dataesr/scientific-paragraphs-categorization huggingface dataset:
| Round 41 (targets 126-130) The targets of round 41 consisted entirely of protein domains in complex with carbohydrate chains. Although we designed recently a coarsegrained ATTRACT force field for protein-carbohydrate docking at the time of the round 41 challenge no such force field was available. In case of the AbnB protein (1,5-alpha-arabinanase catalytic domain), the structure of the 1,5-alpha-arabinanase catalytic mutant (AbnBE201A) complexed to arabinotriose (pdb3d5z, res. 1. 9 Å) served as protein receptor template. The structure of the protein was energy-minimized in the absence and in the presence of arabinotriose using the Amber16 package (see section 2). All-atom RMSD between the resulting two structures was 1. 07 Å, which predominantly accounted for the residues side chains involved in the arabinotriose binding.
It was reasonable to assume that also for targets 134 and 135, the binding mode will be conserved and we used a template-based protocol instead of a docking approach for this target. Based on the program Pep-Fold,  51  extended beta-sheet regions in the 50-residue sequence were predicted. Using an alignment of the known DCL8-peptide complexes, conserved residue positions in the bound peptides were identified and this allowed us to predict the most likely bound peptide segment and the register of binding. For refinement, we used both implicit and explicit solvent approaches (see section 2). For target 134, our top 1 model was a high (***) quality model (second: medium quality model). For target 135, we got three high quality models and seven medium quality models in the top 10 predictions.
ACKNOWLEDGMENTS We thank the organizers of the CAPRI challenge for this opportunity and the assessors for the hard work to evaluate the predictions. We thank all structural biologists who contributed target structures for the CAPRI experiment. The work was supported in part by a grant ( SFB1035/B02 ) to M. Z. from the  DFG (Deutsche Forschungsgemeinschaft ) and by the  CIPSM (Center of Integrated Protein Science Munich ) excellence cluster. This work was supported to S. A. S. by the grant  UMO-2018/30/E/ ST4/00037  from the  National Science Center of Poland (Narodowe Centrum Nauki ). Computational resources were provided by the  Polish Grid Infrastructure  (PL-GRID, grants protgags, gagstr),  ZIH at TU Dresden  (grant p_gag).
The European Space Agency's Planck satellite was launched on 14 May 2009, and surveyed the sky stably and continuously between August 2009 and October 2013. The scientific analysis of the Planck data requires understanding the optical response of its detectors, which originates partly from a physical model of the optical system. In this paper, we use in-flight measurements of planets within ∼1 • of boresight to estimate the geometrical properties of the telescope and focal plane. First, we use observed grating lobes to measure the amplitude of mechanical dimpling of the reflectors, which is caused by the hexagonal honeycomb structure of the carbon fibre reflectors. We find that the dimpling amplitude on the two reflectors is larger than expected from the ground, by 20% on the secondary and at least a factor of 2 on the primary.
Second, we use the main beam shapes of 26 detectors to investigate the alignment of the various elements of the optical system, as well as the large-scale deformations of the reflectors. We develop a metric to guide an iterative fitting scheme, and are able to determine a new geometric model that fits the in-flight measurements better than the pre-flight prediction according to this metric. The new alignment model is within the mechanical tolerances expected from the ground, with some specific but minor exceptions. We find that the reflectors contain large-scale sinusoidal deformations most probably related to the mechanical supports. In spite of the better overall fit, the new model still does not fit the beam measurements at a level compatible with the needs of cosmological analysis. Nonetheless, future analysis of the Planck data would benefit from taking into account some of the features of the new model.
