210 skills found · Page 5 of 7
A-M-Amine / Vehicle Routing OptimizerVehicle Routing Optimizer is a Python project that solves the Vehicle Routing Problem using Google OR-Tools and Open Route Service API. It can handle different VRP variants and generate GeoJSON paths that could be used to visualize and compare solutions. It is a useful tool for logistics and transportation optimization.
patrick-steele-idem / Require Self RefSolves the relative path problem in Node.js by allowing the target module argument of a require call to be relative to the root directory of the containing package
IbrahimSquared / Visibility Based MarchingVBM is an efficient exact wave propagation technique that has an O(n) compute and space complexity. Inherently produces globally optimal paths to all grid points. Solves several shortcomings of state-of-the-art FMM.
mthd98 / Project Algorithm For A Dog Identification AppProject Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.
naimul3070 / Install OpenProject Project Managmen Software Local ServertOpensource for universities, educational institutions, research, IT / technology companies, NGOs, administrations, foundations, public institutions, authorities, banks and insurance companies, and the automotive industry. The platform offers project planning and visualization, application management, release planning, product management, team collaboration, task management, bug tracking, and budget planning. With this open-source solution, the users can record all processes in one central location, create product roadmaps, record all processes in one central location; create project templates; use widgets to visualize project status and progress; create detailed release planning, share the information with team and collect feedback from customers and employees. Apart from this Gantt charts/timeline management; custom fields for work packages; meetings management; scrum (backlogs and task board); calendar, time tracking, cost reporting, budgeting, bug tracking, wiki; twp-factor authentication, and more are some other features community edition offers. In Premium functions, OpenProject includes agile boards, logo and color schemes, your own design and logo, user-defined fields, single sign-on, individual help texts, highlighting of attributes, and more. One can get a complete function comparison amidst three versions, on the official page of this project. Contents [show] Steps to install OpenProject in Ubuntu 20.04 LTS Linux server 1. Add PGP Key The packages to install OpenProject are not available directly using the base repository of Ubuntu 20.04, hence we have to add a repository provided by the developers of this software platform. Well, but the system always needs to ensure that the packages it is getting are authentic and coming only from the source of repository added for it. And for that, we need to add the PGP key used to sign the OpenProject packages. Copy Me wget -qO- https://dl.packager.io/srv/opf/openproject/key | sudo apt-key add - GPG key for OpenProject 2. Integrate OpenProject repository in Ubuntu 20.04 As I mentioned above that we need to add manually a repository to get the OpenProject packages for installation, therefore, for that run the following given command: Copy-Past whole block of given command: Copy Me sudo wget -O /etc/apt/sources.list.d/openproject.list https://dl.packager.io/srv/opf/openproject/stable/12/installer/ubuntu/20.04.repo Add openproject repository on Ubuntu 20.04 3. Run system update To let the system know we have added a new repository to get a third-party application, run once the system update command: Copy Me sudo apt update 4. Command to install OpenProject in Ubuntu 20.04 LTS Finally, all the key things we require to get the OpenProject have been set, it’s time to use the APT package manager to start the installation process. Copy Me sudo apt install openproject sudo apt install openproject ubuntu 20.04 linux server 5. Start configuring OpenProject Well, the installation has been completed but yet has to be configured to get its web interface up and running. To start the further configuration run the given command: Copy Me sudo openproject configure Select Default OpenProject Users from the Construction field can go for the BIM one. default openproject BIM 6. Configure PostgreSQL To store its data we need a database server, here the OpenProject offers you an option to automatically install “Postgres“, however, if you already have an installed Postgres somewhere or on the same server then you can go for “Use an existing PostgreSQL database” option. However, here we are going for “Install a new PostgreSQL server and database locally“. Select it, Okay, and then hit the Enter key. PostgreSQL Auto Install for OpenProject 7. Install Apache Webserver Next, we need a webserver to serve web pages of OpenProject over a network. Hence, the installation wizard will let you install the Apache webserver if it is not already. install apache2 server Set Fully Qualified domain To access the OpenProject using FQDN, mention the same here. For example, here we are using