October 2020 arXiv papers — page 86

Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu

Recent advances in multi-agent reinforcement learning (MARL) have achieved super-human performance in games like Quake 3 and Dota 2. Unfortunately, these techniques require orders-of-magnitude more training rounds than humans and don't generalize to new agent configurations even on the same game. In this work, we propose Collaborative Q-learning (CollaQ)

A. B. Albidah, W. Brevis, V. Fedun, I. Ballai

High resolution solar observations show the complex structure of the magnetohydrodynamic (MHD) wave motion. We apply the techniques of POD and DMD to identify the dominant MHD wave modes in a sunspot using the intensity time series. The POD technique was used to find modes that are spatially orthogonal, whereas the DMD technique identifies temporal orthogona

Elcio Abdalla, Alessandro Marins

The most important problem in fundamental physics is the description of the contents of the Universe. Today, we know that 95% thereof is totally unknown. Two thirds of that amount is the mysterious Dark Energy described in an interesting and important review. We briefly extend here the ideas contained in that review including the more general Dark Sector, th

Md Taufique Hussain, Oguz Selvitopi, Aydin Buluç, Ariful Azad

Sparse matrix-matrix multiplication (SpGEMM) is a widely used kernel in various graph, scientific computing and machine learning algorithms. In this paper, we consider SpGEMMs performed on hundreds of thousands of processors generating trillions of nonzeros in the output matrix. Distributed SpGEMM at this extreme scale faces two key challenges: (1) high comm

Hongming Zhang, Muhao Chen, Haoyu Wang, Yangqiu Song

Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured representations of a sequence of events and on conceptualizing (abstracting) its components into (soft) event categories. Thus, knowledge about a known process such as "buying a car" can be used in the

Gage Bonner, Jean-Luc Thiffeault, Benedek Valko

We study a model for the entanglement of a two-dimensional reflecting Brownian motion in a bounded region divided into two halves by a wall with three or more small windows. We map the Brownian motion into a Markov Chain on the fundamental groupoid of the region. We quantify entanglement of the path with the length of the appropriate element in this groupoid

Alessandro Ravoni

Autocatalytic Sets are reaction networks theorised as networks at the basis of life. Their main feature is the ability of spontaneously emerging and self-reproducing. The Reflexively and Food-generated theory provides a formal definition of Autocatalytic Sets in terms of graphs with peculiar topological properties. This formalisation has been proved to be a

George S. Barron, Christopher J. Wood

Variational Quantum Algorithms (VQAs) are a promising application for near-term quantum processors, however the quality of their results is greatly limited by noise. For this reason, various error mitigation techniques have emerged to deal with noise that can be applied to these algorithms. Recent work introduced a technique for mitigating expectation values

Silvia Paparini, Epifanio G. Virga

Tactoids are pointed, spindle-like droplets of nematic liquid crystal in an isotropic fluid. They have long been observed in inorganic and organic nematics, in thermotropic phases as well as lyotropic colloidal aggregates. The variational problem of determining the optimal shape of a nematic droplet is formidable and has only been attacked in selected classe

Kishan KC, Rui Li, Feng Cui, Anne Haake

Biomedical interaction networks have incredible potential to be useful in the prediction of biologically meaningful interactions, identification of network biomarkers of disease, and the discovery of putative drug targets. Recently, graph neural networks have been proposed to effectively learn representations for biomedical entities and achieved state-of-the

Kishan KC, Feng Cui, Anne Haake, Rui Li

Predicting protein-protein interactions (PPIs) by learning informative representations from amino acid sequences is a challenging yet important problem in biology. Although various deep learning models in Siamese architecture have been proposed to model PPIs from sequences, these methods are computationally expensive for a large number of PPIs due to the pai

Penglong Zhai, Shihua Zhang

Low-rank approximation models of data matrices have become important machine learning and data mining tools in many fields including computer vision, text mining, bioinformatics and many others. They allow for embedding high-dimensional data into low-dimensional spaces, which mitigates the effects of noise and uncovers latent relations. In order to make the

Adrian de Wynter

We consider the problem of finding the set of architectural parameters for a chosen deep neural network which is optimal under three metrics: parameter size, inference speed, and error rate. In this paper we state the problem formally, and present an approximation algorithm that, for a large subset of instances behaves like an FPTAS with an approximation err

Yanxin Li, Stephen G. Walker

In this paper we introduce a new sampling algorithm which has the potential to be adopted as a universal replacement to the Metropolis--Hastings algorithm. It is related to the slice sampler, and motivated by an algorithm which is applicable to discrete probability distributions %which can be viewed as an alternative to the Metropolis--Hastings algorithm in

