dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Assuming an average cluster size of 15 participants, an allocation ratio of 2:1 (control:intervention), a coefficient of variation of cluster size of 0. 4 (cluster size ranging from 5 to 30), and an intra-cluster correlation coefficient of 0. 01, we estimated that with 600 participants [195 intervention arm (13 clusters), 405 control arm (27 clusters)] the trial would have 89% power to detect a difference of 0. 30 standard deviations (S. D. s) in the primary outcome measure (HoNOSCA). Allowing for 30% attrition, we aimed to recruit 21 participants per cluster. The target sample size was, therefore, 840 participants in total (273 intervention; 567 control). We tested the null hypothesis of no difference in mean HoNOSCA score between the MT and UC groups at month 15 post-randomisation, using a generalised linear mixed effect model (GLMM) with random and fixed effects (to allow for the hierarchical structure of the data and adjust for trial arm, time point and baseline characteristics, HoNOSCA score, gender and diagnosis).
Acknowledgements. MILESTONE  Consortium  (full list of members in the Supplementary material): MILESTONE research assistants: UK:  Priya Tah ,  James Griffin ,  Rebecca Appleton  ( University of Warwick ),  Natalie Heaney ,  Kate Lievesley ,  Mathilde Mastroianni ,  Jatinder Singh  ( Kings College London), Laura Adams (University of Plymouth ); Italy:  Giulia Signorini ,  Alessandro Ferrari ,  Elisa Gheza ,  Cecilia Ferrari ,  Laura Rivolta ,  Flavia Levi ,  Maria Cataldo ,  Lidia Manenti ,  Giorgia Morini ,  Adriana Pastore ,  Pamela Stagni ,  Cecilia Toselli ,  Pamela Varvara  ( Unit of Epidemiological and Evaluation Psychiatry -UOPEV, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia ); France:  Frédérick Russet ,  Virginie Maurice ,  Véronique Humbertclaude  ( Centre Hospitalier Universitaire de Montpellier ); Netherlands:  Larissa S.
The reaction was stopped by the addition of 50 μL of 1 M H 3 PO 4. The OD values were read at 450 nm using a spectrophotometer (SpectraMax Plus, Molecular Devices). Cytokine and chemokine analysis. Supernatants collected from tonsils in coculture were analyzed for chemokine/cytokine levels using Bio-Plex Pro magnetic bead assays (Bio-Rad, Hercules, CA USA). The following human chemokine premixed panels was used: BLC (CXCL13), sCD40L (sCD154), MCP-1 (CCL2), MIP-1α (CCL3), MIP-1β (CCL4), SDF-1 (CXCL12), MIP-3β (CCL19), MIP-3α (CCL20), GM-CSF, IP-10 (CXCL10), Fractalkine (CX3CL1), IL-1β, IL-2, IL-4, IL-6, IL-8, IL-9, IL-10, IL-12p70, IL-13, IL-15, IL-17A, IL-21, IL-22, IL-23, TNF-α, IFNα, IFN-γ and TGF-β1, TGF-β2, TGF-β3. The manufacturer's protocol was followed. Data were acquired on a Bio-Plex 200 System (using bead regions defined in the Bio-Rad protocol) and analyzed with the Bio-Plex Manager 6.
0 fluorometer (Life Technologies). Medium depth sequencing (>16 million reads per sample) was performed on a NextSeq 550 System (Illumina) using two High Output flow cells each with a 75 base pair, Paired End run. Demultiplexed fast-q paired end read adapters of length less than 36 and average phred quality score of less than 30 were trimmed and filtered using the skewer software  59. Alignment was performed with HISAT2 to the Homo sapiens NCBI reference genome assembly version GRCh38 and sorted with SAMtools  60, 61. The aligned reads were counted and assigned gene meta-information using the featureCounts software  62  , and analysis was conducted using the R programming language and various packages from the Bioconductor suite. Normalization, weighting, and subsequent differential expression was performed using LIMMA  63  and geneset enrichment analysis was performed using GSVA pathway enrichment sets from MsigDB  64, 65.
