for LDPC codes. More generally, we consider the encoding problem for codes specified by sparse parity-check matrices. We show how to exploit the sparseness of the parity-check matrix to obtain efficient encoders. For the @Q TA-regular LDPC code, for example, the complexity of encoding is essentially

of energy functions for lattice heteropolymer models: Efficient encodings for constraint satisfaction programming and quantum annealing. Advances in Chemical Physics.

Abstract. An Information Dispersal Algorithm (IDA) is developed that breaks a file F of length L = ( F ( into n pieces F,, 1 5 i 5 n, each of length ( F, 1 = L/m, so that every m pieces suffice for reconstructing F. Dispersal and reconstruction are computationally efficient. The sum of the lengths

In this paper, we investigate an efficient encoding approach for generalized low-density (GLD) parity check codes, a generalization of Gallager's low-density parity check (LDPC) codes. We propose a systematic approach to construct approximate upper triangular GLD parity check matrix which

encoding schemes based on scalar quantization that efficiently encode auxiliary numeric properties, such as PageRank, an estimate of page importance used by the Google search engine. Unlike standard scalar quantization algorithms, which concentrate on minimizing the numerical distortion caused by lossy

encoding schemes based on scalar quantization that efficiently encode auxiliary numeric properties, such as PageRank, an estimate of page importance used by the Google search engine. Unlike standard scalar quantization algorithms, which concentrate on minimizing the numerical distortion caused by lossy

. Second, there must be some way of choosing internal representations which allow the preexisting hardware connections to be used efficiently for encoding the con-straints in the domain being searched. We describe a generol parallel search method, based on statistical mechanics, and we show how it leads

to accommodate a wide range of protocols, yet efficient enough to perform competitively with less structured operating systems. 1 Introduction Network software is at the heart of any distributed system. It manages the communication hardware that connects the processors in the system and it defines

with one range image at a time, we first scan-convert it to a distance function, then combine this with the data already acquired using a simple additive scheme. To achieve space efficiency, we employ a run-length encoding of the volume. To achieve time efficiency, we resample the range image to align

. SplitStreamadV esses this problem by striping the content across a forest of interior-nodno# sjoint multicast trees that d stributes the forward ng load among all participating peers. For example, it is possible to construct efficient SplitStream forests in which each peer contributes only as much