Introduction The Planck space mission 1 was launched in May 2009 and ended operations in October 2013. Between August 2009 and January 2012 it observed the sky continuously, completing five surveys of the whole sky with its two instruments: the Low Frequency Instrument (LFI) covering the range 30-70 GHz; and the High Frequency Instrument (HFI) covering the range 100-857 GHz. After January 2012, Planck continued to acquire data with the LFI for more than three additional surveys. The huge Planck data set is dedicated mainly to the study of the cosmic microwave background (CMB), with the primary aim of Planck being to measure the spatial distribution of the temperature anisotropies of the CMB with an accuracy set by astrophysical foregrounds rather than instrument performance. The first Planck maps were released in March 2013, and cosmological results based on these data were published in a series of papers  (Planck Collaboration I 2014).
2010). One of the results of this effort was a physical model of the satellite that could predict to less than 100 dB from peak the response of the system to radiation from the sky. This model (referred to as RFFM for Radio Frequency Flight Model) was implemented in the GRASP software developed by TICRA 2  , and extensively verified by measurements  (Tauber et al. 2010). In spite of the accuracy of the RFFM, it was expected that there would be some unpredicted differences between it and the actual flight performance. As a consequence, the analysis of flight data  (Planck Collaboration II 2014 , 2016; Planck Collaboration VI 2014; Planck Collaboration VII 2016, and references therein)  relies minimally on the RFFM, using instead as much as possible on direct in-flight observations of planets (the brightest point-like objects in the Planck sky) that directly measure each detector's angular response to radiation (generally referred to as a "beam").
g. , bandwidth effects are not taken into account. A physical simulation is therefore required to determine the actual location of the grating lobes. The effect of dimpling was modelled using GRASP, starting in around 2000  (Nielsen 2005 (Nielsen , 2004;; Dubruel 2009). These models were used to understand the main features of the optical response to such distortions and to set requirements on the amplitude of the mechanical distortions of the reflectors. The current implementation of dimpling in the GRASP software (see, e. g. , Fig. 3 ), consists of a 3-dimensional-cosine deformation of the reflector surface added to the nominal reflector, with peaks at the expected physical location of the dimples (the centres of the hexagonal cores) and with zeroes along the edges of the hexagonal quilting.
The method The method used in this work was developed by TICRA for ESA and described in  Jensen et al. (2010). The principle consists of fitting geometrical models of an array of beam patterns iteratively to measurements, varying geometrical parameters (alignment and surface shape) until convergence is achieved. Both the shapes of individual beams and the relative distribution of beams on the sky (the "footprint") contribute information to the process. The method is quite suited to the case of Planck, where the wide frequency range (30-857 GHz) and the large field of view (55 feedhorns spread over 9 • × 8 • on the sky) probe a large range of beam shapes. The fit is carried out by a modified version of the software package POS 7  (which is usually used in the reverse direction, namely to force patterns to a desired shape) and the calculation of patterns using GRASP.
This was an unexpected and remarkable confirmation of the ability of the method to recover in-flight alignment parameters to sub-millimeter accuracy. After the initial tests on simulations, the method was adapted to use the very irregularly sampled Planck data (Borries et al. AMTA 2011). More specifically, a Kriging filter was included to bin and resample the measured samples onto a regular grid, exploiting physical priors on beam and noise properties. The method has also been used to recover grating lobes  (Nielsen et al. 2011 ), but for this purpose the less automated Sect. 2 analysis is preferred. The geometrical parameter space (x in Eq. (  3 )) that is explored includes (see Fig. 14):  -translation of the focal plane in three directions, rotation around the z RDP axis, and a tilt of the x, y plane around the y-axis; -translation of each reflector in three directions and rotation around their respective z axes; -surface distortions of each reflector, modelled as Zernike polynomials up to third order; -displacements of individual horns in x RDP and y RDP.
Note, however, that in the retrieval described in this paper, the scanning-beam-based directions only are used as the initial determination of the focal plane coordinate system -thereafter all beam centroid directions are determined by fitting model beams to the measured ones. For each of the two main Planck data releases so far (in 2013 and 2015), a set of fsbeams was produced specifically for the exercise described here, in both cases based on maps of Saturn and Jupiter. There were significant differences in the way the fsbeams were produced in  2013 and 2015 (Planck Collaboration VII 2016; Planck Collaboration VI 2014) , including: number of planet maps used; size of the fields recovered; and, most importantly, different time transfer func-Fig. 15. Difference in predicted patterns between monochromatic and wideband models.
