October 2020 arXiv papers — page 87

Pramoda Kumar, Shahar Zuri, David Kogan, Moshe Gottlieb

The motion of glaciers over their bedrock or drops of fluid along a solid surface can vary dramatically when these substrates are lubricated. We investigate the coupled flow of a gravity current (GC) of strain-rate softening fluid that is lubricated by a denser, lower-viscosity Newtonian fluid. We present a set of experiments in which such GCs are discharged

Jose Carrasco, Federico Finkel, Artemio González-López, Miguel A. Rodríguez

We study the open version of the su$(m|n)$ supersymmetric Haldane-Shastry spin chain associated to the $BC_N$ extended root system. We first evaluate the model's partition function by modding out the dynamical degrees of freedom of the su$(m|n)$ supersymmetric spin Sutherland model of $BC_N$ type, whose spectrum we fully determine. We then construct a ge

Yifan Chen, Siu Fai Hung, Wing Ki Lo, Yang Chen

Current techniques of patterned material deposition require separate steps for patterning and material deposition. The complexity and harsh working conditions post serious limitations for fabrication. Here, we introduce a novel single-step and easy-to-adapt method that can deposit materials in-situ. Its unique methodology is based on the semiconductor nanopa

Daehoon Gwak, Gyuhyeon Sim, Michael Poli, Stefano Massaroli

By interpreting the forward dynamics of the latent representation of neural networks as an ordinary differential equation, Neural Ordinary Differential Equation (Neural ODE) emerged as an effective framework for modeling a system dynamics in the continuous time domain. However, real-world systems often involves external interventions that cause changes in th

Boyi Liu, Lujia Wang, Xinquan Chen, Lexiong Huang

A technological revolution is occurring in the field of robotics with the data-driven deep learning technology. However, building datasets for each local robot is laborious. Meanwhile, data islands between local robots make data unable to be utilized collaboratively. To address this issue, the work presents Peer-Assisted Robotic Learning (PARL) in robotics,

Xixi Lu, Avigdor Gal, Hajo A. Reijers

Processes, such as patient pathways, can be very complex, comprising of hundreds of activities and dozens of interleaved subprocesses. While existing process discovery algorithms have proven to construct models of high quality on clean logs of structured processes, it still remains a challenge when the algorithms are being applied to logs of complex processe

Kristina O Kvashnina, Piotr M Kowalski, Sergei M Butorin, Gregory Leinders

Valence band electronic structure of mixed uranium oxides (UO2, U4O9, U3O7, U3O8, UO3) has been studied by the resonant inelastic X-ray scattering (RIXS) technique at the U M5 edge and by computational methods. We show here that the RIXS technique and recorded U 5f-O 2p charge transfer excitations can be used to proof the validity of theoretical approximatio

Samrat Mukhopadhyay

This paper considers the task of estimating the $l_2$ norm of a $n$-dimensional random Gaussian vector from noisy measurements taken after many of the entries of the vector are \emph{missed} and only $K\ (0\le K\le n)$ entries are retained and others are set to $0$. Specifically, we evaluate the minimum mean square error (MMSE) estimator of the $l_2$ norm of

Sven Grundmann, Daniel Trabert, Kilian Fehre, Nico Strenger

Photoionization is one of the fundamental light-matter interaction processes in which the absorption of a photon launches the escape of an electron. The time scale of the process poses many open questions. Experiments found time delays in the attosecond ($10^{-18}$ s) domain between electron ejection from different orbitals, electronic bands, or in different

Kazuki Maeda, Masanobu Date, Kazuyasu Sugiyama, Shu Takagi

Series of experiments on turbulent bubbly channel flows observed bubble clusters near the wall which can change large-scale flow structures. To gain insights into clustering mechanisms, we study the interaction of a pair of spherical bubbles rising in a vertical channel through combined experiments and modeling. Experimental imaging identifies that pairwise

Jose Jurandir Alves Esteves, Amina Boubendir, Fabice Guillemin, Pierre Sens

In this demo paper, we consider the network slice placement optimization problem and give some insights into a fast heuristic algorithm tailored to placement in large scale networks. We consider an online optimization scenario with multiple and volatile network slice request arrivals and we showcase the applicability of the proposed Edge-enabled network slic

Laura M. Castro

Programming languages development has intensified in recent years. New ones are created; new features, often cross-paradigm, are featured in old ones. This new programming landscape makes language selection a more complex decision, both from the companies points of view (technical, recruiting) and from the developers point of view (career development). In th

