117,693 research outputs found
Physical-depth architectural requirements for generating universal photonic cluster states
Most leading proposals for linear-optical quantum computing (LOQC) use
cluster states, which act as a universal resource for measurement-based
(one-way) quantum computation (MBQC). In ballistic approaches to LOQC, cluster
states are generated passively from small entangled resource states using
so-called fusion operations. Results from percolation theory have previously
been used to argue that universal cluster states can be generated in the
ballistic approach using schemes which exceed the critical threshold for
percolation, but these results consider cluster states with unbounded size.
Here we consider how successful percolation can be maintained using a physical
architecture with fixed physical depth, assuming that the cluster state is
continuously generated and measured, and therefore that only a finite portion
of it is visible at any one point in time. We show that universal LOQC can be
implemented using a constant-size device with modest physical depth, and that
percolation can be exploited using simple pathfinding strategies without the
need for high-complexity algorithms.Comment: 18 pages, 10 figure
Estimating the Expected Value of Partial Perfect Information in Health Economic Evaluations using Integrated Nested Laplace Approximation
The Expected Value of Perfect Partial Information (EVPPI) is a
decision-theoretic measure of the "cost" of parametric uncertainty in decision
making used principally in health economic decision making. Despite this
decision-theoretic grounding, the uptake of EVPPI calculations in practice has
been slow. This is in part due to the prohibitive computational time required
to estimate the EVPPI via Monte Carlo simulations. However, recent developments
have demonstrated that the EVPPI can be estimated by non-parametric regression
methods, which have significantly decreased the computation time required to
approximate the EVPPI. Under certain circumstances, high-dimensional Gaussian
Process regression is suggested, but this can still be prohibitively expensive.
Applying fast computation methods developed in spatial statistics using
Integrated Nested Laplace Approximations (INLA) and projecting from a
high-dimensional into a low-dimensional input space allows us to decrease the
computation time for fitting these high-dimensional Gaussian Processes, often
substantially. We demonstrate that the EVPPI calculated using our method for
Gaussian Process regression is in line with the standard Gaussian Process
regression method and that despite the apparent methodological complexity of
this new method, R functions are available in the package BCEA to implement it
simply and efficiently
Enriched property ontology for knowledge systems : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Systems in Information Systems, Massey University, Palmerston North, New Zealand
"It is obvious that every individual thing or event has an indefinite number of properties or attributes observable in it and might therefore be considered as belonging to an indefinite number of different classes of things" [Venn 1876]. The world in which we try to mimic in Knowledge Based (KB) Systems is essentially extremely complex especially when we attempt to develop systems that cover a domain of discourse with an almost infinite number of possible properties. Thus if we are to develop such systems how do we know what properties we wish to extract to make a decision and how do we ensure the value of our findings are the most relevant in our decision making. Equally how do we have tractable computations, considering the potential computation complexity of systems required for decision making within a very large domain. In this thesis we consider this problem in terms of medical decision making. Medical KB systems have the potential to be very useful aids for diagnosis, medical guidance and patient data monitoring. For example in a diagnostic process in certain scenarios patients may provide various potential symptoms of a disease and have defining characteristics. Although considerable information could be obtained, there may be difficulty in correlating a patient's data to known diseases in an economic and efficient manner. This would occur where a practitioner lacks a specific specialised knowledge. Considering the vastness of knowledge in the domain of medicine this could occur frequently. For example a Physician with considerable experience in a specialised domain such as breast cancer may easily be able to diagnose patients and decide on the value of appropriate symptoms given an abstraction process however an inexperienced Physician or Generalist may not have this facility.[FROM INTRODUCTION
Rerandomization to improve covariate balance in experiments
Randomized experiments are the "gold standard" for estimating causal effects,
yet often in practice, chance imbalances exist in covariate distributions
between treatment groups. If covariate data are available before units are
exposed to treatments, these chance imbalances can be mitigated by first
checking covariate balance before the physical experiment takes place. Provided
a precise definition of imbalance has been specified in advance, unbalanced
randomizations can be discarded, followed by a rerandomization, and this
process can continue until a randomization yielding balance according to the
definition is achieved. By improving covariate balance, rerandomization
provides more precise and trustworthy estimates of treatment effects.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1008 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Explicit probabilistic models for databases and networks
Recent work in data mining and related areas has highlighted the importance
of the statistical assessment of data mining results. Crucial to this endeavour
is the choice of a non-trivial null model for the data, to which the found
patterns can be contrasted. The most influential null models proposed so far
are defined in terms of invariants of the null distribution. Such null models
can be used by computation intensive randomization approaches in estimating the
statistical significance of data mining results.
Here, we introduce a methodology to construct non-trivial probabilistic
models based on the maximum entropy (MaxEnt) principle. We show how MaxEnt
models allow for the natural incorporation of prior information. Furthermore,
they satisfy a number of desirable properties of previously introduced
randomization approaches. Lastly, they also have the benefit that they can be
represented explicitly. We argue that our approach can be used for a variety of
data types. However, for concreteness, we have chosen to demonstrate it in
particular for databases and networks.Comment: Submitte
Low-Rank Boolean Matrix Approximation by Integer Programming
Low-rank approximations of data matrices are an important dimensionality
reduction tool in machine learning and regression analysis. We consider the
case of categorical variables, where it can be formulated as the problem of
finding low-rank approximations to Boolean matrices. In this paper we give what
is to the best of our knowledge the first integer programming formulation that
relies on only polynomially many variables and constraints, we discuss how to
solve it computationally and report numerical tests on synthetic and real-world
data
- …