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.

在很大程度上，由于隐私问题，很难培训有关疾病诊断或图像分割的医学图像的计算机视觉相关算法。因此，高度寻求生成图像模型以促进数据共享。但是，需要研究3-D生成模型，需要研究其隐私泄漏。我们使用在肿瘤面膜上进行条件研究的头和颈宠物图像介绍了3D生成模型横向gan（TRGAN）。我们为模型定义了图像保真度，实用性和隐私的定量度量。在培训过程中评估了这些指标，以确定理想的保真度，公用事业和隐私权权衡，并建立这些参数之间的关系。我们表明，Trgan的歧视者很容易受到攻击，并且攻击者可以识别哪些样品在训练中几乎完全准确（AUC = 0.99）。我们还表明，仅访问发电机的攻击者无法可靠地分类样品是否已用于训练（AUC = 0.51）。这表明Trgan发电机（而不是歧视者）可以用于共享具有最小隐私风险的合成3-D PET数据，同时保持良好的效用和保真度。

对疾病的诊断或图像分割医学图像训练计算机视觉相关算法是缺乏训练数据，标记的样品，和隐私问题的困难所致。出于这个原因，一个强大的生成方法来创建合成数据后高度寻求。然而，大多数三维图像生成器需要额外的图像输入或者是非常占用大量内存。为了解决这些问题，我们建议调整视频生成技术3-d图像生成。使用时间GAN（TGAN）架构，我们将展示我们能够产生逼真的头部和颈部PET图像。我们还表明，通过调节肿瘤口罩发电机，我们能够控制肿瘤的几何形状和位置，在生成的图像。为了测试合成影像的用途，我们使用合成的图像训练分割模型。空调真实肿瘤掩模合成图像被自动分割，和对应的真实图像也分割。我们评估使用的骰子得分的分割，并找到两个数据集（0.65合成数据，0.70的真实数据）同样的分割算法执行。然后，各种radionomic特征在分割的肿瘤体积为每个数据集来计算。真实的和合成的特征分布的比较显示，8七个特征分布有统计学不显着差异（p> 0.05）。还计算所有radionomic特征之间的相关系数，它是示出了所有在真实数据组中的强统计相关的在合成数据集被保留。

人工智能（AI）技术具有重要潜力，可以实现有效，鲁棒和自动的图像表型，包括识别细微图案。基于AI的检测搜索图像空间基于模式和特征来找到兴趣区域。存在一种良性的肿瘤组织学，可以通过使用图像特征的基于AI的分类方法来识别。图像从图像中提取可用于的可覆盖方式，可以通过显式（手工/工程化）和深度辐射谱系框架来探索途径。辐射瘤分析有可能用作非侵入性技术，以准确表征肿瘤，以改善诊断和治疗监测。这项工作介绍基于AI的技术，专注于肿瘤宠物和PET / CT成像，用于不同的检测，分类和预测/预测任务。我们还讨论了所需的努力，使AI技术转换为常规临床工作流程，以及潜在的改进和互补技术，例如在电子健康记录和神经象征性AI技术上使用自然语言处理。

It does not matter whether it is a job interview with Tech Giants, Wall Street firms, or a small startup; all candidates want to demonstrate their best selves or even present themselves better than they really are. Meanwhile, recruiters want to know the candidates' authentic selves and detect soft skills that prove an expert candidate would be a great fit in any company. Recruiters worldwide usually struggle to find employees with the highest level of these skills. Digital footprints can assist recruiters in this process by providing candidates' unique set of online activities, while social media delivers one of the largest digital footprints to track people. In this study, for the first time, we show that a wide range of behavioral competencies consisting of 16 in-demand soft skills can be automatically predicted from Instagram profiles based on the following lists and other quantitative features using machine learning algorithms. We also provide predictions on Big Five personality traits. Models were built based on a sample of 400 Iranian volunteer users who answered an online questionnaire and provided their Instagram usernames which allowed us to crawl the public profiles. We applied several machine learning algorithms to the uniformed data. Deep learning models mostly outperformed by demonstrating 70% and 69% average Accuracy in two-level and three-level classifications respectively. Creating a large pool of people with the highest level of soft skills, and making more accurate evaluations of job candidates is possible with the application of AI on social media user-generated data.

The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common approaches, but current methods have considerable limitations.For example, fine-tuning an existing policy frequently fails, as the policy can degrade rapidly early in training. In a similar vein, distillation of expert behavior can lead to poor results when given sub-optimal experts. We compare several common approaches for skill transfer on multiple domains including changes in task and system dynamics. We identify how existing methods can fail and introduce an alternative approach to mitigate these problems. Our approach learns to sequence existing temporally-extended skills for exploration but learns the final policy directly from the raw experience. This conceptual split enables rapid adaptation and thus efficient data collection but without constraining the final solution.It significantly outperforms many classical methods across a suite of evaluation tasks and we use a broad set of ablations to highlight the importance of differentc omponents of our method.

人们的行为和反应是由他们的情绪驱动的。在线社交媒体正在成为以书面形式表达情感的绝佳工具。注意上下文和整个句子，帮助我们从文本中检测到情感。但是，这种观点抑制了我们注意文本中的一些情感单词或短语，尤其是当单词隐含地而不是明确地表达情感时。另一方面，仅关注单词并忽略上下文会导致对句子含义和感觉的扭曲理解。在本文中，我们提出了一个框架，该框架分析句子和单词级别的文本。我们将其命名为CEFER（情感识别的上下文和情感嵌入式框架）。我们的四个方法是通过同时考虑整个句子和每个单词以及隐式和明确的情绪来提取数据。从这些数据中获得的知识不仅减轻了前面方法中缺陷的影响，而且还可以增强特征向量。我们使用BERT家族评估几个功能空间，并根据其设计CEFER。 CEFER将每个单词的情感向量（包括明确和隐性情绪）与基于上下文的每个单词的特征向量相结合。 CEFER的表现比Bert家族更好。实验结果表明，识别隐性情绪比检测明确的情绪更具挑战性。 CEFER，提高了隐性情绪识别的准确性。根据结果​​，CEFER在识别明确的情绪和隐性中的3％方面的表现要比BERT家族好5％。

对于在现实世界中运营的机器人来说，期望学习可以有效地转移和适应许多任务和场景的可重复使用的行为。我们提出了一种使用分层混合潜变量模型来从数据中学习抽象运动技能的方法。与现有工作相比，我们的方法利用了离散和连续潜在变量的三级层次结构，以捕获一组高级行为，同时允许如何执行它们的差异。我们在操纵域中展示该方法可以有效地将离线数据脱落到不同的可执行行为，同时保留连续潜变量模型的灵活性。由此产生的技能可以在新的任务，看不见的对象和州内转移和微调到基于视觉的策略，与现有的技能和仿制的方法相比，产生更好的样本效率和渐近性能。我们进一步分析了技能最有益的方式以及何时：他们鼓励定向探索来涵盖与任务相关的国家空间的大区域，使其在挑战稀疏奖励环境中最有效。