Given multiple input signals, how can we infer node importance in a knowledge graph (KG)? Node importance estimation is a crucial and challenging task that can benefit a lot of applications including recommendation, search, and query disambiguation. A key challenge towards this goal is how to effectively use input from different sources. On the one hand, a KG is a rich source of information, with multiple types of nodes and edges. On the other hand, there are external input signals, such as the number of votes or pageviews, which can directly tell us about the importance of entities in a KG. While several methods have been developed to tackle this problem, their use of these external signals has been limited as they are not designed to consider multiple signals simultaneously. In this paper, we develop an end-to-end model MultiImport, which infers latent node importance from multiple, potentially overlapping, input signals. MultiImport is a latent variable model that captures the relation between node importance and input signals, and effectively learns from multiple signals with potential conflicts. Also, MultiImport provides an effective estimator based on attentive graph neural networks. We ran experiments on real-world KGs to show that MultiImport handles several challenges involved with inferring node importance from multiple input signals, and consistently outperforms existing methods, achieving up to 23.7% higher NDCG@100 than the state-of-the-art method.

A federal appeals court on Friday struck down a California law that barred internet sites from disclosing the ages of screen actors. The 2017 law, which the Screen Actors Guild had sought as a means to reduce age discrimination, violates the 1st Amendment, a three-judge panel of the U.S. 9th Circuit Court of Appeals decided unanimously. The law was challenged by the Internet Movie Database -- IMDb.com -- a free website that provides information about movies, television shows and video games and offers encyclopedic profiles of actors. In addition to its publicly available site, IMDb has a subscription-based service for the entertainment industry, known as IMDbPro, which the court described as "Hollywood's version of LinkedIn." Actors, writers, set designers, makeup artists and others create resumes by uploading head shots, prior jobs and biographical information to the site.

I am a recent college graduate with a degree in Software Engineering. Being honest with myself, I would say my programming skills are intermediate, but not expert level in any way (I have about a year of professional experience). I am comfortable with Java and know a bit about JS, HTML/CSS, and Python. Something I've recently come to be very interested in is the field of artificial intelligence. I think I have a basic understanding of what it is, but no clue how it works.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A sizeable chunk of shoreline was spotted by boaters this week in Michigan's Muskegon Lake that could be the result of record water levels and erosion. The floating piece of vegetation was featured in an aerial drone video that shows a pontoon boat circling it. "I've lived my whole life in the Muskegon area, and I've never seen anything like it," said Joe Gee, the photographer who captured footage of the islet.

Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision. INNs combine regression NNs and an iterative model-based image reconstruction (MBIR) algorithm, often leading to both good generalization capability and outperforming reconstruction quality over existing MBIR optimization models. This paper proposes the first fast and convergent INN architecture, Momentum-Net, by generalizing a block-wise MBIR algorithm that uses momentum and majorizers with regression NNs. For fast MBIR, Momentum-Net uses momentum terms in extrapolation modules, and noniterative MBIR modules at each iteration by using majorizers, where each iteration of Momentum-Net consists of three core modules: image refining, extrapolation, and MBIR. Momentum-Net guarantees convergence to a fixed-point for general differentiable (non)convex MBIR functions (or data-fit terms) and convex feasible sets, under two asymptomatic conditions. To consider data-fit variations across training and testing samples, we also propose a regularization parameter selection scheme based on the "spectral spread" of majorization matrices. Numerical experiments for light-field photography using a focal stack and sparse-view computational tomography demonstrate that, given identical regression NN architectures, Momentum-Net significantly improves MBIR speed and accuracy over several existing INNs; it significantly improves reconstruction quality compared to a state-of-the-art MBIR method in each application.

Apple pioneered the voice revolution in 2011 with the introduction of Siri in its iPhone 4s. Today, you tell your iPhone 11, "Hey Siri, Play Bruce Springsteen by Spotify," and it responds, "I can't talk to Spotify, but you can use Apple music instead," politely displaying options on the screena as shown in the figure here. Or, you tell one of your five Amazon Echo devices at home, "Alexa, add pumpkin pie to my Target shopping list,"b then "order AA Duracell batteries," and it adds pumpkin pie and Amazon Basics batteries to your Amazon shopping cart, ignoring your request to shop at Target and be loyal to Duracell. You are the consumer, but your choices have been ignored. Or, consider you are a brand manager.

A second burst of interest in AI authorship broke out in the mid-1980s. Congress once again commissioned a study, this time from its Office of Technology Assessment (OTA), to address this and other controversial computer-related issues. OTA did not offer an answer to the question, perhaps in part because at that time, it was a "toy problem" because no commercially significant outputs of AI or other software programs had yet been generated.5 But deep learning and other AI breakthroughs have caused IP professionals to rethink the AI authorship issue.1,2 For example, The Next Rembrandt video features a group of art experts and computer scientists discussing how they collaborated to digitize many Rembrandt paintings, develop models of particular features of the paintings, and then create a Rembrandt-like portrait of a man with facial hair wearing a hat and looking to the right.6 The resulting AI-generated painting really does look like a Rembrandt.

Researchers from the University of Bath have developed technology that would allow people to digitise their dogs. Such an application could be used for a number of purposes, such as assisting vets in diagnosing issues walking and monitoring their recovery. It could also be used for more entertaining reasons, such as putting digital representations of dogs into movies and video games – without constant use of motion capture suits or expensive equipment. Computer scientists digitised the movement of 14 different breeds of dog, including lurchers and pugs, who were residents of the local animal shelter. The motion capture suits, made especially for dogs, were filmed doing a range of movements as part of their activities.

Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight, and is a consequence of the selective attention that enables you to remain focused on important parts of the world without distraction from irrelevant details. It is believed that this selective attention mechanism enables people to condense broad sensory information into a form that is compact enough to be used for future decision making. While this may seem to be a limitation, such "bottlenecks" observed in nature can also inspire the design of machine learning systems that hope to mimic the success and efficiency of biological organisms. For example, while most methods presented in the deep reinforcement learning (RL) literature allow an agent to access the entire visual input, and even incorporating modules for predicting future sequences of visual inputs, perhaps reducing an agent's access to its visual inputs via an attention constraint could be beneficial to an agent's performance?

For the Hemingway one, please note that the first 3 sentences are me, not GPT-3. In my various tweets, ' ' delimits the prompt (because there's nothing else in the playground UI right now which keeps visual track). Anyway, the interesting thing here is that I spent a good hour trying to get GPT-3 to do this. I knew it could do it based on how it could do Tom Swifties and the Turing dialogue and the other things, but I just couldn't quite figure out how to get it to do so. At one point, I was trying the idea of writing a plot summary and asking it to then "Please write a plot summary of Harry Potter in the style of Ernest Hemingway", and it copied my plot summary verbatim and then added on the sarcastic comment: It still sounds like it came straight from Harry Potter and the Philosopher's Stone.