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

Autor: Grafiati
Publicado: 25 de mayo de 2024

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1

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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2

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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3

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 enc
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4

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 t
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5

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
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6

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 compone
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7

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
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8

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 o
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9

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 a
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10

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
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11

Gupta, Aniket, Alix Reverdy, Jean-Martial Cohard, et al. "Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment." Hydrology and Earth System Sciences 27, no. 1 (2023): 191–212. http://dx.doi.org/10.5194/hess-27-191-2023.

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Abstract. From the micro- to the mesoscale, water and energy budgets of mountainous catchments are largely driven by topographic features such as terrain orientation, slope, steepness, and elevation, together with associated meteorological forcings such as precipitation, solar radiation, and wind speed. Those topographic features govern the snow deposition, melting, and transport, which further impacts the overall water cycle. However, this microscale variability is not well represented in Earth system models due to coarse resolutions. This study explores the impact of precipitation, shortwave
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12

Zheng, Ciyan, Long Peng, Jason K. Eshraghian, Xiaoli Wang, Jian Cen, and Herbert Ho-Ching Iu. "Spiking Neuron Implementation Using a Novel Floating Memcapacitor Emulator." International Journal of Bifurcation and Chaos 32, no. 15 (2022). http://dx.doi.org/10.1142/s0218127422502248.

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Memcapacitors (MCs) are promising candidates for the future design of low-power integrated neuromorphic computing systems, with particular emphasis on dynamical spiking neuron models that exhibit rich temporal behaviors. We present a novel floating flux-controlled MC that is designed using only three current feedback amplifiers, one analog multiplier, one capacitor and one resistor. Compared with existing floating MC emulators, our proposed design has a simpler structure without the need for DC biasing voltage sources, and can operate at higher working frequencies, and therefore enabling rapid
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13

Mintrone, Caterina, Luca Rindi, and Lisandro Benedetti‐Cecchi. "Stabilizing effects of spatially heterogeneous disturbance via reduced spatial synchrony on a rocky shore community." Ecology, January 29, 2024. http://dx.doi.org/10.1002/ecy.4246.

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AbstractUnderstanding how synchronous species fluctuations affect community stability is a main research topic in ecology. Yet experimental studies evaluating how changes in disturbance regimes affect the synchrony and stability of populations and communities remain rare. We hypothesized that spatially heterogeneous disturbances of moderate intensity would promote metacommunity stability by decreasing the spatial synchrony of species fluctuations. To test this hypothesis, we exposed rocky shore communities of algae and invertebrates to homogeneous and gradient‐like spatial patterns of disturba
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14

Bi, Hongjie, Matteo di Volo, and Alessandro Torcini. "Asynchronous and Coherent Dynamics in Balanced Excitatory-Inhibitory Spiking Networks." Frontiers in Systems Neuroscience 15 (December 10, 2021). http://dx.doi.org/10.3389/fnsys.2021.752261.

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Dynamic excitatory-inhibitory (E-I) balance is a paradigmatic mechanism invoked to explain the irregular low firing activity observed in the cortex. However, we will show that the E-I balance can be at the origin of other regimes observable in the brain. The analysis is performed by combining extensive simulations of sparse E-I networks composed of N spiking neurons with analytical investigations of low dimensional neural mass models. The bifurcation diagrams, derived for the neural mass model, allow us to classify the possible asynchronous and coherent behaviors emerging in balanced E-I netwo
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15

Wang, Xingyu, and Lan Zhou. "The Many Roles of Macrophages in Skeletal Muscle Injury and Repair." Frontiers in Cell and Developmental Biology 10 (July 11, 2022). http://dx.doi.org/10.3389/fcell.2022.952249.

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Skeletal muscle is essential to physical activity and energy metabolism. Maintaining intact functions of skeletal muscle is crucial to health and wellbeing. Evolutionarily, skeletal muscle has developed a remarkable capacity to maintain homeostasis and to regenerate after injury, which indispensably relies on the resident muscle stem cells, satellite cells. Satellite cells are largely quiescent in the homeostatic steady state. They are activated in response to muscle injury. Activated satellite cells proliferate and differentiate into myoblasts. Myoblasts fuse to form myotubes which further gr
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16

Imran, Mubashir, Hongzhi Yin, Tong Chen, Nguyen Quoc Viet Hung, Alexander Zhou, and Kai Zheng. "ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences." ACM Transactions on Information Systems, August 29, 2022. http://dx.doi.org/10.1145/3560486.

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Owing to its nature of scalability and privacy by design, federated learning (FL) has received increasing interest in decentralized deep learning. FL has also facilitated recent research on upscaling and privatizing personalized recommendation services, using on-device data to learn recommender models locally. These models are then aggregated globally to obtain a more performant model, while maintaining data privacy. Typically, federated recommender systems (FRSs) do not take into account the lack of resources and data availability at the end-devices. In addition, they assume that the interact
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