dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2021). Some notable artifacts from Planet NICFI data include areas where apparent dust clouds were found nearby cities in Mato Grosso likely due to radiometric correction (Supplementary Fig. S1 ); illumination artifacts in the edges of forests where saturated brighter signals were found (Supplementary Fig. S11 ); and brightness effects created by the mosaicking in locations of severe cloud influence (Supplementary Fig. S3-S5 ). The brightness effects were dealt with by including samples of those locations in the training dataset. To minimize the cloud issues, we developed our own cloud mapping model to mask out remaining clouds in the Planet NICFI product. Other issues related to Amazonian environments that caused the model to produce false positives and commission errors were large flowering trees being confused with logging (likely because their round shape could be confused with a logging deck, as well as having low NIR values), and narrow rivers being confused with roads.
Both products are based on visual interpretation and manual vectorization of maps, but can now be produced in an automatic and timely manner through our approach. In the context of REDD+, policy and decision making, where pixel counting may not be the preferred approach, the maps generated by the DL-DEGRAD approach can be used as a starting point for a sample-based approach to estimating the areas of land cover and change as shown in this paper  (Olofsson et al. , 2014; Global Forest Observations Initiative, 2020). The implementation of near real-time forest monitoring from biannual to monthly, or even weekly, time-steps should be possible with further developments. Planet NICFI provides unprecedented open highresolution data (4. 77-m) at bi-annual (Dec 2015 to Jun 2020) to monthly frequency from September 2020 to recent days, which allows for the continuous monitoring of forest degradation in the future, given the continuity of the program.
Around 24% of these degraded areas have already been deforested by 2021, depicting the pathway of which degradation leads to deforestation. The remaining forests in 2021 account for 301,960 km 2 , of which 14% were assessed as degraded in between 2017 and 2021. Overall, these degradation estimates exceed by a factor of two to three those from official datasets from DETER/INPE. Part of this degradation is in fact legalized though state logging concessions program  (Silgueiro et al. , 2021). However, that does not change the fact that these forests dynamics are being widely changed without much thought to what this will mean for the future of the Amazon forests and potential impacts to the climate. Meanwhile, recent studies point out that logged forests are a net source of carbon to the atmosphere  (Mills et al.
Conclusions Tropical forest degradation can now be mapped at a cost-efficient manner with an unprecedented level of detail in spatial and temporal scales using a trained U-Net deep learning model applied to Planet NICFI data. Moreover, it is possible to provide attributions to the causes of degradation (logging, forest fire, and road construction), which can contribute to ensuring the degradation classes are well represented in the samples being used for area estimates on REDD+ monitoring. We also show that at least annual temporal resolution is necessary to reliably map degraded forests. The performance of our automated approach was found to be consistent with degradation products generated from visual interpretation of 30-m satellite data and substantial manual editing (e. g. , DETER and SIMEX).
However, the NICFI data allows mapping degradation with additional detail which is likely not possible with Landsat. Our new DL-DEGRAD approach shows that the tropical forests of Mato Grosso are being degraded by logging and fire at the rate of 8443 km 2 yr -1. Of the remaining forests in 2021 (301,960 km 2 ), at least 14% are currently degraded. In the period of analysis, 2020 showed the largest area of degradation (13,294 km 2 ), exceeding deforestation twofold. In the future, the model will be evaluated and improved to extend the mapping of forest degradation across the entire pan-tropical forests. 33 and the National Council for Scientific and Technological Development (CNPq), Brazil, productivity grant 314473/2020-3. R. F. is supported by the research grant DeReEco (34306) from Villum Fonden.
Acknowledgements We thank NICFI initiative, and the Level-2 access granted to R. D. , which was imperative to conduct this large-scale study. We thank  SERVIR  and  FAO  for the  Collect Earth Online (CEO)  platform which allowed the collection of reference data for the design-based accuracy and area assessment in this study. We thank four reviewers and editor  Marie Weiss  for insightful comments that helped improve this manuscript.