Slide were subsequently mounted with a glass coverslip using Fluoromount G (Southern Biotech). Confocal images were obtained using a Nikon (A1+) confocal system operated through the NIS-elements AR software. A ×40 (NA 1. 3) objective was used to scan the tissues. Multiple ×40 field of views and Z stack slices generated from each tissue were stitched together via the NIS-elements AR software. Pixel density of each field of view was 512 × 512. No frame averaging or summing was used while obtaining the images. Separation of fluorescence emitted from the fluorophores was achieved utilizing the NIS-elements AR software's "spectral unmixing" function. Briefly, utilizing single fluorophore tissue staining, an emission spectrum database was created, which was subsequently utilized by the program to separate the emission of different fluorophores (simultaneously or sequentially exited) into different channels.
Isotype imaging was on FFPE tonsillar tissue sections that were stained with either a primary isotype control antibody (mouse IgG1 kappa isotype control clone B11/6, abcam, ab91353; or an ID3 specific antibody. Image acquisition was performed on a Leica SP8 confocal microscope with a ×40 objective (NA 1. 30) at 1% zoom. Images were visualized using the software Imaris version 9. 6. 0 (Bitplane). Staining produced a nuclear pattern consistent with transcription factor localization as previously described (https://www. proteinatlas. org/ ENSG00000117318-ID3/antibody). Quantitative imaging analysis (histocytometry). Confocal images were analyzed with Imaris software version 9. 5. 0 (Bitplane). Histocytometry analysis was performed to generate quantitative data from the images. Briefly, 3-dimensional segmented surfaces (based on nuclear signal) of spillover corrected images were generated with Imaris via the Surface Creation module.
Christopher's Hospital for Children (Pennsylvania). Biopsies of palpable inguinal, cervical or axillary lymph nodes were performed at the National Institutes of Health Clinical Research Center in Bethesda, Maryland under protocols approved by the NIAID Institutional Review Board (ClinicalTrials. gov Identifier: NCT00001316). All participants provided written informed consent. The lymph node samples were obtained from individuals with varying levels of plasma HIV viremia, ranging from 700 to 300,000 viral copies/mL, and with CD4 counts ranging from 220 to 470 cells/mm 3. See Table  2  detailing the HIV+ and HIV-patients' immune status. Phenotypic analysis of germinal center. Tonsil cells from adult donors or HIV + lymph nodes were incubated with fluorochrome-conjugated antibodies. The following fluorochrome-conjugated antihuman antibodies were used: CD3 (Clone: HIT3α, Cat: 300324), CD4 (Clone: RPA-T4, Cat: 300520), CD25 (Clone: BC96, Cat: 302608), CD38 (Clone: HIT2, Cat: 303524), PD-1 (Clone: EH12.
For MK3006 wet/dry status was sampled every 3 s and the sum of immersion events for each 10 min received a score between 0 and 200 (wet; immersion data > 0) and was stored. Temperature was recorded after 20 first minutes continuously wet, temperature wet timer reset anytime the device goes dry for > 3 s. For MK3005, wet/dry status was sampled every 3 s and the sum of wet/dry duration was stored. Temperature was recorded after 25 min continuously wet, temperature wet timer reset anytime device went dry for > 6 s.
Acknowledgements We would like to thank all people involved in the fieldwork activities. Fieldwork activities received the administrative and logistical support from the  Fernando de Noronha administration , the  Instituto Chico Mendes de Conservação da Biodiversidade  ( ICM-Bio, Brazil ), the military firemen from  Fernando de Noronha  and the  TAMAR  Project. We also want to express grateful thanks to anonymous reviewers and to colleagues from  IFREMER  and  IRD  for having helped us significantly on the manuscript.
a b s t r a c t The assessment of very low quality visual data is known to be difficult. In particular, the ability of humans to recognize encrypted visual data is currently impossible to determine computationally. The human vision research community has widely studied some particular topics, such as image quality assessment or the determination of a visibility threshold, while others are still barely researched, specifically visual content recognition. To this day, there does not exist a reliable recognition index that can be employed for such tasks. In order to enable the study of human image content recognition, and in an attempt to propose a corresponding recognizability index, we build a dataset of selectively encrypted images together with subjective ground-truth about their human intelligibility.
