dataesr/scientific-paragraphs-categorization huggingface dataset:
Accuracy assessment The performance of the prediction models is evaluated by: Pearson correlation coefficient (R) R = n ∑ n 1 (x i * xi ) - ( ∑ n 1 (x i ) * ∑ n 1 ( xi ) ) ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ ( n ∑ n 1 x i 2 - ( ∑ n 1 x i ) 2 ) * ( n ∑ n 1 xi 2 - ( ∑ n 1 xi ) 2 ) √ (21) Root Mean Square Error (RMSE) RMSE = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ 1 n ⋅ ∑ n 1 (x i -xi ) 2 √ ( 22 ) Mean absolute Error (MAE) MAE = 1 n ⋅ ∑ n 1 (x i -xi ) (23) with xi outputs, x i targets and n is the size of the testing dataset. Low MAE implies good fitting between actual outputs and targets. R value is closed to 1 mean a good relationship among actual data and predicted one.
Data description In this study, the input climatic data  (Dalala et al. , 2020)  are the real measurements of solar irradiance, module temperature, humidity and wind recorded in a weather station located at Jordan valley site (Hashemite Kingdome of Jordan) for the 2017 year. More details are reported in  (Dalala et al. , 2020). The monitored data every 30 minutes, for 12. 238 samples overall, are used as input of the developed electrical and thermal models. The test dataset of 3456 samples are plotted in Fig. 5. The horizontal solar irradiance varies between 0 and 1093. 06 W/m 2 with the mean value is 229. 52 W/m 2. The module temperature is in the range 4. 72 • C ÷ 51. 82 • C with the lowest value in January, the highest value in June and a mean value of 24. 13 • C. The humidity is 11. 33% in September, 99.
81% in March and 54. 12% on annual average. The highest wind value is recorded of 19. 4 m/s with a mean of 1. 1 m/s. The highest electrical and thermal efficiencies are 12. 7% in January and 44. 66% in June with a mean of 5. 27% and 6. 98% respectively. The distribution histograms of the used data are plotted in Fig. 6. The solar irradiance and the wind show a distribution that "leans" to the left (positive skewness) that means the lack of symmetry with respect to the mean, unlike by the module temperature and humidity which present a distribution similar to the uniform one. It is noted that the electrical and thermal efficiencies show the same distribution as the solar irradiance and wind. The Fig. 7  shows the correlation between the input climatic data and the efficiencies (electrical and thermal).
Conclusion The study presents the results of an ANN model to predict the electrical and thermal efficiencies of PVT air systems. The first part of the paper introduces a method to model the electrical and the thermal efficiencies. Such method is evaluated by using actual measurements of weather conditions. Then the efficiency modeled values are applied to train and test a predictive model based on ANN using the weather data as an input for the neural networks. Different combinations of the weather variables were considered to assess the performance of the ANN-based model. The results are presented, and an accuracy analysis is performed by RMSE, R coefficient and MAE. The outcomes demonstrate that the choice of the variables as input of the neural network is very important to perform a model with low error.
As for metropolitan network, WDM technology is used: 80 to 100 channels in C-band (that can double if using L-band) at 100 Gb/s to 400 Gb/s data rate each, are transported on these links for a total traffic of several tens of Terabits/s. When considering the integration of quantum technologies in telecommunications networks, there are two different aspects to take into account. One is the integration at the physical level and the other at the logical level. The first one is related more to the nature of the quantum signals and the second one to the products obtained through the transmission and processing of these quantum signals. Given our focus more on short term technologies, we will concentrate on QKD, although part of the discussion is also applicable to general quantum communications.
The existence of these type of nodes must be considered for the current integration of QKD in telecommunications networks. Due to the difficulty of transmitting and detecting quantum signals, most of the QKD testbeds have been designed as separate networks, avoiding as much as possible the losses and the interaction with classical signals. This situation has been also extended to the control and management structures, that essentially had a minimal connection with the data network where the secret keys are used. It is true that, since the technology has seen a limited deployment, the networks were just a set of point to point connections with pre-fixed key consumers and not much requirements in terms of management. Maintaining a completely separated infrastructure just for QKD and with such a scarce support for connectivity to the data network is a poor proposition, as it incurs in relatively high cost and poor scalability for the services offered.
