Geometry‐specified troposphere decorrelation for subcentimeter real‐time kinematic solutions over long baselines

Real‐time kinematic (RTK) GPS techniques have been extensively developed for applications including surveying, structural monitoring, and machine automation. Limitations of the existing RTK techniques that hinder their applications for geodynamics purposes are twofold: (1) the achievable RTK accuracy is on the level of a few centimeters and the uncertainty of vertical component is 1.5–2 times worse than those of horizontal components and (2) the RTK position uncertainty grows in proportional to the base‐torover distances. The key limiting factor behind the problems is the significant effect of residual tropospheric errors on the positioning solutions, especially on the highly correlated height component. This paper develops the geometry‐specified troposphere decorrelation strategy to achieve the subcentimeter kinematic positioning accuracy in all three components. The key is to set up a relative zenith tropospheric delay (RZTD) parameter to absorb the residual tropospheric effects and to solve the established model as an ill‐posed problem using the regularization method. In order to compute a reasonable regularization parameter to obtain an optimal regularized solution, the covariance matrix of positional parameters estimated without the RZTD parameter, which is characterized by observation geometry, is used to replace the quadratic matrix of their “true” values. As a result, the regularization parameter is adaptively computed with variation of observation geometry. The experiment results show that new method can efficiently alleviate the model’s ill condition and stabilize the solution from a single data epoch. Compared to the results from the conventional least squares method, the new method can improve the longrange RTK solution precision from several centimeters to the subcentimeter in all components. More significantly, the precision of the height component is even higher. Several geosciences applications that require subcentimeter real‐time solutions can largely benefit from the proposed approach, such as monitoring of earthquakes and large dams in real‐time, high‐precision GPS leveling and refinement of the vertical datum. In addition, the high‐resolution RZTD solutions can contribute to effective recovery of tropospheric slant path delays in order to establish a 4‐D troposphere tomography.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

Geometry vs appearance for discriminating between posed and spontaneous emotions

Spontaneous facial expressions differ from posed ones in appearance, timing and accompanying head movements. Still images cannot provide timing or head movement information directly. However, indirectly the distances between key points on a face extracted from a still image using active shape models can capture some movement and pose changes. This information is superposed on information about non-rigid facial movement that is also part of the expression. Does geometric information improve the discrimination between spontaneous and posed facial expressions arising from discrete emotions? We investigate the performance of a machine vision system for discrimination between posed and spontaneous versions of six basic emotions that uses SIFT appearance based features and FAP geometric features. Experimental results on the NVIE database demonstrate that fusion of geometric information leads only to marginal improvement over appearance features. Using fusion features, surprise is the easiest emotion (83.4% accuracy) to be distinguished, while disgust is the most difficult (76.1%). Our results find different important facial regions between discriminating posed versus spontaneous version of one emotion and classifying the same emotion versus other emotions. The distribution of the selected SIFT features shows that mouth is more important for sadness, while nose is more important for surprise, however, both the nose and mouth are important for disgust, fear, and happiness. Eyebrows, eyes, nose and mouth are important for anger.

Evaluation of texture and geometry for dimensional facial expression recognition

Facial expression recognition (FER) algorithms mainly focus on classification into a small discrete set of emotions or representation of emotions using facial action units (AUs). Dimensional representation of emotions as continuous values in an arousal-valence space is relatively less investigated. It is not fully known whether fusion of geometric and texture features will result in better dimensional representation of spontaneous emotions. Moreover, the performance of many previously proposed approaches to dimensional representation has not been evaluated thoroughly on publicly available databases. To address these limitations, this paper presents an evaluation framework for dimensional representation of spontaneous facial expressions using texture and geometric features. SIFT, Gabor and LBP features are extracted around facial fiducial points and fused with FAP distance features. The CFS algorithm is adopted for discriminative texture feature selection. Experimental results evaluated on the publicly accessible NVIE database demonstrate that fusion of texture and geometry does not lead to a much better performance than using texture alone, but does result in a significant performance improvement over geometry alone. LBP features perform the best when fused with geometric features. Distributions of arousal and valence for different emotions obtained via the feature extraction process are compared with those obtained from subjective ground truth values assigned by viewers. Predicted valence is found to have a more similar distribution to ground truth than arousal in terms of covariance or Bhattacharya distance, but it shows a greater distance between the means.

