在过去的十年内，GPU硬件和深神经网络技术的突破已经彻底改变了计算机视野，使图像分析潜力可用于一系列现实应用。电子发现的技术辅助审查（Tar）虽然传统上占据了文本内容，目睹了在范围内纳入多媒体内容的不需要。我们在过去几年中为Tar开发了创新的图像分析应用，例如图像分类，图像聚类和对象检测等。在本文中，我们讨论了使用图像聚类应用程序的使用，以方便基于我们服务客户的经验。我们描述了我们在利用任务中利用图像聚类的一般工作流程，并使用实际项目中的统计信息来展示在焦油中使用图像聚类的有效性。我们还总结了在焦油中使用图像聚类的经验教训和最佳实践。

With increasing number of crowdsourced private automatic weather stations (called TPAWS) established to fill the gap of official network and obtain local weather information for various purposes, the data quality is a major concern in promoting their usage. Proper quality control and assessment are necessary to reach mutual agreement on the TPAWS observations. To derive near real-time assessment for operational system, we propose a simple, scalable and interpretable framework based on AI/Stats/ML models. The framework constructs separate models for individual data from official sources and then provides the final assessment by fusing the individual models. The performance of our proposed framework is evaluated by synthetic data and demonstrated by applying it to a re-al TPAWS network.

We apply Physics Informed Neural Networks (PINNs) to the problem of wildfire fire-front modelling. The PINN is an approach that integrates a differential equation into the optimisation loss function of a neural network to guide the neural network to learn the physics of a problem. We apply the PINN to the level-set equation, which is a Hamilton-Jacobi partial differential equation that models a fire-front with the zero-level set. This results in a PINN that simulates a fire-front as it propagates through a spatio-temporal domain. We demonstrate the agility of the PINN to learn physical properties of a fire under extreme changes in external conditions (such as wind) and show that this approach encourages continuity of the PINN's solution across time. Furthermore, we demonstrate how data assimilation and uncertainty quantification can be incorporated into the PINN in the wildfire context. This is significant contribution to wildfire modelling as the level-set method -- which is a standard solver to the level-set equation -- does not naturally provide this capability.

我们提出了一种新的方法，可以在复杂模型（例如贝叶斯神经网络）中执行近似贝叶斯推断。该方法比马尔可夫链蒙特卡洛更可扩展到大数据，它具有比变异推断更具表现力的模型，并且不依赖于对抗训练（或密度比估计）。我们采用了构建两个模型的最新方法：（1）一个主要模型，负责执行回归或分类； （2）一个辅助，表达的（例如隐式）模型，该模型定义了主模型参数上的近似后验分布。但是，我们根据后验预测分布的蒙特卡洛估计值通过梯度下降来优化后验模型的参数 - 这是我们唯一的近似值（除后模型除外）。只需要指定一个可能性，可以采用各种形式，例如损失功能和合成可能性，从而提供无可能的方法的形式。此外，我们制定了该方法，使后样品可以独立于或有条件地取决于主要模型的输入。后一种方法被证明能够增加主要模型的明显复杂性。我们认为这在诸如替代和基于物理的模型之类的应用中很有用。为了促进贝叶斯范式如何提供不仅仅是不确定性量化的方式，我们证明了：不确定性量化，多模式以及具有最新预测的神经网络体系结构的应用。

野火传播的计算模拟通常在各种条件下（例如地形，燃料类型，天气）采用经验分布计算。条件下的小扰动通常会导致火灾传播（例如速度和方向）的显着变化，因此需要进行计算昂贵的大型模拟以量化不确定性。模型仿真寻求使用机器学习的物理模型的替代表示，旨在提供更有效和/或简化的替代模型。我们提出了一个专用时空神经网络，用于模型仿真，能够捕获火灾传播模型的复杂行为。所提出的方法可以在基于神经网络的方法通常具有挑战性的空间和时间分辨率上进行近似预测。此外，由于新的数据增强方法，即使使用小型训练集，提出的方法也是可靠的。经验实验表明，模拟和模拟的火山之间的良好一致性，平均Jaccard得分为0.76。

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.