[{"citation":{"short":"F. Alimisis, P. Davies, B. Vandereycken, D.-A. Alistarh, in:, Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 2823–2834.","chicago":"Alimisis, Foivos, Peter Davies, Bart Vandereycken, and Dan-Adrian Alistarh. “Distributed Principal Component Analysis with Limited Communication.” In <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>, 4:2823–34. Neural Information Processing Systems Foundation, 2021.","ista":"Alimisis F, Davies P, Vandereycken B, Alistarh D-A. 2021. Distributed principal component analysis with limited communication. Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 4, 2823–2834.","mla":"Alimisis, Foivos, et al. “Distributed Principal Component Analysis with Limited Communication.” <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>, vol. 4, Neural Information Processing Systems Foundation, 2021, pp. 2823–34.","ama":"Alimisis F, Davies P, Vandereycken B, Alistarh D-A. Distributed principal component analysis with limited communication. In: <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>. Vol 4. Neural Information Processing Systems Foundation; 2021:2823-2834.","ieee":"F. Alimisis, P. Davies, B. Vandereycken, and D.-A. Alistarh, “Distributed principal component analysis with limited communication,” in <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>, Virtual, Online, 2021, vol. 4, pp. 2823–2834.","apa":"Alimisis, F., Davies, P., Vandereycken, B., &#38; Alistarh, D.-A. (2021). Distributed principal component analysis with limited communication. In <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i> (Vol. 4, pp. 2823–2834). Virtual, Online: Neural Information Processing Systems Foundation."},"author":[{"last_name":"Alimisis","first_name":"Foivos","full_name":"Alimisis, Foivos"},{"orcid":"0000-0002-5646-9524","full_name":"Davies, Peter","id":"11396234-BB50-11E9-B24C-90FCE5697425","last_name":"Davies","first_name":"Peter"},{"full_name":"Vandereycken, Bart","last_name":"Vandereycken","first_name":"Bart"},{"id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian","last_name":"Alistarh"}],"date_updated":"2022-06-20T08:31:52Z","oa":1,"publication_identifier":{"issn":["1049-5258"],"isbn":["9781713845393"]},"publication":"Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems","day":"01","external_id":{"arxiv":["2110.14391"]},"title":"Distributed principal component analysis with limited communication","project":[{"grant_number":"805223","call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425","name":"Elastic Coordination for Scalable Machine Learning"},{"name":"ISTplus - Postdoctoral Fellowships","_id":"260C2330-B435-11E9-9278-68D0E5697425","grant_number":"754411","call_identifier":"H2020"}],"_id":"11452","year":"2021","date_created":"2022-06-19T22:01:58Z","ec_funded":1,"abstract":[{"text":"We study efficient distributed algorithms for the fundamental problem of principal component analysis and leading eigenvector computation on the sphere, when the data are randomly distributed among a set of computational nodes. We propose a new quantized variant of Riemannian gradient descent to solve this problem, and prove that the algorithm converges with high probability under a set of necessary spherical-convexity properties. We give bounds on the number of bits transmitted by the algorithm under common initialization schemes, and investigate the dependency on the problem dimension in each case.","lang":"eng"}],"publication_status":"published","article_processing_charge":"No","oa_version":"Published Version","quality_controlled":"1","volume":4,"type":"conference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","language":[{"iso":"eng"}],"date_published":"2021-12-01T00:00:00Z","intvolume":"         4","conference":{"start_date":"2021-12-06","location":"Virtual, Online","name":"NeurIPS: Neural Information Processing Systems","end_date":"2021-12-14"},"page":"2823-2834","arxiv":1,"scopus_import":"1","acknowledgement":"We would like to thank the anonymous reviewers for helpful comments and suggestions. We also thank Aurelien Lucchi and Antonio Orvieto for fruitful discussions at an early stage of this work. FA is partially supported by the SNSF under research project No. 192363 and conducted part of this work while at IST Austria under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 805223 ScaleML). PD partly conducted this work while at IST Austria and was supported by the European Union’s Horizon 2020 programme under the Marie Skłodowska-Curie grant agreement No. 754411.","month":"12","department":[{"_id":"DaAl"}],"publisher":"Neural Information Processing Systems Foundation","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2021/file/1680e9fa7b4dd5d62ece800239bb53bd-Paper.pdf"}]},{"publication_status":"published","abstract":[{"text":"Neuronal computations depend on synaptic connectivity and intrinsic electrophysiological properties. Synaptic connectivity determines which inputs from presynaptic neurons are integrated, while cellular properties determine how inputs are filtered over time. Unlike their biological counterparts, most computational approaches to learning in simulated neural networks are limited to changes in synaptic connectivity. However, if intrinsic parameters change, neural computations are altered drastically. Here, we include the parameters that determine the intrinsic properties,\r\ne.g., time constants and reset potential, into the learning paradigm. Using sparse feedback signals that indicate target spike times, and gradient-based parameter updates, we show that the intrinsic parameters can be learned along with the synaptic weights to produce specific input-output functions. Specifically, we use a teacher-student paradigm in which a randomly initialised leaky integrate-and-fire or resonate-and-fire neuron must recover the parameters of a teacher neuron. We show that complex temporal functions can be learned online and without backpropagation through time, relying on event-based updates only. Our results are a step towards online learning of neural computations from ungraded and unsigned sparse feedback signals with a biologically inspired learning mechanism.","lang":"eng"}],"quality_controlled":"1","article_processing_charge":"No","oa_version":"Published Version","date_created":"2022-06-19T22:01:59Z","_id":"11453","year":"2021","title":"Online learning of neural computations from sparse temporal feedback","project":[{"name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","grant_number":"214316/Z/18/Z"}],"date_updated":"2022-06-20T07:12:58Z","citation":{"short":"L. Braun, T.P. Vogels, in:, Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 16437–16450.","ama":"Braun L, Vogels TP. Online learning of neural computations from sparse temporal feedback. In: <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>. Vol 20. Neural Information Processing Systems Foundation; 2021:16437-16450.","apa":"Braun, L., &#38; Vogels, T. P. (2021). Online learning of neural computations from sparse temporal feedback. In <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i> (Vol. 20, pp. 16437–16450). Virtual, Online: Neural Information Processing Systems Foundation.","ieee":"L. Braun and T. P. Vogels, “Online learning of neural computations from sparse temporal feedback,” in <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>, Virtual, Online, 2021, vol. 20, pp. 16437–16450.","mla":"Braun, Lukas, and Tim P. Vogels. “Online Learning of Neural Computations from Sparse Temporal Feedback.” <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>, vol. 20, Neural Information Processing Systems Foundation, 2021, pp. 16437–50.","ista":"Braun L, Vogels TP. 2021. Online learning of neural computations from sparse temporal feedback. Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 20, 16437–16450.","chicago":"Braun, Lukas, and Tim P Vogels. “Online Learning of Neural Computations from Sparse Temporal Feedback.” In <i>Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems</i>, 20:16437–50. Neural Information Processing Systems Foundation, 2021."},"author":[{"last_name":"Braun","first_name":"Lukas","full_name":"Braun, Lukas"},{"first_name":"Tim P","last_name":"Vogels","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","orcid":"0000-0003-3295-6181"}],"day":"01","oa":1,"publication_identifier":{"issn":["1049-5258"],"isbn":["9781713845393"]},"publication":"Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems","month":"12","publisher":"Neural Information Processing Systems Foundation","department":[{"_id":"TiVo"}],"main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2021/file/88e1ce84f9feef5a08d0df0334c53468-Paper.pdf"}],"conference":{"end_date":"2021-12-14","start_date":"2021-12-06","name":"NeurIPS: Neural Information Processing Systems","location":"Virtual, Online"},"page":"16437-16450","acknowledgement":"We would like to thank Professor Dr. Henning Sprekeler for his valuable suggestions and Dr. Andrew Saxe, Milan Klöwer and Anna Wallis for their constructive feedback on the manuscript. Lukas Braun was supported by the Network of European Neuroscience Schools through their NENS Exchange Grant program, by the European Union through their European Community Action Scheme for the Mobility of University Students, the Woodward Scholarship awarded by Wadham College, Oxford and the Medical Research Council [MR/N013468/1]. Tim P. Vogels was supported by a Wellcome Trust Senior Research Fellowship [214316/Z/18/Z].","scopus_import":"1","date_published":"2021-12-01T00:00:00Z","status":"public","language":[{"iso":"eng"}],"intvolume":"        20","type":"conference","volume":20,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"year":"2021","_id":"11458","ec_funded":1,"date_created":"2022-06-20T12:11:53Z","acknowledged_ssus":[{"_id":"ScienComp"}],"oa_version":"Published Version","article_processing_charge":"No","quality_controlled":"1","abstract":[{"lang":"eng","text":"The increasing computational requirements of deep neural networks (DNNs) have led to significant interest in obtaining DNN models that are sparse, yet accurate. Recent work has investigated the even harder case of sparse training, where the DNN weights are, for as much as possible, already sparse to reduce computational costs during training. Existing sparse training methods are often empirical and can have lower accuracy relative to the dense baseline. In this paper, we present a general approach called Alternating Compressed/DeCompressed (AC/DC) training of DNNs, demonstrate convergence for a variant of the algorithm, and show that AC/DC outperforms existing sparse training methods in accuracy at similar computational budgets; at high sparsity levels, AC/DC even outperforms existing methods that rely on accurate pre-trained dense models. An important property of AC/DC is that it allows co-training of dense and sparse models, yielding accurate sparse–dense model pairs at the end of the training process. This is useful in practice, where compressed variants may be desirable for deployment in resource-constrained settings without re-doing the entire training flow, and also provides us with insights into the accuracy gap between dense and compressed models. The code is available at: https://github.com/IST-DASLab/ACDC."