Towards glass-box CNNs

With the substantial performance of neural networks in sensitive fields increases the need for interpretable deep learning models. Major challenge is to uncover the multiscale and distributed representation hidden inside the basket mappings of the deep neural networks. Researchers have been trying to comprehend it through visual analysis of features, mathematical structures, or other data-driven approaches. Here, we work on implementation invariances of CNN-based representations and present an analytical binary prototype that provides useful insights for large scale real-life applications. We begin by unfolding conventional CNN and then repack it with a more transparent representation. Inspired by the attainment of neural networks, we choose to present our findings as a three-layer model. First is a representation layer that encompasses both the class information (group invariant) and symmetric transformations (group equivariant) of input images. Through these transformations, we decrease intra-class distance and increase the inter-class distance. It is then passed through a dimension reduction layer followed by a classifier. The proposed representation is compared with the equivariance of AlexNet (CNN) internal representation for better dissemination of simulation results. We foresee following immediate advantages of this toy version: i) contributes pre-processing of data to increase the feature or class separability in large scale problems, ii) helps designing neural architecture to improve the classification performance in multi-class problems, and iii) helps building interpretable CNN through scalable functional blocks

Visible light-driven photocatalysts, quantum chemical calculations, ADMET-SAR parameters, and DNA binding studies of nickel complex of sulfadiazine

Abstract A 3D-supramolecular nickel integrated Ni-SDZ complex was synthesized using sodium salt of sulfadiazine as the ligand and nickel(II) acetate as the metal salt using a condensation process and slow evaporation approach to growing the single crystal. The metal complex was characterized for its composition, functional groups, surface morphology as well as complex 3D structure, by resorting to various analytical techniques. The interacting surface and stability as well as reactivity of the complex were carried out using the DFT platform. From ADMET parameters, human Intestinal Absorbance data revealed that the compound has the potential to be well absorbed, and also Ni-SDZ complex cannot cross the blood–brain barrier (BBB). Additionally, the complex's DNA binding affinity and in-vivo and in-vitro cytotoxic studies were explored utilizing UV–Vis absorbance titration, viscosity measurements, and S. pombe cells and brine shrimp lethality tests. In visible light radiation, the Ni-SDZ complex displayed exceptional photo-degradation characteristics of approximately 70.19% within 70 min against methylene blue (MB)