Perspective of the Human Body in Sasang Constitutional Medicine

The Sasang constitutional medicine (SCM), a medical tradition originating from Korea, is distinguished from the traditional Chinese medicine in its philosophical background, theoretical development and especially, the fundamental rationale that analyzes the structure and function of the human body within a quadrifocal scheme. In SCM, the structure of the body is comprehended within the Sasang quadrifocal scheme, and the function of the body is understood within the context of the energy–fluid metabolism and the water–food metabolism controlled by the four main organs (lung, spleen, liver and kidney). Also, the concept of Seong–Jeong is used to explain the structural and functional variations between different constitutional types that arise from the constitutional variations in organ system scheme, which are in turn caused by deviations in the constitutional Seong–Jeong. Therefore, understanding the SCM perspective of the human body is essential in order to fully appreciate the advantages of the constitutional typological system (which focuses on individual idiosyncrasies) found in SCM

Family Experiences in End-of-Life Care: A Literature Review

PurposeThe purpose of this study was to summarize and analyze families' experiences of end-of-life care by conducting a systematic review of peer reviewed journals both in Korea and abroad.BackgroundFamilies play an increasingly important role in care and medical treatment, acting as caregivers or decision makers rather than just being passive observers. It is necessary to understand the experiences of family members in order to provide appropriate care for them.MethodsA systematic search of the literature was performed using the Cumulative Index for Nursing and Allied Health Literature (CINAHL) and the Korea Education & Research Information Service (KERIS) for the period of January 1990 through to December 2006. A total of 35 studies met the inclusion criteria.ResultsSeventeen studies used a quantitative design, while 18 studies used qualitative methods. Quantitative studies reported that the family's quality of life was relatively low when the patient was in need of high medical/nursing services. The perceived burden levels were moderately high, and depression levels were high among family caregivers. Various concepts emerged from the 18 qualitative studies, including psychological issues, physical problems, burdens, needs and interpersonal relationships.ConclusionThis study found that most previous research findings were focused on negative and neutral experiences. A few studies identified positive experiences. Based on the study results, we suggest that nurses need to be more aware of the experiences of patients' families and their potential needs

RefQSR: Reference-based Quantization for Image Super-Resolution Networks

Single image super-resolution (SISR) aims to reconstruct a high-resolution image from its low-resolution observation. Recent deep learning-based SISR models show high performance at the expense of increased computational costs, limiting their use in resource-constrained environments. As a promising solution for computationally efficient network design, network quantization has been extensively studied. However, existing quantization methods developed for SISR have yet to effectively exploit image self-similarity, which is a new direction for exploration in this study. We introduce a novel method called reference-based quantization for image super-resolution (RefQSR) that applies high-bit quantization to several representative patches and uses them as references for low-bit quantization of the rest of the patches in an image. To this end, we design dedicated patch clustering and reference-based quantization modules and integrate them into existing SISR network quantization methods. The experimental results demonstrate the effectiveness of RefQSR on various SISR networks and quantization methods.Comment: Accepted by IEEE Transactions on Image Processing (TIP

Human error control in the collaborative workflow modeling tool based on GEMS model

Business process should support the execution of collaboration process with agility and flexibility through the integration of enterprise inner or outer application and human resources from the collaborative workflow view.Although the dependency of enterprise activities to the automated system has been increasing, human role is as important as ever.In the workflow modelling this human role is emphasized and the structure to control human error by analysing decision-making itself is needed.Also, through the collaboration of activities agile and effective communication should be constructed, eventually by the combination and coordination of activities to the aimed process the product quality should be improved.This paper classifies human errors can be occurred in collaborative workflow by applying GEMS(Generic Error Modelling System) to control them, and suggests human error control method through hybrid based modelling as well.On this base collaborative workflow modeling tool is designed and implemented. Using this modelling methodology it is possible to workflow modeling could be supported considering human characteristics has a tendency of human error to be controlled

Proteome-Level Responses of Escherichia coli to Long-Chain Fatty Acids and Use of Fatty Acid Inducible Promoter in Protein Production

In Escherichia coli, a long-chain acyl-CoA is a regulatory signal that modulates gene expression through its binding to a transcription factor FadR. In this study, comparative proteomic analysis of E. coli in the presence of glucose and oleic acid was performed to understand cell physiology in response to oleic acid. Among total of 52 proteins showing altered expression levels with oleic acid presence, 9 proteins including AldA, Cdd, FadA, FadB, FadL, MalE, RbsB, Udp, and YccU were newly synthesized. Among the genes that were induced by oleic acid, the promoter of the aldA gene was used for the production of a green fluorescent protein (GFP). Analysis of fluorescence intensities and confocal microscopic images revealed that soluble GFP was highly expressed under the control of the aldA promoter. These results suggest that proteomics is playing an important role not only in biological research but also in various biotechnological applications

Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization

Large language models (LLMs) face the challenges in fine-tuning and deployment due to their high memory demands and computational costs. While parameter-efficient fine-tuning (PEFT) methods aim to reduce the memory usage of the optimizer state during fine-tuning, the inherent size of pre-trained LLM weights continues to be a pressing concern. Even though quantization techniques are widely proposed to ease memory demands and accelerate LLM inference, most of these techniques are geared towards the deployment phase. To bridge this gap, this paper presents Parameter-Efficient and Quantization-aware Adaptation (PEQA) - a simple yet effective method that combines the advantages of PEFT with quantized LLMs. By updating solely the quantization scales, PEQA can be directly applied to quantized LLMs, ensuring seamless task transitions. Parallel to existing PEFT methods, PEQA significantly reduces the memory overhead associated with the optimizer state. Furthermore, it leverages the advantages of quantization to substantially reduce model sizes. Even after fine-tuning, the quantization structure of a PEQA-tuned LLM remains intact, allowing for accelerated inference on the deployment stage. We employ PEQA-tuning for task-specific adaptation on LLMs with up to 65 billion parameters. To assess the logical reasoning and language comprehension of PEQA-tuned LLMs, we fine-tune low-bit quantized LLMs using a instruction dataset. Our results show that even when LLMs are quantized to below 4-bit precision, their capabilities in language modeling, few-shot in-context learning, and comprehension can be resiliently restored to (or even improved over) their full-precision original performances with PEQA.Comment: Published at NeurIPS 2023. Camera-ready versio

GPS-GLASS: Learning Nighttime Semantic Segmentation Using Daytime Video and GPS data

Semantic segmentation for autonomous driving should be robust against various in-the-wild environments. Nighttime semantic segmentation is especially challenging due to a lack of annotated nighttime images and a large domain gap from daytime images with sufficient annotation. In this paper, we propose a novel GPS-based training framework for nighttime semantic segmentation. Given GPS-aligned pairs of daytime and nighttime images, we perform cross-domain correspondence matching to obtain pixel-level pseudo supervision. Moreover, we conduct flow estimation between daytime video frames and apply GPS-based scaling to acquire another pixel-level pseudo supervision. Using these pseudo supervisions with a confidence map, we train a nighttime semantic segmentation network without any annotation from nighttime images. Experimental results demonstrate the effectiveness of the proposed method on several nighttime semantic segmentation datasets. Our source code is available at https://github.com/jimmy9704/GPS-GLASS.Comment: ICCVW 202