Interactions of Actors and Local Institutions in Policy Process - From Patriotic Health Campaign to Healthy City in Shanghai

The majority of the world’s population lives in urban areas, and more and more people are migrating to urban areas. However, the health hazards of urban life affect the population as well. They often suffer from non-communicable diseases, cardiovascular diseases, cancer and psychosocial problems. To address the increasing concerns about urban health, the WHO developed health promotion initiatives, known as the Healthy Cities programmes in 1986, which aim to place health high on the agendas of decision-makers and to promote comprehensive local strategies for health promotion and sustainable development. It successfully engages local governments in health development from thousands of cities worldwide in both developed and developing countries, including China. In 1994, China started to develop Healthy City pilot projects in the name of Healthy Cities with the suggestion of the WHO. However, the Chinese government started related activities about the environment and health long before WHO introduced the concept of Healthy Cities. The Patriotic Health Campaign was launched in 1952; despite it being a social movement that was not exclusively oriented to urban areas, it paved the way for Healthy Cities programmes in China. Since 1984, the National Government developed more than 40 policies and National Hygienic Cities to improve the urban environment and support Healthy Cities-related activities. However, the implementation of national policies depends on local level actions where collaboration across sectors is problematic, especially since different ministries tend to work separately according to their own prioritized programme. Shanghai is the first mega-city in China to initiate the action for Healthy City development. It was successful in raising high standards for the health status of the population and improving the urban environment in a quantitative way. However, institutional change, especially intersectoral collaboration remains a big challenge for the implementation. Therefore, it would be interesting to know how the local actors develop the Healthy City programme in the specific context of China. However, there is a lack of empirical studies on the Healthy City programme, and few studies focus on intersectoral relationships in Healthy City development; some researches only include limited actors, and some fail to identify the local institutional settings and connect with the international context. On this background, it looks into the policy making processes of making different programmes at different stages as well as the respective modes of policy implementation. This research aims to unfold how local actors develop the Healthy City programme in Shanghai. Two propositions are guiding the analysis: first, whereas policies in China are mainly developed on a national level where everyday challenges of individual local level entities do not play a decisive role, Healthy City policies are implemented on the local level (of cities or city districts) where municipal specificities and local conditions heavily influence the action potentials and actions of authorities and other stakeholders. Second, whereas Healthy City-oriented policies are comprehensive in nature, their implementation is rather fragmented and sectoral. The study applies an approach that is influenced by the discussion about actor-centered institutionalism. The interpretive lens of actor-centered institutionalism is taken to identify the main actors, analyse how they interact with each other, and the underlying institutional settings that are crucial to interpreting policy making and policy implementation. The study will also find out whether the actor-centered institutionalism approach is fully applicable under the conditions of China, or whether certain modifications are to be made. The research follows a qualitative approach, collecting data from multiple sources such as documents, including historic documents in archives, and interviews, combining a variety of research methods including stakeholder analysis, discourse analysis and network analysis. Shanghai is used as a case study as it has the longest experience with the implementation of Healthy City programmes in China, and was also the first to issue a Healthy City Action Plan in 2003. It established the first municipal committee for health promotion in 2005. Whereas the older programmes are analysed based on documents, the latest Healthy City programme is scrutinised by employing document analysis and interviews of different stakeholders in order to get an in-depth understanding of the policy making and implementation processes. This thesis aspires to contribute to the empirical knowledge of the development of public policies, the understanding of actors and actor constellations in Healthy City programmes with reference to specific institutional settings in China, and examining the compatibility and limitations of this interpretive lens in the Chinese context. Moreover, policy recommendations related to practice in Shanghai are provided as further motivation and commitment to Healthy City development in China

Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning

Self-supervised learning has proved effective for skeleton-based human action understanding, which is an important yet challenging topic. Previous works mainly rely on contrastive learning or masked motion modeling paradigm to model the skeleton relations. However, the sequence-level and joint-level representation learning cannot be effectively and simultaneously handled by these methods. As a result, the learned representations fail to generalize to different downstream tasks. Moreover, combining these two paradigms in a naive manner leaves the synergy between them untapped and can lead to interference in training. To address these problems, we propose Prompted Contrast with Masked Motion Modeling, PCM$^{\rm 3}$, for versatile 3D action representation learning. Our method integrates the contrastive learning and masked prediction tasks in a mutually beneficial manner, which substantially boosts the generalization capacity for various downstream tasks. Specifically, masked prediction provides novel training views for contrastive learning, which in turn guides the masked prediction training with high-level semantic information. Moreover, we propose a dual-prompted multi-task pretraining strategy, which further improves model representations by reducing the interference caused by learning the two different pretext tasks. Extensive experiments on five downstream tasks under three large-scale datasets are conducted, demonstrating the superior generalization capacity of PCM$^{\rm 3}$ compared to the state-of-the-art works. Our project is publicly available at: https://jhang2020.github.io/Projects/PCM3/PCM3.html .Comment: Accepted by ACM Multimedia 202

Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations

Contrastive learning has been proven beneficial for self-supervised skeleton-based action recognition. Most contrastive learning methods utilize carefully designed augmentations to generate different movement patterns of skeletons for the same semantics. However, it is still a pending issue to apply strong augmentations, which distort the images/skeletons' structures and cause semantic loss, due to their resulting unstable training. In this paper, we investigate the potential of adopting strong augmentations and propose a general hierarchical consistent contrastive learning framework (HiCLR) for skeleton-based action recognition. Specifically, we first design a gradual growing augmentation policy to generate multiple ordered positive pairs, which guide to achieve the consistency of the learned representation from different views. Then, an asymmetric loss is proposed to enforce the hierarchical consistency via a directional clustering operation in the feature space, pulling the representations from strongly augmented views closer to those from weakly augmented views for better generalizability. Meanwhile, we propose and evaluate three kinds of strong augmentations for 3D skeletons to demonstrate the effectiveness of our method. Extensive experiments show that HiCLR outperforms the state-of-the-art methods notably on three large-scale datasets, i.e., NTU60, NTU120, and PKUMMD.Comment: Accepted by AAAI 2023. Project page: https://jhang2020.github.io/Projects/HiCLR/HiCLR.htm

