Local Modification Research Articles

A Formalism of F-modules for Rings with Complete Local Finite F-Representation Type

Abstract We develop a formalism of unit $F$-modules in the style of Lyubeznik and Emerton-Kisin for rings that have finite $F$-representation type after localization and completion at every prime ideal. As applications, we show that if $R$ is such a ring then the iterated local cohomology modules $H^{n_{1}}_{I_{1}} \circ \cdots \circ H^{n_{s}}_{I_{s}}(R)$ have finitely many associated primes, and that all local cohomology modules $H^{n}_{I}(R / gR)$ have closed support when $g$ is a nonzerodivisor on $R$.

A Comprehensive Exploration of Diverse Skin Cell Types in the Limb of the Desert Tortoise (Testudo graeca) through Light, Transmission, Scanning Electron Microscopy, and Immunofluorescence Techniques

The Greek tortoise, inhabiting harsh desert environments, provides a compelling case for investigating skin adaptations to extreme conditions. We have utilized light microscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and immunofluorescence analysis to describe the structure of the arid-adapted limb skin in the Greek tortoise. Our aim was to identify the cell types that reflect the skin adaptation of this tortoise to arid conditions. Utilizing seven antibodies, we localized and elucidated the functions of various skin cells, shedding light on how the tortoise adapts to adverse environmental conditions. Our findings unveiled numerous scales on the limbs, varying in size and color, acting as protective armor against abrasions, bites, and other potential threats in their rocky habitats. The epidermis comprises four layers: stratum basalis, stratum spinosum, peri-corneous layer, and stratum corneum. Cytokeratin 14 (CK14) was explicitly detected in the basal layer of the epidermis, suggesting a role in maintaining epidermal integrity and cellular function. Langerhans cells were observed between epidermal cells filled with ribosomes and Birbeck granules. Numerous dendritic-shaped Langerhans cells revealed through E-Cadherin signify strong immunity in tortoises' skin. Melanophores were identified using the Melan-A antibody, labeling the cytoplasm, and the SOX10 antibody, labeling the nucleus, providing comprehensive insights into melanophores morphology and distribution. Two types of melanophores were found: dendritic below the stratum basalis of the epidermis and clustered oval melanophores in the deep dermal layer. Varied melanophores distribution resulted in a spotted skin pattern, potentially offering adaptive camouflage and protection against environmental challenges. Numerous myofibroblasts were discerned through alpha-smooth actin (α-SMA) expression, indicating that the Greek tortoise's skin possesses a robust tissue repair and remodeling capacity. B-cell lymphocytes detected via CD20 immunostaining exhibited sporadic distribution in the dermis, concentrating in lymphoid aggregates and around vessels, implying potential roles in local immune responses and inflammation modulation. Employing Tom20 to identify skin cells with abundant mitochondria revealed a notable presence in melanophores and the basal layer of the epidermis, suggesting high metabolic activity in these cell types and potentially influencing cellular functions. These findings contribute to our comprehension of tortoise skin anatomy and physiology, offering insights into the remarkable adaptations of this species finely tuned to their specific environmental habitats.

MCT-VHD: Multi-modal contrastive transformer for video highlight detection

Autonomous highlight detection aims to identify the most captivating moments in a video, which is crucial for enhancing the efficiency of video editing and browsing on social media platforms. However, current efforts primarily focus on visual elements and often overlook other modalities, such as text information that could provide valuable semantic signals. To overcome this limitation, we propose a Multi-modal Contrastive Transformer for Video Highlight Detection (MCT-VHD). This transformer-based network mainly utilizes video and audio modalities, along with auxiliary text features (if exist) for video highlight detection. Specifically, We enhance the temporal connections within the video by integrating a convolution-based local enhancement module into the transformer blocks. Furthermore, we explore three multi-modal fusion strategies to improve highlight inference performance and employ a contrastive objective to facilitate interactions between different modalities. Comprehensive experiments conducted on three benchmark datasets validate the effectiveness of MCT-VHD, and our ablation studies provide valuable insights into its essential components.

A modified vision transformer architecture with scratch learning capabilities for effective fire detection

Fire is considered to be one of the major influencing factors that cause fatalities, property damage, and economic and ecological disruption. To perform the early detection of fire using data from vision sensors to prevent or reduce the damage that fires cause, deep learning models have been widely adopted to overcome the limitations of conventional methods. However, mainstream convolutional neural network (CNN) models have limited generalisation abilities in unseen scenarios and struggle to obtain a good trade-off among the accuracy, inference speed and model size. Currently, vision transformers (ViT) outperform conventional CNN models; however, they are computationally expensive and data-hungry. They provide a limited performance for small and medium-sized datasets, which are very common in the fire scene classification domain. In this work, we develop a novel ViT architecture by combining shifted patch tokenisation and local self-attention modules with a ViT for efficient fire scene classification and enable the model to learn from scratch even on small and medium-sized datasets. Furthermore, to make the model suitable for real-time inferencing, we modify the transformer encoder and eventually achieve a reduced number of floating-point operations and a reduced model size. Additionally, in this work a medium-scale fire dataset is developed that contains complex real-world scenarios. Our model is assessed on three benchmark and a self-created datasets using several evaluation metrics, including a novel cross-corpse evaluation metric, as well as a robustness evaluation metric. The experimental results indicate that our model achieved an overwhelmingly better performance compared to existing methods in terms of the accuracy and model complexity.

Another role of CO-formation catalyst in acidic tandem CO2 electroreduction: Local pH modulator

Another role of CO-formation catalyst in acidic tandem CO2 electroreduction: Local pH modulator

Assessing the Possibility of Implementing Distributed Diagnostic Systems for Hydrogen Fuel Cells Using Local and Global Telecommunication Networks

The development of effective methods and means for diagnosing hydrogen fuel cells during their operation is an urgent task, since their implementation will improve the energy characteristics and stability of operation by detecting incorrect operating modes and malfunctions at the early stages of development. This problem can be solved by using various approaches based on the analysis of impedance, relaxation or fluctuation-noise characteristics. All of these methods require the implementation of rather complex computational procedures and the accumulation of fairly large amount of statistical data, which makes it difficult to implement diagnostic devices on inexpensive embedded microprocessor tools. To overcome this drawback, this work proposes the use of distributed diagnostic systems with high-performance server devices connected to local modules via global or local telecommunication networks. Such an implementation will significantly simplify the requirements for local diagnostic modules and reduce their cost, but this raises the question of the efficiency of making diagnostic decisions by such systems.The work carried out, is a study of delays in making diagnostic and control decisions when using remote server devices for signal processing. It has been shown that the use of such systems makes it possible to achieve delays of less than one second even when using intercontinental communication lines. It should be noted that in applications critical to time delays, the distance between the client and server systems should be reduced. The advantages of the proposed implementation of a diagnostic system for electrochemical energy sources are the possibility of simplifying local measurement systems installed on individual sources, the possibility of accelerating signal processing through the use of more productive computing tools and the accumulation of statistical data.

LIFNS: Design of a novel Lidar-IMU fusion navigation system for AGVs in smart factories

Traditional Automated Guided Vehicle (AGV) robots commonly employ 2D plane navigation systems. However, with the expansion of AGV application scenarios and the increasing complexity of structures, the limitations of existing 2D navigation systems have become more pronounced, rendering them inadequate for addressing these challenges. To tackle this issue, this paper proposes a novel navigation system suitable for AGV robots composed of fused Lidar and Inertial Measurement Unit (IMU), named as Lidar-IMU Fusion Navigation System (LIFNS). LIFNS primarily comprises mapping, localization, and fused path planning modules. In the mapping module, a 3D point cloud map for precise 3D localization and a 2D grid map for path planning are constructed using multiple-line Lidar and IMU. For the localization module, a fusion localization approach is introduced, combining IMU data with Normal Distributions Transform (NDT) point cloud registration through Unscented Kalman Filtering (UKF). Finally, global path planning is executed using the A* algorithm on the grid map, while local path planning utilizes the Timed-Elastic Band (TEB) algorithm. The effectiveness and universality of LIFNS are validated through simulation experiments and real-world deployment tests. The experimental results demonstrate that LIFNS achieves centimeter-level accuracy in both mapping and localization, effectively alleviating the issues of low precision and significant limitations present in traditional AGV robots. This positions LIFNS with promising applications in enclosed settings such as smart factories, industrial parks, and healthcare facilities.

