November 2020 arXiv papers — page 27

Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, José Miguel Hernández-Lobato

Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials. Despite recent progress on leveraging graph representations to design molecules, such methods are fundamentally limited by the lack of three-dimensional (3D) information. In light of this, we propose a novel actor-critic

Asma Bahamyirou, Mireille E. Schnitzer, Edward H. Kennedy, Lucie Blais

Effect modification occurs when the effect of the treatment on an outcome differs according to the level of a third variable (the effect modifier, EM). A natural way to assess effect modification is by subgroup analysis or include the interaction terms between the treatment and the covariates in an outcome regression. The latter, however, does not target a p

Yue Qian, Junhui Hou, Sam Kwong, Ying He

This paper addresses the problem of generating dense point clouds from given sparse point clouds to model the underlying geometric structures of objects/scenes. To tackle this challenging issue, we propose a novel end-to-end learning-based framework. Specifically, by taking advantage of the linear approximation theorem, we first formulate the problem explici

Ian A. Crawford, Katherine H. Joy, Jan H. Pasckert, Harald Hiesinger

The lunar surface has been exposed to the space environment for billions of years and during this time has accumulated records of a wide range of astrophysical phenomena. These include solar wind particles and the cosmogenic products of solar particle events which preserve a record of the past evolution of the Sun, and cosmogenic nuclides produced by high-en

Golnaz Badkobeh, Maxime Crochemore

We extend the left-to-right Lyndon factorisation of a word to the left Lyndon tree construction of a Lyndon word. It yields an algorithm to sort the prefixes of a Lyndon word according to the infinite ordering defined by Dolce et al. (2019). A straightforward variant computes the left Lyndon forest of a word. All algorithms run in linear time on a general al

A. Kudlis, I. Iorsh

We analyze the many-particle correlations that affect the optical properties of two-dimensional semiconductors. These correlations manifest themselves through the specific optical resonances such as excitons, trions, etc. Starting from the generic electron-hole Hamiltonian and employing the microscopic Heisenberg equation of motion the infinite hierarchy of

Brad Weir, David Crisp, Christopher W O'Dell, Sourish Basu

Activity reductions in early 2020 due to the Coronavirus Disease 2019 pandemic led to unprecedented decreases in carbon dioxide (CO2) emissions. Despite their record size, the resulting atmospheric signals are smaller than and obscured by climate variability in atmospheric transport and biospheric fluxes, notably that related to the 2019-2020 Indian Ocean Di

Arthur Bik, Alessandro Danelon, Jan Draisma

In a previous paper, the third author proved that finite-degree polynomial functors over infinite fields are topologically Noetherian. In this paper, we prove that the same holds for polynomial functors from free $R$-modules to finitely generated $R$-modules, for any commutative ring $R$ whose spectrum is Noetherian. As Erman-Sam-Snowden pointed out, when ap

Kamil Wereszczyński, Agnieszka Michalczuk, Damian Pęszor, Marcin Paszkuta

A novel family of Cosine series Quantum Sampling (QCoSamp) operators appropriate for quantum computing is described. The development of quantum algorithms, analogous to classical algorithms, we apply to the harmonic analysis of signals. We show quantum sampling through measurements of a quantum system, and after operators of the family are applied, allow for

Carlos Lassance, Louis Béthune, Myriam Bontonou, Mounia Hamidouche

Measuring the generalization performance of a Deep Neural Network (DNN) without relying on a validation set is a difficult task. In this work, we propose exploiting Latent Geometry Graphs (LGGs) to represent the latent spaces of trained DNN architectures. Such graphs are obtained by connecting samples that yield similar latent representations at a given laye

K. Sieja, S. Goriely

The radiative neutron capture rates for isotopes of astrophysical interest are commonly calculated within the statistical Hauser-Feshbach reaction model. Such an approach, assuming a high level density in the compound system, can be questioned in light and neutron-rich nuclei for which only a few or no resonant states are available. Therefore, in this work w

Victor Saase, Holger Wenz, Thomas Ganslandt, Christoph Groden

Statistical analysis of magnetic resonance imaging (MRI) can help radiologists to detect pathologies that are otherwise likely to be missed. Deep learning (DL) has shown promise in modeling complex spatial data for brain anomaly detection. However, DL models have major deficiencies: they need large amounts of high-quality training data, are difficult to desi

Zhan-Dong Mei

We study the output feedback exponential stabilization for a 1-d wave PDE with dynamic boundary. With only one measurement, we construct an infinite-dimensional state observer to trace the state and design an estimated state based controller to exponentially stabilize the original system. This is an essentially important improvement for the existence literat

