October 2020 arXiv papers — page 35

Esko Gardner, Harry J. Lehto, Kirsi Lehto, Nicolas Fray

Here, we report the detection of phosphorus and fluorine in solid particles collected from the inner coma of comet 67P/Churyumov-Gerasimenko measured with the COmetary Secondary Ion Mass Analyser (COSIMA) instrument on-board the Rosetta spacecraft, only a few kilometers away from the comet nucleus. We have detected phosphorus-containing minerals from the pre

Amir Pouran Ben Veyseh, Nasim Nouri, Franck Dernoncourt, Dejing Dou

Targeted opinion word extraction (TOWE) is a sub-task of aspect based sentiment analysis (ABSA) which aims to find the opinion words for a given aspect-term in a sentence. Despite their success for TOWE, the current deep learning models fail to exploit the syntactic information of the sentences that have been proved to be useful for TOWE in the prior researc

Omid Zabeti

The aim of this note is to consider different notions for the Banach-Saks property in locally solid vector lattices as an extension for the known concepts of the Banach-Saks property in Banach lattices. We investigate relations between them; in particular, we shall characterize spaces in which, these notions agree.

Nicole F. Bell, Matthew J. Dolan, Leon S. Friedrich, Michael J. Ramsey-Musolf

We examine the collider and dark matter phenomenology of the Standard Model extended by a hypercharge-zero SU(2) triplet scalar and gauge singlet scalar. In particular, we study the scenario where the singlet and triplet are both charged under a single $\mathbb{Z}_2$ symmetry. We find that such an extension is capable of generating the observed dark matter d

Nachamma Sockalingam, Junhua Liu

Teaching is about constantly innovating strategies, ways and means to engage diverse students in active and meaningful learning. In line with this, SUTD adopts various student-centric teaching and learning teaching methods and approaches. This means that our graduate/undergraduate instructors have to be ready to teach using these student student-centric teac

Janet Aisbett, Eric J. Drinkwater, Kenneth L. Quarrie, Stephen Woodcock

The magnitude-based decisions (MBD) procedure was developed within sports science as an alternative to null hypothesis significance tests. It aimed to emphasise effect sizes and discourage dichotomous decision-making. The use of MBD was banned by some sports science journals following claims it lacks a theoretical foundation and leads to high Type I error ra

Bruce W. Lee, Jason Lee

Developing a text readability assessment model specifically for texts in a foreign English Language Training (ELT) curriculum has never had much attention in the field of Natural Language Processing. Hence, most developed models show extremely low accuracy for L2 English texts, up to the point where not many even serve as a fair comparison. In this paper, we

Shi Pu, Yijiang He, Zheng Li, Mao Zheng

Facilitated by deep neural networks, video recommendation systems have made significant advances. Existing video recommendation systems directly exploit features from different modalities (e.g., user personal data, user behavior data, video titles, video tags, and visual contents) to input deep neural networks, while expecting the networks to online mine use

Marco Domenico Cirillo, David Abramian, Anders Eklund

Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain. Despite this fact, data augmentation has in our opinion not been fully explored for brain tumor segmentation. In this project we apply different types of data augmentation (flipping, rotation, scaling, brightness adjustment, elastic deformation)

Dr. Joseph A. Esquivel, James A. Esquivel

With the recent implementation of the K to 12 Program, academic institutions, specifically, Colleges and Universities in the Philippines have been faced with difficulties in determining projected freshmen enrollees vis-a-vis decision-making factors for efficient resource management. Enrollment targets directly impacts success factors of Higher Education Inst

Suryaprakash Nagoji Rao

We propose a Law of Nature? Viz., Pure Regularity Occurs at Na\"ive Levels and Regularity has Affinity with Evenness. In a series of three papers, it was established that regular Euler graphs with only one type of (pure) cycles are nonexistent; Regular Euler graphs with only two types of cycles are possible in one of the six cases, viz., regular bipartite Eu

Chelsea L. Miller, Peter J. Smith, Pawel A. Dmochowski

We analyse the behaviour of a massive multi-user MIMO (MU-MIMO) system comprising a base station (BS) equipped with one of five different antenna topologies for which the spatial aperture is either unconstrained, or space-constrained. We derive the normalized mean interference (NMI) with a ray-based channel model, as a metric for topology comparison in each

Dogyun Ko, A. V. Morozov, V. M. Kovalev, I. G. Savenko

We analyze the valley selection rules for optical transitions from impurity states to the conduction band in two-dimensional Dirac materials, taking a monolayer of MoS2 as an example. We employ the analytical model of a shallow impurity potential which localizes electrons described by a spinor wave function, and, first, find the system eigenstates taking int

