A comparative study of the analgesic effects of sevoflurane and propofol in children following otolaryngology surgical procedures: A pilot study

Purpose: To determine the analgesic effects of sevoflurane (Sev) and propofol (Pro) in children whounderwent otolaryngology surgical procedures, and their post-operative conditions.Methods: A total of 62 (ASA I or ASA II) pre-medicated children who were about to undergootolaryngology surgical procedures were chosen and divided equally into Sev and Pro groups, with 31patients per group. During the surgical procedure, Sev was administered via a mask, while Pro wasgiven i.v. Each anesthesia was followed with fentanyl administration.Results: Pain scores such as verbal rating scale (VRS) and visual analogue scale (VAS) were slightlylower in Sev group than in Pro group. However, post-operative conditions such as emergence delirium(ED) and emergence agitation (EA) were significantly elevated in Sev group, when compared to Progroup (p < 0.05). In addition, patients in Sev group had higher levels of hemodynamic parameters(blood pressure), and much higher number of adverse events than those in Pro group. Thus, the overallsatisfaction score and recovery characteristics, i.e., hospitalization time and recovery were slightlybetter in Pro-anesthetized children than in those given Sev.Conclusion: These results suggest that except for pain score, Pro-anesthetized children fared better interms of speedy recovery and reduced adverse effects than those given Pro. Thus, Pro may berecommended as general anaesthetic for children undergoing otolaryngology surgical procedures.Keywords: Sevoflurane, Propofol, Pain score, Emergence agitation, Otolaryngolog

Reinforcement Learning with Human Feedback for Realistic Traffic Simulation

In light of the challenges and costs of real-world testing, autonomous vehicle developers often rely on testing in simulation for the creation of reliable systems. A key element of effective simulation is the incorporation of realistic traffic models that align with human knowledge, an aspect that has proven challenging due to the need to balance realism and diversity. This works aims to address this by developing a framework that employs reinforcement learning with human preference (RLHF) to enhance the realism of existing traffic models. This study also identifies two main challenges: capturing the nuances of human preferences on realism and the unification of diverse traffic simulation models. To tackle these issues, we propose using human feedback for alignment and employ RLHF due to its sample efficiency. We also introduce the first dataset for realism alignment in traffic modeling to support such research. Our framework, named TrafficRLHF, demonstrates its proficiency in generating realistic traffic scenarios that are well-aligned with human preferences, as corroborated by comprehensive evaluations on the nuScenes dataset.Comment: 9 pages, 4 figure

AdvDO: Realistic Adversarial Attacks for Trajectory Prediction

Trajectory prediction is essential for autonomous vehicles (AVs) to plan correct and safe driving behaviors. While many prior works aim to achieve higher prediction accuracy, few study the adversarial robustness of their methods. To bridge this gap, we propose to study the adversarial robustness of data-driven trajectory prediction systems. We devise an optimization-based adversarial attack framework that leverages a carefully-designed differentiable dynamic model to generate realistic adversarial trajectories. Empirically, we benchmark the adversarial robustness of state-of-the-art prediction models and show that our attack increases the prediction error for both general metrics and planning-aware metrics by more than 50% and 37%. We also show that our attack can lead an AV to drive off road or collide into other vehicles in simulation. Finally, we demonstrate how to mitigate the adversarial attacks using an adversarial training scheme.Comment: To appear in ECCV 202

A corpus‐based discourse analysis of liberal studies textbooks in Hong Kong: legitimatizing populism

Researchers have discussed Hong Kong’s localist identities, nativist sentiments, and populism, but have not widely examined the extent to which populism could be perceived in education in Hong Kong. As the chief participants of the Occupying Central and the radical Anti-Extradition Bill movements in Hong Kong were students, this suggests the need to explore the relationship between populism and education, particularly the then-controversial liberal studies textbooks. According to contemporary news reports, liberal studies textbooks contained much content stigmatising the Chinese mainland. Previous studies of liberal studies textbooks applied qualitative discourse analysis methods. In this study, mixed-method analysis was applied to a specialised corpus comprising seven commercial liberal studies textbooks containing 248,339 Chinese characters in total to explore the extent to which liberal studies textbooks contain information concerning the key features of populism—the heightened division between the inner and outer groups. A division was found between positive images of Hong Kong and negative images of China in the narratives of commercial liberal studies textbooks. Accordingly, the textbooks can be understood to contain populism. The present study advocates that relevant educational watchdogs in Hong Kong provide more guidance on the writing and publishing of liberal studies textbooks in the future, keeping the enquiry-based spirit of the liberal studies course fulfilled, and urges stakeholders of Hong Kong education to consider teaching peace education and developing a more inclusive environment

Detecting cyberattacks in industrial control systems using online learning algorithms

Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power grids, and transportation systems. Similar to other information systems, a significant threat to industrial control systems is the attack from cyberspace---the offensive maneuvers launched by "anonymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information. Owing to the importance of industrial control systems, and the possibly devastating consequences of being attacked, significant endeavors have been attempted to secure industrial control systems from cyberattacks. Among them are intrusion detection systems that serve as the first line of defense by monitoring and reporting potentially malicious activities. Classical machine-learning-based intrusion detection methods usually generate prediction models by learning modest-sized training samples all at once. Such approach is not always applicable to industrial control systems, as industrial control systems must process continuous control commands with limited computational resources in a nonstop way. To satisfy such requirements, we propose using online learning to learn prediction models from the controlling data stream. We introduce several state-of-the-art online learning algorithms categorically, and illustrate their efficacies on two typically used testbeds---power system and gas pipeline. Further, we explore a new cost-sensitive online learning algorithm to solve the class-imbalance problem that is pervasive in industrial intrusion detection systems. Our experimental results indicate that the proposed algorithm can achieve an overall improvement in the detection rate of cyberattacks in industrial control systems