dataesr/scientific-paragraphs-categorization huggingface dataset:
Lemma 12. The protocol (rsac, rsas) constructs from [RO 384 , SNET N,ρ,n , PKI F ] the master secret key resource MSK N,ρ,AUX ,n , under the NR-PCA assumption for RSA = (gen, enc, dec), i. e. , for the simulator σ and the reductions C q described in the proof, [RO 384 , SNET N,ρ,n , PKI F ] (rsac,rsas),σ,(0,ε) ==⇒ MSK N,ρ,AUX ,n , where for each distinguisher D, ε(D) = n • |C| • Γ DCq q (G nr-pca ) + q 2 368. Before proceeding with the formal proof, let us discuss some intuition. In the proofs of Lemma 9 and Lemma 11, it is apparent when the pre-master secret value embedded in the challenge to the reduction is delivered to the server. However, as we argue below, this is not necessarily the case for TLS-RSA if one assumes like [JK02, KPW13] that RSA is OW-PCA. Recall that in the RSA mode, the client chooses a random pre-master secret and encrypts it under the server's certified public key.
Oulasvirta et al. [10]  used logs of smartphone interactions to examine users' habits, in particular their habitual checking of their smartphone state and notifications. Woerndl et al. [13]  proposed a unified approach for collecting data about smartphone interactions in an appropriate granularity for user modeling. In this paper we report an investigation into implicit profiling and adaptive user interfaces for mobile news apps. First, a survey was conducted to examine news reading behaviour of users of mobile devices. A cluster analysis revealed three main types of mobile news reader characterized by five factors. Second, a study was conducted to investigate whether users of a news app could be identified in relation to the three types using a dedicated news app (Fig. 1 ), logging user's interactions during two weeks.
Habito News: the mobile news app We implemented an Android news app to log user's interactions whilst reading the news. To ensure its familiarity to users, our news app mimicked the BBC's app in terms of its visual presentation (Fig. 1 ). News stories are organised in rows of thumbnails and are retrieved from the BBC API using an asynchronous background task. To log the interactions we implemented a background service that unobtrusively collects data associated with general preferences and usage of the app, both navigational and reading, based on the five factors found in the survey study.
Deployment and data collection The Habito News app was downloaded from Google Play and used by 23 participants (11 females) aged 22 to 33 years old (M=27, SD=3. 64) for two weeks. Participants were instructed to read the news, as they would do with any other available news app and to use it as their primary news app. After installing the app, users completed a form containing the same questions as contained in the survey. The app then recorded their interactions in relation to the five clustering factors in the survey (including precise scroll usage, reading time, frequency of reading, location, exact category scrolling event such as items browsed and swipe direction, etc). Although, other researchers conducted logging studies  [5, 8] , to the best of our knowledge none of them logged data in such fine granularity.
User Modeling: Predicting News Reader Types The logged interaction data was used to identify each participant's news reader type using a naïve Bayes classifier. The classifier was developed in three steps: (1) The five clustering factors were extracted from the raw interaction data by abstraction. For example, reading style was estimated using the formula 2  in which the proportion of the article exposed to the user by using scroll positions, divided by the total size of the document and multiplied by the number of words to approximate the number of words viewed. The number of words read per (wpm) 3 was estimated by dividing the approximate number of words seen by the reading duration. Finally, the reading speed was interpreted as one of the three reading styles: reading for comprehension (i.
Background: Personalized medicine has become a priority in breast cancer patient management. In addition to the routinely used clinicopathological characteristics, clinicians will have to face an increasing amount of data derived from tumor molecular profiling. The aims of this study were to develop a new gene selection method based on a fuzzy logic selection and classification algorithm, and to validate the gene signatures obtained on breast cancer patient cohorts. Methods: We analyzed data from four published gene expression datasets for breast carcinomas. We identified the best discriminating genes by comparing molecular expression profiles between histologic grade 1 and 3 tumors for each of the training datasets. The most pertinent probes were selected and used to define fuzzy molecular grade 1-like (good prognosis) and fuzzy molecular grade 3-like (poor prognosis) profiles.