The SDN-QKD scheme has been recently demonstrated  [44]. As a comparison with the generic node, Figure  3  represents the structure of a QKD enabled SDN node. The SDN approach has still other aspect to consider: softwarized infrastructures are acknowledged to be weaker from a security perspective, since a possible weak point can affect the whole system. This problem can be tamed when a physical security layer like QKD is made available to the whole network. From a telecom operator's point of view, there are two main parameters which must be differentiated when discussing specific use-cases. The first one stems from the reach limitations of today's QKD technology described in the previous sections. We discern between short-range use-cases, like intra-city data centre interconnection, and long-range use-cases.
We further need to distinguish between 'operator-internal' and 'operator-external' applications. What can be seen as 'operator-internal' application is dedicated to the security of the operator's network itself. This is related to the use of QKD by the operator to protect its own infrastructure (i. e. , protection of the control plane or management plane between operator's assets). The wording 'external' refers to the use-cases dealing with the protection of the data of the customers: in that case, the operator can offer protection of the customer data or offer the key as a service (KaaS). Going into more detail, the first example of a short-reach application is the protection of data centre interconnection (point-to-point application), especially, the traffic exchange between close-by data centres that are acting as disaster recovery backup for each other.
Typically, those replacement data centres are geographically disjoint enough to not suffer the same root cause for a failure. Still, they are close enough to each other to avoid effects on the user experience with respect to latency and delay when a re-routing of data becomes necessary. Those distances (20km-80km) allow for a single QKD hop, i. e. there is no need for intermediate trusted nodes on the link between the data centres involved. Also, today's QKD technology is developed enough to achieve sufficiently high secure key rates. This use-case can further be extended to other applications such as Fibre to the Antenna (FTTA) where a Point of Presence (PoP) or a Cell Site Gateway is linked to the antenna through a fibre (See Figure  4 ). Office to the users. In this case, QKD could be used to transmit securely the keys for the encryption of signals.
Another important aspect of QRNGs is the entropy estimation method that is implemented. There are three approaches that are commonly used today: -Validated physical model (VPM): A detailed physical model of the device is developed and validated by detailed characterization of the device components. Critical parameters of the device are measured, and confidence bounds are placed on their possible values. The entropy resulting solely from trusted quantum physical processes is calculated from the model, making worst-case assumptions about device parameters  [53] [54]. -Fully challenge-response protocol (CRP): Entropy bounds can also be derived by exploiting the unique statistical properties of quantum particles in challenge-response protocols. A typical scenario for such fully CR protocol is a loophole-free Bell test, in which one can derive statistics by processing the input and output data with minimal characterization of the hardware.
Quantum entropy and randomness within the telecommunications industry In contrast to other quantum technologies, QRNGs can be directly used in today's telecommunication systems. In the most simplified form, QRNGs can replace today's PRNGs, bringing the advanced security capabilities and increased performance that a trusted entropy source guarantees. The first and simpler integration scheme to interface QRNG devices with IT equipment is to employ standard network interfaces. There are at least two ways to do this. In the first scheme, the QRNG device is plugged "one-on-one" with the target system, as for instance a hardware security module (HSM), via a dedicated Ethernet interface. The second approach is based on making the QRNGs available as a network resource. Examples of this second approach, applied to network management tasks, were used recently for a demonstration of the Ordered Proof of Transit (OPoT) protocol  [40] , generating the link masks by means of 240 Mb/s of QRNG data to ensure the topology verification of a network service on a 100 Gb/s link.
