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"Yang, Yi"
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Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation
This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation. Existing approaches usually regard the pseudo label as the ground truth to fully exploit the unlabeled target-domain data. Yet the pseudo labels of the target-domain data are usually predicted by the model trained on the source domain. Thus, the generated labels inevitably contain the incorrect prediction due to the discrepancy between the training domain and the test domain, which could be transferred to the final adapted model and largely compromises the training process. To overcome the problem, this paper proposes to explicitly estimate the prediction uncertainty during training to rectify the pseudo label learning for unsupervised semantic segmentation adaptation. Given the input image, the model outputs the semantic segmentation prediction as well as the uncertainty of the prediction. Specifically, we model the uncertainty via the prediction variance and involve the uncertainty into the optimization objective. To verify the effectiveness of the proposed method, we evaluate the proposed method on two prevalent synthetic-to-real semantic segmentation benchmarks, i.e., GTA5 → Cityscapes and SYNTHIA → Cityscapes, as well as one cross-city benchmark, i.e., Cityscapes → Oxford RobotCar. We demonstrate through extensive experiments that the proposed approach (1) dynamically sets different confidence thresholds according to the prediction variance, (2) rectifies the learning from noisy pseudo labels, and (3) achieves significant improvements over the conventional pseudo label learning and yields competitive performance on all three benchmarks.
Journal Article
Electronic metal–support interaction modulates single-atom platinum catalysis for hydrogen evolution reaction
Tuning metal–support interaction has been considered as an effective approach to modulate the electronic structure and catalytic activity of supported metal catalysts. At the atomic level, the understanding of the structure–activity relationship still remains obscure in heterogeneous catalysis, such as the conversion of water (alkaline) or hydronium ions (acid) to hydrogen (hydrogen evolution reaction, HER). Here, we reveal that the fine control over the oxidation states of single-atom Pt catalysts through electronic metal–support interaction significantly modulates the catalytic activities in either acidic or alkaline HER. Combined with detailed spectroscopic and electrochemical characterizations, the structure–activity relationship is established by correlating the acidic/alkaline HER activity with the average oxidation state of single-atom Pt and the Pt–H/Pt–OH interaction. This study sheds light on the atomic-level mechanistic understanding of acidic and alkaline HER, and further provides guidelines for the rational design of high-performance single-atom catalysts.
Insights into the rational design of single-atom metal catalysts remains obscure in heterogeneous catalysis. Here, the authors establish the atomic-level structure–activity relationship for a wide-pH-range hydrogen evolution reaction through the electronic metal–support interaction modulation.
Journal Article
The living record of scientific history : conversations with CN Yang
Professor Chen-Ning Yang is best known for his achievements in Physics. He has also made significant contributions to the development of mathematics, as mathematics is extensively used in his research. In his long and fruitful academic career, he has witnessed many important events in the fields of Physics and Mathematics, and has collaborated or interacted with many great scientists in history. This book records eight interviews with Professor Chen-Ning Yang, which were conducted by the authors from 2016 to 2019. Through Professor Yang's unique perspective, major scientific events in the 20th century were revisited vividly, elaborating the development and mutual influences of mathematics and physics, as well as unveiling the academic work, the daily lives, and the personalities of scientists, as well as their collaboration and competition, some stories unknown to the public before are also revealed in this book.
