We formulate the problem of defogging as state estimation and future state prediction from previous, partial observations in the context of real-time strategy games. We propose to employ encoder-decoder neural networks for this task, and introduce proxy tasks and baselines for evaluation to assess their ability of capturing basic game rules and high-level dynamics. By combining convolutional neural networks and recurrent networks, we exploit spatial and sequential correlations and train well-performing models on a large dataset of human games of StarCraft: Brood War. Finally, we demonstrate the relevance of our models to downstream tasks by applying them for enemy unit prediction in a state-of-the-art, rule-based StarCraft bot. We observe improvements in win rates against several strong community bots.

Attention mechanism is effective in both focusing the deep learning models on relevant features and interpreting them. However, attentions may be unreliable since the networks that generate them are often trained in a weakly-supervised manner. To overcome this limitation, we introduce the notion of input-dependent uncertainty to the attention mechanism, such that it generates attention for each feature with varying degrees of noise based on the given input, to learn larger variance on instances it is uncertain about. We learn this Uncertainty-aware Attention (UA) mechanism using variational inference, and validate it on various risk prediction tasks from electronic health records on which our model significantly outperforms existing attention models. The analysis of the learned attentions shows that our model generates attentions that comply with clinicians' interpretation, and provide richer interpretation via learned variance. Further evaluation of both the accuracy of the uncertainty calibration and the prediction performance with I don't know'' decision show that UA yields networks with high reliability as well.

While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs. Aggregation of outputs makes generalization to new inputs much more difficult. We consider an approach to this problem based on variational learning with a model of output aggregation and Gaussian processes, where aggregation leads to intractability of the standard evidence lower bounds. We propose new bounds and tractable approximations, leading to improved prediction accuracy and scalability to large datasets, while explicitly taking uncertainty into account. We develop a framework which extends to several types of likelihoods, including the Poisson model for aggregated count data. We apply our framework to a challenging and important problem, the fine-scale spatial modelling of malaria incidence, with over 1 million observations.

Meanwhile, we are still waiting on that long-anticipated sequel. A judge has ruled that publisher Krafton must reinstate Ted Gill as CEO of Unknown Worlds Entertainment, . The company fired Gill and two other co-founders last year as part of a shakeup . The Delaware judge said Krafton had violated the terms of its contract with Unknown Worlds when it fired the executives. To remedy these breaches, Gill is reinstated as CEO of Unknown Worlds with full operational authority over the studio, wrote judge Lori W. Will.

The app reads your email inbox and your meeting calendar, then gives you a short audio summary. It can help you spend less time scrolling, but of course, there are privacy drawbacks to consider. WIRED is obsessed with what comes next. Through rigorous investigations and game-changing reporting, we tell stories that don't just reflect the moment--they help create it. When you look back in 10, 20, even 50 years, WIRED will be the publication that led the story of the present, mapped the people, products, and ideas defining it, and explained how those forces forged the future.

We consider a game-theoretical multi-agent learning problem where the feedback information can be lost during the learning process and rewards are given by a broad class of games known as variationally stable games. We propose a simple variant of the classical online gradient descent algorithm, called reweighted online gradient descent (ROGD) and show that in variationally stable games, if each agent adopts ROGD, then almost sure convergence to the set of Nash equilibria is guaranteed, even when the feedback loss is asynchronous and arbitrarily corrrelated among agents. We then extend the framework to deal with unknown feedback loss probabilities by using an estimator (constructed from past data) in its replacement. Finally, we further extend the framework to accomodate both asynchronous loss and stochastic rewards and establish that multi-agent ROGD learning still converges to the set of Nash equilibria in such settings. Together, these results contribute to the broad lanscape of multi-agent online learning by significantly relaxing the feedback information that is required to achieve desirable outcomes.

Societies often rely on human experts to take a wide variety of decisions affecting their members, from jail-or-release decisions taken by judges and stop-and-frisk decisions taken by police officers to accept-or-reject decisions taken by academics. In this context, each decision is taken by an expert who is typically chosen uniformly at random from a pool of experts. However, these decisions may be imperfect due to limited experience, implicit biases, or faulty probabilistic reasoning. Can we improve the accuracy and fairness of the overall decision making process by optimizing the assignment between experts and decisions? In this paper, we address the above problem from the perspective of sequential decision making and show that, for different fairness notions from the literature, it reduces to a sequence of (constrained) weighted bipartite matchings, which can be solved efficiently using algorithms with approximation guarantees. Moreover, these algorithms also benefit from posterior sampling to actively trade off exploitation---selecting expert assignments which lead to accurate and fair decisions---and exploration---selecting expert assignments to learn about the experts' preferences and biases. We demonstrate the effectiveness of our algorithms on both synthetic and real-world data and show that they can significantly improve both the accuracy and fairness of the decisions taken by pools of experts.

A quick inbox search may uncover unused online accounts still tied to your email and personal data, Kurt "CyberGuy" Knutsson writes for Fox News.

Trump announces White House Chief of Staff Susie Wiles diagnosed with'early stage' breast cancer Trump's billionaire adviser publicly rebukes Iran war as JD Vance camp erupts over Israel nuke threat Kristi Noem referred for criminal investigation after'lying under oath' about $220M vanity scheme You don't have to fly to Turkey or Thailand... and can do it on your lunch break! Diet that cures pain and inflammation, devised by experts: Constant sickness and aching joints are the first signs of problems that left unchecked can turn deadly. Timothee Chalamet and Kylie Jenner's strained Oscars chat decoded by lip-reader as he gets snubbed and mocked The snubbed A-lister, drunken pics and C-List stars who plagued the most'exclusive' party: All the Oscars gossip Hollywood didn't want you to see at very messy afterparty Proof Leonardo DiCaprio sent a CLONE to the Oscars... alarming truth about Teyana Taylor's blow up... and a very dirty Barbra Streisand rumor: KENNEDY's most brutal review yet NYC's smiling socialist mayor is VERY different behind the scenes, as progressives who crossed him allege tyrannical and ruthless behavior Awful Timothee Chalamet's ego is bigger than Kylie's inflated butt... but it's so clear what's really going on here. Trump stunned by lurid rumor about Iran's new'gay' ayatollah Chilling new details of dismembered Emily Pike's final hours after she was snatched in Arizona desert and man detectives now believe murdered her'It's like he was possessed': Terrifying moment Alexander brother turned into a'monster' and raped me... and the four chilling words he said after horror attack - alleged victim claims After Oscars 2026, the whispered fear among Hollywood doctors is now massive... this is so much bigger than Ozempic. A-list stars ditch formal Oscars red carpet dresses for sexy party looks - with Jeff Goldblum's wife Emilie Livingston, Heidi Klum, Amelia Gray Hamlin and Kate Hudson turning up the heat at Vanity Fair bash Shock as man begs for death penalty for HIMSELF after pinning dead pastor's hands to wall and targeting other religious leaders How Oscars 2026 proved Hollywood has overdosed on Ozempic: Leading doctors name stars now at'extreme' risk... and reveal terrifying new side effects Billionaire Peter Thiel holds secret'Antichrist' meetings on the Vatican's doorstep READ MORE: Catholic priest warns'the stage is set' for the rise of the Antichrist US billionaire Peter Thiel is hosting a series of closed-door lectures in Rome on the doorstep of the Vatican, focused on the concept of the Antichrist.

The encyclopedia company's lawsuit also said ChatGPT cannibalizes traffic to the Britannica and Merriam-Webster websites. OpenAI has been hit with another lawsuit. According to the lawsuit, ChatGPT generates made-up content or ' hallucinations ' and falsely attributes them to Encyclopedia Britannica. The lawsuit doesn't specify an amount for monetary damages, but Britannica is also seeking an injunction to prevent OpenAI from repeating these accusations. When reached out for comment, a spokesperson for OpenAI told Engadget that, ChatGPT helps enhance human creativity, advance scientific discovery and medical research, and enable hundreds of millions of people to improve their daily lives.