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Wafer map pattern recognition is instrumental for detecting systemic manufacturing process issues. However, high cost in labeling wafer patterns renders it impossible to leverage large amounts of valuable unlabeled data in conventional machine learning based wafer map pattern prediction. We proposed a contrastive learning framework for semi-supervised learning and prediction of wafer map patterns. Our framework incorporates an encoder to learn good representation for wafer maps in an unsupervised manner, and a supervised head to recognize wafer map patterns. In particular, contrastive learning is applied for the unsupervised encoder representation learning supported by augmented data generated by different transformations (views) of wafer maps. We identified a set of transformations to effectively generate similar variants of each original pattern. We further proposed a novel rotation-twist transformation to augment wafer map data by rotating each given wafer map for which the angle of rotation is a smooth function of the radius. Experimental results demonstrate that the proposed semi-supervised learning framework greatly improves recognition accuracy compared to traditional supervised methods, and the rotation-twist transformation further enhances the recognition accuracy in both semi-supervised and supervised tasks. 

DNA strands are polymeric ligands that both protect and tune molecular-sized silver cluster chromophores. We studied single-stranded DNA C4AC4TC3XT4 with X = guanosine and inosine that form a green fluorescent Ag106+ cluster, but these two hosts are distinguished by their binding sites and the brightness of their Ag106+ adducts. The nucleobase subunits in these oligomers collectively coordinate this cluster, and fs time-resolved infrared spectra previously identified one point of contact between the C2–NH2 of the X = guanosine, an interaction that is precluded for inosine. Furthermore, this single nucleobase controls the cluster fluorescence as the X = guanosine complex is ∼2.5× dimmer. We discuss the electronic relaxation in these two complexes using transient absorption spectroscopy in the time window 200 fs–400 µs. Three prominent features emerged: a ground state bleach, an excited state absorption, and a stimulated emission. Stimulated emission at the earliest delay time (200 fs) suggests that the emissive state is populated promptly following photoexcitation. Concurrently, the excited state decays and the ground state recovers, and these changes are ∼2× faster for the X = guanosine compared to the X = inosine cluster, paralleling their brightness difference. In contrast to similar radiative decay rates, the nonradiative decay rate is 7× higher with the X = guanosine vs inosine strand. A minor decay channel via a dark state is discussed. The possible correlation between the nonradiative decay and selective coordination with the X = guanosine/inosine suggests that specific nucleobase subunits within a DNA strand can modulate cluster–ligand interactions and, in turn, cluster brightness. 

Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, i.e., comments. An article may attract an apathetic user engagement (several tens of comments) or a spontaneous fervent user engagement (thousands of comments). In this paper, we study the problem of predicting the total number of user comments a news article will receive. Our main insight is that the early dynamics of user comments contribute the most to an accurate prediction, while news article specific factors have surprisingly little influence. This appears to be an interesting and understudied phenomenon: collective social behavior at a news outlet shapes user response and may even downplay the content of an article. We compile and analyze a large number of features, both old and novel from literature. The features span a broad spectrum of facets including news article and comment contents, temporal dynamics, sentiment/linguistic features, and user behaviors. We show that the early arrival rate of comments is the best indicator of the eventual number of comments. We conduct an in-depth analysis of this feature across several dimensions, such as news outlets and news article categories. We show that the relationship between the early rate and the final number of comments as well as the prediction accuracy vary considerably across news outlets and news article categories (e.g., politics, sports, or health). 

Due to the extreme scarcity of customer failure data, it is challenging to reliably screen out those rare defects within a high-dimensional input feature space formed by the relevant parametric test measurements. In this paper, we study several unsupervised learning techniques based on six industrial test datasets, and propose to train a more robust unsupervised learning model by self-labeling the training data via a set of transformations. Using the labeled data we train a multi-class classifier through supervised training. The goodness of the multi-class classification decisions with respect to an unseen input data is used as a normality score to defect anomalies. Furthermore, we propose to use reversible information lossless transformations to retain the data information and boost the performance and robustness of the proposed self-labeling approach.