dataesr/scientific-paragraphs-categorization huggingface dataset:
Geometric Alignment Measurements For the completeness of time measures of the whole system, we present here the result of our previous work  [22]. The time measures of the geometric alignment module are given in Table  3. The OpenCV  [38]  library 4. 5. 5 version was used during this experiment. The geometric alignment consists of an SIFT  [27]  keypoint extractor, an FLANN matcher  [28] , and a homography estimator. In the first part of Table  3 , we give measures on embedded mobile ZCUs. The left-most column of Table  3  contains the name of the video file. The SIFT points have been detected in the mask, centred on the estimated gaze fixation point in each frame. The radius of the mask was chosen to encompass approximately 100 points. The second column contains the mean mask radius with standard deviation.
Gaze-Driven Faster RCNN Accuracy As Table  7  and Figure  7  show, the current architecture can perform sufficiently well on our real-world data. Reducing the number of channels to 128 does not impoverish the classification accuracy too much, compared with the initial 1024 feature channels of the backbone, as we can see from Table  7. The average accuracy and loss are computed per class of objects. Table  8  shows a comparison between different object recognition methods from the state-of-the-art methods and our method. The state-of-the-art methods, such as lightweight YOLO V3  [20]  and SSD Mobilnet V2  [12] , are trained on the COCO and VOC datasets. We have a specific and very cluttered kitchen environment. For this reason, we do not think that these object detectors are suitable in our case.
What drives ecosystem buildup, diversity, and stability? We assess species arrival and ecosystem changes across 16 millennia by combining regional-scale plant sedimentary ancient DNA from Fennoscandia with near-complete DNA and trait databases. We show that postglacial arrival time varies within and between plant growth forms. Further, arrival times were mainly predicted by adaptation to temperature, disturbance, and light. Major break points in ecological trait diversity were seen between 13. 9 and 10. 8 calibrated thousand years before the present (cal ka BP), as well as break point in functional diversity at 12. 0 cal ka BP, shifting from a state of ecosystem buildup to a state where most habitat types and biotic ecosystem components were in place. Trait and functional diversity stabilized around 8 cal ka BP, after which both remained stable, although changes in climate took place and species inflow continued.
1  and figs. S1 to S3). We expanded plant sedaDNA data from  (20)  and constructed a nearly complete regional DNA reference library (PhyloNorway, 1822 vascular plant taxa) that we used for identification. We combined the taxonomic data with a large trait dataset  (22)  to test which edaphic and biotic factors were important in postglacial ecosystem buildup. Because dwarf shrubs play a key role in the functioning of Fennoscandian ecosystems  (23) , we further investigate the dynamics of their arrival in relation to other groups. specimens from 1899 taxa [PhyloNorway  (24) ], we obtained a P6 loop reference library of 1969 specimens from 1822 taxa (data S1). This DNA reference library not only covers most of the species in Norway but also includes taxa from other polar regions. Our reanalyses of the sedaDNA data of  (20)  coupled with additional samples (see Materials and Methods) resulted in a total dataset of 355 samples containing 42,093,315 reads and 64,338 polymerase chain reaction (PCR) repeats of 315 taxa (data S2).
For up to 227 taxa, data on species traits were available (data S3). As we ran eight PCR repeats per sample, we used these replicates as a proxy for taxon abundance (see Materials and Methods). Sedimentary ancient DNA is a powerful source of information about past floras but, just as with detecting the disappearance of a species, the first arrival is likely to go undetected because of rare and scattered occurrences. Therefore, we estimated likely arrival times on the basis of detectability of each species and used these to analyze the possible effect of species traits on arrival times (data S4).
A past (1934) survey of the flora of Finnmark (48,631 km 2 ) is largely based on material collected more than a hundred years ago and is outdated. A database maintained by T. A. covering Finnmark includes 598 taxa of vascular plants (660 if subspecies are included, excluding apomictic taxa in Ranunculus auricomus coll. , Hieracium and Taraxacum, each counted as one species) and a further 256 introduced taxa, in which some are well established (data S5). The dominant vegetation types in the region are birch forest in the western lowlands (up to 300 to 400 m above sea level) with an oceanic climate, pine forest up to 350 m in the interior with a more continental climate, and alpine tundra above and north of the tree line  (44).
