Vava has unveiled a new pet camera that will let you follow your dog or cat around the house when you're not home. The device, said to be the first mobile pet cam in the world, made its debut at CES in Las Vegas this week. Vava's Pet Cam is equipped with two-way audio so you can hear and speak to your pet remotely, and collision detection will let it roam the house without bumping into anything or falling down the stairs. Vava has unveiled a new pet camera that will let you follow your dog or cat around the house when you're not home. The Vava Pet Cam connects with an app that lets you interact with your pets from afar.

LONDON - The world's oceans are rising in temperature faster than previously believed as they absorb most of the world's growing climate-changing emissions, scientists said Thursday. Ocean heat -- recorded by thousands of floating robots -- has been setting records repeatedly over the last decade, with 2018 expected to be the hottest year yet, displacing the 2017 record, according to an analysis by the Chinese Academy of Sciences. That is driving sea level rise, as oceans warm and expand, and helping fuel more intense hurricanes and other extreme weather, scientists warn. The warming, measured since 1960, is faster than predicted by scientists in a 2013 Intergovernmental Panel on Climate Change report that looked at ocean warming, according to the study, published this week in the journal Science. "It's mainly driven by the accumulation of greenhouse gases such as carbon dioxide in the atmosphere due to human activities," said Lijing Cheng, a lead author of the study from the Chinese Academy of Sciences.

The Tech Tribune staff has compiled the very best tech startups in Cambridge, Massachusetts. Additionally, all companies must be independent (un-acquired), privately owned, at most 10 years old, and have received at least one round of funding in order to qualify. Looking for a badge to celebrate your awesome accomplishment? "Cambridge Mobile Telematics (CMT) pioneered telematics for usage-based and behavior-based programs making roads and drivers safer around the world. Founded in 2010 by two MIT professors, CMT's accomplished team of expert scientists and experienced entrepreneurs developed DriveWell, an advanced mobile-sensing and big data platform delivering an end-to-end smartphone telematics solution. DriveWell provides valuable feedback to users, helping them to improve driving performance and become more aware of unsafe behaviors. DriveWell is the first telematics platform in the industry to provide both traditional vehicle-centric, usage-based-insurance (UBI) and driver-centric, behavior-based insurance (BBI) solutions. Through the DriveWell program, CMT's partners can easily measure mileage, time of day, roadways and risky driving behaviors – giving them a complete picture of every trip and allowing them to segment high-risk vs low-risk customers easily."

The world's oceans are rising in temperature faster than previously believed as they absorb most of the world's growing climate-changing emissions, scientists said Thursday. Ocean heat - recorded by thousands of floating robots - has been setting records repeatedly over the last decade, with 2018 expected to be the hottest year yet, displacing the 2017 record, according to an analysis by the Chinese Academy of Sciences. That is driving sea level rise, as oceans warm and expand, and helping fuel more intense hurricanes and other extreme weather, scientists warn. The world s oceans are rising in temperature faster than previously believed as they absorb most of the world's growing climate-changing emissions, scientists have said. Ocean heating is critical marker of climate change because an estimated 93 percent of the excess solar energy trapped by greenhouse gases accumulates in the world's oceans.

In this paper, we propose deep learning algorithms for ranking response surfaces, with applications to optimal stopping problems in financial mathematics. The problem of ranking response surfaces is motivated by estimating optimal feedback policy maps in stochastic control problems, aiming to efficiently find the index associated to the minimal response across the entire continuous input space $\mathcal{X} \subseteq \mathbb{R}^d$. By considering points in $\mathcal{X}$ as pixels and indices of the minimal surfaces as labels, we recast the problem as an image segmentation problem, which assigns a label to every pixel in an image such that pixels with the same label share certain characteristics. This provides an alternative method for efficiently solving the problem instead of using sequential design in our previous work [R. Hu and M. Ludkovski, SIAM/ASA Journal on Uncertainty Quantification, 5 (2017), 212--239]. Deep learning algorithms are scalable, parallel and model-free, i.e., no parametric assumptions needed on the response surfaces. Considering ranking response surfaces as image segmentation allows one to use a broad class of deep neural networks, e.g., UNet, SegNet, DeconvNet, which have been widely applied and numerically proved to possess high accuracy in the field. We also systematically study the dependence of deep learning algorithms on the input data generated on uniform grids or by sequential design sampling, and observe that the performance of deep learning is {\it not} sensitive to the noise and locations (close to/away from boundaries) of training data. We present a few examples including synthetic ones and the Bermudan option pricing problem to show the efficiency and accuracy of this method.

