Among algorithms for comparing the areas under two or more correlated receiver operating characteristic (ROC) curves, DeLong's algorithm is perhaps the most widely used one due to its simplicity of ...implementation in practice. Unfortunately, however, the time complexity of DeLong's algorithm is of quadratic order (the product of sample sizes), thus making it time-consuming and impractical when the sample sizes are large. Based on an equivalent relationship between the Heaviside function and mid-ranks of samples, we improve DeLong's algorithm by reducing the order of time complexity from quadratic down to linearithmic (the product of sample size and its logarithm). Monte Carlo simulations verify the computational efficiency of our algorithmic findings in this work.
The outbreak of an epidemic disease may pose significant treats to human beings and may further lead to a global crisis. In order to control the spread of an epidemic, the effective management of ...rapidly increased medical waste through establishing a temporary reverse logistics system is of vital importance. However, no research has been conducted with the focus on the design of an epidemic reverse logistics network for dealing with medical waste during epidemic outbreaks, which, if improperly treated, may accelerate disease spread and pose a significant risk for both medical staffs and patients. Therefore, this paper proposes a novel multi-objective multi-period mixed integer program for reverse logistics network design in epidemic outbreaks, which aims at determining the best locations of temporary facilities and the transportation strategies for effective management of the exponentially increased medical waste within a very short period. The application of the model is illustrated with a case study based on the outbreak of the coronavirus disease 2019 (COVID-19) in Wuhan, China. Even though the uncertainty of the future COVID-19 spread tendency is very high at the time of this research, several general policy recommendations can still be obtained based on computational experiments and quantitative analyses. Among other insights, the results suggest installing temporary incinerators may be an effective solution for managing the tremendous increase of medical waste during the COVID-19 outbreak in Wuhan, but the location selection of these temporary incinerators is of significant importance. Due to the limitation on available data and knowledge at present stage, more real-world information are needed to assess the effectiveness of the current solution.
Multistage stochastic integer programming (MSIP) combines the difficulty of uncertainty, dynamics, and non-convexity, and constitutes a class of extremely challenging problems. A common formulation ...for these problems is a dynamic programming formulation involving nested cost-to-go functions. In the linear setting, the cost-to-go functions are convex polyhedral, and decomposition algorithms, such as nested Benders’ decomposition and its stochastic variant, stochastic dual dynamic programming (SDDP), which proceed by iteratively approximating these functions by cuts or linear inequalities, have been established as effective approaches. However, it is difficult to directly adapt these algorithms to MSIP due to the nonconvexity of integer programming value functions. In this paper we propose an extension to SDDP—called stochastic dual dynamic integer programming (SDDiP)—for solving MSIP problems with binary state variables. The crucial component of the algorithm is a new reformulation of the subproblems in each stage and a new class of cuts, termed Lagrangian cuts, derived from a Lagrangian relaxation of a specific reformulation of the subproblems in each stage, where local copies of state variables are introduced. We show that the Lagrangian cuts satisfy a tightness condition and provide a rigorous proof of the finite convergence of SDDiP with probability one. We show that, under fairly reasonable assumptions, an MSIP problem with general state variables can be approximated by one with binary state variables to desired precision with only a modest increase in problem size. Thus our proposed SDDiP approach is applicable to very general classes of MSIP problems. Extensive computational experiments on three classes of real-world problems, namely electric generation expansion, financial portfolio management, and network revenue management, show that the proposed methodology is very effective in solving large-scale multistage stochastic integer optimization problems.
Recent years have witnessed the flourishing of deep learning-based methods in hyperspectral anomaly detection (HAD). However, the lack of available supervision information persists throughout. In ...addition, existing unsupervised learning/semisupervised learning methods to detect anomalies utilizing reconstruction errors not only generate backgrounds but also reconstruct anomalies to some extent, complicating the identification of anomalies in the original hyperspectral image (HSI). In order to train a network able to reconstruct only background pixels (instead of anomalous pixels), in this article, we propose a new blind-spot self-supervised learning network (called BS3LNet) that generates training patch pairs with blind spots from a single HSI and trains the network in self-supervised fashion. The BS3LNet tends to generate high reconstruction errors for anomalous pixels and low reconstruction errors for background pixels due to the fact that it adopts a blind-spot architecture, i.e., the receptive field of each pixel excludes the pixel itself and the network reconstructs each pixel using its neighbors. The above characterization suits the HAD task well, considering the fact that spectral signatures of anomalous targets are significantly different from those of neighboring pixels. Our network can be considered a superb background generator, which effectively enhances the semantic feature representation of the background distribution and weakens the feature expression for anomalies. Meanwhile, the differences between the original HSI and the background reconstructed by our network are used to measure the degree of the anomaly of each pixel so that anomalous pixels can be effectively separated from the background. Extensive experiments on two synthetic and three real datasets reveal that our BS3LNet is competitive with regard to other state-of-the-art approaches.
