dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). Similarly, Plant Disease App targets grapevine diseases on images with more options for the user to achieve the highest accuracy  (Petrellis, 2017). Indeed, deep learning has enabled significant advances in plant disease identification through various approaches  (Martinelli et al. , 2015; Ferentinos, 2018; Golhani et al. , 2018; Ngugi et al. , 2021; Ouhami et al. , 2021). Starting with AlexNet, the first neural network architecture to go beyond the support vector machine known as SVM (a machine learning model)  (Krizhevsky et al. , 2012) , numerous models have been created and evaluated using the public PlantVillage dataset of 54. 306 images  (Hughes et al. , 2015; Yuan et al. , 2022). The focus has always been on proposing a better deep learning model to diagnose the diseases; models such as AlexNet combined with GoogleNet  (Mohanty et al.
, 2016; Brahimi et al. , 2017) , SqueezeNet  (Durmuş et al. , 2017) , or even ResNet almost full  (Zhang et al. , 2018). InceptionV3 also showed a comparable performance  (Brahimi et al. , 2018; Qiang et al. , 2019). DenseNet121 scored nearly perfect  (Too et al. , 2019) , but EfficientNet B4 and B5  (Atila et al. , 2021)  were the best performing models on PlantVillage, with 99. 97% and 99. 91% respectively. This raises many questions about the performance of deep learning on specific crops with a smaller image dataset. In fact, many researchers have proven the effectiveness of transfer learning using variety of models with transfer learning from The ImageNet dataset  (Krizhevsky et al. , 2017). These techniques have been tested on crops for which data are available, such as citrus  (Shrivastava et al.
, 2020). Second, a higher accuracy of 90. 45% on ImageNet classification was recorded after scaling the transformers architecture  (Zhai et al. , 2021). But for ViT to achieve this performances, pre-training on an exceptionally large dataset is required  (Touvron et al. , 2020). The use of transformers in agriculture has successfully detected diseases in vineyards  (Reedha et al. , 2022)  and cassava  (Thai et al. , 2021). Within a few years, transformers will likely to be used for many more tasks in artificial intelligence. To the best of our knowledge, few researchers have addressed the cochineal infection problem from a computer vision perspective. In  (Atitallah et al. , 2021; Kaweesinsakul et al. , 2021; Perez et al. , 2022)  the authors addressed the detection, classification of various cactus species, and segmentation of the plant cactus in complex environments.
Since no public dataset is available, it is necessary to have a dataset of images acquired by smartphone for model training; therefore we have first constructed our image dataset. In this paper, we propose an assistant system to diagnose the health condition of the cacti based on mobile images and Visual image Transformers (VIT). The system we propose is composed of two main components as described in Fig. 2 : a remote server and a mobile application on edge device. The first one is for data collection and training the ViT models. The second one is operated on the mobile application, where a user can take a photo of cactus, obtain information concerning the health status of the cacti and provide a feedback. To avoid running a heavy computations on the smartphone, a remote machine capable of performing the storage and training tasks, was used to store the dataset and regularly re-train the ViT model to perform updates.
The latest update will then be sent to the individual users' s edge device via the application for better accuracy. On the other side, the smartphone will acquire new cactus photos or select some from the gallery and perform the classification. We also contributed with new dataset for images of cochineal infested cacti. The proposed system could be a valuable tool for farmers growing cacti. This work is organized as follows; the first section provides a brief overview of the research. The second section presents a description of the dataset, the materials and the methodology used. The third section details the conduct of experiences. In the fourth section, results are reported with a discussion in the same section. Then conclusions are drawn in the final section.
Dataset description The main dataset for this case study was created manually from the field using a smartphone equipped with an f/1. 9 5 mm ISO-101 camera at several locations in Morocco, precisely in the Guelmim-Oued Noun region, on two dates: 27/03/2021 and 23/05/2021. On each date, we acquired data from different fields as shown in Table  1. The raw images collected from the smartphone had many variations in saturation, angle of shot, resolution, range and zoom. After acquiring the images, they were resized into smaller images in the same shape (256 × 256 × 3) without data augmentation. These resulting images were labeled using our manual classification process shown in Fig. 3  in order to perform a supervised learning approach. Our technique was to some extent based on  (Vasconcelos et al.
