December 2020 arXiv papers — page 130

Alberto Martin Santa Daria, Gustavo Avila, Edit Matyus

Curvilinear kinetic energy models are developed for variational nuclear motion computations including the inter- and the low-frequency intra-molecular degrees of freedom of the formic acid dimer. The coupling of the inter- and intra-molecular modes is studied by solving the vibrational Schrödinger equation for a series of vibrational models, from two up to t

Tristan Bice

Classic work of Pierce and Dauns-Hofmann shows that biregular rings are dual to simple ring bundles over Stone spaces. We extend this duality to Steinberg rings, a purely algebraic generalisation of Steinberg algebras, and ringoid bundles over ample groupoids. We base this largely on an even more general extension of Lawson's noncommutative Stone duality, sp

Zening Yan, Chen Wu, Wenjun Guo

We investigate the quasinormal modes (QNMs) of the scalar field coupled to the Einstein's tensor in the non-commutative geometry inspired black hole spacetime. It is found that the lapse function of the non-commutative black hole metric can be represented by a Kummer's confluent hypergeometric function, which can effectively solve the problem that the numeri

Hyunsung Lee, Michael Wang, Honguk Woo

Deep Learning has been recently recognized as one of the feasible solutions to effectively address combinatorial optimization problems, which are often considered important yet challenging in various research domains. In this work, we first present how to adopt Deep Learning for real-time task scheduling through our preliminary work upon fixed priority globa

Guobin Mou, Wei Wang

Tidal disruption events (TDEs) that occur in active galactic nuclei (AGN) with dusty tori are a special class of sources. TDEs can generate ultrafast and large opening-angle wind, which will almost inevitably collide with the preexisting AGN dusty tori a few years later after the TDE outburst. The wind-torus interactions drive two kinds of shocks: the bow sh

Mitchell J. P. van Zuijlen, Hubert Lin, Kavita Bala, Sylvia C. Pont

A painter is free to modify how components of a natural scene are depicted, which can lead to a perceptually convincing image of the distal world. This signals a major difference between photos and paintings: paintings are explicitly created for human perception. Studying these painterly depictions could be beneficial to a multidisciplinary audience. In this

Jean Renault

This article extends the main results of the publication arXiv:2001.01312 to the case of a twisted groupoid. More precisely, it gives a decomposition of the C*-algebra of a twisted locally compact groupoid with Haar system in presence of a normal subgroupoid. When the normal subgroupoid and the twist over it are abelian, one obtains another twisted groupoid

Dhruval Jain, Arun D Prabhu, Shubham Vatsal, Gopi Ramena

Codeswitching has become one of the most common occurrences across multilingual speakers of the world, especially in countries like India which encompasses around 23 official languages with the number of bilingual speakers being around 300 million. The scarcity of Codeswitched data becomes a bottleneck in the exploration of this domain with respect to variou

Jia Chen, Jian Deng, Zebo Tang, Zhangbu Xu

In this paper, we investigate the kinetic freeze-out properties in relativistic heavy ion collisions at different collision energies. We present a study of standard Boltzmann-Gibbs Blast-Wave (BGBW) fits and Tsallis Blast-Wave (TBW) fits performed on the transverse momentum spectra of identified hadrons produced in Au + Au collisions at collision energies of

Manish Chugani, Shubham Vatsal, Gopi Ramena, Sukumar Moharana

With the overwhelming transition to smart phones, storing important information in the form of unstructured text has become habitual to users of mobile devices. From grocery lists to drafts of emails and important speeches, users store a lot of data in the form of unstructured text (for eg: in the Notes application) on their devices, leading to cluttering of

Nicolas Fournier

We consider the spatially homogeneous Boltzmann equation for hard potentials without cutoff. We prove that an exponential moment of order $\rho=\min\{2\gamma/(2-\nu),2\}$, with the usual notation, is immediately created. This is stronger than what happens in the case with cutoff. We also show that exponential moments of order $\rho\in (0,2]$ are propagated.

