dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2006). Long-term observations at the site provide an extensive dataset of aerosol characteristics and origins. The regular intrusion of the sea breeze is now employed to elucidate of how the evolution of humid conditions accompanied with the intrusion of aged sea-salt and pollution aerosols modifies the mixing state of mineral dust and how this affects the aerosol radiative effect. To address this goal, we employ a multidisciplinary approach by utilizing a combination of comprehensive remote sensing observations coupled with in situ aerosol measurements and offline chemical imaging of atmospheric particles collected at the site. The comprehensive observations were conducted during summer of 2012. Chemical imaging of particles collected before and during sea breeze was carried out by offline scanning electron microscopy, X-ray microanalysis, and Raman microspectroscopy.
Backward trajectories The air mass backward trajectories are obtained using the 3-D HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) model of the US National Oceanic and Atmospheric Administration (NOAA)  (Draxler and Hess, 1998). The runs for backward trajectories are performed using the global data assimilation system. This is performed for altitudes above ground level of 1000, 500, and 10 m, as an indicator of origin of air masses near the surface. The relative humidity at the corresponding altitudes and time is also available along the backward trajectories from HYSPLIT.
According to the diffraction grating of 300 grooves per millimeter used in this work, Raman spectra were acquired in the range 170-2440 cm -1 with a spectral resolution of about 4 cm -1. The data processing of Raman maps was the following. The baseline was estimated individually for each spectrum using asymmetric least squares (ALS) proposed by  Eilers (2003)  and  Eilers and Boelens (2005). The order of differences d was set to 3 (classical value), whereas the trade-off parameter (λ) and the asymmetry parameter (p) were optimized for each map by visual inspection of the estimated baseline and the spectra after correction. Best results were obtained for a p value of 0. 01 and 10 8 for λ. The color map was obtained from the baseline corrected data using the net Raman intensity signal at a specific wavelength over all point spectra.
The calculations of the solar flux employ the aerosol models retrieved by AERONET and the parameters of the gaseous con-centrations and surface reflectance at the site for 16 August that are adopted from the database of the AERONET operational code. The results of the simulated solar flux are superimposed on the results of the measurements in Fig. 13b  and are presented as a function of the corresponding solar zenith angles. The fluxes that are calculated for the aerosol characteristics retrieved just before (red line) and during (blue line) the sea breeze are in good agreement with the measurements and the magnitude of the drop in the measured flux. Thus, the difference between the red and the blue lines, for the same solar zenith angle, corresponds to the loss of solar energy reaching the surface due to the sea breeze.
The sea breeze occurs in many locations around the world and this systematic phenomenon can be used as a natural laboratory to study and evaluate the impact of the aerosol mixing state and hygroscopicity on aerosol optical properties and radiation. Data availability. Data from the ground-based meteorological station, the integrating nephelometer measurements, the laboratory analysis of sampled aerosol, and results of the numerical simulations are available upon request to the corresponding author (yevgeny. derimian@univ-lille1. fr). The air mass backward trajectory, AERONET, MPLNET, and SolRad-Net data are available from http://ready. arl. noaa. gov/HYSPLIT_traj. php, https://aeronet. gsfc. nasa. gov/index. html, https://mplnet. gsfc. nasa. gov, and http:// solrad-net. gsfc. nasa. gov, respectively.
Acknowledgements. The  CaPPA   project (Chemical and Physical Properties of the Atmosphere)  is funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract " ANR-11-LABX-0005-01 " and by the  Regional Council "Nord Pas de Calais -Picardie " and the  European Funds for Regional Economic Development (FEDER). The  SEM facility in Lille (France)  is supported by the  Conseil Regional du Nord-Pas de Calais  and the  European Regional Development Fund (ERDF). Alexander Laskin  acknowledges support from the  W. R. Wiley Environmental Molecular Sciences Laboratory (EMSL) , a national scientific user facility located at  PNNL , and sponsored by the  Office of Biological and Environmental Research of the US DOE. PNNL is operated for the US DOE by the  Battelle Memorial Institute  under contract no.
