77 research outputs found
Hierarchical Graph Structures for Congestion and ETA Prediction
Traffic4cast is an annual competition to predict spatio temporal traffic
based on real world data. We propose an approach using Graph Neural Networks
that directly works on the road graph topology which was extracted from
OpenStreetMap data. Our architecture can incorporate a hierarchical graph
representation to improve the information flow between key intersections of the
graph and the shortest paths connecting them. Furthermore, we investigate how
the road graph can be compacted to ease the flow of information and make use of
a multi-task approach to predict congestion classes and ETA simultaneously. Our
code and models are released here:
https://github.com/floriangroetschla/NeurIPS2022-traffic4cas
Learning Graph Algorithms With Recurrent Graph Neural Networks
Classical graph algorithms work well for combinatorial problems that can be
thoroughly formalized and abstracted. Once the algorithm is derived, it
generalizes to instances of any size. However, developing an algorithm that
handles complex structures and interactions in the real world can be
challenging. Rather than specifying the algorithm, we can try to learn it from
the graph-structured data. Graph Neural Networks (GNNs) are inherently capable
of working on graph structures; however, they struggle to generalize well, and
learning on larger instances is challenging. In order to scale, we focus on a
recurrent architecture design that can learn simple graph problems end to end
on smaller graphs and then extrapolate to larger instances. As our main
contribution, we identify three essential techniques for recurrent GNNs to
scale. By using (i) skip connections, (ii) state regularization, and (iii) edge
convolutions, we can guide GNNs toward extrapolation. This allows us to train
on small graphs and apply the same model to much larger graphs during
inference. Moreover, we empirically validate the extrapolation capabilities of
our GNNs on algorithmic datasets.Comment: Accepted at GCLR@AAAI23, workshop on Graphs and more Complex
structures for Learning and Reasonin
CO2 Reduction Measures in the Aviation Industry: Current Measures and Outlook
This article gives a holistic overview of the current CO2 reduction measures and analyses the effectiveness of measures that are feasible for implementation in the future. To achieve the objectives of the Paris Agreement, the aviation industry needs to implement reduction measures because of its forecasted growth and contribution to global warming. The focus is set on CO2 reduction measures, categorized in technology, operations, infrastructure/air traffic management (ATM), and market-based measures. The most promising long-term technologies to reduce CO2 emissions are hydrogen-powered aircrafts and sustainable aviation fuels (SAF). In terms of operations, CO2 emissions can be reduced through weight savings, either from fuel or payload. For every additional ton on board of an aircraft, an extra 3%â25% of fuel is necessary, depending on the route distance. From an infrastructure/ATM perspective, the goal is to decrease flight times and avoid holdings because every kg of fuel burned produces 3.16 kg of CO2 emissions. Market-based measures have a low impact as a reduction measure, but revenues may be used to accelerate the research and development of more promising reduction measures. The implementation of just one reduction measure is not reasonable. A global approach with reasonable incentives to support CO2 reduction measures is preferable
SALSA-CLRS: A Sparse and Scalable Benchmark for Algorithmic Reasoning
We introduce an extension to the CLRS algorithmic learning benchmark,
prioritizing scalability and the utilization of sparse representations. Many
algorithms in CLRS require global memory or information exchange, mirrored in
its execution model, which constructs fully connected (not sparse) graphs based
on the underlying problem. Despite CLRS's aim of assessing how effectively
learned algorithms can generalize to larger instances, the existing execution
model becomes a significant constraint due to its demanding memory requirements
and runtime (hard to scale). However, many important algorithms do not demand a
fully connected graph; these algorithms, primarily distributed in nature, align
closely with the message-passing paradigm employed by Graph Neural Networks.
Hence, we propose SALSA-CLRS, an extension of the current CLRS benchmark
specifically with scalability and sparseness in mind. Our approach includes
adapted algorithms from the original CLRS benchmark and introduces new problems
from distributed and randomized algorithms. Moreover, we perform a thorough
empirical evaluation of our benchmark. Code is publicly available at
https://github.com/jkminder/SALSA-CLRS.Comment: (Extended Abstract) Presented at the Second Learning on Graphs
Conference (LoG 2023
SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics
Simulating fluid dynamics is crucial for the design and development process,
ranging from simple valves to complex turbomachinery. Accurately solving the
underlying physical equations is computationally expensive. Therefore,
learning-based solvers that model interactions on meshes have gained interest
due to their promising speed-ups. However, it is unknown to what extent these
models truly understand the underlying physical principles and can generalize
rather than interpolate. Generalization is a key requirement for a
general-purpose fluid simulator, which should adapt to different topologies,
resolutions, or thermodynamic ranges. We propose SURF, a benchmark designed to
test the of learned graph-based fluid simulators.
