December 2020 arXiv papers — page 80

Vsevolod Evtushevsky

Among central measures on the path space of the Young--Fibonacci lattice the so-called Plancherel measure has a special role. Its ergodicity was proved by Kerov and Gnedin. The goal of this cycle of two articles is to prove that remaining measures from the Martin boundary of this graph (which were described by Kerov and Goodman) are also ergodic. The measure

Sebastian Fiedler, Roland Clauß, Hartmut Clauß, Stefan Knoblach

Thermography allows for the remote measurement of surface temperatures and is widely used for the identification of energy losses, damage detection or quality control. However, thermal imaging is strongly material dependent and therefore measured and real temperatures can differ significantly. The emission coefficient resides between 0 and 1 and changes main

Kuntal Deka, Sanjeev Sharma

Next-generation internet of things (IoT) applications need trillions of low-powered wireless mobile devices to connect with each other having ultra-reliability and low-latency. Non-orthogonal multiple access (NOMA) is a promising technology to address massive connectivity for 5G and beyond by accommodating several users within the same orthogonal resource bl

Panpan Li, Yikun Gong, Chen Wang

With the continuous development of NoSQL databases, more and more developers choose to use semi-structured data for development and data management, which puts forward requirements for schema management of semi-structured data stored in NoSQL databases. Schema extraction plays an important role in understanding schemas, optimizing queries, and validating dat

Fengchun Mu, Ya Gao, Hong-Da Yin, Gangcheng Wang

We propose a method to create selective interactions with Dicke-Stark model by means of time-dependent perturbation theory. By choosing the proper rotating framework, we find that the time oscillating terms depend on the number of atomic excitations and the number of photonic excitations. Consequently, the Rabi oscillation between selective states can be rea

Soo Ye Kim, Hyeonjun Sim, Munchurl Kim

Blind super-resolution (SR) methods aim to generate a high quality high resolution image from a low resolution image containing unknown degradations. However, natural images contain various types and amounts of blur: some may be due to the inherent degradation characteristics of the camera, but some may even be intentional, for aesthetic purposes (e.g. Bokeh

Narasimha Chary Bonala, Santosha Kumar Pattanayak

In this paper, we consider the GIT quotients of Schubert varieties for the action of a maximal torus. We describe the minuscule Schubert varieties for which the semistable locus is contained in the smooth locus. As a consequence, we study the smoothness of torus quotients of Schubert varieties in the Grassmannian. We also prove that the torus quotient of any

Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt

We consider the problem of online learning in the presence of distribution shifts that occur at an unknown rate and of unknown intensity. We derive a new Bayesian online inference approach to simultaneously infer these distribution shifts and adapt the model to the detected changes by integrating ideas from change point detection, switching dynamical systems

Shunsuke Hirose, Tomotake Kozu, Yingzi Jin

Anomaly detection is necessary for proper and safe operation of large-scale systems consisting of multiple devices, networks, and/or plants. Those systems are often characterized by a pair of multivariate datasets. To detect anomaly in such a system and localize element(s) associated with anomaly, one would need to estimate scores that quantify anomalousness

William Diggin, Michael Diggin

An algorithm to efficiently compute the moments of volumetric images is disclosed. The approach demonstrates a reduction in processing time by reducing the computational complexity significantly. Specifically, the algorithm reduces multiplicative complexity from O(n^3) to O(n). Several 2D projection images of the 3D volume are generated. The algorithm comput

Long Wang

Let $X$ be a smooth projective hypersurface of dimension at least three. We show that every automorphism of the Hilbert square $X^{[2]}$ of $X$ is induced by some automorphism of $X$.

Shentong Mo, Xiaoqing Tan, Jingfei Xia, Pinxu Ren

Spatiotemporal action recognition deals with locating and classifying actions in videos. Motivated by the latest state-of-the-art real-time object detector You Only Watch Once (YOWO), we aim to modify its structure to increase action detection precision and reduce computational time. Specifically, we propose four novel approaches in attempts to improve YOWO

Lu Chen, Jiao Sun, Wei Xu

Deep neural networks (DNNs) significantly improved the accuracy of optical character recognition (OCR) and inspired many important applications. Unfortunately, OCRs also inherit the vulnerabilities of DNNs under adversarial examples. Different from colorful vanilla images, text images usually have clear backgrounds. Adversarial examples generated by most exi

Sehun Chun, Taejin Oh

Additional grid points are often introduced for the higher-order polynomial of a numerical solution with curvilinear elements. However, those points are likely to be located slightly outside the domain, even when the vertices of the curvilinear elements lie within the curved domain. This misallocation of grid points generates a mesh error, called geometric a

Shentong Mo, Haofan Wang, Pinxu Ren, Ta-Chung Chi

Automatic speech verification (ASV) is the technology to determine the identity of a person based on their voice. While being convenient for identity verification, we should aim for the highest system security standard given that it is the safeguard of valuable digital assets. Bearing this in mind, we follow the setup in ASVSpoof 2019 competition to develop

A. C. Robleto-Orús, J. P. Torres-Papaqui, A. L. Longinotti, R. A. Ortega-Minakata

NGC 7469 is a well known Luminous IR Galaxy, with a circumnuclear star formation ring ($\sim 830$ pc radius) surrounding a Seyfert 1 AGN. Nuclear unresolved winds were previously detected in X-rays and UV, as well as an extended biconical outflow in IR coronal lines. We search for extended outflows by measuring the kinematics of the $\mathrm{H\beta}$ and [O

Nimisha Arora, Pintu Das

We have investigated the spin wave modes of strongly dipolar coupled, highly anisotropic nanoislands forming square artificial spin ice system using micromagnetic simulation in MUMAX3 in combination with Matlab coding. Artificial spin ice is considered to be formed by the four square ring-type structure of elliptical cross-section nanoislands. Our results st

Anupam Pandey, Minkush Kansal, Miguel A. Herrada, Jens Eggers

A slender thread of elastic hydrogel is susceptible to a surface instability that is reminiscent of the classical Rayleigh-Plateau instability of liquid jets. The final, highly nonlinear states that are observed in experiments arise from a competition between capillarity and large elastic deformations. Combining a slender analysis and fully three-dimensional

Feng Wang, Huaping Liu

Unsupervised contrastive learning has achieved outstanding success, while the mechanism of contrastive loss has been less studied. In this paper, we concentrate on the understanding of the behaviours of unsupervised contrastive loss. We will show that the contrastive loss is a hardness-aware loss function, and the temperature {\tau} controls the strength of

Zongzheng Zhao, Yixin Liu, Li Guo, Linquan Bai

Large-scale integration of renewable energy sources (RES) brings huge challenges to the power system. A cost-effective reserve deployment and uncertainty pricing mechanism are critical to deal with the uncertainty and variability of RES. To this end, this paper proposes a novel locational marginal pricing mechanism in day-ahead market for managing uncertaint

Sai Kiran Kadambari, Sundeep Prabhakar Chepuri

In this paper, we focus on learning product graphs from multi-domain data. We assume that the product graph is formed by the Cartesian product of two smaller graphs, which we refer to as graph factors. We pose the product graph learning problem as the problem of estimating the graph factor Laplacian matrices. To capture local interactions in data, we seek sp

Pratishtha Batra, Neil A. Spencer, Pritam Ranjan

Factorial designs are often used in various industrial and sociological experiments to identify significant factors and factor combinations that may affect the process response. In the statistics literature, several studies have investigated the analysis, construction, and isomorphism of factorial and fractional factorial designs. When there are multiple cho

Joseph Santos-Sacchi, Dhasakumar Navaratnam, Winston Tan

The outer hair cell (OHC) membrane harbors a voltage-dependent protein, prestin (SLC26a5), in high density, whose charge movement is evidenced as a nonlinear capacitance (NLC). NLC is bell-shaped, with its peak occurring at a voltage, Vh, where sensor charge is equally distributed across the plasma membrane. Thus, Vh provides information on the conformationa

Dinh Dũng, Vu Nhat Huy

We study approximation by arbitrary linear combinations of $n$ translates of a single function of periodic functions. We construct some linear methods of this approximation for univariate functions in the class induced by the convolution with a single function, and prove upper bounds of the $L^p$-approximation convergence rate by these methods, when $n \to \

David Caratelli

MicroBooNE is a neutrino experiment that utilizes a liquid argon time projection chamber (LArTPC) located on-axis in the Booster Neutrino Beam (BNB) at Fermilab. One of the experiment's main goals is to search for excess low-energy electromagnetic-like events as seen by the MiniBooNE experiment, located just downstream of MicroBooNE in the BNB. As MicroBooNE

Bryan Liu, Shuangyang Li, Yixuan Xie, Jinhong Yuan

A deep learning assisted sum-product detection algorithm (DL-SPDA) for faster-than-Nyquist (FTN) signaling is proposed in this paper. The proposed detection algorithm works on a modified factor graph which concatenates a neural network function node to the variable nodes of the conventional FTN factor graph to approach the maximum a posterior probabilities (

Mahmoud Abo-Khamis, Sungjin Im, Benjamin Moseley, Kirk Pruhs

We consider the problem of efficiently estimating the size of the inner join of a collection of preprocessed relational tables from the perspective of instance optimality analysis. The run time of instance optimal algorithms is comparable to the minimum time needed to verify the correctness of a solution. Previously instance optimal algorithms were only know

Kate Storey-Fisher, Marc Huertas-Company, Nesar Ramachandra, Francois Lanusse

We present an anomaly detection method using Wasserstein generative adversarial networks (WGANs) on optical galaxy images from the wide-field survey conducted with the Hyper Suprime-Cam (HSC) on the Subaru Telescope in Hawai'i. The WGAN is trained on the entire sample, and learns to generate realistic HSC-like images that follow the distribution of the train

Catherine Medlock, Alan Oppenheim, Isaac Chuang, Qi Ding

Receiver operating characteristics (ROCs) are a well-established representation of the tradeoff between detection and false alarm probabilities in classical binary hypothesis testing. We use classical ROCs as motivation for two types of operating characteristics for binary hypothesis testing in quantum systems -- decision operating characteristics (QDOCs) an

Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu

Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station-level or regional transportation demands. However, in most of the existing research, the graph convolution was implemented on a heuristically generated adjacency matrix, which cou

A. F. Zadorozhny, V. A. Melent'ev

Aspects of compatibility of topologies of parallel computing systems and tasks are investigated. The introduction of appropriate indexes based on the original topological model of parallel computations and on the nontraditional description of a graph by its projections is proposed and elucidated. On the example of hypercubic computing system (CS) and tasks w

Takeo Sasai, Asuka Matsushita, Masanori Nakamura, Seiji Okamoto

We numerically and experimentally investigate the laser phase noise tolerance of probabilistically shaped (PS) and uniformly shaped (US) quadrature amplitude modulation (QAM) signals. In the simulations, we compare PS-64QAM to US-16QAM, PS-256QAM to US-64QAM, and PS-1024QAM to US-256QAM under the same information rate (IR). We confirm that a sufficient shapi

L. L. Williams, N. Inan

Maxwellian approximations to linear general relativity are revisited in light of relatively recent results on the degrees of freedom in the linear gravitational field. The well-known Maxwellian formalism obtained in harmonic coordinates is compared with a Maxwellian formalism obtained under a coordinate choice where each of the metric components corresponds

Dongkwan Kim

For a coincidental Coxeter group, i.e. of type $A_{n-1}, BC_n, H_3,$ or $I_2(m)$, we define the corresponding $q$-Kreweras numbers attached to limit symbols in the sense of Shoji. The construction of these numbers resembles the argument of Reiner and Sommers and these two share similar properties, but our version is slightly different from and combinatoriall

A. T. Sutinjo, M. Sokolowski, M. Kovaleva, D. C. X. Ung

Aims: This paper aims to derive an expression for the sensitivity of a polarimetric radio interferometer that is valid for all-sky observations of arbitrarily polarized sources, with neither a restriction on FoV nor with any a priori assumption regarding the polarization state of the source. We verify the resulting formula with an all-sky observation using t

U-Rae Kim, Dohyun Kim, Jungil Lee

We employ Dirac's bra-ket notation to define the inertia tensor operator that is independent of the choice of bases or coordinate system. The principal axes and the corresponding principal values for the elliptic plate are determined only based on the geometry. By making use of a general symmetric tensor operator, we develop a method of diagonalization that

Yuan Yao, Xiaolin Sun

We develop an exactly solvable framework of Markov decision process with a finite horizon, and continuous state and action spaces. We first review the exact solution of conventional linear quadratic regulation with a linear transition and a Gaussian noise, whose optimal policy does not depend on the Gaussian noise, which is an undesired feature in the presen

Dohyun Kim, June-Haak Ee, Chaehyun Yu, Jungil Lee

We demonstrate how to make the coordinate transformation or change of variables from Cartesian coordinates to curvilinear coordinates by making use of a convolution of a function with Dirac delta functions whose arguments are determined by the transformation functions between the two coordinate systems. By integrating out an original coordinate with a Dirac

Subhojyoti Mukherjee, Ardhendu Tripathy, Robert Nowak

Active learning can reduce the number of samples needed to perform a hypothesis test and to estimate the parameters of a model. In this paper, we revisit the work of Chernoff that described an asymptotically optimal algorithm for performing a hypothesis test. We obtain a novel sample complexity bound for Chernoff's algorithm, with a non-asymptotic term that

Qicheng Lao, Xiang Jiang, Mohammad Havaei

We propose a hypothesis disparity regularized mutual information maximization~(HDMI) approach to tackle unsupervised hypothesis transfer -- as an effort towards unifying hypothesis transfer learning (HTL) and unsupervised domain adaptation (UDA) -- where the knowledge from a source domain is transferred solely through hypotheses and adapted to the target dom

Matthew Joseph Adiletta, Brian Flanagan

This paper describes an optimized implementation of a Forward Propagating Classification Neural Network which has been previously trained. The implementation described highlights a novel means of using Python scripts to generate a Verilog hardware implementation. The characteristics of this implementation include optimizations to scale input data, use select

Chin-Yao Cheng, Zi-Yu Liu, Pi-Sheng Hu, Tsai-Ni Wang

Efficient frequency conversion of photons has important applications in optical quantum technology because the frequency range suitable for photon manipulation and communication usually varies widely. Recently, an efficient frequency conversion system using a double-$\Lambda$ four-wave mixing (FWM) process based on electromagnetically induced transparency (E

Sergey Belyi, Eduard Tsekanovskii

In this paper we study the L-system realizations generated by the original Weyl-Titchmarsh functions $m_\alpha(z)$ in the case when the minimal symmetric Shr\"o\-dinger operator in $L_2[\ell,+\infty)$ is non-negative. We realize functions $(-m_\alpha(z))$ as impe\-dance functions of Shr\"odinger L-systems and derive necessary and sufficient conditions for $(

Eric Primozic

For $k$ a perfect field of characteristic $p>0$ and $G/k$ a split reductive group with $p$ a non-torsion prime for $G,$ we compute the mod $p$ motivic cohomology of the geometric classifying space $BG_{(r)}$, where $G_{(r)}$ is the $r$th Frobenius kernel of $G.$ Our main tool is a motivic version of the Eilenberg-Moore spectral sequence, due to Krishna. For

Manqing Ma, Gyorgy Korniss, Boleslaw K. Szymanski

Influence maximization is the task of finding the smallest set of nodes whose activation in a social network can trigger an activation cascade that reaches the targeted network coverage, where threshold rules determine the outcome of influence. This problem is NP-hard and it has generated a significant amount of recent research on finding efficient heuristic

Yue Zheng, Wei Chen, D. Y. Xing

Fermi arc surface states are the hallmark of Weyl semimetals, whose identification is usually challenged by their coexistence with gapless bulk states. Surface transport measurements by fabricating setups on the sample boundary provide a natural solution to this problem. Here, we study the Andreev reflection (AR) in a planar normal metal-superconductor junct

Lei Yan, Xin Chen

Sufficient dimension reduction (SDR) is a popular tool in regression analysis, which replaces the original predictors with a minimal set of their linear combinations. However, the estimated linear combinations generally contain all original predictors, which brings difficulties in interpreting the results, especially when the number of predictors is large. I

Kazuhiro Ishige, Yujiro Tateishi

Let $H:=-\Delta+V$ be a nonnegative Schr\"odinger operator on $L^2({\bf R}^N)$, where $N\ge 2$ and $V$ is a radially symmetric inverse square potential. Let $\|\nabla^\alpha e^{-tH}\|_{(L^{p,\sigma}\to L^{q,\theta})}$ be the operator norm of $\nabla^\alpha e^{-tH}$ from the Lorentz space $L^{p,\sigma}({\bf R}^N)$ to $L^{q,\theta}({\bf R}^N)$, where $\alpha\i

Peng Zhang, Jinlong Li, Tengfei Li, Huanhuan Chen

To handle different types of Many-Objective Optimization Problems (MaOPs), Many-Objective Evolutionary Algorithms (MaOEAs) need to simultaneously maintain convergence and population diversity in the high-dimensional objective space. In order to balance the relationship between diversity and convergence, we introduce a Kernel Matrix and probability model call

KyungTae Kim, June-Haak Ee, Kyounghoon Kim, U-Rae Kim

We investigate the motion of a massive particle constrained to move along a path consisting of two line segments on a vertical plane under an arbitrary conservative force. By fixing the starting and end points of the track and varying the vertex horizontally, we find the least-time path. We define the angles of incidence and refraction similar to the refract

Nathalie Majcherczyk, Daniel Jeswin Nallathambi, Tim Antonelli, Carlo Pinciroli

In this paper, we propose an approach to the distributed storage and fusion of data for collective perception in resource-limited robot swarms. We demonstrate our approach in a distributed semantic classification scenario. We consider a team of mobile robots, in which each robot runs a pre-trained classifier of known accuracy to annotate objects in the envir

Claudia del P. Lagos, Eric Emsellem, Jesse van de Sande, Katherine E. Harborne

We use a sample of $z=0$ galaxies visually classified as slow rotators (SRs) in the EAGLE hydrodynamical simulations to explore the effect of galaxy mergers on their formation, characterise their intrinsic galaxy properties, and study the connection between quenching and kinematic transformation. SRs that have had major or minor mergers (mass ratios $\ge 0.3

Marcelo L. Pereira Júnior, Wiliam F. Cunha, Douglas S. Galvão, Luiz A. Ribeiro Júnior

Recently, laser-assisted chemical vapor deposition was used to synthesize a free-standing, continuous, and stable monolayer amorphous carbon (MAC). MAC is a pure carbon structure composed of randomly distributed five, six, seven, and eight atom rings, which differs from disordered graphene. More recently, amorphous MAC-based nanotubes (a-CNT) and nanoscrolls

Shi Shao, Marius Cautun, Carlos S. Frenk, Marta Reina-Campos

It has long been argued that the radial distribution of globular clusters (GCs) in the Fornax dwarf galaxy requires its dark matter halo to have a core of size $\sim 1$ kpc. We revisit this argument by investigating analogues of Fornax formed in E-MOSAICS, a cosmological hydrodynamical simulation that self-consistently follows the formation and evolution of

Peter Moeck

Scanning probe microscopy (SPM) images of regularly arranged spatially periodic objects can be processed crystallographically. The resulting information may be used to remove from the SPM image distortions that are due to a less than perfect imaging process. The combined effects of these distortions result in a point spread function that gives a quantitative

Koichiro Umemoto, Renata M. Wentzcovitch

Here we present an ab initio prediction of an order-disorder transition (ODT) from $I\bar{4}2d$-type to a Th$_3$P$_4$-type phase in the cation sublattices of Mg$_2$GeO$_4$, a post-post-perovskite (post-PPv) phase. This uncommon type of prediction is achieved by carrying out a high-throughput sampling of atomic configurations in a 56-atom supercell followed b

Feixiang Lu, Zongdai Liu, Hui Miao, Peng Wang

Holistically understanding an object and its 3D movable parts through visual perception models is essential for enabling an autonomous agent to interact with the world. For autonomous driving, the dynamics and states of vehicle parts such as doors, the trunk, and the bonnet can provide meaningful semantic information and interaction states, which are essenti

Liang Liao, Jing Xiao, Zheng Wang, Chia-Wen Lin

Existing inpainting methods have achieved promising performance in recovering defected images of specific scenes. However, filling holes involving multiple semantic categories remains challenging due to the obscure semantic boundaries and the mixture of different semantic textures. In this paper, we introduce coherence priors between the semantics and textur

Daito Nakano, Mingyang Yin, Ryosuke Sato, Abram Hindle

Software is prone to bugs and failures. Security bugs are those that expose or share privileged information and access in violation of the software's requirements. Given the seriousness of security bugs, there are centralized mechanisms for supporting and tracking these bugs across multiple products, one such mechanism is the Common Vulnerabilities and Expos

Tomoya Hirota, Riccardo Cesaroni, Luca Moscadelli, Koichiro Sugiyama

We carried out observations of the 22 GHz H2O masers in a high-mass protostar S255 NIRS 3 by using VERA. We measured the proper motions of the 22 GHz H2O masers associated with a bipolar outflow. The expansion velocity of the blueshifted bow shock traced by the 22 GHz H2O masers was 28 km s-1 corresponding to a dynamical timescale of 60 years. The direction

Jose Dolz, Christian Desrosiers, Ismail Ben Ayed

Deep segmentation neural networks require large training datasets with pixel-wise segmentations, which are expensive to obtain in practice. Mixed supervision could mitigate this difficulty, with a small fraction of the data containing complete pixel-wise annotations, while the rest being less supervised, e.g., only a handful of pixels are labeled. In this wo

Mohammad Reza Rahmati

We consider the variation of tropical Hodge structure (TVHS) associated to families of tropical varieties. The family of the tropical intermediate Jacobians of the associated tropical Hodge structure defines a bundle of tropical Jacobians, whose sections we call the tropical normal functions. We define formal sequential derivatives of these functions on the

