dataesr/scientific-paragraphs-categorization huggingface dataset:
Software ecosystem Even the implementation of known algorithms for a swarm can be very challenging, especially considering that swarms are in essence decentralized systems, the behavior of which is based only on local interactions. To the best of our knowledge, only one solution can provide portability, scalability and fast development time: Buzz. Buzz is both a programming language and a virtual machine to run its scripts. It was created by our research group in 2016 to accelerate the implementation of swarm behaviors  [2]. Buzz provides special constructs to address three essential concepts: a) shared memory (virtual stigmergy), b) swarm aggregation, and c) neighbour operations. The Buzz virtual machine (BVM) must run on every unit of the swarm and with the exact same script, but units can differ (i.
e. , a heterogeneous swarm), since the language is platform-agnostic. Example scripts are available online  [43] , as well as the code for the compiler and BVM  [44]. The behaviors described in this section are also open-source  [45]. Using Buzz, we ensure our code can be deployed on many hardware platforms. In this work we also leveraged its swarm-level primitives: virtual stigmergy  [46]  and neighbour operations. We use the former (a tuple space shared across the swarm) to agree on swarm-wide variables, such as the current state in a swarm-wide state machine. We use the latter (a local communication system) as it is the root of most swarm intelligence algorithms: local interaction.
Control algorithms As shown in Figure  2 , we implemented a set of five common swarm behaviors in Buzz scripts: aggregation, formation from graph descriptions, cyclic pursuit, autonomous deployment, and flocking. All scripts require only local interaction with their neighbours: for n robots in the swarm, each pair of robots knows b ij , the bearing between robot i and j, and d ij , the distance between these two. In the following subsections, we detail each algorithm to compute from these inputs, sometimes in conjunction with consen-sus mechanisms, each robot's velocity vector. Their usage for both experiments, such as to which emotional state (fear, anger, happiness, sadness, surprise or disgust) they were associated, is mentioned. In a Buzz script, this velocity vector is an argument to a function dealing with low-level hardware control to actuate the robot.
Hardware selection The robotic platform selected for this study has to be portable and avoid as much as possible any bias due to an anthropomorphic or zoomorphic appearance. We selected the Zooids  [55] , a group of small tabletop cylindrical robots 2. 6 cm in diameter, localized using structured light emitted by a ceiling projector. While our behavioral scripts can be ported on any hardware platform (as explained above), we selected the Zooids for the minimal setup time, their low cost of manufacturing, their open-source controller code, and the simplicity of their manipulation. Even if abstract shapes are a bit less common in human-robot interaction studies, examples showed they can, for instance, generate less embarrassment from the user  [56]. We built a charging station and we made enough Zooid units for two sets: one can charge while the other performs.
Methods To illustrate the different motion patterns in multiple sequential sessions, we alternated between two sets of six Zooids robots. As shown in Table  2 , the three high-level motion attributes described earlier were used as binary in-Brought to you by | Ecole de technologie supérieure (ETS) Authenticated Download Date | 1/20/20 9:03 PM puts, generating 8 possible combinations, i. e. 8 different motion scripts. Each motion followed the same goal sequence (see Figure  4 ): (1) from point A to point B, (2) from point B to point C, (3) from C to B, (4) from B to C, and (5) from C to A. Participants were asked to sit in front of the table on which the Zooids performed. They had a 14 questions to answer on a tablet (available in French and English) after observing each sequence. Each motion script was run only once for the participants, but they were played following one of three possible orders: 1-2-3-4-5-6-7-8, 5-6-7-8-1-2-3-4 and 1-2-7-8-3-4-5-6.
Results This study presents a large number of tied ranks for a relatively small dataset (27 participants). We used Kendall's τ b correlation test to assess the contribution of each parameter in our dataset. We extract the perceived organization from the measures of cohesion and expressivity. As we could not assume that the psychological distance between the scores of expressivity and between those of cohesion were equivalent, we used an ordinal logistic regression to examine the effect of spatial synchronization, temporal synchronization, and distance on both perceived cohesion and expressivity.
