dataesr/scientific-paragraphs-categorization huggingface dataset:
Image processing and segmentation Images were filtered using Fourier filtering approach with either Fiji  (Schindelin et al. , 2012)  or a Fourier filtering Python 3. 7 script kindly provided by Dr. Luca Bertinetti in order to reduce curtaining artifacts  (Spehner et al. , 2020). Stack alignment was performed either by cross-correlation alignment using Align Slices module in Amira 2019. 3 (Thermo Fisher Scientific) or based on Fourier shift theorem as described in Spehner et al. (Spehner et al. , 2020) , Python 3. 7 script was kindly provided by Dr. Luca Bertinetti. Both alignment methods allowed only for translation of the slices and not rotation or scaling. To enhance contrast, unsharp mask filter was applied to the stacks in Fiji software  (Schindelin et al. , 2012). In order to reduce charging artefact, charge correction was performed as described in Spehner et al.
with a Python 3. 7 script kindly provided by by Dr. Luca Bertinetti  (Spehner et al. , 2020). Data segmentation was performed manually using Amira 2019. 3 with the threshold tool on every 3 rd or 4 th slice. The rest of the slices were interpolated. Because intra mitochondrial granules span over 3 slices, owing of their small size, they were segmented without interpolation. All the segmented labels were converted into surfaces using Generate Surface module and presented using a Surface View object. To represent the volume of the nucleus in Figure  3 , volume rendering was applied to the whole stack and the excess volume was cropped out using Volume Edit module.
QUANTIFICATION AND STATISTICAL ANALYSIS Quantifications Intra-mitochondrial granules quantification was performed based on the segmented particles. Volume of each particle was calculated by Amira using Material Statistics feature per particle. To avoid manual errors in the segmentation and exclude objects at the edge of the resolution, objects smaller than 10 voxels were left out of the quantification. The histogram and box plot chart were plotted using MATLAB 2019b (MathWorks). LDs count per cell was made using the following processing steps in Fiji  (Schindelin et al. , 2012) : Cells of interest were cropped and an intensity z-projection was calculated based on standard deviation. The image was then threshold to form a binary image and using the watershed tool. The LDs were counted using the analyze particles tool and corrected manually for false positive and false negatives.
Correlation For 3D correlation, the processed cryo-FIB-SEM was binned in Fiji  (Schindelin et al. , 2012). The center off mass was manually picked in the FIB-SEM dataset and the BODIPY signal was segmented using spot detector (parameters: detect bright spots, scale 2, sensitivity 10, export to ROI) in Icy  (De Chaumont et al. , 2012). Rough, rigid transformation was applied using Auto Finder feature in eC-CLEM (Paul-Gilloteaux et al. , 2017) (parameters: FM with detected ROI spots as source image, FIB-SEM with manually picked ROI as target image, pre-aligned data, max error 10 microns, percentage of point to kept 70%, ''already in the same orientation'' option) following by either rigid or affine transformation with either 9 or 17 LDs identified manually under ec-CLEM v2. 1. 0. Error estimation heatmaps and 95% interval confidence spheres were calculated by eC-CLEM v2.
ACKNOWLEDGMENTS We thank  Dr. Luca Bertinetti  for providing scripts for image processing,  Dr. Martin Schorb  from the  EMBL electron microcopy core facility  for assistance in optimizing the cryo-FM image acquisition, the  EM unit of the Weizmann Institute of Science ,  Dr. Neta Varsano  and  Dr. Mattia Morandi  for critical reading of the manuscript, and members of the  Avinoam team  for fruitful discussions. P. P. -G. is part of  MicroPICell facility (BioGenouest ), member of the national infrastructure  France-BioImaging  ( ANR-10-INBS-04 ), and acknowledges funding from  ANR-18-CE45-0015. This research was supported by the  Minerva Foundation  with funding from the  Federal German Ministry for Education and Research, the David Barton Center for Research on the Chemistry of Life , and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  851080 ) to O.
