Description of the Software. Subject to the terms and conditions of this XXXX, you can download, or attempt to download, the Software on your PC; provided, however, that the Software will only function properly where your PC utilizes a Microsoft Windows operating system. Updates/Bug Fixes. Company reserves the right (but is not obligated) to add additional features or functions to the existing Software, and to provide bug fixes, error corrections, patches, new releases or any other component not specified within this XXXX, from time to time. You understand that we may require your review and acceptance of our then-current XXXX before you will be permitted to use any subsequent versions of the Software. You acknowledge and agree that Company has no obligation to make any subsequent versions of the Software available to you, or to provide bug fixes, error corrections, patches, new releases or any other component not specified within this XXXX. Third Party Software. ANY THIRD PARTY SOFTWARE, AS WELL AS ANY THIRD PARTY PROVIDED PLUG-INS, THAT MAY BE PROVIDED WITH THE SOFTWARE ARE MADE AVAILABLE FOR USE AT YOUR OPTION AND AT YOUR OWN RISK. IF YOU CHOOSE TO USE SUCH THIRD PARTY SOFTWARE, THEN SUCH USE SHALL BE GOVERNED BY SUCH THIRD PARTY'S LICENSING AGREEMENT(S), TERMS AND CONDITIONS AND PRIVACY PRACTICES. COMPANY IS NOT RESPONSIBLE FOR ANY THIRD PARTY SOFTWARE AND SHALL HAVE NO LIABILITY WHATSOEVER FOR YOUR USE OF, OR INABILITY TO USE, THIRD PARTY SOFTWARE. If you experience any problems installing and/or uninstalling the Software, please contact us via e-mail at: xxxxxxx@xxxxxxxxxxx.xxx.xx. THE SOFTWARE IS NOT SPYWARE OR ADWARE. THE SOFTWARE WILL NOT MONITOR HOW YOU USE YOUR PC, NOR WILL IT DELIVER ADVERTISEMENTS TO YOUR PC.
Description of the Software. The "PUBLISHER" has designed and developed 13CFLUX2 which consists of a set of applications for performing 13C‐based Metabolic Flux Analysis (13C‐MFA), i.e. the simulation‐based estimation of in‐ vivo metabolic reaction rates. In particular, the implemented type of 13C‐MFA relies on 13C labeling measurement data obtained from an organism which is kept under metabolic and isotopic stationary conditions. The 13C labeling measurement data are usually (but not necessarily) generated using mass spectrometry (MS) or nuclear magnetic resonance (NMR) measurement devices. The estimation of in‐vivo reaction rates for very large metabolic networks, the evaluation of a series of high throughput experiments, as well as the evaluation of nonlinear statistics and experimental design involves a high computational effort. By implementing newly developed simulation algorithms, which are highly tuned for the underlying mathematical problems, using 13CFLUX2 allows to perform 13C‐MFA studies on a common desktop computer. Network models and measurements are authored in the FluxML document format. Unique characteristics of the 13CFLUX2 software suite are: significantly improved performance and higher numerical accuracy compared to the predecessor version 13CFLUX and other available software packages for 13C‐MFA; professional input and output via XML‐based file formats and HDF5 (hierarchical data format, version 5); a comprehensive error handling including comprehensible error messages; statistical analysis of the obtained reaction rate distribution and tools for performing optimal experimental design; support for multi‐core CPUs and computer clusters. The following license terms apply to the software's executable files as well as to the included manuals and data files (in this agreement referred to as the "SOFTWARE"). Likewise, these terms apply to any 13CFLUX2 software update provided by Forschungszentrum Jülich GmbH, unless separate license terms accompany those items. If so, those license terms apply. BY INSTALLING AND/ OR USING THE SOFTWARE, YOU ACCEPT THE TERMS OF THIS LICENSE AGREEMENT. IF YOU DO NOT ACCEPT IT, DO NOT INSTALL AND USE THE SOFTWARE. IF YOU COMPLY WITH THESE LICENSE TERMS, YOU HAVE THE RIGHTS AS SPECIFIED BELOW.
Description of the Software. The software provides a graphical user interface for visualizing single-channel LFP signals, their time-frequency characteristics; and detect epileptic seizures in zebrafish larvae. The algorithm (feature extraction and classifier) was optimized for detecting seizures in a specific genetic and chemical model of epilepsy as described in the reference paper below. For optimal performance on different zebrafish models and signals recorded under different conditions, the classifier needs to be retrained on data from a similar setting. Reference:
Description of the Software. The MultiBlock Component Analysis (MBCA) software allows to fit a set of component models to multivariate multiblock data (e.g., observations from a set of variables for subjects that are embedded in groups). Given such multivariate multiblock data, the MBCA program performs separate principal component analyses on all data blocks, simultaneous component analyses and/or clusterwise component analyses with increasing complexities (i.e., number of components and, in the latter case, number of clusters) by means of a multi-start alternating least squares algorithm. Missing data are imputed through weighted least squares fitting. In order to help the user in determining the number of components and/or clusters that are present in the data, the results of a model selection procedure are displayed in the output files of the MBCA program. The MBCA program is available in MATLAB and as a standalone (".exe") application. More information about how to handle the MBCA software can be found in De Roover, Ceulemans & Xxxxxxxxx (2011). De Roover, K., Ceulemans, E., Xxxxxxxxx, M. E., Xxxxxxxxxxxx, K., Xxxxxxx, J., & Onghena, P. (2010). Clusterwise simultaneous component analysis for the analysis of structural differences in multivariate multiblock data. Manuscript under second review.
