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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Grant, J"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" 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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.07251">arXiv:2409.07251</a> <span> [<a href="https://arxiv.org/pdf/2409.07251">pdf</a>, <a href="https://arxiv.org/format/2409.07251">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Federated $\mathcal{X}$-armed Bandit with Flexible Personalisation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arabzadeh%2C+A">Ali Arabzadeh</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+J+A">James A. Grant</a>, <a href="/search/cs?searchtype=author&query=Leslie%2C+D+S">David S. Leslie</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.07251v1-abstract-short" style="display: inline;"> This paper introduces a novel approach to personalised federated learning within the $\mathcal{X}$-armed bandit framework, addressing the challenge of optimising both local and global objectives in a highly heterogeneous environment. Our method employs a surrogate objective function that combines individual client preferences with aggregated global knowledge, allowing for a flexible trade-off betw… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.07251v1-abstract-full').style.display = 'inline'; document.getElementById('2409.07251v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.07251v1-abstract-full" style="display: none;"> This paper introduces a novel approach to personalised federated learning within the $\mathcal{X}$-armed bandit framework, addressing the challenge of optimising both local and global objectives in a highly heterogeneous environment. Our method employs a surrogate objective function that combines individual client preferences with aggregated global knowledge, allowing for a flexible trade-off between personalisation and collective learning. We propose a phase-based elimination algorithm that achieves sublinear regret with logarithmic communication overhead, making it well-suited for federated settings. Theoretical analysis and empirical evaluations demonstrate the effectiveness of our approach compared to existing methods. Potential applications of this work span various domains, including healthcare, smart home devices, and e-commerce, where balancing personalisation with global insights is crucial. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.07251v1-abstract-full').style.display = 'none'; document.getElementById('2409.07251v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.05953">arXiv:2312.05953</a> <span> [<a href="https://arxiv.org/pdf/2312.05953">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> RadImageGAN -- A Multi-modal Dataset-Scale Generative AI for Medical Imaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Liu%2C+Z">Zelong Liu</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+A">Alexander Zhou</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+A">Arnold Yang</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+A">Alara Yilmaz</a>, <a href="/search/cs?searchtype=author&query=Yoo%2C+M">Maxwell Yoo</a>, <a href="/search/cs?searchtype=author&query=Sullivan%2C+M">Mikey Sullivan</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+C">Catherine Zhang</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+J">James Grant</a>, <a href="/search/cs?searchtype=author&query=Li%2C+D">Daiqing Li</a>, <a href="/search/cs?searchtype=author&query=Fayad%2C+Z+A">Zahi A. Fayad</a>, <a href="/search/cs?searchtype=author&query=Huver%2C+S">Sean Huver</a>, <a href="/search/cs?searchtype=author&query=Deyer%2C+T">Timothy Deyer</a>, <a href="/search/cs?searchtype=author&query=Mei%2C+X">Xueyan Mei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.05953v1-abstract-short" style="display: inline;"> Deep learning in medical imaging often requires large-scale, high-quality data or initiation with suitably pre-trained weights. However, medical datasets are limited by data availability, domain-specific knowledge, and privacy concerns, and the creation of large and diverse radiologic databases like RadImageNet is highly resource-intensive. To address these limitations, we introduce RadImageGAN, t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.05953v1-abstract-full').style.display = 'inline'; document.getElementById('2312.05953v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.05953v1-abstract-full" style="display: none;"> Deep learning in medical imaging often requires large-scale, high-quality data or initiation with suitably pre-trained weights. However, medical datasets are limited by data availability, domain-specific knowledge, and privacy concerns, and the creation of large and diverse radiologic databases like RadImageNet is highly resource-intensive. To address these limitations, we introduce RadImageGAN, the first multi-modal radiologic data generator, which was developed by training StyleGAN-XL on the real RadImageNet dataset of 102,774 patients. RadImageGAN can generate high-resolution synthetic medical imaging datasets across 12 anatomical regions and 130 pathological classes in 3 modalities. Furthermore, we demonstrate that RadImageGAN generators can be utilized with BigDatasetGAN to generate multi-class pixel-wise annotated paired synthetic images and masks for diverse downstream segmentation tasks with minimal manual annotation. We showed that using synthetic auto-labeled data from RadImageGAN can significantly improve performance on four diverse downstream segmentation datasets by augmenting real training data and/or developing pre-trained weights for fine-tuning. This shows that RadImageGAN combined with BigDatasetGAN can improve model performance and address data scarcity while reducing the resources needed for annotations for segmentation tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.05953v1-abstract-full').style.display = 'none'; document.getElementById('2312.05953v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.18737">arXiv:2305.18737</a> <span> [<a href="https://arxiv.org/pdf/2305.18737">pdf</a>, <a href="https://arxiv.org/format/2305.18737">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Phase Correction using Deep Learning for Satellite-to-Ground CV-QKD </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Long%2C+N+K">Nathan K. Long</a>, <a href="/search/cs?searchtype=author&query=Malaney%2C+R">Robert Malaney</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+K+J">Kenneth J. Grant</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.18737v1-abstract-short" style="display: inline;"> Coherent measurement of quantum signals used for continuous-variable (CV) quantum key distribution (QKD) across satellite-to-ground channels requires compensation of phase wavefront distortions caused by atmospheric turbulence. One compensation technique involves multiplexing classical reference pulses (RPs) and the quantum signal, with direct phase measurements on the RPs then used to modulate a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.18737v1-abstract-full').style.display = 'inline'; document.getElementById('2305.18737v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.18737v1-abstract-full" style="display: none;"> Coherent measurement of quantum signals used for continuous-variable (CV) quantum key distribution (QKD) across satellite-to-ground channels requires compensation of phase wavefront distortions caused by atmospheric turbulence. One compensation technique involves multiplexing classical reference pulses (RPs) and the quantum signal, with direct phase measurements on the RPs then used to modulate a real local oscillator (RLO) on the ground - a solution that also removes some known attacks on CV-QKD. However, this is a cumbersome task in practice - requiring substantial complexity in equipment requirements and deployment. As an alternative to this traditional practice, here we introduce a new method for estimating phase corrections for an RLO by using only intensity measurements from RPs as input to a convolutional neural network, mitigating completely the necessity to measure phase wavefronts directly. Conventional wisdom dictates such an approach would likely be fruitless. However, we show that the phase correction accuracy needed to provide for non-zero secure key rates through satellite-to-ground channels is achieved by our intensity-only measurements. Our work shows, for the first time, how artificial intelligence algorithms can replace phase-measuring equipment in the context of CV-QKD delivered from space, thereby delivering an alternate deployment paradigm for this global quantum-communication application. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.18737v1-abstract-full').style.display = 'none'; document.getElementById('2305.18737v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.07080">arXiv:2206.07080</a> <span> [<a href="https://arxiv.org/pdf/2206.07080">pdf</a>, <a href="https://arxiv.org/ps/2206.07080">ps</a>, <a href="https://arxiv.org/format/2206.07080">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Measuring Inconsistency in Declarative Process Specifications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Corea%2C+C">Carl Corea</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+J">John Grant</a>, <a href="/search/cs?searchtype=author&query=Thimm%2C+M">Matthias Thimm</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2206.07080v1-abstract-short" style="display: inline;"> We address the problem of measuring inconsistency in declarative process specifications, with an emphasis on linear temporal logic on fixed traces (LTLff). As we will show, existing inconsistency measures for classical logic cannot provide a meaningful assessment of inconsistency in LTL in general, as they cannot adequately handle the temporal operators. We therefore propose a novel paraconsistent… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.07080v1-abstract-full').style.display = 'inline'; document.getElementById('2206.07080v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.07080v1-abstract-full" style="display: none;"> We address the problem of measuring inconsistency in declarative process specifications, with an emphasis on linear temporal logic on fixed traces (LTLff). As we will show, existing inconsistency measures for classical logic cannot provide a meaningful assessment of inconsistency in LTL in general, as they cannot adequately handle the temporal operators. We therefore propose a novel paraconsistent semantics as a framework for inconsistency measurement. We then present two new inconsistency measures based on these semantics and show that they satisfy important desirable properties. We show how these measures can be applied to declarative process models and investigate the computational complexity of the introduced approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.07080v1-abstract-full').style.display = 'none'; document.getElementById('2206.07080v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.07882">arXiv:2112.07882</a> <span> [<a href="https://arxiv.org/pdf/2112.07882">pdf</a>, <a href="https://arxiv.org/format/2112.07882">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3462757.3466149">10.1145/3462757.3466149 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Savelka%2C+J">Jaromir Savelka</a>, <a href="/search/cs?searchtype=author&query=Westermann%2C+H">Hannes Westermann</a>, <a href="/search/cs?searchtype=author&query=Benyekhlef%2C+K">Karim Benyekhlef</a>, <a href="/search/cs?searchtype=author&query=Alexander%2C+C+S">Charlotte S. Alexander</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+J+C">Jayla C. Grant</a>, <a href="/search/cs?searchtype=author&query=Amariles%2C+D+R">David Restrepo Amariles</a>, <a href="/search/cs?searchtype=author&query=Hamdani%2C+R+E">Rajaa El Hamdani</a>, <a href="/search/cs?searchtype=author&query=Mee%C3%B9s%2C+S">S茅bastien Mee霉s</a>, <a href="/search/cs?searchtype=author&query=Araszkiewicz%2C+M">Micha艂 Araszkiewicz</a>, <a href="/search/cs?searchtype=author&query=Ashley%2C+K+D">Kevin D. Ashley</a>, <a href="/search/cs?searchtype=author&query=Ashley%2C+A">Alexandra Ashley</a>, <a href="/search/cs?searchtype=author&query=Branting%2C+K">Karl Branting</a>, <a href="/search/cs?searchtype=author&query=Falduti%2C+M">Mattia Falduti</a>, <a href="/search/cs?searchtype=author&query=Grabmair%2C+M">Matthias Grabmair</a>, <a href="/search/cs?searchtype=author&query=Hara%C5%A1ta%2C+J">Jakub Hara拧ta</a>, <a href="/search/cs?searchtype=author&query=Novotn%C3%A1%2C+T">Tereza Novotn谩</a>, <a href="/search/cs?searchtype=author&query=Tippett%2C+E">Elizabeth Tippett</a>, <a href="/search/cs?searchtype=author&query=Johnson%2C+S">Shiwanni Johnson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2112.07882v1-abstract-short" style="display: inline;"> In this paper, we examine the use of multi-lingual sentence embeddings to transfer predictive models for functional segmentation of adjudicatory decisions across jurisdictions, legal systems (common and civil law), languages, and domains (i.e. contexts). Mechanisms for utilizing linguistic resources outside of their original context have significant potential benefits in AI & Law because differenc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.07882v1-abstract-full').style.display = 'inline'; document.getElementById('2112.07882v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.07882v1-abstract-full" style="display: none;"> In this paper, we examine the use of multi-lingual sentence embeddings to transfer predictive models for functional segmentation of adjudicatory decisions across jurisdictions, legal systems (common and civil law), languages, and domains (i.e. contexts). Mechanisms for utilizing linguistic resources outside of their original context have significant potential benefits in AI & Law because differences between legal systems, languages, or traditions often block wider adoption of research outcomes. We analyze the use of Language-Agnostic Sentence Representations in sequence labeling models using Gated Recurrent Units (GRUs) that are transferable across languages. To investigate transfer between different contexts we developed an annotation scheme for functional segmentation of adjudicatory decisions. We found that models generalize beyond the contexts on which they were trained (e.g., a model trained on administrative decisions from the US can be applied to criminal law decisions from Italy). Further, we found that training the models on multiple contexts increases robustness and improves overall performance when evaluating on previously unseen contexts. Finally, we found that pooling the training data from all the contexts enhances the models' in-context performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.07882v1-abstract-full').style.display = 'none'; document.getElementById('2112.07882v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> In Proceedings of ICAIL 2021, pp. 129-138. 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.14412">arXiv:2109.14412</a> <span> [<a href="https://arxiv.org/pdf/2109.14412">pdf</a>, <a href="https://arxiv.org/format/2109.14412">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Grant%2C+J+A">James A. Grant</a>, <a href="/search/cs?searchtype=author&query=Leslie%2C+D+S">David S. Leslie</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2109.14412v2-abstract-short" style="display: inline;"> We consider a variant of online binary classification where a learner sequentially assigns labels ($0$ or $1$) to items with unknown true class. If, but only if, the learner chooses label $1$ they immediately observe the true label of the item. The learner faces a trade-off between short-term classification accuracy and long-term information gain. This problem has previously been studied under the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.14412v2-abstract-full').style.display = 'inline'; document.getElementById('2109.14412v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.14412v2-abstract-full" style="display: none;"> We consider a variant of online binary classification where a learner sequentially assigns labels ($0$ or $1$) to items with unknown true class. If, but only if, the learner chooses label $1$ they immediately observe the true label of the item. The learner faces a trade-off between short-term classification accuracy and long-term information gain. This problem has previously been studied under the name of the `apple tasting' problem. We revisit this problem as a partial monitoring problem with side information, and focus on the case where item features are linked to true classes via a logistic regression model. Our principal contribution is a study of the performance of Thompson Sampling (TS) for this problem. Using recently developed information-theoretic tools, we show that TS achieves a Bayesian regret bound of an improved order to previous approaches. Further, we experimentally verify that efficient approximations to TS and Information Directed Sampling via P贸lya-Gamma augmentation have superior empirical performance to existing methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.14412v2-abstract-full').style.display = 'none'; document.getElementById('2109.14412v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Update to Theorem 1 and experimental work</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.11875">arXiv:2108.11875</a> <span> [<a href="https://arxiv.org/pdf/2108.11875">pdf</a>, <a href="https://arxiv.org/format/2108.11875">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Atmospheric and Oceanic Physics">physics.ao-ph</span> </div> </div> <p class="title is-5 mathjax"> A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hu%2C+Y">Yihao Hu</a>, <a href="/search/cs?searchtype=author&query=O%27Donncha%2C+F">Fearghal O'Donncha</a>, <a href="/search/cs?searchtype=author&query=Palmes%2C+P">Paulito Palmes</a>, <a href="/search/cs?searchtype=author&query=Burke%2C+M">Meredith Burke</a>, <a href="/search/cs?searchtype=author&query=Filgueira%2C+R">Ramon Filgueira</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+J">Jon Grant</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2108.11875v1-abstract-short" style="display: inline;"> This paper presents a novel spatio-temporal LSTM (SPATIAL) architecture for time series forecasting applied to environmental datasets. The framework was evaluated across multiple sensors and for three different oceanic variables: current speed, temperature, and dissolved oxygen. Network implementation proceeded in two directions that are nominally separated but connected as part of a natural envir… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.11875v1-abstract-full').style.display = 'inline'; document.getElementById('2108.11875v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.11875v1-abstract-full" style="display: none;"> This paper presents a novel spatio-temporal LSTM (SPATIAL) architecture for time series forecasting applied to environmental datasets. The framework was evaluated across multiple sensors and for three different oceanic variables: current speed, temperature, and dissolved oxygen. Network implementation proceeded in two directions that are nominally separated but connected as part of a natural environmental system -- across the spatial (between individual sensors) and temporal components of the sensor data. Data from four sensors sampling current speed, and eight measuring both temperature and dissolved oxygen evaluated the framework. Results were compared against RF and XGB baseline models that learned on the temporal signal of each sensor independently by extracting the date-time features together with the past history of data using sliding window matrix. Results demonstrated ability to accurately replicate complex signals and provide comparable performance to state-of-the-art benchmarks. Notably, the novel framework provided a simpler pre-processing and training pipeline that handles missing values via a simple masking layer. Enabling learning across the spatial and temporal directions, this paper addresses two fundamental challenges of ML applications to environmental science: 1) data sparsity and the challenges and costs of collecting measurements of environmental conditions such as ocean dynamics, and 2) environmental datasets are inherently connected in the spatial and temporal directions while classical ML approaches only consider one of these directions. Furthermore, sharing of parameters across all input steps makes SPATIAL a fast, scalable, and easily-parameterized forecasting framework. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.11875v1-abstract-full').style.display = 'none'; document.getElementById('2108.11875v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.05183">arXiv:2108.05183</a> <span> [<a href="https://arxiv.org/pdf/2108.05183">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Deep2Lead: A distributed deep learning application for small molecule lead optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chawdhury%2C+T+K">Tarun Kumar Chawdhury</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+D+J">David J. Grant</a>, <a href="/search/cs?searchtype=author&query=Jin%2C+H+Y">Hyun Yong Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2108.05183v1-abstract-short" style="display: inline;"> Lead optimization is a key step in drug discovery to produce potent and selective compounds. Historically, in silico screening and structure-based small molecule designing facilitated the processes. Although the recent application of deep learning to drug discovery piloted the possibility of their in silico application lead optimization steps, the real-world application is lacking due to the tool… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.05183v1-abstract-full').style.display = 'inline'; document.getElementById('2108.05183v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.05183v1-abstract-full" style="display: none;"> Lead optimization is a key step in drug discovery to produce potent and selective compounds. Historically, in silico screening and structure-based small molecule designing facilitated the processes. Although the recent application of deep learning to drug discovery piloted the possibility of their in silico application lead optimization steps, the real-world application is lacking due to the tool availability. Here, we developed a single user interface application, called Deep2Lead. Our web-based application integrates VAE and DeepPurpose DTI and allows a user to quickly perform a lead optimization task with no prior programming experience. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.05183v1-abstract-full').style.display = 'none'; document.getElementById('2108.05183v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 Pages, 1 figure, 2 images</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.03357">arXiv:2107.03357</a> <span> [<a href="https://arxiv.org/pdf/2107.03357">pdf</a>, <a href="https://arxiv.org/ps/2107.03357">ps</a>, <a href="https://arxiv.org/format/2107.03357">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Performance">cs.PF</span> </div> </div> <p class="title is-5 mathjax"> Performance Evaluation of Mixed-Precision Runge-Kutta Methods </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Burnett%2C+B">Ben Burnett</a>, <a href="/search/cs?searchtype=author&query=Gottlieb%2C+S">Sigal Gottlieb</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+Z+J">Zachary J. Grant</a>, <a href="/search/cs?searchtype=author&query=Heryudono%2C+A">Alfa Heryudono</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2107.03357v1-abstract-short" style="display: inline;"> Additive Runge-Kutta methods designed for preserving highly accurate solutions in mixed-precision computation were proposed and analyzed in [8]. These specially designed methods use reduced precision or the implicit computations and full precision for the explicit computations. We develop a FORTRAN code to solve a nonlinear system of ordinary differential equations using the mixed precision additi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.03357v1-abstract-full').style.display = 'inline'; document.getElementById('2107.03357v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.03357v1-abstract-full" style="display: none;"> Additive Runge-Kutta methods designed for preserving highly accurate solutions in mixed-precision computation were proposed and analyzed in [8]. These specially designed methods use reduced precision or the implicit computations and full precision for the explicit computations. We develop a FORTRAN code to solve a nonlinear system of ordinary differential equations using the mixed precision additive Runge-Kutta (MP-ARK) methods on IBM POWER9 and Intel x86\_64 chips. The convergence, accuracy, runtime, and energy consumption of these methods is explored. We show that these MP-ARK methods efficiently produce accurate solutions with significant reductions in runtime (and by extension energy consumption). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.03357v1-abstract-full').style.display = 'none'; document.getElementById('2107.03357v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">IEEE HPEC 2021 submission</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.02097">arXiv:2010.02097</a> <span> [<a href="https://arxiv.org/pdf/2010.02097">pdf</a>, <a href="https://arxiv.org/format/2010.02097">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> FaNDS: Fake News Detection System Using Energy Flow </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+J">Jiawei Xu</a>, <a href="/search/cs?searchtype=author&query=Zadorozhny%2C+V">Vladimir Zadorozhny</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+D">Danchen Zhang</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+J">John Grant</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2010.02097v1-abstract-short" style="display: inline;"> Recently, the term "fake news" has been broadly and extensively utilized for disinformation, misinformation, hoaxes, propaganda, satire, rumors, click-bait, and junk news. It has become a serious problem around the world. We present a new system, FaNDS, that detects fake news efficiently. The system is based on several concepts used in some previous works but in a different context. There are two… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.02097v1-abstract-full').style.display = 'inline'; document.getElementById('2010.02097v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.02097v1-abstract-full" style="display: none;"> Recently, the term "fake news" has been broadly and extensively utilized for disinformation, misinformation, hoaxes, propaganda, satire, rumors, click-bait, and junk news. It has become a serious problem around the world. We present a new system, FaNDS, that detects fake news efficiently. The system is based on several concepts used in some previous works but in a different context. There are two main concepts: an Inconsistency Graph and Energy Flow. The Inconsistency Graph contains news items as nodes and inconsistent opinions between them for edges. Energy Flow assigns each node an initial energy and then some energy is propagated along the edges until the energy distribution on all nodes converges. To illustrate FaNDS we use the original data from the Fake News Challenge (FNC-1). First, the data has to be reconstructed in order to generate the Inconsistency Graph. The graph contains various subgraphs with well-defined shapes that represent different types of connections between the news items. Then the Energy Flow method is applied. The nodes with high energy are the candidates for being fake news. In our experiments, all these were indeed fake news as we checked each using several reliable web sites. We compared FaNDS to several other fake news detection methods and found it to be more sensitive in discovering fake news items. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.02097v1-abstract-full').style.display = 'none'; document.getElementById('2010.02097v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.03207">arXiv:2009.03207</a> <span> [<a href="https://arxiv.org/pdf/2009.03207">pdf</a>, <a href="https://arxiv.org/format/2009.03207">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Learning to Rank under Multinomial Logit Choice </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Grant%2C+J+A">James A. Grant</a>, <a href="/search/cs?searchtype=author&query=Leslie%2C+D+S">David S. Leslie</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2009.03207v2-abstract-short" style="display: inline;"> Learning the optimal ordering of content is an important challenge in website design. The learning to rank (LTR) framework models this problem as a sequential problem of selecting lists of content and observing where users decide to click. Most previous work on LTR assumes that the user considers each item in the list in isolation, and makes binary choices to click or not on each. We introduce a m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.03207v2-abstract-full').style.display = 'inline'; document.getElementById('2009.03207v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.03207v2-abstract-full" style="display: none;"> Learning the optimal ordering of content is an important challenge in website design. The learning to rank (LTR) framework models this problem as a sequential problem of selecting lists of content and observing where users decide to click. Most previous work on LTR assumes that the user considers each item in the list in isolation, and makes binary choices to click or not on each. We introduce a multinomial logit (MNL) choice model to the LTR framework, which captures the behaviour of users who consider the ordered list of items as a whole and make a single choice among all the items and a no-click option. Under the MNL model, the user favours items which are either inherently more attractive, or placed in a preferable position within the list. We propose upper confidence bound (UCB) algorithms to minimise regret in two settings - where the position dependent parameters are known, and unknown. We present theoretical analysis leading to an $惟(\sqrt{JT})$ lower bound for the problem, an $\tilde{O}(\sqrt{JT})$ upper bound on regret of the UCB algorithm in the known-parameter setting, and an $\tilde{O}(K^2\sqrt{JT})$ upper bound on regret, the first, in the more challenging unknown-position-parameter setting. Our analyses are based on tight new concentration results for Geometric random variables, and novel functional inequalities for maximum likelihood estimators computed on discrete data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.03207v2-abstract-full').style.display = 'none'; document.getElementById('2009.03207v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">updated with new material including regret bound for unknown position bias setting</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.10054">arXiv:2007.10054</a> <span> [<a href="https://arxiv.org/pdf/2007.10054">pdf</a>, <a href="https://arxiv.org/format/2007.10054">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> Parallel Performance of ARM ThunderX2 for Atomistic Simulation Algorithms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Saunders%2C+W+R">William Robert Saunders</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+J">James Grant</a>, <a href="/search/cs?searchtype=author&query=M%C3%BCller%2C+E+H">Eike Hermann M眉ller</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2007.10054v1-abstract-short" style="display: inline;"> Atomistic simulation drives scientific advances in modern material science and accounts for a significant proportion of wall time on High Performance Computing facilities. It is important that algorithms are efficient and implementations are performant in a continuously diversifying hardware landscape. Furthermore, they have to be portable to make best use of the available computing resource. In… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.10054v1-abstract-full').style.display = 'inline'; document.getElementById('2007.10054v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.10054v1-abstract-full" style="display: none;"> Atomistic simulation drives scientific advances in modern material science and accounts for a significant proportion of wall time on High Performance Computing facilities. It is important that algorithms are efficient and implementations are performant in a continuously diversifying hardware landscape. Furthermore, they have to be portable to make best use of the available computing resource. In this paper we assess the parallel performance of some key algorithms implemented in a performance portable framework developed by us. We consider Molecular Dynamics with short range interactions, the Fast Multipole Method and Kinetic Monte Carlo. To assess the performance of emerging architectures, we compare the Marvell ThunderX2 (ARM) architecture to traditional x86_64 hardware made available through the Azure cloud computing service. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.10054v1-abstract-full').style.display = 'none'; document.getElementById('2007.10054v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 3 figures, 1 tables; submitted to EAHPC-2020 (Embracing Arm: a journey of porting and optimization to the latest Arm-based processors 2020)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.09966">arXiv:2007.09966</a> <span> [<a href="https://arxiv.org/pdf/2007.09966">pdf</a>, <a href="https://arxiv.org/format/2007.09966">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Filtered Poisson Process Bandit on a Continuum </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Grant%2C+J+A">James A. Grant</a>, <a href="/search/cs?searchtype=author&query=Szechtman%2C+R">Roberto Szechtman</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2007.09966v1-abstract-short" style="display: inline;"> We consider a version of the continuum armed bandit where an action induces a filtered realisation of a non-homogeneous Poisson process. Point data in the filtered sample are then revealed to the decision-maker, whose reward is the total number of revealed points. Using knowledge of the function governing the filtering, but without knowledge of the Poisson intensity function, the decision-maker se… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.09966v1-abstract-full').style.display = 'inline'; document.getElementById('2007.09966v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.09966v1-abstract-full" style="display: none;"> We consider a version of the continuum armed bandit where an action induces a filtered realisation of a non-homogeneous Poisson process. Point data in the filtered sample are then revealed to the decision-maker, whose reward is the total number of revealed points. Using knowledge of the function governing the filtering, but without knowledge of the Poisson intensity function, the decision-maker seeks to maximise the expected number of revealed points over T rounds. We propose an upper confidence bound algorithm for this problem utilising data-adaptive discretisation of the action space. This approach enjoys O(T^(2/3)) regret under a Lipschitz assumption on the reward function. We provide lower bounds on the regret of any algorithm for the problem, via new lower bounds for related finite-armed bandits, and show that the orders of the upper and lower bounds match up to a logarithmic factor. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.09966v1-abstract-full').style.display = 'none'; document.getElementById('2007.09966v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.02323">arXiv:2001.02323</a> <span> [<a href="https://arxiv.org/pdf/2001.02323">pdf</a>, <a href="https://arxiv.org/format/2001.02323">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> On Thompson Sampling for Smoother-than-Lipschitz Bandits </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Grant%2C+J+A">James A. Grant</a>, <a href="/search/cs?searchtype=author&query=Leslie%2C+D+S">David S. Leslie</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2001.02323v2-abstract-short" style="display: inline;"> Thompson Sampling is a well established approach to bandit and reinforcement learning problems. However its use in continuum armed bandit problems has received relatively little attention. We provide the first bounds on the regret of Thompson Sampling for continuum armed bandits under weak conditions on the function class containing the true function and sub-exponential observation noise. Our boun… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.02323v2-abstract-full').style.display = 'inline'; document.getElementById('2001.02323v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.02323v2-abstract-full" style="display: none;"> Thompson Sampling is a well established approach to bandit and reinforcement learning problems. However its use in continuum armed bandit problems has received relatively little attention. We provide the first bounds on the regret of Thompson Sampling for continuum armed bandits under weak conditions on the function class containing the true function and sub-exponential observation noise. Our bounds are realised by analysis of the eluder dimension, a recently proposed measure of the complexity of a function class, which has been demonstrated to be useful in bounding the Bayesian regret of Thompson Sampling for simpler bandit problems under sub-Gaussian observation noise. We derive a new bound on the eluder dimension for classes of functions with Lipschitz derivatives, and generalise previous analyses in multiple regards. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.02323v2-abstract-full').style.display = 'none'; document.getElementById('2001.02323v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to AISTATS 2020. 