dataesr/scientific-paragraphs-categorization huggingface dataset:
Generation of the pangenome and orthologs analysis. For the generation of the pangenome, additional 5 Italian barn swallow individuals were sampled (Additional file 1: Supplementary Methods). HMW DNA was extracted from the blood samples and sequenced with the PacBio HiFi technology (Additional file 1: Supplementary Methods). HiFi reads were checked for adaptor contamination and trimmed accordingly with cutadapt v3. 2 (135) (Additional file 1: Supplementary Methods). Hifiasm (82) was used to assemble both primary and alternate assemblies which were then purged with purge_dups (118) using custom cutoffs (83) (Table  31 ). Both primary and alternate HiFi-based assemblies were masked with Windowmasker  (123)  and RepeatMasker  (124). The pangenome was generated with the Cactus (50) v1. 3. 0 Pangenome Pipeline (https://github.
com/ComparativeGenomicsToolkit/cactus/blob/master/doc/pangenome. md, Additional file 1: Supplementary Methods) using HiFi-based assemblies and bHirRus1 primary and alternate assemblies. Orthologous genes were found running HALPER  (136)  following the steps described on GitHub (https://github. com/pfenninglab/halLiftover-postprocessing). Briefly, from the HAL alignment, the coverage of bHirRus1 was calculated with halAlignmentDepth  (129). Then, a file for the ortholog extension was generated from the coverage file and halLiftover  (129)  and used to lift bHirRus1 gene coordinates on the aligned HiFi assemblies. Orthologs were then found using the lifted genes. The resulting lists of orthologs were manually evaluated to find genes shared between individuals. The 236 genes that were found only in the bHirRus1 assembly were searched in the HiFi raw reads with BLAST 2.
10. 1+ (84). The alignments were checked to find genes present for more than 80% of their sequence in the reads and 99% identity with the query sequence. SNP catalogue. To generate the catalogue of genetic variants, all publicly available datasets were combined with our newly generated Hifi reads set (see "HiFi reads processing for genetic variants identification" Methods section). For each publicly available dataset, sequencing adapters and low quality bases were trimmed when present, and reads were aligned to the bHirRus1 reference genome. Freebayes v1. 3  (120)  was used on the alignments to call variants, parallelizing the process with a script from the VGP assembly pipeline (Additional file 1: Supplementary Methods). Variants were then split by population and markers were filtered for quality, read depth supporting each variant call, average fraction of missing sites among individuals and minor allele frequency (maf).
Samples showing > 70% of missing genotypes were removed. Variants within repetitive regions were excluded, and only SNPs were extracted for downstream analysis. Details relative to the filters and threshold values used can be found in the Additional file 1: Supplementary Methods section. For SNP density plotting and its correlation with genomic features, all data using the same sequencing technology were merged (HiFi WGS; Illumina WGS; Illumina ddRAD). SNP density was computed across all chromosomes (excluding unlocalized/unplaced scaffolds) over 10 kbp windows and these values were correlated with the GC content per window using the Spearman nonparametric rank test  (126). SNPs falling in genic, intergenic, exonic and intronic regions (as determined from NCBI annotation) for each chromosome in the different datasets were counted.
The results in Figure  1  -right panel show the difference between the global information score obtained with the parameters indicated in the plot (improvements in reliable information acceptance and composition of news) and the information score obtained with the drastic intervention of misinformation for the same composition of news. All results are based on 10 repetitions of simulations for each parameter combination, for T = 1,000. The two extensions of the model are described in Appendix B and C. All the code to run the simulations is written in R, and it is available at https://osf. io/sxbm4/.
The analysis of trunk shape and, in particular, the geometric structures on the bark surface are of main interest for different applications linked to the wood industry or biological studies. Bark singularities are often external records of the history of the development of internal elements. The actors of the forest sector grade the trees by considering these singularities through standards. In this paper, we propose a method using terrestrial LiDAR data to automatically segment singularities on tree surfaces. It is based on the construction of a relief map combined with a convolutional neural network. The algorithms and the source code are available with an online demonstration allowing to test the defect detection without any software installation.
