dataesr/scientific-paragraphs-categorization huggingface dataset:
By contrast, we generate a synthetic background and place the individual diatoms and debris images, while considering the global consistency of the final image. To generate realistic images, we consider different parameters while pasting individual objects, such as scaling, rotation, level of overlap of diatom and diatom-debris ratio. Finally, we perform sensitivity analysis to study the impact of these parameters on the performance of the network by evaluating using real datasets. To summarize, we evaluated the usefulness of synthetic datasets for the automatic detection of diatoms in microscopy images using deep-learning. We first propose a method to generate thousands of synthetic datasets based on an off-the-shelf seamless blending approach. This dataset is used for pre-training a state-of-the-art detection network, YOLOv5  (Jocher et al.
Individual diatom images Individual diatom images were collected from several open-access taxonomic atlases  (Bey and Ector, 2013; Lalanne-Cassou and Voisin, 2013; Peeters and Ector, 2018). From the atlases in PDF format, we automatically extracted diatom images using PDF scraping. Our final dataset consisted of 9230 grayscale images of individual diatoms belonging to 166 diatom species.
Real microscopy images Real images were generated from two types of diatom samples: (1) natural samples collected in the field (Cleurie Stream, Vosges, France, hereafter referred to as CLEURIE) on hard mineral substrates and fixed by adding ethanol, showing natural communities consisting of various diatom species; (2) a sample collected from a lab culture of the diatom Nitzchia palea (hereafter referred to as NPAL). Each liquid sample was subjected to oxidation using hot hydrogen peroxide in order to remove organic material, rinsed with deionized water, then mounted on permanent microscope slides with Naphraxinto, a high refractive index resin in order to be visualized at 1000× magnification using light microscopy. CLEURIE and NPAL datasets finally consist of 120 and 175 images, respectively, each one representing a microscope view field containing multiple diatoms and debris.
Synthetic Microscope Image Generation The synthetic dataset should closely mimic the real images obtained from light microscopy. Let us consider the following procedure to create a seamless image I of width W and height H with images from the individual diatom dataset D: 1. Init I ′ , empty image of dimensions (W, H). • Pick a subset S i ⊂ D composed of individual diatom and debris images. • For each image T ij ∈ S i of width w ij and height h ij , -Apply data augmentation. Refer to Sec. 3. 1 for the different augmentations and the parameters used. -Find a random position P ij for T ij on I ′ such that it does not overlap any previously added diatom or debris images or the borders of I ′ by more than a certain threshold percentage. 2. Init I, empty image of dimensions (W, H). Let B be the set of pixels p I ′ of I ′ comprising the borders of T ij.
Baseline Four augmentation techniques were used in order to generate the synthetic dataset: scaling, rotation, overlap between diatoms and debris, and diatom to debris ratio. To determine the sensitivity of each of the augmentation methods while evaluating using real microscopy images, and to choose the optimal parameter values, we performed a grid-search on the parameters without changing the neural network hyperparameters as summarized in Table  1. The definition and significance of each of these techniques and the parameter values are given below. 1. Scaling: Scaling refers to resizing the images of individual diatoms and debris while creating the synthetic dataset. This was carried out to represent the real microscopy images where diatoms belonging to a species can occur at varying scales.
Diatom-debris ratio: This refers to the ratio between the number of diatoms and the number of debris objects while generating the images. We considered the following range of values:(1) diatom-debris ratio between 2 and 5; (2) diatom-debris ratio between 0. 1 and 0. 5. For the above two cases, the number of diatoms was between 6 and 10, and we varied the number of debris objects to achieve the desired ratio. To study the impact of the synthetic dataset while training on the real datasets, we performed the following experiments: (1) in the 'Synthetic' experiment, we trained and evaluated the network on the synthetic images; (2) in the 'Zero-shot learning' experiment, we trained the network with the synthetic dataset and evaluated using the available real dataset; (3) in the 'Training from scratch' experiment, we trained the network with only the real dataset and evaluated using the real dataset; (4) in the 'Fine-tuning' experiment, we used the model trained on the synthetic.
Implementation Details We used Adam optimizer  (Kingma and Ba, 2014)  and the learning rate was 0. 0002. The batch size for training YOLOv5 (horizontal and rotated) was 16. For every category of synthetic dataset (D1-D16 in Table. 1), we generated 4000 images. While training the networks using synthetic and real datasets, we used 80% of the images for training, 10% for validation and 10% for testing. We trained our networks on GeForce GTX 2080 with 8 Gb RAM and GeForce GTX 1080 with 12 Gb RAM. The total training time for sensitivity analysis was 24 days.
Horizontal bounding box detection Figure  2  shows some examples of the horizontal bounding box detections from YOLOv5. Tables  2  and 3  show the quantitative metrics for horizontal bounding box detection using YOLOv5  (Jocher et al. , 2020). The following observations could be made based on the results: Scaling: When evaluated on synthetic dataset, the precision, and recall (mIoU=0. 50 and mIoU from 0. 50 to 0. 95) was higher when the images were not scaled (D1-D4; D9-D12 in Table. 2). This is because when there is no scaling, the diatoms are of uniform size, which leads to network overfitting. When evaluated on the real microscopy images, the CLEURIE dataset contained diatoms of varying size due to the presence of different species, and hence the presence of scaling in the synthetic dataset leads to better metric values on both zero-shot learning and fine-tuning.
NPAL images contained diatoms of the same species, and all diatoms were of rather uniform size. Hence, the network generalized better when the synthetic dataset had no scaling. Rotation: When the dataset contained no rotated diatoms (D1-D8), the mIoU 0. 5:0. 95 on the synthetic dataset was superior, but it did not generalize well to the CLEURIE and NPAL datasets. This is because networks detecting horizontal bounding boxes are designed to fit non-rotated objects better, hence the network generalizes well. The network draws tighter and more accurate bounding boxes around these objects, resulting in better mIoU values. However, real datasets contain diatoms present in random orientations. Thus, training datasets D9-D16 generalized better to CLEURIE and NPAL datasets compared with D1-D8. Overlap: From the results, having overlaps had no significant impact on the precision and recall scores (mIoU=0.
50 and mIoU from 0. 50 to 0. 95) of the network for synthetic and real datasets. However, the best metric values were obtained when a maximum overlap of 25% was allowed. This means that although overlap helps improve network performance, the network was able to generalize well even when the training datasets do not have overlap of diatoms. Diatom-debris ratio: The network had better metrics when the diatom-debris ratio was in the range  [2, 5]  for both the synthetic and real datasets. For the synthetic datasets, this could be explained by the reduced complexity of the images for the network to detect, since the debris objects were widely distributed. For both the CLEURIE and NPAL datasets, the diatom-debris ratio was greater than 1. Hence, when the synthetic dataset was characterized by a higher diatomdebris ratio, the network generalized better for these datasets.
Scaling: The evaluation metrics follow a similar trend to the horizontal box evaluation. Evaluation on the synthetic dataset achieved the best performance when there was no scaling, since the network overfits to the uniform diatom sizes. Scaling helps the network generalize better to the CLEURIE dataset due to the presence of different taxa of diatoms with varying scales. The NPAL dataset consists of diatoms of the same species with uniform sizes, hence the network generalizes best when trained with no scaling. Overlap: When evaluated on the synthetic dataset, the network performed best when there was no overlap between the diatoms and debris. This is because, when there are no overlaps, there is no ambiguity due to occlusions for the network while learning the rotated bounding box parameters.
For the CLEURIE and NPAL datasets, overlap helps the network generalize better. Diatom-debris ratio: The network performed better when the diatom-debris ratio was in the range  [2, 5]  for the synthetic and real datasets. This is because fewer debris objects means a sparser distribution compared with the diatoms, making them easier to discard. Table  5  shows the results when the network was trained using only the real dataset. Compared with the performance when the network was trained only on real data, there was an improvement of 17% in precision and 22% in recall for the CLEURIE dataset and an improvement of 8% in precision and 23% in recall for the NPAL dataset, for an mIoU threshold of 0. 5 when the network was trained on synthetic data and fine-tuned to real data.
