Search Results (534)

Spectral fidelity is critical for accurate hyperspectral image (HSI) processing. A key characteristic of HSI data is the strong correlation between spectral bands, which manifests as structured symmetry in spectral covariance matrices. While global low-rank tensor decompositions leverage this spectral structure, they often neglect the significant spatial heterogeneity present in real-world scenes. To address this limitation, we propose a Region-Aware 3D Tensor Decomposition (RA-3DTD) framework that balances global spectral consistency with local spatial adaptation. Our approach first performs residual energy-based region detection to identify complex regions within the hyperspectral cube, and then applies localized Higher-Order Orthogonal Iteration (HOOI) specifically to those regions requiring enhanced detail preservation. This two-phase design incorporates global low-rank constraints with local spatial processing, improving denoising accuracy. Extensive experiments on four benchmark datasets (Pavia_80, Indian Pines, Salinas, and Pavia University) demonstrate the effectiveness of our method compared to five leading model-based baselines including BM3D, LRMR, NLR, LRTD, and FastHyDe. Our approach achieves a 1.33 dB increase in PSNR over a leading model-based competitor (FastHyDe) in complex urban scenes while maintaining strong structure fidelity as measured by SSIM and SAM metrics. Full article

This paper investigates scaling symmetry in thermodynamics by unifying constrained Hamiltonian dynamics with symplectic and contact geometries. Through the mathematical processes of contactization and symplectization, we demonstrate that fixing an extended global scale variable effectively recovers the standard thermodynamic description in terms of scale-invariant quantities. The geometric formalism is illustrated by establishing the diffeomorphism between the Lagrangian submanifolds of ideal and van der Waals gases. Finally, applying this framework to a Schwarzschild black hole reveals that changing the scaling weights of entropy and internal energy is a fundamental physical requirement to accommodate non-isothermal dynamics. Full article

Industrial defect image generation is an effective way to alleviate data scarcity and class imbalance in visual inspection. In industrial images, defects usually appear as local asymmetric perturbations on globally regular background structures, which makes defect synthesis dependent on both background consistency and local anomaly fidelity. Existing generative methods still face difficulties when only limited anomalous samples are available, especially in representing fine-grained discrepancies among defect categories, coordinating global and local branches across diffusion stages, and constraining small defect regions and their boundary transitions. To address these issues, this paper develops a symmetry-aware multi-constraint diffusion framework based on the dual-branch architecture of DualAnoDiff. The framework treats multi-class industrial defect generation as a joint optimization problem involving class-conditioned discrepancy representation, diffusion-stage-aware branch coordination, and saliency-guided regional supervision. First, Class-Conditioned Shared-Basis LoRA (CSB-LoRA) models category-specific defect characteristics by combining cross-class shared low-rank bases with class-dependent coefficients, allowing common structural priors and class-specific asymmetric patterns to be represented simultaneously. Second, Temporal Dual-branch Attention Modulation (TDAM) adjusts branch interaction, background information injection, and residual feature fusion according to the denoising stage, so that the generation process can gradually shift from global structure restoration to local defect refinement. Third, Saliency-Guided Reconstruction Loss (SGRL) applies stronger spatial constraints to defect regions and boundary neighborhoods, improving local detail preservation and defect-background continuity. Experiments on the MVTec AD dataset show that the proposed method improves both generation quality and perceptual diversity compared with DualAnoDiff. The average IS increases from 1.93 to 2.07, and IC-LPIPS increases from 0.38 to 0.41. When the generated samples are used for downstream defect segmentation, AP-P improves from 84.5% to 85.7%, and F1-P improves from 78.8% to 79.3%. These results indicate that the generated samples can serve as useful synthetic training data for few-shot and class-imbalanced industrial inspection. Full article

This paper introduces a graphical framework for smooth Lie group actions based on tangent orbit interactions. In contrast with classical intersection graphs, where vertices usually represent algebraic subobjects and edges record set-theoretic intersections, the present construction uses non-trivial orbits as vertices and creates edges from common nonzero tangent directions inside the fixed ambient embedding. Starting from infinitesimal tangent spaces generated by the action, we construct Lie orbit tangent graphs and analyze their adjacency structure, connectedness, completeness, degrees and diameter estimates. To describe local and global interactions, tangent fibers, local tangent orbit cliques, tangent orbit hypergraphs and incidence structures are introduced. We further develop separating tangent paths and use them to construct neighborhood systems and tangent-separating topologies. The framework gives a unified way to encode orbit-level tangent interactions and may be useful in geometric analysis, symmetry-based dynamical systems, differential topology and mathematical physics, where orbits and infinitesimal directions describe invariant motions, constraints or symmetry-reduced configurations. Several examples are included to illustrate how Lie group actions, graph structures, hypergraphs and tangent geometry interact within the proposed scheme. Full article

Dense underwater aquaculture poses significant challenges for intelligent image processing because asymmetric occlusion, turbidity, aeration-like bubbles, and motion blur frequently degrade fish contours and quasi-periodic scale textures. These disturbances often cause conventional detectors to miss detections, merge bounding boxes, experience feature collapse, and exhibit unstable counting. To address this problem, we propose DenseFish-v13, a symmetry-aware NMS-free YOLOv13-Mamba framework for dense underwater fish detection and bio-kinematic behavior recognition. The framework integrates a Bio-Harmonic Frequency Gate to preserve biological texture patterns while suppressing bubble-like frequency noise, a Bi-directional Multi-scale Wavelet Mamba backbone for global occlusion-aware structure recovery, and an asymmetry-aware density repulsion strategy to separate highly overlapping fish instances during bipartite matching. In addition, a lightweight Bio-Kinematic Behavior Head converts continuous detections into interpretable trajectory descriptors for behavior-state recognition. Experiments on the Dense-Aqua benchmark, constructed from public aquaculture datasets, show that DenseFish-v13 achieves 64.8% mAP@50:95 and a Counting MAE of 3.7 on the overall test set, while reaching 64.2% mAP@50:95 and a Counting MAE of 4.1 on the extreme-density split. Under a strong synthetic bubble perturbation, the model shows only a 1.3 percentage-point drop in mAP and maintains 125 FPS on Jetson Orin NX. These results demonstrate its effectiveness in robust, real-time underwater aquaculture monitoring. Full article

The Q-Learning Relational Game (QLRG) framework provides a theoretically rigorous method for identifying minimal winning coalitions in online social networks (OSNs) under the restrictive assumption of global agent symmetry or uniform matroid structure. Real-world OSNs, however, exhibit significant asymmetry. This paper introduces the Cluster-Based Q-Learning Relational Game (C-QLRG), a practical extension that relaxes the global symmetry requirement by leveraging community structure. We partition the agent set into communities with bounded internal variation and represent the state solely by community membership counts of the seed set. Because the closure operator already captures all eventual influence spread, the problem reduces to a sequential seed selection task where the agent decides, at each step, from which community to add the next seed. We prove that the optimal Q-function of a suitably regularized reach-efficiency objective is Lipschitz continuous and derive a performance bound for the learned policy. The full algorithm is presented, and its complexity is analyzed. Empirical evaluations on a synthetic asymmetric network and Zachary’s Karate Club demonstrate that C-QLRG is highly sensitive to reward parameters, where default settings lead to premature stopping, but parameter tuning combined with a corrected minimality verification recovers high-efficiency coalitions by removing non-contributing agents. With tuned parameters, C-QLRG produces a near-winning coalition of size 11 and 99% reach on the synthetic network, surpassing the greedy baseline’s efficiency (size 12) despite a one-node coverage gap, while identifying the optimal winning coalition of size 1 on the Karate Club dataset, matching all baselines. The framework thus offers a principled trade-off between model fidelity and scalability, with the reward design choice being critical for practical deployment. Full article

