dataesr/scientific-paragraphs-categorization huggingface dataset:
Objective The LifeCLEF Bird Recognition Challenge (BirdCLEF) launched in 2014 and has since become the largest bird sound recognition challenge in terms of dataset size and species diversity with multiple tens of thousands of recordings covering up to 1,500 species  [25] ,  [40]. Birds are ideal indicators to identify early warning signs of habitat changes that are likely to affect many other species. They have been shown to respond to various environmental changes over many spatial scales. Large collections of (avian) audio data are an excellent resource to conduct research that can help to deal with environmental challenges of our time. The community platform Xeno-canto 20  launched in 2005 and hosts bird sounds from all continents and daily receives new recordings from some of the remotest places on Earth.
The Xeno-canto archive currently consists of more than 550,000 recordings covering over 10,000 species of birds, making it one of the most comprehensive collections of bird sound recordings worldwide, and certainly the most comprehensive collection shared under Creative Commons licenses. Xeno-canto data was used for BirdCLEF in all past editions to provide researchers with large and diverse datasets for training and testing. The diversity of this data made BirdCLEF a demanding competition and required participating research groups to develop efficient processing and classification pipelines. The large number of recordings often forced participants to reduce the training data and the number of features-strongly implying the deficiencies of low-level audio feature classification for extremely large datasets.
Dataset and Evaluation Protocol Deploying a bird sound recognition system to a new recording and observation site requires classifiers that generalize well across different acoustic domains. Focal recordings of bird species from around the world form an excellent base to develop such a detection system. However, the lack of annotated soundscape data for a new deployment site poses a significant challenge. As in previous editions, training data was provided by the Xeno-canto community and consisted of more than 70,000 recordings covering 960 species from three continents (South and North America and Europe). Participants were allowed to use this and other (meta) data to develop their systems. A representative validation dataset with two hours of soundscape data was also provided, but participants were not allowed to use this data for training-detection systems had to be trained on focal recordings only.
In addition to the 2019 test data, soundscapes from three other recording sites were added in the 2020 edition of BirdCLEF. All audio data were collected with passive acoustic recorders from deployments in Germany (GER), Peru (PER), the High Sierra Nevada (HSN) of California, USA and the Sapsucker Woods area (SSW) in New York, USA. In an attempt to lower the entry level of this challenge, the total amount of soundscape data was reduced to 153 recordings with a duration of ten minutes each. Expert ornithologists provided annotations for often extremely dense acoustic scenes with up to eight species vocalizing at the same time (1. 9 on average, see Figure  4 ). The goal of the task was to localize and identify all audible birds within the provided soundscape test set. Each soundscape was divided into segments of 5 seconds, and a list of species associated to probability scores had to be returned for each segment.
Participants and Results 69 participants registered for the BirdCLEF 2020 challenge and downloaded the dataset. Four teams succeeded in submitting runs. Details of the methods and systems used in the runs are synthesized in the overview working notes paper of the task  [39]  and further developed in the individual working notes of the participants ([1],  [8] ). In Figure  5  we report the performance achieved by the 13 collected runs. All submitted runs featured a CNN classifier trained on extracted audio features and all approaches employ current best practices from past editions. Established neural network architectures like VGG, Inception v3, EfficientNet, Xception, or the baseline repository  [41]  were used in the majority of the submitted runs. Most attempts used log-scale spectrograms as input, only one team used a custom Gabor wavelet layer in their network design.
Data Set and Evaluation Protocol Data collection: A detailed description of the GeoLifeCLEF 2020 dataset is provided in  [9]. In a nutshell, it consists of over 1. 9 million observations in US and France covering 31, 435 plant and animal species (as illustrated in Figure  7 ). Each species observation is paired with high-resolution covariates (RGB-IR imagery, land cover and altitude) as illustrated in Figure  6. These high-resolution covariates are resampled to a spatial resolution of 1 meter per pixel and provided as 256 × 256 images covering a 256m × 256m square centered on each observation. RGB-IR imagery come from the 2009-2011 cycle of the National Agriculture Imagery Program (NAIP) for the U. S. 21  , and from the BD-ORTHO® 2. 0 and ORTHO-HR® 1. 0 databases from the IGN for France  22.
