December 2020 arXiv papers — page 110

Saulo H. S. Silva, Gabriel T. Landi, Raphael C. Drumond, Emmanuel Pereira

In order to better understand the minimal ingredients for thermal rectification, we perform a detailed investigation of a simple spin chain, namely, the open XX model with a Lindblad dynamics involving global dissipators. We use a Jordan-Wigner transformation to derive a mathematical formalism to compute the heat currents and other properties of the steady s

Raffaella Margutti, Ryan Chornock

We describe the first observations of the same celestial object with gravitational waves and light. * GW170817 was the first detection of a neutron star merger with gravitational waves. * The detection of a spatially coincident weak burst of $\gamma$-rays (GRB 170817A) 1.7 s after the merger constituted the first electromagnetic detection of a gravitational

Aaron Sonabend-W, Nilanjana Laha, Ashwin N. Ananthakrishnan, Tianxi Cai

Reinforcement learning (RL) has shown great success in estimating sequential treatment strategies which take into account patient heterogeneity. However, health-outcome information, which is used as the reward for reinforcement learning methods, is often not well coded but rather embedded in clinical notes. Extracting precise outcome information is a resourc

Yichong Xu, Chenguang Zhu, Ruochen Xu, Yang Liu

Commonsense question answering (QA) requires a model to grasp commonsense and factual knowledge to answer questions about world events. Many prior methods couple language modeling with knowledge graphs (KG). However, although a KG contains rich structural information, it lacks the context to provide a more precise understanding of the concepts. This creates

Todd A. Oliynyk, J. Arturo Olvera-Santamaría

We analyze systems of semilinear wave equations in $3+1$ dimensions whose associated asymptotic equation admit bounded solutions for suitably small choices of initial data. Under this special case of the weak null condition, which we refer to as the \textit{bounded weak null condition}, we prove the existence of solutions to these systems of wave equations o

Hsueh-Yung Lin, Evgeny Shinder, Susanna Zimmermann

We initiate the study of factorization centers of birational maps, and complete it for surfaces over a perfect field in this article. We prove that for every birational automorphism $\phi : X \dashrightarrow X$ of a smooth projective surface $X$ over a perfect field $k$, the blowup centers are isomorphic to the blowdown centers in every weak factorization of

Shanmin Wang, Jian Chen, Liusuo Wu, Yusheng Zhao

Coupling of charge and lattice degrees of freedom in materials can produce intriguing electronic phenomena, such as conventional superconductivity where the electrons are mediated by lattice for creating supercurrent. The Mott transition, which is a source for many fascinating emergent behaviors, is originally thought to be driven solely by correlated electr

Harnaik Dhami, Kevin Yu, Troi Williams, Vineeth Vajipey

We study the problem of visual surface inspection of a bridge for defects using an Unmanned Aerial Vehicle (UAV). We do not assume that the geometric model of the bridge is known beforehand. Our planner, termed GATSBI, plans a path in a receding horizon fashion to inspect all points on the surface of the bridge. The input to GATSBI consists of a 3D occupancy

Fumiaki Suzuki

As an application of the theory of Lawson homology and morphic cohomology, Walker proved that the Abel-Jacobi map factors through another regular homomorphism. In this note, we give a direct proof of the theorem.

Jake Grigsby, Brandon Kriesten, Joshua Hoskins, Simonetta Liuti

We present a Machine Learning based approach to the cross section and asymmetries for deeply virtual Compton scattering from an unpolarized proton target using both an unpolarized and polarized electron beam. Machine learning methods are needed to study and eventually interpret the outcome of deeply virtual exclusive experiments since these reactions are cha

Niloy Biswas, Anirban Bhattacharya, Pierre E. Jacob, James E. Johndrow

We consider Markov chain Monte Carlo (MCMC) algorithms for Bayesian high-dimensional regression with continuous shrinkage priors. A common challenge with these algorithms is the choice of the number of iterations to perform. This is critical when each iteration is expensive, as is the case when dealing with modern data sets, such as genome-wide association s

Michael R. Douglas, Subramanian Lakshminarasimhan, Yidi Qi

We propose machine learning inspired methods for computing numerical Calabi-Yau (Ricci flat K\"ahler) metrics, and implement them using Tensorflow/Keras. We compare them with previous work, and find that they are far more accurate for manifolds with little or no symmetry. We also discuss issues such as overparameterization and choice of optimization methods.

