@article{11615, abstract = {The recently published Kepler mission Data Release 25 (DR25) reported on ∼197 000 targets observed during the mission. Despite this, no wide search for red giants showing solar-like oscillations have been made across all stars observed in Kepler’s long-cadence mode. In this work, we perform this task using custom apertures on the Kepler pixel files and detect oscillations in 21 914 stars, representing the largest sample of solar-like oscillating stars to date. We measure their frequency at maximum power, νmax, down to νmax≃4μHz and obtain log (g) estimates with a typical uncertainty below 0.05 dex, which is superior to typical measurements from spectroscopy. Additionally, the νmax distribution of our detections show good agreement with results from a simulated model of the Milky Way, with a ratio of observed to predicted stars of 0.992 for stars with 10<νmax<270μHz. Among our red giant detections, we find 909 to be dwarf/subgiant stars whose flux signal is polluted by a neighbouring giant as a result of using larger photometric apertures than those used by the NASA Kepler science processing pipeline. We further find that only 293 of the polluting giants are known Kepler targets. The remainder comprises over 600 newly identified oscillating red giants, with many expected to belong to the Galactic halo, serendipitously falling within the Kepler pixel files of targeted stars.}, author = {Hon, Marc and Stello, Dennis and García, Rafael A and Mathur, Savita and Sharma, Sanjib and Colman, Isabel L and Bugnet, Lisa Annabelle}, issn = {1365-2966}, journal = {Monthly Notices of the Royal Astronomical Society}, keywords = {Space and Planetary Science, Astronomy and Astrophysics, asteroseismology, methods: data analysis, techniques: image processing, stars: oscillations, stars: statistics}, number = {4}, pages = {5616--5630}, publisher = {Oxford University Press}, title = {{A search for red giant solar-like oscillations in all Kepler data}}, doi = {10.1093/mnras/stz622}, volume = {485}, year = {2019}, } @article{11614, abstract = {The NASA Transiting Exoplanet Survey Satellite (TESS) is about to provide full-frame images of almost the entire sky. The amount of stellar data to be analysed represents hundreds of millions stars, which is several orders of magnitude more than the number of stars observed by the Convection, Rotation and planetary Transits satellite (CoRoT), and NASA Kepler and K2 missions. We aim at automatically classifying the newly observed stars with near real-time algorithms to better guide the subsequent detailed studies. In this paper, we present a classification algorithm built to recognise solar-like pulsators among classical pulsators. This algorithm relies on the global amount of power contained in the power spectral density (PSD), also known as the flicker in spectral power density (FliPer). Because each type of pulsating star has a characteristic background or pulsation pattern, the shape of the PSD at different frequencies can be used to characterise the type of pulsating star. The FliPer classifier (FliPerClass) uses different FliPer parameters along with the effective temperature as input parameters to feed a ML algorithm in order to automatically classify the pulsating stars observed by TESS. Using noisy TESS-simulated data from the TESS Asteroseismic Science Consortium (TASC), we classify pulsators with a 98% accuracy. Among them, solar-like pulsating stars are recognised with a 99% accuracy, which is of great interest for a further seismic analysis of these stars, which are like our Sun. Similar results are obtained when we trained our classifier and applied it to 27-day subsets of real Kepler data. FliPerClass is part of the large TASC classification pipeline developed by the TESS Data for Asteroseismology (T’DA) classification working group.}, author = {Bugnet, Lisa Annabelle and García, R. A. and Mathur, S. and Davies, G. R. and Hall, O. J. and Lund, M. N. and Rendle, B. M.