Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First

Graph Neural Networks (GNNs) have achieved great success in representing data with dependencies by recursively propagating and aggregating messages along the edges. However, edges in real-world graphs often have varying degrees of difficulty, and some edges may even be noisy to the downstream tasks. Therefore, existing GNNs may lead to suboptimal learned representations because they usually treat every edge in the graph equally. On the other hand, Curriculum Learning (CL), which mimics the human learning principle of learning data samples in a meaningful order, has been shown to be effective in improving the generalization ability and robustness of representation learners by gradually proceeding from easy to more difficult samples during training. Unfortunately, existing CL strategies are designed for independent data samples and cannot trivially generalize to handle data dependencies. To address these issues, we propose a novel CL strategy to gradually incorporate more edges into training according to their difficulty from easy to hard, where the degree of difficulty is measured by how well the edges are expected given the model training status. We demonstrate the strength of our proposed method in improving the generalization ability and robustness of learned representations through extensive experiments on nine synthetic datasets and nine real-world datasets. The code for our proposed method is available at https://github.com/rollingstonezz/Curriculum_learning_for_GNNs.Comment: Accepted by NeurIPS 202

Quantum-dot gain without inversion:Effects of dark plasmon-exciton hybridization

We propose an initial-state-dependent quantum-dot gain without population inversion in the vicinity of a resonant metallic nanoparticle. The gain originates from the hybridization of a dark plasmon-exciton and is accompanied by efficient energy transfer from the nanoparticle to the quantum dot. This hybridization of the dark plasmon-exciton, attached to the hybridization of the bright plasmon-exciton, strengthens nonlinear light-quantum emitter interactions at the nanoscale, thus the spectral overlap between the dark and the bright plasmons enhances the gain effect. This hybrid system has potential applications in ultracompact tunable quantum devices.Physics, Condensed MatterSCI(E)[email protected]

Generative-Discriminative Complementary Learning

Majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases. In this paper, we study the complementary learning problem. Unlike ordinary labels, complementary labels are easy to obtain because an annotator only needs to provide a yes/no answer to a randomly chosen candidate class for each instance. We propose a generative-discriminative complementary learning method that estimates the ordinary labels by modeling both the conditional (discriminative) and instance (generative) distributions. Our method, we call Complementary Conditional GAN (CCGAN), improves the accuracy of predicting ordinary labels and can generate high-quality instances in spite of weak supervision. In addition to the extensive empirical studies, we also theoretically show that our model can retrieve the true conditional distribution from the complementarily-labeled data

Emergent social capital from styles of organizing: a case study of creative industry cluster in China

The research presented here was inspired by the growing interest in enterprise cluster research and the need to better understand the creation of social capital in a cluster. By exploring how social capital is created and grown in a specific cluster, which adopts an innovative style of organizing, it will show that the creation and management of clusters is not only an economic issue, but also a social psychological one. This study not only aims to make a contribution to the literature of social capital and cluster, but also seeks to provide practical solutions for building sustainable creative clusters in China. In the theoretical part, an overview of China’s creative clusters and the challenges faced by their top-down style of organizing will be provided. After reviewing the classic literature of cluster and social capital, the potential contribution from social psychology to the construction of social capital in cluster will be examined. In the empirical part, the focus is on a specific bottom-up case named Xindanwei. The present researcher entered into the field as both an academic observer and a wine merchant. Two main research questions have guided the research： 1. What is social capital in the real-world cluster? 2. How can social capital be created in a cluster? Data were collected through semi-structured interviews, participant observation and web documents. Thematic analysis and a theory-driven approach were applied in the interpretation of data. The findings indicate the emergent and context-based nature of social capital, which appears to originate from dynamic guanxi (with physical, institutional and mental elements) rather than static network. Social capital is usually perceived as individually owned asset, but here it can also be co-owned by participating members in cluster. In order to generate social capital, it not only calls for a facilitative platform with multi-level installations, but also requires cluster members to take productive actions and consider the context constraints in the real world. In the end, a facilitative framework to create social capital in clusters is proposed

Degenerate lower dimensional tori in reversible systems

AbstractIn this paper we prove the persistence of lower dimensional invariant tori with prescribed frequencies and singular normal matrices in reversible systems. The normal variable is two-dimensional and the unperturbed nonlinear terms in the differential equation for this variable have a special structure

Morph-specific differences in life history traits between the winged and wingless morphs of the aphid, Sitobion avenae (Fabricius) (Hemiptera: Aphididae)

Life history traits were evaluated in the wing polyphenic aphid, Sitobion avenae (Fabricius), by rearing the winged and wingless morphs under the laboratory conditions. Winged morph with large thoraces exhibited a significantly greater morphological investment in flight apparatus than wingless morph with small thoraces. Compared to the winged morph, the wingless morph produced significantlymore nymphs and exhibited significantly faster nymph development rates. In addition, the age at which reproduction first occurred for the winged morph was significantly delayed, and higher mortality was recorded.The results suggest that the fitness differences associated with wingsmay be related to nymph development, adult fecundity, and mortality. Based on these results, the trends and exceptions of life history traits for the wing polyphenic insects are discussed