September 2019 arXiv papers — page 13

Ahmed Zamzam, Kyri Baker

In this paper, we develop an online method that leverages machine learning to obtain feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps on extremely fast timescales (e.g., milliseconds), bypassing solving an AC OPF altogether. This is motivated by the fact that as the power grid experiences increasing amounts of ren

Oscar Domínguez, Sergey Tikhonov

In this paper we propose a unified approach, based on limiting interpolation, to investigate the embeddings for the Sobolev space $(\dot{W}^k_p(\mathcal{X}))_0, \, \mathcal{X} \in \{\mathbb{R}^d, \mathbb{T}^d, \Omega\}$, in the subcritical case ($k < d/p$), critical case ($k = d/p$) and supercritical case ($k > d/p$). We characterize the Sobolev embeddings i

Thomas W. Mattman

Using the special value at $u=1$ of the Artin-Ihara $L$-function, we give a short proof of the count of the number of spanning trees in the $n$-cube.

Maxime Fortier Bourque, Bram Petri

We prove a sharp upper bound on the number of shortest cycles contained inside any connected graph in terms of its number of vertices, girth, and maximal degree. Equality holds only for Moore graphs, which gives a new characterization of these graphs. In the case of regular graphs, our result improves an inequality of Teo and Koh. We also show that a subsequ

Haridas Umpierrez, Sergio Davis

Nonextensive statistics is a formalism of statistical mechanics that describes the ocurrence of power-law distributions in complex systems, particularly the so-called $q$ exponential family of distributions. In this work we present the use of fluctuation theorems for $q$-canonical ensembles as a powerful tool to readily obtain statistical properties. In part

Torsten V. Zache, Thomas Schweigler, Sebastian Erne, Jörg Schmiedmayer

Quantum field theory is a powerful tool to describe the relevant physics governing complex quantum many-body systems. Here we develop a general pathway to extract the irreducible building blocks of quantum field theoretical descriptions and its parameters purely from experimental data. This is accomplished by extracting the one-particle irreducible (1PI) ver

Guillermo García-Pérez, Matteo A. C. Rossi, Boris Sokolov, Elsi-Mari Borrelli

We introduce the concept of pairwise tomography networks to characterise quantum properties in many-body systems and demonstrate an efficient protocol to measure them experimentally. Pairwise tomography networks are generators of multiplex networks where each layer represents the graph of a relevant quantifier such as, e.g., concurrence, quantum discord, pur

Noé de Rancourt

We give new and simple proofs of some classical properties of hereditarily indecomposable Banach spaces, including the result by W. T. Gowers and B. Maurey that a hereditarily indecomposable Banach space cannot be isomorphic to a proper subspace of itself. These proofs do not make use of spectral theory and therefore, they work in real spaces as well as in c

Boris Müller, Johannes Berner, Clemens Bechinger, Matthias Krüger

A colloidal particle is a prominent example of a stochastic system, and, if suspended in a simple viscous liquid, very closely resembles the case of an ideal random walker. A variety of new phenomena have been observed when such colloid is suspended in a viscoelastic fluid instead, for example pronounced nonlinear responses when the viscoelastic bath is driv

Wendelin Böhmer, Vitaly Kurin, Shimon Whiteson

This paper introduces the deep coordination graph (DCG) for collaborative multi-agent reinforcement learning. DCG strikes a flexible trade-off between representational capacity and generalization by factoring the joint value function of all agents according to a coordination graph into payoffs between pairs of agents. The value can be maximized by local mess

Haoran Ni, Shuangfeng Li, Qihan Zhang, Jiguang Yao

In this paper, we report the observation of spin-orbit magnetoresistance (SOMR) in ferromagnetic metal/heavy metal/MgO system. We measure the magnetoresistance as the function of the thickness of heavy metal (HM) for CoFeB/HM/MgO and CoFeB/HM films where HM = Pt and Ta. Besides the conventional spin Hall magnetoresistance (SMR) peak, the evidence of the SOMR

Shubham Kundal, Raunak Lohiya, Hritik Bansal, Shreya Johri

Computational prediction of origin of replication (ORI) has been of great interest in bioinformatics and several methods including GC-skew, auto-correlation etc. have been explored in the past. In this paper, we have extended the auto-correlation method to predict ORI location with much higher resolution for prokaryotes and eukaryotes, which can be very help

