5G及以后的移动网络将以前所未有的规模支持异质用例,从而要求自动控制和优化针对单个用户需求的网络功能。当前的蜂窝体系结构不可能对无线电访问网络(RAN)进行这种细粒度控制。为了填补这一空白,开放式运行范式及其规范引入了一个带有抽象的开放体系结构,该架构可以启用闭环控制并提供数据驱动和智能优化RAN在用户级别上。这是通过在网络边缘部署在近实时RAN智能控制器(接近RT RIC)上的自定义RAN控制应用程序(即XAPP)获得的。尽管有这些前提,但截至今天,研究界缺乏用于构建数据驱动XAPP的沙箱,并创建大型数据集以有效的AI培训。在本文中,我们通过引入NS-O-RAN来解决此问题,NS-O-RAN是一个软件框架,该框架将现实世界中的生产级近距离RIC与NS-3上的基于3GPP的模拟环境集成在一起,从而实现了XAPPS和XAPPS的开发自动化的大规模数据收集和深入强化学习驱动的控制策略的测试,以在用户级别的优化中进行优化。此外,我们提出了第一个特定于用户的O-RAN交通转向(TS)智能移交框架。它使用随机的合奏混合物,结合了最先进的卷积神经网络体系结构,以最佳地为网络中的每个用户分配服务基站。我们的TS XAPP接受了NS-O-RAN收集的超过4000万个数据点的培训,该数据点在近距离RIC上运行,并控制其基站。我们在大规模部署中评估了性能,这表明基于XAPP的交换可以使吞吐量和频谱效率平均比传统的移交启发式方法提高50%,而动机性开销较少。
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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。
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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。
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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。
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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。
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Existing automated techniques for software documentation typically attempt to reason between two main sources of information: code and natural language. However, this reasoning process is often complicated by the lexical gap between more abstract natural language and more structured programming languages. One potential bridge for this gap is the Graphical User Interface (GUI), as GUIs inherently encode salient information about underlying program functionality into rich, pixel-based data representations. This paper offers one of the first comprehensive empirical investigations into the connection between GUIs and functional, natural language descriptions of software. First, we collect, analyze, and open source a large dataset of functional GUI descriptions consisting of 45,998 descriptions for 10,204 screenshots from popular Android applications. The descriptions were obtained from human labelers and underwent several quality control mechanisms. To gain insight into the representational potential of GUIs, we investigate the ability of four Neural Image Captioning models to predict natural language descriptions of varying granularity when provided a screenshot as input. We evaluate these models quantitatively, using common machine translation metrics, and qualitatively through a large-scale user study. Finally, we offer learned lessons and a discussion of the potential shown by multimodal models to enhance future techniques for automated software documentation.
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Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps, and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing Indirect ImmunoFluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is far from the conventional neural network approach, but it is equivalent to their quantitative and qualitative performance, and it is also solid to adversative noise. The method is robust, based on formally correct functions, and does not suffer from tuning on specific data sets. Results: This work demonstrates the robustness of the method against the variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on two datasets (Neuroblastoma and NucleusSegData) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional to a structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) to segment cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches.
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Detecting anomalous data within time series is a very relevant task in pattern recognition and machine learning, with many possible applications that range from disease prevention in medicine, e.g., detecting early alterations of the health status before it can clearly be defined as "illness" up to monitoring industrial plants. Regarding this latter application, detecting anomalies in an industrial plant's status firstly prevents serious damages that would require a long interruption of the production process. Secondly, it permits optimal scheduling of maintenance interventions by limiting them to urgent situations. At the same time, they typically follow a fixed prudential schedule according to which components are substituted well before the end of their expected lifetime. This paper describes a case study regarding the monitoring of the status of Laser-guided Vehicles (LGVs) batteries, on which we worked as our contribution to project SUPER (Supercomputing Unified Platform, Emilia Romagna) aimed at establishing and demonstrating a regional High-Performance Computing platform that is going to represent the main Italian supercomputing environment for both computing power and data volume.
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Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data across numerous domains. Their underlying ability to represent nodes as summaries of their vicinities has proven effective for homophilous graphs in particular, in which same-type nodes tend to connect. On heterophilous graphs, in which different-type nodes are likely connected, GNNs perform less consistently, as neighborhood information might be less representative or even misleading. On the other hand, GNN performance is not inferior on all heterophilous graphs, and there is a lack of understanding of what other graph properties affect GNN performance. In this work, we highlight the limitations of the widely used homophily ratio and the recent Cross-Class Neighborhood Similarity (CCNS) metric in estimating GNN performance. To overcome these limitations, we introduce 2-hop Neighbor Class Similarity (2NCS), a new quantitative graph structural property that correlates with GNN performance more strongly and consistently than alternative metrics. 2NCS considers two-hop neighborhoods as a theoretically derived consequence of the two-step label propagation process governing GCN's training-inference process. Experiments on one synthetic and eight real-world graph datasets confirm consistent improvements over existing metrics in estimating the accuracy of GCN- and GAT-based architectures on the node classification task.
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Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. A key issue is how to address the inherent non-linearity of classical deep learning, a problem in the quantum domain due to the fact that the composition of an arbitrary number of quantum gates, consisting of a series of sequential unitary transformations, is intrinsically linear. This problem has been variously approached in the literature, principally via the introduction of measurements between layers of unitary transformations. In this paper, we introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning typically associated with superior generalization performance in the classical domain, specifically, hierarchical feature learning. Our approach generalizes the notion of Quantum Neural Tangent Kernel, which has been used to study the dynamics of classical and quantum machine learning models. The Quantum Path Kernel exploits the parameter trajectory, i.e. the curve delineated by model parameters as they evolve during training, enabling the representation of differential layer-wise convergence behaviors, or the formation of hierarchical parametric dependencies, in terms of their manifestation in the gradient space of the predictor function. We evaluate our approach with respect to variants of the classification of Gaussian XOR mixtures - an artificial but emblematic problem that intrinsically requires multilevel learning in order to achieve optimal class separation.
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