April 2020 arXiv papers — page 13

Vikram Waradpande, Daniel Kudenko, Megha Khosla

Deep RL approaches build much of their success on the ability of the deep neural network to generate useful internal representations. Nevertheless, they suffer from a high sample-complexity and starting with a good input representation can have a significant impact on the performance. In this paper, we exploit the fact that the underlying Markov decision pro

S K Tripathy

A model of traversable wormhole in an extended gravity theory has been proposed. The Casimir effect and Generalised Uncertainty Principle (GUP) arising out of the concept of minimal length have been considered to obtain the shape function, radial and tangential pressure of the wormhole. The effects of the GUP parameter and the parameter of the extended gravi

Daniel Adrian Maldonado, Vishwas Rao, Mihai Anitescu, Vivak Patel

In this paper we focus on the parameter estimation of dynamic load models with stochastic terms, in particular, load models where protection settings are uncertain, such as in aggregated air conditioning units. We show how the uncertainty in the aggregated protection characteristics can be formulated as a stochastic differential equation with process noise.

Li Xing, Xuekui Zhang, Ardo van den Hout, Scott Hofer

In longitudinal studies, we observe measurements of the same variables at different time points to track the changes in their pattern over time. In such studies, scheduling of the data collection waves (i.e. time of participants' visits) is often pre-determined to accommodate ease of project management and compliance. Hence, it is common to schedule those vi

Ritwik Banerjee, Joydeep Bhattacharya, Priyama Majumdar

We conduct a unique, Amazon MTurk-based global experiment to investigate the importance of an exponential-growth prediction bias (EGPB) in understanding why the COVID-19 outbreak has exploded. The scientific basis for our inquiry is the well-established fact that disease spread, especially in the initial stages, follows an exponential function meaning few po

Fariza Fauzi, Mohammed Belkhatir

The contextual information of Web images is investigated to address the issue of characterizing their content with semantic descriptors and therefore bridge the semantic gap, i.e. the gap between their automated low-level representation in terms of colors, textures, shapes. . . and their semantic interpretation. Such characterization allows for understanding

Jared D. Huling, Simon Mak

Bias in causal comparisons has a direct correspondence with distributional imbalance of covariates between treatment groups. Weighting strategies such as inverse propensity score weighting attempt to mitigate bias by either modeling the treatment assignment mechanism or balancing specified covariate moments. This paper introduces a new weighting method, call

Xuhao Diao, Jun Hu, Suna Ma

The Legendre spectral Galerkin method of self-adjoint second order elliptic equations usually results in a linear system with a dense and ill-conditioned coefficient matrix. In this paper, the linear system is solved by a preconditioned conjugate gradient (PCG) method where the preconditioner $M$ is constructed by approximating the variable coefficients with

Masahide Manabe

We propose, following the AGT correspondence, how the $\mathcal{W}^{\, para}_{N, n}$ ($n$-th parafermion $\mathcal{W}_N$) minimal model characters are obtained from the $U(N)$ instanton counting on ${\mathbb {C}}^2/{\mathbb {Z}}_n$ with $\Omega$-deformation by imposing specific conditions which remove the minimal model null states.

Mohammadhossein Toutiaee, Abbas Keshavarzi, Abolfazl Farahani, John A. Miller

We propose a novel application of Transfer Learning to classify video-frame sequences over multiple classes. This is a pre-weighted model that does not require to train a fresh CNN. This representation is achieved with the advent of "deep neural network" (DNN), which is being studied these days by many researchers. We utilize the classical approaches for vid

Melissa A. Guidry, Ki Youl Yang, Daniil M. Lukin, Ashot Markosyan

Silicon carbide (SiC) is rapidly emerging as a leading platform for the implementation of nonlinear and quantum photonics. Here, we find that commercial SiC, which hosts a variety of spin qubits, possesses low optical absorption that can enable SiC integrated photonics with quality factors exceeding $10^7$. We fabricate microring resonators with quality fact

Svante Janson, Debleena Thacker

We consider the continuous-time version of the random digital search tree, and construct a coupling with a border aggregation model as studied in Thacker and Volkov (2018), showing a relation between the height of the tree and the time required for aggregation. This relation carries over to the corresponding discrete-time models. As a consequence we find a v

Sima E. Borujeni, Saideep Nannapaneni, Nam H. Nguyen, Elizabeth C. Behrman

Probabilistic graphical models such as Bayesian networks are widely used to model stochastic systems to perform various types of analysis such as probabilistic prediction, risk analysis, and system health monitoring, which can become computationally expensive in large-scale systems. While demonstrations of true quantum supremacy remain rare, quantum computin

S. Anand, S. Porkodi

This paper presents a new auto zeroing technique that combines very high-speed operation, low power consumption, and low input switching interferences. This technique has been applied to the design of CMOS flash Analog-to-Digital converter for Ultra-Wide Band applications. This project is dealt with the design of an auto zeroing amplifier with three stages.

