在信息爆炸的时代，推荐系统通过促进内容探索在人们的日常生活中起着重要作用。众所周知，用户的活动性，即行为数量，倾向于遵循长尾分布，大多数用户的积极性低。在实践中，我们观察到，在联合培训后，尾巴用户的质量推荐率明显低于首席用户。我们进一步确定，由于数据有限，因此在尾巴用户上训练的模型仍然取得了较低的结果。尽管长尾分布在推荐系统中无处不在，但在研究和行业中，提高尾巴用户的推荐性能仍然仍然是挑战。直接应用长尾分配的相关方法可能有可能伤害首席用户的经验，这是不起作用的，因为一小部分具有高积极性的首席用户贡献了平台收入的一部分。在本文中，我们提出了一种新颖的方法，可以显着提高尾巴用户的建议性能，同时至少在基本模型上为首席用户提供至少可比的性能。这种方法的本质是一种新颖的梯度聚合技术，该技术将所有用户共享的常识知识分为主干模型，然后为Head用户和Tail用户个性化提供单独的插件预测网络。至于常识学习，我们利用因果关系理论的向后调整来消除梯度估计，从而掩盖了混杂因素的骨干训练，即用户的积极性。我们对两个公共建议基准数据集和一个从支撑台平台收集的大规模工业数据集进行了广泛的实验。实证研究验证了我们方法的合理性和有效性。

对比度学习是图表学习中的有效无监督方法，对比度学习的关键组成部分在于构建正和负样本。以前的方法通常利用图中节点的接近度作为原理。最近，基于数据增强的对比度学习方法已进步以显示视觉域中的强大力量，一些作品将此方法从图像扩展到图形。但是，与图像上的数据扩展不同，图上的数据扩展远不那么直观，而且很难提供高质量的对比样品，这为改进留出了很大的空间。在这项工作中，通过引入一个对抗性图视图以进行数据增强，我们提出了一种简单但有效的方法，对抗图对比度学习（ARIEL），以在合理的约束中提取信息性的对比样本。我们开发了一种称为稳定训练的信息正则化的新技术，并使用子图抽样以进行可伸缩。我们通过将每个图形实例视为超级节点，从节点级对比度学习到图级。 Ariel始终优于在现实世界数据集上的节点级别和图形级分类任务的当前图对比度学习方法。我们进一步证明，面对对抗性攻击，Ariel更加强大。

对比度学习是图表学习中有效的无监督方法。最近，基于数据增强的对比度学习方法已从图像扩展到图形。但是，大多数先前的作品都直接根据为图像设计的模型进行了调整。与图像上的数据增强不同，图表上的数据扩展远不那么直观，而且很难提供高质量的对比样本，这是对比度学习模型的性能的关键。这为改进现有图形对比学习框架留出了很多空间。在这项工作中，通过引入对抗图视图和信息正常化程序，我们提出了一种简单但有效的方法，即对逆向对比度学习（ARIEL），以在合理的约束中提取信息性的对比样本。它始终优于各种现实世界数据集的节点分类任务中当前的图形对比度学习方法，并进一步提高了图对比度学习的鲁棒性。

动态场景图表形式的结构化视频表示是有关多个视频理解任务的有效工具。与场景图的任务相比，由于场景的时间动态和预测的固有时间波动，动态场景图生成是更具挑战性。我们表明捕获长期依赖性是有效生成动态场景图的关键。我们通过从视频中构造一致的长期对象轨迹来介绍检测跟踪 - 识别范例，然后是捕获对象和视觉关系的动态。实验结果表明，我们的动态场景图检测变压器（DSG-DETR）在基准数据集动作基因组上的显着余量优于最先进的方法。我们还进行消融研究并验证所提出的方法的每个组成部分的有效性。

受到深入学习的巨大成功通过云计算和边缘芯片的快速发展的影响，人工智能研究（AI）的研究已经转移到计算范例，即云计算和边缘计算。近年来，我们目睹了在云服务器上开发更高级的AI模型，以超越传统的深度学习模型，以造成模型创新（例如，变压器，净化家庭），训练数据爆炸和飙升的计算能力。但是，边缘计算，尤其是边缘和云协同计算，仍然在其初期阶段，因为由于资源受限的IOT场景，因此由于部署了非常有限的算法而导致其成功。在本调查中，我们对云和边缘AI进行系统审查。具体而言，我们是第一个设置云和边缘建模的协作学习机制，通过彻底的审查使能够实现这种机制的架构。我们还讨论了一些正在进行的先进EDGE AI主题的潜在和实践经验，包括预先训练模型，图形神经网络和加强学习。最后，我们讨论了这一领域的有希望的方向和挑战。

