Alien Registration- Owens, William H. (Easton, Aroostook County)

https://digitalmaine.com/alien_docs/26556/thumbnail.jp

Ambient Sound Provides Supervision for Visual Learning

The sound of crashing waves, the roar of fast-moving cars -- sound conveys important information about the objects in our surroundings. In this work, we show that ambient sounds can be used as a supervisory signal for learning visual models. To demonstrate this, we train a convolutional neural network to predict a statistical summary of the sound associated with a video frame. We show that, through this process, the network learns a representation that conveys information about objects and scenes. We evaluate this representation on several recognition tasks, finding that its performance is comparable to that of other state-of-the-art unsupervised learning methods. Finally, we show through visualizations that the network learns units that are selective to objects that are often associated with characteristic sounds.Comment: ECCV 201

Visually Indicated Sounds

Objects make distinctive sounds when they are hit or scratched. These sounds reveal aspects of an object's material properties, as well as the actions that produced them. In this paper, we propose the task of predicting what sound an object makes when struck as a way of studying physical interactions within a visual scene. We present an algorithm that synthesizes sound from silent videos of people hitting and scratching objects with a drumstick. This algorithm uses a recurrent neural network to predict sound features from videos and then produces a waveform from these features with an example-based synthesis procedure. We show that the sounds predicted by our model are realistic enough to fool participants in a "real or fake" psychophysical experiment, and that they convey significant information about material properties and physical interactions

The Assessment of Commercial Fishing Effort in Virginia 1987

This report summarizes the assessment of commercial fishing effort in Chesapeake Bay and its Virginia tributaries during the period January 1, 1987 through December 31, 1987. Objectives of this study were: 1) to assess pound net fishing effort in Chesapeake Bay, and in the James, York and Rappahannock rivers; and 2) to assess anchor, drift, and stake gill net fishing effort in the James, York and Rappahannock rivers. The study period covered the calendar year which represents a natural break in fishing effort in Virginia. Data for fyke net and haul seine fisheries, when available, have also been included

The assessment of commercial fishing effort in Virginia Annual Report 1986

This report summarizes the assessment of commercial fishing effort in Chesapeake Bay and its Virginia tributaries during the period October 1, 1985 through December 31, 1986. Objectives of this study were: 1. to assess pound net fishing effort in Chesapeake Bay, and in the James, York and Rappahannock rivers; and 2. to assess anchor, drift, and stake gill net fishing effort in the James, York and Rappahannock rivers. Additionally, two months of data for pound nets and anchor, drift, and stake gill nets have been included in order that the cessation of the study period would occur at a natural break in fishing effort in Virginia. Data for fyke net and haul seine fisheries have also been included

Georgia\u27s Critical Access Hospitals: Financial Performance and Process Improvement

Background: Georgia’s Critical Access Hospitals (CAH) are in crisis. Within the last 2 years, four CAHs have closed their doors due to failed financial and operational performance. Evidence points to the risk that several more are on the brink of closure. CAH closures have far-reaching impact on residents. Negative impacts include the extra distance that patients must travel to seek care, the displacement of health professionals and the unravelling of the entire fabric of the communities these hospitals serve. We hope to help participants understand the financial and operational challenges of CAHs, and to identify realistic strategies to enhance the resilience of these hospitals. Methods: The Georgia Southern team worked with a cohort of CAHs across the state of Georgia to identify financial and operational best practices. Year 1 of this project focused on data collection, analysis and benchmarking. Year 2 is currently focused on performance improvement through Lean Six Sigma. Results: CAHs face financial constraints due to factors such as low volume, declining market share, unfavorable payer mix, challenges relating to collections, and difficulties in recruiting providers. CAHs in Georgia performed more poorly on the financial indicators assessed, in comparison to respective national medians. Many CAHs in our cohort are better organized to deal with crises – utilizing strong executive and bureaucratic structures – than to pursue ongoing improvement through employee empowerment and a process focus. Conclusions: Improvements in the operational and financial management practices of Georgia’s CAHs may significantly improve performance. Evidence-based strategies for operational and financial improvement are vital to sustainability. Opportunities exist for collaboration between public health systems and rural hospitals, including CAHs in assuring healthcare access for rural populations

Operational and Financial Performance of Georgia\u27s Critical Access Hospitals

Background: Georgia’s Critical Access Hospitals (CAHs) face increasingly complex threats to financial sustainability, as demonstrated by the disproportionally high number of closures in comparison to other states in the nation. Methods: Financial performance measures (including profitability, revenue, liquidity, debt, utilization, and productivity), site visits, key personnel interviews, and a revenue cycle management assessment were used to assess the strategic landscape of CAHs in Georgia, analyze financial and operational performance, and provide recommendations. Results: For CAHs in Georgia, financial and operating performance indicators, interviews, and assessments depict a challenging operating environment, but opportunities for improvement exist through implementation of a Lean Six Sigma program and improved benchmarking processes. Conclusions: Georgia’s CAHs operate in a challenging environment, but operational improvement strategies (such as a Lean Six Sigma program) and benchmarking directed towards business processes, including revenue cycle management, provide opportunities for sustainability in the future. Key words: Critical Access Hospital, financial performance, Process Improvement, LEAN Six Sigma, rural hospita

Beewild' bundleflower, a new summer-growing perennial legume for central and south Texas and Mexico

Last updated: 10/19/201

Mapping MOS-HIV to HUI3 and EQ-5D-3L in Patients With HIV

Objectives: The Medical Outcomes Study HIV Health Survey (MOS-HIV) is frequently used in HIV clinical trials; however, scores generated from the MOS-HIV are not suited for cost-effectiveness analyses as they do not assign utility values to health states. Our objective was to estimate and externally validate several mapping algorithms to predict Health Utilities Index Mark 3 (HUI3) and EQ-5D-3L utility values from the MOS-HIV. Methods: We developed and validated mapping algorithms using data from two HIV clinical trials. Data from the first trial (n = 367) formed the estimation data set for the HUI3 (4,610 observations) and EQ-5D-3L (4,662 observations) mapping algorithms; data from the second trial (n = 168) formed the HUI3 (1,135 observations) and EQ-5D-3L (1,152 observations) external validation data set. We compared ordinary least squares (OLS) models of increasing complexity with the more flexible two-part, beta regression, and finite mixture models. We assessed model performance using mean absolute error (MAE) and mean squared error (MSE). Results: The OLS model that used MOS-HIV dimension scores along with squared terms gave the best HUI3 predictions (mean observed 0.84; mean predicted 0.80; MAE 0.0961); the finite mixture model gave the best EQ-5D-3L predictions (mean observed 0.90; mean predicted 0.88; MAE 0.0567). All models produced higher prediction errors at the lower end of the HUI3 and EQ-5D-3L score ranges