符号检测是现代通信系统中的一个基本且具有挑战性的问题，例如多源多输入多输出（MIMO）设置。迭代软干扰取消（SIC）是该任务的最新方法，最近动机的数据驱动的神经网络模型，例如深度，可以处理未知的非线性通道。但是，这些神经网络模型需要在应用之前对网络进行全面的时间量培训，因此在实践中不容易适合高度动态的渠道。我们介绍了一个在线培训框架，该框架可以迅速适应频道中的任何更改。我们提出的框架将最近的深层发展方法与新兴的生成对抗网络（GAN）统一，以捕获频道中的任何变化，并快速调整网络以维持模型的最佳性能。我们证明，我们的框架在高度动态的通道上显着优于最近的神经网络模型，甚至超过了我们实验中静态通道上的神经网络模型。

联合学习（FL）被认为是分布式机器学习（ML）最有前途的解决方案之一。在当前的大多数文献中，FL已被研究用于监督的ML任务，其中边缘设备收集标记的数据。然而，在许多应用中，假设存在跨设备标记的数据是不切实际的。为此，我们开发了一种新颖的方法论，合作联合无监督的对比度学习（CF-CL），用于使用未标记的数据集的跨越边缘设备的FL。 CF-CL采用本地设备合作，其中通过设备到设备（D2D）通信在设备之间进行数据交换，以避免由非独立且相同分布式（非I.I.I.I.D。）本地数据集引起的本地模型偏差。 CF-CL引入了针对无监督的FL设置量身定制的推动力智能数据共享机制，在该设置中，每个设备将其本地数据点的子集推向其邻居，作为保留数据点，并从其邻居中提取一组数据点，并通过其进行采样概率重要性抽样技术。我们证明，CF-CL导致（i）跨设备的无监督的潜在空间对齐，（ii）更快的全局收敛，允许较低的全局模型聚合； （iii）在极端非i.i.d中有效。跨设备的数据设置。

纠错码是现代通信系统中的基本组件，要求极高的吞吐量，超可靠性和低延迟。随着解码器的近期使用机器学习（ML）模型的方法提供了改进的性能和对未知环境的巨大适应性，传统的解码器斗争。我们介绍了一般框架，以进一步提高ML模型的性能和适用性。我们建议将ML解码器与竞争鉴别器网络组合，该网络试图区分码字和嘈杂的单词，因此，指导解码模型以恢复传输的码字。我们的框架是游戏理论，由生成的对抗网络（GANS）有动力，解码器和鉴别者在零和游戏中竞争。解码器学习同时解码和生成码字，而鉴别器学会讲述解码输出和码字之间的差异。因此，解码器能够将嘈杂的接收信号解码为码字，增加成功解码的概率。我们通过证明这解码器定义了我们游戏的NASH均衡点，我们与最佳最大可能性解码器展示了我们的框架的强烈连接。因此，培训均衡具有实现最佳最大可能性性能的良好可能性。此外，我们的框架不需要培训标签，这些标签通常在通信期间通常不可用，因此似乎可以在线培训并适应频道动态。为了展示我们框架的表现，我们将其与最近的神经解码器相结合，并与各种代码上的原始模型和传统解码算法相比，表现出改进的性能。

由于其灵活，安全，表现特性，Edge Computing彻底改变了移动和无线网络世界的世界。最近，我们目睹了越来越多的利用，使得更加努力部署机器学习（ML）技术，例如联邦学习（FL）。与传统的分布式机器学习（ML）相比，FL被宣告以提高通信效率。原始FL假定中央聚合服务器，以聚合本地优化的参数，可能会带来可靠性和延迟问题。在本文中，我们对策略进行了深入的研究，以通过基于当前参与者和/或可用资源进行动态选择的飞行主服务器来替换这一中央服务器。具体来说，我们比较不同的指标来选择该飞行主机并评估共识算法以执行选择。我们的结果表明，使用我们的飞行大师FL框架的运行时显着减少了与我们的EDGEAI测试的测量结果和使用操作边缘测试的Real 5G网络进行的测量结果相比。

作为研究界，我们仍然缺乏对对抗性稳健性的进展的系统理解，这通常使得难以识别训练强大模型中最有前途的想法。基准稳健性的关键挑战是，其评估往往是出错的导致鲁棒性高估。我们的目标是建立对抗性稳健性的标准化基准，尽可能准确地反映出考虑在合理的计算预算范围内所考虑的模型的稳健性。为此，我们首先考虑图像分类任务并在允许的型号上引入限制（可能在将来宽松）。我们评估了与AutoAtrack的对抗鲁棒性，白和黑箱攻击的集合，最近在大规模研究中显示，与原始出版物相比，改善了几乎所有稳健性评估。为防止对自动攻击进行新防御的过度适应，我们欢迎基于自适应攻击的外部评估，特别是在自动攻击稳健性潜在高估的地方。我们的排行榜，托管在https://robustbench.github.io/，包含120多个模型的评估，并旨在反映在$ \ ell_ \ infty $的一套明确的任务上的图像分类中的当前状态 - 和$ \ ell_2 $ -Threat模型和共同腐败，未来可能的扩展。此外，我们开源源是图书馆https://github.com/robustbench/robustbench，可以提供对80多个强大模型的统一访问，以方便他们的下游应用程序。最后，根据收集的模型，我们分析了稳健性对分布换档，校准，分配检测，公平性，隐私泄漏，平滑度和可转移性的影响。

Migraine is a high-prevalence and disabling neurological disorder. However, information migraine management in real-world settings could be limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by migraine sufferers; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts. Our system achieved an F1 score of 0.90 on Twitter and 0.93 on Reddit. Analysis of information posted by our 'migraine cohort' revealed the presence of a plethora of relevant information about migraine therapies and patient sentiments associated with them. Our study forms the foundation for conducting an in-depth analysis of migraine-related information using social media data.

