Neural signed distance functions (SDFs) have been a vital representation to represent 3D shapes or scenes with neural networks. An SDF is an implicit function that can query signed distances at specific coordinates for recovering a 3D surface. Although implicit functions work well on a single shape or scene, they pose obstacles when analyzing multiple SDFs with high-fidelity geometry details, due to the limited information encoded in the latent space for SDFs and the loss of geometry details. To overcome these obstacles, we introduce a method to represent multiple SDFs in a common space, aiming to recover more high-fidelity geometry details with more compact latent representations. Our key idea is to take full advantage of the benefits of generalization-based and overfitting-based learning strategies, which manage to preserve high-fidelity geometry details with compact latent codes. Based on this framework, we also introduce a novel sampling strategy to sample training queries. The sampling can improve the training efficiency and eliminate artifacts caused by the influence of other SDFs. We report numerical and visual evaluations on widely used benchmarks to validate our designs and show advantages over the latest methods in terms of the representative ability and compactness.

Connected and automated vehicles (CAVs) indicate the future of human transportation. There will be a mix of conventional vehicles and CAVs sharing the roads for an extended period before CAVs completely replace all human-driven vehicles (HDVs). It is crucial to investigate how to optimize traffic efficiency across the entire network during this transitional phase. Introducing dedicated lanes (DLs) can be an effective approach. This study aims to provide a comprehensive review of existing studies on the deployment of dedicated CAV lanes. An analysis of the literature shows that there are three main groups of studies related to DLs for CAVs: one group focuses on the deployment of dedicated CAV lanes, the second explores the control policies for DLs for CAVs, and the third examines the impacts of implementing dedicated CAV lanes. The main findings of the analysis indicate that the deployment of DLs for CAVs can enhance traffic performance within a certain range of market penetration rates (MPRs), but this range varies slightly across different studies. It is also found that the MPR of CAVs plays a significant role in the deployment of DLs. It is not recommended to deploy DLs for CAVs if the MPRs are either too low or too high. In addition, the deployment plan of dedicated CAV lanes typically considers the influencing factors such as system cost, safety, environmental factors, and total travel time. This review provides an in-depth examination of DL deployment methodologies, evaluates their impact on traffic systems, and identifies key areas for future research.

Modern cloud service providers increasingly rely on extensive natural language database queries for daily operational workflows, including system maintenance, fault recovery, and customer ticket resolution, all of which are critical for ensuring optimal data availability, service reliability, and customer experience. However, the specialized nature of cloud platform data inherently complicates this task: current practices either depend heavily on Site Reliability Engineer (SRE) experts for manual SQL query or require intensive data collection and manual annotation to train domain-specific Natural Language to SQL (NL2SQL) models for automation-both approaches suffering from high labour costs and limited scalability. To address these challenges, this paper introduces ASASQL, Active Learning-based Data Synthesis and Annotation Strategy for SQL Generation, a novel framework designed to automatically generate training data and select data for annotation to train domain-specific nl2sql models. ASASQL significantly reduces the annotation requirement for training accurate, cost-efficient, and scalable automatic data querying models. Experiments on realworld production datasets from a major cloud service provider demonstrate that ASASQL can reduce the amount of data annotation needed to achieve specific accuracy by $38.7 \%$.

Graph visualization techniques provide a foundational framework for offering comprehensive overviews and insights into cloud computing systems, facilitating efficient management and ensuring their availability and reliability. Despite the enhanced computational and storage capabilities of larger-scale cloud computing architectures, they introduce significant challenges to traditional graph-based visualization due to issues of hierarchical heterogeneity, scalability, and data incompleteness. This paper proposes a novel abstraction approach to visualize large multi-layer networks. Our method leverages graphons, a probabilistic representation of network layers, to encompass three core steps: an inner-layer summary to identify stable and volatile substructures, an inter-layer mixup for aligning heterogeneous network layers, and a context-aware multi-layer joint sampling technique aimed at reducing network scale while retaining essential topological characteristics. By abstracting complex network data into manageable weighted graphs, with each graph depicting a distinct network layer, our approach renders these intricate systems accessible on standard computing hardware. We validate our methodology through case studies, quantitative experiments and expert evaluations, demonstrating its effectiveness in managing large multi-layer networks, as well as its applicability to broader network types such as transportation and social networks.

A concurrent, tunable, tri-band, single chain radio receiver for 5G radio access networks is evaluated. The three concurrent bands are independently tunable over a frequency range from 600 MHz to 2.7 GHz. A hardware-in-the-loop test-bed provides a system level evaluation of the proposed receiver using direct RF digitization. The test-bed emulates a 5G heterogeneous network supporting three wideband, simultaneous connections. By measuring the receiver EVM, we demonstrate sufficient isolation between concurrent bands achieving 60 MHz of aggregated bandwidth as well as strong resilience to adjacent blockers.

A concurrent, tunable, triple-band, single chain radio receiver for 5G radio access networks is presented and its performance is evaluated in a hardware-in-the-loop test-bed. The test-bed emulates a 5G heterogeneous network supporting three independently tunable, wideband, simultaneous connections over a frequency range from 600 MHz to 2.7 GHz. The single chain receiver is able to achieve an aggregate bandwidth of 93.75 MHz, 31.25 MHz per band, and a net data rate of 187.5 Mbit/s through the use of single-carrier QPSK transmissions. The receiver demonstrate sufficient isolation between the concurrent transmissions as well as strong resilience to adjacent blockers through the use of a small guard band.

This invited paper considers a key next step in the design of radio architectures aimed at supporting low energy consumption in 5G heterogeneous radio access networks. State-of-the-art mobile radios usually require one RF transceiver per standard, each working separately at any given time. Software defined radios, while spanning a wide range of standards and frequency bands, also work separately at any specific time. In 5G radio access networks, where continuous, multiband connectivity is envisaged, this conventional radio architecture results in high network power consumption. In this paper, we propose the novel concept of a concurrent multiband frequency-agile radio (CM-FARAD) architecture, which simultaneously supports multiple standards and frequency bands using a single, tunable transceiver. We discuss the subsystem radio design approaches for enabling the CM-FARAD architecture, including antennas, power amplifiers, low noise amplifiers and analogue to digital converters. A working prototype of a dual-band CM-FARAD test-bed is also presented together with measured salient performance characteristics.

In order to meet the high data rate, low latency and high energy efficiency requirements, the future radio units must utilise the frequency spectrum available at certain geographical location efficiently with a minimum amount of hardware. This requires frequency agility and concurrent multi-standard operation capabilities at the base and mobile terminals. To utilise the TV white spaces for mobile and wireless communication and enable their carrier aggregation with sub GHz LTE bands, this paper presents a single-chain, reconfigurable, RF digitising, dual- band receiver comprised of a tunable dual-band antenna, a reconfigurable digital down converter, a baseband processing unit and wideband LNA and ADC in the form of an oscilloscope. The presented dual-band receiver is tested through a hardware- in-the-loop test-bed which shows that up to 20 MHz aggregate bandwidths can be achieved. The receiver is able to provide equivalent error vector magnitude (EVM) performance across a wide range of frequencies avoiding any inter-band interference.