dataesr/scientific-paragraphs-categorization huggingface dataset:
0. Interestingly, ACSN modules related to cell survival processes as WNT-non-canonical pathway and modules related to the invasion and mobility processes as EMT regulators are also enriched by proteins regulating metabolic pathways of ReconMap 2. 0. On the ReconMap 2. 0, the shared proteins are enriched in energy-providing processes such as the citric acid cycle (Krebs cycle) and oxidative phosphorylation and in processes related to pentose phosphate pathway, fructose and mannose metabolism, glycolysis and gluconeogenesis. Finally, the subsystem inositol phosphate metabolism is enriched by the shared proteins (Fig. 3  and Additional file 5). The same trend is observed at the level of the reactions regulation, finding proteins from apoptosis, cell cycle and mitochondrial processes implicated in catalysis of a large number of reactions in the ReconMap 2.
By extracting information about crosstalk between ACSN modules and ReconMap 2. 0 subsystems, it was possible to generate a network where nodes represent ACSN modules and ReconMap 2. 0 subsystems connected by edges if they shared common proteins (Fig. 4 , Additional files 5 and 8). The obtained network contains one large connected component and also a number of modules and subsystems that are not connected to each other. The interconnections between many signalling and metabolic processes on the network form communities. It was possible to identify three major communities each containing ACSN modules interconnected with ReconMap 2. 0 subsystems, that we called ' Apoptosis and Mitochondrial Metabolism' , 'Cell Cycle' and 'Cell Survival and Motility' according to their biological functions (Fig.
NaviCell representation of ACNS-ReconMap 2. 0 resource Due to their corresponding size and technical limitations, the two maps couldn't be merged into one single seamless map. The cross-linking via shared proteins was performed and the two maps were represented as interconnected layers using NaviCell web-based platform, allowing to shuttle between the maps by clicking on a common entity (see next paragraph). Both maps were preserved with their original layout so that their correspondent relevance of the visual organisation was not lost. Furthermore, this allows users to have an easier view and understanding of the whole system. Moreover, entities annotations from ReconMap 2. 0 have been recuperated from the original map and transferring to a NaviCell annotation format (see Materials and Methods), allowing to link entities to corresponding databases (Fig.
5 ). Navigation of ACSN-ReconMap 2. 0 resource using NaviCell platform ACSN and ReconMap 2. 0 maps were linked via the common players. Now that proteins had been displayed on top of the ReconMap 2. 0 network canvas, those common with ACSN will be used as links to shuttle through both maps. Exploration and shuttling between the two map layers is possible using the NaviCell Google Maps-like features  [5]. By clicking on a protein existing in both maps, a window with annotations will appear and a 'globe' icon will be clickable in the list seen in the 'Maps_and_Modules' section. A new window will be opened and the corresponding protein will be shown on this called map. In addition, the exploration of the ACSN-ReconMap 2. 0 resource is facilitated by the semantic zooming principle of NaviCell platform.
0 resource. The developed networks integration methodology and the suggested workflow is a generic mechanism and can be easily applied for integration of other comprehensive maps. The robustness of the method, computational speed and memory usage allows it to be used on any computer with Python and Java installed. Scripts used are open-source and accessible on GitHub (https://github. com/sysbio-curie/ CellDesigner_networks_map_integration_procedure). In this manuscript, we have shown that the merging of metabolic and signalling networks can be achieved and it provides many possibilities for data analyses and comprehension of implicated processes across both maps. In addition, the integrated resource allows to find gaps in connectively between signalling and metabolic processes and suggests exploration of potential links.
Funding This work has been supported by the  COLOSYS  grant  ANR-15-CMED-0001-04 , provided by the  Agence Nationale de la Recherche  under the frame of ERACoSysMed-1, the  ERA-Net  for Systems Medicine in clinical research and medical practice and by  PRECISE   H2020 EU grant. This work received support from  MASTODON program  by  CNRS  (project  APLIGOOGLE ). This article did not receive sponsorship for publication.
It can be assumed that PPP might be an interesting alternative to prevent outliers arising from defects in the baseline geometry in a double-difference processing. The baseline design strategy in a double-difference network processing has a strong impact on the quality and continuity of ZTD time series. Stępniak et al. (2017)  show that ZTD outliers are most of the time caused by sub-daily data gaps at reference stations which provoke disconnections of clusters of stations from the reference network and common-mode biases due to the strong correlation between stations in short baselines. Outliers can reach a few centimetres and more in ZTD and coincide usually with a jump in formal errors. The magnitude and sign of these biases are impossible to predict, because they depend on different errors in the observations and on the geometry of the baselines.
