scispace - formally typeset
Search or ask a question
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
More filters
Journal ArticleDOI
15 Sep 2003
TL;DR: This work proposes a discrete denoising algorithm that does not assume knowledge of statistical properties of the input sequence, and is universal in the sense of asymptotically performing as well as the optimum denoiser that knows theinput sequence distribution, which is only assumed to be stationary.
Abstract: A discrete denoising algorithm estimates the input sequence to a discrete memoryless channel (DMC) based on the observation of the entire output sequence. For the case in which the DMC is known and the quality of the reconstruction is evaluated with a given single-letter fidelity criterion, we propose a discrete denoising algorithm that does not assume knowledge of statistical properties of the input sequence. Yet, the algorithm is universal in the sense of asymptotically performing as well as the optimum denoiser that knows the input sequence distribution, which is only assumed to be stationary. Moreover, the algorithm is universal also in a semi-stochastic setting, in which the input is an individual sequence, and the randomness is due solely to the channel noise. The proposed denoising algorithm is practical, requiring a linear number of register-level operations and sublinear working storage size relative to the input data length.

259 citations

Proceedings ArticleDOI
31 Mar 2009
TL;DR: GViM is presented, a system designed for virtualizing and managing the resources of a general purpose system accelerated by graphics processors and how such accelerators can be virtualized without additional hardware support.
Abstract: The use of virtualization to abstract underlying hardware can aid in sharing such resources and in efficiently managing their use by high performance applications. Unfortunately, virtualization also prevents efficient access to accelerators, such as Graphics Processing Units (GPUs), that have become critical components in the design and architecture of HPC systems. Supporting General Purpose computing on GPUs (GPGPU) with accelerators from different vendors presents significant challenges due to proprietary programming models, heterogeneity, and the need to share accelerator resources between different Virtual Machines (VMs).To address this problem, this paper presents GViM, a system designed for virtualizing and managing the resources of a general purpose system accelerated by graphics processors. Using the NVIDIA GPU as an example, we discuss how such accelerators can be virtualized without additional hardware support and describe the basic extensions needed for resource management. Our evaluation with a Xen-based implementation of GViM demonstrate efficiency and flexibility in system usage coupled with only small performance penalties for the virtualized vs. non-virtualized solutions.

259 citations

Journal ArticleDOI
06 Sep 2017
TL;DR: In this paper, the authors explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control.
Abstract: The research community has considered in the past the application of Artificial Intelligence (AI) techniques to control and operate networks A notable example is the Knowledge Plane proposed by DClark et al However, such techniques have not been extensively prototyped or deployed in the field yet In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control We describe a new paradigm that accommodates and exploits SDN, NA and AI, and provide use-cases that illustrate its applicability and benefits We also present simple experimental results that support, for some relevant use-cases, its feasibility We refer to this new paradigm as Knowledge-Defined Networking (KDN)

258 citations

Patent
08 Apr 1992
TL;DR: In this paper, a biased retention clip was used to remove the physical/electrical media connector from the aperture in a communications card. But the mechanism was not designed to be used in the case of a single-input single-output (SIMO) device.
Abstract: A communications card capable of being mounted in electrical communications with a computer has formed therethrough an aperture so sized and shaped as to be capable of receiving a physical/electrical media connector. The media connector has a biased retention clip, a contact pin block, and contact pins. The retention clip has several standardized characteristics including a broad fixed end protruding from an outer surface of the contact pin block. The broad fixed end tapers abruptly at a transition notch down to a narrow free end, capable of being manipulated by a user to remove the physical/electrical media connector from the aperture in the communications card. In use, a media connector is inserted directly into the aperture in the communications card, the aperture being in contact with a plurality of contact wires fixed within the communications card. The communications card is divided into a retractable access portion of the communications card which can be directly accessed by manipulating an actuating mechanism releasing a retention means thereby allowing a spring to push the retractable access portion of the card outside of the computer housing. The retractable access portion of the communications card may be reinserted back into the computer housing to be carried internally when not in use.

258 citations

Book ChapterDOI
01 Jan 2002
TL;DR: An algorithm is introduced which is both iterative and interactive and lets the user influence future search results by marking some of the results of the current search as 'relevant' or 'irrelevant', thus indicating personal preferences.
Abstract: We examine the problem of searching a database of three-dimensional objects (given in VRML) for objects similar to a given object. We introduce an algorithm which is both iterative and interactive. Rather than base the search solely on geometric feature similarity, we propose letting the user influence future search results by marking some of the results of the current search as 'relevant' or 'irrelevant', thus indicating personal preferences. A novel approach, based on SVM, is used for the adaptation of the distance measure consistently with these markings, which brings the 'relevant' objects closer and pushes the 'irrelevant' objects farther. We show that in practice very few iterations are needed for the system to converge well on what the user "had in mind".

258 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
Network Information
Related Institutions (5)
IBM
253.9K papers, 7.4M citations

94% related

Samsung
163.6K papers, 2M citations

90% related

Carnegie Mellon University
104.3K papers, 5.9M citations

90% related

Microsoft
86.9K papers, 4.1M citations

90% related

Bell Labs
59.8K papers, 3.1M citations

89% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
202223
2021240
20201,028
20191,269
2018964