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Showing papers by "Max Planck Society published in 2017"


Journal ArticleDOI
B. P. Abbott1, Richard J. Abbott1, T. D. Abbott2, Fausto Acernese3  +1131 moreInstitutions (123)
TL;DR: The association of GRB 170817A, detected by Fermi-GBM 1.7 s after the coalescence, corroborates the hypothesis of a neutron star merger and provides the first direct evidence of a link between these mergers and short γ-ray bursts.
Abstract: On August 17, 2017 at 12∶41:04 UTC the Advanced LIGO and Advanced Virgo gravitational-wave detectors made their first observation of a binary neutron star inspiral. The signal, GW170817, was detected with a combined signal-to-noise ratio of 32.4 and a false-alarm-rate estimate of less than one per 8.0×10^{4} years. We infer the component masses of the binary to be between 0.86 and 2.26 M_{⊙}, in agreement with masses of known neutron stars. Restricting the component spins to the range inferred in binary neutron stars, we find the component masses to be in the range 1.17-1.60 M_{⊙}, with the total mass of the system 2.74_{-0.01}^{+0.04}M_{⊙}. The source was localized within a sky region of 28 deg^{2} (90% probability) and had a luminosity distance of 40_{-14}^{+8} Mpc, the closest and most precisely localized gravitational-wave signal yet. The association with the γ-ray burst GRB 170817A, detected by Fermi-GBM 1.7 s after the coalescence, corroborates the hypothesis of a neutron star merger and provides the first direct evidence of a link between these mergers and short γ-ray bursts. Subsequent identification of transient counterparts across the electromagnetic spectrum in the same location further supports the interpretation of this event as a neutron star merger. This unprecedented joint gravitational and electromagnetic observation provides insight into astrophysics, dense matter, gravitation, and cosmology.

7,327 citations


Journal ArticleDOI
TL;DR: The all-atom additive CHARMM36 protein force field is refinement is presented, with improved accuracy in generating polypeptide backbone conformational ensembles for intrinsically disordered peptides and proteins.
Abstract: An all-atom protein force field, CHARMM36m, offers improved accuracy for simulating intrinsically disordered peptides and proteins. The all-atom additive CHARMM36 protein force field is widely used in molecular modeling and simulations. We present its refinement, CHARMM36m ( http://mackerell.umaryland.edu/charmm_ff.shtml ), with improved accuracy in generating polypeptide backbone conformational ensembles for intrinsically disordered peptides and proteins.

3,299 citations


Journal ArticleDOI
TL;DR: This work has shown that liquid–liquid phase separation driven by multivalent macromolecular interactions is an important organizing principle for biomolecular condensates and has proposed a physical framework for this organizing principle.
Abstract: In addition to membrane-bound organelles, eukaryotic cells feature various membraneless compartments, including the centrosome, the nucleolus and various granules. Many of these compartments form through liquid–liquid phase separation, and the principles, mechanisms and regulation of their assembly as well as their cellular functions are now beginning to emerge. Biomolecular condensates are micron-scale compartments in eukaryotic cells that lack surrounding membranes but function to concentrate proteins and nucleic acids. These condensates are involved in diverse processes, including RNA metabolism, ribosome biogenesis, the DNA damage response and signal transduction. Recent studies have shown that liquid–liquid phase separation driven by multivalent macromolecular interactions is an important organizing principle for biomolecular condensates. With this physical framework, it is now possible to explain how the assembly, composition, physical properties and biochemical and cellular functions of these important structures are regulated.

3,294 citations


Journal ArticleDOI
TL;DR: A binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors.
Abstract: On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of $\sim 1.7\,{\rm{s}}$ with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of ${40}_{-8}^{+8}$ Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 $\,{M}_{\odot }$. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at $\sim 40\,{\rm{Mpc}}$) less than 11 hours after the merger by the One-Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient's position $\sim 9$ and $\sim 16$ days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC 4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta.