This will lead to further high-throughput screening of small molecules and for that purpose, specificity and selectivity will need to then be tackled still in vitro and also in vivo. Further experiments are necessary to acquire a better fundamental knowledge of Fusarium cob intron, to improve its splicing efficiency in vitro and to compare with self-splicing introns from the same subgroup or not, and from other pathogen organisms. This will be facilitated by the increasing interest of the scientific community in RNA structure and its relationship with small molecules and by the development of new techniques and tools, such as the recent public database RiboCentre  [67].
RNA Production 4. 2. 1. Construct Preparation All the introns surveyed are resumed in Table  S1  with primers listed in Table  S2. Briefly, a construct consisted in a T7 polymerase promoter followed by one guanosine residue, the last 80 bp of the 5 exon, the intron, and the first 80 bp of the 3 exon of the gene. PCR amplification was conducted using iProof HF Master Mix (Bio-Rad) according to the supplier's recommendations and using in house gDNA, except for Fusarium oxysporum which was obtained through gene synthesis (GeneArt™, Regensburg, Germany), based on mitochondrial genome sequence from F. oxysporum strain UASWS AC1 (GenBank KR952337. 1). The PCR products were purified using QIAquick PCR purification kit (Qiagen, Venlo, The Netherlands) and eluted in 30 µL of RNAse-free water.
The substrate is composed of the first half of the P1 domain (underlined), pursued by the reverse-complement of the 5 Duplex (5 -TGGGTTACCATTGTTGAATATGTAAGGATGA-3 ). RNA binding free energies were computed with IntaRNA, from Vienna RNA package  [70]. The ribozyme was produced from a PCR template as described earlier for the native intron and the unlabeled substrate was ordered as a single strand RNA (ssRNA) from Eurofins Genomics. RNA substrate carries a 5 IowaBlack ® Quencher (in bold) and Internal 6-FAM Fluorophore within the P1 loop (underlined): 5 -UGGGUUACCAUUGUUGAAUAU-GUAAGGAUGA-3 and were ordered from IDT (Integrated DNA Technologies, Coralville, IA, USA). RNA substrate was dissolved in 5 mM HEPES, 20 mM KCl and 0. 2 mM MgCl 2 (pH 7. 4) at 100 µM (stock solution) and aliquoted at 1 µM (working solution) in nuclease-free water.
Acknowledgments: We thank  Quentin Vicens  ( University of Colorado ),  Benoit Castandet  ( Paris-Saclay University ),  Kévin Baudry  ( Boyce Thomson Institute ) and  Vinicius Costa Galvao  ( Bayer CropScience ) for insightful discussions. We are grateful to  Florent Villiers  ( Bayer CropScience ) and  Glen Calvar  ( INSA Lyon ) for comments on the manuscript. We thank  Rachel Baltz ,  Angela Becker  and  Ana Dias  ( Bayer CropScience ) for their support on this project. We are also grateful to  Rhea Stoppel  ( Bayer CropScience ) and  Andreas Timmermann  ( Bayer Pharmaceuticals ) for the funding.
Deng, J. ; Shi, Y. ; Peng, X. ; He, Y. ; Chen, X. ; Li, M. ; Lin, X. ; Liao, W. ; Huang, Y. ; Jiang, T. ; et al. Ribocentre: A Database of Ribozymes. Nucleic Acids Res. 2022, 51, D262-D2681. [CrossRef].
Cannone, J. J. ; Subramanian, S. ; Schnare, M. N. ; Collett, J. R. ; D'Souza, L. M. ; Du, Y. ; Feng, B. ; Lin, N. ; Madabusi, L. V. ; Müller, K. M. ; et al. The Comparative RNA Web (CRW) Site: An Online Database of Comparative Sequence and Structure Information for Ribosomal, Intron, and Other RNAs. BMC Bioinform. 2002, 3, 2. [CrossRef].
We discuss the impact of the recent LHCb update on the two lepton-flavour universality ratios R K and R K * , and the CMS update of B(B s → μ + μ -) regarding the possibility of New Physics in b → s + -decays. We perform global fits of the New Physics Wilson coefficients defined in the model-independent approach of the Weak Effective Theory at the b-quark mass. We discuss three different frameworks for this analysis: (i) an update limited to the experimental data but using the same theoretical framework as in earlier works, (ii) a full update concerning both the experimental inputs and the theoretical framework, (iii) an analysis without the LHCb results on electron modes. The comparison between these various sets of results allows us to identify the differences stemming from the various components of the analysis: new experimental results, new inputs for the hadronic form factors, the role played by LHCb data on electron modes.
