dataesr/scientific-paragraphs-categorization huggingface dataset:
This objective is however only partly fulfilled. The downside of our approach is that it relies on a MCMC algorithm that quickly grinds to a halt under the sheer mass of SNP data  (Lange et al. 2014). Hopefully, the MCMC algorithm is based on a Gibbs sampler that alternates the computation of least square solutions, which are fast to compute. For the HGDP dataset (644, 199 SNPs), the run-time ranges from 13 to 16 hours using a single computer processor (2. 4 GHz 64bit Intel Xeon) when K increases from 1 to 8. Because dataset containing millions of SNPs are becoming available, we are currently working on the development of a faster version of our software. Fitting the factor model to the HGDP data, we kept the 5, 000 SNPs with the largest Bayes factors. The first two factors mainly measure differentiation between Africa, Western Eurasia and East Asia and some outliers related with these two factors are involved in morphological traits, which is a trait often reported to be enriched with genes having signatures of positive selection  (Barreiro et al.
Simulation of the four-population divergence model The first simulation scenario is a divergence model with 4 populations. Populations have a constant effective population sizes of N e = 1, 000 diploid individuals, with 50 individuals sampled in each population. The genotypes consist of 10, 000 independent SNPs. The simulations are performed in two steps. In the first step, we use the software ms to simulate a neutral divergence model  (Hudson 2002). When looking backward in time, we instantly merge population A 1 with A 2 , and population B 1 with B 2 , then after waiting a number T = 20, 80, 120, 160, 200 of generations, we merge the two remaining populations A and B. We keep only variants with a minor allele frequency larger than 5% at the end of this first step. The second step is performed with the software SimuPOP  (Peng and Kimmel 2005).
Simulation of the stepping-stone model The second simulation scenario is a 2-dimensional stepping-stone model with a 10 × 10 grid. Each of the 100 populations has an effective population size of N e = 1, 000 diploid individuals. We sample 10 individuals in each population, and there are 2, 050 independent SNPs. We also consider a 2-step procedure for the simulations. First, we simulate an equilibrium stepping-stone model with the software ms. Neighboring populations exchange migrants with a rate 4N e m = 8 per generation. Then we superimpose a selection gradient using SimuPOP. During 100 generations, we consider that 50 SNPs confer selective advantage. The selection coefficient s = 0. 1 is maximal in population 64, which is located in the lower right quarter of the grid. In the four neighboring population, the selection coefficient is of s = 0.
However, it does mean that the conformational characteristics of the 10 distinct dinucleotide steps embedded in all possible flanking base pairs, i. e. the conformational statistics of at least the 136 distinct tetranucleotides (AAAA, AAAT, AAAC, etc. ), must be examined. Interrogating the Nucleic Acid Database  (13)  for isolated, unmodified B-DNA crystal structures resolved to at least 2. 5 Å shows that while some tetranucleotides are strongly represented (e. g. CGAA, GAAT, etc. ), in only a very few cases are there enough data to guarantee the statistical significance of any derived average value  (14) , and half of the tetranucleotides are completely absent (data from May 2014). Given this state of affairs, a group of laboratories came together in 2002 with the aim of using molecular simulations to create a balanced structural database at the tetranucleotide level that would provide a guide to sequence effects both on the structure and on the dynamic properties of the B-DNA double helix.
The end of the 50 ns simulations was used as the starting point for the current ABC project where each trajectory was extended to 1 s. In the present work, we treat the first 100 ns of the trajectories as an extended equilibration period and analyze only the remaining 900 ns of simulation. Conformational snapshots were saved from each trajectory at 1 ps intervals, leading to a database of 35 million snapshots which represents 9 Tb of data including solvent and 300 Gb if the water molecules are removed. The first stage of conformational analysis was performed using Curves+  (25) , which provides a full set of helical, backbone and groove geometry parameters. Curves+ uses the commonly agreed 'Tsukuba' reference frame to describe each base  (36)  and respects the Cambridge convention for the names and signs of all helical parameters  (37).
