This paper describes a receiver that uses an innovative method to predict,
according to history of receiver operating metrics (packet lost/well received),
the optimum automatic gain control (AGC) index or most appropriate variable
gain range to be used for next packet reception, anticipating an interferer
appearing during the payload reception. This allows the receiver to have higher
immunity to interferers even if they occur during the gain frozen payload
reception period whilst still ensuring an optimum sensitivity level. As a
result, the method allows setting the receiver gain to get an optimum trade-off
between reception sensitivity and random interferer immunity.

这篇论文描述了一种使用创新方法来预测接收机操作指标（丢包率 / 接收良好率）历史数据的接收机，以确定下一个数据包接收时最佳的自动增益控制（AGC）指数或最合适的可变增益范围，预计在有效负载接收期间出现干扰信号。这使得接收机对干扰具有更高的免疫力，即使它们出现在增益冻结的有效负载接收期间，同时仍保证最佳灵敏度水平。因此，该方法允许设置接收机增益，以在接收灵敏度和随机干扰免疫力之间实现最佳权衡。

拥挤无线环境下 AGC 指数管理算法

Algorithm for AGC index management against crowded radio environment

Recent studies have revealed the vulnerability of Deep Neural Networks (DNNs)
to adversarial examples, which can easily fool DNNs into making incorrect
predictions. To mitigate this deficiency, we propose a novel adversarial
defense method called "Immunity" (Innovative MoE with MUtual information \&
positioN stabilITY) based on a modified Mixture-of-Experts (MoE) architecture
in this work. The key enhancements to the standard MoE are two-fold: 1)
integrating of Random Switch Gates (RSGs) to obtain diverse network structures
via random permutation of RSG parameters at evaluation time, despite of RSGs
being determined after one-time training; 2) devising innovative Mutual
Information (MI)-based and Position Stability-based loss functions by
capitalizing on Grad-CAM's explanatory power to increase the diversity and the
causality of expert networks. Notably, our MI-based loss operates directly on
the heatmaps, thereby inducing subtler negative impacts on the classification
performance when compared to other losses of the same type, theoretically.
Extensive evaluation validates the efficacy of the proposed approach in
improving adversarial robustness against a wide range of attacks.

我们在这项工作中提出了一种新的对抗防御方法称为 “Immunity”（基于修改的 Mixture-of-Experts（MoE）架构），通过集成随机开关门（RSGs）和创新的互信息（MI）和位置稳定性（Position Stability）损失函数，提高深度神经网络对广泛攻击的对抗鲁棒性。

增强混合专家网络的 “免疫力” 以进行对抗性防御

Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial  Defense

Poor nutrition can lead to reduced immunity, increased susceptibility to
disease, impaired physical and mental development, and reduced productivity. A
conversational agent can support people as a virtual coach, however building
such systems still have its associated challenges and limitations. This paper
describes the background and motivation for chatbot systems in the context of
healthy nutrition recommendation. We discuss current challenges associated with
chatbot application, we tackled technical, theoretical, behavioural, and social
aspects of the challenges. We then propose a pipeline to be used as guidelines
by developers to implement theoretically and technically robust chatbot
systems.

聊天机器人在健康营养建议中的应用和挑战，提出指导开发者实现技术和理论上健壮的聊天机器人系统的流程。