1,178 research outputs found
Motion Invariance in Visual Environments
The puzzle of computer vision might find new challenging solutions when we
realize that most successful methods are working at image level, which is
remarkably more difficult than processing directly visual streams, just as
happens in nature. In this paper, we claim that their processing naturally
leads to formulate the motion invariance principle, which enables the
construction of a new theory of visual learning based on convolutional
features. The theory addresses a number of intriguing questions that arise in
natural vision, and offers a well-posed computational scheme for the discovery
of convolutional filters over the retina. They are driven by the Euler-Lagrange
differential equations derived from the principle of least cognitive action,
that parallels laws of mechanics. Unlike traditional convolutional networks,
which need massive supervision, the proposed theory offers a truly new scenario
in which feature learning takes place by unsupervised processing of video
signals. An experimental report of the theory is presented where we show that
features extracted under motion invariance yield an improvement that can be
assessed by measuring information-based indexes.Comment: arXiv admin note: substantial text overlap with arXiv:1801.0711
Distributed workload control for federated service discovery
The diffusion of the internet paradigm in each aspect of human life continuously fosters the widespread of new technologies and related services. In the Future Internet scenario, where 5G telecommunication facilities will interact with the internet of things world, analyzing in real time big amounts of data to feed a potential infinite set of services belonging to different administrative domains, the role of a federated service discovery will become crucial. In this paper the authors propose a distributed workload control algorithm to handle efficiently the service discovery requests, with the aim of minimizing the overall latencies experienced by the requesting user agents. The authors propose an algorithm based on the Wardrop equilibrium, which is a gametheoretical concept, applied to the federated service discovery domain. The proposed solution has been implemented and its performance has been assessed adopting different network topologies and metrics. An open source simulation environment has been created allowing other researchers to test the proposed solution
Implications for Electric Dipole Moments of a Leptoquark Scenario for the -Physics Anomalies
Vector leptoquarks can address the lepton flavor universality anomalies in
decays associated with the and
transitions, as observed in recent years. Generically, these leptoquarks yield
new sources of CP violation. In this paper, we explore constraints and
discovery potential for electric dipole moments (EDMs) in leptonic and hadronic
systems. We provide the most generic expressions for dipole moments induced by
vector leptoquarks at one loop. We find that CP-violating phases in tau
and muon couplings can lead to corresponding EDMs within reach of
next-generation EDM experiments, and that existing bounds on the electron EDM
already put stringent constraints on CP-violating electron couplings
Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach
Human visual attention is a complex phenomenon. A computational modeling of
this phenomenon must take into account where people look in order to evaluate
which are the salient locations (spatial distribution of the fixations), when
they look in those locations to understand the temporal development of the
exploration (temporal order of the fixations), and how they move from one
location to another with respect to the dynamics of the scene and the mechanics
of the eyes (dynamics). State-of-the-art models focus on learning saliency maps
from human data, a process that only takes into account the spatial component
of the phenomenon and ignore its temporal and dynamical counterparts. In this
work we focus on the evaluation methodology of models of human visual
attention. We underline the limits of the current metrics for saliency
prediction and scanpath similarity, and we introduce a statistical measure for
the evaluation of the dynamics of the simulated eye movements. While deep
learning models achieve astonishing performance in saliency prediction, our
analysis shows their limitations in capturing the dynamics of the process. We
find that unsupervised gravitational models, despite of their simplicity,
outperform all competitors. Finally, exploiting a crowd-sourcing platform, we
present a study aimed at evaluating how strongly the scanpaths generated with
the unsupervised gravitational models appear plausible to naive and expert
human observers
The impact of land use characteristics for sustainable mobility: the case study of Rome
Sustainable mobility requires actions to reduce the need for travel, to promote modal shift, to reduce trip lengths and to increase efficiency of transport system. Public transport could play an important role to solve part of the needs previously reported. Starting from these remarks, the present paper analyse the role, the importance and the impact of land use characteristics to develop services able to compete with automobile use. This analysis is carried out by studying the real world case of the city of Rome in Italy. The results of the test carried out highlight the importance of density of residences and activities, the need for a good quality access system to the transit services stops and the importance of the configuration of the transit network, identifying the best way to connect the different districts of the urban area. However, single actions are not sufficient to achieve a sustainable transport system: these actions can be successful only if they are planned in a complex unique system that helps the synergic development of the effects of the single actions proposed
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