dataesr/scientific-paragraphs-categorization huggingface dataset:
Communication Overhead Our last set of experiments looks at inter-node communication by measuring the amount of data transmitted during the shuffle phase (Figure  8 ). The goal is to compare these measurements with the theoretical analysis in Section 4. 3, Impact of data size. For Geo dataset (Figure  8a ), H-BNLJ has indeed a lot of communication. For a dataset of 1 * 10 5 records, the shuffle phase transmits almost 4 times the original size. Both RankReduce and H-zkNNJ have a constant factor of 1 because of the duplication of the original dataset to improve the recall. The most efficient algorithm is PGBJ for two reasons. First it does not duplicate the original dataset and second, it relies on various grouping strategies to minimize replication. Impact of k. We have performed another set of experiments, with a fixed dataset of 2 * 10 5 records (only 0.
Image Feature Descriptors (SURF) dataset We now investigate whether the dimension of input data has an impact on the kNN algorithms using the SURF dataset. We used the Euclidian distance between descriptors to measure image similarity. For all experiments in this section, the parameters mentioned in Table  3    Results of experiments when varying the number of descriptors are shown in Figure  9  using a log scale on the x-axis. We omitted H-BkNNJ as it could not process the data in reasonable time. In Figure  9a , we can see that the execution time of the algorithms follows globally the same trend as with the Geo dataset, except for PGBJ. It is a computational intensive algorithm because the replication process implies calculating a lot of Euclidian distances. When in dimension 128, this part tends to dominate the overall computation time.
Regarding disk usage (Figure  9b ), H-zkNNJ is very high because we had to increase the number of shifted copies from 3 to 5 to improve the recall. Indeed, compared to the Geo dataset, it is very low (Figure  9c ). Moreover, as the number of descriptors increases, H-zkNNJ goes from 30% to 15% recall. As explained before, the precision was found to be equal to the recall, which means the algorithm always returned k results. This, together with the improvement using more shifts, proves that the space filling curves using in H-zkNNJ are less efficient with high dimension data.
Communication Overhead With the SURF dataset, we get a very different behavior than with the Geo dataset. The shuffle phase of PGBJ is very costly (Figure  11a ). This is an indication of large replications incurred by the large dimension of the data and a poor choice of pivots. When they are too close to each other, entire cells have to be replicated during the grouping phase. For RankReduce the shuffle is decreased but stay important, essentially because of the replication factor of 5. Finally, the shifts of original data in H-zkNNJ lead to a large communication overhead. Considering now k, we have the same behavior we observed with the Geo dataset. The only difference is PGBJ which now exhibits a large communication overhead (Figure  11b ). This is again because of the choice of pivots and the grouping of the cells.
Impact of Dimension and Dataset We now analyze the behavior of these algorithms according to the dimension of data. Since some algorithms are dataset dependent (i. e the spatial distribution of data has an impact on the outcome), we need to separate data distribution from the dimension. Hence, we use two different kinds of datasets for these experiments. First, we use real world data of various dimensions 10. Second, we have built specific datasets by generating uniformly distributed data to limit the impact of clustering. All the experiments were performed using 0. 5 * 10 5 records and k = 20. Since H-BNLJ relies on the dot product, it is not dataset dependent and its execution time increases with the dimension as seen on Figures  12a  and 13a. PGBJ is heavily dependent on data distribution and on the choice of pivots to build clusters of equivalent size which improves parallelism.
The comparison of execution times for the datasets 128-sift and 281-blog in Figure  12a  shows  one would choose a number of tasks that is a multiple of the number of processing units. The issue with this strategy is that the distribution of the partitions might be unbalanced. Figure  14  shows an experiment with 6 partitions and 6 2 = 36 tasks, each executed on a reducer. Some reducers will have more elements to process than others, slowing the computation. Overall, the challenge with this algorithm is to find the optimal number of partitions for a given dataset.
