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We present 30 newly discovered repeating fast radio burst (FRB) sources from the second catalog of bursts detected by the FRB backend on the Canadian Hydrogen Intensity Mapping Experiment (CHIME/FRB). These repeaters have extragalactic dispersion measures (DMs) spanning 99.4-1446.0 pc cm^-3 and burst rates between 10^-5.7 and 10^-0.5 hr^-1 scaled to a fluence threshold of 5 Jy ms. We report evidence of monotonic, linear DM variations in four repeaters on years-long timescales. The newly discovered sources bring CHIME/FRB's total number of observed repeating FRBs to 80, 79 of which were discovered by CHIME/FRB, between 2018 July 25 and 2023 September 15. In the full CHIME/FRB sample, only 2.4± 0.4% of sources have been observed to repeat, and we do not find evidence for significant evolution of this value over the duration of the experiment. We find no substantial evidence for bimodal populations of one-off and repeating FRBs in their burst rate distributions; the distribution of upper limits on repeat rates implied from observations of as-yet one-offs is entirely contained within the observed range of repeater burst rates and the distributions do not appear inconsistent. Similarly, using the population analysis framework of C. W. James (2023), we find that our observations of repeating and yet-one-off FRBs are equally well fit assuming a power-law distribution of repeat rates with 50-100

In fifth-generation (5G) wireless systems, support for non-terrestrial networks (NTN) was added. Physical layer challenges in the deployment of 5G NTN systems include the dependence of synchronization on the position of the user equipment (UE) that is derived using a global navigation satellite system (GNSS) and on the position of the satellite. In this paper, a solution is presented for position-independent synchronization of sixth-generation (6G) NTN systems. By using a combination of open-loop and closed-loop uplink corrections, the timing and frequency errors can be minimized at the receiver side, without the need for GNSS-based pre-compensation. By means of simulations, lab emulations, and over-satellite testing, we prove that synchronization can be achieved without GNSS and without any signal-to-noise ratio degradation, and with mobility of the satellite and UE.

This paper presents an edge neural architecture search method for human presence detection using Wi-Fi signals. The proposed method, WiFiNAS, is a derivative-free, cell-based technique to design tiny 1D convolutional neural networks for microcontrollers. The process iteratively explores topologies by adjusting the number of convolutional filters and computational layers, based on validation accuracy under micro controller memory constraints. This deployment happens by the unified core technology, which imports and profiles memory usage, operations, and latency on tiny microcontrollers. This information provides feedback to the search process. The final 8bits model achieved a classification accuracy of 98.57

Invasive populations of Anoplophora glabripennis (Motschulsky) threaten forested systems across the globe. Eradication programs in North America can span hundreds of square kilometers, yet eradication efforts are carried out at the individual tree or stand scale. An A. glabripennis infestation was discovered in South Carolina in 2020; within this landscape, a small island was found on which 85% of A. glabripennis host trees were infested. The removal of all infested trees from the island in 2021 provided an opportunity to use this discrete population as a natural microcosm in which the spatial locations for the 14,615 oviposition sites and 1,611 exit holes in the population were documented. Data show A. glabripennis population growth rates varied substantially among individual trees. At the scale of the island, beetle density increased exponentially over time, while the number of infested trees on the island increased at a more moderate rate. The spatial and temporal patterns suggest several causal mechanisms and highlight the need for additional studies including the potential that female preference drives variation among host tree preference and beetle performance. The severity of the A. glabripennis infestation within trees was an inconsistent predictor of the age of an infestation, which is a key assumption used in landscape scale models of A. glabripennis dispersal. However, overall differences between predicted A. glabripennis movement and behavior were similar to that predicted from current risk models, supporting the utility of the landscape scale reconstructions currently used to model A. glabripennis spread and risk.

Microservice systems generate fragmented and misaligned multi-modal data, including metrics, logs, and traces, posing a major challenge for AIOps, which requires coherent system-state representations for downstream analytics. Existing approaches typically model spatio-temporal relations at a coarse granularity or are tailored to specific tasks, limiting their generalizability across different O&M scenarios. To address these limitations, we propose M3RL, a unified pre-training and fine-tuning framework for learning robust and transferable system-state representations. At its core, the spatio-temporal network (STNet) performs fine-grained semantic fusion of multi-modal data along both temporal and spatial dimensions and models evolving communication-driven dependencies among services. We further pre-train STNet with three complementary objectives: denoising reconstruction for robustness, future prediction for temporal evolution modeling, and causal contrastive learning for node-level discriminability. Extensive experiments on two public benchmark datasets demonstrate that M3RL consistently outperforms recent state-of-the-art baselines across key downstream tasks, reducing state prediction MAE by 1.9%, increasing anomaly detection F1 by 4.7%, and improving root cause localization HR@1 by 4.8% on average. These results show that M3RL provides a robust and transferable representation backbone for multiple AIOps tasks.