Online Class-Incremental Continual Learning with Adversarial Shapley Value

As image-based deep learning becomes pervasive on every device, from cell phones to smart watches, there is a growing need to develop methods that continually learn from data while minimizing memory footprint and power consumption. While memory replay techniques have shown exceptional promise for this task of continual learning, the best method for selecting which buffered images to replay is still an open question. In this paper, we specifically focus on the online class-incremental setting where a model needs to learn new classes continually from an online data stream. To this end, we contribute a novel Adversarial Shapley value scoring method that scores memory data samples according to their ability to preserve latent decision boundaries for previously observed classes (to maintain learning stability and avoid forgetting) while interfering with latent decision boundaries of current classes being learned (to encourage plasticity and optimal learning of new class boundaries). Overall, we observe that our proposed ASER method provides competitive or improved performance compared to state-of-the-art replay-based continual learning methods on a variety of datasets.Comment: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21

Dietary Intake of Polyunsaturated Fatty Acids Lowers the Risk of Osteoporosis in Older Korean Females

Objectives: Osteoporosis is an important issue because it is associated with the risk of fractures. However, there is no study in the Republic of Korea on the relationship between PUFAs and the osteoporosis prevalence in middle-aged Korean women using public data. Methods: We intend to prepare basic data for osteoporosis by analyzing the prevalence of osteoarthritis in 3,294 women aged over 50 based on demographic and nutritional characteristics using the 7th KNHANES data using multiple logistic regression. Results: In unadjusted logistic regression analysis, the group with higher than average daily intake of PUFA had a lower prevalence of osteoporosis than the group with lower PUFA intake. In the final model, after adjustment for demographic variables such as sex and age, the group with high PUFA intake had up to 20% lower prevalence of osteoporosis than the other group. However, the effect size in the adjusted and unadjusted models was increased. Conclusions: A higher consumption of omega-3 and omega-6 fatty acids than the average daily intake in Korean women ages 50 years and older was negatively correlated with osteoporosis prevalence. We infer that adequate intake of PUFAs such as omega-3 and omega-6 fatty acids may help prevent osteoporosis

SlAction: Non-intrusive, Lightweight Obstructive Sleep Apnea Detection using Infrared Video

Obstructive sleep apnea (OSA) is a prevalent sleep disorder affecting approximately one billion people world-wide. The current gold standard for diagnosing OSA, Polysomnography (PSG), involves an overnight hospital stay with multiple attached sensors, leading to potential inaccuracies due to the first-night effect. To address this, we present SlAction, a non-intrusive OSA detection system for daily sleep environments using infrared videos. Recognizing that sleep videos exhibit minimal motion, this work investigates the fundamental question: "Are respiratory events adequately reflected in human motions during sleep?" Analyzing the largest sleep video dataset of 5,098 hours, we establish correlations between OSA events and human motions during sleep. Our approach uses a low frame rate (2.5 FPS), a large size (60 seconds) and step (30 seconds) for sliding window analysis to capture slow and long-term motions related to OSA. Furthermore, we utilize a lightweight deep neural network for resource-constrained devices, ensuring all video streams are processed locally without compromising privacy. Evaluations show that SlAction achieves an average F1 score of 87.6% in detecting OSA across various environments. Implementing SlAction on NVIDIA Jetson Nano enables real-time inference (~3 seconds for a 60-second video clip), highlighting its potential for early detection and personalized treatment of OSA.Comment: Accepted to ICCV CVAMD 2023, poste

Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation

As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs). To achieve visual explanations for CNNs, methods based on class activation mapping and randomized input sampling have gained great popularity. However, the attribution methods based on these techniques provide lower resolution and blurry explanation maps that limit their explanation power. To circumvent this issue, visualization based on various layers is sought. In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation. We also propose a layer selection strategy that applies to the whole family of CNN-based models, based on which our extraction framework is applied to visualize the last layers of each convolutional block of the model. Moreover, we perform an empirical analysis of the efficacy of derived lower-level information to enhance the represented attributions. Comprehensive experiments conducted on shallow and deep models trained on natural and industrial datasets, using both ground-truth and model-truth based evaluation metrics validate our proposed algorithm by meeting or outperforming the state-of-the-art methods in terms of explanation ability and visual quality, demonstrating that our method shows stability regardless of the size of objects or instances to be explained.Comment: 9 pages, 9 figures, Accepted at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21

Superaerophobic hydrogels for enhanced electrochemical and photoelectrochemical hydrogen production

The efficient removal of gas bubbles in (photo)electrochemical gas evolution reactions is an important but underexplored issue. Conventionally, researchers have attempted to impart bubble-repellent properties (so-called superaerophobicity) to electrodes by controlling their microstructures. However, conventional approaches have limitations, as they are material specific, difficult to scale up, possibly detrimental to the electrodes' catalytic activity and stability, and incompatible with photoelectrochemical applications. To address these issues, we report a simple strategy for the realization of superaerophobic (photo)electrodes via the deposition of hydrogels on a desired electrode surface. For a proof-of-concept demonstration, we deposited a transparent hydrogel assembled from M13 virus onto (photo)electrodes for a hydrogen evolution reaction. The hydrogel overlayer facilitated the elimination of hydrogen bubbles and substantially improved the (photo)electrodes' performances by maintaining high catalytic activity and minimizing the concentration overpotential. This study can contribute to the practical application of various types of (photo)electrochemical gas evolution reactions

New Finger Biometric Method Using Near Infrared Imaging

In this paper, we propose a new finger biometric method. Infrared finger images are first captured, and then feature extraction is performed using a modified Gaussian high-pass filter through binarization, local binary pattern (LBP), and local derivative pattern (LDP) methods. Infrared finger images include the multimodal features of finger veins and finger geometries. Instead of extracting each feature using different methods, the modified Gaussian high-pass filter is fully convolved. Therefore, the extracted binary patterns of finger images include the multimodal features of veins and finger geometries. Experimental results show that the proposed method has an error rate of 0.13%

Bias-free solar hydrogen production at 19.8???mA???cm???2 using perovskite photocathode and lignocellulosic biomass

Solar hydrogen production is one of the ultimate technologies needed to realize a carbon-neutral, sustainable society. However, an energy-intensive water oxidation half-reaction together with the poor performance of conventional inorganic photocatalysts have been big hurdles for practical solar hydrogen production. Here we present a photoelectrochemical cell with a record high photocurrent density of 19.8???mA???cm???2 for hydrogen production by utilizing a high-performance organic???inorganic halide perovskite as a panchromatic absorber and lignocellulosic biomass as an alternative source of electrons working at lower potentials. In addition, value-added chemicals such as vanillin and acetovanillone are produced via the selective depolymerization of lignin in lignocellulosic biomass while cellulose remains close to intact for further utilization. This study paves the way to improve solar hydrogen productivity and simultaneously realize the effective use of lignocellulosic biomass