All Curves+ parameters are output in a file containing a single record for each snapshot in each oligomer. These files were then analyzed with the program Canal to obtain statistical data on all parameters, as well as time series, parameter distributions (in the form of histograms) and to search for correlations between parameters. Canal is used here to analyze individual trajectories and to make cumulative analyses over many trajectories. The present versions of Curves+, Canal and other related software are available at http://bisi. ibcp. fr/tools/curves plus/. Note that earlier versions of Canal calculated crosscorrelations using standard algorithms for linear variables. These can be applied to angles if their range is small. However, for long simulations of DNA it is necessary to use formulae adapted to circular variables for backbone dihedrals (and, for security, rotational helical parameters).
III. NUMERICAL ILLUSTRATION We would like now to numerically test the cloaking efficiency. For this, we implement the 3 4 spatially varying entries of the transformed tensor in Cartesian coordinates in the finite element package COMSOL MULTIPHYSICS. We mesh the computational domain using 1105932 tetrahedral elements, 36492 triangular elements, 1128 edge elements and 25 vertex elements, see Figure  2. This domain consists of an isotropic homogeneous elastic medium within a sphere of radius r 3 = 10 m, containing a void surrounded by the cloak (a heterogeneous, anisotropic elastic metamaterial in a spherical shell of inner radius r 1 = 2 m and outer radius r 2 = 4 m ) and a point force located at (4 m, -7 m, 0 m), where (0, 0, 0) is the center of the cloak. The sphere of radius r 3 = 10 m is itself surrounded by a spherical shell of inner radius r 3 and outer radius r 4 = 12 m, which is filled with an anisotropic heterogeneous aborptive medium acting as a (reflectionless) perfectly matched layer (PML).
Introduction Multisite capture-recapture studies record encounters with marked animals over several different sites. Generally, animals will return to the previously visited sites rather than randomly selecting a site. If this is the case, transitions will not be Markovian, so that the transition depends on where the animal was at the previous occasion, rather than just where the animal is at the present occasion. Such models are termed "memory models", see for example  Brownie et al. (1993)  and  Rouan et al. (2009) , and we also use this terminology here to represent multisite capture-recapture models where transitions are non-Markovian. The multisite data set on the Canada Goose, Branta canadensis, from  Hestbeck et al. (1991)  has been used by a variety of authors to demonstrate the use of memory models (see for example  Pradel et al.
2005). The Canada Goose data set is an example of a large multisite data set, having an average of around 1200 animals marked per year per site. In this article, we consider whether the memory model can be fitted to smaller data sets and whether diagnostic memory tests and score tests are able to detect memory when sample sizes are considerably smaller. The parameter redundancy of memory models has not previously been formally evaluated. However,  Rouan et al. (2009)  imposed a restriction to ensure that parameters could be estimated, but that was carried out in an arbitrary fashion and, as we show later in this article, a simpler, more effective constraint can be derived using formal procedures. Similarly,  McCrea and Morgan (2011) , who used score tests for model selection, while aware of parameter redundancy issues, did not provide checks using symbolic methods.
Test WBWA (which stands for Where Before Where After) is then a standard test of homogeneity, as if no memory exists within the study (i. e. , site at time t iÀ1 does not affect site at t i+1 ), then the test will be nonsignificant). We note that if large numbers of individuals do exhibit memory, they will tend to visit the same sites repeatedly, and then large observed numbers are expected on the diagonals of the contingency tables. This test is implemented in the computer package U-CARE  (Choquet et al. 2009b ), and we demonstrate how this is carried out in Supporting Information Data S1. Note that, this test is applied before model fitting, and therefore, it does not matter whether or not potential models are parameter redundant.
