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Here, I consider our current view of the universe. I start with the Hubble Ultra-Deep Field, which shows about 10,000 galaxies in a tiny field of view. The whole of the observable universe contains over two trillion galaxies. I discuss two important principles regarding the nature of the universe and our place within it. The cosmological principle holds that the universe is homogeneous provided that we make comparisons at a high enough level of spatial scale. The Copernican principle maintains that our position within the universe is not central. We are certainly not central in the solar system or the galaxy; whether we are central in the universe is a tougher question to grapple with. We are at the centre of our own observable universe, but by definition any other observer is at the centre of theirs. We then turn from seeing galaxies in general to seeing individual events. These include long-known phenomena such as the Crab Nebula, which was produced by a supernova explosion. They also include more recently observed events such as collisions of neutron stars. We end by looking at the relative power of radio signals produced by biological and non-biological sources.
In this chapter the linearized Riemann tensor correlator on a de Sitter background including one-loop corrections from conformal fields is derived. The Riemann tensor correlation function exhibits interesting features: it is gauge-invariant even when including contributions from loops of matter fields, but excluding graviton loops as it is implemented in the 1/N expansion, it is compatible with de Sitter invariance, and provides a complete characterization of the local geometry. The two-point correlator function of the Riemann tensor is computed by taking suitable derivatives of the metric correlator function found in the previous chapter, and the result is written in a manifestly de Sitter-invariant form. Moreover, given the decomposition of the Riemann tensor in terms of Weyl and Ricci tensors, we write the explicit results for the Weyl and Ricci tensors correlators as well as the Weyl–Ricci tensors correlator and study both their subhorizon and superhorizon behavior. These results are extended to general conformal field theories. We also derive the Riemann tensor correlator in Minkowski spacetime in a manifestly Lorentz-invariant form by carefully taking the flat-space limit of our result in de Sitter.
We assemble a novel dataset in order to test theoretical propositions we develop on how states intervene in the elections of others. We start off with a random or representative sample of about 10 per cent of all elections since the end of World War II. Each of these is a case for us. We add a set of potential interveners, powers and organizations that may have a stake in intervening. We scour primary and secondary sources to extract information on how the government and opposition in the target state view relations with the potential interveners. We also extract information on whether and how the intervener acted in support of processes, or of candidates. This chapter is a codebook of how we constructed these novel, theory relevant variables. In addition, we supply extensive case-study notes. In those notes, we connect each of our coding decision to specific strings of text in the sources we used. The resulting database allows us to test the key propositions we develop.
The accuracy of the Global Positioning System (GPS) observable, especially for the code observable, has improved with the development of Global Navigation Satellite System (GNSS) receiver technology. An evaluation of the GPS code observable is presented in this paper, together with a stochastic model for the code and phase observables in Precise Point Positioning (PPP), established using the evaluated results. The results show that the code observables of Leica GNSS receivers are generally better than those of some other brand receivers and the Root Mean Square (RMS) for the code observables of the Leica GRX1200PRO, which includes the multipath effect, reaches 0·71 m, although Coarse/Acquisition (C/A) code observables are tracked. The static positioning of the code observable can reach centimetre level and the convergence time for the JPLM station is just 2·5 hours. The positioning results show that it is difficult to converge the Up direction to the centimetre level, compared with the North and East directions. The results show that static positioning can be correlated with the accumulation characteristic of the error for the code observable, while that that of the kinematic mode can be correlated to the error value. The shortened PPP convergence times verify that the presented stochastic models are effective.
Properties and comparison theorems for the maximal solution of the periodic discrete-time Riccati equation are supplemented by an extension of some earlier results and analysis, for the discrete-time Riccati equation to the periodic case.
The fundamental feature of delirium is disordered attention. Delirium is associated with a variety of medical illnesses including pneumonia, urinary tract infection, sepsis, meningitis, dehydration, congestive heart failure, uremia, liver failure, head injury, and postictal states. Increased mortality and morbidity are likely due to the seriousness of the underlying disease and the difficulty in caring adequately for very agitated patients who often cannot cooperate with treatments. Delirium can occur after an acute stroke and may even be the only observable neurological abnormality. The brain lesions in patients with abulia, when localizable, were located in the upper mesencephalic tegmentum, substantia nigra, medial thalami, striatum, and frontal lobes. Many of the lesions involved or interrupted projecting fibers to the frontal lobes. In contrast, when hyperactive agitated patients had focal brain lesions, the location was most often in the posterior portions of the cerebral hemispheres in the temporal, occipital, and inferior parietal lobes.
