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Gotowa bibliografia na temat „Spatio-temporal tasks”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Spatio-temporal tasks"

Zhang, Zijian, Xiangyu Zhao, Hao Miao, Chunxu Zhang, Hongwei Zhao i Junbo Zhang. "AutoSTL: Automated Spatio-Temporal Multi-Task Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 4 (26.06.2023): 4902–10. http://dx.doi.org/10.1609/aaai.v37i4.25616.

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Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can further promote an intelligent city life by integrating their inseparable relationship. However, existing studies fail to address this joint learning problem well, which generally solve tasks individually or a fixed task combination. The challenges lie in the tangled relation between different properties, the demand for supporting flexible combinations of tasks and the complex spatio-temporal dependency. To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly. Firstly, we propose a scalable architecture consisting of advanced spatio-temporal operations to exploit the complicated dependency. Shared modules and feature fusion mechanism are incorporated to further capture the intrinsic relationship between tasks. Furthermore, our model automatically allocates the operations and fusion weight. Extensive experiments on benchmark datasets verified that our model achieves state-of-the-art performance. As we can know, AutoSTL is the first automated spatio-temporal multi-task learning method.

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Zhang, Yitao, Ruiqing Xu, Wangping Lu, Wolfgang Mayer, Da Ning, Yucong Duan, Xi Zeng i Zaiwen Feng. "Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management". Applied Sciences 13, nr 16 (18.08.2023): 9393. http://dx.doi.org/10.3390/app13169393.

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In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the smooth operation of ships. Currently, ship communication status analysis heavily relies on large-scale, multi-source heterogeneous data with spatio-temporal and multi-modal features, which presents challenges for ship communication quality prediction tasks. To address this issue, this paper constructs a multi-modal spatio-temporal ontology and a multi-modal spatio-temporal knowledge graph for ship communication, guided by existing ontologies and domain knowledge. This approach effectively integrates multi-modal spatio-temporal data, providing support for subsequent efficient data analysis and applications. Taking the scenario of fishing vessel communication activities as an example, the query tasks for ship communication knowledge are successfully performed using a graph database, and we combine the spatio-temporal knowledge graph with graph convolutional neural network technology to achieve real-time communication quality prediction for fishing vessels, further validating the practical value of the multi-modal spatio-temporal knowledge graph.

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Hu, W., S. Winter i K. Khoshelham. "OPTIMIZING URBAN MONITORING BETWEEN STATIONARY, OPPORTUNISTIC VEHICULAR, AND HYBRID SENSING". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W3-2022 (14.10.2022): 65–72. http://dx.doi.org/10.5194/isprs-annals-x-4-w3-2022-65-2022.

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Abstract. Urban monitoring based on wireless sensor networks is a recent paradigm that exploits a large number of low-cost sensors deployed in certain places or/and on mobile devices to collect data ubiquitously at a large scale. In this study, we explore and compare the coverage of stationary and opportunistic vehicular sensing methods with respect to the requirements of a task at hand. We distinguish spatial granularity, temporal granularity, and budget constraints. First we compare the spatio-temporal coverage of stationary sensing and opportunistic vehicular sensing for various tasks, which demonstrates that these two sensing methods are suitable for different tasks. Then we propose a hybrid sensing deployment framework integrating a genetic algorithm to achieve the maximum spatio-temporal coverage for specific tasks. Experiments with a real-world vehicle trajectory dataset demonstrate that the proposed hybrid sensing framework achieves the maximum spatio-temporal coverage in various tasks. Our results provide fundamental guidelines on network planning for urban monitoring applications.

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Li, X. M., W. X. Wang, S. J. Tang, J. Z. Xia, Z. G. Zhao, Y. Li, Y. Zheng i R. Z. Guo. "A NEW CLOUD-EDGE-TERMINAL RESOURCES COLLABORATIVE SCHEDULING FRAMEWORK FOR MULTI-LEVEL VISUALIZATION TASKS OF LARGE-SCALE SPATIO-TEMPORAL DATA". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (25.08.2020): 477–83. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-477-2020.

