dataesr/scientific-paragraphs-categorization huggingface dataset:
The last national survey of agricultural was published in 2000  (RNA, 2000)  and has not been updated since. To gather data on pastoralism at this meso-scale, we designed a protocol in partnership with the different rural councils to co-produce information on campsites and pastoral activities. The research team designed a succinct digital questionnaire administered by trained interviewers from ISRA. Surveys were conducted by a team of 11 trained interviewers, alongside 11 knowledgeable focal points appointed by the municipalities. Over a 2-month period, the questionnaire was administrated in all municipalities and at each campsite encountered, identified by the focal point and by snowballing sampling. The rapid appraisal of campsites was done through a light questionnaire recording the location of camps, number of animals (cattle, sheep, goats), the human population (men, women, children), annual and seasonal displacements, as well as any conflicts encountered.
Data analysis To further analyze the data, we also modeled the information about displacements in a spatialized network. In the database, displacements originate from a specific (geolocalized) camp to a path including one or more Senegalese departments. In the model, we aggregated the information about camps according to their corresponding municipalities to reduce the noise in the network and provide a clearer representation of displacement paths and flow hubs across the entire country. This defined a network where each node represents either a department or a municipality, and each directed edge (a,b) between two nodes represents the presence of one or more displacements from location a to location b. Each edge is weighted according to the number of displacements between the two locations.
Acknowledging current livestock footprint The Delta's land information system, developed by SAED and its technical partners, is unique source of information on land allocation and a knowledge base for monitoring the dynamic of irrigated agriculture. Fig. 3  maps the three different inputs gathered through data collection. It overlays data from the SAED's information system and from the digitized tracks using Google Earth, as well as the location of campsites obtained through fieldwork. One initial observation concerns the spatial extent of SAED's geodata as seen in Fig. 3. This valuable information was assembled in the era of funding from PACR/VFS and then the Millennium Challenge Account (MCA) and covers more than 56,000 ha of agricultural plots. However, it remains limited by SAED's decades of control with its mandate to manage irrigated agriculture.
Acknowledging current trajectories at a territorial scale For more than 50 years, the Senegal River delta and Guiers Lake areas have seen constant growth in hydro-agricultural developments. Propelled by the incentives of various mechanisms facilitating investments, around 25,500 ha of land have been irrigated between 2000 and 2015, almost 11,000 ha of which were developed by foreign agroindustries  (Bourgoin et al. , 2016). In 2019, eleven agro-industries were operating farms that averaged 600 ha. One of these agribusiness, present since early 1970, accounts for more than 14,000 ha on its own  (Bourgoin et al. , 2019). This trajectory for rural development is the result of the territorialization of agricultural programs promoting the development of intensive agriculture in Senegal (e. g. LOASP, 2004; PRACAS, 2014) , and expected spillover effects from agribusinesses adherence to the rationale of mainstream economics  (Pimbert, 2018).
Acknowledgments This project received multiple support: from  Acting for Life and DFID  thought the extension of the  BRACED  project in Senegal; from  AFD  through their support of the scientific program of SAED-UGB, that associated ISRA and CIRAD; from ISRA BAME that contributed to technical and financial means to conduct the surveys; from the  Land Matrix Initiative  and the  Senegal Land Governance Observatory  to access Large-Scale Land Allocation-related data in Senegal.
The Bloom filter, answering whether an item is in a set, has achieved great success in various fields, including networking, databases, and bioinformatics. However, the Bloom filter has two main shortcomings: no support of item deletion and no support of expansion. Existing solutions either support deletion at the cost of using additional memory, or support expansion at the cost of increasing the false positive rate and decreasing the query speed. Unlike existing solutions, we propose the Elastic Bloom filter (EBF) to address the two shortcomings simultaneously. Importantly, when EBF expands, the false positives decrease. Our key technique is Elastic Fingerprints, which dynamically absorb and release bits during compression and expansion. To support deletion, EBF can first delete the corresponding fingerprint and then update the corresponding bit in the Bloom filter.
