dataesr/scientific-paragraphs-categorization huggingface dataset:
The high-likelihoods used here include only T T spectra. H 0 is given in the usual units of km s -1 Mpc -1. [1] Parameter  [2]  2013N(DS)  [3]   [5]  100θ MC. (3) In the 2013 papers, the Planck temperature likelihood was a hybrid: over the multipole range = 2-49, the likelihood was based on the Commander algorithm applied to 87 % of 7 Throughout this paper, we adopt the following labels for likelihoods: (i) Planck TT denotes the combination of the T T likelihood at multipoles ≥ 30 and a low-temperature-only likelihood based on the CMB map recovered with Commander; (ii) Planck TT+lowP further includes the Planck polarization data in the low-likelihood, as described in the main text; (iii) labels such as Planck TE+lowP denote the T E likelihood at ≥ 30 plus the polarization-only component of the map-based low-Planck likelihood; and (iv) Planck TT,TE,EE+lowP denotes the combination of the likelihood at ≥ 30 using T T , T E, and EE spectra and the low-temperature+polarization likelihood.
We make occasional use of combinations of the polarization likelihoods at ≥ 30 and the temperature+polarization data at low-, which we denote with labels such as Planck TE+lowT,P. the sky computed using a Blackwell-Rao estimatorl the likelihood at higher multipoles ( =50-2500) was constructed from cross-spectra over the frequency range 100-217 GHz using the CamSpec software (Planck Collaboration XV 2014), which is based on the methodology developed in  Efstathiou (2004)  and  Efstathiou (2006). At each of the Planck HFI frequencies, the sky is observed by a number of detectors. For example, at 217 GHz the sky is observed by four unpolarized spider-web bolometers (SWBs) and eight polarization sensitive bolometers (PSBs). The TOD from the 12 bolometers can be combined to produce a single map at 217 GHz for any given period of time.
(6) We have made minor changes to the foreground modelling and to the priors on some of the foreground parameters, as discussed in Sect. 2. 3 and Planck Collaboration XI (2016). Given these changes to data processing, mission length, sky coverage, etc. , it is reasonable to ask whether the base ΛCDM parameters have changed significantly compared to the 2013 numbers. In fact, the parameter shifts are relatively small. The situation is summarized in Table  1. The second column of this table lists the Planck+WP parameters, as given in table 5 of PCP13. Since these numbers are based on the 2013 processing of the nominal mission and computed via a DetSet CamSpec likelihood, the column is labelled 2013N(DS). We now make a number of specific remarks about these comparisons. (1) 4-K cooler line systematics.
After the submission of PCP13 we found strong evidence that a residual in the 217 × 217 DetSet spectrum at ≈ 1800 was a systematic caused by electromagnetic interference between the Joule-Thomson 4-K cooler electronics and the bolometer readout electronics. This interference leads to a set of time-variable narrow lines in the power spectrum of the TOD. The data processing pipelines apply a filter to remove these lines; however, the filtering failed to reduce their impact on the power spectra to negligible levels. Incomplete removal of the 4-K cooler lines affects primarily the 217 × 217 PSB×PSB cross-spectrum in Survey 1. The presence of this systematic was reported in the revised versions of 2013 Planck papers. Using simulations and also comparison with the 2013 full-mission likelihood (in which the 217 × 217 power spectrum "dip" is strongly diluted by the additional sky surveys) we assessed that the 4-K line systematic was causing shifts in cosmological parameters of less than 0.
Acknowledgements. The Planck Collaboration acknowledges the support of:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MINECO , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ;  ERC  and  PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www. cosmos. esa. int/web/planck/planck-collaboration. Some of the results in this paper have been derived using the HEALPix package. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013) /  ERC  Grant Agreement No.
