From the early decades of the 20th century, Slovenia's history has primed the nation to become an AI pioneer and has greatly accelerated global progress in artificial intelligence solutions. Not only that, but the entrepreneurial blood that courses through Slovenia, as well as the country's human-centric, society-oriented approach to the digital future, is what has prompted the nation to launch some of the world's most innovative technologies. It is not difficult to see why UNESCO decided to partner strategically into Slovenia's scientific and technological might––there is simply no other nation on Earth that is taking leaps in AI as big as those emanating from the beating heart of Ljubljana. Let Slovenia's IRCAI serve as a premier example of what countries everywhere ought to be doing in order to stay ahead in the digital world of tomorrow. If a mostly unknown nation of just two million strong is capable of positioning itself as an AI powerhouse, so too can other countries.

South Korea is the perfect arena for the field of Artificial Intelligence to flourish. Plenty of talent and capital has been set aside for scientific research and development, the country has the advantage of stable government, while an aging demographic profile presents an opportunity for the technology to be deployed to help meet the challenges of an older population. The Moon administration has reinvigorated the Korean science and technology community in its first three years, demonstrated by activating the first 5G network in April 2019 and the proposed Defense Reform 2.0 for a smaller, yet smarter military. South Korea's Covid-19 response has also added another success in this arena. President Moon Jae-in has made technology a showpiece of his first term, with AI a crucial element.

Working at Shell's Deepwater division in New Orleans gives Barbara Waelde a front-row seat to how the right data can unlock crucial information for the oil giant. So when her supervisor asked her last year if she was interested in a program that could sharpen her digital and data science capabilities, Waelde, 55, jumped at the chance. Since she began her online coursework, the seven-year Shell veteran has learned Python programming, supervised learning algorithms and data modeling, among other skills. Shell began making these online courses available to U.S. employees long before COVID-19 upended daily life. And according to the oil giant, there are no plans to halt or cancel any of them, despite the fact that on March 23 it announced plans to slash operating costs by $9 billion.

San Diego-based TuSimple, which operates a separate unit in China, has 40 18-wheelers operating out of its depot in Tucson, Arizona, and is "essentially running 24/7" carrying loads between Phoenix and El Paso, Texas, chief product officer Chuck Price tells Forbes. It's a tiny freight operation compared to the massive fleets of national haulers like J.B. Hunt, Swift, Werner and Amazon AMZN, each with thousands of trucks and drivers, but no company has more self-driving semis than TuSimple, based on U.S. Transportation Department registry data.

Day after day, the rising number of new cases of the coronavirus in Tokyo and nationwide is making headlines. But what is even more alarming is the increasing number of instances where authorities can't track where the patient got it from. Tracing the source of infection is vital in curbing the outbreak, sparking concerns among medicine and virology experts that the situation in Japan may get out of control like it has in Western countries. In early April, experts from a government-appointed panel said they were not able to verify where or when patients had contracted the virus in 40 percent of reported cases. In Tokyo, the figure is ominously higher than the nation's average. When Tokyo reported a record 197 new coronavirus cases Saturday, 77 percent were cases in which they weren't able to track down the source of infection.

An online chatbot designed to help people living with arthritis and similar conditions during the coronavirus pandemic has been launched in the UK. Developed by charity Versus Arthritis, the the AI tool, called COVA, is the first of its kind which hopes to help people with long-term health condition living in the UK. It was created in 14 days after the charity was inundated with requests for help and information on its helpline, website and social media. The chatbot incorporates health information developed specifically for people with arthritis and musculoskeletal (MSK) conditions on the impact of COVID-19, with answers from Versus Arthritis as well as the British Society for Rheumatology, the NHS and GOV.UK. It is available on the Versus Arthritis website and can be embedded on other websites.