Pasquale Bosso, Octavio Obregón, Saeed Rastgoo, Wilfredo Yupanqui

We consider the classical Hamiltonian of the interior of the Schwarzschild black hole in Ashtekar-Barbero connection formalism. Then, inspired by generalized uncertainty principle models, we deform the classical canonical algebra and derive the effective dynamics of the model under this modification. We show that such a deformation leads to the resolution of

Iori Yanokura, Naoki Wake, Kazuhiro Sasabuchi, Katsushi Ikeuchi

Learning actions from human demonstration video is promising for intelligent robotic systems. Extracting the exact section and re-observing the extracted video section in detail is important for imitating complex skills because human motions give valuable hints for robots. However, the general video understanding methods focus more on the understanding of th

Uwaise Ibna Islam, Iqbal H. Sarker, Enamul Haque, Mohammed Moshiul Hoque

Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol. Continuous use of these substances can ultimately lead a human to disastrous consequences. As patients display a high rate of relapse, prevention at an early stage can be an effective restraint. We therefore propose a binary classifie

Rony Chowdhury Ripan, Iqbal H. Sarker, Md Musfique Anwar, Md. Hasan Furhad

Cybersecurity has recently gained considerable interest in today's security issues because of the popularity of the Internet-of-Things (IoT), the considerable growth of mobile networks, and many related apps. Therefore, detecting numerous cyber-attacks in a network and creating an effective intrusion detection system plays a vital role in today's sec

Anna Mateo-Sanchis, Jordi Muñoz-Marí, Adrián Pérez-Suay, Gustau Camps-Valls

This paper introduces warped Gaussian processes (WGP) regression in remote sensing applications. WGP models output observations as a parametric nonlinear transformation of a GP. The parameters of such prior model are then learned via standard maximum likelihood. We show the good performance of the proposed model for the estimation of oceanic chlorophyll cont

Bing Liu, Yu Tang, Yuxiong Ji, Yu Shen

Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway. Previous studies generally update signal timings in real-time based on predefined traffic measures collected by point detectors, such as traffic volumes and occupancies. Comparing with point detectors, tr

Luis Gómez-Chova, Gonzalo Mateo-García, Jordi Muñoz-Marí, Gustau Camps-Valls

This paper presents the development and implementation of a cloud detection algorithm for Proba-V. Accurate and automatic detection of clouds in satellite scenes is a key issue for a wide range of remote sensing applications. With no accurate cloud masking, undetected clouds are one of the most significant sources of error in both sea and land cover biophysi

David Malmgren-Hansen, Allan Aasbjerg Nielsen, Valero Laparra, Gustau Camps- Valls

The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP). Retrieving accurate atmospheric parameters from the raw data provided by IASI is a large challenge, but necessary in order to use the data in NWP models. Statistical models performance is compromi

Michael Steininger, Daniel Abel, Katrin Ziegler, Anna Krause

Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential

Devis Tuia, Benjamin Kellenberger, Adrian Pérez-Suay, Gustau Camps-Valls

We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation (MSG) satellite. The model provides pixel-level cloud maps with related confidence and propagates information in time via a recurrent neural network structure. With a single model, we are able to

Izabela Babiarz, Wolfgang Schäfer, Antoni Szczurek

We derive the light-front wave function (LFWF) representation of the $γ^{\star} γ^{\star} \to η_{c} (1S),η_{c}(2S)$ transition form factor $F(Q^2_1,Q^2_2)$ for two virtual photons in the initial state. For the LFWF, we use different models obtained from the solution of the Schrödinger equation for a variety of $c\bar{c}$ potentials. We compare our results to

Miles Hansard, Radu Horaud

The receptive fields of simple cells in the visual cortex can be understood as linear filters. These filters can be modelled by Gabor functions, or by Gaussian derivatives. Gabor functions can also be combined in an `energy model' of the complex cell response. This paper proposes an alternative model of the complex cell, based on Gaussian derivatives. It

Yury Kolotaev, Konrad Kollnig

YouTube plays an ever more important role as a political medium. Yet, the implications are to-date not well understood and difficult to analyse, since access to YouTube's statistics is limited. To address this gap, we surveyed 124 people about their views and experiences around YouTube's political influence. Our results revealed diverse, sometimes co

Susmoy Chakraborty, Mir Tafseer Nayeem, Wasi Uddin Ahmad

Determining the readability of a text is the first step to its simplification. In this paper, we present a readability analysis tool capable of analyzing text written in the Bengali language to provide in-depth information on its readability and complexity. Despite being the 7th most spoken language in the world with 230 million native speakers, Bengali suff

Biswajit Koley, A. Satyanarayana Reddy

Let $a,b,c$ be non-zero integers and $f(x)=ax^n+bx^m+c$ be a trinomial of degree $n$. We surveyed the irreducibility criteria of $f(x)$ over rational numbers.