2,746 citations


Journal ArticleDOI
B. P. Abbott1, Richard J. Abbott1, T. D. Abbott2, Fausto Acernese3  +1195 moreInstitutions (139)
TL;DR: In this paper, the authors used the observed time delay of $(+1.74\pm 0.05)\,{\rm{s}}$ between GRB 170817A and GW170817 to constrain the difference between the speed of gravity and speed of light to be between $-3
Abstract: On 2017 August 17, the gravitational-wave event GW170817 was observed by the Advanced LIGO and Virgo detectors, and the gamma-ray burst (GRB) GRB 170817A was observed independently by the Fermi Gamma-ray Burst Monitor, and the Anti-Coincidence Shield for the Spectrometer for the International Gamma-Ray Astrophysics Laboratory. The probability of the near-simultaneous temporal and spatial observation of GRB 170817A and GW170817 occurring by chance is $5.0\times {10}^{-8}$. We therefore confirm binary neutron star mergers as a progenitor of short GRBs. The association of GW170817 and GRB 170817A provides new insight into fundamental physics and the origin of short GRBs. We use the observed time delay of $(+1.74\pm 0.05)\,{\rm{s}}$ between GRB 170817A and GW170817 to: (i) constrain the difference between the speed of gravity and the speed of light to be between $-3\times {10}^{-15}$ and $+7\times {10}^{-16}$ times the speed of light, (ii) place new bounds on the violation of Lorentz invariance, (iii) present a new test of the equivalence principle by constraining the Shapiro delay between gravitational and electromagnetic radiation. We also use the time delay to constrain the size and bulk Lorentz factor of the region emitting the gamma-rays. GRB 170817A is the closest short GRB with a known distance, but is between 2 and 6 orders of magnitude less energetic than other bursts with measured redshift. A new generation of gamma-ray detectors, and subthreshold searches in existing detectors, will be essential to detect similar short bursts at greater distances. Finally, we predict a joint detection rate for the Fermi Gamma-ray Burst Monitor and the Advanced LIGO and Virgo detectors of 0.1–1.4 per year during the 2018–2019 observing run and 0.3–1.7 per year at design sensitivity.

2,633 citations


Journal ArticleDOI
B. P. Abbott1, Richard J. Abbott1, T. D. Abbott2, Fausto Acernese3  +1062 moreInstitutions (115)
TL;DR: The magnitude of modifications to the gravitational-wave dispersion relation is constrain, the graviton mass is bound to m_{g}≤7.7×10^{-23} eV/c^{2} and null tests of general relativity are performed, finding that GW170104 is consistent with general relativity.
Abstract: We describe the observation of GW170104, a gravitational-wave signal produced by the coalescence of a pair of stellar-mass black holes. The signal was measured on January 4, 2017 at 10∶11:58.6 UTC by the twin advanced detectors of the Laser Interferometer Gravitational-Wave Observatory during their second observing run, with a network signal-to-noise ratio of 13 and a false alarm rate less than 1 in 70 000 years. The inferred component black hole masses are 31.2^(8.4) _(−6.0)M_⊙ and 19.4^(5.3)_( −5.9)M_⊙ (at the 90% credible level). The black hole spins are best constrained through measurement of the effective inspiral spin parameter, a mass-weighted combination of the spin components perpendicular to the orbital plane, χ_(eff) = −0.12^(0.21)_( −0.30). This result implies that spin configurations with both component spins positively aligned with the orbital angular momentum are disfavored. The source luminosity distance is 880^(450)_(−390) Mpc corresponding to a redshift of z = 0.18^(0.08)_( −0.07) . We constrain the magnitude of modifications to the gravitational-wave dispersion relation and perform null tests of general relativity. Assuming that gravitons are dispersed in vacuum like massive particles, we bound the graviton mass to m_g ≤ 7.7 × 10^(−23) eV/c^2. In all cases, we find that GW170104 is consistent with general relativity.