A current goal in the graph neural network literature is to enable transformers to operate on graph-structured data, given their success on language and vision tasks. Since the transformer's original sinusoidal positional encodings (PEs) are not applicable to graphs, recent work has focused on developing graph PEs, rooted in spectral graph theory or various spatial features of a graph. In this work, we introduce a new graph PE, Graph Automaton PE (GAPE), based on weighted graph-walking automata (a novel extension of graph-walking automata). We compare the performance of GAPE with other PE schemes on both machine translation and graph-structured tasks, and we show that it generalizes several other PEs. An additional contribution of this study is a theoretical and controlled experimental comparison of many recent PEs in graph transformers, independent of the use of edge features.

Transfer learning on edge is challenging due to on-device limited resources. Existing work addresses this issue by training a subset of parameters or adding model patches. Developed with inference in mind, Inverted Residual Blocks (IRBs) split a convolutional layer into depthwise and pointwise convolutions, leading to more stacking layers, e.g., convolution, normalization, and activation layers. Though they are efficient for inference, IRBs require that additional activation maps are stored in memory for training weights for convolution layers and scales for normalization layers. As a result, their high memory cost prohibits training IRBs on resource-limited edge devices, and making them unsuitable in the context of transfer learning. To address this issue, we present MobileTL, a memory and computationally efficient on-device transfer learning method for models built with IRBs. MobileTL trains the shifts for internal normalization layers to avoid storing activation maps for the backward pass. Also, MobileTL approximates the backward computation of the activation layer (e.g., Hard-Swish and ReLU6) as a signed function which enables storing a binary mask instead of activation maps for the backward pass. MobileTL fine-tunes a few top blocks (close to output) rather than propagating the gradient through the whole network to reduce the computation cost. Our method reduces memory usage by 46% and 53% for MobileNetV2 and V3 IRBs, respectively. For MobileNetV3, we observe a 36% reduction in floating-point operations (FLOPs) when fine-tuning 5 blocks, while only incurring a 0.6% accuracy reduction on CIFAR10. Extensive experiments on multiple datasets demonstrate that our method is Pareto-optimal (best accuracy under given hardware constraints) compared to prior work in transfer learning for edge devices.

The efficient segmentation of foreground text information from the background in degraded color document images is a hot research topic. Due to the imperfect preservation of ancient documents over a long period of time, various types of degradation, including staining, yellowing, and ink seepage, have seriously affected the results of image binarization. In this paper, a three-stage method is proposed for image enhancement and binarization of degraded color document images by using discrete wavelet transform (DWT) and generative adversarial network (GAN). In Stage-1, we use DWT and retain the LL subband images to achieve the image enhancement. In Stage-2, the original input image is split into four (Red, Green, Blue and Gray) single-channel images, each of which trains the independent adversarial networks. The trained adversarial network models are used to extract the color foreground information from the images. In Stage-3, in order to combine global and local features, the output image from Stage-2 and the original input image are used to train the independent adversarial networks for document binarization. The experimental results demonstrate that our proposed method outperforms many classical and state-of-the-art (SOTA) methods on the Document Image Binarization Contest (DIBCO) dataset. We release our implementation code at https://github.com/abcpp12383/ThreeStageBinarization.

The increased importance of mobile photography created a need for fast and performant RAW image processing pipelines capable of producing good visual results in spite of the mobile camera sensor limitations. While deep learning-based approaches can efficiently solve this problem, their computational requirements usually remain too large for high-resolution on-device image processing. To address this limitation, we propose a novel PyNET-V2 Mobile CNN architecture designed specifically for edge devices, being able to process RAW 12MP photos directly on mobile phones under 1.5 second and producing high perceptual photo quality. To train and to evaluate the performance of the proposed solution, we use the real-world Fujifilm UltraISP dataset consisting on thousands of RAW-RGB image pairs captured with a professional medium-format 102MP Fujifilm camera and a popular Sony mobile camera sensor. The results demonstrate that the PyNET-V2 Mobile model can substantially surpass the quality of tradition ISP pipelines, while outperforming the previously introduced neural network-based solutions designed for fast image processing. Furthermore, we show that the proposed architecture is also compatible with the latest mobile AI accelerators such as NPUs or APUs that can be used to further reduce the latency of the model to as little as 0.5 second. The dataset, code and pre-trained models used in this paper are available on the project website: https://github.com/gmalivenko/PyNET-v2