dataesr/scientific-paragraphs-categorization huggingface dataset:
Free (air) cooling Free cooling is an economical method of using low external air temperatures to assist in chilling water, which can then be used for industrial processes, or air conditioning systems. FSI Frequency scanning interferometry. GB A gigabyte is by definition of the IEC, 10 9 B. Gd Gadolinium is a chemical element with symbol Gd and atomic number 64. Gd 2 O 3 Gadolinium(III) oxide is an inorganic compound. It is one of the most commonly available forms of the rare-earth element gadolinium, derivatives of which are potential contrast agents for magnetic resonance imaging. GEANT4 GEANT4 is a platform for the simulation of the passage of particles through matter using Monte Carlo methods. It is the successor of the GEANT series of software toolkits developed by CERN. GeniLac A project planned in Geneva to use the water of lake Geneva for cooling and heating of public buildings and international organisations.
g. injection, extraction, collimation, RF). LTO Lithium titanium oxide. LTS Low temperature superconductors have critical temperatures below 77 K. Luminometer A calorimeter inside the detector to precisely measure the luminosity. Luminosity Luminosity is the rate of collision events normalised to the cross section. It is expressed as inverse square centimetre and inverse second (cm -2 s -1 ) or barn (1 barn = 10 -24 cm 2 ). LV Low voltage. MAC The medium access control sublayer and the logical link control (LLC) sublayer together make up the data link layer (DLL). Within that data link layer, the LLC provides flow control and multiplexing for the logical link (i. e. EtherType, 802. 1Q VLAN tag), while the MAC provides flow control and multiplexing for the transmission medium. MAC is responsible for the transmission of data packets to and from the network-interface card, and to and from another remotely shared channel.
It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. PU Pile-up. PV Photovoltaics. PVSS Prozessvisualisierungs-und steuerungssoftware. A SCADA product from SIEMENS company ETM professional control now called WinCC OA. Px One of the 12 FCC access points. x can be one of A, B, C, D, E, F, G, H, I, J, K, L, e. g. PA is the access point A close to the CERN Meyrin site.
PyHEADTAIL A macroparticle tracking software designed specifically to simulate collective effects in circular accelerators. Pythia A Monte Carlo event generator software. Q The quality factor or Q factor is a dimensionless parameter that describes how underdamped an oscillator or resonator is, and characterises a resonator's bandwidth relative to its centre frequency. Higher Q indicates a lower rate of energy loss relative to the stored energy of the resonator. For an RF cavity it characterises RF losses in the cavity: an RF cavity having a higher Q factor is a more efficient user of RF power. QC1 First final focus quadrupole next to the IP. QC2 Second final focus quadrupole, behind QC1. QCD In theoretical physics, quantum chromodynamics is the theory of the strong interaction between quarks and gluons, the fundamental particles that make up composite hadrons such as the proton, neutron and pion.
ROOT ROOT is a modular scientific software toolkit. It provides functionalities needed to deal with big data processing, statistical analysis, visualisation and storage. ROXIE Software developed at CERN for the electromagnetic simulation and optimisation of accelerator magnets. RPC Resistive plate chamber. RRP The restacked-rod restack process is a manufacturing process for Nb 3 Sn wires developed by the OST company (Oxford instruments technologies), which has been subsequently acquired by BEST Inc. (Bruker Energy and Supercon. Technologies). RRR The residual resistivity ratio is defined as the ratio of the electrical resistivity of a material at room temperature and at a chosen cryogenic temperature. For superconducting materials, a cryogenic temperature above T c must be chosen. RRR serves as a measure of the purity and overall quality of a sample: as electrical resistivity usually increases as defect prevalence increases, a large RRR is associated with a pure sample.
cern. ESRF The European Synchrotron Radiation Facility is a joint research facility situated in Grenoble, France, and supported by 22 countries (13 member countries: France, Germany, Italy, UK, Spain, Switzerland, Belgium, The Netherlands, Denmark, Finland, Norway, Sweden, Russia and 9 associate countries: Austria, Portugal, Israel, Poland, Czech Republic, Hungary, Slovakia, India and South Africa). ESS Energy storage system. ETHZ The Eidgenössische Technische Hochschule Zürich is one of the two Swiss Federal Institutes of Technology. EU European Union. EuCARD2 A FP7 EC funded research and development project in the area of particle accelerator technologies. EuroCirCol A H2020 EC funded research and development project to develop the foundations of a future circular hadron collider. EW Electroweak.
