We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness" of the inner body---challenging for methods based solely on Monte Carlo integration or diffusion theory. We approach the problem differently. Instead of simulating all light transport during rendering, we pre-learn the spatial and directional distribution of radiant flux from tens of cloud exemplars. To render a new scene, we sample visible points of the cloud and, for each, extract a hierarchical 3D descriptor of the cloud geometry with respect to the shading location and the light source. The descriptor is input to a deep neural network that predicts the radiance function for each shading configuration. We make the key observation that progressively feeding the hierarchical descriptor into the network enhances the network's ability to learn faster and predict with high accuracy while using few coefficients. We also employ a block design with residual connections to further improve performance. A GPU implementation of our method synthesizes images of clouds that are nearly indistinguishable from the reference solution within seconds interactively. Our method thus represents a viable solution for applications such as cloud design and, thanks to its temporal stability, also for high-quality production of animated content.

Connected vehicle technology is foreseen to play an important role in reducing the number of traffic accidents while being one of the main enabling components for autonomous driving. One of the application of such connection is to provide accurate information about the road condition such as friction level to drivers or the intelligent systems controlling the car. Road surface friction can be defined as the grip between car tyre and underlying surface. During winter times when the temperature decreases dramatically, friction level reduces substantially, which can increase the risk of car accidents. Studies indicate that road conditions such as surface temperature, type of road, and structure of the road sides play an important role in the measured friction level, and some of these conditions can vary significantly within short distances under specific weather situations. Road friction prediction based on the past sensor measurements available in the cars, e.g., temperature and sun light, has advantages of being independent of the road structure and surrounding infrastructure. Intelligent forecast systems rely on the availability of high quality data in order to allow their multiple actors to make correct decisions in diverse traffic situations. These systems have the potential to increase the safety of roads users by means of the timely sharing of road-related information. With the advances in car-to-car communication technology, today, Volvo cars are equipped with slippery road condition warning system to improve road safety and traffic flow.

Abstract--This paper is concerned with estimation and stochastic control in physical systems which contain unknown input signals or forces. These unknown signals are modeled as Gaussian processes (GP) in the sense that GP models are used in machine learning. The resulting latent force models (LFMs) can be seen as hybrid models that contain a first-principles physical model part and a nonparametric GP model part. The aim of this paper is to collect and extend the statistical inference and learning methods for this kind of models, provide new theoretical results for the models, and to extend the methodology and theory to stochastic control of LFMs. The generalizations of this kind of models to arbitrary differential equations are called latent force models (LFM) [2]-[6] in machine learning literature. In addition to learning problem on the LFMs, we also consider the problem of controlling the LFM using the control functionc(t) . In particular, we consider the problem of optimal stochastic control design for LFMs. The present problem is also closely related to so called input estimation problem that has previously been addressed in target tracking literature (e.g. Simo S arkk a is with the Department of Electrical Engineering and Automation (EEA), Aalto University, Rakentajanaukio 2c, 02150 Espoo, Finland (simo.sarkka@aalto.fi). The difference is that here is no concept of time in this equation, nor a possibility for controlling the equation. A. General problem formulation The models considered in this article can be seen to belong to the following three classes: 1) Basic latent force models which are ordinary differential equations (ODEs) driven by Gaussian input processes u (t) and control inputsc(t) . X, MONTH 20XX 2 2) We also consider are dynamic partial and pseudo differential equation (PDE) based models that can generally be written in form L f (x,t) u (x,t) c(x,t), (7) where L is a linear operator in space and time. The input Gaussian processu (x,t) and control inputc(x,t) are also space-time processes. Typically, the operator has the form L A m d m dt m ··· A 1 d dt A 0, (8) where A 0,...,A m are some spatial partial differential or pseudo-differential operators. This kind of models can often be also written in form of spatiotemporal state-space models f (x,t) t A f f (x,t) B f u (x,t) M f c (x,t), (9) which again is strictly more general than the model (8). For this kind of models there is no control problem per se, because there is no time dependence. These models do not naturally allow for a state-space representation either.

In this work, we develop a novel framework for activity shaping under the Continuous-Time Information Cascades Model which allows the administrator for local control actions by allocating targeted resources that can alter the spread of the process. Our framework employs the optimization of the spectral radius of the Hazard matrix, a quantity that has been shown to drive the maximum influence in a network, while enjoying a simple convex relaxation when used to minimize the influence of the cascade. In addition, use-cases such as quarantine and node immunization are discussed to highlight the generality of the proposed activity shaping framework. Finally, we present the NetShape influence minimization method which is compared favorably to baseline and state-of-the-art approaches through simulations on real social networks.

