November 2020 arXiv papers — page 18

Kai-Xin Gao, Xiao-Lei Liu, Zheng-Hai Huang, Min Wang

Using second-order optimization methods for training deep neural networks (DNNs) has attracted many researchers. A recently proposed method, Eigenvalue-corrected Kronecker Factorization (EKFAC) (George et al., 2018), proposes an interpretation of viewing natural gradient update as a diagonal method, and corrects the inaccurate re-scaling factor in the Kronec

Junjie Huang, Huawei Shen, Qi Cao, Li Cai

While online medical crowdfunding achieved tremendous success, quantitative study about whether and how medical crowdfunding helps people remains little explored. In this paper, we empirically study how online medical crowdfunding helps people using more than 27, 000 fundraising cases in Waterdrop Fundraising, one of the most popular online medical crowdfund

Frank Yu, Mathieu Salzmann, Pascal Fua, Helge Rhodin

Local processing is an essential feature of CNNs and other neural network architectures - it is one of the reasons why they work so well on images where relevant information is, to a large extent, local. However, perspective effects stemming from the projection in a conventional camera vary for different global positions in the image. We introduce Perspectiv

Han Cai, Ying Miao, Moshe Schwartz, Xiaohu Tang

We construct maximally recoverable codes (corresponding to partial MDS codes) which are based on linearized Reed-Solomon codes. The new codes have a smaller field size requirement compared with known constructions. For certain asymptotic regimes, the constructed codes have order-optimal alphabet size, asymptotically matching the known lower bound.

Jessica E. Thorne, Aaron. S. G. Robotham, Luke J. M. Davies, Sabine Bellstedt

We present catalogues of stellar masses, star formation rates, and ancillary stellar population parameters for galaxies spanning $0<z<9$ from the Deep Extragalactic VIsible Legacy Survey (DEVILS). DEVILS is a deep spectroscopic redshift survey with very high completeness, covering several premier deep fields including COSMOS (D10). Our stellar mass and star

Hugo J. Bello, Nora Palomar, Elisa Gallego, Lourdes Jiménez Navascués

While it remains a taboo topic, gender-based violence (GBV) undermines the health, dignity, security and autonomy of its victims. Many factors have been studied to generate or maintain this kind of violence, however, the influence of the media is still uncertain. Here, we use Machine Learning tools to extrapolate the effect of the news in GBV. By feeding neu

Amir Abbass Varshovi

The many-worlds interpretation (MWI) of quantum mechanics is studied from an unprecedented ontological perspective based on the reality of (semi-) deterministic parallel worlds in the interpretation. It is demonstrated that with thanks to the uncertainty principle there would be no consistent way to specify the correct ontology of the Universe, hence the MWI

Elise Beck, Julie Dugdale, Carole Adam, Christelle Gaïdatzis

How should computer science and social science collaborate to build a common model? How should they proceed to gather data that is really useful to the modelling? How can they design a survey that is tailored to the target model? This paper aims to answer those crucial questions in the framework of a multidisciplinary research project. This research addresse

Rustem N. Garifullin, Ismagil T. Habibullin

In the article differential-difference (semi-discrete) lattices of hyperbolic type are investigated from the integrability viewpoint. More precisely we concentrate on a method for constructing generalized symmetries. This kind integrable lattices admit two hierarchies of generalized symmetries corresponding to the discrete and continuous independent variable

Michael Kohler, Sophie Langer

Convolutional neural networks (CNNs) trained with cross-entropy loss have proven to be extremely successful in classifying images. In recent years, much work has been done to also improve the theoretical understanding of neural networks. Nevertheless, it seems limited when these networks are trained with cross-entropy loss, mainly because of the unboundednes

Akihiko Sekine, Kentaro Nomura

One of the intriguing properties characteristic to three-dimensional topological materials is the topological magnetoelectric phenomena arising from a topological term called the $\theta$ term. Such magnetoelectric phenomena are often termed the axion electrodynamics, since the $\theta$ term has exactly the same form as the action describing the coupling bet

Junhao Hua, Chunguang Li

Distributed inference/estimation in Bayesian framework in the context of sensor networks has recently received much attention due to its broad applicability. The variational Bayesian (VB) algorithm is a technique for approximating intractable integrals arising in Bayesian inference. In this paper, we propose two novel distributed VB algorithms for general Ba

Yang Xu, Haibin Kan, Guangyue Han

In $1991$, Wei proved a duality theorem that established an interesting connection between the generalized Hamming weights of a linear code and those of its dual code. Wei's duality theorem has since been extensively studied from different perspectives and extended to other settings. In this paper, we re-examine Wei's duality theorem and its various extensio

