Ray: A Distributed Execution Engine for the Machine Learning Ecosystem

In recent years, growing data volumes and more sophisticated computational procedures have greatly increased the demand for computational power. Machine learning and artificial intelligence applications, for example, are notorious for their computational requirements. At the same time, Moores law is ending and processor speeds are stalling. As a result, distributed computing has become ubiquitous. While the cloud makes distributed hardware infrastructure widely accessible and therefore offers the potential of horizontal scale, developing these distributed algorithms and applications remains surprisingly hard. This is due to the inherent complexity of concurrent algorithms, the engineering challenges that arise when communicating between many machines, the requirements like fault tolerance and straggler mitigation that arise at large scale and the lack of a general-purpose distributed execution engine that can support a wide variety of applications.In this thesis, we study the requirements for a general-purpose distributed computation model and present a solution that is easy to use yet expressive and resilient to faults. At its core our model takes familiar concepts from serial programming, namely functions and classes, and generalizes them to the distributed world, therefore unifying stateless and stateful distributed computation. This model not only supports many machine learning workloads like training or serving, but is also a good t for cross-cutting machine learning applications like reinforcement learning and data processing applications like streaming or graph processing. We implement this computational model as an open-source system called Ray, which matches or exceeds the performance of specialized systems in many application domains, while also offering horizontally scalability and strong fault tolerance properties

Determining molecule diffusion coefficients on surfaces from a locally fixed probe: On the analysis of signal fluctuations

Methods of determining surface diffusion coefficients of molecules from signal fluctuations of a locally fixed probe are revisited and refined. Particular emphasis is put on the influence of the molecule's extent. In addition to the formerly introduced autocorrelation method and residence time method, we develop a further method based on the distribution of intervals between successive peaks in the signal. The theoretical findings are applied to STM measurements of copper phthalocyanine (CuPc) molecules on the Ag(100) surface. We discuss advantages and disadvantages of each method and suggest a combination to obtain accurate results for diffusion coefficients.Comment: 10 pages, 8 figure

A versatile and light-weight slow control system for small-scale applications

We present an open source slow control system for small and medium scale projects. Thanks to its modular and flexible design, where the various instruments are read and controlled by independent plugins, Doberman (Detector OBsERving and Monitoring ApplicatioN) can be quickly adapted for many applications, also making use of existing code or proprietary components. The system uses a SQL database to store the data from the instruments and provides an online application to display and browse through the data. It allows the modification of device settings while the program is running and features a protocol to handle exceptions, including the automated distribution of alarm messages. We present two case studies from astroparticle physics, on which Doberman is successfully deployed: a low-background screening facility installed in a remote underground laboratory and a detector R&D platform using cryogenic liquid xenon

Constructing the space of visual attention

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Page 180 blank. Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 168-171).This thesis explores the nature of a human experience in space through a primary inquiry into vision. This inquiry begins by questioning the existing methods and instruments employed to capture and represent a human experience of space. While existing qualitative and quantitative methods and instruments -- from "subjective" interviews to "objective" photographic documentation -- may lead to insight in the study of a human experience in space, we argue that they are inherently limited with respect to physiological realities. As one moves about the world, one believes to see the world as continuous and fully resolved. However, this is not how human vision is currently understood to function on a physiological level. If we want to understand how humans visually construct a space, then we must examine patterns of visual attention on a physiological level. In order to inquire into patterns of visual attention in three dimensional space, we need to develop new instruments and new methods of representation. The instruments we require, directly address the physiological realities of vision, and the methods of representation seek to situate the human subject within a space of their own construction. In order to achieve this goal we have developed PUPIL, a custom set of hardware and software instruments, that capture the subject's eye movements. Using PUPIL, we have conducted a series of trials from proof of concept -- demonstrating the capabilities of our instruments -- to critical inquiry of the relationship between a human subject and a space. We have developed software to visualize this unique spatial experience, and have posed open questions based on the initial findings of our trials. This thesis aims to contribute to spatial design disciplines, by providing a new way to capture and represent a human experience of space.by Moritz Philipp Kassner [and] William Rhoades Patera.S.M

Sharpening up Galactic all-sky maps with complementary data - A machine learning approach

Galactic all-sky maps at very disparate frequencies, like in the radio and $\gamma$-ray regime, show similar morphological structures. This mutual information reflects the imprint of the various physical components of the interstellar medium. We want to use multifrequency all-sky observations to test resolution improvement and restoration of unobserved areas for maps in certain frequency ranges. For this we aim to reconstruct or predict from sets of other maps all-sky maps that, in their original form, lack a high resolution compared to other available all-sky surveys or are incomplete in their spatial coverage. Additionally, we want to investigate the commonalities and differences that the ISM components exhibit over the electromagnetic spectrum. We build an $n$-dimensional representation of the joint pixel-brightness distribution of $n$ maps using a Gaussian mixture model and see how predictive it is: How well can one map be reproduced based on subsets of other maps? Tests with mock data show that reconstructing the map of a certain frequency from other frequency regimes works astonishingly well, predicting reliably small-scale details well below the spatial resolution of the initially learned map. Applied to the observed multifrequency data sets of the Milky Way this technique is able to improve the resolution of, e.g., the low-resolution Fermi LAT maps as well as to recover the sky from artifact-contaminated data like the ROSAT 0.855 keV map. The predicted maps generally show less imaging artifacts compared to the original ones. A comparison of predicted and original maps highlights surprising structures, imaging artifacts (fortunately not reproduced in the prediction), and features genuine to the respective frequency range that are not present at other frequency bands. We discuss limitations of this machine learning approach and ideas how to overcome them