dataesr/scientific-paragraphs-categorization huggingface dataset:
Even if the all-heterozygous assumption has no impact on their time/space complexities, the comparison between solutions achieved under different hypothesis may produce misleading results. Considering that all the SNPs in the dataset are heterozygous with high confidence, this assumption is not strictly necessary in this case. Figure  6 , built from data in  [33] , shows, for the different tools, the error rate (left histogram) and the percentage of phased positions compared to the total number of positions which can be phased in the input reads (right histogram). Considering this dataset, both HapCol and (P)WHATSHAP achieved very good results in terms of accuracy, reconstructing the haplotypes with high precision and phasing a large number of positions compared with the other two tools. In particular, HapCol and (P)WHATSHAP improved the accuracy of the other two tools by more than 40 %.
Incidentally, WHATSHAP also performed fast, 172 s, behind RefHap, 43 s, and ahead of HapCol, 332 s, and ProbHap, 1205 s. In  [33]  a synthetic dataset was also generated and used for comparative analysis on accuracy. Specifically, the analysis aimed to assess how accuracy changes while varying the coverage of the dataset. Given that the real, standard benchmark dataset previously used relies on the all-heterozygous assumption, and hence contains only heterozygous SNP positions and has low average coverage, a synthetic datasets has been used to characterise the behaviour of tools against the long reads that will be soon available thanks to future-generation sequencing technologies (max coverage 25×, max read length 50,000 bases, max indel rate 10 %, max substitution rate 5 %). The dataset has been generated inserting all the variants of chromosomes 1 and 15 of the Venter's genome into the hg18 assembly genome.
Long reads have been generated at length 1000, 5000, 10,000, and 50,000 using a uniform indel distribution of 10 % and substitution rates 1 and 5 %. These rates have been defined according to the information currently available about the accuracy of long read data generated using future-generation sequencing technologies (see, e. g. ,  [43, 44] ). The final in silico datasets were achieved extracting from each set of simulated reads subsets with maximum coverage of 15×, 20×, and 25×. Since ProbHap and RefHap require that haplotypes are computed outside the all-heterozygous hypothesis, only tests regarding (P)WHATSHAP and HapCol are relevant. Data in  [33]  shows a substantial coherence of (P)WHATSHAP and HapCol in terms of accuracy (less than 1 % of differences), and illustrate how accuracy, measured as error rate, improves with larger coverages.
Trends of such improvements are reported in Fig. 7. Such data provides further grounds to the interest for PWHATSHAP, whose speed-up increases with coverage. Although HapCol, together with WHATSHAP, has high accuracy on these datasets, it is worth recalling that Hap-Col has a couple of substantial drawbacks with respect to WHATSHAP. The first one is the above mentioned requirement for the fragment matrix F to be gapless, which results in the heavy limitation of not being usable with paired end reads. The second one is that HapCol actually solves a constrained version of the MEC problem (which is called k -cMEC in  [33] ) that limits to a given parameter k the amount of errors that can be corrected. This is due to efficiency reasons, because Hap-Col takes time and space exponential in the amount of corrected errors.
Efficiency In this section we outline results of experiments aiming at assessing the performance of the proposed parallel algorithm. All the experiments have been performed on a platform equipped with two E5-2695@2. 40 G Hz Ivy Bridge Intel Xeon processors, each with 12 cores, 64 G Bytes of memory, Linux Red Hat 4. 4. 7 with kernel 2. 6. 32. CPU dynamic frequency scaling and turbo frequency boost have has been disabled to ensure a fair comparison among codes using a different number of cores. Both  parallel and sequential codes have been compiled with gcc 4. 8. 2 using -O3 optimisation level. The parallel version was executed using the shell command numactl -interleave=all to exploit all the available memory bandwidth of the 2 NUMA nodes of the hardware platform. Experiments have been run on a range of synthetic data sets with maximum coverage of 16, 18, 20, 22, 24, 26 and 28, which have been generated from a single data set with an average coverage of 30, mapped to human genome and then pruned to smaller coverage data sets (see  [24]  for details on the construction).
Conversely, for columns with lower coverage the synchronisation overhead exceeds the performance gain, thus they are computed with sequential code. The speedup of the proposed parallel PWHATSHAP against the original sequential WHATSHAP is reported in Table  2. Specifically, the table reports the average computing time for a column for the reference dataset, filtered by different maximum coverages. For each filtering, the WHATSHAP and PWHATSHAP performance is reported together with the speedup of PWHATSHAP over WHAT-SHAP, defined as Speedup = TSeq/TPar. For all coverages, the amount of main memory used was fixed to ∼ 63 GB in all the tested cases. Considering the case of the dataset filtered for max coverage 28, the fraction of sequential time, including both the columns whose construction is not parallelised and inherently sequential parts of the application, amounts to about 15.
Conclusions The work presented in this paper contributes to the haplotype assembly approach, a developing methodology for phasing SNPs based on direct evidence from reads obtained by DNA sequencing. Phasing grants us a better understanding of haplotype information, which is relevant in many contexts, including gene regulation, epigenetic, genome-wide association studies, evolutionary selection, population structure and mutation origin. In this context, our contribution consists of a framework, PWHATSHAP, that improves the efficiency of state of the art haplotype computational analysis. Importantly, PWHATSHAP is aligned with the future trends of sequencing technology, which will provide long reads, i. e. , long fragments of DNA sequences. Building on WHATSHAP, PWHATSHAP improves the efficiency of solving the weighted MEC optimisation problem for haplotyping and supports a faster analysis of datasets with large coverage.
This also caters to the accuracy of the results, which in the current settings, increases with coverage. PWHATSHAP is a multi-core, parallel porting of WHAT-SHAP. Experimental results and benchmark tests show increased performance that can be obtained using computational facilities which are available today at affordable costs. The core haplotyping algorithm is embedded in a larger framework, the same as WHAT-SHAP, which enables the treatment of standard formats for sequencing datasets. As PWHATSHAP is distributed as a freely available toolkit, our contribution aims to be widely accessible to researchers, as well as companies. The development of PWHATSHAP has been a challenging parallelisation exercise for a fine-grained, data intensive algorithm. Such features made the process difficult. We have addressed this by exploiting FastFlow, a highlevel parallel programming framework specifically targeting the parallelisation of fine-grained tasks, which allowed us to develop PWHATSHAP with minimal modifications to the sequential code.
Common to similar frameworks dealing with large datasets, a critical aspect of PWHATSHAP is the trade-off between memory usage and performance. A large amount of information is currently kept in memory for efficient access. However, the amount of available memory represents a rigid limit, after which the necessary virtual memory management and swap to secondary memory devices, i. e. , disks, start to have an impact on performance. We envision two possible approaches to solve this problem and push even further the efficiency of PWHATSHAP. The first one is based on optimised, ad-hoc memory management. The memory access pattern is fully sequential: a large bulk of data is sequentially written, then sequentially read in reverse order to build the solution. Data is never accessed in random order except for the very last column.
An intelligent memory management, aware of such problem-specific information, could maintain relevant data in a limited amount of memory while needed, and swap to disk data outside such a working set (i. e. , almost all but the last two columns). The difficulty lies in providing programmers with suitable abstractions that allow them to transparently deal with data swapping, i. e. , technically, a user-space virtual memory optimised to manage the sequential data scheme used by PWHATSHAP. The second approach is based on memory compression, which is making a comeback mainly because of the availability of multiple core processors. Memory compression has been considered recently in projects regarding Linux, ChromeOS, Android and OS X. Intelligent memory compression would also exploit haplotyping specific information.
Acknowledgments Authors would like to thank the anonymous reviewers for their comments and suggestions that have contributed to improve our paper. This work has been partially supported by the  EU   FP7  project n. 288570 " ParaPhrase: Parallel Patterns for Adaptive Heterogeneous Multicore Systems " (no. 288570 ), and by the  EU H2020  project " Rephrase: Refactoring Parallel Heterogeneous Resource-Aware Applications -a Software Engineering Approach " (no. 644235 ).
