October 2020 arXiv papers — page 46

N. V. Erkaev, M. Scherf, S. E. Thaller, H. Lammer

We apply a 1D upper atmosphere model to study thermal escape of nitrogen over Titan's history. Significant thermal escape should have occurred very early for solar EUV fluxes 100 to 400 times higher than today with escape rates as high as $\approx 1.5\times 10^{28}$ s$^{-1}$ and $\approx 4.5\times 10^{29}$ s$^{-1}$, respectively, while today it is $\approx 7

Giannis Delimpaltadakis, Manuel Mazo

Scheduling communication traffic in networks of event-triggered control (ETC) systems is challenging, as their sampling times are unknown, hindering application of ETC in networks. In previous work, finite-state abstractions were created, capturing the sampling behaviour of LTI ETC systems with quadratic triggering functions. Offering an infinite-horizon loo

Stefano Burrello, Marcella Grasso, Chieh-Jen Yang

We illustrate a step towards the construction of a power counting in energy-density-functional (EDF) theories, by analyzing the equations of state (EOSs) of both symmetric and neutron matter. Within the adopted strategy, next-to-leading order (NLO) EOSs are introduced which contain renormalized first-order-type terms and an explicit second-order finite part.

Christian Uldal Graulund, Dmitrij Szamozvancev, Neel Krishnaswami

Most interaction with a computer is done via a graphical user interface. Traditionally, these are implemented in an imperative fashion using shared mutable state and callbacks. This is efficient, but is also difficult to reason about and error prone. Functional Reactive Programming (FRP) provides an elegant alternative which allows GUIs to be designed in a d

Puhong Duan, Pedram Ghamisi, Xudong Kang, Behnood Rasti

The inclusion of spatial information into spectral classifiers for fine-resolution hyperspectral imagery has led to significant improvements in terms of classification performance. The task of spectral-spatial hyperspectral image classification has remained challenging because of high intraclass spectrum variability and low interclass spectral variability. T

Mohamad Maassarani

We consider orbit configuration spaces $C_n^G(S)$, where $S$ is a surface obtained out of a closed orientable surface $\bar{S}$ by removing a finite number of points (eventually none) and $G$ is a finite group acting freely continuously on $S$. We prove that the fibration $\pi_{n,k} : C_{n}^G(S) \to C_k^G(S)$ obtained by projecting on the first $k$ coordinat

Ryosuke Tsumura, John W. Hardin, Keshav Bimbraw, Olushola S. Odusanya

Novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a pandemic of epic proportions and a global response to prepare health systems worldwide is of utmost importance. In addition to its cost-effectiveness in a resources-limited setting, lung ultrasound (LUS) has emerged as a rapid noninvasive imaging tool for the diagnosis of COVID-1

ACC Coolen, T Nikoletopoulos

Quantum annealing aims to provide a faster method for finding the minima of complicated functions, compared to classical computing, so there is an increasing interest in the relaxation dynamics of quantum spin systems. Moreover, it is known that problems in quantum annealing caused by first order phase transitions can be reduced via appropriate temporal adju

Hanshuang Tong, Yun Zhou, Zhen Wang

Knowledge tracing is a fundamental task in the computer-aid educational system. In this paper, we propose a hierarchical exercise feature enhanced knowledge tracing framework, which could enhance the ability of knowledge tracing by incorporating knowledge distribution, semantic features, and difficulty features from exercise text. Extensive experiments show

Gordon P. S. Gibb, Nick Brown, Rupert W, Nash

In this paper we present a workflow management system which permits the kinds of data-driven workflows required by urgent computing, namely where new data is integrated into the workflow as a disaster progresses in order refine the predictions as time goes on. This allows the workflow to adapt to new data at runtime, a capability that most workflow managemen

Dave Braines, Federico Cerutti, Marc Roig Vilamala, Mani Srivastava

Future coalition operations can be substantially augmented through agile teaming between human and machine agents, but in a coalition context these agents may be unfamiliar to the human users and expected to operate in a broad set of scenarios rather than being narrowly defined for particular purposes. In such a setting it is essential that the human agents

Guiyang Xin, Songyan Xin, Oguzhan Cebe, Mathew Jose Pollayil

In this paper, we aim to improve the robustness of dynamic quadrupedal locomotion through two aspects: 1) fast model predictive foothold planning, and 2) applying LQR to projected inverse dynamic control for robust motion tracking. In our proposed planning and control framework, foothold plans are updated at 400 Hz considering the current robot state and an

