dataesr/scientific-paragraphs-categorization huggingface dataset:
Description of Dataset The dataset used in this study was generated in the LIAS laboratory of Poitiers. The experimental bench in Figure  8  was set up by Pr. Gérard Champenois from the University of Poitiers. It consists of a three phases induction motor (see Table  1 ), a DC motor that acts as a load, an Altivar 58f drive and a dSPACE control board. Table  2  shows the experiment files containing: currents, voltages and time measurements. Different recordings were established corresponding to different loads with speeds close to the nominal functioning of the induction motor since the PLC was not used for control-the IM was supplied directly with necessary power. The dataset is split into three health classes: "Healthy", "one broken rotor bar (1BRB)" and "two broken rotor bars (2 BRB)", as shown in Table  3.
An example of full load current records can be found in Figures  10 11 12. In these figures, the broken bar influence can be seen on the current envelope. The motor was connected to a 50 Hz supply, and the connection between the bench and the Matlab software was established using the dSPACE card. Half of the recordings were designated for the model training, and the other half was used for testing. Tests are divided into three categories corresponding to the three speeds: • Test 1 is low speed (highest load); • Test 2 is medium speed (medium load); • Test 3 is high speed (lowest load). This will allow us to validate the model reliability and robustness in different working conditions.
Comparison In the field of IMs fault detection, the most important criteria for industries are: precision, reliability, execution time and the difficulty of deployment in industrial environments. These criteria have a direct impact on the cost of service and maintenance. The precision of the DICNN is clearly better than that of the PCA's, basing this conclusion on the dataset we have. Furthermore, the DICNN is more reliable due to its reliance on the physical knowledge and isolation of fault information, which the PCA approach lacks, making it more susceptible to mistakes in the presence of noise. Another advantage of the DICNN over PCA is the absence of false alarms when validating with our data. It is true that enlarging the data set or changing other parameters such as the motor type, size or fault position and severity can affect the model's score, but the first obtained results show that even with these different changes, the DICNN remains superior.
Application of Motor Models Motors used in industry today come with different characteristics and sizes. The idea of fault detection and monitoring will always be the same, either by finding differences in the measured signals or finding fault signatures that can indicate the presence and gravity of damage. The PCA method is somehow limited as it is applicable only to motors sharing the same characteristics. In order to obtain a monitoring model, the data have to be collected from a motor close in characteristics to the target. The damage can be either represented by holes in the rotor bars or by overworking the motor. The recording of data has to be under similar working conditions (speed and load). The CNN models discussed in this article are more adaptable to size change. This approach analyses the location of the fault directly, and upon finding the fault signature, the motor's diagnosis can be easier.
In total, 18 different fragility curves have been assigned (3 models for the 83 single-span bridges, 3 for the 7 continuous multi-span bridges, and 12 for the 28 arch bridges), mostly taken from the SYNER-G database  [39]. • Road segments, i. e. , the roads nearby buildings in Bagnères-de-Luchon that might be blocked because of debris from collapsed buildings. These vulnerable segments are identified manually as road segments in Bagnères-de-Luchon that are adjacent to buildings. An inventory of the number of buildings associated with each of the vulnerable road segments has been carried out using Google Maps (see Fig. 5 ). More details on the procedure to estimate the probability of road blockage by debris of collapsed buildings are provided in Argyroudis et al. [10]. In the present case-study, a total of 17 road segments exposed to blockage by debris have been found.
This program features a class based implementation of the impact estimation algorithm that can be integrated in Python projects. It also integrates a reporting tool to create HTML and PDF impact estimation reports of a product. To be functional, this program has been interfaced to the Open Food Facts database  (Open Food Facts, n. d. )  providing packaging information. It uses data from the Ciqual (ANSES, 2020) and FCEN  (Santé Canada, 2015)  nutritional databases, and the Agribalyse (ADEME, 2020b) environmental impact database. All these data are freely available and fit to a French context, but it could be easily adapted to other data sources.