This paper presents an automatic training method based on the Baum–Welch algorithm (also known as EM algorithm) and a robust low-level controller. The method has been applied to the indoor autonomous navigation of a surveillance robot that utilizes a WiFi+Ultrasound Partially Observable Markov Decision Process (POMDP). This method uses a robust local navigation system to automatically provide some WiFi+Ultrasound maps. These maps could be employed within probabilistic global robot localization systems. These systems use a priori probabilistic map in order to estimate the global robot position. The method has been tested in a real environment using two commercial Pioneer 2AT robotic platforms in the premises of the Department of Electronics at the University of Alcalá. Some experimental results and conclusions are presented.
We consider a failure-prone system operating in continuous time. Condition monitoring is conducted at discrete time epochs. The state of the system is assumed to evolve as a continuous-time Markov process with a finite state space. The observation process with continuous-range values is stochastically related to the state process, which, except for the failure state, is unobservable. Combining the failure information and the condition monitoring information, we derive a general recursive filter, and, as special cases, we obtain recursive formulae for the state estimation and other quantities of interest. Updated parameter estimates are obtained using the expectation-maximization (EM) algorithm. Some practical prediction problems are discussed and finally an illustrative example is given using a real dataset.
In this paper, we study the on-line parameter estimation problem for a partially observable system subject to deterioration and random failure. The state of the system evolves according to a continuous-time homogeneous Markov process with a finite state space. The state of the system is hidden except for the failure state. When the system is operating, only the information obtained by condition monitoring, which is related to the working state of the system, is available. The condition monitoring observations are assumed to be in continuous range, so that no discretization is required. A recursive maximum likelihood (RML) algorithm is proposed for the on-line parameter estimation of the model. The new RML algorithm proposed in the paper is superior to other RML algorithms in the literature in that no projection is needed and no calculation of the gradient on the surface of the constraint manifolds is required. A numerical example is provided to illustrate the algorithm.
We consider a failure-prone system which operates in continuous time and is subject to condition monitoring at discrete time epochs. It is assumed that the state of the system evolves as a continuous-time Markov process with a finite state space. The observation process is stochastically related to the state process which is unobservable, except for the failure state. Combining the failure information and the information obtained from condition monitoring, and using the change of measure approach, we derive a general recursive filter, and, as special cases, we obtain recursive formulae for the state estimation and other quantities of interest. Up-dated parameter estimates are obtained using the EM algorithm. Some practical prediction problems are discussed and an illustrative example is given using a real dataset.
According to interpersonal theories on depression, the type of interaction between depression-prone subjects and their social environment plays a causal role in the development and course of depression (e.g. Coyne et al.). So far, interpersonal theories have been tackled mostly by psychometrical methods. However, non-verbal behaviour plays an important role in human social interactions. It is assumed that 60-65% of human communication is non-verbal. Ethological observations have shown that non-verbal interpersonal behaviour of depressed subjects, as assessed prior to treatment, is related to treatment-response or subsequent course of depression. These results are in line with an interpersonal approach of depression.
The effects of endogenous and exogenous factors on mating behaviour were investigated to clarify the mechanisms of mating initiation, as one of the fundamental studies on the rice stem borer, Chilo suppressalis. The periodicity of mating behaviour is controlled basically by an endogenous circadian rhythm, and it is further affected by exogenous factors, such as temperature, day length, light intensity, light wave length and so on. Seasonal variations of mating behaviour can be observed under field conditions. The time of the onset of mating behaviour in August was delayed about 2 hours compared with that of June. Such variation of mating periodicity can be ascribed to the change in the hours of female calling and male pheromone response under different conditions among the seasons. Some of the important behavioural and physiological phenomena concerning mating initiation of Chilo suppressalis are discussed.
The problem of optimal stopping in a Markov chain when there is imperfect state information is formulated as a partially observable Markov decision process. Properties of the optimal value function are developed. It is shown that under mild conditions the optimal policy is well structured. An efficient algorithm, which uses the structural information in the computation of the optimal policy, is presented.
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