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.

Although synthetic aperture imaging (SAI) can achieve the seeing-through effect by blurring out off-focus foreground occlusions while recovering in-focus occluded scenes from multi-view images, its performance is often deteriorated by dense occlusions and extreme lighting conditions. To address the problem, this paper presents an Event-based SAI (E-SAI) method by relying on the asynchronous events with extremely low latency and high dynamic range acquired by an event camera. Specifically, the collected events are first refocused by a Refocus-Net module to align in-focus events while scattering out off-focus ones. Following that, a hybrid network composed of spiking neural networks (SNNs) and convolutional neural networks (CNNs) is proposed to encode the spatio-temporal information from the refocused events and reconstruct a visual image of the occluded targets. Extensive experiments demonstrate that our proposed E-SAI method can achieve remarkable performance in dealing with very dense occlusions and extreme lighting conditions and produce high-quality images from pure events. Codes and datasets are available at https://dvs-whu.cn/projects/esai/.

Machine learning has been widely used in healthcare applications to approximate complex models, for clinical diagnosis, prognosis, and treatment. As deep learning has the outstanding ability to extract information from time series, its true capabilities on sparse, irregularly sampled, multivariate, and imbalanced physiological data are not yet fully explored. In this paper, we systematically examine the performance of machine learning models for the clinical prediction task based on the EHR, especially physiological time series. We choose Physionet 2019 challenge public dataset to predict Sepsis outcomes in ICU units. Ten baseline machine learning models are compared, including 3 deep learning methods and 7 non-deep learning methods, commonly used in the clinical prediction domain. Nine evaluation metrics with specific clinical implications are used to assess the performance of models. Besides, we sub-sample training dataset sizes and use learning curve fit to investigate the impact of the training dataset size on the performance of the machine learning models. We also propose the general pre-processing method for the physiology time-series data and use Dice Loss to deal with the dataset imbalanced problem. The results show that deep learning indeed outperforms non-deep learning, but with certain conditions: firstly, evaluating with some particular evaluation metrics (AUROC, AUPRC, Sensitivity, and FNR), but not others; secondly, the training dataset size is large enough (with an estimation of a magnitude of thousands).

The Interval-valued intuitionistic fuzzy sets (IVIFSs) based on the intuitionistic fuzzy sets combines the classical decision method is in its research and application is attracting attention. After comparative analysis, there are multiple classical methods with IVIFSs information have been applied into many practical issues. In this paper, we extended the classical EDAS method based on cumulative prospect theory (CPT) considering the decision makers (DMs) psychological factor under IVIFSs. Taking the fuzzy and uncertain character of the IVIFSs and the psychological preference into consideration, the original EDAS method based on the CPT under IVIFSs (IVIF-CPT-MABAC) method is built for MAGDM issues. Meanwhile, information entropy method is used to evaluate the attribute weight. Finally, a numerical example for project selection of green technology venture capital has been given and some comparisons is used to illustrate advantages of IVIF-CPT-MABAC method and some comparison analysis and sensitivity analysis are applied to prove this new methods effectiveness and stability.

心电图（ECG）是心脏病的广泛使用的非侵入性诊断工具。许多研究设计了ECG分析模型（例如分类器）来协助诊断。作为一项上游任务，研究建立了生成模型来综合ECG数据，这对提供培训样本，隐私保护和减少注释是有益的。但是，以前的ECG生成方法通常既不合成多视图数据，也不涉及心脏病状况。在本文中，我们提出了一种新型的，用于多视图ECG合成的新型疾病的生成对抗网络，称为ME-GAN，该网络获得了以心脏病为条件的全磁心电图表示，并将其投射到多个标准视图上，以产生ECG信号。由于心脏病的心电图表现通常位于特定波形中，因此我们提出了一种新的“混合标准化”，以精确地注入合适的位置。此外，我们提出了一个视图歧视者，将无序的心电图视图恢复为预定的顺序，监督发电机以获取代表正确视图特征的ECG。此外，提出了一个新的度量RFID，以评估合成的ECG信号的质量。全面的实验验证了我们的ME-GAN在具有可信赖的病态表现的多视图ECG信号合成上表现良好。