Minjia Zhang, Yuxiong He

Recently, Transformer-based language models have demonstrated remarkable performance across many NLP domains. However, the unsupervised pre-training step of these models suffers from unbearable overall computational expenses. Current methods for accelerating the pre-training either rely on massive parallelism with advanced hardware or are not applicable to l

Koichi Hattori, Yoshimasa Hidaka, Naoki Yamamoto, Di-Lun Yang

We derive the Wigner functions of polarized photons in the Coulomb gauge with the $\hbar$ expansion applied to quantum field theory, and identify side-jump effects for massless photons. We also discuss the photonic chiral vortical effect for the Chern-Simons current and zilch vortical effect for the zilch current in local thermal equilibrium as a consistency

Ramu Kumar Yadav, Rajeev Kapri

Using Monte Carlo simulations, we study the hysteresis in unzipping of a double stranded block copolymer DNA with $-A_n B_n-$ repeat units. Here $A$ and $B$ represent two different types of base pairs having two- and three-bonds, respectively, and $2n$ represents the number of such base pairs in a unit. The end of the DNA are subjected to a time dependent pe

Keisuke Kinoshita, Marc Delcroix, Naohiro Tawara

Recent diarization technologies can be categorized into two approaches, i.e., clustering and end-to-end neural approaches, which have different pros and cons. The clustering-based approaches assign speaker labels to speech regions by clustering speaker embeddings such as x-vectors. While it can be seen as a current state-of-the-art approach that works for va

Philipp Benz, Chaoning Zhang, Adil Karjauv, In So Kweon

Convolutional neural networks (CNNs) have made significant advancement, however, they are widely known to be vulnerable to adversarial attacks. Adversarial training is the most widely used technique for improving adversarial robustness to strong white-box attacks. Prior works have been evaluating and improving the model average robustness without class-wise

Tian Tong, Cong Ma, Yuejie Chi

Many problems in data science can be treated as estimating a low-rank matrix from highly incomplete, sometimes even corrupted, observations. One popular approach is to resort to matrix factorization, where the low-rank matrix factors are optimized via first-order methods over a smooth loss function, such as the residual sum of squares. While tremendous progr

Sejun Park, Jaeho Lee, Chulhee Yun, Jinwoo Shin

It is known that $O(N)$ parameters are sufficient for neural networks to memorize arbitrary $N$ input-label pairs. By exploiting depth, we show that $O(N^{2/3})$ parameters suffice to memorize $N$ pairs, under a mild condition on the separation of input points. In particular, deeper networks (even with width $3$) are shown to memorize more pairs than shallow

Raphaël Lachièze-Rey, Giovanni Peccati, Xiaochuan Yang

We establish inequalities for assessing the distance between the distribution of a (possibly multidimensional) functional of a Poisson random measure and that of a Gaussian element. Our bounds only involve add-one cost operators at the order one - that we evaluate and compare at two different scales - and are specifically tailored for studying the Gaussian f

Cole Comfort, Antonin Delpeuch, Jules Hedges

Bimonoidal categories (also known as rig categories) are categories with two monoidal structures, one of which distributes over the other. We formally define sheet diagrams, a graphical calculus for bimonoidal categories that was informally introduced by Staton. Sheet diagrams are string diagrams drawn on a branching surface, which is itself an extruded stri

Joseph Maher, Hidetoshi Masai, Saul Schleimer

We consider three kinds of quotients of the curve complex which are obtained by coning off uniformly quasi-convex subspaces: symmetric curve sets, non-maximal train track sets, and compression body disc sets. We show that the actions of the mapping class group on those quotients are strongly WPD, which implies that the actions are non-elementary and those qu

Xiaojun Chen, Shu Yang, Li Shen, Xuanrong Pang

Training generative adversarial networks (GAN) in a distributed fashion is a promising technology since it is contributed to training GAN on a massive of data efficiently in real-world applications. However, GAN is known to be difficult to train by SGD-type methods (may fail to converge) and the distributed SGD-type methods may also suffer from massive amoun

Ning Chen, Tong Li, Wei Su, Yongcheng Wu

In this work we study the light Higgs scenario in the framework of two Higgs doublet model (2HDM). In this case the heavier CP-even Higgs boson $H$ in 2HDM is the SM-like Higgs with 125 GeV mass and a CP-even Higgs boson $h$ lighter than 125 GeV exhibits in the spectrum. We find that this scenario exists in the alignment limit of $\sin(\beta-\alpha)\simeq 0$

