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The topological model for spatial objects identifies common boundaries between regions, explicitly storing adjacency relations, which not only improves the efficiency of topology-related queries, but also provides advantages such as avoiding data duplication and facilitating data consistency. Recently, a compact representation of the topological model based on planar graph embeddings was proposed.

A binary Write Once Memory (wom) device is a storage mechanism in which a 0-bit can be overwritten much more easily than a 1-bit. A famous example is the flash memory technology, where $0 \rightarrow 1$ transitions are allowed, but $1\rightarrow 0$ transitions require a costly erase procedure and are therefore prohibited. A {\sc wom} code is a coding scheme that permits multiple writes to the {\sc wom} without violating the {\sc wom} rule.
The properties of {\sc wom}
attracted attention even before flash memory was invented.

A particular form of lossless data compression is known as deduplication, which is often applied in a scenario in which a large data repository is given and we wish to store a new, updated, version of it, in which the changes account only for a tiny fraction of the accumulated information. The idea is then to find duplicated parts and store only one copy P of them; the second and subsequent occurrences of these parts can then be replaced by pointers to P.

We describe a grammar for DNA sequencing reads from which we can compute the BWT directly. Our motivation is to perform in succinct space genomic analyses that require complex string queries not yet supported by repetition-based self-indexes. Our approach is to store the set of reads as a grammar, but when required, compute its BWT to carry out the analysis by using self-indexes. Our experiments in real data showed that the space reduction we achieve with our compressor is competitive with LZ-based methods and better than entropy-based approaches.

We introduce a new structural technique for pruning deep neural networks with skip-connections by removing the less informative layers using their Fisher scores. Extensive experiments on the classification of CIFAR-10, CIFAR-100, and SVHN data sets demonstrate the efficacy of our proposed method in compressing deep models, both in terms of the number of parameters and operations.

Data compression is used in a wide variety of tasks, including compression of databases, large learning models, videos, images, etc. The cost of decompressing (decoding) data can be prohibitive for certain real-time applications. In many scenarios, it is acceptable to sacrifice (to some extent) on compression in the interest of fast decoding.

We provide a compact representation of polyominoes with n cells that supports navigation and visibility queries in constant time.

In this paper, we present a coding framework for deep convolutional neural network compression. Our approach utilizes the classical coding theories and formulates the compression of deep convolutional neural networks as a rate-distortion optimization problem. We incorporate three coding ingredients in the coding framework, including bit allocation, dead zone quantization, and Tunstall coding, to improve the rate-distortion frontier without noticeable system-level overhead introduced.