PGBJ A difficulty in PGBJ comes from its sampling-based preprocessing techniques because it impacts the partitioning and thus the load balancing. This raises many challenges. First, how to choose the pivots from the initial dataset. The three techniques proposed by the authors, farthest, k-means and random, lead to different pivots and different partitions and possibly different executions. We found that with our datasets, both k-means and random techniques gave the best performance. Second, the number of pivots is also important because it will impact the number of partitions. A too small or too large number of pivots will decrease performance. Finally, another important parameter is the grouping strategy used (Section 4. 1). In Figure  15 , we can see 0. with Geo dataset, 3000 pivots, KMeans Sampling that the greedy grouping technique has a higher grouping time (bars) than the geo grouping technique.
H-zkNNJ In H-zkNNJ, the z-value transformation leads to information loss. The recall of this algorithm is influenced by the nature, the dimension and the size of the input data. More specifically, this algorithm becomes biased if the distance between initial data is very scattered, and the more input data or the higher the dimension, the more difficult it is to draw the space filling curve. To improve the recall, the authors propose to create duplicates in the original dataset by shifting data. This greatly increases the amount of data to process and has a significant impact on the execution time.
RankReduce RankReduce, with the addition of a third job, can have the best performance of all, provided that it is started with the optimal parameters. The most important ones are W , the size of each bucket, L, the number of hash families and M , the number of hash functions in each family. Since they are dependent on the dataset, experiments are needed to precisely tune them. In  [38] , the authors suggests this can be achieved with a sample dataset and a theoretical model. The first important metric to consider is the number of candidates available in each bucket. Indeed, with some poorly chosen parameter values, it is possible to have less than k elements in each bucket, making it impossible to have enough elements at the end of the computation (there are less than k neighbors in the result).
On the opposite, having too many candidates in each bucket will increase too much the execution time. To illustrate the complexity of the parameter tuning operation, we have run experiments on the Geo and SURF datasets. First, Figure  17  shows that, for the Geo dataset, increasing W improves the recall and the precision at the expense of the execution time, up to an optimal before decreasing. This can be explained by looking at the number of buckets for a given W. As W increases, each bucket contains more elements and thus their number decreases. As a consequence, the probability to have the correct k neighbors inside a bucket increases, which improves the recall. However, the computational load of each bucket also increases. A similar pattern can be observed with the SURF dataset (Figure  18 , left), where increasing W improves the recall (from 5% to 35%) and the precision (from 22% to 35%).
Increasing the number of families L greatly improves both the precision and recall. However, increasing M , the number of hash functions, decreases the number of collisions, reducing execution time but also the recall and precision. Overall, finding the optimal parameters for the LSH part is complex and has to be done for every dataset After finishing all the experiments, we found that the execution time of all algorithms mostly follows the theoretical analysis presented in Section 4. However, as expected, the computationally intensive part, which could not be expressed analytically, has proved to be very sensitive to a lot of different factors. The dataset itself, through its dimension and the data distribution, but also the parameters of some of the pre-processing steps. The magnitude of this sensitivity and its impact on metrics such as recall and precision could not have been inferred without thorough experiments.
CONCLUSION In this paper, we have studied existing solutions to perform the kNN operation in the context of MapReduce. We have first approached this problem from a workflow point of view. We have pointed out that all solutions follow three main steps to compute kNN over MapReduce, namely preprocessing of data, partitioning and actual computation. We have listed and explained the different algorithms which could be chosen for each step, and developed their pros and cons, in terms of load balancing, accuracy of results, and overall complexity. In a second part, we have performed extensive experiments to compare the performance, disk usage and accuracy of all these algorithms in the same environment. We have mainly used two real datasets, a geographic coordinates one (2 dimensions) and an image based one (SURF descriptors, 128 dimensions).
ACKNOWLEDGMENTS This work was partly funded by the  French Government (National Research Agency, ANR)  through the  Investments for the Future Program reference # ANR-11-LABX-0031-01. Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by  INRIA  and including  CNRS ,  RENATER  and several Universities as well as other organizations (see https://www. grid5000. fr).