Haibo Su, Peng Wang, Lingqiao Liu, Hui Li

Fashion products typically feature in compositions of a variety of styles at different clothing parts. In order to distinguish images of different fashion products, we need to extract both appearance (i.e., "how to describe") and localization (i.e.,"where to look") information, and their interactions. To this end, we propose a biologically inspired framework

Ciro Ivan Garcia Lopez

The study of computability has its origin in Hilbert's conference of 1900, where an adjacent question, to the ones he asked, is to give a precise description of the notion of algorithm. In the search for a good definition arose three independent theories: Turing and the Turing machines, G\"odel and the recursive functions, Church and the Lambda Calculus. Lat

Xudong Pan, Mi Zhang, Yifan Yan, Jiaming Zhu

Among existing privacy attacks on the gradient of neural networks, \emph{data reconstruction attack}, which reverse engineers the training batch from the gradient, poses a severe threat on the private training data. Despite its empirical success on large architectures and small training batches, unstable reconstruction accuracy is also observed when a smalle

Guibin Chen, Bosheng Wang, Xiaoliang Wang, Huanjun Deng

Lidar odometry (LO) is a key technology in numerous reliable and accurate localization and mapping systems of autonomous driving. The state-of-the-art LO methods generally leverage geometric information to perform point cloud registration. Furthermore, obtaining point cloud semantic information which can describe the environment more abundantly will help for

Hana Gil, Young-Min Kim, Panagiota Papakonstantinou, Chang Ho Hyun

The KIDS framework for the nuclear equation of state (EoS) and energy density functional (EDF) offers the possibility to explore symmetry-energy (SE) parameters such as J (value at saturation density), L (slope), Ksym (curvature) and so on independently of each other and of assumptions about the effective mass. Here we examine the performance of EoSs with di

Katsuhiro Morita, Shigetoshi Sota, Takami Tohyama

Highly frustrated spin systems such as the kagome lattice (KL) are a treasure trove of new quantum states with large entanglements. We thus study the spin-$\frac{1}{2}$ Heisenberg model on a kagome-strip chain (KSC), which is one-dimensional KL, using the density-matrix renormalization group (DMRG) method. Calculating central charge and entanglement spectrum

Zhaopeng Xing, Xiaojun Yuan, Lisa Vizer

Older adults' need for quality health information has never been more critical as during the COVID-19 pandemic. Yet, they are susceptible to the wide-spread misinformation disseminated through search engines and social media. To build a search-related behavioral profile of older adults, this article surveys the empirical research on age-related differences i

Taichi Nakamura, Kai Fukami, Kazuto Hasegawa, Yusuke Nabae

We investigate the applicability of machine learning based reduced order model (ML-ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel flow at the friction Reynolds number of $Re_\tau=110$ in a minimum domain which can maintain coherent structures of turbulence. Training data set are prepared by direct numerical simulation

Xiong Cai, Dongyang Dai, Zhiyong Wu, Xiang Li

Neural text-to-speech (TTS) approaches generally require a huge number of high quality speech data, which makes it difficult to obtain such a dataset with extra emotion labels. In this paper, we propose a novel approach for emotional TTS synthesis on a TTS dataset without emotion labels. Specifically, our proposed method consists of a cross-domain speech emo

Sagar Paul, Ganesh Kotagiri, Rini Ganguly, Hervè Courtois

Constriction-based Josephson weak-links display a thermal bi-stability between two states exhibiting zero and finite voltages. This manifests in experiments either as hysteresis in weak-links current voltage characteristics or as random telegraphic signal in voltage. In the latter case, a noise-driven amplification of a sinusoidal excitation of the device is

Zhaobin Li, Hongping Wang, Xinlei Zhang, Ting Wu

The dispersion of viral droplets plays a key role in the transmission of COVID-19. In this work, we analyze the dispersion of cough-generated droplets in the wake of a walking person for different space sizes. The air flow is simulated by solving the Reynolds-Averaged Navier-Stokes equations, and the droplets are modelled as passive Lagrangian particles. Sim

Milliam Maxime Zekeng Ndadji, Maurice Tchoupé Tchendji, Clémentin Tayou Djamegni, Didier Parigot

