dataesr/scientific-paragraphs-categorization huggingface dataset:
Method We represent the set of patients by P = {P 1 , P 2 …, P n } and the total number of patients by n. E is the notation of the medical events set, where E = {E 1 , E 2 …, E m } and m is the total number of medical events. Our classification model (Fig. 1 ) has four sequential tasks. a) The model treats structural data and their numeric, date-time, nominal, and Boolean types to represent them more conveniently, b) Distances between patients are computed with the Jaccard distance, c) A dimensionality reduction of the distance matrix is produced on the output of the previous task, and d) The classification is performed on the data in the obtained new embedding space. We detail each of these steps in the sequel. More than the vision of the best processing suite exploration on personalized medicine datasets, the first task applies a recent and promising data representation model that will be applied for the first time in our classification process.
This phase represents the list L i 's dispersion, which is why we refer to it as Data Representation by Dispersion (DRD). With the same linearization mechanism as the first phase of DRR, the DRRD model generates a single global representation table for all nominal events. The notification and marking operations also generate three representations: by a real value, binary, and by a symbol. The DRRD model assembles representations of the same type generated by the DRR and DRD phases to form a single global representation of type by a real value, binary, or by a symbol as needed. In this paper, we use the symbolic representation of the model DRRD (SDRRD). The produced SDRRD table (Equation (  1 )) includes n = |P| rows according to the number of patients and q columns according to the new representation of the m = |E| dataset events, such that q≥ |E|.
The data preparation for the next task of classification requires inserting the fourth column in the RDMP matrix, which carries the labeling information for each patient. This approach will be employed for classifier learning only. Filling in the labeling information based on the dataset EHR produces a labeled RDMP (LRDMP) matrix (refer to Fig. 3 ). It is enough to apply only one reduction technique to the DMP matrix in our model. However, we have considered several reduction techniques for comparison and analysis and selected the best technique. Based on the study of comparison realized by Shaeela et al. [17]  on dimension reduction techniques, we choose four techniques: PCA, KPCA, MDS, and TSNE. Each technique has a strategy that gives us the following four reduced matrices: RPCA (reduction of PCA), RKPCA (reduction of KPCA), RMDS (reduction of MDS), and RTSNE (reduction of TSNE).
Experimental results The Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. usc. edu) is the source that provides the dataset employed in this experimentation. The ADNI was launched in 2003 as a publicprivate partnership led by Principal Investigator Michael W. Weiner, MD. The primary goal of the ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessments can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer's disease (AD)  [31]. The dataset, especially the ADNIMERGE_May15 table, contains 90 attributes. Among these attributes we can cite the following attributes: AGE (patient age), FDG (average FDG-PET of angular, temporal, and posterior cingulate), PTETHCAT (ethnicity), PTEDUCAT (education), COLPROT (study protocol of data collection), Hippocampus_bl (UCSF hippocampus at baseline), APOE4 (apolipoprotein epsilon 4), PET (average PIB SUVR of frontal cortex; anterior cingulate; precuneus cortex; and parietal cortex), AV45 (average AV45 SUVR of frontal; anterior cingulate; precuneus; and parietal cortex relative to the cerebellum), CDRSB (clinical dementia rating scalesum of boxes), ADAS11 (ADAS-Cog-with 11 tasks), GDP (average PIB SUVR of frontal cortex; anterior cingulate; precuneus cortex; and parietal cortex), MMSE (Mini-Mental State Examination), RAVLT_immediate (Rey Auditory Verbal Learning Test immediate)  [31]  and others.
Patients are categorized into five classes: Cognitively Normal (CN), Alzheimer's disease (AD), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC). Based on these classes, we applied data from 500 patients randomly selected (100 patients per class) to evaluate our model. Table  1  describes the statistics of the data line number in this dataset, where each line can contain up to 90 values according to the number of attributes. We have eliminated some attributes that have no medical significance from this set of attributes and can bias the results, such as the patient ID. Only 87 attributes remain after this elimination. The DRRD process directly triggers the transformation operation of date-time and Boolean data, and each line in this step corresponds to a single observation (i.
