From Climate to Global Change:Following the Footprint of Prof.Duzheng YE's Research

To commemorate 100 years since the birth of Professor Duzheng YE, this paper reviews the contribution of Ye and his research team to the development from climate to global change science in the past 30 or so years, including：（1） the role of climate change in global change;（2） the critical time scales and predictability of global change;（3） the sensitive regions of global change—transitional zones of climate and ecosystems; and（4） orderly human activities and adaptation to global change, with a focus on the development of a proactive strategy for adaptation to such change.

Unsupervised Nonlinear Adaptive Manifold Learning for Global and Local Information

In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local information.In this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology structure.Our method aims at minimizing global pairwise data distance errors as well as local structural errors.In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear extractor.Also,we add a feature approximate error that can be used to learn a linear extractor.In addition,we use a method of adaptive neighbor selection to calculate local structural errors.This paper uses the kernel matrix method to optimize the original algorithm.Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.

Population Levels of Climate Change Fear in the United States

There is increasing evidence that climate change, like other natural disasters has the potential for significant human health impacts, including mental health. Fear as a psychological construct concerning climate change is not well understood. An online cross-sectional survey was conducted, targeting a demographically representative sample of Americans (n = 546) in terms of ethnicity, age, and gender. Survey questions included demographic information and global questions regarding self-rated anxiety and fear of climate change. Ordinal logistic models were created to determine which demographic factors were most predictive of climate change fear in the US population. Over half of the study sample (50.9%) indicated being moderately or very afraid of climate change. In the end, only three factors remained significant (p < 0.001) in the model;self-reported level of anxiety, political affiliation, and identifying and Hispanic/Latino. Climate change fear is still not understood, especially in terms of its impact on the mental health of the population in general, though prolonged fear can be an antecedent to other mental health disorders. This study had demonstrated that fear of climate change impacts over half of the U.S population. Level of fear differs significantly by demographic. This study has provided evidence that climate change fear impacts a significant proportion of the US population, prompting a need to investigate the potential acute and long-term impacts of this fear on the human psyche. The harms and benefits of the fear response to climate change should be explored as well as potential responses to fear due to climate change.

Adaptive robust control of nonholonomic systems with stochastic disturbances

This paper deals with nonholonomic systems in chained form with unknown covariance stochastic disturbances The objective is to design the almost global adaptive asymptotical controllers in probability Uo and u1 for the systems by using discontinuous control. A switching control law Uo is designed to almost globally asymptotically stabilize the state x0 in both the singular Xo（t0）=0 case and the non-singular Xo（to）≠O case. Then the state scaling technique is introduced for the discontinuous feedback into the （x1, x2,…, xn）-subsystem. Thereby, by using backstepping technique the global adaptive asymptotical control law u1 has been presented for （x1, x2, …, xn） -subsystem for both different Uo in non-singular x0 （t0）≠0 case and the singular case X0 （t0）=0. The control algorithm validity is proved by simulation.

Adaptive transfer learning framework for dense prediction of human activity recognition

Human activity recognition(HAR)for dense prediction is proven to be of good performance,but it relies on labeling every point in time series with the high cost.In addition,the performance of HAR model will show significant degradation when tested on the sensor data with different distribution from the training data,where the training data and the test data are usually collected from different sensor locations or sensor users.Therefore,the adaptive transfer learning framework for dense prediction of HAR is introduced to implement cross-domain transfer,where the proposed multi-level unsupervised domain adaptation(MLUDA)approach combines the global domain adaptation and the specific task adaptation to adapt the source and target domain in multiple levels.The multi-connected global domain adaptation architecture is proposed for the first time,which can adapt the output layer of the encoder and the decoder in dense prediction model.After this,the specific task adaptation is proposed to ensure alignment of each class centroid in source domain and target domain by introducing the cosine distance loss and the moving average method.Experiments on three public HAR datasets demonstrate that the proposed MLUDA improves the prediction accuracy of target data by 20%compared to the source domain pre-trained model and it is more effective than the other three deep transfer learning methods with an improvement of 10%to 18%in accuracy.

On Orderly Adaptation to Global Warming

Global warming during the last century has been a well-known fact. Despite arguments and uncertainties in explanations, most scientists agree that this century-scale warming trend is attributable to human activities. According to the recent assessment report of the Intergovernmental Panel on Climate Change （IPCC, 2007） based on worldwide scientific results,

The Tea Tree Genome Provides Insights into Tea Flavor and Independent Evolution of Caffeine Biosynthesis

Tea is the world＇s oldest and most popular caffeine-containing beverage with immense economic, medicinal, and cultural importance. Here, we present the first high-quality nucleotide sequence of the repeat-rich （80.9%）, 3.02-Gb genome of the cultivated tea tree Camellia sinensis. We show that an extraordinarily large genome size of tea tree is resulted from the slow, steady, and long-term amplification of a few LTR retrotransposon families. In addition to a recent whole-genome duplication event, lineage-specific expansions of genes associated with flavonoid metabolic biosynthesis were discovered, which enhance catechin production, terpene enzyme activation, and stress tolerance, important features for tea flavor and adaptation. We demonstrate an independent and rapid evolution of the tea caffeine synthesis pathway relative to cacao and coffee. A comparative study among 25 Camellia species revealed that higher expression levels of most flavonoid- and caffeinebut not theanine-related genes contribute to the increased production of catechins and caffeine and thus enhance tea-processing suitability and tea quality. These novel findings pave the way for further metabolomic and functional genomic refinement of characteristic biosynthesis pathways and will help develop a more diversified set of tea flavors that would eventually satisfy and attract more tea drinkers worldwide.