Atinuke Ademola-Idowu, Baosen Zhang

The electrical grid is evolving from a network consisting of mostly synchronous machines to a mixture of synchronous machines and inverter-based resources such as wind, solar, and energy storage. This transformation has led to a decrease in mechanical inertia, which necessitate a need for the new resources to provide frequency responses through their inverte

Asimina Hamakiotes, Aaron Kriegman, Wei-Lun Tsai

The partition crank is a statistic on partitions introduced by Freeman Dyson to explain Ramanujan's congruences. In this paper, we prove that the crank is asymptotically equidistributed modulo Q, for any odd number Q. To prove this, we obtain effective bounds on the error term from Zapata Rolon's asymptotic estimate for the crank function. We then use those

Kevin Namink, Xuanhui Meng, Marc T. M. Koper, Philipp Kukura

The electric double layer (EDL) formed around charged nanostructures at the liquid-solid interface determines their electrochemical activity and influences their electrical and optical polarizability. We experimentally demonstrate that restructuring of the EDL at the nanoscale can be detected by dark-field scattering microscopy. Temporal and spatial characte

Romain Contant, Markus Q. Huber

We investigate quantum chromodynamics with two colors at nonvanishing density using Dyson-Schwinger equations. Lattice methods do not have a complex action problem in this theory. Thus, we can benchmark our results and the effect of truncations directly by comparing with the corresponding lattice results. We do so for the gluon propagator, the chiral condens

Hongki Lee, Changhee Park, Sukyoung Ryu

Researchers have developed various techniques for static analysis of JavaScript to improve analysis precision. To develop such techniques, they first identify causes of the precision losses for unproven properties. While most of the existing work has diagnosed main causes of imprecision in static analysis by manual investigation, manually tracing the impreci

D. Hajdú, E. Dian, K. Gméling, E. Klinkby

The neutron activation of shielding materials and the generated decay gamma radiation are well-known issues in terms of occupational exposure. Though the trace elements of shielding concretes can be dominant sources of the produced activity in such cases, their concentrations are often missing from the input data of shielding-related Monte Carlo simulations.

Amihay Hanany, Rudolph Kalveks

We build on previous studies of the Higgs and Coulomb branches of SUSY quiver theories having 8 supercharges, including $3d~{\cal N}=4$, and Classical gauge groups. The vacuum moduli spaces of many such theories can be parameterised by pairs of nilpotent orbits of Classical Lie algebras; they are transverse to one orbit and intersect the closure of the secon

O Duranthon, M Marsili, R Xie

We show that the mutual information between the representation of a learning machine and the hidden features that it extracts from data is bounded from below by the relevance, which is the entropy of the model's energy distribution. Models with maximal relevance -- that we call Optimal Learning Machines (OLM) -- are hence expected to extract maximally inform

Hortensia Galeana-Sanchez, Hugo Rincon-Galeana, Ricardo Strausz

We prove that, if every cycle of $D$ is an $H$-cycle, then $D$ has an $H$-kernel by walks.

Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, Peter Battaglia

We introduce an approach for imposing physically informed inductive biases in learned simulation models. We combine graph networks with a differentiable ordinary differential equation integrator as a mechanism for predicting future states, and a Hamiltonian as an internal representation. We find that our approach outperforms baselines without these biases in

Yancong Xie, Hongxun Jiang

News items have a significant impact on stock markets but the ways are obscure. Many previous works have aimed at finding accurate stock market forecasting models. In this paper, we use text mining and sentiment analysis on Chinese online financial news, to predict Chinese stock tendency and stock prices based on support vector machine (SVM). Firstly, we col

Bowen Zhao, Jun Takahashi, Anders W. Sandvik

Liu et al. [Phys.Rev.B 98, 241109 (2018)] used Monte Carlo sampling of the physical degrees of freedom of a Projected Entangled Pair State (PEPS) type wave function for the $S=1/2$ frustrated $J_1$-$J_2$ Heisenberg model on the square lattice and found a non-magnetic state argued to be a gapless spin liquid when the coupling ratio $g=J_2/J_1$ is in the range

Ramon Antoine, Francesc Perera, Leonel Robert, Hannes Thiel

We prove that Cuntz semigroups of C*-algebras satisfy Edwards' condition with respect to every quasitrace. This condition is a key ingredient in the study of the realization problem of functions on the cone of quasitraces as ranks of positive elements. In the course of our investigation, we identify additional structure of the Cuntz semigroup of an arbitrary

