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Data Staging on Untrusted Surrogates

by Jason Flinn, Shafeeq Sinnamohideen, Niraj Tolia, M. Satyanarayanan , 2003
"... We show how untrusted computers can be used to facilitate secure mobile data access. We discuss a novel architecture, data staging, that improves the performance of distributed file systems running on small, storage-limited pervasive computing devices. Data staging opportunistically prefetches file ..."
Abstract - Cited by 46 (10 self) - Add to MetaCart
We show how untrusted computers can be used to facilitate secure mobile data access. We discuss a novel architecture, data staging, that improves the performance of distributed file systems running on small, storage-limited pervasive computing devices. Data staging opportunistically prefetches

Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late Nineteenth Century

by N. A. Rayner, D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, A. Kaplan - J. GEOPHYSICAL RESEARCH , 2003
"... ... data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1 ° latitude-longitude g ..."
Abstract - Cited by 539 (4 self) - Add to MetaCart
... data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1 ° latitude

Statistical pattern recognition: A review

by Anil K. Jain, Robert P. W. Duin, Jianchang Mao - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2000
"... The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques ..."
Abstract - Cited by 1035 (30 self) - Add to MetaCart
. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well

Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy

by Hanchuan Peng, Fuhui Long, Chris Ding - IEEE TRANS. PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2005
"... Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first der ..."
Abstract - Cited by 571 (8 self) - Add to MetaCart
derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a

The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomized trials,”

by MSc; David Moher , PhD, MBA; Kenneth F Schulz , Douglas G Altman - Journal of the American Medical Association, , 1987
"... To comprehend the results of a randomized, controlled trial (RCT), readers must understand its design, conduct, analysis, and interpretation. That goal can be achieved only through complete transparency from authors. Despite several decades of educational efforts, the reporting of RCTs needs improv ..."
Abstract - Cited by 787 (15 self) - Add to MetaCart
effect or because the information is essential to judge the reliability or relevance of the findings. We intended the flow diagram to depict the passage of participants through an RCT. The revised flow diagram depicts information from four stages of a trial (enrollment, intervention allocation, follow

The Vector Field Histogram -- Fast Obstacle Avoidance For Mobile Robots

by J. Borenstein, Y. Koren - IEEE JOURNAL OF ROBOTICS AND AUTOMATION , 1991
"... A new real-time obstacle avoidance method for mobile robots has been developed and implemented. This method, named the vector field histogram(VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. The VFH method uses a ..."
Abstract - Cited by 484 (24 self) - Add to MetaCart
a two-dimensional Cartesian histogram gridas a world model. This world model is updated continuously with range data sampled by on-board range sensors. The VFH method subsequently employs a two-stage data-reduction process in order to compute the desired control commands for the vehicle

Data Staging on NFS

by Yunqing Chen, Jian Wu, Cong Yu , 2002
"... Due to the storage limitation and imperfect prediction, mobile computing devices may experience large delays when accessing data on the distributed file systems. Flinn et al. [4] have proposed a novel architecture, called Data Staging, in which nearby untrusted and unmanaged surrogates are used as t ..."
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Due to the storage limitation and imperfect prediction, mobile computing devices may experience large delays when accessing data on the distributed file systems. Flinn et al. [4] have proposed a novel architecture, called Data Staging, in which nearby untrusted and unmanaged surrogates are used

A framework for learning predictive structures from multiple tasks and unlabeled data

by Rie Kubota Ando, Tong Zhang - JOURNAL OF MACHINE LEARNING RESEARCH , 2005
"... One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semi-supervised learning. Although a number of such methods ar ..."
Abstract - Cited by 443 (3 self) - Add to MetaCart
One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semi-supervised learning. Although a number of such methods

Iterative (turbo) soft interference cancellation and decoding for coded CDMA

by Xiaodong Wang, H. Vincent Poor - IEEE Trans. Commun , 1999
"... Abstract — The presence of both multiple-access interference (MAI) and intersymbol interference (ISI) constitutes a major impediment to reliable communications in multipath code-division multiple-access (CDMA) channels. In this paper, an iterative receiver structure is proposed for decoding multiuse ..."
Abstract - Cited by 456 (18 self) - Add to MetaCart
multiuser information data in a convolutionally coded asynchronous multipath DS-CDMA system. The receiver performs two successive softoutput decisions, achieved by a soft-input soft-output (SISO) multiuser detector and a bank of single-user SISO channel decoders, through an iterative process. At each

Image analogies

by Aaron Hertzmann , 2001
"... Figure 1 An image analogy. Our problem is to compute a new “analogous ” image B ′ that relates to B in “the same way ” as A ′ relates to A. Here, A, A ′ , and B are inputs to our algorithm, and B ′ is the output. The full-size images are shown in Figures 10 and 11. This paper describes a new framewo ..."
Abstract - Cited by 455 (8 self) - Add to MetaCart
framework for processing images by example, called “image analogies. ” The framework involves two stages: a design phase, in which a pair of images, with one image purported to be a “filtered ” version of the other, is presented as “training data”; and an application phase, in which the learned filter
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