dataesr/scientific-paragraphs-categorization huggingface dataset:
Variants that were considered to be responsible for the phenotype of the cases were visualized in the Integrative Genomics Viewer (IGV) alongside the exome data of the parents when available for final visual valuation of quality controls (QCs). The ACMG and Association for Molecular Pathology criteria  29  for variant classification were used. Validation through alternative methods was performed locally in the clinical laboratory that submitted the sample to Solve-RD. For each diagnosis achieved through this process, we subsequently attempted to determine the reason why the variants were not identified or discarded at the first analysis. Clinical laboratories, in parallel to submission of the genetically unsolved cases to Solve-RD, also performed local reanalysis. Only information about diagnoses obtained through this reanalysis strategy had to be recorded by the inclusion centers.
Brain magnetic resonance imaging (MRI) at age 2 years and 2 months showed notably corpus callosum hypoplasia, reduced volume of supratentorial white matter, and ventriculomegaly. The reanalysis identified a heterozygous missense variant in TUBB3, reported as likely pathogenic in ClinVar. The inhouse pipeline in the first trio-based analysis (2014) did not call the variant, because it was located in a region not targeted by the enrichment kit. Variant calling in the bioinformatics pipeline was based on this target set ± 200 bases, leaving the variant undetected. We found it in Solve-RD using genome-wide variant calling on raw data sets, leading to the diagnosis of autosomal dominant cortical dysplasia, complex, with other brain malformations type 1 (OMIM 614039).
The reanalysis identified a homozygous pathogenic frameshift in FKBP14. At the time of the first singletonbased analysis, the variant was present in the raw VCF, but it had been filtered out by the in-house pipeline because it was tagged as "common" in the single nucleotide polymorphism database (dbSNP build 151) with an allele frequency in gnomAD of over 10 -4. It appears that this very frequent variant accounts for 70% of alleles involved in the autosomal recessive Ehlers-Danlos syndrome, kyphoscoliotic type 2 (OMIM 614557). 36.
Discussion The aim of Solve-RD is to solve the diagnostic dead end via reanalysis and also to identify new genes and new molecular mechanisms through data sharing and patient inclusion at a pan-European level. The purpose of the "ClinVar lowhanging fruit" analysis of 3576 individuals (including 1522 index cases) from the ERN-ITHACA cohort was not to identify new genes but to decrease the diagnostic dead end. With 59 diagnoses (3. 9%), it shows the usefulness of a systematic reanalysis of negative exomes that is focused on the SNVs and indels reported as being pathogenic and likely pathogenic in ClinVar through systematic realignment, recalling, and reinterpretation using up-to-date bioinformatics pipelines. 19 Between the inclusion of the cases in the Solve-RD project in 2018 and the reanalysis in 2020, 50 candidate cases from the "ClinVar low-hanging fruit" reanalysis were solved in parallel to the Solve-RD project, mainly through co-occurring reanalyzes or re-evaluation of the patients (because individuals without a genetic diagnosis are often advised to recontact their clinician for such analysis).
The third lesson is to never stop reanalyzing genomic data because new genes and new variants are reported all the time. Some tools allow automating the reanalysis in real time,  13  such as Variant Alert,  40  which can highlight updates from ClinVar as soon as variants are submitted at the scale of the complete database. ClinVar-based reanalysis is an effective technique for solving unsolved cases from the ERN-ITHACA cohort with ID and congenital abnormalities, and this strategy should also be effective for other types of cohorts such as those of Solve-RD project. Given the success of the ClinVar-based reanalysis in diagnosing unsolved cases, similar strategies for other variant types, such as CNVs, may be equally successful, which are currently ongoing. This approach should be used as part of a more comprehensive reanalysis strategy, because it only targets variants already reported and cannot resolve all cases.
