Support-Aware Telemetry Compression for 5G Positioning via Conditional Conflict Graphs
本文通过引入条件冲突图和交替码本构造方法，解决了5G定位中地理分离TRP报告量化测量值时的数据压缩问题，同时保持全局零错误解码。
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3D Weighted Geometric Graph Neural Networks for Sheep Facial Pain Assessment
Traditional 2D approaches struggle to model the three-dimensional anatomical structure of sheep faces and the spatial relationships among facial keypoints, limiting the accuracy of Sheep Pain Facial Expression Scale (SPFES)-based pain assessment. This work proposes 3D-SPFES, a novel system that integrates monocular RGB-based depth estimation with a weighted geometric graph neural network (WG-GNN). Leveraging VideoDepthAnything to recover depth, the method embeds facial keypoints into 3D Euclidean space and constructs edge weights that combine Euclidean distances with surface coplanarity. An anatomy-aware scaled dot-product attention mechanism is introduced to facilitate geometric message passing, ultimately yielding a continuous, normalized pain score ranging from 0% to 100%. Requiring no specialized depth-sensing hardware, this approach substantially enhances the modeling of SPFES-relevant features and their 3D spatial dependencies, enabling highly accurate automated pain assessment.
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Price Information Is Not Enough: Ordering and Decision Rules in Storage Bidding
This study addresses the limitation of relying solely on price forecasting for optimizing energy storage bidding, as actual revenue depends critically on the interaction between decision rules and price signals. The work disentangles the theoretical value of price signals from the gains achievable through strategic implementation and proposes an ordinal bidding strategy that leverages only the ranking of prices—rather than their absolute accuracy—under daily throughput constraints. Through a conditional sub-Gaussian price model, mutual information analysis, and Gaussian perturbation experiments, the authors reveal a non-monotonic relationship between information content and realized revenue. Empirical results demonstrate that the ranking-based strategy captures 90% of the attainable revenue; even with perfectly accurate price vectors, swapping the highest and lowest prices reduces revenue by 53%; and current climate-aware benchmarks already achieve 78% of the revenue attainable under ideal forecasts.
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Image selective encryption analysis using mutual information in CNN based embedding space
To address the lack of a unified quantitative standard for information leakage in selectively encrypted images, this paper proposes a privacy risk assessment method integrating information theory and deep learning. The core innovation is a mutual information estimation framework constructed within a CNN embedding space, combining empirical estimators with the Mutual Information Neural Estimation (MINE) optimization strategy to accurately characterize residual spatial dependencies and structural artifacts in encrypted images. Crucially, the method enables end-to-end leakage quantification without requiring access to original plaintext images. Extensive experiments across diverse selective encryption schemes demonstrate its effectiveness and robustness. Results show that the proposed model significantly improves detection sensitivity to information leakage, providing an interpretable, reproducible theoretical tool and practical methodology for evaluating image privacy protection.
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Maximize margins for robust splicing detection
Current image splicing detection models exhibit poor generalization against post-processing operations (e.g., JPEG compression, Gaussian filtering), severely undermining their reliability in real-world deployment. To address this, we propose a robust training paradigm grounded in latent-space decision boundary analysis: model robustness is quantified via boundary width, and models are jointly trained under multiple post-processing perturbations; the optimal checkpoint is selected on the validation set based on maximal boundary width. Crucially, this approach requires no architectural modifications or loss-function redesign—robustness is enhanced solely through refined training strategies and boundary-aware model selection, thereby improving discriminability in the feature space. Extensive experiments across multiple benchmark datasets demonstrate that our method significantly enhances resilience to common post-processing artifacts. Specifically, it yields average AUC improvements of 3.2–5.8 percentage points over state-of-the-art training strategies under JPEG compression and Gaussian blur.
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Support-Aware Telemetry Compression for 5G Positioning via Conditional Conflict Graphs
本文通过引入条件冲突图和交替码本构造方法，解决了5G定位中地理分离TRP报告量化测量值时的数据压缩问题，同时保持全局零错误解码。
0 citationsRead paper
3D Weighted Geometric Graph Neural Networks for Sheep Facial Pain Assessment
Traditional 2D approaches struggle to model the three-dimensional anatomical structure of sheep faces and the spatial relationships among facial keypoints, limiting the accuracy of Sheep Pain Facial Expression Scale (SPFES)-based pain assessment. This work proposes 3D-SPFES, a novel system that integrates monocular RGB-based depth estimation with a weighted geometric graph neural network (WG-GNN). Leveraging VideoDepthAnything to recover depth, the method embeds facial keypoints into 3D Euclidean space and constructs edge weights that combine Euclidean distances with surface coplanarity. An anatomy-aware scaled dot-product attention mechanism is introduced to facilitate geometric message passing, ultimately yielding a continuous, normalized pain score ranging from 0% to 100%. Requiring no specialized depth-sensing hardware, this approach substantially enhances the modeling of SPFES-relevant features and their 3D spatial dependencies, enabling highly accurate automated pain assessment.
0 citationsRead paper
Price Information Is Not Enough: Ordering and Decision Rules in Storage Bidding
This study addresses the limitation of relying solely on price forecasting for optimizing energy storage bidding, as actual revenue depends critically on the interaction between decision rules and price signals. The work disentangles the theoretical value of price signals from the gains achievable through strategic implementation and proposes an ordinal bidding strategy that leverages only the ranking of prices—rather than their absolute accuracy—under daily throughput constraints. Through a conditional sub-Gaussian price model, mutual information analysis, and Gaussian perturbation experiments, the authors reveal a non-monotonic relationship between information content and realized revenue. Empirical results demonstrate that the ranking-based strategy captures 90% of the attainable revenue; even with perfectly accurate price vectors, swapping the highest and lowest prices reduces revenue by 53%; and current climate-aware benchmarks already achieve 78% of the revenue attainable under ideal forecasts.
0 citationsRead paper
Image selective encryption analysis using mutual information in CNN based embedding space
To address the lack of a unified quantitative standard for information leakage in selectively encrypted images, this paper proposes a privacy risk assessment method integrating information theory and deep learning. The core innovation is a mutual information estimation framework constructed within a CNN embedding space, combining empirical estimators with the Mutual Information Neural Estimation (MINE) optimization strategy to accurately characterize residual spatial dependencies and structural artifacts in encrypted images. Crucially, the method enables end-to-end leakage quantification without requiring access to original plaintext images. Extensive experiments across diverse selective encryption schemes demonstrate its effectiveness and robustness. Results show that the proposed model significantly improves detection sensitivity to information leakage, providing an interpretable, reproducible theoretical tool and practical methodology for evaluating image privacy protection.
0 citationsRead paper
Maximize margins for robust splicing detection
Current image splicing detection models exhibit poor generalization against post-processing operations (e.g., JPEG compression, Gaussian filtering), severely undermining their reliability in real-world deployment. To address this, we propose a robust training paradigm grounded in latent-space decision boundary analysis: model robustness is quantified via boundary width, and models are jointly trained under multiple post-processing perturbations; the optimal checkpoint is selected on the validation set based on maximal boundary width. Crucially, this approach requires no architectural modifications or loss-function redesign—robustness is enhanced solely through refined training strategies and boundary-aware model selection, thereby improving discriminability in the feature space. Extensive experiments across multiple benchmark datasets demonstrate that our method significantly enhances resilience to common post-processing artifacts. Specifically, it yields average AUC improvements of 3.2–5.8 percentage points over state-of-the-art training strategies under JPEG compression and Gaussian blur.
0 citationsRead paper