Depending on the use case, it might not be necessary to find the exact solution of a kNN query, and that is why approximate kNN queries have been introduced. The idea is to have the k th approximate neighbor not far from the k th exact one, as shown in the following definition1. Definition 2. The kNN join of two datasets R and S, kNN(R S) is: kNN(R S)={(r,kNN(r,S)), ∀ r ∈ R} Definition 3. The (1 + )-approximate kNN query for a query point r in a dataset S, AkN N (r, S) is a set of approximate k nearest neighbors of r from S, if the k th furthest result.
10. archive. ics. uci. edu/ml/datasets. html H-zkNNJ RankReduce PGBJ H-BNLJ H-zkNNJ RankReduce PGBJ H-BNLJ H-zkNNJ RankReduce recall precision 150 8 0. 8 Time (min) 30 60 90 120 Space requirement 2 4 6 0. 2 0. 4 0. 6 Result size (GB) Recall & Precision 0. 1 0. 3 0. 5 0. 12 0. 25 0. 5 Number of descriptors (*10 5 ) 1 2 4 8 0. 12 0. 25 0. 5 Number of descriptors (*10 5 ) 1 2 4 8 0. 12 0. 25 Number of descriptors (*10 5 ) 0. 5 1 (a) Time (b) Result size and Disk Usage (c) Recall and Precision Fig. 9: Surf, impact of the dataset size 120 H-zkNNJ RankReduce PGBJ H-BNLJ 20 H-zkNNJ RankReduce PGBJ H-BNLJ 1. 6 1. 0 H-zkNNJ RankReduce recall precision Time (min) 30 60 90 Space requirement 2 5 10 15 0. 4 0. 8 1. 2 result size (GB) Recall & Precision 0. 2 0. 4 0. 6 0. 8 2 0 8 32 64 128 256 512 k 2 8 32 64 128 256 512 k 2 8 32 64 128 256 512 k (a) Time (b) Result size and Disk Usage (c) Recall and Precision Fig.
10: Surf dataset with 50k records, impact of K, 4 H-zkNNJ RankReduce PGBJ H-BNLJ 2. 5 9 H-zkNNJ RankReduce PGBJ H-BNLJ Communication Overhead ratio 1 2 3 0. 5 1. 5 |R|+|S| (GB) Communication overhead ratio 3 6 0. 12 0. 25 0. 5 1 Number of descriptors (*10 5 ) 2 4 8 0 2 8 k 32 64 128 256 512 (a) Surf, impact of the dataset size (b) Surf dataset with 50k records, impact of K,.
Here we develop the method, originally presented in  Bird (2016) , to show how the likelihood may be written in a compact mathematical form as a matrix product. This allows for efficient calculation of the posterior distribution from which, for example, the covariance of the inferred parameters can be analyzed. Our complete Bayesian method may be thought of as combining the method for inferring quantal parameters for isolated PSPs developed by  Bhumbra and Beato (2013)  with the method for inferring mean synaptic dynamics (without including correlations) developed by  Costa et al. (2013). As well as describing the mathematical solution we additionally provide the software code to perform Bayesian inference for a variety of models of synaptic dynamics as part of this publication.
Convergence of the sampler was examined by comparing the distributions resulting from chains initiated in different locations. It is straightforward to extend this transparent implementation in our code to include more sophisticated methods such as slice sampling. We provide MATLAB and JULIA code for the Bayesian inference of synaptic parameters from measurements of synaptic amplitudes using the Metropolis-Hastings sampling method  (Metropolis et al. , 1953; Hastings, 1970)  described above as part of the Supplementary Material. The code covers the major synaptic dynamics models including: depression only, depression-facilitation, release-independent depression and frequency-dependent recovery. The models are described in the Appendix.