The key technique of the EBF is called Elastic Fingerprints. EBF consists of a standard Bloom filter and an elastic fingerprint array. To expand the Bloom filter, we first cut one bit from each fingerprint, and appropriately combine the Bloom filter and the cut bits into a larger Bloom filter. To compress the Bloom filter, some bits of the Bloom filter are to be lost, while we append these lost bits to the elastic fingerprints. In other words, EBF dynamically moves bits between the fingerprint array and the Bloom filter during expansion and compression. For deletion, we first delete fingerprints from the fingerprint array, and then determine which bits in the Bloom filter should be cleared. Further, we propose two optimization methods namely lazy update and bucket tree in Section 3. 3. We have open-sourced all code at GitHub [2].
Datasets The following datasets are used in our experiments. • CAIDA: As many papers  [4] ,  [5]  do, we use anonymized IP trace streams from CAIDA  [3] , and then identify each flow of IP trace streams by the five-tuples. • Distinct Stream: we use Distinct Stream, which consists of random distinct elements, to test the worst case performance of each algotirhm. • IMC Data Center IP Trace: We use IP trace streams collected by  [6]  to measure the performance of different kinds of Bloom filters in data centers. Each flow of IP trace streams is identified by the five-tuples.
Since the query of EBF only needs to query one Bloom filter in the fast memory, its query speed is much faster than the other two. As for the deletion speed, we did not compare that of SBF because it does not support deletion operation. The deletion speed of EBF is faster when the relative peak data size is larger than 16. Overall processing speed comparison. EBF vs. DBF vs. SBF (Fig. 6 ): This experiment shows that our EBF is the best queryoriented data structure. Using real traces (CAIDA), we compare the three kinds of Bloom filters' overall performance, i. e. , the speed when the insertions, expansions, and queries are mixed up. We examine the MOPS with the changing ratio of insertion, positive query (i. e. , to query the items which has been inserted) and random query (i. e. , to query random items, most of which hasn't been inserted).
Design Choice of Slow Memory Usage Slow memory consumption. EBF vs. CBF (Fig. 8a ): A question is why we do not use a large CBF instead of buckets/fingerprints in the slow memory, aiming to help the BF expand. This experiment (Fig. 8a ) shows that EBF uses less memory mostly, but CBF make a better use of memory when its size is nearly the maximum size. In this experiment, the BF in the fast memory is 2 12 bits. We allocate 2 22 counters of slow memory for CBF so that CBF can help the BF expand its size to 2 22. We use CAIDA as the dataset. As shown in Fig. 8a , the memory used by EBF varies with the increasing times of expansion, while the memory used by CBF is fixed when it is initialized. Time cost of expanding operations. Lazy update vs. Normal expansion vs. Build EBF (Fig. 8b ): This experiment shows the time cost of 3 kinds of operations to expand the EBF.
CONCLUSION When Bloom filters are used for dynamic sets, deletion and expansion should be supported. In this paper, we propose the Elastic Bloom filter to support item deletion and expansion at the same time without sacrificing the query efficiency. We propose the Elastic Bloom filter which supports item deletion and expansion at the same time. Our key technique is Elastic Fingerprints. Elastic Fingerprints dynamically absorb and release bits during compression and expansion. Mathematically analysis and experimental results show that the Elastic Bloom filter keeps the advantages of the Bloom filter and is suitable to deal with dynamic sets. The experimental results show that the Elastic Bloom filter can outperform the state-of-the-art in terms of query speed and false positives. We have open-sourced all related source code at GitHub [2].
From a database of about 300 foraging trajectories derived from GPS data deployed simultaneously with pressure sensors for the identification of dives, this work has benchmarked deep neural network architectures trained in a supervised manner for the prediction of dives from trajectory data. It first confirms that deep learning allows better dive prediction than usual methods such as Hidden Markov Models. It also demonstrates the generalization properties of the trained networks for inferring dives distribution for seabirds from other colonies and ecosystems. In particular, convolutional networks trained on Peruvian boobies from a specific colony show great ability to predict dives of boobies from other colonies and from distinct ecosystems. We further investigate accross-species generalization using a transfer learning strategy known as 'fine-tuning'.
