Bibliografías temáticas / Asynchronous heterogeneous temporal integrator

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Literatura académica sobre el tema "Asynchronous heterogeneous temporal integrator"

Autor: Grafiati

Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Asynchronous heterogeneous temporal integrator"

Fekak, Fatima-Ezzahra, Michael Brun, Anthony Gravouil, and Bruno Depale. "A new heterogeneous asynchronous explicit–implicit time integrator for nonsmooth dynamics." Computational Mechanics 60, no. 1 (2017): 1–21. http://dx.doi.org/10.1007/s00466-017-1397-0.

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Zafati, Eliass, and Julie Al Hout. "Reflection error analysis for wave propagation problems solved by a heterogeneous asynchronous time integrator." International Journal for Numerical Methods in Engineering 115, no. 6 (2018): 651–94. http://dx.doi.org/10.1002/nme.5820.

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Li, Longyuan, Jihai Zhang, Junchi Yan, et al. "Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 8420–28. http://dx.doi.org/10.1609/aaai.v35i10.17023.

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Time-series is ubiquitous across applications, such as transportation, finance and healthcare. Time-series is often influenced by external factors, especially in the form of asynchronous events, making forecasting difficult. However, existing models are mainly designated for either synchronous time-series or asynchronous event sequence, and can hardly provide a synthetic way to capture the relation between them. We propose Variational Synergetic Multi-Horizon Network (VSMHN), a novel deep conditional generative model. To learn complex correlations across heterogeneous sequences, a tailored encoder is devised to combine the advances in deep point processes models and variational recurrent neural networks. In addition, an aligned time coding and an auxiliary transition scheme are carefully devised for batched training on unaligned sequences. Our model can be trained effectively using stochastic variational inference and generates probabilistic predictions with Monte-Carlo simulation. Furthermore, our model produces accurate, sharp and more realistic probabilistic forecasts. We also show that modeling asynchronous event sequences is crucial for multi-horizon time-series forecasting.

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Agrawal, Shaashwat, Aditi Chowdhuri, Sagnik Sarkar, Ramani Selvanambi, and Thippa Reddy Gadekallu. "Temporal Weighted Averaging for Asynchronous Federated Intrusion Detection Systems." Computational Intelligence and Neuroscience 2021 (December 17, 2021): 1–10. http://dx.doi.org/10.1155/2021/5844728.

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Federated learning (FL) is an emerging subdomain of machine learning (ML) in a distributed and heterogeneous setup. It provides efficient training architecture, sufficient data, and privacy-preserving communication for boosting the performance and feasibility of ML algorithms. In this environment, the resultant global model produced by averaging various trained client models is vital. During each round of FL, model parameters are transferred from each client device to the server while the server waits for all models before it can average them. In a realistic scenario, waiting for all clients to communicate their model parameters, where client models are trained on low-power Internet of Things (IoT) devices, can result in a deadlock. In this paper, a novel temporal model averaging algorithm is proposed for asynchronous federated learning (AFL). Our approach uses a dynamic expectation function that computes the number of client models expected in each round and a weighted averaging algorithm for continuous modification of the global model. This ensures that the federated architecture is not stuck in a deadlock all the while increasing the throughput of the server and clients. To implicate the importance of asynchronicity in cybersecurity, the proposed algorithm is tested using NSL-KDD intrusion detection system datasets. The performance accuracy of the global model is about 99.5% on the dataset, outperforming traditional FL models in anomaly detection. In terms of asynchronicity, we get an increased throughput of almost 10.17% for every 30 timesteps.

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Zhu, Yixin, Dongfen Li, Wenqiang Guo, and Fengli Zhang. "Effect of Heterogeneity of Vertex Activation on Epidemic Spreading in Temporal Networks." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/409510.

