Capturing Topology in Graph Pattern Matching

Graph pattern matching is often defined in terms of subgraph isomorphism, an NP-complete problem. To lower its complexity, various extensions of graph simulation have been considered instead. These extensions allow pattern matching to be conducted in cubic-time. However, they fall short of capturing the topology of data graphs, i.e., graphs may have a structure drastically different from pattern graphs they match, and the matches found are often too large to understand and analyze. To rectify these problems, this paper proposes a notion of strong simulation, a revision of graph simulation, for graph pattern matching. (1) We identify a set of criteria for preserving the topology of graphs matched. We show that strong simulation preserves the topology of data graphs and finds a bounded number of matches. (2) We show that strong simulation retains the same complexity as earlier extensions of simulation, by providing a cubic-time algorithm for computing strong simulation. (3) We present the locality property of strong simulation, which allows us to effectively conduct pattern matching on distributed graphs. (4) We experimentally verify the effectiveness and efficiency of these algorithms, using real-life data and synthetic data.Comment: VLDB201

The role of sustainable development goals, financial knowledge and investment strategies on the organizational profitability: Moderating impact of government support

Recently, sustainable development goals (SDG) and investment strategies and knowledge has become the foremost factors for the high organizational profitability and capture the focus of recent studies and policymakers. Therefore, the current study aims to examine the impact of SDG, investment strategies and financial knowledge on the organizational profitability of manufacturing firms in China. Furthermore, the study examines the role of government support in the interplay between investment plans, financial understanding, and the profitability of organisations. Survey questionnaires and smart-PLS were used to collect data and analyse reliability and correlations. The findings show that SDGs, investment strategies, and financial knowledge all play a substantial role in a company’s profitability.The results also revealed that government support moderates significantly among investment strategies, financial knowledge, and organizational profitability. This study guides the regulators while developing policies regarding SDG and investment strategies with respect to organizational profitability

Reversible Anionic Redox Activities in Conventional LiNi1/3 Co1/3 Mn1/3 O2 Cathodes.

Redox reactions of oxygen have been considered critical in controlling the electrochemical properties of lithium-excessive layered-oxide electrodes. However, conventional electrode materials without overlithiation remain the most practical. Typically, cationic redox reactions are believed to dominate the electrochemical processes in conventional electrodes. Herein, we show unambiguous evidence of reversible anionic redox reactions in LiNi1/3 Co1/3 Mn1/3 O2 . The typical involvement of oxygen through hybridization with transition metals is discussed, as well as the intrinsic oxygen redox process at high potentials, which is 75 % reversible during initial cycling and 63 % retained after 10 cycles. Our results clarify the reaction mechanism at high potentials in conventional layered electrodes involving both cationic and anionic reactions and indicate the potential of utilizing reversible oxygen redox reactions in conventional layered oxides for high-capacity lithium-ion batteries

The operation modal analysis of the structure crack fault diagnosis based on pseudo-successive data

In order to monitor the crack propagation of the structure in the working state for a long time, an operation modal analysis method based on pseudo-successive data is proposed. The vibration response signals of the cantilever beam under white noise excitation are collected and the modal parameters are extracted by the time-frequency operation modal analysis method based on the complex Morlet wavelet. In comparison with the experimental modal analysis results of hammering method, it is revealed that the error of the time-frequency operation modal analysis method is less than 10 %. By setting cracks of different lengths on the cantilever beam, the vibration response signals are extracted, and the modal parameters are extracted by the operation modal analysis method separately. By comparing those modal parameters above, it is found that the natural frequencies of the second, the fourth and the sixth orders decrease with the increase of the crack depth, and the changes of natural frequencies show the monotonicity. So, it can be used as an index for quantitative identification of crack damage. The pseudo continuous data monitoring signals of crack propagation can be constructed by means of “first discrete, then continuous”. The modal parameters changes of the whole crack propagation can be observed in one time plane by means of the operation modal analysis method. Therefore, the effective monitoring and diagnosis of the structure can be completed in case of excessive data of long-time vibration monitoring signals

Improving Radiology Summarization with Radiograph and Anatomy Prompts

The impression is crucial for the referring physicians to grasp key information since it is concluded from the findings and reasoning of radiologists. To alleviate the workload of radiologists and reduce repetitive human labor in impression writing, many researchers have focused on automatic impression generation. However, recent works on this task mainly summarize the corresponding findings and pay less attention to the radiology images. In clinical, radiographs can provide more detailed valuable observations to enhance radiologists' impression writing, especially for complicated cases. Besides, each sentence in findings usually focuses on single anatomy, so they only need to be matched to corresponding anatomical regions instead of the whole image, which is beneficial for textual and visual features alignment. Therefore, we propose a novel anatomy-enhanced multimodal model to promote impression generation. In detail, we first construct a set of rules to extract anatomies and put these prompts into each sentence to highlight anatomy characteristics. Then, two separate encoders are applied to extract features from the radiograph and findings. Afterward, we utilize a contrastive learning module to align these two representations at the overall level and use a co-attention to fuse them at the sentence level with the help of anatomy-enhanced sentence representation. Finally, the decoder takes the fused information as the input to generate impressions. The experimental results on two benchmark datasets confirm the effectiveness of the proposed method, which achieves state-of-the-art results.Comment: 11 pages, ACL2023 Finding