dataesr/scientific-paragraphs-categorization huggingface dataset:
Given that the representations are mathematically equivalent, we will here in supplemental information treat the sequence as one simple sequence x. The inference proceeds by assuming as a working hypothesis that the dataset D is composed by configuration sampled uniformly from the equilibrium Boltzmann-Gibbs distribution P ( x) = exp(-H)/Z (as an inference process, we are free to consider T = β = 1). We are now ready to use D to infer the topology of the network. To do so -as discussed in the main text -in the last years different maximum-likelihood techniques have been proposed  [1, 12, 23, 29, 31, 39]. So far the most promising in terms of accuracy seems to be the pseudo-likelihood maximization introduced in  [12]  where from the previously defined Boltzmann-Gibbs measure we consider the following conditional probability distribution: P i (x i |x \i ) = exp j =i J ij (x i , x j ) + h i (x i ) 21 a=1 exp j =i J ij (x i , a) + h i (a) (8) Given a data set D we can thus maximize the conditional likelihood by maximizing L i (J i,\i , h i ) = 1 M M α=1 log P i (x α i |x α \i ) , (9) as a function of J i,\i , h i.
As customary in many maximum-likelihood inference techniques, we add to the maximization an L 2 regularization term, so that eventually the extremization procedure turns out to be: {J * i,\j , h * i } = argmax J i,\i ,hi {L i -λ J j =i J ij 2 -λ h h i 2 } , (10) with J ij 2 = 21 a,b=1 J 2 ij (a, b) , and h i 2 = 21 a=1 h 2 (a). We refer to the original paper  [12]  for the details of the implementation. We only mention that beside the original MATLAB  [30]  implementation available at http://plmdca. csc. kth. se/, we developed an efficient implementation of the pseudo-likelihood implementation in a new open-source language called Julia  [3]. The package can be downloaded at https://github. com/pagnani/PlmDCA.
Artificial Data An artificial large network consisting of 5 proteins was created in two steps: 1) First, a contact map was defined. This contact map contains the information which residues are in contact. This includes internal residue contacts (where both residues belong to one of the 5 proteins) and inter-protein residue contacts (where one residue belongs to one protein and the other to a different protein). The contact map is therefore a binary, symmetric matrix of size N all × N all with N all = N 1 + N 2 + N 3 + N 4 + N 5 where N i is the number of residues in the i th protein. We decided to use the Kunitz domain (PF00014) as a model for the proteins and set all N i = 53. The 53 × 53 submatrices that define the contacts within each protein were defined by extracting the contacts of the PDB structure 5pti of the Kunitz domain.
This implies that the internal structure of every protein is the same. We defined as contacting proteins the protein pairs 1 -2, 2 -3, 3 -4, 4 -5 and 1 -5. For the 53 × 53 submatrices that define the contacts between contacting protein pairs we used random binary matrices with 10% of the number of internal contacts. This was done individually for each contacting protein pair such that no two contact matrices between two proteins were the same. For non-contacting protein pairs all entries of the contact matrices were set to 0. The resulting contact map can be seen in Fig. 12. 2) Couplings for every contact in the contact map were defined. As a basis for this, couplings and fields inferred from the PF00014 PFAM alignment (Kunitz Domain) were used. This inference was done using a masking with the PDB structure, such that only couplings corresponding to PDB-contacts were allowed to differ from zero.
Acknowledgments CF and AP are supported from the  EU   Marie Curie Training Network NETADIS , ( FP7  Grant  290038 ). HS was supported by grants  GM106085  and  GM019416  from the  US National Institute of General Medical Sciences, National Institutes of Health. MW was supported by the  Agence Nationale de la Recherche  project  COEVSTAT  ( ANR-13-BS04-0012-01 ). CF and AP acknowledge  Riccardo Zecchina  and  Carlo Baldassi  for many interesting discussions.
