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

Minimal codewords in Norm-Trace codes

In this paper, we consider the affine variety codes obtained evaluating the polynomials $by=a_kx^k+\dots+a_1x+a_0$, $b,a_i\in\mathbb{F}_{q^r}$, at the affine \F_{q^r}-rational points of the Norm-Trace curve. In particular, we investigate the weight distribution and the set of minimal codewords. Our approach, which uses tools of algebraic geometry, is based on the study of the absolutely irreducibility of certain algebraic varieties

Minimal codewords in Norm-Trace codes

In this paper, we consider the affine variety codes obtained evaluating the polynomials $by=a_kx^k+\dots+a_1x+a_0$, $b,a_i\in\mathbb{F}_{q^r}$, at the affine \F_{q^r}-rational points of the Norm-Trace curve. In particular, we investigate the weight distribution and the set of minimal codewords. Our approach, which uses tools of algebraic geometry, is based on the study of the absolutely irreducibility of certain algebraic varieties

Experimental utilization of interferometric radar techniques for structural monitoring

SUMMARY This paper presents a new surveying technique that can be employed with hardly reachable structures or extensive structures. The technique is based on an interferometric radar device, which can be positioned up to 2 km away from the structure to be examined and can record displacement measurements with a sampling frequency of up to 100 Hz and an accuracy of 0.1 mm. The results given by the radar are compared with those given by a set of three accelerometers on a test structure (namely, a steel cable-stayed footbridge) for three different loads: a symmetric dynamic load, an eccentric dynamic load and a symmetric quasi-static load. Copyright # 2008 John Wiley & Sons, Ltd

Cement-Based Composites Containing Oxidized Graphene Nanoplatelets: Effects on the Mechanical and Electrical Properties

Graphene nanoplatelets can improve the electrical and mechanical properties of cement matrix composites. The dispersion and interaction of graphene in the cement matrix appears to be difficult due to its hydrophobic nature. By introducing polar groups, graphene oxidation improves the level of dispersion and interaction with the cement. In this work, graphene oxidation using sulfonitric acid for 10, 20, 40, and 60 min was studied. Thermogravimetric Analysis (TGA) and Raman spectroscopy were employed to analyze the graphene before and after the oxidation. The mechanical properties of the final composites showed an improvement of 52% in the flexural strength, 4% in the fracture energy, and 8% in the compressive strength in the case of 60 min of oxidation. In addition, the samples showed a reduction of at least one order of magnitude in electrical resistivity when compared with pure cement