Observational study The first, observational study analyzed the manual annotations of a set of vocal and gestural imitations of a selection of everyday sounds. This set was selected from a larger database (50 imitators imitating 52 sounds under different instructions), which was part of a larger project  [63]. For the current study, we randomly selected a subset of ten imitators imitating eight referent sounds. This selection limited the size of the data (80 audio and video recordings) to make the annotation tractable by human annotators. The coauthors annotated the database to highlight the characteristics of the vocalizations and gestures used to imitate the sounds. The annotation consisted in selecting descriptions from a list of possible voice and gestures phenomena, elaborated during preliminary observations of the database.
Material: The referent sounds The referent sounds used in the observational study were produced by events and products commonly occurring in an everyday environement. They were selected from our previous studies  [26, 33, 64, 65] , commercial or freely available databases (Hollywood Edge, Blue Box, Sound Ideas, Freesound, etc. ), as well as recorded from human computer interfaces (mobile phones, video games, computer operating systems) or synthesized. Our previous studies and pilot observations revealed that two aspects of the referent sounds have an important influence on the imitations: tonalness and morphological profile  [22]. Tonalness refers to the magnitude of the sensation of pitch in a sound (from a weak sensation of pitch to a strong sensation of pitch; tonalness is similar to "pitch strength"  [66] , see  [67]  for a discussion).
Methods: Recording and annotating imitations We recorded and selected vocal and gestural imitations of referent sounds performed by human imitators. The imitations were analyzed by four of the coauthors, who watched each video and annotated them to describe the main characteristics of the vocalizations and gestures. 2. 2. 1 Recordings. Overall, the procedure consisted in listening to the referent sounds and recording an imitation. The database of vocal and gestural imitations has been registered with the French National Commission on Informatics and Liberty (CNIL 1890949).
Each IMU contains 3D accelerometers and 3-axis gyroscopes and transmits the data wirelessly with a latency of about 5-10 ms  [69]. The recording of the IMU data was managed by an Asus PC with Intel Quadcore 3. 2 GHz, running Microsoft Windows 8. The user interface and data stream management were fully developed in Max/MSP v. 6. 1 (Ircam/Cycling74), using Ircam's Mubu Max/MSP externals for the IMUs (http://forumnet. ircam. fr/fr/product/mubu/, last retrieved on  August 17, 2015). We used Harald Meyer's GoPro Camera Control 2 for the wifi control of the GoPro camera (http://www. tequnique. com/gopro, last retrieved on  August 18, 2015). The audio was recorded at a sampling rate of 64 kHz, in 16 bits PCM WAV files, the video at 120 frames per second (1920 x 1080 pixels). Data from the IMUs were collected at 100 Hz.
Procedure. The annotators annotated each video file using three annotation fields. For each of these fields, the annotator selected one response among a list of possible responses (see Table A in S1 Appendix). The annotators also had the possibility to create an ad-hoc response when none of the proposed responses fitted their impression. The annotation fields and the different possible annotations had been created based on pilot observations of the whole database and previous work  [22]. The annotators were aware of which referent sound each imitation referred to. The annotation fields were: • Main vocal feature(s): the annotators described what they perceived as being the most important features of the vocalizations that were characteristic of the referent sounds. They could indicate that the vocalization was tonal or noisy, that the pitch or the spectral centroid of the voice was rising up or down, that it contained some spectral modulation, roughness, or some modulation created by breathing in and out.
Co-occurrences. Next, the analysis counted whenever vocal and gestural features co-occurred. The percentages reported in the next paragraphs are calculated with reference to the total number of imitations per referent sound: therefore, "100%" means that the gestural and vocal features co-occurred in 100% of the imitations of this referent sound. As before, we only report co-occurrences occurring in more than 60% of the imitations. The complete data are reported in Figs A to H in S1 Appendix.
