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As the most common idiopathic inflammatory myopathy in children, juvenile dermatomyositis (JDM) is characterized by skin rashes and muscle weakness. The childhood myositis assessment scale (CMAS) is commonly used to measure the degree of muscle involvement for diagnosis or rehabilitation monitoring. On the one hand, human diagnosis is not scalable and may be subject to personal bias. On the other hand, automatic action quality assessment (AQA) algorithms cannot guarantee 100% accuracy, making them not suitable for biomedical applications. As a solution, we propose a video-based augmented reality system for human-in-the-loop muscle strength assessment of children with JDM. We first propose an AQA algorithm for muscle strength assessment of JDM using contrastive regression trained by a JDM dataset. Our core insight is to visualize the AQA results as a virtual character facilitated by a 3D animation dataset, so that users can compare the real-world patient and the virtual character to understand and verify the AQA results. To allow effective comparisons, we propose a video-based augmented reality system. Given a feed, we adapt computer vision algorithms for scene understanding, evaluate the optimal way of augmenting the virtual character into the scene, and highlight important parts for effective human verification. The experimental results confirm the effectiveness of our AQA algorithm, and the results of the user study demonstrate that humans can more accurately and quickly assess the muscle strength of children using our system.

This report describes the physics case, the resulting detector requirements, and the evolving detector concepts for the experimental program at the Electron-Ion Collider (EIC). The EIC will be a powerful new high-luminosity facility in the United States with the capability to collide high-energy electron beams with high-energy proton and ion beams, providing access to those regions in the nucleon and nuclei where their structure is dominated by gluons. Moreover, polarized beams in the EIC will give unprecedented access to the spatial and spin structure of the proton, neutron, and light ions. The studies leading to this document were commissioned and organized by the EIC User Group with the objective of advancing the state and detail of the physics program and developing detector concepts that meet the emerging requirements in preparation for the realization of the EIC. The effort aims to provide the basis for further development of concepts for experimental equipment best suited for the science needs, including the importance of two complementary detectors and interaction regions. This report consists of three volumes. Volume I is an executive summary of our findings and developed concepts. In Volume II we describe studies of a wide range of physics measurements and the emerging requirements on detector acceptance and performance. Volume III discusses general-purpose detector concepts and the underlying technologies to meet the physics requirements. These considerations will form the basis for a world-class experimental program that aims to increase our understanding of the fundamental structure of all visible matter, 902 pages, 415 authors, 151 institutions

Modeling spatial-temporal relations is imperative for recognizing human actions, especially when a human is interacting with objects, while multiple objects appear around the human differently over time. Most existing action recognition models focus on learning overall visual cues of a scene but disregard a holistic view of human–object relationships and interactions, that is, how a human interacts with respect to short-term task for completion and long-term goal. We therefore argue to improve human action recognition by exploiting both the local and global contexts of human–object interactions (HOIs). In this paper, we propose the Global-Local Interaction Distillation Network (GLIDN), learning human and object interactions through space and time via knowledge distillation for holistic HOI understanding. GLIDN encodes humans and objects into graph nodes and learns local and global relations via graph attention network. The local context graphs learn the relation between humans and objects at a frame level by capturing their co-occurrence at a specific time step. The global relation graph is constructed based on the video-level of human and object interactions, identifying their long-term relations throughout a video sequence. We also investigate how knowledge from these graphs can be distilled to their counterparts for improving HOI recognition. Finally, we evaluate our model by conducting comprehensive experiments on two datasets including Charades and CAD-120. Our method outperforms the baselines and counterpart approaches.

A 5-year-old girl came to the Tianjin Medical University Eye Hospital in May 2021 because of her poor eyesight after birth. The physical examination showed that she had high myopia, esotropia, horizontal tremor, and high myopia retinopathy of both eyes. After inquiring about her medical history, we found that the baby's occipital cystic mass swelled after birth, and CT examination showed that the occipital skull plate defect with meningocele, but without treatment, at present, the occipital mass had subsided by itself. Considering the eye manifestations and skull changes of the child, it may be conformed to Knobloch syndrome, after the detection of V4 by full exon gene, it was found that the child had the compound heterozygous variation of pathogenic gene COL18A1, and Knobloch syndrome was definite, Knobloch syndrome is a rare autosomal recessive hereditary disease with typical features of high myopia, retinal detachment and occipital encephalocele. At present, there is no clear treatment plan, and gene therapy may be an effective treatment for Knobloch syndrome in the future.5岁女童因出生后发现双眼视力差于2021年5月就诊于天津医科大学眼科医院，体检提示：双眼高度近视、双眼内斜视、水平震颤、双眼底高度近视改变，追问病史后发现，患儿出生后头颅枕部囊性肿物膨出，查头颅CT提示：枕部颅板缺损伴脑膜膨出，未作治疗，目前枕部肿物已自行消退。考虑患儿的眼部表现及颅骨改变，可能符合Knobloch综合征，后经全外显子基因检测V4发现，患儿为致病基因COL18A1的复合杂合变异，Knobloch综合征明确，该综合征是一种罕见的常染色体隐性遗传性疾病，典型特征是高度近视、视网膜脱离和枕部脑膨出，目前尚无明确的治疗方案，基因治疗可能是未来应对Knobloch综合征的有效治疗手段。.

The visible world is founded on the proton, the only composite building block of matter that is stable in nature. Consequently, understanding the formation of matter relies on explaining the dynamics and the properties of the proton's bound state. A fundamental property of the proton involves the response of the system to an external electromagnetic field. It is characterized by the electromagnetic polarizabilitiessup1/supthat describe how easily the charge and magnetization distributions inside the system are distorted by the electromagnetic field. Moreover, the generalized polarizabilitiessup2/supmap out the resulting deformation of the densities in a proton subject to an electromagnetic field. They disclose essential information about the underlying system dynamics and provide a key for decoding the proton structure in terms of the theory of the strong interaction that binds its elementary quark and gluon constituents. Of particular interest is a puzzle in the electric generalized polarizability of the proton that remains unresolved for two decadessup2/sup. Here we report measurements of the proton's electromagnetic generalized polarizabilities at low four-momentum transfer squared. We show evidence of an anomaly to the behaviour of the proton's electric generalized polarizability that contradicts the predictions of nuclear theory and derive its signature in the spatial distribution of the induced polarization in the proton. The reported measurements suggest the presence of a new, not-yet-understood dynamical mechanism in the proton and present notable challenges to the nuclear theory.