Location-specific Information Research Articles

The Australian Park Life project: Development of a nationally standardised spatial layer and public participatory GIS for greenspace in Australian capital cities

This paper introduces the ‘Australian Park Life Project’ and describes a protocol to standardise the capture and collation of public open greenspace spatial data across Australian cities. This method will progress greenspace research allowing for unique coherent national analyses and comparative research across Australia. We also outline the development of the Park Life public participatory geographic information system (PPGIS) to spatially explore what, and how, public open greenspaces are being used by Australian communities. The combination of community crowdsourced spatial data providing location-specific information on the green public open spaces used, in combination with the national spatial layer of greenspace allows for unique analyses exploring the role of greenspace provision and design on use and represents a transformative strategy in shaping public open space policy and strategy.•A spatial layer of public open greenspace was created for the eight Australian State and Territory capital cities using a standardised data capture and collection method from local government planning schemes and land use data, and listings of managed and demarcated parks and reserves.•The Park Life public participatory geographic information system (PPGIS) was built to capture spatially referenced information on the use of greenspaces - specifically what spaces are used, and how they are used.

Public perception and acceptance of CCUS: preliminary findings of a qualitative case study in Greece.

The development and implementation of carbon capture, utilisation and storage (CCUS) technologies plays an increasingly important part in European Union (EU) countries' decarbonisation policies and strategies. Several studies have shown the important role social acceptance plays in determining the outcomes of CCUS projects and how social acceptance is shaped by the national and local contexts. Yet most studies on CCUS and social acceptance have focused on a few northern European countries despite the increasing numbers of CCUS projects across the European Union. This study seeks to help address this gap by conducting a case study on how local dynamics shaped people's acceptance and awareness of CCUS in two separate Greek communities. Based on semi-structured interviews with community members near a CCUS pilot plant, and a focus group with community members from a potential storage site, this single case study explores the factors and dynamics that shaped the participants' perceptions of CCUS technologies. Our findings indicate that, despite the low level of awareness of CCUS technologies, participants could draw on their situated knowledge to identify potential drawbacks with their application. We identified scepticism regarding the adoption of new technologies and the organisations involved based on past experiences, and a notable lack of provision of technology and location-specific information as well as public engagement by the project consortium. Our recommendations for future projects and community engagement include the early involvement of the public in project development, location-based transparent information, appropriate channels to facilitate knowledge exchange, and educational initiatives to build communities' capability to influence projects.

Cohort-specific differential vulnerability to capture of mallards and wood ducks in baited swim-in traps

Abstract Attaching leg bands to birds directly before fall hunting seasons is a primary component of monitoring waterfowl in North America. While capture and recovery data are primarily used for estimating survival and harvest distribution, these data may be used to estimate age ratios and other demographic rates, especially if recapture data are available from subsequent trapping and release events. We estimated recapture rates of mallards Anas platyrhynchos and wood ducks Aix sponsa and used those rates to estimate differential capture vulnerability, which is vital to estimate true age ratios from banding data. Posterior estimates of mallard age and sex cohort and location-specific recapture rates varied among capture locations from a mean of 0.004 (0.002- 0.008 95% credible interval) to 0.547 (0.486-0.609). Ratios of recapture rates among cohorts also varied, meaning no single differential vulnerability estimate would be useable across the study area. Our estimates of differential capture vulnerability for mallards, using the ratio of recapture probabilities, averaged 2.64 for adult female to adult male and 5.42 for juvenile female to adult female, with significant variation. Wood duck cohort-specific recapture rates were similar across locations. Similar wood duck recapture rates resulted in similar estimates of differential vulnerability, 1.24 for the adult female to adult male ratio and 1.30 for juvenile female to adult female. The wide range of recapture rate estimates we found for mallards suggests location- specific characteristics may have a strong effect on capture probability. Differences in recapture rates and apparent survival likely resulting from emigration suggest if recapture data are to be used in population modeling, location-specific information is needed. The ability to monitor multiple demographic parameters using a single scheme improves continued assessment of population status. We recommend increased collection of in-season recapture data by biologists during active banding operations. Banders should be aware of the potential value of live, in- season, encounters in monitoring populations and modeling demographic rates.

