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Hole spin qubits based on germanium (Ge) have strong tunable spin orbit interaction (SOI) and ultrafast qubit operation speed. Here we report that the Rabi frequency (f_Rabi) of a hole spin qubit in a Ge hut wire (HW) double quantum dot (DQD) is electrically tuned through the detuning energy and middle gate voltage (V_M). f_Rabi gradually decreases with increasing detuning energy; on the contrary, f_Rabi is positively correlated with V_M. We attribute our results to the change of electric field on SOI and the contribution of the excited state in quantum dots to f_Rabi. We further demonstrate an ultrafast f_Rabi exceeding 1.2 GHz, which evidences the strong SOI in our device. The discovery of an ultrafast and electrically tunable f_Rabi in a hole spin qubit has potential applications in semiconductor quantum computing., 19 pages, 4 figures

Separating audio mixtures into individual instrument tracks has been a standing challenge. We introduce a novel weakly supervised audio source separation approach based on deep adversarial learning. Specifically, our loss function adopts the Wasserstein distance which directly measures the distribution distance between the separated sources and the real sources for each individual source. Moreover, a global regularization term is added to fulfill the spectrum energy preservation property regardless separation. Unlike state-of-the-art weakly supervised models which often involve deliberately devised constraints or careful model selection, our approach need little prior model specification on the data, and can be straightforwardly learned in an end-to-end fashion. We show that the proposed method performs competitively on public benchmark against state-of-the-art weakly supervised methods.

OBJECTIVE: To evaluate the incidence of depression and anxiety caused by pegylated interferon α (PegIFN-α)treatment for chronic hepatitis B (CHB) and assess the efficacy of intervention with escitalopram and alprazolam. METHODS: Atotal of 165 CHB patients receiving PegIFN-α-based treatment for 12 weeks were assessed for moderate to severe depressionand anxiety using Patient Health Questionnaire (PHQ-9) and 7-item Generalized Anxiety Disorder Scale (GAD-7)]. Thepatients identified to have moderate to severe depression and anxiety treated with escitalopram or alprazolam and thepsychological condition of the patients was assessed at the 2nd, 4th and 8th weeks of the treatments. RESULTS: In the 165patients receiving PegIFN-α treatment, 51 patients developed moderate to severe psychiatric symptoms, incuding 37 (22.4%)with depression, 31 (18.8%) with anxiety, and 17 (10.3%) with both. The symptoms of depression and anxiety was bothsignificantly improved by intervention with escitalopram (P=0.000); alprazolam was effective for anxiety (P=0.001) but did notproduce obvious effects on depression (P=0.904). Nevertheless, alprazolam had a much better therapeutic effect thanescitalopram on anxiety in these patients (t=-3.198, P=0.010). CONCLUSION: Psychological symptoms are common in CHBpatients receiving PegIFN-α treatment. The symptoms of depression and anxiety can be ameliorated by intervention withescitalopram and alprazolam, respectively.

The rapid development of the Internet of Things (IoT) has brought great changes for non-contact and non-destructive sensing and diagnosis. For every inanimate object can tell us something by the sound it makes, acoustic sensor demonstrates great advantages comparing to conventional electronic and mechanic sensors in such cases: overcoming environmental obstacles, mapping to existing use cases of detecting problems with human ears, low cost for deployment, etc. It could be widely applied to various domains, such as predictive maintenance of machinery, robot sensory, elderly and baby care in smart home, etc. Whether we can use the acoustic sensor data to understand what is happening and to predict what will happen relies heavily on the analytics capabilities we apply to the acoustic data, which has to overcome the obstacles of noise, disturbance and errors, and has to meet the requirement of real-time processing of high volume signals with large number of sensors. In this paper, we propose a scalable cognitive acoustics analytics service for IoT that provides the user an incremental learning approach to evolve their analytics capability on non-intuitive and unstructured acoustic data through the combination of acoustic signal processing and machine learning technology. It first performs acoustic signal processing and denoising, enables acoustic signal based abnormal detection based on sound intensity, spectral centroid, etc. Then based on the accumulated abnormal data, a supervised learning method is performed as baseline and a neural network based classifier is used to recognize acoustic events in different scenarios with various volume of sample data and requirement of accuracy. In addition, acoustic sensor arrays processing is supported for localization of moving acoustic source in more complex scenario. In this paper, we designed a hybrid computing structure. Finally, we conduct experiments on acoustic event recognition for machinery diagnosis, and show that the proposed system can achieve high accuracy.

