Learning deep dynamical models from image pixels

Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only available through noisy and potentially high-dimensional observations. In these cases, system identification, i.e., finding the measurement mapping and the transition mapping (system dynamics) in latent space can be challenging. For linear system dynamics and measurement mappings efficient solutions for system identification are available. However, in practical applications, the linearity assumptions does not hold, requiring non-linear system identification techniques. If additionally the observations are high-dimensional (e.g., images), non-linear system identification is inherently hard. To address the problem of non-linear system identification from high-dimensional observations, we combine recent advances in deep learning and system identification. In particular, we jointly learn a low-dimensional embedding of the observation by means of deep auto-encoders and a predictive transition model in this low-dimensional space. We demonstrate that our model enables learning good predictive models of dynamical systems from pixel information only.Comment: 10 pages, 11 figure

Modeling and interpolation of the ambient magnetic field by Gaussian processes

Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior on the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications: we demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic

The Influence of Organization and Management on the Safety of NPPS and Other Complex Industrial Systems (Report of an IAEA/IIASA consultants meeting in Laxenburg and Vienna, 18-22 March 1991)

An analysis of causes for human errors reveals that deficiencies in organization and management often provide an environment making errors more likely. There is also a considerable difference between the operational performance of similar industrial plants. A closer analysis often reveals that the differences can be attributed to the managing practices. Accepting organization and management as one important precursor for operational safety, the aim is to identify good managerial structures and practices as well as characteristics of unsafe operational practices. Such information can provide guidance for the operators of the installations and also support regulatory agencies. The ultimate aim should be to detect and correct organizational deficiencies before an incident or accident brings them into the open. It is therefore not sufficient to blame individuals nor training, because management and organization establishes priorities, structures, and practices that enable tasks to be accomplished. A consultants' meeting organized jointly by the International Atomic Energy Agency (IAEA) and the International Institute for Applied Systems Analysis (IIASA) was held in Laxenburg and Vienna, Austria on 18-22 March 1991. The objective of the meeting was to assess the extent to which research within the management sciences -can provide guidance for the practical problems of managing organizations, where safety is the major concern. The influence of organization and management on the safety of complex industrial installations was discussed during the meeting and the exchange of ideas and experience between different industrial sectors and the academia proved fruitful. In spite of the difference among national and company practices it is still expected that there are many possibilities for an exchange of good managerial knowledge, experience, and practices. The report collects both the contributions offered by members of the Expert Task Force and the findings of the discussions that took place during the meeting. Specific reference is in the following text made to the nuclear industry with the understanding that the issues have a wider application to chemical plants, off-shore installations or more generally to industries where safety is a major concern

Economic Aspects of Ecological Risk Due to Nuclear and Coal-Fired Electricity Production (General Comparison Related to the USSR)

It is becoming increasingly important to alleviate the environmental and health impacts of primary energy generation. A comparison of the costs for different abatement measures can provide guidance for the policy makers. This paper provides such a comparison between nuclear and coal-fired electricity production with special application to the USSR. The study is the result of cooperative work between the I.V. Kurchatov Atomic Energy Institute in Moscow, USSR, and the Social and Environmental Dimensions of Technologies (SET) Project at IIASA. It makes use of the methodologies developed for similar studies at the OECD

Surveillance of Echinococcus multilocularis in rodents in the vicinity of the finding of the first infected red fox (Vulpes vulpes) in Sweden

Olsson, G.E., Hörnfeldt, B., Ågren, E., Wahlström, H

Invertible Kernel PCA with Random Fourier Features

Kernel principal component analysis (kPCA) is a widely studied method to construct a low-dimensional data representation after a nonlinear transformation. The prevailing method to reconstruct the original input signal from kPCA -- an important task for denoising -- requires us to solve a supervised learning problem. In this paper, we present an alternative method where the reconstruction follows naturally from the compression step. We first approximate the kernel with random Fourier features. Then, we exploit the fact that the nonlinear transformation is invertible in a certain subdomain. Hence, the name \emph{invertible kernel PCA (ikPCA)}. We experiment with different data modalities and show that ikPCA performs similarly to kPCA with supervised reconstruction on denoising tasks, making it a strong alternative.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl