dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion We have presented the study of a large scale dataset of social interactions via mobile phone calls. We provided a simple empirical characterization of the effects of memory in its microscopic dynamical evolution. Considering the empirical evidences, we defined a novel generative model for time-varying networks with memory. The model mirrors many of the structural properties observed in the real network, like degree and weight heterogeneities, and shows the spontaneous emergence of non-trivial connectivity patterns characterized by strong and weak ties. We characterize the effects of non-Markovian and heterogeneous connectivity patterns on rumor spreading processes. Interestingly, we find that strong ties are responsible for constraining the rumor diffusion within localized groups of individuals.
Methods Dataset. The utilized dataset consists of 633, 986, 311 time stamped mobile-phone call (MPC) events recorded during 182 days with 1 second resolution between 6, 243, 322 individuals connected via 16, 783, 865 edges. The dataset was recorded by a single operator with 20% market share in an undisclosed European country (ethic statement was issued by the Northeastern University Institutional Review Board). To consider only true social interactions, and avoid commercial communications we used interactions between users who had at least one pair of mutual interactions. Data-driven model. In data-driven simulations we initiated the rumor spreading from a randomly selected call event of a randomly selected user in the MPC network. We then run the process for the length of the recorded period.
Acknowledgments This work has been partially funded by the  NSF   CCF-1101743  and  NSF   CMMI-1125095  awards. We acknowledge support from the  FET  project  MULTIPLEX 317532  and  MK acknowledges support from EUs 7th Framework Programs FET-Open ICTeCollective project  (No. 238597 ). We thank  A. -L. Barabási  for the dataset used in this research. We thank  B. Ribeiro ,  R. Burioni  and A. Vezzani for useful discussions.
Acknowledgments This work has been supported by the " Laboratoire d'Excellence NanoSaclay ", in the framework of the project " ONE-FAB-3D ", and by the  "Triangle de la Physique ", in the framework of the project "PUTTON". Special thanks are devoted to  J. -F. Roch  for various equipment helps. M. T. Do  and  T. T. N. Nguyen  acknowledge the fellowship from the  Vietnam International Education Development "322 program " and  Q. Li  acknowledges the fellowship from the  China Scholarship Council.
Our data suggest that the structure rigidity and stability are higher and the D 2 O accessibility to H-bonding network is lower for ASBVd(+) than for ASBVd(-). Remarkably, the Mg 2+ -activated self-cleavage of the viroid does not induce any significant alterations of the secondary viroid structure, as evidenced from the absence of intensity changes of Raman marker bands that, however exhibit small but noticeable frequency downshifts suggesting several minor changes in phosphodioxy, internal loops and hairpins of the cleaved viroids. Conclusions: Our results demonstrate the sensitivity of Raman spectroscopy in monitoring structural and conformational changes of the viroid and constitute the basis for further studies of its interactions with therapeutic agents and cell membranes.
Structure and activity of ASBVd(-) and ASBVd(+) Double helical content From the data of Figure  1 (a) we have calculated that the r 2 parameter for the ASBVd(-) viroid is equal to 1. 23, while it is equal to 1. 25 in formylmethionyl-tRNA  [25]  and 1. 33 in the ribozyme D5-PL-RNA  [12]. For each of the ordered polyribonucleotides listed in Table  IIA  of Thomas and Hartman  [40] , the value of r 2 was found to be constant at 1. 64, indicating the conformational similarity of (-C-O-P-O-C-) linkages. In addition the ratio r 2 approaches zero in completely disordered structures  [36, 41]. Hence, the ratio r 2 observed for ASBVd(-) and model compounds can be converted into the percentage of ordered polyribonucleotide RNAs using the factor 1. 64 as a coefficient of division. The estimate of the number of phosphodiester groups in the ordered region of the viroid RNA, obtained using this procedure, is ~75% whereas it is 76% for formylmethionyl-tRNA and 81% for the ribozyme D5-PL-RNA  [17].
