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The process of grinding silicon carbide(SiC) single crystals containing defects with a single diamond was simulated using molecular dynamics(MD) software. A 4H–SiC simulated workpiece—with transverse, vertical, 45°, and 135° defects—and a diamond tool were established through molecular dynamics, in addition, the models of different depth defects were established. The grinding removal behavior of 4H–SiC in different directions and positions was analyzed. The simulation results demonstrate that there is an elastic deformation interval during 4H–SiC grinding, whereby the defect within the interval will be filled and the defect outside the interval will not. Meanwhile, the resultant force curve, von Mises stress distribution, hydrostatic pressure distribution, surface appearance and temperature distribution were also analyzed. This research has theoretical significance in the optimization of semiconductor SiC process parameters.

Satellite image processing has been widely used in recent years in a number of applications such as land classification, Identification transfer, resource exploration, super‐resolution image, etc. Due to the orbital location, revision time, quick view angle limitations, and weather impact, the satellite images are challenging to manage. There are many types of resolution, such as spatial, spectral, and temporal. Still, in our case, we concentrated on spatial image resolution to super resolve the images from low‐resolution images. For remote sensing image super‐resolution fast wavelet‐based super‐resolution (FWSR), we propose a novel, fast wavelet‐based plexus framework that performs super‐resolution convolutional neural network (SRCNN)‐like extraction of features based on three hidden layers. First, wavelet sub‐band images are combined into a pre‐defined full‐scale data training factor, including approximation and interchangeable stand‐alone units (frequency sub‐bands). Second, to speed up image recovery, mapping the sub‐band image of the wavelet is then measured using its approximate image. Third, the added sub‐pixel layer at the end of the network model is intended to reproduce image quality using a plexus framework. The approximation sub‐band images obtained after discrete wavelet transform wavelet decomposition are used as input rather than the original image because of their high‐frequency data and preserved characteristics. Five current super‐resolution neural network approaches are compared with the proposed technique and tested on three pubic satellite image datasets and two benchmark datasets. The experimental findings are well compared qualitatively and quantitatively.

Super-resolution (SR) has received extensive attention in recent years for satellite image processing in a wide range of application scenarios, such as land classification, identification of changes, the discovery of resources, etc. Satellite images from satellite sensors are mostly low-resolution (LR) images, so they do not completely fulfill object detection and analysis criteria. SR has multiple residual network frameworks in deep learning that have improved performance and can extend thousands of layers in the system. However, each layer improves accuracy by doubling the number of layers, although training thousands of layers are too expensive, the process is slow, and there are functional recovery issues. We proposed a transferred wide residual Single Image Super-Resolution (SISR) remote sensing deep neural network model (WRSR). By increasing the width and reducing the residual network depth, the proposed approach has dramatically reduced memory costs. As a result, our model reduced memory costs by 21% in Enhanced Deep Residual Super-Resolution (EDSR) and 34% in SRResNet as a direct consequence of the in-depth reduction. The proposed architecture improves the efficiency of training loss by performing weight normalization instead of augmentation technology. We compared our method to five recent existing super-resolution (SR) deep neural network methods, tested over three public satellite image datasets and a standard reference (PRIM) dataset. Experiment analysis is evaluated in peak to signal noise ratio (PSNR) and structural similarity index measure (SSIM).

The prevalence of drug-resistant Mycobacterium tuberculosis (Mtb) strains makes disease control more complicated, which is the main cause of death in tuberculosis (TB) patients. Early detection and timely standard treatment are the key to current prevention and control of drug-resistant TB. In recent years, despite the continuous advancement in drug-resistant TB diagnostic technology, the needs for clinical rapid and accurate diagnosis are still not fully met. With the development of sequencing technology, the research of human microecology has been intensified. This study aims to use 16 rRNA sequencing technology to detect and analyze upper respiratory flora of TB patients with anti-TB drug sensitivity (DS, n = 55), monoresistance isoniazide (MR-INH, n = 33), monoresistance rifampin (MR-RFP, n = 12), multidrug resistance (MDR, n = 26) and polyresistance (PR, n = 39) in southern China. Potential microbial diagnostic markers for different types of TB drug resistance are searched by screening differential flora, which provides certain guiding significance for drug resistance diagnosis and clinical drug use of TB. The results showed that the pulmonary microenvironment of TB patients was more susceptible to infection by external pathogens, and the infection of different drug-resistant Mtb leads to changes in different flora. Importantly, seven novel microorganisms (Leptotrichia, Granulicatella, Campylobacter, Delfitia, Kingella, Chlamydophila, Bordetella) were identified by 16S rRNA sequencing as diagnostic markers for different drug resistance types of TB. Leptotrichia, Granulicatella, Campylobacter were potential diagnostic marker for TB patients with INH single-resistance. Delftia was a potential diagnostic marker for TB patients with RFP single drug-resistance. Kingella and Chlamydophila can be used as diagnostic markers for TB patients with PR. Bordetella can be used as a potential diagnostic marker for identification of TB patients with MDR.

