On Scene Segmentation and Histograms-Based Curve Evolution

We consider curve evolution based on comparing distributions of features, and its applications for scene segmentation. In the first part, we promote using cross-bin metrics such as the Earth Mover's Distance (EMD), instead of standard bin-wise metrics as the Bhattacharyya or Kullback-Leibler metrics. To derive flow equations for minimizing functionals involving the EMD, we employ a tractable expression for calculating EMD between one-dimensional distributions. We then apply the derived flows to various examples of single image segmentation, and to scene analysis using video data. In the latter, we consider the problem of segmenting a scene to spatial regions in which different activities occur. We use a nonparametric local representation of the regions by considering multiple one-dimensional histograms of normalized spatiotemporal derivatives. We then obtain semisupervised segmentation of regions using the flows derived in the first part of the paper. Our results are demonstrated on challenging surveillance scenes, and compare favorably with state-of-the-art results using parametric representations by dynamic systems or mixtures of them.     

Prioritizing agile benefits and limitations in relation to practice usage

In recent years, there has been significant shift from rigid development (RD) toward agile. However, it has also been spotted that agile methodologies are hardly ever followed in their pure form. Hybrid processes as combinations of RD and agile practices emerge. In addition, agile adoption has been reported to result in both benefits and limitations. This exploratory study (a) identifies development models based on RD and agile practice usage by practitioners; (b) identifies agile practice adoption scenarios based on eliciting practice usage over time; (c) prioritizes agile benefits and limitations in relation to (a) and (b). Practitioners provided answers through a questionnaire. The development models are determined using hierarchical cluster analysis. The use of practices over time is captured through an interactive board with practices and time indication sliders. This study uses the extended hierarchical voting analysis framework to investigate benefit and limitation prioritization. Four types of development models and six adoption scenarios have been identified. Overall, 45 practitioners participated in the prioritization study. A common benefit among all models and adoption patterns is knowledge and learning, while high requirements on professional skills were perceived as the main limitation. Furthermore, significant variances in terms of benefits and limitations have been observed between models and adoption patterns. The most significant internal benefit categories from adopting agile are knowledge and learning, employee satisfaction, social skill development, and feedback and confidence. Professional skill-specific demands, scalability, and lack of suitability for specific product domains are the main limitations of agile practice usage. Having a balanced agile process allows to achieve a high number of benefits. With respect to adoption, a big bang transition from RD to agile leads to poor quality in comparison with the alternatives.     

Hierarchical mapping of Northern Eurasian land cover using MODIS data

The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI). The Northern Eurasia Land Cover (NELC) database developed in this study follows the FAO-Land Cover Classification System and provides nested groupings of land cover characteristics, with separate layers for land use, wetlands, and tundra. The database implementation is substantially different from other large-scale land cover datasets that provide maps based on a single set of discrete classes. By providing a database consisting of nested maps and complementary layers, the NELC database provides a flexible framework that allows users to tailor maps to suit their needs. The methods used to create the database combine empirically derived climate–vegetation relationships with results from supervised classifications based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The hierarchical approach provides an effective framework for integrating climate–vegetation relationships with remote sensing-based classifications, and also allows sources of error to be characterized and attributed to specific levels in the hierarchy. The cross-validated accuracy was 73% for the land cover map and 73% and 91% for the agriculture and wetland classifications, respectively. These results support the use of hierarchical classification and climate–vegetation relationships for mapping land cover at continental scales.     

Localized coarsening of conforming all-hexahedral meshes

Finite element mesh adaptation methods can be used to improve the efficiency and accuracy of solutions to computational modeling problems. In many applications involving hexahedral meshes, localized modifications which preserve a conforming all-hexahedral mesh are desired. Effective hexahedral refinement methods that satisfy these criteria have recently become available; however, due to hexahedral mesh topology constraints, little progress has been made in the area of hexahedral coarsening. This paper presents a new method to locally coarsen conforming all-hexahedral meshes. The method works on both structured and unstructured meshes and is not based on undoing previous refinement. Building upon recent developments in quadrilateral coarsening, the method utilizes hexahedral sheet and column operations, including pillowing, column collapsing, and sheet extraction. A general algorithm for automated coarsening is presented and examples of models that have been coarsened with this new algorithm are shown. While results are promising, further work is needed to improve the automated process.     

A maximum-likelihood algorithm for reduction of Langmuir probe data

The reduction of Langmuir triple and quadruple probe data, i.e., the determination of the electron temperature T(e) from the measured voltages and currents, requires the solution of an implicit transcendental equation in T(e), at every point in time. Random errors and noise in the measurements occasionally precludes solution of the equation, resulting in an indeterminate temperature at those times. We present a method for overcoming this problem that uses the method of maximum likelihood. The experimental uncertainties, assumed to be normally distributed, are used in solving the implicit equation in T(e). At every point in time, a likelihood function is calculated, and the temperature which maximizes this function is taken to be the solution T(e). The uncertainty in the resulting measurement is taken to be the width of the likelihood function. Examples of this technique are shown.     

