187037 research outputs found
Sort by
Multi-Camera Pedestrian Full Trajectory Perception Using YOLO, DeepSORT, and Homography Transform
No full textA biomimetic gut-associated lymphoid tissue chip for monitoring mucosal immune activation against bacterial stimuli
No full textVideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding
Get PDFEstimation of Electromagnetic Field Strength: Experiments Using Vision Transformers
No full textThe commercialization of fifth-generation (5G) mobile communication technology has enabled ultrafast data transmission and massive connectivity, while simultaneously introducing new challenges, such as exposure to electromagnetic fields (EMF). In particular, the use of massive multiple-input and multiple-output antennas and dense small cell-based 5G networks has increased the risk of EMF concentration in specific areas, thereby increasing the possibility of exceeding EMF regulations. Traditionally, EMF evaluations have relied on measurement-based approaches. However, the rapid deployment of 5G base stations and complex propagation environments pose significant challenges to these methods. Moreover, calculation-based approaches are limited to specific scenarios. Therefore, more accurate and efficient evaluation methods for EMF measurements are required. This study aims to overcome the limitations of existing evaluation methods by applying various vision transformer-based models, that have demonstrated outstanding performance in the field of image processing. Using physically-informed data representations, this study systematically applied notable vision transformer-based models, including the Vision Transformer, Swin Transformer, SegFormer, HRFormer, and Mask2Former for EMF prediction. The performance of the models was compared using a diverse array of metrics, including regression, classification, and perceptual quality analyses. Among these models, HRFormer achieved the best performance, recording a masked MAE of 5.45% and outperforming a baseline U-Net model by approximately 31%, while yielding the most perceptually robust estimations. To the best of our knowledge, this is the first comprehensive comparative study of ViT architectures applied to physically informed EMF estimation. The outcomes of this study are expected to contribute to a more accurate and efficient EMF management system by addressing the limitations of traditional evaluation methods.
Evaluation of Pedestrian Future Conflict at Non-signalized Intersections based on Probabilistic Trajectory Prediction
No full textEarly Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editing
No full textDespite recent advances in diffusion models, achieving reliable image generation and editing remains challenging due to the inherent diversity induced by stochastic noise in the sampling process. Instruction-guided image editing with diffusion models offers user-friendly capabilities, yet editing failures, such as background distortion, frequently occur. Users often resort to trial and error, adjusting seeds or prompts to achieve satisfactory results, which is inefficient. While seed selection methods exist for Text-to-Image (T2I) generation, they depend on external verifiers, limiting applicability, and evaluating multiple seeds increases computational complexity. To address this, we first establish a multiple-seed-based image editing baseline using background consistency scores, achieving Best-of-N performance without supervision. Building on this, we introduce ELECT (Early-timestep Latent Evaluation for Candidate Selection), a zero-shot framework that selects reliable seeds by estimating background mismatches at early diffusion timesteps, identifying the seed that retains the background while modifying only the foreground. ELECT ranks seed candidates by a background inconsistency score, filtering unsuitable samples early based on background consistency while preserving editability. Beyond standalone seed selection, ELECT integrates into instruction-guided editing pipelines and extends to Multimodal Large-Language Models (MLLMs) for joint seed and prompt selection, further improving results when seed selection alone is insufficient. Experiments show that ELECT reduces computational costs (by 41 percent on average and up to 61 percent) while improving background consistency and instruction adherence, achieving around 40 percent success rates in previously failed cases - without any external supervision or training
From weak to strong signals: Exploring R&D projects with research equipment
No full textWeak signals have received growing attention as early indicators of future change, yet prior research has treated them as static indicators. Given their fragmented and uncertain nature, weak signals do not consistently evolve into strong signals. This study investigates whether weak signals can transition into strong ones, with particular attention to the role of research equipment. Building on TF-DoV-based analytical methods, we develop a longitudinal framework and analyze a decade of nationally funded R&D projects in the chemical sector. The results show that projects involving equipment exhibit a higher transition rate from weak to strong signals. This finding highlights the enabling role of equipment, functioning both as material infrastructure and as a cognitive foundation that facilitates the recognition and maturation of emerging topics. Overall, this study reconceptualizes weak signals as dynamic trajectories and demonstrates the catalytic influence of research equipment in their evolution. Further, it utilizes nationally funded R&D projects as a novel policy-relevant data source that captures emerging technological trajectories. These contributions advance the theoretical understanding of weak signals, extend methods for analyzing signal transitions, and provide practical guidance for R&D policy and strategy.
Ipso facto clauses in cross-border insolvency: From STX Pan Ocean to Hanjin Shipping and beyond
No full textThis article examines the enforceability of ipso facto clauses in cross-border insolvency, with a focus on the landmark STX Pan Ocean case. This case highlighted divergent judicial approaches and the critical role of legal frameworks in conflict-of-laws scenarios, as evidenced by the contrasting outcome in the Hanjin Shipping case. In Fibria Celulose S/A v Pan Ocean Co Ltd, an English court ruled that the Korean administrator had failed to demonstrate that Korean law would render the ipso facto clause invalid. This outcome exposes a fundamental question: should the effect of insolvency on contracts be governed by the contract's governing law (lex contractus) or the law of the insolvency forum (lex fori concursus)? This article addresses the scholarly debate surrounding South Korean law's treatment of ipso facto clauses. It highlights the challenges and potential solutions to the lex contractus versus lex fori concursus dilemma.