Yamini Bansal, Gal Kaplun, Boaz Barak

We prove a new upper bound on the generalization gap of classifiers that are obtained by first using self-supervision to learn a representation $r$ of the training data, and then fitting a simple (e.g., linear) classifier $g$ to the labels. Specifically, we show that (under the assumptions described below) the generalization gap of such classifiers tends to

Evan Coleman, Vasudev Shyam

We construct a particular flow in the space of 2D Euclidean QFTs on a torus, which we argue is dual to a class of solutions in 3D Euclidean gravity with conformal boundary conditions. This new flow comes from a Legendre transform of the kernel which implements the $T\bar{T}$ deformation, and is motivated by the need for boundary conditions in Euclidean gravi

D. Kantzas, S. Markoff, T. Beuchert, M. Lucchini

Cygnus X--1 is the first Galactic source confirmed to host an accreting black hole. It has been detected across the entire electromagnetic spectrum from radio to GeV $γ$-rays. The source's radio through mid-infrared radiation is thought to originate from the relativistic jets. The observed high degree of linear polarisation in the MeV X-rays suggests tha

Esteban Jimenez, Nelson Padilla, Sergio Contreras, Idit Zehavi

The next generation of spectroscopic surveys will target emission-line galaxies (ELGs) to produce constraints on cosmological parameters. We study the large scale structure traced by ELGs using a combination of a semi-analytical model of galaxy formation, a code that computes the nebular emission from HII regions using the properties of the interstellar medi

Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier

We consider the problem of online learning with non-convex losses. In terms of feedback, we assume that the learner observes - or otherwise constructs - an inexact model for the loss function encountered at each stage, and we propose a mixed-strategy learning policy based on dual averaging. In this general context, we derive a series of tight regret minimiza

Fan Yin, Guanyu Hu, Weining Shen

We propose a Bayesian nonparametric matrix clustering approach to analyze the latent heterogeneity structure in the shot selection data collected from professional basketball players in the National Basketball Association (NBA). The proposed method adopts a mixture of finite mixtures framework and fully utilizes the spatial information via a mixture of matri

Juan Diego Arias Espinoza, Koen Groenland, Matteo Mazzanti, Kareljan Schoutens

Conditional multi-qubit gates are a key component for elaborate quantum algorithms. In a recent work, Rasmussen et al. (Phys. Rev. A 101, 022308) proposed an efficient single-step method for a prototypical multi-qubit gate, a Toffoli gate, based on a combination of Ising interactions between control qubits and an appropriate driving field on a target qubit.

P. Chakraborty, G. J. Ferland, S. Bianchi, M. Chatzikos

X-ray missions with microcalorimeter technology will resolve spectral features with unprecedented detail. In this work, we improve the H-like K$α$ energies for elements between 6 $\leq Z \leq$ 30 for the release version of the spectral simulation code Cloudy to match laboratory energies. We update the ionization potential ($I_{\rm ion}$) for these elements a

Maksim Levental, Ryan Chard, Joseph A. Libera, Kyle Chard

Flame Spray Pyrolysis (FSP) is a manufacturing technique to mass produce engineered nanoparticles for applications in catalysis, energy materials, composites, and more. FSP instruments are highly dependent on a number of adjustable parameters, including fuel injection rate, fuel-oxygen mixtures, and temperature, which can greatly affect the quality, quantity

August G. Domel, Samuel J. Raymond, Chiara Giordano, Yuzhe Liu

Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the num

Corey Melnick, Patrick Sémon, Kwangmin Yu, Nicholas D'Imperio

We present ComCTQMC, a GPU accelerated quantum impurity solver. It uses the continuous-time quantum Monte Carlo (CTQMC) algorithm wherein the partition function is expanded in terms of the hybridisation function (CT-HYB). ComCTQMC supports both partition and worm-space measurements, and it uses improved estimators and the reduced density matrix to improve ob

Hikaru Watanabe, Youichi Yanase

Violation of parity symmetry gives rise to various physical phenomena such as nonlinear transport and cross-correlated responses. In particular, the nonlinear conductivity has been attracting a lot of attentions in spin-orbit coupled semiconductors, superconductors, topological materials, and so on. In this paper we present theoretical study of the nonlinear

