Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning

Many problems in machine learning and other fields can be (re)for-mulated as linearly constrained separable convex programs. In most of the cases, there are multiple blocks of variables. However, the traditional alternating direction method (ADM) and its linearized version (LADM, obtained by linearizing the quadratic penalty term) are for the two-block case and cannot be naively generalized to solve the multi-block case. So there is great demand on extending the ADM based methods for the multi-block case. In this paper, we propose LADM with parallel splitting and adaptive penalty (LADMPSAP) to solve multi-block separable convex programs efficiently. When all the component objective functions have bounded subgradients, we obtain convergence results that are stronger than those of ADM and LADM, e.g., allowing the penalty parameter to be unbounded and proving the sufficient and necessary conditions} for global convergence. We further propose a simple optimality measure and reveal the convergence rate of LADMPSAP in an ergodic sense. For programs with extra convex set constraints, with refined parameter estimation we devise a practical version of LADMPSAP for faster convergence. Finally, we generalize LADMPSAP to handle programs with more difficult objective functions by linearizing part of the objective function as well. LADMPSAP is particularly suitable for sparse representation and low-rank recovery problems because its subproblems have closed form solutions and the sparsity and low-rankness of the iterates can be preserved during the iteration. It is also highly parallelizable and hence fits for parallel or distributed computing. Numerical experiments testify to the advantages of LADMPSAP in speed and numerical accuracy.Comment: Preliminary version published on Asian Conference on Machine Learning 201

Combine Target Extraction and Enhancement Methods to Fuse Infrared and LLL Images

For getting the useful object information from infrared image and mining more detail of low light level (LLL) image, we propose a new fusion method based on segmentation and enhancement methods in the paper. First, using 2D maximum entropy method to segment the original infrared image for extracting infrared target, enhancing original LLL image by Zadeh transform for mining more detail information, on the basis of the segmented map to fuse the enhanced LLL image and original infrared image. Then, original infrared image, the enhanced LLL image and the first fused image are used to realize fusion in non-subsampled contourlet transform (NSCT) domain, we get the second fused image. By contrast of experiments, the fused image of the second fused method’s visual effect is better than other methods’ from the literature. Finally, Objective evaluation is used to evaluate the fused images’ quality, its results also show that the proposed method can pop target information, improve fused image’s resolution and contrast

Certain Class of Analytic Functions Based on qq-difference operator

In this paper, we considered a generalized class of starlike functions defined by Kanas and R\u{a}ducanu\cite{10} to obtain integral means inequalities and subordination results. Further, we obtain the for various subclasses of starlike functions.Comment:

BCEdge: SLO-Aware DNN Inference Services with Adaptive Batching on Edge Platforms

As deep neural networks (DNNs) are being applied to a wide range of edge intelligent applications, it is critical for edge inference platforms to have both high-throughput and low-latency at the same time. Such edge platforms with multiple DNN models pose new challenges for scheduler designs. First, each request may have different service level objectives (SLOs) to improve quality of service (QoS). Second, the edge platforms should be able to efficiently schedule multiple heterogeneous DNN models so that system utilization can be improved. To meet these two goals, this paper proposes BCEdge, a novel learning-based scheduling framework that takes adaptive batching and concurrent execution of DNN inference services on edge platforms. We define a utility function to evaluate the trade-off between throughput and latency. The scheduler in BCEdge leverages maximum entropy-based deep reinforcement learning (DRL) to maximize utility by 1) co-optimizing batch size and 2) the number of concurrent models automatically. Our prototype implemented on different edge platforms shows that the proposed BCEdge enhances utility by up to 37.6% on average, compared to state-of-the-art solutions, while satisfying SLOs

Theoretical Analysis on Deflagration-to-Detonation Transition

The study on deflagration-to-detonation transition (DDT) is very important because this mechanism has relevance to safety issues in industries, where combustible premixed gases are in general use. However, the quantitative prediction of DDT is one of the major unsolved problems in combustion and detonation theory to date. In this paper, the DDT process is studied theoretically and the critical condition is given by a concise theoretical expression. The results show that a deflagration wave propagating with about 60% Chapman-Jouguet (C-J) detonation velocity is a critical condition. This velocity is the maximum propagating velocity of a deflagration wave and almost equal to the sound speed of combustion products. When this critical conation is reached, a C-J detonation is triggered immediately. This is the quantitative criteria of the DDT process

Job Satisfaction Among Methadone Maintenance Treatment Clinic Service Providers in Jiangsu, China: A Cross-sectional Survey.

ObjectiveService providers' job satisfaction is critical to the stability of the work force and thereby the effectiveness of methadone maintenance treatment (MMT) programs. This study aimed to explore MMT clinic service providers' job satisfaction and associated factors in Jiangsu, China.MethodsThis secondary study used baseline data of a randomized interventional trial implemented in Jiangsu, China. A survey was conducted among 76 MMT service providers using the computer-assisted self-interview (CASI) method. Job satisfaction responses were assessed via a 30-item scale, with a higher score indicating a higher level of job satisfaction. Perceived institutional support and perceived stigma due to working with drug users were measured using a 9-item scale. Correlation and multiple linear regression analyses were performed to identify factors associated with job satisfaction.ResultsCorrelation analyses found a significant association between job satisfaction and having professional experience in the prevention and control of HIV, other sexually transmitted infections, or other infectious diseases (P = 0.046). Multiple regression analyses revealed that working at MMT clinics affiliated with Center for Disease Control and Prevention sites was associated with a lower level of job satisfaction (P = 0.014), and perception of greater institutional support (P = 0.001) was associated with a higher level of job satisfaction.ConclusionJob satisfaction among MMT clinic service providers was moderate in our study. Our findings suggest that institutional support for providers should be improved, and that acquisition of additional expertise should be encouraged