e. , systems which solve problems by directly manipulating the interactions between qubits rather than breaking actions into a set of gate operations. Gate-based quantum computers, sometimes referred to as universal quantum computers, use logical gate operations (AND, OR, etc. ) on qubits. Quantum logic gates are the building blocks of quantum circuits: for example, CNOTs and unitary single qubit operations form a universal set of quantum computing. It should be mentioned that it is also possible to simulate quantum gate-based computers by using classical computers. There exists a variety of software libraries that can be used, each with different purposes: a comprehensive list of tools is available on Quantiki  [63]. Simulation can be made, for instance, using OpenCL (Open Computing Language)  [43]  which is a general-purpose framework for heterogeneous parallel computing on standard hardware, such as CPUs, GPUs, DSP (Digital Signal Processors) and FPGAs (Field-Programmable Gate Arrays).
However, quantum error correction involves a substantial multiplication of resources: the number of physical qubits required may be orders of magnitude greater than the number of error-free logical qubits seen by the algorithm. In addition, a fairly high fidelity must be realised for any known quantum error correction method to be applied. Because of these problems, many researchers are focusing in the so-called NISQC  [69]  -Noisy Intermediate Scale Quantum Computation-algorithms. This is the field of study interested in the algorithms and devices that can perform useful computations when the resources are constrained by e. g. a limited number of gates that can be performed before decoherence is too high, a limited number of qubits is available, etc. Probably, the two best known algorithms are the Shor's algorithm  [10] , able to factor a number into its prime factors with a superpolinomial gain over the best classical algorithm known, and Grover's algorithm  [70] , able to search a unstructured database with a polynomial gain over the best classical possible.
Voluntary fixations were identified as those followed by a fixation in a special confirmatory area. Spontaneous vs. voluntary fixation-related single-trial 700 ms MEG segments were non-randomly classified in the majority of participants, with the group average cross-validated ROC AUC of 0. 66 ± 0. 07 for LF-CNN and 0. 67 ± 0. 07 for VAR-CNN (M ± SD). When the time interval, from which the MEG data were taken, was extended beyond the onset of the visual feedback, the group average classification performance increased up to 0. 91. Analysis of spatial patterns contributing to classification did not reveal signs of significant eye movement impact on the classification results. We conclude that the classification of MEG signals has a certain potential to support gaze-based interfaces by avoiding false responses to spontaneous eye fixations on a single-trial basis.
, 2016 (Lawhern et al. , , 2018;; Schirrmeister et al. , 2017; Lotte et al. , 2018). When applied to the eye fixation-related data from our earlier experiments  (Shishkin et al. , 2016)  a compact CNN, the EEGNet  (Lawhern et al. , 2016 (Lawhern et al. , , 2018) )  with optimized hyperparameters provided a 16% improvement over the EEG classification results obtained with the previously used linear classifier  (Kozyrskiy et al. , 2018). For the MEG signal, CNNs with a different architecture have been proposed, providing an improvement over the EEGNet results  (Zubarev et al. , 2019). These new networks utilize spatiotemporal structure in the MEG data to extract informative components of the MEG signal. They assume that the MEG measurements are generated by a linear (spatial) mixture of a limited number of latent sources, which evolve non-linearly over time.
Experiment Paradigm and Gaze Data Processing We used MEG data from a study by our group (will be published elsewhere) recorded at MEG Center (a division of the Moscow State University for Psychology and Education, Moscow, Russia) in 32 healthy volunteers. The study was performed according to Helsinki Declaration and with the approval of the local ethical committee (protocol 12. 03. 2015/1). The participants played EyeLines game, the gaze-controlled version of Lines, a turn-based computer board game (also known as Color  Lines -Color Lines, 2003). This study was designed to identify early MEG markers of intention preceding the eye fixation or appearing early in its course. The previous EEG study  (Shishkin et al. , 2016)  was focused on the expectancy-related EEG component that should be prominent later during the fixation, i.
