1,050 research outputs found

    Stream VByte: Faster Byte-Oriented Integer Compression

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    Arrays of integers are often compressed in search engines. Though there are many ways to compress integers, we are interested in the popular byte-oriented integer compression techniques (e.g., VByte or Google's Varint-GB). They are appealing due to their simplicity and engineering convenience. Amazon's varint-G8IU is one of the fastest byte-oriented compression technique published so far. It makes judicious use of the powerful single-instruction-multiple-data (SIMD) instructions available in commodity processors. To surpass varint-G8IU, we present Stream VByte, a novel byte-oriented compression technique that separates the control stream from the encoded data. Like varint-G8IU, Stream VByte is well suited for SIMD instructions. We show that Stream VByte decoding can be up to twice as fast as varint-G8IU decoding over real data sets. In this sense, Stream VByte establishes new speed records for byte-oriented integer compression, at times exceeding the speed of the memcpy function. On a 3.4GHz Haswell processor, it decodes more than 4 billion differentially-coded integers per second from RAM to L1 cache

    Tables of subspace codes

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    One of the main problems of subspace coding asks for the maximum possible cardinality of a subspace code with minimum distance at least dd over Fqn\mathbb{F}_q^n, where the dimensions of the codewords, which are vector spaces, are contained in K{0,1,,n}K\subseteq\{0,1,\dots,n\}. In the special case of K={k}K=\{k\} one speaks of constant dimension codes. Since this (still) emerging field is very prosperous on the one hand side and there are a lot of connections to classical objects from Galois geometry it is a bit difficult to keep or to obtain an overview about the current state of knowledge. To this end we have implemented an on-line database of the (at least to us) known results at \url{subspacecodes.uni-bayreuth.de}. The aim of this recurrently updated technical report is to provide a user guide how this technical tool can be used in research projects and to describe the so far implemented theoretic and algorithmic knowledge.Comment: 44 pages, 6 tables, 7 screenshot

    FIELD EXPERIENCE REPORT: FORT RILEY DEPARTMENT OF PUBLIC HEALTH

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    Master of Public HealthPublic Health Interdepartmental ProgramBrandon IrwinThis field experience report is submitted in partial completion of the degree of Master of Public Health at the Kansas State University. The following report presents my public health field experience report. A separate document will report my Thesis research. The work conducted is, to the best of my knowledge, original except where references are provided. This field report covers my public health field experience at the Fort Riley Department of Public Health [DPH]. The report will discuss the DPH divisions, rotations though those divisions, and DPH’s recent application to become an accredited health department through the Public Health Accreditation Board. Observations of the current state of DPH and potential future direction are discussed

    A new upper bound for subspace codes

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    It is shown that the maximum size A2(8,6;4)A_2(8,6;4) of a binary subspace code of packet length v=8v=8, minimum subspace distance d=4d=4, and constant dimension k=4k=4 is at most 272272. In Finite Geometry terms, the maximum number of solids in PG(7,2)\operatorname{PG}(7,2), mutually intersecting in at most a point, is at most 272272. Previously, the best known upper bound A2(8,6;4)289A_2(8,6;4)\le 289 was implied by the Johnson bound and the maximum size A2(7,6;3)=17A_2(7,6;3)=17 of partial plane spreads in PG(6,2)\operatorname{PG}(6,2). The result was obtained by combining the classification of subspace codes with parameters (7,17,6;3)2(7,17,6;3)_2 and (7,34,5;{3,4})2(7,34,5;\{3,4\})_2 with integer linear programming techniques. The classification of (7,33,5;{3,4})2(7,33,5;\{3,4\})_2 subspace codes is obtained as a byproduct.Comment: 9 page

    Coset construction for subspace codes

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    I\u27m Polynesian Too: Philosophy of Assimilation, Cosmopolitanism, Colonialism, Race, & Culture

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    Finding identity is difficult for mixed race and culture Polynesian Americans because there is no full integration into either racial/cultural side. For many Polynesian Americans (mixed race or not), finding an ethnic, cultural, and philosophical identity is a life-long struggle that constantly toils in matters tied to their souls and well being: issues of right and wrong, gender roles, morals/ethics, acceptance, and what it means to be human. For Polynesians and mixed race Polynesians, tribulation and alienation stem from the assimilation model that is present in the world today. “American Consumerist Cosmopolitanism,” as descended from colonialism, has impacted the well-being of Polynesian Americans (mixed race or not) for the worse. I will argue that the values of Polynesian culture are best preserved by a reevaluation of racial categories and ethnic practices in light of the unique colonialist history of Polynesians and that we need to move toward a model of Pluralistic Cosmopolitanism, which promotes true multicultural autonomy and both inter- and intra-cultural acceptance, rather than elitism. To explain and back this, I give brief histories of the Samoan and Hawaiian people, as well as some background in Polynesian philosophy, relevant sociological issues, assimilation/acculturation models, and look at racial philosophy, particularly in how these issues impact the continuation of Samoan and Hawaiian culture
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