Search Results (968)

Real-valued inter-residue distance maps provide essential spatial information for understanding protein folding mechanisms and guiding downstream applications such as function annotation, drug discovery, and structural modeling. However, existing prediction methods often struggle to capture long-range dependencies and to maintain topological consistency across different structural scales. To address these challenges, we propose a novel prediction framework that integrates a Mamba architecture, based on a selective state space model, to effectively model global interactions, and incorporates the Kolmogorov–Arnold Network (KAN) to enhance nonlinear structural representation. Extensive experiments on standard benchmark datasets, including CASP13, CASP14, and CASP15, demonstrate prediction accuracies of 86.53%, 85.44%, and 82.77%, respectively, outperforming state-of-the-art approaches. These results indicate that the proposed framework substantially improves the fidelity of real-valued distance prediction and offers a promising tool for downstream structural and functional studies. Full article

We introduce Recognition Geometry (RG), an axiomatic framework in which geometric structure is not assumed a priori but derived. The starting point of the theory is a configuration space together with recognizers that map configurations to observable events. Observational indistinguishability induces an equivalence relation, and the observable space is obtained as a recognition quotient. Locality is introduced through a neighborhood system, without assuming any metric or topological structure. A finite local resolution axiom formalizes the fact that any observer can distinguish only finitely many outcomes within a local region. We prove that the induced observable map $\overline{R}:{\mathcal{C}}_R\to \mathcal{E}$ is injective, establishing that observable states are uniquely determined by measurement outcomes with no hidden structure. The framework connects deeply with existing approaches: C^*-algebraic quantum theory, information geometry, categorical physics, causal set theory, noncommutative geometry, and topos-theoretic foundations all share the measurement-first philosophy, yet RG provides a unified axiomatic foundation synthesizing these perspectives. Comparative recognizers allow us to define order-type relations based on operational comparison. Under additional assumptions, quantitative notions of distinguishability can be introduced in the form of recognition distances, defined as pseudometrics. Several examples are provided, including threshold recognizers on ${\mathbb{R}}^n$, discrete lattice models, quantum spin measurements, and an example motivated by Recognition Science. In the last part, we develop the composition of recognizers, proving that composite recognizers refine quotient structures and increase distinguishing power. We introduce symmetries and gauge equivalence, showing that gauge-equivalent configurations are necessarily observationally indistinguishable, though the converse does not hold in general. A significant part of the axiomatic framework and the main constructions are formalized in the Lean 4 proof assistant, providing an independent verification of logical consistency. Full article

Accurate segmentation of the airway tree is crucial for the diagnosis and intervention of pulmonary disease; however, delineating small peripheral airways remains challenging. The small size and complex branching of distal airways, combined with the limitations of CT imaging (partial volume effects, noise), often lead to missed bronchial segments. To address these challenges, we propose AirwaySeekNet, a dual-decoder neural network. The model introduces a Voxel-Selective Supervision (VSS) mechanism, a dynamic reliability-aware strategy that focuses training on uncertain voxels, mitigating annotation bias, and enhancing fine-branch detection. We further incorporate a Signed Distance Field (SDF) loss to enforce tubular shape constraints, improving the boundary delineation and connectivity of the airway tree. In experiments on a pig CT dataset, AirwaySeekNet outperformed state-of-the-art models, achieving higher topological completeness and finer branch detection, and the TD metric increased by 5.55% and the BD metric increased by 8.14%. It maintained high overall segmentation accuracy (Dice), with only a minor increase in false positives from the exploration of the smallest bronchi. Overall, AirwaySeekNet markedly improves airway segmentation accuracy and topology preservation, providing a more complete and reliable mapping of the bronchial tree for clinical applications. Full article

