3D super-resolution aims to reconstruct high-fidelity 3D models from low-resolution (LR) multi-view images. Early studies primarily focused on single-image super-resolution (SISR) models to upsample LR images into high-resolution images. However, these methods often lack view consistency because they operate independently on each image. Although various post-processing techniques have been extensively explored to mitigate these inconsistencies, they have yet to fully resolve the issues. In this paper, we perform a comprehensive study of 3D super-resolution by leveraging video super-resolution (VSR) models. By utilizing VSR models, we ensure a higher degree of spatial consistency and can reference surrounding spatial information, leading to more accurate and detailed reconstructions. Our findings reveal that VSR models can perform remarkably well even on sequences that lack precise spatial alignment. Given this observation, we propose a simple yet practical approach to align LR images without involving fine-tuning or generating `smooth' trajectory from the trained 3D models over LR images. The experimental results show that the surprisingly simple algorithms can achieve the state-of-the-art results of 3D super-resolution tasks on standard benchmark datasets, such as the NeRF-synthetic and Mip-NeRF 360 datasets.

Glaucoma is one of the leading causes of irreversible blindness worldwide, underscoring the need for accurate and accessible diagnostic solutions. In this study, we propose a scalable glaucoma classification pipeline that leverages deep features extracted from sensor-acquired retinal fundus images using pretrained architectures-Swin Transformer V2, ConvNeXt V2, and EfficientNet V2. These models are employed as fixed feature extractors, and the concatenated representations are subsequently used to train and evaluate classical classifiers, including Random Forest, Multilayer Perceptron, and Support Vector Machine (SVM). Among these approaches, the SVM achieved the most balanced performance, which was further improved through hyperparameter optimization. Experiments conducted on the public RIM-ONE DL dataset demonstrated that the optimized SVM attained an accuracy of 0.957, an F1-score of 0.938, and an AUC-ROC of 0.978. These results highlight the effectiveness of integrating transformer-based and CNN-based deep representations with conventional machine learning models, offering a practical and resource-efficient framework for automated glaucoma classification.

Large-scale matrix computations have become indispensable in artificial intelligence and scientific applications. It is of paramount importance to efficiently perform out-of-core computations that often entail an excessive amount of disk I/O. Unfortunately, however, most existing systems do not focus on disk I/O aspects and are vulnerable to performance degradation when the scale of input matrices and intermediate data grows large. To address this problem, we present a new out-of-core matrix computation system called PreVision. The PreVision system can achieve optimal buffer replacement by leveraging the deterministic characteristics of data access patterns, and it can also avoid redundant I/O operations by proactively evicting the pages that are no longer referenced. Through extensive evaluations, we demonstrate that PreVision outperforms the existing out-of-core matrix computation systems and significantly reduces disk I/O operations.

Competitor analysis is an essential component of corporate strategy, providing both offensive and defensive strategic contexts to identify opportunities and threats. The rapid development of social media has recently led to several methodologies and frameworks facilitating competitor analysis through online reviews. Existing studies only focused on detecting comparative sentences in review comments or utilized low-performance models. However, this study proposes a novel approach to identifying the competitive factors using a recent explainable artificial intelligence approach at the comprehensive product feature level. We establish a model to classify the review comments for each corresponding product and evaluate the relevance of each keyword in such comments during the classification process. We then extract and prioritize the keywords and determine their competitiveness based on relevance. Our experiment results show that the proposed method can effectively extract the competitive factors both qualitatively and quantitatively.

This paper introduces an efficient test access mechanism for advanced microcontroller bus architecture (AMBA) based SoC to reduce the test application time while minimally adding a new test interface logic. Testable design technique is applied to an SoC with the advanced high-performance bus (AHB) and PCI bus bridge by maximally reusing the bridge functions. Testing time can be significantly reduced by increasing the test channels and by shortening the test control protocols. Experimental results show that area overhead and testing times in both functional and structural test modes are considerably reduced.