How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study

Bidirectional generative adversarial networks (BiGANs) and cycle generative adversarial networks (CycleGANs) are two emerging machine learning models that, up to now, have been used as generative models, i.e., to generate output data sampled from a target probability distribution. However, these models are also equipped with encoding modules, which, after weakly supervised training, could be, in principle, exploited for the extraction of hidden features from the input data. At the present time, how these extracted features could be effectively exploited for classification tasks is still an unexplored field. Hence, motivated by this consideration, in this paper, we develop and numerically test the performance of a novel inference engine that relies on the exploitation of BiGAN and CycleGAN-learned hidden features for the detection of COVID-19 disease from other lung diseases in computer tomography (CT) scans. In this respect, the main contributions of the paper are twofold. First, we develop a kernel density estimation (KDE)-based inference method, which, in the training phase, leverages the hidden features extracted by BiGANs and CycleGANs for estimating the (a priori unknown) probability density function (PDF) of the CT scans of COVID-19 patients and, then, in the inference phase, uses it as a target COVID-PDF for the detection of COVID diseases. As a second major contribution, we numerically evaluate and compare the classification accuracies of the implemented BiGAN and CycleGAN models against the ones of some state-of-the-art methods, which rely on the unsupervised training of convolutional autoencoders (CAEs) for attaining feature extraction. The performance comparisons are carried out by considering a spectrum of different training loss functions and distance metrics. The obtained classification accuracies of the proposed CycleGAN-based (resp., BiGAN-based) models outperform the corresponding ones of the considered benchmark CAE-based models of about 16% (resp., 14%)

SPIRE Map-Making Test Report

The photometer section of SPIRE is one of the key instruments on board of Herschel. Its legacy depends very much on how well the scanmap observations that it carried out during the Herschel mission can be converted to high quality maps. In order to have a comprehensive assessment on the current status of SPIRE map-making, as well as to provide guidance for future development of the SPIRE scan-map data reduction pipeline, we carried out a test campaign on SPIRE map-making. In this report, we present results of the tests in this campaign.Comment: This document has an executive summary, 6 chapters, and 102 pages. More information can be found at: https://nhscsci.ipac.caltech.edu/sc/index.php/Spire/SPIREMap-MakingTest201

Image estimation in the presence of irregular sampling, noise, and pointing jitter

We consider an acquisition system where a continuous image is reconstructed from a set of irregularly distributed noisy samples. Moreover, the system is affected by a random pointing jitter which makes the actual sampling positions different from the nominal ones. We develop a model for the system and derive the optimal, minimum variance unbiased (MVU) estimate. Unfortunately, the latter estimate is not practical to compute when the data size is large. Therefore, we develop a simplified, low resolution model and derive the corresponding MVU estimate, which has a drastically lower complexity. Moreover, we analyze the estimators' performance by using both theoretical analysis and simulations. Finally, we discuss the application to the data of the Photodetector Array Camera and Spectrometer (PACS) instrument, which is an infrared photometer on board the Herschel satellite

Optimal Fast Algorithm for Power and Bit Allocation in OFDM Systems

In an earlier paper, a simple and fast bit loading algorithm for orthogonal frequency-division-multiplex (OFDM) systems was presented. A drawback of the algorithm is that the solution obtained is suboptimal. This drawback is removed in this paper, where we show that the algorithm can be improved and turned into an optimal algorithm with a negligible impact on the computational complexity

Optimum adaptive OFDM systems

When Orthogonal Frequency Division Multiplexing (OFDM) is used to transmit information over a frequency selective channel, it is convenient to vary the power and the number of bits allocated to each subcarrier in order to optimize the system performance. In this paper, the three classical problems of transmission power minimization, error rate minimization and throughput maximization are investigated in a unified manner. The relations existing among these three problems are clarified and a precise definition of optimum system is given. A general and rigorous way to extend the solution of any of the three problems in order to obtain the solution of the other two is presented. This result is used to devise an efficient algorithm for the error rate minimization

An Alternating Least Squares Algorithm with Application to Image Processing

Metodo di rimozione del drift per fotometri infrarossiLeast Squares (LS) estimation is a classical problem, often arising in practice. When the dimension of the problem is large, the solution may be difficult to obtain, due to complexity reasons. A general way to reduce the complexity is that of breaking the problem in smaller sub-problems. Following this approach, in the paper we introduce an Alternating Least Squares (ALS) algorithm that finds the LS estimate by iteratively solving two sub-problems. The algorithm can speed up the solution of any LS problem, but it is especially well suited for applications where the partition arises naturally so that the sub-problems have a structure and are simple to solve. To illustrate this fact, we discuss the application of the ALS to an image formation system affected by noise and drift, describing an efficient implementation and showing that the ALS is an effective image formation method

Least squares image estimation for large data in the presence of noise and irregular sampling

We consider an acquisition system where a continuous, band-limited image is reconstructed from a set of irregularly distributed, noisy samples. An optimal estimator can be obtained by exploiting Least Squares, but it is not practical to compute when the data size is large. A simpler, widely used estimate can be obtained by properly rounding off the pointing information, but it is suboptimal and is affected by a bias, which may be large and thus limits its applicability. To solve this problem, we develop a mathematical model for the acquisition system, which accounts for the pointing information round off. Based on the model, we derive a novel optimal estimate, which has a manageable computational complexity and is largely immune from the bias, making it a better option than the suboptimal one. Moreover, the model opens a new, fruitful point of view on the estimation performance analysis. Finally, we consider the application of the novel estimate to the data of the Photodetector Array Camera and Spectrometer instrument. In this paper, we discuss several implementation aspects and investigate the performance by using both true and simulated data

Fast algorithm for power and bit allocation in OFDM systems

The problem of power and bit allocation in OFDM systems is analysed. A solution algorithm with substantially lower computational complexity than existing algorithms is proposed