Divergence statistics for testing uniform association in cross-classifications

In this paper, we consider the problem of testing uniform association in cross-classifications having ordered categories, taking as test statistic one in the family proposed by Conde and Salicrú [J. Conde, M. Salicrú, Uniform association in contingency tables associated to Csiszár divergence, Statistics and Probability Letters 37 (1998) 149-154]. We consider two approximations to the null distribution of the test statistics in this family: an estimation of the asymptotic null distribution and a bootstrap estimator. We prove that both approximations are asymptotically equivalent. To study their finite sample performance, we carried out two simulation experiments, whose results are presented. From the simulations it can be concluded that the bootstrap estimator behaves much better than the estimated asymptotic null distribution.     

Correlation analysis of principal components from two populations

We investigate a correlation coefficient of principal components from two sets of variables. Using perturbation expansion, we get a limiting distribution of the correlation. In addition, we obtain a limiting distribution of the Fisher's z transformation of the above correlation. Additionally, we verify the accuracy of the limiting distributions using Monte Carlo simulations. Finally in this study, we present two examples and a bootstrap estimation.     

Separation of synchronous and asynchronous communication via testing

One of the early results concerning the asynchronous π$\pi$-calculus which significantly contributed to its popularity is the capability of encoding the output prefix of the (choiceless) π$\pi$-calculus in a natural and elegant way. Encodings of this kind were proposed by Honda and Tokoro, by Nestmann and (independently) by Boudol. We investigate whether the above encodings preserve De Nicola and Hennessy’s testing semantics. In this sense, it turns out that, under some general conditions, no encoding of the output prefix is able to preserve the must testing. This negative result is due to (a) the non-atomicity of the sequences of steps which are necessary in the asynchronous π$\pi$-calculus to mimic synchronous communication, and (b) testing semantics’ sensitivity to divergence.     

Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients

This paper describes feature extraction methods using higher order statistics (HOS) of wavelet packet decomposition (WPD) coefficients for the purpose of automatic heartbeat recognition. The method consists of three stages. First, the wavelet package coefficients (WPC) are calculated for each different type of ECG beat. Then, higher order statistics of WPC are derived. Finally, the obtained feature set is used as input to a classifier, which is based on k-NN algorithm. The MIT-BIH arrhythmia database is used to obtain the ECG records used in this study. All heartbeats in the arrhythmia database are grouped into five main heartbeat classes. The classification accuracy of the proposed system is measured by average sensitivity of 90%, average selectivity of 92% and average specificity of 98%. The results show that HOS of WPC as features are highly discriminative for the classification of different arrhythmic ECG beats.     

Innovative model to simulate exhalation phase in human respiratory system

In this paper, we present a mathematical model, which mimics the bronchial resistances of human's lung in an expiratory act. The model is implemented in Matlab. The inputs that are used in this model derive from spirometry test. This model is able to study a physiologic condition, a pathologic one and the patient's follow up after drug treatment. We split our study into two parts. The first one focuses the analysis on the gas fluido dynamic inside of the respiratory pathways. The second part takes care of the pressure equilibrium in the exchange zone. We use the outputs that derive from the second subsystem to solve the Bernoulli's equation of the first part. The model was validated with data provided from “Clinical Physiology Institute” of CNR and G. Monasterio Foundation of Pisa.     

A method for modeling and visualizing the three-dimensional organization of neuron populations from replicated data: Properties, implementation and illustration

Understanding how the architecture of neuronal populations contributes to brain function requires three-dimensional representations and analyses. Neuroanatomical techniques are available to locate neurons in animal brains. Repeating an experiment in different individuals yields a collection of point patterns from which common organization principles are generally difficult to extract. We recently addressed the problem of generating statistical density maps to integrate replicated point pattern data into meaningful, interpretable representations. Applications to different neuroanatomical systems illustrated the ability of our method to reveal organization rules that cannot be perceived directly on raw data. To make the method practicable for further applications, the aim of the present paper is to establish general guidelines for appropriate parameter tuning, valid result interpretation as well as efficient implementation. Accordingly, we characterize the method by analyzing the role of its main parameter, by reporting results on its statistical properties and by demonstrating its robustness, using both simulated and real neuroanatomical data.     

A search-based framework for automatic testing of MATLAB/Simulink models

Search-based test-data generation has proved successful for code-level testing but almost no search-based work has been carried out at higher levels of abstraction. In this paper the application of such approaches at the higher levels of abstraction offered by MATLAB/Simulink models is investigated and a wide-ranging framework for test-data generation and management is presented. Model-level analogues of code-level structural coverage criteria are presented and search-based approaches to achieving them are described. The paper also describes the first search-based approach to the generation of mutant-killing test data, addressing a fundamental limitation of mutation testing. Some problems remain whatever the level of abstraction considered. In particular, complexity introduced by the presence of persistent state when generating test sequences is as much a challenge at the Simulink model level as it has been found to be at the code level. The framework addresses this problem. Finally, a flexible approach to test sub-set extraction is presented, allowing testing resources to be deployed effectively and efficiently.     

A combined synthetic and np scheme for detecting increases in fraction nonconforming

The applications of attribute control charts cover a wide variety of manufacturing processes in which quality characteristics cannot be measured on a continuous numerical scale or even a quantitative scale. The np control chart is an attribute chart used to monitor the fraction nonconforming p of a process. This chart is effective for detecting large process shifts in p. The attribute synthetic chart is also proposed to detect p shifts. It utilizes the information about the time interval or the Conforming Run Length (CRL) between two nonconforming samples. During the implementation of a synthetic chart, a sample is classified as nonconforming if the number d of nonconforming units falls beyond a warning limit. Unlike the np chart, the synthetic chart is more powerful to detect small and moderate p shifts. This article proposes a new scheme, the Syn-np chart, which comprises a synthetic chart and an np chart. Since the Syn-np chart has both the strength of the synthetic chart for quickly detecting small p shifts and the advantage of the np chart of being sensitive to large p shifts, it has a better and more uniform overall performance. Specifically, it is more effective than the np chart and synthetic chart by 73% and 31%, respectively, in terms of Weighted Average of Average Time to Signal (WAATS) over a wide range of p shifts under different conditions.     

A robust method for measuring trabecular bone orientation anisotropy at in vivo resolution using tensor scale

Trabecular bone (TB) is a network of interconnected struts and plates that constantly remodels to adapt dynamically to the stresses to which it is subjected in such a manner that the trabeculae are oriented along the major stress lines (Wolff's Law). Next to bone density, TB anisotropy has been found to be one of the most significant determinants of the bone's biomechanical behavior. Typically, orientational anisotropy of TB networks is expressed in terms of the fabric tensor, obtained by measuring the mean intercept length between structure elements along test lines. This method, however, can provide only a global statistical average of TB orientation anisotropy and, in general, requires a large sampling volume. Here, we present a new method, based on the recently conceived notion of “tensor scale”, which provides regional information of TB orientation anisotropy. Regional structure is represented by local best fit ellipsoid (ellipse in 2D) and the structural orientation is determined from the eigenvectors along the semi-axes. The method is found to be remarkably robust over a wide range of resolution regimes and image rotation as shown with micro-CT images from specimens of the human distal radius, the latter showing the characteristic differences in structural anisotropy for transverse and longitudinal sections. Finally, the method's applicability to in vivo MR imaging is demonstrated with data from the distal tibia.     

Contrails of Learning: Using New Technologies for Vertical Knowledge-building

Higher education is still dominated by objectivist models of learning involving experts who convey information to novices. Educational research has shown that this model is less effective than more active, constructivist approaches that help learners to build new knowledge on existing knowledge. Although to a lesser extent, the objectivist model is perpetuated in graduate education, a context where students are, ironically, assumed to be working alongside their mentors and becoming part of the culture of research in their fields. Using a recent report issued by the Woodrow Wilson National Fellowship Foundation, The Responsive Ph.D.: Innovations in Doctoral Education (2005), we argue that emerging technologies can help to create constructivist learning environments that challenge students to participate more actively in their own education. As illustration, we consider a graduate seminar on educational technologies that uses a wiki not only to engage students in knowledge-building but to link subsequent sections of the course into an ongoing, purposeful activity that functions both within and beyond the classroom. We explore some of the challenges we faced in getting students to take control of the wiki and overcome their existing assumptions about power and authority in graduate education.     