Climatic data Climate data ranging from current conditions (1950 to 1990) to the LGM (21 cal ka BP) were represented by monthly mean daily nearsurface (2-m) air temperatures for the month July from the CHELSA-TraCE21k V1. 0 dataset  (45, 46). The monthly mean daily near-surface (2-m) air temperatures for July are shown for three selected time slices in Fig. 1  and for the entire time period from 22 to 0 cal ka BP in fig. S1.
S2, C  and D ). We therefore selected the extrapolated JØP-112 age-depth model with an accMean of 20 for Jøkelvatnet. Revised ages for all samples from the Jøkelvatnet core are presented in table  S4. The core records begin at the following: Langfjordvatnet, 16. 9 cal ka BP (with the oldest sample in the dataset at 16,147 cal BP); Nordvivatnet, 12. 9 cal ka BP; Sandfjorddalen, 12. 4 cal ka BP; Eaštorjávri South, 11. 4 cal ka BP; and Horntjernet, 11. 1 cal ka BP. From around 10. 5 cal ka BP, there are samples from 8 to 10 cores in each millennium (data S2 and fig. S3 ). Two cores cover the Late Holocene period only (Nesservatnet and Sierravannet), which could cause a late detection of taxa restricted to habitats found in these two catchments. However, as only two taxa (Potentilla egedei/anserina and Alopecurus geniculatus) were unique to these two cores, this has a negligible impact on the overall first detection of taxa.
The sedaDNA dataset Our sedaDNA dataset consists of a supplemented version of that presented in  (20). All samples had been analyzed at The Arctic University Museum of Norway, where the dedicated ancient DNA laboratory is in a separate building to post-PCR facilities. DNA was extracted from 0. 25 to 0. 35 g of sediments following  (48)  and amplified using the gh primer targeting the vascular plant trnL p6-loop locus of the chloroplast genome  (49)  and sequenced on Illumina NextSeq platform at either Fasteris, SA (Switzerland) or the Genomics Support Centre Tromsø at the UiT The Arctic University of Norway [see full details in  (20, 48) ]. We used all 371 samples from  (20) , and in contrast to  (20) , we included all extraction duplicates (n = 17). We supplemented this dataset with 53 samples from five of the same lake cores that either predated the Holocene or fell outside of the age-depth models used in (table  S6 )  (20).
We also included 76 samples from three cores taken from Lake Øvre AEråsvatnet lakes on Andøya (table S7)  (48) , which were later excluded because of poor DNA quality (see below). All additional samples were processed using the methods in  (20)  with core depth and other sample metadata given in table S6 and library, sequencing details, and tag-sample lookup presented in table  S8. This gave a total pre-quality control (QC)filtered dataset of 517 samples, together with 76 negative and 28 PCR laboratory (positive and negative) controls (table  S9 ).
Construction of reference library A comprehensive reference library is essential for correct identification to lower taxonomic levels, especially when working with short barcodes as sequence-sharing among taxa is common, and if the true species is missing from the reference library, then a match to a closely related species is likely. For the P6 loop used here, the taxonomic resolution to species level is 33% at a circumpolar scale due to the many congeneric species in Beringia and the Amphi-Atlantic region  (31) , but resolution increases to 77 to 93% when applied to local catchments  (50). Thus, from our recent low-coverage genome skims of 2051 specimens from 1899 taxa [PhyloNorway (  24 )], we extracted the P6 loop of the trnL (UUA) intron from the assembled genomes by running the ecoPCR program  (51).
We kept only sequences of category 1. After collapsing sequences matching the same taxon, our dataset consisted of 342 taxa (48,783,645 reads; 68,937 repeats). We next removed low-quality samples by applying the QC approach described in  (20) , which uses minimum metabarcoding technical and analytical quality (MTQ/MAQ) score thresholds as a filter. All negative controls associated with the additional samples were reported in  (20) , and the Andøya negative controls did not affect the thresholds (table  S7 ). We therefore applied the same MTQ/MAQ score filtering thresholds (MTQ ≥ 0. 75; MAQ ≥ 0. 1). We retained the 316 samples reported in  (20) , five extraction duplicates, and 34 of the additional samples from four cores (tables S6 and S9). As only 16 of the 76 samples from Lake Øvre AEråsvatnet passed the QC thresholds (table  S7 ), we omitted this site from further analysis.
