October 2020 arXiv papers — page 36

Atae Rezaei Aghdam, Jason Watson, Shah J Miah, Cynthia Cliff

This paper provides a novel framework for maximizing the effectiveness of the Diabetes Group Education Program, which could be generalized in any similar problem context.

Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis

It has been consistently reported that many machine learning models are susceptible to adversarial attacks i.e., small additive adversarial perturbations applied to data points can cause misclassification. Adversarial training using empirical risk minimization is considered to be the state-of-the-art method for defense against adversarial attacks. Despite be

Saúl A. Blanco, Charles Buehrle

Here we provide three new presentations of Coxeter groups type $A$, $B$, and $D$ using prefix reversals (pancake flips) as generators. We prove these presentations are of their respective groups by using Tietze transformations on the presentations to recover the well known presentations with generators that are adjacent transpositions.

J. M. Flagg, Louis H. Kauffman, Divyamaan Sahoo

This paper is an exposition and review of the research related to the Riemann Hypothesis starting from the work of Riemann and ending with a description of the work of G. Spencer-Brown, culminating in his Denjoy proof of the RH.

Yuyang Dong, Kunihiro Takeoka, Chuan Xiao, Masafumi Oyamada

Finding joinable tables in data lakes is key procedure in many applications such as data integration, data augmentation, data analysis, and data market. Traditional approaches that find equi-joinable tables are unable to deal with misspellings and different formats, nor do they capture any semantic joins. In this paper, we propose PEXESO, a framework for joi

Shaocong Ma, Yi Zhou, Shaofeng Zou

Variance reduction techniques have been successfully applied to temporal-difference (TD) learning and help to improve the sample complexity in policy evaluation. However, the existing work applied variance reduction to either the less popular one time-scale TD algorithm or the two time-scale GTD algorithm but with a finite number of i.i.d.\ samples, and both

Kyungtaek Jun, Seokhwan Yoon, Jae-Hong Lim, SeungJoon Noh

The recent proposal of a new alignment solution for X-ray tomography, Virtual alignment method (VAM) allowed a more accurate method to remove the possible errors that limit the resolution and clarity of the reconstructed image. In the field of dentistry, the movement of patients during the scanning poses as one of the major factors hindering the final recons

Gayathiri Murugamoorthy, Naimul Khan

COVID-19, due to its accelerated spread has brought in the need to use assistive tools for faster diagnosis in addition to typical lab swab testing. Chest X-Rays for COVID cases tend to show changes in the lungs such as ground glass opacities and peripheral consolidations which can be detected by deep neural networks. However, traditional convolutional netwo

Yosuke Higuchi, Hirofumi Inaguma, Shinji Watanabe, Tetsuji Ogawa

For real-world deployment of automatic speech recognition (ASR), the system is desired to be capable of fast inference while relieving the requirement of computational resources. The recently proposed end-to-end ASR system based on mask-predict with connectionist temporal classification (CTC), Mask-CTC, fulfills this demand by generating tokens in a non-auto

Shih-Gu Huang, Moo K. Chung, Anqi Qiu, Alzheimer's Disease Neuroimaging Initiative

This paper revisits spectral graph convolutional neural networks (graph-CNNs) given in Defferrard (2016) and develops the Laplace-Beltrami CNN (LB-CNN) by replacing the graph Laplacian with the LB operator. We then define spectral filters via the LB operator on a graph. We explore the feasibility of Chebyshev, Laguerre, and Hermite polynomials to approximate

Malsha V. Perera, Ashwin De Silva

Phase unwrapping is a classical ill-posed problem which aims to recover the true phase from wrapped phase. In this paper, we introduce a novel Convolutional Neural Network (CNN) that incorporates a Spatial Quad-Directional Long Short Term Memory (SQD-LSTM) for phase unwrapping, by formulating it as a regression problem. Incorporating SQD-LSTM can circumvent

Hyunjin Lee, Young Jin Suh, Changhwa Woo

In this paper, we introduce a notion of quadratic Killing structure Jacobi operator (simply, Killing structure Jacobi operator) and its geometric meaning for real hypersurfaces in the complex two-plane Grassmannians. In addition, we give a classification theorem for Hopf real hypersurfaces with quadratic Killing structure Jacobi operator in complex two-plane