Liu Yuezhang, Bo Li, Qifeng Chen

It is well known that artificial neural networks are vulnerable to adversarial examples, in which great efforts have been made to improve the robustness. However, such examples are usually imperceptible to humans, and thus their effect on biological neural circuits is largely unknown. This paper will investigate the adversarial robustness in a simulated cere

Matheus Cavalcante, Samuel Riedel, Antonio Pullini, Luca Benini

A key challenge in scaling shared-L1 multi-core clusters towards many-core (more than 16 cores) configurations is to ensure low-latency and efficient access to the L1 memory. In this work we demonstrate that it is possible to scale up the shared-L1 architecture: We present MemPool, a 32 bit many-core system with 256 fast RV32IMA "Snitch" cores featuring appl

Kit T. Rodolfa, Hemank Lamba, Rayid Ghani

Growing use of machine learning in policy and social impact settings have raised concerns for fairness implications, especially for racial minorities. These concerns have generated considerable interest among machine learning and artificial intelligence researchers, who have developed new methods and established theoretical bounds for improving fairness, foc

Alex J. Belfield, Dorje C. Brody

Uncertainty lower bounds for parameter estimations associated with a unitary family of mixed-state density matrices are obtained by embedding the space of density matrices in the Hilbert space of square-root density matrices. In the Hilbert-space setup the measure of uncertainty is given by the skew information of the second kind, while the uncertainty lower

Jinna Fan, Shaoxiong Wu, Chang-shui Yu

We study the effect of an ancillary system on the quantum speed limit time in different non-Markovian environments. Through employing an ancillary system coupled with the quantum system of interest via hopping interaction and investigating the cases that both the quantum system and ancillary system interact with their independent/common environment, and the

Madina Abdrakhmanova, Askat Kuzdeuov, Sheikh Jarju, Yerbolat Khassanov

We present SpeakingFaces as a publicly-available large-scale multimodal dataset developed to support machine learning research in contexts that utilize a combination of thermal, visual, and audio data streams; examples include human-computer interaction, biometric authentication, recognition systems, domain transfer, and speech recognition. SpeakingFaces is

Kiran Manjunatha, Marek Behr, Felix Vogt, Stefanie Reese

From the perspective of coronary heart disease, the development of stents has come significantly far in reducing the associated mortality rate, drug-eluting stents being the epitome of innovative and effective solutions. Within this work, the intricate process of in-stent restenosis is modelled considering one of the significant growth factors and its effect

Guidan Yao, Ahmed M. Bedewy, Ness B. Shroff

In this paper, we consider transmission scheduling in a status update system, where updates are generated periodically and transmitted over a Gilbert-Elliott fading channel. The goal is to minimize the long-run average age of information (AoI) at the destination under an average energy constraint. We consider two practical cases to obtain channel state infor

Junmo Kang, Jeonghwan Kim, Suwon Shin, Sung-Hyon Myaeng

Tag recommendation relies on either a ranking function for top-$k$ tags or an autoregressive generation method. However, the previous methods neglect one of two seemingly conflicting yet desirable characteristics of a tag set: orderlessness and inter-dependency. While the ranking approach fails to address the inter-dependency among tags when they are ranked,

Zhuan Ye

In this paper, we focus on the two-dimensional surface quasi-geostrophic equation with fractional horizontal dissipation and fractional vertical thermal diffusion. On the one hand, when the dissipation powers are restricted to a suitable range, the global regularity of the surface quasi-geostrophic equation is obtained by some anisotropic embedding and inter

Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley

A wide variety of methods have been developed for identifying depression, but they focus primarily on measuring the degree to which individuals are suffering from depression currently. In this work we explore the possibility of predicting future depression using machine learning applied to longitudinal socio-demographic data. In doing so we show that data su

Xiaowei Huang, Lvzhou Li

Discrimination of unitary operations is fundamental in quantum computation and information. A lot of quantum algorithms including the well-known Deutsch-Jozsa algorithm, Simon's algorithm, and Grover's algorithm can essentially be regarded as discriminating among individual, or sets of unitary operations (oracle operators). The problem of discriminating betw

Matheus A. Tunes, Cameron Quick, Lukas Stemper, Diego S. R. Coradini

Microelectromechanical systems (MEMS) are currently supporting ground-breaking basic research in materials science and metallurgy as they allow in situ experiments on materials at the nanoscale within electron-microscopes in a wide variety of different conditions such as extreme materials dynamics under ultrafast heating and quenching rates as well as in com

Lucy L. Gao, Jacob Bien, Daniela Witten

Classical tests for a difference in means control the type I error rate when the groups are defined a priori. However, when the groups are instead defined via clustering, then applying a classical test yields an extremely inflated type I error rate. Notably, this problem persists even if two separate and independent data sets are used to define the groups an

