人们认为，深度学习模型以不可预测的和可能的灾难性方式失败，因此受到了监管AI的监管批准和广泛的临床部署。缺乏统计上严格的不确定性量化是破坏对AI结果的信任的重要因素。无分配不确定性量化的最新发展通过为任意数据分布的黑框模型提供可靠性保证，作为正式有效的有限样本预测间隔，为这些问题提供了实用解决方案。我们的工作将这些新的不确定性定量方法（特别是共形预测）应用于腰椎MRI中脊柱狭窄严重程度的深度学习模型。我们展示了一种用于形成顺序预测集的技术，该技术可以保证在用户定义的概率（置信区间）内包含正确的狭窄严重程度。在通过深度学习模型处理的409个MRI考试的数据集中，共形方法提供了较小的预测集尺寸的紧密覆盖范围。此外，我们通过量化明显成像异常的患病率的提高（例如，运动伪像，金属伪像和肿瘤）可以在预测性能中降低预测性能，从而探索具有高不确定性预测（大预测集）标记病例的潜在临床适用性。与随机病例样本相比。

尽管对临床机器学习研究有强烈的关注和相当大的投资，但在现实世界的临床环境中，在大规模的应用中已经部署了相对较少的应用。虽然研究在推进最先进的情况下很重要，但翻译同样重要的是，使这些技术和技术能够最终影响医疗保健。我们认为对几个考虑缺乏升值是在期望和现实之间这种差异的主要原因。为了更好地描述研究人员和从业者之间的整体视角，我们调查了几个从业人员在开发CML中进行临床部署的商业经验。使用这些洞察力，我们确定了几个主要类别的挑战，以便更好地设计和开发临床机学习应用。

With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring, and an accompanying growth in artificial intelligence applications to automatically derive forest properties of interest from these datasets. Many studies use their own data at small spatio-temporal scales, and demonstrate an application of an existing or adapted data science method for a particular task. This approach often involves intensive and time-consuming data collection and processing, but generates results restricted to specific ecosystems and sensor types. There is a lack of widespread acknowledgement of how the types and structures of data used affects performance and accuracy of analysis algorithms. To accelerate progress in the field more efficiently, benchmarking datasets upon which methods can be tested and compared are sorely needed. Here, we discuss how lack of standardisation impacts confidence in estimation of key forest properties, and how considerations of data collection need to be accounted for in assessing method performance. We present pragmatic requirements and considerations for the creation of rigorous, useful benchmarking datasets for forest monitoring applications, and discuss how tools from modern data science can improve use of existing data. We list a set of example large-scale datasets that could contribute to benchmarking, and present a vision for how community-driven, representative benchmarking initiatives could benefit the field.

National research evaluation initiatives and incentive schemes have previously chosen between simplistic quantitative indicators and time-consuming peer review, sometimes supported by bibliometrics. Here we assess whether artificial intelligence (AI) could provide a third alternative, estimating article quality using more multiple bibliometric and metadata inputs. We investigated this using provisional three-level REF2021 peer review scores for 84,966 articles submitted to the UK Research Excellence Framework 2021, matching a Scopus record 2014-18 and with a substantial abstract. We found that accuracy is highest in the medical and physical sciences Units of Assessment (UoAs) and economics, reaching 42% above the baseline (72% overall) in the best case. This is based on 1000 bibliometric inputs and half of the articles used for training in each UoA. Prediction accuracies above the baseline for the social science, mathematics, engineering, arts, and humanities UoAs were much lower or close to zero. The Random Forest Classifier (standard or ordinal) and Extreme Gradient Boosting Classifier algorithms performed best from the 32 tested. Accuracy was lower if UoAs were merged or replaced by Scopus broad categories. We increased accuracy with an active learning strategy and by selecting articles with higher prediction probabilities, as estimated by the algorithms, but this substantially reduced the number of scores predicted.

In medical image analysis, automated segmentation of multi-component anatomical structures, which often have a spectrum of potential anomalies and pathologies, is a challenging task. In this work, we develop a multi-step approach using U-Net-based neural networks to initially detect anomalies (bone marrow lesions, bone cysts) in the distal femur, proximal tibia and patella from 3D magnetic resonance (MR) images of the knee in individuals with varying grades of osteoarthritis. Subsequently, the extracted data are used for downstream tasks involving semantic segmentation of individual bone and cartilage volumes as well as bone anomalies. For anomaly detection, the U-Net-based models were developed to reconstruct the bone profiles of the femur and tibia in images via inpainting so anomalous bone regions could be replaced with close to normal appearances. The reconstruction error was used to detect bone anomalies. A second anomaly-aware network, which was compared to anomaly-na\"ive segmentation networks, was used to provide a final automated segmentation of the femoral, tibial and patellar bones and cartilages from the knee MR images containing a spectrum of bone anomalies. The anomaly-aware segmentation approach provided up to 58% reduction in Hausdorff distances for bone segmentations compared to the results from the anomaly-na\"ive segmentation networks. In addition, the anomaly-aware networks were able to detect bone lesions in the MR images with greater sensitivity and specificity (area under the receiver operating characteristic curve [AUC] up to 0.896) compared to the anomaly-na\"ive segmentation networks (AUC up to 0.874).

