GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification

Light Detection and Ranging (LiDAR), which applies light in the formation of a pulsed laser to estimate the distance between the LiDAR sensor and objects, is an effective remote sensing technology. Many applications use LiDAR including autonomous vehicles, robotics, and virtual and augmented reality (VR/AR). The 3D point cloud classification is now a hot research topic with the evolution of LiDAR technology. This research aims to provide a high performance and compatible real-world data method for 3D point cloud classification. More specifically, we introduce a novel framework for 3D point cloud classification, namely, GSV-NET, which uses Gaussian Supervector and enhancing region representation. GSV-NET extracts and combines both global and regional features of the 3D point cloud to further enhance the information of the point cloud features for the 3D point cloud classification. Firstly, we input the Gaussian Supervector description into a 3D wide-inception convolution neural network (CNN) structure to define the global feature. Secondly, we convert the regions of the 3D point cloud into color representation and capture region features with a 2D wide-inception network. These extracted features are inputs of a 1D CNN architecture. We evaluate the proposed framework on the point cloud dataset: ModelNet and the LiDAR dataset: Sydney. The ModelNet dataset was developed by Princeton University (New Jersey, United States), while the Sydney dataset was created by the University of Sydney (Sydney, Australia). Based on our numerical results, our framework achieves more accuracy than the state-of-the-art approaches.

Color-validity associations put to the test: Examining the robustness of red-false and green-true associations in the Implicit Association Test

Although there is a considerable amount of research on color associations, not much is known about color-validity associations. Initial work with a Stroop task suggests automatic green-true and red-false associations. In the present work, we were interested in testing the generalizability of these associations by using a different procedure, the Implicit Association Test (IAT). In four IAT-experiments we investigated red-false and green-true associations across different color contexts and stimulus materials. Participants categorized objects presented in two different colors as well as true and false statements (Experiment 1), true and false mathematical equations (Experiment 2), or true-related and false-related words (Experiment 3). The hypothesized green-true and red-false associations were observed in all experiments when both red and green stimuli appeared within the same task but varied in tasks involving only one of both colors and a respective control color. The use of conceptual color representation instead of colored objects (Experiment 4) did not produce a clear pattern. A final, registered experiment aims at finding an explanation in valence- and/or salience-asymmetries as potential confounds of IAT effects.

Real-time polarization microscopy of fibrillar collagen in histopathology

Over the past two decades, fibrillar collagen reorganization parameters such as the amount of collagen deposition, fiber angle and alignment have been widely explored in numerous studies. These parameters are now widely accepted as stromal biomarkers and linked to disease progression and survival time in several cancer types. Despite all these advances, there has not been a significant effort to make it possible for clinicians to explore these biomarkers without adding steps to the clinical workflow or by requiring high-cost imaging systems. In this paper, we evaluate previously described polychromatic polarization microscope (PPM) to visualize collagen fibers with an optically generated color representation of fiber orientation and alignment when inspecting the sample by a regular microscope with minor modifications. This system does not require stained slides, but is compatible with histological stains such as H&E. Consequently, it can be easily accommodated as part of regular pathology review of tissue slides, while providing clinically useful insight into stromal composition.

Skin of Color Representation on Wikipedia: Cross-sectional Analysis

Background Wikipedia is one of the most popular websites and may be a go-to source of health and dermatology education for the general population. Prior research indicates poor skin of color (SOC) photo representation in printed dermatology textbooks and online medical websites, but there has been no such assessment performed to determine whether this discrepancy also exists for Wikipedia. Objective The aim of this study was to investigate the number and quality of SOC photos included in Wikipedia’s skin disease pages and to explore the possible ramifications of these findings. Methods Photos of skin diseases from Wikipedia’s “List of Skin Conditions” were assigned by three independent raters as SOC or non-SOC according to the Fitzpatrick system, and were given a quality rating (1-3) based on sharpness, size/resolution, and lighting/exposure. Results We identified 421 skin disease Wikipedia pages and 949 images that met our inclusion criteria. Within these pages, 20.7% of images of skin diseases (196 of 949 images) were SOC and 79.3% (753 of 949 images) were non-SOC (P<.001). There was no difference in the average quality for SOC (2.05) and non-SOC (2.03) images (P=.81). However, the photo quality criteria utilized (sharpness, size/resolution, and lighting/exposure) did not capture all aspects of photo quality. Another limitation of this analysis is that the Fitzpatrick skin typing system is prone to subjectivity and was not originally intended to be utilized as a non-self SOC metric. Conclusions There is SOC underrepresentation in the gross number of SOC images for dermatologic conditions on Wikipedia. Wikipedia pages should be updated to include more SOC photos to mend this divide to ameliorate access to accurate dermatology information for the general public and improve health equity within dermatology.

