Semantic parsing transforms a natural language question into a formal query over a knowledge base. Many existing methods rely on syntactic parsing like dependencies. However, the accuracy of producing such expressive formalisms is not satisfying on long complex questions. In this paper, we propose a novel skeleton grammar to represent the high-level structure of a complex question. This dedicated coarse-grained formalism with a BERT-based parsing algorithm helps to improve the accuracy of the downstream fine-grained semantic parsing. Besides, to align the structure of a question with the structure of a knowledge base, our multi-strategy method combines sentence-level and word-level semantics. Our approach shows promising performance on several datasets.

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We have updated the free NIR-Predictor-Software Spectral Data format support list for many mobile and benchtop NIR Spectroscopy Sensors. Used in QualityControl for Food Fruits Milk Meat LINK CalibrationModel.com has changed the pricing structure and NIRS-Calibration licensing options (including new perpetual and unlimited systems).

We have updated the free NIR-Predictor-Software Spectral Data format support list for many mobile and benchtop NIR Spectroscopy Sensors. Used in QualityControl for Food Fruits Milk Meat LINK CalibrationModel.com has changed the pricing structure and NIRS-Calibration licensing options (including new perpetual and unlimited systems).

This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this first part, we describe a classification for discoveries of physical matter (molecules, materials, devices), processes, and models and how they are unified as search problems. We then introduce a set of questions and considerations relevant to assessing the extent of autonomy. Finally, we describe many case studies of discoveries accelerated by or resulting from computer assistance and automation from the domains of synthetic chemistry, drug discovery, inorganic chemistry, and materials science. These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modelling. Part two reflects on these case studies and identifies a set of open challenges for the field.

Bhutan is sometimes described as \a pebble between two boulders", a small country caught between the two most populous nations on earth: India and China. This pebble is, however, about to be caught up in a vortex: the transformation of our economic, political and social orders by new technologies like Artificial Intelligence. What can a small nation like Bhutan hope to do in the face of such change? What should the nation do, not just to weather this storm, but to become a better place in which to live?

The artificial segmentation of an investment management process into a workflow with silos of offline human operators can restrict silos from collectively and adaptively pursuing a unified optimal investment goal. To meet the investor's objectives, an online algorithm can provide an explicit incremental approach that makes sequential updates as data arrives at the process level. This is in stark contrast to offline (or batch) processes that are focused on making component level decisions prior to process level integration. Here we present and report results for an integrated, and online framework for algorithmic portfolio management. This article provides a workflow that can in-turn be embedded into a process level learning framework. The workflow can be enhanced to refine signal generation and asset-class evolution and definitions. Our results confirm that we can use our framework in conjunction with resampling methods to outperform naive market capitalisation benchmarks while making clear the extent of back-test over-fitting. We consider such an online update framework to be a crucial step towards developing intelligent portfolio selection algorithms that integrate financial theory, investor views, and data analysis with process-level learning.

Enver Yucel is the founder of BAU Global, a broad education network headquartered in Turkey, consisting of five universities, three language schools, four academic centers and one boarding school spread across North America, Europe, Africa and Asia. Yucel has devoted his life to education, having served an estimated 150,000 students since starting his first institution with three class rooms in Istanbul in 1974. He is also a member of the Advisory Board of the UN Institute for Training and Research. He was invited to speak at the AI World Conference & Expo in Boston in the fall of 2019, the first Turkish speaker in the four years of the conference. He recently took some time to answer questions posed by AI Trends Editor John P Desmond, who was in the audience for his Boston talk.

The 2020 Call for Code Global Challenge has expanded its focus to tackle the effects of COVID-19. Technology solutions can help reduce the impact this pandemic has on our daily lives and the world. COVID-19, which is caused by the novel corona virus, has revealed the limits of the systems we take for granted in a very short period of time. Whether it's the massive increase in demand for information during a time of crisis, educating children when schools are closed, or helping communities best distribute limited resources, technology has a pivotal role to play. Through Call for Code, you can see your idea deployed by a global partner ecosystem.

"Gone are the days of data engineers manually copying data around again and again, delivering datasets weeks after a data scientist requests it"-these are Steven Mih's words about the revolution that artificial intelligence is bringing about, in the scary world of big data. By the time the term "big data" was coined, data had already accumulated massively with no means of handling it properly. In 1880, the US Census Bureau estimated that it would take eight years to process the data it received in that year's census. The government body also predicted that it would take more than 10 years to process the data it would receive in the following decade. Fortunately, in 1881, Herman Hollerith created the Hollerith Tabulating Machine, inspired by a train conductor's punch card.