Thus, these saccades had, on average, much smaller amplitude than the saccades from the game board, and did not differ between the spontaneous and voluntary classes. To support the detection of the user's intention in the context of gaze-based interaction, the BCI must FIGURE 1 | An example of EyeLine game board display and an example of a sequence of eye fixations needed to make a move. 1, a target ball; 2, the confirmatory position; 3, a free cell to which the ball will be re-located. A square frame appeared around a ball when it was fixated for more than 500 ms. make the decision based on the MEG data collected before the time where the feedback is normally given in the case that the BCI is not used. The large saccade to the confirmation position appeared only after the feedback to the eye dwell was given (in our case, at about 500 ms from the fixation onset), plus some reaction time (typically around 300 ms), so the data collected before 500 ms from the fixation onset could be safely used for classification.
MEG Data Processing and Features Extraction Magnetoencephalography was recorded at 1000 Hz sampling rate using an Elekta Neuromag Vectorview (MEGIN/Elekta Oy, Helsinki, Finland) MEG system, with 306 sensors at 102 positions around the head (two orthogonal planar gradiometers and a magnetometer per position). Sensors with contaminated signals were manually rejected, then MaxFilter routine (MEGIN/Elekta Oy, Helsinki, Finland) was applied with the following settings: Temporal Signal Space Separation (tSSS)  (Taulu and Simola, 2006)  with t = 10 s and corr = 0. 9, movement correction (MaxMove) and normalizing head origin to the standard position (more details can be found in MaxFilter User's Guide, The Ohba Analysis Group, 2010). The total trial count, per participant, was (M ± SD) 128 ± 38 for voluntary and 294 ± 77 for spontaneous fixations.
Signals were downsampled to 125 Hz to reduce their dimensionality and expand the CNN receptive field in the time domain  (Luo et al. , 2017). The above procedure resulted in 204 × 87 data dimension (channels × down-sampled timepoints) in the case of -0. 2. 0. 5 s features per trial. Preprocessed MEG epochs of -0. 2. 0. 5 s related to the onset of the fixation were used as input to CNN in the main analysis, where we tried to model real-time use of the MEG-based BCI, which has to classify the fixation-related data immediately after the dwell time threshold (0. 5 s) is exceeded. The left border was placed at -0. 2 s, because, as our preliminary analysis showed, starting the time interval from -0. 4 s provided no improvement, and further extension of the interval to the left could lead to incorporating remnants of previous gaze behavior and the reaction to the feedback to an earlier eye dwell on a ball.
An additional analysis using longer MEG epochs from -0. 2. 1. 0 s interval was performed to check that the classifier was operating correctly. Unlike in the main analysis, this longer time interval also included a response to the presentation of the visual feedback (a square frame appearing around the gazed ball approximately at 0. 5 s). Although the feedback did not differ between voluntary and spontaneous fixations, it likely could lead to significantly different MEG response, in particular in the time range of the P300 wave. Moreover, gaze behavior following the feedback clearly differed between voluntary and spontaneous fixations, so MEG signal could include strongly different patterns of eye movement artifacts. Thus, we expected that the data from the -0. 2. 1. 0 s interval could be easily classified, and the classification result would provide an estimate of the CNN architectures' capability to discriminate single-trial fixation-related MEG data in an "easy classification" scenario.
Hyperparameter Setting First, we attempted to tune several architecture parameters, namely the number of LSs, the length of the temporal filter and max-pooling parameters by means of hyperopt Python package 1  and a grid search. Unfortunately, neither approach resulted in a significant increase of the classifier performance estimated as the ROC AUC values (area under the curve of the receiver operating characteristic). Therefore, we set the number of LSs and the length of the temporal filter to arbitrary values chosen low enough to avoid overfitting, to save the flexibility of convolutions and to decrease the training time. Specifically, for both types of CNN the number of LSs was set to 16, whereas the length of time filters was chosen to be 14, which was equal to 100 ms. A combination of drop-out  (Srivastava et al.
e. , the difference from random performance was not found for any fold) for both testing procedures. CV, one inside another. The inner loop corresponded to 4fold CV (dataset was divided into train/validation subset in ratio 3:1) and was used to determine the optimal number of the training iterations. It was defined as the average number training iterations, where the maximum AUC value was achieved on a given fold, over all CV folds. The outer loop was 5-fold CV, used to assess the classifier performance on the test subset. Such organized CV with several times testing was preferable in order to obtain a more stable estimation of classifier performance as in the situation of small dataset neural networks tend to overfitting and classification results may vary when data are splitted anew. Estimation of the CNN performance on the test dataset was carried out in two different ways, by a naïve approach and by means of a network ensemble.