To evaluate the effect of long-term therapy with entecavir and Fufang Biejia Ruangan tablet in patients with chronic hepatitis B (CHB)-associated fibrosis and explore the synergistic therapy that accelerates the reversion of liver fibrosis.A total of 197 patients with CHB-associated fibrosis were recruited from Nanfang Hospital between June, 2010 and June, 2015. The patients were divided into two groups after matching for age, gender and liver stiffness measurement (LSM), namely group A (n=98) treated with Fufang Biejia Ruangan Tablet plus entecavir, and group B (n=99) to receive entecavir only. HBV DNA quantification, HBV serological indicators, blood biochemical indexes, and results of abdominal ultrasound and FibroScan were recorded every 12 weeks. FibroScan values were converted to Metavir staging.Both groups showed significant decreases in serum levels of HBV DNA, alanine aminotransferase (ALT), and LSM value from baseline (all P0.05). The median time to achieve Metavir fibrosis staging improvement were 72 weeks in group A and 96 weeks in group B (P0.05), and the median time to achieve ALT and AST normalization were 12 and 24 weeks in Group A, respectively, significantly shorter than the time in group B (P0.05). No significant difference was found between the two groups in HBV DNA undetectable rate and HBeAg seroconversion rate.The combination therapy with Fufang Biejia Ruangan tablet and entecavir produces a stronger efficacy than entecavir alone in the treatment of chronic hepatitis B patients with liver fibrosis, and Fufang Biejia Ruangan tablet shows an obvious hepatoprotective effect in these patients.

Emerging Internet of Things(IoT) applications are moving from silo and small scale sensor data sharing to composite and large scale ones. With the rapid growth of application scale, IoT applications is going to leverage cloud infrastructure for scalable solutions and real-time services. Thus large volumes of heterogeneous and high frequency sensor data are fed into IoT cloud services for real-time actionable insight, which raises great challenges of performance and adaptability on cloud solutions. In this paper, we propose a streaming based processing infrastructure for high throughput and low latency IoT real-time analytics services. A data adaptive mechanism is also introduced for heterogeneous data stream integration, interpreting and processing with application logics, as well as context stream. We implemented the proposed mechanisms with spark streaming, and deployed real time IoT analytics service in cloud. Experiment results show that the service has a good scalability and high throughput for IoT data analytics.

The cabbage beetle, Colaphellus bowringi Baly undergoes an imaginal summer and winter diapause in the soil; adult emergence is spread over several months to more than 2 years, with prolonged diapause occurring in certain adults under natural conditions. The relationship between natural duration of diapause and postdiapause reproduction was investigated at 25°C under 12L: 12D photoperiod. The mean body weight of postdiapause adults with the long diapause duration of 21 months was significantly greater than with the shorter diapause duration of 5, 11 and 17 months. The longevity and mean total egg production of the postdia- pause adult females with the long diapause duration of 21 months was significantly greater than that of postdiapause females with shorter diapause duration of 5, 11 and 17 months. All results demonstrate that extended diapause is associated with a high level of postdiapause reproductive performance. There is no negative trade off between diapause duration and the post-diapause reproductive traits that we measured in C. bowringi.

Push notification is an important approach to distribute interesting information to users timely. With the fast development of mobile devices and mobile applications, push notification is getting more and more popular. The convergence of mobile and IoT also bring new challenges on how the system can handle the mixed push channels designed for M2M communication and human interaction, and enable the effective interaction with both human and IoT devices involved. IoT devices may push notifications of sensor data in a high frequency. To enable push notification for both of mobile devices and IoT devices, the push notification system is required to achieve high throughput to handle the frequent notifications, and support content matching to filter out the undesired notifications. To enable effective push notification with both human and IoT devices involved, the system is required to understand the users' interests for notifications with the IoT devices providing the users' contexts. That is to say users need an intelligent push notification system. In this paper we propose a high performance context-aware push notification system for the converged mobile and IoT messaging. We designed high performance content matching engine as the core to enable efficient message dispatching for push notification according to highly personalized interest to ensure IoT messages to be involved in push notification. A user's interest of notifications is highly related with his context. Therefore, based on the content-matching engine, a framework for efficient context information fusion is built to support various types of context-aware push notification, towards intelligent push notification. Also we designed shared connection scheme to reduce the resource cost. Based on the content-match engine and context-aware features, the proposed push notification service can support both of group push notification and bi-directional push notification. Tests are conducted for performance evaluation.

Business agility is of vital importance to chemical and petroleum industry, especially in rapid response to diagnose and exchange of real-time information and other relevant data sources. From IT perspective, emerging delivery models such as cloud computing offer the possibility to build the elastic infrastructure and flexible computing platform, thus enable enterprises to focus on their core competences. In this paper we investigate how the new delivery models of cloud service achieve the improvement of the efficiency by means of Integrated Information Framework (IIF) of chemical and petroleum. In infrastructural level, the capabilities and implementation mechanism of cloud based IIF solution lifecycle management are presented. Based on such management foundation, a business process environment delivered in Platform-as-a-Service business model is provided for rapid development of business agility. Finally, IIF in public and enterprise SaaS models to enable the chemical and petroleum value chain and service market is discussed. This paper demonstrates how the cloud computing technologies and architecture patterns significantly enhance the capability and agility of not only chemical and petroleum using IIF, but also the enterprise with similar requirements, business models and IT architectures in other industries.