Data Analysis Raw DNA sequences were extracted and selected by their length (200-600 bp) using mothur software  [24]. Sequences were also trimmed and retained if they contained no ambiguous base, no homopolymers ≥8 and no more than one mismatch to the sequencing primer. Adaptor sequences were removed from the sequences using the cutadapt tool implemented on the Galaxy server of the Institut de Génétique et Microbiologie (IGM) of the Université Paris-Sud (http://galaxy. igmors. u-psud. fr/). A second trimming, using a 90% quality score with >25, was performed using Condetri V_2. 2  [25]. The sequences were screened for potential chimeric sequences with the Decipher web tool (http://decipher. cee. wisc. edu) [26]. Sequences were classified to the genus level using the Greengenes 2013 release database (202,421 bacterial and archaea sequences) at an 80% confidence threshold with mothur and using the RDP-II Naïve Bayesian Classifier  [27].
To estimate the bacterial population richness and diversity, the RDPII pyro site (https://pyro. cme. msu. edu/index. jsp) was used to calculate the Chao1 estimator and Shannon diversity index. The Jaccard index was also estimated and the relative abundance of operational taxonomic units (OTUs) were determined using a 0. 03 distance limit (97% similarity) by clustering on RDP's pyrosequencing pipeline. All sequences have been deposited in the GenBank Sequence Read Archive (SRA) under the SRP049954 accession number.
Pyrosequencing Data Pyrosequencing was performed on the V1-V3 regions of 16S rDNA genes PCR amplified from total DNA extracted from the five desert samples. The total number of sequences obtained for the five samples were 23,245, with an average length of 469 bases after trimming. After quality score adjustments, approximately 23% of the total sequences were removed. The largest number of sequences were obtained from the Tt sample (6,145 sequences), while the Zs sample contained the smallest number of sequences (3,106 sequences) (Table  2 ).
Bacterial Phylogenetic Composition The bacterial populations in the samples were classified from the 16S rDNA sequences with the mothur algorithm using the Greengenes database for bacteria and archaea using the RDP Naïve Bayesian Classifier with an 80% confidence threshold (Fig. 3 ). The most abundant bacteria were from the Proteobacteria, with a range from 15% in the Pt sample to 41. 5% in the Zs sample. The Pt sample has 79. 1% of its members from the Bacteroidetes phylum, while members of this group are not present at ≥ 30% in the other samples. Following this, we observed members of the Firmicutes, with a range from 0. 7% in the Pt sample to 24. 3% in the Ss sample, plus Actinobacteria with a range from 1. 2% in the Pt sample to 17. 6% in the Tt sample. The unclassified sequences at the phylum level ranged from 0.
In our five samples, the pH ranged between 7. 28 and 7. 69, and is thus considered to be only slightly alkaline. The pH can be an important factor in the composition of the bacterial community and it has been reported that a higher diversity in soils is found in samples with near neutral pHs  [36]. Here, we used DNA sequencing to discern microbial populations of V1-V3 variable regions of PCR amplified 16S rDNA, as it has been reported to perform well for bacterial assignments in a wide range of datasets  [37]. We examined the bacterial richness and diversity in each sample using the Chao1 estimator  [38]  and found a large inter-sample variability (from 319 to 961 OTUs) in the five samples from the same desert. It should be noted that the analysis of these five samples may not be representative of the total microbial diversity of the Jizan desert.
A. Offline Learning Phase The first step consists in defining the training set and compute its features and outputs. For this, we proceed in two phases. In the first phase, we compute the set of SAT features R 1 as defined in SATzilla. Most of them are static, and we keep the features concerning the solving time for SAPS and GSAT as described above in Section II-B2. In the second phase, we run A a certain number p of times, and we collect all these runtimes. From this, we approximate the distribution of the runtime of A on the training instances using the method described in Section II-C. From this, we collect three data points: the type of distribution of the RTD, its expectation and its variance. These three data are the output that we are interested in. Concerning the type of distribution, we make the assumption, that is does not change inside a given class of problem.