, 2009; Akroud et al. , 2021)  since the authors had classified the infestation from 0 to 5 to differentiate between the various heath statuses. For comparison to our work, 0 is healthy, 1 is early stage and from 2 to 5 late stage. The principle is to look at the image and then answer the question to know what the class is for the given image. For example, if cacti are present in the image and there is a whitish sticky residue on them and there are many of them, then it is a late-stage infestation. From each image acquired at a different location labeled as "Field i" where i ∈ 1; 8 ½ , we extracted several smaller images corresponding to the relevant classes. Following our method of labeling, we created the first dataset indicating the level of infestation by the carmine cochineal Dactylopius opuntiae in cacti.
Manual classification with expert verification for each image should give one of the seven outputs shown in the diagram in Fig. 3. Note that the "Confused" class is a set of images with too much noise, a lot of mixed information or simply confusion where we cannot define the exact class just by looking at the image. This is because this class is a filter that eliminates any confusion that requires on-site checking to determine which class it belongs to. This class will not contribute to the learning or testing phase. It is stored in an isolated folder out of the experiences. By having the "Confused" class outside of the prepared dataset, we use 3921 images. In Table  2  we present the number of images generated in each class for the different locations. All the images were divided by the assigned class and not by the relative field of the source.
The final verification was done with the help of the same expert to be sure of the correct labels for each image. An example of each class is presented in Fig. 4. The difference between each class is the level of infestation and the health status of the cacti. In the Healthy class, there is both the spiny rackets and spineless rackets. In the Damaged class, we find any infestation other then the cochineal, while in the Old_Dead class, it is a class of dead cactus rackets. The classes NoCactus, EarlyStage and LateStage contain the most of the images. Our dataset is open-source and available at: https:// github. com/AnasBerka/CactiViT-materials. git.
Model: Visual image transformers The Visual image Transformer opted as the primary model for this study is practically the same as the one proposed by  (Dosovitskiy et al. , 2020)  for the "ViT-Base", as shown in Fig. 5. This model was originally based on the transformer of  (Vaswani et al. , 2017). Let S ¼ X i , y i f g n i¼1 be a set of n ¼ 16 images of the batch extracted from our prepared labeled dataset. The model takes as input i, an image X i of size 224 Â 224 Â 3 ð Þ to predict its true label y i. Were y i ∈ 1; 6 ½ corresponds to the encoded labels of the corresponding class in the data set. In the first step, at the embedding layer, the image X i was reshaped from H Â W Â C ð Þto N Â P Â P Â C ð Þ where H, W ð Þ¼ 224, 224 ð Þthe resolution of the input image, C ¼ 3 the number of channels, P the size of the patch used which can be 16 or 32 and N ¼ H Â W ð Þ = P Â P ð Þthe number of the new patches.
Experiment setup In this work, two main experiments were conducted. The first experiment was limited to the minimum number of images in the folder of a class. In our experiments, we set this number to 100 images per class since the minimum was 100 images in the "Damaged" class. Since we have six classes, the total number of images for the first experiment was 600 images to have the same number of images in each class, this to avoid having an unbalanced dataset. For the second experiment, we used all the data in an unbalanced dataset but in a stratified distribution to evaluate the dataset of 3921 images. For the hyper-parameter search, a grid search was performed to select the best optimizer to use and the corresponding learning rate (LR). Each time, models were reset and a unique random combination of the parameters was used for the search.
At the end of the test, only parameters that allow the model to achieve the best accuracy and lower loss compared to the previous results while searching were recorded and used for the experiments. The search uses 150 images from the dataset in a balanced configuration, and the training was limited to 5 epochs due to limited time and resources. Before feeding the model, a resize to a shape of (224x224x3) was applied, with random horizontal flip and normalization. In addition, in all the experiments, we varied the test size by changing the number of  folds used for cross-validation of the overall datasets to validate the evaluations on all images. For example, by choosing 3 folds for training, the data were divided into 3 sets. These sets were selected in a stratified method using the Sklearn method "StratifiedKFold".
