dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance of Skip-GANomaly on Satellite Imagery First, we examined the performance of Skip-GANomaly on satellite imagery when using different data pre-processing techniques on the baseline dataset (all disasters combined). The main result showed that, especially when strict pre-processing was applied, e. g. , removing all patches that contained more than 10 percent of vegetation or shadow (novegshad@10%), performance improved compared to baseline, although it only reached a recall value of 0. 4 (Figure  7 ). A similar trend was found for aerial imagery in an earlier work  [23]. Their performance improved the most when the novegshad@10% rule was applied. Finally, contrary to expectations and excluding the performance of novegshad@10% on 32 × 32 patches, no clear trend was observed for specific patch sizes.
The Importance of Context We investigated whether the characteristics of the different satellite data sources, especially for different disaster events, could explain why the method worked better for some disasters than the other. Certain disasters, such as floods or fires, induced both building damage and damage to their surroundings. Other disasters such as earthquakes mainly induced damage to buildings only. Largescale damage can be better detected from satellite imagery than small-scale damage, because satellite imagery contains inherently coarse resolutions. Most likely, the ADGAN is more efficient in detecting building damage from large-scale disasters by considering the surroundings of the building.
The Importance of Context We investigated whether the characteristics of the different satellite data sources, especially for different disaster events, could explain why the method worked better for some disasters than the other. Certain disasters, such as floods or fires, induced both building damage and damage to their surroundings. Other disasters such as earthquakes mainly induced damage to buildings only. Large-scale damage can be better detected from satellite imagery than small-scale damage, because satellite imagery contains inherently coarse resolutions. Most likely, the ADGAN is more efficient in detecting building damage from large-scale disasters by considering the surroundings of the building. To investigate this idea, we aimed at understanding how the anomaly score distribution corresponds to the large-scale or small-scale damage pattern, by plotting the anomaly scores over the original satellite image.
Cross-Tests Finally, we evaluated the performance of trained UAV-and satellite-based models on the test set of other data subsets to find out how transferable our proposed method is. First, we highlight a couple of general findings for satellite-based cross-tests. In general, a heterogeneous pattern in performance was found when a trained satellite-based model was tested on the test-set of another satellite data subset. In some cases performance increased compared to the dataset on which it was trained, while for others it decreased. Overall, no noteworthy improvements in performance were observed. Second, we observed that satellite-based models did not transfer well to image datasets that had different patch sizes than the ones on which they were trained. For example, satellite-based models trained on patches of size 32 × 32 performed worse when tested on patches of other sizes.
This could be caused by context and GSD, as explained in Section 4. 3. Finally, in some cross-tests, instead of the model, the dataset seemed to be the driving factor behind systematically high performances. For example, the flooding data subset yielded on average a F1score of 0. 62 for all patch sizes combined. This was systematically higher than for all other datasets. We expect that these results were driven by the flood-induced damage pattern, as explained in section 4. 3. Next, we examined the cross-test results for UAV-based models. Again, general findings are highlighted. First, we observed that UAV-based models trained on specific patch sizes transferred well to patches of other sizes. A model trained on 32 × 32 patches from L'Aquila performed equally well on 64 × 64 patches from L'Aquila.
This could be caused by context and GSD, as explained in Section 4. 3. Finally, in some cross-tests, instead of the model, the dataset seemed to be the driving factor behind systematically high performances. For example, the flooding data subset yielded on average a F1-score of 0. 62 for all patch sizes combined. This was systematically higher than for all other datasets. We expect that these results were driven by the flood-induced damage pattern, as explained in Section 4. 3. Next, we examined the cross-test results for UAV-based models. Again, general findings are highlighted. First, we observed that UAV-based models trained on specific patch sizes transferred well to patches of other sizes. A model trained on 32 × 32 patches from L'Aquila performed equally well on 64 × 64 patches from L'Aquila.
We expect that this can be explained by the level of detail that is retained in UAV patches when the image is up-or down sampled during inference. Second, contrary to the findings for satellite imagery, no dataset seemed to drive the performance of datasets in a specific way. Finally, we examined cross-platform transferability where we observed the performance of trained UAV or satellite models that were tested against the test set of either UAV or satellite imagery. The immediate observation is that the proposed method cannot be transferred cross-platform. In summary, the models showed to transfer well if the test buildings look similar to the ones on which they were trained. Contrary to the desired outcome, transferability to buildings that are different in style was not unilaterally shown.
Applicability and Sensitivity of Skip-GANomaly First, the applicability and sensitivity of satellite-based models are discussed. Satellite imagery is generally a difficult data type for unsupervised damage detection tasks due to their visual complexity, which is subject to temporal variety, and due to large GSDs, which make it difficult to detect detailed damage. This difficulty is reflected by the baseline results obtained using our proposed method. However, we showed how performance can be improved. In line with the results found for aerial imagery  [23] , reducing the complexity of the baseline dataset, e. g. , removing vegetation and shadowed patches, improved performances, especially for the 32 × 32 novegshad@10% based model. Cropping the training patch sizes in order to reduce the visual complexity did not always yield better performances.
735. These results are satisfactory when compared to the F1-score of 0. 931 obtained by  [13] , who used a supervised CNN to detect building damage from similar post-event UAV datasets. Again, considering our unsupervised and single-epoch approach, which only makes use of undamaged buildings for training, our method showed to be promising. The importance of contextual information was explained in Section 4. 3. We showed how flood or fire-induced building damage was likely deduced from contextual information, rather than from the building itself. The contextual information has a negative influence towards the classification in disaster events where the damage is small-scale and the affected area is densely built-up. These findings suggest that, in practice, each event has to be approached case by case.
Transferability In general, a heterogeneous performance was observed for satellite-based models when tested on test-sets of other satellite sub-sets. Performance fluctuated for the different datasets and regardless of whether the model tested well on the dataset for which it was trained. This suggests that satellite-based models do not transfer well to other geographic locations or other typologies of disasters. This finding is in contrast with one specific finding from our preliminary work. There, we found that aerial-based models, trained on patches that were pre-processed according to the novegshad@10% rule, transferred well to other datasets  [23]. We did not observe the same for the satellite-based model novegshad@10%. A possible explanation can be that the novegshad@10% model was not able to find the normalcy within other datasets, because the amount of training samples is small (see Figure  6 ).
Therefore, the learned image distribution is too narrow. This could have led to an overestimation of false positives once this model was tested on other datasets. Contrastingly, a homogeneous performance was observed for UAV-based models when tested on test-sets of other UAV sub-sets. Consistent performance was observed when models were tested on different datasets or different patch sizes. In addition, the model performance stayed high if the performance was high for the dataset on which it was trained. Specifically, we found that models transferred well if the buildings on which the model was tested looked similar to the buildings on which it was trained. For example, locations in Italy (L'Aquila and Pescara del Tronto) looked similar and were shown to transfer well. Locations in Asia (Taiwan and Nepal) looked very dissimilar in appearance and did not transfer well.
Similar conclusions for the transferability of Italian locations were found in  [13]. In line with the conclusion drawn in  [13] , we agree that the transferability of a model depends on whether the test data resemble the data on which it was trained. A model that cannot find the normalcy in other datasets is likely to overestimate damage in this dataset. Therefore, our previously stated assumption that buildings in the same geographic region look alike is not always valid. In future approaches, attention should be given to how geographic regions are defined. Categorizing buildings not based on the continent in which they are located, but on lower geographic units such as municipalities or provinces, might lead to a better approximation by the AGDAN of what constitutes a normal building in that geographic unit.
Practicality in Real-world Operations The general conclusion is drawn that ADGANs can be used for damage detection from satellite images on the condition that the imagery is pre-processed to contain minimal vegetation and shadows. Considering how pre-processing is largely automated, this step is not a limitation. Nonetheless, cases were found where models yielded high performance, regardless of the presence of vegetation and shadows. The performance of satellite-based models trained on original imagery from flood and fire disasters was high and, therefore, these datasets do not have to be pre-processed, thus saving time. We showed that damage maps could be constructed in the cases where context provides a significant contribution. These show, in detail, where damage is located. During inference, these maps can be created instantly, and they can therefore provide valuable information in the post-disaster response and recovery phase.
As stated in the introduction, a main limitation of UAV-based models is that UAV-based imagery needs to be collected in the pre-event stage. Considering how UAV-imagery collection is still a human-driven task, this might be difficult to achieve. However, the advantage is that data acquisitions can take place any time during the pre-event stage. Therefore, practical advice to end-users who wish to apply this methodology is to collect UAV-imagery of buildings in the pre-event stage in advance. A final note of consideration is the following: the assumption is made that the normal dataset is free of anomalies. However, day-to-day activities such as constructions can result in visual deviations from normal that are not strictly damage  [45]. In practice, care has to be taken to make the distinction between what is damaged and what is simply an anomaly.
Patient and family The individuals in this study are participants in an Institutional Review Board-approved longitudinal cohort study at the NCI entitled "Etiologic Investigation of Cancer Susceptibility in Inherited Bone Marrow Failure Syndromes" (www. marrowfailure. cancer. gov, ClinicalTrials. gov NCT00027274)  (7). Patients and their family members enrolled in 2008 and completed detailed family history and medical history questionnaires. Detailed medical record review and thorough clinical evaluations of the proband, his sister, parents, and maternal cousin were conducted at the National Institutes of Health (NIH) Clinical Center. Telomere length was measured by flow cytometry with in situ hybridization (flow-FISH)  (45)  in leukocytes of all patients and family members reported. DNA was extracted from whole blood using standard methods.
