Task Mapping Research Articles

Turn transition time in the map task: visual cues and electronic transmission delays

ABSTRACT Prior research reported longer transition times between speakers over Zoom than in person, for natural conversations, without any explicit task. The current study examined transition times within a cooperative Map Task. In-person conversations were compared with video conferencing or digital phone conversations in Experiments 1 and 2 respectively. In Experiment 1 electronically mediated conversations had more variable transition times, but only Experiment 2, when the interlocutor was not visible, had longer transition times for electronically mediated conversations. Participants wore facial masks, but the available visual cues for turn-taking included posture, gaze, and movement of the head. The increased variability of transition times in the remote conditions of both experiments provides weak, indirect evidence consistent with the use of neural oscillations in the timing of turn-taking, but the most significant finding was that the opportunity to view bodily movements of one’s interlocutor had a strong impact on turn transition times.

CUGUV: A Benchmark Dataset for Promoting Large-Scale Urban Village Mapping with Deep Learning Models.

Delineating the extent of urban villages (UVs) is crucial for effective urban planning and management, as well as for providing targeted policy and financial support. Unlike field surveys, the interpretation of satellite imagery provides an efficient, near real-time, and objective means of mapping UV. However, current research efforts predominantly concentrate on individual cities, resulting in a scarcity of interpretable UV maps for numerous other cities. This gap in availability not only hinders public awareness of the distribution and evolution of UV but also limits the reliability and transferability of models due to the insufficient number and diversity of samples. To address this issue, we developed CUGUV, a benchmark dataset that includes a diverse collection of thousands of UV samples, carefully curated from 15 major cities across various geographical regions in China. The dataset can be accessed through this link: https://doi.org/10.6084/m9.figshare.26198093 . This dataset can serve as a foundation for evaluating and improving the robustness and transferability of models. Subsequently, we present an innovative framework that effectively integrates and learns from multiple data sources to better address the cross-city UV mapping task. Tests show that the proposed models achieve over 92% in overall accuracy, precision, and F1-scores, outperforming state-of-the-art models. This highlights the effectiveness of both the proposed dataset and model. This presented dataset and model bolsters our capability to better understand and accurately model these complex and diverse phenomena, ultimately leading to a notable improvement in the performance of large-scale UV mapping.

Evaluation of mobile mapping point clouds in the context of height difference estimation

Abstract In recent years, the usage of point cloud data for various mapping and other civil engineering tasks has become increasingly popular. The detailed acquisition of the environment forms a great advantage compared to point-wise methods using e.g. total station measurements. The major drawback is, that the uncertainty analysis of the measured points and accordingly the derived parameters is not straightforward. A variance propagation of the observations would not lead to plausible results, since the stochastic model is unknown in most of the cases. In this work, we present an empirical way to determine uncertainty information of the point cloud data captured by a mobile mapping system (MMS) related to height differences by using mainly the road surface, where the system drives on. Height differences between objects are often considered in the context of monitoring of land subsidence and engineering structures or mapping tasks. For the evaluation, height differences between points are analyzed, which differ in three major aspects from each other: the distances between the height observations, the environmental conditions, and the locations in the measurement volume of the system. Repeated measurements of the road surface and artificial targets are used to evaluate the precision of the height differences. Using reference values enables an analysis of the full uncertainty information. The results from two data sets show, that the environmental conditions severely influence the GNSS quality and consequently the precision of height differences decreases. Due to positive correlations between neighboring points, which are caused by the trajectory information, the height difference uncertainty increases concerning the traveled distance between the points. Because of remaining calibration errors, the location of the objects within the measurement volume of the profile laser scanner also influences the uncertainty of the height values and thereby also of height differences.

The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023.

The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023.

On Task Mapping in Multi-chiplet Based Many-Core Systems to Optimize Inter- and Intra-chiplet Communications

On Task Mapping in Multi-chiplet Based Many-Core Systems to Optimize Inter- and Intra-chiplet Communications

Neural evidence for perceiving a vowel merger after a social interaction within a native language.

Neural evidence for perceiving a vowel merger after a social interaction within a native language.

