Catalyst is a PyTorch framework developed with the intent of advancing research and development in the domain of deep learning. It enables code reusability, reproducibility and rapid experimentation so that users can conveniently create deep learning models and pipelines without writing another training loop. Catalyst framework is part of the PyTorch ecosystem – a collection of numerous tools and libraries for AI development. It is also a part of the Catalyst Ecosystem – an MLOps ecosystem that expedites training, analysis and deployment of deep learning experiments through Catalyst, Alchemy and Reaction frameworks respectively. We have used the well-known MNIST dataset having 10 output classes (for classifying images of handwritten digits from 0 to 9).

Qatar is a small peninsular nation on the northeastern coast of the Arabian Peninsula. Qatar is endowed with abundant hydrocarbon resources and is the world's largest producer of liquified natural gas (LNG), which accounts for over 80% of its export earnings. Like many of its wealthy neighbors, Qatar faces a unique dilemma with the onset of artificial intelligence (AI) technologies. Despite having one of the world's highest per-capita income and a highly educated local population, the majority of Qataris are under-employed and working in government white collar jobs where they are unable to fully realize the potential of their level of education. These are precisely the occupations that are likely to be made redundant by AI.1 The bulk of the workforce in Qatar consists of expatriates drawn primarily from South Asia and the Middle East and North Africa (MENA) region.

Background: Non-target screening consists in searching a sample for all present substances, suspected or unknown, with very little prior knowledge about the sample. This approach has been introduced more than a decade ago in the field of water analysis, but is still very scarce for indoor and atmospheric trace gas measurements, despite the clear need for a better understanding of the atmospheric trace gas composition. For a systematic detection of emerging trace gases in the atmosphere, a new and powerful analytical method is gas chromatography (GC) of preconcentrated samples, followed by electron ionisation, high resolution mass spectrometry (EI-HRMS). In this work, we present data analysis tools to enable automated identification of unknown compounds measured by GC-EI-HRMS. Results: Based on co-eluting mass/charge fragments, we developed an innovative data analysis method to reliably reconstruct the chemical formulae of the fragments, using efficient combinatorics and graph theory. The method (i) does not to require the presence of the molecular ion, which is absent in $\sim$40% of EI spectra, and (ii) permits to use all measured data while giving more weight to mass/charge ratios measured with better precision. Our method has been trained and validated on >50 halocarbons and hydrocarbons with a molar masses of 30-330 g mol-1 , measured with a mass resolution of approx. 3500. For >90% of the compounds, more than 90% of the reconstructed signal is correct. Cases of wrong identification can be attributed to the scarcity of detected fragments per compound (less than six measured mass/charge) or the lack of isotopic constrain (no rare isotopocule detected). Conclusions: Our method enables to reconstruct most probable chemical formulae independently from spectral databases. Therefore, it demonstrates the suitability of EI-HRMS data for non-target analysis and paves the way for the identification of substances for which no EI mass spectrum is registered in databases. We illustrate the performances of our method for atmospheric trace gases and suggest that it may be well suited for many other types of samples.

Drought, heat, and pest infestation: Climate change is threatening forests in Germany and represents a big challenge in forest management. A joint project of Karlsruhe Institute of Technology (KIT) and EDI GmbH, a spinoff of KIT, now provides support. Together with partners in the forestry sector, they are developing the EDE 4.0 assistance system. Based on artificial intelligence (AI), it helps foresters preserve and sustainably manage forests. Climate change also affects forests in Germany.

Collision-free path planning is an essential requirement for autonomous exploration in unknown environments, especially when operating in confined spaces or near obstacles. This study presents an autonomous exploration technique using a small drone. A local end-point selection method is designed using LiDAR range measurement and then generates the path from the current position to the selected end-point. The generated path shows the consistent collision-free path in real-time by adopting the Euclidean signed distance field-based grid-search method. The simulation results consistently showed the safety, and reliability of the proposed path-planning method. Real-world experiments are conducted in three different mines, demonstrating successful autonomous exploration flight in environments with various structural conditions. The results showed the high capability of the proposed flight autonomy framework for lightweight aerial-robot systems. Besides, our drone performs an autonomous mission during our entry at the Tunnel Circuit competition (Phase 1) of the DARPA Subterranean Challenge.

Many of the commodities that affect our everyday lives are processed by smelters and fumer-furnace operations. These include products made from copper, nickel, and zinc, such as electrical wiring, kitchen appliances, and some types of batteries. Smelters are also used to produce precious metals, such as gold or silver, and animal feed, and to provide heat for dryers and roasters, which must be operated in concert for efficient production. Despite the importance of smelters, operations are becoming increasingly challenging. Most smelters globally have been in operation for at least 20 years, resulting in higher maintenance requirements. Second, feed quality is declining.

This seminar forms part of the AI3SD Online Seminar Series that will run across the winter (from November 2020 to April 2021). This seminar will be run via zoom, when you register on Eventbrite you will receive a zoom registration email alongside your standard Eventbrite registration email. Where speakers have given permission to be recorded, their talks will be made available on our AI3SD YouTube Channel. The theme for this seminar is Property Prediction. Abstract: Equations of state (EoS) for fluids have been a staple of engineering design and practice for over a century.

The internet of things (IoT) is increasingly becoming a key component of many companies' data-driven transformation strategies. Indeed, organizations that have embraced IoT are already seeing benefits such as improved operational processes, better inventory management, and enhanced equipment maintenance -- to name a few. But a successful IoT strategy is more than just connecting a bunch of devices and sensors to the internet and gathering data from these "things." IT must establish the ability to effectively analyze the vast amounts of data IoT creates in order to make sense of it and gain real business insights. That's why an analytics strategy for IoT should be a top priority for any company looking to get the most out of all the connectivity.

We review our recent work in the area of autonomous materials research, highlighting the coupling of machine learning methods and models and more problem-aware modeling. We review the general Bayesian framework for closed-loop design employed by many autonomous materials platforms. We then provide examples of our work on such platforms. We finally review our approaches to extend current statistical and ML models to better reflect problem-specific structure including the use of physics-based models and incorporation of operational considerations into the decision-making procedure.

Ocean scientists are using robot submariness to detect barrels of toxic chemicals under the sea. Thousands of barrels of DDT and other substances are believed submerged in the Pacific Ocean near Los Angeles, but authorities aren't sure where or how many. To get an idea, researchers have launched two'underwater Roombas,' Remote Environmental Monitoring UnitS (REMUS) that can operate in waters ranging from 80 feet to about 20,000 feet. The vehicles take 12 hours to recharge, so while one is scanning the seafloor with its sonar the other is powering up and passing along its findings. Ocean scientists are using'underwater Roombas' to scan the ocean floor for barrels of toxic chemicals, including the banned pesticide DDT.