Mariano Méndez, Tomaso M. Belloni

Binary systems with a neutron-star primary accreting from a companion star display variability in the X-ray band on time scales ranging from years to milliseconds. With frequencies of up to ~1300 Hz, the kilohertz quasi-periodic oscillations (kHz QPOs) represent the fastest variability observed from any astronomical object. The sub-millisecond time scale of

Marcin Jurdziński, Rémi Morvan, Pierre Ohlmann, K. S. Thejaswini

Progress-measure lifting algorithms for solving parity games have the best worst-case asymptotic runtime, but are limited by their asymmetric nature, and known from the work of Czerwiński et al. (2018) to be subject to a matching quasi-polynomial lower bound inherited from the combinatorics of universal trees. Parys (2019) has developed an ingenious quasi-po

Jose Jurandir Alves Esteves, Amina Boubendir, Fabrice Guillemin, Pierre Sens

We propose an online heuristic algorithm for the problem of network slice placement optimization. The solution is adapted to support placement on large scale networks and integrates Edge-specific and URLLC constraints. We rely on an approach called the Power of Two Choices to build the heuristic. The evaluation results show the good performance of the heuris

Gastón García González, Pedro Casas, Alicia Fernández, Gabriel Gómez

Despite the many attempts and approaches for anomaly detection explored over the years, the automatic detection of rare events in data communication networks remains a complex problem. In this paper we introduce Net-GAN, a novel approach to network anomaly detection in time-series, using recurrent neural networks (RNNs) and generative adversarial networks (G

Mikkel Slot Nielsen, Victor Rohde

This note provides a simple sufficient condition ensuring that solutions of stochastic delay differential equations (SDDEs) driven by subordinators are non-negative. While, to the best of our knowledge, no simple non-negativity conditions are available in the context of SDDEs, we compare our result to the literature within the subclass of invertible continuo

E. N. Alexeev, A. M. Gangapshev, Yu. M. Gavrilyuk, A. M. Gezhaev

Results of a comparative analysis of the $^{214}$Po ($T_{1/2}= 163.47\pm0.03$ $μ$s), $^{213}$Po ($T_{1/2}=3.705 \pm 0.001$ $μ$s) and $^{212}$Po ($T_{1/2}=294.09\pm0.07$ ns) half-life annular variation parameters are presented. It is shown that two independent sequential sets of the $^{214}$Po $τ$-values $(τ\equiv T_{1/2})$ obtained in the spaced laboratories

Emanuele Sobacchi, Yuri Lyubarsky, Andrei M. Beloborodov, Lorenzo Sironi

Fast Radio Bursts (FRBs) are extreme astrophysical phenomena entering the realm of non-linear optics, a field developed in laser physics. A classical non-linear effect is self-modulation. We examine the propagation of FRBs through the circumburst environment using the idealised setup of a monochromatic linearly-polarised GHz wave propagating through a unifor

Satoshi Kura

Refinement types are types equipped with predicates that specify preconditions and postconditions of underlying functional languages. We propose a general semantic construction of dependent refinement type systems from underlying type systems and predicate logic, that is, a construction of liftings of closed comprehension categories from given (underlying) c

Ingrid McKibben Lofnes

Charmonium production is highly sensitive to the hot and dense medium created in (ultra)-relativistic heavy-ion collisions, known as the Quark-Gluon Plasma (QGP). Measurements of the J/$ψ$ production serve as important tools for studying the properties of this medium. In addition to QGP effects, the J/$ψ$ production is modified by the presence of cold nuclea

Cian Ryan, Brian O Sullivan, Amr Elrasad, Joe Lemley

Event cameras contain emerging, neuromorphic vision sensors that capture local light intensity changes at each pixel, generating a stream of asynchronous events. This way of acquiring visual information constitutes a departure from traditional frame based cameras and offers several significant advantages: low energy consumption, high temporal resolution, hig

Hila Gonen, Shauli Ravfogel, Yanai Elazar, Yoav Goldberg

Recent works have demonstrated that multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages. We study the word-level translation information embedded in mBERT and present two simple methods that expose remarkable translation capabilities with no fine-tuning. The results suggest that most of this informati

Elli Androulaki, Angelo De Caro, Kaoutar Elkhiyaoui, Christian Gorenflo

Traditionally, blockchain systems involve sharing transaction information across all blockchain network participants. Clearly, this introduces barriers to the adoption of the technology by the enterprise world, where preserving the privacy of the business data is a necessity. Previous efforts to bring privacy and blockchains together either still leak partia