}, issn = {1432-0746}, journal = {Astronomy & Astrophysics}, keywords = {Space and Planetary Science, Astronomy and Astrophysics}, publisher = {EDP Science}, title = {{FliPerClass: In search of solar-like pulsators among TESS targets}}, doi = {10.1051/0004-6361/201834780}, volume = {624}, year = {2019}, } @article{11623, abstract = {Brightness variations due to dark spots on the stellar surface encode information about stellar surface rotation and magnetic activity. In this work, we analyze the Kepler long-cadence data of 26,521 main-sequence stars of spectral types M and K in order to measure their surface rotation and photometric activity level. Rotation-period estimates are obtained by the combination of a wavelet analysis and autocorrelation function of the light curves. Reliable rotation estimates are determined by comparing the results from the different rotation diagnostics and four data sets. We also measure the photometric activity proxy Sph using the amplitude of the flux variations on an appropriate timescale. We report rotation periods and photometric activity proxies for about 60% of the sample, including 4431 targets for which McQuillan et al. did not report a rotation period. For the common targets with rotation estimates in this study and in McQuillan et al., our rotation periods agree within 99%. In this work, we also identify potential polluters, such as misclassified red giants and classical pulsator candidates. Within the parameter range we study, there is a mild tendency for hotter stars to have shorter rotation periods. The photometric activity proxy spans a wider range of values with increasing effective temperature. The rotation period and photometric activity proxy are also related, with Sph being larger for fast rotators. Similar to McQuillan et al., we find a bimodal distribution of rotation periods.}, author = {Santos, A. R. G. and García, R. A. and Mathur, S. and Bugnet, Lisa Annabelle and van Saders, J. L. and Metcalfe, T. S. and Simonian, G. V. A. and Pinsonneault, M. H.}, issn = {0067-0049}, journal = {The Astrophysical Journal Supplement Series}, keywords = {Space and Planetary Science, Astronomy and Astrophysics, methods: data analysis, stars: activity, stars: low-mass, stars: rotation, starspots, techniques: photometric}, number = {1}, publisher = {IOP Publishing}, title = {{Surface rotation and photometric activity for Kepler targets. I. M and K main-sequence stars}}, doi = {10.3847/1538-4365/ab3b56}, volume = {244}, year = {2019}, } @unpublished{11627, abstract = {For a solar-like star, the surface rotation evolves with time, allowing in principle to estimate the age of a star from its surface rotation period. Here we are interested in measuring surface rotation periods of solar-like stars observed by the NASA mission Kepler. Different methods have been developed to track rotation signals in Kepler photometric light curves: time-frequency analysis based on wavelet techniques, autocorrelation and composite spectrum. We use the learning abilities of random forest classifiers to take decisions during two crucial steps of the analysis. First, given some input parameters, we discriminate the considered Kepler targets between rotating MS stars, non-rotating MS stars, red giants, binaries and pulsators. We then use a second classifier only on the MS rotating targets to decide the best data analysis treatment.}, author = {Breton, S. N. and Bugnet, Lisa Annabelle and Santos, A. R. G. and Saux, A. Le and Mathur, S. and Palle, P. L. and Garcia, R. A.}, booktitle = {arXiv}, keywords = {asteroseismology, rotation, solar-like stars, kepler, machine learning, random forest}, title = {{Determining surface rotation periods of solar-like stars observed by the Kepler mission using machine learning techniques}}, doi = {10.48550/arXiv.1906.09609}, year = {2019}, } @unpublished{11630, abstract = {The second mission of NASA’s Kepler satellite, K2, has collected hundreds of thousands of lightcurves for stars close to the ecliptic plane. This new sample could increase the number of known pulsating stars and then improve our understanding of those stars. For the moment only a few stars have been properly classified and published. In this work, we present a method to automaticly classify K2 pulsating stars using a Machine Learning technique called Random Forest. The objective is to sort out the stars in four classes: red giant (RG), main-sequence Solar-like stars (SL), classical pulsators (PULS) and Other. To do this we use the effective temperatures and the luminosities of the stars as well as the FliPer features, that measures the amount of power contained in the power spectral density. The classifier now retrieves the right classification for more than 80% of the stars.}, author = {Saux, A. Le and Bugnet, Lisa Annabelle and Mathur, S. and Breton, S. N. and Garcia, R. A.