As society has evolved, technology has as well, and there is a growing awareness that already-established police techniques -- if used exclusively -- are somewhat out-of-date and oftentimes quite expensive for what they offer. When departments sink valuable resources into maintaining old systems instead of investing into newer, more efficient, and cost-effective technologies -- especially in an era of budget cuts where law enforcement agencies are forced to make difficult decisions as to where to cut funding -- these agencies are missing out on a valuable source of information. One only needs to look at history to witness the evolution of criminal investigations. Fingerprinting, DNA analysis, and computer information systems such as CODIS (Combined DNA Index System) and NDIS (National DNA Index System) have improved investigatory efforts considerably; however, as technology continues to evolve -- and criminals are openly taking advantage of this new technology -- law enforcement agencies may be missing out on a valuable opportunity if they don't embrace more openly the tremendous benefits such new technology brings. The United States spends more than $100 billion annually on law enforcement and incarceration, and this figure does not even consider other economic impacts of crime in terms of victims' costs, property devaluation, and higher outlays for companies to ensure their security.

Lung cancer continues to be a major healthcare challenge with high morbidity and mortality rates among both men and women worldwide. The majority of lung cancer cases are of non-small cell lung cancer type. With the advent of targeted cancer therapy, it is imperative not only to properly diagnose but also sub-classify non-small cell lung cancer. In our study, we evaluated the utility of using Apple Create ML module to detect and sub-classify non-small cell carcinomas based on histopathological images. After module optimization, the program detected 100% of non-small cell lung cancer images and successfully subclassified the majority of the images. Trained modules, such as ours, can be utilized in diagnostic smartphone-based applications, augmenting diagnostic services in understaffed areas of the world.

We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, iii) a comprehensive evaluation of 15 diverse recent methods that captures the status quo of the field, and iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at bop.felk.cvut.cz.

While deep learning models and techniques have achieved great empirical success, our understanding of the source of success in many aspects remains very limited. In an attempt to bridge the gap, we investigate the decision boundary of a production deep learning architecture with weak assumptions on both the training data and the model. We demonstrate, both theoretically and empirically, that the last weight layer of a neural network converges to a linear SVM trained on the output of the last hidden layer, for both the binary case and the multi-class case with the commonly used cross-entropy loss. Furthermore, we show empirically that training a neural network as a whole, instead of only fine-tuning the last weight layer, may result in better bias constant for the last weight layer, which is important for generalization. In addition to facilitating the understanding of deep learning, our result can be helpful for solving a broad range of practical problems of deep learning, such as catastrophic forgetting and adversarial attacking. The experiment codes are available at https://github.com/lykaust15/NN_decision_boundary

For example, given a view of a chair with two rear legs occluded by front legs, humans are easily able to guess the most likely shape behind the visible parts. Recent advances in deep neural networks and data driven approaches show promising results in dealing with such a task. In this paper, we aim to acquire the complete and highresolution 3D shape of an object given a single depth view. By leveraging the high performance of 3D convolutional neural nets and large open datasets of 3D models, our approach learns a smooth function that maps a 2.5D view to a complete and dense 3D shape. In particular, we train an endto-end model which estimates full volumetric occupancy from a single 2.5D depth view of an object. As a result, the learnt 3D structure tends to be coarse and inaccurate. In order to generate higher resolution 3D objects with efficient computation, Octree representation has been recently introduced in [13] [14] [15]. However, increasing the density of output 3D shapes would also inevitably pose a great challenge to learn the geometric details for high resolution 3D structures, which has yet to be explored. Recently, deep generative models achieve impressive success in modeling complex high-dimensional data distributions, among which Generative Adversarial Networks (GANs) [16] and Variational Autoencoders (VAEs) [17] emerge as two powerful frameworks for generative learning, including image and text generation [18] [19], and latent space learning [20] [21]. In the past few years, a number of works [22] [23] [24] [25] applied such generative models to learn latent space to represent 3D object shapes, in order to solve tasks such as new image generation, object classification, recognition and shape retrieval. Abstract--In this paper, we propose a novel approach, 3D-RecGAN, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks.

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. Our intent is to provide you a one-stop source of late-breaking news to help you keep abreast of this fast-paced ecosystem. We're working hard on your behalf with our extensive vendor network to give you all the latest happenings. Be sure to Tweet Above the Trend Line articles using the hashtag: #abovethetrendline.

Technology experts have uncovered a vast misinformation campaign attempting to spread stories on a hugescale. Facebook, and Twitter have found and removed hundreds of accounts that were apparently set up to target users in the US, UK, Latin America and the Middle East. But it is not clear what the campaign was being used for. The accounts appear to have been tied to Iranian actors and cybersecurity firms said they had appeared to be promoting Iran's geopolitical agenda around the world. But whether the campaign was being set up to launch any more specific or targeted attack remains unclear.

Even though we do our best to bring you a solid 52 Video Fridays every year (which works out to over 1,000 robot videos annually), we can't manage to post everything, and sometimes, we miss out on some awesome stuff. That's just one of the reasons why we always look forward to the Robot Film Festival, and the 2018 event took place in July in Portland, Ore. I showed up and gave a talk (most of which you can see in this article), and then found a seat and watched the film selections. As always, there was an impressive amount of really, really good robot videos that I'd never seen before. The videos have all been posted online, and we've picked out a few of the happiest, saddest, scariest, and cleverest to share.

Aug 20, 2018 (Heraldkeeper via COMTEX) -- A new research document is added in HTF MI database of 90 pages, titled as'Global Deep Learning Market (2018-2023)' with detailed analysis, Competitive landscape, forecast and strategies. The study covers geographic analysis that includes regions like North America, Europe, Asia-Pacific, Latin America, and the Middle East and Africa and important players/vendors such as Google, Microsoft, IBM, Intel etc. The report will help you gain market insights, future trends and growth prospects for forecast period of 2018-2023. In enterprise computing, deep learning is evolving into one of the most advanced technologies. Deep learning is a subset of machine learning in Artificial Intelligence (AI) that has networks capable of learning unsupervised, from data that is unstructured or unlabeled.

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. In this paper, we consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we propose a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We present the new model, its induced likelihood function, and a fitting algorithm which can be applied in the presence of missing data. We demonstrate how to use XPCA to produce an estimated full conditional distribution for each data point, and use this to produce to provide estimates for missing data that are automatically range respecting. We compare the methods as applied to simulated and real-world data sets that have a mixture of discrete and continuous variables.