Introduction Low and middle-income countries are experiencing a substantial increase in cancer incidence  (Sung et al. 2021). In India, many cancers are detected at a late stage and many of those cancers detected early are improperly managed. Inadequate knowledge and training of medical graduates could be one of the many reasons for the unmet need for early detection services. Active engagement of primary care providers can have a significant impact on cancer outcomes. Mobile health technology (mHealth) plays a significant role in the overall development and personal development This manuscript is one component of the mHealth project "Oncology Education and Training for Providers using Mobile Phones" which developed a mobile application (M-OncoEd). Device usability and interactive learning were the key features of the M-OncoED.
Data Processing and Analysis Thematic analysis is done with a combination of the inductive and latent approaches. Six steps of the Permission from state health authorities was taken before approaching physicians during the formative phase and for personal communications via emails, SMS, and WhatsApp groups during the implementation phase. Informed consent was incorporated in the mobile application and all participants who gave consent were allowed to enter the modules. Feedback from participants was also obtained from the mobile application. Continuous technical support was ensured throughout the intervention.
Background: PET/CT image quality is directly influenced by the F-18-FDG injected activity. The higher the injected activity, the less noise in the reconstructed images but the more radioactive staff exposition. A new FDA cleared software has been introduced to obtain clinical PET images, acquired at 25% of the count statistics considering US practices. Our aim is to determine the limits of a deep learning based denoising algorithm (SubtlePET) applied to statistically reduced PET raw data from 3 different last generation PET scanners in comparison to the regular acquisition in phantom and patients, considering the European guidelines for radiotracer injection activities. Images of low and high contrasted (SBR = 2 and 5) spheres of the IEC phantom and high contrast (SBR = 5) of micro-spheres of Jaszczak phantom were acquired on 3 different PET devices.
Thus, optimizing the use of PET CT technology in a clinical setting needs to balance carefully the injected activity/acquisition duration ratio. The current standard approach for image reconstruction of PET Raw data is based on iterative methods. The most commonly used being the Ordered Subset Maximum Expectation (OSEM) algorithm, which requires setting up a global number of iterations to reconstruct the image. Theoretically, the higher the number of iterations, the closest to the expected reconstructed image. However, the increased number of iterations generates noise that worsens image quality and might cause misinterpretations and quantification errors by reducing the signal-to-noise ratio. To reduce PET image noise, different approaches have been proposed. The first approaches incorporate image noise reduction within the reconstruction process.
Few years ago, neural networks (NN) organized in multiple layers, called deep learning (DL), have been introduced as an efficient tool to perform image denoising on PET raw data with low statistic. The results, essentially published for brain PET images, suggest that one can recover the same noise level in the images with lower dose than the one obtained with the standard acquisition dose  [10] [11] [12]. Many NN, from supervised or semi-supervised convolutional neural network (CNN) to unsupervised CNN and their derivatives, have been proposed as post-reconstruction processes  [13] [14] [15]. More recently, authors proposed to use NN, within the reconstruction framework, to improve PET image quality  [16] , or to perform directly the image reconstruction from sinograms with a U-NET approach  [17].
Essentially developed in the framework of cerebral examinations, few teams have so far been interested in the use of neural networks for denoising in the case of whole body examinations  [18, 19]. Based on the work from Stanford University  [20] , a new FDA and CE cleared software has been introduced (SubtlePET, Subtle Medical, Menlo Park, CA, USA) to improve clinical PET images, on wholebody PET exams acquired at 25% of the count statistics. This NN is based on a 2. 5D encoder-decoder U-Net deep convolutional neural network trained on North American datasets and has been evaluated on a small population  [21] [22] [23]. Only a very recent study investigates the ability of SubtlePET to produce images of adequate diagnostic confidence on 61 patients, considering a counting statistic equivalent to 66% of the original statistic (i.
e. a 33% reduction of the original statistic) for old-generation PET scanners (without time of flight-TOF-technology)  [24]. According to SubtlePET manufacturer, a 75% dose reduction can be achieved without any information loss with SubtlePET enhanced images, but to our knowledge, nobody has investigated so far, the important topic of model generalizability, i. e. , how well can an AI model trained on North American PET data generalize to new patients from different practices, different scanners and different injected dose standards. Therefore, our aim in this study was to assess the performance and potential limitations of SubtlePET on phantom datasets and on a large population of whole body PET clinical exams performed under EANM or EU practice guidelines for FDG injected dose, considering count statistics down to -66% and on latest generation of PET systems (TOF and SiPM).