Industrial-grade printed circuit boards (PCBs) exhibit high structural order and inherent geometric symmetry, where minute surface defects essentially constitute symmetry-breaking anomalies that disrupt topological integrity. Detecting these anomalies is quite challenging due to issues like scale variation and low contrast. Therefore, this paper proposes a symmetry-aware object detection framework, DAS-YOLO, based on an improved YOLOv11. The U-shaped adaptive feature extraction module (Def-UAD) reconstructs the C3K2 unit, overcoming the geometric limitations of standard convolutions through a deformation adaptation mechanism. This significantly enhances feature extraction capabilities for irregular defect topologies. A semantic-aware module (SADRM) is introduced at the backbone and neck regions. The lightweight and efficient ESSAttn improves the distinguishability of small or weak targets. At the same time, to address information asymmetry between deep and shallow features, an iterative attention feature fusion module (IAFF) is designed. By dynamically weighting and calibrating feature biases, it achieves structured coordination and balanced multi-scale representation. To evaluate the validity of the proposed method, we carried out comprehensive experiments using publicly accessible datasets focused on PCB defects. The results show that the Recall, mAP@50, and mAP@50-95 of DAS-YOLO reached 82.60%, 89.50%, and 46.60%, respectively, which are 3.7%, 1.8%, and 2.9% higher than those of the baseline model, YOLOv11n. Comparisons with mainstream detectors such as GD-YOLO and SRN further demonstrate a significant advantage in detection accuracy. These results confirm that the proposed framework offers a solution that strikes a balance between accuracy and practicality in addressing the key challenges in PCB surface defect detection. Full article

Out-of-distribution (OOD) detection is essential for ensuring the reliability of machine learning models deployed in safety-critical applications. Existing methods often rely solely on statistical properties of feature distributions while ignoring the geometric structure of learned representations. We propose TopoAD, a topology-aware OOD detection framework that leverages Euler Characteristic Curves (ECCs) extracted from intermediate convolutional activation maps and fuses them with standardized energy scores. Specifically, we employ a computationally efficient superlevel-set filtration with a local estimator to capture topological invariants, avoiding the high cost of persistent homology. Furthermore, we introduce task-adaptive aggregation strategies to effectively integrate multi-scale topological features based on the complexity of distribution shifts. We evaluate our method on CIFAR-10 against four diverse OOD benchmarks spanning far-OOD (Textures), near-OOD (SVHN), and semantic shift scenarios. Our results demonstrate that TopoAD-Gated achieves superior performance on far-OOD data with 89.98% AUROC on Textures, while the ultra-lightweight TopoAD-Linear provides an efficient alternative for near-OOD detection. Comprehensive ablation studies reveal that cross-layer gating effectively captures multi-scale topological shifts, while threshold-wise attention provides limited benefit and can degrade far-OOD performance. Our analysis demonstrates that topological features are particularly effective for detecting OOD samples with distinct structural characteristics, highlighting TopoAD’s potential as a sustainable solution for resource-constrained applications in texture analysis, medical imaging, and remote sensing. Full article

Map matching is a fundamental technique for aligning noisy GPS trajectory data with digital road networks and constitutes a key component of Intelligent Transportation Systems (ITS) and Location-Based Services (LBS). Nevertheless, existing approaches still suffer from notable limitations in complex environments, particularly urban and urban-like scenarios characterized by heterogeneous GPS noise and sparse observations, including inadequate adaptability to dynamically varying noise, unavoidable trade-offs between real-time efficiency and matching accuracy, and limited generalization capability across heterogeneous driving behaviors. To overcome these challenges, this paper presents a Meta-learning-driven Progressive map-Matching (MPM) method with a symmetry-aware design, which integrates a two-layer pattern-mining-based noise-robust meta-learning mechanism with a dynamic weight adjustment strategy. By explicitly modeling topological symmetry in road networks, symmetric trajectory patterns, and symmetric noise variation characteristics, the proposed method effectively enhances prior knowledge utilization, accelerates online adaptation, and achieves a more favorable balance between accuracy and computational efficiency. Extensive experiments on two real-world datasets demonstrate that MPM consistently outperforms state-of-the-art methods, achieving up to 10–15% improvement in matching accuracy while reducing online matching latency by over 30% in complex urban environments. Furthermore, the symmetry-aware design significantly improves robustness against asymmetric interference, thereby providing a reliable and scalable solution for high-precision map matching in complex and dynamic traffic environments. Full article