Land cover data originates from the National Land Cover Database (NLCD)  [31]  for the U. S. and from CESBIO  23  for France. All elevation data comes from the NASA Shuttle Radar Topography Mission (SRTM)  24. In addition, the dataset also includes traditional coarser resolution covariates: bio-climatic rasters (1km 2 /pixel, from WorldClim  [30] ) and pedologic rasters (250m 2 /pixel, from SoilGrids  [29] ). Train-test split: The full set of occurrences was split in a training and testing set using a spatial block holdout procedure (see Figure  7 ). This limits the effect of spatial auto-correlation in the data as explained in  [50]. This means that a model cannot achieve a high performance by simply interpolating between training samples. The split was based on a global grid of 5 km × 5 km quadrats.
Participants and Results 40 participants registered for the GeoLifeCLEF 2020 challenge and downloaded the dataset. Only two of them succeeded in submitting runs: Stanford and LIRMM. A major hindrance to participation was the volume of data as well as the computing power needed to train the models (e. g. almost two weeks to train a convolutional neural network on 8 GPUs). Details of the methods and systems Fig. 6 : Each species observation is paired with high-resolution covariates (clockwise from top left: RGB imagery, IR imagery, altitude, land cover). used in the runs of both participants are synthesized in the overview working note paper for this task  [10]. Runs of the LIRMM team are further developed in the individual working note  [11]. Due to convergence issues for runs of Stanford team, after discussion with the authors, it was mutually agreed that they would not provide additional working notes for their runs.
An important following question would be to know whether the information captured by the high-resolution CNN is complementary to the one captured from the bioclimatic and soil variables. This was the purpose of LIRMM/Inria Run 4 that merged the prediction of both models by averaging their outputs. Unfortunately, this was not really conclusive. Either the high-resolution CNN already captured most of the information contained in the bioclimatic variables, or the fusion method was not able to take the best of each model. 5 SnakeCLEF challenge: Automated snake species identification based on images and two-level geographic location data (continent and country). A detailed description of the task and a more complete discussion of the results can be found in the dedicated overview paper  [48].
Dataset and Evaluation Protocol Dataset Overview: For this challenge we have prepared a dataset with 259,214 images belonging to 783 snake species from 145 countries. The dataset has a heavy long-tailed class distribution, where the most frequent species (Thamnophis sirtalis) is represented by 12,201 images and the least frequent by just 17 (Naja pallida). Such a distribution with small inter-class variance and high intra-class   Testing Dataset: Apart from other LifeCLEF challenges, the final testing set remains undisclosed as it is a composition of private images from individual reporters and natural history museums who have not put those images online in any form. A brief description of this closure method is as follows -twice as big as the validation set, contains all 973 classes, and observations from almost all the countries presented in training and validation sets.
Geographical Information: For approximately 80% of the images we provided a two levels of geographical information -country and continent. We have collected observations across 145 countries and all continents. Such information could be crucial for the AI based recognition as it is useful for human experts. Evaluation: The main goal of this challenge was to build a system that is autonomously able of recognizing 973 snake species based on the given image and geographical location input. Every participant had to submit their whole solution into the GitLab based evaluation system that performed evaluation over the secret testing set. Since data were secret each participated team could submit up to 5 submissions per day. The main evaluation metric for this challenge was the Dice Similarity Coefficient (DSC), also known as F1 score.
With the Imagenet-21k weights, ResNet50 architecture, and naive probability weighting approach, Gokuleloop team achieved top F1 score of 0. 625 while having a Log Loss of 0. 83. The FHDO BCSG team approaches combined two stages. Firstly, they used a Mask R-CNN instance detection method for snake detection. Secondly, different EfficientNet models were used to classify regions detected by the previous stage. Their best submitted model was an EfficientNet-B4 fine-tuned from the ImageNet pre-trained checkpoint. This model achieves F1 score of 0. 404 and a Log-Loss of 6. 650. The high Log-Loss was achieved due to the application of softmax normalization after the multiplication of the location data which leads to small differences in the predictions. All submission and their achieved scores are reported in the Figure  12.
Conclusions and Perspectives The main outcome of this collaborative evaluation is a new snapshot of the performance of state-of-the-art computer vision, bio-acoustic and machine learning techniques towards building real-world biodiversity monitoring systems. This study shows that recent deep learning techniques still allow some consistent progress for most of the evaluated tasks. The results of the PlantCLEF challenge, in particular, revealed that the last advances in domain adaptation enable the use of herbarium data to facilitate the identification of rare tropical species for which no or very few other training images are available. The results of the GeoLifeCLEF challenge were also highly relevant, revealing that deep convolutional neural networks trained on high-resolution geographic images are able to effectively predict species distribution even without using bioclimatic or soil variables.
