dataesr/scientific-paragraphs-categorization huggingface dataset:
They are operated based on the manufacturers' instruction, spread all over the world and have their own standards. These various software applications need to be studied to determine which of them has performed a strong analysis of sustainable design as well as which provides a foundation for interoperability. Today's emphasis on high-performance buildings makes it important to leverage BIM-based thermal analyses during design  [4]. BIM technology involves the creation and use of coordinated, consistent information about a building. It allows for better decision-making, documentation and accurate prediction of building performance. Direct links between BIM and non-BIM modeling tools are an important ramification of BIM technology, enabling the creation of deliverables that have an explicit relationship to each other, resulting in a better coordinated and seamless data exchange that saves time, resources, effort and assures quality based liability and reduces risk  [1].
Both IFC and XML create a common language to transfer BIM information between different BIM and building analyses applications while maintaining the meaning of different pieces of information in the transfer. This reduces the need to remodel the same building in each different application. It also adds transparency to the process  [12]. IFC represents a data schema for sharing construction and facility management data across various applications used in the AEC/FM industry domain. It is an object-oriented data schema based on class definitions representing the objects (such as building elements, spaces, properties, shapes, etc. ) that are used by different software applications in the construction or facility management project. IFC has variant data file formats that facilitate exchange between applications, e.
g. ,. ifc, which is the default IFC exchange format,. ifcXML, IFC data file using the XML document structure, and. ifcZIP, IFC data file using the PKzip 2. 04 g compression algorithm  [11]. The AEC Industry has yielded the proprietary development of energy export and import capabilities in several major engineering modeling tools. A number of these involve the use of gbXML. With the development of integration modules inside major engineering analysis tools, gbXML has become the defacto industry standard schema  [17]. GbXML is based on the XML (Extensible Markup Language) deployed by Green Building Studio Inc. It has the ability to carry building environmental sensing information. It also has a global "language" format, with consistent syntax and can potentially represent any computational building model through translation using appropriate mapping engines.
However, this workflow might not immediately smooth management interoperability. The experiences of many BIM based projects show that interoperable thermal analysis software is not enough for the management of thermal performance during the building process, but also requires tools to manage different revisions of BIMs, to compare thermal performance of these revisions and to visualize this in an easy-to-understand way  [20]. An advanced interoperability method was developed by Welle et al. [4] , with ThermalOpt. ThermalOpt is a methodology for automated BIM-based multidisciplinary thermal simulation that attempts to mitigate several technical barriers to BIM-based multidisciplinary thermal simulation encountered in practice today. In this method, the user can start the process by building a parametric BIM model and assigning analysis information using the BIM Application GUI (Digital Project) and the Analysis Application GUI (ThermalSim Plugin).
Constructions and materials Thus, the goals of the review are to draw a picture of the principles of data exchange in thermal simulation software and to compare the interoperability challenges and opportunities of using selected thermal tools and building performance simulation tools that integrate thermal analysis. All data for the identification of the tools have been gathered and shortlisted in the chart below. The tools listed were selected as those most applicable by professional users in the AEC community (whether they focus on the BIM platform or not). These applications are DPV (Design Performance Viewer), Design Builder, Ecotect, EnergyPlus, eQUEST, EcoDesigner, ESP-r, Green Building Studio, Lesosai, IDA ICE, IES VE, TRACE700, TRNSYS and Riuska. They have also been tested on their reliability according to the basic principal of thermal simulation and data exchange.
The test is to illustrate interchanged between BIM design and thermal tools. The websites of each program were visited and a database with key information was selectively built and enriched by journal updates. In addition, scientific articles about the selected programs were consulted when necessary. As a thermal simulation tool is used within a design process that involves other software (e. g. CAD, spreadsheets, presentation, etc. ) the assessment of the compatibility and interoperability of each tool focuses on typical software used in the industry (Table  1 ). This review indicates that there are many energy simulation tools that are used particularly for thermal calculation. In some cases, thermal tools are not stand-alone, but integrated into or as a part of design software. Among these thermal tools, the link with BIM applications is the most interesting possibility in terms of interoperability for energy simulations.
It can import CAD-BIM models from most CAD software; Revit, AutoCad, ArchiCad and 3DsMax. Ecotect can export to a wide range of other programs and CAD formats. Export is supported to Radiance (ray-traced rendering), POV-Ray and. wrl,. dxf (compatible with most CAD software), GBS, EnergyPlus and eQUEST. • eQUEST was developed by James J. Hirsch & Associates in collaboration with Lawrence Berkeley National Laboratory, with LBNL DOE-2  [28]. eQUEST is based on DOE-2 dynamic energy simulation software and calculates heating and cooling loads during a year based on a data-entry building description. eQUEST has the ability to import. dwg files. It is also possible to export analysis results in Excel (. xls) format. eQUEST can import building geometry via gbXML but it is not as generic as the IFC format.
