dataesr/scientific-paragraphs-categorization huggingface dataset:
In this paper, we are focused on the detection of grape diseases with two models: (1) a semantic segmentation model for disease detection and (2) a model to classify the type of diseases (gray mold, acid rot, powdery mildew). The paper is organized as follows: related works are presented in Section 2, the image acquisition protocol, the datasets, and the proposed methods are presented in Section 3; then the classification and segmentation results are presented in Section 4, before presenting our conclusions and future works.
Related Works Automated vine disease detection is a dynamic sub-field of AI research in agriculture because it would allow for large-scale detection of diseases on the field or after harvest. This work is mainly performed by human operators, whose detection of grape diseases is often biased  [3]. In comparison, computer vision over-performs humans in a single grape leaf disease classification task  [4] , all while ensuring an unbiased evaluation. Plant disease detection from RGB images is a difficult task. Many difficulties have been described in detail in the work of  Barbedo [5]. Building a representative dataset is a complex task because there are many sources of variations (phenological stages, disease severity, weather and lighting conditions, etc. ). This also leads to heavily unbalanced classes because the symptoms represent a small part of the images.
Image acquisition An image acquisition protocol was defined in the summer of 2019 to collect images for supervised learning. This protocol was designed to be applied directly to the existing industrial setting without too much interference. Data collection was performed in Vranken-Pommery Monopole's wine presses during the weighting stage. The existing industrial process is the following: (1) pallets containing one to four levels of four grape crates are delivered on the press site; (2) each pallet is moved to the weighting site to collect relevant information (weight, variety, parcels); (3) a quality grade is attributed to the whole pallet after a quick visual inspection by an employee (potential alcoholic content is also estimated during this stage); (4) the pallet is then moved to the docks before pressing.
Data collection campaigns were carried on in two wine presses in 2019 and 2020 in Tours-sur-Marne and Merrey-sur-Arce in France. A third site in Saudoy was added to the campaign in 2021. The industrial process was not changed. The two first sites are in semi-open areas and can be affected by the sunlight and multiple pallets can be visible in the images. Artificial lighting was only used during the 2021 campaign in Tours-sur-Marne. The resulting datasets were cleaned, few images were removed (redundant takes or dark images taken near sun fall). Each visible crate of the weighted pallet is labeled for binary segmentation with LabelMe polygonal mask  [12]. Most images contain 4 crates. Therefore, the segmentation allows us to process each crate separately to detect the disease. Each crate was extracted from the images and infected grapes were labeled for binary segmentation.
In this manner, a dense prediction can be achieved for the whole input image. Many semantic segmentation models have been proposed since FCN. Those include popular models such as U-Net  [14] , with a symmetrical architecture, or lightweight models such as CGNet  [15]. In our context, we have selected the PSPNet  [16]  model with an Inception-Resnet-V2 backbone because it gives good results in many of our datasets while having a reasonable size (about 3M parameters). It was trained on the 3300 images of healthy and diseased crates collected in 2019, 2020, and 2021. Data augmentation was used to reduce over-fitting. Our aim is to assess the semantic segmentation performances for grape disease detection (the first step toward disease quantification) and to understand if the model is able to differentiate the different types of diseases.
Diseases Classification The images from the three campaigns (2019,2020 and 2021) enabled us to create a large dataset with 3300 images of crates in the "Healthy" class and 1600 in the "Disease" class (about 32. 6%). These classes are based on the actually visible symptoms after image masks were produced with the LabelMe tool. However, this is still limited to binary classification/segmentation. This is insufficient to satisfy winemakers' needs because the quality loss depends on the type of disease affecting the grapes. In our previous work, a pre-trained MobileNet-V2  [18]  model reached 92% F-score for binary classification  [2]. In the current work, we used a similar model with the three following classes: gray mold, acid rot, and powdery mildew. Those labels were based on the quality grades given at the press during the weighting.
The segmentation models also reached better disease detection compared to the classifiers proposed in the previous section, with a 0. 95 F-score compared to the 0. 92 score of the pre-trained MobileNet-V2 model from our previous work(Table  2. Umap was applied to the activations of the convolutional layer that follows the Pyramid Pooling Module (similar results were obtained with activations from different layers of the model). We show in Figure  2  the two same images with different labels: (1) purple for the healthy class and yellow for the infected grapes and (2) the disease class is divided into three sub-classes of different symptoms sizes. We can see that the two classes are not perfectly separated. This is due to two reasons: (1) labeling imprecision and errors and (2) confusion between diseases that were not labeled (such as downy mildew or dry grapes).
