VideoCapsuleNet: A Simplified Network for Action Detection

The recent advances in Deep Convolutional Neural Networks (DCNNs) have shown extremely good results for video human action classification, however, action detection is still a challenging problem. The current action detection approaches follow a complex pipeline which involves multiple tasks such as tube proposals, optical flow, and tube classification. In this work, we present a more elegant solution for action detection based on the recently developed capsule network. We propose a 3D capsule network for videos, called VideoCapsuleNet: a unified network for action detection which can jointly perform pixel-wise action segmentation along with action classification. The proposed network is a generalization of capsule network from 2D to 3D, which takes a sequence of video frames as input. The 3D generalization drastically increases the number of capsules in the network, making capsule routing computationally expensive. We introduce capsule-pooling in the convolutional capsule layer to address this issue which makes the voting algorithm tractable. The routing-by-agreement in the network inherently models the action representations and various action characteristics are captured by the predicted capsules. This inspired us to utilize the capsules for action localization and the class-specific capsules predicted by the network are used to determine a pixel-wise localization of actions. The localization is further improved by parameterized skip connections with the convolutional capsule layers and the network is trained end-to-end with a classification as well as localization loss. The proposed network achieves sate-of-the-art performance on multiple action detection datasets including UCF-Sports, J-HMDB, and UCF-101 (24 classes) with an impressive ~20% improvement on UCF-101 and ~15% improvement on J-HMDB in terms of v-mAP scores

Operaciones Bancarias : Análisis de la apertura y operatividad de la cuenta de ahorro y plan de ahorro meta para una persona natural del banco de producción,S.A. noviembre 2016-marzo 2017

El presente trabajo de seminario de graduación se enfoca en las operaciones que realiza un banco especialmente las operaciones pasivas. En esta investigación se explica de manera clara el proceso de apertura de una cuenta de ahorro, producto ofrecido por el Banco de la Producción Sociedad Anónima. (BANPRO S.A) donde se identifica con en el nombre “Plan Ahorro Meta”. En la elaboración del caso práctico tomamos como referencia la información que proporciona la entidad bancaria en su página web, al mismo tiempo se visitó una sucursal bancaria para indagar más sobre el cálculo del interés escalonado que posee como característica dicha cuenta, ya que es un producto nuevo en el mercado financiero. Se procedió de acorde a las políticas, normas y reglamentos establecidos por la institución bancaria, esta misma respetando las leyes que la rigen; de esta forma se protegen los derechos del público y del banco al momento de iniciar una relación contractual. En Nicaragua existen entes que se encargan de regular y supervisar dichas operaciones bancarias, es por esta razón que se analiza detenidamente las principales funciones que tienen estas entidades en relación a los bancos. Se realizó la apertura de la cuenta de ahorro plan ahorro meta, esta opero por un periodo de 6 meses. Mismos periodo en el que se explica paso a paso la forma de cálculo de los intereses generados diarios y la capitalización al cierre del mes, de manera que los usuarios de las cuentas ya sea de ahorro, corriente o aplazo posean un entendimiento claro del funcionamiento de los tipos de depósitos que poseen los bancos

Capsule Networks for Video Understanding

With the increase of videos available online, it is more important than ever to learn how to process and understand video data. Although convolutional neural networks have revolutionized the representation learning from images and videos, they do not explicitly model entities within the given input. It would be useful for learned models to be able to represent part-to-whole relationships within a given image or video. To this end, a novel neural network architecture - capsule networks - has been proposed. Capsule networks add extra structure to allow for the modeling of entities and has shown great promise when applied to image data. By grouping neural activations and propagating information from one layer to the next through a routing-by-agreement procedure, capsule networks are able to learn part-to-whole relationships as well as robust object representations. In this dissertation, we explore how capsule networks can be generalized to video and be used to effectively solve several video understanding problems. First, we generalize capsule networks from the image domain so that it can process 3-dimensional video data. Our proposed video capsule network (VideoCapsuleNet) tackles the problem of video action detection. We introduce capsule-pooling in the convolutional capsule layer to make the voting algorithm tractable in the 3-dimensional video domain. The network\u27s routing-by-agreement inherently models the action representations and various action characteristics are captured by the predicted capsules. We show that VideoCapsuleNet is able to successfully produce pixel-wise localizations of actions present in videos. While action detection only requires a coarse localization, we show that video capsule networks can generate fine-grained segmentations. To that end, we propose a capsule-based approach for video object segmentation, CapsuleVOS, which can segment several frames at once conditioned on a reference frame and segmentation mask. This conditioning is performed through a novel routing algorithm for attention-based efficient capsule selection. We address two challenging issues in video object segmentation: segmentation of small objects and occlusion of objects across time. The first issue is addressed with a zooming module; the second, is dealt with by a novel memory module based on recurrent neural networks. Above we show that capsule networks can effectively localize actors and objects within videos. Next, we address the problem of integration of video and text for the task of actor and action video segmentation from a sentence. We propose a novel capsule-based approach to perform pixel-level localization based on a natural language query describing the actor of interest. We encode both the video and textual input in the form of capsules, and propose a visual-textual routing mechanism for the fusion of these capsules to successfully localize the actor and action within all frames of a video. The previous works are all fully supervised: they are all trained on manually annotated data, which is often time-consuming and costly to acquire. Finally, we propose a novel method for self-supervised learning which does not rely on manually annotated data. We present a capsule network that jointly learns high-level concepts and their relationships across different low-level multimodal (video, audio, and text) input representations. To adapt the capsules to large-scale input data, we propose a routing by self-attention mechanism that selects relevant capsules which are then used to generate a final joint multimodal feature representation. This allows us to learn robust representations from noisy video data and to scale up the size of the capsule network compared to traditional routing methods while still being computationally efficient

Tratamentos sustentáveis e inovadores para a indústria da madeira

A madeira é um recurso natural e uma das matérias-primas mais utilizadas. É a base de um dos tradicionais setores industriais portugueses, com forte imponência a nível global. Devido ao seu alargado leque de aplicações, este material, conta como a base para variadíssimos projetos, que requerem o aparecimento de produtos que aliem a inovação e a criatividade de forma assegurar a sua crescente procura. É neste sentido que o trabalho descrito neste artigo se torna de grande relevo, pois prima pela preocupação ambiental, desenvolvimento empresarial e inovação no ramo industrial. Neste projeto procedeu-se à síntese e otimização de diferentes pigmentos fosforescentes que foram posteriormente aplicados na formulação de um novo revestimento para a madeira. Este novo revestimento permitirá a valorização deste material de construção que é tão apreciado pelos consumidores acrescentando-lhe caraterísticas únicas e inovadoras.info:eu-repo/semantics/publishedVersio

Ensamblaje automatizado pistón-biela mediante dos brazos robot

Piston-rod assembling process is automated by using two robot arms. The piston and the rod are assembled with bolt. Pieces are identified using a computer vision system based on Sherlock7 software. Comunications between computer vision system and robots arms is done using a TCP / IP connection. An ABB IRB 140 robot will pick up the components and place them on its respective platform, while an UR3 of Universal Robots will be in charge of assembling.Se propone el desarrollo de la automatización de un proceso de ensamblaje de partes de un motor mediante dos brazos robot. Las piezas a ensamblar serían, un pistón y una biela, que irían ensambladas mediante un bulón. Se realiza una identificación de piezas mediante el software Sherlock7 y llevar a cabo una conexión vía TCP/IP con el primer brazo robot. El primer robot recogerá los componentes y los colocará en su respectiva plataforma, mientras que el segundo robot será el encargado de realizar el ensamblaje. Se propone utilizar el robot IRB 140 de ABB como primer robot y el robot UR3 de Universal Robots como segundo robot.Villalba Duarte, KD. (2017). Ensamblaje automatizado pistón-biela mediante dos brazos robot. Universitat Politècnica de València. http://hdl.handle.net/10251/87156TFG

diseño de investigación para la comparación de algoritmos de machine learning aplicados a la predicción del valor del precio de criptomonedas, a través de pruebas estadísticas de contraste y post hoc, para seleccionar aquellos con el mejor desempeño.

Compara algoritmos de machine learning aplicados a la predicción del valor del precio de criptomonedas, a través de pruebas estadísticas de contraste y post hoc, para seleccionar aquellos con el mejor desempeño

The psychobiological model of personality and its association with student approaches to learning : Integrating temperament and character

This document is the authors’ version of the final accepted manuscript published in 2020 by Scandinavian Journal of Educational Research. https://www.tandfonline.com/doi/full/10.1080/00313831.2020.1739137Correspondence concerning this article should be addressed to Prof. Paulo Moreira, Instituto de Psicologia e de Ciências da Educação, Universidade Lusíada, Rua de Moçambique 21 e 71, Porto 4100-348, Portugal. Email: [email protected] results from the complex interactions among multiple learning and memory systems. There is a need to examine the personality-learning association using a personality model that captures this complexity: Cloninger’s psychobiological model. The study addresses this need using a person-centered approach. In total, 686 adolescents completed the Junior Temperament and Character Inventory (JTCI) and a measure of approaches to learning. Students with a ‘steady’ temperament showed a preference for the deep approach. Students with high character coherence also had this preference. A temperament profile-by-character profile interaction was crucial for understanding students’ preferred approach to learning. These findings imply that adaptive learning approaches result from an integration of major systems of learning and memory, as measured by the Temperament and Character Inventory

Assessment of scatter radiation in the shielding of radiography procedures

Scattered radiation is the largest contributor to patient dose and the main cause for stochastic effects (1-3). Evaluate the use of radiological protections and attenuating materials depending on the scattered radiation which the patient is subject, analyzing the appropriate placement of the protections is extremely important for the improvement of the radiographer clinical practice (4-7). Therefore, the purpose of this research is to ascertain the best way to protect irradiated patients, particularly children and pregnant women.info:eu-repo/semantics/publishedVersio

In vitro uptake evaluation of a 18F-Labeled Sulforhodamine 101 in CNS cells

Background: We have previously reported the synthesis and biological evaluation of a sulfonamide derivative of Sulforhodamine 101 (SR101), namely SR101 N-(3-[18F]-Fluoropropyl)sulfonamide ([18F]2B-SRF101), designed as a new positron emission tomography (PET) agent for detecting astrocytosis in early stages of Alzheimer´s disease (AD). The fluorescent dye SR101 is an astroglial marker and has been used for the detection of astrocytes in the neocortex of rodents in numerous work. We have confirmed 2B-SRF101’s ability to detect astrocytes in culture similarly than SR101, using fluorescence microscope images. In vivo biological assessment of [18F]2B-SRF101 using micro-PET/CT revealed a higher uptake in cortex and hippocampus of 10-month-old triple-transgenic (3xTg) mice compared with the control group (1). However, the cellular specificity of this radiotracer in the CNS needs to be elucidated, especially considering that SR101 uptake was reported also in other CNS cells (2). Aims: In this work we aimed to elucidate the cellular specificity of 2B-SRF101 in neurons and astrocytes using isolated mice cortex/hippocampus cells. Methods: Enriched astrocytes cultures were prepared from cortices of P0-P2 3xTg or C57 control mice. Neuronal primary cultures were obtained from C57 embryos. Fluorescence confocal images were acquired after 1 min SR101 or 2B-SRF101 (10 μM) incubation in live cells. Cell uptake was determined after 10, 20 and 40 min incubation of confluent cells with [18F]2B-SRF101 (90 μCi) using a Gamma counter. Results: Astrocyte specific uptake was observed for SR101 and 2B-SRF101 in cells derived from both 3xTg and non-Tg mice, with a preferential cytoplasmic distribution, without showing specific uptake in healthy neurons in culture. This result was also observed in internalization assays with [18F]2B-SRF101 in which radiotracer uptake was higher in astrocytes than in neuronal cultures in the three time points evaluated. Conclusion: In this work we brought evidence of astrocytic preference of both SR101 and 2B-SRF101, validating [18F]2B-SRF101 as a promising candidate tracer for astrocytosis detection. Funding/Acknowledgements: We thank ANII for financial support (FMV_3_2020_1_162870) and Unidad de Bioimagenología Avanzada de IPMon.1 Kreimerman I, et al. Front Neuroscience, 13: 734; 2019. 2 Hill R, et al. Nat Methods, 11: 1081-1082; 2014.Centro Uruguayo de Imagenología MolecularAgencia Nacional de Investigación e Innovació