Local Representation Research Articles

On the Local Boundary Representations of Locally $$C^*$$-Algebras

On the Local Boundary Representations of Locally $$C^*$$-Algebras

A signal-diffusion-based unsupervised contrastive representation learning for spatial transcriptomics analysis.

Spatial transcriptomics allows for the measurement of high-throughput gene expression data while preserving the spatial structure of tissues and histological images. Integrating gene expression, spatial information, and image data to learn discriminative low-dimensional representations is critical for dissecting tissue heterogeneity and analyzing biological functions. However, most existing methods have limitations in effectively utilizing spatial information and high-resolution histological images. We propose a signal-diffusion-based unsupervised contrast learning method (SDUCL) for learning low-dimensional latent embeddings of cells/spots. SDUCL integrates image features, spatial relationships and gene expression information. We designed a signal diffusion microenvironment discovery algorithm, which effectively captures and integrates interaction information within the cellular microenvironment by simulating the biological signal diffusion process. By maximizing the mutual information between the local representation and the microenvironment representation of cells/spots, SDUCL learns more discriminative representations. SDUCL was employed to analyze spatial transcriptomics datasets from multiple species, encompassing both normal and tumor tissues. SDUCL performed well in downstream tasks such as clustering, visualization, trajectory inference, and differential gene analysis, thereby enhancing our understanding of tissue structure and tumor microenvironments. https://github.com/WeiMin-Li-visual/SDUCL. Supplementary data are available at Bioinformatics online.

Speech Enhancement Algorithm Based on Microphone Array and Lightweight CRN for Hearing Aid

To address the performance and computational complexity issues in speech enhancement for hearing aids, a speech enhancement algorithm based on a microphone array and a lightweight two-stage convolutional recurrent network (CRN) is proposed. The algorithm consists of two main modules: a beamforming module and a post-filtering module. The beamforming module utilizes directional features and a complex time-frequency long short-term memory (CFT-LSTM) network to extract local representations and perform spatial filtering. The post-filtering module uses analogous encoding and two symmetric decoding structures, with stacked CFT-LSTM blocks in between. It further reduces residual noise and improves filtering performance by passing spatial information through an inter-channel masking module. Experimental results show that this algorithm outperforms existing methods on the generated hearing aid dataset and the CHIME-3 dataset, with fewer parameters and lower model complexity, making it suitable for hearing aid scenarios with limited computational resources.

LGSur‐Net: A Local Gaussian Surface Representation Network for Upsampling Highly Sparse Point Cloud

AbstractWe introduce LGSur‐Net, an end‐to‐end deep learning architecture, engineered for the upsampling of sparse point clouds. LGSur‐Net harnesses a trainable Gaussian local representation by positioning a series of Gaussian functions on an oriented plane, complemented by the optimization of individual covariance matrices. The integration of parametric factors allows for the encoding of the plane's rotational dynamics and Gaussian weightings into a linear transformation matrix. Then we extract the feature maps from the point cloud and its adjoining edges and learn the local Gaussian depictions to accurately model the shape's local geometry through an attention‐based network. The Gaussian representation's inherent high‐order continuity endows LGSur‐Net with the natural ability to predict surface normals and support upsampling to any specified resolution. Comprehensive experiments validate that LGSur‐Net efficiently learns from sparse data inputs, surpassing the performance of existing state‐of‐the‐art upsampling methods. Our code is publicly available at https://github.com/Rangiant5b72/LGSur-Net.

