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

Large-N CP(N-1) sigma model on a finite interval and the renormalized string energy

We continue the analysis started in a recent paper of the large-N two-dimensional CP(N-1) sigma model, defined on a finite space interval L with Dirichlet (or Neumann) boundary conditions. Here we focus our attention on the problem of the renormalized energy density $\mathcal{E}(x,\Lambda,L)$ which is found to be a sum of two terms, a constant term coming from the sum over modes, and a term proportional to the mass gap. The approach to $\mathcal{E}(x,\Lambda,L)\to\tfrac{N}{4\pi}\Lambda^2$ at large $L\Lambda$ is shown, both analytically and numerically, to be exponential: no power corrections are present and in particular no L\"uscher term appears. This is consistent with the earlier result which states that the system has a unique massive phase, which interpolates smoothly between the classical weakly-coupled limit for $L\Lambda\to 0$ and the "confined" phase of the standard CP(N-1) model in two dimensions for $L\Lambda\to\infty$.Comment: LaTeX: 32 pages, 11 figures; V2: appendices E and F added and typos corrected; V3: grant information adde

CBE Clima Tool: a free and open-source web application for climate analysis tailored to sustainable building design

Buildings that are designed specifically to respond to the local climate can be more comfortable, energy-efficient, and with a lower environmental impact. However, there are many social, cultural, and economic obstacles that might prevent the wide adoption of designing climate-adapted buildings. One of the said obstacles can be removed by enabling practitioners to easily access and analyse local climate data. The CBE Clima Tool (Clima) is a free and open-source web application that offers easy access to publicly available weather files (in EPW format) specifically created for building energy simulation and design. It provides a series of interactive visualization of the variables therein contained and several derived ones. It is aimed at students, educators, and practitioners in the architecture and engineering fields. Since its launch has been consistently recording over 3000 monthly unique users from over 70 countries worldwide, both in professional and educational settings.Comment: Submitted to Software

User indoor localisation system enhances activity recognition: A proof of concept

Older people would like to live independently in their home as long as possible. They want to reduce the risk of domestic accidents because of polypharmacy, physical weakness and other mental illnesses, which could increase the risks of domestic accidents (i.e. a fall). Changes in the behaviour of healthy older people could be correlated with cognitive disorders; consequently, early intervention could delay the deterioration of the disease. Over the last few years, activity recognition systems have been developed to support the management of senior citizensâ\u80\u99 daily life. In this context, this paper aims to go beyond the state-of-the-art presenting a proof of concept where information on body movement, vital signs and userâ\u80\u99s indoor locations are aggregated to improve the activity recognition task. The presented system has been tested in a realistic environment with three users in order to assess the feasibility of the proposed method. These results encouraged the use of this approach in activity recognition applications; indeed, the overall accuracy values, amongst others, are satisfactory increased (+2.67% DT, +7.39% SVM, +147.37% NN)

Wave Propagation of Visual Stimuli in Focus of Attention

Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to most relevant locations in the visual field. While current computational models keep improving their predictive ability thanks to the increasing availability of data, they still struggle approximating the effectiveness and efficiency exhibited by foveated animals. In this paper, we present a biologically-plausible computational model of focus of attention that exhibits spatiotemporal locality and that is very well-suited for parallel and distributed implementations. Attention emerges as a wave propagation process originated by visual stimuli corresponding to details and motion information. The resulting field obeys the principle of "inhibition of return" so as not to get stuck in potential holes. An accurate experimentation of the model shows that it achieves top level performance in scanpath prediction tasks. This can easily be understood at the light of a theoretical result that we establish in the paper, where we prove that as the velocity of wave propagation goes to infinity, the proposed model reduces to recently proposed state of the art gravitational models of focus of attention

Large and Dense Swarms: Simulation of a Shortest Path Alarm Propagation

This paper deals with the transmission of alarm messages in large and dense underwater swarms of Autonomous Underwater Vehicles (AUVs) and describes the verification process of the derived algorithm results by means of two simulation tools realized by the authors. A collision-free communication protocol has been developed, tailored to a case where a single AUV needs to send a message to a specific subset of swarm members regarding a perceived danger. The protocol includes a handshaking procedure that creates a silence region before the transmission of the message obtained through specific acoustic tones out of the normal transmission frequencies or through optical signals. This region will include all members of the swarm involved in the alarm message and their neighbours, preventing collisions between them. The AUV sending messages to a target area computes a delay function on appropriate arcs and runs a Dijkstra-like algorithm obtaining a multicast tree. After an explanation of the whole building of this collision-free multicast tree, a simulation has been carried out assuming different scenarios relevant to swarm density, signal power of the modem and the geometrical configuration of the nodes

Focus of Attention Improves Information Transfer in Visual Features

Unsupervised learning from continuous visual streams is a challenging problem that cannot be naturally and efficiently managed in the classic batch-mode setting of computation. The information stream must be carefully processed accordingly to an appropriate spatio-temporal distribution of the visual data, while most approaches of learning commonly assume uniform probability density. In this paper we focus on unsupervised learning for transferring visual information in a truly online setting by using a computational model that is inspired to the principle of least action in physics. The maximization of the mutual information is carried out by a temporal process which yields online estimation of the entropy terms. The model, which is based on second-order differential equations, maximizes the information transfer from the input to a discrete space of symbols related to the visual features of the input, whose computation is supported by hidden neurons. In order to better structure the input probability distribution, we use a human-like focus of attention model that, coherently with the information maximization model, is also based on second-order differential equations. We provide experimental results to support the theory by showing that the spatio-temporal filtering induced by the focus of attention allows the system to globally transfer more information from the input stream over the focused areas and, in some contexts, over the whole frames with respect to the unfiltered case that yields uniform probability distributions