dataesr/scientific-paragraphs-categorization huggingface dataset:
We developed a Matlab script in order to extract from the probabilistic tractography outputs the number of tracts that leave a voxel from NAcc to reach a given target. A Python script has been developed to calculate the number of voxels within the NAcc that have a probability greater than 1% to be connected to a specific target. Voxels with a connection probability of at least 0. 01 were included as voxels containing anatomically valid pathway. The 1% threshold is a typical threshold used in probabilistic tractography  (Lambert et al. , 2012; Li et al. , 2013). Threshold was set to 1% (out of the 5000 generated from each seed voxel) to reject voxels with low probability. This means that at least 1% of the identified fibers intersect the voxels in the valid pathway. This gave us a matrix of "strengths" of interconnection on a scale of 0-100%.
Text-Mining (TM) TM models were evaluated at different levels. First, the two NERs and three extractors are evaluated against a manually annotated corpus. Second, the complete system is evaluated against invivo connectivity from ABA. The TM models were then applied on two large corpora, and the extracted brain regions and connections are discussed. Last, we compared and analyzed the results between TM and LIT for the three structures. The precision of both NERs was estimated on the WhiteText annotation corpus and is 84. 6% (BraiNER), meaning that 85 out of 100 brain regions are correctly identified. The performance of all three extractors was evaluated on 3097 manually annotated connectivity relations, reaching a precision of 45, 60, and 72%, respectively. The resulting database contains over 4 million (lexical) and 4.
The manual effort can be directed on intelligent tasks like validating and searching proposed connectivity statement, instead of their painstakingly identification from within millions of publications. Using this dual strategy (TM prior to manual review), it took less than 2 h to have proposed a set of 25 potential targets for NAcc. In comparison, it took approximately a week for a user trained in neuroanatomy to conduct the isolated literature review of NAcc as presented in Section Manual Literature Review. Therefore, the connectivity database significantly accelerates the manual search of metascale brain region connectivity, by providing a centralized repository of connectivity data for neuroscientists. Another advantage of this dual approach is the possibility for neuroscientist to collectively curate a knowledge base and therefore improve it.
Each approach then develops along different paths (Fig. 1 ). Risk-based approaches start by identifying the assets to protect in the system under development and the threats that might compromise the accomplishment of the privacy principles on these assets. For instance, given the principle of ensuring the safety of personal data referenced in the EU DPD, a threat might involve an unauthorized party gaining access to some personal data. The threats are analyzed and the relevance of the subsequent risk is assessed, i. e. , the combination of their probability and their impact. Then a treatment is proposed to address the risk associated with the threat. This treatment may range from doing nothing (accept the risk) to including requirements that may avoid or reduce the risk, modifying by its impact or its probability.
B. Bottom-up Approach The bottom-up approach is applicable when a first version of the code (or a model of this code) is available. The goal is then to extract properties from this code showing that the desired privacy requirements are satisfied. This approach has been applied in PRIPARE to an electric vehicle charging scenario  [34]. A major challenge of these vehicles is their somewhat limited range, requiring the deployment of many charging stations. To effectively deliver electricity to vehicles and guarantee payment, a protocol exists as part of the ISO 15118  [35]  standardization effort. A privacy-preserving variant of this protocol, POPCORN  [31] , has been proposed in recent work, claiming to provide significant privacy for the user, whilst maintaining functionality. We have defined a formal model of the protocol and its expected privacy properties in the applied Pi-Calculus  [36]  and used ProVerif  [49]  to check them.
The methodology has also identified specific processes to address the new accountability principle (appearing in the forthcoming EU GDPR [1]), and user-empowerment and usability, one of the major current challenges in PbD. This allows positioning PRIPARE's methodology as a tool to achieve truly user-centric PbD systems, whilst demonstrating compliance with the selected privacy principles. In no case does PRIPARE propose what systems need to be built, but it helps to achieve privacy in those systems that are to be created-and it may impact their design decisions. PRIPARE is moving toward validating its methodology. For that, three research projects have been selected, funded by the European Commission's Research and Technological 7 th Framework Programme (FP7), to embed privacy and security, ensuring both that these projects correctly address privacy issues, and that the methodology is validated in practice, in terms of being efficient, practical and aligned with real-world system engineering practices.
Run 2 Challenges Discoveries and precision studies of rare physics events require rejection of "known" events by more than ten orders of magnitude. To meet the Run 2 challenges, the ATLAS experiment  [1]  will have a two-tier trigger system comprised of the hardware-based Level 1 trigger and the software-based High-Level Trigger (HLT)  [2]. The Run 2 increase in instantaneous luminosities complicates the trigger rejection because of the presence of a large number of simultaneous collisions in the same event, overlapping the hard scatter of interest (such as Z → μμ, see figure  1 ). This phenomenon is usually called "pileup". The Run 1 experience demonstrated that tracking performed within the full acceptance of the ATLAS silicon detectors in real-time is a powerful tool to limit pileup effects on the trigger performance.
Match and Fit Conceptually, the FTK system reconstructs charged tracks in pp collisions in two steps: first comes "pattern matching", i. e. the identification of suitable detector hits combinations; then track parameters are fitted ("track fitting") based on the coordinates of the identified hit patterns. Pattern matching is performed using the capability of finding correlations in the data using Associative Memory (AM) ASICs loaded with about one billion of possible patterns, while the track parameters are calculated by implementing fast fit on an FPGA. In both steps a high degree of parallelism is used. The data flow of the pattern-matching step has preliminary sub-steps of hits clustering (with dedicated input FPGAs) and parallel distribution of oncoming tracker data.
FTK Simulation The entire FTK data processing pipeline has to be simulated to support the hardware design and to develop trigger strategies at high luminosity. These two goals provide complementary requirements. The FTK performance studies require many millions of events, therefore, the efficient use of CPU is important. The FTK design studies require a smaller data sample and accurate, but flexible, emulation so that the impact of FTK design choices is matched by appropriate performance studies. The FTK simulation splits every event into 256 η-ϕ sub-regions of each event. In the three-step workflow (1-3), each event is processed by 64 jobs. To keep the required resources below practical Grid limits, each job simulates FTK tracks in four sub-regions (sub0-sub3) one after another (2a). The sub-region merging is done in two steps: (2b) producing whole regions reg0-reg63 and (3a) whole events in the NTUP_FTK files.
Algorithms To achieve both goals, the FTK simulation closely emulates the hardware. All steps in the FTK data flow are reproduced with the possibility of evaluating the impact of changes in the FTK design and/or parameters. To provide input data for the FTK simulation, the silicon detector hits are skimmed, either from real data (RAW) or from simulated events (RDO), into custom ROOT files (NTUP_FTKIP). Figure  2  shows the FTK simulation data flow steps: • Clustering: identify the hit coordinates based on the firing detector elements (strips or pixels), then simulates the custom clustering algorithm running on input FPGAs  [5]. • Data Distribution: simulate geometrical distribution of clusters to the parallel processing units (FTK towers) to be matched with the track patterns in the AM. • Pattern Matching simulation: read the FTK patterns and find coincidences with clusters in an event.
Challenge The Associative Memory (AM) ASIC uses a content addressable memory architecture where any data inquiry is broadcast to all memory elements simultaneously. As a result, each incoming hit reaches all one billion patterns in the whole FTK AM system within the same 10 ns clock cycle. This feature cannot be matched on a system utilising general-purpose CPUs. Similarly, the track fitting simulation on a single general-purpose CPU is at least one million times slower than the 1 GHz rate obtained in the FPGAs on the FTK fit boards. The storage -in simulation -of one billion patterns requires about 35 GB of memory. There are also two million fit coefficients that require about 2 GB of simulation storage, in addition to smaller configuration data. For these reasons, the simulation of the FTK is resource intensive and has inherent performance bottlenecks due to the limited parallelism on generalpurpose CPUs and low bandwidth for memory access 1.