In Business Process Management (BPM), process modelling has been solved in various ways. However, there are no commonly accepted modelling tools (languages). Some of them are criticized for their inability to capture both the lifecycle, informational and organizational models of processes. For some others, process modelling is generally done using a single g

Sayed Amir Hoseini, Ayub Bokani, Jahan Hassan, Shavbo Salehi

Next-generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes. Despite having advantages of using UAV-BSs, their dependence on the on-board, limited-capacity battery hinders their service continuity. Shorter trajectories can save flying energy, however, UAV-BSs must als

Pavel Galashin

Given a finite rhombus tiling of a polygonal region in the plane, the associated critical $Z$-invariant Ising model is invariant under star-triangle transformations. We give a simple matrix formula describing spin correlations between boundary vertices in terms of the shape of the region. When the region is a regular polygon, our formula becomes an explicit

Keiji Tagami

In this paper, we focus on the Hopf invariant and give an alternative proof for the unboundedness of stabilization heights of fiber surfaces, which was firstly proved by Baader and Misev.

S. Shailja, Jiaxiang Jiang, B. S. Manjunath

We propose a novel weakly supervised method to improve the boundary of the 3D segmented nuclei utilizing an over-segmented image. This is motivated by the observation that current state-of-the-art deep learning methods do not result in accurate boundaries when the training data is weakly annotated. Towards this, a 3D U-Net is trained to get the centroid of t

Emmanuel Agullo, Mirco Altenbernd, Hartwig Anzt, Leonardo Bautista-Gomez

This work is based on the seminar titled ``Resiliency in Numerical Algorithm Design for Extreme Scale Simulations'' held March 1-6, 2020 at Schloss Dagstuhl, that was attended by all the authors. Naive versions of conventional resilience techniques will not scale to the exascale regime: with a main memory footprint of tens of Petabytes, synchronously writing

Manish Mishra, Basudev Pattanayak

Let $G$ be a connected reductive group defined over a non-archimedean local field $F$. Let $B$ be a minimal $F$-parabolic subgroup with Levi factor $T$ and unipotent radical $U$. Let $\psi$ be a non-degenerate character of $U(F)$ and $\lambda$ a character of $T(F)$. Let $(K,\rho)$ be a Bushnell-Kutzko type associated to the Bernstein block of $G(F)$ determin

Naftali Cohen, Simran Lamba, Prashant Reddy

Companies survey their customers to measure their satisfaction levels with the company and its services. The received responses are crucial as they allow companies to assess their respective performances and find ways to make needed improvements. This study focuses on the non-systematic bias that arises when customers assign numerical values in ordinal surve

Bin Su, Shaoguang Mao, Frank Soong, Yan Xia

Sentence level pronunciation assessment is important for Computer Assisted Language Learning (CALL). Traditional speech pronunciation assessment, based on the Goodness of Pronunciation (GOP) algorithm, has some weakness in assessing a speech utterance: 1) Phoneme GOP scores cannot be easily translated into a sentence score with a simple average for effective

Songyan Zhang, Zhicheng Wang, Qiang Wang, Jinshuo Zhang

Existing state-of-the-art disparity estimation works mostly leverage the 4D concatenation volume and construct a very deep 3D convolution neural network (CNN) for disparity regression, which is inefficient due to the high memory consumption and slow inference speed. In this paper, we propose a network named EDNet for efficient disparity estimation. Firstly,

Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang

Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness In this work, we improve robustness-aware self-supervised pre-training by learning representations that are consistent under both data augmentations and adversarial perturbations. Our approach leverages a recent contrast

Hojjat Salehinejad, Edward Ho, Hui-Ming Lin, Priscila Crivellaro

Fractures of the cervical spine are a medical emergency and may lead to permanent paralysis and even death. Accurate diagnosis in patients with suspected fractures by computed tomography (CT) is critical to patient management. In this paper, we propose a deep convolutional neural network (DCNN) with a bidirectional long-short term memory (BLSTM) layer for th

Satoshi Takabe, Tadashi Wadayama

Deep unfolding is a promising deep-learning technique, whose network architecture is based on expanding the recursive structure of existing iterative algorithms. Although convergence acceleration is a remarkable advantage of deep unfolding, its theoretical aspects have not been revealed yet. The first half of this study details the theoretical analysis of th

Xinwei Guo, Minmin Yuan, Chengshi Zheng, Xiaodong Li

This paper derives the analytical solution of a novel distributed node-specific block-diagonal linearly constrained minimum variance beamformer from the centralized linearly constrained minimum variance (LCMV) beamformer when considering that the noise covariance matrix is block-diagonal. To further reduce the computational complexity of the proposed beamfor

