On the identification of multi-output linear time-invariant and periodic dynamic systems /

Includes bibliographies (p. 27-29).

Faustina Hasse.

Mode of access: Internet.

Existence and uniqueness of Q-processes with a given finite μ-invariant measure

Let Q be a stable and conservative Q-matrix over a countable state space S consisting of an irreducible class C and a single absorbing state 0 that is accessible from C. Suppose that Q admits a finite mu-subinvariant measure in on C. We derive necessary and sufficient conditions for there to exist a Q-process for which m is mu-invariant on C, as well as a necessary condition for the uniqueness of such a process.

Invariant risk attitudes

Concepts of constant absolute risk aversion and constant relative risk aversion have proved useful in the analysis of choice under uncertainty, but are quite restrictive, particularly when they are imposed jointly. A generalization of constant risk aversion, referred to as invariant risk aversion is developed. Invariant risk aversion is closely related to the possibility of representing preferences over state-contingent income vectors in terms of two parameters, the mean and a linearly homogeneous, translation-invariant index of riskiness. The best-known index with such properties is the standard deviation. The properties of the capital asset pricing model, usually expressed in terms of the mean and standard deviation, may be extended to the case of general invariant preferences. (C) 2003 Elsevier Inc. All rights reserved.

Relativistically invariant quantum information

We show that quantum information can be encoded into entangled states of multiple indistinguishable particles in such a way that any inertial observer can prepare, manipulate, or measure the encoded state independent of their Lorentz reference frame. Such relativistically invariant quantum information is free of the difficulties associated with encoding into spin or other degrees of freedom in a relativistic context.

Comments on edge-to-edge matching and the equivalence of the invariant line, Delta g and Moire fringe approaches to the crystallographic features of precipitates

Three apparently distinct and different approaches have been proposed to account for the crystallographic features of diffusion-controlled precipitation. These three models are based on (a) an invariant line in the habit plane, (b) the parallelism of a pair of Deltags that are perpendicular to the habit plane and (c) the parallelism of a pair of Moire fringes that are in turn parallel to the habit plane. The purpose of the present paper is to show that these approaches are in fact absolutely equivalent and that when certain conditions are satisfied they are essentially the same as the recent edge-to-edge matching model put forward by the authors. (C) 2004 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Pulsed EPR studies of a bacterial sulfite-oxidizing enzyme with pH-invariant hyperfine interactions from exchangeable protons

Variable-frequency pulsed electron paramagnetic resonance studies of the molybdenum(V) center of sulfite dehydrogenase (SDH) clearly show couplings from nearby exchangeable protons that are assigned to a (MoOHn)-O-v group. The hyperfine parameters for these exchangeable protons of SDH are the same at both low and high pH and similar to those for the high-pH forms of sulfite oxidases (SOs) from eukaryotes. The SDH proton parameters are distinctly different from the low-pH forms of chicken and human so.

On the existence of uni-instantaneous Q-processes with a given finite mu-invariant measure

Let S be a countable set and let Q = (q(ij), i, j is an element of S) be a conservative q-matrix over S with a single instantaneous state b. Suppose that we are given a real number mu >= 0 and a strictly positive probability measure m = (m(j), j is an element of S) such that Sigma(i is an element of S) m(i)q(ij) = -mu m(j), j 0 b. We prove that there exists a Q-process P(t) = (p(ij) (t), i, j E S) for which m is a mu-invariant measure, that is Sigma(i is an element of s) m(i)p(ij)(t) = e(-mu t)m(j), j is an element of S. We illustrate our results with reference to the Kolmogorov 'K 1' chain and a birth-death process with catastrophes and instantaneous resurrection.

Lorentz invariant intrinsic decoherence

We present a Lorentz invariant extension of a previous model for intrinsic decoherence (Milburn 1991 Phys. Rev. A 44 5401). The extension uses unital semigroup representations of space and time translations rather than the more usual unitary representation, and does the least violence to physically important invariance principles. Physical consequences include a modification of the uncertainty principle and a modification of field dispersion relations, similar to modifications suggested by quantum gravity and string theory, but without sacrificing Lorentz invariance. Some observational signatures are discussed.