Xiaoxiao Li, Rabah Al-Zaidy, Amy Zhang, Stefan Baral

Objective: Systematic reviews of scholarly documents often provide complete and exhaustive summaries of literature relevant to a research question. However, well-done systematic reviews are expensive, time-demanding, and labor-intensive. Here, we propose an automatic document classification approach to significantly reduce the effort in reviewing documents.

Harrie Oosterhuis

Ranking systems form the basis for online search engines and recommendation services. They process large collections of items, for instance web pages or e-commerce products, and present the user with a small ordered selection. The goal of a ranking system is to help a user find the items they are looking for with the least amount of effort. Thus the rankings

Agapi Rissaki, Orestis Pavlou, Dimitris Fotakis, Vicky Papadopoulou

We propose an end-to-end approach for solving inverse problems for a class of complex astronomical signals, namely Spectral Energy Distributions (SEDs). Our goal is to reconstruct such signals from scarce and/or unreliable measurements. We achieve that by leveraging a learned structural prior in the form of a Deep Generative Network. Similar methods have bee

Le Trung Hieu

Stock portfolio optimization is the process of constant re-distribution of money to a pool of various stocks. In this paper, we will formulate the problem such that we can apply Reinforcement Learning for the task properly. To maintain a realistic assumption about the market, we will incorporate transaction cost and risk factor into the state as well. On top

Yanbo Song

In this paper, we proved a theorem that every large enough odd number can be represented as the sum of three almost equal Piatetski-Shapiro primes.

Parvathy Prem, Ákos Kereszturi, Ariel N. Deutsch, Charles A. Hibbitts

Understanding the origin and evolution of the lunar volatile system is not only compelling lunar science, but also fundamental Solar System science. This white paper (submitted to the US National Academies' Decadal Survey in Planetary Science and Astrobiology 2023-2032) summarizes recent advances in our understanding of lunar volatiles, identifies outsta

Signe Riemer-Sorensen, Gjert H. Rosenlund

We explore the use of deep reinforcement learning to provide strategies for long term scheduling of hydropower production. We consider a use-case where the aim is to optimise the yearly revenue given week-by-week inflows to the reservoir and electricity prices. The challenge is to decide between immediate water release at the spot price of electricity and st

Yang Yu, Zhenhao Gu, Rong Tao, Jingtian Ge

With the continuous development of machine learning technology, major e-commerce platforms have launched recommendation systems based on it to serve a large number of customers with different needs more efficiently. Compared with traditional supervised learning, reinforcement learning can better capture the user's state transition in the decision-making

Philippe Schwaller, Daniel Probst, Alain C. Vaucher, Vishnu H. Nair

Organic reactions are usually assigned to classes containing reactions with similar reagents and mechanisms. Reaction classes facilitate the communication of complex concepts and efficient navigation through chemical reaction space. However, the classification process is a tedious task. It requires the identification of the corresponding reaction class templ

David Malmgren-Hansen, Valero Laparra, Allan Aasbjerg Nielsen, Gustau Camps-Valls

In this paper we present a combined strategy for the retrieval of atmospheric profiles from infrared sounders. The approach considers the spatial information and a noise-dependent dimensionality reduction approach. The extracted features are fed into a canonical linear regression. We compare Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF)

V. P. Berezovoj, Yu. L. Bolotin, V. A. Cherkaskiy, M. I. Konchantnyi

We consider three different approaches to analyze the quantum mechanical problems in multi-well potentials: i) the standard matrix diagonalization technique in the basis sets of harmonic oscillator eigenfunctions or plain waves; ii) the spectral method, which allows to reconstruct the spectrum and stationary functions based on the time-dependent solution of

