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

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

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

A modified method for CT radiomics region-of-interest segmentation in adrenal lipid-poor adenomas: a two-institution comparative study

ObjectiveThis study aimed to investigate the application of modified region-of-interest (ROI) segmentation method in unenhanced computed tomography in the radiomics model of adrenal lipid-poor adenoma, and to evaluate the diagnostic performance using an external medical institution data set and select the best ROI segmentation method.MethodsThe imaging data of 135 lipid-poor adenomas and 102 non-adenomas in medical institution A and 30 lipid-poor adenomas and 43 non-adenomas in medical institution B were retrospectively analyzed, and all cases were pathologically or clinically confirmed. The data of Institution A builds the model, and the data of Institution B verifies the diagnostic performance of the model. Semi-automated ROI segmentation of tumors was performed using uAI software, using maximum area single-slice method (MAX) and full-volume method (ALL), as well as modified single-slice method (MAX_E) and full-volume method (ALL_E) to segment tumors, respectively. The inter-rater correlation coefficients (ICC) was performed to assess the stability of the radiomics features of the four ROI segmentation methods. The area under the curve (AUC) and at least 95% specificity pAUC (Partial AUC) were used as measures of the diagnostic performance of the model.ResultsA total of 104 unfiltered radiomics features were extracted using each of the four segmentation methods. In the ROC analysis of the radiomics model, the AUC value of the model constructed by MAX was 0.925, 0.919, and 0.898 on the training set, the internal validation set, and the external validation set, respectively, and the AUC value of MAX_E was 0.937, 0.931, and 0.906, respectively. The AUC value of ALL was 0.929, 0.929, and 0.918, and the AUC value of ALL_E was 0.942, 0.926, and 0.927, respectively. In all samples, the pAUCs of MAX, MAX_E, ALL, and ALL_E were 0.021, 0.025, 0.018, and 0.028, respectively.ConclusionThe diagnostic performance of the radiomics model constructed based on the full-volume method was better than that of the model based on the single-slice method. The model constructed using the ALL_E method had a stronger generalization ability and the highest AUC and pAUC value