dataesr/scientific-paragraphs-categorization huggingface dataset:
The comparison with the state-of-the-art shows that a well-tuned NN is competitive with the other machine learning methods if the training set contains hundreds of examples ( N > 300 in our experiments). For small datasets, classical methods are more accu- rate. The NN shows its advantages only for very large datasets ( N > 3000 ). Since most of the current gene expression datasets are far from this size, the usefulness of NN in this context can be discussed. However, the genomics data are considered as a pillar of the future precision medicine  [23]  and the production of data is strongly increasing over the world. In the next ten years, the size of the gene expression datasets will gain one or more orders of magnitude, potentially making NN the method of reference. Note that this is what happened in image analysis and natural language processing, where with the increasing of the training sets size, the NN over-performed the other methods and became the state-of-the-art.
An explanation could be that since the patient data are more complex and diverse than cell lines, the NN trained from cell lines cannot capture all relevant expression patterns and make the generalization to patient data more complex. Since the production of cell line data is much easier than patient data, transfer learning may be a promising approach to develop quickly reliable predictive models. For a given disease, we could produce a large number of cell line data to construct a NN, and then the model would be fine-tuned on a smaller patient dataset to obtain an accurate predictive model of the disease. The transfer between a pan-cancer model to a specific cancer model is also possible, even if there are no samples of specific cancer in the pan-cancer data. This means that the NN can identify a general signature of the presence of cancer from pan-cancer data.
It is important not to over-interpret this result. We do not claim that the NN finds a common biological signature shared by all cancers. The different types of cancer are very heterogeneous and may be biologically very different. We claim that the NN finds a common informative signature of cancer in the gene expression data. That is different from a common biological signature since the NN (or all other machine learning models) identifies only correlations between gene and output and no causalities. However, this informative signature can be transferred to produce an accurate classifier with a very small dataset. This transfer is less robust than in the cell line/patient conditions. The transfer does not improve performance for some cancers and can even produce a negative effect. For the moment, we do not identify the conditions that make the transfer successful or not; this point will be investigated in future works.
Even if we cannot ensure the transfer efficiency for all tissues, it should be possible to get rid of the negative effect with domain adaptation methods  [24]. The transfer from pan-cancer data may particularly help for rare cancers. Even with the rise of the capacity of genomics data production, the datasets of rare cancers will still be small. With this approach, we could transfer signatures from pan-cancer data to small rare cancer data. The transfer from a given cancer to another one does not work. We assume that the NN identifies a signature that is too specific to the source cancer. Since the biology between two cancers may be very different, the signature cannot be transferred to another one. In pan-cancer data, the NN analyses a wide variety of cancer and may identify a general signature.
Conclusion In this study, we evaluated the performance of the deep learning approach for cancer prediction from gene expression data based on an exhaustive set of experiments. We provided several recommendations to optimize the NN construction and training. Given the size of the current gene expression datasets, NN is competitive with other machine learning methods but not significantly better. However, with the increase in the size of the datasets, NN will likely become a reference method in the next years. We showed that transfer learning is possible for gene expression data, mainly between cell lines and patient data, and from pan-cancer to specific cancer. The approach is very promising to develop accurate models, especially for rare cancers where large datasets will never be available.
Transfer learning Transfer learning aims to deal with the problem of small size training datasets. It consists of transferring information from the source domain to the target domain in order to perform a target task  [22]. A domain D = {X, P(X)} is defined by the feature space X and a probability distri- bution associated to this space P(X). A task T = {Y , F(X)} is composed of two parts: the label space Y and the target prediction function F(X). F(X) can be considered as a conditional probability function P(Y|X). Given a target learning task T t = {Y t , F t (X)} based on a target domain D t = {X t , P t (X)} and a source task T s = {Y s , F s (X)} based on a source domain D t = {X s , P s (X)} , the transfer leaning goal is to improve the performance of the model that learns the task T t in D t using the knowledge in D s and T s.
