dataesr/scientific-paragraphs-categorization huggingface dataset:
After 2 h of panobinostat treat- ment, only chromatin modifying processes were affected (e. g. , upregulation of histone acetylation). At the 24-h time point, tran- scription of genes changed that were associated with RNA metabo- lism, possibly compensating the HDAC inhibition-related gene expression changes. Notably, a number of genes involved in BCR signaling and apoptosis were affected. These changes included reac- tivation of BCL2L11, CDKN1A, PIK3CB1, NFKBIA, MEF2C, and IRF3 in CLL, while BCL2, ZAP70, LEF1, ETS1, and RUNX1 were repressed (Dataset EV11). The complex gene expression response after panobinostat treatment can be rationalized by considering that pan- HDAC inhibitors like panobinostat not only induce histone hyper- Differential occupancy of TF binding sites at enhancers We identified TF binding motifs in ATAC-seq peak regions (median size 327 bp) for different potential enhancer loci and excluded a AE 1 kb region around promoters.
The annotations included differen- tial ATAC signals (Dataset EV10) at ChromHMM states 1, 8, 9, and 11 (Fig 5A and C), states 1 and 9, which both carry a strong H3K27ac signal (Appendix Fig S5A), and states 8 and 11 for poten- tial poised or weak enhancers (Appendix Fig S5B). In addition, the consensus ATAC peaks were intersected with DMRs for states 1, 8, 9, and 11 (Fig EV2F) as well as differential H3K27ac regions either genome-wide (excluding promoters; Appendix Fig S5C) or for SEs called as described above (Appendix Fig S5D). These different (Braun et al, 2001;Waby et al, 2010)nslational acetylation of nonhistone proteins including TFs. For example, it has been shown that acetylation changes the activity of NF-jB(Chen et al, 2001), enhances DNA binding and transactivation of MEF2C(Ma et al, 2005), and decreases DNA binding and transcription activity of SP1/ 3(Braun et al, 2001;Waby et al, 2010).
AVAILABLE DATA SETS Case studies were conducted using three data sets (Figure  3 ). The first data set was taken at the Siti Inggil complex of the Kasepuhan Palace, Cirebon, Indonesia. This area dated to the 15 th century and includes several historical pavilions within its 1,200 m 2 brick-walled perimeters. The site was digitised in 2018 using a combination of terrestrial laser scanner (TLS) and photogrammetry (both terrestrial and UAV), and was georeferenced to the Indonesian national projection system. The area was then segmented into individual building clusters which are automatically annotated with semantic information using pre-existing GIS data  (Murtiyoso and Grussenmeyer, 2019). The Royal Pavilion located in these premises was thereafter used as case study for the developed method.
Furthermore, the posterior pillars are attached to iron fences. The third data set was obtained from a laser scanning mission at the Paestum archaeological site in southern Italy. The data was acquired by the 3DOM-FBK Trento team  (Fiorillo et al. , 2013)  and has been shared to the authors to be used as a case study in this paper. Specifically, the point cloud data of the "Basilica" or the Temple of Hera was used in this paper. The Basilica is the ruins of a Greek-style temple with Doric orders; Paestum itself being a Greek colony in the 7 th century BC. The Paestum site is also a UNESCO World Heritage site. The three available data sets provide very different styles of architecture. While Paestum's Doric columns provide a prime example of ancient Greek architecture, it is vastly different to the 15 th century Javanese architecture of the Kasepuhan site.
This preliminary processing will enable a fully automatic workflow which begins with the point cloud of the entire building as input, therefore minimising as much as possible human intervention during the process. In the developed workflow, this step is followed immediately by the structural support detection as expressed in the pseudocode of Figure  2. For the Kasepuhan dataset, the building body part which was previously segmented was used as input, while for the St-Pierre and Paestum data sets the original point clouds were directly used as inputs. As has been described in section 3, there are two main parts of the algorithm which are performed simultaneously; namely the segmentation and the classification. Although both steps were integrally implemented in the same function, the following discussion will be divided into the segmentation and classification sections to help understand the consecutive steps of the workflow.
Point cloud detection and segmentation As regards to the detection and segmentation of the input point cloud into potential building structural supports, a summary of the results from the three case studies is showcased in Table  1. As has been previously described, the three available data sets present a unique set of test data with three very different styles of architecture. That being said, the three shares a common characteristic in that columns within the three architectural types have circular cross-sections. The Kasepuhan data set is the smallest of the three, consisting of only a little over 155k points, while Paestum consists of 1. 1M points. The St-Pierre data set consists of 1. 8M points for the choir part which is used in this study. In total, the algorithm managed to detect 20 supports for Kasepuhan, 8 supports for St-Pierre, and 58 supports for Paestum.
This corresponds well with the ground truth data, where identical numbers of structural supports are found in each data set. While the detection of the supports in Kasepuhan and Paestum are quite straight-forward due to the fact that in both data sets the supports are fairly apart from each other, the case of St-Pierre is more complex. In the St-Pierre choir data, the eight pillars are actually four pairs of twin pillars, each pair consisting of two columns conjoined at the plinth and capital levels. Furthermore, the posterior columns of each pair are attached to an iron fence which links the four pairs and forms a barrier between the choir and the ambulatory located behind it. Difficulties arose when applying the algorithm by default, because the function arbitrarily takes the middle altitude crosssection of the point cloud to perform the detection part.
In this regard, the iron fence hinders a proper detection of the posterior columns as stand-alone supports. A tweak was necessary to be applied to the algorithm in order to properly detect each support, namely by setting the cross-section profile to be used in the detection part to the one just beneath the capitals where the iron fence ends. The buffering of the convex hull cookie cutter polygon also needed to be adjusted as to take into account the short space between two columns in a pair. Figure  5  shows a sample of some structural supports detected by the algorithm on the three data sets, while showcasing some of the problems encountered with each particular case. For the Kasepuhan data set, some erroneous points were segmented together with a pier, effectively presenting a case of overclassification or false positive points.
In this case, the overclassified points are those belonging to a sign post which was attached to the pier. This error typically manifests due to the use of the cookie cutter and Euclidean distance-based region The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W15, 2019 27th CIPA International Symposium "Documenting the past for a better future", 1-5 September 2019, Ávila, Spain growing approaches. Although the RANSAC-based plane fitting filter managed to exclude the floor part of the segmented cluster, the use of distance-based region growing did not manage to exclude that of the sign post. This is because the sign post is attached to the pier, effectively telling the algorithm that these points belong to the same cluster. A similar problem can be observed with the St-Pierre data set.
As has been previously explained, the posterior columns are attached to an iron fence. Here the same problem with the combination of the cookie-cutter and distance-based region growing manifested itself. Indeed, the same reasoning can be followed to explain why a small part of the iron fence on each side of the support was included in the cluster. A similar, albeit more curious, problem can be seen in the Paestum data set. Here the same argument regarding the disadvantages of the cookie-cutter and distance-based region growing segmentation may explain the existence of the false positive points. However, the Paestum data set displays a systematic tendency to this, in that the same case happens to not only one support, but indeed many. This may be explained by the iterative nature of the algorithm, in which the previous iteration includes the left (in the relative directions of Figure  5 ) end of the cluster, while the current iteration takes what is rest.
Ambiguities were evident due to the existence of the iron fence between the choir and the ambulatory, while the twin nature of the columns also generated errors. Furthermore, the input point cloud was not preprocessed or cleaned; several artefacts which can be considered as noise were present in the scene e. g. folded chairs, both open and folded (some are stored between the columns of a twin pair, thereby presenting significant uncleaned noise). This problem can also be observed from the cloud-to-cloud analysis also presented in Table  2. The cloud-to-cloud analysis computed a Euclidean distance for each point to its nearest reference point, and was computed using the software CloudCompare (https://www. danielgm. net/cc/ accessed on 4 June 2019). The standard deviation value for the St-Pierre data is quite high (40.
6 mm). As regards to the results for the Paestum and Kasepuhan data sets, both showed good results in terms of unclassified percentage, amounting to around 10-15%. The Kasepuhan data set also fared well in terms of the cloud-to-cloud analysis, although this may be a little biased due to the fact that Kasepuhan presents the lowest number of points and the supports are located fairly apart from each other, as well as the fact that it is the smallest object of the three in terms of scale. For Paestum, the higher order of values, both for the mean and standard deviation values reflected the larger scale of the object compared to the other two data sets. Indeed, Paestum's dimension is about 5 times that of Kasepuhan and St-Pierre.
Result Table  3. Comparison of the results of the classification part of the algorithm on the three data sets. Red denotes the column class while blue the pier class. Note that processing time is for both the segmentation and classification steps, which were integrated into one function. However, Paestum's high standard deviation once again reflected the problem with the overclassification and its systematic nature, as has been described in the previous paragraph. Overall, the segmentation step presents an average success rate of 81. 61% and median success rate of 85. 61% for three tested data sets which are available.
In this regard, results from all three data sets show promising results as the algorithm managed to correctly identify the form of all structural supports. In the of the Paestum data set, the ground truth data gave a total of 58 supports, comprising of 56 circular columns and 2 rectangular ones. Within the pre-defined context identified at section 3, these two rectangular columns can be considered as piers. The algorithm managed to detect the same number of support types, and correctly determined which support belonged to which class. The whole processing of the Paestum data, comprising the detection, segmentation, and classification, takes a total of 341. 16 seconds. For the Kasepuhan data set, the algorithm managed to detect the 6 columns located at the inner part of the pavilion, of which three are located on an elevated dais.
The surrounding 14 wooden piers were also correctly identified. The algorithm took 38. 28 seconds to generate this result. The St-Pierre data also showed promising results, as the programme managed to identify the eight supports as columns, their twin nature notwithstanding. In this case, the processing time amounts to 83. 56 seconds. Although the algorithm managed to perform the classification task well enough, it should be noted that fine tuning of some important parameters must be performed to address specific cases. As may be observed from the processing time of the three data sets, it is not directly linked to the point count of their respective input data as much as to the number of supports detected and the density of the point cloud. The Paestum data took more time to process compared to the denser St-Pierre data due to the amount of supports detected (58).
CONCLUSIONS AND FURTHER WORK This paper has attempted to describe an algorithm which enables the automation in heritage point cloud segmentation and classification using simple geometrical rules, such as convex hull areas and circularity values. The main focus of the algorithm is the detection of architectural elements; in this preliminary phase it is the structural support of the building. It has also showcased the results generated by said algorithm on three test sites with very different architectural styles with promising results. The method was implemented in the Matlab © language, but the main algorithm is open source and other implementations using other languages such as C ++ and Python is perfectly possible. Tests on the three available data sets showed that in terms of segmentation, the algorithm managed to correctly identify potential structural supports.
The segmentation was then performed using the cookie-cutter approach, followed by additional filtering using RANSAC-based plane fitting segmentation to exclude floors and a Euclidean distance-based region growing segmentation to filter out the noises within the cluster. While this approach provided results in a fairly short processing time, it has its own downfalls as evidenced in section 5. Mainly, the approach is dependent on the particular condition of each case, with a deviation to the ideal condition (e. g. existence of attached sign posts or iron fence) resulting in overclassification. That being said, the algorithm managed to be quite reliable in some specific cases, such as the segmentation of the twin pillars in the St-Pierre data set. As has been stated previously, judging from the three available data sets, the algorithm attained mean and median success rates of 81.
ACKNOWLEDGEMENT This research benefits from the Indonesian Endowment Fund for Education (LPDP), Republic of Indonesia. The authors also wish to thank Fabio Remondino of 3DOM-FBK Trento for agreeing to share the Paestum data set used in this paper. The codes developed for this paper are written in Matlab © but are open source and may be downloaded from the following link: https://github. com/murtiad/M_HERACLES.
Data collection We obtained microCT scans of the periotics -the bone containing the inner ear -of 48 species (comprising 94% of extant genera) of odontocetes by imaging osteological specimens from museum collections (see Additional file 1). Using this data, we reconstructed 3D models of the inner ears using the segmentation and thresholding editors in Avizo 9. 0  [66] , and then cleaned the resulting 3D models using Geomagic Wrap [67]. Next we landmarked the digital models with 40 sliding semilandmark curves comprising a total of 371 landmarks (see Additional file 1), using IDAV Landmark  [68]. The position of these curves followed the protocols of Costeur et al. [7] , using only the curves from the cochlea and the vestibular aqueduct because 1) the focus of this study is hearing ability, i.
Geometric morphometrics We performed all geometric morphometric analyses in R version 3. 4. 3 [69] , using the R package GEOMORPH  [70]. First, we used Generalised Procrustes Analysis (GPA) to remove the effects of position, scale and orientation. The semilandmarks were 'slid' along their tangent vectors between adjacent semilandmarks until their positions minimised the shape difference between specimens (using the Procrustes distance criterion), to reduce the effect of their initially arbitrary placement  [71] [72] [73]. We then performed a principal component analysis (PCA) on the resulting Procrustes coordinates using the 'plotTangentSpace' function; and use these principal components (PCs) in all further analyses.
Identifying convergent regimes In this study, we aimed to identify odontocete cochleae that have convergently evolved a similar shape without defining groups as convergent a priori. To identify convergent regimes in odontocete cochleae we used the R package SURFACE  [75]. The SURFACE approach uses an Ornstein-Uhlenbeck (OU) processa random walk where trait values are pulled back towards an adaptive peak/long term meanto identify groups that share a similar adaptive peak, and can hence be defined as belonging to the same convergent regime Mahler et al. [56]. A SURFACE analysis is split into a forward phase and a backward phase to firstly locate regime shifts on a tree, and secondly identify whether the shifts are convergent. In the forward phase, SURFACE starts with the simplest model where the entire clade is in a single adaptive regime.
Detecting significant levels of convergence Using the convergent regimes identified using SUR-FACE, we next used the measures proposed by Stayton  [20]  in the R package CONVEVOL to quantify the degree of convergence in each putatively convergent group and test for statistical significance. We also additionally tested for convergence in 'river dolphin' (Platanista, Lipotes, Inia and Pontoporia) cochleae as previous analyses had found morphological convergence in skull shape and association of cochlear shape with habitat preference  [7, 17]. The C-metrics (C 1 -C 4 ), are distancebased measures that define convergence as where two or more taxa have evolved to be more similar to one another than their ancestors were to each other  [20, 76]. C 1 represents the proportion of the maximum phenotypic distance between two convergent lineages that has been reduced by subsequent evolution (i.
Investigating the strength of convergence Stayton's  [20]  C-metrics measure the significance of convergence, but do not measure strength of convergent evolution, therefore we calculate this using the Wheatsheaf index in the WINDEX R package  [77] , using the convergent regimes identified in the SURFACE analysis as the focal groups. The Wheatsheaf index is calculated by dividing the mean corrected phenotypic distance matrix for pairwise comparisons between all species in the phylogeny, by the mean corrected phenotypic distance for pairwise comparisons between focal species only. As with the C-metric analyses, the first 30 PCs were used. Additionally, 95% confidence intervals (CI) were obtained by jack-knifing the data and calculating the intervals from the resulting distribution. Confidence intervals are given because the calculation of the Wheatsheaf Index is not amenable to multiple, independent sampling (it uses information from the entire sample).
Acknowledgements We would like to thank:  Oliver Lambert  (IRNSB),  Gabriel Aguirre Fernandez (UZH) ,  Brett Clark  and  Vincent Fernandez  (NHM),  Alistair Evans  ( MU ) and  Georg Schulz  and  Bert Müller  ( BSCUB ) for scanning several of the specimens in this study;  Richard Sabin  and  Roberto Portela Miguez  (NHM),  Erich Fitzgerald  and  Karen Roberts  (NMV),  Nancy Simmons ,  Neil Duncan  and  Eileen Westwig  ( AMNH ) for access to specimens;  Anjali Goswami  (NHM),  Ryan Felice  ( UCL ) and  Morgan Hill  ( AMNH ) for access to and advice on using software;  Flavie Laurens  ( UB ) for reconstruction of several of the inner ear models;  Carl Buell  for permitting the use of his illustrations; and the editor and two anonymous reviewers for their helpful comments and suggestions.
The individual element of the rosette has the gauge length and resistance of 3. 18 mm and 700 Ω, respectively. Figure  1  shows a schematic diagram of the measurement set-up. A programmable power source (AMETEK CSW 5550VA) and a data acquisition system (DAQ-NI USB-6251 BNC) with analogue output were used in conjunction with a PC to control the magnitude and the waveform of the supply voltage so as to produce a sinusoidal induction in the SST sample. The feedback control of the supply voltage was programmed using the Matlab/DAQ toolbox. Low-noise/high-gain signal amplifiers were also used to amplify the signal obtained from the search coils. In addition to that, a high sampling rate DAQ system (DEWETRON) controlled by PC/DEWEsoft was used to retrieve the measured signals for the field strength and the flux density.
The fitting was attempted using both nonlinear least-squares minimization and a genetic algorithm (Matlab functions lsqcurvefit and ga). Figure  3  shows the best results obtained from the fitting. Although the magnetization curves correspond relatively well to the measurements, the magnetostriction curves do not. This is caused by the limitations in the analytical expression (1. 5), which fails to correctly describe the physical behaviour of the material. Derivation of a better expression would require a time-consuming trial and error process and would probably lead to complex analytical formulae. In addition, finding suitable initial values for the nonlinear fitting problem may be difficult. In the following, we propose a new identification approach based on linear least-squares spline fitting which does not require analytically expressing the energy density.
(d) Implementation considerations The fitting of the splines was implemented in Matlab 1  (v. R2017b). For the given measurement grid u, v and w and the desired spline order, the B-spline knots were calculated with the Matlab function optknt. Given the grid, the order and the knots, the collocation matrices A u , A v and A w in (2. 5) were obtained with the function spcol. In order to construct the collocation matrices D u , D v and D w in (2. 6), spcol was first used to obtain collocation matrices for mapping the partial derivative B-spline coefficients into the measured partial derivative values. These were multiplied with matrices obtained by function DerivBKnotDeriv (found in  [41] ), which map the B-spline coefficients into partial derivative B-spline coefficients. Different scaling factors should be chosen for different equations in the systems (2.
Remotely sensed maps of global forest extent are widely used for conservation assessment and planning. Yet, there is increasing recognition that these efforts must now include elements of forest quality for biodiversity and ecosystem services. Such data are not yet available globally. Here we introduce two data products, the Forest Structural Condition Index (SCI) and the Forest Structural Integrity Index (FSII), to meet this need for the humid tropics. the SCI integrates canopy height, tree cover, and time since disturbance to distinguish short, open-canopy, or recently deforested stands from tall, closed-canopy, older stands typical of primary forest. the SCI was validated against estimates of foliage height diversity derived from airborne lidar. the FSII overlays a global index of human pressure on SCI to identify structurally complex forests with low human pressure, likely the most valuable for maintaining biodiversity and ecosystem services.