Most of DC Fandome's big reveals today have focused on its upcoming films, but there was still time to squeeze in a video game announcement or two. Earlier today we got a look at Warner Bros. Montreal's new Bat-Family title, Gotham Knights. Now we've finally gotten more details on Rocksteady's upcoming Suicide Squad title after it was teased earlier this month. The game's relationship to James Gunn's movie, which was also showcased during Fandome, is mostly nonexistent. The game will highlight a smaller roster of Harley Quinn, Deadshot, King Shark and Captain Boomerang.

We are now living in a world where we have the most amazing tools and resources to simplify and streamline the process of building a business to leverage our time more than ever. Artificial Intelligence (AI) is now capable of writing content and used to communicate with customers, AI automation can run complete marketing campaigns, so suffice it to say these new technologies can help you create tremendous success in your business and improve your lifestyle. Does that mean this technology guarantees exceptional, infallible results for our business? Some will tell you it is, but here's the thing... All these modern marketing tools are great and definitely have their place, but consider this; the more AI automated your marketing becomes, the less human it can seem, and that is a big mistake.

Users of music streaming, video streaming, news recommendation, Offline evaluation is challenging because the deployed recommender and e-commerce services often engage with content in a sequential decides which items the user sees, introducing significant manner. Providing and evaluating good sequences of recommendations exposure bias in logged data [7, 16, 22]. Various methods have been is therefore a central problem for these services. Prior proposed to mitigate bias using counterfactual evaluation. In this reweighting-based counterfactual evaluation methods either suffer paper, we use terminology from the multi-armed bandit framework from high variance or make strong independence assumptions to discuss these methods: the recommender performs an action about rewards. We propose a new counterfactual estimator that allows by showing an item depending on the observed context (e.g., user for sequential interactions in the rewards with lower variance covariates, item covariates, time of day, day of the week) and then in an asymptotically unbiased manner. Our method uses graphical observes a reward through the user response (e.g., a stream, a purchase, assumptions about the causal relationships of the slate to reweight or length of consumption) [14]. The recommender follows the rewards in the logging policy in a way that approximates the a policy distribution over actions by drawing items stochastically expected sum of rewards under the target policy. Extensive experiments conditioned on the context. in simulation and on a live recommender system show that The basic idea of counterfactual evaluation is to estimate how a our approach outperforms existing methods in terms of bias and new policy would have performed if it had been deployed instead data efficiency for the sequential track recommendations problem. of the deployed policy.

Multi-view clustering methods have been a focus in recent years because of their superiority in clustering performance. However, typical traditional multi-view clustering algorithms still have shortcomings in some aspects, such as removal of redundant information, utilization of various views and fusion of multi-view features. In view of these problems, this paper proposes a new multi-view clustering method, low-rank subspace multi-view clustering based on adaptive graph regularization. We construct two new data matrix decomposition models into a unified optimization model. In this framework, we address the significance of the common knowledge shared by the cross view and the unique knowledge of each view by presenting new low-rank and sparse constraints on the sparse subspace matrix. To ensure that we achieve effective sparse representation and clustering performance on the original data matrix, adaptive graph regularization and unsupervised clustering constraints are also incorporated in the proposed model to preserve the internal structural features of the data. Finally, the proposed method is compared with several state-of-the-art algorithms. Experimental results for five widely used multi-view benchmarks show that our proposed algorithm surpasses other state-of-the-art methods by a clear margin.

An artist has transformed the chipped stone busts of ancient Roman emperors into photorealistic portraits with the help of historical artefacts and creative software. Daniel Voshart, from Toronto, Canada, says that his project of painstakingly colourising and shaping the faces of 54 Principate rulers was'a quarantine project that got a bit out of hand', but it has attracted attention from hobbyists to historians. And he has now released his completed work in a series of stunning portraits and posters that cover 300 years of Roman history. Though more interested in design work for VR for use in architecture and the film industry, the coronavirus pandemic brought Daniel's work to stop and left him with time to explore his hobby of colourising statues. When he came to pick a subject however, he chose to research the busts of Roman Emperors who controlled its sprawling empire during the first three-century-long Principate, despite not being particularly interested in ancient history.

Machine learning is a fantastic tool for renovating old photos and videos. So much so that it can even bring ancient statues to life, transforming the chipped stone busts of long-dead Roman emperors into photorealistic faces you could imagine walking past on the street. The portraits are the creation of designer Daniel Voshart, who describes the series as a quarantine project that got a bit out of hand. Primarily a VR specialist in the film industry, Voshart's work projects got put on hold because of COVID-19, and so he started exploring a hobby of his: colorizing old statues. Looking for suitable material to transform, he began working his way through Roman emperors. He finished his initial depictions of the first 54 emperors in July, but this week, he released updated portraits and new posters for sale.

DC's Fandome event was intended as a replacement for the company's usual big presence at this year's cancelled San Diego Comic-Con, and within minutes the company came out swinging with its first big announcement: Gotham Knights, a video game about a city without Batman, where his various proteges have to step up to defend their home. In the action-adventure title the player will get to play as one of four prominent members of the Bat-Family: Nightwing, Robin, Batgirl and Red Hood. Bruce Wayne is dead, and it won't take long for the villains of Gotham City to figure out that Batman is gone too. Luckily, he's left behind plenty for his associates to work with, including a well-equipped headquarters and of course, the ever-faithful Alfred. The mechanics lie in the vein of games like Marvel's Spider-Man for PS4 and the Assassin's Creed series, with plenty of stealth and brawling, though we also get to see Batgirl drive a pretty sweet bike through the streets of Gotham. The one standout is that this game will be a co-op title, with players able to work together to clear areas and defeat villains -- in the gameplay footage shown, we got to see Batgirl and Robin make their way through a building taken over by perennial Batman villain Mr. Freeze.

"If I were not a physicist, I would probably be a musician. I often think in music. I live my daydreams in music. I see my life in terms of music." I might not be a physicist like Mr. Einstein, but I wholeheartedly agree with his thoughts on music!

… needed to provide the computational horsepower for accelerating the movement of big data used in emerging machine learning applications.

Among them: artificial intelligence; data science and machine learning; emerging technologies; and privacy. The Raleigh-based company was also …