Parth Asawa, Manas Gaur, Kaushik Roy, Amit Sheth

The COVID-19 pandemic has forced public health experts to develop contingent policies to stem the spread of infection, including measures such as partial/complete lockdowns. The effectiveness of these policies has varied with geography, population distribution, and effectiveness in implementation. Consequently, some nations (e.g., Taiwan, Haiti) have been mo

Ramya Srinivasan, Kanji Uchino

With rapid progress in artificial intelligence (AI), popularity of generative art has grown substantially. From creating paintings to generating novel art styles, AI based generative art has showcased a variety of applications. However, there has been little focus concerning the ethical impacts of AI based generative art. In this work, we investigate biases

Qing Yang, Hao Wang

Heating, Ventilation, and Air Conditioning (HVAC) energy consumption accounts for a significant part of the total energy consumption of buildings and households. The ubiquitous adoption of distributed renewable energy and smart meters helps to decarbonize the HVAC energy consumption and improve energy efficiency. However, how to scale up HVAC energy manageme

Jaehyeok Han, Jieon Kim, Sangjin Lee

Digital forensic investigation is used in various areas related to digital devices including the cyber crime. This is an investigative process using many techniques, which have implemented as tools. The types of files covered by the digital forensic investigation are wide and varied, however, there is no way to express the results into a standardized format.

Qing Yang, Hao Wang

Transactive energy plays a key role in the operation and energy management of future power systems. However, the conventional operational mechanism, which follows a centralized design, is often less secure, vulnerable to malicious behaviors, and suffers from privacy leakage. In this work, we introduce blockchain technology in transactive energy to address th

Vivek Srivastava, Mayank Singh

WhatsApp Messenger is one of the most popular channels for spreading information with a current reach of more than 180 countries and 2 billion people. Its widespread usage has made it one of the most popular media for information propagation among the masses during any socially engaging event. In the recent past, several countries have witnessed its effectiv

Fumihiko Tamura, Hiroki Takahashi, Norihiko Kamikubota, Yuichi Ito

A precise and stable timing system is necessary for high intensity proton accelerators such as the J-PARC. The original timing system, which was developed during the construction period of the J-PARC, has been working without major issues since 2006. After a decade of operation, the optical modules, which are key components for signal transfer, were disconti

Liam M. Cronin, Soheil Sadeghi Eshkevari, Debarshi Sen, Shamim N. Pakzad

This study proposes a learning-based method with domain adaptability for input estimation of vehicle suspension systems. In a crowdsensing setting for bridge health monitoring, vehicles carry sensors to collect samples of the bridge's dynamic response. The primary challenge is in preprocessing; signals are highly contaminated from road profile roughness and

Babak Naderi, Gabriel Mittag, Rafael Zequeira Jim\a'enez, Sebastian Möller

Speech communication systems based on Voice-over-IP technology are frequently used by native as well as non-native speakers of a target language, e.g. in international phone calls or telemeetings. Frequently, such calls also occur in a noisy environment, making noise suppression modules necessary to increase perceived quality of experience. Whereas standard

Kleyton da Costa, Felipe Leite Coelho da Silva, Josiane da Silva Cordeiro Coelho, André de Melo Modenesi

Gross domestic product (GDP) is an important economic indicator that aggregates useful information to assist economic agents and policymakers in their decision-making process. In this context, GDP forecasting becomes a powerful decision optimization tool in several areas. In order to contribute in this direction, we investigated the efficiency of classical t

Alexander Moll

In this paper we determine two asymptotic results for Jack measures on partitions, a model defined by two specializations of Jack polynomials proposed by Borodin-Olshanski in [European J. Combin. 26.6 (2005): 795-834]. Assuming these two specializations are the same, we derive limit shapes and Gaussian fluctuations for the anisotropic profiles of these rando

Nicole F. Bell, Giorgio Busoni, Sandra Robles, Michael Virgato

Neutron stars harbour matter under extreme conditions, providing a unique testing ground for fundamental interactions. We recently developed an improved treatment of dark matter (DM) capture in neutron stars that properly incorporates many of the important physical effects, and outlined useful analytic approximations that are valid when the scattering amplit

Thomas Browning

Billey, Konvalinka, Petersen, Solfstra, and Tenner recently presented a method for counting parabolic double cosets in Coxeter groups, and used it to compute $p_n$, the number of parabolic double cosets in $S_n$, for $n\leq13$. In this paper, we derive a new formula for $p_n$ and an efficient polynomial time algorithm for evaluating this formula. We use thes