g. , galaxies with a low SFR, well below the main sequence, but a substantial gas mass. ). This paper is part of series of papers presenting the first results from the ALMA Spectroscopic Survey Large Program (ASPECS-LP;  Decarli et al. 2019 ). The ASPECS-LP is a spectral line scan targeting the Hubble Ultra Deep Field (HUDF). Here we use the results from the spectral scan of Band 3 (84-115 GHz; 3. 6-2. 6 mm) and investigate the nature and physical properties of galaxies detected in molecular emission lines by ALMA. In order to do so, it is important to know about the physical conditions of the galaxies detected in molecular gas, such as their ISM conditions, their (HST ) morphology and stellar and ionized gas dynamics. The HUDF benefits from the deepest and most extensive multi-wavelength data, and as of recently, ultradeep integral-field spectroscopy.
A critical step in identifying ALMA emission lines with actual galaxies relies on matching the galaxies in redshift. In this context, the Multi Unit Spectroscopic Explorer (MUSE,  Bacon et al. 2010 ) HUDF survey, that provides a deep optical integral-field spectroscopic survey over the HUDF  (Bacon et al. 2017) , is essential. The MUSE HUDF is a natural complement to the ASPECS-LP in the same area on the sky, providing optical spectroscopy for all galaxies within the field of view, also without any preselection. In addition, the integral-field spectrograph provides redshifts for over a thousands of galaxies in the HUDF (increasing the number of previously known redshifts by a factor ∼ 10×;  Inami et al. 2017). Depending on the redshift, these data can provide key information on the ISM conditions (such as metallicity and dynamics) of the galaxies harboring molecular gas.
As we will see throughout this paper, the MUSE data are a significant step forward in our understanding of galaxy population selected with ALMA. The paper is organized as follows: We first introduce the spectroscopic and multi-wavelength data ( § 2). We discuss the redshift identification of the CO-detected galaxies from the line search  (Gonzalez-Lopez et al. 2019) , using the MUSE and multi-wavelength data, in § 3. 1. Next, we leverage the large number of MUSE redshifts to separate real from spurious sources down to a significantly lower signal-to-noise ratios (S/N) than possible in the line search ( § 3. 2). Together, these sources form the full ASPECS-LP Band 3 sample ( § 3. 3). We then move on to the central question(s) of this paper: By doing a survey of molecular gas, in what kind of galaxies do we detect molecular gas emission at different redshifts, and what are the physical properties of these galaxies? We determine stellar masses, SFRs and (where possible) metallicities for all sources in ( § 4) and link these to the molecular gas content (M mol ) to derive the gas fraction (M mol /M * , the molecular-to-stellar mass ratio) and depletion time (t depl = M mol /SFR).
8 mJy and 2. 4 mJy and are limited by confusion. For the flux uncertainties we use the maximum of the local and simulated noise levels for each source, as recommended by the documentation 1. We further include the 1. 2 mm continuum data from the combination of the available ASPECS-LP data with the ALMA observations by  Dunlop et al. (2017) , taken over the same region, as detailed in  Aravena et al. (2019). We also include the ASPECS-LP 3. 0 mm continuum data, as presented in  (Gonzalez-Lopez et al. 2019). For the ASPECS survey we have created a master photometry catalog for the galaxies in the HUDF, adopting the spectroscopic redshifts from MUSE ( § 2. 2) and literature sources, as detailed in  Decarli et al. (2019). We use the high-z extension of the SED-fitting code magphys to infer physical parameters from the photometric information of the galaxies in our field  (Da Cunha et al.
2008 , 2015). The high-z extension of magphys includes a larger library of spectral emission models that extend to higher dust optical depths, higher SFRs and younger ages compared to what is typically found in the local universe. From the spectral emission models, the code can constrain the stellar mass, sSFR and the dust attenuation (A V ) along the line of sight. An energy balance argument ensures that the amount of absorption at rest-frame UV/optical wavelengths is consistent with the light reradiated in the infrared. The code performs a Bayesian inference of the posterior likelihood distribution of the fitted parameter, to account for uncertainties such as degeneracies in the models, missing data and non-detections. We run magphys on all the galaxies in our catalog, using the available photometric information in all the bands (listed in Appendix B).
