Stochastic Feature Selection with Distributed Feature Spacing for Hyperspectral Data

Feature subset selection is a well studied problem in machine learning. One short-coming of many methods is the selection of highly correlated features; a characteristic of hyperspectral data. A novel stochastic feature selection method with three major components is presented. First, we present an optimized feature selection method that maximizes a heuristic using a simulated annealing search which increases the chance of avoiding locally optimum solutions. Second, we exploit local cross correlation pair-wise amongst classes of interest to select suitable features for class discrimination. Third, we adopt the concept of distributed spacing from the multi-objective optimization community to distribute features across the spectrum in order to select less correlated features. The classification performance of our semi-embedded feature selection and classification method is demonstrated on a 12-class textile hyperspectral classification problem under several noise realizations. These results are compared with a variety of feature selection methods that cover a broad range of approaches. Abstract © IEE

Investor Competition Over Information and the Pricing of Information Asymmetry

Whether the information environment affects the cost of capital is a fundamental question in accounting and finance research. Relying on theories about competition between informed investors as well as the pricing of information asymmetry, we hypothesize a cross-sectional variation in the pricing of information asymmetry that is conditional on competition. We develop and validate empirical proxies for competition using the number and concentration of institutional investor ownership. Using these proxies, we find a lower pricing of information asymmetry when there is more competition. Overall, our results suggest that competition between informed investors has an important effect on how the information environment affects the cost of capital.Deloitte Foundatio

Possibility for reverse zoonotic transmission of SARS-CoV-2 to free-ranging wildlife: a case study of bats

The COVID-19 pandemic highlights the substantial public health, economic, and societal consequences of virus spillover from a wildlife reservoir. Widespread human transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also presents a new set of challenges when considering viral spillover from people to naïve wildlife and other animal populations. The establishment of new wildlife reservoirs for SARS-CoV-2 would further complicate public health control measures and could lead to wildlife health and conservation impacts. Given the likely bat origin of SARS-CoV-2 and related beta-coronaviruses (β-CoVs), free-ranging bats are a key group of concern for spillover from humans back to wildlife. Here, we review the diversity and natural host range of β-CoVs in bats and examine the risk of humans inadvertently infecting free-ranging bats with SARS-CoV-2. Our review of the global distribution and host range of β-CoV evolutionary lineages suggests that 40+ species of temperate-zone North American bats could be immunologically naïve and susceptible to infection by SARS-CoV-2. We highlight an urgent need to proactively connect the wellbeing of human and wildlife health during the current pandemic and to implement new tools to continue wildlife research while avoiding potentially severe health and conservation impacts of SARS-CoV-2 "spilling back" into free-ranging bat populations

Possibility for reverse zoonotic transmission of SARS-CoV-2 to free-ranging wildlife: a case study of bats

The COVID-19 pandemic highlights the substantial public health, economic, and societal consequences of virus spillover from a wildlife reservoir. Widespread human transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also presents a new set of challenges when considering viral spillover from people to naïve wildlife and other animal populations. The establishment of new wildlife reservoirs for SARS-CoV-2 would further complicate public health control measures and could lead to wildlife health and conservation impacts. Given the likely bat origin of SARS-CoV-2 and related beta-coronaviruses (β-CoVs), free-ranging bats are a key group of concern for spillover from humans back to wildlife. Here, we review the diversity and natural host range of β-CoVs in bats and examine the risk of humans inadvertently infecting free-ranging bats with SARS-CoV-2. Our review of the global distribution and host range of β-CoV evolutionary lineages suggests that 40+ species of temperate-zone North American bats could be immunologically naïve and susceptible to infection by SARS-CoV-2. We highlight an urgent need to proactively connect the wellbeing of human and wildlife health during the current pandemic and to implement new tools to continue wildlife research while avoiding potentially severe health and conservation impacts of SARS-CoV-2 "spilling back" into free-ranging bat populations

BCL::Conf – Improved Open-Source Knowledge-Based Conformation Sampling using the Crystallographic Open Database

This paper describes recent improvements made to the BCL::Conf rotamer generation algorithm and comparison of its performance against other freely available and commercial conformer generation software. We demonstrate that BCL::Conf, with the use of rotamers derived from the COD, more effectively recovers crystallographic ligand-binding conformations seen in the PDB than other commercial and freely available software. BCL::Conf is now distributed with the COD-derived rotamer library, free for academic use. The BCL can be downloaded at http://meilerlab.org/ bclcommons for Windows, Linux, or Apple operating systems.</div

MOESM1 of BCL::Conf: small molecule conformational sampling using a knowledge based rotamer library

Additional file 1. Supplementary data and protocol capture describing steps to reproduce data

Lasercom Uncertainty Modeling and Optimization Simulation (LUMOS): a Statistical Approach to Risk-tolerant Systems Engineering for Small Satellites

In contrast to large-budget space missions, risk-tolerant platforms such as nanosatellites may be better positioned to exchange moderate performance uncertainty for reduced cost and improved manufacturability. New uncertainty-based systems engineering approaches such as multidisciplinary optimization require the use of integrated performance models with input distributions, which do not yet exist for complex systems, such as laser communications (lasercom) payloads. In this paper, we present our development of a statistical, risk-tolerant systems engineering approach and apply it to nanosatellite-based design and architecture problems to investigate whether adding a statistical element to systems engineering enables improvements in performance, manufacturability, and cost. The scope of this work is restricted to a subset of nanosatellite-based lasercom systems, which are particularly useful given current momentum to field Earth-observing nanosatellite constellations and increasing challenges for data retrieval. We build uncertainty-based lasercom performance models for a low Earth orbiting (LEO) system being developed at MIT called the Nanosatellite Optical Downlink Experiment (NODE) as a reference architecture. Compared with a more traditional, deterministic systems engineering,we find our new Lasercom Uncertainty Modeling and Optimization Simulation (LUMOS) approach yields significant benefits including a lasercom downlink design with a 59% reduction in ground station diameter and a 46% reduction in space terminal power for equivalent probabilities of a LEO-ground system delivering 500 Gb/day. While we focus on a nanosatellite lasercom application, the process for characterizing the input distributions and modeling performance is generalizable to other lasercom systems or space systems