translations. CGN port assignments are often per connection, but they could optionally use port ranges. Research indicates that per-connection logging is not scalable in many residential broadband services. This document suggests a way to manage CGN translations in such a way as to significantly reduce

leaving the block. This paper takes a four-pronged approach to improving performance: (1) we introduce a new ‘direct access ’ implementation that avoids searching thread-private logs, (2) we develop compiler optimizations to reduce the amount of logging (e.g. when a thread accesses the same data

Iterative decoding of two-dimensional systematic convolutional codes has been termed “turbo” (de)coding. Using log-likelihood algebra, we show that any decoder can he used which accepts soft inputs-including a priori values-and delivers soft outputs that can he split into three terms: the soft

A new and conceptually simple data structure, called a suffix array, for on-line string searches is intro-duced in this paper. Constructing and querying suffix arrays is reduced to a sort and search paradigm that employs novel algorithms. The main advantage of suffix arrays over suffix trees

simple notion of monotone reducibility and exhibit complete problems. This provides a framework for stating existing results and asking new questions. We show that mNL (monotone nondeterministic log-space) is not closed under complementation, in contrast to Immerman's and Szelepcs &apos

Abstract. We study the local structure of Mori contractions f: X → Z of relative dimension one under an additional assumption that there exists a reduced divisor S such that KX + S is plt and anti-ample.

Abstract—Generally, the Log-MAP kernel of the turbo decoding consume large memories in hardware implement-tation. In this paper, we propose a new Log-MAP kernel to reduce memory usage. The comparison result shows our proposed architecture can reduce the memory size to 26 % of the classical

COWLES FOUNDATION DISCUSSION PAPER NO. 1263

In Andrews and Guggenberger (2003) a bias-reduced log-periodogram estima-tor d̂LP (r) for the long-memory parameter d in a stationary long-memory time series has been introduced. Compared to the Geweke and Porter-Hudak (1983) estimator d̂GPH = d̂LP (0), the estimator d̂LP (r) for r ≥ 1 generally

, Candes and Tao (2007) propose the Dantzig selector using L1 regularization and show that it achieves the ideal risk up to a logarithmic factor log p. Their innovative procedure and remarkable result are challenged when the dimensionality is ultra high as the factor log p can be large and their uniform