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Development of sensor technologies and the prevalence of electronic communication services provide us with a huge amount of data on human communication behavior, including face-to-face conversations, e-mail exchanges, phone calls, message exchanges, and other types of interactions in various online forums. These indirect or direct interactions form potential bridges of the virus spread. For a long time, the study of virus spread is based on the aggregate static network. However, the interaction patterns containing diverse temporal properties may affect dynamic processes as much as the network topology does. Some empirical studies show that the activation time and duration of vertices and links are highly heterogeneous, which means intense activity may be followed by longer intervals of inactivity. We take heterogeneous distribution of the node interactivation time as the research background to build an asynchronous communication model. The two sides of the communication do not have to be active at the same time. One derives the threshold of virus spreading on the communication mode and analyzes the reason the heterogeneous distribution of the vertex interactivation time suppresses the spread of virus. At last, the analysis and results from the model are verified on the BA network.

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Zang, Yu, Zhe Xue, Shilong Ou, Lingyang Chu, Junping Du, and Yunfei Long. "Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16642–50. http://dx.doi.org/10.1609/aaai.v38i15.29603.

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Asynchronous federated learning (AFL) is a distributed machine learning technique that allows multiple devices to collaboratively train deep learning models without sharing local data. However, AFL suffers from low efficiency due to poor client model training quality and slow server model convergence speed, which are a result of the heterogeneous nature of both data and devices. To address these issues, we propose Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction (FedAC). Our framework consists of three key components. The first component is client weight evaluation based on temporal gradient, which evaluates the client weight based on the similarity between the client and server update directions. The second component is adaptive server update with prospective weighted momentum, which uses an asynchronous buffered update strategy and a prospective weighted momentum with adaptive learning rate to update the global model in server. The last component is client update with fine-grained gradient correction, which introduces a fine-grained gradient correction term to mitigate the client drift and correct the client stochastic gradient. We conduct experiments on real and synthetic datasets, and compare with existing federated learning methods. Experimental results demonstrate effective improvements in model training efficiency and AFL performance by our framework.

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van der Meulen, Nick, Peter van Baalen, Eric van Heck, and Sipko Mülder. "No teleworker is an island: The impact of temporal and spatial separation along with media use on knowledge sharing networks." Journal of Information Technology 34, no. 3 (2019): 243–62. http://dx.doi.org/10.1177/0268396218816531.

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Despite its prevalence, there is a lack of understanding regarding the effect of telework on an organization’s knowledge base. Recognizing the enabling role of electronic communication media, this article therefore addresses the interaction effects of media synchronicity and temporal as well as spatial separation among colleagues on sharing in knowledge networks. Special attention is paid to knowledge awareness (a form of metaknowledge representing “who knows what”) as well as homogeneous and heterogeneous knowledge sources to further explicate the relationship between coworker separation and knowledge sharing. Multiple surveys were placed between two smaller ethnographic investigations and combined with whole network data to form an in-depth study of 64 knowledge workers at a medium-sized European research and advisory organization. The results reveal that spatial separation directly reduces the frequency of knowledge sharing between colleagues, whereas temporal separation affects knowledge sharing through reduced knowledge awareness, resulting in lower job and proactive performance. The use of asynchronous media can serve to mitigate most of the negative effects of spatial separation on knowledge sharing but may also exacerbate the negative effect of temporal separation on teleworkers’ knowledge awareness of colleagues with identical expertise.

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Rezaei, Mohammad R., Reza Saadati Fard, Milos R. Popovic, Steven A. Prescott, and Milad Lankarany. "Synchrony-Division Neural Multiplexing: An Encoding Model." Entropy 25, no. 4 (2023): 589. http://dx.doi.org/10.3390/e25040589.

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Cortical neurons receive mixed information from the collective spiking activities of primary sensory neurons in response to a sensory stimulus. A recent study demonstrated an abrupt increase or decrease in stimulus intensity and the stimulus intensity itself can be respectively represented by the synchronous and asynchronous spikes of S1 neurons in rats. This evidence capitalized on the ability of an ensemble of homogeneous neurons to multiplex, a coding strategy that was referred to as synchrony-division multiplexing (SDM). Although neural multiplexing can be conceived by distinct functions of individual neurons in a heterogeneous neural ensemble, the extent to which nearly identical neurons in a homogeneous neural ensemble encode multiple features of a mixed stimulus remains unknown. Here, we present a computational framework to provide a system-level understanding on how an ensemble of homogeneous neurons enable SDM. First, we simulate SDM with an ensemble of homogeneous conductance-based model neurons receiving a mixed stimulus comprising slow and fast features. Using feature-estimation techniques, we show that both features of the stimulus can be inferred from the generated spikes. Second, we utilize linear nonlinear (LNL) cascade models and calculate temporal filters and static nonlinearities of differentially synchronized spikes. We demonstrate that these filters and nonlinearities are distinct for synchronous and asynchronous spikes. Finally, we develop an augmented LNL cascade model as an encoding model for the SDM by combining individual LNLs calculated for each type of spike. The augmented LNL model reveals that a homogeneous neural ensemble model can perform two different functions, namely, temporal- and rate-coding, simultaneously.