Gary McCormack, Rejish Nath, Weibin Li

We study nonlinear dynamics of Rydberg-dressed Bose-Einstein condensates (BECs) trapped in a triple-well potential in the semiclassical limit. The Rydberg-dressed BECs experience a long-range soft-core interaction, giving rise to strong nearest and next-nearest neighbor interactions in the triple-well system. Using mean-field Gross-Pitaevskii (GP) equations,

Ignasi Rosell, Antonio Pich, Juan José Sanz-Cillero

In the light of the mass gap between Standard Model (SM) states and possible new particles, effective field theories are a suitable approach. We take on the non-linear realization of the electroweak symmetry breaking: the electroweak effective theory (EWET), also known as Higgs effective field theory (HEFT) or electroweak chiral Lagrangian (EWChL). At higher

Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay

The graphlet kernel is a classical method in graph classification. It however suffers from a high computation cost due to the isomorphism test it includes. As a generic proxy, and in general at the cost of losing some information, this test can be efficiently replaced by a user-defined mapping that computes various graph characteristics. In this paper, we pr

Sergio Cristallo, Ambra Nanni, Gabriele Cescutti, Ivan Minchev

The vast majority (>=90%) of presolar SiC grains identified in primitive meteorites are relics of ancient asymptotic giant branch (AGB) stars, whose ejecta were incorporated into the Solar System during its formation. Detailed characterization of these ancient stardust grains has revealed precious information on mixing processes in AGB interiors in great det

M. Reiser, J. Hallmann, J. Möller, K. Kazarian

Solutions of wormlike micelles can form cross-linked networks on microscopic length scales. The unique mechanical properties of these complex fluids are driven by the interplay between the network structure and dynamics which are investigated by plate-plate rheometry and X-ray photon correlation spectroscopy~(XPCS) nanorheology. Intensity auto-correlation fu

Paul Brown, Trevor Fenner

The vertex-deleted subgraph G-v, obtained from the graph G by deleting the vertex v and all edges incident to v, is called a card of G. The deck of G is the multiset of its unlabelled cards. The number of common cards b(G,H) of G and H is the cardinality of the multiset intersection of the decks of G and H. A supercard G+ of G and H is a graph whose deck con

Qiang Wang, Tong Xiao, Jingbo Zhu

The standard neural machine translation model can only decode with the same depth configuration as training. Restricted by this feature, we have to deploy models of various sizes to maintain the same translation latency, because the hardware conditions on different terminal devices (e.g., mobile phones) may vary greatly. Such individual training leads to inc

Xing Yan, Weizhong Zhang, Lin Ma, Wei Liu

We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns. Our model captures well both the time-varying characteristic and the asymmetrical heavy-tail property of financial time series. It combines the merits of a popular sequential neural network model, i.e., LSTM, with a novel parametric quantil

Kuantay Boshkayev, Orlando Luongo, Marco Muccino

We investigate neutrino oscillation in the field of an axially symmetric space-time, employing the so-called $q$-metric, in the context of general relativity. Following the standard approach, we compute the phase shift invoking the weak and strong field limits and small deformation. To do so, we consider neutron stars, white dwarfs and supernovae as strong g

Stefano Bianchi, Alessandro Longo, Wolfango Plastino

Adaptive time series analysis has been applied to investigate variability of CO2 concentration data, sampled weekly at Mauna Loa monitoring station. Due to its ability to mitigate mode mixing, the recent time varying filter Empirical Mode Decomposition (tvf-EMD) methodology is employed to extract local narrowband oscillatory modes. In order to perform data a

Andras Horvath, Jalal Al-afandi

It is a common assumption that the activation of different layers in neural networks follow Gaussian distribution. This distribution can be transformed using normalization techniques, such as batch-normalization, increasing convergence speed and improving accuracy. In this paper we would like to demonstrate, that activations do not necessarily follow Gaussia

Mario Milazzo, Giuseppe Gallone, Elena Marcello, Maria Donatella Mariniello

Bacterial colonization ofimplanted biomedical devicesis themain cause of healthcare-associated infections, estimated to be 8.8 million per year in Europe. Many infections originate from damaged skin, which lets microorganisms exploit injuries and surgical accesses as passageways to reach the implant site and inner organs. Therefore, an effective treatment of

Jiale Cheng, Nan Liu, Wei Kang

We study the symmetric private information retrieval (SPIR) problem under arbitrary collusion and eavesdropping patterns for replicated databases. We find its capacity, which is the same as the capacity of the original SPIR problem with the number of databases $N$ replaced by a number $F^*$. The number $F^*$ is the optimal solution to a linear programming pr

Filipe Martinho, Simon Lopez-Marino, Moises Espíndola-Rodríguez, Alireza Hajijafarassar

In kesterite CZTSSe solar cell research, an asymmetric crystallization profile is often obtained after annealing, resulting in a bilayered or double-layered absorber. So far, only segregated pieces of research exist to characterize this double layer, its formation dynamics and its effect on the performance of devices. Here, we review the existing research on

O. I. Hryhorchak

The approximate numerical method for a calculation of a quantum wave impedance in a case of a potential energy with a complicated spatial structure is considered. It was proved that the approximation of a real potential by a piesewise constant function is also reasonable in a case of using a quantum impedance approach.The dependence of an accuracy of numeric

Baifeng Shi, Judy Hoffman, Kate Saenko, Trevor Darrell

Supervised learning requires a large amount of training data, limiting its application where labeled data is scarce. To compensate for data scarcity, one possible method is to utilize auxiliary tasks to provide additional supervision for the main task. Assigning and optimizing the importance weights for different auxiliary tasks remains an crucial and largel

Shusheng Xu, Xingxing Zhang, Yi Wu, Furu Wei

Unsupervised extractive document summarization aims to select important sentences from a document without using labeled summaries during training. Existing methods are mostly graph-based with sentences as nodes and edge weights measured by sentence similarities. In this work, we find that transformer attentions can be used to rank sentences for unsupervised

David J. W. Simpson

We show how the existence of three objects, $Ω_{\rm trap}$, ${\bf W}$, and $C$, for a continuous piecewise-linear map $f$ on $\mathbb{R}^N$, implies that $f$ has a topological attractor with a positive Lyapunov exponent. First, $Ω_{\rm trap} \subset \mathbb{R}^N$ is trapping region for $f$. Second, ${\bf W}$ is a finite set of words that encodes the forward

Nobutaka Asano

We classify the $3$-manifolds obtained as the preimages of arcs on the plane for simplified $(2, 0)$-trisection maps, which we call vertical $3$-manifolds. Such a $3$-manifold is a connected sum of a $6$-tuple of vertical $3$-manifolds over specific $6$ arcs. Consequently, we show that each of the $6$-tuples determines the source $4$-manifold uniquely up to

Sven Becker, Andrew Ross, Romain Lebrun, Lorenzo Baldrati

TmFeO$_3$ (TFO) is a canted antiferromagnet that undergoes a spin reorientation transition (SRT) with temperature between 82 K and 94 K in single crystals. In this temperature region, the Néel vector continuously rotates from the crystallographic $c$-axis (below 82 K) to the $a$-axis (above 94 K). The SRT allows for a temperature control of distinct antiferr

Lucia Amidani, Tatiana V. Plakhova, Anna Yu. Romanchuk, Evgeny Gerber

Developing characterization techniques and analysis methods adapted to the investigation of nanoparticles (NPs) is of fundamental importance considering the role of these materials in many fields of research. The study of actinide based NPs, despite their environmental relevance, is still underdeveloped compared to that of NPs based on stable and lighter ele

Youngjin Park, Deokjun Eom, Byoungki Seo, Jaesik Choi

Forecasting with multivariate time series, which aims to predict future values given previous and current several univariate time series data, has been studied for decades, with one example being ARIMA. Because it is difficult to measure the extent to which noise is mixed with informative signals within rapidly fluctuating financial time series data, designi

Dat Quoc Nguyen, Thanh Vu, Afshin Rahimi, Mai Hoang Dao

In this paper, we provide an overview of the WNUT-2020 shared task on the identification of informative COVID-19 English Tweets. We describe how we construct a corpus of 10K Tweets and organize the development and evaluation phases for this task. In addition, we also present a brief summary of results obtained from the final system evaluation submissions of

Lucia Amidani, Gavin B. M. Vaughan, Tatiana V. Plakhova, Anna Yu. Romanchuk

Structural characterization of actinide nanoparticles (NPs) is of primary importance and hard to achieve, especially for non-homogeneous samples with NPs below 3 nm. By combining High Energy X-ray Scattering (HEXS) and High-Energy-Resolution Fluorescence Detected X-ray Near-Edge Structure (HERFD XANES), we characterized for the first time both short- and med