The isotopic composition of the world’s highest river basins: Role of hydrological mixing ratios and transit time

Hydrological mixing models rely on assumptions governing the rate and timing of mixing proportions, the degree to which isotope ratios conserve the mixing ratios and the estimation of the prior behaviour of meteoric reservoirs. However, these models only describe the integrated catchment response based on different sources having unique and constant isotopic compositions as opposed to location-specific information. Erroneous projection of the hydrological budget can have severe repercussions on climate and eco-hydrological studies, estimation of freshwater availability, and civil and engineering work. Consequently, we design a real-time, multi-parameter, hydrometeorological-constrained, Bayesian-mixing model that can relate meteoric reservoirs to streamflow, and temporal and spatial behaviour of flow paths. Developed for pan-Himalayan catchments (up to 5200 m), with four end-members and pre/post-monsoon seasonality, the isotopic analysis reveals distinct δ18O values: stream water (–9 ± 2.6 ‰), snowpack (–10.7 ± 8.1 ‰), lake water (–12.2 ± 2.5 ‰), and groundwater (–8.4 ± 1.6 ‰). Initial results based on the isotope-enabled, flux-weighted mixing didn’t fully capture spatiotemporal isotopic heterogeneity, affecting hydrograph separation. A revised model, considering the transit time of meteoric reservoirs before their mixing with river discharge, was developed. The revised model illustrates that isotope-enabled mixing models must also consider the hydrometeorological properties of individual reservoirs, the high probability of intermixing between the various reservoirs, and the variable transit time of different meteoric waters in a catchment. With implications for isotope-enabled hydrology, this work describes a novel method applicable to high-mountain hydrology, separating them from other montane or lowland geographies.

Spatial transcriptomics reveals gene interactions and signaling pathway dynamics in rat embryos with anorectal malformation

Anorectal malformation (ARM) is a prevalent early pregnancy digestive tract anomaly. The intricate anatomy of the embryonic cloaca region makes it challenging for traditional high-throughput sequencing methods to capture location-specific information. Spatial transcriptomics was used to sequence libraries of frozen sections from embryonic rats at gestational days (GD) 14 to 16, covering both normal and ARM cases. Bioinformatics analyses and predictions were performed using methods such as WGCNA, GSEA, and PROGENy. Immunofluorescence staining was used to verify gene expression levels. Gene expression data was obtained with anatomical annotations of clusters, focusing on the cloaca region's location-specific traits. WGCNA revealed gene modules linked to normal and ARM cloacal anatomy development, with cooperation between modules on GD14 and GD15. Differential gene expression profiles and functional enrichment were presented. Notably, protein levels of Pcsk9, Hmgb2, and Sod1 were found to be downregulated in the GD15 ARM hindgut. The PROGENy algorithm predicted the activity and interplay of common signaling pathways in embryonic sections, highlighting their synergistic and complementary effects. A competing endogenous RNA (ceRNA) regulatory network was constructed from whole transcriptome data. Spatial transcriptomics provided location-specific cloaca region gene expression. Diverse bioinformatics analyses deepened our understanding of ARM's molecular interactions, guiding future research and providing insights into gene regulation in ARM development.

Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease Detection.

Accurately identifying and locating lesions in chest X-rays has the potential to significantly enhance diagnostic efficiency, quality, and interpretability. However, current methods primarily focus on detecting of specific diseases in chest X-rays, disregarding the presence of multiple diseases in a single chest X-ray scan. Moreover, the diversity in lesion locations and attributes introduces complexity in accurately discerning specific traits for each lesion, leading to diminished accuracy when detecting multiple diseases. To address these issues, we propose a novel detection framework that enhances multi-scale lesion feature extraction and fusion, improving lesion position perception and subsequently boosting chest multi-disease detection performance. Initially, we construct a multi-scale lesion feature extraction network to tackle the uniqueness of various lesion features and locations, strengthening the global semantic correlation between lesion features and their positions. Following this, we introduce an instance-aware semantic enhancement network that dynamically amalgamates instance-specific features with high-level semantic representations across various scales. This adaptive integration effectively mitigates the loss of detailed information within lesion regions. Additionally, we perform lesion region feature mapping using candidate boxes to preserve crucial positional information, enhancing the accuracy of chest disease detection across multiple scales. Experimental results on the VinDr-CXR dataset reveal a 6% increment in mean average precision (mAP) and an 8.4% improvement in mean recall (mR) when compared to state-of-the-art baselines. This demonstrates the effectiveness of the model in accurately detecting multiple chest diseases by capturing specific features and location information.

