708 research outputs found
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 -layer construction
For every physical model defined on a generic graph or factor graph, the
Bethe -layer construction allows building a different model for which the
Bethe approximation is exact in the large limit and it coincides with the
original model for . The 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 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 proprietĂ ottiche ed umiditĂ 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
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