对计算的需求仍在呈指数增长。这种增长将转化为计算能源消耗的指数增长，除非其能源效率的提高可以超过其需求增加。然而，经过数十年的研究，由于已经进行了高度优化，因此进一步提高能源效率变得越来越具有挑战性。结果，在某个时候，计算需求的增加可能会超过其能源效率的增加，这可能会大大增加。这种指数增长（如果不受组织）将把计算定位为全球碳排放的重要贡献者。尽管著名的技术公司已经意识到了这一问题并试图减少其碳排放，但可以理解的是，他们的成功是可以无意间传达出现在或很快就会解决问题的错误印象的潜力。如果这种错误的印象有助于阻止在这一领域进行进一步研究，因为我们讨论了消除计算机，而且更普遍地社会的碳排放远非解决问题。为了更好地理解问题的范围，本文提炼了决定计算的碳足迹及其对实现可持续计算的影响的基本趋势。

Common measures of brain functional connectivity (FC) including covariance and correlation matrices are semi-positive definite (SPD) matrices residing on a cone-shape Riemannian manifold. Despite its remarkable success for Euclidean-valued data generation, use of standard generative adversarial networks (GANs) to generate manifold-valued FC data neglects its inherent SPD structure and hence the inter-relatedness of edges in real FC. We propose a novel graph-regularized manifold-aware conditional Wasserstein GAN (GR-SPD-GAN) for FC data generation on the SPD manifold that can preserve the global FC structure. Specifically, we optimize a generalized Wasserstein distance between the real and generated SPD data under an adversarial training, conditioned on the class labels. The resulting generator can synthesize new SPD-valued FC matrices associated with different classes of brain networks, e.g., brain disorder or healthy control. Furthermore, we introduce additional population graph-based regularization terms on both the SPD manifold and its tangent space to encourage the generator to respect the inter-subject similarity of FC patterns in the real data. This also helps in avoiding mode collapse and produces more stable GAN training. Evaluated on resting-state functional magnetic resonance imaging (fMRI) data of major depressive disorder (MDD), qualitative and quantitative results show that the proposed GR-SPD-GAN clearly outperforms several state-of-the-art GANs in generating more realistic fMRI-based FC samples. When applied to FC data augmentation for MDD identification, classification models trained on augmented data generated by our approach achieved the largest margin of improvement in classification accuracy among the competing GANs over baselines without data augmentation.

Quantitative cephalometric analysis is the most widely used clinical and research tool in modern orthodontics. Accurate localization of cephalometric landmarks enables the quantification and classification of anatomical abnormalities, however, the traditional manual way of marking these landmarks is a very tedious job. Endeavours have constantly been made to develop automated cephalometric landmark detection systems but they are inadequate for orthodontic applications. The fundamental reason for this is that the amount of publicly available datasets as well as the images provided for training in these datasets are insufficient for an AI model to perform well. To facilitate the development of robust AI solutions for morphometric analysis, we organise the CEPHA29 Automatic Cephalometric Landmark Detection Challenge in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI 2023). In this context, we provide the largest known publicly available dataset, consisting of 1000 cephalometric X-ray images. We hope that our challenge will not only derive forward research and innovation in automatic cephalometric landmark identification but will also signal the beginning of a new era in the discipline.

Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control, monitor, and analyze software-based, "open", communication systems. Notably, DT platforms provide a sandbox in which to test artificial intelligence (AI) solutions for communication systems, potentially reducing the need to collect data and test algorithms in the field, i.e., on the physical twin (PT). A key challenge in the deployment of DT systems is to ensure that virtual control optimization, monitoring, and analysis at the DT are safe and reliable, avoiding incorrect decisions caused by "model exploitation". To address this challenge, this paper presents a general Bayesian framework with the aim of quantifying and accounting for model uncertainty at the DT that is caused by limitations in the amount and quality of data available at the DT from the PT. In the proposed framework, the DT builds a Bayesian model of the communication system, which is leveraged to enable core DT functionalities such as control via multi-agent reinforcement learning (MARL), monitoring of the PT for anomaly detection, prediction, data-collection optimization, and counterfactual analysis. To exemplify the application of the proposed framework, we specifically investigate a case-study system encompassing multiple sensing devices that report to a common receiver. Experimental results validate the effectiveness of the proposed Bayesian framework as compared to standard frequentist model-based solutions.

Chromosome analysis is essential for diagnosing genetic disorders. For hematologic malignancies, identification of somatic clonal aberrations by karyotype analysis remains the standard of care. However, karyotyping is costly and time-consuming because of the largely manual process and the expertise required in identifying and annotating aberrations. Efforts to automate karyotype analysis to date fell short in aberration detection. Using a training set of ~10k patient specimens and ~50k karyograms from over 5 years from the Fred Hutchinson Cancer Center, we created a labeled set of images representing individual chromosomes. These individual chromosomes were used to train and assess deep learning models for classifying the 24 human chromosomes and identifying chromosomal aberrations. The top-accuracy models utilized the recently introduced Topological Vision Transformers (TopViTs) with 2-level-block-Toeplitz masking, to incorporate structural inductive bias. TopViT outperformed CNN (Inception) models with >99.3% accuracy for chromosome identification, and exhibited accuracies >99% for aberration detection in most aberrations. Notably, we were able to show high-quality performance even in "few shot" learning scenarios. Incorporating the definition of clonality substantially improved both precision and recall (sensitivity). When applied to "zero shot" scenarios, the model captured aberrations without training, with perfect precision at >50% recall. Together these results show that modern deep learning models can approach expert-level performance for chromosome aberration detection. To our knowledge, this is the first study demonstrating the downstream effectiveness of TopViTs. These results open up exciting opportunities for not only expediting patient results but providing a scalable technology for early screening of low-abundance chromosomal lesions.