Because many of the morphological measurements and meristic characters were non-independent, we also used linear discriminant analysis (LDA) to explore multivariate differences in morphology. LDA determines the trait combinations that best discriminate among levels of a categorical variable and thus was ideally suited to our dataset where we aimed to find linear combinations of morphological or meristic measurements that discriminated among the four Chrysiptera taxa. For both the morphological measurements and the meristic counts, we used the lda function of the MASS package in R  (Venables and Ripley 2002 ) and leave-one-out cross-validation to determine our prediction accuracy. For the meristic characters, as in canonical correspondence analysis, we v 2 standardised the character indicator matrix before LDA.
123 (Accessions: KM455353-KM455364, MK657005, MK657007, MK657116, MK657705, MK657836, MK658695, MZ832606-MZ832613, MZ852575-MZ852644) and metadata uploaded to the Genomic Observatories Metadatabase (GEOME,  Deck et al. 2017; Riginos et al. 2020 ; accessioned as Chrysiptera_CYB_CO1_lliggins at https://n2t. net/ark:/21547/ Dvj2).
Phylogeographic analyses To   (Leigh and Bryant 2015)  using the TCS algorithm option  (Clement et al. 2002)  and PopArtHelper (https://github. com/ignacio3437/PopArtHelper) was used to code the locality data into a nexus file that could be loaded into PopArt. The R package ape  (Paradis and Schliep 2019)  was used to calculate pairwise genetic distances among taxa based on COI and CytB and using the  Tamura and Nei (1993)  substitution model (selected using the IQ-TREE2 -m TESTONLY function as having the best BIC score).
Phylogenetic analysis The average gene region occupancy for our phylogenetic dataset was 88% for all taxa and 32% for four focal Chrysiptera taxa, with 100% of the most variable sites included (COI and CytB; Online Resource 4). The topology of our phylogeny was similar to previous phylogenies of Pomacentrinae and was well supported (Fig. 4 ). Accordingly, the Chrysiptera genus was polyphyletic. The four focal Chrysiptera taxa formed a monophyletic clade, sister to C. starcki (corresponding to the Oceanic ''Chrysiptera'' described by  Tang et al. 2021). Chrysiptera rapanui of Rapa Nui was sister to the other three focal Chrysiptera taxa, which were indistinguishable from each other in the phylogeny.
In temperate, mainland Aotearoa New Zealand, stray C. notialis and the Rangita ¯hua demoiselle co-occur within a few kilometres of each other  (Francis et al. 1999; Middleton et al. 2021) ; in this setting, their ecological function and any evolution of traits associated with reproduction will have the greatest bearing on their future evolutionary trajectories. material or data. The BC Notice recognises the rights of Indigenous peoples to permit the use of information, collections, data and digital sequence information generated from the biodiversity or genetic resources associated with traditional lands, waters and territories. The BC Notice may indicate that BC (Biocultural) Labels are in development and their implementation is being negotiated. For more information about the BC Notices, visit https://localcontexts.
galba. We used the regPhylo R package (version 1. 0. 4; Eme et al. 2019) to query Barcode of Life Database (BOLD, https://www. boldsystems. org/; Ratnasingham et al. 2007) and National Center for Biotechnology Information (NCBI, https://www. ncbi. nlm. nih. gov/) for sequences from all other taxa identified within Pomacentrinae according to the World Register of Marine Species (WoRMS, http://www. marinespecies. org/; WoRMS Editorial Board 2021), representatives from the four other Pomacentridae subfamilies (Lepidozyginae, Stegastinae, Abudefdufinae and Chrominae) and other Percomorpha out-group taxa (as per Cooper et al. 2009; Frederich et al. 2013.
Acknowledgements This research was informed by the helpful advice of  Malcolm Francis ,  Clinton Duffy  and  Gerry Allen. Expeditions  to  Rangita ¯hua ,  Rapa Iti ,  Pitcairn Islands  and New Caledonia were made possible by the crew of the  RV Braveheart, Stoney Creek Shipping Company. For field assistance to collect specimens in New Caledonia we thank  Jeremy Barker ,  Carl Struthers ,  Irene Middleton ,  Mark McGrouther  and  Sally Reader ; in Norfolk Island we thank  Mark Scott ; in Rangita ¯hua we thank  Alison Ballance ,  Charles Bedford ,  Clinton Duffy ,  Malcolm Francis ,  Sam McCormack ,  Mark McGrouther ,  Phil Ross ,  Carl Struthers ,  Stephen Ullrich ,  Ged Wiren  and  Vincent Zintzen ; in Rapa Iti we thank  Charles Bedford ,  Anna Berthelsen ,  Amelia Connell ,  Mark McGrouther ,  Sally Reader ,  Carina Sim-Smith  and  Stephen Ullrich.
