Experimental Investigation of Dynamic Properties of AerMet 100 Steel

AbstractThe dynamic properties characterization of AerMet 100 Steel at four different strain rates, namely 560/s, 1200/s, 2500/s and 4200/s were carried by a SHPB system. The stress-strain curves at these strain rates are obtained and they were compared with the quasi-static test results to analyze its strain effects. By comparing the stress-strain curves under different strain rates and quasi-static results, it can be found that the AerMet 100 shows strong strain rate sensitivity. For example, its yield stress under quasi-static compression is 1800MP while the yield stress is around 2300MP under strain rate 4200/s with an incensement of 24%. The hardening modulus under static compression is larger than that under dynamic compression due to the softening by the high temperature during high velocity impacting

Towards Interpretable Natural Language Understanding with Explanations as Latent Variables

Recently generating natural language explanations has shown very promising results in not only offering interpretable explanations but also providing additional information and supervision for prediction. However, existing approaches usually require a large set of human annotated explanations for training while collecting a large set of explanations is not only time consuming but also expensive. In this paper, we develop a general framework for interpretable natural language understanding that requires only a small set of human annotated explanations for training. Our framework treats natural language explanations as latent variables that model the underlying reasoning process of a neural model. We develop a variational EM framework for optimization where an explanation generation module and an explanation-augmented prediction module are alternatively optimized and mutually enhance each other. Moreover, we further propose an explanation-based self-training method under this framework for semi-supervised learning. It alternates between assigning pseudo-labels to unlabeled data and generating new explanations to iteratively improve each other. Experiments on two natural language understanding tasks demonstrate that our framework can not only make effective predictions in both supervised and semi-supervised settings, but also generate good natural language explanation

Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring

Coarse-to-fine schemes are widely used in traditional single-image motion deblur; however, in the context of deep learning, existing multi-scale algorithms not only require the use of complex modules for feature fusion of low-scale RGB images and deep semantics, but also manually generate low-resolution pairs of images that do not have sufficient confidence. In this work, we propose a multi-scale network based on single-input and multiple-outputs(SIMO) for motion deblurring. This simplifies the complexity of algorithms based on a coarse-to-fine scheme. To alleviate restoration defects impacting detail information brought about by using a multi-scale architecture, we combine the characteristics of real-world blurring trajectories with a learnable wavelet transform module to focus on the directional continuity and frequency features of the step-by-step transitions between blurred images to sharp images. In conclusion, we propose a multi-scale network with a learnable discrete wavelet transform (MLWNet), which exhibits state-of-the-art performance on multiple real-world deblurred datasets, in terms of both subjective and objective quality as well as computational efficiency

Evidence for Charging and Discharging of MoS2 and WS2 on Mica by Intercalating Molecularly Thin Liquid Layers

Transition metal dichalcogenides (TMDCs) are often mechanically exfoliated on mica and examined under ambient conditions. It is known that above a certain relative humidity, a molecularly thin layer of water intercalates between the mica and the TMDC. Herein, the effect of molecularly thin liquid layers on the optical spectra of MoS2 and WS2 exfoliated on dry mica and exposed to the vapors of water, ethanol, and tetrahydrofuran (THF) is investigated. Photoluminescence and differential reflectance (ΔR/R) spectra on the TMDCs on dry mica show dominant trion emission due to n-doping. Intercalation of water removes charge doping and results in purely neutral exciton emission, while an ethanol layer, which can be reversibly exchanged with water, does not completely suppress charge. Similarly, THF intercalates between TMDC and mica, as shown by atomic force microscopy, but it does not suppress the charging of mica. In MoS2 bi- and trilayers, an intercalated water layer leads to a near doubling of the intensity of the indirect band transition. The described charging/discharging of TMDCs by molecular thin liquid layers can provide important clues to better control the optical properties of TMDCs under environmental conditions.Peer Reviewe

Pushing the Limits of Valiant\u27s Universal Circuits: Simpler, Tighter and More Compact

A universal circuit (UC) is a general-purpose circuit that can simulate arbitrary circuits (up to a certain size $n$). Valiant provides a $k$-way recursive construction of universal circuits (STOC 1976), where $k$ tunes the complexity of the recursion. More concretely, Valiant gives theoretical constructions of 2-way and 4-way UCs of asymptotic (multiplicative) sizes $5n\log n$ and $4.75 n\log n$ respectively, which matches the asymptotic lower bound $\Omega(n\log n)$ up to some constant factor. Motivated by various privacy-preserving cryptographic applications, Kiss et al. (Eurocrypt 2016) validated the practicality of 2-way universal circuits by giving example implementations for private function evaluation. G{ü}nther et al. (Asiacrypt 2017) and Alhassan et al. (J. Cryptology 2020) implemented the 2-way/4-way hybrid UCs with various optimizations in place towards making universal circuits more practical. Zhao et al. (Asiacrypt 2019) optimized Valiant\u27s 4-way UC to asymptotic size $4.5 n\log n$ and proved a lower bound $3.64 n\log n$ for UCs under Valiant framework. As the scale of computation goes beyond 10-million-gate ($n=10^7$) or even billion-gate level ($n=10^9$), the constant factor in circuit size plays an increasingly important role in application performance. In this work, we investigate Valiant\u27s universal circuits and present an improved framework for constructing universal circuits with the following advantages. [*Simplicity*] Parameterization is no longer needed. In contrast to that previous implementations resort to a hybrid construction combining $k=2$ and $k=4$ for a tradeoff between fine granularity and asymptotic size-efficiency, our construction gets the best of both worlds when configured at the lowest complexity (i.e., $k=2$). [*Compactness*] Our universal circuits have asymptotic size $3n\log n$, improving upon the best previously known $4.5n\log n$ by 33\% and beating the $3.64n\log n$ lower bound for UCs constructed under Valiant\u27s framework (Zhao et al., Asiacrypt 2019). [*Tightness*] We show that under our new framework the universal circuit size is lower bounded by $2.95 n\log n$, which almost matches the $3n\log n$ circuit size of our 2-way construction. We implement the 2-way universal circuits and evaluate its performance with other implementations, which confirms our theoretical analysis