2,569 citations


Journal ArticleDOI
Shadab Alam1, Metin Ata2, Stephen Bailey3, Florian Beutler3, Dmitry Bizyaev4, Dmitry Bizyaev5, Jonathan Blazek6, Adam S. Bolton7, Joel R. Brownstein7, Angela Burden8, Chia-Hsun Chuang9, Chia-Hsun Chuang2, Johan Comparat9, Antonio J. Cuesta10, Kyle S. Dawson7, Daniel J. Eisenstein11, Stephanie Escoffier12, Héctor Gil-Marín13, Héctor Gil-Marín14, Jan Niklas Grieb15, Nick Hand16, Shirley Ho1, Karen Kinemuchi4, D. Kirkby17, Francisco S. Kitaura16, Francisco S. Kitaura3, Francisco S. Kitaura2, Elena Malanushenko4, Viktor Malanushenko4, Claudia Maraston18, Cameron K. McBride11, Robert C. Nichol18, Matthew D. Olmstead19, Daniel Oravetz4, Nikhil Padmanabhan8, Nathalie Palanque-Delabrouille, Kaike Pan4, Marcos Pellejero-Ibanez20, Marcos Pellejero-Ibanez21, Will J. Percival18, Patrick Petitjean22, Francisco Prada20, Francisco Prada9, Adrian M. Price-Whelan23, Beth Reid16, Beth Reid3, Sergio Rodríguez-Torres9, Sergio Rodríguez-Torres20, Natalie A. Roe3, Ashley J. Ross6, Ashley J. Ross18, Nicholas P. Ross24, Graziano Rossi25, Jose Alberto Rubino-Martin20, Jose Alberto Rubino-Martin21, Shun Saito15, Salvador Salazar-Albornoz15, Lado Samushia26, Ariel G. Sánchez15, Siddharth Satpathy1, David J. Schlegel3, Donald P. Schneider27, Claudia G. Scóccola28, Claudia G. Scóccola29, Claudia G. Scóccola9, Hee-Jong Seo30, Erin Sheldon31, Audrey Simmons4, Anže Slosar31, Michael A. Strauss23, Molly E. C. Swanson11, Daniel Thomas18, Jeremy L. Tinker32, Rita Tojeiro33, Mariana Vargas Magaña1, Mariana Vargas Magaña34, Jose Alberto Vazquez31, Licia Verde, David A. Wake35, David A. Wake36, Yuting Wang37, Yuting Wang18, David H. Weinberg6, Martin White16, Martin White3, W. Michael Wood-Vasey38, Christophe Yèche, Idit Zehavi39, Zhongxu Zhai33, Gong-Bo Zhao18, Gong-Bo Zhao37 
TL;DR: In this article, the authors present cosmological results from the final galaxy clustering data set of the Baryon Oscillation Spectroscopic Survey, part of the Sloan Digital Sky Survey III.
Abstract: We present cosmological results from the final galaxy clustering data set of the Baryon Oscillation Spectroscopic Survey, part of the Sloan Digital Sky Survey III. Our combined galaxy sample comprises 1.2 million massive galaxies over an effective area of 9329 deg^2 and volume of 18.7 Gpc^3, divided into three partially overlapping redshift slices centred at effective redshifts 0.38, 0.51 and 0.61. We measure the angular diameter distance and Hubble parameter H from the baryon acoustic oscillation (BAO) method, in combination with a cosmic microwave background prior on the sound horizon scale, after applying reconstruction to reduce non-linear effects on the BAO feature. Using the anisotropic clustering of the pre-reconstruction density field, we measure the product D_MH from the Alcock–Paczynski (AP) effect and the growth of structure, quantified by fσ_8(z), from redshift-space distortions (RSD). We combine individual measurements presented in seven companion papers into a set of consensus values and likelihoods, obtaining constraints that are tighter and more robust than those from any one method; in particular, the AP measurement from sub-BAO scales sharpens constraints from post-reconstruction BAOs by breaking degeneracy between D_M and H. Combined with Planck 2016 cosmic microwave background measurements, our distance scale measurements simultaneously imply curvature Ω_K = 0.0003 ± 0.0026 and a dark energy equation-of-state parameter w = −1.01 ± 0.06, in strong affirmation of the spatially flat cold dark matter (CDM) model with a cosmological constant (ΛCDM). Our RSD measurements of fσ_8, at 6 per cent precision, are similarly consistent with this model. When combined with supernova Ia data, we find H_0 = 67.3 ± 1.0 km s^−1 Mpc^−1 even for our most general dark energy model, in tension with some direct measurements. Adding extra relativistic species as a degree of freedom loosens the constraint only slightly, to H_0 = 67.8 ± 1.2 km s^−1 Mpc^−1. Assuming flat ΛCDM, we find Ω_m = 0.310 ± 0.005 and H_0 = 67.6 ± 0.5 km s^−1 Mpc^−1, and we find a 95 per cent upper limit of 0.16 eV c^−2 on the neutrino mass sum.

2,413 citations


Journal ArticleDOI
13 Sep 2017-Nature
TL;DR: The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers.
Abstract: Recent progress implies that a crossover between machine learning and quantum information processing benefits both fields. Traditional machine learning has dramatically improved the benchmarking an ...

2,162 citations


Journal ArticleDOI
B. P. Abbott1, Richard J. Abbott1, T. D. Abbott2, Fausto Acernese3  +1113 moreInstitutions (117)
TL;DR: For the first time, the nature of gravitational-wave polarizations from the antenna response of the LIGO-Virgo network is tested, thus enabling a new class of phenomenological tests of gravity.
Abstract: On August 14, 2017 at 10∶30:43 UTC, the Advanced Virgo detector and the two Advanced LIGO detectors coherently observed a transient gravitational-wave signal produced by the coalescence of two stellar mass black holes, with a false-alarm rate of ≲1 in 27 000 years. The signal was observed with a three-detector network matched-filter signal-to-noise ratio of 18. The inferred masses of the initial black holes are 30.5-3.0+5.7M⊙ and 25.3-4.2+2.8M⊙ (at the 90% credible level). The luminosity distance of the source is 540-210+130 Mpc, corresponding to a redshift of z=0.11-0.04+0.03. A network of three detectors improves the sky localization of the source, reducing the area of the 90% credible region from 1160 deg2 using only the two LIGO detectors to 60 deg2 using all three detectors. For the first time, we can test the nature of gravitational-wave polarizations from the antenna response of the LIGO-Virgo network, thus enabling a new class of phenomenological tests of gravity.