Symbolic method Memory models provide examples of models that are structurally too complex for use of the symbolic method developed in  Catchpole and Morgan (1997). Instead, we need first to find a set of parameter combinations, which can be used to investigate parameter redundancy. Cole (2012)  derived one such set of parameter combinations for multistate models using the methods described by  Cole et al. (2010). In Supporting Information Data S1, a suitable set of parameter combinations is derived for multisite models. The parameter combinations for model B are given in equation (  1 ) below; the combinations for model AS and model P are given in Supporting Information Data S1. MAPLE code is given in Supporting Information Data S2 or S3 for automatically creating these sets of parameter combinations.
Cole (2012)  shows how this method of generalization can also be used to extend to any number of states, and we can similarly extend to number of sites. In the example above for N ≥ 2 sites and T ≥ N occasions, the rank of the derivative matrix is N 3 + N 2 + 2NÀ1, which is also the number of parameters, so the model will always have deficiency d = 0. The symbolic algebra involved can be carried out using a symbolic algebra package such as MAPLE (see for example  Catchpole et al. 2002; Cole et al. 2010). Note that any alternative parameterization of the models may be used. One such alternative, involving a reparameterization of the transition matrix to separate survival probability and movement probabilities between sites, is given in Supporting Information Data S1.
The hybrid symbolic-numerical method can be implemented in E-SURGE or MAPLE. In Supporting Information Data S1, we provide a guide to how the hybrid method can be implemented in E-SURGE, and we also provide a MAPLE program that can be used to test whether a specific data set is parameter redundant or not using the hybrid symbolic-numeric method. In this article, the symbolic method is used to find general results about a model. For example, the general result for the example above is that the deficiency of model B is always d = 0. The general result will be the smallest possible deficiency, as it is based on the assumption that all possible histories are observed. Of course, in many real data sets, all the possible histories are not observed, and we shall use the hybrid symbolic-numeric method to investigate the size of a data set that is required for the general parameter redundancy result to still hold.
If the average number of animals marked per year per site is within the range, then the deficiency may change depending on the underlying parameter values. It is recommended in this case that the MAPLE code in Supporting Information Data S4 or S5 be used to check the deficiency for the specific data set used. If the average number of animals marked per year per site is less than the range, then the data set is very likely to have a lower deficiency compared with the general result. C C 0 0 0 C C T 0 0 0 C T C 0 0 N 3 ÀN 2 C T T N N N 3 ÀN 2 + N T C C 0 0 ( NÀ1) 2 T C T 0 0 ( NÀ1) 2 T T C 0 0 N 3 + N 2 À3N + 1 T T T N N N 3 + N 2 À2N + 1 (b) For the simplest case with no time-dependent parameters, a very small number of an animals can be marked per site per year and it is still possible to fit a memory model.
For more complex models, larger sample sizes are needed; however, we do not need as large a sample size as the Canada Goose data set. Model AS needs the smallest sample sizes for deficiency to remain unchanged, then Model P, with model B needing the largest sample sizes. This corresponds to the number of parameters in the respective models, models with more parameters need larger sample sizes, as we might expect. From the results in Table  3 (b), we deduce that for smaller data sets, it may be preferable to use model P with the above constraints rather than model B.
For smaller sample sizes, model P with the constraints given in Section 3. 1 would be better in terms of parameter redundancy than model B. What is clear from this study is that we do not need data sets as large as the Canada Goose data set to use memory models. In terms of general parameter redundancy, model B has more models with deficiency 0, so would be the preferred model to fit. However, we have given constraints that allow model P to still be used. Model P does better than model B in terms of the sample size needed to achieve the general results. Therefore, model P with constraints may be preferable to model B for smaller sample sizes. To use the WBWA test, it is recommended that a sample size of at least 100 animals marked per site per year is used. Whereas to use a score test, 50 animals per year per site is sufficient for the score test to identify memory.
The score test is better than the WBWA test at identifying memory in smaller data sets. However, the diagnostic test is easy to apply as it is available in U-CARE. Bespoke MATLAB code was written for the score test. The two approaches also employ a different strategy for subsequent model selection, with diagnostic tests potentially suggesting a range of possible model extensions relative to the AS model, all of which then need consideration in a second stage of model fitting and comparison. By contrast, score tests which start with a test of memory can then be developed through a succession of step-up stages, involving model elaboration, each time only fitting the most significant model (see for example  McCrea and Morgan 2011). We note finally that our focus in this article has been on exploring tests for memory and considering the parameter redundancy of appropriate models.