Lionel Haemmerlé

Supermassive black holes might form by direct collapse, with a supermassive star (SMS) as progenitor. In this scenario, the SMS accretes at > 0.1 Msun/yr until it collapses into a massive black hole seed due to the general-relativistic (GR) instability. However, the exact mass at which the collapse occurs is not known, as existing numerical simulations give

Filipe Martinho, Alireza Hajijafarassar, Simón Lopez-Marino, Moises Espíndola-Rodríguez

The monolithic tandem integration of third-generation solar energy materials on silicon holds great promise for photoelectrochemistry and photovoltaics. However, this can be challenging when it involves high-temperature reactive processes, which would risk damaging the Si bottom cell. One such case is the high-temperature sulfurization/selenization in thin f

Ting-Wen Wang, Tomotsugu Goto, Seong Jin Kim, Tetsuya Hashimoto

In order to understand the interaction between the central black hole and the whole galaxy or their co-evolution history along with cosmic time, a complete census of active galactic nuclei (AGN) is crucial. However, AGNs are often missed in optical, UV and soft X-ray observations since they could be obscured by gas and dust. A mid-infrared (mid-IR) survey su

M. Haim, D. Möckli, M. Khodas

The few-layer transition metal dichalcogenides (TMDs) have been recently suggested as a platform for controlled unconventional superconductivity. We study the manifestations of unconventional triplet pairing in the density of states of a disordered TMD based monolayer. The conventional singlet pairing attraction is assumed to be the dominant pairing interact

A. V. Nesterenok

The collisional pumping of OH masers in non-dissociative C-type shocks near supernova remnants is considered. The emergence of maser emission in OH lines is investigated for various shock parameters - the shock speed, the preshock gas density, the cosmic-ray ionization rate, and the magnetic field strength. The largest optical depth in the 1720 MHz line is r

Michael Fürst, Shriya T. P. Gupta, René Schuster, Oliver Wasenmüller

In-the-wild human pose estimation has a huge potential for various fields, ranging from animation and action recognition to intention recognition and prediction for autonomous driving. The current state-of-the-art is focused only on RGB and RGB-D approaches for predicting the 3D human pose. However, not using precise LiDAR depth information limits the perfor

Sunny Verma, Jiwei Wang, Zhefeng Ge, Rujia Shen

Multimodal sentiment analysis utilizes multiple heterogeneous modalities for sentiment classification. The recent multimodal fusion schemes customize LSTMs to discover intra-modal dynamics and design sophisticated attention mechanisms to discover the inter-modal dynamics from multimodal sequences. Although powerful, these schemes completely rely on attention

F. Chiaruttini, T. Guillet, C. Brimont, D. Scalbert

The Mott transition from a dipolar excitonic liquid to an electron-hole plasma is demonstrated in a wide GaN/(Al,Ga)N quantum well at $T=7$K by means of spatially-resolved magneto-photoluminescence spectroscopy. Increasing optical excitation density we drive the system from the excitonic state, characterized by a diamagnetic behavior and thus a quadratic ene

Anne Broise-Alamichel, Jouni Parkkonen, Frédéric Paulin

In this survey based on the book by the authors [BPP], we recall the Patterson-Sullivan construction of equilibrium states for the geodesic flow on negatively curved orbifolds or tree quotients, and discuss their mixing properties, emphazising the rate of mixing for (not necessarily compact) tree quotients via coding by countable (not necessarily finite) top

Weiru Fan, Tianrun Chen, Eddie Gil, Shiyao Zhu

Wavefront sensing and reconstruction are widely used for adaptive optics, aberration correction, and high-resolution optical phase imaging. Traditionally, interference and/or microlens arrays are used to convert the optical phase into intensity variation. Direct imaging of distorted wavefront usually results in complicated phase retrieval with low contrast a

Mengge Xue, Bowen Yu, Zhenyu Zhang, Tingwen Liu

More recently, Named Entity Recognition hasachieved great advances aided by pre-trainingapproaches such as BERT. However, currentpre-training techniques focus on building lan-guage modeling objectives to learn a gen-eral representation, ignoring the named entity-related knowledge. To this end, we proposea NER-specific pre-training framework to in-ject coarse

Ruohua Shi, Wenyao Wang, Zhixuan Li, Liuyuan He

Computer vision technology is widely used in biological and medical data analysis and understanding. However, there are still two major bottlenecks in the field of cell membrane segmentation, which seriously hinder further research: lack of sufficient high-quality data and lack of suitable evaluation criteria. In order to solve these two problems, this paper

