最近，出于手术目的，基于视频的应用程序的发展不断增长。这些应用程序的一部分可以在程序结束后离线工作，其他应用程序必须立即做出反应。但是，在某些情况下，应在过程中进行响应，但可以接受一些延迟。在文献中，已知在线访问性能差距。我们在这项研究中的目标是学习绩效 - 延迟权衡并设计一种基于MS-TCN ++的算法，该算法可以利用这种权衡。为此，我们使用了开放手术模拟数据集，其中包含96个参与者的视频，这些视频在可变的组织模拟器上执行缝合任务。在这项研究中，我们使用了从侧视图捕获的视频数据。对网络进行了训练，以识别执行的手术手势。幼稚的方法是减少MS-TCN ++深度，结果减少了接受场，并且还减少了所需的未来帧数。我们表明该方法是最佳的，主要是在小延迟情况下。第二种方法是限制每个时间卷积中可访问的未来。这样，我们在网络设计方面具有灵活性，因此，与幼稚的方法相比，我们的性能要好得多。

目的：这项工作的目标是使用多摄像机视频来分类开放式手术工具，并确定每只手中的哪个工具。多摄像机系统有助于防止在开放的外科视频数据中闭塞。此外，组合多个视图，例如覆盖完整操作场的俯视摄像机和聚焦在手动运动和解剖结构上的特写相机，可以提供更全面的手术工作流程。然而，多摄像机数据融合构成了新的挑战：一个工具可以在一个相机中可见，而不是另一个。因此，我们将全球原始事实定义为使用的工具，无论他们的可见性如何。因此，在系统中应在广泛的时间段内记住超出图像的工具，而系统响应在视频中可见的变化。方法：参与者（n = 48）进行了模拟开放肠道修复。使用顶视图和特写摄像头。 YOLOV5用于工具和手动检测。具有每秒30帧（FPS）的1秒窗口的高频LSTM和3个FPS的40秒窗口的低频LSTM用于空间，时间和多摄像头集成。结果：六个系统的精度和F1是：俯视图（0.88 / 0.88），特写（0.81,0.83），摄像机（0.9 / 0.9），高FPS LSTM（0.92 / 0.93），低FPS LSTM （0.9 / 0.91），我们的最终体系结构多相机分类器（0.93 / 0.94）。结论：通过将具有高FP的系统与多个摄像机阵列的低FPS组合，我们提高了全球地面真理的分类能力。

本研究的目标是开发新的可靠开放式手术缝合培训医学院的仿真系统，以便在资源有限或国内设置。即，我们开发了一种工具和手本地化的算法，以及根据简单的网络摄像头视频数据，计算出用于评估外科技能的运动指标。二十五位参与者使用我们的模拟器执行多个缝合任务。 yolo网络已被修改为多任务网络，以便工具本地化和工具手动交互检测。这是通过分割YOLO检测头来实现的，使得它们支持两项任务，以对计算机运行时间最小的添加。此外，基于系统的结果，计算了运动指标。这些指标包括传统的指标，如时间和路径长度以及评估技术参与者使用的新度量来控制工具。双重任务网络性能与两个网络的性能类似，而计算负载仅略大于一个网络。此外，运动指标显示专家和新手之间的显着差异。虽然视频捕获是微创手术的重要组成部分，但它不是开放手术的整体组成部分。因此，需要新的算法，重点关注当前的独特挑战，是开放的手术视频存在。在本研究中，开发了一种双任务网络来解决本地化任务和手动工具交互任务。双网络可以很容易地扩展到多任务网络，这可能对具有多个层的图像有用，并且用于评估这些不同层之间的交互。

A Digital Twin (DT) is a simulation of a physical system that provides information to make decisions that add economic, social or commercial value. The behaviour of a physical system changes over time, a DT must therefore be continually updated with data from the physical systems to reflect its changing behaviour. For resource-constrained systems, updating a DT is non-trivial because of challenges such as on-board learning and the off-board data transfer. This paper presents a framework for updating data-driven DTs of resource-constrained systems geared towards system health monitoring. The proposed solution consists of: (1) an on-board system running a light-weight DT allowing the prioritisation and parsimonious transfer of data generated by the physical system; and (2) off-board robust updating of the DT and detection of anomalous behaviours. Two case studies are considered using a production gas turbine engine system to demonstrate the digital representation accuracy for real-world, time-varying physical systems.