Patients were studied at least 3 months after their last stroke. All subjects provided written consent, and the study was approved by the local ethics committee. Subjects were invited annually for repeated cognitive testing and MRI scanning. Recruitment began in December 2007 and ended in August 2010. MRI scanning began in January 2008 and was completed in October 2013. In this analysis follow-up data to 3 years were used, providing a maximum of four datasets per individual. Of these, 99 subjects returned at one or more time-points: 98 at Year 1, 77 at Year 2 and 71 at Year 3. One subject attended the baseline and missed the Year 1 follow-up, but attended all subsequent sessions. Four subjects missed the Year 2 follow-up, but subsequently attended at Year 3. These are summarized with the reasons for dropout in Fig.
Preprocessing Initially the raw DICOMs were imported using Statistical Parametric Mapping 8 (SPM8) software (http://www. fil. ion. ucl. ac. uk/spm/software/spm8/), and each image checked to ensure a common orientation. The T 1 -weighted and FLAIR images were aligned using an affine co-registration in SPM for each individual, before being co-registered to MNI orientation and reslicing to 1 mm isotropic voxel resolution in subject space using fourth degree b-spline interpolation.
Segmentation The segmentation steps were adapted and optimized to our study population as described in  Lambert et al. (2015) , but are fully summarized below for reference. In essence, the adapted segmentation pipeline consisted of four steps. First, a group average template was created and the T 1 -weighted and FLAIR images were warped to this space. Second, the warped T 1 -weighted and FLAIR images were used to create population-specific tissue probability maps (TPMs). Third, the new TPMs were used to resegment the native images creating grey matter, white matter, CSF and WMH tissue classes. These were then combined with the manually defined lacune regions of interest, resulting in five tissue classes per individual. Finally, a tissue repair step was performed to generate repaired grey matter, white matter and CSF maps for each individual dataset.
Whole brain volumetric MRI parameters Total cerebral volume, grey matter, white matter, lacunar infarct, and WMH volumes were calculated for each individual and used to calculate percentage change from baseline and annualized rate of volumetric change. For each MRI parameter annualized rate of change was calculated from all available time points (e. g. time in years from baseline) using a least squares linear fit in MATLAB 2013a. These were calculated both using each time point cohort (i. e. only taking data from baseline to the corresponding time-point) to assess how these measures changed as the cohort declined due to dropout, and also using the entire population over all available time points to generate a single summative measure.
Statistical data analysis All analyses were performed in MATLAB 2013a unless specified, and the data were grouped either according to time-point or outcome as specified. Tests between time points used a onetailed test given the a priori expectation that brain volumes decline and lesioned tissue increases with time, whereas all other t-tests were two-tailed. Data distributions were assessed using histograms. The normally distributed data were explored initially using an ANOVA, and if significant a two-sample ttest was used to examine differences between groups. Nonparametric data were tested using a Kruskal-Wallis test. Pearson's correlation was used to calculate correlation coefficients (r). Results significant at P 5 0. 05 were reported.
Limitations There are several limitations with this current work. First, while every effort was made to develop a fully automated, non-biased approach, the segmentations still required a degree of manual correction. However, better quality isotropic data using higher field strengths and image resolutions resulting in improved tissue contrast would be required to refine this approach further to allow a fully automated, non-biased pipeline that would enable increasingly accurate quantification of disease. Additionally, the lacune correction method used will only provide an approximation of the expected tissue type. This could be potentially improved in the future by using a more elaborate model, for example using large datasets of normal brain MRI scans to train a classifier to estimate a missing voxel information (due to an ischaemic lesion) based upon surrounding neighbourhood information.
Data and methods In order to meet the proposed objectives, a three-step methodology is used. First, taking into account the geometrical configurations of the ECO-BB and Hydroscat sensors, the SimulO Monte Carlo code  [12]  is used to test and parameterize the optimal correction method for accurate retrieval of the β p signal in a wide range of natural waters. Second, in the case of Hydroscat measurements, the standard sigma and a new correction methods are both applied to two field data sets of IOPs representative of turbid coastal waters. Results are then used as inputs in the Hydrolight radiative transfer code  [13]  to compute the remote sensing reflectance (Rrs) signal. The validity of the results obtained is finally assessed comparing Hydrolight outputs to Rrs spectral values independently measured in the field (optical closure).