Funding The  Solve-RD  project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement number  779257. Data were analyzed using the  RD-Connect Genome-Phenome Analysis Platform , which received funding from the  EU  projects  RD-Connect ,  Solve-RD , and  European Joint Programme on Rare Diseases  (grant numbers  FP7 305444 ,  H2020 779257 ,  H2020 825575 ),  Instituto de Salud Carlos III  (grant numbers  PT13/0001/0044 ,  PT17/0009/0019 ;  Instituto Nacional de Bioinformática) , and  ELIXIR Implementation Studies. The collaborations in this study were facilitated by the European Reference Network for Intellectual disability, TeleHealth, Autism and  Congenital Anomalies , one of the 24 European Reference Networks approved by the European Reference Network Board of Member States, cofunded by the  European Commission.
AD, autosomal disease; AF, allelic frequency; AR, autosomal recessive; NDD, neurodevelopmental disease; DX, dominant X-linked; gnomAD, Genome Aggregation Database; ID, intellectual disability; ITHACA, Intellectual disability, TeleHealth, Autism and Congenital Anomalies; SNV, single-nucleotide variant; VUS, variant of uncertain significance. ERN-ITHACA, European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies.
Early demonstration on complex dataset like Imagenet or CIFAR-10 showed that SNNs almost match the accuracy of ANN but at the cost of a higher latency. As an example,  Sengupta et al. (2019)  was able to achieve competitive results on CIFAR-10 with a latency of 2,500 timesteps. Han et al. (2020)  proposed a conversion-based training using soft-reset spiking neurons. The Integrate and Fire (IF) with softreset neuron implements a uniform quantization scheme between its analog input and its spiking output, thus leading to a reduced quantization error compared to hard-reset neurons. Moreover, to ensure that spiking neurons operate in the linear regime the authors proposed a technique to balance the firing threshold (V th ). With the proposed model, the authors were able to achieve an accuracy of 60.
It can therefore be considered as a spiking-specific form of quantization aware training. Rathi and Roy (2021)  use direct learning to fine tune network parameters transferred from an ANN. They are able to achieve an accuracy of 92. 70% at 5 timesteps on CIFAR-10 with VGG-16. The authors of  Fang et al. (2021)  used direct learning to train ResNet without any previous conversion from ANN. They proposed SEW-ResNet, a spiking adaptation of ResNet that overcomes the vanishing/exploding gradient problem that occurs with directly trained spiking deep residual networks. They achieved an accuracy of 67. 04% at 4 timesteps on Imagenet with a ResNet34. Their results show that it is possible to train deep spiking neural networks and obtain very competitive results on complex datasets. Finally, in our work we make use of computationally efficient spiking neuron models that approximate the behavior observed in real neurons.
Experimental setup We trained both VGG-16 and ResNet-18 models using direct training on two different image classification problems with increasing complexity: CIFAR-10, CIFAR-100. We trained both networks with the neuron models described in Sections 3. 3 and 3. 4. In our setup, the threshold of neurons are trained layer-wise, so that all the spiking neurons of a given layer share the same V th. For each model and dataset, we also trained a formal version of the network, where spiking neuron are replaced with ReLU activation to compare SNN with a full precision ANN. Both SNN and ANN were trained using stochastic gradient descent (SGD), with a learning rate of 8 • 10 -2. The learning rate is exponentially decayed with a factor of 0. 9 each 30 epochs. Each network is trained for 900 epochs. In SNNs, the input is analog coded  (Rueckauer et al.
, 2017) , that is the spiking neurons of the first layer receive a constant input current. We use data augmentation (random resize and horizontal flip) as well as mixup with α = 1. In addition to the image classifications problems described above, carried out experiments on a keyword spotting (KWS) dataset. In an automatic speech recognition system, KWS consists in detecting a relatively small set of predefined keywords. We used Google Speech Commands (GSC)  (Warden, 2018)  V2, a dataset of audio signals sampled at 16 kHz composed of 1-s recordings of 35 spoken keywords. Raw audio signals are preprocessed to extract Mel Frequency Cepstral Coefficients (MFCC). We used 10 MFC Coefficients, FFT of size 1024, a window size of 640 with a hop of 320, and a padding of 320 on both sides. The pre-processing generates a 48 × 10 coefficients matrix that is subsequently processed by the neural networks.