Sebastian Andres, Noah Halberstam

We study the random conductance model on $\mathbb{Z}^d$ with ergodic, unbounded conductances. We prove a Gaussian lower bound on the heat kernel given a polynomial moment condition and some additional assumptions on the correlations of the conductances. The proof is based on the well-established chaining technique. We also obtain bounds on the Green's functi

Dominik Dannheim, Katharina Dort, Lennart Huth, Daniel Hynds

Corryvreckan is a versatile, highly configurable software with a modular structure designed to reconstruct and analyse test beam and laboratory data. It caters to the needs of the test beam community by providing a flexible offline event building facility to combine detectors with different read-out schemes, with or without trigger information, and includes

Alexander Y Klimenko, Dimitri A Klimenko

We discuss conceptual limitations of generic learning algorithms pursuing adversarial goals in competitive environments, and prove that they are subject to limitations that are analogous to the constraints on knowledge imposed by the famous theorems of G\"odel and Turing. These limitations are shown to be related to intransitivity, which is commonly present

Christian Schimpf, Marcus Reindl, Francesco Basso Basset, Klaus D. Jöns

The generation and long-haul transmission of highly entangled photon pairs is a cornerstone of emerging photonic quantum technologies, with key applications such as quantum key distribution and distributed quantum computing. However, a natural limit for the maximum transmission distance is inevitably set by attenuation in the medium. A network of quantum rep

Yoshio Ebihara, Hayato Waki, Victor Magron, Ngoc Hoang Anh Mai

In this paper, we focus on the "positive" $l_2$ induced norm of discrete-time linear time-invariant systems where the input signals are restricted to be nonnegative. To cope with the nonnegativity of the input signals, we employ copositive programming as the mathematical tool for the analysis. Then, by applying an inner approximation to the copositive cone,

Ke Liu, Mengyu Cao, Mei Lu

Let $n$, $k$ and $t$ be integers with $1\leq t< k \leq n$. The \emph{generalized Kneser graph} $K(n,k,t)$ is a graph whose vertices are the $k$-subsets of a fixed $n$-set, where two $k$-subsets $A$ and $B$ are adjacent if $|A\cap B|<t$. The graph $K(n,k,1)$ is the well-known \emph{Kneser graph}. In 2014, Harvey and Wood determined the exact treewidth of the

Tommaso Bradde, Samuel Chevalier, Marco De Stefano, Stefano Grivet-Talocia

This paper develops a predictive modeling algorithm, denoted as Real-Time Vector Fitting (RTVF), which is capable of approximating the real-time linearized dynamics of multi-input multi-output (MIMO) dynamical systems via rational transfer function matrices. Based on a generalization of the well-known Time-Domain Vector Fitting (TDVF) algorithm, RTVF is suit

Yonatan Harpaz, Joost Nuiten, Matan Prasma

Every $(\infty, n)$-category can be approximated by its tower of homotopy $(m, n)$-categories. In this paper, we prove that the successive stages of this tower are classified by k-invariants, analogously to the classical Postnikov tower for spaces. Our proof relies on an abstract analysis of Postnikov-type towers equipped with k-invariants, and also yields a

Anzhu Yu, Wenyue Guo, Bing Liu, Xin Chen

We present an efficient multi-view stereo (MVS) network for 3D reconstruction from multiview images. While previous learning based reconstruction approaches performed quite well, most of them estimate depth maps at a fixed resolution using plane sweep volumes with a fixed depth hypothesis at each plane, which requires densely sampled planes for desired accur

A. Pakalniskis A. Lukowiak, G. Niaura, P. Gluchowski, D. V. Karpinsky

Single phase barium titanate-bismuth ferrite ((1-x)BaTi$_3$-(x)BiFe$_3$, BTO-BFO) solid solutions were prepared using citric acid and ethylene glycol assisted sol-gel synthesis method. Depending on the dopant content the samples are characterized by tetragonal, tetragonal-pseudocubic, pseudocubic and rhombohedral structure as confirmed by Raman spectroscopy

Eric Liang, Zhanghao Wu, Michael Luo, Sven Mika

Researchers and practitioners in the field of reinforcement learning (RL) frequently leverage parallel computation, which has led to a plethora of new algorithms and systems in the last few years. In this paper, we re-examine the challenges posed by distributed RL and try to view it through the lens of an old idea: distributed dataflow. We show that viewing