All four measurements show no deviations from the SM expectations, in stark contrast with the earlier LHCb measurements. LHCb can identify more easily the properties of muons than electrons, so that the observables involving electrons may be affected by large experimental systematics (related to missing backgrounds and particle identification) that have proved difficult to estimate and have required a long dedicated experimental effort to pin down. The phenomenological implications of this result w. r. t selected WET, LFU and LFU Violating (LFUV) scenarios as well as within the framework of specific dynamical models has been studied in Ref. [59]. These new results have an impact on the NP scenarios assuming a large LFUV between muons and electrons, as the new data suggest NP contributions of similar sizes to both types of modes.
[50]  by including the recent results on R K , R K * and B s → μ + μ -, but keeping the same theoretical inputs. We discuss the modifications induced by this partial update of our framework in a schematical way. In Sect. 3, we fully update our analysis by considering not only the experimental measurements included in the previous section, but also the theoretical improvements concerning the determination of form factors from B-meson Light-Cone Sum Rules  [16, 19]  and lattice computations  [60, 61]. The results obtained in this fully updated framework should be considered as the main outcome of this article. In Sect. 4, given the systematics attached to electrons at LHCb, we change the perspective and explore global fits which do not involve LHCb measurements with electrons. At the end of this section, we predict the LFU ratios R X (instead of taking them as inputs) for a specific LFU NP scenario which is favoured by current data and can be connected with the deviations observed in R D ( * ) ratios.
In Sect. 5 we perform a mode-by-mode and bin-by-bin extraction of C 9μ to check that all values are consistent with each other and do not exhibit a dependence on the mode or the bin, in opposition to the expected behaviour of a long-distance charm-loop contribu-tion. We draw a few conclusions in Sect. 6. The appendices provide further detail on: (i) the results of our updated global fits without LHCb data on electron-dependent observables from LHCb in Appendix A and (ii) a summary of our updated SM predictions with our updated experimental inputs and theoretical framework in Appendix B.
The allowed parameter space for C NP 9μ has reduced w. r. t Table  3  of Ref. [50] , due to the new R K ( * ) measurement from LHCb. The allowed 1σ range for C NP 10μ has shifted from positive  [50]  to negative values. The current central values of the 6D fit yield R K ∼ 0. 92 within this theoretical framework, as can be seen from the explicit expression of R K in Ref. [69]  (with a particular role played by the sign change in C NP 10μ compared to Ref. [50] ). Table  4  shows the results for some prominent LFU and LFUV scenarios corresponding to fits to the "All" dataset, motivated by theoretical models and introduced in previous works. The Pull SM follow exactly the same trend of a 2. 8σ reduction as compared to Ref. [50] , like in the case of the 2D fits, with two main exceptions: Scenarios 7 and 8.
The latest measurement of the R K ( * ) observables clearly favours this scenario to explain the "All" dataset. Scenario 0 is on a similar footing as Scenarios 7 and 8 regarding Pull SM as well as p-values. If R K ( * ) is confirmed to be compatible with the SM in other isospin channels and/or different experimental environments, Scenario 0 may become a better fit to the data and the Pull SM may increase significantly as compared to Scenarios 7 and 8. Table  4  Experimental update only. Most prominent patterns for LFU and LFUV NP contributions from Fit "All". Scenarios 5 to 8 were introduced in Ref. [49]. Scenarios 9 (motivated by 2HDMs  [70] ) and 10 to 13 (motivated by Z models with vector-like quarks  [71] ) were introduced in Ref. [64]  Scenario Best-fit point 1σ Pull SM p-value Scenario 0 3 Full update including both experimental and theoretical new inputs C NP 9μ = C NP 9e = C U 9 -1.