Maulik Parikh, Frank Wilczek, George Zahariade

We develop a formalism to calculate the response of a model gravitational wave detector to a quantized gravitational field. Coupling a detector to a quantum field induces stochastic fluctuations ("noise") in the length of the detector arm. The statistical properties of this noise depend on the choice of quantum state of the gravitational field. We ch

Tatiana V Plakhova, Anna Yu Romanchuk, Sergei M Butorin, Anastasia D Konyukhova

Understanding the complex chemistry of functional nanomaterials is of fundamental importance. Controlled synthesis and characterization at the atomic level is essential to gain deeper insight into the unique chemical reactivity exhibited by many nanomaterials. Cerium oxide nanoparticles have many industrial and commercial applications,resulting from very str

Maulik Parikh, Frank Wilczek, George Zahariade

For the purpose of analyzing observed phenomena, it has been convenient, and thus far sufficient, to regard gravity as subject to the deterministic principles of classical physics, with the gravitational field obeying Newton's law or Einstein's equations. Here we treat the gravitational field as a quantum field and determine the implications of such

D Grapsas, R Herbin, J. -C Latché, Y Nasseri

We build in this paper a numerical solution procedure to compute the flow induced by a spherical flame expanding from a point source at a constant expansion velocity, with an instantaneous chemical reaction. The solution is supposed to be self-similar and the flow is split in three zones: an inner zone composed of burnt gases at rest, an intermediate zone wh

Muhammad Abbas, Asifullah Khan, Aqsa Saeed Qureshi, Muhammad Waleed Khan

Higgs boson is a fundamental particle, and the classification of Higgs signals is a well-known problem in high energy physics. The identification of the Higgs signal is a challenging task because its signal has a resemblance to the background signals. This study proposes a Higgs signal classification using a novel combination of random forest, auto encoder a

Wanyun Cui, Guangyu Zheng, Wei Wang

We propose to solve the natural language inference problem without any supervision from the inference labels via task-agnostic multimodal pretraining. Although recent studies of multimodal self-supervised learning also represent the linguistic and visual context, their encoders for different modalities are coupled. Thus they cannot incorporate visual informa

Chaitanya Manapragada, Geoffrey I Webb, Mahsa Salehi, Albert Bifet

Hoeffding trees are the state-of-the-art methods in decision tree learning for evolving data streams. These very fast decision trees are used in many real applications where data is created in real-time due to their efficiency. In this work, we extricate explanations for why these streaming decision tree algorithms for stationary and nonstationary streams (H

Ludovic Godard-Cadillac, Philippe Gravejat, Didier Smets

We provide a variational construction of special solutions to the generalized surface quasi-geostrophic equations. These solutions take the form of N vortex patches with N-fold symmetry , which are steady in a uniformly rotating frame. Moreover, we investigate their asymptotic properties when the size of the corresponding patches vanishes. In this limit, we

Yossi Lonke

The main result is that a finite dimensional normed space embeds isometrically in $\ell_p$ if and only if it has a discrete Levy $p$-representation. This provides an alternative answer to a question raised by Pietch, and as a corollary, a simple proof of the fact that unless $p$ is an even integer, the two-dimensional Hilbert space $\ell_2^2$ is not isometri

Yuang Shi, Chen Zu, Mei Hong, Luping Zhou

With the increasing amounts of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become an important research direction in medical image analysis. Traditional methods usually depict the data structure using fixed and predefined similarity matrix for each modality separately, without considering the potential relationsh

Wei Chen, Weiping Wang, Li Liu, Michael S. Lew

The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text. Unlike classic reviews of deep learning where monomodal image classifiers such as VGG, ResNet and Inception module are central topics, this paper will examine recent multimodal deep models and structures, including auto-encoders, generative adversarial

Guangneng Hu, Qiang Yang

Transfer learning is an effective technique to improve a target recommender system with the knowledge from a source domain. Existing research focuses on the recommendation performance of the target domain while ignores the privacy leakage of the source domain. The transferred knowledge, however, may unintendedly leak private information of the source domain.

