Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Weighted Negative Samples

Heterogeneous graph contrastive learning has received wide attention recently. Some existing methods use meta-paths, which are sequences of object types that capture semantic relationships between objects, to construct contrastive views. However, most of them ignore the rich meta-path context information that describes how two objects are connected by meta-paths. On the other hand, they fail to distinguish hard negatives from false negatives, which could adversely affect the model performance. To address the problems, we propose MEOW, a heterogeneous graph contrastive learning model that considers both meta-path contexts and weighted negative samples. Specifically, MEOW constructs a coarse view and a fine-grained view for contrast. The former reflects which objects are connected by meta-paths, while the latter uses meta-path contexts and characterizes the details on how the objects are connected. We take node embeddings in the coarse view as anchors, and construct positive and negative samples from the fine-grained view. Further, to distinguish hard negatives from false negatives, we learn weights of negative samples based on node clustering. We also use prototypical contrastive learning to pull close embeddings of nodes in the same cluster. Finally, we conduct extensive experiments to show the superiority of MEOW against other state-of-the-art methods.Comment: Accepted by SDM 202

Privacy-Preserving Model Aggregation for Asynchronous Federated Learning

We present a novel privacy-preserving model aggregation for asynchronous federated learning, named PPA-AFL that removes the restriction of synchronous aggregation of local model updates in federated learning, while enabling the protection of the local model updates against the server. In PPA-AFL, clients can proactive decide when to engage in the training process, and sends local model updates to the server when the updates are available. Thus, it is not necessary to keep synchronicity with other clients. To safeguard client updates and facilitate local model aggregation, we employ Paillier encryption for local update encryption and support homomorphic aggregation. Furthermore, secret sharing is utilized to enable the sharing of decryption keys and facilitate privacy-preserving asynchronous aggregation. As a result, the server remains unable to gain any information about the local updates while asynchronously aggregating to produce the global model. We demonstrate the efficacy of our proposed PPA-AFL framework through comprehensive complexity analysis and extensive experiments on a prototype implementation, highlighting its potential for practical adoption in privacy-sensitive asynchronous federated learning scenarios

Urban Building Energy and Climate (UrBEC) simulation: Example application and field evaluation in Sai Ying Pun, Hong Kong

The energy performance of a building in a dense city depends to some extent on its surroundings. The impact of the built form, together with anthropogenic heat gains from traffic and building HVAC exhaust, determines external environmental conditions at the Urban Canopy Layer. Existing building energy models are limited in accounting for micro-scale variations of the urban microclimate, which may significantly modify a building's energy performance in density cities. This paper presents the Urban Building Energy and Climate (UrBEC) model, a coupled urban microclimate model (UMM) and building energy model (HTB2) developed to assess the time varying energy performance of a cluster of buildings and the combined heat gains to the external space from direct and reflected solar radiation, traffic and the exhaust from HVAC systems in a high-density city. The simulation results were evaluated by comparison with field measurement data collected from the Sai Ying Pun neighbourhood in Hong Kong, on a summer and winter day. Predicted and measured air and surface temperature at the four locations were found to be in reasonable agreement. Simulation results indicate an average of 1-3 ºC of temperature rise in street canyons compared with the ambient air in summer. Street level air is predicted to be 0.6 ºC warmer than those at higher levels (20m +). Anthropogenic heat from traffic and building HVAC exhaust are the dominant contributors to temperature rise in street canyons in summer, exceeding the contribution from urban surfaces. The predicted building cooling demand is expected to increase up to 15 % in summer due to the warming effect in street canyons. The UrBEC model runs significantly faster than current CFD-based approaches. Therefore, the model has the potential to support early stage design and planning decisions in a dense city