Jayaraman J. Thiagarajan, Peer-Timo Bremer, Rushil Anirudh, Timothy C. Germann

A crucial aspect of managing a public health crisis is to effectively balance prevention and mitigation strategies, while taking their socio-economic impact into account. In particular, determining the influence of different non-pharmaceutical interventions (NPIs) on the effective use of public resources is an important problem, given the uncertainties on wh

Gaëtan Gras, Davide Rusca, Hugo Zbinden, Félix Bussières

Detector blinding attacks have been proposed in the last few years, and they could potentially threaten the security of QKD systems. Even though no complete QKD system has been hacked yet, it is nevertheless important to consider countermeasures to avoid information leakage. In this paper, we present a new countermeasure against these kind of attacks based o

Caleb Wagner, Neel Dhanaraj, Trevor Rizzo, Josue Contreras

We present a novel concept of a heterogeneous, distributed platform for autonomous 3D construction. The platform is composed of two types of robots acting in a coordinated and complementary fashion: (i) A collection of communicating smart construction blocks behaving as a form of growable smart matter, and capable of planning and monitoring their own state a

Anne-Sophie Bonnet-Ben Dhia, Lucas Chesnel, Mahran Rihani

In this work, we are interested in the analysis of time-harmonic Maxwell's equations in presence of a conical tip of a material with negative dielectric constants. When these constants belong to some critical range, the electromagnetic field exhibits strongly oscillating singularities at the tip which have infinite energy. Consequently Maxwell's equa

Ana Lourenço, Eric Kerfoot, Irina Grigorescu, Cian M Scannell

Delayed-enhancement cardiac magnetic resonance (DE-CMR)provides important diagnostic and prognostic information on myocardial viability. The presence and extent of late gadolinium enhancement (LGE)in DE-CMR is negatively associated with the probability of improvement in left ventricular function after revascularization. Moreover, LGE findings can support the

Christian L. Vásconez, Denise Perrone, Raffaele Marino, Dimitri Laveder

The nature of the turbulent energy transfer rate is studied using direct numerical simulations of weakly collisional space plasmas. This is done comparing results obtained from hybrid Vlasov-Maxwell simulations of colissionless plasmas, Hall-magnetohydrodynamics, and Landau fluid models reproducing low-frequency kinetic effects, such as the Landau damping. I

N. Gilbert, D. Marenduzzo

This is an introduction to the special issue Genome organization: experiments and simulations, published in Chromosome Research, volume 25, issue 1 (2017).

Cinzia Bisi, Joerg Winkelmann

We prove a Runge theorem for and describe the homology of axially symmetric open subsets of H.

Enamul Haque

The demand for green energy increases day by day due to environmental concern and thermoelectric (TE) materials are one of the eco-friendly energy resources. Few authors reported high TE performance in TmCu$_3$Ch$_4$, reaching the figure of merit (ZT) above 2 at 1000K, from first-principles calculations neglecting electron-phonon scattering, spin-orbit coupl

Philipp Jeitner, Haya Shulman, Michael Waidner

The critical role that Network Time Protocol (NTP) plays in the Internet led to multiple efforts to secure it against time-shifting attacks. A recent proposal for enhancing the security of NTP with Chronos against on-path attackers seems the most promising one and is on a standardisation track of the IETF. In this work we demonstrate off-path attacks against

Congyu Wu, Hagen Fritz, Zoltan Nagy, Juan P. Maestre

As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users' daily lives with unprecedented comprehensiveness, unobtrusiveness, and ecological validity. A number of human-subject studies have been conducted in the past decade to examine the use of mobile sensing to unc

Alexander Collins, Vinod Grover

Probabilistic Programming Languages (PPLs) are a powerful tool in machine learning, allowing highly expressive generative models to be expressed succinctly. They couple complex inference algorithms, implemented by the language, with an expressive modelling language that allows a user to implement any computable function as the generative model. Such language

Martin Rosso, Michele Campobasso, Ganduulga Gankhuyag, Luca Allodi

In this paper we introduce SAIBERSOC, a tool and methodology enabling security researchers and operators to evaluate the performance of deployed and operational Security Operation Centers (SOCs) (or any other security monitoring infrastructure). The methodology relies on the MITRE ATT&CK Framework to define a procedure to generate and automatically inject sy

W. A. T. Gibby, M. L. Barabash, C. Guardiani, D. G. Luchinsky

We introduce a statistical and linear response theory of selective conduction in biological ion channels with multiple binding sites and possible point mutations. We derive an effective grand-canonical ensemble and generalised Einstein relations for the selectivity filter, assuming strongly coordinated ionic motion, and allowing for ionic Coulomb blockade. T

