dataesr/scientific-paragraphs-categorization huggingface dataset:
For both kinds of NN, it can be seen clearly that the interval diameters are much smaller in the domains in which dense measurements are available. Outside those domains, the sigmoid version of the NN has the advantage of providing tighter bounds due to the inherited saturation of the hyperbolic tangent function (Eq. 7). For that reason, we use only the sigmoid implementation in the context of SOFC modeling (cf. Sec. 4). In Table  1 , there is a summary of the computing times 3  for evaluating the trained neural networks. Obviously, the slowest version is the naive evaluation of the point-valued networks in the form of the MATLAB network structure using the command sim. This can be accelerated significantly by employing automatic code generation (genFunction with the option MatrixOnly), which, however, pre-evaluates all NN-internal relations with the point-valued parameters.
To obtain an implementation that is directly suitable for replacing the NN parameters with intervals, an extension of the Matlab functionalities by a vectorized implementation is necessary to which all parameters are passed as function arguments. Compared with this version, the interval-valued realization increases computing times by a factor 5 for ReLU activation functions and by a factor 10 in the nonlinear, sigmoid case. Remark 4. Note that the true function (usually not available for the identification procedure) is not necessarily fully included in the determined interval outputs if the number of training points is insufficient in certain domains. If this phenomenon is detected at runtime after new measured data arrive, the model can be adapted by a re-application of Step B1. If computational resources allow this, Step B3, hot-started with the previously identified interval bounds, can also be re-applied.
Point-Valued Static Neural Network Model As the fundamental, static electric power model, the NN representation in Figure  7  is employed. This network is trained with the measured data described above by using the MATLAB NN toolbox. We rely on the Bayesian regularization back-propagation algorithm (parallelized on four CPU cores) with a maximum number of 5,000 epochs to solve the training task. During the network training, a worsening of the validation performance has been allowed in 50 subsequent iterations with a random subdivision into training (70%), test (15%), and validation (15%) data. For further details, see  Rauh (2021). The visualization of the training results in Figure  9  reveals that the largest approximation errors occur during those operating phases of the SOFC stack in which either the hydrogen mass flow or the electric stack current show significant temporal variations.
Point-Valued Dynamic NARX Model As indicated in Figure  6 , two different types of NARX models can be considered. These are either models with fully nonlinear characteristics of both the system inputs and the fed-back stack voltage (N d,A ) or a model simplification in which only nonlinearities with respect to the input variables are accounted for (N d,B ). To ensure comparability between  the previous static system model and the NARX representation, L = 7 is again used as the fixed number of hidden layer neurons. In addition, M = 10 (for the data set with the reduced sampling frequency of 1 Hz) serves as the memory length of the autoregressive model. This heuristically chosen value is motivated by a delay of a few seconds between the fuel cell stack's inputs and outputs that became visible during the experimental data acquisition.
Compared with the static NN model, both NARX options reduce the deviations between measured and simulated data significantly, especially during the dynamic operating phases. This is confirmed in Table  2  that provides information on the roots of the corresponding mean square approximation errors (RMS). It can be clearly seen that approximation errors for both the voltage and the power are approximately 30% lower if the dynamic model N d,B with the linear output feedback is used instead of the static representation. These errors can be reduced even further if the fully nonlinear representation N d,A is used instead (cf. the visualization of the approximation errors in Figures  10B, 11B ).
CONCLUSION AND OUTLOOK ON FUTURE WORK In this paper, we have presented a novel possibility for extending both static NN models and NARX system approximations by error correction intervals. These corrections are chosen in such a way that the computed system outputs enclose available measured data and can be adjusted so that a certain percentage of outliers is permitted. In future work, we plan to use the interval-valued NN models to assess the reliability of optimization-based control strategies which (in the sense of a predictive controller) adjust system inputs-such as the hydrogen mass flow-to implement tracking controllers for a nonconstant electric power under the simultaneous minimization of the fuel consumption. For this kind of application, it is necessary to avoid overshooting the maximum power point with certainty.