With the popularity of cloud computing, Platform-as-a Service (PaaS) becomes one of the core technical enablers by enterprise to change the services to both customers and internal organizations. An application in an enterprise needs to take into account various specific requirements for hosting in private and hybrid cloud, with unique requirements on rapid development, simplicity for deployment and management, integration with existing solution and compliance to industry standards, etc. In this paper, a novel business process centric PaaS model is introduced, which is targeted at supporting above requirements for cloud enabled industry solutions in an enterprise. Firstly, the emerging requirements of PaaS for cloud enabled industry solutions and the general features to meet such requirements are discussed. Then, the architecture and patterns for integrating with existing solutions are introduced. And the technologies to implement such PaaS model are presented including codeless developer workspace and automatic application generator. As well, to enable this PaaS model for “programmable SaaS”, BPM multi-tenancy is introduced. Based on the model and technologies, we designed and implemented cloud enabled industry solutions for Telecommunication, Chemical and Petroleum, Financial and Healthcare industries. This paper demonstrates how these technologies and architectures significantly enhance the capability of PaaS in the context of industry solutions and enterprise environments.

While service oriented computing is gaining popularity and design methodology for functional features is becoming mature, design of non-functional features and governance capabilities is increasingly important. WS-policy has been touted as a standard way to specify service policy for these features and map service policy to upper layers of SOA policy model for SOA governance. This paper introduces a novel model-driven method to orchestrate service policy within system context in design time. By applying top-down SOA design methodology, firstly the logical service policy model is created to describe relation among service policy in logical level, and then it is transformed to physical service policy model with more factors of real system topology considered. Finally, service policy deployment model is generated to describe the relation among policy related artifacts and policy repositories, and guide and automate the deployment of policies in runtime environment as well. Leveraging system topology and behavior information, service policies are orchestrated vertically and horizontally according to service policy dependency, business policy, service level agreement (SLA), and domain specific constraints. Meta-policy based service policy conflict detection and resolution is highlighted. We demonstrate how this approach significantly enhances the capability to ensure the correctness and consistency of service policy within service oriented computing environment.

Content management and protection are important features of emerging home network technologies. In this paper, we present the design of content protection for workplace client technology (CPWCT), a novel secure content management system. In CPWCT, devices in a home network are organized in a secure P2P cluster leveraging the xCP cluster protocol. Additionally, a P2P content management mechanism is implemented which includes lightweight secure streaming and RDF based capability-aware content indexing technologies. We show how this approach can significantly enhance the ability to dynamically self-reorganize the underlying topology and deploy context sensitive functionality

Stripe rust is one of the most destructive diseases for wheat crops in China. Two stripe rust physiological strains, i.e. CYR30 (intern. name: 175E191) and CYR31 (intern. name: 293E175) have been the dominant and epidemic physiological strains since 1994. One Aegilops tauschii accession (SQ-214) from CIMMYT was found immune from or highly resistant to Chinese new stripe rust races CYR30 and CYR31 at adult stage. SQ-214 was crossed with a highly susceptible Ae. tauschii accession As-80. Analysis of data from F1-F2 populations of SQ-214/As-80 revealed that the resistance was controlled by a single dominant gene. To exploit the resistance for wheat breeding, SQ-214 was crossed with Chinese Spring (CS) and backcrossed by two Chinese commercial wheat varieties MY26 and SW3243. The resistance from SQ-214 was suppressed in the F1 hybrids (CS/SQ-214) and the F2 population of CS/SQ-214//MY26. However, the resistance of SQ-214 was expressed in several F2 individuals of CS/SQ-214//SW3243. Eleven advanced lines with high level of resistance to the Chinese stripe rust CYR30 and CYR31 have been developed. This result suggested that SW3243 does not suppress the expression of the Chinese stripe rust and should be used as wheat germplasm for exploiting resistance of Ae. tauschii in wheat breeding. The gliadin electrophoretic pattern of the eleven advanced lines with high stripe rust resistances was compared with their parents SQ-214, CS and SW3243 by acid polyacrylamide gel electrophoresis. The omega-gliadin bands of Gli-Dt1 in Ae. tauschii SQ-214 were transferred to some advanced lines and freely expressed in common wheat genetic background. One of advanced lines possesses a null Gli-D1 allele, where the omega-gliadin bands encoding by the Gli-D1 allele were absent. The potential utilization of this advanced line for wheat quality and stripe rust resistance breeding is also discussed in this paper.

In the paper, some factors affecting electrode’s hydrogen absorption performances were studied making use of the techniques of galvanostatic cycling, XRD and SEM. Conditions of hydrogen storage alloy manufacture, composition of binder and its concentration forming pressure to prepare foamed MH electrode etc. were studied. It was concluded that (1) heat treatment and rapid cooling down of melt could improve discharge capacity of hydrogen storage alloy respectively, while mechanical grinding or hydrogen absorption-desorption crush had no obvious effect. (2) Combined binder is proved to be better than single one, the best content of binder is 3 – 5 wt%. (3) Forming pressure to prepare foamed MH electrode was suggested to be 1 – 5 tons/cm2.