The Raman light was focused on the spectrograph entrance (slit width 30 μm) by an achromatic lens with f = 75 mm. The Raman signal was separated from the laser light by two Semrock RazorEdge long pass filters (grade U). One filter, with λ laser = 780 nm, was placed perpendicularly to the optical beam just before the focusing lens; another one, with λ laser = 830 nm, was used as a dichroic beamsplitter at an angle of incidence of 45°: it reflected laser light at 780 nm and transmitted all the wavelengths longer than 785 nm. Raman spectra were acquired with the WinSpec software; further data treatment was performed using Igor Pro for Windows software. To obtain the viroid Raman spectrum, the contributions from the PBS and quartz cell were subtracted from the recorded spectrum, normalized on the water bending band around 1640 cm -1.
Acknowledgements H. Kaddour was recipient of a fellowship from the  Centre National de la Recherche Scientifique (CNRS). This study was supported by the  University Paris 6 , the  CNRS  (grant PID EPOV), and the  Centre National d'Etudes Spatiales. We are grateful to  Professor Pierre-Yves Turpin  for critical and helpful comments and to  Anne-Lise Haenni  for careful reading of the manuscript.
Acknowledgements This work was carried out with fi nancial support from the  French State , managed by the  French National Research Agency (ANR)  in the frame of the "Investments for the future"  Programme IdEx Bordeaux  ( ANR-10-IDEX-03-02 ), Cluster of excellence LAPHIA. We are grateful to  Christophe Boyaval  from  IEMN in Lille  for HRSEM measurements. Confocal images of helicoïdal structures were performed at the  Photonic Imaging platform of the Bordeaux Imaging Center  ( UMS 3420 CNRS ). The authors are grateful for the help in FDTD calculations from Lumerical Solutions, Inc.
In this analogy, nucleotides are spins with q = 4 colors. In practice, the probability of observing a given word (s 1. s L ) in the dataset is expressed as P [s 1. s L ] = (1/Z) exp(-H[s 1. s L ]) , where Z is a normalization constant. H is formally equivalent to a Hamiltonian in the language of statistical mechanics, and reads: H[s 1. s L ] = - L i=1 h i (s i ) - L i=1 j<i J i,j (s i , s j ), s i ∈ {A, C, G, T } (1) The "magnetic fields" h i at each site i, along with the interaction parameters J ij between nucleotides at positions i and j, are computed so as to reproduce the frequency of nucleotide usage at each position in the TFBS as well as the pairwise correlations between nucleotides at different positions (see Methods). In principle, the number of parameters in the model is sufficient to reproduce the observed values of all pairwise correlations between nucleotides.
Similarly to the pairwise interactions, the number of clusters K was constrained, to avoid over-fitting, by penalizing the corresponding model score using the BIC. For a given TF, the PWM mixture and the collection of TFBSs in the ChIPSeq data were refined iteratively until convergence, usually reached after 10 iterations. The results are shown in Figure  5A   Twi, Esrrb and MyoD. The best description of Twi ChIPSeq data is, for instance, provided by a mixture of 5 PWMs, which corresponds to 184 independent parameters. The mixture model yields a significant improvement when compared to the single-PWM model for Twi, and milder ones for Essrb and MyoD. In the three cases however, it proves inferior to the pairwise model. More generally, Figure  5B  shows the performances of the different models for all studied TFs using the Kullback-Leibler Divergence or DKL between the data distribution P (s) and the models distributions P m (s).
On the one hand, the mixture model improves the de-scription of the binding data for 12 out of 27 TFs as compared to the single PWM model. The mixture model gives in particular strong improvements in the cases for which the binding sites have a palindromic structure (eg Twi, MyoD, Myog, Max, USF1). This feature often stems from the fact that the TF binds DNA as a dimer, which could give some concreteness to the mixture model: the recruitment of different partners by bHLH factors like MyoD or Myog could indeed lead to a mixture of TFs binding the same sites. On the other hand, the pairwise model clearly outperforms the other models in all cases studied. As in the PWM case, the finite size of the datasets leads us to expect fluctuations in the estimation of the DKL. In order to assess the magnitude of these finitesize fluctuations, we computed the average DKL between the best-fitting (pairwise) model and a finite-size artificial sample drawn from its own distribution, as shown in Figure  5B.