TRINI Overview The objective of our research work was to define a GC-aware load balancing strategy (TRINI) which is able to dynamically adjust to the specific GC characteristics of the underlying application. This strategy would allow the load balancer to forecast the occurrence of the MaGC events with enough accuracy to exploit that information for improving the performance of a cluster. In Figure  2 , we depict the conceptual view of our solution. TRINI periodically retrieves information from the application nodes in order to characterise it. Then, it identifies the most suitable policy based on the GC characteristics of the application running on each node (termed as program family). Finally, the chosen policy is used to forecast the MaGC events and balance the incoming workload among the available application nodes.
In this context, a program family encompasses a set of programs which can be treated similarly because they share some common GC characteristics. For example, a set of program families might be defined according to the duration of the MaGCs. One family can be defined for those programs which tend to suffer MaGCs of small duration (e. g. a few hundreds milliseconds). This is because these MaGCs do not normally represent a major performance issue. On the contrary, another family can be defined for those programs which tend to suffer MaGCs of longer duration. Each program family has two properties: (1) An evaluation criteria to determine if the GC behaviour of an application qualifies for that family. In our previous example, a possible evaluation criterion might be the comparison of the MaGC duration of the monitored application against the duration ranges of each defined program family.
TRINI Core Process TRINI has a core process which coordinates its MAPE-K elements. This process (depicted in Figure  3 ) is triggered when the load balancer starts. As an initial step, it uses a default policy (e. g. , all the available MiGC history might be used to forecast the MaGCs). This initial policy considers any additional configuration provided at start-up time (e. g. , information base such as the load balancing algorithm to use) and it is utilised for all the application nodes. Next, the loop Copyright c 0000 John Wiley & Sons, Ltd. Softw. Pract. Exper. (0000) Prepared using speauth. cls DOI: 10. 1002/spe TRINI 7 specified in the monitor and analyse phases starts for all the application nodes (in parallel), until the load balancing finishes: A new set of data samples is collected, based on the program GC characteristics used to define the set of available program families (e.
g. , GC and memory snapshots). After the collection occurs, the analyser process checks if the current program family suits the GC characteristics of the underlying program. If it is not the case, the evaluation criteria of the other program families are assessed to identify the new program family, which is then used until the next evaluation phase occurs. These actions retrieve their configurations from the database of program families (represented as dashed arrows in Figure  3 ). Furthermore, any exceptions are internally handled and reported.
An example of this process is shown in Figure  4. Firstly, the algorithm uses the OldGen historical data within the FWS (represented as a rectangle) to feed a linear regression model (LRM). This is done to predict the rate of increase in the YoungGen, as a function of the OldGen, and thus extrapolate the data to the point where the OldGen will exceed its maximum threshold (90 MB in our example) and trigger a MaGC. This yields a prediction that the next MaGC will occur when the YoungGen reaches 225 MB in our example. Secondly, the algorithm feeds this YoungGen threshold to another LRM which extrapolates the time series of the YoungGen memory and predicts that a new MaGC event will occur at a time of 600 ms (as shown in Figure  5 ). This forecast process continues iteratively until the monitored application finishes or the forecast is no longer needed.
MiGC-CV Program Families Among the alternative strategies to develop policies for TRINI, we initially concentrated on automating the selection of the FWS. This is because our work at  [39]  showed that the accuracy of the MaGA algorithm is particularly sensitive to this configuration. This sensitivity occurs because the FWS delimits the degree of knowledge (in terms of historical memory data) which is used to forecast the MaGCs (as explained in Section 4. 3). Also, in those experiments no single FWS achieved the lowest forecast error in all the cases, showing that there is no "best-fit-for-all" FWS. Meanwhile, the results of our work at  [13]  showed that the MaGA algorithm tends to benefit from having more historical data available. However, this growth is usually not monotonic. On the contrary, the optimal FWS might experience troughs.
