Wide field of view and light weight optics are critical for advanced eyewear,with applications in augmented/virtual reality and night *** refractive lenses are often stacked to correct aberrations at a wide field of v...
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Wide field of view and light weight optics are critical for advanced eyewear,with applications in augmented/virtual reality and night *** refractive lenses are often stacked to correct aberrations at a wide field of view,leading to limited performance and increased size and *** particular,simultaneously achieving a wide field of view and large aperture for light collection is desirable but challenging to realize in a compact ***,we demonstrate a wide field of view(greater than 60°)meta-optic doublet eyepiece with an entrance aperture of 2.1 *** the design wavelength of 633 nm,the meta-optic doublet achieves comparable performance to a refractive lens-based eyepiece *** meta-doublet eyepiece illustrates the potential for meta-optics to play an important role in the development of high-quality monochrome near-eye displays and night vision systems.
Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions a...
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Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions are firstly detected using visual primitives such as color and shape and then grouped and merged into a co-saliency map. However, co-saliency is intrinsically perceived complexly with bottom-up and top-down strategies combined in human vision. To address this problem, this study proposes a novel end-toend trainable network comprising a backbone net and two branch nets. The backbone net uses ground-truth masks as top-down guidance for saliency prediction, whereas the two branch nets construct triplet proposals for regional feature mapping and clustering, which drives the network to be bottom-up sensitive to co-salient regions. We construct a new dataset of 2019 natural images with co-saliency in each image to evaluate the proposed method. Experimental results show that the proposed method achieves state-of-the-art accuracy with a running speed of 28 fps.
The competition between scrambling and projective measurements can lead to measurement-induced entanglement phase transitions (MIPT). In this work, we show that the universality class of the MIPT can be drastically al...
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The competition between scrambling and projective measurements can lead to measurement-induced entanglement phase transitions (MIPT). In this work, we show that the universality class of the MIPT can be drastically altered when the system has a diffusing conserved density. As a numerical tractable model of this, we study a 1+1d random Clifford circuit locally monitored by classically diffusing particles (“measurers”). The resulting diffusive correlations in the measurement density are a relevant perturbation to the usual space-time random MIPT critical point, producing a new universality class for this phase transition. We find “Griffiths-like” effects due to rare space-time regions where, e.g., the diffusive measurers have a low or high density, but these are considerably weaker than the Griffiths effects that occur with quenched randomness that produce rare spatial regions with infinite lifetime.
The ground states of interacting one-dimensional metals are generically Luttinger liquids. Luttinger-liquid theory is usually considered for translation invariant systems. The Luttinger-liquid description remains vali...
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The ground states of interacting one-dimensional metals are generically Luttinger liquids. Luttinger-liquid theory is usually considered for translation invariant systems. The Luttinger-liquid description remains valid for weak quasiperiodic modulations; however, as the quasiperiodic modulation gets increasingly strong, it is increasingly renormalized and eventually fails, as the system becomes localized. We explore how quasiperiodic modulation renormalizes the Luttinger parameter characterizing this emergent Luttinger liquid, using the renormalization of transmission coefficients across a barrier as a proxy that remains valid for general quasiperiodic modulation. We find, unexpectedly, that quasiperiodic modulation weakens the effects of short-range interactions, but enhances those of long-range interactions. We support the former finding with matrix-product numerics. We also discuss how interactions affect the localization phase boundary.
Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal **...
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Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal *** at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks,an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5(YOLOv5)is ***,we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network(Real-ESRGAN)by changing the U-shaped network(U-Net)to Attention U-Net,to preprocess the images;secondly,we propose a new Focus module that introduces the Marr operator,which can provide more boundary information for the network;again,because Complete Intersection over Union(CIOU)cannot accommodate target borders that are increasing and decreasing,replace CIOU with Extended Intersection over Union(EIOU),while the loss function is changed to Focal and Efficient IOU(Focal-EIOU)due to the different difficulty of sample *** the homemade dataset,the precision of our method is 94%,the recall is 70.8%,and the map@.5 is 83.6%,which is an improvement of 1.3%in precision,9.7%in recall,and 7%in map@.5 over the original *** algorithm can meet the needs of electrolysis tank pole plate abnormal temperature detection,which can lay a technical foundation for improving production efficiency and reducing production waste.
In the electric power equipment industry,various insulating materials and accessories are manufactured using petroleum-based epoxy ***,petrochemical resources are gradually becoming *** addition,the global surge in pl...
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In the electric power equipment industry,various insulating materials and accessories are manufactured using petroleum-based epoxy ***,petrochemical resources are gradually becoming *** addition,the global surge in plastic usage has consistently raised concerns regarding greenhouse gas emissions,leading to worsening global ***,to facilitate eco-friendly policies,industrialising epoxy systems applicable to high-pressure components using bio-based epoxy composites is *** results of the characterisation conducted in this research regarding bio-content were confirmed through thermogravimetric analysis and differential scanning calorimetry,which showed that as the bio-content increased,the thermal stability *** the operating temperature of 105℃ for the insulation spacer,structurally,no issues would be encountered if the spacer was manufactured with a bio-content of 20%(bio 20%).Subsequent tensile and flexural strength measurements revealed mechanical properties equivalent to or better than those of their petroleum-based *** impact strength tended to decrease with increasing *** the dielectric properties confirmed that the epoxy composite containing 20%biomaterial is suitable for manufacturing insulation ***,a series of tests conducted after spacer fabrication confirmed the absence of internal metals and bubbles with no external discolouration or cracks observed.
Research has demonstrated the positive influence of Undergraduate Research Experience (URE) programs in science, Technology, engineering, and Mathematics (STEM) on students' educational journey and their developme...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.
High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation lear...
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High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.
This paper presents a novel integrated sensing and communication (ISAC) framework that leverages recent advancements in reconfigurable distributed antennas and reflecting surfaces (RDARS). RDARS is a programmable stru...
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