Assessing consistency or shifts in foraging locations  [5] [6] [7] , and in the resource spatial partitioning  [8, 9]  provide indeed crucial information for understanding marine ecosystems. This has been particularly enabled by great technical advances in the miniaturization and autonomy of biologging devices  [10, 11]. GPS loggers have been at the forefront of this breakthrough, and can now provide precise and accurate data on the foraging trajectories of many free-ranging species, such as seabirds  [12, 13]. Detailed information on the diving behaviour has also been gained through the additional use of pressure sensors, such as Time Depth Recorders (TDR) devices  [14] [15] [16]. Yet, for historical, financial and ethical reasons, the deployment of several sensors has not always been possible and a substantial amount of tracking datasets consist in GPS tracks only.
More occasionally, supervised machine learning approaches such as artificial neural research and innovation program -grant agreement No. 817578), and to the Young Team IRD Programm (JEAI) for TABASCO project. RF was supported by LEFE program (LEFE MANU project IA-OAC), CNES (grant SWOT-DIEGO) and ANR Projects Melody and OceaniX. Fieldworks have been conducted thanks to the cooperative agreement between IRD, the Agence Nationale de la Recherche (ANR) project TOPINEME, and of the International Joint Laboratory LMI-DISCOH (to SLB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. networks, support vector machines and random forests have also been used  [30, 31]. We may refer the reader to  [32]  for a more detailed review of these methods.
In particular, a central challenge in deep learning is to make algorithms that will not only perform well on the training data, but also on new datasets  [42]. Generalization properties are indeed crucial for deep networks to tackle a wide range of problems. For example, it would be relevant to develop a neural network for the segmentation of behavioral patterns of certain species and whose characteristics are transferable to the analysis of the behavior of another species. Transfer learning refers to the fact of using knowledge that was gained from solving one problem and applying it to a new but related problem. For this purpose, a solution known as 'fine-tuning' consists in using a pre-trained model as the initialization of the training scheme rather than training a new model from scratch  [43].
As in  [17] , this work addresses the inference of seabird diving behaviour from GPS data using Deep Learning methods. Besides, their FCNet architecture, we investigated Convolutional Neural Networks and U-Networks  [44] , which are state-of-the-art architectures for time series and image data processing and shall better account for the time structure of trajectory data. As case-studies, we considered two tropical seabird genus with distinct diving behaviour (Boobies vs Cormorants). The associated datasets comprised 297 foraging trips derived from GPS data deployed simultaneously with pressure sensors for the identification of dives. Our specific objectives were therefore (a) to confirm the performance of deep networks over stateof-the-art tools for dives identification, (b) to demonstrate generalization properties of trained network to predict dives of seabirds from other colonies and (c) to evaluate the benefits of a transfer learning strategy known as 'fine-tuning' for accross-species generalization.
Dataset GPS and TDR devices were jointly fitted to breeding tropical seabirds both in Peru (92 Peruvian boobies, 106 Guanay cormorants) and Brazil (37 masked boobies). Peruvian boobies (Sula Variegata) and Guanay cormorants (Leucocarbo Bougainvilli) were captured at Isla Pescadores (11˚46'30. 34"S, 77˚'51. 22"W) every year in December from 2008 to 2013 and at Isla Guanuape (8˚'18. 92"S, 78˚'42. 72"W) in December 2007, while masked boobies (Sula dactylatra) were captured at Fernando de Noronha archipelago (3˚'9. 71"S, 32˚'36. 11"W) every year in April from 2017 to 2019. The GPS were attached with Tesa tape on the tail feathers for boobies and on the back feathers for cormorants for 1 to 2 days and the TDR were fixed on the bird's leg with a metal band. In total, GPS devices (Gipsy GPS, 25-30 g, Technosmart, Rome, Italy; i-gotU GPS GT 600, 25-30 g, Mobile action Technology, NewTaipei City, Taiwan; MiniGPSlog 30 g, Earth and Ocean GPS, Kiel, Germany; Axy-trek 14g, Technosmart, Rome, Italy) and time-depth recorders (TDRs, 3 g; resolution, 4 cm; G5 CEFAS Technology, Lowesoft, UK) were fitted to 235 seabirds.