图是对物体之间关系的强大表示，吸引了很多关注。图形学习的一个基本挑战是如何在没有标签的情况下训练有效的图形神经网络（GNN）编码器，这些标签既昂贵又耗时。对比学习（CL）是应对这一挑战的最受欢迎的范式之一，该挑战通过区分正和负节点对来训练GNN。尽管最近的CL方法取得了成功，但仍然存在两个爆炸案。首先，如何减少基于随机拓扑的数据增强引入的语义错误。传统CL通过节点级拓扑接近定义正和负节点对，该节点拓扑接近度仅基于图形拓扑，而不论节点属性的语义信息如何，因此某些语义上相似的节点可能被错误地视为负对。其次，如何有效地对现实图形的多重性进行建模，其中节点通过各种关系连接，并且每个关系都可以形成均匀的图层。为了解决这些问题，我们提出了一种新型的多重异质图原型对比度倾斜（X-GAL）框架来提取节点嵌入。 X-GOAL由两个组成部分组成：目标框架，该目标框架学习每个均匀图层的节点嵌入，以及一个对齐正则化，通过对齐层特定的节点嵌入来共同对不同的层进行模拟不同的层。具体而言，目标框架通过简洁的图形转换技术捕获节点级信息，并通过将节点拉到嵌入空间中的同一语义簇中，从而捕获群集级信息。对齐正则化在节点和群集级别的层上对齐嵌入。我们在各种现实世界数据集和下游任务上评估X-GAL，以证明其有效性。

Accurate determination of a small molecule candidate (ligand) binding pose in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose generation using molecular dynamics is hindered by slow protein dynamics. We develop a tiered tensor transform (3T) algorithm to rapidly generate diverse protein-ligand complex conformations for both pose and affinity estimation in drug screening, requiring neither machine learning training nor lengthy dynamics computation, while maintaining both coarse-grain-like coordinated protein dynamics and atomistic-level details of the complex pocket. The 3T conformation structures we generate are closer to experimental co-crystal structures than those generated by docking software, and more importantly achieve significantly higher accuracy in active ligand classification than traditional ensemble docking using hundreds of experimental protein conformations. 3T structure transformation is decoupled from the system physics, making future usage in other computational scientific domains possible.

Increasing research interests focus on sequential recommender systems, aiming to model dynamic sequence representation precisely. However, the most commonly used loss function in state-of-the-art sequential recommendation models has essential limitations. To name a few, Bayesian Personalized Ranking (BPR) loss suffers the vanishing gradient problem from numerous negative sampling and predictionbiases; Binary Cross-Entropy (BCE) loss subjects to negative sampling numbers, thereby it is likely to ignore valuable negative examples and reduce the training efficiency; Cross-Entropy (CE) loss only focuses on the last timestamp of the training sequence, which causes low utilization of sequence information and results in inferior user sequence representation. To avoid these limitations, in this paper, we propose to calculate Cumulative Cross-Entropy (CCE) loss over the sequence. CCE is simple and direct, which enjoys the virtues of painless deployment, no negative sampling, and effective and efficient training. We conduct extensive experiments on five benchmark datasets to demonstrate the effectiveness and efficiency of CCE. The results show that employing CCE loss on three state-of-the-art models GRU4Rec, SASRec, and S3-Rec can reach 125.63%, 69.90%, and 33.24% average improvement of full ranking NDCG@5, respectively. Using CCE, the performance curve of the models on the test data increases rapidly with the wall clock time, and is superior to that of other loss functions in almost the whole process of model training.

In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget. Due to the limited feedback information, existing query-based black-box attack methods often require many queries for attacking each benign example. To reduce query cost, we propose to utilize the feedback information across historical attacks, dubbed example-level adversarial transferability. Specifically, by treating the attack on each benign example as one task, we develop a meta-learning framework by training a meta-generator to produce perturbations conditioned on benign examples. When attacking a new benign example, the meta generator can be quickly fine-tuned based on the feedback information of the new task as well as a few historical attacks to produce effective perturbations. Moreover, since the meta-train procedure consumes many queries to learn a generalizable generator, we utilize model-level adversarial transferability to train the meta-generator on a white-box surrogate model, then transfer it to help the attack against the target model. The proposed framework with the two types of adversarial transferability can be naturally combined with any off-the-shelf query-based attack methods to boost their performance, which is verified by extensive experiments.

Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hinders their applications due to the generalization problem. Recently, Implicit Neural Representation (INR) has appeared as a powerful DL-based tool for solving the inverse problem by characterizing the attributes of a signal as a continuous function of corresponding coordinates in an unsupervised manner. In this work, we proposed an INR-based method to improve dynamic MRI reconstruction from highly undersampled k-space data, which only takes spatiotemporal coordinates as inputs. Specifically, the proposed INR represents the dynamic MRI images as an implicit function and encodes them into neural networks. The weights of the network are learned from sparsely-acquired (k, t)-space data itself only, without external training datasets or prior images. Benefiting from the strong implicit continuity regularization of INR together with explicit regularization for low-rankness and sparsity, our proposed method outperforms the compared scan-specific methods at various acceleration factors. E.g., experiments on retrospective cardiac cine datasets show an improvement of 5.5 ~ 7.1 dB in PSNR for extremely high accelerations (up to 41.6-fold). The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.