Hewlett-Packard

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.

Local and systemic insulin resistance resulting from hepatic activation of IKK-β and NF-κB

Abstract: We show that NF-κB and transcriptional targets are activated in liver by obesity and high-fat diet (HFD). We have matched this state of chronic, subacute 'inflammation' by low-level activation of NF-κB in the liver of transgenic mice, designated LIKK, by selectively expressing constitutively active IKK-b in hepatocytes. These mice exhibit a type 2 diabetes phenotype, characterized by hyperglycemia, profound hepatic insulin resistance, and moderate systemic insulin resistance, including effects in muscle. The hepatic production of proinflammatory cytokines, including IL-6, IL-1β and TNF-α, was increased in LIKK mice to a similar extent as induced by HFD in in wild-type mice. Parallel increases were observed in cytokine signaling in liver and mucscle of LIKK mice. Insulin resistance was improved by systemic neutralization of IL-6 or salicylate inhibition of IKK-β. Hepatic expression of the IκBα superrepressor (LISR) reversed the phenotype of both LIKK mice and wild-type mice fed an HFD. These findings indicate that lipid accumulation in the liver leads to subacute hepatic 'inflammation' through NF-κB activation and downstream cytokine production. This causes insulin resistance both locally in liver and systemically.

PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth

Abstract: Sequential pattern mining is an important data mining problem with broad applications. It is challenging since one may need to examine a combinatorially explosive number of possible subsequence patterns. Most of the previously developed sequential pattern mining methods follow the methodology of A priori which may substantially reduce the number of combinations to be examined. Howeve6 Apriori still encounters problems when a sequence database is large andor when sequential patterns to be mined are numerous ano we propose a novel sequential pattern mining method, called Prefixspan (i.e., Prefix-projected - Ettern_ mining), which explores prejxprojection in sequential pattern mining. Prefixspan mines the complete set of patterns but greatly reduces the efforts of candidate subsequence generation. Moreover; prefi-projection substantially reduces the size of projected databases and leads to efJicient processing. Our performance study shows that Prefixspan outperforms both the Apriori-based GSP algorithm and another recently proposed method; Frees pan, in mining large sequence data bases.

Usage patterns of collaborative tagging systems

Abstract: Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.

Predicting the Future with Social Media

Abstract: In recent years, social media has become ubiquitous and important for social networking and content sharing. And yet, the content that is generated from these websites remains largely untapped. In this paper, we demonstrate how social media content can be used to predict real-world outcomes. In particular, we use the chatter from Twitter.com to forecast box-office revenues for movies. We show that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors. We further demonstrate how sentiments extracted from Twitter can be utilized to improve the forecasting power of social media.

Practical Numerical Algorithms for Chaotic Systems

Abstract: The goal of this book qre to present an elementary introduction on chaotic systems for the non-specialist, and to present and extensive package of computer algorithms ( in the form of pseudocode) for simulating and characterizing chaotic phenomena. These numerical algorithms have been implemented in a software package called INSITE (Interactive Nonlinear System Investigative Toolkit for Everyone) which is being distributed separately.