"Electron-less" global fits The notorious difficulties encountered in the proper identification of electrons at LHCb have been confirmed by the significant change of the values of the LFU ratios R K and R K *. In this section, we attempt at apprehending the role of the LHCb measurements of electron modes and determining the impact of the rest of the data in our earlier results. We thus perform the same analysis of 1D, 2D, 6D and LFU global fits in the updated theoretical framework discussed in Sect. 3. 1, but with a restricted data set compared to Sects. 2 and 3. 2: • We remove all LFU ratios R X (with X = K , K S , K * , K * + ) observables from LHCb  [46, 57, 58]  but keep the ones from Belle  [42] [43] [44]  (we thus call the fit "electronless" in a slightly improper manner). • We remove also all electronic observables from b → se + e -modes coming from LHCb  [33].
Prediction of R K and R K * in a scenario relating charged and neutral anomalies As discussed in Sect. 3. 2, Scenario 8 provides a natural link between charged and neutral anomalies  [64, 77]. If we consider the results obtained in Sect. 4 from a data set without LHCb data on electrons, we can predict R K ( * ). Indeed, the indirect implications from the set of all other observables in some NP hypothesis may have consequences on observables involving electrons due to the specific structure of the NP hypothesis. In this scenario (C V 9μ = -C V 10μ , C U 9 ), the SM-like value for B(B s → μ + μ -) constrains NP in the only Wilson coefficient sensitive to this branching ratio here, namely C V 10μ. The link with C V 9μ in this scenario constrains then the size of LFUV NP. Finally, the remaining observables, mainly P μ 5 and the link with R D ( * )  [64, 77] , constrain C U 9.
In order to identify the impact of each component of the analysis, we consider three different cases: a partial update concerning only the experimental data and in particular the new R K and R K * data, a full update concerning both experimental and theoretical inputs, and an analysis without LHCb results on electron modes. Most scenarios and hypotheses exhibit a reduction of the Pull SM between 2 to 3σ when we consider both partial and full updates. In particular the LFUV fits remain below the 1. 5σ level of significance for our main update. Our new theoretical framework does not yield significant differences in terms of Pull SM and hierarchy of hypotheses compared to our previous framework. There are however differences in terms of pulls for some observables. We can see that the tension for B(B → K μ + μ -) increases due to the new determination of the form factors based on lattice QCD.
A Global fits without LHCb data on electrons We present the full list of results related to fits presented in Sect. 4. We perform the fits in the updated theoretical framework discussed in Sect. 3. 1, with a restricted data set compared to Sects. 2 and 3. 2, removing all LHCb electronrelated results as described in Sect. 4. The main objective of this exercise is to gauge the coherence of the current LHCb b → se + e -data with the rest of the dataset and see whether an improvement of the internal coherence of the fit can be reached by excluding this data. See Tables  11, 12 , 13, 14, 15, 16.
pean Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 860881-HIDDeN [S. D. G. ]. This study has been carried out within the INFN project (Iniziativa Specifica) QFT-HEP [M. N. B. ]. J. M. gratefully acknowledges the financial support from ICREA under the ICREA Academia programme and from the Pauli Center (Zurich) and the Physics Department of University of Zurich. J. M. and A. B. also received financial support from Spanish Ministry of Science, Innovation and Universities (project PID2020-112965GB-I00) and from the Research Grant Agency of the Government of Catalonia (project SGR 1069). The work of B. C. is supported by the Margarita Salas postdoctoral program funded by the European Union-NextGenerationEU. B. C. also thanks the CERN Theory Division, where part of this work was done, for hospitality and the Cambridge Pheno Working Group for helpful discussions.
I. INTRODUCTION Artificial Neural Networks (ANNs) have enabled complex computations but require significant computational resources for both training and inference  [1]. The main bottleneck in these networks is the transfer of large amounts of data to support different tasks. However, the trend in computing is moving towards edge devices, such as the Internet of Things (IoT), to improve security and reduce power consumption and latency  [2]. Furthermore, there is a growing demand for powerful yet energy-efficient accelerators in various fields, including fault detection in microprocessors  [3]  and intrusion detection systems  [4]. The design space for ANNs is vast, and it involves choices in four fundamental aspects: the neuron model, the architecture This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No.
101070238. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. It was also partially supported by the ANR within the EMINENT Project ANR-19-CE24-0001 structure, the information encoding, and the training method  [5]. These choices can significantly impact the hardware design and optimization process. Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Spiking Neural Networks (SNNs) are three popular types of ANNs, each with unique characteristics and applications. CNNs are commonly used in image recognition and processing tasks, and they rely on convolutional layers to extract spatial features from input images  [6].
