Meteor radiant mapping with MU radar

The radiant point mapping of meteor showers with the MU radar by using a modified mapping method originally proposed by Morton and Jones (1982) was carried out. The modification is that each meteor echo was weighted by using the beam pattern of the radar system. A preliminary result of the radiant point mapping of the Geminids meteor shower in 1989 is presented

Revisiting a kNN-based Image Classification System with High-capacity Storage

In existing image classification systems that use deep neural networks, the knowledge needed for image classification is implicitly stored in model parameters. If users want to update this knowledge, then they need to fine-tune the model parameters. Moreover, users cannot verify the validity of inference results or evaluate the contribution of knowledge to the results. In this paper, we investigate a system that stores knowledge for image classification, such as image feature maps, labels, and original images, not in model parameters but in external high-capacity storage. Our system refers to the storage like a database when classifying input images. To increase knowledge, our system updates the database instead of fine-tuning model parameters, which avoids catastrophic forgetting in incremental learning scenarios. We revisit a kNN (k-Nearest Neighbor) classifier and employ it in our system. By analyzing the neighborhood samples referred by the kNN algorithm, we can interpret how knowledge learned in the past is used for inference results. Our system achieves 79.8% top-1 accuracy on the ImageNet dataset without fine-tuning model parameters after pretraining, and 90.8% accuracy on the Split CIFAR-100 dataset in the task incremental learning setting.Comment: 16 pages, 7 figures, 6 table

SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI Tool

Large Language Model (LLM) based Generative AI systems have seen significant progress in recent years. Integrating a knowledge retrieval architecture allows for seamless integration of private data into publicly available Generative AI systems using pre-trained LLM without requiring additional model fine-tuning. Moreover, Retrieval-Centric Generation (RCG) approach, a promising future research direction that explicitly separates roles of LLMs and retrievers in context interpretation and knowledge memorization, potentially leads to more efficient implementation. SimplyRetrieve is an open-source tool with the goal of providing a localized, lightweight, and user-friendly interface to these sophisticated advancements to the machine learning community. SimplyRetrieve features a GUI and API based RCG platform, assisted by a Private Knowledge Base Constructor and a Retrieval Tuning Module. By leveraging these capabilities, users can explore the potential of RCG for improving generative AI performance while maintaining privacy standards. The tool is available at https://github.com/RCGAI/SimplyRetrieve with an MIT license.Comment: 12 pages, 6 figure

RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models

Retrieval-augmented large language models (R-LLMs) combine pre-trained large language models (LLMs) with information retrieval systems to improve the accuracy of factual question-answering. However, current libraries for building R-LLMs provide high-level abstractions without sufficient transparency for evaluating and optimizing prompts within specific inference processes such as retrieval and generation. To address this gap, we present RaLLe, an open-source framework designed to facilitate the development, evaluation, and optimization of R-LLMs for knowledge-intensive tasks. With RaLLe, developers can easily develop and evaluate R-LLMs, improving hand-crafted prompts, assessing individual inference processes, and objectively measuring overall system performance quantitatively. By leveraging these features, developers can enhance the performance and accuracy of their R-LLMs in knowledge-intensive generation tasks. We open-source our code at https://github.com/yhoshi3/RaLLe.Comment: 18 pages, 2 figures, see https://youtu.be/JYbm75qnfTg for the demonstration screencas

Initial Experience of Percutaneous Extraction of Infected Cardiac Implantable Electric Devices Using Excimer Laser

Article信州医学雑誌 63(2):103-108 (2015)journal articl

Importance of Fatty Acid Compositions in Patients with Peripheral Arterial Disease

Objective: Importance of fatty acid components and imbalances has emerged in coronary heart disease. In this study, we analyzed fatty acids and ankle-brachial index (ABI) in a Japanese cohort. Methods: Peripheral arterial disease (PAD) was diagnosed in 101 patients by ABI <= 0.90 and/or by angiography. Traditional cardiovascular risk factors and components of serum fatty acids were examined in all patients (mean age 73.2 +/- 0.9 years; 81 males), and compared with those in 373 age- and sex-matched control subjects with no evidence of PAD. Results: The presence of PAD (mean ABI: 0.71 +/- 0.02) was independently associated with low levels of gamma-linolenic acid (GLA) (OR: 0.90; 95% CI: 0.85-0.96; P = 0.002), eicosapentaenoic acid: arachidonic acid (EPA: AA) ratio (OR: 0.38; 95% CI: 0.17-0.86; P = 0.021), and estimated glomerular filtration rate (OR: 0.97; 95% CI: 0.96-0.98; P<0.0001), and with a high hemoglobin A1c level (OR: 1.34; 95% CI: 1.06-1.69; P = 0.013). Individuals with lower levels of GLA (<= 7.95 mu g/mL) and a lower EPA: AA ratio (<= 0.55) had the lowest ABI (0.96 +/- 0.02, N = 90), while the highest ABI (1.12 +/- 0.01, N = 78) was observed in individuals with higher values of both GLA and EPA: AA ratio (P<0.0001). Conclusion: A low level of GLA and a low EPA: AA ratio are independently associated with the presence of PAD. Specific fatty acid abnormalities and imbalances could lead to new strategies for risk stratification and prevention in PAD patients.ArticlePLOS ONE. 9(9):e107003 (2014)journal articl