A majority of microrobots are constructed using compliant materials that are difficult to model analytically, limiting the utility of traditional model-based controllers. Challenges in data collection on microrobots and large errors between simulated models and real robots make current model-based learning and sim-to-real transfer methods difficult to apply. We propose a novel framework residual model learning (RML) that leverages approximate models to substantially reduce the sample complexity associated with learning an accurate robot model. We show that using RML, we can learn a model of the Harvard Ambulatory MicroRobot (HAMR) using just 12 seconds of passively collected interaction data. The learned model is accurate enough to be leveraged as "proxy-simulator" for learning walking and turning behaviors using model-free reinforcement learning algorithms. RML provides a general framework for learning from extremely small amounts of interaction data, and our experiments with HAMR clearly demonstrate that RML substantially outperforms existing techniques.

Kernel methods and neural networks are two of the most prevalent and versatile machine learning techniques. While various recent publications focus on invariant or equivariant deep learning algorithms, our goal is to derive kernel-based methods that exploit symmetries. Symmetries play an important role in many research areas such as physics and chemistry [1, 2, 3], but also point cloud classification problems [4] or problems defined on sets [5] are naturally permutation-invariant. One of the most prominent applications is in quantum physics. Systems of bosons require symmetric wave functions, whereas systems of fermions are represented by antisymmetric wave functions. Exploiting such symmetries of the underlying system is a popular and powerful approach that has been used to improve the performance of kernel-based methods as well as deep-learning algorithms.

Optimized performance, reliability, availability and safety can be achieved with the automated operation. Digitalization in the mining industry is allowing mines to expand the utilization of technologies being used successfully both at the mine and plant levels, one of the most significant of which is mine equipment autonomy. While an exciting prospect, the mining industry is still having challenges as it works to integrate new technologies onto its mining equipment, such as shovels, drills, and trucks for capabilities like communications and positioning. Consider, too, that just because a mine has become automated does not mean that maintenance programs should be eliminated. On the contrary, they are perhaps more important than ever to support the optimized factors like availability and utilization of mine equipment now-automated workings as customers demand more than ever from their technology.

My primary interests are in the fields of spatial intelligence – how we can develop better navigation and positioning systems for robots and autonomous vehicles. My main research approach involves using a combination of traditional algorithmic approaches, modern deep learning and biologically-inspired approaches, both in terms of software and hardware. Spatial intelligence is one of the most tangible aspects of general intelligence, and hence it's a great gateway by which to progress our understanding and development of intelligence in robotics. For example, spatial intelligence can be directly observed in the brain, where multiple navigationally-relevant neurons like "place cells" can be observed, and modelled in software to create better performing robotic systems. From a technical point of view, autonomous vehicles are very good but not yet sufficiently perfect to be practicable.

Technology is at the center of the changing world. As this understanding and acceptance has started picking up steam in recent years, even those professions that are manual in nature are making use of technology to drive better business results. One such organization, Vale, S.A., which is one of the largest producers of iron ore in the world, is adapting to the times and adopting technology on the way. In a conversation with Infosys' Ashiss Kumar Dash, Gustavo Vieira, Chief Information Officer, Vale, shared his thoughts on how the mining industry is transforming, and technology is playing an increasingly important role in it. "(It's in an interesting moment in) the mining industry now… where we want to use technology really to bring the value, and also reduce the risks of our operation," says Vieira.

Freeport-McMoRan Inc – often shorthanded as Freeport – closed down 1.83% on Thursday to $31.61 per share, dipping harder than the broader markets. The day's end marked a staggering 27 million trades for the mining company, despite continuing a recent pattern of falling stock prices as seen against the 10-day price average of $35.22. However, stock prices are still up almost 16.5% for the year. Currently, the company is trading with a forward 12-month P/E of 12.65. Freeport-McMoRan is a leading international mining company with headquarters in Phoenix, Arizona.

The article provides a review of the publications on the current trends and developments in Dempster-Shafer theory and its different applications in science, engineering, and technologies. The review took account of the following provisions with a focus on some specific aspects of the theory. Firstly, the article considers the research directions whose results are known not only in scientific and academic community but understood by a wide circle of potential designers and developers of advanced engineering solutions and technologies. Secondly, the article shows the theory applications in some important areas of human activity such as manufacturing systems, diagnostics of technological processes, materials and products, building and construction, product quality control, economic and social systems. The particular attention is paid to the current state of research in the domains under consideration and, thus, the papers published, as a rule, in recent years and presenting the achievements of modern research on Dempster-Shafer theory and its application are selected and analyzed.

The platform uses geospatial data and satellite imagery to provide data-based applications for mineral exploration and discovery and promises to increase hypothesis testing and the speed of the exploration lifecycle. "Traditionally, remote sensing is carried out by specialists (remote sensing geologists) on behalf of the mineral exploration team. Although they still have a role in supporting the process, the Descartes Labs platform puts the technology into the hands of the exploration geologists who know the project areas the best. By leveraging the data obtained from satellite and airborne imagery, they can accelerate their hypothesis formulation and exploration strategies to find new deposits," James Orsulak, senior director of business and sales at Descartes Labs, told MINING.COM. MDC: Your platform puts emphasis on the data refinery.

We introduce the problem of robust subgroup discovery, i.e., finding a set of interpretable descriptions of subsets that 1) stand out with respect to one or more target attributes, 2) are statistically robust, and 3) non-redundant. Many attempts have been made to mine either locally robust subgroups or to tackle the pattern explosion, but we are the first to address both challenges at the same time from a global perspective. First, we formulate a broad model class of subgroup lists, i.e., ordered sets of subgroups, for univariate and multivariate targets that can consist of nominal or numeric variables. This novel model class allows us to formalize the problem of optimal robust subgroup discovery using the Minimum Description Length (MDL) principle, where we resort to optimal Normalized Maximum Likelihood and Bayesian encodings for nominal and numeric targets, respectively. Notably, we show that our problem definition is equal to mining the top-1 subgroup with an information-theoretic quality measure plus a penalty for complexity. Second, as finding optimal subgroup lists is NP-hard, we propose RSD, a greedy heuristic that finds good subgroup lists and guarantees that the most significant subgroup found according to the MDL criterion is added in each iteration, which is shown to be equivalent to a Bayesian one-sample proportions, multinomial, or t-test between the subgroup and dataset marginal target distributions plus a multiple hypothesis testing penalty. We empirically show on 54 datasets that RSD outperforms previous subgroup set discovery methods in terms of quality and subgroup list size.

A Swedish physician who helped pioneer chemistry 200 years ago just got another opportunity to innovate. A supercomputer officially christened in honor of Jöns Jacob Berzelius aims to establish AI as a core technology of the next century. Berzelius (pronounced behr-zeh-LEE-us) invented chemistry's shorthand (think H20) and discovered a handful of elements including silicon. A 300-petaflops system now stands on the Linköping University (LiU) campus, less than 70 kilometers from his birthplace in south-central Sweden, like a living silicon tribute to innovations yet to come. "Many cities in Sweden have a square or street that bears Berzelius's name, but the average person probably doesn't know much about him," said Niclas Andersson, technical director at the National Supercomputer Centre (NSC) at Linköping University, which is home to the system based on the NVIDIA DGX SuperPOD.