}, booktitle = {arXiv}, keywords = {asteroseismology - methods, data analysis - thecniques, machine learning - stars, oscillations}, title = {{Automatic classification of K2 pulsating stars using machine learning techniques}}, doi = {10.48550/arXiv.1906.09611}, year = {2019}, } @inproceedings{11826, abstract = {The diameter, radius and eccentricities are natural graph parameters. While these problems have been studied extensively, there are no known dynamic algorithms for them beyond the ones that follow from trivial recomputation after each update or from solving dynamic All-Pairs Shortest Paths (APSP), which is very computationally intensive. This is the situation for dynamic approximation algorithms as well, and even if only edge insertions or edge deletions need to be supported. This paper provides a comprehensive study of the dynamic approximation of Diameter, Radius and Eccentricities, providing both conditional lower bounds, and new algorithms whose bounds are optimal under popular hypotheses in fine-grained complexity. Some of the highlights include: - Under popular hardness hypotheses, there can be no significantly better fully dynamic approximation algorithms than recomputing the answer after each update, or maintaining full APSP. - Nearly optimal partially dynamic (incremental/decremental) algorithms can be achieved via efficient reductions to (incremental/decremental) maintenance of Single-Source Shortest Paths. For instance, a nearly (3/2+epsilon)-approximation to Diameter in directed or undirected n-vertex, m-edge graphs can be maintained decrementally in total time m^{1+o(1)}sqrt{n}/epsilon^2. This nearly matches the static 3/2-approximation algorithm for the problem that is known to be conditionally optimal.}, author = {Ancona, Bertie and Henzinger, Monika H and Roditty, Liam and Williams, Virginia Vassilevska and Wein, Nicole}, booktitle = {46th International Colloquium on Automata, Languages, and Programming}, isbn = {978-3-95977-109-2}, issn = {1868-8969}, location = {Patras, Greece}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{Algorithms and hardness for diameter in dynamic graphs}}, doi = {10.4230/LIPICS.ICALP.2019.13}, volume = {132}, year = {2019}, } @inproceedings{11850, abstract = {Modern networked systems are increasingly reconfigurable, enabling demand-aware infrastructures whose resources can be adjusted according to the workload they currently serve. Such dynamic adjustments can be exploited to improve network utilization and hence performance, by moving frequently interacting communication partners closer, e.g., collocating them in the same server or datacenter. However, dynamically changing the embedding of workloads is algorithmically challenging: communication patterns are often not known ahead of time, but must be learned. During the learning process, overheads related to unnecessary moves (i.e., re-embeddings) should be minimized. This paper studies a fundamental model which captures the tradeoff between the benefits and costs of dynamically collocating communication partners on l servers, in an online manner. Our main contribution is a distributed online algorithm which is asymptotically almost optimal, i.e., almost matches the lower bound (also derived in this paper) on the competitive ratio of any (distributed or centralized) online algorithm.}, author = {Henzinger, Monika H and Neumann, Stefan and Schmid, Stefan}, booktitle = {SIGMETRICS'19: International Conference on Measurement and Modeling of Computer Systems}, isbn = {978-1-4503-6678-6}, location = {Phoenix, AZ, United States}, pages = {43–44}, publisher = {Association for Computing Machinery}, title = {{Efficient distributed workload (re-)embedding}}, doi = {10.1145/3309697.3331503}, year = {2019}, } @inbook{11847, abstract = {This paper serves as a user guide to the Vienna graph clustering framework. We review our general memetic algorithm, VieClus, to tackle the graph clustering problem. A key component of our contribution are natural recombine operators that employ ensemble clusterings as well as multi-level techniques. Lastly, we combine these techniques with a scalable communication protocol, producing a system that is able to compute high-quality solutions in a short amount of time. After giving a description of the algorithms employed, we establish the connection of the graph clustering problem to protein–protein interaction networks and moreover give a description on how the software can be used, what file formats are expected, and how this can be used to find functional groups in protein–protein interaction networks.