On the Serverless Nature of Blockchains and Smart Contracts

Serverless architecture is more frequently associated with the architectural style for developing cloud-native applications . Blockchains are distributed systems designed to enable collaborative scenarios involving untrusted parties . The decentralizedpeer-to-peer nature of blockchains makes it interesting to consider them inserverless architectures, since resource allocation and management tasks are not required to be performed by users .…

Exploring the landscapes of computing digital neuromorphic unconventional and beyond

The acceleration race of digital computing technologies seems to be steeringtoward impasses — technological, economical and environmental — a condition that has spurred research efforts in alternative, “neuromorphic” (brain-like)computing technologies . The idea of exploiting nonlinear physical phenomena “directly” for non-digital computing has been explored under names like “unconventional computing”, “natural computing”,”physical computing”, or “in-materio computing” I stake out how a general concept of “computing” can bedeveloped which comprises digital, neuromorphic, unconventional and possible future paradigms .…

The Reads From Equivalence for the TSO and PSO Memory Models

The verification of concurrent programs remains an open challenge due to thenon-determinism in inter-process communication . The reads-from (RF) equivalence was recently shownto be coarser than the Mazurkiewicz equivalence, leading to impressivescalability improvements for SMC under SC . For TSO and PSO, the standard equivalence has been Shasha-Snir traces .…

Non Invertible Element Constacyclic Codes over Finite PIRs

In this paper we introduce the notion of $\lambda$-constacyclic codes overfinite rings $R$ for arbitary element $R$. We study thenon-invertible-element constacyClic codes (NIE-consticclic code) over finiteprincipal ideal rings (PIRs) We determine the algebraic structures of all NIE codes over finite chain rings, give the unique form of thesets of the defining polynomials and obtain their minimum Hamming distances .…

Uniform auxiliary space preconditioning for HDG methods for elliptic operators with a parameter dependent low order term

The auxiliary space preconditioning (ASP) technique is applied to the HDGschemes for three different elliptic problems with a parameter dependent loworder term . Uniformpreconditioners are obtained for each case and the general ASP theory by J. Xu[21] is used to prove the optimality with respect to the mesh size and uniformity of the low order parameter .…

Two Way Neural Machine Translation A Proof of Concept for Bidirectional Translation Modeling using a Two Dimensional Grid

Neural translation models have proven to be effective in capturing sufficient information from a source sentence and generating a high-quality targetsentence . However, it is not easy to get the best effect for bidirectional translation using a single model . This paper proposes to build a single end-to-endbidirectionaltranslation model using a two-dimensional grid .…

Deep learning based discovery of partial differential equations in integral form from sparse and noisy data

Data-driven discovery of partial differential equations (PDEs) has attracted attention in recent years . For PDEs with high-orderderivatives, the performance of existing methods is unsatisfactory, especially when the data is sparse and noisy . New framework combining deep-learning and integral form is proposed to handle the above-mentionedproblems simultaneously, and improve the accuracy and stability of PDEdiscovery .…

Stochastic sparse adversarial attacks

Stochastic sparse adversarial attacks (SSAA) are simple, fast and purely noise-based targeted and untargeted attacks of NNC . SSAA offer new examples of sparse (or $L_0$) attacks for which only few methodshave been proposed previously . These attacks are devised by exploiting asmall-time expansion idea widely used for Markov processes .…

Health Focused Optimal Power Flow

Health-Focused Optimal Power Flow (HF-OPF) proposed to take into account equipment health in operational and physical constraints . The paper addresses theneed for understanding the relationship between health condition index and theoperational constraints in OPF problems . The results show that health conditioninflicts high cost of generation and can lead to infeasibility even with lesscritical faults .…

Reinforced optimal control

Least squares Monte Carlo methods are a popular numerical approximationmethod for solving stochastic control problems . The choice of basis functions is crucial for the accuracy of the method . We extend the reinforced regression method to a generalclass of stochastically control problems, while considerably improving the method’s efficiency .…

Approximation of a Multivariate Function of Bounded Variation from its Scattered Data

Radial basis function(RBF) interpolationmethods are known to approximate only functions in their native spaces . To date, there has been no known proof that they can approximate functions outsidethe native space associated with the particular RBF being used . In this paper, we describe a scattered data interpolation method which can approximate anyfunction of bounded variation from its scattered data as the data points growdense .…

Algorithmic random duality theory large scale CLuP

Based on our Random Duality Theory (RDT), we developed a powerful algorithmic mechanism (called CLuP) that can be utilized to solve NP hard optimization problems in polynomial time . Here we move things further and utilize another ofremarkable RDT features that we established in a long line of work in the past .…

On inverse problems for semiconductor equations

This paper is devoted to the investigation of inverse problems related to drift-diffusion equations modeling semiconductor devices . In thiscontext we analyze several identification problems corresponding to different types of measurements, where the parameter to be reconstructed is aninhomogeneity in the PDE model (doping profile) For a particular type ofmeasurement (related to the voltage-current map) we consider special cases of Drift-Diffusion equations .…

Corona Warn App Erste Ergebnisse einer Onlineumfrage zur Nicht Nutzung und Gebrauch

In this study, the German “Corona-Warn-App” of the German Federal Government and the Robert-Koch-Institute is examined by means of a non-representative online survey with 1482 participants for reasons of use and non-use . The study provides insights into user behavior with the app during the Corona pandemic, highlights the topic of data protection and how the app is used in general .…

Reduced Order Modeling for Parameterized Time Dependent PDEs using Spatially and Memory Aware Deep Learning

