dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular we adapted two example simulations that come with the current NEST release  (Gewaltig and Diesmann, 2007; Gewaltig et al. , 2012). Both simulations implement the same balanced random network model based on work by  Brunel (2000). In one case all connections are static whereas in the other case excitatory-to-excitatory connections evolve according to a weight dependent STDP rule. We created the same network model in Auryn. For a better comparison we wrote an Auryn class comparable to the NEST poisson_generator and implemented the same neuron model and integration scheme as used in NEST (both classes are openly available in the Auryn code base). However, due to differences in the implementation of STDP (Methods), Auryn assumes a purely axonal synaptic delay, whereas NEST assumes a dendritic delay.
DISCUSSION In this paper we have shown that small or medium size recurrent networks with STDP can be simulated in, or faster than real-time if performance-optimized parallel code is used. However, we also show that the margin for speed-up through parallelization on standard hardware is limited due to finite communication delays and the deviation from strong scaling in the mechanisms of spike propagation. In particular we compared the run times of several standard simulators (Brian, NEST and Neuron) and our own simulator Auryn, when simulating a classic Vogels-Abbott benchmark network. We illustrated that the choice of integration algorithm has a considerable effect on performance. Specifically simulation speed can be increased substantially when numerical precision is not a primary concern.
We report on the design, verification and performance of MuMax 3 , an open-source GPUaccelerated micromagnetic simulation program. This software solves the time-and space dependent magnetization evolution in nano-to micro scale magnets using a finite-difference discretization. Its high performance and low memory requirements allow for large-scale simulations to be performed in limited time and on inexpensive hardware. We verified each part of the software by comparing results to analytical values where available and to micromagnetic standard problems. MuMax 3 also offers specific extensions like MFM image generation, moving simulation window, edge charge removal and material grains.
I. INTRODUCTION MuMax 3 is a GPU-accelerated micromagnetic simulation program. It calculates the space-and time-dependent magnetization dynamics in nano-to micro-sized ferromagnets using a finite-difference discretization. A similar technique is used by the open-source programs OOMMF  [15]  (CPU) and MicroMagnum  [5]  (GPU), and the commercial GpMagnet  [25]  (GPU). MuMax 3 is open-source software written in Go  [4]  and CUDA  [6] , and is freely available under the GPLv3 license on http://mumax. github. io. In addition to the terms of the GPL, we kindly request that any work using MuMax 3 refers to the latter website and this paper. An nVIDIA GPU and a Linux, Windows or Mac platform is required to run the software. Apart from nVIDIA's GPU driver, no other dependencies are required to run MuMax 3.
Finite-element micromagnetic software exists as well like, e. g. , NMag  [17] , TetraMag  [19] , MagPar  [32]  and FastMag  [11]. They offer more geometrical flexibility than finite-difference methods, at the expense of performance. In this paper we first describe each of MuMax 3 's components and assert their individual correctness and accuracy. Then we address the micromagnetic standard problems  [2] , where all software components have to work correctly together to solve real-world simulations. We typically compare against OOMMF  [15]  which has been widely used and profoundly tested for over more than a decade. Finally, we report on the performance in terms of speed and memory consumption. The complete input files used to generate the graphs in this paper are available in appendix A, allowing for each of the presented results to be reproduced independently.
C. Interface Input scripts MuMax 3 provides a dedicated scripting language that resembles a subset of the Go programming language. The script provides a simple means to define fairly complex simulations. This is illustrated by the code snippet below where we excite a Permalloy ellipse with a 1 GHz RF field: setgridsize(128, 32, 1) setcellsize(5e-9, 5e-9, 8e-9) setGeom(ellipse(500e-9, 160e- Programming The MuMax 3 libraries can also be called from native Go. In this way, the full Go language and libraries can be leveraged for more powerful input generation and output processing than the built-in scripting. Web interface MuMax 3 provides web-based HTML 5 user interface. It allows to inspect and control simulations from within a web browser, whether they are running locally or remotely. Simulations may also be entirely constructed and run from within the web GUI.
