Although substantial efforts have been made using graph neural networks (GNNs) for AI-driven drug discovery (AIDD), effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules. Recent studies suggest that big GNN models pre-trained by self-supervised learning on unlabeled datasets enable better transfer performance in downstream molecular property prediction tasks. However, they often require large-scale datasets and considerable computational resources, which is time-consuming, computationally expensive, and environmentally unfriendly. To alleviate these limitations, we propose a novel pre-training model for molecular representation learning, Bi-branch Masked Graph Transformer Autoencoder (BatmanNet). BatmanNet features two tailored and complementary graph autoencoders to reconstruct the missing nodes and edges from a masked molecular graph. To our surprise, BatmanNet discovered that the highly masked proportion (60%) of the atoms and bonds achieved the best performance. We further propose an asymmetric graph-based encoder-decoder architecture for either nodes and edges, where a transformer-based encoder only takes the visible subset of nodes or edges, and a lightweight decoder reconstructs the original molecule from the latent representation and mask tokens. With this simple yet effective asymmetrical design, our BatmanNet can learn efficiently even from a much smaller-scale unlabeled molecular dataset to capture the underlying structural and semantic information, overcoming a major limitation of current deep neural networks for molecular representation learning. For instance, using only 250K unlabelled molecules as pre-training data, our BatmanNet with 2.575M parameters achieves a 0.5% improvement on the average AUC compared with the current state-of-the-art method with 100M parameters pre-trained on 11M molecules.

关于时间知识图（TKGQA）的问题回答最近发现兴趣越来越大。 TKGQA需要时间推理技术来从时间知识库中提取相关信息。唯一现有的TKGQA数据集，即cronquestions，由基于固定时间段内的事实组成，其中跨越同一时期的时间知识图（TKG）可以完全使用用于答案推断，允许使用TKGQA模型。即将根据过去事实回答问题的未来知识。但是，在现实世界的情况下，鉴于到目前为止的知识也很常见，我们希望TKGQA系统回答询问未来的问题。随着人类不断寻求未来计划，建立用于回答此类预测问题的TKGQA系统很重要。然而，这在先前的研究中仍未得到探索。在本文中，我们提出了一个新的任务：关于时间知识图的预测问题。我们还为此任务提出了一个大规模的TKGQA基准数据集，即预测。它包括三种类型的问题，即实体预测，不是和事实推理问题。对于我们数据集中的每个预测问题，QA模型只能在给定问题中注释的时间戳以进行答案推理之前访问TKG信息。我们发现，最先进的TKGQA方法在预测问题上的表现较差，并且他们无法回答不是问题和事实推理问题。为此，我们提出了一种TKGQA模型预测，该模型采用TKG预测模块进行未来推断，以回答所有三种类型的问题。实验结果表明，预测到实体预测问题的最新方法优于最近的TKGQA方法，并且在回答其他两种类型的问题方面也显示出很大的有效性。

真实世界的文本应用程序通常涉及组成广泛的文本控制操作，例如编辑文本W.R.T.属性，操纵关键字和结构，并生成所需属性的新文本。事先的工作通常会学习/芬太尼语言模型（LM）以执行操作的个人或特定子集。最近的研究以插件方式研究了合并操作，通常在复杂序列空间中以昂贵的搜索或优化进行了研究。本文提出了一种新的有效方法，用于在紧凑的文本潜在空间中进行可复合的文本操作。文本潜在矢量的低维度和不同性使我们能够基于给定的任意插入运算符（例如属性分类器）基于普通微分方程（ODE）开发有效的采样器。通过通过有效的适应性将预告片的LMS（例如GPT2）连接到潜在空间，然后我们将采样向量解码为所需的文本序列。灵活的方法允许使用来自不同域中的任何相关数据获取的各种控制操作员（情感，时态，形式，关键字等）。实验表明，在我们的方法中构成这些操作员可以生成或编辑高质量文本，从而在发电质量和效率方面显着改善了以前的方法。

人类的行动识别是一个非常受过大量研究的领域，在该领域中，最引人注目的动作识别网络通常使用日常人类行动的大规模粗粒度动作数据集作为陈述其网络优势的输入。我们打算使用神经网络识别我们的小规模细粒Tai Chi动作数据集，并使用NTU RGB+D数据集提出一种转移学习方法，以预先培训我们的网络。更具体地说，提出的方法首先使用大规模的NTU RGB+D数据集来预先培训基于变压器的网络以进行动作识别，以在人类运动中提取共同的特征。然后，我们冻结除完全连接（FC）层以外的网络权重，并将我们的Tai Chi动作作为输入，仅用于训练初始化的FC权重。实验结果表明，我们的通用模型管道可以达到高度精细的Tai Chi Action识别的高精度，甚至很少输入，并证明我们的方法与先前的Tai Chi Action识别相比实现了最先进的性能方法。

产品图像对于在电子商务平台中提供理想的用户体验至关重要。对于拥有数十亿种产品的平台，手动挑选和组织合格的图像非常耗时且耗尽劳动力。此外，要生成/选择的产品图像需要遵守众多且复杂的图像规则。为了解决这些挑战，在本文中，我们提出了一个新的学习框架，以便在电子商务中自动生成产品图像序列（AGPI）。为此，我们提出了一个多模式统一的图像序列分类器（MUISC），该分类器能够通过学习同时检测所有规则违规的类别。 MUISC利用文本审查反馈作为额外的培训目标，并利用产品文本描述提供额外的语义信息。根据离线评估，我们表明拟议的MUISC显着优于各种基线。除MUISC外，我们还将其他一些重要的模块集成在提出的框架中，例如主图像选择，不合格的内容检测和图像重复数据删除。借助所有这些模块，我们的框架在JD.com推荐平台中有效，有效地工作。到2021年12月，我们的AGPIS框架为约150万种产品生成了高标准图像，并获得了13.6％的拒绝率。

布换人员重新识别（CC-REID）旨在在长时间匹配不同地点的同一个人，例如，超过日子，因此不可避免地满足换衣服的挑战。在本文中，我们专注于处理更具有挑战性的环境下的CC-Reid问题，即，只有一个图像，它可以实现高效和延迟的行人确定实时监控应用。具体而言，我们将步态识别作为辅助任务来驱动图像Reid模型来通过利用个人独特和独立布的步态信息来学习布不可知的表现，我们将此框架命名为Gi-Reid。 Gi-Reid采用两流架构，该架构由图像Reid-Stream和辅助步态识别流（步态流）组成。在推理的高计算效率中丢弃的步态流充当调节器，以鼓励在训练期间捕获捕获布不变的生物识别运动特征。为了从单个图像获取时间连续运动提示，我们设计用于步态流的步态序列预测（GSP）模块，以丰富步态信息。最后，为有效的知识正则化强制执行两个流的高级语义一致性。基于多种图像的布更换Reid基准测试的实验，例如LTCC，PRCC，Real28和VC衣服，证明了GI-REID对最先进的人来说。代码在https://github.com/jinx-ustc/gi -reid提供。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

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.

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.