arXiv subjects

Hongwei Zhang

Publications and source records attributed to Hongwei Zhang.

At least 55 records · Page 3Linked to original sources

arXiv2024

A Universal Method to Transform Aromatic Hydrocarbon Molecules into Confined Carbyne inside Single-Walled Carbon Nanotubes

Carbyne, a sp1-hybridized allotrope of carbon, is a linear carbon chain with exceptional theoretically predicted properties that surpass those of sp2-hybridized graphene and carbon nanotubes (CNTs). However, the existence of carbyne has been debated due to its instability caused by Peierls distortion, which limits its practical development. The only successful synthesis of carbyne has been achieved inside CNTs, resulting in a form known as confined carbyne (CC). However, CC can only be synthesized inside multi-walled CNTs, limiting its property-tuning capabilities to the inner tubes of the CNTs. Here, we present a universal method for synthesizing CC inside single-walled carbon nanotubes (SWCNTs) with diameter of 0.9-1.3 nm. Aromatic hydrocarbon molecules are filled inside SWCNTs and subsequently transformed into CC under low-temperature annealing. A variety of aromatic hydrocarbon molecules are confirmed as effective precursors for formation of CC, with Raman frequencies centered around 1861 cm-1. Enriched (6,5) and (7,6) SWCNTs with diameters less than 0.8 nm are less effective than the SWCNTs with diameter of 0.9-1.3 nm for CC formation. Furthermore, resonance Raman spectroscopy reveals that optical band gap of the CC at 1861 cm-1 is 2.353 eV, which is consistent with the result obtained using a linear relationship between the Raman signal and optical band gap. This newly developed approach provides a versatile route for synthesizing CC from various precursor molecules inside diverse templates, which is not limited to SWCNTs but could extend to any templates with appropriate size, including molecular sieves, zeolites, boron nitride nanotubes, and metal-organic frameworks.

cond-mat.mtrl-sci↗

arXiv2024

Scalable Analysis of Urban Scaling Laws: Leveraging Cloud Computing to Analyze 21,280 Global Cities

Cities play a pivotal role in human development and sustainability, yet studying them presents significant challenges due to the vast scale and complexity of spatial-temporal data. One such challenge is the need to uncover universal urban patterns, such as the urban scaling law, across thousands of cities worldwide. In this study, we propose a novel large-scale geospatial data processing system that enables city analysis on an unprecedented scale. We demonstrate the system's capabilities by revisiting the urban scaling law across 21,280 cities globally, using a range of open-source datasets including road networks, nighttime light intensity, built-up areas, and population statistics. Analyzing the characteristics of 21,280 cities involves querying over half a billion geospatial data points, a task that traditional Geographic Information Systems (GIS) would take several days to process. In contrast, our cloud-based system accelerates the analysis, reducing processing time to just minutes while significantly lowering resource consumption. Our findings reveal that the urban scaling law varies across cities in under-developed, developing, and developed regions, extending the insights gained from previous studies focused on hundreds of cities. This underscores the critical importance of cloud-based big data processing for efficient, large-scale geospatial analysis. As the availability of satellite imagery and other global datasets continues to grow, the potential for scientific discovery expands exponentially. Our approach not only demonstrates how such large-scale tasks can be executed efficiently but also offers a powerful solution for data scientists and researchers working in the fields of city and geospatial science.

cs.DC↗

arXiv2024

AraSync: Precision Time Synchronization in Rural Wireless Living Lab

Time synchronization is a critical component in network operation and management, and it is also required by Ultra-Reliable, Low-Latency Communications (URLLC) in next-generation wireless systems such as those of 5G, 6G, and Open RAN. In this context, we design and implement AraSync as an end-to-end time synchronization system in the ARA wireless living lab to enable advanced wireless experiments and applications involving stringent time constraints. We make use of Precision Time Protocol (PTP) at different levels to achieve synchronization accuracy in the order of nanoseconds. Along with fiber networks, AraSync enables time synchronization across the AraHaul wireless x-haul network consisting of long-range, high-capacity mmWave and microwave links. In this paper, we present the detailed design and implementation of AraSync, including its hardware and software components and the PTP network topology. Further, we experimentally characterize the performance of AraSync from spatial and temporal dimensions. Our measurement and analysis of the clock offset and mean path delay show the impact of the wireless channel and weather conditions on the PTP synchronization accuracy.