In any case an input file corresponding to the user's clicks is generated, which may later be used to repeat the simulation in an automated fashion. Data format MuMax 3 uses OOMMF's "OVF" data format for input and output of all space-dependent quantities. This allows to leverage existing tools. Additionally a tool is provided to convert the output to several other data formats like paraview's VTK[3], gnuplot  [1] , comma-separated values (CSV), Python-compatible JSON,. , and to image formats like PNG, JPG and GIF. Finally, the output is compatible with the 3D rendering software MuView, contributed by Graham Rowlands  [31].
IX. ACKNOWLEDGEMENTS This work was supported by the  Flanders Research Foundation (FWO). The authors would like to cordially thank  Ahmad Syukri bin Abdollah ,  Alex Mellnik ,  Aurelio Hierro ,  Ben Van de Wiele ,  Colin Jermain ,  Damien Louis ,  Ezio Iacocca ,  Gabriel Chaves ,  Graham Rowlands ,  Henning Ulrichs ,  Joo-Von Kim ,  Lasse Laurson ,  Mathias Helsen ,  Raffaele Pellicelli ,  Rémy Lassalle-Balier ,  Robert Stamps  and  Xuanyao (Kelvin) Fong  for the fruitful discussions, contributions or feedback, as well as all others who tested early MuMax versions. MuMax 3 uses svgo (http://github. com/ajstarks/svgo),copyright  Anthony Starks , and freetype-go (http:// code. google. com/p/freetype-go),copyright  Google Inc. ,  Jeff R. Allen ,  Rémy Oudompheng ,  Roger Peppe.
2000 and Peery et al. 2012). But they estimate neither the severity of the contraction nor its age or duration. Although much more mathematically difficult and computationally demanding, likelihood-based methods outperform these moment-based methods by considering all available information in the genetic data (see  Felsenstein 1992; Griffiths and Tavar e 1994a; Emerson et al. 2001 , and the review of  Marjoram and Tavar e 2006). Among others, the software package MSVAR  (Beaumont 1999; Storz and Beaumont 2002)  has been increasingly used to infer past demographic changes. MSVAR assumes a demographic model consisting of a single isolated population, which has undergone a change in effective population size at some time in the past. It is dedicated to the analysis of microsatellite loci that are assumed to follow a strict stepwise mutation model (SMM,  Ohta and Kimura 1973).
The third study tests the robustness of our method against misspecification of the mutation model, and against the existence of a population structure not considered in the model. Finally, we have applied our methodology on the orangutan data set of  Goossens et al. (2006)  and compared our results with those obtained with MSVAR. All analyses in these studies were performed using the latest implementation of the MIGRAINE software package, available at http://kimura. univ-montp2. fr/~rousset/Migraine. htm (last accessed July 28, 2014).
Section A3 in the supplementary material, Supplementary Material online, explains how we tuned the parameters of the algorithm, namely the range of parameters, the size of parameter points, and the number of genealogical histories explored by the IS algorithm. A genuine issue, when facing genetic data, is to test whether the sampled population has undergone size changes or not. Thus, we derived a statistical test from the methodology presented above. It aims at testing between the null hypothesis that no size change occurred (i. e. , N ¼ N anc ) and alternatives such as a population decline or expansion (i. e. , N 6 ¼ N anc ). At level , our test rejects the null hypothesis if and only if 1 lies outside the 1 À CI of the ratio N ratio ¼ N=N anc. All those developments are implemented in the MIGRAINE software package.
Finally and for the same reason, inference of contraction time is expected to be more precise for intermediate timings. This is exactly what is observed in figures 4 and 5. One important result of our study is that high CDRs as well as good inference precision for both the time and the actual population size parameters are still expected for relatively ancient contraction (e. g. , 1:25 4 D 4 5:0). Finally, many recent software packages that make demographic inferences from genetic data, such as MIGRATE  (Beerli and Felsenstein 2001) , IM  (Hey and Nielsen 2004, 2007; Hey 2010) , or LAMARC  (Kuhner 2006) , do not use the classical coalescent parameter scaling by population size (i. e. , 4Nm and T=2N) but rather use scaling by the mutation rate (i. e. , m= and T), or propose both options, as MIGRAINE does.
Comparison with Previous Methods In the past decade, the use of likelihood-based methods to analyze genetic data under a single population model with past variation in population size emerged with the release of the MSVAR software  (Beaumont 1999; Storz and Beaumont 2002). As expected, this coalescent-based MCMC method has been shown to be much more powerful than using summary statistics in detecting past contractions or expansions  (Girod et al. 2011; Peery et al. 2012 ). Moreover, model-based approaches can also infer model parameters, such as current and past population sizes, and the timing of the demographic change. In this study, we compared performances in terms of CDRs, parameter inference precision, and computation times of MSVAR and our IS method. Our simulations globally show similar behavior of the two methods, with slight but clear advantages for MIGRAINE in terms of power of contraction detection, parameter estimation, and computation times.
Robustness to Mutational Processes Although many models have been developed to describe microsatellite mutation processes  (Bhargava and Fuentes 2010) , most programs that analyze microsatellite data use the SMM (e. g. , IM, MIGRATE, LAMARK, see references above, but see DIYABC, Cornuet et al. 2008, and BEAST,  Drummond et al. 2012). However, it has been recognized that violations of the SMM assumptions might induce severe bias in the inference of demographic history  (Gonser et al. 2000). Indeed, mutations of more than one step of the GSM can produce gaps in allele length distribution, which are typically often observed after a population decline under an SMM  (Garza and Williamson 2001). Peery et al. (2012)  recently showed that identification of past contractions using the summary statistic-based BOTTLENECK  (Cornuet and Luikart 1996 ) and M-RATIO softwares  (Garza and Williamson 2001)  is highly biased by deviations from the mutation models implemented in those softwares, often leading to significant contraction detection in samples simulated under a stable population model.
Moreover, our results show complex interactions between levels of population structure, total genetic diversity, mutation processes, and sampling scales that strongly limit practical recommendations for the detection and characterization of past changes in population size in the presence of unaccounted population structure. Such confounding effects of population structure and past changes in population sizes have already been observed. First, the effect of small-scale IBD population structure on CDRs obtained with the BOTTLENECK and M-RATIO softwares has been tested by simulations in  Leblois et al. (2006). Our results are globally in agreement with this previous study, except that they found large FEDRs when using BOTTLENECK on IBD samples and that considering large scale samples makes FEDRs even larger.
Analysis of the Orangutan Data Set Our analyses of the orangutan data set show that 1) all sampled sites, except S9, exhibit a clear signal of a strong and recent population size contraction; and 2) parameter inferences are extremely consistent among sites, and among the different pooled samples. Those results are in good agreement with equivalent analyses using MSVAR published in  Goossens et al. (2006)  and  Sharma et al. (2012) : All analyses indicate 1) ancestral population sizes of about a few thousand individuals (i. e. ,  [2, 900]  for MIGRAINE and [3,100-13,400] for MSVAR), 2) much smaller current population sizes (i. e. ,  individuals for MIGRAINE and [22-1,400] with median values between 60 and 200 individuals for MSVAR), and 3) a relatively recent timing of the contraction (i. e.
Acknowledgments The authors are grateful to  L. Chikhi ,  J. -M. Cornuet ,  A. Estoup ,  J. -M. Marin , and three anonymous reviewers for their constructive discussions about this work. This study was supported by the  Agence Nationale de la Recherche  ( EMILE 09-blan-0145-01  and  IM-Model@CORAL. FISH 2010-BLAN-1726-01  projects) and by the  Institut National de Recherche en Agronomie  (Project  INRA Starting Group "IGGiPop "). Part of this work was carried out by using the resources of the Computational Biology Service Unit from the  MNHN (CNRS Unit e Mixte de Service 2700) , the  INRA MIGALE  and  GENOTOUL bioinformatics platforms , and the computing grids of ISEM and CBGP labs.