As for software, the Python 3 language was chosen because its libraries, such as Timm  (Wightman, 2019) , facilitate obtaining pre-trained models weights on public datasets to apply transfer learning. The comparison between the models was evaluated using the log files of the evaluation of each model. Having the y pred and y true values, we were able to calculate the traditional metrics: accuracy, precision, recall, F1-score and the Matthews correlation coefficient to evaluate the confusion matrix  (Zhu, 2020). All the metrics were used to obtain the model performance using the equations: Precision ¼ The setup we used for our CactiVIT mobile application was different. CactiVIT was built for Android 5. 0 Lollipop (API 21) users to ensure that all users could use our app. The software application used to create it was Android Studio 2021.
Comparison of ViT with other models To compare the selected model "ViT-B/16 mill" with other CNN models, we performed our experiences on the local machine. The criterion was the same: models that could be tested on the local machine. The results of the first experiment using transfer learning presented in Table  3  show the comparison of the selected models in various configurations. The ViT-B/16 model performed best using 50%, 33% and 25% of the dataset as test. But the ViT-B/32 outperformed it while using only 20% as test. This is because the ViT-B/32 had a larger input patches size in this configuration that required more data for training. Overall, for this balanced dataset of 600 images, the ViT-B/16 has an average accuracy of 73%, followed by the ResNet26 with 70. 66% while the third was DenseNet169 with 70.
25%. The results of the second experiment presented in Table  4 , show a higher performance due to the use of a larger dataset. We compared the same selected models in the various configuration, but this time using the large, unbalanced dataset of 3921 images. Once again, the ViT-B/16 model performed the best with an average accuracy of 88. 73%. It was followed by the ViT-B/32_in21k model with 87. 57% while the third is DenseNet169 with 87. 04%. We can assume from the results in Tables  3  and 4  that ViT performed best because it was trained on more data. Also, looking at the classification metrics for each class in Table  5  despite the low accuracy on half of the classes, the ViT had the best results we had seen. Conversely, CNN models like DenseNet can obtain acceptable results without transfer learning, but by adding this knowledge, additional performance was proven, but still inferior to the performance obtained by ViT.
Discussion The experiences revealed in Tables  3  and 4  that having a larger dataset can improve the models' performances significantly. Interestingly, ViT remains in the lead in both experiences. The main difference is that ViT analyzes the entire image and then uses the attention mechanism to understand the semantics in the provided data, like natural language processing (NLP), which leads to better results. CNN, on the other hand, focuses locally on the neighbors of the pixels, so it may miss useful information. The problem arises when we have multi-features, such as having an early-stage cochineal infestation on the top of the cactus with a spiny rackets, in this cases the model cannot predict with a high accuracy. This precision is crucial for agents since we cannot wait for the cochineal to spread.
Conclusion Diagnosing the health statuses for vegetation product using only mobile phone images is the main goal of preventing losses and improving the quality and quantity of plant resources. The proposed application can help farmers to have an idea about the cactus in remote field by showing the probabilities of the health statuses, in case of an infestation exist the farmer can take decision for an early intervention. It has a complete system that acquires data, train it on a remote machine using the ViT model, notifies landowners of a developing infestation and helps conserve resources. In this paper, we have contributed with the application and the dataset used so that researchers can make further improvements. In addition, we have compared the classification models on our dataset, and ViT-B/16 was the best performing model with a score of 88.
Cognitive satellite communication (SatCom) is rapidly emerging as a promising technology to overcome the scarcity of the exclusive licensed band model in order to fulfill the increasing demand for high data rate services. The paper addresses power allocation methods for multi-operator multi-beam uplink satellite communication systems co-existing with a Ka-band terrestrial network, using cognitive radio paradigm. Such a scenario is especially challenging because of i) the coexisting multiple SatCom operators over the cognitive band need to coordinate the use of their resources under limited inter-operator information exchange, and ii) nonlinear onboard high power amplifier (HPA) which leads to nonlinear interference between users and beams. In order to tackle the first challenge, we propose distributed power allocation algorithms including the standard Alternate Direction Multiplier Method (ADMM); Regarding the HPA nonlinear impairment, we propose nonlinear-aware power allocation based on Signomial Programming.
