In this paper, we analyze the impact of data freshness on remote inference
systems, where a pre-trained neural network infers a time-varying target (e.g.,
the locations of vehicles and pedestrians) based on features (e.g., video
frames) observed at a sensing node (e.g., a camera). One might expect that the
performance of a remote inference system degrades monotonically as the feature
becomes stale. Using an information-theoretic analysis, we show that this is
true if the feature and target data sequence can be closely approximated as a
Markov chain, whereas it is not true if the data sequence is far from
Markovian. Hence, the inference error is a function of Age of Information
(AoI), where the function could be non-monotonic. To minimize the inference
error in real-time, we propose a new "selection-from-buffer" model for sending
the features, which is more general than the "generate-at-will" model used in
earlier studies. In addition, we design low-complexity scheduling policies to
improve inference performance. For single-source, single-channel systems, we
provide an optimal scheduling policy. In multi-source, multi-channel systems,
the scheduling problem becomes a multi-action restless multi-armed bandit
problem. For this setting, we design a new scheduling policy by integrating
Whittle index-based source selection and duality-based feature
selection-from-buffer algorithms. This new scheduling policy is proven to be
asymptotically optimal. These scheduling results hold for minimizing general
AoI functions (monotonic or non-monotonic). Data-driven evaluations demonstrate
the significant advantages of our proposed scheduling policies.

分析数据新鲜度对远程推理系统的影响，提出了一种新的选择 - 缓冲区模型和调度策略来降低推理误差并优化信息时效性。

远程推理的及时通信

Timely Communications for Remote Inference

Unmanned Aerial Vehicles (UAVs) are integral in various sectors like
agriculture, surveillance, and logistics, driven by advancements in 5G.
However, existing research lacks a comprehensive approach addressing both data
freshness and security concerns. In this paper, we address the intricate
challenges of data freshness, and security, especially in the context of
eavesdropping and jamming in modern UAV networks. Our framework incorporates
exponential AoI metrics and emphasizes secrecy rate to tackle eavesdropping and
jamming threats. We introduce a transformer-enhanced Deep Reinforcement
Learning (DRL) approach to optimize task offloading processes. Comparative
analysis with existing algorithms showcases the superiority of our scheme,
indicating its promising advancements in UAV network management.

本研究论文讨论了无人机网络数据新鲜度和安全性的挑战，引入了基于变换增强深度强化学习方法的框架以解决窃听和干扰问题，并通过与现有算法的对比分析展示了其在无人机网络管理方面的优越性和前景。

保障天空：具有 AoI 感知的 IRS 辅助安全多无人机系统和高效任务卸载

Securing the Skies: An IRS-Assisted AoI-Aware Secure Multi-UAV System  with Efficient Task Offloading

The delayed feedback problem is one of the most pressing challenges in
predicting the conversion rate since users' conversions are always delayed in
online commercial systems. Although new data are beneficial for continuous
training, without complete feedback information, i.e., conversion labels,
training algorithms may suffer from overwhelming fake negatives. Existing
methods tend to use multitask learning or design data pipelines to solve the
delayed feedback problem. However, these methods have a trade-off between data
freshness and label accuracy. In this paper, we propose Delayed Feedback
Modeling by Dynamic Graph Neural Network (DGDFEM). It includes three stages,
i.e., preparing a data pipeline, building a dynamic graph, and training a CVR
prediction model. In the model training, we propose a novel graph convolutional
method named HLGCN, which leverages both high-pass and low-pass filters to deal
with conversion and non-conversion relationships. The proposed method achieves
both data freshness and label accuracy. We conduct extensive experiments on
three industry datasets, which validate the consistent superiority of our
method.

在本研究中，我们提出了一种名为 DGDFEM 的延迟反馈建模方法，它使用动态图神经网络来解决预测转化率中的延迟反馈问题，并在模型训练中提出了一种名为 HLGCN 的新型图卷积方法，实现了数据新鲜度和标签准确性的平衡。我们在三个行业数据集上进行了大量实验证明了我们方法的一致优越性。