dataesr/scientific-paragraphs-categorization huggingface dataset:
36 1. 79 87. 57 98. 84 TB-Profiler 89. 71 95. 02 10. 29 4. 98 71. 25 98. 53 (b) global dataset Tool Sensitivity Specificity VME ME PPV NPV ARIBA 87. 30 96. 22 12. 70 3. 78 91. 64 94. 11 KvarQ 79. 99 95. 81 20. 01 4. 19 90. 05 90. 99 MTBseq 81. 34 96. 03 18. 66 3. 97 90. 67 91. 56 Mykrobe 88. 12 96. 16 11. 88 3. 84 91. 58 94. 47 TB-Profiler 87. 39 92. 89 12. 61 7. 11 85. 36 93. 95 (c) prospective dataset Tool Sensitivity Specificity VME ME PPV NPV ARIBA 90. 10 98. 87 9. 90 1. 13 78. 74 99. 54 KvarQ 84. 11 99. 21 15. 89 0. 79 83. 19 99. 26 MTBseq 85. 82 99. 00 14. 18 1. 00 80. 05 99. 34 Mykrobe 90. 22 99. 16 9. 78 0. 84 83. 39 99. 54 TB-Profiler 83. 01 97. 24 16. 99 2. 76 58. 33 99. 19.
37urce data Figshare: sample_data. tsv. https://doi. org/10. 6084/m9. figshare. 7556789. v129. This project contains European Nucleotide Archive accession numbers for Illumina sequence data and associated metatdata. Figshare: ont. tsv. https://doi. org/10. 6084/m9. figshare. 7605443. v137. This file contains the run time and peak memory usage of each tool on each sample. This file contains the number of true-and false-positive calls for each tool and each reported variant. This file contains the otuput of the benchmarking pipeline. Figshare: who_regimens. tsv. https://doi. org/10. 6084/m9. figshare. 7605476. v2 54. This file contains drug regimens defined by the WHO. This project contains European Nucleotide Archive accession numbers for ONT data, and matched illumina Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.
Figshare: removed_variants. tsv. https://doi. org/10. Figshare: who_regimen_counts. tsv. https://doi. org/10. 6084/m9. figshare. 7851689. v143. This file contains WHO regimen counts for each tool and dataset. Figshare: pipeline. json. gz. https://doi. org/10. 6084/m9. figshare. 7605509. v1 50.
This is particularly problematic in nursing homes as most of the activity is generally generated by medical staff. To solve this problem, in this paper, we introduce our system named the Non-invasive Unit Recognition System for the Elderly (NURSENET), which combines a piezoelectric floor sensor and a deep learning-based algorithm to recognize non-elderly activity with very high accuracy (AUC 0. 94). This paper is organized as follows. Section 2 first gives the settings of our installation in the nursing home and introduces our algorithm and its training procedure. The performance of NURSENET is evaluated in Section 3 by computing its accuracy over a database containing multiple types of activity. Finally, the main advantages and drawbacks of our system are discussed in Section 4.
The NURSENET Algorithm In this subsection, we present our neural network NURSENET, which is used to identify and characterize activity signals, particularly between medical staff and elderly individuals. This subsection is organized as follows. First, the structure of NURSENET is introduced. Then, the data embedding used to encode the signals is described, and finally, the subnetwork used as the first layers of NURSENET is explained. Most of the strategies developed in the training process aim at circumventing our main issues, which are the small Signal-to-Noise Ratio (SNR) (approximately 20 dB in our walking dataset) and the external alterations of the signal.
This was due to the small signal-to-noise ratio, the perturbations discussed in the previous section, and the limited size of the dataset. The first phase of the training was to learn the embedding of the data. This is detailed in Section 2. 2. 2. Then, SPN was trained separately on a different task, in order to take advantage of the gait structure (see Section 2. 2. 3). The resulting weights were used to initialize the first three layers of NURSENET, and these layers were frozen during the rest of the training process to limit the number of free parameters, henceforth reducing overfitting  [59]. Finally, the rest of the network was initialized using i. i. d. centered Gaussian variables with a standard deviation of 0. 2. For the training phase, regularization was done using dropout  [60]  (with p = 0.