“The heart of the city, where the accent’s the strongest”: localizing perceptions of the “strong Boston accent” in a changing city

Abstract This paper examines folk perceptions of language in the Greater Boston Area. In particular, it seeks to understand which areas are associated with a “Boston” accent, and whether associations are changing given recent shifts in ethnic and economic demographics. A total of 111 Greater Boston residents completed a survey and map task asking what constitutes a “Boston” accent, who has one, and in which areas one can be heard. Results show that the majority of participants perceive the neighborhood of South Boston to be the geographic epicenter of the “Boston” accent, and generally associate accents with historically White working-class areas, despite sometimes changing demographics within them. There is also evidence that participant ethnic background may play a role in perceptions of speech in some areas, with White men less likely to choose South Boston, widely viewed as gentrifying, as accented, and Black and Asian participants less likely to choose the increasingly ethnically diverse neighborhood of North Dorchester. These results demonstrate the importance of eliciting folk perceptions from residents of color to obtain a fuller picture of the language attitudes in a given community.

MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time Systems

Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today’s Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this paper, we consider the challenge of scheduling a RT application modelled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTGs on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6 % and area usage reductions of 46.3 % are possible compared to full Software SW and HW execution, respectively.

GreenDBCE-Net: A Novel Deep Learning Framework for Annual Mapping of Coastal Aquaculture Ponds in China with Sentinel-2 Data

Despite the promising advancements of deep learning techniques in coastal aquaculture pond extraction, their capacity for large-scale mapping tasks remains relatively limited. To address this challenge, this study developed a novel deep learning framework, Dual-Branch Enhanced Network (DBCE-Net), for mapping the annual aquaculture ponds at the national scale using Sentinel-2 imagery. The DBCE-Net framework effectively mitigates the contextual information loss inherent in traditional methods and reduces classification errors by processing both down-sampled large-scale images and block images at their original resolution. The architecture comprises local feature extraction and global feature extraction, along with feature fusion and decoding. The pivotal Multi-scale Dynamic Feature Fusion (DFF) module synthesizes local and global features while incorporating complementary information, demonstrating strong robustness with smaller training areas, compared to previous methods that required a larger number of samples distributed across different regions. By applying the DBCE-Net to Sentinel-2 imagery from 2017 to 2023, we mapped the spatiotemporal distribution of coastal aquaculture ponds across all coastal counties in China, achieving an overall classification accuracy approximately 93%. The results demonstrate substantial changes in the area of coastal aquaculture ponds in China from 2017 to 2023, with the total area declining from 8970.25 km2 to 8261.17 km2, representing a notable decrease of 7.90%. The most pronounced reduction was observed in Shanghai, with a decrease of 38.92%, followed by Zhejiang (31.57%) and Jiangsu (19.07%). These reductions are primarily attributed to policies converting aquaculture ponds into natural wetlands. In contrast, the area of coastal aquaculture ponds in Liaoning Province slightly increased by 5.75%. This DBCE-Net demonstrates good accuracy and generalizability and is promising to further expand its application to the extraction of coastal aquaculture areas worldwide, providing important scientific value and practical significance for the global coastal aquaculture industry.

Extended Study of a Multi-Modal Loop Closure Detection Framework for SLAM Applications

Loop Closure (LC) is a crucial task in Simultaneous Localization and Mapping (SLAM) for Autonomous Ground Vehicles (AGV). It is an active research area because it improves global localization efficiency. The consistency of the global map and the accuracy of the AGV’s location in an unknown environment are highly correlated with the efficiency and robustness of Loop Closure Detection (LCD), especially when facing environmental changes or data unavailability. We propose to introduce multimodal complementary data to increase the algorithms’ resilience. Various methods using different data sources have been proposed to achieve precise place recognition. However, integrating a multimodal loop-closure fusion process that combines multiple information sources within a SLAM system has been explored less. Additionally, existing multimodal place recognition techniques are often difficult to integrate into existing frameworks. In this paper, we propose a fusion scheme of multiple place recognition methods based on camera and LiDAR data for a robust multimodal LCD. The presented approach uses Similarity-Guided Particle Filtering (SGPF) to identify and verify candidates for loop closure. Based on the ORB-SLAM2 framework, the proposed method uses two perception sensors (camera and LiDAR) under two data representation models for each. Our experiments on both KITTI and a self-collected dataset show that our approach outperforms the state-of-the-art methods in terms of place recognition metrics or localization accuracy metrics. The proposed Multi-Modal Loop Closure (MMLC) framework enhances the robustness and accuracy of AGV’s localization by fusing multiple sensor modalities, ensuring consistent performance across diverse environments. Its real-time operation and early loop closure detection enable timely trajectory corrections, reducing navigation errors and supporting cost-effective deployment with adaptable sensor configurations.

