Scale Variations Research Articles

Multiscale off-fault brecciation records coseismic energy budget of principal fault zone

Breccia and pulverized rock are typical textures in off-fault damage adjacent to a main seismogenic zone. Previously, by estimating the energy required to advance the rupture in this zone using particle size distribution at sub-millimeter/micrometer scales, we could constrain the energy budget during coseismic events. However, whether microscopic estimation is sufficient to capture surface energy fragmentation during an earthquake and the effect of measurement scale variation on calculation of co-seismic energy partitioning remained unclear. Here, we investigated the mechanism of coseismic off-fault damage based on field and microstructural observations of a well-exposed breccia body in Ichinokawa, Japan. We used in situ clast measurements coupled with thin-section analysis of breccia clasts to estimate the energy budget of the damage zone adjacent to the principal slip zone of the Median Tectonic Line (MTL). The total surface energy density and corresponding surface energy per unit fault for a width of ~ 500 m of the dynamical damage zone were estimated. The moment magnitude estimated based on surface energy was 5.8–8.3 Mw. In Ichinokawa, off-fault fragmentation is initiated by coseismic activity and is followed by fluid activity. Under dynamic fragmentation conditions, the scale is important to calculate the surface energy.

Improving remote sensing object detection by using feature extraction and rotational equivariant attention

ABSTRACT In recent years, computer vision has witnessed significant attention in the research on object detection in remote-sensing images. Unlike objects in traditional natural images, those in remote sensing images are captured vertically by spacecraft, introducing arbitrary directionality, substantial scale variation, and a more complex background. We propose a rotation-equivariant detector enhanced with feature fusion and attention modules to address remote sensing image object detection challenges. Specifically, we introduce a Rotation-Enhanced Feature Extraction (REFE) module and a Rotational Equivariant Attention (REA) module. These enhancements empower the detector to extract object information more effectively from remotely sensed images, filtering out complex background information and improving detection accuracy and stability. Through extensive experiments on diverse and challenging remote sensing image datasets, our method outperforms the task of object detection. Remarkably, our network achieves 1.0, 2.91, and 1.11 mean average precision (mAP) improvements on the DOTA-v1.5, HRSC2016 and DIOR-R datasets. These experimental results robustly demonstrate the effectiveness and superiority of our proposed method.

Determination of the theory uncertainties from missing higher orders on NNLO parton distributions with percent accuracy

We include uncertainties due to missing higher order corrections to QCD computations (MHOU) used in the determination of parton distributions (PDFs) in the recent NNPDF4.0 set of PDFs. We use our previously published methodology, based on the treatment of MHOUs and their full correlations through a theory covariance matrix determined by scale variation, now fully incorporated in the new NNPDF theory pipeline. We assess the impact of the inclusion of MHOUs on the NNPDF4.0 central values and uncertainties, and specifically show that they lead to improved consistency of the PDF determination. PDF uncertainties on physical predictions in the data region are consequently either unchanged or moderately reduced by the inclusion of MHOUs.

CC-DETR: DETR with Hybrid Context and Multi-Scale Coordinate Convolution for Crowd Counting

Prevailing crowd counting approaches primarily rely on density map regression methods. Despite wonderful progress, significant scale variations and complex background interference within the same image remain challenges. To address these issues, in this paper we propose a novel DETR-based crowd counting framework called Crowd Counting DETR (CC-DETR), which aims to extend the state-of-the-art DETR object detection framework to the crowd counting task. In CC-DETR, a DETR-like encoder–decoder structure (Hybrid Context DETR, i.e., HCDETR) is proposed to tackle complex visual information by fusing features from hybrid semantic levels through a transformer. In addition, we design a Coordinate Dilated Convolution Module (CDCM) to effectively employ position-sensitive context information in different scales. Extensive experiments on three challenging crowd counting datasets (ShanghaiTech, UCF-QNRF, and NWPU) demonstrate that our model is effective and competitive when compared against SOTA crowd counting models.

