dataesr/scientific-paragraphs-categorization huggingface dataset:
To address the need for traceability of PQ measurements, a European wide network of such standards is required and it was with this in mind that the TracePQM project, described below, was conceived. Performing the research in the frame of a joint research project will pool existing knowledge and resources and avoid duplication of research effort and the consequent waste of resources The overall objective of the project is to design, construct, validate and describe a modular, metrology grade measurement system for power quality parameters by building on existing knowledge and expertise in the field. The main features of the measurement set-up will be its modular and flexible design, to cater for commonly used hardware components, to allow new digitizers or PQ algorithms to be easily incorporated, and an open software tool for instrument control, data acquisition and data processing.
Project Objectives and Work Programme Successful implementation of a reference standard for power and PQ parameters based on digitising techniques requires the development of both hardware and software elements. To meet the project goal, TracePQM has four main technical objectives: 1. To design a modular, metrology grade measurement setup for sampled electrical power and PQ parameters measurements, including a review of existing measurement and calibration methods, associated hardware and software, investigation of the optimum use of equipment already available within the NMIs and extension of traceability for power and PQ measurements up to 1 MHz. 2. To develop and validate a modular measurement setup for sampled electrical power and PQ parameters measurements, which can be easily established at NMIs and at other organisations.
The triggering is set to AUTO (TRIG AUTO) and arming to SYN (TARM SYN). This configuration starts sampling immediately after the USB port is ready to receive the data. The samples are then transferred from DMM to internal PC's memory one by one until the predefined total number of samples is read. However, the maximal NRDGS supported by the 3458 is limited to a maximum of 16777 512. If a higher number of samples is required the reading loop needs to be repeated. This approach is compatible for different generations of 3458A models and allows continuous sampling even at the highest supported sampling frequencies (i. e. 100 kHz for SINT and 50 kHz for DINT memory format) since the USB bus allows smooth data transfer in real time. Additionally, the total number of samples is virtually limited only by the memory size of the PC.
Conclusion The objectives and planned work programme of TracePQM, "Traceability Routes for Power Quality Measurements", a joint research project to develop a modular metrology grade measurement system for PQ parameters, has been described. Based on a comprehensive review of existing PQ measurement systems, three candidates for an LF measurement set-up based on Keysight 3458A multimeter, and one for a HF system based on an NI-5922 digitizer have been identified. A software structure has been outlined which is flexible and can be readily adapted to incorporate hardware changes or the addition of new algorithms. Performance tests on the NI-5922 digitizer have shown a relative temperature coefficient in the range (-8 to -14) μV/(V°C). The variability of the digitizer gain due to repeated operation of its self-calibration feature has been studied and a method of improving its performance is suggested.
Once crxd supports the entire documented NRxD interface and has been thoroughly tested, it will replace the rxd module and thus become NEURON's default module for specifying reaction-diffusion kinetics. spatio-temporal information. In particular, cells in CA1 become active sequentially in a stable unidirectional order during spontaneous run periods and under minimal external cues  [1]. This sequential activity seems to integrate either the distance that the animal has run or the time that has elapsed, two related coding states that can be separated through the change in cellular dynamics with the animals' speed. Other studies indicate that these cell sequences depend on theta oscillation from the medial septum and may reflect input from CA3  [2] [3] [4]. Running speed of the animal has also shown to influence theta oscillation frequency and amplitude.
Our results demonstrate that the TMD can enhance our understanding of the anatomy of neuronal morphologies. Brain Project, NeuroMorpho, NeuroElectro, Cell Image Library, NIF Integrated Connectivity, Ion Channel Genealogy, ModelDB, Open Source Brain, GenSat, BrainMaps, NeuronDB, The Human Brain Atlas, and Pub-Med. Initial content included in KnowledgeSpace covers ion channels, neuron types, and microcircuitry. For each content type, physiology, gene expression, anatomy, models, and morphology data sources are available. Going forward we will enhance atlas representations of the mouse brain linking concepts to data, models, and literature, and an atlas representation of the human brain that links to available data, models, and literature will be implemented. Links to analysis tools will also be integrated into the KnowledgeSpace data section.
e. higher clustering, lower path length respect to the remaining network) that favor the amplification of asynchronous firing and determine the onset of a network event. Our modeling study suggests that the 'hot spots' might naturally result from the simple constraints on the network topology and the sparseness of the network. Over the last decade graphics processing units (GPUs) have evolved into powerful, massively parallel co-processors that are increasingly used for scientific computing and machine learning. But it has also become quite clear that writing efficient code for GPU accelerators is difficult even with APIs designed for general purpose computing, such as CUDA and OpenCL. As a consequence, frameworks are being developed for making GPU acceleration available for specific applications without complex parallel code design.
P202 Towards online accurate spike sorting for hundreds of channels Baptiste Lefebvre,, Marcel Stimberg, Olivier Marre, Pierre Yger Institut de la Vision, INSERM UMRS 968, CNRS UMR 7210, Paris, France Correspondence: Pierre Yger (pierre. yger@inserm. com) BMC Neuroscience 2017, 18 (Suppl 1):P202 Understanding how assemblies of neurons encode information requires recording of large populations of cells in the brain. In recent years, multi-electrode arrays and large silicon probes have been developed to record simultaneously from thousands of electrodes packed with a high density. To tackle the fact that these new devices challenge the classical way to perform spike sorting, we recently developed a fast and accurate spike sorting algorithm (available as an open source software, called SpyKING CIRCUS), validated both with in vivo and in vitro ground truth experiments  [1].
Efficient coupling requires precise and sustained synchronization across all subfields, suggesting global integration. The Purkinje cell (PC) is one of the most complex neurons of the brain and integrates more than 100000 synaptic inputs coming from granule cell (GrC) ascending axons (aa), parallel fibers (pf ), climbing fibers (cf ) and molecular layer interneurons (mli). The synapses are distributed, with zone specific limitations, on a large dendritic tree and exploit different neurotransmission mechanisms to modulate the PC discharge in way that remains largely unknown. Here we have explored this wide synaptic computational space using a detailed PC model built in Python -NEURON  [1]. The GrC inputs generated the characteristic burst-pause in the simple spike (SS) responses that were accentuated by MLI inhibition  [2].
Let us describe how MPFR data (numbers and NaN) are represented. In addition to the precision field (regarded An important point is that the exponent range can be very large in MPFR: up to 1 -2 62 , 2 62 -1 on 64-bit machines. In addition to some theoretical issues for the evaluation of trigonometric functions, this introduces difficulties in the implementation of various functions (including mpfr_sum), but is more or less needed as a consequence of arbitrary precision. On this subject, Section Extended and extendable precisions of IEEE 754-2008  [1, Clause 3. 7]  requires the support of a maximum exponent to be at least 1000 times the precision for extendable precision formats. In MPFR, exponents are stored in a signed integer type mpfr_exp_t. If this type has N value bits, i. e. , the maximum value is 2 N -1, then the maximum exponent range is defined so that any valid exponent fits in N -1 bits (sign bit excluded), i.
Preliminary Steps We start by detecting the special cases. The mpfr_sum function does the following. If n 2, we can use existing MPFR functions and macros, mainly for better efficiency since the algorithm described below can work with any number of inputs (only minor changes would be needed): • if n = 0: return +0 (by using MPFR macros); • if n = 1: use mpfr_set (which copies a number, with rounding to the target precision); • if n = 2: use mpfr_add (which adds two numbers, with rounding to the target precision). Now, we have n 3. We iterate over the n input numbers to: (A) detect singular values (NaN, infinity, zero); (B) among the regular values, get the maximum exponent. Such information can be retrieved very quickly and this does not need to look at the significand. Moreover, in the current internal number representation, the kind of a singular value is represented as special values of the exponent field, so that (B) does not need to fetch more data in memory after doing (A).
In practice, one should not see the difference; this second solution was chosen just because it was implemented first, and on a test, it made the code slightly shorter. When the loop has terminated "normally", the result cannot be NaN. We do in the following order: 1) If sign_inf = 0, then the result is an infinity of this sign, and we return it. 2) If rn = 0, then all the inputs are zeros, so that we return the result zero whose sign is given by sign_zero. 3) If rn 2, then one can use mpfr_set or mpfr_add as an optimization, similarly to what was done for n 2. We reiterate in order to find the concerned input(s), call the function and return. 4) Otherwise we call a function sum_aux, which implements the generic case. In addition to the parameters of mpfr_sum, we pass to this function: • the maximum exponent; • the number rn of regular inputs, i.
Exponent Consideration Finally, we set the (maybe out-of-range) exponent of the MPFR number to e, and check whether e is in the current exponent range with the mpfr_check_range function as usual; this function takes the necessary data to be able to handle a possible overflow or underflow: the current result (assumed to be correctly rounded with an unbounded exponent range), the current ternary value (giving the sign of the error), and the rounding mode.
TESTING Different kinds of tests are done. First, there are usual generic random tests, with limited precisions and exponent range: the exact sum is computed with basic additions (mpfr_add) with enough precision, then rounded to the target precision, allowing us to check the result of mpfr_sum. Note that this test could be able to detect bugs in either mpfr_add or mpfr_sum; it is very unlikely to get a same wrong result for both computations, because completely different algorithms are used (when the array has at least 3 regular numbers). As usual, cases involving singular values are also tested. In particular, tests are done with an array of 6 values and every combination of values among NaN, +∞, -∞, +0, -0, +1 and -1. We have some specific tests to trigger particular cases in the implementation, the goal being to have a high code coverage.
For instance, the sum of 4 numbers i•2 46 +j •2 45 + k • 2 44 + f • 2 -2 with -1 i, j, k 1, i = 0 and -3 f 3 is tested with the target precision chosen to have the ulp of the exact sum equal to 2 0 or to 2 44 (all the cases satisfying these conditions are tested). Code (not enabled by default) has been introduced in the mpfr_sum implementation to be able to check some combined parameter value coverage in the TMD cases, allowing us to make sure that all allowed combinations of rounding mode, tmd value (1 or 2), rbit value, sign of the secondary term and sign of the sum are tested. We have generic random tests with cancellations. This is done by starting with some array of random numbers, then computing a correctly rounded sum with mpfr_sum, and appending the opposite value to the array, so that the next mpfr_sum call will have cancellations.
For this reason, a macroscopic-microscopic model has been selected to replace the current phenomenological description of fission barriers. The Finite-Range Liquid-Drop Model (FRLDM) has been implemented in the CONRAD evaluation code and its present implementation shows remarkable consistency with experimental and published benchmark data. The CONRAD code can be used to provide expectation values but also related uncertainties and covariance data. Sensitivity of FRLDM parameters and the correlation matrix between these parameters have been obtained so that further uncertainty propagation on barrier heights can be carried out in the near future.
The macroscopic-microscopic Finite-Range Liquid-Drop Model  [2]  (FRLDM) is a good candidate to achieve this goal as demonstrated by correct nucleus mass predictions, i. e. the binding energy of atomic nuclei. As this model can be used to calculate the nucleus energy as it deforms, it can be also used to obtain an estimation of fission barrier heights V f. a e-mail: pierre. tamagno@cea. fr Best estimate values are not the only required data to perform a proper evaluation. Indeed evaluators must also propagate uncertainties on model parameters to the evaluated quantities. Therefore we have implemented the FRLDM in the CONRAD evaluation code  [3] , as this was designed to perform data assimilation and uncertainty propagation. The proper method to propagate uncertainties on fission barrier heights would consist in adjusting model parameters for masses to obtain both central values and uncertainties on parameters.
Macroscopic model parameters Data analysis requires to know how model parameters impact theoretical values. CONRAD needs then to compute sensitivities i. e. derivatives of theoretical values M th with respect of model parameters. The code offers two possibilities; it can perform numerical derivatives using the model as a black-box (readily available for all new models), or one can bypass this calculation by implementing analytical derivatives if available. In the present case, we used this latter solution as singleparticle energies computation takes significant amount of time. A first simplification is to avoid single-particle energies computations whenever macroscopic parameters p macro are varied, ∂ M th /∂ p macro = ∂ E FRLDM macro /∂ p macro. This can be further improved as most macroscopic-parameters derivatives are straightforward, as long as no shape variation is assumed.
EAGLE The EAGLE simulations are a suite of hydrodynamical simulations of the formation and evolution of galaxies within the context of the ΛCDM cosmological model. Performed using a modified version of the GADGET-3 code (last presented in  Springel et al. 2005) , they include a state-of-the-art implementation of smoothed particle hydrodynamics  (SPH, Dalla Vecchia in prep, Schaller et al. 2015a). They also include a set of subgrid models that account for the physics of photo-heating/ionization from an evolving, uniform background radiation field, radiative cooling from metal lines and atomic processes, star formation, stellar and supermassive black hole evolution and feedback. Subgrid models are included to compensate for the limited resolution of cosmological simulations and the prohibitive computational cost of performing detailed on-thefly radiative transfer.
(2016). Briefly, GALFORM was calibrated to match the observed local luminosity functions in the bJ and K bands from  Norberg et al. (2002)  and  Driver et al. (2012) , as well as the SMBH-bulge mass relation from  Häring & Rix (2004). EA-GLE was calibrated to match the local stellar mass functions inferred from observations by  Li & White (2009)  and  Baldry et al. (2012) , the local stellar mass-size relation from  Shen et al. (2003)  and  Baldry et al. (2012)  and the SMBH mass versus total stellar mass relation from  McConnell & Ma (2013)   7. While EAGLE and GALFORM were calibrated using different observational datasets,  Trayford et al. (2015)  have demonstrated that EAGLE agrees well with observed u and K-band luminosity functions. The GALFORM model used here predicts a similar stellar mass function to EAGLE at z = 0, albeit with a slight deficit of galaxies around the knee.
such, gas surface densities (for a given ISM mass) will be unrealistically low in low-mass galaxies, potentially leading to unrealistically low molecular gas fractions. Observational data from the Herschel Reference Survey (HRS,  Boselli et al. 2014) , the APEX low-redshift legacy survey for molecular gas (ALLSMOG,  Bothwell et al. 2014) , the Galex Aricebo SDSS survey (GASS,  Catinella et al. 2010 ) and the CO legacy database for GASS (COLD GASS,  Saintonge et al. 2011)  indicate that is indeed the case, with a significant number of detected galaxies with higher molecular-to-atomic gas fractions than is predicted by GALFORM for stellar masses lower than 10 10 M. The data also indicates that there are massive galaxies with lower molecular-to-total ISM gas fractions than those predicted by either GALFORM or EAGLE (taking molecular-to-total ratio as a proxy for the star-forming to total ISM ratios shown for EAGLE).
ACKNOWLEDGEMENTS We thank  Joop Schaye  for reading and providing comments that helped improve the clarity of this paper. PM acknowledges the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ) of the  Universit de Lyon  for its financial support within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) of the French government operated by the  National Research Agency (ANR). This work was supported by the  Science and Technology Facilities Council  [ ST/L00075X/1 ,  ST/P000451/1 ]. This work used the DiRAC Data Centric system at Durham University, operated by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk). This equipment was funded by  BIS National E-infrastructure capital  grant  ST/K00042X/1 , STFC capital grants  ST/H008519/1  and  ST/K00087X/1 ,  STFC DiRAC Operations  grant  ST/K003267/1  and  Durham University.
The analysis of existing approaches in the field of biohybrid systems  [12] ,  [13] ,  [14]  shows that generally researchers develop the necessary software to perform a closedloop control of the robots in bio-hybrid systems by themselves, as no such specific software is available commercially. This in-house software is usually not distributed, the only exception being the MADTraC library  [15]  developed at Princeton University, Princeton, NJ, and that was used in  [12]  to track a group of golden shiners (Notemigonus crysoleucas) and a model of a three-spined sticklebacks (Gasterosteus aculeatus L. ) moved by a MiaBot Pro wheeled robot. MAD-TraC provides a desired functionality, but unfortunately its support has been discontinued for a long time with the last changes going back to 2011. An option would be using ROS (Robotic Operating System)  [16]  that has a distributed and modular design, and implements its own navigation stack and bindings with the OpenCV computer vision library  [17] , but the current version doesn't have support for multi-robot systems  [18] , also it is still rather robotic research oriented and thus potentially demanding a significant learning effort; we were on the contrary willing to build a tool easy to use by researchers in the field of behavioural biology.
Also ROS is only fully supported on the Unix-like systems, which is rather limiting, as we target biologists who are often Windows or MacOS users. In this paper we present a novel framework for experimentation on bio-hybrid systems consisting of fish and robots; this framework includes a robotic fish system, an experimental setup and the CATS (Control and Tracking Software) software serving to track animals and robots, and to control the robots. This work is the continuation of the earlier results presented in  [19] ,  [20] ,  [21] ; the first novelty here is the series of adaptations applied to the robots, the setup and the software to perform a robust tracking of robots and fish, and the second is that the robotic control implements a twolevel bio-mimetic animal behavioural model that selects the motion target based on the other fish positions in the same way as they do it, and that commands robot's locomotion pattern to correspond to one of the fish.
The modular event-based architecture for mobile robots library (ASEBA) has been used to individually control the FishBots in real-time and re-program them during an experiment without flashing the microcontrollers firmware. The control of FishBots motion is done through events that are sent from CATS and that contain the parameters for the locomotion (Fig. 3 ). Most of the complex behaviours of FishBot are implemented in CATS as described in the next section, and the robots receive the resulted motor commands thanks to the ASEBA event-based protocol. However the collision avoidance behaviour is fully implemented on-board of each robot. The reason for this is that we are using arenas that can be composed of corridors or small rooms, and thus a fast and reliable obstacle avoidance behaviour is required to avoid the collision of the robots with the boarders of the arena or other robots, that otherwise could generate a failure of the experiment with a FishBot blocked or a decoupling between a RiBot and a FishBot.
We implement a local obstacle avoidance mechanism based on the IR proximity sensors of the FishBot. Once an obstacle is detected, a corresponding event is sent over the ASEBA network to inform CATS, and the collision avoidance routine is executed: the robot will avoid the obstacle autonomously using a Braitenberg based obstacle avoidance algorithm  [22]. Other event emitted by FishBot is "power-down" sent in case of the powering issues, in this case the control software can modify the behaviour of the robot to overcome this situation; also the user is informed on the issue via the user interface. The RiBot design was first introduced in  [20]  and  [21] , and is presented in Fig. 1  (left). This device was designed based on a 3D scanned zebrafish in order to mimic the external shape of the animal.
This modification allowed us to track the LEDs of FishBots with a 180 degree Fisheye camera (elpcctv, China) installed under the setup while still being able to continuously power the robots. As in our previous studies, an overhead camera acA2040-25gm monochrome GigE CCD camera (Basler AG, Germany) with a maximum resolution of 2048×2048 pixels and equipped with low distortion lenses CF12. 5HA-1 (Fujinon, Tokyo, Japan) grabs frames from above the setup that are then processed to track robots and fish. The experimental PC runs the Control And Tracking Software (CATS) that performs a closed-loop control of the robots by tracking fish and robots and by triggering the corresponding response in the robots.
The overview of CATS software is presented on Fig. 3. It consists of two main components: Tracking that tracks the agents, and Robot control that generates commands to control the robots. Both tracking and control run at a frequency of 15 Hz, which is enough to make the robots reproduced most of the zebrafish movements. CATS is implemented in C++ with extensive use of the Qt framework  [23]. We use the library aravis  [24]  to access the main Basler camera. All video stream operations are handled using the GStreamer library  [25]. We tuned the parameters of the GStreamer media components to have a very low latency. Inside CATS the video frames transfer between different modules is done via single-producer, single-consumer lock-free queues developed in  [26]. The tracking routines are based on the OpenCV 3 library  [17].