Wanli Ni, Yuanwei Liu, Zhaohui Yang, Hui Tian

This paper investigates the problem of model aggregation in federated learning systems aided by multiple reconfigurable intelligent surfaces (RISs). The effective integration of computation and communication is achieved by over-the-air computation (AirComp). Since all local parameters are transmitted over shared wireless channels, the undesirable propagation

Tianchen Xu, Kun Chen, Gen Li

We compare two deletion-based methods for dealing with the problem of missing observations in linear regression analysis. One is the complete-case analysis (CC, or listwise deletion) that discards all incomplete observations and only uses common samples for ordinary least-squares estimation. The other is the available-case analysis (AC, or pairwise deletion)

C. S. Kubrusly, P. C. M. Vieira

The purpose of this paper is to characterize weak supercyclicity for Hilbert-space contractions, which is shown to be equivalent to characterizing weak supercyclicity for unitary operators$.$ This is naturally motivated by an open question that asks whether every weakly supercyclic power bounded operator is weakly stable (which in turn is naturally motivated

Keng-Yu Yeh, Yan-Rai Chen, Tung-Sheng Lo, Phillip M. Wu

We have carried out a detailed study to investigate the existence of an insulating parent phase for FeSe superconductor. The insulating Fe4Se5 with specific Fe-vacancy order shows a 3D-Mott variable range hopping behavior with a Verwey-like electronic correlation at around 45 K. The application of the RTA process at 450 celcius degree results in the destruct

Ali Lafzi, Amir Hossein Raffiee, Sadegh Dabiri

Dynamics of a deformable capsule in an oscillatory flow of a Newtonian fluid in a microchannel has been studied numerically. The effects of oscillation frequency, capsule deformability, and channel flow rate have been explored by simulating the capsule within a microchannel. In addition, the simulation captures the effect of the type of imposed pressure osci

Keita Kishigi, Yasumasa Hasegawa

We study the de Haas-van Alphen (dHvA) oscillations in the two-dimensional compensated metal with overtilted Dirac cones near the Lifshitz transition. We employ the tight-binding model of $\alpha$-(BEDT-TTF)$_2$I$_3$, in which the massless Dirac fermions are realized. When a uniaxial pressure $(P)$ along the $y$-axis is applied above $P\simeq 0.2$ kbar in $\

Michael Fuchs, Guan-Ru Yu, Louxin Zhang

We show a first-order asymptotics result for the number of galled networks with $n$ leaves. This is the first class of phylogenetic networks of {\it large} size for which an asymptotic counting result of such strength can be obtained. In addition, we also find the limiting distribution of the number of reticulation nodes of a galled networks with $n$ leaves

Isaac D. Gerg, Vishal Monga

Deep learning has been recently shown to improve performance in the domain of synthetic aperture sonar (SAS) image classification. Given the constant resolution with range of a SAS, it is no surprise that deep learning techniques perform so well. Despite deep learning's recent success, there are still compelling open challenges in reducing the high false ala

Vicenc Rubies-Royo, Eric Mazumdar, Roy Dong, Claire Tomlin

In this work we present a multi-armed bandit framework for online expert selection in Markov decision processes and demonstrate its use in high-dimensional settings. Our method takes a set of candidate expert policies and switches between them to rapidly identify the best performing expert using a variant of the classical upper confidence bound algorithm, th

Benjamin Briggs, Srikanth B. Iyengar

This work concerns maps $\varphi \colon R\to S$ of commutative noetherian rings, locally of finite flat dimension. It is proved that the Andr\'e-Quillen homology functors are rigid, namely, if $\mathrm{D}_n(S/R;-)=0$ for some $n\ge 2$, then $\mathrm{D}_n(S/R;-)=0$ for all $n\ge 2$ and $\varphi$ is locally complete intersection. This extends Avramov's theorem

Ziwen Xu, Beiji Zou, Qing Liu

Retinal image quality assessment is an essential task in the diagnosis of retinal diseases. Recently, there are emerging deep models to grade quality of retinal images. Current state-of-the-arts either directly transfer classification networks originally designed for natural images to quality classification of retinal images or introduce extra image quality

Sheyda Shams, Masoud Movahhedi

The dispersive meshless method with scalar basis function has been successfully applied for analysis of frequency dependent media. However, as scalar based meshless methods are not always divergence-free in the absence of source,inaccurate or even wrong solutions may be found in the results. To overcome this problem, in this paper, a new dispersive formulati