26 pages, 2 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.06821">arXiv:1905.06821</a> <span> [<a href="https://arxiv.org/pdf/1905.06821">pdf</a>, <a href="https://arxiv.org/format/1905.06821">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Adaptive Sensor Placement for Continuous Spaces </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Grant%2C+J+A">James A Grant</a>, <a href="/search/cs?searchtype=author&query=Boukouvalas%2C+A">Alexis Boukouvalas</a>, <a href="/search/cs?searchtype=author&query=Griffiths%2C+R">Ryan-Rhys Griffiths</a>, <a href="/search/cs?searchtype=author&query=Leslie%2C+D+S">David S Leslie</a>, <a href="/search/cs?searchtype=author&query=Vakili%2C+S">Sattar Vakili</a>, <a href="/search/cs?searchtype=author&query=de+Cote%2C+E+M">Enrique Munoz de Cote</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1905.06821v1-abstract-short" style="display: inline;"> We consider the problem of adaptively placing sensors along an interval to detect stochastically-generated events. We present a new formulation of the problem as a continuum-armed bandit problem with feedback in the form of partial observations of realisations of an inhomogeneous Poisson process. We design a solution method by combining Thompson sampling with nonparametric inference via increasing… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.06821v1-abstract-full').style.display = 'inline'; document.getElementById('1905.06821v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.06821v1-abstract-full" style="display: none;"> We consider the problem of adaptively placing sensors along an interval to detect stochastically-generated events. We present a new formulation of the problem as a continuum-armed bandit problem with feedback in the form of partial observations of realisations of an inhomogeneous Poisson process. We design a solution method by combining Thompson sampling with nonparametric inference via increasingly granular Bayesian histograms and derive an $\tilde{O}(T^{2/3})$ bound on the Bayesian regret in $T$ rounds. This is coupled with the design of an efficent optimisation approach to select actions in polynomial time. In simulations we demonstrate our approach to have substantially lower and less variable regret than competitor algorithms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.06821v1-abstract-full').style.display = 'none'; document.getElementById('1905.06821v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, accepted to ICML 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.04065">arXiv:1905.04065</a> <span> [<a href="https://arxiv.org/pdf/1905.04065">pdf</a>, <a href="https://arxiv.org/format/1905.04065">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.jcp.2020.109379">10.1016/j.jcp.2020.109379 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Fast electrostatic solvers for kinetic Monte Carlo simulations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Saunders%2C+W+R">William Robert Saunders</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+J">James Grant</a>, <a href="/search/cs?searchtype=author&query=M%C3%BCller%2C+E+H">Eike Hermann M眉ller</a>, <a href="/search/cs?searchtype=author&query=Thompson%2C+I">Ian Thompson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1905.04065v2-abstract-short" style="display: inline;"> Kinetic Monte Carlo (KMC) is an important computational tool in physics and chemistry. In contrast to standard Monte Carlo, KMC permits the description of time dependent dynamical processes and is not restricted to systems in equilibrium. Recently KMC has been applied successfully in modelling of novel energy materials such as Lithium-ion batteries and solar cells. We consider general solid state… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.04065v2-abstract-full').style.display = 'inline'; document.getElementById('1905.04065v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.04065v2-abstract-full" style="display: none;"> Kinetic Monte Carlo (KMC) is an important computational tool in physics and chemistry. In contrast to standard Monte Carlo, KMC permits the description of time dependent dynamical processes and is not restricted to systems in equilibrium. Recently KMC has been applied successfully in modelling of novel energy materials such as Lithium-ion batteries and solar cells. We consider general solid state systems which contain free, interacting particles which can hop between localised sites in the material. The KMC transition rates for those hops depend on the change in total potential energy of the system. For charged particles this requires the frequent calculation of electrostatic interactions, which is usually the bottleneck of the simulation. To avoid this issue and obtain results in reasonable times, many studies replace the long-range potential by a short range approximation. This, however, leads to systematic errors and unphysical results. On the other hand standard electrostatic solvers such as Ewald summation or fast Poisson solvers are highly inefficient or introduce uncontrollable systematic errors at high resolution. In this paper we describe how the Fast Multipole Method by Greengard and Rokhlin can be adapted to overcome this issue by dramatically reducing computational costs. We exploit the fact that each update in the transition rate calculation corresponds to a single particle move and changes the configuration only by a small amount. This allows us to construct an algorithm which scales linearly in the number of charges for each KMC step, something which had not been deemed to be possible before. We demonstrate the performance and parallel scalability of the method by implementing it in a performance portable software library. We describe the high-level Python interface of the code which makes it easy to adapt to specific cases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.04065v2-abstract-full').style.display = 'none'; document.getElementById('1905.04065v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">26 pages, 19 figures, 7 tables; accepted for publication in Computer Physics Communications</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 78M16; 82C80; 82D37; 65Y05; 65Y20 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> J.2; G.4; D.1.3; D.2.11 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.03403">arXiv:1904.03403</a> <span> [<a href="https://arxiv.org/pdf/1904.03403">pdf</a>, <a href="https://arxiv.org/ps/1904.03403">ps</a>, <a href="https://arxiv.org/format/1904.03403">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> </div> </div> <p class="title is-5 mathjax"> Inconsistency Measures for Relational Databases </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Parisi%2C+F">Francesco Parisi</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+J">John Grant</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.03403v1-abstract-short" style="display: inline;"> In this paper, building on work done on measuring inconsistency in knowledge bases, we introduce inconsistency measures for databases. In particular, focusing on databases with denial constraints, we first consider the natural approach of virtually transforming a database into a propositional knowledge base and then applying well-known measures. However, using this method, tuples and constraints a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.03403v1-abstract-full').style.display = 'inline'; document.getElementById('1904.03403v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.03403v1-abstract-full" style="display: none;"> In this paper, building on work done on measuring inconsistency in knowledge bases, we introduce inconsistency measures for databases. In particular, focusing on databases with denial constraints, we first consider the natural approach of virtually transforming a database into a propositional knowledge base and then applying well-known measures. However, using this method, tuples and constraints are equally considered in charge of inconsistencies. Then, we introduce a version of inconsistency measures blaming database tuples only, i.e., treating integrity constraints as irrefutable statements. We analyze the compliance of database inconsistency measures with standard rationality postulates and find interesting relationships between measures. Finally, we investigate the complexity of the inconsistency measurement problem as well as of the problems of deciding whether the inconsistency is lower than, greater than, or equal to a given threshold. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.03403v1-abstract-full').style.display = 'none'; document.getElementById('1904.03403v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1902.02905">arXiv:1902.02905</a> <span> [<a href="https://arxiv.org/pdf/1902.02905">pdf</a>, <a href="https://arxiv.org/format/1902.02905">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Mobile Artificial Intelligence Technology for Detecting Macula Edema and Subretinal Fluid on OCT Scans: Initial Results from the DATUM alpha Study </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Odaibo%2C+S+G">Stephen G. Odaibo</a>, <a href="/search/cs?searchtype=author&query=MomPremier%2C+M">Mikelson MomPremier</a>, <a href="/search/cs?searchtype=author&query=Hwang%2C+R+Y">Richard Y. Hwang</a>, <a href="/search/cs?searchtype=author&query=Yousuf%2C+S+J">Salman J. Yousuf</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+S+L">Steven L. Williams</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+J">Joshua Grant</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1902.02905v2-abstract-short" style="display: inline;"> Artificial Intelligence (AI) is necessary to address the large and growing deficit in retina and healthcare access globally. And mobile AI diagnostic platforms running in the Cloud may effectively and efficiently distribute such AI capability. Here we sought to evaluate the feasibility of Cloud-based mobile artificial intelligence for detection of retinal disease. And to evaluate the accuracy of a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.02905v2-abstract-full').style.display = 'inline'; document.getElementById('1902.02905v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1902.02905v2-abstract-full" style="display: none;"> Artificial Intelligence (AI) is necessary to address the large and growing deficit in retina and healthcare access globally. And mobile AI diagnostic platforms running in the Cloud may effectively and efficiently distribute such AI capability. Here we sought to evaluate the feasibility of Cloud-based mobile artificial intelligence for detection of retinal disease. And to evaluate the accuracy of a particular such system for detection of subretinal fluid (SRF) and macula edema (ME) on OCT scans. A multicenter retrospective image analysis was conducted in which board-certified ophthalmologists with fellowship training in retina evaluated OCT images of the macula. They noted the presence or absence of ME or SRF, then compared their assessment to that obtained from Fluid Intelligence, a mobile AI app that detects SRF and ME on OCT scans. Investigators consecutively selected retinal OCTs, while making effort to balance the number of scans with retinal fluid and scans without. Exclusion criteria included poor scan quality, ambiguous features, macula holes, retinoschisis, and dense epiretinal membranes. Accuracy in the form of sensitivity and specificity of the AI mobile App was determined by comparing its assessments to those of the retina specialists. At the time of this submission, five centers have completed their initial studies. This consists of a total of 283 OCT scans of which 155 had either ME or SRF ("wet") and 128 did not ("dry"). The sensitivity ranged from 82.5% to 97% with a weighted average of 89.3%. The specificity ranged from 52% to 100% with a weighted average of 81.23%. CONCLUSION: Cloud-based Mobile AI technology is feasible for the detection retinal disease. In particular, Fluid Intelligence (alpha version), is sufficiently accurate as a screening tool for SRF and ME, especially in underserved areas. Further studies and technology development is needed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.02905v2-abstract-full').style.display = 'none'; document.getElementById('1902.02905v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Initial results of the DATUM alpha Study were initially presented on August 13th 2018 in the Keynote Address at the 116th National Medical Association Annual Meeting & Scientific Assembly's New Innovations in Ophthalmology Session. The results were also presented on September 21st 2018 in a Podium Lecture during Alumni Day at the University of Michigan--Ann Arbor Kellogg Eye Center</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1810.02176">arXiv:1810.02176</a> <span> [<a href="https://arxiv.org/pdf/1810.02176">pdf</a>, <a href="https://arxiv.org/format/1810.02176">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Adaptive Policies for Perimeter Surveillance Problems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Grant%2C+J+A">James A. Grant</a>, <a href="/search/cs?searchtype=author&query=Leslie%2C+D+S">David S. Leslie</a>, <a href="/search/cs?searchtype=author&query=Glazebrook%2C+K">Kevin Glazebrook</a>, <a href="/search/cs?searchtype=author&query=Szechtman%2C+R">Roberto Szechtman</a>, <a href="/search/cs?searchtype=author&query=Letchford%2C+A+N">Adam N. Letchford</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1810.02176v2-abstract-short" style="display: inline;"> Maximising the detection of intrusions is a fundamental and often critical aim of perimeter surveillance. Commonly, this requires a decision-maker to optimally allocate multiple searchers to segments of the perimeter. We consider a scenario where the decision-maker may sequentially update the searchers' allocation, learning from the observed data to improve decisions over time. In this work we pro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.02176v2-abstract-full').style.display = 'inline'; document.getElementById('1810.02176v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1810.02176v2-abstract-full" style="display: none;"> Maximising the detection of intrusions is a fundamental and often critical aim of perimeter surveillance. Commonly, this requires a decision-maker to optimally allocate multiple searchers to segments of the perimeter. We consider a scenario where the decision-maker may sequentially update the searchers' allocation, learning from the observed data to improve decisions over time. In this work we propose a formal model and solution methods for this sequential perimeter surveillance problem. Our model is a combinatorial multi-armed bandit (CMAB) with Poisson rewards and a novel filtered feedback mechanism - arising from the failure to detect certain intrusions. Our solution method is an upper confidence bound approach and we derive upper and lower bounds on its expected performance. We prove that the gap between these bounds is of constant order, and demonstrate empirically that our approach is more reliable in simulated problems than competing algorithms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.02176v2-abstract-full').style.display = 'none'; document.getElementById('1810.02176v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1708.01135">arXiv:1708.01135</a> <span> [<a href="https://arxiv.org/pdf/1708.01135">pdf</a>, <a href="https://arxiv.org/format/1708.01135">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> Long range forces in a performance portable Molecular Dynamics framework </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Saunders%2C+W+R">William R. Saunders</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+J">James Grant</a>, <a href="/search/cs?searchtype=author&query=M%C3%BCller%2C+E+H">Eike H. M眉ller</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1708.01135v1-abstract-short" style="display: inline;"> Molecular Dynamics (MD) codes predict the fundamental properties of matter by following the trajectories of a collection of interacting model particles. To exploit diverse modern manycore hardware, efficient codes must use all available parallelism. At the same time they need to be portable and easily extendible by the domain specialist (physicist/chemist) without detailed knowledge of this hardwa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1708.01135v1-abstract-full').style.display = 'inline'; document.getElementById('1708.01135v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1708.01135v1-abstract-full" style="display: none;"> Molecular Dynamics (MD) codes predict the fundamental properties of matter by following the trajectories of a collection of interacting model particles. To exploit diverse modern manycore hardware, efficient codes must use all available parallelism. At the same time they need to be portable and easily extendible by the domain specialist (physicist/chemist) without detailed knowledge of this hardware. To address this challenge, we recently described a new Domain Specific Language (DSL) for the development of performance portable MD codes based on a "Separation of Concerns": a Python framework automatically generates efficient parallel code for a range of target architectures. Electrostatic interactions between charged particles are important in many physical systems and often dominate the runtime. Here we discuss the inclusion of long-range interaction algorithms in our code generation framework. These algorithms require global communications and careful consideration has to be given to any impact on parallel scalability. We implemented an Ewald summation algorithm for electrostatic forces, present scaling comparisons for different system sizes and compare to the performance of existing codes. We also report on further performance optimisations delivered with OpenMP shared memory parallelism. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1708.01135v1-abstract-full').style.display = 'none'; document.getElementById('1708.01135v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 August, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 3 figures, submitted to ParCo 2017 Parallel Computing Conference</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> D.1.3; D.2.11; J.2; G.4 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1705.09605">arXiv:1705.09605</a> <span> [<a href="https://arxiv.org/pdf/1705.09605">pdf</a>, <a href="https://arxiv.org/ps/1705.09605">ps</a>, <a href="https://arxiv.org/format/1705.09605">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Combinatorial Multi-Armed Bandits with Filtered Feedback </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Grant%2C+J+A">James A. Grant</a>, <a href="/search/cs?searchtype=author&query=Leslie%2C+D+S">David S. Leslie</a>, <a href="/search/cs?searchtype=author&query=Glazebrook%2C+K">Kevin Glazebrook</a>, <a href="/search/cs?searchtype=author&query=Szechtman%2C+R">Roberto Szechtman</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1705.09605v1-abstract-short" style="display: inline;"> Motivated by problems in search and detection we present a solution to a Combinatorial Multi-Armed Bandit (CMAB) problem with both heavy-tailed reward distributions and a new class of feedback, filtered semibandit feedback. In a CMAB problem an agent