Mapping of Agate-like Soil Cover Structures Based on a Multitemporal Soil Line Using Neural Network Filtering of Remote Sensing Data

The present study focuses on analysis of the soil cover structure (SCS, SCSs), which is the most detailed level of soil organization in space. The detail in which complex SCS can be studied is often insufficient, since until now it has not been possible to map it over large areas at scales larger than 1:10,000. To increase the detail in which SCS can be studied, the methods of identifying the bare soil surface (BSS) and averaging its multitemporal spectral characteristics were used, which opens up new possibilities for mapping complex SCS over large areas. New SCSs of leached chernozems (Luvic Chernic Phaeozem) were discovered, which can produce patterns on satellite images similar to sections of Timan agate—agate-like soil cover structures (ASCS, ASCSs). ASCSs are formed on Quaternary sediments of varying thickness from 0.3 to 6 m, underlain by carbonate and red sediments of the Permian period. The ASCS pattern is formed by ring-shaped stripes (rings) of different colors and brightness, which are determined by the carbonate and red-colored inclusions involved in the arable horizon. Eight soil varieties were identified to describe ASCSs during the study. According to the WRB, there are six main soil types, and according to the classification of Russian soils in 1977, there are four types. ASCSs were identified over large areas and soil maps of ASCSs were constructed using multitemporal spectral characteristics of the BSS in the form of multitemporal soil line coefficients. Neural networks were used to identify BSS on big remote sensing data. ASCSs have contrasting soil properties and contrasting fertility (productivity of agricultural crops). ASCS maps can serve as the basis for task maps of precision farming systems. Perhaps ASCSs are unique objects for the area of chernozem distribution, where in one soil profile there are rocks with an age from the first thousand years (Quaternary) to 250 million years (Permian). Chernozems are fertile, studied, mercilessly exploited, but sometimes they are simply beautiful—agate-like.

Cognitive Task Complexity and Learner Engagement: Evidence From Direction‐Giving Map Tasks

ABSTRACTTask‐based language teaching (TBLT), a pedagogy focused on learner needs and communicative abilities, influences language education in many locales. TBLT researchers have independently pursued, and yielded valuable insights concerning, the roles of cognitive task complexity (CTC) and learner engagement, although less work has been carried out to weave these strands together. Based on a thorough review of the literature, this study aimed to contribute to work on CTC and engagement by hypothesizing that differences in learner engagement across simpler versus more complex tasks should be clearer on measures of cognitive engagement than on those of affective engagement. To test this hypothesis, a study was conducted with learners of English as an additional language in Japan who performed a series of direction‐giving map tasks. The analyses leveraged robust statistics appropriate to the dataset to uncover larger effect sizes for behavioral and cognitive measures of engagement than for affective measures. These findings are discussed in terms of the intersection of CTC and engagement. The article concludes with a discussion of pedagogical implications, noting, as well, the limitations of the study.