Jin Zhang, Yanhui Lv

For a singularly perturbed elliptic model problem with two small parameters, we analyze finite element methods of any order on a Bakhvalov-type mesh. For convergence analysis, we construct a new interpolation by using the characteristics of layers. Besides, a more subtle analysis of the mesh scale near the exponential layer is carried out. Based on the inter

Damian Dąbrowski

In a recent article we introduced and studied conical energies. We used them to prove three results: a characterization of rectifiable measures, a characterization of sets with big pieces of Lipschitz graphs, and a sufficient condition for boundedness of nice singular integral operators. In this note we give two examples related to sharpness of these results

Maxim Dvornikov, V. B. Semikoz

We study the evolution of axions interacting with primordial magnetic fields (PMFs) starting just from the QCD phase transition in the expanding universe. This interaction is owing to the Primakoff effect. Adopting the zero mode approximation for axions, we derive the system of equations for axions and magnetic fields, where the expansion of the universe and

Weizhi Du, Harvey Tian

Optimization of resource distribution has been a challenging topic in current society. To explore this topic, we develop a Coalition Control Model(CCM) based on the Model Predictive Control(MPC) and test it using a fishing model with linear parameters. The fishing model focuses on the problem of distributing fishing fleets in certain regions to maximize fish

Sandeep Howlader, Goutam Sheet

It is widely believed that topological superconductivity, a hitherto elusive phase of quantum matter, can be achieved by inducing superconductivity in topological materials. In search of such topological superconductors, certain topological insulators (like, Bi$_2$Se$_3$) were successfully turned into superconductors by metal-ion (Cu, Pd, Sr, Nb etc. ) inter

Ryosuke Kiyama, Kazuhisa Hoshi, Yosuke Goto, Yoshikazu Mizuguchi

We investigate the superconducting properties and possible nematic superconductivity of self-doped BiCh2-based (Ch: S, Se) superconductor CeOBiS1.7Se0.3 through the measurements of in-plane anisotropy of magnetoresistance. Single crystals of CeOBiS1.7Se0.3 were grown using a flux method. Single-crystal structural analysis revealed that the crystal structure

Girmaw Abebe Tadesse, Celia Cintas, Skyler Speakman, Komminist Weldemariam

Existing datasets available to address crucial problems, such as child mortality and family planning discontinuation in developing countries, are not ample for data-driven approaches. This is partly due to disjoint data collection efforts employed across locations, times, and variations of modalities. On the other hand, state-of-the-art methods for small dat

Johanna Rock, Wolfgang Roth, Paul Meissner, Franz Pernkopf

Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous vehicles. Key performance factors are weather resistance and the possibility to directly measure velocity. With a rising number of radar sensors and the so far unregulated automotive radar frequency band, mutual interference is inevitable and must be dealt

Yonghui Zhou, Shuguan Ji

This paper is concerned with the globally exponential stability of traveling wave fronts for a class of population dynamics model with quiescent stage and delay. First, we establish the comparison principle of solutions for the population dynamics model. Then, by the weighted energy method combining comparison principle, the globally exponential stability of

Srijita Kundu, Ernest Y. -Z. Tan

We study the task of encryption with certified deletion (ECD) introduced by Broadbent and Islam (2020), but in a device-independent setting: we show that it is possible to achieve this task even when the honest parties do not trust their quantum devices. Moreover, we define security for the ECD task in a composable manner and show that our ECD protocol satis

Haiqin Wang, Xinpeng Xu

We review continuum elastic models for the transmission of both external forces and internal active cellular forces in biopolymer gels, and relate them to recent experiments. Rather than being exhaustive, we focus on continuum elastic models for small affine deformations and intend to provide a systematic continuum method and some analytical perspectives to

Katerina Papagiannouli

We suppose that a L\'evy process is observed at discrete time points. Starting from an asymptotically minimax family of estimators for the continuous part of the L\'evy Khinchine characteristics, i.e., the covariance, we derive a data-driven parameter choice for the frequency of estimating the covariance. We investigate a Lepski\u{i}-type stopping rule for t

Manuel Giollo, Deniz Gunceler, Yulan Liu, Daniel Willett

Bootstrapping speech recognition on limited data resources has been an area of active research for long. The recent transition to all-neural models and end-to-end (E2E) training brought along particular challenges as these models are known to be data hungry, but also came with opportunities around language-agnostic representations derived from multilingual d

