Telcordia Technologies

About: Telcordia Technologies is a based out in . It is known for research contribution in the topics: Network packet & Node (networking). The organization has 3097 authors who have published 4737 publications receiving 237882 citations.

Photonic band structure: The face-centered-cubic case employing nonspherical atoms.

Abstract: We introduce a practical, new, face-centered-cubic dielectric structure which simultaneously solves two of the outstanding problems in photonic band structure. In this new ``photonic crystal'' the atoms are nonspherical, lifting the degeneracy at the W point of the Brillouin zone, and permitting a full photonic band gap rather than a pseudogap. Furthermore, this fully three-dimensional fcc structure lends itself readily to microfabrication on the scale of optical wavelengths. It is created by simply drilling three sets of holes 35.26\ifmmode^\circ\else\textdegree\fi{} off vertical into the top surface of a solid slab or wafer, as can be done, for example, by chemical-beam-assisted ion etching.

Externalities in Economies with Imperfect Information and Incomplete Markets

Abstract: This paper presents a simple, general framework for analyzing externalities in economies with incomplete markets and imperfect infonnation. By identifying the pecuniary effects of these externalities that net out, the paper simplifies the problem of determining when tax interventions are Pareto improving. The approach indicates that such tax interventions almost always exist and that equilibria in situations of imperfect information are rarely constrained Pareto optima. It can also lead to simple tests, based on readily observable indicators of the efficacy of particular tax policies in situations involving adverse selection, signaling, moral hazard, incomplete contingent claims markets, and queue rationing equilibria. Traditional discussions of externalities have emphasized the distinction between technological externalities, in which the action of one individual or firm directly affects the utility or profit ofanother, and pecuniary externalities, in which one individual's or firm's actions affect another only through effects on prices. While the presence of technological externalities imply, in general, that a competitive equilibrium may not be Pareto efficient, pecuniary externalities hy themselves are not a source of inefficiency. The fact that prices change has, of course important consequences: there are both distributional and allocational effects. But, the distribution effects "net" out: gains for example, hy firms whose prices increase—are precisely offset by losses—e.g., to individuals who must pay higher prices. And, there are no welfare losses from the allocation effects as long as the price changes involved are small: if firms are maximizing profits and individuals are maximizing utility, both facing prices that correctly refiect opportunity costs, then standard envelope theorem arguments imply that changes in profits or utility induced by changes in allocations (resulting from any small change in prices) are negligible.

Dynamic program slicing

Abstract: Program slices are useful in debugging, testing, maintenance, and understanding of programs. The conventional notion of a program slice, the static slice, is the set of all statements that might affect the value of a given variable occurrence. In this paper, we investigate the concept of the dynamic slice consisting of all statements that actually affect the value of a variable occurrence for a given program input. The sensitivity of dynamic slicing to particular program inputs makes it more useful in program debugging and testing than static slicing. Several approaches for computing dynamic slices are examined. The notion of a Dynamic Dependence Graph and its use in computing dynamic slices is discussed. The Dynamic Dependence Graph may be unbounded in length; therefore, we introduce the economical concept of a Reduced Dynamic Dependence Graph, which is proportional in size to the number of dynamic slices arising during the program execution.

On the self-similar nature of Ethernet traffic

Abstract: We demonstrate that Ethernet local area network (LAN) traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal behavior, and that such behavior has serious implications for the design, control, and analysis of high-speed, cell-based networks. Intuitively, the critical characteristic of this self-similar traffic is that there is no natural length of a "burst": at every time scale ranging from a few milliseconds to minutes and hours, similar-looking traffic bursts are evident; we find that aggregating streams of such traffic typically intensifies the self-similarity ("burstiness") instead of smoothing it.Our conclusions are supported by a rigorous statistical analysis of hundreds of millions of high quality Ethernet traffic measurements collected between 1989 and 1992, coupled with a discussion of the underlying mathematical and statistical properties of self-similarity and their relationship with actual network behavior. We also consider some implications for congestion control in high-bandwidth networks and present traffic models based on self-similar stochastic processes that are simple, accurate, and realistic for aggregate traffic.

Recommendation as classification: using social and content-based information in recommendation

Abstract: Recommendation systems make suggestions about artifacts to a user. For instance, they may predict whether a user would be interested in seeing a particular movie. Social recomendation methods collect ratings of artifacts from many individuals, and use nearest-neighbor techniques to make recommendations to a user concerning new artifacts. However, these methods do not use the significant amount of other information that is often available about the nature of each artifact - such as cast lists o r movie reviews, for example. This paper presents an inductive learning approach to recommendation that is able to use both ratings information and other forms of information about each artifact in predicting user preferences. We show that our method outperforms an existing social-filtering method in the domain of movie recommendations on a dataset of more than 45,000 movie ratings collected from a community of over 250 users.