Regulatory Outlook for Derivatives Based on Sports Outcomes

This Note dives into the intersection between two distinct areas: sports gambling and derivatives. ErisX had the keen idea to use sports-based futures contracts for hedging purposes, and thought that because of these hedging purposes, it would not be rubbing up against any sports gambling laws. However, the Commodity Futures Trading Commission (CFTC), in charge of regulating the trading of futures contracts, still presented a problem for ErisX. The CFTC prohibits futures contracts involving “gaming,” and while the purpose of these contracts are not speculative, they still involve sports. Therefore, a new CFTC body equipped to deal with sports-based contracts would go a long way in promoting strong and stable markets in the world of sports gambling

Estimation of sparse generalized linear models: the dglars package

dglars is a public available R package that implements the method proposed in Augugliaro, Mineo and Wit (2013) developed to study the sparse structure of a generalized linear model. This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method (LARS). The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve; specifically a predictor-corrector algorithm and a cyclic coordinate descent algorithm

Using differential geometric lars algorithm to study the expression profile of a sample of patients with latex-fruit syndrome

Natural rubber latex IgE-mediated hypersensitivity is one of the most important health problems in allergy during recent years. The prevalence of individuals allergic to latex shows an associated hypersensitivity to some plant-derived foods, especially freshly consumed fruit. This association of latex allergy and allergy to plant-derived foods is called latex-fruit syndrome. The aim of this study is to use the differential geometric generalization of the LARS algorithm to identify candidate genes that may be associated with the pathogenesis of allergy to latex or vegetable

SPARSE INFERENCE IN COVARIATE ADJUSTED CENSORED GAUSSIAN GRAPHICAL MODELS

The covariate adjusted glasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. In this paper we propose an extension to censored data

DgCox: a differential geometric approach for high-dimensional Cox proportional hazard models

Many clinical and epidemiological studies rely on survival modelling to detect clinically relevant factors that affect various event histories. With the introduction of high-throughput technologies in the clinical and even large-scale epidemiological studies, the need for inference tools that are able to deal with fat data-structures, i.e., relatively small number of observations compared to the number of features, is becoming more prominent. This paper will introduce a principled sparse inference methodology for proportional hazards modelling, based on differential geometrical analyses of the high-dimensional likelihood surface

Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experimental testing in real-world warehouse environments. Experimental testing reveals that our proposed solution can deliver fast and robust aerial robot autonomous navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field Robotic

The combination of heterogeneous photocatalysis with chemical and physical operations: a tool for improving the photoprocess performance

Heterogeneous photocatalysis is a process of great potential for pollutant abatement and waste treatment. In order to improve the overall performance of the photoprocess, heterogeneous photocatalysis is being combined with physical or chemical operations, which affect the chemical kinetics and/or the overall ef\ufb01ciency. This review addresses the various possibilities to couple heterogeneous photocatalysis with other technologies to photodegrade organic and inorganic pollutants dissolved in actual or synthetic aqueous ef\ufb02uents. These combinations increase the photoprocess ef\ufb01ciency by decreasing the reaction time in respect to the separated operations or they decrease the cost in respect of heterogeneous photocatalysis alone, generally in terms of light energy. Depending on the operation coupled with heterogeneous photocatalysis, two categories of combinations exist. When the coupling is with ultrasonic irradiation, photo-Fenton reaction, ozonation, or electrochemical treatment, the combination affects the photocatalytic mechanisms thus improving the ef\ufb01ciency of the photocatalytic process. When the coupling is with biological treatment, membrane reactor, membrane photoreactor, or physical adsorption, the combination does not affect the photocatalytic mechanisms but it improves the ef\ufb01ciency of the overall process. The choice of the coupling is related to the type of wastewater to be treated. A synergistic effect, giving rise to an improvement of the ef\ufb01ciency of the photocatalytic process, has been reported in the literature for many cases