MCDAN: a Multi-scale Context-enhanced Dynamic Attention Network for Diffusion Prediction

Information diffusion prediction aims at predicting the target users in the information diffusion path on social networks. Prior works mainly focus on the observed structure or sequence of cascades, trying to predict to whom this cascade will be infected passively. In this study, we argue that user intent understanding is also a key part of information diffusion prediction. We thereby propose a novel Multi-scale Context-enhanced Dynamic Attention Network (MCDAN) to predict which user will most likely join the observed current cascades. Specifically, to consider the global interactive relationship among users, we take full advantage of user friendships and global cascading relationships, which are extracted from the social network and historical cascades, respectively. To refine the model's ability to understand the user's preference for the current cascade, we propose a multi-scale sequential hypergraph attention module to capture the dynamic preference of users at different time scales. Moreover, we design a contextual attention enhancement module to strengthen the interaction of user representations within the current cascade. Finally, to engage the user's own susceptibility, we construct a susceptibility label for each user based on user susceptibility analysis and use the rank of this label for auxiliary prediction. We conduct experiments over four widely used datasets and show that MCDAN significantly overperforms the state-of-the-art models. The average improvements are up to 10.61% in terms of Hits@100 and 9.71% in terms of MAP@100, respectively

Robust Counterfactual Explanations on Graph Neural Networks

Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuition. Most existing methods generate explanations by identifying a subgraph of an input graph that has a strong correlation with the prediction. These explanations are not robust to noise because independently optimizing the correlation for a single input can easily overfit noise. Moreover, they do not align well with human intuition because removing an identified subgraph from an input graph does not necessarily change the prediction result. In this paper, we propose a novel method to generate robust counterfactual explanations on GNNs by explicitly modelling the common decision logic of GNNs on similar input graphs. Our explanations are naturally robust to noise because they are produced from the common decision boundaries of a GNN that govern the predictions of many similar input graphs. The explanations also align well with human intuition because removing the set of edges identified by an explanation from the input graph changes the prediction significantly. Exhaustive experiments on many public datasets demonstrate the superior performance of our method

Automatic Data Transformation Using Large Language Model: An Experimental Study on Building Energy Data

Existing approaches to automatic data transformation are insufficient to meet the requirements in many real-world scenarios, such as the building sector. First, there is no convenient interface for domain experts to provide domain knowledge easily. Second, they require significant training data collection overheads. Third, the accuracy suffers from complicated schema changes. To bridge this gap, we present a novel approach that leverages the unique capabilities of large language models (LLMs) in coding, complex reasoning, and zero-shot learning to generate SQL code that transforms the source datasets into the target datasets. We demonstrate the viability of this approach by designing an LLM-based framework, termed SQLMorpher, which comprises a prompt generator that integrates the initial prompt with optional domain knowledge and historical patterns in external databases. It also implements an iterative prompt optimization mechanism that automatically improves the prompt based on flaw detection. The key contributions of this work include (1) pioneering an end-to-end LLM-based solution for data transformation, (2) developing a benchmark dataset of 105 real-world building energy data transformation problems, and (3) conducting an extensive empirical evaluation where our approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the effectiveness of utilizing LLMs in complex, domain-specific challenges, highlighting the potential of their potential to drive sustainable solutions.Comment: 10 pages, 7 figure

Non-universal gauge bosons Z′Z^{\prime} and lepton flavor-violation tau decays

There are many models beyond the standard model predicting the existence of non-universal gauge bosons $Z^{\prime}$, which can give rise to very rich phenomena. We calculate the contributions of the non-universal gauge bosons $Z^{\prime}$, predicted by topcolor-assisted technicolor (TC2) models and flavor-universal TC2 models, to the lepton flavor-violation tau decays $\tau\to l_{i}\gamma$ and $\tau\to l_{i}l_{j}l_{k}$. We find that the branching ratio $B_{r}(\tau\longrightarrow l_{i}l_{j}l_{k})$ is larger than that of the process $\tau\longrightarrow l_{i}\gamma$ in all of the parameter space. Over a sizable region of the parameter space, we have $B_{r}(\tau\longrightarrow l_{i}l_{j}l_{k})\sim 10^{-8}$, which may be detected in the future experiments.Comment: Final version to appear in Phys. Lett. B. References added and typos correcte

Research on the Composition and Distribution of Organic Sulfur in Coal

The structure and distribution of organic sulfur in coals of different rank and different sulfur content were studied by combining mild organic solvent extraction with XPS technology. The XPS results have shown that the distribution of organic sulfur in coal is related to the degree of metamorphism of coal. Namely, thiophenic sulfur content is reduced with decreasing metamorphic degree; sulfonic acid content rises with decreasing metamorphic degree; the contents of sulfate sulfur, sulfoxide and sulfone are rarely related with metamorphic degree. The solvent extraction and GC/MS test results have also shown that the composition and structure of free and soluble organic sulfur small molecules in coal is closely related to the metamorphic degree of coal. The free organic sulfur small molecules in coal of low metamorphic degree are mainly composed of aliphatic sulfides, while those in coal of medium and high metamorphic degree are mainly composed of thiophenes. Besides, the degree of aromatization of organic sulfur small molecules rises with increasing degree of coalification

The Argument and Consensus of Lymphadenectomy on Lung Cancer Surgery

Lymph node metastasis is an important route of metastasis of lung cancer. Lymphadenectomy has become the standard surgical procedure for lung cancer. The way of intraoperative lymph node assessment also affects the prognosis and treatment strategy of lung cancer. In clinical practice, the way of intraoperative lymph node assessment ranges from selected lymph node biopsy to extended lymph node dissection. The advantages and disadvantages of different lymph node assessment are still controversial. In this article, the argument and consensus of lymphadenectomy on lung cancer operation are summarized

Personalized Cross-Silo Federated Learning on Non-IID Data

Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing to facilitate similar clients to collaborate more. We establish the convergence of FedAMP for both convex and non-convex models, and propose a heuristic method to further improve the performance of FedAMP when clients adopt deep neural networks as personalized models. Our extensive experiments on benchmark data sets demonstrate the superior performance of the proposed methods.Comment: Accepted by AAAI 2021. The API of this work is available at Huawei Cloud (https://developer.huaweicloud.com/develop/aigallery/notebook/detail?id=6d4a9521-6a4d-4b6d-b84d-943d7c7b1cbd), free registration is required before us