Riuska allows the direct transfer and reuse of digital building geometry data from IFC-compliant architectural software (Autodesk Revit, ArchiCAD, Nemetschek`s Allplan and Visio). RIUSKA can also export main spatial thermal parameters to IFC to be used in other applications, such as building services system modeling or requirement management tools. (Limitation: This test and review covers most of the tools widely known and used by the AEC community. However, a comprehensive review of all available tools world-wide is nearly impossible due to the amount of information that would need to be collected). The review has indicated that there are many design or modeling tools commonly used today include some form of algorithm for the prediction of building energy simulations. These include the three CAD software programs widely used by the AEC community: Revit, Archicad, and Sketch-Up.
Thus, to obtain thermal simulation results, the thermal analysis proceeds either by the thermal engine that has been integrated into them (in this case the mass model is automatically converted into the energy model) or by transferring it to another thermal engine that is interoperable with BIM models. Fortunately, many manufacturers and third-party vendors are beginning to provide BIM object libraries populated with many of the physical and analytical characteristics needed to perform HVAC analysis. This review has also indicated that when the model is used to perform analysis, either an integrated analytical modeling tool within the BIM software runs the analysis, or the information in the model is exported out of the physical model in a file format that the analytical modeling software accepts.
The challenge that is facing the BIM is to assure utmost interoperability by fluidizing model representation, allowing low and high resolution building models that correspond to all design phases and allow a design team based model. Several technical barriers that BIM-based thermal simulation tools continue to face today are: long analytical model preparation times, missing or invalid data in architectural models and inconsistent conversion of architectural models to thermal models. Automated transactions between the tools, however, require that the information be improved, so as not to be fragmented in the passage between architectural data and thermal engineering data and to guarantee the tool's workflow. These improvements must be evaluated through a series of interoperability test to ensure consistency between all participants using AEC software.
Interoperability between BIM and energy analysis programs is a developing area and the potential of BIM in terms of energy analysis has not been completely optimized as yet. Until now, BIM software has been especially oriented toward architecture, with such developments as Autodesk Revit, Bentley Architecture, Graphisoft/Bentley ArchiCAD, and Vectorworks have focused primarily on modeling and refining building geometry. For example, to perform an energy analysis especially a more comprehensive, detailed and specialized thermal analysis, users have sometimes relied on stand-alone thermal software that creates its own geometry. Although the functionality of the popular building thermal tools has progressed significantly in the past few years, much of the potential of their interoperability through BIM remains largely untapped.
They allow us to see interactions not only between large cultures but also to see links on a small scale. The KO and RU editions allow us to compare views from European and Asian continents. We also note that in part these 9 editions reflect the languages present in the EC NADINE collaboration. We understand that the present selection of Wikipedia editions does represent a complete view of all 250 languages present at Wikipedia. However, we think that this selection allows us to perform the quantitative statistical analysis of interactions between cultures making a first step in this direction. To analyze these interactions we select the fist top 30 persons (or articles about persons) appearing in the top ranking list of each of 9 editions for 3 ranking algorithms of PageRank, CheiRank and 2DRank.
All listings of person distributions over the above categories are presented at the web page given at Supporting Information (SI) and in 27 tables given in SI. Unfortunately, we were obliged to construct these distributions manually following each person individually at the Wikipedia ranking listings. Due to that we restricted our analysis only to top 30 persons. We think that this number is sufficiently large so that the statistical fluctuations do not generate significant changes. Indeed, we find that our EN distribution over field activities is close to the one obtained for 100 top persons of English Wikipedia dated by Aug 2009  [17]. To perform additional tests we use the database of about 250000 person names in English, Italian and Dutch from the research work  [14]  provided to us by P.
Aragón and A. Kaltenbrunner. Using this database we were able to use computerized (automatic) selection of top 100 persons from the ranking lists and to compare their distributions over activities and languages with our case of 30 persons. The comparison is presented in SI in figures S1,S2,S3. For these 3 cultures we find that our top 30 persons data are statistically stable even if the fluctuations are larger for CheiRank lists. This is in an agreement with the fact that the CheiRank probabilities. related to the outgoing links, are more fluctuating (see discussion at  [19] ). Of course, it would be interesting to extend the computerized analysis of personalities to a larger number of top persons and larger number of languages. However, the database of persons in various languages still should be cleaned and checked and also attribution of persons to various activities and languages still requires a significant amount of work.