Deep neural networks (DNN) have outstanding performance in various applications. Despite numerous efforts of the research community, out-of-distribution (OOD) samples remain significant limitation of DNN classifiers. The ability to identify previously unseen inputs as novel is crucial in safety-critical applications such as self-driving cars, unmanned aerial vehicles and robots. Existing approaches to detect OOD samples treat a DNN as a black box and assess the confidence score of the output predictions. Unfortunately, this method frequently fails, because DNN are not trained to reduce their confidence for OOD inputs. In this work, we introduce a novel method for OOD detection. Our method is motivated by theoretical analysis of neuron activation patterns (NAP) in ReLU based architectures. The proposed method does not introduce high computational workload due to the binary representation of the activation patterns extracted from convolutional layers. The extensive empirical evaluation proves its high performance on various DNN architectures and seven image datasets. ion.

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.

Graph Neural Networks (GNNs) have shown great potential in the field of graph representation learning. Standard GNNs define a local message-passing mechanism which propagates information over the whole graph domain by stacking multiple layers. This paradigm suffers from two major limitations, over-squashing and poor long-range dependencies, that can be solved using global attention but significantly increases the computational cost to quadratic complexity. In this work, we propose an alternative approach to overcome these structural limitations by leveraging the ViT/MLP-Mixer architectures introduced in computer vision. We introduce a new class of GNNs, called Graph MLP-Mixer, that holds three key properties. First, they capture long-range dependency and mitigate the issue of over-squashing as demonstrated on the Long Range Graph Benchmark (LRGB) and the TreeNeighbourMatch datasets. Second, they offer better speed and memory efficiency with a complexity linear to the number of nodes and edges, surpassing the related Graph Transformer and expressive GNN models. Third, they show high expressivity in terms of graph isomorphism as they can distinguish at least 3-WL non-isomorphic graphs. We test our architecture on 4 simulated datasets and 7 real-world benchmarks, and show highly competitive results on all of them.

In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks. Such challenges can be potentially overcome by integrating communication, computing, caching, and control (i4C) technologies. In this survey, we first give a snapshot of different aspects of the i4C, comprising background, motivation, leading technological enablers, potential applications, and use cases. Next, we describe different models of communication, computing, caching, and control (4C) to lay the foundation of the integration approach. We review current state-of-the-art research efforts related to the i4C, focusing on recent trends of both conventional and artificial intelligence (AI)-based integration approaches. We also highlight the need for intelligence in resources integration. Then, we discuss integration of sensing and communication (ISAC) and classify the integration approaches into various classes. Finally, we propose open challenges and present future research directions for beyond 5G networks, such as 6G.

In the recent years, various gradient descent algorithms including the methods of gradient descent, gradient descent with momentum, adaptive gradient (AdaGrad), root-mean-square propagation (RMSProp) and adaptive moment estimation (Adam) have been applied to the parameter optimization of several deep learning models with higher accuracies or lower errors. These optimization algorithms may need to set the values of several hyperparameters which include a learning rate, momentum coefficients, etc. Furthermore, the convergence speed and solution accuracy may be influenced by the values of hyperparameters. Therefore, this study proposes an analytical framework to use mathematical models for analyzing the mean error of each objective function based on various gradient descent algorithms. Moreover, the suitable value of each hyperparameter could be determined by minimizing the mean error. The principles of hyperparameter value setting have been generalized based on analysis results for model optimization. The experimental results show that higher efficiency convergences and lower errors can be obtained by the proposed method.

Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples. Novelty manifests in HAR as unseen samples, activities, objects, environments, and sensor changes, among other ways. Novelty may be task-relevant, such as a new class or new features, or task-irrelevant resulting in nuisance novelty, such as never before seen noise, blur, or distorted video recordings. To perform HAR optimally, algorithmic solutions must be tolerant to nuisance novelty, and learn over time in the face of novelty. This paper 1) formalizes the definition of novelty in HAR building upon the prior definition of novelty in classification tasks, 2) proposes an incremental open world learning (OWL) protocol and applies it to the Kinetics datasets to generate a new benchmark KOWL-718, 3) analyzes the performance of current state-of-the-art HAR models when novelty is introduced over time, 4) provides a containerized and packaged pipeline for reproducing the OWL protocol and for modifying for any future updates to Kinetics. The experimental analysis includes an ablation study of how the different models perform under various conditions as annotated by Kinetics-AVA. The protocol as an algorithm for reproducing experiments using the KOWL-718 benchmark will be publicly released with code and containers at https://github.com/prijatelj/human-activity-recognition-in-an-open-world. The code may be used to analyze different annotations and subsets of the Kinetics datasets in an incremental open world fashion, as well as be extended as further updates to Kinetics are released.