MUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methods.

Gradient-type distributed optimization methods have blossomed into one of the most important tools for solving a minimization learning task over a networked agent system. However, only one gradient update per iteration makes it difficult to achieve a substantive acceleration of convergence. In this article, we propose an accelerated framework named multiupdates single-combination (MUSIC) allowing each agent to perform multiple local updates and a single combination in each iteration. More importantly, we equip inexact and exact distributed optimization methods into this framework, thereby developing two new algorithms that exhibit accelerated linear convergence and high communication efficiency. Our rigorous convergence analysis reveals the sources of steady-state errors arising from inexact policies and offers effective solutions. Numerical results based on synthetic and real datasets demonstrate both our theoretical motivations and analysis, as well as performance advantages.

Air quality prediction by integrating mechanism model and machine learning model.

AQP (Air Quality Prediction) is a very challenging project, and its core issue is how to solve the interaction and influence among meteorological, spatial and temporal factors. To address this central conundrum, we make full use of the characteristics of mechanism model and machine learning and propose a new AQP method based on DM_STGNN (Dynamic Multi-granularity Spatio-temporal Graph Neural Network). This method is the first time to use the air quality model HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory Model) to assist in building a dynamic spatio-temporal graph structure to learn the spatiotemporal relationship of pollutants. DM_STGNN is based on an elaborate encoder-decoder architecture. At the encoder, in order to better mine the spatial dependency, we built a multi-granularity graph structure, used meteorological, time and geographical features to establish node attributes, used well-known HYSPLIT model to dynamically establish the edges among nodes, and used LSTM (Long Short Term Memory) to learn the time-series relationship of pollutant concentrations. At the decoder, in order to better mine the temporal dependency, we built an attention mechanism based LSTM for decoding and AQP. Additionally, in order to efficiently learn the temporal patterns from very long-term historical time series and generate rich contextual information, an unsupervised pre-training model is used to enhance DM_STGNN. The proposed model makes full use of and fully considers the influence of meteorological, spatial and temporal factors, and integrates the advantages of mechanism model and machine learning. On a project-based dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in AQP. We also compare the proposed model with the state-of-the-art AQP methods on the dataset of Yangtze River Delta city group, the experimental results show the appealing performance of our model over competitive baselines.

RNN-K: A Reinforced Newton Method for Consensus-Based Distributed Optimization and Control Over Multiagent Systems.

With the rise of the processing power of networked agents in the last decade, second-order methods for machine learning have received increasing attention. To solve the distributed optimization problems over multiagent systems, Newton's method has the benefits of fast convergence and high estimation accuracy. In this article, we propose a reinforced network Newton method with K -order control flexibility (RNN-K) in a distributed manner by integrating the consensus strategy and the latest knowledge across the network into local descent direction. The key component of our method is to make the best of intermediate results from the local neighborhood to learn global knowledge, not just for the consensus effect like most existing works, including the gradient descent and Newton methods as well as their refinements. Such a reinforcement enables revitalizing the traditional iterative consensus strategy to accelerate the descent of the Newton direction. The biggest difficulty to design the approximated Newton descent in distributed settings is addressed by using a special Taylor expansion that follows the matrix splitting technique. Based on the truncation on the Taylor series, our method also presents a tradeoff effect between estimation accuracy and computation/communication cost, which provides the control flexibility as a practical consideration. We derive theoretically the sufficient conditions for the convergence of the proposed RNN-K method of at least a linear rate. The simulation results illustrate the performance effectiveness by being applied to three types of distributed optimization problems that arise frequently in machine-learning scenarios.

A structure fidelity approach for big data collection in wireless sensor networks.

One of the most widespread and important applications in wireless sensor networks (WSNs) is the continuous data collection, such as monitoring the variety of ambient temperature and humidity. Due to the sensor nodes with a limited energy supply, the reduction of energy consumed in the continuous observation of physical phenomenon plays a significant role in extending the lifetime of WSNs. However, the high redundancy of sensing data leads to great waste of energy as a result of over-deployed sensor nodes. In this paper, we develop a structure fidelity data collection (SFDC) framework leveraging the spatial correlations between nodes to reduce the number of the active sensor nodes while maintaining the low structural distortion of the collected data. A structural distortion based on the image quality assessment approach is used to perform the nodes work/sleep scheduling, such that the number of the working nodes is reduced while the remainder of nodes can be put into the low-power sleep mode during the sampling period. The main contribution of SFDC is to provide a unique perspective on how to maintain the data fidelity in term of structural similarity in the continuous sensing applications for WSNs. The simulation results based on synthetic and real world datasets verify the effectiveness of SFDC framework both on energy saving and data fidelity.

