dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, as a proof-of-concept, we demonstrate the applicability of the proposed algorithm in a real load balancing scenario. We provide a set of measurements showcasing improvements in terms of the clients' connectivity time to download specific content, without a significant impact on the utilization of the content servers. This is achieved due to the deployment of additional content caches, an event triggered by the output of the proposed RCPD detector. The rest of the paper is organized as follows. In Section II, we discuss our approach with respect to related works. In Section III, we present the training phase of the RCPD algorithm, while the on-line phase is discussed in Section IV. In Section V, we present four experiments over synthetic data, providing an extensive validation of the RCPD and its subroutines, while in Section VI, we discuss corresponding experiments using a database of real YouTube video views.
Furthermore, it is confirmed in the real YouTube datasets used in the present work through the evaluation of the time-series's Hurst exponents, as will be discussed in Section VI-A. The off-line analysis tests the constancy (or not) of the mean values up to the current time N. Hence, we define the following null hypothesis of constant mean, H 0 : µ 1 =. = µ N , against the alternative, H 1 : µ 1 =. = µ k * of f = µ k * of f +1 =. = µ N , indicating that the mean value changed at the unknown (time) point k * of f ∈ {1,. , N }. Considering (1) and the corresponding assumptions for the stochastic process X n , we develop a non-parametric CUSUM test statistic following  [27]. The test statistic T S of f , can be viewed as a max-type procedure, T S of f = max 1 n N C T n Ω -1 N C n , (2) where the parameter C n is the retrospective CUSUM detector, C n = 1 √ N n i=1 X i -nX 1,N , (3) while X 1,N = 1 N N i=1 X i denotes the sample mean.
A. On-line Analysis We rewrite equation (1) in the form, X n = µ + Y n , n = 1,. , m + k * -1 µ + Y n + I, n = m + k * ,. (11) where µ, I ∈ R r represents the mean parameters before and after the unknown time of possible change k * ∈ N * respectively. As a reminder, the first dimension of the timeseries represents the video views; the rest could be likes, comments, etc. , and {Y n : n ∈ N} is a random component. The term m ∈ N denotes the length of the training period, i. e. , an interval of length m over the historical period during which the mean is assumed to remain unchanged, so that, µ 1 = • • • = µ m. (12) To satisfy this assumption, the modified off-line CP test previously presented is run in order to identify a suitable m. With m determined, the on-line procedure can be used to check whether  (12)  holds as new data become available.
Next, in the second experiment, using the same test sets as above, we measure the success rates achieved by the proposed trend indicators T I ts in  (27)  and T I f in  (31)  for h = 0 (larger thresholds provided the same true identification rates). The results are summarized in Table  II. The two trend indicators successfully identify the direction of a change in more than 99% of the cases, which shows that they can be interchangeably employed. In the assessment of the performance using real datasets in Section VI, we solely employ the T I f trend indicator. We proceed by assessing the proposed RCPD algorithm using both the standard and the ratio type CUSUM. In this third experiment, we measure the average number of CPs detected, averaged over 1, 000 simulations when a single CP is introduced in the ARMA time-series at the time instance N 2 = 300.
A. Statistical Properties of the Real Dataset First, we evaluate the validity of the most important underlying assumption of this analysis, that the content popularity can be modelled as the sum of a constant mean and a weakdependent (t-dependent) stochastic process, as given in  (1). A first intuitive method to test whether the time-series is shortrange dependent (SRD) is through its autocorrelation function (ACF). The ACF for a weakly-stationary process {X t : t ∈ N with mean value µ is given by, ρ(k) = (X t -µ)(X t+k -µ) σ 2. Note that if ∞ k=-∞ ρ(k) → ∞ the process has long-range dependence (LRD), while if ∞ k=-∞ |ρ(k)| < ∞ it exhibits SRD. To distinguish between these two phenomena, we use the following functional form of the ACF, ρ(k) ∼ C 2H-2 i , as i → ∞, where C i > 0 and H ∈ (0, 1) is the Hurst exponent characterizing the LRD,i.