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Abstract. To address the multi-modal spatio-temporal data efficient scheduling problem of the diverse and highly concurrent visualization applications in cloud-edge-terminal environment, this paper systematically studies the cloud-edge-terminal integrated scheduling model of multi-level visualization tasks of multi-modal spatio-temporal data. By accurately defining the hierarchical semantic mapping relationship between the diverse visual application requirements of different terminals and scheduling tasks, we propose a multi-level task-driven cloud-edge-terminal multi-granularity storage-computing-rendering resource collaborative scheduling method. Based on the workflow, the flexible allocation strategy of cloud-edge-terminal scheduling service chain that consider the characteristics of spatio-temporal task is constructed. Finally, we established a cloud-edge-terminal scheduling adaptive optimization mechanism based on the service quality evaluation model, and developed a prototype system. Experiments are conducted with the urban construction and construction management, the results show that the new method breaks through the bottleneck of traditional spatio-temporal data visualization scheduling, and it can provide theoretical and methodological support for the visualization and scheduling of spatio-temporal big data.

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Feng, Bin, Qing Zhu, Mingwei Liu, Yun Li, Junxiao Zhang, Xiao Fu, Yan Zhou, Maosu Li, Huagui He i Weijun Yang. "An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization". ISPRS International Journal of Geo-Information 7, nr 9 (8.09.2018): 371. http://dx.doi.org/10.3390/ijgi7090371.

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Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries.

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Zhang, Yujia, Lai-Man Po, Xuyuan Xu, Mengyang Liu, Yexin Wang, Weifeng Ou, Yuzhi Zhao i Wing-Yin Yu. "Contrastive Spatio-Temporal Pretext Learning for Self-Supervised Video Representation". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 3 (28.06.2022): 3380–89. http://dx.doi.org/10.1609/aaai.v36i3.20248.

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Spatio-temporal representation learning is critical for video self-supervised representation. Recent approaches mainly use contrastive learning and pretext tasks. However, these approaches learn representation by discriminating sampled instances via feature similarity in the latent space while ignoring the intermediate state of the learned representations, which limits the overall performance. In this work, taking into account the degree of similarity of sampled instances as the intermediate state, we propose a novel pretext task - spatio-temporal overlap rate (STOR) prediction. It stems from the observation that humans are capable of discriminating the overlap rates of videos in space and time. This task encourages the model to discriminate the STOR of two generated samples to learn the representations. Moreover, we employ a joint optimization combining pretext tasks with contrastive learning to further enhance the spatio-temporal representation learning. We also study the mutual influence of each component in the proposed scheme. Extensive experiments demonstrate that our proposed STOR task can favor both contrastive learning and pretext tasks and the joint optimization scheme can significantly improve the spatio-temporal representation in video understanding. The code is available at https://github.com/Katou2/CSTP.

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Baek, Seung-Hwan, i Felix Heide. "Polarimetric spatio-temporal light transport probing". ACM Transactions on Graphics 40, nr 6 (grudzień 2021): 1–18. http://dx.doi.org/10.1145/3478513.3480517.

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Light emitted from a source into a scene can undergo complex interactions with multiple scene surfaces of different material types before being reflected towards a detector. During this transport, every surface reflection and propagation is encoded in the properties of the photons that ultimately reach the detector, including travel time, direction, intensity, wavelength and polarization. Conventional imaging systems capture intensity by integrating over all other dimensions of the incident light into a single quantity, hiding this rich scene information in these aggregate measurements. Existing methods are capable of untangling these measurements into their spatial and temporal dimensions, fueling geometric scene understanding tasks. However, examining polarimetric material properties jointly with geometric properties is an open challenge that could enable unprecedented capabilities beyond geometric scene understanding, allowing for material-dependent scene understanding and imaging through complex transport, such as macroscopic scattering. In this work, we close this gap, and propose a computational light transport imaging method that captures the spatially- and temporally-resolved complete polarimetric response of a scene, which encodes rich material properties. Our method hinges on a novel 7D tensor theory of light transport. We discover low-rank structure in the polarimetric tensor dimension and propose a data-driven rotating ellipsometry method that learns to exploit redundancy of polarimetric structure. We instantiate our theory with two imaging prototypes: spatio-polarimetric imaging and coaxial temporal-polarimetric imaging. This allows us, for the first time, to decompose scene light transport into temporal, spatial, and complete polarimetric dimensions that unveil scene properties hidden to conventional methods. We validate the applicability of our method on diverse tasks, including shape reconstruction with subsurface scattering, seeing through scattering media, untangling multi-bounce light transport, breaking metamerism with polarization, and spatio-polarimetric decomposition of crystals.

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Ikura, Mikihiro, Sarthak Pathak, Jun Younes Louhi Kasahara, Atsushi Yamashita i Hajime Asama. "AdjustSense: Adaptive 3D Sensing System with Adjustable Spatio-Temporal Resolution and Measurement Range Using High-Speed Omnidirectional Camera and Direct Drive Motor". Sensors 21, nr 21 (21.10.2021): 6975. http://dx.doi.org/10.3390/s21216975.

