查看更多>>摘要：News editors obtained the following quote from the background information suppli ed by the inventors: “The present invention relates to an acousto-optic transduc er bonding techniques. “An acousto-optic deflector (AOD) is a device that can steer a laser beam in a s pecific direction through the use of an acoustic wave. In the context of acousto -optic (AO) devices, bonding the transducer to the AO base is a critical step in the manufacturing process. The transducer is responsible for converting electri cal signals into acoustic waves, which interact with the optical signal passing through the AO device. The quality of the bond between the transducer and the AO base directly affects the performance and reliability of the AO device. The AOD consists of an acousto-optic crystal, usually made of an optical material like germanium, tellurite, or silica, that is designed to propagate an acoustic wave through the crystal generated by an RF signal. When a laser beam is directed thr ough the crystal, the acoustic wave creates a spatially varying refractive index in the crystal, which diffracts the laser beam in a specific direction.
查看更多>>摘要：The following quote was obtained by the news editors from the background informa tion supplied by the inventors: “Use of robotic simulators has been proposed to generate simulated training data that is utilized in training of machine learnin g model(s). The machine learning model(s), once trained based on simulated train ing data, can then be utilized during perception, planning, and/or acting stage( s) of robotic control of real robot(s) (i.e., non-simulated physical real-world robot(s)). However, utilization of the machine learning model(s) by the real rob ot(s) in robotic control does not actively involve robotic simulator(s). Rather, the machine learning model(s) are merely trained based on simulated training da ta that is generated using the robotic simulator(s). Further, use of robotic sim ulator(s) has been proposed to test/validate robotic control stacks prior to imp lementation of the control stacks on real robots. However, once the robotic cont rol stacks are implemented on the real robots, the robotic simulator(s) are no l onger actively utilized.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from os f.io: “Gradient boosting is a widely used machine learning algorithm for tabular regre ssion, classification and ranking. Although, most of the open source implementat ions of gradient boosting such as XGBoost, LightGBM and others have used decisio n trees as the sole base estimator for gradient boosting. “This paper, for the first time, takes an alternative path of not just relying o n a static base estimator (usually decision tree), and rather trains a list of m odels in parallel on the residual errors of the previous layer and then selects the model with the least validation error as the base estimator for a particular layer.
NETL
NSTL