Local Modification Research Articles

Bending Moiré in Twisted Bilayer Graphene.

Moiré potentials caused by lattice mismatches significantly alter electrons in two-dimensional materials, inspiring the discovery of numerous unique physical properties. While strain modulates the structure and symmetry of the moiré potential, serving as a tuning mechanism for interactions, the impact of out-of-plane deformation, e.g., bending, on the moiré superlattice remains unknown. Here, we performed large-scale molecular dynamics simulations to study the evolution of the moiré superlattice of twisted bilayer graphene under out-of-plane bending deformation. Our findings indicated that curvature-dependent bending caused both global and local lattice structure modifications in the moiré superlattice. We revealed a linear relationship between lattice displacement and bending curvature across varying initial twist angles along with precise regulation of local interlayer rotation. Additionally, the atomic potential energy landscape revealed that the localized atomic stacks underwent a whirlpool-like transformation, becoming a relaxed superlattice. This work opens up new opportunities for tailoring moiré superlattices by using out-of-plane bending engineering.

Local Charge Regulation in Selenides via High‐Entropy Engineering to Boost Electromagnetic Wave Absorption

AbstractLocal charge modulation is an effective method to improve the polarization loss and electromagnetic (EM) absorption characteristics. However, the use of conventional means (doping, defects, heterojunction surfaces) remains singular and limited in achieving local charge modulation. Surprisingly, lattice distortions induced by high‐entropy engineering can intrinsically regulate local charge states and improve EM wave absorption performances. Herein, a series of selenides with enhanced configuration entropy are designed and prepared to improve polarization loss capacity. Due to the different radii of the mixed ions and the Jahn‐Teller distortion effect, the local charge of the selenides is effectively modulated by the presence of defects and strong lattice distortions inside the lattice. Uneven distribution of charge at defects and formation of boundaries promotes defect‐induced polarization and interface polarization, respectively. Compared to single NiSe2, high‐entropy (NiFeCuCoMn)Se2 with an entropy value of 1.53R has more severe lattice defects and suitable impedance matching properties, exhibiting good EM absorption properties both in the absorption intensity and respond frequency. Tailoring the local charge density via an entropy strategy paves a new way for the high‐performance EM absorption materials.

Switchable Pancharatnam-Berry Phases in Heterogeneously Integrated THz Metasurfaces.

The Pancharatnam-Berry (PB) phase has revolutionized the design of metasurfaces, offering a straightforward and robust method for controlling wavefronts of electromagnetic waves. However, traditional metasurfaces have fixed PB phases determined by the orientation of their individual elements. In this study, an innovative structural design and integration scheme is proposed that utilizes vanadium dioxide, a phase-change material, to achieve thermally controlled dynamic PB phase control within the metasurface. By leveraging the material's properties, this can dynamically alter the optical orientation of individual elements of the metasurface and achieve temperature-dependent local phase modulation based on the geometric phase principle. This approach, combined with advanced fabrication processing technology, paves the way for next-generation dynamic devices with customizable functions.

GLCANet: Global–Local Context Aggregation Network for Cropland Segmentation from Multi-Source Remote Sensing Images

Cropland is a fundamental basis for agricultural development and a prerequisite for ensuring food security. The segmentation and extraction of croplands using remote sensing images are important measures and prerequisites for detecting and protecting farmland. This study addresses the challenges of diverse image sources, multi-scale representations of cropland, and the confusion of features between croplands and other land types in large-area remote sensing image information extraction. To this end, a multi-source self-annotated dataset was developed using satellite images from GaoFen-2, GaoFen-7, and WorldView, which was integrated with public datasets GID and LoveDA to create the CRMS dataset. A novel semantic segmentation network, the Global–Local Context Aggregation Network (GLCANet), was proposed. This method integrates the Bilateral Feature Encoder (BFE) of CNNs and Transformers with a global–local information mining module (GLM) to enhance global context extraction and improve cropland separability. It also employs a multi-scale progressive upsampling structure (MPUS) to refine the accuracy of diverse arable land representations from multi-source imagery. To tackle the issue of inconsistent features within the cropland class, a loss function based on hard sample mining and multi-scale features was constructed. The experimental results demonstrate that GLCANet improves OA and mIoU by 3.2% and 2.6%, respectively, compared to the existing advanced networks on the CRMS dataset. Additionally, the proposed method also demonstrated high precision and practicality in segmenting large-area croplands in Chongzhou City, Sichuan Province, China.

Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention Mechanism

The segmentation and recognition of power lines are crucial for the UAV-based inspection of overhead power lines. To address the issues of class imbalance, low sample quantity, and long-range dependency in images, a specialized semantic segmentation network for power line segmentation called Axial-UNet++ is proposed. Firstly, to tackle the issue of long-range dependencies in images and low sample quantity, a gated axial attention mechanism is introduced to expand the receptive field and improve the capture of relative positional biases in small datasets, thereby proposing a novel feature extraction module termed axial-channel local normalization module. Secondly, to address the imbalance in training samples, a new loss function is developed by combining traditional binary cross-entropy loss with focal loss, enhancing the precision of image semantic segmentation. Lastly, ablation and comparative experiments on the PLDU and Mendeley datasets demonstrate that the proposed model achieves 54.7% IoU and 80.1% recall on the PLDU dataset, and 79.3% IoU and 93.1% recall on the Mendeley dataset, outperforming other listed models. Additionally, robustness experiments show the adaptability of the Axial-UNet++ model under extreme conditions and the augmented image dataset used in this study has been open sourced.

From promise to practice: CAR T and Treg cell therapies in autoimmunity and other immune-mediated diseases.

Autoimmune diseases, characterized by the immune system's attack on the body's own tissues, affect millions of people worldwide. Current treatments, which primarily rely on broad immunosuppression and symptom management, are often associated with significant adverse effects and necessitate lifelong therapy. This review explores the next generation of therapies for immune-mediated diseases, including chimeric antigen receptor (CAR) T cell and regulatory T cell (Treg)-based approaches, which offer the prospect of targeted, durable disease remission. Notably, we highlight the emergence of CD19-targeted CAR T cell therapies, and their ability to drive sustained remission in B cell-mediated autoimmune diseases, suggesting a possible paradigm shift. Further, we discuss the therapeutic potential of Type 1 and FOXP3+ Treg and CAR-Treg cells, which aim to achieve localized immune modulation by targeting their activity to specific tissues or cell types, thereby minimizing the risk of generalized immunosuppression. By examining the latest advances in this rapidly evolving field, we underscore the potential of these innovative cell therapies to address the unmet need for long-term remission and potential tolerance induction in individuals with autoimmune and immune-mediated diseases.

Durable antitumor response via an oncolytic virus encoding decoy-resistant IL-18

BackgroundInterleukin-18 (IL-18), or interferon (IFN)-γ-inducing factor, potentiates T helper 1 and natural killer cell activation as well as CD8+ T-cell proliferation. Recombinant IL-18 has displayed limited clinical efficacy in part...

One-Stage Anchor-Free Online Multiple Target Tracking With Deformable Local Attention and Task-Aware Prediction.

The tracking-by-detection paradigm currently dominates multiple target tracking algorithms. It usually includes three tasks: target detection, appearance feature embedding, and data association. Carrying out these three tasks successively usually leads to lower tracking efficiency. In this paper, we propose a one-stage anchor-free multiple task learning framework which carries out target detection and appearance feature embedding in parallel to substantially increase the tracking speed. This framework simultaneously predicts a target detection and produces a feature embedding for each location, by sharing a pyramid of feature maps. We propose a deformable local attention module which utilizes the correlations between features at different locations within a target to obtain more discriminative features. We further propose a task-aware prediction module which utilizes deformable convolutions to select the most suitable locations for the different tasks. At the selected locations, classification of samples into foreground or background, appearance feature embedding, and target box regression are carried out. Two effective training strategies, regression range overlapping and sample reweighting, are proposed to reduce missed detections in dense scenes. Ambiguous samples whose identities are difficult to determine are effectively dealt with to obtain more accurate feature embedding of target appearance. An appearance-enhanced non-maximum suppression is proposed to reduce over-suppression of true targets in crowded scenes. Based on the one-stage anchor-free network with the deformable local attention module and the task-aware prediction module, we implement a new online multiple target tracker. Experimental results show that our tracker achieves a very fast speed while maintaining a high tracking accuracy.

Rational design of static wetting on roughness-engineered heterogeneous surfaces

Surface roughness and chemical composition are crucial in controlling the static wetting properties of surfaces. Here, conventional surface structuring methods used in Si microfabrication are used as a reference to analyze the impact of precisely engineered surface roughness. The static wettability of rough chemically heterogeneous surfaces is experimentally studied through contact angle measurements and compared against computational simulations to categorize the wetting behavior of water droplets. Heterogeneous samples are observed to already show significant dependence on the surface fraction covered by each material. Furthermore, owing to the presence of a resist layer on top of the Si pillars, intermediate states between the Wenzel (W) and Cassie–Baxter (CB) models are observed. Consistent with these models, we find that local chemical modifications of microstructured surfaces are crucial for controlling their surface wettability properties. Additionally, a comparison of equivalent microstructures made of Si or polydimethylsiloxane (PDMS) reveals the quantitative impact of the hydrophilic/hydrophobic nature of the material on the evolution of the wetting properties with increasing roughness factors. While Si surfaces behave according to the W model, PDMS surfaces show intermediate wetting states at significantly lower roughness levels. Bubbles trapped beneath water droplets demonstrate the existence of intermediate states that cannot be defined by either the W or CB models. By combining experimental results with finite element simulations, we not only demonstrate wettability control through specific roughness and chemical modifications but also provide insight into how these parameters interact to accurately predict and adjust static wetting properties.

Geological remote sensing interpretation via a local-to-global sensitive feature fusion network

Interpreting surface geological elements (such as rocks, minerals, soils, and water bodies) is the main task of geological survey, which plays a crucial role in geological environment remote sensing (GERS). However, the characteristics of geological elements, including high variabilities, various morphology, complicated boundaries and imbalanced class distribution, make it still a challenge for deep learning methods to interpret GERS images. Considering the correlations of geological elements as the regionalized variables in geostatistics, the sensitive features of GERS interpretation mainly include three aspects: tonal, textural and structural characteristics within a singular-class elements, spatial and spectral correlations of adjacent elements, and their global tectonic or spatial distribution. Thus, to simulate the manual interpretation process of geologists from local to global and promote GERS interpretation performance, we propose a local-to-global multi-scale feature fusion network (LGMSFNet). A geological object context represents the intra-class semantic dependencies of pixel sets with the same class. And a local feature aggregation module models the channel and spatial association. Then discriminative features are integrated by a global feature fusion module. For the model optimization, we focus on hard examples during the training process to achieve the balanced optimization of various categories. Two research areas that include large-scale rocks, soils and water exposed on the surface are selected. Massive experiments demonstrate the superiority of the LGMSFNet in GERS interpretation.

Advancing Real-World Image Dehazing: Perspective, Modules, and Training.

Restoring high-quality images from degraded hazy observations is a fundamental and essential task in the field of computer vision. While deep models have achieved significant success with synthetic data, their effectiveness in real-world scenarios remains uncertain. To improve adaptability in real-world environments, we construct an entirely new computational framework by making efforts from three key aspects: imaging perspective, structural modules, and training strategies. To simulate the often-overlooked multiple degradation attributes found in real-world hazy images, we develop a new hazy imaging model that encapsulates multiple degraded factors, assisting in bridging the domain gap between synthetic and real-world image spaces. In contrast to existing approaches that primarily address the inverse imaging process, we design a new dehazing network following the "localization-and-removal" pipeline. The degradation localization module aims to assist in network capture discriminative haze-related feature information, and the degradation removal module focuses on eliminating dependencies between features by learning a weighting matrix of training samples, thereby avoiding spurious correlations of extracted features in existing deep methods. We also define a new Gaussian perceptual contrastive loss to further constrain the network to update in the direction of the natural dehazing. Regarding multiple full/no-reference image quality indicators and subjective visual effects on challenging RTTS, URHI, and Fattal real hazy datasets, the proposed method has superior performance and is better than the current state-of-the-art methods.

Development of A Science Module Based on Local Culture of NTT to Enhance Critical Thinking Skills of Prospective Elementary School Teachers

The research aims to develop a Science Module based on Local Culture to enhance the Critical Thinking Skills of prospective Elementary School Teachers. This type of research is development research, using the 4D model (Define, Design, Develop, and Disseminate). The subjects of this study were 30 fifth-semester students of the Elementary School Teacher Education Program, who acted as prospective teachers and used the Local Culture-Based Science Module in their coursework as part of a trial of the module's use. The data collection methods employed were questionnaires to gather information on validation, attractiveness, and practicality, as well as tests to assess effectiveness. The data analysis technique used was descriptive quantitative analysis, where total scores and values were calculated and then averaged to be interpreted according to predetermined criteria. The results obtained from the research process showed that the Local Culture-Based Science Module was valid with a validity score of 3.85, attractive with a score of 82.2, practical with a score of 80, and effective with an average final test score of 83.06. In conclusion, the Local Culture-Based Science Module is valid, attractive, practical, and effective for use as a teaching guide for prospective elementary school teachers

MGCNet: Multi-granularity consensus network for remote sensing image correspondence pruning

Correspondence pruning aims to remove false correspondences (outliers) from an initial putative correspondence set. This process holds significant importance and serves as a fundamental step in various applications within the fields of remote sensing and photogrammetry. The presence of noise, illumination changes, and small overlaps in remote sensing images frequently result in a substantial number of outliers within the initial set, thereby rendering the correspondence pruning notably challenging. Although the spatial consensus of correspondences has been widely used to determine the correctness of each correspondence, achieving uniform consensus can be challenging due to the uneven distribution of correspondences. Existing works have mainly focused on either local or global consensus, with a very small perspective or large perspective, respectively. They often ignore the moderate perspective between local and global consensus, called group consensus, which serves as a buffering organization from local to global consensus, hence leading to insufficient correspondence consensus aggregation. To address this issue, we propose a multi-granularity consensus network (MGCNet) to achieve consensus across regions of different scales, which leverages local, group, and global consensus to accomplish robust and accurate correspondence pruning. Specifically, we introduce a GroupGCN module that randomly divides the initial correspondences into several groups and then focuses on group consensus and acts as a buffer organization from local to global consensus. Additionally, we propose a Multi-level Local Feature Aggregation Module that adapts to the size of the local neighborhood to capture local consensus and a Multi-order Global Feature Module to enhance the richness of the global consensus. Experimental results demonstrate that MGCNet outperforms state-of-the-art methods on various tasks, highlighting the superiority and great generalization of our method. In particular, we achieve 3.95% and 8.5% mAP5° improvement without RANSAC on the YFCC100M dataset in known and unknown scenes for pose estimation, compared to the second-best models (MSA-LFC and CLNet). Source code: https://github.com/1211193023/MGCNet.

A proton exchange membrane fuel cells degradation prediction method based on multi-scale temporal information merging network

Performance degradation prediction is considered as an effective method to improve the service life of proton exchange membrane fuel cells (PEMFCs). A hybrid method Multi-scale Temporal Information Merging Network (MTIMN) is then proposed for PEMFC degradation prediction, which is designed to merge information from both macro and micro perspectives in PEMFC historical working data. Firstly, a data preprocessing method based on P-M scores and min-max wavelet denoising is designed to eliminate redundant variables and noise considering the structure of PEMFC and data statistics. Secondly, aiming at the voltage recovery phenomenon during the aging process, a Multi-scale Exponential Decomposition Encoding Module (MEDEM) is presented to extract and encode different time scales of sequences from the PEMFC degradation data. Thirdly, a two-layer Historical Information Integration Module (HIIM) is established, which consists of the trend extraction module and the local extraction module, to extract deep dynamic nonlinear characteristics. Finally, the Merging Prediction Module (MPM) merges information from trend and local modules for the final output. The experimental results show that Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are 0.0107 and 0.0079, respectively. Therefore, MTIMN can effectively extract deep dynamic nonlinear characteristics in PEMFC degradation data and improve prediction accuracy greatly.

