A lifted Bregman formulation for the inversion of deep neural networks

We propose a novel framework for the regularized inversion of deep neural networks. The framework is based on the authors' recent work on training feed-forward neural networks without the differentiation of activation functions. The framework lifts the parameter space into a higher dimensional space by introducing auxiliary variables, and penalizes these variables with tailored Bregman distances. We propose a family of variational regularizations based on these Bregman distances, present theoretical results and support their practical application with numerical examples. In particular, we present the first convergence result (to the best of our knowledge) for the regularized inversion of a single-layer perceptron that only assumes that the solution of the inverse problem is in the range of the regularization operator, and that shows that the regularized inverse provably converges to the true inverse if measurement errors converge to zero

Single Particle Tracking: Analysis Techniques for Live Cell Nanoscopy.

Single molecule experiments are a set of experiments designed specifically to study the properties of individual molecules. It has only been in the last three decades where single molecule experiments have been applied to the life sciences; where they have been successfully implemented in systems biology for probing the behaviors of sub-cellular mechanisms. The advent and growth of super-resolution techniques in single molecule experiments has made the fundamental behaviors of light and the associated nano-probes a necessary concern among life scientists wishing to advance the state of human knowledge in biology. This dissertation disseminates some of the practices learned in experimental live cell microscopy. The topic of single particle tracking is addressed here in a format that is designed for the physicist who embarks upon single molecule studies. Specifically, the focus is on the necessary procedures to generate single particle tracking analysis techniques that can be implemented to answer biological questions. These analysis techniques range from designing and testing a particle tracking algorithm to inferring model parameters once an image has been processed. The intellectual contributions of the author include the techniques in diffusion estimation, localization filtering, and trajectory associations for tracking which will all be discussed in detail in later chapters. The author of this thesis has also contributed to the software development of automated gain calibration, live cell particle simulations, and various single particle tracking packages. Future work includes further evaluation of this laboratory\u27s single particle tracking software, entropy based approaches towards hypothesis validations, and the uncertainty quantification of gain calibration

The potential of car advertising in pursuing transport policy goals: Code of good practices in the Spanish context

Climate change, which is mostly caused by CO2 emissions, continues to be a serious environmental problem. The dissemination of correct information regarding the environmental impact of car CO2 emissions and fuel consumption promoted by responsible advertising plays an important role in this problem. This article proposes that the promotion of responsible car advertising could serve as a tool for reducing climate change and decarbonizing transport. Thus, a qualitative and quantitative study was performed to analyse the influence of car advertising on CO2 emissions and attitudes regarding such emissions. The results of this study add value to the limited literature in the field of advertising, cars, and the environment. The analysis of the car advertising sector in Spain in 2007, 2015, and 2016 detected a low presence of good practices. Advertisements lack information regarding energy problems related to mobility, emissions, and climate change. There is an effort to hide CO2 emissions and fuel consumption information in advertising, energy labels are not presented, and information related to efficient driving or moderate vehicle use is lacking. Although the evolution of information regarding emissions and consumption has improved over time with respect to size and location in the advertisements, such data remains marginal. Recent car advertising does not highlight the environmental consequences of the products or offer advice to consumers regarding habits that can help reduce pollution or emissions. Finally, based on this analysis, a detailed code of 28 good practices for the responsible advertising of cars is propose

Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight

The Integrated Health Management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. The complexity of modern aeronautic and aircraft systems (including e.g. the power distribution, flight control, solid and liquid motors) dictates employment of hybrid models and high-level reasoners for analysing mixed continuous and discrete information flow involving multiple modes of operation in uncertain environments, unknown state variables, heterogeneous software and hardware components. To provide the information link between key design/performance parameters and high-level reasoners we rely on development of multi-physics performance models, distributed sensors networks, and fault diagnostic and prognostic (FD&P) technologies in close collaboration with system designers. The main challenges of our research are related to the in-flight assessment of the structural stability, engine performance, and trajectory control. The main goal is to develop an intelligent IHM that not only enhances components and system reliability, but also provides a post-flight feedback helping to optimize design of the next generation of aerospace systems. Our efforts are concentrated on several directions of the research. One of the key components of our strategy is an innovative approach to the diagnostics/prognostics based on the real time dynamical inference (DI) technologies extended to encompass hybrid systems with hidden state trajectories. The major investments are into the multiphysics performance modelling that provides an access of the FD&P technologies to the main performance parameters of e.g. solid and liquid rocket motors and composite materials of the nozzle and case. Some of the recent results of our research are discussed in this chapter. We begin by introducing the problem of dynamical inference of stochastic nonlinear models and reviewing earlier results. Next, we present our analytical approach to the solution of this problem based on the path integral formulation. The resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. In the following Section the strengths of the algorithm are illustrated illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. Next, we discuss a number of recent results in application to the development of the IHM for aerospace system. Firstly, we apply dynamical inference approach to a solution of classical three tank problems with mixed unknown continuous and binary parameters. The problem is considered in the context of ground support system for filling fuel tanks of liquid rocket motors. It is shown that the DI algorithm is well suited for successful solution of a hybrid version of this benchmark problem even in the presence of additional periodic and stochastic perturbation of unknown strength. Secondly, we illustrate our approach by its application to an analysis of the nozzle fault in a solid rocket motor (SRM). The internal ballistics of the SRM is modelled as a set of one-dimensional partial differential equations coupled to the dynamics of the propellant regression. In this example we are specifically focussed on the inference of discrete and continuous parameters of the nozzle blocking fault and on the possibility of an application of the DI algorithm to reducing the probability of "misses" of an on-board FD&P for SRM. In the next section re-contact problem caused by first stage/upper stage separation failure is discussed. The reaction forces imposed on the nozzle of the upper stage during the re-contact and their connection to the nozzle damage and to the thrust vector control (TVC) signal are obtained. It is shown that transient impact induced torquean be modelled as a response of an effective damped oscillator. A possible application of the DI algorithm to the inference of damage parameters and predicting fault dynamics ahead of time using the actuator signal is discussed. Finally, we formulate Bayesian inferential framework for development of the IHM system for in-flight structural health monitoring (SHM) of composite materials. We consider the signal generated by piezoelectric actuator mounted on composite structure generating elastic waves in it. The signal received by the sensor is than compared with the baseline signal. The possibility of damage inference is discussed in the context of development of the SHM

Elastic flow instabilities of non-Newtonian fluids in shear flows

The present work deals with the investigation of flow phenomena of non-Newtonian, viscoelastic fluids and particularly with the emergence of elastic flow instabilities and their underlying mechanism. It is mainly focused on the experimental and theoretical investigation of the viscoelastic Taylor-Couette flow for which previous approaches exhibited some unanswered questions and apparent discrepancies to theoretical predictions. The present study is based on experiments in different Taylor-Couette cells using various polymer solutions with diverse rheological properties. Here, the various cells differ from each other just by their characteristic curvature. The objective is the experimental verification of the universal criterion which predicts the onset of elastic instability of a viscoelastic flow with curved streamlines. Based on a customized, constitutive model which is able to describe the explicit shear rheology of the used polymer solutions, linear stability analysis is performed with aiming at an interpretation of the experimental findings. A supplementary study is presented which addresses the possible degradation of the polymer solutions during the Taylor-Couette measurements. Its results are essential to provide a consistent interpretation of the Taylor-Couette measurements.Die vorliegende Arbeit befasst sich mit verschiedenen Strömungsphänomenen nicht-Newtonscher, viskoelastischer Flüssigkeiten, insbesondere mit elastischen Strömungsinstabilitäten und deren zugrundeliegendem Mechanismus. Der Fokus liegt hierbei auf der experimentellen wie theoretischen Untersuchung des viskoelastischen Taylor-Couette-Flusses, dessen bisherige Behandlung offene Fragen und Unstimmigkeiten gegenüber theoretischen Vorhersagen aufweist. Grundlage unserer Studie bilden Experimente mit mehreren Polymerflüssigkeiten unterschiedlicher rheologischer Eigenschaften in verschiedenen Taylor-Couette-Geometrien, die sich in ihrem Krümmungsparameter unterscheiden. Zielsetzung ist insbesondere die experimentelle Verifizierung eines universellen Kriteriums, das die lineare elastische Instabilität einer viskoelastischen Strömung mit gekrümmten Stromlinien vorhersagt. Basierend auf einem angepassten, konstitutiven Flüssigkeitsmodell, welches die Scherrheologie der verwendeten Flüssigkeiten zu erfassen vermag, wird mit Hilfe einer linearen Stabilitätsanalyse versucht, die experimentellen Ergebnisse nachzuvollziehen. Ergänzend dazu werden die Ergebnisse einer Studie vorgestellt, die die Degradation der Polymerflüssigkeiten im Verlauf der Taylor-Couette Messungen zum Gegenstand hat. Erst diese lassen eine stimmige Interpretation der vorherigen Resultate zu