P. Gil-Pons, C. L. Doherty, J. Gutiérrez, S. W. Campbell

Abridged: Observed abundances of extremely metal-poor (EMP) stars in the Halo hold clues for the understanding of the ancient universe. Interpreting these clues requires theoretical stellar models at the low-Z regime. We provide the nucleosynthetic yields of intermediate-mass Z=$10^{-5}$ stars between 3 and 7.5 $M_{sun}$, and quantify the effects of the unce

Frédéric Bernicot, Yujia Zhai

In this work, we aim to study the action of composing by a rotation on the biparameter $\text{BMO}$ space in $\mathbb{R}^2$. This $\text{BMO}$ space is not preserved by a rotation since it relies on the structure of axis-parallel rectangles. We will quantify this fact by interpolation inequalities. One straightforward application of the interpolation inequal

Atreyee Kundu, Daniel E. Quevedo

This paper deals with Networked Control Systems (NCSs) whose shared networks have limited communication capacity and are prone to data losses. We assume that among (N) plants, only (M < N) plants can communicate with their controllers at any time instant. In addition, a control input, at any time instant, is lost in a channel with a probability (p). Our cont

Lalit K. Rajendran, Bhavini Singh, Pavlos P. Vlachos, Sally P. M. Bane

Nanosecond Surface Dielectric Barrier Discharge (ns-SDBDs) are a class of plasma actuators that utilize a high-voltage pulse of nanosecond duration between two surface-mounted electrodes to create an electrical breakdown of air, along with rapid heating. These actuators usually produce multiple filaments when operated at high pulse frequencies, and the rapid

Pavel Dvořák, Michal Koucký

In this paper we study the computational complexity of functions that have efficient card-based protocols. Card-based protocols were proposed by den Boer [EUROCRYPT &#39;89] as a means for secure two-party computation. Our contribution is two-fold: We classify a large class of protocols with respect to the computational complexity of functions they compute,

Giovanni Saraceno, Claudio Agostinelli, Luca Greco

A weighted likelihood technique for robust estimation of a multivariate Wrapped Normal distribution for data points scattered on a p-dimensional torus is proposed. The occurrence of outliers in the sample at hand can badly compromise inference for standard techniques such as maximum likelihood method. Therefore, there is the need to handle such model inadequ

Santiago Paternain, Juan Andres Bazerque, Alejandro Ribeiro

Reinforcement learning considers the problem of finding policies that maximize an expected cumulative reward in a Markov decision process with unknown transition probabilities. In this paper we consider the problem of finding optimal policies assuming that they belong to a reproducing kernel Hilbert space (RKHS). To that end we compute unbiased stochastic gr

Zhijingcheng Yu, Shweta Shinde, Trevor E. Carlson, Prateek Saxena

Trusted-execution environments (TEE), like Intel SGX, isolate user-space applications into secure enclaves without trusting the OS. Thus, TEEs reduce the trusted computing base, but add one to two orders of magnitude slow-down. The performance cost stems from a strict memory model, which we call the spatial isolation model, where enclaves cannot share memory

Hal Finkel, Alexander McCaskey, Tobi Popoola, Dmitry Lyakh

Domain-specific languages (DSLs) are both pervasive and powerful, but remain difficult to integrate into large projects. As a result, while DSLs can bring distinct advantages in performance, reliability, and maintainability, their use often involves trading off other good software-engineering practices. In this paper, we describe an extension to the Clang C+

Koosha Zarei, Reza Farahbakhsh, Noel Crespi, Gareth Tyson

Impersonators are playing an important role in the production and propagation of the content on Online Social Networks, notably on Instagram. These entities are nefarious fake accounts that intend to disguise a legitimate account by making similar profiles and then striking social media by fake content, which makes it considerably harder to understand which

Chia-Yen Chiang, Chloe Barnes, Plamen Angelov, Richard Jiang

Global climate change has had a drastic impact on our environment. Previous study showed that pest disaster occured from global climate change may cause a tremendous number of trees died and they inevitably became a factor of forest fire. An important portent of the forest fire is the condition of forests. Aerial image-based forest analysis can give an early

Danilo Greco, Jacopo Cavazza, Alessio Del Bue

Given an unknown audio source, the estimation of time differences-of-arrivals (TDOAs) can be efficiently and robustly solved using blind channel identification and exploiting the cross-correlation identity (CCI). Prior &#34;blind&#34; works have improved the estimate of TDOAs by means of different algorithmic solutions and optimization strategies, while alwa

