Channel and spatial attention mechanism has proven to provide an evident performance boost of deep convolution neural networks (CNNs). Most existing methods focus on one or run them parallel (series), neglecting the collaboration between the two attentions. In order to better establish the feature interaction between the two types of attention, we propose a plug-and-play attention module, which we term "CAT"-activating the Collaboration between spatial and channel Attentions based on learned Traits. Specifically, we represent traits as trainable coefficients (i.e., colla-factors) to adaptively combine contributions of different attention modules to fit different image hierarchies and tasks better. Moreover, we propose the global entropy pooling (GEP) apart from global average pooling (GAP) and global maximum pooling (GMP) operators, an effective component in suppressing noise signals by measuring the information disorder of feature maps. We introduce a three-way pooling operation into attention modules and apply the adaptive mechanism to fuse their outcomes. Extensive experiments on MS COCO, Pascal-VOC, Cifar-100, and ImageNet show that our CAT outperforms existing state-of-the-art attention mechanisms in object detection, instance segmentation, and image classification. The model and code will be released soon.

Data uncertainty is commonly observed in the images for face recognition (FR). However, deep learning algorithms often make predictions with high confidence even for uncertain or irrelevant inputs. Intuitively, FR algorithms can benefit from both the estimation of uncertainty and the detection of out-of-distribution (OOD) samples. Taking a probabilistic view of the current classification model, the temperature scalar is exactly the scale of uncertainty noise implicitly added in the softmax function. Meanwhile, the uncertainty of images in a dataset should follow a prior distribution. Based on the observation, a unified framework for uncertainty modeling and FR, Random Temperature Scaling (RTS), is proposed to learn a reliable FR algorithm. The benefits of RTS are two-fold. (1) In the training phase, it can adjust the learning strength of clean and noisy samples for stability and accuracy. (2) In the test phase, it can provide a score of confidence to detect uncertain, low-quality and even OOD samples, without training on extra labels. Extensive experiments on FR benchmarks demonstrate that the magnitude of variance in RTS, which serves as an OOD detection metric, is closely related to the uncertainty of the input image. RTS can achieve top performance on both the FR and OOD detection tasks. Moreover, the model trained with RTS can perform robustly on datasets with noise. The proposed module is light-weight and only adds negligible computation cost to the model.

链接的语音实体旨在识别和消除语言中的命名实体。常规方法严重遭受了不受限制的语音样式和ASR系统产生的嘈杂笔录。在本文中，我们提出了一种名为“知识增强命名实体识别”（KENER）的新颖方法，该方法致力于通过在实体识别阶段无痛地纳入适当的知识来改善鲁棒性，从而改善实体联系的整体性能。肯纳（Kener）首先检索未提及的句子的候选实体，然后利用实体描述作为额外的信息来帮助识别提及。当输入短或嘈杂时，由密集检索模块检索的候选实体特别有用。此外，我们研究了各种数据采样策略和设计有效的损失功能，以提高识别和歧义阶段中检索实体的质量。最后，将与过滤模块的链接作为最终保障措施应用，从而可以过滤出错误认可的提及。我们的系统在NLPCC-2022共享任务2的轨道1中获得第一名，并在轨道1中获得第一名。

语言模型（LMS）被证明具有对物理世界的常识知识，这对于在日常情况下完成任务至关重要。但是，LMS是否有能力为具体任务生成扎根的可执行计划，这仍然是一个悬而未决的问题。这是非常具有挑战性的，因为LMS没有“眼睛”或“手”来感知现实的环境。在这项工作中，我们展示了有关这个重要研究问题的第一个研究。我们首先提出了一个名为G-Planet的新型问题公式，它将其作为输入一个高级目标和在特定环境中的对象表。预期输出是一个计划，该计划包括逐步指令供代理执行。为了实现此问题的研究，我们建立了一个评估协议，并设计了一个专门的指标来评估计划的质量。在我们的广泛实验中，我们表明，为编码环境添加扁平表并使用迭代解码策略都可以提高LMS的基础计划能力。我们对结果的分析也导致有趣的非平凡发现。

营销活动是一系列战略活动，可以促进企业的目标。在真正的工业场景中，营销活动的效果预测非常复杂且具有挑战性，因为通常从观察数据中学到了先验知识，而没有任何营销活动干预。此外，每个主题始终在几个营销活动的干预下同时受到干扰。因此，我们无法轻松解析和评估单个营销活动的效果。据我们所知，目前尚无有效的方法来解决此类问题，即，基于具有多个相互缠绕事件的层次结构对个体级别的预测任务进行建模。在本文中，我们对效果预测任务中涉及的基础解析树的结构进行了深入的分析，并进一步建立了一个层次结构胶囊预测网络（HAPNET）来预测营销活动的影响。基于合成数据和实际数据的广泛结果证明了我们模型比最新方法的优越性，并在实际工业应用中表现出显着的实用性。

我们在位于和交互式多模式对话2.0（SIMMC 2.0）数据集中的多模式Coreference分辨率任务中的工作，作为第十个对话系统技术挑战（DSTC10）的一部分。我们提出了一种利用富有的多模式上下文，例如文本对话历史，对象知识库和可视对话识场景，以确定当前场景中的每个对象是否在当前对话框转弯中提到的基于富有的多模式的模型。结果表明，该方法实质上优于官方DSTC10基线，对象F1得分从开发集中提升了36.6％至77.3％，展示了拟议的对象表示从丰富的多式联算输入的有效性。我们的型号在对象Coreference解决方案任务上的官方评估中排名第二，模型合并后F1分数为73.3％。

随着深度学习技术的快速发展，各种最近的工作试图应用图形神经网络（GNN）来解决诸如布尔满足（SAT）之类的NP硬问题，这表明了桥接机器学习与象征性差距的潜力。然而，GNN预测的解决方案的质量并未在文献中进行很好地研究。在本文中，我们研究了GNNS在学习中解决最大可满足性（MaxSAT）问题的能力，从理论和实践角度来看。我们构建了两种GNN模型来学习来自基准的MaxSAT实例的解决方案，并显示GNN通过实验评估解决MaxSAT问题的有吸引力。我们还基于算法对准理论，我们还提出了GNNS可以在一定程度上学会解决MaxSAT问题的影响的理论解释。

风险的准确器官（OAR）分割对于减少治疗后并发症的放射治疗至关重要。达人指南推荐头部和颈部（H＆N）区域的一套超过40桨的桨，然而，由于这项任务的可预测的禁止劳动力成本，大多数机构通过划定较小的桨子和忽视的少数，选择了大量简化的协议与其他桨相关的剂量分布。在这项工作中，我们提出了一种使用深度学习的新颖，自动化和高效的分层OAR分段（SOARS）系统，精确地描绘了一套全面的42 H＆N OAR。 SOARS将42桨分层进入锚，中级和小型和硬质子类别，通过神经结构搜索（NAS）原则，专门为每个类别提供神经网络架构。我们在内在机构中使用176名培训患者建立了SOAR模型，并在六个不同的机构中独立评估了1327名外部患者。对于每个机构评估，它始终如一地表现出其他最先进的方法至少3-5％的骰子得分（在其他度量的相对误差减少36％）。更重要的是，广泛的多用户研究明显证明，98％的SOARE预测只需要非常轻微或没有直接临床验收的修订（节省90％的辐射脑神经工作负载），并且它们的分割和剂量准确度在于或小于帧 - 用户的变化。这些调查结果证实了H＆N癌症放射疗法工作流OAR描绘过程的强烈临床适用性，提高了效率，全面性和质量。

标准自动指标，例如BLEU对于文档级MT评估不可靠。他们既不能区分翻译质量的文档级改进与句子级别的改进，也不能确定引起上下文反应翻译的话语现象。本文介绍了一种新颖的自动公制金发，以扩大自动MT评估的范围，从句子到文档级别。金发女郎通过对与话语相关的跨度进行分类并计算基于相似性的F1分类跨度来考虑话语一致性。我们对新建的数据集BWB进行了广泛的比较。实验结果表明，金发女郎在文档级别具有更好的选择性和可解释性，并且对文档级别的细微差别更为敏感。在一项大规模的人类研究中，与以前的指标相比，金发碧眼的皮尔逊与人类判断的相关性也明显更高。

Future work sentences (FWS) are the particular sentences in academic papers that contain the author's description of their proposed follow-up research direction. This paper presents methods to automatically extract FWS from academic papers and classify them according to the different future directions embodied in the paper's content. FWS recognition methods will enable subsequent researchers to locate future work sentences more accurately and quickly and reduce the time and cost of acquiring the corpus. The current work on automatic identification of future work sentences is relatively small, and the existing research cannot accurately identify FWS from academic papers, and thus cannot conduct data mining on a large scale. Furthermore, there are many aspects to the content of future work, and the subdivision of the content is conducive to the analysis of specific development directions. In this paper, Nature Language Processing (NLP) is used as a case study, and FWS are extracted from academic papers and classified into different types. We manually build an annotated corpus with six different types of FWS. Then, automatic recognition and classification of FWS are implemented using machine learning models, and the performance of these models is compared based on the evaluation metrics. The results show that the Bernoulli Bayesian model has the best performance in the automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT model has the best performance in the automatic classification task, with the weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and gain a deep understanding of the key content described in FWS, and we also demonstrate that content determination in FWS will be reflected in the subsequent research work by measuring the similarity between future work sentences and the abstracts.