607 research outputs found
Facilitation Team Roles in Online GMB
The COVID-19 pandemic ushered in the new reality of remote working and learning, forcing group model building practitioners to make an abrupt shift to online workshops. Like our peers, the Social System Design Lab (SSDL) at Washington University in St. Louis confronted this challenge by exploring what tools existed for adaptation and continued collaboration. The shift has not been easy, but it has revealed new insights that suggest areas to leverage the strengths of online GMB long after the pandemic comes to an end.
The purpose of this brief is to compare facilitation team roles in traditional, in-person GMB sessions with those in online GMB sessions, shedding particular light on how the realities of online platforms shift what tools facilitators have at their disposal when engaging a group of participants
An Overview of Platforms to Support Online GMB
The COVID-19 pandemic ushered in the new reality of remote working and learning, forcing group model building practitioners to make an abrupt shift to online workshops. Like our peers, the Social System Design Lab (SSDL) at Washington University in St. Louis confronted this challenge by exploring what tools existed for adaptation and continued collaboration. The shift has not been easy, but it has revealed new insights that suggest areas to leverage the strengths of online GMB long after the pandemic comes to an end.
The purpose of this brief is to review a number of online platforms that group model building practitioners have used since the rise of remote work, and to suggest promising combinations for groups who are hoping to implement their own online system dynamics work
Online GMB: Challenges, Opportunities, and Barriers
The COVID-19 pandemic ushered in the new reality of remote working and learning, forcing group model building practitioners to make an abrupt shift to online workshops. Like our peers, the Social System Design Lab (SSDL) at Washington University in St. Louis confronted this challenge by exploring what tools existed for adaptation and continued collaboration. The shift has not been easy, but it has revealed new insights that suggest areas to leverage the strengths of online GMB long after the pandemic comes to an end.
The purpose of this brief is to provide some general points of comparison between in-person and online group model building and introduce challenges and opportunities that practitioners in the SSDL have faced when translating workshops to online spaces
Novel deep learning methods for track reconstruction
For the past year, the HEP.TrkX project has been investigating machine
learning solutions to LHC particle track reconstruction problems. A variety of
models were studied that drew inspiration from computer vision applications and
operated on an image-like representation of tracking detector data. While these
approaches have shown some promise, image-based methods face challenges in
scaling up to realistic HL-LHC data due to high dimensionality and sparsity. In
contrast, models that can operate on the spacepoint representation of track
measurements ("hits") can exploit the structure of the data to solve tasks
efficiently. In this paper we will show two sets of new deep learning models
for reconstructing tracks using space-point data arranged as sequences or
connected graphs. In the first set of models, Recurrent Neural Networks (RNNs)
are used to extrapolate, build, and evaluate track candidates akin to Kalman
Filter algorithms. Such models can express their own uncertainty when trained
with an appropriate likelihood loss function. The second set of models use
Graph Neural Networks (GNNs) for the tasks of hit classification and segment
classification. These models read a graph of connected hits and compute
features on the nodes and edges. They adaptively learn which hit connections
are important and which are spurious. The models are scaleable with simple
architecture and relatively few parameters. Results for all models will be
presented on ACTS generic detector simulated data.Comment: CTD 2018 proceeding
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Pattern recognition problems in high energy physics are notably different
from traditional machine learning applications in computer vision.
Reconstruction algorithms identify and measure the kinematic properties of
particles produced in high energy collisions and recorded with complex detector
systems. Two critical applications are the reconstruction of charged particle
trajectories in tracking detectors and the reconstruction of particle showers
in calorimeters. These two problems have unique challenges and characteristics,
but both have high dimensionality, high degree of sparsity, and complex
geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of
deep learning architectures which can deal with such data effectively, allowing
scientists to incorporate domain knowledge in a graph structure and learn
powerful representations leveraging that structure to identify patterns of
interest. In this work we demonstrate the applicability of GNNs to these two
diverse particle reconstruction problems
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Pattern recognition problems in high energy physics are notably different
from traditional machine learning applications in computer vision.
Reconstruction algorithms identify and measure the kinematic properties of
particles produced in high energy collisions and recorded with complex detector
systems. Two critical applications are the reconstruction of charged particle
trajectories in tracking detectors and the reconstruction of particle showers
in calorimeters. These two problems have unique challenges and characteristics,
but both have high dimensionality, high degree of sparsity, and complex
geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of
deep learning architectures which can deal with such data effectively, allowing
scientists to incorporate domain knowledge in a graph structure and learn
powerful representations leveraging that structure to identify patterns of
interest. In this work we demonstrate the applicability of GNNs to these two
diverse particle reconstruction problems.Comment: Presented at NeurIPS 2019 Workshop "Machine Learning and the Physical
Sciences
The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking
Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data
Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers
This paper presents a graph neural network (GNN) technique for low-level
reconstruction of neutrino interactions in a Liquid Argon Time Projection
Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown
great promise for similar reconstruction tasks in the LHC. In this paper, a
multihead attention message passing network is used to classify the
relationship between detector hits by labelling graph edges, determining
whether hits were produced by the same underlying particle, and if so, the
particle type. The trained model is 84% accurate overall, and performs best on
the EM shower and muon track classes. The model's strengths and weaknesses are
discussed, and plans for developing this technique further are summarised.Comment: 7 pages, 3 figures, submitted to the 25th International Conference on
Computing in High-Energy and Nuclear Physic
Track Seeding and Labelling with Embedded-space Graph Neural Networks
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is
investigating a variety of machine learning approaches to particle track
reconstruction. The most promising of these solutions, graph neural networks
(GNN), process the event as a graph that connects track measurements (detector
hits corresponding to nodes) with candidate line segments between the hits
(corresponding to edges). Detector information can be associated with nodes and
edges, enabling a GNN to propagate the embedded parameters around the graph and
predict node-, edge- and graph-level observables. Previously, message-passing
GNNs have shown success in predicting doublet likelihood, and we here report
updates on the state-of-the-art architectures for this task. In addition, the
Exa.TrkX project has investigated innovations in both graph construction, and
embedded representations, in an effort to achieve fully learned end-to-end
track finding. Hence, we present a suite of extensions to the original model,
with encouraging results for hitgraph classification. In addition, we explore
increased performance by constructing graphs from learned representations which
contain non-linear metric structure, allowing for efficient clustering and
neighborhood queries of data points. We demonstrate how this framework fits in
with both traditional clustering pipelines, and GNN approaches. The embedded
graphs feed into high-accuracy doublet and triplet classifiers, or can be used
as an end-to-end track classifier by clustering in an embedded space. A set of
post-processing methods improve performance with knowledge of the detector
physics. Finally, we present numerical results on the TrackML particle tracking
challenge dataset, where our framework shows favorable results in both seeding
and track finding.Comment: Proceedings submission in Connecting the Dots Workshop 2020, 10 page
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