Evidence for reversible oxygen ion movement during electrical pulsing: enabler of emerging ferroelectricity in binary oxides

Ferroelectric HfO2-based materials and devices show promising potential for applications in information technology but face challenges with inadequate electrostatic control, degraded reliability, and serious variation in effective oxide thickness scaling. We demonstrate a novel interface-type switching strategy to realize ferroelectric characteristics in atomic-scale amorphous binary oxide films, which are formed in oxygen-deficient conditions by atomic layer deposition at low temperatures. This approach can avoid the shortcomings of reliability degradation and gate leakage increment in scaling polycrystalline doped HfO2-based films. Using theoretical modeling and experimental characterization, we show the following. (1) Emerging ferroelectricity exists in ultrathin oxide systems as a result of microscopic ion migration during the switching process. (2) These ferroelectric binary oxide films are governed by an interface-limited switching mechanism, which can be attributed to oxygen vacancy migration and surface defects related to electron (de)trapping. (3) Transistors featuring ultrathin amorphous dielectrics, used for non-volatile memory applications with an operating voltage reduced to ±1 V, have also been experimentally demonstrated. These findings suggest that this strategy is a promising approach to realizing next-generation complementary metal-oxide semiconductors with scalable ferroelectric materials.

Enhancing flood verification using Signal Detection Theory (SDT) and IoT Sensors: A spatial scale evaluation

Inundation mapping by flood simulation is subject to uncertainties which spatial accuracy assessment could not be processed as normal mapping does. This paper presents a comprehensive study on the spatial accuracy of flood verification using Internet of Things (IoT) sensors in several flood-prone regions in Taiwan, including Changhua, Yunlin, Chiayi, Tainan, Kaohsiung, and Pingtung, during three storm events in 2021. The study adopts Signal Detection Theory (SDT) and proposes an ensemble-based approach that combines multiple evaluation metrics to enhance the accuracy and reliability of flood verification in these regions. The study examines the inundation availabilities between flood observation from IoT sensors and flood simulation. A range of spatial scales varies from 100 m to 1000 m and also administrative divisions are considered including neighborhood-level and village-level scales. The results show that increasing the spatial scale generally leads to higher True Positives, indicating improved flood detection, but it also results in increased False Positives, indicating more misclassification of non-flooded points. The findings reveal that the optimal spatial scale for all cities/counties is 450 m which indicates the averaged spatial accuracy of the flood simulation. However, there is notable variation in the optimal spatial scale across different regions. The ensemble approach demonstrates high consistency in determining the optimal spatial scale, as eight out of ten metrics consistently identify the same value. Additionally, the study compares flood verification across administrative divisions in Taiwan, finding that the village-level spatial scale performs best in Changhua and Yunlin counties, while the neighborhood-level scale is preferred in Tainan, Kaohsiung, and Pingtung. This research provides a valuable framework to estimate inundation mapping accuracy under flood verification using IoT sensors in the specified region of Taiwan and highlights the importance of considering local conditions and administrative divisions in determining the optimal spatial scale. These findings can contribute to the improvement of flood detection and monitoring systems in the area with IoT flood sensors.

Transforming Poultry Farming: A Pyramid Vision Transformer Approach for Accurate Chicken Counting in Smart Farm Environments.

