The analysis of multidimensional data is becoming a more and more relevant topic in statistical and machine learning research. Given their complexity, such data objects are usually reshaped into matrices or vectors and then analysed. However, this methodology presents several drawbacks. First of all, it destroys the intrinsic interconnections among datapoints in the multidimensional space and, secondly, the number of parameters to be estimated in a model increases exponentially. We develop a model that overcomes such drawbacks. In particular, we proposed a parsimonious tensor regression based model that retains the intrinsic multidimensional structure of the dataset. Tucker structure is employed to achieve parsimony and a shrinkage penalization is introduced to deal with over-fitting and collinearity. An Alternating Least Squares (ALS) algorithm is developed to estimate the model parameters. A simulation exercise is produced to validate the model and its robustness. Finally, an empirical application to Foursquares spatio-temporal dataset and macroeconomic time series is also performed. Overall, the proposed model is able to outperform existing models present in forecasting literature.

Elephants mourn their dead even if they did not have a close bond and continue to take an interest long after their bodies start to decay, a new study finds. Experts from the San Diego Zoo Institute for Conservation Research looked at 32 wild elephant carcasses from 12 different sources across Africa. They monitored the way in which the animals interacted with the carcasses and found that, in all cases, they would touch and examine the remains. They were also seen vocalising and attempting to lift or pull fallen elephants that had just died, according to researchers. New research has shown they mourn their dead even if they don't know them well (stock image) The idea that elephants have a'unique relationship' with the dead has been touted for a number of years, but this new study is the first to examine it in detail.

GNW • Which global factors are expected to impact the artificial intelligence market for automotive and transportation industry in the future? What are the key market strategies being adopted by them? Global Artificial Intelligence Market for Automotive and Transportation Industry Forecast, 2019-2029 In terms of value, the global artificial intelligence market for automotive and transportation industry is expected to grow at a CAGR of 13.12% during the forecast period 2019-2029. The growth in the market is attributable to the ongoing demand for innovative and technologically advanced automotive solutions. Moreover, intelligent solutions which reduce the incidences of human mistakes while driving, along with providing additional features for enhancing ease-of-driving, have driven the market.

AI to counter unmet clinical demand 3.5.1.5

We work in the area of specialized algorithms in ML/AI for Life Sciences and the financial markets.

We study the problem of community detection in multi-layer networks, where pairs of nodes can be related in multiple modalities. We introduce a general framework, i.e., mixture multi-layer stochastic block model (MMSBM), which includes many earlier models as special cases. We propose a tensor-based algorithm (TWIST) to reveal both global/local memberships of nodes, and memberships of layers. We show that the TWIST procedure can accurately detect the communities with small misclassification error as the number of nodes and/or the number of layers increases. Numerical studies confirm our theoretical findings. To our best knowledge, this is the first systematic study on the mixture multi-layer networks using tensor decomposition. The method is applied to two real datasets: worldwide trading networks and malaria parasite genes networks, yielding new and interesting findings.

Training deep neural networks on large-scale datasets requires significant hardware resources whose costs (even on cloud platforms) put them out of reach of smaller organizations, groups, and individuals. Backpropagation (backprop), the workhorse for training these networks, is an inherently sequential process that is difficult to parallelize. Furthermore, it requires researchers to continually develop various tricks, such as specialized weight initializations and activation functions, in order to ensure a stable parameter optimization. Our goal is to seek an effective, parallelizable alternative to backprop that can be used to train deep networks. In this paper, we propose a gradient-free learning procedure, recursive local representation alignment, for training large-scale neural architectures. Experiments with deep residual networks on CIFAR-10 and the massive-scale benchmark, ImageNet, show that our algorithm generalizes as well as backprop while converging sooner due to weight updates that are parallelizable and computationally less demanding. This is empirical evidence that a backprop-free algorithm can scale up to larger datasets. Another contribution is that we also significantly reduce total parameter count of our networks by utilizing fast, fixed noise maps in place of convolutional operations without compromising generalization.

Yoshua Bengio: Yoshua Bengio OCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3] He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.[4] He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.

Since 2014, Russia has played a dominant role in the civil war hostilities in Syria where the testing of technology fresh out of research and development has been applied to measure results, graded by software systems. Such military upgrades launched in Syria and in Yemen include the SS-21 Scarab, the Uran-9 and the Ratnick-4 (robotics). A four day drill was held in December 2019 in the Gulf of Oman and the Indian Ocean. Participants were Russia, Iran, and China whose cooperation, unity, and military exchanges were evident during the drills. Russia's involvement should be considered in the light of a strategy.

The disruptive technologies these days are getting lots of attention in the global technology market. Particularly, artificial intelligence is one such technology that is making headlines every day. With new inventions and innovations, more and more companies are emerging across the industry to offer something that was never explored before. Most of all, various rising start-ups and other AI-based companies are securing hefty amounts of investment from significant investors every now and then. The beginning of new year marked the commencement of new era of innovation with several investors coming forward to contribute to the transformative journey of emerging innovators.