Dungeon slaying video games have severely lacked one essential element, until now: toasting an epic monster battle with a well-deserved schnitzel. At least, that's what the team at Vienna-based Microbird Games has decided, prompting the creation of forthcoming action role-playing game, Dungeons of Hinterberg. Looking like a Saturday morning cartoon come to life, with a visual style inspired by the clear lines and vivid colours of European comic artists, the indie adventure promises a mix of hack'n' slash action RPG and social sim, against the backdrop of the Austrian Alps – which as video games go, is a setting as fresh as recently fallen snow. Players can explore dungeons, solve puzzles, slay huge bosses … and then enjoy a schnitzel with the local people and other visiting slayers. Fighting monsters has never sounded so delicious.

The deepening cooperation between China and Russia threatens to overturn decades of international stability in nuclear arms control, according to a top adviser to U.S. President Joe Biden. To avert miscalculations, nuclear-weapons states must engage on existing and potential threats, from Iran's atomic ambitions to the use of artificial intelligence for decision-making during crises, Pranay Vaddi, the National Security Council's senior director for arms control, said in an interview in Vienna. "We're entering a different period," Vaddi said after talks at the International Atomic Energy Agency. "It requires a little bit of experimentation." This could be due to a conflict with your ad-blocking or security software.

With the London Marathon coming up this weekend, many may be wondering if we will see a runner achieve a time under two hours. The world record for the fastest 26.2 mile (42.2 km) run is 2 hours, 1 minute and 9 seconds, as set by Eliud Kipchoge during the 2022 Berlin Marathon. He actually beat this time, and achieved the elusive sub-two hour milestone, three year's prior in a park in Vienna, Austria, but this was not recognised as a record. The London race would meet the record requirements if someone beat Kipchoge's time, and with technological advancements, we are closer than we have ever been. Here, MailOnline takes a look at some of the unusual technologies and inventions that may one day help an athlete finally reach the finish line in under two hours.

Parenting is beautiful, joyous, and overwhelming at times. When you embrace parenthood, the biggest task that you can ever imagine is putting your ward to sleep. It's true, putting kids to bed is a huge challenge, especially when they request a new and interesting bedtime story every night. But thanks to artificial intelligence and Oscar, this just became a cakewalk. A Vienna-based firm has streamlined the process of customized storytelling by developing an AI-powered app called Oscar.

This article introduces an energy-based model that is adversarial regarding data: it minimizes the energy for a given data distribution (the positive samples) while maximizing the energy for another given data distribution (the negative or unlabeled samples). The model is especially instantiated with autoencoders where the energy, represented by the reconstruction error, provides a general distance measure for unknown data. The resulting neural network thus learns to reconstruct data from the first distribution while crushing data from the second distribution. This solution can handle different problems such as Positive and Unlabeled (PU) learning or covariate shift, especially with imbalanced data. Using autoencoders allows handling a large variety of data, such as images, text or even dialogues. Our experiments show the flexibility of the proposed approach in dealing with different types of data in different settings: images with CIFAR-10 and CIFAR-100 (not-in-training setting), text with Amazon reviews (PU learning) and dialogues with Facebook bAbI (next response classification and dialogue completion).

This paper designed a model with goal of forecasting on high dimensional time series data. To achieve its goal the model used LSTM network to capture the transition of latent states. In addition, to convert the latent states to observation domain, it has used the Gaussian copula process in which Gaussian process models a low rank covariance matrix which is computationally less complex to infer the parameters. Also the authors used Gaussian copula to convert the non-Gaussian observation to an standard Gaussian distribution. This will help them to enhance prediction power of the model since it converts non-Gaussian observation to have a standard Gaussian distribution.

Strengths: The deep information maximization objective combined with noise contrastive estimation (InfoNCE) is a fairly new unsupervised learning loss that has yet to be thoroughly explored in deep reinforcement learning. The main value of the paper is the study of the representations learned when optimizing the InfoNCE loss and how those representations can be used for continual learning. Moreover, the paper introduces a novel architecture that uses the action information as part of the InfoNCE loss. These two ideas are novel and, to my knowledge, they haven't been presented in the literature before. In terms of significance, there has been growing interest in the representations learned by the InfoNCE loss in the context of reinforcement learning; see, Oord, Li, and Vinyals (2018), Anand et.

Finding accurate solutions to the Schrödinger equation is the key unsolved challenge of computational chemistry. Given its importance for the development of new chemical compounds, decades of research have been dedicated to this problem, but due to the large dimensionality even the best available methods do not yet reach the desired accuracy. Recently the combination of deep learning with Monte Carlo methods has emerged as a promising way to obtain highly accurate energies and moderate scaling of computational cost. In this paper we significantly contribute towards this goal by introducing a novel deep-learning architecture that achieves 40-70% lower energy error at 6x lower computational cost compared to previous approaches. Using our method we establish a new benchmark by calculating the most accurate variational ground state energies ever published for a number of different atoms and molecules. We systematically break down and measure our improvements, focusing in particular on the effect of increasing physical prior knowledge. We surprisingly find that increasing the prior knowledge given to the architecture can actually decrease accuracy.

Back in 2018, I had the privilege of keynoting at one of Semantic Web Company's events in Vienna, as well as attending the full event. It was a great opportunity to immerse myself in the Central European perspective on the utility of Linked Open Data standards and how those standards were being applied. I got to meet innovators at enterprises making good use of Linked Open Vocabularies with the help of SWC's PoolParty semantics platform, Ontotext's GraphDB and Semiodesk's semantic graph development acceleration software, for example. There is so much that is impactful and powerful going on at these kinds of semantic technology events. So many people in the audience grasp the importance of a semantic layer to findable, accessible, interoperable, and reusable (FAIR) data, regardless of its origin and its original form–whether structured data, or document and multimedia content.

The use of adjunctive artificial intelligence (AI) doubled the positive predictive value (PPV) of digital mammography (DM) exams overall and led to greater than 90 percent accuracy for DM and digital breast tomosynthesis (DBT) in detecting breast cancer in women with elevated risk, according to research findings presented recently at the European Congress of Radiology (ECR) conference in Vienna, Austria. For the study, researchers compared the use of adjunctive AI (Transpara version 1.7.0, ScreenPoint Medical) in 11,988 women (between the ages of 50 and 74) who had DM or DBT screening exams versus 16,555 women screened with DM or DBT the year before without AI support. In the AI group, 5,049 women had DM screening with the Hologic Selenia device and 6,949 women had DBT screening with the Hologic Selenia Dimensions device, according to the study. For the non-AI cohort, 7,229 women had DM screening and 9,326 women had DBT.