Event extraction is typically modeled as a multi-class classification problem where both event types and argument roles are treated as atomic symbols. These approaches are usually limited to a set of pre-defined types. We propose a novel event extraction framework that takes event types and argument roles as natural language queries to extract candidate triggers and arguments from the input text. With the rich semantics in the queries, our framework benefits from the attention mechanisms to better capture the semantic correlation between the event types or argument roles and the input text. Furthermore, the query-and-extract formulation allows our approach to leverage all available event annotations from various ontologies as a unified model. Experiments on two public benchmarks, ACE and ERE, demonstrate that our approach achieves state-of-the-art performance on each dataset and significantly outperforms existing methods on zero-shot event extraction. We will make all the programs publicly available once the paper is accepted.

Statistical modeling of outcomes based on a patient's presenting symptoms (symptomatology) can help deliver high quality care and allocate essential resources, which is especially important during the COVID-19 pandemic. Patient symptoms are typically found in unstructured notes, and thus not readily available for clinical decision making. In an attempt to fill this gap, this study compared two methods for symptom extraction from Emergency Department (ED) admission notes. Both methods utilized a lexicon derived by expanding The Center for Disease Control and Prevention's (CDC) Symptoms of Coronavirus list. The first method utilized a word2vec model to expand the lexicon using a dictionary mapping to the Uni ed Medical Language System (UMLS). The second method utilized the expanded lexicon as a rule-based gazetteer and the UMLS. These methods were evaluated against a manually annotated reference (f1-score of 0.87 for UMLS-based ensemble; and 0.85 for rule-based gazetteer with UMLS). Through analyses of associations of extracted symptoms used as features against various outcomes, salient risks among the population of COVID-19 patients, including increased risk of in-hospital mortality (OR 1.85, p-value < 0.001), were identified for patients presenting with dyspnea. Disparities between English and non-English speaking patients were also identified, the most salient being a concerning finding of opposing risk signals between fatigue and in-hospital mortality (non-English: OR 1.95, p-value = 0.02; English: OR 0.63, p-value = 0.01). While use of symptomatology for modeling of outcomes is not unique, unlike previous studies this study showed that models built using symptoms with the outcome of in-hospital mortality were not significantly different from models using data collected during an in-patient encounter (AUC of 0.9 with 95% CI of [0.88, 0.91] using only vital signs; AUC of 0.87 with 95% CI of [0.85, 0.88] using only symptoms). These findings indicate that prognostic models based on symptomatology could aid in extending COVID-19 patient care through telemedicine, replacing the need for in-person options. The methods presented in this study have potential for use in development of symptomatology-based models for other diseases, including for the study of Post-Acute Sequelae of COVID-19 (PASC).

One of the main issues of every business process is to be compliant with legal rules. This work presents a methodology to check in a semi-automated way the regulatory compliance of a business process. We analyse an e-Health hospital service in particular: the Hospital at Home (HaH) service. The paper shows, at first, the analysis of the hospital business using the Business Process Management and Notation (BPMN) standard language, then, the formalization in Defeasible Deontic Logic (DDL) of some rules of the European General Data Protection Regulation (GDPR). The aim is to show how to combine a set of tasks of a business with a set of rules to be compliant with, using a tool.

Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems. Existing environments feature subsets of these properties, but Neural MMO is the first to combine them all. We present Neural MMO as free and open source software with active support, ongoing development, documentation, and additional training, logging, and visualization tools to help users adapt to this new setting. Initial baselines on the platform demonstrate that agents trained in large populations explore more and learn a progression of skills. We raise other more difficult problems such as many-team cooperation as open research questions which Neural MMO is well-suited to answer. Finally, we discuss current limitations of the platform, potential mitigations, and plans for continued development.

A classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros, and superpositions of ones and zeros, which enables quantum computers to perform a vast number of calculations simultaneously compared to classical computers. In a cloud-supported cyber-physical system environment, running a machine learning application in quantum computers is often difficult, due to the existing limitations of the current quantum devices. However, with the combination of quantum-classical neural networks (NN), complex and high-dimensional features can be extracted by the classical NN to a reduced but more informative feature space to be processed by the existing quantum computers. In this study, we develop a hybrid quantum-classical NN to detect an amplitude shift cyber-attack on an in-vehicle control area network (CAN) dataset. We show that using the hybrid quantum classical NN, it is possible to achieve an attack detection accuracy of 94%, which is higher than a Long short-term memory (LSTM) NN (87%) or quantum NN alone (62%)

Finding sources of pollution across vast supply chains may be one of the largest barriers to eliminating carbon pollution. But for others like agriculture or consumer electronics, tracing and quantifying greenhouse gas emissions can be a time-consuming, laborious process. It generally takes an expert around three to six months--sometimes more--to come up with an estimate for a single product. Typically, researchers have to probe vast supply chains, comb the scientific literature, digest reports, and even interview suppliers. They may have to dive into granular details, estimating the footprint of everything from gypsum in drywall to tin solder on circuit boards.

Artificial intelligence (AI) uses algorithms and software to perform certain tasks without human intervention and instructions. AI represents the integration of technologies such as machine learning, natural language processing, reasoning, and perception. It is used in healthcare for approximation of human cognition as well as the analysis of complex medical and diagnostic imaging data. The artificial intelligence in healthcare diagnosis market is driven by the ability of AI to provide improved outcomes; moreover, the growing need to increase coordination between healthcare workforce and patients also supports the market growth. The rise in the importance of Big Data in healthcare, increase in the adoption of precision medicine, and surge in venture capital investments also contribute to the market growth.

U.S. safety investigators want to know why Tesla didn't file recall documents when it updated Autopilot software to better identify parked emergency vehicles, escalating a simmering clash between the automaker and regulators. In a letter to Tesla, the National Highway Traffic Safety Administration told the electric car maker Tuesday that it must recall vehicles if an over-the-internet update deals with a safety defect. "Any manufacturer issuing an over-the-air update that mitigates a defect that poses an unreasonable risk to motor vehicle safety is required to timely file an accompanying recall notice to NHTSA," the agency said in a letter to Eddie Gates, Tesla's director of field quality. The agency also ordered Tesla to provide information about its "Full Self-Driving" software that's being tested on public roads with some owners. The latest clash is another sign of escalating tensions between Tesla and the agency that regulates vehicle safety and partially automated driving systems.

Intelligence comes in many forms. It's what we use to measure a person's intelligence quotient. Developed in France in the early 1900s, it is as a way of tracking a child's intellectual growth. It is supposed to tell you how smart you are. Then there is emotional intelligence, known as EQ, which measures how well you relate to others -- how you assess emotional cues and respond to the feelings of others, known in business lingo as "how you read the room."

Buying a home is an important milestone many Americans dream about. Kids grow up doodling images of their dream home. College students start building their credit early so they can apply for a mortgage in the future. People save money for years so they can afford a downpayment. But, imagine if after all that dreaming and hard work, your hopes of buying a home are dashed by a biased lending algorithm that uses your race, or where you grew up, to determine your future. According to a recent investigation conducted by The Markup, this nightmare is a reality for many prospective borrowers in the United States.