Yuta Murakami, Shintaro Takayoshi, Akihisa Koga, Philipp Werner

We study high-harmonic generation (HHG) in the one-dimensional Hubbard model in order to understand its relation to elementary excitations as well as the similarities and differences to semiconductors. The simulations are based on the infinite time-evolving block decimation (iTEBD) method and exact diagonalization. We clarify that the HHG originates from the

Takahiro Yabe, Kota Tsubouchi, Satish V Ukkusuri

COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts the locations of outbreaks is of crucial importance. Studies have shown the effectiveness of using large-scale mobility data to monitor the impacts of non-pharmaceutical interventi

David A. Anderson, Rachel E. Sapiro, Luís F. Gonçalves, Ryan Cardman

We realize and model a Rydberg-state atom interferometer for measurement of phase and intensity of radio-frequency (RF) electromagnetic waves. A phase reference is supplied to the atoms via a modulated laser beam, enabling atomic measurement of the RF wave's phase without an external RF reference wave. The RF and optical fields give rise to closed interf

Julian Berberich, Frank Allgöwer

The vector space of all input-output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given that the respective input signal is persistently exciting. This fact, which was proven in the behavioral control framework, shows that a single measured trajectory can capture the full behavi

Hao Wang, Baosen Zhang

In this paper, we derive a temporal arbitrage policy for storage via reinforcement learning. Real-time price arbitrage is an important source of revenue for storage units, but designing good strategies have proven to be difficult because of the highly uncertain nature of the prices. Instead of current model predictive or dynamic programming approaches, we us

Wei Zhao, Tianling Lyu, Yang Chen, Lei Xing

In a standard computed tomography (CT) image, pixels having the same Hounsfield Units (HU) can correspond to different materials and it is therefore challenging to differentiate and quantify materials. Dual-energy CT (DECT) is desirable to differentiate multiple materials, but DECT scanners are not widely available as single-energy CT (SECT) scanners. Here w

Shawn S. Wirts

In this paper, we derive the quadratic formula as a consequence of constructively proving the existence of standard and factored forms for general form real quadratic functions. Emphasis is put on connections to graphing of corresponding parabolas through function transformation perspectives of scaling and translation.

Nick Barrowman, Richard J. Webster

Variable trees are a new method for the exploration of discrete multivariate data. They display nested subsets and corresponding frequencies and percentages. Manual calculation of these quantities can be laborious, especially when there are many multi-level factors and missing data. Here we introduce variable trees and their implementation in the vtree R pac

Jianzhou Qu, Alexander Urban

Semipermeable silica membranes are attractive as protective coatings for metal electrocatalysts such as platinum but their impact on the catalytic properties has not been fully understood. Here, we develop a first principles formalism to investigate how silica membranes interact with the surface of platinum metal electrocatalysts to develop a better understa

Jinho Choi

In this paper, we study grant-free random access with massive multiple input multiple output (MIMO) systems. We first show that the performance of massive MIMO based grant-free random access is mainly decided by the probability of preamble collision. The implication of this is that although the number of antennas can be arbitrarily large, the (average) numbe

Xiaoyu He, Yuzu Ido, Benjamin Przybocki

The hat-guessing number is a graph invariant defined by Butler, Hajiaghayi, Kleinberg, and Leighton. We determine the hat-guessing number exactly for book graphs with sufficiently many pages, improving previously known lower bounds of He and Li and exactly matching an upper bound of Gadouleau. We prove that the hat-guessing number of $K_{3,3}$ is $3$, making

Richard Cushman, Jedrzej Snaitycki

We construct a natural transformation between the category of Aronszajn subcartesian spaces and the category of subcartesian differential spaces, which is a subcategory of Sikorski differential spaces.

