dataesr/scientific-paragraphs-categorization huggingface dataset:
To address this issue, we propose another physical-informed loss function from the perspective of magnetic field energy, that is, the MEE loss function: MEE ¼ 1 N X N i¼1 δ QOI 1 2 1 μ i kB dl ðiÞj 2 -jB fem ðiÞj 2 j ð9Þ The EPINN with MEE can naturally encode some underlying physical laws as prior information into the DL network. In this way, during the network training procedure, the target is no longer simply the magnetic field distribution, but the implied magnetic field energy. The MENE and MEE are adopted as the loss function for the hyperparameter investigation in Section 4. 1. With fine-tuned hyperparameters, the EPINN can be effectively trained and the converged model can be used to predict the magnetic field distribution. As a comparison, with the same network architecture and hyperparameter configuration, MAE and MSE are respectively utilized to train the DL network from the same database with the same training-validation strategy.
| NUMERICAL EXPERIMENTS AND DISCUSSION As mentioned before, there are pros and cons to introducing the physics-informed loss functions into the EPINN. On the one hand, physics-informed learning has the advantage of strong generalization in the small data regime: by embedding physics, the EPINN is more effectively concentrated on the regions or physical quantities that we are interested in, thus improving the accuracy of the converged DL network with the same database. On the other hand, it is observed that it can make the DL network more sensitive to the input data and some of the relevant network hyperparameters. As discussed in  [15] , one of the necessary premises for a DL network to achieve good accuracy and reproducibility is a proper set of hyperparameters. Before comparing the learning performance of the EPINN and traditional CNN, we must first ensure its convergence.
The generation of Gaussian distribution is mainly controlled by the following two parameters: the STD and the normalization range. The former controls the size and gradient of bright spots on the Gaussian distribution and the latter determines the movement range and step size of the bright spot. Among them, the normalization range consists of two numbers that are often in the range between 0. 0 and 1. 0, which determines the upper and lower limits of the moving position of the bright spot in the Gaussian distribution map. Taking values of 0. 0 and 1. 0 means that the centre of the bright spot moves from the leftmost to the rightmost and from the top to the bottom of the picture in the 1000 Gaussian distribution input database. Based on the network baseline configuration as shown in Table  2 , these two hyperparameters are respectively investigated with the two proposed loss functions.
25-0. 475. The fine-tuned DL network can be effectively trained by only 50% of the datasets (40% samples for training, 10% samples for validation), the successfully trained EPINN models can be used to predict the distribution of the magnetic flux density with different geometry parameters, hence accelerating the computation. Comparison of the magnetic field distribution around the transformer windings computed by FEM and the corresponding DL prediction results are demonstrated in Figures  10  and 11. It can be observed that the DL predictions differ little from the FEM computations, the errors mainly come from the winding area, which is where the magnetic field distribution is concentrated. The relative errors are below 4% for both physical-informed loss functions. In addition, in order to compare systematically the DL performance and generalization capacity of EPINN with different loss functions in detail, post-tests will be performed to test the interpolation and extrapolation in the next section.
| Post-test with MENE After the training of the network, and taking the geometric label parameters as input, the corresponding magnetic field distribution is generated using the trained EPINN network and compared with the ground truth to calculate the error. What's more, in order to compare the performance of EPINN and   3. As mentioned before, when facing the transformer winding leakage field problem, researchers tend to focus more on the magnetic field distribution near the winding than the other regions. Therefore, the error evaluation between the DL prediction results and the database ground truth is calculated using the MENE loss function. It should be mentioned here that the capability of the interpolation is evaluated when the test samples are inside the training samples and validation samples.
On the other hand, the capacity of extrapolation is evaluated when the test samples are outside the target. In order to evaluate the generalization capacity of the EPINN in a To begin with, the validation samples (i. e. Group #7) are used to compute the post error. The Gaussian distribution generated by the test sample labels is fed into the four separate network models trained with different loss functions to obtain the corresponding outputs, and then the MENE is utilized to calculate the error between these four sets of prediction results that output from the DL network with different loss functions and the corresponding ground truth in the database. The posttest evaluation results of the DL model that trained with MAE, MSE, MENE and MENE in functions of the sample index are separately shown in Figure  12.