Geographies of discourse revisited

Abstract This study explores the role of Geographies of Discourse (GoD) (Scollon, Ron. 2013. Geographies of discourse: Action across layered spaces. In Ingrid De Saint-Georges & Jean-Jacques Weber (eds.), Multilingualism and multimodality: Current challenges for educational studies, 183–198. Rotterdam: Sense Publishers) in creating spaces for multilingualism. Building on work that examined the role of multimodality in civic participation (e.g., de Groot and Jocuns 2023. Multimodality as civic participation: The case of Thailand’s rap against dictatorship. Journal of Language and Politics 22(1). 107–128) we show how mapping, analyzing and connecting the multimodal geographies of discourse within language portraits and a mapping task in Kurdistan create insight into the historical, present and future linkages that create a network of mobile language repertoires. We discuss how geographies of discourse (GoD) emerged from how multilingualism in Iraqi Kurdistan map these GoD between the different material objects and create historical and future connections that emerge as small stories. Describing and mapping these intersections and transformations reifies Latour’s (2005. Reassembling the social: An introduction to actor-network-theory. Oxford: Oxford University Press) notion that neither the material nor the immaterial realizes these transformations or gives meaning to them, instead, the transformative actions and meanings indexed are situated in the social relationships between actors that produce these intersections. We argue that this way of understanding the complex networks of discourse practices that produce GoD is important presently when social interaction is situated in a nexus of online and offline spaces.

Energy-Aware Fault-Tolerant Mapping of Mixed-Criticality Tasks on Heterogeneous Multicores

Energy-Aware Fault-Tolerant Mapping of Mixed-Criticality Tasks on Heterogeneous Multicores

Curiosity shapes spatial exploration and cognitive map formation in humans

AbstractCognitive maps are thought to arise, at least in part, from our intrinsic curiosity to explore unknown places. However, it remains untested how curiosity shapes aspects of spatial exploration in humans. Combining a virtual reality task with indices of exploration complexity, we found that pre-exploration curiosity states predicted how much individuals spatially explored environments, whereas markers of visual exploration determined post-exploration feelings of interest. Moreover, individual differences in curiosity traits, particularly Stress Tolerance, modulated the relationship between curiosity and spatial exploration, suggesting the capacity to cope with uncertainty enhances the curiosity-exploration link. Furthermore, both curiosity and spatial exploration predicted how precisely participants could recall spatial-relational details of the environment, as measured by a sketch map task. These results provide new evidence for a link between curiosity and exploratory behaviour, and how curiosity might shape cognitive map formation.

Development and evaluation of an implementation strategy to increase HPV vaccination among underserved youth across Texas: a protocol paper

BackgroundAll for Them is a theory-based and evidence-informed multilevel, multicomponent program delivered through schools to increase HPV vaccination among medically underserved youth across Texas. Given the potential logistical challenges of program implementation, understanding how to best support the implementation and sustainment of the program is critical. The overall goals of this study are twofold: 1) develop a multifaceted implementation strategy, Implementing All for Them (IM-AFT); and 2) evaluate the impact of IM-AFT on implementation outcomes for schools and healthcare providers to successfully implement All for Them in their respective settings.MethodsThis study is underpinned by a comprehensive and synergistic conceptual framework. We will use Social Cognitive Theory and the Interactive Systems Framework for Dissemination and Implementation to inform important individual and organizational factors to target as part of implementation strategy development. We will use this formative work and Implementation Mapping, a systematic, iterative process that guides the use of theories, models, and frameworks, to develop IM-AFT. The three core aims of the present study are connected to the five tasks of Implementation Mapping. For Aim 1, we will develop IM-AFT using a community-based participatory research-informed approach, including a qualitative assessment of needs and assets associated with program implementation and identification of behavioral and psychosocial objectives to determine implementation outcomes. For Aim 2, we will use a mixed-methods approach to assess user experience with the IM-AFT prototype to test its feasibility, usability, and acceptability. For Aim 3, we will use a descriptive checklist to assess the impact of IM-AFT on user fidelity of program implementation.DiscussionThis paper presents the detailed protocol for developing and evaluating IM-AFT to successfully implement All for Them, leveraging a systematic, community- and theory-based approach and user experience with the strategy prototype. This study will contribute to expanding limited scientific knowledge about using multiple sources to develop and evaluate specified implementation strategies for effective implementation of school-based vaccination programs. Theory-based IM-AFT will guide collaborations between schools and community health centers to improve HPV and other adolescent vaccination rates in underserved communities in Texas.