Nilesh Pandey, Karishma Qureshi, Yogesh Singh Chauhan

A simulation-based study of variability of remnant polarization $\left (P_r \right)$ in a multi-granular 3-D ultra-thin ferroelectric (FE) capacitor is presented in this paper. The Poisson Voronoi Tessellation Diagram (PVD) is used for the nucleation of grains in the FE region, which corresponds to the physical growth mechanism. The PVD algorithm implemented

M. Serpelloni, M. Arricca, C. Bonanno, A. Salvadori

The response of cells during spreading and motility is dictated by several multi-physics events, which are triggered by extracellular cues and occur at different time-scales. For this sake, it is not completely appropriate to provide a cell with classical notions of the mechanics of materials, as for "rheology" or "mechanical response". Rather, a cell is an

Elena Cukanovaite, Pier-Emmanuel Tremblay, Pierre Bergeron, Bernd Freytag

In this paper, we present corrections to the spectroscopic parameters of DB and DBA white dwarfs with -10.0 < log(H/He) < -2.0, 7.5 < log(g) < 9.0 and 12000 K < T_eff < 34000 K, based on 282 3D atmospheric models calculated with the CO5BOLD radiation-hydrodynamics code. These corrections arise due to a better physical treatment of convective energy transport

Xuefei Ning, Changcheng Tang, Wenshuo Li, Songyi Yang

Neural Architecture Search (NAS) has received extensive attention due to its capability to discover neural network architectures in an automated manner. aw_nas is an open-source Python framework implementing various NAS algorithms in a modularized manner. Currently, aw_nas can be used to reproduce the results of mainstream NAS algorithms of various types. Al

Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen

MOBA games, e.g., Honor of Kings, League of Legends, and Dota 2, pose grand challenges to AI systems such as multi-agent, enormous state-action space, complex action control, etc. Developing AI for playing MOBA games has raised much attention accordingly. However, existing work falls short in handling the raw game complexity caused by the explosion of agent

Yong Xiao, Yingyu Li, Guangming Shi, H. Vincent Poor

This paper studies an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) based on the datasets uploaded from a multi-technology-supported IoT network. The data uploading performance of IoT network and the computational capacity of edge servers are entangled with each other in influencing the

Bas van der Heijden, Laura Ferranti, Jens Kober, Robert Babuska

This paper presents DeepKoCo, a novel model-based agent that learns a latent Koopman representation from images. This representation allows DeepKoCo to plan efficiently using linear control methods, such as linear model predictive control. Compared to traditional agents, DeepKoCo learns task-relevant dynamics, thanks to the use of a tailored lossy autoencode

Cornélio Rodrigues Filho

We discuss the $0^{+-}$ singlet sector of glueballs in the Klebanov-Strassler theory. We report the results of a numerical study of the linearized equations in the Klebanov-Strasller background and make a comparison with the spectrum of the scalar sector. While for four towers of the total six towers of massive pseudoscalar states our results match the spect

Qi Liu, Hui Yuan, Raouf Hamzaoui, Honglei Su

In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bit rate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with perceptual quality. While several quality measur

Aziz Ikemakhen, Mohamed Bellaihou

Many applications of geometry modeling and computer graphics necessite accurate curvature estimations of curves on the plane or on manifolds. In this paper, we define the notion of the discrete geodesic curvature of a geodesic polygon on a smooth surface. We show that, when a geodesic polygon P is closely inscribed on a $C^2$-regular curve, the discrete geod

László Gyulai

The production of heavy-flavour hadrons in high-energy hadronic collisions is a unique source of information on various aspects of quantum chromodynamics (QCD). Production of heavy-flavour hadrons in proton--proton collisions allows the test of perturbative QCD models, while the comparison of mesons and baryons with heavy-flavour quarks can differentiate bet

Reyhaneh Rigia, Mehrdad Jalali, Mohammad Hosein Moattar

Social networks are the social structures which are composed of people and their relationships and nowadays, play an important role in data extension. In such networks, the communities are recognized as the groups of users who are often interacting with each other. In this article, a method will be introduced for community detection, which has the capability

Lucas Chaves Lima, Casper Hansen, Christian Hansen, Dongsheng Wang

This report describes the participation of two Danish universities, University of Copenhagen and Aalborg University, in the international search engine competition on COVID-19 (the 2020 TREC-COVID Challenge) organised by the U.S. National Institute of Standards and Technology (NIST) and its Text Retrieval Conference (TREC) division. The aim of the competitio

