933 research outputs found
Impact of Operational Speed Characteristics of Heavy Vehicles on High-Speed Highways
This thesis explores the safety impact of differential speed limit (DSL) strategy by considering gross vehicle weight (GVW) combined with average speed enforcement (ASE) for heavy vehicles. The study used one-year of Weigh-in-Motion (WIM) data (2014) and one-month of Global Positioning System (GPS) data (Mar 2016) collected from along the Trans-Canada Highway 1 in British Columbia.
The research consisted of a data-driven analysis and a two-part simulation analysis. As the DSL investigated was based on GVW, a Modified-Federal Highway Administration (M-FHWA) classification that explicitly considered GVW was tested alongside the FHWA classification regarding average speed and GVW. The simulation analysis assessed the DSL strategy associated with M-FHWA classification and ASE strategys impact on the safety of heavy vehicles.
In general, the analyses showed that DSL adopted with M-FHWA classes combined with ASE would be effective in reducing heavy vehicle speed and improving highway safety
A Unified Analysis of Federated Learning with Arbitrary Client Participation
Federated learning (FL) faces challenges of intermittent client availability
and computation/communication efficiency. As a result, only a small subset of
clients can participate in FL at a given time. It is important to understand
how partial client participation affects convergence, but most existing works
have either considered idealized participation patterns or obtained results
with non-zero optimality error for generic patterns. In this paper, we provide
a unified convergence analysis for FL with arbitrary client participation. We
first introduce a generalized version of federated averaging (FedAvg) that
amplifies parameter updates at an interval of multiple FL rounds. Then, we
present a novel analysis that captures the effect of client participation in a
single term. By analyzing this term, we obtain convergence upper bounds for a
wide range of participation patterns, including both non-stochastic and
stochastic cases, which match either the lower bound of stochastic gradient
descent (SGD) or the state-of-the-art results in specific settings. We also
discuss various insights, recommendations, and experimental results.Comment: Accepted to NeurIPS 202
A Lightweight Method for Tackling Unknown Participation Probabilities in Federated Averaging
In federated learning (FL), clients usually have diverse participation
probabilities that are unknown a priori, which can significantly harm the
performance of FL if not handled properly. Existing works aiming at addressing
this problem are usually based on global variance reduction, which requires a
substantial amount of additional memory in a multiplicative factor equal to the
total number of clients. An important open problem is to find a lightweight
method for FL in the presence of clients with unknown participation rates. In
this paper, we address this problem by adapting the aggregation weights in
federated averaging (FedAvg) based on the participation history of each client.
We first show that, with heterogeneous participation probabilities, FedAvg with
non-optimal aggregation weights can diverge from the optimal solution of the
original FL objective, indicating the need of finding optimal aggregation
weights. However, it is difficult to compute the optimal weights when the
participation probabilities are unknown. To address this problem, we present a
new algorithm called FedAU, which improves FedAvg by adaptively weighting the
client updates based on online estimates of the optimal weights without knowing
the probabilities of client participation. We provide a theoretical convergence
analysis of FedAU using a novel methodology to connect the estimation error and
convergence. Our theoretical results reveal important and interesting insights,
while showing that FedAU converges to an optimal solution of the original
objective and has desirable properties such as linear speedup. Our experimental
results also verify the advantage of FedAU over baseline methods
Expression model for multiple relationships in the ontology of traditional Chinese medicine knowledge
AbstractObjectiveTo explore multiple relationships in traditional Chinese medicine (TCM) knowledge by comparing binary and multiple relationships during knowledge organization.MethodsCharacteristics of binary and multiple semantic relationships as well as their associations are described. A method to classify multiple relationships based on the involvement of time is proposed and theoretically validated using examples from the ancient TCM classic Important Formulas Worth a Thousand Gold Pieces. The classification includes parallel multiple relationships, restricted multiple relationships, multiple relationships that involve time, and multiple relationships that involve time restriction. Next, construction of multiple semantic relationships for TCM concepts in each classification using Protégé, an ontology editing tool is described.ResultsProtégé is superior to a binary relationship and less than ideal with multiple relationships during the constitution of concept relationships.ConclusionWhen applied in TCM, the semantic relationships constructed by Protégé are superior than those constructed by correlation and/or attribute relationships, but less ideal than those constructed by the human cognitive process
Rapid Invasion of Spartina Alterniflora in the Coastal Zone of Mainland China: Spatiotemporal Patterns and Human Prevention
Given the extensive spread and ecological consequences of exotic Spartina alterniflora (S. alterniflora) over the coast of mainland China, monitoring its spatiotemporal invasion patterns is important for the sake of coastal ecosystem management and ecological security. In this study, Landsat series images from 1990 to 2015 were used to establish multi-temporal datasets for documenting the temporal dynamics of S. alterniflora invasion. Our observations revealed that S. alterniflora had a continuous expansion with the area increasing by 50,204 ha during the considered 25 years. The largest expansion was identified in Jiangsu Province during the period of 1990-2000, and in Zhejiang Province during the periods 2000-2010 and 2010-2015. Three noticeable hotspots for S. alterniflora invasion were Yancheng of Jiangsu, Chongming of Shanghai, and Ningbo of Zhejiang, and each had a net area increase larger than 5000 ha. Moreover, an obvious shrinkage of S. alterniflora was identified in three coastal cities including the city of Cangzhou of Hebei, Dongguan, and Jiangmen of Guangdong. S. alterniflora invaded mostly into mudflats (>93%) and shrank primarily due to aquaculture (55.5%). This study sheds light on the historical spatial patterns in S. alterniflora distribution and thus is helpful for understanding its invasion mechanism and invasive species management
Aero-engine rotor-static rubbing characteristic analysis based on casing acceleration signal
The rotor experiment rig of aero-engine was used to simulate rubbing faults in different rotational speeds, rubbing intensities, rubbing positions and casing thickness. The casing acceleration signal was collected and subjected to the analysis by auto-correlation function frequency spectrum. The result indicates that the auto-correlation function frequency spectrum shows significant characteristic frequency in rubbing frequency (product between blade number and rotating frequency) and its integer multiple. The location of each characteristic frequency is characterized by band-frequency characteristic with rotating frequency as interval. The characteristic is not affected by sensor installed position, rotational speed, rubbing position and casing thickness
Local Averaging Helps: Hierarchical Federated Learning and Convergence Analysis
Federated learning is an effective approach to realize collaborative learning
among edge devices without exchanging raw data. In practice, these devices may
connect to local hubs instead of connecting to the global server (aggregator)
directly. Due to the (possibly limited) computation capability of these local
hubs, it is reasonable to assume that they can perform simple averaging
operations. A natural question is whether such local averaging is beneficial
under different system parameters and how much gain can be obtained compared to
the case without such averaging. In this paper, we study hierarchical federated
learning with stochastic gradient descent (HF-SGD) and conduct a thorough
theoretical analysis to analyze its convergence behavior. In particular, we
first consider the two-level HF-SGD (one level of local averaging) and then
extend this result to arbitrary number of levels (multiple levels of local
averaging). The analysis demonstrates the impact of local averaging precisely
as a function of system parameters. Due to the higher communication cost of
global averaging, a strategy of decreasing the global averaging frequency and
increasing the local averaging frequency is proposed. Experiments validate the
proposed theoretical analysis and the advantages of HF-SGD.Comment: 42 pages, 13 figure
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