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Many types of 3D sensing devices are commercially available and were utilized in various technical fields. In most conventional systems with a 3D sensing device, the spatio-temporal resolution and the measurement range are constant during operation. Consequently, it is necessary to select an appropriate sensing system according to the measurement task. Moreover, such conventional systems have difficulties dealing with several measurement targets simultaneously due to the aforementioned constants. This issue can hardly be solved by integrating several individual sensing systems into one. Here, we propose a single 3D sensing system that adaptively adjusts the spatio-temporal resolution and the measurement range to switch between multiple measurement tasks. We named the proposed adaptive 3D sensing system “AdjustSense.” In AdjustSense, as a means for the adaptive adjustment of the spatio-temporal resolution and measurement range, we aimed to achieve low-latency visual feedback for the adjustment by integrating not only a high-speed camera, which is a high-speed sensor, but also a direct drive motor, which is a high-speed actuator. This low-latency visual feedback can enable a large range of 3D sensing tasks simultaneously. We demonstrated the behavior of AdjustSense when the positions of the measured targets in the surroundings were changed. Furthermore, we quantitatively evaluated the spatio-temporal resolution and measurement range from the 3D points obtained. Through two experiments, we showed that AdjustSense could realize multiple measurement tasks: 360∘ 3D sensing, 3D sensing at a high spatial resolution around multiple targets, and local 3D sensing at a high spatio-temporal resolution around a single object.

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Li, He, Xuejiao Li, Liangcai Su, Duo Jin, Jianbin Huang i Deshuang Huang. "Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction". ACM Transactions on Intelligent Systems and Technology 13, nr 2 (30.04.2022): 1–21. http://dx.doi.org/10.1145/3510829.

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Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi’an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 baselines.

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Chen, Nanyu, Anran Yang, Luo Chen, Wei Xiong i Ning Jing. "STO2Vec: A Multiscale Spatio-Temporal Object Representation Method for Association Analysis". ISPRS International Journal of Geo-Information 12, nr 5 (21.05.2023): 207. http://dx.doi.org/10.3390/ijgi12050207.

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Spatio-temporal association analysis has attracted attention in various fields, such as urban computing and crime analysis. The proliferation of positioning technology and location-based services has facilitated the expansion of association analysis across spatio-temporal scales. However, existing methods inadequately consider the scale differences among spatio-temporal objects during analysis, leading to suboptimal precision in association analysis results. To remedy this issue, we propose a multiscale spatio-temporal object representation method, STO2Vec, for association analysis. This method comprises of two parts: graph construction and embedding. For graph construction, we introduce an adaptive hierarchical discretization method to distinguish the varying scales of local features. Then, we merge the embedding method for spatio-temporal objects with that for discrete units, establishing a heterogeneous graph. For embedding, to enhance embedding quality for homogeneous and heterogeneous data, we use biased sampling and unsupervised models to capture the association strengths between spatio-temporal objects. Empirical results using real-world open-source datasets show that STO2Vec outperforms other models, improving accuracy by 16.25% on average across diverse applications. Further case studies indicate STO2Vec effectively detects association relationships between spatio-temporal objects in a range of scenarios and is applicable to tasks such as moving object behavior pattern mining and trajectory semantic annotation.

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Rozprawy doktorskie na temat "Spatio-temporal tasks"

Misra, Navendu. "Comparison of motor-based versus visual sensory representations in object recognition tasks". Texas A&M University, 2005. http://hdl.handle.net/1969.1/2544.

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Various works have demonstrated the usage of action as a critical component in allowing autonomous agents to learn about objects in the environment. The importance of memory becomes evident when these agents try to learn about complex objects. This necessity primarily stems from the fact that simpler agents behave reactively to stimuli in their attempt to learn about the nature of the object. However, complex objects have the property of giving rise to temporally varying sensory data as the agent interacts with the object. Therefore, reactive behavior becomes a hindrance in learning these complex objects, thus, prompting the need for memory. A straightforward approach to memory, visual memory, is where sensory data is directly represented. Another mechanism is skill-based memory or habit formation. In the latter mechanism the sequence of actions performed for a task is retained. The main hypothesis of this thesis is that since action seems to play an important role in simple perceptual understanding it may also serve as a good memory representation. In order to test this hypothesis a series of comparative tests were carried out to determine the merits of each of these representations. It turns out that skill memory performs significantly better at recognition tasks than visual memory. Furthermore, it was demonstrated in a related experiment that action forms a good intermediate representation of the sensory data. This provides support to theories that propose that various sensory modalities can ideally be represented in terms of action. This thesis successfully extends action to the role of understanding of complex objects.