2014; Walter et al. 2016 ) and MF3D  (Pavesi et al. 2018). The fidelity 2  of these linecandidates was estimated from the ratio of the number of lines with a negative and positive flux detected at a given S/N. Lastly, the completeness of the sample was estimated by ingesting simulated emission lines into the real data cube. In total, there are 16 emission line candidates for which the fidelity is ≥ 0. 9. Statistical analysis shows that this sample is free from false positives (the sum of their fidelities, based on the ALMA data alone, is 15. 9;  Gonzalez-Lopez et al. 2019 ). These 16 sources form the primary, line search-sample and are shown in Fig. 2. All these candidates have a S/N ≥ 6. 4. For all sources in the primary sample, one or multiple potential counterpart galaxies are visible in the deep HST imaging shown in Fig.
SUMMARY In this paper we use two spectroscopic integral-field observations of the Hubble Ultra Deep Field, ALMA in the millimeter, and MUSE in the optical regime, to further our understanding of the properties of the galaxy population at the peak of cosmic star formation (1 < z < 3). We start with the line emitters identified from the ASPECS-LP Band 3 (3 mm) data without any preselection  (Gonzalez-Lopez et al. 2019). By using the MUSE data, as well as the deep multi-wavelength data that is available for the HUDF, we find that all ALMAselected sources are associated with a counterpart in the optical/near-IR imaging. The spectroscopic information from MUSE enables us to associate all ALMA line emitters with emission coming from rotational transitions of carbon monoxide (CO) that result in unique redshift identifications: We identify 10 line emitters as CO(2-1) at 1 < z < 2, five as CO(3-2) at 2 < z < 3 and one as CO(4-3) at z = 3.
6. The line search done using the ALMA data is conservative, to avoid contamination by spurious sources in the very large 3 mm data cube  (Gonzalez-Lopez et al. 2019). We therefore also use the MUSE data as a positional and redshift prior to push the detection limit of the ALMA data to greater depth and identify additional CO emitters at z < 2. 9, increasing the total number of ALMA line detections in the field to 18. We present MUSE spectra of all CO-selected galaxies, and use the diagnostic emission lines covered by MUSE to constrain the physical properties of the ALMA line emitters. In particular, for galaxies with coverage of [O ii] λ3727/[Ne iii] λ3869 at z ≤ 1. 5 in the MUSE data, we infer metallicities consistent with being (super-)solar, which motivates our choice of a Galactic conversion factor to transform CO luminosities to molecular (H 2 ) gas masses for these galaxies in this series of ASPECS-LP papers  (Decarli et al.
2017)  and also detected at 5 cm  (Rujopakarn et al. 2016). ASPECS-LP. 3mm. 08: CO(2-1) at z = 1. 382. The source has a more complex morphology which was already discussed in the ASPECS-Pilot program  (Decarli et al. 2016 , see their Fig. 3 ). The CO emission is spatially consistent with a system of spiral galaxies. MUSE reveals that the south-west spiral is in the foreground at z = 1. 087. Careful examination of the MUSE cube reveals [O ii] emission matching the CO redshift in an arc north of the galaxies and possibly towards the south-west, which is ∼ 1. 8 away of peak of the CO emission (∼ 15 kpc at the redshift of the source). A potential scenario is that the north-east spiral galaxies is the background source, in which case the ionized gas emission of the spiral is completely obscured by the disk of the (south-west) foreground spiral.
2014; Straatman et al. 2016) , with z BPZ = 3. 67 +0. 74  -0. 24  (Rafelski et al. 2015). These all suggest a detection of CO(4-3) at z = 3. 601. In order the spectroscopically confirm this redshift, we extract a MUSE spectrum at the position of the source. The strongest UV emission line observed by MUSE at these redshifts is Lyα, while it also covers the much weaker C iii] line. Both are not detected in the spectrum of ASPECS-LP. 3mm. 13. The non-detection of C iii] at the 10 h depth of the mosaic is understandable, as robustly detecting C iii] at these redshifts is challenging (see  Maseda et al. 2017  for a in-depth discussion, which finds the highest redshift detection of C iii] in the deep 30 h MUSE data to be at z ∼ 2. 9). Unfortunately, at z = 3. 601 the expected position of Lyα in MUSE falls close to the [O i] λ5577 skyline (Fig.
