Search Results (5,781)

Image inpainting aims to recover missing regions in damaged images while preserving structural coherence and textural authenticity. Although deep learning methods based on generative adversarial networks (GAN) have made significant progress, they still face challenges in modeling long-range dependencies and maintaining semantic consistency, especially when large areas are missing. To address these issues, we propose an innovative multi-stage restoration framework. The coarse restoration stage incorporates attention via a transformer architecture, while the refinement stage introduces a plug-and-play channel-frequency encoder (CF-Encoder). This encoder effectively models both global structure and local details by hierarchically extracting and enhancing features through frequency-domain decomposition combined with an adaptive spatial-channel attention mechanism. Furthermore, we employ a bi-discriminator fusion mechanism to stabilize training and enhance perceptual quality. Experiments across multiple benchmark datasets demonstrate our method’s superior performance in both quantitative metrics and visual fidelity, with particularly notable advantages in high-missing-value scenarios. Full article

The growing number of on-orbit spacecraft and the increasing volume of telemetry data have made intelligent anomaly detection in multi-channel telemetry essential for mission operations. Current spacecraft anomaly detection methods primarily rely on statistical models or time-series deep learning approaches, which often fail to explicitly model spatiotemporal dependencies across multiple telemetry channels. This shortcoming limits their ability to capture the dynamically evolving and intricately coupled relationships between variables. To overcome this limitation, a Progressive Spatiotemporal Graph (PSTG) model is proposed for anomaly detection in multi-channel spacecraft telemetry. PSTG employs a multi-scale patch embedding module to extract hierarchical semantic features from multi-channel time series, effectively reducing the dimensionality of the spatiotemporal graph. It constructs a sparse adjacency matrix using a multi-head attention mechanism that integrates intra-channel temporal dynamics, inter-channel spatial correlations, and cross-channel spatiotemporal interactions. An improved multi-head graph attention network then captures pairwise dependencies among nodes within the adjacency matrix. As a result, PSTG encodes rich spatiotemporal representations derived from intricate variable interactions, enabling accurate, real-time prediction of multi-channel telemetry. Furthermore, a dynamic thresholding mechanism is incorporated into PSTG to perform online anomaly detection based on prediction residuals. Extensive experiments on real-world spacecraft telemetry data collected over 84 months show that PSTG outperforms eleven state-of-the-art benchmark methods in almost all cases across multiple evaluation metrics. Finally, visualizations of the learned adjacency and attention matrices are presented to interpret the spatiotemporal modeling process, providing operators with actionable insights into the detected anomalies and facilitating root cause analysis. Full article

Cloud cover significantly reduces the electrical power output of photovoltaic systems, making accurate short-term cloud movement predictions essential for reliable solar energy production planning. This article presents a deep learning framework that directly estimates cloud movement from ground-based all-sky camera images, rather than predicting future production from past power data. The system is based on a three-step process: First, a lightweight Convolutional Neural Network segments cloud regions and produces probabilistic masks that represent the spatial distribution of clouds in a compact and computationally efficient manner. This allows subsequent models to focus on the geometry of clouds rather than irrelevant visual features such as illumination changes. Second, a Vector Quantized Variational Autoencoder compresses these masks into discrete latent token sequences, reducing dimensionality while preserving fundamental cloud structure patterns. Third, a GPT-style autoregressive transformer learns temporal dependencies in this token space and predicts future sequences based on past observations, enabling iterative multi-step predictions, where each prediction serves as the input for subsequent time steps. Our evaluations show an average intersection-over-union ratio of 0.92 and a pixel accuracy of 0.96 for single-step (5 s ahead) predictions, while performance smoothly decreases to an intersection-over-union ratio of 0.65 and an accuracy of 0.80 in 10 min autoregressive propagation. The framework also provides prediction uncertainty estimates through token-level entropy measurement, which shows positive correlation with prediction error and serves as a confidence indicator for downstream decision-making in solar energy forecasting applications. Full article

(1) Background: This study investigates the development of spatial navigation and long-term memory in the leopard gecko (Eublepharis macularius) to address gaps in understanding reptilian cognitive ontogeny. We aimed to determine if early-life training enhances long-term memory retention and to evaluate the repeatability of individual cognitive performance over time. (2) Methods: Using a modified Morris Water Maze with visual landmarks, we tested 39 individuals across three life stages: juveniles (20 trials), subadults, and adults (10 trials in each later phase). Long-term memory retention was assessed after four and fourteen months. (3) Results: A strong learning effect was observed during the juvenile stage, with geckos significantly improving speed and navigational efficiency. Spatial memory remained stable at the subadult stage (four months post-training), but declined significantly by adulthood (fourteen months post-training), returning to baseline levels. Individual success rates were significantly repeatable during juvenile (R = 0.192) and subadult phases (R = 0.071), although this consistency disappeared in adulthood. (4) Conclusions: These findings indicate that leopard geckos possess substantial spatial learning abilities early in life and exhibit individual cognitive differences. However, spatial memory decays over time without reinforcement. The results highlight the importance of considering developmental stages when evaluating the evolutionary and ecological constraints of reptilian cognition. Full article