For this purpose, we used the Java Management Extension (JMX)  [43]  to interact with the monitored JVM. JMX was chosen because it is a standard Java technology which can retrieve all the information needed for predicting the MaGC events (e. g. , memory usages or GC snapshots). Environment. All the experiments were performed in an isolated test environment, so that the entire load was controlled. This environment was composed of fifty two virtual machines (VM): A cluster of fifty application nodes with one load balancer, and one load tester node (as shown in Figure  6 ). All the VMs had the following characteristics: 4 virtual CPUs at 2. 20GHz, 3GB of RAM, and 50GB of HD; running Linux Ubuntu 12. 04L, and OpenJDK JVM 7u25-2. 3. 10 with a 1. 6GB heap. Each JVM was configured to initialise its heap to its maximum size, and the calls to programmatically request a MaGC were disabled.
The load tester node also used an Apache JMeter 2. 9  [44]  (a leading open source tool used for application performance testing), and the application nodes ran an Apache Tomcat 6. 0. 35  [45]  (a popular open source Web Application Server for Java). The VMs were located on two Dell PowerEdge M620 servers inside a Dell PowerEdge VRTX chassis. Each server was equipped with 2 Intel Xeon E5-2660 v2 CPUs at 2. 20GHz (10 cores/20 threads), 192 GB of RAM, a 10 GbE network card, and VMware ESXi 5. 5. 0 as hypervisor. Additionally, the chassis provided 12. 3 TB of SAS storage (composed of a hard-drive backplane with 14 hard drives) through a 6 Gbps PERC adapter. Load Balancing Algorithms. The four algorithms discussed in Section 2 were tested: Round robin (RR), random (RAN), weighted round robin (WRR) and weighted random (WRAN); as well as their developed GC-aware counterparts (GC-RR, GC-RAN, GC-WRR, GC-WRAN).
Unlike other benchmarks (which are synthetically generated), these are real-life programs from different business domains and which are widely used in the industry. Appendix A presents a summary of these benchmarks and their respective set of programs. In order to be able to call a program from within a JMeter HTTP test script (so that multiple concurrent calls could be invoked per application node), a wrapper JSP was developed and installed in the Tomcat instance of each application node. For each program, a JMeter test script was created, adding some controlled diversity to the workload. For the DaCapo programs, it involved varying the workload size between program calls (using the available pre-defined workload sizes of DaCapo). In the case of the SPECJVM programs, the controlled diversity involved varying the execution time (in the range of 30 to 90 seconds).
Each JMeter test run lasted 60 minutes and used 500 concurrent users. Finally, each individual program call was considered a transaction. GC Strategies. The three strategies discussed in Section 2 were used: Serial GC (sGC), Parallel GC (pGC), and Concurrent GC (cGC). This was done with the aim of diversifying more the evaluated GC behaviours (as the GC strategy is a major factor affecting the GC behaviour  [12] ). Evaluation Criteria. In terms of performance, our main metrics were throughput per second (tps) and response time (ms). Concerning response time, lower values are better; while for throughput, higher values are better. These metrics were collected with JMeter. In terms of overhead, our main metrics were CPU (%) and memory (MB) utilisations. In both cases, lower values are better.
More importantly, the forecast accuracy reaches practically 100% when the variability is below 0. 1. This behaviour persisted irrespective of the chosen load balancing algorithm or GC strategy. These results show how M iGC CV is an appropriate metric to characterise the program behaviours into families. In our experiments, we also identified that the performance improvements yielded by TRINI are mainly driven by two factors: 1. The total time spent on MaGC in all the application nodes (M aGC D ), as it captures the amount of potential gain that can be obtained. 2. The forecast accuracy (FA) of TRINI, which is the actual enabler that allows the potential gains to be converted into actual gains (by diverting the workload from any node which is suffering a MaGC). In general terms, the performance improvements tend to be bigger when the M aGC D is long (as there is more potential gain to exploit).
Likewise, we concentrated on the Serial GC strategy because it tends to suffer the longest pauses  [7] , hence benefiting more from our work. Finally, we focused on 5 programs which were representative of the program classification that we presented in our previous work  [13]. This configuration allowed us to test a diverse set of GC behaviours with a smaller set of experimental configurations. That classification (shown in Table  I ) grouped the programs of the DaCapo and SPECJVM benchmarks according to their GC characteristics (the M aGC D and the M iGC CV ). In Table  I , the programs underlined are the ones used in this experiment. Evaluating the performance improvements yielded by TRINI in different sizes of clusters; and assessing the behaviour of the overhead in such clusters. Performance Improvements.
