dataesr/scientific-paragraphs-categorization huggingface dataset:
7 mm NMR tube which was then capped. Urine samples were thawed, centrifuged and diluted 1:4 with the NMR buffer detailed above. Samples were then carefully pH adjusted to 7. 0 until stable  27  and then frozen at -80 °C until analysed. Urine and Serum metabolomics data analysis. One-dimensional 1H NMR spectra were acquired at 300 K using a NOESY pulse sequence including water suppression with pre-saturation on a Bruker DRX 600 MHz NMR spectrometer equipped with a TXI 1. 7 mm cryoprobe. 2D homonuclear (1J-resolved) spectra were also acquired for each sample and heteronuclear (1H_1H_13H TOCSY and C HSQC) spectra were also recorded for selected samples to aid spectral assignment. Samples were processed and data calibrated with respect to the DSS signal. Spectra were read into Metabolab  28  , in Matlab (version 2016a, The Mathworks, Natick, MA), and spectra were segmented into 0.
006-ppm (3 Hz) chemical shift 'bins' and the spectral area within each bin was integrated. Spectra were corrected for baseline offset using a spline fit and then normalised using Probabilistic Quotient Normalisation (PQN)  29, 30  and a generalised log transformation was applied  31, 32. Binned data were then compiled into a matrix, with each row representing an individual sample. NMR databases (Human Metabolome Database version 3) and the Chenomx NMR suite (Chenomx, Professional version 8. 0)  33  were used to identify and quantify metabolites present in each sample. Automated metabolite identification with Chenomx was used to produce an initial fit and then manual fitting of the Chenomx provided metabolite spectral library was done. This focussed on a set of metabolites previously identified as being present in human serum  34  and urine  35.
Other published data on metabolites identified in human sera were also used to guide identification  36, 37. Following automated and manual metabolite identification, 40 known metabolites were identified and quantified in sera and 42 in urine. Genotyping. Genotyping was performed using the Illumina HumanCoreExome-24-v1-0 (Batch 1) or Illumina InfiniumCoreExome-24-v1-1 (Batch 2) according to the manufacturer's SOP. Raw intensity data files (idat format) from the Illumina iScan instrument were imported into GenomeStudio (v2011. 1). Samples <90% call rate were excluded. Data was exported to PLINK PED/MAP format on the forward strand. Data was converted from PED/ MAP to BED/BIM/FAM using PLINK v1. 07. HumanCoreExome-24v1-0_A_PopulationReport_MAF_022015. txt or InfiniumCoreExome-24v1-1_A_PopulationReport_MAF.
txt was used to obtain a list of all variants on the array with a MAF >0. 005. This list was used to extract the variants from the genotype file. Variants with a GenomeStudio Cluster Separation <0. 3 were excluded. Variants with <98% call rate were excluded. Individuals with <98% call rate were excluded. Palindromic SNPs (AT/CG) were excluded and the file split per chromosome. Variant IDs were updated to match Haplotype Reference Consortium v1. 1 using GenotypeHarmonizer. SNPs in common between the two arrays were extracted from each dataset and the files combined (TACERA_combined). W. Rayner's script HRC-1000G-check-bim-4. 23. pl was used to align SNPs to HRC v1. 1 panel. PLINK v1. 9 was used to convert the PLINK files to VCF. The Wellcome Trust Sanger Institute imputation server was used to impute the data to the HRC v1.
1 panel using SHAPEIT2 for phasing and PBWT for imputation. HLA imputation methods. HLA imputation was performed using PLINK to extract 28Mb-34Mb on chromosome 6. SNP2HLA was used for imputation with the Type 1 Diabetes Genetics Consortium (T1DGC) Panel  38. Custom bash and STATA scripts were used to extract the HLA haplotypes from the bgl. phased file.