Vicenţiu D. Rădulescu, Dušan D. Repovš

We study the existence of nontrivial weak solutions for a class of generalized $p(x)$-biharmonic equations with singular nonlinearity and Navier boundary condition. The proofs combine variational and topological arguments. The approach developed in this paper allows for the treatment of several classes of singular biharmonic problems with variable growth ari

Oleg Vasilyev, Kathryn Evans, Anna Venancio-Marques, John Bohannon

We present an approach to generating topics using a model trained only for document title generation, with zero examples of topics given during training. We leverage features that capture the relevance of a candidate span in a document for the generation of a title for that document. The output is a weighted collection of the phrases that are most relevant f

Mohammadhossein Toutiaee, Soheyla Amirian, John A. Miller, Sheng Li

Equal Opportunity and Fairness are receiving increasing attention in artificial intelligence. Stereotyping is another source of discrimination, which yet has been unstudied in literature. GAN-made faces would be exposed to such discrimination, if they are classified by human perception. It is possible to eliminate the human impact on fictitious faces classif

Atanu Betal, Jayanta Bera, Satyajit Sahu

Two-dimensional (2D) metal halides have received more attention because of their electronic and optoelectronic properties. Recently, researchers are interested to investigate the thermoelectric properties of metal halide monolayers because of their ultralow lattice conductivity, high Seebeck coefficient and figure of merit. Here, we have investigated thermoe

Balsam Alkouz, Athman Bouguettaya, Sajib Mistry

We propose a novel framework for composing Swarm-based Drone-as-a-Service (SDaaS) for delivery. Two composition approaches, i.e., sequential and parallel are designed considering the different behaviors of drone swarms. The proposed framework considers various constraints, e.g., recharging time and limited battery to meet delivery deadlines. We propose SDaaS

Xiao Zhang, Haoyi Xiong, Dongrui Wu

Over-parameterized deep neural networks (DNNs) with sufficient capacity to memorize random noise can achieve excellent generalization performance, challenging the bias-variance trade-off in classical learning theory. Recent studies claimed that DNNs first learn simple patterns and then memorize noise; some other works showed a phenomenon that DNNs have a spe

Chien-Ming Chi, Patrick Vossler, Yingying Fan, Jinchi Lv

As a flexible nonparametric learning tool, the random forests algorithm has been widely applied to various real applications with appealing empirical performance, even in the presence of high-dimensional feature space. Unveiling the underlying mechanisms has led to some important recent theoretical results on the consistency of the random forests algorithm a

Baolin Peng, Chenguang Zhu, Michael Zeng, Jianfeng Gao

The training of spoken language understanding (SLU) models often faces the problem of data scarcity. In this paper, we put forward a data augmentation method using pretrained language models to boost the variability and accuracy of generated utterances. Furthermore, we investigate and propose solutions to two previously overlooked semi-supervised learning sc

Driss Takir, Theodore Kareta, Joshua P. Emery, Josef Hanus

Asteroid (3200) Phaethon is an active near-Earth asteroid and the parent body of the Geminid Meteor Shower. Because of its small perihelion distance, Phaethon's surface reaches temperatures sufficient to destabilize hydrated materials. We conducted rotationally resolved spectroscopic observations of this asteroid, mostly covering the northern hemisphere and

Jia Song, Luyu Wang, Liang Zhang, Kaiqi Wu

We systematically calculate the structure, formation enthalpy, formation free energy, elastic constants and electronic structure of Ti$_{0.98}$X$_{0.02}$ system by density functional theory (DFT) simulations to explore the effect of transition metal X (X=Ag, Cd, Co, Cr, Cu, Fe, Mn, Mo, Nb, Ni, Pd, Rh, Ru, Tc, and Zn) on the stability mechanism of $\beta$-tit

Fariza Fauzi, Mohammed Belkhatir

The contextual information of Web images is investigated to address the issue of enriching their index characterizations with semantic descriptors and therefore bridge the semantic gap (i.e. the gap between the low-level content-based description of images and their semantic interpretation). Although we are highly motivated by the availability of rich knowle

