dataesr/scientific-paragraphs-categorization huggingface dataset:
2 , where the plasma biomarkers with a significant influence of the nutritional challenge group are highlighted with a black star). In addition, the sPLS-DA analysis applied to the dataset of the 14 plasma biomarker ratio traits at each sampling time point and in relation to the two groups of nutritional challenge (C and NPR) selected one component for the sPLS-DA discriminating groups, whereas the optimal number of selected variables for component 1 was 20. The individual plot of PC1 and PC2, which is given in Fig. 3 , shows the separation of the sample groups. As it can be seen, PC1 (20% of total variability) allows the discrimination of one group from the other, whereas PC2 (7% of total variability) Fig. 1. Graphical representation of the profile of logarithm of the somatic cell count ratio (r_logSCC) measured in both the LPS-injected udder (r_logSCC_i) and the contralateral udder (r_logSCC_c) across the seven milk sampling points considered in the present study for the two studied groups, the nutritional protein restriction (NPR) group (NPR; n = 13) and the control group (C; n = 11).
Multiple benchmarks and datasets exist to evaluate models on image classification. The main ones include MNIST  [26] , ImageNet  [27] , and CIFAR-10. MNIST contains grayscale 28 9 28 labeled images of handwritten digits, while ImageNet and CIFAR-10 classify objects using more complex colored images. They are all used to a large extent for assessing AI-model performances. Even though image processing and AAM are two different tasks, authors in  [28]  adapted HNN to solve a simplified MNIST classification task using the unsupervised Storkey rule. They obtain 61. 5% precision using the Storkey learning algorithm, while typically CNNs achieve around 99% on the standard MNIST classification task  [1]. More recently, authors in  [29]  adapted the contrastive Hebbian rule (CHR)  [30]  to perform supervised learning with energy-based recurrent neural network (RNN) models, using the socalled equilibrium propagation (EP) learning algorithm.
Our first approach consists of utilizing AAM-EP with random weights at the start. Next, we explore the use of AAM-EP on AAM networks pre-trained with unsupervised learning to fine-tune weight values and assess whether AAM-EP can increase the accuracy of pre-trained AAM networks. We evaluate the precision on simplified MNIST test sets with HNN simulated with MATLAB, and digital ONN design implemented on FPGA  [31]. Note, we consider only a simplified 10 9 10 MNIST dataset for image classification with HNN and ONN due to networks and implementation constraints. First, HNN is limited to bipolar values {-1,1}, and thus, it can consider only black and white images. Also, up to our knowledge, there is no efficient training algorithm for ONN to settle in continuous phase. Usual ONN training algorithms are HNN unsupervised training algorithms constraining training patterns to binary values, and so constraining ONN output to binary phases {0°, 180°}.
EP learning algorithms also considered in this work, should in principle be suitable for continuous ONN outputs. However, we believe it requires further investigations, and it is out of the scope of the paper. Also, our current ONN implementation limits ONN size to a hundred neurons. Thus, with our current ONN implementation, the MNIST dataset is the most suitable for image classification with HNN and ONN. Considering additional image datasets necessitates other ONN implementations and adaptations. Our contributions can be summarized as:  (1)  we perform a first study to explore MNIST classification task with ONNs. We adapt the method introduced by  [28]  for HNNs to solve handwritten digits classification task with ONN. (2) We study precision performance of HNN and ONN using unsupervised learning algorithms.
There also exist multiple architectures, and computing methods using ONN. In this work, we only consider fully connected recurrent architecture with phase computation, see Fig. 1 , as it can compute AAM tasks  [18]. We encode data in phase relationships between oscillators. For example, for bipolar values, considering a reference oscillator with phase 0°([? 1]), another oscillator with 180°phase shift from the reference oscillator would represent the opposite bipolar value ([-1]). Similarly, an oscillator with 0°phase shift represents the same bipolar reference value ([? 1]). Phase computation allows to reduce signal amplitude and consequently the energy consumption of the system  [9]. ONN computes based on the dynamics of the coupled oscillators. Couplings between oscillators are set during the learning step and depending on the task.
Auto-associative memory (AAM) task and learning AAM tasks involve memorization of patterns and retrieval of the memorized patterns from the corrupted input information. AAM processes in two main steps. The first is the learning step to memorize patterns in the network, and the second is the inference step to retrieve one of the memorized patterns from a corrupted input, see Fig. 1. Considering HNN or ONN, we associate each image pixel to a neuron, and the color of the pixels to the neuron value, either the binary value for HNN, or the phase value for ONN. AAM tasks are usually solved using unsupervised learning algorithms. Unsupervised learning algorithms differentiate from supervised learning algorithms as they only use data to compute, without any external interaction to evaluate it. ONN-oriented rules were introduced recently, especially for ONNs  [36].
Storkey is also local, incremental, with symmetric weights and is similar to Hebbian as: W ij ¼ X k 1 N n k i n k j À 1 N n i h ij À 1 N h ij n j ð4Þ with h ij : h ij ¼ X j W ij n j ð5Þ In the contrary, the pseudo-inverse learning rule is neither local, nor incremental, but has symmetric weights. Thus, it is not biologically plausible. The pseudo-inverse learning rule is as: W ¼ N Ã n Ã pinvðnÞ ð 6Þ The biological plausibility is useful for online learning applications. Though, in this paper, we process weights offline using MATLAB software, before using them in the HNN MATLAB model or in the digital ONN. In the digital ONN design, we normalize weights in 5-bits signed format. We compare the three above-mentioned learning algorithms with HNN and ONN on the simplified MNIST set.
MNIST database The MNIST set was created to assess neural networks performances on image classification task. It contains 28 9 28 Gy scale labeled images of handwritten digits, from 0 to 9. It is organized in two sets, a training set with 60,000 images, and a test set with 10,000 images. State-ofthe-art solves the MNIST classification problem with CNN models. They can achieve more than 99% of accuracy when trained by supervised back-propagation algorithms  [1].
MNIST dataset preparation In this work, we employ a simplified MNIST set containing the same number of training and test images, where each image is pre-processed to be transformed in a 10 9 10 black and white image in order to be compatible with our digital ONN design, see Fig. 2. We focus on a 10 9 10 format because the digital ONN design is limited in size and resources. The transformation of each image follows three steps. First, each 28 9 28 image is cropped to 20 9 20 removing the black background to reduce similarities among images. Then, the 20 9 20 image is resized to a 10 9 10 by taking average values of 2 9 2 neighbor pixels. Finally, we binarize each image into black and white using a threshold. MNIST grayscale pixels take values in the range of [0,255], thus, for example, if we use a threshold of 128, a pixel value under 128 will become black, otherwise white.
Training patterns for MNIST classification In this work, we use HNN and ONN configured for pattern recognition task, while MNIST is a classification task. Thus, we need to adapt the MNIST classification dataset to be solved as a pattern recognition task. MNIST classification with AAM starts by the training step to configure the weights, and so the choice of the training patterns. The training pattern choice is a key for high precision. Each training pattern must be the best representation of its digit, such that each image of that digit from MNIST set will stabilize to that training pattern. Authors in  [28]  propose to perform a mean on each grayscale image with same label from the MNIST training set of 60,000 images, and we reuse the same method. We define 10 training patterns corresponding to the 10 digits which will be learnt as stable points for both HNN and ONN networks.
Unsupervised learning Authors in  [28]  use the Storkey unsupervised algorithm after stating Hebbian does not have enough memory capacity. Here, we study three training algorithm Hebbian, Storkey, and Pseudo-inverse on HNN. Note, incremental learning algorithms can be sensitive to iterative learning. We call iterative learning the possibility to learn same patterns multiple times. Thus, we study the impact of iterative learning on the Hebbian and Storkey learning algorithms, as Pseudo-inverse is not incremental. From HNN MATLAB simulations, we extract the weights giving best HNN precision and integrate them inside the digital ONN design to compare ONN precision with HNN precision on the simplified MNIST classification task. Chosen weights are normalized into a 5-bits signed representation to be compatible with digital ONN design.