Procedures Blood and urine samples were obtained and stored in a centralised biobank (Cell & Co Biorepository, Pont du Château, France). Detailed demographic, clinical, and biological data were obtained during the inclusion visit using an electronic casereport form. Follow-up included systematic visits annually and spontaneous reports for particular events, which were recorded on specific data forms. 7 his study was observational, and decisions about treatment combination, treatment timing, and screening for the progression of fibrosis were made by the clinician, according to French national recommendations, based on EASL guidelines. 10 5] [16] [17]  The FibroTest TM is approved by European guidelines and by the French national healthcare system for the surveillance of CHC. Several expert centres routinely perform these tests.
Outcomes The co-primary study outcome was the NPV of LCR1-LCR2 at 5 years for the occurrence of HCC as well as survival without HCC according to the predetermined LCR1-LCR2 cut-off, adjusted for HCC risk variables and for the response to HCV treatment, quantified using time-dependent Cox proportional hazards models. This core analysis used the algorithm assuming that only patients with cirrhosis (FibroTest TM >0. 74), and patients without cirrhosis (FibroTest TM < -0. 74) and high LCR1-LCR2 would require screening. The binary result of the LCR1-LCR2 algorithm, the risk of HCC, was identified here as either a 'low' or 'high' risk. This study was registered with ClinicalTrials. gov (NCT01953458). Data for incident HCC included the number of tumours at diagnosis, the largest nodule size, total size, and diagnostic imaging procedures.
The fifth analysis evaluated the subset of patients with and without SVR. The sixth analysis compared the risk of HCC according to LCR1-LCR2 results to the risk of HCC in the general population obtained from national cancer registries, with standardisation for age and sex. 19 n a final pooled analysis, we combined the present external validation database with the updated Original Study to check the overall NPV of LCR1-LCR2 with the best power.
19, 21 SIR results were compared with the Chi-squared test. Baseline characteristics were compared using the Mann-Whitney test for quantitative variables or the Fisher's exact test for categorical variables. Kaplan-Meier curves were drawn to compare survival without HCC. Incidence and 95% CIs were estimated with an exact method based on the Poisson distribution. For the pooled analysis of the updated Original Study cohort, which started in 1997, the SVR was not limited to DAA treatments. Analyses were performed in duplicate, blind to LCR1-LCR2 values, with SAS version 9. 4. NCSS-12. 0 and R software, including timeROC library.
Acknowledgements CM, TB and VD acknowledge support from  Total. This work was supported by the  Dutch Research Council (NWO/OCW) , as part of the  Quantum Software Consortium programme  (project number  024. 003. 037 ). This research is also supported by the project NEASQC funded from the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement No  951821 ).
The popularity of social networks (SNs), amplified by the ever-increasing use of smartphones, has intensified online cybercrimes. This trend has accelerated digital forensics through SNs. One of the areas that has received lots of attention is camera fingerprinting, through which each smartphone is uniquely characterized. Hence, in this paper, we compare classification-based methods to achieve smartphone identification (SI) and user profile linking (UPL) within the same or across different SNs, which can provide investigators with significant clues. We validate the proposed methods by two datasets, our dataset and the VISION dataset, both including original and shared images on the SN platforms such as Google Currents, Facebook, WhatsApp, and Telegram. The obtained results show that k-medoids achieves the best results compared with k-means, hierarchical approaches, and different models of convolutional neural network (CNN) in the classification of the images.
From the forensics point of view, the images shared by users on SN platforms could be considered as complementary clues to detect the evidence referenced in a digital crime  [4]. In a real scenario, once a digital crime is reported on an SN platform, the police may identify a number of suspects (e. g. , friends, relatives and most active users) and collect the electronic devices and the respective profile information on the SNs. With a set of "original images" coming directly from a specific number of the collected devices and the "shared images" taken from suspects' profiles, smartphone identification (SI) and user profile linking (UPL) could be achieved. These tasks represent an orthogonal work compared with the work presented in  [5]  and can provide the police with significant findings and the opportunity to update their dataset to apply to future investigations by creating new fingerprints of the criminals' smartphones.
