March 2020 arXiv papers — page 83

Raphael Memmesheimer, Nick Theisen, Dietrich Paulus

We present a simple, yet effective and flexible method for action recognition supporting multiple sensor modalities. Multivariate signal sequences are encoded in an image and are then classified using a recently proposed EfficientNet CNN architecture. Our focus was to find an approach that generalizes well across different sensor modalities without specific

Vincenzo Ambrosio

In this paper we study the following class of fractional relativistic Schr\"odinger equations: \begin{equation*} \left\{ \begin{array}{ll} (-\Delta+m^{2})^{s}u + V(\varepsilon x) u= f(u) &\mbox{ in } \mathbb{R}^{N}, \\ u\in H^{s}(\mathbb{R}^{N}), \quad u>0 &\mbox{ in } \mathbb{R}^{N}, \end{array} \right. \end{equation*} where $\varepsilon>0$ is a small param

R. Davies, D. Baron, T. Shimizu, H. Netzer

We report on the determination of electron densities, and their impact on the outflow masses and rates, measured in the central few hundred parsecs of 11 local luminous active galaxies. We show that the peak of the integrated line emission in the AGN is significantly offset from the systemic velocity as traced by the stellar absorption features, indicating t

Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni

The notion of implicit bias, or implicit regularization, has been suggested as a means to explain the surprising generalization ability of modern-days overparameterized learning algorithms. This notion refers to the tendency of the optimization algorithm towards a certain structured solution that often generalizes well. Recently, several papers have studied

Roberto Iaconi, Keiichi Maeda, Takaya Nozawa, Orsola De Marco

We study the formation of dust in the expanding gas ejected as a result of a common envelope binary interaction. In our novel approach, we apply the dust formation model of Nozawa et al. to the outputs of the 3D hydrodynamic SPH simulation performed by Iaconi et al., that involves a giant of 0.88~\ms \ and 83~\rs, with a companion of 0.6~\ms \ placed on the

Benjamin Sambale

For a finite group $G$ we investigate the smallest positive integer $e(G)$ such that the map sending $g\in G$ to $e(G)|G:C_G(g)|$ is a generalized character of $G$. It turns out that $e(G)$ is strongly influenced by local data, but behaves irregularly for non-abelian simple groups. We interpret $e(G)$ as an elementary divisor of a certain non-negative integr

Maria Bånkestad, Jens Sjölund, Jalil Taghia, Thomas Schön

We present the elliptical processes -- a family of non-parametric probabilistic models that subsumes the Gaussian process and the Student-t process. This generalization includes a range of new fat-tailed behaviors yet retains computational tractability. We base the elliptical processes on a representation of elliptical distributions as a continuous mixture o

Alexander M. Akulshin, Nafia Rahaman, Sergey A. Suslov, Dmitry Budker

We report on spiking dynamics of frequency up-converted emission at 420 nm generated on the 6P3/2-5S1/2 transition in Rb vapour two-photon excited to the 5D5/2 level with laser light at 780 and 776 nm. The spike duration is less than the natural lifetime of any excited level involved in the interaction with both continuous and pulsed pump radiation. The spik

Lu Qi, Yi Wang, Yukang Chen, Yingcong Chen

In this paper, we explore the mask representation in instance segmentation with Point-of-Interest (PoI) features. Differentiating multiple potential instances within a single PoI feature is challenging because learning a high-dimensional mask feature for each instance using vanilla convolution demands a heavy computing burden. To address this challenge, we p

Shihao Xu, Jing Fang, Xiping Hu, Edith Ngai

Human gait refers to a daily motion that represents not only mobility, but it can also be used to identify the walker by either human observers or computers. Recent studies reveal that gait even conveys information about the walker's emotion. Individuals in different emotion states may show different gait patterns. The mapping between various emotions and ga

Debojyoti Bhattacharya, Sarbeswar Pal

Let $S \subset \mathbb P^3$ be a very general sextic surface over complex numbers. Let $\mathcal{M}(H, c_2)$ be the moduli space of rank $2$ stable bundles on $S$ with fixed first Chern class $H$ and second Chern class $c_2$. In this article we study the configuration of points of certain reduced zero dimensional subschemes on $S$ satisfying Cayley-Bacharach