Optimization Approach First, our mathematical model was programmed in CPLEX. Then, MAPPOINT software is used to represent manufacturers and depot locations geographically. Then, the software provides a distance matrix between the various locations according to their address. Then, this distance matrix with other data from the case study is introduced in our model via the optimization software CPLEX. The execution of our optimization program provides the optimal delivery routes depending on selected objective (economic, environmental or social). After that, all scenarios can be represented geographically in MAPPOINT software. To operate our case study, we used a set of parameters, summarized in the table 2. Table. 2. Parameters of optimization (adapted from  [13]  and  [14] ) 703 fleet of type 1 vehicles gives better results than homogeneous fleets that consist sequentially of type 2 and type 3 vehicles.
Among them, only  (Kim et al. 2010 ) is available online and can be applied on a structural complex from a PDB structure. SymD is based on a fitting and permutation of monomers algorithm to identify the symmetries. In spite of its relative efficiency, SymD cannot deal with structures of more than 10 000 atoms and misses most of the large complexes with the most remarkable symmetries. Moreover, the program outputs only symmetry axes, whereas several structures rather present centres of symmetry. Symmetries being the starting point for the design of navigation guides that will be described, the automatic detection of these geometric features have been developed by our team but will not enter in the scope of this study.
Context support Visualisation of molecular complexes has been brought to everyone's hands thanks to numerous programs such as PyMol  (DeLano 2002)  or VMD  (Humphrey et al. 1996). As a first step to assess navigation paradigms we chose to develop a PyMol plugin based on the python language, using the API available for this program. PyMol was chosen because it is recognized as broadly used software in the structural biology community and could thus be easily evaluated by scientists in this field. Our developments are, however, completely independent of the visualization software used and we expect to port the paradigm on a large-scale visualization platform very soon. A short-term use case will see the integration of our paradigm in the specific and emergent case of visual analytics. This development will take place in an immersive context represented by a CAVE-like system.
By doing this, memory waste caused by the static pre-reservation can be avoided. Since the dynamic-stack scheduling has higher run-time overhead, the hybrid scheduling mechanism, which can reduce the thread preemption frequency, is implemented in LiveOS. With the hybrid scheduling, the memory cost of the dynamic-stack scheduling can be decreased, while the performance of the dynamic-stack scheduling is maintained. To achieve the energy-efficient objective, LiveOS implements two energy-conservation approaches: the multi-core "context-aware" approach and the multi-core "power-off/wakeup" approach. As opposed to the other conservation approaches in which energy is conserved only from the software aspect, energy conservation in LiveOS is achieved from both the software aspect and the multi-core hardware aspect.
Cooperative Multithreading Approach The cooperative multithreading approach is commonly used to solve the deadlock and synchronization problem in the multithreaded OS. However, it can also be used to optimize the memory cost of thread stacks. Typically, this optimization approach has been used in the Embedded Java Virtual Machine (EJVM). EJVM is attractive to the WSN as it not only simplifies the WSN programming process (programming by using Java without considering the low-level details), but also improves the WSN reprogramming performance (only Java application image rather than the monolithic software image needs to be updated during the reprogramming process). Currently, the cooperative multithreading approach (also named "atomic instruction" or "bytecode-granularity switch" in the EJVM terminologies) has been applied in several EJVMs, including the simpleRTJ  [13] , the Darjeeling VM  [14]  and the TakaTuka JVM  [15].