Occupancy Map Guided Attributes Artifacts Removal for Video-Based Point Cloud Compression

Point clouds offer realistic 3D representations of objects and scenes at the expense of large data volumes. To represent such data compactly in real-world applications, Video-Based Point Cloud Compression (V-PCC) converts their texture into 2D attributes and occupancy maps before applying lossy video compression. Unfortunately, the coding artifacts introduced in the decoded attribute maps eventually degrade the quality of the reconstructed point cloud, thereby influencing its immersive experience. This article proposes a deep learning-based attribute map enhancement method that fully leverages the occupancy map's guidance. The design philosophy is that the cross-modality guidance from occupancy can be leveraged as critical information to enhance the attribute. Therefore, instead of treating attribute and occupancy as two separate sources of signals, occupancy serves as an indispensable auxiliary, such that the proposed framework explicitly provides the model with abundant clues by conducting local feature modification and global dependencies aggregation. In particular, the proposed framework is compatible with existing V-PCC bitstreams and can be feasibly incorporated into the standardized decoder pipeline. Extensive evaluations show the effectiveness of the proposed framework in attribute enhancement, with equivalently 6.0% Bjontegaard Delta-rate (BD-rate) savings obtained.

Learning to zoom: Exploiting mixed-scale contextual information for object detection

With the development of deep neural networks, object detection has made a significant leap. However, apart from intrinsic intra and inter class differences, the objects in real-world scenarios may encounter diverse scales, blurred appearance or even severe occlusion. Inspired by the human behavior of looking at blurred images, i.e., zooming in/out, we propose a Mixed-Scale Network (dubbed for MSNet) to tackle these issues. Specifically, MSNet employs the zoom strategy to exploit mixed-scale contextual information, which fully unleashes the representation ability of deep neural networks. Firstly, the global feature aggregation module and global feature enhancement module aims at aggregating mixed-scale features from the global perspective, which learns the transform offset of pixels to align the higher-res features contextually. Moreover, the local feature aggregation module enriches the instance-level feature by adaptively aligning the features of different receptive fields. Extensive experiments on MS COCO demonstrated the effectiveness of MSNet, yielding significant improvements of 0.7–2.8 points in APbox over baselines when paired with different detectors, backbones, and schedules. In addition, for pixel-level prediction tasks including semantic segmentation and instance segmentation, the proposed method gains consistent improvements of 0.6–2.7 points in mIoU/APmask over baselines, which substantiates the effectiveness of MSNet further.

Security analysis and adaptive false data injection against multi-sensor fusion localization for autonomous driving

Multi-sensor Fusion (MSF) algorithms are critical components in modern autonomous driving systems, particularly in localization and AI-powered perception modules, which play a vital role in ensuring vehicle safety. The Error-State Kalman Filter (ESKF), specifically employed for localization fusion, is widely recognized for its robustness and accuracy in MSF implementations. While existing studies have demonstrated the vulnerability of ESKF to sensor spoofing attacks, these works have primarily focused on a black-box implementation, leading to an insufficient security analysis. Specifically, due to the lack of theoretical guidance in previous methods, these studies have consistently relied on exponential functions to fit attack sequences across all scenarios. As a result, the attacker had to explore an extensive parameter space to identify effective attack sequences, lacking the ability to adaptively generate optimal ones. This paper aims to fill this crucial gap by conducting a thorough security analysis of the ESKF model and presenting a simple approach for modeling injection errors in these systems. By utilizing this error modeling, we introduce a new attack strategy that employs constrained optimization to reduce the energy needed to reach the same deviation target, guaranteeing that the attack is both efficient and effective. This method increases the stealthiness of the attack, making it harder to detect. Unlike previous methods, our approach can dynamically produce nearly perfect injection signals without requiring multiple attempts to find the best parameter combination in different scenarios. Through extensive simulations and real-world experiments, we demonstrate the superiority of our method compared to state-of-the-art attack strategies. Our results indicate that our approach requires significantly less injection energy to achieve the same deviation target. Additionally, we validate the practical applicability and impact of our method through end-to-end testing on an AI-powered autonomous driving system.

