The Internet of Things (IoT) possesses enormous potential for a variety of smart agriculture use cases such as pest control, soil management, and autonomous irrigation. Due to the complex terrain and cost constraints, traditional infrastructure-based ubiquitous communication network has poor scalability and cost-efficiency. In such contexts, finding alternative economic and sustainable data collection mechanism becomes paramount. In this paper, the predefined trajectory of UAV equipped with storage capability is opportunistically utilized as a delay-tolerant data transportation channel. We propose an air-ground collaborative data delivery framework and maximize the end-to-end (E2E) data transmission efficiency through jointly optimizing the terrestrial subnet transmission scheduling strategy, subnet resource allocation strategy, subnet formation strategy, and flight speed control of opportunistic UAV. On account of the heterogeneous transmission demands and the task-oriented mobility, the terrestrial IoTs actively pre-network and aggregate the environmental information towards the cluster heads with the position advantage, to improve the data uploading efficiency. We derive the closed-form subnet transmission scheduling and subnet resource allocation strategy. The subnet formation sub-problem is constructed as a coalition formation game, which can be efficiently solved by the better response method. Considering the limited sojourn time in the farmland, the opportunistic UAV dynamically adjusts the flight speed to strike a balance between the traffic distribution, data uploading capability, and data downloading capability. We derive the closed-form solution of the flight speed control strategy. Numerical simulations demonstrate that the proposed algorithm can expand the E2E data delivery volume and outperform the benchmark algorithms.

The automation of logic circuit design enhances chip performance, energy efficiency, and reliability, and is widely applied in the field of Electronic Design Automation (EDA). And-Inverter Graphs (AIGs) efficiently represent, optimize, and verify the functional characteristics of digital circuits, enhancing the efficiency of EDA development. Due to the complex structure and large scale of nodes in real-world AIGs, accurate modeling is challenging, leading to existing work lacking the ability to jointly model functional and structural characteristics, as well as insufficient dynamic information propagation capability. To address the aforementioned challenges, we propose AIGer, with the aim to enhance the expression of AIGs and thereby improve the efficiency of EDA development. Specifically, AIGer consists of two components: 1) Node logic feature initialization embedding component and 2) AIGs feature learning network component. The node logic feature initialization embedding component projects logic nodes, such as AND and NOT, into independent semantic spaces, to enable effective node embedding for subsequent processing. Building upon this, the AIGs feature learning network component employs a heterogeneous graph convolutional network, designing dynamic relationship weight matrices and differentiated information aggregation approaches to better represent the original structure and information of AIGs. The combination of these two components enhances AIGer’s ability to jointly model functional and structural characteristics and improves its message passing capability, thereby strengthening its expressive power for AIGs and enhancing the development efficiency of logic circuits. Experimental results indicate that AIGer outperforms the current best models in the Signal Probability Prediction (SPP) task, improving MAE and MSE by 18.95% and 44.44%, respectively. In the Truth Table Distance Prediction (TTDP) task, AIGer achieves improvements of 33.57% and 14.79% in MAE and MSE, respectively, compared to the best-performing models1.

ABSTRACT In this paper, we investigate the anti‐jamming communication challenge for unmanned aerial vehicles (UAVs) in urban environments with strong jammers. Jamming power often far exceeds the UAVs' inherent anti‐jamming capability threshold, causing anti‐jamming measures to fail and even interrupt normal communication. To address this challenge, we propose an innovative strategy that leverages the natural shielding effect of urban buildings to enhance the anti‐jamming performance of UAV communication links. The core of this strategy lies in leveraging multiple UAVs working collaboratively to form an end‐to‐end anti‐jamming communication network in the urban environment. Specifically, we first introduce a UAV formation control mechanism for end‐to‐end collaboration—‘resonant motion’ transmission. Second, we propose an anti‐jamming algorithm for urban environments with strong jammers, combining ‘resonant motion’ transmission with the artificial potential field (APF) algorithm and rapidly exploring random tree star (RRT*) to develop a novel anti‐jamming path planning algorithm. Finally, we leverage prior knowledge of jammers, UAV formation and urban environment to enable UAV formation to evade obstacles and strong jammers in urban environment, find optimal communication positions and thereby build more robust communication links. The anti‐jamming strategy proposed in this paper provides a practical new approach to addressing the technical challenge of difficult UAV communication in urban environment with strong jammers. Simulation experiments demonstrate that UAVs can effectively address the challenge of UAV formation in urban environment through collaborative operations and intelligent algorithms, achieving reliable end‐to‐end transmission for UAV formation, outperforming traditional algorithms in both anti‐jamming performance and energy consumption.

