Search Results (205)

We present a comprehensive general relativistic analysis of the Lemaître–Tolman–Bondi (LTB) metric, incorporating a cosmological constant $\mathrm{\Lambda }$ and a central pointlike mass $M_{\mathrm{d}}$ at the geometric origin. Within this framework, $M_{\mathrm{d}}$ is identified as the material source of dark matter in cosmology, yielding a scale-dependent total matter–density parameter ${\mathrm{\Omega }}_{\mathrm{m}}\left(L\right)$ characterized by an $L^{-3}$ decay of its dark component ${\mathrm{\Omega }}_{\mathrm{d}}\left(L\right)$. We demonstrate that the Hubble and $S_8$ tensions are not independent anomalies but interconnected consequences of spacetime inhomogeneity. These discrepancies arise from a combination of physical and methodological factors: the probing of radial gradients at different characteristic scales and the subsequent interpretation of these data through a global FLRW template. This approach, compounded by the practice of isotropic sky averaging, masks the underlying LTB geometry and converts the physical variation of the manifold into the observed cosmological tensions. Our framework provides a self-consistent geometric explanation for current anomalies while preserving the Copernican principle, identifying the crisis in cosmology as arising from the application of homogeneous models to a manifold characterized by radial gradients and scale-dependent dynamics, where the observer and probes reside within the same inhomogeneous regime. Full article

Vision–language models (VLMs) have demonstrated remarkable performance on a wide range of multimodal reasoning tasks, yet their visual grounding mechanisms remain poorly understood and are often unreliable for fine-grained visual concepts. Existing approaches typically rely on raw attention maps or gradient-based saliency, which provide heuristic explanations but lack a causal interpretation of how visual evidence contributes to model predictions. In this paper, we propose an Information Bottleneck Score (IBS) framework that explicitly quantifies the causal importance of visual patches through interventional analysis. By masking candidate image patches and measuring the induced change in the model prediction, the IBS captures patch-level causal contributions rather than correlation-based signals. We further lift patch-level importance to the attention-head level by aggregating the IBS with text-to-image attention, enabling the identification of a small subset of information-transmitting attention heads responsible for visual grounding. Building on the selected heads, we construct refined importance maps that guide visual cropping in a fully training-free manner. Extensive experiments on multiple detail-sensitive benchmarks, including TextVQA, V*, POPE, and DocVQA, demonstrate consistent improvements in fine-grained visual understanding, while evaluations on general-purpose datasets such as GQA, AOKVQA, and VQAv2 confirm that overall reasoning performance is preserved. Additional ablation studies further validate the effectiveness of each component in the proposed framework. Overall, our work provides a causal perspective on visual grounding in VLMs and offers a model-agnostic, training-free approach for both interpreting and enhancing multimodal reasoning. Full article

Cataract diagnosis requires a comprehensive dilated examination by an ophthalmologist using a slit lamp; there is currently no effective means to objectively screen for cataracts in the community using portable devices without dilation. We hypothesized that it would be possible to predict cataract severity using deep learning on images taken using a portable smartphone-based slit lamp prototype, with and without dilation. In this prospective cross-sectional pilot study, slit lamp images were captured from eligible patients with cataracts in a tertiary clinic using a portable slit lamp prototype attached to a smartphone. The Pentacam nuclear staging score (PNS, Pentacam^®, Oculus, Inc., Arlington, WA, USA) was taken from the dilated pupils and served as ground truth. A transformer prototypical network with the Swin transformer on the images was trained to assign the class label corresponding to the highest predicted probability. Heat maps were generated based on attribution masks to identify the anatomical areas of concern. A total of 1900 images from 198 eyes of 99 patients were captured. The average age was 65.3 ± 10.4 years (range, 41.0 to 88.0 years) and the average PNS score was 1.57 ± 0.81 (range, 0 to 4). The model achieved an average accuracy of 81.25% and 74.38% for undilated and dilated eyes, respectively. Heat map visualization using the integrated gradient method successfully identified the anatomical area of interest in certain images. This study suggests the possibility of estimating cataract density using a portable smartphone slit lamp device without dilation. Further work is under way to validate this technique in a larger and more diverse group of eyes with cataracts. Full article