Conclusion In this work, we assessed the use of a synthetic dataset for diatom detection using deep-learning. When the labelled dataset available for training the network is limited, one can use a synthetic dataset for training and fine-tune it to the real dataset. We proposed a method to generate thousands of synthetic datasets and performed a comprehensive analysis on the impact of the synthetic dataset when training horizontal and rotated object detection networks. Our results show that when the available labeled dataset is limited, fine-tuning from a network trained on synthetic dataset can significantly improve the performance of the network. This approach will thus be of interest for future work, which will focus on the implementation of automatic diatom segmentation and classification networks.
In this complex elimination setting, environmental changes (meteorological, landscape, etc. ), socio-political events or METF interventions occurrence induced differential dynamic variations and invalidated the stationarity assumption. It prevented the use of classical time series methods (e. g. ARIMA). Functional data transformation allowed to remove sharp irregularities, with smoothing parameters identical and optimized to the entire set of time series, and without stationarity assumptions. Analyses were conducted using R 4. 0 (R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria) and {fda}, {TSclust} and {dtw} packages. An R script is available in supplementary material to facilitate the implementation of the clustering method (Sup. Met. 1).
e. seasonality with time lag) (Sup. Fig. 4, 5 ) This study presented several strengths. First, the unique dataset collected by the METF communitybased MP network allowed studying malaria incidence at an unprecedented granularity (village-scale) in the GMS. Beyond identifying the northern area of the region with the highest malaria burden (already shown elsewhere  8, 10  ), clustering highlighted localized groups of few villages relevant for operation planning. P. falciparum and P. vivax dynamics also could be studied separately thanks to data richness. Second, clustering analysed only clinical malaria incidence and thus transmission characteristics. According to WHO guidelines, taking into account malaria receptivity will improve this clustering. Combining P. falciparum and P. vivax analysis brings some information about receptivity, but this should to be completed with more detailed data including environmental characteristics.
The method proposed here provides a thorough tool to exploit longitudinal routine case data and extract an untapped wealth of information for operational planning, allowing to gauge trends, seasonality shifts or periodic outbreaks. We applied it to a fine-grained village-level malaria incidence dataset. This approach relies on village-level data yet unavailable in many settings. Our method could be used to identify longitudinal incidence patterns at coarser scale in a country-wide approach (e. g. village tract or health area). WHO "high burden to high impact" approach promotes targeting areas where malaria burden is highest, leading to stratification beyond district level, e. g. health areas or health facility catchment areas. 24, 25  Other infectious diseases with highly heterogeneous dynamics driven by multiple factors could also be analysed in this workflow.
Data sources and variable definitions The data used to estimate (2) are described next. Output is GDP in constant 2005 USD, capital stock is derived from the accumulation of investment flows (inv), which are expressed in constant 2005 USD. The perpetual inventory equation for deriving the capital stock series is specified as 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑖𝑡 = (1 -𝛿)𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑖𝑡-1 + 𝑖𝑛𝑣 𝑖𝑡-1 , where 𝛿 is the rate of physical depreciation assumed to be constant across countries and years at 15%. The initial capital stock is derived from the steady state condition: 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 0𝑖 = 𝑖𝑛𝑣 0𝑖 ∕( ḡ𝑖 + 𝛿), where ḡ𝑖 is the average growth rate of investment in country i over the sample period and 𝑖𝑛𝑣 0𝑖 is investment in the first year of the sample in each country. Labor is the number of workers employed and human capital is the average years of schooling for the population (15 years old and above).
