Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks

Representation learning on networks aims to derive a meaningful vector representation for each node, thereby facilitating downstream tasks such as link prediction, node classification, and node clustering. In heterogeneous text-rich networks, this task is more challenging due to (1) presence or absence of text: Some nodes are associated with rich textual information, while others are not; (2) diversity of types: Nodes and edges of multiple types form a heterogeneous network structure. As pretrained language models (PLMs) have demonstrated their effectiveness in obtaining widely generalizable text representations, a substantial amount of effort has been made to incorporate PLMs into representation learning on text-rich networks. However, few of them can jointly consider heterogeneous structure (network) information as well as rich textual semantic information of each node effectively. In this paper, we propose Heterformer, a Heterogeneous Network-Empowered Transformer that performs contextualized text encoding and heterogeneous structure encoding in a unified model. Specifically, we inject heterogeneous structure information into each Transformer layer when encoding node texts. Meanwhile, Heterformer is capable of characterizing node/edge type heterogeneity and encoding nodes with or without texts. We conduct comprehensive experiments on three tasks (i.e., link prediction, node classification, and node clustering) on three large-scale datasets from different domains, where Heterformer outperforms competitive baselines significantly and consistently.Comment: KDD 2023. (Code: https://github.com/PeterGriffinJin/Heterformer

Research methods in economics to evaluate the role of energy efficiency and financial inclusion in achieving China’s carbon neutrality target

In the recent literature, energy efficiency got the attention of scholars due to its discouraging impact on CO2 emissions, which is considered the most prevalent greenhouse gas that human activities produce. Data reports that China is the leading CO2 emitting country across the globe, and still the environmental degradation is in progress. Thus, the current paper empirically investigates the impact of energy efficiency (ENEF), financial inclusion (FD), GDP, export diversification (EXD), and human capital index (HCI) on the environmental degradation of China over the period from 1988 to 2018. This study uses various time-series tests to empirically investigate the determinant of CO2 emissions, including normality tests, unit root tests, and combined cointegration tests. Besides, the long-run coefficients are analyzed via the fully modified ordinary least square (FMOLS), dynamic OLS (DOLS), and the Canonical Cointegrating Regression (CCR) estimators. The empirical findings reveal that all the variables are cointegrated in the long run. However, the coefficient estimate shows that ENEF and HCI significantly promote environmental sustainability. While GDP, FD, and EXD significantly promote environmental degradation by enhancing the CO2 level in the atmosphere. This study recommends practical policy implications based on the empirical findings: energy-efficient products and energy sources could be promoted

Spatial Variability of Relative Sea-Level Rise in Tianjin, China: Insight from InSAR, GPS, and Tide-Gauge Observations

The Tianjin coastal region in Bohai Bay, Northern China, is increasingly affected by storm-surge flooding which is exacerbated by anthropogenic land subsidence and global sea-level rise (SLR). We use a combination of synthetic aperture radar interferometry (InSAR), continuous GPS (CGPS), and tide-gauge observations to evaluate the spatial variability of relative SLR (RSLR) along the coastline of Tianjin. Land motion obtained by integration of 2 tracks of Sentinel-1 SAR images and 19 CGPS stations shows that the recent land subsidence in Tianjin downtown is less than 8 mm/yr, which has significantly decreased with respect to the last 50 years (up to 110 mm/yr in the 1980s). This might benefit from the South-to-North Water Transfer Project which has provided more than 1.8 billion cubic meters of water for Tianjin city since 2014 and reduced groundwater consumption. However, subsidence centers have shifted to suburbs, especially along the coastline dominated by reclaimed harbors and aquaculture industry, with localized subsidence up to 170 mm/yr. Combining InSAR observations with sea level records from tide-gauge stations reveals spatial variability of RSLR along the coastline. We find that, in the aquaculture zones along the coastline, the rates of land subsidence are as high as 82 mm/yr due to groundwater extraction for fisheries, which subsequently cause local sea levels to rise nearly 30 times faster than the global average. New insights into land subsidence and local SLR could help the country's regulators to make decisions on ensuring the sustainable development of the coastal aquaculture industry

Experimental generation of 6 dB continuous variable entanglement from a nondegenerate optical parametric amplifier

We experimentally demonstrated that the quantum correlations of amplitude and phase quadratures between signal and idler beams produced from a non-degenerate optical parametric amplifier (NOPA) can be significantly improved by using a mode cleaner in the pump field and reducing the phase fluctuations in phase locking systems. Based on the two technical improvements the quantum entanglement measured with a two-mode homodyne detector is enhanced from ~ 4 dB to ~ 6 dB below the quantum noise limit using the same NOPA and nonlinear crystal.Comment: 7 pages, 5 figure

The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study

Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature. Scientific literature tagging is beyond a pure multi-label text classification task because papers on the Web are prevalently accompanied by metadata information such as venues, authors, and references, which may serve as additional signals to infer relevant tags. Although there have been studies making use of metadata in academic paper classification, their focus is often restricted to one or two scientific fields (e.g., computer science and biomedicine) and to one specific model. In this work, we systematically study the effect of metadata on scientific literature tagging across 19 fields. We select three representative multi-label classifiers (i.e., a bag-of-words model, a sequence-based model, and a pre-trained language model) and explore their performance change in scientific literature tagging when metadata are fed to the classifiers as additional features. We observe some ubiquitous patterns of metadata's effects across all fields (e.g., venues are consistently beneficial to paper tagging in almost all cases), as well as some unique patterns in fields other than computer science and biomedicine, which are not explored in previous studies.Comment: 11 pages; Accepted to WWW 202

"Why Should I Review This Paper?" Unifying Semantic, Topic, and Citation Factors for Paper-Reviewer Matching

As many academic conferences are overwhelmed by a rapidly increasing number of paper submissions, automatically finding appropriate reviewers for each submission becomes a more urgent need than ever. Various factors have been considered by previous attempts on this task to measure the expertise relevance between a paper and a reviewer, including whether the paper is semantically close to, shares topics with, and cites previous papers of the reviewer. However, the majority of previous studies take only one of these factors into account, leading to an incomprehensive evaluation of paper-reviewer relevance. To bridge this gap, in this paper, we propose a unified model for paper-reviewer matching that jointly captures semantic, topic, and citation factors. In the unified model, a contextualized language model backbone is shared by all factors to learn common knowledge, while instruction tuning is introduced to characterize the uniqueness of each factor by producing factor-aware paper embeddings. Experiments on four datasets (one of which is newly contributed by us) across different fields, including machine learning, computer vision, information retrieval, and data mining, consistently validate the effectiveness of our proposed UniPR model in comparison with state-of-the-art paper-reviewer matching methods and scientific pre-trained language models

Submicron silicon powder production in an aerosol reactor

Powder synthesis by thermally induced vapor phase reactions is described. The powder generated by this technique consists of spherical, nonagglomerated particles of high purity. The particles are uniform in size, in the 0.1–0.2 µm size range. Most of the particles are crystalline spheres. A small fraction of the spheres are amorphous. Chain agglomerates account for less than 1% of the spherules