Image analysis by moment invariants using a set of step-like basis functions

Moment invariants have been thoroughly studied and repeatedly proposed as one of the most powerful tools for 2D shape identification. In this paper a set of such descriptors is proposed, being the basis functions discontinuous in a finite number of points. The goal of using discontinuous functions is to avoid the Gibbs phenomenon, and therefore to yield a better approximation capability for discontinuous signals, as images. Moreover, the proposed set of moments allows the definition of rotation invariants, being this the other main design concern. Translation and scale invariance are achieved by means of standard image normalization. Tests are conducted to evaluate the behavior of these descriptors in noisy environments, where images are corrupted with Gaussian noise up to different SNR values. Results are compared to those obtained using Zernike moments, showing that the proposed descriptor has the same performance in image retrieval tasks in noisy environments, but demanding much less computational power for every stage in the query chain.

Joint 4-D variational stereo reconstruction and camera calibration refinement for oceanic sea state measurements

Validating modern oceanographic theories using models produced through stereo computer vision principles has recently emerged. Space-time (4-D) models of the ocean surface may be generated by stacking a series of 3-D reconstructions independently generated for each time instant or, in a more robust manner, by simultaneously processing several snapshots coherently in a true ?4-D reconstruction.? However, the accuracy of these computer-vision-generated models is subject to the estimations of camera parameters, which may be corrupted under the influence of natural factors such as wind and vibrations. Therefore, removing the unpredictable errors of the camera parameters is necessary for an accurate reconstruction. In this paper, we propose a novel algorithm that can jointly perform a 4-D reconstruction as well as correct the camera parameter errors introduced by external factors. The technique is founded upon variational optimization methods to benefit from their numerous advantages: continuity of the estimated surface in space and time, robustness, and accuracy. The performance of the proposed algorithm is tested using synthetic data produced through computer graphics techniques, based on which the errors of the camera parameters arising from natural factors can be simulated.

Estimating risks in declining populations with poor data

Census data on endangered species are often sparse, error-ridden, and confined to only a segment of the population. Estimating trends and extinction risks using this type of data presents numerous difficulties. In particular, the estimate of the variation in year-to-year transitions in population size (the “process error” caused by stochasticity in survivorship and fecundities) is confounded by the addition of high sampling error variation. In addition, the year-to-year variability in the segment of the population that is sampled may be quite different from the population variability that one is trying to estimate. The combined effect of severe sampling error and age- or stage-specific counts leads to severe biases in estimates of population-level parameters. I present an estimation method that circumvents the problem of age- or stage-specific counts and is markedly robust to severe sampling error. This method allows the estimation of environmental variation and population trends for extinction-risk analyses using corrupted census counts—a common type of data for endangered species that has hitherto been relatively unusable for these analyses.

Misleaders of labor,

A corrupted labor leadership.--Class collaboration.--Reactionary labor politics.--Bribery and betrayal in various industries.--Organized graft in the building trades.--Plundering the workers.Trade union capitalism swindle.--Tainted labor journalism.--Autocratic control of the unions.--What must be done.

The works of William Shakespeare : the plays edited from the folio of MDCXXIII, with various readings from all the editions and all the commentators, notes, introductory remarks, a historical sketch of the text, an account of the rise and progress of the English drama, a memoir of the poet, and an essay upon his genius /

Publisher's advertisement (12 p.) at beginning of v. 2.

The writings of Mark Twain [pseud.]

Each vol. has general half-title and special t.-p.

The complete works of Mark Twain.

v.1. The adventures of Tom Sawyer -- v.2. The innocents abroad -- v.3. Pudd'nhead Wilson -- v.4. The America claimant -- v.5. A Connecticut Yankee in King Arthur's court -- v.6. Roughing it -- v.7. Life on the Mississippi -- v.8. The mysterious stranger -- v.9. The adventures of Huckleberry Finn -- v.10. The gilded age -- v.11. A tramp abroad -- v.12. What is man? -- v.13. Following the equator -- v.14. Tom Sawyer abroad -- v.15. The man that corrupted Hadleyburg -- v.16. In defense of Harriet Shelley -- v.17. Joan of Arc -- v.18. The $30,000 bequest -- v.19. Sketches old and new -- v.20. Europe and elsewhere -- v.21. The prince and the pauper -- v.22. Mark Twain's notebook -- v.23. Christian science -- v.24. Mark Twain's speeches.

The writings of Mark Twain.

URL additional copies of v.6 and 19: Hillcrest Edition.

The complete works of Mark Twain

- Autobiography. - [v.1.] The adventures of Tom Sawyer. -[v.2.] The innocents abroad. - [v.3.] Pudd'nhead Wilson. - [v.4.] The American claimant. - [v.5.] Connecticut Yankee in King Arthur's court. -[v.6.] Roughing it. - [v.7.] Life on the Mississippi. - [v.8.] The mysterious stranger. -[v.9.] The adventures of Huckleberry Finn. - [v.10.] The gilded age. - [v.11.] A tramp abroad. -[v.12.] What is man? - [v.13.] Following the equator. -[v.14.] Tom Sawyer abroad. - [v.15.] The man that corrupted Hadleybrug. -[v.16.] In defense of Harriet Shelley. - [v.17.] Joan of Arc. - [v.18.] The 30,000 bequest. - [v.19.] Sketches, new and old. - [v.20.] Europe and elsewhere. - [v.21.] - The Prince and the pauper. - [v.22.] Mark Twain's notebook. - [v.23.] Christian Science. -[v.24.] Mark Twain's speeches.

Essays on Procurement, Scoring Auction, and Quality Manipulation Corruption

Thesis (Ph.D.)--University of Washington, 2016-06

Educação e Religião em Comenius na sua Didática Magna

Este trabalho é uma pesquisa sobre Religião e Educação em João Amós Comenius na obra Didática Magna. No primeiro capítulo, são apresentados o contexto histórico, social, político e religioso em que Comenius estava inserido e as influências que marcaram seu pensamento. Também sua vida e obra. No segundo capítulo é analisada a constituição da educação na Idade Média. Para tanto, são descritos os principais desenvolvimentos ocorridos na educação, bem como as manifestações de cada movimento, caracterizando como a educação se difundia no período anterior ao de Comenius. No terceiro capítulo são apresentadas sínteses e algumas análises teológicas e pedagógicas da obra Didática Magna, tendo como proposta proporcionar uma pedagogia educacional transformadora, uma arte de ensinar tudo a todos . Um ponto de real importância a saber é que a salvação tinha como seu instrumento humano a educação. A educação e apresentada como a salvação para a corrupção do gênero humano. Na obra são descritos os princípios que devem colaborar para a formação do ser humano. A pesquisa conclui que em Comenius houve um impulso para a educação. Mas este impulso não pode ser atribuído apenas à força do pedagogo e teologo João Amós Comenius. Deve-se ser compreendido em conjunto com os fatores políticos, religiosos e culturais.

Learning with noise and regularizers in multilayer neural networks

We study the effect of two types of noise, data noise and model noise, in an on-line gradient-descent learning scenario for general two-layer student network with an arbitrary number of hidden units. Training examples are randomly drawn input vectors labeled by a two-layer teacher network with an arbitrary number of hidden units. Data is then corrupted by Gaussian noise affecting either the output or the model itself. We examine the effect of both types of noise on the evolution of order parameters and the generalization error in various phases of the learning process.

Learning with regularizers in multilayer neural networks

We study the effect of regularization in an on-line gradient-descent learning scenario for a general two-layer student network with an arbitrary number of hidden units. Training examples are randomly drawn input vectors labelled by a two-layer teacher network with an arbitrary number of hidden units which may be corrupted by Gaussian output noise. We examine the effect of weight decay regularization on the dynamical evolution of the order parameters and generalization error in various phases of the learning process, in both noiseless and noisy scenarios.

Noisy fitness evaluation in genetic algorithms and the dynamics of learning

A theoretical model is presented which describes selection in a genetic algorithm (GA) under a stochastic fitness measure and correctly accounts for finite population effects. Although this model describes a number of selection schemes, we only consider Boltzmann selection in detail here as results for this form of selection are particularly transparent when fitness is corrupted by additive Gaussian noise. Finite population effects are shown to be of fundamental importance in this case, as the noise has no effect in the infinite population limit. In the limit of weak selection we show how the effects of any Gaussian noise can be removed by increasing the population size appropriately. The theory is tested on two closely related problems: the one-max problem corrupted by Gaussian noise and generalization in a perceptron with binary weights. The averaged dynamics can be accurately modelled for both problems using a formalism which describes the dynamics of the GA using methods from statistical mechanics. The second problem is a simple example of a learning problem and by considering this problem we show how the accurate characterization of noise in the fitness evaluation may be relevant in machine learning. The training error (negative fitness) is the number of misclassified training examples in a batch and can be considered as a noisy version of the generalization error if an independent batch is used for each evaluation. The noise is due to the finite batch size and in the limit of large problem size and weak selection we show how the effect of this noise can be removed by increasing the population size. This allows the optimal batch size to be determined, which minimizes computation time as well as the total number of training examples required.

The belief in TAP

We show the similarity between belief propagation and TAP, for decoding corrupted messages encoded by Sourlas's method. The latter is a special case of the Gallager error- correcting code, where the code word comprises products of K bits selected randomly from the original message. We examine the efficacy of solutions obtained by the two methods for various values of K and show that solutions for K>=3 may be sensitive to the choice of initial conditions in the case of unbiased patterns. Good approximations are obtained generally for K=2 and for biased patterns in the case of K>=3, especially when Nishimori's temperature is being used.