Oh, the Places They’ll Go: A Geographic Analysis of Gubernatorial Campaigns

Geography has long played a significant role in American electoral politics. Candidates engage in “retail politics” by visiting fairs, festivals, schools, churches, and businesses, to meet and greet potential voters. However, systematically understanding where candidates are actually going has plagued prior work due to small sample sizes and lack of data availability. Presidential elections have been used with success in previous scholarship, but there are few viable candidates to compare within a single cycle. Utilizing competitive gubernatorial elections, this work attempts to address these deficiencies by utilizing social media posts to track multiple candidates in real-time across the 2018 campaign cycle at the state level. The paper tests competing theories of candidate engagement regarding travel decisions: do candidates (1) focus their attention on their partisan base or (2) try to attract independent or “swing” voters? Following an original and intensive data collection effort, we identified location-specific information for over 4700 campaign stops made by major-party candidates across seventeen states. Our results lend support to candidates spending time with their respective bases of partisan support, with Republicans going to rural areas with non-college educated individuals while Democrats travel to urban counties with a more diverse electorate.

Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning

Coffee leaf diseases are a significant challenge for coffee cultivation. They can reduce yields, impact bean quality, and necessitate costly disease management efforts. Manual monitoring is labor-intensive and time-consuming. This research introduces a pioneering mobile application equipped with global positioning system (GPS)-enabled reporting capabilities for on-site coffee leaf disease detection. The application integrates advanced deep learning (DL) techniques to empower farmers and agronomists with a rapid and accurate tool for identifying and managing coffee plant health. Leveraging the ubiquity of mobile devices, the app enables users to capture high-resolution images of coffee leaves directly in the field. These images are then processed in real-time using a pre-trained DL model optimized for efficient disease classification. Five models, Xception, ResNet50, Inception-v3, VGG16, and DenseNet, were experimented with on the dataset. All models showed promising performance; however, DenseNet proved to have high scores on all four-leaf classes with a training accuracy of 99.57%. The inclusion of GPS functionality allows precise geotagging of each captured image, providing valuable location-specific information. Through extensive experimentation and validation, the app demonstrates impressive accuracy rates in disease classification. The results indicate the potential of this technology to revolutionize coffee farming practices, leading to improved crop yield and overall plant health.

PDMSC: privacy-preserving decentralized multi-skill spatial crowdsourcing

Purpose The purpose of this paper is to study the following two issues regarding blockchain crowdsourcing. First, to design smart contracts with lower consumption to meet the needs of blockchain crowdsourcing services and also need to design better interaction modes to further reduce the cost of blockchain crowdsourcing services. Second, to design an effective privacy protection mechanism to protect user privacy while still providing high-quality crowdsourcing services for location-sensitive multiskilled mobile space crowdsourcing scenarios and blockchain exposure issues. Design/methodology/approach This paper proposes a blockchain-based privacy-preserving crowdsourcing model for multiskill mobile spaces. The model in this paper uses the zero-knowledge proof method to make the requester believe that the user is within a certain location without the user providing specific location information, thereby protecting the user’s location information and other privacy. In addition, through off-chain calculation and on-chain verification methods, gas consumption is also optimized. Findings This study deployed the model on Ethereum for testing. This study found that the privacy protection is feasible and the gas optimization is obvious. Originality/value This study designed a mobile space crowdsourcing based on a zero-knowledge proof privacy protection mechanism and optimized gas consumption.

End-to-end Point Supervised Object Detection with low-level instance features

The use of point-level annotations in Point Supervision Object Detection (PSOD) has gained popularity due to its low labeling cost and the provision of specific location information. Previous studies have often relied on Multiple Instance Learning (MIL) to select and refine a low-quality pseudo box, which heavily relies on category information. This method concentrates on identifying the most distinctive objects in each category within an image. However, without the boundary constraints provided by Ground-Truth (GT), the detector may suffer from instance ambiguity problems, which can significantly deteriorate the model’s performance. To tackle these concerns, we propose an end-to-end object detection model called low-level instance features object detector (LIFOD) to mime low-level instance features information to refine the pseudo box. LIFOD utilizes a Low-level Feature Extractor (LFE) module to exploit low-level instance feature information to combine low-level feature information and category information, thus solving the challenge of instance ambiguity. To further alleviate the problem of local minima, we introduce bounding box regression in LIFOD and implement an end-to-end training strategy for point-supervised models to mitigate local minima. We evaluate our proposed LIFOD method on the public datasets COCO 2014 and 2017 and show that it outperforms the existing image-level and point-level supervised detection methods, and significantly reduces the gap in performance between PSOD and box-level object detection.