To model high dimensional data, Gaussian methods are widely used since they remain tractable and yield parsimonious models by imposing strong assumptions on the data. Vine copulas are more flexible by combining arbitrary marginal distributions and (conditional) bivariate copulas. Yet, this adaptability is accompanied by sharply increasing computational effort as the dimension increases. The approach proposed in this paper overcomes this burden and makes the first step into ultra high dimensional non-Gaussian dependence modeling by using a divide-and-conquer approach. First, we apply Gaussian methods to split datasets into feasibly small subsets and second, apply parsimonious and flexible vine copulas thereon. Finally, we reconcile them into one joint model. We provide numerical results demonstrating the feasibility of our approach in moderate dimensions and showcase its ability to estimate ultra high dimensional non-Gaussian dependence models in thousands of dimensions.

Most popular word embedding techniques involve implicit or explicit factorization of a word co-occurrence based matrix into low rank factors. In this paper, we aim to generalize this trend by using numerical methods to factor higher-order word co-occurrence based arrays, or \textit{tensors}. We present four word embeddings using tensor factorization and analyze their advantages and disadvantages. One of our main contributions is a novel joint symmetric tensor factorization technique related to the idea of coupled tensor factorization. We show that embeddings based on tensor factorization can be used to discern the various meanings of polysemous words without being explicitly trained to do so, and motivate the intuition behind why this works in a way that doesn't with existing methods. We also modify an existing word embedding evaluation metric known as Outlier Detection [Camacho-Collados and Navigli, 2016] to evaluate the quality of the order-$N$ relations that a word embedding captures, and show that tensor-based methods outperform existing matrix-based methods at this task. Experimentally, we show that all of our word embeddings either outperform or are competitive with state-of-the-art baselines commonly used today on a variety of recent datasets. Suggested applications of tensor factorization-based word embeddings are given, and all source code and pre-trained vectors are publicly available online.

DOI: 10.18713/JIMIS-010917-3-1 Submitted: 12/2/2016 - Published: 6/2/2017 Volume: 3 - Year: 2017 Issue: Digital Contextualization Editors: Frédéric Lebaron, Brigitte Le Roux, Fionn Murtagh, Evelyn Ruppert The relevance and importance of contextualizing data analytics is described. Qualitative characteristics might form the context of quantitative analysis. Topics that are at issue include: contrast, baselining, secondary data sources, supplementary data sources; dynamic and heterogeneous data. In geometric data analysis, especially with the Correspondence Analysis platform, various case studies are both experimented with, and are reviewed. In such aspects as paradigms followed, and technical implementation, implicitly and explicitly, an important point made is the major relevance of such work for both burgeoning analytical needs and for new analytical areas including Big Data analytics, and so on. For the general reader, it is aimed to display and describe, first of all, the analytical outcomes that are subject to analysis here, and then proceed to detail the more quantitative outcomes that fully support the analytics carried out.

Of the many, many (many many many) challenges that are inherent to urban drone delivery, safety is one of the most important. Nobody has a reliable, cost-effective solution for this, although we've seen some unreliable ones (dangling packages on strings) and cumbersome ones (dedicated, protected landing pads), so we've been missing an elegant way of protecting end users from robots that fly with spinning blades of death. EPFL in Switzerland has had a solution for this for years--drones surrounded by protective cages that allow them to bounce off of obstacles. As far as the drones are concerned, humans are obstacles as well, so a protective cage does pretty well at protecting them from us (and vice versa). The annoying thing about these cages has always been that they're all kinds of bulky, especially if they're protecting a quadrotor beefy enough to be useful.

I'm working for a small start up and trying to follow some of the results in the literature that reconstruct 3D models from videos or images but it seems all of them use some datasets (mostly the Basel Face Model or facewarehouse) that are not accessible for commercial usage. Did anyone else encounter this issue? is there an affordable (or free) model available?

In the last instalment of this blog series, we discussed objectives and accuracy in machine learning. And we described two crucial tests for the utility of a machine learning model: The model must be sufficiently accurate and we must be able to deploy the model so that it can produce actionable outputs from the available data. We then introduced a real-world scenario -- predicting train failures up to 36 hours in advance of their occurrence using sensor data -- to illustrate the application of those tests. But how did we decide which of the multitude of machine learning algorithms to use to train our model in the first place? To answer this question, we need to revisit the main classes of machine learning algorithms.