Machine learning driven trading strategies have gar-nered a lot of interest over the past few years. There is, however, limited consensus on the ideal approach for the development of such trading strategies. Further, most literature has focused on trading strategies for short-term trading, with little or no focus on strategies that attempt to build long-term wealth. Our paper proposes a new approach for developing long-term investment strategies using an ensemble of evolutionary algorithms and a deep learning model by taking a series of short-term purchase decisions. Our methodology focuses on building long-term wealth by improving systematic investment planning (SIP) decisions on Exchange Traded Funds (ETF) over a period of time. We provide empirical evidence of superior performance (around 1 % higher returns) and robustness using our ensemble approach as compared to the traditional daily systematic investment practice on a given ETF. Our results are based on live trading decisions made by our algorithm and executed on the Robinhood trading platform.

The current pandemic has introduced substantial uncertainty to traditional methods for demand planning. These uncertainties stem from the disease progression, government interventions, economy and consumer behavior. While most of the emerging literature on the pandemic has focused on disease progression, a few have focused on consequent regulations and their impact on individual behavior. The contributions of this paper include a quantitative behavior model of fear of COVID-19, impact of government interventions on consumer behavior, and impact of consumer behavior on consumer choice and hence demand for goods. It brings together multiple models for disease progression, consumer behavior and demand estimation-thus bridging the gap between disease progression and consumer demand. We use panel regression to understand the drivers of demand during the pandemic and Bayesian inference to simplify the regulation landscape that can help build scenarios for resilient demand planning. We illustrate this resilient demand planning model using a specific example of gas retailing. We find that demand is sensitive to fear of COVID-19 - as the number of COVID-19 cases increase over the previous week, the demand for gas decreases-though this dissipates over time. Further, government regulations restrict access to different services, thereby reducing mobility, which in itself reduces demand.

Artificial intelligence (AI) is increasingly being adopted for automation and decision-making tasks across all industries, public sector, and law. Applications range from hiring and credit limit decisions, to loan and healthcare claim approvals, to criminal sentencing, and even the selective provision of information by social media companies to different groups of viewers. The increased adoption of AI, affecting so many aspects of our daily lives, highlights the potential risks around automated decision making and the need for better governance and ethical standards when deploying such systems. In response to that need, governments, states, municipalities, private sector organizations, and industry groups around the world have drafted hundreds, perhaps even thousands at this point - of new, regulatory proposals and guidelines; many already in effect and more on the way. The data-driven and often black box nature of these systems does not absolve organizations from the social responsibility or increasingly commonplace regulatory requirements to confirm they work as intended and are deployed in a responsible manner, lest they run the risk of reputational damage, regulatory fines, and/or legal action. The legal community should have a good understanding of the responsible development and deployment of artificial intelligence in order to inform, translate, and advise on the legal implications of AI systems.

Adverse Events (AEs) are a significant concern in healthcare, since it is among the leading causes of morbidity and mortality[12]. According to the Food and Drug Administration (FDA), between 2006 and 2014, there was a 232% increase in AE cases reported to have caused mortality[13]. In fact, the volume of all AE cases reported to the FDA has increased by almost five fold since 1997[13]. Pharmaceutical companies are struggling to handle the increased case volume due to manual logging of individual cases. This is not a sustainable solution as we see the volume of AE case logs increase exponentially [12,13]. In this paper, we discuss our work and findings for implementing a pharmacovigilance automation solution. This solution explores machine learning techniques in being able to identify serious vs non-serious adverse event narrative logs. While developing our methodology, we explored both traditional machine learning and deep learning techniques. Our final model achieved a mean F1-Score of 95% and an MCC score of 0.80 on the AE case narratives1.

Adverse Events (AEs) are a significant concern in healthcare, since it is among the leading causes of morbidity and mortality[12]. According to the Food and Drug Administration (FDA), between 2006 and 2014, there was a 232% increase in AE cases reported to have caused mortality[13]. In fact, the volume of all AE cases reported to the FDA has increased by almost five fold since 1997[13]. Pharmaceutical companies are struggling to handle the increased case volume due to manual logging of individual cases. This is not a sustainable solution as we see the volume of AE case logs increase exponentially [12,13]. In this paper, we discuss our work and findings for implementing a pharmacovigilance automation solution. This solution explores machine learning techniques in being able to identify serious vs non-serious adverse event narrative logs. While developing our methodology, we explored both traditional machine learning and deep learning techniques. Our final model achieved a mean F1-Score of 95% and an MCC score of 0.80 on the AE case narratives.1

The study of computational agents capable of rational behaviour has received increasing attention in recent years. A number of theoretical formalizations for such multi-agent systems have been proposed. However, most of these formalizations do not have a strong semantic basis nor a sound and complete axiomatization. Hence, it has not been clear as to how these formalizations could assist in building agents in practice. This paper explores a particular type of multi-agent system, in which each agent is viewed as having the three mental attitudes of belief (B), desire (D), and intention (I). It provides a family of multi-modal branching-time BDI logics with a possible-worlds semantics, categorizes them, provides sound and complete axiomatizations, and gives constructive tableau-based decision procedures for testing the satisfiability and validity of formulas. The computational complexity of these decision procedures is no greater than the complexity of their underlying temporal logic component.

The aim of this paper is to provide a basis for a theory of events and systems of events that can be used for reasoning about arbitrarily complex dynamic domains involving multiple agents. The approach is based on a model of events that explicitly represents the domain of influence of each event. By restricting the scope of an event's domain of influence, most of the problems that have seriously troubled the more conventional state-transition models of events can be avoided. The effect of performing events, either in isolation or in parallel with other events, is described. A formalism is developed that allows the reasoning about arbitrarily complex behaviours. It is shown how this formalism avoids the frame problem yet allows the ramifications of any given event occurrence to be modelled without the introduction of non-monotonic mechanisms. Finally, the notion of system is introduced to provide a compositional means for reasoning about complex domains