The raw data were reconstructed according to the routinely used OSEM or BPL protocols (cf. Table  1 ) and for the 1/2 and 1/3 acquisition duration, post-processed with Sub-tlePET (named PET50 + SP and PET33 + SP). We used PET/CT images from the first 110 patients who agreed to participate to this study. Those patients benefited from PET examination addressed for various pathologies (oncology or internal medicine representative of the clinical activity) during October 2020. All patients were informed that their data were fully anonymized for research purposes and gave their approval (IRB approval was obtained for this study). PET/CT scans were acquired 60 min after the injection of 3 MBq/kg of F-18-FDG, with an acquisition time varying from 1. 5 min/bed position for the DMI4 to 2 min/bed position for D710 and DIQ4 (cf.
SubtlePET algorithm SubtlePET uses a 2. 5D encoder-decoder U-Net deep convolutional neural network to perform denoising. The software takes a low count PET image (from shorter scan or lower dose) as input and generates a high quality PET image (close to full dose image) as output. It employs a convolutional neural network (CNN)-based method in a pixel's neighborhood to reduce noise and increase image quality. Using a residual learning approach and optimized for quantitative (L1 norm) as well as structural similarity (SSIM), the software learns to separate and suppress the noise components while preserving and enhancing the structural components. The networks were trained with paired low-and high-count PET series coming from a wide range of clinical indications and patient BMI and from a large variety of PET/CT and PET/MR devices (10 General Electric, 5 Siemens and 2 Philips models).
The training data included millions of paired image patches derived from hundreds of patient scans with multi-slice PET data and data augmentation. All the training PET data was acquired in the USA or Canada with the average injected FDG dose ~ 6 MBq/kg and acquisition time per bed of 2-3 min/bed. For the training regime, low count data was either retrospectively reconstructed or prospectively acquired at 1/4th the acquisition time or dose (i. e. , 1. 5 MBq/kg at 2-3 min/bed or 6 MBq/kg at 30-45 s/bed).
For each VOI, the SUV max , SUV mean , SUV peak and standard deviation (SD) were recorded to derive: the sphere contrast recovery coefficient (CRC) for the phantom data only, the contrast to noise ratio (CNR) and the background variability (BV) by using: We also calculated the percentage variation of SUV max , SUV peak , CRC, CNR and BV (ΔSUV max , ΔSUV peak , ΔCRC, ΔCRC and ΔBV respectively) regularly used in clinical practice, between the SubtlePET-enhanced images (PET50 + SP, PET33 + SP, PET100 + SP) and the standard PET100 images. We studied the correlation between BV and SUV variations (ΔBV and ΔSUV) as a function of the patient BMI to evaluate the efficiency of SubtlePET on patient with noisier images. Additionally, quantitative image quality metrics like peak signal to noise ratio (PSNR) and structural similarity index (SSIM) were also calculated between the regular duration PET scan (PET100) and the faster PET processed and unprocessed series to assess for the presence or the absence of absolute errors (data loss, corruption, alteration, or exaggeration).
They did not investigated a larger dose reduction due to practical considerations, but not from objective reasons. Our results on phantom experiments, clearly confirm that a dose reduction up to 50% can be achieve without any lost in the lesion detectability, signal to noise ratio or quantitation. Our clinical results are in line with this work, since they reported no significant difference between datasets in lesions'detectability, target lesion mean SUV max value, and liver mean SUV mean values. No false-positive lesions were neither reported in PET enhanced with SubtlePET. Compared to other deep learning based denoising approaches  [18, 19] , the optimization steps implemented in SubtlePET were able to correctly recover the SUV max values of the lesions maintaining a noise level equivalent to full-time images.