Accurate extraction of crop planting structures is important for crop area and yield estimation, but complex and fragmented cropping patterns with overlapping phenology in the Hetao Irrigation District hinder reliable crop discrimination. This study proposes a hierarchical workflow that integrates vegetation masking with multi-source feature optimization for crop mapping. First, dual-temporal Sentinel-2 imagery (May and August) is used to generate a vegetation region-of-interest(ROI) mask via Otsu thresholding applied to the Normalized Difference Vegetation Index (NDVI), combined with pixel-wise maximum-value fusion to reduce phenology-driven omissions and background interference. Second, within the vegetation mask, Sentinel-2 spectral, vegetation-index, and texture features are combined with Sentinel-1 synthetic aperture radar (SAR) backscatter and SAR texture features to construct a multi-source feature set. Random Forest(RF) feature-importance ranking is used to select an effective feature subset, and four classifiers (RF, support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and convolutional neural network (CNN)) are compared under the same training/validation setting. The vegetation extraction achieves an overall accuracy of 91% (Kappa = 0.80). Using Sentinel-2 features only, the optimized subset with CNN attains the best performance (overall accuracy = 95%, Kappa = 0.93). Adding Sentinel-1 SAR texture features provides an additional improvement (overall accuracy = 96%, Kappa = 0.94), particularly for classes prone to confusion in fragmented plots. Area proportions derived from the final map are consistent with statistical yearbook data (percentage errors: maize 3.45%, sunflower 2.66%, wheat 0.11%, tomato 0.92%) under the study conditions. This workflow supports practical crop-structure monitoring in complex irrigation districts. Full article

We present frequency- and magnetic field-dependent measurements of the complex dielectric permittivity ε*(f, H) of a kerosene-based ferrofluid, containing Mn₀.₆Fe₀.₄Fe₂O₄ nanoparticles, over 0.8–5 GHz and static fields up to ~91 kA/m. The imaginary part, ${\epsilon }_F^{″}$, shows a peak at a characteristic frequency that shifts towards higher frequencies with increasing H, revealing a magnetic field-dependent relaxation process, interpreted using the Maxwell–Wagner–Sillars model. The dielectrophoretic extraction of nanoparticles was evaluated via the squared electric field gradient, and a threshold, ${\left(\nabla E^2\right)}_{\min }$, dependent on particle size was determined. Below that threshold, Brownian forces dominate, so the ferrofluid acts as a homogeneous dielectric. For this case, the Clausius–Mossotti factor (CM) was calculated for ferrofluid droplets in air and in water as a function of frequency and magnetic field. In air, CM exhibits modest but systematic magnetic field dependence, indicating a magnetically modulated dielectric response at GHz frequencies. In contrast, when water is used as the reference medium, CM remains negative and essentially independent of H across the entire frequency range, suggesting that the high permittivity of water masks the magneto-dielectric effects in the ferrofluid. These findings provide insight into the interplay between the magnetic field and the permittivity of ferrofluids, with implications for high-frequency applications. Moreover, using a λ/4 antenna connected to a network analyzer, the existence of the dielectrophoretic force acting on a ferrofluid-impregnated textile thread at microwave frequencies was experimentally demonstrated. Full article

This study provides a comprehensive assessment of 12 metal(loid)s in the muscle tissue of the commercially vital shrimp, Penaeus semisulcatus, from four stations (Bozyazi, Silifke, Karatas, and Iskenderun) along the Northeastern Mediterranean. Metal concentrations were evaluated separately for males and females, utilizing Estimated Weekly Intake (EWI), Target Hazard Quotient (THQ), Carcinogenic Risk (CR), and Selenium Health Benefit Value (HBV_Se) indices. While the species is generally safe for consumption across the region, a striking, localized bioaccumulation of Chromium (Cr) was identified specifically in Iskenderun Bay, where male shrimps exhibited concentrations (1.209 mg/kg wet weight) approximately 10-fold higher than females, highlighting a sex-specific sensitivity likely linked to metabolic and physiological differences. By adopting a precautionary risk assessment framework—considering the region’s intense industrial profile—this localized spike resulted in a Total Carcinogenic Risk (∑CR = 5.15 × 10⁻⁴) for this group, exceeding the priority threshold. Furthermore, widespread Lead (Pb) contamination was detected across all stations, with several samples surpassing EU maximum levels (0.50 mg/kg). Regarding Arsenic (As), while high total concentrations led to THQ values > 1 across the regional gradient, this was characterized as a conservative modeling artifact rather than a physiological threat, as Arsenic in crustaceans is predominantly in the non-toxic organic form. Conversely, any potential risk from Mercury (Hg) was conclusively mitigated by an overwhelming molar excess of Selenium (Se) at all locations, confirmed by consistently positive HBV_Se values (0.312–0.658). In conclusion, our findings demonstrate that seafood safety is conditional and region-specific. The study underscores that localized contamination “hotspots” can be easily masked by non-sex-specific sampling and emphasizes the necessity of moving beyond simplistic risk models by incorporating selenium-mercury antagonism and precautionary risk assumptions for industrial pollutants. Full article

In photovoltaic remote sensing image segmentation tasks, fully supervised methods can achieve high accuracy. However, the high cost of pixel-level annotation significantly limits their scalability in large-scale scenarios. To overcome this annotation bottleneck, this paper proposes a zero-shot cross-modal segmentation framework based on the visual-language pre-trained foundation model (CLIP). This approach harnesses CLIP’s cross-modal knowledge transfer capabilities to achieve precise extraction of photovoltaic targets without requiring any downstream training. This paper first introduces the Layer-wise Augmented Residual Attention (LARA) mechanism to enhance fine-grained detail representation in the feature space. Subsequently, a Cross-modal Semantic Attribution Module (CMSA) is designed to generate precise activation maps by leveraging image-text alignment gradient information. Finally, the Confidence-Aware Refinement Strategy (CARS) replaces the conventional training-based denoising process, directly producing high-quality binary segmentation masks through adaptive thresholding. Comparative experiments were conducted to evaluate the proposed method against various baselines using several public datasets with varying resolutions in Jiangsu Province including Unmanned Aerial Vehicles imagery, Beijing-2, Gaofen-2, and a self-created Sentinel-2 imagery covering multiple countries. Notably, the proposed method achieved an IoU of 70.3% on the Gaofen-2 PV03 dataset with a spatial resolution of approximately 0.3 m and 50.8% on the self-created Sentinel-2 PV_Sentinel-2 dataset with a spatial resolution of 10 m. Experimental results demonstrate that our proposed approach maintains excellent cross-domain generalisation capabilities while reducing annotation costs, thereby providing an efficient and viable technical pathway for the automated monitoring of large-scale photovoltaic facilities. Full article

Landslides are characterized by strong suddenness and a wide range of damage; accurate prediction of their susceptibility is an important prerequisite for regional risk prevention and control. To address the difficulties in acquiring landslide inventories in complex terrain areas and the insufficient interpretability of existing prediction models, this study proposes a landslide susceptibility assessment (LSA) framework that integrates automated sample detection and interpretability analysis. The proposed framework is applied to Moxi Town, a typical alpine valley area in Sichuan Province, China. A Mask R-CNN instance segmentation model was introduced to achieve automated detection of landslide samples, resulting in a high-quality inventory containing 923 landslides. Based on the spatial relationships between the landslide inventory and influencing factors, a convolutional neural network (CNN) landslide susceptibility assessment model incorporating Shapley Additive exPlanations (SHAP) interpretability analysis was constructed. The CNN model was further compared with random forest (RF) and extreme gradient boosting (XGBoost) machine learning models. The results show that the AUC value of the CNN model has increased by 4.3% and 3.2% compared with the RF and XGBoost models, respectively, and it significantly reduces the pretzel effect of landslide susceptibility mapping (LSM). The results validate the reliability of the proposed framework, which can provide technical support for landslide disaster prevention and monitoring. Full article

One of the major current societal challenges concerns the reuse of waste materials and valuable substances to mitigate the environmental impact of human activities, which has led to the increasing release of pollutants, from plastics to pharmaceuticals. In this study, we report a simple recycling strategy for surgical masks to obtain an activated carbon-like material, suitable for the solid-phase extraction (SPE) of Phthalic acid esters (PAEss) from plastic bottled water. The sorbent was produced by high-temperature calcination after sulfuric acid treatment to enhance the thermal stability of polypropylene. The sorbent was characterized by thermal analysis, Raman spectroscopy, FTIR and scanning electron microscopy. SPE was used to preconcentrate the analytes, and the main parameters affecting the extraction, such as pH, sorbent amount, organic modifier percentage, ionic strength and elution volume, were optimized. PAEs were determined by UHPLC-PDA under gradient elution. The developed method was linear in the range 0.25–1000 ng/mL, with LOQs between 0.25 and 0.10 ng/mL and LODs between 0.008 and 0.003 ng/mL. Recovery ranged from 95.9 to 104.7%, the precision expressed as RSD% was below 7.32, and the accuracy expressed as BIAS% ranged from −5.75 to 5.93. The proposed approach provides a simple and low-cost valorization route for PPE waste, while enabling reliable PAEs analysis in drinking water. Full article

