COVRATIO Statistic as a Discrimination Method for Multivariate Normal Distribution

The COVRATIO statistic has been used to identify the presence of outlier in data, which is based on deletion approach, where the determinant of covariance matrix for the full dataset excludes i-th row. This study proposes a novel discrimination method for the multivariate normal (MVN) distribution using the idea of COVRATIO statistic, denoted as . The linear discrimination function (LDF) for MVN distribution will be compared to the statistic. Simulation results showed that the as discrimination method performs better than the LDF with lower misclassification probabilities in all cases considered. The interest in the discrimination method arose in connection with the study of an application to discriminate the shape of the human maxillary dental arches, thus statistic may be considered as an alternative.

Cryptospace Invertible Steganography with Conditional Generative Adversarial Networks

Deep neural networks have become the foundation of many modern intelligent systems. Recently, the author has explored adversarial learning for invertible steganography (ALIS) and demonstrated the potential of deep neural networks to reinvigorate an obsolete invertible steganographic method. With the worldwide popularisation of the Internet of things and cloud computing, invertible steganography can be recognised as a favourable way of facilitating data management and authentication due to the ability to embed information without causing permanent distortion. In light of growing concerns over cybersecurity, it is important to take a step forwards to investigate invertible steganography for encrypted data. Indeed, the multidisciplinary research in invertible steganography and cryptospace computing has received considerable attention. In this paper, we extend previous work and address the problem of cryptospace invertible steganography with deep neural networks. Specifically, we revisit a seminal work on cryptospace invertible steganography in which the problem of message decoding and image recovery is viewed as a type of binary classification. We formulate a general expression encompassing spatial, spectral, and structural analyses towards this particular classification problem and propose a novel discrimination function based on a recurrent conditional generative adversarial network (RCGAN) which predicts bit-planes with stacked neural networks in a top-down manner. Experimental results evaluate the performance of various discrimination functions and validate the superiority of neural-network-aided discrimination function in terms of classification accuracy.

A New Cycle-Slip Repair Method for Dual-Frequency BDS Against the Disturbances of Severe Ionospheric Variations and Pseudoranges with Large Errors

Many Beidou navigation satellite system (BDS) receivers or boards provide dual-frequency measurements to conduct precise positioning and navigation for low-power consumption. Cycle-slip processing is a primary work to guarantee consistent, precise positioning with the phase data. However, the cycle-slip processing of BDS dual-frequency phases still follows with those of existing GPS methods. For single-satellite data, cycle-slip detection (CSD) with the geometry-free phase (GF) is disturbed by severe ionospheric delay variations, while CSD or cycle-slip repair (CSR) with the Melbourne–Wubbena combination (MW) must face the risk of the tremendous disturbance from large pseudorange errors. To overcome the above limitations, a new cycle-slip repair method for BDS dual-frequency phases (BDCSR) is proposed: (1) An optimal model to minimize the variance of the cycle-slip calculation was established to the dual-frequency BDS, after correcting the ionospheric variation with a reasonable and feasible way. (2) Under the BDS dual-frequency condition, a discrimination function was built to exclude the adverse disturbance from the pseudorange errors on the CSR, according to the rankings of the absolute epoch-difference GFs calculated by the searched cycle-slip candidates after correcting the ionospheric variation. Subsequently, many compared CSR tests were implemented in conditions of low and medium elevations during strong geomagnetic storms. Comparisons from the results of different methods show that: (1) The variations of ionospheric delays are intolerable in the cycle-slip calculation during the geomagnetic storm, and the tremendous influence from the ionospheric variation should be corrected before calculating the cycle-slip combination with the BDS dual-frequency data. (2) Under the condition of real dual-frequency BDS data during the geomagnetic storm, the actual success rate of the conventional dual-frequency CSR (CDCSR) by employing the optimized combinations, but absenting from the discrimination function, is lower than that of BDCSR by about 2%; The actual success rate of the CSD with MW (MWCSD), is lower than that of BDCSR by about 2%. (3) After adding gross errors of 0.7 m to all real epoch-difference pseudoranges epoch-by-epoch, results of CDCSR and MWCSD showed many errors. However, BDCSR achieved a higher actual success rate than those of CDCSR and MWCSD, about 43% and 16%, respectively, and better performance of refraining the disturbance of large pseudorange error on the cycle-slip determination was achieved in the BDCSR methodology.

Colour vision and information theory: the receptor noise-limited model implies optimal colour discrimination by opponent channels

In order to interpret animal behaviour we need to understand how they see the world. As colour discrimination is almost impossible to test directly in animals, it is important to develop theoretical models based in the properties of visual systems. One of the most successful is the receptor noise-limited (RNL) model, which depends only on the level of noise in photoreceptors and opponent mechanisms. Here optimal colour discrimination properties are obtained using information theoretical tools, for the early stages of visual systems with and without colour opponent mechanisms. For most biologically relevant conditions the optimal discrimination function of an ideal observer coincides with the one obtained with the RNL model. Many variants of the model can be cast into the same framework, which permits meaningful comparisons across species. For example, it is shown that the presence of opponency seems to be the preferred hypothesis for bees, but not for budgerigars. Since this is a consequence of the presence of oil droplets, this could also be true for most other species of birds.

Chromatic information processing in the first optic ganglion of the butterfly Papilio xuthus

The butterfly Papilio xuthus has acute tetrachromatic color vision. Its eyes are furnished with eight spectral classes of photoreceptors, situated in three types of ommatidia, randomly distributed in the retinal mosaic. Here, we investigated early chromatic information processing by recording spectral, angular, and polarization sensitivities of photoreceptors and lamina monopolar cells (LMCs). We identified three spectral classes of LMCs whose spectral sensitivities corresponded to weighted linear sums of the spectral sensitivities of the photoreceptors present in the three ommatidial types. In ~ 25% of the photoreceptor axons, the spectral sensitivities differed from those recorded at the photoreceptor cell bodies. These axons showed spectral opponency, most likely mediated by chloride ion currents through histaminergic interphotoreceptor synapses. The opponency was most prominent in the processes of the long visual fibers in the medulla. We recalculated the wavelength discrimination function using the noise-limited opponency model to reflect the new spectral sensitivity data and found that it matched well with the behaviorally determined function. Our results reveal opponency at the first stage of Papilio’s visual system, indicating that spectral information is preprocessed with signals from photoreceptors within each ommatidium in the lamina, before being conveyed downstream by the long visual fibers and the LMCs.

Effects of an 8-week yoga program on sustained attention and discrimination function in children with attention deficit hyperactivity disorder

This study investigated whether a yoga exercise intervention influenced the sustained attention and discrimination function in children with ADHD. Forty-nine participants (mean age = 10.50 years) were assigned to either a yoga exercise or a control group. Participants were given the Visual Pursuit Test and Determination Test prior to and after an eight-week exercise intervention (twice per week, 40 min per session) or a control intervention. Significant improvements in accuracy rate and reaction time of the two tests were observed over time in the exercise group compared with the control group. These findings suggest that alternative therapies such as yoga exercises can be complementary to behavioral interventions for children with attention and inhibition problems. Schools and parents of children with ADHD should consider alternatives for maximizing the opportunities that children with ADHD can engage in structured yoga exercises.