Memory Based Online Learning of Deep Representations from Video Streams

We present a novel online unsupervised method for face identity learning from video streams. The method exploits deep face descriptors together with a memory based learning mechanism that takes advantage of the temporal coherence of visual data. Specifically, we introduce a discriminative feature matching solution based on Reverse Nearest Neighbour and a feature forgetting strategy that detect redundant features and discard them appropriately while time progresses. It is shown that the proposed learning procedure is asymptotically stable and can be effectively used in relevant applications like multiple face identification and tracking from unconstrained video streams. Experimental results show that the proposed method achieves comparable results in the task of multiple face tracking and better performance in face identification with offline approaches exploiting future information. Code will be publicly available.Comment: arXiv admin note: text overlap with arXiv:1708.0361

Context-Aware Trajectory Prediction

Human motion and behaviour in crowded spaces is influenced by several factors, such as the dynamics of other moving agents in the scene, as well as the static elements that might be perceived as points of attraction or obstacles. In this work, we present a new model for human trajectory prediction which is able to take advantage of both human-human and human-space interactions. The future trajectory of humans, are generated by observing their past positions and interactions with the surroundings. To this end, we propose a "context-aware" recurrent neural network LSTM model, which can learn and predict human motion in crowded spaces such as a sidewalk, a museum or a shopping mall. We evaluate our model on a public pedestrian datasets, and we contribute a new challenging dataset that collects videos of humans that navigate in a (real) crowded space such as a big museum. Results show that our approach can predict human trajectories better when compared to previous state-of-the-art forecasting models.Comment: Submitted to BMVC 201

Wide-address spaces - exploring the design space

In a recent issue of Operating System Review, Hayter and McAuley [1991] argue that future high-performance systems trade a traditional, bus-based organization for one where all components are linked together by network switches (the Desk-Area Network). In this issue of Operating System Review, Leslie, McAuley and Mullender conclude that DAN-based architectures allow the exploitation of shared memory on a wider scale than just a single (multi)processor. In this paper, we will explore how emerging 64-bit processors can be used to implement shared address spaces spanning multiple machines

A Short Review on Nanostructured Carbon Containing Biopolymer Derived Composites for Tissue Engineering Applications

The development of new scaffolds and materials for tissue engineering is a wide and open realm of material science. Among solutions, the use of biopolymers represents a particularly interesting area of study due to their great chemical complexity that enables creation of specific molecular architectures. However, biopolymers do not exhibit the properties required for direct application in tissue repair—such as mechanical and electrical properties—but they do show very attractive chemical functionalities which are difficult to produce through in vitro synthesis. The combination of biopolymers with nanostructured carbon fillers could represent a robust solution to enhance composite properties, producing composites with new and unique features, particularly relating to electronic conduction. In this paper, we provide a review of the field of carbonaceous nanostructure-containing biopolymer composites, limiting our investigation to tissue-engineering applications, and providing a complete overview of the recent and most outstanding achievements

A Short Review on Biomedical Applications of Nanostructured Bismuth Oxide and Related Nanomaterials

In this review, we reported the main achievements reached by using bismuth oxides and related materials for biological applications. We overviewed the complex chemical behavior of bismuth during the transformation of its compounds to oxide and bismuth oxide phase transitions. Afterward, we summarized the more relevant studies regrouped into three categories based on the use of bismuth species: (i) active drugs, (ii) diagnostic and (iii) theragnostic. We hope to provide a complete overview of the great potential of bismuth oxides in biological environments