Iris Ren, Anja Volk, Wouter Swierstra, Remco C. Veltkamp

Pattern discovery algorithms in the music domain aim to find meaningful components in musical compositions. Over the years, although many algorithms have been developed for pattern discovery in music data, it remains a challenging task. To gain more insight into the efficacy of these algorithms, we introduce three computational methods for examining their ou

Giuseppe Dibitetto, Nicolò Petri

M-theory is known to possess supersymmetric solutions where the geometry is $\mathrm{AdS}_3\times S^3\times S^3$ warped over a Riemann surface $\Sigma_{2}$. The simplest examples in this class can be engineered by placing M2 and M5 branes as defects inside of a stack of background M5 branes. In this paper we show that a generalization of this construction yi

Lukas Lange, Xiang Dai, Heike Adel, Jannik Strötgen

The recognition and normalization of clinical information, such as tumor morphology mentions, is an important, but complex process consisting of multiple subtasks. In this paper, we describe our system for the CANTEMIST shared task, which is able to extract, normalize and rank ICD codes from Spanish electronic health records using neural sequence labeling an

Moussa Kamal Eddine, Antoine J. -P. Tixier, Michalis Vazirgiannis

Inductive transfer learning has taken the entire NLP field by storm, with models such as BERT and BART setting new state of the art on countless NLU tasks. However, most of the available models and research have been conducted for English. In this work, we introduce BARThez, the first large-scale pretrained seq2seq model for French. Being based on BART, BART

Feng Liu, Xiaoming Liu

The goal of this paper is to learn dense 3D shape correspondence for topology-varying objects in an unsupervised manner. Conventional implicit functions estimate the occupancy of a 3D point given a shape latent code. Instead, our novel implicit function produces a part embedding vector for each 3D point, which is assumed to be similar to its densely correspo

A. L. Solovjov, E. V. Petrenko, L. V. Omelchenko, E. Nazarova

Temperature dependencies of excess conductivity, Sigma', have been studied in detail for three FeSe_{0.94} textured polycrystalline samples prepared by partial melting and solid state reaction. It was revealed that both Sigma' and its temperature dependence are extremely sensitive to the method of sample preparation. Then, it was shown that in the range from

Paul R. Burd, Luca Kohlhepp, Sarah M. Wagner, Karl Mannheim

Context. Monthly-binned gamma-ray light curves of 236 bright gamma-ray sources, particularly blazars, selected from a sample of 2278 high-galactic latitude objects observed with Fermi-LAT, show flux variability characterized by power spectral densities consisting of a single power-law component, ranging from Brownian to white noise. Aims. The main goal here

Nikolas Klug, Moritz Einfalt, Stephan Brehm, Rainer Lienhart

The current state-of-the-art in monocular 3D human pose estimation is heavily influenced by weakly supervised methods. These allow 2D labels to be used to learn effective 3D human pose recovery either directly from images or via 2D-to-3D pose uplifting. In this paper we present a detailed analysis of the most commonly used simplified projection models, which

Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov, Thomas Seidl

Unlabeled data is often abundant in the clinic, making machine learning methods based on semi-supervised learning a good match for this setting. Despite this, they are currently receiving relatively little attention in medical image analysis literature. Instead, most practitioners and researchers focus on supervised or transfer learning approaches. The recen

Daniel Burk, Andreas Völz, Knut Graichen

The modular open-source framework GRAMPC-D for model predictive control of distributed systems is presented in this paper. The modular concept allows to solve optimal control problems (OCP) in a centralized and distributed fashion using the same problem description. It is tailored to computational efficiency with the focus on embedded hardware. The distribut

Raphaël T. Leriche, Alexandra Palacio-Morales, Marco Campetella, Cesare Tresca

Transition metal dichalcogenides (TMDs) display a rich variety of instabilities such as spin and charge orders, Ising superconductivity and topological properties. Their physical properties can be controlled by doping in electric double-layer field-effect transistors (FET). However, for the case of single layer NbSe$_2$, FET doping is limited to $\approx 1\t

Rick M. Bütler, Christian Häger, Henry D. Pfister, Gabriele Liga

In this paper, we propose a model-based machine-learning approach for dual-polarization systems by parameterizing the split-step Fourier method for the Manakov-PMD equation. The resulting method combines hardware-friendly time-domain nonlinearity mitigation via the recently proposed learned digital backpropagation (LDBP) with distributed compensation of pola