As we can see, among the 100 samples in Group #7, except a few, the network trained by MENE generates the best performing output with the smallest post error, as the blue line plots in Figure  12. The same with the aforementioned hyperparameter investigation process, in order to avoid the influence of randomness on the conclusion, the same set of networks is trained 10 times with the same hyperparameters and the same database, then the post-test error is calculated for these 10 trained model respectively. The results are shown in Table  11 , each cell reports the mean (min, max) of summary performance calculated over 10 times post-test. Next, four groups of training samples are adopted as test samples to carry out the post-test, which is to evaluate the interpolation capability of the network.
In other words, we get the same results as theoretically expected. In addition, we can also find that the interpolation performance of the network is very stable. For all the five groups of samples involved in the training procedure, including the training samples and validations samples, the post-test loss with MENE is in the range from 1. 5e-4 to 3e-4, with little difference in the results between groups. This indicates that the network performs well in the interpolation test after effective convergence. The above five groups of samples are all involved in the training process to update the weight of the EPINN network. The post-test process is to test the interpolation capacity of the network after effectively training. The purpose of employing DL is to provide an efficient surrogate model for complex and time-consuming computations, utilizing parts of the computation results as the database to train the DL network and then using the trained model to predict the results of subsequent samples, thus accelerating the computation and reducing the cost.
from the same database with the same network architecture and configurations. The numerical experiments demonstrate that the designed physics-informed loss functions MENE and MEE have significant advantages over conventional DL loss functions MAE and MSE in terms of prediction for magnetic field distribution. Besides, the EPINN has shown good potential and stability can obtain outstanding performance in interpolation tests. As for the extrapolation, the prediction performance of the network is limited by the input label values of the test samples; good results can be obtained only for samples with label values in the vicinity of the training samples.
from the same database with the same network architecture and configurations. The numerical experiments demonstrate that the designed physics-informed loss functions MENE and MEE have significant advantages over conventional DL loss functions MAE and MSE in terms of prediction for magnetic field distribution. Besides, the EPINN has shown good potential and stability can obtain outstanding performance in interpolation tests. As for the extrapolation, the prediction performance of the network is limited by the input label values of the test samples; good results can be obtained only for samples with label values in the vicinity of the training samples. 8. 60e-3 (8. 23e-3, 8. 83e-3) Abbreviations: MAE, mean absolute error; MEE, magnetic energy error; MENE, magnetic energy norm error; MSE, mean square error.
Collecting relations between chemicals and drugs is crucial in biomedical research. The pre-trained transformer model, e. g. Bidirectional Encoder Representations from Transformers (BERT), is shown to have limitations on biomedical texts; more specifically, the lack of annotated data makes relation extraction (RE) from biomedical texts very challenging. In this paper, we hypothesize that enriching a pre-trained transformer model with syntactic information may help improve its performance on chemical-drug RE tasks. For this purpose, we propose three syntax-enhanced models based on the domain-specific BioBERT model: Chunking-Enhanced-BioBERT and Constituency-Tree-BioBERT in which constituency information is integrated and a Multi-Task-Learning framework Multi-Task-Syntactic (MTS)-BioBERT in which syntactic information is injected implicitly by adding syntax-related tasks as training objectives.
(7)  propose to append to BERT attention layers in which the word-to-word attention matrix is replaced by the dependency graph. Simply fine-tuning their proposed network on the TAC Relation Extraction Dataset (TACRED) RE data set  (10)  performed slightly better than the vanilla BERT. They also claim that dependency information helps improve the performance of BERT on RE tasks, but the improvement heavily depends on the quality of the dependency trees. Yu et al. (11)  propose Dynamically Pruned Graph Convolutional Network to average the dependency graph with the attention matrix and control the information flow between words by applying a binary gate on each word. Guo et al. (12)  propose Attention Guided Graph Convolutional Network (AGGCN), which consists of several stacked multi-head attention layers.