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Chen, Yuanyuan. "The modulation of spatio-temporal brain dynamics in visual word recognition by psycholinguistic variables and tasks studies using EEG/MEG and fMRI". Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607863.

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Griggs, David. "Aging and Spatio-temporal Vision: Effects of Blur on Localization Task Performance". TopSCHOLAR®, 1987. http://digitalcommons.wku.edu/theses/1997.

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The aim of this project was to examine age-related declines in the processing of spatial frequency information. Some current theories of spatial vision state that humans process high spatial frequency information separately or differently from low spatial frequency information. There is also evidence that normal aging may affect the processing of some spatial frequencies more than others. Specifically, it has been proposed that older adults have deficits in their ability to process low spatial frequency information, and that older adults process visual information more slowly in general than young adults. Eight observers in each of three age groups were tested on a localization task. The spatial frequency content of distractors presented in the visual field was varied along with speed of presentation and clarity of the display. A progressive loss in the extent of the functional visual field was demonstrated. Results were consistent with the position that older adults experience declines in their ability to process temporal information, and that older adults do process visual information at a slower rate than young adults.

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Dutia, Dharini. "Multi-Robot Task Allocation and Scheduling with Spatio-Temporal and Energy Constraints". Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1298.

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Autonomy in multi-robot systems is bounded by coordination among its agents. Coordination implies simultaneous task decomposition, task allocation, team formation, task scheduling and routing; collectively termed as task planning. In many real-world applications of multi-robot systems such as commercial cleaning, delivery systems, warehousing and inventory management: spatial & temporal constraints, variable execution time, and energy limitations need to be integrated into the planning module. Spatial constraints comprise of the location of the tasks, their reachability, and the structure of the environment; temporal constraints express task completion deadlines. There has been significant research in multi-robot task allocation involving spatio-temporal constraints. However, limited attention has been paid to combine them with team formation and non- instantaneous task execution time. We achieve team formation by including quota constraints which ensure to schedule the number of robots required to perform the task. We introduce and integrate task activation (time) windows with the team effort of multiple robots in performing tasks for a given duration. Additionally, while visiting tasks in space, energy budget affects the robots operation time. We map energy depletion as a function of time to ensure long-term operation by periodically visiting recharging stations. Research on task planning approaches which combines all these conditions is still lacking. In this thesis, we propose two variants of Team Orienteering Problem with task activation windows and limited energy budget to formulate the simultaneous task allocation and scheduling as an optimization problem. A complete mixed integer linear programming (MILP) formulation for both variants is presented in this work, implemented using Gurobi Optimizer and analyzed for scalability. This work compares the different objectives of the formulation like maximizing the number of tasks visited, minimizing the total distance travelled, and/or maximizing the reward, to suit various applications. Finally, analysis of optimal solutions discover trends in task selection based on the travel cost, task completion rewards, robot's energy level, and the time left to task inactivation.

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Marton, Zoltan-Csaba [Verfasser], Michael [Akademischer Betreuer] Beetz i Darius [Akademischer Betreuer] Burschka. "Multi-cue Perception for Robotic Object Manipulation : How Spatio-temporal Integration of Multi-modal Information Aids Task Execution / Zoltan-Csaba Marton. Gutachter: Michael Beetz ; Darius Burschka. Betreuer: Michael Beetz". München : Universitätsbibliothek der TU München, 2014. http://d-nb.info/1070639028/34.

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Hossain, Mohammad Zahid. "FlockViz: A Visualization Technique to Facilitate Multi-dimensional Analytics of Spatio-temporal Cluster Data". 2014. http://hdl.handle.net/1993/23591.

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Visual analytics of large amounts of spatio-temporal data is challenging due to the overlap and clutter from movements of multiple objects. A common approach for analyzing such data is to consider how groups of items cluster and move together in space and time. However, most methods for showing Spatio-temporal Cluster (STC) properties, concentrate on a few dimensions of the cluster (e.g. the cluster movement direction or cluster density) and many other properties are not represented. Furthermore, while representing multiple attributes of clusters in a single view existing methods fail to preserve the original shape of the cluster or distort the actual spatial covering of the dataset. In this thesis, I propose a simple yet effective visualization, FlockViz, for showing multiple STC data dimensions in a single view by preserving the original cluster shape. To evaluate this method I develop a framework for categorizing the wide range of tasks involved in analyzing STCs. I conclude this work through a controlled user study comparing the performance of FlockViz with alternative visualization techniques that aid with cluster-based analytic tasks. Finally the exploration capability of FlockViz is demonstrated in some real life data sets such as fish movement, caribou movement, eagle migration, and hurricane movement. The results of the user studies and use cases confirm the advantage and novelty of the novel FlockViz design for visual analytic tasks.

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Hsiao-ChiehPan i 潘曉潔. "The effect of spatio-temporal task constraints on reaching performance in patients with stroke". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/t8t4vx.

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碩士
國立成功大學
職能治療學系
104
SUNMMARY The purpose of this research was to examine the effect of spatio-temporal task constraints on reaching performance in patients with stroke. This project utilized repeated-measures design and kinematic analysis. We included 20 stroke patients and 20 age-matched controls. All participants were instructed to reach balls under three conditions with different spatio-temporal constraints: dropping ball, moving ball, and stationary ball. Their motor performance was recorded for kinematic analysis. Our results revealed that stroke group could move their hemiparetic arm with shorter reaction time and movement time, and higher peak velocity under the moving ball condition than the dropping and stationary ball conditions. These findings give new ideas for developing intervention strategies to facilitate better performance of stroke patients, and extend the application in therapeutic activities by giving tasks with spatio-temporal constraints. Key Words: stroke, task constraint, reaching, kinematic analysis

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Zeman, Philip Michael. "Feasibility of Multi-Component Spatio-Temporal Modeling of Cognitively Generated EEG Data and its Potential Application to Research in Functional Anatomy and Clinical Neuropathology". Thesis, 2009. http://hdl.handle.net/1828/5010.

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This dissertation is a compendium of multiple research papers that, together, address two main objectives. The first objective and primary research question is to determine whether or not, through a procedure of independent component analysis (ICA)-based data mining, volume-domain validation, and source volume estimation, it is possible to construct a meaningful, objective, and informative model of brain activity from scalpacquired EEG data. Given that a methodology to construct such a model can be created, the secondary objective and research question investigated is whether or not the sources derived from the EEG data can be used to construct a model of complex brain function associated with the spatial navigation and the virtual Morris Water Task (vMWT). The assumptions of the signal and noise characteristics of scalp-acquired EEG data were discussed in the context of what is currently known about functional brain activity to identify appropriate characteristics by which to separate the activities comprising EEG data into parts. A new EEG analysis methodology was developed using both synthetic and real EEG data that encompasses novel algorithms for (1) data-mining of the EEG to obtain the activities of individual areas of the brain, (2) anatomical modeling of brain sources that provides information about the 3-dimensional volumes from which each of the activities separated from the EEG originates, and (3) validation of data mining results to determine if a source activity found via the data-mining step originates from a distinct modular unit inside the head or if it is an artefact. The methodology incorporating the algorithms developed was demonstrated for EEG data collected from study participants while they navigated a computer-based virtual maze environment. The brain activities of participants were meaningfully depicted via brain source volume estimation and representation of the activity relationships of multiple areas of the brain. A case study was used to demonstrate the analysis methodology as applied to the EEG of an individual person. In a second study, a group EEG dataset was investigated and activity relationships between areas of the brain for participants of the group study were individually depicted to show how brain activities of individuals can be compared to the group. The results presented in this dissertation support the conclusion that it is feasible to use ICA-based data mining to construct a physiological model of coordinated parts of the brain related to the vMWT from scalp-recorded EEG data. The methodology was successful in creating an objective and informative model of brain activity from EEG data. Furthermore, the evidence presented indicates that this methodology can be used to provide meaningful evaluation of the brain activities of individual persons and to make comparisons of individual persons against a group. In sum, the main contributions of this body of work are 5 fold. The technical contributions are: (1) a new data mining algorithm tailored for EEG, (2) an EEG component validation algorithm that identifies noise components via their poor representation in a head model, (3) a volume estimation algorithm that estimates the region in the brain from which each source waveform found via data mining originates, (4) a new procedure to study brain activities associated with spatial navigation. The main contribution of this work to the understanding of brain function is (5) evidence of specific functional systems within the brain that are used while persons participate in the vMWT paradigm (Livingstone and Skelton, 2007) examining spatial navigation.
Graduate
0541
0622
0623

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Nayak, Akshata. "A pilot study: Effect of a novel dual-task treadmill walking program on balance, mobility, gaze and cognition in community dwelling older adults". 2015. http://hdl.handle.net/1993/30691.