The mini PSPs induced by different vesicles are assumed to be independently identically distributed. The mean quantal amplitude is µ a and the standard deviation is σ a. In addition there is a normally-distributed background noise with standard deviation σ b that is uncorrelated with EPSP amplitude. For illustrative purposes we focus on a model of synaptic dynamics that features depression and facilitation  (Tsodyks and Markram, 1997; Fuhrmann et al. , 2002) , though other models for which computer code is also provided are described in Appendix A. Activity reduces synaptic efficacy through vesicle depletion; however, the build-up of Ca 2+ in a presynaptic terminal means that the probability of release given that a vesicle is present u is increased by presynaptic activity. Thus, the response to sustained activity can involve larger individual PSPs than the response to Probability that an empty release site is restocked isolated spikes.
This is equally true of the correlated Bayesian method. Estimates of σ a could be improved by a very high stimulation rate that typically causes either 0 or 1 vesicles to release with each spike. Note that with typical delays between sweeps of 15 s, collecting this dataset required just over a minute of experimental time, giving a relatively sparse dataset that nevertheless still allows good estimates of the underlying synaptic parameters. A major advantage of the Bayesian method over a maximum likelihood approach is that it can recover the full distribution of parameters. This allows determination of the covariances between different parameters. Figure  2  plots the joint posterior distributions of certain pairs of parameters (in total there are 28 possible pairs for the synaptic-dynamics model considered here).
Adenosine (100 µM) was then bath-applied to the slice (see Methods) and the stimulus protocol repeated. Figure  3A  plots individual postsynaptic voltage traces before and after the application of adenosine; Figure  3B  shows the change in average EPSP size. The marginal maximumlikelihood estimates for the depression timescale τ D and mean quantal amplitude µ a are similar between the control and adenosine datasets (Figure  3C ). However, the suppressive effect of adenosine on synaptic transmission is clearly visible in the effective number of release sites n and the initial release probability p 0 that drives the shift from predominantly depressing to weakly facilitating synapses. It is also possible to examine the changes in covariance between pairs of parameters inferred from the experimental data (Figure  3D ).
The advance renders the calculation of the likelihood required for Bayesian inference practical for a variety of models of short-term synaptic plasticity. Moreover, unlike approaches that have relied on mean-variance analysis, it is applicable to single-sweep experiments and so is suitable for in-vivo scenarios where presynaptic firing is uncontrolled, but can be monitored. The likelihood calculation that makes this inference possible is flexible and can be extended to a number of common synaptic models, allowing for examination of augmented recovery  (Wang and Kaczmarek, 1998; Hosoi et al. , 2007) , releaseindependent depression with frequency-dependent recovery  (Fuhrmann et al. , 2004) , and receptor desensitization  (Jones and Westbrook, 1996; Otis et al. , 1996). Four such models are described in Appendix A with associated computer code in the MATLAB and JULIA environments to be found in the Supplementary Material.
Study area and 239þ240 Pu measurements The authors used the dataset established by  Alewell et al. (2014). The targeted study area is the Urseren Valley (Canton Uri), in Central Switzerland Alps, where 6 reference sites and 9 sampling points were sampled in 2007. The 9 sampling points were collected on three different land use types with three replicates each: hayfield (HF1, HF2, HF3), pasture with dwarf shrubs (PAW1, PAW2, PAW3) and pasture without dwarf shrubs (PAWO1, PAWO2, PAWO3). For a more detailed description of the study site, see  Meusburger and Alewell (2008). The reference soil cores were sectioned into 3 cm increments down to a depth of 30 cm to obtain detailed information on the 239þ240 Pu depth distribution. The cores of the sampling sites were sectioned in 10 cm increments down to a depth of 40 cm.
Conclusions Four widely recognized models to convert FRN inventories into soil redistribution rates have been described and applied to a 239þ240 Pu dataset collected in Swiss Alpine grasslands. In presenting the first application of the new model MODERN to assess soil erosion or deposition rates from 239þ240 Pu inventories, the authors compare this innovative model to commonly used and established ones. Regarding erosion rates, MODERN and the IM (which was specifically designed for Pu) compare best. Both models consider the specific mass depth of the sampling site to derive erosion rates. The deviation between the models may be explained by the fact that IM assumes an exponential distribution of the Pu distribution at reference sites, whereas MODERN reproduces precisely the depth profile following the accuracy of the incremental measurements.
