Local Transformations Research Articles

Boundary Element Method for 3D Laplace and Stokes Flow Problems with Analytical Technique

The analytical technique of element integral in the boundary element method for 3D Laplace and Stokes flow problems is investigated. The boundary integral equations are discretized with constant elements. The local coordinate transformation and polar coordinate transformation techniques are adopted to induce the analytical expressions of element integrals. For 3D Laplace problems, the analytical integral formulae, which are valid for an arbitrary source point, are derived. For 3D Stokes flow problems, analytical evaluation of the integrals, in which the source point and the boundary element are in the same plane, are proposed. These analytical formulae are applicable to arbitrary convex polygonal planar elements. The purpose of numerical examples is to make the case that the analytical expressions are accurate.

Coarse-to-Fine Homography Estimation for Infrared and Visible Images

Homography estimation for infrared and visible images is a critical and fundamental task in multimodal image processing. Recently, the coarse-to-fine strategy has been gradually applied to the homography estimation task and has proved to be effective. However, current coarse-to-fine homography estimation methods typically require the introduction of additional neural networks to acquire multi-scale feature maps and the design of complex homography matrix fusion strategies. In this paper, we propose a new unsupervised homography estimation method for infrared and visible images. First, we design a novel coarse-to-fine strategy. This strategy utilizes different stages in the regression network to obtain multi-scale feature maps, enabling the progressive refinement of the homography matrix. Second, we design a local correlation transformer (LCTrans), which aims to capture the intrinsic connections between local features more precisely, thus highlighting the features crucial for homography estimation. Finally, we design an average feature correlation loss (AFCL) to enhance the robustness of the model. Through extensive experiments, we validated the effectiveness of all the proposed components. Experimental results demonstrate that our method outperforms existing methods on synthetic benchmark datasets in both qualitative and quantitative comparisons.

AstroYOLO: A hybrid CNN–Transformer deep-learning object-detection model for blue horizontal-branch stars

Abstract Blue horizontal-branch stars (BHBs) are ideal tracers for studying the Milky Way (MW) due to their bright and nearly constant magnitude. However, an incomplete screen of BHBs from a survey would result in bias of estimation of the structure or mass of the MW. With surveys of large sky telescopes like the Sloan Digital Sky Survey (SDSS), it is possible to obtain a complete sample. Thus, detecting BHBs from massive photometric images quickly and effectually is necessary. The current acquisition methods of BHBs are mainly based on manual or semi-automatic modes. Therefore, novel approaches are required to replace manual or traditional machine-learning detection. The mainstream deep-learning-based object-detection methods are often vanilla convolutional neural networks whose ability to extract global features is limited by the receptive field of the convolution operator. Recently, a new Transformer-based method has benefited from the global receptive field advantage brought by the self-attention mechanism, exceeded the vanilla convolution model in many tasks, and achieved excellent results. Therefore, this paper proposes a hybrid convolution and Transformer model called AstroYOLO to take advantage of the convolution in local feature representation and Transformer’s easier discovery of long-distance feature dependences. We conduct a comparative experiment on the 4799 SDSS DR16 photometric image dataset. The experimental results show that our model achieves 99.25% AP@50, 93.79% AP@75, and 64.45% AP@95 on the test dataset, outperforming the YOLOv3 and YOLOv4 object-detection models. In addition, we test on larger cutout images based on the same resolution. Our model can reach 99.02% AP@50, 92.00% AP@75, and 61.96% AP@95 respectively, still better than YOLOv3 and YOLOv4. These results also suggest that an appropriate size for cutout images is necessary for the performance and computation of object detection. Compared with the previous models, our model has achieved satisfactory object-detection results and can effectively improve the accuracy of BHB detection.