Saleh Shahinfar, Paul Meek, Greg Falzon

Deep learning (DL) algorithms are the state of the art in automated classification of wildlife camera trap images. The challenge is that the ecologist cannot know in advance how many images per species they need to collect for model training in order to achieve their desired classification accuracy. In fact there is limited empirical evidence in the context

Tanfang Chen, Weiwei Wang, Wenyang Wei, Xing Shi

This paper describes DiDi AI Labs' submission to the WMT2020 news translation shared task. We participate in the translation direction of Chinese->English. In this direction, we use the Transformer as our baseline model, and integrate several techniques for model enhancement, including data filtering, data selection, back-translation, fine-tuning, model

Derek Weber, Frank Neumann

Political astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Common approaches examining these phenomena focus on broad campaigns rather than the small groups responsible. To reveal latent networks of cooperating accounts, we propose a novel temporal window approach that relies on account interactions and

Rui Wang, Zhihua Wei, Yibin Zhan, Zhuoxi Chen

In this report, we describe the submission of Tongji University team to the CLOSE track of the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020 at Interspeech 2020. We investigate different speaker recognition systems based on the popular ResNet-34 architecture, and train multiple variants via various loss functions. Both Offline and online data augmenta

Xuanfu Wu, Yang Feng, Chenze Shao

Despite the improvement of translation quality, neural machine translation (NMT) often suffers from the lack of diversity in its generation. In this paper, we propose to generate diverse translations by deriving a large number of possible models with Bayesian modelling and sampling models from them for inference. The possible models are obtained by applying

Leepakshi Bindra, Kalvin Eng, Omid Ardakanian, Eleni Stroulia

Large commercial buildings are complex cyber-physical systems containing expensive and critical equipment that ensure the safety and comfort of their numerous occupants. Yet occupant and visitor access to spaces and equipment within these buildings are still managed through unsystematic, inefficient, and human-intensive processes. As a standard practice, lon

Antonios Katsianis, Haojie Xu, Xiaohu Yang, Yu Luo

We present the eddington bias corrected Specific Star Formation Rate Function (sSFRF) at different stellar mass scales from a sub-sample of the Sloan Digital Sky Survey Data Release DR7 (SDSS), which is considered complete both in terms of stellar mass (${\rm M_{\star}}$) and star formation rate (SFR). The above enable us to study qualitatively and quantitat

Ross Berkowitz, Pat Devlin

Given a graph $G$ and some initial labelling $σ: V(G) \to \{Red, Blue\}$ of its vertices, the \textit{majority dynamics model} is the deterministic process where at each stage, every vertex simultaneously replaces its label with the majority label among its neighbors (remaining unchanged in the case of a tie). We prove---for a wide range of parameters---that

Yoshifusa Ita, John W. Menzies, Patricia A. Whitelock, Noriyuki Matsunaga

The goal of this paper is to characterise the light variation properties of Mira variables in the Small Magellanic Cloud. We have investigated a combined optical and near infrared multi-epoch dataset of Mira variables based on our monitoring data obtained over 15 years. Bolometric correction relations are formulated for various near-infrared colours. We find

Jad Salman, Cayla A. Stifler, Alireza Shahsafi, Chang-Yu Sun

Structural characterization of biologically formed materials is essential for understanding biological phenomena and their environment, and generating new bio-inspired engineering concepts. For example, nacre -- formed by mollusks in the ocean -- encodes local environmental conditions throughout its formation and has exceptional strength due to its nanoscale

Esther Rolf, Jonathan Proctor, Tamma Carleton, Ian Bolliger

Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of satellite imagery can generalize across diverse prediction task

Bachir El Khadir, Jean Bernard Lasserre, Vikas Sindhwani

We propose a novel method for planning shortest length piecewise-linear motions through complex environments punctured with static, moving, or even morphing obstacles. Using a moment optimization approach, we formulate a hierarchy of semidefinite programs that yield increasingly refined lower bounds converging monotonically to the optimal path length. For co

Xueying Zhan, Antoni Bert Chan

Active learning (AL) is a subfield of machine learning (ML) in which a learning algorithm could achieve good accuracy with less training samples by interactively querying a user/oracle to label new data points. Pool-based AL is well-motivated in many ML tasks, where unlabeled data is abundant, but their labels are hard to obtain. Although many pool-based AL

Ruizhi Dong, Dongxing Mao, Xu Wang, Yong Li

Ventilation barriers allowing simultaneous sound blocking and free airflow passage are of great challenge but necessary for particular scenarios calling for sound-proofing ventilation. Previous works based on local resonance or Fano-like interference serve a narrow working range around the resonant or destructive-interference frequency. Efforts made on broad

Yanbo Zhou, Qu Li, Xuhua Yang, Hongbing cheng

Predicting the popularity of scientific publications has attracted many attentions from various disciplines. In this paper, we focus on the popularity prediction problem of scientific papers, and propose an age-based diffusion (AD) model to identify which paper will receive more citations in the near future and will be popular. The AD model is a mimic of the