To reduce the overall number of steps in the FTK simulation workflow, both merging steps are joined with adjacent workflow steps (figure  3 ): the first merging step is done at the end of the FTK simulation step, while the second merging step is done at the beginning of the data reconstruction step, which includes the trigger simulation using FTK tracks. Thus, the data reconstruction step uses 64 files from each tower region of the same event plus the RDO or RAW data file of the same event (i. e. the file that was used to skim the silicon detector hits as inputs to the FTK simulation). As a result, single workflow jobs can be sequentially executed on a single Grid node or distributed concurrently to different Grid nodes. The workflow reduces the resources required by the FTK simulation application through increasing parallelism.
Figure3. The FTK simulation splits every event into 256 η-ϕ sub-regions of each event. In the three-step workflow (1-3), each event is processed by 64 jobs. To keep the required resources below practical Grid limits, each job simulates FTK tracks in four sub-regions (sub0-sub3) one after another (2a). The sub-region merging is done in two steps: (2b) producing whole regions reg0-reg63 and (3a) whole events in the NTUP_FTK files. The final step (3) uses FTK tracks in trigger simulations producing the reconstructed data in DESD_FTK files or adds FTK tracks to the simulated events in the RDO_FTK files.
Final SaWLens Input and Results We summarize the basic parameters of each cluster reconstruction in Table  5 , including input data, reconstructed field sizes and the refinement levels of the multiscale grid. For two sample clusters, Abell 1423 and CL J1226, no multi-band wide-field weak lensing data with acceptable seeing and exposure time levels is available. In the case of CL J1226 this is less severe since we have access to a rather wide HST/ACS mosaic and, since the cluster resides at high redshift, the angular size of the reconstruction refers to a large physical size of the system. We therefore include CL J1226 in our following mass-concentration analysis, while we drop Abell 1423 from this sample. The output of the reconstruction is the lensing potential on a multi-scale grid, which is then converted into a convergence or surface-mass density map via Eq.
2 Mpc/h and where we divided the radial range into 11 bins. The center for the radial profile is the dominant peak in the convergence map. We applied this binning scheme to all 1000 convergence realizations for each cluster and derived the covariance matrix for the convergence bins. Both the convergence data points and the convergence matrix are shown in Fig. 15. To the convergence bins and the corresponding covariance matrix we fit a NFW profile given by Eq. 1. We numerically project the NFW profile on a sphere along the line-of-sight and thereby introduce the assumption     5. of spherical symmetry in our cluster mass profiles. This is certainly not justified for all sample clusters and may introduce biases. We discuss this issue in further detail in Sec. 6. We perform the profile fitting using the least-squares formalism by minimizing χ 2 ( p) = N bin i,j=0 (κ bin -κ( p)) i C -1 ij (κ bin -κ( p)) j , (22) where p = (ρ s , r s ) and C is the covariance matrix of the binned data.
For the comparison we calculate the enclosed mass within 1. 5 Mpc of the cluster center following  Applegate et al. (2014)  and show the mass comparison for the three data sets in Fig. 7. We find median values for the ratios M SaWLens /M WtG and M SaWLens /M U14 of 0. 88 and 0. 93, respectively. For the unweighted geometric mean 1  of these ratios we find 0. 94 ± 0. 11 and 0. 96 ± 0. 05. Although we see significant scatter between the different studies, there is good general agreement. We emphasize that this comparison just serves as a quick cross-check between the different mass estimates. We leave a detailed statistical comparison between different CLASH lensing, X-ray and SZ studies, as well as a comparison to earlier results from the literature to a follow-up work by the CLASH collaboration.
c-M Relation of the Full Samples First, we compare the CLASH data set to the full sample c-M relations of D08 and B13. As one can see from visual inspection of Fig. 9  already, there is good agreement between the CLASH data and the theoretical c-M relations derived from the simulations. To statistically quantify the agreement we calculate the ratio c obs /c sim as a function of cluster redshift. This ratio for each data point is shown in the bottom panel of Fig. 9. Next, we calculate the mean, standard deviation, first, second (median) and third quartiles of all these ratios and report them in Table  8. The median is also shown as horizontal pink line in the bottom panel of Fig. 9  with the error range defined by the first and third quartiles. As a last test we perform a Pearson's χ 2 test, with the null hypothesis that the theoretical c-M relation is a good fit to our data and report the p-value in Table  8.