Adsorption is a relevant process in many fields, such as product manufacturing or pollution remediation in porous materials. Adsorption takes place at the molecular scale, amenable to be modeled by Lagrangian numerical methods. We have proposed a chemical diffusion-reaction model for the simulation of adsorption, based on the combination of a random walk particle tracking method involving the use of Gaussian Kernel Density Estimators. The main feature of the proposed model is that it can effectively reproduce the nonlinear behavior characteristic of the Langmuir and Freundlich isotherms. In the former, it is enough to add a finite number of sorption sites of homogeneous sorption properties, and to set the process as the combination of the forward and the backward reactions, each one of them with a prespecified reaction rate. To model the Freundlich isotherm instead, typical of low to intermediate range of solute concentrations, there is a need to assign a different equilibrium constant to each specific sorption site, provided they are all drawn from a truncated power-law distribution. Both nonlinear models can be combined in a single framework to obtain a typical observed behavior for a wide range of concentration values.

Network method of moments arXiv:1202.5101 is an important tool for nonparametric network inferences. However, there has been little investigation on accurate descriptions of the sampling distributions of network moment statistics. In this paper, we present the first higher-order accurate approximation to the sampling CDF of a studentized network moment by Edgeworth expansion. In sharp contrast to classical literature on noiseless U-statistics, we showed that the Edgeworth expansion of a network moment statistic as a noisy U-statistic can achieve higher-order accuracy without non-lattice or smoothness assumptions but just requiring weak regularity conditions. Behind this result is our surprising discovery that the two typically-hated factors in network analysis, namely, sparsity and edge-wise observational errors, jointly play a blessing role, contributing a crucial self-smoothing effect in the network moment statistic and making it analytically tractable. Our assumptions match the minimum requirements in related literature. For practitioners, our empirical Edgeworth expansion is highly accurate and computationally efficient. It is also easy to implement. These were demonstrated by comprehensive simulation studies. We showcase three applications of our results in network inference. We proved, to our knowledge, for the first time that some network bootstraps enjoy higher-order accuracy, and provided theoretical guidance for tuning network sub-sampling. We also derived a one-sample test and Cornish-Fisher confidence interval for any given moment, both with analytical formulation and explicit error rates.

We present the Dayton Annotated LiDAR Earth Scan (DALES) data set, a new large-scale aerial LiDAR data set with over a half-billion hand-labeled points spanning 10 square kilometers of area and eight object categories. Large annotated point cloud data sets have become the standard for evaluating deep learning methods. However, most of the existing data sets focus on data collected from a mobile or terrestrial scanner with few focusing on aerial data. Point cloud data collected from an Aerial Laser Scanner (ALS) presents a new set of challenges and applications in areas such as 3D urban modeling and large-scale surveillance. DALES is the most extensive publicly available ALS data set with over 400 times the number of points and six times the resolution of other currently available annotated aerial point cloud data sets. This data set gives a critical number of expert verified hand-labeled points for the evaluation of new 3D deep learning algorithms, helping to expand the focus of current algorithms to aerial data. We describe the nature of our data, annotation workflow, and provide a benchmark of current state-of-the-art algorithm performance on the DALES data set.

The big problem for neural network models which are trained to count instances is that whenever test range goes high training range generalization error increases i.e. they are not good generalizers outside training range. Consider the case of automating cell counting process where more dense images with higher cell counts are commonly encountered as compared to images used in training data. By making better predictions for higher ranges of cell count we are aiming to create better generalization systems for cell counting. With architecture proposal of neural arithmetic logic units (NALU) for arithmetic operations, task of counting has become feasible for higher numeric ranges which were not included in training data with better accuracy. As a part of our study we used these units and different other activation functions for learning cell counting task with two different architectures namely Fully Convolutional Regression Network and U-Net. These numerically biased units are added in the form of residual concatenated layers to original architectures and a comparative experimental study is done with these newly proposed changes . This comparative study is described in terms of optimizing regression loss problem from these models trained with extensive data augmentation techniques. We were able to achieve better results in our experiments of cell counting tasks with introduction of these numerically biased units to already existing architectures in the form of residual layer concatenation connections. Our results confirm that above stated numerically biased units does help models to learn numeric quantities for better generalization results.