对话推荐系统（CRS）的注意力日益增长，该系统可作为基于对话和建议的以任务为基础的工具，以提供感兴趣的项目并探索用户偏好。但是，CRS中现有的工作未能向用户明确显示推理逻辑，并且整个CRS仍然是黑匣子。因此，我们提出了一个基于生成对话代理的解释，以解释他们为何采取行动的解释，提出了一个名为“解释建议”（EGCR）的新颖端到端框架。 EGCR结合了用户评论，以增强项目表示并提高整个对话的信息。据我们所知，这是对现实世界数据集上可解释的对话建议的第一个框架。此外，我们在一个基准的对话推荐数据集上评估了EGCR，并比其他最先进的模型在建议准确性和对话质量上获得更好的性能。最后，广泛的实验表明，生成的解释不仅具有高质量和解释性，而且使CRS更加值得信赖。我们将使我们的代码可为CRS社区做出贡献

随着推荐系统变得越来越复杂和复杂，它们通常会缺乏公平和透明度。为建议提供强大而公正的解释，人们越来越关注，因为它可以帮助解决这些问题并提高推荐系统的信任度和信息性。然而，尽管事实是为人类生成了这种解释，这些人类对具有适当情绪的信息做出更强烈反应，但在为建议解释时，人们缺乏对情绪的考虑。发现当前的解释生成模型可以夸大某些情绪，而无需准确捕获基本的语调或含义。在本文中，我们提出了一种基于多头变压器的新方法，称为“情感感知变压器”，以解释推荐（情感者），以产生更健壮，公平和情感增强的解释。为了衡量产生的解释的语言质量和情感公平性，我们采用自动文本指标和人类的看法进行评估。在具有多个评估指标的三个广泛使用基准数据集上进行的实验表明，情感者在文本质量，解释性和对情感分布的公平性方面始终优于现有的最新解释生成模型。 Emoter的实施将作为开源工具包发布，以支持进一步的研究。

变压器被认为是自2018年以来最重要的深度学习模型之一，部分原因是它建立了最先进的记录（SOTA）记录，并有可能取代现有的深神经网络（DNNS）。尽管取得了显着的胜利，但变压器模型的延长周转时间是公认的障碍。序列长度的多样性施加了其他计算开销，其中需要将输入零填充到批处理中的最大句子长度，以容纳并行计算平台。本文针对现场可编程的门阵列（FPGA），并提出了一个连贯的序列长度自适应算法 - 硬件与变压器加速度的共同设计。特别是，我们开发了一个适合硬件的稀疏注意操作员和长度意识的硬件资源调度算法。提出的稀疏注意操作员将基于注意力的模型的复杂性降低到线性复杂性，并减轻片外记忆流量。提出的长度感知资源硬件调度算法动态分配了硬件资源以填充管道插槽并消除了NLP任务的气泡。实验表明，与CPU和GPU实施相比，我们的设计准确度损失很小，并且具有80.2 $ \ times $和2.6 $ \ times $速度，并且比先进的GPU加速器高4 $ \ times $ $ $ \ times $通过Cublas Gemm优化。

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.

For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there is no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion-batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.

In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G.

In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are embedded in the pilot sequences and the base station simultaneously detects active devices and their embedded data bits without explicit channel estimation. Due to the correlated sparsity pattern introduced by the non-coherent transmission scheme, the traditional approximate message passing (AMP) algorithm cannot achieve satisfactory performance. Therefore, we propose a deep learning (DL) modified AMP network (DL-mAMPnet) that enhances the detection performance by effectively exploiting the pilot activity correlation. The DL-mAMPnet is constructed by unfolding the AMP algorithm into a feedforward neural network, which combines the principled mathematical model of the AMP algorithm with the powerful learning capability, thereby benefiting from the advantages of both techniques. Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient. Moreover, a refinement module is designed to further advance the performance by utilizing the spatial feature caused by the correlated sparsity pattern. Simulation results demonstrate that the proposed DL-mAMPnet can significantly outperform traditional algorithms in terms of the symbol error rate performance.

Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this paper, we propose Discriminative Radial Domain Adaptation (DRDR) which bridges source and target domains via a shared radial structure. It's motivated by the observation that as the model is trained to be progressively discriminative, features of different categories expand outwards in different directions, forming a radial structure. We show that transferring such an inherently discriminative structure would enable to enhance feature transferability and discriminability simultaneously. Specifically, we represent each domain with a global anchor and each category a local anchor to form a radial structure and reduce domain shift via structure matching. It consists of two parts, namely isometric transformation to align the structure globally and local refinement to match each category. To enhance the discriminability of the structure, we further encourage samples to cluster close to the corresponding local anchors based on optimal-transport assignment. Extensively experimenting on multiple benchmarks, our method is shown to consistently outperforms state-of-the-art approaches on varied tasks, including the typical unsupervised domain adaptation, multi-source domain adaptation, domain-agnostic learning, and domain generalization.

Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services which require low delay and high accuracy. Sampling rate adaption which dynamically configures the sampling rates of industrial IoT devices according to network conditions, is the key in minimizing the service delay. In this paper, we investigate the collaborative DNN inference problem in industrial IoT networks. To capture the channel variation and task arrival randomness, we formulate the problem as a constrained Markov decision process (CMDP). Specifically, sampling rate adaption, inference task offloading and edge computing resource allocation are jointly considered to minimize the average service delay while guaranteeing the long-term accuracy requirements of different inference services. Since CMDP cannot be directly solved by general reinforcement learning (RL) algorithms due to the intractable long-term constraints, we first transform the CMDP into an MDP by leveraging the Lyapunov optimization technique. Then, a deep RL-based algorithm is proposed to solve the MDP. To expedite the training process, an optimization subroutine is embedded in the proposed algorithm to directly obtain the optimal edge computing resource allocation. Extensive simulation results are provided to demonstrate that the proposed RL-based algorithm can significantly reduce the average service delay while preserving long-term inference accuracy with a high probability.

As an important variant of entity alignment (EA), multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) with multiple modalities like images. However, current MMEA algorithms all adopt KG-level modality fusion strategies but ignore modality differences among individual entities, hurting the robustness to potential noise involved in modalities (e.g., unidentifiable images and relations). In this paper we present MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, to dynamically predict the mutual correlation coefficients among modalities for instance-level feature fusion. A modal-aware hard entity replay strategy is also proposed for addressing vague entity details. Extensive experimental results show that our model not only achieves SOTA performance on multiple training scenarios including supervised, unsupervised, iterative, and low resource, but also has limited parameters, optimistic speed, and good interpretability. Our code will be available soon.