Development of exploratory data analysis methods for chemical, spatial and temporal analysis of surface water quality data: the Ontario Provincial Water Quality Monitoring Network

Surface water quality (SWQ) databases have been widely compiled to provide information characterizing environmental conditions. But SWQ databases appear to be under-utilized, given the large investment in their creation. One reason is that database spatial, temporal, and compositional dimension vary through time, reflecting changing priorities through time and contrasts between different agencies, making coherent analysis challenging. This thesis explores the Ontario Provincial Water Quality Monitoring Network (PWQMN) to derive higher order hydrochemical properties, to render SWQ data in “network space” permitting catchment-wide visualization, and in undertaking temporal trend analysis. Rivers play a critical role in the terrestrial carbon cycle, but the level and role of dissolved carbon dioxide is poorly understood because it is difficult to measure or estimate. A stepwise algorithm was developed to extract an exceptionally large and accurate PCO2 data set from the PWQMN. The results showed ubiquitous supersaturation and decrease downstream, implying high rates of organic matter import into surface waters. The spatial pattern of surface water monitoring shows a close relationship to a novel upstream ordering system that was exploited to develop a “network space” transformation of rivers and SWQ data. Mapping of chloride, carbon dioxide, oxygen and total phosphorus data in network space showed spatial coherence, clear urban impact, and systematic inter-catchment differences. A complementary mixing algorithm allowed budgeting for high-resolution data sets, but was less successful for general mapping where its value was in auditing the data for point sources or poor monitoring. Rendering of SWQ data in time using network space was very effective, but risky due to bias and possible errors in the data. Overall, PCO2 levels peaked in the mid-1990s, then fell dramatically to variable, but non-treading levels. These changes were associated with significant transitions in monitoring policy and priorities, so were investigated as possible artifacts. Inter-catchment and epochal differences in PCO2 (and its determinants: alkalinity and pH) were unexpected. This may arise from regional acid rain control programs, but may be a result of contrasting field protocols in different agencies

AWESOME: GPU Memory-constrained Long Document Summarization using Memory Mechanism and Global Salient Content

Long document summarization systems are critical for domains with lengthy and jargonladen text, yet they present significant challenges to researchers and developers with limited computing resources. Existing solutions mainly focus on efficient attentions or divide-and-conquer strategies. The former reduces theoretical time complexity, but is still memory-heavy. The latter methods sacrifice global context, leading to uninformative and incoherent summaries. This work aims to leverage the memory-efficient nature of divide-and-conquer methods while preserving global context. Concretely, our framework AWESOME uses two novel mechanisms: (1) External memory mechanisms track previously encoded document segments and their corresponding summaries, to enhance global document understanding and summary coherence. (2) Global salient content is further identified beforehand to augment each document segment to support its summarization. Extensive experiments on diverse genres of text, including government reports, transcripts, scientific papers, and novels, show that AWESOME produces summaries with improved informativeness, faithfulness, and coherence than competitive baselines on longer documents, while having a similar or smaller GPU memory footprint

An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading

We propose an ensemble method to improve the generalization performance of trading strategies trained by deep reinforcement learning algorithms in a highly stochastic environment of intraday cryptocurrency portfolio trading. We adopt a model selection method that evaluates on multiple validation periods, and propose a novel mixture distribution policy to effectively ensemble the selected models. We provide a distributional view of the out-of-sample performance on granular test periods to demonstrate the robustness of the strategies in evolving market conditions, and retrain the models periodically to address non-stationarity of financial data. Our proposed ensemble method improves the out-of-sample performance compared with the benchmarks of a deep reinforcement learning strategy and a passive investment strategy

Tail processes for stable-regenerative model

We investigate a family of discrete-time stationary processes, known as stable-regenerative model, that may exhibit typical behaviors of short-range or long-range dependence, respectively, depending on the parameters. We elaborate the phase transition in terms of the tail processes that characterize local clustering of extremes. In particular, in the sub-critical regime, we compute the candidate extremal index and the extremal index, and they are not the same.Comment: Minor revision. 21 pages. Inconsistent notions of tail processes in the previous versions were now corrected. Proofs in Section 3 were modified accordingl

Time-aware Prompting for Text Generation

In this paper, we study the effects of incorporating timestamps, such as document creation dates, into generation systems. Two types of time-aware prompts are investigated: (1) textual prompts that encode document timestamps in natural language sentences; and (2) linear prompts that convert timestamps into continuous vectors. To explore extrapolation to future data points, we further introduce a new data-to-text generation dataset, TempWikiBio, containing more than 4 millions of chronologically ordered revisions of biographical articles from English Wikipedia, each paired with structured personal profiles. Through data-to-text generation on TempWikiBio, text-to-text generation on the content transfer dataset, and summarization on XSum, we show that linear prompts on encoder and textual prompts improve the generation quality on all datasets. Despite having less performance drop when testing on data drawn from a later time, linear prompts focus more on non-temporal information and are less sensitive to the given timestamps, according to human evaluations and sensitivity analyses. Meanwhile, textual prompts establish the association between the given timestamps and the output dates, yielding more factual temporal information in the output.Comment: EMNLP 2022 Findings (short paper

Shaping a Smart Transportation System for Sustainable Value Co-Creation

The smart transportation system (STS) leverages ubiquitous and networked computing to improve the efficiency of urban mobility. Whilst existing IS work has explored various factors influencing STS development, there is a lack of consideration of how value can be created for building a more sustainable STS. Drawing upon the value co-creation theory and stakeholder theory, we seek to understand the socio-technical shaping of the STS ecosystem and how government, firms and citizens collaboratively create sustainable value for designing and implementing STS initiatives. To reach this aim, we carry out a longitudinal case study over 2016–2018 in Shijiazhuang, China. We offer both theoretical and practical explanations on (i) key value facets with regard to sustainable STS design and implementation; and (ii) a holistic view of iterative value co-creation process pushed by key stakeholders. This study makes particular contributions to the IS, marketing and transportation literature by offering a critical understanding of the social dynamics for shaping a big data-driven STS ecosystem.</p