Implicit Continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like, fingerprint or face. Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users’ identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session. However, divergent issues remain unaddressed. This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called, Gaussian Weighted Cauchy Kriging-based Continuous Czekanowski’s (GWCK-CC). First, a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise present in the raw input face images. Cauchy Kriging Regression function is employed to reduce the dimensionality. Finally, Continuous Czekanowski’s Classification is utilized for proficient classification between the genuine user and attacker. By this way, the proposed GWCK-CC method achieves accurate authentication with minimum error rate and time. Experimental assessment of the proposed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset. The results confirm that the proposed GWCK-CC method enhances authentication accuracy, by 9%, reduces the authentication time, and error rate by 44%, and 43% as compared to the existing methods.     

Col4a3-/- Mice on Balb/C Background Have Less Severe Cardiorespiratory Phenotype and SGLT2 Over-Expression Compared to 129x1/SvJ and C57Bl/6 Backgrounds

Alport syndrome (AS) is a hereditary renal disorder with no etiological therapy. In the preclinical Col4a3^-/- model of AS, disease progression and severity vary depending on mouse strain. The sodium-glucose cotransporter 2 (SGLT2) is emerging as an attractive therapeutic target in cardiac/renal pathologies, but its application to AS remains untested. This study investigates cardiorespiratory function and SGLT2 renal expression in Col4a3^-/- mice from three different genetic backgrounds, 129x1/SvJ, C57Bl/6 and Balb/C. male Col4a3^-/- 129x1/SvJ mice displayed alterations consistent with heart failure with preserved ejection fraction (HFpEF). Female, but not male, C57Bl/6 and Balb/C Col4a3^-/- mice exhibited mild changes in systolic and diastolic function of the heart by echocardiography. Male C57Bl/6 Col4a3^-/- mice presented systolic dysfunction by invasive hemodynamic analysis. All strains except Balb/C males demonstrated alterations in respiratory function. SGLT2 expression was significantly increased in AS compared to WT mice from all strains. However, cardiorespiratory abnormalities and SGLT2 over-expression were significantly less in AS Balb/C mice compared to the other two strains. Systolic blood pressure was significantly elevated only in mutant 129x1/SvJ mice. The results provide further evidence for strain-dependent cardiorespiratory and hypertensive phenotype variations in mouse AS models, corroborated by renal SGLT2 expression, and support ongoing initiatives to develop SGLT2 inhibitors for the treatment of AS.     

Learning from Examples with Information Theoretic Criteria

This paper discusses a framework for learning based on information theoretic criteria. A novel algorithm based on Renyi's quadratic entropy is used to train, directly from a data set, linear or nonlinear mappers for entropy maximization or minimization. We provide an intriguing analogy between the computation and an information potential measuring the interactions among the data samples. We also propose two approximations to the Kulback-Leibler divergence based on quadratic distances (Cauchy-Schwartz inequality and Euclidean distance). These distances can still be computed using the information potential. We test the newly proposed distances in blind source separation (unsupervised learning) and in feature extraction for classification (supervised learning). In blind source separation our algorithm is capable of separating instantaneously mixed sources, and for classification the performance of our classifier is comparable to the support vector machines (SVMs).     