R. M. L. N. Kumari, G. A. C. T. Bandara, Maheshi B. Dissanayake

Fabric defect detection is a crucial quality control step in the textile manufacturing industry. In this article, machine vision system based on the Sylvester Matrix Based Similarity Method (SMBSM) is proposed to automate the defect detection process. The algorithm involves six phases, namely resolution matching, image enhancement using Histogram Specificati

Johannes Braathen, Shinya Kanemura

The Higgs trilinear coupling provides a unique opportunity to study the structure of the Higgs sector and probe indirect signs of BSM Physics -- even if new states are somehow hidden. In models with extended Higgs sectors, large deviations in the Higgs trilinear coupling can appear at one loop because of non-decoupling effects in the radiative corrections in

Wlodek W. Zadrozny

This article contains a proposal to add coinduction to the computational apparatus of natural language understanding. This, we argue, will provide a basis for more realistic, computationally sound, and scalable models of natural language dialogue, syntax and semantics. Given that the bottom up, inductively constructed, semantic and syntactic structures are b

M. Hameeda, Q. Gani, B. Pourhassan, M. C. Rocca

Gravity is so different from other fundamental forces that it is now essentially treated as a non-fundamental force of entropic origin. A number of good studies have been carried out in this direction. Quantum gravity has also significantly improved our understanding by combining gravity well with quantum physics. However, there are still many impediments to

Michael G. Raymer, Tiemo Landes, Markus Allgaier, Sofiane Merkouche

While two-photon absorption (TPA) and other forms of nonlinear interactions of molecules with isolated time-frequency-entangled photon pairs (EPP) have been predicted to display a variety of fascinating effects, their potential use in practical quantum-enhanced molecular spectroscopy requires close examination. This paper presents a detailed theoretical stud

CNP Slagle, Lance Fortnow

Herein we explore a dual tree algorithm for matrix multiplication of $A\in \mathbb{R}^{M\times D}$ and $B\in\mathbb{R}^{D\times N}$, very narrowly effective if the normalized rows of $A$ and columns of $B$, treated as vectors in $\mathbb{R}^{D}$, fall into clusters of order proportionate to $Ω(D^τ)$ with radii less than $\arcsin(ε/\sqrt{2})$ on the surface o

Ali Zeynali, Bo Sun, Mohammad Hajiesmaili, Adam Wierman

The design of online algorithms has tended to focus on algorithms with worst-case guarantees, e.g., bounds on the competitive ratio. However, it is well-known that such algorithms are often overly pessimistic, performing sub-optimally on non-worst-case inputs. In this paper, we develop an approach for data-driven design of online algorithms that maintain nea

Torsten Asselmeyer-Maluga, Jerzy Krol

In this paper, we will describe the idea that dark matter partly consists of gravitational solitons (gravisolitons). The corresponding solution is valid for weak gravitational fields (weak field limit) with respect to a background metric. The stability of this soliton is connected with the existence of a special foliation and amazingly with the smoothness pr

Catherine Medlock, Alan Oppenheim, Petros Boufounos

It is well-known in classical frame theory that overcomplete representations of a given vector space provide robustness to additive noise on the frame coefficients of an unknown vector. We describe how the same robustness can be shown to exist in the context of quantum state estimation. A key element of the discussion is the application of classical frame th

Jiaruo Li, Oleg Yu. Gorobtsov, Sheena K. K. Patel, Nelson Hua

Electronic instabilities drive ordering transitions in condensed matter. Despite many advances in the microscopic understanding of the ordered states, a more nuanced and profound question often remains unanswered: how do the collective excitations influence the electronic order formation? Here, we experimentally show that a phonon affects the spin density wa

Junzhe Shi, Min Tian, Sangwoo Han, Tung-Yan Wu

Electric vehicles (EVs) have been growing rapidly in popularity in recent years and have become a future trend. It is an important aspect of user experience to know the Remaining Charging Time (RCT) of an EV with confidence. However, it is difficult to find an algorithm that accurately estimates the RCT for vehicles in the current EV market. The maximum RCT

Alberto J Hernández, Maikol Solís, Ronald A. Zúñiga-Rojas

In this paper we estimate the first-order sensitivity index of random variables within a model by reconstructing the embedding manifold of a two-dimensional cloud point. The model assumed has p predictors and a continuous outcome Y . Our method gauges the manifold through a Vietoris-Rips complex with a fixed radius for each variable. With this object, and us

