MSNBC's "Morning Joe" reacted to the networks upcoming name change, "My Source News Opinion World," or MS NOW, on Monday. But don't shed a tear (not that you would, anyway), it's turning into MS NOW. Or, as the New York Times put it, "Goodbye, MSNBC. The far-left network lost its tie to the newsy term "NBC" and looks more like some feminist retread site. Or, as MSNBC President Rebecca Kutler put it, "While our name will be changing, who we are and what we do will not." So, maybe my viewership assessment is correct. Sure, the ship might have made a career of hitting icebergs, but it's got a new name. The fallout from the change was swift. The Times even took a swipe with the follow-up headline: "MSNBC's Rebrand Invites Bemusement and Ridicule." The name switch reflects marketing nonsense as part of the corporate split. It also eliminates the long-standing comparison to MSDNC. The rationalization for the new name is: "My Source for News, Opinion, and the World." CNBC is going to keep its name, according to the Wall Street Journal, but the initials mean something else – "Consumer News and Business Channel," another marketing nuance. The new company will include, "NBCUniversal's cable television networks, including USA Network, CNBC, MSNBC, Oxygen, E!, SYFY and Golf Channel" along with a few other properties, including the formerly useful Rotten Tomatoes movie site. Nobody sane wants MSNBC/MS NOW connected in any way to NBC. It's been a corporate embarrassment for years. They're OK with it looking like the rational folks at CNBC are still connected, but the lunacy of MSNBC gets rebranded. It removes the stain for NBC. The more things change, the more they remain the same. This is the same network where they repeatedly compare President Donald Trump to monsters like Hitler and Stalin. Hosts regularly throw around charges of dictatorship like we are living in 1930s Germany – although somehow they are allowed to say it. Host Tiffany Cross recently claimed the government was grabbing people and "transporting them to concentration camps." And the face of the franchise, MSNBC host Rachel Maddow, told viewers, "We have a consolidating dictatorship in our country." Remember, "Morning Joe" host Joe Scarborough made the most-embarrassing quote of the entire failed Joe Biden presidency: "I've said it for years now, he's cogent.

In this paper, we introduce STREETS, a novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. We seek to address the limitations of existing datasets in this area. Many such datasets lack a coherent traffic network graph to describe the relationship between sensors.

After learning the novel classes, the model is then evaluated on the overall classification performance on both base and novel classes. To this end, we propose a meta-learning model, the Attention Attractor Network, which regularizes the learning of novel classes. In each episode, we train a set of new weights to recognize novel classes until they converge, and we show that the technique of recurrent back-propagation can back-propagate through the optimization process and facilitate the learning of these parameters.

We collect a dataset of over one million unique images and associated news articles from left-and right-leaning news sources, and develop a method to predict the image's political leaning. This problem is particularly challenging because of the enormous intra-class visual and semantic diversity of our data. We propose a two-stage method to tackle this problem. In the first stage, the model is forced to learn relevant visual concepts that, when joined with document embeddings computed from articles paired with the images, enable the model to predict bias. In the second stage, we remove the requirement of the text domain and train a visual classifier from the features of the former model. We show this two-stage approach facilitates learning and outperforms several strong baselines.

We thank all reviewers for their detailed and constructive comments. "problem [...] is relevant and important," "dataset is original," Apologies for the confusion; we will clarify. We will include results for the upper bound in Table 2 as requested by R2 . R1: Contribution of stage 2: If we remove stage 2 and zero out weights for text embedding, acc. is only 0.677. R1: "Sweet spot" for text data: We will include an experiment that trains with the first k sentences (varying k).

Neural Information Processing Systems http://nips.cc/

MND left her without a voice. After such a long time, I couldn't really remember my voice, Sarah Ezekiel tells BBC Access All. When I first heard it again, I felt like crying. The onset of motor neurone disease (MND) left Sarah without a voice and the use of her hands at the age of 34. It was within months of her becoming a mum for the second time.

Work done while with Baidu Research.

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