Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatio-temporal invariant patterns. Specifically, we first design the environment-aware EA-DGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.Comment: Accepted by the 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification

Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topological properties of the nodes (e.g., locations, roles) are unbalanced (topology-imbalance), other than the number of training labeled nodes (quantity-imbalance). Existing studies on topology-imbalance focus on the location or the local neighborhood structure of nodes, ignoring the global underlying hierarchical properties of the graph, i.e., hierarchy. In the real-world scenario, the hierarchical structure of graph data reveals important topological properties of graphs and is relevant to a wide range of applications. We find that training labeled nodes with different hierarchical properties have a significant impact on the node classification tasks and confirm it in our experiments. It is well known that hyperbolic geometry has a unique advantage in representing the hierarchical structure of graphs. Therefore, we attempt to explore the hierarchy-imbalance issue for node classification of graph neural networks with a novelty perspective of hyperbolic geometry, including its characteristics and causes. Then, we propose a novel hyperbolic geometric hierarchy-imbalance learning framework, named HyperIMBA, to alleviate the hierarchy-imbalance issue caused by uneven hierarchy-levels and cross-hierarchy connectivity patterns of labeled nodes.Extensive experimental results demonstrate the superior effectiveness of HyperIMBA for hierarchy-imbalance node classification tasks.Comment: Accepted by Web Conference (WWW) 202

A Carbon Nanotube-based Hundred Watt-level Ka-band Backward Wave Oscillator

Carbon nanotube (CNT) cold-cathodes hold much promise in a variety of millimeter-wave and terahertz vacuum electronic radiation devices due to their inherent near instantaneous temporal turn-on and near-ideal ideal field electron emission performance. Here we report on the development of a CNT cold-cathode Ka -band backward-wave oscillator (BWO). Using a novel beam compression stage, theoretical studies, simulation results, and empirical findings collectively demonstrate that this device affords an unprecedentedly high output power of 230 W at a technologically important operating frequency of 33.65 GHz. The developed magnetic injection electron gun achieves a high emission current of 265.5 mA (emission current density of 188.3 mA/cm 2 ) and a high focused beam current density of 18.5 A/cm 2 , which our studies suggest, is essential to the BWOs high output power

Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective

Off-policy Learning to Rank (LTR) aims to optimize a ranker from data collected by a deployed logging policy. However, existing off-policy learning to rank methods often make strong assumptions about how users generate the click data, i.e., the click model, and hence need to tailor their methods specifically under different click models. In this paper, we unified the ranking process under general stochastic click models as a Markov Decision Process (MDP), and the optimal ranking could be learned with offline reinforcement learning (RL) directly. Building upon this, we leverage offline RL techniques for off-policy LTR and propose the Click Model-Agnostic Unified Off-policy Learning to Rank (CUOLR) method, which could be easily applied to a wide range of click models. Through a dedicated formulation of the MDP, we show that offline RL algorithms can adapt to various click models without complex debiasing techniques and prior knowledge of the model. Results on various large-scale datasets demonstrate that CUOLR consistently outperforms the state-of-the-art off-policy learning to rank algorithms while maintaining consistency and robustness under different click models

A Highly Overmoded Structure for Hundred-Kilowatt-Class <italic>Ka</italic>-Band Extended Interaction Klystron

Here, we report on the development of highly overmoded structure for a millimeter-wave (MMW) extended interaction klystron (EIK). To enhance electron beam loading, a new method for designing an oversized beam tunnel in a large cavity by concentrating the axial field is demonstrated. The transmission and oscillation characteristics of the interaction circuit operating in the quasi-TM04 mode are tested by the developed mode conversion circuit. Results suggest that a five-cavity EIK based on this highly overmoded structure can achieve an output power of 289 kW at 32.92 GHz with a saturated gain of 51.6 dB by injecting a 3.3-mm-diameter electron beam with a current of 18 A. The output power exceeds 100 kW at a bandwidth of 100 MHz.</p