}],"related_material":{"record":[{"id":"13074","status":"public","relation":"dissertation_contains"}]},"publication_status":"published","publication":"35th Conference on Neural Information Processing Systems","publication_identifier":{"isbn":["9781713845393"],"issn":["1049-5258"]},"oa":1,"day":"6","author":[{"full_name":"Peste, Elena-Alexandra","id":"32D78294-F248-11E8-B48F-1D18A9856A87","first_name":"Elena-Alexandra","last_name":"Peste"},{"last_name":"Iofinova","first_name":"Eugenia B","orcid":"0000-0002-7778-3221","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","full_name":"Iofinova, Eugenia B"},{"full_name":"Vladu, Adrian","last_name":"Vladu","first_name":"Adrian"},{"last_name":"Alistarh","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X"}],"citation":{"ieee":"E.-A. Peste, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating Compressed/DeCompressed training of deep neural networks,” in <i>35th Conference on Neural Information Processing Systems</i>, Virtual, Online, 2021, vol. 34, pp. 8557–8570.","apa":"Peste, E.-A., Iofinova, E. B., Vladu, A., &#38; Alistarh, D.-A. (2021). AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In <i>35th Conference on Neural Information Processing Systems</i> (Vol. 34, pp. 8557–8570). Virtual, Online: Curran Associates.","ama":"Peste E-A, Iofinova EB, Vladu A, Alistarh D-A. AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In: <i>35th Conference on Neural Information Processing Systems</i>. Vol 34. Curran Associates; 2021:8557-8570.","mla":"Peste, Elena-Alexandra, et al. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” <i>35th Conference on Neural Information Processing Systems</i>, vol. 34, Curran Associates, 2021, pp. 8557–70.","ista":"Peste E-A, Iofinova EB, Vladu A, Alistarh D-A. 2021. AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 8557–8570.","chicago":"Peste, Elena-Alexandra, Eugenia B Iofinova, Adrian Vladu, and Dan-Adrian Alistarh. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” In <i>35th Conference on Neural Information Processing Systems</i>, 34:8557–70. Curran Associates, 2021.","short":"E.-A. Peste, E.B. Iofinova, A. Vladu, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Curran Associates, 2021, pp. 8557–8570."},"date_updated":"2023-06-01T12:54:45Z","project":[{"name":"Elastic Coordination for Scalable Machine Learning","grant_number":"805223","_id":"268A44D6-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"external_id":{"arxiv":["2106.12379"]},"title":"AC/DC: Alternating Compressed/DeCompressed training of deep neural networks","scopus_import":"1","acknowledgement":"This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML), and a CNRS PEPS grant. This research was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp). We would also like to thank Christoph Lampert for his feedback on an earlier version of this work, as well as for providing hardware for the Transformer-XL experiments.","page":"8557-8570","conference":{"end_date":"2021-12-14","location":"Virtual, Online","start_date":"2021-12-06","name":"NeurIPS: Neural Information Processing Systems"},"arxiv":1,"main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2021/file/48000647b315f6f00f913caa757a70b3-Paper.pdf"}],"department":[{"_id":"GradSch"},{"_id":"DaAl"}],"publisher":"Curran Associates","month":"12","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","volume":34,"type":"conference","intvolume":"        34","language":[{"iso":"eng"}],"status":"public","date_published":"2021-12-06T00:00:00Z"},{"scopus_import":"1","acknowledgement":"We gratefully acknowledge funding the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML), as well as computational support from Amazon Web Services (AWS) EC2.","conference":{"name":"NeurIPS: Neural Information Processing Systems","location":"Virtual, Online","start_date":"2021-12-06","end_date":"2021-12-14"},"page":"14873-14886","arxiv":1,"main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2021/file/7cfd5df443b4eb0d69886a583b33de4c-Paper.pdf"}],"publisher":"Curran Associates","department":[{"_id":"DaAl"}],"month":"12","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","volume":34,"type":"conference","intvolume":"        34","language":[{"iso":"eng"}],"status":"public","date_published":"2021-12-06T00:00:00Z","year":"2021","_id":"11463","ec_funded":1,"date_created":"2022-06-26T22:01:35Z","oa_version":"Published Version","article_processing_charge":"No","quality_controlled":"1","abstract":[{"lang":"eng","text":"Efficiently approximating local curvature information of the loss function is a key tool for optimization and compression of deep neural networks. Yet, most existing methods to approximate second-order information have high computational\r\nor storage costs, which limits their practicality. In this work, we investigate matrix-free, linear-time approaches for estimating Inverse-Hessian Vector Products (IHVPs) for the case when the Hessian can be approximated as a sum of rank-one matrices, as in the classic approximation of the Hessian by the empirical Fisher matrix. We propose two new algorithms: the first is tailored towards network compression and can compute the IHVP for dimension d, if the Hessian is given as a sum of m rank-one matrices, using O(dm2) precomputation, O(dm) cost for computing the IHVP, and query cost O(m) for any single element of the inverse Hessian. The second algorithm targets an optimization setting, where we wish to compute the product between the inverse Hessian, estimated over a sliding window of optimization steps, and a given gradient direction, as required for preconditioned SGD. We give an algorithm with cost O(dm + m2) for computing the IHVP and O(dm + m3) for adding or removing any gradient from the sliding window. These\r\ntwo algorithms yield state-of-the-art results for network pruning and optimization with lower computational overhead relative to existing second-order methods. Implementations are available at [9] and [17]."}],"publication_status":"published","publication":"35th Conference on Neural Information Processing Systems","publication_identifier":{"issn":["1049-5258"],"isbn":["9781713845393"]},"oa":1,"day":"06","date_updated":"2022-06-27T07:05:12Z","citation":{"ieee":"E. Frantar, E. Kurtic, and D.-A. Alistarh, “M-FAC: Efficient matrix-free approximations of second-order information,” in <i>35th Conference on Neural Information Processing Systems</i>, Virtual, Online, 2021, vol. 34, pp. 14873–14886.","ama":"Frantar E, Kurtic E, Alistarh D-A. M-FAC: Efficient matrix-free approximations of second-order information. In: <i>35th Conference on Neural Information Processing Systems</i>. Vol 34. Curran Associates; 2021:14873-14886.","apa":"Frantar, E., Kurtic, E., &#38; Alistarh, D.-A. (2021). M-FAC: Efficient matrix-free approximations of second-order information. In <i>35th Conference on Neural Information Processing Systems</i> (Vol. 34, pp. 14873–14886). Virtual, Online: Curran Associates.","mla":"Frantar, Elias, et al. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” <i>35th Conference on Neural Information Processing Systems</i>, vol. 34, Curran Associates, 2021, pp. 14873–86.","ista":"Frantar E, Kurtic E, Alistarh D-A. 2021. M-FAC: Efficient matrix-free approximations of second-order information. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 14873–14886.","chicago":"Frantar, Elias, Eldar Kurtic, and Dan-Adrian Alistarh. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” In <i>35th Conference on Neural Information Processing Systems</i>, 34:14873–86. Curran Associates, 2021.","short":"E. Frantar, E. Kurtic, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Curran Associates, 2021, pp. 14873–14886."},"author":[{"first_name":"Elias","last_name":"Frantar","full_name":"Frantar, Elias","id":"09a8f98d-ec99-11ea-ae11-c063a7b7fe5f"},{"first_name":"Eldar","last_name":"Kurtic","full_name":"Kurtic, Eldar","id":"47beb3a5-07b5-11eb-9b87-b108ec578218"},{"last_name":"Alistarh","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X"}],"project":[{"grant_number":"805223","call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425","name":"Elastic Coordination for Scalable Machine Learning"}],"external_id":{"arxiv":["2010.08222"]},"title":"M-FAC: Efficient matrix-free approximations of second-order information"},{"publication_identifier":{"issn":["1049-5258"],"isbn":["9781713845393"]},"publication":"35th Conference on Neural Information Processing Systems","oa":1,"day":"06","author":[{"last_name":"Alistarh","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X"},{"id":"C5402D42-15BC-11E9-A202-CA2BE6697425","full_name":"Korhonen, Janne","first_name":"Janne","last_name":"Korhonen"}],"date_updated":"2022-06-27T06:54:31Z","citation":{"mla":"Alistarh, Dan-Adrian, and Janne Korhonen. “Towards Tight Communication Lower Bounds for Distributed Optimisation.” <i>35th Conference on Neural Information Processing Systems</i>, vol. 34, Curran Associates, 2021, pp. 7254–66.","ieee":"D.-A. Alistarh and J. Korhonen, “Towards tight communication lower bounds for distributed optimisation,” in <i>35th Conference on Neural Information Processing Systems</i>, Virtual, Online, 2021, vol. 34, pp. 7254–7266.","apa":"Alistarh, D.-A., &#38; Korhonen, J. (2021). Towards tight communication lower bounds for distributed optimisation. In <i>35th Conference on Neural Information Processing Systems</i> (Vol. 34, pp. 7254–7266). Virtual, Online: Curran Associates.","ama":"Alistarh D-A, Korhonen J. Towards tight communication lower bounds for distributed optimisation. In: <i>35th Conference on Neural Information Processing Systems</i>. Vol 34. Curran Associates; 2021:7254-7266.","chicago":"Alistarh, Dan-Adrian, and Janne Korhonen. “Towards Tight Communication Lower Bounds for Distributed Optimisation.” In <i>35th Conference on Neural Information Processing Systems</i>, 34:7254–66. Curran Associates, 2021.","ista":"Alistarh D-A, Korhonen J. 2021. Towards tight communication lower bounds for distributed optimisation. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 7254–7266.","short":"D.