Algorithmic Guarantees for Inverse Imaging with Untrained Neural Network Priors

Deep neural networks as image priors have been recently introduced for problems such as denoising, super-resolution and inpainting with promising performance gains over hand-crafted image priors such as sparsity and low-rank. Unlike learned generative priors they do not require any training over large datasets. However, few theoretical guarantees exist in the scope of using untrained network priors for inverse imaging problems. We explore new applications and theory for untrained neural network priors. Specifically, we consider the problem of solving linear inverse problems, such as compressive sensing, as well as non-linear problems, such as compressive phase retrieval. We model images to lie in the range of an untrained deep generative network with a fixed seed. We further present a projected gradient descent scheme that can be used for both compressive sensing and phase retrieval and provide rigorous theoretical guarantees for its convergence. We also show both theoretically as well as empirically that with deep network priors, one can achieve better compression rates for the same image quality as compared to when hand crafted priors are used

A Survey on Program Slicing

Program slicing is an important technique for untangling programs by only focusing on selected aspects of semantics. The processing flow of slicing deletes those parts of the program that have no effect upon the semantics that are required to execute. For program slicing it is important to understand the important aspects that are related to execution and relationship of variable involved in the program. Slicing has applications in software maintenance, testing and debugging. Program slicing is a process of extracting parts of programs by tracing the programs in which the main task is to find out all statements in a program that directly or indirectly influence the value of a variable at some point in a program. In proposed paper a detailed survey is done on various slicing techniques and understanding the applications in various areas such as debugging, program comprehension and understanding, program integration

Exploring of Antimicrobial Activity of Triphala Mashi—an Ayurvedic Formulation

Triphala Mashi is an ayurvedic formulation that was prepared in our lab. Aqueous and alcoholic extracts of both Triphala and Triphala Mashi were used, to evaluate antimicrobial activity. Comparative phytochemical profile of Triphala and Triphala Mashi was done by preliminary phytochemical screening, total phenolic content and thin layer chromatography (TLC). Antimicrobial activity includes isolation of pathogens from clinical samples, its characterization, testing its multiple drug resistance against standard antibiotics and antimicrobial activity of aqueous and alcoholic extracts of both Triphala and Triphala Mashi against these organisms by using agar gel diffusion method. Triphala Mashi containing phenolic compounds, tannins exhibited comparable antimicrobial activity in relation to Triphala against all the microorganisms tested. It inhibits the dose-dependent growth of Gram-positive and Gram-negative bacteria. In conclusion, it appears that Triphala Mashi has non-specific antimicrobial activity