dataesr/scientific-paragraphs-categorization huggingface dataset:
We also ensure that the units of measure used to record quantities are consistent over time. Metadata provided by the ONS for Prodcom links each product-year with a unit of measure and where these units change over time within a  15  Office for National Statistics (2018c). 16 This introduces a discrepancy with the ARDx. In Prodcom, firms report calendar-year product sales and quantities, while in the ARDx firms can report either calendar year or financial year revenue figures. We deal with this by dropping firms that report values for ARDx sales that are outside a range of ±30% of total Prodcom sales. This also has the effect of removing manufacturing firms with a high proportion of services in revenues. product we define a new product, leaving us a total of 5,028 product-units. Some products are reported within Prodcom without quantity data and we drop these products, leaving 3,239 consistent product-units with non-missing quantity data.
'All variables available' refer to manufacturing firms in the ARDx that have (i) at least 10 employees and (ii) have the following variables available: employment, total turnover ex. VAT, purchases of goods and materials, capital stock, total wages and salaries. '. plus Prodcom' adds the requirement that the firm-year observation is also in the Prodcom dataset. '. plus single product' adds the requirement that at least 90% of a firm's output at basic prices is accounted for by sales of a single product. '. plus data constraints' adds the requirement that Prodcom measures a non-zero quantity of production, and that firm revenues reported by Prodcom are within 30% of the output calculated from the ARDx. Revenue TFP estimated using ordinary least squares (OLS) or the Wooldridge procedure (WLD) in revenue and value added forms changes in the sample composition.
Using the Translog production function for manufacturing The limited overlap between the Prodcom and ARDx data sets forces us to estimate a unique production function for manufacturing firms rather than estimate different production functions for different two-digit industries. In this respect, results provided in Table  A27  and Figure  A8 , and obtained using the more flexible Translog production function, allay concerns about the issue of heterogeneity in output elasticities across firms and industries in manufacturing.
10. 61. 32 Yarn of uncombed cotton, not per retail sale, for woven fabrics (excluding for carpets and floor coverings) 13. 10. 61. 33 Yarn of uncombed cotton, not per retail sale, for knitted fabrics and hosiery 13. 10. 61. 35 Yarn of uncombed cotton, not per retail sale, for other uses (including carpets and floor coverings) 13. 10. 61. 52 Yarn of combed cotton, not per retail sale, for woven fabrics (excluding for carpets and floor coverings) 13. 10. 61. 53 Source: EC RAMON Database (2009 Prodcom List).
Office for National Statistics. Virtual Microdata Laboratory (University of the West of England), 2017. © 2022 The Authors. Oxford Bulletin of Economics and Statistics published by Oxford University and John Wiley & Sons Ltd. 14680084, 0, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/obes. 12533 by Test, Wiley Online Library on [07/12/2022]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License.
Dataset The dataset was built from 180 pictures chosen from Flickr (selection criterion was picture with balanced hue content, saturated and good looking). As test sets, we used the MCM [7] and the KODIM  [8]  datasets. No image from the KODIM dataset was used when this dataset was the one tested, similarly for MCM. Since the reconstructed outputs are tiny, this small number of images becomes a large dataset. For each train/validation procedure, we randomly selected about 2 million 2x2 "center" and 16x16 "neighborhood" patches. To do so, each image was sampled into many 2x2x3 super pixels plus their corresponding 16x16x3 neighborhood. Therefore, there were enough images patches to perform training within reasonable time delays on a recent personal computer.
The mosaic was applied on each image of the dataset by element-wise multiplication of each color channel with the associated CFA. For experiment 1, the periodical model, the 4x4#2 mosaic was used. This mosaic was proposed by Amba & al. [3]  as an alternative to the more traditional Bayer pattern. We chose this pattern for our periodic model as it was the one which provided the best performances after training. During experiment 2, we also used the Bayer CFA to mosaic the image and evaluate the capabilities of both our models to generalize to another kind of periodically mosaicked image. In experiment 2, our objective is to confirm that the random network has indeed learned to demosaic under an approximation of luminance, and independently from the kind CFA. To do so, we test the capabilities of both the periodic and random model to perform demosaicking of images filtered through CFAs that are different from the one the models have been trained on.
Experiment 1 In this first experiment, we provide the results of RanColDem when demosaicking random CFA and periodic 4x4-2 CFA, with quality measured by the widely used Peak Signal to Noise Ratio (PSNR) metric. We then focus on the differences between how both models function. Our analysis concludes that the periodical and the random model proceed in a completely different ways, more specifically with regards to how luminance is handled. We highlight this by analysing the results of a post-training degradation of the CFA mosaic's colors. Table  1  shows the results for the KODIM and the MCM datasets. The periodical model reaches slightly higher values (about 2 points PSNR) but the differences between both models in similar conditions is small. For the two models, reconstruction quality is good, despite a relatively low PSNR, as confirmed by the visual evaluation provided in figure  3.