All the analysis components, described in this paragraph shall serve as the prototype for all following comparisons between our data and c-M relations from simulations. To quantify how well we can expect the data and c-M relation to agree, we show in the very top of Table  8  the comparison to the c-M relation which we derived in Sec. 5. 1 from the CLASH data itself. Finally, Fig. 9  also shows the c-M relation of  Prada et al. (2012)  since it is widely used in the literature. One can easily see that there is a discrepancy between this re-   lation and the CLASH results, especially when the good agreement with the D08 and B13 relations is considered. However, we refer to  Meneghetti & Rasia (2013)  which argues that a direct comparison in the c-M plane is not a meaningful comparison in the case of the Prada et al.
However, when restricting ourselves to the strictly relaxed clusters the 2D c-M relation is in tension with observations. The p-value drops from 0. 87 to 0. 01 and the difference in the median concentration ratio increases to 12%. Furthermore, the trend in the difference has changed. The expected values from the simulations are now higher than the observations. The situation improves significantly to only 7% overestimation in the concentration ratio and a p-value of 0. 26 once we pick only those simulated clusters which are able to produce strong-lensing features by demanding that the cluster produces a critical line (comp. Eq. 10). This selection is appropriate since all but one CLASH cluster allowed us to identify strong lensing features. However, the observational data is clearly in tension with a simulated cluster sample selected after the three relaxation criteria of  Neto et al.
Using this set of simulated X-ray images we apply the very same selection criteria which were used to select the CLASH X-ray selected clusters. For a more detailed description of these criteria and the selection process see M14. This CLASH-like, X-ray selected sample in 2D is the one simulated sample which comes closest to the real CLASH clusters, both with respect to the selection criteria and the analysis method. The comparison between the c-M relation of this sample and the observed CLASH clusters shows indeed significant improvement over the limiting case of the fully relaxed sample in the last section. The qualitative agreement between the data points and the X-ray selected c-M relation in Fig. 12  is quite obvious. The median concentration ratio shows that the observed CLASH concentrations are only 4% lower than the ones from the X-ray selected simulation sample and the p-value 0.
CONCLUSIONS The HST multi-cycle treasury program CLASH was in part designed to shed light on the dark matter density profile of galaxy clusters by combining the enormous resolving power of the HST with wide-field Subaru imaging. The CLASH X-ray selected sample of galaxy clusters was specifically selected to have a mostly undisturbed Xray morphology, suggesting that this sub-sample represents an undisturbed and unbiased set of clusters in terms of their density profile. This choice was made since former studies of lensing clusters with exquisite data quality were inconsistent with the predictions of ΛCDM, and selection effects were thought to be a possible cause of this disagreement. In this work we applied advanced lensing reconstruction techniques to this CLASH data set. Our reconstructions combines weak and strong lensing to fully exploit the lensing data provided by the CLASH program.
This work was supported in part by contract research "Internationale Spitzenforschung II/2-6" of the  Baden Württemberg Stiftung. The  Dark Cosmology Centre  is funded by the  DNRF. J. S. was supported by  NSF /AST1313447 ,  NASA/NNX11AB07G , and the  Norris Foundation CCAT Postdoctoral Fellowship. E. R. acknowledges support from the  National Science Foundation   AST-1210973 ,  SAO TM3-14008X  (issued under  NASA  Contract No. NAS8-03060 ).