However, the sigmoid and tanh functions are still used in certain NNs, such as Recurrent Neural Networks (RNNs) and autoencoders  [17]. State-of-the-art implementations of CNNs and DNNs often use 32-bit floating-point numbers in software or model-based approaches. However, implementing such algorithms in hardware is challenging due to their extensive data requirements, high energy consumption, and large memory bandwidth. To address these challenges, quantization, and regularization techniques have been explored, using fixed-point computations with 16 bits, 8 bits, or lower precision. Although these methods reduce the precision of synaptic weights and inter-layer signals, IBM's TrueNorth  [18]  chip has achieved acceptable precision using only five synaptic states, albeit at high design and energy costs  [19] ,  [20].
Commonly used gradient descent methods include Stochastic Gradient Descent (SGD)  [31] , AdaGrad  [32] , etc. Regularization techniques such as Dropout  [33] , etc. , are often used to prevent overfitting and improve generalization. Additionally, data augmentation methods such as flipping, rotating, and cropping the input images are employed to increase the size of the training dataset and improve model robustness  [34]. Unsupervised learning approaches, such as Autoencoders  [17]  and Restricted Boltzmann Machines  [35] , can also be used for pretraining the model parameters. Transfer learning approaches can also be employed, where a pre-trained network is fine-tuned on a new dataset with similar or related features  [36]. Finally, reinforcement learning can also be used to train CNNs and DNNs, where the model learns to take actions based on a reward signal, such as in game-playing agents  [37].
Regarding SNNs, several software frameworks natively support the deployment of the code on GPUs, for example, the ones based on pyTorch, like snnTorch  [39]  and spikeTorch, or CUDA accelerated C++ frameworks, like SLAYER  [40]  and CARLsim 4  [41]. However, SNNs have many features unsuitable with a GPU execution. For example, spikes can be represented in the digital domain as single-bit events (high in the presence of a spike and low otherwise). The numerical representation used in GPU, based on words with 8, 16, 32, 64, or similar bit widths, is inefficient. Moreover, the computation can be based on events: a neuron can react only in the presence of an active input spike, remaining in a quiescent state otherwise. Such an update policy would allow exploiting the sparsity of spikes typical of SNNs, with considerable savings in switching power, but again is not supported by GPUs.
However, it is always necessary to validate the calculated mineral proportions from the data processing software. In fact, calculations and mineral proportion interpretations are based on the chemistry and density of the minerals. Therefore, the calculated composition requires a cross validation by comparing to an independent bulk geochemical analysis using X-ray fluorescence or ICP-MS. Another approach is to back calculate the modal mineralogical composition from the bulk geochemical analyses using element-to-mineral (EML) conversion methodologies. While EML can be easily applied to large geochemical datasets, limitations arise from the difficulty to assign certain elements to the mineral phases that are present in the samples. The example in this article is taken from Eramet's Grande Côte Operation (GCO) heavy mineral sands mine, in which primary detrital minerals, such as ilmenite, are subject to alteration, therefore producing a range of Fe and Ti-bearing minerals and are often accompanied by changes in oxidation state.
Data Acquisition and Processing XRF-XRD combined measurements were performed by the SOLSA combined instruments (Figures  1  and 2 ) allowing for the simultaneous measurement of XRF and XRD signals. X-ray fluorescence spectra were acquired using a Mo micro-source radiation and an Amptek X-123 (AMPTEK INC, Bedford, MA, USA) SDD (Silicon Drift Detector) detector placed over the sample to ensure the measurement of lighter elements down to Z = 12 (Mg). Data were collected for a quantitative XRF model from 0. 1 to 15 keV. X-ray powder diffraction data were acquired in asymmetric mode using Co Kα average radiation (Kα1 = 1. 78900 Å and Kα2 = 1. 79289 Å) and equipped with an INEL curved position sensitive detector which spans the entire 5 ≤ 2θ ≤ 120° range simultaneously. For this diffractometer, the instrumental function was also defined using yttrium oxide (Y2O3) standard powder following the analytical procedure described in  [7].