We have successfully passed the challenge of choosing the similarity distance, reduction plan and classifiers to apply. The three selected use scenarios confirm our success against the problem of choosing the best processing series. For Alzheimer's disease described by the chosen dataset, the resulting performances demonstrate the importance of these diagnoses for judging patients' medical statuses. These diagnoses may include elements that are weakly associated with this disease, but the resulting performance indicates that some of them are strongly correlated to the different categories if it is not the complete set.
Comparison To judge our model and discuss its characteristics compared to current research, we have examined previous works  [34] [35] [36] [37]  that address the same classification problem based on patient and participant data. Globally, Table  7  summarizes the four targeted approaches and our contribution. The points adopted in this table are the characteristics considered almost typical among all the state-of-the-art works. Joloudari et al. [34]  proposed a work to predict the patients' state and the possibility of their liver disease suffering. Their process utilized data for 583 patients collected from 3 data sources, described 14 attributes and tested five classifiers for comparison. With an attribute selection strategy, the particle swarm optimization (PSO)-SVM is the best classifier that achieves the performance FM = 0.
958. During processing, missing values, numeric and nominal data types are all treated, but Boolean, date, and time-series data are not treated. Terrada et al. [35]  proposed an automatic process to boost atherosclerosis diagnosis. Data from 835 patients were employed, including 29 attributes, and seven classifiers were tested for preference. This model applied an attribute selection strategy, and an ANN classifier generated a maximum performance of FM = 0. 98. Except for numeric, nominal, and Boolean data, this process does not operate on the date data type, time series, and missing values. The approach of Carvalho et al. [36]  is a dynamic decision model that is based on supervised learning. For the experimentation, a data set of 319 patients was employed, but this approach does not specify the adopted number of attributes for evaluating the eight tested classifiers.
The best performance obtained was FM = 0. 95 by the A1DE classifier within 145 min. In addition to disregarding time series, Boolean and date data are not considered in this approach. For 349 concerned patients, Lu et al. [37]  classified cancer patients into two categories: ovarian cancer and benign ovarian tumor. For the relevant attribute selection among the 49 classifiers employed, the results of three classifiers are compared. The logistic regression classifier (log reg) returned the best performance FM = 0. 97. This model processes only missing values and numeric and nominal data and disregards Boolean, date, and time-series data. Compared to our contribution, we utilized the richest dataset in terms of attribute number (87 attributes) without taking into account the approach  [36] , which does not include this detail.
Our model classifies patient data into five distinct categories and tests four classifiers against other approaches. The TSNE technique adopted for data reduction allowed us to minimize the calculation time up to 32 ms. Although we did not use the same dataset as the other approaches, our dataset has a special quality (considered types and structures), but the excellent performance obtained indicates that our proposal succeeds in terms of selecting the most appropriate processing series. The complete variety of data types processed, the consideration of time series and missing values, and processing performance and elapsed time of treatment are all factors that highlight the exceptional characteristics of our model and its advantages compared to other works. This modular rating allows us to consider our model to be more reliable and more effective.
Conclusion Automatic medical decision-making based on a personalized medicine dataset is the main purpose of this work. Our proposal applies a treatment series to achieve this goal. Applied tasks process structured data that have several types. They also consider time series and missing values. We applied a current data representation model named DRRD. We tested the following three distance measures to calculate the patients' similarity: Jaccard distance, Hamming distance, and Levenshtein distance. Subsequently, we tested the following four reduction techniques: PCA, KPCA, MDS, and TSNE. We examined four classifiers-NB, SVM, 3-NN, and RF-to categorize patients. By experimentation on an Alzheimer's disease dataset, we defined three use scenarios that were formed according to both performance requirements and processing time.