AIRHF-Net: an adaptive interaction representation hierarchical fusion network for occluded person re-identification

To tackle the high resource consumption in occluded person re-identification, sparse attention mechanisms based on Vision Transformers (ViTs) have become popular. However, they often suffer from performance degradation with long sequences, omission of crucial information, and token representation convergence. To address these issues, we introduce AIRHF-Net: an Adaptive Interaction Representation Hierarchical Fusion Network, named AIRHF-Net, designed to enhance pedestrian identity recognition in occluded scenarios. Our approach begins with the development of an Adaptive Local-Window Interaction Encoder (AL-WIE), which aims to overcome the inherent subjective limitations of traditional sparse attention mechanisms. This innovative encoder merges window attention, adaptive local attention, and interaction attention, facilitating automatic localization and focusing on visible pedestrian regions within images. It effectively extracts contextual information from window-level features while minimizing the impact of occlusion noise. Additionally, recognizing that ViTs may lose spatial information in deeper structural layers, we implement a Local Hierarchical Encoder (LHE). This component segments the input sequence in the spatial dimension, integrating features from various spatial positions to construct hierarchical local representations that substantially enhance feature discriminability. To further augment the quality and breadth of datasets, we adopt an Occlusion Data Augmentation Strategy (ODAS), which bolsters the model’s capacity to extract critical information under occluded conditions. Extensive experiments demonstrate that our method achieves improved performance on the Occluded-DukeMTMC dataset, with a rank-1 accuracy of 69.6% and an mAP of 61.6%.

The imaginaries of the city of Norilsk, from the Soviet industrial utopia to the contemporary nickel legend

Norilsk is a polar city in Russia, located to the South of the Taimyr Peninsula in the Arctic Circle. The city’s surroundings are rich in unique deposits of polymetallic ores, and life in the city is organised around metal extraction. This is a reason why Norilsk is an interesting object for analysis of the social representation of the city’s image. Social representation is a fairly new socio-philosophical concept. By the word ‘representation’ we mean perceptions that are formed on the basis of reality. In the case of Norilsk, it is the result of all perceptions of the city and its environment. The study is based on contemporary documentary films, geographical, historical and journalistic documents. The analysis of perceptions of Norilsk allows us to understand the reasons for ideological debates around the history of Russia, such as discussions about memorials to victims of political repressions. Statements about Norilsk are contradictory. Negative statements are related to negative perceptions of the city, while positive statements are related to the residents’ love for their city. The analysis of representations related to the architecture of Norilsk allows us to identify gaps in the official discourse about the foundation of the city and explore its historical background. The study has revealed that the Soviet architectural heritage is well-preserved and documented, while statements about buildings associated with the Gulag are relegated to a marginalised space. Consideration of external and local representations of Norilsk allows us to understand the process of rewriting the history of the city.

Adaptive Granularity-Fused Keypoint Detection for 6D Pose Estimation of Space Targets

Estimating the 6D pose of a space target is an intricate task due to factors such as occlusions, changes in visual appearance, and background clutter. Accurate pose determination requires robust algorithms capable of handling these complexities while maintaining reliability under various environmental conditions. Conventional pose estimation for space targets unfolds in two stages: establishing 2D–3D correspondences using keypoint detection networks and 3D models, followed by pose estimation via the perspective-n-point algorithm. The accuracy of this process hinges critically on the initial keypoint detection, which is currently limited by predominantly singular-scale detection techniques and fails to exploit sufficient information. To tackle the aforementioned challenges, we propose an adaptive dual-stream aggregation network (ADSAN), which enables the learning of finer local representations and the acquisition of abundant spatial and semantic information by merging features from both inter-layer and intra-layer perspectives through a multi-grained approach, consolidating features within individual layers and amplifying the interaction of distinct resolution features between layers. Furthermore, our ADSAN implements the selective keypoint focus module (SKFM) algorithm to alleviate problems caused by partial occlusions and viewpoint alterations. This mechanism places greater emphasis on the most challenging keypoints, ensuring the network prioritizes and optimizes its learning around these critical points. Benefiting from the finer and more robust information of space objects extracted by the ADSAN and SKFM, our method surpasses the SOTA method PoET (5.8°, 8.1°/0.0351%, 0.0744%) by 0.5°, 0.9°, and 0.0084%, 0.0354%, achieving 5.3°, 7.2° in rotation angle errors and 0.0267%, 0.0390% in normalized translation errors on the Speed and SwissCube datasets, respectively.