The methods of acquisition, setup, protocol, outlier detection, are described and we suggest how to calculate a recognition score as well as a recognition threshold. The performance of traditional visual quality indices to predict human visual content recognition is assessed on these data and found to be inapt to estimate recognition of visual content. Contrasting, structure based recognition indices as proposed for this task are shown to represent a promising starting point for further research. To facilitate the creation of a recognition index and to foster further research into human visual content recognition and its relation to the human visual system we will make the database publicly available.
Here, the security metric is the biometric comparison accuracy obtained. In this work, we focus solely on human recognition in the context of confidential encryption. Application scenarios include to prevent leakage of movie content when distributing pre-release trailers or to prevent human security personnel from recognizing people in surveillance data. To automatically ascertain the recognizability of image content, the use of a visual recognition index (VRI), similar to a VQI, would be desirable. In order to come up with such a tool, similar to the process of establishing and assessing VQIs (compare the LIVE  [31]  and TID  [24]  datasets, respectively), a dataset with protected/ encrypted visual material together with subjective ground-truth on the human intelligibility of these data is required.
However, currently, neither proper VRI nor suited datasets to facilitate their developments do exist. In  [34] , a dataset of transparently encrypted images together with human perception ground-truth data has been released. However, this data is entirely unsuited for the human recognition context and VRI development (as transparently encrypted imagery is required to be recognizable by the application context). Also, the usage of the lower quality spectrum of impairments of the LIVE and TID datasets as done in  [6]  is of no use for the confidential encryption scenario as the intelligibility of the visual information in these two datasets is out of question. Therefore, in this work, we intend to provide a corresponding dataset of encrypted visual data to facilitate the development of a VRI.
The provided data also comprises results of human observer experiments at the recognition threshold of image content. Currently, as preparatory work, there only exists a set of recommendations for the acquisition of such a database,  [5] , which were adapted from the International Telecommunication Union (ITU) and International Electrotechnical Commission (IEC) recommendations for image quality evaluations to meet the confidential encryption context. The rest of the paper is structured as follows: Section 2 describes the database, how the images were created and how the acquisition of subjective recognition scores were performed; Section 3 gives details on how to analyze the raw observer scores from the previous section, how outliers are found and how the content recognition threshold is estimated; Section 4 uses the database to test the capabilities of visual quality indices (VQI) to separate recognizable and unrecognizable images.
Images in the database One major aim when selecting target images for the inclusion in our database is that we wanted to have a considerable overlap with a database from literature, the LIVE database  [31]. We have thus selected a set of images from the Kodak database  1  which are contained in the LIVE database as well. We also included an additional test image to have a non-natural image containing large single color patches, which can for example be found in drawings or animated films, and a more structured representation of frequency. To gauge the effect of color on the recognition we additionally included two gray-scale versions of images in the database. Overall, 12 color images, and 2 gray-scale ones were used. Despite the reduced number of images, the experiments had to be split into two sessions to combat viewer fatigue.
Fig. 1a  shows the reference images contained in the database. In order to maintain the experiment within a reasonable time-frame, we have selected 6 encryption methods operating with 9 protection strength steps (overall, 10 images including the original). The range was chosen ''by hand" to include at least a clearly intelligible, albeit strongly distorted, version of the image and then in distinct steps towards an obviously unrecognizable image. This was done by selecting a parameter set for the encryption manually based on a single image and then applying this set of parameters to the whole database. The results for the jpg (described later) encryption can be seen in Fig. 1b. The choice of encryption techniques was a difficult one and was limited by practical means. The encryption had to have settings allowing for a gradual change to get roughly 9 different levels of quality as required.
