We consider a preference learning setting where every participant chooses an ordered listofkmost preferred items among adisplayed setofcandidates.

We consider a preference learning setting where every participant chooses an ordered list of $k$ most preferred items among a displayed set of candidates.

Surprise Adequacy (SA) has been widely studied as a test adequacy metric that can effectively guide software engineers towards inputs that are more likely to reveal unexpected behaviour of Deep Neural Networks (DNNs). Intuitively, SA is an out-of-distribution metric that quantifies the dissimilarity between the given input and the training data: if a new input is very different from those seen during training, the DNN is more likely to behave unexpectedly against the input. While SA has been widely adopted as a test prioritization method, its major weakness is the fact that the computation of the metric requires access to the training dataset, which is often not allowed in real-world use cases. We present DANDI, a technique that generates a surrogate input distribution using Stable Diffusion to compute SA values without requiring the original training data. An empirical evaluation of DANDI applied to image classifiers for CIFAR10 and ImageNet-1K shows that SA values computed against synthetic data are highly correlated with the values computed against the training data, with Spearman Rank correlation value of 0.852 for ImageNet-1K and 0.881 for CIFAR-10. Further, we show that SA value computed by DANDI achieves can prioritize inputs as effectively as those computed using the training data, when testing DNN models mutated by DeepMutation. We believe that DANDI can significantly improve the usability of SA for practical DNN testing.

We consider a preference learning setting where every participant chooses an ordered list of k most preferred items among a displayed set of candidates. We identify a distance-based ranking model for the population's preferences and their (ranked) choice behavior. The ranking model resembles the Mallows model but uses a new distance function called Reverse Major Index (RMJ). We find that despite the need to sum over all permutations, the RMJ-based ranking distribution aggregates into (ranked) choice probabilities with simple closed-form expression. We develop effective methods to estimate the model parameters and showcase their generalization power using real data, especially when there is a limited variety of display sets.

After a four-year hiatus, Black Mirror is back. Season six is now on Netflix, along with the whole back catalog--including one Christmas special and an interactive movie. The show, created by Charlie Brooker and producer Annabel Jones, is a modern take on classic anthology series like The Twilight Zone. Through Brooker's dark, playful, and sometimes uplifting lens, the show examines the unintended ways technology impacts our lives. Because it's an anthology series--in which each installment has new subject matter and a slightly different tone--each episode has its fans.

DALL-E 2 lets you create images you never thought possible, all from a little bit of original text. OpenAI originally introduced DALL-E in January 2021, and then one year later introduced the new and improved DALL-E 2, which generates more realistic photos with four times greater resolution. When you look at the photos produced by the original DALL-E tool, they are much blurrier and less recognizable than the photos produced by DALL-E 2 with the same text prompts. To use DALL-E, all you have to do is create an account to start a free trial with 50 free credits for the first month and 15 free credits every following month. When you have an idea for a photo, you'll type a detailed text description and enter it into DALL-E 2 to produce many AI variations to choose from.

Yes, you have your R2-D2, your BB-8, Data (Brent Spiner), even WALL-E. So while we still can, take notes on these robots before they become our technological overlords. Not only are the Fem-bots evil, they are Evil's evil. Dr. Evil's (Mike Myers), to be precise. Attractive and seductive, the Fem-bots were a means of distracting, and killing, Austin Powers (Mike Myers), not only with their agility but with their "machine gun jubblies," guns protruding from their breasts.

Robots are a common factor in many sci-fi movies, as they've always seemed like something a futuristic society would have. Of course, as society progresses, there are some real life robots now, maybe even within your own home. While we may have numerous examples of artificial intelligence nowadays, one thing we still haven't discovered (for better or worse) is making these robots actually sentient, something we still only see in fiction. While this topic remains fictional, there are many movies who like to play with the idea of robots coming to life and gaining their own sentience. There are many different ways to play with the subject too.

As a good battle shonen should, One Piece has a variety of iconic fights that are complemented by some flashy and exciting finishing moves. A staple of any anime fight, the idea of the finishing move fuses both brutal power and explicit branding to create some of the most exciting and recognizable attacks ever seen in fiction. In One Piece's world, Devil Fruits, Haki, martial arts, robotic enhancements, and some very loose interpretations of physics all contribute to One Piece's own, colorful gallery of finishing moves. And while rating each one's power and effectiveness is a large discussion within its own right, it's also really fun just looking at which finishing moves are just the coolest and most memorable. Monkey D. Luffy's Gum-Gum Gatling doesn't have the awe-inspiring, simplistic appeal of a one-hit attack; but what it lacks in brevity, it more than makes up for with raw, visceral spectacle.

Since the superhero genre is rooted in science fiction, the Marvel Cinematic Universe features a lot of futuristic technology. One type of tech that has appeared in almost every MCU project and has had a large impact on the franchise is Artificial Intelligence. There are a bunch of different AI systems featured in the MCU that serve various purposes, each with its own distinct personality. Much like the main characters, no two AIs are exactly the same, even if they were created by the same person which usually happens to be Tony Stark. Wizey is a smart home system created by Tina Minoru and her tech company, Wizard, on Marvel's Runaways. The system is installed in the Minoru household and runs security as well as gives them updates on things like news and weather.