Brain-inspired semantic data augmentation for multi-style images.

Data augmentation is an effective technique for automatically expanding training data in deep learning. Brain-inspired methods are approaches that draw inspiration from the functionality and structure of the human brain and apply these mechanisms and principles to artificial intelligence and computer science. When there is a large style difference between training data and testing data, common data augmentation methods cannot effectively enhance the generalization performance of the deep model. To solve this problem, we improve modeling Domain Shifts with Uncertainty (DSU) and propose a new brain-inspired computer vision image data augmentation method which consists of two key components, namely, using Robust statistics and controlling the Coefficient of variance for DSU (RCDSU) and Feature Data Augmentation (FeatureDA). RCDSU calculates feature statistics (mean and standard deviation) with robust statistics to weaken the influence of outliers, making the statistics close to the real values and improving the robustness of deep learning models. By controlling the coefficient of variance, RCDSU makes the feature statistics shift with semantic preservation and increases shift range. FeatureDA controls the coefficient of variance similarly to generate the augmented features with semantics unchanged and increase the coverage of augmented features. RCDSU and FeatureDA are proposed to perform style transfer and content transfer in the feature space, and improve the generalization ability of the model at the style and content level respectively. On Photo, Art Painting, Cartoon, and Sketch (PACS) multi-style classification task, RCDSU plus FeatureDA achieves competitive accuracy. After adding Gaussian noise to PACS dataset, RCDSU plus FeatureDA shows strong robustness against outliers. FeatureDA achieves excellent results on CIFAR-100 image classification task. RCDSU plus FeatureDA can be applied as a novel brain-inspired semantic data augmentation method with implicit robot automation which is suitable for datasets with large style differences between training and testing data.

A Fully Automated Mini-Mental State Examination Assessment Model Using Computer Algorithms for Cognitive Screening.

Background: Rapidly growing healthcare demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults. Objective: To develop a fully automated Mini-Mental State Examination (MMSE) assessment model and validate the model's rating consistency. Methods: The Automated Assessment Model for MMSE (AAM-MMSE) was an about 10-min computerized cognitive screening tool containing the same questions as the traditional paper-based Chinese MMSE. The validity of the AAM-MMSE was assessed in term of the consistency between the AAM-MMSE rating and physician rating. Results: A total of 427 participants were recruited for this study. The average age of these participants was 60.6 years old (ranging from 19 to 104 years old). According to the intraclass correlation coefficient (ICC), the interrater reliability between physicians and the AAM-MMSE for the full MMSE scale AAM-MMSE was high [ICC (2,1)=0.952; with its 95% CI of (0.883,0.974)]. According to the weighted kappa coefficients results the interrater agreement level for audio-related items showed high, but for items "Reading and obey", "Three-stage command", and "Writing complete sentence" were slight to fair. The AAM-MMSE rating accuracy was 87%. A Bland-Altman plot showed that the bias between the two total scores was 1.48 points with the upper and lower limits of agreement equal to 6.23 points and -3.26 points. Conclusions: Our work offers a promising fully automated MMSE assessment system for cognitive screening with pretty good accuracy.

Nanoscale Phase Change Material Array by Sub-Resolution Assist Feature for Storage Class Memory Application.

High density phase change memory array requires both minimized critical dimension (CD) and maximized process window for the phase change material layer. High in-wafer uniformity of the nanoscale patterning of chalcogenides material is challenging given the optical proximity effect (OPE) in the lithography process and the micro-loading effect in the etching process. In this study, we demonstrate an approach to fabricate high density phase change material arrays with half-pitch down to around 70 nm by the co-optimization of lithography and plasma etching process. The focused-energy matrix was performed to improve the pattern process window of phase change material on a 12-inch wafer. A variety of patternings from an isolated line to a dense pitch line were investigated using immersion lithography system. The collapse of the edge line is observed due to the OPE induced shrinkage in linewidth, which is deteriorative as the patterning density increases. The sub-resolution assist feature (SRAF) was placed to increase the width of the lines at both edges of each patterning by taking advantage of the optical interference between the main features and the assistant features. The survival of the line at the edges is confirmed with around a 70 nm half-pitch feature in various arrays. A uniform etching profile across the pitch line pattern of phase change material was demonstrated in which the micro-loading effect and the plasma etching damage were significantly suppressed by co-optimizing the etching parameters. The results pave the way to achieve high density device arrays with improved uniformity and reliability for mass storage applications.

Polyaniline/reduced graphene oxide foams as metal-free cathodes for stable lithium-oxygen batteries.

Lithium-oxygen batteries (LOBs) are considered as next-generation energy storage devices owing to their high-energy densities, yet they generally suffer from low actual specific capacity and poor cycle performance. To solve these issues, a range of electrocatalysts have been introduced in the cathode to reduce the overpotential during charge/discharge cycles and minimize unwanted side reactions. Due to relative high costs and limited reserves of noble metals and their compounds, it is important to develop low-cost and efficient metal-free electrocatalysts. Here, we report a simple method to prepare three-dimensional porous polyaniline (PANI)/reduced graphene oxide foams (PPGFs) with different PANI contents via a two-step self-assembly process. When these foams are tested as the cathode in LOBs, the device using the PPGF with 50% PANI content exhibits a discharge capacity up to 36 010 mAh g-1 and an excellent cycling stability (260 cycles at 1000 mAh g-1 and 500 cycles at 500 mAh g-1), provid ing new insights into the design of next-generation metal-free cathodes for LOBs.

Graphitic carbon nitride as polysulfide anchor and barrier for improved lithium-sulfur batteries.

The poor conductivity of sulfur and the shuttle effect of soluble polysulfides have considerably hindered the practical application of lithium-sulfur (Li-S) batteries. Here, we have fabricated a three-dimensional graphitic carbon nitride/reduced graphene oxide (GCN@rGO) network as the sulfur host in Li-S batteries, where the bifunctional GCN strongly binds polysulfides through a chemical interaction and catalyzes the redox reactions of polysulfides. Additionally, GCN coating is also applied to different membranes and when these GCN-coated-membranes (GCMs) are used as separators, they are found to effectively act as the polysulfide barrier to suppress the diffusion of polysulfide intermediates to the Li anode and thus ameliorate the shuttle effect. As a result, the Li-S battery assembled from the GCN@rGO/S cathode and GCM separator exhibited a high initial specific capacity of 1000.6 mAh g-1 at 0.1 C and 87% capacity retention with 0.066% decay per cycle over 200 charge-discharge cycles at 0.5 C.