Some Flight Characteristics of a Deflected Slipstream V/STOL Aircraft

The Ryan VZ-3RY V/STOL test vehicle was flight tested over the airspeed range from 80 knots to below 6 knots. The deflected slipstream concept proved to be better suited to STOL than VTOL operation. Adverse ground effects prevented operation close to the ground at speeds less than 20 knots and below approximately 15 feet altitude. Steep glide slopes to landing (up to -16 deg) at approximately 40 knots were achieved, but steep, slow, descending flight did not appear feasible. Full-span leading-edge slats markedly increased the descent capability and reduced the minimum level flight speed

EEG Interictal Spike Detection Using Artificial Neural Networks

Epilepsy is a neurological disease causing seizures in its victims and affects approximately 50 million people worldwide. Successful treatment is dependent upon correct identification of the origin of the seizures within the brain. To achieve this, electroencephalograms (EEGs) are used to measure a patient’s brainwaves. This EEG data must be manually analyzed to identify interictal spikes that emanate from the afflicted region of the brain. This process can take a neurologist more than a week and a half per patient. This thesis presents a method to extract and process the interictal spikes in a patient, and use them to reduce the amount of data for a neurologist to manually analyze. The effectiveness of multiple neural network implementations is compared, and a data reduction of 3-4 orders of magnitude, or upwards of 99%, is achieved

Consequences of Information Asymmetry on Corporate Risk Management

This paper will demonstrate the impact information asymmetry has on risk management. There is a noticeable impact within the context of consumer credit risk. If a firm is able to recognize this, they can make improved credit decisions that will reduce the consequences. The theoretical impact will be presented while depicting areas of risk management that are susceptible to information asymmetry. We find a direct impact on the development of scoring models, credit policies, and origination volume. These results hold for banks with portfolios consisting of consumer credit products and small business loans. Once known, banks can better tailor their credit policies and underwriting guidelines to reduce the impact. This will provide the blueprints for empirical research into the fiscal consequences, particularly concerning loss provisioning and the charge-off of consumer loans

The Immunity of Local Governments and Their Officials from Antitrust Claims After City of Boulder

On January 13, 1982, the United States Supreme Court rendered an opinion against the City of Boulder, Colorado, which expanded the potential liability of local governmental entities and their officials to claims under the federal antitrust laws. The Supreme Court essentially held that a municipality cannot obtain immunity from antitrust claims unless it satisfies a stringent test. Due to the broad language of the opinion, virtually every activity in which a local governmental entity engages, including the traditional activities of zoning, licensing, franchising, purchasing and operating public utilities, has become subject to antitrust challenges that may require a trial on the merits

Antitrust & Local Government, Search and Seizure

The United States Supreme Court\u27s recent decision in Community Communications Co. v. City of Boulder has been aptly described as a lightning bolt with potentially thunderous repercussions. City of Boulder basically established that local government activity is not immune from federal antitrust laws unless it is undertaken pursuant to a clearly articulated and affirmatively expressed state policy to replace competition with regulation or monopoly public service

CLUSTERED HIERARCHICAL ANOMALY AND OUTLIER DETECTION ALGORITHMS

Anomaly and outlier detection is a long-standing problem in machine learning. In some cases, anomaly detection is easy, such as when data are drawn from well-characterized distributions such as the Gaussian. However, when data occupy high-dimensional spaces, anomaly detection becomes more difficult. We present CLAM (Clustered Learning of Approximate Manifolds), a manifold mapping technique in any metric space. CLAM begins with a fast hierarchical clustering technique and then induces a graph from the cluster tree, based on overlapping clusters as selected using several geometric and topological features. Using these graphs, we implement CHAODA (Clustered Hierarchical Anomaly and Outlier Detection Algorithms), exploring various properties of the graphs and their constituent clusters to find outliers. CHAODA employs a form of transfer learning based on a training set of datasets, and applies this knowledge to a separate test set of datasets of different cardinalities, dimensionalities, and domains. On 24 publicly available datasets, we compare CHAODA (by measure of ROC AUC) to a variety of state-of-the-art unsupervised anomaly-detection algorithms. Six of the datasets are used for training. CHAODA outperforms other approaches on 16 of the remaining 18 datasets. CLAM and CHAODA scale to large, high-dimensional “big data” anomalydetection problems, and generalize across datasets and distance functions. Source code to CLAM and CHAODA are freely available on GitHub1