Procedure and experimental setup. The experiment used the same interface, experimental setup, and the same procedure as in the observational study. In addition, the righthand position was tracked using a depth camera (Microsoft Kinect v2). The Kinect data (position of the limbs in three dimensions over time) were acquired and managed using Dale Phurrough's Max/MSP external (https://hidale. com, last retrieved on June 14, 2014). As in the observational study, the instructions emphasized the communicative goal of the imitations. The task for the participants was to imitate the referent sounds so that somebody else could recognize them only by listening and watching the imitation. They were not allowed to use onomatopoeias. They were not allowed to pantomime the imagined sound-producing action.
For each gesture, we computed the power spectrum of CWT of the acceleration in the x, y and z axes, from the acceleration data of the IMUs. We used the Complex Morlet wavelet basis with a carrier frequency ω 0 = 5Hz, 8 bands per octave, on the 0. 1-50 hz frequency range  [78]. This provides us with a time-frequency representation of the gesture data in each direction, called scalogram. In a scalogram, the spectral dimension (the wavelet spectrum) is characterized by spectral bands that are distributed in "scales" that evolve as the inverse of a frequency (a low scale therefore corresponds to a high frequency). We focused on the extraction of gesture-level descriptors from the scalogram, using the moments (centroid and spread) of the average wavelet spectrum, as described below.
Given a query object q, reverse k-nearest neighbor (RkNN) search aims to locate those objects of the database that have q among their k-nearest neighbors. In this paper, we propose an approximation method for solving RkNN queries, where the pruning operations and termination tests are guided by a characterization of the intrinsic dimensionality of the data. The method can accommodate any index structure supporting incremental (forward) nearest-neighbor search for the generation and verification of candidates, while avoiding impractically-high preprocessing costs. We also provide experimental evidence that our method significantly outperforms its competitors in terms of the tradeoff between execution time and the quality of the approximation. Our approach thus addresses many of the scalability issues surrounding the use of previous methods in data mining.
INTRODUCTION The reverse k-nearest neighbor (RkNN) similarity query -that is, the computation of all objects of a data set that have a given query object amongst their respective k-nearest neighbors (kNNs) -is a fundamental operation in data mining. Even though RkNN and kNN queries may seem to be equivalent at first glance, they require different algorithms and data structures for their implementation. Intuitively speaking, RkNN queries attempt to determine those data objects that are most 'influenced' by the query object. Such notions of influence arise in many important applications. In graph mining, the degree of hubness of a node  [46]  can be computed by means of RkNN queries. RkNN queries are also crucial for many existing data mining models, particularly in the areas of clustering and outlier detection  [18, 27, 37].
Virtually all existing RkNN query strategies aggregate information from the kNN sets (the 'forward' neighborhoods) of data objects in order to determine and verify RkNN candidate objects. Some strategies require the computation of exact or approximate kNN distances for all data objects  [29, 51, 3, 44, 2, 4] , whereas others apply distance-based pruning techniques with index structures such as R-trees  [17]  for Euclidean data, or M-trees  [10]  for general metric data  [41, 33, 40, 43]. Although they are required for the determination of exact RkNN query results, the use of exact kNN sets generally involves significant overheads for precomputation and storage, especially if the value of k is not known beforehand. In recent years, data sets have grown considerably in complexity, with the feature spaces often being of high dimensionality.
For the many data mining applications that depend on indices for similarity search, overall performance can be sharply limited by the curse of dimensionality. For reverse neighborhood search, the impact of the curse of dimensionality is felt in the high cost of computing the forward kNN distances needed for the generation and verification of RkNN candidates. Most practical RkNN approaches work with approximations to the true kNN distance values. The quality of the approximate RkNN results, and thus the quality of the method as a whole, naturally depends heavily on the accuracy of the kNN distance approximation. Theoreticians and practitioners have put much effort into finding workarounds for the difficulties caused by the curse of dimensionality. One approach to the problem has been to characterize the difficulty of datasets not in terms of the representational dimension of the data, but instead in terms of a more compact model of the data.
