Tracking the industrial growth of modern China with high-resolution panchromatic imagery: A sequential convolutional approach

Due to insufficient or difficult to obtain data on development in inaccessible regions, remote sensing data is an important tool for interested stakeholders to collect information on economic growth. To date, no studies have utilized deep learning to estimate industrial growth at the level of individual sites. In this study, we harness high-resolution panchromatic imagery to estimate development over time at 419 industrial sites in the People's Republic of China using a multi-tier computer vision framework. We present two methods for approximating development: (1) structural area coverage estimated through a Mask R-CNN segmentation algorithm, and (2) imputing development directly with visible & infrared radiance from the Visible Infrared Imaging Radiometer Suite (VIIRS). Labels generated from these methods are comparatively evaluated and tested. On a dataset of 2,078 50 cm resolution images spanning 19 years, the results indicate that two dimensions of industrial development can be estimated using high-resolution daytime imagery, including (a) the total square meters of industrial development (average error of 0.021 $\textrm{km}^2$), and (b) the radiance of lights (average error of 9.8 $\mathrm{\frac{nW}{cm^{2}sr}}$). Trend analysis of the techniques reveal estimates from a Mask R-CNN-labeled CNN-LSTM track ground truth measurements most closely. The Mask R-CNN estimates positive growth at every site from the oldest image to the most recent, with an average change of 4,084 $\textrm{m}^2$.Comment: Fixed typo

pyShore: A deep learning toolkit for shoreline structure mapping with high-resolution orthographic imagery and convolutional neural networks

The process of mapping shoreline structures (i.e., riprap, groins, breakwaters or bulkheads) is heavily reliant on in-situ field surveys and manual delineation using orthoimagery or aerial imagery. These processes are time and resource intensive, resulting in update times of longer than a decade for larger waterbodies. In this study, we explore the effectiveness of a deep learning approach to map shoreline armoring structures from remotely sensed high-resolution imagery. We focus on computationally efficient techniques which can be deployed in desktop environments similar to those used by human coders today, with the goal of providing a semi-automated technique which reduces the total amount of time required to delineate shoreline structures. We test a range of architectures using a dataset of over 10,000 observations of four classes of shoreline structure, finding that a ResNet18 based Pyramid Attention Network (PAN) architecture achieves 72% overall accuracy (60 cm resolution), with 80% and 94% prediction accuracy in breakwater and groins, respectively. This relatively lightweight implementation enabled a 1.5 kilometers of shoreline to be processed in 1.4 s (GPU) to 2.16 s (CPU) in simulated user environments. Finally, we present pyShore, an implementation of this deep learning algorithm made available for human coders to apply as a part of a semi-automated workflow

Phototunable biomemory based on light-mediated charge trap

Phototunable biomaterial‐based resistive memory devices and understanding of their underlying switching mechanisms may pave a way toward new paradigm of smart and green electronics. Here, resistive switching behavior of photonic biomemory based on a novel structure of metal anode/carbon dots (CDs)‐silk protein/indium tin oxide is systematically investigated, with Al, Au, and Ag anodes as case studies. The charge trapping/detrapping and metal filaments formation/rupture are observed by in situ Kelvin probe force microscopy investigations and scanning electron microscopy and energy‐dispersive spectroscopy microanalysis, which demonstrates that the resistive switching behavior of Al, Au anode‐based device are related to the space‐charge‐limited‐conduction, while electrochemical metallization is the main mechanism for resistive transitions of Ag anode‐based devices. Incorporation of CDs with light‐adjustable charge trapping capacity is found to be responsible for phototunable resistive switching properties of CDs‐based resistive random access memory by performing the ultraviolet light illumination studies on as‐fabricated devices. The synergistic effect of photovoltaics and photogating can effectively enhance the internal electrical field to reduce the switching voltage. This demonstration provides a practical route for next‐generation biocompatible electronics

Genome-Wide Histone H3K27 Acetylation Profiling Identified Genes Correlated With Prognosis in Papillary Thyroid Carcinoma

Thyroid carcinoma (TC) is the most common endocrine malignancy, and papillary TC (PTC) is the most frequent subtype of TC, accounting for 85–90% of all the cases. Aberrant histone acetylation contributes to carcinogenesis by inducing the dysregulation of certain cancer-related genes. However, the histone acetylation landscape in PTC remains elusive. Here, we interrogated the epigenomes of PTC and benign thyroid nodule (BTN) tissues by applying H3K27ac chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) along with RNA-sequencing. By comparing the epigenomic features between PTC and BTN, we detected changes in H3K27ac levels at active regulatory regions, identified PTC-specific super-enhancer-associated genes involving immune-response and cancer-related pathways, and uncovered several genes that associated with disease-free survival of PTC. In summary, our data provided a genome-wide landscape of histone modification in PTC and demonstrated the role of enhancers in transcriptional regulations associated with prognosis of PTC

Variational Information Bottleneck Regularized Deep Reinforcement Learning for Efficient Robotic Skill Adaptation