Simulated train driving: fatigue, self-awareness and cognitive disengagement

Fatigue is a serious issue for the rail industry, increasing inefficiency and accident risk. The performance of 20 train drivers in a rail simulator was investigated at low, moderate and high fatigue levels. Psychomotor vigilance (PVT), self-rated performance and subjective alertness were also assessed. Alertness, PVT reaction times, extreme speed violations (>25% above the limit) and penalty brake applications increased with increasing fatigue level. In contrast, fuel use, draft (stretch) forces and braking errors were highest at moderate fatigue levels. Thus, at high fatigue levels, errors involving a failure to act (errors of omission) increased, whereas incorrect responses (errors of commission) decreased. The differential effect of fatigue on error types can be explained through a cognitive disengagement with the virtual train at high fatigue levels. Interaction with the train reduced dramatically, and accident risk increased. Awareness of fatigue-related performance changes was moderate at best. These findings are of operational concern.     

Unconditional lower bounds for learning intersections of halfspaces

We prove new lower bounds for learning intersections of halfspaces, one of the most important concept classes in computational learning theory. Our main result is that any statistical-query algorithm for learning the intersection of $\sqrt{n}$ halfspaces in n dimensions must make $2^{\varOmega (\sqrt{n})}$ queries. This is the first non-trivial lower bound on the statistical query dimension for this concept class (the previous best lower bound was n ^Ω(log?n)). Our lower bound holds even for intersections of low-weight halfspaces. In the latter case, it is nearly tight. We also show that the intersection of two majorities (low-weight halfspaces) cannot be computed by a polynomial threshold function (PTF) with fewer than n ^{Ω(log?n/log?log?n)} monomials. This is the first super-polynomial lower bound on the PTF length of this concept class, and is nearly optimal. For intersections of k=ω(log?n) low-weight halfspaces, we improve our lower bound to $\min\{2^{\varOmega (\sqrt{n})},n^{\varOmega (k/\log k)}\},$ which too is nearly optimal. As a consequence, intersections of even two halfspaces are not computable by polynomial-weight PTFs, the most expressive class of functions known to be efficiently learnable via Jackson’s Harmonic Sieve algorithm. Finally, we report our progress on the weak learnability of intersections of halfspaces under the uniform distribution.     

Screening of serum samples from Wegener's granulomatosis patients using antibody microarrays

Wegener's Granulomatosis (WG) is an idiopathic granulomatosis autoimmune vasculitis that primarily affects small vessels and is associated with glomerulonephritis and pulmonary granulomatous vasculitis. Anti‐neutrophil cytoplasmic auto‐antibodies (cANCA) against proteinase‐3 are used to identify WG, but ANCA titers are not present in some patients with the localized disease. The objective of this study was to develop an antibody array to help identify protein expression patterns in serum from patients with WG as compared to normals. The arrays were tested for limits of detection, background, and cross reactivity using standard proteins. The arrays were hybridized with either normal patient serum (n = 30) or with serum samples from a population of WG patients (n = 26) that were age and sex matched. Data analysis and curve fitting of the standard dilution series calculated r² values and determined a sensitivity of <50 pg/mL for the majority of proteins. A total of 24 proteins were assessed. Several statistically significant increases (p<0.05) were seen in the expression of: angiotensin converting enzyme‐I, IFN‐γ, IL‐8, s‐ICAM‐1 and s‐VCAM in WG patients as compared to controls. Utilizing the antibody microarray technology has led to the identification of potential biomarkers of vascular injury in the serum of WG patients.     

Programmable logic construction kits for hyper-real-time neuronal modeling

Programmable logic designs are presented that achieve exact integration of leaky integrate-and-fire soma and dynamical synapse neuronal models and incorporate spike-time dependent plasticity and axonal delays. Highly accurate numerical performance has been achieved by modifying simpler forward-Euler-based circuitry requiring minimal circuit allocation, which, as we show, behaves equivalently to exact integration. These designs have been implemented and simulated at the behavioral and physical device levels, demonstrating close agreement with both numerical and analytical results. By exploiting finely grained parallelism and single clock cycle numerical iteration, these designs achieve simulation speeds at least five orders of magnitude faster than the nervous system, termed here hyper-real-time operation, when deployed on commercially available field-programmable gate array (FPGA) devices. Taken together, our designs form a programmable logic construction kit of commonly used neuronal model elements that supports the building of large and complex architectures of spiking neuron networks for real-time neuromorphic implementation, neurophysiological interfacing, or efficient parameter space investigations.