EASe : integrating search with learned episodes

Weak methods are insufficient to solve complex problems. Constrained weak methods, like hill-climbing, search too little of the problem space. Unconstrained weak methods, like breadth-first search, are intractable. Fortunately, through the integration of multiple weak methods more powerful problem solvers can be created. We demonstrate that augmenting a weak constrained search method with episodes provides a tractable method for solving a large class of problems. We demonstrate that these episodes can be generated using an unconstrained weak method while solving simple problems from a domain. We provide an analytical model of our approach and empirical results from the logic synthesis domain of VLSI design as well as the classic tile-sliding domain

Instance-based prediction of real-valued attributes

Instance-based representations have been applied to numerous classification tasks with a fair amount of success. These tasks predict a symbolic class based on observed attributes. This paper presents a method for predicting a numeric value based on observed attributes. We prove that if the numeric values are generated by continuous functions with bounded slope, then the predicted values are accurate approximations of the actual values. We demonstrate the utility of this approach by comparing it with standard approaches for value-prediction. The approach requires no background knowledge

Detecting and removing noisy instances from concept descriptions

Several published results show that instance-based learning algorithms record high classification accuracies and low storage requirements when applied to supervised learning tasks. However, these learning algorithms are highly sensitive to training set noise. This paper describes a simple extension of instance-based learning algorithms for detecting and removing noisy instances from concept descriptions. The extension requires evidence that saved instances be significantly good classifiers before it allows them to be used for subsequent classification tasks. We show that this extension's performance degrades more slowly in the presence of noise, improves classification accuracies, and further reduces storage requirements in several artificial and real-world databases

Comparing instance-averaging with instance-saving learning algorithms

The goal of our research is to understand the power and appropriateness of instance-based representations and their associated acquisition methods. This paper concerns two methods for reducing storage requirements for instance-based learning algorithms. The first method, termed instance-saving, represents concept descriptions by selecting and storing a representative subset of the given training instances. We provide an analysis for instance-saving techniques and specify one general class of concepts that instance-saving algorithms are capable of learning. The second method, termed instance-averaging, represents concept descriptions by averaging together some training instances while simply saving others. We describe why analyses for instance-averaging algorithms are difficult to produce. Our empirical results indicate that storage requirements for these two methods are roughly equivalent. We outline the assumptions of instance-averaging algorithms and describe how their violation might degrade performance. To mitigate the effects of non-convex concepts, a dynamic thresholding technique is introduced and applied in both the averaging and non-averaging learning algorithms. Thresholding increases the storage requirements but also increases the quality of the resulting concept descriptions

Kinematic cascade as a hydrologic model, The

Bibliography: page 22

Energetic efficiency of lactating dairy cows, effects of controlled and ad libitum feeding at 18 degrees C and 31 degrees C temperatures.

This bulletin is a report on Department of Dairy Husbandry Research Project No. 125, 'Climatic Factors'--P. [2].Digitized 2007 AES.Includes bibliographical references (page 16)

A hybrid lagrangian–eulerian particle model for ecosystem simulation

Current numerical methods for simulating biophysical processes in aquatic environments are typically constructed in a grid-based Eulerian framework or as an individual-based model in a particle-based Lagrangian framework. Often, the biogeochemical processes and physical (hydrodynamic) processes occur at different time and space scales, and changes in biological processes do not affect the hydrodynamic conditions. Therefore, it is possible to develop an alternative strategy to grid-based approaches for linking hydrodynamic and biogeochemical models that can significantly improve computational efficiency for this type of linked biophysical model. In this work, we utilize a new technique that links hydrodynamic effects and biological processes through a property-carrying particle model (PCPM) in a Lagrangian/Eulerian framework. The model is tested in idealized cases and its utility is demonstrated in a practical application to Sandusky Bay. Results show the integration of Lagrangian and Eulerian approaches allows for a natural coupling of mass transport (represented by particle movements and random walk) and biological processes in water columns which is described by a nutrient-phytoplankton-zooplankton-detritus (NPZD) biological model. This method is far more efficient than traditional tracer-based Eulerian biophysical models for 3-D simulation, particularly for a large domain and/or ensemble simulations

The Space Operations Simulation Center (SOSC) and Closed-loop Hardware Testing for Orion Rendezvous System Design

The Space Operations Simulation Center (SOSC) at the Lockheed Martin (LM) Waterton Campus in Littleton, Colorado is a dynamic test environment focused on Autonomous Rendezvous and Docking (AR&D) development testing and risk reduction activities. The SOSC supports multiple program pursuits and accommodates testing Guidance, Navigation, and Control (GN&C) algorithms for relative navigation, hardware testing and characterization, as well as software and test process development. The SOSC consists of a high bay (60 meters long by 15.2 meters wide by 15.2 meters tall) with dual six degree-of-freedom (6DOF) motion simulators and a single fixed base 6DOF robot. The large testing area (maximum sensor-to-target effective range of 60 meters) allows for large-scale, flight-like simulations of proximity maneuvers and docking events. The facility also has two apertures for access to external extended-range outdoor target test operations. In addition, the facility contains four Mission Operations Centers (MOCs) with connectivity to dual high bay control rooms and a data/video interface room. The high bay is rated at Class 300,000 (. 0.5 m maximum particles/m3) cleanliness and includes orbital lighting simulation capabilities

