Federated Graph Learning (FGL) has emerged as a powerful paradigm for decentralized training of graph neural networks while preserving data privacy. However, existing FGL methods are predominantly designed for static graphs and rely on parameter averaging or distribution alignment, which implicitly assume that all features are equally transferable across clients, overlooking both the spatial and temporal heterogeneity and the presence of client-specific knowledge in real-world graphs. In this work, we identify that such assumptions create a vicious cycle of spurious representation entanglement, client-specific interference, and negative transfer, degrading generalization performance in Federated Learning over Dynamic Spatio-Temporal Graphs (FSTG). To address this issue, we propose a novel causality-inspired framework named SC-FSGL, which explicitly decouples transferable causal knowledge from client-specific noise through representation-level interventions. Specifically, we introduce a Conditional Separation Module that simulates soft interventions through client conditioned masks, enabling the disentanglement of invariant spatio-temporal causal factors from spurious signals and mitigating representation entanglement caused by client heterogeneity. In addition, we propose a Causal Codebook that clusters causal prototypes and aligns local representations via contrastive learning, promoting cross-client consistency and facilitating knowledge sharing across diverse spatio-temporal patterns. Experiments on five diverse heterogeneity Spatio-Temporal Graph (STG) datasets show that SC-FSGL outperforms state-of-the-art methods.

This article presents a four-phase adaptive on-time (AOT) controlled buck converter with an ultra-fast load-transient response. A dynamic-frequency clock generator is proposed to generate and auto-synchronize three duty signals with 90°, 180°, and 270° phase shifts based on an already-generated duty signal. Interleaving angles between phases are always kept constant even when the switching frequency varies. Unlike the traditional pulse distribution scheme in constant on-time (COT) multiphase buck converters, the generated pulses no longer need to be sequentially distributed to different phases, but synchronized to one phase instead. Therefore, a much faster load-transient response can be achieved. A transient-enhancement scheme is also proposed to remove the speed limitations introduced by the error amplifier (EA) during load transient. The fast response merits of the AOT controller can thereby be fully taken advantage of while a relatively constant operating frequency is maintained under different input and load conditions. Simulated in a 180-nm BCD process, the proposed converter shows a fast load-transient response with 102-mV output voltage droop and 754-ns settling time for a 6A/1ns rapid load current step, where a 22-μF decoupling capacitor with a relatively large equivalent series resistance (ESR) of 15 mΩ is used. Besides, this work achieves a peak efficiency of 89.7% for 5V-to-1.4V conversion, while the efficiency is maintained at >75% over a wide load range from 1 A to 10 A.

Massive wireless connections for Internet of Things (IoT) terminals require large amounts of spectrum resource, Cognitive Radio-Enabled IoT (CR-IoT) is emerged as an effective solution to alleviate the scarcity of spectrum resources. However, CR-IoT faces several problems, such as spectrum sensing data falsification (SSDF) attacks, when using of cooperative spectrum sensing, and energy efficiency due to equipped with low-powered IoT terminals. To tackle these challenges, secure power allocation is considered a promising technique for improving energy efficiency. In this article, a joint spectrum sensing and secure power allocation scheme is proposed under SSDF attacks. First, to ensure fair resource allocation and resistance against SSDF attacks, a weighted data transmission time allocation scheme is designed based on the trust degree, where the trust degree is obtained by using an online learning algorithm and is updated in the sensed power allocation process. Then, a joint optimization problem of spectrum sensing time, number of cooperative nodes, and transmit power is formulated to maximize the CR-IoT sum energy efficiency, subject to the constraints of CR-IoT node’s average transmission power, minimum data rate, and the interference to the primary user. To handle the nonconvexity of the resulting problem, an alternating iterative optimization algorithm is proposed to iteratively solve three subproblems: 1) spectrum sensing time optimization; 2) number of cooperative nodes optimization; and 3) transmission power optimization. Simulation results demonstrate that the proposed scheme is effective in improving energy efficiency under various SSDF attack scenarios.

