Signal-to-Noise Eigenmode Analysis of the Two-Year COBE Maps

To test a theory of cosmic microwave background fluctuations, it is natural to expand an anisotropy map in an uncorrelated basis of linear combinations of pixel amplitudes --- statistically-independent for both the noise and the signal. These $S/N$-eigenmodes are indispensible for rapid Bayesian analyses of anisotropy experiments, applied here to the recently-released two-year COBE {\it dmr} maps and the {\it firs} map. A 2-parameter model with an overall band-power and a spectral tilt $\nu_{\Delta T}$ describes well inflation-based theories. The band-powers for {\it all} the {\it dmr} $53,90,31$ $a$+$b$ GHz and {\it firs} 170 GHz maps agree, $\{(1.1\pm 0.1)\times 10^{-5}\}^{1/2}$, and are largely independent of tilt and degree of (sharp) $S/N$-filtering. Further, after optimal $S/N$-filtering, the {\it dmr} maps reveal the same tilt-independent large scale features and correlation function. The unfiltered {\it dmr} $53$ $a$+$b$ index $\nu_{\Delta T}+1$ is $1.4\pm 0.4$; increasing the $S/N$-filtering gives a broad region at (1.0--1.2)$\pm$0.5, a jump to (1.4--1.6)$\pm$0.5, then a drop to 0.8, the higher values clearly seen to be driven by $S/N$-power spectrum data points that do not fit single-tilt models. These indices are nicely compatible with inflation values ($\sim$0.8--1.2), but not overwhelmingly so.Comment: submitted to Phys.Rev.Letters, 4 pages, uuencoded compressed PostScript; also bdmr2.ps.Z, via anonymous ftp to ftp.cita.utoronto.ca, cd to /pub/dick/yukawa; CITA-94-2

Public/private sector partnership for emerging infections.

This paper gives examples of public/private partnerships that support research, support drug development and that advance policy development, suggesting that such partnerships can advance our understanding and control of emerging infections. The investment in emerging infectious diseases from government and from industry is currently much larger than that from philanthropy. Nevertheless philanthropy, even with limited dollars, is able to play a catalytic function and provide risk capitol for innovative partnerships and could in the future play an even larger role if the value of such investment is better defined and argued to recruit additional dollars to this area

Functional reasoning in diagnostic problem solving

This work is one facet of an integrated approach to diagnostic problem solving for aircraft and space systems currently under development. The authors are applying a method of modeling and reasoning about deep knowledge based on a functional viewpoint. The approach recognizes a level of device understanding which is intermediate between a compiled level of typical Expert Systems, and a deep level at which large-scale device behavior is derived from known properties of device structure and component behavior. At this intermediate functional level, a device is modeled in three steps. First, a component decomposition of the device is defined. Second, the functionality of each device/subdevice is abstractly identified. Third, the state sequences which implement each function are specified. Given a functional representation and a set of initial conditions, the functional reasoner acts as a consequence finder. The output of the consequence finder can be utilized in diagnostic problem solving. The paper also discussed ways in which this functional approach may find application in the aerospace field

Using decision-tree classifier systems to extract knowledge from databases

One difficulty in applying artificial intelligence techniques to the solution of real world problems is that the development and maintenance of many AI systems, such as those used in diagnostics, require large amounts of human resources. At the same time, databases frequently exist which contain information about the process(es) of interest. Recently, efforts to reduce development and maintenance costs of AI systems have focused on using machine learning techniques to extract knowledge from existing databases. Research is described in the area of knowledge extraction using a class of machine learning techniques called decision-tree classifier systems. Results of this research suggest ways of performing knowledge extraction which may be applied in numerous situations. In addition, a measurement called the concept strength metric (CSM) is described which can be used to determine how well the resulting decision tree can differentiate between the concepts it has learned. The CSM can be used to determine whether or not additional knowledge needs to be extracted from the database. An experiment involving real world data is presented to illustrate the concepts described

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base

Using output to evaluate and refine rules in rule-based expert systems

The techniques described provide an effective tool which knowledge engineers and domain experts can utilize to help in evaluating and refining rules. These techniques have been used successfully as learning mechanisms in a prototype adaptive diagnostic expert system and are applicable to other types of expert systems. The degree to which they constitute complete evaluation/refinement of an expert system depends on the thoroughness of their use

Simulation of seismic events induced by CO2 injection at In Salah, Algeria

Date of Acceptance: 18/06/2015 Acknowledgments The authors would like to thank the operators of the In Salah JV and JIP, BP, Statoil and Sonatrach, for providing the data shown in this paper, and for giving permission to publish. Midland Valley Exploration are thanked for the use of their Move software for geomechanical restoration. JPV is a Natural Environment Research Council (NERC) Early Career Research Fellow (Grant NE/I021497/1) and ALS is funded by a NERC Partnership Research Grant (Grant NE/I010904).Peer reviewedPublisher PD