dataesr/scientific-paragraphs-categorization huggingface dataset:
These precise radial-velocity data also allow us to rule out the presence of any other nearby stellar or brown dwarf object orbiting the primary star. We note that observations will continue when the system is again observable to provide a robust mass for the planet. We explored a number of other methods to determine from which star the transit originates. Because our system has high proper motion, we were able to investigate whether the host star had moved on top of a background star which could be the source of the transit signal. We compared the position of the primary star in digitally-scanned SuperCOSMOS  (Hambly et al. 2001 ) archival POSS-1 images (taken 1953. 93) with the star's position at the time of the T ESS observations (we chose November 1 -2018. 83 -the effective mid-point of sector 4).
The system had moved 29. 6 since the POSS-1 image was taken. No background star was seen at the current position of LTT 1445A in the POSS-1 image, which has a magnitude limit of roughly 19. 5 mag at 620-670 nm. The data validation (DV) report from the SPOC pipeline provides a number of tests to aid in analyzing planet candidates. Two of the tests in the DV report use a difference image to analyze the centroid shift that occurs during the transit event to determine from which star the transit signal originates. The difference image provides the flux value for each pixel by taking the difference between the mean out-of-transit flux value and the mean in-transit value. Therefore, the star that is the planet host will have residual flux in the difference image. A centroid is then determined for the difference image.
Light Curve Modeling Because of the complex nature of the light curve, we used a combination of exoplanet (Foreman-Mackey et al. 2017a) and EXOFASTv2 (Eastman 2017) for our light curve modeling. The purpose of using exoplanet was to fit and remove the photometric modulation in the T ESS light curve using Gaussian Processes (GP) while preserving the planetary transit signal. We then used EXOFASTv2, which does not currently have GP capability, to simultaneously fit the de-trended transit data from exoplanet and the HARPS RV data. exoplanet also has the capability to fit RV data, but we did not get convergence when including the HARPS data in the model. We elected not to include the third transit in our analysis because the light curve baseline showed a strong slope at egress; we note that this transit was also omitted from the results in the SPOC DV report.
exoplanet For the first part of the light curve analysis, we used the python package exoplanet  (Foreman-Mackey et al. 2019). Exoplanet employs probabilistic methods to model exoplanet transit and radial velocity data sets. It has the additional capability to incorporate Gaussian Processes (GP) with celerite (Foreman-Mackey et al. 2017a) and limb-darkened light curves with starry  (Luger et al. 2018). We used the SPOC-generated Pre-Search Data Conditioning Simple Aperture Photometry (PDCSAP) light curve  (Smith et al. 2012; Stumpe et al. 2014) , corrected with our calculated dilution factor. Before fitting, we removed positive outliers (flares) deviating by more than 3. 0-σ above the median absolute deviation of the PDCSAP light curve. To model the planetary transit, we used a limb-darkened transit model and a Keplerian orbit.
5 + τ 3 + τ 4 (3) ω 1 = 4πQ 1 τ 2 4Q 2 1 -1 (4) S 1 = τ 1 ω 1 Q 1 (5) Q 2 = 0. 5 + τ 3 (6) ω 2 = 8πQ 2 τ 2 4Q 2 2 -1 (7) S 2 = τ 5 τ 1 ω 2 Q 2 (8) This is an appropriate kernel for data that are quasiperiodic in nature, such as the observed rotational modulation in the light curve of LTT 1445ABC. The hyperparameters for this GP are the amplitude of variability τ 1 , the primary period of the variability τ 2 , the quality factor τ 3 , the difference between the quality factors of the first and second modes of the two oscillators τ 4 , the fractional amplitude of the secondary mode to the primary mode τ 5 , and a jitter term added to account for excess white noise τ 6. We placed a uniform prior on τ 5 and Gaussian priors on τ 1 , τ 2 , τ 3 , τ 4 , and τ 6. The mean (µ) and standard deviation (σ) value we set for the Gaussian prior on τ 1 , τ 2 , τ 3 , τ 4 , and τ 6 are shown in Table  7.