PSNR can also be improved by simply augmenting the number of kernels at each convolution layer. A third way to increase PSNR would be to provide a more carefully crafted dataset of images as input. These implementations would also raise the PSNR to values closer to the best demosaicking models (i. e. DMCNN-VD  [9] , whose values for both KODIM and MCM are provided in Table  1 ), at the cost of simplicity. Performance is not our main objective, rather, obtaining an equal baseline to compare and analyze the demosaicking process of periodical and fully random models is. [9]  Both networks indeed learn the most efficient way to achieve the required task. For the periodical model, the better approximation to reconstruct the image is to simply guess which color is missing out of the repetitive pattern which is present both at the central super-pixel location and at the neighborhood, so the pattern can be deciphered without need to focus on the image statistics.
The network has learned several different input patterns despite it being impossible to have trained on all patterns. To be specific, this is impossible because there are 3 1616 > 1. 3122 different possibilities of placing three colors on a 16x16 grid, while our training set has about 2 million elements. We can affirm that the network is able to generalize based on a reduced set of examples. Learning rate is lower in random case than in regular case (50 epochs for the regular net and twice this number for the random case), and the reconstruction quality is lower in random case compared to the regular case (~2dB less for KODIM database). But it remains spectacular that the network can reconstruct raw image having any arrangement of colors. This raises similar questions at the level of human retina that is able to learn the demosaicking process from natural images on the basis of a single random mosaic of photoreceptors (for a specific individual, and different mosaics among different individuals).
, 2018). Over the last decade, artificial intelligence has revolutionised the way we use computers to identify features and patterns in ecological datasets automatically, accurately and reliably  (Christin et al. , 2019). Using computer audition, computer vision and machine learning algorithms, ecologists can today automate complex tasks covering the detection, identification, counting and measurement of individuals from images and audio recordings  (Brodrick et al. , 2019; Lürig, 2022; Mcloughlin et al. , 2019; Peters et al. , 2014). Deep learning, a branch of machine learning based on multilayer artificial neural networks, has been particularly successful at performing these tasks  (Christin et al. , 2019; Scholl et al. , 2021; Tuia et al. , 2022). While these approaches are becoming increasingly popular in ecology, their use often requires expertise from multiple disciplines (e.
Tools for automatic extraction of ecological knowledge Fully automated monitoring of ecological communities requires computational analysis pipelines that can process and extract knowledge from the large datasets generated by sensing technologies. Diverse computational methods exist for feature extraction, as well as classifying and measuring the characteristics of those features  (Lürig et al. , 2021). These have recently been dominated and greatly improved by deep learning approaches, based on models such as Convolutional Neural Networks (CNNs)  (Brodrick et al. , 2019)  (Figure  3a-e ). Excellent reviews about the usage of deep learning in bioacoustics and computer vision, as well as current trends and limitations already exist  (Christin et al. , 2019; Gibb et al. , 2019; Høye et al. , 2021; Mcloughlin et al.
, 2019; Stowell, 2022; Stowell et al. , 2019; Tuia et al. , 2022; Wäldchen & Mäder, 2018; Weinstein, 2018). Therefore, reviewing these methodologies and their technical characteristics is beyond the scope of this work. However, there are several key considerations when using machine learning methods in the context of automated analyses in ecology and the types of data captured by remote and distributed sensing systems. First, effective machine learning models typically require large amounts of training data where a ground truth is known. For imaging datasets, this would involve the annotation of large numbers of images with the specific features that need to be extracted (e. g. classification of individual pixels as 'organism' or 'background' if segmentation is the goal). While a single training set might allow for a model to accurately extract features for similar types of images, it is rare that a single model can generalise well to vastly different environments.
g. different organisms). Beyond extraction of simple features like these, processed data can also form input to other analyses such as tracking and interaction mapping algorithms, as well as other machine learning models able to capture higher-level characteristics (e. g. behavioural traits). Recurrent Neural Networks (RNNs) and Transformer models have become commonplace in natural language processing and image analysis to aid in machine translation  (Young et al. , 2018)  and the understanding of video content  (Khan et al. , 2022). While their use in ecology to date has been limited, it is likely their application will grow as large multidimensional datasets become available through automated sensing technologies. For example, Transformer models for species classification and distribution prediction from image and sound recordings in the field have already begun to emerge  (Conde & Turgutlu, 2021; Elliott et al.
Instead, various computational pipelines, each suited to dealing with a specific context or processing step, depending on data types and on the organisms being monitored, are likely to be needed. Contrary to intuition and similarly to sensor deployment and maintenance, the full automation of knowledge extraction from ecological dataset initially depends on people and labour. With labour ranging from data annotation and management to model development, training ecologists for achieving fully automated monitoring of ecological communities might trend towards literacy in the relevant data types, as well as collaborations with engineers and computer scientists. Therefore, we argue that there is a timely need for dedicated funding streams to both train ecologists in these methods and to develop coordinated research networks with such standardised data acquisition protocols.