Lingbo Li, Ying Fan, An Zeng, Zengru Di

This paper studies an integrated system of political and economic systems from a systematic perspective to explore the complex interaction between them, and specially analyzes the case of the US presidential election forecasting. Based on the signed association networks of industrial structure constructed by economic data, our framework simulates the diffusi

Spyros Tserkis, Jayne Thompson, Austin P. Lund, Timothy C. Ralph

We quantify the maximum amount of entanglement of formation (EoF) that can be achieved by continuous-variable states under passive operations, which we refer to as EoF-potential. Focusing, in particular, on two-mode Gaussian states we derive analytical expressions for the EoF-potential for specific classes of states. For more general states, we demonstrate t

Yian Li, Hai Zhao

Pre-trained contextualized language models such as BERT have shown great effectiveness in a wide range of downstream Natural Language Processing (NLP) tasks. However, the effective representations offered by the models target at each token inside a sequence rather than each sequence and the fine-tuning step involves the input of both sequences at one time, l

Chuan-Peng Zhang, Guang-Xing Li, Thushara Pillai, Timea Csengeri

The initial stage of star formation is a complex area study because of its high density and low temperature. Under such conditions, many molecules become depleted from the gas phase by freezing out onto dust grains. However, the deuterated species could remain gaseous and are thus ideal tracers. We investigate the gas dynamics and NH$_2$D chemistry in eight

Rajesh Kumar Mundotiya, Manish Kumar Singh, Rahul Kapur, Swasti Mishra

Corpus preparation for low-resource languages and for development of human language technology to analyze or computationally process them is a laborious task, primarily due to the unavailability of expert linguists who are native speakers of these languages and also due to the time and resources required. Bhojpuri, Magahi, and Maithili, languages of the Purv

T. Ishikawa, D. R. Brumley, T. J. Pedley

A concentrated, vertical monolayer of identical spherical squirmers, which may be bottom-heavy, and which are subjected to a linear shear flow, is modelled computationally by two different methods: Stokesian dynamics, and a lubrication-theory-based method. Inertia is negligible. The aim is to compute the effective shear viscosity and, where possible, the nor

Saman Jahani, Arkadev Roy, Alireza Marandi

Despite recent progress in nonlinear optics in wavelength-scale resonators, there are still open questions on the possibility of parametric oscillation in such resonators. We present a general approach to predict the behavior and estimate the oscillation threshold of multi-mode subwavelength and wavelength-scale optical parametric oscillators (OPOs). As an e

David Gómez-Gutiérrez

This paper aims to introduce a design methodology to stabilize a chain of integrators in a fixed-time with predefined Upper Bound for the Settling-Time (UBST). This approach is based on time-varying gains (time-base generator) that become singular as the time approaches the predefined convergence time. We present the conditions such that every nonzero trajec

Madhur Parashar, Kasturi Saha, Sharba Bandyopadhyay

Magnetic field sensing, with its recent advances, is emerging as a viable alternative to measure functional activity of single neurons in the brain by sensing action potential associated magnetic fields (APMFs). Measurement of APMFs of large axons of worms have been possible due to their size. In the mammalian brain, axon sizes, their numbers and routes, res

Praneeth Ranga, Sung Beom Cho, Rohan Mishra, Sriram Krishnamoorthy

We report on the modeling of polarization-induced two-dimensional electron gas (2DEG) formation at ${\epsilon}$-AlGaO3 / ${\epsilon}$-Ga2O3 heterointerface and the effect of spontaneous polarization (Psp) reversal on 2DEG density in ${\epsilon}$-Ga2O3 /${\epsilon}$-AlGaO3 / ${\epsilon}$-Ga2O3 double heterostructures. Density-functional theory (DFT) is utiliz

Parameswaran Raman, S. V. N. Vishwanathan

Factorization Machines (FM) are powerful class of models that incorporate higher-order interaction among features to add more expressive power to linear models. They have been used successfully in several real-world tasks such as click-prediction, ranking and recommender systems. Despite using a low-rank representation for the pairwise features, the memory o

Sophie Groenwold, Samhita Honnavalli, Lily Ou, Aesha Parekh

As NLP tools become ubiquitous in today's technological landscape, they are increasingly applied to languages with a variety of typological structures. However, NLP research does not focus primarily on typological differences in its analysis of state-of-the-art language models. As a result, NLP tools perform unequally across languages with different syntacti