The model also indicates that the offsets between the LFI and HFI FPUs are slightly larger than expected in the x and z directions. Overall, there remain significant uncertainties in the physical model of the main beams derived from this analysis (RFFM2), which are reflected in the remaining differences between the best-fit model and the planet measurements. These differences are of order 0. 3% in integrated power in the main beam at 30 GHz, rising to about 1% at 70-100 GHz, then 2% at 143 GHz, and decreasing again to 1% at 353 GHz. One of the main factors contributing to these differences is the effect of the deconvolution of the time response from the HFI bolometer data. The remaining differences between model and data are much larger than the uncertainties that are required for cosmological analysis of the Planck data.
Acknowledgements. This paper has been reviewed internally by the  Planck Collaboration. 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. 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 ,  MICINN , and  JA (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) ; and  DEISA (EU). The GRASP-based modelling work described here was carried out largely by  Per Nielsen  of  TICRA , and benefitted from TICRA's long-standing, dedicated, and very productive involvement in the Planck mission covering, among others the specification, prediction, and characterization of the optical system of Planck.
SOLUTION APPROACH Problem (1) is a mixed-integer linear program which can theoretically be solved directly by a mathematical programming solver such as CPLEX. However, its size grows exponentially fast with the size of the network due to constraints (1. 3). Even for medium size instances, the explicit enumeration of all possible paths between the source and the target of an attack requires a prohibitive computation time. We thus propose in what follows a constraint generation approach in which only a small subset of constraints (1. 3) are added to the formulation during the course of the Branch & Bound algorithm. Initialization For each attack (s,t), we look for the shortest path (in terms of the number of hops) between s and t and add the constraint of type (1. 3) corresponding to this shortest path in the formulation.
All the communicating components have been described in the Promela language (required by Spin). The derived composition has been later on verified by the Spin model checker. Once a counterexample is returned by Spin, the proactive race testing is performed, i. e. , counterexamples for concurrency violations are applied to the implementations under study. These counterexamples in fact serve as test sequences to be applied to the implementation under test, in order to make sure the property violation over the model indeed corresponds to a race condition in the implementation. The main contributions of this work are therefore i) a proactive model checking based testing method for SDN race detection; ii) extended automata specifications describing the behavior of an SDN controller, an SDN switch and a communication channel (southbound interface), as well as their Promela specifications that can be downloaded from  [17]  (which can be easily extended for specific needs); iii) the description of the potential races of two types for the considered composition in LTL, and iv) experimental results identifying an inconsistency in a common SDN composition (ONOS with Open vSwitch) with respect to the time constraints and delays for the rules to be pushed.
In this case, the Promela code contains the variable IDflow, the rule's ID. Note that for convenience and without loss of generality the value of the IDflow is equal to the rule's priority. Moreover, the Promela description has an array app_table that contains all the current switch flow rules. The description of the controller behavior includes one process. A fragment  4  of the Promela description of the controller is illustrated in Figure  3. The complete version of the re-lated program, as well as the Promela code for other interacting components, are available at  [17]. The EIOA that models channel 1's behavior has two states, namely q 0 and q 1. These states correspond to the state of the buffer. In particular, state q 0 corresponds to the situation when the buffer is empty while state q 1 reflects the opposite.