Neil J. Y. Fan, Peter L. Guo, Nicolas Y. Liu

Non-attacking skyline fillings were used by Haglund, Haiman and Loehr to establish a combinatorial formula for nonsymmetric Macdonald polynomials. Semistandard skyline fillings are non-attacking skyline fillings with both major index and coinversion number equal to zero, which serve as a combinatorial model for key polynomials. In this paper, we construct an

Yoshiko Kanada-En'yo, Kazuyuki Ogata

Background: The triaxial and hexadecapole deformations of the K=0+ and K=2+ bands of 24Mg have been investigated by the inelastic scatterings of various probes, including electrons, protons, and alpha particles, for a prolonged time. However, it has been challenging to explain the unique properties of the scatterings observed for the $4^+_1$ state through re

Melia E. Bonomo

Physicist Isabelle Stone forged ahead in what was largely a men's profession in the early 1900s. She is credited as the first woman in the United States to obtain a Ph.D. in physics, one of the two women founders of the American Physical Society, and one of only two women to attend the first International Congress of Physics. Hers is the story of an unma

Farhana Faruqe, Ryan Watkins, Larry Medsker

The work reported here addresses the capacity of psychophysiological sensors and measures using Electroencephalogram (EEG) and Galvanic Skin Response (GSR) to detect levels of trust for humans using AI-supported Human-Machine Interaction (HMI). Improvements to the analysis of EEG and GSR data may create models that perform as well, or better than, traditiona

Anh Nguyen, Anh Tran

In recent years, neural backdoor attack has been considered to be a potential security threat to deep learning systems. Such systems, while achieving the state-of-the-art performance on clean data, perform abnormally on inputs with predefined triggers. Current backdoor techniques, however, rely on uniform trigger patterns, which are easily detected and mitig

Razieh Torkamani, Hadi Zayyani

This paper investigates the problem of graph signal recovery (GSR) when the topology of the graph is not known in advance. In this paper, the elements of the weighted adjacency matrix is statistically related to normal distribution and the graph signal is assumed to be Gaussian Markov Random Field (GMRF). Then, the problem of GSR is solved by a Variational B

Shengkui Zhao, Trung Hieu Nguyen, Hao Wang, Bin Ma

Recent state-of-the-art neural text-to-speech (TTS) synthesis models have dramatically improved intelligibility and naturalness of generated speech from text. However, building a good bilingual or code-switched TTS for a particular voice is still a challenge. The main reason is that it is not easy to obtain a bilingual corpus from a speaker who achieves nati

Razieh Torkamani, Hadi Zayyani, Ramazan Ali Sadeghzadeh

In this paper, a novel model-based distributed compressive sensing (DCS) algorithm is proposed. DCS exploits the inter-signal correlations and has the capability to jointly recover multiple sparse signals. Proposed approach is a Bayesian decentralized algorithm which uses the type 1 joint sparsity model (JSM-1) and exploits the intra-signal correlations, as

Aditya Mishra, Dipak K. Dey, Yong Chen, Kun Chen

Multivariate regression techniques are commonly applied to explore the associations between large numbers of outcomes and predictors. In real-world applications, the outcomes are often of mixed types, including continuous measurements, binary indicators, and counts, and the observations may also be incomplete. Building upon the recent advances in mixed-outco

Ajai Choudhry, Iliya Bluskov, Alexander James

In this paper we obtain several parametric solutions of the quartic diophantine equation $(x_1^4+x_2^4)(y_1^4+y_2^4)=z_1^4+z_2^4$. We also show how infinitely many parametric solutions of this equation may be obtained by using elliptic curves.

Jianfeng He, Xuchao Zhang, Shuo Lei, Shuhui Wang

Semantic editing on segmentation map has been proposed as an intermediate interface for image generation, because it provides flexible and strong assistance in various image generation tasks. This paper aims to improve quality of edited segmentation map conditioned on semantic inputs. Even though recent studies apply global and local adversarial losses exten

James Otterson

We use Hölder's inequality to get simple derivations of certain economic formulas involving CES, Armington, or $n$-stage Armington functions.

Madeline Navarro, Yuhao Wang, Antonio G. Marques, Caroline Uhler

Inferring graph structure from observations on the nodes is an important and popular network science task. Departing from the more common inference of a single graph and motivated by social and biological networks, we study the problem of jointly inferring multiple graphs from the observation of signals at their nodes (graph signals), which are assumed to be