B. Performance of the Off-line Training Phase First, we test the hypothesis H 0 of no change in the mean structure on our dataset. H 0 is rejected in approximately 70% of the cases, for a significance level of a = 0. 05. This outcome indicates that CP algorithms can identify changing content dynamics in real times series. Next, we estimate the number of CPs, by applying the extended off-line algorithm. The corresponding results are illustrated in Fig. 1  and indicate a sufficiently high number of content popularity anomalies (i. e. , mean changes). Hence, a CP analysis is indeed a suitable tool for content popularity detection. To evaluate the performance of the proposed trend indicator T I f , we need a baseline independent assessment of the direction of change. We declare that a real increase in the mean value of content visit exists if E[X( k * i-1,of f ) : X( k * i,of f )] < E[X( k * i,of f ) : X( k * i+1,of f )], (32) or , that a real decrease in the number of visits exists if E[X( k * i-1,of f ) : X( k * i,of f )] > E[X( k * i,of f ) : X( k * i+1,of f )], ( 33 ) where i = 1, • • • , card( k * of f ), k * 0 = 1, k * s+1 = N and E[•] denotes the numerical average.
We test the modified MACD T I f on two sets of videos. The first set, Video Set 1, comprises the whole dataset, while the second set, Video Set 2, comprises only the videos with a considerable average number of visits (> 10), i. e. , for which, E[X(1) : X(1000)] > 10. The percentage of successful T I f identifications are tabulated in Table  V  for five values of the parameter h, namely h = 0, 3, 5, 7 and 10, where h denotes the T I f 's calculation threshold introduced in Section IV-B. Commenting on the results for Video Set 1, the T I f trend indicator works well, except for h = 0, providing at least 90% correct direction identifications. As expected, as h increases the procedure works better. More specifically, an h ≥ 5 parameter choice yields a success rate of 95%, while if a more agile estimation is needed then an h ≥ 3 still maintains a 91% accuracy.
D. Time Dependencies of Piecewise time-series We also measure the autocorrelation function of the piecewise -divided by the detected CPs -time-series. Results are tabulated in Table VII and verify the short dependence structure of the dataset; significant lags in time dependencies higher than 30 instances can be found in less than 5% of the time-series. Furthermore, the fact that the ACF of the piecewise time-series drops to zero quickly indicates that the detected CPs split the time-series into stationary segments, which, additionally, confirms indirectly the accuracy of the off-line CP estimations over the changes in the real data.
E. Computational Complexity and Scalability Finally, we present a MATLAB implementation of the overall algorithm with a large number of time-series (882 in this experiment) to quantify its performance in terms of processing cost. The computational time is measured on a Lenovo IdeaPad 510-15IKB laptop, with an Intel Core i7-7500U @ 2. 70 GHz processor and 12 GB RAM. In Fig. 6 , we show the aggregate processing cost per time instance for the two on-line methods and the total number of time-series. For the first 100 time instances, the algorithm collects the initial data, since it bootstraps. The peaks indicate the off-line part of the algorithm, which is more processing demanding mainly due to the segmentation algorithms running in parallel. The online part in the standard on-line algorithm indicates a linear complexity, since it is based on  (18) , while the equivalent quantity in  (21)  of the ratio-type is more CPU intensive, justifying the comparatively higher processing cost of the latter algorithm.
VII. THE RCPD ALGORITHM IN A LOAD BALANCING SCENARIO In this Section, we demonstrate our proposal in a real content distribution scenario, balancing the traffic between web clients and content caches with a bespoke DNS-based load-balancer. We implement the RCPD algorithm as a clientserver MATLAB application. The RCPD engine receives periodic content popularity measurements; if a CP is detected, the corresponding upward or downward changes are signalled to the load balancer. The load balancer: (i) distributes the load between the deployed content caches, in a round-robin fashion; (ii) tracks content visits and communicates them to the RCPD engine; and (iii) deploys or removes content caches based on the RCPD outputs. We implement the web clients using with the httpperf tool (https://github.
VIII. CONCLUSIONS AND FUTURE WORK In this paper, we developed the RCPD, a novel algorithm for the real-time detection of changes in the mean value of content popularity. Approaching the problem statistically, we efficiently combined off-line and on-line non-parametric CUSUM procedures to avoid restrictive assumptions for content popularity behavior and to reduce the overall computational cost. We divided the algorithm in two phases. The first phase is an extended retrospective (off-line) procedure with a modified BS algorithm and is used to adjust online parameters, based on historical data of the particular video. The second phase integrates one of two alternative trend indicators to the sequential (on-line) procedure, to reveal the direction of a detected change. We provided extensive simulations, using synthetic and real data, that demonstrated the performance of the proposed algorithm for the successful identification of content popularity changes in real-time.