Jin-Lei Wu, Yan Wang, Jin-Xuan Han, Yongyuan Jiang

Quantum holonomic gates hold built-in resilience to local noises and provide a promising approach for implementing fault-tolerant quantum computation. We propose to realize high-fidelity holonomic $(N+1)$-qubit controlled gates using Rydberg atoms confined in optical arrays or superconducting circuits. We identify the scheme, deduce the effective multi-body

Isaías Rodríguez, Renela M. Valladares, Alexander Valladares, David Hinojosa-Romero

In general, conventional superconductivity and magnetism are competing phenomena. In some alloys this competition is a function of the concentration of the elements. Here we show that in the palladium alloys Pd$_{1-c}$(H/D/T)$_{c}$ (Pd-ides) the increase in the concentration $c$ of the ides: hydrogen, deuterium, tritium (H/D/T), lowers the predicted magnetis

Heiko Dietrich, Bettina Eick, Xueyu Pan

The groups whose orders factorise into at most four primes have been described (up to isomorphism) in various papers. Given such an order n, this paper exhibits a new explicit and compact determination of the isomorphism types of the groups of order n together with effective algorithms to enumerate, construct, and identify these groups. The algorithms are im

Zhilin Zhang, Xiangyu Liu, Zhenzhe Zheng, Chenrui Zhang

In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue. However, most of the state-of-the-art auction mechanisms only focus on optimizing a single performance metric, e.g., either social welfare or revenue, and are not suitabl

Kazuki Ikeda, Dmitri E. Kharzeev, Yuta Kikuchi

The real-time topological susceptibility is studied in $(1+1)$-dimensional massive Schwinger model with a $θ$-term. We evaluate the real-time correlation function of electric field that represents the topological Chern-Pontryagin number density in $(1+1)$ dimensions. Near the parity-breaking critical point located at $θ=π$ and fermion mass $m$ to coupling $g

Bokui Shen, Fei Xia, Chengshu Li, Roberto Martín-Martín

We present iGibson 1.0, a novel simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes. Our environment contains 15 fully interactive home-sized scenes with 108 rooms populated with rigid and articulated objects. The scenes are replicas of real-world homes, with distribution and the layout of objects aligned

Kyung-Yong Park, Iksu Jang, Ki-Seok Kim, S. Kettemann

The competition between the indirect exchange interaction (IEC) of magnetic impurities in metals and the Kondo effect gives rise to a rich quantum phase diagram, the Doniach Diagram. In disordered metals, both the Kondo temperature and the IEC are widely distributed due to the scattering of the conduction electrons from the impurity potential. Therefore, it

Jinho Yang, Iksu Jang, Jae-Ho Han, Ki-Seok Kim

We investigate the role of generic scale invariance in a Mott transition from a U(1) spin-liquid insulator to a Landau Fermi-liquid metal, where there exist massless degrees of freedom in addition to quantum critical fluctuations. Here, the Mott quantum criticality is described by critical charge fluctuations, and additional gapless excitations are U(1) gaug

Sizhe An, Ganapati Bhat, Suat Gumussoy, Umit Ogras

Human activity recognition (HAR) research has increased in recent years due to its applications in mobile health monitoring, activity recognition, and patient rehabilitation. The typical approach is training a HAR classifier offline with known users and then using the same classifier for new users. However, the accuracy for new users can be low with this app

Gowoon Cheon, Lusann Yang, Kevin McCloskey, Evan J. Reed

Crystal structure search is a long-standing challenge in materials design. We present a dataset of more than 100,000 structural relaxations of potential battery anode materials from randomized structures using density functional theory calculations. We illustrate the usage of the dataset by training graph neural networks to predict structural relaxations fro

Michael Vennettilli, Soutick Saha, Ushasi Roy, Andrew Mugler

Temperature sensing is a ubiquitous cell behavior, but the fundamental limits to the precision of temperature sensing are poorly understood. Unlike in chemical concentration sensing, the precision of temperature sensing is not limited by extrinsic fluctuations in the temperature field itself. Instead, we find that precision is limited by the intrinsic copy n

John D. Treado, Dong Wang, Arman Boromand, Michael P. Murrell

Soft, amorphous solids such as tissues, foams, and emulsions are composed of deformable particles. However, the effect of single-particle deformability on the collective behavior of soft solids is still poorly understood. We perform numerical simulations of two-dimensional jammed packings of explicitly deformable particles to study the mechanical response of

Huan Wang, Suhas Lohit, Mike Jones, Yun Fu

Knowledge distillation (KD) is a general neural network training approach that uses a teacher model to guide the student model. Existing works mainly study KD from the network output side (e.g., trying to design a better KD loss function), while few have attempted to understand it from the input side. Especially, its interplay with data augmentation (DA) has

Chaoyue Niu, Danesh Tarapore, Klaus-Peter Zauner

Robot swarms to date are not prepared for autonomous navigation such as path planning and obstacle detection in forest floor, unable to achieve low-cost. The development of depth sensing and embedded computing hardware paves the way for swarm of terrestrial robots. The goal of this research is to improve this situation by developing low cost vision system fo

Sandipan Banerjee, Ajjen Joshi, Jay Turcot, Bryan Reimer

Distracted drivers are dangerous drivers. Equipping advanced driver assistance systems (ADAS) with the ability to detect driver distraction can help prevent accidents and improve driver safety. In order to detect driver distraction, an ADAS must be able to monitor their visual attention. We propose a model that takes as input a patch of the driver's face alo

Jinhyuk Lim, J. S. Kim, Ajinkya C. Hire, Yundi Quan

A15 Nb$_3$Si is, until now, the only high temperature superconductor produced at high pressure (~110 GPa) that has been successfully brought back to room pressure conditions in a metastable condition. Based on the current great interest in trying to create metastable-at-room-pressure high temperature superconductors produced at high pressure, we have restudi

Christine Allen-Blanchette, Kostas Daniilidis

Images encode both the state of the world and its content. The former is useful for tasks such as planning and control, and the latter for classification. The automatic extraction of this information is challenging because of the high-dimensionality and entangled encoding inherent to the image representation. This article introduces two theoretical approache

Hongda Qiu

We develop a new framework for multi-agent collision avoidance problem. The framework combined traditional pathfinding algorithm and reinforcement learning. In our approach, the agents learn whether to be navigated or to take simple actions to avoid their partners via a deep neural network trained by reinforcement learning at each time step. This framework m

Dieuthuy Pham, Minhtuan Ha, Changyan Xiao

Accurate detection of the feature points of the projected pattern plays an extremely important role in one-shot 3D reconstruction systems, especially for the ones using a grid pattern. To solve this problem, this paper proposes a grid-point detection method based on U-net. A specific dataset is designed that includes the images captured with the two-shot ima

Baldur Steingrimsson, Xuesong Fan, Anand Kulkarni, Michael C. Gao

This chapter presents an innovative framework for the application of machine learning and data analytics for the identification of alloys or composites exhibiting certain desired properties of interest. The main focus is on alloys and composites with large composition spaces for structural materials. Such alloys or composites are referred to as high-entropy

Clifford M. Krowne, Xianwei Sha

Moving beyond traditional 2D materials is now desirable to have switching capabilities (e.g., transistors). Here we propose using borophene because, as we will show in this letter, obtaining regions of the electronic bandstructure which act as valence and conduction bands, with an apparent bandgap, may be obtainable in the foreseeable future. Here for partic

Luk R. Arnaut

The excess power, energy and intensity of a random electromagnetic field above a high threshold level are characterized based on a Slepian--Kac model for upcrossings. For quasi-static fields, the probability distribution of the excess intensity in its regression approximation evolves from $χ^2_3$ to $χ^2_2$ when the threshold level increases. The excursion a

Parisa Majari, Emerson Sadurni, Mohammad Reza Setare, John Alexander Franco-Villafane

We show an implementation of a kappa-deformed Dirac equation in tight-binding arrays of photonic waveguides. This is done with a special configuration of couplings extending to second nearest neighbors. Geometric manipulations can control these evanescent couplings. A careful study of wave packet propagation is presented, including the effects of deformation

Saeed Ullah Khan, Jingli Ren

This article explores the characteristics of ergoregion, horizons and circular geodesics around a Kerr-Newman-Kasuya black hole. We investigate the effect of spin and dyonic charge parameters on ergoregion, event horizon and static limit surface of the said black hole. We observed that both electric, as well as magnetic charge parameters, results in decreasi

Erfan Mohagheghi, Joan Gubianes Gasso, Pu Li

Different types of energy sources (e.g., batteries, supercapacitors, fuel cells) can be utilized in electric vehicles to store and provide energy in the e-powertrain through power electronic devices [1-6]. The lifetime of the components in the e-powertrain depends on their load profile [7,8]. For instance, the lifetime of a battery highly depends on the dept

