THE MANIFESTATION OF URBAN SHAMANISM “DHARMA WHEEL” IN CONTEMPORARY TRADITIONAL DANCE CREATION OF KOREA

As one of the important religions of Korea, Shamanism has collided with Buddhism and Confucianism in the special historical development, which has resulted in localized “difference”. This cultural phenomenon has triggered diversified discussions among the international academia of different fields. During the study in Korea, the author has felt that the traditional dance of Korea, as an important symbol of Korean culture, not only expresses the traditional elements of Korea with an aesthetic attitude in the contemporary society, but also keeps developing by spreading the “contemporary traditional dance creation of Korea”. The author’s research intention has thus been evoked. Through literature review, the author has found that the Korean culture scholars and dancers who are studying Korean culture share a consensus at different levels. On the basis of previous argumentation of other scholars, the research starts from the observation of the “Dharma wheel” manifestation in the contemporary traditional dances of Korea, and explores the seemingly simple relationship between the “manifestation of urban Shamanism Dharma wheel” and the contemporary traditional dance creation of Korea. It makes an attempt to expound the new relationships of Korean dance creation that keeps reshaping the manifestation of dance’s life taking the elements in “cities” as a social mirror, by analyzing and surveying different literature and objects from a cross-cultural perspective. This research is believed to highlight the multiple meanings of disciplines such as anthropology, history and social science to the performing art studies and is expected to fill the research gap in the contemporary Shaman “Dharma wheel” and performing arts through the connection with “contemporary traditional dance creation of Korea”

The alignment between the distribution of satellites and the orientation of their central galaxy

We use galaxy groups selected from the Sloan Digital Sky Survey to examine the alignment between the orientation of the central galaxy (defined as the brightest group member) and the distribution of satellite galaxies. By construction, we therefore only address the alignment on scales smaller than the halo virial radius. We find a highly significant alignment of satellites with the major axis of their central galaxy. This is in qualitative agreement with the recent study of Brainerd, but inconsistent with several previous studies who detected a preferential minor-axis alignment. The alignment strength in our sample is strongest between red central galaxies and red satellites. On the contrary, the satellite distribution in systems with a blue central galaxy is consistent with isotropic. We also find that the alignment strength is stronger in more massive haloes and at smaller projected radii from the central galaxy. In addition, there is a weak indication that fainter (relative to the central galaxy) satellites are more strongly aligned. We present a detailed comparison with previous studies, and discuss the implications of our findings for galaxy formatio

Structural Properties of Central Galaxies in Groups and Clusters

Using a representative sample of 911 central galaxies (CENs) from the SDSS DR4 group catalogue, we study how the structure of the most massive members in groups and clusters depend on (1) galaxy stellar mass (Mstar), (2) dark matter halo mass of the host group (Mhalo), and (3) their halo-centric position. We establish and thoroughly test a GALFIT-based pipeline to fit 2D Sersic models to SDSS data. We find that the fitting results are most sensitive to the background sky level determination and strongly recommend using the SDSS global value. We find that uncertainties in the background translate into a strong covariance between the total magnitude, half-light size (r50), and Sersic index (n), especially for bright/massive galaxies. We find that n depends strongly on Mstar for CENs, but only weakly or not at all on Mhalo. Less (more) massive CENs tend to be disk (spheroid)-like over the full Mhalo range. Likewise, there is a clear r50-Mstar relation for CENs, with separate slopes for disks and spheroids. When comparing CENs with satellite galaxies (SATs), we find that low mass (<10e10.75 Msun/h^2) SATs have larger median n than CENs of similar Mstar. Low mass, late-type SATs have moderately smaller r50 than late-type CENs of the same Mstar. However, we find no size differences between spheroid-like CENs and SATs, and no structural differences between CENs and SATs matched in both mass and colour. The similarity of massive SATs and CENs shows that this distinction has no significant impact on the structure of spheroids. We conclude that Mstar is the most fundamental property determining the basic structure of a galaxy. The lack of a clear n-Mhalo relation rules out a distinct group mass for producing spheroids, and the responsible morphological transformation processes must occur at the centres of groups spanning a wide range of masses. (abridged)Comment: 22 pages, 14 figures, submitted to MNRA

Sketched Ridgeless Linear Regression: The Role of Downsampling

Overparametrization often helps improve the generalization performance. This paper presents a dual view of overparametrization suggesting that downsampling may also help generalize. Focusing on the proportional regime $m\asymp n \asymp p$, where $m$ represents the sketching size, $n$ is the sample size, and $p$ is the feature dimensionality, we investigate two out-of-sample prediction risks of the sketched ridgeless least square estimator. Our findings challenge conventional beliefs by showing that downsampling does not always harm generalization but can actually improve it in certain cases. We identify the optimal sketching size that minimizes out-of-sample prediction risks and demonstrate that the optimally sketched estimator exhibits stabler risk curves, eliminating the peaks of those for the full-sample estimator. To facilitate practical implementation, we propose an empirical procedure to determine the optimal sketching size. Finally, we extend our analysis to cover central limit theorems and misspecified models. Numerical studies strongly support our theory.Comment: Add more numerical experiments and some discussions, relax the Gaussian assumption of coefficient vector to moment condition

