Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification

Accurate and robust classification of diseases is important for proper diagnosis and treatment. However, medical datasets often face challenges related to limited sample sizes and inherent imbalanced distributions, due to difficulties in data collection and variations in disease prevalence across different types. In this paper, we introduce an Iterative Online Image Synthesis (IOIS) framework to address the class imbalance problem in medical image classification. Our framework incorporates two key modules, namely Online Image Synthesis (OIS) and Accuracy Adaptive Sampling (AAS), which collectively target the imbalance classification issue at both the instance level and the class level. The OIS module alleviates the data insufficiency problem by generating representative samples tailored for online training of the classifier. On the other hand, the AAS module dynamically balances the synthesized samples among various classes, targeting those with low training accuracy. To evaluate the effectiveness of our proposed method in addressing imbalanced classification, we conduct experiments on the HAM10000 and APTOS datasets. The results obtained demonstrate the superiority of our approach over state-of-the-art methods as well as the effectiveness of each component. The source code will be released upon acceptance

Unsigned Orthogonal Distance Fields: An Accurate Neural Implicit Representation for Diverse 3D Shapes

Neural implicit representation of geometric shapes has witnessed considerable advancements in recent years. However, common distance field based implicit representations, specifically signed distance field (SDF) for watertight shapes or unsigned distance field (UDF) for arbitrary shapes, routinely suffer from degradation of reconstruction accuracy when converting to explicit surface points and meshes. In this paper, we introduce a novel neural implicit representation based on unsigned orthogonal distance fields (UODFs). In UODFs, the minimal unsigned distance from any spatial point to the shape surface is defined solely in one orthogonal direction, contrasting with the multi-directional determination made by SDF and UDF. Consequently, every point in the 3D UODFs can directly access its closest surface points along three orthogonal directions. This distinctive feature leverages the accurate reconstruction of surface points without interpolation errors. We verify the effectiveness of UODFs through a range of reconstruction examples, extending from simple watertight or non-watertight shapes to complex shapes that include hollows, internal or assembling structures.Comment: accepted by CVPR 202

Seizing the window of opportunity to mitigate the impact of climate change on the health of Chinese residents

The health threats posed by climate change in China are increasing rapidly. Each province faces different health risks. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate and even prevent the achievement of the Healthy and Beautiful China initiatives. The 2021 China Report of the Lancet Countdown on Health and Climate Change is the first annual update of China’s Report of the Lancet Countdown. It comprehensively assesses the impact of climate change on the health of Chinese households and the measures China has taken. Invited by the Lancet committee, Tsinghua University led the writing of the report and cooperated with 25 relevant institutions in and outside of China. The report includes 25 indicators within five major areas (climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement) and a policy brief. This 2021 China policy brief contains the most urgent and relevant indicators focusing on provincial data: The increasing health risks of climate change in China; mixed progress in responding to climate change. In 2020, the heatwave exposures per person in China increased by 4.51 d compared with the 1986–2005 average, resulting in an estimated 92% increase in heatwave-related deaths. The resulting economic cost of the estimated 14500 heatwave-related deaths in 2020 is US$176 million. Increased temperatures also caused a potential 31.5 billion h in lost work time in 2020, which is equivalent to 1.3% of the work hours of the total national workforce, with resulting economic losses estimated at 1.4% of China’s annual gross domestic product. For adaptation efforts, there has been steady progress in local adaptation planning and assessment in 2020, urban green space growth in 2020, and health emergency management in 2019. 12 of 30 provinces reported that they have completed, or were developing, provincial health adaptation plans. Urban green space, which is an important heat adaptation measure, has increased in 18 of 31 provinces in the past decade, and the capacity of China’s health emergency management increased in almost all provinces from 2018 to 2019. As a result of China’s persistent efforts to clean its energy structure and control air pollution, the premature deaths due to exposure to ambient particulate matter of 2.5 μm or less (PM2.5) and the resulting costs continue to decline. However, 98% of China’s cities still have annual average PM2.5 concentrations that are more than the WHO guideline standard of 10 μg/m3. It provides policymakers and the public with up-to-date information on China’s response to climate change and improvements in health outcomes and makes the following policy recommendations. (1) Promote systematic thinking in the related departments and strengthen multi-departmental cooperation. Sectors related to climate and development in China should incorporate health perspectives into their policymaking and actions, demonstrating WHO’s and President Xi Jinping’s so-called health-in-all-policies principle. (2) Include clear goals and timelines for climate-related health impact assessments and health adaptation plans at both the national and the regional levels in the National Climate Change Adaptation Strategy for 2035. (3) Strengthen China’s climate mitigation actions and ensure that health is included in China’s pathway to carbon neutrality. By promoting investments in zero-carbon technologies and reducing fossil fuel subsidies, the current rebounding trend in carbon emissions will be reversed and lead to a healthy, low-carbon future. (4) Increase awareness of the linkages between climate change and health at all levels. Health professionals, the academic community, and traditional and new media should raise the awareness of the public and policymakers on the important linkages between climate change and health.</p

Disposal constructions in Chaoshan Southern Min: An apparent time study of the Chenghai dialect

This study examines the use of various types of disposal constructions in the Chenghai dialect of Chaoshan Southern Min. Based on the distinction between head-marking and dependent-marking grammar, we identify four types of disposal constructions, depending on the position of the marker. We performed the fruit cart task to elicit disposal constructions from 30 native speakers of this dialect. Our results indicate that zero-marking is the most dominant construction type, where topicalization represents the most common subtype; this observation is in line with Southern Min’s strong tendency towards topicalized structures. Nonetheless, despite its dominance at present, the frequency of this construction type increases with age, which suggests that it may be losing ground. Notably, according to our preliminary observation, another topicalized structure in Chenghai Southern Min also seems falling into disfavour, suggesting that the declining use of topicalization in this Chaoshan dialect may be systemic

A dual quantum image feature extraction method: PSQIFE

In digital image processing, feature extraction occupies a very important position, which is related to the effect of image classification or recognition. At present, effective quantum feature extraction methods are relatively lacking. And the current feature extraction methods are mainly devoted to the extraction of basic features of images, failing to consider the global features of classical images and the global features of quantum images comprehensively. In this paper, we propose a dual quantum image feature extraction method named PSQIFE, which focuses on the global energy representation of images by constructing dual quantum image global features. The representation of the global features of the dual quantum image is obtained by quantum superposition of two parts of quantum state features. In this paper, quantum image reconstruction and quantum image fidelity tests are performed on the extracted global features by 9 classes of classical images, and the overall fidelity is above 95%. In addition, the effectiveness of PSQIFE dual quantum image feature extraction method is verified by comparing the image classification test with convolutional feature extraction method on Mnist dataset. The method has some reference significance for the research of quantum image feature extraction and classification.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/174937/1/ipr212561_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/174937/2/ipr212561.pd