Saheb Chhabra, Akshay Agarwal, Richa Singh, Mayank Vatsa

The high susceptibility of deep learning algorithms against structured and unstructured perturbations has motivated the development of efficient adversarial defense algorithms. However, the lack of generalizability of existing defense algorithms and the high variability in the performance of the attack algorithms for different databases raises several questi

Mohammad Salahshour

A large body of empirical evidence suggests that humans are willing to engage in costly punishment of defectors in public goods games. Based on such pieces of evidence, it is suggested that punishment serves an important role in promoting cooperation in humans, and possibly other species. Nevertheless, theoretical work has been unable to show how this is pos

Nilay Sanghvi, Sushant Kumar Singh, Akshay Agarwal, Mayank Vatsa

The non-intrusive nature and high accuracy of face recognition algorithms have led to their successful deployment across multiple applications ranging from border access to mobile unlocking and digital payments. However, their vulnerability against sophisticated and cost-effective presentation attack mediums raises essential questions regarding its reliabili

Zhipu Zhou, Alexander Shkolnik, Sang-Yun Oh

Factor modeling of asset returns has been a dominant practice in investment science since the introduction of the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT). The factors, which account for the systematic risk, are either specified or interpreted to be exogenous. They explain a significant portion of the risk in large portfolios

Mehak Gupta, Vishal Singh, Akshay Agarwal, Mayank Vatsa

Presentation attacks are posing major challenges to most of the biometric modalities. Iris recognition, which is considered as one of the most accurate biometric modality for person identification, has also been shown to be vulnerable to advanced presentation attacks such as 3D contact lenses and textured lens. While in the literature, several presentation a

L. Chierchia, C. E. Koudjinan

This paper continues the discussion started in [CK19] concerning Arnold's legacy on classical KAM theory and (some of) its modern developments. We prove a detailed and explicit `global' Arnold's KAM Theorem, which yields, in particular, the Whitney conjugacy of a non-degenerate, real-analytic, nearly-integrable Hamiltonian system to an integrable system on a

Xiaodong Jiang, Ronghang Zhu, Pengsheng Ji, Sheng Li

Graph, as an important data representation, is ubiquitous in many real world applications ranging from social network analysis to biology. How to correctly and effectively learn and extract information from graph is essential for a large number of machine learning tasks. Graph embedding is a way to transform and encode the data structure in high dimensional

Aref Ghaderi, Vahid Morovati, Roozbeh Dargazany

In this study, a stochastic constitutive modeling approach for elastomeric materials is developed to consider uncertainty in material behavior and its prediction. This effort leads to a demonstration of the deterministic approaches error compared to probabilistic approaches in order to calculate the probability of failure. First, the Bayesian linear regressi

Burkhard Militzer, Felipe Gonzalez-Cataldo, Shuai Zhang, Heather D. Whitley

We study nonideal mixing effects in the regime of warm dense matter (WDM) by computing the shock Hugoniot curves of BN, MgO, and MgSiO_3. First, we derive these curves from the equations of state (EOS) of the fully interacting systems, which were obtained using a combination of path integral Monte Carlo calculations at high temperature and density functional

Wesley G. Lautenschlaeger, Thaísa Tamusiunas

A Galois correspondence theorem is proved for the case of inverse semigroups acting orthogonally on commutative rings as a consequence of the Galois correspondence theorem for groupoid actions. To this end, we use a classic result of inverse semigroup theory that establishes a one-to-one correspondence between inverse semigroups and inductive groupoids.

Vladimir Kovalenko, Alexander Andrianov, Vladimir Andrianov

The properties of the light vector mesons in the presence of local parity breaking medium with a chiral imbalance are considered in the vector-meson dominance model. Applying the finite lowest-order radiatively induced local effective Lagrangian, initially developed for the QED, for the vector $\rho$ and $\omega$ mesons, we obtained the mass spectrum as a fu

Yaoqi Huang, Vineeth Chandran Suja, Javier Tajuelo, Gerald G. Fuller

Droplet interface bilayers are a convenient model system to study the physio-chemical properties of phospholipid bilayers, the major component of the cell membrane. The mechanical response of these bilayers to various external mechanical stimuli is an active area of research due to implications for cellular viability and development of artificial cells. In t

Alexander Kerr, Geo Jose, Colin Riggert, Kieran Mullen

Topological phase transitions, which do not adhere to Landau's phenomenological model (i.e. a spontaneous symmetry breaking process and vanishing local order parameters) have been actively researched in condensed matter physics. Machine learning of topological phase transitions has generally proved difficult due to the global nature of the topological indice

Lakshya Bhardwaj

In Part 1 of this series of papers, we described a general method for determining the flavor symmetry of any 5d SCFT which can be constructed by integrating out BPS particles from some 6d SCFT compactified on a circle. In this part, we apply the method to explicitly determine the flavor symmetry of those 5d SCFTs which reduce, upon a mass deformation, to som