Based on the above definition of the transfer learning, a small number of labeled data in the target domain is required to induce the target predictive function. In our experiments, we define different target tasks (presence of cancer or type of cancer) using the same feature space (same features) with different distributions. We apply different transfer learning approaches using the deep neural network classifier described in Sect. 5. 1 as a target classifier: • Supervised transfer learning: source domain labels are used in the construction of the source model. The NN model is first trained using the source training set. Then, the first F layers are frozen ( F ∈ {0,. , L -1} ), i. e. , the weights of these layers are kept fixed. Finally, a second network training, called fine-tuning, is performed using a target dataset.
Runtime Comparison We start by comparing the runtime cost of our scheme with that of PS, highlighting the overhead resulting from the issuer-indistinguishability property. As both schemes are implemented using the same tools, this is the most accurate manner to identify the overhead of our scheme, regardless of implementation details. We start by comparing the non-interactive and interactive variants. Then we analyze the cost of each operation: issuer-key generation, verifier-key generation, signature issuance, and signature presentation. We base our comparisons on an implementation in Rust (at: https://gitlab. inria. fr/mgestin/rust_hidden_ issuer_signature). We use the BLS12-381 curve  [22] , a widely used curve for pairing-based cryptography with efficient computation time. BLS is a type-3-pairingfriendly curve with 256-bit-prime group size.
1 with respect to our implementation. The one with IHABC depends on whether we analyze the user, the verifer, or the issuer. We analyze the data with reference to Appendix D. On the user's side, the most frequent operations is Randomize. Albeit short, its running time is 15% faster for our scheme for 10 trusted issuers. Even if less frequent, the IntegrityVerification operation runs 3 times faster in our scheme than in IHABC. On the verifier's side, the most frequent operation is VerifyRandomized. The IHABC implementation of this algorithm is 20% more efficient. In the interactive version we are considering, VerifierSetup is executed relatively rarely but it runs 4 times faster in our scheme than in IHABC. Finally, on the issuer's side, Sign runs twice as fast in IHABC as in our scheme, while IssuerKeyGen runs four times faster in IHABC.
Acknowledgments We wish to thank the anonymous reviewers and our shepherd,  Christina Garman , for their insightful comments and remarks that led to significant improvements to our paper. This work was partially funded by the  SOTERIA  project. SOTERIA has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  101018342. This content reflects only the author's view. The European Agency is not responsible for any use that may be made of the information it contains.
33, which is optimum for processing numerical models in mountainous areas  (Perko et al. , 2019). The relative orientation of the Pléiades images was performed using the rational polynomial coefficients (RPCs) provided by the an-cillary information. Finally, stereo-matching algorithms enabled the generation of a 3D point cloud by means of pixelto-pixel correspondences between the images. The 3D mapping procedure for the stereoscopic pair was performed using the Ames Stereo Pipeline (ASP), free open-source software developed by the National Aeronautics and Space Administration (NASA;  Beyer et al. , 2018). The Pléiades images were acquired in the framework of the ISIS programme of the French Space Agency (CNES) in the framework of the Pléiades Glacier Observatory initiative  (Berthier et al.
Then, the xyz adjustment factors are computed as follows: x = a • sin (b) , (2) y = a • cos (b) , (3) z = c tan(α). (4) As the 2009 geodetic survey presents the best photogrammetric adjustment (Table  1 ), the DEM generated from these data was used as a reference to evaluate the bias in the geodetic data of the other acquisitions. Because high-spatialresolution data were used in this study (Table  1 ), an elevation difference layer of 10 × 10 m cell size was computed for each geodetic comparison using the universal kriging technique based on an exponential semi-variogram model. After co-registration, analysis revealed that the geodetic datasets were slightly unaligned with respect to the reference, with a mean elevation difference ranging from -1. 8 to 3. 2 m on stable terrain. Consequently, we decided to perform 3D geometric adjustment to minimise the difference in the geodetic data.