So far, strategies have been developed which make use of a Kalman filterbased optimization method  (Rauh, 2021). Generalizations to intervalvalued NN models can lead to the implementation of sensitivitybased predictive control procedures such as the one published in  Rauh et al. (2011). Note that this approach would require intervalvalued partial derivatives of the NN outputs with respect to the gas mass flow and the electric current. This, however, can be achieved in a straightforward manner by overloading the operators using the interval data type of INTLAB with the functionalities for automatic differentiation available in the same toolbox.
Time synchronization between excitation pulses and luminescence detection was provided by a home-made triggering device. Tunable excitation permitted us to separate the new emission features from those of the already known phosphorescence of the parent molecule  [21]  or of the chain-growth products  [22]. In luminescence decay time measurements, phosphorescence light, selected with the grating monochromator, was detected by the photomultiplier. Photons arriving after the excitation laser pulse were counted in successive time gates using a National Instrument acquisition card (PCIe-6251) and a homemade LabView-based software.
We then used the "dapc" function while retaining 250 PCs and five discriminant analysis eigenvalues. In the sPCA analysis, we ran the "spca" function by choosing a Delaunay triangulation as a connection network and kept the first positive eigenvalue. To assess similarities between geographical and genetic distributions, we performed a Procrustes analysis and a Mantel test  (Lisboa et al. , 2014). We used the "procrustes" function in the R package MCMCpack 1. 4-7  (Martin et al. , 2011)  to perform a Procrustes transformation analysis on the first two PCs identified in the PCA analysis, in which the PCA coordinates were scaled to geographical coordinates. We determined the Pearson correlation coefficient between the pairwise genetic distance (F ST ) and the geographical distance between populations using stat_cor() in R.
7. 1  (Rambaut et al. , 2018). We used Bayesian inference with the structured coalescent estimator Migrate 4. 4. 4 (Beerli et al. , 2019)  to test population history models for the four regions N, S, NE, and NW based on our population structure results. Thus, the following different population scenarios were evaluated: seven scenarios (Figure  S1 , models 1-7) that assumed the most recent common ancestry occurred in the N or NW regions based on their high genetic diversity, and among which, four of the scenarios (Figure  S1 , models 2-5) assumed that the S and NE populations were from different sources; we included two admixture models (Figure  S1 , models 8-9) to assess whether population admixture has occurred within the North region, given our admixture result. We randomly selected 20 individuals from each region and ran the models with a strictly filtered SNP dataset that contained no missing data (n = 422 SNPs).
We used one long chain with four replicates for each run with 1,000,000 iterations and a burn-in of 10,000. We compared the models using the log marginal likelihood  (Beerli et al. , 2019). To further estimate effective population sizes, divergence time, and migration rates of the optimal model identified with Migrate, we used an alternative coalescent-based simulation method, Fastsimcoal 2. 6  (Excoffier & Foll, 2011). We applied easySFS. py (https://github. com/isaac overc ast/easySFS) to generate the joint site frequency spectrum (SFS). We performed 100 independent parameter estimations to obtain the maximum composite likelihood of the joint SFS, each run with 100,000 iterations and 40 cycles of expectation conditional maximization (ECM)  (Lanier et al. , 2015)  using a maximum likelihood method.
The SnpEff database for ALB was built manually with the reference genome GCA_000390285. 1  (McKenna et al. , 2016). The flanking sequence for each locus identified was compared to the NCBI database using blastn to identify similar insect genes  (Altschul et al. , 1990) , and details for the annotated regions were derived from insect-based records from the Gene Ontology database  (Ashburner et al. , 2000; Gene Ontology Consortium, 2021) , UniProt database (UniProt Consortium, 2021), and FlyBase  (Larkin et al. , 2021)  to determine molecular function and biological processes. We identified a potentially adaptive outlier SNP within glycerol kinase and conducted a phylogenetic analysis for related insect genes using the Neighbor-joining method with 1000 bootstraps in the MEGA X software  (Kumar et al.