1,979 citations


Journal ArticleDOI
TL;DR: The new version of the MPI Bioinformatics Toolkit is introduced, focusing on improved features for the comprehensive analysis of proteins, as well as on promoting teaching.

1,757 citations


Journal ArticleDOI
TL;DR: The web application GeSeq combines batch processing with a fully customizable reference sequence selection of organellar genome records from NCBI and/or references uploaded by the user to support high-quality annotations of chloroplast genomes.
Abstract: We have developed the web application GeSeq (https://chlorobox.mpimp-golm.mpg.de/geseq.html) for the rapid and accurate annotation of organellar genome sequences, in particular chloroplast genomes. In contrast to existing tools, GeSeq combines batch processing with a fully customizable reference sequence selection of organellar genome records from NCBI and/or references uploaded by the user. For the annotation of chloroplast genomes, the application additionally provides an integrated database of manually curated reference sequences. GeSeq identifies genes or other feature-encoding regions by BLAT-based homology searches and additionally, by profile HMM searches for protein and rRNA coding genes and two de novo predictors for tRNA genes. These unique features enable the user to conveniently compare the annotations of different state-of-the-art methods, thus supporting high-quality annotations. The main output of GeSeq is a GenBank file that usually requires only little curation and is instantly visualized by OGDRAW. GeSeq also offers a variety of optional additional outputs that facilitate downstream analyzes, for example comparative genomic or phylogenetic studies.

Journal ArticleDOI
TL;DR: Because MMseqs2 needs no random memory access in its innermost loop, its runtime scales almost inversely with the number of cores used, which enables sensitive protein sequence searching for the analysis of massive data sets.
Abstract: Sequencing costs have dropped much faster than Moore's law in the past decade, and sensitive sequence searching has become the main bottleneck in the analysis of large (meta)genomic datasets. While previous methods sacrificed sensitivity for speed gains, the parallelized, open-source software MMseqs2 overcomes this trade-off: In three-iteration profile searches it reaches 50% higher sensitivity than BLAST at 83-fold speed and the same sensitivity as PSI-BLAST at 270 times its speed. MMseqs2 therefore offers great potential to increase the fraction of annotatable (meta)genomic sequences.

Proceedings ArticleDOI
21 Jul 2017
TL;DR: The utility of the OctNet representation is demonstrated by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.
Abstract: We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, we exploit the sparsity in the input data to hierarchically partition the space using a set of unbalanced octrees where each leaf node stores a pooled feature representation. This allows to focus memory allocation and computation to the relevant dense regions and enables deeper networks without compromising resolution. We demonstrate the utility of our OctNet representation by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.

Journal ArticleDOI
TL;DR: The study shows that the combination of robust topological surface states and large room temperature carrier mobility, both of which originate from bulk Dirac bands of the Weyl semimetal, is a recipe for high activity HER catalysts.
Abstract: The search for highly efficient and low-cost catalysts is one of the main driving forces in catalytic chemistry. Current strategies for the catalyst design focus on increasing the number and activity of local catalytic sites, such as the edge sites of molybdenum disulfides in the hydrogen evolution reaction (HER). Here, the study proposes and demonstrates a different principle that goes beyond local site optimization by utilizing topological electronic states to spur catalytic activity. For HER, excellent catalysts have been found among the transition-metal monopnictides-NbP, TaP, NbAs, and TaAs-which are recently discovered to be topological Weyl semimetals. Here the study shows that the combination of robust topological surface states and large room temperature carrier mobility, both of which originate from bulk Dirac bands of the Weyl semimetal, is a recipe for high activity HER catalysts. This approach has the potential to go beyond graphene based composite photocatalysts where graphene simply provides a high mobility medium without any active catalytic sites that have been found in these topological materials. Thus, the work provides a guiding principle for the discovery of novel catalysts from the emerging field of topological materials.

