A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing

Compared to traditional distributed computing environments such as grids, cloud computing provides a more cost-effective way to deploy scientific workflows. Each task of a scientific workflow requires several large datasets that are located in different datacenters from the cloud computing environment, resulting in serious data transmission delays. Edge computing reduces the data transmission delays and supports the fixed storing manner for scientific workflow private datasets, but there is a bottleneck in its storage capacity. It is a challenge to combine the advantages of both edge computing and cloud computing to rationalize the data placement of scientific workflow, and optimize the data transmission time across different datacenters. Traditional data placement strategies maintain load balancing with a given number of datacenters, which results in a large data transmission time. In this study, a self-adaptive discrete particle swarm optimization algorithm with genetic algorithm operators (GA-DPSO) was proposed to optimize the data transmission time when placing data for a scientific workflow. This approach considered the characteristics of data placement combining edge computing and cloud computing. In addition, it considered the impact factors impacting transmission delay, such as the band-width between datacenters, the number of edge datacenters, and the storage capacity of edge datacenters. The crossover operator and mutation operator of the genetic algorithm were adopted to avoid the premature convergence of the traditional particle swarm optimization algorithm, which enhanced the diversity of population evolution and effectively reduced the data transmission time. The experimental results show that the data placement strategy based on GA-DPSO can effectively reduce the data transmission time during workflow execution combining edge computing and cloud computing

IMPACT OF PERCEIVED DYADIC COPING ON WELL-BEING AND WORK ENGAGEMENT: A MULTILEVEL MODERATED MEDIATION APPROACH

Ph.DDOCTOR OF PHILOSOPH

Meeting Action Item Detection with Regularized Context Modeling

Meetings are increasingly important for collaborations. Action items in meeting transcripts are crucial for managing post-meeting to-do tasks, which usually are summarized laboriously. The Action Item Detection task aims to automatically detect meeting content associated with action items. However, datasets manually annotated with action item detection labels are scarce and in small scale. We construct and release the first Chinese meeting corpus with manual action item annotations. In addition, we propose a Context-Drop approach to utilize both local and global contexts by contrastive learning, and achieve better accuracy and robustness for action item detection. We also propose a Lightweight Model Ensemble method to exploit different pre-trained models. Experimental results on our Chinese meeting corpus and the English AMI corpus demonstrate the effectiveness of the proposed approaches.Comment: 5 pages, 2 figures. Paper accepted to the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023), Rhodes, Greec

Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling

Topic segmentation is critical for obtaining structured documents and improving downstream tasks such as information retrieval. Due to its ability of automatically exploring clues of topic shift from abundant labeled data, recent supervised neural models have greatly promoted the development of long document topic segmentation, but leaving the deeper relationship between coherence and topic segmentation underexplored. Therefore, this paper enhances the ability of supervised models to capture coherence from both logical structure and semantic similarity perspectives to further improve the topic segmentation performance, proposing Topic-aware Sentence Structure Prediction (TSSP) and Contrastive Semantic Similarity Learning (CSSL). Specifically, the TSSP task is proposed to force the model to comprehend structural information by learning the original relations between adjacent sentences in a disarrayed document, which is constructed by jointly disrupting the original document at topic and sentence levels. Moreover, we utilize inter- and intra-topic information to construct contrastive samples and design the CSSL objective to ensure that the sentences representations in the same topic have higher similarity, while those in different topics are less similar. Extensive experiments show that the Longformer with our approach significantly outperforms old state-of-the-art (SOTA) methods. Our approach improve $F_1$ of old SOTA by 3.42 (73.74 -> 77.16) and reduces $P_k$ by 1.11 points (15.0 -> 13.89) on WIKI-727K and achieves an average relative reduction of 4.3% on $P_k$ on WikiSection. The average relative $P_k$ drop of 8.38% on two out-of-domain datasets also demonstrates the robustness of our approach.Comment: Accepted by EMNLP 2023. Codes is available at https://github.com/alibaba-damo-academy/SpokenNLP