We present results of an all-sky search for continuous gravitational waves (CWs), which can be produced by fast spinning neutron stars with an asymmetry around their rotation axis, using data from the second observing run of the Advanced LIGO detectors. Three different semi-coherent methods are used to search in a gravitational-wave frequency band from 20 to 1922 Hz and a first frequency derivative from -1 × 10 -8 to 2 × 10 -9 Hz/s. None of these searches has found clear evidence for a CW signal, so upper limits on the gravitational-wave strain amplitude are calculated, which for this broad range in parameter space are the most sensitive ever achieved.
A cleaning procedure was applied to H1 data during post-processing in order to remove jitter noise and some noise lines (more details are given in  [47] ). All of the searches of this paper used this cleaned dataset. The calibration of this dataset and its uncertainties on amplitude and phase are described in  [48]. These searches don't use all the data from the observing run, since times where the detectors are known to be poorly behaving are removed from the analysis. This means that the effective amount of data used is smaller than the full duration of the run. As in previous observing runs, several artifical signals (called hardware injections) have been physically injected in the detectors in order to test their response and to validate the different pipelines. These hardware injections are described in Sections III, V and in the Appendix.
Pairs of candidates with distance d FH < 3 are considered coincident. This value was chosen based on a study with software simulated signals and allows, on one hand, to reduce the false alarm probability and, on the other, to be sufficiently robust with respect to the fact that a signal can be found with slightly different parameters in the two detectors. Coincident candidates are subject to a ranking procedure, based on the value of a statistic built using the distance and the FrequencyHough histogram weighted number count of the coincident candidates, as described in  [41]. In this analysis, after the ranking the eight candidates in each 0. 1-Hz band with the highest values of the statistic have been selected. The second column is the increased FFT duration, the third is the enhancement factor E, with respect to the original duration.
Upper limit computation Upper limits are computed in each 1-Hz band between 20 Hz and 1000 Hz by injecting software simulated signals, with the same procedure used in  [36]. For each 1 Hz band 20 sets of 100 signals each are generated, with fixed amplitude within each set and random parameters (sky location, frequency, spin-down/up, and polarization parameters). These are generated in time domain and then added to the data of both detectors in the frequency domain. For each injected signal in a set of 100, an analysis is done using the FrequencyHough pipeline over a frequency band of 0. 1 Hz around the injection frequency, the full spin-down/up range used in the real analysis, and nine sky points around the injection position  [36]. Candidates are selected as in the real analysis, except that no clustering is applied, as it would have been affected by the presence of too many signals.
B. SkyHough The SkyHough method has been used in other searches using data from the Initial LIGO S2, S4 and S5 and Advanced LIGO O1 observing runs  [27, 28, 32, 38, 40]. Its main description is given in  [42]. Here we summarize its main characteristics and the new features that have been implemented in this search. The code for the main part of the search is called lalapps DriveHoughMulti and is part of the publicly available LALSuite package  [57].
Post-processing The post-processing consists of several steps: 1. The output of the main SkyHough search is one toplist for each dataset (there are two datasets, each one with data from two detectors, detailed in Table  V ) and each region in parameter space. We search for coincidental pairs between these toplists, by calculating the distance in parameter space and selecting the pairs which are closer than a certain threshold called d co. For the coincidental pairs the centers (average locations in parameter space weighted by significance) are calculated. The parameter space distance is calculated as: d SH = ∆f δf 2 + ∆ ḟ δ ḟ 2 + ∆x δx 2 + ∆y δy 2 , (17) where the numbers in the numerators represent the difference between to templates and the numbers in the denominators represent the parameter resolution (this distance is unitless and is given as a number of bins).
Follow-up We describe a follow-up method based on the Fstatistic (described in more detail in subsection IV C) and the GCT metric method  [56]. This method uses the lalapps HierarchSearchGCT code, part of the publicly available LALSuite  [57] , and it is similar in spirit to the multi-step follow-up methods described in  [58]  or  [59]. The goal is to compare the F-statistic values obtained from software injected signals to the F-statistic values obtained from the outliers. We want to compare the F-statistic obtained at different stages which scale the coherent time. It is expected that for an astrophysical signal the F-statistic value should increase if the coherent time increases. The resolution in parameter space is given by  [56] : δf = √ 12m πT c ( 18 ) δ ḟ = √ 720m πT 2 c γ (19) δθ = √ m sky πf τ e , (20) where m and m sky are mismatch parameters, γ is a parameter which gives the refinement between the coherent and semi-coherent stages and τ e = 0.
C. Time-Domain F-statistic The Time-Domain F-statistic search method uses the algorithms described in  [33, 43, 60, 61]  and has been applied to an all-sky search of VSR1 data  [33]  and an all-sky search of the LIGO O1 data  [38, 40]. The main tool is the F-statistic  [43]  by which one can search coherently the data over a reduced parameter space consisting of signal frequency, its derivatives, and the sky position of the source. The F-statistic eliminates the need for a grid search over remaining parameters (see Eqs. 1 and 4), in particular, the inclination angle ι and polarization ψ. Once a signal is identified the estimates of those four parameters are obtained from analytic formulae. However, a coherent search over the whole LIGO O2 data set is computationally prohibitive and we need to apply a semi-coherent method, which consists of dividing the data into shorter time domain segments.
