A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data.

Fiber-optic distributed acoustic sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis, including microseismicity detection, ambient noise tomography, earthquake source characterization, and active source seismology. Using laser-pulse techniques, DAS turns (commercial) fiber-optic cables into seismic arrays with a spatial sampling density of the order of meters and a time sampling rate up to one thousand Hertz. The versatility of DAS enables dense instrumentation of traditionally inaccessible domains, such as urban, glaciated, and submarine environments. This in turn opens up novel applications such as traffic density monitoring and maritime vessel tracking. However, these new environments also introduce new challenges in handling various types of recorded noise, impeding the application of traditional data analysis workflows. In order to tackle the challenges posed by noise, new denoising techniques need to be explored that are tailored to DAS. In this work, we propose a Deep Learning approach that leverages the spatial density of DAS measurements to remove spatially incoherent noise with unknown characteristics. This approach is entirely self-supervised, so no noise-free ground truth is required, and it makes no assumptions regarding the noise characteristics other than that it is spatio-temporally incoherent. We apply our approach to both synthetic and real-world DAS data to demonstrate its excellent performance, even when the signals of interest are well below the noise level. Our proposed methods can be readily incorporated into conventional data processing workflows to facilitate subsequent seismological analyses.

Nonlinear Unmixing of Hyperspectral Data With Vector-Valued Kernel Functions.

This paper presents a kernel-based nonlinear mixing model for hyperspectral data, where the nonlinear function belongs to a Hilbert space of vector valued functions. The proposed model extends the existing ones by accounting for band-dependent and neighboring nonlinear contributions. The key idea is to work under the assumption that nonlinear contributions are dominant in some parts of the spectrum, while they are less pronounced in other parts. In addition to this, we motivate the need for taking into account nonlinear contributions originating from the ground covers of neighboring pixels by practical considerations, precisely the adjacency effect. The relevance of the proposed model is that the nonlinear function is associated with a matrix valued kernel that allows to jointly model a wide range of nonlinearities and includes prior information regarding band dependences. Furthermore, the choice of the nonlinear function input allows to incorporate neighboring effects. The optimization problem is strictly convex and the corresponding iterative algorithm is based on the alternating direction method of multipliers. Finally, experiments conducted using synthetic and real data demonstrate the effectiveness of the proposed approach.

Proximity operators for phase retrieval.

We present a new formulation of a family of proximity operators that generalize the projector step for phase retrieval. These proximity operators for noisy intensity measurements can replace the classical "noise-free" projection in any projection-based algorithm. They are derived from a maximum-likelihood formulation and admit closed form solutions for both the Gaussian and the Poisson cases. In addition, we extend these proximity operators to under-sampled intensity measurements. To assess their performance, these operators are exploited in a classical Gerchberg-Saxton algorithm. We present numerical experiments showing that the reconstructed complex amplitudes with these proximity operators always perform better than using the classical intensity projector, while their computational overhead is moderate.

PAINTER: a spatiospectral image reconstruction algorithm for optical interferometry.

Astronomical optical interferometers sample the Fourier transform of the intensity distribution of a source at the observation wavelength. Because of rapid perturbations caused by atmospheric turbulence, the phases of the complex Fourier samples (visibilities) cannot be directly exploited. Consequently, specific image reconstruction methods have been devised in the last few decades. Modern polychromatic optical interferometric instruments are now paving the way to multiwavelength imaging. This paper is devoted to the derivation of a spatiospectral (3D) image reconstruction algorithm, coined Polychromatic opticAl INTErferometric Reconstruction software (PAINTER). The algorithm relies on an iterative process, which alternates estimation of polychromatic images and complex visibilities. The complex visibilities are not only estimated from squared moduli and closure phases, but also differential phases, which helps to better constrain the polychromatic reconstruction. Simulations on synthetic data illustrate the efficiency of the algorithm and, in particular, the relevance of injecting a differential phases model in the reconstruction.

Blind and fully constrained unmixing of hyperspectral images.

This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images. Unmixing is performed without the use of any dictionary, and assumes that the number of constituent materials in the scene and their spectral signatures are unknown. The estimated abundances satisfy the desired sum-to-one and nonnegativity constraints. Two models with increasing complexity are developed to achieve this challenging task, depending on how noise interacts with hyperspectral data. The first one leads to a convex optimization problem and is solved with the alternating direction method of multipliers. The second one accounts for signal-dependent noise and is addressed with a reweighted least squares algorithm. Experiments on synthetic and real data demonstrate the effectiveness of our approach.

Melanoma anorretal: relato de 2 casos e revisão da literatura / Anorectal melanoma: two case reports and review of the literature

Os tumores malignos do canal anal e do anus são muito raros, não ultrapassam 2 por cento de todos os tumores do colo, reto e anus; segundo os principais levantamentos os melanomas não ultrapassam a incidência de 0,1 a 1,2 por cento dos tumores malignos. Os autores descrevem 2 casos de melanoma, discutindo os principais dados da literatura, enfocando os aspectos diagnósticos, tratamento, evolução e prognostico. Os indicies de cura s são baixos e com elevados índices de mortalidade a curto prazo.

Malignant tumors of the anal canal and anus are rare pathologic events, representing less than 2 percent of all tumors of the colon, rectum and anus; according to reports, the incidence of melanoma does not achieve 0,1 to 1,2 percent of malignant tumors. Authors describe two cases of melanoma, discussing the data described in literature, focusing on the diagnosis aspects, treatment, evolution and prognosis. Cure rates are low and the disease presents high rates of mortality in short term.

Bivariate gamma distributions for image registration and change detection.

This paper evaluates the potential interest of using bivariate gamma distributions for image registration and change detection. The first part of this paper studies estimators for the parameters of bivariate gamma distributions based on the maximum likelihood principle and the method of moments. The performance of both methods are compared in terms of estimated mean square errors and theoretical asymptotic variances. The mutual information is a classical similarity measure which can be used for image registration or change detection. The second part of the paper studies some properties of the mutual information for bivariate Gamma distributions. Image registration and change detection techniques based on bivariate gamma distributions are finally investigated. Simulation results conducted on synthetic and real data are very encouraging. Bivariate gamma distributions are good candidates allowing us to develop new image registration algorithms and new change detectors.

Influência materna e os fatores de risco de cárie dentária / Maternal influence and the risk factors of dental decay

Por ser multifatorial e transmissível, a cárie dentária deve ter seus métodos preventivos baseados principalmente na educação materna, uma vez que a mãe é a pessoa mais intimamente relacionada à criança. O presente trabalho foi realizado em 153 crianças, de zero a cinco anos de idade, de seis creches públicas da cidade de Diamantina/MG, abordando os fatores comportamentais de risco das mães, em ralação à transmissibilidade de cárie, dieta cariogênica noturna, higiene bucal das crianças e o nível de instrução materno em relação à doença. Para sua execução, aplicou-se um questionário às mães e, posteriormente, foi realizado o exame físico intra-bucal nas crianças, onde foi observado elevado índice ceo-d (4,83) sendo que, deste total, 84,57 por cento estavam cariados, 13,53 por cento extraídos/extração indicada e somente 1,90 por cento obturados. Algumas mães, apesar de conhecerem os métodos corretos de higiene bucal, bem como a idade correta para o seu início, não os aplicam aos filhos. Foi constatado que o número de crianças assistidas por serviço odontológico é muito baixo, refletindo assim o desinteresse ou falta de conhecimento das mães sobre a necessidade de assistência odontológica precoce, bem como a deficiência dos programas de saúde, tanto em nível preventivo quanto curativo