This paper presents a method for measuring the spatial distribution of surface normal vectors with high angular accuracy. The measured data are visualized using a color-mapping technique and represented as normal maps, which are commonly used in computer graphics. Reliable methods for evaluating material surface properties have long been sought in industrial applications where visual assessments of reflective properties are still widely employed, particularly in appearance-critical fields. Motivated by this need, we introduce an imaging-based technique for measuring the high-resolution spatial distribution of surface normal vectors from specular reflection. A dedicated measurement apparatus was developed to capture surface normal vectors at 1024 × 1024 sampling points with a spatial resolution of 0.02 × 0.02 mm and an angular resolution of approximately 0.1°. Using this apparatus, normal maps were obtained for various materials, including plastic, ceramic tile, inkjet paper, and aluminum sheets. The spatial distribution of surface normal vectors reflects surface roughness, which strongly influences perceived texture. The resulting normal maps enable not only quantitative surface analysis for industrial inspection but also the physical reproduction of gloss in computer graphics. Full article

Worldwide visual geo-localization aims to predict the geographic coordinates of an image capture location from visual content alone, posing unique challenges due to the vast scale of the Earth’s surface and pervasive visual ambiguity across distant regions. Existing approaches face distinct limitations as follows: retrieval-based methods demand massive geo-tagged databases and scale poorly; alignment-based models lack interpretability and are vulnerable to visually similar scenes; and large vision-language models (LVLMs) offer semantic reasoning but suffer from hallucination. A natural solution is retrieval-augmented generation (RAG), yet we observe that directly injecting retrieved candidates as context causes severe context poisoning. To address this, we propose HybridGeo, a dual-stream late-fusion framework that decouples retrieval from reasoning. A retrieval stream applies continuous alignment with spatial–semantic clustering to produce stable regional anchors; a reasoning stream performs context-free Chain-of-Thought inference to yield an independent coordinate estimate. The two streams are fused only at the decision stage via a spatial–consistency module that triggers weighted averaging under agreement or confidence-based arbitration under conflict. Experiments on Im2GPS3k show that HybridGeo achieves 73.89% Country@750km accuracy, outperforming the retrieval baseline by 7.27% and 8.23%, and surpassing both VLM-only and RAG baselines. These results demonstrate that late fusion effectively avoids context poisoning while enabling complementary benefits from both streams. Full article

The Historic Urban Landscape (HUL) approach provides a vital and extensive framework for heritage conservation. However, local practices often struggle to spatially translate qualitative assessments into quantitative controls at the urban block level, the most effective basic scale for administrative implementation, thereby limiting effective responses to the Management of Change. By integrating HUL with the theory of Dynamic Integrity, this study constructs a multi-dimensional evaluation index system and proposes a HUL evaluation method based on Character-Based Zoning. Taking the 125 urban block units of the historic urban area of Jingdezhen as a case study, this research integrates historical mapping, GIS spatial analysis, and Co-occurrence Network Analysis to reveal the internal structural logic of the heritage system. The study finds that the HUL of Jingdezhen is a multi-nodal dynamic system driven by four core elements: ritual beliefs, administrative management, production activities, and commercial guilds. Critically, modern visual intrusions severely impact the core heritage components within this system, specifically the Dubang and ritual culture. Based on the three dimensions of Heritage Richness, Landscape Sensitivity and Value Centrality, the study systematically identifies a total of 11 types of urban block units within the plots that characterize distinct historic landscape features and transformation patterns. This research not only deepens the localized application of HUL theory but also provides a scientific basis and methodological support for the Management of Change and periodic assessment in dynamic heritage environments. Full article

Non-invasive magnetic resonance imaging (MRI), as an extension of nuclear magnetic resonance (NMR) technology, enables detailed characterization of lithium-ion batteries (LIBs) in model systems. This review summarizes the fundamental principles of MRI and its applications in liquid/solid electrolytes, electrodes, and limited commercial diagnostics. Key capabilities include quantifying ion diffusion coefficients and mobility numbers in electrolytes, visualizing dendrite growth in lithium metal, and tracking lithium distribution in porous electrodes such as graphite and LiCoO₂. However, spatial and temporal resolution (typically 10–100 μm with acquisition times ranging from minutes to hours) and metal-induced shielding effects severely limit direct imaging in complete commercial batteries. Indirect methods like magnetic field imaging (MFI) show potential for defect detection. Future work should focus on sequence optimization and multimodal fusion, while emphasizing MRI’s primary role in fundamental research rather than conventional industrial testing. Full article