Smart farm environments, equipped with cutting-edge technology, require proficient techniques for managing poultry. This research investigates automated chicken counting, an essential part of optimizing livestock conditions. By integrating artificial intelligence and computer vision, it introduces a transformer-based chicken-counting model to overcome challenges to precise counting, such as lighting changes, occlusions, cluttered backgrounds, continual chicken growth, and camera distortions. The model includes a pyramid vision transformer backbone and a multi-scale regression head to predict precise density maps of the crowded chicken enclosure. The customized loss function incorporates curriculum loss, allowing the model to learn progressively, and adapts to diverse challenges posed by varying densities, scales, and appearances. The proposed annotated dataset includes data on various lighting conditions, chicken sizes, densities, and placements. Augmentation strategies enhanced the dataset with brightness, contrast, shadow, blur, occlusion, cropping, and scaling variations. Evaluating the model on the proposed dataset indicated its robustness, with a validation mean absolute error of 27.8, a root mean squared error of 40.9, and a test average accuracy of 96.9%. A comparison with the few-shot object counting model SAFECount demonstrated the model's superior accuracy and resilience. The transformer-based approach was 7.7% more accurate than SAFECount. It demonstrated robustness in response to different challenges that may affect counting and offered a comprehensive and effective solution for automated chicken counting in smart farm environments.

A novel MO-YOLOv4 for segmentation of multi-class bridge damages

Most studies of multi-class damage segmentation overlook two prominent and critical challenges: the substantial variations in damage scale and the susceptibility of network training to background interferences. As a consequence, achieving the desired performance in multi-class damage segmentation becomes a difficult task. To tackle these challenges, a novel multi-class damage segmentation method, MO-YOLOv4, is proposed based on the widely used YOLOv4 structure. MO-YOLOv4 incorporates the Multi-Scale Attention (MSA) module to greatly enhance the feature extraction ability for multi-class damages, and employs the Oriented Bounding Box (OBB)-based segmentation strategy for effectively reducing the influence of background interferences during the network training phase. The effectiveness of the MSA module and OBB-based segmentation strategy is validated through verification and ablation experiments. In the comparative study, the Mean Precision rate (MP), Mean Recall rate (MR), Mean F1-score (MF) and Mean Intersection over Union rate (MIoU) of the proposed MO-YOLOv4 reach up to 82.49%, 72.73%, 77.04% and 64.28%, respectively, indicating the superior level of segmentation performance offered by MO-YOLOv4 in comparison to the mainstream semantic segmentation methods for multi-class damages.

Fragmentation and reorganization of agglomerates in catalyst inks: Understanding the time-scale and temperature-dependent effects on the dispersion process

The time and temperature of the dispersive shear process of catalyst inks have a great influence on the agglomerate structure of the ink and the rheological properties of the ink. In this paper, three different catalyst inks were prepared at 20 °C, 30 °C and 40 °C, and tested using characterization such as rheological techniques, zeta (ξ) potential, and particle size measurements. As the temperature rises, the median particle size inside the ink at the end of shear was 400, 308.05 and 157.3 nm, and the zeta potential was −37, −33 and −25 mV, with a decrease in viscosity and a deterioration in thixotropy. The catalyst layers prepared from inks dispersed at different temperatures had the smallest cracks and the flattest surface at 30 °C, and the largest cracks, the largest contact angle, the roughest surface, and the poorest polarization properties at 40 °C. Through the comprehensive analysis of the experimental data, the fragmentation and reorganization processes of agglomerates during the internal dispersion of catalyst ink were revealed from the perspectives of time scale and temperature variation, respectively. This study provides an experimental basis for a deeper understanding of the dispersion performance of catalyst ink at different temperatures.

Deciphering spatial heterogeneity of maritime accidents considering impact scale variations

ABSTRACT Ensuring maritime safety has ascended as a preeminent concern within the global maritime sector. Understanding how factors affect maritime accidents’ consequences in different water areas would be of great benefit to preventing the occurrence or reducing the consequences. This study thus employed a multi-scale geographically weighted regression (MGWR) model on the accident dataset from Fujian waters in the East China Sea, to quantify the influences of different factors as well as the spatial heterogeneity in the effects of key factors on maritime accident consequence. The performances of MGWR are compared with multiple linear regression (MLR) and GWR. As expected, MGWR outperforms the other two models in terms of its ability to clearly capture the unobserved spatial heterogeneity in the effects of factors. Results reveal notably distinct influences of some factors on maritime accident consequences in different locations. An intuitive indication by MGWR is that approximately 50% of the accidents present positive coefficients of good visibility while other locations are negative, which are failed to recognize by MLR. The outcomes provide insights for making appropriate safety countermeasures and policies customized for different water areas.

