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Shengrui Wang

Shengrui Wang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Machine Learning Social and Information Networks Artificial Intelligence Biomolecules Distributed, Parallel, and Cluster Computing Genomics Neural and Evolutionary Computing Populations and Evolution

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preprint2026arXiv

CAAFC: Chronological Actionable Automated Fact-Checker for misinformation / non-factual hallucination detection and correction

With the vast amount of content uploaded every hour, along with the AI generated content that can include hallucinations, Automated Fact-Checking (AFC) has become increasingly vital, as it is infeasible for human fact-checkers to manually verify the sheer volume of information generated online. Professional fact-checkers have identified several gaps in existing AFC systems, noting a misalignment between how these systems operate and how fact-checking is performed in practice. In this paper, we introduce CAAFC (Chronological Actionable Automated Fact-Checker), a frame-work designed to bridge these gaps. It surpasses SOTA AFC and hallucination detection systems across multiple benchmark datasets. CAAFC operates on claims, conversations, and dialogues, enabling it not only to detect factual errors and hallucinations, but also to correct them by providing actionable justifications supported by primary information sources. Furthermore, CAAFC can update evidence and knowledge bases by incorporating recent and contextual information when necessary, thereby enhancing the reliability of fact verification.

preprint2022arXiv

Modeling Regime Shifts in Multiple Time Series

We investigate the problem of discovering and modeling regime shifts in an ecosystem comprising multiple time series known as co-evolving time series. Regime shifts refer to the changing behaviors exhibited by series at different time intervals. Learning these changing behaviors is a key step toward time series forecasting. While advances have been made, existing methods suffer from one or more of the following shortcomings: (1) failure to take relationships between time series into consideration for discovering regimes in multiple time series; (2) lack of an effective approach that models time-dependent behaviors exhibited by series; (3) difficulties in handling data discontinuities which may be informative. Most of the existing methods are unable to handle all of these three issues in a unified framework. This, therefore, motivates our effort to devise a principled approach for modeling interactions and time-dependency in co-evolving time series. Specifically, we model an ecosystem of multiple time series by summarizing the heavy ensemble of time series into a lighter and more meaningful structure called a \textit{mapping grid}. By using the mapping grid, our model first learns time series behavioral dependencies through a dynamic network representation, then learns the regime transition mechanism via a full time-dependent Cox regression model. The originality of our approach lies in modeling interactions between time series in regime identification and in modeling time-dependent regime transition probabilities, usually assumed to be static in existing work.

preprint2022arXiv

Neural forecasting at scale

We study the problem of efficiently scaling ensemble-based deep neural networks for multi-step time series (TS) forecasting on a large set of time series. Current state-of-the-art deep ensemble models have high memory and computational requirements, hampering their use to forecast millions of TS in practical scenarios. We propose N-BEATS(P), a global parallel variant of the N-BEATS model designed to allow simultaneous training of multiple univariate TS forecasting models. Our model addresses the practical limitations of related models, reducing the training time by half and memory requirement by a factor of 5, while keeping the same level of accuracy in all TS forecasting settings. We have performed multiple experiments detailing the various ways to train our model and have obtained results that demonstrate its capacity to generalize in various forecasting conditions and setups.

preprint2021arXiv

COVID-19: Detecting Depression Signals during Stay-At-Home Period

The new coronavirus outbreak has been officially declared a global pandemic by the World Health Organization. To grapple with the rapid spread of this ongoing pandemic, most countries have banned indoor and outdoor gatherings and ordered their residents to stay home. Given the developing situation with coronavirus, mental health is an important challenge in our society today. In this paper, we discuss the investigation of social media postings to detect signals relevant to depression. To this end, we utilize topic modeling features and a collection of psycholinguistic and mental-well-being attributes to develop statistical models to characterize and facilitate representation of the more subtle aspects of depression. Furthermore, we predict whether signals relevant to depression are likely to grow significantly as time moves forward. Our best classifier yields F-1 scores as high as 0.8 and surpasses the utilized baseline by a considerable margin, 0.173. In closing, we propose several future research avenues.

preprint2021arXiv

Investigating Moral Foundations from Web Trending Topics

Moral foundations theory helps understand differences in morality across cultures. In this paper, we propose a model to predict moral foundations (MF) from social media trending topics. We also investigate whether differences in MF influence emotional traits. Our results are promising and leave room for future research avenues.

preprint2012arXiv

Deep-sequencing of the Peach Latent Mosaic Viroid Reveals New Aspects of Population Heterogeneity

Viroids are small circular single-stranded infectious RNAs that are characterized by a relatively high mutation level. Knowledge of their sequence heterogeneity remains largely elusive, and, as yet, no strategy attempting to address this question from a population dynamics point of view is in place. In order to address these important questions, a GF305 indicator peach tree was infected with a single variant of the Avsunviroidae family member Peach latent mosaic viroid (PLMVd). Six months post-inoculation, full-length circular conformers of PLMVd were isolated, deep-sequenced and the resulting sequences analyzed using an original bioinformatics scheme specifically designed and developed in order to evaluate the richness of a given the sequence's population. Two distinct libraries were analyzed, and yielded 1125 and 1061 different PLMVd variants respectively, making this study the most productive to date (by more than an order of magnitude) in terms of the reporting of novel viroid sequences. Sequence variants exhibiting up to ~20% of mutations relative to the inoculated viroid were retrieved, clearly illustrating the high divergence dynamic inside a unique population. Using a novel hierarchical clustering algorithm, the different variants obtained were grouped into either 7 or 8 clusters depending on the library being analyzed. Most of the sequences contained, on average, between 4.6 and 6.3 mutations relative to the variant used initially to inoculate the plant. Interestingly, it was possible to reconstitute the sequence evolution between these clusters. On top of providing a reliable pipeline for the treatment of viroid deep-sequencing, this study sheds new light on the importance of the sequence variation that may take place in a viroid population and which may result in the formation of a quasi-species.