Acknowledgements This work has been conducted funded by the " Edge AI Technologies for Optimised Performance Embedded Processing " project, received funding from  KDT JU  under grant agreement No  101097300. The  KDT  JU receives support from the  European Union 's  Horizon Europe research and innovation program  and  Austria ,  Belgium ,  France ,  Greece ,  Italy ,  Latvia ,  Luxembourg ,  Netherlands ,  Norway. We want to thank  Vranken-Pommery Monopole , our partner in the EdgeAI project, for allowing image collection in their sites. We also thank the  ROMEO Computing Center 2 of Université de Reims Champagne-Ardenne , whose Nvidia DGX-1 server allowed us to accelerate the training steps and compare several model approaches.
Contrary to APT, EELS provides direct information on inter-atomic bonding or on local modes of electronic or phonon vibration  [28, 29]. Nevertheless, the chemical sensitivity of (S)TEM-based methods is only around 1at. %  [29] , and the projected image makes the precise analysis of small microstructural features embedded in a matrix challenging. Here, we showcase how APT can readily bring insights into the binding of surface atoms by reprocessing existing dataset. From the physics of field evaporation, we introduce an energetic parameter, which has a direct link to the field evaporation energy barrier and encodes information on the local surface atomic neighborhoods that can be mapped in 3D at the nanometer scale, leading to a new approach termed field evaporation energy loss spectroscopy (FEELS).
Q(F ) = C(1 -F F EV ) Φ = exp( -Q(F ) k B T ) (1) Experimentally, the field evaporation process is described by the Arrhenius law  [1]  (equation 1). By precisely measuring the evaporation flux as a function of the specimen's base temperature and applied field, the binding energy of surface atoms can be evaluated. This latter parameter influences the field evaporation sequences in APT experiments. Data were already reported for W  [44]  and Rh  [45]  for temperatures up to 100K and both show the predicted linear decrease of Q(F ) with F over 0. 85F EV. We also performed a series of APT experiments on a pure Al specimen at increasing temperatures from 25K to 100K to verify the linearity of Q(F) (Supplementary Information 1). The asymptotic zero field energy barrier C can be extracted from Q(F ) experimental graph (Supplementary Information 1).
e. edges of the detector, due to poorer counting statistics. We first revisited the APT analysis of a Σ3[110](111) grain boundary, i. e. a region separating two grains of similar crystal structure, in pure Al specimen  [51]. In the reconstructed volume (Figure  3. a), the grain boundary is made visible by a lower point-density caused by trajectory aberrations  [52]. Previous report on this specific dataset  [51]  showed no change in composition at the grain boundary itself. Figure  3. b corresponds to the mass spectra plotted in each two regions-of-interest identified in blue and in red in the inset. The slope is identical over most of the mass peak, but strongly differ above 27. 75amu, showcasing an increased energy loss for atoms on the grain boundary. FEELS is sensitive enough to reveal the difference in the field evaporation process between the grain boundary and the metallic matrix, and can hence image structural defects.
This is reflected by the presence of a larger tail of F e 2+ , i. e. higher energy loss compared to the FeSi. Considering the mass spectrum of the entire dataset ( Surprisingly, FEELS also highlights the presence of a grain boundary inside the FeSi phase (figure  4. c), i. e. enabling to perform structural analysis that can be elusive to conventional APT. A 3D-reconstructed volume, Supplementary Information 6, reveals the entire FeBSi structure imaged by FEELS. Another example of application on a FeBSi sample annealed at 500 • is presented in Supplementary Information 7. In this experiment, we were expected two crystalline phases FeSi and FeB, however FEELS reveals us a third structure. The composition measurement shows no distinction with a FeB phase, so we believe that the crystalline structure is locally different.
Acknowledgments. We thank  Uwe Tezins ,  Christian Broß , and  Andreas Sturm  for their support to the FIB and APT facilities at MPIE. L. T. S. and B. G. acknowledge financial support from the  ERC-CoG-SHINE-771602. F. F. M. acknowledges financial support from the  International Max Planck Research School for Interface Controlled Materials for Energy Conversion (IMPRS-SurMat).
It relies on an effective differential elimination theory using, e. g. , Gröbner or Janet basis methods. Different softwares can nowadays handle effective aspects of algebraic analysis: Macaulay2, Maple (OreModules), Singular, HomAlg, etc. Based on our experience in effective algebraic 𝐷-modules theory, we aim at extending the algebraic analysis approach to handle linear systems of ordinary integro-differential equations with polynomial coefficients. In other words, we would like to replace the Weyl algebra by the ring of ordinary integro-differential operators with polynomial coefficients, denoted by I 1 in what follows. Contrary to the Weyl algebra case, Bavula proved in  [1]  that I 1 is not a noetherian ring, a fact which seems to compromise the possibility to develop an effective integro-differential elimination theory, and thus, an effective algebraic analysis approach to ordinary integro-differential linear systems.
Theorem 3. 6 ([1] ). I 1 is a coherent ring. Using the involution 𝜃 of I 1 (see Proposition 2. 6), the left coherence property yields the right coherence property, and vice versa. We point out that the proof of Theorem 3. 6 given in  [1]  is not constructive. The main goal of the present paper is to contribute to the development of an effective version of the coherence property of I 1 and its implementation in the computer algebra software Maple. Here, we shall focus on Condition (2)(a) of Proposition 3. 5. The second condition will be studied in a future work. Note that, to our knowledge, I 1 would be the first example of a coherent but not noetherian ring implemented in a computer algebra system, which has important applications (e. g. , calculus).
ANNIHILATOR OF AN EVALUATION OPERATOR 4. 1 Preliminary remarks and results In Section 3, we have recalled that the ring I 1 was coherent. Yet, the proof of the coherence property given in  [1]  remains not algorithmic. To make it so, we shall rely on Proposition 3. 5 which shows that the coherence property is equivalent to two conditions: one on the annihilator of elements of I 1 and one on the intersection of finitely generated ideals. In this paper, we shall only focus on the first one, letting the second one for a future work. Let us then consider Condition (2) (a) of Proposition 3. 5. In the case 𝑎 ∈ I 1 \ ⟨𝑒⟩, the characterization of a finite set of generators for ann I 1 (. 𝑎) was obtained in  [17]  and implemented in the IntDiffOp package  [10]. Hence, it remains to study the case 𝑎 ∈ ⟨𝑒⟩.
For more details, see, e. g. ,  [11]. Such a computation is implemented in computer algebra softwares. Example 4. 6. Let us consider 𝑎 0 = 𝑡, 𝑎 1 = 𝑡 2 + 1, and 𝑎 2 = 𝑡 3 -2 𝑡. Then, we have 𝑚 = max 𝑖 ∈ 0, 2 {deg 𝑡 (𝑎 𝑖 )} = 3 and 𝐶 = 𝑡 1 0 0 0 𝑡 2 + 1 2 𝑡 2 0 0 𝑡 3 -2 𝑡 3 𝑡 2 -2 6 𝑡 6 0 𝑇 ∈ k[𝑡] 5×3. Using, e. g. , Maple, we can compute a matrix 𝐷 whose rows generate the k[𝑡]-module ker k[𝑡 ] (. 𝐶). We find , for 𝑘 = 0,. , 𝑟 , 𝑚 = max 𝑖 ∈ 0, 𝑟 {deg 𝑡 (𝑎 𝑖 )}, and 𝐶 ∈ k[𝑡] (𝑚+2) × (𝑟 +1) and 𝐽 𝑚+1 be the matrices defined by  (9). If 𝐸 ∈ k 𝑣× (𝑚+2) is a full row rank matrix such that ker k (. 𝑒 (𝐶)) = im k (. 𝐸) (where 𝑒 (𝐶) is the evalutation of C at 𝑡 = 0), then we have 𝐷 = -6 6 𝑡 -3 𝑡 2 + 3 𝑡 3 -3 𝑡 0 0 0 0 0 1. Thus, two ann I 1 (. 𝑎) ⟨𝑒⟩ = 𝑣 ∑︁ 𝑘=1 I 1 𝑔 𝑘 , where the 𝑔 𝑘 's are defined by 𝑔 1.
