Attention-based arbitrary style transfer studies have shown promising performance in synthesizing vivid local style details. They typically use the all-to-all attention mechanism: each position of content features is fully matched to all positions of style features. However, all-to-all attention tends to generate distorted style patterns and has quadratic complexity. It virtually limits both the effectiveness and efficiency of arbitrary style transfer. In this paper, we rethink what kind of attention mechanism is more appropriate for arbitrary style transfer. Our answer is a novel all-to-key attention mechanism: each position of content features is matched to key positions of style features. Specifically, it integrates two newly proposed attention forms: distributed and progressive attention. Distributed attention assigns attention to multiple key positions; Progressive attention pays attention from coarse to fine. All-to-key attention promotes the matching of diverse and reasonable style patterns and has linear complexity. The resultant module, dubbed StyA2K, has fine properties in rendering reasonable style textures and maintaining consistent local structure. Qualitative and quantitative experiments demonstrate that our method achieves superior results than state-of-the-art approaches.

在这项研究中，提出了一种集成检测模型，即Swin-Transformer-Yolov5或Swin-T-Yolov5，用于实时葡萄酒葡萄束检测，以继承Yolov5和Swin-Transformer的优势。该研究是针对2019年7月至9月的两种不同的霞多丽（始终白色或白色混合浆果皮肤）和梅洛（白色或白色混合浆果皮肤）的研究。从2019年7月至9月。 -yolov5，其性能与几个常用/竞争性对象探测器进行了比较，包括更快的R-CNN，Yolov3，Yolov4和Yolov5。在不同的测试条件下评估了所有模型，包括两个不同的天气条件（阳光和多云），两个不同的浆果成熟度（不成熟和成熟）以及三个不同的阳光方向/强度（早晨，中午和下午）进行全面比较。此外，Swin-t-Yolov5的预测葡萄束数量与地面真实值进行了比较，包括在注释过程中的现场手动计数和手动标记。结果表明，拟议的SWIN-T-YOLOV5的表现优于所有其他研究的葡萄束检测模型，当天气多云时，最高平均平均精度（MAP）和0.89的F1得分的97％。该地图分别比更快的R-CNN，Yolov3，Yolov4和Yolov5大约大约44％，18％，14％和4％。当检测到未成熟的浆果时，Swin-T-Yolov5获得了最低的地图（90％）和F1分数（0.82），其中该地图大约比相同的浆果大约40％，5％，3％和1％。此外，在将预测与地面真相进行比较时，Swin-T-Yolov5在Chardonnay品种上的表现更好，最多可达到R2的0.91和2.36根均方根误差（RMSE）。但是，它在Merlot品种上的表现不佳，仅达到R2和3.30的RMSE的0.70。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

作为混合成像技术，光声显微镜（PAM）成像由于激光强度的最大允许暴露，组织中超声波的衰减以及换能器的固有噪声而受到噪声。去噪是降低噪声的后处理方法，并且可以恢复PAM图像质量。然而，之前的去噪技术通常严重依赖于数学前导者以及手动选择的参数，导致对不同噪声图像的不令人满意和慢的去噪能，这极大地阻碍了实用和临床应用。在这项工作中，我们提出了一种基于深度学习的方法，可以从PAM图像中除去复杂的噪声，没有数学前导者，并手动选择不同输入图像的设置。注意增强的生成对抗性网络用于提取图像特征并去除各种噪声。在合成和实际数据集上证明了所提出的方法，包括幻影（叶静脉）和体内（小鼠耳血管和斑马鱼颜料）实验。结果表明，与先前的PAM去噪方法相比，我们的方法在定性和定量上恢复图像时表现出良好的性能。此外，为256次\ times256 $像素的图像实现了0.016 s的去噪速度。我们的方法对于PAM图像的去噪有效和实用。

深度加强学习（DRL）的框架为连续决策提供了强大而广泛适用的数学形式化。本文提出了一种新的DRL框架，称为\ emph {$ f $-diveliventcence加强学习（frl）}。在FRL中，通过最大限度地减少学习政策和采样策略之间的$ F $同时执行策略评估和政策改进阶段，这与旨在最大化预期累计奖励的传统DRL算法不同。理论上，我们证明最小化此类$ F $ - 可以使学习政策会聚到最佳政策。此外，我们将FRL框架中的培训代理程序转换为通过Fenchel Concugate的特定$ F $函数转换为鞍点优化问题，这构成了政策评估和政策改进的新方法。通过数学证据和经验评估，我们证明FRL框架有两个优点：（1）政策评估和政策改进过程同时进行，（2）高估价值函数的问题自然而缓解。为了评估FRL框架的有效性，我们对Atari 2600的视频游戏进行实验，并显示在FRL框架中培训的代理匹配或超越基线DRL算法。

联合学习允许多个参与者在不公开数据隐私的情况下协作培训高效模型。但是，这种分布式的机器学习培训方法容易受到拜占庭客户的攻击，拜占庭客户通过修改模型或上传假梯度来干扰全球模型的训练。在本文中，我们提出了一种基于联邦学习（CMFL）的新型无服务器联合学习框架委员会机制，该机制可以确保算法具有融合保证的鲁棒性。在CMFL中，设立了一个委员会系统，以筛选上载已上传的本地梯度。 The committee system selects the local gradients rated by the elected members for the aggregation procedure through the selection strategy, and replaces the committee member through the election strategy.基于模型性能和防御的不同考虑，设计了两种相反的选择策略是为了精确和鲁棒性。广泛的实验表明，与典型的联邦学习相比，与传统的稳健性相比，CMFL的融合和更高的准确性比传统的稳健性，以分散的方法的方式获得了传统的耐受性算法。此外，我们理论上分析并证明了在不同的选举和选择策略下CMFL的收敛性，这与实验结果一致。

本文回顾了关于压缩视频质量增强质量的第一个NTIRE挑战，重点是拟议的方法和结果。在此挑战中，采用了新的大型不同视频（LDV）数据集。挑战有三个曲目。Track 1和2的目标是增强HEVC在固定QP上压缩的视频，而Track 3旨在增强X265压缩的视频，以固定的位速率压缩。此外，轨道1和3的质量提高了提高保真度（PSNR）的目标，以及提高感知质量的2个目标。这三个曲目完全吸引了482个注册。在测试阶段，分别提交了12个团队，8支球队和11支球队，分别提交了轨道1、2和3的最终结果。拟议的方法和解决方案衡量视频质量增强的最先进。挑战的首页：https：//github.com/renyang-home/ntire21_venh

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.