Dmitry Vorobiev, Zoran Ninkov, Lee Bernard, Neal Brock

In the last decade, imaging polarimeters based on micropolarizer arrays have been developed for use in terrestrial remote sensing and metrology applications. Micropolarizer-based sensors are dramatically smaller and more mechanically robust than other polarimeters with similar spectral response and snapshot capability. To determine the suitability of these n

Yuanbin Wu, Christoph H. Keitel, Adriana Pálffy

X-ray assisted nuclear excitation by electron capture (NEEC) into inner-shell atomic holes in a plasma environment generated by strong optical lasers is investigated theoretically. The considered scenario involves the interaction of a strong optical laser with a solid-state nuclear target leading to the generation of a plasma. In addition, intense x-ray radi

Jamison Barsotti, Rob Carman

For a saturated fusion system $\mathcal F$ on a $p$-group $S$, we study the Burnside ring of the fusion system $B(\mathcal F)$, as defined by Matthew Gelvin and Sune Reeh, which is a subring of the Burnside ring $B(S)$. We give criteria for an element of $B(S)$ to be in $B(\mathcal F)$ determined by the $\mathcal F$-automorphism groups of essential subgroups

Givi Meishvili, Simon Jenni, Paolo Favaro

We propose a novel method to use both audio and a low-resolution image to perform extreme face super-resolution (a 16x increase of the input size). When the resolution of the input image is very low (e.g., 8x8 pixels), the loss of information is so dire that important details of the original identity have been lost and audio can aid the recovery of a plausib

Richard Benjamins, Alberto Barbado, Daniel Sierra

Recently, a lot of attention has been given to undesired consequences of Artificial Intelligence (AI), such as unfair bias leading to discrimination, or the lack of explanations of the results of AI systems. There are several important questions to answer before AI can be deployed at scale in our businesses and societies. Most of these issues are being discu

Reuben Tan, Huijuan Xu, Kate Saenko, Bryan A. Plummer

The goal of weakly-supervised video moment retrieval is to localize the video segment most relevant to the given natural language query without access to temporal annotations during training. Prior strongly- and weakly-supervised approaches often leverage co-attention mechanisms to learn visual-semantic representations for localization. However, while such a

Hendrik Spende, Christoph Margenfeld, Tobias Meyer, Irene Manglano Clavero

Plasma profiling time of flight mass spectrometry (PP-TOFMS) has recently gained interest, as it enables the elemental profiling of semiconductor structures with high depth resolution in short acquisition times. As recently shown by Tempez et al., PP-TOFMS can be used to obtain the composition in the structures for modern field effect transistors [1]. There,

Xiaohan Ding, Guiguang Ding, Xiangxin Zhou, Yuchen Guo

Deep Neural Network (DNN) is powerful but computationally expensive and memory intensive, thus impeding its practical usage on resource-constrained front-end devices. DNN pruning is an approach for deep model compression, which aims at eliminating some parameters with tolerable performance degradation. In this paper, we propose a novel momentum-SGD-based opt

S. A. Braeuninger, A. Jesche, S. Kamusella, F. Seewald

We present a systematic 57Fe-Moessbauer study on highly diluted Fe centers in Li2(Li1-xFex)N single-crystals as a function of temperature and magnetic field applied transverse and longitudinal with respect to the single-ion anisotropy axis. Below 30 K the Fe centers exhibit a giant magnetic hyperfine field of E_A = 70.25(2)T parallel to the axis of strongest

Riley Patrick, Andre Scaffidi, Pankaj Sharma

In this letter we explore beyond the Standard Model top-Higgs Yukawa couplings as a function of a CP-mixing parameter $\xi_t$ at the 14 TeV HL-LHC in the process $pp\to thj$. We observe that angular variables of the decay products of the top are non-trivially sensitive to $\xi_t$. This fact is exploited in a full detector level analysis that employs machine

Oleg Andreev

We consider the string breaking phenomenon within effective string models which purport to mimic QCD with two light flavors, with a special attention to baryon modes. We make some estimates of the string breaking distances at zero and non-zero baryon chemical potentials. Our estimates point towards the enhancement of baryon production in strong decays of hea

Maiara Mitiko Taniguchi, Vitor Santaella Zanuto, Pablo Portes, Luis Carlos Malacarne

Spectral converters are known to increase photovoltaic energy conversion by minimizing losses due to fundamental non-absorption and thermalization processes, and have been suggested to surpass the Shockley-Queisser efficiency limit in single junction solar cells. Here we present a detailed spectroscopic study of photoluminescence in tellurite-tungstate glass

