作为史上史上的医学历史之一，放射学，目睹了巨大的技术进步，并彻底改变了我们今天练习医学的方式。在过去的几十年中，医学成像方式产生了地震量的医疗数据。使用此数据的人工智能（AI）应用程序的开发和采用将导致放射学中的下一阶段进化。它将包括自动化诸如注释，报告 - 生成等的费力的手动任务，以及初始放射学评估案件的援助放射科学家在评估工作流程中。我们为放射学自动化进展提出了一项级别的分类，解释了每个级别的AI援助，具有相应的挑战和解决方案。我们希望这样的讨论可以帮助我们以结构化的方式解决挑战，并采取必要的步骤，以确保在放射学中顺利采用新技术。

A popular approach to creating a zero-shot cross-language retrieval model is to substitute a monolingual pretrained language model in the retrieval model with a multilingual pretrained language model such as Multilingual BERT. This multilingual model is fined-tuned to the retrieval task with monolingual data such as English MS MARCO using the same training recipe as the monolingual retrieval model used. However, such transferred models suffer from mismatches in the languages of the input text during training and inference. In this work, we propose transferring monolingual retrieval models using adapters, a parameter-efficient component for a transformer network. By adding adapters pretrained on language tasks for a specific language with task-specific adapters, prior work has shown that the adapter-enhanced models perform better than fine-tuning the entire model when transferring across languages in various NLP tasks. By constructing dense retrieval models with adapters, we show that models trained with monolingual data are more effective than fine-tuning the entire model when transferring to a Cross Language Information Retrieval (CLIR) setting. However, we found that the prior suggestion of replacing the language adapters to match the target language at inference time is suboptimal for dense retrieval models. We provide an in-depth analysis of this discrepancy between other cross-language NLP tasks and CLIR.

Negotiation is one of the crucial abilities in human communication, and there has been a resurgent research interest in negotiation dialogue systems recently, which goal is to empower intelligent agents with such ability that can efficiently help humans resolve conflicts or reach beneficial agreements. Although there have been many explorations in negotiation dialogue systems, a systematic review of this task has to date remained notably absent. To this end, we aim to fill this gap by reviewing contemporary studies in the emerging field of negotiation dialogue systems, covering benchmarks, evaluations, and methodologies. Furthermore, we also discuss potential future directions, including multi-modal, multi-party, and cross-cultural negotiation scenarios. Our goal is to provide the community with a systematic overview of negotiation dialogue systems and to inspire future research.

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.

Real engineering and scientific applications often involve one or more qualitative inputs. Standard Gaussian processes (GPs), however, cannot directly accommodate qualitative inputs. The recently introduced latent variable Gaussian process (LVGP) overcomes this issue by first mapping each qualitative factor to underlying latent variables (LVs), and then uses any standard GP covariance function over these LVs. The LVs are estimated similarly to the other GP hyperparameters through maximum likelihood estimation, and then plugged into the prediction expressions. However, this plug-in approach will not account for uncertainty in estimation of the LVs, which can be significant especially with limited training data. In this work, we develop a fully Bayesian approach for the LVGP model and for visualizing the effects of the qualitative inputs via their LVs. We also develop approximations for scaling up LVGPs and fully Bayesian inference for the LVGP hyperparameters. We conduct numerical studies comparing plug-in inference against fully Bayesian inference over a few engineering models and material design applications. In contrast to previous studies on standard GP modeling that have largely concluded that a fully Bayesian treatment offers limited improvements, our results show that for LVGP modeling it offers significant improvements in prediction accuracy and uncertainty quantification over the plug-in approach.

