Compressed data aggregation (CDA) over wireless sensor networks (WSNs) is
task-specific and subject to environmental changes. However, the existing
compressed data aggregation (CDA) frameworks (e.g., compressed sensing-based
data aggregation, deep learning(DL)-based data aggregation) do not possess the
flexibility and adaptivity required to handle distinct sensing tasks and
environmental changes. Additionally, they do not consider the performance of
follow-up IoT data-driven deep learning (DL)-based applications. To address
these shortcomings, we propose OrcoDCS, an IoT-Edge orchestrated online deep
compressed sensing framework that offers high flexibility and adaptability to
distinct IoT device groups and their sensing tasks, as well as high performance
for follow-up applications. The novelty of our work is the design and
deployment of IoT-Edge orchestrated online training framework over WSNs by
leveraging an specially-designed asymmetric autoencoder, which can largely
reduce the encoding overhead and improve the reconstruction performance and
robustness. We show analytically and empirically that OrcoDCS outperforms the
state-of-the-art DCDA on training time, significantly improves flexibility and
adaptability when distinct reconstruction tasks are given, and achieves higher
performance for follow-up applications.

提出了一种名为 OrcoDCS 的物联网边缘协调在线深度压缩感知框架，通过利用特别设计的非对称自编码器，降低编码开销、提高重构性能和鲁棒性，实现了对不同物联网设备组和其感知任务的高度灵活性和适应性，同时在训练时间上优于现有的压缩数据聚合方法并为后续应用提供更高性能。

OrcoDCS：一种物联网边缘编排的在线深度压缩感知框架

OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing  Framework

The spatiotemporal resolution of Partial Differential Equations (PDEs) plays
important roles in the mathematical description of the world's physical
phenomena. In general, scientists and engineers solve PDEs numerically by the
use of computationally demanding solvers. Recently, deep learning algorithms
have emerged as a viable alternative for obtaining fast solutions for PDEs.
Models are usually trained on synthetic data generated by solvers, stored on
disk and read back for training. This paper advocates that relying on a
traditional static dataset to train these models does not allow the full
benefit of the solver to be used as a data generator. It proposes an open
source online training framework for deep surrogate models. The framework
implements several levels of parallelism focused on simultaneously generating
numerical simulations and training deep neural networks. This approach
suppresses the I/O and storage bottleneck associated with disk-loaded datasets,
and opens the way to training on significantly larger datasets. Experiments
compare the offline and online training of four surrogate models, including
state-of-the-art architectures. Results indicate that exposing deep surrogate
models to more dataset diversity, up to hundreds of GB, can increase model
generalization capabilities. Fully connected neural networks, Fourier Neural
Operator (FNO), and Message Passing PDE Solver prediction accuracy is improved
by 68%, 16% and 7%, respectively.

该论文提出了一个开源在线培训框架，用于快速解决偏微分方程组，可以提高深度代理模型的数据多样性，对于 Fully connected neural networks、Fourier Neural Operator (FNO) 和 Message Passing PDE Solver 的预测准确度分别提高了 68％、16％和 7％。

利用大规模在线学习训练深度代理模型

Training Deep Surrogate Models with Large Scale Online Learning

Symbol detection is a fundamental and challenging problem in modern
communication systems, e.g., multiuser multiple-input multiple-output (MIMO)
setting. Iterative Soft Interference Cancellation (SIC) is a state-of-the-art
method for this task and recently motivated data-driven neural network models,
e.g. DeepSIC, that can deal with unknown non-linear channels. However, these
neural network models require thorough timeconsuming training of the networks
before applying, and is thus not readily suitable for highly dynamic channels
in practice. We introduce an online training framework that can swiftly adapt
to any changes in the channel. Our proposed framework unifies the recent deep
unfolding approaches with the emerging generative adversarial networks (GANs)
to capture any changes in the channel and quickly adjust the networks to
maintain the top performance of the model. We demonstrate that our framework
significantly outperforms recent neural network models on highly dynamic
channels and even surpasses those on the static channel in our experiments.

本研究提出了一种在线训练框架，用于快速适应于任何通道变化，以保持模型的最佳性能，该框架将最新的深度展开方法与对抗生成网络相结合，以捕获通道的任何变化并迅速调整网络，在高动态通道上显著优于最近的神经网络模型，并在我们的实验中甚至超过静态通道的性能。