Search Results (1,666)

Programmable quantum simulators based on Rydberg atom arrays provide a versatile platform for data-driven quantum simulation of strongly correlated systems, combinatorial optimization problems, and artificial quantum materials. In this review, we present a unified perspective on how materials-inspired effective Hamiltonians can be engineered and probed in Rydberg arrays, highlighting representative phenomena such as quantum phase transitions, frustrated spin-liquid–like states, symmetry-protected topological phases, and nonequilibrium dynamics. We further discuss recent progress in machine learning-based approaches, including phase identification from experimental snapshots, neural network quantum states, Hamiltonian learning, and quantum reservoir computing. A central theme is the emergence of closed-loop classical–quantum hybrid workflows, in which quantum simulation, measurement, and classical inference are integrated through iterative feedback. These developments position Rydberg atom arrays not only as programmable simulators but also as data-driven platforms for the scalable exploration, characterization, and design of complex quantum materials. Full article

The skin–brain axis constitutes a complex, bidirectional network integrating cutaneous sensory, immune, and neuroendocrine systems with central neural circuits involved in emotion regulation, stress responsivity, and social cognition. Advances in psychodermatology and cosmetic science have progressively extended this framework to the emerging field of neurocosmetics, which explores how topical formulations, sensorial properties, and cutaneous neuromodulators may influence psychological well-being, affective states, and perceived stress. The aim of this narrative review is to synthesize current evidence on the biological foundations of the skin–brain axis and to critically examine the implications of these mechanisms for neurocosmetic interventions from a psychological and psychiatric perspective. It describes the biological substrates underlying skin–brain communication, including the cutaneous hypothalamic–pituitary–adrenal axis, neuropeptides, neurotrophins, transient receptor potential channels, and endocannabinoid signaling, and examines how these pathways are targeted by neurocosmetic interventions. Particular attention is devoted to neuroactive compounds, such as peptides, cannabinoids, botanicals, and aromatherapeutic molecules, as well as to sensorial strategies involving texture, temperature, and olfactory cues, which may modulate mood, anxiety, and self-perception through peripheral mechanisms. From a psychological and psychiatric perspective, the review discusses the intersection between stress-related skin conditions, body image disturbances, and emotional dysregulation, highlighting how cosmetic practices may influence subjective well-being beyond purely aesthetic outcomes. Methodological limitations of the existing literature, including the heterogeneity of study designs and outcome measures, as well as ethical considerations related to mood- and stress-related claims in cosmetic products, are critically examined. Finally, future research directions are outlined, and a translational framework is proposed to integrate dermatology, neuroscience, and mental health within next-generation cosmetic science. Full article

Unmanned aerial vehicles (UAVs) operating in safety-critical missions require effective anomaly detection methods to identify propulsion-system faults before they cause catastrophic failures. However, current vibration-based diagnostic models typically rely on datasets representing only discrete, isolated fault states, and do not capture the continuous structural degradation that occurs during real flight operations. To address this gap, this study proposes a severity-ordered vibration data augmentation framework for anomaly detection in rotary-wing UAV propulsion systems. Controlled experiments were conducted under healthy, tape-induced imbalance, scratch, and cut propeller conditions using stepped throttle excitation from 10% to 100% in 10% increments, with 40 s per level. A severity-ordered arrangement strategy based on throttle level and a robust peak-to-peak severity metric generated approximately 7.5 h of augmented vibration data per axis, representing a continuous degradation trajectory. Three-axis continuous wavelet transform (CWT) scalograms of size $48\times 96\times 3$ were used to train an unsupervised anomaly detection framework. Comparative experiments with Isolation Forest, One-Class SVM, and LSTM–AE demonstrated that the proposed Convolutional Neural Network (CNN)–Bidirectional Gated Recurrent Unit (BiGRU)–State-Space Model (SSM)–Autoencoder (AE) architecture achieved the best performance, reaching 0.9959 precision, 0.4428 recall, 0.6131 F1-score, and 0.9284 Area Under the Receiver Operating Characteristic Curve (AUROC). The ablation study further showed that incorporating temporal modeling and state-space dynamics improves detection robustness compared with CNN–AE and CNN–BiGRU–AE baselines. These results show that combining severity-ordered augmentation with deep temporal learning improves progressive propulsion anomaly detection in UAV vibration monitoring. This work introduces a methodology that connects rotor dynamics principles with deep learning, providing a continuous degradation manifold that improves early-stage detection and condition monitoring of UAV propulsion systems. Full article