Candidate shortages and the electoral consequences for radical right-wing parties: insights from Sweden

ABSTRACT While much research has explored factors that increase support for radical right-wing parties, the role of their local candidates has received less attention. This study contends that parties, particularly those distancing themselves from extremist elements, often struggle with a shortage of candidates, leading to both premature resignations and a lack of suitable replacements. This shortage negatively affects voter support in elections following candidate resignation. To illustrate this issue, the study utilizes a novel dataset examining the Sweden Democrats (SD), a party frequently grappling with “empty seats” in local assemblies, which are allocated seats after elections that remain unfilled or are prematurely vacated by local politicians. The analysis shows that these vacant seats reduce the party’s support by approximately 1.7 percentage points in local elections. This effect is compounded by voter turnout; when turnout is low and SD has previously failed to maintain their seats, their support decreases by as much as 10 percentage points. These findings emphasize the significant impact of a robust pool of local candidates on party performance, highlighting the critical role of local candidate representation for the electoral success of radical right-wing parties.

VMC‐UNet: A Vision Mamba‐CNN U‐Net for Tumor Segmentation in Breast Ultrasound Image

ABSTRACTBreast cancer remains one of the most significant health threats to women, making precise segmentation of target tumors critical for early clinical intervention and postoperative monitoring. While numerous convolutional neural networks (CNNs) and vision transformers have been developed to segment breast tumors from ultrasound images, both architectures encounter difficulties in effectively modeling long‐range dependencies, which are essential for accurate segmentation. Drawing inspiration from the Mamba architecture, we introduce the Vision Mamba‐CNN U‐Net (VMC‐UNet) for breast tumor segmentation. This innovative hybrid framework merges the long‐range dependency modeling capabilities of Mamba with the detailed local representation power of CNNs. A key feature of our approach is the implementation of a residual connection method within the U‐Net architecture, utilizing the visual state space (VSS) module to extract long‐range dependency features from convolutional feature maps effectively. Additionally, to better integrate texture and structural features, we have designed a bilinear multi‐scale attention module (BMSA), which significantly enhances the network's ability to capture and utilize intricate feature details across multiple scales. Extensive experiments conducted on three public datasets demonstrate that the proposed VMC‐UNet surpasses other state‐of‐the‐art methods in breast tumor segmentation, achieving Dice coefficients of 81.52% for BUSI, 88.00% for BUS, and 88.96% for STU. The source code is accessible at https://github.com/windywindyw/VMC‐UNet.

Hierarchical Fusion of Infrared and Visible Images Based on Channel Attention Mechanism and Generative Adversarial Networks.

In order to solve the problem that existing visible and infrared image fusion methods rely only on the original local or global information representation, which has the problem of edge blurring and non-protrusion of salient targets, this paper proposes a layered fusion method based on channel attention mechanism and improved Generative Adversarial Network (HFCA_GAN). Firstly, the infrared image and visible image are decomposed into a base layer and fine layer, respectively, by a guiding filter. Secondly, the visible light base layer is fused with the infrared image base layer by histogram mapping enhancement to improve the contour effect. Thirdly, the improved GAN algorithm is used to fuse the infrared and visible image refinement layer, and the depth transferable module and guided fusion network are added to enrich the detailed information of the fused image. Finally, the multilayer convolutional fusion network with channel attention mechanism is used to correlate the local information of the layered fusion image, and the final fusion image containing contour gradient information and useful details is obtained. TNO and RoadSence datasets are selected for training and testing. The results show that the proposed algorithm retains the global structure features of multilayer images and has obvious advantages in fusion performance, model generalization and computational efficiency.