Naotaka Kajino, Kosei Konishi, Makoto Nakashima

We study the directed polymers in random environment on an infinite graph $G=(V,E)$ on which the underlying random walk satisfies sub-Gaussian heat kernel bounds with spectral dimension $d_{s}$ strictly less than two. Our goal in this paper is to show (i) the existence and the coincidence of the quenched and the annealed free energy $F_q(\beta)$, $F_a(\beta)

Annalisa Amadori, Francesca Gladiali, Massimo Grossi, Angela Pistoia

In this paper we consider nodal radial solutions of the problem $$ \begin{cases} -\Delta u=|u|^{2^*-2}u+\lambda u&\text{ in }B,\\ u=0&\text{ on }\partial B \end{cases} $$ where $2^*=\frac{2N}{N-2}$ with $3\le N\le6$ and $B$ is the unit ball of $\R^N$. We compute the asymptotics of the solution $u$ as well as $||u||_\infty$, its first zero and other relevant

A. P. Moina

A pseudospin model for description of the influence of the electric field, confined to the plane of sublattice polarization, on the two-dimensional squaric acid antiferroelectrics is developed. The system behavior is analyzed in terms of the parameters of ferroelectric and antiferroelectric ordering, as well as the non-collinearity angle $\theta$, which is t

Michael A. Hedderich, Lukas Lange, Heike Adel, Jannik Strötgen

Deep neural networks and huge language models are becoming omnipresent in natural language applications. As they are known for requiring large amounts of training data, there is a growing body of work to improve the performance in low-resource settings. Motivated by the recent fundamental changes towards neural models and the popular pre-train and fine-tune

M. Robbe-Saule, C. Morize, R. Henaff, Y. Bertho

The generation of a tsunami wave by an aerial landslide is investigated through model laboratory experiments. We examine the collapse of an initially dry column of grains into a shallow water layer and the subsequent generation of waves. The experiments show that the collective entry of the granular material into water governs the wave generation process. We

Yufeng Zheng, Seonwook Park, Xucong Zhang, Shalini De Mello

Many computer vision tasks rely on labeled data. Rapid progress in generative modeling has led to the ability to synthesize photorealistic images. However, controlling specific aspects of the generation process such that the data can be used for supervision of downstream tasks remains challenging. In this paper we propose a novel generative model for images

Virginia Bordignon, Stefan Vlaski, Vincenzo Matta, Ali H. Sayed

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to streaming data during testing and continue to improve their performance even during this phase. By doing

Lukas Lange, Heike Adel, Jannik Strötgen, Dietrich Klakow

Combining several embeddings typically improves performance in downstream tasks as different embeddings encode different information. It has been shown that even models using embeddings from transformers still benefit from the inclusion of standard word embeddings. However, the combination of embeddings of different types and dimensions is challenging. As an

Janus J. Eriksen

We present a perspective on what the future holds for full configuration interaction (FCI) theory, with an emphasis on conceptual rather than technical details. Upon revisiting the early history of FCI, a number of its key contemporary approximations are compared on as equal a footing as possible, using a recent blind challenge on the benzene molecule as a t

Gabriel Budel, Maksim Kitsak, Rodrigo Aldecoa, Konstantin Zuev

We consider random hyperbolic graphs in hyperbolic spaces of any dimension $d+1\geq 2$. We present a rescaling of model parameters that casts the random hyperbolic graph model of any dimension to a unified mathematical framework, leaving the degree distribution invariant with respect to the dimension. Unlike the degree distribution, clustering does depend on

Thomas Martynec, Sabine H. L. Klapp, Sarah A. M. Loos

Understanding nonequilibrium systems and the consequences of irreversibility for the system's behavior as compared to the equilibrium case, is a fundamental question in statistical physics. Here, we investigate two types of nonequilbrium phase transitions, a second-order and an infinite-order phase transition, in a prototypical q-state vector Potts model whi

Sravanthi Gurugubelli, Sundeep Prabhakar Chepuri

In this paper, we focus on graph learning from multi-view data of shared entities for spectral clustering. We can explain interactions between the entities in multi-view data using a multi-layer graph with a common vertex set, which represents the shared entities. The edges of different layers capture the relationships of the entities. Assuming a smoothness

Neil Walton, Yuqing Zhang

We propose a simple randomized rule for the optimization of prices in revenue management with contextual information. It is known that the certainty equivalent pricing rule, albeit popular, is sub-optimal. We show that, by allowing a small amount of randomization around these certainty equivalent prices, the benefits of optimal pricing and low regret are ach