Experiments In this paper, we report post-BioCreative VII experiments in which we investigate the contribution of syntax rather than aim to maximize performance. However, to give an insight into what constitutes a high performance on the DrugProt data set, we present the performance of the top performing DrugProt participants in Table  3. We see that F1-scores on this dataset range from 77. 4 to 79. 7 for the top six teams. During the challenge, our best result was obtained with an ensemble system containing BioBERT and SciBERT models, which outperformed systems containing models of a same type. It demonstrates that having different types of models vote may counteract the systematic errors of each model and therefore boost the overall performance. We test ensemble systems consisting of syntax-enhanced models and non-syntaxenhanced models based on the hypothesis that these two types of model will act in a complementary way.
Cerebral palsy, a common physical disability in childhood, often causes abnormal patterns of movement and posture. To better understand the pathology and improve rehabilitation of patients, a comprehensive bone shape analysis approach is proposed in this article. First, a group analysis is performed on a clinical MRI dataset using two state-of-the-art shape analysis methods: ShapeWorks and a voxel-based method relying on Advanced Normalization Tools (ANTs) registration. Second, an analysis of three bones of the ankle is done to provide a complete view of the ankle joint. Third, a bone shape analysis is carried out at subject level to highlight variability patterns for personnalized understanding of deformities.
Acquisition and preprocessing of clinical data In this work, we focus on three bone shapes of the ankle joint: calcaneus, talus and tibia. Eleven TD children and nine children with CP with age ranged from 6 to 14 years old participated in this study which was approved by the regional ethics committee. The CP group includes seven males and two females and the TD group consists of seven males and four females with no history of pathology of the lower limbs. The demographic characteristics including age, weight, height and BMI of two groups are demonstrated in Table  1. The T-test is performed on these characteristics and no significant inter-group difference is noticed (p > 0. 05). The details of demographic characteristics of each child included in the experiment are available in Supplementary Materials.
All children were selected with no contraindications to MRI and with no history of lower limb musculo-skeletal injury or surgery in the past 6 months. MRI data were acquired in a single visit after parents signed informed consent forms. 3D MRI data have been acquired using a 3T MR scanner (Achieva dStream, Philips Medical Systems, Best, Netherlands) with a resolution of 0. 26 × 0. 26 × 0. 8 mm 3 and resampled to 0. 5 × 0. 5 × 0. 5 mm 3 for the purpose of adaptation to clinic (T1weighted gradient-echo, flip angle 10, matrix 576 × 576, FOV 150 mm × 150 mm, TR/TE 7. 81/2. 75 ms, mean acquisition duration: 424. 32 s). Images of the ankle were taken on the CP group's paretic lower limb, and on the non-dominant lower limb for the TD group. The acquisition protocol was detailed in  Makki et al. (2019)  and  Garetier et al.
(2020). In order to extract the shape of the 3 bones of interest, we make use of a semi-automatic segmentation approach. Age variability induces developmental variability of bones and cartilages. To overcome such shape variability, the considered regions of interest include bones and growth cartilages. First, 3 subjects at different ages (7, 10 and 12) are manually segmented by an expert. A registration-based label propagation method is then used to segment the subjects of the data sets, with manual correction if necessary. Newly segmented subjects are included at each propagation stage. Finally, smooth segmentation maps are computed by training a standard patch-based unet network  Ronneberger et al. (2015)  trained on the expert-based segmentation maps and manually corrected if required (see Figure  2 ).
Surface-based shape analysis Statistical Shape Modeling is a mathematical approach to quantify 3D shape variation. One approach for SSM is to analyze meshes computed for image segmentation. In this work, we rely on the surface-based SSM software called Shapeworks  (Cates et al. (2017) ) 1 that has been recently used for ankle joint analysis using weightbearing computed tomography  (Krähenbühl et al. (2020) ;  Lenz et al. (2021) ). Recent benchmarking study has shown the potential of Shapeworks with respect to other surface-based SSM  (Goparaju et al. (2022) ). ShapeWorks is a groupwise particle-based shape modeling method that does not rely on surface parameterizations. Shapeworks handles each surface mesh as a set of particles that describe the surface geometry. Such a particle-based representation avoids many of the problems inherent in parametric representations (i.
