Significant advancements in the field of wood species identification are needed worldwide to support sustainable timber trade. In this work we contribute to automate the identification of wood species via high-resolution macroscopic images of timber. The main challenge of this problem is that fine-grained patterns in timber are crucial in order to accurately identify wood species, and these patterns are not properly learned by traditional convolutional neural networks (CNNs) trained on low/medium resolution images. We propose a Timber Deep Learning Identification with Patch-based Inference Voting methodology, abbreviated TDLI-PIV methodology. Our proposal exploits the concept of patching and the availability of high-resolution macroscopic images of timber in order to overcome the inherent challenges that CNNs face in timber identification. The TDLI-PIV methodology is able to capture fine-grained patterns in timber and, moreover, boosts robustness and prediction accuracy via a collaborative voting inference process. In this work we also introduce a new data set of marcroscopic images of timber, called GOIMAI-Phase-I, which has been obtained using optical magnification in order to capture fine-grained details, which contrasts to the other datasets that are publicly available. More concretely, images in GOIMAI-Phase-I are taken with a smartphone with a 24x magnifying lens attached to the camera. Our data set contains 2120 images of timber and covers 37 legally protected wood species. Our experiments have assessed the performance of the TDLI-PIV methodology, involving the comparison with other methodologies available in the literature, exploration of data augmentation methods and the effect that the dataset size has on the accuracy of TDLI-PIV.

A novel method for SPECT angle interpolation based on deep learning methodologies is presented. Projection data from software phantoms were used to train the proposed model. For evaluation of the efficacy of the method, phantoms based on Shepp Logan, with various noise levels added were used, and the resulting interpolated sinograms are reconstructed using Ordered Subset Expectation Maximization (OSEM) and compared to the reconstructions of the original sinograms. The proposed method can quadruple the projections, and denoise the original sinogram, in the same process. As the results show, the proposed model significantly improves the reconstruction accuracy. Finally, to demonstrate the efficacy and capability of the proposed method results from real-world DAT-SPECT sinograms are presented.

Why Is Deep Learning Called Deep? You must have asked that question.

There have been more than 850,000 confirmed cases and over 48,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there are currently no proven effective medications against COVID-19. Drug repurposing offers a promising way for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, genes, pathways, and expressions, from a large scientific corpus of 24 million PubMed publications. Using Amazon AWS computing resources, we identified 41 repurposable drugs (including indomethacin, toremifene and niclosamide) whose therapeutic association with COVID-19 were validated by transcriptomic and proteomic data in SARS-CoV-2 infected human cells and data from ongoing clinical trials. While this study, by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential of accelerating therapeutic development for COVID-19.

Deep learning is already helping the designers to make a new edition of a product. With the help of data from the sensors and some of the finest deep learning methods, the experts are able to optimize the designs. We have already seen that deep learning, AI, ML and big data have augmented creativity the design space. The biggest example is the IBM's machine learning system, Watson. A large number of images were inserted into the system, and of the images were the work of artist Gaudi.

We are on the brink of a major disruption, which we think might be bigger than the industrial revolution. At BootstrapLabs we are focusing heavily on a major shift that is impacting almost every sector: Artificial Intelligence – AI. AI has reached an inflection point, where it can now be applied to quickly drive efficient returns, and in our book, is ripe for building startups that will disrupt major markets and their incumbents. Let's go back: The Industrial Revolution & Apple During the Industrial Revolution, the steam engine enabled a major technological shift as a large amount of manual labor was now able to be automated. Yet, few know that the first version of the steam engine was actually built the 1st century CE and was called Aeolipile.

Still think the Great Pyramid is an ancient tomb? Some experts in deep learning may beg to differ. They have discovered that a particular application of deep learning methodology applied to the ancient structures on the Giza Plateau, including the Great Pyramid, may in fact show that it is a three dimensional process diagram. "We were using a deep learning methodology that helps us identify and categorize unknown symbols and processes. We thought it would be fun to use it on something everyone knows. We were amazed to find that the Giza Plateau may in fact be a three dimensional process diagram."