dataesr/scientific-paragraphs-categorization huggingface dataset:
Intra-epoch tie points Inter-epoch tie points are not fully rotational invariant and also require adjustments for processing the original image size. With our proposed workflow using the combination of SuperGlue on downsampled images to determine the rotation and the subsequent DISK matching procedure applied to the full resolution images, we are able to estimate the camera parameters for all historical aerial images of the original Occitanie dataset in one single automatic run (Fig. 16 ). A comparison of the final models reveals the increase in registered images, number of observations per images, estimated 3D points, and also a increase in the final reprojection error (Table  4 ). However, this is to be expected for larger datasets as a higher number of camera parameters needs to be optimized in the bundle adjustment procedure because we consider a separate single camera for every epoch.
Accuracies achieved for a scaled model The previously presented results only show results in image space and in the local, arbitrary coordinate system defined by COLMAP. Here, we give insights into the tie-point accuracy, compare multi-temporal DSMs, and perform change detection using the Occitanie dataset. To assess the final tie point accuracy, a few stable points were measured in the historical images (Fig. 17 ). Note that conventionally used natural GCPs such as manholes or street corners were often not visible due to insufficient image quality or plant growth, or changes due to construction work during the 50 year   observation period (i. e. , from the most recent images used to extract the scale and to the earliest images dating back to the 1970's). Three different three-dimensional objects (pit, statue, fountain) were used for the tie point based accuracy assessment.
While usually flat objects are advantageous to assess the reprojection error, such targets could not be identified in the dataset with sufficient quality. The reprojection errors excluding epoch 1971 range between 0. 4 and 1. 6 pixels. They coincide with or are even superior to the overall mean reprojection error of the model estimated in COLMAP. When epoch 1971 is also included into the error analysis, the reprojection error increases to values between 1. 2 and 2. 1 pixels. The lower quality of the 1971 dataset could also be observed in the DSMs, because the overall image quality of the aerial photographs from 1971 is considerable worse compared to the later epochs. Furthermore, the object pit is located closer to the border of the image block of the 1971 epoch, which is another potential reason for the lower performance because a decrease of accuracy can be expected due to lower image overlap.
Implications and limitations Working on the two different datasets using learned feature matching methods leads to several findings. Both, DISK and SuperGlue are able to find feature matches between historical aerial images of different epochs and within the same epoch as highlighted with the Occitanie dataset. The Congo dataset reveals that SIFT (not shown in the publication) and SuperGlue are not able to find feature matches in texture-less scenes of the tropical rainforest. Also, the default configuration of DISK results only in few correct feature correspondences. However, an extensive parameter testing enabled the finding of the optimal settings for historical aerial images. Some limitations of our workflow have to be emphasized. Feature matching becomes more computationally expensive the more images are compared to each other.
The number of exhaustive matching of image pairs is calculated as (𝑛 2 + 𝑛)∕2 and hence increases significantly for large datasets, which is also the case for our presented tile-based approach. We intercept this increase slightly by including assumptions about the image overlap within and across flight strips and by using downsampled images to generate matches. It is recommended to use one or multiple GPUs for feature matching. As an example, the calculation of SuperGlue feature matches for one single image pair takes about 10 s on a common Intel i7-1165Gz @ 2. 80 GHz while it reduces significantly to 0. 1 s when using a single NVidia A100 GPU. Due to the synergetic characteristics of SuperGlue and DISK, our workflow requires setting up both matching strategies, which can be Further, the applied learned feature matching methods were originally trained on terrestrial images.
Although they provide good results on the aerial datasets of our case studies, the approaches might fail on other data and the general transferability from terrestrial to aerial perspective needs to be investigated more broadly. A future strategy could be the training of DISK with historical aerial images. However, this requires a (historical) ground truth dataset providing the images, depths and calibration protocols. One option for such a training procedure might be the newly established TIME benchmark dataset  (Farella et al. , 2022). In order to reach a comparable size of training data as large as the MegaDepth dataset with 135 scenes used for DISK, a similar amount of reconstructed historical aerial scenes would be necessary. With the 28 different scenes from the TIME benchmark and more historical data openly available, our workflow could possibly be used for generating this large amount of COLMAP reconstructions.
