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.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

面部影响的成像可用于通过成年后的儿童进行心理生理属性，特别是用于监测自闭症谱系障碍等终身疾病。深度卷积神经网络在对成年人的面部表情进行分类方面表现出了令人鼓舞的结果。但是，经过成人基准数据培训的分类器模型由于心理物理发展的差异而不适合学习儿童表情。同样，接受儿童数据训练的模型在成人表达分类中的表现较差。我们建议适应域，以同时对齐成人和儿童表达式在共享潜在空间中的分布，以确保对任何一个领域的稳健分类。此外，在成年子女表达分类中研究了面部图像的年龄变化，但仍无法掌握。我们从多个领域中汲取灵感，并提出深层自适应面部表情，以融合betamix选定的地标特征（面部自我），以进行成人的面部表情分类。在文献中，基于与表达，域和身份因素的相关性，beta分布的混合物首次用于分解和选择面部特征。我们通过两对成人孩子数据集评估面对面的自我。我们提出的面对面的方法在对齐成人和儿童表情的潜在表示方面优于成人孩子转移学习和其他基线适应方法。

近年来，全球医学事物（IOMT）行业已经以极大的速度发展。由于IOMT网络的庞大规模和部署，安全和隐私是IOMT的关键问题。机器学习（ML）和区块链（BC）技术已大大提高了Healthcare 5.0的功能和设施，并产生了一个名为“ Smart Healthcare”的新领域。通过早期确定问题，智能医疗保健系统可以帮助避免长期损害。这将提高患者的生活质量，同时减少压力和医疗保健费用。 IOMT在信息技术领域中启用了一系列功能，其中之一是智能和互动的医疗保健。但是，将医疗数据合并到单个存储位置以训练强大的机器学习模型，这引起了人们对隐私，所有权和更加集中的遵守的担忧。联合学习（FL）通过利用集中式聚合服务器来传播全球学习模型，从而克服了前面的困难。同时，本地参与者可以控制患者信息，从而确保数据机密性和安全性。本文对与医疗保健中联邦学习纠缠的区块链技术的发现进行了全面分析。 5.0。这项研究的目的是利用区块链技术和入侵检测系统（IDS）在医疗保健5.0中构建安全的健康监测系统，以检测医疗保健网络中的任何恶意活动，并使医生能够通过医疗传感器监控患者并采取必要的措施。定期通过预测疾病。

上下文：大数据的有效处理是SQL和NOSQL数据库的一项具有挑战性的任务，在这种数据库中，有效的软件体系结构起着至关重要的作用。 SQL数据库设计用于构建数据和支持垂直可扩展性。相反，水平可伸缩性由NOSQL数据库支持，并且可以有效地处理较大的非结构化数据。可以根据组织的需求选择正确的范式；但是，做出正确的选择通常可能具有挑战性。 SQL和NOSQL数据库遵循不同的体系结构。同样，混合模型之后是NOSQL数据库的每个类别。因此，对于多个云服务提供商（CSP）的云消费者来说，数据移动变得困难。此外，每个云平台IAAS，PAAS，SaaS和DBAAS还监视各种范式。目的：该系统文献综述（SLR）旨在研究与SQL和NOSQL数据库软件体系结构相关的相关文章，并解决各种云平台之间的数据可移植性和互操作性。最新的状态通过观察缩放，性能，可用性，一致性和分片特性，介绍了SQL和NOSQL数据库的许多性能比较研究。根据研究研究，NOSQL数据库设计的结构可以是大数据分析的正确选择，而SQL数据库适合OLTP数据库。研究人员提出了许多与云中数据流动相关的方法。开发了基于平台的API，这使用户的数据移动变得困难。因此，在跨多个CSP的数据移动期间发现了数据可移植性和互操作性问题。为了最大程度地减少开发人员的努力和互操作性，要求统一的API使数据移动在各种云平台之间相对易于访问。

快速准确地检测该疾病可以大大帮助减少任何国家医疗机构对任何大流行期间死亡率降低死亡率的压力。这项工作的目的是使用新型的机器学习框架创建多模式系统，该框架同时使用胸部X射线（CXR）图像和临床数据来预测COVID-19患者的严重程度。此外，该研究还提出了一种基于nom图的评分技术，用于预测高危患者死亡的可能性。这项研究使用了25种生物标志物和CXR图像，以预测意大利第一波Covid-19（3月至6月2020年3月至6月）在930名Covid-19患者中的风险。提出的多模式堆叠技术分别产生了89.03％，90.44％和89.03％的精度，灵敏度和F1分数，以识别低风险或高危患者。与CXR图像或临床数据相比，这种多模式方法可提高准确性6％。最后，使用多元逻辑回归的列线图评分系统 - 用于对第一阶段确定的高风险患者的死亡风险进行分层。使用随机森林特征选择模型将乳酸脱氢酶（LDH），O2百分比，白细胞（WBC）计数，年龄和C反应蛋白（CRP）鉴定为有用的预测指标。开发了五个预测因素参数和基于CXR图像的列函数评分，以量化死亡的概率并将其分为两个风险组：分别存活（<50％）和死亡（> = 50％）。多模式技术能够预测F1评分为92.88％的高危患者的死亡概率。开发和验证队列曲线下的面积分别为0.981和0.939。

近年来，基于复杂的卷积神经网络架构的越来越复杂的方法一直在缓慢推动良好的基准数据集的性能。在本文中，我们返回返回检查真正需要这种复杂性。我们呈现RC-Net，一个完全卷积的网络，其中每层过滤器数量被优化，以减少特征重叠和复杂性。我们还使用跳过连接来将空间信息丢失保持为最小，通过将网络中的汇集操作保持到最小。在我们的实验中使用了两个公开的视网膜血管分段数据集。在我们的实验中，RC-Net是非常有竞争力的，表现优于替代方案的分割方法，具有两种甚至三个数量级的训练参数。

深度学习（DL）模型为各种医学成像基准挑战提供了最先进的性能，包括脑肿瘤细分（BRATS）挑战。然而，局灶性病理多隔室分割（例如，肿瘤和病变子区）的任务特别具有挑战性，并且潜在的错误阻碍DL模型转化为临床工作流程。量化不确定形式的DL模型预测的可靠性，可以实现最不确定的地区的临床审查，从而建立信任并铺平临床翻译。最近，已经引入了许多不确定性估计方法，用于DL医学图像分割任务。开发指标评估和比较不确定性措施的表现将有助于最终用户制定更明智的决策。在本研究中，我们探索并评估在Brats 2019-2020任务期间开发的公制，以对不确定量化量化（Qu-Brats），并旨在评估和排列脑肿瘤多隔室分割的不确定性估计。该公制（1）奖励不确定性估计，对正确断言产生高置信度，以及在不正确的断言处分配低置信水平的估计数，（2）惩罚导致更高百分比的无关正确断言百分比的不确定性措施。我们进一步基准测试由14个独立参与的Qu-Brats 2020的分割不确定性，所有这些都参与了主要的Brats细分任务。总体而言，我们的研究结果证实了不确定性估计提供了分割算法的重要性和互补价值，因此突出了医学图像分析中不确定性量化的需求。我们的评估代码在HTTPS://github.com/ragmeh11/qu-brats公开提供。

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .