Comparing and Combining Lexicase Selection and Novelty Search

Lexicase selection and novelty search, two parent selection methods used in evolutionary computation, emphasize exploring widely in the search space more than traditional methods such as tournament selection. However, lexicase selection is not explicitly driven to select for novelty in the population, and novelty search suffers from lack of direction toward a goal, especially in unconstrained, highly-dimensional spaces. We combine the strengths of lexicase selection and novelty search by creating a novelty score for each test case, and adding those novelty scores to the normal error values used in lexicase selection. We use this new novelty-lexicase selection to solve automatic program synthesis problems, and find it significantly outperforms both novelty search and lexicase selection. Additionally, we find that novelty search has very little success in the problem domain of program synthesis. We explore the effects of each of these methods on population diversity and long-term problem solving performance, and give evidence to support the hypothesis that novelty-lexicase selection resists converging to local optima better than lexicase selection

Estimating Historical Sage-Grouse Habitat Abundance Using a State-and-Transition Model

Use of reference conditions to compare current conditions what managers believed represented healthy and functioning systems has become a common approach to evaluate vegetation and habitat conditions and aid development of land management plans. Often reference conditions attempt to describe landscapes as they existed and functioned prior to about 1850, and often largely rely on expert opinion. We developed reference conditions for sagebrush (Artemisia spp. L.) ecosystems in eastern Oregon based on ecological site descriptions, soil surveys, climate data, wildfire records, expert opinion, and literature using a state-and-transition (STM) modeling framework. Using ecological site descriptions for the Malheur High Plateau Major Land Resource Area (MHP), we divided sagebrush communities into four groups based on grass productivity in low, average and high productivity years. Literature helped us determine which disturbance factors to include, the community phases for each model, and associated seasonal habitat for greater sage-grouse (Centrocercus urophasianus). We developed successional timelines in the absence of disturbance, and determined the probable outcomes of a given type of disturbance event. We used fire records and climate data to develop disturbance event probabilities and periodicities. Contrary to our expectations, fire did not appear to be the most important factor influencing sagebrush ecosystems under reference conditions in our models. The modeled historical abundance of sage-grouse breeding and brood-rearing habitat was within range of or greater than the amount recommended by sage-grouse biologists, but the abundance of wintering habitat was less. By using objective criteria as much as possible, our approach should also be repeatable in other locations. Since we used climate criteria to define most disturbance probabilities, our models provide an opportunity to examine how changes in climate could affect plant communities, disturbance regimes, and the quality and quantity of sage-grouse habitat in future modeling efforts

Columbia River Rhyolites: Age-Distribution Patterns and Their Implications for Arrival, Location, and Dispersion of Continental Flood Basalt Magmas in the Crust

Columbia River province magmatism is now known to include abundant and widespread rhyolite centers even though the view that the earliest rhyolites erupted from the McDermitt Caldera and other nearby volcanic fields along the Oregon–Nevada state border has persisted. Our study covers little-studied or unknown rhyolite occurrences in eastern Oregon that show a much wider distribution of older centers. With our new data on distribution of rhyolite centers and ages along with literature data, we consider rhyolites spanning from 17.5 to 14.5 Ma of eastern Oregon, northern Nevada, and western Idaho to be a direct response to flood basalts of the Columbia River Basalt Group (CRBG) and collectively categorize them as Columbia River Rhyolites. The age distribution patterns of Columbia River Rhyolites have implications for the arrival, location, and dispersion of flood basalt magmas in the crust. We consider the period from 17.5 to 16.4 Ma to be the waxing phase of rhyolite activity and the period from 15.3 to 14.5 Ma to be the waning phase. The largest number of centers was active between 16.3–15.4 Ma. The existence of crustal CRBG magma reservoirs beneath rhyolites seems inevitable, and hence, rhyolites suggest the following. The locations of centers of the waxing phase imply the arrival of CRBG magmas across the distribution area of rhyolites and are thought to correspond to the thermal pulses of arriving Picture Gorge Basalt and Picture-Gorge-Basalt-like magmas of the Imnaha Basalt in the north and to those of Steens Basalt magmas in the south. The earlier main rhyolite activity phase corresponds with Grande Ronde Basalt and evolved Picture Gorge Basalt and Steens Basalt. The later main phase rhyolite activity slightly postdated these basalts but is contemporaneous with icelanditic magmas that evolved from flood basalts. Similarly, centers of the waning phase span the area distribution of earlier phases and are similarly contemporaneous with icelanditic magmas and with other local basalts. These data have a number of implications for long-held notions about flood basalt migration through time and the age-progressive Snake River Plain Yellowstone rhyolite trend. There is no age progression in rhyolite activity from south-to-north, and this places doubt on the postulated south-to-north progression in basalt activity, at least for main-phase CRBG lavas. Furthermore, we suggest that age-progressive rhyolite activity of the Snake River Plain–Yellowstone trend starts at ~12 Ma with activity at the Bruneau Jarbidge center, and early centers along the Oregon–Nevada border, such as McDermitt, belong to the early to main phase rhyolites identified here

