This study demonstrates the feasibility of point cloud-based proactive link quality prediction for millimeter-wave (mmWave) communications. Image-based methods to quantitatively and deterministically predict future received signal strength using machine learning from time series of depth images to mitigate the human body line-of-sight (LOS) path blockage in mmWave communications have been proposed. However, image-based methods have been limited in applicable environments because camera images may contain private information. Thus, this study demonstrates the feasibility of using point clouds obtained from light detection and ranging (LiDAR) for the mmWave link quality prediction. Point clouds represent three-dimensional (3D) spaces as a set of points and are sparser and less likely to contain sensitive information than camera images. Additionally, point clouds provide 3D position and motion information, which is necessary for understanding the radio propagation environment involving pedestrians. This study designs the mmWave link quality prediction method and conducts two experimental evaluations using different types of point clouds obtained from LiDAR and depth cameras, as well as different numerical indicators of link quality, received signal strength and throughput. Based on these experiments, our proposed method can predict future large attenuation of mmWave link quality due to LOS blockage by human bodies, therefore our point cloud-based method can be an alternative to image-based methods.

本文提出了一种用于拆分计算的神经体系结构搜索（NAS）方法。拆分计算是一种新兴的机器学习推理技术，可解决在物联网系统中部署深度学习的隐私和延迟挑战。在拆分计算中，神经网络模型通过网络使用Edge服务器和IoT设备进行了分离和合作处理。因此，神经网络模型的体系结构显着影响通信有效载荷大小，模型准确性和计算负载。在本文中，我们解决了优化神经网络体系结构以进行拆分计算的挑战。为此，我们提出了NASC，该NASC共同探讨了最佳模型架构和一个拆分点，以达到延迟需求（即，计算和通信的总延迟较小，都比某个阈值较小）。 NASC采用单发NAS，不需要重复模型培训进行计算高效的体系结构搜索。我们使用硬件（HW） - 基准数据的NAS基础的绩效评估表明，拟议的NASC可以改善``通信潜伏期和模型准确性''的权衡，即，将延迟降低了约40-60％，从基线降低了约40-60％有轻微的精度降解。

分布式推理（DI）框架已经获得了牵引力作为用于实时应用的技术，用于在资源受限的内容（物联网）设备上的尖端深机学习（ML）。在DI中，计算任务通过IOT设备通过有损的物联网网络从物联网设备卸载到边缘服务器。然而，通常，在通信延迟和可靠性之间存在通信系统级权衡;因此，为了提供准确的DI结果，需要一种可靠和高等待的通信系统来调整，这导致DI的不可忽略的端到端潜伏期。这激励我们通过ML技术的努力来改善通信延迟与准确性之间的权衡。具体而言，我们提出了一种以通信为导向的模型调谐（ComTune），其旨在通过低延迟但不可靠的通信链路实现高度精确的DI。在Comtune中，关键的想法是通过应用辍学技术的应用来微调不可靠通信链路的效果。这使得DI系统能够针对不可靠的通信链路获得鲁棒性。我们的ML实验表明，ComTune使得能够以低延迟和有损网络在低延迟和损失网络下准确预测。

本文提出了一种完全分散的联邦学习（FL）方案，用于通过多跳网络连接的所有内容（IOE）设备。由于FL算法几乎没有收敛机器学习（ML）模型的参数，因此本文侧重于功能空间中ML模型的收敛性。考虑到ML任务的代表性损失函数例如，均方误差（MSE）和Kullback-Leibler（KL）发散，是凸起的功能，直接更新功能空间中的功能的算法可以收敛到最佳解决方案。本文的关键概念是定制基于共识的优化算法，可以在功能空间中工作，以分布式方式实现全局最佳。本文首先分析了函数空间中所提出的算法的收敛，其被称为元算法，并且示出了频谱图理论可以以类似于数值矢量的方式应用于函数空间。然后，为神经网络（NN）开发了基于共识的多跳联盟蒸馏（CMFD）以实现元算法。 CMFD利用知识蒸馏来实现相邻器件之间的功能聚集而没有参数平均。 CMFD的一个优点是它即使在分布式学习者中使用不同的NN模型也是如此。虽然CMFD不完全反映元算法的行为，但元算法的融合属性的讨论促进了对CMFD的直观理解，并且模拟评估表明，NN模型会聚使用CMFD进行多种任务。仿真结果还表明，CMFD比弱连接网络的参数聚合实现更高的准确性，CMFD比参数聚合方法更稳定。

We envision a mobile edge computing (MEC) framework for machine learning (ML) technologies, which leverages distributed client data and computation resources for training high-performance ML models while preserving client privacy. Toward this future goal, this work aims to extend Federated Learning (FL), a decentralized learning framework that enables privacy-preserving training of models, to work with heterogeneous clients in a practical cellular network. The FL protocol iteratively asks random clients to download a trainable model from a server, update it with own data, and upload the updated model to the server, while asking the server to aggregate multiple client updates to further improve the model. While clients in this protocol are free from disclosing own private data, the overall training process can become inefficient when some clients are with limited computational resources (i.e., requiring longer update time) or under poor wireless channel conditions (longer upload time). Our new FL protocol, which we refer to as FedCS, mitigates this problem and performs FL efficiently while actively managing clients based on their resource conditions. Specifically, FedCS solves a client selection problem with resource constraints, which allows the server to aggregate as many client updates as possible and to accelerate performance improvement in ML models. We conducted an experimental evaluation using publicly-available large-scale image datasets to train deep neural networks on MEC environment simulations. The experimental results show that FedCS is able to complete its training process in a significantly shorter time compared to the original FL protocol.