Federated Learning (FL) is to collaboratively train a global model among distributed clients by iteratively aggregating their local updates without sharing their raw data, whereby the global modal can approximately converge to the centralized training way over a global dataset that composed of all local datasets (i.e., union of all users’ local data). However, in real-world scenarios, the distributions of the data classes are often imbalanced not only locally, but also in the global dataset, which severely deteriorate the FL performance due to the conflicting knowledge aggregation. Existing solutions for FL class imbalance either focus on the local data to regulate the training process or purely aim at the global datasets, which often fail to alleviate the class imbalance problem if there is mismatch between the local and global imbalance. Considering these limitations, this paper proposes a Global-Local Joint Learning method, namely GLJL, which simultaneously harmonizes the global and local class imbalance issue by jointly embedding the local and the global factors into each client’s loss function. Through extensive experiments over popular datasets with various class imbalance settings, we show that the proposed method can significantly improve the model accuracy over minority classes without sacrificing the accuracy of other classes.

Estimating causal effects among different events is of great importance to critical fields such as drug development. Nevertheless, the data features associated with events may be distributed across various silos and remain private within respective parties, impeding direct information exchange between them. This, in turn, can result in biased estimations of local causal effects, which rely on the characteristics of only a subset of the covariates. To tackle this challenge, we introduce an innovative disentangle architecture designed to facilitate the seamless cross-silo transmission of model parameters, enriched with causal mechanisms, through a combination of shared and private branches. Besides, we introduce global constraints into the equation to effectively mitigate bias within the various missing domains, thereby elevating the accuracy of our causal effect estimation. Extensive experiments conducted on new semi-synthetic datasets show that our method outperforms state-of-the-art baselines.

Recent advances in mobile technologies such as 5G and WiFi 6E do not seem to deliver the promised mobile access bandwidth. To effectively characterize mobile access bandwidth in the wild, we work with a major commercial mobile bandwidth testing app to analyze mobile access bandwidths of 3.54M end users in China, based on fine-grained measurement and diagnostic information. Our analysis presents a surprising and frustrating fact---in the past two years, the average WiFi bandwidth remains largely unchanged, while the average 4G/5G bandwidth decreases remarkably. Our analysis further reveals the root causes---the bottlenecks in the underlying infrastructure (e.g., devices and wired Internet access) and side effects of aggressively migrating radio resources from 4G to 5G---with implications on closing the technology gaps. Additionally, our analysis provides insights on building ultra-fast, ultra-light bandwidth testing services (BTSes) at scale. Our new design dramatically reduces the test time of the commercial BTS from 10 seconds to 1 second on average, with a 15× reduction on the backend cost.

In recent years, 3-D synthetic aperture radar (SAR) imaging has proved its great potential in monitoring, security inspection, and radar cross section (RCS) measurement. However, 3-D SAR images based on matched filter (MF) methods have high sidelobes and are susceptible to background noise. Therefore, in this article, we propose a novel 3-D sparse SAR imaging method to improve the image quality, which combines the plug-and-play framework and the improved alternating direction method of multiplier (ADMM). First, the plug-and-play framework allows one to use state-of-the-art denoisers instead of proximal operators to improve the image quality. Second, we linearize the subproblem of ADMM involving forward imaging model. Compared with the traditional ADMM method, the improved ADMM, namely, linear ADMM (LADMM), avoids the inversion of high-dimensional matrix and is more suitable for solving high-dimensional imaging problems. Simulation and real data experiments show that the proposed method can effectively improve the image quality. Numerical analysis and 3-D visualization results are presented, which prove the impressive performance of plug-and-play LADMM.

VR applications rely on the user's ability to explore the virtual scene efficiently. In complex scenes, occlusions limit what the user can see from a given location, and the user has to navigate the viewpoint around occluders to gain line of sight to the hidden parts of the scene. When the disoccluded regions prove to be of no interest, the user has to retrace their path, making scene exploration inefficient. Furthermore, the user might not be able to assume a viewpoint that would reveal the occluded regions due to physical limitations, such as obstacles in the real world hosting the VR application, viewpoints beyond the tracked area, or viewpoints above the user's head that cannot be reached by walking. Several occlusion management methods have been proposed in visualization research, such as top view, X-ray, and multiperspective visualization, which help the user see more from the current position, having the potential to improve the exploration efficiency of complex scenes. This paper reports on a study that investigates the potential of these three occlusion management methods in the context of VR applications, compared to conventional navigation. Participants were required to explore two virtual scenes to purchase five items in a virtual Supermarket, and to find three people in a virtual parking garage. The task performance metrics were task completion time, total distance traveled, and total head rotation. The study also measured user spatial awareness, depth perception, and simulator sickness. The results indicate that users benefit from top view visualization which helps them learn the scene layout and helps them understand their position within the scene, but the top view does not let the user find targets easily due to occlusions in the vertical direction, and due to the small image footprint of the targets. The X-ray visualization method worked better in the garage scene, a scene with a few big occluders and a low occlusion depth complexity' and less well in the Supermarket scene, a scene with many small occluders that create high occlusion depth complexity. The multi-perspective visualization method achieves better performance than the top view method and the X-ray method, in both scenes. There are no significant differences between the three methods and the conventional method in terms of spatial awareness, depth perception, and simulator sickness.