We present a novel reduced order model (ROM) approach for parameterizedtime-dependent PDEs based on modern learning . The ROM is suitable formulti-query problems and is nonintrusive . It is divided into two distinct stages: a nonlinear dimensionality reduction stage that handles the spatiallydistributed degrees of freedom based on convolutional autoencoders .…

Automatic differentiation of Sylvester Lyapunov and algebraic Riccati equations

Sylvester, Lyapunov, and algebraic Riccati equations are the bread and butter of control theorists . They are used to compute infinite-horizon Gramians, solve optimal control problems in continuous or discrete time, and design observers . Here, we derive the forward and reverse-mode derivatives of the solutions to all three types of equations, and showcase their application on an inverse control problem .…

Evolutionary Planning in Latent Space

Planning is a powerful approach to reinforcement learning with several desirable properties . However, it requires a model of the world, which is not available in many real-life problems . In this paper, we propose tolearn a world model that enables Evolutionary Planning in Latent Space (EPLS) We use a Variational Auto Encoder (VAE) to learn a compressed latent latentrepresentation of individual observations and extend a Mixture DensityRecurrent Neural Network (MDRNN) The planning agents are better than standard model-freereinforcement learning approaches demonstrating the viability of our approach .…

A sequential sensor selection strategy for hyper parameterized linear Bayesian inverse problems

We consider optimal sensor placement for hyper-parameterized linear Bayesianinverse problems . We link the numerical stability of themaximum a posterior point and A-optimal experimental design to an observabilitycoefficient that describes the influence of the chosen sensors . Wepropose an algorithm that iteratively chooses the sensor locations to improve this coefficient and thereby decrease the eigenvalues of the posteriorcovariance matrix .…

Efficient Broadcast for Timely Updates in Mobile Networks

This letter considers a wireless network where an access point (AP)broadcasts timely updates to several mobile users . The timeliness ofinformation owned by a user is characterized by the recently proposed age ofinformation . While frequently broadcasting the timely updates and always using the maximum power can minimize the age of information for all users, that can waste valuable communication resources .…

An off the grid approach to multi compartment magnetic resonance fingerprinting

We propose a novel numerical approach to separate multiple tissuecompartments in image voxels . We estimate quantitatively their nuclearmagnetic resonance (NMR) properties and mixture fractions, given magneticresonance fingerprinting (MRF) measurements . The number of tissues, their types or quantitative properties are not a-priori known, but the image is assumed to be composed of sparse compartments with linearly mixed Bloch magnetisationresponses .…

On cutting blocking sets and their codes

Cutting blocking sets give rise to saturating sets and minimal linearcodes and those having size as small as possible are of particular interest . We observe that from a cutting blocking set obtained by Fancsali and Sziklai, byusing a set of pairwise disjoint lines, there arises a minimal linear codewhose length grows linearly with respect to its dimension .…

A Mathematical Dashboard for the Analysis of Italian COVID 19 Epidemic Data

A data analysis of the COVID-19 epidemic is proposed on the basis of thedashboard publicly accessible at https://www.epimox.polimi.it that focuses on the characterization of the first and second epidemic outbreaks in Italy . Thescope of this tool is to foster a deeperinterpretation of available data as well as to provide a hint on the nearfuture evolution of the most relevant epidemic indicators .…

DeepClimGAN A High Resolution Climate Data Generator

Earth system models (ESMs) are often used to generate future projections of climate change scenarios . Emulators are substantially less expensive but may not have all of the complexity of anESM . Here we demonstrate the use of a conditional generative adversarialnetwork (GAN) to act as an ESM emulator .…

V3H Incomplete Multi view Clustering via View Variation and View Heredity

Real data often appear in the form of multiple incomplete views . Previous clustering methods only learn the consistent information between different views and ignore the unique information of each view . We propose a novel View Variation and View Heredity approach (V 3H) Inspired by the variation and the heredity in genetics, V 3H first decomposes each subspace into a variation matrix for the corresponding view and a redity matrix for all the views .…

The Dynamic of Body and Brain Co Evolution

Method permits to co-evolve the body and the controlproperties of robots . It can be used to adapt the morphological traits of robots with a hand-designed morphological bauplan . Our results indicate that robots with co-adapted body and control traits outperform robots with fixed morphologies .…

MEG Multi Evidence GNN for Multimodal Semantic Forensics

Fake news often involves semantic manipulations across modalities such asimage, text, location etc and requires the development of multimodal semanticforensics for its detection . The proposed model outperforms existing state-of-the-art algorithms with an error reduction of up to 25% . Existing methods arelimited to using a single evidence (retrieved package) which ignores potential improvement from the use of multiple evidences .…

Sparse linear regression CLuP achieves the ideal emph exact ML

In this paper we revisit one of the classical statistical problems, theso-called sparse maximum-likelihood (ML) linear regression . As a way ofattacking this type of regression, we present a novel CLuP mechanism that relies on the Random Duality Theory (RDT) basedalgorithmic machinery that we recently introduced in  Stojnicclupint19, StojnicClupcmpl19,StojNicclupplt19, and Stojnicluprephased20 .…

Investigating Emotion Color Association in Deep Neural Networks

It has been found that representations learned by Deep Neural Networks (DNNs)correlate very well to neural responses measured in primates’ brains . Past studies have shown that particular colors can be associated with specific emotion arousal in humans . Do deep neural networks also learn implicitassociations in stimuli, particularly, an emotion-color association betweenimage stimuli?…

A finite element scheme for an initial value problem

A new Hamilton principle of convolutional type, completely compatible with the initial conditions of an IVP, has been proposed in a recent publicationarXiv:1912.08490v1 [math-ph]. In the present paper the possible use of thisprinciple for the formulation of a FE scheme adjusted to dynamical problems is investigated .…