The aim of our data embedding phase was to replace the first layers of the CNN with some already trained filters, here the atoms of the dictionary. Since the training was done on a small dataset, reducing the number of layers in the network may improve the results. Hence, in order to mimic the behavior of the first layers of the CNN, regular convolution was used instead of convolutional sparse coding. All data embedding details were given below. Preprocessing. It has been shown that preprocessing improves the performance of CNN networks  [65] , particularly when dealing with a small labeled dataset. The preprocessing of data was done as follows. Let (C k t ) T t=1 denote the signal produced by the channel k (1 ≤ k ≤ K = 5), where t = 1,. , T represent the data points recorded with a frequency of 100 Hz.
1. Each channel (C k t ) T t=1 was filtered with a low-pass Butterworth filter with a 10-Hz cutoff frequency, fifth order, and zero lag. This step aimed to limit the amount of electronic noise present in the signal, as the 10-Hz cutoff frequency is the reference in gait-related signals  [66]. 2. The linear trend of each channel was removed using a least squares model. 3. Each channel whose signal maximum amplitude was small was then set to zero, as the channel was assumed to only account for noise. 4. The resulting signal s was obtained as the sum of all the channels: s. = C t t = K ∑ k=1 C k t t Figure  3  shows an example of the resulting signal. Signal embedding. Data augmentation is a key part of training complex neural networks, particularly when only small labeled datasets are available  [67].
However, these techniques are difficult to apply in our setting, as it is unclear that commonly-used transformations would preserve the nature and structure of the data. For instance, a re-scaled step signal may be confused with a fall signal, as the amplitude of the applied force is the main difference between the two signals (see  [68] ). In our case, we are dealing with a small dataset, which is composed of complex signals, thus making end-to-end learning a difficult task. To circumvent this difficulty, the data were first transformed using convolution with atoms learned from Convolutional Dictionary Learning (CDL) in order to extract key elements and features of the signal. This first phase was intended to improve the quality of the CNN input to balance out the limited amount of available data.
In our setting, the recording was of lower quality, and nearly-identical steps, such as the ones produced by the walking of young healthy individual  [70] , were recorded with significant variations between them (see Figure  3 ). Besides, a large number of signals in the dataset contained elements that were not related to steps (e. g. , pushing a wheelchair or a cart). Finding a suitable dictionary was therefore significantly more challenging, and this is why atoms were created by selecting signals that were almost entirely walk related. Learning step atoms. As previously said, in order to learn atoms relevant to our step description process, we used a CDL approach on a subset of the walk signals, to guide the learning towards step-related atoms. Equation  (2)  shows that when learning a dictionary, we learn at the same time the dictionary and sparse representations of input signals.
We used the implementation of Wohlberg  [73]  of the algorithm, available in a Python package named SPORCO (SParse Optimization Research COde). The number of atoms m was considered a hyperparameter of our CDL problem, and we chose it using cross-validation. Our experiments showed that m = 3 achieved the best trade-off between the quality of the reconstruction of the training set and the one of the validation set, while maintaining the sparsity of the representation. Using less atoms led to either poor reconstruction or non-sparse representation of the training set, whereas using more atoms significantly decreased the reconstruction quality of the validation set. These three atoms were 0. 7 s long, which corresponded to an upper bound of the duration of a step in the walking of a healthy young individual  [74].
Data were labeled according to the type of event (walking, rolling a cart, pushing a chair, etc. ), the status of the person (patient or caregiver), and the number of persons on the floor. The final database contained 93 labeled signals with a labeling resolution of 1 s for event labels (e. g walk, rolling a cart, etc. ) and 10 ms for the steps of the concerned signals. Table  1  shows the number of events of each main class. It should be noted that the majority of signals are from caregivers. Consequently, it is crucial to identify them as accurately as possible in order not to alter a potential elderly activity monitoring system. Figure  9  shows two examples of signals recorded in the nursing home. Table  1. Number of instances in the dataset. Signals have an average duration of 10 s. Note that in the walking, >1 person category, all the walkers were either all elderly or all medical staff.