Data Records Figure  2  summarises the number of samples processed on each omic platform. All raw data and processed mRNA and small RNA data are available in the NCBI GEO series accession number GSE97476  39. Serum and urine metabolomic data have been deposited at the MetaboLights database of the European Bioinformatics Institute (EBI) under MTBLS1497  40. Genotype data has been deposited in the European Genome-phenome Archive (EGA) with ID number EGAS00001004424 41. De-Identified clinical data, X-ray, Somalogic proteomic data, including aptamer annotation and Protagen autoantibody data are available in the RA-MAP project in figshare  16. technical Validation exploratory data analysis across omic platforms. Whole blood, cell subsets and serum readouts from each individual omic data platform were compared at baseline and six months, using a unified exploratory data analysis (EDA) approach employing a range of multi-dimensional visualisation and dimensionality reduction methods implemented in the bioplotr package (https://github.
com/dswatson/bioplotr). The technical validity of each platform was explored using visualisations of mean variance / dispersion plots, density plots, subject similarity and principal component analysis (PCA). Full EDA markdown documents and data files for each platform are available in the RA-MAP GitHub. In Fig. 3 , PCA is used to gain an overview of each omics platform at baseline and 6-months in whole blood and cell subsets in the TACERA early RA cohort (a, c-h) and across a range of time points in the vaccine cohort (b, k-o). Similar separation by cell type is evident in both RA and vaccine cohorts. After QC, including limited outlier removal, all Omics platforms appear relatively homogeneous with no unexpected structure in PCA projections. Some evidence of separation by time is seen in the RA cohort in whole blood and cell subset mRNA, serum miRNA, serum autoantibodies and urine metabolomics.
In the TACERA cohort, the larger PCs, representing the largest source of variance, are closely associated with time and DAS28 and related disease activity scores in whole blood and cell subset mRNA and whole blood miRNA; and rather less closely associated with the other platforms. Notably time and DAS28 correlate closely with the exception of the urine metabolome where changes in time, including the joint strongest association seen in the dataset, are less well correlated with disease activity. This leads us to conclude that changes over time in the urine metabolome driven by PC2 are unlikely to be disease related. We note that region hub shows some correlation with measures of disease activity in some Omic platforms. Samples from 28 patient recruiting centres were processed in 7 regional hubs  14  , we hypothesize that this may reflect a higher proportion of more severe RA cases seen in tertiary referral centres.
Usage Notes Genotype data is made available through the European Genome-phenome Archive (EGA). Request for data access will be referred directly to our Data Access Committee: https://ega-archive. org/datasets/ EGAD00001006736. If you need to request access to this data set, please contact the RA-MAP Data Access Committee (Contact person:m. r. barnes@qmul. ac. uk). Applicants will be asked to complete the Data Access Agreement (DAA) (including a brief summary of the proposal, proposed usage of the dataset, the storage of data, so the DAC can determine if the planned usage falls within the consents) and to agree to the terms and conditions of the DAA. The DAA must be signed by the applicant and the relevant Head of Department, or equivalent. If applications include a named collaborator then the collaborator's Institution must sign and submit a separate DAA.
For microarray analysis, amplified RNA was hybridized to Illumina HumanHT-12 V4. 0 expression beadchips and scanned on an Illumina Beadstation 500. Illumina's GenomeStudio version 2011. 1 with the Gene Expression Module v1. 9. 0 was used to generate signal intensity values. TACERA samples were randomized across the analysis plates, with samples from same RA subject (baseline and 6-month time points) assigned within same plate. Vaccine samples were run in separate batches, but also randomized across analysis plates. Non-normalised control and sample probe data was exported from GenomeStudio.
Acknowledgements The programme of research described in this article is funded by the  Medical Research Council, UK (An Immunological Toolkit for Clinical Application , grant number  G1001518  and Towards a cure for early rheumatoid arthritis, grant number  G1001516 ). The  RA-MAP Consortium  would particularly like to thank members of the  MRC Immunity and Inflammation Stratified Medicine Steering Group and Officers of the Medical Research.
