Gross Biomass and Root/Shoot Ratio Mediated Drought Sensitivities of Ecosystem Carbon Exchange in a Meadow Steppe

According to IPCC’s Report (2007), global precipitation regimes will change largely in the future, with more annual precipitation at the mid-latitude regions. Simultaneously, due to the accelerating industrialization and use of nitrogen (N) fertilizer, significant increase in nitrogen deposition has been widely documented (Liu et al., 2013). Water and nitrogen are the two most important limiting factors for the ecological processes of arid and semi-arid grassland ecosystems; therefore, altered precipitation regimes and enhanced nitrogen deposition are likely to change vegetation composition, ecosystem productivity, and aboveground vs belowground biomass distribution. In addition to these long-term changes, short-term climate extremes, such as drought, are projected to increase in frequency and intensity in the future, and thus there is a clear need to understand how they will impact ecosystem carbon exchange, especially after the vegetation structure has been modified by altered precipitation regimes and nitrogen deposition (Reichstein et al., 2013). However, not much information is available in the literature about the sensitivity of ecosystem carbon exchange to extreme drought, particularly when the ecosystem productivity and biomass distribution were altered by nitrogen deposition and changed precipitation regimes

Tactile feedback display with spatial and temporal resolutions.

We report the electronic recording of the touch contact and pressure using an active matrix pressure sensor array made of transparent zinc oxide thin-film transistors and tactile feedback display using an array of diaphragm actuators made of an interpenetrating polymer elastomer network. Digital replay, editing and manipulation of the recorded touch events were demonstrated with both spatial and temporal resolutions. Analog reproduction of the force is also shown possible using the polymer actuators, despite of the high driving voltage. The ability to record, store, edit, and replay touch information adds an additional dimension to digital technologies and extends the capabilities of modern information exchange with the potential to revolutionize physical learning, social networking, e-commerce, robotics, gaming, medical and military applications

Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View

Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks. Despite their success, one severe limitation of GNNs is the over-smoothing issue (indistinguishable representations of nodes in different classes). In this work, we present a systematic and quantitative study on the over-smoothing issue of GNNs. First, we introduce two quantitative metrics, MAD and MADGap, to measure the smoothness and over-smoothness of the graph nodes representations, respectively. Then, we verify that smoothing is the nature of GNNs and the critical factor leading to over-smoothness is the low information-to-noise ratio of the message received by the nodes, which is partially determined by the graph topology. Finally, we propose two methods to alleviate the over-smoothing issue from the topological view: (1) MADReg which adds a MADGap-based regularizer to the training objective;(2) AdaGraph which optimizes the graph topology based on the model predictions. Extensive experiments on 7 widely-used graph datasets with 10 typical GNN models show that the two proposed methods are effective for relieving the over-smoothing issue, thus improving the performance of various GNN models.Comment: Accepted by AAAI 2020. This complete version contains the appendi

Incorporating Fine-grained Events in Stock Movement Prediction

Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.Comment: Accepted by 2th ECONLP workshop in EMNLP201

Effect of acupuncture on lung cancer-related fatigue: study protocol for a multi-center randomized controlled trial

BACKGROUND: Fatigue is one of the primary symptoms in lung cancer, with a prevalence of 88.0% in survivors of cancer, and an even higher prevalence post resection surgery. Effective fatigue control after lung cancer surgery is important for patient recovery and quality of life. Some studies have shown that acupuncture might be effective in treating cancer-related fatigue; however, randomized controlled trials (RCTs) of suitable sample size are limited. METHOD/DESIGN: This is a multi-center, patient-blinded RCT. A total of 320 eligible patients will be recruited in four centers and randomly assigned to either the acupuncture group or the sham acupuncture group in a 1:1 ratio. Treatment will be given twice per week for 12 sessions. Treatment will be given at acupoints GV20, GV29, CV12, CV6, CV4, and bilateral LI4, LR3, SP6, ST36. The primary outcome will be assessed using the Chinese version of The Brief Fatigue Inventory. The secondary outcomes will be measured using The European Organization for Research and The Treatment of Cancer Quality of Life Questionnaire, and the Hamilton Rating Scale for Depression. The primary outcome will be assessed at all main points (baseline, the 3rd week, the 6th week, and at follow up time points) and the secondary outcomes will be assessed at baseline and the 6th week. Intention-to-treat analysis will be used in this RCT. DISCUSSION: This trial protocol provides an example of the clinical application acupuncture treatment in the management of lung cancer-related fatigue. If the acupuncture treatment protocol confirms that acupuncture is an effective and safe option for lung cancer-related fatigue, it can be adopted as a standardized treatment. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR1900022831. Registered on 27 April 2019. URL: http://www.chictr.org.cn/showproj.aspx?proj=37823

Three‐dimensional Zn O / S i broom‐like nanowire heterostructures as photoelectrochemical anodes for solar energy conversion

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102038/1/pssa201329214.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102038/2/pssa201329214-sm-0001-SupFigs.pd