5 Search Results for "West, Richard"


Document
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382)

Authors: Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, and Jessica Montgomery

Published in: Dagstuhl Reports, Volume 12, Issue 9 (2023)


Abstract
This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling". Today’s scientific challenges are characterised by complexity. Interconnected natural, technological, and human systems are influenced by forces acting across time- and spatial-scales, resulting in complex interactions and emergent behaviours. Understanding these phenomena - and leveraging scientific advances to deliver innovative solutions to improve society’s health, wealth, and well-being - requires new ways of analysing complex systems. The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains. AI for science is a rendezvous point. It brings together expertise from AI and application domains; combines modelling knowledge with engineering know-how; and relies on collaboration across disciplines and between humans and machines. Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers working together to design and deploy effective AI tools. This report summarises the discussions from the seminar and provides a roadmap to suggest how different communities can collaborate to deliver a new wave of progress in AI and its application for scientific discovery.

Cite as

Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, and Jessica Montgomery. Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). In Dagstuhl Reports, Volume 12, Issue 9, pp. 150-199, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@Article{berens_et_al:DagRep.12.9.150,
  author =	{Berens, Philipp and Cranmer, Kyle and Lawrence, Neil D. and von Luxburg, Ulrike and Montgomery, Jessica},
  title =	{{Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382)}},
  pages =	{150--199},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{9},
  editor =	{Berens, Philipp and Cranmer, Kyle and Lawrence, Neil D. and von Luxburg, Ulrike and Montgomery, Jessica},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.9.150},
  URN =		{urn:nbn:de:0030-drops-178125},
  doi =		{10.4230/DagRep.12.9.150},
  annote =	{Keywords: machine learning, artificial intelligence, life sciences, physical sciences, environmental sciences, simulation, causality, modelling}
}
Document
PAStime: Progress-Aware Scheduling for Time-Critical Computing

Authors: Soham Sinha, Richard West, and Ahmad Golchin

Published in: LIPIcs, Volume 165, 32nd Euromicro Conference on Real-Time Systems (ECRTS 2020)


Abstract
Over-estimation of worst-case execution times (WCETs) of real-time tasks leads to poor resource utilization. In a mixed-criticality system (MCS), the over-provisioning of CPU time to accommodate the WCETs of highly critical tasks may lead to degraded service for less critical tasks. In this paper we present PAStime, a novel approach to monitor and adapt the runtime progress of highly time-critical applications, to allow for improved service to lower criticality tasks. In PAStime, CPU time is allocated to time-critical tasks according to the delays they experience as they progress through their control flow graphs. This ensures that as much time as possible is made available to improve the Quality-of-Service of less critical tasks, while high-criticality tasks are compensated after their delays. This paper describes the integration of PAStime with Adaptive Mixed-criticality (AMC) scheduling. The LO-mode budget of a high-criticality task is adjusted according to the delay observed at execution checkpoints. This is the first implementation of AMC in the scheduling framework of LITMUS^RT, which is extended with our PAStime runtime policy and tested with real-time Linux applications such as object classification and detection. We observe in our experimental evaluation that AMC-PAStime significantly improves the utilization of the low-criticality tasks while guaranteeing service to high-criticality tasks.

Cite as

Soham Sinha, Richard West, and Ahmad Golchin. PAStime: Progress-Aware Scheduling for Time-Critical Computing. In 32nd Euromicro Conference on Real-Time Systems (ECRTS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 165, pp. 3:1-3:24, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


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@InProceedings{sinha_et_al:LIPIcs.ECRTS.2020.3,
  author =	{Sinha, Soham and West, Richard and Golchin, Ahmad},
  title =	{{PAStime: Progress-Aware Scheduling for Time-Critical Computing}},
  booktitle =	{32nd Euromicro Conference on Real-Time Systems (ECRTS 2020)},
  pages =	{3:1--3:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-152-8},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{165},
  editor =	{V\"{o}lp, Marcus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2020.3},
  URN =		{urn:nbn:de:0030-drops-123668},
  doi =		{10.4230/LIPIcs.ECRTS.2020.3},
  annote =	{Keywords: progress-aware scheduling, code instrumentation, timing annotation}
}
Document
smARTflight: An Environmentally-Aware Adaptive Real-Time Flight Management System

Authors: Anam Farrukh and Richard West

Published in: LIPIcs, Volume 165, 32nd Euromicro Conference on Real-Time Systems (ECRTS 2020)


Abstract
Multi-rotor drones require real-time sensor data processing and control to maintain flight stability, which is made more challenging by external disturbances such as wind. In this paper we introduce smARTflight: an environmentally-aware adaptive real-time flight management system. smARTflight adapts the execution frequencies of flight control tasks according to timing and safety-critical constraints, in response to transient fluctuations of a drone’s attitude. In contrast to current state-of-the-art methods, smARTflight’s criticality-aware scheduler reduces the latency to return to a steady-state target attitude. The system also improves the overall control accuracy and lowers the frequency of adjustments to motor speeds to conserve power. A comparative case-study with a well-known autopilot shows that smARTflight reduces unnecessary control loop executions under stable conditions, while reducing response time latency by as much as 60% in a given axis of rotation when subjected to a 15° step attitude disturbance.