ACKNOWLEDGMENT This work has received funding from the  EU 's  Horizon 2020 research and innovation programme  through the 4th open call scheme of the FED4FIRE+ (grant agr. n o 732638), the  EU-BRA Horizon 2020   NECOS  Project (grant agr. n o  777067 ) and the project " GSRT  funding for the years 2016-2017 (Award for the participation in competitive E. U. projects) [ Measuring Mobile Broadband Networks in Europe (MONROE ,  H2020 , grant agreement no. 644399 )]",  Ministry of Education, Research and Religious Affairs, General Secretariat for Research and Technology (GSRT), Greece.
Sampling strategy A quota telephone survey was conducted between March and September 2017 with participants from eight European countries, namely France, Ireland, Italy, the Netherlands, Poland, Portugal, Spain, and Sweden. Adult participants (18 years and older) were randomly selected from online research panels of operational landline and mobile phone numbers so as to be representative of the general population in each country with respect to gender and age. Interviews were conducted using Computer Assisted Telephone Interviews (CATI) software tailored for telephone surveys. All interviews were conducted by bilingual interviewers, recruited and trained to conduct the survey. Respondents were informed of the purpose of the study, the intended use of the data, and assured of anonymity. A pilot study was conducted with a sample of 30 individuals in France to assess the length of the questionnaire and its intelligibility (face validity).
Covariates at the individual level We investigated the influence of gender, age, education level, marital and child status, living area, employment status, and either individual income or being taxable (yes/no). Individual incomes were compared to the mean income in each country at the time of collection and percentage of the mean income was considered for the analysis (2017 mean incomes were: Netherlands €52,900; France €43,800; Sweden €42,400; Spain: €38,500; Ireland €37,700; Italy €36,700; Poland €27,000 and Portugal €25,400). However, due to a large amount of missing data for this variable (30%) a proxy variable was used in the final model, which was whether participants paid national taxes. Furthermore, we investigated the role of two additional variables as potential predictors of WTP: respondents' exposure to homelessness and their general attitudes about homelessness.
The value is expressed per 100 persons of working age (15-64); and 6) housing cost overburden rate: this indicator is defined as the percentage of the population living in a household where the total housing costs (net of housing allowances) represent more than 40% of the total disposable household income (net of housing allowances). These dummy variables were estimated from official data from Eurostat databases (European Commission, 2019) (eMethods2 [INSERT LINK TO ONLINE FILE]).
Statistical analysis Since discrepancies were found between the distribution of sociodemographic variables (i. e. gender, age, and education) and the 2017 census data obtained through the World Bank (World Bank, 2019) and Eurostat (European Commission, 2017), weights were applied. The survey sample was weighted by strata, which were defined by official national population strata on age, gender and education level. Data analyses were conducted using R 3. 6. 0, 'Survey' package that allowed us to incorporate a complex sampling design (clustered and weighted data). Descriptive analyses were carried out, presenting the results as percentages (N (%)) for the qualitative variables or of mean and standard deviations (m ± standard deviation) for the quantitative variables. Distributions of participants across the binary WTP variables (WTP yes/no) were cross-tabulated and tested using chi2 (χ2).
Analyses of variances (ANOVA) were used for the continuous variables (WTP valuation revealed through the bidding process). A series of diagnostic tests were performed, such as tests on the presence of heteroscedasticity using the Breusch-Pagan test (bptest() in R) and correlation in explanatory variables using the Pearson's correlation. The 'Survey' package in R produces the sandwich estimator and provides standard errors robust to heteroscedasticity, and possibly clustering. We addressed the issue of protest responses by two approaches: 1) assignment of a zero value prior to estimating mean and median WTP; 2) exclusion of individuals with protest answers, in order to analyse the specific impact of the protest responses on the estimated mean WTP value. Five outcome variables were considered in the following analysis (Table  1 ).
