Emotion recognition based on electroencephalography (EEG) signals is one of the current research challenges in this field. In order to learn the optimal graph structure information for each subject, we propose a dynam...
Emotion recognition based on electroencephalography (EEG) signals is one of the current research challenges in this field. In order to learn the optimal graph structure information for each subject, we propose a dynamic graph attention neural network model. The model utilizes a graph attention neural network as a feature learner, dynamically learning channel connections, and enriching feature representations between channels through global attention. To verify the effectiveness of the proposed method, we conducted experiments on the publicly available emotion recognition dataset SEED. The experimental results show that the average accuracy and standard deviation of the 15 subjects are 94.6% and 4.98%, respectively. The results indicate that our proposed dynamic graphical attention neural network outperforms existing methods.
Hands are paramount for dexterous interactions that humans exhibit in daily life. Understanding the intricacies of human hand-object interactions is therefore necessary. Unfortunately, the limitations of state-of-the-...
Hands are paramount for dexterous interactions that humans exhibit in daily life. Understanding the intricacies of human hand-object interactions is therefore necessary. Unfortunately, the limitations of state-of-the-art technologies make capturing the full hand-object complexity unfeasible, giving rise to the need for new technological means to achieve this aim. In this work, we propose an end-to-end framework in which individualized hand models are derived and used to capture quantitative personalized hand-object interaction information, precisely, hand shape, kinematics, and contact surfaces. The results of this study serve as a proof of concept that such a framework can significantly deepen personalized hand-object interaction analyses, providing, in perspective, insights for medical diagnoses and rehabilitation, among *** relevance— Our work showcases the need to incorporate bespoke human hand models in individualized hand function assessment technologies, as hand-object interaction information is subject-dependent.
Ferroelectric Field-Effect Transistors (FeFETs) having the same structure as normal transistors are affected by self-heating. Consequently, there is a reduction in the ON current and a decrease in the sensing margin w...
Ferroelectric Field-Effect Transistors (FeFETs) having the same structure as normal transistors are affected by self-heating. Consequently, there is a reduction in the ON current and a decrease in the sensing margin which increases the probability of error for in-memory computing applications. In this paper, we have investigated for the first time how self-heating impacts the reliability of Fe-FinFET and Fe-FDSOI-based systems. Our analysis unveils that Fe-FinFET devices are more prone to self-heating effects as compared to Fe-FDSOI devices due to channel confinement. Furthermore, we have evaluated how self-heating impacts the performance of Fe-FET-based various in-memory computing systems.
The heterogeneous integration of advanced CMOS and emerging technology-based circuits as well as of chiplets at package level present promising avenues to meet the high computational intensity demands of AI applicatio...
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ISBN:
(数字)9798331594503
ISBN:
(纸本)9798331594510
The heterogeneous integration of advanced CMOS and emerging technology-based circuits as well as of chiplets at package level present promising avenues to meet the high computational intensity demands of AI applications, in particular due the strict constraints in terms of area, power consumption, and reliability. We classify heterogeneous integration at both the chip level, encompassing novel transistor types such as complementary FETs (CFETs) and novel devices, such as resistive memories, and at the packaging level, spanning from 2.5D to 3D and 5.5D approaches. In this context, the paper presents two case studies assuming the idea of vertical and horizontal integration at chip and packaging level. In more detail, case study I presents the design of vertically integrated inverters based on CFET and thin-film transistor (TFT), as well as the design of SRAM cells based on similar monolitic 3D integrations. In addition, case study II explores the main concepts and the state-of-the-art of heterogeneous integration of chiplets at package level. Quality and reliability aspects that are specific to the heterogeneous integration of advanced CMOS and emerging technologies are further discussed assuming a holistic approach based on circuits’ lifecycle phases. Finally, this paper provides insights and summarizes strategies to address some of these challenges, ensuring high quality and reliable integration for the next-generation of AI hardware.
Urban road traffic systems are advancing into sophisticated networks, underscoring the importance of real-time collaborative decision-making. This study tackles the intricate challenge of cooperative path planning und...