Development of an in situ alloying method for high-performance welding processes to achieve an LTT effect by local modification of the alloy content

One possible option for increasing the fatigue strength of welded joints is the use of so-called low transformation temperature (LTT) alloys. The aim is to introduce residual compressive stresses into the weld to counteract crack initiation and propagation. Until now, there has been no application of an LTT effect to high-performance welding processes such as the laser beam submerged arc hybrid welding process (LUPuS hybrid). First, the LUPuS hybrid single-wire process was further developed into the LUPuS tandem hybrid process. This makes it possible to equip the two submerged arc welding torches with different commercially available filler wires. The aim of the work is to further develop the LUPuS tandem hybrid welding process to enable the use of the LTT effect. The in situ alloying process for obtaining the LTT effect from commercially available material combinations was extended to the two-wire process. The alloy obtained was investigated by means of energy dispersive x-ray spectroscopy and hardness measurements and the influence on residual stresses was determined by the borehole method supported by electronic speckle pattern interferometry.

From Lewis Acid to Lewis Base by La3+-to-Y3+ Substitution in α-YB5O9: Local Structure Modification Induced Lewis Basicity.

Different from the common perspective of average structure, we propose that the locally elongated metal-oxygen bonds induced by La3+-to-Y3+ substitution to a Lewis acid α-YB5O9 generate medium-strength basic sites. Experimentally, NH3- and CO2-TPD experiments prove that the La3+ doping of α-Y1-xLaxB5O9 (0 ≤ x ≤ 0.24) results in the emergence of new medium-strength basic sites and the increasing La3+ concentration modifies the number, not the strength, of the acidic and basic sites. The catalytic IPA conversion exhibits a reversal of the product selectivity, i.e., from 93% of propylene for α-YB5O9 to ∼90% of acetone for α-Y0.76La0.24B5O9, which means the La3+ doping gradually turns the solid from a Lewis acid to a Lewis base. Besides, α-Y0.76RE0.24B5O9 (RE = Ce, Eu, Gd, Tm) compounds were prepared to consolidate the above conjecture, where the acetone selectivity exhibits a linear dependence on the ionic radius (or electronegativity). This work suggests that the substitution-induced local structure change deserves more attention.

Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum

Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. In recent years, Graph Neural Networks (GNN) have been widely applied to GFD, characterizing the anomalous possibility of a node by aggregating neighbor information. However, fraud graphs are inherently heterophilic, thus most of GNNs perform poorly due to their assumption of homophily. In addition, due to the existence of heterophily and class imbalance problem, the existing models do not fully utilize the precious node label information. To address the above issues, this paper proposes a semi-supervised GNN-based fraud detector SEC-GFD. This detector includes a hybrid filtering module and a local environmental constraint module, the two modules are utilized to solve heterophily and label utilization problem respectively. The first module starts from the perspective of the spectral domain, and solves the heterophily problem to a certain extent. Specifically, it divides the spectrum into various mixed-frequency bands based on the correlation between spectrum energy distribution and heterophily. Then in order to make full use of the node label information, a local environmental constraint module is adaptively designed. The comprehensive experimental results on four real-world fraud detection datasets denote that SEC-GFD outperforms other competitive graph-based fraud detectors. We release our code at https://github.com/Sunxkissed/SEC-GFD.

Recasting Regional Lighting for Shadow Removal

Removing shadows requires an understanding of both lighting conditions and object textures in a scene. Existing methods typically learn pixel-level color mappings between shadow and non-shadow images, in which the joint modeling of lighting and object textures is implicit and inadequate. We observe that in a shadow region, the degradation degree of object textures depends on the local illumination, while simply enhancing the local illumination cannot fully recover the attenuated textures. Based on this observation, we propose to condition the restoration of attenuated textures on the corrected local lighting in the shadow region. Specifically, We first design a shadow-aware decomposition network to estimate the illumination and reflectance layers of shadow regions explicitly. We then propose a novel bilateral correction network to recast the lighting of shadow regions in the illumination layer via a novel local lighting correction module, and to restore the textures conditioned on the corrected illumination layer via a novel illumination-guided texture restoration module. We further annotate pixel-wise shadow masks for the public SRD dataset, which originally contains only image pairs. Experiments on three benchmarks show that our method outperforms existing state-of-the-art shadow removal methods. Project page in: yuhaoliu7456.github.io/RRL-Net.

Privileged Prior Information Distillation for Image Matting

Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance. In this paper, we propose a novel framework named Privileged Prior Information Distillation for Image Matting (PPID-IM) that can effectively transfer privileged prior environment-aware information to improve the performance of trimap-free students in solving hard foregrounds. The prior information of trimap regulates only the teacher model during the training stage, while not being fed into the student network during actual inference. To achieve effective privileged cross-modality (i.e. trimap and RGB) information distillation, we introduce a Cross-Level Semantic Distillation (CLSD) module that reinforces the students with more knowledgeable semantic representations and environment-aware information. We also propose an Attention-Guided Local Distillation module that efficiently transfers privileged local attributes from the trimap-based teacher to trimap-free students for the guidance of local-region optimization. Extensive experiments demonstrate the effectiveness and superiority of our PPID on image matting. The code will be released soon.

BCLNet: Bilateral Consensus Learning for Two-View Correspondence Pruning

Correspondence pruning aims to establish reliable correspondences between two related images and recover relative camera motion. Existing approaches often employ a progressive strategy to handle the local and global contexts, with a prominent emphasis on transitioning from local to global, resulting in the neglect of interactions between different contexts. To tackle this issue, we propose a parallel context learning strategy that involves acquiring bilateral consensus for the two-view correspondence pruning task. In our approach, we design a distinctive self-attention block to capture global context and parallel process it with the established local context learning module, which enables us to simultaneously capture both local and global consensuses. By combining these local and global consensuses, we derive the required bilateral consensus. We also design a recalibration block, reducing the influence of erroneous consensus information and enhancing the robustness of the model. The culmination of our efforts is the Bilateral Consensus Learning Network (BCLNet), which efficiently estimates camera pose and identifies inliers (true correspondences). Extensive experiments results demonstrate that our network not only surpasses state-of-the-art methods on benchmark datasets but also showcases robust generalization abilities across various feature extraction techniques. Noteworthily, BCLNet obtains significant improvement gains over the second best method on unknown outdoor dataset, and obviously accelerates model training speed.

Multi-Modal Disordered Representation Learning Network for Description-Based Person Search

Description-based person search aims to retrieve images of the target identity via textual descriptions. One of the challenges for this task is to extract discriminative representation from images and descriptions. Most existing methods apply the part-based split method or external models to explore the fine-grained details of local features, which ignore the global relationship between partial information and cause network instability. To overcome these issues, we propose a Multi-modal Disordered Representation Learning Network (MDRL) for description-based person search to fully extract the visual and textual representations. Specifically, we design a Cross-modality Global Feature Learning Architecture to learn the global features from the two modalities and meet the demand of the task. Based on our global network, we introduce a Disorder Local Learning Module to explore local features by a disordered reorganization strategy from both visual and textual aspects and enhance the robustness of the whole network. Besides, we introduce a Cross-modality Interaction Module to guide the two streams to extract visual or textual representations considering the correlation between modalities. Extensive experiments are conducted on two public datasets, and the results show that our method outperforms the state-of-the-art methods on CUHK-PEDES and ICFG-PEDES datasets and achieves superior performance.