Hyperspectral Image Classification Algorithm for Forest Analysis Based on a Group-Sensitive Selective Perceptual Transformer

Substantial advancements have been achieved in hyperspectral image (HSI) classification through contemporary deep learning techniques. Nevertheless, the incorporation of an excessive number of irrelevant tokens in large-scale remote sensing data results in inefficient long-range modeling. To overcome this hurdle, this study introduces the Group-Sensitive Selective Perception Transformer (GSAT) framework, which builds upon the Vision Transformer (ViT) to enhance HSI classification outcomes. The innovation of the GSAT architecture is primarily evident in several key aspects. Firstly, the GSAT incorporates a Group-Sensitive Pixel Group Mapping (PGM) module, which organizes pixels into distinct groups. This allows the global self-attention mechanism to function within these groupings, effectively capturing local interdependencies within spectral channels. This grouping tactic not only boosts the model’s spatial awareness but also lessens computational complexity, enhancing overall efficiency. Secondly, the GSAT addresses the detrimental effects of superfluous tokens on model efficacy by introducing the Sensitivity Selection Framework (SSF) module. This module selectively identifies the most pertinent tokens for classification purposes, thereby minimizing distractions from extraneous information and bolstering the model’s representational strength. Furthermore, the SSF refines local representation through multi-scale feature selection, enabling the model to more effectively encapsulate feature data across various scales. Additionally, the GSAT architecture adeptly represents both global and local features of HSI data by merging global self-attention with local feature extraction. This integration strategy not only elevates classification precision but also enhances the model’s versatility in navigating complex scenes, particularly in urban mapping scenarios where it significantly outclasses previous deep learning methods. The advent of the GSAT architecture not only rectifies the inefficiencies of traditional deep learning approaches in processing extensive remote sensing imagery but also markededly enhances the performance of HSI classification tasks through the deployment of group-sensitive and selective perception mechanisms. It presents a novel viewpoint within the domain of hyperspectral image classification and is poised to propel further advancements in the field. Empirical testing on six standard HSI datasets confirms the superior performance of the proposed GSAT method in HSI classification, especially within urban mapping contexts, where it exceeds the capabilities of prior deep learning techniques. In essence, the GSAT architecture markedly refines HSI classification by pioneering group-sensitive pixel group mapping and selective perception mechanisms, heralding a significant breakthrough in hyperspectral image processing.

Nondirect-Product Local Diabatic Representation with Smolyak Sparse Grids.

Modeling nonadiabatic conical intersection dynamics is critical for understanding a wide range of photophysical, photochemical, and biological phenomena. Here we develop a nonadiabatic conical intersection wave packet dynamic method in the local diabatic representation using Smolyak sparse grids. Employing sparse grids avoids the direct-product grids in configuration space and alleviates the exponential scaling of computation costs with the molecular size. Numerical demonstrations are first performed for a two-dimensional vibronic model of pyrazine, where the results using sparse grids are in excellent agreement with those using direct-product grids, with sparse grids being much faster. Moreover, we demonstrate that for a four-dimensional pyrazine model, where direct-product grids are computationally infeasible, sparse grids can provide almost exact results. The sparse grid local diabatic representation method is further applied to a realistic model system of phenol photodissociation with much more complex potential energy surfaces; the results using sparse grids still agree very well with the direct-product grids. Finally, by combining with electronic structure calculations, we apply our method to the Shin-Metiu model without quasi-diabatization. The sparse grid and direct-product grid results are in good agreement, with the sparse grid computational cost being half of the full grid.

Reshaping disaster management: An integrated community‐led approach

AbstractThe management of disasters has traditionally involved public, private, and nongovernmental organisations working together. While scholars have examined the value of collaborations among these entities, less is known about how to successfully engage and empower communities in disaster management. Based on network governance theory, this article contributes to the growing body of public management literature on community engagement by presenting findings from an Australian research initiative conducted after the 2019/20 Black Summer bushfires in New South Wales. Through workshops and semi‐structured interviews with a total of 58 members from local communities and emergency agencies, this paper identifies differing perspectives on power distribution among stakeholders, indicating complexities in achieving an integrated and community‐led disaster management approach. The findings underscore the need to shift from exclusively centralised to more inclusive systems, recognising the unique contributions of nonofficial community‐based groups. To address this, the study suggests: a funded community consultation committee, ensuring government and local community representation; collaborative debriefing sessions, leveraging technology for knowledge capture; and the adoption of different leadership styles able to identify, include, and integrate communities as both steerers and rowers within established hierarchical arrangements.Points for practitioners The current centralised emergency management system, which relies on recognised experts and state‐controlled facilities, limits the integration of nonofficial resources and community‐based knowledge. A shift towards a more community‐centric and integrated approach (collaborative polycentric governance) is needed to enhance disaster resilience and response in Australia. Different stages of disaster reduction could and should have different leadership styles: a transformational, collaborative community‐based style should be implemented before and after the disaster, while a transactional leadership style, more focused on restructuring the system or how it is applied, should be adopted during the disaster.

Neural Predictors of Fear Depend on the Situation.

The extent to which neural representations of fear experience depend on or generalize across the situational context has remained unclear. We systematically manipulated variation within and across three distinct fear-evocative situations including fear of heights, spiders, and social threats. Participants (n = 21; 10 females and 11 males) viewed ∼20 s clips depicting spiders, heights, or social encounters and rated fear after each video. Searchlight multivoxel pattern analysis was used to identify whether and which brain regions carry information that predicts fear experience and the degree to which the fear-predictive neural codes in these areas depend on or generalize across the situations. The overwhelming majority of brain regions carrying information about fear did so in a situation-dependent manner. These findings suggest that local neural representations of fear experience are unlikely to involve a singular pattern but rather a collection of multiple heterogeneous brain states.

Multi-granularity self-attention mechanisms for few-shot Learning

Few-shot learning aims to classify novel data categories with limited labeled samples. Although metric-based meta-learning has shown better generalization ability as a few-shot classification method, it still faces challenges in handling data noise and maintaining inter-sample distance stability. To address these issues, our study proposes an innovative few-shot learning approach to enhance image features' global and local semantic representation. Initially, our method employs a multiscale residual module to facilitate extracting multi-granularity features within images. Subsequently, it optimizes the fusion of local and global features using the self- attention mechanism inherent in the Transformer module. Additionally, a weighted metric module is integrated to improve the model's resilience against noise interference. Empirical evaluations on CIFAR-FS and MiniImageNet few-shot datasets using 5-way 1-shot and 5-way 5-shot scenarios demonstrate the effectiveness of our approach in capturing multi-level and multi-granularity image representations. Compared to other methods, our method improves accuracy by 2.63% and 1.27% for 5-shot scenes on these two datasets. The experimental results validate the efficacy of our model in significantly enhancing few-shot image classification performance.

An attention-based hybrid model for spatial and temporal sentiment analysis of COVID-19 related tweets in the contiguous United States

ABSTRACT Understanding the sentiments of social media posts can help health authorities respond to disease outbreaks, through a proxy measure of fear, confidence, and community compliance. Sentiment analysis identifies a pattern of emotion through the written word and assigns a positive, neutral, or negative value to it. As of February 2023, there were 677.7 million confirmed cases of coronavirus disease (COVID-19) and more than 6.7 million confirmed deaths. In this paper, around 170,000 COVID-19-related tweets were collected between September 2020 and January 2021 in the contiguous United States. Data preprocessing and exploratory investigation were completed for analysis of the collected dataset. Further, a novel and unified architecture called attention-based one-dimensional convolution with bidirectional long short-term memory layers (CNN-BiLSTM-ATT) is proposed to classify people’s sentiments as positive, neutral, and negative based on COVID-19-related tweets. In the CNN-BiLSTM-ATT model, the CNN layer can extract the low-level semantic features from textual data, and the BiLSTM layer can extract both the previous and future contextual representations. The attention module can improve the information focus from the outputted layer of the BiLSTM. The proposed method can extract both the local phrase representations and the global feature of sentences. Numerical experiments were conducted on COVID-19-related tweets using the proposed method and other baseline models to compare their performances. Our experimental results demonstrate that the CNN-BiLSTM-ATT model achieves an average accuracy of 95.16% and a macro-average F1-score of 95.12%, which outperforms the baseline models.

