Stochastic Linear-quadratic Control Problems with Affine Constraints

In this paper, we investigate the stochastic linear-quadratic control problems with affine constraints in random coefficients case. With the help of the Pontryagin maximum principle and stochastic Riccati equation, the dual problem of original problem is established and the feedback solution of the optimal control problem is obtained. Under the Slater condition, the equivalence is proved between the solutions to the original problem and the ones of the dual problem, and the KKT condition is also provided for the dual problem. Finally, an invertibility assumption is given for ensuring the uniqueness of the solutions to the dual problem

Deep Learning-Based Building Footprint Extraction With Missing Annotations

Most state-of-the-art deep learning-based methods for extraction of building footprints are aimed at designing proper convolutional neural network (CNN) architectures or loss functions able to effectively predict building masks from remote sensing (RS) images. To properly train such CNN models, large-scale and pixel-level building annotations are required. One common approach to obtain scalable benchmark data sets for the segmentation of buildings is to register RS images with auxiliary geospatial information data, such as those available from OpenStreetMaps (OSM). However, due to land-cover changes, urban construction, and delayed geospatial information updating, some building annotations may be missing in the corresponding ground-truth building mask layers. This will likely introduce confusion in the training of CNN models for discriminating between background and building pixels. To solve this important issue, we first formulate the problem as a long-tailed classification one. Then, we introduce a new joint loss function based on three terms: 1) logit adjusted cross entropy (LACE) loss, aimed at discriminating between building and background pixels from a long-tailed label distribution; 2) weighted dice loss, aimed at increasing the F₁ scores of the predicted building masks; and 3) boundary (BD) alignment loss, which is optimized for preserving the fine-grained structure of building boundaries. Our experiments, conducted on two benchmark building segmentation data sets, validate the effectiveness of our newly proposed loss with respect to other state-of-the-art losses commonly used for extracting building footprints. The codes of this letter will be publicly available from https://github.com/jiankang1991/GRSL_BFE_MA

PiCoCo: Pixelwise Contrast and Consistency Learning for Semisupervised Building Footprint Segmentation

Building footprint segmentation from high-resolution remote sensing (RS) images plays a vital role in urban planning, disaster response, and population density estimation. Convolutional neural networks (CNNs) have been recently used as a workhorse for effectively generating building footprints. However, to completely exploit the prediction power of CNNs, large-scale pixel-level annotations are required. Most state-of-the-art methods based on CNNs are focused on the design of network architectures for improving the predictions of building footprints with full annotations, while few works have been done on building footprint segmentation with limited annotations. In this article, we propose a novel semisupervised learning method for building footprint segmentation, which can effectively predict building footprints based on the network trained with few annotations (e.g., only 0.0324 km2 out of 2.25-km2 area is labeled). The proposed method is based on investigating the contrast between the building and background pixels in latent space and the consistency of predictions obtained from the CNN models when the input RS images are perturbed. Thus, we term the proposed semisupervised learning framework of building footprint segmentation as PiCoCo, which is based on the enforcement of Pixelwise Contrast and Consistency during the learning phase. Our experiments, conducted on two benchmark building segmentation datasets, validate the effectiveness of our proposed framework as compared to several state-of-the-art building footprint extraction and semisupervised semantic segmentation methods

Rotation-Invariant Deep Embedding for Remote Sensing Images

Endowing convolutional neural networks (CNNs) with the rotation-invariant capability is important for characterizing the semantic contents of remote sensing (RS) images since they do not have typical orientations. Most of the existing deep methods for learning rotation-invariant CNN models are based on the design of proper convolutional or pooling layers, which aims at predicting the correct category labels of the rotated RS images equivalently. However, a few works have focused on learning rotation-invariant embeddings in the framework of deep metric learning for modeling the fine-grained semantic relationships among RS images in the embedding space. To fill this gap, we first propose a rule that the deep embeddings of rotated images should be closer to each other than those of any other images (including the images belonging to the same class). Then, we propose to maximize the joint probability of the leave-one-out image classification and rotational image identification. With the assumption of independence, such optimization leads to the minimization of a novel loss function composed of two terms: 1) a class-discrimination term and 2) a rotation-invariant term. Furthermore, we introduce a penalty parameter that balances these two terms and further propose a final loss to Rotation-invariant Deep embedding for RS images, termed RiDe. Extensive experiments conducted on two benchmark RS datasets validate the effectiveness of the proposed approach and demonstrate its superior performance when compared to other state-of-the-art methods. The codes of this article will be publicly available at https://github.com/jiankang1991/TGRS_RiDe

Skp2 expression unfavorably impacts survival in resectable esophageal squamous cell carcinoma

<p>Abstract</p> <p>Background</p> <p>The correlation of S-phase kinase–associated protein 2 (Skp2) with metastasis and prognosis in esophageal squamous cell carcinoma (ESCC) is controversial. The purpose of this study was to explore whether there was a correlation between the expression of Skp2 evaluated by immunohistochemistry and the clinical outcome of patients with operable ESCC, and to further determine the possible mechanism of the impact of Skp2 on survival.</p> <p>Methods</p> <p>Tissue microarrays that included 157 surgically resected ESCC specimens was successfully generated for immunohistochemical evaluation. The clinical/prognostic significance of Skp2 expression was analyzed. Kaplan-Meier analysis was used to compare the postoperative survival between groups. The prognostic impact of clinicopathologic variables and Skp2 expression was evaluated using a Cox proportional hazards model. A cell proliferation assay and a colony formation assay were performed in ESCC cell lines to determine the function of Skp2 on the progression of ESCC <it>in vitro</it>.</p> <p>Results</p> <p>Skp2 expression correlated closely with the T category (<it>p</it> = 0.035) and the pathological tumor-node-metastasis (TNM) stage (<it>p</it> = 0.027). High expression of Skp2 was associated with poor overall survival in resectable ESCC (<it>p</it> = 0.01). The multivariate Cox regression analysis demonstrated that pathological T category, pathological N category, cell differentiation, and negative Skp2 expression were independent factors for better overall survival. <it>In vitro</it> assays of ESCC cell lines demonstrated that Skp2 promoted the proliferative and colony-forming capacity of ESCCs.</p> <p>Conclusions</p> <p>Negative Skp2 expression in primary resected ESCC is an independent factor for better survival. Skp2 may play a pro-proliferative role in ESCC cells.</p