Jiarui Jin, Kounianhua Du, Weinan Zhang, Jiarui Qin

Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations. Recent attempts either rely on explicit path reachability to leverage path-based semantic relatedness or graph neighborhood to learn heterogeneous network representations before predictions. These weakly coupled manners overlook

Amaury Hayat

In this paper we study the global exponential stability in the $L^{2}$ norm of semilinear $1$-$d$ hyperbolic systems on a bounded domain, when the source term and the nonlinear boundary conditions are Lipschitz. We exhibit two sufficient stability conditions: an internal condition and a boundary condition. This result holds also when the source term is nonlo

Zhen Zhang, Renata M. Wentzcovitch

We present an $\textit{ab initio}$ study of the thermodynamic properties of cubic CaSiO$_3$ perovskite (CaPv) over the pressure and temperature range of the Earth's lower mantle. We compute the anharmonic phonon dispersions throughout the Brillouin zone by utilizing the phonon quasiparticle approach, which characterizes the intrinsic temperature dependence o

Morgan Frearson, Kien Nguyen

The use of deep learning for human identification and object detection is becoming ever more prevalent in the surveillance industry. These systems have been trained to identify human body's or faces with a high degree of accuracy. However, there have been successful attempts to fool these systems with different techniques called adversarial attacks. This pap

Thomas Quella

We study the phase diagram of the $SO_q(3)$ quantum group invariant spin-1 bilinear-biquadratic spin chain for real values of $q>1$. Numerical computations suggest that the chain has at least three clearly distinguished phases: A chiral analogue of the Haldane phase, a dimerized phase and a ferromagnetic phase. In contrast, the counterpart of the extended cr

Maxime Debertolis, Serge Florens, Izak Snyman

The quenching of degenerate impurity states in metals generally induces a long-range correlated quantum state known as the Kondo screening cloud. While a macroscopic number of particles clearly take part in forming this extended structure, assessing the number of truly entangled degrees of freedom requires a careful analysis of the relevant many-body wavefun

Sandra Carillo, Cornelia Schiebold

An explicit solution formula for the matrix modified KdV equation is presented, which comprises the solutions given in Ref. 7 (S. Carillo, M. Lo Schiavo, and C. Schiebold. Matrix solitons solutions of the modified Korteweg-de Vries equation. In: Nonlinear Dynamics of Structures, Systems and Devices, edited by W. Lacarbonara, B. Balachandran, J. Ma, J. Tenrei

Mohammadreza Edalati, Mahdi Mahmoudzadeh, Javad Safaie, Fabrice Wallois

Rhythm processing involves building expectations according to the hierarchical temporal structure of auditory events. Although rhythm processing has been addressed in the context of predictive coding, the properties of the oscillatory response in different cortical areas is still not clear. We explored the oscillatory properties of the neural response to rhy

Pawel Duch, Michael Duetsch, Jose M. Gracia-Bondia

We revisit a nearly ten-year old controversy on the diphoton decay of the Higgs particle. To a large extent, the controversy turned around the respective merits of the regularization techniques employed. The novel aspect of our approach is that no regularization techniques are brought to bear: we work within the Bogoliubov--Epstein--Glaser scheme of renormal

Di Liu, Hao Kong, Xiangzhong Luo, Weichen Liu

Edge computing and artificial intelligence (AI), especially deep learning for nowadays, are gradually intersecting to build a novel system, called edge intelligence. However, the development of edge intelligence systems encounters some challenges, and one of these challenges is the \textit{computational gap} between computation-intensive deep learning algori

Yu Mei, Weihua Gu, Michael Cassidy, Wenbo Fan

Transit vehicles operating under skip-stop service visit only a subset of the stops residing along a corridor. It is a strategy commonly used to increase vehicle speeds and reduce patron travel times. The present paper develops a continuous approximation model to optimally design a select form of skip-stop service, termed AB-type service. The model accounts

Simon Feigl, Radovan Vranik, Bareld Wit, Stefan Müllegger

Radio-frequency (rf) scanning tunneling microscopy has recently been advanced to methods such as single-atom spin resonance. Such methods benefit from a frequency-independent rf voltage amplitude across the tunnel junction, which is challenging to achieve due to the strong frequency dependence of the rf attenuation in a transmission line. Two calibration met

Mattia Segu, Alessio Tonioni, Federico Tombari

Domain generalization aims at training machine learning models to perform robustly across different and unseen domains. Several recent methods use multiple datasets to train models to extract domain-invariant features, hoping to generalize to unseen domains. Instead, first we explicitly train domain-dependant representations by using ad-hoc batch normalizati