Thibault Randrianarisoa

Recently, S. Arlot and R. Genuer have shown that a model of random forests outperforms its single-tree counterpart in the estimation of $\alpha-$H\"older functions, $\alpha\leq2$. This backs up the idea that ensembles of tree estimators are smoother estimators than single trees. On the other hand, most positive optimality results on Bayesian tree-based metho

M. Holschneider, K. Ferrat, G. Zöller, Ch. Molkenthin

We develop a technique to estimate spatially varying seismicity patterns. It is based on a Gaussian approximation of the underlying Poisson Process. A link function is used to estimate local moments of the seismicity from observed catalogues. These are modeled by a nonstationary Gaussian field. We construct a prior based on the local distribution of seismic

Xiongwei Wu, Xiuhua Li, Jun Li, P. C. Ching

In most Internet of Things (IoT) networks, edge nodes are commonly used as to relays to cache sensing data generated by IoT sensors as well as provide communication services for data consumers. However, a critical issue of IoT sensing is that data are usually transient, which necessitates temporal updates of caching content items while frequent cache updates

Arnaud Roisin, Anne-Sophie Libert

Aims. There are a growing number of giant planets discovered moving around one stellar component of a binary star, most of which have very diverse eccentricity. These discoveries raise the question of their formation and long-term evolution because the stellar companion can strongly affect the planet formation process. We aim to study the dynamical influence

P. Prelovšek, M. Mierzejewski, J. Krsnik, O. S. Barišić

We examine the standard model of many-body localization (MBL), i.e., the disordered chain of interacting spinless fermions, by representing it as the network in the many-body (MB) basis of noninteracting localized Anderson states. By studying eigenstates of the full Hamiltonian, for strong disorders we find that the dynamics is confined up to very long times

Bastian Schäfermeier, Gerd Stumme, Tom Hanika

The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely

Jake P. Solomon, Amitai M. Yuval

We construct families of imaginary special Lagrangian cylinders near transverse Maslov index $0$ or $n$ intersection points of positive Lagrangian submanifolds in a general Calabi-Yau manifold. Hence, we obtain geodesics of open positive Lagrangian submanifolds near such intersection points. Moreover, this result is a first step toward the non-perturbative c

Eduard Gorbunov, Dmitry Kovalev, Dmitry Makarenko, Peter Richtárik

In this paper, we propose a unified analysis of variants of distributed SGD with arbitrary compressions and delayed updates. Our framework is general enough to cover different variants of quantized SGD, Error-Compensated SGD (EC-SGD) and SGD with delayed updates (D-SGD). Via a single theorem, we derive the complexity results for all the methods that fit our

Kimet Jusufi, Saurabh

We study the effect of baryonic matter and apparent dark matter on black hole shadow in Verlinde's Emergent Gravity. To do so, we consider different baryonic mass profiles and an optically-thin disk region described by a gas in a radial free fall around the black hole. Assuming that most of the baryonic matter in the galaxy is located near the galactic cente

Ghanashyam Meher, Udit Raha

In the strangeness $S=-2$ sector, we study the $\Xi^{-}nn$ ($I=3/2, J^P={1/2}^+$) three-body system using pionless halo effective field theory (EFT), which provides a systematic model independent framework for assessing the feasibility of light particle-stable three-body bound states, utilizing low-energy universality. Here we take recourse to a simplistic s

Zhen Wang, Ben Teng, Yun Zhou, Hanshuang Tong

Balancing group teaching and individual mentoring is an important issue in education area. The nature behind this issue is to explore common characteristics shared by multiple students and individual characteristics for each student. Biclustering methods have been proved successful for detecting meaningful patterns with the goal of driving group instructions

Jiemin Li, Lei Xu, Mirian Garcia-Fernandez, Abhishek Nag

We explore the existence of the collective orbital excitations, orbitons, in the canonical orbital system KCuF$_3$. Using the Cu $L_3$-edge resonant inelastic X-ray scattering we show that the non-dispersive high-energy peaks result from the Cu$^{2+}$ $dd$ orbital excitations. These high-energy modes show good agreement with the {\it ab-initio} quantum chemi

Stefan Vlaski, Ali H. Sayed

Decentralized algorithms for stochastic optimization and learning rely on the diffusion of information as a result of repeated local exchanges of intermediate estimates. Such structures are particularly appealing in situations where agents may be hesitant to share raw data due to privacy concerns. Nevertheless, in the absence of additional privacy-preserving

Vladimir A. Zolotarev

For a Dirac operator with non-local potential on a finite segment, a method of reconstruction of non-local potential from the spectral data is developed. Description of spectral data for such class of operators is given.

