Unsupervised Feature Selection with Adaptive Structure Learning

The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods

Forcing and Velocity Correlations in a Vibrated Granular Monolayer

The role of forcing on the dynamics of a vertically shaken granular monolayer is investigated. Using a flat plate, surprising negative velocity correlations are measured. A mechanism for this anti-correlation is proposed with support from both experimental results and molecular dynamics simulations. Using a rough plate, velocity correlations are positive, and the velocity distribution evolves from a gaussian at very low densities to a broader distribution at high densities. These results are interpreted as a balance between stochastic forcing, interparticle collisions, and friction with the plate.Comment: 4 pages, 5 figure

Preventing Advanced Persistent Threats in Complex Control Networks

An Advanced Persistent Threat (APT) is an emerging attack against Industrial Control and Automation Systems, that is executed over a long period of time and is difficult to detect. In this context, graph theory can be applied to model the interaction among nodes and the complex attacks affecting them, as well as to design recovery techniques that ensure the survivability of the network. Accordingly, we leverage a decision model to study how a set of hierarchically selected nodes can collaborate to detect an APT within the network, concerning the presence of changes in its topology. Moreover, we implement a response service based on redundant links that dynamically uses a secret sharing scheme and applies a flexible routing protocol depending on the severity of the attack. The ultimate goal is twofold: ensuring the reachability between nodes despite the changes and preventing the path followed by messages from being discovered.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Interplay of Spin-Orbit Interactions, Dimensionality, and Octahedral Rotations in Semimetallic SrIrO3_3

We employ reactive molecular-beam epitaxy to synthesize the metastable perovskite SrIrO$_{3}$ and utilize {\it in situ} angle-resolved photoemission to reveal its electronic structure as an exotic narrow-band semimetal. We discover remarkably narrow bands which originate from a confluence of strong spin-orbit interactions, dimensionality, and both in- and out-of-plane IrO$_6$ octahedral rotations. The partial occupation of numerous bands with strongly mixed orbital characters signals the breakdown of the single-band Mott picture that characterizes its insulating two-dimensional counterpart, Sr$_{2}$IrO$_{4}$, illustrating the power of structure-property relations for manipulating the subtle balance between spin-orbit interactions and electron-electron interactions

Tunnel splitting and quantum phase interference in biaxial ferrimagnetic particles at excited states

The tunneling splitting in biaxial ferrimagnetic particles at excited states with an explicit calculation of the prefactor of exponent is obtained in terms of periodic instantons which are responsible for tunneling at excited states and is shown as a function of magnetic field applied along an arbitrary direction in the plane of hard and medium axes. Using complex time path-integral we demonstrate the oscillation of tunnel splitting with respect to the magnitude and the direction of the magnetic field due to the quantum phase interference of two tunneling paths of opposite windings . The oscillation is gradually smeared and in the end the tunnel splitting monotonously increases with the magnitude of the magnetic field when the direction of the magnetic field tends to the medium axis. The oscillation behavior is similar to the recent experimental observation with Fe$_8$ molecular clusters. A candidate of possible experiments to observe the effect of quantum phase interference in the ferrimagnetic particles is proposed.Comment: 15 pages, 5 figures, acceptted to be pubblished in Physical Review