dataesr/scientific-paragraphs-categorization huggingface dataset:
This can be achieved using classification and regression tree analysis (CART). CART is a technique used in data mining that is commonly used as a rule-based classification design. The output of a CART analysis is a set of logical if-then conditions that end in terminal nodes and predict the value of the response variable. These conditional rules can be implemented using map algebra to create a quality assessment map. CART analysis is commonly applied to remote sensing data because it makes no assumptions about input data or their statistical distribution, and is well suited for dealing with collinear datasets, outliers, and potentially insignificant predictors  [37] [38] [39] [40] [41]. CART was successfully used to derive the DEM uncertainty prediction map. Bater and Coops used CART analysis and identified ground point density and slope as the two conditions that have the greatest impact on error when the DEM is interpolated from airborne LiDAR data.
Software Tool The tools required to compute a DFM confidence map-reclassification, slope, and raster calculator-are included in most GIS packages, and the process involves relatively low computational costs. However, the pipeline is relatively complex, consisting of 21 individual steps and 40 connections (Figure  4 ). To facilitate the computation, we developed an open source tool in the form of a QGIS plug-in, which is available as a ZIP file via the Github repository  [44]. In the first step, we built the pipeline in the QGIS graphical model designer. The first calculation uses the GRASS r. resample algorithm to unify the cell size (default 0. 5 m) and the extent of the input rasters (DFM, vegetation density, and ground point density). The slope was then calculated using the QGIS slope algorithm.
Each raster was then reclassified based on the above parameters or ranges using QGIS reclassify by table. The GDAL raster calculator was used to calculate the resulting confidence maps, which were styled according to the colour scheme introduced in this article (Figures  3  and 4 ). The results were loaded as layers into QGIS. In the second step, the pipeline was implemented in the Python programming language to create a QGIS plug-in  [45]. The plug-in has been tested in the latest long-term release of QGIS 3. 16. x on Windows 10, macOS Big Sur, and Linux. After installing the tool using the "Install from ZIP" dialog, the user first selects the input layers, either from files or from already loaded map layers. Depending on the options selected, the tool delivers up to four DFM confidence maps for resolutions of 0.
DFM A DFM is an archaeology-specific gridded surface model-a product of archaeologyspecific LiDAR data processing. It combines ground features with archaeological microrelief features and buildings. To properly describe a DFM, therefore, archaeological features need to be specified. There are many different archaeological features, but four morphological types can be distinguished (Table  2 ; Figure  5 ). Most archaeological features detected using airborne LiDAR data fall into the first two types-embedded or partially embedded features-which are to some degree part of the ground and, hence, the DTM. This explains why archaeologists are able to detect many archaeological features using a general purpose DTM. However, standing features and standing objects are off-terrain features. As such, they are intentionally excluded from DTMs.
Optimal DFM Resolution In spatial sciences, the term "resolution" initially referred to the level of detail or to the smallest object that can be recognized on an aerial photograph. In a DEM or DFM, it refers to the grid cell size, and is expressed in terms of ground spacing. The smaller the grid size, the higher the resolution  [47]. The selection of resolution is based on point density and distribution, horizontal accuracy, spatial autocorrelation, terrain complexity, or a combination of these. The computational cost and data storage should also be considered  [48]. The first problem in generating a DEM is to determine a resolution that can represent the terrain features at the desired level of detail  [49]. There are a number of studies on this topic, but they are field-and case-study specific (overview in  [47] ).
In another study, the recognition rate of archaeological features was found to decrease slowly when the point density was reduced from 7. 3 to 1. 8 pnts/m 2 , and to decrease more rapidly with further reduction  [54]. A Slovenian study demonstrated a decrease in the precision of DEMs when the pulse density was halved from 16 to 8 pulses per m 2  [55]. A comparison of datasets at 0. 7 and 2 pnts/m 2 with a UAV dataset at 22 ground pnts/m 2 showed little improvement in detection success  [56]. Similarly, a comparison of a 0. 5 pnt/m 2 dataset with a 13 pnts/m 2 dataset showed that the accuracy of archaeological interpretation was significantly better with the latter  [57]. Some ongoing studies in Finland demonstrated stark improvement and numerous new features observed when comparing a 2 m DEM with 0.