While convolutional neural networks (CNNs) excel at image classification, their generalization across domains and robustness to nonlinear degradation remain challenges in art media classification (AMC). To address these challenges, this article presents a dual-stage analytical framework: first, an evaluation of seven discrete CNN architectures—ranging from VGG16 to ConvNeXt—subjected to domain shift using the New Spain (Mexico) Art Media Dataset; and second, a formal robustness analysis using an artistic corruption benchmark (Art-C). This benchmark simulates nonlinear degradations, including cracking, oxidized varnish, and pictorial abstraction. Our results demonstrate that while deep convolutional representations maintain acceptable transferability (accuracy $>70\%$), significant variability exists in architectural stability (mean 0.0607) under progressive stochastic degradation. Notably, Xception exhibited the highest robustness (Art-mCE = 0.8039), whereas VGG16 showed the greatest relative performance decay. Severity analysis further indicates that structural perturbations induce higher error rates than chromatic shifts, suggesting that CNNs are more sensitive to topological features (depth and residual connections) than color-space distributions. We provide quantitative evidence characterizing the relationship between architectural topology and empirical stability in non-natural image domains. Full article

Modern neural networks have achieved remarkable success in visual recognition; however, due to their sensitivity to domain shifts, Unsupervised Domain Adaptation (UDA) remains an open research problem. A key reason for this limitation is that source-trained models rely primarily on texture, lacking the explicit geometric information required for object recognition. To overcome this problem, we introduce CoDA, an object-centric learning framework inspired by infant cognitive development, specifically the process of object individuation. By introducing a geometric prior, our approach employs a physically grounded generation pipeline that uses a textureless “Sculpture Mode” and object isolation to complement textural information with 3D geometric features, capturing shape information that is often ignored during training. To enable robust training from scratch, we further integrate two control mechanisms: a Network Stability Scheduler to orchestrate training progression based on convergence stability, and a Dynamic Top-K Pseudo-Labeling strategy that adapts confidence thresholds for each individual class. Extensive evaluations on three real-world target datasets (VegFru, Fruits-262, and Open Images v7) demonstrate that CoDA, trained on a source dataset of just 12,000 synthetic images, achieves comparable results to (and in specific domains surpasses) ImageNet-pretrained models (leveraging 1.2 million images), significantly outperforming state-of-the-art adversarial and semi-supervised domain adaptation methods. Full article

Cardiovascular and cerebrovascular diseases are serious threats to human health and impose a significant burden on individuals and society. As the two critical and complex organs with the highest metabolic demands, the brain and the heart form an interactive relationship through metabolic networks. As a core prerequisite for maintaining the normal physiological functions of the body, metabolic homeostasis is also a crucial foundation for ensuring the brain–heart synergy. When the human metabolism is in a stable state, the energy supply and material exchange of the brain and the heart can accurately match demand, the neural signal transmission is smooth, and the myocardial contraction is strong and regular—thus ensuring the coordinated and unified functions of these two vital organs. However, once metabolic homeostasis is disrupted, problems such as energy metabolism disorders will arise, which will then become a core inducing mechanism for cardiovascular and cerebrovascular comorbidities. This article presents a review of the research progress on the potential mechanisms of brain-heart interactions based on metabolic regulation from three aspects: neurometabolic, endocrino-metabolic and immune–metabolic regulation, the impact of cardiac function on brain metabolism, and the bidirectional regulation of brain-heart metabolism. Full article