Quantification of sedaDNA data Regarding abundance, sedaDNA studies may be taxonomically biased for various reasons, for example, unequal preservation, taphonomy, primer binding, marker length, or bias in the DNA reference library used  (10, 61). The PCR step in the metabarcoding approach may especially cause biases in quantities and can underestimate graminoids  (50, 62) , although the close similarity of past circumarctic vegetation reconstructed using metabarcoding  (53)  and shotgun metagenomic sequencing  (11)  suggests that this might be less of a problem than previously assumed. Possible ways to quantify metabarcoding data are using raw read counts, proportion of reads, or Downloaded from https://www. science. org at Havforskningsinstituttet on October 25, 2022 number of PCR repeats where species occur (any of these can also be transformed or weighted).
This requires that a sufficient number of PCR repeats have been analyzed  (68)  and that they have not been merged before sequencing  (10). Thus, while the actual contribution of the dominant taxa may be underestimated when using proportion of PCR repeats, this approach highlights the full diversity of the taxa detected. In the Results and Discussion section, we therefore chose to quantify the sedaDNA data using our eight PCR repeats. Similar patterns in growth form and traits were observed when using the proportion of reads (data S2) or only the qualitative information, which is the presence of taxa (figs. S6 and S11). The total number of repeats across multiple samples and lakes within a time slice was used while calculating the proportion of repeats for each 500-year time slice. A similar approach was used after taking the sum of repeats of all species within a growth form while calculating repeat proportion for growth forms within a 500-year time slice.
Number of samples and sites over time As our selection of sites represented the regional variation in deglaciation times, the earliest sample varied among sites. Both the number of sites and the number of samples increased over time (fig. S3 ). To assess the impact of increasing sampling intensity on species accumulation curves, we resampled the data to a maximum of nine samples for each time slice of 500 years representing all the sampled lakes. Thus, if a time slice contained more than nine samples, then we randomly selected only nine samples by choosing at least one sample from each lake that was sampled during this time. The resampled curves fitted very well with the curves generated from the full dataset (Fig. 1B ). Similarly, trait accumulation based on resampled data (see below) showed no effect of sampling (fig.
Observed temporal patterns of taxa occurrence The temporal patterns of occurrences are plotted as the number of PCR repeats (1 to 8) using the rioja (v0. 9. 26) and ggplot2 (v3. 3. 2) packages in R. Note that sequences assigned to Salix are included in "Trees & shrubs" although this genus also includes some dwarf shrubs. We performed multivariate regression tree (MRT) analysis (fig. S4 ) to find optimal partition in the plant sedaDNA data as it does not make any assumptions about species-environment relations and provides robust results  (69). While performing MRT in the mvpart (v1. 6. 2) package in R, we considered species assemblage as the response and sample age as the predictor variables. A major change in the vegetation was found at 7. 8 cal ka BP, with secondary changes at 13. 0, 9. 7, 3.
Plant trait data We used species traits from the Flora of Sweden  (22). If a sequence matched to several taxa or was identified only to genus level, then we included the relevant taxa if they were found in the region today  (56). In this case, we used mean trait values if there was a <3 category difference within the genus/taxon group, except for moisture where we used <4 and climatic variables where we used <5. If the trait variation within a genus/taxon group was larger than these values, then we did not average but considered the trait value for this taxon as missing, as averaging may reduce model fit  (70). In a few instances, the sequence matched to several taxa, but only one of them was present in  (22). In this case, we used the trait values of the taxon present in the database for all the taxa represented by the sequence (see notes in data S3).
To minimize redundancy, we carried out a multivariate analysis with mixed quantitative variables and factors [fig. S8 ; using the function dudi. mix in the package ade4 in R (71) to identify correlated variables and keep a simplified set of variables]. For this analysis, missing values for continuous traits were replaced by the mean and taxa with missing values for categorical traits were excluded (two taxa). A single taxon with orchid mycorrhiza was also excluded.