cs.NI↗

arXiv2024

Linear hypothesis testing in high-dimensional heteroscedastics via random integration

In this paper, for the problem of heteroskedastic general linear hypothesis testing (GLHT) in high-dimensional settings, we propose a random integration method based on the reference L2-norm to deal with such problems. The asymptotic properties of the test statistic can be obtained under the null hypothesis when the relationship between data dimensions and sample size is not specified. The results show that it is more advisable to approximate the null distribution of the test using the distribution of the chi-square type mixture, and it is shown through some numerical simulations and real data analysis that our proposed test is powerful.

math.ST↗

arXiv2024

Wireless Spectrum in Rural Farmlands: Status, Challenges and Opportunities

Due to factors such as low population density and expansive geographical distances, network deployment falls behind in rural regions, leading to a broadband divide. Wireless spectrum serves as the blood and flesh of wireless communications. Shared white spaces such as those in the TVWS and CBRS spectrum bands offer opportunities to expand connectivity, innovate, and provide affordable access to high-speed Internet in under-served areas without additional cost to expensive licensed spectrum. However, the current methods to utilize these white spaces are inefficient due to very conservative models and spectrum policies, causing under-utilization of valuable spectrum resources. This hampers the full potential of innovative wireless technologies that could benefit farmers, small Internet Service Providers (ISPs) or Mobile Network Operators (MNOs) operating in rural regions. This study explores the challenges faced by farmers and service providers when using shared spectrum bands to deploy their networks while ensuring maximum system performance and minimizing interference with other users. Additionally, we discuss how spatiotemporal spectrum models, in conjunction with database-driven spectrum-sharing solutions, can enhance the allocation and management of spectrum resources, ultimately improving the efficiency and reliability of wireless networks operating in shared spectrum bands.

cs.NI↗

arXiv2024

Learning to Rank for Maps at Airbnb

As a two-sided marketplace, Airbnb brings together hosts who own listings for rent with prospective guests from around the globe. Results from a guest's search for listings are displayed primarily through two interfaces: (1) as a list of rectangular cards that contain on them the listing image, price, rating, and other details, referred to as list-results (2) as oval pins on a map showing the listing price, called map-results. Both these interfaces, since their inception, have used the same ranking algorithm that orders listings by their booking probabilities and selects the top listings for display. But some of the basic assumptions underlying ranking, built for a world where search results are presented as lists, simply break down for maps. This paper describes how we rebuilt ranking for maps by revising the mathematical foundations of how users interact with search results. Our iterative and experiment-driven approach led us through a path full of twists and turns, ending in a unified theory for the two interfaces. Our journey shows how assumptions taken for granted when designing machine learning algorithms may not apply equally across all user interfaces, and how they can be adapted. The net impact was one of the largest improvements in user experience for Airbnb which we discuss as a series of experimental validations.

cs.IR↗

arXiv2024

ARA-O-RAN: End-to-End Programmable O-RAN Living Lab for Agriculture and Rural Communities

As wireless networks evolve towards open architectures like O-RAN, testing, and integration platforms are crucial to address challenges like interoperability. This paper describes ARA-O-RAN, a novel O-RAN testbed established through the NSF Platforms for Advanced Wireless Research (PAWR) ARA platform. ARA provides an at-scale rural wireless living lab focused on technologies for digital agriculture and rural communities. As an O-RAN Alliance certified Open Testing and Integration Centre (OTIC), ARA launched ARA-O-RAN -- the first public O-RAN testbed tailored to rural and agriculture use cases, together with the end-to-end, whole-stack programmability. ARA-O-RAN uniquely combines support for outdoor testing across a university campus, surrounding farmlands, and rural communities with a 50-node indoor sandbox. The testbed facilitates vital R\&D to implement open architectures that can meet rural connectivity needs. The paper outlines ARA-O-RAN's hardware system design, software architecture, and enabled research experiments. It also discusses plans aligned with national spectrum policy and rural spectrum innovation. ARA-O-RAN exemplifies the value of purpose-built wireless testbeds in accelerating impactful wireless research.