Efficient soil fertility monitoring is essential for sustainable agriculture, food security, and environmental management across Africa, yet conventional laboratory methods remain prohibitively costly and slow for continental-scale applications. Soil spectroscopy is considered as a rapid, non-destructive alternative with transformative potential. This review provides a systematic synthesis of spectroscopic applications across Africa, encompassing laboratory, field, airborne, and satellite-based platforms, while examining major data sources including the Africa Soil Information Service (AfSIS) and GEO-CRADLE spectral libraries. We critically evaluate the evolution of modeling approaches, revealing that Partial Least Squares Regression (PLSR) dominates, but a shift toward advanced frameworks like hybrid physically based models, ensemble learning and deep neural networks is essential. Critically, we identify a pronounced imbalance wherein laboratory spectroscopy prevails while imaging and satellite-based approaches remain comparatively underutilized, despite their unparalleled potential for scaling point measurements to continental extents. The review consolidates findings on key soil properties, demonstrating consistent successes for primary constituents with direct spectral responses (i.e., organic carbon), while revealing relative uncertainty for properties inferred indirectly via covariance (e.g., available phosphorus, potassium). Despite significant local and regional progress, the absence of a standardized pan-African spectral library and the intractable transferability problem remain formidable barriers. Future research must pivot decisively toward imaging spectroscopy and satellite platforms, mitigating PLSR dominance through systematic adoption of ensemble methods, transfer learning, and model harmonization frameworks to fully operationalize these technologies in support of Africa’s sustainable development goals. Full article

Nanophotonics, an interdisciplinary field merging nanotechnology and photonics, has enabled transformative advancements across diverse sectors, including green energy, biomedicine, and optical computing. This review comprehensively examines recent progress in nanophotonic principles and applications, highlighting key innovations in material design, device engineering, and system integration. In renewable energy, nanophotonics allows the use of light-trapping nanostructures and spectral control in perovskite solar cells, concentrating solar power systems, and thermophotovoltaics. This has significantly enhanced solar conversion efficiencies, approaching theoretical limits. In biosensing, nanophotonic platforms achieve unprecedented sensitivity in detecting biomolecules, pathogens, and pollutants, enabling real-time diagnostics and environmental monitoring. Medical applications leverage tailored light–matter interactions for precision photothermal therapy, image-guided surgery, and early disease detection. Furthermore, nanophotonics underpins next-generation optical neural networks and neuromorphic computing, offering ultrafast, energy-efficient alternatives to von Neumann architectures. Despite rapid growth, challenges in scalability, fabrication costs, and material stability persist. Future advancements will rely on novel materials, AI-driven design optimization, and multidisciplinary approaches to enable scalable, low-cost deployment. This review summarizes recent progress and highlights future trends, including novel material systems, multidisciplinary approaches, and enhanced computational capabilities, paving the way for transformative applications in this rapidly evolving field. Full article

Hyperspectral unmixing aims to estimate endmember spectra and their corresponding abundance fractions at the subpixel scale, which is a critical preprocessing step for quantitative analysis of hyperspectral remote sensing imagery. While deep learning-based methods have achieved remarkable progress, three fundamental challenges remain: (i) reliance on a single shared spatial prior that cannot decouple the heterogeneous spatial patterns of different land covers; (ii) the lack of synergy in jointly optimizing endmember extraction and abundance estimation; (iii) the poor robustness of unsupervised training to complex mixtures, noise, and class imbalance. To address these issues, we propose a novel unsupervised unmixing framework that integrates adaptive orthogonal multi-faceted graph representation with curriculum learning. Specifically, we design an Adaptive Orthogonal Multi-Faceted Graph Generator (AOMFG) to learn a set of independent orthogonal graph structures, achieving spatially informed decoupling of land cover patterns. Then, a dual-branch collaborative optimization network is constructed: a Graph Convolutional Network (GCN) branch that incorporates the learned spatial topological priors for abundance estimation, and a 1D Convolutional Neural Network (1DCNN) branch that employs a query-attention mechanism to adaptively aggregate pure spectral features for endmember extraction. Finally, we introduce a three-stage curriculum learning strategy that progressively fine-tunes the model, which significantly enhances its performance. Extensive experiments on three widely used real-world benchmark datasets demonstrate that our proposed framework consistently outperforms state-of-the-art methods in both endmember extraction and abundance estimation accuracy. Comprehensive ablation studies, parameter sensitivity analysis, and noise robustness tests further validate the effectiveness of each core component. Full article

