Understanding Delegation Through Machine Learning: A Method and Application to the European Union

2019 ◽  
Vol 114 (1) ◽  
pp. 291-301 ◽  
Author(s):  
L. JASON ANASTASOPOULOS ◽  
ANTHONY M. BERTELLI
Keyword(s):  
Machine Learning ◽  
European Union ◽  
Empirical Studies ◽  
Learning Approach ◽  
The European Union ◽  
Empirical Estimation ◽  
Eu Legislation ◽  
Machine Learning Approach ◽  
Eu Directives ◽  
Delegation Of Powers

Delegation of powers represents a grant of authority by politicians to one or more agents whose powers are determined by the conditions in enabling statutes. Extant empirical studies of this problem have relied on labor-intensive content analysis that ultimately restricts our knowledge of how delegation has responded to politics and institutional change in recent years. We present a machine learning approach to the empirical estimation of authority and constraint in European Union (EU) legislation, and demonstrate its ability to accurately generate the same discretionary measures used in an original study directly using all EU directives and regulations enacted between 1958–2017. We assess validity by training our classifier on a random sample of only 10% of hand-coded provisions and replicating an important substantive finding. While our principal interest lies in delegation, our method is extensible to any context in which human coding has been profitably produced.

Harmonization of the Ukrainian Legislation with the Legislation of the European Union with Regard to Trade Defence

2020 ◽  
Vol 82 ◽  
pp. 174-188
Author(s):  
Iuliia Lokshyna
Keyword(s):  
European Union ◽  
The European Union ◽  
Normative Documents ◽  
Eu Legislation ◽  
Eu Directives ◽  
The Eu

The issue of the necessity of approximation, adaptation or harmonization of the Ukrainian legislation with the EU legislation has been tackled by a number of scholars in Ukraine. A number of normative documents also paid considerable attention to this issue in general. However, there is still an issue of defining the most suitable term which would better purpose bringing legislation into conformity with the requirements of the EU. According to some scholars the notion “harmonization” could better reflect this process. This view is also shared by the author of this article. The article also discusses the importance and the need to pass new draft laws in the field of trade defence in Ukraine, in particular, regarding anti-dumping, countervailing measures and safeguards. Since some of the new articles correspond to similar provisions in the EU directives, this is viewed as an important step to harmonize the Ukrainian legislation with the legislation of the European Union in this sphere.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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