Suchergebnisse

When you are browsing social media websites such as Twitter and Facebook, have you ever seen hashtags like #NeverAgain and #EnoughIsEnough? Do you know what they mean? Never Again is an American student-led political movement for gun control to prevent gun violence. In the United States, gun control has long been debated. According to the data from the Gun Violence Archive (http://www.shootingtracker.com/), in 2017, the U.S. saw a total of 346 mass shootings. Supporters claim that the proliferation of firearms is the direct spark of a series of social unrest factors such as robbery, sexual crimes, and theft, while others believe the gun culture represents an integral part of their freedom. For the Never Again Gun Control Movement, we would like to generate a human readable summary based on deep learning methods so that one can study incidents of gun violence that shocked the world such as the 2017 Las Vegas shooting, in order to figure out the impact of gun proliferation. Our project includes three steps: pre-processing, topic modeling, and abstractive summarization using deep learning. We began with a large collection of news articles associated with the #NeverAgain movement. The raw news articles needed to be pre-processed in multiple ways. An ArchiveSpark script was used to convert the WARC and CDX files to a readable and parseable JSON. However, we figured out that at least forty percent of the data was noise. A series of restrictive word filters was applied to remove noise. After noise removal, we identified the most frequent words to get a preliminary idea whether we were filtering noise properly. We used the Natural Language Toolkits (NLTK) Named Entity chunker to generate named entities, which are phrases that form important nouns (people, places, organizations, etc.) in a sentence. For Topic Modeling, we classified sentences into different buckets or topics, which identified distinct themes in the collection. While we were performing the dictionary creation and document vectorization, the Latent Dirichlet allocation algorithm (for topic modeling) did not take the normalized and tokenized word corpus directly. It had to be converted into a vector for each article in the collection. We chose to use the Bag Of Words (BOW) approach. The Bag Of Words method is a simplifying representation used in natural language processing and information retrieval. In this model, text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order, but keeping multiplicity. According to topic modeling, we needed to choose the number of topics, which means one must guess how many topics are present in a collection. There is no foolproof way of replacing human logic to weave keywords into topics with semantic meaning. To address this we tried the coherence score approach. Coherence score is an attempt to mimic the human readability of the topic, and the higher the coherence score, the more coherent the topics are considered. The last step for topic modeling is Latent Dirichlet Allocation (LDA). Latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. Compared with some other algorithms, LDA is a probabilistic one, which means that LDA is better at handling topic mixtures in different documents. In addition, LDA identifies topics coherently whereas the topics from other algorithms are more disjoint. After we had our topics (three in total), we filtered the article collection based on these topics. What resulted was three distinct collections of articles on which we could apply an abstractive summarization algorithm to produce a coherent summary. We chose to use a Pointer-Generator Network (PGN), a deep learning approach designed to create abstractive summaries, to produce said summaries. We created a summary for each identified topic and performed post-processing to produce one summary that connected the three topics (which are related) into a summary that flowed. The result was a summary that reflected the main themes of the article collection and informed the reader of the contents of said collection in less than two pages. ; NSF IIS-1619028 ; Description of files of this collection: - NeverAgain_report_in_PDF_format.pdf: The final report of the project in PDF format. - NeverAgain_Report_Latex_Material.zip: A zip file containing the source material of the LaTeX version of the final report. - NeverAgain_ ZIP_file_of_source_code.zip: A zip file containing all the source code of the project. - NeverAgain_presentation_in_powerpoint: The final presentation in Microsoft PowerPoint format. - NeverAgain_presentation_in_pdf: The final presentation in PDF format.

Endorsing employee voice is one thing; implementation of endorsed ideas is another. Although organizational research has paid increasing attention to examining managers' psychological endorsement of employee voice, the factors that can affect managers' actual implementation of endorsed employee voice remain unclear. Drawing on the theory of planned behavior, we develop a conceptual model of managerial voice implementation and conceptualize it as a manager's planned behavior that is affected by the manager's motivation, felt obligation, and perceived control in relation to implementation. We further apply social network approaches to explain how social network characteristics across multiple levels in the team (i.e. dyadic ties, network centrality, and network closure) can facilitate the manager's psychological impetus for voice implementation – transforming endorsed voice into managerial practices in the workplace. Finally, we discuss the theoretical and practical implications of this manager-centric and social network-based framework of managerial voice implementation.

Jian Li,1 Hong Ye Ren,2 Juanjuan Zhang,2 Peng Dong,2 Yan Wang,3 Andrea L Stevens,3 Yi Han,3 Min Huang4 1Laboratory of Carcinogenesis and Translational Research for the Ministry of National Education, Department of GI Oncology, Peking University School of Oncology, Beijing Cancer Hospital & Institute, 2Pfizer Inc., Beijing, People's Republic of China; 3WG Consulting, New York, NY, USA; 4School of Pharmacy, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China Objective: To evaluate the cost-effectiveness of sunitinib as a second-line treatment in patients with advanced gastrointestinal stromal tumors that no longer respond to imatinib 400 mg/d, compared with imatinib 600 mg/d, 800 mg/d, or best supportive care (BSC) in the People's Republic of China. Methods: This study was conducted from the government payer's perspective with a time horizon of 5 years. Three health states were considered: progression-free survival, disease progression survival, and death, with a cycle length of 6 weeks. Probabilities of disease progression and death were estimated based on survival functions using exponential distribution and progression survival data in the clinical trials. Drug costs were based on drug retail prices and the patient assistance program in the People's Republic of China, and adverse event management costs were based on published data and/or expert opinion. Uncertainties for parameters in the study were addressed through one-way deterministic and probabilistic sensitivity analysis. Results: When sunitinib was compared with imatinib 600 mg/d and BSC, the incremental cost-effectiveness ratio was RMB75,715 with RMB121,080 per quality-adjusted life-year (QALY) gained. Sunitinib demonstrated lower costs and higher QALYs than imatinib 800 mg/d. In the probabilistic sensitivity analysis, the willingness-to-pay per QALY gained was set to be three times the per capita gross domestic product of the People's Republic of China, that is, RMB46,510 in 2014. Sunitinib was demonstrated to be cost-effective compared with imatinib 600 mg/d, imatinib 800 mg/d, and BSC, with probabilities of 82.3%, 95.6%, and 78.2%, respectively. Limitations: Clinical data for imatinib 800 mg/d and BSC in the analysis were based upon studies in non-Chinese populations. Because of the unavailability of utility data from Chinese gastrointestinal stromal tumor patients, the analysis used the utility estimates from studies performed in other countries. Conclusion: Sunitinib provides greater clinical benefit than high-dose imatinib or BSC as a second-line treatment. In the Chinese setting, sunitinib is estimated to be cost-effective compared with imatinib 800 mg/d, imatinib 600 mg/d, or BSC. Keywords: cost-effectiveness analysis, gastrointestinal stromal tumor, imatinib, sunitinib, best supportive care, second-lineCorrigendum for this paper has been published