Silver Standard Masks for Data Augmentation Applied to Deep-Learning-Based Skull-Stripping

The bottleneck of convolutional neural networks (CNN) for medical imaging is the number of annotated data required for training. Manual segmentation is considered to be the "gold-standard". However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. We propose in this work the use of what we refer to as silver standard masks for data augmentation in deep-learning-based skull-stripping also known as brain extraction. We generated the silver standard masks using the consensus algorithm Simultaneous Truth and Performance Level Estimation (STAPLE). We evaluated CNN models generated by the silver and gold standard masks. Then, we validated the silver standard masks for CNNs training in one dataset, and showed its generalization to two other datasets. Our results indicated that models generated with silver standard masks are comparable to models generated with gold standard masks and have better generalizability. Moreover, our results also indicate that silver standard masks could be used to augment the input dataset at training stage, reducing the need for manual segmentation at this step

Full-Scale and Wind Tunnel Investigation of the Flow Field Over a Coastal Escarpment

A multiscale experimental approach, consisting of full-scale measurements and physical modeling in a laboratory environment, was conducted to investigate the flow field over a coastal escarpment on the Wind Energy Institute of Canada’s research and development wind park. The influence of sea breeze inflow conditions, thermal stability and local topographic features on the flow field were examined. The results of the full-scale study show that the near surface flow field is significantly influenced by the sea breeze circulatory coastal flow regime, creating larger shear layers with a smaller recirculation regions compared to cases of typical boundary layer flow typical boundary layer flow. Results of the physical modeling show an agreeable comparison between the hub height wind speeds at the location of a turbine was achieved under non-ABL conditions by simulated the non-ABL inflow. Additionally, significantly different mean and turbulent flow fields were observed for three different escarpment geometries

DDT and Other Organohalogen Pesticides in Aquatic Organisms

Organohalogen (OH) compounds are persistent hydrocarbon compounds containing a halogen group, often chlorine or bromine, that substitutes for hydrogen atoms in different positions in the hydrocarbon. They may occur naturally, but this chapter\u27s focus is on synthetically produced compounds, mainly organochlorines, that were produced for use as pesticides. Nine OH compounds (aldrin, chlordane, dichlorodiphenyltrichloroethane [DDT], dieldrin, endrin, heptachlor, hexachlorobenzene, mirex, and toxaphene) are in the top 12 list of particularly toxic and persistent organic pollutants (POPs) identified by the Stockholm Convention treaty implemented in 2004 under the United Nations Environment Program (UNEP). More than 90 countries have signed on to this treaty as Parties. These chemicals became classified as POPs because they may remain in the environment for decades following their use, they accumulate in fatty tissues of exposed organisms, they have a variety of toxic endpoints, and they travel long distances from source areas through atmospheric or aqueous transport

DDT and Other Organohalogen Pesticides in Aquatic Organisms

Organohalogen (OH) compounds are persistent hydrocarbon compounds containing a halogen group, often chlorine or bromine, that substitutes for hydrogen atoms in different positions in the hydrocarbon. They may occur naturally, but this chapter\u27s focus is on synthetically produced compounds, mainly organochlorines, that were produced for use as pesticides. Nine OH compounds (aldrin, chlordane, dichlorodiphenyltrichloroethane [DDT], dieldrin, endrin, heptachlor, hexachlorobenzene, mirex, and toxaphene) are in the top 12 list of particularly toxic and persistent organic pollutants (POPs) identified by the Stockholm Convention treaty implemented in 2004 under the United Nations Environment Program (UNEP). More than 90 countries have signed on to this treaty as Parties. These chemicals became classified as POPs because they may remain in the environment for decades following their use, they accumulate in fatty tissues of exposed organisms, they have a variety of toxic endpoints, and they travel long distances from source areas through atmospheric or aqueous transport

NeuralMind-UNICAMP at 2022 TREC NeuCLIR: Large Boring Rerankers for Cross-lingual Retrieval

This paper reports on a study of cross-lingual information retrieval (CLIR) using the mT5-XXL reranker on the NeuCLIR track of TREC 2022. Perhaps the biggest contribution of this study is the finding that despite the mT5 model being fine-tuned only on query-document pairs of the same language it proved to be viable for CLIR tasks, where query-document pairs are in different languages, even in the presence of suboptimal first-stage retrieval performance. The results of the study show outstanding performance across all tasks and languages, leading to a high number of winning positions. Finally, this study provides valuable insights into the use of mT5 in CLIR tasks and highlights its potential as a viable solution. For reproduction refer to https://github.com/unicamp-dl/NeuCLIR22-mT