The grumpy bin:reducing food waste through playful social interactions

Domestic food waste is a world-wide problem that is complex and difficult to tackle as it touches diverse habits and social behaviors. This paper introduces the Grumpy Bin, a smart food waste bin designed for the context of student housing. The Grumpy Bin1 contributes to the state of the art of food waste prevention solutions by challenging the traditional approach on pervasive technology, which is commonly based on system-driven judgements and persuasive data representations. Instead, this design empowers users and their social acquaintances to collectively judge their actions, hence adding a layer of social mediation that is likely to increase the chance for behavior change.</p

Efficient On-the-fly Category Retrieval using ConvNets and GPUs

We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval - where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. We make three contributions: (i) we present an evaluation of state-of-the-art image representations for object category retrieval over standard benchmark datasets containing 1M+ images; (ii) we show that ConvNets can be used to obtain features which are incredibly performant, and yet much lower dimensional than previous state-of-the-art image representations, and that their dimensionality can be reduced further without loss in performance by compression using product quantization or binarization. Consequently, features with the state-of-the-art performance on large-scale datasets of millions of images can fit in the memory of even a commodity GPU card; (iii) we show that an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel with downloading the new training images, allowing for a continuous refinement of the model as more images become available, and simultaneous training and ranking. The outcome is an on-the-fly system that significantly outperforms its predecessors in terms of: precision of retrieval, memory requirements, and speed, facilitating accurate on-the-fly learning and ranking in under a second on a single GPU.Comment: Published in proceedings of ACCV 201

Singularities and n-dimensional black holes in torsion theories

In this work we have studied the singular behaviour of gravitational theories with non symmetric connections. For this purpose we introduce a new criteria for the appearance of singularities based on the existence of black/white hole regions of arbitrary codimension defined inside a spacetime of arbitrary dimension. We discuss this prescription by increasing the complexity of the particular torsion theory under study. In this sense, we start with Teleparallel Gravity, then we analyse Einstein-Cartan theory, and finally dynamical torsion models

Fermion dynamics in torsion theories

In this work we have studied the non-geodesical behaviour of particles with spin 1/2 in Poincar\'e gauge theories of gravity, via the WKB method and the Mathisson-Papapetrou equation. We have analysed the relation between the two approaches and we have argued the different advantages associated with the WKB approximation. Within this approach, we have calculated the trajectories in a particular Poincar\'e gauge theory, discussing the viability of measuring such a motion.Comment: 17 pages, 2 figures, minor changes, conclusions unchanged. It matches the version published in JCA

Analyzing First-Person Stories Based on Socializing, Eating and Sedentary Patterns

First-person stories can be analyzed by means of egocentric pictures acquired throughout the whole active day with wearable cameras. This manuscript presents an egocentric dataset with more than 45,000 pictures from four people in different environments such as working or studying. All the images were manually labeled to identify three patterns of interest regarding people's lifestyle: socializing, eating and sedentary. Additionally, two different approaches are proposed to classify egocentric images into one of the 12 target categories defined to characterize these three patterns. The approaches are based on machine learning and deep learning techniques, including traditional classifiers and state-of-art convolutional neural networks. The experimental results obtained when applying these methods to the egocentric dataset demonstrated their adequacy for the problem at hand.Comment: Accepted at First International Workshop on Social Signal Processing and Beyond, 19th International Conference on Image Analysis and Processing (ICIAP), September 201

Evaluation of a Multicommuted Flow System for Photometric Environmental Measurements

A portable flow analysis instrument is described for in situ photometric measurements. This system is based on light-emitting diodes (LEDs) and a photodiode detector, coupled to a multipumping flow system. The whole equipment presents dimensions of 25  cm × 22  cm × 10  cm, weighs circa 3 kg, and costs 650 €. System performance was evaluated for different chemistries without changing hardware configuration for determinations of (i) Fe3+ with SCN-, (ii) iodometric nitrite determination, (iii) phenol with sodium nitroprusside, and (iv) 1-naphthol-N-methylcarbamate (carbaryl) with p-aminophenol. The detection limits were estimated as 22, 60, 25, and 60 ng mL -1 for iron, nitrite, phenol, and carbaryl at the 99.7% confidence level with RSD of 2.3, 1.0, 1.8, and 0.8%, respectively. Reagent and waste volumes were lower than those obtained by flow systems with continuous reagent addition. Sampling rates of 100, 110, 65, and 72 determinations per hour were achieved for iron, nitrite, phenol, and carbaryl determination

Evaluation of the health status of Araucaria araucana trees using hyperspectral images

