A reduced-complexity and asymptotically efficient time-delay estimator

This paper considers the problem of estimating the time delays of multiple replicas of a known signal received by an array of antennas. Under the assumptions that the noise and co-channel interference (CCI) are spatially colored Gaussian processes and that the spatial signatures are arbitrary, the maximum likelihood (ML) solution to the general time delay estimation problem is derived. The resulting criterion for the delays yields consistent and asymptotically efficient estimates. However, the criterion is highly non-linear, and not conducive to simple minimization procedures. We propose a new cost function that is shown to provide asymptotically efficient delay estimates. We also outline a heuristic way of deriving this cost function. The form of this new estimator lends itself to minimization by the computationally attractive iterative quadratic maximum likelihood (IQML) algorithm. The existence of simple yet accurate initialization schemes based on ESPRIT and identity weightings makes the approach viable for practical implementation.Peer ReviewedPostprint (published version

From Collapse to Freezing in Random Heteropolymers

We consider a two-letter self-avoiding (square) lattice heteropolymer model of N_H (out ofN) attracting sites. At zero temperature, permanent links are formed leading to collapse structures for any fraction rho_H=N_H/N. The average chain size scales as R = N^{1/d}F(rho_H) (d is space dimension). As rho_H --> 0, F(rho_H) ~ rho_H^z with z={1/d-nu}=-1/4 for d=2. Moreover, for 0 < rho_H < 1, entropy approaches zero as N --> infty (being finite for a homopolymer). An abrupt decrease in entropy occurs at the phase boundary between the swollen (R ~ N^nu) and collapsed region. Scaling arguments predict different regimes depending on the ensemble of crosslinks. Some implications to the protein folding problem are discussed.Comment: 4 pages, Revtex, figs upon request. New interpretation and emphasis. Submitted to Europhys.Let

Overview of ImageCLEFcoral 2019 task

Understanding the composition of species in ecosystems on a large scale is key to developing effective solutions for marine conservation, hence there is a need to classify imagery automatically and rapidly. In 2019, ImageCLEF proposed for the first time the ImageCLEFcoral task. The task requires participants to automatically annotate and localize benthic substrate (such as hard coral, soft coral, algae and sponge) in a collection of images originating from a growing, large-scale dataset from coral reefs around the world as part of monitoring programmes. In its first edition, five groups participated submitting 20 runs using a variety of machine learning and deep learning approaches. Best runs achieved 0.24 in the annotation and localisation subtask and 0.04 on the pixel-wise parsing subtask in terms of MAP 0.5 IoU scores which measures the Mean Average Precision (MAP) when using the performance measure of Intersection over Union (IoU) bigger to 0.5 of the ground truth

Sustainability indicators of subsurface flow constructed wetlands in Portuguese small communities

The discharge of untreated domestic wastewater in the receiving waters creates a negative and environmental impact, inversely proportional to its autodepuration ability. Conventional wastewater treatment plants involve large capital investments and operating costs, and could be economically unsustainable for small-medium communities. So, constructed wetlands as natural low-cost systems can be an appropriate alternative, because they require low maintenance, give rise to good performances and provide a natural appearance. This work presents a synthesis of data obtained through an extensive survey performed in twenty Portuguese constructed wetlands utilities. Based on this information, some sustainable indicators and removal pollutant efficiencies were calculated. Besides identifying the main operational problems observed, it was also possible to detect inadequate monitoring procedures, aiming, with some proposed corrections, to improve the performance of these low-cost wastewater treatment utilities. The results obtained in this work encourage the development of future studies to increase the performance of these wastewater systems based on a better knowledge of the influence of hydraulic parameters, like flow, retention time and hydraulic application rate, in the pollutants removal efficiencies.(undefined

SlideImages: A Dataset for Educational Image Classification

In the past few years, convolutional neural networks (CNNs) have achieved impressive results in computer vision tasks, which however mainly focus on photos with natural scene content. Besides, non-sensor derived images such as illustrations, data visualizations, figures, etc. are typically used to convey complex information or to explore large datasets. However, this kind of images has received little attention in computer vision. CNNs and similar techniques use large volumes of training data. Currently, many document analysis systems are trained in part on scene images due to the lack of large datasets of educational image data. In this paper, we address this issue and present SlideImages, a dataset for the task of classifying educational illustrations. SlideImages contains training data collected from various sources, e.g., Wikimedia Commons and the AI2D dataset, and test data collected from educational slides. We have reserved all the actual educational images as a test dataset in order to ensure that the approaches using this dataset generalize well to new educational images, and potentially other domains. Furthermore, we present a baseline system using a standard deep neural architecture and discuss dealing with the challenge of limited training data.Comment: 8 pages, 2 figures, to be presented at ECIR 202

EGS4 and MCNP4b MC Simulation of a Siemens KD2 Accelerator in 6 MV Photon Mode

The geometry of a Siemens Mevatron KD2 linear accelerator in 6 MV photon mode was modeled with EGS4 and MCNP4b. Energy spectra and other phase space distributions have been extensively compared in different plans along the beam line. The differences found have been evaluated both qualitative and quantitatively. The final aim was that both codes, running in different operating systems and with a common set of simulation conditions, met the requirement of fitting the experimental depth dose curves and dose profiles, measured in water for different field sizes. Whereas depth dose calculations are in a certain extent insensible to some simulation parameters like electron nominal energy, dose profiles have revealed to be a much better indicator to appreciate that feature. Fine energy tuning has been tried and the best fit was obtained for a nominal electron energy of 6.15 MeV

An Overview of Lipid Droplets in Cancer and Cancer Stem Cells

For decades, lipid droplets have been considered as the main cellular organelles involved in the fat storage, because of their lipid composition. However, in recent years, some new and totally unexpected roles have been discovered for them: (i) they are active sites for synthesis and storage of inflammatory mediators, and (ii) they are key players in cancer cells and tissues, especially in cancer stem cells. In this review, we summarize the main concepts related to the lipid droplet structure and function and their involvement in inflammatory and cancer processes

Bose-Einstein condensation in 87Rb: characterization of the Brazilian experiment

We describe the experimental apparatus and the methods to achieve Bose-Einstein condensation in 87Rb atoms. Atoms are first laser cooled in a standard double magneto-optical trap setup and then transferred into a QUIC trap. The system is brought to quantum degeneracy selectively removing the hottest atoms from the trap by radio-frequency radiation. We also present the main theoretical aspects of the Bose-Einstein condensation phenomena in atomic gases