London (CNN Business)Some of the leading artificial intelligence experts from Africa and South America have been denied visas to attend a major industry conference in Canada, dealing a setback to efforts to prevent bias from taking root in the new technology. Conference organizers say Canadian immigration authorities have denied visas to two dozen academics from countries such as Nigeria and Brazil, preventing them from attending the event next month in Vancouver. Katherine Heller, a professor who serves as co-chair of diversity and inclusion at the Neural Information Processing Systems conference, said organizers "are trying extremely hard" to have the visa denials overturned. "It is very significant for the field of AI that all voices be heard," she said. The problem of algorithmic bias in data science has become more pronounced, and there's mounting evidence that AI-powered algorithms display bias against women and some racial groups.

Because tensor data appear more and more frequently in various scientific researches and real-world applications, analyzing the relationship between tensor features and the univariate outcome becomes an elementary task in many fields. To solve this task, we propose \underline{Fa}st \underline{S}parse \underline{T}ensor \underline{R}egression model (FasTR) based on so-called unit-rank CANDECOMP/PARAFAC decomposition. FasTR first decomposes the tensor coefficient into component vectors and then estimates each vector with $\ell_1$ regularized regression. Because of the independence of component vectors, FasTR is able to solve in a parallel way and the time complexity is proved to be superior to previous models. We evaluate the performance of FasTR on several simulated datasets and a real-world fMRI dataset. Experiment results show that, compared with four baseline models, in every case, FasTR can compute a better solution within less time.

The gradient noise (GN) in the stochastic gradient descent (SGD) algorithm is often considered to be Gaussian in the large data regime by assuming that the \emph{classical} central limit theorem (CLT) kicks in. This assumption is often made for mathematical convenience, since it enables SGD to be analyzed as a stochastic differential equation (SDE) driven by a Brownian motion. We argue that the Gaussianity assumption might fail to hold in deep learning settings and hence render the Brownian motion-based analyses inappropriate. Inspired by non-Gaussian natural phenomena, we consider the GN in a more general context and invoke the \emph{generalized} CLT, which suggests that the GN converges to a \emph{heavy-tailed} $\alpha$-stable random vector, where \emph{tail-index} $\alpha$ determines the heavy-tailedness of the distribution. Accordingly, we propose to analyze SGD as a discretization of an SDE driven by a L\'{e}vy motion. Such SDEs can incur `jumps', which force the SDE and its discretization \emph{transition} from narrow minima to wider minima, as proven by existing metastability theory and the extensions that we proved recently. In this study, under the $\alpha$-stable GN assumption, we further establish an explicit connection between the convergence rate of SGD to a local minimum and the tail-index $\alpha$. To validate the $\alpha$-stable assumption, we conduct experiments on common deep learning scenarios and show that in all settings, the GN is highly non-Gaussian and admits heavy-tails. We investigate the tail behavior in varying network architectures and sizes, loss functions, and datasets. Our results open up a different perspective and shed more light on the belief that SGD prefers wide minima.

Public road authorities and private mobility service providers need information derived from the current and predicted traffic states to act upon the daily urban system and its spatial and temporal dynamics. In this research, a real-time parking area state (occupancy, in- and outflux) prediction model (up to 60 minutes ahead) has been developed using publicly available historic and real time data sources. Based on a case study in a real-life scenario in the city of Arnhem, a Neural Network-based approach outperforms a Random Forest-based one on all assessed performance measures, although the differences are small. Both are outperforming a naive seasonal random walk model. Although the performance degrades with increasing prediction horizon, the model shows a performance gain of over 150% at a prediction horizon of 60 minutes compared with the naive model. Furthermore, it is shown that predicting the in- and outflux is a far more difficult task (i.e. performance gains of 30%) which needs more training data, not based exclusively on occupancy rate. However, the performance of predicting in- and outflux is less sensitive to the prediction horizon. In addition, it is shown that real-time information of current occupancy rate is the independent variable with the highest contribution to the performance, although time, traffic flow and weather variables also deliver a significant contribution. During real-time deployment, the model performs three times better than the naive model on average. As a result, it can provide valuable information for proactive traffic management as well as mobility service providers.