V. V. Khruschov, S. V. Fomichev, S. V. Semenov

Light sterile neutrinos contributions both for active neutrinos oscillations and neutrinoless double beta decay characteristics are estimated in the framework of the phenomenological model with three active and three sterile neutrinos assuming the Majorana nature of neutrino. Appearance and survival probabilities for active neutrinos with contributions of eV

Sai Yalamanchi, Tzu-Kuo Huang, Galen Clark Haynes, Nemanja Djuric

Motion prediction of surrounding vehicles is one of the most important tasks handled by a self-driving vehicle, and represents a critical step in the autonomous system necessary to ensure safety for all the involved traffic actors. Recently a number of researchers from both academic and industrial communities have focused on this important problem, proposing

Zhao Yuan

We give the definition of s-Vector control by using the complex power vector in the p-q plane to quantify the feasible operational region of battery energy storage system (BESS) and to find the optimal power set-point solution. The s-Vector control features in the avoidance of using costly optimization solvers to solve optimization models online which is als

Juan De Vicente

One of the keys to understanding the universe is the proper measurement of the relation between the luminosity distance $d_L$ and the angular diameter distance $d_A$. In 1933 Etherington deduced from general relativity the reciprocity equation $d_L=d_A(1+z)^{\gamma}$, with $\gamma=1$ for a local (non-expanding) universe. This relation has been adapted to an

Patrick Mehlitz, Leonid I. Minchenko, Alain B. Zemkoho

Partial calmness is a celebrated but restrictive property of bilevel optimization problems whose presence opens a way to the derivation of Karush--Kuhn--Tucker-type necessary optimality conditions in order to characterize local minimizers. In the past, sufficient conditions for the validity of partial calmness have been investigated. In this regard, the pres

Hyunjin Lee, Young Jin Suh

In this paper, first, we investigate the commuting property between the normal Jacobi operator~${\bar R}_N$ and the structure Jacobi operator~$R_{\xi}$ for Hopf real hypersurfaces in the complex quadric~$Q^m = SO_{m+2}/SO_mSO_2$, $m \geq 3$, which is defined by ${\bar R}_N R_{\xi} = R_{\xi}{\bar R}_N$. Moreover, a new characterization of Hopf real hypersurfa

Han Chen, Peng Lu

For accomplishing a variety of missions in challenging environments, the capability of navigating with full autonomy while avoiding unexpected obstacles is the most crucial requirement for UAVs in real applications. In this paper, we proposed such a computationally efficient obstacle avoidance trajectory planner that can be used in cluttered unknown environm

Nikhel Gupta, Christian L. Reichardt

We present a new application of deep learning to infer the masses of galaxy clusters directly from images of the microwave sky. Effectively, this is a novel approach to determining the scaling relation between a cluster's Sunyaev-Zel'dovich (SZ) effect signal and mass. The deep learning algorithm used is mResUNet, which is a modified feed-forward dee

Tatsushi Ikeda, Gregory D. Scholes

The hierarchical equations of motion (HEOM) theory is one of the standard methods to rigorously describe open quantum dynamics coupled to harmonic environments. Such a model is used to capture non-Markovian and non-perturbative effects of environments appearing in ultra-fast phenomena. In the regular framework of the HEOM theory, the environment correlation

Jean-Louis Clerc

A new formula is obtained for the holomorphic bi-differential operators on tube-type domains which are associated to the decomposition of the tensor product of two scalar holomorphic representations, thus generalizing the classical Rankin-Cohen brackets. The formula involves a family of polynomials of several variables which may be considered as a (weak) gen

Gen Wang

The Heisenberg uncertainty principle and its extensions are all still inequalities form which hold the superior approximate estimations. Based on quantum covariant Poisson bracket theory, we propose quantum geomertainty relation to modify and explain the uncertainty relation to positively give a complete description of reality that enhances the outcome of ea

Meng Bao, Fucai Lin

Topological gyrogroups, with a weaker algebraic structure without associative law, have been investigated recently. We prove that each $T_{0}$-strongly topological gyrogroup is completely regular. We also prove that every $T_{0}$-strongly topological gyrogroup with a countable pseudocharacter is submetrizable. Finally, we prove that the left coset space $G/H

Markus Haase

A consistent functional calculus approach to the spectral theorem for strongly commuting normal operators on Hilbert spaces is presented. In contrast to the common approaches using projection-valued measures or multiplication operators, here the functional calculus is not treated as a subordinate but as the central concept. Based on five simple axioms for a

