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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 paper considers the problem of tracking a moving target using a pair of cooperative bearing-only mobile sensors. Sensor trajectory optimisation plays the central role in this problem, with the objective to minimize the estimation error of the target state. Two approximate closed-form statistical reward functions, referred to as the Expected Rényi information divergence (RID) and the Determinant of the Fisher Information Matrix (FIM), are analysed and discussed in the paper. Being available analytically, the two reward functions are fast to compute and therefore potentially useful for longer horizon sensor trajectory planning. The paper demonstrates, both numerically and from the information geometric viewpoint, that the Determinant of the FIM is a superior reward function. The problem with the Expected RID is that the approximation involved in its derivation significantly reduces the correlation between the target state estimates at two sensors, and consequently results in poorer performance.

Sintering of advanced thermal barrier coatings (TBCs) at high temperatures is key challenge as it can adversely affect service performance and thermal fatigue resistance of TBCs. In this study, sintering behavior of pyrochlore-type La2(Zr0.7Ce0.3)2O7 (LZ7C3) was investigated using experiments and molecular dynamics. Meanwhile, the corresponding dynamic process and behind mechanism were uncovered. Results showed that novel LZ7C3 exhibited significantly higher sintering resistance than host La2Zr2O7 (LZ) and typical 8 wt% yttria-stabilized zirconia (8YSZ) at temperature up to 1773 K, which indicated that pyrochlore-type LZ7C3 is a promising TBC candidate to replace conventional 8YSZ at high temperatures. Further study also revealed that initial stage played crucial role in sintering process, and the sintering mainly occurred at grain boundary (GB) region. Intrinsic sintering activation energy of LZ7C3 GB (695.248 J mol−1) is larger than that of LZ GB (384.171 J mol−1) and 8YSZ GB (173.303 J mol−1), which resulted in outstanding sintering resistance for LZ7C3. Furthermore, no obvious enrichment of second phase was observed at the GB of LZ7C3. This study thus concluded that hindering the atomic diffusion of GB, as well as introducing foreign atom with larger mass and bond energy may act as effective strategy to enhance the sintering resistance of TBCs materials.

In this work, the authors study the problem of detecting and tracking small targets using high-resolution maritime radar, where sea clutter is correlated in range, and its amplitude fluctuations are characterised by occasional spikes. To tackle this problem, they develop a Bernoulli track-before-detect filter, as the optimal recursive Bayesian detector/estimator of target state and its presence in noise. A realistic clutter model, in the form of the K -distribution with unknown distribution parameters, is adopted. Target amplitude fluctuations are also included in the model. The detection and tracking improvement are demonstrated by simulations and compared against a conventional point target tracking algorithm.

Zirconia ceramics has an important position in the field of biomaterials because of excellent mechanical properties. Because of abrasive resistance and hardness, conventional machining of zirconia ceramic is difficult and time-consuming. A nontraditional hybrid laser-assisted grinding (LAG) system combining laser and cubic boron nitride (CBN) grinding wheel was developed for machining zirconia ceramic. This hybrid system utilizes the outstanding thermostability of zirconia ceramic: The material present on the cutting path is rapidly removed by local laser heating and grinding wheel. A combined theoretical and experimental study was performed to evaluate different modes of machining of zirconia ceramic. The machining parameters were predicted by numerical analysis. The surface quality, surface morphology, and subsurface damage of zirconia ceramic specimens were analyzed and compared. The results show that LAG can easily achieve ductile regime grinding in the same machine tool. Compared with conventional grinding, the surface integrity of zirconia ceramic was significantly improved by the LAG process. LAG could achieve ductile regime grinding with a large depth-of-cut. It changes the machinability of zirconia ceramic.

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.

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.

It has been an important technique to reduce sidelobes for processing nonzero Doppler target returns under Golay complementary waveforms. While the pointwise minimization processor in our early work shows an enhanced performance on the suppression of range sidelobes compared with existing approaches, it has a reduced detection probability to target with location uncertainties. In this paper, we present an alternative processing method which combines the radar returns in two separated pairs of Golay complementary waveforms through a pointwise addition processor. Our simulation result shows that this alternative method has a similar performance to that of the pointwise minimum processor for range sidelobes suppression and computational complexity but results in an improved target detection probability, with an acceptable decrease on the peak to peak-sidelobe ratio.

