Integer Linear Programming in Computational and Systems Biology: An Entry-Level Text and Course. By Dan Gusfield. Cambridge and New York: Cambridge University Press. $64.99. xviii + 412 p.; ill.; index. ISBN: 9781108421768. 2019.

2020 ◽  
Vol 95 (3) ◽  
pp. 254-254
Keyword(s):  
New York ◽  
Linear Programming ◽  
Systems Biology ◽  
Integer Linear Programming ◽  
Cambridge University ◽  
Entry Level

scholarly journals Mapping Entity Sets in News Archives Across Time

2019 ◽  
Vol 4 (3) ◽  
pp. 208-222 ◽  
Author(s):  
Yijun Duan ◽  
Adam Jatowt ◽  
Sourav S. Bhowmick ◽  
Masatoshi Yoshikawa
Keyword(s):  
New York ◽  
Linear Programming ◽  
Integer Linear Programming ◽  
Orthogonal Transformation ◽  
New York Times ◽  
The New York Times ◽  
Qualitative And Quantitative ◽  
The Past ◽  
Programming Framework

Abstract We propose a novel way of utilizing and accessing information stored in news archives as well as a new style of investigating the history. Our idea is to automatically generate similar entity pairs given two sets of entities, one from the past and one representing the present. This allows performing entity-oriented mapping between different times. We introduce an effective method to solve the aforementioned task based on a concise integer linear programming framework. In particular, our model first conducts typicality analysis to estimate entity representativeness. It next constructs orthogonal transformation between the two entity collections. The result is a set of typical across-time comparables. We demonstrate the effectiveness of our approach on the New York Times dataset through both qualitative and quantitative tests.

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