BOOK
A Review of SARS-CoV-2 and the Ongoing Clinical Trials
The sudden outbreak of 2019 novel coronavirus (2019-nCoV, later named SARS-CoV-2) in Wuhan, China, which rapidly grew into a global pandemic, marked the third introduction of a virulent coronavirus into the human society, affecting not only the healthcare system, but also the global economy. Although our understanding of coronaviruses has undergone a huge leap after two precedents, the effective approaches to treatment and epidemiological control are still lacking. In this article, we present a succinct overview of the epidemiology, clinical features, and molecular characteristics of SARS-CoV-2. We summarize the current epidemiological and clinical data from the initial Wuhan studies, and emphasize several features of SARS-CoV-2, which differentiate it from SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), such as high variability of disease presentation. We systematize the current clinical trials that have been rapidly initiated after the outbreak of COVID-19 pandemic. Whereas the trials on SARS-CoV-2 genome-based specific vaccines and therapeutic antibodies are currently being tested, this solution is more long-term, as they require thorough testing of their safety. On the other hand, the repurposing of the existing therapeutic agents previously designed for other virus infections and pathologies happens to be the only practical approach as a rapid response measure to the emergent pandemic, as most of these agents have already been tested for their safety. These agents can be divided into two broad categories, those that can directly target the virus replication cycle, and those based on immunotherapy approaches either aimed to boost innate antiviral immune responses or alleviate damage induced by dysregulated inflammatory responses. The initial clinical studies revealed the promising therapeutic potential of several of such drugs, including favipiravir, a broad-spectrum antiviral drug that interferes with the viral replication, and hydroxychloroquine, the repurposed antimalarial drug that interferes with the virus endosomal entry pathway. We speculate that the current pandemic emergency will be a trigger for more systematic drug repurposing design approaches based on big data analysis.
Journal Article
أفكار حول تعميق الإصلاح
يناقش الكتاب سلسلة من الإيضاحات الهامة قدمها الرئيس الصيني والأمين العام للجنة المركزية للحزب الشيوعي الصيني، شي جين بينغ، وتدور حول أفكار الإصلاح وتوسيع الانفتاح على نحو شامل في الصين. يضم الكتاب أكثر من 70 وثيقة هامة على صورة كلمات شي جين بينغ وخطاباته وتعليقاته وتوجيهاته وينقسم الكتاب إلى 12 موضوعا خاصا تتضمن 274 قطعة من مقتطفات الأقوال، نشر بعضها لأول مرة.
Book
Comprehensive landscape of extracellular vesicle-derived RNAs in cancer initiation, progression, metastasis and cancer immunology
Extracellular vesicles (EVs), a class of heterogeneous membrane vesicles, are generally divided into exosomes and microvesicles on basis of their origination from the endosomal membrane or the plasma membrane, respectively. EV-mediated bidirectional communication among various cell types supports cancer cell growth and metastasis. EVs derived from different cell types and status have been shown to have distinct RNA profiles, comprising messenger RNAs and non-coding RNAs (ncRNAs). Recently, ncRNAs have attracted great interests in the field of EV-RNA research, and growing numbers of ncRNAs ranging from microRNAs to long ncRNAs have been investigated to reveal their specific functions and underlying mechanisms in the tumor microenvironment and premetastatic niches. Emerging evidence has indicated that EV-RNAs are essential functional cargoes in modulating hallmarks of cancers and in reciprocal crosstalk within tumor cells and between tumor and stromal cells over short and long distance, thereby regulating the initiation, development and progression of cancers. In this review, we discuss current findings regarding EV biogenesis, release and interaction with target cells as well as EV-RNA sorting, and highlight biological roles and molecular mechanisms of EV-ncRNAs in cancer biology.
Journal Article
Sensor Classification Using Convolutional Neural Network by Encoding Multivariate Time Series as Two-Dimensional Colored Images
This paper proposes a framework to perform the sensor classification by using multivariate time series sensors data as inputs. The framework encodes multivariate time series data into two-dimensional colored images, and concatenate the images into one bigger image for classification through a Convolutional Neural Network (ConvNet). This study applied three transformation methods to encode time series into images: Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), and Markov Transition Field (MTF). Two open multivariate datasets were used to evaluate the impact of using different transformation methods, the sequences of concatenating images, and the complexity of ConvNet architectures on classification accuracy. The results show that the selection of transformation methods and the sequence of concatenation do not affect the prediction outcome significantly. Surprisingly, the simple structure of ConvNet is sufficient enough for classification as it performed equally well with the complex structure of VGGNet. The results were also compared with other classification methods and found that the proposed framework outperformed other methods in terms of classification accuracy.
Journal Article