Our method goes further by using natural visual cues (marker-free solution) and a global estimation approach based on sequential Monte Carlo methods for the model adaptation. We apply our method to the iCub humanoid robot  (Metta et al. , 2010) , depicted in Figure  1. Instead of learning an internal model from scratch, we exploit a computer graphics (CG) model of the robot that includes the CAD kinematics [provided within the YARP/iCub software framework as described in  Pattacini (2011) ] and an appearance model of the hand shape and texture. This model is adapted in real-time during reaching movements using data from the motor encoders and from the cameras located in the robot eyes. The model adaptation consists in estimating a set of joint offsets to be added to the CAD kinematics to better describe the real robot.
Simulation Results In the simulation experiments, we use the iCub simulator both as robot and as internal mental simulation. The iCub simulator is a realistic software that uses ODE (Open Dynamic Engine) for simulating the motion of rigid bodies and their physical interaction. It uses the same software and control architecture of the real iCub robot. In order to consider the iCub simulator a realistic model of the real robot, we introduce artificial angular offsets in the 7 DOFs of the right arm kinematic chain. We define ETA = [5, 4, 3, -2, 3, -7, 3]; these offsets have the same order of magnitude of the calibration errors, we typically encounter on the real robot. We use the same set of offsets in all the simulation experiments, in order to be able to compare the different results. Therefore, in these experiments the only difference between the robot and the internal mental simulation is the set of artificial offset; the goal of the body schema online adaptation is to compensate for these offsets.
Background: Genome layout and gene regulation appear to be interdependent. Understanding this interdependence is key to exploring the dynamic nature of chromosome conformation and to engineering functional genomes. Evidence for non-random genome layout, defined as the relative positioning of either co-functional or co-regulated genes, stems from two main approaches. Firstly, the analysis of contiguous genome segments across species, has highlighted the conservation of gene arrangement (synteny) along chromosomal regions. Secondly, the study of long-range interactions along a chromosome has emphasised regularities in the positioning of microbial genes that are co-regulated, co-expressed or evolutionarily correlated. While one-dimensional pattern analysis is a mature field, it is often powerless on biological datasets which tend to be incomplete, and partly incorrect.
Moreover, there is a lack of comprehensive, user-friendly tools to systematically analyse, visualise, integrate and exploit regularities along genomes. Results: Here we present the Genome REgulatory and Architecture Tools SCAN (GREAT:SCAN) software for the systematic study of the interplay between genome layout and gene expression regulation. GREAT:SCAN is a collection of related and interconnected applications currently able to perform systematic analyses of genome regularities as well as to improve transcription factor binding sites (TFBS) and gene regulatory network predictions based on gene positional information.
We developed GREAT:SCAN (Genome REgulatory Architecture Tools:SCAN), a collection of on-line software tools designed to perform systematic detection of regular patterns along genomes, integrate and interconnect results between available methods and provide informative visualisations. GREAT:SCAN extends two algorithms previously developed by our team for the detection of periodically arranged genes  [12]  and the prediction of transcription factor binding sites (TFBS)  [13]. It provides a web user interface which streamlines the usage of these algorithms, performs a fully automated analysis of regularities among genomic features, extends with novel functionalities the analytical capabilities of the previous software and reports results in human-(plots and graphs) as well as in machine-(tables) readable formats.
GREAT:SCAN is available in two versions: a) running as an online application integrated in the computational framework of the GREAT portal in the servers of abSYNTH platform (absynth. issb. genopole. fr/Bioinformatics/tools/GREAT); b) as a downloadable stand-alone command line Docker image of each individual tool, to facilitate incorporation into pipelines. Here, we introduce this new collection of tools called GREAT:SCAN, we describe their novel features and we demonstrate their use and analytical capabilities by a) calculating regularities on the regulons of the seven major transcription factors (TFs) in Escherichia coli; and b) predicting new target genes in the corresponding regulons by using data from two different sources: local TFBS sequence and global gene position along the genome.