Corrado Pezzato, Riccardo Ferrari, Carlos Hernandez

More adaptive controllers for robot manipulators are needed, which can deal with large model uncertainties. This paper presents a novel active inference controller (AIC) as an adaptive control scheme for industrial robots. This scheme is easily scalable to high degrees-of-freedom, and it maintains high performance even in the presence of large unmodeled dyna

Michael Fuchs, Cecilia Holmgren, Dieter Mitsche, Ralph Neininger

We identify the mean growth of the independence number of random binary search trees and random recursive trees and show normal fluctuations around their means. Similarly we also show normal limit laws for the domination number and variations of it for these two cases of random tree models. Our results are an application of a recent general theorem of Holmgr

Christoph Hauser, Camillo Ballani, Philipp Dürrenfeld, Frank Heyroth

We investigate spin pumping and the effective spin mixing conductance in heterostructures based on magnetic oxide trilayers composed of La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO), LaNiO$_3$ (LNO), and SrRuO$_3$ (SRO). The heterostructures serve as a model system for an estimation of the effective spin mixing conductance at the different interfaces. Our results show

Johannes Köhler, Matthias A. Müller, Frank Allgöwer

In this paper, we present a quasi infinite horizon nonlinear model predictive control (MPC) scheme for tracking of generic reference trajectories. This scheme is applicable to nonlinear systems, which are locally incrementally stabilizable. For such systems, we provide a reference generic offline procedure to compute an incrementally stabilizing feedback wit

Huseyin A. Inan, Gaurav Singh Tomar, Huapu Pan

Semantic parsing is the problem of deriving machine interpretable meaning representations from natural language utterances. Neural models with encoder-decoder architectures have recently achieved substantial improvements over traditional methods. Although neural semantic parsers appear to have relatively high recall using large beam sizes, there is room for

Yi Guo, Xinyang Zhou, Changhong Zhao, Yue Chen

Conventional optimal power flow (OPF) solvers assume full observability of the involved system states. However, in practice, there is a lack of reliable system monitoring devices in the distribution networks. To close the gap between the theoretic algorithm design and practical implementation, this work proposes to solve the OPF problems based on the state e

Renaud Dubé, Andrei Cramariuc, Daniel Dugas, Hannes Sommer

Precisely estimating a robot's pose in a prior, global map is a fundamental capability for mobile robotics, e.g. autonomous driving or exploration in disaster zones. This task, however, remains challenging in unstructured, dynamic environments, where local features are not discriminative enough and global scene descriptors only provide coarse information. We

Lanoir Addala, Jean Dolbeault, Xingyu Li, Mohamed Lazhar Tayeb

This paper is devoted to the linearized Vlasov-Poisson-Fokker-Planck system in presence of an external potential of confinement. We investigate the large time behaviour of the solutions using hypocoercivity methods and a notion of scalar product adapted to the presence of a Poisson coupling. Our framework provides estimates which are uniform in the diffusion

Yaadhav Raaj

Pose Priors are critical in human pose estimation, since they are able to enforce constraints that prevent estimated poses from tending to physically impossible positions. Human pose generally consists of up to 22 Joint Angles of various segments, and their respective bone lengths, but the way these various segments interact can affect the validity of a pose

Buddhima Gamlath, Sagar Kale, Ola Svensson

We consider the maximum bipartite matching problem in stochastic settings, namely the query-commit and price-of-information models. In the query-commit model, an edge e independently exists with probability $p_e$. We can query whether an edge exists or not, but if it does exist, then we have to take it into our solution. In the unweighted case, one can query

Ivan Šupić, Daniel Cavalcanti, Joseph Bowles

Self-testing protocols are methods to determine the presence of shared entangled states in a device independent scenario, where no assumptions on the measurements involved in the protocol are made. A particular type of self-testing protocol, called parallel self-testing, can certify the presence of copies of a state, however such protocols typically suffer f

Andreas Fischer, Friederike Schmid, Thomas Speck

We develop a dynamic mean-field theory for polar active particles that interact through a self-generated field, in particular one generated through emitting a chemical signal. While being a form of chemotactic response, it is different from conventional chemotaxis in that particles discontinuously change their motility when the local concentration surpasses

Francisco Marmolejo, Matías Menni

Lawvere has observed that certain 'gros' toposes in algebraic geometry suggest the existence of an 'infinitesimal level', closely related to finite-dimensional local algebras. Motivated by this observation we propose an elementary definition of level {\epsilon} associated to a local geometric morphism, establish some relevant basic properties suggested by ge