demo.how2shout.com. You can use whatever you have. Alternatively, if you want to access it using a server IP address then mention that instead of a domain name. set fully qualified domain for OpenProject on Ubuntu Server Path (optional) This is optional. If you want to access your OpenProject web interface under some folder then you can mention it here. For example, let say you already have some website running on your server and to access it you are using your root domain then we cannot use the same domain to access another web platform. Therefore, to solve we can install another website under a subfolder. And the name of that subfolder you can mention here. server path prefix 8. Server SSL Those who already have SSL for the domain they want to use with OpenProject, do not need to install a new SSL certificate, even the ones who are using either Let’s Encrypt or Cloudflare. However, if you don’t have any existing SSL certificate then of course go for the Yes option otherwise NO. Server SSL for Project management Application 9. Install Subversion Just select the “Install Subversion repository support”. Subversion support Again hit the Enter key to set the default path and then install Git repository support, if you want. 10. STMP for Sending Emails Users who want to send emails to others from the web interface of OpenProject need to configure either SendMail or SMTP. We recommend using SMTP to route mail through your mail servers. Select it and configure the same. Or else just SKIP who don’t require emails service, right now. Next, select to install Memcache server for better cache performance or just skip if you don’t need it. Install a new memcached server Wait for a few minutes and the OpenProject open source project management will be on your server. 11. Access OpenProject Web interface Once the installation is completed, it’s time to access the Web interface of OpenProject to start managing our project through it. So, open any web browser on your local system that can access the server IP address where OpenProject is installed. In the URL either type the server IP address or Fully Qualified domain name associated with it. http://server-ip-address or http://your-domain.com If you have installed the OpenProject not in the root directory and with some server suffix or in simple words mentioned the folder name you have assigned during the installation of this project management platform. example: http://server-ip-address/your-sub-folder or http://your-domain.com/your-sub-foler Note: Replace- your-domain. com with the Domain you have added to use with OpenProject while configuring it. Whereas the sub-folder is the Server path suffix if you have mentioned while setting it up. Dashboard of project management Linux 12. Sign-in or Login OpenProject backend Now, let’s log in to the backend. The default username is admin and the password is also admin. Login openProject Backend Admin Change the default Admin password to something strong. Change Admin User 13. Admin Dashboard Finally, you have successfully installed the OpenProject on your Ubuntu 20.04 LTS Linux. Now you can start going through its learning curves to efficiently manage your projects. For more information once can visit its documentation page. OpenProject Installed in Ubuntu 20.04 Linux 14. Video Tutorial Video Player 00:00 14:15 Other Articles: • Top 3 Command Line Ubuntu Package Manager tools • How to install Gparted on Ubuntu 20.04 LTS • How to install Bitwarden server on Ubuntu 20.04 • Install VNC Server on Ubuntu 20.04 | 18.04 RELATED POSTS DaloRADIUS and FreeRADIUS install on Ubuntu 20.04 serverHeyan Maurya UBUNTUInstall FreeRadius & web GUI daloRADIUS on Ubuntu 20.04 serverSet Default Kernel Version of UbuntuHeyan Maurya UBUNTUHow to change default kernel in Ubuntu 22.04 | 20.04 LTSWSL Ubuntu 22.04 LTS Jammy Jelly FIshHeyan Maurya UBUNTUHow to Upgrade WSL 2 or 1 Ubuntu 20.04 to 22.04 LTSGoogle Drive in Ubuntu 20.04 LTSHeyan Maurya UBUNTU4153 VIEWSHow to Setup and use Google Drive on Ubuntu 20.04 LEAVE A REPLY Comment Text* Name* Email* Website Save my name, email, and website in this browser for the next time I comment. This site uses Akismet to reduce spam. Learn how your comment data is processed.
okanulas / Pathfind3rPathfinder is a Lego Mindstorms based printing system that can generate and solve 2D mazes. Grid size and dimensions are cusomizable. Maze width and height is limited to 20 tiles to support international A4 sized paper. In addition to draw and solve mazes Pathfinder can also draw simple SVG paths as well. Draw area of the SVG file should be set as 1750x1650.
kavyadevd / AcmeRoboticsPathPlannerA C++ based manipulator arm path planner with IK solver and output simulator
tassoneroberto / Critical Path MethodJava application designed to solve the Critical Path Method (CPM) problem.
surynek / ReLOCAn experimental package for solving various versions of multi-agent path finding problem (MAPF). Most of solvers in the package are based on the reduction to propositional satisfiability (SAT). This is the original implementation of the MDD-SAT solver.
