On the spectrum of the twisted Dolbeault Laplacian over K\"ahler manifolds

We use Dirac operator techniques to a establish sharp lower bound for the first eigenvalue of the Dolbeault Laplacian twisted by Hermitian-Einstein connections on vector bundles of negative degree over compact K\"ahler manifolds.Comment: 14 pages. Completely revised: estimates corrected and shown to be shar

New divisors in the boundary of the instanton moduli space

Let ${\mathcal I}(n)$ denote the moduli space of rank $2$ instanton bundles of charge $n$ on ${\mathbb P}^3$. It is known that ${\mathcal I}(n)$ is an irreducible, nonsingular and affine variety of dimension $8n-3$. Since every rank $2$ instanton bundle on ${\mathbb P}^3$ is stable, we may regard ${\mathcal I}(n)$ as an open subset of the projective Gieseker-Maruyama moduli scheme ${\mathcal M}(n)$ of rank $2$ semistable torsion free sheaves $F$ on ${\mathbb P}^3$ with Chern classes $c_1=c_3=0$ and $c_2=n$, and consider the closure $\overline{{\mathcal I}(n)}$ of ${\mathcal I}(n)$ in ${\mathcal M}(n)$. We construct some of the irreducible components of dimension $8n-4$ of the boundary $\partial{\mathcal I}(n):=\overline{{\mathcal I}(n)}\setminus{\mathcal I}(n)$. These components generically lie in the smooth locus of ${\mathcal M}(n)$ and consist of rank $2$ torsion free instanton sheaves with singularities along rational curves

Cyber-Physical Systems: a multi-criteria assessment for Internet-of-Things (IoT) systems

This research work was partially supported by funds provided by the European Commission in the scope of FoF/H2020-636909 C2NET, FoF/H2020-723710 vf-OS and ICT/H2020-825631 ZDMP.This article addresses a multi-criteria decision problem regarding the more suitable device (system) to perform a task for cyber-physical systems. New embedded systems provided everyday makes engineers’ decision very difficult. Components are proposed to formally describe solutions, criteria, constraints and priorities, taking into account users’ specific aspects. To materialise all formal descriptions, a model-driven approach is followed, allowing the design of enablers for interoperability with standards. It is enabled the use of different software languages and decision methods. Proposed framework enables a better Internet-of-Things system selection, and therefore stakeholders can perform a more suitable design of their cyber-physical enterprise systems.authorsversioninpres

Impact of automated action labeling in classification of human actions in RGB-D videos

For many applications it is important to be able to detect what a human is currently doing. This ability is useful for applications such as surveillance, human computer interfaces, games and healthcare. In order to recognize a human action, the typical approach is to use manually labeled data to perform supervised training. This paper aims to compare the performance of several supervised classifiers trained with manually labeled data versus the same classifiers trained with data automatically labeled. In this paper we propose a framework capable of recognizing human actions using supervised classifiers trained with automatically labeled data in RGB-D videos.info:eu-repo/semantics/publishedVersio

Predicting human activities in sequences of actions in RGB-D videos

In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset.info:eu-repo/semantics/acceptedVersio