dataesr/scientific-paragraphs-categorization huggingface dataset:
Previous Work in Secure Linear Regression As linear regression is one of the most commonly used statistical tools in data analysis, there are many attempts in the literature at obtaining secure linear regression protocols over distributed databases. Many of these protocols do not offer adequate privacy guarantees  [31, 32]  as they share intermediate results or rely on a trusted third party to handle these intermediate results  [33]. In  [34] , El Emam et al provide some scenarios where privacy can be breached by sharing intermediate aggregate results (refer to  [35]  for a decomposition of available secure regressions based on privacy and accuracy). The first linear regression algorithm with cryptographic security was developed by Hall et al  [15] ; it makes heavy usage of SMCs, particularly secure matrix multiplication protocol.
The theoretical complexity of the algorithm is O (Np 2 ); however, the protocol is heavy on communication. In fact, the matrix multiplication protocol requires each party to send 2 matrices to every other party (of size, p 2 ), such protocol is repeated, O (k), where k is the maximal number of bits required to represent the largest integer. However, their algorithm performs better than all previous secure linear regression algorithms  [37]. Experiments done by the authors themselves indicated a capacity to handle over 4 million records with 16 features in a range of 3 hours (to provide some perspective, our algorithm requires less than 3 min for the same dataset and same settings and for both feature selection and parameter estimation). It is very important to note that all cited secure regression algorithms do not perform feature selection.
Our Algorithm The goal of distributed statistical learning algorithms is to scale up computations by distributing the data over multiple machines. The underlying assumption is that all data are owned by the same organization. Thus, sharing of intermediate and local results between the different machines is allowed. In our setting, the dataset (X, Y) is owned by m≥ 2 data holders (or sites) S 1 ,. ,S m and the different sites are interested in cooperatively performing linear regression on the union of their datasets; however, they are not willing or able to share their data. Only the final result of the computation should be revealed to all parties. In line with the DSC theory, we assume that all the samples in all sites are independent and identically distributed (randomly drawn from the same [population] distribution).
Experiments We evaluated our secure multiparty linear regression algorithm (SMA) by implementing it and analyzing the results using real and synthetic datasets. The real datasets are used to test the accuracy of the algorithm whereas the synthetic datasets are used to analyze its performance. To analyze the accuracy of the algorithm, we needed real datasets that originated from multiple different sources (different data owners). The sources' IDs had to be included to inform the actual data division along the different sites. For the synthetic experiment, data were generated and randomly allocated along the different sites, as the purpose was solely to evaluate the efficiency of the algorithm for various numbers of records and features. We used Python3 as our programming language, which we augmented with the Scikit-learn, numpy, pandas, gmpy2, and phe libraries for functionality such as socket programming, homomorphic encryption, and for dealing with negative and real floating-point arithmetic.
We built our system on top of 10 Linux machines with Intel Core i5-4570 CPU, 3. 20GHz processor, and 8GB RAM, 4 cores each. To increase the number of possible sites to 20, we installed 2 Linux virtual machines on each machine with 4 GB memory each (note that the Paillier encryption library handles real-number values with arbitrarily high precision). To test our SMA, we compared its performance with the central algorithm (CA). The CA performs linear regression on 1 machine using the same approach as the SMA (ie, it uses lasso for feature selection and linear least squares method for XSL • FO RenderX parameter calculation  [39] ). We opted not to test the accuracy or the efficiency of our SMA algorithm against existing secure linear regression algorithms as none of the existing algorithms perform model selection.
Real Datasets To test the accuracy of our algorithm, we collected real datasets that include information about the original collection site. Then, we treated each site as an independent data owner. We succeeded in finding 4 real datasets: 3 public datasets contained within the University of California Irvine repository (student performance in Portuguese, student performance in Math, and automobile fuel consumption data) and 1 from Cerner clinical database (the Diabetes dataset, where the number of sites included was varied between 3, 6, and 12, and the weight variable was excluded in some experiments because of excessive missing values). The datasets are presented in detail in Multimedia Appendix 2. In the experiments on real datasets, we randomly divided the datasets into 0. 7 training set and 0.