Abstract
Severe events of wintertime particulate air pollution in Beijing (winter haze) are associated with high relative humidity (RH) and fast production of particulate sulfate from the oxidation ...of sulfur dioxide (SO
2
) emitted by coal combustion. There has been considerable debate regarding the mechanism for SO
2
oxidation. Here we show evidence from field observations of a haze event that rapid oxidation of SO
2
by nitrogen dioxide (NO
2
) and nitrous acid (HONO) takes place, the latter producing nitrous oxide (N
2
O). Sulfate shifts to larger particle sizes during the event, indicative of fog/cloud processing. Fog and cloud readily form under winter haze conditions, leading to high liquid water contents with high pH (>5.5) from elevated ammonia. Such conditions enable fast aqueous-phase oxidation of SO
2
by NO
2
, producing HONO which can in turn oxidize SO
2
to yield N
2
O.This mechanism could provide an explanation for sulfate formation under some winter haze conditions.
Unit commitment (UC) is a key operational problem in power systems for the optimal schedule of daily generation commitment. Incorporating uncertainty in this already difficult mixed-integer ...optimization problem introduces significant computational challenges. Most existing stochastic UC models consider either a two-stage decision structure, where the commitment schedule for the entire planning horizon is decided before the uncertainty is realized, or a multistage stochastic programming model with relatively small scenario trees to ensure tractability. We propose a new type of decomposition algorithm, based on the recently proposed framework of stochastic dual dynamic integer programming (SDDiP), to solve the multistage stochastic unit commitment (MSUC) problem. We propose a variety of computational enhancements to SDDiP, and conduct systematic and extensive computational experiments to demonstrate that the proposed method is able to handle elaborate stochastic processes and can solve MSUCs with a huge number of scenarios that are impossible to handle by existing methods.
Conducting hydrogels have attracted much attention for the emerging field of hydrogel bioelectronics, especially poly(3,4‐ethylenedioxythiophene): poly(styrene sulfonate) (PEDOT:PSS) based hydrogels, ...because of their great biocompatibility and stability. However, the electrical conductivities of hydrogels are often lower than 1 S cm−1 which are not suitable for digital circuits or applications in bioelectronics. Introducing conductive inorganic fillers into the hydrogels can improve their electrical conductivities. However, it may lead to compromises in compliance, biocompatibility, deformability, biodegradability, etc. Herein, a series of highly conductive ionic liquid (IL) doped PEDOT:PSS hydrogels without any conductive fillers is reported. These hydrogels exhibit high conductivities up to ≈305 S cm−1, which is ≈8 times higher than the record of polymeric hydrogels without conductive fillers in literature. The high electrical conductivity results in enhanced areal thermoelectric output power for hydrogel‐based thermoelectric devices, and high specific electromagnetic interference (EMI) shielding efficiency which is about an order in magnitude higher than that of state‐of‐the‐art conductive hydrogels in literature. Furthermore, these stretchable (strain >30%) hydrogels exhibit fast self‐healing, and shape/size‐tunable properties, which are desirable for hydrogel bioelectronics and wearable organic devices. The results indicate that these highly conductive hydrogels are promising in applications such as sensing, thermoelectrics, EMI shielding, etc.
PEDOT:PSS hydrogels exhibit high conductivities up to ≈305 S cm−1, which is ≈8 times higher than the record of polymeric hydrogels without conductive fillers. The high electrical conductivity results in enhanced areal thermoelectric output power for hydrogel‐based thermoelectric devices, and high specific electromagnetic interference shielding efficiency (an order in magnitude higher than that of reported of state‐of‐the art conductive hydrogels).
Hyperspectral target detection can be described as locating targets of interest within a hyperspectral image based on prior information of targets. The complexity of actual scenes limits the ...performance of traditional statistical methods that rely on model assumptions, and traditional machine learning methods rely on mapping functions with limited complexity. To address these problems, we propose a Siamese transformer network for hyperspectral image target detection (STTD). The contribution of this article is threefold. First, we propose a novel method of constructing training samples using only the image itself and the limited prior information, which is suitable for target detection based on the Siamese network framework. Second, the Siamese network framework is utilized to solve the problem of similarity metric learning, i.e., make homogeneous features as close as possible and heterogeneous features as far as possible. Third, the most state-of-the-art network, transformer, is applied as the backbone of our proposed Siamese network to extract global features from spectra with long-range dependencies to achieve target detection. Furthermore, we make adaptive improvements to transformer for hyperspectral images. The proposed method shows its unique advantages in suppressing the background to a low level and highlighting the target with high probability. Experiments on five different datasets demonstrate the superiority of the proposed STTD as compared to the state-of-the-art.
Super-resolution (SR) techniques play a crucial role in increasing the spatial resolution of remote sensing data and overcoming the physical limitations of the spaceborne imaging systems. Though the ...convolutional neural network (CNN)-based methods have obtained good performance, they show limited capacity when coping with large-scale super-resolving tasks. The more complicated spatial distribution of remote sensing data further increases the difficulty in reconstruction. This article develops a dense-sampling super-resolution network (DSSR) to explore the large-scale SR reconstruction of the remote sensing imageries. Specifically, a dense-sampling mechanism, which reuses an upscaler to upsample multiple low-dimension features, is presented to make the network jointly consider multilevel priors when performing reconstruction. A wide feature attention block (WAB), which incorporates the wide activation and attention mechanism, is introduced to enhance the representation ability of the network. In addition, a chain training strategy is proposed to optimize further the performance of the large-scale models by borrowing knowledge from the pretrained small-scale models. Extensive experiments demonstrate the effectiveness of the proposed methods and show that the DSSR outperforms the state-of-the-art models in both quantitative evaluation and visual quality.
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