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Deformation Behavior and Effect of Strain Path on Strain-Induced Martensite Transformation in Austenitic Stainless Steel
, M.Sc. Thesis Sharif University of Technology ; Serajzadeh, Siamak (Supervisor)
Abstract
In some of the austenitic stainless steels, the strain-induced martensite transformation occurs during the deformation processing owing to instability of austenite phase at low temperatures. In this study, formation of strain-induced martensite as well as the impact of strain path on the rate of martensite transformation in 316L austenitic stainless steel were investigated. At first, the multi-pass cold rolling experiments at room temperature and -8 ℃ were conducted in which both direct and reverse rolling layouts were taken into account. Then, as the third path, the uni-axial tensile loading with an appropriate strain rate was considered. After that, in order to examine the mechanical and...
How will your tweet be received? predicting the sentiment polarity of tweet replies
, Article 15th IEEE International Conference on Semantic Computing, ICSC 2021, 27 January 2021 through 29 January 2021 ; 2021 , Pages 370-373 ; 9781728188997 (ISBN) ; Monajem, M ; Christlein, V ; Heinrich, P ; Nicolaou, A ; Naderi Boldaji, H ; Lotfinia, M ; Evert, S ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2021
Abstract
Twitter sentiment analysis, which often focuses on predicting the polarity of tweets, has attracted increasing attention over the last years, in particular with the rise of deep learning (DL). In this paper, we propose a new task: predicting the predominant sentiment among (first-order) replies to a given tweet. Therefore, we created RETwEET, a large dataset of tweets and replies manually annotated with sentiment labels. As a strong baseline, we propose a two-stage DL-based method: first, we create automatically labeled training data by applying a standard sentiment classifier to tweet replies and aggregating its predictions for each original tweet; our rationale is that individual errors...