Public perception and acceptance of CCUS: preliminary findings of a qualitative case study in Greece

The development and implementation of carbon capture, utilisation and storage (CCUS) technologies plays an increasingly important part in European Union (EU) and global decarbonisation policies and strategies that seek to address climate change to achieve the Paris Agreement goals. Several studies have shown the important role social acceptance plays in determining the outcomes of CCUS projects and how social acceptance is shaped by the national and local contexts. Yet most studies on CCUS and social acceptance have focused on countries in northern Europe and North America despite the increasing numbers of CCUS projects in other regions of the world. This study seeks to help address this gap by conducting a case study on how local dynamics shaped people's acceptance and awareness of CCUS in a Greek community. Based on semi-structured interviews with six community members near a CCUS pilot plant, this single case study explores the factors and dynamics that shaped the interviewees' perceptions of CCUS technologies. Our findings indicate that, despite the low level of awareness of CCUS technologies, participants could draw on their situated knowledge to identify potential drawbacks with their application. We identified scepticism regarding the adoption of new technologies and the organisations involved based on past experiences, and a notable lack provision of technology and location-specific information as well public engagement by the project consortium. Our recommendations for future projects and community engagement include the early involvement of the public in project development, location-based transparent information, appropriate channels to facilitate knowledge exchange, and educational initiatives to build communities' capability to influence projects.

Visualization of Nanocrystallites in Organic Semiconducting Blends Using Cryo-Electron Microscopy.

The efficiency of an organic solar cell is highly dependent on the complex, interpenetrating morphology, and molecular order within the composite phases of the bulk heterojunction (BHJ) blend. Both these microstructural aspects are strongly influenced by the processing conditions and chemical design of donor/acceptor materials. To establish improved structure-function relationships, it is vital to visualize the local microstructural order to provide specific local information about donor/acceptor interfaces and crystalline texture in BHJ blend films. The visualization of nanocrystallites, however, is difficult due to the complex semi-crystalline structure with few characterization techniques capable of visualizing the molecular ordering of soft materials at the nanoscale. Here, it is demonstrated how cryo-electron microscopy can be utilized to visualize local nanoscale order. This method is used to understand the distribution/orientation of crystallites in a BHJ blend. Long-range (>300nm) texturing of IEICO-4F crystallites oriented in an edge-on fashion is observed, which has not previously been observed for spin-coated materials. This approach provides a wealth of quantitative information about the texture and size of nanocrystallites, which can be utilized to understand charge generation and transport in organic film. This study guides tailoring the material design and processing conditions for high-performance organic optoelectronic devices.

Considering collaborative incident management: A study of the COVID-19 response and initial recovery in a rural West Virginia community.

Hierarchical command-style structures are commonplace in the management of disasters, though researchers have begun to recommend the exploration of networked approaches to incident management. Furthermore, few studies are explicitly looking at the rural context of disaster management. This study seeks to contribute to both topics by examining the motivations for emergent collaboration in the direction of the response and initial recovery to the coronavirus disease 2019 pandemic in a rural West Virginia community. Between March 2020 and March 2022, the primary investigator moderated regular briefings of a community task force convened to coordinate the pandemic response and recovery. As regular operations concluded, members completed a survey, and 10 randomly selected members participated in semistructured interviews regarding their experiences in the task force. Survey responses suggest that common motivations for collaboration in nondisaster contexts (as they appear in the scholarly literature) and potential benefits of networked approaches highlighted in the incident management literature could also serve as motivators within the disaster context. Qualitative interview data extend that discussion and identify the need to gain clear information regarding the concerned authorities and specific local information to better inform the expenditure of limited resources as two additional motivators for collaboration.