Tanvir Mahmud, Shaikh Anowarul Fattah

Malaria, a life-threatening disease, infects millions of people every year throughout the world demanding faster diagnosis for proper treatment before any damages occur. In this paper, an end-to-end deep learning-based approach is proposed for faster diagnosis of malaria from thin blood smear images by making efficient optimizations of features extracted fro

Carlos M. Massera, Marco H. Terra, Denis F. Wolf

We propose a tube-based guaranteed cost model predictive controller considering a homothetic formulation for constrained linear systems subject to multiplicative structured norm-bounded uncertainties. It provides an upper bound to the general min-max model predictive control. The invariance property of the proposed tube holds for any arbitrary scaling. It yi

Toni Tan, Rene Weller, Gabriel Zachmann

We present a novel representation of compressed data structure for simultaneous bounding volume hierarchy (BVH) traversals like they appear for instance in collision detection & proximity query. The main idea is to compress bounding volume (BV) descriptors and cluster BVH into a smaller parts 'treelet' that fit into CPU cache while at the same time m

Eklavya Sarkar, Pavel Korshunov, Laurent Colbois, Sébastien Marcel

Morphing attacks is a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in face morphing attack detection is developing rapidly, however very few datasets with severa

Ravi G. Patel, Indu Manickam, Nathaniel A. Trask, Mitchell A. Wood

Physics-informed neural network architectures have emerged as a powerful tool for developing flexible PDE solvers which easily assimilate data, but face challenges related to the PDE discretization underpinning them. By instead adapting a least squares space-time control volume scheme, we circumvent issues particularly related to imposition of boundary condi

Hongzi Mao, Chenjie Gu, Miaosen Wang, Angie Chen

In modern video encoders, rate control is a critical component and has been heavily engineered. It decides how many bits to spend to encode each frame, in order to optimize the rate-distortion trade-off over all video frames. This is a challenging constrained planning problem because of the complex dependency among decisions for different video frames and th

Anatoly Fomenko, Irina Kharcheva, Vladislav Kibkalo

In the paper we discuss Fomenko conjecture on realization of topology of topology of Liouville foliaions of smooth and real-analytic integrable Hamiltonian systems by integrable billiards. Vedyushkina-Kharcheva algorithm of 3-atom realization by billiard books is described clearly in the terms of $f$-graphs. Note, that an arbitrary type of base of Liouville

Anthony Corso, Mykel J. Kochenderfer

Safety validation is important during the development of safety-critical autonomous systems but can require significant computational effort. Existing algorithms often start from scratch each time the system under test changes. We apply transfer learning to improve the efficiency of reinforcement learning based safety validation algorithms when applied to re

Carlos M. Massera, Tiago C. dos Santos, Marco H. Terra, Denis F. Wolf

The development of control techniques to maintain vehicle stability under possible loss-of-control scenarios is essential to the safe deployment of autonomous ground vehicles in public scenarios. In this paper, we propose a tube-based guaranteed cost model predictive controller for autonomous vehicles able to avoid front and rear tire saturation and to track

Harish Haresamudram, Irfan Essa, Thomas Ploetz

Feature extraction is crucial for human activity recognition (HAR) using body-worn movement sensors. Recently, learned representations have been used successfully, offering promising alternatives to manually engineered features. Our work focuses on effective use of small amounts of labeled data and the opportunistic exploitation of unlabeled data that are st

Niko Partanen, Mika Hämäläinen, Tiina Klooster

Our study presents a series of experiments on speech recognition with endangered and extinct Samoyedic languages, spoken in Northern and Southern Siberia. To best of our knowledge, this is the first time a functional ASR system is built for an extinct language. We achieve with Kamas language a Label Error Rate of 15\%, and conclude through careful error anal

Gonzalo Mateo-García, Luis Gómez-Chova, Gustau Camps-Valls

Convolutional neural networks (CNN) have proven to be state of the art methods for many image classification tasks and their use is rapidly increasing in remote sensing problems. One of their major strengths is that, when enough data is available, CNN perform an end-to-end learning without the need of custom feature extraction methods. In this work, we study

Bum Chul Kwon, Peter Achenbach, Jessica L. Dunne, William Hagopian

Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for prevention trials or the development and personalization of treatments for those affected. We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinc

Chi Zhang, Yanji Wei, Frederic Dias, Xiangyu Hu

In this paper, we present an efficient, accurate and fully Lagrangian numerical solver for modeling wave interaction with oscillating wave energy converter (OWSC). The key idea is to couple SPHinXsys, an open-source multi-physics library in unified smoothed particle hydrodynamic (SPH) framework, with Simbody which presents an object-oriented Application Prog

