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  1. This article presents a novel system,LLDPC,1which brings Low-Density Parity-Check (LDPC) codes into Long Range (LoRa) networks to improve Forward Error Correction, a task currently managed by less efficient Hamming codes. Three challenges in achieving this are addressed: First, Chirp Spread Spectrum (CSS) modulation used by LoRa produces only hard demodulation outcomes, whereas LDPC decoding requires Log-Likelihood Ratios (LLR) for each bit. We solve this by developing a CSS-specific LLR extractor. Second, we improve LDPC decoding efficiency by using symbol-level information to fine-tune LLRs of error-prone bits. Finally, to minimize the decoding latency caused by the computationally heavy Soft Belief Propagation (SBP) algorithm typically used in LDPC decoding, we apply graph neural networks to accelerate the process. Our results show thatLLDPCextends default LoRa’s lifetime by 86.7% and reduces SBP algorithm decoding latency by 58.09×.

     
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    Free, publicly-accessible full text available July 31, 2025
  2. Free, publicly-accessible full text available August 1, 2025
  3. Free, publicly-accessible full text available June 3, 2025
  4. Graph Neural Networks (GNNs) have emerged as powerful tools for processing graph-structured data, enabling applications in various domains. Yet, GNNs are vulnerable to model extraction attacks, imposing risks to intellectual property. To mitigate model extraction attacks, model ownership verification is considered an effective method. However, throughout a series of empirical studies, we found that the existing GNN ownership verification methods either mandate unrealistic conditions or present unsatisfactory accuracy under the most practical settings—the black-box setting where the verifier only requires access to the final output (e.g., posterior probability) of the target model and the suspect model. Inspired by the studies, we propose a new, black-box GNN ownership verification method that involves local independent models and shadow surrogate models to train a classifier for performing ownership verification. Our method boosts the verification accuracy by exploiting two insights: (1) We consider the overall behaviors of the target model for decision-making, better utilizing its holistic fingerprinting; (2) We enrich the fingerprinting of the target model by masking a subset of features of its training data, injecting extra information to facilitate ownership verification. To assess the effectiveness of our proposed method, we perform an intensive series of evaluations with 5 popular datasets, 5 mainstream GNN architectures, and 16 different settings. Our method achieves nearly perfect accuracy with a marginal impact on the target model in all cases, significantly outperforming the existing methods and enlarging their practicality. We also demonstrate that our method maintains robustness against adversarial attempts to evade the verification. 
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    Free, publicly-accessible full text available May 19, 2025
  5. Free, publicly-accessible full text available May 19, 2025
  6. Abstract

    The water vapor transport associated with latent heat flux (LE) in the planetary boundary layer (PBL) is critical for the atmospheric hydrological cycle, radiation balance, and cloud formation. The spatiotemporal variability of LE and water vapor mixing ratio (rv) are poorly understood due to the scale‐dependent and nonlinear atmospheric transport responses to land surface heterogeneity. Here, airborne in situ measurements with the wavelet technique are utilized to investigate scale‐dependent relationships among LE, vertical velocity (w) variance (), andrvvariance () over a heterogeneous surface during the Chequamegon Heterogeneous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign. Our findings reveal distinct scale distributions of LE, , and at 100 m height, with a majority scale range of 120 m–4 km in LE, 32 m–2 km in , and 200 m–8 km in . The scales are classified into three scale ranges, the turbulent scale (8–200 m), large‐eddy scale (200 m–2 km), and mesoscale (2–8 km) to evaluate scale‐resolved LE contributed by and . The large‐eddy scale in PBL contributes over 70% of the monthly mean total LE with equal parts (50%) of contributions from and . The monthly temporal variations mainly come from the first two major contributing classified scales in LE, , and . These results confirm the dominant role of the large‐eddy scale in the PBL in the vertical moisture transport from the surface to the PBL, while the mesoscale is shown to contribute an additional ∼20%. This analysis complements published scale‐dependent LE variations, which lack detailed scale‐dependent vertical velocity and moisture information.

     
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    Free, publicly-accessible full text available February 16, 2025
  7. This paper presents GeoDMA , which processes the GPS data from multiple vehicles to detect anomalous driving maneuvers, such as rapid acceleration, sudden braking, and rapid swerving. First, an unsupervised deep auto-encoder is designed to learn a set of unique features from the normal historical GPS data of all drivers. We consider the temporal dependency of the driving data for individual drivers and the spatial correlation among different drivers. Second, to incorporate the peer dependency of drivers in local regions, we develop a geographical partitioning algorithm to partition a city into several sub-regions to do the driving anomaly detection. Specifically, we extend the vehicle-vehicle dependency to road-road dependency and formulate the geographical partitioning problem into an optimization problem. The objective of the optimization problem is to maximize the dependency of roads within each sub-region and minimize the dependency of roads between any two different sub-regions. Finally, we train a specific driving anomaly detection model for each sub-region and perform in-situ updating of these models by incremental training. We implement GeoDMA in Pytorch and evaluate its performance using a large real-world GPS trajectories. The experiment results demonstrate that GeoDMA achieves up to 8.5% higher detection accuracy than the baseline methods. 
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  8. null (Ed.)
    Zero-knowledge (ZK) proofs with an optimal memory footprint have attracted a lot of attention, because such protocols can easily prove very large computation with a small memory requirement. Such ZK protocol only needs O(M) memory for both parties, where M is the memory required to verify the statement in the clear. In this paper, we propose several new ZK protocols in this setting, which improve the concrete efficiency and, at the same time, enable sublinear amortized communication for circuits with some notion of relaxed uniformity. 1. In the circuit-based model, where the computation is represented as a circuit over a field, our ZK protocol achieves a communication complexity of 1 field element per non-linear gate for any field size while keeping the computation very cheap. We implemented our protocol, which shows extremely high efficiency and affordability. Compared to the previous best-known implementation, we achieve 6×–7× improvement in computation and 3×– 7× improvement in communication. When running on intro-level AWS instances, our protocol only needs one US dollar to prove one trillion AND gates (or 2.5 US dollars for one trillion multiplication gates over a 61-bit field). 2. In the setting where part of the computation can be represented as a set of polynomials, we can achieve communication sublinear to the polynomial size: the communication only depends on the input size and the highest degree of all polynomials, independent of the number of polynomials and the number of multiplications in the polynomials. Using the improved ZK protocol, we can prove matrix multiplication with communication proportional to the input size, rather than the number of multiplications. Proving the multiplication of two 1024 × 1024 matrices, our implementation, with one thread and 1 GB of memory, only needs 10 seconds and communicates 25 MB, 35× faster than the state-of-the-art protocol Virgo that would need more than 140 GB of memory for the same task. 
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