The California judge presiding over Elon Musk's lawsuit against OpenAI and its CEO, Sam Altman, has removed himself from the case. Judge Ethan Schulman on Monday sustained a challenge from Musk's lawyers, which cited a California state law that allows plaintiffs and defendants to remove a judge they believe cannot grant an impartial trial. The law, known as California Code of Civil Procedure 170.6, does not require the person issuing the challenge to provide any factual basis for their claim that the judge is prejudiced against them. Each side in a case gets one such peremptory challenge, which is granted as long as it is filed with correct language and within a certain time frame. Lawyers for Altman and Musk did not respond to requests for comment.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. President Biden took major hits this week, from the Pentagon confirming that a "tragic mistake" led to 10 civilians in Afghanistan dying in a drone strike, to the Food and Drug Administration rejecting his vaccine booster proposal, with much of the news breaking as the president headed to the beach for vacation. "So the U.S. drone strike did NOT kill any ISIS-K but did kill 10 innocent civilians, including 7 children. The Biden administration is a sad, tragic mess and an utter embarrassment on the world stage!,"

This paper studies the scheduling of jobs of different families on parallel machines with qualification constraints. Originating from semiconductor manufacturing, this constraint imposes a time threshold between the execution of two jobs of the same family. Otherwise, the machine becomes disqualified for this family. The goal is to minimize both the flow time and the number of disqualifications. Recently, an efficient constraint programming model has been proposed. However, when priority is given to the flow time objective, the efficiency of the model can be improved. This paper uses a polynomial-time algorithm which minimize the flow time for a single machine relaxation where disqualifications are not considered. Using this algorithm one can derived filtering rules on different variables of the model. Experimental results are presented showing the effectiveness of these rules. They improve the competitiveness with the mixed integer linear program of the literature.

This paper considers the scheduling of job families on parallel machines with time constraints on machine qualifications. In this problem, each job belongs to a family and a family can only be executed on a subset of qualified machines. In addition, machines can lose their qualifications during the schedule. Indeed, if no job of a family is scheduled on a machine during a given amount of time, the machine loses its qualification for this family. The goal is to minimize the sum of job completion times, i.e. the flow time, while maximizing the number of qualifications at the end of the schedule. The paper presents a new Constraint Programming (CP) model taking more advantages of the CP feature to model machine disqualifications. This model is compared with two existing models: an Integer Linear Programming (ILP) model and a Constraint Programming model. The experiments show that the new CP model outperforms the other model when the priority is given to the number of disqualifications objective. Furthermore, it is competitive with the other model when the flow time objective is prioritized.