Tsinghua University
Canvas Category Consultancy : Research : Academic
Tsinghua University is a university in Beijing, People’s Republic of China. Tsinghua University was established in 1911, originally under the name “Tsinghua Xuetang”. The school was renamed the “Tsinghua School” in 1912. The university section was founded in 1925 and the name “National Tsinghua University” adopted in 1928. With a motto of Self-Discipline and Social Commitment and in the spirit of the Latin Facta Non Verba, Tsinghua University is dedicated to academic excellence, the well-being of Chinese society and to global development. Today, most national and international rankings place Tsinghua as one of the best universities in China.
Assembly Line
PhaBuilder collaborates with Solenis and Hengxin
Solenis, a leading global producer of specialty chemicals for water-intensive industries, has signed an agreement with Beijing PhaBuilder Biotechnology Co., Ltd., (hereinafter referred to as PhaBuilder), an innovative synthetic biology enterprise, to collaborate on further developing key PHA (Polyhydroxyalkanoate) - based technology for the paper packaging market.
DIN Germany announced that Hefei Hengxin Life Science and Technology Co., Ltd. (hereinafter referred to as Hengxin) has obtained the world’s first DIN certificate of PHA coated paper products. Chairman Yan Deping of Hengxin stated that this achievement is the result of years of research, continuous innovation, and pursuit of excellence by Tsinghua University, PhaBuilder, and Hengxin. This product not only has excellent physical properties and environmental benefits, but also has made a significant breakthrough in the processing techniques, filling the gap in PHA market.
PHA is a green and sustainable material manufactured through green and low-carbon bio-fermentation. Its application in paper products will help promote the eco-friendly development of the paper industry. The PHA coating of the paper products manufactured by Hengxin is made from the PHA raw material of PhaBuilder. PhaBuilder was established by Professor George Guo-Qiang Chen from Tsinghua University who specializes in synthetic biology.
TsFile: A Standard Format for IoT Time Series Data
TsFile is a columnar storage file format designed for time series data, featuring advanced compression to minimize storage, high throughput of read and write, and deep integration with processing and analysis tools such as Apache projects Spark and Flink. TsFile is designed to support a “high ingestion rate up to tens of million data points per second and rare updates only for the correction of low-quality data; compact data packaging and deep compression for long-live historical data; traditional sequential and conditional query, complex exploratory query, signal processing, data mining and machine learning.”
TsFile is the underlying storage file format for the Apache IoTDB time-series database. IoTDB represents more than a decade of work at China’s Tsinghua University School of Software. It became a top-level project with the Apache Software Foundation in 2020.
DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models
Nature evolves creatures with a high complexity of morphological and behavioral intelligence, meanwhile computational methods lag in approaching that diversity and efficacy. Co-optimization of artificial creatures’ morphology and control in silico shows promise for applications in physical soft robotics and virtual character creation; such approaches, however, require developing new learning algorithms that can reason about function atop pure structure. In this paper, we present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. DiffuseBot bridges the gap between virtually generated content and physical utility by (i) augmenting the diffusion process with a physical dynamical simulation which provides a certificate of performance, and ii) introducing a co-design procedure that jointly optimizes physical design and control by leveraging information about physical sensitivities from differentiable simulation. We showcase a range of simulated and fabricated robots along with their capabilities.