Shiwen He, Zhenyu An, Jianyue Zhu, Jian Zhang

Driven by the explosive growth of Internet of Things (IoT) devices with stringent requirements on latency and reliability, ultra-reliability and low latency communication (uRLLC) has become one of the three key communication scenarios for the fifth generation (5G) and beyond 5G communication systems. In this paper, we focus on the beamforming design problem

Bohdan Slavko, Kirill Glavatskiy, Mikhail Prokopenko

The spatial structure of modern cities exhibits highly diverse patterns and keeps evolving under numerous constraints. Two key dimensions have recently achieved prominence in characterizing this diversity: heterogeneity and spreading. However, modern settlements do not fill the entire heterogeneity--spreading space. Yet, the dynamic mechanisms leading to eme

Tommaso Di Noto, Guillaume Marie, Sébastien Tourbier, Yasser Alemán-Gómez

A commonly adopted approach to carry out detection tasks in medical imaging is to rely on an initial segmentation. However, this approach strongly depends on voxel-wise annotations which are repetitive and time-consuming to draw for medical experts. An interesting alternative to voxel-wise masks are so-called "weak" labels: these can either be coarse or over

Ibtissem Chouba, Lionel Amodeo, Farouk Yalaoui, Taha Arbaoui

The discrepancy between patient demand and the emergency departments (ED) capacity, that mainly depends on human resources and on beds available for patients, often lead to ED's overcrowding and to the increase in waiting time. In this paper, we focus on the optimization of the human (medical and paramedical staff) and material resources (beds) in the ED of

Jean-Baptiste Gouéré, Marie Théret

For a given dimension d $\ge$ 2 and a finite measure $\nu$ on (0, +$\infty$), we consider $\xi$ a Poisson point process on R d x (0, +$\infty$) with intensity measure dc $\otimes$ $\nu$ where dc denotes the Lebesgue measure on R d. We consider the Boolean model $\Sigma$ = $\cup$ (c,r)$\in$$\xi$ B(c, r) where B(c, r) denotes the open ball centered at c with r

Andreas Buchberger, Christian Häger, Henry D. Pfister, Laurent Schmalen

We consider near maximum-likelihood (ML) decoding of short linear block codes. In particular, we propose a novel decoding approach based on neural belief propagation (NBP) decoding recently introduced by Nachmani et al. in which we allow a different parity-check matrix in each iteration of the algorithm. The key idea is to consider NBP decoding over an overc

S. Yu, Z. Zhang, H. Xia, X. Dou

Spectroscopy is a well-established nonintrusive tool that has played an important role in identifying substances and quantifying their compositions, from quantum descriptions to chemical and biomedical diagnostics. Challenges exist in accurate measurements in dynamic environments, especially for understanding chemical reactions in arbitrary free-space. We de

Sarah Juricic, Jeanne Goffart, Simon Rouchier, Aurélie Foucquier

The thermal characterisation of a building envelope is usually best performed from on site measurements with controlled heating power set points. Occupant-friendly measurement conditions provide on the contrary less informative data. Notwithstanding occupancy, the boundary conditions alone contribute to a greater extent to the energy balance. Non intrusive c

J. Hem, C. Crauste-Thibierge, Clement Florence, D. Long

We report experimental evidence that a polymer stretched at constant strain rate $\dot\lambda$ presents complex memory effects after that $\dot\lambda$ is set to zero at a specific strain $\lambda_w$ for a duration $t_w$, ranging from $100$s to $ 2.2\times10^5$s. When the strain rate is resumed, both the stress and the dielectric constant relax to the unpert

Shengran Hu, Ran Cheng, Cheng He, Zhichao Lu

In the recent past, neural architecture search (NAS) has attracted increasing attention from both academia and industries. Despite the steady stream of impressive empirical results, most existing NAS algorithms are computationally prohibitive to execute due to the costly iterations of stochastic gradient descent (SGD) training. In this work, we propose an ef

Orlando Luongo, Marco Muccino

We alleviate the circularity problem, whereby gamma-ray bursts are not perfect distance indicators, by means of a new model-independent technique based on B\'ezier polynomials. To do so, we use the well consolidate \textit{Amati} and \textit{Combo} correlations. We consider improved calibrated catalogs of mock data from differential Hubble rate points. To ge

Kengo Matsumoto

We introduce a notion of coded equivalence in one-sided topological Markov shifts. The notion is inspired by coding theory. One-sided topological conjugacy implies coded equivalence. We will show that coded equivalence implies continuous orbit equivalence of one-sided topological Markov shifts.