Wissam Siblini, Mohamed Challal, Charlotte Pasqual

Open Domain Question Answering (ODQA) on a large-scale corpus of documents (e.g. Wikipedia) is a key challenge in computer science. Although transformer-based language models such as Bert have shown on SQuAD the ability to surpass humans for extracting answers in small passages of text, they suffer from their high complexity when faced to a much larger searc

Goran Zuzic, Di Wang, Aranyak Mehta, D. Sivakumar

We address the challenge of finding algorithms for online allocation (i.e. bipartite matching) using a machine learning approach. In this paper, we focus on the AdWords problem, which is a classical online budgeted matching problem of both theoretical and practical significance. In contrast to existing work, our goal is to accomplish algorithm design {\em ta

Martin Smuda, Florian Kummer

In this work a solver for instationary two-phase flows on the basis of the extended Discontinuous Galerkin (extended DG/XDG) method is presented. The XDG method adapts the approximation space conformal to the position of the interface. This allows a sub-cell accurate representation of the incompressible Navier-Stokes equations in their sharp interface formul

Takashi Kono, Masaaki Kakoki, Tomoki Yoshikawa, Xiaoxiao Wang

Using a well-focused soft X-ray synchrotron radiation beam, angle-resolved photoelectron spectroscopy was applied to a full-Heusler-type Co$_2$MnGe alloy to elucidate its bulk band structure. A large parabolic band at the Brillouin zone center and several bands that cross the Fermi level near the Brillouin zone boundary were identified in line with the resul

Kevin Roux, Victor Helson, Hideki Konishi, Jean-Philippe Brantut

We report on the fast production and weakly destructive detection of a Fermi gas with tunable interactions in a high finesse cavity. The cavity is used both with far off-resonant light to create a deep optical dipole trap, and with near-resonant light to reach the strong light-matter coupling regime. The cavity-based dipole trap allows for an efficient captu

B. Lott, D. Gasparrini, S. Ciprini

An incremental version (4LAC-DR2) of the fourth catalog of active galactic nuclei (AGNs) detected by the Fermi-LAT is presented. This version is associated with the second release of the 4FGL general catalog (based on 10 years of data), where the spectral parameters, spectral energy distributions, yearly light curves, and associations have been updated for a

Ryuji Miyauchi, Makoto Kishimoto

Spatially-resolved velocity maps at high resolutions of 1-10 pc are becoming available for many nearby AGNs in both optical/infrared atomic emission lines and sub-mm molecular lines. For the former, it has been known that a linear relationship appears to exist between the velocity of the ionized gas clouds and the distance from the nucleus in the inner ~100

Xiao Liu, Jiajie Zhang, Siting Li, Zuotong Wu

What mechanisms causes GAN&#39;s entanglement? Although developing disentangled GAN has attracted sufficient attention, it is unclear how entanglement is originated by GAN transformation. We in this research propose a difference-in-difference (DID) counterfactual framework to design experiments for analyzing the entanglement mechanism in on of the Progressiv

Paul Kilgarriff, Rémi Lemoy, Geoffrey Caruso

Seen from a satellite, observing land use in the daytime or at night, most cities have circular shapes, organised around a city centre. A radial analysis of artificial land use growth is conducted in order to understand what the recent changes in urbanisation are across Europe and how it relates to city size. We focus on the most fundamental differentiation

Karol Arndt, Ali Ghadirzadeh, Murtaza Hazara, Ville Kyrki

Few-shot adaptation is a challenging problem in the context of simulation-to-real transfer in robotics, requiring safe and informative data collection. In physical systems, additional challenge may be posed by domain noise, which is present in virtually all real-world applications. In this paper, we propose to perform few-shot adaptation of dynamics models i

A. Miyata, T. Hikihara, S. Furukawa, R. K. Kremer

We investigated the magnetoelastic properties of the quasi-one-dimensional spin-1/2 frustrated magnet LiCuVO$_4$. Longitudinal-magnetostriction experiments were performed at 1.5 K in high magnetic fields of up to 60 T applied along the $b$ axis, i.e., the spin-chain direction. The magnetostriction data qualitatively resemble the magnetization results, and sa

Joseph D. O&#39;Brien, James P. Gleeson

A detailed analysis of matches played in the sport of Snooker during the period 1968-2020 is used to calculate a directed and weighted dominance network based upon the corresponding results. We consider a ranking procedure based upon the well-studied PageRank algorithm that incorporates details of not only the number of wins a player has had over their caree