After recovery, each GPS track was split into foraging trips by selecting locations further than a given distance to the colony and longer than a given time. Foraging trips were linearly interpolated to the TDR sampling resolution (i. e. 1s) and the coverage ratio was computed as in  [17]. It is defined as the ratio between the number of recorded fixes and the number of fixes that should have been recorded with a perfectly regular sampling in a fixed temporal window. Amount of missing data is detailed in Table  1. True dives were defined by depth measured by TDR higher than 2 meters. Each GPS position was thus associated with a boolean value detailing the 'dive' status. This dataset consists therefore in a total of 297 foraging trips of seabirds with doubled-deployment GPS and TDR (see Table  1 ).
s-1 associated to non-diving behaviour. https://doi. org/10. 1371/journal. pcbi. 1009890. t001 Fully-connected network (FCNet). The first architecture implemented was similar to the fully-connected network presented by  [17]. As input vector, we used the concatenation of step speed, turning angle and coverage time series for over a 20-second window. This input vector is fed to a layer of 100 nodes followed by 3 layers of 500 nodes. Each node applies a linear transformation to the incoming data and a non-linear activation chosen as a Rectified Linear Unit (ReLU -RELU(x) = max(0, x)). The last layer applied a softmax binary function so that the output of the vector is a time series of values between 0 and 1, which can be interpreted as binary classification probabilities. This architecture is a classical example of a so-called multilayer perceptron, with a rectified linear activation which is the default activation in deep learning architectures.
In the reported experiments, the weight was empirically set to 5 for cormorants datasets and 30 for boobies dataset. The minimisation of the training loss exploits the Adam stochastic optimizer  [47]. A fixed learning rate of 0. 001 was used for all training procedures. Networks were evaluated on training and validation datasets every epoch (defined as one pass through the entire train dataset). We consider an early-stopping criterion such that the training procedure was stopped as soon as the validation loss started increasing. Overall, given a trajectory the diving probability at a given location was assessed by computing the mean probability of all predictions derived from all 20 positions windows. These models were implemented, trained and tested with python using pytorch library  [45].
Benchmarked methods Two classical methods for dive prediction First-Passage Time (FPT), and Hidden Markov Models (HMM) were evaluated for intercomparison purposes. FPT was computed following  [48] , by selecting the radius that maximizes the variance of passage times. Time passage values were converted into a probability of dives with min-max normalization. Regarding HMMs, we applied the momentuHMM R package  [28]. We implemented HMMs with 3 (resp. 4) behavioural modes for boobies (resp. cormorants) associated to traveling, searching, diving and resting behaviours. This approach represents trajectories as a sequence of steps and angles. It models steps as random variables following a gamma marginal distribution and angle following a von mises marginal distribution. We may point out that the HMMs directly provide as outputs a likelihood value of the diving behaviour.
Evaluation scheme We describe below the evaluation scheme we implemented to assess the performance of the proposed neural network approaches. We first focus on the benchmarking of the performance of the considered approaches in terms of dive prediction accuracy for different data input. For the proposed neural network architectures, we further analyze their generalization properties. The methodological framework is exposed in Fig 2. (a) Network training. We assessed the dive prediction performance of the 5 benchmarked methods (FPT, HMM, FCNet, CNNet and UNet) considering trajectory data derived from the two dataset from Pescadores Island (see Table  1 ). To test for the effect of temporal resolution, the two datasets have been downsampled every 5, 15 and 30s. When downsampling, temporal windows containing at least one dive were classified as dives.
Each dataset were then splitted into training, validation and test datasets with respective size of 50%, 30% and 20%. Deep networks were trained and selected based on the training and validation datasets. All approaches were finally compared on the testing dataset. Overall, this led to the quantitative comparison of the performance of 5 models on 6 datasets all listed in Table  2. As evaluation metrics for dive prediction, we evaluated the receiver operating characteristics curve (ROC) which describes the performance of a binary classifier. It consists in plotting the true positive rate (i. e. true predicted dives) against the false positive rate (i. e. false predicted dives). We obtain this curve by varying the probability threshold defining dive/no dive behaviours. Moreover, we evaluate the area under the curve (AUC) as well as the binary cross entropy (BCE) for the test datasets.