042. 611 , which is (partly) financed by the  Netherlands Organization for Scientific Research (NWO)  D. R. acknowledges support from the  National Science Foundation  under grant number  AST-1614213. I. R. S. acknowledges support from the  ERC Advanced  Grant  DUSTYGAL (321334 ) and  STFC  ( ST/P000541/1 ) Work on Gnuastro has been funded by the  Japanese MEXT scholarship  and its  Grant-in-Aid for Scientific Research  ( 21244012 ,  24253003 ), the  ERC  advanced grant 339659-MUSICOS, European Unions  Horizon 2020 research and innovation programme  under  Marie Sklodowska-Curie  grant agreement No  721463  to the SUNDIAL ITN, and from the  Spanish MINECO  under grant number  AYA2016-76219-P.
We named this concatenation and bootstrapping method, Weighted Partial Super Bootstrap (wpSBOOT). We show on three simulated datasets of 16, 32 and 64 tips that our method improves the predictive power of bootstrap values. We also used as a benchmark an empirical collection of 853 one to one orthologous genes from seven yeast species and found wpSBOOT to significantly improve discrimination capacity between topologically correct and incorrect trees. Bootstrap values of wpSBOOT are comparable to similar readouts estimated using a single method. However, for reduced trees by 50 and 95% bootstrap thresholds, wpSBOOT comes out the lowest Type I error (less FP).
Phylogeny has the aim of reconstructing the scenario best supported by variations measured across homologous sequences. Given the alignment of the set sequences, the task of estimating hidden mutations and searching the most likely tree is NP-Complete under its most common formulations [e. g. Maximum parsimony  (Graham and Foulds, 1982) ]. Computing accurate Multiple Sequence Alignments (MSAs) comes, however, with issues of its own  (Chatzou et al. , 2016). Not being able to define and estimate a correct MSA unambiguously is a major problem when doing phylogenetic reconstruction. Wong et al. have previously exposed a few practical consequences of this situation when reconstructing phylogenetic trees  (Wong et al. , 2008). They have shown that given a collection of 1502 datasets of homologous sequences, the seven most widely used aligners result in unexpectedly diverse phylogenetic trees with 46% of the datasets associated with more than one topology.
Trees are even unstable in large scale data and their topology is affected by only changing sequence input order. Chatzou et al. have shown over 38% of branches are sensitive to the sequence input order in the dataset of HOMFAM with larger than 100 sequences  (Chatzou et al. , 2018). Until recently, this problem had been all but ignored by the community, with the vast majority of published trees relying on a single aligner, frequently ClustalW. When doing so, the issue of MSA reliability is usually addressed using a post-processing method, such as column trimming, in order to systematically remove the portions of an MSA unlikely to be correct  (Capella-Gutierrez et al. , 2009; Castresana, 2000). However recent results suggest trimming to have only a limited impact on phylogenetic estimation  (Dessimoz and Gil, 2010; Liu et al.
, 2009; Saurabh et al. , 2012). Moreover, it has been shown that excessive trimming can worsen the inferred phylogenetic tree  (Tan et al. , 2015). Some protocols address this problem through systematic MSA sampling. For instance, the Heads-or-Tails procedure involves aligning the reversed sequences and comparing the direct and reverse versions of the MSA  (Landan and Graur, 2007, 2008). This makes it the equivalent of a two-replicates sampling strategy. PRANK employs a more sophisticated approach with each MSA replicate estimated by randomly breaking all dynamic programing ties  (Lo ¨ytynoja and Goldman, 2008). The most elaborate (and time intensive) protocol is probably GUIDANCE where MSA replicates are obtained by re-estimating progressive MSAs using guide trees obtained from the bootstrap replicates of the default MSA  (Penn et al.
If a dataset is so complex that seven aligners can produce seven MSAs different enough to support more than a single tree, one needs to know how such variation affects phylogenetic reconstruction. In this work, we are proposing a bootstrap method that precisely addresses this issue. We show that rather than combining the alternative MSAs into a unique consensus model or locally trimming them on the basis of their agreement, one can concatenate those alternative MSAs into a Super-MSA which is used to infer an tree. Then, the tree's confidence is estimated by using replicates drawn from the Super-MSA. Doing so results in bootstrap values that simultaneously reflect site sampling (as regular bootstrap) along with the MSA induced uncertainty. While this procedure does not significantly improve the accuracy of the inferred tree, it makes the global bootstrap index more informative in identifying problematic branches or trees.