Emna Baccour, Aiman Erbad, Amr Mohamed, Mounir Hamdi

With the emergence of smart cities, Internet of Things (IoT) devices as well as deep learning technologies have witnessed an increasing adoption. To support the requirements of such paradigm in terms of memory and computation, joint and real-time deep co-inference framework with IoT synergy was introduced. However, the distribution of Deep Neural Networks (D

Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik, Pietro Liò

Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering DNN-based approaches is improving their explainability. In this work we present CME: a concept-based model extraction framework, used for analysing DNN models via concept-based extracted models. Using two case studies (dSprites, and Caltech

Mehmet Ozan Unal, Metin Ertas, Isa Yildirim

Ionizing radiation has been the biggest concern in CT imaging. To reduce the dose level without compromising the image quality, low-dose CT reconstruction has been offered with the availability of compressed sensing based reconstruction methods. Recently, data-driven methods got attention with the rise of deep learning, the availability of high computational

Luis A. Leiva, Moises Diaz, Miguel A. Ferrer, Réjean Plamondon

Online fraud often involves identity theft. Since most security measures are weak or can be spoofed, we investigate a more nuanced and less explored avenue: behavioral biometrics via handwriting movements. This kind of data can be used to verify whether a user is operating a device or a computer application, so it is important to distinguish between human an

Lakshya Bhardwaj

A large class of 5d superconformal field theories (SCFTs) can be constructed by integrating out BPS particles from 6d SCFTs compactified on a circle. We describe a general method for extracting the flavor symmetry of any 5d SCFT lying in this class. For this purpose, we utilize the geometric engineering of 5d N=1 theories in M-theory, where the flavor symmet

Nathan Osborne, Christine B. Peterson, Marina Vannucci

Network estimation and variable selection have been extensively studied in the statistical literature, but only recently have those two challenges been addressed simultaneously. In this paper, we seek to develop a novel method to simultaneously estimate network interactions and associations to relevant covariates for count data, and specifically for composit

Efthymios Tzinis, Dimitrios Bralios, Paris Smaragdis

Recent deep learning approaches have shown great improvement in audio source separation tasks. However, the vast majority of such work is focused on improving average separation performance, often neglecting to examine or control the distribution of the results. In this paper, we propose a simple, unified gradient reweighting scheme, with a lightweight modif

Thomas Speck

At thermal equilibrium, intensive quantities like temperature and pressure have to be uniform throughout the system, restricting inhomogeneous systems composed of different phases. The paradigmatic example is the coexistence of vapor and liquid, a state that can also be observed for active Brownian particles steadily driven away from equilibrium. Recently, a

Taoufik Chtioui, Sami Mabrouk, Abdenacer Makhlouf

The purpose of this paper is to provide and study a Hom-type generalization of Jordan-Malcev-Poisson algebras, called Hom-Jordan-Malcev-Poisson algebras. We show that they are closed under twisting by suitable self-maps and give a characterization of admissible Hom-Jordan-Malcev-Poisson algebras. In addition, we introduce the notion of pseudo-Euclidian Hom-J

Jorge Alfaro, Robinson Mancilla

In this work, we present the thermodynamic study of a model that considers the black hole as a condensate of gravitons. In this model, the spacetime is not asymptotically flat because of a topological defect that introduces an angle deficit in the spacetime like in Global Monopole solutions. We have obtained a correction to the Hawking temperature plus a neg

Raúl Antonio Amaya-Almazán, Vahram Chavushyan, Victor Manuel Patiño-Álvarez

The flat-spectrum radio quasar 3C~454.3 throughout the years has presented very high activity phases (flares) in which the different wavebands increase their flux dramatically. In this work, we perform multiwavelength analysis from radio to gamma-rays and study the Mg~II~$\lambda 2798$\AA\ emission line and the UV~Fe~II band from 2008-2018. We found that an

Anastasios Papazafeiropoulos, Pandelis Kourtessis, Marco Di Renzo, Symeon Chatzinotas

Cell-free (CF) massive multiple-input-multiple-output (MIMO) has emerged as an alternative deployment for conventional cellular massive MIMO networks. Prior works relied on the strong assumption (quite idealized) that the APs are uniformly distributed, and actually, this randomness was considered during the simulation and not in the analysis. However, in pra

