Feasibility of Controlling Gas Concentration and Temperature Distributions in a Semiconductor Chamber with CT-TDLAS

The feasibility to control the gas concentration and temperature distributions in a semiconductor process chamber by measuring them was investigated. Gas concentration and temperature distributions for various flow rates were measured with the computed tomography-tunable diode laser absorption spectroscopy (CT-TDLAS). The infrared absorption spectra of multiple laser paths passing through the measured area were collected and the distributions of methane concentration and temperature in the chamber were reconstructed with the computed tomography (CT) calculations. The measured results indicated that the distributions can be independently controlled by measuring with the CT-TDLAS and adjusting the flow rates and the susceptor temperature

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

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

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

Improvement of Open Bite and Stomatognathic Function in an Axenfeld- Rieger Syndrome Patient by Orthodontic Sectional Arch Mechanics: Clinical Considerations and the Risk of Orthodontic Tooth Movement

Orthodontists need to understand the orthodontic risks associated with systemic disorders. Axenfeld-Rieger syndrome (ARS) is a rare autosomal dominant disorder with genetic and morphological variability. The risks of orthodontic treatment in ARS patients have been unclear. Here we describe the correction of an anterior open bite in a 15-year-old Japanese female ARS patient by molar intrusion using sectional archwires with miniscrew implants. An undesirable development of external apical root resorption (EARR) was observed in all intrusive force-applied posterior teeth during the patient’s orthodontic treatment, suggesting that ARS patients have a higher risk of EARR than the general population

Visibility Estimation of Traffic Signals under Rainy Weather Conditions for Smart Driving Support

Abstract-The aim of this work is to support a driver by notifying the information of traffic signals in accordance with their visibility. To avoid traffic accidents, the driver should detect and recognize surrounding objects, especially traffic signals. However, when driving a vehicle under rainy weather conditions, it is difficult for drivers to detect or to recognize objects existing in the road environment in comparison with fine weather conditions. Therefore, this paper proposes a method for estimating the visibility of traffic signals for drivers under rainy weather conditions by image processing. The proposed method is based on the concept of visual noise known in the field of cognitive science, and extracts two types of visual noise features which ware considered that they affect the visibility of traffic signals. We expect to improve the accuracy of visibility estimation by combining the visual noise features with the texture feature introduced in a previous work. Experimental results showed that the proposed method could estimate the visibility of traffic signals more accurately under rainy weather conditions

Attribute-Aware Loss Function for Accurate Semantic Segmentation Considering the Pedestrian Orientations

Numerous applications such as autonomous driving, satellite imagery sensing, and biomedical imaging use computer vision as an important tool for perception tasks. For Intelligent Transportation Systems (ITS), it is required to precisely recognize and locate scenes in sensor data. Semantic segmentation is one of computer vision methods intended to perform such tasks. However, the existing semantic segmentation tasks label each pixel with a single object's class. Recognizing object attributes, e.g., pedestrian orientation, will be more informative and help for a better scene understanding. Thus, we propose a method to perform semantic segmentation with pedestrian attribute recognition simultaneously. We introduce an attribute-aware loss function that can be applied to an arbitrary base model. Furthermore, a re-annotation to the existing Cityscapes dataset enriches the ground-truth labels by annotating the attributes of pedestrian orientation. We implement the proposed method and compare the experimental results with others. The attribute-aware semantic segmentation shows the ability to outperform baseline methods both in the traditional object segmentation task and the expanded attribute detection task

Association between Household Exposure to Secondhand Smoke and Dental Caries among Japanese Young Adults: A Cross-Sectional Study

The long-term effects of secondhand smoke (SHS) on dental caries among Japanese young adults remain unclear. The purpose of this cross-sectional study was to evaluate whether household exposure to SHS is associated with dental caries in permanent dentition among Japanese young adults. The study sample included 1905 first-year university students (age range: 18-19 years) who answered a questionnaire and participated in oral examinations. The degree of household exposure to SHS was categorized into four levels according to the SHS duration: no experience (-), past, current SHS = 10 years. Dental caries are expressed as the total number of decayed, missing, and filled teeth (DMFT) score. The relationships between SHS and dental caries were determined by logistic regression analysis. DMFT scores (median (25th percentile, 75th percentile)) were significantly higher in the current SHS >= 10 years (median: 1.0 (0.0, 3.0)) than in the SHS-(median: 0.0 (0.0, 2.0)); p = 0.001). DMFT >= 1 was significantly associated with SHS >= 10 years (adjusted odds ratio: 1.50, 95% confidence interval: 1.20-1.87, p = 10 years) was associated with dental caries in permanent dentition among Japanese young adults

Decreases in blood perfusion of the anterior cingulate gyri in Anorexia Nervosa Restricters assessed by SPECT image analysis

BACKGROUND: It is possible that psychopathological differences exist between the restricting and bulimic forms of anorexia nervosa. We investigated localized differences of brain blood flow of anorexia nervosa patients using SPECT image analysis with statistic parametric mapping (SPM) in an attempt to link brain blood flow patterns to neurophysiologic characteristics. METHODS: The subjects enrolled in this study included the following three groups: pure restrictor anorexics (AN-R), anorexic bulimics (AN-BP), and healthy volunteers (HV). All images were transformed into the standard anatomical space of the stereotactic brain atlas, then smoothed. After statistical analysis of each brain image, the relationships among images were evaluated. RESULTS: SPM analysis of the SPECT images revealed that the blood flow of frontal area mainly containing bilateral anterior cingulate gyri (ACC) was significantly decreased in the AN-R group compared to the AN-BP and HV groups. CONCLUSIONS: These findings suggest that some localized functions ofthe ACCare possibly relevant to the psychopathological aspects of AN-R