(Rodriguez and Rodrik, 2000; Li and Liu, 2005; Busse and Groizard, 2008). FDI is defined as the share of FDI inflows to GDP and data are collected from various issues of the International Monetary Fund (IMF) and International Financial Statistics (IFS). Trade improves the allocation of resources through specialization as well as allows developing countries to assimilate knowledge from their trading partners  (Fagan et al. , 2016; Bournakis et al. , 2018). Trade is defined as the share of imports to GDP and data are obtained from the World Bank Development Indicators (WDI). Natural resources include the sum of rents from gas, coal, minerals, and forestry as a share of GDP, and data are obtained from WDI. Finally, Credit captures the development of financial markets. The financial system is the mediator between savers and borrowers channeling credit into investment projects with high return rates  (Rajan and Zingales, 1998).
II. BACKGROUND AND MOTIVATION Two different kinds of techniques can be used to introduce variability into programming/modeling languages  [9] : amalgamated and separated (aka orthogonal). The amalgamated approach proposes to augment the base language with variability concepts. In  [7] ,  [10] ,  [11] , the authors extend the UML metamodel for modeling variability in multiple UML diagrams, e. g. , Class or Sequence diagrams. In  [12] ,  [13] , the authors propose a generic solution that can be applied to any kind of language and is fully supported by a tool. In a separated approach (aka. orthogonal approach), the base language and the variability language are kept distinct and are related via a mapping. An illustration of this second approach can be found in  [14]  (supported by the FeatureMapper tool) or in  [15].
These approaches directly relate features and model elements and derive product models by removing all the model elements associated with non-selected features. Another example of the separated approach is VML*  [16] , which proposes a family of textual languages dedicated to the modeling of relationships between elements. VML* and FeatureMapper have been compared in  [17]  in terms of automation, scalability, expressiveness and evolution of the mapping. In this paper we set ourselves in a context of an orthogonal approach where feature models are then linked to a base model through a realization model. The Common Variability Language (CVL)  [18]  has been unsuccessfully proposed as a standard for this kind of approaches, but it is nevertheless an excellent illustration of the important concepts that we need, as illustrated in Fig.
B. Implementation To perform the evaluation of our approach, we developed both a generic implementation of Greal and set of specialisations for BPMN. The implementation is based on Eclipse (https://www. eclipse. org/) and Xtext (https://www. eclipse. org/ Xtext/) for the definition of the realization language. For the context of the case study built on top of BPMN, we use the Eclipse BPMN editor (https://www. eclipse. org/ bpmn2-modeler/) and the ELK project (https://www. eclipse. org/elk/). The motivation for these choices was mainly driven by the possibility to have access to the source code of these tools. Feature models are defined using the open-source tool FeatureIDE  [27]  (https://featureide. github. io/). Feature IDE provides an attributed feature model editor with support for constraints based on boolean expressions.
This effort required a lot of back and forth and exchanges with various Airbus stakeholders to refine the tooling according to the practices at Airbus. The current implementation contains 45652 lines of Java code for the generic part (incl. 42088 generated Xtext code, using cloc to compute the number of lines of code) and 903 for the specialization for BPMN. This illustrates the reuse provided by Greal.
The advantage of an internal DSL is a smooth integration to the host language (e. g. , Java) and a possible reuse of mainstream tooling. However, an internal DSL might be more verbose and disconnected from a specific ecosystem (e. g. , the BPMN tooling). A possibility is thus to offer internal DSLs, hosted on top of Java, Python, etc. , in addition to the external DSL. A "must" would be to transition from external to internal DSL in an automated way: a realization model written in the external DSL could well be edited through a fluent API (and back again). There are software language engineering works for supporting the construction of several "shapes" of a language  [29] , and there have been some attempts to provide different shapes of a feature modeling language  [30]. Second, the use of another "shape" of the realization language can be envisioned into spreadsheet tools like Excel.
Participants One hundred and ninety-one participants (after removal of 1 participant with high overall errors, see Data Analysis section) were recruited online via Prolific. co, completed the study online via Pavlovia. org, and were paid £2 for their participation. The entire experiment lasted less than 20 minutes. Participants were randomly assigned to one of the six contingency groups  (50, 60, 70, 80, 90, or 100%). We aimed for a minimum of 28 participants per group based on rough power estimates on data from our prior study  (Iorio et al. , 2023) , which would represent high power (. 8) to detect an effect size as small as η p 2 =. 055 for the interaction between contingency and group in response times. The final sample included 31, 29, 33, 37, 32, and 29 participants,  respectively. 4 All participants were native English speakers, were not musicians, and did not know how to read musical notation, as determined via the screening questions required prior to participation.
