dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods In this section we first introduce the non uniform sampling problem of the Wii Board Balance (Section 2. 1), then we discuss the resampling method commonly used in the Posturography literature (Section 2. 2) and their limitation. In Section 2. 3, we introduce a new algorithm, Sliding Window Average with Relevance Interval Interpolation (SWARII), designed to better apprehend the non constant time frequency acquisition of the WBB. Finally, we describe experiments that we conducted both on simulated and real dataset ( Sections 2. 4 and 2. 5) in order to compare the different resampling algorithms, and we discuss their respective analysis.
Simulated Data In order to analyse the respective performances and relative merits of the algorithms presented in Sections 2. 2 and 2. 3, we compare them on some simulated dataset (comparisons on real WBB acquisitions are performed in the next subsection). The signals (piecewise linear and sinusoidal) are generated as follows: first, the ground truth signal f is defined as: f linear (t) = 10 min(t -6 t/6 , 6 t/6t) f sine (t) = 10 sin(πt/3) Then to generate the time intervals δ i between any two successive samples t i+1 and t i , we use a mixture of the absolute values of two independent Gaussian distributions, to represent the multi-regimes distribution of the acquisition frequency of the WBB: t i+1 -t i = δ i ∼ p i |N (0. 02, 0. 01)| + (1 -p i )|N (0. 1, 0. 01)| where p i ∼ B(0. 5), i. e.
, is the realization of 0. 5-mean independent Bernoulli random variable. Finally, the noisy signal is generated by adding to every data point an independent Gaussian random variable of mean 0 and standard deviation 0. 1: x i ∼ f (t i ) + N (0, 0. 1) On each of the linear and sinusoidal datasets, we apply a resampling algorithm (PCI, LI, SWAI, SWARII or cubic splines) to obtain a uniform signal sampled at 25 Hz. With the PCI and LI methods the resampled signal is then filtered using a 4th order 12 Hz low pass Butterworth. No filter is applied to SWAI and SWARII, as doing averages over a window already denoises the signal, or to the cubic splines, which already includes a regularisation component. Let x1 ,. , xN be the resulting values at the resampled time instants s 1 ,. , s N. To derive the speed at those time instants, we use the formula: d x dt (s i ) = xi+1 -xi-1 s i+1 -s i-1 and we compare the different results, using the root mean square error (RMSE) R defined as: R(d x/dt, d f /dt) = 1 N N ∑ i=1 d x dt (s i ) - d f dt (s i ) 2 In addition to the speed, we also compare the resulting Approximate Entropy (ApEn) of the different resampling methods.
Toy Dataset Figure  3  shows the sinusoidal signal, during a change of direction, both observed and reconstructed. It is interesting to note that all the methods give reasonable estimates of the position of the CoP. However, when the reconstruction is differentiated to obtain the speed (Figure  4 ), their respective performances greatly differ. The same remarks hold true for the piecewise linear signal. The Root Mean Square Error (RMSE) of the difference between the real speed of the signal and the speed derived from the reconstructed signals are presented in Tables  1  and 2. Table  3  shows the ApEn of the true signal, sampled at 25 Hz, compared to the ApEn of the reconstructed signals. The PCI method gets the worse RMSE, both for speed and position. Since this method only use one point of the original signal for the interpolation, it reconstruction of the observed signal is heavily influenced by the noise, despite the low pass filter.
Static Signal In this setting, an inanimate object was recorded during 20 s with the WBB. Hence, the speed and the ApEn are known to be zero, as the signal is constant. This allow for an easy evaluation of the different methods. Figure  5  shows the speed obtained by differentiating the signal reconstructed with the use of the different methods. The Root Mean Square Error (RMSE) of the difference between the real speed of the signal and the speed derived from the reconstructed signal are presented in Table  4 , and the ApEn of the respective signals can be found in Table  3. The performances of the respective algorithms confirm the result of the experiment-PCI and LI vastly overestimate the speed and the ApEn of the signal-on the toy dataset, expect for the cubic splines. Its clearly suboptimal results for speed estimation, and its poor estimation of the ApEn, are directly related to a poor choice of lambda (here λ = 2.
