Mediation through Computerized Dynamic Assessment (C-DA) for Second and Foreign Language Learning: A Literature Review

This paper examines research on the effects and roles of computerized dynamic assessment (C-DA) in mediating second language (L2) learning and development. The review synthesizes studies on C-DA interventions for developing language skills including reading comprehension, vocabulary and grammar skills. C-DA studies indicate the promotion of language development, but the effectiveness is not all attributed to C-DA mediations. Effectiveness seems dependent on both C-DA and additional factors like noticing skills or teacher mediation. In these studies, most C-DA prompts are pre-constructed and not fully attuned to learners’ zone of proximal development, and some C-DA studies address this by complementing C-DA with human mediator interaction. Given the limited number of C-DA studies, the findings of this review are limited. More research is needed, especially on integrating human and computer mediation. However, this review offers implications for designing responsive C-DA platforms to better attune to learners’ needs

Does Synthetic Data Generation of LLMs Help Clinical Text Mining?

Recent advancements in large language models (LLMs) have led to the development of highly potent models like OpenAI's ChatGPT. These models have exhibited exceptional performance in a variety of tasks, such as question answering, essay composition, and code generation. However, their effectiveness in the healthcare sector remains uncertain. In this study, we seek to investigate the potential of ChatGPT to aid in clinical text mining by examining its ability to extract structured information from unstructured healthcare texts, with a focus on biological named entity recognition and relation extraction. However, our preliminary results indicate that employing ChatGPT directly for these tasks resulted in poor performance and raised privacy concerns associated with uploading patients' information to the ChatGPT API. To overcome these limitations, we propose a new training paradigm that involves generating a vast quantity of high-quality synthetic data with labels utilizing ChatGPT and fine-tuning a local model for the downstream task. Our method has resulted in significant improvements in the performance of downstream tasks, improving the F1-score from 23.37% to 63.99% for the named entity recognition task and from 75.86% to 83.59% for the relation extraction task. Furthermore, generating data using ChatGPT can significantly reduce the time and effort required for data collection and labeling, as well as mitigate data privacy concerns. In summary, the proposed framework presents a promising solution to enhance the applicability of LLM models to clinical text mining.Comment: 10 pages, 8 tables, 4 figure

MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization

Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming. This is attributed to overheads caused by sparse matrix multiplication, which are sidestepped when training multi-layer perceptrons (MLPs) with only node features. MLPs, by ignoring graph context, are simple and faster for graph data, however they usually sacrifice prediction accuracy, limiting their applications for graph data. We observe that for most message passing-based GNNs, we can trivially derive an analog MLP (we call this a PeerMLP) with an equivalent weight space, by setting the trainable parameters with the same shapes, making us curious about \textbf{\emph{how do GNNs using weights from a fully trained PeerMLP perform?}} Surprisingly, we find that GNNs initialized with such weights significantly outperform their PeerMLPs, motivating us to use PeerMLP training as a precursor, initialization step to GNN training. To this end, we propose an embarrassingly simple, yet hugely effective initialization method for GNN training acceleration, called MLPInit. Our extensive experiments on multiple large-scale graph datasets with diverse GNN architectures validate that MLPInit can accelerate the training of GNNs (up to 33X speedup on OGB-Products) and often improve prediction performance (e.g., up to $7.97\%$ improvement for GraphSAGE across $7$ datasets for node classification, and up to $17.81\%$ improvement across $4$ datasets for link prediction on metric Hits@10). The code is available at \href{https://github.com/snap-research/MLPInit-for-GNNs}.Comment: Accepted by ICLR202

18F-FDG PET/CT revealed sporadic schwannomatosis involving the lumbar spinal canal and both lower limbs: a case report

Schwannomatosis is a rare autosomal dominant hereditary syndrome disease characterized by multiple schwannomas throughout the body, without bilateral vestibular schwannoma or dermal schwannoma. The most common location of schwannomatosis is the head and neck, as well as the limbs, while multiple schwannomas in the lumbosacral canal and lower extremities are relatively rare. In this study, we report a 79-year-old woman diagnosed with schwannomatosis. MRI and contrast-enhanced imaging revealed multiple schwannomas in both lower extremities. An 18F-FDG PET/CT examination revealed that in addition to multiple tumors with increased 18F-FDG uptake in both lower extremities, there was also an increased 18F-FDG uptake in a mass in the lumbosacral canal. These masses were confirmed to be schwannomas by pathology after surgery or biopsy. 18F-FDG PET/CT findings of schwannomas were correlated with MRI and pathological components. Antoni A area rich in tumor cells showed significant enhancement on contrast-enhanced T1WI, and PET/CT showed increased uptake of 18F-FDG in the corresponding area, while Antoni B region rich in mucus showed low enhancement on contrast-enhanced T1WI, accompanied by a mildly increased 18F-FDG uptake

