预示着在不同时间尺度上作用的软件化，可编程网络控制和使用作用的全包装控制器的使用，作为下一代蜂窝网络发展的关键驱动力。这些技术已经培养了新设计的智能数据驱动的解决方案，用于管理大量各种蜂窝功能，基本上不可能在传统上闭合的蜂窝体系结构中实施。尽管行业对人工智能（AI）和机器学习（ML）解决方案具有明显的兴趣，该解决方案是对无线电访问网络（RAN）的闭环控制，并且该领域的几项研究工作远非主流，但仍然是一个复杂的操作，而且经常被忽略。在本文中，我们讨论了如何为开放式RAN的智能闭环控制设计AI/ML解决方案，从而根据具有高性能记录的示例解决方案提供指南和见解。然后，我们展示如何通过OpenRan Gym在O-RAN近实时RAN智能控制器（RIC）上实例化这些解决方案，Openran Gym是第一个用于数据驱动的O-RAN实验的公共可用工具箱。我们展示了一个由OpenRan Gym开发的XAPP的用例，并在蜂窝网络上进行了测试，其中有7个基站和42位用户部署在Colosseum Wireless网络模拟器上。我们的演示表明，位于Openran的XAPP开发环境的高度灵活性，该环境与部署方案和交通需求无关。

开放式无线电访问网络（RAN）体系结构将在下一代蜂窝网络中启用互操作性，开放性和可编程数据驱动控制。但是，开发和测试有效的解决方案，这些解决方案跨越了异质的细胞部署和量表，并在如此多样化的环境中优化网络性能是一项复杂的任务，这是一项复杂的任务，仍然在很大程度上没有探索。在本文中，我们介绍了OpenRan Gym，这是一个统一，开放和O-Ran符合的实验工具箱，用于数据收集，设计，原型设计和测试下一代Open RAN Systems的端到端数据驱动的控制解决方案。 OpenRan Gym扩展并结合了一个独特的解决方案，几个软件框架用于数据收集统计和控制控制，以及轻巧的O-Ran近实时RAN智能控制器（RIC）量身定制，可在实验性无线平台上运行。我们首先概述了OpenRan Gym的各种建筑组件，并描述了如何按大规模收集数据和设计，训练和测试人工智能和机器学习O-Ran-Commiate应用程序（XAPP）。然后，我们详细描述了如何在SoftWarized Rans上测试开发的XAPP，并提供了一个使用OpenRan Gym开发的两个XAPP的示例，这些XAPP用于控制一个具有7个基站的网络，并在奥马斗马会测试中部署了42个用户。最后，我们展示了如何通过罗马竞技场上的Openran Gym开发的解决方案，可以将其导出到现实世界中的异质无线平台，例如Arena Testbed以及PAWR计划的粉末和宇宙平台。 OpenRan Gym及其软件组件是开源的，并且对研究社区公开可用。

尽管开放式运输所带来的新机遇，但基于ML的网络自动化的进步已经缓慢，主要是因为大规模数据集和实验测试基础设施的不可用。这减缓了实际网络上的深度加强学习（DRL）代理的开发和广泛采用，延迟了智能和自主运行控制的进展。在本文中，我们通过提出用于开放式RAN基于DRL基闭环控制的设计，培训，测试和实验评估的实用解决方案和软件管道来解决这些挑战。我们介绍了Colo-RAN，这是一个具有软件定义的无线电循环的第一个公开的大型O-RAN测试框架。在ColoSseum无线网络仿真器的规模和计算能力上，Colo-RAN使用O-RAN组件，可编程基站和“无线数据厂”来实现ML研究。具体而言，我们设计并开发三种示例性XApp，用于基于DRL的RAN切片，调度和在线模型培训，并评估其在具有7个软化基站和42个用户的蜂窝网络上的性能。最后，我们通过在竞技场上部署一个室内可编程测试平台来展示Colo-RAN到不同平台的可移植性。我们的一类大型评估的广泛结果突出了基于DRL的自适应控制的益处和挑战。他们还提供关于无线DRL管道的开发的见解，从数据分析到DRL代理商的设计，以及与现场训练相关的权衡。 Colo-RAN和收集的大型数据集将公开向研究界公开提供。

Colorsseum是一种开放式和公开可用的大型无线无线测试，可通过虚拟化和软载波形和协议堆栈进行实验研究，在完全可编程的“白盒子”平台上。通过256最先进的软件定义的无线电和巨大的通道仿真器核心，罗马斗兽场几乎可以模拟任何方案，在各种部署和渠道条件下，可以在规模上进行设计，开发和测试解决方案。通过有限脉冲响应滤波器通过高保真FPGA的仿真再现这些罗马孔射频场景。过滤器模拟所需的无线通道的抽头，并将它们应用于无线电节点生成的信号，忠实地模拟现实世界无线环境的条件。在本文中，我们将罗马斗兽场介绍为测试楼，这是第一次向研究界开放。我们描述了罗马斗兽场的建筑及其实验和仿真能力。然后，我们通过示例性用例证明了罗马斗兽场对实验研究的有效性，包括频谱共享和无人空中车辆场景的普遍用途用例，包括普遍的无线技术（例如，蜂窝和Wi-Fi）。斗兽索斗兽场未来更新的路线图总结了这篇论文。

