如今，基础模型已成为人工智能中的基本基础设施之一，铺平了通往通用情报的方式。但是，现实提出了两个紧急挑战：现有的基础模型由英语社区主导；用户通常会获得有限的资源，因此不能总是使用基础模型。为了支持中文社区的发展，我们介绍了一个名为Fengshenbang的开源项目，该项目由认知计算与自然语言研究中心（CCNL）领导。我们的项目具有全面的功能，包括大型预培训模型，用户友好的API，基准，数据集等。我们将所有这些都包装在三个子项目中：风水次模型，风水框架和狂热基准。 Fengshenbang的开源路线图旨在重新评估中国预培训的大型大型模型的开源社区，促使整个中国大型模型社区的发展。我们还希望构建一个以用户为中心的开源生态系统，以允许个人访问所需的模型以匹配其计算资源。此外，我们邀请公司，大学和研究机构与我们合作建立大型开源模型的生态系统。我们希望这个项目将成为中国认知情报的基础。

为了应对人类检测对标签数据和隐私问题的不断增长的需求，合成数据已被用作替代品，并在人类检测和跟踪任务中显示出令人鼓舞的结果。我们参加了第七届基准测试多目标跟踪（BMTT）的研讨会，主题是“合成数据可以带我们多远”？我们的解决方案Pietrack是根据合成数据开发的，而无需使用任何预训练的权重。我们提出了一种自我监督的域适应方法，该方法能够减轻合成（例如Motsynth）和真实数据（例如Mot17）之间的域移位问题，而无需涉及额外的人类标签。通过利用拟议的多尺度合奏推理，我们在MOT17测试集中获得了58.7的最终HOTA得分，在挑战中排名第三。

在本文中，我们为开关系统提供了一种基于神经网络的自适应学习（DNN-AL）方法。当前，基于神经网络的深度方法是为了学习未知动态系统中的方程式而积极开发的，但是它们的效率可能会因离散时间时存在结构变化而对开关系统退化。在这种新的DNN-AL策略中，观察到的数据集被自适应分解为子集，因此每个子集中没有结构性变化。在自适应过程中，DNN是层次结构的，并逐渐识别出未知的切换时间。尤其是，重复使用先前迭代步骤的网络参数以初始化后期迭代步骤的网络，从而为DNN提供有效的培训程序。对于通过我们的DNN-AL获得的DNN，建立了预测误差的界限。进行了数值研究以证明DNN-AL的效率。

网络在许多现实世界应用程序中无处不在（例如，编码信任/不信任关系的社交网络，由时间序列数据引起的相关网络）。尽管许多网络都是签名或指示的，或者两者都在图形神经网络（GNN）上缺少统一的软件包，专门为签名和定向网络设计。在本文中，我们提出了Pytorch几何签名的指示，这是一个填补此空白的软件包。在此过程中，我们还提供了简短的审查调查，以分析签名和定向网络的分析，讨论相关实验中使用的数据，提供提出的方法概述，并通过实验评估实施方法。深度学习框架包括易于使用的GNN模型，合成和现实世界数据，以及针对签名和定向网络的特定任务评估指标和损失功能。作为Pytorch几何形状的扩展库，我们提出的软件由开源版本，详细文档，连续集成，单位测试和代码覆盖范围检查维护。我们的代码可在\ url {https://github.com/sherylhyx/pytorch_geometric_signed_directed}上公开获得。

过去几年的技术创新的巨大浪潮，标志着AI技术的进展，是深刻的重塑行业和社会。然而，在路上，一个关键的挑战等待着我们，即我们满足快速增长的情景的能力的能力受到收购培训数据的成本的严重限制。由于主流学习范式的局限性，这一困难的局面是基于主流学习范式的局限性：我们需要根据大量注释的数据以及通常从头来训练每个新场景的新模型。在解决这一基本问题时，我们超越并开发一个名为实习生的新学习范式。通过在多个阶段的来自多个来源的监控信号学习，培训的模型将产生强大的相互性。我们在26个众所周知的数据集中评估我们的模型，该数据集涵盖计算机视觉中的四类任务。在大多数情况下，我们的模型仅适用于目标域中的培训数据的10％，始终以完整的数据培训的对应物，通常由显着的边距。这是一个重要前景的重要一步，其中具有一般视觉能力的这种模型可以大大降低对数据的依赖，从而加速通过AI技术的采用。此外，围绕我们的新范式旋转，我们还介绍了一个新的数据系统，新的架构和新的基准，以及一起形成一般愿景生态系统，以开放和包容性的方式支持其未来的发展。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

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.

Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations in this framework.

This paper illustrates the technologies of user next intent prediction with a concept knowledge graph. The system has been deployed on the Web at Alipay, serving more than 100 million daily active users. Specifically, we propose AlipayKG to explicitly characterize user intent, which is an offline concept knowledge graph in the Life-Service domain modeling the historical behaviors of users, the rich content interacted by users and the relations between them. We further introduce a Transformer-based model which integrates expert rules from the knowledge graph to infer the online user's next intent. Experimental results demonstrate that the proposed system can effectively enhance the performance of the downstream tasks while retaining explainability.