Ehtijad-Ali / MazeSolver DFS BFS AI Pathfinding VisualizerNo description available
lmbek / PathfindingProjectComputer Science subject module at Roskilde University. Was made as part of a 15ETCS course where we applied Different Pathfinding strategies to solve shortest path problem. Contains Dijkstra and A* implemented in Java with a JavaFX User Interface to demonstrate shortest path visualization. Note: this project is not maintained
Ahmed-S-Shamsaldin / Donkey And Smuggler Optimization Algorithm A Collaborative Working Approach To Path Finding MATLABSwarm Intelligence is a metaheuristic optimization approach that has become very predominant over the last few decades. These algorithms are inspired by animals’ physical behaviors and their evolutionary perceptions. The simplicity of these algorithms allows researchers to simulate different natural phenomena to solve various real-world problems. This paper suggests a novel algorithm called Donkey and Smuggler Optimization Algorithm (DSO). The DSO is inspired by the searching behavior of donkeys. The algorithm imitates transportation behavior such as searching and selecting routes for movement by donkeys in the actual world. Two modes are established for implementing the search behavior and route-selection in this algorithm. These are the Smuggler and Donkeys. In the Smuggler mode, all the possible paths are discovered and the shortest path is then found. In the Donkeys mode, several donkey behaviors are utilized such as Run, Face & Suicide, and Face & Support. Real world data and applications are used to test the algorithm. The experimental results consisted of two parts, firstly, we used the standard benchmark test functions to evaluate the performance of the algorithm in respect to the most popular and the state of the art algorithms. Secondly, the DSO is adapted and implemented on three real-world applications namely; traveling salesman problem, packet routing, and ambulance routing. The experimental results of DSO on these real-world problems are very promising. The results exhibit that the suggested DSO is appropriate to tackle other unfamiliar search spaces and complex problems. This file contains the MATLAB code of the algorithm, the test functions and the TSP application.
fazeelkhalid / Graph Real Time ProblemsPoints: 100 Topics: Graphs, topological sort, freedom to decide how to represent data and organize code (while still reading in a graph and performing topological sort) PLAGIARISM/COLLUSION: You should not read any code (solution) that directly solves this problem (e.g. implements DFS, topological sorting or other component needed for the homework). The graph representation provided on the Code page (which you are allowed to use in your solution) and the pseudocode and algorithm discussed in class provide all the information needed. If anything is unclear in the provided materials check with us. You can read materials on how to read from a file, or read a Unix file or how to tokenize a line of code, BUT not in a sample code that deals with graphs or this specific problem. E.g. you can read tutorials about these topics, but not a solution to this problem (or a problem very similar to it). You should not share your code with any classmate or read another classmate's code. Part 1: Main program requirements (100 pts) Given a list of courses and their prerequisites, compute the order in which courses must be taken so that when taking a courses, all its prerequisites have already been taken. All the files that the program would read from are in Unix format (they have the Unix EOL). Provided files: ● Grading Criteria ● cycle0.txt ● data0.txt ● data0_rev.txt ● data1.txt - like data0.txt but the order of the prerequisite courses is modified on line 2. ● slides.txt (graph image) - courses given in such a way that they produce the same graph as in the image. (The last digit in the course number is the same as the vertex corresponding to it in the drawn graph. You can also see this in the vertex-to-course name correspondence in the sample run for this file.) ● run.html● data0_easy.txt - If you cannot handle the above file format, this is an easier file format that you can use, but there will be 15 points lost in this case. More details about this situation are given in Part 3. ● Unix.zip - zipped folder with all data files. ● For your reference: EOL_Mac_Unix_Windows.png - EOL symbols for Unix/Mac/Windows Specifications: 1. You can use structs, macros, typedef. 2. All the code must be in C (not C++, or any other language) 3. Global or static variables are NOT allowed. The exception is using macros to define constants for the size limits (e.g. instead of using 30 for the max course name size). E.g. #define MAX_ARRAY_LENGTH 20 4. You can use static memory (on the frame stack) or dynamic memory. (Do not confuse static memory with static variables.) 5. The program must read from the user a filename. The filename (as given by the user) will include the extension, but NOT the path. E.g.: data0.txt 6. You can open and close the file however many times you want. 7. File format: 1. Unix file. It will have the Unix EOL (end-of-line). 