-A. Alistarh, J. Korhonen, in:, 35th Conference on Neural Information Processing Systems, Curran Associates, 2021, pp. 7254–7266."},"project":[{"name":"Elastic Coordination for Scalable Machine Learning","call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223"}],"title":"Towards tight communication lower bounds for distributed optimisation","external_id":{"arxiv":["2010.08222"]},"year":"2021","_id":"11464","date_created":"2022-06-26T22:01:35Z","ec_funded":1,"oa_version":"Published Version","article_processing_charge":"No","quality_controlled":"1","abstract":[{"text":"We consider a standard distributed optimisation setting where N machines, each holding a d-dimensional function\r\nfi, aim to jointly minimise the sum of the functions ∑Ni=1fi(x). This problem arises naturally in large-scale distributed optimisation, where a standard solution is to apply variants of (stochastic) gradient descent. We focus on the communication complexity of this problem: our main result provides the first fully unconditional bounds on total number of bits which need to be sent and received by the N machines to solve this problem under point-to-point communication, within a given error-tolerance. Specifically, we show that Ω(Ndlogd/Nε) total bits need to be communicated between the machines to find an additive ϵ-approximation to the minimum of ∑Ni=1fi(x). The result holds for both deterministic and randomised algorithms, and, importantly, requires no assumptions on the algorithm structure. The lower bound is tight under certain restrictions on parameter values, and is matched within constant factors for quadratic objectives by a new variant of quantised gradient descent, which we describe and analyse. Our results bring over tools from communication complexity to distributed optimisation, which has potential for further applications.","lang":"eng"}],"publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","volume":34,"type":"conference","intvolume":"        34","language":[{"iso":"eng"}],"status":"public","date_published":"2021-12-06T00:00:00Z","scopus_import":"1","acknowledgement":"We thank the NeurIPS reviewers for insightful comments that helped us improve the positioning of our results, as well as for pointing out the subsampling approach for complementing the randomised lower bound. We also thank Foivos Alimisis and Peter Davies for useful discussions. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML).","page":"7254-7266","conference":{"location":"Virtual, Online","start_date":"2021-12-06","name":"NeurIPS: Neural Information Processing Systems","end_date":"2021-12-14"},"arxiv":1,"main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2021/file/3b92d18aa7a6176dd37d372bc2f1eb71-Paper.pdf"}],"department":[{"_id":"DaAl"}],"publisher":"Curran Associates","month":"12"},{"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2104.06966","open_access":"1"}],"article_processing_charge":"No","oa_version":"Preprint","month":"04","related_material":{"record":[{"relation":"dissertation_contains","id":"12072","status":"public"}]},"department":[{"_id":"TiBr"}],"publication_status":"submitted","abstract":[{"text":"We find an asymptotic formula for the number of primitive vectors $(z_1,\\ldots,z_4)\\in (\\mathbb{Z}_{\\neq 0})^4$ such that $z_1,\\ldots, z_4$ are all squareful and bounded by $B$, and $z_1+\\cdots + z_4 = 0$. Our result agrees in the power of $B$ and $\\log B$ with the Campana-Manin conjecture of Pieropan, Smeets, Tanimoto and V\\'{a}rilly-Alvarado.","lang":"eng"}],"date_created":"2022-09-09T10:42:51Z","_id":"12076","year":"2021","arxiv":1,"article_number":"2104.06966","date_published":"2021-04-15T00:00:00Z","doi":"10.48550/arXiv.2104.06966","external_id":{"arxiv":["2104.06966"]},"title":"Sums of four squareful numbers","status":"public","language":[{"iso":"eng"}],"day":"15","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"arXiv","date_updated":"2023-02-21T16:37:30Z","citation":{"chicago":"Shute, Alec L. “Sums of Four Squareful Numbers.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2104.06966\">https://doi.org/10.48550/arXiv.2104.06966</a>.","ista":"Shute AL. Sums of four squareful numbers. arXiv, 2104.06966.","mla":"Shute, Alec L. “Sums of Four Squareful Numbers.” <i>ArXiv</i>, 2104.06966, doi:<a href=\"https://doi.org/10.48550/arXiv.2104.06966\">10.48550/arXiv.2104.06966</a>.","ama":"Shute AL. Sums of four squareful numbers. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2104.06966\">10.48550/arXiv.2104.06966</a>","ieee":"A. L. Shute, “Sums of four squareful numbers,” <i>arXiv</i>. .","apa":"Shute, A. L. (n.d.). Sums of four squareful numbers. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2104.06966\">https://doi.org/10.48550/arXiv.2104.06966</a>","short":"A.L. Shute, ArXiv (n.d.)."},"type":"preprint","author":[{"orcid":"0000-0002-1812-2810","full_name":"Shute, Alec L","id":"440EB050-F248-11E8-B48F-1D18A9856A87","first_name":"Alec L","last_name":"Shute"}]},{"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2104.14946"}],"article_processing_charge":"No","oa_version":"Preprint","month":"04","related_material":{"record":[{"relation":"dissertation_contains","id":"12072","status":"public"}]},"department":[{"_id":"TiBr"}],"publication_status":"submitted","abstract":[{"text":"We compare the Manin-type conjecture for Campana points recently formulated\r\nby Pieropan, Smeets, Tanimoto and V\\'{a}rilly-Alvarado with an alternative\r\nprediction of Browning and Van Valckenborgh in the special case of the orbifold\r\n$(\\mathbb{P}^1,D)$, where $D =\\frac{1}{2}[0]+\\frac{1}{2}[1]+\\frac{1}{2}[\\infty]$. We find that the two predicted leading constants do not agree, and we discuss whether thin sets\r\ncould explain this discrepancy. Motivated by this, we provide a counterexample\r\nto the Manin-type conjecture for Campana points, by considering orbifolds\r\ncorresponding to squareful values of binary quadratic forms.","lang":"eng"}],"acknowledgement":"The author would like to thank Damaris Schindler and Florian Wilsch for their helpful comments on the heights and Tamagawa measures used in Section 3, together with Marta Pieropan, Sho Tanimoto and Sam Streeter for providing valuable feedback on an earlier version of this paper, and Tim Browning for many useful comments and discussions during the development of this work. The author is also grateful to the anonymous referee for providing many valuable comments and suggestions that improved the quality of the paper.","date_created":"2022-09-09T10:43:17Z","_id":"12077","year":"2021","arxiv":1,"article_number":"2104.14946","date_published":"2021-04-30T00:00:00Z","doi":"10.48550/arXiv.2104.14946","status":"public","external_id":{"arxiv":["2104.14946"]},"title":"On the leading constant in the Manin-type conjecture for Campana points","language":[{"iso":"eng"}],"day":"30","oa":1,"publication":"arXiv","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"preprint","date_updated":"2023-02-21T16:37:30Z","citation":{"short":"A.L. Shute, ArXiv (n.d.).","chicago":"Shute, Alec L. “On the Leading Constant in the Manin-Type Conjecture for Campana Points.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2104.14946\">https://doi.org/10.48550/arXiv.2104.14946</a>.","ista":"Shute AL. On the leading constant in the Manin-type conjecture for Campana points. arXiv, 2104.14946.","mla":"Shute, Alec L. “On the Leading Constant in the Manin-Type Conjecture for Campana Points.” <i>ArXiv</i>, 2104.14946, doi:<a href=\"https://doi.org/10.48550/arXiv.2104.14946\">10.48550/arXiv.2104.14946</a>.","apa":"Shute, A. L. (n.d.). On the leading constant in the Manin-type conjecture for Campana points. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2104.14946\">https://doi.org/10.48550/arXiv.2104.14946</a>","ieee":"A. L. Shute, “On the leading constant in the Manin-type conjecture for Campana points,” <i>arXiv</i>. .","ama":"Shute AL. On the leading constant in the Manin-type conjecture for Campana points. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2104.14946\">10.48550/arXiv.2104.14946</a>"},"author":[{"first_name":"Alec L","last_name":"Shute","orcid":"0000-0002-1812-2810","id":"440EB050-F248-11E8-B48F-1D18A9856A87","full_name":"Shute, Alec L"}]},{"abstract":[{"text":"Activation of cell-surface and intracellular receptor-mediated immunity results in rapid transcriptional reprogramming that underpins disease resistance. However, the mechanisms by which co-activation of both immune systems lead to transcriptional changes are not clear. Here, we combine RNA-seq and ATAC-seq to define changes in gene expression and chromatin accessibility. Activation of cell-surface or intracellular receptor-mediated immunity, or both, increases chromatin accessibility at induced defence genes. Analysis of ATAC-seq and RNA-seq data combined with publicly available information on transcription factor DNA-binding motifs enabled comparison of individual gene regulatory networks activated by cell-surface or intracellular receptor-mediated immunity, or by both. These results and analyses reveal overlapping and conserved transcriptional regulatory mechanisms between the two immune systems.","lang":"eng"}],"publication_status":"published","article_type":"original","oa_version":"None","keyword":["Plant Science","Physiology"],"article_processing_charge":"No","quality_controlled":"1","year":"2021","_id":"12186","date_created":"2023-01-16T09:14:35Z","pmid":1,"external_id":{"pmid":["34387350"]},"title":"Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors","issue":"22","date_updated":"2023-05-08T11:01:18Z","author":[{"first_name":"Pingtao","last_name":"Ding","full_name":"Ding, Pingtao"},{"first_name":"Toshiyuki","last_name":"Sakai","full_name":"Sakai, Toshiyuki"},{"full_name":"Krishna Shrestha, Ram","first_name":"Ram","last_name":"Krishna Shrestha"},{"last_name":"Manosalva Perez","first_name":"Nicolas","full_name":"Manosalva Perez, Nicolas"},{"full_name":"Guo, Wenbin","last_name":"Guo","first_name":"Wenbin"},{"full_name":"Ngou, Bruno Pok Man","first_name":"Bruno Pok Man","last_name":"Ngou"},{"first_name":"Shengbo","last_name":"He","full_name":"He, Shengbo"},{"last_name":"Liu","first_name":"Chang","full_name":"Liu, Chang"},{"last_name":"Feng","first_name":"Xiaoqi","orcid":"0000-0002-4008-1234","full_name":"Feng, Xiaoqi","id":"e0164712-22ee-11ed-b12a-d80fcdf35958"},{"full_name":"Zhang, Runxuan","first_name":"Runxuan","last_name":"Zhang"},{"full_name":"Vandepoele, Klaas","first_name":"Klaas","last_name":"Vandepoele"},{"full_name":"MacLean, Dan","first_name":"Dan","last_name":"MacLean"},{"full_name":"Jones, Jonathan D G","first_name":"Jonathan D G","last_name":"Jones"}],"citation":{"mla":"Ding, Pingtao, et al. “Chromatin Accessibility Landscapes Activated by Cell-Surface and Intracellular Immune Receptors.” <i>Journal of Experimental Botany</i>, vol. 72, no. 22, Oxford University Press, 2021, pp. 7927–41, doi:<a href=\"https://doi.org/10.1093/jxb/erab373\">10.1093/jxb/erab373</a>.","ieee":"P. Ding <i>et al.