| Microsatellite discovery and analysis To independently assess population structure with a different set of data, we genotyped 232 new ALB specimens collected independently from 16 locations in China (Table  S3 ) within the biogeographic regions (i. e. , NW, N, NE, S) using highly polymorphic microsatellite markers. Here, we sampled within the same regions, but with limited overlap with localities of the GBS study, with only five duplicated sampling locations, Beijing (BJ), Hengshui (HS), Cixi (CIX), Tongliao (TOL), and Yanchi (YC). DNA was extracted using   S4 ) for 202 individuals (1-32 individuals per location, average 12. 6). We assessed linkage disequilibrium between all pairs of microsatellite loci in the R package genepop 1. 1. 7 (Rousset, 2008)  to avoid genetically linked loci. Mean number of alleles per locus (A n ), average gene diversity over loci (D), observed heterozygosity (H o ), and expected heterozygosity (H e ) under HWE were estimated in Arlequin 3.
5  (Excoffier & Lischer, 2010). The probability test for deviation from HWE was calculated in the R package genepop 1. 1. 7 (Rousset, 2008). We used the R package PopGenReport 3. 0. 4 (Adamack & Gruber, 2014)  to estimate mean allelic richness (A s ) for each location and pairwise F ST after correcting for sample size differences. Samples with fewer than five specimens were omitted from the estimation. We applied DAPC to characterize genetic clusters using Ward's hierarchical clustering method  (Ward, 1963). We further assessed the distribution of genetic variance between and within genetic clusters as assessed through an AMOVA based on the clusters according to our DAPC results. Statistical significance of covariance associated with each hierarchical level was calculated with 1000 permutations.
| Congruence between genome-wide marker sets To assess the congruence between the two marker sets, we performed a Mantel test using a Monte-Carlo method on the pairwise F ST generated from SNPs and microsatellites in ade4  (Chessel et al. , 2004). We used F ST values calculated from the five sampling locations (i. e. , BJ, HS, CIX, TOL, and YC) present in both datasets. We performed the 'mantel. rtest' analysis on the two F ST matrices with | 941 CUI et al.
| Population assignment To identify markers for downstream diagnostic tool development, we wished to identify SNPs that could accurately assign individuals to their source populations. To do so, we used Mycorrhiza 0. 0. 28, a genotype assignment software that employs machine learning and phylogenetic networks to identify informative SNPs that can assign individuals to their source population  (Georges-Filteau et al. , 2020). The SNPs were ranked by discriminatory power based on mutual information. Mycorrhiza works in two steps. First, it generates a pairwise genetic distance matrix from the genotype data and uses it to construct a phylogenetic split system using the Neighbor-Net method in the program SplitsTree 4. 14. 6  (Huson, 1998; Huson & Bryant, 2006). Second, the split system is used in a two-fold crossvalidation procedure via a random split method in the scikit-learn library.
| Gene flow and population history We estimated contemporary gene flow within and among regions using our SNP dataset. The migration rates ranged from 0. 004 ± 0. 004 to 0. 124 ± 0. 060 migrants per generation (Nm), with an average across all populations estimate of 0. 01 ± 0. 0006 Nm (Figure  5a ). Out of the nine scenarios that included present-day populations tested in Migrate (Figure  S1 ), model3 was the most probable based on marginal likelihood (Table  S7 ). This model assumes that the North population first diverged from the Northwest population with ongoing gene flow and an estimated migration rate of 5e -03 between populations (Fastsimcoal output). Through modeling by Fastsimcoal, the divergence between the North and the Northwest populations was traced back to 16,349 generations ago (Figure  5b ).
We assigned functional annotations to each locus based on flanking sequence similarity with previously published results. Over half of the flanking sequences (n = 213) could not be characterized. For loci that were functionally similar to previously annotated regions, we identified loci associated with a range of molecular functions and biological processes (Table  S9 ). Of particular interest was the glycerol kinase (GLK) gene (AGLA000593, i5K database, https:// i5k. nal. usda. gov/Anopl ophora_glabr ipennis, Accessed Jan 2022). The SNP under selection was annotated as a missense mutation under positive selection (F ST = 0. 335) (Table  S9 ). We compared the ALB GLK gene to previously published insect GLK genes. We observed that the ALB GLK gene is closely related to genes described in other beetle taxa (Figure  6a ), including Dendroctonus ponderosae and Tribolium castaneum, in agreement with the species' phylogenetic relationships shown in  McKenna et al.
