dataesr/scientific-paragraphs-categorization huggingface dataset:
(d) Hysteresis thresholding: After non-maximum suppression, the highly weighted pixels are confirmed to be in the final map of the edges. However, weak pixels are further analysed in terms of whether they contribute to noise or the image. Applying two pre-defined threshold values, any pixels with an intensity gradient which is higher than the maximum value are edges, and those with lower than minimum values are not edges, not well defined, and hence discarded. Python code for canny edge detector: Region of interest (segmentation): This step takes into account only the region covered by the road lane and the image is divided into segments for processing  [47]. A mask is created in this ROI. Furthermore, a bit-wise AND operation is performed between each pixel of the canny image and this mask  [48].
This function masks the canny edge and shows only the required polygon ROI. Python code for defining ROI: def def do_segment ( frame ) : Hough line transform: The Hough line transform  [49]  is a transform used to detect straight lines. The probabilistic Hough line transform is used here; this gives the extremes of the highlighted pixels of the image. This is the final step in the lane detection process and is done to find the "Lane markings" on the road. This is given mathematically by r = Xcos(θ) + Ysin(θ) The Hough space lines intersect at θ = 0. 925 and r = 9. 6. The curve in the polar coordinate system is given as r = xcosθ + ysinθ, and a single line crossing through all these points can be given as The output of the Hough line transform is shown in Figure  3  and the final output is as shown in Figure  4.
1. Correcting the camera's distortion: This involves undistorting the camera view to obtain a distinct sky view and vehicle views of the road ahead. The main cause of the change in size and shape of an object is mainly because of image distortion while being captured. This leads to a major problem: the object may appear to be closer or farther away than it actually is. All the distortion image data points can be extracted by theoretically comparing them to the actual data points which can be calculated. This is done by calling the pickle function, which is shown by the Python code below: def u n d i s t o r t ( img , c a l _ d i r =r ' c a l _ p i c k l e. p ' ) : with open ( c a l _ d i r , mode= ' rb ' ) as f : f i l e = p i c k l e. load ( f ) mtx = f i l e [ ' mtx ' ] d i s t = f i l e [ ' d i s t ' ] d s t = cv2.
Phase 1: Model Learning Phase In the model learning phase, important input parameters are collected and used for training the model. This trained model is then tested on a validation dataset to further fine-tune the hyper parameters and measure the model's accuracy. If the trained model is not at the required accuracy level, more data points are included in the validation set to further tune the parameters. A model is constructed and trained on the dynamics through repeated regressions on the interaction data. This is mainly done in five consecutive steps, which is explained in detail below.
3. Fit the model on the validation and training dataset: The built dynamic model (f) is trained in a supervised fashion to minimise the loss over the experience batch (D) by using stochastic gradient descent, i. e. , one example at a time for 2000 epochs, as shown in Figure  6. As there is only one training set, as the number of epochs increased, the validation error also increased (above 2000), which indicated over fitting of the data. Thus, to avoid this, the number of epochs is set to be 2000, since, higher than this, an increased deviation in the validation dataset compared to the training dataset was observed.
2. Minimizing the cross-entropy  [55]  between the given and the target distribution to better the sample in the next distribution. This distribution is compared with the given target distribution. The target distribution is defined by selecting the top performing sampled sequences. After reducing the entropy to the required level, the trained model is simulated in three different environments, such as a highway, merging of lanes on a highway, and a roundabout. The rendering of these environments is done using Pygame, and the required graphics are designed and scaled as required, finally importing the required modules for the environments, agents, and visualisation. The abovementioned environments are simulated with these defined parameters, as in Tables  1  and 2  and are discussed as follows:  In the highway environment, the ego vehicle (indicated in green as shown in Figure  8 ) is being driven on a four-lane, one-way highway with all incoming vehicles in the same direction.
Case studies, via an ego vehicle through the use of MRLs, were performed as part of the analysis, where the algorithm was tested in two phases over 2000 epochs to reduce the variance between the validation and training datasets. Furthermore, the planning phase oversaw the ego vehicle's manoeuvrability, where the MDP algorithm was tested in three environments: velocity variance, lane changing, and lane merging. With the application of a robust control framework, it was noted that the ego vehicle was able to predict the states of its surrounding vehicles, hence gaining the ability to predict the possible paths of surrounding vehicles. It is noted that there is no single pathway towards achieving autonomous driving, where a multitude of methods may be utilised. However, human safety should be optimised at all levels, where AV companies should prioritise this aspect, which will then lead to increased adoption and subsequently the further development of the concept.
[35]ifferentiate between NLZs and box labels, a Boolean data type is explicated to each LIDAR point to indicate whether it is an NLZ or not. Two-dimensional camera labelling is done by providing 2D bounding box labels in the captured camera image[34]. These labels are highly defined, have a specific fit, and have global unique tracking IDs. Usually vehicles, pedestrians, and cyclists are the objects which have 2D labels. Then these labelled data are divided into training and test datasets. The training set is used to train the model, and the test set is used to measure the accuracy of the trained model[35]. It is noted that the test set should be large enough to yield statistically meaningful results, it should contain all the characteristics of the complete dataset, and the training dataset should not exclude any characteristics from the test dataset; in other words, the test dataset should be the subset of the training dataset.
Introduction From the perspective of human evolution, art is mysterious. People in all known populations invest substantial time, energy and effort into generating abstract patterns and performances  (Brown, 1991) , to no obvious benefit. In biology, the study of seemingly non-functional traits in social communication relies on the evolutionary theory of signaling, a framework for understanding how reproductive trade-offs produce phenomena such as warning displays, mating calls, and specialized adaptations such as bright, colorful plumage  (Zahavi, 1975). It is currently unclear whether human art is comparable to signaling behaviours, what features they have in common with each other, or if art is even something that can be usefully understood using an evolutionary approach. In recent years, the availability of large art datasets has enabled large-scale quantitative analysis  (Liu et al.
Kolam Dataset We (TW) interviewed 312 artists in the Kodaikanal region in Tamil Nadu in 2009, collecting a total of 6,393 kolam drawings from the ner pulli nelevu or sikku kolam family, along with details of each woman's education, kolam-making experience, place of origin and household demographic background, including caste. Using the lexicon of 29 kolam gestures developed in  Waring (2012b) , each kolam was digitally transcribed into a sequence of gestures, and transferred into a database using the kolam R package (see http://github. com/nhtran93/kolam for more details). An example of transcribed kolam drawings can be seen in Figure  1. The geometry of the kolam can be divided into three geometric spaces (orthogonal, diagonal, transitional) with their specific corresponding gestures. Each set of gestures is represented by a letter (O, D, T, respectively), while special variations of these moves are given special letters (C, H, P).
The statistical models were implemented in the probabilistic programming language Stan (v2. 18)  (Carpenter et al. , 2017) , using 6000 samples in four independent A c c e p t e d M a n u s c r i p t chains. We applied an iterative process of model building, inference, model checking and evaluation, and model expansion to ensure a principled and robust Bayesian workflow  (Gabry, Simpson, Vehtari, Betancourt, & Gelman, 2019; Talts, Betancourt, Simpson, Vehtari, & Gelman, 2018). Prior predictive simulations and fitted models to simulated data were used to determine reasonable and regularizing priors for the parameters that respects the parameter type's bounds. We present a complete description of the statistical models and the priors in the SI. Analyses were performed in R (R Core Team, 2019).
Generative simulations could approximate temporal data and provide more in-depth understanding on how artistic traditions could have theoretically evolved, specifically in regards to the diversity or the complexity and the stability of the kolam in the population across time. In order to infer the underlying generative processes, a probabilistic model, in which the hypothesized causal mechanisms (i. e. , cognitive constraints, aesthetic preferences or other potential constraints) are explicitly defined, needs to be built  (Kandler & Powell, 2015). Such a probabilistic model can allow us to repeatedly simulate datasets with known parameters and compare the resulting distribution with observed data to infer the most likely hypothesized causal mechanisms. Furthermore, measuring the signaling value of specific kolam motifs for coordinating using classification tasks  (Bell, 2020)  could be a promising endeavor to explain the role of kolam art for social coordination.
Conclusion Using quantitative measures to systematically study material art in a large-scale anthropological dataset, our findings inform discussions on entropy trade-offs and various constraints on complexity operating on artistic traditions. In the case study of the hand-drawn Tamil artistic tradition, our findings are consistent with evolutionary signaling theories of constrained optimization and lend support that artistic complexity, measured by Shannon information entropy, is optimized through an observable, apparently unconscious trade-off between two standard ecological and information-theoretic measures: richness and evenness. This trade off between richness and evenness can potentially be explained by cognitive constraints and aesthetic preferences. Variation in structural and information-theoretic properties of kolam drawings are small, and evidence of social structures reflected in the information measures we employ, are weak.
Acknowledgements We specifically want to thank the many Tamil kolam artists who were willing to share their repertoires, and the local collaborators who participated in data collection, including:  Kousalia Devi ,  Subashini Vetrivel ,  Matthew ,  Shankar ,  Arun ,  Sudarshan ,  Senthil , and  Michael. We thank the team of transcribers  Anja Becker ,  Anne Büchner ,  Carlotta Rück ,  Claudia Bavero ,  Franziska Meier ,  Kristina Kunze ,  Karin Kämmerling ,  Leonie Ette  and  Maria Fröhlich. We thank  Cody T. Ross  for his Stan code review and the  Department of Human Behavior, Ecology and Culture  for stimulating discussions and feedback.