Experimental results on CIFAR-/ Benchmark results on CIFAR-10/100 datasets are shown in Table  1. Our models perform consistently better than recent state of the art ANN-SNN conversion methods on both datasets. As an example, we improve the top-1 accuracy of ResNet-18 on CIFAR-10 by 1. 5% with respect to QFFS  (Li et al. , 2022). Experimental results also show that our quantization approaches outperform previous methods regardless of the network architecture. As an example, we improve the top-1 accuracy on CIFAR-10 for both ResNet-18 and VGG-16 architectures. The non-uniform quantization scheme provides the best accuracy scores in 3 out of four configurations, while the uniform scheme provides a slightly better accuracy score on ResNet-18 and CIFAR-100. It is worth noting that, these improvements are obtained using only 4 timesteps.
Experimental results on Google Speech Commands The experimental results on the Google Speech Commands dataset are shown in Table  2. We compared both our quantization schemes, ATIF-u and ATIF-nu, with two recently published SNNs. The first network, called  Recurrent (Cramer et al. , 2022) , is a single layer recurrent-SNN composed of 128 Leaky-Integrate-and-Fire (LIF) neurons and trained using surrogate gradient and BPTT. E2E is based on a ResNet-8 architecture with IF spiking neurons and is trained using ANN-SNN conversion. Notably, neither networks use threshold balancing to mitigate the quantization error introduced by the spiking neurons. As we can observe from Table  2 , Recurrent SNN proposed in  Cramer et al. (2022) , which features a very simple architecture, is not able to match current state of the art results on this particular task even with a latency of 200 timesteps.
Ablation studies We decompose the effects of our methods using an ablation study on the CIFAR-10 dataset. Three ResNet-18 networks are trained with different spiking neuron models. The baseline corresponds to a network with IF soft-reset spiking neurons, where all neurons share a fixed V th = 1. We compare this network with both uniform and non-uniform quantization schemes described in Sections 3. 3 and 3. 4, respectively. In those schemes V th is a trainable parameter: it is trained layer-wise for each network. All three networks are trained using surrogate gradient learning and use the same training setup described in Section 4. 1. As shown on Table  3 , a significant accuracy improvement can be obtained by optimizing the V th of spiking neurons during training. As an example, comparing the uniform and fixed threshold quantization scheme with the trainable one we can observe that the accuracy increases by 4.
A framework to predict binary liquidus from the properties of the pure elements and estimates from Miedema's model is proposed. The liquidus prediction is broken down into three simpler prediction tasks to improve both accuracy and interpretability. The equilibrium onset temperature of solidification is predicted with a mean absolute error of 102 K. A large dataset comprising data on 2016 binary liquidus between 64 elements is shared in open access. CALPHAD assessments enable the creation of large machine learning datasets for predicting phase equilibria.
The first is high-throughput DFT calculations. Here, the thermodynamic stability of compounds is evaluated from their enthalpy of formation calculated at 0 K, as in Ref. [11]. The great advantage of this approach is that large amounts of data can be generated in an efficient way. For instance, databases such as The Materials Project  [12]  contain data on more than 100,000 compounds, and it is possible to create tailor-made datasets of more than 10,000 entries  [13]. A disadvantage of this approach is that the data can be of limited accuracy  [14]  and do not provide information on the thermal stability of the compounds. The second source is the experimental literature. High-quality data can be obtained from it, but it is very time consuming to collect and assess the data. Therefore, the datasets, often collected from non-digital compilations, are relatively small, comprising tens  [15] [16] [17]  to thousands  [8, 18]  of entries on phase equilibria.
This can be detrimental to the performance of ML models  [19]. An interesting alternative to collecting phase diagram data is to rely on CALPHAD-based thermodynamic assessments. A major advantage of this strategy is that CALPHAD assessments integrate and combine both thermodynamic and phase equilibria data, obtained from both experimental measurements and firstprinciples calculations  [20]. Consistency between data of different nature can be evaluated as they are all translated in terms of Gibbs energy. Moreover, compatibility between binary data and ternary data can be evaluated, as illustrated in Ref. [21]. It is therefore suggested that CALPHAD assessments can be used to create large and unique datasets for predicting phase equilibria and thermodynamic properties. However, to the best of our knowledge, there have been only few attempts to predict phase diagrams properties from datasets collected from CALPHAD assessments  [22] [23] [24] [25].
