STMixer: A One-Stage Sparse Action Detector

Traditional video action detectors typically adopt the two-stage pipeline, where a person detector is first employed to generate actor boxes and then 3D RoIAlign is used to extract actor-specific features for classification. This detection paradigm requires multi-stage training and inference, and cannot capture context information outside the bounding box. Recently, a few query-based action detectors are proposed to predict action instances in an end-to-end manner. However, they still lack adaptability in feature sampling and decoding, thus suffering from the issues of inferior performance or slower convergence. In this paper, we propose a new one-stage sparse action detector, termed STMixer. STMixer is based on two core designs. First, we present a query-based adaptive feature sampling module, which endows our STMixer with the flexibility of mining a set of discriminative features from the entire spatiotemporal domain. Second, we devise a dual-branch feature mixing module, which allows our STMixer to dynamically attend to and mix video features along the spatial and the temporal dimension respectively for better feature decoding. Coupling these two designs with a video backbone yields an efficient end-to-end action detector. Without bells and whistles, our STMixer obtains the state-of-the-art results on the datasets of AVA, UCF101-24, and JHMDB.Comment: Accepted by CVPR 202

Model predictive current control based on a generalised adjacent voltage vectors approach for multilevel inverters

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163810/1/pel2bf01679.pd

Excited-state spectroscopy of spin defects in hexagonal boron nitride

We used optically detected magnetic resonance (ODMR) technique to directly probe electron-spin resonance transitions in the excited state of negatively-charged boron vacancy (VB-) defects in hexagonal boron nitride (hBN) at room temperature. The data showed that the excited state has a zero-field splitting of ~ 2.1 GHz, a g factor similar to the ground state and two types of hyperfine splitting ~ 90 MHz and ~ 18.8 MHz respectively. Pulsed ODMR experiments were conducted to further verify observed resonant peaks corresponding to spin transitions in the excited state. In addition, negative peaks in photoluminescence and ODMR contrast as a function of magnetic field magnitude and angle at level anti-crossing were observed and explained by coherent spin precession and anisotropic relaxation. This work provided significant insights for studying the structure of VB- excited states, which might be used for quantum information processing and nanoscale quantum sensing

Controllable sliding transfer of wafer‐size graphene

The innovative design of sliding transfer based on a liquid substrate can succinctly transfer high‐quality, wafer‐size, and contamination‐free graphene within a few seconds. Moreover, it can be extended to transfer other 2D materials. The efficient sliding transfer approach can obtain high‐quality and large‐area graphene for fundamental research and industrial applications

Impact Loads on the Occupant under the Protection of an Inversion Tube Energy Absorber during a Helicopter Crash

The objective of this paper is to investigate the impact loads on the occupants’ head, neck, and spine under the protection of an inversion tube energy absorber during a helicopter crash landing. Due to the high vertical acceleration, the head, neck and spine are the most vulnerable parts of a body, so that an energy absorber is needed to dissipate the kinetic energy of the occupant and the seat to minimize the impact loads. In this paper, an inversion tube was adopted as an energy-absorbing device. The occupant injury conditions were evaluated by a numerical simulation. The result indicates that the impact loads on occupant’s head, neck and spine are below the regulated thresholds under the protection of the energy absorber when the helicopter crash at a speed of 12.81m/s in vertical direction. As a consequence, the design of the occupant protection system has been proven reliable

An efficient fuzzy optimization algorithm based on convolutional neural network

The paper proposes a method based on dense-sparse-dense optimization algorithm. It uses sparsity to tune network weights. By adding fuzzy membership, the optimization strategy can enhance the feature information with larger weights and weaken the feature information with less weight. Through accurate cutting of network weights, parameters in network are effectively reduced. The experimental results show that the performance of this method is better than the existing method