Description of the Software. The LMPCA software program is a MATLAB graphical user interface for fitting the linked- mode PARAFAC-PCA model (Wilderjans et al., 2009) to a coupled data set consisting of a real-valued three-way data array and a real-valued two-way data matrix that have one mode in common. Given such a coupled data set, the LMPCA software program estimates a user- specified range of linked-mode PARAFAC-PCA solutions with increasing complexities (i.e., number of components) by making use of a multistart alternating least squares algorithm. In order to help the user in determining the number of components that are present in the data, a scree plot and fit information for all complexities considered, are displayed in the output files that are created by the LMPCA program. For more information about how to handle the LMPCA software program, see Wilderjans, Ceulemans, Kiers, & Xxxxx (2009). References Wilderjans, T. F., Ceulemans, E., Xxxxx, X. X. X., & Xxxxx, K. (2009). The LMPCA program: A graphical user interface for fitting the linked-mode PARAFAC-PCA model to coupled real- valued data. Behavior Research Methods, 41, 1073-1082.
Description of the Software. This HG3D software stands for head pose and gaze tracking in 3D. It contains the implementation of algorithms related to those tasks, while profiting from calibrated RGB-D data (standard vision and depth). The head pose tracking is based on an implementation of the Iterative Closest Points (ICP) algorithm, for which a user specific facial template (face 3D mesh) is needed. The gaze tracking is based on a sparse reconstruction algorithm from head pose rectified eye images. This methodology is proposed and described in the following publication: [1] Gaze Estimation From Multimodal Kinect Data, Xxxxxxx Xxxxx and Xxxx-Xxxx Odobez, in: IEEE Conference in Computer Vision and Pattern Recognition, Workshop on Gesture Recognition, Providence, RI, USA, June 2012. Nevertheless, depends on the usage (such as combining appearance models from different users), the following publication is also relevant: [1] Person Independent 3D Gaze Estimation from Remote RGB-D Cameras, Xxxxxxx Xxxxx and Xxxx-Xxxx Odobez, in: IEEE International Conference on Image Processing, Melbourne, Australia, September 2013. As the algorithms require the learning of user specific models of the facial shape and the eyes appearance (while gazing at different directions), we provide auxiliary tools for the models learning. In the case of the facial shape learning, we provide an standalone application to collect some RGB-D frame samples of the subject, place a small set of landmarks and then run the fitting of a 3D Morphable Model (from which we recommend the Basel Face Model, developed at the University of Basel). For the learning of gaze appearance models we provide both a simple strategy, in which the user is requested to gaze at the camera while rotating the head, and the capabilities for extension, in which the user can define a callback function from which to extract the gazed 3D point. There is compatibility with the EYEDIAP dataset and further extensions of the methodology are possible. The software can indeed be improved, especially with the addition of different gaze estimation methods, head pose tracking and respective training methodologies. It was also designed such that users can take the obtained head pose and gaze parameters and use them in applications of their own (e.g. human robot interaction). There is indeed some commercial potential for this system, as a Kinect like camera is a cheap device and they are becoming available everywhere. Even though there are some strong competitors ...
Description of the Software. ViTRaM is a tool that is capable of visualizing overlapping transcriptional modules in a very intuitive way. ViTraM allows for a dynamic visualization of overlapping transcriptional modules in a 2D gene-experiment matrix. Multiple methods are included for obtaining the optimal layout of the overlapping modules. features: -In addition to the previously developed tools for visualizing multiple modules, ViTraM also allows to display additional information on the regulatory program of the modules. The regulatory program consists of the transcription factors and their corresponding motifs. -A first way of obtaining information on the regulatory program is by using the information from curated databases. This information can be used to further analyze modules inferred by biclustering algorithms. -Secondly, information on the regulatory program can also be the outcome of a module inference tool itself. Both types of information on the regulatory program can be included by ViTraM. -By visualizing not only the modules but also the regulatory program, ViTraM can provide more insight into the modules and makes the biological interpretation of the identified modules less complicated for biologists. Reference:
Description of the Software. The Software may include, without limitation,: (a) "Server Software" that provides document management services to other programs; (b) "Client Software" that allows a computer or workstation to access or utilize the services functionality provided by the Server Software; (c) "Stand-alone Software" that operates on a single computer; (d) "Demonstration Software" that is provided only for demonstration, testing and feedback purposes; (e) "Distributed Computing Cluster Software" that allows distribution of processing work for certain Laserfiche application tasks onto other machines; and/or (f) "Plug-in Software Modules" that can be added to the previously mentioned types of software. Specific additional terms that accompany a software development kit or Laserfiche software designated for "application service provider" purposes will also apply to Licensee.
Description of the Software. Win!Factor will be used to collect, store, retrieve and manage information regarding the clients of Customer. All references in this Agreement to the "Software" means Win!Factor , as modified for Customer. It does not include any other software (such as Microsoft Word) or any other operating systems.
Description of the Software. The nonnegative real-valued model of hierarchical classes (NNRV-HICLAS) software allows to fit nonnegative real-valued hierarchical classes models to two-way two-mode data (e.g., a rectangular objects by attributes data matrix). Given such data, the NNRV-HICLAS program performs nonnegative real-valued hierarchical classes analyses of a prespecified complexity (i.e., number of object and attribute clusters) by means of a multi-start two-stage algorithm combining a simulated annealing and an alternating local descent stage. The NNRV-HICLAS program is available in MATLAB.
