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Applied Longitudinal Data Analysis Modeling Change And Event Occurrence

Rapid digital technology advancement has resulted in a tremendous increase in computing tasks imposing stringent energy efficiency and area efficiency requirements on next-generation computing. To meet the growing data-driven demand, in-memory computing and transistor-based computing have emerged as potent technologies for the implementation of matrix and logic computing. However, to fulfil the future computing requirements new materials are urgently needed to complement the existing Si complementary metal–oxide–semiconductor technology and new technologies must be developed to enable further diversification of electronics and their applications. The abundance and rich variety of electronic properties of two-dimensional materials have endowed them with the potential to enhance computing energy efficiency while enabling continued device downscaling to a feature size below 5 nm. In this Review, from the perspective of matrix and logic computing, we discuss the opportunities, progress and challenges of integrating two-dimensional materials with in-memory computing and transistor-based computing technologies. This Review discusses the recent progress and future prospects of two-dimensional materials for next-generation nanoelectronics.

Two-dimensional materials for next-generation computing technologies.

This article provides a review of current development and challenges in brain-inspired computing with memristors. We review the mechanisms of various memristive devices that can mimic synaptic and neuronal functionalities and survey the progress of memristive spiking and artificial neural networks. Different architectures are compared, including spiking neural networks, fully connected artificial neural networks, convolutional neural networks, and Hopfield recurrent neural networks. Challenges and strategies for nanoelectronic brain-inspired computing systems, including device variations, training, and testing algorithms, are also discussed.

Brain-inspired computing with memristors: Challenges in devices, circuits, and systems

The continued growth in the demand of data storage and processing has spurred the development of high-performance storage technologies and brain-inspired neuromorphic hardware. Semiconductor quantum dots (QDs) offer an appealing option for these applications since they combine excellent electronic/optical properties and structural stability and can address the requirements of low-cost, large-area, and solution-based manufactured technologies. Here, we focus on the development of nonvolatile memories and neuromorphic computing systems based on QD thin-film solids. We introduce recent advances of QDs and highlight their unique electrical and optical features for designing future electronic devices. We also discuss the advantageous traits of QDs for novel and optimized memory techniques in both conventional flash memories and emerging memristors. Then, we review recent advances in QD-based neuromorphic devices from artificial synapses to light-sensory synaptic platforms. Finally, we highlight major challenges for commercial translation and consider future directions for the postsilicon era.

Semiconductor Quantum Dots for Memories and Neuromorphic Computing Systems

The development of brain network hubs.

As the research on artificial intelligence booms, there is broad interest in brain-inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re-visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges.

Variation in longitudinal trajectories of cortical sulci in normal elderly

The high inter-subject variability in emotional EEG activities has posed great challenges for practical EEG-based affective computing applications. The recently popular domain adaptation strategy seemed to be a promising technique for addressing this issue, by minimizing the discrepancy of EEG data from different subjects. The present study proposed and implemented an extended Domain Adaptation method by introducing Subject Clustering (DASC). By clustering subjects based on the similarity of their emotion-specific EEG activities, the DASC method could make a flexible use of the available source domain information towards an optimized target domain application. Using the publicly available EEG dataset of DEAP, the DASC method achieved an average accuracy of $73.9\pm 13.5\%$ and $68.8\pm 11.2\%$ for binary classifications of the high or low levels of valence and arousal. Comparison with the state-of-the-art performance as well as the ablation experiments suggest the proposed DASC method as an effective extension to the conventional domain adaptation methods for EEG-based emotion recognition.

Domain Adaptation for Cross-Subject Emotion Recognition by Subject Clustering

Emotion recognition plays a vital role in human-machine interactions and daily healthcare. EEG signals have been reported to be informative and reliable for emotion recognition in recent years. However, the inter-subject variability of emotion-related EEG signals poses a great challenge for the practical use of EEG-based emotion recognition. Inspired by the recent neuroscience studies on inter-subject correlation, we proposed a Contrastive Learning method for Inter-Subject Alignment (CLISA) for reliable cross-subject emotion recognition. Contrastive learning was employed to minimize the inter-subject differences by maximizing the similarity in EEG signals across subjects when they received the same stimuli in contrast to different ones. Specifically, a convolutional neural network with depthwise spatial convolution and temporal convolution layers was applied to learn inter-subject aligned spatiotemporal representations from raw EEG signals. Then the aligned representations were used to extract differential entropy features for emotion classification. The performance of the proposed method was evaluated on our THU-EP dataset with 80 subjects and the publicly available SEED dataset with 15 subjects. Comparable or better cross-subject emotion recognition accuracy (i.e., 72.1% and 47.0% for binary and nine-class classification, respectively, on the THU-EP dataset and 86.3% on the SEED dataset for three-class classification) was achieved as compared to the state-of-the-art methods. The proposed method could be generalized well to unseen emotional stimuli as well. The CLISA method is therefore expected to considerably increase the practicality of EEG-based emotion recognition by operating in a "plug-and-play" manner. Furthermore, the learned spatiotemporal representations by CLISA could provide insights into the neural mechanisms of human emotion processing.

Xinke Shen

Papers

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges.

Variation in longitudinal trajectories of cortical sulci in normal elderly

Domain Adaptation for Cross-Subject Emotion Recognition by Subject Clustering

Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition.

Short-term PM2.5 exposure and cognitive function: Association and neurophysiological mechanisms.