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Ask HN: Who wants to be hired? (February 2024)

Ask HN: Who wants to be hired? (February 2024)
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Mount Sinai develops AI that can predict which drugs cause birth defects

Researchers from Mount Sinai Health System have developed an AI model to predict which drugs may cause birth defects. ....

Icahn School Of Medicine At Mount Sinai , York Mount Sinai Health System , Common Fund , Communications Medicine , Icahn School , Mount Sinai , Tai Model , Birth Defects , Mount Sinai , New York City , Data Scientists , Ih Common Fund , Eprotox Kg , Semi Supervised Learning ,

"LaSSL: Label-Guided Self-Training for Semi-supervised Learning" by Zhen Zhao, Luping Zhou et al.

The key to semi-supervised learning (SSL) is to explore adequate information to leverage the unlabeled data. Current dominant approaches aim to generate pseudo-labels on weakly augmented instances and train models on their corresponding strongly augmented variants with high-confidence results. However, such methods are limited in excluding samples with low-confidence pseudo-labels and under-utilization of the label information. In this paper, we emphasize the cruciality of the label information and propose a Label-guided Self-training approach to Semi-supervised Learning (LaSSL), which improves pseudo-label generations from two mutually boosted strategies. First, with the ground-truth labels and iteratively-polished pseudo-labels, we explore instance relations among all samples and then minimize a class-aware contrastive loss to learn discriminative feature representations that make same-class samples gathered and different-class samples scattered. Second, on top of improved feature re ....

Label Guided Self Training , Semi Supervised Learning ,

"DAGAD: Data Augmentation for Graph Anomaly Detection" by Fanzhen Liu, Xiaoxiao Ma et al.

Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of background knowledge about them, which causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority of objects in real-world graphs are normal, bringing the class imbalance problem as well. To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data aug ....

Data Augmentation Based Graph Anomaly Detection , Anomalous Sample Scarcity , Anomaly Detection , Class Imbalance , Data Augmentation , Graph Mining , Graph Neural Networks , Semi Supervised Learning ,

"Automatic driver cognitive fatigue detection based on upper body postu" by Shahzeb Ansari, Haiping Du et al.

Driver cognitive fatigue can significantly affect driving and may lead to fatal accidents. In this regard, automatic detection of underload driver cognitive fatigue based on upper body posture dynamics is studied in this paper, where a semi-supervised approach is developed to identify the cognitive fatigue patterns of driver posture. Initially, an unsupervised Gaussian Mixture Model (GMM) clustering is applied to the acceleration data representing the driver's head, neck, and sternum obtained in a simulated driving through a motion capture suit. This provides the optimum clusters of the most-similar and correlated time-series data of driver upper posture. Then, an automatic labelling algorithm is developed that mines the maximal value and the standard deviation of each GMM cluster and assigns a symbol according to the discrepancy in postural behaviour. Finally, novel machine learning supervised classifiers, including Gaussian Support Vector Machines, and Bootstrap-Aggregating base ....

Gaussian Mixture Model , Gaussian Support Vector Machines , Ensemble Classifiers , Ognitive Driver Fatigue , River Posture , Atigue Posture Patterns , Semi Supervised Learning ,