Acknowledgments This work was completed as part of the  Hubert Curien Partnership (PHC) TASSILI cooperation program between France and Algeria  under the project code 19MDU212. Data collection and sharing for this project was funded by the  ADNI (National Institutes of Health  Grant  U01 AG024904 ) and  Department of Defense (DOD) ADNI  ( DOD award number   W81XWH-12-2-0012 ). The ADNI was funded by the  National Institute on Aging , the  National Institute of Biomedical Imaging and Bioengineering , and by generous contributions from the following organizations:  AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation ;  Araclon Biotech ;  BioClinica, Inc. ; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc. ; Cogstate;  Eisai Inc. ;  Elan Pharmaceuticals, Inc. ;  Eli Lilly and Company; EuroImmun; F.
Introduction Progress in science and technology depends on research teams working on specific topics of interest and on the publication of their results in peer-reviewed articles  [1]. The rapidly growing body of scientific information  [2]  reflects past and current states of the art and represents an invaluable socio-economic resource guiding future research activities, policies and investments  [3] [4] [5] [6] [7] [8]. Its utility relies on the quality and accessibility of bibliographic databases  [9, 10]  and on refined methods to search and analyse the content of scientific articles  [3, 6, [11] [12] [13] [14] [15] [16]. Authorship on these articles credits contributions of individual team members with diverse expertise and skills  [17] [18] [19] [20] [21] , but choosing the best method to evaluate research, for example to identify potential experts, recruits and collaborators, remains a challenge  [22].
Presently, the impact of individual contributors  [23] , journals  [24] , institutions and nations  [25]  is predominantly estimated based on citation counts of scientific articles (for reviews see  [5, [26] [27] [28] ). In a frequent scenario, a user interested in a specific topic queries a bibliographic database, scrutinizes the resulting list of relevant publications and learns readily about scientific advances. But, it is very difficult for the user to learn about the contributing teams and their impact. To address this recurring issue, I propose a new bibliometric approach, further referred to as TeamTree analysis (TTA). Using author names and publication years of scientific articles related to a field of interest, TTA reveals the development and composition of the workforce with new visuals, named TeamTree graphs (TTGs), and estimates the impact of authors with a new metric named TeamTree product (TTP).
Methodology The principal steps and key features of TTA are introduced in Fig 1 using generic publications. The TTA-derived parameters are summarized in Table  1. Typically, scientific articles related to a user-defined topic of interest are retrieved from a bibliographic database (Fig 1A ; Table  2 ). From each article, TTA extracts the authors, the year of publication and a database-specific article identifier (Fig  1A ). TTA includes author initials to reduce author ambiguity  [29]. For some fields, frequent ambiguous author names were removed. TTA categorizes authors according to their byline position and sorts publications by year. Then, it assigns a chronologic author index (AI) and a randomly generated color (Col) to each last author  (Fig 1B). TTA focuses on authors on the last byline position as they are mostly responsible for the research  [19].
TTGs provide a framework to illustrate an author's contributions to each category TTA is implemented with custom-written routines based on the open source software R  [31]  and selected R packages for data handling (data. table [32] ), statistical and network analyses (igraph  [33] ; dunn. test [34] ) and data visualization (eulerr  [35] ; ggfortify  [36] ; ggplot2  [37] ; ggrepel  [38] ; igraph  [33] ; plot3D  [39] ). The R script is freely available upon request to the author and at https://github. com/fw-pfrieger/TeamTree. It can be used to analyse publications in a user-defined field of interest. Bibliographic records were obtained from MEDLINE using year of publication in ascending order, assigns to each last author a chronologic author index (AI) and a unique color (Col) and counts the number of articles per author per year (PCy).
Acknowledgments The author thanks  V. Demais ,  S. Eglen ,  N. Elghobashi-Meinhardt ,  J. Jouzel ,  J. M. Lehn ,  M. Muzet ,  V. Pallottini ,  J. L. Paluh ,  B. Poulain ,  H. Runz ,  J. P. Sauvage ,  D. Schulte ,  S. Silber  and  M. Slezak  for helpful discussions and comments on previous versions of the manuscript.