Results In this section, we thoroughly evaluate the performance and behavior of NURSENET on multiple types of signals. First, the accuracy of NURSENET is studied over each subgroup of our database, and we take a closer look at misclassified signals. Then, a complete ablation analysis is done to show the relative improvement added by each part of the algorithm, and our approach is compared to a another robust off-the-shelf classification algorithm. The algorithm was tested and run with a Python implementation on a laptop with a 2. 4-GHz Intel Core i7 processor. The training of the several stages (i. e. , CDL, SPN, and NURSENET) took a few hours. However, the complete inference process was very fast (nearly instantaneous for a 10-s signal).
Performance Evaluation The algorithm was evaluated by performing a stratified split of the dataset into training and testing sets (70 and 30%), resulting in similar distributions of data in both sets. The hyperparameters of CDL, SPN, and NURSENET were chosen using cross-validation on the training set. The separation was done using stratified random sampling along the three labels single walk, where one individual was walking on the sensor, multiple walks, where strictly more than one individual was walking at the same time on the sensor, and others, where one non-walk-related event occurred (e. g. , pushing a wheelchair or a cart). The signals were labelled as staff if all the persons involved were parts of the medical staff and elderly otherwise. The demographics are detailed in Table  2.
NURSENET achieved a global AUC of 0. 94(±0. 02) on the test set for the classification between staff and non-staff activities. This high performance was evenly distributed on all the labels, as shown by Figure  10. Additionally, it should be noted that the accuracy of NURSENET was similar for both the test set and the entire dataset, which highlights the generalizability of our approach. Table  3  presents two examples of classification obtained by NURSENET for different thresholds, and Figure  11  shows examples of misclassified signals. Note that the signal is irregular. (Right) Elderly walking. Contrary to the previous example (Figure  9 ), this signal presents higher periodicity and amplitude, and the recorded individual is healthier. Table  3. Confusion matrix for different thresholds τ, i.
It is important to notice that while all the previously-described algorithms achieved good performance on the entire dataset (which includes the training set), their AUCs significantly dropped on the test set, hence suggesting significant overfitting. This was particularly true for RF, whose AUC was only 0. 72(±0. 04) on the test set, highlighting the advantage of a CNN-based approach in our setting. Additionally, NURSENET significantly outperformed both its simplified versions, which illustrates the advantages of our training method. Finally, it should be noted that NURSENET without embedding, which is a CNN directly applied to the raw signal, achieved the surprisingly high AUC of 0. 84(±0. 03).
In-depth classification. As seen in Section 3, NURSENET was able to separate medical staff and elderly activities with very high accuracy (AUC 0. 94). Future work will focus on separating different categories of activity as each one of them entails different indicators regarding elderly activity monitoring. However, this task requires a significantly larger dataset, and therefore, the installation of NURSENET on multiple locations. Signal Quality. Serra et al. [10]  used a similar sensor to NURSENET and showed examples of footstep recordings that proved to be very reliable in their interpretability. For instance, different phases of a footstep could be seen: heel, toe, weight transfer, and foot removal. Moreover, these footsteps were recorded multiple times with different persons, showing that the generated signal was repeatable, illustrating it with a superimposition of several footsteps of one person (see Figure  13 ).
Indeed, in this setting, the signal was collected through crimps hooked to the sensor and linked to a printed circuit. This connection was vulnerable to humidity that may be in the surfaces in contact with the system (e. g. , adjacent walls), which may even lead to corrosion. Besides, this humidity may enter in contact with the edges of the sensor, thus creating an additional resistance between the two sides of the device. Depending on the characteristics of the humidity, these two phenomenons may significantly alter the output of the unit. Future improvement of the sensor would aim to reduce these effects. Individual identification. While NURSENET achieved very high accuracy for classifying the elderly and medical staff, it is not able at the moment to recognize individuals. This is due to the fact that the dataset used only included a handful of signals for each individual.
Model Specification Hi-sAFe simulates a 3D agroforestry environment by coupling the existing STICS (version 8) crop model  [22, 45]  with a new tree model (sAFe-Tree) on a daily time-step (Figure  1 ). The integration of STICS and sAFe-Tree occurs via three tree-crop interaction modules that govern the dynamics of light, water, and nitrogen via simple, well-established equations. Hi-sAFe is written in Java and runs on the Capsis platform  [46]. Here, we introduce Hi-sAFe, a three-dimensional (3D), process-based, biophysical model that integrates tree-crop interactions in agroforestry systems. Hi-sAFe has been under development since 2002 via the Silvoarable Agroforestry for Europe (SAFE) project  [41]  and was partially described by Talbot  [42]. The model attempts to overcome the gaps and weaknesses of existing agroforestry models by capturing spatial and temporal heterogeneity.
Model Specification Hi-sAFe simulates a 3D agroforestry environment by coupling the existing STICS (version 8) crop model  [22, 45]  with a new tree model (sAFe-Tree) on a daily time-step (Figure  1 ). The integration of STICS and sAFe-Tree occurs via three tree-crop interaction modules that govern the dynamics of light, water, and nitrogen via simple, well-established equations. Hi-sAFe is written in Java and runs on the Capsis platform  [46].
Model Implementation and Distribution Hi-sAFe version 4. 0 is available online free of charge (at www1. montpellier. inra. fr/wp-inra/hisafe/en/). The model runs on PC, Mac, and Linux platforms and requires Java version 1. 8 or later. A suite of tools for building, running, and analyzing Hi-sAFe simulations is also available via the hisafer R package (www. github. com/kevinwolz/hisafer). The package includes a vignette to introduce users to the available functions and an online issue tracker where users can report bugs or suggest changes.
These houses are influenced by the architecture of the French houses and they also reflect the wealth of their owners  (Dinh & Groves, 2006). From 2007 to 2018, Bui Quang Khiem has taken more than 500 pictures of the façades of the old houses around Hanoi. The process of photo-taking is meticulous and time-consuming, given the lively economic life of the city and the country  (Vuong, 2014). Many of the houses are covered in advertisement boards and the photographer had to wait for the moment when the advertisements boards were put away. Many of the houses have been destroyed during the time and the pictures are the limited documents left of them. All of the pictures have been deposited openly online in the Open Science Framework (OSF)'s database and can be accessed at https://osf. io/tfy6k/.
• PL: Short for "Pillar. " This categorical variable takes on one of these values: French, Chinese or Hybrid (coded as "PL_FR," "PL_CN" and "PL_HY" respectively). • RF: Short for "Roof. " This categorical variable takes on one of these values: French, Chinese or Hybrid (coded as "RF_FR," "RF_CN" and "RF_HY" respectively). In the initial model, the door, pillar and roof (DO, PL and RF) variables probabilistically influence the modern feeling  (MD)  variable. The decoration variable (DC) also probabilistically influences both the traditional (TR) and the modern  (MD)  feeling variable. This study deployed the bnlearn package (short for "Bayesian network learning") to create the DAG, for further technical explanation, see Bayesian Networks-With Examples in R of  Scutari and Denis (2014).
modelstring(dag) [1] "[B][T][C][RF][DO][PL][DC|B:T:C][MD|DC:RF:DO:PL][TR|DC][CE|MOD:TR]" plot(dag) Figure  4 : The DAG created using the bnlearn package. This directed acyclic graph is identical to the one presented in Figure  1. The formal definition of how the dependencies are encoded in the map into the probability space via conditional independence relationships is provided in Equation  1 : Pr (, , , , , , , , , ) = Pr() Pr() Pr() Pr() Pr() Pr() Pr(|, , ) Pr(|, , , )Pr(TR|DC) Pr (|, ) The conditional probabilities in the local distributions can be estimated with the empirical frequencies in the datasets, for example, Equation  2 shows the conditional probabilities of a façade exhibits a strong presence of cultural evolution given it also gives off a strong feeling of modern: 𝑃𝑅 (𝐶𝐸 = 𝐶𝐸 |MD=MD_S) = 𝑃𝑟 (𝐶𝐸 = "CE_E", 𝑀𝐷 = MD_S) 𝑃𝑟 (𝑀𝐷 = MD_S) = CE_E MD_S.
Trait-by-trait analysis of phenotypic variation within and among populations. In order to investigate phenotypic variation, we set up two common garden  We gathered phenotypic data during two consecutive years (2013 and 2014). We targeted 18 phenotypic traits that included six traits related to plant architecture, three traits related to leaves, three traits related to reproduction, five traits related to grains, and one trait related to stomata (S2 Table ). Each of the four experimental assays (year-field combinations) encompassed four blocks. In each block, we evaluated one offspring (half-sibs) of ~15 mother plants from each of the 11 teosinte populations using a semi-randomized design. After filtering for missing data, the association panel included 1664 teosinte individuals. We found significant effects of Field, Year and/or their interaction for most traits, and a highly significant Population effect for all of them (model M1, S3 Table ).