g. ,  [16, 17] ). As seen in Section 3, those methods lead to significant error when deriving the mean, standard deviation and quantiles of the speed, and are very inaccurate for computing the instantaneous speed. The use of a sliding window, as in SWAI or SWARII, gives significantly better results. Since SWAI is widely available in most data analysis software, it should always be preferred to CPI and LI when resampling data from the WBB. The cubic splines approach, while providing solid theoretical guarantees, produces disappointing results in practice. This is largely due to the difficulty of choosing the parameter λ, and the fact that the quality of the signal of the WBB varies greatly during a record (e. g. , different sampling rate). It is worth noting that SWARII outperforms SWAI by a careful use of the relevance intervals of the data points.
To achieve a better accuracy when acquiring a signal from the WBB, we highly recommend to use SWARII (An implementation of SWARII for Python 2. 7 is available for download at: https://reine. cmla. ens-cachan. fr/j. audiffren/SWARII. git) or SWAI whenever possible as those methods require only one parameter, the length of the window. It is the authors belief that more recent and state-of-the-art approaches to resampling can lead to better performances. However the complexity of those models, and the number of required parameters that need to be carefully tuned to prevent overfitting or flattening of the derivatives, make them difficult to use for non experts. Hence we have promoted in this paper the use of sliding window methods for their robustness as they rely on the proper calibration of only one simple parameter.
Acknowledgments: This work was funded by specific grant for the SmartCheck project thanks to  SATT-IDF INNOV. We would like to thank in particular  Robert Marino  and  Charles-Henri Michel  for their support. We also thank  Danping Wang  and  Xavier Lioneton  for their help in using the ATMI platform, as well as  Damien Ricard ,  Nicolas Vayatis , and  Pierre-Paul Vidal  for useful discussions. Finally, we would like to thank the reviewers of this papers for their insightful suggestion.
We also benchmarked MLspike against Peeling, yet in a slightly simpler situation, that is, with provided baseline level, thus reproducing the published results  32  using the code available online (Supplementary Fig. 2 ). With those settings, MLspike outperformed Peeling by B20%. Next, we characterized MLspike's performance with respect to the noise's frequency spectrum (Fig. 3a-c  and Supplementary Fig. 3 ): white noise, low-frequency drifts (that is, slow baseline fluctuations) together with white noise, and pink noise (which has equal power in all octaves and includes complex baseline fluctuations and photonic noise). As expected, pink noise induced by far the largest ERs when noise was quantified by RMS power calculated over the entire frequency spectrum (Supplementary Fig. 3 ). However, when noise was quantified by RMS power restricted to the 0.
Methods Software. MATLAB implementation of the MLspike and autocalibration algorithms are available as Supplementary Software, and can also be found on the depository https://github. com/MLspike. OGB-1-AM recordings were performed on juvenile Wistar rats (P28-40) of either sex. Those were anaesthetized with Urethane (2 g kg À 1 body weight). Body temperature was monitored and maintained at 37. 5 °C with a heat controller and heating pad (CWE). A metal chamber was attached with dental cement to the exposed skull above the primary somatosensory cortex (2. 5 mm posterior and 5. 5 mm lateral to the bregma). A 3-mm-wide craniotomy was opened and the dura mater was carefully removed. The chamber was then filled with agarose (2% in artificial cerebrospinal fluid) and stabilized under a cover glass.
Stimulation. In the in vitro experiment, cells were stimulated using a tungsten electrode placed in the stratum radiatum of CA1 400 mm away towards the CA3 region from the imaged area. Ten stimulus pulses of 100 mA amplitude were applied at 1 Hz with 50 ms pulse width at using a stimulus isolator (WPI A365). In the anaesthetized rat experiments, activity was recorded in the absence of a stimulus. For imaging and stimulation in anaesthetized mice, see ref. 13. In the experiments performed on awake head-restrained mouse, a visual stimulus was delivered during data acquisition, in form of drifting gratings (spatial frequency: 0. 25cyc/°, eight possible orientations). Those appeared after 2 s of dark screen, drifted for 5 s at 1 cyc s À 1 , stopped for 1 s, and were then replaced by the dark screen.
