Natural language computational chemistry command line interface

Install Must have Open-AI Codex key: export OPENAI_API_KEY= then nlcc key bindings ctrl-w copy to clipboard (Note, you may need to install xsel on linux) ctrl-q help ctrl-o reset context ctrl-z execute python code ctrl-t adjust temperature ctrl-u status ctrl-c quit ctrl-x write to file (same output as copy to clipboard) ctrl-l load from file ctrl-n update number of code responses ctrl-down Enter multiline. escape-enter to enter prompt or ctrl-down to leave GitHub https://github.com/whitead/nlcc    

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Adaptive Graph Convolution for Point Cloud Analysis

This repository contains the implementation of AdaptConv for point cloud analysis. Adaptive Graph Convolution (AdaptConv) is a point cloud convolution operator presented in our ICCV2021 paper. If you find our work useful in your research, please cite our paper. Installation Classification classification.md Part Segmentation Indoor Segmentation coming soon GitHub https://github.com/hrzhou2/AdaptConv-master    

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A deep stable learning method for out-of-distribution generalization

StableNet is a deep stable learning method for out-of-distribution generalization. This is the official repo for CVPR21 paper “Deep Stable Learning for Out-Of-Distribution Generalization” and the arXiv version can be found at https://arxiv.org/abs/2104.07876. Introduction Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise. Therefore, eliminating the impact of distribution shifts between training and testing data is crucial for building performance-promising deep models. Conventional methods assume […]

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Revolt.py: An async library to interact with the revolt.chat api

An async library to interact with the https://revolt.chat api. This library will be focused on making bots and i will not implement anything only for user accounts. Support server: https://app.revolt.chat/invite/FDXER6hr Documentation is here Example More examples in the examples folder import revolt import asyncio import aiohttp class Client(revolt.Client): async def on_message(self, message: revolt.Message): if message.content == “hello”: await message.channel.send(“hi how are you”) async def main():    

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An experimental Fang Song style Chinese font generated with skeleton-tracing and pix2pix

An experimental Fang Song style Chinese font generated with skeleton-tracing and pix2pix, with glyphs based on cwTeXFangSong. The font is optimised for vertical typesetting. Below is a sample: The font contains roughly 13,000 glyphs, mostly for traditional Chinese. I created the font for one of my own projects. The font is admittedly not perfect, but nevertheless have many ineteresting features; therefore I am sharing the font file and programs used to generate it. Download the font directly at dist/tkFangSong.ttf. The […]

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Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting

ICCV 2021 Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting Baseline of DKPNet is available. Currently, only code of DKPNet-baseline is released. In fact, MSE in our paper is equivalent to RMSE in academic papers. Please use the word RMSE instead of MSE when refering to the corresponding numerical values in our paper. We are sorry for the mistake and can do nothing to corret it after the camera-ready version deadline. Download the datasets ShanghaiTech A, ShanghaiTech […]

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Asyncio client for Deta Cloud in python

Unofficial client for Deta Clound Install Supported functionality Deta Base Deta Drive Decorator for cron tasks Examples import asyncio from aiodeta import Deta DETA_PROJECT_KEY = “xxx_yyy” async def go(): db_name = “users” # Initialize Deta client deta = Deta(DETA_PROJECT_KEY) # Initialize Deta Base client base = deta.Base(db_name)

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