Kenan Doğan, Murat Şahin, Oğuz Yayla

In this paper we study pseudorandomness of a family of sequences in terms of two measures, the family complexity ($f$-complexity) and the cross-correlation measure of order $\ell$. We consider sequences not only on binary alphabet but also on $k$-symbols ($k$-ary) alphabet. We first generalize some known methods on construction of the family of binary pseudo

Jihyung Moon, Hyunchang Cho, Eunjeong L. Park

Quality estimation (QE) is the task of automatically evaluating the quality of translations without human-translated references. Calculating BLEU between the input sentence and round-trip translation (RTT) was once considered as a metric for QE, however, it was found to be a poor predictor of translation quality. Recently, various pre-trained language models

Marcella Scoczynski Ribeiro Martins, Mohamed El Yafrani, Myriam R. B. S. Delgado, Ricardo Luders

A Local Optima Network (LON) is a graph model that compresses the fitness landscape of a particular combinatorial optimization problem based on a specific neighborhood operator and a local search algorithm. Determining which and how landscape features affect the effectiveness of search algorithms is relevant for both predicting their performance and improvin

Sam Spiro

Consider the following iterated process on a hypergraph $H$. Each vertex $v$ has an initial vertex weight. At each step, we uniformly at random select an edge $F$ in $H$, and for each vertex $v$ in $F$ we replace the weight of $v$ by the average value of the vertex weights over all vertices in $F$. This is a generalization of an interactive process on graphs

T. -S. H. Lee

Based on the vector meson dominance assumption, a Hamiltonian model has been developed to investigate $J/\Psi$ photo-production reaction on the nucleon by using the $J/\Psi$-nucleon potential extracted from a lattice QCD calculation of Phys. Rev. D{\bf 82}, 091501 (2010). It is found that the predicted total cross sections are comparable to the recent data o

Chinmay Sonar, Palash Dey, Neeldhara Misra

The Chamberlin-Courant and Monroe rules are fundamental and well-studied rules in the literature of multi-winner elections. The problem of determining if there exists a committee of size k that has a Chamberlin-Courant (respectively, Monroe) score of at most r is known to be NP-complete. We consider the following natural problems in this setting: a) given a

Md. Yasin Kabir, Sanjay Madria

Due to the nature of the data and public interaction, twitter is becoming more and more useful to understand and model various events. The goal of CoronaVis is to use tweets as the information shared by the people to visualize topic modeling, study subjectivity, and to model the human emotions during the COVID-19 pandemic. The main objective is to explore th

Hao Zhang, Aixin Sun, Wei Jing, Joey Tianyi Zhou

Given an untrimmed video and a text query, natural language video localization (NLVL) is to locate a matching span from the video that semantically corresponds to the query. Existing solutions formulate NLVL either as a ranking task and apply multimodal matching architecture, or as a regression task to directly regress the target video span. In this work, we

Zhiyong Yang, Qianqian Xu, Xiaochun Cao, Qingming Huang

As an effective learning paradigm against insufficient training samples, Multi-Task Learning (MTL) encourages knowledge sharing across multiple related tasks so as to improve the overall performance. In MTL, a major challenge springs from the phenomenon that sharing the knowledge with dissimilar and hard tasks, known as negative transfer, often results in a

Tulkin H. Rasulov, Elyor B. Dilmurodov

We consider a $2 \times 2$ operator matrix ${\mathcal A}_\mu,$ $\mu>0$ related with the lattice systems describing two identical bosons and one particle, another nature in interactions, without conservation of the number of particles. We obtain an analogue of the Faddeev equation and its symmetric version for the eigenfunctions of ${\mathcal A}_\mu$. We desc

Lachlan Ewen MacDonald

We give a new construction of the holonomy groupoid of a regular foliation in terms of a partial connection on a diffeological principal bundle of germs of transverse parametrisations. We extend these ideas to construct a novel holonomy groupoid for any foliated bundle, which we prove sits at the top of a hierarchy of diffeological jet holonomy groupoids ass

Daigorou Hirai, Hajime Sagayama, Shang Gao, Hiroyuki Ohsumi

In electronic solids with strong spin-orbit interactions (SOIs), the spin and orbital degrees of freedom of an electron are quantum mechanically entangled, which may result in an exotic multipolar order instead of a conventional dipolar order such as a magnetic order. Such a higher-degree order is called "hidden order" because of difficulties in experimental