S. Calder, A. V. Haglund, A. I. Kolesnikov, D. Mandrus

Two-dimensional van der Waals compounds with magnetic ions on a honeycomb lattice are hosts to a variety of exotic behavior. The magnetic interactions in one such compound, MnPSe$_3$, are investigated with elastic and inelastic neutron scattering. Magnetic excitations are observed in the magnetically ordered regime and persist to temperatures well above the

C. Barbarino, J. Sollerman, F. Taddia, C. Fremling

Type Ic supernovae represent the explosions of the most stripped massive stars, but their progenitors and explosion mechanisms remain unclear. Larger samples of observed supernovae can help characterize the population of these transients. We present an analysis of 44 spectroscopically normal Type Ic supernovae, with focus on the light curves. The photometric

Eleni Bohacek, Andrew J. Coates, David R. Selviah

This paper investigates how the inherent quantization of camera sensors introduces uncertainty in the calculated position of an observed feature during 3-D mapping. It is typically assumed that pixels and scene features are points, however, a pixel is a two-dimensional area that maps onto multiple points in the scene. This uncertainty region is a bound for q

Gaëtan Rensonnet, Jonathan Rafael-Patiño, Benoît Macq, Jean-Philippe Thiran

Diffusion-weighted MRI (DW-MRI) has recently seen a rising interest in planar, spherical and general B-tensor encodings. Some of these sequences have aided traditional linear encoding in the estimation of white matter microstructural features, generally by making DW-MRI less sensitive to the orientation of axon fascicles in a voxel. However, less is known ab

Giovanni S. Alberti, Paolo Campodonico, Matteo Santacesaria

Photoacoustic tomography (PAT) is an emerging imaging modality that aims at measuring the high-contrast optical properties of tissues by means of high-resolution ultrasonic measurements. The interaction between these two types of waves is based on the thermoacoustic effect. In recent years, many works have investigated the applicability of compressed sensing

Johnny Tang

I study the heterogeneity of credence goods provision in taxi drivers taking detours in New York City. First, I document that there is significant detouring on average by drivers. Second, there is significant heterogeneity in cheating across individuals, yet each individual&#39;s propensity to take detours is stable: drivers who detour almost always detour,

Juan Luis Gonzalo, Camilla Colombo, Pierluigi Di Lizia

The core aspects and latest developments of Manoeuvre Intelligence for Space Safety (MISS), a new software tool for collision avoidance analysis and design, are presented. The tool leverages analytical and semi-analytical methods for the efficient modelling of the orbit modifications due to different control strategies, such as impulsive or low-thrust manoeu

Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Matthias Bethge

How do humans learn to acquire a powerful, flexible and robust representation of objects? While much of this process remains unknown, it is clear that humans do not require millions of object labels. Excitingly, recent algorithmic advancements in self-supervised learning now enable convolutional neural networks (CNNs) to learn useful visual object representa

Oliver Bachtler, Irene Heinrich

We present an interactive framework that, given a membership test for a graph class $\mathcal{G}$ and a number $k$, finds and tests unavoidable sets for the class of graphs in $\mathcal{G}$ of path-width at most $k$. We put special emphasis on the case that $\mathcal{G}$ is the class of cubic graphs and tailor the algorithm to this case. In particular, we in

I. A. Solovev, I. I. Yanibekov, Yu. P. Efimov, S. A. Eliseev

Larmor precession of the quasiparticle spin about a transverse magnetic field leads to the oscillations in the spontaneous photon echo signal due to the shuffling of the optical coherence between optically accessible (bright) and inaccessible (dark) states. Here we report on a new non-oscillating photon echo regime observed in the presence of non-equal depha

Li Yuan, Yichen Zhou, Shuning Chang, Ziyuan Huang

Detecting and recognizing human action in videos with crowded scenes is a challenging problem due to the complex environment and diversity events. Prior works always fail to deal with this problem in two aspects: (1) lacking utilizing information of the scenes; (2) lacking training data in the crowd and complex scenes. In this paper, we focus on improving sp

Pim L. Joostens, Egbert Westerhof

The Warm Plasma Dispersion Relation, for waves in the electron cyclotron resonance range of frequencies, can be cast into the form of a bi-quadratic equation for $N_\perp$, where the coefficients are a function of $N_\perp^2$ and an iterative procedure is required to obtain a solution. However, this iterative procedure is not well understood and fails to con

