dataesr/scientific-paragraphs-categorization huggingface dataset:
Metrics Cell lineages can be used for a wide variety of analyses, and different kinds of errors can affect downstream results differently; therefore, reducing performance of a tracking method to a single number that represents "overall performance" is generally not possible. Therefore, we distinguish five types of tracking errors: false positive edges (FP), false negative edges (FN), identity switches (IS)-when one reconstructed track takes over following a cell from another reconstructed track-false positive divisions (FP-D) and false negative divisions (FN-D), as shown in Fig. 3. To allow comparison across datasets, we normalize the number of errors by the number of ground truth edges, resulting in an errors per edge metric. Additionally, we compute the fraction of ground truth lineages that were perfectly reconstructed over T time points, for a range of values for T.
Second, we cannot use segmentation overlap to match ground truth to reconstructed cells. Instead, we choose a matching threshold that is a bit larger than the radius of a nucleus in the dataset. Considering only nodes within this threshold as potential matching endpoints, we pair. Splits are named for the evaluation set, so early 1 and early 2 are both evaluated on the early section. ground truth and reconstruction edges using Hungarian Matching to minimize the sum of endpoint distance. Both reconstruction and ground truth edges can be matched to a dummy edge, allowing detection of false negative ground truth edges and reconstructions that do not match to any ground truth. In addition to evaluating tracking performance, we examine the performance of the cell indicator and movement vector networks.
Baselines Due to the size of our datasets and nature of our ground truth, we can only compare against cell tracking methods that can be run efficiently on multi-terabyte 3D datasets, and that do not require dense annotations or segmentations for training. Tracking with Gaussian Mixture Models (TGMM)  (Amat et al. , 2014)  was previously run on certain time regions of D and M , and we were able to extend those results to the full time series. Because TGMM cannot process multi-channel input, for ZF we produced tracks for each of the two views separately, and reported the best result for each evaluation region. More recently, the tracking method included in the ELEPHANT framework has potential to be scalable to multi-terabyte datsets  (Sugawara et al. , 2021). The cell detection step requires sparse nuclear segmentations by manual ellipsoid fitting, preventing us from comparing directly with the full method, so instead we run a greedy nearest-neighbor linking algorithm inspired by this work on our cell candidates.
Results Fig. 2  shows the sum of errors per edge for each organism, averaged over the train/validation/test splits. Across all datasets, our method produces significantly fewer errors per edge than TGMM and the greedy baseline, with the greedy baseline landing between TGMM and our method. Our method performs similarly between M and D (0. 049 and 0. 050 total errors per edge) and slightly worse on ZF (0. 078). Considering individual types of errors provides more insight into the performance of the different methods. For both TGMM and our method, false negative edges (FN) are the most common error type. In every case, our method produces fewer FN and fewer false positive divisions (FP-D) than TGMM. The false negative division (FN-D) performance is similar between our method and TGMM -in absolute numbers, neither our method nor TGMM correctly identifies more than a third of the divisions, but divisions are so underrepresented in the evaluation sets that this error type does not significantly affect the overall sum of errors.
On M , our method does not produce any divisions, and thus the FP-D rate is always zero, while TGMM has a very high FP-D rate and still does not detect many of the true divisions. While our method does not always have fewer identity switches (IS) than TGMM, exmaning performance by dataset shows clear trends. For M , our method always produces fewer IS than TGMM. However, for D and ZF , TGMM produces hardly any IS, likely due to the high number of FN. Since an IS can only occur when two neighboring ground truth edges are matched to different reconstructed tracks, a high number of FN reduces the opportunities for IS to occur. Our method significantly reduces the number of FN on these datasets, resulting in slightly more IS than TGMM but fewer overall errors. Fig. 2  also shows the track accuracy, or fraction of perfectly reconstructed tracks, for a range of track lengths.