Yafu Tian, Alexander Carballo, Ruifeng Li, Kazuya Takeda

Rich semantic information extraction plays a vital role on next-generation intelligent vehicles. Currently there is great amount of research focusing on fundamental applications such as 6D pose detection, road scene semantic segmentation, etc. And this provides us a great opportunity to think about how shall these data be organized and exploited. In this pap

Cheng Jiang, Yu-Long Liu, Mika A. Sillanpää

We study the energy-level evolution and ground-state cooling of mechanical resonators under a synthetic phononic gauge field. The tunable gauge phase is mediated by the phase difference between the $\mathcal{PT}$- and anti-$\mathcal{PT}$-symmetric mechanical couplings in a multimode optomechanical system. The transmission spectrum then exhibits the asymmetri

Lang Liu, Øyvind Christiansen, Wen-Hong Ruan, Zong-Kuan Guo

Extending the electromagnetic and gravitational radiations from binary black holes with electric and magnetic charges in circular orbits in Phys. Rev. D {\bf 102}, 103520 (2020), we calculate the total emission rates of energy and angular momentum due to gravitational and electromagnetic radiations from dyonic binary black holes in precessing elliptical orbi

Debasis Mazumdar

Representation of 2D frame less visual space as neural manifold and its modelling in the frame work of information geometry is presented. Origin of hyperbolic nature of the visual space is investigated using evidences from neuroscience. Based on the results we propose that the processing of spatial information, particularly estimation of distance, perceiving

Jeffrey Fong, Siwei Chen, Kaiqi Chen

Training neural networks with large batch is of fundamental significance to deep learning. Large batch training remarkably reduces the amount of training time but has difficulties in maintaining accuracy. Recent works have put forward optimization methods such as LARS and LAMB to tackle this issue through adaptive layer-wise optimization using trust ratios.

Margot Hanley, Apoorv Khandelwal, Hadar Averbuch-Elor, Noah Snavely

Important ethical concerns arising from computer vision datasets of people have been receiving significant attention, and a number of datasets have been withdrawn as a result. To meet the academic need for people-centric datasets, we propose an analytical framework to guide ethical evaluation of existing datasets and to serve future dataset creators in avoid

Zhigang Li, Yi Ou, Xubiao Peng, Jianyu Fu

Microelectromechanical system (MEMS) focal plane array (FPA) with optical readout offers exciting opportunities for real-time terahertz (THz) imaging. However, conventional FPA suffers from a low THz absorption ratio, which further decreases the performance of THz imaging. Here, we present a simple and scalable approach for the realization of THz focal plane

Alexander Zeifman

In this note, a general approach to the study of non-stationary Markov chains with catastrophes and the corresponding queuing models is considered, as well as to obtain estimates of the limiting regime itself. As an illustration, an example of a queuing model is studied.

Foisal B. T. Siddiki, I. Shesterikov, J. -M. Noterdaeme, K. Crombé

IShTAR (Ion cyclotron Sheath Test ARrangement) is a dedicated test facility to investigate the interaction of ICRF wave and plasma at the Max-Planck Institute for Plasma Physics in Garching, Germany. Plasma is provided by a plasma source (length= 0.1m, diameter = 0.4m) and it is responsible to create the necessary plasma environment in front of the ICRF ante

Chuan-Shen Hu, Yu-Min Chung

This paper concerns a theoretical approach that combines topological data analysis (TDA) and sheaf theory. Topological data analysis, a rising field in mathematics and computer science, concerns the shape of the data and has been proven effective in many scientific disciplines. Sheaf theory, a mathematics subject in algebraic geometry, provides a framework f

Alireza Mohammadidoost, Matin Hashemi

Many research works have been performed on implementation of Vitrerbi decoding algorithm on GPU instead of FPGA because this platform provides considerable flexibility in addition to great performance. Recently, the recently-introduced Tensor cores in modern GPU architectures provide incredible computing capability. This paper proposes a novel parallel imple

Ashvin Swaminathan

We give a parametrization of square roots of the ideal class of the inverse different of rings defined by binary forms in terms of the orbits of a coregular representation. This parametrization, which can be construed as a new integral model of a ``higher composition law'' discovered by Bhargava and generalized by Wood, was the missing ingredient needed to s

Belinda Stapelberg, Katherine M. Malan

Reinforcement learning has recently experienced increased prominence in the machine learning community. There are many approaches to solving reinforcement learning problems with new techniques developed constantly. When solving problems using reinforcement learning, there are various difficult challenges to overcome. To ensure progress in the field, benchmar

