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Empathy for children is critical for designing AI technologies that may affect children. This paper presents the work in progress of a study on the feasibility of a new method to provide objective understanding of people’s empathy for children based on functional near infrared spectroscopy (fNIRS). Adult participants (n=13) were presented with benign or concerning scenarios involving children interacting with AI technologies. Their brain activation patterns were recorded and analyzed. Preliminary data analysis revealed distinctive patterns in the mPFC region, which justifies future work to fully realize the potential of this method. 

For semantic segmentation, label probabilities are often uncalibrated as they are typically only the by-product of a segmentation task. Intersection over Union (IoU) and Dice score are often used as criteria for segmentation success, while metrics related to label probabilities are not often explored. However, probability calibration approaches have been studied, which match probability outputs with experimentally observed errors. These approaches mainly focus on classification tasks, but not on semantic segmentation. Thus, we propose a learning-based calibration method that focuses on multi-label semantic segmentation. Specifically, we adopt a convolutional neural network to predict local temperature values for probability calibration. One advantage of our approach is that it does not change prediction accuracy, hence allowing for calibration as a postprocessing step. Experiments on the COCO, CamVid, and LPBA40 datasets demonstrate improved calibration performance for a range of different metrics. We also demonstrate the good performance of our method for multi-atlas brain segmentation from magnetic resonance images. 

Photons at microwave and optical frequencies are principal carriers for quantum information. While microwave photons can be effectively controlled at the local circuit level, optical photons can propagate over long distances. High-fidelity conversion between microwave and optical photons will allow the distribution of quantum states across different quantum technology nodes and enhance the scalability of hybrid quantum systems toward a future “Quantum Internet.” Despite a frequency difference of five orders of magnitude, there has been significant progress recently toward the transfer between microwave and optical photons with steadily improved efficiency in a coherent and bidirectional manner. In this review, we summarize this progress, emphasizing integrated device approaches, and provide a perspective for device implementation that enables quantum state transfer and entanglement distribution across microwave and optical domains.