Generalizability of time series forecasting models depends on the quality of model selection. Temporal cross validation (TCV) is a standard technique to perform model selection in forecasting tasks. TCV sequentially partitions the training time series into train and validation windows, and performs hyperparameter optmization (HPO) of the forecast model to select the model with the best validation performance. Model selection with TCV often leads to poor test performance when the test data distribution differs from that of the validation data. We propose a novel model selection method, H-Pro that exploits the data hierarchy often associated with a time series dataset. Generally, the aggregated data at the higher levels of the hierarchy show better predictability and more consistency compared to the bottom-level data which is more sparse and (sometimes) intermittent. H-Pro performs the HPO of the lowest-level student model based on the test proxy forecasts obtained from a set of teacher models at higher levels in the hierarchy. The consistency of the teachers' proxy forecasts help select better student models at the lowest-level. We perform extensive empirical studies on multiple datasets to validate the efficacy of the proposed method. H-Pro along with off-the-shelf forecasting models outperform existing state-of-the-art forecasting methods including the winning models of the M5 point-forecasting competition.

Each student matters, but it is hardly for instructors to observe all the students during the courses and provide helps to the needed ones immediately. In this paper, we present StuArt, a novel automatic system designed for the individualized classroom observation, which empowers instructors to concern the learning status of each student. StuArt can recognize five representative student behaviors (hand-raising, standing, sleeping, yawning, and smiling) that are highly related to the engagement and track their variation trends during the course. To protect the privacy of students, all the variation trends are indexed by the seat numbers without any personal identification information. Furthermore, StuArt adopts various user-friendly visualization designs to help instructors quickly understand the individual and whole learning status. Experimental results on real classroom videos have demonstrated the superiority and robustness of the embedded algorithms. We expect our system promoting the development of large-scale individualized guidance of students.

在医学图像分析中，许多疾病的微妙视觉特征要具有挑战性，尤其是由于缺乏配对数据。例如，在温和的阿尔茨海默氏病（AD）中，很难从纯成像数据中观察到脑组织萎缩，尤其是没有配对的AD和认知正常（CN）数据以进行比较。这项工作介绍了疾病发现甘（Didigan），这是一种基于弱的基于风格的框架，可发现和可视化细微的疾病特征。 Didigan了解了AD和CN视觉特征的疾病歧管，并将此歧管采样的样式代码施加到解剖结构“蓝图”上，以综合配对AD和CN磁共振图像（MRIS）。为了抑制生成的AD和CN对之间的非疾病相关变化，Didigan利用具有循环一致性和抗偏置的结构约束来实施解剖对应关系。当对阿尔茨海默氏病神经影像学计划（ADNI）数据集进行测试时，Didigan通过合成的配对AD和CN扫描显示了关键的AD特征（减少海马体积，心室增大和皮质结构的萎缩）。定性结果通过自动化的大脑体积分析来支持，其中还测量了脑组织结构的系统成对降低

近年来，深度学习导致了在城市驾驶场景中移动（即具有运动能力）物体的检测方面取得的巨大进展。监督方法通常需要大型培训集的注释；因此，人们对利用弱，半或自我监督的方法避免这种情况非常兴趣，并取得了很大的成功。虽然弱和半监督的方法需要一些注释，但自我监督的方法已经使用了诸如运动之类的线索来完全减轻注释的需求。但是，完全没有注释通常会降低其性能，而在运动组进行分组期间出现的歧义可以抑制其找到准确的物体边界的能力。在本文中，我们提出了一种称为SCT的新的自制移动对象检测方法。这同时使用运动提示和预期对象大小来提高检测性能，并预测3D方向边界框的密集网格以改善对象发现。我们在Kitti跟踪基准上的最先进的自我监督的移动对象检测方法TCR极大地超过了，并且实现了全面监督的PV-RCNN ++方法的30％以内IOUS <= 0.5。

FP8是加速深度学习训练推论以外的16位格式的自然发展。在本文中，我们提出了一个8位浮点（FP8）二进制互换格式，该格式由两个编码组成-E4M3（4位指数和3位Mantissa）和E5M2（5位指数和2位指数和2位Mantissa）。尽管E5M2遵循IEEE 754惯例代表特殊值的惯例，但E4M3的动态范围是通过不代表无限态，只有一个Mantissa Bit-Pattern来扩展NAN。我们证明了FP8格式对各种图像和语言任务的功效，从而有效地匹配了16位培训课程所达到的质量。我们的研究涵盖了主要的现代神经网络体系结构 - CNN，RNN和基于变压器的模型，使所有超参数与16位基线训练课程保持不变。我们的培训实验包括大型，最多175b参数，语言模型。我们还检查了使用16位格式训练的语言模型的FP8训练后定量化，该格式抗拒固定点INT8量化。