Negative to Positive Crossover of Magnetoresistance in Layered WS2 with Ohmic Contact

The discovery of graphene has ignited intensive investigation on two dimensional (2D) materials. Among them, transition metal dichalcogenide (TMDC), a typical representative, attracts much attention due to the excellent performance in field effect transistor (FET) related measurements and applications. Particularly, when TMDC eventually reaches few-layer dimension, a wide range of electronic and optical properties, in striking contrast to bulk samples, are detected. In this Letter, we synthesized single crystalline WS2 nanoflakes by physical vapor deposition (PVD) method and carried out a series of transport measurements of contact resistance and magnetoresistance. Focused ion beam (FIB) technology was applied to deposit Pt electrodes on WS2 flakes. Different from the electron beam lithography (EBL) fabricated electrodes, FIB-deposited leads exhibited ohmic contact, resolving the dilemma of Schottky barrier. Furthermore, a temperature-modulated negative-to-positive transition of magnetoresistance (MR) associated with a crossover of carrier type at similar temperature was demonstrated. Our work offers a pathway to optimize the contact for TMDC and reveals the magnetoresistance characteristics of WS2 flakes, which may stimulate further studies on TMDC and corresponding potential electronic and optoelectronic applications

Research on Method of Health Assessment about the Destruction Equipment for High-risk Hazardous Chemical Waste

AbstractThe destroying tasks of high-risk hazardous chemical waste have a strict request to the health status of destruction equipment.The paper proposes the health status classification method based on time between failures for the destruction of equipment, set up health status assessment model based on Time-varying Bayesian Networks and the time slice, which can take advantage of history fault information and health status monitoring indicator information to health status assessment for the destruction equipment, and which provides a reliable and safe evaluation method

Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement Study

Object tracking is an important functionality of edge video analytic systems and services. Multi-object tracking (MOT) detects the moving objects and tracks their locations frame by frame as real scenes are being captured into a video. However, it is well known that real time object tracking on the edge poses critical technical challenges, especially with edge devices of heterogeneous computing resources. This paper examines the performance issues and edge-specific optimization opportunities for object tracking. We will show that even the well trained and optimized MOT model may still suffer from random frame dropping problems when edge devices have insufficient computation resources. We present several edge specific performance optimization strategies, collectively coined as EMO, to speed up the real time object tracking, ranging from window-based optimization to similarity based optimization. Extensive experiments on popular MOT benchmarks demonstrate that our EMO approach is competitive with respect to the representative methods for on-device object tracking techniques in terms of run-time performance and tracking accuracy. EMO is released on Github at https://github.com/git-disl/EMO

Hierarchical Pruning of Deep Ensembles with Focal Diversity

Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing popularity to study deep ensemble techniques in the deep learning community. Some mission-critical applications utilize a large number of deep neural networks to form deep ensembles to achieve desired accuracy and resilience, which introduces high time and space costs for ensemble execution. However, it still remains a critical challenge whether a small subset of the entire deep ensemble can achieve the same or better generalizability and how to effectively identify these small deep ensembles for improving the space and time efficiency of ensemble execution. This paper presents a novel deep ensemble pruning approach, which can efficiently identify smaller deep ensembles and provide higher ensemble accuracy than the entire deep ensemble of a large number of member networks. Our hierarchical ensemble pruning approach (HQ) leverages three novel ensemble pruning techniques. First, we show that the focal diversity metrics can accurately capture the complementary capacity of the member networks of an ensemble, which can guide ensemble pruning. Second, we design a focal diversity based hierarchical pruning approach, which will iteratively find high quality deep ensembles with low cost and high accuracy. Third, we develop a focal diversity consensus method to integrate multiple focal diversity metrics to refine ensemble pruning results, where smaller deep ensembles can be effectively identified to offer high accuracy, high robustness and high efficiency. Evaluated using popular benchmark datasets, we demonstrate that the proposed hierarchical ensemble pruning approach can effectively identify high quality deep ensembles with better generalizability while being more time and space efficient in ensemble decision making.Comment: To appear on ACM Transactions on Intelligent Systems and Technolog