EXOFASTv2 We used the output light curve data from exoplanet with the stellar variability removed as input for the software package exofastv2  (Eastman et al. 2013; Eastman 2017). exofastv2 is a suite of IDL routines that simultaneously fits exoplanetary transit and radial velocity data using a differential Markov Chain Monte Carlo (MCMC) code. Because we derived the stellar parameters as described in §4. 2, we did not include a spectral energy distribution in the fit, and we disabled the default MIST stellar evolutionary models that use isochrones to constrain the stellar parameters. We placed Gaussian priors on the mass, radius, effective temperature, and metallicity that were equal to the uncertainties noted in §4. 2. The quadratic limb darkening coefficients were constrained by the T ESS data and penalized for straying from the values predicted by the  Claret (2017)  limb darkening tables at a given logg, T eff , and [Fe/H], as is standard within EXOFASTv2.
In Figure  7 , we show the best-fit model to the RV data, which we acknowledge is marginal. The uncertainties illustrated are the RV uncertainties from Table  5  added in quadrature with the fitted RV jitter. The uncertainty on the planet's mass is largely due to the sparse RV coverage, so we place a 3-σ upper limit on the RV semi-amplitude K, planet mass, and planet density. The 3-σ upper limit is where 99. 73% of all links of all chains, after discarding the burn-in, are smaller than the quoted value. Listed in Table  8  are the 68% confidence values from the exo-fastv2 fit. In addition we list the 3-σ upper limits for K and its derived parameters. To confirm that the two light curve fitting packages produce the same result, we also ran exofastv2 on the GP-corrected transit data without including the radial velocity data.
The 0. 038 AU separation of LTT 1445Ab is significantly less than one-third the 21 AU separation between A and BC (the smallest separation measured between A and B by Rossiter in 1943). The preliminary spectroscopic orbit of LTT 1445Ab from HARPS data, fit simultaneously with the T ESS transit data using exofastv2. The orbit and its residuals are shown in the top panel, while the velocity drift of the primary star due to the stellar BC components is evident in the bottom panel, along with the residuals. Therefore, the planet is likely in a dynamically stable orbit, even given the presence of two other stars in the system. M dwarfs are thought to be more likely than more massive stars to host multiple planets in compact orbits of less than roughly ten days  (Ballard 2019; Muirhead et al. 2015 , respectively, for early and mid-to-late M dwarfs).
LTT 1445A may very well host multiple planets, but with only one sector of data from T ESS, we have not yet seen indications of other planets in the system. The alignment of the three stellar components and the edge-on orbit of the BC pair and planet is suggestive of the co-planarity of the system. Including LTT 1445ABC, there are six 'pure' M dwarf triples known within 10 pc. 10 Among these are the very tight systems LTT 12352 and GJ 866 that have separations much smaller than LTT 1445. The remaining three systems -GJ 2005, GJ 1230, and GJ 1245 -all have archival HST NICMOS images available. Remarkably, none appear to exhibit the possible co-planarity we observe in LTT 1445ABC. The presence of a transiting planet in this system raises the possibility that the entire system is co-planar, which has intriguing implications for planet formation scenarios.
Acknowledgments The authors thank the anonymous referee for their prompt response and for their comments and suggestions. We are indebted to  Guillermo Torres  for developing the M dwarf-specific relations from which we calculated the T ESS magnitudes. We thank  Mark Everett  for his role in obtaining the DSSI speckle data. We are extremely grateful to  Matthew Payne ,  Chelsea Huang ,  Joseph Rodriguez ,  Samuel Quinn , and  Jacob Bean  for illuminating conversations and suggestions that helped improve the analysis and interpretation of the results presented here. The  MEarth Team  gratefully acknowledges funding from the  David and Lucille Packard Fellowship  for Science and Engineering (awarded to D. C. ). This material is based upon work supported by the  National Science Foundation  under grants  AST-0807690 ,  AST-1109468 ,  AST-1004488  ( Alan T.