IV. EXPERIMENTAL EVALUATION We focus our attention on three SAT local search solvers (Sparrow  [21] , probSAT  [22] , and g2wsat  [23] ), and a TSP local search solver LKH  [10]. We recall that these solvers are among the best solvers in their particular domains and we use WEKA (version 3. 6. 2), a popular ML library to build the ML models. In particular, we use the Pace regression algorithm available in WEKA with its default parameters for the machine learning models to estimate the parameters of the distributions, i. e. , lognormal and shifted exponential. In our experiments we use random and structured instances. The random SAT set of instances has been used previously in  [24]  and  [22]  and we use the software portgen to randomly generate TSP instances. The set of structured SAT instances has been proposed in  [25]  and previously in  [26] , and we collected structured TSP instances from OpenStreetMaps (osm).
B. Testing Phase In order to evaluate the accuracy of the machine learning models, we perform 10 runs of the multi-walk parallel algorithms and report the mean runtime for each actual empirical evaluation of the algorithms. We recall that for each instance in the testing set, we collect the set of features as described in Section II, and use the regression models to compute the parameters of the best distribution from the learning phase. We begin our analysis with random instances. To this end, Figure  1  shows the actual vs. predicted RTD for probSAT to  solve a typical random 3-SAT instance. Clearly, the predicted RTD closely matches the actual empirical RTD. Similar behaviour has been observed for other instances in the dataset. Certainly, this figure visually confirms the accuracy of the proposed framework to estimate the sequential RTD.
We evaluated the quality of the ML models using the expected RTDs to predict the mean runtime of SAT solvers namely Sparrow, probSAT, and g2wsat with up to 40 cores and the TSP solver LKH with up to 16 cores. Interestingly, we have observed a correlation coefficient of up to 0. 85 for SAT solvers and up to 0. 95 for the TSP solver for random instances, and a CC of about 0. 65 for g2wsat and a CC of about 0. 88 for LKH on structured instances. Currently, the feature computation code relies heavily in the use of SAPS and GSAT. We plan to introduce similar features with our target solvers, i. e. , Sparrow and probSAT, to improve the quality of the ML models. Furthermore, we plan to extend our study to optimisation problems where the optimal value is unknown, and use the ML to predict the upper and lower bounds of the solutions.
Declarative language for indicators definition Our second result is a formal model and a declarative language to finely define indicators as aggregations along dimensions for VGS data. It is based on the relational algebra (a foundation concept for relational databases, like SQL databases). In our current prototype, we implemented this language by automatically translating it to complex nested SQL aggregation queries. It enables to easily integrate new domain-specific dimensions and/or adapted existing dimensions (like Time and Location). Due to space limitations, we do not describe the formal definition of this language. We sketch its expressiveness with an example: a user can define an indicator as a 2-step aggregation along the Time dimension for temperature sensor data, with an average at the minute level, and then a formula like "(MAX+MIN)/2" at the hour level, applied to the previous average at the minute level.
The model was also later used in  (Rodriguez, 2013)  and  (Gutierrez, 2007)  and improved to support a methodology for evaluation of the quality of sensor data for the environmental phenomena monitoring systems. These ontologies and models inspired our proposed data-centric VGS (Virtual Generic Sensor) model detailed in Section 2. 1. For analyzing sociological phenomenon, one of the commonly adapted tools is the survey. A survey corresponds to collection and analysis of answers to questionnaires. Several survey softwares have been proposed for the creation of surveys and the exploration of collected data. LimeSurvey (LimeSurvey 2015) is a free software package that permits to publish a questionnaire online and to collect their answers. SPHINX iQ (Sphinx. 2015) is a commercial software that permits to create surveys and analyze the data and their nature (quantitative or qualitative).
Another group of users of surveys use spreadsheets for managing the questionnaires. We studied closely the database schema of LimeSurvey to obtain a conceptual model of survey data. The documentation of the SPHINX software permits us to extend the specifications linked to the conception of questionnaires to know the different types of answers (to questions). We preview the importance of the analysis phase of questionnaire data that constitute an important objective of this data collection. However, these tools are not destined towards the management, exploration and cross-analysis of voluminous heterogeneous data. Concerning data management, the use of data warehouses for sensor data storage and analysis can be seen in various works like monitoring of pollinators  (Da Costa, 2010) , building energy and maintenance  (Gökçe, 2009)  and  (Stack, 2012) , soil ecosystem  (Szlavecz, 2007).
