Antiferromagnetic effects in Chaotic Map lattices with a conservation law

Some results about phase separation in coupled map lattices satisfying a conservation law are presented. It is shown that this constraint is the origin of interesting antiferromagnetic effective couplings and allows transitions to antiferromagnetic and superantiferromagnetic phases. Similarities and differences between this models and statistical spin models are pointed out.Comment: 14 pages including 9 figure

A Framework For Abstracting, Designing And Building Tangible Gesture Interactive Systems

This thesis discusses tangible gesture interaction, a novel paradigm for interacting with computer that blends concepts from the more popular fields of tangible interaction and gesture interaction. Taking advantage of the human innate abilities to manipulate physical objects and to communicate through gestures, tangible gesture interaction is particularly interesting for interacting in smart environments, bringing the interaction with computer beyond the screen, back to the real world. Since tangible gesture interaction is a relatively new field of research, this thesis presents a conceptual framework that aims at supporting future work in this field. The Tangible Gesture Interaction Framework provides support on three levels. First, it helps reflecting from a theoretical point of view on the different types of tangible gestures that can be designed, physically, through a taxonomy based on three components (move, hold and touch) and additional attributes, and semantically, through a taxonomy of the semantic constructs that can be used to associate meaning to tangible gestures. Second, it helps conceiving new tangible gesture interactive systems and designing new interactions based on gestures with objects, through dedicated guidelines for tangible gesture definition and common practices for different application domains. Third, it helps building new tangible gesture interactive systems supporting the choice between four different technological approaches (embedded and embodied, wearable, environmental or hybrid) and providing general guidance for the different approaches. As an application of this framework, this thesis presents also seven tangible gesture interactive systems for three different application domains, i.e., interacting with the In-Vehicle Infotainment System (IVIS) of the car, the emotional and interpersonal communication, and the interaction in a smart home. For the first application domain, four different systems that use gestures on the steering wheel as interaction means with the IVIS have been designed, developed and evaluated. For the second application domain, an anthropomorphic lamp able to recognize gestures that humans typically perform for interpersonal communication has been conceived and developed. A second system, based on smart t-shirts, recognizes when two people hug and reward the gesture with an exchange of digital information. Finally, a smart watch for recognizing gestures performed with objects held in the hand in the context of the smart home has been investigated. The analysis of existing systems found in literature and of the system developed during this thesis shows that the framework has a good descriptive and evaluative power. The applications developed during this thesis show that the proposed framework has also a good generative power.Questa tesi discute l’interazione gestuale tangibile, un nuovo paradigma per interagire con il computer che unisce i principi dei più comuni campi di studio dell’interazione tangibile e dell’interazione gestuale. Sfruttando le abilità innate dell’uomo di manipolare oggetti fisici e di comunicare con i gesti, l’interazione gestuale tangibile si rivela particolarmente interessante per interagire negli ambienti intelligenti, riportando l’attenzione sul nostro mondo reale, al di là dello schermo dei computer o degli smartphone. Poiché l’interazione gestuale tangibile è un campo di studio relativamente recente, questa tesi presenta un framework (quadro teorico) che ha lo scopo di assistere lavori futuri in questo campo. Il Framework per l’Interazione Gestuale Tangibile fornisce supporto su tre livelli. Per prima cosa, aiuta a riflettere da un punto di vista teorico sui diversi tipi di gesti tangibili che possono essere eseguiti fisicamente, grazie a una tassonomia basata su tre componenti (muovere, tenere, toccare) e attributi addizionali, e che possono essere concepiti semanticamente, grazie a una tassonomia di tutti i costrutti semantici che permettono di associare dei significati ai gesti tangibili. In secondo luogo, il framework proposto aiuta a concepire nuovi sistemi interattivi basati su gesti tangibili e a ideare nuove interazioni basate su gesti con gli oggetti, attraverso linee guida per la definizione di gesti tangibili e una selezione delle migliore pratiche per i differenti campi di applicazione. Infine, il framework aiuta a implementare nuovi sistemi interattivi basati su gesti tangibili, permettendo di scegliere tra quattro differenti approcci tecnologici (incarnato e integrato negli oggetti, indossabile, distribuito nell’ambiente, o ibrido) e fornendo una guida generale per la scelta tra questi differenti approcci. Come applicazione di questo framework, questa tesi presenta anche sette sistemi interattivi basati su gesti tangibili, realizzati per tre differenti campi di applicazione: l’interazione con i sistemi di infotainment degli autoveicoli, la comunicazione interpersonale delle emozioni, e l’interazione nella casa intelligente. Per il primo campo di applicazione, sono stati progettati, sviluppati e testati quattro differenti sistemi che usano gesti tangibili effettuati sul volante come modalità di interazione con il sistema di infotainment. Per il secondo campo di applicazione, è stata concepita e sviluppata una lampada antropomorfica in grado di riconoscere i gesti tipici dell’interazione interpersonale. Per lo stesso campo di applicazione, un secondo sistema, basato su una maglietta intelligente, riconosce quando due persone si abbracciano e ricompensa questo gesto con uno scambio di informazioni digitali. Infine, per l’interazione nella casa intelligente, è stata investigata la realizzazione di uno smart watch per il riconoscimento di gesti eseguiti con oggetti tenuti nella mano. L’analisi dei sistemi interattivi esistenti basati su gesti tangibili permette di dimostrare che il framework ha un buon potere descrittivo e valutativo. Le applicazioni sviluppate durante la tesi mostrano che il framework proposto ha anche un valido potere generativo

Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI

Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficult to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state fMRI data from the Human Connectome Project, showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, whilst synergy occurs mainly between non-homologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits.Comment: 6 figures. arXiv admin note: text overlap with arXiv:1403.515

Information transfer of an Ising model on a brain network

We implement the Ising model on a structural connectivity matrix describing the brain at a coarse scale. Tuning the model temperature to its critical value, i.e. at the susceptibility peak, we find a maximal amount of total information transfer between the spin variables. At this point the amount of information that can be redistributed by some nodes reaches a limit and the net dynamics exhibits signature of the law of diminishing marginal returns, a fundamental principle connected to saturated levels of production. Our results extend the recent analysis of dynamical oscillators models on the connectome structure, taking into account lagged and directional influences, focusing only on the nodes that are more prone to became bottlenecks of information. The ratio between the outgoing and the incoming information at each node is related to the number of incoming links

Consensus clustering approach to group brain connectivity matrices

A novel approach rooted on the notion of consensus clustering, a strategy developed for community detection in complex networks, is proposed to cope with the heterogeneity that characterizes connectivity matrices in health and disease. The method can be summarized as follows: (i) define, for each node, a distance matrix for the set of subjects by comparing the connectivity pattern of that node in all pairs of subjects; (ii) cluster the distance matrix for each node; (iii) build the consensus network from the corresponding partitions; (iv) extract groups of subjects by finding the communities of the consensus network thus obtained. Differently from the previous implementations of consensus clustering, we thus propose to use the consensus strategy to combine the information arising from the connectivity patterns of each node. The proposed approach may be seen either as an exploratory technique or as an unsupervised pre-training step to help the subsequent construction of a supervised classifier. Applications on a toy model and two real data sets, show the effectiveness of the proposed methodology, which represents heterogeneity of a set of subjects in terms of a weighted network, the consensus matrix

Synergy as a warning sign of transitions: the case of the two-dimensional Ising model

We consider the formalism of information decomposition of target effects from multi-source interactions, i.e. the problem of defining redundant and synergistic components of the information that a set of source variables provides about a target, and apply it to the two-dimensional Ising model as a paradigm of a critically transitioning system. Intuitively, synergy is the information about the target variable that is uniquely obtained taking the sources together, but not considering them alone; redundancy is the information which is shared by the sources. To disentangle the components of the information both at the static level and at the dynamical one, the decomposition is applied respectively to the mutual information and to the transfer entropy between a given spin, the target, and a pair of neighbouring spins (taken as the drivers). We show that a key signature of an impending phase transition (approached from the disordered size) is the fact that the synergy peaks in the disordered phase, both in the static and in the dynamic case: the synergy can thus be considered a precursor of the transition. The redundancy, instead, reaches its maximum at the critical temperature. The peak of the synergy of the transfer entropy is far more pronounced than those of the static mutual information. We show that these results are robust w.r.t. the details of the information decomposition approach, as we find the same results using two different methods; moreover, w.r.t. previous literature rooted on the notion of Global Transfer Entropy, our results demonstrate that considering as few as three variables is sufficient to construct a precursor of the transition, and provide a paradigm for the investigation of a variety of systems prone to crisis, like financial markets, social media, or epileptic seizures

An Impedimetric Biosensing Strategy Based on Bicyclic Peptides as Bioreceptors for Monitoring huPA Cancer Biomarker

In the era of liquid biopsies, the reliable and cost-effective detection and screening of cancer biomarkers has become of fundamental importance, thus paving the way for the advancement of research in the field of point-of-care testing and the development of new methodologies and technologies. Indeed, the latter ones can help designing advanced diagnostic tools that can offer portability, ease of use with affordable production and operating costs. In this respect, impedance-based biosensing platforms might represent an attractive alternative. In this work, we describe a proof-of-concept study aimed at designing portable impedimetric biosensors for the monitoring of human urokinase-type plasminogen activator (h-uPA) cancer biomarker by employing small synthetic receptors. Aberrant levels of h-uPA were correlated with different types of cancers. Herein, we report the use of two bicyclic peptides (P2 and P3) which have been engineered to bind h-uPA with high affinity and exquisite specificity. The synthetic receptors were immobilized via biotin-streptavidin chemistry on the surface of commercial screen-printed electrodes. The impedimetric changes in the electrode/solution interface upon incubation of spiked h-uPA samples in the presence of a redox probe were followed via electrochemical impedance spectroscopy. The P3-based impedimetric assay showed the best outcomes in terms of dynamic range and linearity (0.01–1 μg mL−1) and sensitivity (LOD = 9 ng mL−1). To fully assess the performances of P3 over P2, and to compare the label-free architecture vs. labelled architecture, a voltammetric assay was also developed

Information Flow in Networks and the Law of Diminishing Marginal Returns: Evidence from Modeling and Human Electroencephalographic Recordings

We analyze simple dynamical network models which describe the limited capacity of nodes to process the input information. For a proper range of their parameters, the information flow pattern in these models is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. We apply this analysis to effective connectivity networks from human EEG signals, obtained by Granger Causality, which has recently been given an interpretation in the framework of information theory. From the distributions of the incoming versus the outgoing values of the information flow it is evident that the incoming information is exponentially distributed whilst the outgoing information shows a fat tail. This suggests that overall brain effective connectivity networks may also be considered in the light of the law of diminishing marginal returns. Interestingly, this pattern is reproduced locally but with a clear modulation: a topographic analysis has also been made considering the distribution of incoming and outgoing values at each electrode, suggesting a functional role for this phenomenon