Alexander Carballo, Jacob Lambert, Abraham Monrroy-Cano, David Robert Wong

In this work, we present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors, covering a range of manufacturers, models, and laser configurations. Data captured independently from each sensor includes three different environments and configurations: static targets, where objects were placed at known dista

Daniël Reijsbergen, Tien Tuan Anh Dinh

Consensus protocols are currently the bottlenecks that prevent blockchain systems from scaling. However, we argue that transaction execution is also important to the performance and security of blockchains. In other words, there are ample opportunities to speed up and further secure blockchains by reducing the cost of transaction execution. Our goal is to un

Jiang Chao, Liang Huawei, Wang Zhiling

In recent years, the use of object proposal as a preprocessing step for target detection to improve computational efficiency has become an effective method. Good object proposal methods should have high object detection recall rate and low computational cost, as well as good localization quality and repeatability. However, it is difficult for current advance

Tran Xuan Bach, Nguyen Duc Anh, Ngo Van Linh, Khoat Than

We consider how to effectively use prior knowledge when learning a Bayesian model from streaming environments where the data come infinitely and sequentially. This problem is highly important in the era of data explosion and rich sources of precious external knowledge such as pre-trained models, ontologies, Wikipedia, etc. We show that some existing approach

Robi Bhattacharjee, Kamalika Chaudhuri

A growing body of research has shown that many classifiers are susceptible to {\em{adversarial examples}} -- small strategic modifications to test inputs that lead to misclassification. In this work, we study general non-parametric methods, with a view towards understanding when they are robust to these modifications. We establish general conditions under wh

Anirban Mukherjee, Siddhartha Lal

We introduce a novel momentum space entanglement renormalization group (MERG) scheme for the topologically ordered (T.O.) ground state of the 2D Hubbard model on a square lattice (\cite{anirbanmotti,anirbanmott2}) using a unitary quantum circuit comprised of non-local unitary gates. At each MERG step, the unitary quantum circuit disentangles a set of electro

Prasuna Bandi, Anish Ghosh, Jiyoung Han

We prove a uniform effective density theorem as well as an effective counting result for a generic system comprising a polynomial with a mild homogeneous condition and several linear forms using Roger's second moment formula for the Siegel transform on the space of unimodular lattices.

Tiehang Duan, Mihir Chauhan, Mohammad Abuzar Shaikh, Jun Chu

The pattern of Electroencephalogram (EEG) signal differs significantly across different subjects, and poses challenge for EEG classifiers in terms of 1) effectively adapting a learned classifier onto a new subject, 2) retaining knowledge of known subjects after the adaptation. We propose an efficient transfer learning method, named Meta UPdate Strategy (MUPS

Ngo Van Linh, Tran Xuan Bach, Khoat Than

Learning hidden topics from data streams has become absolutely necessary but posed challenging problems such as concept drift as well as short and noisy data. Using prior knowledge to enrich a topic model is one of potential solutions to cope with these challenges. Prior knowledge that is derived from human knowledge (e.g. Wordnet) or a pre-trained model (e.

A. Smolyanitsky, A. Fang, A. F. Kazakov, E. Paulechka

We combine quantum-chemical calculations and molecular dynamics simulations to consider aqueous ion flow across non-axisymmetric nanopores in monolayer graphene and MoS$_2$. When the pore-containing membrane is subject to uniaxial tensile strains applied in various directions, the corresponding permeability exhibits considerable directional dependence. This

Sheng-cai Zhu, Xiao-zhi Yan, Jin Liu, Artem R. Oganov

Graphite and diamond are two well-known allotropes of carbon with distinct physical properties due to different atomic connectivity. Graphite has a layered structure in which the honeycomb carbon sheets can easily glide, while atoms in diamond are strongly bonded in all three dimensions. The transition from graphite to diamond has been a central subject in p

Beihao Xia, Conghao Wang, Qinmu Peng, Xinge You

It remains challenging to automatically predict the multi-agent trajectory due to multiple interactions including agent to agent interaction and scene to agent interaction. Although recent methods have achieved promising performance, most of them just consider spatial influence of the interactions and ignore the fact that temporal influence always accompanie

Hang Yuan

Given a Lagrangian fibration, we provide a natural construction of a mirror Landau-Ginzburg model consisting of a rigid analytic space, a superpotential function, and a dual fibration based on Fukaya's family Floer theory. The mirror in the B-side is constructed by the counts of holomorphic disks in the A-side together with the non-archimedean analysis and t

Viet Vo, Xingliang Yuan, Shi-Feng Sun, Joseph K. Liu

The security of our data stores is underestimated in current practice, which resulted in many large-scale data breaches. To change the status quo, this paper presents the design of ShieldDB, an encrypted document database. ShieldDB adapts the searchable encryption technique to preserve the search functionality over encrypted documents without having much imp

Emily A. Hobbs, Alexander Christensen, Brian C. Utter

The sudden arrest of motion due to confinement is commonly observed via the clogging transition in the flow of particles through a constriction. We present results of a simple experiment to elucidate a similar transition in the bidirectional flow of two species in which two species of macroscopic particles with different densities are confined in a tube and

I. A. Rather, A. A. Usmani, S. K. Patra

We study the nuclear matter properties like symmetry energy, slope parameter, curvature, skewness and incompressibility for Hybrid EoS. The hybrid EoS is constructed using the recently proposed Effective-Field-Theory motivated Relativistic Mean-Field model (E-RMF) for the hadron phase with different parameter sets and MIT Bag model for the quark phase with d

Xiaoming Liu, Qirui Li, Chao Shen, Xi Peng

Graph convolution network (GCN) attracts intensive research interest with broad applications. While existing work mainly focused on designing novel GCN architectures for better performance, few of them studied a practical yet challenging problem: How to learn GCNs from data with extremely limited annotation? In this paper, we propose a new learning method by

Florent P. Baudier, Pavlos Motakis, Thomas Schlumprecht, András Zsák

We observe that embeddings into random metrics can be fruitfully used to study the $L_1$-embeddability of lamplighter graphs or groups, and more generally lamplighter metric spaces. Once this connection has been established, several new upper bound estimates on the $L_1$-distortion of lamplighter metrics follow from known related estimates about stochastic e

Samuel M. Fischer, Martina Beck, Leif-Matthias Herborg, Mark A. Lewis

Aquatic invasive species (AIS) cause significant ecological and economic damages around the world. A major spread mechanism for AIS is traffic of boaters transporting their watercraft from invaded to uninvaded waterbodies. To inhibit the spread of AIS, several Canadian provinces and American states set up watercraft inspection stations at roadsides, where po

Zdzislaw Brzezniak, Ben Goldys, Liang Li

We consider 3D stochastic Landau-Lifshitz-Gilbert equations coupled with the Maxwell equations with the full energy. We have proved the existence and some further regularities of the weak solution.

Andrés A. Plazas

Gravitational lensing has been identified as a powerful tool to address fundamental problems in astrophysics at different scales, ranging from exoplanet identification to dark energy and dark matter characterization in cosmology. Image simulations have played a fundamental role in the realization of the full potential of gravitational lensing by providing a

Genki Kikumoto, Naohiro Torii, Koji Fukao, C. Patrick Royall

We use a mixture of a polymer and its dimer to control dynamics in a manner inspired by \emph{pinning} a fraction of the system. In our system of $\alpha$-methyl styrene, where the polymer has a glass transition at higher temperature than the dimer, at intermediate temperatures, the polymer acts to "pin" the dimer. Within this temperature range, we use diffe

Emily J. Davis, Avikar Periwal, Eric S. Cooper, Gregory Bentsen

Using an ensemble of atoms in an optical cavity, we engineer a family of nonlocal Heisenberg Hamiltonians with continuously tunable anisotropy of the spin-spin couplings. We thus gain access to a rich phase diagram, including a paramagnetic-to-ferromagnetic Ising phase transition that manifests as a diverging magnetic susceptibility at the critical point. Th

Feng Mei, Qihao Guo, Ya-Fei Yu, Liantuan Xiao

Simulating the topological phases of matter in synthetic quantum simulators is a topic of considerable interest. Given the universality of digital quantum simulators, the prospect of digitally simulating exotic topological phases is greatly enhanced. However, it is still an open question how to realize digital quantum simulation of topological phases of matt

Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín, Silvio Savarese

Imitation learning is an effective and safe technique to train robot policies in the real world because it does not depend on an expensive random exploration process. However, due to the lack of exploration, learning policies that generalize beyond the demonstrated behaviors is still an open challenge. We present a novel imitation learning framework to enabl

J. B. Rosenzweig, N. Majernik, R. R. Robles, G. Andonian

In the field of beam physics, two frontier topics have taken center stage due to their potential to enable new approaches to discovery in a wide swath of science. These areas are: advanced, high gradient acceleration techniques, and x-ray free electron lasers (XFELs). Further, there is intense interest in the marriage of these two fields, with the goal of pr

Bernadette Bucher, Karl Schmeckpeper, Nikolai Matni, Kostas Daniilidis

Model-based curiosity combines active learning approaches to optimal sampling with the information gain based incentives for exploration presented in the curiosity literature. Existing model-based curiosity methods look to approximate prediction uncertainty with approaches which struggle to scale to many prediction-planning pipelines used in robotics tasks.

