肺气道树建模对于诊断肺部疾病的诊断至关重要，尤其是对于X射线计算机断层扫描（CT）。 CT图像上的气道树建模可以为专家提供3维测量，例如壁厚等。此信息可以极大地帮助诊断诸如慢性阻塞性肺疾病等肺部疾病[1-4]。许多学者尝试了各种方法来建模肺气道树，可以根据其性质将其分为两个主要类别。也就是说，基于模型的方法和深度学习方法。基于典型模型的方法的性能通常取决于模型参数的手动调整，这可能是其优点和缺点。优势是它不需要大量的培训数据，这可能对像医学成像这样的小数据集有益。另一方面，基于模型的性能可能是错误的[5,6]。近年来，深度学习在医学图像处理领域取得了良好的结果，许多学者在医学图像分割中使用了基于UNET的方法[7-11]。在UNET的所有变化中，UNET 3+ [11]具有相对较好的结果，与UNET的其余部分相比。因此，为了进一步提高肺气道建模的准确性，本研究将Frangi滤波器[5]与UNET 3+ [11]结合在一起，以开发双通道3D UNET 3+。 Frangi过滤器用于提取类似容器的特征。然后，类似容器的功能用作指导双通道UNET 3+训练和测试程序的输入。

This paper introduces a learned hierarchical B-frame coding scheme in response to the Grand Challenge on Neural Network-based Video Coding at ISCAS 2023. We address specifically three issues, including (1) B-frame coding, (2) YUV 4:2:0 coding, and (3) content-adaptive variable-rate coding with only one single model. Most learned video codecs operate internally in the RGB domain for P-frame coding. B-frame coding for YUV 4:2:0 content is largely under-explored. In addition, while there have been prior works on variable-rate coding with conditional convolution, most of them fail to consider the content information. We build our scheme on conditional augmented normalized flows (CANF). It features conditional motion and inter-frame codecs for efficient B-frame coding. To cope with YUV 4:2:0 content, two conditional inter-frame codecs are used to process the Y and UV components separately, with the coding of the UV components conditioned additionally on the Y component. Moreover, we introduce adaptive feature modulation in every convolutional layer, taking into account both the content information and the coding levels of B-frames to achieve content-adaptive variable-rate coding. Experimental results show that our model outperforms x265 and the winner of last year's challenge on commonly used datasets in terms of PSNR-YUV.

Recently, e-scooter-involved crashes have increased significantly but little information is available about the behaviors of on-road e-scooter riders. Most existing e-scooter crash research was based on retrospectively descriptive media reports, emergency room patient records, and crash reports. This paper presents a naturalistic driving study with a focus on e-scooter and vehicle encounters. The goal is to quantitatively measure the behaviors of e-scooter riders in different encounters to help facilitate crash scenario modeling, baseline behavior modeling, and the potential future development of in-vehicle mitigation algorithms. The data was collected using an instrumented vehicle and an e-scooter rider wearable system, respectively. A three-step data analysis process is developed. First, semi-automatic data labeling extracts e-scooter rider images and non-rider human images in similar environments to train an e-scooter-rider classifier. Then, a multi-step scene reconstruction pipeline generates vehicle and e-scooter trajectories in all encounters. The final step is to model e-scooter rider behaviors and e-scooter-vehicle encounter scenarios. A total of 500 vehicle to e-scooter interactions are analyzed. The variables pertaining to the same are also discussed in this paper.

As one of the most popular micro-mobility options, e-scooters are spreading in hundreds of big cities and college towns in the US and worldwide. In the meantime, e-scooters are also posing new challenges to traffic safety. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than the pedestrains and bicyclists. These features make e-scooters challenging for human drivers, pedestrians, vehicle active safety modules, and self-driving modules to see and interact. To study this new mobility option and address e-scooter riders' and other road users' safety concerns, this paper proposes a wearable data collection system for investigating the micro-level e-Scooter motion behavior in a Naturalistic road environment. An e-Scooter-based data acquisition system has been developed by integrating LiDAR, cameras, and GPS using the robot operating system (ROS). Software frameworks are developed to support hardware interfaces, sensor operation, sensor synchronization, and data saving. The integrated system can collect data continuously for hours, meeting all the requirements including calibration accuracy and capability of collecting the vehicle and e-Scooter encountering data.

