Functional Soft Nano-hybrids: Synthesis and Biological Applications

Nature implements the route of self-assembly in several fundamental processes. Researchers around the globe are always fascinated by the subtle and intricate mysteries of Nature. They try to mimic natural ways by building supramolecular self-assembly of molecules, which bear resemblance to those occurring in Nature. In this regard, amphiphilic molecules comprised of polar hydrophilic head and hydrophobic tail self assembles in water to form different supramolecular structures. These structures are widely diverse and are utilized to understand structurefunction relation of biological processes. On the other hand, the birth of nanotechnology has revolutionized almost every domain of research, especially from material science to biomedicinal arena. However, the potential of any nanomaterial is extremely constricted without the use of supramolecular chemistry. From the very synthesis and stabilization of any nanomaterial the invisible bonds play the central role. Hence the fundamental process of self assembly is the key towards utilizing nanomaterials in almost any direction of research. In this respect, carbon nanomaterials and metal nanoparticles have gained major attention owing to their amazing optical and electronic properties. Recently, it has also gained huge impetus in biomedicinal arena. The present thesis gives an overview on the development of novel self-assembled aggregates with a particular focus on gelation and some of their task specific applications. Also, it deals with amalgamation of supramolecular self-assembled systems with nanomaterials like carbon nanotube, graphene, silver nanoparticles (AgNPs) and thereby developing soft nanocomposites having superior physicochemical and biochemical properties.Research was conducted under the supervision of Prof. P K Das of Biological Chemistry division under SBS [School of Biological Sciences]Research was carried out under CSIR fellowshi

Learning to reason over visual objects

A core component of human intelligence is the ability to identify abstract patterns inherent in complex, high-dimensional perceptual data, as exemplified by visual reasoning tasks such as Raven's Progressive Matrices (RPM). Motivated by the goal of designing AI systems with this capacity, recent work has focused on evaluating whether neural networks can learn to solve RPM-like problems. Previous work has generally found that strong performance on these problems requires the incorporation of inductive biases that are specific to the RPM problem format, raising the question of whether such models might be more broadly useful. Here, we investigated the extent to which a general-purpose mechanism for processing visual scenes in terms of objects might help promote abstract visual reasoning. We found that a simple model, consisting only of an object-centric encoder and a transformer reasoning module, achieved state-of-the-art results on both of two challenging RPM-like benchmarks (PGM and I-RAVEN), as well as a novel benchmark with greater visual complexity (CLEVR-Matrices). These results suggest that an inductive bias for object-centric processing may be a key component of abstract visual reasoning, obviating the need for problem-specific inductive biases.Comment: ICLR 202

Structural review of relics tourism by text mining and machine learning

Purpose: The objective of the paper is to find trends of research in relic tourism-related topics. Specifically, this paper uncovers all published studies having latent issues with the keywords "relic tourism" from the Web of Science database. Methods: A total of 109 published articles (2002-2021) were collected related to "relic tourism." Machine learning tools were applied. Network analysis was used to highlight top researchers in this field, their citations, keyword clusters, and collaborative networks. Text analysis and Bidirectional Encoder Representation from Transformer (BERT) of artificial intelligence model were used to predict text or keyword-based topic reference in machine learning. Results: All the papers are published basically on three primary keywords such as "!relics," "culture," and "heritage." Secondary keywords like "protection" and "development" also attract researchers to research this topic. The co-author network is highly significant for diverse authors, and geographically researchers from five countries are collaborating more on this topic. Implications: Academically, future research can be predicated with dense keywords. Journals can bring more special issues related to the topic as relic tourism still has some unexplored areas

Bacterial biofilms: role of quorum sensing and quorum quenching

Bacterial biofilms provide an adjustable strategy to manage themselves in the existing conditions. Biofilms of pathogenic bacteria act as a reservoir for various device and non-device related diseases which are tough to cure. Exposure to a high dose of antibiotics is not an appropriate solution to this problem as high antibiotic concentrations lead to the generation of Multi-drug resistant strains as well as affect the human body. So, it is needed to bypass the use of antibiotics to prevent bacterial biofilms. In this context, Quorum Sensing (QS) may be a potential target since biofilm formation is regulated by QS. N-acyl homoserine lactones (N-AHL) act as predominant QS signal molecules in Gram-negative bacteria. Counteraction of the QS-regulated activities using quorum quenching may be an alternative way to combat biofilm formation in bacteria. Quorum sensing inhibitors (QSIs) and QQ enzymes play a significant role in this regard either by interference with the signal generation, perception, or by degradation, and chemical modification, respectively. Many quorum quenching enzymes have been reported from bacteria. Extremophilic bacteria have also been reported to produce potent quorum quenching enzymes which can effectively break down N-AHLs

Systematic Visual Reasoning through Object-Centric Relational Abstraction

Human visual reasoning is characterized by an ability to identify abstract patterns from only a small number of examples, and to systematically generalize those patterns to novel inputs. This capacity depends in large part on our ability to represent complex visual inputs in terms of both objects and relations. Recent work in computer vision has introduced models with the capacity to extract object-centric representations, leading to the ability to process multi-object visual inputs, but falling short of the systematic generalization displayed by human reasoning. Other recent models have employed inductive biases for relational abstraction to achieve systematic generalization of learned abstract rules, but have generally assumed the presence of object-focused inputs. Here, we combine these two approaches, introducing Object-Centric Relational Abstraction (OCRA), a model that extracts explicit representations of both objects and abstract relations, and achieves strong systematic generalization in tasks (including a novel dataset, CLEVR-ART, with greater visual complexity) involving complex visual displays

Determinantal Point Process Attention Over Grid Codes Supports Out of Distribution Generalization

Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of which humans are capable. One such case is out-of-distribution (OOD) generalization -- successful performance on test examples that lie outside the distribution of the training set. Here, we identify properties of processing in the brain that may contribute to this ability. We describe a two-part algorithm that draws on specific features of neural computation to achieve OOD generalization, and provide a proof of concept by evaluating performance on two challenging cognitive tasks. First we draw on the fact that the mammalian brain represents metric spaces using grid-like representations (e.g., in entorhinal cortex): abstract representations of relational structure, organized in recurring motifs that cover the representational space. Second, we propose an attentional mechanism that operates over these grid representations using determinantal point process (DPP-A) -- a transformation that ensures maximum sparseness in the coverage of that space. We show that a loss function that combines standard task-optimized error with DPP-A can exploit the recurring motifs in grid codes, and can be integrated with common architectures to achieve strong OOD generalization performance on analogy and arithmetic tasks. This provides both an interpretation of how grid codes in the mammalian brain may contribute to generalization performance, and at the same time a potential means for improving such capabilities in artificial neural networks.Comment: 24 pages (including Appendix), 19 figure

DeepPlace: Learning to Place Applications in Multi-Tenant Clusters

Large multi-tenant production clusters often have to handle a variety of jobs and applications with a variety of complex resource usage characteristics. It is non-trivial and non-optimal to manually create placement rules for scheduling that would decide which applications should co-locate. In this paper, we present DeepPlace, a scheduler that learns to exploits various temporal resource usage patterns of applications using Deep Reinforcement Learning (Deep RL) to reduce resource competition across jobs running in the same machine while at the same time optimizing for overall cluster utilization.Comment: APSys 201