90 research outputs found
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 and lepton flavor-violation tau decays
There are many models beyond the standard model predicting the existence of
non-universal gauge bosons , which can give rise to very rich
phenomena. We calculate the contributions of the non-universal gauge bosons
, predicted by topcolor-assisted technicolor (TC2) models and
flavor-universal TC2 models, to the lepton flavor-violation tau decays and . We find that the branching ratio
is larger than that of the process
in all of the parameter space. Over a sizable
region of the parameter space, we have , 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
Microstructure and Textural Properties of Yoghurts Produced by Exopolysaccharides- Producing Starter Cultures
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),
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