Masahiro Kobayashi, Kazuho Watanabe

We discuss unbiased estimation equations in a class of objective function using a monotonically increasing function $f$ and Bregman divergence. The choice of the function $f$ gives desirable properties such as robustness against outliers. In order to obtain unbiased estimation equations, analytically intractable integrals are generally required as bias corre

Hadi Papi, Virgile Yves Favre, Hossein Ahmadvand, Mojtaba Alaei

The magnetic and structural properties of polycrystalline Co$_{4-x}$ Ni$_x$ Nb$_2$ O$_9$ (x=1,2) have been investigated by neutron powder diffraction, magnetization and heat capacity measurements, and density functional theory (DFT) calculations. For x=1, the compound crystallizes in the trigonal P$\bar{3}$c1 space group. Below T$_N$ = 31 K it develops a wea

Yong Liu, Susen Yang, Chenyi Lei, Guoxin Wang

Side information of items, e.g., images and text description, has shown to be effective in contributing to accurate recommendations. Inspired by the recent success of pre-training models on natural language and images, we propose a pre-training strategy to learn item representations by considering both item side information and their relationships. We relate

Minjeong Kim, Gyuwan Kim, Sang-Woo Lee, Jung-Woo Ha

Language model pre-training has shown promising results in various downstream tasks. In this context, we introduce a cross-modal pre-trained language model, called Speech-Text BERT (ST-BERT), to tackle end-to-end spoken language understanding (E2E SLU) tasks. Taking phoneme posterior and subword-level text as an input, ST-BERT learns a contextualized cross-m

Sarah Meldrum, Sherlock A. Licorish, Bastin Tony Roy Savarimuthu

Platforms such as Stack Overflow are available for software practitioners to solicit solutions to their challenges and knowledge needs. The practices therein have in recent times however triggered quality related concerns. This is a noteworthy issue when considering that the Stack Overflow platform is used by numerous software developers. Academic research t

Kresten Lindorff-Larsen, Kaare Teilum

We review the background, theory and general equations for the analysis of equilibrium protein unfolding experiments, focusing on denaturant and heat-induced unfolding. The primary focus is on the thermodynamics of reversible folding/unfolding transitions and the experimental methods that are available for extracting thermodynamic parameters. We highlight th

Z. Motaqy, G. Almashaqbeh, B. Bahrak, N. Yazdani

Smart contract-enabled blockchains allow building decentralized applications in which mutually-distrusted parties can work together. Recently, oracle services emerged to provide these applications with real-world data feeds. Unfortunately, these capabilities have been used for malicious purposes under what is called criminal smart contracts. A few works expl

J. A. Aguilar, P. Allison, J. J. Beatty, H. Bernhoff

This article presents the design of the Radio Neutrino Observatory Greenland (RNO-G) and discusses its scientific prospects. Using an array of radio sensors, RNO-G seeks to measure neutrinos above 10 PeV by exploiting the Askaryan effect in neutrino-induced cascades in ice. We discuss the experimental considerations that drive the design of RNO-G, present fi

G. Buonaiuto, I. Lesanovsky, B. Olmos

We theoretically investigate measurement-based feedback control of a laser-driven one-dimensional atomic chain interfaced with a nanofiber. The interfacing leads to all-to-all interactions among the atomic emitters and induces chirality, i.e. the directional emission of photons into a preferred guided mode of the nanofiber. In the setting we consider, the me

Seyyed Saeed Sarfjoo, Srikanth Madikeri, Petr Motlicek

To better model the contextual information and increase the generalization ability of Speech Activity Detection (SAD) system, this paper leverages a multi-lingual Automatic Speech Recognition (ASR) system to perform SAD. Sequence discriminative training of Acoustic Model (AM) using Lattice-Free Maximum Mutual Information (LF-MMI) loss function, effectively e

Ludovic Huguet, Victor Barge-Zwick, Michael Le Bars

Motivated by numerous geophysical applications, we have carried out laboratory experiments of a reactive (i.e. melting) solid sphere freely falling by gravity in a stratified environment, in the regime of large Reynolds ($Re$) and Froude numbers. We compare our results to non-reactive spheres in the same regime. First, we confirm for larger values of $Re$, t

Gesualdo Delfino

The two-dimensional case occupies a special position in the theory of critical phenomena due to the exact results provided by lattice solutions and, directly in the continuum, by the infinite-dimensional character of the conformal algebra. However, some sectors of the theory, and most notably criticality in systems with quenched disorder and short range inte

Thien-Minh Nguyen, Shenghai Yuan, Muqing Cao, Yang Lyu

In recent years, Onboard Self Localization (OSL) methods based on cameras or Lidar have achieved many significant progresses. However, some issues such as estimation drift and feature-dependence still remain inherent limitations. On the other hand, infrastructure-based methods can generally overcome these issues, but at the expense of some installation cost.