In the surveyed countries, the rate of homelessness varied from 0. 04% in Portugal to 0. 36% in Sweden (FEANTSA, 2018; Feantsa and The Foundation Abbé Pierre, 2018). However, there is a wide heterogeneity in both counting methodologies and utilised time-frames behind this prevalence data which warns against any possible comparisons. Our study found no effect for graduate employment on WTP. A possible explanation is the inverse correlation (-0. 67) between graduate-employment and risk-of-poverty rates. Risk-of-poverty rate has a consistently positive and significant effect across all of our models, and this variable may be acting as a proxy for graduate-employment rate. Fig. 2. Percentage of respondents which were willing to pay for the Housing First program. Distinct histograms were built by including or excluding protest answers; 95% Confidence intervals were reported.
This solution has been praised by the International Society for Applied Cytometry for its discriminative abilities and operatorfriendly application. 10 FlowSOM can be programmed to extract up to 100 nodes ordered in minimal spanning trees (MST). Applied to classical MFC, it can also, unlike MC, take into account the scatter properties (SSC) of the cells. Here we report how unsupervised FlowSOM analysis can guide in depth subsets identification by the combined use of a classical MFC software. In a first step, four 10-color antibody combinations reported previously  11  were applied to normal BM samples (Table  S1 , supplemental Digital Content, http://links. lww. com/HS/A23). The latter had been obtained from adults without any hematological disorder during thoracic surgery, collected on EDTA-K, stained in a lysis no wash manner and acquired on a Navios instrument (Beckman Coulter, Miami, FL) according to Harmonemia recommendations.
5 The flow cytometry standard (fcs) files obtained after acquisition of these normal BM samples with the 4 panels were merged and submitted to the unsupervised analysis of FlowSOM scripts, resulting in 4 reference MST. New dedicated R scripts were developed to obtain a representation of the global unsupervised multiparametric analysis of each panel as an MST according to FlowSOM strategy and integrated in the classical MFC software Kaluza (Beckman Coulter). This tool was then used to further identify each of the MST subsets (ie, "nodes") according to its whole immunophenotypic characteristics (mean fluorescence intensity, cell numbers, percentages). As shown in Figure  1A , the CD45/SSC biparametric histogram of merged normal BM yielded 100 MST unsupervised subsets or "nodes," highlighting the complexity of normal BM.
5 Here, the delineation is straightforward and comes directly from the unsupervised analysis, followed by operatordriven examination of cells' characteristics. Based on these new reference BM display patterns, the corresponding FlowSOM R-script was applied to acute myeloid leukemia (AML) BM samples investigated at diagnosis and follow-up with the same AML panels in the course of routine laboratory analysis. One example is shown in Figure  2 , where the leukemic population appeared as a single cluster on a classical CD45/SSC cartography. FlowSOM unsupervised analysis allowed to observe, in the expected nodes, elements of residual hematopoiesis. Color-backgating of the CD45/SSC leukemic cluster highlighted several nodes on the MST, providing a direct visualization of AML immunophenotypic heterogeneity.
Concomitant analysis of a follow-up sample from the same patient allowed to clearly see which of these subclones had disappeared or survived upon chemotherapy (see figure legend). The same strategy can be successfully applied to acute lymphoblastic leukemia (data not shown) where the tracking of MRD has been largely published and is much easier than in AML. In summary, we report here on the bioinformatics innovation of an original combination of available software, likely to be universally implemented for a new, objective and comprehensive (CD14 dim , CD16 + ) segregated by FlowSOM as Mo3. Right: colored MST of AML-B stained normal merged BM, with a focus on the tree nodes of immature progenitors (bermudes). 5 The biparametric CD34/CD38 histogram shows the superimposition of the 3 subsets  13  , respectively, CD34 + CD38 À (dark blue), CD34 + CD38 + (gold), and CD34 lo CD38 + (cyan).
For the three hot Jupiters Qatar-3b, -4b, and -5b, the orbital period uncertainty was not provided in their discovery paper  Alsubai et al. (2017). These planets are included here for a new analysis because new results published at the Exoplanet Transit Database (ETD,  Poddaný et al. 2010)  indicate deviations from the currently known ephemerides for two of the three targets. We finish the ranking in Table  1  at an uncertainty of 11 minutes. This value is rather arbitrary, but we consider uncertainties smaller than this value to cause only minor problems in the scheduling of transit follow-up observations. The targets of our sample are very diverse in their planetary and stellar parameters, therefore they are of interest for a broad bandwidth of investigations: from investigations on radius inflation mechanisms of hot Jupiters (Qatar-4b and Kelt-8b have radii larger than 1.