Bayesian optimization (BO) based on Gaussian process models is a powerful paradigm to optimize black-box functions that are expensive to evaluate. While several BO algorithms provably converge to the global optimum of the unknown function, they assume that the hyperparameters of the kernel are known in advance. This is not the case in practice and misspecification often causes these algorithms to converge to poor local optima. In this paper, we present the first BO algorithm that is provably no-regret and converges to the optimum without knowledge of the hyperparameters. We slowly adapt the hyperparameters of stationary kernels and thereby expand the associated function class over time, so that the BO algorithm considers more complex function candidates. Based on the theoretical insights, we propose several practical algorithms that achieve the empirical data efficiency of BO with online hyperparameter estimation, but retain theoretical convergence guarantees. We evaluate our method on several benchmark problems.

Renewable energy is the future, but at present no one is tracking just who's got solar panels on their roof, in their back yard, or a shared neighborhood installation. Fortunately, solar panels generally work best when exposed to the light. That makes them easy to spot, and count, from orbit -- which is just what the DeepSolar project is doing. There are a number of initiatives for collecting this information -- some regulated, some voluntary, some automated. But none of them is comprehensive enough or accurate enough to base policy or business decisions on at a national or state level.

Planning under model uncertainty is a fundamental problem across many applications of decision making and learning. In this paper, we propose the Robust Adaptive Monte Carlo Planning (RAMCP) algorithm, which allows computation of risk-sensitive Bayes-adaptive policies that optimally trade off exploration, exploitation, and robustness. RAMCP formulates the risk-sensitive planning problem as a two-player zero-sum game, in which an adversary perturbs the agent's belief over the models. We introduce two versions of the RAMCP algorithm. The first, RAMCP-F, converges to an optimal risk-sensitive policy without having to rebuild the search tree as the underlying belief over models is perturbed. The second version, RAMCP-I, improves computational efficiency at the cost of losing theoretical guarantees, but is shown to yield empirical results comparable to RAMCP-F. RAMCP is demonstrated on an n-pull multi-armed bandit problem, as well as a patient treatment scenario.

We present a novel technique for assessing the dynamics of multiphase fluid flow in the oil reservoir. We demonstrate an efficient workflow for handling the 3D reservoir simulation data in a way which is orders of magnitude faster than the conventional routine. The workflow (we call it "Metamodel") is based on a projection of the system dynamics into a latent variable space, using Variational Autoencoder model, where Recurrent Neural Network predicts the dynamics. We show that being trained on multiple results of the conventional reservoir modelling, the Metamodel does not compromise the accuracy of the reservoir dynamics reconstruction in a significant way. It allows forecasting not only the flow rates from the wells, but also the dynamics of pressure and fluid saturations within the reservoir. The results open a new perspective in the optimization of oilfield development as the scenario screening could be accelerated sufficiently.

The scientific and technological revolution of the Internet of Things has begun in the area of oceanography. Historically, humans have observed the ocean from an external viewpoint in order to study it. In recent years, however, changes have occurred in the ocean, and laboratories have been built on the seafloor. Approximately 70.8% of the Earth's surface is covered by oceans and rivers. The Ocean of Things is expected to be important for disaster prevention, ocean-resource exploration, and underwater environmental monitoring. Unlike traditional wireless sensor networks, the Ocean Network has its own unique features, such as low reliability and narrow bandwidth. These features will be great challenges for the Ocean Network. Furthermore, the integration of the Ocean Network with artificial intelligence has become a topic of increasing interest for oceanology researchers. The Cognitive Ocean Network (CONet) will become the mainstream of future ocean science and engineering developments. In this article, we define the CONet. The contributions of the paper are as follows: (1) a CONet architecture is proposed and described in detail; (2) important and useful demonstration applications of the CONet are proposed; and (3) future trends in CONet research are presented.