Abstract This paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle‐enabled access network. Since different user equipments performing different tasks and have different data rate requirements, the priority‐based traffic fairness problem is investigated. This problem, however, is a mixed integer nonlinear programming problem with NP‐hard complexity, making it challenging to be solved. To address this issue, a self‐organized and distributed framework “sense‐as‐you‐fly” based on the decomposition process, which divides the original problem into several subproblems, is proposed. Assuming without central controller, we derive the closed‐form resource allocation scheme and propose distributed many‐to‐one matching to optimize user association subproblem. Considering the coupled characteristics, the multi‐unmanned aerial vehicle deployment and channel allocation subproblems are modelled as a local altruistic game. The existence of Nash equilibrium is proved with the aid of exact potential game and efficient best response learning‐based algorithm is proposed. The original problem is finally addressed by solving the sub‐problems alternately and iteratively. Simulation results verify its effectiveness. By jointly optimizing multidimensional variables, the proposed algorithm unlocks network performance gains, especially in resource‐limited regimes.

Big data applications have differentiated requirements for I/O resources in cloud environments. For instance, data analytic and AI/ML applications usually have periodical burst I/O traffic, and data stream processing and database applications often introduce fluctuating I/O loads based on a guaranteed I/O bandwidth. However, the existing resource isolation model (i.e., RLW) and methods (e.g., Token-bucket, mClock, and cgroup) cannot support the fluctuating I/O load and differentiated I/O demands well, and thus cannot achieve fairness, high resource utilization, and high performance for applications at the same time. In this paper, we propose a novel efficient and fair I/O resource isolation model and method called R2B, which can adapt to the differentiated I/O characteristics and requirements of different applications in a shared resource environment. R2B can simultaneously satisfy the fairness and achieve both high application efficiency and high bandwidth utilization.

The task of infomin learning aims to learn a representation with high utility while being uninformative about a specified target, with the latter achieved by minimising the mutual information between the representation and the target. It has broad applications, ranging from training fair prediction models against protected attributes, to unsupervised learning with disentangled representations. Recent works on infomin learning mainly use adversarial training, which involves training a neural network to estimate mutual information or its proxy and thus is slow and difficult to optimise. Drawing on recent advances in slicing techniques, we propose a new infomin learning approach, which uses a novel proxy metric to mutual information. We further derive an accurate and analytically computable approximation to this proxy metric, thereby removing the need of constructing neural network-based mutual information estimators. Compared to baselines, experiments on algorithmic fairness, disentangled representation learning and domain adaptation verify that our method can more effectively remove unwanted information with limited time budget.

Perioperative complications are associated with increased patient morbidity and mortality, and result in substantial healthcare resource utilization. With the aim to facilitate medical decision-making and improve health outcomes, machine learning methods are used to train prediction models to inform healthcare professionals and patients about the risks, which require both inner- and outer-hospital information, e.g., daily performance and clinical tests. For sake of data security and privacy, the Hospital Information System (HIS) is usually isolated from the public Internet and the raw patient samples are forbidden to transfer directly, which limits the integration of inner-and outer-hospital information. In this paper, we propose a learning framework named FedIO which bridges Inner- and Outer-hospital information via vertical Federated Learning for perioperative complications prognostic prediction. Instead of transmitting data into one cloud center, FedIO leverages the locally kept data to train a prediction model, during which only the intermediate parameters are transmitted and integrated. Extensive experiments are conducted on real-world datasets and the results manifest that combining inner- and outer-hospital knowledge is better than either of them, and FedIO shows the same-level performance as the cloud-based methods but without sharing raw data.