Automated video acquisition The robotic gantry and the camera are controlled by an in-house-developed Python program that consists of the following steps (i. e. first part of the automation workflow): a. Parametrisation: A two-column data frame containing the X and Y locations where we want to move the gantry is loaded into the program (i. e. gantry location loop). A one-column data frame containing the GMT times for which the gantry will loop over the different locations is loaded into the program. Video duration is then selected, as well as a file path for where to save the video that will be recorded by the camera. b. Video acquisition loop: The gantry then starts its loop at the times indicated in a. The gantry moves the camera to the first X/Y location and starts the camera. A first check controlling whether the camera is well positioned over a microcosm is performed using another in-house-developed OpenCV algorithm  (Bradski, 2000).
If the position is not correct, the gantry moves the platform until the camera field of view matches with a microcosm. Once the position is correct, a second check is performed by reading a QRcode fixed to the microcosm. This QRcode contains information about the microcosm ID, treatment and replicate, which are stored as variables to properly name the video that is then recorded. Once the video is saved, the camera turns off and the gantry moves to the next location, repeating this same procedure. c. End of the loop: Once all locations have been navigated to, the gantry moves back to its home location, before starting the video acquisition loop again as many times as listed in the time data frame loaded during the parametrisation step.
Automated video analysis The second part of the automation workflow consists of processing and extracting ecological knowledge from the videos that were automatically acquired. To achieve this, we firstly developed a computer vision methodology using OpenCV in Python  (Bradski, 2000). Since the model species are in constant motion, we used background subtraction to segment objects corresponding to living organisms from the background (Figure  4c ). The segmented objects are then measured (e. g. centroid location, length, width, surface area, orientation) using basic OpenCV functions, and tracked using a custom algorithm based on Kalman-Filtering  (Patel & Thakore, 2013). Tracking objects allows us to calculate morphometric, velocity and trajectory metrics for each segmented object over the entire video (Figure  4d-g ).
Classifying objects by assigning them a species name is operated by sending each object's images into a CNN based on MobilenetV2 and pretrained on ImageNet  (Deng et al. , 2009; Howard et al. , 2017). This CNN was fine-tuned using automatically generated training protist image datasets that we obtained by recording videos of single species and using the same segmentation/tracking methodology described above. Classifying all objects in all frames allows the collection of multiple classification data: one per frame for each single object, increasing classification accuracy by looking at the classification time series of each object (Figure  4f ). Scandinavia, this system helps to understand effects of landscape composition and competition on pollination. In the UK, the system is being extended to monitor pest control in crop fields, showing clear potential to monitor ecological processes such as herbivory, predation and detritivory at hitherto impossible sites and resolutions.
e-butte rfly. org/), the Global Biodiversity Information Diversity (https:// www. gbif. org/), iNaturalist (https://www. inatu ralist. org/), Pl@ntNet (https://plant net. org/) and Zooniverse (https://www. zooni verse. org/). However, a major challenge is the generalisation of such automated solutions to a wide diversity of natural ecosystems. Specifically, even with an exceptionally large training dataset, the system will encounter unfamiliar species, including some that are inseparable within high-resolution imagery. Three emerging approaches will help this challenge to be overcome: (1) open-set classification can allow specimens, even those that are not present in training data, to be classified to the lowest possible taxonomic level  (Lee et al. , 2018) ; (2) synthetic image datasets can be generated using images of specimens with validated species-level identification  (Skovsen et al.
Towards the fully automated monitoring of any community in any ecosystem The previous examples of fully automated monitoring of multidimensional data from multispecies systems pose the question of whether such frameworks could be developed for almost any other ecosystem. A first limitation is obviously the requirement for large and properly labelled training datasets when implementing accurate and reliable deep learning classifiers, preventing the monitoring of communities for which such data is not available  (McKibben & Frey, 2021). Second, the environmental complexity of natural systems could hamper such designs, generating data with a very low signal-to-noise ratio in comparison with experimental systems. Nevertheless, initiatives aiming at capturing and monitoring wildlife habitat complexity do exist and represent great research avenues to combine automated wildlife monitoring and habitat mapping within complex environments.
System presentation The system collects non-invasive, high-resolution data on flowers and pollinating insects. High-volume acquisition of images to train CNNs is achieved using a camera, mounted on a steel frame, with a power supply and a memory storage unit. By incorporating computers into on-site hardware, CNNs can then detect, classify and track insects and flowers in real-time. The system allows rigorous monitoring of abundance, diversity and phenology of plants and pollinators. By automatically generating entomological data at unprecedented spatial and temporal resolutions, real-time tracking can revolutionise our understanding of not only plant-pollinator interactions, but pollinator-pollinator interactions. 2. Automated image acquisition Affordable webcams (e. g. Logitech C920 HD Pro Webcam ~$60) and wildlife time-lapse cameras (e.