The index, f irstIndexN oN ull, is the index at the position of the cell '1' of the pattern '01'. The shift is then given by shif t = 2π - f irstIndexN oN ull pad * 2π. (4) Finally, posX and posY are given by    posX = width -1 upperBA * ((θ P + shif t) mod 2π -upperBA) + width -1, posY = z P -minH. (5) This step of discretization is described in Algorithm 1. In the code, it corresponds to the function computeDicretisation of the class UnrolledMap in the package ReliefMap.
We see in Figure  7  (b), a relief map obtained from a partial trunk, the red pixels are those whose intensity will not be calculated during the discretization process. Algorithm 3 is used to check if a cell of the discretizationMap could be represented. The function corresponding to this algorithm is detectCellsIn in the class UnrolledMap of the package ReliefMap. For each cell, a value is chosen according to the delta distance (δ P ) of points in the cell. This distance δ P represents the local relief of each point, its calculation is detailed in  [11]  and reported in Section 3. 1. 1. If the current cell is not empty in the discretizationMap, we choose the maximum δ P of all points contained in the cell. Otherwise, a multi-resolution analysis is performed. It consists in looking for the missing information in cells resulting from the discretizationMap reduced by a factor 1 2 n , with n = 1,.
, M axdF until the cell contains at least one point (M axdF is a parameter of the multi-resolution search function, it allows to limit the search). More precisely, the points contained in the larger cells are obtained by accumulating those of the unreduced cells of the discretizationMap on a square window of width 2 n. Finally, we choose the maximum δ P of points contained in the studied cell. Algorithm 5 allows to extract the points in lower resolution and Algorithm 4 gives the maximum relief representation of a set of points. They correspond respectively to the functions getIndPointsInLowerResolution and maxReliefRepresentation in the class UnrolledMap of the package ReliefMap. A grayscale image is generated from relief map, by using a single static intensity scale where intensityP erCm grayscale levels correspond to one centimeter.
Associated to this parameter, a cutoff interval of minimal and maximal heights ([minRV, maxRV ]) is considered to ensure that all levels can be represented in the usual [0, 255] range. The minimal value minRV is a parameter of our method and the maximal value maxRV is simply given by maxRV = 255 * 1 intensityP erCm + minRV. (6) The resulting grayscale value is deduced as follows grayscaleV alue = relief V alue -minRV maxRV -minRV * 255. (7) Algorithm 6 allows to generate the grayscale relief map. It is implemented in the function toGrayscaleImageFixed, in the UnrolledMap class of the package ReliefMap.
Dataset To test our method we have 25 trunk meshes: 10 from  [11]  and 15 new data manually annotated. This database contains 11 tree species: beech, birch, elm, fir, red oak, wild cherry, aspen, horn beam, lime, and alder. The studied trunks have between 366 and 896 cm of circumference and between 694 and 1150 cm of height. The visible surface singularities are mainly branch scars and burls, there are also some small singularities whose type is not distinguishable. In Figure  13 , we can see some examples of 3D point clouds from the dataset. We randomly pick 5 trunks among the 25 to test the method and kept the others for training. structures which increases the false positive rate and therefore the accuracy decreases. Despite this difference in performance, the low values of standard variation show that the 5 models provide close performance measures even for Aspen2 and Alder2.
Materials The archive examples_IPOL. tar. gz contains the tree meshes and the ground-truth data files. They are in two directories: INRAE1a, INRAE1b. The data come from the same acquisition device and have been transformed into a 3D mesh using the process described in  [10]. Each tree trunk is associated to three files: the mesh (with extension. off), the point indices corresponding to singularities (suffixed by -groundtruth-points. id), the face indices corresponding to singularities (suffixed by -groundtruth. id). The experiments in Section 4 use the files suffixed by-groundtruth. id for evaluating the performance of our method. The scaledMeshes directory contains scaled meshes used in Section 6. 4. The archive model_IPOL. tar. gz contains the models to reproduce the results. They are suffixed by K following a number between 0 and 4.
Dependencies For compilation of the C++ sources these libraries must be installed: • DGtal 1_1_0 or later: https://github. com/DGtal-team/DGtal • GNU GSL: https://www. gnu. org/software/gsl/ • PCL: https://pointclouds. org/downloads/ To use our trained model for the segmentation of singularities on the relief map these libraries need to be installed (we recommend the user to use a virtual environnement as described in the tensorflow guide): • Tensorflow 2. 2: https://www. tensorflow. org/install/pip • tensorflow-addons: https://www. tensorflow. org/addons/overview • OpenCv: https://pypi. org/project/opencv-python/.
Generate the Relief Map (C++) Once the dependencies are installed and the sources downloaded, the project is built with the following commands: cd TLDDC mkdir build cd build cmake. -DDGtal_DIR=/path/to/DGtalSourceBuild make Three executable files are generated in the build directory: segunroll , groundtruth, segToMesh. segunroll allows the user to build the grayscale relief map. The image format is pgm. Three files are generated: reliefmap_info, cylPoint, discretize. The file reliefmap_info contains data relative to the construction of the relief map: height, width, upper bound of the angle of the mesh points (see Section 3. 1. 2). All the points of the mesh converted into cylindrical coordinates are stored in cylPoint using one point per line with: radius, angle and height. The file discretize contains height × width lines.
2. 2). Two files are generated. A file suffixed by -defect. id contains the indices of points considered as singularities. A file suffixed by defect. off contains the trunk mesh with the singularities colored in green. The command to do this operation is the following (after generating the singularity segmentation by an already trained model):. /segToMesh -i InputMesh -s segmentationMapName -o outputName • InputMesh: path to the same mesh used to generate the reliefmap. • -s: path to the segmentation file. • -o: prefix the output. The interaction between these different executables is summarized in Figure  16. To generate the relief map and labeled images of a whole directory at once, you can use the script makeAllPair. sh (in the run directory) with the following command. The relief map will have the same name as the mesh and the labeled image will be fixed by _GT.
Scale Influence It should be mentioned that the trunks in our database present comparable scale size, with handannotated singularities (see Section 4. 1). In order to evaluate the robustness of our method with respect to the mesh scale, we have changed the scale of the WildCherry1 mesh using the following values: 0. 8, 1. 2, 1. 5, 3, 5. This experiment is done for the purpose of verifying whether our method is able to perform the singularity segmentation on larger trunks. Figure  18  shows the relief maps generated from the enlarged mesh and the associated segmentation. The relief map seems to be impacted by the scale factor. The perturbation of the relief map by the scale change impacts the quality of the segmentation. We think that this is due to the fixed size of the rectangular patch used to calculate the delta distance (see Section 3.
Conclusion In this article, we proposed a method allowing to automatically segment the singularities on the tree bark surface. It is adapted from  [1]. More precisely, the method is based on the construction of a relief map able to locally adapt itself to the global shape of the trunk, and combines with a convolutional neural network to improve the resulting segmentation quality. Furthermore, we bring an improvement in generating relief maps compared to  [1]. This improvement allows to build relief maps from partial meshes of trunks where a cylindrical sector may be missing. Moreover, we detail the algorithms and the code used to generate the relief map. The cross validation shows that the training is robust to our database (the standard deviation is low). However, we believe that our segmentation method would benefit from using more 3D information.
However, these two types of discriminators only score one sequence at a time, given as input of the model. This limits the number of possible tokens to be considered at each decoding step, to avoid a computationally prohibitive cost. Solving this issue, recently introduced Generative Discriminators (GeDi)  [19]  give scores for all tokens from the vocabulary at once, hence dramatically reducing the cost of width exploration. In this paper, we explore the pros and cons of these three types of discriminators (bidirectional, unidirectional, generative) when used in cooperative language decoding. In parallel, approaches relying on Monte Carlo Tree Search algorithm (MCTS)  [6]  have been used for cooperative generation with more sophisticated exploration strategies than beam search. This non-myopic discriminator-guided decoding lead to state-of-the-art results in different applications  [3, 20, 21, 32].
|V | being in the order of ten thousand, discriminating every possible continuation of decoding sequences is too costly. Thus, cooperative approaches have to circumvent this issue by limiting the number of sequences actually evaluated by the discriminator. For example, DAS pre-filters potential continuations on the nucleus of the LM distribution  [16]. This choice necessarily biases the resulting generated distribution. Recently,  [19]  introduced Generative Discriminators (GeDi) that exploit Class-Conditionnal Language Models (CC-LMs)  [18]  to discriminate every token at once. CC-LMs condition distributions of sequence  on a desired class of interest :  ( | ) =   (  |  1: -1 , ). Assuming a uniform prior distribution of classes  ∈ C, Bayes' rule enables to use this for discrimination:  ( |  1: ) ∝  ( 1: | ).