The results are presented in Table  2  and in the Supplement. In all cases, there was a reduction of around 8 % in the standard deviation and the mean difference in elevation was improved, and a normal distribution of the dh on the glacier forelands is better shaped with an average standard error of 0. 03 m after 3D geometric adjustment (Fig. S1  in the Supplement). The co-registration results showed a similar displacement vector of around 8 m in all five datasets. For the aerial-based geodetic data, this was attributed to the number and distribution of the GCPs used to solve the stereo orientation. For the Pléiades-based data, this is probably explained by the fact that the triangulation and geometric adjustment of Pléiades stereo-images were based on the RPC model without including GCPs.
As the 2009 geodetic it well represents the long-term climatology of this region (Fig. S3  in the Supplement); moreover it covers several geopotential levels and the entire study period. Reanalysis data are prone to systematic biases due to the limited spatial resolution and simplified physics used to represent climate, particularly in mountain areas where orographic factors drive climate conditions. In practice, temperature (precipitation) measurements made near glaciers are somewhat higher (lower) than reanalysis data. To adjust the ERA5 dataset to observations, we compared the reanalysis data close to the grid cell to the station location and the in situ meteorological data at a monthly time step for the nine weather stations with the longest series that recorded climate variability from the western and the eastern sides of the cordillera between 1950 and 2018 (Fig.
On the other hand, the temperature recorded by the M188 weather station (Papallacta) was satisfactorily captured by ERA5 reanalysis. To reduce the bias between the ERA5 data and reference observations, we retain M003 data and we applied the following approaches: (i) for temperature calibration we applied a bias-correction method which uses the mean differences and variability in the data series  (Hawkins et al. , 2013) ; and (ii) for precipitation calibration, we applied a quantile mapping approach which uses a statistical transformation function to adjust the distribution of ERA5 data to match the distribution of observations  (Thrasher et al. , 2012). To define the function, we applied a non-parametric transformation termed QUANT in the R package qmap  (Gudmundsson et al. , 2012). Once biases were reduced, a simple statistical metric enabled us to confirm that the mean difference between ERA5 data and in situ observations was improved by 90 % and the root mean square error (RMSE) reduced by 60 %, showing that the calibration methods performed quite well except for the RMSE of M188 station, which showed no improvement (Table 3).
Code and data availability. The changes in annual surface elevation of all glaciers on the Antisana ice cap in each study period produced by this research can be found at and downloaded from https://doi. org/10. 5281/zenodo. 7122112 (Basantes-Serrano et al. , 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-16-4659-2022-supplement. Author contributions. RBS designed the study, produced the datasets, led the geodetic and climate analysis, and wrote the manuscript. AR and BF designed the study and contributed to writing the manuscript. CV provided recommendations on the glaciological and geodetic interpretations. AS provided guidance on the photogrammetric processing. TC and JCR contributed with climatic discussion. All authors contributed to the data interpretation and participated in discussions for improving the manuscript.
Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements. This study was funded by the Universidad Regional Amazónica Ikiam and the Laboratoire Mixte International GREAT-ICE (http://www. great-ice. ird. fr/, last access: 30 July 2019) led by the French Institute of Research for Development (IRD). We acknowledge the Service National d'Observation GLACIO-CLIM (http://glacioclim. osug. fr/, last access: 30 July 2019) and the contribution of the Labex OSUG@2020 (Investissements d'Avenir -ANR10 LABX56). Special thanks to the institutions that provided access to the aerial photographs and meteorological observations: the Ministerio del Ambiente, Agua y Transición Ecológica (MAE), the Empresa Pública Metropolitana de Agua Potable y Saneamiento de Quito (EPMAPS) and the Instituto Nacional de Meteorología e Hidrología (INAMHI).
We acknowledge Etienne Berthier (CNRS, LEGOS) for the Pléiades Glacier Observatory initiative via the French Space Agency (CNES) ISIS programme, which facilitated access to Pléiades data. ERA5 reanalysis data were acquired through the Copernicus Climate Change Service implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF). Comments and suggestions from the two anonymous reviewers and editors helped improve the manuscript. Financial support. This study was funded by the Universidad Regional Amazónica Ikiam and the Service National d'Observation.
