240 research outputs found

    Strassen's Matrix Multiplication Algorithm Is Still Faster

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    Recently, reinforcement algorithms discovered new algorithms that really jump-started a wave of excitements and a flourishing of publications. However, there is little on implementations, applications, and, especially, no absolute performance and, we show here they are not here to replace Strassen's original fast matrix multiplication yet. We present Matrix Flow, this is a simple Python project for the automatic formulation, design, implementation, code generation, and execution of fast matrix multiplication algorithms for CPUs, using BLAS interface GPUs, and in the future other accelerators. We shall not play with module-2 (Z2) algorithms and, for simplicity, we present only square double-precision matrices. By means of factorizing the operand matrices we can express many algorithms and prove them correct. These algorithms are represented by Data Flows and matrix data partitions: a Directed Acyclic Graph. We show that Strassen's original algorithm is still the top choice even for modern GPUs. We also address error analysis in double precision, because integer computations are correct, alwaysComment: 8 pages, 2 images, mathematical softwar

    Integrating rather than collecting: statistical matching in the data flood era

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    Statistical matching is progressively emerging as a straightforward approach to data integration. This method of increasing importance and interest is useful to address the unsolved challenges posed by data shortage as well as the several opportunities occurring in the present data flood era. This paper offers an exhaustive review of the methodology from its early beginnings up to the most recent developments, considering also the most relevant applications. The links that statistical matching has with other integration methods are discussed, analysing how a 50-year-old method has been only recently proposed under a consistent but (yet) incomplete framework. Strengths and weaknesses of statistical matching are compared, considering different data features and sample representativeness frameworks, also, given future research ideas, always keeping an eye on uncertainty, the key problem to which statistical matching tries to answer

    Conservation biology of wedgefishes (Family Rhinidae) and giant guitarfishes (Family Glaucostegidae)

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    Brooke D'Alberto investigated the conservation biology of two families of Critically Endangered shark-like rays. The project provided life history, species distribution, and population productivity for these rays. This information will guide global conservation efforts for these threatened species. Her research has directly assisted in the species listing on multilateral trade agreements

    CONSOLE_WP3_Task3.2_Pan-EU survey of farmers and other rural landowners_common_db_2022.10.19_v10

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    The data set contains the information about 2,721 respondents observed with respect to 157 variables. Data were collected at national level on the case studies of the CONSOLE Consortium partners by means of a survey on farmers/land managers/forests owners. The surveys were carried out with a common questionnaire that was built within the framework of the CONSOLE project. The surveys were carried out in 13 countries of the European Union. Data were properly anonymized by each CONSOLE partner and assembled together by the UNIBO team. They have been operated through proper data management and assembled as a coherent and cohesive data set. The latter contains variables on the socio-demographic characteristics of the respondents, variables related to the structural features of the agricultural/forests holdings as well as behavioural information and variables related to the opinions expressed by the respondents on the four innovative contract solutions investigated by the CONSOLE project
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