Cite as

Anam Farrukh and Richard West. smARTflight: An Environmentally-Aware Adaptive Real-Time Flight Management System. In 32nd Euromicro Conference on Real-Time Systems (ECRTS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 165, pp. 24:1-24:22, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


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@InProceedings{farrukh_et_al:LIPIcs.ECRTS.2020.24,
  author =	{Farrukh, Anam and West, Richard},
  title =	{{smARTflight: An Environmentally-Aware Adaptive Real-Time Flight Management System}},
  booktitle =	{32nd Euromicro Conference on Real-Time Systems (ECRTS 2020)},
  pages =	{24:1--24:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-152-8},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{165},
  editor =	{V\"{o}lp, Marcus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2020.24},
  URN =		{urn:nbn:de:0030-drops-123874},
  doi =		{10.4230/LIPIcs.ECRTS.2020.24},
  annote =	{Keywords: adaptive real-time systems, safety criticality, flight controller, multi-rotor drones, environmental awareness}
}
Document
Bayesian ACRONYM Tuning

Authors: John Gamble, Christopher Granade, and Nathan Wiebe

Published in: LIPIcs, Volume 135, 14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019)


Abstract
We provide an algorithm that uses Bayesian randomized benchmarking in concert with a local optimizer, such as SPSA, to find a set of controls that optimizes that average gate fidelity. We call this method Bayesian ACRONYM tuning as a reference to the analogous ACRONYM tuning algorithm. Bayesian ACRONYM distinguishes itself in its ability to retain prior information from experiments that use nearby control parameters; whereas traditional ACRONYM tuning does not use such information and can require many more measurements as a result. We prove that such information reuse is possible under the relatively weak assumption that the true model parameters are Lipschitz-continuous functions of the control parameters. We also perform numerical experiments that demonstrate that over-rotation errors in single qubit gates can be automatically tuned from 88% to 99.95% average gate fidelity using less than 1kB of data and fewer than 20 steps of the optimizer.

Cite as

John Gamble, Christopher Granade, and Nathan Wiebe. Bayesian ACRONYM Tuning. In 14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 135, pp. 7:1-7:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{gamble_et_al:LIPIcs.TQC.2019.7,
  author =	{Gamble, John and Granade, Christopher and Wiebe, Nathan},
  title =	{{Bayesian ACRONYM Tuning}},
  booktitle =	{14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019)},
  pages =	{7:1--7:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-112-2},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{135},
  editor =	{van Dam, Wim and Man\v{c}inska, Laura},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TQC.2019.7},
  URN =		{urn:nbn:de:0030-drops-103995},
  doi =		{10.4230/LIPIcs.TQC.2019.7},
  annote =	{Keywords: Quantum Computing, Randomized Benchmarking}
}
Document
Terminal Embeddings

Authors: Michael Elkin, Arnold Filtser, and Ofer Neiman

Published in: LIPIcs, Volume 40, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2015)


Abstract
In this paper we study terminal embeddings, in which one is given a finite metric (X,d_X) (or a graph G=(V,E)) and a subset K of X of its points are designated as terminals. The objective is to embed the metric into a normed space, while approximately preserving all distances among pairs that contain a terminal. We devise such embeddings in various settings, and conclude that even though we have to preserve approx |K| * |X| pairs, the distortion depends only on |K|, rather than on |X|. We also strengthen this notion, and consider embeddings that approximately preserve the distances between all pairs, but provide improved distortion for pairs containing a terminal. Surprisingly, we show that such embeddings exist in many settings, and have optimal distortion bounds both with respect to X \times X and with respect to K * X. Moreover, our embeddings have implications to the areas of Approximation and Online Algorithms. In particular, Arora et. al. devised an ~O(sqrt(log(r))-approximation algorithm for sparsest-cut instances with r demands. Building on their framework, we provide an ~O(sqrt(log |K|)-approximation for sparsest-cut instances in which each demand is incident on one of the vertices of K (aka, terminals). Since |K| <= r, our bound generalizes that of Arora et al.

Cite as

Michael Elkin, Arnold Filtser, and Ofer Neiman. Terminal Embeddings. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 40, pp. 242-264, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)


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@InProceedings{elkin_et_al:LIPIcs.APPROX-RANDOM.2015.242,
  author =	{Elkin, Michael and Filtser, Arnold and Neiman, Ofer},
  title =	{{Terminal Embeddings}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2015)},
  pages =	{242--264},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-89-7},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{40},
  editor =	{Garg, Naveen and Jansen, Klaus and Rao, Anup and Rolim, Jos\'{e} D. P.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2015.242},
  URN =		{urn:nbn:de:0030-drops-53064},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2015.242},
  annote =	{Keywords: embedding, distortion, terminals}
}
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