Erfan Mohagheghi, Joan Gubianes Gasso, Abebe Geletu, Pu Li

E-powertrain of future electric vehicles could consist of energy generation units (e.g., fuel cells and photovoltaic modules), energy storage systems (e.g., batteries and supercapacitors), energy conversion units (e.g., bidirectional DC/DC converters and DC/AC inverters) and an electric machine, which can work in both generating and motoring modes [1- 6]. An

Aninda Chakraborty, Sayan Goswami

In a recent work, N. Hindman, D. Strauss and L. Zamboni have shown that the Hales-Jewett theorem can be combined with a sufficiently well behaved homomorphisms. In this paper we will show that those combined extensions can be made if we replace the alphabet by an increasing sequence of alphabets, infact it holds for some Ramsey theoretic small sets. To obtai

Benjamin Nasmith

Three-graded root systems can be arranged into nested sequences. One exceptional sequence provides a natural means to recover some structures and symmetries familiar in the context of particle physics.

David R. Perticone

We study the properties of charmed, nonstrange, $\rm D$ mesons produced in continuum $\rm {e^+}{e^-}$ annihilations. The $ \rm {e^+}{e^-}$ collisions were generated in the energy region of the $ \rm Υ(3S)$ and $ \rm Υ(4S)$ resonances by the Cornell Electron Storage Ring, and were analyzed by the CLEO detector. We make extensive study of the decay topology $\

Chaitanya Thammineni, Hemanth Manjunatha, Ehsan T. Esfahani

This paper presents the selective use of eye-gaze information in learning human actions in Atari games. Vast evidence suggests that our eye movement convey a wealth of information about the direction of our attention and mental states and encode the information necessary to complete a task. Based on this evidence, we hypothesize that selective use of eye-gaz

Misnal Munir, Amaliyah, Moses Glorino Rumambo Pandin

This study aimed to find new perspectives on the use of humor through digital media. A qualitative approach was used to conduct this study, where data were collected through a literature review. Stress is caused by the inability of a person to adapt between desires and reality. All forms of stress are basically caused by a lack of understanding of human'

Ahad N. Zehmakan

We introduce and study a novel majority-based opinion diffusion model. Consider a graph $G$, which represents a social network. Assume that initially a subset of nodes, called seed nodes or early adopters, are colored either black or white, which correspond to positive or negative opinion regarding a consumer product or a technological innovation. Then, in e

Prasad Jayanti, Anup Joshi

Recent advances in non-volatile main memory (NVRAM) technology have spurred research on designing algorithms that are resilient to process crashes. This paper is a fuller version of our conference paper \cite{jayanti:rmeabort}, which presents the first Recoverable Mutual Exclusion (RME) algorithm that supports abortability. Our algorithm uses only the read,

Stefan Neumann, Pauli Miettinen

We study the clustering of bipartite graphs and Boolean matrix factorization in data streams. We consider a streaming setting in which the vertices from the left side of the graph arrive one by one together with all of their incident edges. We provide an algorithm that, after one pass over the stream, recovers the set of clusters on the right side of the gra

Chun Kai Ling, Fei Fang, J. Zico Kolter

A central problem in machine learning and statistics is to model joint densities of random variables from data. Copulas are joint cumulative distribution functions with uniform marginal distributions and are used to capture interdependencies in isolation from marginals. Copulas are widely used within statistics, but have not gained traction in the context of

Maya Glinchuk, Anna Morozovska, Lesya Yurchenko

The observation of ferroelectric, ferromagnetic and ferroelastic phases in thin films of binary oxides attract the broad interest of scientists and engineers. However, the theoretical consideration of observed behaviour physical nature was performed mainly for HfO2 thin films from the first principles, and in the framework of Landau-Ginzburg-Devonshire (LGD)

Pengzhan Jin, Zhen Zhang, Ioannis G. Kevrekidis, George Em Karniadakis

We propose the Poisson neural networks (PNNs) to learn Poisson systems and trajectories of autonomous systems from data. Based on the Darboux-Lie theorem, the phase flow of a Poisson system can be written as the composition of (1) a coordinate transformation, (2) an extended symplectic map and (3) the inverse of the transformation. In this work, we extend th

