Fiber Optic Fluid Level Sensor

A fiber optic fluid level sensor based on transmission attenuation due to bending loss is described. Fibers formed with reverse curvatures of decreasing radii will induce an increasing amount of lower mode light loss to the cladding as the light propagates along the multimode fiber. The sensor is arranged in the fluid in a vertical orientation such that the light travels along the fiber from the bottom or low fluid point to the top or full point. As the fluid covers increasing lengths of the exposed fiber, it strips ever more power from the cladding (assuming the fluid refractive index is greater than the cladding). Data taken with a sensor of this configuration show a monotonic decrease of output intensity as a function of increasing fluid level. As much as a -14dB change occurred over a one-foot fluid level change. Comparison of these results with a mathematical model shows good agreement. © 1986 SPIE

Approximating interactive human evaluation with self-play for open-domain dialog systems

© 2019 Neural information processing systems foundation. All rights reserved. Building an open-domain conversational agent is a challenging problem. Current evaluation methods, mostly post-hoc judgments of static conversation, do not capture conversation quality in a realistic interactive context. In this paper, we investigate interactive human evaluation and provide evidence for its necessity; we then introduce a novel, model-agnostic, and dataset-agnostic method to approximate it. In particular, we propose a self-play scenario where the dialog system talks to itself and we calculate a combination of proxies such as sentiment and semantic coherence on the conversation trajectory. We show that this metric is capable of capturing the human-rated quality of a dialog model better than any automated metric known to-date, achieving a significant Pearson correlation (r >.7, p <.05). To investigate the strengths of this novel metric and interactive evaluation in comparison to state-of-the-art metrics and human evaluation of static conversations, we perform extended experiments with a set of models, including several that make novel improvements to recent hierarchical dialog generation architectures through sentiment and semantic knowledge distillation on the utterance level. Finally, we open-source the interactive evaluation platform we built and the dataset we collected to allow researchers to efficiently deploy and evaluate dialog models

A data mining approach for diagnosis of coronary artery disease

Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to achieve methods with high accuracy and less side effects and costs. In this paper, a dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features. Also, a feature creation method is proposed to enrich the dataset. Then Information Gain and confidence were used to determine the effectiveness of features on CAD. Typical Chest Pain, Region RWMA2, and age were the most effective ones besides the created features by means of Information Gain. Moreover Q Wave and ST Elevation had the highest confidence. Using data mining methods and the feature creation algorithm, 94.08 accuracy is achieved, which is higher than the known approaches in the literature. © 2013 Elsevier Ireland Ltd