High-fidelity 4D reconstruction of dynamic scenes is pivotal for immersive simulation yet remains challenging due to the photometric inconsistencies inherent in multi-view sensor arrays. Standard 3D Gaussian Splatting (3DGS) strictly adheres to the brightness constancy assumption, failing to distinguish between intrinsic scene radiance and transient brightness shifts caused by independent auto-exposure (AE), auto-white-balance (AWB), and non-linear ISP processing. This misalignment often forces the optimization process to compensate for spectral discrepancies through incorrect geometric deformation, resulting in severe temporal flickering and spatial floating artifacts. To address these limitations, we present Lumina-4DGS, a robust framework that harmonizes spatiotemporal geometry modeling with a hierarchical exposure compensation strategy. Our approach explicitly decouples photometric variations into two levels: a Global Exposure Affine Module that neutralizes sensor-specific AE/AWB fluctuations and a Multi-Scale Bilateral Grid that residually corrects spatially varying non-linearities, such as vignetting, using luminance-based guidance. Crucially, to prevent these powerful appearance modules from masking geometric flaws, we introduce a novel SSIM-Gated Optimization mechanism. This strategy dynamically gates the gradient flow to the exposure modules based on structural similarity. By ensuring that photometric enhancement is only activated when the underlying geometry is structurally reliable, we effectively prioritize geometric accuracy over photometric overfitting. Extensive experiments validate the quantitative superiority of Lumina-4DGS. On the Waymo Open Dataset, our method achieves a state-of-the-art Full Image PSNR of 31.12 dB while minimizing geometric errors to a Depth RMSE of 1.89 m and Chamfer Distance of 0.215 m. Furthermore, on our highly challenging self-collected surround-view dataset featuring severe unconstrained illumination shifts, Lumina-4DGS yields a significant 2.13 dB PSNR improvement over recent driving-scene baselines. These results confirm that our framework achieves photorealistic, exposure-invariant novel view synthesis while maintaining superior geometric consistency across heterogeneous camera inputs. Full article

Convolutional neural networks (CNNs) can achieve high accuracy in photovoltaic (PV) fault recognition from RGB imagery, yet their decisions may rely on shortcut cues induced by heterogeneous backgrounds, viewpoints, and class imbalance. This work presents a multi-method explainability audit on the Kaggle PV Panel Defect Dataset (six classes), comparing five architectures (Baseline CNN, VGG16, ResNet50, InceptionV3, EfficientNetB0). Explanations are obtained with LIME superpixel surrogates (reported together with kernel-weighted surrogate fidelity), occlusion sensitivity (quantified via IoU@Top10% against consistent proxy masks, Shannon entropy, and Hoyer sparsity), and Integrated Gradients evaluated by deletion–insertion faithfulness and a Faithfulness Gap. While ResNet50 yields the best predictive performance, EfficientNetB0 shows the most consistent faithfulness evidence and stable panel-centered attributions. The analysis highlights class-dependent vulnerability to context cues, especially for the Clean and damaged classes, and supports using quantitative explainability diagnostics during model selection and dataset curation to mitigate shortcuts in vision-based PV monitoring. Full article

Large language models (LLMs) are increasingly deployed as information systems that evolve over time, where managing internal knowledge—acquisition, retention, and removal—becomes essential. In practice, these processes are primarily realized through continual learning and machine unlearning mechanisms. Despite this, these two mechanisms are often studied in isolation, limiting both interpretability and controllability. In this work, we present a parameter-efficient knowledge management framework where continual learning and machine unlearning—despite employing distinct task-specific objectives—are integrated through a shared retention-controlled parameter evolution mechanism. We ground these structural constraints in a drift-aware design principle: under a model smoothness assumption, we establish a formal upper bound showing that Kullback–Leibler (KL) divergence on retained knowledge is controlled by the magnitude and direction of parameter updates, providing a principled rationale for combining Low-Rank Adaptation (LoRA) freezing, sparse masking, and orthogonal gradient projection into a unified constraint system. Experiments on the Task of Fictitious Unlearning (TOFU) benchmark and real-world benchmarks demonstrate effective knowledge acquisition, selective removal, and robust retention across sequential tasks with strong overall performance and stability. This work provides a practical parameter-efficient recipe and a drift-aware design principle validated on controlled interleaved benchmarks, offering insights toward reliable knowledge management in evolving deployment scenarios. Full article

