Sharpening Wald-type inference in robust regression for small samples

The datasets used in statistical analyses are often small in the sense that the number of observations n is less than 5 times the number of parameters p to be estimated. In contrast, methods of robust regression are usually optimized in terms of asymptotics with an emphasis on efficiency and maximal bias of estimated coefficients. Inference, i.e., determination of confidence and prediction intervals, is proposed as complementary criteria. An analysis of MM-estimators leads to the development of a new scale estimate, the Design Adaptive Scale Estimate, and to an extension of the MM-estimate, the SMDM-estimate, as well as a suitable ψ-function. A simulation study shows and a real data example illustrates that the SMDM-estimate has better performance for small n/p and that the use the new scale estimate and of a slowly redescending ψ-function is crucial for adequate inference.     

Face tracking with automatic model construction

This paper describes an active model with a robust texture model built on-line. The model uses one camera and it is able to operate without active illumination. The texture model is defined by a series of clusters, which are built in a video sequence using previously encountered samples. This model is used to search for the corresponding element in the following frames. An on-line clustering method, named leaderP is described and evaluated on an application of face tracking. A 20-point shape model is used. This model is built offline, and a robust fitting function is used to restrict the position of the points. Our proposal is to serve as one of the stages in a driver monitoring system. To test it, a new set of sequences of drivers recorded outdoors and in a realistic simulator has been compiled. Experimental results for typical outdoor driving scenarios, with frequent head movement, turns and occlusions are presented. Our approach is tested and compared with the Simultaneous Modeling and Tracking (SMAT) [1], and the recently presented Stacked Trimmed Active Shape Model (STASM) [2], and shows better results than SMAT and similar fitting error levels to STASM, with much faster execution times and improved robustness.     

On Solutions of Linear Ordinary Difference Equations in their Coefficient Field

We extend the notion of monomial extensions of differential fields, i.e. simple transcendental extensions in which the polynomials are closed under differentiation, to difference fields. The structure of such extensions provides an algebraic framework for solving generalized linear difference equations with coefficients in such fields. We then describe algorithms for finding the denominator of any solution of those equations in an important subclass of monomial extensions that includes transcendental indefinite sums and products. This reduces the general problem of finding the solutions of such equations in their coefficient fields to bounding their degrees. In the base case, this yields in particular a new algorithm for computing the rational solutions of q -difference equations with polynomial coefficients.     

Compile and Run-Time Support for the Parallelization of Sparse Matrix Updating Algorithms

This work presents a survey of the capabilities that the sparse computation offers for improving performance when parallelized, either automatically or through a data-parallel compiler. The characterization of a sparse code gets more complicated as code length increases: Access patterns change from loop to loop, thus making necessary to redefine the parallelization strategy. While dense computation solely offers the possibility of redistributing data structures, several other factors influence the performance of a code excerpt in the sparse field, like source data representation on file, compressed data storage in memory, the creation of new nonzeroes at run-time (fill-in) or the number of processors available. We analize the alternatives that arise from each issue, providing a guideline for the underlying compilation work and illustrating our techniques with examples on the Cray T3E.     

An evolutionary approach to the delineation of functional areas based on travel-to-work flows

This paper presents a new approach to the delineation of local labor markets based on evolutionary computation. The aim of the exercise is the division of a given territory into functional regions based on travel-to-work flows. Such regions are defined so that a high degree of inter-regional separation and of intra-regional integration in both cases in terms of commuting flows is guaranteed. Additional requirements include the absence of overlap between delineated regions and the exhaustive coverage of the whole territory. The procedure is based on the maximization of a fitness function that measures aggregate intra-region interaction under constraints of inter-region separation and minimum size. In the experimentation stage, two variations of the fitness function are used, and the process is also applied as a final stage for the optimization of the results from one of the most successful existing methods, which are used by the British authorities for the delineation of travel-to-work areas （TTWAs）. The empirical exercise is conducted using real data for a sufficiently large territory that is considered to be representative given the density and variety of travel-to-work patterns that it embraces. The paper includes the quantitative comparison with alternative traditional methods, the assessment of the performance of the set of operators which has been specifically designed to handle the regionalization problem and the evaluation of the convergence process. The robustness of the solutions, something crucial in a research and policy-making context, is also discussed in the paper.     

Simulations of extensional flow in microrheometric devices

We present a detailed numerical study of the flow of a Newtonian fluid through microrheometric devices featuring a sudden contraction–expansion. This flow configuration is typically used to generate extensional deformations and high strain rates. The excess pressure drop resulting from the converging and diverging flow is an important dynamic measure to quantify if the device is intended to be used as a microfluidic extensional rheometer. To explore this idea, we examine the effect of the contraction length, aspect ratio and Reynolds number on the flow kinematics and resulting pressure field. Analysis of the computed velocity and pressure fields show that, for typical experimental conditions used in microfluidic devices, the steady flow is highly three-dimensional with open spiraling vortical structures in the stagnant corner regions. The numerical simulations of the local kinematics and global pressure drop are in good agreement with experimental results. The device aspect ratio is shown to have a strong impact on the flow and consequently on the excess pressure drop, which is quantified in terms of the dimensionless Couette and Bagley correction factors. We suggest an approach for calculating the Bagley correction which may be especially appropriate for planar microchannels. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Electrospinning hollow and core/sheath nanofibers using hydrodynamic fluid focusing

Hollow poly (vinylpyrrolidone) (PVP) + TiO₂ and polypyrrole (core)/PVP (sheath) nanofibers were successfully electrospun using hydrodynamic fluid focusing. Utilizing a two-dimensional fluid focusing technique previously applied to aqueous solutions, intersecting microchannels cast in (poly)dimethylsiloxane were utilized to dynamically center core fluids in immiscible sheath fluids prior to electrospinning at the channel outlet. Advantages of using microfluidic channel networks for the electrospinning of composite nanofibers include spatiotemporal control over input reagents, ease of fabrication and the ability to focus the core stream into sheath layer without the need of complex co-annular nozzles.     

A novel induction motor control scheme using IDA-PBC

A new control scheme for induction motors is proposed in the present paper, applying the interconnection and damping assignment-passivity based control （IDA-PBC） method. The scheme is based exclusively on passivity based control, without restricting the input frequency as it is done in field oriented control （FOC）. A port-controlled Hamiltonian （PCH） model of the induction motor is deduced to make the interconnection and damping of energy explicit on the scheme. The proposed controller is validated under computational simulations and experimental tests using an inverter prototype.     

Proteomic analysis of secretory proteins and vesicles in vascular research

The release of proteins and membrane vesicles in the bloodstream regulates diverse vascular processes, both physiological, such as angiogenesis and haemostasis, and pathological, such as atherosclerosis and atherothrombosis. Proteomics, beside its canonical application for the expression profiling in cells and organs, can be applied to the study of secreted proteins and microvesicles, which play a significant role in the homeostasis of the vasculature, and the development of the atherosclerotic disease.     

Induction charge detector with multiple sensing stages

An induction charge detector yields the net charge and the time of flight of a particle. The unique ability to independently measure these two parameters sets apart this rather simple detection technique. The main shortcoming of this instrument is its high charge detection limit, resulting from the intrinsic noise of the detector electronics and the low signal associated with the charge to measure. The goal of the present work is to lower the detection limit of this detector. This article describes an induction charge detector whose main novelty is a sequence of aligned cylindrical electrodes for measuring the charge of a particle n times. In a time domain analysis, this feature reduces both the detection limit and the standard error of the charge measurement by factors of square root of 2 and square root of n. More importantly, sensing stages could be added to arbitrarily lower the detection limit in a frequency domain analysis.