Apparatus The experiment was programmed in PsychoJS (i. e. , JavaScript-converted PsychoPy experiment for data collection online) and was run online. The experiment worked on desktop or laptop computers. Responses for the practice, learning, and objective awareness phases were made with the D, F, G, H, J, K, and L keys for fa, sol, la, si, do, ré, and mi, respectively. Of course, we did not use a real instrument for this study, but these arbitrary keyboard responses are analogous to a piano keyboard response modality (i. e. , with the same left-to-right key ordering). The Y ("yes") and N ("no") keys were used for the subjective awareness question.
Data Analysis Raw CSV data files were first merged into a single dataset using CSVDataMerge  (Schmidt, 2021c ) and then analysed with R (R Core Team, 2018). We performed analyses on the learning phase for mean correct response times and mean error rates. Trials on which participants failed to respond before the 3000 ms deadline were excluded from both dependant measures. All statistical tests are two-tailed. Group was treated as a linear factor (i. e. , with 1 degree of freedom) in the initial analyses. In subsequent analyses on all dependent measures, we compared a linear effect with power, exponential, and quadratic functions. Bayes factors were conducted with matched models using bayesfactor_inclusion from the bayestestR package for ANOVA results and with ttestBF from the BayesFactor package for t tests.
A quadratic model was also tested on the current dataset, though we note that we only present results for a quadratic model with an intercept, c, fixed at 0, or ax 2 + bx. This was because (a) this simpler model always fit better and (b) a model with the added intercept fit rather nonsensical values for the in-tercept (e. g. , a 134 ms contingency effect with a null contingency manipulation in the RT data). In addition to a linear and quadratic model, we also tested a power function, ax b (where x = 101 -group contingency percentage), and exponential function, e bx (where x = 100 -group contingency percentage). Note that both of these latter functions could theoretically have an added intercept, k, however we only present the simpler models because (a) a priori the intercept should be zero (i.
Data Accessibility Statement All the stimuli, presentation materials, participant data, and analysis scripts can be found in the following Open Science Framework repository: https://osf. io/42fc3/. Submitted: June 22, 2023 PST, Accepted: September 28, 2023 PST This is an open-access article distributed under the terms of the Creative Commons Attribution 4. 0 International License (CCBY-4. 0). View this license's legal deed at http://creativecommons. org/licenses/by/4. 0 and legal code at http://creativecommons. org/licenses/by/4. 0/legalcode for more information. Automatizing Sight Reading: Contingency Proportion and Task Relevance in the Music Contingency Learni… Collabra: Psychology.
The superiority of compound VIIIb over compound IIIa in both docking score and biological activity is attributed to the hydrazide linker that was engaged in extra H-bond with Cys773 as also seen with erlotinib. In addition, the methyl group into the pyridine ring played an important role in the superior binding of compound VIIIb, as it was involved in two H-bonds in addition to various hydrophobic interactions with key residues (Figure  6d ). In this study, to compare the effect of each ligand on the stability of the EGFR enzyme, the latter was subjected to MDs using the X-ray coordinates of EGFR-erlotinib complex. Therefore, three MD simulations were performed for 150 ns using GROMACS 5. 1. 1 software. [53] As shown in Figure  7 , compound VIIIb had the privilege of forming a stable complex with EGFR, as indicated by its low RMSD values that nearly matches that of erlotinib.
| Computational studies The physicochemical characteristics and drug-likeness of selected compounds were predicted by using the web-based program SwissADME (SIB Swiss Institute of Bioinformatics, Écublens, Switzerland, www. swissadme. ch/, accessed on April 23, 2023). [60] Docking study, MDs, binding free energy calculations were conducted as reported previously. All detailed procedures were provided in the supplementary data.