Rida El-Allami, Alberto Marchisio, Muhammad Shafique, Ihsen Alouani

Deep Learning (DL) algorithms have gained popularity owing to their practical problem-solving capacity. However, they suffer from a serious integrity threat, i.e., their vulnerability to adversarial attacks. In the quest for DL trustworthiness, recent works claimed the inherent robustness of Spiking Neural Networks (SNNs) to these attacks, without considerin

Naif Alshammari, Samet Akcay, Toby P. Breckon

Robust semantic scene segmentation for automotive applications is a challenging problem in two key aspects: (1) labelling every individual scene pixel and (2) performing this task under unstable weather and illumination changes (e.g., foggy weather), which results in poor outdoor scene visibility. Such visibility limitations lead to non-optimal performance o

Mika Hämäläinen, Niko Partanen, Khalid Alnajjar

Our study presents a dialect normalization method for different Finland Swedish dialects covering six regions. We tested 5 different models, and the best model improved the word error rate from 76.45 to 28.58. Contrary to results reported in earlier research on Finnish dialects, we found that training the model with one word at a time gave best results. We b

Numan Celik, Soumya Gupta, Sharib Ali, Jens Rittscher

Barrett's oesophagus (BE) is one of the early indicators of esophageal cancer. Patients with BE are monitored and undergo ablation therapies to minimise the risk, thereby making it eminent to identify the BE area precisely. Automated segmentation can help clinical endoscopists to assess and treat BE area more accurately. Endoscopy imaging of BE can inclu

T. Glaser, J. Meinecke, C. Länger, J. Heep

Synthesis of organic bi-layers on silicon was realized by a combination of surface functionalization under ultra-high vacuum (UHV) conditions and solution-based click chemistry. The silicon (001) surface was prepared with a high degree of perfection in UHV and functionalized via chemoselective adsorption of ethinyl cyclopropyl cyclooctyne from the gas phase.

Ufuk Beyaztas, Han Lin Shang

A partial least squares regression is proposed for estimating the function-on-function regression model where a functional response and multiple functional predictors consist of random curves with quadratic and interaction effects. The direct estimation of a function-on-function regression model is usually an ill-posed problem. To overcome this difficulty, i

Denis S. Grebenkov

We consider a particle diffusing outside a compact planar set and investigate its boundary local time $\ell_t$, i.e., the rescaled number of encounters between the particle and the boundary up to time $t$. In the case of a disk, this is also the (rescaled) number of encounters of two diffusing circular particles in the plane. For that case, we derive explici

Matthew J. Wahila, Nicholas F. Quackenbush, Jerzy T. Sadowski, Jon-Olaf Krisponeit

Transition metal oxides such as vanadium dioxide (VO$_2$), niobium dioxide (NbO$_2$), and titanium sesquioxide (Ti$_2$O$_3$) are known to undergo a temperature-dependent metal-insulator transition (MIT) in conjunction with a structural transition within their bulk. However, it is not typically discussed how breaking crystal symmetry via surface termination a

A. V. Sarantsev

The covariant operator expansion method used by the Bonn-Gatchina group for the analysis of the meson photoproduction data is extended on the case of meson electro-production reactions. The angular dependence of the partial waves is deduced and the obtained amplitudes are compared with those used in other analyses of the electro-production reactions

Naif Alshammari, Samet Akcay, Toby P. Breckon

Automotive scene understanding under adverse weather conditions raises a realistic and challenging problem attributable to poor outdoor scene visibility (e.g. foggy weather). However, because most contemporary scene understanding approaches are applied under ideal-weather conditions, such approaches may not provide genuinely optimal performance when compared

Javier Montalt-Tordera, Vivek Muthurangu, Andreas Hauptmann, Jennifer Anne Steeden

Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20 years, parallel imaging, temporal encoding and compressed sensing have enabled substantial speed-ups in the acquisition of MRI data, by accurately recovering missing lines of k-spa

Olga Golovneva, Charith Peris

Data sparsity is one of the key challenges associated with model development in Natural Language Understanding (NLU) for conversational agents. The challenge is made more complex by the demand for high quality annotated utterances commonly required for supervised learning, usually resulting in weeks of manual labor and high cost. In this paper, we present ou