</i>, “Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors,” <i>Journal of Experimental Botany</i>, vol. 72, no. 22. Oxford University Press, pp. 7927–7941, 2021.","ama":"Ding P, Sakai T, Krishna Shrestha R, et al. Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors. <i>Journal of Experimental Botany</i>. 2021;72(22):7927-7941. doi:<a href=\"https://doi.org/10.1093/jxb/erab373\">10.1093/jxb/erab373</a>","apa":"Ding, P., Sakai, T., Krishna Shrestha, R., Manosalva Perez, N., Guo, W., Ngou, B. P. M., … Jones, J. D. G. (2021). Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors. <i>Journal of Experimental Botany</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/jxb/erab373\">https://doi.org/10.1093/jxb/erab373</a>","chicago":"Ding, Pingtao, Toshiyuki Sakai, Ram Krishna Shrestha, Nicolas Manosalva Perez, Wenbin Guo, Bruno Pok Man Ngou, Shengbo He, et al. “Chromatin Accessibility Landscapes Activated by Cell-Surface and Intracellular Immune Receptors.” <i>Journal of Experimental Botany</i>. Oxford University Press, 2021. <a href=\"https://doi.org/10.1093/jxb/erab373\">https://doi.org/10.1093/jxb/erab373</a>.","ista":"Ding P, Sakai T, Krishna Shrestha R, Manosalva Perez N, Guo W, Ngou BPM, He S, Liu C, Feng X, Zhang R, Vandepoele K, MacLean D, Jones JDG. 2021. Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors. Journal of Experimental Botany. 72(22), 7927–7941.","short":"P. Ding, T. Sakai, R. Krishna Shrestha, N. Manosalva Perez, W. Guo, B.P.M. Ngou, S. He, C. Liu, X. Feng, R. Zhang, K. Vandepoele, D. MacLean, J.D.G. Jones, Journal of Experimental Botany 72 (2021) 7927–7941."},"publication_identifier":{"issn":["0022-0957","1460-2431"]},"publication":"Journal of Experimental Botany","day":"13","publisher":"Oxford University Press","department":[{"_id":"XiFe"}],"month":"08","page":"7927-7941","scopus_import":"1","acknowledgement":"We thank the Gatsby Foundation (UK) for funding to the JDGJ laboratory. PD acknowledges support from the European Union’s Horizon 2020 Research and Innovation Program under Marie Skłodowska Curie Actions (grant agreement: 656243) and a Future Leader Fellowship from the Biotechnology and Biological Sciences Research Council (BBSRC) (grant agreement: BB/R012172/1). TS, RKS, DM, and JDGJ were supported by the Gatsby Foundation funding to the\r\nSainsbury Laboratory. NMP and KV were supported by a BOF grant from Ghent University (grant agreement: BOF24Y2019001901). WG and RZ were supported by the Scottish Government Rural and Environment Science and Analytical Services division (RESAS), and RZ also acknowledges the support from a BBSRC Bioinformatics and Biological Resources Fund (grant agreement: BB/S020160/1).BPMN was supported by the Norwich Research Park (NRP) Biosciences Doctoral Training Partnership (DTP) funded by the BBSRC (grant agreement: BB/M011216/1). SH and XF were supported by a BBSRC Responsive Mode grant (grant agreement: BB/S009620/1) and a European Research Council Starting grant ‘SexMeth’ (grant agreement: 804981). CL was supported by Deutsche Forschungsgemeinschaft (grant agreement: LI 2862/4). ","language":[{"iso":"eng"}],"status":"public","doi":"10.1093/jxb/erab373","date_published":"2021-08-13T00:00:00Z","intvolume":"        72","extern":"1","volume":72,"type":"journal_article","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"author":[{"full_name":"Long, Jincheng","last_name":"Long","first_name":"Jincheng"},{"full_name":"Walker, James","first_name":"James","last_name":"Walker"},{"full_name":"She, Wenjing","last_name":"She","first_name":"Wenjing"},{"last_name":"Aldridge","first_name":"Billy","full_name":"Aldridge, Billy"},{"first_name":"Hongbo","last_name":"Gao","full_name":"Gao, Hongbo"},{"full_name":"Deans, Samuel","last_name":"Deans","first_name":"Samuel"},{"full_name":"Vickers, Martin","first_name":"Martin","last_name":"Vickers"},{"orcid":"0000-0002-4008-1234","id":"e0164712-22ee-11ed-b12a-d80fcdf35958","full_name":"Feng, Xiaoqi","first_name":"Xiaoqi","last_name":"Feng"}],"citation":{"ama":"Long J, Walker J, She W, et al. Nurse cell--derived small RNAs define paternal epigenetic inheritance in Arabidopsis. <i>Science</i>. 2021;373(6550). doi:<a href=\"https://doi.org/10.1126/science.abh0556\">10.1126/science.abh0556</a>","ieee":"J. Long <i>et al.</i>, “Nurse cell--derived small RNAs define paternal epigenetic inheritance in Arabidopsis,” <i>Science</i>, vol. 373, no. 6550. American Association for the Advancement of Science (AAAS), 2021.","apa":"Long, J., Walker, J., She, W., Aldridge, B., Gao, H., Deans, S., … Feng, X. (2021). Nurse cell--derived small RNAs define paternal epigenetic inheritance in Arabidopsis. <i>Science</i>. American Association for the Advancement of Science (AAAS). <a href=\"https://doi.org/10.1126/science.abh0556\">https://doi.org/10.1126/science.abh0556</a>","mla":"Long, Jincheng, et al. “Nurse Cell--Derived Small RNAs Define Paternal Epigenetic Inheritance in Arabidopsis.” <i>Science</i>, vol. 373, no. 6550, American Association for the Advancement of Science (AAAS), 2021, doi:<a href=\"https://doi.org/10.1126/science.abh0556\">10.1126/science.abh0556</a>.","ista":"Long J, Walker J, She W, Aldridge B, Gao H, Deans S, Vickers M, Feng X. 2021. Nurse cell--derived small RNAs define paternal epigenetic inheritance in Arabidopsis. Science. 373(6550).","chicago":"Long, Jincheng, James Walker, Wenjing She, Billy Aldridge, Hongbo Gao, Samuel Deans, Martin Vickers, and Xiaoqi Feng. “Nurse Cell--Derived Small RNAs Define Paternal Epigenetic Inheritance in Arabidopsis.” <i>Science</i>. American Association for the Advancement of Science (AAAS), 2021. <a href=\"https://doi.org/10.1126/science.abh0556\">https://doi.org/10.1126/science.abh0556</a>.","short":"J. Long, J. Walker, W. She, B. Aldridge, H. Gao, S. Deans, M. Vickers, X. Feng, Science 373 (2021)."},"date_updated":"2023-05-08T10:56:39Z","publication":"Science","publication_identifier":{"issn":["0036-8075","1095-9203"]},"day":"02","title":"Nurse cell--derived small RNAs define paternal epigenetic inheritance in Arabidopsis","external_id":{"pmid":["34210850"]},"issue":"6550","year":"2021","_id":"12187","date_created":"2023-01-16T09:15:14Z","pmid":1,"abstract":[{"text":"Genomes of germ cells present an existential vulnerability to organisms because germ cell mutations will propagate to future generations. Transposable elements are one source of such mutations. In the small flowering plant Arabidopsis, Long et al. found that genome methylation in the male germline is directed by small interfering RNAs (siRNAs) imperfectly transcribed from transposons (see the Perspective by Mosher). These germline siRNAs silence germline transposons and establish inherited methylation patterns in sperm, thus maintaining the integrity of the plant genome across generations.","lang":"eng"}],"article_type":"original","publication_status":"published","oa_version":"None","keyword":["Multidisciplinary"],"article_processing_charge":"No","quality_controlled":"1","volume":373,"type":"journal_article","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"status":"public","doi":"10.1126/science.abh0556","date_published":"2021-07-02T00:00:00Z","intvolume":"       373","extern":"1","scopus_import":"1","acknowledgement":"We thank the John Innes Centre Bioimaging Facility (S. Lopez, E. Wegel, and K. Findlay) for their assistance with microscopy and the Norwich BioScience Institute Partnership Computing Infrastructure for Science Group for high-performance computing resources. Funding: This work was funded by a European Research Council Starting Grant (“SexMeth” 804981; J.L., J.W., and X.F.), a Sainsbury Charitable Foundation studentship (J.W.), two Biotechnology and Biological Sciences Research Council (BBSRC) grants (BBS0096201 and BBP0135111; W.S., M.V., and X.F.), two John Innes Foundation studentships (B.A. and S.D.), and a BBSRC David Phillips Fellowship (BBL0250431; H.G. and X.F.). Author contributions: J.L., J.W., and X.F. designed the study and wrote the manuscript; J.L., W.S., B.A., H.G., and S.D. performed the experiments; and J.L., J.W., B.A., H.G., S.D., M.V., and X.F. analyzed the data. Competing interests: The authors declare no competing interests. Data and material availability: All sequencing data have been deposited in the Gene Expression Omnibus (GEO) under accession no. GSE161625. Accession nos. of published datasets used in this study are listed in table S6. Published software used in this study include Bowtie v1.2.2 (https://doi.org/10.1002/0471250953.bi1107s32), Bismark v0.22.2 (https://doi.org/10.1093/bioinformatics/btr167), Kallisto v0.43.0 (https://doi.org/10.1038/nbt0816-888d), Shortstack v3.8.5 (https://doi.org/10.1534/g3.116.030452), and Cutadapt v1.15 (https://doi.org/10.1089/cmb.2017.0096). TrimGalore v0.4.1 and MarkDuplicates v1.141 are available from https://github.com/FelixKrueger/TrimGalore and https://github.com/broadinstitute/picard, respectively. All remaining data are in the main paper or the supplementary materials.","publisher":"American Association for the Advancement of Science (AAAS)","department":[{"_id":"XiFe"}],"month":"07"},{"oa_version":"Published Version","article_processing_charge":"No","quality_controlled":"1","file":[{"date_updated":"2023-03-28T11:00:33Z","file_id":"12777","success":1,"relation":"main_file","date_created":"2023-03-28T11:00:33Z","file_name":"2021_LNCS_Bansal.pdf","access_level":"open_access","creator":"dernst","content_type":"application/pdf","file_size":747418,"checksum":"b020b78b23587ce7610b1aafb4e63438"}],"abstract":[{"text":"Several problems in planning and reactive synthesis can be reduced to the analysis of two-player quantitative graph games. Optimization is one form of analysis. We argue that in many cases it may be better to replace the optimization problem with the satisficing problem, where instead of searching for optimal solutions, the goal is to search for solutions that adhere to a given threshold bound.