Effect of Li-deficiency impurities on the electron-overdoped LiFeAs superconductor

We use transport, inelastic neutron scattering, and angle resolved photoemission experiments to demonstrate that the stoichiometric LiFeAs is an intrinsically electron-overdoped superconductor similar to those of the electron-overdoped NaFe1-xTxAs and BaFe2-xTxAs2 (T = Co,Ni). Furthermore, we show that although transport properties of the stoichiometric superconducting LiFeAs and Li-deficient nonsuperconducting Li1-xFeAs are different, their electronic and magnetic properties are rather similar. Therefore, the nonsuperconducting Li1-xFeAs is also in the electron overdoped regime, where small Li deficiencies near the FeAs octahedra can dramatically suppress superconductivity through the impurity scattering effect.Comment: 5 figures,5 page

Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach

Fairness in machine learning has attracted increasing attention in recent years. The fairness methods improving algorithmic fairness for in-distribution data may not perform well under distribution shifts. In this paper, we first theoretically demonstrate the inherent connection between distribution shift, data perturbation, and model weight perturbation. Subsequently, we analyze the sufficient conditions to guarantee fairness (i.e., low demographic parity) for the target dataset, including fairness for the source dataset, and low prediction difference between the source and target datasets for each sensitive attribute group. Motivated by these sufficient conditions, we propose robust fairness regularization (RFR) by considering the worst case within the model weight perturbation ball for each sensitive attribute group. We evaluate the effectiveness of our proposed RFR algorithm on synthetic and real distribution shifts across various datasets. Experimental results demonstrate that RFR achieves better fairness-accuracy trade-off performance compared with several baselines. The source code is available at \url{https://github.com/zhimengj0326/RFR_NeurIPS23}.Comment: NeurIPS 202

Chasing Fairness in Graphs: A GNN Architecture Perspective

There has been significant progress in improving the performance of graph neural networks (GNNs) through enhancements in graph data, model architecture design, and training strategies. For fairness in graphs, recent studies achieve fair representations and predictions through either graph data pre-processing (e.g., node feature masking, and topology rewiring) or fair training strategies (e.g., regularization, adversarial debiasing, and fair contrastive learning). How to achieve fairness in graphs from the model architecture perspective is less explored. More importantly, GNNs exhibit worse fairness performance compared to multilayer perception since their model architecture (i.e., neighbor aggregation) amplifies biases. To this end, we aim to achieve fairness via a new GNN architecture. We propose \textsf{F}air \textsf{M}essage \textsf{P}assing (FMP) designed within a unified optimization framework for GNNs. Notably, FMP \textit{explicitly} renders sensitive attribute usage in \textit{forward propagation} for node classification task using cross-entropy loss without data pre-processing. In FMP, the aggregation is first adopted to utilize neighbors' information and then the bias mitigation step explicitly pushes demographic group node presentation centers together. In this way, FMP scheme can aggregate useful information from neighbors and mitigate bias to achieve better fairness and prediction tradeoff performance. Experiments on node classification tasks demonstrate that the proposed FMP outperforms several baselines in terms of fairness and accuracy on three real-world datasets. The code is available in {\url{https://github.com/zhimengj0326/FMP}}.Comment: Accepted by AAAI Conference on Artificial Intelligence (AAAI) 2024. arXiv admin note: substantial text overlap with arXiv:2202.0418

Retiring Δ\DeltaDP: New Distribution-Level Metrics for Demographic Parity

Demographic parity is the most widely recognized measure of group fairness in machine learning, which ensures equal treatment of different demographic groups. Numerous works aim to achieve demographic parity by pursuing the commonly used metric $\Delta DP$. Unfortunately, in this paper, we reveal that the fairness metric $\Delta DP$ can not precisely measure the violation of demographic parity, because it inherently has the following drawbacks: i) zero-value $\Delta DP$ does not guarantee zero violation of demographic parity, ii) $\Delta DP$ values can vary with different classification thresholds. To this end, we propose two new fairness metrics, Area Between Probability density function Curves (ABPC) and Area Between Cumulative density function Curves (ABCC), to precisely measure the violation of demographic parity at the distribution level. The new fairness metrics directly measure the difference between the distributions of the prediction probability for different demographic groups. Thus our proposed new metrics enjoy: i) zero-value ABCC/ABPC guarantees zero violation of demographic parity; ii) ABCC/ABPC guarantees demographic parity while the classification thresholds are adjusted. We further re-evaluate the existing fair models with our proposed fairness metrics and observe different fairness behaviors of those models under the new metrics. The code is available at https://github.com/ahxt/new_metric_for_demographic_parityComment: Accepted by TMLR. Code available at https://github.com/ahxt/new_metric_for_demographic_parit