Posterior distributions of arrival time  were obtained using a Bayesian approach, that is, assuming a prior distribution for  and  given in the study by  Wang et al. (72) π(θ, λ ) = 1 ─ 2θ √ _ 2π e -λ 2 /8 We used the R script provided by Wang et al. (72)  to estimate arrival time. This method allows the estimation of arrival time and the associated uncertainty (90% credible interval-the interval is bounded on the earliest observed date). To account for the uncertainty Downloaded from https://www. science. org at Havforskningsinstituttet on  October 25, 2022  in the estimated arrival times in the analyses relating traits and arrival times, we considered the width of credible intervals as an approximation of the precision of each estimate. For some species, estimated arrival times predated the oldest analyzed sample.
The metric is very similar to other commonly used metrics such as Rao's functional diversity  (79, 80). We used the mFD package  (81)  to calculate functional dispersion. We then used break point analysis to explore whether a joint measure of functional diversity, i. e. , functional dispersion, provides comparable break points to the break points of individual traits. As trait similarity of the assemblage may enhance temporal stability  (82) , we also calculated functional redundancy to assess stability of northern ecosystem using adiv package  (83)  and performed break point analysis. The break point analysis was performed using the library "segmented"  (84). All the analyses in this work were performed in R v4. 1. 3 (85).
Acknowledgments: We thank  F. J. Ancin Murguzur  for field assistance at Nordvivatnet,  K. Lorberau  for assistance in generating the additional sedaDNA data from Sandfjorddalen, and  K. Helmens  for access to and assistance with the Kuutsjärvi core. Bioinformatic analyses were performed on resources provided by  UNINETT Sigma2 -the National Infrastructure  for High-Performance Computing and Data Storage in Norway. Funding: This work was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  grant agreement no. 819192  (to I. G. A. , A. G. B. , L. P. , and E. C. ) for the IceAGenT project;  Research Council of Norway  grant  250963/F20  (to I. G. A. , K. A. B. , E. C. , N. G. Y. , D. E. , and L. P. ; supported D. P. R. and P.
The PhyloNorway P6 loop of trnL (UAA) intron is given in data S1. Final sedaDNA data used are given as data S2 and final trait data as data S3. First apperance and estimated first arrival of all taxa are given in data S4. A list of all taxa in the region is given as data S5. All newly generated raw sedaDNA sequence data have been deposited in the  European Nucleotide Archive (ENA)  (www. ebi. ac. uk/ena/ browser/home) with BioProject accession PRJEB39329. R scripts used for estimated first arrival times and linear models are given as Supplementary R script. Submitted 22 February 2022 Accepted 12 August 2022 Published 28 September 2022 10. 1126/sciadv. abo7434Downloaded from https://www. science. orgat Havforskningsinstituttet on October 25, 2022.
This research field is essential in data mining for quickly isolating irregular or suspicious segments in large amounts of the database. Many anomaly detection schemes have been proposed in the literature  [7] ,  [8] ,  [9] ,  [10] ,  [11] ,  [12] ,  [13] ,  [14] ,  [15] ,  [16] ,  [17]. Among them, the unsupervised methods are the most interesting since they are widely applicable and do not require labeling the data. Deep learning techniques for anomaly detection are often based on an encoding-decoding network architecture to learn healthy data or images that do not contain anomalies, such as references  [14] ,  [15]. Another approach is based on the one-class SVM method in the latent space  [17]. Anomaly detection in SAR using the difference between the input and the reconstructed image obtained through the autoencoder scheme is also proposed in  [16].
Still, it could suffer from the SAR speckle noise. In the literature, most model-based algorithms face three main issues. First, the speckle noise corrupts the image and potentially leads to numerous false detections. Second, statistical approaches may exhibit deficiencies in modeling spatial correlation in SAR images. Finally, neural networks based on supervised learning approaches are not recommended due to the lack of annotated SAR data, notably for the class of abnormal patterns. Our proposed method aims to address these issues through a self-supervised algorithm. Our approach consists in mitigating this annoying speckle with  [22]. The algorithm is adapted to our dataset and trained so that every polarization can be despeckled with the same weights. After, an Adversarial Autoencoder (AAE) is trained from these despeckled data.