\r\nThis work defines and investigates the satisficing problem on a two-player graph game with the discounted-sum cost model. We show that while the satisficing problem can be solved using numerical methods just like the optimization problem, this approach does not render compelling benefits over optimization. When the discount factor is, however, an integer, we present another approach to satisficing, which is purely based on automata methods. We show that this approach is algorithmically more performant – both theoretically and empirically – and demonstrates the broader applicability of satisficing over optimization.","lang":"eng"}],"publication_status":"published","year":"2021","_id":"12767","date_created":"2023-03-26T22:01:09Z","ec_funded":1,"has_accepted_license":"1","project":[{"name":"Formal Methods for Stochastic Models: Algorithms and Applications","grant_number":"863818","call_identifier":"H2020","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E"}],"external_id":{"arxiv":["2101.02594"]},"title":"On satisficing in quantitative games","publication_identifier":{"issn":["0302-9743"],"eissn":["1611-3349"],"isbn":["9783030720155"]},"publication":"27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems","oa":1,"day":"21","author":[{"full_name":"Bansal, Suguman","last_name":"Bansal","first_name":"Suguman"},{"last_name":"Chatterjee","first_name":"Krishnendu","orcid":"0000-0002-4561-241X","full_name":"Chatterjee, Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Vardi","first_name":"Moshe Y.","full_name":"Vardi, Moshe Y."}],"date_updated":"2025-07-14T09:09:51Z","citation":{"short":"S. Bansal, K. Chatterjee, M.Y. Vardi, in:, 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Springer Nature, 2021, pp. 20–37.","chicago":"Bansal, Suguman, Krishnendu Chatterjee, and Moshe Y. Vardi. “On Satisficing in Quantitative Games.” In <i>27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems</i>, 12651:20–37. Springer Nature, 2021. <a href=\"https://doi.org/10.1007/978-3-030-72016-2\">https://doi.org/10.1007/978-3-030-72016-2</a>.","ista":"Bansal S, Chatterjee K, Vardi MY. 2021. On satisficing in quantitative games. 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. TACAS: Tools and Algorithms for the Construction and Analysis of Systems, LNCS, vol. 12651, 20–37.","mla":"Bansal, Suguman, et al. “On Satisficing in Quantitative Games.” <i>27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems</i>, vol. 12651, Springer Nature, 2021, pp. 20–37, doi:<a href=\"https://doi.org/10.1007/978-3-030-72016-2\">10.1007/978-3-030-72016-2</a>.","apa":"Bansal, S., Chatterjee, K., &#38; Vardi, M. Y. (2021). On satisficing in quantitative games. In <i>27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems</i> (Vol. 12651, pp. 20–37). Luxembourg City, Luxembourg: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-72016-2\">https://doi.org/10.1007/978-3-030-72016-2</a>","ieee":"S. Bansal, K. Chatterjee, and M. Y. Vardi, “On satisficing in quantitative games,” in <i>27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems</i>, Luxembourg City, Luxembourg, 2021, vol. 12651, pp. 20–37.","ama":"Bansal S, Chatterjee K, Vardi MY. On satisficing in quantitative games. In: <i>27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems</i>. Vol 12651. Springer Nature; 2021:20-37. doi:<a href=\"https://doi.org/10.1007/978-3-030-72016-2\">10.1007/978-3-030-72016-2</a>"},"license":"https://creativecommons.org/licenses/by/4.0/","publisher":"Springer Nature","department":[{"_id":"KrCh"}],"month":"03","scopus_import":"1","acknowledgement":"We thank anonymous reviewers for valuable inputs. This work is supported in part by NSF grant 2030859 to the CRA for the CIFellows Project, NSF grants IIS-1527668, CCF-1704883, IIS-1830549, the ERC CoG 863818 (ForM-SMArt), and an award from the Maryland Procurement Office.","conference":{"location":"Luxembourg City, Luxembourg","start_date":"2021-03-27","name":"TACAS: Tools and Algorithms for the Construction and Analysis of Systems","end_date":"2021-04-01"},"page":"20-37","arxiv":1,"alternative_title":["LNCS"],"intvolume":"     12651","ddc":["000"],"language":[{"iso":"eng"}],"status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"doi":"10.1007/978-3-030-72016-2","date_published":"2021-03-21T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2023-03-28T11:00:33Z","volume":12651,"type":"conference"},{"article_processing_charge":"No","oa_version":"Published Version","main_file_link":[{"open_access":"1","url":"https://vsc.ac.at/fileadmin/user_upload/vsc/conferences/ashpc21/BOOKLET_ASHPC21.pdf"}],"file":[{"checksum":"ba73f85858fb9d5737ebc7724646dd45","file_size":422761,"content_type":"application/pdf","creator":"dernst","access_level":"open_access","file_name":"2021_ASHPC_Schloegl.pdf","date_created":"2023-05-16T07:36:34Z","success":1,"relation":"main_file","file_id":"12971","date_updated":"2023-05-16T07:36:34Z"}],"month":"06","department":[{"_id":"ScienComp"}],"publisher":"University of Ljubljana","publication_status":"published","_id":"12909","year":"2021","date_created":"2023-05-05T13:17:36Z","page":"5","conference":{"name":"ASHPC - Austrian-Slovenian HPC Meeting","start_date":"2021-05-31","location":"Virtual","end_date":"2021-06-02"},"ddc":["000"],"has_accepted_license":"1","title":"Managing software on a heterogenous HPC cluster","status":"public","language":[{"iso":"eng"}],"date_published":"2021-06-02T00:00:00Z","doi":"10.3359/2021hpc","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"ASHPC21 – Austrian-Slovenian HPC Meeting 2021","publication_identifier":{"isbn":["978-961-6980-77-7","978-961-6133-48-7"]},"file_date_updated":"2023-05-16T07:36:34Z","day":"02","type":"conference_abstract","date_updated":"2023-05-16T07:43:54Z","citation":{"ieee":"A. Schlögl, S. Elefante, A. Hornoiu, and S. Stadlbauer, “Managing software on a heterogenous HPC cluster,” in <i>ASHPC21 – Austrian-Slovenian HPC Meeting 2021</i>, Virtual, 2021, p. 5.","apa":"Schlögl, A., Elefante, S., Hornoiu, A., &#38; Stadlbauer, S. (2021). Managing software on a heterogenous HPC cluster. In <i>ASHPC21 – Austrian-Slovenian HPC Meeting 2021</i> (p. 5). Virtual: University of Ljubljana. <a href=\"https://doi.org/10.3359/2021hpc\">https://doi.org/10.3359/2021hpc</a>","ama":"Schlögl A, Elefante S, Hornoiu A, Stadlbauer S. Managing software on a heterogenous HPC cluster. In: <i>ASHPC21 – Austrian-Slovenian HPC Meeting 2021</i>. University of Ljubljana; 2021:5. doi:<a href=\"https://doi.org/10.3359/2021hpc\">10.3359/2021hpc</a>","mla":"Schlögl, Alois, et al. “Managing Software on a Heterogenous HPC Cluster.” <i>ASHPC21 – Austrian-Slovenian HPC Meeting 2021</i>, University of Ljubljana, 2021, p. 5, doi:<a href=\"https://doi.org/10.3359/2021hpc\">10.3359/2021hpc</a>.","ista":"Schlögl A, Elefante S, Hornoiu A, Stadlbauer S. 2021. Managing software on a heterogenous HPC cluster. ASHPC21 – Austrian-Slovenian HPC Meeting 2021. ASHPC - Austrian-Slovenian HPC Meeting, 5.","chicago":"Schlögl, Alois, Stefano Elefante, Andrei Hornoiu, and Stephan Stadlbauer. “Managing Software on a Heterogenous HPC Cluster.” In <i>ASHPC21 – Austrian-Slovenian HPC Meeting 2021</i>, 5. University of Ljubljana, 2021. <a href=\"https://doi.org/10.3359/2021hpc\">https://doi.org/10.3359/2021hpc</a>.","short":"A. Schlögl, S. Elefante, A. Hornoiu, S. Stadlbauer, in:, ASHPC21 – Austrian-Slovenian HPC Meeting 2021, University of Ljubljana, 2021, p. 5."},"author":[{"orcid":"0000-0002-5621-8100","full_name":"Schlögl, Alois","id":"45BF87EE-F248-11E8-B48F-1D18A9856A87","first_name":"Alois","last_name":"Schlögl"},{"full_name":"Elefante, Stefano","id":"490F40CE-F248-11E8-B48F-1D18A9856A87","last_name":"Elefante","first_name":"Stefano"},{"full_name":"Hornoiu, Andrei","id":"77129392-B450-11EA-8745-D4653DDC885E","first_name":"Andrei","last_name":"Hornoiu"},{"full_name":"Stadlbauer, Stephan","id":"4D0BC184-F248-11E8-B48F-1D18A9856A87","first_name":"Stephan","last_name":"Stadlbauer"}]},{"abstract":[{"lang":"eng","text":"Chromosomal inversion polymorphisms, segments of chromosomes that are flipped in orientation and occur in reversed order in some individuals, have long been recognized to play an important role in local adaptation. They can reduce recombination in heterozygous individuals and thus help to maintain sets of locally adapted alleles. In a wide range of organisms, populations adapted to different habitats differ in frequency of inversion arrangements. However, getting a full understanding of the importance of inversions for adaptation requires confirmation of their influence on traits under divergent selection. Here, we studied a marine snail, Littorina saxatilis, that has evolved ecotypes adapted to wave exposure or crab predation. These two types occur in close proximity on different parts of the shore. Gene flow between them exists in contact zones. However, they exhibit strong phenotypic divergence in several traits under habitat-specific selection, including size, shape and behaviour. We used crosses between these ecotypes to identify genomic regions that explain variation in these traits by using QTL analysis and variance partitioning across linkage groups. We could show that previously detected inversion regions contribute to adaptive divergence. Some inversions influenced multiple traits suggesting that they contain sets of locally adaptive alleles. Our study also identified regions without known inversions that are important for phenotypic divergence. Thus, we provide a more complete overview of the importance of inversions in relation to the remaining genome."}],"month":"04","publisher":"Dryad","department":[{"_id":"NiBa"}],"related_material":{"record":[{"status":"public","id":"9394","relation":"used_in_publication"}]},"article_processing_charge":"No","oa_version":"Published Version","main_file_link":[{"url":"https://doi.org/10.5061/dryad.zgmsbccb4","open_access":"1"}],"license":"https://creativecommons.org/publicdomain/zero/1.0/","_id":"12987","year":"2021","date_created":"2023-05-16T12:34:09Z","status":"public","tmp":{"image":"/images/cc_0.png","name":"Creative Commons Public Domain Dedication (CC0 1.0)","short":"CC0 (1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"title":"Data from: Genetic variation for adaptive traits is associated with polymorphic inversions in Littorina saxatilis","date_published":"2021-04-10T00:00:00Z","doi":"10.5061/DRYAD.ZGMSBCCB4","ddc":["570"],"has_accepted_license":"1","citation":{"short":"E. Koch, H.E. Morales, J. Larsson, A.M. Westram, R. Faria, A.R. Lemmon, E.M. Lemmon, K. Johannesson, R.K. Butlin, (2021).","mla":"Koch, Eva, et al. <i>Data from: Genetic Variation for Adaptive Traits Is Associated with Polymorphic Inversions in Littorina Saxatilis</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/DRYAD.ZGMSBCCB4\">10.5061/DRYAD.ZGMSBCCB4</a>.","ama":"Koch E, Morales HE, Larsson J, et al. Data from: Genetic variation for adaptive traits is associated with polymorphic inversions in Littorina saxatilis. 