Classifying densities using functional regression trees: Applications in oceanology

The problem of building a regression tree is considered when the response variable is a probability density function. Splitting criteria which are well adapted to measure the dissimilarity between densities are proposed using the Csiszár's f-divergence. The comparison between performances of trees constructed with various criteria is tackled through numerical simulations. Afterwards, a tree is constructed to predict the size distribution of a zooplankton community using a set of explanatory environmental variables. Functional PCA is used in order to interpret the main modes of variation of the size spectra around the predicted density in each terminal node. Finally, a bagging procedure is used to increase the accuracy of the tree-based model.     

Araneola: A scalable reliable multicast system for dynamic environments

This paper presents Araneola (Araneola means “little spider” in Latin.), a scalable reliable application-level multicast system for highly dynamic wide-area environments. Araneola supports multi-point to multi-point reliable communication in a fully distributed manner, while incurring constant load (in terms of message and space complexity) on each node. For a tunable parameter k≥3, Araneola constructs and dynamically maintains a basic overlay structure in which each node’s degree is either k or k+1, and roughly 90% of the nodes have degree k. Empirical evaluation shows that Araneola’s basic overlay achieves three important mathematical properties of k-regular random graphs (i.e., random graphs in which each node has exactly k neighbors) with N nodes: (i) its diameter grows logarithmically with N; (ii) it is generally k-connected; and (iii) it remains highly connected following random removal of linear-size subsets of edges or nodes. The overlay is constructed and maintained at a low cost: each join, leave, or failure is handled locally, and entails the sending of only about 3k messages in total, independent of N. Moreover, this cost decreases as the churn rate increases.The low degree of Araneola’s basic overlay structure allows for allocating plenty of additional bandwidth for specific application needs. In this paper, we give an example for such a need — communicating with nearby nodes; we enhance the basic overlay with additional links chosen according to geographic proximity and available bandwidth. We show that this approach, i.e., a combination of random and nearby links, reduces the number of physical hops messages traverse without hurting the overlay’s robustness, as compared with completely random Araneola overlays (in which all the links are random) with the same average node degree.Given Araneola’s overlay, we sketch out several message dissemination techniques that can be implemented on top of this overlay. We present a full implementation and evaluation of a gossip-based multicast scheme, with up to 10,000 nodes. We show that compared with a (non-overlay-based) gossip-based multicast protocol, gossiping over Araneola achieves substantial improvements in load, reliability, and latency.     

Performance evaluation for a transportation system in stochastic case

In a single-commodity multistate flow network, the arc capacity is stochastic and thus the system capacity (i.e. the maximum flow from the source to the sink) is not a fixed number. This paper constructs a multicommodity multistate flow network with weighted capacity allocation to model a transportation system. Each arc with cost attribute has several possible capacities. The capacity weight, the consumed amount of arc capacity by per commodity, varies with the arcs and types of commodity. We define the system capacity as a demand vector d if the system fulfills at most d. The addressed problem in this work is to measure the service quality of a transportation system. We propose a performance index, the probability that the upper bound of the system capacity equals a demand vector d subject to the budget constraint. A simple algorithm based on minimal cuts is presented to generate all (d,B)-MC that are the maximal capacity vectors meeting exactly the demand d under the budget B. The proposed performance index can be subsequently evaluated in terms of such (d,B)-MC.     

