48,124 research outputs found
Congruence Testing of Point Sets in 4-Space
We give a deterministic O(n log n)-time algorithm to decide if two n-point sets in 4-dimensional Euclidean space are the same up to rotations and translations. It has been conjectured that O(n log n) algorithms should exist for any fixed dimension. The best algorithms in d-space so far are a deterministic algorithm by Brass and Knauer [Int. J. Comput. Geom. Appl., 2000] and a randomized Monte Carlo algorithm by Akutsu [Comp. Geom., 1998]. They take time O(n^2 log n) and O(n^(3/2) log n) respectively in 4-space. Our algorithm exploits many geometric structures and properties of 4-dimensional space
Fair Testing
In this paper we present a solution to the long-standing problem of characterising the coarsest liveness-preserving pre-congruence with respect to a full (TCSP-inspired) process algebra. In fact, we present two distinct characterisations, which give rise to the same relation: an operational one based on a De Nicola-Hennessy-like testing modality which we call should-testing, and a denotational one based on a refined notion of failures. One of the distinguishing characteristics of the should-testing pre-congruence is that it abstracts from divergences in the same way as Milner¿s observation congruence, and as a consequence is strictly coarser than observation congruence. In other words, should-testing has a built-in fairness assumption. This is in itself a property long sought-after; it is in notable contrast to the well-known must-testing of De Nicola and Hennessy (denotationally characterised by a combination of failures and divergences), which treats divergence as catrastrophic and hence is incompatible with observation congruence. Due to these characteristics, should-testing supports modular reasoning and allows to use the proof techniques of observation congruence, but also supports additional laws and techniques. Moreover, we show decidability of should-testing (on the basis of the denotational characterisation). Finally, we demonstrate its advantages by the application to a number of examples, including a scheduling problem, a version of the Alternating Bit-protocol, and fair lossy communication channel
Schwarzschild Tests of the Wahlquist-Estabrook-Buchman-Bardeen Tetrad Formulation for Numerical Relativity
A first order symmetric hyperbolic tetrad formulation of the Einstein
equations developed by Estabrook and Wahlquist and put into a form suitable for
numerical relativity by Buchman and Bardeen (the WEBB formulation) is adapted
to explicit spherical symmetry and tested for accuracy and stability in the
evolution of spherically symmetric black holes (the Schwarzschild geometry).
The lapse and shift which specify the evolution of the coordinates relative to
the tetrad congruence are reset at frequent time intervals to keep the
constant-time hypersurfaces nearly orthogonal to the tetrad congruence and the
spatial coordinate satisfying a kind of minimal rate of strain condition. By
arranging through initial conditions that the constant-time hypersurfaces are
asymptotically hyperbolic, we simplify the boundary value problem and improve
stability of the evolution. Results are obtained for both tetrad gauges
(``Nester'' and ``Lorentz'') of the WEBB formalism using finite difference
numerical methods. We are able to obtain stable unconstrained evolution with
the Nester gauge for certain initial conditions, but not with the Lorentz
gauge.Comment: (accepted by Phys. Rev. D) minor changes; typos correcte
Intrinsic data depth for Hermitian positive definite matrices
Nondegenerate covariance, correlation and spectral density matrices are
necessarily symmetric or Hermitian and positive definite. The main contribution
of this paper is the development of statistical data depths for collections of
Hermitian positive definite matrices by exploiting the geometric structure of
the space as a Riemannian manifold. The depth functions allow one to naturally
characterize most central or outlying matrices, but also provide a practical
framework for inference in the context of samples of positive definite
matrices. First, the desired properties of an intrinsic data depth function
acting on the space of Hermitian positive definite matrices are presented.
Second, we propose two computationally fast pointwise and integrated data depth
functions that satisfy each of these requirements and investigate several
robustness and efficiency aspects. As an application, we construct depth-based
confidence regions for the intrinsic mean of a sample of positive definite
matrices, which is applied to the exploratory analysis of a collection of
covariance matrices associated to a multicenter research trial
Measuring the relationship conditions in person-centred and experiential psychotherapies : past, present, and future
This chapter looks at measuring the relationship conditions in person-centred and experiential psychotherapie
State Space Reduction For Parity Automata
Exact minimization of ?-automata is a difficult problem and heuristic algorithms are a subject of current research. We propose several new approaches to reduce the state space of deterministic parity automata. These are based on extracting information from structures within the automaton, such as strongly connected components, coloring of the states, and equivalence classes of given relations, to determine states that can safely be merged. We also establish a framework to generalize the notion of quotient automata and uniformly describe such algorithms. The description of these procedures consists of a theoretical analysis as well as data collected from experiments
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