To join the sprint, please bring your laptop and sign up. You are also very welcome to bring your project. Please contact us if you have any questions.
Scisprint, hosted by the sciwork community, is a monthly coding sprint. It would like to facilitate discussions and exchanges among people in the fields of science, numerical computation, and engineering. Participants, regardless of experience level, can gain valuable development insights in this event.
We would like to provide a supportive and friendly environment for all attendees to support more developers to join in the open-source communities.
It aims to encourage collaboration and interaction among developers through project participation. The projects cover various fields, including but not limited to science, numerical computation, and engineering. You are also encouraged to share your own projects in scisprint. Refer to project list below for more details.
modmesh seamlessly mixes C++ and Python through pybind11, allowing you to leverage the strengths of both programming languages for efficient PDE solving. We use Qt and Python to visualize the computation results to give you a better understanding of your PDE solution. modmesh also supports mesh visualization, currently in the Gmsh mesh file format. We have recently made efforts to improve the modmesh UI/UX.
The design allows it to run on Windows, Linux, and MacOS. Everyone can use or contribute to modmesh.
Common errors such as the misuse of synonyms and homophones are prevalent for both native and non-native Taiwan Mandarin users. This issue is indirectly attributed to the fact that large language models (LLMs) trained on data primarily sourced from the internet struggle to play a reliable role in Mandarin grammar checking tasks. Moreover, the token-by-token and linear training process of the LLMs cannot provide valid knowledge based on a thorough understanding of the non-linear structure of human language. However, from a linguistic perspective, training a model solely based on a mass amount of data is not the most direct solution. In reality, such errors are directly related to the non-linear structures of word classes and phrase structures. By analyzing and simplifying the structures of correct usages into code instructions, a linguistics-based model can effectively grasp the use of language in a manner similar to human native speakers. This enables the model to accomplish efficient Mandarin grammar checking tasks with minimal resources.
uTensor is an extremely lightweight machine learning inference framework built on C++11. It simplifies model deployment by seamlessly converting TensorFlow-trained models into efficient C++ files that can be used to infer on the embedding device and integrate with optimized libraries such as CMSIS-NN by ARM with ease. Compared with the binary files, C++ source code will provide greater flexibility to modify the trained model for the embedding engineers.
We provide the defaults for tensors, operators, and memory allocation. Just like the booming development of machine learning, we are also actively developing the above functions. Welcome to join us.
Sciwork Portal is a project for maintaining our official website - Sciwork.dev, which was built by Pelican with tailwindCSS, and deployed by Netfliy. We create the promotional pages for meetup and sprint events. Our team also maintains the sciwork conference page - conf.sciwork.dev.
We have always been actively trying to provide users a better web browsing experience, including information presentation and visual experience. Welcome to join us if you are interested in website maintence.
Please register at kktix.
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Center for Theory and Computation (Rm. P518, 3rd General Building, National Tsing Hua University).
We will provide more detailed instructions on how to get to the venue in the event notification email.