Kisnney Almeida, Igor Lima

We find a condition on the underlying graph of an Artin group that fully determines if it is subgroup separable. As a consequence, an Artin group is subgroup separable if and only if it can be obtained from Artin groups of ranks at most 2 via a finite sequence of free products and direct products with the infinite cyclic group. This result generalizes the Me

Qiuhan He, Andrew Robertson, James Nightingale, Shaun Cole

A fundamental prediction of the cold dark matter (CDM) model of structure formation is the existence of a vast population of dark matter haloes extending to subsolar masses. By contrast, other dark matter models, such as a warm thermal relic (WDM), predict a cutoff in the mass function at a mass which, for popular models, lies approximately between $10^7$ an

Safi Shams Muhtasimul Hoque, Chao-Yu Chen, Alphan Sahin

In this study, we propose a wideband index modulation (IM) based on circularly-shifted chirps. To derive the proposed method, we first prove that a Golay complementary pair (GCP) can be constructed by linearly combining the Fourier series of chirps. We show that Fresnel integrals and/or Bessel functions, arising from sinusoidal and linear chirps, respectivel

Anton Ratnarajah, Zhenyu Tang, Dinesh Manocha

We present a Generative Adversarial Network (GAN) based room impulse response generator (IR-GAN) for generating realistic synthetic room impulse responses (RIRs). IR-GAN extracts acoustic parameters from captured real-world RIRs and uses these parameters to generate new synthetic RIRs. We use these generated synthetic RIRs to improve far-field automatic spee

Stanley F. Dermott, Apostolos A. Christou, Dan Li, Thomas J. J. Kehoe

All asteroids are currently classified as either family, originating from the disruption of known bodies, or non-family. An outstanding question is the origin of these non-family asteroids. Were they formed individually, or as members of known families but with chaotically evolving orbits, or are they members of old ghost families, that is, asteroids with a

Andrei Okounkov

We generalize the construction of elliptic stable envelopes to actions of connected reductive groups and give a direct inductive proof of their existence and uniqueness in a rather general situation. We show these have powerful enumerative applications, in particular, to the computation of vertex functions and their monodromy.

Ayaz Akram, Anna Giannakou, Venkatesh Akella, Jason Lowe-Power

Scientific computing sometimes involves computation on sensitive data. Depending on the data and the execution environment, the HPC (high-performance computing) user or data provider may require confidentiality and/or integrity guarantees. To study the applicability of hardware-based trusted execution environments (TEEs) to enable secure scientific computing

Jana N. Guenther, Christian Hoelbling, Lukas Varnhorst

We present a method to study the semiclassical gravitational collapse of a radially symmetric scalar quantum field in a coherent initial state. The formalism utilizes a Fock space basis in the initial metric, is unitary and time reversal invariant up to numerical precision. It maintains exact compatibility of the metric with the expectation values of the ene

Ruangrawee Kitichotkul, Christopher A. Metzler, Frank Ong, Gordon Wetzstein

Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand black-boxes. Accordingly, it is challenging to know when they will work and, more importantly, when they will fail. This limitation is a major barrier to their use in safety-critica

Ke Yu, Benedikt Dorschner, Tim Colonius

We propose a multi-resolution strategy that is compatible with the lattice Green's function (LGF) technique for solving viscous, incompressible flows on unbounded domains. The LGF method exploits the regularity of a finite-volume scheme on a formally unbounded Cartesian mesh to yield robust and computationally efficient solutions. The original method is spat

Steve Zelditch

Husimi distributions of Laplace eigenfunctions are special types of `microlocal lifts' of eigenfunctions to phase space. Their weak * limits are the well-known quantum limits or microlocal defect measures of an orthonormal basis $\{ \phi_j\}$ of eigenfunctions on a Riemannian manifold $(M,g)$ . Husimi distributions are normalized mod squares of analytic cont

Christopher A. Metzler, Gordon Wetzstein

Plug and play (P&P) algorithms iteratively apply highly optimized image denoisers to impose priors and solve computational image reconstruction problems, to great effect. However, in general the "effective noise", that is the difference between the true signal and the intermediate solution, within the iterations of P&P algorithms is neither Gaussian nor whit

Matthew J. Gursky, Samuel Pérez-Ayala

Let $(M^n,g)$ be a closed Riemannian manifold of dimension $n\ge 3$. Assume $[g]$ is a conformal class for which the Conformal Laplacian $L_g$ has at least two negative eigenvalues. We show the existence of a (generalized) metric that maximizes the second eigenvalue of $L_g$ over all conformal metrics (the first eigenvalue is maximized by the Yamabe metric).