In addition, a classification approach based on artificial neural networks (ANNs) effectively achieves social-by-original SI and inter-layer UPL (i. e. , the blue and red straight arrows in Figure  2 ). In particular, we classify the "shared images" by exploiting the fingerprints derived from the obtained classes, refer to Section 3. 4 for more details. Analyzing a huge number of images on all SN platforms is an unfeasible task; for this reason, in a real-world scenario investigators identify a restricted number of suspects and collect the relative devices and user profile information. Accordingly, to evaluate the proposed methods, we use our real dataset that consists of 4500 images captured by 18 different smartphones. The dataset was uploaded and downloaded on 4 of the most popular SNs, namely Google Currents (Google+ was discontinued in April 2019 and enterprise accounts were transitioned to Google Currents ), Facebook, WhatsApp, and Telegram.
In addition, we validate our proposed methods by the VISION image dataset  [12]. The obtained results show the effectiveness of the proposed methods, even for the images degraded through the compression process on the applied SNs. Moreover, the methods are device-independent and able to distinguish the same model of smartphones. An important result of our work is applying the inter-layer UPL task to link a given set of user profiles on different SN platforms. This is more desirable in digital investigations because on average, users are active on multiple SNs  [1]. The rest of the paper is organized as follows. Section 2 provides a summary of the SI and UPL tasks proposed in the literature. In Section 3, we describe the proposed methods. Experiments and their results are discussed in Section 4.
The authors of  [32]  introduced a method that was not dependent on login credentials. The behavioral traits of users were applied to link users. Zafarani et al. [33]  applied behavioral patterns to establish a mapping among identities of individuals across social media sites. The authors of  [34]  used datasets such as call records and matched the obtained histograms of users' data representing their fingerprints to identify users. In  [35] , user activities on SNs were analyzed to find trust interactions between the users. However, there are still some problems with these approaches. The information of users' identities could be diverse on different SNs  [36]. The users may select different nicknames and E-mail addresses, resulting in incorrect matching between the real person and the accounts  [33].
Methodology We first provide a brief background on RN extraction and PN computation, namely smartphone camera fingerprinting. Then, we describe the pre-processing phase that enables definition of several parameters, such as the orientation, size, and channel of the images. Finally, we explain SI and UPL across SNs based on classification techniques. To evaluate our methods, we gathered a dataset including 4500 images from 18 different smartphones. Through the paper, we call our dataset "Lab Dataset", i. e. , D L. Based on our previous work  [40] , the minimum number of images per samrtphone to get a reliable fingerprint is 50. Hence, for each smartphone, we collected 250 images. Then, we kept a subset of 50 "original images" (O) and uploaded and downloaded 50 images on each of the four selected SNs: Google Currents (G), WhatsApp (W), Facebook High Resolution (FH), and Telegram (T).
Correspondingly, we have the datasets D O L , D G L , D W L , D FH L , and D T L. The characteristics of the applied smartphones in D L are shown in Table  1. We use also the VISON image dataset including a different number of images taken by 35 smartphones. The images are divided into flat, which is a set of images of walls and skies, and generic, which is a set of images without limitations on orientation or scenario. The images were shared through WhatsApp and Facebook (in both high and low resolutions). We use only generic images in our experiments. We call the datasets D O V , D W V , D FH V , and D FL V corresponding to the O, W, FH, and Facebook Low Resolution (FL) images. The lowest and the highest resolutions of images for each SN in the datasets D L and D V are presented in Table  2.
Original-By-Original Smartphone Identification and Intra-Layer User Profile Linking We apply supervised and unsupervised classification to images (see Figure  1b ). More specifically, we do supervised classification using different pre-trained convolutional neural networks (CNN) such as GoogleNet [47], SqueezeNet  [48] , Densenet201  [49] , and Mobilenetv2  [50]. In particular, we have added a convolutional layer to adapt the size of the images to the network input, retaining the weights from the previous training on the ImageNet dataset. As an unsupervised classification, we use k-means, k-medoids, and hierarchical techniques, which are performed based on a similarity measure in such a way that the objects in the same class have more similarity compared with those in different classes  [51].
