Monte Carlo algorithms are very effective in finding the largest independent set in sparse random graphs

The effectiveness of stochastic algorithms based on Monte Carlo dynamics in solving hard optimization problems is mostly unknown. Beyond the basic statement that at a dynamical phase transition the ergodicity breaks and a Monte Carlo dynamics cannot sample correctly the probability distribution in times linear in the system size, there are almost no predictions nor intuitions on the behavior of this class of stochastic dynamics. The situation is particularly intricate because, when using a Monte Carlo based algorithm as an optimization algorithm, one is usually interested in the out of equilibrium behavior which is very hard to analyse. Here we focus on the use of Parallel Tempering in the search for the largest independent set in a sparse random graph, showing that it can find solutions well beyond the dynamical threshold. Comparison with state-of-the-art message passing algorithms reveals that parallel tempering is definitely the algorithm performing best, although a theory explaining its behavior is still lacking.Comment: 14 pages, 12 figure

One-loop topological expansion for spin glasses in the large connectivity limit

We apply for the first time a new one-loop topological expansion around the Bethe solution to the spin-glass model with field in the high connectivity limit, following the methodological scheme proposed in a recent work. The results are completely equivalent to the well known ones, found by standard field theoretical expansion around the fully connected model (Bray and Roberts 1980, and following works). However this method has the advantage that the starting point is the original Hamiltonian of the model, with no need to define an associated field theory, nor to know the initial values of the couplings, and the computations have a clear and simple physical meaning. Moreover this new method can also be applied in the case of zero temperature, when the Bethe model has a transition in field, contrary to the fully connected model that is always in the spin glass phase. Sharing with finite dimensional model the finite connectivity properties, the Bethe lattice is clearly a better starting point for an expansion with respect to the fully connected model. The present work is a first step towards the generalization of this new expansion to more difficult and interesting cases as the zero-temperature limit, where the expansion could lead to different results with respect to the standard one.Comment: 8 pages, 1 figur

Ensemble renormalization group for disordered systems

We propose and study a renormalization group transformation that can be used also for models with strong quenched disorder, like spin glasses. The method is based on a mapping between disorder distributions, chosen such as to keep some physical properties (e.g., the ratio of correlations averaged over the ensemble) invariant under the transformation. We validate this ensemble renormalization group by applying it to the hierarchical model (both the diluted ferromagnetic version and the spin glass version), finding results in agreement with Monte Carlo simulations.Comment: 7 pages, 10 figure

Optimization of laser wavelength, power and pulse duration for eye-safe Raman spectroscopy

Abstract Raising the interest in remote chemical analysis, in particular through Raman and fluorescence spectroscopy, the opportunity of increasing the exposure represents an important step for an easier and more reliable spectrum analysis. However, the European directive 2006/25/EC defines the maximum permitted exposure (MPE) to artificial radiations according to exposure duration, wavelength, coherence of the radiation and beam divergence. Though the Raman cross section scales in general according to the fourth power of the excitation wavelength, promoting the use of deep UV radiation, a synergy between wavelength and exposure time can raise the Raman signal in the near UV or in the near IR if compliance to eye-safety directives is requested. In this work we will analyze the possibilities offered by commercially available components for enhancing the Raman scattering under eye-safe conditions

R&D Subsidization effect and network centralization. Evidence from an agent-based micro-policy simulation

This paper presents an agent-based micro-policy simulation model assessing public R&D policy effect when R&D and non-R&D performing companies are located within a network. We set out by illustrating the behavioural structure and the computational logic of the proposed model; then, we provide a simulation experiment where the pattern of the total level of R&D activated by a fixed amount of public support is analysed as function of companies’ network topology. More specifically, the suggested simulation experiment shows that a larger “hubness” of the network is more likely accompanied with a decreasing median of the aggregated total R&D performance of the system. Since the aggregated firm idiosyncratic R&D (i.e., the part of total R&D independent of spillovers) is slightly increasing, we conclude that positive cross-firm spillover effects - in the presence of a given amount of support - have a sizeable impact within less centralized networks, where fewer hubs emerge. This may question the common wisdom suggesting that larger R&D externality effects should be more likely to arise when few central champions receive a support