Therefore, SBOOT is against bootstrap assumption, columns drawn independently and identically distributed  (Felsenstein, 1985). To reduce this bias, we designed two additional protocols, partial SBOOT (pSBOOT) and its weighted version, Weighted Partial SBOOT (wpSBOOT). pSBOOT involves generating replicates having a length equal to the average length of the alternative MSAs (Fig. 1a. 2, if the average alignment length of seven aligners is 150, then we sample 150 columns for each replicate instead of 1050. For compromising lost information by partial sampling, we generate seven times more replicates, 700, to have the same amount information with SBOOT). wpSBOOT considers the high of correlation existing between some methods that tend to produce similar or identical MSAs. Under this scheme, the probability of drawing a column from an MSA is inversely proportional to its average similarity with other alternatives (Fig.
1a. 3), the sampling weight of the MSA, MSA x , is obtained using the following formula: SampleWeiðMSA x Þ ¼ 100 À P N y6 ¼x ColumnSimðMSA x ; MSA y Þ N À 1 (3) where N is the set of concatenated MSAs, MSA y is an MSA from the set N expect to MSA x and ColumnSim is the percentage of columns in MSA x that are found identically aligned within MSA y , as returned by the aln_compare function of T-Coffee  (Taly et al. , 2011). The weights are normalized to sum up to 100 for avoiding zero-weight of all identical alignments. The resulting weight is then used when drawing replicates from the concatenated dataset, with each column having a probability to be selected proportional to the weight of the MSA it comes from. According to this weighting scheme, an alignment program that produces alignments similar to others programs will contribute proportionally less to the bootstrap value although this downweighting will be compensated by the presence of many similar MSAs.
Individual aligners Phylogenetic reconstructions were evaluated using ClustalW  (Thompson et al. , 1994) , Dca  (Stoye, 1998) , Dialign2  (Morgenstern, 1999) , Mafft  (Katoh, 2002) , Muscle  (Edgar, 2004) , ProbCons  (Do et al. , 2005) , T-Coffee  (Notredame et al. , 2000) , PRANK (Lo ¨ytynoja and Goldman, 2008), SATe  (Liu et al. , 2009)  and M-Coffee  (Wallace et al. , 2006). M-Coffee and Super-MSA share a dependence on the primary MSAs either used to build T-Coffee library for the consensus MSA or be joined as the concatenated MSA. M-Coffee is a meta-method which combines the outputs of Wong's seven MSA methods into one single consensus MSA  (Wallace et al. , 2006). We compare M-Coffee with Super-MSA to investigate two ways for assembling alignment uncertainty, a consensus or a concatenation.
Simulated reference dataset The simulated datasets used to test Gblocks  (Talavera and Castresana, 2007)  and trimAl  (Capella-Gutierrez et al. , 2009)  were combined. They contain three different simulated ROSE-sets  (Stoye, 1998) , made of datasets having 16, 32 and 64 tips, respectively. Sequence evolutionary rate patterns were extracted from real The bootstrap value is measured in entire tree and clade levels. The bootstrap of the entire tree is the fraction of trees with an identical topology among bootstrap replicates. The bootstrap value of a clade is the fraction of its corresponding species partition among bootstrap replicated trees. (c) We measure discrimination power of bootstrap according to two loss functions. All-or-nothing loss function would assign a loss of 1 or 0 if the inference tree is not identical or identical to the Yeast ToL, respectively.
Per Branch loss function assigns a penalty for each clade in the true tree that is missing and another penalty for each clade in the inference tree that is not present in the true tree, typical the Robinson-Foulds metric alignments using TreePuzzle  (Schmidt et al. , 2002)  with an amongsite heterogeneity model that assumed a Gamma distribution of 16 rate categories. Then, ROSE created conserved and divergent regions by simulating different positions with above rates and PAM  (Dayhoff et al. , 1978)  evolutionary model. Each set contains 100 datasets generated under a purely asymmetric tree topology (nonultrametric model with the average and maximum length from the root to the tips, 0. 89 and 1. 30 substitutions/position; MSA length 400 amino acids). These datasets were aligned with the selected aligners and the resulting models were used to estimate phylogenetic trees using the PROTGAMMAJTT model of RAxML  (Stamatakis, 2006)  that supports the JTT matrix and the gamma-distributed rate variation.