SimWall is a user-friendly, stereo tiled display wall system composed of 18 commodity projectors operated by a Linux graphics cluster. Collaborating together, these projectors work as a single logical display capable ...SimWall is a user-friendly, stereo tiled display wall system composed of 18 commodity projectors operated by a Linux graphics cluster. Collaborating together, these projectors work as a single logical display capable of giving a high-resolution show, large-scale, and passive stereo scene. In order to avoid tedious system setup and maintenance, software-based automatic geometry and photometric calibration are used. The software calibration is integrated to the system seamlessly by an on-card transform method and is transparent to users. To end-users, SimWall works just as a common PC, but provides super computing, rendering and displaying ability. In addition, SimWall has stereoscopic function that gives users a semi-immersive experience in polarized passive way. This paper presents system architecture, implementation, and other technical issues such as hardware constraints, projectors alignment, geometry and photometric calibration, implementation of passive stereo, and development of overall soft- ware environment.展开更多
We study the area-minimization property of the cones over Stiefel manifolds V_(m)(F^(n))(F=R,C or H)and their products,where the Stiefel manifolds are embedded into the unit sphere of Euclidean space in a standard way...We study the area-minimization property of the cones over Stiefel manifolds V_(m)(F^(n))(F=R,C or H)and their products,where the Stiefel manifolds are embedded into the unit sphere of Euclidean space in a standard way.We will show that these cones are areaminimizing if the dimension is at least 7,using the Curvature Criterion of[Mem.Amer.Math.Soc.,1991,91(446):vi+111 pp.].This extends the results of corresponding references,where the cones over products of Grassmann manifolds were considered.展开更多
Adversarial approaches,which intentionally challenge machine learning models by generating difficult examples,are increasingly being adopted to improve machine learning interatomic potentials(MLIPs).While already prov...Adversarial approaches,which intentionally challenge machine learning models by generating difficult examples,are increasingly being adopted to improve machine learning interatomic potentials(MLIPs).While already providing great practical value,little is known about the actual prediction errors of MLIPs on adversarial structures and whether these errors can be controlled.We propose the Calibrated Adversarial Geometry Optimization(CAGO)algorithm to discover adversarial structures with userassigned errors.Through uncertainty calibration,the estimated uncertainty of MLIPs is unified with real errors.By performing geometry optimization for calibrated uncertainty,we reach adversarial structures with the user-assigned target MLIP prediction error.Integrating with active learning pipelines,we benchmark CAGO,demonstrating stable MLIPs that systematically converge structural,dynamical,and thermodynamical properties for liquid water and water adsorption in a metal-organic framework within only hundreds of training structures,where previously many thousands were typically required.展开更多
基金Project supported by the National Natural Science Foundation of China for Distinguished Young Scholars (No. 60225009)the Major Research Plan of China (No. 90405003)
文摘SimWall is a user-friendly, stereo tiled display wall system composed of 18 commodity projectors operated by a Linux graphics cluster. Collaborating together, these projectors work as a single logical display capable of giving a high-resolution show, large-scale, and passive stereo scene. In order to avoid tedious system setup and maintenance, software-based automatic geometry and photometric calibration are used. The software calibration is integrated to the system seamlessly by an on-card transform method and is transparent to users. To end-users, SimWall works just as a common PC, but provides super computing, rendering and displaying ability. In addition, SimWall has stereoscopic function that gives users a semi-immersive experience in polarized passive way. This paper presents system architecture, implementation, and other technical issues such as hardware constraints, projectors alignment, geometry and photometric calibration, implementation of passive stereo, and development of overall soft- ware environment.
基金Supported by NSFC(No.11871450)Project of Stable Support for Youth Team in Basic Research Field,CAS(No.YSBR-001).
文摘We study the area-minimization property of the cones over Stiefel manifolds V_(m)(F^(n))(F=R,C or H)and their products,where the Stiefel manifolds are embedded into the unit sphere of Euclidean space in a standard way.We will show that these cones are areaminimizing if the dimension is at least 7,using the Curvature Criterion of[Mem.Amer.Math.Soc.,1991,91(446):vi+111 pp.].This extends the results of corresponding references,where the cones over products of Grassmann manifolds were considered.
基金supported by the Research Council of Norway through the Centre of Excellence Hylleraas Centre for Quantum Molecular Sciences(Grant 262695)the Young Researcher Talent grants 344993 and 354100+2 种基金We acknowledge the EuroHPC Joint Undertaking for awarding this project access to the EuroHPC supercomputer LUMI,hosted by CSC(Finland)and the LUMI consortium through a EuroHPC Regular Access call(Grants EHPC-REG-2023R02-088,EHPC-REG-2023R03-146)Support was also received from the Centre for Advanced Study in Oslo,Norway,which funded and hosted the SLB Young CAS Fellow research project during the academic year of 23/24 and 24/25Part of the simulations were performed on resources provided by Sigma2—the Norwegian National Infrastructure for High-Performance Computing and Data Storage(grant numbers NN4654K and NS4654K).
文摘Adversarial approaches,which intentionally challenge machine learning models by generating difficult examples,are increasingly being adopted to improve machine learning interatomic potentials(MLIPs).While already providing great practical value,little is known about the actual prediction errors of MLIPs on adversarial structures and whether these errors can be controlled.We propose the Calibrated Adversarial Geometry Optimization(CAGO)algorithm to discover adversarial structures with userassigned errors.Through uncertainty calibration,the estimated uncertainty of MLIPs is unified with real errors.By performing geometry optimization for calibrated uncertainty,we reach adversarial structures with the user-assigned target MLIP prediction error.Integrating with active learning pipelines,we benchmark CAGO,demonstrating stable MLIPs that systematically converge structural,dynamical,and thermodynamical properties for liquid water and water adsorption in a metal-organic framework within only hundreds of training structures,where previously many thousands were typically required.
