我们通过补充每个图像的弱点将内扫描（iOS）和牙科锥形电脑层析术（CBCT）图像集成到一个图像中的完全自动化方法。单独的牙科CBCT可能无法通过有限的图像分辨率和各种CBCT伪像（包括金属诱导的伪像）来描绘牙齿表面的精确细节。 iOS非常准确地扫描窄区域，但它在全拱扫描过程中产生累积缝合误差。该方法不仅要补偿具有iOS的CBCT衍生的牙齿表面的低质量，而且还要校正整个牙弓的IOS的累积拼接误差。此外，整合提供了一种图像中CBCT的IOS和齿根的牙龈结构。所提出的全自动方法包括四个部分; （i）iOS数据（TSIM-iOS）的单个牙齿分割和识别模块; （ii）CBCT数据（TSIM-CBCT）的个体齿分割和识别模块; （iii）IOS和CBCT之间的全球到局部牙齿登记; （iv）全拱ios的缝合纠错。实验结果表明，该方法分别达到了0.11mm和0.30mm的地标和表面距离误差。

由于锥形光束计算机断层扫描（CBCT）图像的三维（3D）单个齿的准确和自动分割是一个具有挑战性的问题，因为难以将个体齿与相邻齿和周围的肺泡骨分开。因此，本文提出了一种从牙科CBCT图像识别和分割3D个体齿的全自动方法。所提出的方法通过开发基于深度学习的分层多步模型来解决上述难度。首先，它自动生成上下钳口全景图像，以克服由高维数据和与有限训练数据集相关的维度的诅咒引起的计算复杂度。然后使用所获得的2D全景图像来识别2D单独的牙齿并捕获3D个体齿的兴趣和紧密区域（ROIS）。最后，使用松动和紧密的ROI实现了精确的3D个体齿分割。实验结果表明，牙齿识别的牙齿识别的F1分数为93.35％，对于各个3D齿分割，骰子相似度系数为94.79％。结果表明，该方法为数字牙科提供了有效的临床和实用框架。

标记大量数据很昂贵。主动学习旨在通过要求注释未标记的集合中最有用的数据来解决这个问题。我们提出了一种新颖的活跃学习方法，该方法利用自我监督的借口任务和独特的数据采样器来选择既困难又具有代表性的数据。我们发现，简单的自我监督借口任务（例如旋转预测）的损失与下游任务损失密切相关。在主动学习迭代之前，对未标记的集合进行了借口任务学习者进行培训，并且未标记的数据被分类并通过其借口任务损失分组成批处理。在每个主动的学习迭代中，主要任务模型用于批评要注释的批次中最不确定的数据。我们评估了有关各种图像分类和分割基准测试的方法，并在CIFAR10，CALTECH-101，IMAGENET和CITYSCAPES上实现引人注目的性能。我们进一步表明，我们的方法在不平衡的数据集上表现良好，并且可以有效地解决冷启动问题的解决方案，在这种问题中，主动学习性能受到随机采样的初始标记集的影响。

由于新兴的深度神经网络（DNN）模型的规模继续增大，使用大型GPU集群培训DNN是实现可接受培训时间的基本要求。在本文中，我们考虑了集群大小的未来增加的情况将导致全局批量大小用于培训模型以达到基本限制：超出某个点，更大的全球批量尺寸会导致样品效率降低，总体上升准确性的时间。因此，为了实现培训性能的进一步改进，我们必须考虑“强大的缩放”策略，该策略保持全局批量大小常量，并将较小的批次分配给每个GPU。不幸的是，这使得能够有效地使用群集资源。我们呈现DeepPool，通过两个关键思想解决这种效率挑战的系统。首先，突发并行性将大量GPU分配给突发中的前景作业，以利用整个层的并行性的不均匀性。其次，GPU多路复用优先考虑前台培训工作的吞吐量，而背景培训作业包装以回收未充分利用的GPU资源，从而提高集群范围利用率。这两个想法在一起使DeepPool能够在群集刻度大的单一任务中通过标准数据并行度进行2.2 - 2.4倍的完整性。

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJJLST20, ACJJS21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^d$, we obtain the following results (up to polylogarithmic factors). We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text{opt}} \in [d^{-1}, d^{-1/4}]$, our algorithm matches the state-of-the-art oracle depth of [BJLLS19] while maintaining the optimal total work of stochastic gradient descent. We give an $(\epsilon_{\text{dp}}, \delta)$-differentially private algorithm which, given $n$ samples of Lipschitz loss functions, obtains near-optimal optimization error and makes $\min(n, n^2\epsilon_{\text{dp}}^2 d^{-1}) + \min(n^{4/3}\epsilon_{\text{dp}}^{1/3}, (nd)^{2/3}\epsilon_{\text{dp}}^{-1})$ queries to the gradients of these functions. In the regime $d \le n \epsilon_{\text{dp}}^{2}$, where privacy comes at no cost in terms of the optimal loss up to constants, our algorithm uses $n + (nd)^{2/3}\epsilon_{\text{dp}}^{-1}$ queries and improves recent advancements of [KLL21, AFKT21]. In the moderately low-dimensional setting $d \le \sqrt n \epsilon_{\text{dp}}^{3/2}$, our query complexity is near-linear.

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.

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/ .

In this paper, we develop an efficient multi-scale network to predict action classes in partial videos in an end-to-end manner. Unlike most existing methods with offline feature generation, our method directly takes frames as input and further models motion evolution on two different temporal scales.Therefore, we solve the complexity problems of the two stages of modeling and the problem of insufficient temporal and spatial information of a single scale. Our proposed End-to-End MultiScale Network (E2EMSNet) is composed of two scales which are named segment scale and observed global scale. The segment scale leverages temporal difference over consecutive frames for finer motion patterns by supplying 2D convolutions. For observed global scale, a Long Short-Term Memory (LSTM) is incorporated to capture motion features of observed frames. Our model provides a simple and efficient modeling framework with a small computational cost. Our E2EMSNet is evaluated on three challenging datasets: BIT, HMDB51, and UCF101. The extensive experiments demonstrate the effectiveness of our method for action prediction in videos.

Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior policy. In some cases, there may be unmeasured variables that can confound the action-reward or action-next-state relationships, rendering many existing OPE approaches ineffective. This paper develops an instrumental variable (IV)-based method for consistent OPE in confounded Markov decision processes (MDPs). Similar to single-stage decision making, we show that IV enables us to correctly identify the target policy's value in infinite horizon settings as well. Furthermore, we propose an efficient and robust value estimator and illustrate its effectiveness through extensive simulations and analysis of real data from a world-leading short-video platform.