Monte Carlo simulations SimulO is a Monte Carlo code developed to easily simulate many types of optical devices, especially in the field of marine optics  [12]. It is a natural three-dimensional (3D) forward Monte Carlo code, which implies that each photon is followed, one at a time, from the source to the point where it is absorbed. Using this code, complex optical devices can be simulated by positioning and sizing any number of elementary virtual objects. It allows reproducing IOP sensors taking into account their specifications in terms of geometry, dimensions, light source and detectors, imposing true IOPs typical of natural waters. Computations reproduce the IOPs that would actually be measured by the sensor after correcting for photon losses. The difference between the true and corrected IOPs is representative of the measurement uncertainty associated to the virtual sensor.
In this case the dimensions of the HS-6 sensor are simply too large for measuring light backscattering due to complex trajectories of photons in multi-scattering regimes and significant variations of the nominal photon pathlength. However, overall, results are satisfactory and both the ECO-BB and HS-4 sensors (when applying the new data correction method proposed in this study) should be able to perform reliable measurements in turbid waters to accurately estimate the particulate VSF. For such measurements in turbid waters the instrument pathlength is a key issue in the data correction: this pathlength should be as short as possible to minimize loss of photons due to scattering, which is the case with the ECO-BB sensor. This loss due to scattering must be taken into account and corrected for in the case of Hydroscat data.
The new correction method developed in the present study (Eqs. (  9 ) and (  10 )) assumes the loss of photons due to scattering along the instrument pathlength is, as a first approximation, a simple linear function of the particulate backscattering coefficient. Such a method, if proved to be valid based on real measurements, is convenient as it only requires measuring light absorption on top of Hydroscat data. The next step is to test on field measurements the validity of our Monte Carlo simulation results. This test is made only considering Hydroscat measurements (HS-4) as to date only this sensor with its five adaptative gains has the capability to measure data without saturation in highly turbid scattering waters.
86): despite some scatter, a linear relationship is established with a negligible intercept and a slope (0. 99) very close to 1 (Fig. 8(a) ). At 1020 nm the correlation between measured and computed Rrs values significantly decreases due to a lower signal to noise ratio but similar results are obtained: linear relationships with negligible intercepts and a slope of 2. 37 and 0. 95, respectively, between the computed Rrs_s and Rrs_n (y axis) and the measured Rrs signal (x axis). The Bourgneuf Bay data set tends to confirm these results. Combining the visible and NIR wavelengths, a rather poor linear relationship (R 2 = 0. 48) is observed between the measured Rrs (x axis) and computed Rrs_s (y axis) with a negligible intercept and the slope close to 2 (1. 98) (Fig. 8(b) ). Actually the difference between Rrs and Rrs_s increases with increasing Rrs, i.
e. , with increasing b bp. The more light is scattered in the water the more Rrs_s vs. Rrs departs from the 1:1 relationship. In contrast, once again, a linear relationship (R 2 = 0. 74) with negligible intercept and a slope close to 1 (0. 86) is obtained between Rrs and Rrs_n. The new correction method applied to Hydroscat data allows a more accurate retrieval of the 'true' Rrs signal at 550, 700, 850 and 1020 nm while the standard sigma correction leads to an overestimation by a factor 2. This factor 2 difference between the computed Rrs_s and Rrs_n values results from the factor 2 to 4 difference between the b bp values produced using the standard and new sigma corrections. The standard sigma correction clearly overestimates light attenuation along the photons pathlength due to particulate scattering, overcorrects for it and thus dramatically overestimates b bp values in highly scattering waters.
9 (a)-9(b)). In terms of magnitude, b bp _s/b p typically varies from 5 up to 15-20% in the Río de la Plata, from 3 up to 25-35% in the slightly more turbid waters of the Bourgneuf Bay. Such high values are definitely unexpected in the case of marine particles, which suggests once again that the sigma correction overcorrects Hydroscat data for light attenuation and provides dramatically overestimated b bp _s coefficients in highly scattering waters. Less pronounced spectral variations of the particulate backscattering ratio are obtained when the new correction method is applied to Hydroscat data (Fig. 9(c-d )): b bp _s/b p spectra are almost flat in the Río de la Plata and only show a weak decrease with increasing wavelength in the Bourgneuf Bay, as already observed in U. S. coastal waters  [40, 41].