SCALP boards can be interconnected in a modular way to support the structure of the neural model. Such a unified software and hardware approach enables the processing to be scaled and allows information from several modalities to be merged dynamically. The deployment on hardware boards provides performance results of parallel execution on several devices, with the communication between each board through dedicated serial links. The proposed unified architecture, composed of the ReSOM model and the SCALP hardware platform, demonstrates a significant increase in accuracy thanks to multimodal association, and a good trade-off between latency and power consumption compared to a centralized GPU implementation.
Thanks to this ability, mammals and many higher animals create neural maps that represent their environment. Each map stores relevant information that, when combined, builds a model of the experienced objects and concepts. These maps can be built by the brain without the need for explicit data annotation or object labels. This property would be extremely useful for training on large amounts of data, where annotating itself becomes a difficult and expensive task. The Self-Organizing Map (SOM)  [4]  is one of the main unsupervised neural models based on such principles of cortical self-organization in the brain. It dynamically adapts to represent the input data according to its distribution. This paper explores the efficiency of the proposed brain-inspired computing software and hardware framework based on SOMs with application to the specific case of multimodal association problems.
D. Outline of the paper The next section presents an overview of the state of the art related to the scientific questions addressed in this paper. Section III describes the ReSOM model for multimodal unsupervised learning based on local computations. Section IV gives general information about the SCALP board used for the hardware solution. Section V describes both software simulations on a CPU/GPU and the results of hardware deployment on FPGA-based boards. Section VI briefly summarizes the tests conducted and discusses possible paths for future development.
It is worth mentioning that other solutions tackling this issue have been proposed in the literature, although none of them meet all our criteria. One of them is the Elastic Map  [22]  method that creates a nonlinear distortion of the observed space, and another is the Generative Topographic Map  [23]  that builds a map based on statistical principles, without an inspiration from neural systems. Separately, we note the Neural Gas (NG) model  [24] , which works on similar principles as the SOM, but allows more freedom in the neural structures achievable. The NG model on the other hand, lacks the topological properties inherent in cortical regions, since its structure depends on the distance of each prototype from stimuli. This makes it complex to implement based only on local interactions of neurons, therefore complex to program in a distributed manner.
In parallel, it has been shown that SOMs perform better at representing overlapping structures compared to classical clustering techniques such as partitive clustering or K-means  [31]. Recently, we showed that the SOM combined with transfer learning reaches a competitive accuracy on complex few-shot learning problems  [32]. In addition, SOMs were directly inspired by work on the cerebral cortex  [4] , and can hence be potentially applied in the future for the development of biologically compatible components. The KSOM proposed more than thirty years ago  [33]  can serve as the main reference for our work, even though different adaptations of the original SOM model have since been proposed. We can distinguish several models offering some interesting and unique behaviors. The Dynamic Self-organizing Map (DSOM)  [34] , for example, dynamically adapts to changes in the dataset over time, continuously rebuilding the map structure if needed.
For example, as part of the DARPA's SyNAPSE project roadmap, the IBM's TrueNorth neuromorphic chip (  [60] )(  [61] ) can implement 1 million digital neurons. The ThrueNorth chip implements a very rich neural model, capable of reproducing many behaviors observed in biological neurons. As part of the Eureopean Human Brain Project, the SpiNNAker (  [62] ) project aims to model a billion biological real-time impulse neurons using a million ARM968 cores. More recently, Intel's Loihi neuromorphic chip (  [63] ) implements 130K Leaky Integrate and Fire (LIF) neurons and 130M synapses capable of online learning and inference. Its successor Loihi2 (  [64] ) reaches 1M neurons and introduces a more flexible microcode programmable neural engine that allows simulation of a wide range of different neural models.
More original vector codings have been proposed by  [70]  which proposes to encode the vector components as the duty cycle of square waveforms and to use Digital Phase-Locked Loops (DPLL) as parallel computing elements to measure and update the distances between vectors. Other approaches focus on learning performance (expressed in millons of connection updates per second -MCUPSper watt consumption). Lachmair et al. (  [71] ) propose a bus-interconnected multi-FPGA hardware SOM that allows the implementation of large reconfigurable networks. Abady et al. propose a layered description of the HW SOM (  [72] ,  [73] ) where the computational neurons are decoupled from the communication that is implemented with a NoC. The same idea is exploited by Jovanovic et al. (  [74] ,  [75] ) in a more hierarchical manner as the interconnected computational cells no longer implement neurons but clusters of neurons.
