Dongkui Ma

Inspired to the work of Ma and Wu\cite{Ma} and Climenhaga\cite{Climenhaga}, we introduce the new nation of topological pressure of a semigroup of maps by using the Carath\'{e}odory-Pesin structure (C-P structure) with respect to arbitrary subset in this paper. Moreover, by Bowen's equation, we characterize the Hausdorff dimension of an arbitrary subset, wher

Caleb Bowyer

The equivalence of a systematic convolutional encoder as linear state-space control system is first realized and presented through an example. Then, utilizing this structure, a new optimal state-sequence estimator is derived, in the spirit of the Viterbi algorithm. Afterwords, a novel way to perform optimal decoding is achieved, named the Bowyer Decoder, whi

Jianfeng Lu, Lihan Wang

We study the computational complexity of zigzag sampling algorithm for strongly log-concave distributions. The zigzag process has the advantage of not requiring time discretization for implementation, and that each proposed bouncing event requires only one evaluation of partial derivative of the potential, while its convergence rate is dimension independent.

Junyu Lin, Junyu He, Xin Ye, Dajun Wang

We report measurements of the ac polarizabilities of ultracold ground-state $^{23}\rm{Na}^{87}\rm{Rb}$ molecules. While the polarizability of the ground rotational state $J = 0$ is isotropic, that of the first excited rotational state $J = 1$ is anisotropic and depends strongly on the light polarization angle. We obtain both polarizabilities precisely by com

Tiffany Frugé Jones, Joshua Lee Padgett, Qin Sheng

Norm estimates for strongly continuous semigroups have been successfully studied in numerous settings, but at the moment there are no corresponding studies in the case of solution operators of singular integral equations. Such equations have recently garnered a large amount of interest due to their potential to model numerous physically relevant phenomena wi

Jonathan M. Mousley, Manuel A. Santana, LeRoy B. Beasley, David E. Brown

In this article we investigate the existence of (2,3)-cordial labelings of oriented hypercubes. In this investigation, we determine that there exists a (2,3)-cordial oriented hypercube for any dimension divisible by 3. Next, we provide examples of (2,3)-cordial oriented hypercubes of dimension not divisible by 3 and state a conjecture on existence for dimens

Kohsuke Shibata

It is known that a two-dimensional $F$-rational ring has a rational singularity. However a two-dimensional ring with a rational singularity is not $F$-rational in general. In this paper, we investigate $F$-rationality of a two-dimensional graded ring with a rational singularity in terms of the multiplicity. Moreover, we determine when a two-dimensional grade

Changchang Xi, Jinbi Zhang

Given an $n\times n$ matrix $c$ over a unitary ring $R$, the centralizer of $c$ in the full $n\times n$ matrix ring $M_n(R)$ is called a principal centralizer matrix ring, denoted by $S_n(c,R)$. We investigate its structure and prove: $(1)$ If $c$ is an invertible matrix with a $c$-free point, or if $R$ has no zero-divisors and $c$ is a Jordan-similar matrix

Marco A. Rodríguez-García, Isaac Pérez Castillo, P. Barberis-Blostein

Estimating correctly the quantum phase of a physical system is a central problem in quantum parameter estimation theory due to its wide range of applications from quantum metrology to cryptography. Ideally, the optimal quantum estimator is given by the so-called quantum Cram\'er-Rao bound, so any measurement strategy aims to obtain estimations as close as po