Accurate and robust detection of leaf diseases is a key enabler for precision agriculture and large-scale crop health monitoring. Despite the strong generalization of modern one-stage detectors (e.g., YOLOv8), two domain-specific challenges remain: (i) weak or blurry lesion boundaries hinder precise localization, and (ii) low color contrast between diseased and healthy tissues forces models to rely on subtle texture patterns rather than salient shapes. To tackle these challenges, we reframe the core agricultural disease detection task as the identification of “asymmetric morphological anomalies” and propose a domain-tailored enhancement framework. First, we introduce an Edge Enhancement Module (EEM) that explicitly strengthens boundary-aware representations. Inspired by the natural symmetry of healthy leaves, our EEM is specifically designed to capture symmetry-breaking boundary discontinuities and localized asymmetric edges caused by disease lesions. Our method enhances edge and texture cues that are indicative of disease lesions, which often exhibit local asymmetries and boundary discontinuities. The EEM includes a Differential Normalized Pooling Block (DNPB) that highlights edge responses through discrepancies between max pooling and average pooling, which also models cross-group edge correlations. Second, the Lightweight Texture-Sensitive Feature Enhancement (LTSFE) mechanism amplifies texture-discriminative channels under low-contrast conditions by leveraging complementary global statistics and efficient channel mixing, all with negligible computational overhead. We evaluated our method on a self-constructed dataset of 106,434 images with 225,640 annotations covering diverse crops. Experiments show that the proposed method achieves state-of-the-art accuracy (81.54% mAP@0.5:0.95) while maintaining real-time inference (142 FPS), consistently outperforming strong baselines. Ablations confirm the effectiveness and complementarity of EEM and LTSFE, demonstrating that domain-specific architectural design, inspired by biological symmetry, can substantially improve agricultural vision systems. Full article

Euclidean plane geometry problem solving is a challenging benchmark for artificial intelligence because it requires complex diagram understanding, symbolic deduction, and multi-step reasoning. Constructing effective datasets for this task requires geometric instances that are realizable, non-degenerate, structurally diverse, and paired with human-like proofs. However, existing random or template-based generation pipelines often produce redundant, singular, or infeasible candidates, causing substantial computation to be spent before useful reasoning trajectories can be extracted. To address these limitations, we present FGeo-GCG, a hybrid geometric data synthesis framework built on the FormalGeo-V2 deductive engine. It formulates Geometric Configuration Generation as an incremental linear construction process that decomposes global constraint satisfaction into local construction steps, thereby pruning invalid branches during the generation process. To improve reliability and efficiency, FGeo-GCG combines two validation stages: a safe stochastic Jacobian-rank filter estimates whether local candidate constraints contribute independent algebraic restrictions, and progressive geometric validation checks whether the resulting partial construction remains realizable and non-degenerate. By encoding incidence-, metric-, and symmetry-related dependencies within unified constraint graphs, the framework also connects geometric data synthesis with structural symmetry analysis. Validated constraint graphs are then converted into problem instances through forward deduction, goal decomposition, and multi-dimensional complexity filtering, producing proof targets without manual annotation. Experiments show that the full validation pipeline reduces the failure rate for highly constrained instances. The resulting FGeo-GCG dataset contains more than 50,000 formally validated plane geometric configurations and provides engine-derived reasoning traces and targets for future training and evaluation of neuro-symbolic geometry problem-solving systems. Full article

Background/Objectives: Traditional protein contact analysis often fails to distinguish between local, sequence-driven motifs and global, tertiary scaffolding, which ensures structural determinism. While deep-learning models do not fully elucidate the ‘why’, they do reveal the underlying directional rules of the stability landscape. In this study, we analyzed 475 non-redundant Protein Data Bank (PDB) structures categorized into SCOP classes (all-α, all-β, α/β, α+β) of the hydrolase superfamily. Methods: To isolate the structural anchors of the global fold, we applied a sequence separation filter of ∣i − j∣ ≥ 6 and a precise spatial cutoff of 3–5 Å between Cα-only to construct asymmetric 20 × 20 frequency matrices, both raw and normalized, then present the former using a violin diagram. We developed a Pearson Correlation (PC) framework to analyze these matrices, providing high correlation when considered as vectors and giving the directionality (N-to-C vs. C-to-N) in protein folding when considered as matrices. Results: Our results reveal a hierarchical organization of tertiary determinism. Initial visualization of Residue–Residue Contact Frequency Matrices (RRCFMs), Z-score normalization (NRRCFM), and violin plots reveal the Universal Structural Grammar (USG) of interaction. Furthermore, a near-perfect PC (r = 0.99) as determined via inter-class Z-score correlation and inter-class PC demonstrates shared statistical interaction laws. In addition, PC Stage 1 (intra-class) analysis identified high symmetry, with around 80% of contacts exhibiting a very strong to strong positive correlations, while PC Stage 2 (inter-class) analysis demonstrated that around 50% of contacts exhibited very strong to strong positive correlations. Finally, we identified universal druggable pockets for drug discovery. Conclusions: This powerful mathematical framework provides a robust analytical tool for structure-based drug design. Full article