SuperGlue does not allow for such a training procedure because the architecture and corresponding weights of the neural network model are not openly available. However, first available open variants of SuperGlue might be alternatives  (Viniavskyi et al. , 2022). Generation of DSMs becomes possible when using the proposed method with post-processing in Agisoft Metashape. It could be observed, that the comparison of DSMs between  2001, 1990 and 1981  of the Occitanie dataset lead to reasonable results with standard deviations in Z-direction of twice the GSD. However, for epoch 1971 with actually a lower GSD the final dense point cloud was more erroneous with visible holes and larger radiometric differences between overlapping areas. Consequently, the result for tie point accuracy and DSM comparison were worse than for the other epochs.
This could not be directly observed during feature matching but only after detailed analysis of the final sparse and dense point clouds. We agree with  Feurer and Vinatier (2018)  and Knuth et al. (  2023 ) who state that processing multiple epochs simultaneously is advantageous, as it may help to cope with the correlation between flying height and focal length. In the multi-temporal dataset less systematic errors occur, like e. g. , the dome effect for the Congo flight strip. The multitemporal feature matching enabled a reasonable convergence of the bundle adjustment with small final mean reprojection errors already in COLMAP without prior definition of camera parameters opposed to the Congo dataset.
Conclusions Historical aerial images offer the potential to enable long-term observations of environments as early flight campaigns date back to the 19th century. However, this data often suffers from degradation, inadequate digitization quality, and missing flight information. The study presents a comprehensive workflow for processing these historical aerial images and unlocking the potential of existing data lying in numerous archives. It especially focuses on mono-temporal and multi-temporal data with difficult radiometric properties and unknown camera parameters. We significantly improve the feature matching in the SfM workflow enabling the automatic detection of tie points between image pairs that could not yet be matched using existing strategies. Our workflow does initially not need any other information except that the processed data is an aerial image dataset.
This will allow an extended observation period of different study areas and a monitoring of time-dependent environmental changes. The results show a significant increase in tie points found in historical aerial image pairs and a low mean reprojection error of 1. 1 pixels for the final 3D model determined by COLMAP. The accuracy investigation shows that this reprojection error can also be reproduced for several stable regions. After metrically scaling the point cloud, the comparison of multi-temporal DSMs reveal that our method is capable of producing reliable DSMs with a standard deviation of 0. 8 meters in stable areas. Additionally, environmental changes in larger areas can be detected. As the presented work only deals with two datasets (and the TIME benchmark dataset integrated in the open-source code), we intend to test on further historical aerial images in the future.
Acknowledgments The authors express their gratitude to  Fabrice Vinatier  for providing information and images of the Occitanie dataset and to  Maxime Réjou-Méchain  for providing the Congo dataset. Special thanks go to  Elisa Mariarosaria Farella  for supporting with the TIME benchmark. The provided sample images are part of the TIME dataset realized with the support of EuroSDR and different European Mapping Agencies. In particular, the reported images are part of the Norway dataset, kindly provided by  Mikko Sippo. The authors gratefully acknowledge the GWK's support for this project by providing computing time through the  Center for Information Services and HPC (ZIH)   at TU Dresden  on HRSK-II.
Abstract We present observations of polarized dust emission at 850 μm from the L43 molecular cloud, which sits in the Ophiuchus cloud complex. The data were taken using SCUBA-2/POL-2 on the James Clerk Maxwell Telescope as a part of the BISTRO large program. L43 is a dense  (N 10 H 22  2 ~-10 23 cm -2 ) complex molecular cloud with a submillimeter-bright starless core and two protostellar sources. There appears to be an evolutionary gradient along the isolated filament that L43 is embedded within, with the most evolved source closest to the Sco OB2 association. One of the protostars drives a CO outflow that has created a cavity to the southeast. We see a magnetic field that appears to be aligned with the cavity walls of the outflow, suggesting interaction with the outflow. We also find a magnetic field strength of up to ∼160 ± 30 μG in the main starless core and up to ∼90 ± 40 μG in the more diffuse, extended region.
