Software Workshop

The Columbia Computer Center IBM Machine Room, about 1980. Photo: Bob Resnikoff.

Our goal is to increase the impact of the research made at the Max Planck Institute for Intelligent Systems by

creating innovative software solutions for scientific purposes, always following industry standards
improving existing codes written by researchers (port to a new framework, optimization, parallelization...)
disseminating expertise and promoting good practices in software development (wiki, reviews, trainings)
assisting scientists to design, deploy, and maintain their software
providing scientists with a state-of-the-art infrastructure for software development
developing tools that improve the workflow of scientists

Fields of expertise

Programming languages: C++, Python, CMake, C#, Matlab, LaTeX, CUDA, bash, powershell
Technologies: git, django, celery, Qt, PyQt, REST API, docker, Ansible, Kinect2, OpenCV, Caffe2, pytorch, iOS
Tools: Confluence, Jira, Bamboo, Crucible, GitLab, download center
Methodologies: Agile software development, sprints, daily scrums

Members:

Jean-Claude Passy

Research Engineer @ Software Workshop

	Tübingen

	+49 7071 601 1852

Keerthana Jaganathan

	Tübingen

Ivan Oreshnikov

	Tübingen

Alumni:

CityGraph

2020-05-21

A Python framework for representing a city (real or virtual) and moving around it.

https://github.com/MPI-IS/CityGraph

Haptipedia

2020-04-06

An online, open-source, visualization of a growing database of haptic devices invented since 1992.

https://haptipedia.org/

python django javascript

NuGridPy

2019-12-27

Python framework for analyzing data produced by stellar astrophysical codes.

https://github.com/NuGrid/NuGridPy

python hdf5

Sleep Learning

2019-09-04

An application for collecting, organizing, and post-processing EEG and EMG data from animal sleep.

python django celery dash post-processing

Attention Training

2019-06-17

A productivity application for tracking and training the ability of the user to focus on a chosen task.

python pyqt multi-threading

Code Cov

2019-05-08

A tool to analyze the coverage of python code by extracting and visualizing using an interactive web interface.

python dash

Monocle

2019-02-24

Application used for capturing from the Kinect 2.0 device.

https://github.com/MPI-IS/monocle

c# kinect sdk makefile

Robot Data Collector

2019-01-25

A web application for managing, organizing, and sharing robotic data.

python django celery rest api

When working in 3D graphics, one needs to load raw data, conduct various processing on it, visualize the results to help understanding, then save the output in different kinds of formats. Here we release the Mesh Library to facilitate all these aforementioned operations. This library is built on top of OpenGL and CGAL, with an easy-to-use Python interface. Other than the basic usages like data IO and interactive visualization, it also supports other more complex functionalities like texture rendering, visibility computation, and geometry arithmetic. We hope the release of this tool makes the entry to 3D world smoother for interested people.

https://github.com/MPI-IS/mesh

mesh library 3d graphics

Accelerated K-means

2018-06-15

An efficient and generic implementation of the k-means clustering algoriothm.

c++ boost cmake cython python matlab

Coffee tracker

2017-11-28

An application for tracking consumption across several "coffee rooms" and facilitating payments.

python django celery

OpenPhd Guiding

2017-08-01

Algorithm using a learned motion correction based on a Gaussian Process to produce better images from a telescope.

https://openphdguiding.org/

c++ boost cmake

Robot Analysis Infrastructure

2017-06-30

An application for analyzing simultaneously videos and sensors data from robotic experiments.

python pyqt opencv

Scan Manager

2017-04-07

A web application for being able to manage the increasing amount of body scans.

python django celery rabbitmq

Distributed brain-computer interface

2017-02-20

A brain-computer interface (BCI) to assist and interpret thoughts from patients suffering diseases such as amyotrophic lateral sclerosis. This monitoring tool is especially suited for research and for reaching patients living in remote locations.

https://is.mpg.de/person/mhohmann

c++ qt mqtt

livius

2015-07-13

The tool we developed for generating the videos on MLSS 2015

python batch processing video ffmpeg

Code Doc

2015-04-01

A Django application for hosting and accessing documentation and distributions generated from source code.