ExpCLIP: Bridging Text and Facial Expressions via Semantic Alignment

The objective of stylized speech-driven facial animation is to create animations that encapsulate specific emotional expressions. Existing methods often depend on pre-established emotional labels or facial expression templates, which may limit the necessary flexibility for accurately conveying user intent. In this research, we introduce a technique that enables the control of arbitrary styles by leveraging natural language as emotion prompts. This technique presents benefits in terms of both flexibility and user-friendliness. To realize this objective, we initially construct a Text-Expression Alignment Dataset (TEAD), wherein each facial expression is paired with several prompt-like descriptions.We propose an innovative automatic annotation method, supported by Large Language Models (LLMs), to expedite the dataset construction, thereby eliminating the substantial expense of manual annotation. Following this, we utilize TEAD to train a CLIP-based model, termed ExpCLIP, which encodes text and facial expressions into semantically aligned style embeddings. The embeddings are subsequently integrated into the facial animation generator to yield expressive and controllable facial animations. Given the limited diversity of facial emotions in existing speech-driven facial animation training data, we further introduce an effective Expression Prompt Augmentation (EPA) mechanism to enable the animation generator to support unprecedented richness in style control. Comprehensive experiments illustrate that our method accomplishes expressive facial animation generation and offers enhanced flexibility in effectively conveying the desired style

Perioperative bilateral medial medullary infarction with “snake eyes appearance”: a case report

Perioperative bilateral medial medullary infarction (BMMI) cases mimicking acute motor axonal neuropathy (AMAN) under general anesthesia have not been reported. We describe a patient who suffered flaccid quadriplegia and could not wean from mechanical ventilation after emergence from general anesthesia in cardiac surgery. A diagnosis of AMAN was considered, but intravenous immunoglobulin showed little efficacy. Magnetic resonance imaging of the patient later revealed BMMI with “snake eyes appearance,” and he was found to have severe vertebral artery stenosis. Considering the association between severe coronary heart disease and cerebrovascular stenosis, we highlight the significance of preoperative evaluation and comprehensive management of the cerebrovascular system for certain patients

Dataset and Baselines for IID and OOD Image Classification Considering Data Quality and Evolving Environments.

At present, artificial intelligence is in a period of rapid development, and deep learning has begun to be applied in various fields. Data, as a key part of the deep learning, its efficiency and stability, will directly affect the performance of the model, so it is valued by people. In order to make the dataset efficient, many active learning methods have been proposed, the dataset containing independent identically distribution (IID) samples is reduced with excellent performance; in order to make the dataset more stable, it should be solved that the model encounters out-of-distribution (OOD) samples to improve generalization performance. However, the current active learning method design and the method of adding OOD samples lack guidance, and people do not know what samples should be selected and which OOD samples will be added to better improve the generalization performance. In this paper, we propose a dataset containing a variety of elements called a dataset with Complete Sample Elements(CSE), the labels such as rotation angle and distance in addition to the common classification labels. These labels can help people analyze the distribution characteristics of each element of an efficient dataset, thereby inspiring new active learning methods; we also construct a corresponding OOD test set, which can not only detect the generalization performance of the model, but also helps explore metrics between OOD samples and existing dataset to guide the selected method of OOD samples, so that it can improve generalization efficiently. In this paper, we explore the distribution characteristics of efficient datasets in terms of angle element, and confirm that an efficient dataset tends to contain samples with different appearance. At the same time, experiments have proved the positive influence of the addition of OOD samples on the generalization performance of dataset

Hydrophilic domains compose of interlocking cation-? blocks for constructing hard actuator with robustness and rapid humidity responsiveness

Biomimetic actuators have seemingly infinite potential for use in previously unexplored areas. However, large stresses and a rapid water response are difficult to realize in soft actuators, owing to which their practical applicability is currently limited. In this paper, a new method for designing and fabricating humidity-responsive sturdy hard actuator. By combining a rigid matrix and hydrophilic water domains consisting of dynamic interlocking cation-π blocks, high-performance polymer actuator was synthesized that swell rapidly in response to a water gradient in their environment, resulting in unprecedentedly large stresses. More critically, the strong interlocking cation-π blocks reform and the intermolecular distance is reduced when the water is removed, allowing the deformed actuator to revert its original shape. The proposed design principle can potentially be extended to produce different types of sturdy actuators with rapid water responsiveness