Arjun Balaji, Lachlan Kelsey, Kamran Majeed, Carl Schultz

The segmentation and analysis of coronary arteries from intravascular optical coherence tomography (IVOCT) is an important aspect of diagnosing and managing coronary artery disease. Current image processing methods are hindered by the time needed to generate expert-labelled datasets and the potential for bias during the analysis. Therefore, automated, robust

Yuxi Fu, Qizhe Yang

A complete characterization of the complexity of the reachability problem for vector addition system has been open for a long time. The problem is shown to be Tower complete.

Michael Mitzenmacher, Charalampos E. Tsourakakis

In this work we consider the problem of recovering $n$ discrete random variables $x_i\in \{0,\ldots,k-1\}, 1 \leq i \leq n$ (where $k$ is constant) with the smallest possible number of queries to a noisy oracle that returns for a given query pair $(x_i,x_j)$ a noisy measurement of their modulo $k$ pairwise difference, i.e., $y_{ij} = (x_i-x_j) \mod k$. This

Christopher Yeung, Ju-Ming Tsai, Brian King, Yusaku Kawagoe

A central challenge in the development of nanophotonic structures is identifying the optimal design for a target functionality, and understanding the physical mechanisms that enable the optimized device's capabilities. Previously investigated design methods for nanophotonic structures, including both conventional optimization approaches as well as nascen

Yixuan Wang, Zhaoyang Qiu

We study the three-dimensional compressible Navier-Stokes equations coupled with the $Q$-tensor equation perturbed by a multiplicative stochastic force, which describes the motion of nematic liquid crystal flows. The local existence and uniqueness of strong pathwise solution up to a positive stopping time is established where ``strong" is in both PDE and pro

A. S. Rubtsova, K. S. Ryutin, V. N. Temlyakov

This paper is devoted to the study of a discrepancy-type characteristic -- the fixed volume discrepancy -- of the Korobov point sets in the unit cube. It was observed recently that this new characteristic allows us to obtain optimal rate of dispersion from numerical integration results. This observation motivates us to thoroughly study this new version of di

Yulong Gu, Yu Guan, Paolo Missier

Efficiently inducing high-level interpretable regularities from knowledge graphs (KGs) is an essential yet challenging task that benefits many downstream applications. In this work, we present GPFL, a probabilistic rule learner optimized to mine instantiated first-order logic rules from KGs. Instantiated rules contain constants extracted from KGs. Compared t

Shichen Deng, Jiale Yuan, Yuli Lin, Xiaoxiang Yu

Phonon engineering focuses on heat transport modulation on atomic-scale. Different from reported methods, it is shown that electric field can also modulate heat transport in ferroelectric polymers, poly(vinylidene fluoride), by both simulation and measurement. Interestingly, thermal conductivities of poly(vinylidene fluoride) array can be enhanced by a facto

Xin Guo, Anran Hu, Renyuan Xu, Junzi Zhang

This paper presents a general mean-field game (GMFG) framework for simultaneous learning and decision-making in stochastic games with a large population. It first establishes the existence of a unique Nash Equilibrium to this GMFG, and demonstrates that naively combining reinforcement learning with the fixed-point approach in classical MFGs yields unstable a

Lambert T. Leong

The purpose of this work was to perform a network analysis on the rapidly growing bitcoin transaction network. Using a web-socket API, we collected data on all transactions occurring during a six hour window. Sender and receiver addresses as well as the amount of bitcoin exchanged were record. Graphs were generated, using R and Gephi, in which nodes represen

Po-Ming Law, Sana Malik, Fan Du, Moumita Sinha

Machine learning models often make predictions that bias against certain subgroups of input data. When undetected, machine learning biases can constitute significant financial and ethical implications. Semi-automated tools that involve humans in the loop could facilitate bias detection. Yet, little is known about the considerations involved in their design.

