A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields

Extreme mechanical processes such as strong lattice distortion and bond breakage during fracture are ubiquitous in nature and engineering, which often lead to catastrophic failure of structures. However, understanding the nucleation and growth of cracks is challenged by their multiscale characteristics spanning from atomic-level structures at the crack tip to the structural features where the load is applied. Molecular simulations offer an important tool to resolve the progressive microstructural changes at crack fronts and are widely used to explore processes therein, such as mechanical energy dissipation, crack path selection, and dynamic instabilities (e.g., kinking, branching). Empirical force fields developed based on local descriptors based on atomic positions and the bond orders do not yield satisfying predictions of fracture, even for the nonlinear, anisotropic stress-strain relations and the energy densities of edges. High-fidelity force fields thus should include the tensorial nature of strain and the energetics of rare events during fracture, which, unfortunately, have not been taken into account in both the state-of-the-art empirical and machine-learning force fields. Based on data generated by first-principles calculations, we develop a neural network-based force field for fracture, NN-F$^3$, by combining pre-sampling of the space of strain states and active-learning techniques to explore the transition states at critical bonding distances. The capability of NN-F$^3$ is demonstrated by studying the rupture of h-BN and twisted bilayer graphene as model problems. The simulation results confirm recent experimental findings and highlight the necessity to include the knowledge of electronic structures from first-principles calculations in predicting extreme mechanical processes

Bat azimuthal echolocation using interaural level differences: modeling and implementation by a VLSI-based hardware system

Bats have long fascinated both scientists and engineers due to their superb ability to use echolocation to fly with speed and agility through complex natural environments in complete darkness. This dissertation presents a neuromorphic VLSI circuit model of bat azimuthal echolocation. Interaural level differences (ILDs) are the cues for bat azimuthal echolocation and are also the primary cues used by other mammals to localize high frequency sounds. The fact that neurons in bats respond to short echoes by one or two spikes strongly suggests that the conventionally used firing rate is an unlikely code. The operation of first spike latency in ILD computation and transformation is investigated in a network of spiking neurons linking the lateral superior olive (LSO), dorsal nucleus of the lateral lemniscus (DNLL), and inferior colliculus (IC). The results of the investigation suggest that spatially distributed first spike latencies can serve as a fast code for azimuth that can be ``read-out'' by ascending stages. With the hardware echolocation model that uses spike timing representation, we study how multiple echoes can affect bat echolocation and demonstrate that the response to multiple sounds is not a simple linear addition of the response to single sounds. By developing functional models of the bat echolocation system, we can study the efficient implementation demonstrated by nature. For example, variations among analog VLSI circuit units due to the unavoidable transistor mismatch - traditionally thought of as a hurdle to overcome - have been found beneficial in generating the desired diversity of response that is similar to their neural counterparts. This work advocates the use and design of summating and exponentially decaying synapses. A compact and easily controllable synapse circuit has found an application in achieving a linear temporal spike summation by operating with a very short time constants. It has also been applied in modeling a nonlinear intensity-latency trading by working with a long synaptic time constant. We propose a new synapse circuit model that is compatible with those used in computational models and implementable by CMOS transistors operating in the subthreshold region

RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo

Learning-based multi-view stereo (MVS) has by far centered around 3D convolution on cost volumes. Due to the high computation and memory consumption of 3D CNN, the resolution of output depth is often considerably limited. Different from most existing works dedicated to adaptive refinement of cost volumes, we opt to directly optimize the depth value along each camera ray, mimicking the range finding of a laser scanner. This reduces the MVS problem to ray-based depth optimization which is much more light-weight than full cost volume optimization. In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth. This sequential modeling, conducted based on transformer features, essentially learns the epipolar line search in traditional multi-view stereo. We devise a multi-task learning for better optimization convergence and depth accuracy. We found the monotonicity property of the SDFs along each ray greatly benefits the depth estimation. Our method ranks top on both the DTU and the Tanks & Temples datasets over all previous learning-based methods, achieving an overall reconstruction score of 0.33mm on DTU and an F-score of 59.48% on Tanks & Temples. It is able to produce high-quality depth estimation and point cloud reconstruction in challenging scenarios such as objects/scenes with non-textured surface, severe occlusion, and highly varying depth range. Further, we propose RayMVSNet++ to enhance contextual feature aggregation for each ray through designing an attentional gating unit to select semantically relevant neighboring rays within the local frustum around that ray. RayMVSNet++ achieves state-of-the-art performance on the ScanNet dataset. In particular, it attains an AbsRel of 0.058m and produces accurate results on the two subsets of textureless regions and large depth variation.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence. arXiv admin note: substantial text overlap with arXiv:2204.0132

MicroTEE: Designing TEE OS Based on the Microkernel Architecture

ARM TrustZone technology is widely used to provide Trusted Execution Environments (TEE) for mobile devices. However, most TEE OSes are implemented as monolithic kernels. In such designs, device drivers, kernel services and kernel modules all run in the kernel, which results in large size of the kernel. It is difficult to guarantee that all components of the kernel have no security vulnerabilities in the monolithic kernel architecture, such as the integer overflow vulnerability in Qualcomm QSEE TrustZone and the TZDriver vulnerability in HUAWEI Hisilicon TEE architecture. This paper presents MicroTEE, a TEE OS based on the microkernel architecture. In MicroTEE, the microkernel provides strong isolation for TEE OS's basic services, such as crypto service and platform key management service. The kernel is only responsible for providing core services such as address space management, thread management, and inter-process communication. Other fundamental services, such as crypto service and platform key management service are implemented as applications at the user layer. Crypto Services and Key Management are used to provide Trusted Applications (TAs) with sensitive information encryption, data signing, and platform attestation functions. Our design avoids the compromise of the whole TEE OS if only one kernel service is vulnerable. A monitor has also been added to perform the switch between the secure world and the normal world. Finally, we implemented a MicroTEE prototype on the Freescale i.MX6Q Sabre Lite development board and tested its performance. Evaluation results show that the performance of cryptographic operations in MicroTEE is better than it in Linux when the size of data is small.Comment: 8 pages, 8 figure

Formal Kinematic Analysis of a General 6R Manipulator Using the Screw Theory

Kinematic analysis is a significant method when planning the trajectory of robotic manipulators. The main idea behind kinematic analysis is to study the motion of the robot based on the geometrical relationship of the robotic links and their joints, such as the Denavit-Hartenberg parameters. Given the continuous nature of kinematic analysis and the shortcoming of the traditional verification methods, we propose to use high-order-logic theorem proving for conducting formal kinematic analysis. Based on the screw theory in HOL4, which is newly developed by our research institute, we utilize the geometrical theory of HOL4 to develop formal reasoning support for the kinematic analysis of a robotic manipulator. To illustrate the usefulness of our fundamental formalization, we present the formal kinematic analysis of a general 6R manipulator

Research on the Experimental Teaching Method of Vibration Damping Fastener for Undergraduates Majoring in Rail Transit

Experiment is an important teaching link in talent training. Aiming at the current situation and problems of the experimental teaching of rail transit major, taking the experimental teaching of vibration damping fastener drop weight for railway engineering major of Central South University as an example, the specific methods of the new experimental teaching mode for undergraduates majoring in rail transit are expounded: Improve the subject experimental system, build an open experimental platform, and improve the school-enterprise resource sharing system, etc. This model is conducive to the reform and development of the experimental teaching model for rail transit majors and related science and engineering majors