F I G U R E 4 Maximum likelihood phylogenetic tree for Chinese Asian longhorned beetles analyzed in RAxML. A total of 6102 SNPs were used in the analysis. Each branch represents a sample. The branches are colored by their population codes (explained in Table  1 ). The width of each branch corresponds with their bootstrap value (widest branch with a bootstrap value of 100). Fan-like sections indicate subregions NE, S, and NW (whereby SHI is actually in the N region). The rest of the populations are from the N region our marker and specimen sets (Tables  S5  and S11 ). Here, we observed a significant congruence between our datasets (r = 0. 951, p-value = 0. 039) based on 9999 replicates between the two datasets. We observed similar, although not identical, clustering in our two DAPC analyses on these distinct datasets (Figures  3b  and 7 ).
, 2014; Populus, Hou et al. , 2018). In our SNP dataset, this North-South division aligned with the hypothesized location of an "aridity belt" along the Yellow River (= Huang He) in East Asia  (Milne & Abbott, 2002). Geographic barriers such as mountain chains, arid regions, rivers, and geographic distance are key factors driving population structure in many widely distributed Asian insect species (e. g. ,  Du et al. , 2021). Population structure can be driven by a combination of geographic, spatial, and environmental conditions. Often, these vary along spatial or temporal gradients; temperature conditions, light regime, and moisture availability can show latitudinal, altitudinal, or longitudinal gradients across a species range  (De Frenne et al. , 2013; Laiolo & Obeso, 2017). Geographic barriers such as rivers and mountains, particularly when aligned along these longitudinal or latitudinal axes, can profoundly influence population structuring and gene flow within populations  (Bester-van der Merwe et al.
, 2000). As climatic restrictions eased, ALB expanded its range in concert with its host plants, leading to localized admixture between nearby regions while still maintaining distinct regional structure. Similar population structuring, including regional admixture, has been reported in temperate forest trees, including Juglans species  (Bai et al. , 2016)  and Betula platyphylla  (Chen & Lou, 2019). We also observed evidence of movement out of the North to outlying regions in Zunyi (ZUY) and Pengzhou (PEZ) in the microsatellite dataset (Figure  7 ), suggesting a possible long-distance spread pathway. Similarly, we detected two distinct genomic signatures in our South Korean samples. We had specimens collected from sites within the historic forested range of ALB (i. e. , Kangwon, Pocheon), shipping hub between China and South Korea  (Li, 2012).
| Dataset congruence Here, we used two different genetic marker systems and slightly different sampling strategies within the main geographic regions to verify the overall genetic pattern of ALB in its native range. In addition to complementary findings, in both cases, the overall genetic structure differentiated NE, NW, and S regions. Given the highly congruent results from the two sets of independent markers, it is worthwhile to consider the pros and cons of the two approaches. The biggest advantage of GBS is undoubtedly its capability to produce thousands of SNPs. Genome-wide SNP markers provide in-depth insights into the level of population structure that may not be revealed by other types of genetic markers. For example, our SNP data show a genetic cline along a latitudinal gradient in Eastern China, a result not reported by Sanger sequencing or microsatellite data  (Carter et al.
, 2010; Javal, Lombaert, et al. , 2019). For organisms whose population structure is obscured by recent long-distance dispersal (such as human-aided movement as seen in many invasive species), SNP-based analysis proves to be particularly powerful in resolving the underlying genetic pattern  (Picq et al. , 2018). Additionally, GBS can generate SNPs that are candidate markers for identifying associations between genotypes and phenotypes that may point to processes of selection or adaptation to novel environmental conditions  (Wickland et al. , 2017) , which can ultimately improve our assessment of invasive risk. However, microsatellite markers do have their own merits. Using the designed PCR primers, there is no need to sequence the genomes and run the bioinformatic pipeline again for joint SNP calling when new samples are added to the existing database.