Young-Jun Choi, Kang-Hyurk Lee

In this paper, we study the existence of a complete holomorphic vector fields on a strongly pseudoconvex complex manifold admitting a negatively curved complete K\"ahler-Einstein metric and a discrete sequence of automorphisms. Using the method of potential scaling, we will show that there is a potential function of the K\"ahler-Einstein metric whose differe

Axel Ringh, Li Qiu

In this work we study the set of strictly accretive matrices, that is, the set of matrices with positive definite Hermitian part, and show that the set can be interpreted as a smooth manifold. Using the recently proposed symmetric polar decomposition for sectorial matrices, we show that this manifold is diffeomorphic to a direct product of the manifold of (H

Yixin Cao, Jun Kuang, Ming Gao, Aoying Zhou

Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts.However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, leading to the lackof sufficient annotations for the remaining types of relations. In this paper, we propose a general appr

Xiongtao Cui, Jungang Han

Chinese medical question-answer matching is more challenging than the open-domain question answer matching in English. Even though the deep learning method has performed well in improving the performance of question answer matching, these methods only focus on the semantic information inside sentences, while ignoring the semantic association between question

Sijie Mai, Songlong Xing, Jiaxuan He, Ying Zeng

In this paper, we study the task of multimodal sequence analysis which aims to draw inferences from visual, language and acoustic sequences. A majority of existing works generally focus on aligned fusion, mostly at word level, of the three modalities to accomplish this task, which is impractical in real-world scenarios. To overcome this issue, we seek to add

Koichi Kano, Satoshi Hagiwara, Takahiro Igarashi, Minoru Otani

We investigated the free corrosion potential at an interface between an Al electrode and an aqueous NaCl solution (NaCl(aq)) under acidic conditions via density functional theory combined with the effective screening medium and reference interaction site model (ESM-RISM). Firstly, the electrode potentials for the anodic and cathodic corrosion reactions were

Yekyung Kim

Recently, several studies have investigated active learning (AL) for natural language processing tasks to alleviate data dependency. However, for query selection, most of these studies mainly rely on uncertainty-based sampling, which generally does not exploit the structural information of the unlabeled data. This leads to a sampling bias in the batch active

Tetsuya Fujie, Yuya Higashikawa, Naoki Katoh, Junichi Teruyama

This paper addresses the minmax regret 1-sink location problem on dynamic flow path networks with parametric weights. We are given a dynamic flow network consisting of an undirected path with positive edge lengths, positive edge capacities, and nonnegative vertex weights. A path can be considered as a road, an edge length as the distance along the road and a

Yu-Hsiu Huang, Hiroyuki Hirashita, Yun-Hsin Hsu, Yen-Ting Lin

We model dust evolution in Milky Way-like galaxies by post-processing the IllustrisTNG cosmological hydrodynamical simulations in order to predict dust-to-gas ratios and grain size distributions. We treat grain-size-dependent dust growth and destruction processes using a 64-bin discrete grain size evolution model without spatially resolving each galaxy. Our

C. Foster, J. T. Mendel, C. D. P. Lagos, E. Wisnioski

We present an overview of the Middle Ages Galaxy Properties with Integral Field Spectroscopy (MAGPI) survey, a Large Program on ESO/VLT. MAGPI is designed to study the physical drivers of galaxy transformation at a lookback time of 3-4 Gyr, during which the dynamical, morphological, and chemical properties of galaxies are predicted to evolve significantly. T

Hai-Han Sun, Yee Hui Lee, Abdulkadir C. Yucel, Genevieve Ow

In this paper, a compact dual-polarized Vivaldi antenna is presented. Four Vivaldi elements are used as radiators, and are positioned obliquely and connected in a horn shape. By exciting two sets of elements, two orthogonally polarized radiations can be achieved. The dual-polarized antenna features a low operating frequency band with a wide bandwidth, high p

G. Saxena, P. K. Sharma, Prafulla Saxena

Latest experimental and evaluated $\alpha$-decay half-lives between 82$\leq$Z$\leq$118 have been used to modify two empirical formulas: (i) Horoi scaling law [J. Phys. G \textbf{30}, 945 (2004)], and Sobiczewski formula [Acta Phys. Pol. B \textbf{36}, 3095 (2005)] by adding asymmetry dependent terms ($I$ and $I^2$) and refitting of the coefficients. The resu

Yuanhao Shen, Jungang Han

To solve the problem of redundant information and overlapping relations of the entity and relation extraction model, we propose a joint extraction model. This model can directly extract multiple pairs of related entities without generating unrelated redundant information. We also propose a recurrent neural network named Encoder-LSTM that enhances the ability

