Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis

Aiming at solving large-scale learning problems, this paper studies distributed optimization methods based on the alternating direction method of multipliers (ADMM). By formulating the learning problem as a consensus problem, the ADMM can be used to solve the consensus problem in a fully parallel fashion over a computer network with a star topology. However, traditional synchronized computation does not scale well with the problem size, as the speed of the algorithm is limited by the slowest workers. This is particularly true in a heterogeneous network where the computing nodes experience different computation and communication delays. In this paper, we propose an asynchronous distributed ADMM (AD-AMM) which can effectively improve the time efficiency of distributed optimization. Our main interest lies in analyzing the convergence conditions of the AD-ADMM, under the popular partially asynchronous model, which is defined based on a maximum tolerable delay of the network. Specifically, by considering general and possibly non-convex cost functions, we show that the AD-ADMM is guaranteed to converge to the set of Karush-Kuhn-Tucker (KKT) points as long as the algorithm parameters are chosen appropriately according to the network delay. We further illustrate that the asynchrony of the ADMM has to be handled with care, as slightly modifying the implementation of the AD-ADMM can jeopardize the algorithm convergence, even under a standard convex setting.Comment: 37 page

Asynchronous Distributed ADMM for Large-Scale Optimization- Part II: Linear Convergence Analysis and Numerical Performance

The alternating direction method of multipliers (ADMM) has been recognized as a versatile approach for solving modern large-scale machine learning and signal processing problems efficiently. When the data size and/or the problem dimension is large, a distributed version of ADMM can be used, which is capable of distributing the computation load and the data set to a network of computing nodes. Unfortunately, a direct synchronous implementation of such algorithm does not scale well with the problem size, as the algorithm speed is limited by the slowest computing nodes. To address this issue, in a companion paper, we have proposed an asynchronous distributed ADMM (AD-ADMM) and studied its worst-case convergence conditions. In this paper, we further the study by characterizing the conditions under which the AD-ADMM achieves linear convergence. Our conditions as well as the resulting linear rates reveal the impact that various algorithm parameters, network delay and network size have on the algorithm performance. To demonstrate the superior time efficiency of the proposed AD-ADMM, we test the AD-ADMM on a high-performance computer cluster by solving a large-scale logistic regression problem.Comment: submitted for publication, 28 page

Influence of electrode thermal conductivity on resistive switching behavior during reset process

Resistive random access memory (RRAM) is the most promising candidate for non-volatile memory (NVM) due to its extremely low operation voltage, extremely fast write/erase speed, and excellent scaling capability. However, an obstacle hindering mass production of RRAM is the non-uniform physical mechanism in its resistance switching process. This study examines the influence of different electrode thermal conductivity on switching behavior during the reset process. Electrical analysis methods and an analysis of current conduction mechanism indicate that better thermal conductivity in the electrode will require larger input power in order to induce more active oxygen ions to take part in the reset process. More active oxygen ions cause a more complete reaction during the reset process, and cause the effective switching gap (dsw) to become thicker. The effect of the electrode thermal conductivity and input power are explained by our model and clarified by electrical analysis methods. Please click Additional Files below to see the full abstract

Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

The traffic classification based on the network applications is one important issue for network management. In this paper, we propose an application-based online and offline traffic classification, based on deep learning mechanisms, over software-defined network (SDN) testbed. The designed deep learning model, resigned in the SDN controller, consists of multilayer perceptron (MLP), convolutional neural network (CNN), and Stacked Auto-Encoder (SAE), in the SDN testbed. We employ an open network traffic dataset with seven most popular applications as the deep learning training and testing datasets. By using the TCPreplay tool, the dataset traffic samples are re-produced and analyzed in our SDN testbed to emulate the online traffic service. The performance analyses, in terms of accuracy, precision, recall, and F1 indicators, are conducted and compared with three deep learning models

Separation, characterization and leaching behaviors of heavy metals in contaminated river sediments

In this research, the sequential extraction test was conducted to understand the characteristic of heavy metals in the sediment. Subsequently, the pH-dependent leaching test, percolation test were subjected to explore the possible leaching of heavy metals and stabilizing mechanism. Finally, based on the resuts of pH dependent test,the acid/chemical washing were applied to predict long-term, leaching characteristics. The results from the sediment characteristic analyses showed that the concentrations of heavy metals (such as Cu, Pb, Zn, Ni, and Cr) in river sediments exceeded the upper limit of Sediment Quality Standard of Taiwan, implying further decontamination works should be addressed. Results from the chemical washing (extraction) showed that the heavy metal removal efficiency was good when washed with 2N HCl for 120 minutes; the order of removal efficiency was Ni 90% > Zn 87% > Pb 85% > Cu 83% > Cr 70%. For chelation extraction, the suitable operating condition was achieved with 0.5M Citric Acid after 120 minutes contact; the order of heavy metal ion capturing efficiency was Zn 61% > Ni 54% > Pb 40% > Cu 36% > Cr 24%. Comparing the heavy metal bonding types before and after chemical washing (extraction) showed that some metal ions exist in residual forms in the sediments (Ni, Zn, Cu); however, after the washing process, the heavy metal ions became more exchangeable forms with higher bioavailability. Keywords: sediment, heavy metal, leaching test, chemical washing

The effectiveness of different health education strategies in people with pre-diabetes: A randomized controlled trial

Background. People with pre-diabetes often lack knowledge of their risks of developing diabetes. In one of our previous study, Multi-Approach Health Education was shown evidence to be effective on health behavior of reducing risks of developing diabetes. However, which one approach is really effective and efficient need further investigation. Purpose. To examine the effects of different intervention strategies on diabetes prevention knowledge, exercise, dietary behavior, and physiological indicators for people with pre-diabetes. Methods. This was a randomly controlled trial. People who received health examination and were found fasting blood glucose higher than normal, between 100 - 125 mg/dl in 2011 were recruited. Three types of intervention were randomly assigned to 3 groups respectively. The control group (n=51) received a health education lecture. One experimental group (n=48) received the identical lecture plus telephone encouragement. The second experimental group (n=41) received the identical lecture plus a health reminder poster in their daily life. The outcomes were evaluated for the change in knowledge of diabetes prevention, exercise behavior, dietary behavior, and physiological outcomes at 6 and 12 weeks after the lecture of three groups, respectively. Results. Three intervention strategies were equally efficacious at inducing positive behavioral changes but overall the magnitudes of physiological changes were the same. In general, the maximum change in parameters was achieved after 6 weeks and maintained in the second 6 weeks of the study. Conclusions. Educating people with pre-diabetes about their condition can have a positive effect upon their health behaviors. However, education lecture coupled with a telephone follow up or plus educational posters were found no more effective than lecture alone. The lecture alone of health education may be enough for people with pre-diabetes, but the long term effect needs further investigation

Cost and Benefit Analysis of Line Arresters for a 69kV Transmission System in Taiwan

This paper presents a systematical evaluation experience on the performance and economic studies of line arresters for a 69 kV transmission system in Taiwan. The transient over-voltage phenomenon in high voltage transmission lines under lightning by using the Electro-Magnetic Transients Program (EMTP) package is well modeled and analyzed. The modeling for the simulated system including lightning, transmission line, transmission tower and line arrester are all considered to have more practical results. The cost and benefit evaluation for line arresters installation  is then conducted to provide a reasonable suggestion for lightning protection.  The performance of line arresters is evaluated by considering different installation schemes and lightning currents. Finally, a Taiwan’s experience is illustrated from both of the cost and benefit points of view