Remote sensing imagery provides comprehensive views of the Earth, where different sensors collect complementary data at different spatial scales. Large, pretrained models are commonly finetuned with imagery that is heavily augmented to mimic different conditions and scales, with the resulting models used for various tasks with imagery from a range of spatial scales. Such models overlook scale-specific information in the data. In this paper, we present Scale-MAE, a pretraining method that explicitly learns relationships between data at different, known scales throughout the pretraining process. Scale-MAE pretrains a network by masking an input image at a known input scale, where the area of the Earth covered by the image determines the scale of the ViT positional encoding, not the image resolution. Scale-MAE encodes the masked image with a standard ViT backbone, and then decodes the masked image through a bandpass filter to reconstruct low/high frequency images at lower/higher scales. We find that tasking the network with reconstructing both low/high frequency images leads to robust multiscale representations for remote sensing imagery. Scale-MAE achieves an average of a $5.0\%$ non-parametric kNN classification improvement across eight remote sensing datasets compared to current state-of-the-art and obtains a $0.9$ mIoU to $3.8$ mIoU improvement on the SpaceNet building segmentation transfer task for a range of evaluation scales.

Functionality and dialogue experience are two important factors of task-oriented dialogue systems. Conventional approaches with closed schema (e.g., conversational semantic parsing) often fail as both the functionality and dialogue experience are strongly constrained by the underlying schema. We introduce a new paradigm for task-oriented dialogue - Dialog2API - to greatly expand the functionality and provide seamless dialogue experience. The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs. The model also manages the dialogue policy and interact with the user through generating appropriate natural language responses. By allowing generating free-form programs, Dialog2API supports composite goals by combining different APIs, whereas unrestricted program revision provides natural and robust dialogue experience. To facilitate Dialog2API, the core model is provided with API documents, an execution environment and optionally some example dialogues annotated with programs. We propose an approach tailored for the Dialog2API, where the dialogue states are represented by a stack of programs, with most recently mentioned program on the top of the stack. Dialog2API can work with many application scenarios such as software automation and customer service. In this paper, we construct a dataset for AWS S3 APIs and present evaluation results of in-context learning baselines.

The widespread use of information and communication technology (ICT) over the course of the last decades has been a primary catalyst behind the digitalization of power systems. Meanwhile, as the utilization rate of the Internet of Things (IoT) continues to rise along with recent advancements in ICT, the need for secure and computationally efficient monitoring of critical infrastructures like the electrical grid and the agents that participate in it is growing. A cyber-physical system, such as the electrical grid, may experience anomalies for a number of different reasons. These may include physical defects, mistakes in measurement and communication, cyberattacks, and other similar occurrences. The goal of this study is to emphasize what the most common incidents are with power systems and to give an overview and classification of the most common ways to find problems, starting with the consumer/prosumer end working up to the primary power producers. In addition, this article aimed to discuss the methods and techniques, such as artificial intelligence (AI) that are used to identify anomalies in the power systems and markets.

Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanations to detect the important decision boundaries of a pre-trained black-box model. This information is used to build a supervised discretization of the features in the dataset with a tunable granularity. Using the discretized dataset, a smaller, therefore more interpretable Decision Tree can be trained, which, in addition, enhances the stability and robustness of the baseline Decision Tree. Numerical results on real-world datasets show the effectiveness of the approach in terms of accuracy and sparsity compared to the baseline Decision Tree.

Predicting the presence of major depressive disorder (MDD) using behavioural and cognitive signals is a highly non-trivial task. The heterogeneous clinical profile of MDD means that any given speech, facial expression and/or observed cognitive pattern may be associated with a unique combination of depressive symptoms. Conventional discriminative machine learning models potentially lack the complexity to robustly model this heterogeneity. Bayesian networks, however, may instead be well-suited to such a scenario. These networks are probabilistic graphical models that efficiently describe the joint probability distribution over a set of random variables by explicitly capturing their conditional dependencies. This framework provides further advantages over standard discriminative modelling by offering the possibility to incorporate expert opinion in the graphical structure of the models, generating explainable model predictions, informing about the uncertainty of predictions, and naturally handling missing data. In this study, we apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.

许多微体系式优化为深度神经网络解锁了巨大的处理能力，从而促进了AI革命。随着这种优化的精疲力尽，现代AI的增长现在是通过培训系统的性能，尤其是其数据流动的。我们没有专注于单个加速器，而是研究了全系统规模的大规模培训的数据移动特征。基于我们的工作量分析，我们设计了HammingMesh，这是一种新颖的网络拓扑，以低成本提供高的带宽，并具有很高的工作计划灵活性。具体而言，HammingMesh可以支持具有两个并行性的两个维度的深度学习培训工作的完整带宽和隔离。此外，它还为通用流量的高全球带宽提供支持。因此，HammingMesh将为未来的大规模深度学习系统供电，并具有极端的带宽要求。