Kaikai Pan, Peter Palensky, Peyman Mohajerin Esfahani

The main objective of this article is to develop scalable dynamic anomaly detectors when high-fidelity simulators of power systems are at our disposal. On the one hand, mathematical models of these high-fidelity simulators are typically "intractable" to apply existing model-based approaches. On the other hand, pure data-driven methods developed primarily in

Shuo Wan, Jiaxun Lu, Pingyi Fan, Khaled B. Letaief

Mobile edge computing (MEC) has been considered as a promising technique for internet of things (IoT). By deploying edge servers at the proximity of devices, it is expected to provide services and process data at a relatively low delay by intelligent networking. However, the vast edge servers may face great challenges in terms of cooperation and resource all

Jeisson Prieto, Jonatan Gomez

The major difficulty in Multi-objective Optimization Evolutionary Algorithms (MOEAs) is how to find an appropriate solution that is able to converge towards the true Pareto Front with high diversity. Most existing methodologies, which have demonstrated their niche on various practical problems involving two and three objectives, face significant challenges i

Giovanni M. Strampelli, Jonathan Aguilar, Laurent Pueyo, Antonio Aparicio

We present new results concerning the sub-stellar binary population in the Orion Nebula Cluster (ONC). Using the Karhunen-Lo\`{e}ve Image Projection (KLIP) algorithm, we have reprocessed images taken with the IR channel of the Wide Field Camera 3 mounted on the Hubble Space Telescope to unveil faint close companions in the wings of the stellar PSFs. Starting

Yury E. Geints, Alexander A. Zemlyanov, Igor V. Minin, Oleg V. Minin

The physical origin of subwavelength photonic nanojet in retrograde-reflection mode (retro-PNJ) is theoretically considered. This specific type of photonic nanojet emerges upon sequential double focusing of a plane optical wave by a transparent dielectric microparticle located near a flat reflecting mirror. For the first time to the best of our knowledge, we

Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models. In this paper, we target on revisiting Chinese pre-trained language models to examine their effectiveness in a non-English

Wen Yu Kon, Charles Ci Wen Lim

Private information retrieval (PIR) is a database query protocol that provides user privacy, in that the user can learn a particular entry of the database of his interest but his query would be hidden from the data centre. Symmetric private information retrieval (SPIR) takes PIR further by additionally offering database privacy, where the user cannot learn a

Mario Gennaro, Massimo Robberto

We exploit the ability of the Hubble Space Telescope to probe near infrared water absorption present in the atmosphere of low-mass stars, brown dwarfs and planetary mass objects to create a very pure sample of Orion Nebula Cluster (ONC) members, not affected by contamination from background stars and galaxies which lack water absorption. Thanks to these data

Anuraag Singh, Giorgio Triulzi, Christopher L. Magee

In this work, we attempt to provide a comprehensive granular account of the pace of technological change. More specifically, we survey estimated yearly performance improvement rates for nearly all definable technologies for the first time. We do this by creating a correspondence of all patents within the US patent system to a set of technology domains. A tec

Jun-Ho Choi, Jong-Seok Lee

Human activity recognition using multiple sensors is a challenging but promising task in recent decades. In this paper, we propose a deep multimodal fusion model for activity recognition based on the recently proposed feature fusion architecture named EmbraceNet. Our model processes each sensor data independently, combines the features with the EmbraceNet ar

Gabriela Garcia-Arroyo, Jorge L. Cervantes-Cota, Ulises Nucamendi, Alejandro Aviles

We study the effects of dark energy (DE) anisotropic stress on features of the matter power spectrum (PS). We employ the Parametrized Post-Friedmannian (PPF) formalism to emulate an effective DE, and model its anisotropic stress properties through a two-parameter equation that governs its overall amplitude ($g_0$) and transition scale ($c_g$). For the backgr

Hajime Nagoya

We construct a fundamental system of a $q$-difference Lax pair of rank $N$ in terms of 5d Nekrasov functions with $q=t$. Our fundamental system degenerates by the limit $q\to 1$ to a fundamental system of a differential Lax pair, which yields the Fuji-Suzuki-Tsuda system. We introduce tau functions of our system as Fourier transforms of 5d Nekrasov functions

Massimo Robberto, Mario Gennaro, Maria Giulia Ubeira Gabellini, Lynne A. Hillenbrand

In order to obtain a complete census of the stellar and sub-stellar population, down to a few M$_{Jup}$ in the $\sim1$ Myr old Orion Nebula Cluster, we used the infrared channel of the Wide Field Camera 3 of the Hubble Space Telescope with the F139M and F130N filters. These bandpasses correspond to the $1.4 \mu$m H$_2$O absorption feature and an adjacent lin

