An Approximate Execution of Rule-Based Multi-level Models

Author(s):  
Tobias Helms ◽  
Martin Luboschik ◽  
Heidrun Schumann ◽  
Adelinde M. Uhrmacher
Keyword(s):  
2011 ◽  
Vol 5 (1) ◽  
pp. 166 ◽  
Author(s):  
Carsten Maus ◽  
Stefan Rybacki ◽  
Adelinde M Uhrmacher

EPE Journal ◽  
2000 ◽  
Vol 10 (1) ◽  
pp. 26-31 ◽  
Author(s):  
Moinuddin ◽  
A.S. Siddiqui ◽  
A.K. Sharma ◽  
J.P. Gupta

2013 ◽  
Vol 105 ◽  
pp. 304-318 ◽  
Author(s):  
João P. Trovão ◽  
Paulo G. Pereirinha ◽  
Humberto M. Jorge ◽  
Carlos Henggeler Antunes

2020 ◽  
Author(s):  
R. Bianco ◽  
G. Novembre ◽  
H. Ringer ◽  
N. Kohler ◽  
P.E. Keller ◽  
...  

Complex sequential behaviours, such as speaking or playing music, often entail the flexible, rule-based chaining of single acts. However, it remains unclear how the brain translates abstract structural rules into concrete series of movements. Here we demonstrate a multi-level contribution of anatomically distinct cognitive and motor networks to the execution of novel musical sequences. We combined functional and diffusion-weighted neuroimaging to dissociate high-level structural and low-level motor planning of musical chord sequences executed on a piano. Fronto-temporal and fronto-parietal neural networks were involved when sequences violated pianists’ structural or motor plans, respectively. Prefrontal cortex is identified as a hub where both networks converge within an anterior-to-posterior gradient of action control linking abstract structural rules to concrete movement sequences.


Author(s):  
Honglei Guo ◽  
Bang An ◽  
Zhili Guo ◽  
Zhong Su

Unstructured document compliance checking is always a big challenge for banks since huge amounts of contracts and regulations written in natural language require professionals' interpretation and judgment. Traditional rule-based or keyword-based methods cannot precisely characterize the deep semantic distribution in the unstructured document semantic compliance checking due to the semantic complexity of contracts and regulations. Deep Semantic Compliance Advisor (DSCA) is an unstructured document compliance checking platform which provides multi-level semantic comparison by deep learning algorithms. In the statement-level semantic comparison, a Graph Neural Network (GNN) based syntactic sentence encoder is proposed to capture the complicate syntactic and semantic clues of the statement sentences. This GNN-based encoder outperforms existing syntactic sentence encoders in deep semantic comparison and is more beneficial for long sentences. In the clause-level semantic comparison, an attention-based semantic relatedness detection model is applied to find the most relevant legal clauses. DSCA significantly enhances the productivity of legal professionals in the unstructured document compliance checking for banks.


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