Xianghui Zhong

The $k$-Opt and Lin-Kernighan algorithm are two of the most important local search approaches for the Metric TSP. Both start with an arbitrary tour and make local improvements in each step to get a shorter tour. We show that for any fixed $k\geq 3$ the approximation ratio of the $k$-Opt algorithm for Metric TSP is $O(\sqrt[k]{n})$. Assuming the Erd\H{o}s gir

Jiancheng Lyu, Jack Xin, Yifeng Yu

It is expensive to compute residual diffusivity in chaotic in-compressible flows by solving advection-diffusion equation due to the formation of sharp internal layers in the advection dominated regime. Proper orthogonal decomposition (POD) is a classical method to construct a small number of adaptive orthogonal basis vectors for low cost computation based on

Alireza Ahmadi, Lorenzo Nardi, Nived Chebrolu, Cyrill Stachniss

Autonomous navigation is a pre-requisite for field robots to carry out precision agriculture tasks. Typically, a robot has to navigate through a whole crop field several times during a season for monitoring the plants, for applying agrochemicals, or for performing targeted intervention actions. In this paper, we propose a framework tailored for navigation in

W. H. Dickhoff, M. C. Atkinson

A review of recent applications of the nonlocal dispersive optical model (DOM) is presented that allows a simultaneous description of nuclear structure and nuclear reactions. An assessment of the quality of the resulting potentials for $^{40}$Ca and $^{48}$Ca is discussed for the description of the $(e,e'p)$ reaction to valence hole states and the possibilit

Ridha Nasri, Jalal Rachad, Laurent Decreusefond

The upcoming fifth generation (5G) New Radio (NR) interface inherits many concepts and techniques from 4G systems such as the Orthogonal Frequency Division Multiplex (OFDM) based waveform and multiple access. Dimensioning 5G NR interface will likely follow the same principles as in 4G networks. It aims at finding the number of radio resources required to car

Marco Maggesi, Donato Pertici, Giuseppe Tomassini

We prove some extension theorems for quaternionic holomorphic functions in the sense of Fueter. Starting from the existence theorem for the nonhomogeneous Cauchy-Riemann-Fueter Problem, we prove that an $\mathbb{H}$-valued function $f$ on a smooth hypersurface, satisfying suitable tangential conditions, is locally a jump of two $\mathbb{H}$-holomorphic funct

J. C. Andrade, J. R. P. Hanslope

In this paper, we consider a general form of the analogue of Ramanujan's sum in the ring of polynomials over a finite field. We first prove some multiplicative properties of such functions before considering their finite Fourier series and some specific examples. In the end, we also prove a result about the Dirichlet series of such functions.

J. E. Proctor, C. G. Pruteanu, I. Morrison, I. F. Crowe

We have studied in detail the transition from gas-like to rigid liquid-like behaviour in supercritical $N_2$ at 300 K (2.4 $T_C$). Our study combines neutron diffraction and Raman spectroscopy with empirical potential structure refinement and ab-initio molecular dynamics simulations. We observe a narrow transition from gas-like to rigid liquid-like behaviour

Stéphane Clinchant, Kweon Woo Jung, Vassilina Nikoulina

Exploiting large pretrained models for various NMT tasks have gained a lot of visibility recently. In this work we study how BERT pretrained models could be exploited for supervised Neural Machine Translation. We compare various ways to integrate pretrained BERT model with NMT model and study the impact of the monolingual data used for BERT training on the f

Reza Bahmanyar, Elenora Vig, Peter Reinartz

In spite of the many advantages of aerial imagery for crowd monitoring and management at mass events, datasets of aerial images of crowds are still lacking in the field. As a remedy, in this work we introduce a novel crowd dataset, the DLR Aerial Crowd Dataset (DLR-ACD), which is composed of 33 large aerial images acquired from 16 flight campaigns over mass

Matthew Proctor, Paloma A. Huidobro, Stefan A. Maier, Richard V. Craster

The coupled light-matter modes supported by plasmonic metasurfaces can be combined with topological principles to yield subwavelength topological valley states of light. We give a systematic presentation of the topological valley states available for lattices of metallic nanoparticles: All possible lattices with hexagonal symmetry are considered, as well as