Loop expansion around the Bethe approximation through the MM-layer construction

For every physical model defined on a generic graph or factor graph, the Bethe $M$-layer construction allows building a different model for which the Bethe approximation is exact in the large $M$ limit and it coincides with the original model for $M=1$. The $1/M$ perturbative series is then expressed by a diagrammatic loop expansion in terms of so-called fat-diagrams. Our motivation is to study some important second-order phase transitions that do exist on the Bethe lattice but are either qualitatively different or absent in the corresponding fully connected case. In this case the standard approach based on a perturbative expansion around the naive mean field theory (essentially a fully connected model) fails. On physical grounds, we expect that when the construction is applied to a lattice in finite dimension there is a small region of the external parameters close to the Bethe critical point where strong deviations from mean-field behavior will be observed. In this region, the $1/M$ expansion for the corrections diverges and it can be the starting point for determining the correct non-mean-field critical exponents using renormalization group arguments. In the end, we will show that the critical series for the generic observable can be expressed as a sum of Feynman diagrams with the same numerical prefactors of field theories. However, the contribution of a given diagram is not evaluated associating Gaussian propagators to its lines as in field theories: one has to consider the graph as a portion of the original lattice, replacing the internal lines with appropriate one-dimensional chains, and attaching to the internal points the appropriate number of infinite-size Bethe trees to restore the correct local connectivity of the original model

Aerosol nello strato limite planetario: relazione tra proprietà ottiche ed umidità relativa

Lo studio degli aerosol da Terra con l’utilizzo simultaneo di più tecniche di telerilevamento – un lidar Rayleigh, un lidar Raman ed un radiometro a microonde – ha permesso di caratterizzare l’accrescimento igroscopico di aerosol in differenti condizioni meteorologiche. L’accrescimento igroscopico degli aerosol è ritenuto responsabile di variazioni dell’albedo planetaria e pertanto importante come forzante radiativo per il pianeta. Misurando contemporaneamente l’umidità relativa atmosferica ed il coefficiente di retrodiffusione rispettivamente con un lidar Raman e con un lidar Rayleigh è stato possibile mettere in relazione la sezione d’urto aerosolica con l’umidità relativa, secondo l’andamento proposto da Kasten (1969). Sotto differenti condizioni meteorologiche sono stati rilevati comportamenti diversi a seconda della provenienza delle masse d’aria osservate, ed è stato estrapolato il valore dell’esponente della funzione di Kasten per le diverse tipologie di aerosol studiate

Novel methods for posture-based human action recognition and activity anomaly detection

PhD ThesisArti cial Intelligence (AI) for Human Action Recognition (HAR) and Human Activity Anomaly Detection (HAAD) is an active and exciting research eld. Video-based HAR aims to classify human actions and video-based HAAD aims to detect abnormal human activities within data. However, a human is an extremely complex subject and a non-rigid object in the video, which provides great challenges for Computer Vision and Signal Processing. Relevant applications elds are surveillance and public monitoring, assisted living, robotics, human-to-robot interaction, prosthetics, gaming, video captioning, and sports analysis. The focus of this thesis is on the posture-related HAR and HAAD. The aim is to design computationally-e cient, machine and deep learning-based HAR and HAAD methods which can run in multiple humans monitoring scenarios. This thesis rstly contributes two novel 3D Histogram of Oriented Gradient (3D-HOG) driven frameworks for silhouette-based HAR. The 3D-HOG state-of-the-art limitations, e.g. unweighted local body areas based processing and unstable performance over di erent training rounds, are addressed. The proposed methods achieve more accurate results than the baseline, outperforming the state-of-the-art. Experiments are conducted on publicly available datasets, alongside newly recorded data. This thesis also contributes a new algorithm for human poses-based HAR. In particular, the proposed human poses-based HAR is among the rst, few, simultaneous attempts which have been conducted at the time. The proposed HAR algorithm, named ActionXPose, is based on Convolutional Neural Networks and Long Short-Term Memory. It turns out to be more reliable and computationally advantageous when compared to human silhouette-based approaches. The ActionXPose's exibility also allows crossdatasets processing and more robustness to occlusions scenarios. Extensive evaluation on publicly available datasets demonstrates the e cacy of ActionXPose over the state-of-the-art. Moreover, newly recorded data, i.e. Intelligent Sensing Lab Dataset (ISLD), is also contributed and exploited to further test ActionXPose in real-world, non-cooperative scenarios. The last set of contributions in this thesis regards pose-driven, combined HAR and HAAD algorithms. Motivated by ActionXPose achievements, this thesis contributes a new algorithm to simultaneously extract deep-learningbased features from human-poses, RGB Region of Interests (ROIs) and detected objects positions. The proposed method outperforms the stateof- the-art in both HAR and HAAD. The HAR performance is extensively tested on publicly available datasets, including the contributed ISLD dataset. Moreover, to compensate for the lack of data in the eld, this thesis also contributes three new datasets for human-posture and objects-positions related HAAD, i.e. BMbD, M-BMdD and JBMOPbD datasets