Search results for "Benchmark data"

Monte Carlo calculation of dose rate distributions around 192Ir wires.

1997

Monte Carlo calculations of absolute dose rate in liquid water are presented in the form of away-along tables for 1 and 5 cm 192 Ir wires of 0.3 mm diameter. Simulated absolute dose rate values can be used as benchmark data to verify the calculation results of treatment planning systems or directly as input data for treatment planning. Best fit value of attenuation coefficient suitable for use in Sievert-integrals-type calculations has been derived based on Monte Carlo calculation results. For the treatment planning systems that are based on TG43 formalism we have also calculated the required dosimetry parameters.

Monte Carlo calculation of dose rate distributions around 0.5 and 0.6 mm in diameter 192Ir wires

1999

Monte Carlo simulations of absolute dose rate in liquid water are presented in the form of away-along tables for 1 and 5 cm 192 Ir wires of 0.5 and 0.6 mm diameter. Simulated absolute dose rate values can be used as benchmark data to verify the calculation results of treatment planning systems or directly as input data for treatment planning. Best fit value of an attenuation coefficient suitable for use in Sievert integral-type calculations has been derived based on Monte Carlo simulation results. For the treatment planning systems that are based on the TG43 formalism we have also computed the required dosimetry parameters.

Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones

2014

Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities. Nowadays, there is a pressing need to identify and develop new drug-based antiprotozoan therapies. In …

Benchmark database for fine-grained image classification of benthic macroinvertebrates

2018

Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categori…

2014

Large data sets classification is widely used in many industrial applications. It is a challenging task to classify large data sets efficiently, accurately, and robustly, as large data sets always contain numerous instances with high dimensional feature space. In order to deal with this problem, in this paper we present an online Logdet divergence based metric learning (LDML) model by making use of the powerfulness of metric learning. We firstly generate a Mahalanobis matrix via learning the training data with LDML model. Meanwhile, we propose a compressed representation for high dimensional Mahalanobis matrix to reduce the computation complexity in each iteration. The final Mahalanobis mat…

Human experts vs. machines in taxa recognition

2020

The step of expert taxa recognition currently slows down the response time of many bioassessments. Shifting to quicker and cheaper state-of-the-art machine learning approaches is still met with expert scepticism towards the ability and logic of machines. In our study, we investigate both the differences in accuracy and in the identification logic of taxonomic experts and machines. We propose a systematic approach utilizing deep Convolutional Neural Nets with the transfer learning paradigm and extensively evaluate it over a multi-pose taxonomic dataset with hierarchical labels specifically created for this comparison. We also study the prediction accuracy on different ranks of taxonomic hier…