Alois Brunel, Damiano Mazza, Michele Pagani

Backpropagation is a classic automatic differentiation algorithm computing the gradient of functions specified by a certain class of simple, first-order programs, called computational graphs. It is a fundamental tool in several fields, most notably machine learning, where it is the key for efficiently training (deep) neural networks. Recent years have witnes

Rémi Bernhard, Pierre-Alain Moellic, Jean-Max Dutertre

As the will to deploy neural networks models on embedded systems grows, and considering the related memory footprint and energy consumption issues, finding lighter solutions to store neural networks such as weight quantization and more efficient inference methods become major research topics. Parallel to that, adversarial machine learning has risen recently

Danny R. Ramasawmy, Ben T. Cox, Bradley E. Treeby

Simulating the propagation of elastic waves in multi-layered media has many applications. A common approach is to use matrix methods where the elastic wave-field within each material layer is represented by a sum of partial-waves along with boundary conditions imposed at each interface. While these methods are well-known, coding the required matrix formation

Adrian Kent

Models in which causation arises from higher level structures as well as from microdynamics may be relevant to unifying quantum theory with classical physics or general relativity. They also give a way of defining a form of panprotopsychist property dualism, in which consciousness and material physics causally affect one another. I describe toy models based

Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini, Sergio Tessaris

Recent advances in the field of Business Process Management have brought about several suites able to model complex data objects along with the traditional control flow perspective. Nonetheless, when it comes to formal verification there is still the lack of effective verification tools on imperative data-aware process models and executions: the data perspec

Gonçalo Faria

This project considers Capsule Networks, a recently introduced machine learning model that has shown promising results regarding generalization and preservation of spatial information with few parameters. The Capsule Network's inner routing procedures thus far proposed, a priori, establish how the routing relations are modeled, which limits the expressivenes

V. Klimashonok, I. Perapechka, Ya. Shnir

We study fermion modes localized on the kink in the 1+1 dimensional $\phi^4$ model, coupled to the Dirac fermions with backreaction. Using numerical methods we construct self-consistent solutions of the corresponding system of coupled integral-differential equations and study dependencies of the scalar field of the kink and the normalizable fermion bound sta

Jipeng Cheng, Todor Milanov

In a companion paper to this one, we proved that the Gromov--Witten theory of a Fano orbifold line of type $D$ is governed by a system of Hirota Bilinear Equations. The goal of this paper is to prove that every solution to the Hirota Bilinear Equations determines a solution to a new integrable hierarchy of Lax equations. We suggest the name extended D-Toda h

Indu Ilanchezian, Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta

In this paper we investigate the usage of adversarial perturbations for the purpose of privacy from human perception and model (machine) based detection. We employ adversarial perturbations for obfuscating certain variables in raw data while preserving the rest. Current adversarial perturbation methods are used for data poisoning with minimal perturbations o

Lorenzo Nardi, Cyrill Stachniss

Nowadays, mobile robots are deployed in many indoor environments, such as offices or hospitals. These environments are subject to changes in the traversability that often happen by following repeating patterns. In this paper, we investigate the problem of navigating in such environments over extended periods of time by capturing these patterns and exploiting

Shruti Tople, Amit Sharma, Aditya Nori

Machine learning models, especially deep neural networks have been shown to be susceptible to privacy attacks such as membership inference where an adversary can detect whether a data point was used for training a black-box model. Such privacy risks are exacerbated when a model's predictions are used on an unseen data distribution. To alleviate privacy attac

David Soriano-Paños, Gourab Ghoshal, Alex Arenas, Jesús Gómez-Gardeñes

Human mobility plays a key role on the transformation of local disease outbreaks into global pandemics. Thus, the inclusion of human movements into epidemic models has become mandatory for understanding current epidemic episodes and to design efficient prevention policies. Following this challenge, here we develop a Markovian framework which enables to addre

John Armstrong, Cristin Buescu

We quantify the benefit of collectivised investment funds, in which the assets of members who die are shared among the survivors. For our model, with realistic parameter choices, an annuity or individual fund requires approximately 20\% more initial capital to provide as good an outcome as a collectivised investment fund. We demonstrate the importance of the

Cyril Imbert, Luis Silvestre

We derive $C^\infty$ a priori estimates for solutions of the inhomogeneous Boltzmann equation without cut-off, conditional to point-wise bounds on their mass, energy and entropy densities. We also establish decay estimates for large velocities, for all derivatives of the solution.