Acknowledgments This study is part of the  SeaSWIR  project funded by the  BELSPO agency  and  HIGHROC  project funded by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement n°  606797. We gratefully thank  D. R. Dana  and  R. A. Maffione  ( HOBI Labs Inc. ) and  M. Twardowski  ( WET Labs Inc. ) for providing detailed information on the Hydroscat ECO-BB sensors design. Field experiments in the  Río de la Plata  and  Bourgneuf Bay  were respectively carried out as part of the  SeaSWIR (P. I. E. Knaeps)  and  ANR-12-AGRO-0001 GIGASSAT (P. I. F. Pernet ) research projects.
Below, I present a quick description of the USINE propagation code along with some of its outputs. USINE was started and developed fifteen years ago to interpret nuclear data, and to test the astrophysical or DM origin of the GCR anti-protons and anti-deuterons. Some key results obtained with this (or previous versions of this) code are (i) the first automatic scan of the transport parameter space  [6] , (ii) the use of these parameters to show the compatibility of the models with antiprotons data  [1, 5] , (iii) providing benchmark propagation models (mid/med/max) for dark matter studies  [3] , (iv) the first use of a Markov Chain Monte Carlo in the context of GCR physics  [9, 10, 11].
After their transport in the Galaxy, GCRs are also modulated by the Solar activity. Only at this stage can the model predictions be compared to data (taken from balloon, satellite, or ground-based experiments). To handle all these ingredients, the USINE code was developed and first applied to the thin disc/thick halo diffusion/convection model  [6]. The first version was written in 1999 in C. The second version was written (between 2003 and 2005) C++ and interfaced with ROOT  4  for the plotting routines and with the MCMC engine GreAT  [12]. The current version (V3) is based on a complete rewriting of V2, which I started in 2011, in order to provide the community with a more generic and flexible code. The code structure is illustrated in figure  1. The C++ classes are sorted according to the ingredients needed for propagation and are organised as follows: • GCR data read from CRDB 5  exports  [8] ; • CR description (charts) and interaction cross sections, ISM description, and GCR sources (spatial distribution, spectra); • Propagation models and Solar modulation models;.
PoS(ICRC2015)484 USINE propagation code and associated tools David Maurin All these plots are automatically available for any model (and combination of parameters) developed in the USINE framework. Plots for antinuclei are also provided (details on secondary and tertiary production, etc. ). Thanks to a text user-interface, any combination (ratio) of nuclei and elements can also be displayed for any modulation level (in the Force-field).
Conclusion USINE is a C++/ROOT code dedicated to the propagation of GCR nuclei and anti-nuclei 7. Although USINE was developed for two zones (thin disc and thick halo) 1D and 2D models, nothing prevents it to be extended to more complex modelling. A lot of efforts were dedicated towards flexibility (the USINE classes for the propagation ingredients are designed to support 3D and time dependence), simplicity of the inputs (ASCII files for cross-sections, CR data, and initialisation and description of the free parameters of the study), and comparisons to data (import from CRDB, plots, and additional free parameters easily handled in the minimisation). In the context of AMS-02 data, and the many young researchers entering the field, USINE should prove a fast and useful tool to learn or better understand the richness of the physics of GCR nuclei.
However, SDN and the OpenFlow protocol, a de-facto standard for the southbound interface, do not allow to trivially acquire services requirements and translate them into forwarding policies. Indeed, among the previous examples, writing applications that could be directly enforced in the network usually relies on a domain-specific language, slowing its adoption. On the other hand, when high-level languages are provided to express policies, they can only be translated to the extent of a service chain, which is not directly deployable to the forwarding plane. Therefore, there is a need for a policy-based management framework able to collect high-level inputs from operators and provide appropriate reaction scripts that can configure SDN equipments. Additionally, the programmibility of the network is rarely exploited to introduce autonomicity: a policy management approach should be context-aware, so that policies are dynamically enforced depending on the network and security states.