Hung Hung, Su-Yun Huang, Ching-Kang Ing

Principal component analysis (PCA) is the most commonly used statistical procedure for dimension reduction. An important issue for applying PCA is to determine the rank, which is the number of dominant eigenvalues of the covariance matrix. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) are among the most widely used rank sele

Guo-Jian Wang, Xiao-Jiao Ma, Jun-Qing Xia

In this work, we achieve the determination of the cosmic curvature $\Omega_K$ in a cosmological model-independent way, by using the Hubble parameter measurements $H(z)$ and type Ia supernovae (SNe Ia). In our analysis, two nonlinear interpolating tools are used to reconstruct the Hubble parameter, one is the Artificial Neural Network (ANN) method, and the ot

Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang

Deep neural networks (DNNs) are powerful black-box predictors that have achieved impressive performance on a wide variety of tasks. However, their accuracy comes at the cost of intelligibility: it is usually unclear how they make their decisions. This hinders their applicability to high stakes decision-making domains such as healthcare. We propose Neural Add

Yoshinori Miyazaki, Jun Korenaga

Chondrites are the likely building blocks of Earth, and identifying the group of chondrite that best represents Earth is a key to resolving the state of the early Earth. The origin of chondrites, however, remains controversial partly because of their puzzling major element compositions, some exhibiting depletion in Al, Ca, and Mg. Based on a new thermochemic

BESIII Collaboration

Using $2.93\,\rm fb^{-1}$ of $e^+e^-$ collision data taken at a center-of-mass energy of 3.773\,GeV with the BESIII detector, we report the first measurements of the absolute branching fractions of fourteen hadronic $D^{0(+)}$ decays to exclusive final states with an $\eta$, e.g., $D^0\to K^-\pi^+\eta$, $K^0_S\pi^0\eta$, $K^+K^-\eta$, $K^0_SK^0_S\eta$, $K^-\

Yiyan Yao, Xin-long Luo

In this paper, a method of improving vertical positioning accuracy with the Global Positioning System (GPS) information and barometric pressure values is proposed. Firstly, we clear null values for the raw data collected in various environments, and use the 3$\sigma$-rule to identify outliers. Secondly, the Weighted Multinomial Logistic Regression (WMLR) cla

Daniel Chin, Yian Zhang, Tianyu Zhang, Jake Zhao

Learning to play an instrument is intrinsically multimodal, and we have seen a trend of applying visual and haptic feedback in music games and computer-aided music tutoring systems. However, most current systems are still designed to master individual pieces of music; it is unclear how well the learned skills can be generalized to new pieces. We aim to explo

Mohammadreza Soltaniyeh, Richard P. Martin, Santosh Nagarakatte

This paper describes REAP, a software-hardware approach that enables high performance sparse linear algebra computations on a cooperative CPU-FPGA platform. REAP carefully separates the task of organizing the matrix elements from the computation phase. It uses the CPU to provide a first-pass re-organization of the matrix elements, allowing the FPGA to focus

Hiroshi Miki

Particular class of skew orthogonal polynomials are introduced and investigated, which possess Laurent symmetry. They are also shown to appear as eigenfunctions of symplectic generalized eigenvalue problems. The modification of these polynomials gives some symplectic eigenvalue problem and the corresponding matrix is shown to be equivalent to butterfly matri

Franco Marchesoni-Acland, Camilo Mariño, Elías Masquil, Pablo Masaferro

Improving energy efficiency is a necessity in the fight against climate change. Non Intrusive Load Monitoring (NILM) systems give important information about the household consumption that can be used by the electric utility or the end users. In this work the implementation of an end-to-end NILM system is presented, which comprises a custom high frequency me

Benjamin A. Mazin

The superconducting materials that make up an MKID have a significant effect on its performance. The $T_\textrm{c}$ and normal state resistivity $\rho_\textrm{N}$ of the film determine the penetration depth $\lambda$ and therefore how much kinetic inductance it has. The ratio of kinetic inductance to total inductance ($\alpha$), the volume of the inductor, a

Rosemary A. Renaut, Jarom D. Hogue, Saeed Vatankhah

Focusing inversion of potential field data for the recovery of sparse subsurface structures from surface measurement data on a uniform grid is discussed. For the uniform grid the model sensitivity matrices exhibit block Toeplitz Toeplitz block structure, by blocks for each depth layer of the subsurface. Then, through embedding in circulant matrices, all forw

Evan M. Yu, Juan Eugenio Iglesias, Adrian V. Dalca, Mert R. Sabuncu

Deep neural networks are powerful tools for biomedical image segmentation. These models are often trained with heavy supervision, relying on pairs of images and corresponding voxel-level labels. However, obtaining segmentations of anatomical regions on a large number of cases can be prohibitively expensive. Thus there is a strong need for deep learning-based

Alvaro Suarez, Daniel Baccino, Martin Monteiro, Arturo C. Marti

Experimental analysis of the motion in a system of two coupled oscillators with arbitrary initial conditions was performed and the normal coordinates were obtained directly. The system consisted of two gliders moving on an air track, joined together by a spring and joined by two other springs to the fixed ends. From the positions of the center of mass and th

Li Yu

We develop a new master equation as a unified description of the effects of both quantum noise (system-bath interaction) and classical noise on a system's dynamics, using a two-dimensional series expansion method. When quantum and classical noises are both present, their combined effect on a system's dynamics is not necessarily a simple sum of the two indivi

Erick Schmidt, Nikolaos Gatsis, David Akopian

This work proposes a global navigation satellite system (GNSS) spoofing detection and classification technique for single antenna receivers. We formulate an optimization problem at the baseband correlator domain by using the Least Absolute Shrinkage and Selection Operator (LASSO). We model correlator tap outputs of the received signal to form a dictionary of

Tanay Dey, Supratik Mukhopadhyay, Subhasis Chattopadhyay, Jhilam Sadukan

Resistive plate chamber (RPC) is one of the state-of-the-art particle detection technology for the High Energy Physics (HEP) experiments. The basic operating mechanism of an RPC involves ionization of gas due to the passage of charged particles followed by electron transport , avalanche, and subsequent electromagnetic induction on readout strips due to the m

D. G. Cocks, R. P. McEachran, G. J. Boyle, E. Cheng

In previous papers we have proposed a method for the ab initio calculation of fully differential cross-sections for electron scattering in liquids and applied it to liquid argon, xenon and krypton. In this paper, we extend the procedure to the consideration of positron scattering in liquid helium, which is complicated by the annihilation process as well as t

Yunyi Zhang, Jiaming Shen, Jingbo Shang, Jiawei Han

Entity set expansion, aiming at expanding a small seed entity set with new entities belonging to the same semantic class, is a critical task that benefits many downstream NLP and IR applications, such as question answering, query understanding, and taxonomy construction. Existing set expansion methods bootstrap the seed entity set by adaptively selecting con

Fabian Bolte, Stefan Bruckner

Storyline visualizations display the interactions of groups and entities and their development over time. Existing approaches have successfully adopted the general layout from hand-drawn illustrations to automatically create similar depictions. Ward Shelley is the author of several diagrammatic paintings that show the timeline of art-related subjects, such a

X. Shi, M. K. Ayoub, J. Barreiro Guimarães da Costa, H. Cui

We report on the results of a radiation campaign with neutrons and protons of Low Gain Avalanche Detectors (LGAD) produced by Hamamatsu (HPK) as prototypes for the High-Granularity Timing Detector (HGTD) in ATLAS. Sensors with an active thickness of 50~$\mu$m were irradiated in steps of roughly 2$\times$ up to a fluence of $3\times10^{15}~\mathrm{n_{eq}cm^{-

Ruoyu P. T. Wang

We study the decay of the global energy for the damped Klein-Gordon equation on non-compact manifolds with finitely many cylindrical and subconic ends up to bounded perturbation. We prove that under the Geometric Control Condition, the decay is exponential, and that under the weaker Network Control Condition, the decay is logarithmic, by developing the globa

Kenneth F. Caluya, Abhishek Halder

We consider the problem of steering the joint state probability density function of a static feedback linearizable control system over finite time horizon. Potential applications include controlling neuronal populations, swarm guidance, and probabilistic motion planning. Our theoretical developments reveal the structure of the minimum energy controller for t

Xudong Chen

We introduce in the paper a novel observability problem for a continuum ensemble of nonholonomic control systems with unknown population density. We address the problem by focussing on a prototype of such ensemble system, namely, the ensemble of Bloch equations. The dynamics of the equations are structurally identical, but show variations in Larmor dispersio

Etika Agarwal, S. Sivaranjani, Vijay Gupta, Panos Antsaklis

We consider the problem of designing distributed controllers to guarantee dissipativity of a networked system comprised of dynamically coupled subsystems. We require that the control synthesis is carried out locally at the subsystem-level, without explicit knowledge of the dynamics of other subsystems in the network. We solve this problem in two steps. First