Chao-Yang Kao, Huang-Chih Kuo, Jian-Wen Chen, Chiung-Liang Lin

Many edge devices employ Recurrent Neural Networks (RNN) to enhance their product intelligence. However, the increasing computation complexity poses challenges for performance, energy efficiency and product development time. In this paper, we present an RNN deep learning accelerator, called RNNAccel, which supports Long Short-Term Memory (LSTM) network, Gate

Mikhail N. Shneider, Mikhail Pekker

In this paper, we propose a model for the initial stage of the development of the Universe, analogous to cavitation in a liquid in a negative pressure field. It is assumed that at the stage of inflation, multiple breaks of the metric occur with the formation of areas of physical vacuum in which the generation of matter occurs. The proposed model explains the

Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Pin-Yu Chen

We propose a novel decentralized feature extraction approach in federated learning to address privacy-preservation issues for speech recognition. It is built upon a quantum convolutional neural network (QCNN) composed of a quantum circuit encoder for feature extraction, and a recurrent neural network (RNN) based end-to-end acoustic model (AM). To enhance mod

Jun Zhuang, Dali Wang

Microscopic images from multiple modalities can produce plentiful experimental information. In practice, biological or physical constraints under a given observation period may prevent researchers from acquiring enough microscopic scanning. Recent studies demonstrate that image synthesis is one of the popular approaches to release such constraints. Nonethele

Aleksander Simonič

Assuming the Riemann Hypothesis, we provide explicit upper bounds for moduli of $S(t)$, $S_1(t)$, and $\zeta\left(1/2+\mathrm{i}t\right)$ while comparing them with recently proven unconditional ones. As a corollary we obtain a conditional explicit bound on gaps between consecutive zeros of the Riemann zeta-function.

Alexandru B. Georgescu, Peiwen Ren, Aubrey R. Toland, Shengtong Zhang

Metal-insulator transition (MIT) compounds are materials that may exhibit insulating or metallic behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics. There is a dearth of thermally-driven MIT materials, however, which makes delineating these compounds from

Zhihan Wang

We study min-max theory for area functional among hypersurfaces constrained in a smooth manifold with boundary. A Schoen-Simon-type regularity result is proved for integral varifolds which satisfy a variational inequality and restrict to a stable minimal hypersurface in the interior. Based on this, we show that for any admissible family of sweepouts $\Pi$ in

Xiaoyun Fu, Madhavan Rajagopal Padmanabhan, Raj Gaurav Kumar, Samik Basu

Influence diffusion has been central to the study of propagation of information in social networks, where influence is typically modeled as a binary property of entities: influenced or not influenced. We introduce the notion of attitude, which, as described in social psychology, is the degree by which an entity is influenced by the information. We present an

Younggyo Seo, Kimin Lee, Ignasi Clavera, Thanard Kurutach

Model-based reinforcement learning (RL) has shown great potential in various control tasks in terms of both sample-efficiency and final performance. However, learning a generalizable dynamics model robust to changes in dynamics remains a challenge since the target transition dynamics follow a multi-modal distribution. In this paper, we present a new model-ba

Zhe Zhang, Chunyu Wang, Weichao Qiu, Wenhu Qin

Occlusion is probably the biggest challenge for human pose estimation in the wild. Typical solutions often rely on intrusive sensors such as IMUs to detect occluded joints. To make the task truly unconstrained, we present AdaFuse, an adaptive multiview fusion method, which can enhance the features in occluded views by leveraging those in visible views. The c

Ang Yang

This thesis focuses on Bayesian optimization with the improvements coming from two aspects:(i) the use of derivative information to accelerate the optimization convergence; and (ii) the consideration of scalable GPs for handling massive data.

Philip Blanco, Carl Mungan

You have a rocket in a high circular orbit around a massive central body (a planet, or the Sun) and wish to escape with the fastest possible speed at infinity for a given amount of fuel.