To further the state of the art, we first propose using a set of advanced image features that are inspired from the field of image quality assessment, which effectively contributes to rumor detection. These metrics are good indicators for the detection of fake images, even for those generated by advanced techniques like generative adversarial networks (GANs). Then, we introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features (i. e. , text, social context, and image features) by supervised machine learning. Such algorithms provide interpretability and explainability in the decisions taken, which we believe is particularly important in the context of rumor verification. Experimental results show that MONITOR can detect rumors with an accuracy of 96% and 89% on the MediaEval benchmark and the FakeNewsNet dataset, respectively.
In this paper, we second the hypothesis that the use of image properties is important in rumor verification. Images may indeed attract more attention than texts  [2]. Furthermore, images play a crucial role in the news diffusion process. For example, in the dataset collected by  [12] , the average number of messages with an attached image is more than 11 times that of plain text ones. Figure  1  shows two sample rumors posted on Twitter. In Figure  1 (a), it is hard to assess veracity from the text, but the likely-manipulated image hints at a rumor. In Figure  1 (b), it is hard to assess veracity from both the text or the image because the image has been taken out of its original context. Based on the above observations, we aim to leverage all the modalities of microblog messages for verifying rumors; that is, features extracted from textual and social context content of messages, and up to now unused visual and statistical features derived from images.
e. , text, social context and image features) by supervised machine learning. This choice is motivated by two factors. First, these techniques provide explainability and interpretability about the decisions taken. We believe that such explanations are necessary, especially in the context of rumors, with people's privacy in line. Second, we do also want to explore the performance of deep machine learning methods in the near future, especially to study the tradeoff between classification accuracy, computing complexity, and explainability. Eventually, extensive experiments conducted on two real-world datasets demonstrate the effectiveness of our rumor detection approach. MONITOR indeed outperforms all state-of-the-art machine learning baselines with an accuracy and F1-score of up to 96% and 89% on the MediaEval benchmark  [6]  and the FakeNewsNet dataset  [26] , respectively.
Because our focus is the visual features set, we keep all these features in the learning process. Fig. 2 : Overview of MONITOR Message Content Features Content features are extracted from the message's text. Aiming to arouse much attention and stimulate the public mood, rumor texts tend to have certain patterns in contrast to non-rumors. We extract characteristics such as the length of a tweet text and the number of its words. These characteristics also include statistics such as the number of exclamation and question marks, as well as binary features indicating the existence or not of emoticons. Furthermore, other features are extracted from the linguistics of a text, including the number of positive and negative sentiment words. For the English language, we use Liu and Hu's opinion lexicon list 3  , for German the Leipzig Affective Norms  [3] , and for Spanish the adaptation of ANEW  [4].
In no-reference algorithms, the only input is the image whose quality we want to measure, these algorithms compare statistical features of the input image against a set of features derived from an image database. In our case, we do not have the original version of the posted image; therefore, the approach that is fitting for our context is the no-reference IQA metric. For this purpose, we use three no-reference algorithms that have been demonstrated to be highly efficient. The Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE)  [20]  is trained on a database of images with known distortions, and is limited to evaluating the quality of images with the same type of distortion. BRISQUE is opinion-aware, which means that subjective quality scores accompany the training images. The Naturalness Image Quality Evaluator (NIQE)  [21]  is trained on a database of pristine images and can measure the quality of images with arbitrary distortion.
Model Training So far, we have obtained a first set of relevant textual features through a feature selection process performed on text content and social context features. We have also obtained a second set of image features composed of statistical and visual features driven from the field of image quality evaluation. These two sets of features are scaled, normalized, and concatenated to form the multimodal representation for a given message, which is fed to learn a supervised classifier for the rumor verification goal. Several learning algorithms can be implemented for the classification task of message veracity. In the experimental part, we investigate the algorithms that provide the best performance. In summary, MONITOR takes as input training data with contents from three different modalities: text, social context and image.
Experiments In this section, we conduct extensive experiments on two public datasets, to validate the effectiveness of the image features derived from IQA (Section 3. 2) and the relevance of fusing several features. First, we present statistics about the datasets we used. Then, we describe the experimental settings: a brief review of state-of-the-art features for news verification and a selection of the best of these textual features as baselines. Finally, we present experimental results and analyze the features to achieve insights into MONITOR.