Regarding the AUC, it was estimated by integrating the ROC curve along the x axis using the composite trapezoidal rule. For neural network approaches, we also analyzed the value of the training loss for the training and test datasets. (b) Generalization performance of pre-trained networks. In this section, we evaluated the generalization performance through the application of models trained on Pescadores dataset to data that have not been used during the training process. In particular, we evaluate previously fitted deep networks performance on the two datasets from Guañape Island and from Fernando de Noronha Archipelago, composed of Peruvian boobies and masked boobies trajectories, respectively. In this experiment, we compared the dive prediction performance of FPT and HMM methods to the best FCNet, CNNet and UNet models.
Beyond AUC and BCE performance metrics, we also evaluated the relevance of the estimated maps of dive distributions. The later were computed using a weighted Kernel Density Estimator (KDE) using dive probabilities as weighing factor. As groundtruth, we considered the map of dive distributions estimated from true dive locations defined by TDR data. Kernel densities were estimated using a 0. 01×0. 01˚grid and a bandwith of 0. 25˚. From these maps, we evaluated an means square error (MSE) as an integrated performance metrics for the different approaches  [49]. (c) Network fine-tuning. We evaluated the benefits of fine-tuning for predicting dives of the 15s-resampled Brazilian dataset (Table  1 ) based on the deep networks fitted on the dataset from Pescadores. The Brazilian dataset was therefore split into training, validation and test datasets with respective size of 50%, 30% and 20%.
Results We detail below the numerical experiments performed in this study to assess the relevance of the proposed neural network approaches to predict dive behaviour of boobies and cormorants from trajectory data. (a) Network training. On the Pescadores Island dataset used for network training, we reported a contrasted performance of the different methods, with AUC going from 0. 61 to 0. 96 (see Table  2 ), which corresponds in the best cases to correct prediction rates of diving and non-diving behaviour of approximately 95% and of 60% in the worst cases (see  Fig 3). Overall, all methods performed better at predicting the dives of boobies than those of cormorants. The UNet obtained systematically the best prediction performance with averaged AUC of 0. 93 (resp. 0. 90) for boobies and cormorants respectively.
The CNNet also achieved very good predictions, consistently performing at least as well as state-of-the-art tools with averaged AUC of 0. 9 (resp. 0. 85). The lowest performance was reported for the FPT approach, which never predicted dives with AUC higher than 0. 73. The HMM obtained relatively good performance on the boobies dataset with AUC indices around 0. 85, yet it did not get AUC higher than 0. 76 on the cormorants dataset. It also obtained the highest BCE, approximately 2 to 10 times higher than the UNet. Regarding the FCNet, the AUC index ranged from 0. 65 to 0. 89, showing a much greater variability than for CNNet and UNet architectures. The best neural network predictions for Pescadores dataset are illustrated in Fig 4. Interestingly, deep networks were relatively sensitive to the sampling resolution, whereas it did not affect much the performance of the FPT and HMM approaches.
For both species, the higher the resolution, the better the performance for UNets, CNNets and FCNets. For instance, we reported a mean AUC of 0. 92 for a 5s resolution vs. 0. 81 for a 30s resolution. This was particularly true for the CNNet and mostly for the FCNet which did not performed better than HMM on the 30s-resoluted datasets, whereas they were able to outperform state-of-the-art approaches on the 5s-resoluted datasets. (b) Generalization performance of pre-trained networks. Overall, all networks trained with data from Pescadores reported a AUC performance higher than 0. 78 (resp. 0. 56) when applied to Guañape (resp. Fernando de Noronha) dataset (Fig  5 ). AUC performance averaged 0. 85 when using deep networks trained with the boobies dataset and 0. 72 with cormorants data (see Table  3 ).