Luca Belli, Sofia Ira Ktena, Alykhan Tejani, Alexandre Lung-Yut-Fong

Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception. Despite the fact that Twitter data has been extensively used to understand socioeconomic and political phenomena and user behaviour, the implicit feedback provided by

J. de-la-Cruz-Moreno, H. García-Compeán, E. López

The perturbative expansion of Chern-Simons gauge theory leads to invariants of knots and links, the finite type invariants or Vassiliev invariants. It has been proven that at any order in perturbation theory the resulting expression is an invariant of that order. Bott-Taubes integrals on configuration spaces are introduced in the present context to write Fey

Jiahua Chen, Yukun Liu, James Zidek

Consider a population consisting of clusters of sampling units, evolving temporally, spatially, or according to other dynamics. We wish to monitor the evolution of its means, medians, or other parameters. For administrative convenience and informativeness, clustered data are often collected via a rotating plan. Under rotating plans, the observations in the s

Janardhan Kulkarni, Shi Li, Jakub Tarnawski, Minwei Ye

We consider the classic problem of scheduling jobs with precedence constraints on a set of identical machines to minimize the makespan objective function. Understanding the exact approximability of the problem when the number of machines is a constant is a well-known question in scheduling theory. Indeed, an outstanding open problem from the classic book of

Ajay Kumar Shrestha, Sandhya Joshi, Julita Vassileva

We propose a new free eCommerce platform with blockchains that allows customers to connect to the seller directly, share personal data without losing control and ownership of it and apply it to the domain of shopping cart. Our new platform provides a solution to four important problems: private payment, ensuring privacy and user control, and incentives for s

Tasnim Mohiuddin, M Saiful Bari, Shafiq Joty

Most of the successful and predominant methods for bilingual lexicon induction (BLI) are mapping-based, where a linear mapping function is learned with the assumption that the word embedding spaces of different languages exhibit similar geometric structures (i.e., approximately isomorphic). However, several recent studies have criticized this simplified assu

Kairong Jiang, Matthew Berger, Joshua A. Levine

We introduce a new technique to visualize complex flowing phenomena by using concepts from shape analysis. Our approach uses techniques that examine the intrinsic geometry of manifolds through their heat kernel, to obtain representations of such manifolds that are isometry-invariant and multi-scale. These representations permit us to compute heat kernel sign

Andrew Vardy, Dalia S. Ibrahim

We present a new version of our previously proposed algorithm enabling a swarm of robots to construct a desired shape from objects in the plane. We also describe a hardware realization for this system which makes use of simple and readily sourced components. We refer to the task as planar construction which is the gathering of ambient objects into some desir

Roman Frolov, Peter Minev, Aziz Takhirov

This study proposes an algorithm for modeling compressible flows in spherical shells in nearly incompressible and weakly compressible regimes based on an implicit direction splitting approach. The method retains theoretically expected convergence rates and remains stable for extremely small values of the characteristic Mach number. The staggered spatial disc

Bradley Hauer, Grzegorz Kondrak

Synonymy and translational equivalence are the relations of sameness of meaning within and across languages. As the principal relations in wordnets and multi-wordnets, they are vital to computational lexical semantics, yet the field suffers from the absence of a common formal framework to define their properties and relationship. This paper proposes a unifyi

Yunyoung Hwang, Jorge Puebla, Mingran Xu, Aurelien Lagarrigue

Surface acoustic waves (SAWs) in the GHz frequency range can inject spin currents dynamically into adjacent nonmagnetic layers via spin pumping effect associated with ferromagnetic resonance. Here, we demonstrate an enhancement of acoustic ferromagnetic resonance and spin current generation by a pair of SAW reflector gratings, which form an acoustic analogue

Boris S. Mordukhovich, Dao Nguyen

The main goal of this paper is developing the method of discrete approximations to derive necessary optimality conditions for a class of constrained sweeping processes with nonsmooth perturbations. Optimal control problems for sweeping processes have been recently recognized among the most interesting and challenging problems in modern control theory for dis

Jonathan Gorard

The Wolfram Model, which is a slight generalization of the model first introduced by Stephen Wolfram in A New Kind of Science (NKS), is a discrete spacetime formalism in which space is represented by a hypergraph whose dynamics are determined by abstract replacement operations on set systems, and in which the conformal structure of spacetime is represented b