The object identification within an image captured during rough weather conditions (such as haze, fog) poses difficulty due to the reduction of an image. The rough weather conditions lead not only to the variation of the image's visual effect but also to the disadvantage of post‐processing of an image. Furthermore, it causes inconvenience of all types of instruments that rely on optical imaging, such as satellite remote‐sensing systems, aerial photo systems, outdoor monitoring systems, and object identification systems, respectively. Hence, the improvement and restorement of the visual effects and enhanced post‐processing are needed. This research introduces a new image enhancement approach for image dehazing based on dark channel prior and piecewise linear transformation; also, the histogram equalisation technique, i.e. contrast limited adaptive histogram equalisation is applied. A dark channel prior is well known for its simplicity and productivity. In this work, the dark channel prior to a new angle is analysed in the first step, where average patch sizes are estimated for the computation of haze densities. Furthermore, the sky is approximated up to 5–10% of the hazy images, which has a good effect in removing the haze from the image. Using the dark channel, the proposed algorithm significantly boosted the effects of the dark images as well as reduced the influence of haze and noise. Eventually, for colour correction, the piecewise linear transformation technique is applied, which enhances the colour close to the original image. Experimental results demonstrate that the proposed method significantly improves the visibility of the algorithm on dark remote‐sensing images as well as on hazy natural images.

We introduce the problem of anti-knowledge mining. Our goal is to create an "anti-knowledge base" that contains factual mistakes. The resulting data can be used for analysis, training, and benchmarking in the research domain of automated fact checking. Prior data sets feature manually generated fact checks of famous misclaims. Instead, we focus on the long tail of factual mistakes made by Web authors, ranging from erroneous sports results to incorrect capitals. We mine mistakes automatically, by an unsupervised approach, from Wikipedia updates that correct factual mistakes. Identifying such updates (only a small fraction of the total number of updates) is one of the primary challenges. We mine anti-knowledge by a multi-step pipeline. First, we filter out candidate updates via several simple heuristics. Next, we correlate Wikipedia updates with other statements made on the Web. Using claim occurrence frequencies as input to a probabilistic model, we infer the likelihood of corrections via an iterative expectation-maximization approach. Finally, we extract mistakes in the form of subject-predicate-object triples and rank them according to several criteria. Our end result is a data set containing over 110,000 ranked mistakes with a precision of 85% in the top 1% and a precision of over 60% in the top 25%. We demonstrate that baselines achieve significantly lower precision. Also, we exploit our data to verify several hypothesis on why users make mistakes. We finally show that the AKB can be used to find mistakes on the entire Web.

The extensive deployment of wireless infrastructure provides a low-cost approach to tracking of mobile phone users in indoor environments using received signal strength (RSS). Crowdsourcing has been promoted as an efficient way to reduce the labor-intensive site survey process in conventional fingerprint-based localization systems. Despite its stated advantages, use of crowdwourcing for localization has issues of accuracy and reliability in indoor applications, in large part because of multipath propagation. This paper discusses and evaluates a Bayesian approach to localization of mobile users based on a crowdsourced fingerprint, in which environmental constraints as well as dynamic property of the mobile are incorporated as priors. Both a Markov chain and a semi-Markov chain approach are applied for modeling the transition and duration statistics of the mobile user across different location cells. Field test results demonstrate the effectiveness of introducing this additional information for localization in a real-world wireless network.

Video analytics with Deep Neural Networks (DNNs) empowers many vision-based applications. However, deploying DNN models for video analytics services must address the challenges of computational capacity, service delay, and cost. Leveraging the edge-cloud collaboration to address these problems has become a growing trend. This paper provides the multimedia research community with an open source framework named SmartEye for real-time video analytics by leveraging the edge-cloud collaboration. The system consists of 1) an edge layer which enables video preprocessing, model selection, on-edge inference, and task offloading; 2) a request forwarding layer which serves as a gateway of the cloud and forwards the offloaded tasks to backend workers; and 3) a backend worker layer that processes the offloaded tasks with specified DNN models. One can easily customize the policies for preprocessing, offloading, model selection, and request forwarding. The framework can facilitate research and development in this field. The project is released as an open source project on GitHub at https://github.com/MSNLAB/SmartEye.

In order to better simulate the vertical ejection process of a certain type of surface-to-air missile, this article establishes a certain type of surface-to-air missile ejection device high-pressure indoor ballistic equation, low-pressure indoor ballistic equation, missile motion equation and pressure relief hole exhaust equation, and use MATLAB software to program. The pressure curves of the high and low pressure chambers with time, the gunpowder combustion curve and the missile movement curve are obtained. Comparing the data simulation results with the research results of other scholars, the results show that the algorithm can better reflect the change trend of each key parameter in the missile ejection process, and the obtained data can be used as the experimental basis for missile launch dynamics simulation.

With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it’s still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use.

Absolute localization of a flying UAV on its own in a global-navigation-satellite-system (GNSS)-denied environment is always a challenge. In this paper, we present a landmark-based approach where a UAV is automatically locked into the landmark scene shown in a georeferenced image via a feedback control loop, which is driven by the output of an aerial image registration. To pursue a real-time application, we design and implement a speeded-up-robust-features (SURF)-based image registration algorithm that focuses efficiency and robustness under a 2D geometric transformation. A linear UAV controller with signals of four degrees of freedom is derived from the estimated transformation matrix. The approach is validated in a virtual simulation environment, with experimental results demonstrating the effectiveness and robustness of the proposed UAV self-localization system.

This paper considers the two-dimensional (2D) anchorless localization problem for sensor networks in global positioning system (GPS)-denied environments. We present an efficient method, based on the multidimensional scaling (MDS) algorithm, in order to estimate the positions of the nodes in the network using measurements of the inter-node distances. The proposed method takes advantage of the mobility of the nodes to address the location ambiguity problem, i.e., rotation and flip ambiguity, which arises in the anchorless MDS algorithm. Knowledge of the displacement of the moving node is used to produce an analytical solution for the noise-free case. Subsequently, a least squares estimator is presented for the noisy scenario and the associated closed-form solution derived. The simulations show that the proposed algorithm accurately and efficiently estimates the locations of nodes, outperforming alternative methods.