Discussion The Buzz programming language and its virtual machine proved a versatile tool to design expressive sequences. Due to the simplified design parameters, the choreographers achieved a mapping between the target emotional states and the control algorithms, without requiring decentralized programming expertise. Each available control algorithm was in the end associated with a unique target emotional state, such as fear with 'aggregation', whereas anger was uniquely associated with the 'random deployment' algorithm. However, this mapping between emotions and control algorithms did not translate into a unique recognition profile for the emotional states: in general participants had difficulty identifying the emotions intended by the choreographers, while still being able to differentially respond to these emotions.
, 2015). For quantitative data, linear mixed models are typically employed with several factors modelled in including genotype, sex, sexgenotype interaction, body weight and batch (i. e. phenotype measures collected on the same day). Mutant mouse lines found to have a significant deviation in phenotype measurements are assigned a phenotype term from the Mammalian Phenotype Ontology  (Blake et al. , 2017). These associations, as well as the raw data, are disseminated via the web portal (https://www. mousephenotype. org) using application programming interfaces and data downloads. A challenge with high-throughput phenotyping efforts is the small sample size for the experimental group (i. e. the knockout mice) that is produced to maximize the use of finite resources, considering biological relevance and power analysis  (Charan and Kantharia, 2013).
All mice generated by the IMPC are on the inbred C57BL/6N strain. To reduce genetic drift, IMPC centers maintain wild-type C57BL/6N production colonies that are periodically rederived using commercial vendors  (Dickinson et al. , 2016; Kurbatova et al. , 2015). Mutant F0 mice are bred with wild-type mice from the production colonies to reduce the confounding effects of any de novo, non-targeted mutations. In addition, the IMPC centres are encouraged to measure these knockout mice in two or more batches, as this improves the false discovery rate by modelling in the random effect of day-to-day variation  (Karp et al. , 2014). In contrast, large control sample sizes accumulate as they provide a strong internal control of the pipeline and typically generated with every experimental batch. Such large control groups represent a unique dataset that increase the power of the subsequent analyses and allow the construction of a robust baseline  (Bradley et al.
Thus, more tuneable approaches were needed. In this study, we address the complexity of the data collected over time by proposing a novel windowing strategy that we call 'soft windowing'. This approach utilizes a weighting function to assign flexible weights, ranging from 0 to 1, to the control data points. Controls that are collected on or near the date of mutants are assigned the maximal weights, whereas controls at earlier or later dates are assigned less weight. In contrast to the hard windowing, the weighting function in the soft windowing allows for different shapes and bandwidths by alternating the tuning parameters. In addition, we demonstrate how to tune parameters and demonstrate the implementation of the soft windowing on the IMPC data.
The same approach can be generalized to multiple signals  (Huang et al. , 2007; Li et al. , 2007; Tang et al. , 2009)  or applied as a rolling window  (Harel et al. , 2008)  in the presence of exogenous variables to account for time dependency in the regression coefficients  (Brown et al. , 2018). Alternatively, we propose a soft windowing approach for the regression methods by defining a weighting function that applies less weight to the residuals outside the window of interest. This leads to distinct advantages over the hard windowing. First, the entire dataset is included in the analysis in contrast to the limited data points in the hard windowing. Second, the windowing and the parameter estimation are coupled, which is a direct result of using the weighted least squares (WLS). Critically, by bounding the controls in a window, we freeze the analysis and abrogate the need for further analysis assuming no new experimental data are generated within the time window.
We should stress that the definition of TðlÞ in the soft windowing can be challenging because the WGF assigns weights to the entire dataset in the final fitting. To address this complexity, we propose the Sum of Weights Score by SWSðk; lÞ ¼ P n i¼1 WGFðt i ; k; l; mÞ, that is the summation of weights from WGF for specific l and k. Note that SWSðl; kÞ ! TðlÞ with the equality for sufficiently large k. Because l is generally unknown, a value of TðlÞ ¼ T independent of l needs to be decided before the analysis. Our experiments, inspired by the z-test minimal sample size ðn > 30Þ, show that setting SWS ! T with T % maxð35; ffiffiffiffiffiffiffi ffi np 2 p Þ Single peak 35p Multiple peaks ( provides sufficient statistical power and precision for the analysis of each sex-parameter in IMPC. Once the bandwidth, l, is selected, the shape parameter, k, can be optimized on a grid of values similar to l.
This algorithm is implemented for a broad range of models in the R package SmoothWin that is available from https://cran. r-pro ject. org/package¼SmoothWin. The main function of the package, SmoothWinð. Þ, allows an initial model for the input and, given a range of values for the bandwidth and shape, it performs soft windowing on the input model. Furthermore, it allows plotting of the results for diagnostics and further inspections. One also can generate the weights from SWGF using the expWeighð. Þ function.
Sensitivity analysis The sensitivity of the soft windowing to the tuning parameters in particular, the minimum observation required in the window (T), is tested on the two IMPC examples introduced in Figure  1   where n is the total observation in the dataset. We should stress that l and k are selected to cover the entire experiment range and avoid bias by selecting the incomplete ranges. Then we only study the effect of T on the final fittings. Figure  5  shows the sensitive of soft windowing to the T, for instance, selection of a high value for T could lead to including too much noise in the final fitting.
We create artificial mutant groups by randomly sliding the true mutant structure over the time domain of controls, collecting as many controls as there were mutants in the original set and repeating this procedure five times per dataset (Supplementary Fig. S1  shows an illustration of three iterations of the structured resampling on the Bone Mineral Content parameter). For non-windowed and soft-windowed analyses, the same statistical model is fitted. That is the linear mixed model implemented in the R package PhenStat with genotype, sex, genotype-sex interactions and body weight for the fixed effect terms and the batch in the random effect. This setup implies that the difference in the results is a direct consequence of the control selection strategy by soft windowing. The outcome of the simulation study consists of 18 IMPC procedures across 11 centres and over 2:5 million analyses and P-values.
Soft windowing as part of the IMPC statistics pipeline We next show the performance of the soft windowing approach on IMPC data by integrating it into the standard IMPC statistics pipeline in PhenStat  (Kurbatova et al. , 2016). To this end, each dataset is processed by the PhenStat for the initial estimation of a fully saturated linear mixed model including genotype, sex, genotype-sex interaction and body weight in the fixed effect term and the batch in the random effect. The resulting fit is then passed into the soft windowing algorithm in the R package SmoothWin for the determination of the optimal windowing weights. After determining the optimal weights, the final model is fitted using a weighted linear mixed model and utilizing a backward elimination approach to optimize the final model.
To explore these differences further, we created an online tool that displays the entire control dataset for a given mouse line-parameter assay with the statistical summaries for both the non-windowed methodology and the soft-windowed approach. Users may filter on a number of attributes, arrange filter order, zoom in on data visualization or navigate directly to the results (https://wwwdev. ebi. ac. uk/mi/impc/ dev/phenotype-archive/media/images/windowing/). Figure  6  shows the corresponding visualization on the IMPC website for the complete dataset (including males and females) previously shown for males only in Figure  1  (top) for the Forelimb grip strength normalized against body weight parameter from the IMPC Grip Strength procedure. The soft window is indicated, as well as   changes in the total number of controls (here 1; 572 fewer after soft windowing-counting soft windowing weights >10 À7 ).
Furthermore, the P-value corresponding to the genotype effect shows a significant change in magnitude, from 2:05 Â 10 À4 to 6:75 Â 10 À18 after applying the soft windowing. We then tested if our soft-windowed analysis changed our human disease model discovery rate. We have previously described the IMPC Phenodigm translational pipeline that automatically detects phenotypic similarities between the IMPC strains and over 7 000 rare diseases described in the Online Mendelian Inheritance in Man (OMIM), Orphanet and the Deciphering Developmental Disorders (DDD) databases  (Meehan et al. , 2017). This pipeline generates qualitative scores on how well a mouse line's associated phenotypes overlap with the phenotypes of the human rare disease populations  (Akawi et al. , 2015; Firth et al. , 2009; Meehan et al.
, 2017; Mungall et al. , 2015; OMIM Browser, 2017; Rath et al. , 2012). By comparing the disease model resulting from our soft-windowed analysis versus non-windowed analysis for IMPC data release 9:2, we find a slight increase in the number of disease models (106 versus 99 models using a threshold of 50% phenotype overlap from a set of 2082 mouse lines that contain mutations-Supplementary File SI).
Discussion High-throughput phenomics is a powerful tool for the discovery of new genotype-phenotype associations and there is an increasing need for innovative analyses that make effective use of the voluminous data being generated. Batch effects are inevitable when a large amount of data is collected at different times and/or sites and, therefore, need to be accounted for in the statistical analysis. In this study, we developed a novel 'soft windowing' method that selects a window of time to include controls that are locally selected with respect to experimental animals, thus reducing the noise level in the data collected over long periods of time (years). Soft windowing has notable advantages over a more traditional hard windowing approach. In contrast to the limited data points included in the hard windowing method, the entire dataset is considered for the analysis.
To this end, we engineered a weighting function to produce weights in the form of a window of time. Control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. This method has the capability of producing indivdual windows as well as merging intersected ones. Moreover, the method was implemented to automatically select window size and shape. The performance of the method was shown on a simulated scenario that uses real control data collected by the IMPC highthroughput pipelines to assess detection of false positives. We also showed the enhancements to the IMPC statistical pipeline that establishes genotype-phenotype associations by comparing mutants versus control data using our soft-windowed approach. There are two known conditions that affect the method: (i) the WGF can be slow when there are too many (>20) distinct windows, however, we have optimized the algorithm to be fast enough for the typical IMPC number of peaks (%3s for 1500 samples and 16 peaks under k ¼ 1 and l ¼ 30); and (ii) our resampling scenario indiciated that our soft windowing approach is sensitive to the data that have a high level of outliers or random deviation from the mean.
This may result from a bias in the design of the resampling but may also indicate that using all available controls may be appropriate for the cases with extreme variability. Our soft windowing approach addresses the scaling issues associated with analysing an ever-increasing set of control data in longterm projects by eliminating controls with weights sufficiently close to zero from future analysis. In the case of the IMPC, once a window of control data is determined for a dataset, there would be no further requirement to re-analyse the dataset with each subsequent data release. This will reduce the computational resources needed with the resulting gene-phenotype associations remaining stable, greatly facilitating data exchange with research groups trying to functionally validate genes and their disease variants.
Our findings also have important implications for such efforts as the UK BioBank and the All of Us initiatives where large cohort sizes coupled with mobile medical sensors are generating phenotype data at an unprecedented rate  (Sankar and Parker, 2017; Sudlow et al. , 2015). Researchers performing restrospective analysis to analyse exposures for a defined outcome group (e. g. metabolic disease) are challenged by the variability and longitudinal characteristics associated with these datasets. The methods described here can be used with these human health resources to maximize analytical power and help researchers find the genetic and environmental contributers to human diseases.
; 1Þ and I is the indicator function, Iðx 2 ½a; bÞ ¼ 1 x 2 ½a; b 0 o:w : & In other words, the model is piecewise linear and only significant in the t 2 20; 40 ð Þinterval. Figure 4 (top) shows the global estimation of the linear regression from the entire data (dotted black line) and the WLR by WGFðt; 9; 5; 30Þ (dashed blue line) as well as weights from the WGF on the bottom. This plot shows that the non-WLR leads to a horizontal line, where no significant gradient is detected, whereas the WLR tends to model the significant section of the data that leads to fitting the true line. Figure 4 compares the effect of windowing versus considering the entire dataset, showing the different conclusions.
ILC and CTS Primer In this section we exemplify ILC by applying it on an average function, show why the resulting incremental program is asymptotically inefficient, and use CTS conversion and differentiation to incrementalize our example efficiently and speed it up asymptotically (as confirmed by benchmarks in Sect. 4. 1). Further examples in Sect. 4 apply CTS to higher-order programs and suggest that CTS enables incrementalizing efficiently some core database primitives such as joins.
Incrementalization Case Studies In this section, we investigate two questions: whether our transformations can target a typed language like Haskell and whether automatically transformed programs can perform well. We implement by hand primitives on sequences, bags and maps in Haskell. The input terms in all case studies are written in a deep embedding of λ AL into Haskell. The transformations generate Haskell code that uses our primitives and their derivatives. We run the transformations on three case studies: a computation of the average value of a bag of integers, a nested loop over two sequences and a more involved example inspired by Koch et al. [17] 's work on incrementalizing database queries. For each case study, we make sure that results are consistent between from scratch recomputation and incremental evaluation; we measure the execution time for from scratch recomputation and incremental computation as well as the space consumption of caches.
Unlike DBToaster, we assume our program is already transformed to explicitly use indexes, as above. Because our indexes are maps, we implemented a change structure, CTS primitives and their CTS derivatives for maps. To build the indexes, we use a groupBy function built from primitive functions foldMapGroup on bags and singleton for bags and maps respectively. The CTS function groupByC and the CTS derivative dgroupByC are automatically generated. While computing the indexes with groupBy is self-maintainable, merging them is not. We need to cache and incrementally update the intermediately created indexes to avoid recomputing them. We evaluate the performance in the same way we did in the other case studies. The input of size n is a pair of bags where both contain the pairs (i , i ) for i between 1 and n.
Incremental computation on collections or databases by finite differencing has a long tradition  [6, 22]. The most recent and impressive line of work is the one on DBToaster  [16, 17] , which is a highly efficient approach to incrementalize queries over bags by combining iterated finite differencing with other program transformations. They show asymptotic speedups both in theory and through experimental evaluations. Changes are only allowed for datatypes that form groups (such as bags or certain maps), but not for instance for lists or sets. Similar ideas were recently extended to higher-order and nested computation  [18] , though only for datatypes that can be turned into groups. Koch et al. [18]  emphasize that iterated differentiation is necessary to obtain efficient derivatives; however, ANF conversion and remembering intermediate results appear to address the same problem, similarly to the field of automatic differentiation  [27].
Acknowledgments. We are grateful to anonymous reviewers: they made important suggestions to help us improve our technical presentation. We also thank  Cai Yufei ,  Tillmann Rendel ,  Lourdes del Carmen González Huesca ,  Klaus Ostermann ,  Sebastian Erdweg  for helpful discussions on this project. This work was partially supported by  DFG  project  282458149  and by  SNF  grant No. 200021_166154.
Various metagratings have been used to demonstrate efficient light deflection at visible regime but the performance does not exceed 80%  40. Here, we provide optimized 3D metasurface designs with record efficiency above 87%. To the best of our knowledge, this is the highest expected performance reported in the literature at visible regime for 3D gradient metasurfaces. Our global optimization techniques rely respectively on advanced evolutionary strategies and statistical learning, coupled with a high order Discontinuous Galerkin Time-Domain (DGTD) solver from the DIOGENeS software suite dedicated to computational nanophotonics  41. We apply the optimization solution to design optimal phase gradient metasurfaces made of scatterers of different shaped, such as rectangular and cylindrical nanopillars.