Unveiling mechanisms of self-healing in CoCrFeMnNi/HfNbTaTiZr dual-phase high-entropy alloys: A molecular dynamics simulation study

This study investigates the mechanical properties and self-healing behavior of FCC CoCrFeMnNi/BCC HfNbTaTiZr dual-phase high-entropy alloy (DP-HEA) multilayers using molecular dynamics simulations. The relaxed DP-HEA multilayers have low hexagonal close-packed (HCP) percentages and decreasing "Other" fractions with increasing thickness. DP-HEA multilayers exhibit ductile behavior under uniaxial tensile loading, with stress increasing linearly until the yield strain and then gradually decreasing. Dislocation motion, twinning in the FCC phase, and BCC to HCP local structural transformation contribute to this behavior. Single crystal FCC CoCrFeMnNi and BCC HfNbTaTiZr alloys show different stress-strain responses, while DP-HEAs exhibit no significant stress drop beyond the ultimate strain. Self-healing is observed in DP-HEAs with 5 and 10 nm thickness, with a transformation from HCP to BCC structures. The DP-HEA with 15 nm thickness shows less pronounced self-healing. The interlayer distance between BCC/FCC interfaces influences the self-healing ability of the BCC phase. These findings enhance our understanding of the mechanical behavior and self-healing mechanisms in DP-HEA multilayers, facilitating the design of advanced materials with improved properties.

Nanoscale phase transition in impact indented Mo/Cu thin films

The study reveals a size effect in deformation mechanisms via unique structural transitions in sputter deposited fcc/bcc nanolayers under high strain rate impact nanoindentation as layer thickness is reduced from around 100–200 nm to around 5 nm. The indented 5 nm Cu/5 nm Mo multilayer films characterized via cross-sectional scanning/transmission electron microscopy have revealed a phase transition in the 5 nm thick Mo layers from BCC to a coherent metastable FCC structure and fine deformation twins in the 5 nm Cu layers inside a shear band that is approximately 500 nm long and inclined to the layer interfaces. In contrast, thicker layers, 80 nm Cu/180 nm Mo, show different deformation behavior with uneven layer thickness reduction, shearing of Cu layers, and bending of the columnar Mo grains beneath the indentation without any sign of shear band and localized phase transformation.

Holographic Schwinger effect in spinning black hole backgrounds* *Supported by the National Natural Science Foundation of China (NSFC) (12375140) and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (G1323523064)

We perform a potential analysis for the holographic Schwinger effect in spinning Myers-Perry black holes. We compute the potential between the produced pair by evaluating the classical action of a string attached on a probe D3-brane at an intermediate position in the AdS bulk. We find that increasing the angular momentum reduces the potential barrier, thus enhancing the Schwinger effect, consistent with previous findings obtained via the local Lorentz transformation. In particular, these effects are more visible for the particle pair lying in the transversal plane compared with that along the longitudinal orientation. In addition, we discuss how the Schwinger effect changes with the shear viscosity to entropy density ratio at strong coupling under the influence of angular momentum.

A Multimodal Robust Simultaneous Localization and Mapping Approach Driven by Geodesic Coordinates for Coal Mine Mobile Robots

Mobile robots in complex underground coal mine environments are still unable to achieve accurate pose estimation and the real-time reconstruction of scenes with absolute geographic information. Integrated terrestrial-underground localization and mapping technologies have still not been effectively developed. This paper proposes a multimodal robust SLAM method based on wireless beacon-assisted geographic information transmission and lidar-IMU-UWB elastic fusion mechanism (LIU-SLAM). In order to obtain the pose estimation and scene models consistent with the geographic information, the construction of two kinds of absolute geographic information constraints based on UWB beacons is proposed. An elastic multimodal fusion state estimation mechanism is designed based on incremental factor graph optimization. A tightly coupled lidar-inertial odometry is firstly designed to construct the lidar-inertial local transformation constraints, which are further integrated with the absolute geographic constraints by UWB anchors through a loosely coupled approach. Extensive field tests based on coal mine robots have been conducted in scenarios such as underground garages and underground coal mine laneways. The results show that the proposed geodesic-coordinate driven multimode robust SLAM method can obtain absolute localization accuracy within 25 cm with practical robustness and real-time performance in different underground application scenarios. The wireless beacon-assisted geodesic-coordinate transmission strategy can provide a plug-and-play customized solution for precise positioning and scene modeling in complex scenarios of various coal mine robot application.