python django documentation continuous-integration reat api

Robust and scalable PCA using Grassmann averages

2014-06-01

The Grassmann Averages PCA is a method for extracting the principal components from a sets of vectors, with the nice following properties: 1) it is of linear complexity wrt. the dimension of the vectors and the size of the data, which makes the method highly scalable, 2) It is more robust to outliers than PCA in the sense that it minimizes an L1 norm instead of the L2 norm of the standard PCA. It comes with two variants: 1) the standard computation, that coincides with the PCA for normally distributed data, also referred to as the GA, 2) a trimmed variant, that is more robust to outliers, referred to the TGA. We provide implementations for the Grassmann Average, the Trimmed Grassmann Average, and the Grassmann Median. The simplest is the Matlab implementation used in the CVPR 2014 paper, but we also provide a faster C++ implementation, which can be used either directly from C++ or through a Matlab wrapper interface. The repository contains the following:

a C++ multi-threaded implementation of the GA and TGA
a C++ multi-threaded implementation of the EM-PCA (for comparisons)
binaries that computes the GA, TGA and EM-PCA on a set of images (frames of a video)
Matlab bindings
Documentation of the C++ API

https://github.com/MPI-IS/Grassmann-Averages-PCA

8 results

2020

How to navigate everyday distractions: Leveraging optimal feedback to train attention control

Wirzberger, M., Lado, A., Eckerstorfer, L., Oreshnikov, I., Passy, J., Stock, A., Shenhav, A., Lieder, F.

Annual Meeting of the Cognitive Science Society, July 2020 (conference) Accepted

Abstract

To stay focused on their chosen tasks, people have to inhibit distractions. The underlying attention control skills can improve through reinforcement learning, which can be accelerated by giving feedback. We applied the theory of metacognitive reinforcement learning to develop a training app that gives people optimal feedback on their attention control while they are working or studying. In an eight-day field experiment with 99 participants, we investigated the effect of this training on people's productivity, sustained attention, and self-control. Compared to a control condition without feedback, we found that participants receiving optimal feedback learned to focus increasingly better (f = .08, p < .01) and achieved higher productivity scores (f = .19, p < .01) during the training. In addition, they evaluated their productivity more accurately (r = .12, p < .01). However, due to asymmetric attrition problems, these findings need to be taken with a grain of salt.

How to navigate everyday distractions: Leveraging optimal feedback to train attention control DOI Project Page [BibTex]

2020

re sf Wirzberger, M., Lado, A., Eckerstorfer, L., Oreshnikov, I., Passy, J., Stock, A., Shenhav, A., Lieder, F. How to navigate everyday distractions: Leveraging optimal feedback to train attention control Annual Meeting of the Cognitive Science Society, July 2020 (conference) Accepted

How to navigate everyday distractions: Leveraging optimal feedback to train attention control DOI Project Page [BibTex]

ACTrain: Ein KI-basiertes Aufmerksamkeitstraining für die Wissensarbeit [ACTrain: An AI-based attention training for knowledge work]

Wirzberger, M., Oreshnikov, I., Passy, J., Lado, A., Shenhav, A., Lieder, F.

66th Spring Conference of the German Ergonomics Society, 2020 (conference)

Abstract

Unser digitales Zeitalter lebt von Informationen und stellt unsere begrenzte Verarbeitungskapazität damit täglich auf die Probe. Gerade in der Wissensarbeit haben ständige Ablenkungen erhebliche Leistungseinbußen zur Folge. Unsere intelligente Anwendung ACTrain setzt genau an dieser Stelle an und verwandelt Computertätigkeiten in eine Trainingshalle für den Geist. Feedback auf Basis maschineller Lernverfahren zeigt anschaulich den Wert auf, sich nicht von einer selbst gewählten Aufgabe ablenken zu lassen. Diese metakognitive Einsicht soll zum Durchhalten motivieren und das zugrunde liegende Fertigkeitsniveau der Aufmerksamkeitskontrolle stärken. In laufenden Feldexperimenten untersuchen wir die Frage, ob das Training mit diesem optimalen Feedback die Aufmerksamkeits- und Selbstkontrollfertigkeiten im Vergleich zu einer Kontrollgruppe ohne Feedback verbessern kann.