Rethinking prediction-based video anomaly detection from local–global normality perspective

Video anomaly detection (VAD) has been intensively studied for years because of its potential applications in intelligent video systems. Prediction-based VAD methods have gained a lot of attention in recent years due to their simplicity and effectiveness, which learn normality through a frame prediction tasks. However, we find that they often consider only local or global normality in the temporal dimension thus are difficult to generalize for various scenarios. Specifically, some of them focus on learning local spatiotemporal representations from consecutive frames to enhance the representation for normal events while the other methods are devoted to memorizing prototypical normal patterns of whole training videos to weaken the generalization for anomalies. In this paper, we rethink these two types of methods and find that the former methods are difficult to adapt to simple scenarios due to powerful representation allows these methods to represent some anomalies and causes miss detection while the latter struggle with complex scenarios due to the limitations for diverse normal patterns and causes false alarm. To verify our discoveries, we design a two-branch model, Local–Global Normality Network (LGN-Net), to simultaneously learn local and global normality. We verified the effect of local and global normality on the VAD model by ablation study and find that the model can be more adaptive to different scenarios when accounting for both normality. The code is available online: https://github.com/Myzhao1999/LGN-Net.

Electoral Systems and the Autocrat’s Trade-Off: Evidence from the Russian Duma

abstract: Legislative elections sustain authoritarian regimes, but distinct electoral systems simultaneously benefit and hurt the political prospects of those regimes. The author theorizes about a trade-off between electoral systems that facilitate information on local elite preferences, and systems that maintain legislative control. An electoral system can achieve either, but not at the same time. The author undertakes an empirical study of Russian federal deputies, half of whom are elected in single-member districts, the other half on a nationwide closed-party list. Candidates can register on both lists (N = 515), and district results determine final list affiliation. Electoral incentives change abruptly for deputies who are barely winning or losing their district, a fact that the author exploits to identify the effects of local representation on pro- and antiregime parliamentary behavior. The results support a trade-off: district-list deputies convey more information on local preferences in their speeches than party-list deputies do, but they oppose law proposals more frequently. How autocrats weigh this trade-off determines the electoral system and illuminates electoral reform in autocracies.

DAUP: Enhancing point cloud homogeneity for 3D industrial anomaly detection via density-aware point cloud upsampling

The use of 3D information in industrial anomaly detection tasks has been shown to enhance performance by uncovering unseen abnormal patterns in the RGB modality. Despite the focus on detection pipeline design and multimodal fusion schemes in previous approaches, explorations of dataset characteristics were often overlooked. In contrast to RGB images where pixels form regular grids, point clouds intrinsically lack order and exhibit inhomogeneous densities across regions, thereby adversely affecting the feature extraction process. In this work, we propose a learning-based density-aware point cloud upsampling module (DAUP) to address the inhomogeneous problem. A learning-based neural shape function is developed to generate a local representation of the surface for point upsampling purposes. Utilizing the points generated by the neural shape function, we devise a density-aware resampling mechanism aimed at selecting a diverse number of points from varied regions to facilitate adaptive upsampling within regions of varying densities. DAUP can substantially reducing the misclassification rate for off-the-shelf anomaly detection pipelines. Extensive experiments confirm the effectiveness of our upsampling method on the benchmark dataset MVTec 3D-AD. Notably, our method surpasses previous state-of-the-art methods in terms of image-level AUROC based on the feature bank-based anomaly detection pipeline.

Materialism versus memory: Collecting football shirts in the age of consumerism

While the connection between consumption and travel has been documented, there has been little work on how consumption can go beyond simple materialism and sometimes even represent a reaction to the dominant trends of late modern consumer society. I use Walter Benjamin’s essay “Unpacking My Library” as an inspiration to understand the emotive connection between collecting, consumption, and memory. This paper uses an auto-ethnographic method, showing how the goal of furthering a collection of football shirts serves as a way to give agency to the consumer. Since collecting is typically related to the collector’s memory, consuming in the context of a collection is not simple consumerism or materialism. In this case, the object being collected is a football shirt, a representation of a specific locality. Collecting as a motivation for consumption serves as a way for the individual to respond to the dominant trends of homogenization and mass culture in sport created by globalization.