Lung nodule detection remains a critical challenge in clinical diagnostics due to the small size, weak contrast, and high background interference of nodules in CT scans. To address these issues, a novel deep neural network architecture, termed Daisy-Net, is proposed. This model incorporates dual attention mechanisms and inter-scale feature perception, consisting of two primary components: the Parallelized Patch and Spatial Context Aware (PPSCA) module and the Omni-domain Multistage Fusion (OMF) module. The PPSCA module enhances the extraction of fine-grained textures and boundary information through multi-branch patch perception and spatial attention. The OMF module employs omni-domain feature fusion and progressive stage-wise supervision to improve robustness and discrimination under complex conditions. The lung nodule detection task is formulated as a two-dimensional segmentation problem and evaluated on the LUNA16 dataset. In the post-binarization comparative evaluation, Daisy-Net achieves the best overall performance among all compared methods, with an Intersection over Union (IoU) of 81.41, a Dice coefficient of 89.75, a precision of 95.34, a sensitivity of 84.78, and a specificity of 99.9974. These findings indicate the model’s strong capability in detecting small pulmonary nodules accurately and reliably. Full article

Neural language models have achieved remarkable progress in semantic representation learning. However, cross-domain representation learning still suffers from prominent semantic noise propagation issues. Existing methods still face challenges in cross-domain semantic modeling, including limited robustness across different semantic granularities, difficulty in separating transferable semantics from task-irrelevant semantic interference, and insufficient adaptability to specialized scenarios. These issues may reduce feature discriminability in fine-grained semantic tasks and complex application settings. To address these problems, we propose the Diffusion-Coded Attention Network (DCANet), a novel cross-domain representation learning architecture with three synergistic core modules: a multi-granular parallel diffusion masking mechanism for cross-scale context fusion via stochastic path activation, an implicit semantic encoder that distills domain-invariant patterns into adaptive bias codes via shared latent manifolds, and a self-correcting attention topology realizing dynamic semantic purification via closed-loop interactions between local features and global bias states. Extensive evaluations are conducted on nine well-recognized benchmark datasets to verify DCANet’s effectiveness and reliability. Experimental results show that DCANet attains state-of-the-art results on the majority of the benchmark datasets, with significant accuracy improvements on text classification and sentiment analysis tasks. Full article

The Persistent Scatterers InSAR (PSInSAR) technology, which utilizes pixels with stable phases to extract ground deformation, is an effective tool for large-scale, long-period surface monitoring applications. It has been widely applied to land subsidence monitoring, earthquake research, and infrastructure risk management. Furthermore, some studies have successfully employed this method to monitor the progressive motion of creeping in landslide areas. However, these regions containing active landslides are usually covered by canopy layers, which cause low coherence in InSAR processing and reduce the number of stable pixels, thereby preventing long-term period monitoring in those areas. In this study, the supervised deep learning model, U-Net, based on a convolutional neural network, is applied to the differential InSAR dataset acquired from Sentinel-1 to improve persistent scatterer selection. A well-processed PSInSAR result, utilizing 55 Sentinel-1 images acquired from 5 November 2014 to 19 December 2017, is introduced as a dataset for model training. The pixel-based Persistent Scatterer (PS) labels used for model training are identified using the StaMPS software. The model is designed to identify the distributed scatterer (iDS) index using a single pair of SAR images. As a result, more iDS pixels can be obtained from a single interferogram, indicating a significant improvement over the StaMPS algorithm. The line-of-sight velocity and time series of PS pixels from the model prediction show a long-term uplift on the upper slope, which represents downslope sliding in the target area. Furthermore, some iDS pixels exhibit a seasonal deformation on the lower part of the slope. The capability for these additional deformation analyses underscores the potential of this new deep-learning-based approach. Full article