Conclusion We assessed the performances and potential limitations of a new neural networkbased denoising approach on phantom datasets and on a large patient population from daily practice, benefiting from F18-FDG PET exams based on EU recommended standard of 3 MBq/kg injected activity. In our phantom study and patient image quality analysis, reducing the count statistic as low as 33% leads to a loss of detectability and loss of image quality, making the image not good enough for interpretation. Compared to other similar architectures, for the half-duration acquisitions, the optimization steps implemented in SubtlePET statistically recover the quantification of the phantom experiments and correctly but not statistically recover the SUV max values of the lesions. It also maintain a noise level equivalent to full-time images, yielding a lesion contrast-to-noise ratio comparable to that of full statistical PET acquisition and did not induce any modification of the final clinical report.
We assume the baryon perturbation traces the cold dark matter on large scales, so d = + - ⎜ ⎟ ⎛ ⎝ ⎞ ⎠ ( ) ˜( ) ( )[ ( )] ( ) x x x T z T z x T T , 1 1 d d = r H. In this paper, we focus on the limit where the spin temperature is T S ? T CMB , which is valid soon after reionization begins. As such, we can neglect the dependence on the spin temperature. Also, for simplicity, we ignore the effect of peculiar velocity, because it only weakly affects the light-cone effect. We generate a realization of the 21 cm brightness temperature fields using the density and ionized fraction fields from seminumerical simulations with the code 21cmFAST, given an initial condition in the density fields. We then interpolate the snapshots at different time to construct the light-cone data cube along the line of sight (LOS) within a comoving distance of a simulation box.
These ranges are consistent with  Greig & Mesinger (2018). Other parameters that have less impact on reionization are fixed throughout this paper, e. g. , R mfp = 15 Mpc (the mean free path of ionizing photons). Also, cosmological parameters are fixed as (Ω Λ , Ω m , Ω b , n s , σ 8 , h) = (0. 692, 0. 308, 0. 0484, 0. 968, 0. 815, 0. 678)  (Planck Collaboration et al. 2016). Figure  1. The workflow of the DELFI-3D CNN. The data d are prepared using the simulator (21cmFAST herein) with the parameters θ. The data are compressed to the low-dimensional summaries t using some compressors (trained 3D CNN herein). The parameter-summary pairs (θ, t) are fed to an ensemble of NDEs that are trained to learn the data likelihood p(t|θ). The posterior distribution is inferred from the learned data likelihood and parameter prior using Bayes' theorem.
For each given set of reionization parameters, we generate a mock light-cone data cube of 3D 21 cm images, or a sample. We use the Latin Hypercube Sampling  (McKay et al. 1979)  to scan the EoR parameter space. For the 3D CNN, we generate 10,000 samples, in which 8000 samples are used for training the 3D CNN, 1000 for validation, and 1000 for testing the 3D CNN. After the 3D CNN is trained, its network weights are fixed. Then we generate 10,000 new samples which are compressed into parameter-data summaries pairs by the trained 3D CNN and then provided to DELFI for training the density estimators (with 8100 samples), validation (with 900 samples), and testing the DELFI-3D CNN (with 1000 samples). The initial conditions for all realizations were independently generated by sampling spatially correlated Gaussian random fields with the power spectrum given by linear theory.
Network Training For regression problems, loss functions like the mean absolute error and mean squared error aim to penalize the deviation between the true and predicted values. Our objective is to attain a loss minimum where the loss function reaches the smallest possible value, which may be facilitated by using a proper optimizer like RMSprop  (Tieleman & Hinton 2012). The key ingredient for an optimizer is the recipe for an appropriate learning rate: too large a learning rate often leads to local minima, but convergence is hard to achieve if the learning rate is too small. In this paper, we choose a variable learning rate that decreases by a factor of 10 once the learning begins to stagnate. Generally, a validation data set that is independent of the training and test data sets is used during the training process, for two purposes-to choose the optimal model on the 9 The effect of initial conditions on the parameter estimation is discussed in Appendix D.