Autocalibration of s. Parameter s was estimated by computing the RMS of the fluorescence signals filtered between 3 and 20 Hz, and multiplying this quantity by a corrective factor. Indeed, our model considers fluorescence signals as the sum of calcium-related signals (possibly modulated by the baseline drifts) and of a white noise with s. d. s. It is possible to consider that the calcium-related part of the signals contributes significantly less to the high-frequency content of the signals (for example, above 3 Hz, see Fig. 3d ) than the noise, so it appears justified to calculate the s. d. over high-pass filtered signals, and afterwards multiply it by a corrective term. The highest frequencies were also eliminated in this calculation, because the noise present in our data is actually not purely white (see the spectra in Fig.
5a ), implying that it might not be accurate to use signal power calculated in the highest frequencies to estimate the noise level expected in the crucial band B1 Hz. The corrective factor was determined such that when the method is applied to a white noise signal, the estimated s value corresponds to its true standard deviation. However, at low SNR, estimating s to this 'correct' value can lead to an excessive number of misses, the algorithm 'not trusting the data enough to assign spikes'. Therefore for the OGB and GCaMP6f data set, we slightly biased this factor (multiplying it by 0. 7), to force s to be underestimated. This resulted in a more equilibrated number of misses and false detections. Autocalibration of A and t. As shown in Fig. 4a  (see also Supplementary Fig. 5 ), the autocalibration of A takes advantage of the discrete nature of spikes, namely that calcium transient amplitudes can take only a fix set of values depending on whether they are caused by 1, 2, 3 and so on spikes.
This was done by counting together all the true/detected/missed/falsely detected spikes from all the sampled spike trains generated by the algorithm. This resulted in an average ER, noted oER4, that reflected the average accuracy over this distribution of spike trains. Correlation. To compare estimation accuracies to algorithms estimating spiking probabilities, we used correlation between the vectors of real spike counts after binning to 40 ms (or other if specified), and the estimated instantaneous spiking rates. These instantaneous spiking rates were either directly provided by the algorithm (MCMC, CD and SMC) or obtained by low-pass filtering estimated spike counts (MLspike and Peeling) with a kernel of 100 ms. Quantification of the noise level. Noise level. We quantified the noise level in the real data by taking the RMS of the difference between the measured fluorescence signals and those predicted by the electrically recorded spikes (using the calibrated parameter values).
Acknowledgements We thank  T. -W. Chen ,  K. Svoboda  and the  GENIE  project at  Janelia Research Campus  for sharing their published GCaMP5 (ref. 15) and GCaMP6 (ref. 13) data, now available at http://crcns. org/, T. -W. Chen  for sharing with us his ideas on the nonlinear modelling of GCaMP Ca 2 þ sensors and  B. Bathellier ,  B. Kampa ,  G. Masson ,  K. O ´csai ,  T. -W. Chen  and  K. Svoboda  for discussions and comments on the manuscript,  K. O ´csai  for help with programming and  A. Meso  for English editing. This work was supported by recurrent  Aix-Marseille Universiteand CNRS  funding and by a  French-Hungarian international ANR  Grant MULTISCALEFUNIM to B. R. and I. V. ; an  ANR  Grant  BALAV1  to I. V. ;  SH/7/2/8 ,  KMR_0214 ,  FP7-ICT-2011-C 323945  and  KTIA_NAP_12-2-2015-0006 ; A.
The first US FDA authorisation of an hESC derived cell product in a clinical phase 1/2a safety/efficacy (i. e. an oligodendroglial progenitor for spinal cord repair sponsored first by the Geron Corporation and then Asterias Biotherapeutics, http://www. nature. com/news/fundingwindfall-rescues-abandoned-stem-cell-trial-1. 15350) was founded on hESC source material first isolated using research grade reagents and laboratory conditions and subsequently transitioned into current Good Tissue Practice (GTP) and GMP. More recently, there have been several independent authorisations in the US and the EU of hESC based cell therapy products for the treatment of variant forms of age-related macular degeneration with comparable or improved provenance (www. clinicaltrials. gov). Assessment of risk and retrospective testing, such as for adventitious pathogens, can help qualify source cell material and reagents not originally isolated under standards suitable for clinical use.