Large language models (LLMs) have become central to modern artificial intelligence, yet their internal decision-making processes remain difficult to interpret. As interest grows in making these models more transparent and reliable, topological data analysis (TDA) has emerged as a promising mathematical approach for exploring their structure. This scoping review maps the current landscape of research where TDA tools—such as persistent homology and Mapper—are used to examine LLM components like attention patterns, latent representations, and training dynamics. By analyzing topological features across layers and tasks, these methods provide new ways to understand how language models generalize, respond to unfamiliar inputs, and shift under fine-tuning. The review also considers how TDA-based techniques contribute to broader goals in interpretability and robustness, especially in detecting hallucinations, out-of-distribution behavior, and representational collapse. Overall, the findings suggest that TDA offers a rigorous and versatile framework for studying LLMs, helping researchers uncover deeper patterns in how these models learn and reason. Full article

Five prevention is an important technical means to prevent maloperations in substations, and knowledge extraction from wiring diagrams is the key to intelligent “five prevention logic verification”. To address the error accumulation caused by multimodal object matching in traditional methods, this paper proposes a hierarchical recognition-based approach for topological knowledge extraction. This method establishes a multi-level recognition framework utilizing image tiling, decomposing the wiring diagram recognition task into three hierarchical levels from top to bottom: connection modes, bay types, and switching devices. A depth-first strategy is employed to establish parent–child node relationships, forming an initial topological structure. Based on the recognition results, the proposed approach performs regularized parsing and leverages a bay topology knowledge base to achieve automated matching of inter-device topological relationships. To enhance recognition accuracy, the model incorporates a Swin Transformer block to strengthen global feature perception and adds an ultra-small target detection layer to improve small-object recognition. The experimental results demonstrate that all recognition layers achieve mAP@0.5 exceeding 90%, with an overall precision of 93.9% and a recall rate of 91.7%, outperforming traditional matching algorithms and meeting the requirements for wiring diagram topology knowledge extraction. Full article

In freeform optical metrology, wavefront fitting over non-circular apertures is hindered by the loss of Zernike polynomial orthogonality and severe sampling grid distortion inherent in standard conformal mappings. To address the resulting numerical instability and fitting bias, we propose a unified framework curve-shortening flow (CSF)-guided progressive quasi-conformal mapping (CSF-QCM), which integrates geometric boundary evolution with topology-aware parameterization. CSF-QCM first smooths complex boundaries via curve-shortening flow, then solves a sparse Laplacian system for harmonic interior coordinates, thereby establishing a stable diffeomorphism between physical and canonical domains. For doubly connected apertures, it preserves topology by computing the conformal modulus via Dirichlet energy minimization and simultaneously mapping both boundaries. Benchmarked against state-of-the-art methods (e.g., Fornberg, Schwarz–Christoffel, and Ricci flow) on representative irregular apertures, CSF-QCM suppresses area distortion and restores discrete orthogonality of the Zernike basis, reducing the Gram matrix condition number from >900 to <8. This enables high-precision reconstruction with RMS residuals as low as $3\times {10}^{-3}\lambda$ and up to 92% lower fitting errors than baselines. The framework provides a unified, computationally efficient, and numerically stable solution for wavefront reconstruction in complex off-axis and freeform optical systems. Full article

Accurate river network mapping is essential for hydrological modeling, flood risk assessment, and watershed environment management. However, conventional methods based on either optical imagery or digital elevation models (DEMs) often suffer from river network discontinuity and poor representation of morphologically complex rivers. To overcome this limitation, this study proposes a novel method integrating the river-oriented Gradient Boosting Tree model (RGBT) and adaptive stream burning algorithm for high-precision and topologically consistent river network extraction. Water-oriented multispectral indices and multi-scale linear geometric features are first fused and input for a river-oriented Gradient Boosting Tree model to generate river probability maps. A direction-constrained region growing strategy is then applied to derive spatially coherent river vectors. These vectors are finally integrated into a spatially adaptive stream burning algorithm to construct a conditional DEM for hydrological coherent river network extraction. We select eight representative regions with diverse topographical characteristics to evaluate the performance of our method. Quantitative comparisons against reference networks and mainstream hydrographic products demonstrate that the method achieves the highest positional accuracy and network continuity, with errors mainly focused within a 0–40 m range. Significant improvements are primarily for narrow tributaries, highly meandering rivers, and braided channels. The experiments demonstrate that the proposed method provides a reliable solution for high-resolution river network mapping in complex environments. Full article

Steel pipes play a vital role in energy and industrial transportation systems, where undetected defects such as cracks and wall thinning may lead to severe safety hazards. Although ultrasonic guided waves enable long-range inspection, their defect imaging performance is often limited by dispersion, multimode interference, and strong noise. In this work, a multi-frequency fusion imaging method integrating time-reversal, topological derivative, and sparse Bayesian learning is proposed for guided wave-based defect detection in steel pipes. Multi-frequency guided waves are employed to enhance defect sensitivity and suppress frequency-dependent ambiguity. Time-reversal focusing is used to concentrate scattered energy at defect locations, while the topological derivative provides a global sensitivity map as physics-guided prior information. These results are further fused within a sparse Bayesian learning framework to achieve probabilistic defect imaging and uncertainty quantification. Dispersion compensation based on the semi-analytical finite element method is introduced to ensure accurate wavefield reconstruction at different frequencies. Domain randomization is also incorporated to improve robustness against uncertainties in material properties, temperature, and measurement noise. Numerical simulation results verify that the proposed method achieves high localization accuracy and significantly outperforms conventional TR-based imaging in terms of resolution, false alarm suppression, and stability. The proposed approach provides a reliable and robust solution for guided wave inspection of steel pipelines and offers strong potential for engineering applications in nondestructive evaluation and structural health monitoring. Full article

Intercity population flows are playing an increasingly pivotal role in shaping the spatial evolution and structural dynamics of urban networks. Drawing upon Amap Migration Data (2018–2023), this study maps China’s urban networks using social network analysis and identifies their key drivers using a temporal exponential random graph model. The findings reveal three primary insights: First, the overall network exhibits “high connectivity and strong clustering” traits. Enhanced efficiency in intercity resource allocation fosters cross-regional factor flows, resulting in multi-tiered connectivity corridors. Industrial linkages and policy interventions drive the development of a polycentric and clustered configuration. Second, the individual city network exhibits a core–periphery dynamic structure. A diamond-shaped framework dominated by hub cities in the national strategic regions directs factor flows. Development of strategic corridors enables peripheral cities to evolve into secondary hubs by leveraging structural hole advantages, reflecting the continuous interplay between network structure and geo-economic factors. Third, driving factors involve nonlinear interactions within a multi-layered system. Path dependence in topology, gradient potential from nodal attributes, spatial counterbalance between geographic decay laws and multidimensional proximity, and adaptive self-organization are collectively associated with the transition of the urban network toward a multi-tiered synergistic pattern. By revealing the dynamic interplay between network topology and multidimensional driving factors, this study deepens and advances the theoretical connotations of the “Space of Flows” theory, providing an empirical foundation for optimizing regional governance strategies and promoting high-quality coordinated development of Chinese cities. Full article

Background: Cardiovascular polypharmacy inherently amplifies the risk of drug–drug interactions (DDIs), yet most studies remain limited to isolated drug pairs or predefined high-risk classes, without mapping the systemic architecture through which interactions accumulate. Objectives: To characterize the burden, severity, and network structure of potential DDIs in a real-world cohort of hospitalized cardiovascular patients using interaction profiling combined with graph-theoretic network analysis. Methods: This retrospective observational study included 250 hospitalized cardiovascular patients. All home medications at admission were analyzed using the Drugs.com interaction database, and a drug interaction network was constructed to compute topological metrics (i.e., degree, betweenness, and eigenvector centrality). Results: Polypharmacy was highly prevalent, with a mean of 7.7 drugs per patient, and 98.4% of patients exhibited at least one potential DDI. A total of 4353 interactions were identified, of which 12.1% were classified as major, and 35.2% of patients presented high-risk profiles with ≥3 major interactions. Interaction burden showed a strong correlation with medication count (r = 0.929). Network analysis revealed a limited cluster of hub medications, particularly pantoprazole, furosemide, spironolactone, amiodarone, and perindopril, that disproportionately governed both interaction density and high-severity risk. Conclusions: These findings move beyond conventional pairwise screening by demonstrating how interaction risk propagates through interconnected therapeutic networks. The study supports the integration of hub-focused deprescribing, targeted monitoring strategies, and network-informed clinical decision support to mitigate DDI risk in cardiovascular polypharmacy. Future studies should link potential DDIs to clinical outcomes and validate network-based prediction models in prospective settings. Full article

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article