急诊科（EDS）的表现对于任何医疗保健系统都非常重要，因为它们是许多患者的入口处。但是，除其他因素外，患者敏锐度水平和访问患者的相应治疗要求的变异性对决策者构成了重大挑战。平衡患者的等待时间首先是由医生与所有敏锐度水平的总长度相处的，对于维持所有患者的可接受的操作表现至关重要。为了解决这些要求在为患者分配空闲资源时，过去提出了几种方法，包括累积的优先排队（APQ）方法。 APQ方法在系统和敏锐度水平方面将优先评分线性分配给患者。因此，选择决策基于一个简单的系统表示，该表示作为选择功能的输入。本文研究了基于机器学习（ML）的患者选择方法的潜力。它假设对于大量的培训数据，包括多种不同的系统状态，（接近）最佳分配可以通过（启发式）优化器计算出关于所选的性能指标，并旨在模仿此类最佳行为。应用于新情况。因此，它结合了系统的全面状态表示和复杂的非线性选择函数。拟议方法的动机是，高质量的选择决策可能取决于描述ED当前状态的各种因素，而不仅限于等待时间，而这些因素可以由ML模型捕获和利用。结果表明，所提出的方法显着优于大多数评估设置的APQ方法

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.

The application of Natural Language Processing (NLP) to specialized domains, such as the law, has recently received a surge of interest. As many legal services rely on processing and analyzing large collections of documents, automating such tasks with NLP tools emerges as a key challenge. Many popular language models, such as BERT or RoBERTa, are general-purpose models, which have limitations on processing specialized legal terminology and syntax. In addition, legal documents may contain specialized vocabulary from other domains, such as medical terminology in personal injury text. Here, we propose LegalRelectra, a legal-domain language model that is trained on mixed-domain legal and medical corpora. We show that our model improves over general-domain and single-domain medical and legal language models when processing mixed-domain (personal injury) text. Our training architecture implements the Electra framework, but utilizes Reformer instead of BERT for its generator and discriminator. We show that this improves the model's performance on processing long passages and results in better long-range text comprehension.

Much recent work in task-oriented parsing has focused on finding a middle ground between flat slots and intents, which are inexpressive but easy to annotate, and powerful representations such as the lambda calculus, which are expressive but costly to annotate. This paper continues the exploration of task-oriented parsing by introducing a new dataset for parsing pizza and drink orders, whose semantics cannot be captured by flat slots and intents. We perform an extensive evaluation of deep-learning techniques for task-oriented parsing on this dataset, including different flavors of seq2seq systems and RNNGs. The dataset comes in two main versions, one in a recently introduced utterance-level hierarchical notation that we call TOP, and one whose targets are executable representations (EXR). We demonstrate empirically that training the parser to directly generate EXR notation not only solves the problem of entity resolution in one fell swoop and overcomes a number of expressive limitations of TOP notation, but also results in significantly greater parsing accuracy.

从教育和研究的角度来看，关于硬件的实验是机器人技术和控制的关键方面。在过去的十年中，已经介绍了许多用于车轮机器人的开源硬件和软件框架，主要采用独轮车和类似汽车的机器人的形式，目的是使更广泛的受众访问机器人并支持控制系统开发。独轮车通常很小且便宜，因此有助于在较大的机队中进行实验，但它们不适合高速运动。类似汽车的机器人更敏捷，但通常更大且更昂贵，因此需要更多的空间和金钱资源。为了弥合这一差距，我们介绍了Chronos，这是一种具有定制开源电子设备的新型汽车的1/28比例机器人，以及CRS是用于控制和机器人技术的开源软件框架。 CRS软件框架包括实施各种最新的算法，以进行控制，估计和多机构协调。通过这项工作，我们旨在更轻松地使用硬件，并减少启动新的教育和研究项目所需的工程时间。

基础模型在AI的所有应用中都被认为是一个突破性的突破性，有望进行功能提取的可重复使用的机制，从而减轻了对特定于任务的预测模型的大量高质量培训数据的需求。但是，基础模型可能可能编码甚至加强历史数据集中存在的现有偏见。鉴于仔细检查基础模型的能力有限，尚不清楚机会是否超过了临床决策等安全关键应用中的风险。在我们对最近发布且可公开可用的胸部X射线基础模型的统计偏差分析中，我们发现了关注的原因，因为该模型似乎编码了受保护特征，包括生物学性别和种族认同，这可能会导致下游亚组的各个子群体不同申请。尽管针对医疗保健应用的基础模型的研究处于早期阶段，但我们认为，让社区意识到这些风险以避免伤害很重要。

我们提出了Blenderbot 3，这是一个175B参数对话模型，能够通过访问Internet和长期内存进行开放域对话，并接受了大量用户定义的任务的培训。我们同时发布了模型权重和代码，还将模型部署在公共网页上，以与有机用户进行交互。该技术报告描述了该模型的构建方式（建筑，模型和培训计划）以及其部署的细节，包括安全机制。人类评估表明，它优于现有的开放域对话代理，包括其前身（Roller等，2021； Komeili等，2022）。最后，我们使用部署收集的数据详细介绍了持续学习的计划，该数据也将公开发布。因此，该研究计划的目标是使社区能够研究通过互动学习的不断改进的负责任的代理商。

机器学习模型的预测失败通常来自训练数据中的缺陷，例如不正确的标签，离群值和选择偏见。但是，这些负责给定失败模式的数据点通常不知道先验，更不用说修复故障的机制了。这项工作借鉴了贝叶斯对持续学习的看法，并为两者开发了一个通用框架，确定了导致目标失败的培训示例，并通过删除有关它们的信息来修复模型。该框架自然允许将最近学习的最新进展解决这一新的模型维修问题，同时将现有的作品集成了影响功能和数据删除作为特定实例。在实验上，提出的方法优于基准，既可以识别有害训练数据，又要以可普遍的方式固定模型失败。

AI的长期目标是建立以人类方式理解概念的系统。搁置建立这种系统的困难，即使试图评估一个系统也是一个挑战，这是由于当今的AI相对不透明度及其在寻找快捷键解决方案的倾向。假设可以识别一个概念实例的系统也必须像人类一样理解其他实例，那么人类倾向于拟人化的趋势会加剧这一点。在本文中，我们认为理解一个概念需要在各种环境中使用它的能力。因此，我们通过探测系统在许多不同的实例化中使用给定概念的能力来提出以概念为中心的系统评估。我们介绍了对两个领域的评估的案例研究 - 乌鸦（受乌鸦的渐进式矩阵）和抽象和推理语料库（ARC） - 用于开发和评估AI系统中的抽象能力。我们基于概念的评估方法揭示了有关常规测试集将隐藏的AI系统的信息。

我们研究了可以写入欧几里得凸函数的差异的地质凸（G-Convex）问题。这种结构出现在统计和机器学习中的几个优化问题中，例如，用于矩阵缩放，协方差的M估计器和Brascamp-Lieb不平等。我们的工作提供有效的算法，一方面利用G-Convexity来确保全球最优性以及保证迭代复杂性。另一方面，拆分结构使我们能够开发欧几里得最小化算法，这些算法可以帮助我们绕开计算昂贵的Riemannian操作（例如指数型地图和并行运输）的需求。我们通过将其专门针对机器学习文献中以前研究过的一些具体优化问题来说明我们的结果。最终，我们希望我们的工作有助于激励人们更广泛地寻找混合的欧几罗南优化算法。