Fabio Rodríguez, José-Miguel Díaz-Báñez, Ernesto Sanchez-Laulhe, Jesús Capitán

This paper presents a novel algorithm to plan energy-efficient trajectories for autonomous ornithopters. In general, trajectory optimization is quite a relevant problem for practical applications with \emph{Unmanned Aerial Vehicles} (UAVs). Even though the problem has been well studied for fixed and rotatory-wing vehicles, there are far fewer works exploring

Zhen Ke, Liang Shi, Songtao Sun, Erli Meng

Recent researches show that pre-trained models (PTMs) are beneficial to Chinese Word Segmentation (CWS). However, PTMs used in previous works usually adopt language modeling as pre-training tasks, lacking task-specific prior segmentation knowledge and ignoring the discrepancy between pre-training tasks and downstream CWS tasks. In this paper, we propose a CW

Fabian Freund

Trees corresponding to $\Lambda$- and $\Xi$-$n$-coalescents can be both quite similar and fundamentally different compared to bifurcating tree models based on Kingman's $n$-coalescent. This has consequences for inference of a well-fitting gene genealogy as well as for assessing biological properties of species having such sample genealogies. Here, mathematic

Jun-ichi Maskawa, Koji Kuroda

This article present a continuous cascade model of volatility formulated as a stochastic differential equation. Two independent Brownian motions are introduced as random sources triggering the volatility cascade. One multiplicatively combines with volatility; the other does so additively. Assuming that the latter acts perturbatively on the system, then the m

Dario Pasquini, Marco Cianfriglia, Giuseppe Ateniese, Massimo Bernaschi

Password security hinges on an in-depth understanding of the techniques adopted by attackers. Unfortunately, real-world adversaries resort to pragmatic guessing strategies such as dictionary attacks that are inherently difficult to model in password security studies. In order to be representative of the actual threat, dictionary attacks must be thoughtfully

Jianan Wang, Eren Sezener, David Budden, Marcus Hutter

Human intelligence is characterized not only by the capacity to learn complex skills, but the ability to rapidly adapt and acquire new skills within an ever-changing environment. In this work we study how the learning of modular solutions can allow for effective generalization to both unseen and potentially differently distributed data. Our main postulate is

Xinsheng Wang, Siyuan Feng, Jihua Zhu, Mark Hasegawa-Johnson

This paper proposes a new model, referred to as the show and speak (SAS) model that, for the first time, is able to directly synthesize spoken descriptions of images, bypassing the need for any text or phonemes. The basic structure of SAS is an encoder-decoder architecture that takes an image as input and predicts the spectrogram of speech that describes thi

Merve Nur Cakir, Mehwish Saleemi, Karl-Heinz Zimmermann

Dynamic programming is a mathematical optimization method and a computer programming method as well. In this paper, the notion of sheaf programming in topological spaces is introduced and it is demonstrated that it relates very well to the concept of dynamic programming.

Pablo Barceló, Mikaël Monet, Jorge Pérez, Bernardo Subercaseaux

In spite of several claims stating that some models are more interpretable than others -- e.g., "linear models are more interpretable than deep neural networks" -- we still lack a principled notion of interpretability to formally compare among different classes of models. We make a step towards such a notion by studying whether folklore interpretability clai

George Vahala, Linda Vahala, Min Soe, Abhay K. Ram

A quantum lattice algorithm (QLA) is developed for the solution of Maxwell equations in scalar dielectric media using the Riemann-Silberstein representation. For x-dependent and y-dependent inhomogeneities, the corresponding QLA requries 8 qubits/spatial lattice site. This is because the corresponding Pauli spin matrices have off-diagonal components which pe

Anthoulla Phella

This paper considers forecasts of the growth and inflation distributions of the United Kingdom with factor-augmented quantile autoregressions under a model averaging framework. We investigate model combinations across models using weights that minimise the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Quantile Regression I