Yao Lei Xu, Kriton Konstantinidis, Danilo P. Mandic

The irregular and multi-modal nature of numerous modern data sources poses serious challenges for traditional deep learning algorithms. To this end, recent efforts have generalized existing algorithms to irregular domains through graphs, with the aim to gain additional insights from data through the underlying graph topology. At the same time, tensor-based m

Ruben Henrard, Adam-Christiaan van Roosmalen

It is well known that a resolving subcategory $\mathcal{A}$ of an abelian subcategory $\mathcal{E}$ induces several derived equivalences: a triangle equivalence $\mathbf{D}^-(\mathcal{A})\to \mathbf{D}^-(\mathcal{E})$ exists in general and furthermore restricts to a triangle equivalence $\mathbf{D}^{\mathsf{b}}(\mathcal{A})\to \mathbf{D}^{\mathsf{b}}(\mathca

Tytus Pikies, Krzysztof Turowski, Marek Kubale

In this paper we consider the problem of scheduling on parallel machines with a presence of incompatibilities between jobs. The incompatibility relation can be modeled as a complete multipartite graph in which each edge denotes a pair of jobs that cannot be scheduled on the same machine. Our research stems from the work of Bodlaender et al.~[1992, 1993]. In

Ferdinand Claude, Sergei V. Koniakhin, Anne Maître, Simon Pigeon

The dark solitons observed in a large variety of nonlinear media are unstable against the modulational (snake) instabilities and can break in vortex streets. This behavior has been investigated in nonlinear optical crystals and ultracold atomic gases. However, a deep characterization of this phenomenon is still missing. In a resonantly pumped 2D polariton su

Nikolai Larkin

An initial-boundary value problem for the generalized 2D Zakharov-Kuznetsov equation posed on the right half-strip is considered. Existence, uniqueness and the exponential decay rate of global regular solutions for small initial data are established.

Deepan Muthirayan, Pramod P. Khargonekar

In this paper, we investigate a novel control architecture and algorithm for incorporating preadaption functions. We propose a preadaptation mechanism that can augment any adaptive control scheme and improve its resilience. Through simulations of a flight control system we illustrate the effectiveness of the preadaptation mechanism in improving the adaptatio

Guido Mueller

ALPS II, the Any Light Particle Search, is a second-generation Light Shining through a Wall experiment that hunts for axion-like particles. It uses two optical cavities; one on each side of the wall, to first generate light particles from a very strong intra-cavity optical field and then turn these particles back into photons in the second cavity called the

Javad Asadollahi, Somayeh Sadeghi

Let ${\mathscr{C}}$ be an $n$-cluster tilting subcategory of an exact category $({\mathscr{A}}, {\mathscr{E}})$, where $n \geq 1$ is an integer. It is proved by Jasso that if $n> 1$, then ${\mathscr{C}}$ although is no longer exact, but has a nice structure known as $n$-exact structure. In this new structure conflations are called admissible $n$-exact sequen

Ruofeng Wen

We developed a machine learning approach that quantifies multiple aspects of the success or values in Customer Service contacts, at anytime during the interaction. Specifically, the value/reward function regarding to the turn-level behaviors across human agents, chatbots and other hybrid dialog systems is characterized by the incremental information and conf

Sergey V. Erohin, Qiyuan Ruan, Pavel B. Sorokin, Boris I. Yakobson

Nearly two-dimensional diamond, or diamane, is coveted as ultrathin $sp^3$-carbon film with unique mechanics and electro-optics. The very thinness ($~h$) makes it possible for the surface chemistry, e.g. adsorbed atoms, to shift the bulk phase thermodynamics in favor of diamond, from multilayer graphene. Thermodynamic theory coupled with atomistic first prin

Anton Baranov, Yurii Belov, Aleksei Kulikov

We study hereditary completeness of systems of exponentials on an interval such that the corresponding generating function $G$ is small outside of a lacunary sequence of intervals $I_k$. We show that, under some technical conditions, an exponential system is hereditarily complete if and only if the logarithmic length of the union of these intervals is infini