With the development of scientific big data technology, problem-oriented analysis becomes normal case. iAnalysis V1.0, an interactive analysis cloud service system, gives a unified cloud resource management service for scientific data analysis. It can not only be used directly by end-user scientists through the service portal, but also be called by other existing data systems in the form of docker container. In this paper, there are presented the key technical methods of iAnalysis V1.0, including the interactive analysis service solution based on container technology, the interactive analysis components and storage & high-performance computing services for interactive analysis. There are also introduced the specific application services of iAnalysis V1.0 such as lightweight deployment service, management and monitoring services, external application programming interface services and so on. Some applications of iAnalysis V1.0 are also presented. The basic deployment images of iAnalysis V1.0 can be download from https://hub.docker.com/repository/docker/bioinf/ianalysis.

Training and evaluation of fair classifiers is a challenging problem. This is partly due to the fact that most fairness metrics of interest depend on both the sensitive attribute information and label information of the data points. In many scenarios it is not possible to collect large datasets with such information. An alternate approach that is commonly used is to separately train an attribute classifier on data with sensitive attribute information, and then use it later in the ML pipeline to evaluate the bias of a given classifier. While such decoupling helps alleviate the problem of demographic scarcity, it raises several natural questions such as: how should the attribute classifier be trained?, and how should one use a given attribute classifier for accurate bias estimation? In this work we study this question from both theoretical and empirical perspectives. We first experimentally demonstrate that the test accuracy of the attribute classifier is not always correlated with its effectiveness in bias estimation for a downstream model. In order to further investigate this phenomenon, we analyze an idealized theoretical model and characterize the structure of the optimal classifier. Our analysis has surprising and counter-intuitive implications where in certain regimes one might want to distribute the error of the attribute classifier as unevenly as possible among the different subgroups. Based on our analysis we develop heuristics for both training and using attribute classifiers for bias estimation in the data scarce regime. We empirically demonstrate the effectiveness of our approach on real and simulated data.

Antarctica plays an important role in research on global change, and its unique geography, ocean, climate, and environment provide an ideal place for humankind to understand Earth’s evolution. Remote sensing provides an effective means to monitor and observe large-scale processes on the continent. Synthetic aperture radar (SAR) in particular provides the capability for all-weather Earth observation. The Sentinel-1A and Sentinel-1B SAR satellites have ideal ground coverage and imaging frequency for observing Antarctica. This study developed a dataset of 69,586 Sentinel-1 EW mode satellite images of the Antarctic ice sheet from October 2014 to December 2020. The dataset was processed with the European Space Agency Sentinel Application Platform (SNAP) and a Python batch scheduling tool on the Big Earth Data Cloud Service Platform of the Chinese Academy of Sciences Big Earth Data Science Engineering Program (CASEarth). Several data processing operations were implemented to process the raw dataset, including radiometric calibration, invalid edge removal, geocoding, data re-projection to an Antarctic projection, data compression to TIFF format, and construction of image pyramids. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00085.

Continual learning has become increasingly important as it enables NLP models to constantly learn and gain knowledge over time. Previous continual learning methods are mainly designed to preserve knowledge from previous tasks, without much emphasis on how to well generalize models to new tasks. In this work, we propose an information disentanglement based regularization method for continual learning on text classification. Our proposed method first disentangles text hidden spaces into representations that are generic to all tasks and representations specific to each individual task, and further regularizes these representations differently to better constrain the knowledge required to generalize. We also introduce two simple auxiliary tasks: next sentence prediction and task-id prediction, for learning better generic and specific representation spaces. Experiments conducted on large-scale benchmarks demonstrate the effectiveness of our method in continual text classification tasks with various sequences and lengths over state-of-the-art baselines. We have publicly released our code at https://github.com/GT-SALT/IDBR., NAACL 2021

As multi-task models gain popularity in a wider range of machine learning applications, it is becoming increasingly important for practitioners to understand the fairness implications associated with those models. Most existing fairness literature focuses on learning a single task more fairly, while how ML fairness interacts with multiple tasks in the joint learning setting is largely under-explored. In this paper, we are concerned with how group fairness (e.g., equal opportunity, equalized odds) as an ML fairness concept plays out in the multi-task scenario. In multi-task learning, several tasks are learned jointly to exploit task correlations for a more efficient inductive transfer. This presents a multi-dimensional Pareto frontier on (1) the trade-off between group fairness and accuracy with respect to each task, as well as (2) the trade-offs across multiple tasks. We aim to provide a deeper understanding on how group fairness interacts with accuracy in multi-task learning, and we show that traditional approaches that mainly focus on optimizing the Pareto frontier of multi-task accuracy might not perform well on fairness goals. We propose a new set of metrics to better capture the multi-dimensional Pareto frontier of fairness-accuracy trade-offs uniquely presented in a multi-task learning setting. We further propose a Multi-Task-Aware Fairness (MTA-F) approach to improve fairness in multi-task learning. Experiments on several real-world datasets demonstrate the effectiveness of our proposed approach.

The vibration response of the launcher is one of the important sources of the initial disturbance of the missile launch. Based on the theory of multi-body dynamics and finite element analysis, a rigid-flexible coupling model of a vehicle-mounted missile launcher is established, and the vibration response of the launching tube is simulated. Using the method of replacing the rigid and flexible body models of the key components, the order of the main factors affecting the subsidence of the launch tube is obtained, and the conclusion that the subsidence can be reduced by increasing the rigidity of the frame and chassis is proposed.