Zhe Mei, Alex T. Grigas, John D. Treado, Gabriel Melendez Corres

Globular proteins undergo thermal fluctuations in solution, while maintaining an overall well-defined folded structure. In particular, studies have shown that the core structure of globular proteins differs in small, but significant ways when they are solved by x-ray crystallography versus solution-based NMR spectroscopy. Given these discrepancies, it is unc

Amir Esteghamatian, Tamer A. Zaki

The dynamics of dense finite-size particles in vertical channel flows of Newtonian and viscoelastic carrier fluids are examined using particle resolved simulations. Comparison to neutrally buoyant particles in the same configuration highlights the effect of settling. The particle volume fraction is $5\%$, and a gravity field acts counter to the flow directio

Nathan Kallus

I provide a rejoinder for discussion of "More Efficient Policy Learning via Optimal Retargeting" to appear in the Journal of the American Statistical Association with discussion by Oliver Dukes and Stijn Vansteelandt; Sijia Li, Xiudi Li, and Alex Luedtkeand; and Muxuan Liang and Yingqi Zhao.

Pauli Miettinen, Stefan Neumann

The goal of Boolean Matrix Factorization (BMF) is to approximate a given binary matrix as the product of two low-rank binary factor matrices, where the product of the factor matrices is computed under the Boolean algebra. While the problem is computationally hard, it is also attractive because the binary nature of the factor matrices makes them highly interp

Rishi Sonthalia, Anna C. Gilbert

In this paper, we present a new formulation of unbalanced optimal transport called Dual Regularized Optimal Transport (DROT). We argue that regularizing the dual formulation of optimal transport results in a version of unbalanced optimal transport that leads to sparse solutions and that gives us control over mass creation and destruction. We build intuition

Kaiwen Xu, Riqiang Gao, Mirza S. Khan, Shunxing Bao

A major goal of lung cancer screening is to identify individuals with particular phenotypes that are associated with high risk of cancer. Identifying relevant phenotypes is complicated by the variation in body position and body composition. In the brain, standardized coordinate systems (e.g., atlases) have enabled separate consideration of local features fro

Meytal Rapoport-Lavie, Dan Raviv

Modern perception systems in the field of autonomous driving rely on 3D data analysis. LiDAR sensors are frequently used to acquire such data due to their increased resilience to different lighting conditions. Although rotating LiDAR scanners produce ring-shaped patterns in space, most networks analyze their data using an orthogonal voxel sampling strategy.

Andrey Tremba

Robustness of linear systems with constant coefficients is considered. There exist methods and tools for analyzing the stability of systems with random or deterministic uncertainties. At the same time, there are no approaches for the analysis of systems containing both types of parametric uncertainty. The types of robustness are reviewed and new type of &#34

Nicolas Prevot

We describe a SAT solver using both the GPU (CUDA) and the CPU with a new clause exchange strategy. The CPU runs a classic multithreaded CDCL SAT solver. EachCPU thread exports all the clauses it learns to the GPU. The GPU makes a heavy usage of bitwise operations. It notices when a clause would have been used by a CPU thread and notifies that thread, in whi

Takashi Kodama, Ribeka Tanaka, Sadao Kurohashi

Intelligent dialogue systems are expected as a new interface between humans and machines. Such an intelligent dialogue system should estimate the user's internal state (UIS) in dialogues and change its response appropriately according to the estimation result. In this paper, we model the UIS in dialogues, taking movie recommendation dialogues as examples

Ilias, Chenn, Israel Michael Sigal

We consider the Bogoliubov-de Gennes equations giving an equivalent formulation of the BCS theory of superconductivity. We are interested in static solutions with the magnetic field present. We carefully formulate the equations in the basis independent form, discuss their general features and isolate key physical classes of solutions (normal and vortex latti

Kazuma Nagao, Ludwig Mathey

The concept of Tomonaga--Luttinger liquids (TLL) on the basis of the free-boson models is ubiquitous in theoretical descriptions of low-energy properties in one-dimensional quantum systems. In this work, we develop a squeezed-field path-integral description for gapless one-dimensional systems beyond the free-boson picture of the TLL paradigm. In the squeezed

Hamed Alemohammad, Kevin Booth

Regularly updated and accurate land cover maps are essential for monitoring 14 of the 17 Sustainable Development Goals. Multispectral satellite imagery provide high-quality and valuable information at global scale that can be used to develop land cover classification models. However, such a global application requires a geographically diverse training datase