Methods with Heavy Precomputation The basic observation motivating all RkNN methods is that if the distance of an object v to the query object q is smaller than the actual kNN distance of v, then v is a RkNN of q. This relationship between RkNN queries and the kNN distances of data objects was first formalized in  [29]. The authors proposed a solution (originally for the special case k = 1) in which each database object is transformed into the smallest possible hypersphere covering the k-neighborhood of the object. For this purpose, the (exact) kNN distance of each object must be precomputed. By indexing the obtained collection of hyperspheres in an R-Tree  [17] , the authors reduced the RkNN problem to that of answering point containment queries for hyperspheres. The authors of the RdNN-Tree  [51]  extend this idea: at each index node, the maximum of the kNN distances of the points (hypersphere radii) is aggregated within the subtree rooted at this node.
In this way, smaller bounding rectangles can be maintained at the R-Tree nodes, with the potential of higher pruning power when processing queries. An obvious limitation of the aforementioned solutions is that an independent R-Tree would be required for each possible value of k -which is generally infeasible in practice. Approaches such as  [3, 44, 2, 4]  attempt to overcome this limitation by allowing approximate kNN distances (for any choice of k up to a predefined maximum). Exact RkNN results can still be achieved if the approximate kNN distances are guaranteed to be lower bounds of the exact distances. If so, the method returns a superset of the exact query result as candidates; the query result can then be isolated from the candidate set in a subsequent refinement phase. However, determining the final exact result requires a kNN query to be performed for each candidate against the entire database.
Most methods for RkNN search use such lower bounding approximations. Of these methods, the MRkNNCoP algorithm  [3]  is of special interest to us, as it is the only RkNN method presented to date that implicitly uses estimation of intrinsic dimensionality to improve the pruning power of the index. MRkNNCoP uses a progressive refinement of upper and lower bounds to kNN distances to the query object so as to identify database objects that can be safely discarded. The pruning strategy relies on the assumption that the kNN distances for query q fit a formula for the fractal dimension FD involving the neighborhood size k: more precisely, the kNN distance d k (q) is determined according to a relationship of the form FD = c log k log d k (q) for some constant c. All these solutions have difficulties in handling high dimensional data.
Solving for the dimension gives m = log(V2/V1) log(r2/r1). ( 1 ) Equation 1 is not in itself immediately useful, as the representational dimension m of a feature space is almost always known. However, under a distributional point of view in which the dataset S is regarded as having been produced by a statistical generation process, the formulation of Equation 1 leads to a measure of the intrinsic dimensionality (ID) of S. The ratio of the volumes V2/V1 of the neighborhood balls B ≤ S (q, ri) can be substituted by the ratio of the probability measures associated with the two balls. Under this substitution, with appropriate assumptions on the continuity of the distribution of distances with respect to q, as r2 → r1 → 0, m tends to the local intrinsic dimensionality (LID)  [20, 5] , which can be defined as follows: LID lim r→0 + lim →0 + log (F ((1 + )r) /F (r)) log(1 + ) , where F (r) represents the probability measure associated with the ball centered at q with radius r.
Candidate Set Reduction In Algorithm 1, many unnecessary verification operations can be avoided through the use of witnesses. However, as the number of candidates increases, the cost of maintaining witnesses can become too expensive, particularly when the dataset size or dimensionality is large. To overcome this problem, we introduce the following heuristic variation, which we refer to as RDT+: in order to reduce the costs associated with witness operations, any newly-retrieved object v that accumulates k or more witnesses in its first cycle through the witness procedure (Lines 8-19 of Algorithm 1) is excluded from the filter set. More precisely, Line 20 is not executed unless W (v) < k. The rationale is that objects that have been excluded as potential reverse k-nearest neighbors are themselves unlikely to be decisive witnesses for the rejection of other objects.