This paper presents results of the ongoing development of a crowdsourcing platform for labelling remote sensing images. In order to perform fast spatial and temporal retrieval and read data efficiently, this paper proposes an adaptive grid design method to enable the remote sensing images being segmented into tiles, which supports parallel reading and writing, and uses Ceph cluster for storage. To guarantee crowdsourcing results' quality, a quality control mechanism combining expertsourcing and crowdsourcing is proposed. The online labelling system is designed and a prototype has been developed. Users neither have to install any software, nor download large remote sensing images. They can directly label the images by just selecting tags and drawing polygons. By providing such a platform, a large volume of training dataset for remote sensing classification can be obtained.

Acupuncture is a medical modality based on the theory of traditional Chinese medicine, and its effect is relatively dependent on acupoint specificity. However, there is little knowledge on acupoint specificity versus acupuncture outcomes because of the deficiency of rigorous investigation on this topic, which has impeded the growing legitimacy of acupuncture in the mainstream of medicine as an evidence-based therapy. Therefore, it is of utmost importance to clarify this critical issue. The present study aims to verify the phenomenon of acupoint specificity in acupuncture-induced cardiovascular regulation and explore the biological mechanism by measuring mast cells’ degranulation and adenosine release. This study was conducted to explore the specificity of acupoints in an acute bradycardia rabbit model. After electroacupuncture (EA) stimulation at PC6, PC control (con) 1, PC con 2, LU7, LI11, and nonacupoint, only the PC6 group showed a significant improvement in relative heart rate as compared to that of the model group. There was no significant difference between the relative heart rate of other EA groups and that of the model group. Historical results also showed that the ratio of degranulated mast cells in PC6 was significantly higher than other acupoints and control points. From the results of high-performance liquid chromatography (HPLC), a transient elevation of adenosine concentration during EA was only observed on acupoints and control points ( P < 0.05 ) along the pericardium meridian. The EA-induced adjustment on acute bradycardia exhibits a relative specificity of acupoints, which may be related to mast cell degranulation and adenosine release in local acupoint areas. Increased degranulation of mast cells and augmentation of adenosine release during EA may be the mechanisms for PC6 having significantly better acupuncture effects than other acupoints and nonacupoints.

We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. To obtain generated targets that are natural but also faithful to the source table, we introduce a dataset construction process where annotators directly revise existing candidate sentences from Wikipedia. We present systematic analyses of our dataset and annotation process as well as results achieved by several state-of-the-art baselines. While usually fluent, existing methods often hallucinate phrases that are not supported by the table, suggesting that this dataset can serve as a useful research benchmark for high-precision conditional text generation., Accepted to EMNLP 2020

NLP models are shown to suffer from robustness issues, i.e., a model's prediction can be easily changed under small perturbations to the input. In this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model that, given an input text, generates adversarial texts through controllable attributes that are known to be invariant to task labels. For example, in order to attack a model for sentiment classification over product reviews, we can use the product categories as the controllable attribute which would not change the sentiment of the reviews. Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches. We further use our generated adversarial examples to improve models through adversarial training, and we demonstrate that our generated attacks are more robust against model re-training and different model architectures., Comment: 6 pages, accepted to EMNLP 2020

Acupuncture is one of the most promising modalities in complimentary medicine. However, the underlying mechanisms are not well understood yet. We found that in TRPV2 knockout male mice, acupuncture-induced analgesia was suppressed with a decreased activation of mast cells in the acupoints stimulated. The mast cell stabilizer sodium cromolyn could suppress the release of adenosine in the acupoints on male rats. A direct injection of adenosine A1 receptor agonist or histamine H1 receptor agonist increased β-endorphin in the cerebral-spinal fluid in the acute adjuvant arthritis male rats and thus replicated the analgesic effect of acupuncture. These observations suggest that the mast cell is the central structure of acupoints and is activated by acupuncture through TRPV2 channels. The mast cell transduces the mechanical stimuli to acupuncture signal by activating either H1 or A1 receptors, therefore triggering the acupuncture effect in the subject. These findings might open new frontiers for acupuncture research.

Anaerobic digestion (AD) is an effective biological process to recover energy from organic solid wastes. There is a growing interest in utilization of renewable or sustainable bioenergy sources as future alternatives to fossil fuels. However, low hydrolysis rate, high heterogeneity of plant cell-wall structure of lignocellulose, process instability at high organic loading rate, and low metabolic activity of microbes limit the practical application of AD of organic solid wastes. A novel nanobubble water (NBW) based-AD system has been proposed to achieve enhanced methane production from typical organic solid wastes. This review summarizes the recent NBW studies, particularly on its utilization in AD processes and its stimulating effects on microorganisms and enzyme activities. The NBW generation approaches and its unique properties are firstly reviewed; then the roles of O2 in the O2-containing gas NBW in AD are also outlined and discussed. A deep insight into the feasibility of applying NBW-based AD system in the promotion of energy production from organic solid wastes is provided. Based on the reviewed literature works, the challenges and prospects of adopting the NBW-based AD system as a promising and sustainable technology for energy recovery from organic solid wastes are highlighted.