cs.NI↗

arXiv2024

StructComp: Substituting Propagation with Structural Compression in Training Graph Contrastive Learning

Graph contrastive learning (GCL) has become a powerful tool for learning graph data, but its scalability remains a significant challenge. In this work, we propose a simple yet effective training framework called Structural Compression (StructComp) to address this issue. Inspired by a sparse low-rank approximation on the diffusion matrix, StructComp trains the encoder with the compressed nodes. This allows the encoder not to perform any message passing during the training stage, and significantly reduces the number of sample pairs in the contrastive loss. We theoretically prove that the original GCL loss can be approximated with the contrastive loss computed by StructComp. Moreover, StructComp can be regarded as an additional regularization term for GCL models, resulting in a more robust encoder. Empirical studies on various datasets show that StructComp greatly reduces the time and memory consumption while improving model performance compared to the vanilla GCL models and scalable training methods.

cs.LG↗

arXiv2024

Exploring Wireless Channels in Rural Areas: A Comprehensive Measurement Study

The study of wireless channel behavior has been an active research topic for many years. However, there exists a noticeable scarcity of studies focusing on wireless channel characteristics in rural areas. With the advancement of smart agriculture practices in rural regions, there has been an increasing demand for affordable, high-capacity, and low-latency wireless networks to support various precision agriculture applications such as plant phenotyping, livestock health monitoring, and agriculture automation. To address this research gap, we conducted a channel measurement study on multiple wireless frequency bands at various crop and livestock farms near Ames, Iowa, based on Iowa State University~(ISU)'s ARA Wireless Living lab - one of the NSF PAWR platforms. We specifically investigate the impact of weather conditions, humidity, temperature, and farm buildings on wireless channel behavior. The resulting measurement dataset, which will soon be made publicly accessible, represents a valuable resource for researchers interested in wireless channel prediction and optimization.

cs.NI↗

arXiv2024

Backdoor Attack with Mode Mixture Latent Modification

Backdoor attacks become a significant security concern for deep neural networks in recent years. An image classification model can be compromised if malicious backdoors are injected into it. This corruption will cause the model to function normally on clean images but predict a specific target label when triggers are present. Previous research can be categorized into two genres: poisoning a portion of the dataset with triggered images for users to train the model from scratch, or training a backdoored model alongside a triggered image generator. Both approaches require significant amount of attackable parameters for optimization to establish a connection between the trigger and the target label, which may raise suspicions as more people become aware of the existence of backdoor attacks. In this paper, we propose a backdoor attack paradigm that only requires minimal alterations (specifically, the output layer) to a clean model in order to inject the backdoor under the guise of fine-tuning. To achieve this, we leverage mode mixture samples, which are located between different modes in latent space, and introduce a novel method for conducting backdoor attacks. We evaluate the effectiveness of our method on four popular benchmark datasets: MNIST, CIFAR-10, GTSRB, and TinyImageNet.

cs.CR↗

arXiv2023

Understanding Community Bias Amplification in Graph Representation Learning

In this work, we discover a phenomenon of community bias amplification in graph representation learning, which refers to the exacerbation of performance bias between different classes by graph representation learning. We conduct an in-depth theoretical study of this phenomenon from a novel spectral perspective. Our analysis suggests that structural bias between communities results in varying local convergence speeds for node embeddings. This phenomenon leads to bias amplification in the classification results of downstream tasks. Based on the theoretical insights, we propose random graph coarsening, which is proved to be effective in dealing with the above issue. Finally, we propose a novel graph contrastive learning model called Random Graph Coarsening Contrastive Learning (RGCCL), which utilizes random coarsening as data augmentation and mitigates community bias by contrasting the coarsened graph with the original graph. Extensive experiments on various datasets demonstrate the advantage of our method when dealing with community bias amplification.