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A growing body of literature suggests that aging causes restrictions in mobility, gaze, and cognitive functions, increasing the risk of falls and adverse health events. A novel Dual-Task Treadmill walking (DT-TW) program was designed to train balance, gaze, cognition, and gait simultaneously. Eleven healthy community-dwelling older adults (age 70-80 yrs) were recruited to play a variety of computer games while standing on a sponge surface and walking on a treadmill. Data on centre of pressure (COP) excursion for core balance, spatio-temporal gait variability parameters, head tracking performances, and neuropsychological tests were collected pre and post intervention. A significant improvement in balance, gaze, cognition, and gait performance was observed under dual-task conditions. The study thus concludes that DT-TW is a novel intervention program which combines interactive games with exercises to train dual-task abilities in community dwelling older adults.
October 2015

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Książki na temat "Spatio-temporal tasks"

DiFrisco, James. Biological Processes. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779636.003.0004.

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This chapter investigates the identity and persistence conditions for processes as a task of biological process ontology. It argues that the problem of intrinsic variation in evolution, development, and metabolism motivates viewing biological individuals as processes rather than as substances. Different criteria of identity for processes are then evaluated, including causal and spatio-temporal relations. The chapter ultimately settles on the view that processes are individuated by causal cohesion and are identical if they share the same cohesive properties and spatio-temporal region. The persistence of processes is interpreted on the model of perdurance, as a form of causal continuity or genidentity.

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Części książek na temat "Spatio-temporal tasks"

Filatov, Valentin, i Andriy Kovalenko. "Fuzzy Systems in Data Mining Tasks". W Advances in Spatio-Temporal Segmentation of Visual Data, 243–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35480-0_6.

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Sao, Ashutosh, Simon Gottschalk, Nicolas Tempelmeier i Elena Demidova. "MetaCitta: Deep Meta-Learning for Spatio-Temporal Prediction Across Cities and Tasks". W Advances in Knowledge Discovery and Data Mining, 70–82. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33383-5_6.

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AbstractAccurate spatio-temporal prediction is essential for capturing city dynamics and planning mobility services. State-of-the-art deep spatio-temporal predictive models depend on rich and representative training data for target regions and tasks. However, the availability of such data is typically limited. Furthermore, existing predictive models fail to utilize cross-correlations across tasks and cities. In this paper, we propose MetaCitta, a novel deep meta-learning approach that addresses the critical challenges of data scarcity and model generalization. MetaCitta adopts the data from different cities and tasks in a generalizable spatio-temporal deep neural network. We propose a novel meta-learning algorithm that minimizes the discrepancy between spatio-temporal representations across tasks and cities. Our experiments with real-world data demonstrate that the proposed MetaCitta approach outperforms state-of-the-art prediction methods for zero-shot learning and pre-training plus fine-tuning. Furthermore, MetaCitta is computationally more efficient than the existing meta-learning approaches.

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Frisone, F., P. G. Morasso, G. Iannò, M. Marongiu, P. Vitali i G. Rodriguez. "Spatio-temporal cortical activity patterns in cognitive tasks using fMRI". W Perspectives in Neural Computing, 126–31. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0811-5_10.

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Bosveld-de Smet, Leonie, i Daniël Houben. "Map or Gantt? Which Diagram Helps Viewers Best in Spatio-Temporal Data Exploration Tasks?" W Diagrammatic Representation and Inference, 357–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54249-8_28.

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Ramo, Mirco, i Guénolé C. M. Silvestre. "A Transformer Architecture for Online Gesture Recognition of Mathematical Expressions". W Communications in Computer and Information Science, 55–67. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_5.

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AbstractThe Transformer architecture is shown to provide a powerful framework as an end-to-end model for building expression trees from online handwritten gestures corresponding to glyph strokes. In particular, the attention mechanism was successfully used to encode, learn and enforce the underlying syntax of expressions creating latent representations that are correctly decoded to the exact mathematical expression tree, providing robustness to ablated inputs and unseen glyphs. For the first time, the encoder is fed with spatio-temporal data tokens potentially forming an infinitely large vocabulary, which finds applications beyond that of online gesture recognition. A new supervised dataset of online handwriting gestures is provided for training models on generic handwriting recognition tasks and a new metric is proposed for the evaluation of the syntactic correctness of the output expression trees. A small Transformer model suitable for edge inference was successfully trained to an average normalised Levenshtein accuracy of 94%, resulting in valid postfix RPN tree representation for 94% of predictions.

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Zhu, Chen, Yue Cui, Yan Zhao i Kai Zheng. "Task Assignment with Spatio-temporal Recommendation in Spatial Crowdsourcing". W Web and Big Data, 264–79. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25158-0_21.

