Linear complexity universal decoding with exponential error probability decay

In this manuscript we consider linear complexity binary linear block encoders and decoders that operate universally with exponential error probability decay. Such scenarios may be relevant in wireless scenarios where probability distributions may not be fully characterized due to the dynamic nature of wireless environments. More specifically, we consider the setting of fixed length-to-fixed length near-lossless data compression of a memoryless binary source of unknown probability distribution as well as the dual setting of communicating on a binary symmetric channel (BSC) with unknown crossover probability. We introduce a new 'min-max distance' metric, analogous to minimum distance, that addresses the universal binary setting and has the same properties as that of minimum distance on BSCs with known crossover probability. The code construction and decoding algorithm are universal extensions of the 'expander codes' framework of Barg and Zemor and have identical complexity and exponential error probability performance

Towards practical minimum-entropy universal decoding

Minimum-entropy decoding is a universal decoding algorithm used in decoding block compression of discrete memoryless sources as well as block transmission of information across discrete memoryless channels. Extensions can also be applied for multiterminal decoding problems, such as the Slepian-Wolf source coding problem. The 'method of types' has been used to show that there exist linear codes for which minimum-entropy decoders achieve the same error exponent as maximum-likelihood decoders. Since minimum-entropy decoding is NP-hard in general, minimum-entropy decoders have existed primarily in the theory literature. We introduce practical approximation algorithms for minimum-entropy decoding. Our approach, which relies on ideas from linear programming, exploits two key observations. First, the 'method of types' shows that that the number of distinct types grows polynomially in n. Second, recent results in the optimization literature have illustrated polytope projection algorithms with complexity that is a function of the number of vertices of the projected polytope. Combining these two ideas, we leverage recent results on linear programming relaxations for error correcting codes to construct polynomial complexity algorithms for this setting. In the binary case, we explicitly demonstrate linear code constructions that admit provably good performance

Right Without Remedy? The Development of the Presumption of Innocence at the International Criminal Court

This article examines the presumption of innocence’s development at the InternationalCriminal Court. While the presumption of innocence was hardly debated at the RomeConference, several issues surrounding the presumption have been open to wideinterpretation by the Court. This article argues that since the Rome Statute’s entry intoforce, the presumption of innocence goes beyond the text of Article 66 and hasbecome a robust right that has application both inside and outside of the courtroomand has effect during the Situation, Pre-Trial and Trial phases. Despite thesedevelopments, what happens when the right is violated remains an open question. Thepaper will conclude that while the presumption of innocence may be better defined andmore protective than it was 20 years ago, what happens in the case of a violationcontinues to be an area for further development

A new source-splitting approach to the Slepian-Wolf problem

It is shown that achieving an arbitrary rate-point in the achievable region of the M-source Slepian-Wolf [1] problem may be reduced via a practical source-splitting transformation to achieving a corner point in a 2M − 1 source Slepian-Wolf problem. Moreover, each source must be split at most once. This approach extends the ideas introduced in [2] to a practical setting: it does not require common randomness shared between splitters and the decoders, the cardinality of each source split is strictly smaller than the original, and practical iterative decoding methods can achieve rates near the theoretical bound

Rate-splitting for the deterministic broadcast channel

We show that the deterministic broadcast channel, where a single source transmits to M receivers across a deterministic mechanism, may be reduced, via a rate-splitting transformation, to another (2M−1)-receiver deterministic broadcast channel problem where a successive encoding approach suffices. Analogous to rate-splitting for the multiple access channel and source-splitting for the Slepian-Wolf problem, all achievable rates (including non-vertices) apply. This amounts to significant complexity reduction at the encoder

Time-sharing vs. source-splitting in the Slepian-Wolf problem: error exponents analysis

We discuss two approaches for decoding at arbitrary rates in the Slepian-Wolf problem - time sharing and source splitting - both of which rely on constituent vertex decoders. We consider the error exponents for both schemes and conclude that source-splitting is more robust at coding at arbitrary rates, as the error exponent for time-sharing degrades significantly at rates near vertices. As a by-product of our analysis, we exhibit an interesting connection between minimum mean-squared error estimation and error exponents

On some new approaches to practical Slepian-Wolf compression inspired by channel coding

This paper considers the problem, first introduced by Ahlswede and Körner in 1975, of lossless source coding with coded side information. Specifically, let X and Y be two random variables such that X is desired losslessly at the decoder while Y serves as side information. The random variables are encoded independently, and both descriptions are used by the decoder to reconstruct X. Ahlswede and Körner describe the achievable rate region in terms of an auxiliary random variable. This paper gives a partial solution for the optimal auxiliary random variable, thereby describing part of the rate region explicitly in terms of the distribution of X and Y

Low-Complexity Approaches to Slepian–Wolf Near-Lossless Distributed Data Compression

This paper discusses the Slepian–Wolf problem of distributed near-lossless compression of correlated sources. We introduce practical new tools for communicating at all rates in the achievable region. The technique employs a simple “source-splitting” strategy that does not require common sources of randomness at the encoders and decoders. This approach allows for pipelined encoding and decoding so that the system operates with the complexity of a single user encoder and decoder. Moreover, when this splitting approach is used in conjunction with iterative decoding methods, it produces a significant simplification of the decoding process. We demonstrate this approach for synthetically generated data. Finally, we consider the Slepian–Wolf problem when linear codes are used as syndrome-formers and consider a linear programming relaxation to maximum-likelihood (ML) sequence decoding. We note that the fractional vertices of the relaxed polytope compete with the optimal solution in a manner analogous to that observed when the “min-sum” iterative decoding algorithm is applied. This relaxation exhibits the ML-certificate property: if an integral solution is found, it is the ML solution. For symmetric binary joint distributions, we show that selecting easily constructable “expander”-style low-density parity check codes (LDPCs) as syndrome-formers admits a positive error exponent and therefore provably good performance

The 1980\u27s And Today; An Analysis Of Women\u27s Subjective Well-being

The purpose of this study is to augment the existing literature concerning the relationship between marital status, gender, social networks, and cohort effect on dimensions of subjective well-being for women. Multiple dimensions of subjective well-being are examined. Multiple regression and logistic regression are employed to examine the effects of marital status, social networks, and cohort effects on the dependent variables that tap the dimensions of subjective well-being. The analysis controls for age, race, education, income, religious attendance and region of residence. The findings report some inconsistency in regards to the current literature. Social networks and support are found to be the most constant independent predictor of subjective well-being. While the effects of being divorced and separated, as well as cohort membership, are not as consistent, the findings are notable and should be addressed in future research addressing subjective well-being