cs.LG↗

arXiv2023

Scheduling with Probabilistic Per-Packet Real-Time Guarantee for URLLC

For ultra-reliable, low-latency communications (URLLC) applications such as mission-critical industrial control and extended reality (XR), it is important to ensure the communication quality of individual packets. Prior studies have considered Probabilistic Per-packet Real-time Communications (PPRC) guarantees for single-cell, single-channel networks, but they have not considered real-world complexities such as inter-cell interference in large-scale networks with multiple communication channels and heterogeneous real-time requirements. To fill the gap, we propose a real-time scheduling algorithm based on \emph{local-deadline-partition (LDP)}, and the LDP algorithm ensures PPRC guarantee for large-scale, multi-channel networks with heterogeneous real-time constraints. We also address the associated challenge of schedulability test. In particular, we propose the concept of \emph{feasible set}, identify a closed-form sufficient condition for the schedulability of PPRC traffic, and then propose an efficient distributed algorithm for the schedulability test. We numerically study the properties of the LDP algorithm and observe that it significantly improves the network capacity of URLLC, for instance, by a factor of 5-20 as compared with a typical method. Furthermore, the PPRC traffic supportable by the LDP algorithm is significantly higher than that of state-of-the-art comparison schemes. This demonstrates the potential of fine-grained scheduling algorithms for URLLC wireless systems regarding interference scenarios.

cs.NI↗

arXiv2023

Towards Deterministic Communications in 6G Networks: State of the Art, Open Challenges and the Way Forward

Over the last decade, society and industries are undergoing rapid digitization that is expected to lead to the evolution of the cyber-physical continuum. End-to-end deterministic communications infrastructure is the essential glue that will bridge the digital and physical worlds of the continuum. We describe the state of the art and open challenges with respect to contemporary deterministic communications and compute technologies: 3GPP 5G, IEEE Time-Sensitive Networking, IETF DetNet, OPC UA as well as edge computing. While these technologies represent significant technological advancements towards networking Cyber-Physical Systems (CPS), we argue in this paper that they rather represent a first generation of systems which are still limited in different dimensions. In contrast, realizing future deterministic communication systems requires, firstly, seamless convergence between these technologies and, secondly, scalability to support heterogeneous (time-varying requirements) arising from diverse CPS applications. In addition, future deterministic communication networks will have to provide such characteristics end-to-end, which for CPS refers to the entire communication and computation loop, from sensors to actuators. In this paper, we discuss the state of the art regarding the main challenges towards these goals: predictability, end-to-end technology integration, end-to-end security, and scalable vertical application interfacing. We then present our vision regarding viable approaches and technological enablers to overcome these four central challenges. Key approaches to leverage in that regard are 6G system evolutions, wireless friendly integration of 6G into TSN and DetNet, novel end-to-end security approaches, efficient edge-cloud integrations, data-driven approaches for stochastic characterization and prediction, as well as leveraging digital twins towards system awareness.

cs.NI↗

arXiv2023

Microwave heating as a universal method to transform confined molecules into armchair graphene nanoribbons

Armchair graphene nanoribbons (AGNRs) with sub-nanometer width are potential materials for fabrication of novel nanodevices thanks to their moderate direct band gaps. AGNRs are usually synthesized by polymerizing precursor molecules on substrate surface. However, it is time-consuming and not suitable for large-scale production. AGNRs can also be grown by transforming precursor molecules inside single-walled carbon nanotubes via furnace annealing, but the obtained AGNRs are normally twisted. In this work, microwave heating is applied for transforming precursor molecules into AGNRs. The fast heating process allows synthesizing the AGNRs in seconds. Several different molecules were successfully transformed into AGNRs, suggesting that it is a universal method. More importantly, as demonstrated by Raman spectroscopy, aberration-corrected high-resolution transmission electron microscopy and theoretical calculations, less twisted AGNRs are synthesized by the microwave heating than the furnace annealing. Our results reveal a route for rapid production of AGNRs in large scale, which would benefit future applications in novel AGNRs-based semiconductor devices.