[Determination methods for erythropoietin receptor activator in human urine].

In the present study, isoelectronic focusing with different pH gradients (pH 3-5, 2-6) or migrating distances (8.5, 12 and 17 cm) and SDS-PAGE was used to separate continuous erythropoietin receptor activator (CERA), recombinant human erythropoietin (rhEPO), darbepoetin and endogenous EPO spiked in human urine with 37 degrees C overnight incubation. Double blotting and chemiluminescent visualization were used to detect the IEF and SDS-PAGE profiles. The bands of CERA profile were detected and well separated from the endogenous EPO and the other two EPO preparations with both SDS-PAGE and the IEF method using a gradient pH 3-5 and a migrating distance of 17 cm, and a significant particular band of CERA profile was found in the IEF result. These preliminary results indicated that the methods were reliable and reproducible for detecting CERA, and could be used as a routine procedure for anti-doping analysis.

[Determination of lead in microemulsified rapeseed oil and bio-diesel oil by GFAAS].

Bio-diesel oil has attracted much attention as a substitutable energy sources for its renewable and eco-friendly property. However, problems of lead contamination in fuel are also emphasized increasingly at present. So it was of quite significance to determine the contents of lead in bio-diesel oil and its raw material rapeseed oil. An effective method was developed for the rapid determination of lead in rapeseed oil and bio-diesel oil by graphite furnace atomic absorption spectrometry (GFAAS) after their stabilization as microemulsions. In this research work, polyethyleneglycol octyl phenyl ether and n-butanol were used for emulsifier and auxiliary emulsifying agent, respectively. For Pb, efficient thermal stabilization was obtained using NH4H2PO4 as matrix modifier. Sample stabilization was necessary because of evident analyte losses that occurred immediately after sampling. Excellent long-term sample stabilization and the influence of the microemulsion composition on the GFAAS response were observed by mixing different organic solvents. The ashing and atomization temperature and ramp rate influenced the sensitivity obtained for Ph. Take this into account, the optimum conditions of the graphite furnace atomic absorption spectrometric determination of Pb in rapeseed oil and bio-diesel oil samples were investigated. The results showed that the microemulsion was quite stable when the value of V(20% polyethyleneglycol octyl phenyl ether), V(n-butanol), V(oil) and V(water) was 0.1: 8.9: 0.5: 0.5, without matrix interference effect. The determination limit of the proposed method was 126.2 microg x L(-1) for Pb, comfortably below the values found in the analyzed samples. The recoveries were from 81.8% to 109.0%, which performed using the addition of different concentrations of lead to bio-diesel oil, rapeseed oil and petrochemical diesel samples. The relative standard deviation of determination was 5.84%. This work showed the great efficiency of the microemulsion, indicating that it is possible to extract lead from the oil phase. The method was applied to the determination of lead in oil samples with satisfactory results.

[Detection of the metabolites of dehydroepiandrosterone in urine with gas chromatography-combustion-isotope ratio mass spectrometry].

AIM: To establish a method to determine the isotope ratios of 13C to 12C of dehydroepiandrosterone and its metabolites in urine, for detecting the source of dehydroepiandrosterone or its metabolites. METHODS: Preliminary separation of endogenous anabolic androgenic steroids could be achieved using solid phase extraction, enzymolysis and thin layer chromatography. The source of dehydroepiandrosterone and other endogenous anabolic androgenic steroids could be detected by their delta values with gas chromat ography-combustion-isotope ratio mass spectrometry. RESULTS: The 5 values of some metabolites of dehydroepiandrosterone reduced after the administration of dehydroepiandrosterone preparation. In these cases the data indicated that exogenous anabolic androgenic steroids were administrated. CONCLUSION: The source of dehydroepiandrosterone or its metabolites in urine could be detected by measuring their delta values with this method.