One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth's energy balance. Aerosols provide the `seeds' on which cloud droplets form, and changes in the amount of aerosol available to a cloud can change its brightness and other physical properties such as optical thickness and spatial extent. Clouds play a critical role in moderating global temperatures and small perturbations can lead to significant amounts of cooling or warming. Uncertainty in this effect is so large it is not currently known if it is negligible, or provides a large enough cooling to largely negate present-day warming by CO2. This work uses deep convolutional neural networks to look for two particular perturbations in clouds due to anthropogenic aerosol and assess their properties and prevalence, providing valuable insights into their climatic effects.

Motivated by applications in various scientific fields having demand of predicting relationship between higher-order (tensor) feature and univariate response, we propose a \underline{S}parse and \underline{L}ow-rank \underline{T}ensor \underline{R}egression model (SLTR). This model enforces sparsity and low-rankness of the tensor coefficient by directly applying $\ell_1$ norm and tensor nuclear norm on it respectively, such that (1) the structural information of tensor is preserved and (2) the data interpretation is convenient. To make the solving procedure scalable and efficient, SLTR makes use of the proximal gradient method to optimize two norm regularizers, which can be easily implemented parallelly. Additionally, a tighter convergence rate is proved over three-order tensor data. We evaluate SLTR on several simulated datasets and one fMRI dataset. Experiment results show that, compared with previous models, SLTR is able to obtain a solution no worse than others with much less time cost.

The Dead Sea Scrolls, found by a shepherd boy in 1947, dating from roughly 200 BC through 100 AD, were remarkably well-preserved. Exciting finds like the Scrolls and the hieroglyphs of ancient Egypt tempt us to think that when a lost document is found, we will easily physically read it once we understand the language. Sadly, many surviving documents are so damaged that they cannot be read using traditional methods. All we know is that they are/were documents. Nowadays, the 1,700-year-old En-Gedi Scroll--one of the most ancient snippets of the Old Testament ever uncovered--isn't much to look at.

The firm has also developed a drone able to fly around the underground corridors of coal mines to detect gas emissions and other potential threats. Marcin Dziekanski, coordinator of the drone project of the Silesian metropolis, an alliance of more than 40 cities in the coal-mining Katowice region, said they use drones to monitor the smoke produced by coal-heated individual houses. "They fly over Katowice, over the buildings, as well as over other cities, enabling us to intervene, in cooperation with the city police, showing that we are monitoring our space, our environment," he told AFP, adding that "we are creating a set of good practices that we are sharing with others." Spartaqs considers itself above all a research firm looking into new technologies, though it has already sold a dozen drones--at an average price of 50,000 euros ($55,000) a pop--in Poland and Georgia. But the company has realised that buyers like the Saudis and the Americans, who are very interested in certain models, want to see "the plant where they are produced." So they have begun looking for investors, including abroad, who would like to participate in the development of a serial production line.

By Country (United States; Canada; Mexico and Rest of North America) 6.3.

A collection of 5,000-year-old cave paintings depicting various figures and symbols has been discovered in Spain. The drawings were discovered in the rocky area of San Juan, near the town of Albuquerque in the province of Badajoz in western Spain. They are around 4 inches in length and include some anthropomorphic figures, as well as an arrow and other symbols, according to Spanish daily newspaper La Vanguardia. The doodlings were discovered by Agustín Palomo, an historic researcher who lives locally to the caves, while he was looking for a type of tomb known as a Dolmen. Mr Palomo immediately recognised their significance, given their location not far from two other well-known sets of cave drawings - 'Risco de San Blas', of the Sierra de la Carava and those of Azagala - the latter of which were only discovered around 20 years ago.