Activity of Marphysa gravelyi Southern (Polychaeta) under heterosmotic conditions

Marphysa gravely; Southern is a common polychaete which occurs in the muddy substratum of the brackish-water regions of Madras, where salinities fluctuate over a wide range (Panikkar and Aiyar, 1937). In the laboratory under experimental conditions the worm is able to tolerate dilutions of sea-water ranging from 20-70% without any ill effect

Damping in Torsional Vibrations of Embedded Footings

The existing theoretical models to explain the dynamic behavior of embedded footings, overestimate the real response by neglecting damping forces which are inevitable as a result of slip at the interface of the embedded footing and soil. Many researchers in the field of Soil Dynamics have suggested that the inclusion of friction damping and internal damping in the mathematical model is necessary to improve the reliability of theoretical predictions. In this paper, results of the experimental investigations on full scale model embedded footings subjected to torsional mode of vibration have been presented. The results have been analyzed making use of three theoretical models, as developed by, Novak and Sachs (1973); Sankaran et al (1978) and Sankaran et al (1980). The importance of d-ping in predicting the dynamic response is brought out by a comparison of field vibratory test data with the corresponding values predicted by each of the above mentioned theoretical models

Self-Path: self-supervision for classification of pathology images with limited annotations

While high-resolution pathology images lend themselves well to ‘data hungry’ deep learning algorithms, obtaining exhaustive annotations on these images for learning is a major challenge. In this paper, we propose a self-supervised convolutional neural network (CNN) frame-work to leverage unlabeled data for learning generalizable and domain invariant representations in pathology images. Our proposed framework, termed as Self-Path, employs multi-task learning where the main task is tissue classification and pretext tasks are a variety of self-supervised tasks with labels inherent to the input images.We introduce novel pathology-specific self-supervision tasks that leverage contextual, multi-resolution and semantic features in pathology images for semi-supervised learning and domain adaptation. We investigate the effectiveness of Self-Path on 3 different pathology datasets. Our results show that Self-Path with the pathology-specific pretext tasks achieves state-of-the-art performance for semi-supervised learning when small amounts of labeled data are available. Further, we show that Self-Path improves domain adaptation for histopathology image classification when there is no labeled data available for the target domain. This approach can potentially be employed for other applications in computational pathology, where annotation budget is often limited or large amount of unlabeled image data is available