BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading

Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microaneurysms, are essential to DR grading in medical imaging, but they could easily be influenced by other objects. To address these challenges, we propose a new deep learning architecture, called BiRA-Net, which combines the attention model for feature extraction and bilinear model for fine-grained classification. Furthermore, in considering the distance between different grades of different DR categories, we propose a new loss function, called grading loss, which leads to improved training convergence of the proposed approach. Experimental results are provided to demonstrate the superior performance of the proposed approach.Comment: Accepted at ICIP 201

SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery

Abstract. Building height and footprint are two fundamental urban morphological features required by urban climate modelling. Although some statistical methods have been proposed to estimate average building height and footprint from publicly available satellite imagery, they often involve tedious feature engineering which makes it hard to achieve efficient knowledge discovery in a changing urban environment with ever-increasing earth observations. In this work, we develop a deep-learning-based (DL) Python package – SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery) to extract such information. Multi-task deep-learning (MTDL) models are proposed to automatically learn feature representation shared by building height and footprint prediction. Besides, we integrate digital elevation model (DEM) information into developed models to inform models of terrain-induced effects on the backscattering displayed by Sentinel-1 imagery. We set conventional machine-learning-based (ML) models and single-task deep-learning (STDL) models as benchmarks and select 46 cities worldwide to evaluate developed models’ patch-level prediction skills and city-level spatial transferability at four resolutions (100, 250, 500 and 1000 m). Patch-level results of 43 cities show that DL models successfully produce discriminative feature representation and improve the coefficient of determination (R2) of building height and footprint prediction more than ML models by 0.27–0.63 and 0.11–0.49, respectively. Moreover, stratified error assessment reveals that DL models effectively mitigate the severe systematic underestimation of ML models in the high-value domain: for the 100 m case, DL models reduce the root mean square error (RMSE) of building height higher than 40 m and building footprint larger than 0.25 by 31 m and 0.1, respectively, which demonstrates the superiority of DL models on refined 3D building information extraction in highly urbanized areas. For the evaluation of spatial transferability, when compared with an existing state-of-the-art product, DL models can achieve similar improvement on the overall performance and high-value prediction. Furthermore, within the DL family, comparison in building height prediction between STDL and MTDL models reveals that MTDL models achieve higher accuracy in all cases and smaller bias uncertainty for the prediction in the high-value domain at the refined scale, which proves the effectiveness of multi-task learning (MTL) on building height estimation

Zero-noise Extrapolation Assisted with Purity for Quantum Error Mitigation

Quantum error mitigation is a technique used to post-process errors occurring in the quantum system, which reduces the expected errors and achieves higher accuracy. One method of quantum error mitigation is zero-noise extrapolation, which involves amplifying the noise and then extrapolating the observable expectation of interest back to a noise-free point. This method usually relies on the error model of the noise, as error rates for different levels of noise are assumed during the noise amplification process. In this paper, we propose that the purity of output states in noisy circuits can assist in the extrapolation process, eliminating the need for assumptions about error rates. We also introduce the quasi-polynomial model from the linearity of quantum channel for extrapolation of experimental data, which can be reduced to other proposed models. Furthermore, we verify our purity-assisted zero-noise extrapolation by performing numerical simulations and experiments on the online public quantum computation platform, Quafu, to compare it with the routine zero-noise extrapolation and virtual distillation methods. Our results demonstrate that this modified method can suppress the random fluctuation of operator expectation measurement, and effectively reduces the bias in extrapolation to a level lower than both the zero-noise extrapolation and virtual distillation methods, especially when the error rate is moderate

(E)-2-Acetyl­pyrazine 4-nitro­phenyl­hydrazone

In the title compound, C12H11N5O2, the mol­ecule adopts an E configuration, with the benzene and pyrazine rings located on opposite sides of the N=C double bond. The face-to-face separations of 3.413 (14) and 3.430 (8) Å, respectively between parallel benzene rings and between pyrazine rings indicate the existence of π–π stacking between adjacent mol­ecules. The crystal structure also contains N—H⋯N and C—H⋯O hydrogen bonding