Quantum Direct Communication with Authentication

We propose two Quantum Direct Communication (QDC) protocols with user authentication. Users can identify each other by checking the correlation of Greenberger-Horne-Zeilinger (GHZ) states. Alice can directly send a secret message to Bob using the remaining GHZ states after authentication. Our second QDC protocol can be used even though there is no quantum link between Alice and Bob. The security of the transmitted message is guaranteed by properties of entanglement of GHZ states.Comment: 9 pages, 3 figures and 2 table

Rethinking Fashion Therapy: Theoretical and Practical Foundations for Value Creations in Clothing and Textiles Discipline

As a type of psychotherapy, fashion therapy (hereafter FT) improves mental health to enhance self-­concepts through grooming behaviors in all parts of the human body including physical appearance management behaviors and fashion product consumption (Horn & Gurel, 1981;; Thompson, 1962). Until now, similar disciplines, such as psychology, women\u27s studies, and art therapy studies, have provided academic and empirical grounds for FT studies. Based on the literature reviews, this study suggests academic reasons why CT should prosper FT studies

The Meaning of Fashion: Implicit and Explicit Self-esteem and Depression

This study investigates the relationship between the implicit self-esteem and the depression to fill the gap. In psychological field, the therapy is considered to be effective as both external and internal selves are healed. Hence, this study employed implicit self-reported method to examine the genuine therapeutic effect of fashion. This study is significant as it facilitated the implicit association test (IAT) in first place in fashion field. The purpose of the study is to develop the foundation of positive effect of fashion by revealing the relationship between the fashion and the substantial self

Fast Diffusion Sampler for Inverse Problems by Geometric Decomposition

Diffusion models have shown exceptional performance in solving inverse problems. However, one major limitation is the slow inference time. While faster diffusion samplers have been developed for unconditional sampling, there has been limited research on conditional sampling in the context of inverse problems. In this study, we propose a novel and efficient diffusion sampling strategy that employs the geometric decomposition of diffusion sampling. Specifically, we discover that the samples generated from diffusion models can be decomposed into two orthogonal components: a ``denoised" component obtained by projecting the sample onto the clean data manifold, and a ``noise" component that induces a transition to the next lower-level noisy manifold with the addition of stochastic noise. Furthermore, we prove that, under some conditions on the clean data manifold, the conjugate gradient update for imposing conditioning from the denoised signal belongs to the clean manifold, resulting in a much faster and more accurate diffusion sampling. Our method is applicable regardless of the parameterization and setting (i.e., VE, VP). Notably, we achieve state-of-the-art reconstruction quality on challenging real-world medical inverse imaging problems, including multi-coil MRI reconstruction and 3D CT reconstruction. Moreover, our proposed method achieves more than 80 times faster inference time than the previous state-of-the-art method.Comment: 21 page

Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis

Recently, diffusion models have shown remarkable results in image synthesis by gradually removing noise and amplifying signals. Although the simple generative process surprisingly works well, is this the best way to generate image data? For instance, despite the fact that human perception is more sensitive to the low frequencies of an image, diffusion models themselves do not consider any relative importance of each frequency component. Therefore, to incorporate the inductive bias for image data, we propose a novel generative process that synthesizes images in a coarse-to-fine manner. First, we generalize the standard diffusion models by enabling diffusion in a rotated coordinate system with different velocities for each component of the vector. We further propose a blur diffusion as a special case, where each frequency component of an image is diffused at different speeds. Specifically, the proposed blur diffusion consists of a forward process that blurs an image and adds noise gradually, after which a corresponding reverse process deblurs an image and removes noise progressively. Experiments show that the proposed model outperforms the previous method in FID on LSUN bedroom and church datasets. Code is available at https://github.com/sangyun884/blur-diffusion

A Subtle Difference between Russia and Chinas Stances toward the Korean Peninsula and Its Strategic Implications for South Korea

In a New Cold War, Northeast Asia becomes a battlefield among great powers. China no longer seems to accept any further erosion of its strategic advantages, particularly the deployment of THAAD in South Korea. Thus South Korea is finding no recourse for ameliorating the North Korean nuclear problem within a great game between the US and China. But there is a difference between Russia and Chinas strategic position. Russia is relatively detached from the security dilemma unfolding in Northeast Asia. While Beijing perceives the THAAD as a fundamental threat, Moscows strategic sensitivity is lower. Moreover, Russia is able to keep North Korea at a greater distance than China, which faces difficulty in neglecting its buffer state. Additionally, Moscows growing economic influence in North Korea recently assists in maximizing its strategic goals. Indeed Russia could conceivably reap big rewards by supplanting China and adopting a new role as regional balancer. Thus South Korea is able to secure its strategic autonomy by using Russia as a bulwark against the current geopolitical dilemma.This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2009-362-A00002)

CXR-LLAVA: a multimodal large language model for interpreting chest X-ray images

Purpose: This study aimed to develop an open-source multimodal large language model (CXR-LLAVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists Materials and Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLAVA network. Then, the model was fine-tuned, primarily using Dataset 2. The model's diagnostic performance for major pathological findings was evaluated, along with the acceptability of radiologic reports by human radiologists, to gauge its potential for autonomous reporting. Results: The model demonstrated impressive performance in test sets, achieving an average F1 score of 0.81 for six major pathological findings in the MIMIC internal test set and 0.62 for seven major pathological findings in the external test set. The model's F1 scores surpassed those of GPT-4-vision and Gemini-Pro-Vision in both test sets. In human radiologist evaluations of the external test set, the model achieved a 72.7% success rate in autonomous reporting, slightly below the 84.0% rate of ground truth reports. Conclusion: This study highlights the significant potential of multimodal LLMs for CXR interpretation, while also acknowledging the performance limitations. Despite these challenges, we believe that making our model open-source will catalyze further research, expanding its effectiveness and applicability in various clinical contexts. CXR-LLAVA is available at https://github.com/ECOFRI/CXR_LLAVA