link (url) Project Page [BibTex]

re sf Wirzberger, M., Oreshnikov, I., Passy, J., Lado, A., Shenhav, A., Lieder, F. ACTrain: Ein KI-basiertes Aufmerksamkeitstraining für die Wissensarbeit [ACTrain: An AI-based attention training for knowledge work] 66th Spring Conference of the German Ergonomics Society, 2020 (conference)

link (url) Project Page [BibTex]

2018

The effect of binding energy and resolution in simulations of the common envelope binary interaction

Iaconi, R., De Marco, O., Passy, J., Staff, J.

Monthly Notices of the Royal Astronomical Society, 477, June 2018 (article)

Abstract

The common envelope binary interaction remains one of the least understood phases in the evolution of compact binaries, including those that result in Type Ia supernovae and in mergers that emit detectable gravitational waves. In this work, we continue the detailed and systematic analysis of 3D hydrodynamic simulations of the common envelope interaction aimed at understanding the reliability of the results. Our first set of simulations replicate the five simulations of Passy et al. (a 0.88 M☉, 90 R☉ red giant branch (RGB) primary with companions in the range 0.1-0.9 M☉) using a new adaptive mesh refinement gravity solver implemented on our modified version of the hydrodynamic code ENZO. Despite smaller final separations obtained, these more resolved simulations do not alter the nature of the conclusions that are drawn. We also carry out five identical simulations but with a 2.0 M☉ primary RGB star with the same core mass as the Passy et al. simulations, isolating the effect of the envelope binding energy. With a more bound envelope, all the companions in-spiral faster and deeper, though relatively less gas is unbound. Even at the highest resolution, the final separation attained by simulations with a heavier primary is similar to the size of the smoothed potential even if we account for the loss of some angular momentum by the simulation. As a result, we suggest that an ∼2.0 M☉ RGB primary may possibly end in a merger with companions as massive as 0.6 M☉, something that would not be deduced using analytical arguments based on energy conservation.

DOI [BibTex]

2018

sf Iaconi, R., De Marco, O., Passy, J., Staff, J. The effect of binding energy and resolution in simulations of the common envelope binary interaction Monthly Notices of the Royal Astronomical Society, 477, June 2018 (article)

DOI [BibTex]

2017

Design of a visualization scheme for functional connectivity data of Human Brain

Bramlage, L.

Hochschule Osnabrück - University of Applied Sciences, 2017 (thesis)

Bramlage_BSc_2017.pdf [BibTex]

2017

sf Bramlage, L. Design of a visualization scheme for functional connectivity data of Human Brain Hochschule Osnabrück - University of Applied Sciences, 2017 (thesis)

Bramlage_BSc_2017.pdf [BibTex]

The effect of a wider initial separation on common envelope binary interaction simulations

Iaconi, R., Reichardt, T., Staff, J., De Marco, O., Passy, J., Price, D., Wurster, J., Herwig, F.

Monthly Notices of the Royal Astronomical Society, 464, pages: 4028-4044, 2017 (article)

Abstract

We present hydrodynamic simulations of the common envelope binary interaction between a giant star and a compact companion carried out with the adaptive mesh refinement code enzo and the smooth particle hydrodynamics code phantom. These simulations mimic the parameters of one of the simulations by Passy et al. but assess the impact of a larger, more realistic initial orbital separation on the simulation outcome. We conclude that for both codes the post-common envelope separation is somewhat larger and the amount of unbound mass slightly greater when the initial separation is wide enough that the giant does not yet overflow or just overflows its Roche lobe. phantom has been adapted to the common envelope problem here for the first time and a full comparison with enzo is presented, including an investigation of convergence as well as energy and angular momentum conservation. We also set our simulations in the context of past simulations. This comparison reveals that it is the expansion of the giant before rapid in-spiral and not spinning up of the star that causes a larger final separation. We also suggest that the large range in unbound mass for different simulations is difficult to explain and may have something to do with simulations that are not fully converged.