SURF comprises individual datasets and provides specific performance and
generalization metrics for evaluating and comparing different models. We
empirically demonstrate the applicability of SURF by thoroughly investigating
the two state-of-the-art graph-based models, yielding new insights into their
generalization.Comment: Accepted at LoG 2023, Learning on Graphs Conferenc
Liebe deinen NĂ€chsten wie dich selbst. Untersuchungen zum alttestamentlichen Gebot der NĂ€chstenliebe (Lev 19,18)
Die vorliegende Studie bĂŒndelt die Auslegungsgeschichte zu einem Bibelvers, der in seiner Rezeption stark nachgewirkt hat. Hinzu treten zahlreiche weiterfĂŒhrende Erkenntnisse, die Verfasser durch das Integrieren biblischer und ausserbiblischer Perspektiven formulieren konnte. Neben intensiven Ăbersetzungsstudien zum Liebesgebot, wird vor allem durch die Diskussion der Kontextbegriffe das theologische Feld umrissen, das zu einer adĂ€quaten Deutung und zum theologischen VerstĂ€ndnis beitrĂ€gt. Studien zum VerhĂ€ltnis von Liebesgebot und Heiligkeitsgesetz sowie zur historischen Verortung des Liebesgebotes beleuchten die literarischen Kontexte und die Entstehungsgeschichte eindrĂŒcklich. Abschliessend nimmt Verf. eine systematische Verortung des Liebesgebotes vor: In diesem Rahmen gelingt es ihm, eine weiterfĂŒhrende theologische Perspektive zu prĂ€sentieren, die letztlich anknĂŒpfungsfĂ€hig fĂŒr die umliegenden theologischen Disziplinen ist
Modeling subjective relevance in schizophrenia and its relation to aberrant salience
In schizophrenia, increased aberrant salience to irrelevant events and reduced learning of relevant information may relate to an underlying deficit in relevance detection. So far, subjective estimates of relevance have not been probed in schizophrenia patients. The mechanisms underlying belief formation about relevance and their translation into decisions are unclear. Using novel computational methods, we investigated relevance detection during implicit learning in 42 schizophrenia patients and 42 healthy individuals. Participants underwent functional magnetic resonance imaging while detecting the outcomes in a learning task. These were preceded by cues differing in color and shape, which were either relevant or irrelevant for outcome prediction. We provided a novel definition of relevance based on Bayesian precision and modeled reaction times as a function of relevance weighted unsigned prediction errors (UPE). For aberrant salience, we assessed responses to subjectively irrelevant cue manifestations. Participants learned the contingencies and slowed down their responses following unexpected events. Model selection revealed that individuals inferred the relevance of cue features and used it for behavioral adaption to the relevant cue feature. Relevance weighted UPEs correlated with dorsal anterior cingulate cortex activation and hippocampus deactivation. In patients, the aberrant salience bias to subjectively task-irrelevant information was increased and correlated with decreased striatal UPE activation and increased negative symptoms. This study shows that relevance estimates based on Bayesian precision can be inferred from observed behavior. This underscores the importance of relevance detection as an underlying mechanism for behavioral adaptation in complex environments and enhances the understanding of aberrant salience in schizophrenia
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
The global trends of urbanization and increased personal mobility force us to
rethink the way we live and use urban space. The Traffic4cast competition
series tackles this problem in a data-driven way, advancing the latest methods
in machine learning for modeling complex spatial systems over time. In this
edition, our dynamic road graph data combine information from road maps,
probe data points, and stationary vehicle detectors in three cities
over the span of two years. While stationary vehicle detectors are the most
accurate way to capture traffic volume, they are only available in few
locations. Traffic4cast 2022 explores models that have the ability to
generalize loosely related temporal vertex data on just a few nodes to predict
dynamic future traffic states on the edges of the entire road graph. In the
core challenge, participants are invited to predict the likelihoods of three
congestion classes derived from the speed levels in the GPS data for the entire
road graph in three cities 15 min into the future. We only provide vehicle
count data from spatially sparse stationary vehicle detectors in these three
cities as model input for this task. The data are aggregated in 15 min time
bins for one hour prior to the prediction time. For the extended challenge,
participants are tasked to predict the average travel times on super-segments
15 min into the future - super-segments are longer sequences of road segments
in the graph. The competition results provide an important advance in the
prediction of complex city-wide traffic states just from publicly available
sparse vehicle data and without the need for large amounts of real-time
floating vehicle data.Comment: Pre-print under review, submitted to Proceedings of Machine Learning
Researc
Measurement of the cosmic ray spectrum above eV using inclined events detected with the Pierre Auger Observatory
A measurement of the cosmic-ray spectrum for energies exceeding
eV is presented, which is based on the analysis of showers
with zenith angles greater than detected with the Pierre Auger
Observatory between 1 January 2004 and 31 December 2013. The measured spectrum
confirms a flux suppression at the highest energies. Above
eV, the "ankle", the flux can be described by a power law with
index followed by
a smooth suppression region. For the energy () at which the
spectral flux has fallen to one-half of its extrapolated value in the absence
of suppression, we find
eV.Comment: Replaced with published version. Added journal reference and DO
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