In this paper, the formulation of reverse time migration (RTM) is improved for impulse borehole radar imaging in the subsurface scenarios with layered media. By fully adopting the prior information of surrounding media, the time gating function is designed and applied to the incident wave field and scattering wave field for each imaging point, which strengthens the correlation between the wave fields in the time domain. The clutters partly caused by the multiple reflections between different media layers are suppressed due to the gating function. A normalized zero-offset cross correlation with gated samples is conducted and used to weight the result of RTM. The improved approach is compared with the conventional RTM, the back-projection method, and the Stolt migration algorithm with synthetic data and is then validated by a single-borehole radar experiment in a layered media scenario. The results demonstrate that the developed approach is superior to the conventional methods in locating targets and robust in complex subsurface environments.

In this paper, the formulation of Stolt migration is modified for impulse borehole radar imaging in large nonuniform subsurface scenarios. By applying the nonuniform fast Fourier transform (FFT) to the acquisition of the frequency-wavenumber spectrum (FWS), the efficiency of Stolt migration for nonuniform surveys is improved. First, each nonuniform exponent basis in Fourier transform is approximated with a weighted summation of several uniform exponent bases. Then, the nonuniform samples with the same uniform exponent basis are accumulated to generate a larger virtual uniform sample set. The FWS of nonuniform samples is approximated with the virtual sample set by FFT. Finally, angular frequency interpolation and inverse FFT are performed over a sample FWS to reconstruct the reflectivity map of the imaging area. The selection of approximation parameters is discussed to make a tradeoff between approximation error and computational cost. The improved Stolt migration technique is compared with the conventional backprojection method and the Kirchhoff migration method on synthetic data and validated by a single borehole radar experiment in a subsurface scenario. The results show that the developed Stolt migration is superior to the conventional methods in terms of computational cost, cross-range resolution, and the ability to reconstruct the targets.

A novel data acquisition and imaging method based on compressive sensing is utilized for impulse borehole radar (IBR). With the sparse transform that we present for IBR systems, only 50% or even less samples are needed to be collected and transmitted to reconstruct the target space, which reduces the sampling rate and data transmission rate of IBR systems. The simulation and experiment results show that the proposed method is more robust in noise environment and the reconstructed target spaces have less artifacts compared with the solutions of the traditional Stolt migration method.

In this paper, the formulation of Stolt migration is modified for impulse borehole radar near-field imaging in the subsurface scenarios where the transceiver is widely separated with respect to the detection range. The proposed approach consists of the following aspects. First, the locations of the transmitter and receiver in the survey are regarded as independent sample dimensions, and the original sample set is converted to an enlarged virtual sample set. The frequency-wavenumber spectrum (FWS) of the virtual sample set is available via multidimensional fast Fourier transform (FFT). Then, the relation between the angular frequency and wavenumbers of the transmitter and receiver is derived in the frame of the virtual sample set, which provides the basis for the interpolation in angular frequency and weighting process of FWS. By applying multidimensional inverse FFT (IFFT) to the interpolated and weighted FWS of the virtual sample set, the energy of target responses will focus in some profile of the IFFT result, the position of which is related with the separation between the transmitter and receiver. Finally, the desired target space can be extracted from the IFFT result. The improved Stolt migration technique is compared with the conventional Stolt migration algorithm, back-projection method, and Kirchhoff migration algorithm on synthetic data and validated by single-borehole radar experiment in the subsurface scenario. The results show that the developed Stolt migration is superior to the conventional methods in cross-range resolution, computational cost, and the ability to reconstruct locations and shapes of targets.