Influence of neotectonic activity on groundwater salinity and playa development in the Mendha river catchment,western India

The groundwater salinity of Mendha river, one of the important streams that feed Sambhar playa in Rajasthan in western India, was studied to understand the effect of neotectonic activity on groundwater quality and salinity. We attempted to decouple the tectonic control of salinity and its contribution in the development of playa deposits in Rajasthan. Multiresolution, multidate satellite data products such as IRS‐1C, IRS‐1D LISS‐III, PAN and Landsat MSS were digitally enhanced and analysed to model the morphotectonic evolution and hydrological regime of the region. Electrical conductance data from spatially distributed points in the Mendha river basin were correlated with the aquifer geometry deciphered from borehole lithologs and lineaments and major geomorphic features interpreted from satellite images. The results of the study reveal that the aquifer geometry is controlled by subsurface structures that have been influenced by neotectonic activity in the past 8–9 ka, significantly influencing the hydrological regime and salinity of Sambhar playa.     

TopoAct: Visually Exploring the Shape of Activations in Deep Learning

Deep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive performance in tasks such as image and text classification. To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i.e.combinations of neuron firings, at various layers of the network in response to a particular input. With a large number of inputs, we aim to obtain a global view of what neurons detect by studying their activations. In particular, we develop visualizations that show the shape of the activation space, the organizational principle behind neuron activations, and the relationships of these activations within a layer. Applying tools from topological data analysis, we present TopoAct , a visual exploration system to study topological summaries of activation vectors. We present exploration scenarios using TopoAct that provide valuable insights into learned representations of neural networks. We expect TopoAct to give a topological perspective that enriches the current toolbox of neural network analysis, and to provide a basis for network architecture diagnosis and data anomaly detection.     

Hybrid NSCT domain multiple watermarking for medical images

Multimedia Tools and Applications - Medical image watermarking is a challenging area of research. High bandwidth, secure transmission of patient’s data among hospitals and hiding capacity are...     

When simulated environments make the difference: the effectiveness of different types of training of car service procedures

An empirical analysis was performed to compare the effectiveness of different approaches to training a set of procedural skills to a sample of novice trainees. Sixty-five participants were randomly assigned to one of the following three training groups: (1) learning-by-doing in a 3D desktop virtual environment, (2) learning-by-observing a video (show-and-tell) explanation of the procedures, and (3) trial-and-error. In each group, participants were trained on two car service procedures. Participants were recalled to perform a procedure either 2 or 4 weeks after the training. The results showed that: (1) participants trained through the virtual approach of learning-by-doing performed both procedures significantly better (i.e. p < .05 in terms of errors and time) than people of non-virtual groups, (2) the virtual training group, after a period of non-use, were more effective than non-virtual training (i.e. p < .05) in their ability to recover their skills, (3) after a (simulated) long period from the training—i.e. up to 12 weeks—people who experienced 3D environments consistently performed better than people who received other kinds of training. The results also suggested that independently from the training group, trainees’ visuospatial abilities were a predictor of performance, at least for the complex service procedure, adj R ² = .460, and that post-training performances of people trained through virtual learning-by-doing are not affected by learning styles. Finally, a strong relationship (p < .001, R ² = .441) was identified between usability and trust in the use of the virtual training tool—i.e. the more the system was perceived as usable, the more it was perceived as trustable to acquire the competences.     

Towards a super-resolution based approach for improved face recognition in low resolution environment

Multimedia Tools and Applications - The video surveillance activity generates a vast amount of data, which can be processed to detect miscreants. The task of identifying and recognizing an object...     

Environment-friendly and cost-effective solution for flexible packaging printing process by advancement in engraving process

Clean Technologies and Environmental Policy - Organic solvents have been commonly used in the printing process for a long time in gravure printing applications. Using organic solvents in...