A Manifold of α α α's for which ∃x ∈ X: V (φ(x) -φ φ φ) = α α α. A N Empirical manifold of α α α's which are the coordinates of the φ(x (i) )'s in the eigenbasis. Shape induced by the x parameterization. φ(•) High-dimensional shape mapping, φ : X → Φ Φ Space of shape discretizations, Φ ⊂ R D. φ φ φ High-dimensional shape representation of one design (φ φ φ ∈ R D ). φ φ φ Mean shape in the Φ Φ Φ database. Φ Φ Φ Shape database (N × D matrix whose i-th row is φ(x (i) )). V D × D matrix whose columns (v 1 ,. , v D ) are the eigenvectors of the covariance matrix of Φ Φ Φ. They are the vectors of the orthonormal V basis. x Design vector in the space of CAD parameters, x ∈ X. X Original search space (of CAD parameters), X ⊂ R d. While these parameters are intuitive to a designer, they are not chosen to achieve any specific mathematical property and in particular do not let themselves interpret to reduce dimensionality.
Experiments In this section, all the parametric design problems used in the experiments throughout this paper are introduced and discussed in terms of significant dimensions. Unless stated otherwise, the database Φ Φ Φ is made of N = 5000 designs sampled uniformly in X. We start with 3 test cases of known intrinsic dimension, which will be complemented by 4 other test cases. The metamodeling and the optimization will be addressed later in Sections 3 and 4.
This additive model can be interpreted as a GP in the α α α a space, with an inhomogeneous noise fitted by the Y a (•) GP  [15]. It aims at modeling a function that varies primarily along the active dimensions, and fluctuates only marginally along the inactive ones, as illustrated in Figure  32. Denoting k a and k a the kernels of the GPs, the hyper-parameters ϑ a = (θ a1 ,. , θ a δ , σ 2 a ) and ϑ a = (θ a , σ 2 a ) are estimated by maximizing the log-likelihood of (9) given the observed data y 1:n , l Y (α α α (1:n) , y 1:n ; ϑ a , ϑ a ) = - n 2 log 2π - 1 2 log(|K|) - 1 2 (y 1:n -1 β) K -1 (y 1:n -1 β), using the kergp package  [14]. K = K a + K a , with K aij = σ 2 a k a (α α α a(i) , α α α a(j) ), and K aij = σ 2 a k a (α α α a (i) , α α α a (j) ), and β is given by Equation  (7). The correlation between α α α and α α α being k(α α α, α α α ) = σ 2 a k a (α α α a , α α α a ) + σ 2 a k a (α α α a , α α α a ), the kriging predictor and variance of this additive GP are  [37] m(α α α) = 1 n β + k(α α α, α α α (1:n) ) K -1 (y 1:n -1 n β) s 2 (α α α) = σ 2 a + σ 2 a -k(α α α, α α α (1:n) ) K -1 k(α α α, α α α (1:n) ) (10).
Here, the difference between α α α (n+1) * and α α α (n+1) is calculated as the distance between the associated shapes φ φ φ (n+1) * and φ φ φ (n+1). Since the database Φ Φ Φ contains the shape representation of N distinct designs, d 0 := min i,j=1,. ,N i =j Φ Φ Φ i -Φ Φ Φ j R D , the minimal distance between two different designs in Φ Φ Φ is used as a threshold beyond which φ φ φ (n+1) and φ φ φ (n+1) * are considered to be different. The replication strategy is further motivated by the fact that since x (n+1) = arg min x∈X (φ(x) -φ φ φ) -Vα α α (n+1) * 2 R D = arg min x∈X V (φ(x) -φ φ φ) α α α(x) -α α α (n+1) * 2 R D , where the last equality expresses just a change of basis since V is orthogonal, α α α (n+1) is an orthogonal projection 6  of α α α (n+1) * on A, see Figure  38. This is somehow similar to  [35, 36]  where the authors project non realistic shapes on a smooth surface built via diffuse approximation or a local polynomial fitting, using the points of A N , to retrieve a realistic design.
