Conditional Deep 3D-Convolutional Generative Adversarial Nets for RGB-D Generation

Generation of synthetic data is a challenging task. There are only a few significant works on RGB video generation and no pertinent works on RGB-D data generation. In the present work, we focus our attention on synthesizing RGB-D data which can further be used as dataset for various applications like object tracking, gesture recognition, and action recognition. This paper has put forward a proposal for a novel architecture that uses conditional deep 3D-convolutional generative adversarial networks to synthesize RGB-D data by exploiting 3D spatio-temporal convolutional framework. The proposed architecture can be used to generate virtually unlimited data. In this work, we have presented the architecture to generate RGB-D data conditioned on class labels. In the architecture, two parallel paths were used, one to generate RGB data and the second to synthesize depth map. The output from the two parallel paths is combined to generate RGB-D data. The proposed model is used for video generation at 30 fps (frames per second). The frame referred here is an RGB-D with the spatial resolution of 512 × 512.

"In-vitro Effects of Chlorpyrifos and Monocrotophos on the Activity of Acetylcholinesterase (AChE) in Different Tissues of Apple Snail Pila globosa (Swainson, 1822)"

The impact of two organophosphorus insecticides [Chlorpyrifos (CPF) and Monocrotophos (MCP)] on non-target wild natural gastropod, Pila globosa (apple snail) from the paddy fields was studied. The activity of acetylcholinesterase (AChE) was monitored on foot-muscle and hepatopancreas tissues of control and exposed snails. In the foot- muscle AChE inhibition progressed and reached 54.19% and 63.13% of the control, whereas, the AChE inhibition in the hepatopancreas reached 46.96% and 53.67% over control after 48 hours of exposure to 1.5 mL.L-1 and 2.5 mL.L-1 CPF respectively. After 48 hours of MCP exposure at 1.5 mL.L-1 and 2.5 mL.L-1 separately, the AChE inhibition of foot muscle was 49.07% and 57.59% respectively while in hepatopancreas it was 44.65% and 48.84% respectively. Our results show more inhibition of AChE activities on the foot-muscle than hepatopancreas in a concentration and time-dependent manner with greater severity by CPF in comparison to MCP. AChE inhibition increased with the increasing exposure time.

An Ontology Representation Language for Multimedia Event Applications

This paper presents formalization of a new Multimedia Web Ontology Language (E-MOWL) to handle events with media depictions. The temporal, spatial and entity aspects that are implicitly linked to an event are represented through this language to model the context of events. The already existing Multimedia Web Ontology Language (MOWL) can be leveraged for perceptual modelling of a domain, where the concepts manifest into media patterns in the multimedia document and helps in semantic processing of the contents. The language E-MOWL provides a rich method for representing knowledge corresponding to a specific domain wherein the context specifies the intended meaning of each element of the domain of discourse; an element in different context may correspond to different functional role. The context information associated with an event ties the audiovisual data with event related aspects. All these aspects when considered altogether provide the evidence and contribute towards recognizing an event from multimedia documents. The language also enables reasoning with the uncertainty associated with the events and is organized in the form of Bayesian Network (BN). The media items that are semantically relevant can be assimilated together on the basis of their association with events. We have demonstrated the efficacy of our approach by utilizing an ontology for the entertainment category in news domain to offer an application \textit{news aggregation} and event-based book recommendations.

A Novel Abnormality Annotation Database for COVID-19 Affected Frontal Lung X-rays.. [DOI: 10.1101/2021.01.07.21249323]

Purpose

To advance the usage of CXRs as a viable solution for efficient COVID-19 diagnostics by providing large-scale annotations of the abnormalities in frontal CXRs in BIMCV-COVID19+ database, and to provide a robust evaluation mechanism to facilitate its usage.

Materials and Methods

We provide the abnormality annotations in frontal CXRs by creating bounding boxes. The frontal CXRs are a part of the existing BIMCV-COVID19+ database. We also define four different protocols for robust evaluation of semantic segmentation and classification algorithms. Finally, we benchmark the defined protocols and report the results using popular deep learning models as a part of this study.

Results

For semantic segmentation, Mask-RCNN performs the best among all the models with a DICE score of 0.43 ± 0.01. For classification, we observe that MobileNetv2 yields the best results for 2-class and 3-class classification. We also observe that deep models report a lower performance for classifying other classes apart from the COVID class.

Conclusion

By making the annotated data and protocols available to the scientific community, we aim to advance the usage of CXRs as a viable solution for efficient COVID-19 diagnostics. This large-scale data will be useful for ML algorithms and can be used for learning radiological patterns observed in COVID-19 patients. Further, the protocols will facilitate ML practitioners for unified large-scale evaluation of their algorithms.

Data Availability Statement

The data associated with this work is available here : Radiologists’ Annotations on COVID-19+ X-rays https://osf.io/b35xu/ via @OSFramework andhttp://covbase4all.igib.res.in/.