Integrated optic/nanofluidic detection device with plasmonic readout

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references.Integrated lab-on-a-chip devices provide the promise of many benefits in many application areas. A low noise, high resolution, high sensitivity integrated optical microfluidic device would not only improve the capabilities of existing procedures but also enable new applications. This thesis presents an architecture and fabrication process for such a device. Previously, the possibilities for such integrated systems were limited by existing fabrication technologies. An integrated fabrication process including glass nanofluidics, diffused waveguides and metal structures was developed. To enable this process a voltage-assisted polymer bond procedure was developed. This bond process enables high strength, robust, optically clear, low temperature bonding of glass - a capability that was not possible before. Bond strength was compared with a glass-to-glass anodic type bond using various materials and a polymer bond using two polymers: Cytop and PMMA. Bond strength was far superior to standard polymer bonding procedures. Design considerations to minimize background noise are presented, analyzed and implemented. Using Cytop as an index-matched polymer layer reduces scattered light in the device. Plasmonic devices driven via evanescent fields were designed, simulated, fabricated, and tested in isolation as well as in the integrated system. A sample device was made to demonstrate applicability of this process to direct linear analysis of DNA. The device was shown to provide enhanced and confined electromagnetic excitation as well as the capability to excite submicron particles. A demonstrated excitation spot of 200nm is the best we have seen in this type of device. Further work is suggested that can improve this resolution further.by Jonathan S. Varsanik.Ph.D

Fiscal year 1973 scientific and technical reports, articles, papers, and presentations

Formal NASA technical reports, papers published in technical journals, and presentations by MSFC personnel in FY73 are presented. Papers of MSFC contractors are also included