To address the challenges of sensor installation limitations, severe background noise interference, and low model deployment efficiency in rolling bearing fault diagnosis in industrial environments, this paper proposes a lightweight, progressive fusion and knowledge-distillation diagnostic framework that integrates vibration and sound signals. First, considering the differences in physical characteristics between vibration and sound signals, a feature-extraction network for heterogeneous modality perception is designed: the vibration branch employs a large-kernel one-dimensional convolutional neural network, while the sound branch uses a small-kernel stacked two-dimensional convolutional neural network, with depthwise separable convolutions introduced for lightweight modification. Second, an attention-gated progressive feedback fusion strategy is proposed. Learnable gating units are used to filter the confidence of the fused features, feeding them back to the original input as residuals, effectively suppressing noise accumulation and improving fusion quality. Finally, a cross-architecture knowledge-distillation scheme is constructed, transferring the fault feature-discrimination ability from the deep heterogeneous fusion network (teacher network GAF-Net) to the lightweight LightGBM (student network Distilled-LGB). Combined with a normal sample statistical feature alignment mechanism, the student model can independently complete end-to-end fault diagnosis only with online-extractable handcrafted features, achieving microsecond-level pure model inference speed while ensuring diagnostic accuracy, fully meeting industrial edge deployment requirements. Experiments on a self-built industrial dataset and the public UOEMD-VAFCVS dataset show that GAF-Net achieves 97.89% (A → B) and 96.72% (15 Hz → 30 Hz) accuracy. Distilled-LGB achieves 21 ms inference time and 4.2 MB model size with <1% accuracy loss, demonstrating noise robustness, cross-condition generalization, and edge deployment capability. Full article

Differentiable Neural Architecture Search has emerged as a powerful paradigm for automated network design, yet it suffers from a fundamental optimization inconsistency problem: Architectures optimized under continuous relaxation often fail to maintain their performance after discretization. To address this challenge, we propose Fisher-DARTS—a Fisher information-driven differentiable NAS framework. The proposed method introduces three key innovations: (1) a Fisher information-based momentum update mechanism that guides architectural parameters toward statistically significant operations, aligning the search objective with discrete deployment; (2) a progressive three-region pruning strategy that adaptively eliminates redundant operations with low Fisher information, ensuring architectural compactness; and (3) a cell-weighted fusion module that preserves multi-scale features across stacked cells. Additionally, the search space is expanded by incorporating attention mechanisms to enhance feature representation capability. The proposed framework is generic and applicable to a wide range of vision tasks. To validate its effectiveness, we apply it to gaze estimation—a core technology in multimodal human–computer interaction. Experimental results on three public datasets, MPIIFaceGaze, RT-GENE, and ETH-XGaze, demonstrate that Fisher-DARTS achieves mean angular errors of 3.22°, 5.45°, and 4.12°, respectively, outperforming hand-designed networks and existing NAS-based gaze estimation models. These results validate the effectiveness of the proposed Fisher-driven NAS framework and its generalization capability across diverse scenarios. Full article

As the scale of graph-structured data continues to grow, graph summarization has become an important technique for storage efficiency and high-level visualization. This study investigates a Node Importance (NI) approach to graph summarization that prioritizes structural integrity over simple size reduction. The NI approach selects super nodes by ranking vertices through centrality and propagation metrics. Experimental results demonstrate that the proposed NI method achieves compression rates comparable to or slightly lower than traditional Minimum Description Length (MDL) methods across various datasets while maintaining structural integrity. However, today, the high dimensionality and complexity of modern graph data are making deep learning techniques more popular. Great progress in deep learning summarization techniques is achieved with Graph Neural Networks (GNNs). This study investigates the structure and suitability of different GNN architectures for graph summarization using the NI approach. Graph Attention Networks (GATs) and their variants are discussed as a flexible, learned notion of node importance via attention. We present an examination of GATs, covering both diverse approaches and improvements. This study also discusses extensions that enhance the concept of node importance established by the GAT model, GAT variants for node importance estimation, and application-specific GAT research. Full article