To evaluate the prognostic performance of the fuzzy grade signatures in breast cancer tumors, a Kaplan-Meier analysis was conducted to compare the relapse-free survival deduced from histologic grade and fuzzy molecular grade classification. Results: We applied the fuzzy logic selection on breast cancer databases and obtained four new gene signatures. Analysis in the training public sets showed good performance of these gene signatures for grade (sensitivity from 90% to 95%, specificity 67% to 93%). To validate these gene signatures, we designed probes on custom microarrays and tested them on 150 invasive breast carcinomas. Good performance was obtained with an error rate of less than 10%. For one gene signature, among 74 histologic grade 3 and 18 grade 1 tumors, 88 cases (96%) were correctly assigned.
This grade is associated with an unclear risk of recurrence and is thus not informative for clinical decision making. Individualized medicine, not only based on clinicopathological characteristics, but also using information derived from tumor molecular profiles could improve patient management and increase survival. However it remains a clinical challenge. We have applied this new fuzzy logic methodology on gene expression data information obtained from public available datasets and our own patients cohort. We identified gene signatures enabling accurate discrimination of patients with breast cancer into either grade 1 or grade 3 and to help classifying patients with histologic grade 2 tumors into grade 1 or grade 3 like. This classification could lead to a reduction of over-and under-treated patients.
Tumor data were derived from four independent cohorts: NKI2-Agilent (113 samples)  [16] , KJX64KJ125-GSE2990 (103)  [14] , Uppsala-GSE4922 (123)  [13] , and Transbig-GSE7390 (113)  [6]. The training sets were used to identify groups of genes whose expression allowed distinguishing histologic grade 1 from grade 3 tumors. MEMBAS algorithm ranked in decreasing order the probes and a posterior probability was iteratively estimated for each tumor by leave-one-out cross validation (LOOCV). We performed selection of the minimal number of probes which achieved the highest possible sensitivity (Histologic Grade HG3 patients correctly identified) and an acceptable level of specificity (HG1 patients correctly classified), in order to have accurate grade assignments (Figure  1 ). In breast cancer context, a high sensitivity is privileged over high specificity in order to be sure that patients with high risk of recurrence would be thoroughly treated.
Gene signature D  [6]  presented a high rate of sensitivity (93%) and a lower specificity (67%), likely due to an imbalance in the composition of grade 1 and 3 tumors in this cohort and to a lower number of grade 1 tumors. To sum up, cancer grades prediction obtained from the four signatures strongly correlated with those obtained from histologic data. As detailed in the Methods section (Figure  2 ), to quantify the degree of membership to molecular grade 1-like (fMG1) and 3-like (fMG3), a molecular grade score was developed. As shown in Figure  2 , a tumor displaying a grade 3-like gene expression profile is assigned with a score (membership degree) greater than or equal to 0. 5, whereas a score below 0. 5 corresponded to a tumor displaying a grade 1-like gene expression profile. The values of molecular grade score for all datasets are shown in Figure  2.
Gene signature (GS) performance assessment in independent validation sets An important aspect of our fuzzy gene signatures (fGS) is their performance. It has to be assessed on cohorts from various origins, including a validation set. Therefore, we tested the four fuzzy gene signatures (fGS) on several independent patient cohorts. We used exactly the same probes than the ones included in each fGS in order to strictly respect selected molecular profiles. Only gene signature A could not be validated on an independent dataset due to the deficiency of other Agilent platform datasets. Furthermore, redundant patients present in KJX64/KJ125 and Uppsala datasets were removed from the validation tests so they were only considered once. Validation tests showed that gene signatures B, C and D achieved similar performances to those obtained in training sets with about 80-90% of sensitivity (classification of HG3 tumors) and specificity (classification of HG1 tumors), and error rates between 7 to 17% (Additional file 2: Table  S2 ).
