dataesr/scientific-paragraphs-categorization huggingface dataset:
CoastCams is an open-source collection of existing MATLAB scripts to quantify key wave parameters (e. g. , wave height, wave period), mean water levels, and morphology (e. g. , shoreline positions) in the nearshore environment. The repository performs the analysis on oblique orthorectified timestack images from land-based coastal monitoring systems. The proposed approach is a combination of several key parameters that aims to get a better understanding of nearshore processes by leveraging the strength of existing codes. CoastCams provides a unified and simplified method that is accessible for coastal managers, engineers, and scientists with a user-friendly and practical method to monitor and identify key drivers in coastal zone. In this paper, we present the standalone remote video-based method and validate the estimated hydro parameters with sensors deployed in the nearshore on a rocky platform in Socoa, France.
However, collecting reliable in-situ measurements in highly dynamic and complex coastal zones is challenging. The interaction of various coastal processes (e. g. , tides, waves, currents) can cause rapid changes in wave climate and sea levels at different spatial and temporal scales  (Woodworth et al. , 2019). Traditional in-situ measurements and survey techniques often provide spatially and temporally sparse datasets, failing to capture the dynamic behaviour of coastal processes. In addition, existing long-term coastal monitoring programmes, based on in-situ measurements, are limited to only a few sites worldwide (e. g. ,  Barnard et al. , 2015; Pianca et al. , 2015; Turner et al. , 2016). Ideally, coastal monitoring programmes should span years to decades and consider local environmental factors, such as coastal morphology, rates of sea level rise, and nearshore and swash dynamics  (Farrell et al.
, 2021). However, the logistics of field monitoring, including time, cost, and travel, often hinder the collection of high-temporal-resolution datasets. Nonetheless, the advent of commercial, low-cost remote sensing techniques in coastal research has partially overcome this obstacle. Remote sensing techniques, including radar, satellite imagery, and video cameras, have already successfully been employed in coastal monitoring to measure various parameters. For instance, they have been used for shoreline position detection  (Boak and Turner, 2005) , intertidal beach morphology analysis  (Uunk et al. , 2010; Osorio et al. , 2012) , (breaking) wave height estimation  (Almar et al. , 2012; Chen et al. , 2021) , nearshore currents measurements  (Radermacher et al. , 2014; Almar et al. , 2016 ) swash zone water level assessment  (Ibaceta et al.
, 2018) , coastal sea level monitoring  (Abessolo Ondoa et al. , 2019; Thuan et al. , 2019; Abessolo et al. , 2020) , as well as obtaining bathymetry estimation through depth-inversion algorithms  (Holman and Haller, 2011; Bergsma et al. , 2016; Brodie et al. , 2018) , and wave celerity  (Holman et al. , 2013). Other tools have also been developed to address specific elements of coastal processes, such as sediment transport and beach profile changes  (Li et al. , 2002) , diffusion of pollution on coastal waters  (Yuan, 2007) , and coastal flooding  (Smith et al. , 2012) , to support coastal managers and decision makers. Furthermore, open-source tools like CoastSat  (Vos et al. , 2019)  and CASSIE  (Almeida et al. , 2021)  provide high-temporal-resolution datasets, albeit with lower spatial resolution.
However, accurately measuring multiple key parameters simultaneously remains a major challenge in comprehensively monitoring coastal environments. To address this gap in coastal monitoring, this study introduces CoastCams, an open-source MATLAB software toolkit designed for analysing timestack images collected from fixed video cameras along the coast. While previous efforts, such as the Coastal Imaging Research Network (CIRN), have made strides in providing tools for coastal analysis through a community-GitHub repository  (Bruder and Brodie, 2020; Palmsten and Brodie, 2022) , CoastCams offers a simplified and unified method to capture multiple key parameters simultaneously from timestack images that complement the existing capabilities of CIRN, including image georectification as well as creating timestack image.
To demonstrate the potential of the toolkit, this study focuses on the validation of the calculated parameters. The case study particularly focuses on the hydrodynamic outputs from the toolkit, allowing CoastCams to be used as a 'virtual' tide gauge. More information on specific calculations can be found in the code and the accompanying README file. The case study's location along a rocky platform proved advantageous in eliminating the uncertainties associated with changing bathymetry, ensuring the accuracy and reliability of the wave conditions and mean water levels derived from the coastal monitoring system. This successful validation against in-situ data from the sensors deployed in the study area highlights the potential of CoastCams to effectively estimate key parameters from coastal monitoring systems.