The context variable buffer. size corresponds to the number of messages in the buffer. The EIOA has four transitions, and two of them are unconditional. As an example, a transition (q 1 , PF, q 0 ) can be considered: it models the situation when channel 1 has sent a PostFlow request for a rule with ID id moving to state q 0. The transition diagram of the corresponding EIOA is shown in Figure  4a. This EIOA is also described in (a fragment of) the Promela language. In this case, the Promela code contains the IDflow variable, the identifier for a given rule. Similarly, the Promela description has an array buffer that contains the messages in the buffer. A fragment of the Promela description of channel 1 (channel 2) is illustrated in Fig. 4b. Given the room constraints, we do not include the detailed EIOA descriptions of other interacting components, such as the switch or application.
2), we propose an algorithmic solution (Algorithm 3). As the approach proposed in this paper is proactive, we execute the derived counterexample α potentially leading to a race against the implementation of the SDN framework. For that reason, we rely on an automated script; the script is executed at most N times, simulating α as an input to the component of interest. If during the execution of the script the competition between an input and output can be observed, then the script returns T RUE. Therefore, when Execute script(S ) returns T RUE a race is indeed detected in the implementation. Note that the execution of the script can be performed against any application, controller, and switch, from different as long as the corresponding versions are compatible. An important step of Algorithm 1 concerns the choice of the component of interest and its state (or a subset of those) for further verification.
Experimental Results Experiments were performed with the ONOS Controller and the Mininet  [12]  simulator, executed under a virtual machine running on VirtualBox Version 5. 1. 34 for Ubuntu 16. 04 LTS with 4GB of RAM, and a quad AMD A6-7310 APU with AMD Radeon R4 Graphics processor. Our experimental setup corresponds to the topology in Figure  1 , i. e. , the simulated network contains a single switch running Open vSwitch version v2. 11. 0 and a single application implemented as a Perl  [18]  script. The goal of the experiments was to estimate the efficiency of the proposed algorithms for race detection and their impact on available SDN components of wide use, namely, on the communication between the ONOS controller and an Open vSwitch. All the Perl scripts utilized in the experimental setup are accessible at  [17].
To detect such a race we used three rules (n = 3). The probability of success of the race detection can be estimated using the EIOA in Figure  3. Assume that the transitions at each state are equally probable, then the stochastic matrix Π = 1,3 = 1 3. Given the probability of success, we executed the model checking and Spin found a counterexample (available at  [17] ) of length 21. To comply with the order of the requests in the counterexample α, following Algorithm 1 we derived a vector of timeouts τ that were implemented with the sleep() procedure. The counterexample α (together with the values τ i in the comments of the Perl script) can be checked at  [17]. The script of interest was executed 10 times, i. e. , N = 10 and at the 4-th iteration an unexpected behavior was observed. A rule deleted via a timeout, produced a FlowExpire message from the switch to the controller, however, the controller at a later time instance produced a FlowDelete message for the same rule ID.
The latter means that the flow table of the controller can be dis-synchronized, i. e. , the controller's knowledge of the network state is not always relevant and up-to-date. Moreover, it cannot be predicted which action removed the rule: FlowExpire or DelFlow. The detailed logs of the script execution and race detection are also available at  [17]. In the files logs/test_1. txt, logs/test_2. txt and logs/test_3. txt the FLOW REMOV ED signal is sent 5 times, however, in logs/test_4. txt the FLOW REMOV ED signal is sent 3 times. This means that to delete a rule with number 5000 in first 3 cases the controller sent FLOW REMOV ED signal and in the fourth case the switch sent a FLOW EXPIRE message earlier. Thus, the input / output race is possible.
We executed the Perl script N = 10 times and at the 8-th execution an unexpected behavior was observed. Figure  5  showcases the flow table in the controller, which exhibits a race condition; Fig. 5a  presents the rules as initially installed while Fig. 5b  presents the rules at the end. Note that the rules 5003 and 5004 expired, however, the rule 5002 is still present, although the value of the timeout in the rule 5002 is set to 7 seconds while the timeout of the rules 5003 and 5004 are set to 8 and 9 seconds, correspondingly. Indeed, the nondeterministic behavior of the controller can lead to a rule permutation in the channel. In the counterexample of interest, two rules with the IDs 2 and 3 accordingly happened to permutate. The iterative execution of a Perl script allows to detect such permutations.