Several ML studies have been conducted to predict the congruent melting point of compounds corresponding to a local maximum in the liquidus. Most of these studies focus on ionic liquids, salts melting below 373 K and composed of organic and inorganic ions, as in Ref. [46]. Studies on the prediction of the melting point of equimolar binary compounds have been carried out on the basis of limited datasets containing less than 50 entries  [16, 17]. An ML model was developed based on a dataset of 248 melting points of pure elements and binary compounds  [18]. Despite the limited size of the dataset, performance was improved after including physical properties calculated by DFT as features. An ensemble model of 30 graph neural network models was trained on a much larger dataset of 10,000 melting points of compounds  [47].
A neural network was developed to predict binary liquidus from a dataset collected from CALPHAD assessments comprising 287 binaries and 57 elements  [23]. However, this ML model failed to reproduce miscibility gaps. This limitation can be explained as follows. A maximum in the liquidus can correspond to two very different cases. The first is the presence of a compound that melts congruently, which suggests some affinity between the elements, while the second is the presence of a liquid miscibility gap that comes from repulsive interactions between the elements. Despite the importance of the liquidus for materials design, the prediction of liquidus temperatures in a vast compositional space has only been attempted once in 2020  [23]. That is because it is challenging to collect data of sufficient quantity and quality to build models upon.
In this study, we propose a framework to predict binary liquidus including miscibility gaps (Fig. 1 ). From data collected from CALPHAD assessments on 466 binary systems and 64 elements, our framework is tested, and 1563 binary liquidus not included in our dataset are predicted. Our framework consists of three ML prediction models. The first, referred to as Model 1, is a classification model to predict the formation of liquid miscibility gaps. The second, Model 2, predicts the equilibrium onset temperature of solidification, noted T S+L , disregarding the fact the liquid may not be homogeneous. This information is obtained from Model 1 and from our third model, Model 3. Model 3 predicts the critical temperature above which the elements are completely miscible in the liquid, noted T L+L , whether the miscibility gap is metastable (T L+L < T S+L = T liquidus ) or stable (T S+L < T L+L = T liquidus ).
Dataset Training datasets are collected from 466 CALPHAD assessments of binary phase diagrams obtained from the Computational Phase Diagram Database (CPDDB)  [48]  using the Pandat software  [49]. A reference list of the assessments can be found in the data repository associated with this article  [50]. First, in these binaries, a stable liquid miscibility gap is found in 44 systems, and this information is used to develop Model 1. Second, for each binary, the liquidus is calculated over the entire compositional range in steps of 0. 5 at. %. The data on the 13 systems in which there is a very steep miscibility gap, stable above 4000 K, are removed. The liquidus temperature in the remaining 453 binaries are used to develop Model 2 after removing the data corresponding to miscibility gaps. Last, liquid miscibility gap temperatures from 33 binaries in which the miscibility gap is eventually stable and 171 binaries in which it is metastable are used to develop Model 3.
Descriptors Up to 58 descriptors are obtained from the properties of the pure elements weighted by the composition. The melting properties of the pure elements are calculated from the SGTE unary database  [51]. In this way, feature extraction is consistent with data collection, as this database is the backbone of CALPHAD assessments. It is pointed out that the melting point of C is set at 4765 K, which is an upper limit  [52] , and that for P, data on the red allotrope which melts at 852 K are adopted. The other properties are calculated using matminer  [53]  from the data accumulated in the magpie repository  [54]. Up to 4 descriptors are obtained from Miedema's semiempirical model  [55]. The element-dependent parameters of the model are taken from the matminer repository  [53]. To compute the variables of the model, we follow the procedure of Ref.
ML methods The following algorithms are used: k-nearest neighbor (KNN) and random forest (RF) as implemented in scikit-learn  [57] , lightGBM  [58] , and artificial neural network (ANN) with two hidden layers as implemented in tensorflow  [59]. All models are optimized and tested from a nested crossvalidation (CV) approach schematized in Fig. S4. Data on each binary system form a group and are never split between the training, validation, or test sets. A group 10-fold CV is performed in the inner loop to determine the best set of hyperparameters from grid search, except for lightGBM for which the Optuna package  [60]  is used. A group 20-fold CV is performed in the outer loop to evaluate model performance. More details are in Supplementary Note B.