Interactions between Helicobacter pylori infection and host metabolic homeostasis: A comprehensive review.

The microbiota actively and extensively participates in the regulation of human metabolism, playing a crucial role in the development of metabolic diseases. Helicobacter pylori (H. pylori), when colonizing gastric epithelial cells, not only induces local tissue inflammation or malignant transformation but also leads to systemic and partial changes in host metabolism. These shifts can be mediated through direct contact, toxic components, or indirect immune responses. Consequently, they influence various molecular metabolic events that impact nutritional status and iron absorption in the host. Unraveling the intricate and diverse molecular interaction links between H.pylori and human metabolism modulation is essential for understanding pathogenesis mechanisms and developing targeted treatments for related diseases. However, significant challenges persist in comprehensively understanding the complex association networks among H. pylori itself, the infected host's status, the host microbiome, and the immune response. Previous metabolomics research has indicated that H. pylori infection and eradication may selectively shape the metabolite and microbial profiles of gastric lesions. Yet, it remains largely unknown how these diverse metabolic pathways, including isovaleric acid, cholesterol, fatty acids, and phospholipids, specifically modulate gastric carcinogenesis or affect the host's serum metabolism, consequently leading to the development of metabolic-associated diseases. The direct contribution of H. pylori to metabolisms still lacks conclusive evidence. In this review, we summarize recent advances in clinical evidence highlighting associations between chronic H. pylori infection and metabolic diseases, as well as its potential molecular regulatory patterns.

Dislocation breakaway from nanoparticle array linear complexions: Plasticity mechanisms and strength scaling laws

Linear complexions are stable defect states, where the stress field associated with a dislocation induces a local phase transformation that remains restricted to nanoscale dimensions. As these complexions are born at the defects which control plasticity in metals, it is crucial to understand their impact on subsequent mechanical properties. In this work, atomistic modeling is used to understand how dislocation mechanics are altered by the presence of nanoparticle array linear complexions in a Ni-Al alloy. Molecular dynamics simulations are used to identify the critical shear stress needed to drive dislocation breakaway, first for nanoparticle arrays formed by Monte Carlo/molecular dynamics methods to represent realistic configurations and subsequently for simplified models that allow the effects of particle spacing and size to be varied in a controlled manner. A combined bowing and progressive unpinning mechanism is uncovered, leading to the demonstration of a new strength scaling law that differs in keys ways from classical Orowan bowing.

FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising

Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count PET image quality, but acquiring a large, centralized, and diverse dataset from multiple institutions for training a robust model is difficult due to privacy and security concerns of patient data. Moreover, low-count PET data at different institutions may have different data distribution, thus requiring personalized models. While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored. In this work, we propose FedFTN, a personalized federated learning strategy that addresses these challenges. FedFTN uses a local deep feature transformation network (FTN) to modulate the feature outputs of a globally shared denoising network, enabling personalized low-count PET denoising for each institution. During the federated learning process, only the denoising network’s weights are communicated and aggregated, while the FTN remains at the local institutions for feature transformation. We evaluated our method using a large-scale dataset of multi-institutional low-count PET imaging data from three medical centers located across three continents, and showed that FedFTN provides high-quality low-count PET images, outperforming previous baseline FL reconstruction methods across all low-count levels at all three institutions.