Life Cycle Assessment of Hydrothermal Carbonization: A Review of Product Valorization Pathways

Hydrothermal carbonization (HTC) has the potential to be a sustainable and environmentally beneficial approach for organic waste treatment. It is likely that HTC product use will dictate the viability of large-scale HTC facilities; therefore, understanding the viability and environmental implications associated with HTC product valorization pathways is critical. The overall goal of this review is to gain an understanding of how HTC product valorization is currently being modeled in life cycle assessment studies, and to use such information to assess current research and/or data needs associated with product valorization. To accomplish this, a review of existing HTC literature was conducted and used to assess the current state of knowledge surrounding the environmental implications of HTC product use. From this review of the literature, it is clear that potential exists for HTC product valorization. To realize this potential in a full-scale application, research gaps and data needs were identified that included a system-level integration to evaluate location-specific information as well as more extensive characterization of the impact of HTC product properties on valorization impacts.

Effects of Intercropping on Maize and Soybean Yield Performance, Land Equivalent Ratio, and Maize Leaf Area in Conservation Agriculture

Maize-soybean intercropping systems as a conservation farming practice are receiving increased focus from the scientific community. This is because of the advantages of intercropping, especially nutrient benefits through cereal-legume interactions, alternative sustainable methods to manage biotic stress (pests, diseases, weeds), and crop failure risk management due to erratic weather. In addition, smallholders in developing countries commonly use intercropping to produce crops. In Uganda, 40% of smallholder farmers are practicing intercropping yet no adequate location-specific information is available to inform their practice. Farmers who adopted conservation farming practices did not mulch their maize due to drudgery associated with collecting mulch. This study evaluated the effect of two tillage methods (T1 = Conventional tillage using ox drawn mould board plough, T2 = Minimum tillage using ox drawn ripper) and five soil cover practices (SC1 = Mulched Maize, SC2 = Control no mulch, SC3 = Two rows of soybean in between one row of maize, SC4 = One row of soybean in between one row of maize, SC5 = Sole soybean) on maize and soybean yield performance. The trials were established for 4 rainy seasons on a sandy loam ferrosol at National Agricultural Research Organization Institute in Lira, Uganda. Soil cover practice had a significant effect on maize and soybean crop Yield. Mulching significantly increased maize yield and LAI. The LER for both intercropping partterns were above 1.2. Tillage methods were not significantly different in determining crop performance. The practice of minimum tillage should also be adopted because it enhances the positive effects of soil cover (intercropping). We recommend farmers to adopt the intercropping pattern of one row of soybean in between maize row spaced at 75 × 30 cm for better LER, and crop performance. This intercropping pattern maximizes on available resources to deliver better output in conservation farming. Maize crop generally performed better during first season as compared to second season. We recommend farmers to utilize the first rains as the main maize production season.

Public perception and acceptance of CCUS: preliminary findings of a qualitative case study in Greece

The development and implementation of carbon capture, utilisation and storage (CCUS) technologies plays an increasingly important part in European Union (EU) and global decarbonisation policies and strategies that seek to address climate change to achieve the Paris Agreement goals. Several studies have shown the important role social acceptance plays in determining the outcomes of CCUS projects and how social acceptance is shaped by the national and local contexts. Yet most studies on CCUS and social acceptance have focused on countries in northern Europe and North America despite the increasing numbers of CCUS projects in other regions of the world. This study seeks to help address this gap by conducting a case study on how local dynamics shaped people's acceptance and awareness of CCUS in a Greek community. Based on semi-structured interviews with community members near a CCUS pilot plant, this single case study explores the factors and dynamics that shaped the interviewees' perceptions of CCUS technologies. Our findings indicate that, despite the low level of awareness of CCUS technologies, participants could draw on their situated knowledge to identify potential drawbacks with their application. We identified scepticism regarding the adoption of new technologies and the organisations involved based on past experiences, and a notable lack provision of technology and location-specific information as well public engagement by the project consortium. Our recommendations for future projects and community engagement include the early involvement of the public in project development, location-based transparent information, appropriate channels to facilitate knowledge exchange, and educational initiatives to build communities' capability to influence projects.