Image dehazing is a fast-growing research area in image processing and computer vision. Due to the extreme fog, haze, and air dispersion within an environment, the hazy image raises several challenges in retrieving original image information type. However, past techniques endure massive computation complexity and even the distortion of original images such as halos and over-saturation. In this research work, a new wavelet Hybrid (Local-Global Combined) Network is proposed for single image dehazing using a convolution neural network (CNN) in the wavelet domain (WH-Net). It is observed that low-level features such as edges are more important than high-level features such as texture. So we have used 2-DWT to decompose the single image model into the frequency subbands, which performs more quickly. It is demonstrated that the estimation of wavelet sub-bands reformulates the trainable end-to-end learning with a special architecture where DWT and IDWT are the feature extraction layers instead of Conv and Deconv, distinguishing it from classical CNN networks. The WHNet method is designed to achieve multi-level representations of hazy images to provide local and global information. The proposed network prominent features are designed with fewer convolution layers without decreasing performance relative to the commonly observed deeper learning models. Compared to several state-of-the-art algorithms, our proposed WHNet neural network surpasses in visual and quantitative performances on three public datasets.

Airborne LiDAR technology is a popular technique to quickly acquire high-precision information of the ground and the objects above it. Moreover, filtering the data is one of the necessary core processing steps. In recent years, many algorithms have been derived from traditional filtering algorithms. However, when applied to LiDAR data obtained from mountainous regions, most algorithms generate numerous omissions and errors when attempting to retain steep terrain features and filter vegetation information. This paper aims to quickly and accurately extract the digital terrain model (DTM) in mountainous regions. To accomplish this goal, a filtering algorithm based on a regular mesh generation strategy is proposed. The proposed algorithm initially divides the huge amount of point cloud data into strips and selects the appropriate spacing to subdivide each strip into equidistant grid data. The grid data is used as the input to an iterative polynomial fitting process, after which the point cloud is classified based on a controlled threshold. The experimental results show that the proposed method can quickly and efficiently classify data of mountains with different characteristics while retaining terrain feature information better than other algorithms. The average accuracy of recognition is greater than 92%. In addition, this algorithm also applies to mountains with lush vegetation.

The Chinese remainder theorem (CRT) is often used to find unwrap signal phase over multiple wavelengths as a means of estimating total travel time of a signal. Existing approaches, implemented efficiently in closed form using either standard CRT or methods derived from lattice theory, require the wavelengths involved to be coprime. This can be a limiting factor in real applications, especially where the available signal bandwidth is limited. In this letter, we derive a new algorithm to extend the closed-form lattice algorithm to accommodate non-coprime wavelengths. The benefit of this development is demonstrated with a Wi-Fi wireless device localization example.

Recently, there has been increasing development of positioning technology, which enables us to collect large scale trajectory data for moving objects. Efficient processing and analysis of massive trajectory data has thus become an emerging and challenging task for both researchers and practitioners. Therefore, in this paper, we propose an efficient data processing framework for mining massive trajectory data. This framework includes three modules: (1) a data distribution module, (2) a data transformation module, and (3) a high performance I/O module. Specifically, we first design a two-step consistent hashing algorithm, which takes into account load balancing, data locality, and scalability, for a data distribution module. In the data transformation module, we present a parallel strategy of a linear referencing algorithm with reduced subtask coupling, easy-implemented parallelization, and low communication cost. Moreover, we propose a compression-aware I/O module to improve the processing efficiency. Finally, we conduct a comprehensive performance evaluation on a synthetic dataset (1.114 TB) and a real world taxi GPS dataset (578 GB). The experimental results demonstrate the advantages of our proposed framework.

Semantic segmentation in high-resolution remote-sensing (RS) images is a fundamental task for RS-based urban understanding and planning. However, various types of artificial objects in urban areas make this task quite challenging. Recently, the use of Deep Convolutional Neural Networks (DCNNs) with multiscale information fusion has demonstrated great potential in enhancing performance. Technically, however, existing fusions are usually implemented by summing or concatenating feature maps in a straightforward way. Seldom do works consider the spatial importance for global-to-local context-information aggregation. This paper proposes a Learnable-Gated CNN (L-GCNN) to address this issue. Methodologically, the Taylor expression of the information-entropy function is first parameterized to design the gate function, which is employed to generate pixelwise weights for coarse-to-fine refinement in the L-GCNN. Accordingly, a Parameterized Gate Module (PGM) was designed to achieve this goal. Then, the single PGM and its densely connected extension were embedded into different levels of the encoder in the L-GCNN to help identify the discriminative feature maps at different scales. With the above designs, the L-GCNN is finally organized as a self-cascaded end-to-end architecture that is able to sequentially aggregate context information for fine segmentation. The proposed model was evaluated on two public challenging benchmarks, the ISPRS 2Dsemantic segmentation challenge Potsdam dataset and the Massachusetts building dataset. The experiment results demonstrate that the proposed method exhibited significant improvement compared with several related segmentation networks, including the FCN, SegNet, RefineNet, PSPNet, DeepLab and GSN.For example, on the Potsdam dataset, our method achieved a 93.65% F 1 score and 88.06% I o U score for the segmentation of tiny cars in high-resolution RS images. As a conclusion, the proposed model showed potential for object segmentation from the RS images of buildings, impervious surfaces, low vegetation, trees and cars in urban settings, which largely varies in size and have confusing appearances.

Modern search engines increasingly incorporate tabular content, which consists of a set of entities each augmented with a small set of facts. The facts can be obtained from multiple sources: an entity's knowledge base entry, the infobox on its Wikipedia page, or its row within a WebTable. Crucially, the informativeness of a fact depends not only on the entity but also the specific context(e.g., the query).To the best of our knowledge, this paper is the first to study the problem of contextual fact ranking: given some entities and a context (i.e., succinct natural language description), identify the most informative facts for the entities collectively within the context.We propose to contextually rank the facts by exploiting deep learning techniques. In particular, we develop pointwise and pair-wise ranking models, using textual and statistical information for the given entities and context derived from their sources. We enhance the models by incorporating entity type information from an IsA (hypernym) database. We demonstrate that our approaches achieve better performance than state-of-the-art baselines in terms of MAP, NDCG, and recall. We further conduct user studies for two specific applications of contextual fact ranking-table synthesis and table compression-and show that our models can identify more informative facts than the baselines.