Preparation and properties of copper-aluminum composite strips and foils by horizontal continuous composite casting and rolling

Cu-Al composite strips and foils show broad application prospects in electric power, new energy and other fields. However, they are difficult to prepare because of brittle interfacial intermetallic compounds and deformation ability difference between two matrix metals. In this study, Cu-Al composite strips and foils with the thickness of 350 µm and 50 µm are successfully prepared by horizontal continuous composite casting and rolling. Moreover, the effects of as-cast interfacial layer thickness on the properties of Cu-Al composite strips are studied. In the rolling, the as-cast interfacial layer is broken into fragments, so as to promote the Cu-Al matrix bonding at atomic scale. The local solid-state amorphous transformation is also observed at the newly formed Cu-Al interface. Therefore, with the as-cast interfacial layer thickness decreasing from 400 µm to 25 µm, the peeling strength of Cu-Al composite strips increasing from 15.1 N/mm to 17.5 N/mm. With the as-cast interfacial layer thickness decreasing from 400 µm to 65 µm, the conductivity of Cu-Al composite strips increases from 62.36%IACS to 72.58%IACS because the size of interfacial layer fragments is reduced. However, with the as-cast interfacial layer thickness decreasing from 65 µm to 25 µm, the conductivity decreases from 72.58%IACS to 68.65%IACS because the copper layer thickness after rolling is reduced. Large interfacial layer fragments hinder current conduction, but a suitable interfacial layer thickness can inhibit the coordinated deformation of Cu and Al, so as to increase the proportion of copper layer thickness in the entire composite strip.

Effect of phase transformation on the ductile to brittle transition behavior of Ti–V–Nb–Zrx body-centered cubic high-entropy alloys at elevated temperatures

Single-phase body-centered cubic (BCC) lightweight Ti–V–Nb–Zrx solid-solution alloys were fabricated by reducing the Zr concentration within the range of 0–5 at% to improve the mechanical properties of equimolar TiVNbZr alloy at elevated temperatures. The fabricated Zrx alloys indeed exhibited greater strength above 800 °C than TiVNbZr, but some special mechanical responses occurred at 800 °C: (ⅰ) superficial cracks with different lengths and amounts along the high-angle grain boundaries (HAGBs) on the compressed sample surface, and (ⅱ) near-brittle fracture of the tensile stress-strain curves. Based on the analysis of the deformed and undeformed surface, local stress concentration and phase transformation were responsible for the cracks and difference in compression behavior, and atmospheric oxygen, deformation localization and phase transformation were mainly responsible for the tensile fracture difference at 800 °C. Phase transformation promote stress dispersion, combined with better oxidation resistance, which make the Zr-containing alloys fractured via the transgranular mode in tensile test. In particular, the Zr3 alloy exhibited work hardening and tensile ductility. Based on the tensile and compression properties of the Zrx alloys, it was found that at RT and 600 °C, atomic size difference (δ) governed alloy strength; from 800 to 1000 °C, δ and melting temperature (Tm) co-operatively governed alloy strength; at 1200 °C, alloy strength was independent of δ, but dependent on Tm. The fabricated Ti–V–Nb–Zrx alloys have medium mechanical properties but a small strength loss at high temperatures, and are suitable for various RT deformation processing methods.

Islamic Dialectics and Local Cultural Transformation in the Matajang Community, Enrekang Regency

This article aims to (i) describe the dialectic of Islam with local culture in the Matajang community. (2) describe the form of local cultural transformation as a result of dialectics with Islam in the Matajang community. This research is qualitative with a single case study design. The research locations are in the Matajang community which includes Labuku Village, Baringeng Village, and Tapong Village and Matakali Village. The research informants consisted of 4 traditional leaders. Data collection used in-depth interviews, observation, and literature studies. Data analysis uses a three-plot technique, namely data reduction, categorization. The results of this study are (1) the dialectic of Islam and local culture has a meeting point in the occult aspect. Both acknowledge the existence of the supernatural and it is in this aspect that Islam is accepted by the Matajang people. (2) The transformation of local ritual culture occurs in several rituals gradually, starting from changes in prayers and matra to changes in some parts of the ritual but the local aspects are still visible. The conclusion of cultural change is a form of negotiating Islamic culture with local culture because not all forms are taken, only those that are appropriate to local characteristics.

