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Institution

Hewlett-Packard

CompanyPalo Alto, California, United States
About: Hewlett-Packard is a company organization based out in Palo Alto, California, United States. It is known for research contribution in the topics: Signal & Layer (electronics). The organization has 34663 authors who have published 59808 publications receiving 1467218 citations. The organization is also known as: Hewlett Packard & Hewlett-Packard Company.


Papers
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Journal ArticleDOI
TL;DR: Heterogeneous (or asymmetric) chip multiprocessors present unique opportunities for improving system throughput, reducing processor power, and mitigating Amdahl's law.
Abstract: Heterogeneous (or asymmetric) chip multiprocessors present unique opportunities for improving system throughput, reducing processor power, and mitigating Amdahl's law. On-chip heterogeneity allow the processor to better match execution resources to each application's needs and to address a much wider spectrum of system loads - from low to high thread parallelism - with high efficiency.

368 citations

Journal ArticleDOI
TL;DR: The thermal challenges in next-generation electronic systems, as identified through panel presentations and ensuing discussions at the workshop, Thermal Challenges in Next Generation Electronic Systems, held in Santa Fe, NM, January 7-10, 2007, are summarized in this article.
Abstract: Thermal challenges in next-generation electronic systems, as identified through panel presentations and ensuing discussions at the workshop, Thermal Challenges in Next Generation Electronic Systems, held in Santa Fe, NM, January 7-10, 2007, are summarized in this paper. Diverse topics are covered, including electrothermal and multiphysics codesign of electronics, new and nanostructured materials, high heat flux thermal management, site-specific thermal management, thermal design of next-generation data centers, thermal challenges for military, automotive, and harsh environment electronic systems, progress and challenges in software tools, and advances in measurement and characterization. Barriers to further progress in each area that require the attention of the research community are identified.

368 citations

Journal ArticleDOI
TL;DR: In this paper, a superoscillatory function is defined as a band-limited function oscillating faster than its fastest Fourier component, which is the initial state of a freely-evolving quantum wavefunction ψ.
Abstract: A superoscillatory function—that is, a band-limited function f(x) oscillating faster than its fastest Fourier component—is taken to be the initial state of a freely-evolving quantum wavefunction ψ. The superoscillations persist for unexpectedly long times, but eventually disappear through the interaction of contributions to ψ with complex momenta that are exponentially disparate in magnitude; this is established by applying the asymptotics of integrals, supported by numerics. f(x) can alternatively be regarded as the wave generated by a diffraction grating, propagating paraxially and without evanescence as ψ in the space beyond. The persistence of superoscillations is then interpreted as the propagation of sub-wavelength structure farther into the field than the more familiar evanescent waves.

367 citations

Journal ArticleDOI
TL;DR: It is shown by experiment that all but one of these computation methods leads to biased measurements, especially under high class imbalance, which is of particular interest to those designing machine learning software libraries and researchers focused onhigh class imbalance.
Abstract: Cross-validation is a mainstay for measuring performance and progress in machine learning. There are subtle differences in how exactly to compute accuracy, F-measure and Area Under the ROC Curve (AUC) in cross-validation studies. However, these details are not discussed in the literature, and incompatible methods are used by various papers and software packages. This leads to inconsistency across the research literature. Anomalies in performance calculations for particular folds and situations go undiscovered when they are buried in aggregated results over many folds and datasets, without ever a person looking at the intermediate performance measurements. This research note clarifies and illustrates the differences, and it provides guidance for how best to measure classification performance under cross-validation. In particular, there are several divergent methods used for computing F-measure, which is often recommended as a performance measure under class imbalance, e.g., for text classification domains and in one-vs.-all reductions of datasets having many classes. We show by experiment that all but one of these computation methods leads to biased measurements, especially under high class imbalance. This paper is of particular interest to those designing machine learning software libraries and researchers focused on high class imbalance.

367 citations

Patent
02 Mar 1998
TL;DR: In this paper, a pre-backup check is performed prior to running the actual scheduled backup job so that any faults which have developed since the initial configuration can be remedied.
Abstract: A backup system includes backup application software operating on a host computer, which is configurable to store data to be backed up to a backup apparatus. The system is configurable to schedule and enact a pre-backup check, prior to running the actual scheduled backup job so that any faults which have developed since the initial configuration can be remedied. The pre-backup check preferably occurs on a daily basis prior to every scheduled backup job.

367 citations


Authors

Showing all 34676 results

NameH-indexPapersCitations
Andrew White1491494113874
Stephen R. Forrest1481041111816
Rafi Ahmed14663393190
Leonidas J. Guibas12469179200
Chenming Hu119129657264
Robert E. Tarjan11440067305
Hong-Jiang Zhang11246149068
Ching-Ping Wong106112842835
Guillermo Sapiro10466770128
James R. Heath10342558548
Arun Majumdar10245952464
Luca Benini101145347862
R. Stanley Williams10060546448
David M. Blei98378111547
Wei-Ying Ma9746440914
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
202223
2021240
20201,028
20191,269
2018964