For length 1, this metric is the fraction of false negative edges, and thus our method and greedy outperform TGMM. However, as the time window increases, the greedy method's track accuracy drops quickly for all datasets, reflecting the lack of global track optimization in this per-frame tracking algorithm. TGMM's accuracy is similar to the greedy baseline on M , but for D and ZF , the slope of the decline is much flatter, indicating that TGMM perfectly reconstructs more long track segments on these datasets. Combining the fairly high track accuracy of TGMM with the large number of false negative edges, we can infer that on D and ZF , TGMM's errors are grouped together, resulting in some tracks being faithfully reconstructed and others missed completely. Our method has the highest track accuracy across all datasets, with a similar rate of decline as TGMM on D and ZF but a higher starting accuracy.
D shows similar results between the two evaluation regions, but the same is not true for ZF. TGMM performs much better on ZF side 2 than side 1 in both track accuracy and sum of errors. Indeed, on side 2, TGMM and our method have a similar track accuracy, while TGMM performance on side 1 degrades significantly using both metrics. Manual examination of the raw data shows that the tracks on side 1 are harder for a human to identify due to less clear signal on that side of the dataset; thus, the relative results indicate that our method is more robust to varied imaging conditions than TGMM. Unexpectedly, the greedy baseline performs worse on the easier side 2. To explain this, we observe that the cell indicator model for side 2 predicts significantly more candidate cells, and more false positive candidates, than the model for side 1, likely due to randomness in the training pipeline (see Supplementary Note 3).
The greedy method creates many false positive divisions involving those candidates, while ours does not, showing that the optimization step can extract coherent tracks from a noisy candidate graph. Fig. 6  shows the standalone performance of the cell indicator and movement vector networks. Cell recall for all mouse and Drosophila models exceeds 0. 99, indicating that nearly all ground truth cells in these datasets have a nearby candidate cell. Recall is slightly lower for both zebrafish models, but still exceeds 0. 96. The movement vector network has a smaller mean distance between predicted and actual parent location than the baseline for all models, and the distribution of distances is concentrated closer to zero. The mouse cells move further on average than the Drosophila and zebrafish cells, so the magnitude of improvement compared for mouse is greater.
Because divisions are underrepresented in the training data and particularly difficult, we try sampling batches specifically at divisions 25% of the time (+D). We also simulate more cell movement by adding a random shift augmentation between the previous frame and the target frame (+S). Results for each combination of these training and architecture variations on one M split are shown in Fig. 7b. To draw conclusions about the effect of any of these variations, the resulting change in performance has to be greater than the effect of random retraining shown in Fig. 7a. In general, none of the models produced a large, consistent difference in tracking score, although division sampling seems to produce worse results in general. Due to training time and expense, we were not able to train every model in the ablation study multiple times or on every dataset, which would have allowed more conclusive comparisons.
, 2021 ) is a possible solution, since annotators could easily generate negative training examples in regions where the network most needs guidance. Using targeted negative examples, we expect the cell indicator network could learn to suppress cell prediction in these high intensity regions with limited fine-tuning. When applying our method to a new dataset, one bottleneck of the method is the need to grid search the hyperparameters of the ILP. Even with blockwise processing, solving the ILP on the whole validation set takes tens of minutes per run, so we limited the grid search to four values per hyperparameter, resulting in 256 runs. We were guided by experience in choosing the range of values to search, but there is no guarantee that our solution was optimal, or that the same range would apply to different datasets.
In the future, we will examine alternatives to grid searching a manually selected range of values, such as using a structured support vector machine to find the best set of ILP parameters on a given dataset. Finally, identifying divisions is an important question for developmental analysis, but divisions are underrepresented compared to non-dividing cells, and have distinct movement and appearance. To address the difficulties that divisions present, we tried sampling divisions more frequently during training and adding a shift augmentation to mimic the movement of dividing cells, as discussed in Supplementary Note 3. However, even with these tactics, our method does not consistently identify divisions. One possible explanation is that the failure to identify divisions occurs in the optimization step, while our interventions focus on improving network predictions.