Datasets To evaluate MONITOR's performance, we conduct experiments on two well-established public benchmark datasets for rumor detection, i. e. , the MediaEval Verifying Multimedia Use  [6]  and the FakeNewsNet  [26]  benchmarks. Their statistics are shown in Table  4. MediaEval is collected from Twitter and includes all three characteristics: text, social context and images. It is designed for message-level verification. The dataset has two parts: a development set containing about 9,000 rumor and 6,000 non-rumor tweets from 17 rumor-related events; a test set containing about 2,000 tweets from another batch of 35 rumor-related events. We remove tweets without any text or image, thus obtaining a final dataset including 411 distinct images associated with 6,225 real and 7,558 fake tweets, respectively.
FakeNewsNet is one of the most comprehensive fake news detection benchmark. Fake and real news articles are collected from the fact-checking websites PolitiFact and GossipCop. Since we are particularly interested in images in this work, we extract and exploit the image information of all tweets. To keep the dataset balanced, we randomly choose 2,566 real and 2,587 fake news events. After removing tweets without images, we obtain 56,369 tweets and 59,838 images.
Feature Sets The features labeled Textual are the best features selected among message content and social context features (Tables  1  and 2 ). We select them with the information gain ratio method  [13] , which is commonly used for measuring the goodness of attributes in decision tree learning, over both datasets. It helps select a subset of 15 relevant textual features with an information gain larger than zero (Table  6 ). The features labeled Image are all the image features listed in Table  3. The features labeled MONITOR are the feature set that we propose, consisting of the fusion of textual and image feature sets. The features labeled Castillo are the above-mentioned best 15 textual features. Eventually, the features labeled Wu are the 40 textual features identified in literature. Classification Model To assess the robustness of our proposal, we execute various learning algorithms for each feature set.
Feature Analysis The advantage of our approach is that we can achieve some elements of interpretability, especially to explain the contribution of image and textual features in the prediction process. Thus, we conduct an analysis to illustrate the importance of each feature set. To identify what features have the most predictive power, we depict the first most 15 important features achieved by the random forest. Variables of high importance are drivers of the classification and their values have a significant impact on the prediction. Figure  5  shows that, for both datasets, visual characteristics are in the top five features. The remaining features are a mix of text content and social context features. These results validate the effectiveness of the IQA image features issued, as well as the the importance of fusing several modalities in the process of rumor verification.
Conclusion and Perspectives To assess the veracity of messages posted on social networks, most machine learning techniques ignore the images attached to messages. In this paper, to improve the performance of the message verification, we propose a multimodal fusion framework called MONITOR that uses features extracted from the textual content of the message, the social context, and also image features have not been considered until now. Extensive experiments conducted on the MediaEval benchmark and FakeNewsNet dataset demonstrated that: 1) the image features that we introduce play a key role in message veracity assessment; and 2) no single homogeneous feature set can generate the best results alone. They also show that with a classification accuracy higher than 96% on MediaEval, and 89% on FakeNewsNet, MONITOR outperforms state-of-the-art machine learning methods.
Cytometry Analysis To analyze the fluorescence profile of GFP and tdTomato strains, we used the MACSQuant R VYB with the MACSQuantify TM Software (Miltenyi Biotec). A total of 20,000 cells were analyzed per sample, by using the Y2-A Channel (615/20 nm) for tdTomato strains and the B1-A channel (525/50 nm) for the GFP strains. The fsc files were exported and analyzed by the FlowJo software. All the figures were drawn based on log-transformed values of fluorescence intensity.
Microcolonies Growth Measurement Microcolony growth analysis was performed on the Morphologi G3-S microscope (Malvern Panalytical). The parameters used for the microscope (x20 magnification, Nikon CFI60 camera, bright field, brightness 70%) and the pre-filtering of the acquired data were the same for all the experiments. A manual prefiltering was indeed performed directly during image acquisition using the microscope software (Malvern Morphologi v7. 21), all the particles with a diameter of <1 µm being considered as background noise and hence excluded from the analysis. For non-sorted cells, a quick sonication was applied to dissociate clumps before plating (two times 10 s sonication at 20% amplitude). Sorted fluorescent cells did not require this preliminary step and were directly spread on rehydrated slides.
Data Treatment Detailed methodology for microcolony growth analysis performed in this project can be found in the Supplementary Methods section in Supplementary Material. In brief, we developed a Python script to calculate individual microcolony growth rates from the experimental datasets (see "individual_cluster_growth_rates_code. zip" in the Supplementary files in Supplementary Material that includes the input data and the Python script called "analysis. py"). The raw experimental datasets include the ID number, area, center X coordinate, and center Y coordinate for each cell cluster (i. e. , single cell or microcolony) at each timepoint. As the cluster ID numbers are different at each timepoint, they could not be used to match clusters across timepoints. The first goal of our Python script was therefore to match clusters across timepoints.