Alan Rezazadeh

This research paper explores application of multivariable regression models using only reservoir temperatures for predicting oil and gas production in a Toe-Heal-Air-Injection (THAI) enhanced oil recovery process. This paper discusses effects of statistical interaction between thermocouples by using second degree quadratic polynomials, which showed significa

Armin Karamzade, Amir Najafi, Seyed Abolfazl Motahari

In this paper, we extend a class of celebrated regularization techniques originally proposed for feed-forward neural networks, namely Input Mixup (Zhang et al., 2017) and Manifold Mixup (Verma et al., 2018), to the realm of Recurrent Neural Networks (RNN). Our proposed methods are easy to implement and have a low computational complexity, while leverage the

Mingfu Xue, Yushu Zhang, Jian Wang, Weiqiang Liu

The training and creation of deep learning model is usually costly, thus it can be regarded as an intellectual property (IP) of the model creator. However, malicious users who obtain high-performance models may illegally copy, redistribute, or abuse the models without permission. To deal with such security threats, a few deep neural networks (DNN) IP protect

Chiara Ledesma, Oshean Lee Garonita, Lorenzo Jaime Flores, Isabelle Tingzon

Access to accurate, granular, and up-to-date poverty data is essential for humanitarian organizations to identify vulnerable areas for poverty alleviation efforts. Recent works have shown success in combining computer vision and satellite imagery for poverty estimation; however, the cost of acquiring high-resolution images coupled with black box models can b

Vasyl Tyberkevych, Andrei Slavin, Petro Artemchuk, Graham Rowlands

Vector Hamiltonian formalism (VHF) for the description of a weakly nonlinear magnetization dynamics has been developed. Transformation from the traditional Landau-Lifshitz equation, describing dynamics of a magnetization vector $\vec{m}(\vec{r}, t)$ on a sphere, to a vector Hamiltonian equation, describing dynamics of a \emph{spin excitation vector} $\vec{s}

Hongyang Zhang, Xiaojing Huang, J. Andrew Zhang

Wireless communications over fast fading channels are challenging, requiring either frequent channel tracking or complicated signaling schemes such as orthogonal time frequency space (OTFS) modulation. In this paper, we propose low-complexity frequency domain equalizations to combat fast fading, based on novel discrete delay-time and frequency-Doppler channe

Mingfu Xue, Shichang Sun, Zhiyu Wu, Can He

The popularity of various social platforms has prompted more people to share their routine photos online. However, undesirable privacy leakages occur due to such online photo sharing behaviors. Advanced deep neural network (DNN) based object detectors can easily steal users' personal information exposed in shared photos. In this paper, we propose a novel adv

Slavko Simic

In this article we give some refinements of Simpson's Rule in cases when it is not applicable in it's classical form i.e., when the target function is not four times differentiable on a given interval. Some sharp two-sided inequalities for an extended form of Simpson's Rule are also proven.

Nicolás Morales-Durán, Pawel Potasz, Allan H. MacDonald

Moir\'e superlattices formed in two-dimensional semiconductor heterobilayers provide a new realization of Hubbard model physics in which the number of electrons per effective atom can be tuned at will. We report on an exact diagonalization study of the electronic properties of half-filled narrow moir\'e bands in which correlation strengths are varied by chan

Stephan Eismann, Patricia Suriana, Bowen Jing, Raphael J. L. Townshend

Proteins are miniature machines whose function depends on their three-dimensional (3D) structure. Determining this structure computationally remains an unsolved grand challenge. A major bottleneck involves selecting the most accurate structural model among a large pool of candidates, a task addressed in model quality assessment. Here, we present a novel deep

Pablo Moscato, Mohammad Nazmul Haque, Kevin Huang, Julia Sloan

In the field of Artificial Intelligence (AI) and Machine Learning (ML), the approximation of unknown target functions $y=f(\mathbf{x})$ using limited instances $S={(\mathbf{x^{(i)}},y^{(i)})}$, where $\mathbf{x^{(i)}} \in D$ and $D$ represents the domain of interest, is a common objective. We refer to $S$ as the training set and aim to identify a low-complex

R K Ghosh, Vinay R, Arnab Bhattacharyya

A vehicle's fuel consumption depends on its type, the speed, the condition, and the gradients of the road on which it is moving. We developed a Routing Engine for finding an eco-route (one with low fuel consumption) between a source and a destination. Open Street Maps has data on road conditions. We used CGIAR-CSI road elevation data 16[4] to integrate the r