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Jiang, Mingxin, Shimin Yang, Zhongbo Zhao, Jiadong Yan, Yuzhong Chen, Tuo Zhang, Shu Zhang, Benjamin Becker, Keith M. Kendrick i Xi Jiang. "Exploring Gyro-Sulcal Functional Connectivity Differences Across Task Domains via Anatomy-Guided Spatio-Temporal Graph Convolutional Networks". W Machine Learning in Medical Imaging, 130–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87589-3_14.

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Vafaeinezhad, Ali Reza, Ali Asghar Alesheikh, Majid Hamrah, Reza Nourjou i Rouzbeh Shad. "Using GIS to Develop an Efficient Spatio-temporal Task Allocation Algorithm to Human Groups in an Entirely Dynamic Environment Case Study: Earthquake Rescue Teams". W Computational Science and Its Applications – ICCSA 2009, 66–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02454-2_5.

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Ahmed, Khadeer. "Brain-Inspired Spiking Neural Networks". W Biomimetics [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93435.

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Brain is a very efficient computing system. It performs very complex tasks while occupying about 2 liters of volume and consuming very little energy. The computation tasks are performed by special cells in the brain called neurons. They compute using electrical pulses and exchange information between them through chemicals called neurotransmitters. With this as inspiration, there are several compute models which exist today trying to exploit the inherent efficiencies demonstrated by nature. The compute models representing spiking neural networks (SNNs) are biologically plausible, hence are used to study and understand the workings of brain and nervous system. More importantly, they are used to solve a wide variety of problems in the field of artificial intelligence (AI). They are uniquely suited to model temporal and spatio-temporal data paradigms. This chapter explores the fundamental concepts of SNNs, few of the popular neuron models, how the information is represented, learning methodologies, and state of the art platforms for implementing and evaluating SNNs along with a discussion on their applications and broader role in the field of AI and data networks.

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Nanni, Mirco, Roberto Trasarti, Paolo Cintia, Barbara Furletti, Chiara Renso, Lorenzo Gabrielli, Salvatore Rinzivillo i Fosca Giannotti. "Mobility Profiling". W Data Science and Simulation in Transportation Research, 1–29. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4920-0.ch001.

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The ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network.

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Streszczenia konferencji na temat "Spatio-temporal tasks"

Glake, Daniel, Mareike Schmidt, Felix Kiehn, Fabian Panse, Ulfia Lenfers, Thomas Clemen i Norbert Ritter. "Operator Placement for Spatio-temporal Tasks". W 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020279.

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Xu, Jia, Chengcheng Guan, Haipeng Dai, Dejun Yang, Lijie Xu i Jianyi Kai. "Incentive Mechanisms for Spatio-Temporal Tasks in Mobile Crowdsensing". W 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2019. http://dx.doi.org/10.1109/mass.2019.00016.

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Saha, Sujan Kumar, Yecheng Xiang i Hyoseung Kim. "STGM: Spatio-Temporal GPU Management for Real-Time Tasks". W 2019 IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). IEEE, 2019. http://dx.doi.org/10.1109/rtcsa.2019.8864564.

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Wang, Leye, Xu Geng, Xiaojuan Ma, Feng Liu i Qiang Yang. "Cross-City Transfer Learning for Deep Spatio-Temporal Prediction". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/262.

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Spatio-temporal prediction is a key type of tasks in urban computing, e.g., traffic flow and air quality. Adequate data is usually a prerequisite, especially when deep learning is adopted. However, the development levels of different cities are unbalanced, and still many cities suffer from data scarcity. To address the problem, we propose a novel cross-city transfer learning method for deep spatio-temporal prediction tasks, called RegionTrans. RegionTrans aims to effectively transfer knowledge from a data-rich source city to a data-scarce target city. More specifically, we first learn an inter-city region matching function to match each target city region to a similar source city region. A neural network is designed to effectively extract region-level representation for spatio-temporal prediction. Finally, an optimization algorithm is proposed to transfer learned features from the source city to the target city with the region matching function. Using citywide crowd flow prediction as a demonstration experiment, we verify the effectiveness of RegionTrans. Results show that RegionTrans can outperform the state-of-the-art fine-tuning deep spatio-temporal prediction models by reducing up to 10.7% prediction error.

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Naji, Yassine, Aleksandr Setkov, Angelique Loesch, Michele Gouiffes i Romaric Audigier. "Spatio-temporal predictive tasks for abnormal event detection in videos". W 2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2022. http://dx.doi.org/10.1109/avss56176.2022.9959669.

