The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propose a learning-based dynamic time-frequency division duplexing (D-TFDD) scheme that adaptively allocates the uplink and downlink time-frequency resources of base stations (BSs) to meet the asymmetric and heterogeneous traffic demands while alleviating the inter-cell interference. We formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP) that maximizes the long-term expected sum rate under the users' packet dropping ratio constraints. In order to jointly optimize the global resources in a decentralized manner, we propose a federated reinforcement learning (RL) algorithm named federated Wolpertinger deep deterministic policy gradient (FWDDPG) algorithm. The BSs decide their local time-frequency configurations through RL algorithms and achieve global training via exchanging local RL models with their neighbors under a decentralized federated learning framework. Specifically, to deal with the large-scale discrete action space of each BS, we adopt a DDPG-based algorithm to generate actions in a continuous space, and then utilize Wolpertinger policy to reduce the mapping errors from continuous action space back to discrete action space. Simulation results demonstrate the superiority of our proposed algorithm to benchmark algorithms with respect to system sum rate.

Deep learning-based linkage of records across different databases is becoming increasingly useful in data integration and mining applications to discover new insights from multiple sources of data. However, due to privacy and confidentiality concerns, organisations often are not willing or allowed to share their sensitive data with any external parties, thus making it challenging to build/train deep learning models for record linkage across different organizations' databases. To overcome this limitation, we propose the first deep learning-based multi-party privacy-preserving record linkage (PPRL) protocol that can be used to link sensitive databases held by multiple different organisations. In our approach, each database owner first trains a local deep learning model, which is then uploaded to a secure environment and securely aggregated to create a global model. The global model is then used by a linkage unit to distinguish unlabelled record pairs as matches and non-matches. We utilise differential privacy to achieve provable privacy protection against re-identification attacks. We evaluate the linkage quality and scalability of our approach using several large real-world databases, showing that it can achieve high linkage quality while providing sufficient privacy protection against existing attacks.

以任务为导向的通信，主要是使用基于学习的联合源通道编码（JSCC），旨在通过将与任务相关的信息传输到接收方来设计通信有效的边缘推理系统。但是，只有在不引入任何冗余的情况下传输与任务相关的信息可能会导致由于渠道变化引起的学习鲁棒性问题，而JSCC将源数据直接映射到连续的通道输入符号中会对现有数字通信系统提出兼容性问题。在本文中，我们通过首先调查编码表示形式的信息性与接收到的信息失真的鲁棒性之间的固有权衡解决这两个问题，然后提出一种具有任务调制的导向的通信方案，名为Inveete Task-定向的JSCC（DT-JSCC），其中发射器将功能编码为离散表示形式，并使用数字调制方案将其传输到接收器。在DT-JSCC方案中，我们开发了一个可靠的编码框架，称为强大的信息瓶颈（rib），以改善对信道变化的稳健性，并使用变量近似来得出肋骨目标的可拖动变异上限，以克服克服相互信息的计算棘手性。实验结果表明，所提出的DT-JSCC比具有低通信延迟的基线方法更好的推理性能更好，并且由于施加的肋骨框架而表现出对通道变化的鲁棒性。

本文回顾了AIM 2022上压缩图像和视频超级分辨率的挑战。这项挑战包括两条曲目。轨道1的目标是压缩图像的超分辨率，轨迹〜2靶向压缩视频的超分辨率。在轨道1中，我们使用流行的数据集DIV2K作为培训，验证和测试集。在轨道2中，我们提出了LDV 3.0数据集，其中包含365个视频，包括LDV 2.0数据集（335个视频）和30个其他视频。在这一挑战中，有12支球队和2支球队分别提交了赛道1和赛道2的最终结果。所提出的方法和解决方案衡量了压缩图像和视频上超分辨率的最先进。提出的LDV 3.0数据集可在https://github.com/renyang-home/ldv_dataset上找到。此挑战的首页是在https://github.com/renyang-home/aim22_compresssr。

预测不同托卡马克人的破坏是要克服的巨大障碍。未来的Tokamaks在高性能排放时几乎无法忍受中断。很少有高性能的破坏排放几乎无法构成丰富的训练集，这使得当前数据驱动的方法难以获得可接受的结果。能够将在一个Tokamak训练的中断预测模型转移到另一种训练的机器学习方法以解决该问题。关键是一个包含特征提取器的破坏预测模型，该模型能够在Tokamak诊断数据中提取常见的破坏前体痕迹，并具有可转移的破坏分类器。基于上面的问题，该论文首先提出了专门针对Tokamaks上的普通诊断中的破坏前体特征而设计的深融合功能提取器，该特征是根据当前已知的破坏前体，为可转移模型提供了有希望的基础。通过与J-Text上的手动特征提取进行比较，可以证明融合功能提取器。基于在J-TEXT上训练的功能提取器，将中断预测模型转移到East数据中，仅来自East实验的20次放电。该性能与经过1896年出院的模型相当。从其他模型培训方案之间的比较，转移学习表明了其在预测不同托卡马克人的破坏方面的潜力。