D. Application of ReSOM to classification tasks In order to make a quantitative analysis of our proposed model, we must use a quality metric of the resulting space clustering. To do that, we apply our framework to three subsets of data: a training set used for unsupervised learning composed of multimodal unlabeled data; a small annotated dataset used to attribute labels to neurons; and a testing set used for the validation and the evaluation of the clustering. This specific validation process leads at the end to the evaluation of a single metric corresponding to the accuracy of classification. That also means that this application consists in mapping the continuous space representation onto the discrete label space by assigning some class labels to the representation neurons. To do this, we use a certain amount of labeled data.
In fact, for any classification task based on unsupervised learning where the results need to be communicated in the so-called discrete label space, we need to find a method of attributing the identified clusters to existing labels. Different methods may be used to solve this problem, such as having an external expert who manually labels the obtained representations, or an automated process based on some labeled data. We use the latter approach, trying to minimize the number of required labels. The labeled data space should be large enough to represent all possible patterns of the available data. In practice, our results showed that only 1% of data for the MNIST dataset and 10% for the SMNIST dataset is enough to reach the best quality of classification  [19]. To have a uniform structure this work uses about 10% of labels for all the data modalities (precise numbers are given in section V).
25 Gb/s. A layered hardware/software architecture enables applications to be deployed on SCALP in a seamless manner. The ARM Cortex-A9 runs a Petalinux OS allowing users to write applications in C, C++ or Python. A set of libraries and drivers enables the use of services deployed on the FPGA. These services may be hardware accelerators or specific interfaces. In the case of the work presented in this paper, they will permit access to the router built on the FPGA. Figure  5  depicts the SCALP node internal architecture in the case of a 2D SCALP array (a third dimension is also supported). It is composed of two main layers, routing and computation: • The routing layer includes the equivalent of routing + link + physical layers in the OSI model, it guarantees link integrity between two neighbor nodes, and can redirect packets to neighbors in order to permit remote node communications without using intermediate computation nodes.
A. Simulation results 1) ReSOM framework and dataset description. : To test the model, a Python-based framework was implemented using the PyTorch library  [78]  to speed up matrix calculations. Some simple datasets are used for the tests, such as the MNIST  [79]  and Spoken-MNIST (SMNIST) datasets. The SMINST dataset is a subsample of Speech Commands  [80]  reduced to pronounced numbers from 0 to 9. The numbers were transformed to the Mel Frequence Cepstral Coefficients. The final version of the joint dataset in this configuration for two modalities was presented in a previous work  [81]. The Fashion MNIST dataset  [82] , representing images of different clothes, and the hand signs dataset  [83] , representing numbers shown by hands, were used as the third and fourth modalities. Even if F-MNIST does not represent the same type of information as other considered datasets, it is one of the simplest datasets in the literature and it uses the same format as MNIST (10 classes and 28x28 images).
It is assumed that each of the objects present in this dataset is arbitrarily associated to a specific class number between 0 and 9. More information about the dataset size can be found in Table  I. All the datasets have been subsampled or oversampled (if needed) to have the same number of training/test vectors (60 000/10 000), but the data vector size is left unchanged. All the vectors have sizes comprised between 100 and 2000 depending on the dataset used. We applied our labeling algorithm on a part of 5000 training data vectors (about 8% of data were labeled). 2) Preliminary tests of ReSOM with 2 modalities: Due to the use of a new framework that accelerates matrix calculations, it was decided to return to the problem of 2 modalities, previously resolved with ReSOM. We check if the model improves with a wider search for the hyperparameters, keeping the old sizes of the SOMs (10 × 10 and 16 × 16 for MNIST and SMNIST, respectively).
