Epistemic logics are a primary formalism for multi-agent systems but major reasoning tasks in such epistemic logics are intractable, which impedes applications of multi-agent epistemic logics in automatic planning. Knowledge compilation provides a promising way of resolving the intractability by identifying expressive fragments of epistemic logics that are tractable for important reasoning tasks such as satisfiability and forgetting. The property of logical separability allows to decompose a formula into some of its subformulas and thus modular algorithms for various reasoning tasks can be developed. In this paper, by employing logical separability, we propose an approach to knowledge compilation for the logic Kn by defining a normal form SDNF. Among several novel results, we show that every epistemic formula can be equivalently compiled into a formula in SDNF, major reasoning tasks in SDNF are tractable, and formulas in SDNF enjoy the logical separability. Our results shed some lights on modular approaches to knowledge compilation. Furthermore, we apply our results in the multi-agent epistemic planning. Finally, we extend the above result to the logic K45n that is Kn extended by introspection axioms 4 and 5.

Researchers from Australia have discovered a new species of wasp named'Xenomorph' because of its similarities to the monster from the Alien movie franchise. Dolichogenidea xenomorph injects its eggs into live caterpillars, and then the larvae slowly eat the other bug from the inside out. When they've had their fill, they burst out of the caterpillar and grow into adult wasps - before repeating the cycle all over again. Researchers from Australia have discovered a new species of wasp named'Xenomorph' because of its similarities to the monster from the Alien movie franchise Xenomorph is one of three newly documented parasitoid wasp species - a kind of wasp that needs to kill their host to complete their life cycle. 'Dolichogenidea xenomorph acts as a parasite in caterpillars in a similar way that the fictional Alien creature does in its human host,' said lead researcher Erinn Fagan-Jeffries, PhD student in the University of Adelaide's School of Biological Sciences.

Elon Musk is reportedly launching an investigation into an employee who sabotaged the company. Elon Musk, Chief Executive Officer of Space Exploration Technologies Corporation, speaks on the final day of the 68th International Astronautical Congress in Adelaide, Australia, on Sept. 29, 2017. Elon Musk has called lidar a crutch. The Tesla CEO believes he can build self-driving and semi-autonomous cars without relying on the technology, which uses lasers to help the cars map and navigate their surroundings. Instead, Tesla has looked to cameras and radar -- without lidar -- to do much of the work needed for its Autopilot driver assistance system.

Embryologists manually assess each embryo based on its physical appearance at a limited number of critical development check points, using a standard grading system together with digital time-lapse imagery to select the best embryo to transfer to the woman. Ivy AI observes the embryo's development while the embryo remains completely undisturbed in the time-lapse incubator. Ivy looks at the first five days of the embryo's life, and does this for 2000 embryos. Ivy takes millions of pieces of information and correlates this data, which then will tell the embryologist which embryo will have a successful fetal heart and accelerate the chance of a healthy baby. Pre-clinical validation of the technology has been conducted in Virtus Health clinics using the data obtained from more than 2600 embryos from NSW, the ACT and Queensland laboratories.

Google is working on some nifty new features for Google Maps, including a short list of your favorite places, the possibility of a'virtual positioning system' and more. Assistant is coming to Google Maps in a big way, with a ton of new shortcuts, as well as the ability for the digital assistant to text your friend when you're on your way. Google is rolling out a tool called'Your Match', which uses machine learning to determine your location and interests, serving up targeted suggestions for new businesses opening up in your area and more.

While a decade ago China was known to be the "world's factory," manufacturing everyday household goods for companies across the globe, in recent years tech and internet companies have redefined the face of Chinese industry. Both Tencent and Alibaba are now among the world's top 10 most valuable companies. Indeed, Chinese companies are now leading the way in the most disruptive global tech trends, including autonomous vehicles, machine learning and blockchain. According to Deloitte, global CFOs' optimism about the Chinese market has never been higher. But all this innovation has come with a side effect: Chinese consumers' expectations of brands and businesses have risen to match the market's optimism.

Digital banking offerings might not be new in countries like the U.S. and U.K., but it's still an emerging offering in some. Recent partnerships and product launches are bringing digital capabilities to financial institutions (FIs) and consumers in countries where access to financial tools was previously limited to brick-and-mortar branches. And, in regions where digital tools have long been available, new innovations are providing more intelligent financial insights than ever before. In the June edition of the Digital Banking Tracker, PYMNTS explores the latest digital developments in the banking world -- and the roadblocks standing in the way of widespread tech adoption. Digital banking capabilities are currently making their big debut in markets that had yet to tap in to the potential of mobile and online finance management solutions.

We define a new class of Bayesian point estimators, which we refer to as risk-averse estimators. We then use this definition to formulate several axioms that we claim to be natural requirements for good inference procedures, and show that for two classes of estimation problems the axioms uniquely characterise an estimator. Namely, for estimation problems with a discrete hypothesis space, we show that the axioms lead to the MAP estimate, whereas for well-behaved, purely continuous estimation problems the axioms lead to the Wallace-Freeman estimate. Interestingly, this combined use of MAP and Wallace-Freeman estimation reflects the common practice in the Minimum Message Length (MML) community, but there these two estimators are used as approximations for the information-theoretic Strict MML estimator, whereas we derive them exactly, not as approximations, and do so with no use of encoding or information theory. Keywords: Bayes estimation, risk-averse, inference, axiomatic approach, MML, Wallace-Freeman, invariance 1. Introduction One of the fundamental statistical problems is point estimation. In a Bayesian setting, this can be described as follows. Let (x,θ) X Θ be a pair of random variables with a known joint distribution that assigns positive probability / probability density to any (x,θ) X Θ.

Estimating the influence of a given feature to a model prediction is challenging. We introduce ROAR, RemOve And Retrain, a benchmark to evaluate the accuracy of interpretability methods that estimate input feature importance in deep neural networks. We remove a fraction of input features deemed to be most important according to each estimator and measure the change to the model accuracy upon retraining. The most accurate estimator will identify inputs as important whose removal causes the most damage to model performance relative to all other estimators. This evaluation produces thought-provoking results -- we find that several estimators are less accurate than a random assignment of feature importance. However, averaging a set of squared noisy estimators (a variant of a technique proposed by Smilkov et al. (2017)), leads to significant gains in accuracy for each method considered and far outperforms such a random guess.

When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or another. The mounting evidence for each of the classes helps us make our final decision. In this work, we introduce a deep network architecture that reasons in a similar way: the network dissects the image by finding prototypical parts, and combines evidence from the prototypes to make a final classification. The algorithm thus reasons in a way that is qualitatively similar to the way ornithologists, physicians, geologists, architects, and others would explain to people on how to solve challenging image classification tasks. The network uses only image-level labels for training, meaning that there are no labels for parts of images. We demonstrate the method on the CIFAR-10 dataset and 10 classes from the CUB-200-2011 dataset.