Mangesh Bhattarai, Vineet Bharti, Sambit Banerjee, Vasant Natarajan

We experimentally demonstrate electromagnetically induced absorption (EIA) in a vee + ladder system. The experiment is done using the low-lying energy levels of $^{87}$Rb. A theoretical model of the system is made that reproduces the experimental results. We study the dependence of the characteristics of the EIA resonance on various combinations of the diffe

Om Ji Omer, Prashant Laddha, Gurpreet S Kalsi, Anirud Thyagharajan

Semantic understanding and completion of real world scenes is a foundational primitive of 3D Visual perception widely used in high-level applications such as robotics, medical imaging, autonomous driving and navigation. Due to the curse of dimensionality, compute and memory requirements for 3D scene understanding grow in cubic complexity with voxel resolutio

Erwan Brugallé, Andrés Jaramillo Puentes

Tropical refined invariants of toric surfaces constitute a fascinating interpolation between real and complex enumerative geometries via tropical geometry. They were originally introduced by Block and G\"ottsche, and further extended by G\"ottsche and Schroeter in the case of rational curves. In this paper, we study the polynomial behavior of coefficients of

Dmitry K. Efimkin, Emma K. Laird, Jesper Levinsen, Meera M. Parish

Recently, it has been demonstrated that the absorption of moderately doped two-dimensional semiconductors can be described in terms of exciton-polarons. In this scenario, attractive and repulsive polaron branches are formed due to interactions between a photo-excited exciton and a Fermi sea of excess charge carriers. These interactions have previously been t

Andre G. Campos, Karen Z. Hatsagortsyan, Christoph H. Keitel

Exact solutions of the Dirac equation, a system of four partial differential equations, are rare. The vast majority of them are for highly symmetric stationary systems. Moreover, only a handful of solutions for time dependent dynamics exists. Given the growing number of applications of high energy electron beams interacting with a variety of quantum systems

Yanir A. Rubinstein, Kewei Zhang

We construct a family of K\"ahler-Einstein edge metrics on all Hirzebruch surfaces using the Calabi ansatz and study their angle deformation. This allows us to verify in some special cases a conjecture of Cheltsov-Rubinstein that predicts convergence towards a non-compact Calabi-Yau fibration in the small angle limit. We also give an example of a K\"ahler-Ei

Calin-Ioan Gheorghiu

We comparatively use some classical spectral collocation methods as well as highly performing Chebfun algorithms in order to compute the eigenpairs of second order singular Sturm-Liouville problems with separated self-adjoint boundary conditions. For both the limit-circle non oscillatory and oscillatory cases we pay a particular attention. Some "hard" benchm

Jonas Wätzel, Jamal Berakdar

Laser fields can be shaped on a subwavelength scale as to have a specific distribution in spin angular momentum, orbital structure, or topology. We study how these various features affect the strongly nonlinear electron dynamics. Specifically, we derive closed expressions for the wave function of an unbound electron subject to a generally structured, intense

V. Jacques, E. Wu, T. Toury, F. Treussart

We present a new realization of the textbook experiment consisting in single-photon interference based on the pulsed, optically excited photoluminescence of a single colour centre in a diamond nanocrystal. Interferences are created by wavefront-splitting with a Fresnel's biprism and observed by registering the "single-photon clicks" with an intensified CCD c

Frederik Warburg, Martin Jørgensen, Javier Civera, Søren Hauberg

Uncertainty quantification in image retrieval is crucial for downstream decisions, yet it remains a challenging and largely unexplored problem. Current methods for estimating uncertainties are poorly calibrated, computationally expensive, or based on heuristics. We present a new method that views image embeddings as stochastic features rather than determinis

Jie Ma, Qi Chai, Jun Liu, Qingyu Yin

Textbook Question Answering (TQA) is a task that one should answer a diagram/non-diagram question given a large multi-modal context consisting of abundant essays and diagrams. We argue that the explainability of this task should place students as a key aspect to be considered. To address this issue, we devise a novel architecture towards span-level eXplanati

Jay Santokhi, Pankaj Daga, Joned Sarwar, Anna Jordan

Finding sustainable and novel solutions to predict city-wide mobility behaviour is an ever-growing problem given increased urban complexity and growing populations. This paper seeks to address this by describing a traffic frame prediction approach that uses Convolutional LSTMs to create a Temporal Autoencoder with U-Net style skip-connections that marry toge