INCENFA-SCALE is a five-year project funded by the EC Horizon2020 programme, successor of the INCEFA-PLUS project. INCEFA-SCALE started in October 2020. The objective of this project is to improve the capacity to predict the lifetime of Nuclear Power Plant (NPP) components subjected to environmental assisted fatigue. The project starts off by analysing the existing data and then provides new environmentally assisted fatigue data which allow the laboratory test outcomes to be applied to components with real geometries and loads. So far, the data mining of different finished projects (INCEFA-PLUS, USNRC, EPRI, MHI and AdFaM) has been carried out, and test conditions for filling the knowledge gaps have been established. Moreover, the test matrix for 2022 has been defined. In this first phase, tests are focused on producing reference data, analysing complex waveforms (variable amplitude) and the effect of the surface finish.
Introduction INCEFA-SCALE is a five-year project funded by the Horizon 2020 program of the European Commission. This project is the successor to INCEFA-PUS, a project developed between 2015 and 2020. INCEFA-SCALE began in October 2020 and its objective is to continue the study of environmental-assisted fatigue (EAF), making progress in the capacity to predict the lifetime of components in nuclear power plants affected by this phenomenon. Nuclear power plant operators have observed that failures attributable to EAF are fewer than those estimated by the calculation methods used so far  (Tice et al. (2018) ). One possible cause of this discrepancy is the transfer of laboratory test results to real-scale components. Additionally, the ability to address transferability between laboratory and component scales, both in geometry and loads, remains limited by data availability.
This knowledge gap is addressed by INCEFA-SCALE. The plan of the project is, on the one hand, to further the knowledge of the mechanical behaviour through the exhaustive analysis of tested specimens and data mining, and, on the other hand, to perform tests which are focused on specific aspects of the loading conditions. At present, the project is analyzing a large amount of previous test data collected in the MatDB database, managed by the JRC, from the INCEFA-PLUS project (INCEFA-PLUS  Consortium (2020) ) and other sources, such as USNRC, EPRI, MHI and the AdFaM project. The testing program has been defined and will be developed over the next three years. In addition, a series of protocols have been agreed: management of experimental data, testing protocol and analysis of the tested specimens.
Project organisation The project is composed of six work packages: WP1: Project Management; WP2: Data Mining; WP3: Test Program; WP4: Modeling and Developing Assessment Rules; WP5: Mechanical understanding; and WP6: Dissemination and Training. The INCEFA-SCALE project consortium comprises seventeen organizations: Jacobs (UK, project coordinator), PSI (Switzerland), UJV Rez (Czech Republic), VTT (Finland), CIEMAT (Spain), IRSN (France), University of Cantabria (Spain), CEA (France), JRC (the Netherlands), Framatome (France), EDF (France), Inesco Ingenieros (Spain), Rolls-Royce (UK), Framatome GmbH (Germany), Technological University of Kaunas (Lithuania), KAERI (South Korea), and University of Manchester (UK). The good cooperation and communication between partners, which characterised the INCEFA-PLUS project, is a key aspect in developing INCEFA-SCALE, since there is a close interdependence between the activities of data mining, testing, modeling and mechanical understanding.
Project details Most of the tests will be carried out on a common plant material, 316L stainless steel, supplied by EDF. In addition, some partners will test a specific 300 series stainless steel for their own national interest. This will make it possible to study the differences between the previous material tested in the INCEFA-PLUS project (304L) and the new material to be characterised. Following the practice developed in INCEFA-PLUS, the participants will organise themselves into committees to define common testing methods and protocols. In addition, there is a committee, the Experts Panel, which will review the generated results to define its quality. Finally, all the organisations remain committed to agreeing on a common data format, as far as possible, since INCEFA-SACALE will have different test methods, as well as publishing the data in the MatDB repository, managed by the JRC.
Data mining The analysis of data from prior test campaigns has been completed. The sources of these data were: INCEFA-PLUS project, different materials from national programs and MHI (all of them available in the MatDB database), in addition to data from VTT, USNRC, ANL and EPRI. These data come from both fatigue tests in air and in PWR environment. Fig. 1  shows a selection of fatigue test data in PWR conditions from the different sources. From the analysis carried out, a lack of data at low strains and an effect of the cycle waveform on the fatigue life have been identified.
