Siwon Kim

Deep learning (DL) has been extensively adopted in many applications, including disease prediction. Most DL-based applications are executed on a cloud server because the DL models are too large and complicated to be executed on the client-side. De facto cloud-hosted inferences lead to privacy concerns regarding services that operate on personal medical data. Nevertheless, given the recent development of DL-based applications for health-diagnosis services, these applications have become a dominant means of healthcare support in our daily lives. To prevent the misuse of personal medical data, several techniques have been developed to preserve sensitive information, with a trade-off between privacy and utility. A simple method that offers privacy preservation and good prediction performance involves the deployment of a diagnostic method to the client side. However, doing so makes DL models more vulnerable to adversaries. To this end, we propose a deep private generative framework that guarantees user-data privacy while maintaining the original class information and protecting the models from reverse engineering. Experimentation with practical deep neural networks on benchmark disease datasets demonstrates that the proposed method decreases the mutual information between the original data and synthetic data by nearly 80% while preserving a prediction accuracy of nearly 95% of the original prediction accuracy.

In addition to its success in representation learning, contrastive learning is effective in image anomaly detection. Although contrastive learning depends significantly on data augmentation methods, time-series data augmentation for time-series anomaly detection is not investigated sufficiently. Additionally, although time-series data share a temporal context, the existing contrastive loss contrasts temporally related samples, in which deteriorated anomaly detection performance is observed on time-series data. Herein, we propose contrastive multivariate time-series anomaly detection (CTAD), a multivariate time-series anomaly detection framework that addresses these challenges by incorporating a one-class learning scheme into the contrastive loss based on meticulously designed time-series data augmentations. Specifically, we propose seven types of general time-series data augmentations to be applied variable- and point- wise, and provide guidance on data augmentation methods for contrastive time-series anomaly detection. The superiority of the one-class contrastive loss and the appropriate selection of time-series data augmentation allow CTAD to achieve outstanding performance in multiple datasets, even using a simple long short-term memory network. Furthermore, CTAD is robust to noise as it trains a noise-invariant network. This enables up to 47× faster and 20× more memory-efficient anomaly detection performance compared with existing methods while affording robustness, which are essential considerations in real-world applications.

The uncontrollable dendritic growth of metallic lithium during repeated cycling in carbonate electrolytes is a crucial obstacle hindering the practical use of Li‐metal batteries (LMBs). Among numerous approaches proposed to mitigate the intrinsic constraints of Li metal, the design of a functional separator is an attractive approach to effectively suppress the growth of Li dendrites because direct contact with both the Li metal surface and the electrolyte is maintained. Here, a newly designed all‐in‐one separator containing bifunctional CaCO₃ nanoparticles (CPP separator) is proposed to achieve the flattening of Li deposits on the Li electrode. Strong interactions between the highly polar CaCO₃ nanoparticles and the polar solvent reduces the ionic radius of the Li⁺‐solvent complex, thus increasing the Li⁺ transference number and leading to a reduced concentration overpotential in the electrolyte‐filled separator. Furthermore, the integration of CaCO₃ nanoparticles into the separator induces the spontaneous formation of mechanically‐strong and lithiophilic CaLi₂ at the Li/separator interface, which effectively decreases the nucleation overpotential toward Li plating. As a result, the Li deposits exhibit dendrite‐free planar morphologies, thus enabling excellent cycling performance in LMBs configured with a high‐Ni cathode in a carbonate electrolyte under practical operating conditions.

Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling. In these studies, models were pre-trained with a substantial amount of unlabeled data, and the representations were transferred to various downstream tasks. Most pre-training methods solely rely on language modeling and often exhibit limited performance. In this paper, we introduce a novel pre-training scheme called PLUS , which stands for P rotein sequence representations L earned U sing S tructural information. PLUS consists of masked language modeling and a complementary protein-specific pre-training task, namely same-family prediction. PLUS can be used to pre-train various model architectures. In this work, we use PLUS to pre-train a bidirectional recurrent neural network and refer to the resulting model as PLUS-RNN. Our experiment results demonstrate that PLUS-RNN outperforms other models of similar size solely pre-trained with the language modeling in six out of seven widely used protein biology tasks. Furthermore, we present the results from our qualitative interpretation analyses to illustrate the strengths of PLUS-RNN. PLUS provides a novel way to exploit evolutionary relationships among unlabeled proteins and is broadly applicable across a variety of protein biology tasks. We expect that the gap between the numbers of unlabeled and labeled proteins will continue to grow exponentially, and the proposed pre-training method will play a larger role. All the data and codes used in this study are available at https://github.com/mswzeus/PLUS .

