A data‐centric framework for debugging highly parallel applications

Contemporary parallel debuggers allow users to control more than one processing thread while supporting the same examination and visualisation operations of that of sequential debuggers. This approach restricts the use of parallel debuggers when it comes to large scale scientific applications run across hundreds of thousands compute cores. First, manually observing the runtime data to detect error becomes impractical because the data is too big. Second, performing expensive but useful debugging operations becomes infeasible as the computational codes become more complex, involving larger data structures, and as the machines become larger. This study explores the idea of a data‐centric debugging approach, which could be used to make parallel debuggers more powerful. It discusses the use of ad hoc debug‐time assertions that allow a user to reason about the state of a parallel computation. These assertions support the verification and validation of program state at runtime as a whole rather than focusing on that of only a single process state. Furthermore, the debugger's performance can be improved by exploiting the underlying parallel platform because the available compute cores can execute parallel debugging functions, while a program is idling at a breakpoint. We demonstrate the system with several case studies and evaluate the performance of the tool on a 20 000 cores Cray XE6. Copyright © 2013 John Wiley & Sons, Ltd.     

Interval functional observers for time-delay systems with additive disturbances

This article considers the design of interval functional observers to estimate a linear function of the state vector of time-delay systems subject to both input and output additive disturbances. Two novel functional observers are proposed and designed such that they bound the set of all admissible values of a linear function of the state vector at each instant of time. By contrast to interval observers currently available in the literature, both observers proposed in this article utilize multiple delayed output measurement and have a more general structure. This trade-off feature overcomes some drawbacks in previous work and enables interval functional observers to be designed for a wider class of time-delay systems. Conditions for the existence of interval functional observers are derived and an effective design algorithm for computing unknown observer matrices is provided. Two illustrative examples are given to show the advantages and effectiveness of our design method.     

Collineation measurement: A method for constructing the imperial citadel of Thang Long in harmony with nature

The Imperial Citadel of Thang Long is a crucial case in ancient Vietnam’s planning and design history. Although historical materials indicate that the orientation of the Imperial Citadel of Thang Long has a dialectical unity relationship with the surroundingmountains, current research is only speculative generalization and lacks empirical analysis. Based on existing findings, this paper identifies the collineation measurement as a generalmethod in the Sinosphere countries for determining spatial orientation. Using a mixed-method of historical archives, fieldwork and simulation model, this paper summarizeshistorical cluesandthreedesignperspectives related to spatial orientation by statistical analysis. Further, it analyzes the logic and application of collineation measurement in constructing the Imperial Citadel of Thang Long. The results show that Vietnamese designers used Tàn Viên Mountain as a component of Thang Long city by collineating the twomountain peaks to the west. Tàn Viên Mountain and the highlands extending eastward fromit are used as the key to establishing the position of the Imperial Citadel, setting the spatial structure of human settlements, and the development of city space. The location, layout, and formof important buildings in the Imperial Citadel are also closely related to the surrounding landscape within 50 km.     

Detection of cross-channel anomalies

The data deluge has created a great challenge for data mining applications wherein the rare topics of interest are often buried in the flood of major headlines. We identify and formulate a novel problem: cross-channel anomaly detection from multiple data channels. Cross-channel anomalies are common among the individual channel anomalies and are often portent of significant events. Central to this new problem is a development of theoretical foundation and methodology. Using the spectral approach, we propose a two-stage detection method: anomaly detection at a single-channel level, followed by the detection of cross-channel anomalies from the amalgamation of single-channel anomalies. We also derive the extension of the proposed detection method to an online settings, which automatically adapts to changes in the data over time at low computational complexity using incremental algorithms. Our mathematical analysis shows that our method is likely to reduce the false alarm rate by establishing theoretical results on the reduction of an impurity index. We demonstrate our method in two applications: document understanding with multiple text corpora and detection of repeated anomalies in large-scale video surveillance. The experimental results consistently demonstrate the superior performance of our method compared with related state-of-art methods, including the one-class SVM and principal component pursuit. In addition, our framework can be deployed in a decentralized manner, lending itself for large-scale data stream analysis.     

Regularized nonnegative shared subspace learning

Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining.     

A novel inverse modeling control for piezo positioning stage

Journal of Mechanical Science and Technology - This paper focuses on the application of a novel inverse nonlinear autoregressive with exogenous input (NARX) structure and a fuzzy inference system...     

Synthesis and characterization of core/shell structured nanophosphors CePO_4:Tb@LaPO_4 by solvothermal method

Core/shell structured CePO4:Tb(III) @LaPO4 and CePO4:Tb(III) were successfully synthesized in tris(2-ethylhexyl) phosphate(TEHP) and diethylene glycol(DEG) solvents for comparison of the two techniques in open air and closed reaction vessel.Morphology and crystal structure of the core/shell nanophosphors were determined by using X-ray diffraction(XRD) and transmission electron microscopy(TEM) ,which showed that nanophosphors had diameter of about 5-10 nm with the monoclinic monazite phase.The nanophosphors ...