During the previous project, this was an excellent method to ensure the creativity of the analysis. Afterwards, the members of the Expert Panel will meet periodically to discuss the ideas and come up with a uniform set of conclusions. From the data generated in the INCEFA-PLUS and INCEFA-SCALE projects, together with the incorporation of external data, new S-N curves (stress versus number of cycles) will be developed for different conditions. Mechanistic understanding will include the effect of environmental factors. The final purpose is to take an important step forward in evaluating the fatigue of nuclear power plant components under real conditions. To do this, the results obtained in INCEFA-SCALE will be used to develop a Fatigue Assessment Procedure (FAP).
Project status Since its beginning in October 2020, many virtual meetings of different subgroups with interests in mechanistic understanding, testing tasks, analysis of existing data and modeling have been held. The achievements so far (June 2022) have been: • Collaboration with EPRI, under a confidentiality agreement (under development). • WP2 has completed the development of a software that will facilitate data mining activities, using the information uploaded in MatDB. Additionally, external data will be available for reviewing (EPRI, USNRC and NRA). • Members of WP3, WP4 and WP5 have shared ideas on the objectives of the respective WPs and the study priority. • Uniaxial tests belonging to WP3 have started, with the support of the Expert Panel. • Tests in featured conditions of the WP3 are being defined by a working group.
stsci. edu/jwst-near-infrared-camera/nircam-features-andcaveats/nircam-claws-and-wisps 97 ceers. github. io/hdr1. html and F356W images as the detection image, with photometry measured on all seven bands. Colors were measured in small Kron apertures with a Kron factor of 0. 8 and a Kron minimum radius of 1. 1 pixels; this is smaller than previous studies, which we found necessary to keep the elliptical aperture close to the significant isophotes of small, faint galaxies. An aperture correction was derived in the F356W catalog as the ratio between the flux measured in the default Kron aperture (with PHOT_AUTOPARAMS 2. 5, 3. 5) and that in our small Kron aperture. This correction was applied to all fluxes and uncertainties. We use the CEERS simulated imaging 98 to test the accuracy of this procedure, finding that after this aperture correction, the total fluxes were underestimated by ∼10%-15%, rising to 22% in F444W (understandable due to the larger PSF in F444W as the photometric apertures were defined on F356W).
Photometric Redshifts We use the EAZY  (Brammer et al. 2008 ) software package to estimate photometric redshifts for all sources in our photometric catalog. EAZY fits nonnegative linear combinations of user-supplied templates to derive probability distribution functions (PDFs) for the redshift based on the quality of fit of the various template combinations to the observed photometry for a given source. The template set we use includes the "tweak_fsps_QSF_12_v3" set of 12 FSPS  (Conroy & Gunn 2010)  templates recommended by the EAZY documentation. To this, we add a set of six additional templates spanning bluer colors than the FSPS models, as R. Larson et al. (2022, in preparation)  found that these improve the accuracy of photometric redshift fits for the expected blue colors of z > 9 galaxies.
Photometric Accuracy While our fiducial photometric measurements were derived in as robust a manner as possible, different software packages require different parameters and assumptions, which could lead to unknown systematic biases. We thus independently derive NIRCam photometry from our images with two independent software packages. The first method is PHOTUTILS from Python's astropy package  (Bradley et al. 2020). Source detection was performed on a combined F277W and F356W image and the resulting segmentation image passed to the PHOTUTILS SourceCatalog routine, which carried out aperturematched photometry on the background-subtracted, PSFmatched images in each filter. The second method is a custom photometry package, where photometry is measured in circular apertures with radii ranging from 0 10 to 0 35, applying aperture corrections for pointlike sources (<0.