Analyzing real-time video microscopy : the dynamics and geometry of vesicles and tubules in endocytosis

With the advent of live cell imaging microscopy, new types of mathematical analyses and measurements are possible. Many of the real-time movies of cellular processes are visually very compelling, but elementary analysis of changes over time of quantities such as surface area and volume often show that there is more to the data than meets the eye. This unit outlines a geometric modeling methodology and applies it to tubulation of vesicles during endocytosis. Using these principles, it has been possible to build better qualitative and quantitative understandings of the systems observed, as well as to make predictions about quantities such as ligand or solute concentration, vesicle pH, and membrane trafficked. The purpose is to outline a methodology for analyzing real-time movies that has led to a greater appreciation of the changes that are occurring during the time frame of the real-time video microscopy and how additional quantitative measurements allow for further hypotheses to be generated and tested.

Practical improvements to simultaneous computation of multi-view geometry and radial lens distortion

This paper discusses practical issues related to the use of the division model for lens distortion in multi-view geometry computation. A data normalisation strategy is presented, which has been absent from previous discussions on the topic. The convergence properties of the Rectangular Quadric Eigenvalue Problem solution for computing division model distortion are examined. It is shown that the existing method can require more than 1000 iterations when dealing with severe distortion. A method is presented for accelerating convergence to less than 10 iterations for any amount of distortion. The new method is shown to produce equivalent or better results than the existing method with up to two orders of magnitude reduction in iterations. Through detailed simulation it is found that the number of data points used to compute geometry and lens distortion has a strong influence on convergence speed and solution accuracy. It is recommended that more than the minimal number of data points be used when computing geometry using a robust estimator such as RANSAC. Adding two to four extra samples improves the convergence rate and accuracy sufficiently to compensate for the increased number of samples required by the RANSAC process.

Effect of geometry on the melting rates of iron rods burning in high pressure oxygen

The effect of sample geometry on the melting rates of burning iron rods was assessed. Promoted-ignition tests were conducted with rods having cylindrical, rectangular, and triangular cross-sectional shapes over a range of cross-sectional areas. The regression rate of the melting interface (RRMI) was assessed using a statistical approach which enabled the quantification of confidence levels for the observed differences in RRMI. Statistically significant differences in RRMI were observed for rods with the same cross-sectional area but different cross-sectional shape. The magnitude of the proportional difference in RRMI increased with the cross-sectional area. Triangular rods had the highest RRMI, followed by rectangular rods, and then cylindrical rods. The dependence of RRMI on rod shape is shown to relate to the action of molten metal at corners. The corners of the rectangular and triangular rods melted faster than the faces due to their locally higher surface area to volume ratios. This phenomenon altered the attachment geometry between liquid and solid phases, increasing the surface area available for heat transfer, causing faster melting. Findings relating to the application of standard flammability test results in industrial situations are also presented.

Facilitating growth in prospective teachers’ knowledge : teaching geometry in primary schools

This paper reports on a study that focused on growth of understanding about teaching geometry by a group of prospective teachers engaged in lesson plan study within a computer-supported collaborative learning (CSCL) environment. Participation in the activity was found to facilitate considerable growth in the participants’ pedagogical-content knowledge (PCK). Factors that influenced growth in PCK included the nature of the lesson planning task, the cognitive scaffolds inserted into the CSCL virtual space, the meta-language scaffolds provided to the participants, and the provision of both private and public discourse spaces. The paper concludes with recommendations for enhancing effective knowledge-building discourse about mathematics PCK within prospective teacher education CSCL environments.

'The war of Canudos: a Euclidean interpretation.' [Guerra de Canudos: uma leitura euclidiana]

The publication of the book The interior, in 1902, would change the course of thinking about the War of Canudos, who for many years, had been known simply as' the history of Euclid. President Getulio Vargas became interested in the backwoods bloodbath after reading the book avenger-Euclidean. Liked the work he visited the place of occurrence of war promising enjoy the river poured-Barris with the construction of the weir Cocorobo. Euclides da Cunha lived and produced his work in a time of great change in thought, politics and technology. Despite having worked in the press throughout his life, was best known as an engineer, for having exercised the office during the reconstruction of the bridge, in Sao Jose do Rio Pardo. This article aims to illuminate the event of war in light of the Euclidean work. We will examine the trajectory of Euclides da Cunha in journalism. Your learning process to execute the office newsreader and war correspondent, the newspaper O Estado de S. Paul, as well as their reports and work-monument the hinterlands. Resumo: A publicação da obra Os sertões, em 1902, mudaria os rumos do pensamento sobre a Guerra de Canudos, que, por muitos anos, ficara conhecida, simplesmente, como ‘história de Euclides’. O presidente Getúlio Vargas interessou-se pela hecatombe sertaneja após ter lido o livro-vingador euclidiano. Gostou tanto da obra que visitou o lugar de acontecimento da guerra prometendo aproveitar as águas do rio Vaza-Barris com a construção do açude de Cocorobó. Euclides da Cunha viveu e produziu a sua obra em um momento de grandes transformações no pensamento, na política e na tecnologia. Apesar de ter atuado na imprensa ao longo de toda a sua vida, ficou mais conhecido como engenheiro, por ter exercido o ofício, durante a reconstrução da ponte, em São José do Rio Pardo. O presente artigo visa iluminar o acontecimento da guerra à luz da obra euclidiana. Examinaremos a trajetória de Euclides da Cunha no jornalismo. O seu processo de aprendizagem para exercer o ofício de noticiarista e correspondente de guerra, pelo jornal O Estado de S. Paulo, bem como, as suas reportagens e obra-monumento Os sertões.