Journal ArticleDOI
TL;DR: SDSS-IV as mentioned in this paper is a project encompassing three major spectroscopic programs: the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA), the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), and the Time Domain Spectroscopy Survey (TDSS).
Abstract: We describe the Sloan Digital Sky Survey IV (SDSS-IV), a project encompassing three major spectroscopic programs. The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is observing hundreds of thousands of Milky Way stars at high resolution and high signal-to-noise ratios in the near-infrared. The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is obtaining spatially resolved spectroscopy for thousands of nearby galaxies (median $z\sim 0.03$). The extended Baryon Oscillation Spectroscopic Survey (eBOSS) is mapping the galaxy, quasar, and neutral gas distributions between $z\sim 0.6$ and 3.5 to constrain cosmology using baryon acoustic oscillations, redshift space distortions, and the shape of the power spectrum. Within eBOSS, we are conducting two major subprograms: the SPectroscopic IDentification of eROSITA Sources (SPIDERS), investigating X-ray AGNs and galaxies in X-ray clusters, and the Time Domain Spectroscopic Survey (TDSS), obtaining spectra of variable sources. All programs use the 2.5 m Sloan Foundation Telescope at the Apache Point Observatory; observations there began in Summer 2014. APOGEE-2 also operates a second near-infrared spectrograph at the 2.5 m du Pont Telescope at Las Campanas Observatory, with observations beginning in early 2017. Observations at both facilities are scheduled to continue through 2020. In keeping with previous SDSS policy, SDSS-IV provides regularly scheduled public data releases; the first one, Data Release 13, was made available in 2016 July.

Journal ArticleDOI
TL;DR: The first unambiguous coincident observation of gravitational waves and electromagnetic radiation from a single astrophysical source and marks the start of gravitational-wave multi-messenger astronomy was reported in this article.
Abstract: On 2017 August 17 at 12:41:06 UTC the Fermi Gamma-ray Burst Monitor (GBM) detected and triggered on the short gamma-ray burst (GRB) 170817A. Approximately 1.7 s prior to this GRB, the Laser Interferometer Gravitational-wave Observatory triggered on a binary compact merger candidate associated with the GRB. This is the first unambiguous coincident observation of gravitational waves and electromagnetic radiation from a single astrophysical source and marks the start of gravitational-wave multi-messenger astronomy. We report the GBM observations and analysis of this ordinary short GRB, which extraordinarily confirms that at least some short GRBs are produced by binary compact mergers.

Journal ArticleDOI
TL;DR: In this paper, the authors review the basic concepts and compare these topological states of matter from the materials perspective with a special focus on Weyl semimetals, and introduce the signatures of Weyl points in a pedagogical way, from Fermi arcs to the chiral magnetotransport properties.
Abstract: Topological insulators and topological semimetals are both new classes of quantum materials, which are characterized by surface states induced by the topology of the bulk band structure. Topological Dirac or Weyl semimetals show linear dispersion around nodes, termed the Dirac or Weyl points, as the three-dimensional analog of graphene. We review the basic concepts and compare these topological states of matter from the materials perspective with a special focus on Weyl semimetals. The TaAs family is the ideal materials class to introduce the signatures of Weyl points in a pedagogical way, from Fermi arcs to the chiral magnetotransport properties, followed by hunting for the type-II Weyl semimetals in WTe2, MoTe2, and related compounds. Many materials are members of big families, and topological properties can be tuned. As one example, we introduce the multifunctional topological materials, Heusler compounds, in which both topological insulators and magnetic Weyl semimetals can be found. Instead of a comp...

Journal ArticleDOI
Steven R. Majewski1, Ricardo P. Schiavon2, Peter M. Frinchaboy3, Carlos Allende Prieto4, Carlos Allende Prieto5, Robert H. Barkhouser6, Dmitry Bizyaev7, Dmitry Bizyaev8, Basil Blank, Sophia Brunner1, Adam Burton1, Ricardo Carrera5, Ricardo Carrera4, S. Drew Chojnowski1, S. Drew Chojnowski7, Katia Cunha9, Courtney R. Epstein10, Greg Fitzgerald, Ana E. García Pérez5, Ana E. García Pérez1, Fred Hearty11, Fred Hearty1, Chuck Henderson, Jon A. Holtzman7, Jennifer A. Johnson10, Charles R. Lam1, James E. Lawler12, Paul Maseman9, Szabolcs Mészáros5, Szabolcs Mészáros13, Szabolcs Mészáros4, Matthew J. Nelson1, Duy Coung Nguyen14, David L. Nidever15, David L. Nidever1, Marc H. Pinsonneault10, Matthew Shetrone16, Stephen A. Smee6, Verne V. Smith9, T. Stolberg, Michael F. Skrutskie1, E. Walker1, John C. Wilson1, Gail Zasowski1, Gail Zasowski6, Friedrich Anders17, Sarbani Basu18, Stephane Beland19, Michael R. Blanton20, Jo Bovy21, Jo Bovy14, Joel R. Brownstein22, Joleen K. Carlberg1, Joleen K. Carlberg23, William J. Chaplin24, William J. Chaplin25, Cristina Chiappini17, Daniel J. Eisenstein26, Yvonne Elsworth24, Diane Feuillet7, Scott W. Fleming27, Scott W. Fleming28, Jessica Galbraith-Frew22, Rafael A. García29, D. Anibal García-Hernández5, D. Anibal García-Hernández4, Bruce Gillespie6, Léo Girardi30, James E. Gunn21, Sten Hasselquist7, Sten Hasselquist1, Michael R. Hayden7, Saskia Hekker25, Saskia Hekker31, Inese I. Ivans22, Karen Kinemuchi7, Mark A. Klaene7, Suvrath Mahadevan11, Savita Mathur32, Benoit Mosser33, Demitri Muna10, Jeffrey A. Munn, Robert C. Nichol, Robert W. O'Connell1, John K. Parejko18, Annie C. Robin34, H. J. Rocha-Pinto35, M. Schultheis36, Aldo Serenelli5, Neville Shane1, Victor Silva Aguirre25, Jennifer Sobeck1, Benjamin A. Thompson3, Nicholas W. Troup1, David H. Weinberg10, Olga Zamora5, Olga Zamora4 
TL;DR: In this article, the Hungarian National Research, Development and Innovation Office (K-119517) and Hungarian National Science Foundation (KNFI) have proposed a method to detect the presence of asteroids in Earth's magnetic field.
Abstract: National Science Foundation [AST-1109178, AST-1616636]; Gemini Observatory; Spanish Ministry of Economy and Competitiveness [AYA-2011-27754]; NASA [NNX12AE17G]; Hungarian Academy of Sciences; Hungarian NKFI of the Hungarian National Research, Development and Innovation Office [K-119517]; Alfred P. Sloan Foundation; National Science Foundation; U.S. Department of Energy Office of Science

