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We propose a new structured pruning framework for compressing Deep Neural Networks
(DNNs) with skip-connections, based on measuring the statistical dependency of hidden
layers and predicted outputs. The dependence measure defined by the energy statistics of
hidden layers serves as a model-free measure of information between the feature maps and
the output of the network. The estimated dependence measure is subsequently used to
prune a collection of redundant and uninformative layers. Model-freeness of our measure

Genomic sequencing data contain three different data fields: read names, quality values, and nucleotide sequences. In this work, a variety of entropy encoders and compression algorithms were benchmarked in terms of compression-decompression rates and times separately for each data field as raw data from FASTQ files (implemented in the Fastq analysis script) and in MPEG-G uncompressed descriptor symbols decoded from MPEG-G bitstreams (implemented in the symbols analysis script).

With the widespread application of next generation sequencing technologies, the volume of sequencing data became comparable to that of big data domains. The compression of sequencing reads (nucleotide sequences, quality values, read names), in both raw and aligned data, is a way to alleviate bandwidth, transfer, and storage requirements of genomics pipelines. ISO/IEC MPEG-G standardizes the compressed representation (i.e. storage and streaming) of structured, indexed sets of genomic sequencing data for both raw and aligned data.

x3 is a lossless optimizing dictionary-based data compressor. The algorithm uses a combination of a dictionary, context modeling, and arithmetic coding. Optimization adds the ability to find the most appropriate parameters for each file. Even without optimization, x3 can compress data with a compression ratio comparable to the best dictionary compression methods like LZMA, zstd, or Brotli.

The study of three-dimensional folding of chromosomes is important to understand genomics processes. This is done through techniques, such as Hi-C, that analyze the spatial organization of chromosomes in a cell. The data coming from the study is a 2-dimensional quantitative maps with genomic coordinate systems. We present a novel approach called Contact Matrix Compressor(CMC) for the efficient compression of Hi-C data. By exploiting the properties of the data, such as diagonally dominant and symmetrical, CMC achieves a much higher compression.