cond-mat.mtrl-sci↗

arXiv2023

Leaderless Consensus of Heterogeneous Multiple Euler-Lagrange Systems with Unknown Disturbance

This paper studies the leaderless consensus problem of {heterogeneous} multiple networked Euler-Lagrange systems subject to persistent disturbances with unknown constant biases, amplitudes, initial phases, and frequencies. The main characteristic of this study is that none of the agents has information of a common reference model or of a common reference trajectory. Therefore, the agents must simultaneously and in a distributed way: achieve consensus to a common reference model (group model); achieve consensus to a common reference trajectory; {and} reject the unknown disturbances. We show that this is possible via a suitable combination of techniques of distributed `observers', internal model principle, and adaptive regulation. The proposed design generalizes recent results on group model learning, which have been studied for linear agents over undirected networks. In this work, group model learning is achieved for Euler-Lagrange dynamics over directed networks in the presence of persistent unknown disturbances.

math.OC↗

arXiv2022

Active Reconfigurable Intelligent Surface Aided Surveillance Scheme

This letter attempts to design a surveillance scheme by adopting an active reconfigurable intelligent surface (RIS). Different from the conventional passive RIS, the active RIS could not only adjust the phase shift but also amplify the amplitude of the reflected signal. With such reflecting, the reflected signal of active RIS could jointly adjust the signal to interference plus noise ratio (SINR) of the suspicious receiver and the legitimate monitor, hence the proactive eavesdropping at the physical layer could be effectively realized. We formulate the optimization problem with the target of maximizing the eavesdropping rate to obtain the optimal reflecting coefficient matrix of the active RIS. The formulated optimization problem is nonconvex fractional programming and challenging to deal with. We then solve the problem by approximating it as a series of convex constraints. Simulation results validate the effectiveness of our designed surveillance scheme and show that the proposed active RIS aided surveillance scheme has good performance in terms of eavesdropping rate compared with the scheme with passive RIS.

cs.IT↗

arXiv2022

Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data

Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic information. In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter. The actual observable data at the transmitter can also have non-identical distribution with the empirical data in the shared background knowledge library. To address these practical issues, this paper proposes a new neural network-based semantic communication system for image transmission, where the task is unaware at the transmitter and the data environment is dynamic. The system consists of two main parts, namely the semantic coding (SC) network and the data adaptation (DA) network. The SC network learns how to extract and transmit the semantic information using a receiver-leading training process. By using the domain adaptation technique from transfer learning, the DA network learns how to convert the data observed into a similar form of the empirical data that the SC network can process without retraining. Numerical experiments show that the proposed method can be adaptive to observable datasets while keeping high performance in terms of both data recovery and task execution.

cs.IT↗

arXiv2022

Secure Transmission Design for Virtual Antenna Array-aided Device-to-device Multicast Communications

This paper investigates the physical-layer security in a Virtual Antenna Array (VAA)-aided Device- to-Device Multicast (D2DM) communication system, where the User Equipments (UEs) who cache the common content will form a VAA system and cooperatively multicast the content to UEs who desire it. Specifically, with the target of securing the VAA-aided D2DM communication under the threat of multiple eavesdroppers, we propose a secure beamforming scheme by jointly considering the formed VAA and the Base Station (BS). For obtaining the optimal beamforing vectors, a nonsmooth and nonconvex Weight Sum Rate Maximization Problem (WRMP) is formulated and solved using Successive Convex Approximation (SCA) approach. Furthermore, we consider the worst case that eavesdroppers cooperatively form a eavesdrop VAA to enhance the overhearing capacity. In this case, we modify the securing beamforming scheme, formulate the corresponding WRMP and solve it using a two-level optimization. Simulation results validate the improvements of the VAA-aided D2DM scheme in terms of communication security compared with conventional D2DM schemes.

cs.IT↗