The PageRank (CheiRank) probability distributions are similar for all editions. However, the fluctuations of P * are stronger that is related to stronger fluctuations of outgoing edges  [19]. The top article of PageRank is usually USA or the name of country of a given language (FR, RU, KO). For NL we have at the top beetle, species, France. The top articles of CheiRank are various listings. Since each article has its PageRank ranking K and CheiRank ranking K * , we can assign two dimensional coordinates to all the articles. Fig. 2  shows the density of articles in the two dimensional plane (K, K * ) for each Wikipedia edition. The density is computed for 100 × 100 logarithmically equidistant cells which cover the whole plane (K, K * ). The density plot represents the locations of articles in the plane.
The transitions inside each culture (persons of the same language as language edition) are omitted since we are analyzing the interlinks between cultures. Then the Google matrix of cultures is constructed by the standard rule for the directed networks: all links are treated democratically with the same weight, sum of links in each column is renormalized to unity, α = 0. 85. Even if this network has only 10 nodes we still can find for it PageRank and CheiRank probabilities P and P * and corresponding indexes K and K *. The matrix elements of G matrix, written in order of index K, are shown in Fig. 6  for the corresponding networks of cultures presented in Fig. 5. We note that we consider all cultures on equal democratic grounds. The decays of PageRank and CheiRank probabilities with the indexes K, K * are shown in Fig.
We make a conjecture that the Zipf law with the generalized exponents z, z * will work in a better way for a larger number of multilingual Wikipedia editions which now have about 250 languages. The distributions of cultures on the PageRank -CheiRank plane (K, K * ) are shown in Fig. 8. For the network of cultures constructed from top 30 PageRank persons we obtain the following ranking. The node WR is located at the top PageRank K = 1 and it stays at the last CheiRank position K * = 10. This happens due to the fact that such persons as Carl Linnaeus, Jesus, Aristotle, Plato, Alexander the Great, Muhammad are not native for our 9 Wikipedia editions so that we have many nodes pointing to WR node, while WR has no outgoing links. The next node in PageRank is FR node at K = 2, K * = 5, then DE node at K = 3, K * = 4 and only then we find EN node at K = 4, K * = 7.
Attributing top persons of the ranking list to different cultures we construct the network of cultures and characterize entanglement of cultures on the basis of Google matrix analysis of this directed network. We considered only 9 Wikipedia editions selecting top 30 persons in a "manual" style. It would be useful to analyze a larger number of editions using an automatic computerized selection of persons from prefabricated listing in many languages developing lines discussed in  [14]. This will allow to analyze a large number of persons improving the statistical accuracy of links between different cultures. The importance of understanding of cultural diversity in globalized world is growing. Our computational, data driven approach can provide a quantitative and efficient way to understand diversity of cultures by using data created by millions of Wikipedia users.
We believe that our results shed a new light on how organized interactions and links between different cultures. Table  1. Considered Wikipedia networks from language editions: English (EN), French (FR), German (DE), Italian (IT), Spanish (ES), Dutch (NL), Russian (RU), Hungarian (HU), Korean (KO). Here N A is number of articles, N L is number of hyperlinks between articles, κ is the correlator between PageRank and CheiRank. Date represents the time in which data are collected.
The distributed nature of nervous systems makes it necessary to record from a large number of sites in order to decipher the neural code, whether single cell, local field potential (LFP), micro-electrocorticograms (µECoG), electroencephalographic (EEG), magnetoencephalographic (MEG) or in vitro micro-electrode array (MEA) data are considered. High channel-count recordings also optimize the yield of a preparation and the efficiency of time invested by the researcher. Currently, data acquisition (DAQ) systems with high channel counts (>100) can be purchased from a limited number of companies at considerable prices. These systems are typically closed-source and thus prohibit custom extensions or improvements by end users. We have developed MANTA, an open-source MATLAB-based DAQ system, as an alternative to existing options.
On this basis we have developed an open-source DAQ system, which is easy to extend and use. MANTA is written in MATLAB and makes use of a range of efficient coding strategies to allow fast plotting for 100's of channels. It supports natural visualization of arbitrary 1D, 2D and 3D electrode array geometries, integrates filtering, spike-sorting and denoising for online display, synchronization and communication with a control unit, and can acquire data from both analog and digital headstages. It is built using a modular design to facilitate and encourage the addition of new hardware and analysis tools. Being based on MATLAB, debugging and improving MANTA is a rapid process aided by an integrated simulation mode, and is thus easily accessible to the plethora of neuroscientists versed in MATLAB.
METHODS SYSTEM CONFIGURATION Assembling a MANTA system requires making a few choices, including the workstation configuration, headstage type (digital/analog, and for analog headstages the second level amplifiers) and vendor, and choosing an appropriate DAQ card, schematically shown in Figure  1  (see more details below). MANTA can be used with either analog or digital front-end amplifiers (headstages) and runs on 32-bit and 64-bit platforms. Windows is fully supported. On Linux and Mac OS X, National Instruments only provides limited support for the analog M-series cards (NI-DAQmx base driver), hence only the analog section below applies to these operating systems. In addition to MATLAB itself, the Signal Processing, the Instrument Control (for TCP/IP communication) and the DAQ (for audio output) toolboxes are required, although we are working to overcome this dependence.