The short time domain data are analyzed coherently with the F-statistic. Then the output from the coherent search from time domain segments is analyzed by a different, computationally-manageable method. Moreover, to reduce the computer memory required to do the search, the data are divided into narrow-band segments that are analyzed separately. Thus our search method consists primarily of two parts. The first part is the coherent search of narrow-band, time-domain segments. The second part is the search for coincidences among the candidates obtained from the coherent search. The pipeline is described in Section IV of  [38]  (see also Fig. 13  of  [38]  for the flow chart of the pipeline). The same pipeline is used for the search of LIGO O2 data presented here except that a number of parameters of the search are different.
Our O2 results are comparable with the upper limits obtained in O1 by the Einstein@Home project  [39]  over the range 20-100 Hz. Note, however, that the Einstein@Home search covered a spin-down/up range smaller by almost one order of magnitude. Moreover, while the Einstein@Home search is, in principle, more sensitive due to the use of much longer data segments (compared to the FrequencyHough and SkyHough pipelines), with 210 hr duration, it is also less robust in case of deviations from the assumed signal model described in Section III. At frequencies higher than 100 Hz, the previous best upper limits were obtained in  [62]  using O1 data. Our results improve on those upper limits by approximately 17%. The 95% CL upper limits on h 0 can be converted to upper limits on ellipticity by using equation (  2 ) with a canonical value for the moment of inertia of 10 38 kg•m 2 and by using different distances: = c 4 4π 2 G h 0 d I zz f 2.
B. SkyHough SkyHough has analyzed frequencies from 50 to 1500 Hz and spin-down/up values from -10 -8 to 10 -9 Hz/s as shown in Fig. 2. The four different coherent times that have been used are shown in Table  III. This analysis uses the C02 cleaned dataset  [48] , and splits the data from H1 and L1 in two datasets, divided by time as shown in Table  V , where the start and stop times for each dataset are indicated. The main search generates a toplist per dataset per 0. 1 Hz band of 10000 candidates with a maximum of 1000 per sky-patch. The number of sky-patches depends on the frequency: to minimize the computational cost of the search, we try to minimize the number of skypatches for a limited amount of RAM. From 50 to 850 Hz, there are 28 sky-patches; from 850 to 1000 Hz, 31 sky-patches; from 1000 to 1150 Hz, 38 sky-patches; from 1150 to 1250 Hz, 45 sky-patches; from 1300 to 1500 Hz, 28 sky-patches.
After applying the post-processing stage previously described (with distance thresholds of d co = 3 and d cl = √ 14), we are left with 4548 0. 1 Hz bands (from a total of 14500) having coincidental pairs. We apply the population veto, used in many past searches, which demands that each dataset contributes to each cluster with at least two different templates. Af-   16 ) is T obs = 7915032 s, the maximum span of these datasets. ter applying this veto, only 1539 outliers remain. The next step is to apply the F-statistic follow-up method described in Section IV B 3 to these 1539 outliers. The thresholds obtained are 1. 47 and 3. 66 for the first and second comparison respectively, as shown in Fig 7. Only 17 outliers are above the threshold at 3. 66, as shown in Fig. 8. All of the outliers which are above the final threshold correspond to one of the hardware injections listed in Table VII or to one known source of instrumental noise, listed in  [46].
1 Hz band). Although no detections were made, we produce all-sky averaged upper limits on the strain of the signal h 0 (these upper limits are valid for all the frequency bands except the ones which have one of the 1539 outliers). We add software simulated signals to the original data by using lalapps Makefakedata v5. We have injected signals at 10 different 0. 1 Hz bands for each of the four coherent times (a total of 40 bands), which can be seen in Table  VI. These are bands which don't have outliers or instrumental known sources of lines or combs. We have used 5 different sensitivity depths at each coherent time, with 400 signals per sensitivity depth. The sensitivity depth is given by: D = √ S n h 0 , (25) where S n is the one-sided power spectral density. We inject signals at random positions in the sky, covering the full spin-down/up range and with random polarization, inclination and initial phase.