SCALE PARAMETER ESTIMATION From the analysis in the previous section, one may be tempted to conclude that the most effective way of choosing values for the scale parameter t in practice is by estimating MaxGed. However, estimation of MaxGed is extremely impractical due to the potentially infinite number of query locations over which the maximum GED value is to be estimated. Moreover, MaxGed is an extremely conservative and loose upper bound on the intrinsic dimensionality in the vicinity of the query. Instead, a more favorable tradeoff between accuracy and execution time may be achieved through the direct estimation of the intrinsic dimension itself, and not through an upper bound on GED estimates. For this purpose, we precompute estimates of the intrinsic dimensionality for the entire dataset using existing estimators for other expansion-based models: the maximum likelihood estimator (MLE) for the local intrinsic dimensionality (LID)  [5] , and two estimators of the correlation dimension (CD), the Grassberger-Procaccia algorithm  [16]  and the Takens estimator  [45, 42].
With these estimators guiding the choice of t, the RDT termination criterion based on Theorem 1 is no longer a guarantee but a heuristic requiring experimental validation. An overall measure of ID can be obtained by averaging LID values over a sufficiently large sample of points drawn from the dataset S. For this purpose, we chose the MLE (Hill) estimator described in  [5] , due to its relative stability and convergence properties: IDx = - 1 n n i=1 ln xn w -1 , where x1. xn are observations of a random distance variable X taking values in the range (0, w]. The experimental results of  [5]  show that a neighborhood set size of n = 100 is generally sufficient for convergence. The runtime of the estimator scales linearly. The Grassberger-Procaccia (GP) algorithm  [16]  estimates the dimension in terms of the proportion of pairwise distance values falling below a supplied small threshold r > 0: C(r) = 2 N (N -1) i<j H(r -xi -xj ), where H is the Heaviside step function that is 0 for values below 0, and 1 for values greater or equal to zero.
All methods in the experimental study were implemented in Java using the ELKI data mining framework  [39]. Excluding the cost of maintaining the index supporting forward kNN queries, the asymptotic storage cost of the algorithms is dominated by the number of candidates generated throughout the execution. As explained in Section 4, the number of candidates generated depends on the input parameters t and k, and is linear in n in the worst case. The estimators of correlation dimension were implemented by adapting the source code 1 used in  [19]. To produce the final averaged estimate of local intrinsic dimensionality, MLE estimation was performed over a random sample of size equal to ten percent of each dataset; for each estimate, 100 nearest neighbors were used. Back-end support for incremental kNN queries can be provided in a great variety of ways.
For our experimentation, we chose as examples two different methods: the Cover Tree  [6] , and straightforward sequential database scan. For all datasets except for MNIST and Imagenet (described below), the Cover Tree was found to perform substantially better than sequential scan. Thus, for these two sets, all experimental results were reported using sequential scan, while for the remaining sets, the results reported are for the Cover Tree. So that our method could be tested against competitors that require it, the Euclidean distance was used for all experiments. In the literature on RkNN search, experimentation usually targeted combinations of datasets and queries of low scale in terms of the reverse neighbor rank k, as well as the dataset size and representational dimension. We based the first group of experiments on the following collection of publicly available datasets of low to medium scale, selected so as to span a variety of potential target applications.
From among the objects contained in each dataset, we extracted 100 randomly chosen points to serve as query objects for RkNN queries using k ∈ {10, 50, 100}. • The Sequoia dataset contains a total of 62,174 locations of interest from within California 2. Individual locations in the dataset are represented by a normalized pair of latitude and longitude values. • The Amsterdam Library of Object Images (ALOI )  [14]  consists of 110,250 images of physical objects taken under varying lighting conditions. Each element is described by a vector spanning 641 features, including texture and color histograms. Details regarding the feature generation process are available in  [8]. • The Forest Cover Type (FCT ) dataset  [7] , available as part of the UCI Machine Learning Repository 2  [32] , describes the topographical features of 581,012 forest cells, each 900m 2 in area.