Approximate inference in astronomy

This thesis utilizes the rules of probability theory and Bayesian reasoning to perform inference about astrophysical quantities from observational data, with a main focus on the inference of dynamical systems extended in space and time. The necessary assumptions to successfully solve such inference problems in practice are discussed and the resulting methods are applied to real world data. These assumptions range from the simplifying prior assumptions that enter the inference process up to the development of a novel approximation method for resulting posterior distributions. The prior models developed in this work follow a maximum entropy principle by solely constraining those physical properties of a system that appear most relevant to inference, while remaining uninformative regarding all other properties. To this end, prior models that only constrain the statistically homogeneous space-time correlation structure of a physical observable are developed. The constraints placed on these correlations are based on generic physical principles, which makes the resulting models quite flexible and allows for a wide range of applications. This flexibility is verified and explored using multiple numerical examples, as well as an application to data provided by the Event Horizon Telescope about the center of the galaxy M87. Furthermore, as an advanced and extended form of application, a variant of these priors is utilized within the context of simulating partial differential equations. Here, the prior is used in order to quantify the physical plausibility of an associated numerical solution, which in turn improves the accuracy of the simulation. The applicability and implications of this probabilistic approach to simulation are discussed and studied using numerical examples. Finally, utilizing such prior models paired with the vast amount of observational data provided by modern telescopes, results in Bayesian inference problems that are typically too complex to be fully solvable analytically. Specifically, most resulting posterior probability distributions become too complex, and therefore require a numerical approximation via a simplified distribution. To improve upon existing methods, this work proposes a novel approximation method for posterior probability distributions: the geometric Variational Inference (geoVI) method. The approximation capacities of geoVI are theoretically established and demonstrated using numerous numerical examples. These results suggest a broad range of applicability as the method provides a decrease in approximation errors compared to state of the art methods at a moderate level of computational costs.Diese Dissertation verwendet die Regeln der Wahrscheinlichkeitstheorie und Bayes’scher Logik, um astrophysikalische Größen aus Beobachtungsdaten zu rekonstruieren, mit einem Schwerpunkt auf der Rekonstruktion von dynamischen Systemen, die in Raum und Zeit definiert sind. Es werden die Annahmen, die notwendig sind um solche Inferenz-Probleme in der Praxis erfolgreich zu lösen, diskutiert, und die resultierenden Methoden auf reale Daten angewendet. Diese Annahmen reichen von vereinfachenden Prior-Annahmen, die in den Inferenzprozess eingehen, bis hin zur Entwicklung eines neuartigen Approximationsverfahrens für resultierende Posterior-Verteilungen. Die in dieser Arbeit entwickelten Prior-Modelle folgen einem Prinzip der maximalen Entropie, indem sie nur die physikalischen Eigenschaften eines Systems einschränken, die für die Inferenz am relevantesten erscheinen, während sie bezüglich aller anderen Eigenschaften agnostisch bleiben. Zu diesem Zweck werden Prior-Modelle entwickelt, die nur die statistisch homogene Raum-Zeit-Korrelationsstruktur einer physikalischen Observablen einschränken. Die gewählten Bedingungen an diese Korrelationen basieren auf generischen physikalischen Prinzipien, was die resultierenden Modelle sehr flexibel macht und ein breites Anwendungsspektrum ermöglicht. Dies wird anhand mehrerer numerischer Beispiele sowie einer Anwendung auf Daten des Event Horizon Telescope über das Zentrum der Galaxie M87 verifiziert und erforscht. Darüber hinaus wird als erweiterte Anwendungsform eine Variante dieser Modelle zur Simulation partieller Differentialgleichungen verwendet. Hier wird der Prior als Vorwissen benutzt, um die physikalische Plausibilität einer zugehörigen numerischen Lösung zu quantifizieren, was wiederum die Genauigkeit der Simulation verbessert. Die Anwendbarkeit und Implikationen dieses probabilistischen Simulationsansatzes werden diskutiert und anhand von numerischen Beispielen untersucht. Die Verwendung solcher Prior-Modelle, gepaart mit der riesigen Menge an Beobachtungsdaten moderner Teleskope, führt typischerweise zu Inferenzproblemen die zu komplex sind um vollständig analytisch lösbar zu sein. Insbesondere ist für die meisten resultierenden Posterior-Wahrscheinlichkeitsverteilungen eine numerische Näherung durch eine vereinfachte Verteilung notwendig. Um bestehende Methoden zu verbessern, schlägt diese Arbeit eine neuartige Näherungsmethode für Wahrscheinlichkeitsverteilungen vor: Geometric Variational Inference (geoVI). Die Approximationsfähigkeiten von geoVI werden theoretisch ermittelt und anhand numerischer Beispiele demonstriert. Diese Ergebnisse legen einen breiten Anwendungsbereich nahe, da das Verfahren bei moderaten Rechenkosten eine Verringerung des Näherungsfehlers im Vergleich zum Stand der Technik liefert

Mining Technologies Innovative Development

The present book covers the main challenges, important for future prospects of subsoils extraction as a public effective and profitable business, as well as technologically advanced industry. In the near future, the mining industry must overcome the problems of structural changes in raw materials demand and raise the productivity up to the level of high-tech industries to maintain the profits. This means the formation of a comprehensive and integral response to such challenges as the need for innovative modernization of mining equipment and an increase in its reliability, the widespread introduction of Industry 4.0 technologies in the activities of mining enterprises, the transition to "green mining" and the improvement of labor safety and avoidance of man-made accidents. The answer to these challenges is impossible without involving a wide range of scientific community in the publication of research results and exchange of views and ideas. To solve the problem, this book combines the works of researchers from the world's leading centers of mining science on the development of mining machines and mechanical systems, surface and underground geotechnology, mineral processing, digital systems in mining, mine ventilation and labor protection, and geo-ecology. A special place among them is given to post-mining technologies research