3. The improved results over the O1 search are due to the better sensitivity of the detectors, the use of a longer dataset and improvements of the pipelines. For the semi-coherent methods used in this analysis, the strain sensitivity is proportional to  [65] [66] [67] : h 0 ∝ S n T coh N ∼1/4. (28) For the same observing time, an improvement of 1. 15 in the noise floor of both detectors translates to an expected improvement of 1. 15 of the minimum detectable strain (looking at Fig. 1 , we see that H1 has improved by ∼1. 15, while L1 has improved less, so at most a factor of 1. 15 is what we expect from the improved noise floors). To ascertain the influence of the run duration (assuming that the same coherent time has been used), we compare the different number of segments used. Comparing O1  [40]  where ∼6600 segments were used by the SkyHough pipeline (similar numbers apply also for the other pipelines), with O2 where ∼10600 segments have been used, the expected improvement is of ∼1.
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory  and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium.
Local pattern mining on attributed networks is an important and interesting research area combining ideas from network analysis and data mining. In particular, local patterns on attributed networks allow both the characterization in terms of their structural (topological) as well as compositional features. In this paper, we present MinerLSD, a method for efficient local pattern mining on attributed networks. In order to prevent the typical pattern explosion in pattern mining, we employ closed patterns for focusing pattern exploration. In addition, we exploit efficient techniques for pruning the pattern space: We adapt a local variant of the standard Modularity metric used in community detection that is extended using optimistic estimates, and furthermore include graph abstractions. Our experiments on several standard datasets demonstrate the efficacy of our proposed novel method MinerLSD as an efficient method for local pattern mining on attributed networks.
Compared to our work described in  Atzmueller et al. (2018) , we have added onto the discussion of the MinerLSD algorithm, also considering further related approaches for putting the proposed method into context. Furthermore, we have considerably extended the evaluation and discussion of the proposed novel algorithm with new experiments, also using new (larger) datasets, and by illustrating the pattern mining approach using exemplary patterns. MinerLSD focuses both on local pattern mining (e. g. , for local community detection) using the local modularity metric  (Newman 2004; Newman and Girvan 2004; Atzmueller et al. 2016) , as well as graph abstraction that reduces graphs to k-core subgraphs  (Soldano et al. 2015). In order to prevent the typical pattern explosion in pattern mining, we employ closed patterns.
In addition, we exploit optimistic estimates for the local modularity for focussing pattern exploration inspired by community detection methods and for pruning the pattern space. Essentially, the optimistic estimate technique provides two advantages: First, it neglects the importance of a minimal support threshold which is typically applied in pattern mining. Second, it enables a very efficient pattern exploration approach, given a suitable threshold for the local modularity, as we will show below. Then, this threshold can of course alternatively be entirely eliminated in a top-k approach. We demonstrate the efficacy of our presented novel method MinerLSD by performing experiments on several standard datasets, in relation to two baselines for local pattern mining. Our contributions are summarized as follows: 1.
For local pattern mining on attributed graphs, we analyze the impact of generating closed patterns compared to standard pattern mining in terms of the search effort. 2. Using two baseline algorithms, we further investigate the impact of pruning the pattern exploration space using an optimistic estimate of the local modularity measure with different thresholds. 3. Finally, we propose the MinerLSD method for efficient local pattern mining on attributed graphs. MinerLSD relies on closed pattern mining, optimistic estimate pruning, and graph abstraction. The rest of this paper is organized as follows: Section "Related Work" discusses related work, before section "Background" introduces basic notions and concepts. After that, "The MinerLSD Algorithm" section presents the novel MinerLSD method. Next, section "Datasets" introduces the applied datasets.
The algorithm works on an edge dataset that is attributed with common attributes of the respective nodes. Then, communities are detected in a top-k approach maximizing a given community interestingness measure. This includes, among others, the local modularity, which is derived from the (global) measure, i. e. , the (Newman) Modularity  (Newman 2004; Newman and Girvan 2004). For an efficient community detection approach, COMODO utilizes optimistic estimate pruning. In this paper, we adapt the COMODO approach integrating optimistic estimate pruning for the local modularity as proposed by COMODO with closed abstract pattern mining of the MinerLC algorithm. This results in the efficient and effective MinerLSD algorithm, making use of efficient techniques based on abstract closed pattern mining and branch-and-bound pruning according to the local modularity.
The MinerLSD Algorithm In the following, we describe our proposed novel method MinerLSD in detail. MinerLSD integrates core subgraph closed pattern mining with pattern selection according to the local modularity MODL function, and optimistic estimate pruning according to a specific optimistic estimator, i. e. , oe(MODL). As input parameters, MinerLSD requires a graph G = (V , E), a set of items I, a dataset D describing vertices as itemsets and a core operator p. p depends on G and to any image p(X) = W we associate the core subgraph C whose vertex set is vs(C) = W. In our experiments, p(X) returns the k-core of X. As further parameters, MinerLSD considers the corresponding value k as well as a frequency threshold s (defaulting to 0) and a local modularity threshold lm. The algorithm outputs the frequent pairs (c, W ) where c is a core closed pattern and W = p • ext(c) its associated k-core.