In theory, the LASSO L 1 -penalty part forces most of the coecients to 0. Nevertheless, in a more practical approach, the algorithm employed does not lead to exact zeroes. Thus, the R-package glmnet  (Friedman et al. , 2010)  applies a threshold on the coecients. Furthermore, following Chatterjee and Lahiri (  2011 ), the sparsevar R-package  (Vazzoler et al. , 2016)  enables to apply a more tailor-made threshold for time-series estimation which equals to 1 c pd ln T , that we retain for our model. The hyper-parameter α is determined through a grid search. For every value α h of a predened grid tα 1 ,. , α H u, we estimate the parameters of the VAR model 3 Ĉ, Â1 ,. , Âp , λA α h , as explained just before, and deduce the residuals Êα h ,t for t tt min p,. , t max u. In the applications, we estimate the tuning parameters by minimizing the prediction error, that we obtain by computing the root-mean-square error RMSE pα h q g f f e 1 d pT ¡ pq tmax ţt min p } Êα h ,t } 2 2.
Data The datasets that we analyze comes from the Human Mortality Database (2016). We choose to illustrate our approach with historical mortality data from the England and Wales (UK), the United States (US) and France (FR), as these populations have been largely studied. At rst, the overall population is considered, and then both males and females are segregated. We select the age-period observation t45,. , 99u ¢ t1950,. , 2012u which was available for these 3 countries when the data was extracted. We begin our analysis by a visual inspection of our data. Figure  1  describes the shape of the period log-mortality improvements for populations on a Lexis diagram where the trajectory of one cohort follows a 45 degree line. Dierent cohort eects can be observed for these countries with pink (resp.
(2017). This latter eect, underlined by our data-driven approach, has not been well documented in the literature yet to our knowledge. More concretely, a vertical pattern on the i th column of A 1 reveals a persistent eect from the term ∆y i,t¡1 on ∆y j,t , j t45,. 99u, meaning an eect on the older cohorts for j i and on the younger cohorts for j ¡ i. Similar structures can be observed on the matrices A k for k t2,. , 7u. On these datasets, these eects are quite dicult to interpret, but one can note that the transfers from the older to the younger cohorts are scattered, whereas some vertical lines are generally captured for columns corresponding to the ages between 45 and 65. Disaggregated data would be very useful to explore these eects further. Indeed, these patterns could result from biological, environmental or societal causes, unless it is due to some anomalies in the HMD  (Cairns et al.
In-sample model comparison We now study how our approach ts well and captures better the mortality pattern on these subsets compared to the classical Lee-Carter (LC) model (4. 1) and the M7 model (4. 2). These last models are estimated using the R-package StMoMo  (Villegas et al. , 2017)  following their usual two-stage tting procedure: rst, we estimate the factor coecients of each model, and then we forecast it using univariate ARIMA processes, automatically selected by the R-package using an AIC criterion. To be comparable with our one-stage tting approach, the results for these models are those obtained after tting the time-series parameters. Table  2  contains the values of the RMSE, as dened in Equation (3. 2) for each model and each population. The LC and M7 models have quite comparable results, whereas the VAR-ENET model has the lowest RMSE value in each country.
One of the main challenges in anomaly detection is the difficulty to clearly distinguish normal instances from anomalous ones, as the boundary between the two is usually imprecise and evolves over the time in some application domains. In addition, anomalies are often rare events, so labelled datasets for model training and validation are either unavailable or severely imbalanced in favor of normal instances. As a consequence, semi-supervised or unsupervised learning is more frequently used than supervised learning. In a semi-supervised approach for anomaly detection, it is assumed that in the training set labels exist only for the normal class. If there are any unlabelled instances, then they are assumed to have the same label as the labelled instances that are nearby in terms of distribution.