Code Memory Size of the Multithreaded Scheduling The current popular WSN multithreaded systems include the TinyOS TOSThread  [25] , Contiki multithreading  [17]  and mantisOS  [12]. MantisOS is a purely traditional multithreaded WSN OS. TinyOS TOSThread and Contiki multithreading are the multithreaded systems which are developed complementarily on the basis of the event-driven kernels (Figure  15 ), and the purpose of these two systems is to add the preemption functionality (required by the long-time tasks which cannot be split by the split-phase programming  [9] ) into the fully event-driven systems. LiveOS in which the event-driven scheduler and the multithreaded scheduler are implemented in parallel, the multithreaded scheduler in TinyOS and Contiki is implemented on the top of the event-driven scheduler.
Since the event-driven scheduler is still used in the native scheduling layer, the real-time scheduling cannot be achieved in these two OSes as of yet. In Figure  16 , the code memory size of the different systems is shown. MantisOS consumes the most code memory resources, this is because it is a pure multithreaded WSN OS. In mantisOS, all system tasks (either RT tasks or non-RT tasks) are executed by threads, thus the thread number can be large. In order to manage these threads efficiently, the multi-level queue scheduling approach is used (Figure  17 ). This approach improves the scheduling efficiency, but increases the code memory size. Code memory size of TinyOS TOSThread is also large because the application space is decoupled from the underlying system space in TOSThread  [25]  (Figure  15a ), and the message communication mechanism is applied for the interaction between the application space and the system space.
With these mechanisms, the application development process in TinyOS is simplified, but the software architecture becomes more complicated, and more code memory resources are needed. Contiki multithreading system is simply implemented as a library, and the code memory size is the minimum. In LiveOS, the dynamic-stack hybrid scheduling mechanism is implemented. The code memory size of LiveOS scheduler is not large if compared to the others, and there are two main reasons: First, the thread number in LiveOS is small (all non-RT tasks are executed by only one thread after the hybrid scheduling mechanism is applied, Figure  11 ), thus thread scheduling architecture is simple, and the code memory size of the multithreaded scheduler is not large (986 bytes). Second, the event control flag mechanism (Figure  11 ) rather than the scheduling queue mechanism (Figure  1 ) is used in LiveOS for the implementation of the event-driven scheduler.
In LiveOS, every non-RT task has one corresponding event control flag. Once an event is triggered, the related control flag will be set. An event-driven scheduler polls the control flags in loop. If a control flag is observed to be set, the related task will be executed. By using this mechanism, the event-driven scheduling architecture becomes simple, and the event-driven scheduler costs only 150 bytes of code memory in LiveOS (TinyOS event-driven scheduler: 178 bytes; Contiki event-driven scheduler: 936 bytes). Figure  17. Multi-level round-robin scheduling in mantisOS. By using these scheduling queues, threads in mantis OS can be managed efficiently.
With these approaches, more than 30% of the energy resource can be conserved by LiveOS when compared to a typical single-core WSN system. With the energy optimization strategy, the lifetime of WSN nodes can be prolonged. Consequently, the WSN nodes become more competent to be deployed in harsh outdoor environments. Currently, an online demo about LiveOS can be accessed from the websites  [34]. The ongoing work of LiveOS will focus on the following topics: (1) Optimizing the memory cost further by implementing a dynamic memory allocator which has high memory utilization efficiency, yet keeps low allocation overhead; (2) Combining the multi-core hardware technique and the state-machine validation software technique to improve the reliability of the WSN nodes; (3) Developing an integrated System on Chip (SoC) multi-core node.
Acknowledgments This work has been sponsored by the  French government research program  " Investissements d'avenir " through the  IMobS3 Laboratory of Excellence  ( ANR-10-LABX-16-01 ), by the  European Union  through the program Regional competitiveness and employment 2007-2013 (ERDF-Auvergne region). Our thanks also to the support from China Advanced  Technology 863 Research Program  ( 2012AA010904 ),  China National Natural Science  (No. 60903195 ),  China Scholarship Council  PhD Program (2009627016), and the " Space satellite research on collaborative ad hoc networks " project hosted by the  Innovation Fund of Satellite Application Institute in China Aerospace Science and Technology Corporation.