Automated diagnosis using deep neural networks in chest radiography can help radiologists detect life-threatening diseases. However, existing methods only provide predictions without accurate explanations, undermining the trustworthiness of the diagnostic methods. Here, we present XProtoNet, a globally and locally interpretable diagnosis framework for chest radiography. XProtoNet learns representative patterns of each disease from X-ray images, which are prototypes, and makes a diagnosis on a given X-ray image based on the patterns. It predicts the area where a sign of the disease is likely to appear and compares the features in the predicted area with the prototypes. It can provide a global explanation, the prototype, and a local explanation, how the prototype contributes to the prediction of a single image. Despite the constraint for interpretability, XProtoNet achieves state-of-the-art classification performance on the public NIH chest X-ray dataset.

Learning classifiers with imbalanced data can be strongly biased toward the majority class. To address this issue, several methods have been proposed using generative adversarial networks (GANs). Existing GAN-based methods, however, do not effectively utilize the relationship between a classifier and a generator. This article proposes a novel three-player structure consisting of a discriminator, a generator, and a classifier, along with decision boundary regularization. Our method is distinctive in which the generator is trained in cooperation with the classifier to provide minority samples that gradually expand the minority decision region, improving performance for imbalanced data classification. The proposed method outperforms the existing methods on real data sets as well as synthetic imbalanced data sets.

Instantaneous drowsiness (i.e., lapse or micro-sleep) during various activities such as driving or construction causes enormous socioeconomic losses. Thus, a virtuous cycle system that monitors a subject’s drowsiness can improve work efficiency and safety. We propose a novel framework to detect instantaneous drowsiness with only a two-second length of electroencephalography (EEG). To achieve reliable performance, we use multitaper power spectral density for feature extraction along with extreme gradient boosting as a machine learning classifier. In addition, we introduce a novel phenotype labeling of instantaneous drowsiness by combining both task dependent and independent measures of alertness (psychomotor vigilance task and electrooculography technique, respectively). The results show that our techniques outperform others used in previous studies. We also identified which spectral components (θ, α, and γ) and channels (Fp1, Fp2, T3, T4, O1, O2, and electrocardiogram) play important roles in our drowsiness detection framework. To verify the applicability for a mobile environment, we implemented our framework on a wireless EEG, as well as on a wired EEG. We hereby present our successful results.

Sleep staging is an important part of clinical neurology. However, it is still performed manually by technical experts and is labor-intensive and time-consuming. To overcome these obstacles in the manual sleep staging process, a large number of machine learning-based classifiers with hand-engineered features have been proposed. Additionally, combinations of a deep neural network (DNN) have been recently highlighted as the state-of-the-art classifiers in view of their effectiveness for automatic sleep staging. In spite of the existence of a large number of these types of classifiers, to-this-date, no prior DNN-based approach has attempted sleep-stage classification using pediatric electroencephalographic (EEG) signals. In this paper, we propose a novel end-to-end classifier based on a multi-domain hybrid neural network (HNN-multi) approach consisting of a convolutional neural network and bidirectional long short-term memory for automatic sleep staging with pediatric scalp EEG recordings. To find effective temporal, spatial, and domain-specific conditions, we investigated noticeable changes in the classification performance corresponding to: 1) the length of input signals; 2) the number of channels; and 3) the types of input signals in the time and frequency domains. Our HNN-based classifier yielded the best performance metrics using 30-s time series in combination with an instantaneous frequency using a 19-channel, three-stage classification, with overall accuracy, F1 score, and Cohen's Kappa, equal to 92.21%, 0.90, and 0.88, respectively. We suggest that an effective combination of temporal and spatial time-domain clues with time-varying frequency domain information plays a pivotal role in pediatric, automatic sleep staging. Sufficiently reasonable performance of our HNN-based approach coping with the highly complicated pediatric EEG signatures hopefully sheds light on the clinical feasibility of the DNN-based automatic sleep staging for pediatric neurology.