It does not need to separate abnormal SAR images from the training dataset. Our method's last step is a change detection between the pairwise original and reconstructed images. This comparison is performed through a distance metric based on covariance matrices estimated locally. We finally compare our method with the conventional RX anomaly detector  [7]  by analyzing their global performance, and we highlight the importance of the despeckling process. This paper is organized as follows. After the introduction in Section I, Section II presents the context of SAR imaging. The proposed methodology being based on despeckling of SAR images, Section III describes the filtering process. Section IV presents the self-supervised neural architecture that aims to reconstruct the final SAR image without anomalies.
B. DATASET In this paper, the analyzed dataset is composed of a time series of X-band SAR images (each of size 4800 × 30000 × 4) acquired by SETHI, the airborne instrument developed by ONERA  [27] ,  [28]. The resolutions of these images are about 20 cm in both azimuth and range domains for the four polarization channels. In the monostatic case, the channels V H and HV are often averaged because they contain the same information. The averaging decreases the speckle impact without degrading the resolution (so-called reciprocity principle  [29] ). The resulting three channels HH, 1 2 (HV + V H ), VV are then thresholded separately using a value λ(I ) chosen as: λ(I ) = μ I + 3 σ I , (3) where μ I and σ I are respectively the estimated mean and the standard deviation of the intensity image I.
Two fake noisy image are created such that y 1 = y + s 1 and y 2 = y + s 2 where s 1 and s 2 are both distributed according the distribution from (2). The network is learning how to denoise data without any knowledge of spatial correlation. r Phase B: The training step is performed with ex- perimental SAR images and a change compensation pre-processing: two same areas acquired at a different time y t n and y t m are used. To compensate for changes that could have occurred between the acquisition at two dates, an estimation of the reflectivity is done beforehand (on sub-sampled images to remove spatial correlation). This leads to y 1 = y t n and y 2 = y t mf θ (y sub t m ) + f θ (y sub t n ) where the sub exponent characterizes sub-sampled data. This finally allows y 2 to have the speckle of y t m and the reflectivity of y t n.
IV. IMAGE RECONSTRUCTION Once the pre-processing step has been performed, the goal is to highlight anomalies. This is obtained in an unsupervised manner, and the network has no information about what should be an anomaly and what shouldn't. This makes the task harder than, for example, with a supervised convolution network. But, if we can reach the state-of-the-art with an unsupervised algorithm, one of the most significant drawbacks of artificial intelligence, corresponding to the need for a highquality labeled dataset will be overcome.
A. DESPECKLING QUALITY The proposed algorithm is applied to images that are decomposed into patches of size 256 × 256. For phase A, we first need to compute a speckle-free image by averaging co-registered images acquired at different dates. Every polarization has been averaged, and we then used the algorithm MuLoG  [21]  to remove the remaining speckle. It gives us training data of 3052 patches grouped in batches of size 32, which leads to a total of 955 batches. The network is then trained for 20 epochs. Phases B and C use images from the dataset previously described, and the pre-processing step is the one described in Phase B and C, Section III. There are four piles of two images, one for each polarization, and each image is decomposed into 33047 patches with a batch size of 32, which leads to a total of 1032 batches.
1) AAE TRAINING AND ARCHITECTURE The training process is self-supervised. The training dataset has no labels and potentially can contain anomalies. In this case, the evaluation and training datasets can be mixed because the training process requires only unlabeled data. In the same way, as for the despeckling algorithm, a log transformation is used, and the input data are normalized between 0 and 1. These images are decomposed into patches of size 64 × 64. The sliding window has a stride of 16. This leads to a total of 163785 patches grouped in batches of size 128, so we get 1279 batches for one training epoch with a total of 20 epochs. The architecture of the network is illustrated in Fig. 2. It has been designed according to  [37]. To update the weights, we use the optimizer Adam  [38]  with a cyclical learning rate  [39]  that goes linearly from 10 -3 to 10 -2.