Learning a complex network representation for shape classification

Shape contour is a key low-level characteristic, making shape description an important aspect in many computer vision problems, with several challenges such as variations in scale, rotation, and noise. In this paper, we introduce an approach for shape analysis and classification from binary images based on representations learned by applying Randomized Neural Networks (RNNs) on feature maps derived from a Complex Network (CN) framework. Our approach models the contour in a complex network and computes their topological measures using a dynamic evolution strategy. This evolution of the CN provides significant information into the physical aspects of the shape’s contour. Therefore, we propose embedding the topological measures computed from the dynamics of the CN evolution into a matrix representation, which we have named the Topological Feature Map (TFM). Then, we employ the RNN to learn representations from the TFM through a sliding window strategy. The proposed representation is formed by the learned weights between the hidden and output layers of the RNN. Our experimental results show performance improvements in shape classification using the proposed method across two generic shape datasets. We also applied our approach to the recognition of plant leaves, achieving high performance in this challenging task. Furthermore, the proposed approach has demonstrated robustness to noise and invariance to transformations in scale and orientation of the shapes.

A three-stream fusion and self-differential attention network for multi-modal crowd counting

Multi-modal crowd counting aims at using multiple types of data, like RGB-Thermal and RGB-Depth, to count the number of people in crowded scenes. Current methods mainly focus on two-stream multi-modal information fusing in the encoder and single-scale semantic features in the decoder. In this paper, we propose an end-to-end three-stream fusion and self-differential attention network to simultaneously address the multi-modal fusion and scale variation problems for multi-modal crowd counting. Specifically, the encoder adopts three-stream fusion to fuse stage-wise modality-paired and modality-specific features. The decoder applies a self-differential attention mechanism on multi-level fused features to extract basic and differential information adaptively, and finally, the counting head predicts the density map. Experimental results on RGB-T and RGB-D benchmarks show the superiority of our proposed method compared with the state-of-the-art multi-modal crowd counting methods. Ablation studies and visualization demonstrate the advantages of the proposed modules in our model.

SCPMan: Shape context and prior constrained multi-scale attention network for pancreatic segmentation

Due to the poor prognosis of Pancreatic cancer, accurate early detection and segmentation are critical for improving treatment outcomes. However, pancreatic segmentation is challenged by blurred boundaries, high shape variability, and class imbalance. To tackle these problems, we propose a multiscale attention network with shape context and prior constraint for robust pancreas segmentation. Specifically, we proposed a Multi-scale Feature Extraction Module (MFE) and a Mixed-scale Attention Integration Module (MAI) to address unclear pancreas boundaries. Furthermore, a Shape Context Memory (SCM) module is introduced to jointly model semantics across scales and pancreatic shape. Active Shape Model (ASM) is further used to model the shape priors. Experiments on NIH and MSD datasets demonstrate the efficiency of our model, which improves the state-of-the-art Dice Score for 1.01% and 1.03% respectively. Our architecture provides robust segmentation performance, against the blurry boundaries, and variations in scale and shape of pancreas.

MsSVT++: Mixed-Scale Sparse Voxel Transformer With Center Voting for 3D Object Detection.