2021. doi:<a href=\"https://doi.org/10.5061/DRYAD.ZGMSBCCB4\">10.5061/DRYAD.ZGMSBCCB4</a>","ieee":"E. Koch <i>et al.</i>, “Data from: Genetic variation for adaptive traits is associated with polymorphic inversions in Littorina saxatilis.” Dryad, 2021.","apa":"Koch, E., Morales, H. E., Larsson, J., Westram, A. M., Faria, R., Lemmon, A. R., … Butlin, R. K. (2021). Data from: Genetic variation for adaptive traits is associated with polymorphic inversions in Littorina saxatilis. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.ZGMSBCCB4\">https://doi.org/10.5061/DRYAD.ZGMSBCCB4</a>","chicago":"Koch, Eva, Hernán E. Morales, Jenny Larsson, Anja M Westram, Rui Faria, Alan R. Lemmon, E. Moriarty Lemmon, Kerstin Johannesson, and Roger K. Butlin. “Data from: Genetic Variation for Adaptive Traits Is Associated with Polymorphic Inversions in Littorina Saxatilis.” Dryad, 2021. <a href=\"https://doi.org/10.5061/DRYAD.ZGMSBCCB4\">https://doi.org/10.5061/DRYAD.ZGMSBCCB4</a>.","ista":"Koch E, Morales HE, Larsson J, Westram AM, Faria R, Lemmon AR, Lemmon EM, Johannesson K, Butlin RK. 2021. Data from: Genetic variation for adaptive traits is associated with polymorphic inversions in Littorina saxatilis, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.ZGMSBCCB4\">10.5061/DRYAD.ZGMSBCCB4</a>."},"author":[{"full_name":"Koch, Eva","last_name":"Koch","first_name":"Eva"},{"full_name":"Morales, Hernán E.","first_name":"Hernán E.","last_name":"Morales"},{"full_name":"Larsson, Jenny","last_name":"Larsson","first_name":"Jenny"},{"orcid":"0000-0003-1050-4969","id":"3C147470-F248-11E8-B48F-1D18A9856A87","full_name":"Westram, Anja M","first_name":"Anja M","last_name":"Westram"},{"first_name":"Rui","last_name":"Faria","full_name":"Faria, Rui"},{"full_name":"Lemmon, Alan R.","last_name":"Lemmon","first_name":"Alan R."},{"first_name":"E. Moriarty","last_name":"Lemmon","full_name":"Lemmon, E. Moriarty"},{"last_name":"Johannesson","first_name":"Kerstin","full_name":"Johannesson, Kerstin"},{"full_name":"Butlin, Roger K.","last_name":"Butlin","first_name":"Roger K."}],"date_updated":"2023-08-08T13:34:07Z","type":"research_data_reference","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"10"},{"date_created":"2023-05-23T13:42:27Z","_id":"13057","year":"2021","month":"10","publisher":"Zenodo","department":[{"_id":"JoFi"}],"related_material":{"record":[{"status":"public","id":"9928","relation":"used_in_publication"}]},"abstract":[{"text":"This dataset comprises all data shown in the figures of the submitted article \"Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction\". Additional raw data are available from the corresponding author on reasonable request.","lang":"eng"}],"main_file_link":[{"url":"https://doi.org/10.5281/zenodo.5592104","open_access":"1"}],"article_processing_charge":"No","oa_version":"Published Version","type":"research_data_reference","date_updated":"2023-08-11T10:44:21Z","citation":{"short":"M. Peruzzo, F. Hassani, G. Szep, A. Trioni, E. Redchenko, M. Zemlicka, J.M. Fink, (2021).","chicago":"Peruzzo, Matilda, Farid Hassani, Grisha Szep, Andrea Trioni, Elena Redchenko, Martin Zemlicka, and Johannes M Fink. “Geometric Superinductance Qubits: Controlling Phase Delocalization across a Single Josephson Junction.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5592103\">https://doi.org/10.5281/ZENODO.5592103</a>.","ista":"Peruzzo M, Hassani F, Szep G, Trioni A, Redchenko E, Zemlicka M, Fink JM. 2021. Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5592103\">10.5281/ZENODO.5592103</a>.","mla":"Peruzzo, Matilda, et al. <i>Geometric Superinductance Qubits: Controlling Phase Delocalization across a Single Josephson Junction</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5592103\">10.5281/ZENODO.5592103</a>.","ieee":"M. Peruzzo <i>et al.</i>, “Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction.” Zenodo, 2021.","apa":"Peruzzo, M., Hassani, F., Szep, G., Trioni, A., Redchenko, E., Zemlicka, M., &#38; Fink, J. M. (2021). Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5592103\">https://doi.org/10.5281/ZENODO.5592103</a>","ama":"Peruzzo M, Hassani F, Szep G, et al. Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5592103\">10.5281/ZENODO.5592103</a>"},"author":[{"first_name":"Matilda","last_name":"Peruzzo","orcid":"0000-0002-3415-4628","full_name":"Peruzzo, Matilda","id":"3F920B30-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Farid","last_name":"Hassani","orcid":"0000-0001-6937-5773","full_name":"Hassani, Farid","id":"2AED110C-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Szep, Grisha","last_name":"Szep","first_name":"Grisha"},{"first_name":"Andrea","last_name":"Trioni","id":"42F71B44-F248-11E8-B48F-1D18A9856A87","full_name":"Trioni, Andrea"},{"last_name":"Redchenko","first_name":"Elena","id":"2C21D6E8-F248-11E8-B48F-1D18A9856A87","full_name":"Redchenko, Elena"},{"id":"2DCF8DE6-F248-11E8-B48F-1D18A9856A87","full_name":"Zemlicka, Martin","first_name":"Martin","last_name":"Zemlicka"},{"first_name":"Johannes M","last_name":"Fink","full_name":"Fink, Johannes M","id":"4B591CBA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8112-028X"}],"day":"22","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2021-10-22T00:00:00Z","doi":"10.5281/ZENODO.5592103","title":"Geometric superinductance qubits: Controlling phase delocalization across a single Josephson junction","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"status":"public","ddc":["530"]},{"abstract":[{"lang":"eng","text":"The zip file includes source data used in the main text of the manuscript \"Theory of branching morphogenesis by local interactions and global guidance\", as well as a representative Jupyter notebook to reproduce the main figures. A sample script for the simulations of branching and annihilating random walks is also included (Sample_script_for_simulations_of_BARWs.ipynb) to generate exemplary branched networks under external guidance. A detailed description of the simulation setup is provided in the supplementary information of the manuscipt."}],"department":[{"_id":"EdHa"}],"related_material":{"record":[{"id":"10402","status":"public","relation":"used_in_publication"}]},"publisher":"Zenodo","month":"08","oa_version":"Published Version","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.5257161"}],"year":"2021","_id":"13058","date_created":"2023-05-23T13:46:34Z","title":"Source data for the manuscript \"Theory of branching morphogenesis by local interactions and global guidance\"","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"status":"public","doi":"10.5281/ZENODO.5257160","date_published":"2021-08-25T00:00:00Z","ddc":["570"],"citation":{"chicago":"Ucar, Mehmet C. “Source Data for the Manuscript ‘Theory of Branching Morphogenesis by Local Interactions and Global Guidance.’” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5257160\">https://doi.org/10.5281/ZENODO.5257160</a>.","ista":"Ucar MC. 2021. Source data for the manuscript ‘Theory of branching morphogenesis by local interactions and global guidance’, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5257160\">10.5281/ZENODO.5257160</a>.","mla":"Ucar, Mehmet C. <i>Source Data for the Manuscript “Theory of Branching Morphogenesis by Local Interactions and Global Guidance.”</i> Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5257160\">10.5281/ZENODO.5257160</a>.","apa":"Ucar, M. C. (2021). Source data for the manuscript “Theory of branching morphogenesis by local interactions and global guidance.” Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5257160\">https://doi.org/10.5281/ZENODO.5257160</a>","ama":"Ucar MC. Source data for the manuscript “Theory of branching morphogenesis by local interactions and global guidance.” 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5257160\">10.5281/ZENODO.5257160</a>","ieee":"M. C. Ucar, “Source data for the manuscript ‘Theory of branching morphogenesis by local interactions and global guidance.’” Zenodo, 2021.","short":"M.C. Ucar, (2021)."},"date_updated":"2023-08-14T13:18:46Z","author":[{"last_name":"Ucar","first_name":"Mehmet C","full_name":"Ucar, Mehmet C","id":"50B2A802-6007-11E9-A42B-EB23E6697425","orcid":"0000-0003-0506-4217"}],"type":"research_data_reference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"day":"25"},{"_id":"13061","year":"2021","date_created":"2023-05-23T16:14:35Z","ec_funded":1,"article_processing_charge":"No","oa_version":"Published Version","main_file_link":[{"url":"https://doi.org/10.5061/dryad.7pvmcvdtj","open_access":"1"}],"abstract":[{"text":"Infections early in life can have enduring effects on an organism’s development and immunity. In this study, we show that this equally applies to developing “superorganisms” – incipient social insect colonies. When we exposed newly mated Lasius niger ant queens to a low pathogen dose, their colonies grew more slowly than controls before winter, but reached similar sizes afterwards. Independent of exposure, queen hibernation survival improved when the ratio of pupae to workers was small. Queens that reared fewer pupae before worker emergence exhibited lower pathogen levels, indicating that high brood rearing efforts interfere with the ability of the queen’s immune system to suppress pathogen proliferation. Early-life queen pathogen-exposure also improved the immunocompetence of her worker offspring, as demonstrated by challenging the workers to the same pathogen a year later. Transgenerational transfer of the queen’s pathogen experience to her workforce can hence durably reduce the disease susceptibility of the whole superorganism.","lang":"eng"}],"month":"10","publisher":"Dryad","department":[{"_id":"SyCr"}],"related_material":{"record":[{"relation":"used_in_publication","id":"10284","status":"public"}]},"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"29","type":"research_data_reference","author":[{"first_name":"Barbara E","last_name":"Casillas Perez","full_name":"Casillas Perez, Barbara E","id":"351ED2AA-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Pull","first_name":"Christopher","orcid":"0000-0003-1122-3982","id":"3C7F4840-F248-11E8-B48F-1D18A9856A87","full_name":"Pull, Christopher"},{"last_name":"Naiser","first_name":"Filip","full_name":"Naiser, Filip"},{"full_name":"Naderlinger, Elisabeth","last_name":"Naderlinger","first_name":"Elisabeth"},{"last_name":"Matas","first_name":"Jiri","full_name":"Matas, Jiri"},{"orcid":"0000-0002-2193-3868","full_name":"Cremer, Sylvia","id":"2F64EC8C-F248-11E8-B48F-1D18A9856A87","first_name":"Sylvia","last_name":"Cremer"}],"citation":{"mla":"Casillas Perez, Barbara E., et al. <i>Early Queen Infection Shapes Developmental Dynamics and Induces Long-Term Disease Protection in Incipient Ant Colonies</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">10.5061/DRYAD.7PVMCVDTJ</a>.","ama":"Casillas Perez BE, Pull C, Naiser F, Naderlinger E, Matas J, Cremer S. Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies. 2021. doi:<a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">10.5061/DRYAD.7PVMCVDTJ</a>","apa":"Casillas Perez, B. E., Pull, C., Naiser, F., Naderlinger, E., Matas, J., &#38; Cremer, S. (2021). Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">https://doi.org/10.5061/DRYAD.7PVMCVDTJ</a>","ieee":"B. E. Casillas Perez, C. Pull, F. Naiser, E. Naderlinger, J. Matas, and S. Cremer, “Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies.” Dryad, 2021.","chicago":"Casillas Perez, Barbara E, Christopher Pull, Filip Naiser, Elisabeth Naderlinger, Jiri Matas, and Sylvia Cremer. “Early Queen Infection Shapes Developmental Dynamics and Induces Long-Term Disease Protection in Incipient Ant Colonies.” Dryad, 2021. <a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">https://doi.org/10.5061/DRYAD.7PVMCVDTJ</a>.","ista":"Casillas Perez BE, Pull C, Naiser F, Naderlinger E, Matas J, Cremer S. 2021. Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">10.5061/DRYAD.7PVMCVDTJ</a>.","short":"B.E. Casillas Perez, C. Pull, F. Naiser, E. Naderlinger, J. Matas, S. Cremer, (2021)."},"date_updated":"2023-08-14T11:45:28Z","ddc":["570"],"title":"Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies","tmp":{"image":"/images/cc_0.png","name":"Creative Commons Public Domain Dedication (CC0 1.0)","short":"CC0 (1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"status":"public","project":[{"grant_number":"771402","_id":"2649B4DE-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"Epidemics in ant societies on a chip"}],"date_published":"2021-10-29T00:00:00Z","doi":"10.5061/DRYAD.7PVMCVDTJ"},{"ddc":["570"],"tmp":{"image":"/images/cc_0.png","name":"Creative Commons Public Domain Dedication (CC0 1.0)","short":"CC0 (1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"title":"Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model","status":"public","doi":"10.5061/DRYAD.8GTHT76P1","date_published":"2021-03-02T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"day":"02","type":"research_data_reference","author":[{"first_name":"Eniko","last_name":"Szep","id":"485BB5A4-F248-11E8-B48F-1D18A9856A87","full_name":"Szep, Eniko"},{"last_name":"Sachdeva","first_name":"Himani","id":"42377A0A-F248-11E8-B48F-1D18A9856A87","full_name":"Sachdeva, Himani"},{"first_name":"Nicholas H","last_name":"Barton","orcid":"0000-0002-8548-5240","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","full_name":"Barton, Nicholas H"}],"date_updated":"2023-09-05T15:44:05Z","citation":{"apa":"Szep, E., Sachdeva, H., &#38; Barton, N. H. (2021). Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">https://doi.org/10.5061/DRYAD.8GTHT76P1</a>","ama":"Szep E, Sachdeva H, Barton NH. Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model. 2021. doi:<a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">10.5061/DRYAD.8GTHT76P1</a>","ieee":"E. Szep, H. Sachdeva, and N. H. Barton, “Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model.” Dryad, 2021.","mla":"Szep, Eniko, et al. <i>Supplementary Code for: Polygenic Local Adaptation in Metapopulations: A Stochastic Eco-Evolutionary Model</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">10.5061/DRYAD.8GTHT76P1</a>.","ista":"Szep E, Sachdeva H, Barton NH. 2021. Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">10.5061/DRYAD.8GTHT76P1</a>.","chicago":"Szep, Eniko, Himani Sachdeva, and Nicholas H Barton. “Supplementary Code for: Polygenic Local Adaptation in Metapopulations: A Stochastic Eco-Evolutionary Model.” Dryad, 2021. <a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">https://doi.org/10.5061/DRYAD.8GTHT76P1</a>.","short":"E. Szep, H. Sachdeva, N.H. Barton, (2021)."},"oa_version":"Published Version","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5061/dryad.8gtht76p1"}],"abstract":[{"lang":"eng","text":"This paper analyzes the conditions for local adaptation in a metapopulation with infinitely many islands under a model of hard selection, where population size depends on local fitness. Each island belongs to one of two distinct ecological niches or habitats. Fitness is influenced by an additive trait which is under habitat-dependent directional selection. Our analysis is based on the diffusion approximation and  accounts for both genetic drift and demographic stochasticity. By neglecting linkage disequilibria, it yields the joint distribution of allele frequencies and population size on each island. We find that under hard selection, the conditions for local adaptation in a rare habitat are more restrictive for more polygenic traits: even moderate migration load per locus at very many loci is sufficient for population sizes to decline. This further reduces the efficacy of selection at individual loci due to increased drift and because smaller populations are more prone to swamping due to migration, causing a positive feedback between increasing maladaptation and declining population sizes. Our analysis also highlights the importance of demographic stochasticity, which  exacerbates the decline in numbers of maladapted populations, leading to population collapse in the rare habitat at significantly lower migration than predicted by deterministic arguments."}],"department":[{"_id":"NiBa"}],"related_material":{"record":[{"status":"public","id":"9252","relation":"used_in_publication"}]},"publisher":"Dryad","month":"03","year":"2021","_id":"13062","date_created":"2023-05-23T16:17:02Z"},{"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"04","date_updated":"2023-09-26T10:36:15Z","citation":{"short":"M.R. Robinson, (2021).","mla":"Robinson, Matthew Richard. <i>Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>.","ama":"Robinson MR. Probabilistic inference of the genetic architecture of functional enrichment of complex traits. 2021. doi:<a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>","ieee":"M. R. Robinson, “Probabilistic inference of the genetic architecture of functional enrichment of complex traits.” Dryad, 2021.","apa":"Robinson, M. R. (2021). Probabilistic inference of the genetic architecture of functional enrichment of complex traits. Dryad. <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">https://doi.org/10.5061/dryad.sqv9s4n51</a>","chicago":"Robinson, Matthew Richard. “Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits.” Dryad, 2021. <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">https://doi.org/10.5061/dryad.sqv9s4n51</a>.","ista":"Robinson MR. 2021. Probabilistic inference of the genetic architecture of functional enrichment of complex traits, Dryad, <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>."},"author":[{"first_name":"Matthew Richard","last_name":"Robinson","orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard"}],"type":"research_data_reference","ddc":["570"],"tmp":{"image":"/images/cc_0.png","name":"Creative Commons Public Domain Dedication (CC0 1.0)","short":"CC0 (1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"status":"public","title":"Probabilistic inference of the genetic architecture of functional enrichment of complex traits","date_published":"2021-11-04T00:00:00Z","doi":"10.5061/dryad.sqv9s4n51","_id":"13063","year":"2021","date_created":"2023-05-23T16:20:16Z","article_processing_charge":"No","oa_version":"Published Version","main_file_link":[{"url":"https://doi.org/10.5061/dryad.sqv9s4n51","open_access":"1"}],"abstract":[{"text":"We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only $\\leq$ 10\\% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having &gt;95% probability of contributing &gt;0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data.","lang":"eng"}],"month":"11","publisher":"Dryad","department":[{"_id":"MaRo"}],"related_material":{"link":[{"relation":"software","url":"https://github.com/medical-genomics-group/gmrm"}],"record":[{"id":"8429","status":"public","relation":"used_in_publication"}]}},{"day":"30","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Randriamanantsoa","first_name":"Samuel","full_name":"Randriamanantsoa, Samuel"},{"full_name":"Papargyriou, Aristeidis","last_name":"Papargyriou","first_name":"Aristeidis"},{"full_name":"Maurer, Carlo","first_name":"Carlo","last_name":"Maurer"},{"last_name":"Peschke","first_name":"Katja","full_name":"Peschke, Katja"},{"full_name":"Schuster, Maximilian","first_name":"Maximilian","last_name":"Schuster"},{"full_name":"Zecchin, Giulia","last_name":"Zecchin","first_name":"Giulia"},{"last_name":"Steiger","first_name":"Katja","full_name":"Steiger, Katja"},{"full_name":"Öllinger, Rupert","last_name":"Öllinger","first_name":"Rupert"},{"first_name":"Dieter","last_name":"Saur","full_name":"Saur, Dieter"},{"first_name":"Christina","last_name":"Scheel","full_name":"Scheel, Christina"},{"full_name":"Rad, Roland","last_name":"Rad","first_name":"Roland"},{"id":"3A9DB764-F248-11E8-B48F-1D18A9856A87","full_name":"Hannezo, Edouard B","orcid":"0000-0001-6005-1561","last_name":"Hannezo","first_name":"Edouard B"},{"last_name":"Reichert","first_name":"Maximilian","full_name":"Reichert, Maximilian"},{"last_name":"Bausch","first_name":"Andreas R.","full_name":"Bausch, Andreas R."}],"type":"research_data_reference","citation":{"short":"S. Randriamanantsoa, A. Papargyriou, C. Maurer, K. Peschke, M. Schuster, G. Zecchin, K. Steiger, R. Öllinger, D. Saur, C. Scheel, R. Rad, E.B. Hannezo, M. Reichert, A.R. Bausch, (2021).","mla":"Randriamanantsoa, Samuel, et al. <i>Spatiotemporal Dynamics of Self-Organized Branching in Pancreas-Derived Organoids</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5148117\">10.5281/ZENODO.5148117</a>.","ieee":"S. Randriamanantsoa <i>et al.</i>, “Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids.” Zenodo, 2021.","apa":"Randriamanantsoa, S., Papargyriou, A., Maurer, C., Peschke, K., Schuster, M., Zecchin, G., … Bausch, A. R. (2021). Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5148117\">https://doi.org/10.5281/ZENODO.5148117</a>","ama":"Randriamanantsoa S, Papargyriou A, Maurer C, et al. Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5148117\">10.5281/ZENODO.5148117</a>","chicago":"Randriamanantsoa, Samuel, Aristeidis Papargyriou, Carlo Maurer, Katja Peschke, Maximilian Schuster, Giulia Zecchin, Katja Steiger, et al. “Spatiotemporal Dynamics of Self-Organized Branching in Pancreas-Derived Organoids.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5148117\">https://doi.org/10.5281/ZENODO.5148117</a>.","ista":"Randriamanantsoa S, Papargyriou A, Maurer C, Peschke K, Schuster M, Zecchin G, Steiger K, Öllinger R, Saur D, Scheel C, Rad R, Hannezo EB, Reichert M, Bausch AR. 2021. Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5148117\">10.5281/ZENODO.5148117</a>."},"date_updated":"2023-08-04T09:25:23Z","ddc":["570"],"date_published":"2021-07-30T00:00:00Z","doi":"10.5281/ZENODO.5148117","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"status":"public","title":"Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids","date_created":"2023-05-23T16:39:24Z","_id":"13068","year":"2021","main_file_link":[{"url":"https://doi.org/10.5281/zenodo.6577226","open_access":"1"}],"article_processing_charge":"No","oa_version":"Published Version","month":"07","publisher":"Zenodo","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"12217"}]},"department":[{"_id":"EdHa"}],"abstract":[{"text":"Source data and source code for the graphs in \"Spatiotemporal dynamics of self-organized branching pancreatic cancer-derived organoids\".","lang":"eng"}]},{"ddc":["570"],"doi":"10.5281/ZENODO.5519410","date_published":"2021-12-25T00:00:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"status":"public","title":"Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans","day":"25","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"date_updated":"2023-08-14T11:53:26Z","type":"research_data_reference","author":[{"last_name":"Chauve","first_name":"Laetitia","full_name":"Chauve, Laetitia"},{"first_name":"Francesca","last_name":"Hodge","full_name":"Hodge, Francesca"},{"full_name":"Murdoch, Sharlene","last_name":"Murdoch","first_name":"Sharlene"},{"full_name":"Masoudzadeh, Fatemah","last_name":"Masoudzadeh","first_name":"Fatemah"},{"full_name":"Mann, Harry-Jack","last_name":"Mann","first_name":"Harry-Jack"},{"full_name":"Lopez-Clavijo, Andrea","first_name":"Andrea","last_name":"Lopez-Clavijo"},{"first_name":"Hanneke","last_name":"Okkenhaug","full_name":"Okkenhaug, Hanneke"},{"first_name":"Greg","last_name":"West","full_name":"West, Greg"},{"last_name":"Sousa","first_name":"Bebiana C.","full_name":"Sousa, Bebiana C."},{"full_name":"Segonds-Pichon, Anne","last_name":"Segonds-Pichon","first_name":"Anne"},{"last_name":"Li","first_name":"Cheryl","full_name":"Li, Cheryl"},{"first_name":"Steven","last_name":"Wingett","full_name":"Wingett, Steven"},{"first_name":"Hermine","last_name":"Kienberger","full_name":"Kienberger, Hermine"},{"last_name":"Kleigrewe","first_name":"Karin","full_name":"Kleigrewe, Karin"},{"last_name":"de Bono","first_name":"Mario","full_name":"de Bono, Mario","id":"4E3FF80E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8347-0443"},{"full_name":"Wakelam, Michael","first_name":"Michael","last_name":"Wakelam"},{"full_name":"Casanueva, Olivia","first_name":"Olivia","last_name":"Casanueva"}],"citation":{"mla":"Chauve, Laetitia, et al. <i>Neuronal HSF-1 Coordinates the Propagation of Fat Desaturation across Tissues to Enable Adaptation to High Temperatures in C. Elegans</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5519410\">10.5281/ZENODO.5519410</a>.","ieee":"L. Chauve <i>et al.</i>, “Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans.” Zenodo, 2021.","ama":"Chauve L, Hodge F, Murdoch S, et al. Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5519410\">10.5281/ZENODO.5519410</a>","apa":"Chauve, L., Hodge, F., Murdoch, S., Masoudzadeh, F., Mann, H.-J., Lopez-Clavijo, A., … Casanueva, O. (2021). Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5519410\">https://doi.org/10.5281/ZENODO.5519410</a>","chicago":"Chauve, Laetitia, Francesca Hodge, Sharlene Murdoch, Fatemah Masoudzadeh, Harry-Jack Mann, Andrea Lopez-Clavijo, Hanneke Okkenhaug, et al. “Neuronal HSF-1 Coordinates the Propagation of Fat Desaturation across Tissues to Enable Adaptation to High Temperatures in C. Elegans.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5519410\">https://doi.org/10.5281/ZENODO.5519410</a>.","ista":"Chauve L, Hodge F, Murdoch S, Masoudzadeh F, Mann H-J, Lopez-Clavijo A, Okkenhaug H, West G, Sousa BC, Segonds-Pichon A, Li C, Wingett S, Kienberger H, Kleigrewe K, de Bono M, Wakelam M, Casanueva O. 2021. Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5519410\">10.5281/ZENODO.5519410</a>.","short":"L. Chauve, F. Hodge, S. Murdoch, F. Masoudzadeh, H.-J. Mann, A. Lopez-Clavijo, H. Okkenhaug, G. West, B.C. Sousa, A. Segonds-Pichon, C. Li, S. Wingett, H. Kienberger, K. Kleigrewe, M. de Bono, M. Wakelam, O. Casanueva, (2021)."},"main_file_link":[{"url":"https://doi.org/10.5281/zenodo.5547464","open_access":"1"}],"oa_version":"Published Version","article_processing_charge":"No","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"10322"}]},"department":[{"_id":"MaDe"}],"publisher":"Zenodo","month":"12","abstract":[{"lang":"eng","text":"To survive elevated temperatures, ectotherms adjust the fluidity of membranes by fine-tuning lipid desaturation levels in a process previously described to be cell-autonomous. We have discovered that, in Caenorhabditis elegans, neuronal Heat shock Factor 1 (HSF-1), the conserved master regulator of the heat shock response (HSR)- causes extensive fat remodelling in peripheral tissues. These changes include a decrease in fat desaturase and acid lipase expression in the intestine, and a global shift in the saturation levels of plasma membrane’s phospholipids. The observed remodelling of plasma membrane is in line with ectothermic adaptive responses and gives worms a cumulative advantage to warm temperatures. We have determined that at least six TAX-2/TAX-4 cGMP gated channel expressing sensory neurons and TGF-β/BMP are required for signalling across tissues to modulate fat desaturation. We also find neuronal hsf-1  is not only sufficient but also partially necessary to control the fat remodelling response and for survival at warm temperatures. This is the first study to show that a thermostat-based mechanism can cell non-autonomously coordinate membrane saturation and composition across tissues in a multicellular animal."}],"date_created":"2023-05-23T16:40:56Z","year":"2021","_id":"13069"},{"abstract":[{"text":"CpGs and corresponding mean weights for DNAm-based prediction of cognitive abilities (6 traits)","lang":"eng"}],"month":"12","department":[{"_id":"MaRo"}],"publisher":"Zenodo","related_material":{"record":[{"status":"public","id":"10702","relation":"used_in_publication"}]},"article_processing_charge":"No","oa_version":"Published Version","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.5794029"}],"_id":"13072","year":"2021","date_created":"2023-05-23T16:46:20Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"status":"public","title":"Blood-based epigenome-wide analyses of cognitive abilities","date_published":"2021-12-20T00:00:00Z","doi":"10.5281/ZENODO.5794028","ddc":["570"],"citation":{"mla":"McCartney, Daniel L., et al. <i>Blood-Based Epigenome-Wide Analyses of Cognitive Abilities</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>.","ama":"McCartney DL, Hillary RF, Conole EL, et al. Blood-based epigenome-wide analyses of cognitive abilities. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>","ieee":"D. L. McCartney <i>et al.</i>, “Blood-based epigenome-wide analyses of cognitive abilities.” Zenodo, 2021.","apa":"McCartney, D. L., Hillary, R. F., Conole, E. L., Trejo Banos, D., Gadd, D. A., Walker, R. M., … Marioni, R. E. (2021). Blood-based epigenome-wide analyses of cognitive abilities. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5794028\">https://doi.org/10.5281/ZENODO.5794028</a>","chicago":"McCartney, Daniel L, Robert F Hillary, Eleanor LS Conole, Daniel Trejo Banos, Danni A Gadd, Rosie M Walker, Cliff Nangle, et al. “Blood-Based Epigenome-Wide Analyses of Cognitive Abilities.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5794028\">https://doi.org/10.5281/ZENODO.5794028</a>.","ista":"McCartney DL, Hillary RF, Conole EL, Trejo Banos D, Gadd DA, Walker RM, Nangle C, Flaig R, Campbell A, Murray AD, Munoz Maniega S, del C Valdes-Hernandez M, Harris MA, Bastin ME, Wardlaw JM, Harris SE, Porteous DJ, Tucker-Drob EM, McIntosh AM, Evans KL, Deary IJ, Cox SR, Robinson MR, Marioni RE. 2021. Blood-based epigenome-wide analyses of cognitive abilities, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>.","short":"D.L. McCartney, R.F. Hillary, E.L. Conole, D. Trejo Banos, D.A. Gadd, R.M. Walker, C. Nangle, R. Flaig, A. Campbell, A.D. Murray, S. Munoz Maniega, M. del C Valdes-Hernandez, M.A. Harris, M.E. Bastin, J.M. Wardlaw, S.E. Harris, D.J. Porteous, E.M. Tucker-Drob, A.M. McIntosh, K.L. Evans, I.J. Deary, S.R. Cox, M.R. Robinson, R.E. Marioni, (2021)."},"type":"research_data_reference","date_updated":"2023-08-02T14:05:12Z","author":[{"last_name":"McCartney","first_name":"Daniel L","full_name":"McCartney, Daniel L"},{"first_name":"Robert F","last_name":"Hillary","full_name":"Hillary, Robert F"},{"last_name":"Conole","first_name":"Eleanor LS","full_name":"Conole, Eleanor LS"},{"first_name":"Daniel","last_name":"Trejo Banos","full_name":"Trejo Banos, Daniel"},{"last_name":"Gadd","first_name":"Danni A","full_name":"Gadd, Danni A"},{"last_name":"Walker","first_name":"Rosie M","full_name":"Walker, Rosie M"},{"full_name":"Nangle, Cliff","first_name":"Cliff","last_name":"Nangle"},{"full_name":"Flaig, Robin","last_name":"Flaig","first_name":"Robin"},{"first_name":"Archie","last_name":"Campbell","full_name":"Campbell, Archie"},{"full_name":"Murray, Alison D","first_name":"Alison D","last_name":"Murray"},{"last_name":"Munoz Maniega","first_name":"Susana","full_name":"Munoz Maniega, Susana"},{"full_name":"del C Valdes-Hernandez, Maria","last_name":"del C Valdes-Hernandez","first_name":"Maria"},{"full_name":"Harris, Mathew A","last_name":"Harris","first_name":"Mathew A"},{"full_name":"Bastin, Mark E","first_name":"Mark E","last_name":"Bastin"},{"first_name":"Joanna M","last_name":"Wardlaw","full_name":"Wardlaw, Joanna M"},{"full_name":"Harris, Sarah E","last_name":"Harris","first_name":"Sarah E"},{"full_name":"Porteous, David J","last_name":"Porteous","first_name":"David J"},{"full_name":"Tucker-Drob, Elliot M","first_name":"Elliot M","last_name":"Tucker-Drob"},{"first_name":"Andrew M","last_name":"McIntosh","full_name":"McIntosh, Andrew M"},{"full_name":"Evans, Kathryn L","first_name":"Kathryn L","last_name":"Evans"},{"first_name":"Ian J","last_name":"Deary","full_name":"Deary, Ian J"},{"first_name":"Simon R","last_name":"Cox","full_name":"Cox, Simon R"},{"last_name":"Robinson","first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813"},{"first_name":"Riccardo E","last_name":"Marioni","full_name":"Marioni, Riccardo E"}],"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"20"}]