Abstractive summarization for long-form narrative texts such as movie scripts is challenging due to the computational and memory constraints of current language models. A movie script typically comprises a large number of scenes; however, only a fraction of these scenes are salient, i.e., important for understanding the overall narrative. The salience of a scene can be operationalized by considering it as salient if it is mentioned in the summary. Automatically identifying salient scenes is difficult due to the lack of suitable datasets. In this work, we introduce a scene saliency dataset that consists of human-annotated salient scenes for 100 movies. We propose a two-stage abstractive summarization approach which first identifies the salient scenes in script and then generates a summary using only those scenes. Using QA-based evaluation, we show that our model outperforms previous state-of-the-art summarization methods and reflects the information content of a movie more accurately than a model that takes the whole movie script as input.

With the freight delivery demands and shipping costs increasing rapidly, intelligent control of fleets to enable efficient and cost-conscious solutions becomes an important problem. In this paper, we propose DeepFreight, a model-free deep-reinforcement-learning-based algorithm for multi-transfer freight delivery, which includes two closely-collaborative components: truck-dispatch and package-matching. Specifically, a deep multi-agent reinforcement learning framework called QMIX is leveraged to learn a dispatch policy, with which we can obtain the multi-step joint vehicle dispatch decisions for the fleet with respect to the delivery requests. Then an efficient multi-transfer matching algorithm is executed to assign the delivery requests to the trucks. Also, DeepFreight is integrated with a Mixed-Integer Linear Programming optimizer for further optimization. The evaluation results show that the proposed system is highly scalable and ensures a 100\% delivery success while maintaining low delivery-time and fuel consumption. The codes are available at https://github.com/LucasCJYSDL/DeepFreight.

The Data Scientist applies strong expertise in machine learning, data mining, and information retrieval to design, prototype, and build next generation advanced analytics engine and services. They collaborate with translators to define technical problem statement and hypothesis to test and develops efficient and accurate analytical models that mimic business decisions.

Twelve candidates are divided into three teams and given the task of assembling a box. Twelve Rolls Royce employees stand around them, one assigned to each candidate, taking notes. The box is a prop, and the test has nothing to do with programming or repairing the robots that make engine parts here. "We are looking at what they say, we are looking at what they do, we are looking at the body language of how they are interacting," says Lorin Sodell, the plant manager. This story is part of the The New Economy podcast series.

The bone fragments found in the Natalee Holloway case could match the DNA of any one of the four missing people who disappeared in or near Aruba. Holloway went missing during her vacation in Aruba, a Dutch Carribean island off Venezuela, on May 30, 2005. The investigation into Holloway's disappearance new human remains, which her parents hoped belonged to their daughter, but a subsequent forensic analysis proved they were not a match. Holloway's father and his private detective had uncovered human bone fragments in Aruba as part of the investigation that was chronicled on Oxygen's "The Disappearance of Natalee Holloway." One of the four bone samples recovered in Aruba was that of human, and Dr. Jason Kolowski, a forensic scientist, said the human bone fragments belong to a single individual.