Pedro Baptista de Castro, Kensei Terashima, Takafumi D Yamamoto, Suguru Iwasaki

Intending to optimize the giant magnetocaloric properties of HoB$_{2}$, we synthesized and magnetocalorically characterized Ho$_{1-x}$Gd$_{x}$B$_{2}$(0.1 $\leq$ x $\leq$ 0.4) alloys. We found out that Gd enters stoichiometrically and randomly into the Ho site, leading to a Vegard-type structural change. The addition of spherical S$^{7/2}$ Gd$^{3+}$ moments p

Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens

We introduce Constr-DRKM, a deep kernel method for the unsupervised learning of disentangled data representations. We propose augmenting the original deep restricted kernel machine formulation for kernel PCA by orthogonality constraints on the latent variables to promote disentanglement and to make it possible to carry out optimization without first defining

Ruoyi Zhang, Haibo Yuan

M31 and M33 serve as ideal places to study distributions of dust in the outskirts of spiral galaxies. In this letter, using about 0.2 million stars selected from the LAMOST data and combining precise photometry and parallaxes from the Gaia DR2, we have constructed a two-dimensional foreground dust reddening map towards the M31 and M33 region ($111.2{\deg} {\

Huang Xie, Okko Räsänen, Tuomas Virtanen

In this paper, we study zero-shot learning in audio classification through factored linear and nonlinear acoustic-semantic projections between audio instances and sound classes. Zero-shot learning in audio classification refers to classification problems that aim at recognizing audio instances of sound classes, which have no available training data but only

David Pask, Adam Sierakowski, Aidan Sims

We study the structure and compute the stable rank of C*-algebras of finite higher-rank graphs. We completely determine the stable rank of the C*-algebra when the k-graph either contains no cycle with an entrance, or is cofinal. We also determine exactly which finite, locally convex k-graphs yield unital stably finite C*-algebras. We give several examples to

Yanping Chen, Zhijie Fan, Ji Li

In this paper, we study the $L^{p}$ boundedness and $L^{p}(w)$ boundedness ($1<p<\infty$ and $w$ a Muckenhoupt $A_{p}$ weight) of fractional maximal singular integral operators $T_{\Omega,\alpha}^{\#}$ with homogeneous convolution kernel $\Omega(x)$ on an arbitrary homogeneous group $\mathbb H$ of dimension $\mathbb{Q}$. We show that if $0<\alpha<\mathbb{Q}$

Ryo. Ogawa, Tatsunori Okada, Hideyuki Takahashi, Fuyuki Nabeshima

We developed a new microwave Hall effect measurement method for materials in the skin depth region at low temperatures using a cross-shaped bimodal cavity. We analytically calculated electromagnetic fields in the cross-shaped cavity, and the response of the cavity including the sample, whose property is represented by the surface impedance tensor; further, w

Sedigheh Barootkoob

In this paper, we introduce the concept of biamenability of Banach algebras and we show that despite the apparent similarities between amenability and biamenability of Banach algebras, they lead to very different, and somewhat opposed, theories. In this regard, we show that commutative Banach algebras such as R and C tend to lack biamenability, while they ma

Sharu Theresa Jose, Osvaldo Simeone

The goal of these lecture notes is to review the problem of free energy minimization as a unified framework underlying the definition of maximum entropy modelling, generalized Bayesian inference, learning with latent variables, statistical learning analysis of generalization,and local optimization. Free energy minimization is first introduced, here and histo

Giuliana Ramella

Visual quality evaluation is one of the challenging basic problems in image processing. It also plays a central role in the shaping, implementation, optimization, and testing of many methods. The existing image quality assessment methods focused on images corrupted by common degradation types while little attention was paid to color quantization. This in spi

Patrick Gelß, Stefan Klus, Ingmar Schuster, Christof Schütte

We propose a method for the approximation of high- or even infinite-dimensional feature vectors, which play an important role in supervised learning. The goal is to reduce the size of the training data, resulting in lower storage consumption and computational complexity. Furthermore, the method can be regarded as a regularization technique, which improves th

Stephane Geudens

This paper is devoted to coregular submanifolds in Poisson geometry. We show that their local Poisson saturation is an embedded Poisson submanifold, and we give a normal form for this Poisson submanifold around the coregular submanifold. This result recovers the normal form around Poisson transversals, and it yields Poisson versions of some normal form/rigid