Journal ArticleDOI
TL;DR: This review introduces readers to the basic principles and fundamentals of flow chemistry and critically discusses recent flow chemistry accounts.
Abstract: Flow chemistry involves the use of channels or tubing to conduct a reaction in a continuous stream rather than in a flask Flow equipment provides chemists with unique control over reaction parameters enhancing reactivity or in some cases enabling new reactions This relatively young technology has received a remarkable amount of attention in the past decade with many reports on what can be done in flow Until recently, however, the question, “Should we do this in flow?” has merely been an afterthought This review introduces readers to the basic principles and fundamentals of flow chemistry and critically discusses recent flow chemistry accounts

Journal ArticleDOI
TL;DR: Nayaket et al. as mentioned in this paper revealed the existence of surface states of LaBi through the observation of three Dirac cones: two coexist at the corners and one appears at the centre of the Brillouin zone, by employing angle-resolved photoemission spectroscopy in conjunction with ab initio calculations.
Abstract: The rare-earth monopnictide LaBi exhibits exotic magneto-transport properties, including an extremely large and anisotropic magnetoresistance. Experimental evidence for topological surface states is still missing although band inversions have been postulated to induce a topological phase in LaBi. In this work, we have revealed the existence of surface states of LaBi through the observation of three Dirac cones: two coexist at the corners and one appears at the centre of the Brillouin zone, by employing angle-resolved photoemission spectroscopy in conjunction with ab initio calculations. The odd number of surface Dirac cones is a direct consequence of the odd number of band inversions in the bulk band structure, thereby proving that LaBi is a topological, compensated semimetal, which is equivalent to a time-reversal invariant topological insulator. Our findings provide insight into the topological surface states of LaBi’s semi-metallicity and related magneto-transport properties. The magnetoresistance suggests an exotic topological phase in LaBi, but the evidence is still missing. Here, Nayaket al. report the existence of surface states of LaBi through the observation of three Dirac cones, confirming it a topological semimetal.

Journal ArticleDOI
20 Jul 2017-Nature
TL;DR: A complete electronic band theory is proposed, which builds on the conventional band theory of electrons, highlighting the link between the topology and local chemical bonding and can be used to predict many more topological insulators.
Abstract: Since the discovery of topological insulators and semimetals, there has been much research into predicting and experimentally discovering distinct classes of these materials, in which the topology of electronic states leads to robust surface states and electromagnetic responses. This apparent success, however, masks a fundamental shortcoming: topological insulators represent only a few hundred of the 200,000 stoichiometric compounds in material databases. However, it is unclear whether this low number is indicative of the esoteric nature of topological insulators or of a fundamental problem with the current approaches to finding them. Here we propose a complete electronic band theory, which builds on the conventional band theory of electrons, highlighting the link between the topology and local chemical bonding. This theory of topological quantum chemistry provides a description of the universal (across materials), global properties of all possible band structures and (weakly correlated) materials, consisting of a graph-theoretic description of momentum (reciprocal) space and a complementary group-theoretic description in real space. For all 230 crystal symmetry groups, we classify the possible band structures that arise from local atomic orbitals, and show which are topologically non-trivial. Our electronic band theory sheds new light on known topological insulators, and can be used to predict many more.