Plant species providing Non-Timber Forest Products (NTFP) are economically important across Africa. How this heterogeneous and understudied resource will respond to ongoing climate change remains understudied. Here, we modelled the impact of endof-the-century climate change on the distribution of 40 NTFP plant species distributed across tropical Africa. Occurrence data were extracted from a taxonomically verified database and three different ecological niche modelling algorithms were used. Species distributions were modelled under two end-of-century (2085) climate change models (RCP4. 5 and RCP8. 5) and two dispersal scenarios (limited and expanded). We show that for the 40 NTFP plant species studied here, different responses are modelled with some species gaining in suitable habitats (47.
| Non-timber forest products plant species distribution data A total of 40 NTFP species considered as 'priority' were selected relying on the classification of  Ingram (2014)  based on a database of around 500 species from Cameroon. This database provides a ranking of the species importance, or 'priority', based on five criteria: species (1) with products that have a high economic trade value or are important for autoconsumption;  (2)  with products that are overexploited (this is a function of in situ conservation priority status and domestication); (3) with products that have multiple uses (including conflicting); (4) with uses; (5) that are classified as threatened (e. g. , on the IUCN Red List, CITES listed and/or protected by national laws). In this study, we selected the top priority NTFP species for which we could retrieve sufficient data about their distribution (see below).
Occurrence data for each species were extracted from the RAINBIO database  (Dauby et al. , 2016). RAINBIO is a synthesis of several databases from GBIF and several major herbaria of vascular plant occurrence data. It has the advantage of being curated and taxonomically and geographically verified. For each species, we estimated the percentage of their occurrence within (referred to as Forest Fraction) or outside forested regions using polygons representing the map of different ecoregions found in Africa based on  (Olson et al. , 2001).
| Climatic data Climatic data were extracted from AFRICLIM ver. 3 (www. afric lim. org), a data set of high-resolution ensemble climate projections for Africa  (Platts et al. , 2015). This database comprises monthly data on temperature and rainfall together with derived bioclimatic summary variables such as moisture indices and dry season length. Ten variables related to temperature and 11 related to precipitation were downloaded (see  Platts et al. , 2015  for more details) which are mainly derived from the WorldClim-Global Climate Database. Finally, all climatic layers were downloaded at 10′ of spatial resolution.
| Species distribution modelling For each selected NTFP plant species, distribution modelling was undertaken using three different algorithms: Maximum Entropy (MaxEnt;  Phillips et al. , 2006) , Generalised Boosting Model (GBM;  Ridgeway, 1999)  and Generalised Linear Models (GLM;  Hastie & Tibshirani, 1990)  which were shown to be amongst the best performing SDM algorithms  (Elith et al. , 2006; Elith & Graham, 2009). Models were run using the package Biomod2 ver 3. 5. 1  (Thuiller et al. , 2020)  in the R environment (R Core Team, 2021). For each species, models were built following four steps: 1. We defined a calibration area by defining the Minimum Convex Polygon or convex hull  (Kremen et al. , 2008)  around the occurrence points of each species. A buffer zone of 5° degrees was created around the convex hull defining the calibration area; 2.
We generated pseudo-absence data using the 'Target group sampling' method  (Ponder et al. , 2011). The number of pseudoabsence cells (or background cells for MaxEnt) was variable amongst species depending on the species-specific calibration (1) rd = ∑ 12 i=1 max 0,PET i -PRi area. The sampling of a species was weighted by the density of all occurrences contained in the RAINBIO database and used as a proxy for botanical sampling effort across the region. 3. Model evaluation was achieved through a cross-validation procedure by partitioning the presence and pseudo-absence data into calibration and validation data. We used a checkerboard approach for partitioning which has the advantage of maintaining the spatial structure of the data  (Muscarella et al. , 2014). The performance of models was estimated using two statistics calculated for each pair of presence and pseudo-absence data: the area under the receiver operating characteristics curve (AUC,  Fielding & Bell, 1997)  and the true skill statistic (TSS,  Allouche et al.
