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Recent trends have shown that autonomous agents, such as Autonomous Ground Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), and mobile robots, effectively improve human productivity in solving diverse tasks. However, since these agents are typically powered by portable batteries, they require extremely low power/energy consumption to operate in a long lifespan. To solve this challenge, neuromorphic computing has emerged as a promising solution, where bio-inspired Spiking Neural Networks (SNNs) use spikes from event-based cameras or data conversion pre-processing to perform sparse computations efficiently. However, the studies of SNN deployments for autonomous agents are still at an early stage. Hence, the optimization stages for enabling efficient embodied SNN deployments for autonomous agents have not been defined systematically. Toward this, we propose a novel framework called SNN4Agents that consists of a set of optimization techniques for designing energy-efficient embodied SNNs targeting autonomous agent applications. Our SNN4Agents employs weight quantization, timestep reduction, and attention window reduction to jointly improve the energy efficiency, reduce the memory footprint, optimize the processing latency, while maintaining high accuracy. In the evaluation, we investigate use cases of event-based car recognition, and explore the trade-offs among accuracy, latency, memory, and energy consumption. The experimental results show that our proposed framework can maintain high accuracy (i.e., 84.12% accuracy) with 68.75% memory saving, 3.58x speed-up, and 4.03x energy efficiency improvement as compared to the state-of-the-art work for the NCARS dataset. In this manner, our SNN4Agents framework paves the way toward enabling energy-efficient embodied SNN deployments for autonomous agents. [ABSTRACT FROM AUTHOR]

Exaggerated reactivity to drug-cues and emotional dysregulations represent key symptoms of early stages of substance use disorders. The diagnostic criteria for (Internet) gaming disorder strongly resemble symptoms for substance-related addictions. However, previous cross-sections studies revealed inconsistent results with respect to neural cue reactivity and emotional dysregulations in these populations. To this end, the present fMRI study applied a combined cross-sectional and longitudinal design in regular online gamers (n = 37) and gaming-naïve controls (n = 67). To separate gaming-associated changes from predisposing factors, gaming-naive subjects were randomly assigned to 6 weeks of daily Internet gaming or a non-gaming condition. At baseline and after the training, subjects underwent an fMRI paradigm presenting gaming-related cues and non-gaming-related emotional stimuli. Cross-sectional comparisons revealed gaming-cue specific enhanced valence attribution and neural reactivity in a parietal network, including the posterior cingulate in regular gamers as compared to gaming naïve-controls. Longitudinal analysis revealed that 6 weeks of gaming elevated valence ratings as well as neural cue-reactivity in a similar parietal network, specifically the posterior cingulate in previously gaming-naïve controls. Together, the longitudinal design did not reveal supporting evidence for altered emotional processing of non-gaming associated stimuli in regular gamers whereas convergent evidence for increased emotional and neural reactivity to gaming-associated stimuli was observed. Findings suggest that exaggerated neural reactivity in posterior parietal regions engaged in default mode and automated information processing already occur during early stages of regular gaming and probably promote continued engagement in gaming behavior. [ABSTRACT FROM AUTHOR]

Robust and accurate step counting is important for indoor localization algorithms that rely on smartphone inertial sensors. Existing solutions for step counting do not consider users’ false walking state (e.g., when a user in still state uses her phone for texting, playing games, and watching movies), which results in the over-counting problem. In this paper, we propose a robust and accurate step counting algorithm to solve the overcounting problem caused by false walking. Experimental results show that the proposed algorithm outperforms the commonly-used peak detection-based method and can improve the step counting accuracy by 6.56% for normal walking case, 9.54% for free walking case, and by 58.92% for false walking case, which is a significant improvement in the accuracy and robustness. We also compare the proposed method with popular commercial step counting applications including S Health, i-Health, and Pedometer++, which shows that our method can achieve better accuracy. [ABSTRACT FROM PUBLISHER]