Xingran Zhu

Cross-lingual word sense disambiguation (WSD) tackles the challenge of disambiguating ambiguous words across languages given context. The pre-trained BERT embedding model has been proven to be effective in extracting contextual information of words, and have been incorporated as features into many state-of-the-art WSD systems. In order to investigate how syn

Wenlong Mou, Ashwin Pananjady, Martin J. Wainwright

Linear fixed point equations in Hilbert spaces arise in a variety of settings, including reinforcement learning, and computational methods for solving differential and integral equations. We study methods that use a collection of random observations to compute approximate solutions by searching over a known low-dimensional subspace of the Hilbert space. Firs

Jim Wiseman

We can approximate a continuous self-map $f$ of a compact metric space by discretizing the space into a grid. Through either the map itself or a time series, $f$ induces a multivalued grid map $\mathcal F$. The dynamical properties of $\mathcal F$ depend on the resolution of the grid, and we study the persistence of these properties as we change the resoluti

Jesus Rodriguez Sanchez, Fredrik Rusek, Ove Edfors, Liang Liu

The Large Intelligent Surface (LIS) is a promising technology in the areas of wireless communication, remote sensing and positioning. It consists of a continuous radiating surface located in the proximity of the users, with the capability to communicate by transmission and reception (replacing base stations). Despite of its potential, there are numerous chal

Joshua B. Surya, Juanjuan Lu, Yuntao Xu, Hong X. Tang

Cavity nonlinear optics enables intriguing physical phenomena to occur at micro- or nano-scales with modest input powers. While this enhances capabilities in applications such as comb generation, frequency conversion and quantum optics, undesired nonlinear effects including photorefraction and thermal bistability are exacerbated. In this letter, we propose a

Kevin Chen, Junshen K. Chen, Jo Chuang, Marynel Vázquez

Conventional approaches to vision-and-language navigation (VLN) are trained end-to-end but struggle to perform well in freely traversable environments. Inspired by the robotics community, we propose a modular approach to VLN using topological maps. Given a natural language instruction and topological map, our approach leverages attention mechanisms to predic

Usmann Khan, Lun Wang, Jithendaraa Subramanian, Joseph P. Near

Today's massive scale of data collection coupled with recent surges of consumer data leaks has led to increased attention towards data privacy and related risks. Conventional data privacy protection systems focus on reducing custodial risk and lack features empowering data owners. As an end user there are limited options available to specify and enforce

Hikmet Yucel, Gulin Elibol, Ugur Yayan

Mobile robots have the capability to work in real-time autonomously. Autonomous behavior is strictly dependent on knowing the position of the mobile robot. The positioning of a mobile robot in an indoor area is a difficult task for only one sensor information is used. We proposed a system and method to locate the mobile robot via fusing signals from WIFI and

Bingcong Li, Lingda Wang, Georgios B. Giannakis, Zhizhen Zhao

Aiming at convex optimization under structural constraints, this work introduces and analyzes a variant of the Frank Wolfe (FW) algorithm termed ExtraFW. The distinct feature of ExtraFW is the pair of gradients leveraged per iteration, thanks to which the decision variable is updated in a prediction-correction (PC) format. Relying on no problem dependent par

Yuri Alexandre Aoto, Márcio Fabiano da Silva

The set of all electronic states that can be expressed as a single Slater determinant forms a submanifold, isomorphic to the Grassmannian, of the projective Hilbert space of wave functions. We explored this fact by using tools of Riemannian geometry of Grassmannians as described by Absil et. al [Acta App. Math. 80, 199 (2004)], to propose an algorithm that c

Christoph Aistleitner, Nina Gantert, Zakhar Kabluchko, Joscha Prochno

Let $(a_k)_{k\in\mathbb N}$ be a sequence of integers satisfying the Hadamard gap condition $a_{k+1}/a_k>q>1$ for all $k\in\mathbb N$, and let $$ S_n(ω) = \sum_{k=1}^n\cos(2πa_k ω),\qquad n\in\mathbb N,\;ω\in [0,1]. $$ The lacunary trigonometric sum $S_n$ is known to exhibit several properties typical for sums of independent random variables. In this paper w

Rizal Afgani

In this article we are defining a refinement of Kool-Thomas invariants of local surfaces via the equivariant $K$-theoretic invariants proposed by Nekrasov and Okounkov. Kool and Thomas defined the reduced obstruction theory for the moduli of stable pairs $\mathcal{P}_χ(X,i_{*}β)$ as the degree of the virtual class $\left[\mathcal{P}_χ(S,β)\right]^{red}$ afte

Michael Albert, Vincent Vatter

Pop-stacks are variants of stacks that were introduced by Avis and Newborn in 1981. Coincidentally, a 1982 result of Unger implies that every permutation of length n can be sorted by n-1 passes through a deterministic pop-stack. We give a new proof of this result inspired by Knuth's zero-one principle.