Comparison of both unsupervised and supervised methods In this work, we highlighted that the supervised AAM-EP learning algorithm can help increasing precision of HNN and ONN networks pre-trained with unsupervised learning algorithms for the MNIST classification task. However, using supervised learning can be more demanding in terms of computational efforts. In this work, training was performed in MATLAB. However, we can derive from results obtained in MATLAB the computational efforts for the different learning algorithms, as well as an estimation of the learning latency if learning was implemented on the digital ONN. We evaluate the computational efforts using the metric of the number of multiply and accumulate operations (NMAC OP ) required for each learning method. Table  2  shows a comparison of the computational efforts for the various learning methods for a general case, as well as for the MNIST classification application.
Conclusion This work proposes a study of solving classification of handwritten digits images from MNIST dataset using single-layer energy-based AAM networks. In particular, we analyze the behavior of an HNN tested on MATLAB software and a digital ONN design simulated on Vivado software. To classify with single-layer AAM network, we define training patterns for each label. So, after initialization, the network evolves to one of the training patterns corresponding to one label. We evaluate network precision to classify 10 9 10 black and white images of handwritten digits from a simplified MNIST set. We compare and assess precision performances for different learning configurations. First, we compute precision for different unsupervised learning algorithms. We obtain a maximum HNN precision of 65.
2009; Ricca et al. 2012) , neutral or charged  (Banhatti et al. 2021) , with varying hydrogenation degree  (Mackie et al. 2015; Pla et al. 2020) , and incorporating heteroatoms  (Hudgins et al. 2005; Oliveira et al. 2020)  or complexed to a metal atom  (Simon & Joblin 2010). Such calculations were also used to build spectroscopic databases of PAHs  (Malloci et al. 2007a; Bauschlicher et al. 2018). However, the AIBs not only include discrete bands but also broad plateaus, which are a collection of broad blended features whose carriers remain elusive  (Shannon et al. 2018). For instance, fullerene-rich planetary nebulae were observed to exhibit a broad plateau with substructure in the 6-9 µm range  (García-Hernández et al. 2010 , 2011b ,a, 2012; Bernard-Salas et al. 2012). Several types of carriers were proposed, including hydrogenated amorphous carbon (HAC) compounds  (Bernard-Salas et al.
Acknowledgements. The authors gratefully acknowledge financial support by the  Agence Nationale de la Recherche (ANR)  Grant No. ANR-16-CE29-0025  and the  Spanish MICINN  ( PID2019-110091GB-I00 ). They also thank the computing mesocenter CALMIP (" CALcul en MIdi Pyrénées ",  UMS CNRS 3667 ) for generous allocation of computer resources (p0059 project).
Finally, the operating temperature of the photodiode chip can be controlled at -40 ± 0. 01 °C. We tested and evaluated the performance of the cooled Si photodiode. In the absence of light, the dark current and shunt resistance were measured under a reverse voltage of -100 mV, and the test results were 5 TΩ and 20 fA, respectively. Using a light source composed of a halogen lamp and a monochromator to output monochromatic light of 380 nm~1000 nm, we recorded the spectral response data of a standard Si-trap detector and cooled Si photodiode. The absolute spectral responsivity of the standard Si-trap detector was calibrated by the National Institute of Metrology of China. Comparing the data of the two detectors, the relative spectral responsivity of the cooled Si A temperature control circuit based on the PID algorithm is designed, as shown in Figure  2.
Finally, the operating temperature of the photodiode chip can be controlled at -40 ± 0. 01 °C. We tested and evaluated the performance of the cooled Si photodiode. In the absence of light, the dark current and shunt resistance were measured under a reverse voltage of -100 mV, and the test results were 5 TΩ and 20 fA, respectively. Using a light source composed of a halogen lamp and a monochromator to output monochromatic light of 380 nm~1000 nm, we recorded the spectral response data of a standard Si-trap detector and cooled Si photodiode. The absolute spectral responsivity of the standard Si-trap detector was calibrated by the National Institute of Metrology of China. Comparing the data of the two detectors, the relative spectral responsivity of the cooled Si We tested and evaluated the performance of the cooled Si photodiode.