These field strengths give magnetically super-and subcritical values, respectively, and both are found to be roughly trans-Alfvénic. We also present a new method of data reduction for these denser but fainter objects like starless cores. Unified Astronomy Thesaurus concepts: Stellar-interstellar interactions (1576); Interstellar magnetic fields (845); Young stellar objects (1834); Dust continuum emission (412); Starlight polarization (1571); Molecular clouds (1072).
Data Reduction To reduce the data, we used the Submillimetre User Reduction Facility (SMURF) package  (Chapin et al. 2013)  from the Starlink software  (Currie et al. 2014). The SMURF package contains the data reduction routine for SCUBA-2/ POL-2 observations named pol2map. 101 In the first step, the raw bolometer time streams are separated into Stokes I , Q, and U time streams. The command makemap  (Chapin et al. 2013 ) is then called to create an initial Stokes I map from the Stokes I time streams. The Stokes I map from the first step is used to create an AST and PCA mask at a fixed signal-to-noise ratio (S/N), which is then used to mask out nonastronomical signals (we refer to these as the autogenerated masks). The AST mask is used to define background regions that are forced to zero after each iteration in order to prevent growth of spurious structures in the map.
Distinguishing such artificial structures from real astronomical signals requires care. Our solution to this problem is to re-use the 4″ pixel autogenerated masks when creating externally masked maps with 8″ pixels, rather than using new masks based on the automasked 8″ maps. The smaller 4″ masks will then restrict the growth of artificial extended structures giving us more confidence in the remaining extended structure. To do this, we ran the entire reduction using the standard 4″ pixel size. We then regridded the AST and PCA masks from that reduction to 8″ using the command compave from the KAPPA package  (Currie & Berry 2014). We then ran the second step of the reduction using the regridded AST and PCA masks to create the externally masked Stokes I, Q, and U maps as well as the polarization vector catalogs, using a pixel size of 8″.
CO Observations We used archival observations of the CO J = 1-0 line carried out with the Berkely Illinois Maryland Array (BIMA) 10 antenna interferometry array. The CO J = 1-0 data were obtained from  Lee et al. (2002) , and details of the observations and data reduction can be found therein. BIMA has a similar beam size to that of JCMT at 12 8. We also used archival observations of the CO J = 3-2 line carried out with the Heterodyne Array Receiver Program (HARP) to remove CO contribution from the 850 μm Stokes I map. This is discussed further in Section 4. 3. The data were accessed from the Canadian Astronomy Data Centre database 104  (Project ID: M07AU11) and were downloaded as reduced spectral cubes that were then mosaicked using the PICARD recipe MOSAIC_JCMT_IMAGES. 105.
We also proposed an evolutionary gradient across the isolated filament starting with the most evolved source RNO 90, which is closest to the Sco OB2 association and moving away from Sco OB2 toward RNO 91 and then eventually the starless core. Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. The data taken in this paper were observed under the project code M20AL018. The Starlink software  (Currie et al. 2014 ) is currently supported by the East Asian Observatory. Facilities: JCMT (SCUBA-2, POL-2), Herschel (SPIRE, PACS). Software: Starlink  (Currie et al. 2014 ), Astropy (Astropy Collaboration et al. 2013 , 2018).
Acknowledgments We thank the referee for providing thoughtful and constructive feedback that helped improve this article. J. K. acknowledges funding from the  Moses Holden Studentship  for his PhD. D. W. -T. acknowledge  Science and Technology Facilities Council (STFC)  support under grant No. STR0007861. K. P. is a  Royal Society University Research Fellow , supported by grant No. URFR1211322. C. W. L. acknowledges the support by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education, Science and Technology  ( NRF-2019R1A2C1010851 ), and support by the  Korea Astronomy and Space Science Institute  grant funded by the  Korea government (MSIT ; project No. 2022-1-840-05 ). W. K. was supported by the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIT)  ( NRF-2021R1F1A1061794 ).