In this work, we use WALKIMYA-2D, a new 2D hydrodynamical code coupled to a reduced gas phase chemical network allowing for the evolution of the CO chemistry in molecular outflows to be followed  (Castellanos-Ramírez et al. 2018). The advantage of this code is that it computes the time evolution of chemical species in a full gas dynamic simulation. The new functionalities of WALKIMYA-2D allow us to investigate more thoroughly the dynamics and the physical structure of protostellar outflows. We have selected the high-velocity outflow associated with the intermediate-mass Class 0 protostellar system Cep E-mm, located in the Cepheus OB3 association at a distance of 819 ± 16 pc  (Karnath et al. 2019) , whose CO emission observed at 1 ′′ with the Institut de Radioastronomie Millimétrique (IRAM) interferometer is displayed in Fig.
For computational reasons (to keep computational times within reach), the network is reduced to 14 chemical species, including C, O, H 2 O, OH, and CO  (Castellanos-Ramírez et al. 2018 ). The reaction rates were obtained from the UMIST database  (McElroy et al. 2013). WALKIMYA-2D and its chemical network were successfully benchmarked against laboratory experiments to study the NO formation by electrical discharges using both a zero-dimensional and a gas dynamic model approach  Castellanos-Ramírez et al. (2018). Also, in the astrochemical context, the code was successfully benchmarked against the model of dark molecular cloud presented in  McElroy et al. (2013) , and has been used to explain the CO emission produced by the Orion Fingers in the Orion KL region  (Rodríguez-González et al. 2022).
Initial conditions: the protostellar core In order to describe the protostellar core, we have adopted the physical conditions derived by  Crimier et al. (2010)  from a simple 1D modeling of the dust spectral energy distribution between 24 and 1300 µm, using the radiative transfer code 1D DUSTY  (Ivezic & Elitzur 1997). The temperature is fitted by a broken radial power law T ∝ r β with β = -0. 8 in the range 50-300 K and β = -0. 4 in the range 7-50 K. In Crimier's model, the density profile is assumed to start at a radius r in = 70 au and to follow a single radial power law distribution n(r)= n 0 × (r 0 /r) α. In order to avoid density singularities in the limit r → 0 in the numerical modeling, we have adopted the slightly modified density profile n(r) = n 0 1 + (r/r 0 ) α , (1) where n 0 = 10 9 cm -3 , r 0 = 100 au and α = 1.
Acknowledgments. P. R. -R. O. , A. S. , and B. L. acknowledge support from (a) the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  811312  for the project " Astro-Chemical Origins " (ACO); (b) the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  for the  Project "The Dawn of Organic Chemistry" (DOC)  grant agreement No  741002 ; (c) the  UNAMPAPIIT  grant  IN110722. Some of the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad. univ-grenoble-alpes. fr), which is partly supported by the  Equip@Meso  project (reference  ANR-10-EQPX-29-01 ) of the programme  Investissements d'Avenir  supervised by the  Agence Nationale de la Recherche  and the  Miztli-UNAM supercomputer , project  LANCADUNAM-DGTIC-123 2022-1.
| Statistical analysis TL measurements and ages were used to fit Von Bertalanffy growth curve model (Von  Bertalanffy, 1938) : where L t is the estimated total length at time t, L ∞ the maximum asymptotic size (i. e. , the total length for fish with an ∞ age), K is the initial growth rate, and t 0 is the theoretical age at which body size is null. Von Bertallanfy growth curves parameters (L ∞ , K, t 0 ) were estimated by Bayesian inference using the Bayesian software JAGS and the "R2jags" package  (Su & Yajima, 2015)  in R software (version 4. 0. 2; R development Core Team). We assumed that the asymptotic size L ∞ , the initial growth rate K, and the theoretical age at null size t 0 could vary between temperature (T) and food (C) conditions. Consequently, four values of L ∞ , K, and t 0 (one for each combination [CT] of temperature and food condition) were fitted.