Christopher D. Cooper, Jaydeep P. Bardhan

We propose a new model for estimating the free energy of forming a molecular cavity in a solvent, by assuming this energy is dominated by the electrostatic energy associated with creating the static (interface) potential inside the cavity. The new model approximates the cavity-formation energy as that of a shell capacitor: the inner, solute-shaped conductor

Shreya Roy, Anirban Chakraborty

The goal of our work is to perform pixel label semantic segmentation on 3D biomedical volumetric data. Manual annotation is always difficult for a large bio-medical dataset. So, we consider two cases where one dataset is fully labeled and the other dataset is assumed to be fully unlabelled. We first perform Semantic Segmentation on the fully labeled isotropi

M. Cheng, F. Fang, C. C. Pain, I. M. Navon

Deep learning techniques for improving fluid flow modelling have gained significant attention in recent years. Advanced deep learning techniques achieve great progress in rapidly predicting fluid flows without prior knowledge of the underlying physical relationships. Advanced deep learning techniques achieve great progress in rapidly predicting fluid flows w

Ilya Sychugov

Luminescence solar concentrators act as semi-transparent photovoltaic cells and can be applied to large surface areas in modern urban environment. In this paper their optical efficiencies were analytically derived for different unit shapes as simple, integral-free expressions. Perimeter length increase for the same enclosed area requires more photodetectors,

Iroro Orife, Julia Kreutzer, Blessing Sibanda, Daniel Whitenack

Africa has over 2000 languages. Despite this, African languages account for a small portion of available resources and publications in Natural Language Processing (NLP). This is due to multiple factors, including: a lack of focus from government and funding, discoverability, a lack of community, sheer language complexity, difficulty in reproducing papers and

Jinyi Hu, Maosong Sun

Poetry generation is an interesting research topic in the field of text generation. As one of the most valuable literary and cultural heritages of China, Chinese classical poetry is very familiar and loved by Chinese people from generation to generation. It has many particular characteristics in its language structure, ranging from form, sound to meaning, th

Yang Chen, Emerson Li

Stock prices are influenced over time by underlying macroeconomic factors. Jumping out of the box of conventional assumptions about the unpredictability of the market noise, we modeled the changes of stock prices over time through the Markov Decision Process, a discrete stochastic control process that aids decision making in a situation that is partly random

Jerrin Thomas Panachakel, Nandagopal Netrakanti Vinayak, Maanvi Nunna, A. G. Ramakrishnan

This work proposes improvements in the electroencephalogram (EEG) recording protocols for motor imagery through the introduction of actual motor movement and/or somatosensory cues. The results obtained demonstrate the advantage of requiring the subjects to perform motor actions following the trials of imagery. By introducing motor actions in the protocol, th

AJ Timothy Jull, Irina Panyushkina, Matthew Salzer, Chris Baisan

Excursions in the carbon-14 record measured in tree rings are attributed to various high energy but short-lived cosmic effects. So far, rapid changes at 774-775 CE, 993-994 CE and 660 BCE have been convincingly interpreted as due to rapid changes in solar cosmic-ray flux, usually accredited with reproduction of the events in trees at different locations and

Xuanqing Liu, Hsiang-Fu Yu, Inderjit Dhillon, Cho-Jui Hsieh

We introduce a new way of learning to encode position information for non-recurrent models, such as Transformer models. Unlike RNN and LSTM, which contain inductive bias by loading the input tokens sequentially, non-recurrent models are less sensitive to position. The main reason is that position information among input units is not inherently encoded, i.e.,

Ovidiu Racorean

We argue, in the context of AdS/CFT correspondence, that the structure of the geometry dual to two entangled CFTs is a time non-orientable spacetime. Further, we elevate this argument to whatever entangled quantum systems. Accordingly, we should expect that entangled quantum systems (particles in subsidiary) to not experience the flow of time. As a result, t

D. da Silva, A. Barrie, J. Shuster, C. Schiff

Space plasma data analysis and mission operations are aided by the categorization of plasma data between different regions of the magnetosphere and identification of the boundary regions between them. Without computerized automation this means sorting large amounts of data to hand-pick regions. Using hand-labeled data created to support calibration of the Fa