Advanced visual localization techniques encompass image retrieval challenges and 6 Degree-of-Freedom (DoF) camera pose estimation, such as hierarchical localization. Thus, they must extract global and local features from input images. Previous methods have achieved this through resource-intensive or accuracy-reducing means, such as combinatorial pipelines or multi-task distillation. In this study, we present a novel method called SuperGF, which effectively unifies local and global features for visual localization, leading to a higher trade-off between localization accuracy and computational efficiency. Specifically, SuperGF is a transformer-based aggregation model that operates directly on image-matching-specific local features and generates global features for retrieval. We conduct experimental evaluations of our method in terms of both accuracy and efficiency, demonstrating its advantages over other methods. We also provide implementations of SuperGF using various types of local features, including dense and sparse learning-based or hand-crafted descriptors.

Community Question Answering (CQA) sites have spread and multiplied significantly in recent years. Sites like Reddit, Quora, and Stack Exchange are becoming popular amongst people interested in finding answers to diverse questions. One practical way of finding such answers is automatically predicting the best candidate given existing answers and comments. Many studies were conducted on answer prediction in CQA but with limited focus on using the background information of the questionnaires. We address this limitation using a novel method for predicting the best answers using the questioner's background information and other features, such as the textual content or the relationships with other participants. Our answer classification model was trained using the Stack Exchange dataset and validated using the Area Under the Curve (AUC) metric. The experimental results show that the proposed method complements previous methods by pointing out the importance of the relationships between users, particularly throughout the level of involvement in different communities on Stack Exchange. Furthermore, we point out that there is little overlap between user-relation information and the information represented by the shallow text features and the meta-features, such as time differences.

3D点云可以灵活地表示连续表面，可用于各种应用；但是，缺乏结构信息使点云识别具有挑战性。最近的边缘感知方法主要使用边缘信息作为描述局部结构以促进学习的额外功能。尽管这些方法表明，将边缘纳入网络设计是有益的，但它们通常缺乏解释性，使用户想知道边缘如何有所帮助。为了阐明这一问题，在这项研究中，我们提出了以可解释方式处理边缘的扩散单元（DU），同时提供了不错的改进。我们的方法可以通过三种方式解释。首先，我们从理论上表明，DU学会了执行任务呈纤维边缘的增强和抑制作用。其次，我们通过实验观察并验证边缘增强和抑制行为。第三，我们从经验上证明，这种行为有助于提高绩效。在具有挑战性的基准上进行的广泛实验验证了DU在可解释性和绩效增长方面的优势。具体而言，我们的方法使用S3DIS使用Shapenet零件和场景分割来实现对象零件分割的最新性能。我们的源代码将在https://github.com/martianxiu/diffusionunit上发布。

开放式对象检测（OSOD）最近引起了广泛的关注。它是在正确检测/分类已知对象的同时检测未知对象。我们首先指出，最近的研究中考虑的OSOD方案，该方案考虑了类似于开放式识别（OSR）的无限种类的未知物体，这是一个基本问题。也就是说，我们无法确定要检测到的内容，而对于这种无限的未知对象，这是检测任务所必需的。这个问题导致了对未知对象检测方法的性能的评估困难。然后，我们介绍了OSOD的新颖方案，该方案仅处理与已知对象共享超级类别的未知对象。它具有许多真实的应用程序，例如检测越来越多的细粒对象。这个新环境摆脱了上述问题和评估困难。此外，由于已知和未知对象之间的视觉相似性，它使检测到未知对象更加现实。我们通过实验结果表明，基于标准检测器类别预测的不确定性的简单方法优于先前设置中测试的当前最新OSOD方法。

图像字幕的当前最新方法采用基于区域的特征，因为它们提供了对象级信息，对于描述图像的内容至关重要；它们通常由对象检测器（例如更快的R-CNN）提取。但是，他们有几个问题，例如缺乏上下文信息，不准确检测的风险以及高计算成本。可以通过使用基于网格的功能来解决前两个。但是，如何提取和融合这两种功能是未知的。本文提出了一种仅使用变压器的神经结构，称为砂砾（基于网格和区域的图像字幕变压器），该构建物有效地利用了两个视觉特征来生成更好的字幕。粒度用基于DITR的方法代替了以前方法中使用的基于CNN的检测器，从而使其更快地计算。此外，它的整体设计仅由变压器组成，可以对模型进行端到端的训练。这种创新的设计和双重视觉功能的集成带来了重大的性能提高。几个图像字幕基准的实验结果表明，砂砾的推论准确性和速度优于先前的方法。