Further, we used publicly available encryption methods only for reproducibility and ease of use. Even so, we had to ''cheat" a bit with some encryption methods by iterating the application of the encryption to push it beyond the recognizability boundary. Let us now provide some details on the six encryption types being used in this database: jpg: This method  [40]  encrypts, based on AES, the AC coefficient coding of JPEG encoded images. The order of run-length coded symbols, and corresponding values, are permuted, as are coefficient value bits. In addition, the order of blocks using the same Huffman table with an interleaved minimum coded unit are permuted. The parameters for this encoding are either 'low' or the number of rounds. For low, only the first 8 AC coefficients were encrypted to get a proper high quality representation of the image.
Split of the Database for Acquisition: To prevent viewer fatigue we aimed to keep a single session of ground truth acquisition well below the one hour mark, in order to achieve this we split the database into two test sets: SPLIT1 contains the encryption types fake, j2k and j2kne, SPLIT2 is composed of jxr, jpg and H. 265. This means a session should take at most 50 min: 14 images with 9 distortion steps for each of the three encryption types and maximum 8 s per image leads to 50 min and 24 s. In practice most sessions stayed well below this time since most pairs in the recognizable range can easily be selected faster than the allotted 8 s.
Setup for recognition groundtruth acquisition The Video Quality Experts Group (VQEG) and ITU groups regularly issue recommendations  [39, 14, 1, 13]  concerning the subjective setups and protocols for quality assessment. These recommendations are aimed towards the quality estimation based on human observer scores, or the visibility threshold tracking, but none are issued for a recognition task. In  [5]  these recommendations were evaluated in terms of applicability for the acquisition of a database for image content recognition. We will follow these combined guidelines  [5]  and adapt them where necessary. In the following we will briefly describe the environment and procedure used during the acquisition of the subjective recognition data. It should be noted that the subjective acquisitions were collected in two different locations: One had the option to have a more stringent setup (a controlled environment CE), the other only to a lesser degree (semi-controlled environment SE).
The Snellen eye chart was used to control the acuity, and Ishihara color plates were used to validate a normal color vision. For the SE setup the means were more limited but we utilized an online vision  3  test to check visual acuity, near vision and color vision. About the Observers: The minimum number of observers recommended by all standards is 15 and was exceeded in all environments. In CE, 90 observers took part in the study with 45 observers enrolled per half session (SPLIT1 and SPLIT2, see Split of the Database for Acquisition in Section 2). As previously mentioned, the observers were screened for correct color vision and acuity. Except for 3 observers who had a 20/25 vision, all other observers had at least a 20/20. Three observers had a red/green color deficiency. The observers' average age was 33:6 and 34:8 for SPLIT1 and SPLIT2 respectively.
Where to get the Database? The database is available online as University Salzburg Encryption Evaluation Database (USEE DB) at http://www. wavelab. at/sources/USEE. The database contains the original images, encrypted images and the individual binary, correct/incorrect match, and score per user (the output of the experiments). It also contains a score for each image based on the  analysis methods in the following chapters and likewise a classification into recognizable or hidden image content. The encryption parameters for each file is explained in the database documentation.
Outlier detection Outlier detection is performed per session and per environment based on the clustering technique and opinion difference expressed by Hamming distance as described earlier. This is done since, due to data retention policy, anonymised IDs from one set of experiments could not be linked to those from another. The results of the outlier detection are given in Table  2 , we give the number of outliers as well as the intermediate values, l and r, (which give the distance between pairwise observer results) and the resulting cut off threshold T. The experiments were not of the same difficulty, as exhibited by the mean number of errors over all observers -for SPLIT1, this is 76:22 and for SPLIT2 it is 48:55. The dendrogram representations of the clustering are given in Fig. 4. Interestingly, we find that the difference between the two environments overall is small.
Otherwise, common operating points on the receiver operating characteristic (ROC) curve based on the full database should be used. Two points on the ROC curve make sense: 1) the equal error rate, because it makes different methods comparable by computing bounds for the significant difference without needing the exact experimental results  [8] ; and 2) the false positive rate at zero false negative rate (0FNR), which is basically the desired output of the system, i. e. no false negatives means no breach in security. In addition to the classification accuracy provided by the MCC a more direct comparison of the scores from human observers and algorithms can be taken into account. Given that the Human Visual System (HVS) is non-linear and the evaluation tool is not required to be, a non-linear correspondence measure should be used, usually the Spearman rank order correlation  [33]  or the Kendall s  [20].
