Emergence of crowding: the role of contrast and orientation salience

Crowding causes difficulties in judging attributes of an object surrounded by other objects. We investigated crowding for stimuli that isolated either S-cone or luminance mechanisms or combined them. By targeting different retinogeniculate mechanisms, we aim to determine the earliest site at which crowding emerges. Discrimination was measured in an orientation judgement task where Gabor-targets were presented parafoveally among flankers. In the first experiment, we assessed flanked and unflanked orientation discrimination thresholds for S-cone, achromatic and combined stimuli. In the second experiment, we captured individual differences by measuring unflanked detection and orientation sensitivity, and performance under flanker-interference for stimuli containing luminance only or combined with S-cone contrast. We confirmed that orientation sensitivity was lower for unflanked S-cone stimuli. When flanked, the pattern of results for S-cone stimuli was the same as for achromatic stimuli with comparable (i.e., low) contrast levels. We also found that flanker interference exhibited a genuine signature of crowding only when orientation discrimination threshold was reliably surpassed. Crowding, therefore, emerges at a stage that operates on signals representing task-relevant featural (here, orientation) information. Since luminance and S-cone mechanisms have very different spatial tuning properties, it is most parsimonious to conclude that crowding takes place at a neural processing stage after they have been combined.

USE OF CONVOLUTIONAL NEURAL NETWORKS FOR X-RAY IMAGE ORIENTATION DETERMINATION

The fact that Artificial Intelligence (AI) based algorithms exhibit a high performance on image classification tasks has been shown many times. Still, certain issues exist with the application of machine learning (ML) artificial neural network (ANN) algorithms. The best known is the need for a large amount of statistically varied data, which can be addressed with expanded collection or data augmentation. Other issues are also present. Convolutional neural networks (CNNs) show extremely high performance on image-shaped data. Despite their performance, CNNs exhibit a large issue which is the sensitivity to image orientation. Previous research shows that varying the orientation of images may greatly lower the performance of the trained CNN. This is especially problematic in certain applications, such as X-ray radiography, an example of which is presented here. Previous research shows that the performance of CNNs is higher when used on images in a single orientation (left or right), as opposed to the combination of both. This means that the data needs to be differentiated before it enters the classification model. In this paper, the CNN-based model for differentiation between left and right-oriented images is presented. Multiple CNNs are trained and tested, with the highest performing being the VGG16 architecture which achieved an Accuracy of 0.99 (+/- 0.01), and an AUC of 0.98 (+/- 0.01). These results show that CNNs can be used to address the issue of orientation sensitivity by splitting the data in advance of being used in classification models.

Neuroscienze per l’architettura

Discusses the Indian design treatise the Vaastu Veda in relation to visual neuroscience. Relates visual perception in architecture to functional organisation of the brain. Relates Hubel and Weisel’s orientation sensitivity to the sense of order and pleasure imparted by the regularity of colonnades and cable stay bridges. Suggests aspects of perception facilitated by neuronal architecture and the dynamic between familiarity and novelty, plasticity and visual attunement.

Assessing the Molecular Specificity and Orientation Sensitivity of Infrared, Raman, and Vibrational Sum-Frequency Spectra

Linear programming was used to assess the ability of polarized infrared absorption, Raman scattering, and visible–infrared sum-frequency generation to correctly identify the composition of a mixture of molecules adsorbed onto a surface in four scenarios. The first two scenarios consisted of a distribution of species where the polarity of the orientation distribution is known, both with and without consideration of an arbitrary scaling factor between candidate spectra and the observed spectra of the mixture. The final two scenarios have repeated the tests, but assuming that the polarity of the orientation is unknown, so the symmetry-breaking attributes of the second-order nonlinear technique are required. The results indicate that polarized Raman spectra are more sensitive to orientation and molecular identity than the other techniques. However, further analysis reveals that this sensitivity is not due to the high-order angle dependence of Raman, but is instead attributed to the number of unique projections that can be measured in a polarized Raman experiment.

Reducing SARS-CoV-2 infectious spreading patterns by removing S and R compartments from SIR model equation

This research points to the asymptotic instability of SIR model and its variants to predict the behavior of SARS-CoV-2 infection spreading patterns over the population and time aspects. Mainly for the “S” and “R” terms of the equation, the predictive results fail due to confounding environment of variables that sustain the virus contagion within population complex network basis of analysis. While “S” and “R” are not homologous data of analysis, thus with improper topological metrics used in many researches, these terms leads to the asymptotic feature of “I” term as the most stable point of analysis to achieve proper predictive methods. Having in its basis of formulation the policies adopted by countries, “I” therefore presents a stable fixed point orientation in order to be used as a predictive analysis of nearby future patterns of SARS-CoV-2 infection. New metrics using a Weinbull approach for “I” are presented and fixed point orientation (sensitivity of the method) are demonstrated empirically by worldwide statistical data.

Selective etching and hardness properties of quenched SAC305 solder joints

Purpose The purpose of this paper is to investigate the morphology of intermetallic (IMC) compounds and the mechanical properties of SAC305 solder alloy under different cooling conditions. Design/methodology/approach SAC305 solder joints were prepared under different cooling conditions/rates. The performance of three different etching methods was investigated: simple chemical etching, deep etching based on the Jackson method and selective removal of β-Sn by a standard three-electrode cell method. Phase and structural analyses were conducted by X-ray diffraction (XRD). The morphology of etched solder was examined by a field emission scanning electron microscope. The hardness evaluations of the solder joints were conducted by a Vickers microhardness tester. Findings The Ag3Sn network was significantly refined by the ice-quenching process. Further, the thickness of the Cu6Sn5 layer decreased with an increase in the cooling rate. The finer Ag3Sn network and the thinner Cu6Sn5 IMC layer were the results of the reduced solidification time. The ice-quenched solder joints showed the highest hardness values because of the refinement of the Ag3Sn and Cu6Sn5 phases. Originality/value The reduction in the XRD peak intensities showed the influence of the cooling condition on the formation of the different phases. The micrographs prepared by electrochemical etching revealed better observations regarding the shape and texture of the IMC phases than those prepared by the conventional etching method. The lower grain orientation sensitivity of the electrochemical etching method (unlike chemical etching) significantly improved the micrographs and enabled accurate observation of IMC phases.