Data acquisition We follow the taxonomy and species list of lacertids from the Reptile Database  (Uetz et al. , 2020). We removed from this list several categories of species that are expected to have speciation modes differing from the rest of the species and driven by mechanisms presumably not be affected by the presence of Thomas de Solan et al. polymorphism. We first removed the parthenogenetic species, as they arise by interspecific hybridization and result in unisexual clones providing "instant" reproductive isolation from their parent species (several species of the genus Darevskia, noted as "parthenogenetic" in Table  S1 ). We also removed strictly insular species (species noted as "insular" in Table  S1 ). Although morphic speciation might have helped the divergence of some insular species, we believe that geographic isolation remains the primary factor in insular speciation processes.
Supporting this idea, no case of in situ (intra-island) speciation is known in insular lacertids. We also removed four species for which we could not find any accurate information on colouration and five species with an uncertain taxonomic status (as judged by two of us, PAC and PG). Insularity was assessed using the distribution information available in the reptile-database website. After these steps, we retained 295 species for the speciation analysis. The final list of all species, including all species removed from the dataset, can be found in Supplementary Table  S1. We scored the presence of sexual colourations and polymorphism from multiple sources, such as scientific and naturalist papers and field guides, but also multiple photographs taken by the authors in the field (sources listed in Table  S1 ).
We used the phylogeny provided by Garcia-Porta et al. (  2019 ) as backbone phylogeny. We subsequently added the 85 non-insular and non-parthenogenetic species that were not sampled by  Garcia-Porta et al. (2019). These species were added to the backbone tree using the function add. species. to. genus from the phytools package  (Revell, 2012). For 17 species (listed in Table  S1 ) where the phylogenetic position was known from previous works, the species was added with its sister taxa at a random divergence date. If no phylogenetic information was available, the species was randomly located in its genus; this applied to 68 species in our phylogeny, of which only four were treated as polymorphic (see Table  S1 ). We repeated this operation one hundred times in order to account for phylogenetic uncertainty generated by randomly added species in all analyses.
Trait evolution and ancestral state reconstruction We investigated the evolutionary inertia for conspicuous colourations and polymorphism using the δ values  (Borges et al. , 2019). This index is designed to measure phylogenetic signal in categorical traits, with high δ value indicating high phylogenetic signal (i. e. strong phylogenetic constraint to trait evolution). To test the significance of the observed δ, we compared with a Wilcoxon test the distribution of the 100 observed δ, measured on the 100 phylogenetic trees, against a null distribution obtained by measuring δ after randomization of the trait data among species (phylogenetic signal = zero). In addition, we reconstructed the ancestral state of the colouration with the make. simmap function from the phytools package  (Revell, 2012).
It should be noted that the diversification rates estimated by SDD and CID models are not time dependent, and only vary depending on characters states (unlike speciation/extinction analysis of BAMM;  Rabosky, 2014). We selected the best-fit model among these four models based on the Akaike Information Criterion corrected for sample size (AICc, models were preferred when delta AICc > 2). All the models were made using the R package Hisse  (Beaulieu, 2017). To account for phylogenetic uncertainty, we fitted the four SDD models, as well as STRAPPS and FiSSE analysis for each trait and phylogenetic tree. For SDD models, we assessed the best-fitting model each time.
(2022). For this analysis we used species coloration character states as coded in our database and the R script as in our main analysis. (ii) Our coding of character states differed from  Brock et al. (2022) 's in some species. Among the species we had in common, 10 species were coded as polymorphic in our dataset while they were coded as monomorphic in Brock et al. dataset, and 7 species were coded as monomorphic in our dataset while they were coded as polymorphic in Brock et al. dataset (see Table  S2  for coloration data of Brock et al. , and the rational of our coding). We tested how these differences affected our results by repeating the analysis with both the species and the character states used by  Brock et al. (2022). The R script used for this analysis was the same as for our main analysis.
(iii) Although we used the same R package (HiSSE;  Beaulieu, 2017) , the HiSSE package was recently updated, and our analysis was based a more recent version than  Brock et al. (2022). As a result, there are several differences in the R function between Brock et al. 's analysis and our analysis. This results in differences of functions (the function specifically built for CID4 models was removed in the recent version), but also in differences in the number of transition rates between states in SDD models. We tested how differences in R script affected our results by re-running the original Brock et al. 's analysis using their script, species and character states (hence repeating their analysis entirely without changing anything). All the analyses were made on the phylogenetic tree produced by Garcia-Porta et al.