In the absence of light, the dark current and shunt resistance were measured under a reverse voltage of -100 mV, and the test results were 5 TΩ and 20 fA, respectively. Using a light source composed of a halogen lamp and a monochromator to output monochromatic light of 380~1000 nm, we recorded the spectral response data of a standard Si-trap detector and cooled Si photodiode. The absolute spectral responsivity of the standard Si-trap detector was calibrated by the National Institute of Metrology of China. Comparing the data of the two detectors, the relative spectral responsivity of the cooled Si photodiode can be obtained. Then, by using a laser light source, the absolute spectral responsivity ratio of the Si-trap detector to the cooled Si photodiode at 632. 8 nm was tested. Combining the relative spectral responsivity and single-wavelength absolute spectral responsivity ratio, the absolute spectral responsivity of the cooled Si photodiode can be calculated, and the results are shown in Figure  3.
Uncertainty Caused by the Drift of the Light Source The drift of the light source may be caused by the drift of the supply curren drift of the reflectivity of the inner wall of the integrating sphere, etc. It is diffic evaluate each influencing factor separately, so we monitor the light source throug photodiode and evaluate the relative drift of the light source by analyzing the pho rent. The light source is warmed for 1800 s. Then, the Si photodiode is placed at th let of the light source and the photocurrent is collected using an am (Keithley-6517b). The nonlinearity test time for each step is less than 200 s, so we divide the cu data into groups for every 200 s, making a linear fit to the current data over 200 s tain a slope of a. The uncertainty caused by the drift of the light source is expressed 200 ( ) r mean a u S I ⋅ = , where Imean is the mean current over 200 s.
Uncertainty Caused by the Drift of the Light Source The drift of the light source may be caused by the drift of the supply current, the drift of the reflectivity of the inner wall of the integrating sphere, etc. It is difficult to evaluate each influencing factor separately, so we monitor the light source through a Si photodiode and evaluate the relative drift of the light source by analyzing the photocurrent. The light source is warmed for 1800 s. Then, the Si photodiode is placed at the outlet of the light source and the photocurrent is collected using an ammeter (Keithley-6517b). The nonlinearity test time for each step is less than 200 s, so we divide the current data into groups for every 200 s, making a linear fit to the current data over 200 s to obtain a slope of a. The uncertainty caused by the drift of the light source is expressed as: u r (S) = 200 • |a| I mean , (14) where I mean is the mean current over 200 s.
Satellites have witnessed the impact of the amplification on phytoplankton and vegetation productivity as well as on human activity and infrastructure. Satellite missions of the European Space Agency (ESA) are increasingly contributing to amplification monitoring and assessment. The ESA Climate Change Initiative has become an essential provider of long-term climatic-quality remote-sensing data products for essential climate variables. Still, such synthesis has found that additional efforts are needed to improve cross-sensor calibrations and retrieval algorithms and to reduce uncertainties. As the amplification is set to continue into the 21st century, a new generation of satellite instruments with improved revisiting time and spectral and spatial resolutions are in high demand in both research and stakeholders' communities.
One such impactful phenomenon is a climatic transition from multiyear to seasonal sea ice in the Arctic Ocean  [8] , which unlocked surface feedback leading to the emergence of the apparent amplification in the 21st century  [9]. The longest time series (since 1966) of satellite observations exists for snow cover  [10]. Figure  2  presents the temporal coverage for essential climate variables (ECVs) collected in the ESA Climate Change Initiative (CCI). The ESA CCI efforts are central for synthesis; ECVs are considered from the perspective of physical climatology of the Arctic amplification. ECVs provide reliable, traceable, observation-based evidence for a range of climate Capturing the climate change over a relatively short period-the majority of remotesensing data products have become available since 1979  [3] -satellite observations have proven to be crucial for the discovery and monitoring of important changes in the earth's climate system  [4].
The ESA CCI efforts are central for synthesis; ECVs are considered from the perspective of physical climatology of the Arctic amplification. ECVs provide reliable, traceable, observation-based evidence for a range of climate applications, including monitoring and attributing of climate change phenomena  [11]. The ECV concept has been adopted by space agencies operating Earth observation satellites. At present, ESA CCI comprises 23 parallel ECV projects, a dedicated climate-modeling project for the assessment of products, a portal (https://climate. esa. int/en/odp/#/dashboard (accessed on 8 January 2023)) providing the products, a toolbox to facilitate the combining and analysis of the products, and a visualization tool supporting outreach. Although climaticquality ECV records require data fusion from many space-born sensors and missions, the ESA satellite missions were of critical importance for many ECVs.