Methods The collection currently includes two tools. GREAT:-SCAN:PATTERNS, a package for the systematic analyses of regular patterns on genomes, and GREAT:SCAN:-PRECISION, a multi-view machine learning tool to predict novel TFBSs. GREAT:SCAN:PATTERNS GREAT:SCAN:PATTERNS performs a complete analysis of periodic patterns along genomes. The analysis comprises three steps: 1) The systematic detection and visualisation of all possible periods from the genome positions of features of interest (such as co-regulated genes); 2) The clustering and visualisation of genomic features which are "in-phase" in the phase coordinates; 3) The mapping of any sub-region of the genome where a periodic pattern can be detected. The first step commences by exhaustively evaluating all the possible periods in the dataset.
By reporting the boundaries of the regions where periodicities are detected, this approach is able to map the observed periods on their respective genomic regions. GREAT:SCAN:PRECISION GREAT:SCAN:PRECISION ("PRECISION" stands for "PREdiction of CIS-regulatory elements improved by gene positiON") is a novel implementation in the R language  [21]  of PRECISION  [13] , a multi-view learning algorithm for TFBS prediction which incorporates two views: a) DNA sequence motif readout calculated by a TFBS position weight matrix (local sequence classifier) and b) individual gene contribution to overall genome periodic pattern calculated as the positional score by Fig. 2  "Periodobar" of the detected periods for CRP target genes. Height of the bars indicates the significance of each period (-log(p-value) means that higher bars will depict lower normalised p-values -see text).
These qualities of the PRECISION boosting algorithm make it suitable to incorporate a diverse set of classifiers with input data from multi-omics studies. To explore further the interplay between the two views currently used by PRECISION (i. e. sequence and position), two sets of variables were extracted. One set contains the classifier prediction scores, for each gene, calculated during the particular iteration where the position classifier was selected and a second set containing the classifier prediction scores calculated during the iterations when the sequence classifier was selected. At the end of boosting PRECISION constructs a linear combination of all the selected weak classifiers at each iteration to form a strong classifier. Then a per feature multivariate statistical analysis method called canonical correlation analysis (CCA)  [31]  was applied on this mixed dataset of the positional and the sequence scores.
Acknowledgements We thank  François Bucchini  for his help with the web application,  Ivan Junier  for sharing his preliminary observations on the coincidence of macrodomain and periodic region boundaries, Genopole and the abSYNTH platform for hosting the applications and all the members of MEGA team at iSSB for being avid beta-testers of the tools. This study was supported by the  EU   FP7  project  ST-FLOW.
Availability and requirements The software is available to the community as free online tools (Additional file 1) which can be found on the abSYNTH platform af the institute of Systems and Synthetic Biology (iSSB). The software runs as a web application freely for any non-commercial use (i. e. academic, teaching). No installation is required as all computations are performed by the abSYNTH servers (access at: absynth. issb. genopole. fr/Bioinformatics/tools/ GREAT). Every user can, after the end of the computations, download a compressed file with all the plots and the tables the program has generated. All input data and results are kept for one week and are available for downloading by the user with the job specific URL that the portal provides (Additional file 2).
102 Rh In order to investigate the level scheme reported in Fig. 1  and electromagnetic properties of the transitions of interest in 102 Rh we have performed four types of data analyses. The ordering of the transitions in the level scheme was determined according to γ-ray relative intensities, γ-γ coincidence relationships, and γ-ray energy sums. The electric or magnetic character and multipolarity of the transitions were deduced by linear polarization and angular correlations measurements, respectively. The lifetimes were derived by using the DSAM. Such complex approach is employed for the first time in the case of the investigation of chirality in nuclei. The angular correlation function for two successive transitions from oriented states depends on the spins of initial, intermediate and final levels, and on the multipole mixing ratios  [34].