DOI [BibTex]

sf Iaconi, R., Reichardt, T., Staff, J., De Marco, O., Passy, J., Price, D., Wurster, J., Herwig, F. The effect of a wider initial separation on common envelope binary interaction simulations Monthly Notices of the Royal Astronomical Society, 464, pages: 4028-4044, 2017 (article)

DOI [BibTex]

Common Envelope Light Curves. I. Grid-code Module Calibration

Galaviz, P., De Marco, O., Passy, J., Staff, J., Iaconi, R.

Astrophysical Journal, Supplement, 229, pages: 36, 2017 (article)

Abstract

The common envelope (CE) binary interaction occurs when a star transfers mass onto a companion that cannot fully accrete it. The interaction can lead to a merger of the two objects or to a close binary. The CE interaction is the gateway of all evolved compact binaries, all stellar mergers, and likely many of the stellar transients witnessed to date. CE simulations are needed to understand this interaction and to interpret stars and binaries thought to be the byproduct of this stage. At this time, simulations are unable to reproduce the few observational data available and several ideas have been put forward to address their shortcomings. The need for more definitive simulation validation is pressing and is already being fulfilled by observations from time-domain surveys. In this article, we present an initial method and its implementation for post-processing grid-based CE simulations to produce the light curve so as to compare simulations with upcoming observations. Here we implemented a zeroth order method to calculate the light emitted from CE hydrodynamic simulations carried out with the 3D hydrodynamic code Enzo used in unigrid mode. The code implements an approach for the computation of luminosity in both optically thick and optically thin regimes and is tested using the first 135 days of the CE simulation of Passy et al., where a 0.8 M ⊙ red giant branch star interacts with a 0.6 M ⊙ companion. This code is used to highlight two large obstacles that need to be overcome before realistic light curves can be calculated. We explain the nature of these problems and the attempted solutions and approximations in full detail to enable the next step to be identified and implemented. We also discuss our simulation in relation to recent data of transients identified as CE interactions.

DOI [BibTex]

sf Galaviz, P., De Marco, O., Passy, J., Staff, J., Iaconi, R. Common Envelope Light Curves. I. Grid-code Module Calibration Astrophysical Journal, Supplement, 229, pages: 36, 2017 (article)

DOI [BibTex]

Nonparametric Disturbance Correction and Nonlinear Dual Control

Klenske, E. D.

(24098), ETH Zurich, 2017 (phdthesis)

DOI [BibTex]

ei pn sf Klenske, E. D. Nonparametric Disturbance Correction and Nonlinear Dual Control (24098), ETH Zurich, 2017 (phdthesis)

DOI [BibTex]

2015

Scalable Robust Principal Component Analysis using Grassmann Averages

Hauberg, S., Feragen, A., Enficiaud, R., Black, M.

IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), December 2015 (article)

Abstract

In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average (GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average (TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.

preprint pdf from publisher supplemental Project Page [BibTex]

2015

ps sf Hauberg, S., Feragen, A., Enficiaud, R., Black, M. Scalable Robust Principal Component Analysis using Grassmann Averages IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), December 2015 (article)

preprint pdf from publisher supplemental Project Page [BibTex]

Members:

Alumni:

CityGraph

Haptipedia

NuGridPy

Sleep Learning

Attention Training

Code Cov

Monocle

Robot Data Collector

Mesh Library

Accelerated K-means

Coffee tracker

OpenPhd Guiding

Robot Analysis Infrastructure

Scan Manager

Distributed brain-computer interface

livius

Code Doc

Robust and scalable PCA using Grassmann averages

2020

How to navigate everyday distractions: Leveraging optimal feedback to train attention control

2020

ACTrain: Ein KI-basiertes Aufmerksamkeitstraining für die Wissensarbeit [ACTrain: An AI-based attention training for knowledge work]

2018

The effect of binding energy and resolution in simulations of the common envelope binary interaction

2018

2017

Design of a visualization scheme for functional connectivity data of Human Brain

2017

The effect of a wider initial separation on common envelope binary interaction simulations

Common Envelope Light Curves. I. Grid-code Module Calibration

Nonparametric Disturbance Correction and Nonlinear Dual Control

2015

Scalable Robust Principal Component Analysis using Grassmann Averages

2015

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