Parallel Algorithms for Generating Random Networks with Given Degree Sequences

Random networks are widely used for modeling and analyzing complex processes. Many mathematical models have been proposed to capture diverse real-world networks. One of the most important aspects of these models is degree distribution. Chung--Lu (CL) model is a random network model, which can produce networks with any given arbitrary degree distribution. The complex systems we deal with nowadays are growing larger and more diverse than ever. Generating random networks with any given degree distribution consisting of billions of nodes and edges or more has become a necessity, which requires efficient and parallel algorithms. We present an MPI-based distributed memory parallel algorithm for generating massive random networks using CL model, which takes $O(\frac{m+n}{P}+P)$ time with high probability and $O(n)$ space per processor, where $n$, $m$, and $P$ are the number of nodes, edges and processors, respectively. The time efficiency is achieved by using a novel load-balancing algorithm. Our algorithms scale very well to a large number of processors and can generate massive power--law networks with one billion nodes and $250$ billion edges in one minute using $1024$ processors.Comment: Accepted in NPC 201

Bayesian learning of joint distributions of objects

There is increasing interest in broad application areas in defining flexible joint models for data having a variety of measurement scales, while also allowing data of complex types, such as functions, images and documents. We consider a general framework for nonparametric Bayes joint modeling through mixture models that incorporate dependence across data types through a joint mixing measure. The mixing measure is assigned a novel infinite tensor factorization (ITF) prior that allows flexible dependence in cluster allocation across data types. The ITF prior is formulated as a tensor product of stick-breaking processes. Focusing on a convenient special case corresponding to a Parafac factorization, we provide basic theory justifying the flexibility of the proposed prior and resulting asymptotic properties. Focusing on ITF mixtures of product kernels, we develop a new Gibbs sampling algorithm for routine implementation relying on slice sampling. The methods are compared with alternative joint mixture models based on Dirichlet processes and related approaches through simulations and real data applications.Comment: Appearing in Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS) 2013, Scottsdale, AZ, US

Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes

There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model

Using the SWAT model to improve process descriptions and define hydrologic partitioning in South Korea

Watershed-scale modeling can be a valuable tool to aid in quantification of water quality and yield; however, several challenges remain. In many watersheds, it is difficult to adequately quantify hydrologic partitioning. Data scarcity is prevalent, accuracy of spatially distributed meteorology is difficult to quantify, forest encroachment and land use issues are common, and surface water and groundwater abstractions substantially modify watershed-based processes. Our objective is to assess the capability of the Soil and Water Assessment Tool (SWAT) model to capture event-based and long-term monsoonal rainfall–runoff processes in complex mountainous terrain. To accomplish this, we developed a unique quality-control, gap-filling algorithm for interpolation of high-frequency meteorological data. We used a novel multi-location, multi-optimization calibration technique to improve estimations of catchment-wide hydrologic partitioning. The interdisciplinary model was calibrated to a unique combination of statistical, hydrologic, and plant growth metrics. Our results indicate scale-dependent sensitivity of hydrologic partitioning and substantial influence of engineered features. The addition of hydrologic and plant growth objective functions identified the importance of culverts in catchment-wide flow distribution. While this study shows the challenges of applying the SWAT model to complex terrain and extreme environments; by incorporating anthropogenic features into modeling scenarios, we can enhance our understanding of the hydroecological impact