Digital pathology provides an excellent opportunity for applying fully convolutional networks (FCNs) to tasks, such as semantic segmentation of whole slide images (WSIs). However, standard FCNs face challenges with respect to multi-resolution, inherited from the pyramid arrangement of WSIs. As a result, networks specifically designed to learn and aggregate information at different levels are desired. In this paper, we propose two novel multi-resolution networks based on the popular 'U-Net' architecture, which are evaluated on a benchmark dataset for binary semantic segmentation in WSIs. The proposed methods outperform the U-Net, demonstrating superior learning and generalization capabilities.

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An investigation on how to produce a fast and accurate prediction of user behaviour on the Web is conducted. First, the problem of predicting user behaviour as a classification task is formulated and then the main problems of such real-time predictions are specified: the accuracy and time complexity of the prediction. Second, a method for comparison of online and batch (offline) algorithms used for user behaviour prediction is proposed. Last, the performance of these algorithms using the data from a popular question and answer platform, Stack Overflow, is empirically explored. It is demonstrated that a simple online learning algorithm outperforms state-of-the-art batch algorithms and performs as well as a deep learning algorithm, Deep Belief Networks. The proposed method for comparison of online and offline algorithms as well as the provided experimental evidence can be used for choosing a machine learning set-up for predicting user behaviour on the Web in scenarios where the accuracy and the time performance are of main concern.

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Complexity and scale of modern data is at its highest level but its temporal properties are often neglected. As a result, it is often hard for a user to make an informed decision about its time related characteristics. However, an aesthetic and efficient visualization can mitigate this drawback of data representation. For example, an informative graphical visualization based on user’s interaction with a computer interface can dramatically improve user experience with temporal data. In this paper, I propose such visualization of temporal data for reasoning. I developed a temporal model supporting different temporal entities for this data. These include timestamps, intervals, different time granularity and uncertainty of time. I proposed a multimodal visualization based on this abstract time model so a user will have the functionality to reason on temporal properties of visualized data from different points of view

This article summarizes a research topic which discusses one of the most common problems in multiple micro-robots' navigation, namely congestion avoidance. This situation occurs when troops of micro-robots moving in different directions meet each other at a common area causing congestion situations. To avoid this risky statewhich can create an inextricable scenario -firstly, we established a local and a global communication network that manages the robots' displacement through formation control. This warns each of them of the existence of a risk of congestion and manages the choice of the priority group, which is a new concept that we introduce in order to deal with this kind of congestion conflict. Secondly, we consider a new algorithm based on chaotic equations and inspired by the behaviour of schools of fish, which solves the congestion problem by creating a bifurcation of the troop's configuration and enables the proper avoidance of the conflict exhibited by congestion.