How negative sampling provides class balance to rare event case data using a vehicular accident prediction project as a use case scenario

Rare event case data occur at such an infrequent rate that even having high amounts of it can leave researchers starving for more information. There has always existed a tug and pull relationship among rare event case data, where a higher count of entries often leads to a lack of explanatory variables, and vice versa. In the research spectrum of rare event case probability prediction, several methods of data sampling exist to remedy the main issue of rare event case data: a lack of data to collect and learn from. The most effective methods often involve altering the distribution of the training samples in a data set. The least utilized of these methods is negative sampling, where positive entries in a data set are used to generate negative entries. To outline the utility of negative sampling, this work discusses the application of five types of negative sampling on a vehicular accident prediction project, where non-accident records are generated through manipulating the temporal and spatial attributes of existing accident records. Moreover, different methods of data manipulation, including feature selection and different negative to positive data ratios, are used to explore what types of explanatory variables are most important when predicting vehicular accidents. Additionally, two types of predictive models, a Multilayer Perceptron and a Logistic Regression model, are created and directly compared in terms of predictive capability. Ultimately, the best model for predictive performance is heavily dependent on the specific implementation and desired results

EFFECTS OF RISK ON OPTIMAL NITROGEN FERTILIZATION DATES IN WINTER WHEAT PRODUCTION AS AFFECTED BY DISEASE AND NITROGEN SOURCE

Optimal fertilization dates were found for two nitrogen sources in the presence of two diseases for wheat farmers with different risk preferences. Risk was independent of fertilization date. Ammonium Nitrate and Urea-Ammonium Nitrate did not affect risk differently. Ammonium Nitrate applied on March 9 was optimal regardless of risk preferences.Crop Production/Industries,

EFFECTS OF RISK, DISEASE, AND NITROGEN SOURCE ON OPTIMAL NITROGEN FERTILIZATION RATES IN WINTER WHEAT PRODUCTION

Interactions among nitrogen (N) fertilization rate, N source, and disease severity can affect mean yield and yield variance in conservation tillage wheat production. A Just-Pope model was used to evaluate the effects of N rate, N source, and disease on the spring N-fertilization decision. Ammonium nitrate (AN) was the utility-maximizing N source regardless of risk preferences. The net-return-maximizing AN rate was 92 lb N/acre, providing $0.52/acre higher net returns than the best alternative N source (urea). If a farmer could anticipate a higher than average Take-All infection, the difference in optimal net-returns between AN and urea would increase to$35.11/acre.Crop Production/Industries,

The HSR on chromosome 1 of the house mouse, Mus domesticus: distribution and frequency in Switzerland

A total of 357 house mice (Mus domesticus) from 83 localities uniformly distributed throughout Switzerland were screened for the presence of a homogenously staining region (HSR) on chromosome 1. Altogether 47 mice from 11 localities were HSR/ + or HSR/HSR. One sample of 11 individuals all had an HSR/HSR karyotype. Almost all mice with the variant were collected from the Rhone valley (HSR frequency: 61%) and Val Bregaglia (HSR frequency: 81%). For samples from most of thearea of Switzerland, the HSR was absent. There was no strong association between the geographic distribution of the HSR and the areas of occurrence of metacentrics. However, at Chiggiogna the HSR was found on Rb (1·3). Possible explanations for the HSR polymorphism are discusse

Vision-Aided Autonomous Landing and Ingress of Micro Aerial Vehicles

Micro aerial vehicles have limited sensor suites and computational power. For reconnaissance tasks and to conserve energy, these systems need the ability to autonomously land at vantage points or enter buildings (ingress). But for autonomous navigation, information is needed to identify and guide the vehicle to the target. Vision algorithms can provide egomotion estimation and target detection using input from cameras that are easy to include in miniature systems

BOSS-LDG: A Novel Computational Framework that Brings Together Blue Waters, Open Science Grid, Shifter and the LIGO Data Grid to Accelerate Gravitational Wave Discovery

We present a novel computational framework that connects Blue Waters, the NSF-supported, leadership-class supercomputer operated by NCSA, to the Laser Interferometer Gravitational-Wave Observatory (LIGO) Data Grid via Open Science Grid technology. To enable this computational infrastructure, we configured, for the first time, a LIGO Data Grid Tier-1 Center that can submit heterogeneous LIGO workflows using Open Science Grid facilities. In order to enable a seamless connection between the LIGO Data Grid and Blue Waters via Open Science Grid, we utilize Shifter to containerize LIGO's workflow software. This work represents the first time Open Science Grid, Shifter, and Blue Waters are unified to tackle a scientific problem and, in particular, it is the first time a framework of this nature is used in the context of large scale gravitational wave data analysis. This new framework has been used in the last several weeks of LIGO's second discovery campaign to run the most computationally demanding gravitational wave search workflows on Blue Waters, and accelerate discovery in the emergent field of gravitational wave astrophysics. We discuss the implications of this novel framework for a wider ecosystem of Higher Performance Computing users.Comment: 10 pages, 10 figures. Accepted as a Full Research Paper to the 13th IEEE International Conference on eScienc

Mechanism of lignin inhibition of enzymatic biomass deconstruction

Background The conversion of plant biomass to ethanol via enzymatic cellulose hydrolysis offers a potentially sustainable route to biofuel production. However, the inhibition of enzymatic activity in pretreated biomass by lignin severely limits the efficiency of this process. Results By performing atomic-detail molecular dynamics simulation of a biomass model containing cellulose, lignin, and cellulases (TrCel7A), we elucidate detailed lignin inhibition mechanisms. We find that lignin binds preferentially both to the elements of cellulose to which the cellulases also preferentially bind (the hydrophobic faces) and also to the specific residues on the cellulose-binding module of the cellulase that are critical for cellulose binding of TrCel7A (Y466, Y492, and Y493). Conclusions Lignin thus binds exactly where for industrial purposes it is least desired, providing a simple explanation of why hydrolysis yields increase with lignin removal