The rapid proliferation of text-to-image generative systems has transformed visual content production, yet the structural characteristics embedded in their compositional outputs remain insufficiently understood. Rather than approaching human–AI differentiation as a purely classification problem, this study investigates whether a controlled set of AI-generated and human-designed posters exhibits measurable structural divergence under thematically matched conditions. A dataset of jazz festival posters was analyzed using interpretable geometric and information-theoretic descriptors, including spatial density (padding ratio), edge density, chromatic dispersion, and entropy-based measures. Instead of relying on deep neural detection architectures, we employed a transparent machine-learning framework to examine intrinsic structural separability within feature space. Results demonstrated highly stable group separation (ROC-AUC = 0.99; 95% CI: 0.978–0.999) under cross-validated evaluation. Distributional analysis further revealed a pronounced divergence in spatial density allocation (Kolmogorov–Smirnov statistic = 0.76, p < 10⁻²⁸), accompanied by a very large effect size (Cohen’s d = 1.365). While padding ratio emerged as the dominant discriminative factor, additional entropy- and chromatic-based descriptors contributed to group separation even when spatial density was excluded (AUC = 0.903). These findings indicate that AI-generated and human-designed posters can diverge in negative space allocation and chromatic organization under controlled thematic and platform-specific conditions. The study contributes to the explainable analysis of generative visual systems by reframing human–AI differentiation as a structural divergence problem grounded in interpretable image statistics rather than as a model-specific artifact detection task. Full article

The damage caused by herbivores is generally measured as the amount of leaf tissue consumed, without accounting for the fate of the leftover tissue. As a result, the plant defense mechanisms that promote resistance to herbivore feeding by photosynthetically acclimating the rest of the plant to the feeding spot leaf area have not been well exploited. Plant-insect interactions are now becoming better defined with the development of visualization methods that permit spatial whole-leaf assessment of photosynthetic efficiency after herbivore attack. The purpose of our study was to evaluate the spatial heterogeneity of photosystem II (PSII) function at the whole-leaf level before and after herbivory by the Colorado potato beetles. Twenty minutes after Colorado potato beetle (Leptinotarsa decemlineata) feeding, the maximum efficiency of PSII photochemistry (Fv/Fm) decreased significantly, suggesting photoinhibition due to reduced efficiency of the oxygen-evolving complex (OEC). The decreased quantum yield of PSII photochemistry (Φ_PSII) after feeding, at the neighboring area of the feeding spot and at the rest of the leaf area, was attributed to the reduced efficiency of the open PSII reaction centers (Fv′/Fm′), since there was no change in the fraction of open PSII reaction centers (qp). Nevertheless, plant defense elicitation was activated by the photoprotective mechanism of non-photochemical quenching (NPQ) that reduced the singlet oxygen (¹O₂) formation in potato plants in the neighboring area of the feeding spot and at the rest of the leaf area. In addition, the increased production of hydrogen peroxide (H₂O₂) triggered by this increase suggests that it acted as a signaling molecule in the biotic stress defense response. Full article

Deepfake technology poses significant threats to information authenticity and social trust, necessitating effective detection methods. However, existing detection approaches predominantly rely on high-complexity network architectures that, while accurate in controlled environments, suffer from prohibitive computational costs that hinder deployment in resource-constrained scenarios such as social media platforms. To address this efficiency-accuracy dilemma, we propose a lightweight face forgery detection method that systematically learns multi-scale forgery traces. Our approach features a four-stage lightweight architecture that hierarchically extracts features from local textures to global semantics, mimicking the human visual system. Within each stage, a multi-scale dynamic perception mechanism divides feature channels into parallel groups equipped with lightweight attention modules to capture forgery cues spanning pixel-level anomalies, local structures, regional patterns, and semantic inconsistencies. Furthermore, rather than relying on conventional feature fusion that risks suppressing subtle artifacts, we introduce a novel Context-Guided Dynamic Convolution. This mechanism uses mid-level spatial anomalies as active anchors to dynamically modulate high-level semantic filters, with the goal of mitigating the disconnect between semantic content and forgery evidence. Our model achieves strong performance, yielding an AUC of 91.98% on FaceForensics++ and 93.50% on DeepFake Detection Challenge, outperforming current state-of-the-art lightweight methods. Furthermore, compared to heavy Vision Transformers, our model achieves a superior performance-efficiency trade-off, requiring only 3.06 M parameters and 1.36 G FLOPs, making it highly suitable for real-time, resource-constrained deployment. Full article