C-0488 ,  183. C-0437 , and  1102. C-0339. This research has made use of the  Washington Double Star Catalog , maintained at the  U. S. Naval Observatory. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. AAM and HD-L are supported by  NSF   Graduate Research Fellowship  grants  DGE1745303  and  DGE1144152 , respectively. NA-D acknowledges the support of  FONDECYT  project  3180063. EPH grate-fully acknowledges funding from  NSF  grant  AST-1517824. The RECONS team is indebted to long-term support from the  NSF , most recently under grant  AST-1715551.
Work by JNW was partly supported by the  Heising-Simons Foundation. Support for JKT and the acquistion of the speckle data was provided by  NASA  through  Hubble Fellowship grant   HST-HF2-51399. 001  awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , for  NASA , under contract  NAS5-26555. XD is supported by the  French National Research Agency  in the framework of the  Investissements dAvenir program  ( ANR-15-IDEX-02 ), through the funding of the " Origin of Life " project of the  Université Grenoble-Alpes. CDD acknowledges support from the  T ESS   Guest Investigator Program  through grant  80NSSC18K1583.
Our method does not run real-life applications to generate traffic but uses a single program that reproduces model data while respecting some properties of that data (size, duration, acknowledgment by services, etc. ). Thus, this method significantly reduces the resource requirements for the generation of evaluation data. In this paper, we propose a methodology to eval-uate security products and services with the proposed network simulation. This methodology aims to help the evaluator to identify the inputs required by the network simulation but also to choose a correct simulation topology according to the evaluation goals (use of external components, selection of inputs, the constitution of the ground truth, etc. ). We illustrate that methodology with the example of the evaluation of the Intrusion Detection System (IDS) Suricata with a network simulation prototype.
Evaluation Environment As previously mentioned, the evaluation of targets like services and security products verify all kinds of aspects. A large variety of tools exists to evaluate those aspects. Workloads drivers like ApacheBench and SPEC CPU2017 are drivers that produce large amounts of data to stress specific resources of the target (CPU, network, memory, etc. ). However, the produced traffic is dependent on the tool and is detrimental to inline security products that require a learning phase, such as anomaly-based IDS. Also, it only tests some of the functionalities of the target. To test all functionalities of a target, we previously mentioned that model validation and verification tools exist, but evaluators usually have to manually test the functions or draft homegrown scripts to do so.
The best way to generate attacks on a target is to either manually generate attacks or use an exploit script. Exploit databases like Metasploit allow the evaluator to test a large variety of attacks targeting specific products. However, exploits and homegrown scripts are specific and not scalable. Other methods also exist to look for vulnerabilities in a target like fuzzing and vulnerability scanners (e. g. ,  Arachni, OWASP, Wapiti, OpenVAS, etc. ). All these tools and methods require a network or virtualized structure. It can be a physical network dedicated to the evaluation of products, a virtual network existing above a physical network (overlay networks) or a virtual network with solely virtual network equipment. The choice of the network will depend on the resources of the evaluator, but it can be costly and time-consuming to set up and maintain.
The virtual host generates the data on the client side of A 1 with the data generating function f corresponding to the selected level realism. The host gives f the model data of the action A 1 and the elementary action parameters in the scenario. With an appropriate scenario, the network simulation can make a large number of clients use all the functionalities of the service over the required period of time, verifying both the compliance to the specifications and the workload processing capacity. For the production of attacks, there are two solutions: the evaluator can either provide inputs for attacks as he would for other elementary actions or connect an external source of attacks (exploit database, physical attacker, vulnerability scanner, etc. ) to the simulated network. The simulation model uses the same data generating function to produce malicious or benign data.
EVALUATION OF AN IDS: EXPERIMENTAL RESULTS In this section, we present an application of the previous methodology for the evaluation of a security product, focusing on the IDS Suricata. The goal of this evaluation is to verify the impact of a consequent volume of benign data on the detection rate of Suricata. We also want to look for differences in the analysis by Suricata between live and offline traffic. We call live analysis the alerts generated by Suricata during the simulation and offline analysis the alerts generated by Suricata from the network traces captured from the simulation. The main difference between the two analysis is that during the live analysis, Suricata is under stress to keep up with the flow of data passing through it, while in the offline analysis Suricata reads the traces at its own pace.
