Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network

With more and more household objects built on planned obsolescence and consumed by a fast-growing population, hazardous waste recycling has become a critical challenge. Given the large variability of household waste, current recycling platforms mostly rely on human operators to analyze the scene, typically composed of many object instances piled up in bulk. Helping them by robotizing the unitary extraction is a key challenge to speed up this tedious process. Whereas supervised deep learning has proven very efficient for such object-level scene understanding, e.g., generic object detection and segmentation in everyday scenes, it however requires large sets of per-pixel labeled images, that are hardly available for numerous application contexts, including industrial robotics. We thus propose a step towards a practical interactive application for generating an object-oriented robotic grasp, requiring as inputs only one depth map of the scene and one user click on the next object to extract. More precisely, we address in this paper the middle issue of object seg-mentation in top views of piles of bulk objects given a pixel location, namely seed, provided interactively by a human operator. We propose a twofold framework for generating edge-driven instance segments. First, we repurpose a state-of-the-art fully convolutional object contour detector for seed-based instance segmentation by introducing the notion of edge-mask duality with a novel patch-free and contour-oriented loss function. Second, we train one model using only synthetic scenes, instead of manually labeled training data. Our experimental results show that considering edge-mask duality for training an encoder-decoder network, as we suggest, outperforms a state-of-the-art patch-based network in the present application context.Comment: This is a pre-print of an article published in Human Friendly Robotics, 10th International Workshop, Springer Proceedings in Advanced Robotics, vol 7. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-89327-3\_16, Springer Proceedings in Advanced Robotics, Siciliano Bruno, Khatib Oussama, In press, Human Friendly Robotics, 10th International Workshop,

A Generalized Statistical Complexity Measure: Applications to Quantum Systems

A two-parameter family of complexity measures $\tilde{C}^{(\alpha,\beta)}$ based on the R\'enyi entropies is introduced and characterized by a detailed study of its mathematical properties. This family is the generalization of a continuous version of the LMC complexity, which is recovered for $\alpha=1$ and $\beta=2$. These complexity measures are obtained by multiplying two quantities bringing global information on the probability distribution defining the system. When one of the parameters, $\alpha$ or $\beta$, goes to infinity, one of the global factors becomes a local factor. For this special case, the complexity is calculated on different quantum systems: H-atom, harmonic oscillator and square well.Comment: 15 pages, 3 figure

Friendship selection and influence processes for popularity in early and mid-adolescents

Introduction This study examined the effect of popularity levels on friendship selection and friends' influence on popularity levels in early and mid-adolescence. Methods Participants were 4205 Spanish adolescents (M-age = 13.1 years at Wave 1; 48% girls) belonging to 160 classrooms in two waves. Adolescents were asked about their friendships and the popularity of their classmates. Results Longitudinal social network analyses showed that adolescents preferred similarly popular peers as friends. High popular classmates were more attractive as friends, particularly in early adolescence. Popular adolescents were more selective in their friendship nominations and adolescents with popular friends became more popular over time. These two effects were only significant in mid-adolescents, although comparative analyses showed a similar tendency at both age groups. Conclusions This study highlights the importance of popularity levels in adolescents' friendship selection and suggests that popularity, at the individual and group level, plays a relevant role in social development. Implications adapted to the different selection and influence processes in early and mid-adolescence are discussed

Avaliação dos impactos ambientais e econômicos do controle químico do percevejo-barriga-verde (Dichelops melacanthus) em trigo e milho safrinha.

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Dos nuevas especies de Pseudosinella Schäffer, 1897 (Collembola, Entomobryidae) de Castilla-La Mancha

Two new species of Pseudosinella (Collembola, Entomobryidae), P. lafargensis sp. n. and P. cementensis sp. n., have been found in Castilla-La Mancha, where the number of citations of this genus is very few.Se han encontrado dos nueva especies de Pseudosinella (Collembola, Entomobryidae), P. lafargensis sp. n. y P. cementensis sp. n., en Castilla-La Mancha, en donde el número de citas de este género es muy reducido

Fluorescence: Absorption coefficient ratio — Tracing photochemical and microbial degradation processes affecting coloured dissolved organic matter in a coastal system

Original research paperThe optical properties of coloured dissolved organic matter (CDOM) – absorption coefficient, induced fluorescence, and fluorescence quantum yield – were determined in the coastal eutrophic system of the Ría de Vigo (NW Spain) under two contrasting situations: a downwelling event in September 2006 and an upwelling event in June 2007. Significantly different optical properties were recorded in the shelf surface (higher absorption coefficient and lower quantum yield) and bottom (lower absorption coefficient and higher quantum yield) waters that entered the embayment during downwelling and upwelling conditions, respectively. Continental waters presented distinctly high CDOM levels. The spatial and temporal variability of the induced fluorescence to absorption coefficient ratio during the mixing of shelf and continental waters was used to quantify the relative importance of photochemical and microbial processes under these contrasting hydrographic conditions. Photochemical processes were dominant during the downwelling episode: 86% of the variability of CDOM can be explained by photochemical degradation. On the contrary, microbial processes prevailed during the upwelling event: 77% of the total variability of CDOM was explained by microbial respiration.The Xunta de Galicia, grant number PGIDIT-05MA40201PR; the project SUMMER, grant number CTM2008-03309/MAR; a I3P-CSIC predoctoral fellowship and a Marie Curie I.O.F.Versión del editor2,75

Zero-Shot Hashing via Transferring Supervised Knowledge

Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge e.g. semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions. However, confronted with the rapid growth of newly-emerging concepts and multimedia data on the Web, existing supervised hashing approaches may easily suffer from the scarcity and validity of supervised information due to the expensive cost of manual labelling. In this paper, we propose a novel hashing scheme, termed \emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of "seen" categories. Specifically, we project independent data labels i.e. 0/1-form label vectors) into semantic embedding space, where semantic relationships among all the labels can be precisely characterized and thus seen supervised knowledge can be transferred to unseen classes. Moreover, in order to cope with the semantic shift problem, we rotate the embedded space to more suitably align the embedded semantics with the low-level visual feature space, thereby alleviating the influence of semantic gap. In the meantime, to exert positive effects on learning high-quality hash functions, we further propose to preserve local structural property and discrete nature in binary codes. Besides, we develop an efficient alternating algorithm to solve the ZSH model. Extensive experiments conducted on various real-life datasets show the superior zero-shot image retrieval performance of ZSH as compared to several state-of-the-art hashing methods.Comment: 11 page

Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing System