dataesr/scientific-paragraphs-categorization huggingface dataset:
Beyond playing techniques, we should note that SOL erases other factors of acoustic variability, such as identity of performer, identity of instrument manufacturer, audio acquisition equipment, and room response characteristics, which are all restricted to singletons. Addressing these factors of variability is beyond the scope of this paper, which focuses on the influence of playing technique. Despite this restriction, the SOL dataset remains impractically large for collecting human similarity judgments. Our protocol addresses this problem by means of three complementary approaches: disentanglement of factors, expert pre-screening, and the use of an efficient annotation interface.
Disentanglement of factors First, we purposefully disentangle categorical variables (IMTs) from continuous variables (pitch and intensity) in the SOL dataset. Indeed, under first approximation, the perception of timbre is invariant to pitch and intensity. Therefore, we select auditory stimuli according to a reference pitch and a reference intensity; in our case, middle C (C 4 ) and mf. After this selection, every IMT triplet contains a single acoustic exemplar, regarded as canonical in the following. The number of canonical stimuli for the entire SOL dataset is equal to 235. We should note, however, that the proposed pitch and intensity cannot be strictly enforced across all IMTs. Indeed, as explained above, a fraction of IMTs can only be achieved at restricted values of pitch and intensity parameters, e.
Efficient annotation interface Thirdly, we design a graphical user interface for partitioning a corpus of short audio samples. The need for such an interface arises from the unscalability of Likert scales in the context of pairwise similarity judgments. Assuming that similarity is a symmetric quantity, collecting a dense matrix of continuously valued ratings of similarity among a dataset of N items would require 1 2 (N 2 -N ) Likert scales. In the case of N = 78 IMTs, the task would amount to about 3k horizontal sliders, i. e. , several hours of cumbersome work for the human annotator. Engaging as many participants as possible in our study called for a more streamlined form of human-computer interaction, even if it sacrificed the availability of continuously valued ratings. To this end, we implemented a web application, named Cyberlioz, in which the user can spontaneously listen and arrange sounds into clusters of timbre similarity.
We refer to  [13]  for more details on the implementation of joint time-frequency scattering. We adopt the multi-index notation p = (λ, α, β) as a shorthand for the tuple of frequency, rate, and scale. The tuple p is known as a scattering path (see  [68] ), and may apply to index both first-order (S 1 ) and second-order (S 2 ) coefficients. Given an input waveform x, we denote by Sx the feature vector resulting from the concatenation of S 1 x and S 2 x: Sx t, p = (λ, α, β) = S 1 x(t, λ, α, β) if α = 0, S 2 x(t, λ, α, β) otherwise. (8) Median-based logarithmic compression and affine standardization Now, we apply a pointwise nonlinear transformation on averaged joint timefrequency scattering coefficients. The role of this transformation, which is adapted to the dataset in an unsupervised way, is to Gaussianize the histogram of amplitudes of each scattering path p.
On a dataset of environmental audio recordings, a previous publication has shown empirically that Equation 10 brings the histogram of Sx n (p) closer to a Gaussian distribution  [14]. Since then, this finding has also been confirmed in the case of musical sounds  [4]. Lastly, we standardize every feature Sx n to null mean and unit variance, across the dataset X = {x 1. x N }, independently for each scattering path p. Again, if X is split between training and test sets, we measure means and variances over the training set only and propagate them as constants to the test set. With a slight abuse of notation, we still denote by Sx n (p) the standardized log-scattering features at path p for sample n, even though its value differs from Equation 10 by an affine transformation.
Metric learning with large-margin nearest neighbors (LMNN) Let x be some arbitrary audio sample in the dataset X. Let G be a cluster graph with N = card X vertices and C clusters in total. We denote by G(x) the cluster to which the sample x belongs. Given another sample y in X , y is similar to x if and only if belongs to the cluster G(x). Because G is a disjoint union of complete graphs, this relation is symmetric: x ∈ G(y) is equivalent to y ∈ G(x). In our protocol, x contains the sound of an isolated musical note and the cluster graph G encodes auditory similarities within the dataset X. Moreover, we take G to be the equal to the "consensus" cluster graph G 0 , i. e. , arising from the partition of a hypergraph H which contains the judgments of all K participants from our perceptual study (see perceptual data collection section).
Extension to diverse pitches and dynamics In order to suit the practical needs of contemporary music composers, computerassisted orchestration must draw from a diverse realm of instruments and techniques. Therefore, whereas our data collection procedure for timbre similarity judgments focused on a single pitch (middle C) and a single intensity level (mf ), we formulate our machine listening experiment on an expanded dataset of audio samples, containing variations in pitch and dynamics. Given an audio stimulus x n from our perceptual study, we seek its position in the cluster graph G 0. Then, we identify its IMT triplet, scrape for audio samples in SOL matching this triplet, and assign them all to the same cluster as the original audio stimulus. We repeat the same procedure for all N = 78 nodes in G 0 , resulting in N = 9346 samples in total.
Thus, from a limited amount of human annotation, we curate a relatively large subset of SOL, amounting to about one third of the entire dataset (9346 out of 25444 samples). In doing so, we assume that the perception of timbre is fully invariant to frequency transposition as well as changes in dynamics. This assumption coincides with the commonplace definition of timbre as an autonomous parameter of musical sound. Previous studies on real-world musical sounds have confirmed that listeners are able to ignore salient pitch differences while rating timbre similarity  [71, 72] , insofar as these differences do not exceed one octave. Since then, another study has shown that trained musicians can identify the similarity within pairs of notes from two instruments (horn and bassoon) spanning a range of 2.
Evaluation metric Let us denote by x 1 ,. , x N the N audio samples associated with our annotated dataset. Given a sample n and a human subject k, we denote by G k the cluster graph associated to the subject k, and by G k (n) the cluster to which the sample x n belongs. Our machine listening system takes the waveform x n as input and returns a ranked list of nearest neighbors: Φ 1 (x n ), Φ 2 (x n ), Φ 3 (x n ), and so forth. In the context of browsing an audio collection by timbre similarity, x n is a userdefined query while the function Φ plays the role of a search engine. We consider the first retrieved sample, Φ 1 (x n ), to be relevant to user k if and only if it belongs to the same cluster as x n in the cluster graph G k ; hence the Boolean condition Φ 1 (x n ) ∈ G k (n). Likewise, the second retrieved sample is relevant if and only if Φ 2 (x n ) ∈ G k (n).
In the scientific literature on STRF, the matrix Vx is known as the "cortical output collapsed on the rate-scale axes"  [75]. Previous publications on STRFs have demonstrated the interest of visualizing the slice Vx in the case of speech  [76] , lung sounds  [77] , and music  [18]. However, the visualization of musical sounds has been restricted to some of most common playing techniques, i. e. , piano played staccato and violin played pizzicato. Furthermore, prior publications on time-frequency scattering have displayed slices of S 2 x in the case of synthetic signals; but there is a gap in literature as regards the interpretability of the scale-rate domain in the case of real-world signals. To remedy this gap, we select twelve isolated notes from the SOL dataset from two instruments: violin (Figure  3 ) and flute (Figure  4 ).
Likewise, impulsive flute sounds such as sforzando (Figure  4(d)) , key click (Figure  4 (e)), and vibrato (Figure  4 (f)) create ridges in the scale-rate domain of varying cutoff rates α. We distribute the implementation of these figures as part of the MATLAB library scattering. m, which is released under the MIT license. [5]   Ablation study We now turn to alter certain key choices in the design of the above-described computational blocks, and discuss their respective impacts on downstream performance. Figure  5  summarizes our results. Interestingly, the system Φ is not only best on average, but also best for every subject in the cohort. Specifically, replacing Φ by a simpler model Φ (see subsections below for examples of such models) results in P Φ (k, 5) < P Φ (k, 5) for every k. Borrowing from the terminology of welfare economics, Φ can be said to be uniquely Pareto-efficient  [78].
Role of dataset size First of all, it is worth noting that the presented AP@5 figure of 99. 0% ± 1 does not abide to a conventional training set vs. test set paradigm, as is most often done in machine learning research. Rather, the LMNN algorithm is trained on all available samples (N = 9346 isolated notes) with the "consensus" cluster graph as training objective (G = G 0 ). Then, it is evaluated on the same samples with individual cluster graphs as ground truth (G = G k for k ≥ 1). The reason behind this choice is that, in the context of computer-assisted orchestration, the collection of audio samples has a fixed size, as it is shipped alongside the software itself. This is the case, for example, of Orchidea, [6] which comes paired with its own version of SOL, named OrchideaSOL  [79]. Henceforth, our main goal was to evaluate the generalization ability of our metric learning algorithm beyond the restricted set of samples for which human annotations are directly available (see perceptual data collection section); that is, beyond one pitch class (middle C) and one intensity level (mf ).
Despite this caveat, we may adopt a "query-by-example" (QbE) paradigm by partitioning the database and audio samples in half (  1 2 N = 4673), training the LMNN algorithm on the first half, and querying it with samples from the other [5] Link to download the scattering. m library: https://github. com/lostanlen/scattering. m [6] Link to Orchidea software and OrchideaSOL dataset: www. orch-idea. org half. In this evaluation framework, our system reaches an AP@5 of 96. 2% ± 2. Interestingly, querying the system with samples from the training set leads to an AP@5 of 96. 5% ± 2, i. e. , roughly on par with the test set. Thus, it appears that the gap in performance between the evaluation presented in the results section (99. 0% ± 1) and query-by-example evaluation (96. 2% ± 2) is primarily attributable to a reduction of the size of training set by half, whereas statistical overfitting of the training set with respect to the test set likely plays a minor role.
Comparison with mel-frequency cepstral coefficient (MFCC) baseline Lastly, we train a baseline system in which joint time-frequency scattering coefficients are replaced by MFCCs. Specifically, we extract a 40-band mel-frequency spectrogram by means of the RASTAMAT library; apply the pointwise logarithm; and compute a discrete cosine transform (DCT) over the mel-frequency axis. [7]   [7] Link to RASTAMAT library: http://www. ee. columbia. edu/ ~dpwe/resources/matlab/rastamat This operation results in a 40-dimensional feature vector over frames of duration T = 25 ms. Over our cohort of K = 31 participants, we report an AP@5 of 81. 8% ± 7. Arguably, this figure is not directly comparable with our best performing system (AP@5 of 99. 0% ± 1), due to the mismatch in dimensionality between MFCC and joint time-frequency scattering coefficients.
This is in line with other classification results, where linear support vector machines applied to scattering coefficients have achieved significant success  [12, 13]. We also see the necessity of a fully joint time-frequency model for accessing timbre, as opposed to a purely spectral model or one that treats the two axes in a separable manner. This fact has also been observed in other contexts, such as the work of Patil et al. [18]. A related observation is the need for capturing large-scale structure. Indeed, reducing the window size to 25 ms means that we lose a great deal of time-frequency structure, bringing results closer to that of the separable model. The success of the above system in identifying timbral similarities has immediate applications in browsing music databases. These are typically organized based on instrumental and playing techniques taxonomies, with additional keywords offering a more flexible organization.
Accessing the sounds in these databases therefore requires some knowledge of the taxonomy and keywords used. Furthermore, the user needs to have some idea of the particular playing technique they are searching for. As an alternative, content-based searches allow the user to identify sounds based on similarity to some given query sound. This query-by-example approach provides an opportunity to search for sounds without having a specific instrument of playing technique in mind, yielding a wider range of available sounds. A composer with access to such a system would therefore be able to draw on a more diverse palette of musical timbre. The computational model proposed in this work is well suited to such a query-byexample task. We have shown that it is able to adequately approximate the timbral judgments of a wide range of participants included in our study.
Whole-exome sequencing of patient fibroblasts. Whole-exome sequencing of fibroblasts was performed by Novogene (Novogene (UK) Company Ltd. ). Exome capture was performed with the SureSelect Human All Exon v6 kit (Agilent) and sequenced on the Illumina HiSeq platform. Sequencing reads were trimmed using Trimmomatic  53  , aligned to the hg38 reference genome using BWA  54  , and duplicate reads were marked using Picard Tools (http://broadinstitute. github. io/picard). Somatic mutations were called using the Varscan 2 pipeline  55. Identified somatic variants were annotated using Variant Effect Predictor  56  and variants present in dbSNP (but not in the COSMIC database) were excluded. Lentiviral shRNA transfection. Knockdown of MMP1 expression in HFF cells was performed using shRNA Lentiviral Particles (Santa Cruz Biotechnology) and siGENOME siRNA (Horizon Discovery).
c Melanoma spheroid invasion in noCSD and isogenic noCSD-UV fibroblast secretomes in the presence of Batimastat or DMSO vehicle, (two-sided Mann-Whitney U **p = 0. 0011, *p = 0. 0104, data represents eight replicate spheroids measured across two-independent experiments per condition). d Melanoma spheroid invasion in CSD and isogenic CSD-UV fibroblast secretomes in the presence of 8 nM Batimastat or DMSO vehicle, (two-sided Mann-Whitney U, ns not significant). Data represents eight replicate spheroids measured across two-independent experiments per condition, box plots represent 25th to 75th percentiles with median, whiskers represent minimum and maximum values. e Representative images of collagen degradation in shCtrl-HFF, shCtrl-UV-HFF, shMMP1-HFF and shMMP1-UV-HFF fibroblasts. Green: intact DQ collagen; red: phalloidin; blue: Hoechst.
Each HFF condition was created in biological duplicates. Secretome collection. To collect secretomes 1 × 10 6 cells were plated in a 100 mm dish and cultured for 72 h in DMEM without FBS to limit cell proliferation. Secretomes were collected in duplicate, aliquoted and stored at -80 °C until used. Batimastat treatment. MMP inhibitor Batimastat (Sigma Aldrich, SML0041) was resuspended in DMSO at 15 mg/ml and a stock was diluted to 1 mM. Batimastat was added to fibroblast secretomes to a final concentration of 8 nM in secretome volume and control secretomes had equal volume of DMSO added as a vehicle control. RNA-sequencing data analysis. RNA-seq data from ENA project PRJEB13731; also at https://www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-4652/ were downloaded. Data are single-end RNA-seq from short-term cultivated fibroblasts sequenced on an Illumina HiSeq 2000 sequencer.
COSMIC mutational signatures v2 (https://cancer. sanger. ac. uk/cosmic/ signatures_v2) were identified using the MutationalPatterns 60 package (version 1. 8. 0) in R (version 3. 5. 1, RStudio v1. 2. 5001, RStudio Inc). Differential expression analysis was performed using the DESeq2 package (version 1. 22. 2, ref. 61  ) in R (version 3. 5. 1). Reads counts of genes were filtered for genes expressed in fibroblasts by removing any gene with <100 counts across all samples. For pathway enrichment analysis genes that were significantly differentially expressed in by signature 7 mutation count (false discovery rates (FDR) p value < 0. 1) were compared against the Reactome Database 62 using the Molecular Signatures Database v7. 0 (Msigdb, Broad Institute, https://www. gsea-msigdb. org/gsea/msigdb/ index.
9 μm (Dr Maisch GmbH) and eluted with a flow of 300 nl/min from 5 to 30% of buffer (80% ACN and 0. 1% formic acid), in a 90 min linear gradient. MS raw data were acquired using the XCalibur software (Thermo Fisher Scientific). MS raw files were processed using MaxQuant software 69 (version 1. 6. 3. 3) and searched against the human UniProt database (release 2016_07, 70,630 sequences), using the Andromeda search engine  70  with the following settings: the parent mass and fragment ions were searched with an initial mass deviation of 4. 5 and 20 p. p. m. , respectively. Carbamidomethyl (C) was added as a fixed modification and acetyl (N-term) and oxidation (M) as variable modifications. The minimum peptide length was set to seven amino acids and a maximum of two missed cleavages, and specificity for trypsin cleavage were required.
The FDR at the protein and peptide level were set to 1%. The LFQ setting was enabled for protein quantification  71. Razor and unique peptides were used for quantification. Perseus software 72 (version 1. 6. 2. 2) was used for statistical analysis. Data were filtered to remove potential contaminants, reverse peptides that match a decoy database and peptides only identified in their modified Fig. 4  Low collagen correlates with low invasion and improved outcome in aged melanoma patients. a H&E top panel: primary cutaneous melanoma and inset (box) with single cell invasion (arrows) in a collagen-rich dermis with no chronic sun damage (noCSD), collagen: red (scale bars left: 4000 µm, right: 400 µm). Lower panel: melanoma in a collagen-poor dermis with chronic sun damage (CSD) and inset (box), no single cell invasion; collagen: red (scale bars left: 3000 µm, right: 300 µm).
form. LFQ intensities were transformed by log 2. A two-sample t test was used to determine significantly regulated proteins, with the permutation-based FDR ≤ 0. 05 and S0 = 0. 1 being considered significant. Atomic force microscopy. For imaging purposes, the ECMs prepared as above were fixed with 2% PFA and stored with PBS containing 1% penicillin/streptomycin at 4 °C. A day prior to imaging, the dishes were washed with distilled water five times to wash off any salt and then air dried overnight. Samples were imaged by intermittent contact mode in air using a Bruker ScanAsyst 9. 1. The probe was auto-tuned using Nanoscope software (version 1. 4). Images were taken at 10 × 10 μm and 2 × 2 μm area at least two sites. Data were processed using Nanoscope analysis software 1. 4 prior to image export.
For concentration gradient collagen was diluted to 0. 25, 0. 5, 1, 1. 5, 2 or 2. 5 mg/ml in phenol-free DMEM without FCS. For degraded collagen invasion collagen was diluted to 1. 5 mg/ml in phenol-free DMEM containing collagenase I (Gibco) to final concentration in the collagen of 0. 5, 1, 5 or 10 µg/ml. For invasion using fibroblast secretomes collagen was diluted to 1. 5 mg/ml using secretome collected from various fibroblast cell lines instead of DMEM. Spheroids were allowed to invade over 72 h and light microscopy photographs were taken. Images were analysed to quantify the invasive area around the spheroid by creating a layered mask for the spheroid core and invasive area, and quantifying the invasive area as a percentage of the total size of the spheroid in ImageJ software (1. 53c).
For single cell invasion analysis, spheroids in the above conditions were imaged using an Opera Phenix (Perkin Elmer) with a 5× (0. 16 NA) lens. Spheroids were stained with Hoechst 33342 for 1 h prior to imaging. Stacks of images were acquired through the full depth of the spheroids, and images were analysed using Columbus software (Perkin Elmer). A custom analysis pipeline was used to detect individually invading cells. In brief, a maximal intensity projection of the Hoechst stain was processed (Flatfield correction: Basic, Guassian blur: 2px) was used to determine the perimeter of the spheroid, and nuclei were detected in the remaining portion of the image. Experiments with varying levels of collagen matrices or collagenase were repeated, and confirmed by an independent laboratory, blinded for matrix composition.
After 10 days constructs were fixed in 4% PFA, and embedded in paraffin and stained with H&E and stained for Fibronectin with FN1 antibody (F3648, Sigma Aldrich). For each construct, the number of cells invading into the collagen was counted in at least five different fields of view under light microscopy, by two-independent scorers. Leica SCN400 was used for whole slide imaging alongside ImageScope software v12. 3 (Leica Microsystems). Second-harmonic generation imaging. Second-harmonic generation imaging of collagen in 3D organotypic models was performed using a Leica SP8 upright confocal microscope with 25× NA 0. 95 water objectives controlled by Leica LAS X software. The images were acquired with 880 nm illumination laser line from MaiTai Ti:Sapphire laser (Spectra Physics) and HyD-RLD detector installed 440/ 20 nm filter cube (Leica), also used 483/32 nm filter (Leica) collecting autofluorescence signals at the same time.
Acknowledgements A. V. is a  Wellcome Beit Fellow  and personally funded by a  Wellcome Trust   Intermediate Fellowship  ( 110078/Z/15/Z ), and has work funded by  Cancer Research UK  ( A27412 ) and  Leo Pharma Foundation. E. N. is funded by  Fondo de Investigación en Salud (FIS)   PI15/ 01860 ,  Instituto Carlos III, Spain. C. G. -M. funded by the  French Dermatology Society, Collège des Enseignants en Dermatologie de France (CEDEF)  and  UNICANCER France. S. J. F. acknowledges support from the  European Commission  ( FP7-PEOPLE-2013-IEF-6270270 ) and the  Royal College of Surgeons in Ireland StAR programme. P. M. is funded by the  Manchester Cancer Research Centre , supported by the  CRUK Manchester Institute. We acknowledge the generous contribution of  Christie UK Biobank, APHM Biobank (France)  and  IVO Biobank (Spain).
Materials and methods Model nanocomposites were prepared with binary mixtures of silica nanoparticles and a PMMA matrix. Specifically, a silica nanoparticle of 3 nm in size was embedded into a PMMA matrix, made of ten and twenty chains, respectively, and each chain composed of two hundred monomers. The PMMA chain was built using the polymer builder package in the Schrodinger Maestro tool  (Matestro, 2018 ). An atactic configuration was chosen with random backbone dihedral distribution, as this allows for a more accurate representation of the real polymer conformation. The silica particle was prepared and cut from a cristobalite supercell  (Pedone et al. , 2008)  as presented in Fig. 1. The preparation protocol included heating the bulk supercell to 5000 K for 20 ps and cooling from 5000 K to 300 K, with isothermal control at intermediate temperatures  (3000, 2000, 1500, 1000, 900, 500 K)  for 20 ps to obtain amorphous silica (validated against experimental densities between 1.