2. 2). However, to demonstrate the effect of reduced point density on ground point filtering, the data in the left column lacks manual reclassification. This is consistent with the vast majority of published studies that use grids with 0. 5 or 1 m resolution. Lower resolution is only used when better data are not available, and there are no examples that clearly show significant benefits of increasing resolution beyond 0. 5 m. It can be said that 0. 5 m DEM is currently the gold standard in archaeological practice. Archaeology-specific data acquisition is usually planned to produce a 0. 5 m DFM or better. The types of archaeological features that can be recorded with such data can be estimated by applying the cardinal theorem of interpolation  [59]  and its implementation in archaeological field survey practice: The survey grid must be half the size of the archaeological features to be recorded.
Therefore, current archaeological practice most commonly uses airborne LiDAR data to detect features either at least 1 m in diameter or linear features at least approximately 0. 5 m wide. In practice, the most commonly acquired features are larger. The second problem is determining the appropriate source data density. It is believed that for DEMs with lower resolution-for example, 5 m or lower-a point density as low as 0. 36 points per grid cell may be sufficient  [51]. For high-resolution DEMs, one point per grid cell is required  [60]. Above this value, there is no appreciable improvement in accuracy  [61]. On the other hand, if the point density is much lower, the surface will be representative of the specific interpolator used rather than the target terrain, because there will be many interpolation artefacts  [62].
However, aside from data point density, there are several factors that affect the accuracy of DEMs: the morphology of the terrain, the interpolation method  [63] , and the distribution of the data points  [64]. In the case of airborne LiDAR data, the latter is very important. This is because of non-uniform point distribution due to inter-scan-line spacing and flight strip overlap (Figure  7 : AT)  [20, 65] , and because data points are typically undersampled in non-vegetated areas and oversampled in densely vegetated areas (Figure  3 : SI1 and SI2). This makes the typical airborne LiDAR data an inefficient data source, to say the least. For example, 4 pnts/m 2 measured in a regular grid is a data source superior to a typical airborne LiDAR dataset with an average density of 4 ground pnts/m 2.
In a typical airborne LiDAR project, this fact is compensated for by brute force, so to speak-namely, by oversampling. In the above example, the LiDAR dataset would have to be densified to about 20 pulses/m 2 in order to match the use case scenario of 4 manually measured ground pnts/m 2. However, many, if not most archaeologists use LiDAR data that have been acquired for non-archaeological purposes. For example, general purpose data are available for many countries and regions. For such projects, the question of resolution arises in reverse: How to determine the resolution that maximizes the use of the available data? The simplified answer is that the resolution should be approximately equal to the average point density; for example, a 0. 5 m DFM should be interpolated if the data have 4 pnts/m 2.
For archaeology, a minimum of 2 pnts/m 2 for a 0. 5 m DFM is suggested  [18]. However, as mentioned above, airborne LiDAR point data are extremely unevenly distributed. This is exacerbated to an extreme in terrain where densely forested steep slopes are mixed with fields and meadows interspersed with hedgerows. For example, in SI2 the density varies between 0. 3 and 15 pnts/m 2 within the boundaries of a single archaeological site, let alone a single 1 km 2 tile (Figure  7 : SI2). 3: SI1 and SI2). This makes the typical airborne LiDAR data an inefficient data source, to say the least. For example, 4 pnts/m 2 measured in a regular grid is a data source superior to a typical airborne LiDAR dataset with an average density of 4 ground pnts/m 2. In a typical airborne LiDAR project, this fact is compensated for by brute force, so to speaknamely, by oversampling.
DFM Resolution As mentioned, airborne LiDAR point data are extremely unevenly distributed, and it is difficult to determine optimal DFM resolution. There are several possible approaches to this  [69] :  (1)  The conventional wisdom for DEM interpolation described above would suggest a grid resolution suitable for the vast majority of the dataset-for example, by fitting to the 95th percentile of point spacing in the dataset. The disadvantage is the loss of archaeological information in areas of locally high data density, such as meadows. (  2 ) The second option is to segment the data into areas of similar point density, interpolate each area separately, and merge them for a final DEM. However, the result of this complex procedure would hardly differ from the simple (3) interpolation of the whole dataset with the resolution suitable for the areas with the highest continuous point density.
We have therefore taken care to ensure that our definitions are broad enough to encompass all existing morphological types of archaeological features, and potentially those that we are not yet aware of. The meaning of DFM, we believe, extends beyond the mere dictionary definition. It seems that the "revolution" that airborne LiDAR data have achieved in archaeology to date has been largely due to the enormous amount of archaeological information that has been obtained from general-purpose DEMs. This was possible because the vast majority of archaeological features are embedded features that form part of any general purpose DEM. We hope that these and other studies calling for archaeology-specific data processing will spur the next "evolutionary" step that will bring additional data by mapping archaeological features that can only be detected in DFMs.
The primary application of the DFM confidence map is interpretive mapping, or the archaeological interpretation of DFMs. In particular, it allows the (automatic) determination of the confidence level for each mapped archaeological feature. This does not replace the confidence level determined by the operator, but can be used either in addition to it or as part of it. It also provides important metadata for interpretive mapping. For example, the density of a particular type of archaeological feature mapped in a given area may reflect past human activity, but it may also reflect bias in the data-e. g. , fewer features in areas with a lower quality DFMand such bias can be revealed by the DFM confidence map. In fact, any archaeological interpretation based on feature density and not matched with data quality (e.
g. ,  [71] ) can be considered incomplete. The third issue, which has been largely neglected in the archaeological literature, is the question of optimal DFM resolution. Regardless, in archaeological practice 0. 5 m is an established gold standard. We have reasoned that the archaeology-specific optimal resolution should lean heavily towards undersampling in order to extract the maximum amount of archaeological information from a given dataset. The DFM confidence map can be used as a tool to determine the optimal DFM resolution. As defined above, the optimal archaeology-specific resolution for visualization will have a confidence level of three or four for approximately 1  2 of the map. If there are no computational cost constraints, the optimal resolution can be increased until at least one contiguous area remains at confidence level six, regardless of undersampling in other areas.
Numerical experiments We now study the numerical behavior of Algorithm 2. We have implemented Algorithm 2 in MATLAB, using vectorized assembling functions and the mesh generator provided in  [12, 13] , on a computer running Linux (Ubuntu 16. 04) with 3. 00 GHz clock frequency and 32GB RAM. The test problem used is designed in order to illustrate the numerical behavior of the algorithms more than to model actual bonded structures. The test problem used is illustrated in Fig. 2 , derived from  [11]. The adherents and the interface are Ω 1 = (-10, 0) × (0, 50), Ω 2 = (0, 10) × (0, 50), Γ = {0} × (0, 50). The materials are isotropic and the materials constants of the adherents are E 1 = 50000MPA, ν 1 = 0. 3, E 2 = 25000MPA ν 2 = 0. 3. There are no volume forces, i. e. f i = 0 for i = 1, 2. On Γ i , Dirichlet boundary conditions u i = 0 are prescribed while Γ σ i subjected to surface traction σ i (u i )n i = -100 ( 0 1 ) on Γ σ i , i = 1, 2.
e. , Shape-from-Shading (SfS), from a slightly lower resolution imaging dataset such as CTX or HRSC, to retrieve compatible or even more detailed 3D information than stereo-derived 1 m/pixel HiRISE DTM. The proposed DTM processing system uses a combination of photogrammetric 3D reconstruction, image co-registration, image SRR, SfS DTM refinement, and 3D co-alignment methods. In particular, for photogrammetric 3D reconstruction, we use the open-source Co-registered Ames Stereo Pipeline  [6]  with Gotcha matching  [7]  and Optimisations (CASP-GO)  [8]. For image co-registration, we use an in-house mutual shape adaptive scale invariant feature transform (MSA-SIFT) algorithm that was described in  [9]. For image SRR, we use the multi-scale adaptive-weighted residual super-resolution generative adversarial network (GAN) single-image SRR system (MARSGAN) described in  [10].
For SfS DTM refinement, we use the HDEM algorithm described in  [11, 12]. Finally, for 3D co-alignment, we use the B-Spline fitting algorithm described in  [13]. In this paper, we demonstrate the proposed DTM processing system with 4 m/pixel CaSSIS image crops and an 18 m/pixel stereo-derived CTX DTM to produce ~1 m/pixel CaSSIS DTMs over the ExoMars 2022 Rosalind Franklin  [14]  rover's landing site, Oxia Planum  [15]. A cropped example of the 4 m/pixel CaSSIS image (panchromatic band) and our DTM result (colourised and hill-shaded) is shown in Figure  1. The layout of the paper is as follows. In Section 1. 1, we review previous work that is relevant to the proposed processing system. In Section 2. 1, we introduce the input and validation datasets used in this work. From Sections 2. 2-2. 6 and 2.
Datasets In this work, we use the 6 m/pixel CTX  [2]  and 4 m/pixel CaSSIS  [2]  images for experiments and the 1 m/pixel stereo-derived HiRISE  [4]  DTM products [5] for intercomparison. The CTX images are accessible through the U. S. National Aeronautics and Space Administration (NASA) Planetary Data System (PDS)  [72]   Our experiments were performed using the CTX and CaSSIS data over the Exo-Mars 2022 Rosalind Franklin  [14]  rover landing site at Oxia Planum  [15]. Many CaSSIS, HiRISE, and CTX images have been repeatedly captured over Oxia Planum (please refer to Supplementary Materials for a complete list of available CaSSIS images, HiRISE DTMs and potential stereo pairs, and CTX stereo pairs). In order to provide intercomparison of the resultant CaSSIS DTM, we demonstrate the new functionality with a specific CTX stereo pair, F23_044811_1985_XN_18N024W and F23_044956_1984_XN_18N024W, and CaS-SIS image MY34_001934_162_0_PAN, for areas that overlap with the PDS HiRISE DTM DTEEC_036925_1985_037558_1985_L01.
Acknowledgments: The research leading to these results is receiving funding from the  UKSA Aurora programme  ( 2018-2021 ) under grant  ST/S001891/1 , as well as partial funding from the  STFC MSSL  Consolidated Grant  ST/K000977/1. SJC is grateful to the  French Space Agency CNES  for supporting her CaSSIS and HiRISE related work. CaSSIS is a project of the  University of Bern  and funded through the  Swiss Space Office via ESA 's  PRODEX programme. The instrument hardware development was also supported by the  Italian Space Agency (ASI) (ASI-INAF  agreement no. 2020-17-HH. 0 ), INAF/Astronomical Observatory of Padova , and the  Space Research Center (CBK) in Warsaw. Support from SGF (Budapest) , the  University of Arizona (Lunar and Planetary Lab. ), and  NASA  are also gratefully acknowledged.
The research leading to these results received funding from the  UKSA Aurora programme  ( 2018-2021 ) under grant no. ST/S001891/1  as well as partial funding from the  STFC MSSL  Consolidated Grant  ST/K000977/1. S. J. C. is grateful to the  French Space Agency CNES  for supporting her CaSSIS and HiRISE related work. Institutional Review Board Statement: Not applicable.
Our model relies on joint time-frequency scattering features to extract spectrotemporal modulations as acoustic features. Furthermore, it minimizes triplet loss in the cluster graph by means of the large-margin nearest neighbor (LMNN) metric learning algorithm. Over a dataset of 9346 isolated notes, we report a state-of-the-art average precision at rank five (AP@5) of 99. 0% ± 1. An ablation study demonstrates that removing either the joint time-frequency scattering transform or the metric learning algorithm noticeably degrades performance.
Use case Behind the overarching challenge of coming up with a robust predictive model for listening behaviors in humans, the main practical application of timbre similarity retrieval lies in the emerging topic of computer-assisted orchestration  [10]. In such context, the composer queries the software with an arbitrary audio signal. The outcome is another audio signal which is selected from a database of instrumental samples and perceptually similar to the query. The advantage of this search is that, unlike the query, the retrieved sound is precisely encoded in terms of duration, pitch, intensity, instrument, and playing technique. Thus, following the aesthetic tradition of spectralism in contemporary music creation, the computer serves as a bridge from the auditory level to the symbolic level, i.
e. , from a potentially infinite realm of timbral sensations to a musical score of predefined range  [11]. Composers may personalize their search engine by manually defining a cluster graph via the Cyberlioz interface (see perceptual data collection section). This preliminary annotation lasts 30 to 60 minutes, which is relatively short in comparison with the duration of the N = 9346 audio samples in the database: i. e. , roughly three hours of audio.