For the production of attacks, there are two solutions: the evaluator can either provide inputs for attacks as he would for other elementary actions or connect an external source of attacks (exploit database, physical attacker, vulnerability scanner, etc. ) to the simulated network. The simulation model uses the same data generating function to produce malicious or benign data. The last step of every evaluation is the analysis of the experimental results. This step is a comparison of the ground truths of the different components of.
Data analysis To assess the participants' ability to perform our hand-tracking task, the following dependent variables were extracted from each trial. For all trials we computed the mean Euclidian distance between cursor position and target position. The temporal relationship between cursor and target movement, and between eye and target movement was estimated by means of cross correlations that simultaneously took into account the vertical and horizontal axes. To simultaneously cross correlate horizontal (x) and vertical (y) position signals between effectors, we interleaved the x and y signals, and always time shifted these interleaved signals by a multiple of two samples  (Danion & Flanagan, 2018; Flanagan, Terao, & Johansson, 2008). A positive value indicates that the cursor was lagging on the target.
Acknowledgments We would like to thank  Martin York ,  Justin Peterson , and  Tayler Jarvis  for technical support and logistical support. Support for this research was provided by the  CNRS  ( PICS N8 191607 ), the  Natural Sciences and Engineering Research Council of Canada  ( RGPIN/04837 ), and the  Canadian Institutes of Health Research  ( 82837 ). JM was supported by the  Innovative Training Network Perception and Action in Complex Environment (PACE ) that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant  agreement  N8 642961. This paper reflects only the authors' view and that the  Research Executive Agency (REA)  of the  European Commission  is not responsible for any use that may be made of the information it contains.
In particular, it allows CONSENT to efficiently scale to Oxford Nanopore ultra-long reads. Our experiments show that CONSENT compares well to the latest state-of-the-art self-correction methods, and even outperforms them on real Oxford Nanopore datasets. In particular, they show that CONSENT is the only method able to scale to a human dataset containing Oxford Nanopore ultra-long reads, reaching lengths up to 340 kbp.
Alignment piles An alignment pile represents a set of reads that overlap with a given read A. More formally, it can be defined as follows. For any given read A, we define an alignment pile for A as a set of alignment tuples (A, R, Ab, Ae, Rb, Re, S) where R is a long read id, Ab and Ae represent respectively the start and the end positions of the alignment on A, Rb and Re represent respectively the start and the end positions of the alignment on R, and S indicates whether R aligns forward (0) or reverse complement (1) to A. One can remark that, compared to Daccord, this definition is slightly. Re] if S = 1 (where R represents the reverse-complement of read R). This edit script can easily be retrieved by Daccord, as it relies on DALIGNER  (Myers, 2014)  to compute actual alignments between the long reads.
However, as CONSENT relies on a mapping strategy, it does not have access to such an information, and we thus chose to exclude the edit script from the definition of a pile. In its alignment pile, we call the read A the template read. The alignment pile of a given template read A thus contains all the necessary information needed for its correction. An example of an alignment pile is given in Figure  2. A R1 R2 R3 R4 R5 R6 Fig. 2 : An alignment pile for a template read A. The pile is delimited by vertical lines at the extremities of A. Prefixes and suffixes of reads overlapping A outside of the pile are not considered during the next steps, as the data they contain will not be useful for correcting A.
Consensus computation In order to compute the consensus of a window, CONSENT uses POAv2  (Lee et al. , 2002) , an implementation of a multiple sequence alignment strategy based on partial order graphs. These graphs are directed acyclic graphs, and are used as data structures containing all the information of the multiple sequence alignment. This way, at each step (i. e. at each alignment of a new sequence), the graph contains the current multiple sequence alignment result. To add a new sequence to the multiple alignment, the sequence is aligned to the DAG, using a generalization of the Smith-Waterman algorithm. Unlike other methods that compute 1V1 alignments between the read to be corrected and other reads mapping to it, and then build a result DAG to represent the multiple sequences alignment, this strategy allows CONSENT to directly build the result DAG, during the multiple alignment.