K. Q. is supported by  National Key R&D Program of China  Nos. 2022YFA1603100  and  2017YFA0402604 , the  National Natural Science Foundation of China (NSFC)  grant  U1731237 , and the science research grant from the  China Manned Space  Project with No. CMS-CSST-2021-B06. C. L. H. H. acknowledges the support of the  NAOJ Fellowship  and  JSPS KAKENHI  grants  KG18K13586 ,  KG20K14527 , and  KG22H01271. M. R. is supported by the international  Gemini Observatory , a program of  NSFʼs NOIRLab , which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation , on behalf of the Gemini partnership of Argentina,  Brazil ,  Canada ,  Chile , the  Republic of Korea , and the  United States of America. L. F.
acknowledges support from the  Ministry of Science and Technology of Taiwan , under grant Nos. 111-2811-M-005-007  and  109-2112-M-005-003 -MY3. M. T. is supported by  JSPS KAKENHI  grant Nos. 18H05442 ,  15H02063 , and  22000005. J. K. is supported by  JSPS KAKENHI  grant No. 19K14775. D. J. is supported by  NRC Canada  and by an  NSERC   Discovery Grant. E. J. C. is supported by  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  (grant No. NRF-2022R1I1A1A01053862 ). F. P. acknowledges support from the  Spanish State Research Agency (AEI)  under grant No. PID2019-105552RB-C43  and from the  Agencia Canaria de Investigación, Innovación y Sociedad de la Información (ACIISI)  under the  European FEDER (FONDO EUROPEO DE DESARROLLO REGIONAL) de Canarias   2014-2020  grant No.
PROID2021010078. C. E. acknowledges the financial support from grant  RJF/2020/000071  as a part of  Ramanujan Fellowship  awarded by  Science and Engineering Research Board (SERB) ,  Department of Science and Technology (DST), Govt. of India. The James Clerk Maxwell Telescope is operated by the  East Asian Observatory  on behalf of The  National Astronomical Observatory of Japan ;  Academia Sinica Institute of Astronomy and Astrophysics ; the  Korea Astronomy and Space Science Institute ; the  National Astronomical Research Institute of Thailand ;  Center for Astronomical Mega-Science  (as well as the  National Key R&D Program of China  with No. 2017YFA0402700 ). Additional funding support is provided by the  Science and Technology Facilities Council of the United Kingdom  and participating universities and organizations in the  United Kingdom, Canada , and Ireland.
(J) Adults continue to express nppc and scg2a in CSF-cNs. See also Figures  S3-S6 , Table  S1 , and Data S1. enriched in this dataset, and we found numerous taste 1 and 2 receptor families such as tas2r200. 1, tas1r2. 2, and tas1r2. 1 (Figure  5A ; Data S1), while transcripts for receptors (pkd2l1, pkd1l2, tas1r2. 1, tas2r200. 1, and tas1r2. 2) did not increase upon infection in CSF-cNs (Figure  5B ). By contrast, numerous transcripts encoding immune-related secretory compounds were upregulated in CSF-cNs upon infection (76 out of 231, 33%; Figures  5C  and 5D ; Data S1): multiple cytokines including IL-1b and IL-8/CXCL8 (Figure  5D , right), and complement factors C3 (alternative pathway), C4 (classical and lectin pathway), and C6 (terminal pathway) (Figure  5D , left).
m. or i. v. for imaging. Subsequently, the larvae were embedded in 1. 5% low-melting point agarose dissolved in egg water (60 mg/mL sea salts, Sigma-Aldrich, S9883) in MilliQ) in an open uncoated 8-well chamber slide (ibidi, 80826) and kept at 28 C in a temperature-controlled chamber (Okolab). Larvae were imaged on a Nikon AIR confocal microscope system. FIJI/MATLAB was used for image analysis. Imaging involved several experimental conditions, unless otherwise noted, all solutions were injected into the hindbrain ventricle of 2 dpf larvae. Pneumococcal meningitis 2000 colony forming units (CFU) of red fluorescent S. pneumoniae D39 HlpA-HlpA-mCherry or vehicle-injected with sterile 0. 5% (w/v) phenol red solution in PBS were injected into the hindbrain ventricle after anesthesia of zebrafish larvae with tricaine followed by paralysis with a-bungarotoxin injection, before being embedded in low-melting point agarose as described previously.
