15,132 research outputs found
Fast Decoder for Overloaded Uniquely Decodable Synchronous Optical CDMA
In this paper, we propose a fast decoder algorithm for uniquely decodable
(errorless) code sets for overloaded synchronous optical code-division
multiple-access (O-CDMA) systems. The proposed decoder is designed in a such a
way that the users can uniquely recover the information bits with a very simple
decoder, which uses only a few comparisons. Compared to maximum-likelihood (ML)
decoder, which has a high computational complexity for even moderate code
lengths, the proposed decoder has much lower computational complexity.
Simulation results in terms of bit error rate (BER) demonstrate that the
performance of the proposed decoder for a given BER requires only 1-2 dB higher
signal-to-noise ratio (SNR) than the ML decoder.Comment: arXiv admin note: substantial text overlap with arXiv:1806.0395
Quantum computing classical physics
In the past decade quantum algorithms have been found which outperform the
best classical solutions known for certain classical problems as well as the
best classical methods known for simulation of certain quantum systems. This
suggests that they may also speed up the simulation of some classical systems.
I describe one class of discrete quantum algorithms which do so--quantum
lattice gas automata--and show how to implement them efficiently on standard
quantum computers.Comment: 13 pages, plain TeX, 10 PostScript figures included with epsf.tex;
for related work see http://math.ucsd.edu/~dmeyer/research.htm
Probabilities and health risks: a qualitative approach
Health risks, defined in terms of the probability that an individual will suffer a particular type of adverse health event within a given time period, can be understood as referencing either natural entities or complex patterns of belief which incorporate the observer's values and knowledge, the position adopted in the present paper. The subjectivity inherent in judgements about adversity and time frames can be easily recognised, but social scientists have tended to accept uncritically the objectivity of probability. Most commonly in health risk analysis, the term probability refers to rates established by induction, and so requires the definition of a numerator and denominator. Depending upon their specification, many probabilities may be reasonably postulated for the same event, and individuals may change their risks by deciding to seek or avoid information. These apparent absurdities can be understood if probability is conceptualised as the projection of expectation onto the external world. Probabilities based on induction from observed frequencies provide glimpses of the future at the price of acceptance of the simplifying heuristic that statistics derived from aggregate groups can be validly attributed to individuals within them. The paper illustrates four implications of this conceptualisation of probability with qualitative data from a variety of sources, particularly a study of genetic counselling for pregnant women in a U.K. hospital. Firstly, the official selection of a specific probability heuristic reflects organisational constraints and values as well as predictive optimisation. Secondly, professionals and service users must work to maintain the facticity of an established heuristic in the face of alternatives. Thirdly, individuals, both lay and professional, manage probabilistic information in ways which support their strategic objectives. Fourthly, predictively sub-optimum schema, for example the idea of AIDS as a gay plague, may be selected because they match prevailing social value systems
OneMax in Black-Box Models with Several Restrictions
Black-box complexity studies lower bounds for the efficiency of
general-purpose black-box optimization algorithms such as evolutionary
algorithms and other search heuristics. Different models exist, each one being
designed to analyze a different aspect of typical heuristics such as the memory
size or the variation operators in use. While most of the previous works focus
on one particular such aspect, we consider in this work how the combination of
several algorithmic restrictions influence the black-box complexity. Our
testbed are so-called OneMax functions, a classical set of test functions that
is intimately related to classic coin-weighing problems and to the board game
Mastermind.
We analyze in particular the combined memory-restricted ranking-based
black-box complexity of OneMax for different memory sizes. While its isolated
memory-restricted as well as its ranking-based black-box complexity for bit
strings of length is only of order , the combined model does not
allow for algorithms being faster than linear in , as can be seen by
standard information-theoretic considerations. We show that this linear bound
is indeed asymptotically tight. Similar results are obtained for other memory-
and offspring-sizes. Our results also apply to the (Monte Carlo) complexity of
OneMax in the recently introduced elitist model, in which only the best-so-far
solution can be kept in the memory. Finally, we also provide improved lower
bounds for the complexity of OneMax in the regarded models.
Our result enlivens the quest for natural evolutionary algorithms optimizing
OneMax in iterations.Comment: This is the full version of a paper accepted to GECCO 201
Optimal Nested Test Plan for Combinatorial Quantitative Group Testing
We consider the quantitative group testing problem where the objective is to
identify defective items in a given population based on results of tests
performed on subsets of the population. Under the quantitative group testing
model, the result of each test reveals the number of defective items in the
tested group. The minimum number of tests achievable by nested test plans was
established by Aigner and Schughart in 1985 within a minimax framework. The
optimal nested test plan offering this performance, however, was not obtained.
In this work, we establish the optimal nested test plan in closed form. This
optimal nested test plan is also order optimal among all test plans as the
population size approaches infinity. Using heavy-hitter detection as a case
study, we show via simulation examples orders of magnitude improvement of the
group testing approach over two prevailing sampling-based approaches in
detection accuracy and counter consumption. Other applications include anomaly
detection and wideband spectrum sensing in cognitive radio systems
Data Discovery and Anomaly Detection Using Atypicality: Theory
A central question in the era of 'big data' is what to do with the enormous
amount of information. One possibility is to characterize it through
statistics, e.g., averages, or classify it using machine learning, in order to
understand the general structure of the overall data. The perspective in this
paper is the opposite, namely that most of the value in the information in some
applications is in the parts that deviate from the average, that are unusual,
atypical. We define what we mean by 'atypical' in an axiomatic way as data that
can be encoded with fewer bits in itself rather than using the code for the
typical data. We show that this definition has good theoretical properties. We
then develop an implementation based on universal source coding, and apply this
to a number of real world data sets.Comment: 40 page
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