Dual-comb spectroscopy over 100km open-air path

Satellite-based greenhouse gases (GHG) sensing technologies play a critical role in the study of global carbon emissions and climate change. However, none of the existing satellite-based GHG sensing technologies can achieve the measurement of broad bandwidth, high temporal-spatial resolution, and high sensitivity at the same time. Recently, dual-comb spectroscopy (DCS) has been proposed as a superior candidate technology for GHG sensing because it can measure broadband spectra with high temporal-spatial resolution and high sensitivity. The main barrier to DCS's display on satellites is its short measurement distance in open air achieved thus far. Prior research has not been able to implement DCS over 20 km of open-air path. Here, by developing a bistatic setup using time-frequency dissemination and high-power optical frequency combs, we have implemented DCS over a 113 km turbulent horizontal open-air path. Our experiment successfully measured GHG with 7 nm spectral bandwidth and a 10 kHz frequency and achieved a CO2 sensing precision of <2 ppm in 5 minutes and <0.6 ppm in 36 minutes. Our results represent a significant step towards advancing the implementation of DCS as a satellite-based technology and improving technologies for GHG monitoringComment: 24 pages, 6 figure

Mid-Infrared Self-Similar Compression of Picosecond Pulse in an Inversely Tapered Silicon Ridge Waveguide

On chip high quality and high degree pulse compression is desirable in the realization of integrated ultrashort pulse sources, which are important for nonlinear photonics and spectroscopy. In this paper, we design a simple inversely tapered silicon ridge waveguide with exponentially decreasing dispersion profile along the propagation direction, and numerically investigate self-similar pulse compression of the fundamental soliton within the mid-infrared spectral region. When higher-order dispersion (HOD), higher-order nonlinearity (HON), losses (α), and variation of the Kerr nonlinear coefficient γ(z) are considered in the extended nonlinear Schrödinger equation, a 1 ps input pulse at the wavelength of 2490 nm is successfully compressed to 57.29 fs in only 5.1-cm of propagation, along with a compression factor Fc of 17.46. We demonstrated that the impacts of HOD and HON are minor on the pulse compression process, compared with that of α and variation of γ(z). Our research results provide a promising solution to realize integrated mid-infrared ultrashort pulse sources

A Molecular Design Approach Towards Elastic and Multifunctional Polymer Electronics

Next-generation wearable electronics require enhanced mechanical robustness and device complexity. Besides previously reported softness and stretchability, desired merits for practical use include elasticity, solvent resistance, facile patternability and high charge carrier mobility. Here, we show a molecular design concept that simultaneously achieves all these targeted properties in both polymeric semiconductors and dielectrics, without compromising electrical performance. This is enabled by covalently-embedded in-situ rubber matrix (iRUM) formation through good mixing of iRUM precursors with polymer electronic materials, and finely-controlled composite film morphology built on azide crosslinking chemistry which leverages different reactivities with C–H and C=C bonds. The high covalent crosslinking density results in both superior elasticity and solvent resistance. When applied in stretchable transistors, the iRUM-semiconductor film retained its mobility after stretching to 100% strain, and exhibited record-high mobility retention of 1 cm2 V−1 s−1 after 1000 stretching-releasing cycles at 50% strain. The cycling life was stably extended to 5000 cycles, five times longer than all reported semiconductors. Furthermore, we fabricated elastic transistors via consecutively photo-patterning of the dielectric and semiconducting layers, demonstrating the potential of solution-processed multilayer device manufacturing. The iRUM represents a molecule-level design approach towards robust skin-inspired electronics

The use of MRI apparent diffusion coefficient (ADC) in monitoring the development of brain infarction

<p>Abstract</p> <p>Background</p> <p>To study the rules that apparent diffusion coefficient (ADC) changes with time and space in cerebral infarction, and to provide the evidence in defining the infarction stages.</p> <p>Methods</p> <p>117 work-ups in 98 patients with cerebral infarction (12 hyperacute, 43 acute, 29 subacute, 10 steady, and 23 chronic infarctions) were imaged with both conventional MRI and diffusion weighted imaging. The average ADC values, the relative ADC (rADC) values, and the ADC values or rADC values from the center to the periphery of the lesion were calculated.</p> <p>Results</p> <p>The average ADC values and the rADC values of hyperacute and acute infarction lesion depressed obviously. rADC values in hyperacute and acute stage was minimized, and increased progressively as time passed and appeared as "pseudonormal" values in approximately 8 to 14 days. Thereafter, rADC values became greater than normal in chronic stage. There was positive correlation between rADC values and time (P < 0.01). The ADC values and the rADC values in hyperacute and acute lesions had gradient signs that these lesions increased from the center to the periphery. The ADC values and the rADC values in subacute lesions had adverse gradient signs that these lesions decreased from the center to the periphery.</p> <p>Conclusion</p> <p>The ADC values of infarction lesions have evolution rules with time and space. The evolution rules with time and those in space can be helpful to decide the clinical stage, and to provide the evidence in guiding the treatment or judging the prognosis in infarction.</p