Goal This article proposes a machine listening system which computes the dissimilarity in timbre between two audio samples. [1] Crucially, this dissimilarity is not evaluated in terms of acoustic tags, but in terms of ad hoc clusters, as defined by a human consensus of auditory judgments. Our system consists of two stages: unsupervised feature extraction and supervised metric learning. The feature extraction stage is a nonlinear map which relies on the joint time-frequency scattering transform  [12, 13] , followed by per-feature Gaussianization  [14]. It encodes patterns of spectrotemporal modulation in the acoustic query while offering numerical guarantees of stability [1] For the sake of research reproducibility, the source code for the experimental protocol of this paper is available online, alongside anonymized data from human subjects: https://github.
Approach The main contribution of this article can be formulated as the intersection between three topics. To the best of our knowledge, prior literature has addressed these topics separately, but never in combination. First, our dataset encompasses a broad range of extended playing techniques, well beyond the so-called "ordinary" mode of acoustic production. Specifically, we fit pairwise judgments for 78 different techniques arising from 16 instruments, some of which include removable timbre-altering devices such as mutes. Secondly, we purposefully disregard the playing technique metadata underlying each audio sample during the training phase of our model. In other words, we rely on listeners, not performers, to define and evaluate the task at hand. Thirdly, we supplement our quantitative benchmark with visualizations of timefrequency scattering coefficients in the rate-scale domain for various typical samples of instrumental playing techniques.
Furthermore, supplementing user-generated content with the outcome of a crowdsourced annotation campaign allows an explicit verification of instrument tags. For instance, the Open-MIC dataset  [31] , maintained by the Community for Open and Sustainable Music Information Research (COSMIR)  [32] , comprises a vast corpus of 20k polyphonic music excerpts spanning 20 instruments as a derivative of the Free Music Archive (FMA) dataset  [33]. Another example is the Medley-solos-DB dataset  [34] , which comprises 21k monophonic excerpts from eight instruments as a derivative of the MedleyDB dataset of multitrack music  [35]. Over the past decade, the availability of large digital audio collections, together with the democratization of high-performance computing on dedicated hardware, has spurred the development of deep learning architectures in music instrument recognition  [36, 37, 38].
Dataset We consider a list of 16 instruments: violin (Vn), viola (Va), cello (Vc), contrabass (Cb), concert harp (Hp), Spanish guitar (Gtr), accordion (Acc), flute (Fl), soprano clarinet (BbCl), alto saxophone (ASax), oboe (Ob), bassoon (Bn), trumpet in C (TpC), French horn (Hn), tenor trombone (TTbn), and bass tuba (BBTb). Among this list, the first six are strings, the next six are woodwind, and the last four are brass. Some of these instruments may be temporarily equipped with timbre-altering mutes, such as a rubber sordina on the bridge of a violin or an aluminium "wahwah", also known as harmon, inside in the bell of a trumpet. Once augmented with mutes, the list of 16 instruments grows to 33. Furthermore, every instrument, whether equipped with a mute or not, affords a panel of playing techniques ranging in size between 11 (for the accordion) and 41 (for the bass tuba).
The resort to a non-ordinary playing technique may restrict both the tessitura and the dynamics range of the instrument-mute pair under consideration. For example, the pitch of pedal tones in brass instruments is tied to the fundamental mode of the bore, i. e. , usually B Z or F. Likewise, the intensity of key clicks in the oboe is necessarily pp, while the intensity of snap pizzicato à la Bartók in plucked strings is necessarily ff. In summary, audio signals from isolated musical notes may vary across three categorical variables (instrument, mute, and technique) and two quantitative variables (intensity and pitch). The Studio On Line dataset (SOL), recorded at Ircam in 1998, offers a joint sampling of these variables. The version of SOL that we use throughout this paper, named "0. 9 HQ", amounts to a total of 25444 audio signals.