V. SIMULATION RESULTS In this section, we evaluate the Bit Error Rate (BER) and the computational complexity of the proposed detector based on quadratic minimization. We consider N × n MIMO systems, where N and n are the number of transmit and receive antennas, respectively. The channel coefficients are i. i. d. circularly symmetric complex Gaussian distributed with zero mean and unit variance, and the data symbols belong to a finite constellation. The number of transmitted symbols is equal to N at one channel use. For our proposed decoding scheme, we use the cvx toolbox, which is a Matlab-based modeling system for convex optimization  [15] ,  [16]. We use the Gurobi optimizer to solve the [Quad-min] problem under convex constraints  [17]. This optimizer employs the PDIP methods for the quadratic programming.
The most well-known one is due to  Silvescu and Honavar (2001). To understand precisely the dynamics of some biological processes (like cell development) while considering the memory of the system, the authors take their inspiration from the works about Markov models. TDN (n, m, k, D) , where each gene can be expressed at levels 0, 1,. , D -1. Their main results, however, focus on TBN (n, m, k): they design a decision tree learning algorithm that infers a temporal Boolean network from time series data. They also give some bounds on the size of the necessary data to infer temporal Boolean networks. They illustrate their results through artificially generated networks, claiming that the main limit of their method is the lack of real data on large datasets. Other authors addressed the idea of modeling delayed and indirect influences.
ALGORITHM Here, we briefly summarize the essence of LFkT. Because of the lack of space, the details of the algorithm, its pseudo-code, and the proofs of correctness are given as Supplementary Material. We refer to the pseudo-code of the appendix as follows: (algo. N l. x-y) for Algorithm N, line x to y. LFkT is an algorithm that can learn the dynamics of a Markov(k) system from its traces of executions. LFkT takes a set of traces of executions O as input, where each trace is a sequence of state transitions. If all traces are consistent, the algorithm outputs a logic program P that realizes all transitions of O. The learned influences can be at most k-step relations, where k is the size of the longest trace of O. The main idea is to extract n-step interpretation transitions, 1 ≤ n ≤ k, from the traces of executions of the system.
After analyzing all interpretation transitions, the programs that have been learned are merged into a unique logic program (algo. 1 l. 24-29). This operation ensures that the rules outputted are consistent with all observations. It can be checked by comparing each rule with other logic programs. If an n-step rule R is more general than an n -step rules R , n < n, then R is not consistent with the observations from which R has been learned. To avoid this case, we can remove n-step rules that have no variable of the form v t -n. Indeed, if such rules are consistent with the observations, then they should also have been learned from (n -1)-step rules. Finally, LFkT outputs a logic program that realizes all consistent traces of execution of O.
EVALUATION AND BIOLOGICAL CASE STUDY In the previous subsections, we have illustrated step by step how the LFkT algorithm is able to learn Markov(k) systems. To illustrate the merits of our work, we now apply this approach to the analysis of the yeast cell cycle dataset from  Spellman et al. (1998)  and  Cho et al. (1998) , which have been previously analyzed in  Li et al. (2006). In this paper, Li et al. tackle the inference of gene regulatory networks from temporal gene expression data. The originality of their work lies in the fact they consider delayed correlations between genes. The methodology can capture gene regulations that are delayed of k time units. The limits of the approach is that the authors only consider pairwise overlaps of expression levels shifted in time relative to each other.
In these experiments, for each Boolean network, the number of variables is 16 and the length of traces in input is five states. The five Boolean networks have been implemented as a logic program using Answer Set Programing  (Brewka et al. , 2011). The source code of these programs is given as Supplementary Material. Traces of executions of these programs have been computed using the answer set solver clasp  (Gebser et al. , 2012). All experiments are run with a C++ implementation of LFkT on a processor Intel Xeon (X5650, 2. 67GHz) with 12 GB of RAM. The main purpose of these experiments is to assess the efficiency of our approach, i. e. , how many traces LFkT can handle for a given k. Complete output of LFkT for these experiments is accessible as textfile at http://tony. research. free. fr/paper/Frontier/output.