Luke Nicholas Darlow, Amos Storkey

Recently, a number of competitive methods have tackled unsupervised representation learning by maximising the mutual information between the representations produced from augmentations. The resulting representations are then invariant to stochastic augmentation strategies, and can be used for downstream tasks such as clustering or classification. Yet data au

Camila Fernandez, Chung Shue Chen, Pierre Gaillard, Alonso Silva

In this paper, we make an experimental comparison of semi-parametric (Cox proportional hazards model, Aalen's additive regression model), parametric (Weibull AFT model), and machine learning models (Random Survival Forest, Gradient Boosting with Cox Proportional Hazards Loss, DeepSurv) through the concordance index on two different datasets (PBC and GBCS

Boaz Fish, Ben Zion Bobrovsky

In this paper we present a Neural Network design that can be used to track the location of a moving object within a given range based on the object's noisy coordinates measurement. A function commonly performed by the KLMn filter, our goal is to show that our method outperforms the Kalman filter in certain scenarios.

P. P. Ebner, A. Eltelt

We study the ability of Wasserstein Generative Adversarial Network (WGAN) to generate missing audio content which is, in context, (statistically similar) to the sound and the neighboring borders. We deal with the challenge of audio inpainting long range gaps (500 ms) using WGAN models. We improved the quality of the inpainting part using a new proposed WGAN

Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon

The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep learning. Deep learning methods are remarkably accurate, but also opaque, which limits their potential use in safety-critical applications. To achieve trust and accountability, designers and operators of machine learning algorithms must be able to explain the inner

Anirudh Mani, Shruti Palaskar, Nimshi Venkat Meripo, Sandeep Konam

Off-the-shelf pre-trained Automatic Speech Recognition (ASR) systems are an increasingly viable service for companies of any size building speech-based products. While these ASR systems are trained on large amounts of data, domain mismatch is still an issue for many such parties that want to use this service as-is leading to not so optimal results for their

Kiran Karra, Chace Ashcraft, Neil Fendley

In this paper, we introduce the TrojAI software framework, an open source set of Python tools capable of generating triggered (poisoned) datasets and associated deep learning (DL) models with trojans at scale. We utilize the developed framework to generate a large set of trojaned MNIST classifiers, as well as demonstrate the capability to produce a trojaned

Hanbin Dai, Liangbo Zhou, Feng Zhang, Zhengyu Zhang

In this paper, we focus on the coordinate representation in human pose estimation. While being the standard choice, heatmap based representation has not been systematically investigated. We found that the process of coordinate decoding (i.e. transforming the predicted heatmaps to the coordinates) is surprisingly significant for human pose estimation performa

Nikolai Vavilov, Zuhong Zhang

In the present paper, which is a direct sequel of our papers [10,11,35] joint with Roozbeh Hazrat, we achieve a further dramatic reduction of the generating sets for commutators of relative elementary subgroups in Chevalley groups. Namely, let $Φ$ be a reduced irreducible root system of rank $\ge 2$, let $R$ be a commutative ring and let $A,B$ be two ideals

Sven Nordebo, Mats Gustafsson, Yevhen Ivanenko

This paper reformulates and extends some recent analytical results concerning a new optical theorem and the associated physical bounds on absorption in lossy media. The analysis is valid for any linear scatterer (such as an antenna), consisting of arbitrary materials (bianisotropic, etc.) and arbitrary geometries, as long as the scatterer is circumscribed by

Noah Schwartz, Jean-François Sauvage, Edgard Renault, Carlos Correia

Current designs for all three extremely large telescopes show the overwhelming adoption of the pyramid wavefront sensor (P-WFS) as the WFS of choice for adaptive optics (AO) systems sensing on natural guide stars (NGS) or extended objects. The key advantages of the P-WFS over the Shack-Hartmann are known and are mainly provided by the improved sensitivity (f

V. G. Dubrovsky, A. V. Topovsky

We proposed general scheme for construction of exact real periodical solutions of mKP-1 equation via Zakharov-Manakov $\overline{\partial}$-dressing method, derived convenient determinant formula for calculation of such solutions and demonstrated how reality and boundary conditions for the field $u(x,y,t)$ can be satisfied. We calculated the new classes of e

V. G. Dubrovsky, A. V. Topovsky

We constructed new classes of exact multi-lump solutions of mKP-1,2 equations with integrable boundary condition $u(x,y,t)\big|_{y=0}=0$ by the use of $\overline\partial$-dressing method of Zakharov and Manakov. We exactly satisfied reality and boundary conditions for the field $u(x,y,t)$ using general determinant formula for multi-lump solutions. We illustr