Self-supervised Heterogeneous Graph Variational Autoencoders

Heterogeneous Information Networks (HINs), which consist of various types of nodes and edges, have recently demonstrated excellent performance in graph mining. However, most existing heterogeneous graph neural networks (HGNNs) ignore the problems of missing attributes, inaccurate attributes and scarce labels for nodes, which limits their expressiveness. In this paper, we propose a generative self-supervised model SHAVA to address these issues simultaneously. Specifically, SHAVA first initializes all the nodes in the graph with a low-dimensional representation matrix. After that, based on the variational graph autoencoder framework, SHAVA learns both node-level and attribute-level embeddings in the encoder, which can provide fine-grained semantic information to construct node attributes. In the decoder, SHAVA reconstructs both links and attributes. Instead of directly reconstructing raw features for attributed nodes, SHAVA generates the initial low-dimensional representation matrix for all the nodes, based on which raw features of attributed nodes are further reconstructed to leverage accurate attributes. In this way, SHAVA can not only complete informative features for non-attributed nodes, but rectify inaccurate ones for attributed nodes. Finally, we conduct extensive experiments to show the superiority of SHAVA in tackling HINs with missing and inaccurate attributes

Influence of the interannual variability of the Kuroshio Extension on the Mediterranean trough in the cold season

The impacts of the Kuroshio Extension (KE) interannual fluctuation on the Mediterranean trough (MedT) and associated Euro-Mediterranean climate in wintertime are analyzed in this study. It appears that when the KE index is positive (corresponds to a stable KE state with sharp sea surface temperature front and subsided oceanic eddies), the MedT is observed to be stronger in the west of its climatological position, resulting in a colder condition to the west of the Black Sea and more precipitation in the East Mediterranean and Anatolia Plateau. The opposite atmospheric pattern holds when the KE index is negative (unstable KE state). The empirical orthogonal function analysis is performed on 500-hPa geopotential height to obtain the dominant modes of MedT. The results show the KE index is significantly correlated with the intensity and zonal displacement of MedT, with correlation coefficients of 0.57 and 0.40, respectively. The low-frequency Rossby wave activity and high-frequency eddies are revealed as the prominent contributors to this co-variability between the KE and MedT. The upward turbulent heat flux transport from the ocean to the atmosphere strengthened by the stable KE activates the Rossby waves propagating upward and westward to the MedT region, contributing to approximately 30% of the MedT deepening. Meanwhile, the KE-driven Rossby waves intensify the background baroclinicity to the north of the Mediterranean Sea and thus enhance the transient eddy activity, consequently leading to another 20% of the MedT amplification via the eddy–mean flow interaction. The findings highlight the impacts of the KE state on the meteorological conditions in the Euro-Mediterranean region, which can potentially optimize the forecast accuracies of the MedT and associated Euro-Mediterranean climate variability

The Interesting Influence of Nanosprings on the Viscoelasticity of Elastomeric Polymer Materials: Simulation and Experiment

Among all carbon nanostructured materials, helical nanosprings or nanocoils have attracted particular interest as a result of their special mechanical behavior. Here, carbon nanosprings are used to adjust the viscoelasticity and reduce the resulting hysteresis loss (HL) of elastomeric polymer materials. Two types of nanospring‐filled elastomer composites are constructed as follows: system I is obtained by directly blending polymer chains with nanosprings; system II is composed of the self‐assembly of a tri‐block structure such as chain‐nanospring‐chain. Coarse‐grained molecular dynamics simulations show that the incorporation of nanosprings can improve the mechanical strength of the elastomer matrix through nanoreinforcement and considerably decrease the hysteresis loss. This finding is significant for reducing fuel consumption and improving fuel efficiency in the automobile tire industry. Furthermore, it is revealed that the spring constant of nanosprings and the interfacial chemical coupling between chains and nanosprings both play crucial roles in adjusting the viscoelasticity of elastomers. It is inferred that elastomer/carbon nanostructured materials with good flexibility and reversible mechanical response (carbon nanosprings, nanocoils, nanorings, and thin graphene sheets) have both excellent mechanical and low HL properties; this may open a new avenue for fabrication of high performance automobile tires and facilitate the large‐scale industrial application of these materials. Carbon nanosprings are found to have the capability to tune the mechanical and viscoelastic properties of elastomeric polymer materials. It is inferred that elastomer/carbon nanostructured materials with good flexibility and reversible mechanical response (i.e., carbon nanosprings, nanocoils, nanorings, and thin graphene sheets) have both excellent mechanical properties and low hysteresis loss.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96669/1/adfm_201201438_sm_suppl.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96669/2/1156_ftp.pd