Voxel-based shape analysis For the voxel-based approach, we make use of an image registration-based framework to compute a mean image model of the TD population. To deal with the high variability in shape and appearance of the bones, we compute the TD template using a group-wise diffeomorphic algorithm  (Avants and Gee (2004) ) by not only considering image intensity but also bone segmentation maps using signed distance maps. Such a multivariate approach ensures realistic template estimation (see Figure  3 ) with sharp details of cortical bones and cartilage. Then, for each subject of the dataset, deformation fields are estimated by non-linear multivariate registration onto the mean TD template previously computed. The template estimation and the patient-to-template registration stages are performed with Advanced Normalization Tools (ANTs)2.
Limitations and perspectives This study offers exciting new information concerning the bone morphology of children with cerebral palsy and equinus. However, it is necessary to continue such a study in order to confirm these results. Firstly, this study is performed on a limited-size dataset due to the scarcity of pediatric datasets. Although the similar deformation pattern is revealed from our dataset with different tools, it would be better to confirm our results by increasing the number of subjects included. The intergroup difference (in terms of age and sex) of the subjects who participated in the study requires caution in interpreting the results. The results must therefore be analysed in relation to the study population and must be confirmed on the basis of a larger data set. Secondly, as already highlighted in  Gao et al.
Interestingly, a previous study reported altered expression of nuclear genes encoding mitochondrial proteins (e. g. , SLC25A1, ECHS1, MRPL45, TIMM23) in the Nr2f1-null embryonic neocortex  (Montemayor et al. , 2010). Moreover, by interrogating a recently published RNA-sequencing dataset  (Chen et al. , 2020) , we found altered expression of multiple genes coding for mitochondrial proteins (e. g. , OPA1, GDAP1, ACO2, ATP5G2) in the hippocampi of adult Nr2f1-heterozygous mice. Taken together, these data suggest that Nr2f1 may exert a direct role on the transcriptional control of nuclear-encoded mitochondrial genes in neurons.
The mitochondrial fusion factor Mfn2 is downregulated in mouse models of BBSOAS Mitochondria undergo continuous morphological transition by dynamic processes thanks to the coordinated action of proteins promoting mitochondrial fission (e. g. , Drp1, Fis1) and fusion (e. g. , Mfn1/2, Opa1), as well as their transport (e. g. , Miro, Milton)  (Course and Wang, 2016; Giacomello et al. , 2020; Misgeld and Schwarz, 2017). It is well known that altered expression and function of those factors leads to permanent changes in the overall shape of mitochondria  (Chan, 2020)  that could underlie the observed mitochondrial phenotype following Nr2f1 LOF and GOF. We thus wondered whether altered Nr2f1 activity might impinge on mitochondrial dynamics. To this purpose, we exploited our ChIP-seq dataset for Nr2f1 (Figure  1 ) and unveiled that three of the key nuclear genes encoding proteins crucial for mitochondrial dynamics, namely dynamin-2 (Dnm2), mitochondrial dynamics protein of 51kDa (MiD5) and mitofusin-2 (Mfn2), are indeed bound by Nr2f1 on their promoters (Figure  4a ).
Based on the fragmented and elongated mitochondrial shape observed in Nr2f1-icKO and Nr2f1-O/E neurons respectively, we focused our attention on the fusion factor Mfn2 as a putative target for Nr2f1 action. Notably, Nr2f1 binds to a promoter region closed to TSS of the Mfn2 gene, which is associated to the H3K4me3 in the adult neural tissue (Figure  4a ). We next crossed our Chip-seq dataset with previously published transcriptome data on the hippocampi of adult Nr2f1-heterozygous mice (Nr2f1-het)  (Chen et al. , 2020) , a validated BBSOAS mouse model  (Bertacchi et al. , 2019a; Jurkute et al. , 2021; Tocco et al. , 2021). By Gene Set Enrichment Analysis (GSEA), we found that among the 1053 enriched genes approximately the 8% (82/1053) codes for mitochondrial proteins, including the Mfn2 gene (Figure  4b ).