The latter was founded on Q-Pulse software, adapted for use as guided by partners at the SNBTS with prior experience of its application in adult cell and tissue manufacture and therapy. Key internal operational tasks consisted of: i) recruitment, training and management of staff dedicated to research and development, quality control (QC), QA and cell production; ii) cell manufacturing facility design, installation, operation and performance qualification (DQ, IQ, OQ, PQ); and iii) protocol development, qualification and implementation in the production facility. Key external engagements and associated tasks included establishment and maintenance of: i) institutional advisory and regional health service ethics committee approval of information, consent and procedures for tissue procurement; ii) contractual agreements with collaborators (assisted conception units), suppliers and service providers and specification of associated technical protocols and schedules; and iii) relationships with professional forums (i.
Live cells at 60-70% confluency were shipped in warm containers, fixed and analysed by standard G-banding analysis. For research grade lines, 20 spreads were analysed whereas for clinical grade lines, 30 spreads were analysed. Viral screening for cytomegalovirus (CMV), human T-cell lymphotropic virus (HTLV)-1, human immunodeficiency virus (HIV)-1, hepatitis C virus (HCV), hepatitis B virus (HBV) and Epstein-Barr virus (EBV) was carried out by The Doctors Laboratory (London, UK). European pharmacopoeia (EP) sterility testing was carried out by Moredun Scientific Ltd. (Penicuik, Scotland) using the culture method. 2. 9. SNP genotyping and analysis DNA samples were assayed using the Illumina HumanCytoSNP-12 v2. 1 BeadChip. Genotyping data was initially assessed using GenomeStudio genotyping module (v1.
94, Illumina). Karyostudio (v1. 4, Illumina) was employed to perform automatic normalisation and to identify genomic aberrations utilising default settings of the built-in cnvPartition algorithm (3. 07, Illumina) to generate B-allele frequency and smoothened Log R ratio plots for detected regions. These parameters are designed to detect CNVs greater than 75 kb and CN-LOH regions larger than 1 MB with a confidence value greater than 35. All identified regions were first cross-matched to the Database of Genomic Variants (DGV; http://dgv. tcag. ca) to identify naturally-occurring structural variations in human. CNVs that were not identified on the DGV were then checked against a list of ES cell-associated culture adaptation genomic variants published by the International Stem Cell Initiative  (ISCI, Amps et al.
Acknowledgements The authors gratefully acknowledge the leadership and commitment of  Mr Aidan Courtney  as co-founder and chief executive officer (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016), as well as the voluntary commitment of expertise, time and effort of past and current executive and non-executive directors and observers on the Roslin Cells board of governors. This includes  Prof. Charles  ffrench-Constant,  Dr Andrew Henderson , and  Dr Wendy Nicholson  as non-executive directors;  Mr Paul Williams  as Finance Director;  Dr Ed Hutchinson ,  Dr John Campbell , and  Mr Bruce Gellatly  as observers. The project would not have been possible without the generous voluntary donation of embryos from donors undergoing assisted conception and the staff in HFEA licensed centres in the United Kingdom involved in ethical provision of patient information and consent and.
In order to do that, they need to spend a lot of resources in order to analyze a huge amount of data (data that they own and that is not accessible to anybody). In this paper we try to reconstruct these observations and these tendencies by exploiting other data sources publicly available like in Social Medias (especially Twitter). We propose a study in which we compare the figures related to exploits, published in different security reports from 2014 to 2015, with the presence and the popularity of these kits on Social Media. Through this study we first want to answer to the question: Does the popularity of an exploit published on Social Medias has an impact on the severity of the attacks resulting from these exploits? Our hypothesis concerns the simplicity of consumption offered by Social Media platforms to dangerous toolkits that can damage systems.
STATE OF THE ART The concept of software vulnerability monitoring through Social Media analysis already spotted new phenomena's related to the way how security information (vulnerabilities, zero-days, exploit codes, bugs etc. ) are shared and exploited by attackers. In  (Trabelsi, 2015)  authors conducted a study to evaluate the quality and the relevance of security information related on Twitter. They discovered that 75% of the CVEs 1 related to the Linux-kernel software component in 2014 were already disclosed on twitter before and more globally 41% of the CVEs published by the NIST NVD were already present on Social media before. The same authors were using a real time vulnerability monitoring system that crawls and analyse Twitter streams  (Trabelsi2, 2015). In  (Edkrantz, 2015)  authors made a study that correlates the existence of exploit code on twitter with real-word attacks.
