22267679
Multicausal Prior Families, Randomisation and Essential Graphs
45728102
Causal Identification in Design Networks
When planning and designing a policy intervention and evaluation, the policy maker will have to define a strategy which will define the (conditional independence) structure of the available data. Here, Dawid’s extended influence diagrams are augmented by including ‘experimental design’ decisions nodes within the set of intervention strategies to provide semantics to discuss how a ‘design’ decision strategy (such as randomisation) might assist the systematic identification of intervention causal effects. By introducing design decision nodes into the framework, the experimental design underlying the data available is made explicit. We show how influence diagrams might be used to discuss the efficacy of different designs and conditions under which one can identify ‘causal’ effects of a future policy intervention. The approach of this paper lies primarily within probabilistic decision theory.
2206785
What Have Gene Libraries Done for AIS?
Artificial Immune Systems (AIS) have been shown to be useful, practical and realisable approaches to real-world problems. Most AIS implementations are based around a canonical algorithm such as clonotypic learning, which we may think of as individual, lifetime learning. Yet a species also learns. Gene libraries are often thought of as a biological mechanism for generating combinatorial diversity of antibodies. However, they also bias the antibody creation process, so that they can be viewed as a way of guiding the lifetime learning mechanisms. Over time, the gene libraries in a species will evolve to an appropriate bias for the expected environment (based on species memory). Thus gene libraries are a form of meta-learning which could be useful for AIS. Yet they are hardly ever used. In this paper we consider some of the possible benefits and implications of incorporating the evolution of gene libraries into AIS practice. We examine some of the issues that must be considered if the implementation is to be successful and beneficial.
5328327
Fault-tolerant parallel applications using queues and actions
There are many techniques supporting execution of large computations over a network of workstations (NOW) but data intensive computations are usually run on high performance parallel machines. A NOW comprising individual user's machines typically has a low performance interconnect and suffers arbitrary changes of availability. Exploiting such resources to execute data intensive computations is difficult but even in a more constrained environment there is an unfulfilled need for fault-tolerance. The structuring approach presented fulfills this need. Performance exceeding 100 Mflop/s is demonstrated for large fault-tolerant out of core examples of matrix multiplication and Cholesky factorisation using five 133 MHz Pentium compute machines.
1492870
Dynamic Resource Allocation Heuristics for Maximizing Robustness with an Overall Makespan Constraint in an Uncertain Environment
Heterogeneous parallel and distributed computing systems may operate in an environment where certain system performance features degrade due to unpredictable circumstances. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. This work uses a mathematic expression of robustness for a dynamic environment where task execution time estimates are known to contain errors. Several heuristic solutions to the problem are presented that utilize this expression of robustness to influence mapping decisions. These solutions are then compared to a bound on the highest attainable robustness of the described system.
17817110
MPEG-7 video automatic labeling system
In this demo, we show a novel end-to-end video automatic labeling system, which accepts MPEG-1 sequence inputs and generates MPEG-7 XML metadata files. Detections are based on the prior established anchor models. This system has two parts: model training process and labeling process. They are comprised of seven modules: Shot Segmentation, Region Segmentation, Annotation, Feature Extraction, Model Learning, Classification, and XML Rendering.
17464838
VisualSEEk: a fully automated content-based image query system
We describe a highly functional prototype system for searching by visual features in an image database. The VisualSEEk system is novel in that the user forms the queries by diagramming spatial arrangements of color regions. The system nds the images that contain the most similar arrangements of similar regions. Prior to the queries, the system automatically extracts and indexes salient color regions from the images. By utilizing e cient indexing techniques for color information, region sizes and absolute and relative spatial locations, a wide variety of complex joint color/spatial queries may be computed.