最近在视觉跟踪中成功的关键因素之一是专用基准的可用性。尽管对跟踪研究有很大的受益，但现有的基准并没有与以前相同的难度，而最近的跟踪器的性能则主要是由于（i）引入了更复杂的基于变形金刚的方法，并且（ii）缺乏各种情况，因此缺乏各种情况。不良的可见性，例如恶劣的天气条件，伪装和成像效应。我们介绍了Avist，这是一个专门的基准，用于在具有不良可见性的不同情况下进行视觉跟踪。 Avist包括120个具有80k注释框架的具有挑战性的序列，涵盖了18种不同的方案，这些场景大致分为五个具有42个对象类别的属性。远景的主要贡献是涵盖恶劣天气条件的多样化和挑战性的情况，例如浓雾，大雨和沙尘暴；阻塞效应，包括火，阳光和溅水；不利成像效应，例如，低光；目标效应，包括小目标和干扰物对象以及伪装。我们进一步基准了17个关于Avist的流行和最新跟踪器，对它们跨属性的跟踪性能进行了详细分析，这表明了性能改善的巨大空间。我们认为，远景可以通过补充现有的基准，开发新的创意跟踪解决方案，以继续推动最先进的界限，从而极大地使跟踪社区受益。我们的数据集以及完整的跟踪性能评估可在以下网址提供：https：//github.com/visionml/pytracking

这项研究旨在使用人工智能（AI）和多视图图像实现更可靠的自动化后建筑物损害分类。当前的实践和研究工作在采用AI进行灾后损害评估的AI方面通常是（a）定性，基于标准损害量表缺乏建筑物损害水平的精制分类，并且（b）基于空中或卫星图像培训，具有有限的视图，视图有限，尽管有指示性，但并不完全描述损伤量表。为了使损伤水平的更准确和可靠的自动量化量化，本研究提出了以多种地面和建筑物的空中视图形式使用更全面的视觉数据。为了具有这样的空间感知的损害预测模型，使用了多视图卷积神经网络（MV-CNN）体系结构，结合了损坏建筑物不同视图的信息。这种空间3D上下文损害信息将导致更准确地识别损害和可靠的损害水平量化。拟议的模型经过训练和验证，并在侦察视觉数据集上进行了验证，其中包含飓风哈维后检查的建筑物的专家标签，地理标记的图像。开发的模型在预测损害水平方面表现出合理的准确性，可用于支持更加知识和可靠的AI-AI-AS辅助灾害管理实践。

推荐兴趣点是一个困难的问题，需要从基于位置的社交媒体平台中提取精确的位置信息。对于这种位置感知的推荐系统而言，另一个具有挑战性和关键的问题是根据用户的历史行为对用户的偏好进行建模。我们建议使用Transformers的双向编码器表示的位置感知建议系统，以便为用户提供基于位置的建议。提出的模型包含位置数据和用户偏好。与在序列中预测每个位置的下一项（位置）相比，我们的模型可以为用户提供更相关的结果。基准数据集上的广泛实验表明，我们的模型始终优于各种最新的顺序模型。

车祸（IOV）可以促进连接车辆（CV），自动驾驶汽车（AV）和其他IOV实体之间的无缝连通性。 IOV网络的入侵检测系统（IDS）可以依靠机器学习（ML）来保护车辆内网络免受网络攻击。基于区块链的联合森林（BFF）可用于根据IOV实体的数据训练ML模型，同时保护数据的机密性并降低对数据篡改的风险。但是，以这种方式创建的ML模型仍然容易受到逃避，中毒和探索性攻击的影响。本文研究了各种可能的对抗性示例对BFF-ID的影响。我们提出了整合统计检测器来检测和提取未知的对抗样品。通过将未知检测的样品包括在检测器的数据集中，我们使用附加模型来增强BFF-ID，以检测原始已知攻击和新的对抗性输入。统计对手检测器以50和100个输入样本的样本量确信对对抗性示例。此外，增强的BFF-IDS（BFF-IDS（AUG））成功地减轻了以上96％的精度。通过这种方法，每当检测到对抗样本并随后采用BFF-ID（AUG）作为主动安全模型时，该模型将继续在沙箱中增强。因此，统计对抗检测器的拟议集成以及随后使用检测到的对抗样本对BFF-ID的增强，为对抗性例子和其他未知攻击提供了可持续的安全框架。

推荐系统，信息检索和其他信息访问系统提出了在非结构化文本中检查和应用公平和偏见缓解概念的独特挑战。本文介绍了DBIAS，这是一个Python包，可确保新闻文章的公平性。DBIAS是一种受过训练的机器学习（ML）管道，可以使用文本（例如，段落或新闻故事），并检测文本是否有偏见。然后，它检测到文本中的有偏见的单词，掩盖它们，并推荐一组带有新单词的句子，这些句子是无偏见或至少偏见的句子。我们结合了数据科学最佳实践的要素，以确保该管道可再现和可用。我们在实验中表明，该管道可以有效缓解偏见，并优于确保新闻文章公平性的常见神经网络体系结构。