Ramil M. Khusnutdinoff, Anatolii V. Mokshin

The elastic properties of the Zr$_{50}$Cu$_{40}$Ag$_{10}$ metallic alloy, such as the bulk modulus $B$, the shear modulus $G$, the Young's modulus $E$ and the Poisson's ratio $σ$, are investigated by molecular dynamics simulation in the temperature range $T=250-2000$ K and at an external pressure of $p = 1.0$ bar. It is shown that the liquid-glass tr

Tharun Mohandoss, Aditya Kulkarni, Daniel Northrup, Ernest Mwebaze

Multi-spectral satellite imagery provides valuable data at global scale for many environmental and socio-economic applications. Building supervised machine learning models based on these imagery, however, may require ground reference labels which are not available at global scale. Here, we propose a generative model to produce multi-resolution multi-spectral

Luana Persano, Adam Szukalski, Michele Gaio, Maria Moffa

Lasers based on biological materials are attracting an increasing interest in view of their use in integrated and transient photonics. DNA as optical biopolymer in combination with highly-emissive dyes has been reported to have excellent potential in this respect, however achieving miniaturized lasing systems based on solid-state DNA shaped in different geom

Poojith Kotikalapudi, Vinayak Elangovan

This paper investigates different methods to detect obstacles ahead of a robot using a camera in the robot, an aerial camera, and an ultrasound sensor. We also explored various efficient path finding methods for the robot to navigate to the target source. Single and multi-iteration angle-based navigation algorithms were developed. The theta-based path findin

Onur Danaci, Sanjaya Lohani, Brian T. Kirby, Ryan T. Glasser

Two-qubit systems typically employ 36 projective measurements for high-fidelity tomographic estimation. The overcomplete nature of the 36 measurements suggests possible robustness of the estimation procedure to missing measurements. In this paper, we explore the resilience of machine-learning-based quantum state estimation techniques to missing measurements

Jeremy Heng, Pierre E. Jacob, Nianqiao Ju

We consider a vector of $N$ independent binary variables, each with a different probability of success. The distribution of the vector conditional on its sum is known as the conditional Bernoulli distribution. Assuming that $N$ goes to infinity and that the sum is proportional to $N$, exact sampling costs order $N^2$, while a simple Markov chain Monte Carlo

Yipin Su, Ray W. Ogden, Michel Destrade

A rectangular plate of dielectric elastomer exhibiting gradients of material properties through its thickness will deform inhomogeneously when a potential difference is applied to compliant electrodes on its major surfaces, because each plane parallel to the major surfaces will expand or contract to a different extent. Here we study the voltage-induced bendi

Dan Raphaeli, Snir Nisim

In this paper we present SPIRAP, SPinal Random Access Protocol, a new method for multiuser detection over wireless fading channel. SPIRAP combines sequential decoding with rateless Spinal code. SPIRAP appears to be an efficient protocol for transmitting small packets in a minimally controlled network and can be attractive for the Internet of Things (IOT) app

Faraj. A. Abdunabi

In this paper, we will be delving deeper into the connection between the rough theory and the ring theory precisely in the principle and maximal ideal. The rough set theory has shown by Pawlak as good formal tool for modeling and processing incomplete information in information system. The rough theory is based on two concepts the upper approximation of a gi

Yaroslav Granovskyi, Mark Malamud, Hagen Neidhardt

Let $\mathcal{G}$ be a metric noncompact connected graph with finitely many edges. The main object of the paper is the Hamiltonian ${\bf H}_α$ associated in $L^2(\mathcal{G};\mathbb{C}^m)$ with a matrix Sturm-Liouville expression and boundary delta-type conditions at each vertex. Assuming that the potential matrix is summable and applying the technique of bo

Cody Blakeney, Xiaomin Li, Yan Yan, Ziliang Zong

Deep neural networks (DNNs) have been extremely successful in solving many challenging AI tasks in natural language processing, speech recognition, and computer vision nowadays. However, DNNs are typically computation intensive, memory demanding, and power hungry, which significantly limits their usage on platforms with constrained resources. Therefore, a va

Aditya Kulkarni, Tharun Mohandoss, Daniel Northrup, Ernest Mwebaze

Semantic segmentation of satellite imagery is a common approach to identify patterns and detect changes around the planet. Most of the state-of-the-art semantic segmentation models are trained in a fully supervised way using Convolutional Neural Network (CNN). The generalization property of CNN is poor for satellite imagery because the data can be very diver