A design of scientific analysis service based on container technology in data cloud is presented: resource nodes are scaled up by means of automatic registration; resource nodes can be physical hosts or virtual hosts. When the utilization rate of computing resources reaches the threshold, the management node starts resource registration, subsequently, a resource host is to be registered and the available container instances are into the pool. The health of the container instance pool is maintained inside of the host. The instance lifecycle management is performed according to the maximum usage time and maximum silent time from each instance. A user can not only do analysis jobs through the interactive component, the interactive programming component or the interactive application component, which is in a separate container instance for efficient isolation of the resources, but also use data storage clusters and the high-performance computing clusters in data cloud.

How can we build recommender systems to take into account fairness? Real-world recommender systems are often composed of multiple models, built by multiple teams. However, most research on fairness focuses on improving fairness in a single model. Further, recent research on classification fairness has shown that combining multiple "fair" classifiers can still result in an "unfair" classification system. This presents a significant challenge: how do we understand and improve fairness in recommender systems composed of multiple components? In this paper, we study the compositionality of recommender fairness. We consider two recently proposed fairness ranking metrics: equality of exposure and pairwise ranking accuracy. While we show that fairness in recommendation is not guaranteed to compose, we provide theory for a set of conditions under which fairness of individual models does compose. We then present an analytical framework for both understanding whether a real system's signals can achieve compositional fairness, and improving which component would have the greatest impact on the fairness of the overall system. In addition to the theoretical results, we find on multiple datasets -- including a large-scale real-world recommender system -- that the overall system's end-to-end fairness is largely achievable by improving fairness in individual components., Comment: WSDM 2021

A multi-region scene matching-based localisation system for automated navigation of Unmanned Aerial Vehicles (UAV) is proposed. This system may serve as a backup navigation error correction system to support autonomous navigation in the absence of a global positioning system such as a Global Navigation Satellite System. Conceptually, the system computes the location of the UAV by comparing the sensed images taken by an on board optical camera with a library of pre-recorded geo-referenced images. Several challenging issues in building such a system are addressed, including the colour variability problem and elimination of time-varying details from the pairs of images. The overall algorithm is an iterative process involving four sub-processes: firstly, exact histogram matching is applied to sensed images to overcome the colour variability issues; secondly, regions are automatically extracted from the sensed image where landmarks are detected via their colour histograms; thirdly, these regions are matched against the library, while eliminating inconsistent regions between underlying image pairs in the registration process; and finally the location of the UAV is computed using an optimisation procedure which minimises the localisation error using affine transformations. Experimental results demonstrate the proposed system in terms of accuracy, robustness and computational efficiency.

In this paper, we present a method for estimating the location of a WiFi transmitter by a receiver using the radio resource and knowledge of the indoor room structure. We derive a three-ray path propagation model for the received radio signal in a known indoor environment. We show that the position of the transmitter could be localized using the received radio signal measurements. The likelihood under this model exhibits multiple local peaks when only few frequencies are used, which leads to the location ambiguities under the Maximum Likelihood criterion. We observed in simulation that the ambiguous locations under the Maximum Likelihood estimation vary with the WiFi radio frequency used but the ground truth location is always presented as a peak. Therefore, we use multiple WiFi frequency bands to resolve the localization ambiguity. A subspace based method is applied in combination with Maximum Likelihood method utilizing the same set of measurements to improve localization efficiency. Simulation using commercial ray tracing software presents promising result.

Hydrometeor classification methods using polarimetric radar variables rely on probability density functions (PDFs) or membership functions derived empirically or by using electromagnetic scattering calculations. This paper describes an objective approach based on cluster analysis to deriving the PDFs. An iterative procedure with K-means clustering and expectation–maximization clustering based on Gaussian mixture models is developed to generate a series of prototypes for each hydrometeor type from several radar scans. The prototypes are then grouped together to produce a PDF for each hydrometeor type, which is modeled as a Gaussian mixture. The cluster-based method is applied to polarimetric radar data collected with the CP-2 S-band radar near Brisbane, Queensland, Australia. The results are illustrated and compared with theoretical classification boundaries in the literature. Some notable differences are found. Automated hydrometeor classification algorithms can be built using the PDFs of polarimetric variables associated with each hydrometeor type presented in this paper.

The Radio Frequency Identification (RFID) is a rapidly developing technology with growing applications in several fields. One of the key applications is the localisation of tagged objects using signal phase difference information via dual-frequency technology. In this application, unwrapping signal phases to acquire the range between the reader and RFID tag is the major issue that has typically been addressed in the literature using either the Chinese Remainder Theorem or Lattice Theory. In this paper, a lattice-based method robust to phase measurement noise is presented to resolve the wrapped range. The proposed algorithm is shown to be more robust and efficient than existing approaches in terms of the reconstruction probability. Simulations are presented to illustrate the performance of the proposed algorithm.

Current airborne laser scanning system is able to acquire three dimensional geographic information of areas and objects on the ground quickly with the form of three dimensional discrete point clouds which densities is up to 40 points per square meter. The main problem is the classification and identification of these objects in the point cloud. In this paper, we use the curved surface approximation method to classify point clouds into terrain and off-terrain points. A dimensionality fuzzy clustering method is introduced to classify buildings and vegetations. The method of surface approximation makes a distinguishing surface from the scanned data themselves, and makes the surface approach the ground by iteration. The surface made by his method fits the shape of the ground better. This paper compares the distinguishing effects of the method with and without dimensionality reduction, and result showed that the method with the dimensionality reduction is effective.