Alkyl radical geometry controls geminate cage recombination in alkylcobalamins

Abstract: The radical pair that results from photolysis of adenosylcob(II1)alamin (AdoCbl"') undergoes primary geminate recombination with a first-order rate constant of 1 x lo9 s-l. In contrast, methylcob(II1)alamin (CH3Cbl"') and aristeromicylcob(II1)alamin (AriCblII', the carbocyclic analogue of AdoCbl"' in which the ribofuranose ring oxygen has been replaced with a methylene group) does not undergo primary geminate recombination. The ribofwanose group enables a high rate of geminate recombination in the [Ado' Cbl"'] radical pair. This may be due to a stereoelectronic (p-anomeric) effect that maintains a pyramidal geometry at the 5'-carbon of the 5'-deoxyadenosyl radical, or it may be due to hindered rotation about the C4t-C5, bond such that /?-elimination to the olefin is prevented. Recombination in the geminate singlet radical pair is in competition with diffusive escape to form a solvent-separated radical pair. Hyperfine coupling from Co" promotes intersystem crossing to the triplet radical pair (Chagovetz, A. M.; Grissom, C. B. J. Am. Chem. SOC. 1993, 115, 12152). Recombination of the [CH3' Cbl"] radical pair is not prevented by a lack of intersystem crossing, as neither unlabeled or I3C-labeled CH3Cbl"' undergoes geminate recombination. There is only a small difference in the rate of diffusive recombination in the solvent cage for AdoCbl"', AriCbl"', and CH3Cbl"' following photolysis: 2.01 x 10" s-l, 2.20 x lo4 s-l, and 1.16 x lo4 s-l. The rate of diffusive recombination is limited by productive collisions and not by radical geometry or intersystem crossing. The CF3' radical that results from photolysis of (trifluoromethyl)cob(III)alamin (CF3Cbl"') maintains its pyramidal geometry and undergoes faster diffusive recombination in the solvent cage at 51 x lo4 s-l. The C-Co bond dissociation enthalpy in AriCbl"' is 37 f 1.4 kcaymol. The profound difference in geminate recombination rates for AdoCbl"' and CH3Cbl"' is consistent with their different biological roles as enzymatic cofactors: AdoCbl"' is an initiator of radical chain chemistry in the active site, whereas CH3Cbl"' is a methyl group donor in an S~2-type process.

The analogue shear zone : from rheology to associated geometry

The geometry of ductile strain localization phenomena is related to the rheology of the deformed rocks. Both qualitative and quantitative rheological properties of natural rocks have been estimated from finite field structures such as folds and shear zones. We apply physical modelling to investigate the relationship between rheology and the temporal evolution of the width and transversal strain distribution in shear zones, both of which have been used previously as rheological proxies. Geologically relevant materials with well-characterized rheological properties (Newtonian, strain hardening, strain softening, Mohr-Coulomb) are deformed in a shear box and observed with Particle Imaging Velocimetry (PIV). It is shown that the width and strain distribution histories in model shear zones display characteristic finite responses related to material properties as predicted by previous studies. Application of the results to natural shear zones in the field is discussed. An investigation of the impact of 3D boundary conditions in the experiments demonstrates that quantitative methods for estimating rheology from finite natural structures must take these into account carefully.

A multi-layered approach for site detection in UAS emergency landing scenarios using geometry-based image segmentation

This paper presents an alternative approach to image segmentation by using the spatial distribution of edge pixels as opposed to pixel intensities. The segmentation is achieved by a multi-layered approach and is intended to find suitable landing areas for an aircraft emergency landing. We combine standard techniques (edge detectors) with novel developed algorithms (line expansion and geometry test) to design an original segmentation algorithm. Our approach removes the dependency on environmental factors that traditionally influence lighting conditions, which in turn have negative impact on pixel-based segmentation techniques. We present test outcomes on realistic visual data collected from an aircraft, reporting on preliminary feedback about the performance of the detection. We demonstrate consistent performances over 97% detection rate.