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

Mechanism of deep eutectic solvent on coal spontaneous combustion

Chemical inhibition is one of the important measures for the prevention and control of coal spontaneous combustion. This paper proposed a quasi-ionic liquid inhibition method based on deep eutectic solvents (DES). First, seven kinds of room temperature deep eutectic solvents were prepared and screened using a heating method. The changes in the functional groups and thermodynamic characteristics of different DES-treated coal samples were analyzed. On this basis, the density functional theory was utilized to analyze the differences in the modification of coal's physicochemical properties by the hydrogen bond strength in the DES, and the inhibition mechanism of deep eutectic solvents and their optimal hydrogen bond strength were deduced. The results showed that after the DES treatment, the hydrogen bond network in coal was disrupted and rearranged. The relative abundance of aliphatic and aromatic hydrocarbons increased by 10%−37%, the content of aliphatic side chains decreased by 9.38%−20.65%, the relative abundance of oxygen-containing functional groups (C=O and C—O) decreased by 22.88%−56.94%, and free low-molecular compound and minerals were leached out. After the DES treatment, the mass loss during the evaporation and desorption stage of coal and the oxygen uptake during the oxygen absorption stage decreased. The heat release during the low temperature oxidation stage and the thermal decomposition stage was reduced by 8.94%−77.51% and 5.40%−26.20%, respectively. The stronger the electronegativity of the hydrogen bond acceptor site in the HBA, the greater the hydrogen bond strength formed between HBA and HBD. The hydrogen bond strength in the DES was positively correlated with the degree of destruction of the hydrogen bond network in coal, and was locally correlated with the oxygen uptake during the oxygen absorption stage, the heat release during low temperature oxidation, and the mineral removal rate. The DES weakened the low-temperature oxidation reactivity of coal by dissolving its active components, and increased the bond dissociation enthalpy of coal by promoting the rearrangement of hydrogen bonds into more thermally stable [OH]4 and OH—N hydrogen bonds. However, the excessive strong hydrogen bond strength would inhibit the removal and dissolution of active side chains. Therefore, the hydrogen bond strength of deep eutectic solvents used to inhibit coal spontaneous combustion should be controlled between 69.45 kJ/mol and 160.00 kJ/mol

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

Peripheral cutaneous synucleinopathy characteristics in genetic Parkinson’s disease

BackgroundCutaneous phosphorylated alpha-synuclein (p-α-syn) deposition is an important biomarker of idiopathic Parkinson’s disease (iPD). Recent studies have reported synucleinopathies in patients with common genetic forms of PD.ObjectiveThis study aimed to detect p-α-syn deposition characteristic in rare genetic PD patients with CHCHD2 or RAB39B mutations. Moreover, this study also aimed to describe peripheral alpha-synuclein prion-like activity in genetic PD patients, and acquire whether the cutaneous synucleinopathy characteristics of genetic PD are consistent with central neuropathologies.MethodsWe performed four skin biopsy samples from the distal leg (DL) and proximal neck (C7) of 161 participants, including four patients with CHCHD2 mutations, two patients with RAB39B mutations, 16 patients with PRKN mutations, 14 patients with LRRK2 mutations, five patients with GBA mutations, 100 iPD patients, and 20 healthy controls. We detected cutaneous synucleinopathies using immunofluorescence staining and a seeding amplification assay (SAA). A systematic literature review was also conducted, involving 64 skin biopsies and 205 autopsies of genetic PD patients with synucleinopathy.ResultsP-α-syn was deposited in the peripheral cutaneous nerves of PD patients with CHCHD2, LRRK2, or GBA mutations but not in those with RAB39B or PRKN mutations. There were no significant differences in the location or rate of α-syn-positive deposits between genetic PD and iPD patients. Peripheral cutaneous synucleinopathy appears to well represent brain synucleinopathy of genetic PD, especially autosomal dominant PD (AD-PD). Cutaneous α-synuclein SAA analysis of iPD and LRRK2 and GBA mutation patients revealed prion-like activity.ConclusionP-α-syn deposition in peripheral cutaneous nerves, detected using SAA and immunofluorescence staining, may serve as an accurate biomarker for genetic PD and iPD in the future

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