An experimental approach to quantify strain transfer efficiency of fibre bragg grating sensors to host structures

This paper developed a method to evaluate the strain transfer efficiency of fibre Bragg grating sensors to host structures. Various coatings were applied to fibre Bragg grating sensors after being fabricated. They were epoxy, silane agent and polypropylene, representing different surface properties. A neat epoxy resin plate was used as the host in which the coated fibre sensors were embedded in the central layer. The tensile strain output from the FBGs was compared with that obtained from electrical strain gauges which were attached on the surface of the specimen. A calculating method based on the measured strains was developed to quantify the strain transfer function of different surface coatings. The strain transfer coefficient obtained from the proposed method provided a direct indicator to evaluate the strain transfer efficiency of different coatings used on the FBG sensors, under either short or long-term loading. The results demonstrated that the fibre sensor without any coating possessed the best strain transfer, whereas, the worst strain transfer was created by polypropylene coating. Coatings play a most influential role in strain measurements using FBG sensors

A Generalized Wine Quality Prediction Framework by Evolutionary Algorithms

Wine is an exciting and complex product with distinctive qualities that makes it different from other manufactured products. Therefore, the testing approach to determine the quality of wine is complex and diverse. Several elements influence wine quality, but the views of experts can cause the most considerable influence on how people view the quality of wine. The views of experts on quality is very subjective, and may not match the taste of consumer. In addition, the experts may not always be available for the wine testing. To overcome this issue, many approaches based on machine learning techniques that get the attention of the wine industry have been proposed to solve it. However, they focused only on using a particular classifier with a specific set of wine dataset. In this paper, we thus firstly propose the generalized wine quality prediction framework to provide a mechanism for finding a useful hybrid model for wine quality prediction. Secondly, based on the framework, the generalized wine quality prediction algorithm using the genetic algorithms is proposed. It first encodes the classifiers as well as their hyperparameters into a chromosome. The fitness of a chromosome is then evaluated by the average accuracy of the employed classifiers. The genetic operations are performed to generate new offspring. The evolution process is continuing until reaching the stop criteria. As a result, the proposed approach can automatically find an appropriate hybrid set of classifiers and their hyperparameters for optimizing the prediction result and independent on the dataset. At last, experiments on the wine datasets were made to show the merits and effectiveness of the proposed approach

Accelerating Diffusion Sampling with Classifier-based Feature Distillation

Although diffusion model has shown great potential for generating higher quality images than GANs, slow sampling speed hinders its wide application in practice. Progressive distillation is thus proposed for fast sampling by progressively aligning output images of $N$-step teacher sampler with $N/2$-step student sampler. In this paper, we argue that this distillation-based accelerating method can be further improved, especially for few-step samplers, with our proposed \textbf{C}lassifier-based \textbf{F}eature \textbf{D}istillation (CFD). Instead of aligning output images, we distill teacher's sharpened feature distribution into the student with a dataset-independent classifier, making the student focus on those important features to improve performance. We also introduce a dataset-oriented loss to further optimize the model. Experiments on CIFAR-10 show the superiority of our method in achieving high quality and fast sampling. Code will be released soon

Demonstration of Einstein-Podolsky-Rosen Steering with Enhanced Subchannel Discrimination

Einstein-Podolsky-Rosen (EPR) steering describes a quantum nonlocal phenomenon in which one party can nonlocally affect the other's state through local measurements. It reveals an additional concept of quantum nonlocality, which stands between quantum entanglement and Bell nonlocality. Recently, a quantum information task named as subchannel discrimination (SD) provides a necessary and sufficient characterization of EPR steering. The success probability of SD using steerable states is higher than using any unsteerable states, even when they are entangled. However, the detailed construction of such subchannels and the experimental realization of the corresponding task are still technologically challenging. In this work, we designed a feasible collection of subchannels for a quantum channel and experimentally demonstrated the corresponding SD task where the probabilities of correct discrimination are clearly enhanced by exploiting steerable states. Our results provide a concrete example to operationally demonstrate EPR steering and shine a new light on the potential application of EPR steering.Comment: 16 pages, 8 figures, appendix include

Vinyl Ester Oligomer Crosslinked Porous Polymers Prepared via Surfactant-Free High Internal Phase Emulsions

Using vinyl ester resin (VER) containing styrene (or methyl methacrylate) and vinyl ester oligomer (VEO) as external phase, Pickering high internal phase emulsions (Pickering HIPEs) having internal phase volume fraction of up to 95 vol% were prepared with copolymer particles as sole stabilizer. Polymerizing the external phase of these Pickering HIPEs led to porous polymers (poly-Pickering-HIPEs). Compared to the polystyrene- (PS-) based poly-Pickering-HIPEs which were prepared with mixture of styrene and divinylbenzene (DVB) as crosslinker, the poly-Pickering-HIPEs herein showed much higher elastic modulus and toughness. The elastic modulus of these poly-Pickering-HIPEs increased with increasing the VEO concentration in the external phase, while it decreased with increasing internal phase volume fraction. Increasing VEO concentration in the external phase also resulted in a decrease in the average void diameter as well as a narrow void diameter distribution of the resulting poly-Pickering-HIPEs. In addition, there were many small pores in the voids surface caused by the volume contraction of VER during the polymerization, which suggests a new method to fabricate porous polymers having a well-defined hierarchical pore structure

Beyond Gisin's Theorem and its Applications: Violation of Local Realism by Two-Party Einstein-Podolsky-Rosen Steering

We demonstrate here that for a given mixed multi-qubit state if there are at least two observers for whom mutual Einstein-Podolsky-Rosen steering is possible, i.e. each observer is able to steer the other qubits into two different pure states by spontaneous collapses due to von Neumann type measurements on his/her qubit, then nonexistence of local realistic models is fully equivalent to quantum entanglement (this is not so without this condition). This result leads to an enhanced version of Gisin's theorem (originally: all pure entangled states violate local realism). Local realism is violated by all mixed states with the above steering property. The new class of states allows one e.g. to perform three party secret sharing with just pairs of entangled qubits, instead of three qubit entanglements (which are currently available with low fidelity). This significantly increases the feasibility of having high performance versions of such protocols. Finally, we discuss some possible applications.Comment: 9 pages, 1 figur