, 2006). The AUC and TSS were averaged for all validation-calibration data sets to have a unique AUC and TSS for each algorithm and for each species. We excluded the models with an AUC value < 0. 7; models with an AUC value ≥0. 7 were considered reliable  (Elith, 2000). TSS values range from -1 to +1, where +1 is an indication of a perfect model fit and values ≤0 are an indication of models which are no better than random  (Allouche et al. , 2006). 4. We constructed the potential present-day distribution of each species by projecting in the calibration area and the niche models built were evaluated as robust. Binary maps were produced to predict the presence or absence of each species. This conversion was done using the optimised ROC threshold of Biomod2  (Thuiller et al. , 2020) , which minimises the absolute difference between sensitivity and specificity and is ranked amongst the most reliable thresholds  (Liu et al.
| Future climate data and distribution modelling Future climate variables were also derived from the AFRICLIM data set for the period 2070-2100 (averaged at 2085). The models were built using coupled Atmosphere-Ocean global circulation models (GCM) produced by Swedish Meteorological and Hydrological Institute (SMHI). However, there is a variability in global circulation models  (IPCC, 2007)  linked to a different set of parameters to represent landuse scenarios and key ecosystem processes  (Doherty et al. , 2010). Thus, 10 different GCMs were used. In addition, for each GCM, we used two greenhouse gas emission scenarios named representative concentration pathways (RCP 4. 5 and RCP 8. 5). RCP 4. 5 is a gas emission scenario that limits the increase in mean temperature to 1. 8°C for 2085s, whereas RCP 8.
Relative change in suitable habitat (CSH) using MaxEnt for: (a) considering RCP 4. 5 and expanded dispersion scenario; (b) considering RCP 4. 5 and limited dispersion scenario considering RCP 4. 5; (c) considering RCP 8. 5 and expanded dispersion scenario and (d) considering RCP 8. 5 and limited dispersion scenario. Values represent arithmetic means of all model combinations from 10 general circulation models. The box indicates the interquartile range. Colour gradients of boxes show a range of species from 'loser species' (CSH < 0) in red to 'winner species' (CSH > 0) in green We used a curated and verified tropical African occurrence database (Dauby et al. , 2016) combined with three SDM algorithms, and 10 different Global Climate Models (GCMs) under two different future predictions (RCP 4.
F I G U R E 1 1 Relative change in suitable habitat (CSH) using MaxEnt for: (a) considering RCP 4. 5 and expanded dispersion scenario; (b) considering RCP 4. 5 and limited dispersion scenario considering RCP 4. 5; (c) considering RCP 8. 5 and expanded dispersion scenario and (d) considering RCP 8. 5 and limited dispersion scenario. Values represent arithmetic means of all model combinations from 10 general circulation models. The box indicates the interquartile range. Colour gradients of boxes show a range of species from 'loser species' (CSH < 0) in red to 'winner species' (CSH > 0) in green We used a curated and verified tropical African occurrence database (Dauby et al. , 2016) combined with three SDM algorithms, and 10 different Global Climate Models (GCMs) under two different future predictions (RCP 4.
2000) , but there are others that take advantage of infrared colors, depending on the stellar population (e. g. , cold brown dwarfs-  Leggett et al. 2010). More recently, large-scale surveys have taken these photometric parameter determination strategies to the next level by building databases with millions of spectroscopically observed objects. Two such examples are the Sloan Digital Sky Survey (SDSS;  York et al. 2000)  in the Northern Hemisphere and the SkyMapper Sky Survey (SMSS;  Wolf et al. 2018)  in the Southern Hemisphere. Both of these surveys conducted separate subsurveys aiming to perform medium-resolution (R ∼ 1500) spectroscopic follow-up of stars in the the Milky Way: the Sloan Extension for Galactic Understanding and Exploration (SEGUE-1;  Yanny et al. 2009 ) and SEGUE-2  (Rockosi et al.