An Integrated Feasibility Study of Reservoir Thermal Energy Storage (RTES) in Portland, OR, USA

In regions with long cold overcast winters and sunny summers, Deep Direct-Use (DDU) can be coupled with Reservoir Thermal Energy Storage (RTES) technology to take advantage of pre-existing subsurface permeability to save summer heat for later use during cold seasons. Many aquifers worldwide are underlain by permeable regions (reservoirs) containing brackish or saline groundwater that has limited beneficial use due to poor water quality. We investigate the utility of these relatively deep, slow flowing reservoirs for RTES by conducting an integrated feasibility study in the Portland Basin, Oregon, USA, developing methods and obtaining results that can be widely applied to RTES systems elsewhere. As a case study, we have conducted an economic and social cost-benefit analysis for the Oregon Health and Science University (OHSU), a teaching hospital that is recognized as critical infrastructure in the Portland Metropolitan Area. Our investigation covers key factors that influence feasibility including 1) the geologic framework, 2) heat and fluid flow modeling, 3) capital and maintenance costs, 4) the regulatory framework, and 5) operational risks. By pairing a model of building seasonal heat demand with an integrated model of RTES resource supply, we determine that the most important factors that influence RTES efficacy in the study area are operational schedule, well spacing, the amount of summer heat stored (in our model, a function of solar array size), and longevity of the system. Generally, heat recovery efficiency increases as the reservoir and surrounding rocks warm, making RTES more economical with time. Selecting a base-case scenario, we estimate a levelized cost of heat (LCOH) to compare with other sources of heating available to OHSU and find that it is comparable to unsubsidized solar and nuclear, but more expensive than natural gas. Additional benefits of RTES include energy resiliency in the event that conventional energy supplies are disrupted (e.g., natural disaster) and a reduction in fossil fuel consumption resulting in a smaller carbon footprint. Key risks include reservoir heterogeneity and a possible reduction in permeability through time due to scaling (mineral precipitation). Lastly, a map of thermal energy storage capacity for the Portland Basin yields a total of 43,400 GWh, suggesting tremendous potential for RTES in the Portland Metropolitan Area

Connecting and dating with tephras: principles, functioning, and application of tephrochronology in Quaternary research

Tephrochronology, the characterisation and use of volcanic-ash layers as a unique chronostratigraphic linking, synchronizing, and dating tool, has become a globally-practised discipline of immense practical value in a wide range of subjects including Quaternary stratigraphy, palaeoclimatology, palaeoecology, palaeolimnology, physical geography, geomorphology, volcanology, geochronology, archaeology, human evolution, anthropology, and human disease and medicine. The advent of systematic studies of cryptotephras – the identification, correlation, and dating of sparse, fine-grained glass-shard concentrations ‘hidden’ within sediments or soils – over the past ~20 years has been revolutionary. New cryptotephra techniques developed in northwestern Europe and Scandinavia in particular and in North America most recently adapted or improved to help solve problems as they arose, have now been applied to sedimentary sequences (including ice) on all the continents. The result has been the extension of tephra isochrons over wide areas hundreds to several thousands of kilometres from source volcanoes. Taphonomic and other issues, such as quantifying uncertainties in correlation, provide scope for future work. Developments in dating and analytical methods have led to important advances in the application of tephrochronology in recent times. In particular: (i) the ITPFT (glass fission-track) method has enabled landscapes and sequences to be dated where previously no dates were obtainable or where dating was problematic; (ii) new EMPA protocols enabling narrow-beam analyses (<5 um) of glass shards, or small melt inclusions, have been developed, meaning that small (typically distal) glass shards or melt inclusions <~10 um in diameter can now be analysed more efficaciously than previously (and with reduced risk of accidentally including microlites in the analysis as could occur with wide-beam analyses); (iii) LA-ICPMS method for trace element analysis of individual shards <~10 um in diameter is generating more detailed ‘fingerprints’ for enhancing tephra-correlation efficacy (Pearce et al., 2011, 2014; Pearce, 2014); and (iv) the revolutionary rise of Bayesian probability age modelling has helped to improve age frameworks for tephras of the late-glacial to Holocene period especially

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein--protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence ``Protein A causes protein B to bind protein C&#39;&#39; can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP&#39;09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing.&nbsp; Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP&#39;09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP&#39;11 and BioNLP&#39;13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.</p

2011 Annual Report

https://digitalrepository.unm.edu/msb_annual_reports/1008/thumbnail.jp