(1) In the case of the naive method, after obtaining the optimal number of training iterations CNN was retrained anew, using the obtained iterations number, on the entire training sample. The trained network was then used to calculate ROC AUC on the test sample in each of the five folds of outer CV loop. (2) To construct an ensemble of the neural networks, we used the weights obtained on each fold of inner CV loop. Thus, an ensemble of 4 CNNs was obtained. Their predictions on the test subset were averaged to calculate the target class probability. The obtained probabilities for each MEG trial from the test set were used for classification. A permutation test was applied to examine whether the classifier had learned a significant predictive pattern in the data, that is, a real connection between the data and the class labels  (Ojala and Garriga, 2010).
We assumed that the topographical maps would present spatial patterns of features, most useful for the classifier, and that if they showed increased weights for frontal sensors, it would point out that the classifier heavily relied on eye movement artifacts, which mostly affect these sensors. Note that using gaze features to classify the voluntary and spontaneous gaze fixations could be, in principle, beneficial, but it deserves an additional study that should consider different issues, such as possible differences in gaze behavior patterns between the data used for classifier training and its application. At the current stage of the EBCI development it seemed reasonable to consider the classification of MEG data alone. To take into account all LSs obtained we considered two variants of LSs averaging: weighted averaging, when weights were calculated by eq.
DISCUSSION In this study, MEG co-registered with the eye tracker data was utilized to distinguish voluntary eye fixations used for sending commands to a computer from spontaneous ones. Above-chance classification results were obtained for 21 out of 25 participants using 700 ms (-0. 2. 0. 5 s) epochs of MEG data centered on the onset of the fixation. AUC values (M ± SD) for naïve and ensemble testing procedures appeared to be the same and equal to 0. 66 ± 0. 07 and 0. 67 ± 0. 07 for LF-CNN and VAR-CNN, respectively. Extending MEG epochs up to 1000 ms from the fixation onset to include the evoked response to the visual feedback to fixation improved the classification performance dramatically (Table  2 ) for both CNN architectures: for LF-CNN AUC appeared to be 0. 91 ± 0. 06 and 0. 91 ± 0. 06, for VAR-CNN -0.
At the same time, a full FVE obtained for signal reconstructed from spatially filtered data showed that 35. 12% and 31. 92% of the input information passed through the spatial filter and was utilized by the classifier in case of intervals -0. 2. 0. 5 s and -0. 2. 1. 0 s, respectively. Thus, the LF-CNN network likely classified the voluntary and spontaneous eye fixations based mainly on MEG components originating from brain sources and not oculomotor activity. As we made no prior assumptions regarding the MEG signatures that might inform the classification, we were interested in spatiotemporal features of the signal that were extracted from MEG by CNN. It should be also emphasized, that the pattern obtained from the forward model does not reflect the full spatiotemporal data/feature structure, but only shows from where the signal features were taken by the classifier.
Gaze data are typically considered to be not different between voluntary gazebased control and spontaneous gaze behaviors. (2) Use of MEG and EEG spectral features. Some advanced methods of deep learning sensitivity analysis, e. g. , the gradient-based attribution methods  (Ancona et al. , 2018)  and methods for frequency spectral analysis in deep architectures developed for brain signals application in works  (Schirrmeister et al. , 2017; Hartmann et al. , 2018)  can also help to reveal discriminative signal features in the experiment paradigm described in this work. (3) Use of more data for classifiers training, especially by using several sessions recorded on different days in the same participant or a large group of participants. The latter approach, i. e. , transfer learning, was shown to be feasible for the CNNs used in this work  (Zubarev et al.