A Hybrid EEG-Based Emotion Recognition Approach Using Wavelet Convolutional Neural Networks (WCNN) and Support Vector Machine

Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool which makes processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. In this paper, a hybrid approach based on deep features extracted from Wavelet CNNs (WCNNs) weighted layers and multiclass support vector machine (MSVM) is proposed to improve recognition of emotional states from electroencephalogram (EEG) signals. First, EEG signals were preprocessed and converted to time-frequency (T-F) color representation or scalogram using the continuous wavelet transform (CWT) method. Then, scalograms were fed into four popular pre-trained CNNs, AlexNet, ResNet-18, VGG-19 and Inception-v3 to fine-tune them. Then, the best feature layer from each one was used as input to the MSVM method to classify four quarters of the valence-arousal model. Finally, subject-independent Leave-One-Subject-Out criterion was used to evaluate the proposed method on DEAP and MAHNOB-HCI databases. Results show that extracting deep features from the earlier convolutional layer of ResNet-18 (Res2a) and classifying using the MSVM increases the average accuracy, precision and recall about 20% and 12% for MAHNOB-HCI and DEAP databases, respectively. Also, combining scalograms from four regions of pre-frontal, frontal, parietal and parietal-occipital and two regions of frontal and parietal achieved the higher average accuracy of 77.47% and 87.45% for MAHNOB-HCI and DEAP databases, respectively. Combining CNN and MSVM increased recognition of emotion from EEG signal and results were comparable to state-of-the-art studies.

Neural Dynamics of Conflict Control in Working Memory

Attention and working memory (WM) have classically been considered as two separate cognitive functions, but more recent theories have conceptualized them as operating on shared representations and being distinguished primarily by whether attention is directed internally (WM) or externally (attention, traditionally defined). Supporting this idea, a recent behavioral study documented a “WM Stroop effect,” showing that maintaining a color word in WM impacts perceptual color-naming performance to the same degree as presenting the color word externally in the classic Stroop task. Here, we employed ERPs to examine the neural processes underlying this WM Stroop task compared to those in the classic Stroop and in a WM-control task. Based on the assumption that holding a color word in WM would (pre-)activate the same color representation as by externally presenting that color word, we hypothesized that the neural cascade of conflict–control processes would occur more rapidly in the WM Stroop than in the classic Stroop task. Our behavioral results replicated equivalent interference behavioral effects for the WM and classic Stroop tasks. Importantly, however, the ERP signatures of conflict detection and resolution displayed substantially shorter latencies in the WM Stroop task. Moreover, delay-period conflict in the WM Stroop task, but not in the WM control task, impacted the ERP and performance measures for the WM probe stimuli. Together, these findings provide new insights into how the brain processes conflict between internal representations and external stimuli, and they support the view of shared representations between internally held WM content and attentional processing of external stimuli.

New class of magenta dyestuffs based on azomethine pyrazolone disperse dyes: Synthesis, characterization, and dyeing performance on polyester fabrics with ultraviolet protection and pH-dependent properties

Eight azomethine pyrazolone magenta dyes with iminodiethanol groups have been synthesized from 1,3-disubstituted- 1H-pyrazol-5( 4H)-ones in good yield. The newly synthesized dyes were characterized using spectroscopic data and elemental analyses techniques. All dyes have been successfully applied to polyester fabrics as disperse dyes where their dyeing performances have been discussed and evaluated in detail. The shades of these dyes ranged from red violet, purple and dark purple colors with good depth, brightness and good leveling properties. Multifunctional properties such as color representation, colorimetric data (L*, a*, b*, C*, h*, K/S), fastness properties of the dyed samples with respect to washing, perspiration, rubbing and light fastness have been discussed and evaluated in detail. The degree of exhaustion and fixation was also achieved after establishing the optimal dyeing conditions at 130°C, high pressure, 2% shade, and pH ≈5. As well, the influence of the dye bath pH on the K/S percentage and color intensity was estimated and discussed. Furthermore, the dyed fabrics were tested for ultraviolet protection factor and the results showed that these dyes gave excellent ultraviolet protection.

Real-Time Polarization Microscopy of Fibrillar Collagen in Histopathology

Over the past two decades, fibrillar collagen reorganization parameters such as the amount of collagen deposition, fiber angle and alignment have been widely explored in numerous studies. These parameters are now widely accepted as stromal biomarkers and linked to disease progression and survival time in several cancer types. Despite all these advances, there has not been a significant effort to make it possible for clinicians to explore these biomarkers without adding steps to the clinical workflow or by requiring high-cost imaging systems. In this paper, we evaluate previously described polychromatic polarization microscope (PPM) to visualize collagen fibers with an optically generated color representation of fiber orientation and alignment when inspecting the sample by a regular microscope with minor modifications. This system does not require stained slides, but is compatible with histological stains such as H&E. Consequently, it can be easily accommodated as part of regular pathology review of tissue slides, while providing clinically useful insight into stromal composition.