Nanobubble water (NBW) has been applied in various fields due to the unique properties of nanobubbles (NBs) including long-term stability, negative zeta potential and generation of free radicals. In this study, the performance of four kinds of NBW from different gases (air, N2, H2, and CO2) in addition to deionized water (DW) were investigated and compared in terms of the growth of the probiotic Lactobacillus acidophilus 1028. The NB density, size distribution, zeta potential, pH and dissolved oxygen (DO) of the NBW were firstly investigated. Results indicate that N2-NBW had the highest absolute value of zeta potential and NB density (−25.3 ± 5.43 mV and 5.73 ± 1.0 × 107 particles per mL, respectively), while the lowest was detected in CO2-NBW (−6.96 ± 2.36 mV and 3.39 ± 1.73 × 107 particles per mL, respectively). With the exception of CO2-NBW, all the other types of NBW showed promotion effects on the growth of the strain at the lag and logarithmic phases. Among them, N2-NBW demonstrated the best performance, achieving the highest increase ratio of 51.1% after 6 h cultivation. The kinetic models (Logistic and Gompertz) indicate that the culture with N2-NBW had the shortest lag phase and the maximum specific growth rate when compared to the H2-NBW and DW groups under the same cultivation conditions. Preliminary analysis on the mechanisms suggested that these effects were related to the properties (zeta potential and density) of the NBs, which might affect the transport of substances. This study suggests that NBW has the potential for promoting the production efficiency of probiotics via fermentation.

Vitrified bond CBN grinding wheels are being widely used due to their superior performance. Also, advantages of vitrified grinding wheels are high elastic modulus, stable chemical property, and low thermal expansion coefficient. Brittleness and low strength are key factors restricting the development of vitrified bond CBN grinding wheels. In this paper, the sintering in a high magnetic field was innovatively introduced into the manufacturing of vitrified bond CBN grinding wheels, and the effects of sintering in a high magnetic field on properties on vitrified bond and vitrified CBN composites were systematically investigated. Vitrified bond was characterized using three-point bending, scanning electron microscopy, X-ray diffraction. It was observed that microstructure of vitrified bond could be changed, grain orientation could be controlled and average grain size could be decreased in a high magnetic field, while vitrified bond strength could be simultaneously improved. High quality vitrified bond could be obtained by appropriately adjusting the strength and direction of high magnetic field. Results demonstrated that vitrified bond properties were improved when the magnetic field strength was 6 T. In order to highlight the high magnetic field effect on the vitrified CBN composites, the ordinary CBN abrasives and nickel plated CBN abrasives were used respectively. Microstructures, bending strengths of vitrified CBN composites were compared in different high magnetic fields. When the magnetic field strength was appropriate (less than 6 T), the binding characteristic of vitrified bond CBN composites with nickel plated CBN abrasives was greatly improved. The highest bending strength value of vitrified CBN composites was 79.5 MPa in 6 T high magnetic field.

Vitrified bond CBN composites, with different amounts of TiO 2 doping, were prepared by conventional sintering and high magnetic field sintering processes. Mechanical properties, cross-sectional morphology, refractoriness, fluidity and structural characterization have been carried out to understand the role of TiO 2 addition and sintering conditions. Results show that TiO 2 addition significantly affected bending strength, refractoriness and fluidity of vitrified bonds. In addition, high magnetic field sintering improved bending strength and the microstructure of vitrified bond CBN composites. Due to high magnetic field sintering, CBN grains were completely covered by vitrified bond and exhibited fewer pores. In addition, high magnetic field promoted the growth of specific grains, such as SiO 2 , whereas suppressed grain growth of other crystal phases, such as NaAlSi 3 O 8 .