In this paper, we propose SceNDD: a scenario-based naturalistic driving dataset that is built upon data collected from an instrumented vehicle in downtown Indianapolis. The data collection was completed in 68 driving sessions with different drivers, where each session lasted about 20--40 minutes. The main goal of creating this dataset is to provide the research community with real driving scenarios that have diverse trajectories and driving behaviors. The dataset contains ego-vehicle's waypoints, velocity, yaw angle, as well as non-ego actor's waypoints, velocity, yaw angle, entry-time, and exit-time. Certain flexibility is provided to users so that actors, sensors, lanes, roads, and obstacles can be added to the existing scenarios. We used a Joint Probabilistic Data Association (JPDA) tracker to detect non-ego vehicles on the road. We present some preliminary results of the proposed dataset and a few applications associated with it. The complete dataset is expected to be released by early 2023.

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.

As the demand for user privacy grows, controlled data removal (machine unlearning) is becoming an important feature of machine learning models for data-sensitive Web applications such as social networks and recommender systems. Nevertheless, at this point it is still largely unknown how to perform efficient machine unlearning of graph neural networks (GNNs); this is especially the case when the number of training samples is small, in which case unlearning can seriously compromise the performance of the model. To address this issue, we initiate the study of unlearning the Graph Scattering Transform (GST), a mathematical framework that is efficient, provably stable under feature or graph topology perturbations, and offers graph classification performance comparable to that of GNNs. Our main contribution is the first known nonlinear approximate graph unlearning method based on GSTs. Our second contribution is a theoretical analysis of the computational complexity of the proposed unlearning mechanism, which is hard to replicate for deep neural networks. Our third contribution are extensive simulation results which show that, compared to complete retraining of GNNs after each removal request, the new GST-based approach offers, on average, a $10.38$x speed-up and leads to a $2.6$% increase in test accuracy during unlearning of $90$ out of $100$ training graphs from the IMDB dataset ($10$% training ratio).

随着机器学习的应用程序价值的增加，深神经网络（DNN）的知识产权（IP）权利正在越来越关注。通过我们的分析，大多数现有的DNN水印方法都可以抵抗微调和修剪攻击，但蒸馏攻击。为了解决这些问题，我们提出了一个新的DNN水印框架，统一的软标签扰动（USP），与探测器与要水印的模型配对，并定制了软标签扰动（CSP），通过将watermark嵌入WaterMark，将摄入量嵌入到水中模型输出概率分布。实验结果表明，我们的方法可以抵抗所有水印去除攻击，并且在蒸馏攻击中表现跑赢大盘。此外，我们在主要任务和水印之间还取决了出色的权衡，达到98.68％的水印准确性，而仅影响主要任务准确性0.59％。

培训和评估语言模型越来越多地要求构建元数据 - 多样化的策划数据收集，并具有清晰的出处。自然语言提示最近通过将现有的，有监督的数据集转换为多种新颖的预处理任务，突出了元数据策划的好处，从而改善了零击的概括。尽管将这些以数据为中心的方法转化为生物医学语言建模的通用域文本成功，但由于标记的生物医学数据集在流行的数据中心中的代表性大大不足，因此仍然具有挑战性。为了应对这一挑战，我们介绍了BigBio一个由126个以上的生物医学NLP数据集的社区库，目前涵盖12个任务类别和10多种语言。 BigBio通过对数据集及其元数据进行程序化访问来促进可再现的元数据策划，并与当前的平台兼容，以及时工程和端到端的几个/零射击语言模型评估。我们讨论了我们的任务架构协调，数据审核，贡献指南的过程，并概述了两个说明性用例：生物医学提示和大规模，多任务学习的零射门评估。 BigBio是一项持续的社区努力，可在https://github.com/bigscience-workshop/biomedical上获得。

图形结构化数据在实践中无处不在，并且经常使用图神经网络（GNN）处理。随着最近的法律确保``被遗忘的权利''的法律，删除图数据的问题已变得非常重要。为了解决该问题，我们介绍了GNNS的\ emph {认证图形}的第一个已知框架。与标准机器学习相反，在处理复杂的图形数据时，出现了新的分析和启发式学位挑战。首先，需要考虑三种不同类型的未学习请求，包括节点功能，边缘和节点学习。其次，为了建立可证明的绩效保证，需要解决与传播过程中功能混合相关的挑战。简单的图卷积（SGC）及其广泛的Pagerank（GPR）扩展的示例说明了基本分析，从而为GNN的认证未学习奠定了理论基础。我们对六个基准数据集的实证研究表明，与不利用图形信息的完整再培训方法和方法相比，相比之下，表现出色的性能复杂性权衡。例如，当在CORA数据集上学习$ 20 \％$的节点时，我们的方法仅遭受$ 0.1 \％$ $的测试准确性损失，而与完整的再培训相比，提供了$ 4 $倍的加速。我们的方案还胜过未利用图形信息的学习方法，其测试准确性提高了$ 12 \％$，以相当的时间复杂性。