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。

鉴于其精确，效率和客观性，深入学习（DL）在重塑医疗保健系统方面具有很大的承诺。然而，DL模型到嘈杂和分发输入的脆性是在诊所的部署中的疾病。大多数系统产生点估计，无需进一步了解模型不确定性或信心。本文介绍了一个新的贝叶斯深度学习框架，用于分割神经网络中的不确定量化，特别是编码器解码器架构。所提出的框架使用一阶泰勒级近似传播，并学习模型参数分布的前两个矩（均值和协方差，通过最大化培训数据来最大限度地提高界限。输出包括两个地图：分段图像和分段的不确定性地图。细分决定中的不确定性被预测分配的协方差矩阵捕获。我们评估了从磁共振成像和计算机断层扫描的医学图像分割数据上提出的框架。我们在多个基准数据集上的实验表明，与最先进的分割模型相比，所提出的框架对噪声和对抗性攻击更加稳健。此外，所提出的框架的不确定性地图将低置信度（或等效高不确定性）与噪声，伪像或对抗攻击损坏的测试输入图像中的贴片。因此，当通过在不确定性地图中呈现更高的值，该模型可以自评测出现错误预测或错过分割结构的一部分，例如肿瘤。

This paper proposes a perception and path planning pipeline for autonomous racing in an unknown bounded course. The pipeline was initially created for the 2021 evGrandPrix autonomous division and was further improved for the 2022 event, both of which resulting in first place finishes. Using a simple LiDAR-based perception pipeline feeding into an occupancy grid based expansion algorithm, we determine a goal point to drive. This pipeline successfully achieved reliable and consistent laps in addition with occupancy grid algorithm to know the ways around a cone-defined track with an averaging speeds of 6.85 m/s over a distance 434.2 meters for a total lap time of 63.4 seconds.

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.

标准人工神经网络（ANNS）使用无内存非线性激活的总和产生或多功能节点操作。这些神经网络已知具有通用功能近似功能。先前提出的形态学感知器使用Max-sum，代替总产量，节点处理，并具有有希望的电路实现属性。在本文中，我们表明这些Max-SUM ANN没有通用近似功能。此外，我们考虑了形态学上的签名签名的最大和最大 - 明星和最大 - 星级概括，并表明这些变体也没有通用的近似能力。我们将这些变化与对数数字系统（LNS）的实现进行对比，这些变化也避免了乘法，但确实具有通用的近似功能。

定量超声（QUS）提供了有关组织特性的重要信息。可以通过将包络数据分为小重叠贴片并计算不同的斑点统计信息，例如中Nakagami的参数和knody k-Distribution（HK-Distribution）来形成QUS参数图像。计算出的QUS参数图像可能是错误的，因为补丁中只有几个独立的样本可用。另一个挑战是，假定斑块内的包膜样品来自相同的分布，这一假设通常会违反，因为该组织通常不是同质的。在本文中，我们提出了一种基于卷积神经网络（CNN）的方法，以估算QUS参数图像而无需修补。我们构建一个从HK分布中采样的大数据集，具有随机形状和QUS参数值的区域。然后，我们使用众所周知的网络以多任务学习方式估算QUS参数。我们的结果证实，所提出的方法能够减少错误并改善QUS参数图像中的边界定义。

自动化的脑肿瘤检测已成为一项高度可观的医学诊断研究。在最近的医学诊断中，高度考虑检测和分类用于采用机器学习和深度学习技术。然而，需要改善当前模型的准确性和性能以进行合适的治疗。在本文中，通过采用增强的优化算法来确保深度卷积学习的改进，因此，基于改进的Harris Hawks优化（HHO），深度卷积神经网络（DCNN）被认为是G-HHO。这种杂交具有灰狼优化（GWO）和HHO，以提供更好的结果，从而限制了收敛速度和增强性能。此外，采用大小阈值来分割强调脑肿瘤检测的肿瘤部分。进行了实验研究，以验证2073年总数增强MRI图像的建议方法的性能。通过将其与巨大增强MRI图像上的九种现有算法进行比较，以准确性，精度，召回，F量，执行时间和内存使用情况进行比较，可以确保该技术的性能。性能比较表明，DCNN-G-HHO比现有方法更成功，尤其是在97％的评分精度下。此外，统计性能分析表明，建议的方法更快，并且在MR图像上识别和分类脑肿瘤癌的记忆力较少。此验证的实施是在Python平台上进行的。建议方法的相关代码可在以下网址提供：https：//github.com/bryarahassan/dcnn-g-hho。

尽管自动图像分析的重要性不断增加，但最近的元研究揭示了有关算法验证的主要缺陷。性能指标对于使用的自动算法的有意义，客观和透明的性能评估和验证尤其是关键，但是在使用特定的指标进行给定的图像分析任务时，对实际陷阱的关注相对较少。这些通常与（1）无视固有的度量属性，例如在存在类不平衡或小目标结构的情况下的行为，（2）无视固有的数据集属性，例如测试的非独立性案例和（3）无视指标应反映的实际生物医学领域的兴趣。该动态文档的目的是说明图像分析领域通常应用的性能指标的重要局限性。在这种情况下，它重点介绍了可以用作图像级分类，语义分割，实例分割或对象检测任务的生物医学图像分析问题。当前版本是基于由全球60多家机构的国际图像分析专家进行的关于指标的Delphi流程。

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.