Structural reconstruction helps infer the spatial relationships and object layouts in a scene, which is an essential computer vision task for understanding visual content. However, it remains challenging due to the high complexity of scene structural topologies in real-world environments. To address this challenge, this paper proposes RegionGraph, a novel method for structural reconstruction of buildings from a satellite image. It utilizes a layout region graph construction and graph contraction approach, introducing a primitive (layout region) estimation network named ConPNet for detecting and estimating different structural primitives. By combining structural extraction and rendering synthesis processes, RegionGraph constructs a graph structure with layout regions as nodes and adjacency relationships as edges, and transforms the graph optimization process into a node-merging-based graph contraction problem to obtain the final structural representation. The experiments demonstrated that RegionGraph achieves a 4% improvement in average F1 scores across three types of primitives and exhibits higher regional completeness and structural coherency in the reconstructed structure. Full article

In the context of urban renewal, the spatial interface of historic commercial districts functions as both a carrier of historical character and a key setting for commercial activity, public life, and local cultural expression. To address the limitations of conventional studies that rely heavily on field observation and qualitative description, this study takes Xiaohe Zhijie in Hangzhou as a case and develops a multi-level street-view recognition framework for the quantitative analysis of spatial interface characteristics. Based on street-view image collection and standardized preprocessing, a sample database was established at the sampling-point scale. Semantic segmentation, automated commercial object detection, and manual interpretation were combined to identify interface elements, including buildings, sky, greenery, pavement, vehicles, pedestrians, and commercial objects, while commercial content was assessed in terms of locality and homogenization. The results show that Xiaohe Zhijie exhibits a building-dominated and relatively enclosed interface pattern, with greenery and pavement forming the basic environmental ground, weak vehicle interference, and localized enhancement of vitality through commercial objects and pedestrian activities. Significant differences were found among street segments in openness, commercial coverage, and local expression. Three interface types were identified: commercial–cultural composite, local life-oriented, and waterfront landscape–cultural composite. The main challenge lies not in commercialization itself, but in stronger visual locality than content locality and increasing homogenization, resulting in a pattern of “localized form but homogenized content.” Full article

Urban soundscape management is a central challenge to the livability and sustainability of cities and requires approaches that complement level indicators with frameworks capable of integrating context, use and experience. In this framework, the present work applies a multilayer methodology to the Old Town of Bilbao, understood as a useful case study to explore the applicability of soundscape reading in historic centers with intense coexistence of commercial, hospitality and catering uses, pedestrian, logistical and cultural uses. The methodology is organized into two phases. The first focuses on the recording and documentation of control points and routes through sound fieldwork, perceptual descriptions and homogeneous systematization of information. From this corpus, a qualified sound map and a first visual characterization of the sound identity are elaborated. The second phase presented in this article, consists of the interpretative synthesis of the corpus through five analytical dimensions and the preparation of fragments and sound sequences conceived for future application through reactivated listening. The results are presented at three levels: (1) a traceable documentary corpus of records, files and synthetic representations; (2) a comparative reading by dimensions that identifies spatial contrasts between interior, exterior and perimeter, as well as relationships between urban form, uses, persistence, masking and salience; and (3) a set of operational audio materials prepared for subsequent comparison with inhabitants and users. In a transversal way, type–token reading distinguishes between the diversity of sounds and dominance by repetition. The article does not yet carry out participatory validation of these materials; its contribution consists of proposing and applying a traceable analytical protocol as a basis for future phases of social contrast and applied discussion. Full article

Brain-Derived Neurotrophic Factor (BDNF), a key member of the neurotrophin family, is critically involved in neuronal survival, synaptic plasticity, learning, and memory. While its roles in mammals have been extensively documented, the molecular regulatory mechanisms governing BDNF expression and its causal contributions to complex cognitive behaviors remain poorly understood in non-mammalian vertebrates—particularly for the domestic pigeon (Columba livia domestica), a species distinguished by its remarkable spatial navigation and homing capabilities. This review synthesizes the current evidence on BDNF in the pigeon central nervous system across five thematic domains: molecular structure and isoform diversity, transcriptional and epigenetic regulatory networks, involvement in neural development, associations with cognitive and navigational behaviors, and potential translational applications. A particular emphasis is placed on the region-specific and activity-dependent expression patterns of BDNF in brain structures such as the hippocampal formation (HF), optic tectum, and striatum, and their functional relevance to visual processing, homing behavior, and stress adaptation. To date, most findings remain correlational; therefore, establishing a mechanistic understanding necessitates the integration of advanced methodologies—including single-cell omics, CRISPR-based gene editing, and high-resolution behavioral phenotyping—to causally link BDNF dynamics, neural circuit modulation, and spatial cognition. This synthesis aims to bridge gaps in comparative neurobiology, inform molecular approaches to avian cognitive enhancement, and support evidence-based strategies for racing pigeon breeding and welfare assessment. Full article