MuCoGraph: A multi-scale constraint enhanced pose-graph framework for MLS point cloud inconsistency correction

The position consistency of mobile laser scanning (MLS) point clouds is crucial for large-scale applications, and is normally guaranteed by the global navigation satellite system (GNSS) and high-precision inertial measurement unit (IMU) in the data acquisition. However, GNSS-denied environments such as city valleys result in significant position inconsistency for overlapping areas, and it is difficult to automatically locate these inconsistent areas. In this paper, to overcome these problems, we present MuCoGraph, which introduces multi-scale constraints to establish the correct correspondences for revisited areas, and formulates an enhanced pose graph for position inconsistency correction. The georeferenced MLS point cloud is first sliced into segments adaptively, and these segments are then abstracted as graph vertices, which satisfy local geometric consistency and rigid transformation hypotheses. Accurate revisited graph edges are then constructed hierarchically under multi-scale scenery consistency constraints. These revisited edges are initialized based on feature-based correspondence estimation and further unreliable edge pruning. Finally, through combination with virtual co-observations, correspondence-enhanced pose-graph optimization is introduced to globally redistribute the errors and obtain a high-precision point cloud. The proposed method was used to correct the MLS point cloud position inconsistency in three datasets. The average three-dimensional distance of the checkpoints was reduced from 0.362 m, 0.108 m, and 1.027 m to 0.057 m, 0.033 m, and 0.051 m for datasets I, II, and III respectively. In addition, the root-mean-square error of all three datasets was less than 0.04 m after correction. The experiments confirmed that the proposed method can automatically locate and correct the position inconsistency of MLS point clouds, showing good robustness and effectiveness.

Fubini–Study metric and topological properties of flat band electronic states: the case of an atomic chain with s − p orbitals

The topological properties of the flat band states of a one-electron Hamiltonian that describes a chain of atoms with s − p orbitals are explored. This model is mapped onto a Kitaev–Creutz type model, providing a useful framework to understand the topology through a nontrivial winding number and the geometry introduced by the Fubini–Study (FS) metric. This metric allows us to distinguish between pure states of systems with the same topology and thus provides a suitable tool for obtaining the fingerprint of flat bands. Moreover, it provides an appealing geometrical picture for describing flat bands as it can be associated with a local conformal transformation over circles in a complex plane. In addition, the presented model allows us to relate the topology with the formation of compact localized states and pseudo-Bogoliubov modes. Also, the properties of the squared Hamiltonian are investigated in order to provide a better understanding of the localization properties and the spectrum. The presented model is equivalent to two coupled SSH chains under a change of basis.

GFFT: Global-local feature fusion transformers for facial expression recognition in the wild

Facial expression recognition in the wild has become more challenging owing to various unconstrained conditions, such as facial occlusion and pose variation. Previous methods usually recognize expressions by holistic or relatively coarse local methods, but only capture limited features and are susceptible to be influenced. In this paper, we propose the Global–local Feature Fusion Transformers (GFFT) that is centered on cross-patch communication between features by self-attentive fusion. This method solves the problems of facial occlusion and pose variation effectively. Firstly, the Global Contextual Information Perception (GCIP) is designed to fuse global and local features, learning the relationship between them. Subsequently, the Facial Salient Feature Perception (FSFP) module is proposed to guide the fusion features to understand the key regions of facial features using facial landmark features to further capture face-related salient features. In addition, the Multi-scale Feature Fusion (MFF) is constructed to combine different stages of fusion features to reduce the sensitivity of the deep network to facial occlusion. Extensive experiments show that our GFFT outperforms existing state-of-the-art methods with 92.05% on RAF-DB, 67.46% on AffectNet-7, 63.62% on AffectNet-8, and 91.04% on FERPlus, demonstrating its effectiveness and robustness.

