A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation). We also present two detailed extensions of Bayesian optimization, with experiments---active user modelling with preferences, and hierarchical reinforcement learning---and a discussion of the pros and cons of Bayesian optimization based on our experiences

Geographic information system (GIS) integration of geological, geochemical and geophysical data from the Aggeneys base metal province, South Africa

Geographic Information System (GIS) technology aids in storage, manipulation, processing, analysis and presentation of spatial data sets. GIS can effectively interrogate large multidisciplinary exploration data sets in the search for new mineral exploitation targets. A spatial database, the AGGeneys Exploration Database (AGGED), has been created, comprising exploration data gathered during two decades of exploration for base-metals in the Aggeneys area, Bushmanland, South Africa. AGGED includes data extracted from analog maps, as well as digital remotely sensed sources, stored in vector and raster data structures, respectively. Vector data includes field based observations such as the extent of outcropping geological units, litho- and chrono-stratigraphic data; structural data; laboratory data based on regional geochemical stream sediment and traverse sampling; cadastral data and known mineral occurrences. Raster data includes Landsat satellite TM imagery and airborne magnetic data. Spatial variation within single data maps are examined. Spatial correlation between three different data maps are facilitated using colour analysis of hue, saturation and value components in a perceptual colour model. Simultaneously combining lead and zinc data with Landsat TM and geophysical magnetic data spatially delineates four new "geoscience" anomalies in the area under investigation. Two distinctive anomalies occur on the farms Aroams and Aggeneys. The Aroams anomaly (GSAl) has not been previously recognised, whereas the Aggeneys anomaly (GSA2) has been located before. The two other "geoscience" anomalies, on the farm Haramoep (GSA3 and GSA4 ), are slightly less distinct. Overlaying fold axial trace patterns and anomalies on the farm Haramoep, indicate that F2 and F3 fold structures are closely associated with these two anomalies. The location of the Aroams anomaly occurs along the same east-west trend of the four known major ore-deposits viz. Big Syncline, Broken Hill, Black Mountain and Gamsberg. Extrapolating F2 and F3 fold patterns using magnetic data locates this Aroams anomaly along the F3 axial trace extending from Big Syncline through to Gamsberg. The elevated Pb-Zn geochemical anomaly and structural data associated with the Aroams anomaly makes it a promising future exploitation target. The AGGED database can be expanded both in geographic extent to include surrounding areas, and to allow for inclusion of future surveys. Analytical processing of data in AGGED can also be continued and expanded. GIS is a burgeoning field and developments in GIS technology will impact on the explorationist. Developments in object-oriented and knowledge-based database technologies, visualisation techniques and artificial intelligence, incorporated in future GIS need to be closely monitored and evaluated by geoscience explorationists

Extrapolation of Stationary Random Fields

We introduce basic statistical methods for the extrapolation of stationary random fields. For square integrable fields, we set out basics of the kriging extrapolation techniques. For (non--Gaussian) stable fields, which are known to be heavy tailed, we describe further extrapolation methods and discuss their properties. Two of them can be seen as direct generalizations of kriging.Comment: 52 pages, 25 figures. This is a review article, though Section 4 of the article contains new results on the weak consistency of the extrapolation methods as well as new extrapolation methods for $\alpha$-stable fields with $0<\alpha\leq 1

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 341)

This bibliography lists 133 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during September 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Automatic Performance Optimization on Heterogeneous Computer Systems using Manycore Coprocessors

Emerging computer architectures and advanced computing technologies, such as Intel’s Many Integrated Core (MIC) Architecture and graphics processing units (GPU), provide a promising solution to employ parallelism for achieving high performance, scalability and low power consumption. As a result, accelerators have become a crucial part in developing supercomputers. Accelerators usually equip with different types of cores and memory. It will compel application developers to reach challenging performance goals. The added complexity has led to the development of task-based runtime systems, which allow complex computations to be expressed as task graphs, and rely on scheduling algorithms to perform load balancing between all resources of the platforms. Developing good scheduling algorithms, even on a single node, and analyzing them can thus have a very high impact on the performance of current HPC systems. Load balancing strategies, at different levels, will be critical to obtain an effective usage of the heterogeneous hardware and to reduce the impact of communication on energy and performance. Implementing efficient load balancing algorithms, able to manage heterogeneous hardware, can be a challenging task, especially when a parallel programming model for distributed memory architecture. In this paper, we presents several novel runtime approaches to determine the optimal data and task partition on heterogeneous platforms, targeting the Intel Xeon Phi accelerated heterogeneous systems

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Motion Segmentation Aided Super Resolution Image Reconstruction

This dissertation addresses Super Resolution (SR) Image Reconstruction focusing on motion segmentation. The main thrust is Information Complexity guided Gaussian Mixture Models (GMMs) for Statistical Background Modeling. In the process of developing our framework we also focus on two other topics; motion trajectories estimation toward global and local scene change detections and image reconstruction to have high resolution (HR) representations of the moving regions. Such a framework is used for dynamic scene understanding and recognition of individuals and threats with the help of the image sequences recorded with either stationary or non-stationary camera systems. We introduce a new technique called Information Complexity guided Statistical Background Modeling. Thus, we successfully employ GMMs, which are optimal with respect to information complexity criteria. Moving objects are segmented out through background subtraction which utilizes the computed background model. This technique produces superior results to competing background modeling strategies. The state-of-the-art SR Image Reconstruction studies combine the information from a set of unremarkably different low resolution (LR) images of static scene to construct an HR representation. The crucial challenge not handled in these studies is accumulating the corresponding information from highly displaced moving objects. In this aspect, a framework of SR Image Reconstruction of the moving objects with such high level of displacements is developed. Our assumption is that LR images are different from each other due to local motion of the objects and the global motion of the scene imposed by non-stationary imaging system. Contrary to traditional SR approaches, we employed several steps. These steps are; the suppression of the global motion, motion segmentation accompanied by background subtraction to extract moving objects, suppression of the local motion of the segmented out regions, and super-resolving accumulated information coming from moving objects rather than the whole scene. This results in a reliable offline SR Image Reconstruction tool which handles several types of dynamic scene changes, compensates the impacts of camera systems, and provides data redundancy through removing the background. The framework proved to be superior to the state-of-the-art algorithms which put no significant effort toward dynamic scene representation of non-stationary camera systems