SHREC'16: partial matching of deformable shapes

Matching deformable 3D shapes under partiality transformations is a challenging problem that has received limited focus in the computer vision and graphics communities. With this benchmark, we explore and thoroughly investigate the robustness of existing matching methods in this challenging task. Participants are asked to provide a point-to-point correspondence (either sparse or dense) between deformable shapes undergoing different kinds of partiality transformations, resulting in a total of 400 matching problems to be solved for each method - making this benchmark the biggest and most challenging of its kind. Five matching algorithms were evaluated in the contest; this paper presents the details of the dataset, the adopted evaluation measures, and shows thorough comparisons among all competing methods

Tethers in Space Handbook

A new edition of the Tethers in Space Handbook was needed after the last edition published in 1989. Tether-related activities have been quite busy in the 90's. We have had the flights of TSSI and TSSI-R, SEDS-1 and -2, PMG, TIPS and OEDIPUS. In less than three years there have been one international Conference on Tethers in Space, held in Washington DC, and three workshops, held at ESA/Estec in the Netherlands, at ISAS in Japan and at the University of Michigan, Ann Harbor. The community has grown and we finally have real flight data to compare our models with. The life of spaceborne tethers has not been always easy and we got our dose of setbacks, but we feel pretty optimistic for the future. We are just stepping out of the pioneering stage to start to use tethers for space science and technological applications. As we are writing this handbook TiPs, a NRL tether project is flying above our heads. There is no emphasis in affirming that as of today spacebome tethers are a reality and their potential is far from being fully appreciated. Consequently, a large amount of new information had to be incorporated into this new edition. The general structure of the handbook has been left mostly unchanged. The past editors have set a style which we have not felt needed change. The section on the flights has been enriched with information on the scientific results. The categories of the applications have not been modified, and in some cases we have mentioned the existence of related flight data. We felt that the section contributed by Joe Carroll, called Tether Data, should be maintained as it was, being a "classic" and still very accurate and not at all obsolete. We have introduced a new chapter entitled Space Science and Tethers since flight experience has shown that tethers can complement other space-based investigations. The bibliography has been updated. Due to the great production in the last few years %e had to restrict our search to works published in refereed journal. The production, however, is much more extensive. In addition, we have included the summary of the papers presented at the last International Conference which was a forum for first-hand information on all the flights

Detecção de Pedestres Utilizando Descritores de Orientação do Gradiente e Auto Similaridade de Cor

Detecção de pedestres é um problema muito abordado na atualidade, possuindo diversas aplicações com potencial para melhorar a qualidade de vida da sociedade. Algumas dessas aplicações se encontram nas áreas de sistemas de auxílio ao motorista, reconhecimento de pessoas em fotos e vídeos, e vigilância. Atualmente existe um grande número de pesquisas envolvendo este assunto, trazendo muitas ramificações ao estado da arte no que diz respeito a detecção de pedestres. Esta dissertação apresenta um sistema de detecção de pedestres em ambientes não controlados baseado em janelas deslizantes. Sistemas deste tipo são compostos por dois blocos principais: um para a extração de características e outro para classificação das janelas. Duas técnicas de extração de características são usadas, sendo elas: HOG (Histogram of Oriented Gradient) e CSS (Color Self Similarities), e para classificar as janelas é usado o SVM (Support Vector Machine) linear. Além dessas técnicas, são também utilizadas: mean shift e agrupamento hierárquico, para a fusão de múltiplas detecções sobrepostas; e filtro bilateral, para pré-processamento da imagem. Os resultados obtidos sobre o banco de dados INRIA Person Database mostram que o sistema proposto, usando somente o descritor HOG, apresenta melhorias em relação a sistemas semelhantes, com um log average miss rate igual a 41,8%, contra 46% da literatura. Este resultado foi possível devido ao corte das detecções finais para melhor adequação às anotações modificadas, e também a algumas modificações feitas nos parâmetros dos descritores. A adição do descritor CSS modificado ao HOG aumenta a eficácia do sistema, levando a um log average miss rate igual a 36,2%, classificando separadamente cada descrito

Quantum reading under a local energy constraint

Nonclassical states of light play a central role in many quantum information protocols. Their quantum features have been exploited to improve the readout of information from digital memories, modelled as arrays of microscopic beam splitters [S. Pirandola, Phys. Rev. Lett. 106, 090504 (2011)]. In this model of quantum reading, a nonclassical source of light with Einstein-Podolski-Rosen correlations has been proven to retrieve more information than any classical source. In particular, the quantum-classical comparison has been performed under a global energy constraint, i.e., by fixing the mean total number of photons irradiated over each memory cell. In this paper we provide an alternative analysis which is based on a local energy constraint, meaning that we fix the mean number of photons per signal mode irradiated over the memory cell. Under this assumption, we investigate the critical number of signal modes after which a nonclassical source of light is able to beat any classical source irradiating the same number of signals.Comment: REVTeX. Published versio

Differentiable Graph Module (DGM) for Graph Convolutional Networks

Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-Euclidean structured data. One of the limitations of the majority of current graph neural network architectures is that they are often restricted to the transductive setting and rely on the assumption that the underlying graph is known and fixed. Often, this assumption is not true since the graph may be noisy, or partially and even completely unknown. In such cases, it would be helpful to infer the graph directly from the data, especially in inductive settings where some nodes were not present in the graph at training time. Furthermore, learning a graph may become an end in itself, as the inferred structure may provide complementary insights next to the downstream task. In this paper, we introduce Differentiable Graph Module (DGM), a learnable function that predicts edge probabilities in the graph which are optimal for the downstream task. DGM can be combined with convolutional graph neural network layers and trained in an end-to-end fashion. We provide an extensive evaluation on applications in healthcare, brain imaging, computer graphics, and computer vision showing a significant improvement over baselines both in transductive and inductive settings

In-Space Transportation with Tethers

The annual report covers the research conducted on the following topics related to the use of spaceborne tethers for in-space transportation: ProSEDS tether modeling (current collection analyses, influence of a varying tether temperature); proSEDS mission analysis and system dynamics (tether thermal model, thermo-electro-dynamics integrated simulations); proSEDS-tether development and testing (tether requirements, deployment test plan, tether properties testing, deployment tests); and tethers for reboosting the space-based laser (mission analysis, tether system preliminary design, evaluation of attitude constraints)

A Music index of periodicals for the first six months of 1948 /

Thesis (M.M.)--Boston Universit

Channel Simulation in Quantum Metrology

In this review we discuss how channel simulation can be used to simplify the most general protocols of quantum parameter estimation, where unlimited entanglement and adaptive joint operations may be employed. Whenever the unknown parameter encoded in a quantum channels is completely transferred in an environmental program state simulating the channel, the optimal adaptive estimation cannot beat the standard quantum limit. In this setting, we elucidate the crucial role of quantum teleportation as a primitive operation which allows one to completely reduce adaptive protocols over suitable teleportation-covariant channels and derive matching upper and lower bounds for parameter estimation. For these channels, we may express the quantum Cramer Rao bound directly in terms of their Choi matrices. Our review considers both discrete- and continuous-variable systems, also presenting some new results for bosonic Gaussian channels using an alternative sub-optimal simulation. It is an open problem to design simulations for quantum channels that achieve the Heisenberg limit