Journal ArticleDOI
02 Jan 2017-PeerJ
TL;DR: The architecture of SymPy is presented, a description of its features, and a discussion of select domain specific submodules are discussed, to become the standard symbolic library for the scientific Python ecosystem.
Abstract: SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.


Proceedings ArticleDOI
01 Jul 2017
TL;DR: The Spatial Pyramid Network (SPyNet) is much simpler and 96% smaller than FlowNet in terms of model parameters, which makes it more efficient and appropriate for embedded applications.
Abstract: We learn to compute optical flow by combining a classical spatial-pyramid formulation with deep learning. This estimates large motions in a coarse-to-fine approach by warping one image of a pair at each pyramid level by the current flow estimate and computing an update to the flow. Instead of the standard minimization of an objective function at each pyramid level, we train one deep network per level to compute the flow update. Unlike the recent FlowNet approach, the networks do not need to deal with large motions, these are dealt with by the pyramid. This has several advantages. First, our Spatial Pyramid Network (SPyNet) is much simpler and 96% smaller than FlowNet in terms of model parameters. This makes it more efficient and appropriate for embedded applications. Second, since the flow at each pyramid level is small (

Journal ArticleDOI
TL;DR: In this article, a deep tensor neural network is used to predict atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure.
Abstract: Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems. Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical systems, beyond properties trivially contained in the training data.

Journal ArticleDOI
Elena Aprile1, Jelle Aalbers2, F. Agostini, M. Alfonsi3, F. D. Amaro4, M. Anthony1, F. Arneodo5, P. Barrow6, Laura Baudis6, Boris Bauermeister7, M. L. Benabderrahmane5, T. Berger8, P. A. Breur2, April S. Brown2, Ethan Brown8, S. Bruenner9, Giacomo Bruno, Ran Budnik10, L. Bütikofer11, J. Calvén7, João Cardoso4, M. Cervantes12, D. Cichon9, D. Coderre11, Auke-Pieter Colijn2, Jan Conrad7, Jean-Pierre Cussonneau13, M. P. Decowski2, P. de Perio1, P. Di Gangi14, A. Di Giovanni5, Sara Diglio13, G. Eurin9, J. Fei15, A. D. Ferella7, A. Fieguth16, W. Fulgione, A. Gallo Rosso, Michelle Galloway6, F. Gao1, M. Garbini14, Robert Gardner17, C. Geis3, Luke Goetzke1, L. Grandi17, Z. Greene1, C. Grignon3, C. Hasterok9, E. Hogenbirk2, J. Howlett1, R. Itay10, B. Kaminsky11, Shingo Kazama6, G. Kessler6, A. Kish6, H. Landsman10, R. F. Lang12, D. Lellouch10, L. Levinson10, Qing Lin1, Sebastian Lindemann9, Manfred Lindner9, F. Lombardi15, J. A. M. Lopes4, A. Manfredini10, I. Mariș5, T. Marrodán Undagoitia9, Julien Masbou13, F. V. Massoli14, D. Masson12, D. Mayani6, M. Messina1, K. Micheneau13, A. Molinario, K. Morâ7, M. Murra16, J. Naganoma18, Kaixuan Ni15, Uwe Oberlack3, P. Pakarha6, Bart Pelssers7, R. Persiani13, F. Piastra6, J. Pienaar12, V. Pizzella9, M.-C. Piro8, Guillaume Plante1, N. Priel10, L. Rauch9, S. Reichard6, C. Reuter12, B. Riedel17, A. Rizzo1, S. Rosendahl16, N. Rupp9, R. Saldanha17, J.M.F. dos Santos4, Gabriella Sartorelli14, M. Scheibelhut3, S. Schindler3, J. Schreiner9, Marc Schumann11, L. Scotto Lavina19, M. Selvi14, P. Shagin18, E. Shockley17, Manuel Gameiro da Silva4, H. Simgen9, M. V. Sivers11, A. Stein20, S. Thapa17, Dominique Thers13, A. Tiseni2, Gian Carlo Trinchero, C. Tunnell17, M. Vargas16, N. Upole17, Hui Wang20, Zirui Wang, Yuehuan Wei6, Ch. Weinheimer16, J. Wulf6, J. Ye15, Yanxi Zhang1, T. Zhu1 
TL;DR: The first dark matter search results from XENON1T, a ∼2000-kg-target-mass dual-phase (liquid-gas) xenon time projection chamber in operation at the Laboratori Nazionali del Gran Sasso in Italy, are reported and a profile likelihood analysis shows that the data are consistent with the background-only hypothesis.
Abstract: We report the first dark matter search results from XENON1T, a ∼2000-kg-target-mass dual-phase (liquid-gas) xenon time projection chamber in operation at the Laboratori Nazionali del Gran Sasso in Italy and the first ton-scale detector of this kind The blinded search used 342 live days of data acquired between November 2016 and January 2017 Inside the (1042±12)-kg fiducial mass and in the [5,40] keVnr energy range of interest for weakly interacting massive particle (WIMP) dark matter searches, the electronic recoil background was (193±025)×10-4 events/(kg×day×keVee), the lowest ever achieved in such a dark matter detector A profile likelihood analysis shows that the data are consistent with the background-only hypothesis We derive the most stringent exclusion limits on the spin-independent WIMP-nucleon interaction cross section for WIMP masses above 10 GeV/c2, with a minimum of 77×10-47 cm2 for 35-GeV/c2 WIMPs at 90% CL

