Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.

Studying the non-thermal lobes of IRAS 16547-4247 through a multi-wavelength approach

In recent years, massive protostars have turned out to be a possible population of high-energy emitters. Among the best candidates is IRAS 16547-4247, a protostar that presents a powerful outflow with clear signatures of interaction with its environment. This source has been revealed to be a potential high-energy source because it displays non-thermal radio emission of synchrotron origin, which is evidence of relativistic particles. To improve our understanding of IRAS 16547-4247 as a high-energy source, we analyzed XMM-Newton archival data and found that IRAS 16547-4247 is a hard X-ray source. We discuss these results in the context of a refined one-zone model and previous radio observations. From our study we find that it may be difficult to explain the X-ray emission as non-thermal radiation coming from the interaction region, but it might be produced by thermal Bremsstrahlung (plus photo-electric absorption) by a fast shock at the jet end. In the high-energy range, the source might be detectable by the present generation of Cherenkov telescopes, and may eventually be detected by Fermi in the GeV range.

Deep GMRT radio observations and a multi-wavelength study of the region around HESS J1858+020

Context. There are a number of very high energy sources in the Galaxy that remain unidentified. Multi-wavelength and variability studies, and catalogue searches, are powerful tools to identify the physical counterpart, given the uncertainty in the source location and extension. Aims. This work carries out a thorough multi-wavelength study of the unidentified, very high energy source HESS J1858+020 and its environs. Methods. We have performed Giant Metrewave Radio Telescope observations at 610 MHz and 1.4 GHz to obtain a deep, low-frequency radio image of the region surrounding HESS J1858+020. We analysed archival radio, infrared, and X-ray data as well. This observational information, combined with molecular data, catalogue sources, and a nearby Fermi gamma-ray detection of unidentified origin, are combined to explore possible counterparts to the very high energy source. Results. We provide with a deep radio image of a supernova remnant that might be related to the GeV and TeV emission in the region. We confirm the presence of an H ii region next to the supernova remnant and coincident with molecular emission. A potential region of star formation is also identified. We identify several radio and X-ray sources in the surroundings. Some of these sources are known planetary nebulae, whereas others may be non-thermal extended emitters and embedded young stellar objects. Three old, background Galactic pulsars also neighbour HESS J1858+020 along the line of sight. Conclusions. The region surrounding HESS J1858+020 is rich in molecular structures and non-thermal objects that may potentially be linked to this unidentified very high energy source. In particular, a supernova remnant interacting with nearby molecular clouds may be a good candidate, but a star forming region, or a non-thermal radio source of yet unclear nature, may also be behind the gamma-ray source. The neighbouring pulsars, despite being old and distant, cannot be discarded as candidates. Further observational studies are needed, however, to narrow the search for a counterpart to the HESS source.

One-seller assignment markets with multi-unit demands

We consider one-seller assignment markets with multi-unit demands and prove that the associated game is buyers-submodular. Therefore the core is non-empty and it has a lattice structure which contains the allocation where every buyer receives his marginal contribution. We prove that in this kind of market, every pairwise-stable outcome is associated to a competitive equilibrium and viceversa. We study conditions under which the buyers-optimal and the seller-optimal core allocations are competitive equilibrium payoff vectors. Moreover, we characterize the markets for which the core coincidences with the set of competitive equilibria payoff vectors. When agents behave strategically, we introduce a procedure that implements the buyers-optimal core allocation as the unique subgame perfect Nash equilibrium outcome.

Statistical mechanics of multi-edge networks

Statistical properties of binary complex networks are well understood and recently many attempts have been made to extend this knowledge to weighted ones. There are, however, subtle yet important considerations to be made regarding the nature of the weights used in this generalization. Weights can be either continuous or discrete magnitudes, and in the latter case, they can additionally have undistinguishable or distinguishable nature. This fact has not been addressed in the literature insofar and has deep implications on the network statistics. In this work we face this problem introducing multiedge networks as graphs where multiple (distinguishable) connections between nodes are considered. We develop a statistical mechanics framework where it is possible to get information about the most relevant observables given a large spectrum of linear and nonlinear constraints including those depending both on the number of multiedges per link and their binary projection. The latter case is particularly interesting as we show that binary projections can be understood from multiedge processes. The implications of these results are important as many real-agent-based problems mapped onto graphs require this treatment for a proper characterization of their collective behavior.