L. E. Abramson, D. D. Kelson

Via numerical experiments, we show that the $\sim$10%-20% passive fraction seen at $z>3$ is consistent with galaxy star formation histories being maximally correlated stochastic processes. If so, this fraction should reflect a time-independent baseline that holds at any epoch or mass regime where mean star formation rates are rising. Data at $\log M_\ast<10$

Mertcan Yetkin, Sudharsan Kalidoss, Frank E. Curtis, Lawrence V. Snyder

A generic formulation for the optimal control of a single wave-energy converter (WEC) is proposed. The formulation involves hard and soft constraints on the motion of the WEC to promote reduced damage and fatigue to the device during operation. Most of the WEC control literature ignores the cost of the control and could therefore result in generating less po

Rui Li, Qing Mao, Pei Wang, Xiantuo He

Self-supervised depth estimation has shown its great effectiveness in producing high quality depth maps given only image sequences as input. However, its performance usually drops when estimating on border areas or objects with thin structures due to the limited depth representation ability. In this paper, we address this problem by proposing a semantic-guid

A. Kashlinsky

In the inflation-based cosmology the dark matter (DM) density component starts moving with respect to the universal expansion at $z_{\rm eq}\sim 3,200$ while baryons remain frozen until $z_{\rm rec}\sim 1,100$. It has been suggested that in this case post-linear corrections to the evolution of small fluctuations would result, for the standard $\Lambda$-domin

Takeo Sasai, Masanori Nakamura, Etsushi Yamazaki, Asuka Matsushita

We present a simple nonlinear digital pre-distortion (DPD) of optical transmitter components, which consists of concatenated blocks of a finite impulse response (FIR) filter, a memoryless nonlinear function and another FIR filter. The model is a Wiener-Hammerstein (WH) model and has essentially the same structure as neural networks or multilayer perceptions.

André-Nicolas Chené, Shunyuan Mao, Michael Lundquist, Eder Martioli

This paper describes the software DRAGraces (Data Reduction and Analysis for GRACES), which is a pipeline reducing spectra from GRACES (Gemini Remote Access to the CFHT ESPaDOnS Spectrograph) at the Gemini North Telescope. The code is written in the IDL language. It is designed to find all the GRACES frames in a given directory, automatically determine the l

Salman Mohamadi, Hamidreza Amindavar

Active learning frameworks offer efficient data annotation without remarkable accuracy degradation. In other words, active learning starts training the model with a small size of labeled data while exploring the space of unlabeled data in order to select most informative samples to be labeled. Generally speaking, representing the uncertainty is crucial in an

Walter Tholen, Leila Yeganeh

Expanding on the comprehensive factorization of functors internal to a category C, under fairly mild conditions on a monad T on C we establish that this orthogonal factorization system exists even in Burroni's category Cat(T) of (internal) T-categories and their functors. This context provides for some expected applications and some unexpected connections. F

Noah Oghenfego Ogwara, Krassie Petrova, Mee Loong, Yang

This paper reviews existing Intrusion Detection Systems (IDS) that target the Mobile Cloud Computing (MCC), Cloud Computing (CC), and Mobile Device (MD) environment. The review identifies the drawbacks in existing solutions and proposes a novel approach towards enhancing the security of the User Layer (UL) in the MCC environment. The approach named MINDPRES

Xinyu Li, Chunhui Liu, Bing Shuai, Yi Zhu

In the world of action recognition research, one primary focus has been on how to construct and train networks to model the spatial-temporal volume of an input video. These methods typically uniformly sample a segment of an input clip (along the temporal dimension). However, not all parts of a video are equally important to determine the action in the clip.

Guillermo Angeris, Alex Evans, Tarun Chitra

Liquidity and trading activity on constant function market makers (CFMMs) such as Uniswap, Curve, and Balancer has grown significantly in the second half of 2020. Much of the growth of these protocols has been driven by incentivized pools or 'yield farming', which reward participants in crypto assets for providing liquidity to CFMMs. As a result, CFMMs and a

Yukiko Obata, Michiko Sato, Yuji Kondo, Yuta Yamaguchi

The strong fascination exerted by the binary compound of FeSe demands reliable engineering protocols and more effective approaches towards inducing superconductivity in FeSe thin films. Our study addresses the peculiarities in pulsed laser deposition which determine FeSe thin film growth and focuses on the film/substrate interface, the tendency for domain ma

Nikolai V. Ivanov

The paper is devoted to a generalized and simplified version of author's approach to covering theorems in bounded cohomology theory. The amenability assumptions are replaced by weaker and more natural acyclicity assumprions. In the case of open coverings the paracompactness assumption is removed. It is shown that for paracompact spaces the case of closed cov

Takayuki Osogami

We provide complete proofs of the lemmas about the properties of the regularized loss function that is used in the second order techniques for learning time-series with structural breaks in Osogami (2021). In addition, we show experimental results that support the validity of the techniques.