Observations and data acquisition Transit time-series photometry in the course of this work has been obtained with the 1. 2-m STELLA telescope, the 0. 8-m Joan Oró telescope (TJO) of the Montsec Astronomical Observatory, the 2. 2-m telescope of the Calar Alto observatory, the   (Strassmeier et al. 2004 (Strassmeier et al. , 2010) )  observed 19 transits in total in the Sloan r' filter. The original field of view of WiFSIP of 22 ′ × 22 ′ was reduced in all observations to 15 ′ × 15 ′ to shorten the read-out time. A mild defocus was always applied to spread the PSF to an artificial FWHM of about 3 ′′. Five transit light curves in Johnson V filter were obtained with the TJO and its main imager MEIA2. The instrument has a field of view of 12. 3 x 12. 3 arcmin and a resolution of 0. 36 arcsec/pixel.
Two transit light curves were observed with the CHAT, which is a newly commissioned 0. 7 telescope at Las Campanas Observatory, Chile, built by members of the HATSouth  (Bakos et al. 2013 ) team, and dedicated to the follow-up of transiting exoplanets. A more detailed account of the CHAT facility will be published at a future date (Jordán et al. , in prep 1  ). Data were obtained with a Finger Lakes Microline CCD camera equipped with a back-illuminated CCD and a Sloan i filter. The camera, which has a field of view of ≈ 21 ′ × 21 ′ , was slightly defocused during the observations. A light curve of WASP-117b transit was obtained in July 2017 at the LCO, which is a fully robotic network of telescopes  (Brown et al. 2013) , deployed around the globe in both hemispheres 2. We used a 1. 0-m telescope of the network at the South African Astrophysical Observatory (SAAO) and the Sinistro camera.
The telescope was defocused by 3. 0 mm. One transit light curve was obtained with 1-m telescope of Yunnan Observatories, China, and its 2Kx2K CCD camera using the Cousins R filter. The instrument offers a field of view of 7. 2 , ′ ×7. 2 ′ and a resolution of 0. 2 ′′ /pixel. We complemented our data sample with a large number of amateur light curves, which we obtained from the ETD 3   (Poddaný et al. 2010). We selected 85 light curves from this database by visual inspection. While the ETD offers an online fitting routine and provides the derived transit parameter, we downloaded the reduced light curves and re-analyzed them for their timing information homogeneously to the newly obtained light curves with professional telescopes (see  Section 4). A summary of all 120 light curves and their properties is given in Table  B.
Light-curve analysis In this work, we analyzed the photometric transit light curves with the publicly available software tool JKTEBOP  (Southworth et al. 2004; Southworth 2008). Fit parameters were the orbital semi-major axis scaled by the stellar radius a/R ⋆ , the orbital inclination i, the planet-star radius ratio k, the midpoint of the transit T , the orbital period P, the eccentricity of the orbit e, the argument of periastron ω, and coefficients of the detrending function c 0,1,2. Many studies suggest that trends in small-telescope transit photometry of 1 to 2 mmag photometric precision is fit by simple detrending functions with very few coefficients (e. g. ,  Juvan et al. 2018; Southworth et al. 2016; Mancini et al. 2016; Maciejewski et al. 2016). For STELLA/WiFSIP photometry, several studies used the Bayesian Information Criterion to show that first or second order polynomials over time form the best representation of trends or systematics in the light curves (e.
A χ 2 red slightly larger than unity can be caused by starspots in the host-star photosphere that deform the shape of photometric transit curve  (Oshagh et al. 2013; Holczer et al. 2015). For the majority of the targets investigated in this work, the photometric quality of the light curves only allowed for a slight improvement on the precision of T 0 compared to the discovery papers. However, including the timing measurement of these publications, our data expand the time interval of observed transit events for all targets significantly. Therefore, the uncertainty of the estimated orbital period P could be lowered by an order of magnitude for all targets. Objects worth emphasizing are WASP-117b, HAT-P-31b, and HAT-P-29b, for which we measured the predicted transit times to be off by more than 2 hours.