Their position was taken with a DGPS-RTK, with an accuracy of 5 mm for vertical positioning. The images were captured using the SIRENA software  (Nieto et al. , 2010) , which generates four types of images from each camera; (1) Snapshot: an instantaneous image; (2) Timex: a single image averaging multiple snapshot images over one period, typically 10 or 15 min, (3) Variance: similarly to timex images, variance images contain the standard deviation computed in time from the same set of snapshots used to generate the timex image; and (4) Timestack: intensity values saved at each time step at a selected array of pixels (i. e. transect) from snapshot. For this study, only timestack images were used. A transect was considered for each camera along the rocky platform and offshore area in order to generate timestack images, at 2 Hz with a total of 14 min per timestack (Fig.
Quantification parameters The timestack images were analysed using in-house (https://github. com/NuytsSiegmund/CoastCams. git) developed MATLAB scripts (version R2020b). The toolkit requires the following toolboxes in MATLAB. • Image_toolbox; • Map_toolbox • Signal_toolbox; and • Statistics_toolbox. Fig. 5  shows the workflow of the different steps undertaken in this study and Table  3  gives a detailed overview of the different parameters that were quantified and their method. CoastCams is then an integration of these previously published methods, highlighted in Table  3  and in order to provide a unified and simplified approach. Note that georectified timestack images can be produced with the tools available at CIRN  (Bruder and Brodie, 2020). CoastCams outputs the wave climate parameters, mean water levels, and morphological parameters as a.
Results Fig. 6  highlights the key parameters from the timestack image estimations with the measurements from the sensors deployed during the   field campaign. Note that these outputs focus on the hydro parameters as they can be correlated with the analysis from the pressure sensors but that multiple other parameters are analysed with the toolkit as well. In   these parameters are calculated (e. g. , the significant wave height from the timestack images is compared with the pressure sensor closest to the breakpoint location for each time step). The x-axis represents the time period of the field campaign, noting that timestack images were only available during daylight hours (6h-19h) but are shown here as a continuous dataset. Additionally, it is worth noting that occasionally the water level did not reach EZP13 (i.
Firstly, the data point used to derive mean water levels from the timestack images is located in the centre of the FoV of the camera and coincides with the location of pressure sensor EZP13. Fig. 11  indicates that the r 2 indeed decreases further away from the camera as well as an increasing RMSE. As a result, it is recommended to extract mean water levels close to the image centre of the camera, while taking into account bottom friction and, potentially, a changing bathymetry when implementing on sandy coasts. Note that the size of the timestack image used for cross-correlation calculations decreases in size in order to eliminate noise near the edge of the timestack images. Secondly, it is recommended to select a transect in the cross-shore direction with a sufficient length in order to always cover the full extent of the breaker zone.
Conclusion This study presents a toolkit to estimate key parameters in the nearshore by leveraging the strength of existing codes and providing a unified and simplified method that can be utilised by a wide range of users. Identifying parameters, such as wave height, mean water levels, and morphological parameters, in the nearshore using a coastal monitoring system will allow us to obtain remotely sensed datasets to better understand coastal processes in a particular location. Historically, measuring these parameters required in-situ instruments (e. g. , pressure sensors, buoys, wave gauges), which are generally expensive and lead to spatially scarce datasets. Moreover, these instruments are subject to corrosion and biofouling, which increases their maintenance costs. This publicly available MATLAB code estimated the mean water level with a good degree of accuracy (r 2 = 0.
95 and RMSE = 0. 289 m) and the significant wave height that are not restricted by the timestack image dimensions by r 2 = 0. 78 and RMSE = 0. 162 m, while also providing the specific location of wave breaking. Therefore, this approach is suitable for use in the swash and surf zone with variations in mean water levels, wave breaking heights, and wave breaking positions. However, errors may arise during low peak wave periods and when the timestack image dimensions are too restricted to identify the breaker zone. In addition, this method is not able to measure during night-time. The software is easy to use, requires limited inputs from the user, is freely available and open source. For these reasons, CoastCams represents a valuable tool for a range of potential users such as researcher evaluating impacts of rising sea levels, hydrodynamic forcing, and shoreline positions in the coastal zone, as well as for coastal managers and policy makers for regulating coastal development or implementing coastal protection.