Deep Reinforcement Learning (DRL) algorithms have been widely studied for sequential decision-making problems, and substantial progress has been achieved, especially in autonomous robotic skill learning. However, it is always difficult to deploy DRL methods in practical safety-critical robot systems, since the training and deployment environment gap always exists, and this issue would become increasingly crucial due to the ever-changing environment. Aiming at efficiently robotic skill transferring in a dynamic environment, we present a meta-reinforcement learning algorithm based on a variational information bottleneck. More specifically, during the meta-training stage, the variational information bottleneck first has been applied to infer the complete basic tasks for the whole task space, then the maximum entropy regularized reinforcement learning framework has been used to learn the basic skills consistent with that of basic tasks. Once the training stage is completed, all of the tasks in the task space can be obtained by a nonlinear combination of the basic tasks, thus, the according skills to accomplish the tasks can also be obtained by some way of a combination of the basic skills. Empirical results on several highly nonlinear, high-dimensional robotic locomotion tasks show that the proposed variational information bottleneck regularized deep reinforcement learning algorithm can improve sample efficiency by 200–5000 times on new tasks. Furthermore, the proposed algorithm achieves substantial asymptotic performance improvement. The results indicate that the proposed meta-reinforcement learning framework makes a significant step forward to deploy the DRL-based algorithm to practical robot systems

Stimuli-Directed Helical Chirality Inversion and Bio-Applications

Helical structure is a sophisticated ubiquitous motif found in nature, in artificial polymers, and in supramolecular assemblies from microscopic to macroscopic points of view. Significant progress has been made in the synthesis and structural elucidation of helical polymers, nevertheless, a new direction for helical polymeric materials, is how to design smart systems with controllable helical chirality, and further use them to develop chiral functional materials and promote their applications in biology, biochemistry, medicine, and nanotechnology fields. This review summarizes the recent progress in the development of high-performance systems with tunable helical chirality on receiving external stimuli and discusses advances in their applications as drug delivery vesicles, sensors, molecular switches, and liquid crystals. Challenges and opportunities in this emerging area are also presented in the conclusion

Fusing Appearance and Prior Cues for Road Detection

Road detection is a crucial research topic in computer vision, especially in the framework of autonomous driving and driver assistance. Moreover, it is an invaluable step for other tasks such as collision warning, vehicle detection, and pedestrian detection. Nevertheless, road detection remains challenging due to the presence of continuously changing backgrounds, varying illumination (shadows and highlights), variability of road appearance (size, shape, and color), and differently shaped objects (lane markings, vehicles, and pedestrians). In this paper, we propose an algorithm fusing appearance and prior cues for road detection. Firstly, input images are preprocessed by simple linear iterative clustering (SLIC), morphological processing, and illuminant invariant transformation to get superpixels and remove lane markings, shadows, and highlights. Then, we design a novel seed superpixels selection method and model appearance cues using the Gaussian mixture model with the selected seed superpixels. Next, we propose to construct a road geometric prior model offline, which can provide statistical descriptions and relevant information to infer the location of the road surface. Finally, a Bayesian framework is used to fuse appearance and prior cues. Experiments are carried out on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) road benchmark where the proposed algorithm shows compelling performance and achieves state-of-the-art results among the model-based methods

A survey on complex factual question answering

Answering complex factual questions has drawn a lot of attention. Researchers leverage various data sources to support complex QA, such as unstructured texts, structured knowledge graphs and relational databases, semi-structured web tables, or even hybrid data sources. However, although the ideas behind these approaches show similarity to some extent, there is not yet a consistent strategy to deal with various data sources. In this survey, we carefully examine how complex factual question answering has evolved across various data sources. We list the similarities among these approaches and group them into the analysis–extend–reason framework, despite the various question types and data sources that they focus on. We also address future directions for difficult factual question answering as well as the relevant benchmarks

Z-Schemed WO₃/rGO/SnIn₄S₈ Sandwich Nanohybrids for Efficient Visible Light Photocatalytic Water Purification

Semiconductor photocatalysis has received much attention as a promising technique to solve energy crisis and environmental pollution. This work demonstrated the rational design of “sandwich„ WO3/rGO/SnIn4S8 (WGS) Z-scheme photocatalysts for efficient purification of wastewater emitted from tannery and dyeing industries. Such materials were prepared by a combined protocol of the in situ precipitation method with hydrothermal synthesis, and structurally characterized by XRD, SEM, HRTEM, UV-vis DRS, and PL spectroscopy. Results showed that the Z-schemed nanohybrids significantly enhanced the photocatalytic activity compared to the single component photocatalysts. An optimized case of the WGS-2.5% photocatalysts exhibited the highest Cr(VI) reduction rate, which was ca. 1.8 and 12 times more than those of pure SnIn4S8 (SIS) and WO3, respectively. Moreover, the molecular mechanism of the enhanced photocatalysis was clearly revealed by the radical-trapping control experiments and electron paramagnetic resonance (ESR) spectroscopy. The amount of superoxide and hydroxyl radicals as the major reactive oxygen species performing the redox catalysis was enhanced significantly on the Z-scheme WGS photocatalysts, where the spatial separation of photoinduced electron⁻hole pairs was therefore accelerated for the reduction of Cr(VI) and degradation of Rhodamine B (RhB). This study provides a novel strategy for the synthesis of all-solid-state Z-scheme photocatalysts for environmental remediation