Building a database for product design knowledge retrieval—A case study in robotic design database

In product design process, when dealing with technical problems or initiating a new design, R&D personnel would often turn to technical database to seek inspiration. The building of a database with such documents has not been systematically dealt with. In this paper, several issues on how to build up a product design database are investigated: input source, sampling scheme and quality control. A case study of building a database for robotic design is used to demonstrate the concept. It is an archive of more than 1500 relevant technical papers. A total of 16 graduates are employed as operators in the labeling process and subsequently the hypothesis tests are utilized to process the labeling results. To ensure this database quality, the labeling consistency of each operator and the understanding of each category are tested. With the use of statistical methods, this work proposes a feasible and practical way to create such a database for product design.     

Using compressed index structures for processing moving objects in large spatio-temporal databases

This paper develops a novel, compressed B⁺-tree based indexing scheme that supports the processing of moving objects in one-, two-, and multi- dimensional spaces. The past, current, and anticipated future trajectories of movements are fully indexed and well organized. No parameterized functions and geometric representations are introduced in our data model so that update operations are not required and the maintenance of index structures can be accomplished by basic insertion and deletion operations. The proposed method has two contributions. First, the spatial and temporal attributes of trajectories are accurately preserved and well organized into compact index structures with very efficient memory space utilization and storage requirement. Second, index maintenance overheads are more economical and query performance is more responsive than those of conventional methods. Both analytical and empirical studies show that our proposed indexing scheme outperforms the TPR-tree.     

Remediating Knowledge-Making Spaces in the Graduate Curriculum: Developing and Sustaining Multimodal Teaching and Research

Functional, critical, and rhetorical training in multimodal teaching and research must span all aspects of professional development within the graduate curriculum in order to best prepare emerging professionals for success in the age of digital media. The authors call upon multiple perspectives of their home program and offer a range of benchmarks for other programs that want to promote digital teaching and research as integral, sustainable components of their knowledge-making spaces.     

Use of integrator in nonlinear H∞ design for disturbance rejection

The disturbance suppression problem for nonlinear systems is examined in this paper. We review the so-called nonstandard mixed sensitivity problem, which introduces an integrator to a selected weight, as well as the linear classical disturbance suppression problem and the linear H_∞ disturbance suppression problem. We extend this H_∞ problem to the nonlinear case, and present a method to reduce the order of the state feedback Hamilton-Jacobi (HJ) partial differential equation for this nonlinear H_∞ problem by extending the concept of comprehensive stability (Proceedings of the 36th Conference on Decision and Control, 1997, p. 4653; IEEE Trans. Ind. Electron. (1998) 488). Finally, we investigate the structure of the output feedback H_∞ controller for disturbance suppression, and draw the conclusion that, as in the linear case, there must also be an integrator in the controller.     

A novel ocean color index to detect floating algae in the global oceans

Various types of floating algae have been reported in open oceans and coastal waters, yet accurate and timely detection of these relatively small surface features using traditional satellite data and algorithms has been difficult or even impossible due to lack of spatial resolution, coverage, revisit frequency, or due to inherent algorithm limitations. Here, a simple ocean color index, namely the Floating Algae Index (FAI), is developed and used to detect floating algae in open ocean environments using the medium-resolution (250- and 500-m) data from operational MODIS (Moderate Resolution Imaging Spectroradiometer) instruments. FAI is defined as the difference between reflectance at 859 nm (vegetation “red edge”) and a linear baseline between the red band (645 nm) and short-wave infrared band (1240 or 1640 nm). Through data comparison and model simulations, FAI has shown advantages over the traditional NDVI (Normalized Difference Vegetation Index) or EVI (Enhanced Vegetation Index) because FAI is less sensitive to changes in environmental and observing conditions (aerosol type and thickness, solar/viewing geometry, and sun glint) and can “see” through thin clouds. The baseline subtraction method provides a simple yet effective means for atmospheric correction, through which floating algae can be easily recognized and delineated in various ocean waters, including the North Atlantic Ocean, Gulf of Mexico, Yellow Sea, and East China Sea. Because similar spectral bands are available on many existing and planned satellite sensors such as Landsat TM/ETM+ and VIIRS (Visible Infrared Imager/Radiometer Suite), the FAI concept is extendable to establish a long-term record of these ecologically important ocean plants.