pulls a combination of arms from a set $\{1,...,k\}$ in each round, generating random outcomes from probability distributions associated with these a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.09605v1-abstract-full').style.display = 'inline'; document.getElementById('1705.09605v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1705.09605v1-abstract-full" style="display: none;"> Motivated by problems in search and detection we present a solution to a Combinatorial Multi-Armed Bandit (CMAB) problem with both heavy-tailed reward distributions and a new class of feedback, filtered semibandit feedback. In a CMAB problem an agent pulls a combination of arms from a set $\{1,...,k\}$ in each round, generating random outcomes from probability distributions associated with these arms and receiving an overall reward. Under semibandit feedback it is assumed that the random outcomes generated are all observed. Filtered semibandit feedback allows the outcomes that are observed to be sampled from a second distribution conditioned on the initial random outcomes. This feedback mechanism is valuable as it allows CMAB methods to be applied to sequential search and detection problems where combinatorial actions are made, but the true rewards (number of objects of interest appearing in the round) are not observed, rather a filtered reward (the number of objects the searcher successfully finds, which must by definition be less than the number that appear). We present an upper confidence bound type algorithm, Robust-F-CUCB, and associated regret bound of order $\mathcal{O}(\ln(n))$ to balance exploration and exploitation in the face of both filtering of reward and heavy tailed reward distributions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.09605v1-abstract-full').style.display = 'none'; document.getElementById('1705.09605v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 May, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1704.03329">arXiv:1704.03329</a> <span> [<a href="https://arxiv.org/pdf/1704.03329">pdf</a>, <a href="https://arxiv.org/format/1704.03329">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.cpc.2017.11.006">10.1016/j.cpc.2017.11.006 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Domain Specific Language for Performance Portable Molecular Dynamics Algorithms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Saunders%2C+W+R">William R. Saunders</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+J">James Grant</a>, <a href="/search/cs?searchtype=author&query=M%C3%BCller%2C+E+H">Eike H. M眉ller</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1704.03329v2-abstract-short" style="display: inline;"> Developers of Molecular Dynamics (MD) codes face significant challenges when adapting existing simulation packages to new hardware. In a continuously diversifying hardware landscape it becomes increasingly difficult for scientists to be experts both in their own domain (physics/chemistry/biology) and specialists in the low level parallelisation and optimisation of their codes. To address this chal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1704.03329v2-abstract-full').style.display = 'inline'; document.getElementById('1704.03329v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1704.03329v2-abstract-full" style="display: none;"> Developers of Molecular Dynamics (MD) codes face significant challenges when adapting existing simulation packages to new hardware. In a continuously diversifying hardware landscape it becomes increasingly difficult for scientists to be experts both in their own domain (physics/chemistry/biology) and specialists in the low level parallelisation and optimisation of their codes. To address this challenge, we describe a "Separation of Concerns" approach for the development of parallel and optimised MD codes: the science specialist writes code at a high abstraction level in a domain specific language (DSL), which is then translated into efficient computer code by a scientific programmer. In a related context, an abstraction for the solution of partial differential equations with grid based methods has recently been implemented in the (Py)OP2 library. Inspired by this approach, we develop a Python code generation system for molecular dynamics simulations on different parallel architectures, including massively parallel distributed memory systems and GPUs. We demonstrate the efficiency of the auto-generated code by studying its performance and scalability on different hardware and compare it to other state-of-the-art simulation packages. With growing data volumes the extraction of physically meaningful information from the simulation becomes increasingly challenging and requires equally efficient implementations. A particular advantage of our approach is the easy expression of such analysis algorithms. We consider two popular methods for deducing the crystalline structure of a material from the local environment of each atom, show how they can be expressed in our abstraction and implement them in the code generation framework. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1704.03329v2-abstract-full').style.display = 'none'; document.getElementById('1704.03329v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 April, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">24 pages, 12 figures, 11 tables, accepted for publication in Computer Physics Communications on 12 Nov 2017</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> D.1.3, D.2.11, J.2, G.4 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1605.06052">arXiv:1605.06052</a> <span> [<a href="https://arxiv.org/pdf/1605.06052">pdf</a>, <a href="https://arxiv.org/format/1605.06052">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Hierarchical Clustering in Face Similarity Score Space </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Grant%2C+J">Jason Grant</a>, <a href="/search/cs?searchtype=author&query=Flynn%2C+P">Patrick Flynn</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1605.06052v1-abstract-short" style="display: inline;"> Similarity scores in face recognition represent the proximity between pairs of images as computed by a matching algorithm. Given a large set of images and the proximities between all pairs, a similarity score space is defined. Cluster analysis was applied to the similarity score space to develop various taxonomies. Given the number of subjects in the dataset, we used hierarchical methods to aggreg… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1605.06052v1-abstract-full').style.display = 'inline'; document.getElementById('1605.06052v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1605.06052v1-abstract-full" style="display: none;"> Similarity scores in face recognition represent the proximity between pairs of images as computed by a matching algorithm. Given a large set of images and the proximities between all pairs, a similarity score space is defined. Cluster analysis was applied to the similarity score space to develop various taxonomies. Given the number of subjects in the dataset, we used hierarchical methods to aggregate images of the same subject. We also explored the hierarchy above and below the subject level, including clusters that reflect gender and ethnicity. Evidence supports the existence of clustering by race, gender, subject, and illumination condition. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1605.06052v1-abstract-full').style.display = 'none'; document.getElementById('1605.06052v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 May, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1409.1456">arXiv:1409.1456</a> <span> [<a href="https://arxiv.org/pdf/1409.1456">pdf</a>, <a href="https://arxiv.org/format/1409.1456">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1371/journal.pone.0124219">10.1371/journal.pone.0124219 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Accurate, fully-automated NMR spectral profiling for metabolomics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ravanbakhsh%2C+S">Siamak Ravanbakhsh</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+P">Philip Liu</a>, <a href="/search/cs?searchtype=author&query=Bjorndahl%2C+T">Trent Bjorndahl</a>, <a href="/search/cs?searchtype=author&query=Mandal%2C+R">Rupasri Mandal</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+J+R">Jason R. Grant</a>, <a href="/search/cs?searchtype=author&query=Wilson%2C+M">Michael Wilson</a>, <a href="/search/cs?searchtype=author&query=Eisner%2C+R">Roman Eisner</a>, <a href="/search/cs?searchtype=author&query=Sinelnikov%2C+I">Igor Sinelnikov</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+X">Xiaoyu Hu</a>, <a href="/search/cs?searchtype=author&query=Luchinat%2C+C">Claudio Luchinat</a>, <a href="/search/cs?searchtype=author&query=Greiner%2C+R">Russell Greiner</a>, <a href="/search/cs?searchtype=author&query=Wishart%2C+D+S">David S. Wishart</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1409.1456v3-abstract-short" style="display: inline;"> Many diseases cause significant changes to the concentrations of small molecules (aka metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile". This information can be extracted from a biofluid's NMR spectrum. Today, this is often done manually by trained human experts, which means this process is relatively slow,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1409.1456v3-abstract-full').style.display = 'inline'; document.getElementById('1409.1456v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1409.1456v3-abstract-full" style="display: none;"> Many diseases cause significant changes to the concentrations of small molecules (aka metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile". This information can be extracted from a biofluid's NMR spectrum. Today, this is often done manually by trained human experts, which means this process is relatively slow, expensive and error-prone. This paper presents a tool, Bayesil, that can quickly, accurately and autonomously produce a complex biofluid's (e.g., serum or CSF) metabolic profile from a 1D1H NMR spectrum. This requires first performing several spectral processing steps then matching the resulting spectrum against a reference compound library, which contains the "signatures" of each relevant metabolite. Many of these steps are novel algorithms and our matching step views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures, show that Bayesil can autonomously find the concentration of all NMR-detectable metabolites accurately (~90% correct identification and ~10% quantification error), in <5minutes on a single CPU. These results demonstrate that Bayesil is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively -- with an accuracy that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. Available at http://www.bayesil.ca. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1409.1456v3-abstract-full').style.display = 'none'; document.getElementById('1409.1456v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 September, 2014; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 September, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2014. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> PLoS ONE 10(5): e0124219, 2015 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/cs/0511065">arXiv:cs/0511065</a> <span> [<a href="https://arxiv.org/pdf/cs/0511065">pdf</a>, <a href="https://arxiv.org/ps/cs/0511065">ps</a>, <a href="https://arxiv.org/format/cs/0511065">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TCOMM.2007.892450">10.1109/TCOMM.2007.892450 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Performance Analysis of MIMO-MRC in Double-Correlated Rayleigh Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=McKay%2C+M+R">Matthew R. McKay</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+A+J">Alex J. Grant</a>, <a href="/search/cs?searchtype=author&query=Collings%2C+I+B">Iain B. Collings</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="cs/0511065v1-abstract-short" style="display: inline;"> We consider multiple-input multiple-output (MIMO) transmit beamforming systems with maximum ratio combining (MRC) receivers. The operating environment is Rayleigh-fading with both transmit and receive spatial correlation. We present exact expressions for the probability density function (p.d.f.) of the output signal-to-noise ratio (SNR), as well as the system outage probability. The results are… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cs/0511065v1-abstract-full').style.display = 'inline'; document.getElementById('cs/0511065v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="cs/0511065v1-abstract-full" style="display: none;"> We consider multiple-input multiple-output (MIMO) transmit beamforming systems with maximum ratio combining (MRC) receivers. The operating environment is Rayleigh-fading with both transmit and receive spatial correlation. We present exact expressions for the probability density function (p.d.f.) of the output signal-to-noise ratio (SNR), as well as the system outage probability. The results are based on explicit closed-form expressions which we derive for the p.d.f. and c.d.f. of the maximum eigenvalue of double-correlated complex Wishart matrices. For systems with two antennas at either the transmitter or the receiver, we also derive exact closed-form expressions for the symbol error rate (SER). The new expressions are used to prove that MIMO-MRC achieves the maximum available spatial diversity order, and to demonstrate the effect of spatial correlation. The analysis is validated through comparison with Monte-Carlo simulations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cs/0511065v1-abstract-full').style.display = 'none'; document.getElementById('cs/0511065v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 November, 2005; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2005. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">25 pages. Submitted to the IEEE Transactions on Communications</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/cs/0510060">arXiv:cs/0510060</a> <span> [<a href="https://arxiv.org/pdf/cs/0510060">pdf</a>, <a href="https://arxiv.org/ps/cs/0510060">ps</a>, <a href="https://arxiv.org/format/cs/0510060">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Optimal Transmit Covariance for Ergodic MIMO Channels </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hanlen%2C+L+W">Leif W Hanlen</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+A+J">Alex J Grant</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="cs/0510060v1-abstract-short" style="display: inline;"> In this paper we consider the computation of channel capacity for ergodic multiple-input multiple-output channels with additive white Gaussian noise. Two scenarios are considered. Firstly, a time-varying channel is considered in which both the transmitter and the receiver have knowledge of the channel realization. The optimal transmission strategy is water-filling over space and time. It is show… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cs/0510060v1-abstract-full').style.display = 'inline'; document.getElementById('cs/0510060v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="cs/0510060v1-abstract-full" style="display: none;"> In this paper we consider the computation of channel capacity for ergodic multiple-input multiple-output channels with additive white Gaussian noise. Two scenarios are considered. Firstly, a time-varying channel is considered in which both the transmitter and the receiver have knowledge of the channel realization. The optimal transmission strategy is water-filling over space and time. It is shown that this may be achieved in a causal, indeed instantaneous fashion. In the second scenario, only the receiver has perfect knowledge of the channel realization, while the transmitter has knowledge of the channel gain probability law. In this case we determine an optimality condition on the input covariance for ergodic Gaussian vector channels with arbitrary channel distribution under the condition that the channel gains are independent of the transmit signal. Using this optimality condition, we find an iterative algorithm for numerical computation of optimal input covariance matrices. Applications to correlated Rayleigh and Ricean channels are given. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cs/0510060v1-abstract-full').style.display = 'none'; document.getElementById('cs/0510060v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 October, 2005; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2005. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages, 14 figures, Submitted to IEEE Transactions on Information Theory</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/cs/0110032">arXiv:cs/0110032</a> <span> [<a href="https://arxiv.org/pdf/cs/0110032">pdf</a>, <a href="https://arxiv.org/ps/cs/0110032">ps</a>, <a href="https://arxiv.org/format/cs/0110032">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> A logic-based approach to data integration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Grant%2C+J">J. Grant</a>, <a href="/search/cs?searchtype=author&query=Minker%2C+J">J. Minker</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="cs/0110032v1-abstract-short" style="display: inline;"> An important aspect of data integration involves answering queries using various resources rather than by accessing database relations. The process of transforming a query from the database relations to the resources is often referred to as query folding or answering queries using views, where the views are the resources. We present a uniform approach that includes as special cases much of the p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cs/0110032v1-abstract-full').style.display = 'inline'; document.getElementById('cs/0110032v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="cs/0110032v1-abstract-full" style="display: none;"> An important aspect of data integration involves answering queries using various resources rather than by accessing database relations. The process of transforming a query from the database relations to the resources is often referred to as query folding or answering queries using views, where the views are the resources. We present a uniform approach that includes as special cases much of the previous work on this subject. Our approach is logic-based using resolution. We deal with integrity constraints, negation, and recursion also within this framework. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cs/0110032v1-abstract-full').style.display = 'none'; document.getElementById('cs/0110032v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 October, 2001; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2001. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">47 pages, Accepted for publication in the Theory and Practice of Logic Programming</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> H.2, I.2.4 </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: 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