Developing a Shoreline Restoration Suitability Model for North Indian River and Mosquito Lagoon, Phase II

This project successfully created a living shoreline restoration prioritization model and a mangrove hydrodynamic habitat suitability model for 180 miles of estuarine shorelines in Mosquito Lagoon and northern Indian River. Shoreline model data are available for direct download as a spatial dataset (https://stars.library.ucf.edu/shorelines/), or for online viewing in a GIS storymap: (https://ucfonline.maps.arcgis.com/apps/MapSeries/index.html?appid=45caa29e80e6441c8bf6f75c542860af). New empirical wave data were created through hydrodynamic modeling. Frequency analysis was applied to characterize wave climate in study area shorelines. Wind-wave measurements observed in the field validated that actual wave heights above 2 cm were well represented by the model. Modelled hydrodynamic data were combined with shoreline data (collected in the field during the project Phase I) to develop fundamental knowledge regarding hydrodynamic habitat suitability of IRL shoreline species. Through this analysis, strong relationships between mangrove presence and wind wave hydrodynamics were illuminated, such that the probability of mangrove persistence was predicted at the project site scale based on wave climate. Additionally, the influential role of site intertidal slope and its interaction with site hydrodynamics was confirmed. This is a transformative source of information from the perspective of Planning, Design and Engineering (PD&E) of shoreline stabilization projects and regional-scale restoration planning. Mangroves were found on shorelines with overall lower incoming wave height distributions as compared to shorelines without mangrove vegetation. Mangrove presence became less likely as wave height increased, suggesting that there is a critical wave magnitude-frequency combination above which it is increasingly unlikely that mangrove vegetation will persist. Where wave heights exceeded 5 cm 20% of time, there was over an 80% chance of mangrove persistence. Where wave heights were 8 cm 20% of time, chance of mangrove persistence dropped to 50%. Where wave heights were over 15 cm 20% of time, there was less than 10% chance of mangrove persistence. While wave climate was found to explain the greatest variance within a generalized linear model of mangrove distribution, the influence of shoreline slope was also found to be significant. Low shoreline intertidal slopes were found to increase the threshold wave climate mangroves can survive. For example, the 80th percentile wave height associated with 50% probability of mangrove survival was 8 cm when slope was 0.2, increased to 9 cm when slope was lower than 0.2, and decreased to 4 cm when slope was greater. The presence of oysters or seagrasses at the shoreline were also correlated with wave height; however, conditions within the project area were insufficient to create robust hydrodynamic habitat thresholds for these important coastal ecosystem engineers. There are therefore future research opportunities to apply frameworks developed herein to broader study areas, which will potentially lead to discovery of flow-ecology relationships for a more diverse suite of coastal ecosystem engineers. All study shorelines were classified within a prioritization model according to need and urgency of stabilization. Shoreline sites classified in Urgent need (18% of study shoreline) should be triaged for immediate stabilization. Shoreline sites classified as Priority (10% of study shoreline) will eventually move to the Urgent category without intervention. Shorelines classified as Vulnerable (6% of study shorelines) are sites for pre-emptive restoration. Sites within the Wetland category (38% of study shorelines) do not need to be restored at this time and can serve as reference sites for living shoreline stabilization. Shorelines with hard armoring (28% of study shorelines) may represent opportunities to increase long-term shoreline resilience or restore shoreline ecotone functionality. Analysis of Hardened shorelines in context of local wave climate and slope indicate that many hardened shorelines in the project study area may not actually require armoring. Living shoreline containing mangrove forest could be expected to stabilize many currently hardened shorelines. All study shorelines were classified according to likelihood of mangrove persistence based on hydrodynamic habitat suitability. Within the study area, 68% of the shoreline was characterized by 50% or greater probability of mangrove persistence. At the site scale, likelihood of mangrove persistence can also be increased by design of an equilibrium shoreline slope, adding elasticity to stabilization site designs in areas that are on the borderline of mangrove hydrodynamic habitat suitability. Severe erosion was three times more likely to be observed on shorelines without mangrove vegetation, where over 60% of sites had escarpment heights greater than 30 cm. Similarly, shorelines with mangrove were more than two times as likely to be characterized by no to low levels of erosion. Managers and practitioners within and outside of the direct project area can benefit from this work. First, the actual hydrodynamic habitat thresholds for mangrove discovered in this study can be transferred to other locations within and outside of the Indian River Lagoon system. Locations throughout Florida that fit within the mangrove temperature, salinity and hydrology habitat zones may apply the hydrodynamic habitat knowledge developed herein to site-scale project planning. Second, the synergy between regional-scale project prioritization data and site-scale habitat suitability design tools demonstrated in this project can be a framework for future restoration planning efforts. Provision of information both at a broad geographic scale for use in regional planning, and making the information sufficiently detailed such that it can be applied at the site scale can help managers and practitioners understand when and where restoration is needed, and also the appropriateness of nature-based or green-grey hybrid designs on a site-by-site basis. Widespread investment in this type of information, and dedicated strategies to adopt such information in project PD&E may increase restoration success and impact on a regional scale