VI. EXPERIMENTS AND RESULTS To verify the concept of the energy harvesting architecture described in the previous section we conducted a series of experiments. At first we verified the correctness of open circuit voltage estimation calculated by LT Spice for different Peltier modules, as shown in Fig. 9. Tests confirmed results obtained from simulations. To ensure stability of test conditions -including proper temperature on both sides -tested modules were put between a pair of other Peltier modules which were connected to a pair of laboratory power supplies. Current flowing through them generated a temperature difference between their inner sides. Heat and cold from their outer sides were dissipated by large heat sinks as shown in Fig. 10. The second set of experiments was about verification of the idea of having net power gain with a DC/DC boost converter driven by the MCU at low power mode.
Initially the switching transistor has been driven by an arbitrary function generator    8 ). The second probe is connected to the drain of Q1. After driving the gate low, the voltage at the inductor L1 is increasing very rapidly and goes through the Schottky diode D1 to the capacitor C2. The third probe is connected to C2. This experiment was conducted without the battery B1, so the energy that keeps the MCU working comes only from the capacitor C2 (2200 µF, 16 V). to find the best configuration of inductor value and duty cycle of pulse-width modulated signal. The same experiments have been conducted using LTSpice simulations. We used a testbed with a Peltier module as shown on Fig. 11 , and more convenient laboratory power supply with a resistor connected in series that mimics internal resistance of a Peltier module.
The final alignments were manually checked and analysed by using the Randomized Accelerated Maximum likelihood method (RaxML 50 ) and PhyML 51 based on the General time reversible model (1000 bootstraps), Neighbour-Joining 52 method based on the Tamura-Nei model (1000 boostraps) and Bayesian inference using MrBayes V3. 1. 6  53 (settings: chain length 2000, subsample frequency 1000, burn in of 10%), implemented in GENEIOUS. Sequences were deposited in GenBank with accession numbers MT250555-MT250568 and MT253647-MT253654.
Metabolomic analyses. Selected individuals looked clean and were free of endo-epiphytes. About 100 g FW (blades) per individual were dried with paper towel, weighted and chopped in strips of ca. 0. 5 × 2 cm using a sterile razor blade. Biomass was extracted 2 times in 100% methanol using a sonicator for 1 hr. A third extraction was carried out using 100% acetone. The three final solutions were combined, filtered using cotton plugs and solvent-evaporated in a rotatory evaporator water bath at 40 °C. To remove the dried extract from the evaporator, a few drops of methanol were added. The final liquid was put in 50 ml Falcon tubes, air-dried for few days under a fume hood and stored at 5 °C in darkness. Mass spectrometry analyses were carried out according to Macintyre et al. 54. High resolution mass spectrometric data were obtained using a THERMO SCIENTIFIC MS system (LTQ XL/LTQ ORBITRAP DISCOVERY) coupled to a THERMO SCIENTIFIC HPLC system (ACCELA PDA DETECTOR, ACCELA PDA AUTOSAMPLER, and ACCELA PUMP).
The following conditions were used: capillary voltage 45 V, capillary temperature 260 °C, and auxiliary gas flow rate 10-20 arbitrary units, sheath gas flow rate 40-50 arbitrary units, spray voltage 4. 5 kV, mass range 100-2000 amu (maximum resolution 30000). For LC/MS; WATERS SUNFIRE C18 analytical HPLC column (5 µm, 4. 6 × 150 mm) was used with a mobile phase of 0-100% MeOH over 30 min at a flow rate of 1 ml/min. Mass spectrometry data were processed using the predefined metabolomics workflow described previously  54. Raw data were converted to mzml format using the MassConvert tool from ProteoWizard  55  then imported and processed in MZmine 2. 40  56  using predefined settings to extract the features. The processed data from MZmine were incorporated into the customized Dictionary of Natural Products Library version June 2019 through the built-in Excel macro for peak identification and dereplication.
"Hits" were taxonomically filtered, and unidentified peaks were double checked against the MS raw data in Xcalibur 2. 2. An algorithm was employed to use the molecular formula data set from these databases for dereplication. The data set was further analyzed using SIMCA V 15. 0 (Umetrics, Umeå, Sweden) using the unsupervised statistical analysis method, principal component analysis (PCA) and supervised analysis method Orthogonal partial least squares discrimination analysis (OPLS-DA). Heat maps were plotted using the programming software R (version ×64 2. 15. 2) using a script in the ggplot2 package  57. Venn diagrams were generated using BioVenn  58.
