NEC

About: NEC is a company organization based out in Tokyo, Japan. It is known for research contribution in the topics: Signal & Layer (electronics). The organization has 33269 authors who have published 57670 publications receiving 835952 citations. The organization is also known as: NEC Corporation & NEC Electronics Corporation.

Processor sharing flows in the internet

Abstract: Most congestion control algorithms try to emulate processor sharing (PS) by giving each competing flow an equal share of a bottleneck link. This approach leads to fairness, and prevents long flows from hogging resources. For example, if a set of flows with the same round trip time share a bottleneck link, TCP’s congestion control mechanism tries to achieve PS; so do most of the proposed alternatives, such as eXplicit Control Protocol (XCP). But although they emulate PS well in a static scenario when all flows are long-lived, they do not come close to PS when new flows arrive randomly and have a finite amount of data to send, as is the case in today’s Internet. Typically, flows take an order of magnitude longer to complete with TCP or XCP than with PS, suggesting large room for improvement. And so in this paper, we explore how a new congestion control algorithm — Rate Control Protocol (RCP) — comes much closer to emulating PS over a broad range of operating conditions. In RCP, a router assigns a single rate to all flows that pass through it. The router does not keep flow-state, and does no per-packet calculations. Yet we are able to show that under a wide range of traffic characteristics and network conditions, RCP’s performance is very close to ideal processor sharing.

Social Choice Theory and Recommender Systems: Analysis of the Axiomatic Foundations of Collaborative Filtering

Abstract: The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the behavior of multiple users to recommend items of interest to individual users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed several variations of the technology. We take the perspective of CF as a methodology for combining preferences. The preferences predicted for the end user is some function of all of the known preferences for everyone in a database. Social Choice theorists, concerned with the properties of voting methods, have been investigating preference aggregation for decades. At the heart of this body of work is Arrow's result demonstrating the impossibility of combining preferences in a way that satisfies several desirable and innocuous-looking properties. We show that researchers working on CF algorithms often make similar assumptions. We elucidate these assumptions and extend results from Social Choice theory to CF methods. We show that only very restrictive CF functions are consistent with desirable aggregation properties. Finally, we discuss practical implications of these results.

Compressibility and Polygonization of Single-Walled Carbon Nanotubes under Hydrostatic Pressure

Abstract: Single-walled carbon nanotubes show linear elasticity under hydrostatic pressure up to 1.5 GPa at room temperature. The volume compressibility, measured by in situ synchrotron x-ray diffraction, has been determined to be $0.024\mathrm{GPa}{}^{\ensuremath{-}1}$. Theoretical calculations suggest that single-walled carbon nanotubes are polygonized when they form bundles of hexagonal close-packed structure and the intertubular gap is smaller than the equilibrium spacing of graphite (002) $(d\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}3.35\phantom{\rule{0ex}{0ex}}\AA{})$. It has also been determined that the deformation of the trigonal nanotube lattice under hydrostatic pressure is reversible up to 4 GPa, beyond which the nanotube lattice is destroyed.

System and method for intelligent caching and refresh of dynamically generated and static web content

Abstract: A system for updating Web pages stored in cache based on modifications to data stored in a database is disclosed. The system for updating stored Web pages may be part of a larger system having a database management system for storing data used to generate Web pages. The database management system is capable of identifying modified data stored in the database. The system for updating stored Web pages is comprised of one or more servers programmed for maintaining associations between the stored Web pages and the stored data, and receiving the identity of modified data from the memory management system. In addition, the servers are capable of determining, from the identified modified data and the maintained associations, which stored Web pages are associated with the identified modified data. Furthermore, the servers are capable of communicating an update command to the cache that contains the stored Web pages associated with the identified modified data, for the purpose of updating the stored Web pages.

A unifying framework for detecting outliers and change points from time series

Abstract: We are concerned with the issue of detecting outliers and change points from time series. In the area of data mining, there have been increased interest in these issues since outlier detection is related to fraud detection, rare event discovery, etc., while change-point detection is related to event/trend change detection, activity monitoring, etc. Although, in most previous work, outlier detection and change point detection have not been related explicitly, this paper presents a unifying framework for dealing with both of them. In this framework, a probabilistic model of time series is incrementally learned using an online discounting learning algorithm, which can track a drifting data source adaptively by forgetting out-of-date statistics gradually. A score for any given data is calculated in terms of its deviation from the learned model, with a higher score indicating a high possibility of being an outlier. By taking an average of the scores over a window of a fixed length and sliding the window, we may obtain a new time series consisting of moving-averaged scores. Change point detection is then reduced to the issue of detecting outliers in that time series. We compare the performance of our framework with those of conventional methods to demonstrate its validity through simulation and experimental applications to incidents detection in network security.