Motivation: Biologists often wish to use their knowledge on a few experimental models of a given molecular system to identify homologs in genomic data. We developed a generic tool for this purpose. Results: Macromolecular System Finder (MacSyFinder) provides a flexible framework to model the properties of molecular systems (cellular machinery or pathway) including their components, evolutionary associations with other systems and genetic architecture. Modelled features also include functional analogs, and the multiple uses of a same component by different systems. Models are used to search for molecular systems in complete genomes or in unstructured data like metagenomes. The components of the systems are searched by sequence similarity using Hidden Markov model (HMM) protein profiles. The assignment of hits to a given system is decided based on compliance with the content and organization of the system model.
A graphical interface, MacSyView, facilitates the analysis of the results by showing overviews of component content and genomic context. To exemplify the use of MacSyFinder we built models to detect and class CRISPR-Cas systems following a previously established classification. We show that MacSyFinder allows to easily define an accurate ''Cas-finder'' using publicly available protein profiles. Availability and Implementation: MacSyFinder is a standalone application implemented in Python. It requires Python 2. 7, Hmmer and makeblastdb (version 2. 2. 28 or higher). It is freely available with its source code under a GPLv3 license at https://github. com/gem-pasteur/macsyfinder. It is compatible with all platforms supporting Python and Hmmer/ makeblastdb. The ''Cas-finder'' (models and HMM profiles) is distributed as a compressed tarball archive as Supporting Information.
In Prokaryotes, organelles and viruses, macromolecular systems are often encoded in one or a few conserved neighbouring operons ensuring tight regulation and correct assembly/functioning. This facilitates the assignment of certain components to a system  [6] [7] [8] [9]. We have developed a program named Macromolecular System Finder (MacSyFinder) to detect molecular systems in genome data from user-defined biological models. The components of the systems are searched using protein profiles encoded as hidden Markov models (HMM), such as those available in databases like PFAM, TIGRFAM or PRODOM  [10] [11] [12]. Protein profiles provide a compressed way to represent a database of homologous sequences, giving increased sensitivity and specificity  [13]. MacSyFinder identifies the presence of a given system according to the specifications of the input model, which includes customizable information on the type and number of components, on their genetic organization, and other relevant discriminating traits.
Implementation, system requirements and availability MacSyFinder was coded in Python, and details on its objectoriented implementation are available in Text S1 and in File S1. MacSyFinder requires Python version 2. 7, the formatdb or makeblastdb tools (version 2. 2. 28 or better for the latter)  [20, 21]  and the program Hmmer  [13, 22]. MacSyFinder is freely available. Its source code is distributed under a GPLv3 license at https:// github. com/gem-pasteur/macsyfinder and updated versions will be accessible there. MacSyFinder is compatible with all platforms supporting Python, Hmmer, and makeblastdb. The MacSyFinder release used in this paper is provided in Data S1. MacSyView's source code is freely available at https://github. com/gempasteur/macsyview but it is also distributed in the MacSyFinder's package.
Input and output The MacSyFinder program (Data S1) receives as input a list of systems defined in XML files (see above), protein profiles, command-line parameters and a file with protein sequences in fasta format (see the documentation in File S1). The parameters can be specified in the command-line or in a configuration file. System and component parameters specified in the command-line override model specifications in the XML files. MacSyFinder manages three different types of protein datasets. The unordered dataset lacks information on gene order and genome origin. This mode is useful to study large sequence databanks or metagenomic data. Naturally, in these datasets the notions of co-localization and quorum are not relevant. The unordered replicon dataset includes protein sequences from one single genome.
This is useful to analyse unassembled genomes with large numbers of contigs. In this case the notion of quorum is relevant (albeit with certain limitations), but co-localization is not. The ordered replicon dataset includes proteins from one single replicon that are ordered according to the position of the corresponding genes in the genome. This is the most powerful mode and can be used to analyse complete or nearly complete genomes. Another related mode (gembase) requires a specific input file format and allows the analysis of multiple ordered replicons in a single step (see File S1). The output of MacSyFinder includes log files, intermediate results, the number of detected systems, and the information on each detected component from each instance of the system. This information is made available in the form of text tables and JSON files.