A prediction model for N2 disease in T1 non–small cell lung cancer

ObjectiveControversy remains over the routine use of mediastinoscopy or positron emission tomography in T1 non–small cell lung cancer without lymph node enlargement on computed tomography because the risk of N2 involvement is comparatively low. We aimed to develop a prediction model for N2 disease in cT1N0 non–small cell lung cancer to aid in the decision-making process.MethodsWe reviewed the records of 530 patients with computed tomography–defined T1N0 non–small cell lung cancer who underwent surgical resection with systematic lymph node dissection. Correlations between N2 involvement and clinicopathologic parameters were assessed using univariate analysis and binary logistic regression analysis. A prediction model was built on the basis of logistic regression analysis and was internally validated using bootstrapping.ResultsThe incidence of N2 disease was 16.8%. Four independent predictors were identified in multivariate logistic regression analysis and included in the prediction model: younger age at diagnosis (odds ratio, 0.974; 95% confidence interval, 0.952-0.997), larger tumor size (odds ratio, 2.769; 95% confidence interval, 1.818-4.217), central tumor location (odds ratio, 3.204; 95% confidence interval, 1.512-6.790), and invasive adenocarcinoma histology (odds ratio, 3.537; 95% confidence interval, 1.740-7.191). This model shows good calibration (Hosmer–Lemeshow test: P = .784), reasonable discrimination (area under the receiver operating characteristic curve, 0.726; 95% confidence interval, 0.669-0.784), and minimal overfitting demonstrated by bootstrapping.ConclusionsWe developed a 4-predictor model that can estimate the probability of N2 disease in computed tomography–defined T1N0 non–small cell lung cancer. This prediction model can help to determine the cost-effective use of mediastinal staging procedures

Fatigue Assessment of Traffic Signal Mast Arms based on Field Test Data under Natural Wind Gusts

In recent years, several states including Missouri, Wyoming, California, and Texas experienced fracture failures of traffic signal mast arms. Almost all the failures are associated with the propagation of defects or cracks. It is therefore imperative to evaluate existing mast arms using a simple yet accurate procedure. A statistical methodology is proposed to predict the fatigue life of signal mast arm structures on the basis of field-measured strain data. The annual occurrence of various stress levels is determined using the historical wind speed data in the vicinity of a mast arm structure and the strain readings of the structure under specific wind gusts. For each stress level, the crack initiation and propagation lives are estimated with the strain-life approach and the Paris crack-growth-rate model. They are combined to account for variable stresses by means of Miner\u27s rule and the root-mean-square model, respectively. The stress concentration factor around the arm-post connection is determined using a finite element model. The parameters in the life prediction models are determined with ASTM flat tension and compact tension tests. The proposed methodology was applied to a 12.8-m (42-ft) long octagonal mast arm and a 16.5-m (54-ft) long circular mast arm in Missouri. It is concluded that signal structures in perfect condition will not crack under natural wind gusts during their service life. However, the 16.5-m-long arm is likely to be vulnerable to tiny defects around the weld connection, but the 12.8-m-long arm is safe unless a visible crack exists

Prevalence of lymph node metastases in superficial esophageal squamous cell carcinoma

ObjectiveEndoscopic treatment of superficial esophageal carcinoma has been increasingly conducted around the world. Because no lymph nodes are removed in such a procedure, the risk of lymph node metastases (LNMs) should be clearly understood. The aim of the present study was to accurately clarify the pattern of lymphatic spread in patients with superficial esophageal squamous cell carcinoma and analyze the factors potentially related to LNMs.MethodsThe pattern of lymphatic spread was studied in 189 patients who had undergone radical lymphadenectomy from 2006 to 2011. The risk factors associated with LNMs were determined by multivariate logistic regression analysis. According to the depth of tumor invasion, mucosal tumors were classified as M1, M2, and M3 and submucosal tumors as SM1, SM2, and SM3.ResultsA total of 4252 lymph nodes were resected (average, 23 ± 9; range, 12-68). LNMs occurred in 49 patients (25.9%). The frequency of LNMs was 4.3% in those with mucosal and 33.1% in those with submucosal cancer. LNMs were found in 0%, 0%, 11.8%, 24.0%, 20.5%, and 43.8% of the M1, M2, M3, SM1, SM2, and SM3 cancer, respectively. For submucosal cancer, SM3 cancer (P = .006) and lymphovascular invasion (P = .001) were significant independent risk factors for LNMs. Paratracheal nodes were the most frequently involved. “Skip” metastases occurred in 20 of 49 patients (40.8%).ConclusionsEndoscopic treatment can be attempted when the tumor is limited to the lamina propria mucosa. However, 2-field radical lymphadenectomy with careful upper mediastinal lymph node resection should be conducted for submucosal squamous cell carcinoma