O. I. Hryhorchak, V. S. Pastukhov

A finit periodic $\delta-\delta'$ comb was solved by the help of both classical approach based on a direct solving of a Sr\"{odinger} equation and a quantum wave impedance method. It was demonstrated that the violation of a periodicity leads to the formation of energy levels in band gaps. The expresion for a surface levels (Tamm's levels) were found in this

Anca-Voichita Matioc, Bogdan-Vasile Matioc

In this paper we establish the well-posedness of the Muskat problem with surface tension and equal viscosities in the subcritical Sobolev spaces $W^s_p(\mathbb{R})$, where ${p\in(1,2]}$ and ${s\in(1+1/p,2)}$. This is achieved by showing that the mathematical model can be formulated as a quasilinear parabolic evolution problem in $W^{\overline{s}-2}_p(\mathbb

Yurika Sakai, Andrey Kormilitzin, Qiang Liu, Alejo Nevado-Holgado

The most successful methods such as ReLU transfer functions, batch normalization, Xavier initialization, dropout, learning rate decay, or dynamic optimizers, have become standards in the field due, particularly, to their ability to increase the performance of Neural Networks (NNs) significantly and in almost all situations. Here we present a new method to ca

Sébastian de Bone, Runsheng Ouyang, Kenneth Goodenough, David Elkouss

The distribution of high-quality Greenberger-Horne-Zeilinger (GHZ) states is at the heart of many quantum communication tasks, ranging from extending the baseline of telescopes to secret sharing. They also play an important role in error-correction architectures for distributed quantum computation, where Bell pairs can be leveraged to create an entangled net

Sun-Goo Lee, Seong-Han Kim, Chul-Sik Kee

Bound states in the continuum (BICs) and Fano resonances in planar photonic lattices, including metasurfaces and photonic crystal slabs, have been studied extensively in recent years. Typically, the BICs and Fano resonances are associated with the second stop bands open at the second-order Gamma ($\Gamma$) point. This paper address the fundamental properties

Baptiste Schubnel, Rafael E. Carrillo, Paolo Taddeo, Lluc Canals Casals

Power consumption in buildings show non-linear behaviors that linear models cannot capture whereas recurrent neural networks (RNNs) can. This ability makes RNNs attractive alternatives for the model-predictive control (MPC) of buildings. However RNN models lack mathematical regularity which makes their use challenging in optimization problems. This work ther

Jinbo Song, Chao Chang, Fei Sun, Xinbo Song

Learning informative representations (aka. embeddings) of users and items is the core of modern recommender systems. Previous works exploit user-item relationships of one-hop neighbors in the user-item interaction graph to improve the quality of representation. Recently, the research of Graph Neural Network (GNN) for recommendation considers the implicit col

Ilya Ivantsov, Alvaro Ferraz, Evgenii Kochetov

The Hubbard model and the lattice Kondo model are shown to become identical in the strong-coupling limit. A departure from the strong-coupling regime produces distinct theories, however: the relevant perturbation terms give rise to either short-range spin exchange $(t-J$ model) or short-range charge exchange ($t-V$ model), respectively.

Andrius Žemaitis, Mantas Gaidys, Paulius Gečys, Martynas Barkauskas

Here, to the best of our knowledge, for the first time we report the in-depth experimental study of high ultrafast laser ablation efficiency for processing of copper and steel with single-pulses, MHz-, GHz- and burst in the burst (biburst) regime. The comparison of burst, biburst, and single-pulse ablation efficiencies was performed for beam-size-optimised r

Salvatore Siciliano, David A. Towers

In this paper we begin to study the subalgebra lattice of a Leibniz algebra. In particular, we deal with Leibniz algebras whose subalgebra lattice is modular, upper semi-modular, lower semi-modular, distributive, or dually atomistic. The fact that a non-Lie Leibniz algebra has fewer one-dimensional subalgebras in general results in a number of lattice condit

Tobias Graf

We present a geometric framework to model the optical effects of deformations of planar holographic optical elements (HOE) into curved surfaces, such as sphere segments. In particular, we consider deformations which do not preserve the Gaussian curvature.