Values of this DKL that are larger than the one obtained with the real dataset are indicative of overfitting, while the opposite case would suggest that the model is incomplete. In all cases, however, the DKL obtained with this control procedure was within error bars of the value computed with respect to the observed sample, with the exception of NRSF, MyoD, and Myog, as seen in Figure  5B. Thus, the pairwise model is generally the best possible model, insofar as the available dataset allows us to probe.
DISCUSSION The availability of ChIPseq data for many TFs has led us to revisit the question of nucleotide correlations in TFBSs. In order to perform a fully consistent analysis of this type of data, we have developed a workflow in which the TFBS collection and the model describing them are simultaneously obtained and refined together. We have found that when a sufficiently large number of TFBSs is available, the PWM description does not account well for their statistics. The general presence of correlations that follows from this finding, agrees with previous reports for particular transcription factors  [6, 8, 24]  and with the conclusions of large scale in vitro TF binding studies  [9, 10]. In order to refine the PWM description, we have analyzed a model with pairwise interactions  [23] , and a PWM mixture model  [14].
In the case of TFBSs, comparison between in vitro  [9, 10]  and in vivo binding data may help to disentangle the different possible origins of the found correlations and seems worth pursuing. It appears similarly interesting to study how much of the found pair correlations can be explained on the basis of structural data. Finally, the role of nucleotide interaction in TFBS evolution  [41]  should be considered and could improve the reconstruction of TFBSs from multi-species comparison  [42] [43] [44]. Independently of these future prospects, we have found that the TFBSs predicted from ChIP-seq data significantly depended on the model used to extract them. Since the pairwise interaction model and the developed workflow significantly improve TFBS description and require a modest computational effort, they should prove worthy tools in future data analyses.
Genome-wide data retrieval We use both ChIP-on-chip data from Drosophila Melanogaster and ChIPseq data from Mus Musculus. Data was retrieved from the litterature  [26, 27]  and from ENCODE data accessible through the UCSC website http://hgdownload. cse. ucsc. edu/goldenPath/ mm9/encodeDCC/wgEncodeCaltechTfbs/, for a total of 27 TFs. Among them, there are 5 developmental Drosophilae TFs: Bap, Bin, Mef2, Tin and Twi, 11 mammalian stem cells TFs: c-Myc, E2f1, Esrrb, Klf4, Nanog, n-Myc, Oct4, Sox2, Stat3, Tcfcp2l1, Zfx, and 11 factors involved in mammalian myogenesis: Cebpb, E2f4, Fosl1, Max, MyoD, Myog, Nrsf, Smad1, Srf, Tcf3, Usf1. Overall, there are between 678 and 38292 ChIP peaks, with average size 280bp. DNA sequences were masked for repeats using RepeatMasker  [45].
Initial PWM refinement Along with the ChIPseq data for the different factors, we also retrieved corresponding PWMs from the literature  [26]  or from TRANSFAC database  [46]. These initial PWMs were refined according to the following protocol. Given ChIPseq data (bound regions) for a given TF and an initial PWM of length L (L = 12 was taken for all computations in the present paper), we scanned both strands of each bound region and attributed to all observed L-mers a score defined as the ratio between the PWM and background models probabilities. A cutoff was set such that half of the bound regions had at least one predicted TFBS with a score above the cutoff, setting a True Positive Rate (TPR) of 50%. This heuristic criterium overcame the problem of False Positives among the ChIPseq peaks that might have polluted the data.
P i,j (a, a )) is the probability of having nucleotide a at position i (resp. nucleotides a and a at position i and j) in the TFBS data set. The function δ denotes the Kronecker δ-function defined by δ(a, a ) = 1 if a = a ,and 0 otherwise. The first term in Eq. (  5 ) is the entropy of the probability distribution to be found and the other terms are the given constraints along with their Lagrangian multipliers. Maximization of the functional L is performed in a usual way by setting the functional derivative with respect to the probability distribution P to zero: δL δP (s) = 0 = -ln P (s)-1+λ+ L i=1 h i (s i )+ L-1 i=1 j>i J i,j (s i , s j ). (6) Finally, using the constraint {s} P (s) = 1, one finds the probability distribution that maximizes entropy given the constraints that it reproduces the observed one-and two-point correlations: P [s] = exp[-H(s)]/Z, (7) where H(s) is the inhomogeneous Potts model Hamiltonian, H[s 1.