Multi-UAV target localization relies on cooperative fusion of local, perception-derived geometric measurements over an edge network. While distributed fusion improves scalability and robustness compared with a centralized architecture, the iterative message exchanges may leak sensitive information to external eavesdroppers or honest-but-curious peers. This paper proposes a privacy-preserving distributed fusion method for multi-UAV localization via free-noise masking. The key idea is a double-injection mechanism. Specifically, each UAV masks its transmitted iterate with a locally generated bounded noise vector, while injecting the same noise into its local update so that the perturbations cancel exactly in the network-average dynamics under doubly stochastic mixing. As a result, the proposed PPDO-FN scheme preserves the practical convergence and weighted least squares localization accuracy of non-private distributed gradient descent, without requiring heavy cryptography or a trusted server. We further introduce reconstruction-based privacy metrics under transcript attacks and quantify the privacy–accuracy tradeoff. Simulation results demonstrate (i) near-identical accuracy and consensus behavior to the non-private baseline, (ii) monotonic privacy improvement with increasing masking strength, and (iii) the necessity of double-injection canceling compared with a naive single-injection baseline. Finally, we provide an end-to-end case study to connect the image-level detection to the geometric localization and then to privacy-preserving distributed fusion, illustrating engineering viability for our proposed approach. Full article

No-reference image quality assessment (NR-IQA) models achieve high correlation with human mean opinion scores (MOS) on clean benchmarks, yet recent work shows they can be highly vulnerable to small adversarial perturbations that severely degrade ranking consistency, including in black-box settings. We introduce the Spectral Robustness Mixer (SRM), a lightweight neck inserted between an NR-IQA backbone and regression head, designed to reduce adversarial sensitivity without changing the dataset, label format, or target metric. SRM couples (i) deep-to-shallow cross-scale fusion via a Nyström low-rank attention surrogate, (ii) ridge-conditioned landmark kernels with ridge regularization, solved via numerically stable small-matrix factorization (SVD/LU) to improve conditioning, and (iii) variance-aware entropy-regularized fusion gates with a bounded gain cap to limit gradient amplification. We evaluate SRM on TID2013 and KonIQ-10k under a white-box ${\mathcal{l}}_{\infty }/{\mathcal{l}}_2$ attack ensemble that includes per-image regression objectives and a correlation-aware pairwise inversion objective (a ranking-inspired surrogate for correlation inversion), with expectation-over-transformation (EOT) and anti-gradient masking checks. At $ϵ=4/255$ (${\mathcal{l}}_{\infty }$), SRM improves worst-case robust Spearman’s rank-order correlation coefficient (SROCC; defined as the minimum over our fixed attack ensemble) by an absolute $0.06$–$0.08$ SROCC points (i.e., correlation-coefficient units, not percentage gain) across datasets/backbones, while keeping clean SROCC within $0.00$–$0.01$ of the baseline. We observe similar trends for Pearson linear correlation coefficient (PLCC). Full article

Targetless LiDAR–camera extrinsic calibration remains challenging due to unreliable cross-modal correspondences and sensitivity to initialization. We present a targetless extrinsic calibration framework based on class-agnostic boundary mask alignment in a shared image-plane representation. This scheme first constructs consistent LiDAR–camera mask pairs from image-plane depth and intensity projections of LiDAR data and camera images. It then obtains robust initial pose candidates through bounded rotation-only global initialization and refines them using a computationally efficient stochastic gradient approximation to estimate the optimal extrinsic parameters. Experiments on the KITTI benchmark demonstrate a superior accuracy–runtime trade-off compared with a segmentation-based global optimization baseline, while real-world driving tests confirm stable cross-modal alignment under vibration and inter-modal timing jitter. Full article