The analysis of dynamic laser speckle is widely known as an effective non-contact technique for detecting micro-activity on a sample surface. However, the analysis obtained using conventional statistical methods is heavily influenced by the properties of the sample, laser beam and ambient lighting. Recently, we proposed a new normal vector based dynamic speckle analysis method and demonstrated its ability to remove interference due to non-uniform reflectivity, illumination, and time-varying ambient lighting. In this paper, the normal vector based statistical technique for dynamic speckle analysis is further justified by the application to leaf surface micro-motion detection for monitoring plant water status. We carried out experiments for surface activity detection of attached leaves to monitor variation of leaf water status. The presented results highlight the advantage of the normal vector based analysis over image intensity based methods and demonstrate the potential of measuring plant water status via dynamic laser speckle analysis.

Reed-Muller codes were used in radar signal processing to determine the sequence transmitting order of Golay complementary waveforms for radar illumination to get an improved detection performance on atargetofnonzero Doppler. In this paper, we consider the detection case where multiple targets with nonzero Doppler are present. We propose a signal processing procedure which achieves an enhanced illumination performance by applying the combination of the Reed-Muller codes and the Binominal Designto the Golay omplementary waveform transmission sequences. The procedure consists of two processes. A Reed-Muller sequence is selected according to a weighted average Doppler algorithm. In the meantime, the Binominal Design algorithmis used to the receiving weights of Golay complementary waveforms as well. After match filtering, the minimum output values of the two processes are point-wisely operated as thefinal output. Simulated results show that the proposed signal processing procedure has a better detection performance in the sense of lower sidelobes and higher Doppler resolution for nonzero Doppler targets against the existing methods.

China is experiencing severe PM 2 . 5 (fine particles with a diameter of 2.5 &mu, g or smaller) pollution problem. Little is known, however, about how the increasing concentration trend is spatially distributed, nor whether there are some areas that experience a stable or decreasing concentration trend. Managers and policymakers require such information to make strategic decisions and monitor progress towards management objectives. Here, we present a pixel-based linear trend analysis of annual PM 2 . 5 concentration variation in China during the period 1999&ndash, 2016, and our results provide guidance about where to prioritize management efforts and affirm the importance of controlling coal energy consumption. We show that 87.9% of the whole China area had an increasing trend. The drastic increasing trends of PM 2 . 5 concentration during the last 18 years in the Beijing&ndash, Tianjin&ndash, Hebei region, Shandong province, and the Three Northeastern Provinces are discussed. Furthermore, by exploring regional PM 2 . 5 pollution, we find that Tarim Basin endures a high PM 2 . 5 concentration, and this should have some relationship with oil exploration. The relationship between PM 2 . 5 pollution and energy consumption is also discussed. Not only energy structure reconstruction should be repeatedly emphasized, the amount of coal burned should be strictly controlled.

Dynamic laser speckle analysis is a useful measurement technique which can be used to analyze micro-motion on a sample surface via temporal statistics based on a sequence of speckle images. The existing analysis methods in the literature are based on intensity variation of the speckle images over time. It is observed that the robustness of these methods can be significantly influenced by the presence of non-uniform incidence and reflectivity, and unrelated image intensity variations. Hence, their applicable circumstances are limited. In this paper, a novel method for dynamic speckle analysis is proposed to address the interference of non-uniform incidence and reflectivity, and unrelated intensity variations. The dynamics of the speckle pattern at a pixel is measured by the temporal statistics for the directions of local normal vectors (rather the intensities) on every speckle image over time. The proposed method can effectively eliminate the aforementioned impacts in a dynamic speckle analysis process. Moreover, the local normal vector at a particular pixel in a speckle image also carries local geometry information regarding to its neighboring pixels. Three algorithms for computing the statistics of the directions of local normal vectors over time are subsequently presented. The effectiveness of the proposed method is demonstrated via the analysis of dynamic speckle patterns measured from a paint drying experiment. The robustness of the proposed method is compared with that of the typical existing methods through processing the dynamic speckle of simulation experiments under non-uniform and varying lighting conditions.

t is known that dynamic speckle patterns can be used to identify the temporal evolution of an active sample and some desired properties of the sample may therefore be detected via the dynamic laser speckle analysis. Mathematically understand the structure of sample surface which gives arise to the dynamic speckle patterns is crucial to the analysis. In this paper, a simulation model of the dynamic speckle patterns for plant leaves is proposed. The developed model is derived from the principle of coherent electromagnetic wave scattering off a randomly textured and time varying surface and is demonstrated via the simulation of dynamic speckle patterns of a fresh unattached leaf surface along the true laser speckle measurements. Both simulated and true measurements of the dynamic speckle patterns of the leaf are analyzed via several statistical techniques and a consistent agreement between the results of the two is observed. The proposed statistical model is helpful to gain the insight of the relationship between speckle dynamics and the activity of the leaf surface which is supposed to be an measurement of plant water stress and water status.

Inverse synthetic aperture radar (ISAR) imaging is conventionally addressed by means of fast Fourier transform (FFT) method. In this paper, a frame processing method derived based on frame theory for ISAR imaging is proposed as an alternative to FFT based approaches. In this method, a finite set of K consecutive target returns after a front-end processing by radar receiver are treated as a vector in a Hilbert space where a frame structure is defined. A frame and its dual are constructed as the functions of waveform and processing parameters and the target rotation rate. The latter can be estimated online. For certain classes of high resolution radar (HRR) waveforms, we show that it is possible to use standard methods of frame analysis and synthesis to reconstruct the ISAR image from radar returns. This method is analogous to those of reflective tomography, and does not suffer from the “range walk” problem exhibited by the FFT based approaches which poses constraints on the feasible aperture size for observing a target. Both simulated and experimental results are presented to demonstrate the effectiveness of the proposed method. Practical implementation and issues of the proposed frame processing technique are also discussed.

In target detection and tracking, the resolvability of multiple closely spaced targets of the sensor measurement is an important criterion for measuring the sensing ability of sensor systems like radar and sonar. The concept of information resolution for a sensor measurement system, which is defined in the framework of information geometry, is introduced. In particular, the information resolution of radar/sonar systems is generalised from the work on existing radar resolution pioneered by Woodward, et al. [1, 2] and defined on statistical manifolds where the intrinsic geometrical structure of both waveform, measurement, and noise models of the underlying sensing devices are conveniently characterized in terms of the Fisher information metric. This work is motivated by the fact that existing radar resolution identifies the ambiguities of the underlying waveforms regardless of the influence of noise but the latter often has significant contributions to outcomes such as target detection and tracking, radar imaging, etc. To this end, information resolution provides a unified statistical measure for the capability of sensing devices for a given application. Examples of basic radar measurements are presented to highlight the importance of information resolution for measurement systems in practical applications. Refereed/Peer-reviewed

The massive data is constantly and rapidly growing in remote sensing field. How to achieve efficient storage and rapid retrieval of massive remote sensing image metadata is a difficult problem. Big Data technologies provide convenient and fast tools for the storage, retrieval and analysis of remote sensing image metadata. According to the feature of remote sensing image metadata, we first give new concepts of fat grid and thin grid and propose a grid indexing method called Spark-Fat-Thin-Grid-Index (SFTGridIndex). In SFTGridIndex, the index file and partitions files are stored in HDFS. We then design an optimized retrieval method based on SFTGrid-Index. The method can avoid the intersection computing of a large number of polygons. This research can ensure efficient storage and fast query of massive remote sensing image metadata and has good scalability.

Optimal determination of a UAV using a vision-based system to match images against a database is an important problem. It can be reformulated to the problem of using multiregion scene registration to match areas of a noisy and distorted image to a geo-referenced image. Under the assumptions that the mapping between sensed and geo-referenced images preserves gradients of straight lines cross mapping points on images and registration errors are all Gaussian distributed, we derive a two-stage weighted linear least square algorithm which localises the UAV optimally. Performance of the proposed algorithm is demonstrated via Monte Carlo multiple runs along with those available in literature.

With the emerging of vast quantities of geospatial data, large temporal and spatial scale of data are used in geosciences research nowadays. As a lot of data processing tasks such as image interpretation are hard to be processed automatically, and the data process workload is huge, crowdsourcing is studied as a supplement tool of cloud computing technology and advanced algorithms. This paper outlines the procedure and methodology of applying crowdsourcing in geoscientific data process. And based on the GSCloud platform, a case study of Qinghai-Tibetan Lake Extraction task has been carried out to explore the feasibility of the application of crowdsourcing in geosciences. By analyzing the case, the paper summarizes the problems and characteristics, and advantages and challenges are also presented at last.

Automatic optical and infrared (IR) image registration is a crucial requirement in the estimation of plant canopy temperature. The latter is used to compute the plant water stress status which can potentially be applied to an automated control system for optimal crop irrigation management. Due to the nature of image sources and plant scene, it is difficult to find enough common features between the pair of images to register them using a simple method. In this paper an improved registration algorithm is described, where a modified Sobel edge detector is adopted and a variable resolution scheme is applied. The algorithm can improve the registration efficiency considerably compared to our previous developed registration method. Experiment results show that the modified algorithm can achieve the same registration accuracy and a better success rate compared to that from the original algorithm with a substantial reduction of computational complexity.

Name ambiguity is a critical problem in many applications, in particular in online bibliography systems, such as DBLP, ACM, and CiteSeerx. Despite the many studies, this problem is still not resolved and is becoming even more serious, especially with the increasing popularity of Web 2.0. This paper addresses the problem in the academic researcher social network ArnetMiner using a supervised method for exploiting all side information including co-author, organization, paper citation, title similarity, author's homepage, web constraint, and user feedback. The method automatically determines the person number k. Tests on the researcher social network with up to 100 different names show that the method significantly outperforms the baseline method using an unsupervised attribute-augmented graph clustering algorithm.

Automatic registration of optical and IR images is a crucial step towards constructing an automated irrigation control system where plant water information is sensed via thermal imaging. The scene of the IR image is assumed to be completely included in the optical image and the alignment between the common scene in the two images may involve translation and rotation by a small angle, though a small scale difference may also be present. This automatic registration of data from two quite different, non-rigid imaging regimes presents several challenges, which cannot be overcome using common image processing techniques. In this paper, a fully automatic image registration algorithm for the alignment of optical and IR image pairs is described, where Pearson's cross-correlation between a pair of images serves as the similarity measure. A computationally efficient algorithm is designed and packaged as a software application. This work provides an intervention free process for extracting plant water stress information which can be fed into an automated irrigation scheduling program. The proposed algorithm is justified by the comparison of its registration performance with that of other potential algorithm techniques using several experimental data collections. Our results demonstrate the effectiveness of the proposed algorithm and efficiency of its application to the registration of IR and optical images.