We compared the growth curves among our four experimental treatments by investigating the overlap among their CIs. Curves were considered significantly different when their CIs did not overlap  (Pritchard et al. , 2017). We investigated the effects of temperature, feeding frequency, and their interaction (fixed effects) on mean daily clutch size per female (log-transformed) and survival probabilities using a linear mixed effects model (lmer function in the "lme4" package,  Bates et al. , 2015)  and a mixed effects Cox proportional hazards model (coxme function in the "coxme" package  [Therneau, 2015] ), respectively, with tank as a random factor. For both models, analyses of deviance using Wald tests were provided to test the significance of fixed parameters. We tested the assumptions of the mixed effects Cox proportional hazards model using the cox.
For the fecundity function (f_n), we used a Poisson regression model to describe the link between fish size and egg number. Egg hatching rate and survival probability (f_g) and the body size distribution of juveniles (f_d) were estimated from unpublished data from the same experimental populations. We used the "ipmr" R package  (Levin et al. , 2021)  functions to define the kernels (define_kernel), the domain (define_domains), and the initial state of the population (define_pop_state) and to compute the IPMs (make_ipm). The number of iterations of the IPMs was fixed per treatment to achieve asymptotic dynamics according to the is_conv_ to_asymptotic function. We used the gen_time and lambda functions from the "Rage" and "ipmr" R packages  (Jones et al. , 2022; Levin et al. , 2021)  to quantify the generation time T and the asymptotic per capita population growth rate λ.
We quantified the uncertainty of T and λ by bootstrapping 1000 combinations of L ∞ , K, and t 0 from the Bayesian model posterior distributions (with replacement) and by using 1000 random sample of each vital rate data set (survival, reproduction, and fecundity) and refitting all demographic functions s, g, f_p, f_n. For each new iteration, we ran an IPM and estimated T and λ. This yielded 1000 estimates of T and λ for each experimental treatment. We next calculated the 95% confidence intervals of T and λ and compared their mean values across experimental treatments based on the overlap of their 95% confidence intervals. We also performed a sensitivity analysis to investigate the sensitivity of T and λ to small changes in the vital rate estimates (see Figure  S5 ). Data and scripts used to build the IPMs and perform the sensitivity analysis are available online.
, 2018). This shift is partly driven by the recognition that traditional classroom-based teaching methods are not always effective in engaging students and helping them develop the knowledge and skills needed in an ever-changing work A few years ago, a product-based educational framework called Reproduction of an Innovation Environment in the Classroom (RAIS in Spanish) was developed and implemented in the Chemical Engineering School at Universidad de Los Andes in Venezuela  (Márquez et al. , 2016). RAIS is an educational strategy that enables the integral management of knowledge through the development of a product in the classroom as the primary focus of university education. This strategy relies on the access to information through resources such as the internet, databases, texts, and publications, transforming the traditional transmitting teaching and learning process to a process based on the integral management of knowledge and application  (Márquez et al.
Recently, this idea has been pushed in generative adversarial networks (GANs)  (Goodfellow et al. , 2020) , making the computer iterate through a competitive process involving a pair of networks, one trying to generate computational models of real samples (e. g. , images of cats) until the other is not able to recognize the difference of real sample from a fake (computer generated). In the last few years, natural language processing algorithms, which enable machines to understand and generate human language, have become more advanced and were used in applications such as language translation and virtual assistants. DNNs and GANs has led to breakthroughs such as language generation models and AI image generators (e. g. https://www. midjourney. com/ https://openai. com/product/dall-e-2). Speci cally, GPT (Generative Pre-trained Transformer) is a type of language model that uses a deep learning algorithm trained on a large amount of text data to learn patterns and relationships in language  (Floridi & Chiriatti, 2020).