Estimating Effects of Long-Term Treatments

Estimating the effects of long-term treatments in A/B testing presents a significant challenge. Such treatments -- including updates to product functions, user interface designs, and recommendation algorithms -- are intended to remain in the system for a long period after their launches. On the other hand, given the constraints of conducting long-term experiments, practitioners often rely on short-term experimental results to make product launch decisions. It remains an open question how to accurately estimate the effects of long-term treatments using short-term experimental data. To address this question, we introduce a longitudinal surrogate framework. We show that, under standard assumptions, the effects of long-term treatments can be decomposed into a series of functions, which depend on the user attributes, the short-term intermediate metrics, and the treatment assignments. We describe the identification assumptions, the estimation strategies, and the inference technique under this framework. Empirically, we show that our approach outperforms existing solutions by leveraging two real-world experiments, each involving millions of users on WeChat, one of the world's largest social networking platforms

Continuous use of authoring for adaptive educational hypermedia : a long-term case study

Adaptive educational hypermedia allows lessons to be personalized according to the needs of the learner. However, to achieve this, content must be split into stand-alone fragments that can be processed by a course personalization engine. Authoring content for this process is still a difficult activity, and it is essential for the popularization of adaptive educational hypermedia that authoring is simplified, so that the various stakeholders in the educational process, students, teachers, administrators, etc. can easily work with such systems. Thus, real-world testing with these stakeholders is essential. In this paper we describe recent extensions and improvements we have implemented in the My Online Teacher MOT3.0 adaptation authoring tool set, based on an initial set of short-term evaluations, and then focus on describing a long-term usage and assessment of the system

Modelling and testing of long range battery electric vehicle performance

There are two significant issues facing road transport in the medium to long term: the depletion of cheap oil reserves and the need to reduce carbon emissions. A long term solution for passenger cars could be the introduction of battery electric vehicles (BEVs). However, one of the main problems associated with the current generation of BEVs is their short range relative to conventional internal combustion engine (ICE) cars.To investigate this issue, a long range battery electric vehicle, the UltraCommuter (UC), was constructed by the University of Waikato in partnership with HybridAuto Ltd. This paper describes the development, modelling and testing of the UC and its performance in the 2007 World Solar Challenge.<br /

Has Trade Openness Increased all Portuguese Public Expenditures? A Detailed Time-Series Study

This work aims at identifying the public outlays that has been influenced by the growth of Portuguese trade openness since the end of World War II. For the Portuguese reality, it is one of the first attempts to discuss a large set of simultaneously tested control variables. For this purpose, the methodology started from a model that tries to the public expenditures to a system of simultaneous macroeconomic forces and, for testing, it followed the steps associated with cointegration analysis. Using the most convenient techniques, a restrictive set of four expenditures (subsidies, interest payments, other current expenditures, and total public expenditures as a proportion of GDP) was found among the wider set suggested by the Literature. The nature of these expenditures supports the claim that, for the Portuguese case, a particular validity of the compensation hypothesis has been observed. The achieved evidence promotes an important rule: in addition to there being a long-term relation between (some) public expenditures and trade openness, short-term relations may also appear.globalization, economic policy, government expenditure composition

Representing temporal dependencies in smart home activity recognition for health monitoring.

Long term health conditions, such as fall risk, are traditionally diagnosed through testing performed in hospital environments. Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared overtime. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs which consider the temporal dependencies present in the sensor data in order to produce richer representations and improved classification accuracy. The LSTM approaches are compared to the performance of a selection of base line classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier

BIDIRECTIONAL LSTM AND KALMAN FILTER FOR PASSENGER FLOW PREDICTION ON BUS TRANSPORTATION SYSTEMS

Forecasting travel demand is a complex problem facing public transit operators. Passenger flow prediction is useful not only for operators, used for long-term planning and scheduling, but also for transit users. The time is quickly approaching that short-term passenger flow prediction will be expected as a matter of course by transit users. To address this expectation, a Bi-directional Long Short-Term Memory Neural Network model (BDLSTM NN) and a Bi-directional Long Short-Term Memory Neural Network Kalman Filter model (BDLSTM KF) predict short-term passenger flow and based on the dependencies between passenger count and spatial-temporal features. A comprehensive preprocessing framework is proposed leveraging historical data and extracting bidirectional features of passenger flow. The proposed model is based on [1] but adapted, applied, and analysed to produce optimal results for passenger flow forecasting on a bus route. Building on [2], a BDLSTM architecture is then combined with a Kalman filter. The Kalman filter reduces the training and testing complexity required for passenger flow forecasting. The BDLSTM-based Kalman filter produces predictions with less uncertainty than each method alone. Evaluating the BDLSTM-based Kalman filter with two months of real-world data, one year apart shows positive improvements for short-term forecasting in high complexity bus networks. It is possible to see that the BDLSTM outperforms traditional machine and deep learning techniques used in this context

Experimental simulations of the weathering of volcanic ash : a case study to better understand short- and long-term impacts of ash-leachable elements on the environment : a thesis presented in the partial fulfilment of the requirements for a degree of Master of Science in Earth Science at Massey University, Palmerston North, New Zealand

The aim of this project is the development and testing of a new methodology for the investigation of the short- to long-term leaching behaviour of volcanic ash. Previous research has demonstrated that volcanic eruptions can have strong impacts on the environment, which result from elements that have been leached from volcanic ash. To date, there is relatively little understanding of the minor and trace element composition of ash-leached brines, and how this varies over time. These gaps in knowledge currently preclude an estimate of both the detrimental and the beneficial impacts of volcanic ash fall due to leaching on the environment, agriculture, as well as on human and animal health. An adaption of a soxhlet reactor was found to be an adequate experimental technique for the constant flushing of volcanic ash samples with deionised water. This was designed to accelerate the weathering of a volcanic material in a laboratory setting. A number of shortcomings in the experimental method could be identified through the course of this research and should be considered in future investigations. In this experiment nine volcanic ash samples from four different and highly active volcanoes have been tested. These volcanoes are Mt. Ruapehu and White Island in New Zealand, Mt. Kelut in Indonesia and Mt. Sakurajima in Japan. All volcanic ash samples were found to release elements into brine over the experimental time in a strongly non-linear fashion. Based on the current data set of nine ash samples, three main classes of time-variant element release behaviour are here suggested and defined, whose characteristics are primarily controlled by the element, rather than volcanic source or ash characteristics. A preliminary interpretation of these different element release pattern is that their temporal changes are most likely restrained by the strength of chemical and mechanical bond of elements to the surface of juvenile and non-juvenile ash material. Moreover, significant controls on the long-term leaching concentrations of elements were found to be by the style of eruption as well as the nature of the volcano plumbing system, confirming results of earlier batch leaching experiments. The 1995-96 Mt. Ruapehu eruption sequence in particular illustrated some significant variability in leaching behaviour as a result of specific eruption parameters. Volcanic ash samples that have been derived from a phreatomagmatic style eruption have been found to have a higher short-to long-term impact than those volcanic ash samples derived from dry magmatic eruptions. A simple method was developed to estimate the real–world equivalent weathering time corresponding to the duration of a soxhlet reactor leaching experiment. The method, which is primarily based on the total volume of water percolating through an ash sample, and to relate this to local annual rain fall data, was found to estimate real-world weathering times in the natural environment fairly accurately. Based on these natural time constraints, detrimental short-term impacts (months to years) are concluded for lead and fluoride, and beneficial short-term impacts for calcium and manganese. Long-term beneficial effects (up to 20 years) are seen for zinc, copper and iron, while long-term detrimental impacts are concluded for the cases of lead and fluoride. The strong dependence of the leaching rate on the effective ash surface area precludes that future forecasts of short- and long-term impacts should be made by considering local soil permeability and ash grain-size characteristics. In that way future modelling approaches via reactive and non-reactive porous media flow of ash-leached brines into soil and groundwater may form an interesting avenue for future developments of this pilot study. This approach may hold potential to give quantitative advice to regional councils, the agricultural industry and governmental agencies on detrimental and beneficial short- to long-term impacts of volcanic ash