Acknowledgements This work was financially supported by the  Swiss National Science Foundation (SNF) , project no. 200021-146018. The authors also acknowledge the  International Atomic Energy Agency (IAEA)  for support provided through the technical project 20435 and the Coordinated Research Project (CRP) D1. 50. 17 on "Nuclear techniques for a better understanding of the impact of climate change on soil erosion in upland agro-ecosystems".
High-throughput data production technologies, particularly 'next-generation' DNA sequencing, have ushered in widespread and disruptive changes to biomedical research. Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods, as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible, transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication, or reuse.
INTRODUCTION Started in 2005, Galaxy (https://galaxyproject. org) enables biologists without programming and systems administra-tion expertise to perform computational analysis through the Web  (1). Existing analysis tools are defined for Galaxy and made available with a consistent Web interface. Multistep analyses can be performed by running tools in succession, and Galaxy preserves the complete provenance of each analysis step  (2, 3). By bridging the gap between tool developers and researchers, Galaxy helps both constituencies accelerate their research. The project consists of several components: The Public Galaxy Server: This is the focus of this paper. It is located at https://usegalaxy. org and is an installation (or instance) of the Galaxy software combined with many common tools, visualizations and data sources; this site has been available since 2007 for anyone to analyze their data free of charge.
The site provides substantial CPU and disk space, making it possible to analyze large datasets. The public site supports thousands of users and hundreds of thousands of jobs per month (https://bit. ly/gxystats). The Galaxy software framework: Galaxy is an opensource application (distributed under the terms of the permissive Academic Free License; https://getgalaxy. org) that can be deployed on any Unix system. The Galaxy software is highly customizable and integrates with a wide variety of compute environments ranging from laptop computers to clusters to compute clouds. The Galaxy Tool Shed: The Tool Shed (https://usegalaxy. org/toolshed) facilitates sharing tools between instances of Galaxy by providing a central location where tool developers can upload both their tool configurations and 'recipes' W4 Nucleic Acids  Research, 2016, Vol.
Goals of the Galaxy project Three broad goals drive the development of Galaxy. First, Galaxy seeks to increase access to complex computational analyses for all scientists, including those with limited or no programming knowledge. Galaxy's Web-based graphical user interface (GUI) makes it simple to do everything needed for relatively large data analyses. Using the Galaxy GUI, users can upload their own data or retrieve data from public databases, choose analysis tools, set tool inputs and parameters and run tools. The Galaxy GUI also includes a workflow editor where users can create automated, multistep analyses using drag and drop. Galaxy analyses are completely reproducible. All analysis parameters and inputs are permanently recorded, and analyses can be precisely repeated using the GUI. Finally, Galaxy provides collaborative and transparent analyses by enabling users to share and publish their analyses via the Web.
Analysis interface and history The Galaxy analysis interface (Figure  1 ) is divided into three panels that show available tools, a main window for running tools and viewing datasets and a history of datasets created by running tools. Analysis tools are grouped into categories and can be searched. When a tool is selected, it is shown in the main window, where its inputs and parameters are set and the tool is executed. When a user executes a tool, its output datasets are added to the history panel. Color coding and animations show the state of tool execution. Clicking on a dataset in the history panel provides a wealth of information, including the tool and parameter settings used to create it, its size, and a small dataset preview.
Workflows Galaxy's graphical workflow editor (Figure  2 ) makes it simple to create multi-step analyses using drag and drop functionality. Using the editor, tools can be added and connected to each other so that the output of one tool becomes the input of other tools. Tool parameters can be set, and common actions such as hiding intermediate datasets, changing data formats, and renaming datasets are supported as well. Workflows enable automating and repeatedly running large analyses. Once created, workflows function like tools: they can be accessed and run from Galaxy's main analysis interface.