(  2019 ), which was used in both our main analysis and  Brock et al. (2022). Brock et al. (2022)  built a second phylogeny on which they repeated all their analysis, without affecting their conclusion. We did not used this phylogeny in the comparison because it was built with only five genes, while Garcia-Porta et al. ' phylogeny was built with one dataset of 324 anonymous nuclear loci and one dataset of 6269 protein-coding nuclear loci. In addition, some clades identified in Brock et al. ' phylogeny were at odd with previous results: for example, the Gallotiinae are nested within Lacertinae in  Brock et al (2022) , while they are sister clades in Garcia-Porta et al. (  2019 ),  Arnold et al. (2007)  and  Kapli et al. (2011).
Inconsistent results on Lacertids The lack of effect of colour polymorphism on speciation rates contradicts the conclusions of  Brock et al. (2022)  obtained on the same family Lacertidae. Although  Brock et al. (2022)  used a different phylogeny, they also used the same phylogeny as us and this did not affect their conclusion. We also excluded insular species and parthenogenetic species, because speciation mechanisms in these species are unlikely to depend on polymorphism, unlike  Brock et al. (2022). In addition, their phylogeny only included species that were represented in the molecular dataset. While this avoided placing species randomly within their genus, as we did (we checked that our results were robust to the random position of species with no genetic data within their genus by repeating all analyses on 100 trees, see methods), this also resulted in removing a considerable fraction of the Lacertidae species from their data.
Worryingly, this fraction of species excluded from their analysis is biased between the two main clades of Lacertidae (Table  S1 ): their dataset lacks 17 species of Lacertini out of 143 but 66 species of Eremiadini out of 203. Since polymorphic species are mostly Lacertini, it seems dangerous to underrepresent the mostly non-polymorphic Eremiadini when comparing the diversification rate of polymorphic and non-polymorphic species. If we wanted to maintain our choice to include as many species as possible in the dataset, switching from Garcia-Porta et al's tree to Brock et al's tree would only decrease the number of randomly added species from 68 to 57. Last, Garcia-Porta et al's backbone is based on genomic data while Brock et al's tree, based on a small amount of sequence data, has obtained some spurious relationships.
Finally, when we tested how this difference of species sampling affected our results by repeating our analysis using only the species included by  Brock et al. (2022)  and using species coloration character states as coded in our database, we found the same conclusion of no effect of polymorphism on diversification. We are thus confident that our choice of using Garcia-Porta et al. 's (2019) phylogeny and including species that have no genetic data available was the best methodological choice, but the use of different phylogenies and different species did not cause the differences in the two studies. Another possible reason is that our attributions of character states (polymorphic versus nonpolymorphic) differed in some cases from Brock et al. (17 species highlighted in yellow in Table  S2 ), and indeed repeating our analysis with the species and character coding of Brock et al.
's original results (their Table  2 , repeated here in Table  S3 ). Especially worrying is the fact that the net turnover parameters are not estimated (parameters at boundary) for the two state classes (polymorphic and non-polymorphic) of the A class of the hidden state. The higher diversification of polymorphic species is thus only supported in one of the two classes of the hidden state using the new version of the R package HiSSE, which does not allow to conclude on the impact of polymorphism on diversification rates according to  Beaulieu & O'Meara (2017). FiSSE and STRAPP have been developed to avoid this kind of drawbacks and provide a more robust, and nonparametric, way of testing character-dependent speciation rates  (Rabosky & Huang, 2016; Rabosky & Goldberg, 2017). The fact that these methods do not recover an effect of polymorphism, whatever the data and character states used (ours or  Brock et al's) , and that  Brock et al.
results  are less robust when using their species and trait coding with the new version of HiSSE suggests that the result of  Brock et al (2022)  might have been partly (mostly?) driven by a type-1 error. In conclusion, the differences between the results of  Brock et al. (2022)  and our results concerning the impact of colour polymorphism on diversification in Lacertidae originate partly from Brock et al. 's choice to restrain colour polymorphism to throat colour polymorphism only and partly from methodological issues associated with the R package HiSSE.