Photometric Redshift Accuracy Similar to photometry, different photometric redshift packages can also impart biases on results. While we have used a well-tested fiducial package in EAZY and implemented a new set of templates customized for very high redshift galaxies, it is prudent to explore whether other packages would find different photometric redshift results. As we discuss below, we have run the PROSPECTOR  (Johnson et al. 2021) , BAGPIPES  (Carnall et al. 2018) , CIGALE  (Burgarella et al. 2005; Noll et al. 2009; Boquien et al. 2019) , and DENSE BASIS  (Iyer & Gawiser 2017; Iyer et al. 2019 ) SED-fitting codes on our fiducial photometry. While for the stellar population properties discussed below, we use our EAZY-derived  z ( ) as a redshift prior, we also performed an independent run with the redshift as a free parameter.
Physical Properties The five photometric detections afforded by NIRCam allow us the unprecedented opportunity to study the physical properties of a galaxy potentially only ∼400 Myr after the Big Bang. Our fiducial stellar population modeling is done with the PROSPECTOR Bayesian SED-fitting code  (Johnson et al. 2021). We follow the same procedures as in  Tacchella et al. (2022) , and we refer the reader there for more details. Briefly, we model the SED with a 13-parameter model that includes redshift (where here prior is set to the posterior of EAZY, unlike in Section 4. 2. 3, where it was free), stellar mass, stellar and gas-phase metallicities, dust attenuation (two-component dust model including birth-cloud dust attenuating young stars (<10 Myr) and nebular emission, a diffuse component for the whole galaxy with a flexible attenuation law; three parameters), and an ionization parameter for the nebular emission.
We estimate the uncertainty on our fits by conducting a Monte Carlo analysis where we modify the input F277W science image to randomly vary the pixel-to-pixel noise, recompute the parameters, and then repeat this analysis 40 times. Following this procedure, we measure a half-light radius of 3. 0 ± 0. 12 pixels (0 09 ± 0 0036), which corresponds to a physical size of 340 ± 14 pc at z = 11. 8. We check these results using the standard configuration of STATMORPH, a Python package developed to calculate the nonparametric morphology of galaxies, as well as compute single Sérsic fits. Using the same images as input, we find a half-light radius of 2. 9 pixels, in good agreement with the measurement from GALFITM. We repeat this measurement for the F200W filter and a stacked F200W + F277W image and find consistent results.
Conclusions We present the results from a search for ultrahigh-redshift galaxy candidates from the first epoch of NIRCam imaging from the JWST CEERS survey. We use imaging from both the SW and LW cameras over four pointings covering 34. 5 arcmin 2 in the F115W, F150W, F200W, F277W, F356W, F410W, and F444W filters, reaching m ∼ 29 (5σ) in the deepest bands. We measure photometry using SE, with an emphasis on robust measurements of colors, total fluxes, and uncertainties. We estimate photometric redshifts with the EAZY software package, including new blue templates designed to better recover the colors of very distant galaxies. We iteratively develop a set of conservative selection criteria to select candidate galaxies at z 12. We find one candidate galaxy satisfying stringent nondetections (S/N < 1.
2. Error map values of <1000 (indicating coverage by the majority of exposures) in F115W, F150W, F200W, F277W, and the detection image. 3. Initial more inclusive photometric redshift cuts of ò >  z 8 ( ) 0. 9, z best > 8. 5, and c < eazy 2 20 (to reject poor EAZY fits), and that the intergrated  z ( ) in a Δz = 1 98 Simulated Data Release 3; ceers. github. io/sdr3. html.
This linker DNA is likely hypersensitive to MNase digestion, which separates the two hemisomes from each other and results in their co-segregation within the low sedimentation rate fractions of the gradient. We took advantage of our high-coverage histone MNase ChIP-seq datasets to explore subnucleosomal organization at CTCF-binding sites and promoters. This analysis revealed the presence of 50-80 bp subnucleosomes having distribution patterns specific to each category of cis-regulatory elements (Extended Data Fig. 6 ). At CTCF-binding sites, a single low sedimentation rate particle was detected in the central region coinciding with the location of the CTCF motif (Extended Data Fig. 6a, b ). Promoters also contain a single low sedimentation rate particle rather than the paired particle pattern of enhancers, coinciding in genomic position with nucleosome -1 located upstream of the TSS (Extended Data Fig.