Chanyong Park

In expanding universes, the entanglement entropy must be time-dependent because the background geometry changes with time. For understanding time evolution of quantum correlations, we take into account two distinct holographic models, the dS boundary model and the braneworld model. In this work, we focus on two-dimensional expanding universes for analytic ca

Dongya Jia, Jun Hyoung Park, Harsimran Kaur, Kwang Hwa Jung

Cancer cells have the plasticity to adjust their metabolic phenotypes for survival and metastasis. During metastasis, a developmental program known as the epithelial-mesenchymal transition (EMT) plays a critical role. There is extensive cross-talk between metabolism and EMT, but how this leads to coordinated physiological changes is still uncertain. The elus

Yi Gu, Jie Li, Yuting Gao, Ruoxin Chen

Neural networks are susceptible to catastrophic forgetting. They fail to preserve previously acquired knowledge when adapting to new tasks. Inspired by human associative memory system, we propose a brain-like approach that imitates the associative learning process to achieve continual learning. We design a heuristics mechanism to potentiatively stimulate the

Abhijeet Sahu, Patrick Wlazlo, Zeyu Mao, Hao Huang

A power system is a complex cyber-physical system whose security is critical to its function. A major challenge is to model and analyze its communication pathways with respect to cyber threats. To achieve this, the design and evaluation of a cyber-physical power system (CPPS) testbed called Resilient Energy Systems Lab (RESLab) is presented that captures rea

Shao-Long Chen, Amit Dutta Banik, Ze-Kun Liu

We explore the phenomenology of the Georgi-Machacek model extended with two Higgs doublets and vector fermion doublets invariant under $SU(2)_L \times U(1)_Y\times \mathcal {Z}_4 \times \mathcal {Z}_2$. The $\mathcal {Z}_4$ symmetry is broken spontaneously while the imposed $\mathcal {Z}_2$ symmetry forbids triplet fields to generate any vacuum expectation v

Surbhi Goel, Adam Klivans, Pasin Manurangsi, Daniel Reichman

We prove several hardness results for training depth-2 neural networks with the ReLU activation function; these networks are simply weighted sums (that may include negative coefficients) of ReLUs. Our goal is to output a depth-2 neural network that minimizes the square loss with respect to a given training set. We prove that this problem is NP-hard already f

Farhad Moghimifar, Gholamreza Haffari, Mahsa Baktashmotlagh

Current approaches which are mainly based on the extraction of low-level relations among individual events are limited by the shortage of publicly available labelled data. Therefore, the resulting models perform poorly when applied to a distributionally different domain for which labelled data did not exist at the time of training. To overcome this limitatio

Haoyi Fan, Fengbin Zhang, Yue Gao

Self-supervised learning achieves superior performance in many domains by extracting useful representations from the unlabeled data. However, most of traditional self-supervised methods mainly focus on exploring the inter-sample structure while less efforts have been concentrated on the underlying intra-temporal structure, which is important for time series

Xuanlong Ma, Zhonghua Wang

Let $G$ be a finite group. The co-prime order graph of $G$ is the graph whose vertex set is $G$, and two distinct vertices $x,y$ are adjacent if gcd$(o(x),o(y))$ is either $1$ or a prime, where $o(x)$ and $o(y)$ are the orders of $x$ and $y$, respectively. In this paper, we characterize all finite groups whose co-prime order graphs are complete and classify

Behzad Azmi, Karl Kunisch, Sérgio S. Rodrigues

It is shown that an internal control based on a moving indicator function is able to stabilize the state of parabolic equations evolving in rectangular domains. For proving the stabilizability result, we start with a control obtained from an oblique projection feedback based on a finite number of static actuators, then we used the continuity of the state whe

Dounnu Sasaki

The space $\mathrm{GC} (\Sigma)$ of geodesic currents on a hyperbolic surface $\Sigma$ can be considered as a completion of the set of weighted closed geodesics on $\Sigma$ when $\Sigma$ is compact, since the set of rational geodesic currents on $\Sigma$, which correspond to weighted closed geodesics, is a dense subset of $\mathrm{GC}(\Sigma )$. We prove tha

Zhenqiang Ying, Maniratnam Mandal, Deepti Ghadiyaram, Alan Bovik

No-reference (NR) perceptual video quality assessment (VQA) is a complex, unsolved, and important problem to social and streaming media applications. Efficient and accurate video quality predictors are needed to monitor and guide the processing of billions of shared, often imperfect, user-generated content (UGC). Unfortunately, current NR models are limited