Prediction of the formation of liquid miscibility gaps To train Model 1, five descriptors are used. Two descriptors are obtained from the absolute difference of the enthalpy of fusion of the pure elements and of their entropy of fusion. The remaining three descriptors obtained from Miedema's model  [55, 61]  are (i) the enthalpy of mixing in the liquid phase estimated at equiatomic composition, and the square of the difference (ii) of the so-called work function, denoted u, and (iii) of the cube root of the average electron density at the boundary of the Wigner-Seitz cell, denoted n 1=3 ws. It has been shown that the latter two descriptors allow to distinguish between binary systems in which compounds exist from others  [55, 62]. In the dataset, the classes are imbalanced as liquid miscibility gaps are stable in only 44 of the 466 included binaries.
It is pointed out that similar results are obtained with the lightGBM algorithm (Supplementary Note C). In a previous study  [66] , an empirical model called Mott model was proposed to predict liquid immiscibility in binaries. It was applied to the present dataset to allow for a direct comparison with our ML model. For each binary, the Mott number was calculated from the data in the magpie repository  [54] , and compared with ''Mott criterion 1" from Ref. [66]. An accuracy of 74. 2% was obtained, which is significantly lower than the accuracy of our ML model. Furthermore, an advantage of RF is that the probability of belonging to each class can be evaluated. This is more informative than simply knowing whether a miscibility gap is stable or not. For instance, our model predicts a probability of having a miscibility gap of 0.
Over MAPE el A ð Þ¼100 X el BjA-B2Dataset f g 1 199 X 0:5 x B 99:5 Â T CALPHAD ðx B Þ À T ML ðx B Þ j j T CALPHAD ðx B Þ ð1Þ with T CALPHAD the liquidus temperature calculated from CALPHAD assessments, T ML the liquidus temperature predicted from Model 2. The predictions are especially accurate for rare earths, with an MAE of 72 K calculated over all the 158 binaries in the dataset between a lanthanide and any other element, which can be as low as 30 K for Ho and Er (Fig. S6 ). The predicted Au-Tb liquidus, which has an MAE of 35 K, is shown in Fig. 3(a). This good agreement is explained by the fact that there are particularly strong For the elements that are underrepresented in the dataset, such as As, Ba, Cs, Rh and Tl for which only one binary is included (Fig. S4 ), the calculated MAPE may not be representative.
trends in phase diagrams throughout the lanthanide series  [69, 70]. However, the predictions are less reliable for group (1) and group 15 elements. This is because these electropositive and electronegative elements tend to form complex liquids with short-range ordering  [71, 72] , and they are underrepresented in the dataset (Fig. S2 ). An important feature of Model 2 is that can give indications of the presence of congruent melting phases and eutectics that correspond to maxima and minima of the liquidus. This is true even when the liquidus temperature is not closely predicted, as shown in Fig. 3(b)  with the Cu-Y case. Fig. 4  shows a scatter plot between the liquidus temperatures predicted by Model 2 and calculated from CALPHAD assessments across the whole dataset. The absolute error is less than 100 K in 63% of the cases, and less than 200 K in 85% of the cases.
[81] , whereas a temperature of 1966 K is predicted from Model 3. This illustrates how our ML models can provide CALPHAD assessors with reasonable liquidus estimates where no experimental data exist. On the other hand, in the Al-Ga system, the liquidus calculated from Ref. [82]  is reliable, as it is supported by several consistent experimental datasets. Model 2 greatly overestimates the liquidus temperature in this system. This is explained by the fact that our model was trained to reproduce the overestimated B-Ga liquidus and assumes similarities between two binaries of group 13 neighbors. If data on B-Ga are removed from the training set, the MAE for Al-Ga decreases from 724 K to 330 K, a value still high, but more consistent with the expected accuracy for group 13 elements (Fig. 2 ). This illustrates how unreliable data in the training set can mislead the model.