In the hierarchical classification technique, the objects are typically organized into a dendrogram (tree structure), where leaf nodes represent the individual data and the root is the whole dataset. The middle nodes show merged groups of similar objects  [52]. In partitional classification such as k-means  [53] , and k-medoids the objects are divided into some partitions, each of which is considered as a group. The partitional classification starts by initializing a set of k class centers. Then, each object is assigned to the class whose center is the nearest  [11, 54]. K-medoids is an expensive approach, but it is a more reliable technique in the presence of noise and outliers compared to the other unsupervised classification methods  [55]. We compare the CNN, hierarchical, k-means, and k-medoids techniques to classify the "original images" and achieve original-by-original SI and select the best technique for classification of smartphone camera fingerprints.
Then, in a similar way, we classify the "shared images" to achieve intra-layer UPL. Figure  3  shows the task of original-by-original SI. Through the proposed methods, the number of smartphones under investigation has to be provided. Let I be a set of the "original images", and S = {S 1 , S 2 ,. , S m } be a set of m camera sources. We aim to classify the images of I into the right sources of S, where each camera source S i has its own set of images, that is I 1,i ,. , I j,i ,. , I n,i ∈ S i. Thus, we have the full dataset I = I i,j , ∀ i = 1,. , n and j = 1,. , m, where n is the number of the collected images for each of the m smartphones. Firstly, we extract the RNs of the "original images" such that RN <i,j> is the RN corresponding to ith image taken by jth smartphone. Then, we use correlation as the similarity measure because it is the optimal metric for multiplicative signals such as PRNU  [41].
Experimental Results In this section, the results of SI and UPL are presented. In particular, the results of original-by-original SI, social-by-original SI, intra-layer UPL, and inter-layer UPL are provided, respectively. The proposed methods were implemented in MATLAB R , version R2019a on a laptop with the following characteristics: Intel Core i7-6500U (2. 93 GHz), 16 GB of RAM, and Windows 10 operating system. In each of these tests, to evaluate the classification processes, we calculate several measures. Let TP be a set of images to which the method has correctly assigned class labels, while that it has correctly not assigned is represented by TN. In addition, FP is the set of images to which the method has wrongly assigned class labels and FN is the set of images that the method has wrongly not assigned.
Accordingly, Sensitivity (S E ), Specificity (S P), Rand Index (RI), Adjusted Rand Index (ARI), F1measure (F ), and Purity (P) are defined as follows: SE = |TP| |TP| + |FN| (8) SP = |TN| |TN| + |FP| (9) RI = |TP| + |TN| |TP| + |FP| + |TN| + |FN| (10) where |. | denotes cardinality of the related set, i. e. , True Positive (TP), True Negative (TN), False Positive (FP), or False Negative (FN). The value of RI varies between 0 and 1, respectively showing no agreement and full agreement between the classification results and the ground truth. For two random classes, the average of RI, i. e. , RI is a non-zero value. To get rid of this bias, ARI was proposed by  [63] : ARI = RI -RI 1 -RI (11) F = 2. |TP| 2. |TP| + |FP| + |FN| (12) P = ∑ |C| i=1 | c i | |c i | |C| (13) where C = {c 1 , c 2 ,. , c m } is the set of the obtained classes corresponding to m smartphones in dataset, c i denotes the number of RNs with the dominant class label in the class c i , and |c i | is the total number of RNs in c i.
Original-By-Original Smartphone Identification Results In this experiment, we use "original images" to identify their acquisition smartphones, which is called the original-by-original SI task. As shown in Table  1 , these images have a high resolution, so the results can be considered as a benchmark for the capability of the classification in the best case. Furthermore, we exploit this experiment to perform some pre-processing in terms of size for all the applied images in the datasets. In particular, in the pre-processing phase, we use the k-medoids method because it is a more reliable technique in the presence of noise and outliers. Based on Table  4 , to obtain the optimal resolution in resizing, we resize the extracted RNs form the images in D O L with different resolutions, i. e. , 128 × 128, 256 × 256, 512 × 512, 960 × 544, 1024 × 1024, 1280 × 1024, and 1536 × 1536.