Hongxin Wei, Lei Feng, Rundong Wang, Bo An

Deep neural networks have been shown to easily overfit to biased training data with label noise or class imbalance. Meta-learning algorithms are commonly designed to alleviate this issue in the form of sample reweighting, by learning a meta weighting network that takes training losses as inputs to generate sample weights. In this paper, we advocate that choo

Khyati Malhan, Rodrigo A. Ibata, Nicolas F. Martin

We measure the Sun's velocity with respect to the Galactic halo using Gaia Early Data Release 3 (EDR3) observations of stellar streams. Our method relies on the fact that, in low-mass streams, the proper motion of stars should be directed along the stream structure in a non-rotating rest frame of the Galaxy, but the observed deviation arises due to the S

Ahmad F. Taha, Shen Wang, Yi Guo, Tyler H. Summers

Real-time water quality (WQ) sensors in water distribution networks (WDN) have the potential to enable network-wide observability of water quality indicators, contamination event detection, and closed-loop feedback control of WQ dynamics. To that end, prior research has investigated a wide range of methods that guide the geographic placement of WQ sensors. T

Siyuan Qiao, Yukun Zhu, Hartwig Adam, Alan Yuille

In this paper, we present ViP-DeepLab, a unified model attempting to tackle the long-standing and challenging inverse projection problem in vision, which we model as restoring the point clouds from perspective image sequences while providing each point with instance-level semantic interpretations. Solving this problem requires the vision models to predict th

Thayne Currie, Olivier Guyon, Julien Lozi, Ananya Sahoo

We describe the current on-sky performance of the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) instrument on the Subaru telescope on Maunakea, Hawaii. SCExAO is continuing to advance its AO performance, delivering H band Strehl ratios in excess of 0.9 for bright stars. We describe new advances with SCExAO's wavefront control that lead to a more

Rene Carmona

This is an expanded version of the lecture given at the AMS Short Course on Mean Field Games, on January 13, 2020 in Denver CO. The assignment was to discuss applications of Mean Field Games in finance and economics. I need to admit upfront that several of the examples reviewed in this chapter were already discussed in book form. Still, they are here accompa

Zhixiang Hu, Qianheng Du, Yu Liu, D. Graf

We report quantum oscillation measurements of LaAlGe, a Lorentz-violating type-II Weyl semimetal with tilted Weyl cones. Very small quasiparticle masses and very high Fermi velocities were detected at the Fermi surface. Whereas three main frequencies have been observed, angular dependence of two Fermi surface sheets indicates possible two-dimensional (2D) ch

Sebastian Copei, Albert Zündorf

At Kassel University we are working on a solution for bidirectional transformations based on event sourcing for about a year, now. It turned out, that the TTC 2020 migration case is a special case of a bidirectional transformation and that our approach provides a reasonable solution for it.

James B. Hartle

Many scientists seeking to understand the quantum mechanics of measurement situations (Copenhagen quantum theory) agree on its overwhelmingly successful algorithms to predict the outcomes of laboratory measurements but disagree on what these algorithms mean and how they are to be interpreted. Some of these problems are briefly described and resolutions sugge

Jean-François Fortin, Wen-Jie Ma, Valentina Prilepina, Witold Skiba

We study conformal conserved currents in arbitrary irreducible representations of the Lorentz group using the embedding space formalism. With the help of the operator product expansion, we first show that conservation conditions can be fully investigated by considering only two- and three-point correlation functions. We then find an explicitly conformally-co

Jianhang Xu, Chunyi Zhang, Linfeng Zhang, Mohan Chen

Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H$_2$O) and heavy water (D$_2$O) within the isothermal-isobaric ensemble. In particular, the deep neural network is trained based on ab initio data obtained from the strongly constrained and appropriately normed (SCAN) exchange-correlation f