Liu Liu, Lin Li, Weinian Zhang

In this paper we investigate iterative roots of strictly monotone upper semi-continuous multifunctions having finitely many jumps. Known results are concerning roots of order 2 for multifunctions of exact one jump. For the general investigation, we introduce a concept `intensity' to formulate the growth of jumps under iteration and find a class of strictly m

Lei Qian, Rui Yao, Jinghai Sun, Jinlong Xu

FAST is the largest single-dish radio telescope in the world. The characteristics of FAST are presented and analyzed in the context of the parameter space to show how FAST science achievements are affected. We summarize the scientific achievements of FAST and discuss its future science based on the new parts of the parameter space that can be explored by FAS

R. D. Yamaletdinov, Y. V. Pershin

In this Chapter we provide a review of the main results obtained in the modeling of graphene kinks and antikinks, which are elementary topological excitations of buckled graphene membranes. We introduce the classification of kinks, as well as discuss kink-antikink scattering, and radiation-kink interaction. We also report some new findings including i) the e

Weiqiang Ma, Yibin Qian

The shell evolution has been studied extensively within the framework of interacting shell model, while the studies from the single particle viewpoint is relatively lacking or neglected. In particular, the isospin dependence of spin-orbit splitting has become increasingly important as $N/Z$ increases in neutron-rich nuclei. Following the initial independent-

Xiangchen Lu, Liang Chen, Nan Shen, Lei Wang

With the rapid development of China's BeiDou Navigation Satellite System(BDS), the application of real-time precise point positioning (RTPPP) based on BDS has become an active research area in the field of Global Navigation Satellite System (GNSS). BDS has provided the service of broadcasting RTPPP information. It indicates that BDS has become the second sat

Yilun Jin, Lixin Fan, Kam Woh Ng, Ce Ju

Deep neural networks (DNNs) are known to be prone to adversarial attacks, for which many remedies are proposed. While adversarial training (AT) is regarded as the most robust defense, it suffers from poor performance both on clean examples and under other types of attacks, e.g. attacks with larger perturbations. Meanwhile, regularizers that encourage uncerta

Xin Fu, Tseleung So, Jongbaek Song

Let $X$ be a $4$-dimensional toric orbifold. If $H^3(X)$ has a non-trivial odd primary torsion, then we show that $X$ is homotopy equivalent to the wedge of a Moore space and a CW-complex. As a corollary, given two 4-dimensional toric orbifolds having no 2-torsion in the cohomology, we prove that they have the same homotopy type if and only their integral co

Wei-Yang Liu, Jiunn-Wei Chen

Large momentum effective field theory (LaMET) enables the extraction of parton distribution functions (PDFs) directly on a Euclidean lattice through a factorization theorem that relates the computed quasi-PDFs to PDFs. We apply chiral perturbation theory (ChPT) to LaMET to further separate soft scales, such as light quark masses and lattice size, to obtain l

Kazuki Shigyou, Linhao Sun, Riku Yajima, Shohei Takigaura

Glass nanopipettes are widely used for various applications in nanosciences. In most of the applications, it is important to characterize their geometrical parameters, such as the aperture size and the inner cone angle at the tip region. For nanopipettes with sub-10 nm aperture and thin wall thickness, transmission electron microscopy (TEM) must be most inst

Zohreh Raziei, Mohsen Moghaddam

Recent advances in deep Reinforcement Learning (RL) have created unprecedented opportunities for intelligent automation, where a machine can autonomously learn an optimal policy for performing a given task. However, current deep RL algorithms predominantly specialize in a narrow range of tasks, are sample inefficient, and lack sufficient stability, which in

Nishant Subramani, Alexandre Matton, Malcolm Greaves, Adrian Lam

Documents are a core part of many businesses in many fields such as law, finance, and technology among others. Automatic understanding of documents such as invoices, contracts, and resumes is lucrative, opening up many new avenues of business. The fields of natural language processing and computer vision have seen tremendous progress through the development

Weixiang Jin, Michael Zucker, Arnd Pralle

Some anesthetics bind and potentiate gamma-aminobutyric-acid-type receptors, but no universal mechanism for general anesthesia is known. Furthermore, often encountered complications such as anesthesia induced amnesia are not understood. General anesthetics are hydrophobic molecules easily dissolving into lipid bilayers. Recently, it was shown that general an

Bojing Feng, Haonan Xu, Wenfang Xue, Bindang Xue

Credit rating is an analysis of the credit risks associated with a corporation, which reflects the level of the riskiness and reliability in investing, and plays a vital role in financial risk. There have emerged many studies that implement machine learning and deep learning techniques which are based on vector space to deal with corporate credit rating. Rec