2. Size limits: 1. The file name will be at most 30 characters. 2. A course name will be at most 30 characters 3. A line in the file will be at most 1000 characters. 3. The file ends with an empty new line. 4. Each line (except for the last empty line) has one or more course names. 5. Each course name is a single word (without any spaces). E.g. CSE1310 (with no space between CSE and 1310). 6. There is no empty space at the end of the line. 7. There is exactly one empty space between any two consecutive courses on the same line. (You do not need to worry about having tabs or more than one empty space between 2 courses.) The first course name on each line is the course being described and the following courses are the prerequisites for it. E.g. CSE2315 CSE1310 MATH1426 ENGL13018. The first line describes course CSE2315 and it indicates that CSE2315 has 2 prerequisite courses, namely: CSE1310 and MATH1426. The second line describes course ENG1301 and it indicates that ENG1301 has no prerequisites. 9. You can assume that there is exactly one line for every course, even for those that do not have prerequisites (see ENGL1301 above). Therefore you can count the number of lines in the file to get the total number of courses. 10.The courses are not given in any specific order in the file. 8. You must create a directed graph corresponding to the data in the file. 1. The graph will have as many vertices as different courses listed in the file. 2. You can represent the vertices and edges however you want. 3. You do NOT have to use a graph struct. If you can do all the work with just the 2D table (the adjacency matrix) that is fine. You HAVE TO implement the topological sorting covered in class (as this assignment is on Graphs), but you can organize, represent and store the data however you want. 4. For the edges, you can use either the adjacency matrix representation or the adjacency list. If you use the adjacency list, keep the nodes in the list sorted in increasing order. 5. For each course that has prerequisites, there is an edge, from each prerequisite to that course. Thus the direction of the edge indicates the dependency. The actual edge will be between the vertices in the graph corresponding to these courses. E.g. file data0.txt has: c100 c300 c200 c100 c200 c100 Meaning: c100-----> c200 \ | \ | \ | \ | \ | \ | V V c300(The above drawing is provided here to give a picture of how the data in the file should be interpreted and the graph that represents this data. Your program should *NOT* print this drawing. See the sample run for expected program output.) From this data you should create the correspondence: vertex 0 - c100 vertex 1 - c300 vertex 2 - c200 and you can represent the graph using adjacency matrix (the row and column indexes are provided for convenience): | 0 1 2 ----------------- 0| 0 1 1 1| 0 0 0 2| 0 1 0 e.g. E[0][1] is 1 because vertex 0 corresponds to c100 and vertex 1 corresponds to c300 and c300 has c100 as a prerequisite. Notice that E[1][0] is not 1. If you use the adjacency list representation, then you can print the adjacency list. The list must be sorted in increasing order (e.g. see the list for 0). It should show the corresponding node numbers. E.g. for the above example the adjacency list will be: 0: 1, 2, 1: 2: 1, 6. 7. In order for the output to look the same for everyone, use the correspondence given here: vertex 0 for the course on the first line, vertex 1 for the course on the second line, etc. 1. Print the courses in topological sorted order. This should be done using the DFS (Depth First Search) algorithm that we covered in class and the topological sorting based on DFS discussed in class. There is no topological order if there is a cycle in the graph; in this case print an error message. If in DFV-visit when looking at the (u,v) edge, if the color of v is GRAY then there is a cycle in the graph (and therefore topological sorting is not possible). See the Lecture on topological sorting (You can find the date based on the table on the Scans page and then watch the video from that day. I have also updated the pseudocodein the slides to show that. Refresh the slides and check the date on the first page. If it is 11/26/2020, then you have the most recent version.) 8. (6 points) create and submit 1 test file. It must cover a special case. Indicate what special case you are covering (e.g. no course has any prerequisite). At the top of the file indicate what makes it a special case. Save this file as special.txt. It should be in Unix EOL format. Part 2: Suggestions for improvements (not for grade) 1. CSE Advisors also are mindful and point out to students the "longest path through the degree". That is longest chain of course prerequisites (e.g. CSE1310 ---> CSE1320 --> CSE3318 -->...) as this gives a lower bound on the number of semesters needed until graduation. Can you calculate for each course the LONGEST chain ending with it? E.g. in the above example, there are 2 chains ending with c300 (size 2: just c100-->c300, size 3: c100-->c200-->c300) and you want to show longest path 3 for c300. Can you calculate this number for each course? 