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Di Carvalho, Josie Antonucci, and Stephen A. Wickham. "Does spatiotemporal nutrient variation allow more species to coexist?" Oecologia 194, no. 4 (2020): 695–707. http://dx.doi.org/10.1007/s00442-020-04768-9.

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AbstractTemporal heterogeneity in nutrient availability is known to increase phytoplankton diversity by allowing more species to coexist under different resource niches. Spatial heterogeneity has also been positively correlated with species diversity. Here we investigated how temporal and spatial differences in nutrient addition together impact biodiversity in metacommunities varying in the degree of connectivity among the patches. We used a microcosm experimental design to test two spatiotemporal ways of supplying nutrients: synchronously (nutrients were added regionally—to all four patches at the same time) and asynchronously (nutrients were added locally—to a different patch each time), combined with two different degrees of connectivity among the patches (low or high connectivity). We used three species of algae and one species of cyanobacteria as the primary producers; and five ciliate and two rotifer species as the grazers. We expected higher diversity in metacommunities receiving an asynchronous nutrient supply, assuming stronger development of heterogeneous patches with this condition rather than with synchronous nutrient supply. This result was expected, however, to be dependent on the degree of connectivity among patches. We found significant effects of nutrient addition in both groups of organisms. Phytoplankton diversity increased until the fourth week (transiently) and zooplankton richness was persistently higher under asynchronous nutrient addition. Our results were consistent with our hypothesis that asynchronicity in nutrient supply would create a more favorable condition for species to co-occur. However, this effect was, in part, transient and was not influenced by the degree of connectivity.

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Mueen, Ahmed, Mohammad Awedh, and Bassam Zafar. "Multi-obstacle aware smart navigation system for visually impaired people in fog connected IoT-cloud environment." Health Informatics Journal 28, no. 3 (2022): 146045822211126. http://dx.doi.org/10.1177/14604582221112609.

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Design of smart navigation for visually impaired/blind people is a hindering task. Existing researchers analyzed it in either indoor or outdoor environment and also it’s failed to focus on optimum route selection, latency minimization and multi-obstacle presence. In order to overcome these challenges and to provide precise assistance to visually impaired people, this paper proposes smart navigation system for visually impaired people based on both image and sensor outputs of the smart wearable. The proposed approach involves the upcoming processes: (i) the input query of the visually impaired people (users) is improved by the query processor in order to achieve accurate assistance. (ii) The safest route from source to destination is provided by implementing Environment aware Bald Eagle Search Optimization algorithm in which multiple routes are identified and classified into three different classes from which the safest route is suggested to the users. (iii) The concept of fog computing is leveraged and the optimal fog node is selected in order to minimize the latency. The fog node selection is executed by using Nearest Grey Absolute Decision Making Algorithm based on multiple parameters. (iv) The retrieval of relevant information is performed by means of computing Euclidean distance between the reference and database information. (v) The multi-obstacle detection is carried out by YOLOv3 Tiny in which both the static and dynamic obstacles are classified into small, medium and large obstacles. (vi) The decision upon navigation is provided by implementing Adaptive Asynchronous Advantage Actor-Critic (A3C) algorithm based on fusion of both image and sensor outputs. (vii) Management of heterogeneous is carried out by predicting and pruning the fault data in the sensor output by minimum distance based extended kalman filter for better accuracy and clustering the similar information by implementing Spatial-Temporal Optics Clustering Algorithm to reduce complexity. The proposed model is implemented in NS 3.26 and the results proved that it outperforms other existing works in terms of obstacle detection and task completion time.

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