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 continuous development of Earth science, soil temperature has received more and more attention in Earth system research as an important parameter. The change of soil temperature (Ts) in different regions and related time series is affected by many factors, which bring certain difficulties to the accuracy of soil temperature prediction and the robustness of the algorithm. In this paper, an embedded network prediction model based on the gated recurrent unit (GRU) model is proposed to learn the local and global features of historical temperature for improving the prediction performance of soil temperature. We input different steps into the GRU model, and the output is weighted to obtain the final prediction result. In order to obtain the global characteristics of soil temperature, we connect the previous steps to the output layer directly, and the local characteristics of soil temperature are obtained through the following steps. This paper uses the soil temperature data from two meteorological stations (Laegern and Fluehli) in Switzerland as the input data to predict the soil temperature for different soil depths (5 cm, 10 cm, and 15 cm) at different time points (6 hrs, 12 hrs, and 24 hrs), using RMSE, MAE, MSE, and R 2 performance indicators as evaluation criteria to verify the accuracy of prediction. As the experimental results show, our method has the best performance compared to the others (artificial neural networks (ANN), extreme learning machine model (ELM), long short-term memory network (LSTM), gated recurrent unit network (GRU)). In particular, we estimated the soil temperature at the soil depth of 10 cm of the Fluehli station in the coming 6 hrs; our method achieved the best performance; and, meanwhile, our model achieved the maximum value of R 2 (0.9914) and the minimum values of RMSE (0.4668), MAE (0.2585), and MSE (0.2214) compared with the other four models. Therefore, our model can not only predict the soil temperature at different depths but also improve the accuracy.

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

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.

Hidden Markov Chains (HMCs) and, more recently, Hidden semi-Markov Chains (HsMCs) have been used by several groups of researchers to provide a model for indoor localization. A homogeneous HMC is completely determined by the state initial probability vector and the state transition probability matrix. This is also true for the HsMC provided the state duration probability is given. These parameters are often chosen heuristically but when sufficient measurement training data are available, they can be learned using the well-known Baum-Welch algorithm. Given the model parameters, approaches such as the forward-only algorithm, the forward-backwards algorithm and the Viterbi algorithm can be applied for state sequence inference under the HMC/HsMC framework. In indoor localization applications, there is often insufficient prior information to specify such parameters in advance of the application and they have to be learned from limited amounts of training data. In this paper, we endeavour to evaluate the parameter learning accuracy of the Baum-Welch algorithm using varying amounts of training data, and evaluate the influence of applying inaccurate model parameters on these typical state estimation algorithms under both the HMC and HsMC frameworks. All of the evaluations are based on received signal strength (RSS) for application to indoor localization.

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

In order to optimize the structure performances of ceramic cubic boron nitride (CBN) grinding wheels, the effects of ZrO2 and Y2O3 addition on the physical and mechanical properties of ceramic bond and ceramic CBN composites were systematically investigated. The basic ceramic bond was composed of B2O3, Al2O3, SiO2, Li2O and Na2O. The additions of ZrO2 and Y2O3 had a certain effect on refractoriness, liquidity, flexural strength of ceramic bonds. The experimental results demonstrated that in the ceramic bonds, both ZrO2 and Y2O3 could promote the formation of glass grid, while the ceramic bond strength would further increase. In addition, the new phase of YBO3 appeared in the ceramic bond, due to the filling of Y2O3. When the contents of ZrO2 and Y2O3 were 4 wt% respectively, the strength of the ceramic bond was highest, increasing by 23% (16.1 Mpa) compared to the basic ceramic bond. The last point required to be pointed out was that the phase transformation toughening of ZrO2 cannot be achieved and it was limited by low temperature sintering.

The paper is devoted to Bayesian target motion analysis (TMA) for the case when the variance of additive white zero-mean Gaussian measurement noise is unknown. Two Rao–Blackwellised particle filters for TMA are developed, which jointly estimate the target state and the measurement variance. The error performance of the two particle filters is compared against the theoretical Cramer–Rao lower bound. The bound suggests the error in target state estimation is not affected by the ignorance of the measurement noise variance. Both developed TMA algorithms reach this theoretical bound, however, one is significantly faster.

In this paper, the porosity effects on oxygen ions diffusion was investigated in the yttria-stabilized zirconia (YSZ) by molecular dynamics simulation (MD). Different models were simulated to elucidate the role of yttria (Y2O3) concentration (3 to 8 mol% Y2O3), number of pores and pores density and structure (cubic and tetragonal lattice) on the oxygen ions diffusion in YSZ system. MD simulation results showed that the diffusion coefficient of the tetragonal YSZ was smaller than that of the cubic YSZ, the maximum of oxygen ions diffusion coefficient exhibited in the 6 mol% Y2O3 among all the tetragonal YSZ systems. From MD simulation results, it was found that the pores in the YSZ system were retarding the oxygen ions diffusion. For constant porosity, smaller single pore volume resulted in lower D, while higher total porosity also led to smaller D.