Supeng Liu, Ryo Matsumoto, Rajveer Jha, Aichi Yamashita

The effects of pressure on the superconducting properties of a Bi-based layered superconductor La2O2Bi3Ag0.6Sn0.4S6, which possesses a four-layer-type conducting layer, have been studied through the electrical resistance and magnetic susceptibility measurements. The crystal structure under pressure was examined using synchrotron X-ray diffraction at SPring-8

Yuli B. Rudyak, Soumen Sarkar

In this paper, we introduce relative LS category of a map and study some of its properties. Then we introduce `higher topological complexity' of a map, a homotopy invariant. We give a cohomological lower bound and compare it with previously known `topological complexity' of a map. Moreover, we study the relation between Lusternik-Schnirelmann category and to

R. Sharma, M. Massi, M. Chernyakova, D. Malyshev

The high-mass X-ray binary LS I +61{\deg}303 exhibits variability in its radio and X-ray emissions, ranging from minute to hour time-scales. At such short time-scales, not much is known about the possible correlations between these two emissions from this source, which might offer hints to their origin. Here, we study the relationship between these emissions

Zhuo Huang, Ying Tai, Chengjie Wang, Jian Yang

Semi-Supervised Learning (SSL) with mismatched classes deals with the problem that the classes-of-interests in the limited labeled data is only a subset of the classes in massive unlabeled data. As a result, the classes only possessed by the unlabeled data may mislead the classifier training and thus hindering the realistic landing of various SSL methods. To

Lichao Wang, Lanxin Lei, Hongli Song, Weibao Wang

With the gradual maturity of 5G technology,autonomous driving technology has attracted moreand more attention among the research commu-nity. Autonomous driving vehicles rely on the co-operation of artificial intelligence, visual comput-ing, radar, monitoring equipment and GPS, whichenables computers to operate motor vehicles auto-matically and safely without

Chun-Hsing Lin, Siang-Ruei Wu, Hung-Yi Lee, Yun-Nung Chen

Score function-based natural language generation (NLG) approaches such as REINFORCE, in general, suffer from low sample efficiency and training instability problems. This is mainly due to the non-differentiable nature of the discrete space sampling and thus these methods have to treat the discriminator as a black box and ignore the gradient information. To i

Meng Shen, Hao Yu, Liehuang Zhu, Ke Xu

Deep neural networks (DNNs) have been increasingly used in face recognition (FR) systems. Recent studies, however, show that DNNs are vulnerable to adversarial examples, which can potentially mislead the FR systems using DNNs in the physical world. Existing attacks on these systems either generate perturbations working merely in the digital world, or rely on

Victoria Cantoral-Farfan, Seoyoung Kim

In this paper, we clarify and build connections between various conjectures largely motivated by the works of Jean-Pierre Serre and John Tate. We closely study the Tate conjecture for algebraic cycles as well as their motivic generalizations along with various links to Nagao's conjecture.

Yasunari Suzuki, Yoshiaki Kawase, Yuya Masumura, Yuria Hiraga

To explore the possibilities of a near-term intermediate-scale quantum algorithm and long-term fault-tolerant quantum computing, a fast and versatile quantum circuit simulator is needed. Here, we introduce Qulacs, a fast simulator for quantum circuits intended for research purpose. We show the main concepts of Qulacs, explain how to use its features via exam

San-Dong Guo, Wen-Qi Mu, Yu-Tong Zhu, Ru-Yue Han

Janus two-dimensional (2D) materials have attracted much attention due to possessing unique properties caused by their out-of-plane asymmetry, which have been achieved in many 2D families. In this work, the Janus monolayers are predicted in new 2D $\mathrm{MA_2Z_4}$ family by means of first-principles calculations, $\mathrm{MoSi_2N_4}$ and $\mathrm{WSi_2N_4}

Zhiyao Xie, Rongjian Liang, Xiaoqing Xu, Jiang Hu

Net length is a key proxy metric for optimizing timing and power across various stages of a standard digital design flow. However, the bulk of net length information is not available until cell placement, and hence it is a significant challenge to explicitly consider net length optimization in design stages prior to placement, such as logic synthesis. This w

Alex Zunger, Oleksandr I. Malyi

Doping mobile carriers into ordinary semiconductors such as Si, GaAs, and ZnO was the enabling step in the electronic and optoelectronic revolutions. The recent emergence of a class of "Quantum Materials", where uniquely quantum interactions between the components produce specific behaviors such as topological insulation, unusual magnetism, superconductivity