Emilia Leogrande, Philipp Roloff, Ulrike Schnoor, Matthias Weber

The Compact Linear Collider, CLIC, is a multi-TeV electron-positron collider proposed for construction at CERN. A detector model, CLICdet, that is suited for the experimental conditions at CLIC and is based on realistic performance, has been developed. This paper describes the implementation of CLICdet in a fast simulation tool for particle physics collider

Ouraman Hajizadeh, Markus Q. Huber, Axel Maas, Jan M. Pawlowski

Reliably computing the free energy in a gauge theory like QCD is a challenging and resource-demanding endeavor. As an alternative, we explore here the possibility to obtain the associated thermodynamic anomaly by exploiting its relation to the Tan contact. Optimally, this would reduce the determination of the free energy to a high-precision calculation of tw

S. Alex Breitweiser, Annemarie L. Exarhos, Raj N. Patel, Jennifer Saouaf

Defect-based quantum emitters in solid state materials offer a promising platform for quantum communication and sensing. Confocal fluorescence microscopy techniques have revealed quantum emitters in a multitude of host materials. In some materials, however, optical properties vary widely between emitters, even within the same sample. In these cases, traditio

Isabela Cunha Maia Nobre, Pascal Frossard

Distributed graph signal processing algorithms require the network nodes to communicate by exchanging messages in order to achieve a common objective. These messages have a finite precision in realistic networks, which may necessitate to implement message quantization. Quantization, in turn, may generate distortion and performance penalty in the distributed

Jure Brence, Luka Cmok, Nerea Sebastian, Alenka Mertelj

A comparative experimental investigation of the dependence of second harmonic generation (SHG) on applied external voltage between the standard nematic liquid crystalline material and an analogue ferromagnetic nematic liquid crystalline material was performed by using a fundamental optical beam at 800 nm wavelength. For a ferromagnetic material, the dependen

Pu Yang, Krishnamurthy Iyer, Peter Frazier

We consider the information design problem in spatial resource competition settings. Agents gather at a location deciding whether to move to another location for possibly higher level of resources, and the utility each agent gets by moving to the other location decreases as more agents move there. The agents do not observe the resource level at the other loc

Yuyang Gao, Giorgio A. Ascoli, Liang Zhao

Deep neural networks (DNNs) are known for extracting useful information from large amounts of data. However, the representations learned in DNNs are typically hard to interpret, especially in dense layers. One crucial issue of the classical DNN model such as multilayer perceptron (MLP) is that neurons in the same layer of DNNs are conditionally independent o

Shubhayan Sarkar, Debashis Saha, Jędrzej Kaniewski, Remigiusz Augusiak

Bell nonlocality as a resource for device independent certification schemes has been studied extensively in recent years. The strongest form of device independent certification is referred to as self-testing, which given a device certifies the promised quantum state as well as quantum measurements performed on it without any knowledge of the internal working

Federico Paolucci, Giorgio De Simoni, Paolo Solinas, Elia Strambini

Despite metals are believed to be insensitive to field-effect and conventional Bardeen-Cooper-Schrieffer (BCS) theories predict the electric field to be ineffective on conventional superconductors, a number of gating experiments showed the possibility of modulating the conductivity of metallic thin films and the critical temperature of conventional supercond

Eugenio Borghini

We extend a systolic inequality of Guth for Riemannian manifolds of maximal $\mathbb{Z}_2$ cup-length to piecewise Riemannian complexes of dimension 2. As a consequence we improve the previous best universal lower bound for the systolic area of groups for a large class of groups, including free abelian and surface groups, most of irreducible 3-manifold group

Yong Cai, Yun-Song Piao

We investigate the implication of Trans-Planckian Censorship Conjecture (TCC) for the initial state of primordial perturbations. It is possible to set the state of perturbation modes in the infinite past as the Minkowski vacuum, only if the pre-inflationary era is past-complete. We calculate the evolution of the perturbation modes in such a pre-inflationary

Wolfgang Kastaun, Frank Ohme

After the detection of gravitational waves caused by the coalescence of compact objects in the mass range of neutron stars, GW170817, several studies have searched for an imprint of tidal effects in the signal, employing different model assumptions. One important distinction is whether or not to assume that both objects are neutron stars and obey the same eq

R. J. Graham, Tiffany Shaw, Dorian Abbot

According to the Snowball Earth hypothesis, Earth has experienced periods of low-latitude glaciation in its deep past. Prior studies have used general circulation models (GCMs) to examine the effects such an extreme climate state might have on the structure and dynamics of Earth's troposphere, but the behavior of the stratosphere has not been studied in deta

