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Livres sur le sujet « Partitioning and placement algorithms »

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Auteur : Grafiati

Publié le 14 mars 2023

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1

1934-, Zobrist George W., dir. Routing, placement, and partitioning. Norwood, N.J : Ablex, 1994.

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2

Center, Langley Research, dir. Approximate algorithms for partitioning and assignment problems. Hampton, Va : National Aeronautics and Space Administration, Langley Research Center, 1986.

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3

Walshaw, C. Parallel optimisation algorithms for multilevel mesh partitioning. London : CMS Press, 1999.

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4

S, Lueker George, dir. Probabilistic analysis of packing and partitioning algorithms. New York : Wiley, 1991.

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5

S, Agaian S., dir. Multidimensional discrete unitary transforms : Representation, partitioning, and algorithms. New York : Marcel Dekker, 2003.

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6

Schwartz, Victor Scott. Dynamic platform-independent meta-algorithms for graph-partitioning. Monterey, Calif : Naval Postgraduate School, 1998.

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7

H, Bohkari Shahid, et Langley Research Center, dir. Efficient algorithms for a class of partitioning problems. Hampton, Va : National Aeronautics and Space Administration, Langley Research Center, 1990.

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8

United States. National Aeronautics and Space Administration., dir. Parallel algorithms for placement and routing in VLSI design. Urbana, Ill : [University of Illinois at Urbana-Champaign, 1991.

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9

Tang, Xiaowei. Thre e extensions to force-directed placement for general graphs. Dublin : University College Dublin, 1998.

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10

El-Darzi, Elia. Methods for solving the set covering and set partitioning problems using graph theoretic (relaxation) algorithms. Uxbridge : Brunel University, 1988.

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11

Bader, David A., 1969- editor of compilation, Meyerhenke, Henning, 1978- editor of compilation, Sanders, Peter, editor of compilation et Wagner, Dorothea, 1957- editor of compilation, dir. Graph partitioning and graph clustering : 10th DIMACS Implementation Challenge Workshop, February 13-14, 2012, Georgia Institute of Technology, Atlanta, GA. Providence, Rhode Island : American Mathematical Society, 2013.

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12

Bahoshy, N. M. Parallelization of algorithms by explicit partitioning. 1993.

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13

Dynamic Platform-Independent Meta-Algorithms for Graph-Partitioning. Storming Media, 1998.

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14

Grigoryan, Artyom M., et Sos S. Agaian. Multidimensional Discrete Unitary Transforms : Representation : Partitioning, and Algorithms. Taylor & Francis Group, 2003.

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15

F, Pierce John. Partitioning Algorithms for a Class of Knapsack Problems. Creative Media Partners, LLC, 2015.

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16

Grigoryan, Artyom M., et Sos S. Agaian. Multidimensional Discrete Unitary Transforms : Representation : Partitioning, and Algorithms. Taylor & Francis Group, 2003.

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17

Aradau, Claudia, et Tobias Blanke. Algorithmic Reason. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192859624.001.0001.

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Are algorithms ruling the world today? Is artificial intelligence making life-and-death decisions? Are social media companies able to manipulate elections? As we are confronted with public and academic anxieties about unprecedented changes, this book offers a different analytical prism to investigate these transformations as more mundane and fraught. Aradau and Blanke develop conceptual and methodological tools to understand how algorithmic operations shape the government of self and other. While disperse and messy, these operations are held together by an ascendant algorithmic reason. Through a global perspective on algorithmic operations, the book helps us understand how algorithmic reason redraws boundaries and reconfigures differences. The book explores the emergence of algorithmic reason through rationalities, materializations, and interventions. It traces how algorithmic rationalities of decomposition, recomposition, and partitioning are materialized in the construction of dangerous others, the power of platforms, and the production of economic value. The book shows how political interventions to make algorithms governable encounter friction, refusal, and resistance. The theoretical perspective on algorithmic reason is developed through qualitative and digital methods to investigate scenes and controversies that range from mass surveillance and the Cambridge Analytica scandal in the UK to predictive policing in the US, and from the use of facial recognition in China and drone targeting in Pakistan to the regulation of hate speech in Germany. Algorithmic Reason offers an alternative to dystopia and despair through a transdisciplinary approach made possible by the authors’ backgrounds, which span the humanities, social sciences, and computer sciences.

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18

Aspden, Richard, et Jenny Gregory. Morphology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199668847.003.0011.

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The study of joint morphology can help us to understand the risk factors for osteoarthritis (OA), how it progresses, and aids in developing imaging biomarkers for study of the disease. OA results in gross structural changes in affected joints. Growth of osteophytes, deformation of joint components, and loss of joint space where cartilage has broken down are all characteristics of the disorder. Certain bone shapes as well as malalignment predispose people to future OA, or may be a marker for early OA. Geometrical measures, such as the alpha angle or Wiberg’s CE angle, used to be the primary tool for investigating morphology. In recent years, however, statistical shape modelling (SSM) has become increasingly popular. SSM can be used with any imaging modality and has been successfully applied to a number of musculoskeletal conditions. It uses sets of landmark points denoting the anatomy of one or more bones to generate new variables (modes) that describe and quantify the shape variation in a set of images via principal components analysis. With the aid of automated search algorithms for point placement, the use of SSMs is expanding and provides a valuable and versatile tool for exploration of bone and joint morphometry. Whilst the majority of research has focused on hip and knee OA, this chapter provides an overview of joint morphology through the whole skeleton and how it has helped our ability to understand and quantify the risk and progression of osteoarthritis.

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