Robert van Meer, Jeng-Da Chai

A two-orbital two-electron diatomic model resembling LiH is used to investigate the differences between the exact Löwdin-Shull and approximate Hartree-Fock-Bogoliubov and Baerends-Buijse density matrix functionals in the medium- to long-distance dissociation region. In case of homolytic dissociation (one electron on each atom), the approximate functionals fa

Jesus Gorronogoitia Cruz, Andrey Sadovykh, Dragos Truscan, Hugo Bruneliere

In this paper, we overview our experiences of developing large set of open source tools in ECSEL JU European project called MegaM@Rt2 whose main objective is to propose a scalable model-based framework incorporating methods and tools for the continuous development and runtime support of complex software-intensive Cyber-Physical Systems (CPSs). We briefly pre

Nazife Erkurşun-Özcan, Farrukh Mukhamedov

In the present paper deals with asymptotical stability of Markov operators acting on abstract state spaces (i.e. an ordered Banach space, where the norm has an additivity property on the cone of positive elements). Basically, we are interested in the rate of convergence when a Markov operator $T$ satisfies the uniform $P$-ergodicity, i.e. $\|T^n-P\|\to 0$, h

Bryce Doerr, Richard Linares

Controlling large swarms of robotic agents presents many challenges including, but not limited to, computational complexity due to a large number of agents, uncertainty in the functionality of each agent in the swarm, and uncertainty in the swarm's configuration. The contribution of this work is to decentralize Random Finite Set (RFS) control of large co

Nikolai Yakovenko, Avantika Lal, Johnny Israeli, Bryan Catanzaro

Variant calling, the problem of estimating whether a position in a DNA sequence differs from a reference sequence, given noisy, redundant, overlapping short sequences that cover that position, is fundamental to genomics. We propose a deep averaging network designed specifically for variant calling. Our model takes into account the independence of each short

Suat Gumussoy, Hitay Ozbay

This paper investigates stable suboptimal H-infinity controllers for a class of single-input single-output time-delay systems. For a given plant and weighting functions, the optimal controller minimizing the mixed sensitivity (and the central suboptimal controller) may be unstable with finitely or infinitely many poles in complex right half-plane. For each o

Chang Xu, Cecile Paris, Surya Nepal, Ross Sparks

We address the issue of having a limited number of annotations for stance classification in a new domain, by adapting out-of-domain classifiers with domain adaptation. Existing approaches often align different domains in a single, global feature space (or view), which may fail to fully capture the richness of the languages used for expressing stances, leadin

Cristina Mollica, Luca Tardella

The forward order assumption postulates that the ranking process of the items is carried out by sequentially assigning the positions from the top (most-liked) to the bottom (least-liked) alternative. This assumption has been recently relaxed with the Extended Plackett-Luce model (EPL) through the introduction of the discrete reference order parameter, descri

Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang

We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file. Unlike existing AutoML frameworks that primarily focus on model/hyperparameter selection, AutoGluon-Tabular succeeds by ensembling multiple models and

Miloslav Znojil

Non-separable $D-$dimensional partial differential Schrödinger equations are considered at $D=2$ and $D=3$, with the even-parity local potentials $V(x,y,\ldots)$ which are polynomials of degree four (cusp catastrophe resembling case) and six (butterfly resembling case). Their extremes (i.e., minima and maxima) are assumed pronounced, localized via a suitable

Dirk Eddelbuettel, Alton Barbehenn

We describe how we both use and extend the PrarieLearn framework by taking advantage of its built-in support for external auto-graders. By using a custom Docker container, we can match our course requirements perfectly. Moreover, by relying on the flexibility of the interface we can customize our Docker container. A specific extension for unit testing is des

Hadi Abdi Khojasteh, Ebrahim Ansari, Mahdi Bohlouli

Language recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited in low-resource formal languages. This consists of a significant gap in describing the colloquial language especially for low-resourced ones such as Per

Andrea Zunino, Sarah Adel Bargal, Riccardo Volpi, Mehrnoosh Sameki

Conventionally, AI models are thought to trade off explainability for lower accuracy. We develop a training strategy that not only leads to a more explainable AI system for object classification, but as a consequence, suffers no perceptible accuracy degradation. Explanations are defined as regions of visual evidence upon which a deep classification network m