Promises and perils of Guan

This article examines families’ involvement in the care and management of people with serious mental illnesses in China, and focuses on how that involvement is shaped by changing psychiatric institutions and law. Drawing on 32 months of fieldwork, I show that familial involvement is primarily characterised by guan [管], which can mean ‘care’ and/or ‘control’, and which commonly invokes a particular cultural ideal of parenting. Tracing how the language and practice of guan circulate between different realms, I argue that a ‘biopolitical paternalism’ has emerged in contemporary China. It reduces patients to carriers and manifestations of biomedical/security risk and legitimises the state’s policy of population management as a form of paternalistic intervention, while displacing certain paternalistic responsibilities, such as hospitalisation and ensuring medication compliance, onto patients’ families. This biopolitical paternalism produces vulnerabilities and unease within families and aggravates health disparities between patients. The analytic of biopolitical paternalism has conceptual efficacy and practical implications beyond mental health

Occupational Hazards: Sex, Business, and HIV in Post‐Mao China. Elanah Uretsky. Stanford, CA: Stanford University Press, 2016. 280 pp.

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137431/1/amet12511_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137431/2/amet12511.pd

An Analysis of the Construction and Development of ‘Starry Star’ Training Camp in Zhengzhou City

Since 2017, the Ministry of Education has taken the lead in establishing the \u27Starry Star\u27 pilot training camp. As a leading city of school football campaign/activity, Zhengzhou City takes up heavy responsibility of continuously promoting the rapid development of school football in Henan Province. This paper adopts such methodologies such as literature, fieldwork, mathematical statistics and logical analysis to identify the weaknesses in the current phase of construction and to make reasonable recommendations according to policies and the actual situation for pointing out the direction for the future development. In terms of organizational leadership, all relevant policies and documents are well-developed reflecting the high degree of importance attached to each training camp, but very few of them have omissions in the development of the system of admissions and training management regulations. In terms of condition guarantee, three aspects are prominent, including the treatment of coaches (calculation of hours), the size of the training grounds at each training site not meeting the requirements of the camps\u27 participation groups (due to limited size of the campus), and the lack of implementation of supporting funds (in the surrounding counties). In terms of competition and training, all schools lost points to varying degrees in the various detailed assessment indicators of the survey and assessment such as inappropriate teaching attitude of the teaching staff, unsystematic training syllabus, unfinished work arrangements for two tournaments and one practice and insufficient attention to the campers\u27 performance in cultural subjects. In terms of reserve training and training participation rate, nearly 30% of the camps failed to send outstanding athletes to higher level schools; half of the camps failed to ensure over 90% attendance of campers. It is suggested that relevant authorities should in future give standardized and clear policies regarding admissions and other related systems. Further, the authorities should clarify the way of coaches\u27 salary and title appraisal, focus on the implementation of matching funds for training camps, and gradually adjust the correspondence between matching camper groups and camp site conditions. As the main task of the Starry Star training camps, more attention should be placed in future work, improving policies, seriously correcting and dealing with various situations that affect the quality of race training in each camp. The authorities should optimize the way of supplying outstanding athletes, and at the same time strengthen regular inspections and random checks for the attendance, putting equal emphasis on training quality and participation

CD-Xbar : a converge-diverge crossbar network for high-performance GPUs

Modern GPUs feature an increasing number of streaming multiprocessors (SMs) to boost system throughput. How to construct an efficient and scalable network-on-chip (NoC) for future high-performance GPUs is particularly critical. Although a mesh network is a widely used NoC topology in manycore CPUs for scalability and simplicity reasons, it is ill-suited to GPUs because of the many-to-few-to-many traffic pattern observed in GPU-compute workloads. Although a crossbar NoC is a natural fit, it does not scale to large SM counts while operating at high frequency. In this paper, we propose the converge-diverge crossbar (CD-Xbar) network with round-robin routing and topology-aware concurrent thread array (CTA) scheduling. CD-Xbar consists of two types of crossbars, a local crossbar and a global crossbar. A local crossbar converges input ports from the SMs into so-called converged ports; the global crossbar diverges these converged ports to the last-level cache (LLC) slices and memory controllers. CD-Xbar provides routing path diversity through the converged ports. Round-robin routing and topology-aware CTA scheduling balance network traffic among the converged ports within a local crossbar and across crossbars, respectively. Compared to a mesh with the same bisection bandwidth, CD-Xbar reduces NoC active silicon area and power consumption by 52.5 and 48.5 percent, respectively, while at the same time improving performance by 13.9 percent on average. CD-Xbar performs within 2.9 percent of an idealized fully-connected crossbar. We further demonstrate CD-Xbar's scalability, flexibility and improved performance perWatt (by 17.1 percent) over state-of-the-art GPU NoCs which are highly customized and non-scalable

Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction

Photographs taken with less-than-ideal exposure settings often display poor visual quality. Since the correction procedures vary significantly, it is difficult for a single neural network to handle all exposure problems. Moreover, the inherent limitations of convolutions, hinder the models ability to restore faithful color or details on extremely over-/under- exposed regions. To overcome these limitations, we propose a Macro-Micro-Hierarchical transformer, which consists of a macro attention to capture long-range dependencies, a micro attention to extract local features, and a hierarchical structure for coarse-to-fine correction. In specific, the complementary macro-micro attention designs enhance locality while allowing global interactions. The hierarchical structure enables the network to correct exposure errors of different scales layer by layer. Furthermore, we propose a contrast constraint and couple it seamlessly in the loss function, where the corrected image is pulled towards the positive sample and pushed away from the dynamically generated negative samples. Thus the remaining color distortion and loss of detail can be removed. We also extend our method as an image enhancer for low-light face recognition and low-light semantic segmentation. Experiments demonstrate that our approach obtains more attractive results than state-of-the-art methods quantitatively and qualitatively.Comment: Accepted by ACM MM 202

From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion

With the rapid progression of deep learning technologies, multi-modality image fusion has become increasingly prevalent in object detection tasks. Despite its popularity, the inherent disparities in how different sources depict scene content make fusion a challenging problem. Current fusion methodologies identify shared characteristics between the two modalities and integrate them within this shared domain using either iterative optimization or deep learning architectures, which often neglect the intricate semantic relationships between modalities, resulting in a superficial understanding of inter-modal connections and, consequently, suboptimal fusion outcomes. To address this, we introduce a text-guided multi-modality image fusion method that leverages the high-level semantics from textual descriptions to integrate semantics from infrared and visible images. This method capitalizes on the complementary characteristics of diverse modalities, bolstering both the accuracy and robustness of object detection. The codebook is utilized to enhance a streamlined and concise depiction of the fused intra- and inter-domain dynamics, fine-tuned for optimal performance in detection tasks. We present a bilevel optimization strategy that establishes a nexus between the joint problem of fusion and detection, optimizing both processes concurrently. Furthermore, we introduce the first dataset of paired infrared and visible images accompanied by text prompts, paving the way for future research. Extensive experiments on several datasets demonstrate that our method not only produces visually superior fusion results but also achieves a higher detection mAP over existing methods, achieving state-of-the-art results.Comment: 10 pages, 12 figures, 3 tables, conferenc