Lin Jiang, Jinfu Zhu, Tao Xue, Jingjun Wen

Current Sensitive Preamplifiers (CSPs) are widely used in front-end electronics in data acquire system (DAQ), due to their ability to amplify signals directly. The optimization of energy resolution requires CSPs with suitable parameters such as gain, bandwidth, and low noise, etc. With the rapid improvement of CSP's bandwidth and trans-impedance gain, it is

Neil K. Chada, Yuming Chen, Daniel Sanz-Alonso

This paper provides a unified perspective of iterative ensemble Kalman methods, a family of derivative-free algorithms for parameter reconstruction and other related tasks. We identify, compare and develop three subfamilies of ensemble methods that differ in the objective they seek to minimize and the derivative-based optimization scheme they approximate thr

Liang Li, Tengfei Huang, Pengfei Lan, Yinfu Zhang

We experimentally explore the fingerprint of the microscopic electron dynamics in second-order harmonic generation (SHG). It is shown that the interbond electron hopping induces a novel source of nonlinear polarization and plays an important role even when the driving laser intensity is 2 orders of magnitude lower than the characteristic atomic field. Our mo

Guosheng Cui, Ruxin Wang, Dan Wu, Ye Li

In recent years, semi-supervised multi-view nonnegative matrix factorization (MVNMF) algorithms have achieved promising performances for multi-view clustering. While most of semi-supervised MVNMFs have failed to effectively consider discriminative information among clusters and feature alignment from multiple views simultaneously. In this paper, a novel Disc

Ying Mao, Peizhao Hu

Cloud storage plays an important role in social computing. This paper aims to develop a cloud storage management system for mobile devices to support an extended set of file operations. Because of the limit of storage, bandwidth, power consumption, and other resource restrictions, most existing cloud storage apps for smartphones do not keep local copies of f

Jose Ceniceros, Indu R. Churchill, Mohamed Elhamdadi

We generalize the notion of the quandle polynomial to the case of singquandles. We show that the singquandle polynomial is an invariant of finite singquandles. We also construct a singular link invariant from the singquandle polynomial and show that this new singular link invariant generalizes the singquandle counting invariant. In particular, using the new

Tirumalapudi Raviteja, Rajay Vedaraj . I. S

In autonomous Vehicles technology Image segmentation was a major problem in visual perception. This image segmentation process is mainly used in medical applications. Here we adopted an image segmentation process to visual perception tasks for predicting the agents on the surrounding environment, identifying the road boundaries and tracking the line markings

Andrea Marrama

Let $\mathcal{O}_K$ be a complete discrete valuation ring of mixed characteristic $(0,p)$ with perfect residue field. We prove the existence of the Hodge-Newton filtration for $p$-divisible groups over $\mathcal{O}_K$ with additional endomorphism structure for the ring of integers of a finite, possibly ramified field extension of $\mathbb{Q}_p$. The argument

Keiji Tagami

The $m$-trace of a knot is the $4$-manifold obtained from $\mathbf{B}^4$ by attaching a $2$-handle along the knot with $m$-framing. In 2015, Abe, Jong, Luecke and Osoinach introduced a technique to construct infinitely many knots with the same $m$-trace, which is called the operation $(\ast m)$. In this paper, we prove that their technique can be explained i

T. Naka, T. Nakane, S. Ishii, M. Nakayama

We report magnetic properties in the random spinel magnet CoGa2O4. Rietveld analysis of the x-ray diffraction profile for CoGa2O4 reveals that the Co and Ga ions are distributed randomly in the tetrahedral A-sites and octahedral B-sites in the cubic spinel structure. CoGa2O4 exhibits a spin-glass transition at TSG = 8.2 K that is confirmed by measurements of

David F. Anderson, Badal Joshi, Abhishek Deshpande

This paper is concerned with the utilization of deterministically modeled chemical reaction networks for the implementation of (feed-forward) neural networks. We develop a general mathematical framework and prove that the ordinary differential equations (ODEs) associated with certain reaction network implementations of neural networks have desirable properti

Yue Li, Lin Fu, Nikolaus A. Adams

In this work, a framework to construct arbitrarily high-order low-dissipation shock-capturing schemes with flexible and controllable nonlinear dissipation for convection-dominated problems is proposed. While a set of candidate stencils of incremental width is constructed, each one is indicated as smooth or nonsmooth by the ENO-like stencil selection procedur

Hao Wang, Gonzague Henri, Chin-Woo Tan, Ram Rajagopal

Electricity consumed by residential consumers counts for a significant part of global electricity consumption and utility companies can collect high-resolution load data thanks to the widely deployed advanced metering infrastructure. There has been a growing research interest toward appliance load disaggregation via nonintrusive load monitoring. As the elect

Amir Sepehri, Cyrus DiCiccio

For companies developing products or algorithms, it is important to understand the potential effects not only globally, but also on sub-populations of users. In particular, it is important to detect if there are certain groups of users that are impacted differently compared to others with regard to business metrics or for whom a model treats unequally along