Synthetic Dataset Using synthetic data, we performed a scalability analysis to evaluate the time performance of the proposed solution as the data size and the number of parties increase. The synthetic datasets were generated in Python using sklearn. datasets. make_regression. The number of records was varied between 10 5 and 10 8 , the number of features between 2 and 50, and the number of sites between 2 and 20. The records were always divided equally between the sites. We distinguished between 2 testing strategies: n fixed (Figures  3 4 5 ) and N fixed (Figure  6 ). The algorithm was compared with the CA (where data are shared in the clear) as there exists no other secure linear regression algorithm that performs model selection. For the fixed n strategy (with, p≪n), Figures  3  and 4  show the time performance of CA versus SMA as a function of the sample size N.
Discussion This study introduced a model for privacy-preserving distributed data mining in which local models are produced separately and SMC is used to aggregate the results privately. Theoretical results from statistical theory were used to design the first secure multiparty linear regression model that does model selection and parameter estimation. In general, theoretical results from statistical computing say that the averaged local estimates are as accurate as the centralized estimates when n>p and m<n. In line with the theoretical results, we conducted computations on the distributed sites independently and then combined the results securely to form 1 estimator for the collective dataset. Experiments were conducted with n ≫ p and m < n and they showed the accuracy (using 4 real datasets) and efficiency (using synthetic data) of the algorithm.
The experiments on synthetic data showed very good time performance. When n is fixed, as N increases, the time taken by the CA increases at a much faster rate than SMA. For big N (10 8 ), the algorithm does model selection, and parameter estimation in under 20 min (the algorithm of  [36]  does only parameter estimation in the range of 8 hours). Much of the existing theoretical work in DSC assumes a uniform and random distribution of samples across the different sites or that the m independent sites have n observations each that are independent and identically distributed according to the same distribution P. This assumption certainly facilitates the mathematical analysis but may not be realistic for some applications. In the SMC applications, data are collected and owned by the different sites and may thus have systematic differences across these different sites, in which case, the assumption could be overly restrictive.
Study participants and setting 1st, 3rd and 5th academic year medical students were invited to participate in the study voluntarily and anonymously during mandatory seminars/tutorials and lectures. The study purpose and the consent of participants were declared. The data collection took place in 2014 at all study centers (Dresden, Munich, Budapest and Pécs), targeting about 5000 registered students in total. In addition to German and Hungarian students, we enclosed international students in Hungary (who represent about 40% of students at medical faculties in Hungary).
Data management and statistical analysis The data were recorded anonymously and no personal data was collected. The recorded data is stored at the Department of General Practice/TU Dresden in accordance with the applicable data protection regulations. Only authorized persons have access to the data. The data analysis was performed using SPSS 22. 0. Pearson's chi 2 -tests and z-tests (Bonferroni adjusted in case of comparison of more than 2 subgroups) were used to determine whether there were significant differences between frequencies regarding different subgroups. Due to the high variety of students'backgrounds (85 different nationalities), three categories were created for the variable "nationality": German, Hungarian and "other". The categories German and Hungarian only include single citizenships; cases of dual citizenships are included in "other".
As part of a further analysis, the pertussis vaccination status was examined among the subgroup of Germans studying in Germany and in Hungary. In order to show differences in gender distribution the binomial test was performed. Based on nonparametric distribution of the data Bonferroni adjusted Mann-Whitney-tests and Welch-test were used for comparing means of metric data (e. g. age) of the different subgroups. Logistic regressions were executed to meet the complexity of influencing factors (based on bivariate analysis) on complete vaccination status as well as to control effects of intercorrelation. It was examined, how a complete vaccination status is influenced by variables such as place of study, year of study, nationality, and the subjective assessment of the necessity of being vaccinated against pertussis.
Student population Of about 5000 registered students in 2014, 2961 students completed the survey questionnaire. After data check, this study includes data of a total of 2816 medical students in their first, third and fifth year of study. One thousand sixty-two students participated in Germany (589 in Dresden, 473 in Munich), and 1754 students took part in Hungary (770 in Pécs, 984 in Budapest). Of these, 2677 students (95. 1%) stated their pertussis vaccination status, and 2655 (94. 3%) added information about their nationality. Table  1  shows a summary of sociodemographic characteristics. Averaging 23. 5 years of age, students of other nationalities were significantly older than students from Germany and Hungary. There were considerably more women (62. 3%) than men among the students (binomial test; p ≤ 0.
6% vs 76. 3% German, 60. 3% others). This implies that most Hungarian students do not know their own vaccination status very well, although only 16. 5% reported not knowing their status. Due to the mandatory vaccination program students are most likely still sufficiently vaccinated in their first study years, but low awareness of their vaccination status could have implications for future booster vaccinations. The incomparability of data regarding Hungarian students could be explained by the point of time for the last booster: according to current recommendations this mandatory vaccination takes place on the 6th grade at school  [9]. Nevertheless, students in our study got their booster at the age of 6 years which might not be considered as a booster vaccination. Further, getting a booster every 10 years was not yet recommended in Hungary at the time of the study.
Methods The present was a prospective and longitudinal study on the motor function of individuals with confirmed diagnosis of Spoan syndrome in which data collected in 2007 and 2017 were compared. Of the 61 participants in the first assessment, 47 resided in municipalities in Alto Oeste Potiguar, Rio Grande do Norte, and could be located with the help of community health workers. These patients were assessed at home by occupational therapists. The distribution of the patients per municipality was as follows: São Miguel (n = 17), Serrinha dos Pintos (n = 13), Pau dos Ferros (n = 5), Encanto (n = 4), Coronel João Pessoa (n = 3), Dr. Severiano (n = 3) and Martins (n = 2). Fourteen of the participants in the original study were excluded for the following reasons: not residing in the investigated municipalities (n = 6), death (n = 8) and lack of confirmation of Spoan syndrome on genetic testing (n = 1).
The 47 participants in the present study were assessed using the same protocols as the ones used by Graciani and colleagues (2010). The participants were first interviewed to collect data such as date of birth, educational level, age at onset of symptoms and ages at loss of ambulation and hand function. Next, we applied the following instruments for the measurement of motor function parameters: 1. Modified Barthel Index (MBI), described by  Shah et al. (1989)   [6] : this index determines the degree of dependence of individuals relative to 10 functional categories -(1) personal hygiene, (2) bathing, (3), feeding, (4) toilet use, (5) stair climbing, (6) dressing, (7) bladder control, (8) bowel control, (9) ambulation and (10) transfers. The total score ranges from 0 to 100, and the results are categorised as follows: 0-20total dependence; 21-60severe dependence; 61-90 moderate dependence; 91-99slight dependence; and 100independence.
Statistical tests Descriptive measures are expressed as the means and standard deviations when appropriate, and as medians and interquartile ranges otherwise. Data normality was assessed using the Anderson-Darling test. The paired samples t-test was used to compare between time points when distribution was normal; otherwise, the Wilcoxon signed-rank test was used. Groups (2007 and 2017) within the cohort were compared by means of the independent samples t-test or the Mann-Whitney U test when the data distribution was non-normal. Categorical variables were analysed using Fisher's test. Correlations were investigated with Pearson's or Spearman's tests.
Results The sample consisted of 47 individuals, including 31 females and 16 males. A total of 25 participants were illiterate, 14 had attended approximately 4 years of formal schooling, and four had attended 8 years. Only one participant had completed higher education, and three did not answer this question. Twenty-nine participants received the state-provided Continued Assistance Benefit corresponding to the equivalent of the minimum wage. The remainder of the sample had no source of income but were instead dependent on relatives. All the data are described based on the comparison of the data collected from the same individuals in 2007 and 2017. The mean age of the sample was 36. 87 ± 12. 50 in 2007 and 47. 21 ± 12. 42 years old in 2017. There was no significant difference (p > 0. 05) in the mean age between females and males.
Measurement Results and Discussion During a period of 10 seconds, the set of four consecutive RFID frames are wirelessly transmitted to the tag. The first frame asks for the tag's EPC and received signal strength indicator (RSSI). The second and third frames ask for the temperature measured by the internal node sensor and the data of the external photoresistive sensor, respectively. The last frame asks for the supply voltage of the RFID tag. Figure  4  reports the voltage at the supercapacitor port for three different ERPs: 33 dBm (2W), 31 dBm, and 29 dBm. The resulting power densities at the rectenna location (1 m in front of the reader) were then 12. 6, 7. 9, and 5 µW/cm 2 , respectively. The cold start-up charging times (t c ) were 6, 8, and 12 min, respectively, for a total initial charging time of 8, 10, and 15 min, respectively.
The apertures for fixation and stimulus circles subtended 0. 87° and 5. 2° of visual angle, respectively. The stimulus LED array was placed 5. 2° below the horizontal meridian at an eccentricity of 4. 3°. The luminance of white and blue LEDs was linearized using a quadratic function, such that a linear increase in voltage corresponded to a linear increase in luminance (measured in cd/m 2 ). Flicker was implemented as a sine-wave modulation of the power supply to the LEDs, which was controlled using a microcontroller of the STM32F4-Discovery board by adjusting the width of PWM signal, and linearizing the current using a low-pass filter. MATLAB was used for communication with the STM32F4-Discovery board via a USB virtual com port protocol (programmed in C++). A circuit diagram of the sinewave generator can be found in Supplementary materials (Figure  S1 ).
04, SR Research, Ontario) with a sampling rate of 1000 Hz, and 9-point custom calibration. Gaze data were analyzed offline using EyeLink® Data Viewer to identify the trials during which participants made saccades to one of the lateralized stimuli during the time window used for SSVEP analysis. No trials were rejected based on the inspection of eye movement data. Stimulus presentation, response registration, and interaction with EyeLink 1000 Plus were controlled by custom-written Matlab routines using Psychtoolbox  (Brainard, 1997). EEG data acquisition and preprocessing. EEG data were recorded using a 64-channel ActiveTwo BioSemi system (for detailed description of the hardware, see www. biosemi. com) at 1024 Hz sampling rate. Offline, the data were downsampled to 512 Hz and re-referenced to the average activity of all electrodes.
6 Hz; (2) filter centered at f-1 Hz with FWHM = 2 Hz; (3) filter centered at f+1 Hz with FWHM = 2 Hz. Data filtered at the flicker frequency and the neighboring frequencies are further referred to as "signal" (S) and "reference" (R), respectively. Narrow-band filtering was performed in the frequency domain by multiplying the Fourier coefficients of the channel time series with frequency-domain Gaussian filter:  =  -0. 5((-)/) 2 ) (1) 𝑠 = 𝜎 2𝜋-1 4𝜋 (2) where f is a vector of frequencies from the Fourier transform of the time series signal, m is the peak frequency of the filter, and is the desired spectral FWHM. After frequency-wise multiplication, the inverse Fourier transform was applied to obtain the narrowband filtered time series signal. Third, temporally-filtered data from 1500 to 7000 ms (relative to the cue onset) were used to compute channel covariance matrices (two R matrices that were averaged and one S matrix).
Flickeronset and offset elicits strong visual responses, which affect the quality of the spatial filter and thus were excluded from the analyses  (Cohen and Gulbinaite, 2017). To increase the robustness of the spatial filters, we applied a 1% shrinkage regularization to the R covariance matrix. Shrinkage regularization involves adding a percent of the average eigenvalues onto the diagonals of the R covariance matrix. This reduces the influence of noise on the resulting eigendecomposition. Fourth, generalized eigenvalue decomposition (MATLAB function eig) on the covariance matrices S and R was used to construct spatial filters (W): 𝑆𝑊 = 𝑅𝑊𝛬 (3) where W is the matrix of eigenvectors and Λ is a diagonal matrix of eigenvalues. 𝑦 = 𝑤 𝑐 𝑇 𝑆 (4) where w is the c th column in W matrix. Maps were normalized prior to averaging across participants (Figure  3A  and C, Figure  5D , and Figure  7B ).
Alpha-band power (8-13 Hz) analyses. To evaluate α-band power changes related to the shifts and maintenance of spatial attention separately from attentional modulation of SSVEPs, only 15-80 Hz flicker conditions were included in the α-power analyses, meaning that SSVEPs (1f and harmonicallyrelated responses) in the alpha-band were excluded. We performed two sets of analyses. First, to make alpha-band power results comparable to SSVEP analyses that were done in the frequency domain, we estimated alpha power using short-time FFT in the same time window (1500-7000 ms), with a 500-ms sliding window, and steps of 25 ms (i. e. 95% overlap between successive time segments). Second, we estimated alpha power changes over time by convolving stimulus-locked single-trial data from all electrodes with complex Morlet wavelets, defined as: 𝑒 𝑖2𝜋𝑓 𝑖 𝑡 𝑒 -𝑡 2 (2𝜎 2 ⁄ ) (5) where t is time, fi is frequency which ranged from 2 to 60 Hz in 50 logarithmically spaced steps, and σ is the width of each frequency band defined as n/(2πfi), where n is a number of wavelet cycles that varied from 3 to 15 in logarithmically spaced steps.
The same statistical procedure was performed when evaluating attentional modulation across different flicker frequencies, i. e. comparing SNR spectra for attend flicker and ignore flicker conditions. Statistical analyses of alpha power. Statistical significance of alpha-band lateralization in attend vs. ignore flicker conditions (Figure  5D ) was evaluated using the following nonparametric permutation testing procedure. First, real t-values were obtained at each pixel of the topographical map (electrodelevel alpha-band power extrapolated using Matlab plotting function contourf) by performing a onesample t-test comparing the condition difference against 0. Second, a null hypothesis distribution of tvalues (permuted t-values) was generated by swapping condition labels and rotating the topographies by 90° for a random number of subjects, and re-computing t-values.
The mirror images that have resulted from this encounter have also been used, in some cases, to support ideological 4 The data analyzed in this paper was collected during three short periods of fieldwork in India between November 2016 and March 2018. Along with informal discussions and interviews (often tape-recorded) I have used multiple written sources-judgments, newspapers, websites, legal files, activist pamphlets, etc. For the Tibetan section, most of the interviews were conducted in the Tibetan community of Dehradun. For the hunter section, interviews were held in Dehradun and in various places in Himachal Pradesh. The interviews in the section on activists and on the judiciary were conducted in Delhi. For the legal section, I would like to thank the lawyer Ritwick Dutta for his insights and for sharing with me most of the cases he handles in his work.
Acknowledgements This research was supported by the  Region Pays-de-la-Loire as part of the projects "RHIZOSFER"  ( N°2012-09695 ) and "PLAISIR". We are grateful to the  PLATIN (Plateau d'Isotopie de Normandie) core facility  for element and isotope analysis and the  LUBEM (University of Brest ) for the analysis of bacterial biomass. We sincerely thank the technical staff of LEVA and SESAN for their technical assistance.
Power delivered to the PID was always the same AC line voltage (220-240V). This PID setup consisted of a step down transformer (220 230 (AC) V to 32 24 (DC) V) to power a MODBUS communication interface and a solid state relay. Sensors were fixed on the outer surface of the chamber A. During this process, LABVIEW program was controlling the solid state relay via MODBUS communication and delivering the line voltage to the heating ring around the cell. The heating ring has a resistance of 220 ohms and a power rating of 220 W. The temperature of the cell (T) was controlled with a precision of ± 0. 01 • C.
Low Temperature Experiments For low temperature experiments, we chose the FPPE configuration with a semitransparent LiTaO 3 , coated with ITO on one face, and gold on other was in contact with the sample. The light entered through the transparent ITO and heated the sensor-sample interface. The system was cooled from ambient temperature to -196 • C at a rate of 6 • C min -1 by circulating liquid nitrogen and hold at this temperature for 10 min to avoid any temperature gradient in the sample. The flow was stopped and the cell was allowed to heat to ambient temperature at a rate around 1 • C min -1. Data acquisition was done from -180 • C to 20 • C at every second while heating. Fast cooling of LiTaO 3 results in building up of surface charges on it and one should be careful when establishing a connection to the LOCK-IN amplifier.