Such results are key to correctly assess the accuracy and relevance of the retrieved mineral information. For that purpose, we developed an inversion model based on a Monte Carlo Markov Chains (MCMC) scheme with a Bayesian approach to invert VIS-NIR spectra. This approach allows to properly propagate the uncertainties from the data to the retrieved properties, and finally assess what such uncertainties imply for the interpretation. Different binary and ternary mixtures with minerals of interest in planetary sciences and displaying a large variety of albedos and spectral features were tested. Typical uncertainties, both for the abundance and the grain size, were derived and sensitivities on specific parameters/trends were identified. In particular, the role of absorption features in the spectra is quantified.
Tests were performed using both the Hapke and the Shkuratov radiative transfer models. The case of unidentified endmembers in the mixture is also discussed. In particular, results show that if the unidentified phase does not display any significant spectral feature, the lack of knowledge about its optical properties does not significantly impact the inversion. These different results will be key in the quantitative analyses of VIS-NIR spectra from planetary bodies. Finally, we analyze more specifically the case of phyllosilicates and carbonates, two families of minerals of high importance in understanding the Mars geologic and climate history. Typical uncertainties on their relative abundances and grain sizes are derived in various cases, providing a critical supporting dataset for the characterization of the martian mineralogy and the associated geological processes.
e. , by propagating the uncertainty on the data). Here, the capability to retrieve quantitative properties of mineral mixtures (abundance, grain size) from typical space VIS-NIR reflectance spectroscopic data is explored. As remotely sensed surfaces are commonly composed of assemblages of fine-grained intimately mixed minerals, we decided to focus on intimate mixtures in the present study. For that purpose, we developed a model based on a Monte Carlo Markov Chains (MCMC) scheme with a Bayesian approach to invert VIS-NIR spectra of various compositions. Coupled to a radiative transfer model like the Hapke or Shkuratov model, and assuming a given uncertainty on the data, we can quantitatively evaluate how this uncertainty is propagated to the retrieved parameters and what it implies for their interpretation.
equivalent. It can also be noticed that the standard deviations for the grain size can be different for both endmembers within a given mixture, as shown in Fig. 13  (left). This difference is similar in both models. These results demonstrate that both models are to first order equally sensitive to a 20% uncertainty on the reflectance data. A second run of inversion was then performed, assuming this time an uncertainty of 20% on the reflectance data and a relative uncertainty between the spectral channels of 1% (from each spectral channel to the next), similar to the relative calibration that we generally expect on spaceborn VIS-NIR spectrometers. The spectral sampling here is 10 nm. Such case represents a theoretical case since a 1% relative uncertainty may not apply to other contributors (e.
Fig. 16b  shows that the overall reflectance factor tends to increase as the third endmember is added for the tested grain sizes, but the absolute band depths of the features present in the spectra and due to the two other endmembers experience little variation. Neither the fraction nor the grain size can be well constrained for this third compound (Fig. 19 ). Overall, the addition of a third endmember with no or very shallow absorption features tends to increase the relative uncertainties on the retrievals since there is more flexibility in the combination of the different parameters to fit the spectrum within the allowed data uncertainty. As a second run, 3-endmember mixtures BAS/BasGl/OLV and NG1/OLV/MGC were tested. Results show similar trends as for twoendmember mixtures: both the constraints on the fraction and the grain size depend on the overall absorptivity (through the reflectance) and its variation over the spectral range (i.
As for the magnesite, it is much more difficult to characterize: uncertainties on the abundances are typically between 10% and 35% except in some cases where the grains are large (above a few hundreds of microns in diameter) or when mixed with a highly absorbent phase. Such results will be key in the characterization of the martian mineralogy and the associated geological processes. More generally, models that are capable to propagate the uncertainties on the data to the endmembers properties (abundances and grain sizes), similar to the one presented in this paper, will be critical in the future to correctly assess the accuracy and relevance of the quantitative information retrieved from VIS-NIR reflectance spectra. Areal and intraparticle mixtures, also observed among planetary surfaces, should also be subject to such kind of evaluation in the future.
8, 9 Structural data regarding macromolecular assemblies is available in the Protein Data Bank (PDB),  10  and the number of protein complexes structures released each year has been steadily increasing over the last decade, in particular thanks to recent advances in cryo-EM techniques, which enabled to reach atomic resolution for very large complexes. 11, 12 ] [22]  A central problem when modeling protein interactions is the docking question, where one attempts to predict the atomic structure of a protein complex based on the structure of its individual components. The CAPRI (for Critical Assessment of PRedicted Interactions, https://www. capri-docking. org/) 4] [25]  Over the years, dozens of research groups have had the opportunity to test the performance of their computational procedures against the blind prediction of more than 150 targets, i.
Docking models selection The CAPRI Score-set (http://cb. iri. univ-lille1. fr/Users/ lensink/Score_set/) set up by Lensink and Wodak  40  lists models produced by participants to the CAPRI competition for 15 protein complexes (targets), with between 300 and 2000 docking poses available for each target. In the present study, we selected the three targets (41, 29 and 37, shown in Figure  1 ) for which models are available for the three accuracy categories High, Medium and Acceptable (see the Supplementary information for a reminder of the CAPRI assessment criteria defining theses categories). Target 41 (pdb code 2wpt  41  ) is a complex between the colicin E9 endonuclease, an antibiotic protein, and its bacterial inhibitor, the immunity protein Im2 from E. coli. Target 29 (pdb code 2vdu  42  ) is a tRNA m7G methylation complex of yeast, which is involved in the tRNA degradation pathway.
It comprises a catalytic unit Trm8 (chain D in the pdb file) bound to the non catalytic unit Trm82 (chain F in the pdb file), which modulates the Trm8 activity. Finally, Target 37 (pdb code 2w83  43  ) is a trimeric assembly formed by the human GTP-binding ADP-ribosylation factor ARF6 bound with two leucine zipper domains from the JIP4 protein. This interaction regulates the binding of JIPs with motor proteins such as kinesin or dynactin. These three complexes present buried surfaces areas (BSA) comprised between 1500 and 2000 Å 2 (see Table  1 ), which is relatable to the average BSA of 1600 Å that was observed for single patch interfaces by Chakrabarty and Janin in their review of protein protein recognition sites  44. For each target, ten models were selected that are distributed over the three categories and are listed in Table  1.
In this work, a simpler alternative, more accessible to current multimodal speech applications, combines CNN/DAE with a multimodal source-filter synthesis module. In the method (Figure  1 ), a CNN/DAE architecture first learns LSF, F0, and voiced/unvoiced (U/V) flag from ultrasound tongue and visual lip images, using ground truths derived from an audio track. Subsequently, a ''Convolutional Vocoder'' uses the predicted acoustic parameters to produce an acoustic signal having properties similar to the raw audio input. The method is applied to a ''rare singing'' data set, whose results can be of importance both in the study of rare singing styles, and as an exploratory study of acoustic parameter prediction for an SSI. Spectral Distortion (SD) performance of LSF prediction using the architecture is found to improve as compared to an earlier study  [11]  on the same dataset; and F0 prediction, using only tongue and lip images, is excellent.
The performance of the convolutional vocoder compares favorably to that of a standard MELP-MLSA (Mixed Excitation Linear Prediction -Mel Log Spectrum Approximation) coder  [20] ,  [21]. Example videos of the resulting CNN synthesis and ground truth, illustrating the ultrasound tongue and visual lip images used, are provided online at  [22]. A historical overview of multimodal speech synthesis is presented in the next section, followed by an outline of the datasets used in section III. The training procedure and results for the CNN appear in section IV. A, and vocoder development with its results in section IV. B. Conclusions and future perspectives are discussed in the section V.