It includes the study of various data structures diagrammatically. All the basics data structures, their types, operations are explained diagrammatically Algorithms for various techniques are explained diagrammatically. All the concepts are implemented in C++ programming language. It includes a lot of diagrams and examples. It contains the following chapters :
Domain adaptation deals with training models using large scale labeled data from a specific source domain and then adapting the knowledge to certain target domains that have few or no labels. Many prior works learn domain agnostic feature representations for this purpose using a global distribution alignment objective which does not take into account the finer class specific structure in the source and target domains. We address this issue in our work and propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. ILA-DA simultaneously accounts for intra-class clustering as well as inter-class separation among the categories, resulting in less noisy classifier boundaries, improved transferability and increased accuracy. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets and provide insights into the proposed alignment approach.
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Here, we analyze PPI networks with available data with respect to gene expression, PPIs and network structure (see Fig. 2). We examine the PPI networks of DNA repair, mismatch repair, DNA replication, and the ribosome. We make use of publicly available data (SNAP37 and KEGG38 databases), which are annotated and experimentally verified, for three organisms: Escherichia coli (prokaryote), Saccharomyces cerevisiae (unicellular eukaryote) and Homo sapiens (multicellular eukaryote). We found that the prospective resilience of many of these networks is greater when node addition was based on the gene expression compared to the other node attachment strategies. 2ff7e9595c
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