Deena Nath, U. P. Deshpande, N. V. Chandra Shekar, Sujay Chakravarty

In the present work, a thin film was deposited on quartz substrate by reactive RF magnetron sputtering of high purity (99.999%) aluminium target using ultra-high pure (Ar + N2) gas mixture. The percentage ratio of Ar and N2 in the gas mixture was 95% and 5%, respectively. Chemical characterization using x-ray photoelectron spectroscopy (XPS) and energy-dispe

Zhaowen Wang, Wei Zhang, Zhiming Wang

Differentiable Architecture Search (DARTS) provides a baseline for searching effective network architectures based gradient, but it is accompanied by huge computational overhead in searching and training network architecture. Recently, many novel works have improved DARTS. Particularly, Partially-Connected DARTS(PC-DARTS) proposed the partial channel samplin

Julien Y. Dutheil

The development of coalescent theory paved the way to statistical inference from population genetic data. In the genomic era, however, coalescent models are limited due to the complexity of the underlying ancestral recombination graph. The sequentially Markov coalescent (SMC) is a heuristic that enables the modelling of complete genomes under the coalescent

A. S. Maxwell, G. S. J. Armstrong, M. F. Ciappina, E. Pisanty

We investigate the discrete orbital angular momentum (OAM) of photoelectrons freed in strongfield ionization. We use these `twisted&#39; electrons to provide an alternative interpretation on existing experimental work of vortex interferences caused by strong field ionization mediated by two counterrotating circularly polarized pulses separated by a delay. Us

Zhihao Cheng, Liu Liu, Aishan Liu, Hao Sun

Imitation learning from observation (LfO) is more preferable than imitation learning from demonstration (LfD) due to the nonnecessity of expert actions when reconstructing the expert policy from the expert data. However, previous studies imply that the performance of LfO is inferior to LfD by a tremendous gap, which makes it challenging to employ LfO in prac

Francesco Romor, Marco Tezzele, Gianluigi Rozza

Gaussian processes are employed for non-parametric regression in a Bayesian setting. They generalize linear regression, embedding the inputs in a latent manifold inside an infinite-dimensional reproducing kernel Hilbert space. We can augment the inputs with the observations of low-fidelity models in order to learn a more expressive latent manifold and thus i

Stephan Radonic, Jürgen Besserer, Valeria Meier, Carla Rohrer Bley

TCP models based on Poisson statistics are characterizing the distribution of the surviving clonogens. It enables the calculation of TCP for individuals. In order to describe clinically observed survival data of patient cohorts it is necessary to extend the model. This is typically done by either incorporating variations of various model parameters, or by us

Javier Esparza, Stefan Kiefer, Jan Kretinsky, Maximilian Weininger

We study runtime monitoring of $ω$-regular properties. We consider a simple setting in which a run of an unknown finite-state Markov chain $\mathcal M$ is monitored against a fixed but arbitrary $ω$-regular specification $φ$. The purpose of monitoring is to keep aborting runs that are &#34;unlikely&#34; to satisfy the specification until $\mathcal M$ execute

Sina Foroutan-Barenji, Onur Erdem, Savas Delikanli, Huseyin Bilge Yagci

In this work, we report the first account of monolithically-fabricated vertical cavity surface emitting lasers (VCSELs) of densely-packed, orientation-controlled, atomically flat colloidal quantum wells (CQWs) using a self-assembly method and demonstrate single-mode lasing from a record thin colloidal gain medium with a film thickness of 7 nm under femtoseco

Aru Beri, Sachindra Naik, K. P Singh, Gaurava K. Jaisawal

SwiftJ0243.6+6124, the first Galactic ultra-luminous X-ray pulsar, was observed during its 2017-2018 outburst with \emph{AstroSat} at both sub- and super-Eddington levels of accretionwith X-ray luminosities of $L_{X}{\sim}7{\times}10^{37}$ and $6{\times}10^{38}$$ergs^{-1}$, respectively.Our broadband timing and spectral observations show that X-ray pulsation

Łukasz Pańkowski

We prove that any non-zero complex values $z_1,\ldots,z_n$ can be approximated by the following integral shifts of the Riemann zeta-function $ζ(s+id_1τ),\ldots,ζ(s+id_nτ)$ for infinitely many $τ$, provided $d_1,\ldots,d_n\in\mathbb{N}$ and $s$ is a fixed complex number lying in the right open half of the critical strip.