Whole-exome sequencing WES of blood-derived DNA for family NCI-226 was performed at the NCI's Cancer Genomics Research Laboratory as previously described  (46). Exome enrichment was performed with NimbleGen's SeqCap EZ Human Exome Library v3. 0 + UTR (Roche NimbleGen Inc. , Madison, WI, USA), targeting 96 Mb of exonic sequence and the flanking untranslated regions (UTRs) on an Illumina HiSeq. Annotation of each exome variant locus was performed using a custom software pipeline. WES variants of interest were identified if they met the following criteria: heterozygous in the proband, his mother, and maternal cousin; nonsynonymous; had a minor allele frequency <0. 1% in the Exome Aggregation Consortium databases; and occurred <5 times in our in house database of 4091 individuals. Variants of interest were validated to rule out false-positive findings using an Ion 316 chip on the Ion PGM Sequencer (Life Technologies, Carlsbad, CA, USA).
5 g/ml) and analyzed with an LSR II fluorescence-activated cell sorter. WT and G3 Terc -/-mice were included in all flow-FISH experiments, respectively, as controls of normal and short telomeres. For fluorescence shift analyses, the green histograms (corresponding to cells with the telomeric probe) were sliced into 18 windows of equal width and numbered 0 to 17 according to their distance from the median value in cells without the probe, and the number of cells in each window was quantified with ImageJ. The data from two to five mice per genotype were typically used to calculate mean telomere lengths, expressed relative to the mean in WT cells.
Acknowledgments: We are grateful to the family for valuable contributions to this study. We thank  I. Grandjean ,  C. Caspersen ,  A. Fosse , and  M. Garcia  from the  Animal Facility ,  C. Alberti  and  C. Roulle  from the  Transgenesis Platform ,  M. Richardson  and  A. Nicolas  from the  Pathology Service , and  Z. Maciorowski  from the  Cell-Sorting Facility of the Institut Curie. We thank.
Study population We performed a cross-sectional study of 257 older adults aged ≥70 years who participated in the Coyoacán Cohort, an observational study conducted in the Coyoacán borough located in Southern Mexico City conducted between 2008 and 2009. Complete methodological details for the design and protocols of this study have been published elsewhere, and questionnaire materials were similar for the full cohort  [7, 12]. Briefly, recruited participants were non-institutionalized older adults with residence in Coyoacán who were registered at the comprehensive "Food Support, Medical Care and Free Drugs Program" (FMDP) government program. All participants underwent face-to-face interviews to collect self-reported socio-demographic and health data and a comprehensive geriatric assessment which included physical performance tests, cognitive, nutritional, and medical assessment.
Baseline questionnaire data were collected between April-May 2008; clinical evaluation and biological sample collection were carried out between June 2008 and July 2009. In the original cohort, a sample of 1294 participants was calculated to ensure a sample size which could estimate a prevalence of ~14% of frailty with α = 0. 05 and β = 0. 20. Among contacted potential participants, acceptance rate was 86. 9% and a total of 1124 participants completed the initial interview, which included individuals with and without T2D. For the present study, we included participants with T2D without previous clinical diagnosis of dementia, defined as self-report of previously diagnosed T2D and/or selfreport of taking T2D medications (n = 236). To account for subjects who were not previously diagnosed with T2D, we included subjects with fasting glucose ≥126 mg/dL (n = 21), who had enough information for their dementia-risk stratification using the DSDRS (overall, n = 257).
Observations and data reduction Our ALMA Cycle 3 observations in Band 6 were carried out on 2016 March 5 for 77 minutes on-source integration time, with 36 antennas in the C36-3 configuration. The four 1. 875 GHz spectral windows were tuned to cover the frequency ranges  GHz. The quasars, J1159-0940 and J1229+0203, were used to calibrate the complex gains and bandpass. The coverage of the visibility data in the u-v plane was well covered with baselines with lengths of 15m to 640m. The source was observed at elevations of 58-77 • and the weather was stable with precipitable water vapor, PWV=1. 6-2. 2 mm. We used the supplied calibration script and Common Astronomy Software Applications (CASA;  McMullin et al. 2007)  to reduce the data. We imaged the phase calibrator and determined that the bandpass stability is accurate to ∼0.
Acknowledgements. MDL wishes to thank  Nikos Prantzos  for interesting discussions on the nucleosynthetic origin of fluorine. This paper makes use of the following ALMA data: ADS/JAO. ALMA # 2015. 1. 01489. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA) , and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO , and  NAOJ. The  National Radio Astronomy Observatory  is a facility of the  NSF  operated under cooperative agreement by  AUI. CY acknowledges support from an  ESO  Fellowship. EF acknowledges support from the  European Research Council  in the form of the  Advanced Grant MIST  ( FP7/2017-2022 , No.