Independence of SNPs associated to phenotypes. We computed the pairwise linkage disequilibrium (LD) as measured by r 2 between the 171 outlier SNPs using the R package LDcorSV  [62]. Because we were specifically interested by LD pattern between phenotypically-associated SNPs, as for the association analyses we accounted for structure and kinship computed from SSRs while estimating LD  [63]. The 171 outlier SNPs were distributed along the 10 chromosomes of maize, and exhibited low level of linkage disequilibrium (LD), except for SNPs located on chromosomes eight, nine, and a cluster of SNPs located on chromosome 4 (S12 Fig). Among the 171, the subset of 23 phenotypically-associated SNPs (detected when considering the 11-population structure) displayed an excess of elevated LD values -out of 47 pairs of SNPs phenotypically-associated to a same trait, 16 pairs were contained in the 5% higher values of the LD distribution of all outlier SNP pairs.
Adaptive teosinte trait variation is likely relevant for maize evolution and breeding. Whether the underlying SNPs detected in teosintes bear similar effects in maize or whether their effects differ in domesticated backgrounds will have to be further investigated. Finally, we extracted available SNP genotypes generated with the MaizeSNP50 Genotyping BeadChip for 28 populations out of our 37 populations  [57]  (S1 Table ). From this available SNP dataset, we randomly sampled 1000 SNPs found to display no selection footprint  [57] , hereafter neutral SNPs. Data for neutral SNPs (Data S1) are available at: 10. 6084/m9. figshare. 9901472. We used this panel of 28 populations to investigate correlation with environmental variation. Note that 10 out of the 28 populations were common to our association panel, and genotypes were available for 24 to 34 individuals per population, albeit different from the ones of our association mapping panel.
SSR genotyping and genetic structuring analyses on the association panel In order to quantify the population structure and individual kinship in our association panel, we genotyped 46 SSRs (S4 Table ). Primers sequences are available from the maize database project  [115]  and genotyping protocol were previously published  [116]. Genotyping was done at the GENTYANE platform (UMR INRA 1095, Clermont-Ferrand, France). Allele calling was performed on electropherograms with the GeneMapper® Software Applied Biosystems®. Allele binning was carried out using Autobin software  [117] , and further checked manually. We employed STRUCTURE Bayesian classification software to compute a genetic structure matrix on individual genotypes. Individuals with over 40% missing data were excluded from analysis. For each number of clusters (K from 2 to 13), we performed 10 independent runs of 500,000 iterations after a burn-in period of 50,000 iterations, and combined these 10 replicates using the LargeKGreedy algorithm from the CLUMPP program  [118].
We plotted the resulting clusters using DISTRUCT software. We then used the Evanno method  [119]  to choose the optimal K value. We followed the same methodology to compute a structure matrix from the outlier SNPs. We inferred a kinship matrix K from the same SSRs using SPAGeDI  [120]. Kinship coefficients were calculated for each pair of individuals as correlation between allelic states  [121]. Since teosintes are outcrossers and expected to exhibit an elevated level of heterozygosity, we estimated intra-individual kinship to fill in the diagonal. We calculated ten kinship matrices, each excluding the SSRs from one out of the 10 chromosomes. Microsatellite data (Data S2) are available at: 10. 6084/m9. figshare. 9901472 In order to gain insights into population history of divergence and admixture, we used 1000 neutral SNPs (i.
Analyses were undertaken at the Institute for Evolution and Biodiversity (University of Münster) as followed: 5mm blade discs were cut out from the mid length of one of the leaves and microscopic images were taken after excitation with a 488nm laser. Nine locations (0. 15mm 2 ) per disc were captured with 10 images per location along the z-axis (vertically along the tissue). We automatically filtered images based on quality and estimated leaf stomata density using custom image analysis algorithms implemented in Matlab. For each sample, we calculated the median stomata density over the (up to) nine locations. To verify detection accuracy, manual counts were undertaken for 54 random samples. Automatic and manual counts were highly correlated (R²=0. 82), indicating reliable detection (see S1 Annex StomataDetection, Dittberner and de Meaux, for a detailed description).