Kelp metabolomes. Using mass spectrometry, the dataset obtained for the collected species of Lessonia, Macrocystis and their hybrid was subjected to multivariate analysis (Fig. 4 ). As shown by Principal Component Analysis (PCA) (Fig. 4a ) and Hierarchical Cluster Analysis (HCA) (Fig. 5a ) along with a heatmap dendrogram (Fig. 5b ), the hybrid was found to be more chemically similar to Macrocystis than to Lessonia. The predictability score (Q 2 ) was quite low at 0. 312 due to the observed dispersion of the samples indicating a higher chemical    2a  and Suppl. Figs. 4  and 5 ). In parallel, the numbers of overlapping metabolites of the hybrid samples with Macrocystis and Lessonia strains, which were 15 and 2, respectively, implied that the hybrid had a metabolome more similar to Macrocystis.
01 (Fig. 4b, c ), which were highlighted in grey on Suppl. Table  3. The fitness and predictability scores were at R 2 = 0. 94; Q 2 = 0. 743, respectively, which were typical for metabolomic models using spectral-based datasets  59. Since the difference between R 2 and Q 2 is less than 0. 30, the model is confident not to be over fitted. The model is also considered valid from the permutation test resulting to a Q 2 Y intercept <0 at -0. 11. The percentage variation between classes was 21. 3% while.
Acknowledgements We thank the support from  G. Millne  ( UoA ),  M. Rateb  ( UoA ) and  D. Zagal  ( UACh ) in the histological preparations, mass spectrometry set-up and the cultivation of the hybrid progeny, respectively. PM and LM developed part of this work with  BecasChile (Fondecyt)  funding, specifically grants No. 72130422  (PM) and No. 73140389  (LM). We would like to acknowledge the  British Council Newton Fund Institutional Links , project No. 261781172  for funding SS a postdoctoral research fellow. We are also grateful to the  UK Natural Environment Research Council  for their support to FCK (program Oceans  2025-WP 4. 5  and grants  NE/D521522/1  and  NE/ J023094/1 ). This work also received support from the  Marine Alliance for Science and Technology for Scotland pooling initiative.
Implementation and Experimental Evaluation In this section, we describe the implementation of an SMT-based concolic testing tool for Prolog programs that follows the ideas presented so far. We have implemented our tool in SWI-Prolog  [15]  and have used the C interface of SWI-Prolog in order to call the functions of the Z3 solver  [3]. The scheme of the workflow is shown in Figure  3. Here, "Concolic Tool" is the main module (written in Prolog) which performs concolic execution. Once a constraint is built, it is transformed into an SMT well-formed string and sent to the module "SWIPrologZ3", a module written in C that uses Z3's library to interact with the Z3 solver and solve this constraint. The results are sent back to the main module "Concolic Tool". In contrast to the concolic testing semantics shown in Figure  2 , we have implemented a nondeterministic version of concolic execution which is based on Prolog's backtracking mechanism.
Here, the information that must survive a backtracking step is inserted to the internal database using dynamic predicates and asserted for consistency. Regarding the termination of concolic testing, we impose a maximum term depth for the generated test cases. Since the domain is finite and we do not generate duplicated test cases, termination is trivially ensured. Consider, for instance, the usual specification of natural numbers built from 0 and s( ): ( 1 ) nat(0). ( 2 ) nat(s(X)) ← nat(X). where we assume that the argument of nat/1 is an input argument (and must be ground). Given an initial goal like nat(0), in the first iteration of the algorithm we add, e. g. , the following new test cases: nat(1) and nat(s(0)), where the constant 1 is used to avoid matching any clause. When considering the second test case, we will generate the alternative test cases nat(s(1)) and nat(s(s(0)).
12 enchmarks. We selected six subject programs from previous benchmarks  [9]  and from GitHub. 13 We ran concolic testing between 3 and 100 executions on a MacBook Pro hexacore 2,6 Ghz with 16 GB RAM in order to get reliable results. Reported times, in seconds, are the average of these executions. Our results are reported in Table  1. Here, concolic refers to the tool presented in this paper, while contest refers to the tool introduced in  [9]. The size of a subject program is the number of its source lines of code. The column Ground Args displays the number of arguments of the initial symbolic goal to ground, starting at the first position. #TCs refers to the number of generated test cases. A timeout for contest is set to 1000 seconds (the crash is an overflow). Q1: Performance. The three first lines of Table  1  clearly show the influence of the maximum term depth in a typically recursive program.