Marius Hofert, Avinash Prasad, Mu Zhu

Generative moment matching networks (GMMNs) are suggested for modeling the cross-sectional dependence between stochastic processes. The stochastic processes considered are geometric Brownian motions and ARMA-GARCH models. Geometric Brownian motions lead to an application of pricing American basket call options under dependence and ARMA-GARCH models lead to a

Mehdi Eshaghi, Moslem Zarei, Ahmad Sheykhi

We study a two-field quintessential Higgs inflation model in which a quintessence field with an exponential potential $e^{-\beta\phi/M_P}$ is coupled to the Higgs field from the beginning of inflation. The Higgs field itself is also non-minimally coupled to gravity. The inflationary predictions of this model for $n_s$ and $r$ are in good agreement with Planc

Matthew Joseph Adiletta, Oliver Thomas

This project brings music to sight. Music can be a visual masterpiece. Some people naturally experience a visualization of audio - a condition called synesthesia. The type of synesthesia explored is when sounds create colors in the 'mind's eye.' Project included interviews with people who experience synesthesia, examination of prior art, and topic research t

Blai Bonet, Hector Geffner

It has been observed that in many of the benchmark planning domains, atomic goals can be reached with a simple polynomial exploration procedure, called IW, that runs in time exponential in the problem width. Such problems have indeed a bounded width: a width that does not grow with the number of problem variables and is often no greater than two. Yet, while

Guangchen Wang, Wencan Wang, Zhiguo Yan

In this paper, we study an optimal control problem of linear backward stochastic differential equation (BSDE) with quadratic cost functional under partial information. This problem is solved completely and explicitly by using a stochastic maximum principle and a decoupling technique. By using the maximum principle, a stochastic Hamiltonian system, which is a

Levent Guvenc

Road vehicle safety systems can be broadly classified into the two categories of passive and active systems. The aim of passive safety systems is to reduce risk of injury to the occupants of the vehicle during and after an accident like a crash or rollover. Passive safety systems include the design of safety restraints, design for crashworthiness, seat belts

Mackenzie Simper, Julia A. Palacios

The standard coalescent is widely used in evolutionary biology and population genetics to model the ancestral history of a sample of molecular sequences as a rooted and ranked binary tree. In this paper, we present a representation of the space of ranked trees as a space of constrained ordered matched pairs. We use this representation to define ergodic Marko

K. Horaites, L. Andersson, S. J. Schwartz, S. Xu

This observational study demonstrates that the magnitude and location of energization of electrons in the Martian magnetosheath is more complex than previous studies suggest. Electrons in Mars's magnetosheath originate in the solar wind and are accelerated by an electric field when they cross the bow shock. Assuming that this acceleration is localized solely

Zhiqing Yang, Youngjun Choe, Matthew Martell

To battle with economic challenges during the COVID-19 pandemic, the US government implemented various measures to mitigate economic loss. From issuance of stimulus checks to reopening businesses, consumers had to constantly alter their behavior in response to government policies. Using anonymized card transactions and mobile device-based location tracking d

Peter R. Young, Alexander Ryabtsev, Enrico Landi

The identifications of Fe VII emission lines in the wavelength range 193-197 {\AA} are discussed in the light of new measurements of laboratory spectra and atomic data calculations. This region is of importance to studies of solar spectra from the EUV Imaging Spectrometer (EIS) on board the Hinode spacecraft, which has its peak sensitivity at these wavelengt

Miaowei Wang, Alexander William Mohacey, Hongyu Wang, James Apfel

Since 2014, very deep convolutional neural networks have been proposed and become the must-have weapon for champions in all kinds of competition. In this report, a pipeline is introduced to perform the classification of smoking and calling by modifying the pretrained inception V3. Brightness enhancing based on deep learning is implemented to improve the clas

Antonio Sa Barreto Neto, Felipe Farias, Marco Aurelio Tomaz Mialaret, Bruno Cartaxo

