Survey of water proton longitudinal relaxation in liver in vivo

Objective To determine the variability, and preferred values, for normal liver longitudinal water proton relaxation rate R1 in the published literature. Methods Values of mean R1 and between-subject variance were obtained from literature searching. Weighted means were fitted to a heuristic and to a model. Results After exclusions, 116 publications (143 studies) remained, representing apparently normal liver in 3392 humans, 99 mice and 249 rats. Seventeen field strengths were included between 0.04 T and 9.4 T. Older studies tended to report higher between-subject coefficients of variation (CoV), but for studies published since 1992, the median between-subject CoV was 7.4%, and in half of those studies, measured R1 deviated from model by 8.0% or less. Discussion The within-study between-subject CoV incorporates repeatability error and true between-subject variation. Between-study variation also incorporates between-population variation, together with bias from interactions between methodology and physiology. While quantitative relaxometry ultimately requires validation with phantoms and analysis of propagation of errors, this survey allows investigators to compare their own R1 and variability values with the range of existing literature.

Evaluation and Analysis of Interferograms from an InSAR Radar Encrypted by an AES-Based Cryptosystem with The Five Encryption Modes

This paper falls within the framework of the security of satellite images, in particular interferograms from an Interferometric Synthetic Aperture Radar (inSAR) system. The innovation of this work consists in the application of a cryptosystem based on two algorithms Advanced Encryption Standard (AES) and the Rivest, Shamir and Adleman (RSA) encryption algorithm for securing interferograms of inSAR systems. AES employs five encryption modes Electronic Code Book (ECB), Cipher Bloc Chaining (CBC), Cipher FeedBack (CFB), Output FeedBack (OFB), and counter-mode encryption (CTR). The use of the AES algorithm alone can only ensure the confidentiality function. In the proposed cryptosystem confidentiality is ensured by the AES algorithm, authenticity is guaranteed by the RSA algorithm, and integrity is ensured by two parameters; the correlation function between the adjacent pixels and the SSIM parameters (structural similarity index SSIM). For evaluation and analysis of security performance for interferogram encryption, several test metrics are employed. These metrics are: Analysis of histograms of the encrypted interferograms, correlation between the adjacent pixels, between the original interferogram and the encrypted interferogram, SSIM between the original interferogram and the decrypted one. Moreover, we exploit the analysis of resistance to error propagation for the five modes. The obtained results show a superiority of the OFB and CTR modes for the encryption of inSAR interferograms compared to ECB, CFB, and CBC modes. It is noteworthy, that the main criteria that can be used to choose between OFB and CTR for encryption of satellite images are propagation of errors and the complexity material for their locations on the edges of the satellites propagation of errors and the complexity material for their locations on the edges of the satellites. OFB mode is employed in satellites to minimize the number of on-board circuits, which is decisive for satellites. CTR mode is recommended by the CCSDS (Consultative Committee for Space Data Systems) for telemetry (TM) and remote control (TC) encryption.

Accuracy in Online Media

Accuracy is at the core of what journalists do and it amounts to journalistic commitment to report without errors. This tenet of journalism is now in danger, because of the influence of digitalization, changes in media landscapes, and the utilization of the assertation model of journalism. In this study, we used a combination of content analysis and visual network analysis to investigate how subjective errors are disseminated through an online environment, how time/speed influences the propagation of errors, and what the error correction procedures/routines are. The results demonstrate that 69% of the analyzed stories contained errors, and the main cause of such errors was the use of secondary sources, instead of primary ones, these errors transcend national borders and, time/speed had only a minor role in the emergence and correction of the errors, etc. Out of the 107 media websites analyzed, only seventeen provide certain modalities of requesting error correction.

A comparative study of hyperspectral unmixing using different algorithm approaches

<span>Hyperspectral unmixing (HU) is an important technique for remotely sensed hyperspectral data exploitation. Hyperspectral unmixing is required to get an accurate estimation due to low spatial resolution of hyperspectral cameras, microscopic material mixing, and multiple scattering that cause spectra measured by hyperspectral cameras are mixtures of spectra of materials in a scene. It is a process of estimating constituent endmembers and their fractional abundances present at each pixel in hyperspectral image. Researchers have devised and investigated many models searching for robust, stable, tractable and accurate unmixing algorithm. Such algorithm are highly desirable to avoid propagation of errors within the process. This paper presents the comparison of hyperspectral unmixing method by using different kind of algorithms. These algorithms are named VCA, NFINDR, SISAL, and CoNMF. The performance of unmixing process is evaluated by calculating the SAD (spectral angle distance) for each endmembers by using same input of hyperspectral data for different algorithm.</span>

Assessing slope precision from numerical propagation of DEM (Digital Elevation Model) vertical errors

&lt;p&gt;Digital Elevation Models (DEM) are widely used tools in planetary sciences. Geometric parameters such as pixel orientations and local slopes can be derived from these DEM. However, as any data, DEM have a limited precision (i.e. errors), which yields to the propagation of errors on geometric parameters. Assessing local slopes is necessary for many morphological studies: then, how do the DEM vertical errors propagate on the slope values? To answer to this question, we develop a simple and fast numerical method based on the addition of noise on synthetic DEM. We evaluate the different components of slope errors to define the relationship between input DEM noise and output slope errors. Results can be used as first order estimations to discuss slope maps precision.&lt;/p&gt;

RESEARCH IN-DEPTH TRAINING OF NEURAL NETWORKS

Currently, deep learning of neural networks is one of the most popular methods for speech recognition, natural language processing, and computer vision. The article reviews the history of deep learning of neural networks and the current state in General. We consider algorithms for training neural networks used for deep training of neural networks, followed by fine-tuning using the method of back propagation of errors. Neural networks with large numbers of hidden layers, frequently occurring and disappearing gradients are very difficult to train. In this paper, we consider methods that successfully implement training of neural networks with large numbers of layers (more than one hundred) and vanishing gradients. A review of well-known libraries used for successful deep learning of neural networks is conducted.

An algebraic model for the propagation of errors in matrix calculus

We assume that every element of a matrix has a small, individual error, and model it by an external number, which is the sum of a nonstandard real number and a neutrix, the latter being a convex (external) additive group. The algebraic properties of external numbers formalize common error analysis, with rules for calculation which are a sort of mellowed form of the axioms for real numbers.We model the propagation of errors in matrix calculus by the calculus of matrices with external numbers, and study its algebraic properties. Many classical properties continue to hold, sometimes stated in terms of inclusion instead of equality. There are notable exceptions, for which we give counterexamples and investigate suitable adaptations. In particular we study addition and multiplication of matrices, determinants, near inverses, and generalized notions of linear independence and rank.

Biologically Motivated Algorithms for Propagating Local Target Representations

Finding biologically plausible alternatives to back-propagation of errors is a fundamentally important challenge in artificial neural network research. In this paper, we propose a learning algorithm called error-driven Local Representation Alignment (LRA-E), which has strong connections to predictive coding, a theory that offers a mechanistic way of describing neurocomputational machinery. In addition, we propose an improved variant of Difference Target Propagation, another procedure that comes from the same family of algorithms as LRA-E. We compare our procedures to several other biologicallymotivated algorithms, including two feedback alignment algorithms and Equilibrium Propagation. In two benchmarks, we find that both of our proposed algorithms yield stable performance and strong generalization compared to other competing back-propagation alternatives when training deeper, highly nonlinear networks, with LRA-E performing the best overall.