Extent of Adoption of Agro Advisories Disseminated Through Annapurna Krishi Prasar Seva (AKPS) at Krishi Vigyan Kendra, Palem in Telangana State, India

Agriculture is the most sector in developing countries like India, the majority of them depending on it, to upliftment of their economic status. Most farmers have adopted traditional agricultural practices for farming in the last several years. In this context, the extension system depends on the ICT platform, and it plays a greater role in uplifting the livelihoods of rural people by effectively providing need-based and location-specific information. ICT’s today serving many purposes in agriculture, business, and social development activities, etc., especially in agriculture from crop production technologies to marketing, facilitating forward & backward linkages to the farm produce. In this study impact of agro advisories disseminated through AKPS inferred that 42.50 per cent of the respondents were grouped under the medium category of adoption followed by high (40.00%), low (10.83%) and very low (06.67%) respective categories. Most of the respondents irrespective of the crop had a high level of knowledge about modern technologies. It was also found that the knowledge level of the farmers changed with what they had been growing seasonal crops.

Intra- and inter-session reliability of electrical detection and pain thresholds of cutaneous and muscle primary afferents in the lower back of healthy individuals.

To advance evidence-based practice and targeted treatments of low back pain (LBP), a better pathophysiological understanding and reliable outcome measures are required. The processing of nociceptive information from deeper somatic structures (e.g., muscle, fascia) might play an essential role in the pathophysiology of LBP. In this study, we measured the intra- and inter-session reliability of electrical detection and pain thresholds of cutaneous and muscle primary afferents of the lower back. Twenty healthy participants attended two study visits separated by 27.7 ± 1.7days. To determine the location-specific electrical detection threshold (EDT) and pain threshold (EPT), needle electrodes were inserted in the epidermal layer over, and in the lumbar erector spinae muscle. Additionally, established quantitative sensory testing (QST) parameters were assessed. Reliability was determined by differences between measurements, intraclass correlation coefficients (ICC2,1), Bland-Altman plots, and standard error of measurement (SEM). Correspondence between QST parameters and electrical thresholds was assessed using Pearson's correlation. Except for cutaneous EPT, no significant (p ≤ 0.05) intra- and inter-session differences were observed. Excellent intra-session reliability was shown for cutaneous and intramuscular electrical stimulations and all QST parameters (ICC: 0.76-0.93). Inter-session reliabilities were good (ICC: 0.74-0.75) except for electrical stimulations (ICC: 0.08-0.36). Limits of agreement and SEM were higher for inter-session than intra-session. A medium to strong relationship was found between electrical and mechanical/pressure pain thresholds. In conclusion, cutaneous and intramuscular electrical stimulation will potentially close an important diagnostic gap regarding the selective examination of deep tissue afferents and provide location-specific information for the excitability of non-nociceptive and nociceptive afferents.

Urbanity: automated modelling and analysis of multidimensional networks in cities

Urban networks play a vital role in connecting multiple urban components and developing our understanding of cities and urban systems. Despite the significant progress we have made in understanding how city networks are connected and spread out, we still have a lot to learn about the meaning and context of these networks. The increasing availability of open data offers opportunities to supplement urban networks with specific location information and create more expressive urban machine-learning models. In this work, we introduce Urbanity, a network-based Python package to automate the construction of feature-rich urban networks anywhere and at any geographical scale. We discuss data sources, the features of our software, and a set of data representing the networks of five major cities around the world. We also test the usefulness of added context in our networks by classifying different types of connections within a single network. Our findings extend accumulated knowledge about how spaces and flows within city networks work, and affirm the importance of contextual features for analyzing city networks.

Deep Corner

Recent studies have shown promising results on joint learning of local feature detectors and descriptors. To address the lack of ground-truth keypoint supervision, previous methods mainly inject appropriate knowledge about keypoint attributes into the network to facilitate model learning. In this paper, inspired by traditional corner detectors, we develop an end-to-end deep network, named Deep Corner, which adds a local similarity-based keypoint measure into a plain convolutional network. Deep Corner enables finding reliable keypoints and thus benefits the learning of the distinctive descriptors. Moreover, to improve keypoint localization, we first study previous multi-level keypoint detection strategies and then develop a multi-level U-Net architecture, where the similarity of features at multiple levels can be exploited effectively. Finally, to improve the invariance of descriptors, we propose a feature self-transformation operation, which transforms the learned features adaptively according to the specific local information. The experimental results on several tasks and comprehensive ablation studies demonstrate the effectiveness of our method and the involved components.