Accurate 3D object detection in large-scale outdoor scenes, characterized by considerable variations in object scales, necessitates features rich in both long-range and fine-grained information. While recent detectors have utilized window-based transformers to model long-range dependencies, they tend to overlook fine-grained details. To bridge this gap, we propose MsSVT++, an innovative Mixed-scale Sparse Voxel Transformer that simultaneously captures both types of information through a divide-and-conquer approach. This approach involves explicitly dividing attention heads into multiple groups, each responsible for attending to information within a specific range. The outputs of these groups are subsequently merged to obtain final mixed-scale features. To mitigate the computational complexity associated with applying a window-based transformer in 3D voxel space, we introduce a novel Chessboard Sampling strategy and implement voxel sampling and gathering operations sparsely using a hash map. Moreover, an important challenge stems from the observation that non-empty voxels are primarily located on the surface of objects, which impedes the accurate estimation of bounding boxes. To overcome this challenge, we introduce a Center Voting module that integrates newly voted voxels enriched with mixed-scale contextual information towards the centers of the objects, thereby improving precise object localization. Extensive experiments demonstrate that our single-stage detector, built upon the foundation of MsSVT++, consistently delivers exceptional performance across diverse datasets.

3DSN-Net: A 3-D Scale-Aware convNet With Nonlocal Context Guidance for Kidney and Tumor Segmentation From CT Volumes.

Automatic kidney and tumor segmentation from CT volumes is a critical prerequisite/tool for diagnosis and surgical treatment (such as partial nephrectomy). However, it remains a particularly challenging issue as kidneys and tumors often exhibit large-scale variations, irregular shapes, and blurring boundaries. We propose a novel 3-D network to comprehensively tackle these problems; we call it 3DSN-Net. Compared with existing solutions, it has two compelling characteristics. First, with a new scale-aware feature extraction (SAFE) module, the proposed 3DSN-Net is capable of adaptively selecting appropriate receptive fields according to the sizes of targets instead of indiscriminately enlarging them, which is particularly essential for improving the segmentation accuracy of the tumor with large scale variation. Second, we propose a novel yet efficient nonlocal context guidance (NCG) mechanism to capture global dependencies to tackle irregular shapes and blurring boundaries of kidneys and tumors. Instead of directly harnessing a 3-D NCG mechanism, which makes the number of parameters exponentially increase and hence the network difficult to be trained under limited training data, we develop a 2.5D NCG mechanism based on projections of feature cubes, which achieves a tradeoff between segmentation accuracy and network complexity. We extensively evaluate the proposed 3DSN-Net on the famous KiTS dataset with many challenging kidney and tumor cases. Experimental results demonstrate our solution consistently outperforms state-of-the-art 3-D networks after being equipped with scale aware and NCG mechanisms, particularly for tumor segmentation.

Visual place recognition for autonomous mobile robot navigation using LoFTR and MAGSAC++

Autonomous mobile robots are defined as robotic entities capable of independent movement and intelligent decision-making, relying on their ability to perceive and analyze their surroundings, including objects in their environment. In Simultaneous Localization and Mapping (SLAM) systems, loop closure is often achieved through visual place recognition techniques, where the system compares the current visual input with previously observed scenes to identify matches. In computer vision applications, Speeded-Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT) are popular feature extraction algorithms used for such as key point detection, matching, and image registration tasks. The choice of inlier threshold should be based on the specific characteristics of the application and the nature of the images being processed. It often requires experimentation and tuning to find the optimal balance between robustness and accuracy. It Utilizes the pre-trained Local Feature Transformer (LoFTR) and MAGSAC++ estimator to address these drawbacks by employing the number of inliers to determine the similarity between two images for visual place recognition. Our experiment demonstrates that the number of inliers can determine the similarity of locations between two images. Scale variations and translation in location significantly influence the resulting number of inliers. Comparing images from the same location and from different locations yields varying numbers of inliers. The number of inliers significantly influences the similarity of locations. At the same location, the number of inliers is above 150, while at different locations, the number is below 150.

OptFlowCam: A 3D‐Image‐Flow‐Based Metric in Camera Space for Camera Paths in Scenes with Extreme Scale Variations

AbstractInterpolation between camera positions is a standard problem in computer graphics and can be considered the foundation of camera path planning. As the basis for a new interpolation method, we introduce a new Riemannian metric in camera space, which measures the 3D image flow under a small movement of the camera. Building on this, we define a linear interpolation between two cameras as shortest geodesic in camera space, for which we provide a closed‐form solution after a mild simplification of the metric. Furthermore, we propose a geodesic Catmull‐Rom interpolant for keyframe camera animation. We compare our approach with several standard camera interpolation methods and obtain consistently better camera paths especially for cameras with extremely varying scales.