In this work we use the implementation of the method discussed in the ref. [58]  into the Mathematica package DiffExp  [59]. For a successful implementation the boundary value must be given with a sufficiently high numerical precision. The development of the auxiliary mass flow method  [60] [61] [62]  and in particular the Mathematica package AMFlow  [63]  offers a simple and practical solution to this task. We are therefore able to offer a solution for the master integrals which has the potential for phenomenological applications, as has been done for other processes  [22, 25, [64] [65] [66] [67] [68] [69] [70]. Beyond our semi-analytic solution for the master integrals, we also derive the analytic representation for the system of differential equations in terms of logarithmic one-forms. The alphabet for this system is written in a compact form and it shows the same analytic structure as in the five-point massless  [16]  and in the one-mass  [22]  cases.
Notation and definitions We consider the Feynman integral topology in d = 4 -2ε dimensions with eight propagators as shown in figure  1. This can be written as, I a 9 ,a 10 ,a 11 a 1 ,a 2 ,a 3 ,a 4 ,a 5 ,a 6 ,a 7 ,a 8 = D 4-2 k 1 D 4-2 k 2 D a 9 9 D a 10 10 D a 11 11 D a 1 1 • • • D a 8 8 (2. 1) where a 1 , • • • , a 11 ≥ 0. The propagators, and numerators, are defined as D 1 = k 2 1 , D 2 = (k 1 -p 1 ) 2 -m 2 t , D 3 = (k 1 -p 1 -p 2 ) 2 , D 4 = (k 1 -p 1 -p 2 -p 3 ) 2 , D 5 = k 2 2 , D 6 = (k 2 -p 5 ) 2 , D 7 = (k 2 -p 4 -p 5 ) 2 , D 8 = (k 1 + k 2 ) 2 , D 9 = (k 1 + p 5 ) 2 , D 10 = (k 2 + p 1 ) 2 -m 2 t , D 11 = (k 2 + p 1 + p 2 ) 2 , (2. 2) and the integration measure is: D d k i = d d k i iπ d 2 e εγ E. (2. 3) d ij = p i • p j. (2. 4) The minimal set of master integrals (MIs) is obtained by IBP reduction  [51, 71] , as implemented in the software LiteRed  [72, 73]  and FiniteFlow  [55].
Numerical solution of the differential equations As a proof of concept of our work, we discuss in this section a numerical solution for the system of differential equations associated to the master integrals. The system has been integrated semi-analytically exploiting the generalised power series expansion method  [58] , as implemented in the package DiffExp  [59]. Since we are interested in a numerical evaluation of the master integrals, we integrated the system using high-precision numerical boundary conditions. This evaluation has been done exploiting the auxiliary mass flow method  [60] [61] [62] , by means of the package AMFlow  [63]. The boundary values are evaluated at the rational point chosen arbitrarily in the Euclidean region: x 0 := - 2 17 , - 17 13 , - 7 , - 19 5 , - 23 with a precision of O(100) digits.
All the relevant material for the numerical evaluation is given in the supplementary material attached to this paper: • anc/DiffExp/boundary_value. m: a set of numerical boundary conditions; • anc/DiffExp/DEQs/d_1. m: the dlog matrix in the DiffExp format; • anc/DiffExp/analytic_continuation. m: the list of polynomials needed for the analytic continuation; JHEP01(2023)156 • anc/DiffExp/DIFFEXP_run. wl: a Mathematica file for the numerical evaluation of the MIs with DiffExp; • anc/boundary/run. wl: an AMFlow script to generate high-precision boundary conditions. Our numerical tests with DiffExp have not been optimised for a realistic phase-space integration required by phenomenological studies. As a result it is not possible to quote any sensible analysis of the evaluation times since in our tests, all benchmark points were transported from the same Euclidean boundary point.