Surface roughness of woven fabrics plays a key role in tactile comfort and skin–textile interaction, particularly in medical applications involving prolonged contact with human skin. This study focuses on the surface roughness of woven fabrics in plain and twill (1/3 S) weaves intended for hospital bed sheets and bedding applications. Plain weave represents a structurally symmetric system, while twill weave exhibits a pronounced diagonal structure. Roughness was evaluated using the Fabric Touch Tester (FTT) and further analyzed through amplitude (Rq), height distribution (Rku), and frequency-related parameters (linear peak density) obtained by signal processing and peak analysis in OriginPro 2026. The results showed that weave structure is the dominant factor influencing surface topography. Plain weave fabrics exhibited higher amplitude roughness and more uniform height distribution, while twill fabrics showed lower global roughness but stronger directional dependence, particularly in diagonal directions. Linear peak density was not significantly affected by laundering cycles, fiber composition, or finishing, but was strongly dependent on weave type. The findings demonstrate that due to the orthotropic nature of woven fabrics, surface roughness, derived from surface topography, cannot be adequately described by a single parameter, and that a combined analysis of amplitude and spatial descriptors is required, with the surface being evaluated not only along the principal symmetry directions (warp and weft) but also in off-axis directions. These results provide valuable insight for the design of hospital textiles with improved tactile comfort and reduced risk of skin irritation. Full article

This study presents a structure-aware inference framework for tooth numbering and lesion localization in visible light oral images. Tooth numbering is often compromised by class imbalance and structural inconsistency caused by the uneven distribution of tooth types, motivating the integration of anatomical priors into the inference process. The framework first partitions the dental arch into quadrants using a deep learning-based detection module to establish spatial organization. Based on this, an Anchor-Teeth-Guided Inference (ATGI) strategy reconstructs globally consistent tooth numbering by leveraging dental arch continuity, bilateral symmetry, and confidence-guided anchor selection, thereby improving the recognition of underrepresented tooth classes. Visually suspicious lesion regions are independently detected and spatially associated with numbered teeth, enabling joint structural and lesion-aware analysis. Evaluated on a multi-source dataset, the method achieves a weighted F1-score of 0.813 for 32-class tooth numbering, outperforming end-to-end baselines while improving spatial consistency. Lesion localization yields F1-scores of 0.850 for caries-related regions and 0.789 for gingivitis-related regions. These results demonstrate that incorporating anatomical constraints enhances numbering robustness and improves rare-class recognition in visible light dental image analysis, showing potential for screening-oriented oral assessment and teledentistry applications. Full article

Background: The use of sports supplements among adolescents is rising globally, driven by fitness trends and social media influence, yet knowledge gaps persist. This study aimed to assess supplement usage patterns, knowledge, attitudes, information sources, and the impact of a social media educational intervention among Serbian secondary school students. Methods: A single-group pre–post educational intervention study was conducted in secondary school students (aged 14–18) in Vojvodina, Serbia. A 21-question anonymous questionnaire was distributed to 1000 students along with parental informed consent forms. Pre-intervention survey assessed sociodemographics, physical activity and social media habits, supplement use information sources, and awareness of risks and banned substances. Based on the initial findings, an educational campaign delivered 56 short videos (≈70 s each) on Instagram and TikTok covering most frequently used supplements (e.g., creatine, proteins, caffeine, energy drinks). After, the intervention survey was repeated. The data were analyzed using the McNemar–Bowker test of symmetry. Results: In this study, 65% of Serbian secondary school adolescents reported being physically active, engaging predominantly in gym workouts and team sports. The majority of participants initiate dietary supplement use independently, without consulting healthcare professionals or adults. The most commonly used supplements were vitamins and minerals, while energy drinks ranked notably high. Social media intervention had a limited impact due to its short duration; however, certain changes were detected. Conclusions: Serbian adolescents frequently use sports supplements without adequate professional guidance. Long-term TikTok/Instagram interventions could be used in the future in order to influence behaviors and improve knowledge about sport supplement use. Full article

