Public feedback - but personal feedforward?

Academic feedback is taken here as the reporting to student writers of the strengths and weaknesses of their submitted draft work, while academic feedforward refers to constructive advice regarding possible strengthening of students’ next work. Both originate from a tutor’s initial judgement of a student’s work. Feedback and feedforward on work showing need for improvement are problematic in a Confucian Heritage Culture. Even gently constructive advice within a programme seeking evidence for assessment of critical thinking may lead to perception of hurtful criticism by Taiwanese students. Some could withdraw from class activity accordingly. So the writers adjusted their response style. They now choose between different approaches featuring tutorial feedback or feedforward, depending on the standard of work being judged. When individual postings feature poor critical thinking, the writers opt for private messages concentrating on constructive feedforward. For better postings, they provide positive feedback with reasons for their judgements, and summarise to the class these exemplars of generic strengths in critical thinking. They also offer private prompting when they see scope for further enrichment of an able student’s critical thinking. This might also be a useful practice when tutoring solely in the West

Knowledge-Rich Approach to Automatic Grammatical Information Acquisition: Enriching Chinese Sketch Engine with a Lexical Grammar

PACLIC 20 / Wuhan, China / 1-3 November, 200

Knowledge-Rich Approach to Automatic Grammatical Information Acquisition: Enriching Chinese Sketch Engine with a Lexical Grammar

PACLIC 20 / Wuhan, China / 1-3 November, 200

A positive feedback loop of IL-17B-IL-17RB activates ERK/β-catenin to promote lung cancer metastasis

Inflammation contributes to the development and progression of cancer. Interleukin-17 (IL-17) is an inflammatory cytokine that functions in inflammation and cancer, as well as several other cellular processes. In this study, we investigated the roles and the prognostic value of IL-17 and the IL-17 receptor (IL-17R) in lung cancer. Gene expression microarray analysis followed by Kaplan-Meier survival curve showed that IL-17B was associated with poor patient survival, and IL-17B receptor (IL-17RB) was up-regulated in lung cancer tissue compared with normal tissue. Expression of IL-17RB was associated with lymph node metastasis and distant metastasis, as well as poor patient survival. IL-17RB overexpression significantly increased cancer cell invasion/migration and metastasis in vitro and in vivo. IL-17RB induced ERK phosphorylation, resulting in GSK3β inactivation and leading to β-catenin up-regulation. IL-17RB also participated in IL-17B synthesis via the ERK pathway. IL-17RB activation is required for IL-17B-mediated ERK phosphorylation. Taken together, IL-17B-IL-17RB signaling and ERK participate in a positive feedback loop that enhances invasion/migration ability in lung cancer cell lines. IL-17RB may therefore serve as an independent prognostic factor and a therapeutic target for lung cancer

Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You Where

While image data starts to enjoy the simple-but-effective self-supervised learning scheme built upon masking and self-reconstruction objective thanks to the introduction of tokenization procedure and vision transformer backbone, convolutional neural networks as another important and widely-adopted architecture for image data, though having contrastive-learning techniques to drive the self-supervised learning, still face the difficulty of leveraging such straightforward and general masking operation to benefit their learning process significantly. In this work, we aim to alleviate the burden of including masking operation into the contrastive-learning framework for convolutional neural networks as an extra augmentation method. In addition to the additive but unwanted edges (between masked and unmasked regions) as well as other adverse effects caused by the masking operations for ConvNets, which have been discussed by prior works, we particularly identify the potential problem where for one view in a contrastive sample-pair the randomly-sampled masking regions could be overly concentrated on important/salient objects thus resulting in misleading contrastiveness to the other view. To this end, we propose to explicitly take the saliency constraint into consideration in which the masked regions are more evenly distributed among the foreground and background for realizing the masking-based augmentation. Moreover, we introduce hard negative samples by masking larger regions of salient patches in an input image. Extensive experiments conducted on various datasets, contrastive learning mechanisms, and downstream tasks well verify the efficacy as well as the superior performance of our proposed method with respect to several state-of-the-art baselines