The effects of phonemic awareness instructions on L2 listening comprehension: A meta-analysis

This study examines the effect of phonemic awareness instruction on listening comprehension ability in learning English as a second language or foreign language (L2). The searching procedures were carried out using Publish or Perish with discreetly selected keywords. Eight studies with 13 samples published between 2000 and 2020 were obtained. Hedges' g was calculated, leveraging Comprehensive Meta-Analysis (CMA) software. The overall effect size of phonemic awareness instruction on listening skills was found to be large (Hedges' g = 0.99, lower bound = 0.82, upper bound = 1.61). The result demonstrated that phonemic awareness instructions to beginners (Hedges' g = 0.86) or primary school students (Hedges' g = 3.67) might have a large effect on enhancing their listening skills. Furthermore, research that conducted phonemic awareness instructions (Hedges' g = 1.43) showed a much larger effect size than phonics instructions (Hedges' g = 0.45), suggesting the need for a more focused phonemic instruction for L2 learners. Lastly, the effectiveness of phonemic awareness instruction was better assessed when using intensive listening measurements (Hedges' g = 1.43) rather than selective ones (Hedges' g = 0.80). These results collectively indicate that phonemic awareness instructions could be both effective and practical for helping L2 learners improve listening skills.N

DiffMatch: Diffusion Model for Dense Matching

The objective for establishing dense correspondence between paired images consists of two terms: a data term and a prior term. While conventional techniques focused on defining hand-designed prior terms, which are difficult to formulate, recent approaches have focused on learning the data term with deep neural networks without explicitly modeling the prior, assuming that the model itself has the capacity to learn an optimal prior from a large-scale dataset. The performance improvement was obvious, however, they often fail to address inherent ambiguities of matching, such as textureless regions, repetitive patterns, and large displacements. To address this, we propose DiffMatch, a novel conditional diffusion-based framework designed to explicitly model both the data and prior terms. Unlike previous approaches, this is accomplished by leveraging a conditional denoising diffusion model. DiffMatch consists of two main components: conditional denoising diffusion module and cost injection module. We stabilize the training process and reduce memory usage with a stage-wise training strategy. Furthermore, to boost performance, we introduce an inference technique that finds a better path to the accurate matching field. Our experimental results demonstrate significant performance improvements of our method over existing approaches, and the ablation studies validate our design choices along with the effectiveness of each component. Project page is available at https://ku-cvlab.github.io/DiffMatch/.Comment: Project page is available at https://ku-cvlab.github.io/DiffMatch

LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation

Successfully training a deep neural network demands a huge corpus of labeled data. However, each label only provides limited information to learn from and collecting the requisite number of labels involves massive human effort. In this work, we introduce LEAN-LIFE, a web-based, Label-Efficient AnnotatioN framework for sequence labeling and classification tasks, with an easy-to-use UI that not only allows an annotator to provide the needed labels for a task, but also enables LearnIng From Explanations for each labeling decision. Such explanations enable us to generate useful additional labeled data from unlabeled instances, bolstering the pool of available training data. On three popular NLP tasks (named entity recognition, relation extraction, sentiment analysis), we find that using this enhanced supervision allows our models to surpass competitive baseline F1 scores by more than 5-10 percentage points, while using 2X times fewer labeled instances. Our framework is the first to utilize this enhanced supervision technique and does so for three important tasks -- thus providing improved annotation recommendations to users and an ability to build datasets of (data, label, explanation) triples instead of the regular (data, label) pair.Comment: Accepted to the ACL 2020 (demo). The first two authors contributed equally. Project page: http://inklab.usc.edu/leanlife

High dose concentration administration of ascorbic acid inhibits tumor growth in BALB/C mice implanted with sarcoma 180 cancer cells via the restriction of angiogenesis

To test the carcinostatic effects of ascorbic acid, we challenged the mice of seven experimental groups with 1.7 × 10-4 mol high dose concentration ascorbic acid after intraperitoneal administrating them with sarcoma S-180 cells. The survival rate was increased by 20% in the group that received high dose concentration ascorbic acid, compared to the control. The highest survival rate was observed in the group in which 1.7 × 10-4 mol ascorbic acid had been continuously injected before and after the induction of cancer cells, rather than just after the induction of cancer cells. The expression of three angiogenesis-related genes was inhibited by 0.3 times in bFGF, 7 times in VEGF and 4 times in MMP2 of the groups with higher survival rates. Biopsy Results, gene expression studies, and wound healing analysis in vivo and in vitro suggested that the carcinostatic effect induced by high dose concentration ascorbic acid occurred through inhibition of angiogenesis

The Role of Glial Mitochondria in α-Synuclein Toxicity

The abnormal accumulation of alpha-synuclein (α-syn) aggregates in neurons and glial cells is widely known to be associated with many neurodegenerative diseases, including Parkinson’s disease (PD), Dementia with Lewy bodies (DLB), and Multiple system atrophy (MSA). Mitochondrial dysfunction in neurons and glia is known as a key feature of α-syn toxicity. Studies aimed at understanding α-syn-induced toxicity and its role in neurodegenerative diseases have primarily focused on neurons. However, a growing body of evidence demonstrates that glial cells such as microglia and astrocytes have been implicated in the initial pathogenesis and the progression of α-Synucleinopathy. Glial cells are important for supporting neuronal survival, synaptic functions, and local immunity. Furthermore, recent studies highlight the role of mitochondrial metabolism in the normal function of glial cells. In this work, we review the complex relationship between glial mitochondria and α-syn-mediated neurodegeneration, which may provide novel insights into the roles of glial cells in α-syn-associated neurodegenerative diseases. © Copyright © 2020 Jeon, Kwon, Jo, Lee, Kim and Kim.1