A detailed comparison of the three systems outlined in Figure  1  is given in Table  1. All results reported below were collected on a PC workstation (Core i7 2700 K, GeForce GTS 460, current cost <$1000) running Windows 7 32-bit/64-bit, MATLAB 2010b and the National Instruments drivers NI-DAQmx 9. 5/HSDIO 1. 8. 1. If newer hardware were used, the performance would improve accordingly. Prices for other hardware cited below are accurate as of 02/2013 and are likely to decrease as commercial DAQ hardware continues to improve.
For the digital headstage (bottom), the signal is conditioned using a special circuit (see Figure  A2 ) and then digitized using a high-speed digital DAQ card by National Instruments (656X series). The signals are treated uniformly in MANTA and can then be displayed or sent to audio output. (B) The data flow from the probe to the display is represented schematically here. In the analog path (top), the signal is amplified, but remains analog until it reaches the DAQ card. In the digital path (bottom), the signal is directly digitized at the headstage and then relayed digitally to the digital DAQ card. Next, the raw data is read into MANTA (using custom code to interact with the NI drivers) and directly streamed to disk, followed by several filtering and analysis steps before the data is displayed.
The last steps require the MATLAB Signal Processing Toolbox. is already performed at the headstage, no additional amplification stage is needed. This simplifies the system setup and reduces its costs. Presently, MANTA only supports the digital protocol used in Blackrock headstages. To relay the digital signal to and from the headstage with minimal distortion it is necessary to interject a custom built circuit for signal conditioning/separation between the DAQ card and the headstage. The circuit diagram is provided as Figure  A2  and the PCB-board definitions (in the format used by expresspcb. com) can be found in the software repository (InterfaceBoardDesignX. Y. pcb). The costs for making this box are ∼$60 and its assembly only requires a reasonable acquaintance with soldering (two chips have to be SMD soldered).
SOFTWARE ARCHITECTURE The complexity of a software package determines the likelihood for new developers to participate in an open-source project. To this end, MANTA was designed with simplicity and flexibility in mind. MANTA takes advantage of MATLAB's extensive library of mathematical transformations so that the system currently encompasses <5000 lines of code (including comments/empty lines etc. ). New developers can thus learn the software in a short time before adding their own custom features. The main DAQ loops are written to dynamically adapt their speed to the current load, determined by the number of channels, the chosen display options, and computer performance. All graphics and audio output are embedded inside try-catch environments, which shield acquisition from potential display errors.
CONTROLLING ACQUISITION Two modes of acquisition are supported, locally controlled and remotely triggered. The locally controlled mode uses the buttons and fields in the user interface to start and stop acquisition, display and saving. This mode serves to prepare a recording or collect exploratory data. Acquisitions of precise length are not supported in this mode, since to this end, MANTA will typically be controlled remotely or triggers from the stimulus software will be saved in some analog channels for later synchronization. The remotely triggered mode is used for temporal synchronization and automated collection of many trials. The remotely controlled operation consists of two stages for preparing and starting a recording. First, a TCP/IP communication prepares the recording, i. e. , defining internal structures and setting up the files required for saving the data (see below for details).
PERFORMANCE Prompt audio-visual feedback in a DAQ system permits the user to observe relationships between evoked activity and externallycontrolled stimuli. Conventionally, graphics display in MATLAB is a performance bottleneck, preventing real-time applications which require substantial data display. To improve the speed of the visual display we make use of MATLAB's ability to update an existing plot via references to the graphics objects rather than fully redrawing them. To further optimize the visual display, the number of data points is matched to the size of the plot windows on the screen. These techniques lead to a substantial gain (>20X) in display speed and allow us to draw 128 channels in ∼75 ms. Depending on how much processing (filtering, triggering, threshold-computing, averaging) the user chooses to perform at a given time, the exact processing speed will vary.
Average update intervals for plotting the voltage trace, adding spikes and adding automatic threshold computation are shown in Figure  4. If only the traces (red) or spikes (orange) are displayed, the update speed exceeds 10 Hz for 128 channels. If both are displayed together the performance reduces to ∼6. 5 Hz. Results for analog and digital headstages are not distinguished, since extracting the data from the DAQ cards constitutes only a few percent of the overall processing time and thus leads to very similar overall values. The present performance results were collected on a Windows 7 (64-bit) system. Graphics performance, especially 2D performance, can be substantially faster on Linux systems, which can amount to 30% on the same hardware. However, graphics performance is substantially slower on Mac systems, owing to the suboptimal X11 implementation/integration.