Visual quality indices and recognition There are currently no visual recognition indices as far as the authors are aware. However, visual quality indices (VQIs) have been frequently used for visual security analysis, although it is known that they are not well suited for the task  [9]. According to  [10]  the best suited VQIs for security analysis are the VIF and LEG, the most widely used are the PSNR and the SSIM. In the following we will briefly describe the VQIs and evaluate their usefulness as VRI based on the method outlined in the previous section using the database introduced. Local Edge Gradients (LEG). The LEG  [7]  analyses changes in luminance and edge information. The main part is the edge score which combines the edge change between images, based on the local binary pattern concept, and an edge gradient change score between images.
e. nearby the images edges). Typically, advanced VQIs employ the Contrast Sensitivity Function (CSF) in order to represent the images as contrast gaps. The contour areas are thus presenting higher weights to be pooled into a single quality score. For the images composing this database, we have either high frequency (and high amplitude) noise on top of the image, or some large image areas being wiped out. An advanced HVS based VQI can simply not interpret the huge quantity of high contrast areas that occur in the distorted image and do not match with any existing contrast within the original image. The recognition task is very different from a quality assessment or even from a visibility threshold detection task. For image recognition, an observer only needs to find a match between small portions of the images to recognize the content.
Learning the recognition threshold We do not intend to create a (new) recognition index in this section. Rather, we are interested in whether applying machine learning can improve on the VQIs given above. We directly use the VQI scores on two resolution levels as feature input for a support vector machine (SVM) with a radial basis function. We try to learn a regression model (basically building a new VQI based on the old VQIs). If machine learning can improve the rates above (Table  3 ) substantially, then this is a clear indication that the features used in classical quality indices can be used for recognition tasks but the valuation of features needs to be reassessed. We split the database into non-overlapping sets, based on reference images. One set is removed and all others are used for training, then the removed set is evaluated.
This is done once per set, in essence building a classification for the whole database by proceeding set by set. The final classification will then be evaluated as given above. The input for the learning will be the output of the LEG, PSNR, SSIM and VIF at two resolution steps, once with full resolution and once downsampled by two. The downsampling should remove some of the noise while keeping larger structures relatively unscathed. As a fitness function we used the 0FNR, EER, MCC and SROC in turn. The results can be seen in Table  5 , split by fitness function ('Target' in the table). From the results it would seem that the regular metric scores are an ill fit for the recognition task. While this combination of VQI scores is certainly better than each  VQI individually, the results are still very bad, especially for the typical use-case of 0FNR.
These binary decisions are fused into a final score. This changes the influence of a single observer significantly and likely requires a higher minimum number of observers than recommended for quality assessment. In order to test the impact of the number of observers, we consider that the valid observers (after the dendrogram clustering step) represent the ground truth, i. e. , a reasonable estimation of the recognizability rate. From this retained set of observers we randomly selected several subsets of observers in order to generate reduced panels of assessors. We chose these reduced panels to comprise between 5 and 32 observers. For each panel size, we randomly generate 100 subsets, i. e. , we repeated the test 100 times. For each of the so-obtained subset, we compute both the Pearson correlation and RMSE with the full subjective dataset (#O ¼ 67 for SPLIT1 and #O ¼ 74 for SPLIT2).
They might try to ascertain the ability of the VQI to cope with a specific dataset by evaluating it on a limited subset. Indeed, we can not prove there is not a VQI which, for a specific test set, conforms well to human judgment. There is however a pitfall in such a limited subset test as we will briefly outline. In order to study the feasibility of running a small experiment, and test a few metrics, we have decided to split the whole subjective database into subsets of fewer observers, and most importantly fewer test images. As explained in Section 2. 1, the full database is composed of 14 images, 6 SEnc methods and 9 SEnc parameters, it is thus composed of 756 test images. Overall 150 observers were enrolled to determine the recognizability of these 756 images (90 under CE conditions, and 60 under SE viewing conditions).