Holomorphic general coordinate invariant modified measure gravitational theory

Complexifying space time has many interesting applications, from the construction of higher dimensional unification, to provide a useful framework for quantum gravity and to better define some local symmetries that suffer singularities in real space time. In this context here spacetime is extended to complex spacetime and standard general coordinate invariance is also extended to complex holomorphic general coordinate transformations. This is possible by introducing a non Riemannian Measure of integration, which transforms avoiding non holomorphic behavior. Instead the measure transforms according to the inverse of the jacobian of the coordinate transformation and avoids the traditional square root of the determinant of the metric −g. which is not globally holomorphic, or the determinant of the vierbein which is sensitive to the vierbein orientations and not invariant under local lorentz transformations with negative determinants. A contribution to the cosmological term appears as an integration constant in the equations of motion. A proposed action for Finsler geometry, which involves −g rather than −g will also constitute an example of a Holomorphic General Coordinate Invariant Modified Measure Gravitational Theory.

Unbiased curriculum learning enhanced global-local graph neural network for protein thermodynamic stability prediction.

Proteins play crucial roles in biological processes, with their functions being closely tied to thermodynamic stability. However, measuring stability changes upon point mutations of amino acid residues using physical methods can be time-consuming. In recent years, several computational methods for protein thermodynamic stability prediction (PTSP) based on deep learning have emerged. Nevertheless, these approaches either overlook the natural topology of protein structures or neglect the inherent noisy samples resulting from theoretical calculation or experimental errors. We propose a novel Global-Local Graph Neural Network powered by Unbiased Curriculum Learning for the PTSP task. Our method first builds a Siamese graph neural network to extract protein features before and after mutation. Since the graph's topological changes stem from local node mutations, we design a local feature transformation module to make the model focus on the mutated site. To address model bias caused by noisy samples, which represent unavoidable errors from physical experiments, we introduce an unbiased curriculum learning method. This approach effectively identifies and re-weights noisy samples during the training process. Extensive experiments demonstrate that our proposed method outperforms advanced protein stability prediction methods, and surpasses state-of-the-art learning methods for regression prediction tasks. All code and datais available at https://github.com/haifangong/UCL-GLGNN.

CoupleUNet: Swin Transformer coupling CNNs makes strong contextual encoders for VHR image road extraction

ABSTRACT Accurately segmenting roads is challenging due to substantial intra-class variations, indistinct inter-class distinctions, and occlusions caused by shadows, trees, and buildings. To address these challenges, it is crucial to simultaneously pay attention to important texture details and perceive global geometric context information. Recent research has shown that CNN-Transformer hybrid structures outperform using CNN or Transformer alone. CNN excels at extracting local detail features and Transformer naturally has the ability to perceive global context information. In this paper, we propose a dual-branch network module called ConSwin, which combines the advantages of ResNet and Swin Transformer for better road information extraction. Taking ConSwin as the basic encoding module, we build an hourglass-shaped network with an encoder-decoder structure. In order to better transfer texture and structural detail information between the encoder and decoder, we also design two novel connection blocks. We conduct comparative experiments on the Massachusetts and CHN6-CUG datasets, and the proposed method outperforms state-of-the-art methods in terms of overall accuracy, IOU, and F1 metrics. Other experiments verify the effectiveness of our method, while visualization results demonstrate its ability to obtain better road representations.

Civil society in local sustainable transformation – can bottom-up activities meet top-down expectations?

ABSTRACT The aim of the article is to explore whether and how local civil society – grounded in bottom-up activities – responds to a top-down initiative with expectations of sustainable transformation. The focus of our research is the possible role of civil society organizations as agents in collaborations aiming for a forest-based bioeconomy. The research was conducted in a local area within the sparsely populated Swedish region of Värmland, characterized by a strong tradition of forestry. The study is explorative and uses qualitative methods: interviews with representatives of local civil society actors, participant observations of local meetings and workshops, and document analyses. Our results suggest that general policy expectations concerning the inclusion of civil society become vague when operationalized in documents at the regional level, and lack clarity concerning the roles expected from civil society. Civil society organizations (CSOs) contribute in a multitude of ways to local social, economic, and environmental sustainability. However, the inclusion of CSOs as full actors in collaborations requires a greater knowledge of the traditions and structure of Swedish civil society, as well as its conditions and challenges in different local contexts.