Revista oficial de la Asociación Española de Teledetección[EN] The Araucaria araucana is an endemic species from Chile and Argentina, which has a high biological, scientific and cultural value and since 2016 has shown a severe affection of leaf damage in some individuals, causing in some cases their death. The purpose of this research was to detect, from hyperspectral images, the individuals of the Araucaria species (Araucaria araucana (Molina and K. Koch)) and its degree of disease, by isolating its spectral signature and evaluating its physiological state through indices of vegetation and positioning techniques of the inflection point of the red edge, in a sector of the Ralco National Reserve, Biobío Region, Chile. Seven images were captured with the HYSPEX VNIR-1600 hyperspectral sensor, with 160 bands and a random sampling was carried out in the study area, where 90 samples of Araucarias were collected. In addition, from the remote sensing techniques applied, spatial data mining was used, in which Araucarias were classified without symptoms of disease and with symptoms of disease. A 55.11% overall accuracy was obtained in the classification of the image, 53.4% in the identification of healthy Araucaria and 55.96% in the identification of affected Araucaria. In relation to the evaluation of their sanitary status, the index with the best percentage of accuracy is the MSR (70.73%) and the one with the lowest value is the SAVI (35.47%). The positioning technique of the inflection point of the red edge delivered an accuracy percentage of 52.18% and an acceptable Kappa index.[ES] La Araucaria araucana es una especie endémica de Chile y Argentina, presenta un alto valor biológico, científico, cultural y desde el año 2016 ha evidenciado una severa afección del daño foliar en algunos individuos, causando en ciertos casos su muerte. Esta investigación tiene por objetivo detectar a partir de imágenes hiperespectrales, los individuos de la especie Araucaria (Araucaria araucana (Molina y K. Koch)) y su grado de afección, mediante el aislamiento de su firma espectral y la evaluación de su estado sanitario mediante índices de vegetación y técnicas de posicionamiento del punto de inflexión del red edge, en un sector de la Reserva Nacional Ralco, Región del Biobío, Chile. Se capturaron siete imágenes con el sensor hiperespectral HYSPEX VNIR-1600, con 160 bandas y se realizó un muestreo aleatorio en el área de estudio, donde se recolectaron 90 muestras de Araucarias. Además, de las técnicas de teledetección aplicadas, se utilizó minería de datos espaciales, que permitió clasificar las Araucarias con y sin síntomas de afección. Se logró un 55,11% de exactitud global en la clasificación de la imagen, un 53,4% en la identificación de Araucarias sanas y un 55,96% en la identificación de Araucarias afectadas. En relación a la evaluación de su estado sanitario, el índice con mejor porcentaje de exactitud es el MSR (70,73%) y el con menor porcentaje de exactitud es el SAVI (35,47%). La técnica de posicionamiento del punto de inflexión del red edge entregó un porcentaje de exactitud de 52,18% y un índice de Kappa aceptable.Este artículo se ha realizado en el contexto de fin de grado del Magíster en Teledetección, Facultad de Ciencias de la Universidad Mayor y en el mar-co del Proyecto “Prospección fitosanitaria para determinar los niveles de afección de daño foliar en bosques de Araucaria araucana de las regiones del Biobío, Araucanía y Los Ríos, 2017/ID: 633-32-LE16, financiado por la Corporación Nacional Forestal (CONAF) de Chile. 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Non-destructive estimation of foliar carotenoid content of tree species using merged vegetation indices. Journal of Plant Physiology, 176, 210-217. https://doi.org/10.1016/J.JPLPH.2014.11.003Gholizadeh, A., Mišurec, J., Kopačková, V., Mielke, C., Rogass, C. 2016. Assessment of Red-Edge Position Extraction Techniques: A Case Study for Norway Spruce Forests Using HyMap and Simulated Sentinel-2 Data. Forests, 7(226), 1-17. https://doi.org/10.3390/f7100226Guyot, G., Baret, F., Major, D. 1988. High spectral resolution: Determination of spectral shifts between the red and the near infrared. International Archives of Photogrammetry and Remote Sensing, 11(750-760).Hakkenberg, C. R., Peet, R. K., Urban, D. L., Song, C. 2018. Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing. Ecological Applications, 28(1), 177- 190. https://doi.org/10.1002/eap.1638Hall, M. A. 1998. Correlation-based feature subset selection for machine learning. 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Tantalum and niobium carbides obtention by carbothermic reduction of columbotantalite ores

Presentado al: 8º Congreso Nacional de Ciencia y Tecnología Metalúrgicas. Madrid 27 a 29 de mayo de 1998Los carburos de tantalio y de niobio se caracterizan por su extremada dureza y resistencia al ataque químico. Ambos carburos, pero principalmente el de tantalio, se utilizan en la fabricación de metal duro (carburos cementados), junto con otros carburos, para herramientas de corte, matricería y para operaciones especiales de mecanizado que requieran resistencia al desgaste y resistencia al choque mecánico y a alta temperatura. También se está investigando su uso como refuerzo de aceros rápidos para el desarrollo de materiales resistentes al desgaste por vía pulvimetalúrgica. Sin embargo, el uso de TaC suele venir limitado por su alto precio, por lo que en ocasiones se utiliza carburo de tantalio con conteniendo de niobio, más barato. En el presente trabajo se estudia la obtención de carburos complejos de tantalio y de niobio a partir de un concentrado mineral de columbotantalita de procedencia española, mediante un proceso relativamente barato y sencillo, como es la reducción carbotérmica. Se describe el afino previo del mineral y su posterior reducción, así como la caracterización de los productos obtenidos.Tantalum and niobium carbides are characterized by its high hardness and chemical corrosion resistance. Both carbides, but mainly TaC, are used in hard metals (sintered carbides), together with other carbides, to manufacture cutting tools and dies and in special machining applications involving mechanical shock at high temperature. Its use as reinforcement of wear resistant materials through powder metallurgy techniques are being investigated. However, the use of TaC is usually limited because of its high cost. Therefore tantalum carbide with niobium content, which is cheaper, is used. In this work the obtention of complex tantalum and niobium carbides from a Spanish columbotantalite ore is studied through a relatively cheap and simple process as it is carbothermic reduction. Concentration of the ore, its reduction and the characterization of products are described.Este trabajo ha sido posible gracias a la financiación por la CICYT del proyecto MAT96-0542, ya finalizado, y del proyecto MAT97-0695, actualmente en curso, y a los trabajos realizados con anterioridad por el Dr. J. C. Ruiz Sierra, iniciador e impulsor de ambos proyectos y de esta línea de investigación