Machine Learning For In-Region Location Verification In Wireless Networks

In-region location verification (IRLV) aims at verifying whether a user is inside a region of interest (ROI). In wireless networks, IRLV can exploit the features of the channel between the user and a set of trusted access points. In practice, the channel feature statistics is not available and we resort to machine learning (ML) solutions for IRLV. We first show that solutions based on either neural networks (NNs) or support vector machines (SVMs) and typical loss functions are Neyman-Pearson (N-P)-optimal at learning convergence for sufficiently complex learning machines and large training datasets . Indeed, for finite training, ML solutions are more accurate than the N-P test based on estimated channel statistics. Then, as estimating channel features outside the ROI may be difficult, we consider one-class classifiers, namely auto-encoders NNs and one-class SVMs, which however are not equivalent to the generalized likelihood ratio test (GLRT), typically replacing the N-P test in the one-class problem. Numerical results support the results in realistic wireless networks, with channel models including path-loss, shadowing, and fading

QCD at High Temperature : Results from Lattice Simulations with an Imaginary mu

We summarize our results on the phase diagram of QCD with emphasis on the high temperature regime. For $T \ge 1.5 T_c$ the results are compatible with a free field behavior, while for $T \simeq 1.1 T_c$ this is not the case, clearly exposing the strongly interacting nature of QCD in this regionComment: 7 pages, 2 figures; To appear in the proceedings of QCD@Work 2005,International Workshop on Quantum Chromodynamics, Conversano, Bari, Italy, 16-20 Jun 200

Malleability of the self: electrophysiological correlates of the enfacement illusion

Self-face representation is fundamentally important for self-identity and self-consciousness. Given its role in preserving identity over time, self-face processing is considered as a robust and stable process. Yet, recent studies indicate that simple psychophysics manipulations may change how we process our own face. Specifically, experiencing tactile facial stimulation while seeing similar synchronous stimuli delivered to the face of another individual seen as in a mirror, induces 'enfacement' illusion, i.e. the subjective experience of ownership of the other’s face and a bias in attributing to the self, facial features of the other person. Here we recorded visual Event-Related Potentials elicited by the presentation of self, other and morphed faces during a self-other discrimination task performed immediately after participants received synchronous and control asynchronous Interpersonal Multisensory Stimulation (IMS). We found that self-face presentation after synchronous as compared to asynchronous stimulation significantly reduced the late positive potential (LPP; 450-750 ms), a reliable electrophysiological marker of self-identification processes. Additionally, enfacement cancelled out the differences in LPP amplitudes produced by self- and other-face during the control condition. These findings represent the first direct neurophysiological evidence that enfacement may affect self-face processing and pave the way to novel paradigms for exploring defective self-representation and self-other interactions

Insediamenti rurali a corte nella Sicilia occidentale : la tenuta dello Zucco di Henri d’Orléans, duca d’Aumale

Lo studio indaga sulle trasformazioni del complesso rurale fortifi cato a corte chiusa della Tenuta dello Zucco, fl orida realtà agricola siciliana ottocentesca inserita, in Provincia di Palermo nel territorio di Carini, all’interno di un grande ex feudo. Il bene rurale, oggi in stato di parziale abbandono, conobbe, dalla metà dell’Ottocento, con l’intraprendenza imprenditoriale e la disponibilità fi nanziaria del proprietario Henri d’Orléans, duca d’Aumale, il suo massimo splendore architettonico e produttivo. Dopo una disamina morfo/tipologica sugli insediamenti rurali a corte della Sicilia occidentale, il volume ricostruisce, attraverso fonti bibliografi che, fotografi che, iconografi che e cartografi che e inedite documentazioni d’archivio le trasformazioni cronologiche, le modifi cazioni tipologiche e le stratifi cazioni storiche della residenza rurale, trasferendo, anche in forma di disegni, informazioni metriche desunte da inventari manoscritti. Il processo di conoscenza intrapreso documenta il degrado delle strutture edilizie e dei paramenti murari fornendo utili chiavi di lettura per futuri interventi di riqualifi cazione e di fruizione del sito

Providing Transaction Class-Based QoS in In-Memory Data Grids via Machine Learning

Elastic architectures and the ”pay-as-you-go” resource pricing model offered by many cloud infrastructure providers may seem the right choice for companies dealing with data centric applications characterized by high variable workload. In such a context, in-memory transactional data grids have demonstrated to be particularly suited for exploiting advantages provided by elastic computing platforms, mainly thanks to their ability to be dynamically (re-)sized and tuned. Anyway, when specific QoS requirements have to be met, this kind of architectures have revealed to be complex to be managed by humans. Particularly, their management is a very complex task without the stand of mechanisms supporting run-time automatic sizing/tuning of the data platform and the underlying (virtual) hardware resources provided by the cloud. In this paper, we present a neural network-based architecture where the system is constantly and automatically re-configured, particularly in terms of computing resources

Brain imaging in Kufs disease type B. case reports

The clinical traits of Kufs disease (KD) type B (CLN13), an adult-onset neuronal ceroid lipofuscinosis (NCL), are well established according to the neurological features of the cases reported with mutations in CTSF. The neuroradiological characteristics of this uncommon disease have not yet been outlined

Structured Knowledge Representation for Image Retrieval

We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete clientserver image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval