dataesr/scientific-paragraphs-categorization huggingface dataset:
The MOSAIIC Agent-Based Traffic Model As stated in the previous section, we chose to implement the model with the GAMA platform. The GAMA platform provides modelers-who quite often are not developers-with tools to develop highly complex models. In particular, it offers a complete modeling language (GAML: GAma Modeling Language) and an integrated development environment that allows modelers to quickly and easily build models. Indeed, the GAML language is as simple to use and to understand as ¼ the Netlogo modeling language [14] and does not require high level programming ½ skills. In addition, GAMA provides different features that can be used by modelers ¾ to develop traffic models. In particular, GAMA allows to simply load GIS data ¿ (shapefiles, OSM data ), to define graphs from polyline geometries, to compute Í AE Ç Ê Ê Ì È Ê Ç Ç 24 MOSAIIC: City-Level Agent-Based Traffic Simulation Adapted to.
While recent studies have suggested that the fission yeast diameter is set by the size of Cdc42-GTP domains  [4, 16] , our data refine these views, and demonstrate that domains can also rapidly adapt to cell morphology, highlighting novel functional interplays between shape and polarity  [18, 20]. Accordingly, recent theoretical models predict the existence of stability conditions on the adaptation of Cdc42-GTP domain size to cell geometry required to maintain a stable diameter over generations of dividing cells  [12]. Finally, by analyzing images of active-Cdc42 domains in other cell types we found that our scaling could be valid over a 100-fold range in radii of curvature (Figure  S4C            We note that radius of curvature was determined using the same script for MDCK; and this analysis may be prone to uncertainty given the more irregular shapes of the plasma membrane in these cells.
Image analysis To analyze polarity domain width and curvature, we developed a Matlab package "CurvPol" that we make available to the community. This script starts from a path to a data folder containing many fields of many spores or cells. The script interacts with the user; and first displays a large image of the field and asks how many spores have to be analyzed. The user then draws one ROI around each spore, and one for the background. The script displays a large view of the fluorescent image of the spore expressing CRIB-3GFP (or any other marker) and the user places 20 points around the cell edge following the fluorescence. We confirmed that adding more points did not improve neither analyses of cell shape nor polarity domain size. These discrete points are interpolated using cubic spline method, with an interval of interpolation of one pixel, to smooth the cell contour.
Along the determined cell contour, a one dimensional profile of polarity factors' intensity is computed by integrating the fluorescence signal in a direction perpendicular to the cell contour. The integral area (thickness of cell edge) can be varied in the initial settings and had little effect on the result for CRIB signals (Figure  S1 C-E ). As a default value for all analysis on CRIB presented in the paper the thickness used was 2px. For other markers, like for3-3GFP and bud6-tomato, which are dottier and more weakly associated to the membrane, we used a 8px thickness. Based on the 1D signal profile, singularly polarized spores were selected and analyzed further automatically (see 'Data selection' hereafter). To define the size of polarity domains, the script first searches for the position with the highest signal intensity along the cell contour.
Then the signal profile around the peak is fitted with a Gaussian function (Figure  S1A  left) [S4-S6]. We note that spores pose more challenge to analyze than vegetative cells due to much lower fluorescent signals  [S3]. For images acquired at 250x magnification a default of 60 points (~2. 6 µm) were used for the fit. The domain size was determined as the full width at half maximum (FWHM) of the fitted Gaussian function. Fitting parameters (number of points for the fit and initial width for fitting search) can be changed in the script. As noted above, the outcome was robust against the choice of parameters. In addition, if a fit appears to be off the actual data, the software allows the user to place the analyzed cell in a category for re-analysis, which can be subsequently performed by modifying fitting parameters.
To define cell local curvature at the polar cap, a small number of cell contour points were fitted with a circle. The center of these points was determined as the center of the fitted Gaussian function, and a default of 3 µm contour (~70 points at 250x) around the center were used to compute local curvature radius (Figure  S1F ). This length can be modified in the script input; and in events of unsatisfactory curvature fitting, the user can again place the analyzed cell in a category for reanalysis which can be performed by modifying this length. We found that varying this length between 1-5 µm did not largely influence the result (Figure  S1F  and S1G ).
Data selection Polarized spores were selected in an unbiased manner with this script (Figure  S1A ). The selection was based on the 1D intensity profile of polarity factors at the cell membrane. The first polar domain was identified by Gaussian fitting around the point with the highest intensity. If the fitting was bad (characterized by a failure in the regression), the data was automatically defined as 'nonpolarized'. We note that most of the data classified in this category are non-polarized spores or polarized spores with very low signal. To discard spores with multiple domains, the same domain identification was performed from the 1D intensity profile, in which the first domain approximated by the Gaussian function was subtracted. The ratio of second to first peaks was used to define single vs multipole domains.
ACKNOWLEDGMENTS The authors acknowledge  S. Martin ,  M. Balasubramanian  and  F. Martin-Belmonte  for sharing material. We thank  G. Romet-Lemonne  and  A. Jegou  for careful reading of the manuscript, and  V. Contremoulins  for help with Imaris. Our laboratory is supported by the  CNRS , and grants from the  FP7 CIG program ,  ITN "FungiBrain" , the " Mairie de Paris emergence" program  and the  European Research Council  ( CoG Forcaster   N° 647073 ).
This cross produces female flies that are heterozygous for the dominant Tl 10B allele (sqh-MRLC::eGFP/ þ ; Tl 10B /UASp-Gap43::mCherry), which were crossed to sqh-MRLC::eGFP/Cyo; UASp-Gap43::mCherry/MKRS males to collect ventralized embryos. The stock sqh-Resille::mCherry; bnt/TM3 was a gift from Eric Wieschaus. MuVi-SPIM imaging and data processing. Imaging was performed using a MuVi-SPIM  23. Illumination was done using Nikon Â 10/0. 3w objective lenses and detection with Nikon Â 25/1. 1w objective lenses, resulting in an effective image pixel size of 0. 26 mm Â 0. 26 mm. Optical sections of an embryo were recorded with a typical spacing of 1 mm. Therefore, the effective volume of every voxel was B0. 07 mm 3. Embryos were imaged from two opposing directions simultaneously and successively from two directions normal to the first two (0°¼ dorsal and ventral view; 90°¼ lateral views), with time resolution B15-20 s per complete data set.
2e. We typically found inner layers up to a depth of 20 mm from the apical surface using 1 mm steps along the direction of the normal vectors. For each layer, we performed a cylindrical projection onto a 2D sheet (Supplementary Fig. 2f )  23. Our primary focus for this work was the 180-mm-wide region along the AP axis at the centre of the embryo. This region was only minimally distorted by the cylindrical projection, allowing straightforward calculation of cell properties such as surface area  23. Optical sections of the embryo. Cross, sagittal and coronal sections were obtained using Fiji software, and selecting one plane out of a 3D-fused stack. All cross-sections are taken at half embryo length. Cell segmentation tracking and analysis. Projected surface views of cells were segmented and tracked with the freeware software Ilastik  46  and Cell-Profiler 47 respectively.
Cell shape analysis and cell kinematics were measured using CellProfiler and Matlab. Track rendering was obtained with Imaris (Bitplane) software. The internalization of the ventral furrow was tracked by using the 'Manual Tracking' Fiji plugin that also provided measures of the furrow depth. Imaris (Bitplane) software was used for rendering. Cell orientation (for example, Fig. 3c, d ) is defined by fitting an ellipse to the cell and measuring the angle of the major axis. The axis parallel to the AP axis of the embryo is defined as 90°, the axis parallel to the DV axis as 0°, as illustrated in the schematics (Fig. 3d , bottom, and Supplementary Movie 12). The y axis in the plots is thus cell orientation (average and s. d. ), whereas the colour shows the aspect ratio (ratio between major and minor axis of the fitted ellipse to cells).
These constraints imply that the crosssection area of each cell A c and of yolk A y are fixed too; based on experiments we choose A c/ A y ¼ 11/600 ¼ 0. 0183 (ref. 32). Apart from being subject to these constraints, cell shape is governed by intracellular forces due to the contractility of actomyosin cortical network and cell-cell adhesion mediated by cadherins. We introduce effective surface tensions of the apical, basal and lateral cell sides G a , G b and G l , respectively, which are all different from each other. The total energy reads: W ¼ X N i¼1 G ðiÞ a l ðiÞ a þ G ðiÞ b l ðiÞ b þ 1 2 G ðiÞ l l ðiÞ l ; ð1Þ and the minimal-energy shape of the embryo is computed using Surface Evolver package  49. Apart from the intrinsic preferred cell shape, which is given by the model parameters as studied in ref.
Acknowledgements We thank  D. Gilmour ,  R. Mathew ,  M. Rembold ,  R. Reuter ,  S. Roth  and  C. Collinet  for critical reading of the manuscript;  E. Wieschaus ,  T. Lecuit ,  C. Collinet ,  P. -F. Lenne  and  A. Politi  for fruitful discussions;  E. Wieschaus ,  M. Rembold ,  S. De Renzis  and  T. Lecuit  for the gift of flies;  G. Quintas Glasner de Medeiros ,  C. Tishier  and members of the  EMBL ALMF  for technical support;  E. Mottay  and the  laser company Amplitudes Syste  `mes for their continuous support and collaboration. T. E. S. was funded by the  EMBL Interdisciplinary Postdocs initiative. P. Z. and M. K. were supported by the  Slovenian Research Agency  (grant no. P1-0055 ) and by the  ESF   Research Networking Programme QuanTissue. M. R. is recipient of a co-financed  EMBO-Marie Curie fellowship  and of a  Human Frontier Science Program Long Term  fellowship.
This is important to understand performance limitations and bottlenecks during experiments -both those caused by the experiment itself, and those caused by limitations of the testbed (e. g. network architecture) or other concurrent experiments. It is also a prerequisite to evaluate the impact of various solutions on the environment (e. g. networkefficiency, power-efficiency of applications), and to experiment on applications that try to optimize various criteria using feedback loops. Sometimes measurements can be performed by the experimenters themselves, by instrumenting the application, or through the use of system-level instrumentation and monitoring tools such as DTrace  [7] , systemtap  [14]  or Linux's perf tools  [12]. But this approach has three main limitations. First, the measurement infrastructure is part of the experiment itself, and might cause a non-negligible impact on the experiment execution and results, depending on how the measurements and the instrumentation are performed.
Second, it puts the burden of instrumentation on the experimenters. This can be justified when the instrumentation needs are somehow specific to the experiment, but there are many cases where fairly standard measurements need to be collected. Finally, it cannot provide access to data that is not normally accessible by the experimenter without cooperation from the testbed operators. Grid'5000  [5]  is a testbed for experiment-driven research on HPC, Clouds and Big Data. It is structured in 10 sites (geographical locations) connected together with a dedicated 10-Gbps backbone network. Overall, the testbed contains 1000 nodes (or 8000 CPU cores). The testbed already provides a number of advanced features: resources are automatically described and verified using a Reference API  [19] , experimenters can reconfigure the software stack using Kadeploy  [18]  and isolate their experiments at the physical network level with KaVLAN  [5]  in order to execute complex middlewares  [4].
Overview The Kwapi framework was originally developed to provide power consumption measurements to the cloud computing software platform OpenStack, in the context of the XLCloud project 1. Its architecture (Figure  1 ) is based on a layer of drivers, which retrieve measurements from several devices, and a layer of plugins that collect and process them. The communication between these two layers goes through a bus, The driver layer is controlled by a Driver Manager that reads the configuration file and initializes a thread for every entry found. An entry consists in the list of probes, the kind of driver to use and the relevant parameter (e. g. SNMP OID). Each driver retrieves measurements and pushes them to the forwarder in JSON format, using ZeroMQ as transport layer. The Forwarder is an internal communication bus based on ZeroMQ.
It works using a publish/subscribe pattern where the drivers are publishers and the plugins are subscribers. It can work locally, i. e. with publishers and subscribers on the same machine, or through a gateway machine to connect isolated networks. In the case of a distributed architecture, a plugin can listen to several drivers located at remote locations using the forwarder. A plugin is a data consumer that retrieves and processes measurements from the Forwarder. In the original Kwapi framework, only two plugins were present: a REST API that allows an external system to access real-time measurements, and a visualization plugin based on Round-Robin Database files that expose metrics through a web interface.
The simplest case is a node powered by a single PSU, but commonly, a node has two redundant PSUs. Both must be monitored and their consumption must be added to compute the node's total consumption. More complex situations involve nodes that share one or more individual PSUs, as for blade servers. Thanks to Grid'5000 Reference API which provides an elaborate description of these various monitoring "topologies" 2 , Kwapi's self configuration can automatically handle all cases found on Grid'5000 (9 in total). Through its API and user interface, it also advertises users about these specifics, informing them how energy monitoring data is collected on nodes. Automated configuration is a key feature for large scale platform monitoring tool. Kwapi's automated configuration plugin that reads the Grid'5000 Reference API to build the list of devices to monitor for every nodes efficiently addresses this challenge.
EVALUATION This section provides information about Kwapi's current status and suitability for experimenters. The typical questions that we try to answer are: (0) Does Kwapi introduce monitoring overheads ? (1) Is Kwapi useful to provide detailed information about phenomena observed during experiments? (2) Is it reactive enough to serve as data input to guide experiments in a feedback loop? (3) Could data from its long-term storage be used to understand trends? (4) Is it ready for production?.
Measuring power consumption while powercycling machines Grid'5000 provides experimenters with the ability to turn off and on reserved machines through an interface provided by Kadeploy  [18]. Figure  7  shows the network usage and power consumption on all machines of the Reims Grid'5000 site (44 machines) during the following operations: • 18:39:28 -machines are turned off; • 18:40:28 -machines are turned on again, and generate network traffic as they boot via PXE; • 18:49:28 -machine reservation is terminated, causing a reboot to Grid'5000's default system environment. Those graphs are generated real-time when the experimenter connects to the Kwapi visualization interface. It is also possible to extract this data from the Kwapi API, or to retrieve this data after the experiment using the HDF5 export.
Visualizing TCP congestion control Given its high frequency of measurement, Kwapi makes it possible to observe short lived phenomena. A good example is TCP's congestion control algorithms, which we illustrate by initiating a data transfer using nuttcp between two Grid'5000 sites (Rennes and Nancy). As those sites are connected via a 10-Gbps dedicated backbone, the TCP connection is only limited by the speed of each node's network interface (1 Gbps). In Figure  8 , we compare the bandwidth observed via Kwapi for the start of the TCP connections, with two different configurations of the TCP stack: Hystart enabled, and disabled. Hystart  [16]  is a heuristic added to Linux's implementation of TCP CUBIC in 2008 that measures the connection's RTT to exit slow start as soon as congestion starts happening, and before packet losses are observed.
Unfortunately, the Linux implementation suffered from bugs related to timer precision that resulted in lower performance until they were fixed in March 2011 3. As a result, and as one can observe in Figure  8 , the bandwidth grows much more slowly when Hystart is enabled (with its buggy version, found in Linux 2. 6. 32). One could argue that the resulting measurements are not as accurate as what could have been collected using nuttcp itself, or using Operating Systems counters. That is true. However, the fact that Kwapi measurements are completely passive and do not require any instrumentation on the sending and receiving nodes is a clear advantage in some situations.
Extracting long-term power consumption trends The use of Grid'5000 usage is governed by a set of policies to allow the shared use of the infrastructure by many users. One of these rules distinguish between daytime use of the resources, and night use of the resources. Users must execute large scale jobs during nights (from 19:00pm to 09:00am CET/CEST) and week-ends. During working days (Monday to Friday), users should not use more than the equivalent of 2 hours on all the cores during the given day, to give every user a chance to get some resources to perform smallscale experiments, or do preparatory work for larger-scale experiments. This section presents an analysis of the power consumption of all 44 nodes from the Reims Grid'5000 site, from 2015-01-01 to 2015-02-19. 1,508,225 power consumption measures were captured over that period, and retrieved from the longterm storage database of Kwapi.
In the future, this work could be continued in several ways. 4 https://github. com/lpouillo/kwapi-g5k 5 https://pypi. python. org/pypi/kwapi-g5k First, Kwapi could be extended to capture other metrics on the testbed. Low-hanging fruits are additional metrics related to network traffic (e. g. network errors) and energy consumption (e. g. reactive power, in V. A). There are also network technologies popular in the HPC world such as Infiniband that are not supported yet, despite being available on Grid'5000. While they do not provide fine-grained data, sFlow and NetFlow could be supported to provide insight in the type of traffic. Storage systems could also be monitored, which would be useful to understand the behavior of Big Data experiments. Monitoring the temperature at various points of the server rooms could also be relevant for experiments doing power-aware computing (e.
ACKNOWLEDGMENTS Part of research on Kwapi is supported by the  French FSN (Fonds national pour la Société Numérique)   XLcloud  project. Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including  CNRS ,  RENATER  and several Universities as well as other organizations (see https://www. grid5000. fr).
eScholarship provides open access, scholarly publishing services to the University of California and delivers a dynamic research platform to scholars worldwide. current or future conditions. In this paper, we argue for a "bottom-up" approach that estimates productivity, or potential productivity based on information about the agricultural practices and technologies used in past societies. Of key theoretical interest is using this information to estimate the carrying capacity of a given region, independently of estimates of population size. We outline how explicit crop yield models can be combined with high quality historical and archaeological information about past societies, in order to infer the temporal and geographic patterns of change in agricultural productivity and potential. We discuss the kinds of information we need to collect about agricultural techniques and practices in the past, and introduce a new databank initiative we have developed for collating the best available historical and archaeological evidence.
We introduce a new databank initiative we have developed for collating the best available historical and archaeological evidence. We discuss the kinds of coded information we are collecting about agricultural techniques and practices in order to inform our modelling efforts. We illustrate this task by presenting three short case studies summarizing what is known about agricultural systems in three different regions at various time periods. We discuss the challenges confronting this approach, and the various limitations and caveats that apply to the task at hand. Fourth, we outline how we can combine a statistical approach of modelling past crop productivity based on climate inputs with the kind of historical information we are collecting.
The Need for a Systematic, Comparative Approach Agricultural productivity (and the factors that determine it) varies in space and, importantly, in time. In recent years, a large amount of work has been conducted on historical climate change and the effects of climate on crop productivity (see section 4). This work needs supplementing with information about historical crop yields and the cultural and technological factors that affect agricultural productivity. Unfortunately, such data are not readily available in the kind of systematic manner on a global scale that would aid these endeavors due to the general turn away from broad-scale theorizing and comparative perspectives in disciplines such as anthropology, archaeology, and history. Here, we demonstrate how initiative that we have developed, Seshat: The Global History Databank 2  , can provide a framework for collecting the necessary information to model agricultural productivity in the past and, more generally, to test comparative hypotheses about cultural evolution and human history.
Most historians and archaeologists studying agricultural systems or other aspects of human societies tend to be experts in particular time periods and/or tightly defined regions. Although there are some who argue that there are broadscale patterns and general processes shaping human history, their claims tend to rely on illustrative examples and are not systematically tested in the manner that is common in the natural sciences  (Diamond 2010). However, in order to test competing ideas properly, a more rigorous way of adjudicating between alternative hypotheses is required. A barrier to such an endeavor is the lack of data of suitable quantity and quality in the kind of systematic format that is required. It is for these reasons that the Seshat project aims to work directly with historians and other relevant experts to construct a large-scale database that collates the most up-to-date knowledge and understanding of past human societies in a systematic manner.