Larval zebrafish survival assays Survival (death here is defined as larvae that do not respond to tail touches nor have a beating heart) was assessed every 24 hours. Curves were generated with GraphPad Prism 7. 0 and survival data were analyzed with the log rank (Mantel-Cox) test. Survival experiments in CSF-cN compromised transgenic larvae Zebrafish larvae were infected as described previously by injecting 2000 colony forming units (CFU) wild-type S. pneumoniae D39 into the hindbrain ventricle and kept in 6-well plates at 28 C with 20 larvae in each group per well. 24 The mortality rate was scored by monitoring live and dead embryos at 24 hours intervals post-injection; mortality was determined by heartbeat and response mechanical stimuli. All experiments were performed in triplicate. Survival analyses were performed and involved the following experimental conditions: Nitroreductase Tg(pkd2l1:GAL4, UAS:epNTR-tagRFPT-UTR.
Analysis of calcium transients in vivo To determine the overall activity of a given cell of interest defined within a region of interest (ROI), the DF/F integrated over time was calculated as it more reliable and less sensitive to fluctuations in the level of expression of the fluorescent calcium sensor; comparisons across conditions were made using multifactorial ANOVAs with Scheff e post-hoc tests. To compare isolated large-amplitude calcium transients (as in Figures  2E-2G  and 2N ), a MATLAB script was written to identify features of the Ca 2+ trace where a sustained high-amplitude calcium transient (i. e. > 100% DF/F) was associated with a sharp onset (i. e. differential of DF/F trace > 30). Statistical testing was performed on these isolated events using the Wilcoxon rank sum test with Bonferroni correction for multiple comparisons.
Analysis of calcium transients in vitro Slow translational drifts of the image due to cell movements were corrected using image registration by taking as a reference image a frame of the original position of the CSF-cN. We identified CSF-cN calcium transients in response to 1-second stimulus by using a 100 ms-long flash of green light performed 12. 74s before the stimulus. Frames corresponding to flashes stimuli were removed from the representative traces (see Figures  3B  and 3C ). To determine the response of a given cell (ROI) to a 1-second stimulus (as in Figures  3D  and 3E ), the DF/F amplitude of each peak calcium response after stimulus was calculated using a home-made MATLAB script available on the Wyart lab github: the DF/F average of over six time points around the maximum DF/F of the peak was calculated, and was subtracted to this the DF/F average of six points before the stimulus.
A cell was considered as a responding cell when we observed a response for at least one of the 3 1-second stimuli. Latency was calculated as time between the 1s-stimulus and the beginning of the peak response. Time-to-peak was calculated as the time between the beginning of the peak response and the maximum of the peak. Time decay was calculated by applying an exponential fit of model 'a*exp(-b*x)+c' and extracting the time decay as follow: decay = 1/b. As control for no-stimulus (see Figure  3G , black circles), we measured the time decay of transients corresponding to spontaneous activity with no stimulus (see Figures  3B  and 3C , left). Comparisons across conditions (as in Figures  3D  and 3E ) were made using 2-factor ANOVAs with Turkey HSD post-hoc tests on R. Comparisons for latency, time-to-peak and time decay were made using two-sample Kolmogorov-Smirnov non-parametric test on MATLAB.
ACKNOWLEDGMENTS We wish to thank  Sophie Nunes-Figueiredo ,  Antoine Arneau ,  Monica Dicu , and B en edicte Daboval for keeping fish in excellent housing conditions in the PhenoZfish facility of the  Paris Brain Institute. We thank  Prof. Hitoshi Okamoto  and  Prof. Wilbert Bitter  for fruitful discussions;  Bethany Berry  for her generous gift of gfap promoter construct (originally cloned by  Brooke Gaynes) ;  Prof. Jan-Willem Veening  for providing the S. pneumoniae D39 HlpA-mCherry and S. pneumoniae D39 HlpA-GFP strains;  Dr. Taurai Tasara  for providing the L. monocytogenes LL195 strain;  Yannick Marie ,  Romain Daveau , and  Justine Gu  egan for their critical assistance in completing the transcriptomic analysis;  Dr. Giovanna Barba-Spaeth  for her help with UV-inactivation; as well as  Cora Chadick ,  Jeroen Kole ,  Coen Kuijl , and  Theo Verboom  for technical assistance.