Real-time self-adaptive deep stereo

Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs. These models, however, suffer from a notable decrease in accuracy when exposed to scenarios significantly different from the training set, e.g., real vs synthetic images, etc.). We argue that it is extremely unlikely to gather enough samples to achieve effective training/tuning in any target domain, thus making this setup impractical for many applications. Instead, we propose to perform unsupervised and continuous online adaptation of a deep stereo network, which allows for preserving its accuracy in any environment. However, this strategy is extremely computationally demanding and thus prevents real-time inference. We address this issue introducing a new lightweight, yet effective, deep stereo architecture, Modularly ADaptive Network (MADNet) and developing a Modular ADaptation (MAD) algorithm, which independently trains sub-portions of the network. By deploying MADNet together with MAD we introduce the first real-time self-adaptive deep stereo system enabling competitive performance on heterogeneous datasets.Comment: Accepted at CVPR2019 as oral presentation. Code Available https://github.com/CVLAB-Unibo/Real-time-self-adaptive-deep-stere

Where are your Manners? Sharing Best Community Practices in the Web 2.0

The Web 2.0 fosters the creation of communities by offering users a wide array of social software tools. While the success of these tools is based on their ability to support different interaction patterns among users by imposing as few limitations as possible, the communities they support are not free of rules (just think about the posting rules in a community forum or the editing rules in a thematic wiki). In this paper we propose a framework for the sharing of best community practices in the form of a (potentially rule-based) annotation layer that can be integrated with existing Web 2.0 community tools (with specific focus on wikis). This solution is characterized by minimal intrusiveness and plays nicely within the open spirit of the Web 2.0 by providing users with behavioral hints rather than by enforcing the strict adherence to a set of rules.Comment: ACM symposium on Applied Computing, Honolulu : \'Etats-Unis d'Am\'erique (2009

Neuronal modulation in the prefrontal cortex in a transitive inference task: evidence of neuronal correlates of mental schema management

When informed that A > B and B > C, humans and other animals can easily conclude that A > C. This remarkable trait of advanced animals, which allows them to manipulate knowledge flexibly to infer logical relations, has only recently garnered interest in mainstream neuroscience. How the brain controls these logical processes remains an unanswered question that has been merely superficially addressed in neuroimaging and lesion studies, which are unable to identify the underlying neuronal computations. We observed that the activation pattern of neurons in the prefrontal cortex (PFC) during pair comparisons in a highly demanding transitive inference task fully supports the behavioral performance of the two monkeys that we tested. Our results indicate that the PFC contributes to the construction and use of a mental schema to represent premises. This evidence provides a novel framework for understanding the function of various areas of brain in logic processes and impairments to them in degenerative, traumatic, and psychiatric pathologies. SIGNIFICANCE STATEMENT: In cognitive neuroscience, it is unknown how information that leads to inferential deductions are encoded and manipulated at the neuronal level. We addressed this question by recording single-unit activity from the dorsolateral prefrontal cortex of monkeys that were performing a transitive inference (TI) task. The TI required one to choose the higher ranked of two items, based on previous, indirect experience. Our results demonstrated that single-neuron activity supports the construction of an abstract, mental schema of ordered items in solving the task and that this representation is independent of the reward value that is experienced for the single items. These findings identify the neural substrates of abstract mental representations that support inferential thinking

Optimal Fiscal Policy in a Simple Macroeconomic Context

This article derives optimal fiscal rules within a stochastic model of Keynesian type in the context of Poole (1970) analysis. By using optimal control theory and applying the Hamilton-Jacoby-Bellman equation, we extend the original Poole results concerning the output stabilization properties of monetary policy to the case of fiscal policy. In particular, we look for the optimal setting of government expenditure and lump-sum taxation in the case that the fiscal authority wishes to keep the product close to a reference value and that the economy is assumed to be affected by stochastic disturbances of real and/or monetary type. According to the findings an optimal government expenditure rule is on average preferable to a taxation rule whatever the source of disturbances

A case of acute respiratory failure in a young man

A 40 years old man presents with a few hours history of progressive dyspnea. He was suffering from almost a week of low grade fever. The night before the onset of dyspnea he had high fever (39,5°C), polyuria and dysuria. His blood pressure is 115/65 mmHg and his oxygen saturation while breathing ambient air is 81%. Chest auscultation reveals rales bilaterally. A chest radiography shows bilateral pulmonary infiltrates consistent with pulmonary edema. How should this patient be evaluated to establish the cause of the acute pulmonary edema and to determine appropriate therapy

Continual Adaptation for Deep Stereo

Depth estimation from stereo images is carried out with unmatched results by convolutional neural networks trained end-to-end to regress dense disparities. Like for most tasks, this is possible if large amounts of labelled samples are available for training, possibly covering the whole data distribution encountered at deployment time. Being such an assumption systematically unmet in real applications, the capacity of adapting to any unseen setting becomes of paramount importance. Purposely, we propose a continual adaptation paradigm for deep stereo networks designed to deal with challenging and ever-changing environments. We design a lightweight and modular architecture, Modularly ADaptive Network (MADNet), and formulate Modular ADaptation algorithms (MAD, MAD++) which permit efficient optimization of independent sub-portions of the entire network. In our paradigm, the learning signals needed to continuously adapt models online can be sourced from self-supervision via right-to-left image warping or from traditional stereo algorithms. With both sources, no other data than the input images being gathered at deployment time are needed. Thus, our network architecture and adaptation algorithms realize the first real-time self-adaptive deep stereo system and pave the way for a new paradigm that can facilitate practical deployment of end-to-end architectures for dense disparity regression

Anonymous crypt P2P. A model for a secure and private communication

The aim of this work is to contribute to the modelling of a peer-to-peer protocol in order to fill a lack that still remains in the wide panorama of developed model, i.e. .e. a deterministic anonymous and crypt peer-to-peer communication system. This work considers first the most important model confirmed by the diffusion and the reliability for their purposes, presenting an overview that focuses on the main characteristics. Than an analysis of the requirements is done and two different strategies are analysed, building two models for different anonymity and security levels. The two models are discussed and a communication protocol for a minimal user client interface is described. Finally the scalability problem is discussed

Aspects of geodesical motion with Fisher-Rao metric: classical and quantum

The purpose of this article is to exploit the geometric structure of Quantum Mechanics and of statistical manifolds to study the qualitative effect that the quantum properties have in the statistical description of a system. We show that the end points of geodesics in the classical setting coincide with the probability distributions that minimise Shannon's Entropy, i.e. with distributions of zero dispersion. In the quantum setting this happens only for particular initial conditions, which in turn correspond to classical submanifolds. This result can be interpreted as a geometric manifestation of the uncertainty principle.Comment: 15 pages, 5 figure