Our methodology is agile, incremental, iterative, and interactive to allow knowledge discovery along the way by users. Our VGS model is generic as it enables heterogeneous data handling. The VGS model is semantically compatible with standard sensor ontologies defined by standardization organizations like Open Geospatial Consortium standards (DUL, 2010) and  (Reed , 2007). Our conceptual VGS model has been built from a real multidisciplinary approach and its generic design makes it easy to apply to other phenomena observation. This model, as well as our agile exploration approach, is moreover independent from a specific data management technology. Although it is currently implemented on a SQL database, we aim at implementing it also on Big-Data-oriented databases like MongoDB or Cassandra. Furthermore, concerning energy consumption and monitoring systems, we briefly point at a major challenge of pervasive environments: to take into account energy consumption of the monitoring architecture itself.
We document the model physics and employed parameterizations for the basis module CryoGrid 3, and compare model results with in situ observations of surface energy balance, surface temperatures, and ground thermal regime from the Samoylov permafrost observatory in NE Siberia. The comparison suggests that CryoGrid 3 can not only model the evolution of the ground thermal regime in the last decade, but also consistently reproduce the chain of energy transfer processes from the atmosphere to the ground. In addition, we demonstrate a simple 1-D parameterization for thaw processes in permafrost areas rich in ground ice, which can phenomenologically reproduce both formation of thermokarst ponds and subsidence of the ground following thawing of ice-rich subsurface layers. Long-term simulation from 1901 to 2100 driven by reanalysis data and climate model output demonstrate that the hydrological regime can both accelerate and delay permafrost thawing.
Time integration In CryoGrid 3, only the physical process of heat conduction and the associated surface energy balance are integrated in time, while all other processes, such as the infiltration and refreezing of meltwater (Sect. 2. 5), occur instantly at the end of each time step in the form of a physical consistency check. To make the code intuitive to use and modify, the simplest possible time integration scheme is employed, first-order forward Euler. Since it is neither computationally efficient nor numerically stable, an adaptive integration time step is used, which prevents too large a change in the internal energy of a grid cell and thus allows a certain degree of control over the accuracy of the solution. As a first step, the method of lines  (Schiesser, 1991)  is used to discretize the spatial derivatives in Eqs.
Samoylov Island provides the unique opportunity to force CryoGrid 3 with a long-term in situ record, while at the same time providing comprehensive high-quality observations for validation. An automatic weather station (AWS) has been operational since 1998  (Boike et al. , 2008) , measuring air temperature, net radiation, humidity, and wind speed at hourly intervals. Since 2009, all components of the radiation balance are measured, i. e. , incoming and outgoing short and long-wave radiation. Eddy covariance measurements of sensible and latent heat fluxes have been ongoing since 2007 in close proximity of the AWS  (Langer et al. , 2011a, b) , and gaps in the time series of the climate station have, when pos-sible, been filled with data from the eddy covariance stations. Furthermore, records of temporary climate stations operated in different time periods on Samoylov Island have been used to fill gaps in the AWS record (for details, see  Boike et al.
CryoGrid 3 runs for the Lena River delta To evaluate the performance of CryoGrid 3 and demonstrate the excess ice scheme, two model runs were performed: validation runs for the period 1979-2014 were forced by measured data from the Samoylov AWS and downscaled ERA-Interim reanalysis data to benchmark the model performance. To assess the potential impact of excess ground ice on the trajectories of permafrost thaw, long-term thaw susceptibility runs from 1901 to 2100 were conducted based on reanalysis data and climate model output. A first validation run is conducted for a subsurface stratigraphy typical for Samoylov Island on the first river terrace so that it can be compared to in situ observations. Since the assumed snow density has a pronounced influence on ground temperatures (see also  Langer et al.