This time instead of a hard grid for such parameters as σ and , an advanced algorithm "Optuna"  [84]  was used to optimize their values. As can be seen in Table  II , the accuracy results of the previous similar work (95. 1%) have been achieved and even surpassed (with 96. 6%). Notice that our model has used the datasets in a raw form, without any feature extractor, which might radically enhance the quality of the vectors representing the data. The addition of feature extractors as input of the neural maps increases the algorithm's accuracy, as we showed in  [32] ,  [76]. 3) Tests of ReSOM scalability to more modalities: The following tests presented in Table  III  show the model's scalability. We conducted tests of ReSOM with up to four modalities by adding the Fashion MNIST database (FMNIST) and a hand gesture database (denoted "Gests" in the following).
4) Connection importance and pruning: Reducing the number of values in the matrix can be extremely useful both for speeding up calculations and for reducing energy consumption in future implementations of the system. So next, we analyze the level of sparseness of the connections obtained and its influence on the resulting accuracy. The first thing to look at is the number of unused connections (i. e. , those with zero weight). The fraction of such connections is quite large, as can be observed in Fig. 7A , which shows the evolution of the number of nonzero connections during the training process. We can observe some differences in the density of connections depending on the SOMs to be linked together. For example, the difference in the number of connections about of 1. 3 times between the most linked pair of SMINST and Gests database ("1-2"), and the less linked pair of MNIST and FMNIST ("0-3").
The scheme involves 2 SCALPs passing data directly via the HSSL protocol. They are responsible for processing visual and auditory information and one of them also for the ReSOM prediction inference. The boards are connected to a PC in a local network via an Ethernet connection. The scheme can be seen in Figure  8A. In the proposed scheme, the PC plays the role of program execution controller. It starts the different steps of the algorithm and controls the synchronization of data transfer between the devices. It also sends data vectors for testing and collects the final predictions, playing both the role of sensors (such as a camera) in a real system and the role of a monitoring system for the counted predictions. Some parts of the algorithm (such as the calculation of SOM activations and the modification of the weights while training) are parallelized and so executed on different boards and computed independently of one another.
These processes are controlled in different threads of the controller program written in Python and launched on the PC. The SOM calculations are executed on the boards as a Python server program. The nodes expose SOM and ReSOM computing functions as services that can be called through Python's RPyC library. All requests are sent over the internal LAN network configured for standard communication between Bionic Beaver Linux on the PC and PetaLinux on the SCALP board. For communication between the boards, direct connections with the HSSL protocol are used. Its schema allows the address of a node in the network to be set in three-dimensional coordinates and data to be sent to a desired address. In the current SCALP version, data are sent on a FIFO receiver of a board, where data packets of a size of 64 words of 8 bytes are sequentially recorded.
The packet sending function is a core application service and may be called via C/C++ code (or by a Ctypes call in Python). To send a data object of any type and size in Python, a program wrapper was written that serializes and splits files into the packets, and after that sequentially sends them to a given address. The receiver board reassembles them to the initial format. 2) Deployment scenarios: Several deployment scenarios were tested to verify the global system performance. The first model to be tested was the inference model, to demonstrate the ability of a two-board system to produce values similar to those simulated earlier, using the already trained weights of both SOMs and lateral connections. The schema followed is shown in Table  IV. Further, in a similar way, the model was tested with the addition of the training of ReSOM lateral connections.
Because of some nonoptimal features of the presented framework (which will be discussed later), we did not reach the maximum possible computational speed. Therefore we decided to limit ourselves to a small test dataset (300 samples), sufficient to demonstrate the model's functionality. The accuracy of this on-board prediction test was equal to 96. 6% and the resulting confusion matrix is presented in Figure  9. These results are exactly the same as the ones achieved by the simulation code, both in term of accuracy and the confusion matrix, thus confirming the validity of the deployed model. 4) Analysis of system execution performance: In the previous section, we conducted tests with a fairly small number of samples. This was due to the the hardware architecture of the FPGA and non-optimized usage of the Python language.