我们启动对在线路由问题进行预测的研究，这是受到学习效果算法领域的最新成果的启发。一个学习的在线算法，如果预测是准确的，同时否则可以维持理论保证，即使预测非常错误，则以黑盒方式纳入了预测，以胜过现有的算法。在这项研究中，我们特别开始研究经典的在线旅行推销员问题（OLTSP），其中未来的请求得到了预测。与以前其他研究中的预测模型不同，OLTSP中的每个实际请求与其到达时间和位置相关，可能与预测的每个实际请求不一致，这些预测会导致麻烦的情况。我们的主要结果是研究不同的预测模型和设计算法，以改善不同环境中最著名的结果。此外，我们将提出的结果概括为在线拨号问题。

随着野火产生的大气气溶胶减少了向地球的传入太阳辐射，越来越频繁的野火会显着影响太阳能的产生。通过气溶胶光学深度（AOD）测量大气气溶胶，可以通过地球静止卫星检索和监测AOD数据流。但是，多源遥感数据流通常具有异质特征，包括不同的数据缺失率，测量误差，系统偏见等。为了准确估计和预测潜在的AOD传播过程，存在实践需求和理论利益，以提出一种通过同时利用或融合多种源的异质卫星远程远程远程灵感数据来建模物理信息的统计方法。提出的方法利用光谱方法将多源卫星数据流与控制AOD传播过程的基本对流扩散方程相结合。统计模型中包括一个偏差校正过程，以说明物理模型的偏差和傅立叶系列的截断误差。提出的方法适用于从国家海洋和大气管理局获得的加利福尼亚野火AOD数据流。提供了全面的数值示例，以证明所提出方法的预测能力和模型解释性。计算机代码已在GitHub上提供。

Fisheye镜头由于其广泛的视野（FOV）而增加了计算摄影和辅助驾驶的应用。但是，鱼眼图像通常包含其成像模型引起的无效黑色区域。在本文中，我们提出了一种鱼眼方法，该方法通过超越无效的地区来扩展鱼眼镜头的FOV，从而改善了被捕获的场景的完整性。与矩形和未发生的图像相比，Fisheye图像支出面临两个挑战：不规则的绘画区域和失真合成。在观察鱼眼图像的径向对称性时，我们首先提出了一种极地支出策略，以推断从中心到外部区域的相干语义。这样的支出方式考虑了径向失真和圆边界的分布模式，从而提高了更合理的完成方向。对于失真合成，我们提出了一个螺旋失真感知的感知模块，其中学习路径与Fisheye图像的扭曲保持一致。随后，场景修订模块将生成的像素与估计的失真重新安排以匹配鱼眼图像，从而扩展了FOV。在实验中，我们在三个受欢迎的户外数据集上评估了拟议的fisheeex：CityScapes，BDD100K和Kitti和一个真实世界的Fisheye Image DataSet。结果表明，我们的方法显着优于最先进的方法，超出原始鱼眼图像的内容多约27％。

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。

头发编辑是计算机视觉和图形中有趣和挑战的问题。许多现有方法需要粗略的草图或掩码作为用于编辑的条件输入，但是这些交互既不直接也不高效。为了从繁琐的相互作用过程中获取用户，本文提出了一种新的头发编辑交互模式，其能够基于用户提供的文本或参考图像单独地或共同地操纵头发属性。为此目的，我们通过利用对比语言图像预训练（剪辑）模型的强大图像文本表示能力来编码共享嵌入空间中的图像和文本条件，并提出统一的头发编辑框架。通过精心设计的网络结构和丢失功能，我们的框架可以以脱谕方式执行高质量的头发编辑。广泛的实验在操纵准确性，编辑结果的视觉现实主义和无关的属性保存方面表现出我们的方法的优越性。项目repo是https://github.com/wty-ustc/hairclip。