近年来，自动对色素，非色素和脱发的非胸膜皮肤病变的分类引起了很多关注。但是，皮肤纹理，病变形状，脱位对比度，照明条件等的成像变化。阻碍了鲁棒的特征提取，从而影响分类精度。在本文中，我们提出了一个新的深神经网络，该网络利用输入数据进行鲁棒特征提取。具体而言，我们分析了卷积网络的行为（视野），以找到深度监督的位置，以改善特征提取。为了实现这一目标，首先，我们执行激活映射以生成对象掩码，突出显示对分类输出生成最重要的输入区域。然后，选择层的有效接收场的网络层与对象掩模中的近似对象形状相匹配，以作为我们进行深度监督的焦点。利用三个黑色素瘤检测数据集和两个白癜风检测数据集上的不同类型的卷积特征提取器和分类器，我们验证了新方法的有效性。

在这个大数据时代，当前一代很难从在线平台中包含的大量数据中找到正确的数据。在这种情况下，需要一个信息过滤系统，可以帮助他们找到所需的信息。近年来，出现了一个称为推荐系统的研究领域。推荐人变得重要，因为他们拥有许多现实生活应用。本文回顾了推荐系统在电子商务，电子商务，电子资源，电子政务，电子学习和电子生活中的不同技术和发展。通过分析有关该主题的最新工作，我们将能够详细概述当前的发展，并确定建议系统中的现有困难。最终结果为从业者和研究人员提供了对建议系统及其应用的必要指导和见解。

基于物理学的模型已成为流体动力学的主流，用于开发预测模型。近年来，由于数据科学，处理单元，基于神经网络的技术和传感器适应性的快速发展，机器学习为流体社区提供了复兴。到目前为止，在流体动力学中的许多应用中，机器学习方法主要集中在标准过程上，该过程需要将培训数据集中在指定机器或数据中心上。在这封信中，我们提出了一种联合机器学习方法，该方法使本地化客户能够协作学习一个汇总和共享的预测模型，同时将所有培训数据保留在每个边缘设备上。我们证明了这种分散学习方法的可行性和前景，并努力为重建时空领域建立深度学习的替代模型。我们的结果表明，联合机器学习可能是设计与流体动力学相关的高度准确预测分散的数字双胞胎的可行工具。

在查询图像中检索与感兴趣的对象（OOI）在语义上相似的对象具有许多实际用例。一些示例包括修复失败，例如虚假的负面因素/阳性模型或减轻数据集中的类不平衡。有针对性的选择任务需要从大规模的未标记数据池中找到相关数据。在此规模上进行手动开采是不可行的。此外，OOI通常很小，占据图像区域的1％不到1％，被遮挡，并且在混乱的场景中与许多语义上不同的物体共存。现有的语义图像检索方法通常集中在较大尺寸的地理地标的采矿和/或需要额外的标记数据，例如带有相似对象的图像/图像对，用于带有通用对象的挖掘图像。我们在DNN功能空间中提出了一个匹配算法的快速稳固的模板，该模板从一个大的未标记数据池中检索了对象级的语义相似图像。我们将查询图像中OOI周围的区域投射到DNN功能空间以用作模板。这使我们的方法能够专注于OOI的语义，而无需额外的标记数据。在自主驾驶的背景下，我们通过将对象探测器的故障案例作为OOI评估我们的系统进行靶向选择。我们证明了其在具有2.2m图像的大型未标记数据集上的功效，并在采矿中显示出对具有小型OOI的图像的高回忆。我们将我们的方法与众所周知的语义图像检索方法进行比较，该方法也不需要额外的标记数据。最后，我们证明我们的方法是灵活的，并以一种或多种语义上不同的同时发生的OOI无缝地检索图像。

我们旨在定量衡量医学图像分割模型的实际可用性：可以使用/信任模型的预测在多大程度上，多久和在哪些样品上进行样本。我们首先提出了一个度量，正确的信心等级相关性（CCRC），以捕获预测的置信度估计如何与其正确性分数相关。具有高价值CCRC的模型意味着其预测信心可靠地表明，哪些样本的预测更可能是正确的。由于CCRC没有捕获实际的预测正确性，因此仅仅指示预测模型是否既准确又可靠地用于实践中。因此，我们进一步提出了另一种可用区域估计（URE）的方法，同时量化了预测在一个估计中的置信度评估的正确性和可靠性。 URE提供了有关模型的预测在多大程度上可用的具体信息。此外，可以利用可用区域（UR）的大小来比较模型：具有较大UR的模型可以作为更可用的模型，因此可以将其视为更好的模型。六个数据集的实验验证了所提出的评估方法表现良好，为医学图像分割模型的实际可用性提供了具体和简洁的措施。代码可在https://github.com/yizhezhang2000/ure上提供。