Acknowledgments We thank  Simone Zoia  and  Heribertus Bayu Hartanto  for many helpful discussions. This project received funding from the  European Union 's  Horizon 2020 research and innovation programmes High precision multi-jet dynamics at the LHC  (consolidator grant agreement No  772099 ),  EWMassHiggs (Marie Skłodowska Curie Grant  agreement ID:  101027658 ),  European Research Council starting  grant  BOSON 101041109  as well as from the  Villum Fonden  research grant  00025445.
To push forward the mRNA level prediction, we present miREx, a CNN-based tool that includes information about miRNA targets and expression levels in the model. Indeed, each miRNA can target specific genes, and the model exploits this information to guide the learning process. In detail, not all miRNAs are included, only a selected subset with the highest impact on the model. MiREx has been evaluated on four cancer primary sites from the genomics data commons (GDC) database: lung, kidney, breast, and corpus uteri. Results show that mRNA level prediction benefits from selected miRNA targets and expression information. Future model developments could include other transcriptional regulators or be trained with proteomics data to infer protein levels.
miRNA targeting TargetScan  [27]  is a state-of-the-art database for miRNA annotation and target description. Cumulative Weighted Context++ Scores (CWCS) from TargetScan were downloaded to embed miRNA target-specific information in the model. This score is a metric to assess the likelihood of a given miRNA binding to a target mRNA. Consequently, for each miRNA-gene pair, we have a CWCS. Therefore, our model also considers the effectiveness of miRNAs in the repression of gene expression.
mRNA and miRNA expression values mRNA and miRNA data are obtained from the Genomic Data Commons (GDC) portal  [28]  for four primary sites: lung, kidney, breast, and corpus uteri. Each primary site can contain data from different cancer subtypes. Lung Data from LUng ADenocarcinoma (LUAD) and LUng Squamous Cell carcinoma (LUSC) was downloaded. Mesothelioma (MESO) data was discarded since miRNA expression levels were unavailable. The dataset is composed of 982 samples (507 for LUAD and 475 for LUSC). Kidney Data from KIdney CHromophobe (KICH), KIdney Renal Papillary cell carcinoma (KIRP), and KIdney Renal Clear cell carcinoma (KIRC) was downloaded. Sarcoma (SARC) was excluded due to the low number of samples. The dataset is composed of 871 samples (66 for KICH, 290 for KIRP, and 515 for KIRC).
Breast Data from Breast Invasive Carcinoma (BRCA) was downloaded. Diffuse Large B-Cell Lymphoma (DLBC) was excluded due to the low number of samples. The dataset is composed of 1076 BRCA samples. Corpus Uteri Data from Uterine Corpus Endometrial Carcinoma (UCEC) was downloaded. Sarcoma (SARC) was excluded due to the low number of samples. The dataset is composed of 536 UCEC samples.
Data processing In each of the four datasets acquired, we preserved only those samples that included mRNA and miRNA expression values. To make a fair comparison with Xpresso  [4] , histone and Y chromosome genes were discarded, and only protein-coding genes were kept. Ultimately, 18,377 genes were used by Xpresso. Out of these, 18,347 genes also had corresponding expression values in the GDC dataset and were subsequently employed by miREx. The miRNA expression values data downloaded from GDC comprises 1881 miRNAs. Of those, only 243 had known gene targets in the TargetScan database and were used in our model. The CWCS was extracted for each remaining miRNA-target pair from TargetScan. We computed the mean expression levels of each gene and miRNA across all cancer primary sites. In cases where multiple subtypes were present within a primary site, we also calculated the overall mean expression across all subtypes for that site.
Results In this section, we present the results obtained from the four datasets. As previously outlined in Sect. Materials and methods, our approach involves training four distinct models. miREx, our proposed model, leverages multiple aspects of miR-NAs (expression levels, targeting, and CWCS) as well as sequence and gene features. In contrast, the state-of-the-art model (Xpresso) only uses sequence and gene features. AllMirna includes expression levels and targeting of miRNA but does not take into account the CWCS. Finally, AbsCorr, similarly to miREx, considers all available information for miRNAs; however, AbsCorr examines indirect regulation. In all the plots in the results section, we display the mean of the R 2 values along with their corresponding 95% confidence intervals.