Bayesian invariant measurements of generalisation for discrete distributions

Neural network learning rules can be viewed as statistical estimators. They should be studied in Bayesian framework even if they are not Bayesian estimators. Generalisation should be measured by the divergence between the true distribution and the estimated distribution. Information divergences are invariant measurements of the divergence between two distributions. The posterior average information divergence is used to measure the generalisation ability of a network. The optimal estimators for multinomial distributions with Dirichlet priors are studied in detail. This confirms that the definition is compatible with intuition. The results also show that many commonly used methods can be put under this unified framework, by assume special priors and special divergences.

Bayesian invariant measurements of generalisation for continuous distributions

A family of measurements of generalisation is proposed for estimators of continuous distributions. In particular, they apply to neural network learning rules associated with continuous neural networks. The optimal estimators (learning rules) in this sense are Bayesian decision methods with information divergence as loss function. The Bayesian framework guarantees internal coherence of such measurements, while the information geometric loss function guarantees invariance. The theoretical solution for the optimal estimator is derived by a variational method. It is applied to the family of Gaussian distributions and the implications are discussed. This is one in a series of technical reports on this topic; it generalises the results of ¸iteZhu95:prob.discrete to continuous distributions and serve as a concrete example of a larger picture ¸iteZhu95:generalisation.

Bayesian invariant measurements of generalisation

The problem of evaluating different learning rules and other statistical estimators is analysed. A new general theory of statistical inference is developed by combining Bayesian decision theory with information geometry. It is coherent and invariant. For each sample a unique ideal estimate exists and is given by an average over the posterior. An optimal estimate within a model is given by a projection of the ideal estimate. The ideal estimate is a sufficient statistic of the posterior, so practical learning rules are functions of the ideal estimator. If the sole purpose of learning is to extract information from the data, the learning rule must also approximate the ideal estimator. This framework is applicable to both Bayesian and non-Bayesian methods, with arbitrary statistical models, and to supervised, unsupervised and reinforcement learning schemes.

Preparation, characterisation and entrapment of a non-glycosidic threitol ceramide into liposomes for presentation to invariant natural killer T cells

Dendritic cells (DCs) are able to present glycolipids to invariant natural killer T (iNKT) cells in vivo. Very few compounds have been found to stimulate iNKT cells, and of these, the best characterised is the glycolipid a-galactosylceramide, which stimulates the production of large quantities of interferon-gamma (IFN-?) and interleukin-4 (IL-4). However, aGalCer leads to overstimulation of iNKT cells. It has been demonstrated that the aGalCer analogue, threitol ceramide (ThrCer 2), successfully activates iNKT cells and overcomes the problematic iNKT cell activation-induced anergy. In this study, ThrCer 2 has been inserted into the bilayers of liposomes composed of a neutral lipid, 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC), or dimethyldioctadecylammonium bromide (DDA), a cationic lipid. Incorporation efficiencies of ThrCer within the liposomes was 96% for DSPC liposomes and 80% for DDA liposomes, with the vesicle size (large multilamellar vs. small unilamellar vesicles) making no significant difference. Langmuir-Blodgett studies suggest that both DSPC and DDA stack within the monolayer co-operatively with the ThrCer molecules with no condensing effect. In terms of cellular responses, IFN-? secretion was higher for cells treated with small DDA liposomes compared with the other liposome formulations, suggesting that ThrCer encapsulation in this liposome formulation resulted in a higher uptake by DCs.

A developmental dissociation of view-dependent and view-invariant object recognition in adolescence

Spatial generalization skills in school children aged 8-16 were studied with regard to unfamiliar objects that had been previously learned in a cross-modal priming and learning paradigm. We observed a developmental dissociation with younger children recognizing objects only from previously learnt perspectives whereas older children generalized acquired object knowledge to new viewpoints as well. Haptic and - to a lesser extent - visual priming improved spatial generalization in all but the youngest children. The data supports the idea of dissociable, view-dependent and view-invariant object representations with different developmental trajectories that are subject to modulatory effects of priming. Late-developing areas in the parietal or the prefrontal cortex may account for the retarded onset of view-invariant object recognition. © 2006 Elsevier B.V. All rights reserved.