Generalized energy and gradient flow via graph framelets

In this work, we provide a theoretical understanding of the framelet-based graph neural networks through the perspective of energy gradient flow. By viewing the framelet-based models as discretized gradient flows of some energy, we show it can induce both low-frequency and high-frequency-dominated dynamics, via the separate weight matrices for different frequency components. This substantiates its good empirical performance on both homophilic and heterophilic graphs. We then propose a generalized energy via framelet decomposition and show its gradient flow leads to a novel graph neural network, which includes many existing models as special cases. We then explain how the proposed model generally leads to more flexible dynamics, thus potentially enhancing the representation power of graph neural networks

Generalized Laplacian Regularized Framelet GCNs

This paper introduces a novel Framelet Graph approach based on p-Laplacian GNN. The proposed two models, named p-Laplacian undecimated framelet graph convolution (pL-UFG) and generalized p-Laplacian undecimated framelet graph convolution (pL-fUFG) inherit the nature of p-Laplacian with the expressive power of multi-resolution decomposition of graph signals. The empirical study highlights the excellent performance of the pL-UFG and pL-fUFG in different graph learning tasks including node classification and signal denoising

Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond

Graph Neural Networks (GNNs) have emerged as one of the leading approaches for machine learning on graph-structured data. Despite their great success, critical computational challenges such as over-smoothing, over-squashing, and limited expressive power continue to impact the performance of GNNs. In this study, inspired from the time-reversal principle commonly utilized in classical and quantum physics, we reverse the time direction of the graph heat equation. The resulted reversing process yields a class of high pass filtering functions that enhance the sharpness of graph node features. Leveraging this concept, we introduce the Multi-Scaled Heat Kernel based GNN (MHKG) by amalgamating diverse filtering functions' effects on node features. To explore more flexible filtering conditions, we further generalize MHKG into a model termed G-MHKG and thoroughly show the roles of each element in controlling over-smoothing, over-squashing and expressive power. Notably, we illustrate that all aforementioned issues can be characterized and analyzed via the properties of the filtering functions, and uncover a trade-off between over-smoothing and over-squashing: enhancing node feature sharpness will make model suffer more from over-squashing, and vice versa. Furthermore, we manipulate the time again to show how G-MHKG can handle both two issues under mild conditions. Our conclusive experiments highlight the effectiveness of proposed models. It surpasses several GNN baseline models in performance across graph datasets characterized by both homophily and heterophily

Preclinical Evaluation of Radioiodinated Hoechst 33258 for Early Prediction of Tumor Response to Treatment of Vascular-Disrupting Agents

This study aimed to explore the use of 131I-Hoechst 33258 (131I-H33258) for early prediction of tumor response to vascular-disrupting agents (VDAs) with combretastatin-A4 phosphate (CA4P) as a representative. Necrosis avidity of 131I-H33258 was evaluated in mouse models with muscle necrosis and blocking was used to confirm the tracer specificity. Therapy response was evaluated by 131I-H33258 SPECT/CT imaging 24 h after CA4P therapy in W256 tumor-bearing rats. Radiotracer uptake in tumors was validated ex vivo using γ-counting, autoradiography, and histopathological staining. Results showed that 131I-H33258 had predominant necrosis avidity and could specifically bind to necrotic tissue. SPECT/CT imaging demonstrated that an obvious “hot spot” could be observed in the CA4P-treated tumor. Ex vivo γ-counting revealed 131I-H33258 uptake in tumors was increased 2.8-fold in rats treated with CA4P relative to rats treated with vehicle. Autoradiography and corresponding H&E staining suggested that 131I-H33258 was mainly localized in necrotic tumor area and the higher overall uptake in the treated tumors was attributed to the increased necrosis. These results suggest that 131I-H33258 can be used to image induction of cell necrosis 24 h after CA4P therapy, which support further molecular design of probes based on scaffold H33258 for monitoring of tumor response to VDAs treatment

Engineering grain boundaries at the 2D limit for the hydrogen evolution reaction

Atom-thin transition metal dichalcogenides (TMDs) have emerged as fascinating materials and key structures for electrocatalysis. So far, their edges, dopant heteroatoms and defects have been intensively explored as active sites for the hydrogen evolution reaction (HER) to split water. However, grain boundaries (GBs), a key type of defects in TMDs, have been overlooked due to their low density and large structural variations. Here, we demonstrate the synthesis of wafer-size atom-thin TMD films with an ultra-high-density of GBs, up to ~1012 cm−2. We propose a climb and drive 0D/2D interaction to explain the underlying growth mechanism. The electrocatalytic activity of the nanograin film is comprehensively examined by micro-electrochemical measurements, showing an excellent hydrogen-evolution performance (onset potential: −25 mV and Tafel slope: 54 mV dec−1), thus indicating an intrinsically high activation of the TMD GBs

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Geophysical Interpretation of Horizontal Fractures in Shale Oil Reservoirs Using Rock Physical and Seismic Methods

Horizontal fractures are one of the factors that significantly affect the ultimate productivity of shale oil reservoirs. However, the prediction of horizontal fractures by using seismic methods remains a challenge, which is due to the complex elastic and seismic responses that are associated with horizontal fractures. A framework that predicts horizontal fractures by seismic rock physical methods has been developed in the present study. A shale model is then proposed to quantify the shale elastic responses that are associated with the properties of the horizontal fractures. The modeling results that are based on the logging data validated the applicability of the proposed model, and the predicted fracture properties could be used to evaluate the development of horizontal fractures. According to the framework of the Poisson impedance, a horizontal fracture indicator is suggested to represent the logging-derived fracture density in terms of a combination of elastic properties. By using seismic-inverted elastic properties, the obtained indicator enabled an estimation of zones with the potential development of horizontal fractures. The established indicator showed a good correlation with the fracture density and could be used as an effective indicator in the prediction of horizontal fractures in shale oil reservoirs. Furthermore, seismic data applications show a consistency between the development of horizontal fractures and the production status of the boreholes. This result highlights the importance of horizontal fractures for the ultimate productivity and emphasizes the applicability of the proposed methods