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Xia, Jinfu, Yan Zhao, Guanfeng Liu, Jiajie Xu, Min Zhang i Kai Zheng. "Profit-driven Task Assignment in Spatial Crowdsourcing". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/265.

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In Spatial Crowdsourcing (SC) systems, mobile users are enabled to perform spatio-temporal tasks by physically traveling to specified locations with the SC platforms. SC platforms manage the systems and recruit mobile users to contribute to the SC systems, whose commercial success depends on the profit attained from the task requesters. In order to maximize its profit, an SC platform needs an online management mechanism to assign the tasks to suitable workers. How to assign the tasks to workers more cost-effectively with the spatio-temporal constraints is one of the most difficult problems in SC. To deal with this challenge, we propose a novel Profit-driven Task Assignment (PTA) problem, which aims to maximize the profit of the platform. Specifically, we first establish a task reward pricing model with tasks' temporal constraints (i.e., expected completion time and deadline). Then we adopt an optimal algorithm based on tree decomposition to achieve the optimal task assignment and propose greedy algorithms to improve the computational efficiency. Finally, we conduct extensive experiments using real and synthetic datasets, verifying the practicability of our proposed methods.

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Fang, Ziquan, Dongen Wu, Lu Pan, Lu Chen i Yunjun Gao. "When Transfer Learning Meets Cross-City Urban Flow Prediction: Spatio-Temporal Adaptation Matters". W Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/282.

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Urban flow prediction is a fundamental task to build smart cities, where neural networks have become the most popular method. However, the deep learning methods typically rely on massive training data that are probably inaccessible in real world. In light of this, the community calls for knowledge transfer. However, when adapting transfer learning for cross-city prediction tasks, existing studies are built on static knowledge transfer, ignoring the fact inter-city correlations change dynamically across time. The dynamic correlations make urban feature transfer challenging. This paper proposes a novel Spatio-Temporal Adaptation Network (STAN) to perform urban flow prediction for data-scarce cities via the spatio-temporal knowledge transferred from data-rich cities. STAN encompasses three modules: i) spatial adversarial adaptation module that adopts an adversarial manner to capture the transferable spatial features; ii) temporal attentive adaptation module to attend to critical dynamics for temporal feature transfer; iii) prediction module that aims to learn task-driven transferable knowledge. Extensive experiments on five real datasets show STAN substantially outperforms state-of-the-art methods.

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Guan, Jiazhi, i Dongyao Shen. "Transfer Spatio-Temporal Knowledge from Emotion-Related Tasks for Facial Expression Spotting". W MM '21: ACM Multimedia Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3476100.3484461.

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Hirel, Julien, Philippe Gaussier i Mathias Quoy. "Biologically inspired neural networks for spatio-temporal planning in robotic navigation tasks". W 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2011. http://dx.doi.org/10.1109/robio.2011.6181522.

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Bassem, Christine. "Mobility Coordination of Participants in Mobile CrowdSensing Platforms with Spatio-Temporal Tasks". W the 17th ACM International Symposium. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3345770.3356734.

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Raporty organizacyjne na temat "Spatio-temporal tasks"

Beck, Tanya, i Ping Wang. Morphodynamics of barrier-inlet systems in the context of regional sediment management, with case studies from West-Central Florida, USA. Engineer Research and Development Center (U.S.), wrzesień 2021. http://dx.doi.org/10.21079/11681/41984.

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The temporal and spatial scales controlling the morphodynamics of barrier-inlet systems are critical components of regional sediment management practice. This paper discusses regional sediment management methods employed at multiple barrier-inlet systems, with case studies from West-Central Florida. A decision-support tool is proposed for regional sediment management with discussion of its application to barrier-inlet systems. Connecting multiple barrier islands and inlets at appropriate spatio-temporal scales is critical in developing an appropriately scoped sediment management plan for a barrier-inlet system. Evaluating sediment bypassing capacity and overall inlet morphodynamics can better inform regional sand sharing along barrier-inlet coastlines; particularly where sediment resources are scarce and a close coupling between inlet dredging and beach placement is vital to long-term sustainable management. Continued sea-level rise and anthropogenic activities may intensify the need for investigating longer-term processes and expanding regional planning at a centennial timescale and are acknowledged as challenging tasks for RSM studies. Specifically, we suggested that a regionally focused, multi-inlet study was necessary for management plan of individual inlet for the west-central Florida case studies. Key recommendations based on the case studies are included.

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