Ok Song An, Jin U Kang, Thae Hyok Kim, Ui Ri Mun

We investigate the post-bounce background dynamics in a certain class of single bounce scenarios studied in the literature, in which the cosmic bounce is driven by a scalar field with negative exponential potential such as the ekpyrotic potential. We show that those models can actually lead to cyclic evolutions with repeated bounces. These cyclic evolutions,

Jiabao Zhang, Tianhong Wang, Geng Li, Yue Jiang

In this work, we study the lepton-number-violating processes of $K^\pm$ and $D^\pm$ mesons. Two quasi-degenerate sterile neutrinos are assumed to induce such processes. Different with the case where only one sterile neutrino involves, here, the CP phases of the mixing parameters could give sizable contribution. This, in turn, would affect the absolute values

Wenhao Li, Bincheng Zhang, Jiajie Zhang

Detecting and intercepting malicious requests are one of the most widely used ways against attacks in the network security. Most existing detecting approaches, including matching blacklist characters and machine learning algorithms have all shown to be vulnerable to sophisticated attacks. To address the above issues, a more general and rigorous detection met

Vincent E. Coll, Nicholas Mayers, Nicholas Russoniello, Gil Salgado

A ($2k+1$)$-$dimensional contact Lie algebra is one which admits a one-form $\varphi$ such that $\varphi \wedge (d\varphi)^k\ne0$. Such algebras have index one, but this is not generally a sufficient condition. Here we show that index-one type-A seaweed algebras are necessarily contact. Examples, together with a method for their explicit construction, are pr

Keiji Tagami

The $0$-trace of a knot is the $4$-manifold represented by the $0$-framing of the knot. In this manuscript, we survey methods constructing a pair of knots with diffeomorphic $0$-traces. In particular, we focus on Gompf-Miyazaki's dualizable pattern, Abe-Jong-Omae-Takeuchi's band presentation, and RGB-diagram given by Piccirillo and named by the author, and w

Weiguang Chen, Wenjun Jiang, Xueqi Li, Kenli Li

Disentangled representation has been widely explored in many fields due to its maximal compactness, interpretability and versatility. Recommendation system also needs disentanglement to make representation more explainable and general for downstream tasks. However, some challenges slow its broader application -- the lack of fine-grained labels and the comple

Jinmian Li, Shuo Yang, Rao Zhang

Measuring the vector boson scattering (VBS) precisely is an important step towards understanding the electroweak symmetry breaking of the standard model (SM) and detecting new physics beyond the SM. We propose a neural network which compress the features of the VBS into three dimensional latent space. The consistency of the SM prediction and the experimental

Luanxuan Hou, Jie Cao, Yuan Zhao, Haifeng Shen

We propose an augmented Parallel-Pyramid Net ($P^2~Net$) with feature refinement by dilated bottleneck and attention module. During data preprocessing, we proposed a differentiable auto data augmentation ($DA^2$) method. We formulate the problem of searching data augmentaion policy in a differentiable form, so that the optimal policy setting can be easily up

Naoki Hamada, Naoto Shida, Naohito Tomita

In this paper, the ranges of bilinear pseudo-differential operators of $S_{0,0}$-type on $L^2 \times L^2$ are determined in the framework of Besov spaces. Our result improves the $L^2 \times L^2 \to L^1$ boundedness of those operators with symbols in the bilinear H\"ormander class $BS^m_{0,0}$.

S. Gallo, G. Iacobelli, G. Ost, D. Y. Takahashi

In many dynamical systems in nature, the law of the dynamics changes along with the temporal evolution of the system. These changes are often associated with the occurrence of certain events. The timing of occurrence of these events depends, in turn, on the trajectory of the dynamical system itself, making the dynamics of the system and the timing of a chang

Adel Sohbi, Ruben Ohana, Isabelle Zaquine, Eleni Diamanti

We propose a method to experimentally demonstrate contextuality with a family of tests for qudits. The experiment we propose uses a qudit encoded in the path of a single photon and its temporal degrees of freedom. We consider the impact of noise on the effectiveness of these tests, taking the approach of ontologically faithful non-contextuality. In this appr

Chu Xu, Timothy Cleary, Daiwei Wang, Guoxing Li

This article examines the problem of Lithium-Sulfur (Li-S) battery state estimation. Such estimation is important for the online management of this energy-dense chemistry. The literature uses equivalent circuit models (ECMs) for Li-S state estimation. This article's main goal is to perform estimation using a physics-based model instead. This approach is attr