Programme theories to describe how different general practitioner service models work in different contexts in or alongside emergency departments (GP-ED): realist evaluation

BackgroundAddressing increasing patient demand and improving ED patient flow is a key ambition for NHS England. Delivering general practitioner (GP) services in or alongside EDs (GP-ED) was advocated in 2017...

Cross-level and multiscale CNN-Transformer network for automatic building extraction from remote sensing imagery

ABSTRACT Automatic building detection from remote sensing images holds significant applications in various domains, including cartography, land-use change detection, urban planning, and 3D intelligent city construction. Driven by the innovation of deep learning technology, convolutional neural networks (CNNs) have emerged as a powerful framework for automatic interpretation, exhibiting hierarchical feature generation and robust representation for building extraction. However, the inherent computational limitations of convolutional operators hinder the acquisition of an effective global receptive field. Recent advancements in deep learning have witnessed the adoption of the multi-head self-attention (MHSA) mechanism by Transformer, presenting the superiority of global context construction. Nonetheless, this network structure encounters difficulties in effectively modelling two-dimensional spatial structure features with scale variation in semantic segmentation and often has huge parameters with high algorithm complexity. To address the above problems, this paper proposed a hybrid deep neural network synergistically harnessing the superiority of CNN and Transformer to extract buildings from remote sensing images. Meanwhile, an improved MHSA mechanism is developed to facilitate the construction of multiscale global representations, concurrently reducing computational complexity. Furthermore, the cross-level MHSA modules the semantic correlation between the deep and shallow-level features to refine spatial fine-grained information. The proposed method was comprehensively evaluated on two building datasets via quantitative analysis and visualization. The experimental results confirmed that the developed network and modules outperform other state-of-the-art methods and can effectively improve the accuracy (93.4% F1-score on the WHU dataset, 89.38% F1-score on the CAMB dataset) with high efficiency for building extraction.

A deep learning-based dynamic deformable adaptive framework for locating the root region of the dynamic flames.

Traditional optical flame detectors (OFDs) in flame detection are susceptible to environmental interference, which will inevitably cause detection errors and miscalculations when confronted with a complex environment. The conventional deep learning-based models can mitigate the interference of complex environments by flame image feature extraction, which significantly improves the precision of flame recognition. However, these models focus on identifying the general profile of the static flame, but neglect to effectively locate the source of the dynamic flame. Therefore, this paper proposes a novel dynamic flame detection method named Dynamic Deformable Adaptive Framework (DDAF) for locating the flame root region dynamically. Specifically, to address limitations in flame feature extraction of existing detection models, the Deformable Convolution Network v2 (DCNv2) is introduced for more flexible adaptation to the deformations and scale variations of target objects. The Context Augmentation Module (CAM) is used to convey flame features into Dynamic Head (DH) to feature extraction from different aspects. Subsequently, the Layer-Adaptive Magnitude-based Pruning (LAMP) where the connection with the smallest LAMP score is pruned sequentially is employed to further enhance the speed of model detection. More importantly, both the coarse- and fine-grained location techniques are designed in the Inductive Modeling (IM) to accurately delineate the flame root region for effective fire control. Additionally, the Temporal Consistency-based Detection (TCD) contributes to improving the robustness of model detection by leveraging the temporal information presented in consecutive frames of a video sequence. Compared with the classical deep learning method, the experimental results on the custom flame dataset demonstrate that the AP0.5 value is improved by 4.4%, while parameters and FLOPs are reduced by 25.3% and 25.9%, respectively. The framework of this research extends applicability to a variety of flame detection scenarios, including industrial safety and combustion process control.