The performance of a constraint model can often be improved by converting a subproblem into a single table constraint (referred to as tabulation). Finding subproblems to tabulate is traditionally a manual and time-intensive process, even for expert modellers. This paper presents TabID, an entirely automated method to identify promising subproblems for tabulation in constraint programming. We introduce a diverse set of heuristics designed to identify promising candidates for tabulation, aiming to improve solver performance. These heuristics are intended to encapsulate various factors that contribute to useful tabulation. We also present additional checks to limit the potential drawbacks of suboptimal tabulation. We comprehensively evaluate our approach using benchmark problems from existing literature that previously relied on manual identification by constraint programming experts of constraints to tabulate. We demonstrate that our automated identification and tabulation process achieves comparable, and in some cases improved results. We empirically evaluate the efficacy of our approach on a variety of solvers, including standard CP (Minion and Gecode), clause-learning CP (Chuffed and OR-Tools) and SAT solvers (Kissat). Our findings highlight the substantial potential of fully automated tabulation, suggesting its integration into automated model reformulation tools.

We study a planning problem based on Plotting, a tile-matching puzzle video game published by Taito in 1989. The objective of this game is to remove a target number of coloured blocks from a grid by sequentially shooting blocks into the grid. Plotting features complex transitions after every shot: various blocks are affected directly, while others can be indirectly affected by gravity. We highlight the challenges of modelling Plotting with PDDL and of solving it with a grounding-based state-of-the-art planner.

Free game codes are a time-honored tradition when you're building or upgrading a gaming PC. Odds are pretty good that at any given time, a purchase of a new AMD or Nvidia graphics card will come with a free copy of whatever title is hot that quarter. Intel's latest selection of freebies includes the latest Assassin's Creed stab-em-up and an indie survival crafting game called Nightingale. The "Gamer Days" bundle runs from August 24th to September 4th in the United States. Assassin's Creed: Mirage takes the Ubisoft series back to its Middle Eastern story roots, following assassin Basim Ibn Ishaq as he creeps around Bagdad to uncover conspiracies and ancient relics.

In March 2022, a synthesized video of Ukrainian President Volodymyr Zelenskyy appeared on various social media platforms and a national news website. In the video, Zelenskyy urges his people to surrender in their fight against Russia; however, the speaker is not Zelenskyy at all. The minute-long clip was a deepfake, a synthesized video produced via deep learning models, and the president soon posted a legitimate message reaffirming his nation's commitment to defending its land and people. The Ukrainian government already been had warning the public that state-sponsored deepfakes could be used as part of Russia's information warfare. The video itself was not particularly realistic or convincing, but the quality of deepfakes has been improving rapidly.

It's a classic exercise in high-school art class: a student sits at her desk, charcoal pencil held in one hand, poised over a sheet of paper, while the other hand lies outstretched in front of her, palm up, fingers relaxed so that they curve inward. Then she uses one hand to draw the other. It's a beginner's assignment, but the task of depicting hands convincingly is one of the most notorious challenges in figurative art. I remember it being incredibly frustrating--getting the angles and proportion of each finger right, determining how the thumb connects to the palm, showing one finger overlapping another just so. Too often, I would end up with a bizarrely long pinky, or a thumb jutting out at an impossible angle like a broken bone.

Hazard detection and avoidance is a key technology for future robotic small body sample return and lander missions. Current state-of-the-practice methods rely on high-fidelity, a priori terrain maps, which require extensive human-in-the-loop verification and expensive reconnaissance campaigns to resolve mapping uncertainties. We propose a novel safety mapping paradigm that leverages deep semantic segmentation techniques to predict landing safety directly from a single monocular image, thus reducing reliance on high-fidelity, a priori data products. We demonstrate precise and accurate safety mapping performance on real in-situ imagery of prospective sample sites from the OSIRIS-REx mission. INTRODUCTION Hazard detection and avoidance (HD&A) is a key technology for future robotic small body sample return and lander missions.

Do you have an almost psychic way of reading people, or can you never make out what someone might be thinking? What about how much you should trust them? Maybe the question really should be whether you would trust someone who doesn't even exist. That might sound ridiculous, but ask anyone who was a test subject for an experiment that gauged just how trustworthy random human faces seemed. The thing is that not all of them were actually human.

They can be too opaque, and to raise the signal of what is most important, they end up hiding too much. "The purpose of abstraction is not to be vague, but to create a new semantic level in which one can be absolutely precise."--

Artificial intelligence can create such realistic human faces that people can't distinguish them from real faces – and they actually trust the fake faces more. Fictional, computer-generated human faces are so convincing they can fool even trained observers. They can be easily downloaded online and used for internet scams and fake social media profiles. "We should be concerned because these synthetic faces are incredibly effective for nefarious purposes, for things like revenge porn or fraud, for example," says Sophie Nightingale at Lancaster University in the UK. AI programs called generative adversarial networks, or GANs, can learn to create fake images that are less and less distinguishable from real images, by pitting two neural networks against each other.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Following Tuesday's historic touchdown on the asteroid Bennu, NASA has released never-before-seen images of the OSIRIS-REx spacecraft kicking up rocks and debris on the space rock's surface. The images are from the point in time when OSIRIS-REx approached and touched down on the surface of Bennu, which is more than 200 million miles from Earth. "The spacecraft's sampling arm – called the Touch-And-Go Sample Acquisition Mechanism (TAGSAM) – is visible in the lower part of the frame," NASA wrote on its website.