First applied to monitoring mid-term temporal phenomena (over a few weeks)  [14, 18] , this method is now dedicated to monitoring ''long-term" phenomena (several years)  [19] [20] [21] [22] [23]. For such purposes, it is not always possible to immobilize a capital asset like a resistivity meter. Moreover, when tracking time-dependent phenomena, a resistivity measurement triggered by external sensors would produce a more refined description  [24]  and limit the amount of data to be processed. The aim of this work is to present OhmPi, an open source resistivity meter based on a Raspberry Pi platform. Both the equipment and theory behind the measurement protocol are described first. Next, the design files, a bill of materials and construction instructions are provided, along with a summary description of the software.
On the other hand, most commercial devices are not open source, making it impossible to interface the instrument with other sensors. This point is critical when conducting continuous monitoring, which consists of a series of measurements spaced by relatively regular intervals. If the purpose of the monitoring exercise is to capture a relatively rapid event (e. g. a Fig. 2. Procedure for acquiring a dataset with several quadrupoles using an automatic electrical resistivity meter (as modified according to  [14] ). rainfall) over a long period (several weeks), then the chosen interval will have to be short and thus lead to collecting large amounts of unnecessary data. OhmPi has been designed to be easily controlled by any sensor, yielding a value that may be used to trigger a measurement, hence allowing for more intelligent and flexible data acquisition.
Current injection The next step consists of featuring the 4-channel relay module used for current injection and its assembly. The wiring between the relays must be carried out in strict accordance with Fig. 10. This card must then be connected to the Raspberry Pi and the measurement card. On the Raspberry Pi, it is necessary to connect inputs In1 and In2 to the same GPIO. For this purpose, it is necessary to solder together the two pins on the 4-channel relay shield module and connect them to the Raspberry Pi GPIO-7 (Fig. 10 ). The same must be performed for inputs In3 and In4 with GPIO-8. Connect the GND and 5Vdc pins of the relay card's 4 channels respectively to the GND pin and 5Vcc of the Raspberry Pi. Now connect relays 1, 2, 3 and 4, as shown in the diagram, using 1-mm2 cables (red and black in Fig.
Preliminary procedure (only for the initial operation) The open source code must be downloaded at the Open Science Framework source file repository for this manuscript (https:// osf. io/dzwb4/) or at the following Gitlab repository address: https://gitlab. irstea. fr/reversaal/OhmPi. The code must be then unzipped into a selected folder (e. g. OhmPi-master). A ''readme" file is proposed in the directory to assist with installation of the software and required python packages. It is strongly recommended to create a python virtual environment for installing the required packages and running the code.
24%, with a maximum of 0. 57% and a minimum of 0. 02%. With OhmPi, the average value equals 1. 03%, with a maximum of 2. 07% and a minimum of 0. 11%. The difference is very small within this range of values, with a slightly higher standard deviation for OhmPi, but still <3% in this case study. The two datasets were then inverted using a well-known inversion software (BERT,  [50] ). This inversion was prepared with the same settings for both datasets. Fig. 19  shows the two resulting images; they are similar, as are the RMS errors. Overall, the resistivity distributions over the section are very comparable and of the same order of magnitude. Minor variations are visible and can be assigned to a few errors in our equipment and environmental effects due to delays between the two acquisitions.
Acknowledgments The  REVERSAAL Research unit 's support for this project is gratefully acknowledged. The authors would like to thank the many members of the research team who assisted in the various aspects of this work;  Jérémie Aubert  and  Arnold Imig , as well as  Hélène Guyard  from the  IGE Laboratory in Grenoble  for their advice on electronics. The authors also appreciate the reviewers for their comments, which have considerably helped to improve this article.
Likewise, time to treatment failure has been cited by the US Food and Drug Administration (FDA) as a surrogate endpoint for clinical impact of targeted therapy  [15]. The amount of DNA being shed by the tumor is measured by the variant allele fraction (AF) in plasma, which correlates with the location and volume of disease as well as the amount of nontumoral DNA circulating at the time of the blood draw. The correlation between plasma AF (%) and the response rate on targeted therapies as assessed by Response Evaluation Criteria in Solid Tumors (RECIST) remains inconclusive, with some previous data reporting a lack of correlation  [8, 12]. Herein, we describe clinical outcomes with targeted therapies in a daily clinical practice population of advanced NSCLC patients with actionable alterations identified by an amplicon-based NGS assay, the InVisionFirst 1 -Lung, with the aim to assess the feasibility and clinical relevance of liquid biopsy testing in this population.