任务概括是自然语言处理（NLP）的漫长挑战。最近的研究试图通过将NLP任务映射到人类可读的提示形式中来提高预训练语言模型的任务概括能力。但是，这些方法需要费力且不灵活的提示，并且在同一下游任务上的不同提示可能会获得不稳定的性能。我们提出了统一的架构提示，这是一种灵活且可扩展的提示方法，该方法会根据任务输入架构自动自动自定义每个任务的可学习提示。它在任务之间建模共享知识，同时保持不同任务架构的特征，从而增强任务概括能力。架构提示采用每个任务的明确数据结构，以制定提示，因此涉及几乎没有人类的努力。为了测试模式提示的任务概括能力，我们对各种一般NLP任务进行基于模式提示的多任务预训练。该框架在从8种任务类型（例如QA，NLI等）的16个看不见的下游任务上实现了强劲的零射击和很少的概括性能。此外，全面的分析证明了每个组件在架构提示中的有效性，其在任务组成性方面的灵活性以及在全DATA微调设置下提高性能的能力。

在本文中，我们介绍了人际内和人际关系网络（I^2R-NET），以进行多人姿势估计。它涉及两个基本模块。首先，人类内部关系模块在一个人身上运行，旨在捕获人类内部依赖性。其次，人际关系模块考虑了多个实例之间的关系，并着重于捕获人间的相互作用。人际关系间的关系模块可以通过减少特征图的分辨率来设计非常轻巧，但学习有用的关系信息以显着提高人类内部关系模块的性能。即使没有铃铛和哨子，我们的方法也可以竞争或胜过当前的比赛获胜者。我们对可可，人群和ochuman数据集进行了广泛的实验。结果表明，所提出的模型超过了所有最新方法。具体而言，所提出的方法在众群数据集上达到了77.4％的AP和Ochuman数据集上的67.8％AP，从而超过了现有方法的大幅度优于较大的利润率。此外，消融研究和可视化分析还证明了我们的模型的有效性。

Question Answering (QA) is a longstanding challenge in natural language processing. Existing QA works mostly focus on specific question types, knowledge domains, or reasoning skills. The specialty in QA research hinders systems from modeling commonalities between tasks and generalization for wider applications. To address this issue, we present ProQA, a unified QA paradigm that solves various tasks through a single model. ProQA takes a unified structural prompt as the bridge and improves the QA-centric ability by structural prompt-based pre-training. Through a structurally designed prompt-based input schema, ProQA concurrently models the knowledge generalization for all QA tasks while keeping the knowledge customization for every specific QA task. Furthermore, ProQA is pre-trained with structural prompt-formatted large-scale synthesized corpus, which empowers the model with the commonly-required QA ability. Experimental results on 11 QA benchmarks demonstrate that ProQA consistently boosts performance on both full data fine-tuning, few-shot learning, and zero-shot testing scenarios. Furthermore, ProQA exhibits strong ability in both continual learning and transfer learning by taking the advantages of the structural prompt.

The click-through rate (CTR) prediction task is to predict whether a user will click on the recommended item. As mind-boggling amounts of data are produced online daily, accelerating CTR prediction model training is critical to ensuring an up-to-date model and reducing the training cost. One approach to increase the training speed is to apply large batch training. However, as shown in computer vision and natural language processing tasks, training with a large batch easily suffers from the loss of accuracy. Our experiments show that previous scaling rules fail in the training of CTR prediction neural networks. To tackle this problem, we first theoretically show that different frequencies of ids make it challenging to scale hyperparameters when scaling the batch size. To stabilize the training process in a large batch size setting, we develop the adaptive Column-wise Clipping (CowClip). It enables an easy and effective scaling rule for the embeddings, which keeps the learning rate unchanged and scales the L2 loss. We conduct extensive experiments with four CTR prediction networks on two real-world datasets and successfully scaled 128 times the original batch size without accuracy loss. In particular, for CTR prediction model DeepFM training on the Criteo dataset, our optimization framework enlarges the batch size from 1K to 128K with over 0.1% AUC improvement and reduces training time from 12 hours to 10 minutes on a single V100 GPU. Our code locates at https://github.com/bytedance/LargeBatchCTR.

近年来，异构图形神经网络（HGNNS）一直在开花，但每个工作所使用的独特数据处理和评估设置会让他们的进步完全了解。在这项工作中，我们通过使用其官方代码，数据集，设置和超参数来展示12个最近的HGNN的系统再现，揭示了关于HGNN的进展的令人惊讶的结果。我们发现，由于设置不当，简单的均匀GNN，例如GCN和GAT在很大程度上低估了。具有适当输入的GAT通常可以匹配或优于各种场景的所有现有HGNN。为了促进稳健和可重复的HGNN研究，我们构建异构图形基准（HGB），由具有三个任务的11个不同数据集组成。 HGB标准化异构图数据分割，特征处理和性能评估的过程。最后，我们介绍了一个简单但非常强大的基线简单 - HGN - 这显着优于HGB上以前的所有模型 - 以加速未来HGNN的进步。