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Pro-life publisher facing demands to retract calling abortion 'killing' fires back: 'The answer is No'

FOX News

Live Action refuses demands to retract abortion reporting after attorneys for 13 abortion rights advocates accused the organization of publishing false claims about their pregnancies.


Pro-life journalist assaulted on street assigns blame to Democratic rhetoric

FOX News

'Live Action' journalist Savannah Craven Antao speaks out after being punched by an interviewee on'The Will Cain Show.' Pro-life activist Savannah Craven Antao believes the Democratic Party's recent rhetoric about "punching" at their Republican opponents contributed to the attack that left her bloody during a recent interview. Antao, a young pro-life influencer who was punched in the face by a woman she was interviewing in New York City earlier this month, pointed to Rep. Jasmine Crockett's, D-Texas, recent line about Democrats "punching" as inspiring the attack that happened to her. "She said, 'I think that you punch,'" Antao told Fox News Digital. "'I think you're okay with punching.' So yeah – pretty much just describes the left at this point. They're totally fine with just using force like that to hurt people if they don't agree with them."


Detection of Problem Gambling with Less Features Using Machine Learning Methods

arXiv.org Artificial Intelligence

Analytic features in gambling study are performed based on the amount of data monitoring on user daily actions. While performing the detection of problem gambling, existing datasets provide relatively rich analytic features for building machine learning based model. However, considering the complexity and cost of collecting the analytic features in real applications, conducting precise detection with less features will tremendously reduce the cost of data collection. In this study, we propose a deep neural networks PGN4 that performs well when using limited analytic features. Through the experiment on two datasets, we discover that PGN4 only experiences a mere performance drop when cutting 102 features to 5 features. Besides, we find the commonality within the top 5 features from two datasets.