Implicit Training of Energy Model for Structure Prediction

Most deep learning research has focused on developing new model and training procedures. On the other hand the training objective has usually been restricted to combinations of standard losses. When the objective aligns well with the evaluation metric, this is not a major issue. However when dealing with complex structured outputs, the ideal objective can be hard to optimize and the efficacy of usual objectives as a proxy for the true objective can be questionable. In this work, we argue that the existing inference network based structure prediction methods ( Tu and Gimpel 2018; Tu, Pang, and Gimpel 2020) are indirectly learning to optimize a dynamic loss objective parameterized by the energy model. We then explore using implicit-gradient based technique to learn the corresponding dynamic objectives. Our experiments show that implicitly learning a dynamic loss landscape is an effective method for improving model performance in structure prediction.Comment: AAA

Towards Sustainable Water Supply: Schematic Development of Big Data Collection Using Internet of Things (IoT)

Water supply systems in the United States connect raw water sources to hundreds of millions of water consumers through humongous infrastructure that include approximately one million miles of buried water mains and service connections and thousands of treatment facilities and appurtenances. This enormous set-up is currently operated by more than 170,000 public water systems. Sustainability of the water supply system faces several imminent challenges such as: 1) increasing water main breaks, 2) decreasing fresh water resources, 3) untraceable non-revenue water use, and 4) increasing water demands. However, current water supply management practices are not capable of providing fundamental solutions to the issues identified above. Big Data is a new technical concept to collect massive amounts of relevant data from sensors installed to monitor structural condition, usage, and system performance. This Big Data concept can be realized by deploying Internet of Things (IoT) technology throughout the water supply infrastructure and consumers’ usage. This paper presents a schematic development of IoT application for Big Data collection through a myriad of water clients. The scheme consists of downstream and upstream data collection using Wireless Sensor Network (WSN) technologies connecting to IoT. Downstream data shall provide water usage and performance data to clients and upstream data is similar to traditional SCADA and Automated Meter Reading (AMR) systems. Ultimately, all data will be converged to build a Big Data collection system where data mining identifies 1) local and system performances including pressure and flow, 2) non-revenue and illegitimate water consumption, and 3) locations and quantity of water breaks and water losses. The goal of this development is to enable both utilities and consumers to proactively manage their water usage and achieve higher levels of sustainability in water supply

Sustainability evaluation of pipe asset management strategies

The consequences associated with pipe failures can be widespread impacting service, while potentially causing damage, affecting traffic, and contaminating water. Recently the visibility of pipe failures has increased with social media and 24-hour news coverage. In response, many utilities have adapted pipe asset management strategies to reduce failures. Also, many technologies have emerged that allow for a more proactive pipe asset management. As sustainability has become a focus for many organizations including utilities, the question becomes which pipe asset management strategy is most sustainable. The purpose of this paper is to evaluate three pipe asset management strategies for sustainability using Envision®. The strategies include: a reactive run-to-failure and then replace; a preemptive replacement prior to failure based on assumed condition; and a balanced approach of active condition assessment and action based on the known condition. Envision® will be used to evaluate each approach to determine its sustainability rating

Does construction service provider\u27s response matter? Understanding the influence of anecdotal information on online consumer decisions

The home improvement service industry is growing rapidly, and the advancement in technology has made information about service providers, such as customer reviews, accessible with a few clicks. However, the impact of anecdotal information, like reviews and a service provider\u27s response to a review, have not been studied extensively in the home improvement service industry. Using the Data Frame Theory of Sensemaking, this study investigated the combined effect of these two variables on an online consumer\u27s decision. We recruited 360 participants through Qualtrics Research Services to participate in a 4∗3 between-subjects study. The findings suggest that when all reviews were either entirely positive or negative, i.e., consistent information, the service provider\u27s response did not influence the customer\u27s decision. However, when the reviews were inconsistent, the service provider\u27s response was influential. In addition, negative reviews created a lack of trust in the information, which is a potential area for future research