Distributed learning of CNNs on heterogeneous CPU/GPU architectures

Convolutional Neural Networks (CNNs) have shown to be powerful classification tools in tasks that range from check reading to medical diagnosis, reaching close to human perception, and in some cases surpassing it. However, the problems to solve are becoming larger and more complex, which translates to larger CNNs, leading to longer training times that not even the adoption of Graphics Processing Units (GPUs) could keep up to. This problem is partially solved by using more processing units and distributed training methods that are offered by several frameworks dedicated to neural network training. However, these techniques do not take full advantage of the possible parallelization offered by CNNs and the cooperative use of heterogeneous devices with different processing capabilities, clock speeds, memory size, among others. This paper presents a new method for the parallel training of CNNs that can be considered as a particular instantiation of model parallelism, where only the convolutional layer is distributed. In fact, the convolutions processed during training (forward and backward propagation included) represent from $60$-$90$\% of global processing time. The paper analyzes the influence of network size, bandwidth, batch size, number of devices, including their processing capabilities, and other parameters. Results show that this technique is capable of diminishing the training time without affecting the classification performance for both CPUs and GPUs. For the CIFAR-10 dataset, using a CNN with two convolutional layers, and $500$ and $1500$ kernels, respectively, best speedups achieve $3.28\times$ using four CPUs and $2.45\times$ with three GPUs. Modern imaging datasets, larger and more complex than CIFAR-10 will certainly require more than $60$-$90$\% of processing time calculating convolutions, and speedups will tend to increase accordingly

Video browsing interfaces and applications: a review

We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other

The dynamic right-to-left translocation of Cerl2 is involved in the regulation and termination of nodal activity in the mouse node

The determination of left-right body asymmetry in mouse embryos depends on the interplay of molecules In a highly sensitive structure, the node. Here, we show that the localization of Cerl2 protein does not correlate to its mRNA expression pattern, from 3-somite stage onwards. Instead, Cerl2 protein displays a nodal flow-dependent dynamic behavior that controls the activity of Nodal in the node, and the transmission of the laterality information to the left lateral plate mesoderm (LPM). Our results indicate that Cerl2 initially localizes and prevents the activation of Nodal genetic circuitry on the right side of the embryo, and later its right-to-left translocation shutdowns Nodal activity in the node. The consequent prolonged Nodal activity in the node by the absence of Cerl2 affects local Nodal expression and prolongs its expression in the LPM. Simultaneous genetic removal of both Nodal node inhibitors, Cerl2 and Lefty1, sustains even longer and bilateral his LPM expression.F.C.T.; IBB/CBME, LAinfo:eu-repo/semantics/publishedVersio

Towards the port-city: thirty years of waterfront regeneration projects in Malaga.

A principios de la década de los 80' la ciudad de Málaga, con un centro histórico totalmente degradado, y un puerto, aún sin hacer frente a la demanda de contenedores y cruceros, deciden embaucarse en el desarrollo de un Plan Especial. Este plan permitiría por un lado que el puerto se modernizara y pudiera ser competitivo, liberando los muelles más próximos a la ciudad y adentrándose en el mar; y por otro lado que esos muelles liberados de actividad portuaria pudieran transformarse en terciario para la regeneración del centro histórico tal y como había ocurrido en Baltimore y otras tantas ciudades que habían adaptado ese modelo. Sin embargo, esto que en un principio parecía resolver los problemas de ambas realidades, dio lugar a más de 25 años de discusiones y propuestas distantes. Durante este largo periodo el Plan se quedó obsoleto. El tiempo de aprobación del Plan superó la previsión del mismo. Cuando llegaron a un acuerdo, tanto el puerto como la ciudad ya se habían desarrollado paralelamente, tanto en el tiempo como en el espacio, pero sin ninguna relación. Esto mismo se reflejaba en el Plan acordado que se limitaba a los muelles, sin relacionarse ni con la dársena ni con la ciudad. Sin embargo, a pesar de la discordancia, los ciudadanos han ido conquistando ambos terrenos, y aunque no existe ninguna continuidad física ni funcional, han sido ellos mismos los que han conseguido la integración del puerto-ciudad.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Benthic meiofauna as indicator of ecological changes in estuarine ecosystems: The use of nematodes in ecological quality assessment

a b s t r a c t Estuarine meiofauna communities have been only recently considered to be good indicators of ecological quality, exhibiting several advantages over macrofauna, such as their small size, high abundance, rapid generation times and absence of a planktonic phase. In estuaries we must account not only for a great natural variability along the estuarine gradient (e.g. sediment type and dynamics, oxygen availability, temperature and flow speed) but also for the existence of anthropogenic pressures (e.g. high local population density, presence of harbors and dredging activities). Spatial and temporal biodiversity patterns of meiofauna and freeliving marine nematodes were studied in the Mondego estuary (Portugal). Both taxonomic and functional approaches were applied to nematode communities in order to describe the community structure and to relate it with the environmental parameters along the estuary. At all sampling events, nematode assemblages reflected the estuarine gradient, and salinity and grain size composition were confirmed to be the main abiotic factors controlling the distribution of the assemblages. Moreover, the low temporal variability may indicate that natural variability is superimposed by the anthropogenic pressures present in some areas of the estuary. The characterization of both meiofauna and nematode assemblages highlighted the usefulness of the integration of both taxonomic and functional attributes, which must be taken into consideration when assessing the ecological status of estuaries

Prediction of indicators through machine learning and anomaly detection : a case study in the supplementary health system in Brazil

The research aimed to investigate the stages of a MachineLearning model process creation inordertopredict the indicator over the number of medical appointments per day done in the areaof supplementary health inthe region ofPorto Alegre /RS - Brazil and to propose a metric for anomalies detection. Literature reviewand applied case study wasusedas a methodology inthis paper,besides wasused the statistical software calledR, in order toprepare the data and create the model. Thestages ofthecase study was: database extraction, division of the base in training andtesting, creation of functions and feature engineering,variables selection and correlationanalysis, choiceof the algorithms with cross-validationand tuning, training of models, application of the models in the test data, selection of the best model and proposal of the metric for anomalies detection. At the end of these stages, it was possible to select the best modelin terms ofMAE (MeanAbsolute Error), the Random Forest, which was the algorithm withbetter performance when compared to Linear Regression and Neural Network. It also makes possible to identified nine anomaly points and thirty-eight warning points using the standard deviation metric. It was concluded, through the proposed methodology and the results obtained, that the steps of feature engineering and variables selection were essential for the creation and selection of the model, in addition, the proposed metric achieved the objective of generates alerts in the indicator, showing cases with possible problems or opportunities

BrainCAT: a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis

Multimodal neuroimaging studies have recently become a trend in the neuroimaging field and are certainly a standard for the future. Brain connectivity studies combining functional activation patterns using resting-state or task-related functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) tractography have growing popularity. However, there is a scarcity of solutions to perform optimized, intuitive, and consistent multimodal fMRI/DTI studies. Here we propose a new tool, brain connectivity analysis tool (Brain CAT), for an automated and standard multimodal analysis of combined fMRI/DTI data, using freely available tools. With a friendly graphical user interface, BrainCAT aims to make data processing easier and faster, implementing a fully automated data processing pipeline and minimizing the need for user intervention, which hopefully will expand the use of combined fMRI/DTI studies. Its validity was tested in an aging study of the default mode network (DMN) white matter connectivity. The results evidenced the cingulum bundle as the structural connector of the precuneus/posterior cingulate cortex and the medial frontal cortex, regions of the DMN. Moreover, mean fractional anisotropy (FA) values along the cingulum extracted with BrainCAT showed a strong correlation with FA values from the manual selection of the same bundle. Taken together, these results provide evidence that BrainCAT is suitable for these analyses.The authors thank the developers of all the software tools used by BrainCAT, namely, MRIcron, FSL, Diffusion Toolkit, and TrackVis. This work was supported by SwitchBox-FP7-HEALTH-2010-grant 259772-2

Photonic crystal-driven spectral concentration for upconversion photovoltaics

International audienceThe main challenge for applying upconversion (UC) to silicon photovoltaics is the limited amount of solar energy harvested directly via erbium-based upconverter materials (24.5 W m(-2)). This could be increased up to 87.7 W m(-2) via spectral concentration. Due to the nonlinear behavior of UC, this could increase the best UC emission by a factor 13. In this paper, the combined use of quantum dots (QDs)for luminescent down-shiftingand photonic crystals (PCs)for reshaping the emissionto achieve spectral concentration is shown. This implies dealing with the coupling of colloidal QDs and PC at the high-density regime, where the modes are shifted and broadened. In the first fabricated all-optical devices, the spectral concentration rises by 67%, the QD emission that matches the absorption of erbium-based upconverters increases by 158%, and the vertical emission experiences a 680% enhancement. Remarkably, the PC redshifts the overall emission of the QDs, which could be used to develop systems with low reabsorption losses. In light of this, spectral concentration should be regarded as one of the main strategies for UC photovoltaics

Use of Sentinel-2 Satellite for Spatially Variable Rate Fertiliser Management in a Sicilian Vineyard

Satellites can be used for producing maps of within-field crop and soil parameters and, consequentially, spatially variable rate crop input application maps. The plant vegetative vigour index (i.e., Normalised Difference Vegetation Index—NDVI) and the leaf water content index (i.e., Normalised Difference Water Index—NDWI) maps were used to study—through both time and space—the phenological phases of two plots, with Syrah and Nero d’Avola grapevine varieties, in a Sicilian vineyard farm, located in Naro (Agrigento, Sicily, Italy). The aim of this work is to produce spatially variable rate nitrogen fertiliser maps to be applied in the two vineyard plots under study as well as to understand when they should be fertilised or not according to their target crop yields. The average plant vegetative vigour and leaf water content of both the plots showed a high temporal and spatial variability during all phenological phases and, according to these results, the optimal fertilisation time should have been 12 April 2021. In fact, this crop operation is aimed at supporting the vegetative activity but must be performed when the soil water and, therefore, the plant leaf water content are high. Therefore, spatially variable rate fertilisation should have been performed around 12 April 2021 in both plots, using previous NDVI maps and taking into consideration two management zones. This work demonstrates the usefulness of remote sensing data as Decision Support Systems (DSS) for nitrogen fertilisation in order to reduce the production cost, environmental impact and climate footprints per kg of produced grapes, according to the European Green Deal challenges

High temperature magnetic stabilization of cobalt nanoparticles by an antiferromagnetic proximity effect

Thermal activation tends to destroy the magnetic stability of small magnetic nanoparticles, with crucial implications in ultra-high density recording among other applications. Here we demonstrate that low blocking temperature ferromagnetic (FM) Co nanoparticles (TB<70 K) become magnetically stable above 400 K when embedded in a high N\'eel temperature antiferromagnetic (AFM) NiO matrix. The origin of this remarkable TB enhancement is due to a magnetic proximity effect between a thin CoO shell (with low N\'eel temperature, TN; and high anisotropy, KAFM) surrounding the Co nanoparticles and the NiO matrix (with high TN but low KAFM). This proximity effect yields an effective AFM with an apparent TN beyond that of bulk CoO, and an enhanced anisotropy compared to NiO. In turn, the Co core FM moment is stabilized against thermal fluctuations via core-shell exchange-bias coupling, leading to the observed TB increase. Mean-field calculations provide a semi-quantitative understanding of this magnetic- proximity stabilization mechanism