Then, the generative discriminator is the same as the left-to-right one, the only difference being the size of the output layer that changes from (ℎ_, _) to (ℎ_, _). Experiments are made on two datasets from  [39] : ama-zon_polarity which is a binary (positive or negative) online reviews classification task and AG_news which is a topic classification task with 4 labels (world, sport, business and science). These datasets allows to study results of cooperative generation on two rather different constraints and domains: applying polarity on online reviews and writing news about a specific topic. Also, AG_news allows to study the generalization to non-binary classification and texts with more diverse content. Each model is trained for 20 epochs using AdamW  [23]  with HuggingFace's trainer default parameters ( 1 = 0.
We used a bidirectional BERT model as oracle discriminator to get the most accurate evaluation possible. Language models are also BERT models with an LM head in order to use the same tokenizer. Average results over 500 sampled test texts using each type of discriminator on the two datasets are reported in Tab. 1. We also report results obtained using the vanilla LM likelihood  () as back-propagated score in MCTS evaluation, to provide baseline results achievable without discriminators. Results are obtained using best performing hyper-parameters in the literature (  = 3, temperature  = 1) and 50 iterations of MCTS per token, unless specified otherwise. We report statistical significance between each type of discriminator using t-test with p-value=0. 01. The difference of generation accuracy when using bidirectional and unidirectional discriminator shows that the difference in raw accuracy reflects in resulting samples when used for cooperative generation.
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive version was published in SIGIR 2022, https://doi. org/10. 1145/3477495. 3531858 of GeDi. This means that its signal promotes less good solutions than standard discriminators when guiding the generation. The type of discriminator has no significant impact on the other metrics. Please note that the general difference of Self-BLEU and oracle perplexity between the two datasets is due to the difference in their content: AG_news is more diverse, which results in lower Self-BLEU and higher perplexity. Finally, we notice that doubling the number of MCTS iterations allows to increase the accuracy results of the unidirectional model, bridging the gap between both model for a still lower computational cost (see next section).
Not   usual vocabularies, generative discriminators seem very interesting at first glance to allow wider search. However, while achieving similar results in terms of classification accuracy, scoring the whole vocabulary comes at the price of a less informative signal. Moreover, although counter-intuitive, this width is not necessarily useful as shown by the search performed by the state-of-the-art Monte Carlo Tree Search, which usually explores more in depth than in width. Thus, such models will prove useful when used with methods that make particular use of this width information. We leave such explorations for future work. To allow reproduction and further experiments on this subject, the code used for our experiments is made available for the community at https://github. com/NohTow/PPL-MCTS/tree/main/teammates.
for redistribution. The definitive version was published in SIGIR 2022, https://doi. org/10. 1145/3477495. 3531858 amazon_polarity AG_news Value Accuracy ↑ 5 -Self-BLEU ↓ Oracle perplexity ↓ Accuracy ↑ 5 -Self-BLEU ↓ Oracle perplexity ↓  () Performance of MCTS w. r. t. the metric to optimize on amazon_polarity (left) and AG_news (right) datasets. * indicates statistically significant improvement against Generative Discriminator. Note that no model demonstrated significant improvement over unidirectional discriminator.
This was confirmed in a multivariate analysis that showed miR-126 as an independent predictor of shorter OS (HR: 2. 558, 95% CI: 1. 177-5. 560; p = 0. 018). Another study showed that its low expression was associated with poor metastasis-free survival in BC patients  [21]. Hence, this stimulated our interest in further elucidating the role of miR-126 in BC tumorigenesis, specifically in ER+ BC. In this study, first, we validated the microarray data of miR-126 expression in ER+ BC tissues by RT-qPCR. Then, we identified the effect of overexpressing miR-126 on cell proliferation, cell cycle progression, mammosphere formation, and expression of potential targets. Finally, correlation between the expression of miR-126 or the dysregulated targets and overall survival of ER+ BC patients was assessed.
Decreased MCF-7 Cell Proliferation and No Effect on Cell Cycle Progression upon Overexpressing miR-126 To determine the effect of miR-126 overexpression on the proliferation of MCF-7, MTT assay was done at 48 and 72 h post transfection. miR-126 significantly decreased the proliferation of MCF-7 at 72 h post transfection (p = 0. 034) (Figure  3A ). Then, to determine the effect of miR-126 on the cell cycle of MCF-7, propidium iodide (PI) assay was performed at 24 and 48 h post transfection. Our data showed that there was no difference in the cell cycle phases of miR-126 mimic-transfected cells when compared to NC duplextransfected cells at 24 or 48 h post transfection (Figure  3B, C ).
Decreased MCF-7 Cell Proliferation and No Effect on Cell Cycle Progression upon Overexpressing miR-126 To determine the effect of miR-126 overexpression on the proliferation of MCF-7, MTT assay was done at 48 and 72 h post transfection. miR-126 significantly decreased the proliferation of MCF-7 at 72 h post transfection (p = 0. 034) (Figure  3A ). Then, to determine the effect of miR-126 on the cell cycle of MCF-7, propidium iodide (PI) assay was performed at 24 and 48 h post transfection. Our data showed that there was no difference in the cell cycle phases of miR-126 mimic-transfected cells when compared to NC duplex-transfected cells at 24 or 48 h post transfection (Figure  3B, C ).
SLC7A5 and PLXNB2 as Potential Targets of miR-126 miR-126 targets were selected through two predicted target databases, microT-CDS and TargetScanHuman 7. 2, and an experimentally validated database Tarbase 7. 0. Further selection was performed based on the literature through a PubMed search regarding validated relation of miR-126 with BC and other cancers. As a result, the following targets were selected: SLC7A5, PLXNB2, CRK, PLK2, SPRED1, and IRS1 (Table  1 ).
SLC7A5 and PLXNB2 as Potential Targets of miR-126 miR-126 targets were selected through two predicted target databases, microT-CDS and TargetScanHuman 7. 2, and an experimentally validated database Tarbase 7. 0. Further selection was performed based on the literature through a PubMed search regarding validated relation of miR-126 with BC and other cancers. As a result, the following targets were selected: SLC7A5, PLXNB2, CRK, PLK2, SPRED1, and IRS1 (Table  1 ). To explore whether miR-126 targets SLC7A5, PLXNB2, CRK, PLK2, SPRED1, and IRS1, RT-qPCR was run on miR-126 mimic-transfected cells as compared to NC duplextransfected cells 24 h post transfection with GAPDH as an internal control. SLC7A5 and PLXNB2 mRNA were significantly downregulated in MCF-7. On the other hand, mRNA of PLK2, CRK, SPRED1, and IRS1 did not show any significant change in their expression (Figure  5 ).
Correlation of High Expression of miR-126 or Low Expression of SLC7A5 with Better Overall Survival of ER+ BC Patients To determine whether miR-126 or the dysregulated targets (SLC7A5 and PLXNB2) could predict prognosis, in silico survival analysis was used. ER+ BC patients were selected in a similar way to our clinical BC tissue samples previously discussed. In addition, the METABRIC database was selected since it includes patients with a long-term followup of 94. 2 months. A total of 944 ER+ patients were obtained for miR-126 expression data.
Correlation of High Expression of miR-126 or Low Expression of SLC7A5 with Better Overall Survival of ER+ BC Patients To determine whether miR-126 or the dysregulated targets (SLC7A5 and PLXNB2) could predict prognosis, in silico survival analysis was used. ER+ BC patients were selected in a similar way to our clinical BC tissue samples previously discussed. In addition, the METABRIC database was selected since it includes patients with a long-term follow-up of 94. 2 months. A total of 944 ER+ patients were obtained for miR-126 expression data and a total of 720 ER+ patients were obtained for SLC7A5 and PLXNB2 expression data. BC patients with high expression of miR-126 presented a significant association with better overall survival (OS) compared to patients with low expression of miR-126 (HR = 0.