Hamid Radmard Rahmani, Carsten Koenke, Marco A. Wiering

In many reinforcement learning (RL) problems, it takes some time until a taken action by the agent reaches its maximum effect on the environment and consequently the agent receives the reward corresponding to that action by a delay called action-effect delay. Such delays reduce the performance of the learning algorithm and increase the computational costs, a

Babak Naderi, Ross Cutler

The quality of the speech communication systems, which include noise suppression algorithms, are typically evaluated in laboratory experiments according to the ITU-T Rec. P.835, in which participants rate background noise, speech signal, and overall quality separately. This paper introduces an open-source toolkit for conducting subjective quality evaluation

Ojaswi Acharya, Stella Li, David Meyer, Jasmine Noory

In topological data analysis persistence modules are used to distinguish the legitimate topological features of a finite data set from noise. Interleavings between persistence modules feature prominantly in the analysis. One can show that for $\epsilon$ positive, the collection of $\epsilon$-interleavings between two persistence modules $M$ and $N$ has the s

Klaus Schneider, Beichuan Zhang, Van Sy Mai, Lotfi Benmohamed

State-of-the-art Internet traffic engineering uses source-based explicit routing via MPLS or Segment Routing. Though widely adopted in practice, source routing can face certain inefficiencies and operational issues, caused by its use of bandwidth reservations. In this work, we make the case for Hop-by-Hop (HBH) Traffic Engineering: splitting traffic among ne

Sriram Krishna, Nishant Sinha

The established way of interfacing with most computer systems is a mouse and keyboard. Hand gestures are an intuitive and effective touchless way to interact with computer systems. However, hand gesture based systems have seen low adoption among end-users primarily due to numerous technical hurdles in detecting in-air gestures accurately. This paper presents

Ryota Ono, Alberto Marmodoro, Jakub Schusser, Yositaka Nakata

The layered 2D-material MoTe$_2$ in the T$_d$ crystal phase is a semimetal which has theoretically been predicted to possess topologically non-trivial bands corresponding to Weyl fermions. Clear experimental evidence by angle-resolved photoemission spectroscopy (ARPES) is, however, lacking, which calls for a careful examination of the relation between ground

Daniel McGinnis

We prove that every finite family of convex sets in the plane satisfying the $(4,3)$-property can be pierced by $9$ points. This improves the bound of $13$ proved by Gy\'arf\'as, Kleitman, and T\'oth in 2001.

E. Krotscheck, J. Wang

We apply parquet-diagram summation methods for the calculation of the superfluid gap in $S$-wave pairing in neutron matter for realistic nucleon-nucleon interactions such as the Argonne $v_6$ and the Reid $v_6$ potentials. It is shown that diagrammatic contributions that are outside the parquet class play an important role. These are, in variational theories

Adam Korányi

Based on the material in an old article of Wallach a short proof of the Harish-Chandra condition is given.

Alexandra Chronopoulou, Dario Stojanovski, Viktor Hangya, Alexander Fraser

This paper describes the submission of LMU Munich to the WMT 2020 unsupervised shared task, in two language directions, German<->Upper Sorbian. Our core unsupervised neural machine translation (UNMT) system follows the strategy of Chronopoulou et al. (2020), using a monolingual pretrained language generation model (on German) and fine-tuning it on both Germa

Andrew K. Hirsch, Ethan Cecchetti

Type systems designed for information-flow control commonly use a program-counter label to track the sensitivity of the context and rule out data leakage arising from effectful computation in a sensitive context. Currently, type-system designers reason about this label informally except in security proofs, where they use ad-hoc techniques. We develop a frame

Srikanth Chandar, Muvazima Mansoor, Mohina Ahmadi, Hrishikesh Badve

With the advent of 4G, there has been a huge consumption of data and the availability of mobile networks has become paramount. Also, with the burst of network traffic based on user consumption, data availability and network anomalies have increased substantially. In this paper, we introduce a novel approach, to identify the regions that have poor network con

Athanasios Bakopoulos

In this Ph.D. dissertation we study the emergence of black-hole and wormhole solutions in the framework of the Einstein-scalar-Gauss-Bonnet (EsGB) theory. Particularly we study a family of theories where the coupling function $f(\phi)$ between the scalar field of the theory and the quadratic Gauss-Bonnet gravitational term has an arbitrary form. At first, we