This work presents a Resistive RAM (RRAM)-based Ising machine characterized by high spin density and efficient analog in-memory computing. The proposed design enables compact spin representations that support interactions with up to eight neighboring spins at 1-bit precision. The functionality and performance of the proposed Ising machine is evaluated using comprehensive simulation in 28nm CMOS technology, incorporating measured RRAM variation characteristics to validate its robustness and efficiency. Additionally, software-based simulations are employed to solve classical combinatorial problems, such as the Max-Cut problem, while accounting for the non-idealities inherent in the proposed RRAM-based analog in-memory computing approach. Each proposed spin occupies an area of 13.5 μm2, achieving an area reduction of 16.7% to 92.1% compared to recent works, based on feature size normalization.

Utilizing the multi-dimensional (MD) space for constellation shaping has been proven to be an effective approach for achieving shaping gains. Despite there exists a variety of MD modulation formats tailored for specific optical transmission scenarios, there remains a notable absence of a dependable comparison method for efficiently and promptly re-evaluating their performance in arbitrary transmission systems. In this paper, we introduce an analytical nonlinear interference (NLI) power model-based shaping gain estimation method to enable a fast performance evaluation of various MD modulation formats in coherent dual-polarization (DP) optical transmission system. In order to extend the applicability of this method to a broader set of modulation formats, we extend the established NLI model to take the 4D joint distribution into account and thus able to analyze the complex interactions of non-iid signaling in DP systems. With the help of the NLI model, we conduct a comprehensive analysis of the state-of-the-art modulation formats and investigate their actual shaping gains in two types of optical fiber communication scenarios (multi-span and single-span). The numerical simulation shows that for arbitrary modulation formats, the NLI power and relative shaping gains in terms of signal-to-noise ratio can be more accurately estimated by capturing the statistics of MD symbols. Furthermore, the proposed method further validates the effectiveness of the reported NLI-tolerant modulation format in the literature, which reveals that the linear shaping gains and modulation-dependent NLI should be jointly considered for nonlinearity mitigation.

Autism Spectrum Disorders (ASD) remain a healthcare challenge and gain considerable attention due to the increasing prevalence rates and insupportable burden on families and society. It is noted that the recognition of children’s emotional states plays an important role in the evaluation and intervention process of ASD. In this paper, we aim to address the problem of automatic recognition of the emotional states of ASD children in social interactive scenarios. Since the child can be unconstrained in realistic scenarios, the face occlusion under pose variations and uncertain backgrounds become challenges of this task. To tackle this problem, we employ both facial expressions as well as body poses as cues to recognize the emotional states while most traditional methods only leverage the former. Firstly for the facial information, spatial features are extracted through convolutional neural networks followed by a temporal transformer to extract temporal information. Then for the body pose information, graph convolutional networks combined with the self-attention part are used to represent spatial features and temporal convolutional layers for temporal counterparts. Finally, different multimodal fusion ways are explored to generate final recognition results. We evaluate this method on a challenging database collected by us in real-world child-clinician interactive scenarios and the proposed method achieved significantly better results than baselines using only facial information. Thus it is suggested that there is a potential to assist in clinical practice by providing the recognized emotion as feedback.

Early screening of autism spectrum disorder (ASD) is crucial since early intervention evidently confirms significant improvement of functional social behavior in toddlers. This article attempts to bootstrap the response-to-instructions (RTIs) protocol with vision-based solutions in order to assist professional clinicians with an automatic autism diagnosis. The correlation between detected objects and toddler's emotional features, such as gaze, is constructed to analyze their autistic symptoms. Twenty toddlers between 16-32 months of age, 15 of whom diagnosed with ASD, participated in this study. The RTI method is validated against human codings, and group differences between ASD and typically developing (TD) toddlers are analyzed. The results suggest that the agreement between clinical diagnosis and the RTI method achieves 95% for all 20 subjects, which indicates vision-based solutions are highly feasible for automatic autistic diagnosis.