Lung Figure  3  shows the results obtained on the lung dataset. MiREx and the other models were trained with three targets: LUAD, LUSC, and their mean. Firstly, we compared the Xpresso results with those obtained with the AllMirna model. The R 2 decreased with the inclusion of miRNA expression levels in all three Fig. 3  Results obtained on lung dataset cases (LUAD, LUSC, and mean). The differences are statistically significant, with p-values < 0. 05. MiREx and AbsCorr were trained using only a subset of the miR-NAs, and while both approaches lead to higher R 2 in comparison to Xpresso, statisti- cal tests show that only miREx's results are statistically significant (p-values < 0. 05). Table  3b  contains the subsets of ten miRNAs selected by the two methods in the three categories mean, LUAD and LUSC.
Kidney Figure  4  shows the results obtained on the kidney dataset. The models were trained with four targets: KICH, KIRC, KIRP, and the mean of all three classes. We can see that in all four cases, the AllMirna model leads to worse results. In two classes (KICH and KIRP), we can see that using a selected subset of miRNAs leads to better predictions when compared to Xpresso. In particular, with miREx, the results are statistically significant (p-value << 0. 05). The third class, KIRC, has a different trend. In this case, our model does not seem to bring an improvement in the results. The results on the mean of all three subtypes show an improvement with our model. However, it is not statistically significant. Table  4b  displays the miRNAs selected by MiREx and AbsCorr. Again, there is a high overlap between miRNAs chosen by the same method in the different classes, while there is a low overlap between the two techniques.
Corpus Uteri Figure  6  shows the results obtained on the corpus uteri dataset. The models were trained with a single corpus uteri cancer subtype: UCEC. In this dataset, AllMirna and AbsCorr lead to worse results than Xpresso. MiREx improves the prediction (p-value < 0. 05). Table  6b  displays the miRNAs chosen by miREx and AbsCorr. The overlap of miR-NAs is minimal, as only two are in common.
As expected, the two methods chose different miRNAs since they looked at miRNAs with direct or indirect effects on gene regulation. Instead, when there are multiple classes for the same cancer primary site, the miRNAs chosen by the same method in the different categories are very similar. Many of the miRNAs selected by these methods are known to play various roles in cancer-related processes. For example, miR-23a is involved in many cancers  [29] , and, in particular, in non-small cell lung cancer (NSCLC)  [30, 31] , which is a class of lung cancers that includes LUAD and LUSC, two subtypes used in this work. At least one method chose miR-506 for all four datasets. This miRNA is known for its association with different cancers, including kidney  [32]  and lung  [33].
The knowledge of the material budget with a high precision is fundamental for measurements of direct photon production using the photon conversion method due to its direct impact on the total systematic uncertainty. Moreover, it influences many aspects of the charged-particle reconstruction performance. In this article, two procedures to determine data-driven corrections to the materialbudget description in ALICE simulation software are developed. One is based on the precise knowledge of the gas composition in the Time Projection Chamber. The other is based on the robustness of the ratio between the produced number of photons and charged particles, to a large extent due to the approximate isospin symmetry in the number of produced neutral and charged pions. Both methods are applied to ALICE data allowing for a reduction of the overall material budget systematic uncertainty from 4.
The primary vertex is reconstructed either using global tracks (with ITS and TPC information) or using SPD tracklets. The contamination from in-bunch pile-up events is removed offline by excluding events with multiple vertices reconstructed in the SPD  [25]. In general, MC simulations use a geometrical model of the ALICE detectors, an event generator as input, and a particle transport software, GEANT3  [26]  in the ALICE case.
TPC-gas based calibration weights: ω i The material-budget correction via ω i calibration weights exploits the fact that a well-known and homogeneous part of the detector material can be used as a reference to calibrate the material budget of the rest of the detector known with less precision. The TPC gas volume  [23]  in the fiducial range used for photon reconstruction, 95 < R < 145 cm, is perfectly suited for this purpose. The TPC gas material budget depends on the exact chemical composition, temperature and pressure of the gas. During data taking, variations in the TPC gas composition and pressure are monitored with a gas chromatograph and a pressure gauge, respectively, and applied accordingly in the MC simulations via access to the conditions and calibrations database with a granularity of few hours.