Comparison to the state-of-the-art We now compare CONSENT against state-of-the-art error correction methods. We include the following tools in the benchmark: Canu, Daccord, FLAS, and MECAT. We voluntarly excluded LoRMA from the comparison, as it tends to split the reads a lot, and thus to produce reads that are usually shorter than 500 bp. We also exclude hybrid error correction tools from the benchmark, as we believe it makes more sense to only compare self-correction tools against each other. Comparison on simulated data is presented in Section 3. 2. 2, and comparison on real data in Section 3. 2. 3. Datasets used for these comparisons are presented in Section 3. 2. 1. All tools were ran with default or recommended parameters. For CONSENT, we used a minimum support of 4 to define a window, a window size of 500, an overlap size of 50 between the windows, a k-mer size of 9 for the chaining and the polishing, a threshold of 4 to consider as solid.
Datasets For our experiments, we used both simulated Pacific Biosciences and real Oxford Nanopore long reads. Pacific Biosciences reads were simulated with SimLoRD, using the following parameters: -probability-threshold 0. 3 -prob-ins 0. 145 -prob-del 0. 06, and -prob-sub 0. 02. Two datasets with a 12% error rate were thus generated for E. coli, S. cerevisiae and C. elegans: one with a 30x coverage, and one with a 60x coverage, corresponding to typical sequencing depth in current long reads experiments. As for the real Oxford Nanopore data, a 63x coverage dataset from D. melanogaster, and a 29x coverage from H. sapiens, containing ultra-long reads, reaching lengths up to 340 kbp. Further details and accession numbers for all the datasets are given in Table  2. Details on the used reference sequences are given in Table  3.
Comparison on simulated data To precisely assess the accuracy of the different correction methods, we first tested them on the simulated Pacific Biosciences datasets. LRCstats was used to evaluate the correction accuracy of each method. However, LRCstats did not scale to the C. elegans 60x dataset, requiring more than 4 days and 100 GB of RAM to compute the results. For this dataset, we thus only report throughput and error rate statistics, obtained by aligning the corrected reads to the reference with Minimap2. Correction statistics of all the aforementioned tools on the different datasets, along with their runtime and memory consumption, are given in Table  4. For methods having distinct, easily identifiable, steps for overlapping and correction (i. e. Daccord, MECAT and CONSENT), we additionally report runtime and memory consumption of these two processes apart.
All the correction experiments were run on a computer equipped with 16 2. 39 GHz cores and 32 GB of RAM. Daccord clearly performed the best in terms of throughput and quality, outperforming all the other methods on the E. coli and the S. cerevisiae datasets. However, the overlapping step, relying on full alignment of the long reads against each other, consumed high amounts of memory, 3x to 11x more than CONSENT or MECAT mapping strategies. As a result, Daccord could not scale to the C. elegans datasets, DALIGNER reporting an error upon start. On the contrary, Canu displayed the highest error rates on all the datasets, but consumed relatively stable, low amounts of memory. In particular, on the two C. elegans datasets, it displayed the lower memory consumption among all the other methods. MECAT performed the best in terms of runtime, outperforming all the other tools by several order of magnitudes on all the datasets.
Its overlapping strategy was also highly efficient, and displayed the lowest memory consumption among the other strategies, on all the datasets. However, compared to Minimap2, the overlapping strategy adopted in CONSENT, MECAT's overlapping strategy displayed higher runtimes, raising according to both the size of the dataset and the genome complexity, P. Table  4. LRCStats results on the simulated Pacific Biosciences datasets. Results for the C. elegans 60x datasets were obtained by aligning the reads to the reference, as LRCstats did not scale, and required more than 4 days and 100 GB of memory to compute the results. Only throughput and error rate are thus reported for this dataset. Daccord results are missing for the two C. elegans datasets, as DALIGNER failed to perform alignment, reporting an error upon start, even when ran on a cluster node with 28 2.