Functioning The user runs MacSyFinder from the command-line on a protein sequence dataset for a number of systems of interest. The non-redundant list of components to search is extracted from the XML files. The presence of a given component is determined by similarity search with HMM protein profiles using the program Hmmer  [13]. The hits are filtered according to user-defined i-evalue (for statistical significance) and to the minimal coverage of the profile in the alignment (to control for the minimal size of the profile that must be matched to obtain biologically relevant hits). The components defined in the models are searched in parallel for rapidity (Fig. 3A ). If multiple profiles match the same protein, MacSyFinder selects the hit with the highest score. The subsequent steps depend on whether the input dataset is an ordered replicon, an unordered genome or an unordered genomic dataset (e.
g. , a metagenome). If the dataset is an ordered replicon, the hits are clustered according to the genetic organization specified in the model. Clusters including the components of a single type of system are used to fill inventories of ''compatible'' systems (Fig. 3B ). If multiple systems are compatible with the set of components in the clusters, then the different candidate systems are examined. The order of exam is given by decreasing number of components shared between the cluster and the compatible systems. The cluster will be assigned to the first system in the list that fits its content. A system is regarded as complete if the quorum is respected. When a complete instance of the system has components from a single locus, further new occurrences of the same components in the cluster are used to produce a novel instance.
When a single cluster is not enough to make a complete instance and the multi_loci parameter is turned on, the hits are stored to fill up an instance of the system encoded by multiple distant loci. Clusters with components from multiple systems are split in sub-clusters containing components from a single system. These subclusters are then re-analysed in terms of their components (Fig. 3B ). MacSyFinder can only resolve these complex cases if the components of each system are contiguous, instead of scattered on the cluster. Unordered sequence datasets cannot be analysed with the colocalization criteria. Therefore, hits from the similarity searches are directly used to fill inventories of each system. Systems are complete if the required quorum is respected. The presence of forbidden components is ignored in this mode, even if such occurrences are stored to inform the user.
Data The complete genomes of bacteria (2484) and archaea (159) were downloaded from NCBI RefSeq (ftp://ftp. ncbi. nih. gov/ genomes/, November 2013). Profiles for the Cas protein families were obtained from the TIGRFAM database, version 13. 0 (http://www. jcvi. org/cgi-bin/tigrfams/index. cgi, August 15 ). We illustrate how systems can be modelled and distinguished with two imaginary systems ''A'' and ''B'' that have four homologous components (C1-C4, similar colours for the two systems). The system ''B'' has one component that is not found in ''A''(C5). The parameter inter_gene_max_space (D) defines the maximal number of genes between two consecutive components (d i,j ). The two systems are defined by a set of mandatory (green), accessory (black) and forbidden (red) components. The quorum rules allow relaxing the definition of the system without altering the list of its components (min_genes_required and min_mandatory_genes_required parameters in XML files).
Developing a new system's model The goal of MacSyFinder is to query genomic data using biologically meaningful models of a system. The first step of the model building procedure is therefore to use the available knowledge to identify the system components, their frequency and their genetic architecture. The second step is to obtain protein profiles for the components either by building them specifically for this purpose or by retrieving them from public databases. Protein profiles can be built easily from multiple sequence alignments of homologous proteins using Hmmer  [13]. The third step is to write the model in the simple MacSyFinder's XML grammar (see above and Fig. 1  for an example). The final step is to include information about homologous systems in the model. The use of systemspecific profiles and forbidden attributes facilitates the discrimination between systems (Fig.
Detection and classification of CRISPR-Cas systems The known cas operons have from 3 to 13 genes encoding very diverse proteins, among which several nucleases and helicases with DNA and/or RNA binding domains  [24, 25]. A unified classification of CRISPR-Cas systems has been recently established based on the presence or absence of peculiar Cas protein families, and on the genetic architecture of the cas operon  [25]. Three major types and several subtypes of CRISPR-Cas systems have been described. cas1 and cas2 universally occur across types and subtypes, whereas cas3/cas7, cas9, and cas10 have been defined as the signature genes for type I, type II, and type III, respectively (Fig. 4 ). Protein profiles matching most of these Cas protein families are publicly available in the TIGRFAM database  [11, 24].