The increasing usage of smartphones in everyday tasks has been motivated many studies on energy consumption characterization aiming to improve smartphone devices' effectiveness and increase user usage time. In this scenario, it is essential to study mechanisms capable of characterizing user usage patterns, so smartphones' components can be adapted to promote

Navid C. Constantinou, Andrew McC. Hogg

Atmosphere and ocean are coupled via air-sea interactions. The atmospheric conditions fuel the ocean circulation and its variability, but the extent to which ocean processes can affect the atmosphere at decadal time scales remains unclear. In particular, such low-frequency variability is difficult to extract from the short observational record, meaning that

Yulong Pan, Per-Olof Persson

We present a geometric multigrid solver for the Compact Discontinuous Galerkin method through building a hierarchy of coarser meshes using a simple agglomeration method which handles arbitrary element shapes and dimensions. The method is easily extendable to other discontinuous Galerkin discretizations, including the Local DG method and the Interior Penalty

Fan Chen, Jianguo Huang, Chunmei Wang, Haizhao Yang

This paper proposes Friedrichs learning as a novel deep learning methodology that can learn the weak solutions of PDEs via a minmax formulation, which transforms the PDE problem into a minimax optimization problem to identify weak solutions. The name "Friedrichs learning" is for highlighting the close relationship between our learning strategy and Friedrichs

Philip Erickson

The conditional logit model is a standard workhorse approach to estimating customers' product feature preferences using choice data. Using these models at scale, however, can result in numerical imprecision and optimization failure due to a combination of large-valued covariates and the softmax probability function. Standard machine learning approaches allev

A. A. Lima, G. M. Sotkov, M. Stanishkov

We describe the effect of the marginal deformation of the $\cal N = (4, 4)$ superconformal $(T^4)^N /S_N$ orbifold theory on a doublet of R-neutral twisted Ramond fields, in the large-$N$ approximation. Our analysis of their dynamics explores the explicit analytic form of the genus-zero four-point function involving two R-neutral Ramond fields and two deform

Leonid Boytsov

This short document describes a traditional IR system that achieved MRR@100 equal to 0.298 on the MS MARCO Document Ranking leaderboard (on 2020-12-06). Although inferior to most BERT-based models, it outperformed several neural runs (as well as all non-neural ones), including two submissions that used a large pretrained Transformer model for re-ranking. We

Mengjia Xu

Graph analytics can lead to better quantitative understanding and control of complex networks, but traditional methods suffer from high computational cost and excessive memory requirements associated with the high-dimensionality and heterogeneous characteristics of industrial size networks. Graph embedding techniques can be effective in converting high-dimen

Pavel Pokhilko, Dmitry S. Bezrukov, Anna I. Krylov

Macroscopic assemblies of interacting spins give rise to a broad spectrum of behaviors determined by the spatial arrangement of the magnetic sites and the electronic interactions between them. Compounds of copper (II), in which each copper carries spin $\frac{1}{2}$, exhibit a vast variety of physical properties. For antiferromagnetically coupled spin sites,

S. Ustamujic, S. Orlando, E. Greco, M. Miceli

The morphology and the distribution of material observed in SNRs reflect the interaction of the SN blast wave with the ambient environment, the physical processes associated with the SN explosion and the internal structure of the progenitor star. IC 443 is a MM SNR located in a quite complex environment: it interacts with a molecular cloud in the NW and SE a

The Pierre Auger Collaboration, A. Aab, P. Abreu, M. Aglietta

To obtain direct measurements of the muon content of extensive air showers with energy above $10^{16.5}$ eV, the Pierre Auger Observatory is currently being equipped with an underground muon detector (UMD), consisting of 219 10 $\mathrm{m^2}$-modules, each segmented into 64 scintillators coupled to silicon photomultipliers (SiPMs). Direct access to the showe

Annie Sauer, Robert B. Gramacy, David Higdon

Deep Gaussian processes (DGPs) are increasingly popular as predictive models in machine learning (ML) for their non-stationary flexibility and ability to cope with abrupt regime changes in training data. Here we explore DGPs as surrogates for computer simulation experiments whose response surfaces exhibit similar characteristics. In particular, we transport

Nicolas Laforge, Richard Wiltshaw, Richard V. Craster, Vincent Laude

We systematically engineer a series of square and rectangular phononic crystals to create experimental realisations of complex topological phononic circuits. The exotic topological transport observed is wholly reliant upon the underlying structure which must belong to either a square or rectangular lattice system and not to any hexagonal-based structure. The