Properties of vitrified bond with varying Ni doping amounts were extensively investigated. Effects of Ni addition on microstructures and properties of vitrified bond cubic boron nitride (CBN) composites prepared in strong magnetic field were investigated for applications in CBN grinding tools. Vitrified bond was characterized using three-point bending, scanning electron microscopy, X-ray diffraction and other methods. The refractoriness, fluidity, and bending strength of vitrified bond were evaluated. Bending strengths, microstructures, and phase compositions of vitrified bond CBN composites achieved using conventional and strong electromagnetic sintering techniques were compared. Results show that the addition of Ni to vitrified bond CBN composites improved the fluidity and bending strength of the vitrified bond. Strong electromagnetic sintering improved the mechanical strength and pore structure of vitrified bond CBN composites. Moreover, the introduction of the strong magnetic field facilitated Ni migration and aggregation in vitrified bond, rotated abrasives, and formed new substances, thus increasing the stability of vitrified bond CBN composite thermal material. Also, strong magnetic field inhibit grain growth of non-magnetic and ferromagnetic materials with fine-grain effect.

Work of Pezeshki, Calderbank and others [1, 2] has shown that choice of transmission order of Golay complementary waveforms in radar pulse trains can significantly improve Doppler resilience in detecting nonzero Doppler targets. For multiple targets a Weighted average Doppler (WD) algorithm [3] based on our earlier work has been shown to provide improvements. In this paper, we demonstrate that the Weighted Sidelobe Minimization procedure, developed in our recent work that combines the outputs of WD and the existing Binominal Design algorithms for ordering transmitted complementary waveform sequences using a point-wise minimization process (PMP), is also effective for the elimination of false target returns arising from sea clutter. A detailed analysis based on a Swerling II target model in terms of target detection probability is presented to validate the use of PMP. Our validation is strengthened by numerical simulations for both fixed and randomized scenarios.

This paper presents structure, thermal expansion coefficient and phase stability of La2(Zr0.7Ce0.3)2O7 (LZ7C3) ceramic by both theoretical and experimental results. It was found out that LZ7C3 powders had a pyrochlore structure after being heat-treated at temperatures higher than 1473 K or higher according to XRD and TEM results. The calculated average thermal expansion coefficient (TEC) was 7.12 × 10−6 K−1, which is a little smaller than experiment result, but changes of calculated average TECs of LZ, YSZ and LZ7C3 had the same trend with experimental results. Finally, the radial distribution function (RDF) was calculated to study the phase stability of LZ7C3.

We describe a method for processing radar signals, combining the outputs of two – a Weighted average Doppler algorithm and an existing Binominal Design algorithm via a point-wise minimum processor. The method renders a better performance in range sidelobe suppression for the detection of multiple moving targets using Golay complementary waveforms than the existing Binominal Design algorithm. The effectiveness of the method is justified by analyzing the corresponding ambiguity functions. We also show the performance of the new signal processing method by simulations under a realistic scenario.

Most mechanism contains one or more joints. However, joint clearances which cannot be dislodged make accuracy analysis become complicated. In order to seek a method to investigate the impact of joint clearances on the accuracy of a parallel robot, this paper deals with the 3-SPS-1-S spatial parallel robot’s pose error predicting and sensitivity analyzing. In this paper, a nonlinear method for evaluating the sources of the pose errors is proposed on the condition that the pose errors only come from joint clearances. Firstly, based on the nonlinear contact modes inside the joints, the error equivalent method is outlined for spherical joint and sliding pair. Then the pose error distribution over its workspace is presented. Secondly, those areas where the pose errors present abnormally changing are analyzed. Thirdly, sensitivity models are defined to evaluate how much the external variable load affects the pose errors. Finally, the simulation results, which are consistent with the structural characteristics of the 3-SPS-1-S spatial parallel robot, proved the validity of the equivalent method proposed in this paper.

This paper presents thermal properties in both yttria-stabilized zirconia (YSZ) and the coupled system based on lanthanum zirconate (LZ) and YSZ through molecular dynamics (MD) simulation. Mean square displacement (MSD), thermal transport behavior and thermal conductivity of both YSZ and LZ/YSZ systems were calculated at temperatures ranging from 1273 K to 1473 K. Typical oxygen ion vibration in both YSZ and LZ/YSZ coupled system were studied. Radial distribution functions (RDFs) of Y ions were investigated in both systems. The oxygen ion hopping in YSZ explains the movements of oxygen ions among vacancies in the YSZ easier than in LZ/YSZ coupled system. The vibrational density of state (VDOS) of scattered phonons explains the trends in thermal conductivity for both systems. In the case of LZ/YSZ coupled-system, LZ keeps more ions vibrated on their original position and reduces the hopping of oxygen ions among vacancies in the YSZ unit, while also providing thermal resistance to ensure lower thermal conductivity of the coupled system.