The number of interaction parameters that are being added is controlled by the Bayesian Information Criterion, or BIC (Figure  5 ). The BIC computes the opposite log-likelihood and adds a penalty proportional to the number of parameters involved. This adverts the over-fitting of a finite dataset with an extravagant number of parameters. The procedure is iterated until minimization of the BIC, yielding the best pairwise model with the full set of parameters ({h i (a)}, {J i,j (a, a )}). As in the case of the PWM model, we score each sequence using the ratio between the TF and background pairwise models and impose a score cutoff so as to select a set of bound sites yielding 50% TPR, on which a new pairwise model is learned. This process is iterated until convergence to a stable set of bound sites.
INTRODUCTION There is increasing acceptance that the Nearest Neighbor (NN) algorithm with Dynamic Time Warping (DTW) as the distance measure is the technique of choice for most time series classification problems. The NN-DTW algorithm has been shown to be competitive or superior in domains as diverse as gesture recognition, robotics and ECG classification  [1]. Moreover recent comprehensive studies have validated this idea: • In  [1]  we compare NN-DTW to nearly all of the most highly cited distance measures in the literature on dozens of datasets. They found that no distance measure consistently beats DTW, but DTW almost always outperforms most methods that were originally touted as superior, based on less complete empirical evaluations. • In  [2]  (and to a lesser extent  [3] ) the authors test the assumption that the Nearest Neighbor classifier is the best technique and consider other classifiers, including neural networks and decision trees.
As we shall show, this allows us to condense large datasets into much smaller (as small as a single instance per class) dataset that can produce the same accuracy as the original dataset. Less intuitively, in some domains the reduced datasets may allow greater accuracy, because the averaging combines evidence from all exemplars to produce prototypes that are more like the classes platonic ideal than any individual instance. The rest of this paper is organized as follows. In Section II we review related and background work. In Section III we introduce the necessary definitions and formally define the problem to be solved, allowing us to introduce our solution in Section IV. Section V sees a forceful empirical validation of our claims, and we offer conclusions and directions for future work in Section VI.
Most obviously it is faster, being O(1) rather than O(n). However, and less intuitively, it is also known that some circumstances, the Nearest centroid classifier is more accurate than the Nearest neighbor classifier (NN)  [11]. Because it may be counterintuitive that the nearest centroid classifier can be more accurate than NN, we will demonstrate this in an intuitive setting. Consider a domain in which all exemplars are uniformly distributed in the unit square, with objects having an X-value less than 0. 5 assigned the label A, otherwise B. Figure  2  illustrates an example in which there are just three instances per class. For balanced dataset sizes from 2 to 4,000, we compared the error rates of the NN and the Nearest centroid classifier (NCC) on this domain, each time averaging over 1,000 runs.
The results are shown in Figure  3. Without any experiments we would realize that the two algorithms must agree on the far left side of the figure, since the centroid of a single point is that point, the two algorithms are identical here. A little more introspection tells us that the algorithms will also agree on the far right side of the figure. What is less obvious is that the Nearest centroid classifier is more accurate in between those two extremes. The effect is small, but is statistically significant. It is important to note that the Nearest centroid classifier is not guaranteed to be more accurate than the NN classifier in general. For example, consider the "Japanese flag" dataset (adapted from  [35] ) shown in Figure  4 , here the NN algorithm approaches zero error-rate for large training dataset sizes, in contrast the Nearest centroid classifier steadfastly achieves just the default rate.
In spite of the existence of such pathological cases, the Nearest centroid classifier often outperforms the NN algorithm on real datasets, especially if one is willing (as we are) to generalize it slightly; for example, by using clustering to allow a small number of centroids, rather than just one. Thus our claim is simply:  gains, and the reduced computational requirements come "for free".  Sometimes NCC The above discussion at first may appear to be moot for time series, because the concept of "centroid" for warped time series is ill-defined. It is the central contribution of this paper to show that we can take the "centroid" for warped time series in a principled manner that allows us to achieve both improvements in accuracy and reduced computational requirements at run time. In the last decade the cognitive science community has presented strong evidence that the visual systems remarkable abilities stem, at least in part, from its ability to represent sets of objects by a "gist" or "ensemble" 1  , which may be simply the average of the objects  [12].