On depth of modules in an ideal

Let [Formula: see text] be a commutative Noetherian ring, [Formula: see text] an ideal of [Formula: see text] and [Formula: see text] a finitely generated R-module with dimR(M) = d. Denote by depth[Formula: see text] the depth of [Formula: see text] in [Formula: see text]. In [C. Huneke and V. Trivedi, The height of ideals and regular sequences, Manuscr. Math. 93 (1997) 137–142], Huneke and Trivedi proved that if [Formula: see text] is a quotient of a regular ring then there exists a finite subset [Formula: see text] of [Formula: see text] such that [Formula: see text] Denote by [Formula: see text] the [Formula: see text]th pseudo support of [Formula: see text] defined by Brodmann and Sharp [On the dimension and multiplicity of local cohomology modules, Nagoya Math. J. 167 (2002) 217–233]. In this paper, we prove that if [Formula: see text] is closed for all [Formula: see text] then the above formula of [Formula: see text] holds true, where [Formula: see text]. In particular, if [Formula: see text] is a quotient of a Cohen–Macaulay local ring then [Formula: see text]. We also give some examples to clarify the results.

Diffusion-based framework for weakly-supervised temporal action localization

Weakly supervised temporal action localization aims to localize action instances with only video-level supervision. Due to the absence of frame-level annotation supervision, how effectively separate action snippets and backgrounds from semantically ambiguous features becomes an arduous challenge for this task. To address this issue from a generative modeling perspective, we propose a novel diffusion-based network with two stages. Firstly, we design a local masking mechanism module to learn the local semantic information and generate binary masks at the early stage, which (1) are used to perform action-background separation and (2) serve as pseudo-ground truth required by the diffusion module. Then, we propose a diffusion module to generate high-quality action predictions under the pseudo-ground truth supervision in the second stage. In addition, we further optimize the new-refining operation in the local masking module to improve the operation efficiency. The experimental results demonstrate that the proposed method achieves a promising performance on the publicly available mainstream datasets THUMOS14 and ActivityNet. The code is available at https://github.com/Rlab123/action_diff.

ESL-YOLO: Small Object Detection with Effective Feature Enhancement and Spatial-Context-Guided Fusion Network for Remote Sensing

Improving the detection of small objects in remote sensing is essential for its extensive use in various applications. The diminutive size of these objects, coupled with the complex backgrounds in remote sensing images, complicates the detection process. Moreover, operations like downsampling during feature extraction can cause a significant loss of spatial information for small objects, adversely affecting detection accuracy. To tackle these issues, we propose ESL-YOLO, which incorporates feature enhancement, fusion, and a local attention pyramid. This model includes: (1) an innovative plug-and-play feature enhancement module that incorporates multi-scale local contextual information to bolster detection performance for small objects; (2) a spatial-context-guided multi-scale feature fusion framework that enables effective integration of shallow features, thereby minimizing spatial information loss; and (3) a local attention pyramid module aimed at mitigating background noise while highlighting small object characteristics. Evaluations on the publicly accessible remote sensing datasets AI-TOD and DOTAv1.5 indicate that ESL-YOLO significantly surpasses other contemporary object detection frameworks. In particular, ESL-YOLO enhances mean average precision mAP by 10% and 1.1% on the AI-TOD and DOTAv1.5 datasets, respectively, compared to YOLOv8s. This model is particularly adept at small object detection in remote sensing imagery and holds significant potential for practical applications.