University of Cagliari

About: University of Cagliari is a education organization based out in Cagliari, Italy. It is known for research contribution in the topics: Population & Dopamine. The organization has 11029 authors who have published 29046 publications receiving 771023 citations. The organization is also known as: Università degli Studi di Cagliari & Universita degli Studi di Cagliari.

The Role of Ligands in the Chemical Synthesis and Applications of Inorganic Nanoparticles.

Abstract: The design of nanoparticles is critical for their efficient use in many applications ranging from biomedicine to sensing and energy. While shape and size are responsible for the properties of the inorganic nanoparticle core, the choice of ligands is of utmost importance for the colloidal stability and function of the nanoparticles. Moreover, the selection of ligands employed in nanoparticle synthesis can determine their final size and shape. Ligands added after nanoparticle synthesis infer both new properties as well as provide enhanced colloidal stability. In this article, we provide a comprehensive review on the role of the ligands with respect to the nanoparticle morphology, stability, and function. We analyze the interaction of nanoparticle surface and ligands with different chemical groups, the types of bonding, the final dispersibility of ligand-coated nanoparticles in complex media, their reactivity, and their performance in biomedicine, photodetectors, photovoltaic devices, light-emitting devices, sensors, memory devices, thermoelectric applications, and catalysis.

Wild patterns: Ten years after the rise of adversarial machine learning half-day tutorial

Abstract: Deep neural networks and machine-learning algorithms are pervasively used in several applications, ranging from computer vision to computer security. In most of these applications, the learning algorithm has to face intelligent and adaptive attackers who can carefully manipulate data to purposely subvert the learning process. As these algorithms have not been originally designed under such premises, they have been shown to be vulnerable to well-crafted, sophisticated attacks, including training-time poisoning and test-time evasion attacks (also known as adversarial examples). The problem of countering these threats and learning secure classifiers in adversarial settings has thus become the subject of an emerging, relevant research field known as adversarial machine learning. The purposes of this tutorial are: (a) to introduce the fundamentals of adversarial machine learning to the security community; (b) to illustrate the design cycle of a learning-based pattern recognition system for adversarial tasks; (c) to present novel techniques that have been recently proposed to assess performance of pattern classifiers and deep learning algorithms under attack, evaluate their vulnerabilities, and implement defense strategies that make learning algorithms more robust to attacks; and (d) to show some applications of adversarial machine learning to pattern recognition tasks like object recognition in images, biometric identity recognition, spam and malware detection.

ALICE: Physics Performance Report, Volume II

Abstract: ALICE is a general-purpose heavy-ion experiment designed to study the physics of strongly interacting matter and the quark–gluon plasma in nucleus–nucleus collisions at the LHC. It currently involves more than 900 physicists and senior engineers, from both the nuclear and high-energy physics sectors, from over 90 institutions in about 30 countries.The ALICE detector is designed to cope with the highest particle multiplicities above those anticipated for Pb–Pb collisions (dNch/dy up to 8000) and it will be operational at the start-up of the LHC. In addition to heavy systems, the ALICE Collaboration will study collisions of lower-mass ions, which are a means of varying the energy density, and protons (both pp and pA), which primarily provide reference data for the nucleus–nucleus collisions. In addition, the pp data will allow for a number of genuine pp physics studies.The detailed design of the different detector systems has been laid down in a number of Technical Design Reports issued between mid-1998 and the end of 2004. The experiment is currently under construction and will be ready for data taking with both proton and heavy-ion beams at the start-up of the LHC.Since the comprehensive information on detector and physics performance was last published in the ALICE Technical Proposal in 1996, the detector, as well as simulation, reconstruction and analysis software have undergone significant development. The Physics Performance Report (PPR) provides an updated and comprehensive summary of the performance of the various ALICE subsystems, including updates to the Technical Design Reports, as appropriate.The PPR is divided into two volumes. Volume I, published in 2004 (CERN/LHCC 2003-049, ALICE Collaboration 2004 J. Phys. G: Nucl. Part. Phys. 30 1517–1763), contains in four chapters a short theoretical overview and an extensive reference list concerning the physics topics of interest to ALICE, the experimental conditions at the LHC, a short summary and update of the subsystem designs, and a description of the offline framework and Monte Carlo event generators.The present volume, Volume II, contains the majority of the information relevant to the physics performance in proton–proton, proton–nucleus, and nucleus–nucleus collisions. Following an introductory overview, Chapter 5 describes the combined detector performance and the event reconstruction procedures, based on detailed simulations of the individual subsystems. Chapter 6 describes the analysis and physics reach for a representative sample of physics observables, from global event characteristics to hard processes.