Turlock Irrigation District

Presented at the 2002 USCID/EWRI conference, Energy, climate, environment and water - issues and opportunities for irrigation and drainage on July 9-12 in San Luis Obispo, California.The Turlock Irrigation District (TID), California's first irrigation district, was established in 1887. In 1997, the TID began to investigate what improvements could be made to its water measurement facilities, some of which dated to the early part of the 20th century. The plan that was developed consisted of the installation of telemetry to existing concrete weirs, construction of new long-throated flumes, and installation of solid-state devices to replace existing systems. The data collected in the field is transmitted via a spread-spectrum radio to the District's operation center where it is loaded into the Irrigation SCADA system. Office staff can access the data and use it to monitor and analyze the operation of the irrigation system. The experience gained in data monitoring and collection will be used as the foundation for further improvements in operation of the TID canal system

Proposing a New Algorithm for Premanipulative Testing in Physical Therapy Practice

In the field of physical therapy, there is debate as to the clinical utility of premanipulative vascular assessments. Cervical artery dysfunction (CAD) risk assessment involves a multi-system approach to differentiate between spontaneous versus mechanical events. The purposes of this inductive analysis of the literature are to discuss the link between cervical spine manipulation (CSM) and CAD, to examine the literature on premanipulative vascular tests, and to suggest an optimal sequence of premanipulative testing based on the differentiation of a spontaneous versus mechanical vascular event. Knowing what premanipulative vascular tests assess and the associated clinical application facilitates an evidence-informed decision for clinical application of vascular assessment before CSM

Controllable Neural Story Plot Generation via Reinforcement Learning

Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model towards a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.Comment: Published in IJCAI 201