[9]. Their early state-of-theart analysis of the OpenFlow controller languages has exposed some substantial difficulties: (1) network functions written in these languages do not compose; (2) low-level hardware-dependent semantics complexify how rules are written and distributed; (3) network programs' two-tiered architecture (spanning both the controller and the switch) incurs concurrency issues. To address these issues, they proposed Frenetic  [9] , a high-level network programming language, and in particular, a network policy management library that includes a set of sourcelevel operators for constructing and manipulating streams of network traffic. They later extend their work in the Pyretic language  [10]  by introducing an abstract packet model, a sequential composition operator and a topology abstraction using network objects.
Concurrently, Voellmy et al. have proposed Procera  [11] , a functional reactive programming language like Frenetic, with which it shares similar constructs. But unlike Frenetic which applies only to packets, Procera handles any event stream allowing for both sequential and parallel composition, and uses function values to represent high-level policy. While these languages were all declarative, Voellmy et al. took another direction when proposing the Maple language  [12]  which introduces algorithmic policies, high-level policies that decide the behaviors of an entire network. Kang et al. have considered an abstraction similar to Maple, i. e. , the whole network is one big switch  [14] , but are more concerned than other proposals on how to satisfy user-defined policies and adhere to the underlying hardware resources constraints at the same time.
This engine has to manipulate a set of pre-defined • SDN Security Module -SSM: SSM is responsible for the implementation of the current policy in the Policy Enforcement Points (PEPs). PEPs comprise the OpenFlow switches and the SDN controller. The PEP will enforce automatically the selected policy by the SPM using the OpenFlow scripts. More details about this module could be found in Section V. Fig. 1  illustrates the workflow of our approach. A monitoring engine continuously supervises the data plane (2) to detect any failures, that may be system-or networkbased. This component updates accordingly the virtual machines' status, as well as the network status based on alerts received from local or external sources  (1). When resources are critically damaged (failure, error, etc. ), the OrBAC-PDP (Policy Decision Point) is notified (3).
1 <xml version ="1. 0" encoding="UTF-8"standalone ="Yes"?> This module aims to deploy the selected rules by the SPM into the SDN switches. Indeed, any OrBAC security rule should be translated to a set of three possible actions: adding, deleting and/or modifying a rule from the flow table of a SDN switch. In this module, we have designed three SDN templates for each action. The deployment of an OrBAC security is based on the Algorithm 1. It will take two inputs: the security policy to deploy and the network topology that will permit to configure OpenFlow scripts. After this, for each rule, it will translate it to a set of actions with the needed parameters extracted from the network topology (as the server IP address, the switch Identity, the ports, etc). At the end of this loop, it will deploy the needed strategy in the controlled switches based on the rest API of the controller.
A. Implementation In order to implement the use case, we have set up a testbed (see Figure  2 ) featuring several hardware servers and OpenFlow (OF) switches: • 4 Alcatel OS6860 switches, OF-capable with 24 ports running at 1 Gbps. • Two web servers running Apache v2. 4. 7 providing a set of video files. (Named main server and second server in the following) • Two clients streaming video from the server using Tapas  [5] , an adaptive video streaming client. It simulate a video player without adding the computational overhead of the decoding process. • An SDN controller managing the switches. In these experiments, we have installed Floodlight 1  on a virtual machine. The OrBAC PDP, the security policies and the SDN security module are deployed in the same virtual machine for eased communication.
B. Evaluation Network Failure Scenario: In this experiment, we want to prove the feasibility of our solution when several non-simultaneous network failures occur. The situation is presented in the second availability service (load balancing on demand). Initial situation: During the initial deployment, the context is set as normal-context. Based on this context the OrBAC-PDP will activate a set of rule. Among them, we find the following rule: • P 2 : Permission(CSP, SDN-Controller, activate, loadBalancing-on-demand, normal-context) These latter will be mapped to openflow scripts that will deploy the best path for the client. As shown in Figure  5 , flow rules are pushed toward the different switches during the first period. During this phase, only one client is using the web service. Figure  3  shows an average latency of around 500 µs between client 1 and the server.