Analyzing the Potential of Using Social Robots in Autism Classroom Settings

In recent years, social robots have rapidly advanced alongside the progress of artificial intelligence. Countries around the world have been enacting strategic initiatives that combine robotics and artificial intelligence, leading to an increasing exploration of the application of AI technology in the field of education. In the context of autism intervention, social robots have shown promising results in intervention programs and behavior therapy for children with autism. However, there is a lack of research specifically focusing on the use of social robots in autism classroom settings. Therefore, we have synthesized existing studies and proposed the integration of social robots into autism classrooms. Through the collaboration between robots and teachers, as well as the interaction between robots and students, we aim to enhance the attention of children with autism in the classroom and explore new impacts on their classroom performance, knowledge acquisition, and generalization of after-class skills

Network Pruning Spaces

Network pruning techniques, including weight pruning and filter pruning, reveal that most state-of-the-art neural networks can be accelerated without a significant performance drop. This work focuses on filter pruning which enables accelerated inference with any off-the-shelf deep learning library and hardware. We propose the concept of \emph{network pruning spaces} that parametrize populations of subnetwork architectures. Based on this concept, we explore the structure aspect of subnetworks that result in minimal loss of accuracy in different pruning regimes and arrive at a series of observations by comparing subnetwork distributions. We conjecture through empirical studies that there exists an optimal FLOPs-to-parameter-bucket ratio related to the design of original network in a pruning regime. Statistically, the structure of a winning subnetwork guarantees an approximately optimal ratio in this regime. Upon our conjectures, we further refine the initial pruning space to reduce the cost of searching a good subnetwork architecture. Our experimental results on ImageNet show that the subnetwork we found is superior to those from the state-of-the-art pruning methods under comparable FLOPs

BOLD-fMRI activity informed by network variation of scalp EEG in juvenile myoclonic epilepsy

Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME

Analyzing the Potential of Using Social Robots in Autism Classroom Settings

In recent years, social robots have rapidly advanced alongside the progress of artificial intelligence. Countries around the world have been enacting strategic initiatives that combine robotics and artificial intelligence, leading to an increasing exploration of the application of AI technology in the field of education. In the context of autism intervention, social robots have shown promising results in intervention programs and behavior therapy for children with autism. However, there is a lack of research specifically focusing on the use of social robots in autism classroom settings. Therefore, we have synthesized existing studies and proposed the integration of social robots into autism classrooms. Through the collaboration between robots and teachers, as well as the interaction between robots and students, we aim to enhance the attention of children with autism in the classroom and explore new impacts on their classroom performance, knowledge acquisition, and generalization of after-class skills

Transcranial deep-tissue phototherapy for Alzheimer's disease using low-dose X-ray-activated long-afterglow scintillators

Non-invasive phototherapy has been emerging as an ambitious tactic for suppression of amyloid-β (Aβ) self-assembly against Alzheimer's disease (AD). However, it remains a daunting challenge to develop efficient photosensitizers for Aβ oxygenation that are activatable in a deep brain tissue through the scalp and skull, while reducing side effects on normal tissues. Here, we report an Aβ targeted, low-dose X-ray-excitable long-afterglow scintillator (ScNPs@RB/Ab) for efficient deep-brain phototherapy. We demonstrate that the as-synthesized ScNPs@RB/Ab is capable of converting X-rays into visible light to activate the photosensitizers of rose bengal (RB) for Aβ oxygenation through the scalp and skull. We show that the ScNPs@RB/Ab persistently emitting visible luminescence can substantially minimize the risk of excessive X-ray exposure dosage. Importantly, peptide KLVFFAED-functionalized ScNPs@RB/Ab shows a blood-brain barrier permeability. In vivo experimental results validated that ScNPs@RB/Ab alleviated Aβ burden and slowed cognitive decline in triple-transgenic AD model mice at extremely low X-ray doses without side effects. Our study paves a new pathway to develop high-efficiency transcranial AD phototherapy. STATEMENT OF SIGNIFICANCE: Non-invasive phototherapy has been emerging as an ambitious tactic for suppression of amyloid-β (Aβ) self-assembly against Alzheimer's disease (AD). However, it remains a daunting challenge to develop efficient photosensitizers for Aβ oxygenation that are activatable in a deep brain tissue through the scalp and skull, while reducing side effects on normal tissues. Herein, we report an Aβ targeted, low-dose X-ray-excitable long-afterglow scintillators (ScNPs@RB/Ab) for efficient deep-brain phototherapy. In vivo experimental results validated that ScNPs@RB/Ab alleviated Aβ burden and slowed cognitive decline in triple-transgenic AD model mice at extremely low X-ray doses without side effects