Correlation between fuzzy molecular grade score and relapse-free survival To estimate the prognostic performance of gene signatures in breast cancer patients, the Kaplan-Meier method  [54]  was used to compare the relapse-free survival deduced from histologic classification to that from fuzzy molecular classification. The reliability of molecular grades in the different datasets and in pooled datasets was tested. Histologic grade 3 tumors were associated with a high rate of relapse whereas histologic grade 1 tumors were related to a low risk of recurrence. Histologic grade 2 tumors were associated with an intermediate rate of relapse. We performed the same survival analysis with fuzzy molecular grade obtained with the four gene signatures. The results summarized by hazard ratios, 95% confidence intervals and p value, are presented in Table  2.
The hazard ratio from all datasets classified with the four gene signatures showed significant log rank test p value. Only gene signature D failed to reach significance in its training set (Transbig cohort), however histologic grading did too. As shown in Table  2 , discriminating grade 1 from grade 3 tumors with our Fuzzy molecular method exhibited better results than with the histologic one. In addition, while survival curves of molecular grade 1 and 3 were similar to those of histologic grade 1 and 3 respectively, in some cases, fuzzy gene signatures could be better than the histologic grade classification in improving relapse free survival for patients with grade 1 and 3 tumors (Additional file 3: Figure  S1 ). To evaluate the clinical interest of molecular grades, grade 1-like and grade 3-like, we compared the relapsefree survival of grade 2 patients who had grade 1-like profile with that of patients who had grade 3-like profile, according to the four gene signatures.
The four fuzzy molecular grade signatures separated grade 2 tumors into 2 distinguished classes, grade 1-like and grade 3-like, with a statistically significant difference in relapse-free survival across all datasets or when datasets were pooled (Figure  5  and Additional file 4: Table  S3 ). Grade 2 tumors with grade 1-like profile had a lower risk of recurrence than grade 3-like. We examined several variables in a univariate analysis (Additional file 5: Table  S4 ) and found that gene expression grade of our fGS, histologic grade, lymphoma node status and tumor size were all statistically significantly associated with relapse-free survival. However, in a multivariable analysis, only our fuzzy Gene Signature (fGS) and tumor size remained statistically significant associated with relapse-free survival.
Grade 1 & 3 profiles With Nimblegen microarrays constructed with the newly designed probes based on our fuzzy gene selection as described in Material and methods, we examined the expression profiles on ICRs cohorts for consistency with predicted histologic grade. As shown in Figure  6 , the gene expression patterns of patients with histologic grade 1 (n = 18) and grade 3 (n = 74) tumors were similar to those identified previously in the training and validation sets from public datasets. A total of 125 probe sets (representing 122 genes) were identified as genes with the highest discriminating power (i. e. the most significantly differentially expressed genes) between grade 1 and grade 3 tumors. For all fuzzy gene signatures, we could easily discriminate low and high grade from the gene expression patterns of the ICR cohort.
Grade 2 classification in fMG1-like or fMG3-like categories After checking the accuracy of the fuzzy prediction model on grade 1 and 3 tumors with the new design of probes, we tested the profiles of histologic grade 2 tumors (n = 58). As for the public datasets, the histologic grade 2 tumors harbored extreme values than encompassed those of histologic grade 1 and 3 tumors (Figure  6 ). It could be noticed that 69% of grade 2 tumors were classified identically by the four gene signatures. Only 7% (4/58 tumors) of grade 2 tumors were ambiguously classified as fMG1-like by two gene signatures and as fMG3-like by the two others. Among these 4 tumors, 3 were within the equivocal zone. This demonstrates that the use of several gene signatures may prove useful to enhance confidence on prognostic information provided by molecular grade.
We applied this approach to four previously published microarray datasets (Agilent and Affymetrix technologies) and generated four distinct fuzzy gene signatures. We found that all four grade signatures were able to reliably identify histologic grade 1 and 3 tumors and molecularly separate grade 2 tumors into fMG1-like or fMG3-like categories, regardless of the platform technology, experimental protocols or type of cohorts. The fact of applying the membership concept (i. e. a sample belongs simultaneously to each category with a given value of confidence) allowed representing the heterogeneity of tumors without the necessity of preprocessing into molecular subtypes. Hence, only those probes that truly contributed to the characterization of both grade 1 and grade 3 categories were selected.
Breast microarray datasets We collected four public available datasets from patients with primary breast cancer profiled using Affymetrix or Agilent DNA microarrays: the Netherlands Cancer Institute NKI2 dataset from van de Vijver et al. (249 patients) and the Bordet Institute TRANSBIG dataset from  Desmedt et al. (198 patients). Some samples of the NKI2 cohorts were excluded from our study due to missing or biased data. Redundant patients (74 samples) present in KJX64/KJ125 and Uppsala datasets were removed from the validation tests so they were only considered once. Gene expression and clinical data of public series were retrieved from Gene Expression Omnibus (GEO) public database http://www. ncbi. nlm. nih. gov/geo, author's website and publications  [6, 13, 14, 16]. All datasets were retrospective.
Finally a sample (tumor/patient) will be assigned to the class for which its GAD is the highest  [62]. In Hedjazi et al. [60]  and his PhD manuscript  [63]  an extensive experimental study, including a comparison with known feature selection methods has been performed on several datasets presenting mixed-type and high-dimensional data. The experimental results in these works show that MEMBAS leads to a significant improvement of classification performance of LAMDA (fuzzy classifier) as well as other well-known classifiers (k-NN, SVM). Moreover, the combined fuzzy model MEMBAS/LAMDA works well in datasets with mixedtype data, since the same fuzzifying process (membership functions) is used for both feature selection and classification. This provides a similar processing for each feature type with minimal loss of information.
Fuzzy molecular gradegene signature strategy For each breast cancer cohort we identified the most relevant genes using MEMBAS/LAMDA algorithms. The procedure was as follows: 1. The cohort database was separated into a training set (histologic grade 1 and grade 3 patients) and a validation set (histologic grade 2 and unknown grade patients). Gene expression values were then standardized within the interval  [x_imin, x_imax]. This linear re-scaling of the variable into the interval [0,1] was performed according to equation  (1)  where xi is the i-th gene probe (feature) and x i is its normalized value; x_imin and x_imax are the bounds of the i-th probe for the corresponding cohort. x i ¼ xi -x i min xi max -x i min ð1Þ This standardization, based only on histologic grade 1 and grade 3 status, was necessary since gene expression values in different platforms are scaled by unknown parameters and because grade compositions diverge between datasets.
Hence, for each iteration, the number of probes was incremented, following the ranked order, until the whole probe set was tested. Since most cohort datasets are scarce and small (in terms of patient number), classification performance was evaluated with a leave-one-out cross-validation (LOOCV) to estimate the optimal classification parameters as proposed by Wessels et al. [65]. This method consists in holding out one single sample from the histologic grade 1 and grade 3 dataset of the cohort. This sample was considered as the validation data and the remaining histologic grade 1 and grade 3 samples of the cohort were used to estimate the optimal parameters of the classifier (profile of each class, exigency-α). The resulting model was employed to classify the test sample. This was carried out on all samples so that each of them was used once for testing.
This procedure is commonly used to evaluate the generalization performance of a classifier, since it reduces bias from the training set, i. e. data for a sample (tumor) are never used to estimate the classifier parameters (class profile) for its own classification. The resulting probe set, providing the higher classification performance, characterizes the fuzzy Molecular Grade 3-like (fMG3) and fuzzy Molecular Grade 1-like classes. 4. Given that LAMDA classification provides a global adequacy degree (GAD) for each sample to both classes (Figure  2 ), a molecular grade score has been introduced in order to determine the similarity between the Gene Expression Grade and the histologic grade. This score was determined by taking the GAD of each sample (tumor) for the Fuzzy Molecular Grade 3-like (fMG3) class.
NimbleGen Microarrays Gene signatures obtained were validated on an additional cohort. In order to focus the attention to the most informative genes, custom NimbleGen Roche 4x72K gene expression microarray kit (NimbleGen Roche Diagnostics, Meylan, France) was designed by NimbleGen support, based on genes included in gene signatures provided by the proposed fuzzy logic selection strategy (2030 accession numbers). We added controls of microarray experiment and housekeeping genes for normalization data. A mean of 9 probes by sequence were designed.
Validation cohort In order to validate the different generated gene signatures (fGS), 150 frozen breast cancer tumors from the tumor bank of the Claudius Regaud Institute (ICR Toulouse, France) were selected. This cohort consisted of consecutive invasive breast carcinoma patients treated at Claudius Regaud Institute between 2009 and 2011. All patients included in this cohort signed an informed consent. Clinicopathological characteristics of the series were similar to those observed in routine clinical practice (i. e. majority of pre-menopausal patients presenting with T1c, node negative, ER+ invasive ductal carcinoma of intermediate grade). Clinical-pathological data of our samples are available in Additional file 11: Table  S8. The study was approved by the local ethic committee.
One μg ds-cDNA was Cy3 labeled using One-Color DNA labeling kit (NimbleGen Roche Diagnostics, Meylan, France). Hybridization on our Roche NimbleGen 4x72K custom microarray, washing slides and scan were performed according to the manufacturer's protocol (NimbleGen Roche Diagnostics, Meylan, France). DEVA software v1. 2 (NimbleGen Roche Diagnostics, Meylan, France) was used to extract the raw intensity values. Spike in control analyses, normalization and statistical analyses of the data were performed with Bioconductor packages (http://www. bioconductor. org/) and R software (version 2. 14. 1). All data were Robust Multichip Average (RMA) background corrected, log2-transformed, summarized by a robust mean for each probe. Then, housekeeping normalization was done on the experimental data. This method involved subtracting the average from a subset of 20 selected reference genes.
As numerous studies reported that commonly used reference genes are not constantly expressed under different experimental conditions, we selected the 20 most suitable reference genes among 62 candidate genes previously reported as 'housekeeping' genes, based on their expression stability and correlation. Correlation coefficient between fluorescence and quantity of controls were comprised from 0. 96 to 1, providing high confidence in the experimental data. The microarray data have been deposited in the Gene Expression Omnibus (GEO) public database. The GSE53958 study can be found at: http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE53958.
Figure4Cross validation of fGS B. Genes of the fGS B were mapped to three previously published breast cancer microarray datasets: A) Uppsala (GSE4922) [13] ; B) Transbig (GSE7390) [6] ; and C) Stockholm (GSE1456) [13]. HG1 and HG3 tumors from each dataset were used to calculate molecular grade profiles; HG2 tumors were classified as fMG 1-like or fMG 3-like (top panel) and sorted according to their molecular grade score (bottom panel).
Acknowledgements This work was supported by the  National Research Agency (Agence National pour la Recherche) ,  "BIOTECHNOLOGIES" program  (decision n°ANR  2010 BIOT 004 06 : Project  INNODIAG ) and by the  "pôle de compétitivité" Cancer-Bio-Santé from Midi Pyrénées, France. Carine Valle  and  Tatiana Kempowsky-Hamon  were supported by  ANR  fellowship. We thank the ethics committee " Claudius Regaud Cancer Institute Research committee " that approved our study and gave us access to the biological resources and patient database. We also thank our colleagues at the  GeT-Biochip Platform Toulouse Genopole  and  Dendris Company  for fruitful discussion and critical reading of the manuscript.
With the current implementation of The Virtual Brain, it is possible to simulate and study both approaches without the need to modify the architecture of the simulator. It should be noticed that running times of a model including node-to-node propagation, at the spatial resolution of a surface, would be inconveniently long for most purposes. Cortical surface meshes can now routinely be extracted from MRI data, thanks to a number of freely available software packages, e. g. , FreeSurfer  (Dale et al. , 1999). To make these mesh surfaces suitable to support a range of neural field models, additional simplification and regularization is sometimes required. Appendix A of this paper describes a set of methodologies, primarily developed for 3D computer graphics, which can achieve this simplification and regularization.