Accurate prediction of slope displacement from high-frequency GNSS monitoring data is critical for early warning of landslides and tailings dam failures. However, existing deep learning approaches often neglect the spatial coordination imposed by geological structures and fail to decouple abrupt deformation signals from background noise, leading to non-physical oscillations and inconsistent long-term predictions. To address these limitations, this paper proposes a Symmetry-Aware Physics-Guided Spatio-Temporal Graph Network (PG-STGN). First, a geological hierarchy-aware graph is constructed by integrating geometric proximity with prior knowledge of exploration levels, where the resulting adjacency matrix is symmetric by design and reflects the physical symmetry of deformation interactions among monitoring points at the same elevation. A hierarchical masking mechanism restricts feature aggregation to physically connected neighborhoods while preserving this symmetry. Second, an improved dual-path temporal convolutional network (iTCN) decouples high-frequency abrupt variations from low-frequency evolutionary trends, enabling both sensitive detection of sudden deformation and stable tracking of long-term creep. Third, a physics-consistent loss function combining first-order temporal differencing and graph Laplacian regularization enforces kinematic smoothness and spatial coordination; the Laplacian itself is derived from the symmetric adjacency matrix, ensuring symmetric regularization across the monitoring network. Evaluated on a real-world slope GNSS dataset from a large-scale mining project, PG-STGN reduces mean squared error (MSE) by approximately 23.7% and achieves a global R² of 0.924, outperforming state-of-the-art spatio-temporal models. Ablation studies confirm that the symmetric physics-guided graph, dual-path decoupling, and consistency loss are each essential for suppressing spurious correlations and maintaining physically plausible predictions. The proposed framework provides a robust, interpretable, and symmetry-constrained solution for automated slope monitoring under complex geological conditions. Full article

Point cloud completion from partial observations remains challenging due to the trade-off between preserving global structural consistency and recovering fine-grained local details, especially under severe incompleteness. We propose a symmetry-guided progressive refinement network to address this problem by learning flexible structural correspondences and progressively refining incomplete shapes. First, a Symmetry Graph Inference Network (SymGraphNet) constructs a feature-space graph over sampled keypoints and predicts symmetry-guided structural counterparts for robust coarse shape recovery, without explicitly estimating a rigid symmetry plane or axis. Second, a confidence-weighted Cross-Aware Decoder adaptively fuses partial-observation features and symmetry-guided features to balance visible-region fidelity and missing-region completion. Third, a multi-stage residual refinement strategy progressively improves geometric fidelity, local continuity, and point distribution uniformity. Experiments on PCN, MVP, and KITTI datasets demonstrate consistent improvements over representative state-of-the-art methods under both synthetic and real-world incomplete point cloud settings. Full article

The unprecedented precision now being achieved in the measurement of the Pontecorvo–Maki–Nakagawa–Sakata (PMNS) lepton mixing matrix opens a new window onto the underlying structure of the neutrino mass matrix and the possibly associated flavor symmetries. In this work, we investigate the constraints imposed on the unitary matrix $U_{\nu }$ that diagonalizes the neutrino mass matrix, under the hypothesis that the charged-lepton mixing matrix $U_l$ consists of a single two-by-two rotation in one of the three sectors: (1,2), (1,3), or (2,3). For this analysis, we considered the latest global fit, which incorporates the precision measurement of ${\theta }_{12}$ from the JUNO experiment. For each scenario, we also derive analytical expressions for the entries of $U_{\nu }$ in terms of the measured PMNS parameters to obtain compact sum-rule-like formulae. Full article