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ISBN:
(数字)9798350348811
ISBN:
(纸本)9798350348828
Urban road traffic systems are advancing into sophisticated networks, underscoring the importance of real-time collaborative decision-making. This study tackles the intricate challenge of cooperative path planning under complex urban conditions, taking into account a variety of vehicle types and their respective priorities. While conventional path planning techniques struggle with such intricate coordination, reinforcement learning, though theoretically capable, is hindered by its limited model reusability and protracted training times. To address these issues, we present a novel parallel multi-agent reinforcement learning strategy for path planning that is adaptable to various vehicle types. The problem is initially cast as a multi-agent Markov Decision Process (MDP), followed by the introduction of a parallel training approach within the Q-learning framework. This approach leverages tensor computation to transform the Q-table, state, and reward, thereby markedly accelerating the training process. Empirical simulations demonstrate the approach’s efficacy, achieving a 0.84% reduction in training time (from approximately 771.611 seconds to 0.654 seconds), achieving a 93.94% lower probability of path overlap though the total distance increased by 7.69%.
Introduction: Osteosynthesis of the equine femur is still a challenge for veterinary medicine. Even though intramedullary fracture fixation is possible nowadays, the varying geometry of the medullary cavity along the ...
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Pre-defined manipulation primitives are widely used for cloth manipulation. However, cloth properties such as its stiffness or density can highly impact the performance of these primitives. Although existing solutions...
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Unsupervised video anomaly detection aims to train the model on unlabeled videos containing only normal behavior and identify data instances with abnormal objects or behaviors as anomalies. Abnormal events are typical...
Unsupervised video anomaly detection aims to train the model on unlabeled videos containing only normal behavior and identify data instances with abnormal objects or behaviors as anomalies. Abnormal events are typically detected due to their unique appearance and motion features. Existing methods mainly rely on various appearance-motion feature fusion techniques to model the information from both modalities for anomaly detection. However, they overlook the semantic consistency between motion and appearance information, resulting in constrained model performance. To address this limitation, we directly model the relationship of semantic consistency between appearance and motion. Given the varying importance of different pixels, manually designing feature fusion methods to represent their consistency is impractical. So, we designed a two-stream feature fusion module based on cross-attention. This module constructs information fusion between appearance and motion semantics in normal videos, dynamically capturing the inherent appearance-motion semantic consistency. We further believe that, regardless of the perspective, the appearance-motion semantic consistency should be similar. Therefore, this paper introduces a new consistency loss, urging the model to fully model the semantic representation of consistency between appearance and motion in normal data, thereby identifying anomalies with lower consistency. Experimental results on various standard datasets and ablation studies have demonstrated the effectiveness of the proposed method.
Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian assumption for the observation noise ...
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Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian assumption for the observation noise to build the likelihood function for Bayesian inference. However, the electromagnetic measurements of brain activity are usually affected by miscellaneous artifacts, leading to a potentially non-Gaussian distribution for the observation noise. Hence the conventional Gaussian likelihood model is a suboptimal choice for the real-world source imaging task. In this study, we aim to solve this problem by proposing a new likelihood model which is robust with respect to non-Gaussian noises. Motivated by the robust maximum correntropy criterion, we propose a new improper distribution model concerning the noise assumption. This new noise distribution is leveraged to structure a robust likelihood function and integrated with hierarchical prior distributions to estimate source activities by variational inference. In particular, the score matching is adopted to determine the hyperparameters for the improper likelihood model. A comprehensive performance evaluation is performed to compare the proposed noise assumption to the conventional Gaussian model. Simulation results show that, the proposed method can realize more precise source reconstruction by designing known ground-truth. The real-world dataset also demonstrates the superiority of our new method with the visual perception task. This study provides a new backbone for Bayesian source imaging, which would facilitate its application using real-world noisy brain signal.
Multi-Object Tracking (MOT) encompasses various tracking scenarios, each characterized by unique traits. Ef-fective trackers should demonstrate a high degree of gen-eralizability across diverse scenarios. However, exi...
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ISBN:
(数字)9798350353006
ISBN:
(纸本)9798350353013
Multi-Object Tracking (MOT) encompasses various tracking scenarios, each characterized by unique traits. Ef-fective trackers should demonstrate a high degree of gen-eralizability across diverse scenarios. However, existing trackers struggle to accommodate all aspects or necessi-tate hypothesis and experimentation to customize the asso-ciation information (motion and/or appearance) for a given scenario, leading to narrowly tailored solutions with limited generalizability. In this paper, we investigate the factors that influence trackers' generalization to different scenar-ios and concretize them into a set of tracking scenario at-tributes to guide the design of more generalizable trackers. Furthermore, we propose a “point-wise to instance-wise relation” framework for MOT, i.e., GeneralTrack, which can generalize across diverse scenarios while eliminating the need to balance motion and appearance. Thanks to its supe-rior generalizability, our proposed GeneralTrack achieves state-of-the-art performance on multiple benchmarks and demonstrates the potential for domain generalization.
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