Martijn Bartelds, Wietse de Vries, Faraz Sanal, Caitlin Richter

Variation in speech is often quantified by comparing phonetic transcriptions of the same utterance. However, manually transcribing speech is time-consuming and error prone. As an alternative, therefore, we investigate the extraction of acoustic embeddings from several self-supervised neural models. We use these representations to compute word-based pronuncia

F. Alarab, K. Hricovini, B. Leikert, L. Nicolai

We combined photoelemission spectroscopy with first-principle calculations to investigate structural and electronic properties of SrTiO$_{3}$ doped with Ni impurities. In SrTiO$_{3}$ polycrystalline thin films, grown by magnetron sputtering, the mean size of the crystallites increases with the concentration of Ni. To determine the electronic band structure o

R. R. Ismagilov, I. P. Kudarenko, S. A. Malykhin, S. D. Babin

A fast optical spectroscopic method for determination rotational ($T_{rot}$) and vibrational ($T_{vib}$) temperatures in two-temperature Boltzmann distribution of the excited state by using machine learning approach is presented. The method is applied to estimate molecular gas temperatures in a direct current glow discharge in hydrogen-methane gas mixture du

Guillaume Jaume, Pushpak Pati, Behzad Bozorgtabar, Antonio Foncubierta-Rodríguez

Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists. In this work, we address this by adopting bio

Vicente Garzó, Ricardo Brito, Rodrigo Soto

A linear stability analysis of the hydrodynamic equations of a model for confined quasi-two-dimensional granular gases is carried out. The stability analysis is performed around the homogeneous steady state (HSS) reached eventually by the system after a transient regime. In contrast to previous studies (which considered dilute or quasielastic systems), our a

Luis F. Abanto-Leon, Andreas Baeuml, Gek Hong, Sim

The intrinsic hardware imperfection of WiFi chipsets manifests itself in the transmitted signal, leading to a unique radiometric fingerprint. This fingerprint can be used as an additional means of authentication to enhance security. In fact, recent works propose practical fingerprinting solutions that can be readily implemented in commercial-off-the-shelf de

Björn Browatzki, Jörn-Philipp Lies, Christian Wallraven

We propose an encoder-decoder framework for the segmentation of blood vessels in retinal images that relies on the extraction of large-scale patches at multiple image-scales during training. Experiments on three fundus image datasets demonstrate that this approach achieves state-of-the-art results and can be implemented using a simple and efficient fully-con

Naoyuki Itagaki, Tomoya Naito

Cluster dynamics and single-particle correlation are simultaneously treated for the description of the ground state of ${}^{12} \mathrm{C} $. The recent development of the antisymmetrized quasi cluster model (AQCM) makes it possible to generate $jj$-coupling shell-model wave functions from $\alpha$ clusters models. The cluster dynamics and the competition wi

Sixing Yu, Arya Mazaheri, Ali Jannesari

Model compression aims to deploy deep neural networks (DNN) on mobile devices with limited computing and storage resources. However, most of the existing model compression methods rely on manually defined rules, which require domain expertise. DNNs are essentially computational graphs, which contain rich structural information. In this paper, we aim to find

Yutong Xie, Jianpeng Zhang, Zehui Liao, Yong Xia

It has been widely recognized that the success of deep learning in image segmentation relies overwhelmingly on a myriad amount of densely annotated training data, which, however, are difficult to obtain due to the tremendous labor and expertise required, particularly for annotating 3D medical images. Although self-supervised learning (SSL) has shown great po

Jiří Fejlek, Stefan Ratschan

A common approach in robotics is to learn tasks by generalizing from special cases given by a so-called demonstrator. In this paper, we apply this paradigm and present an algorithm that uses a demonstrator (typically given by a trajectory optimizer) to automatically synthesize feedback controllers for steering a system described by ordinary differential equa

Dante M. Kennes, Martin Claassen, Lede Xian, Antoine Georges

Twisted van der Waals heterostructures have latterly received prominent attention for their many remarkable experimental properties, and the promise that they hold for realising elusive states of matter in the laboratory. We propose that these systems can, in fact, be used as a robust quantum simulation platform that enables the study of strongly correlated

Shintaro Eijima, Mikhail Shaposhnikov, Inar Timiryasov

The $\nu$MSM -- an extension of the Standard Model by three relatively light singlet Majorana fermions $N_{1,2,3}$ -- allows for the generation of lepton asymmetry which is several orders of magnitude larger than the observed baryon asymmetry of the Universe. The lepton asymmetry is produced in interactions of $N_{2,3}$ (with masses in the GeV region) at tem