SMOTE: Synthetic Minority Over-sampling Technique

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy

Effective and efficient optics inspection approach using machine learning algorithms

The Final Optics Damage Inspection (FODI) system automatically acquires and utilizes the Optics Inspection (OI) system to analyze images of the final optics at the National Ignition Facility (NIF). During each inspection cycle up to 1000 images acquired by FODI are examined by OI to identify and track damage sites on the optics. The process of tracking growing damage sites on the surface of an optic can be made more effective by identifying and removing signals associated with debris or reflections. The manual process to filter these false sites is daunting and time consuming. In this paper we discuss the use of machine learning tools and data mining techniques to help with this task. We describe the process to prepare a data set that can be used for training and identifying hardware reflections in the image data. In order to collect training data, the images are first automatically acquired and analyzed with existing software and then relevant features such as spatial, physical and luminosity measures are extracted for each site. A subset of these sites is 'truthed' or manually assigned a class to create training data. A supervised classification algorithm is used to test if the features can predict the class membership of new sites. A suite of self-configuring machine learning tools called 'Avatar Tools' is applied to classify all sites. To verify, we used 10-fold cross correlation and found the accuracy was above 99%. This substantially reduces the number of false alarms that would otherwise be sent for more extensive investigation

Local Area Signal-to-Noise Ratio (LASNR) algorithm for Image Segmentation

Many automated image-based applications have need of finding small spots in a variably noisy image. For humans, it is relatively easy to distinguish objects from local surroundings no matter what else may be in the image. We attempt to capture this distinguishing capability computationally by calculating a measurement that estimates the strength of signal within an object versus the noise in its local neighborhood. First, we hypothesize various sizes for the object and corresponding background areas. Then, we compute the Local Area Signal to Noise Ratio (LASNR) at every pixel in the image, resulting in a new image with LASNR values for each pixel. All pixels exceeding a pre-selected LASNR value become seed pixels, or initiation points, and are grown to include the full area extent of the object. Since growing the seed is a separate operation from finding the seed, each object can be any size and shape. Thus, the overall process is a 2-stage segmentation method that first finds object seeds and then grows them to find the full extent of the object. This algorithm was designed, optimized and is in daily use for the accurate and rapid inspection of optics from a large laser system (National Ignition Facility (NIF), Lawrence Livermore National Laboratory, Livermore, CA), which includes images with background noise, ghost reflections, different illumination and other sources of variation

Physical therapy and exercise interventions in Huntington's disease: a mixed methods systematic review protocol

Review question/objective: : The review seeks to evaluate the effectiveness of physical therapy and exercise interventions in Huntington's disease (HD). The review question is: What is the effectiveness of physiotherapy and therapeutic exercise interventions in people with HD, and what are patients’, families’ and caregivers’ perceptions of these interventions? Review question/objective: The specific objectives are: Review question/objective: This mixed methods review seeks to develop an aggregated synthesis of quantitative, qualitative and narrative systematic reviews on physiotherapy and exercise interventions in HD, in an attempt to derive conclusions and recommendations useful for clinical practice and policy decision-making

Final optics damage inspection (FODI) for the National Ignition Facility

The National Ignition Facility (NIF) at the Lawrence Livermore National Laboratory (LLNL) will routinely fire high energy shots (approaching 10 kJ per beamline) through the final optics, located on the target chamber. After a high fluence shot, exceeding 4J/cm2 at 351 nm wavelength, the final optics will be inspected for laser-induced damage. The FODI (Final Optics Damage Inspection) system has been developed for this purpose, with requirements to detect laser-induced damage initiation and to track and size it's the growth to the point at which the optic is removed and the site mitigated. The FODI system is the 'corner stone' of the NIF optic recycle strategy. We will describe the FODI system and discuss the challenges to make optics inspection a routine part of NIF operations

The Impact of Different Types of Assistive Devices on Gait Measures and Safety in Huntington's Disease

BACKGROUND: Gait and balance impairments lead to frequent falls and injuries in individuals with Huntington's disease (HD). Assistive devices (ADs) such as canes and walkers are often prescribed to prevent falls, but their efficacy is unknown. We systematically examined the effects of different types of ADs on quantitative gait measures during walking in a straight path and around obstacles. METHODS: Spatial and temporal gait parameters were measured in 21 subjects with HD as they walked across a GAITRite walkway under 7 conditions (i.e., using no AD and 6 commonly prescribed ADs: a cane, a weighted cane, a standard walker, and a 2, 3 or 4 wheeled walker). Subjects also were timed and observed for number of stumbles and falls while walking around two obstacles in a figure-of-eight pattern. RESULTS: Gait measure variability (i.e., coefficient of variation), an indicator of fall risk, was consistently better when using the 4WW compared to other ADs. Subjects also walked the fastest and had the fewest number of stumbles and falls when using the 4WW in the figure-of-eight course. Subjects walked significantly slower using ADs compared to no AD both across the GAITRite and in the figure-of-eight. Measures reflecting gait stability and safety improved with the 4WW but were made worse by some other ADs

Tools for Predicting Optical Damage on Inertial Confinement Fusion-Class Laser Systems

Operating a fusion-class laser to its full potential requires a balance of operating constraints. On the one hand, the total laser energy delivered must be high enough to give an acceptable probability for ignition success. On the other hand, the laser-induced optical damage levels must be low enough to be acceptably handled with the available infrastructure and budget for optics recycle. Our research goal was to develop the models, database structures, and algorithmic tools (which we collectively refer to as ''Loop Tools'') needed to successfully maintain this balance. Predictive models are needed to plan for and manage the impact of shot campaigns from proposal, to shot, and beyond, covering a time span of years. The cost of a proposed shot campaign must be determined from these models, and governance boards must decide, based on predictions, whether to incorporate a given campaign into the facility shot plan based upon available resources. Predictive models are often built on damage ''rules'' derived from small beam damage tests on small optics. These off-line studies vary the energy, pulse-shape and wavelength in order to understand how these variables influence the initiation of damage sites and how initiated damage sites can grow upon further exposure to UV light. It is essential to test these damage ''rules'' on full-scale optics exposed to the complex conditions of an integrated ICF-class laser system. Furthermore, monitoring damage of optics on an ICF-class laser system can help refine damage rules and aid in the development of new rules. Finally, we need to develop the algorithms and data base management tools for implementing these rules in the Loop Tools. The following highlights progress in the development of the loop tools and their implementation