Leptin Signaling in Obesity and Colorectal Cancer

Obesity and colorectal cancer (CRC) are among the leading diseases causing deaths in the world, showing a complex multifactorial pathology. Obesity is considered a risk factor in CRC development through inflammation, metabolic, and signaling processes. Leptin is one of the most important adipokines related to obesity and an important proinflammatory marker, mainly expressed in adipose tissue, with many genetic variation profiles, many related influencing factors, and various functions that have been ascribed but not yet fully understood and elucidated, the most important ones being related to energy metabolism, as well as endocrine and immune systems. Aberrant signaling and genetic variations of leptin are correlated with obesity and CRC, with the genetic causality showing both inherited and acquired events, in addition to lifestyle and environmental risk factors; these might also be related to specific pathogenic pathways at different time points. Moreover, mutation gain is a crucial factor enabling the genetic process of CRC. Currently, the inconsistent and insufficient data related to leptin’s relationship with obesity and CRC indicate the necessity of further related studies. This review summarizes the current knowledge on leptin genetics and its potential relationship with the main pathogenic pathways of obesity and CRC, in an attempt to understand the molecular mechanisms of these associations, in the context of inconsistent and contradictory data. The understanding of these mechanisms linking obesity and CRC could help to develop novel therapeutic targets and prevention strategies, resulting in a better prognosis and management of these diseases.     

Description and composition of bio-inspired design patterns: a complete overview

In the last decade, bio-inspired self-organising mechanisms have been applied to different domains, achieving results beyond traditional approaches. However, researchers usually use these mechanisms in an ad-hoc manner. In this way, their interpretation, definition, boundary (i.e. when one mechanism stops, and when another starts), and implementation typically vary in the existing literature, thus preventing these mechanisms from being applied clearly and systematically to solve recurrent problems. To ease engineering of artificial bio-inspired systems, this paper describes a catalogue of bio-inspired mechanisms in terms of modular and reusable design patterns organised into different layers. This catalogue uniformly frames and classifies a variety of different patterns. Additionally, this paper places the design patterns inside existing self-organising methodologies and hints for selecting and using a design pattern.     

SYSTEMS MODEL OF COASTAL CIRCULATION

The numerical model presented simulates two-dimensional hydrodynamic conditions in order to predict both components of the average velocity, vertically integrated, as well as the free surface elevation of the sea in coastal waters. The equations supporting the model are the well-known shallow-water equations, or quasi-static equations. In the classical theory for long waves in shallow water, the vertical accelerations of the fluid particles are neglected because these accelerations are very small with respect to gravity. In the same manner, vertical velocities may be neglected when compared with the horizontal ones. Integration of the system of differential equations in partial derivatives has been performed by using a “leapfrog” finite-difference technique under explicit solving. The computer program was developed in FORTRAN 77. Results and their agreement with coastal configuration are shown for two applications of this model: the Delta del Ebro, on the Mediterranean coast of Spain, and the Ria de Pontevedra, on the Atlantic northwestern coast of Spain. Here attention is focused on the application to the Delta del Ebro area.     

Sol-gel Preparation and Preliminary in vitro Evaluation of Fluorapatite／Hydroxyapatite Solid Solution Films

Fluorapatite/hydroxyapatite solid solution has better biological properties than other apatites, especially used as films or coatings. In this work, sol-gel preparation and in vitro behavior of fluorapatite/hydroxyapatite solid solution films on titanium alloy were investigated. Ca(NO3)2-4H20 and PO(OH)x(OEt)3-x were selected as precursors, and hexafluorophosphoric acid (HPFo) was used as a fluorine containing reagent. The Ca and P precursors were mixed with HPFo to keep the Ca/P molar ratio 1.67. The mixtures refluxed for 12 h were used as dipping sols for the preparation of the films. The phase of the films obtained at 600℃ was apatite. The F contents in the films increased with the concentrations of HPF6 in the dipping sols. The solid solution films were shown to have better stability than hydroxyapatite films, and a reasonably good bioactivity in the in vitro evaluation.     

Design of Two‐Stage Class AB CMOS Buffers: A Systematic Approach

A systematic approach for the design of two‐stage class AB CMOS unity‐gain buffers is proposed. It is based on the inclusion of a class AB operation to class A Miller amplifier topologies in unity‐gain negative feedback by a simple technique that does not modify quiescent currents, supply requirements, noise performance, or static power. Three design examples are fabricated in a 0.5 µm CMOS process. Measurement results show slew rate improvement factors of approximately 100 for the class AB buffers versus their class A counterparts for the same quiescent power consumption (< 200 µW).