Name ambiguity is common in academic digital libraries, such as multiple authors having the same name. This creates challenges for academic data management and analysis, thus name disambiguation becomes necessary. The procedure of name disambiguation is to divide publications with the same name into different groups, each group belonging to a unique author. A large amount of attribute information in publications makes traditional methods fall into the quagmire of feature selection. These methods always select attributes artificially and equally, which usually causes a negative impact on accuracy. The proposed method is mainly based on representation learning for heterogeneous networks and clustering and exploits the self-attention technology to solve the problem. The presentation of publications is a synthesis of structural and semantic representations. The structural representation is obtained by meta-path-based sampling and a skip-gram-based embedding method, and meta-path level attention is introduced to automatically learn the weight of each feature. The semantic representation is generated using NLP tools. Our proposal performs better in terms of name disambiguation accuracy compared with baselines and the ablation experiments demonstrate the improvement by feature selection and the meta-path level attention in our method. The experimental results show the superiority of our new method for capturing the most attributes from publications and reducing the impact of redundant information.

Automatic image colorization is a particularly challenging problem. Due to the high illness of the problem and multi-modal uncertainty, directly training a deep neural network usually leads to incorrect semantic colors and low color richness. Existing transformer-based methods can deliver better results but highly depend on hand-crafted dataset-level empirical distribution priors. In this work, we propose DDColor, a new end-to-end method with dual decoders, for image colorization. More specifically, we design a multi-scale image decoder and a transformer-based color decoder. The former manages to restore the spatial resolution of the image, while the latter establishes the correlation between semantic representations and color queries via cross-attention. The two decoders incorporate to learn semantic-aware color embedding by leveraging the multi-scale visual features. With the help of these two decoders, our method succeeds in producing semantically consistent and visually plausible colorization results without any additional priors. In addition, a simple but effective colorfulness loss is introduced to further improve the color richness of generated results. Our extensive experiments demonstrate that the proposed DDColor achieves significantly superior performance to existing state-of-the-art works both quantitatively and qualitatively. Codes will be made publicly available.

The state-of-the-art language model-based automatic metrics, e.g. BARTScore, benefiting from large-scale contextualized pre-training, have been successfully used in a wide range of natural language generation (NLG) tasks, including machine translation, text summarization, and data-to-text. Recent studies show that considering both major errors (e.g. mistranslated tokens) and minor errors (e.g. imperfections in fluency) can produce high-quality human judgments. This inspires us to approach the final goal of the evaluation metrics (human-like evaluations) by automatic error analysis. To this end, we augment BARTScore by incorporating the human-like error analysis strategies, namely BARTScore++, where the final score consists of both the evaluations of major errors and minor errors. Experimental results show that BARTScore++ can consistently improve the performance of vanilla BARTScore and outperform existing top-scoring metrics in 20 out of 25 test settings. We hope our technique can also be extended to other pre-trained model-based metrics. We will release our code and scripts to facilitate the community.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. Intuitively, weakly supervised training is a direct solution to reduce the cost of labeling. However, for weakly supervised large-scale point cloud semantic segmentation, too few annotations will inevitably lead to ineffective learning of network. We propose an effective weakly supervised method containing two components to solve the above problem. Firstly, we construct a pretext task, \textit{i.e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network. In this way, the representation capability of the weakly supervised network can be improved by the guidance from a heterogeneous task. Besides, to generate pseudo label for unlabeled data, a sparse label propagation mechanism is proposed with the help of generated class prototypes, which is used to measure the classification confidence of unlabeled point. Our method is evaluated on large-scale point cloud datasets with different scenarios including indoor and outdoor. The experimental results show the large gain against existing weakly supervised and comparable results to fully supervised methods\footnote{Code based on mindspore: https://github.com/dmcv-ecnu/MindSpore\_ModelZoo/tree/main/WS3\_MindSpore}.

尽管模拟语义通信系统在文献中受到了很大的关注，但在数字语义通信系统上的工作较少。在本文中，我们开发了一个深度学习（DL）启用的矢量量化（VQ）语义通信系统，用于图像传输，名为VQ-Deepsc。具体而言，我们提出了一个基于卷积的神经网络（CNN）的收发器来提取图像的多尺度语义特征，并引入多尺度语义嵌入空间以执行语义特征量化，从而使数据与数字通信系统兼容。此外，我们通过引入Patchgan歧视者来采用对抗训练来提高接收图像的质量。实验结果表明，根据SSIM，所提出的VQ-Deepsc优于传统图像传输方法。

我们开发了NL2接口，以探索从自然语言查询中生成可用的交互式多访问界面的潜力。借助NL2系列，用户可以直接编写自然语言查询，以自动生成完全交互式的多访问界面，而无需任何额外的学习工具或编程语言。此外，用户可以与接口进行交互，以轻松地转换数据并快速看到可视化中的结果。

学习域不变的表示已成为域适应/概括的最受欢迎的方法之一。在本文中，我们表明不变的表示可能不足以保证良好的概括，在考虑标签函数转移的情况下。受到这一点的启发，我们首先在经验风险上获得了新的概括上限，该概括风险明确考虑了标签函数移动。然后，我们提出了特定领域的风险最小化（DRM），该风险最小化（DRM）可以分别对不同域的分布移动进行建模，并为目标域选择最合适的域。对四个流行的域概括数据集（CMNIST，PACS，VLCS和域）进行了广泛的实验，证明了所提出的DRM对域泛化的有效性，具有以下优点：1）它的表现明显超过了竞争性盆地的表现； 2）与香草经验风险最小化（ERM）相比，所有训练领域都可以在所有训练领域中具有可比性或优越的精度； 3）在培训期间，它仍然非常简单和高效，4）与不变的学习方法是互补的。

半监督学习（SSL）通过利用大量未标记数据来增强有限标记的样品来改善模型的概括。但是，目前，流行的SSL评估协议通常受到计算机视觉（CV）任务的约束。此外，以前的工作通常从头开始训练深层神经网络，这是耗时且环境不友好的。为了解决上述问题，我们通过从简历，自然语言处理（NLP）和音频处理（AUDIO）中选择15种不同，具有挑战性和全面的任务来构建统一的SSL基准（USB），我们会系统地评估主导的SSL方法，以及开源的一个模块化和可扩展的代码库，以对这些SSL方法进行公平评估。我们进一步为简历任务提供了最新的神经模型的预训练版本，以使成本负担得起，以进行进一步调整。 USB启用对来自多个域的更多任务的单个SSL算法的评估，但成本较低。具体而言，在单个NVIDIA V100上，仅需要37个GPU天才能在USB中评估15个任务的FIXMATCH，而335 GPU天（除ImageNet以外的4个CV数据集中的279 GPU天）在使用典型协议的5个CV任务上需要进行5个CV任务。

多语言神经机器翻译（MNMT）使一个系统能够将句子从多种源语言转换为多种目标语言，与传统的双语系统相比，大大降低了部署成本。但是，MNMT培训益处通常仅限于多一对一的方向。该模型在一对一的表现不佳，并且在零镜头设置中遭受了多种影响。为了解决这个问题，本文讨论了如何实际构建提供任意X-Y翻译指示的MNMT系统，同时使用预处理和填充的两阶段培训策略利用多语言。尝试WMT'21多语言翻译任务，我们证明我们的系统的表现优于大多数方向的直接双语模型和枢轴翻译模型的传统基线，平均提供+6.0和+4.1 BLEU，而无需进行架构更改或额外的数据收集。 。此外，我们还在极大的数据设置中检查了我们提出的方法，以适应实际的部署方案。