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When Neuroscience ‘Touches’ Architecture: From Hapticity to a Supramodal Functioning of the Human Brain
No full textIn the last decades, the rapid growth of functional brain imaging methodologies allowed cognitive neuroscience to address open questions in philosophy and social sciences. At the same time, novel insights from cognitive neuroscience research have begun to influence various disciplines, leading to a turn to cognition and emotion in the fields of planning and architectural design. Since 2003, the Academy of Neuroscience for Architecture has been supporting ‘neuro-architecture’ as a way to connect neuroscience and the study of behavioral responses to the built environment. Among the many topics related to multisensory perceptual integration and embodiment, the concept of hapticity was recently introduced, suggesting a pivotal role of tactile perception and haptic imagery in architectural appraisal. Arguments have thus risen in favor of the existence of shared cognitive foundations between hapticity and the supramodal functional architecture of the human brain. Precisely, supramodality refers to the functional feature of defined brain regions to process and represent specific information content in a more abstract way, independently of the sensory modality conveying such information to the brain. Here, we highlight some commonalities and differences between the concepts of hapticity and supramodality according to the distinctive perspectives of architecture and cognitive neuroscience. This comparison and connection between these two different approaches may lead to novel observations in regard to people–environment relationships, and even provide empirical foundations for a renewed evidence-based design theory
Mechanical properties of Graphene: Molecular dynamics simulations correlated to continuum based scaling laws
No full textIn this paper, the combined effect of domain size, lattice orientation and crack length on the mechanical properties of Graphene, namely the yield strength and strain, are studied extensively based on molecular dynamics simulations. Numerical predictions are compared with the continuum-based laws of size effect and multifractal scaling. The yield strength is found to vary with the specimen size as ≈L^{−1/3}, which is in agreement with the multifractal scaling law, and with the inverse square of the initial crack length as ≈a^{-1/2}, according to the Griffith’s energy criterion for fracture
La PMI a conduzione familiare di fronte agli accordi di Basilea
No full textLa PMI a conduzIone familiare e il sistema di ring bancario per la connessione di credito: impatto sulle competenze aziendali e implicazioni sui processi di comunicazione
Polarized User and Topic Tracking in Twitter
No full textDigital traces of conversations in micro-blogging platforms and OSNs provide information about user opinion with a high degree of resolution. These information sources can be exploited to understand and monitor collective behaviours. In this work, we focus on polarisation classes, i.e., those topics that require the user to side exclusively with one position. The proposed method provides an iterative classification of users and keywords: first, polarised users are identified, then polarised keywords are discovered by monitoring the activities of previously classified users. This method thus allows tracking users and topics over time. We report several experiments conducted on two Twitter datasets during political election time-frames. We measure the user classification accuracy on a golden set of users, and analyse the relevance of the extracted keywords for the ongoing political discussion
Data Science and Complex Networks. Real Case Studies with Python
No full textThis book provides a comprehensive yet short description of the basic concepts of Complex Network theory. In contrast to other books the authors present these concepts through real case studies. The application topics span from Foodwebs, to the Internet, the World Wide Web and the Social Networks, passing through the International Trade Web and Financial time series. The final part is devoted to definition and implementation of the most important network models.
The text provides information on the structure of the data and on the quality of available datasets. Furthermore it provides a series of codes to allow immediate implementation of what is theoretically described in the book. Readers already used to the concepts introduced in this book can learn the art of coding in Python by using the online material. To this purpose the authors have set up a dedicated web site where readers can download and test the codes. The whole project is aimed as a learning tool for scientists and practitioners, enabling them to begin working instantly in the field of Complex Networks
Statistically validated network of portfolio overlaps and systemic risk
No full textCommon asset holding by financial institutions, namely portfolio overlap, is nowadays regarded as an important channel for financial contagion with the potential to trigger fire sales and thus severe losses at the systemic level. In this paper we propose a method to assess the statistical significance of the overlap between pairs of heterogeneously diversified portfolios, which then allows us to build a validated network of financial institutions where links indicate potential contagion channels due to realized portfolio overlaps. The method is implemented on a historical database of institutional holdings ranging from 1999 to the end of 2013, but can be in general applied to any bipartite network where the presence of similar sets of neighbors is of interest. We find that the proportion of validated network links (i.e., of statistically significant overlaps) increased steadily before the 2007-2008 global financial crisis and reached a maximum when the crisis occurred. We argue that the nature of this measure implies that systemic risk from fire sales liquidation was maximal at that time. After a sharp drop in 2008, systemic risk resumed its growth in 2009, with a notable acceleration in 2013, reaching levels not seen since 2007. We finally show that market trends tend to be amplified in the portfolios identified by the algorithm, such that it is possible to have an informative signal about financial institutions that are about to suffer (enjoy) the most significant losses (gains)
Users Polarization on Facebook and Youtube
No full textOn social media algorithms for content promotion, accounting for users preferences, might limit the exposure to unsolicited contents. In this work, we study how the same contents (videos) are consumed on different platforms -- i.e. Facebook and YouTube -- over a sample of 12M of users. Our findings show that the same content lead to the formation of echo chambers, irrespective of the online social network and thus of the algorithm for content promotion. Finally, we show that the users' commenting patterns are accurate early predictors for the formation of echo-chambers
A Lyapunov method for stability analysis of piecewise-affine systems over non-invariant domains
No full textThis paper analyses stability of discrete-time piecewise-affine systems, defined on possibly non-invariant domains, taking into account the possible presence of multiple dynamics in each of the polytopic regions of the system. An algorithm based on linear programming is proposed, in order to prove exponential stability of the origin and to find a positively invariant estimate of its region of attraction. The results are based on the definition of a piecewise-affine Lyapunov function, which is in general discontinuous on the boundaries of the regions. The proposed method is proven to lead to feasible solutions in a broader range of cases as compared to a previously proposed approach. Two numerical examples are shown, among which a case where the proposed method is applied to a closed-loop system, to which model predictive control was applied without a-priori guarantee of stability
Optimal design of low-frequency band gaps in anti-tetrachiral lattice meta-materials
No full textThe elastic wave propagation is investigated in a beam lattice material characterized by a square periodic cell with anti-tetrachiral microstructure. With reference to the Floquet-Bloch spectrum, focus is made on the band structure enrichments and modifications which can be achieved by equipping the cellular microstructure with tunable local resonators. By virtue of its composite mechanical nature, the so-built inertial meta-material gains enhanced capacities of passive frequency-band filtering. Indeed the number, placement and properties of the inertial resonators can be designed to open, shift and enlarge the band gaps between one or more pairs of consecutive branches in the frequency spectrum. In order to improve the meta-material performance, several nonlinear optimization problems are formulated. The largest among the band gap amplitudes in the low-frequency range is selected as suited objective function. Proper inequality constraints are introduced to restrict the admissible solutions within a compact set of mechanical and geometric parameters, including only physically realistic properties of both the lattice and the resonators. The optimization problems related to full and partial band gaps are solved by using a globally convergent version of the numerical method of moving asymptotes, combined with a quasi-Monte Carlo multi-start technique. The optimal solutions are numerically computed, discussed and compared from the qualitative and quantitative viewpoints, bringing to light the limits and potential of the meta-material performance. The clearest trends emerging from the numerical analyses are pointed out and interpreted from the physical viewpoint. Finally, some specific recommendations about the microstructural design of the meta-material are synthesized