Autopsy Tool As introduced in the previous section, more and more security researchers are now using Social Media to monitor security issues. The advantage of using Social Medias is the diversity and the aggregation of sources; instead of monitoring several websites, blogs, forums, bug trackers, Social Media platforms like Twitter, due to its diversity, offers an aggregator of links and sources coming from all origins. Social Medias offers also a good dataset archive that can be exploited in order to study phenomena related to cybersecurity and threats. For this reason we developed a tool called SMASH-Autopsy (in extension to SMASH-Live (Trabelsi3, 2015) that searches back in Social Media archive and performs automated analytics and statistics on past security events related to software vendors, programs and components.
This tool searches for all the publications related to any security information referring to a software component. The collected information is clustered by content then categorized by topic (vulnerability, zero-day, exploit, patch, bug, data flaw, etc. ) for every topic we have sub-categories related to the different type of topics. For example for the Vulnerability topic, the sub-categories would be SQL Injection, XSS, DDoS, etc. Once the information is clustered and classified, we start a quantitative analysis in order to generate statistics on the number of issues, the frequency, the number of patches and solutions and the impact of the issues. In the current version of the SMASH-Autopsy tool, we mainly rely on a Twitter Search API, but we are currently integrating other sources like Facebook SECRYPT 2016 -International Conference on Security and Cryptography and Google News.
CONCLUSION AND FUTURE WORK In this position paper we demonstrate the influence of Social Media Networks on the cybersecurity landscape. We proposed a study that analyses the presence and the popularity of information related to exploit kits on Twitter in order to correlate these measurements with real data related to the impact of the attacks generated by these kits. This data is provided by security professional reports (from 2014 to 2015). The results obtained are very encouraging especially with regards to the strong correlation between the popularity of an exploit with the importance of the related attack. This led us to comfort our hypothesis: the more an exploit is popular on social media, the more the probability of having attacks generated from it is high. For this reason we started developing a predictive model based on security information collected from Social Medias.
Social Medias tell us what is the favourite exploit kit and we can guess what could be the future attacks. In this paper we describe the concept of threat pre-diction without detailing the predictive model since we need to conduct a long term study in order to validate the predictions generated by this tool, and this requires time. It is not yet clear to us the estimation of the time delay between the first apparition of an exploit on Twitter and the first recorded attack. We need security professional proprietary data to obtain this information. Beside the pure time series based predictive model we are also working on a ma-chine learning based algorithm that tends to adapt the monitoring on the type of security information that is highly changing over the time. We are also experimenting different existing popularity computation algorithms for Social Media is order to verify the existence of a better algorithm that could correspond better to the information distribution of the real attacks.
Physiologically-based biomarker kinetic model development Our model was developed using the SimBiology extension of MatLab (MathWorks, Natick MA), and results were analyzed in the Origin Pro 9. 0 (Northampton, MA) and JMP 11 (SAS) programs. Our methods were derived from a generic Physiologically-Based Pharmacokinetic (PBPK) model developed by others  [14] , which was further based on a previous multi-compartment system in which several organs were represented with realistic dimensions  [15, 16]. In the aforementioned model, the organs were connected by arterial and venous circulation with appropriate hemodynamic values, also obtained from the literature. For the model described herein, we simplified this arterial-to-venous transfer of biomarkers by assuming a homogeneous distribution of the biomarker in the systemic circulation, and that the volume of this idealized vascular compartment was equal to the total volemia.
Discussion The main outcome of this study was the implementation of a MatLab-based pharmacokinetic model that allows to study or interpret the fate and excretion, levels and halflife of markers derived from the CNS but sampled in the blood compartment. A corollary set of hypotheses, which were largely confirmed by cross-validation of the model with existing data, implicated the variation of markers' levels due to: (1) physiological parameters (e. g. , GFR); (2) somatic properties (volumetric size of different organs during development); and (3) environmental factors such as sun exposure.