Semi-Supervised Learning for Mars Imagery Classification and Segmentation

With the progress of Mars exploration, numerous Mars image data are collected and need to be analyzed. However, due to the imbalance and distortion of Martian data, the performance of existing computer vision models is unsatisfactory. In this paper, we introduce a semi-supervised framework for machine vision on Mars and try to resolve two specific tasks: classification and segmentation. Contrastive learning is a powerful representation learning technique. However, there is too much information overlap between Martian data samples, leading to a contradiction between contrastive learning and Martian data. Our key idea is to reconcile this contradiction with the help of annotations and further take advantage of unlabeled data to improve performance. For classification, we propose to ignore inner-class pairs on labeled data as well as neglect negative pairs on unlabeled data, forming supervised inter-class contrastive learning and unsupervised similarity learning. For segmentation, we extend supervised inter-class contrastive learning into an element-wise mode and use online pseudo labels for supervision on unlabeled areas. Experimental results show that our learning strategies can improve the classification and segmentation models by a large margin and outperform state-of-the-art approaches.Comment: Accepted by ACM Trans. on Multimedia Computing Communications and Applications (TOMM

A Meta-Analysis

Background p16INK4a is a tumor suppressor protein which is induced in cells upon the interaction of high-risk HPV E7 with the retinoblastoma protein by a positive feedback loop, but cannot exert its suppressing effect. Previous reports suggested that p16INK4a immunostaining allows precise identification of even small CIN or cervical cancer lesions in biopsies. The prognostic value of overexpressed p16INK4a in cervical cancer has been evaluated for several years while the results remain controversial. We performed a systematic review and meta-analysis of studies assessing the clinical and prognostic significance of overexpression of p16INK4a in cervical cancer. Methods Identification and review of publications assessing clinical or prognostic significance of p16INK4a overexpression in cervical cancer until March 1, 2014. A meta-analysis was performed to clarify the association between p16INK4a overexpression and clinical outcomes. Results A total of 15 publications met the criteria and comprised 1633 cases. Analysis of these data showed that p16INK4a overexpression was not significantly associated with tumor TNM staging (I+II vs. III+IV) (OR = 0.75, 95% confidence interval [CI]: 0.35–1.63, P = 0.47), the tumor grade (G1+ G2 vs. G3) (OR = 0.78, 95% CI: 0.39–1.57, P = 0.49), the tumor size (<4 vs. ≥4 cm) (OR = 1.10, 95% CI: 0.45–2.69, P = 0.83), or vascular invasion (OR = 1.20, 95% CI: 0.69–2.08, P = 0.52). However, in the identified studies, overexpression of p16INK4a was highly correlated with no lymph node metastasis (OR = 0.51, 95% CI: 0.28–0.95, P = 0.04), increased overall survival (relative risk [RR]: 0.42, 95% CI: 0.24–0.72, P = 0.002) and increased disease free survival (RR: 0.60, 95% CI: 0.44–0.82, P = 0.001)

S5^{5}Mars: Semi-Supervised Learning for Mars Semantic Segmentation

Deep learning has become a powerful tool for Mars exploration. Mars terrain semantic segmentation is an important Martian vision task, which is the base of rover autonomous planning and safe driving. However, there is a lack of sufficient detailed and high-confidence data annotations, which are exactly required by most deep learning methods to obtain a good model. To address this problem, we propose our solution from the perspective of joint data and method design. We first present a newdataset S5Mars for Semi-SuperviSed learning on Mars Semantic Segmentation, which contains 6K high-resolution images and is sparsely annotated based on confidence, ensuring the high quality of labels. Then to learn from this sparse data, we propose a semi-supervised learning (SSL) framework for Mars image semantic segmentation, to learn representations from limited labeled data. Different from the existing SSL methods which are mostly targeted at the Earth image data, our method takes into account Mars data characteristics. Specifically, we first investigate the impact of current widely used natural image augmentations on Mars images. Based on the analysis, we then proposed two novel and effective augmentations for SSL of Mars segmentation, AugIN and SAM-Mix, which serve as strong augmentations to boost the model performance. Meanwhile, to fully leverage the unlabeled data, we introduce a soft-to-hard consistency learning strategy, learning from different targets based on prediction confidence. Experimental results show that our method can outperform state-of-the-art SSL approaches remarkably. Our proposed dataset is available at https://jhang2020.github.io/S5Mars.github.io/

GLANT6によるHSPA5のグリコシル化とp53誘導分子HSPB7の発癌における機能解析

学位の種別: 課程博士審査委員会委員 : (主査)東京大学准教授 松田 浩一, 東京大学教授 菅野 純夫, 東京大学教授 古川 洋一, 東京大学准教授 加藤 直也, 東京大学講師 松原 大祐University of Tokyo(東京大学

BaySize: Bayesian Sample Size Planning for Phase I Dose-Finding Trials

We propose BaySize, a sample size calculator for phase I clinical trials using Bayesian models. BaySize applies the concept of effect size in dose finding, assuming the MTD is defined based on an equivalence interval. Leveraging a decision framework that involves composite hypotheses, BaySize utilizes two prior distributions, the fitting prior (for model fitting) and sampling prior (for data generation), to conduct sample size calculation under desirable statistical power. Look-up tables are generated to facilitate practical applications. To our knowledge, BaySize is the first sample size tool that can be applied to a broad range of phase I trial designs