Journal ArticleDOI
08 Sep 2017-Science
TL;DR: In this article, the authors review recent experimental progress in quantum many-body simulation and comment on future directions, and present a review of the current state-of-the-art in this field.
Abstract: Quantum simulation, a subdiscipline of quantum computation, can provide valuable insight into difficult quantum problems in physics or chemistry. Ultracold atoms in optical lattices represent an ideal platform for simulations of quantum many-body problems. Within this setting, quantum gas microscopes enable single atom observation and manipulation in large samples. Ultracold atom–based quantum simulators have already been used to probe quantum magnetism, to realize and detect topological quantum matter, and to study quantum systems with controlled long-range interactions. Experiments on many-body systems out of equilibrium have also provided results in regimes unavailable to the most advanced supercomputers. We review recent experimental progress in this field and comment on future directions.

Journal ArticleDOI
TL;DR: In this article, a review of parity-time symmetry in classical photonic systems is presented, along with some recent developments in non-Hermitian quantum symmetry paradigms for device functionalities.
Abstract: Nearly one century after the birth of quantum mechanics, parity–time symmetry is revolutionizing and extending quantum theories to include a unique family of non-Hermitian Hamiltonians. While conceptually striking, experimental demonstration of parity–time symmetry remains unexplored in quantum electronic systems. The flexibility of photonics allows for creating and superposing non-Hermitian eigenstates with ease using optical gain and loss, which makes it an ideal platform to explore various non-Hermitian quantum symmetry paradigms for novel device functionalities. Such explorations that employ classical photonic platforms not only deepen our understanding of fundamental quantum physics but also facilitate technological breakthroughs for photonic applications. Research into non-Hermitian photonics therefore advances and benefits both fields simultaneously. General concepts and recent developments of parity–time symmetry in classical photonics are reviewed.

Proceedings ArticleDOI
08 May 2017
TL;DR: In this paper, a relatively simple deep feed-forward network was proposed to estimate 3D human pose from 2D joint locations with a remarkably low error rate, achieving state-of-the-art results on Human3.6M.
Abstract: Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations given raw image pixels. Despite their excellent performance, it is often not easy to understand whether their remaining error stems from a limited 2d pose (visual) understanding, or from a failure to map 2d poses into 3- dimensional positions.,,With the goal of understanding these sources of error, we set out to build a system that given 2d joint locations predicts 3d positions. Much to our surprise, we have found that, with current technology, "lifting" ground truth 2d joint locations to 3d space is a task that can be solved with a remarkably low error rate: a relatively simple deep feedforward network outperforms the best reported result by about 30% on Human3.6M, the largest publicly available 3d pose estimation benchmark. Furthermore, training our system on the output of an off-the-shelf state-of-the-art 2d detector (i.e., using images as input) yields state of the art results – this includes an array of systems that have been trained end-to-end specifically for this task. Our results indicate that a large portion of the error of modern deep 3d pose estimation systems stems from their visual analysis, and suggests directions to further advance the state of the art in 3d human pose estimation.

Proceedings Article
01 Jan 2017
TL;DR: In this paper, the authors describe how to use Relation Networks (RNs) as a simple plug-and-play module to solve problems that fundamentally hinge on relational reasoning.
Abstract: Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn. In this paper we describe how to use Relation Networks (RNs) as a simple plug-and-play module to solve problems that fundamentally hinge on relational reasoning. We tested RN-augmented networks on three tasks: visual question answering using a challenging dataset called CLEVR, on which we achieve state-of-the-art, super-human performance; text-based question answering using the bAbI suite of tasks; and complex reasoning about dynamical physical systems. Then, using a curated dataset called Sort-of-CLEVR we show that powerful convolutional networks do not have a general capacity to solve relational questions, but can gain this capacity when augmented with RNs. Thus, by simply augmenting convolutions, LSTMs, and MLPs with RNs, we can remove computational burden from network components that are not well-suited to handle relational reasoning, reduce overall network complexity, and gain a general ability to reason about the relations between entities and their properties.