}, author = {Biedermann, Sonja and Henzinger, Monika H and Schulz, Christian and Schuster, Bernhard}, booktitle = {Protein-Protein Interaction Networks}, editor = {Canzar, Stefan and Rojas Ringeling, Francisca}, isbn = {9781493998722}, issn = {1940-6029}, pages = {215–231}, publisher = {Springer Nature}, title = {{Vienna Graph Clustering}}, doi = {10.1007/978-1-4939-9873-9_16}, volume = {2074}, year = {2019}, } @inproceedings{11853, abstract = {We present a deterministic dynamic algorithm for maintaining a (1+ε)f-approximate minimum cost set cover with O(f log(Cn)/ε^2) amortized update time, when the input set system is undergoing element insertions and deletions. Here, n denotes the number of elements, each element appears in at most f sets, and the cost of each set lies in the range [1/C, 1]. Our result, together with that of Gupta~et~al.~[STOC'17], implies that there is a deterministic algorithm for this problem with O(f log(Cn)) amortized update time and O(min(log n, f)) -approximation ratio, which nearly matches the polynomial-time hardness of approximation for minimum set cover in the static setting. Our update time is only O(log (Cn)) away from a trivial lower bound. Prior to our work, the previous best approximation ratio guaranteed by deterministic algorithms was O(f^2), which was due to Bhattacharya~et~al.~[ICALP`15]. In contrast, the only result that guaranteed O(f) -approximation was obtained very recently by Abboud~et~al.~[STOC`19], who designed a dynamic algorithm with (1+ε)f-approximation ratio and O(f^2 log n/ε) amortized update time. Besides the extra O(f) factor in the update time compared to our and Gupta~et~al.'s results, the Abboud~et~al.~algorithm is randomized, and works only when the adversary is oblivious and the sets are unweighted (each set has the same cost). We achieve our result via the primal-dual approach, by maintaining a fractional packing solution as a dual certificate. This approach was pursued previously by Bhattacharya~et~al.~and Gupta~et~al., but not in the recent paper by Abboud~et~al. Unlike previous primal-dual algorithms that try to satisfy some local constraints for individual sets at all time, our algorithm basically waits until the dual solution changes significantly globally, and fixes the solution only where the fix is needed.}, author = {Bhattacharya, Sayan and Henzinger, Monika H and Nanongkai, Danupon}, booktitle = {60th Annual Symposium on Foundations of Computer Science}, isbn = {978-1-7281-4953-0}, issn = {2575-8454}, location = {Baltimore, MD, United States}, pages = {406--423}, publisher = {Institute of Electrical and Electronics Engineers}, title = {{A new deterministic algorithm for dynamic set cover}}, doi = {10.1109/focs.2019.00033}, year = {2019}, } @inproceedings{11851, abstract = {The minimum cut problem for an undirected edge-weighted graph asks us to divide its set of nodes into two blocks while minimizing the weighted sum of the cut edges. In this paper, we engineer the fastest known exact algorithm for the problem. State-of-the-art algorithms like the algorithm of Padberg and Rinaldi or the algorithm of Nagamochi, Ono and Ibaraki identify edges that can be contracted to reduce the graph size such that at least one minimum cut is maintained in the contracted graph. Our algorithm achieves improvements in running time over these algorithms by a multitude of techniques. First, we use a recently developed fast and parallel inexact minimum cut algorithm to obtain a better bound for the problem. Afterwards, we use reductions that depend on this bound to reduce the size of the graph much faster than previously possible. We use improved data structures to further lower the running time of our algorithm. Additionally, we parallelize the contraction routines of Nagamochi et al. . Overall, we arrive at a system that significantly outperforms the fastest state-of-the-art solvers for the exact minimum cut problem.}, author = {Henzinger, Monika H and Noe, Alexander and Schulz, Christian}, booktitle = {33rd International Parallel and Distributed Processing Symposium}, isbn = {978-1-7281-1247-3}, issn = {1530-2075}, location = {Rio de Janeiro, Brazil}, publisher = {Institute of Electrical and Electronics Engineers}, title = {{Shared-memory exact minimum cuts}}, doi = {10.1109/ipdps.2019.00013}, year = {2019}, }