I. Zborovsky, M. Tokarev

$z$-Scaling of inclusive spectra as a manifestation of self-similarity and fractality of hadron interactions is illustrated. The scaling for negative particle production in $Au+Au$ collisions from BES-I program at RHIC is demonstrated. The scaling variable $z$ depends on the momentum fractions of the colliding objects carried by the interacting constituents,

H. Fukazawa, K. Kumeda, N. Shioda, YS Lee

Nuclear quadrupole resonance (NQR) measurements were performed on the heavy fermion superconductor Ce3PtIn11 with Tc = 0.32 K. The temperature dependence of both spin-lattice relaxation rate 1/T1 and NQR spectra evidences the occurrence of two successive magnetic transitions with TN1 = 2.2 K and TN2 = 2.0 K. In successive magnetic transitions, even though th

R. A. Nyman, H. S. Dhar, J. D. Rodrigues, F. Mintert

Photon thermalisation and condensation in dye-filled microcavities is a growing area of scientific interest, at the intersection of photonics, quantum optics and statistical physics. We give here a short introduction to the topic, together with an explanation of some of our more important recent results. A key result across several projects is that we have a

Saeedeh Shekarpour, Abhishek Nadgeri, Kuldeep Singh

In the era of Big Knowledge Graphs, Question Answering (QA) systems have reached a milestone in their performance and feasibility. However, their applicability, particularly in specific domains such as the biomedical domain, has not gained wide acceptance due to their &#34;black box&#34; nature, which hinders transparency, fairness, and accountability of QA

Masahiro Takeda

Let $G$ be the classical group, and let Hom$(\mathbb{Z}^m,G)$ denote the space of commuting $m$-tuples in $G$. Baird proved that the cohomology of Hom$(\mathbb{Z}^m,G)$ is identified with a certain ring of invariants of the Weyl group of $G$. In this paper by using the result of Baird we give the cohomology ring of Hom$(\mathbb{Z}^2,G)$ for simple Lie group

Tim Nugent, Nicole Stelea, Jochen L. Leidner

Despite recent advances in deep learning-based language modelling, many natural language processing (NLP) tasks in the financial domain remain challenging due to the paucity of appropriately labelled data. Other issues that can limit task performance are differences in word distribution between the general corpora - typically used to pre-train language model

Matheus Henrique Junqueira Saldanha, Adriano Kamimura Suzuki

There is a myriad of phenomena that are better modelled with semi-infinite distribution families, many of which are studied in survival analysis. When performing inference, lack of knowledge of the populational minimum becomes a problem, which can be dealt with by making a good guess thereof, or by handcrafting a grid of initial parameters that will be usefu

Matthias Grundmann, Hannes Hartenstein

Payment channel networks are a highly discussed approach for improving scalability of cryptocurrencies such as Bitcoin. As they allow processing transactions off-chain, payment channel networks are referred to as second layer technology, while the blockchain is the first layer. We uncouple payment channel networks from blockchains and look at them as first-c

Jingjing Cui, Dong Liu, Jiankang Zhang, Halil Yetgin

Relying on multi-hop communication techniques, aeronautical ad hoc networks (AANETs) seamlessly integrate ground base stations (BSs) and satellites into aircraft communications for enhancing the on-demand connectivity of planes in the air. In this integrated AANET context we investigate the shortest-path routing problem with the objective of minimizing the t

Tobias Scharff, Wolfram Ratzke, Jonas Zipfel, Philippe Klemm

At low temperatures and high magnetic fields, electron and hole spins in an organic light-emitting diode (OLED) become polarized so that recombination preferentially forms molecular triplet excited-state species. For low device currents, magnetoelectroluminescence (MEL) perfectly follows Boltzmann activation, implying a virtually complete polarization outcom

Rajesh K Vatsa, Deepak Mathur

Zhang and coworkers have recently reported results of experiments involving irradiation of argon clusters doped with bromofluorene chromophores by nanosecond-long pulses of 532 nm laser light. Multiply-charged ions of atomic argon (charge states, n, up to 7) and carbon (charge states up to 4) are observed which are sought to be rationalised using an evaporat

David Gillsjö, Kalle Åström

This work studies Semantic Scene Completion which aims to predict a 3D semantic segmentation of our surroundings, even though some areas are occluded. For this we construct a Bayesian Convolutional Neural Network (BCNN), which is not only able to perform the segmentation, but also predict model uncertainty. This is an important feature not present in standar

Bei Han, Lingen Luo, Gehao Sheng, Xiuchen Jiang

The quick detection and localization of partial discharge (PD) in an air-insulated substation (AIS) based on ultrahigh-frequency (UHF) sensor arrays are efficient for power equipment monitoring. The adopted UHF PD time difference of arrival (TDOA) methods mainly use the time difference of electromagnetic wave signals. Thus, the system requires both a high sa