C. DBA: the best-so-far method to average time series for Dynamic Time Warping DTW Barycenter Averaging (DBA), introduced in  [8] , exploits the parallels between time series and computational biology, while taking account of the unique properties of the former. We have shown in  [8]  that DBA outperforms all existing averaging techniques on all datasets of the UCR Archive  [22]. In particular it always obtained lower residuals (Equation  2 ) than the state-of-theart methods, with a typical margin of about 30%, making it the best method to date for time series averaging for DTW. DBA iteratively refines an average sequence  ̅ and follows an expectation-maximization scheme: 1. Consider  ̅ fixed and find the best multiple alignment  2   of the set of sequences  consistently with  ̅. 2. Now consider  fixed and update  ̅ as the best average sequence consistent with.
Problem Statement Contract Time Series Classification: Given (1) a large time series training dataset, (2) the maximum amount of computation resources available, and (3) as much training time as needed, produce the most accurate classifier possible. We assume that the computational resource constraint will be time, not space, and that it will be given to us in the form of the number of CPU cycles available each second. For ease of exposition we assume that the constraint will be given as a positive integer C, which is the number of exemplars per class that we can examine when asked to classify a new object. Figure  5  illustrates this problem statement. As we explained in the introduction, based on the consensus of the literature and our own experiments, we believe that the best solution will be a variant of Nearest Neighbor classification.
A. Why K-Means Based Approach While it is possible that for some datasets, a single prototype may capture the "essence" of a class, for other datasets it may require a small number of prototypes. Moreover, a single dataset may exhibit both possibilities on a class-by-class basis. For example for the "Japanese flag" dataset shown in Figure  4 , a single centroid is clearly optimal for the circle/red class, but we would need, say eight suitably arranged examples from the green/square class arranged in an octagon to carve out a decision boundary that approximates the true circular decision boundary. To give a more concrete example in a domain we explore in this work, consider the case study in insect surveillance in Section V. A. Here we may have what appears to be a single class, Culex stigmatosoma, the mosquito that spreads West Nile virus.
However, this insect, like most mosquitoes, is highly sexually dimorphic. If we try to create a single template to represent both males and females we are condemned to have a template that represents neither. However, by clustering each individual class, we hope to be able to account for any natural polymorphism within the class. In Table  II  we show such a clustering-based approach to condensing a dataset. It is important to note, however, that we see our main contribution as proposing a warping-invariant-averaging based condensation framework, of which Table  II  is simply one concrete and straightforward partitional clustering example. To further reinforce this notation in our experimental section we also consider a warping-invariantaveraging hierarchical clustering based condensation framework.
We consider situations where we can only visit a small handful of exemplars, as few as just one per class; this is the defining characteristic of our problem setting. In any case, we expect (and empirically demonstrate) that all algorithms converge as we allow the size of the reduced dataset used to increase. That is to say, if we randomly sample as many time series as there are in the training set, we actually obtain the full training set, which is logically equivalent to the 1-NN classifier. The behavior is similar for the other techniques: the reduced sets of time series all tend to the initial training set as their sizes increase. Our experiments will be divided into three parts: A. We begin with a case study, to ground the utility of our ideas in the real world. B. Having shown that average-based methods outperform sampling-based ones on our case study, we further assess the performance of the different methods on a full-scale experiment with 42 datasets.
Wingbeat frequency at 354Hz Harmonics Single-Sided Amplitude Spectrum that using the entire spectrum, and treating the problem as a time series classification problem, significantly reduces the error rate. In retrospect this is not surprising. A G note on a piano and an open string G note on a guitar have the same frequency of 196Hz (about the same frequency as a honey bee), but are easy to tell apart. The ability to automatically classify insects has potential implications for agricultural and human health, as many plant/human diseases are vectored by insects. The promising results presented in  [31]  are demonstrated in the laboratory settings, and exploit large training datasets to archive high accuracy. However, field deployments must necessarily be on inexpensive resource-constrained hardware, which may not have the ability to allow nearestneighbor search on large training datasets, up to hundreds of times a second.