Liupeng Yang, Yining Fei, Kaiyuan Zhou, Lina Chen

Spin-orbit torques due to interfacial Rashba and spin Hall effects have been widely considered as a potentially more efficient approach than the conventional spin-transfer torque to control the magnetization of ferromagnets. We report a comprehensive study of spin-orbit torque efficiency in Ta(O)/Ni81Fe19 bilayers by tuning low-oxidation of \b{eta}-phase tan

Dabao Wang, Siwei Wu, Ziling Lin, Lei Wu

Flash Loan, as an emerging service in the decentralized finance ecosystem, allows users to request a non-collateral loan. While providing convenience, it also enables attackers to launch malicious operations with a large amount of asset that they do not have. Though there exist spot media reports of attacks that leverage Flash Loan, there lacks a comprehensi

Anatolij Dvurečenskij, Dominik Lachman

In the paper, we investigate a one-to-one correspondence between $n$-dimensional observables and $n$-dimensional spectral resolutions with values in a kind of a lexicographic form of quantum structures like perfect MV-algebras or perfect effect algebras. The multidimensional version of this problem is more complicated than a one-dimensional one because if ou

Sunghyun Park, Han Li, Ameen Patel, Sidharth Mudgal

Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding of a user request. We propose a scalable and automatic approach for improving NLU in a large-scale conversational AI system by leveraging implicit user feedback, with an insight th

Chaoyun Zhang

The next generation of mobile networks is set to become increasingly complex, as these struggle to accommodate tremendous data traffic demands generated by ever-more connected devices that have diverse performance requirements in terms of throughput, latency, and reliability. This makes monitoring and managing the multitude of network elements intractable wi

Lukas J. Maczewsky, Matthias Heinrich, Mark Kremer, Sergey K. Ivanov

The hallmark feature of topological insulators renders edge transport virtually impervious to scattering at defects and lattice disorder. In our work, we experimentally demonstrate a topological system, using a photonic platform, in which the very existence of the topological phase is brought about by nonlinearity. Whereas in the linear regime, the lattice s

Vahid Reza Khazaie, Nicky Bayat, Yalda Mohsenzadeh

State-of-the-art deep neural network models have reached near perfect face recognition accuracy rates on controlled high-resolution face images. However, their performance is drastically degraded when they are tested with very low-resolution face images. This is particularly critical in surveillance systems, where a low-resolution probe image is to be matche

Bruno Colbois, Katie Gittins

We obtain upper bounds for the Steklov eigenvalues $\sigma_k(M)$ of a smooth, compact, connected, $n$-dimensional submanifold $M$ of Euclidean space with boundary $\Sigma$ that involve the intersection indices of $M$ and of $\Sigma$. One of our main results is an explicit upper bound in terms of the intersection index of $\Sigma$, the volume of $\Sigma$ and

Anatolij Dvurečenskij, Dominik Lachman

The paper is a continuation of the research on a one-to-one correspondence between $n$-dimensional spectral resolutions and $n$-dimensional observables on lexicographic types of quantum structures which started in \cite{DvLa4}. In Part I, we presented the main properties of $n$-dimensional spectral resolutions and observables, and we deeply studied character

Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric

In the contextual linear bandit setting, algorithms built on the optimism principle fail to exploit the structure of the problem and have been shown to be asymptotically suboptimal. In this paper, we follow recent approaches of deriving asymptotically optimal algorithms from problem-dependent regret lower bounds and we introduce a novel algorithm improving o

Xutao Yu, Haonan Zheng, Yanghua Lu, Jiaqi Si

As the fast development of internet of things (IoTs), distributed sensors have been frequently used and the small and portable power sources are highly demanded. However, the present portable power source such as lithium battery has low capacity and need to be replaced or recharged frequently. A portable power source which can continuously generate electrica

Edoardo Vittori, Michele Trapletti, Marcello Restelli

In this paper we show how risk-averse reinforcement learning can be used to hedge options. We apply a state-of-the-art risk-averse algorithm: Trust Region Volatility Optimization (TRVO) to a vanilla option hedging environment, considering realistic factors such as discrete time and transaction costs. Realism makes the problem twofold: the agent must both min

Ludovic Berthier

A theoretical treatment of deeply supercooled liquids is difficult because their properties emerge from spatial inhomogeneities that are self-induced, transient, and nanoscopic. I use computer simulations to analyse self-induced static and dynamic heterogeneity in equilibrium systems approaching the experimental glass transition. I characterise the broad sam

Márton Elekes, János Benjamin Antal, Gábor Szárnyas

This report contains an analysis of the queries defined in the SIGMOD 2014 Programming Contest. We first describe the data set, then present the queries, providing graphical illustrations for them and pointing out their caveats. Our intention is to document our lessons learnt and simplify the work of those who will attempt to create a solution to this contes