Charles N. C. Freitas, Filipe R. Cordeiro, Valmir Macario

The absence of food monitoring has contributed significantly to the increase in the population's weight. Due to the lack of time and busy routines, most people do not control and record what is consumed in their diet. Some solutions have been proposed in computer vision to recognize food images, but few are specialized in nutritional monitoring. This wor

Modestas Filipavicius, Matteo Manica, Joris Cadow, Maria Rodriguez Martinez

Less than 1% of protein sequences are structurally and functionally annotated. Natural Language Processing (NLP) community has recently embraced self-supervised learning as a powerful approach to learn representations from unlabeled text, in large part due to the attention-based context-aware Transformer models. In this work we present a modification to the

Lourival Lima, Paulo Ruffino, Francys Souza

In this survey we present the near-optimal stochastic control problem according to some recent tools in the literature. In particular, we focus on the approach of a discretization of the noise values instead of the canonical time-discretization. This is the so called {\it skeleton} structure. This allows to obtain an $ε$-optimal control in non-Markovian syst

Gabriel F. Magno, Carlos H. Grossi, Gerardo Adesso, Diogo O. Soares-Pinto

Information geometry promotes an investigation of the geometric structure of statistical manifolds, providing a series of elucidations in various areas of scientific knowledge. In the physical sciences, especially in quantum theory, this geometric method has an incredible parallel with the distinguishability of states, an ability of great value for determini

Vasiliki Petrotou

Unprojection is a theory due to Reid which constructs more complicated rings starting from simpler data. The idea of unprojection is intended for serial use. Papadakis and Neves developed a theory of parallel unprojection. In the present work we develop a new method of unprojection. Starting from a codimension 3 ideal defined by the pfaffians of a 5x5 skewsy

Michal Balcerak, Thomas Schmelzer

Rather than directly predicting future prices or returns, we follow a more recent trend in asset management and classify the state of a market based on labels. We use numerous standard labels and even construct our own ones. The labels rely on future data to be calculated, and can be used a target for training a market state classifier using an appropriate s

J. Baranski, M. Baranska, T. Zienkiewicz, R. Taranko

We study a hybrid structure, comprising the single-level quantum dot attached to the topological superconducting nanowire, inspecting dynamical transfer of the Majorana quasiparticle onto normal region. Motivated by the recent experimental realization of such heterostructure and its investigation under the stationary conditions [L. Schneider et al., https://

Xander Faber, Keith Pardue, David Zelinsky

The classical theory of the cross-ratio is a beautiful case study of the moduli of ordered points of the projective line and of invariants of the action of $PGL_2$. We generalize the theory of the cross-ratio to the setting of $S$-valued points for an arbitrary scheme $S$. To accomplish this goal, we provide a comprehensive and computationally focused treatm

Swarnendu Banerjee, Bapi Saha, Max Rietkerk, Mara Baudena

Abrupt transitions leading to algal blooms are quite well known in aquatic ecosystems and have important implications for the environment. These ecosystem shifts have been largely attributed to nutrient dynamics and food web interactions. Contamination with heavy metals such as copper can modulate such ecological interactions which in turn may impact ecosyst

Kai Sun, Yingjie Hu, Jia Song, Yunqiang Zhu

Historical maps contain rich geographic information about the past of a region. They are sometimes the only source of information before the availability of digital maps. Despite their valuable content, it is often challenging to access and use the information in historical maps, due to their forms of paper-based maps or scanned images. It is even more time-

Gal Dor

We categorify the Hecke L-functions of $\mathrm{GL}(1)$ by replacing the L-functions with "modules of zeta integrals". These modules of zeta integrals are generated by the classical L-function. This approach allows us to categorify questions regarding L-functions, as well as make their construction more canonical by avoiding the GCD procedure usually

Anna Moliterni, Davide Altamura, Rocco Lassandro, Vincent Olieric

Anthracene derivative compounds are currently investigated because of their unique physical properties (e.g., bright luminescence and emission tunability), which make them ideal candidates for advanced optoelectronic devices. Intermolecular interactions are the basis of the tunability of the optical and electronic properties of these compounds, whose predict

Adugna G. Mullissa, Diego Marcos, Devis Tuia, Martin Herold

Deep learning (DL) has proven to be a suitable approach for despeckling synthetic aperture radar (SAR) images. So far, most DL models are trained to reduce speckle that follows a particular distribution, either using simulated noise or a specific set of real SAR images, limiting the applicability of these methods for real SAR images with unknown noise statis