05/lg(e) ≈ 12%, as the output parameter y is in the logarithmic scale of base 10. We also note in Figure  4  that near both upper and lower limits of the parameter range, the scatters from the perfect recovery become larger, which means worse recovery accuracy. This is probably due to boundary effects in the parameter space, i. e. , the networks cannot take the data outside the boundaries. This could be avoided in practice by choosing the range of the parameter space wide enough around the most likely values indicated by a trial analysis. The recovery performance test can be further made by plotting in Figure  5  the 2D joint probability density distribution of the fractional errors, ò = (y predy true )/y true , where y represents either the parameters directly predicted by the CNN (in the logarithmic scale), ( ) T log vir and z ( ) log , or the deduced parameter (in the linear scale), T vir and ζ.
This level of recovery accuracy improves upon the earlier analysis based on 2D CNN (G19). In their work, the relative error of the parameter ( ) T log 10 vir is 1%, which is comparable to ours, but the relative error of the parameter ζ is ∼10%. While the total light-cone volume therein is much larger than ours (300 versus 100 cMpc per each transverse side to the LOS), the 2D study only extracted five slices along the LOS for each light cone. In other words, for the same LOS redshift interval, the data volume exploited in our 3D CNN study is 100 2 /(5 × 300) ≈ 6. 7 times larger than in the previous 2D study. This explains qualitatively why the DELFI-3D CNN recovery outperforms the 2D CNN study. For the purpose of comparison, we perform an MCMC analysis of the 21 cm power spectrum alone based on the 3D light cone and using a Gaussian likelihood approximation.
We also note that, in principle, the 3D CNN can be replaced by other compressors that optimize a trade-off between image compression and information extraction. This provides room for improving the performance of posterior inference, which will be considered in a follow-up paper. ). BDW acknowledges support from the Simons Foundation. We thank Yangyao Chen, Rui Huang, Benoit Semelin, Hayato Shimabukuro, and Meng Zhou for useful discussions and helps. The deep learning computations and reionization simulations were ran in the R2D2 and Metis GPU workstations and at the Venus cluster at the Tsinghua University, respectively. Software: 21CMMC  (Greig & Mesinger 2015 , 2017 , 2018) , 21cmFAST  (Mesinger & Furlanetto 2007; Mesinger et al. 2011) , pydelfi  (Alsing et al. 2019).
Introduction Nowadays, many bioinformatics pipelines rely on k-mers to perform a multitude of different tasks. Representing sequences as sets of words of length k generally leads to more timeefficient algorithms than relying on traditional alignments. For these reasons, alignment-free algorithms have started to replace their alignment-based counterparts in a wide range of practical applications, from sequence comparison and phylogenetic reconstruction  [34, 36,  Our first contribution is the enhancement of CSFs with a Bloom Filter to deal with datasets of very small entropy and to achieve better space usage. We chose an E. Coli and a C. Elegans genome to test our implementation. With these examples, we demonstrate the advantages of our BCSF implementation over a simple CSF. Our second improvement takes advantage of the fact that similar k-mers tend to have identical (or similar) counts (see also  [23] ).
[1]  proposed a solution for CSF taking an asymptotically optimal nH 0 (f ) + o(nH 0 (f )) space (n size of the underlying value set), however the solution is rather complex and probably not suitable for practical implementation. As of today, to our knowledge, the only practical implementation of a CSF is GV3CompressedFunction  [11] , found in the Java package Sux4J (https://sux. di. unimi. it/). Although entropy-sensitive, the method of  [11] , however, has an intrinsic limitation of using at least 1 bit per element, due to involved coding schemes. This is a serious limitation when dealing with very skewed distributions of values, where one value occurs predominantly often and the empirical entropy can be much smaller than 1. This is precisely the case for count distributions in whole genomes, studied in this paper.