Therefore, we were able to exceed 90% predictive accuracy with only 200 SNP markers for the six genetic groups delimited within our dataset. We needed even fewer SNP markers to confidently assign individuals to Northern or Southern regions with 95% accuracy. A study on Lymantria dispar spp. also showed that a high degree of genetic differentiation led to high population assignment success  (Picq et al. , 2018). They used laboratory colonies that consisted of eight groups that were genetically distinct, and the assignment success was generally high (86%-100%) using different sets of  SNPs (12, 24, 48, and 96). However, for populations of white bass (Morone chrysops) with a moderate genetic differentiation between six populations (F ST = 0. 083), the assignment accuracy reached 99. 78% with 57 SNPs  (Zhao et al.
, 2019b). Moreover, progress has been made in developing assignment tools using SNP markers. For example, an assay that contains 324 SNPs, allowing for predictions of phenotype, biogeographical ancestry, and male lineage has been demonstrated to be a robust and sensitive human forensics tool  (Diepenbroek et al. , 2020). Overall, such an approach is also very promising for translating our results into developing SNP panels for a biosurveillance purpose in forest protection. While our sampling design covered most of the regions in China, there are records of ALB throughout China  (Yan, 1985) , and we have not fully sampled the entire distribution. Given our microsatellite results, the NW population Pengyang (PEY) that is not included in the SNP dataset formed a distinct cluster. There are likely other populations with a different genetic make-up that have not been collected.
| CON CLUS ION Our study revealed clear regional differentiation among native ALB populations using two independent datasets that comprised complementary genetic data and sample sets. Biogeography, drift, and limited dispersal capacity are likely key factors that aided the formation and subsequent maintenance of the ALB population structure within its native range. Future research is poised to explore the environmental factors, including both abiotic (e. g. , temperature, precipitation, etc. ) and biotic (e. g. , host plants), that shaped the complex genetic structure we observed in this pest. The patterns of genetic structure varied among regions and while large-scale human-assisted ALB population migration was limited, we were able to detect it in both datasets. Our ability to resolve this complex pattern of movement in the native range demonstrated the ability for our markers set to serve as a diagnostic tool to track invasive ALB populations outside their native range.
DATA AVA I L A B I L I T Y S TAT E M E N T Raw sequence data are available at the Sequence Read Archive (SRA) with the BioProject ID PRJNA824548. Variant Call Format data have been deposited in the Dryad Digital Repository: https:// doi. org/10. 5061/dryad. 866t1 g1rb. Scripts are available at https:// github. com/mimin gcui/nativ eALB.
To allow for quantification of perfusion parameters (i. e. , StO 2 %, NIR%, THI%, and TWI%) in the investigated area, the camera-specific software package (TIVITA™ Suite) allows to define circular regions of interest (ROI) in which the mean value for each parameter is calculated  [22, 26]. Three ROIs were manually positioned in the areas of interest, namely the vertical scar underneath the DIEP flap skin island to the inframammary fold (1), the skin island of the DIEP flap, where the nipple was previously positioned (2), and the medial side of the mastectomy skin flap (3) (Figure  1a ). Clinical evaluation of the flap was performed to assess the vitality of the tissue (i. e. , vital versus necrotic DIEP flap or mastectomy skin flap).
First of all, this study was limited by a small sample size. However, 19 TIVITA™ images per patient were taken in a time window of 72 h postoperatively, which resulted in a large dataset that made it possible to closely monitor postoperative changes. We are aware of the need for studies with a larger sample size to confirm the efficacy of HSI in assessing mastectomy skin flap and DIEP flap viability and determine the exact timing of tissue debridement based on HSI instead of clinical evaluation. Additionally, no continuous HSI measurements were collected to assess tissue perfusion during follow-up. However, we believe that the frequency applied in this study is clinically relevant and logistically feasible. Previous studies also concluded that hyperspectral imaging systems could well detect a decrease in oxygenation before clinical evaluation and Doppler assessment  [20] [21] [22].