2. Allow the user the enter a list of courses taken so far (from the user or from file) and print a list of the courses they can take (they have all the prerequisites for). 3. Ask the user to enter a desired number of courses per semester and suggest a schedule (by semester). Part 3: Implementation suggestions 1. Reading from file: (15 points) For each line in the file, the code can extract the first course and the prerequisites for it. If you cannot process each line in the file correctly, you can use a modified input file that shows on each line, the number of courses, but you would lose the 15 points dedicated to line processing. If your program works with the "easy files", in order to make it easy for the TAs to know which file to provide, please name your C program courses_graph_easy.c. Here is the modification shown for a new example. Instead of c100 c300 c200 c100 c200 the file would have: 1 c1003 c300 c200 c100 1 c200 1. that way the first data on each line is a number that tells how many courses (strings) follow after it on that line. Everything is separated by exactly one space. All the other specifications are the same as for the original file (empty line at the end, no space at the end of any line, length of words, etc). Here is data0_easy.txt Make a direct correspondence between vertex numbers and course names. E.g. the **first** course name on the first line corresponds to vertex 0, the **first** course name on the second line corresponds to vertex 1, etc... 2. 3. The vertex numbers are used to refer to vertices. 4. In order to add an edge in the graph you will need to find the vertex number corresponding to a given course name. E.g. find that c300 corresponds to vertex 1 and c200 corresponds to vertex 2. Now you can set E[2][1] to be 1. (With the adjacency list, add node 1 in the adjacency list for 2 keeping the list sorted.) To help with this, write a function that takes as arguments the list/array of [unique] course names and one course name and returns the index of that course in the list. You can use that index as the vertex number. (This is similar to the indexOf method in Java.) 5. To see all the non-printable characters that may be in a file, find an editor that shows them. E.g. in Notepad++ : open the file, go to View -> Show symbol -> Show all characters. YOU SHOULD TRY THIS! In general, not necessarily for this homework, if you make the text editor show the white spaces, you will know if what you see as 4 empty spaces comes from 4 spaces or from one tab or show other hidden characters. This can help when you tokenize. E.g. here I am using Notepad++ to see the EOL for files saved with Unix/Mac/Windows EOL (see the CR/LF/CRLF at the end of each line): EOL_Mac_Unix_Windows.png How to submit Submit courses_graph.c (or courses_graph_easy.c) and special.txt (the special test case you created) in Canvas . (For courses_graph_easy.c you can submit the "easy" files that you created.)Your program should be named courses_graph.c if it reads from the normal/original files. If instead it reads from the 'easy' files, name it courses_graph_easy.c As stated on the course syllabus, programs must be in C, and must run on omega.uta.edu or the VM. IMPORTANT: Pay close attention to all specifications on this page, including file names and submission format. Even in cases where your answers are correct, points will be taken off liberally for non-compliance with the instructions given on this page (such as wrong file names, wrong compression format for the submitted code, and so on). The reason is that non-compliance with the instructions makes the grading process significantly (and unnecessarily) more time consuming. Contact the instructor or TA if you have any questions
macfreek / Puzzle CodeMiscellaneous Python code for solving diverse puzzles. Includes path finding, iterators and prime numbers.
copa-uniandes / ESPPRC JPulseJava implementation of the pulse algorithm to solve the elementary shortest path problem with resource constraints (ESPPRC). The paper is available at: https://doi.org/10.1287/trsc.2014.0582
ehe3 / Multi Agent PathfindingSimulator that routes multiple robots on a grid without collisions using a SAT-solver, independence detection with A* search, and path length compression.
tremblay17 / FuzzyGA PathPlanningA fuzzy genetic algorithm that attempts to solve a coverage path planning problem
mgrechanik / Ant Colony OptimizationThe implementation of the ant colony optimization algorithm. Allows to solve Travelling Salesman Problem , Shortest path problem, etc.
prateek22sri / Graph Delta Stepping SSSPA python implementation of a graph algorithm for solving the single source shortest path problem called Delta Stepping Algorithm
robert-30 / Physarum MazeSolve mazes. Find shortest paths. Based on a paper on the Physarum Polycephalum slime mold by Tero, Kobayashi and Nakagaki.