Such opportunities to quantify fine-scale pollinatorpollinator interactions are particularly relevant given mounting concerns about the impacts of managed honeybees on wild pollinators  (Ropars et al. , 2022; Thomson, 2016). Collection of sufficient real-time data will even allow individual tracks to be examined in relation to the presence and absence of other individuals or species. In this way, we may begin to grasp the behavioural mechanisms behind competitive exclusion as never before. individually annotated and classified at the species level  (Naughton et al. , 2015). By combining this workflow and the vast amount of labelled 3D coral structures it has generated with approaches aiming at automatically classifying 3D objects such as MeshCNN  (Hanocka et al. , 2019)  and Global point Signature Plus & Deep Wide Residual Network  (Hoang et al.
Funding information Danmarks Frie Forskningsfond , Grant/ Award Number:  8021-00423B ;  Engineering and Physical Sciences Research Council , Grant/Award Number:  BB/W013959/1  and  EP/N510129/1 ;  Natural Environment Research Council , Grant/Award Number:  NE/T003502/1 ,  NE/T006579/1  and  NE/ S01537X/1 ;  Royal Society , Grant/Award Number:  UF160357 ;  Royal Society , Grant/ Award Number: RGS\R2\192033 MB is supported by  NE/T003502Council (NERC)  grant. CC is supported by grants  NE/T006579/1  and  NE/T003502/1  from  NERC , and  RGS\R2\192033  from  The Royal Society. T. E. G. is supported by a  Royal Society University   Research Fellowship  grant  UF160357 , a  Turing Fellowship  from  The Alan Turing Institute  under the  EPSRC grant   EP/ N510129/1  and  BrisEngBio , a  UKRI-funded Engineering Biology Research Centre  under grant  BB/W013959/1.
Others are based on linear programming and optimization processes  [18] , or on artificial intelligence with neural networks and remote sensing data  [19]  or even the Internet of Things and machine learning  [20]. Actually, very few take into account the farmer's decision-making process as proposed by  [21, 22]. Ref. [23]  represents an interesting attempt, but is mainly focused on channel irrigation that is not the only water resource for irrigation. Our paper presents NIRAVARI, a simple and parsimonious biodecisional model, able to test policy schemes regarding pond, borewells and irrigation equipment. The paper mainly focuses on the farmer's decision-making part of the model, and the complexity of dealing with different water resources to irrigate their farms. The initial ideas of the model were initiated with some policy-makers during a workshop held in Bangalore in March 2019.
Instead of using complex modelling languages, we chose a wide-spread language (VBA), allowing for object-oriented modelling and programming. Each element of our farm system is then modeled as an object, and a multiplicity of objects (such as beles or crops) are dealt with as structured containers. The use of Excel allows users to manage simple graphic user interfaces with validation processes in order to avoid any false input to the model parameters. The scenarios tested show that rainfed systems can maintain a balance in the water table, but the income from these systems is very low and highly variable due to climate variability. However, adding a pond to this system reduces the vulnerability of these farms to climate variability while preserving the water table. On the other hand, a 100% irrigated system with high-added value crops leads to a significant improvement in income, but a drastic decrease in the water table.
Network traffic analysis includes network traffic classification, network traffic prediction using time-series data, and intrusion detection systems. Understanding network behavior can improve performance, security, quality of service (QoS) and help avoid violation of SLA (service-level agreements). For example, load traffic prediction plays an important role in network management, such as short-and long-term resource allocation (e. g. , bandwidth), anomaly detection, and traffic routing. To achieve the intelligent management of networks and services in the softwaredefined networking (SDN) environment, ML methods can be used. A centralized SDN controller has a global network view which facilitates the collection of the network traffic. Several researchers argue that, with the introduction of SDN, there is a high potential for collecting data from forwarding devices, which need to be handled by ML due to their complexity  [2, 3].
Related Work Driven by the recent advances in ML/DL in network traffic management, many studies have been conducted to survey the works on this domain. For example, Xie et al. [4]  have provided a comprehensive survey of ML techniques applied to SDN. Different types of ML/DL algorithms and their applications in the SDN environment are presented. Nevertheless, they did not review the traffic prediction application. Additionally, the paper lacks several recent approaches as well as a dataset in the literature review. Latah and Toker  [5]  briefly reviewed the application of AI with SDN. Then, in  [6] , they presented an extensive overview of AI techniques that have been used in the context of SDN. Additionally, Zhao et al. [7]  surveyed ML/DL algorithms and their applications in the SDN environment.