Gabriela Robiolo, Ezequiel Scott, Santiago Matalonga, Michael Felderer

Background: To adequately attend to non-functional requirements (NFRs), they must be documented; otherwise, developers would not know about their existence. However, the documentation of NFRs may be subject to Technical Debt and Waste, as any other software artefact. Aims: The goal is to explore indicators of potential Technical Debt and Waste in NFRs docume

Stephen Eales, Oliver Eales, Pieter de Vis

In this paper, we use the Herschel Reference Survey to make a direct test of the hypothesis that the growth of a stellar bulge leads to a reduction in the star-formation efficiency of a galaxy (or conversely a growth in the gas-depletion timescale) as a result of the stabilisation of the gaseous disk by the gravitational field of the bulge. We find a strong

Paolo Tripicchio, Carlo Alberto Avizzano, Massimo Bergamasco

During the design phase of products and before going into production, it is necessary to verify the presence of mechanical plays, tolerances, and encumbrances on production mockups. This work introduces a multi-modal system that allows verifying assembly procedures of products in Virtual Reality starting directly from CAD models. Thus leveraging the costs an

Stanislav Böhm, Jakub Beránek, Martin Šurkovský

Haydi (http://haydi.readthedocs.io) is a framework for generating discrete structures. It provides a way to define a structure from basic building blocks and then enumerate all elements, all non-isomorphic elements, or generate random elements in the structure. Haydi is designed as a tool for rapid prototyping. It is implemented as a pure Python package and

Jeffrey Bergen, Piotr Grzeszczuk

Let $X$ be a set of noncommuting variables of cardinality $card(X)\geqslant 2$, and ${\mathscr G}=\{\sigma_x\}_{x\in X}$, ${\mathscr D}=\{\delta_x\}_{x\in X}$ be families of automorphisms and skew derivations of the ring $R$. It is proved that if the ring $R$ is semiprime Goldie, then the free skew extension $R[X;{\mathscr G},{\mathscr D}]$ is semiprimitive.

Sheng Rao, Quanting Zhao

We introduce a canonical isomorphism from the space of pure-type complex differential forms on a compact complex manifold to the one on its infinitesimal deformations. By use of this map, we generalize an extension formula in a recent work of K. Liu, X. Yang and the second author. As a direct corollary of the extension formulas, we prove several deformation

Jan van Waaij

Due to their conjugate posteriors, Gaussian process priors are attractive for estimating the drift of stochastic differential equations with continuous time observations. However, their performance strongly depends on the choice of the hyper-parameters. We employ the marginal maximum likelihood estimator to estimate the scaling and/or smoothness parameter(s)

Matteo Rinaldi

In this talk we present the results of the investigation on the so called double parton distribution functions (dPDFs), accessible quantities in high energy proton-proton and proton nucleus collisions, in double parton scattering processes (DPS). These new and almost unknown distributions encode information on how partons inside a proton are correlated among

Simona Nistor, Cezar Oniciuc

We construct simply connected, complete, non-$CMC$ biconservative surfaces in the $3$-dimensional hyperbolic space $\mathbb{H}^3$ in an intrinsic and extrinsic way. We obtain three families of such surfaces, and, for each surface, the set of points where the gradient of the mean curvature function does not vanish is dense and has two connected components. In

Jinn-Ouk Gong

We present analytic integral solutions for the second-order induced gravitational waves (GWs). After presenting all the possible second-order source terms, we calculate explicitly the solutions for the GWs induced by the linear scalar and tensor perturbations during matter- and radiation-dominated epochs.

Amilcar Bedoya-Pinto, Avanindra Kumar Pandeya, Defa Liu, Hakan Deniz

Weyl Semimetals (WSMs), a recently discovered topological state of matter, exhibit an electronic structure governed by linear band dispersions and degeneracy (Weyl) points leading to rich physical phenomena, which are yet to be exploited in thin film devices. While WSMs were established in the monopnictide compound family several years ago, the growth of thi

Marc Henneaux, Victor Lekeu, Amaury Leonard

The (free) graviton admits, in addition to the standard Pauli-Fierz description by means of a rank-two symmetric tensor, a description in which one dualizes the corresponding (2,2)-curvature tensor on one column to get a (D-2,2)-tensor, where D is the spacetime dimension. This tensor derives from a gauge field with mixed Yound symmetry (D-3,1) called the "du