Co-designing a low-intensity psychological therapy for fear of recurrence in psychosis using translational learning from fear of recurrence in oncology: protocol for intervention development for future testing in a feasibility study

IntroductionFear of recurrence is a transdiagnostic problem experienced by people with psychosis, which is associated with anxiety, depression and risk of future relapse events. Despite this, there is a lack...

Exploring Transfer Learning for Anthropogenic Geomorphic Feature Extraction from Land Surface Parameters Using UNet

Semantic segmentation algorithms, such as UNet, that rely on convolutional neural network (CNN)-based architectures, due to their ability to capture local textures and spatial context, have shown promise for anthropogenic geomorphic feature extraction when using land surface parameters (LSPs) derived from digital terrain models (DTMs) as input predictor variables. However, the operationalization of these supervised classification methods is limited by a lack of large volumes of quality training data. This study explores the use of transfer learning, where information learned from another, and often much larger, dataset is used to potentially reduce the need for a large, problem-specific training dataset. Two anthropogenic geomorphic feature extraction problems are explored: the extraction of agricultural terraces and the mapping of surface coal mine reclamation-related valley fill faces. Light detection and ranging (LiDAR)-derived DTMs were used to generate LSPs. We developed custom transfer parameters by attempting to predict geomorphon-based landforms using a large dataset of digital terrain data provided by the United States Geological Survey’s 3D Elevation Program (3DEP). We also explored the use of pre-trained ImageNet parameters and initializing models using parameters learned from the other mapping task investigated. The geomorphon-based transfer learning resulted in the poorest performance while the ImageNet-based parameters generally improved performance in comparison to a random parameter initialization, even when the encoder was frozen or not trained. Transfer learning between the different geomorphic datasets offered minimal benefits. We suggest that pre-trained models developed using large, image-based datasets may be of value for anthropogenic geomorphic feature extraction from LSPs even given the data and task disparities. More specifically, ImageNet-based parameters should be considered as an initialization state for the encoder component of semantic segmentation architectures applied to anthropogenic geomorphic feature extraction even when using non-RGB image-based predictor variables, such as LSPs. The value of transfer learning between the different geomorphic mapping tasks may have been limited due to smaller sample sizes, which highlights the need for continued research in using unsupervised and semi-supervised learning methods, especially given the large volume of digital terrain data available, despite the lack of associated labels.

Active Perception in SLAM Exploration: Problem Formulation and Methods Review

Active SLAM (A-SLAM) aims to enable an agent’s autonomous exploration and mapping task, provide robustness and accuracy in both localization and mapping and enhance environmental adaptivity in applications. Its working principle, as a holistic probabilistic-based problem in model formulation, can be divided into three decoupled working stages and accordingly solved, these stages are brief action identification, cost and utility computation, and action execution. The identification stage needs reasoning on what and where to look at in order to diminish uncertainty in an efficient fashion, by employing sensory methods and performing searching or optimization tasks with appropriate environmental representations, to feed the following stages with a discretized set of planning goals. This is the active perception problem by definition. The activeness of active SLAM is mostly decided at the perception stage with appropriate quantifying measures. This paper revisits the formulation of the A-SLAM problem especially by the stage of active perception, reviews its environmental representations (maps) and related modeling measures for information and uncertainty, underlines a sample-based aim of maximizing the information gain from sensors with respect to corresponding maps, and make comments on some novel approaches.

Collaborative learning in older age and the role of familiarity: evidence from the map task

ABSTRACT As we age, learning new knowledge and skills becomes more difficult due to age-related changes to cognition. Learning collaboratively could counteract these changes, and perhaps more so when working with someone familiar. This study examined whether collaborative learning is affected by age and partner familiarity. Forty-eight participants (younger n = 24, older n = 24) completed the Map Task with a familiar and unfamiliar same-age partner. Participants became more efficient at completing the Map Task over time, regardless of age and partner familiarity. There was no age difference in immediate or 1-hour recall, but younger adults recalled more after 7 days than older adults. Overall, results suggest that collaborative learning outcomes are unaffected by age or partner familiarity and that collaborative learning has short-term protective effects on memory, with age-related declines only emerging after 7 days.