Microscope acquisition and quantifications Images of multiple immunofluorescence on tissue sections were acquired with a TCS SP5 confocal microscope (Leica Microsystems) or a Nikon microscope coupled with a computer-assisted image analysis system (Neurolucida software, MicroBrightField). Confocal z-stacked images used for morphometric analyses on both cellular and mitochondrial architecture were captured through the thickness of the slice (50 µm) at 0. 5-µm optical steps with an objective 63X/1. 4 N. A. oil immersion lens with an additional zoom (2x) and a resolution of 8-bit, 1024/1024 pixels and 50Hz scan-speed (1 voxel=133. 6*133. 6*395. 2 nm, xyz). mitoDsRed+YFP+ newborn granule neurons residing in the upper and lower blades of the DG were used for analyses only when satisfying all the following criteria: (i) to be positive for doublecortin (i.
For the analysis of normalized data (i. e. , fold change) a Kruskal-Wallis test followed by Dunn's post-hoc test was used. Kolmogorov-Smirnov test was carried out to compare cumulative distributions. The statistical significance was defined as follows: *p<0. 05, **p<0. 01, and ***p<0. 001. All statistical analyses were performed using Graphpad Prism 8 software (version 8. 0. 2; GraphPad Software, San Diego was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted May 2, 2022. ; https://doi. org/10. 1101/2022. 03. 25. 485113 doi: bioRxiv preprint 13 California USA, www. graphpad. com). Fisher's exact test, with the Benjamini-Hochberg False Discovery Rate (FDR) correction for multiple testing, has been added as the default algorithm for the over-representation test for GO analyses  (Mi et al.
Acknowledgements This work received the financial support of the  Jerome Lejeune Foundation  (Cycle 2020a -Project # 1977 ) and was supported by the  ex60%  ( University of Turin ) to  S. De Marchis  and the  Foundation Cassa Risparmio Torino  ( CRT -2019/2254 ) to S. Bovetti. Postdoctoral fellowships to  S. Bonzano  were funded by:  Compagnia San Paolo (Turin)  " Bando per l'internazionalizzazione della ricerca ";  Fondazione Umberto Veronesi (Milan) ; the  Accademia Nazionale dei Lincei "Centro Linceo Interdisciplinare Beniamino Segre" (Rome).
Such signatures are also ubiquitous in non-minimal scenarios  [10] [11] [12] [13] [14] [15] [16] [17] [18]  and in a recent example were found to appear naturally within the context of dark matter models arising from Dirac gauginos  [19] , where limits from disappearing tracks were also discussed. ATLAS  [20]  published a search for this signature based on 36. 1 fb -1 , with substantial recasting material including efficiencies (i. e. information about the proportion of events that are selected by the experiments due to the response of the detector rather than just the cuts applied); this material was used and then applied to other models  [21]  in a code available on the LLPrecasting github repository https://github. com/llprecasting/recastingCodes. Very recently a conference note  [22]  with the full run 2 dataset of 136 fb -1 appeared.
Indeed the recasting of LHC searches so that they can be applied to models (or subsets of the parameter space) other than those originally considered by the experimental analysis is by now well developed. It is increasingly common for analyses to provide supplemental information for this purpose, often in the form of efficiencies or even pseudo-code; see  [30]  for a recent review and references therein. There now exist several popular frameworks for this purpose, which differ in their principal objectives: GAMBIT  [31] [32] [33]  is a scanning tool, designed to explore the likelihood space of a model, which can construct likelihoods for model points from collider data through its module ColliderBit  [34] ; CheckMATE  [35] [36] [37]  aims to check models for exclusion against a wide variety of analyses, including now several LLP ones  [38] ; MadAnalysis  [39] [40] [41] [42] [43] [44]  aims at providing a general analysis framework for examining data in detail, which can also be used to check models against many important analyses provided by users in its Public Analysis Database  [45, 46].