- Read more about SnappyR: A New High-Speed Lossless Data Compression Algorithm
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- Read more about LZ4r - A New Fast Compression Algorithm for High-Speed Data Storage Systems
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- Read more about Probabilistic Fine-grained Urban Flow Inference with Normalizing Flows
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Fine-grained urban flow inference (FUFI) aims at enhancing the resolution of traffic flow, which plays an important role in intelligent traffic management. Existing FUFI methods are mainly based on techniques from image super-resolution (SR) models, which cannot fully capture the influence of external factors and face the ill-posed problem in SR tasks. In this paper, we propose UFI-Flow – Urban Flow Inference via normalizing Flow, a novel model for addressing the FUFI problem in a principled manner by using a single probabilistic loss.
ICASSP_slides.pptx
ICASSP_5513_poster.pdf
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- Read more about Fast and Compact Set Intersection through Recursive Universe Partitioning
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We present a data structure that encodes a sorted integer sequence in small space allowing, at the same time, fast intersection operations. The data layout is carefully designed to exploit word-level parallelism and SIMD instructions, hence providing good practical performance. The core algorithmic idea is that of recursive partitioning the universe of representation: a markedly different paradigm than the widespread strategy of partitioning the sequence based on its length.
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- Read more about Compressing and Randomly Accessing Sequences
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In this paper we consider the problem of storing sequences of symbols in
a compressed format, while supporting random access to the symbols without
decompression. Although this is a well-studied problem when the data is
textual, the kind of sequences we look at are not textual, and we argue
that traditional compression methods used in the text algorithms community
(such as compressors targeting $k$-th order empirical entropy) do not
perform as well on these sequential data, and simpler methods such
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- Read more about Decompressing Lempel-Ziv compressed text
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We consider the problem of decompressing the Lempel--Ziv 77 representation of a string $S$ of length $n$ using a working space as close as possible to the size $z$ of the input. The folklore solution for the problem runs in $O(n)$ time but requires random access to the whole decompressed text. Another folklore solution is to convert LZ77 into a grammar of size $O(z\log(n/z))$ and then stream $S$ in linear time. In this paper, we show that $O(n)$ time and $O(z)$ working space can be achieved for constant-size alphabets.
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- Read more about Approximating Optimal Bidirectional Macro Schemes.
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Lempel-Ziv is an easy-to-compute member of a wide family of so-called macro schemes; it restricts pointers to go in one direction only. Optimal bidirectional macro schemes are NP-complete to find, but they may provide much better compression on highly repetitive sequences. We consider the problem of approximating optimal bidirectional macro schemes. We describe a simulated annealing algorithm that usually converges quickly. Moreover, in some cases, we obtain bidirectional macro schemes that are provably a 2-approximation of the optimal.
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- Read more about Grammar compression with probabilistic context-free grammar
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We propose a new approach for universal lossless text compression, based on grammar compression. In the literature, a target string T has been compressed as a context-free grammar G in Chomsky normal form satisfying L(G) = T. Such a grammar is often called a straight-line program (SLP). In this work, we consider a probabilistic grammar G that generates T, but not necessarily as a unique element of L(G). In order to recover the original text T unambiguously, we keep both the grammar G and the derivation tree of T from the start symbol in G, in compressed form.
dcc_poster.pdf
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- Read more about Reverse Multi-Delimiter Compression Codes
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An enhanced version of a recently introduced family of variable length binary codes with multiple pattern delimiters is presented and discussed. These codes are complete, universal, synchronizable, they have monotonic indexing and allow a standard search in compressed files. Comparing the compression rate on natural language texts demonstrates that introduced codes appear to be much superior to other known codes with similar properties. A fast byte-aligned decoding algorithm is constructed, which operates much faster than the one for Fibonacci codes.
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- Read more about An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition
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In this paper, we examine the problem of modeling overdispersed frequency vectors that are naturally generated by several machine learning and computer vision applications.
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