Modeling Temporal Evidence from External Collections

Newsworthy events are broadcast through multiple mediums and prompt the crowds to produce comments on social media. In this paper, we propose to leverage on this behavioral dynamics to estimate the most relevant time periods for an event (i.e., query). Recent advances have shown how to improve the estimation of the temporal relevance of such topics. In this approach, we build on two major novelties. First, we mine temporal evidences from hundreds of external sources into topic-based external collections to improve the robustness of the detection of relevant time periods. Second, we propose a formal retrieval model that generalizes the use of the temporal dimension across different aspects of the retrieval process. In particular, we show that temporal evidence of external collections can be used to (i) infer a topic's temporal relevance, (ii) select the query expansion terms, and (iii) re-rank the final results for improved precision. Experiments with TREC Microblog collections show that the proposed time-aware retrieval model makes an effective and extensive use of the temporal dimension to improve search results over the most recent temporal models. Interestingly, we observe a strong correlation between precision and the temporal distribution of retrieved and relevant documents.Comment: To appear in WSDM 201

SAFA : a semi-asynchronous protocol for fast federated learning with low overhead

Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the unreliable nature of end devices while the cost of device-server communication cannot be neglected. In this paper, we propose SAFA, a semi-asynchronous FL protocol, to address the problems in federated learning such as low round efficiency and poor convergence rate in extreme conditions (e.g., clients dropping offline frequently). We introduce novel designs in the steps of model distribution, client selection and global aggregation to mitigate the impacts of stragglers, crashes and model staleness in order to boost efficiency and improve the quality of the global model. We have conducted extensive experiments with typical machine learning tasks. The results demonstrate that the proposed protocol is effective in terms of shortening federated round duration, reducing local resource wastage, and improving the accuracy of the global model at an acceptable communication cost

Dexmedetomidine alleviates high glucose-induced podocyte damage by inhibiting EDA2R

Purpose: To investigate the effect and mechanism of action of dexmedetomidine (Dex) on podocyte injury. Methods: Cells were incubated with high glucose (50 mM) to induce a podocyte injury model in vitro. Cell viability, apoptosis, the expression of related protein related in podocyte injury and albumin permeability were evaluated by 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium bromide (MTT), flow cytometry, western blot and Transwell assays. Results: Dex administration enhanced HG-induced cell viability and the relative protein expression of Bcl-2, but reduced the HG-induced relative protein level of Bax and apoptosisrate in podocytes (p < 0.05). Besides, Dex incubation compensated HG-induced relative protein expressions of nephrin and podocin in podocytes but did the reverse with regard to relative protein expression of desmin and albumin permeability (p < 0.05). Moreover, Dex treatment resulted in a decrease in ectodysplasin A2 receptor (EDA2R) expression in HG-induced podocytes. The level of EDA2R was upregulated by the transfection of overexpression plasmid containing the EDA2R sequences. Overexpression of EDA2R reversed Dex-induced increase in cell viability, apoptosis, expression of nephrin, podocin and desmin, as well as albumin permeability in HG-stimulated podocytes (p < 0.05). Conclusion: Dex ameliorates HG-induced podocyte injury via inhibition of EDA2R, indicating that Dex is a potential alternative drug for the treatment of podocyte injury

Is Argument Structure of Learner Chinese Understandable: A Corpus-Based Analysis

This paper presents a corpus-based analysis of argument structure errors in learner Chinese. The data for analysis includes sentences produced by language learners as well as their corrections by native speakers. We couple the data with semantic role labeling annotations that are manually created by two senior students whose majors are both Applied Linguistics. The annotation procedure is guided by the Chinese PropBank specification, which is originally developed to cover first language phenomena. Nevertheless, we find that it is quite comprehensive for handling second language phenomena. The inter-annotator agreement is rather high, suggesting the understandability of learner texts to native speakers. Based on our annotations, we present a preliminary analysis of competence errors related to argument structure. In particular, speech errors related to word order, word selection, lack of proposition, and argument-adjunct confounding are discussed.Comment: Proceedings of the 2018 International Conference on Bilingual Learning and Teaching (ICBLT-2018

Creation and Evaluation of Polymer/Multiwall Carbon Nanotube Films for Structural Vibration Control and Strain Sensing Properties

Multifunctional materials both with damping properties and strain sensing properties are very important. They promise to be more weight-efficient, and provide volume-efficient performance, flexibility and potentially, less maintenance than traditional multi-component brass-board systems. The goal of this dissertation work was to design, synthesize, investigate and apply polyaniline/Multiwall carbon nanotube (PANI/MWCNT) and polyurethane (PU) /MWCNT composites films for structural vibration control and strain sensors using free layer damping methods and static and dynamic strain sensing test methods. The PANI/MWCNT was made by in situ polymerization of PANI in the presence of MWCNT, then frit compression was used to make circular and rectangular PANI/MWCNT composite films. PU/MWCNT composites were made by the layer-by-layer method. Free end vibration test results showed both of PANI/MWCNT and PU/MWCNT have better damping ratios than each of their components. Static sensing test indicated that though there appears to be residual strain in both composite sensors after the load is removed, both the sensor and the foil strain gage react linearly when re-engaged. A drift test of the sensor showed that it is stable. The dynamic sensing test results showed that over the range of 10-1000 Hz, the PANI/MWCNT composite sensor was consistently superior to foil strain gage for sensing purposes since the highest peak consistently corresponded to the input frequency and was much higher, for example, at 20 Hz, 820 times higher than those of the strain gage. Using the same criterion, the PU/Buckypaper composite sensor was comparable to or superior to the foil strain gage for sensing purposes over the range of 10 Hz to 200 Hz. The relationship of loss factor, η, and beam coverage length, L1/L, is discussed for single sided and double sided attachment. For both PANI/MWCNT and PU/MWCNT, the loss factor, η, was found to increase as coverage length, L1/L, increases. The loss factor, η, was found to have a maximum as with coverage length, L1/L, as the coverage length continues to increase. The trend for double sided attachment was found to follow the trends discussed by Rao (2004) and Levy and Chen (1994) for viscoelastic material constrained damping

PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer

Recently, the superiority of Transformer for long-term time series forecasting (LTSF) tasks has been challenged, particularly since recent work has shown that simple models can outperform numerous Transformer-based approaches. This suggests that a notable gap remains in fully leveraging the potential of Transformer in LTSF tasks. Consequently, this study investigates key issues when applying Transformer to LTSF, encompassing aspects of temporal continuity, information density, and multi-channel relationships. We introduce the Placeholder-enhanced Technique (PET) to enhance the computational efficiency and predictive accuracy of Transformer in LTSF tasks. Furthermore, we delve into the impact of larger patch strategies and channel interaction strategies on Transformer's performance, specifically Long Sub-sequence Division (LSD) and Multi-channel Separation and Interaction (MSI). These strategies collectively constitute a novel model termed PETformer. Extensive experiments have demonstrated that PETformer achieves state-of-the-art performance on eight commonly used public datasets for LTSF, surpassing all existing models. The insights and enhancement methodologies presented in this paper serve as valuable reference points and sources of inspiration for future research endeavors