Information-theoretic Capacity of Clustered Random Networks

We analyze the capacity scaling laws of clustered ad hoc networks in which nodes are distributed according to a doubly stochastic shot-noise Cox process. We identify five different operational regimes, and for each regime we devise a communication strategy that allows to achieve a throughput to within a poly-logarithmic factor (in the number of nodes) of the maximum theoretical capacity.Comment: 6 pages, in Proceedings of ISIT 201

Belief Dynamics in Social Networks: A Fluid-Based Analysis

The advent and proliferation of social media have led to the development of mathematical models describing the evolution of beliefs/opinions in an ecosystem composed of socially interacting users. The goal is to gain insights into collective dominant social beliefs and into the impact of different components of the system, such as users' interactions, while being able to predict users' opinions. Following this thread, in this paper we consider a fairly general dynamical model of social interactions, which captures all the main features exhibited by a social system. For such model, by embracing a mean-field approach, we derive a diffusion differential equation that represents asymptotic belief dynamics, as the number of users grows large. We then analyze the steady-state behavior as well as the time dependent (transient) behavior of the system. In particular, for the steady-state distribution, we obtain simple closed-form expressions for a relevant class of systems, while we propose efficient semi-analytical techniques in the most general cases. At last, we develop an efficient semi-analytical method to analyze the dynamics of the users' belief over time, which can be applied to a remarkably large class of systems.Comment: submitted to IEEE TNS

Una proposta di web storage mapping per lastre fotografiche in vetro

Il Dipartimento di Scienze Documentarie, Linguistico-Filologiche e Geografiche della Sapienza, Università di Roma, annovera tra il proprio patrimonio documentario un fondo fotografico composto da circa 5.000 lastre in vetro di notevole interesse per la ricerca in campo geografico. Tale materiale, databile tra la fine del 1800 e l’inizio del 1900, è stato raccolto negli anni nell’ambito dell’attività scientifica e didattica dei docenti attivi nell’allora Istituto di Geografia; attualmente, una parte di esso è in fase di catalogazione e valorizzazione poiché si ritiene che possa essere di supporto nella lettura e nella comprensione dei fenomeni spaziali, consentendo una più agevole e accurata analisi geografica relativamente alle dinamiche di utilizzo del territorio, alla fruizione dei beni presenti e alle trasformazioni che le società rappresentate nelle lastre fotografiche hanno attraversato nel tempo.The aim of this paper is to growth up of the consciousness and of the knowledge about the image like further model of analysis and complementary than the word because the analysis’ activity and the photographic sources’ interpretation are a vehicle to study in deep the territorial transformations. Through the analysis of the landscape imprinted on the images it is possible to gain new insights that would otherwise remain invisible. The Department of Documentary Sciences, Linguistics and Philology and Geography of the Sapienza University of Rome has implemented a digital storage process of the image collections preserved in the Library of Geography

Representation of Signals by Local Symmetry Decomposition

In this paper we propose a segmentation of finite support sequences based on the even/odd decomposition of a signal. The objective is to find a more compact representation of information. To this aim, the paper starts to generalize the even/odd decomposition by concentrating the energy on either the even or the odd part by optimally placing the centre of symmetry. Local symmetry intervals are thus located. The sequence segmentation is further processed by applying an iterative growth on the candidate segments to remove any overlapping portions. Experimental results show that the set of segments can be more eficiently compressed with respect to the DCT transformation of the entire sequence, which corresponds to the near optimal KLT transform of the data chosen for the experiment

Even/odd decomposition made sparse: A fingerprint to hidden patterns

The very fundamental operation of even/odd decomposition is at the core of some of the simplest information representation and signal processing tasks. So far most of its use has been for rearranging data to provide fast implementations of various types of transforms (Fourier, DCT, …) or for achieving elementary data transformation, such as the Walsh–Hadamard transform. This work proposes to look into the decomposition framework to obtain a richer perspective. In the context of an iterated even/odd decomposition, it is possible to pinpoint intermediate layered levels of symmetries which cannot be easily captured in the original data. In addition this determines a hierarchical fingerprinting for any sort of continuous finite support analog signal or for any discrete-time sequence which may turn out useful in several recognition or categorization tasks. It also may help to achieve sparsity within a natural hierarchical framework, which could be easily extended for many other types of orthogonal transformations. This paper also suggests a global measure of the energy imbalance across the hierarchy of the decomposition to capture the overall fingerprinting of this interpretation

Image symmetries: The right balance between evenness and perception

A recent and fascinating interest in computational symmetry for computer vision and computer graphics applications has led to a remarkable realization of new symmetry detection algorithms. Such a concern is culminated in a symmetry detection competition as a workshop affiliated with the 2011 and 2013 CVPR Conferences. In this paper, we propose a method based on the computation of the symmetry level associated to each pixel. Such a value is determined through the energy balance of the even/odd decomposition of a patch with respect to a central axis (which is equivalent to estimate the middle point of a row-wise convolution). Peaks localization along the perpendicular direction of each angle allows to identify possible symmetry axes. The evaluation of a feature based on gradient information allows to establish a classification confidence for each detected axis. By adopting the aforementioned rigorous validation framework, the proposed method indicates significant performance increase

Impact load estimation on retention structures with the discrete element method

The design of countermeasures such as barriers and filter dams needs an accurate estimation of the impact load. However, debris flows typically contain poorly sorted grains, whose size can span several orders of magnitude. Large grains can induce impulsive loads on a barrier, and potentially clog the openings designed to induce self-cleaning after an event. The current modeling techniques, mostly based on continuum-based depth-integrated approximations, cannot accurately describe these mechanisms, and analytical approaches often fail to tackle this complexity. In an effort to reproduce a realistic impact load, a sample flow composed of grains is reproduced with a three-dimensional model based on the Discrete Element Method (DEM). The mass impinges upon a barrier with a prescribed velocity. The barrier design is inspired by a monitored dam built on a catchment located in the Italian Alps, which features multiple outlets. The grains can clog the outlets, forming frictional arches. The load pattern on the barrier is analyzed in terms of single-grain impact and of collective behaviors. The impulse transferred by the granular mass to the structure is then used as input for a structural analysis of the barrier through a Finite Element analysis. The results highlight how frictional chains can induce loads that are substantially different from those determined by standard analytical approaches

On reflection symmetry in natural images

Many new symmetry detection algorithms have been recently developed, thanks to an interest revival on computational symmetry for computer graphics and computer vision applications. Notably, in 2013 the IEEE CVPR Conference organized a dedicated workshop and an accompanying symmetry detection competition. In this paper we propose an approach for symmetric object detection that is based both on the computation of a symmetry measure for each pixel and on saliency. The symmetry value is obtained as the energy balance of the even-odd decomposition of a patch w.r.t. each possible axis. The candidate symmetry axes are then identified through the localization of peaks along the direction perpendicular to each considered axis orientation. These found candidate axes are finally evaluated through a confidence measure that also allow removing redundant detected symmetries. The obtained results within the framework adopted in the aforementioned competition show significant performance improvement