A timeline of all ESA satellite missions can be found in the online Earth Observation Handbook, in the CEOS database at http://database. eohandbook. com/measurements/overview. aspx (accessed on 26 November 2022). Starkweather et al. [12]  provided a wider perspective on a value chain for the Arctic Observing Network that combines both satellite and ground-based (in situ) monitoring systems. The value chain traces the impact of satellite observations (in combination with other data sets and models) down to vital signs of climate change and societal impact. applications, including monitoring and attributing of climate change phenomena  [11]. The ECV concept has been adopted by space agencies operating Earth observation satellites. At present, ESA CCI comprises 23 parallel ECV projects, a dedicated climate-modeling project for the assessment of products, a portal (https://climate.
esa. int/en/odp/#/dashboard (accessed on 8 January 2023)) providing the products, a toolbox to facilitate the combining and analysis of the products, and a visualization tool supporting outreach. Although climatic-quality ECV records require data fusion from many space-born sensors and missions, the ESA satellite missions were of critical importance for many ECVs. A timeline of all ESA satellite missions can be found in the online Earth Observation Handbook, in the CEOS database at http://database. eohandbook. com/measurements/overview. aspx (accessed on 26 November 2022). Starkweather et al. [12]  provided a wider perspective on a value chain for the Arctic Observing Network that combines both satellite and ground-based (in situ) monitoring systems. The value chain traces the impact of satellite observations (in combination with other data sets and models) down to vital signs of climate change and societal impact.
One example is given by the Greenland ice sheet studies. An unprecedented loss of Greenland ice (100 to 255 Gt of ice per year) has been inferred from a synthetic data product for ice mass balance (elevation) monitoring  [63]. The first data were collected in the late 1970s by the National Aeronautics and Space Administration (NASA)'s Geodetic and Earth Orbiting Satellite-3 (GEOS-3), NASA's Seasat, and the US Navy's Geosat oceanographic radar altimeters. These data were combined with observations from a fleet of missions that provided for different products, e. g. , GRACE and GRACE-FO  [60]. Another example is given by the University of Alabama in Huntsville (UAH)'s Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit (AMSU) product that records the temperature of upper atmosphere data  [64, 65].
It combines data from NOAA satellite series and data from the TIROS-N  (1978) (1979) ,  Aqua (2002 Aqua ( -2009)) , and MetOP A (2007-2016) and B (2012-2016) satellites, which do not bear identical instrumentation. Yet another example refers to the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index data set (GIMMS3g), which is widely used to assess long-term vegetation changes  [66]. The ESA Copernicus Sentinel missions have opened a new era of polar satellite observations. The missions consist of a family of satellites designed for the operational monitoring of the Earth system with continuity up to 2030 and beyond. On-board sensors include both radar and multi-spectral imagers for land, ocean, sea ice, snow cover, ice sheets, glaciers, and atmospheric monitoring.
Today, almost 40 years (1982 on) of satellite cloud observations are available  [47]. Currently, four long-term climate data records (data sets) exist that are exclusively based on AVHRR data. One is a CM SAF Cloud, Albedo, and Surface Radiation data set from AVHRR data, second edition (CLARA-A2). It applies a hierarchical decision tree thresholding method to retrieve cloud properties  [74]. The other data set-the NOAA's Pathfinder Atmospheres, Extended program (PATMOS-x)-is based on a naïve Bayesian method  [76]. The third is the Extended Advanced Very High-Resolution Radiometer (AVHRR) Polar Pathfinder (APP-x)  [77]. The fourth is the ESA Cloud CCI (version 3) 1982-2017, which uses neural network and optimal estimation techniques to provide cloud property retrievals  [128, 129]. Satellite observations are essential in studies of Arctic cloudiness and its impact  [32, 126, 127].