LGMRec: Local and Global Graph Learning for Multimodal Recommendation

The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchases, clicks) and item various modalities (e.g., visual and textual). The majority of existing studies typically focus on utilizing modal features or modal-related graph structure to learn user local interests. Nevertheless, these approaches encounter two limitations: (1) Shared updates of user ID embeddings result in the consequential coupling between collaboration and multimodal signals; (2) Lack of exploration into robust global user interests to alleviate the sparse interaction problems faced by local interest modeling. To address these issues, we propose a novel Local and Global Graph Learning-guided Multimodal Recommender (LGMRec), which jointly models local and global user interests. Specifically, we present a local graph embedding module to independently learn collaborative-related and modality-related embeddings of users and items with local topological relations. Moreover, a global hypergraph embedding module is designed to capture global user and item embeddings by modeling insightful global dependency relations. The global embeddings acquired within the hypergraph embedding space can then be combined with two decoupled local embeddings to improve the accuracy and robustness of recommendations. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our LGMRec over various state-of-the-art recommendation baselines, showcasing its effectiveness in modeling both local and global user interests.

Removing Interference and Recovering Content Imaginatively for Visible Watermark Removal

Visible watermarks, while instrumental in protecting image copyrights, frequently distort the underlying content, complicating tasks like scene interpretation and image editing. Visible watermark removal aims to eliminate the interference of watermarks and restore the background content. However, existing methods often implement watermark component removal and background restoration tasks within a singular branch, leading to residual watermarks in the predictions and ignoring cases where watermarks heavily obscure the background. To address these limitations, this study introduces the Removing Interference and Recovering Content Imaginatively (RIRCI) framework. RIRCI embodies a two-stage approach: the initial phase centers on discerning and segregating the watermark component, while the subsequent phase focuses on background content restoration. To achieve meticulous background restoration, our proposed model employs a dual-path network capable of fully exploring the intrinsic background information beneath semi-transparent watermarks and peripheral contextual information from unaffected regions. Moreover, a Global and Local Context Interaction module is built upon multi-layer perceptrons and bidirectional feature transformation for comprehensive representation modeling in the background restoration phase. The efficacy of our approach is empirically validated across two large-scale datasets, and our findings reveal a marked enhancement over existing watermark removal techniques.

Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders

Learning 3D representation plays a critical role in masked autoencoder (MAE) based pre-training methods for point cloud, including single-modal and cross-modal based MAE. Specifically, although cross-modal MAE methods learn strong 3D representations via the auxiliary of other modal knowledge, they often suffer from heavy computational burdens and heavily rely on massive cross-modal data pairs that are often unavailable, which hinders their applications in practice. Instead, single-modal methods with solely point clouds as input are preferred in real applications due to their simplicity and efficiency. However, such methods easily suffer from limited 3D representations with global random mask input. To learn compact 3D representations, we propose a simple yet effective Point Feature Enhancement Masked Autoencoders (Point-FEMAE), which mainly consists of a global branch and a local branch to capture latent semantic features. Specifically, to learn more compact features, a share-parameter Transformer encoder is introduced to extract point features from the global and local unmasked patches obtained by global random and local block mask strategies, followed by a specific decoder to reconstruct. Meanwhile, to further enhance features in the local branch, we propose a Local Enhancement Module with local patch convolution to perceive fine-grained local context at larger scales. Our method significantly improves the pre-training efficiency compared to cross-modal alternatives, and extensive downstream experiments underscore the state-of-the-art effectiveness, particularly outperforming our baseline (Point-MAE) by 5.16%, 5.00%, and 5.04% in three variants of ScanObjectNN, respectively. Code is available at https://github.com/zyh16143998882/AAAI24-PointFEMAE.

RetouchFormer: Semi-supervised High-Quality Face Retouching Transformer with Prior-Based Selective Self-Attention

Face retouching is to beautify a face image, while preserving the image content as much as possible. It is a promising yet challenging task to remove face imperfections and fill with normal skin. Generic image enhancement methods are hampered by the lack of imperfection localization, which often results in incomplete removal of blemishes at large scales. To address this issue, we propose a transformer-based approach, RetouchFormer, which simultaneously identify imperfections and synthesize realistic content in the corresponding regions. Specifically, we learn a latent dictionary to capture the clean face priors, and predict the imperfection regions via a reconstruction-oriented localization module. Also based on this, we can realize face retouching by explicitly suppressing imperfections in our selective self-attention computation, such that local content will be synthesized from normal skin. On the other hand, multi-scale feature tokens lead to increased flexibility in dealing with the imperfections at various scales. The design elements bring greater effectiveness and efficiency. RetouchFormer outperforms the advanced face retouching methods and synthesizes clean face images with high fidelity in our list of extensive experiments performed.

Modulation of AQP2 localization and water reabsorption.

Modulation of AQP2 localization and water reabsorption.

Abstract 3996: Secreted cytokine-αPDL1 fusion proteins improve solid tumor chimeric antigen receptor (CAR) T-cell therapy

Abstract Background: Success of Chimeric Antigen Receptor (CAR) T cells therapy for solid tumors are limited by the suppressive solid tumor microenvironment (TME), T cell exhaustion, lack of persistence, and poor trafficking to tumors. Strategies to improve this include therapeutic combinations, such as checkpoint inhibition (CPI) or cytokine support. However, CPI therapy has largely failed because of refractory and resistant tumors and cytokine administration can lead to lethal toxicities. To this end, we propose that enabling CAR T cells to secrete bi-functional fusion proteins consisting of cytokine modifiers (ie: IL12, IL15, or TGFβ-trap) combined with checkpoint inhibition (αPDL1 scFv) can provide tumor PDL1 sequestered cytokine activity and local tumor-immune modulation to boost solid tumor CAR T cell efficacy, enhance CPI impacts, and safely improve durable outcomes. Methods: Mouse T cells dual transduced to express surface CAR targeting prostate or ovarian solid tumors in addition to secretable cytokine fused to an αPDL1 scFv were generated. In vitro, the PDL1 blockade capacity and tumor-surface cytokine presentation of supernatants from dual transduced CAR T cells was assessed on PDL1 induced tumor cells. Further, secreted fusion protein transduced CAR T cells were assessed for function, phenotype, and cytokine release in a repetitive tumor rechallenge assay. In vivo, mouse CAR T cells dual transduced with fusion proteins and appropriate controls were assessed for anti-tumor efficacy, survival kinetics, and toxicity in immunocompetent solid tumor mouse models of prostate cancer and intraperitoneal disseminated ovarian cancer. Results: CAR T cells were shown to be successfully dual transduced, and secreted αPDL1-cytokine fusion proteins exhibiting functional PDL1 binding characteristics in vitro. CAR T cells engineered with αPDL1-IL12 fusion protein had greater anti-tumor activity and CAR specific expansion compared to other tested fusion proteins in in vitro co-culture assays. In a syngeneic prostate tumor model, mice receiving CAR T cells with αPDL1-IL12 fusion T cells out competed other cytokine fusion combinations. In a syngeneic ovarian tumor model we safely achieved 100% curative response rate with CAR secreting αPDL1-IL12 fusion in contrast to CAR with αPDL1mutIL12 control along with tumor regional PDL1 blockade on myeloid subsets within the TME. Conclusions: Our findings suggest that CAR T cells engineered to secrete αPD-L1-IL12 fusion protein show improved tumor control, less systemic toxicity, and enhanced expansion which promotes eradication of disease in two independent in vivo tumor models with two unique solid tumor CAR targets. We believe this strategy has the potential to improve solid tumor CAR T-cell efficacy and enhance durable innate immune responses. Citation Format: Lea Christian, John P. Murad, Lupita Lopez, Anthony Park, Jason Yang, Eric Lee, Candi Trac, Stephen Forman, Saul J. Priceman. Secreted cytokine-αPDL1 fusion proteins improve solid tumor chimeric antigen receptor (CAR) T-cell therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3996.