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.

Distance-based hierarchical clustering (HC) methods are widely used in unsupervised data analysis but few authors take account of uncertainty in the distance data. We incorporate a statistical model of the uncertainty through corruption or noise in the pairwise distances and investigate the problem of estimating the HC as unknown parameters from measurements. Specifically, we focus on single linkage hierarchical clustering (SLHC) and study its geometry. We prove that under fairly reasonable conditions on the probability distribution governing measurements, SLHC is equivalent to maximum partial profile likelihood estimation (MPPLE) with some of the information contained in the data ignored. At the same time, we show that direct evaluation of SLHC on maximum likelihood estimation (MLE) of pairwise distances yields a consistent estimator. Consequently, a full MLE is expected to perform better than SLHC in getting the correct HC results for the ground truth metric., Comment: 21 pages, 6 figures

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.

Thermal properties of yttria stabilized zirconia (YSZ) were investigated using molecular dynamics (MD) simulation. Several YSZ samples were designed to elucidate the effect of porosity on thermal conductivity of the designed YSZ systems. Mean square displacement (MSD), vibrational density of state (VDOS) and atomic movement in designed YSZ and other samples were also investigated. Results of MD simulation indicated the key role of pores in preventing thermal transport behavior. For same volume of a single pore, samples with larger pore volume showed smaller thermal conductivity ( κ ), while for same amount of total porosity, samples with small-sized pores displayed a smaller thermal conductivity. The latter was due to the presence of relatively larger number of smaller-sized pores as compared to larger-sized ones. Samples with smaller-sized pores in greater numbers also exhibited lower vibration frequency and lower amplitude of single atoms, suggesting smaller thermal conductivity ( κ ) values. For constant pore volume, YSZ samples were observed to have a decrease in thermal conductivity with increasing number of pores. Among YSZ samples with same levels of porosity, those with smaller pores displayed smaller thermal conductivity. Similar levels of thermal conductivity could be acquired in samples with smaller pore amounts and/or smaller porosity as compared to those with larger pores.

In this paper, we investigate the problem of detecting and tracking small targets in sea clutter using the Bernoulli Track-Before-Detect filter. This filter is an optimal recursive Bayesian detector / estimator to determine the state of a single target and its presence in noise. Typically, clutter amplitude fluctuations are modelled using the Rayleigh distribution. This assumption, however, is not valid in the maritime domain, where sea-clutter is often spikier with a longer distribution tail. In this work, we develop the Bernoulli filter for a compound Gaussian clutter model with thermal noise and model the target with a Swerling 1 fluctuation. To demonstrate the performance improvement, we model the texture as a gamma distribution, giving an overall K plus noise distribution for the clutter plus noise. The detection and tracking improvement is then demonstrated using Monte Carlo simulation.

The detection and tracking of targets in the maritime environment can be a challenging problem. One approach to improve the tracking ability of weak targets is to use track before detect (TBD). However, different TBD algorithms have different underlying assumptions about the background clutter which are not always valid. In this paper we consider three TBD algorithms: the histogram probabilistic multi-hypothesis tracker (H-PMHT), the Bernoulli filter and the multi-Bernoulli filter. The H-PMHT inherently assumes the clutter is modelled with a distribution derived from the continuous limit of the Poisson distribution, while the two Bernoulli filters both assume Rayleigh distributed clutter. To quantify performance, we use a Monte Carlo simulation with the sea clutter generated using both Rayleigh and K-distributions. The performance is then determined using the optimal sub-pattern assignment (OSPA) distance.

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.

Modern search engines provide contextual information surrounding query entities beyond ten blue links in the form of information cards. Among the various attributes displayed about entities there has been recent interest in providing fun facts. Obtaining such trivia at a large scale is, however, non-trivial: hiring professional content creators is expensive and extracting statements from the Web is prone to uninteresting, out-of-context and/or unreliable facts. In this paper we show how fun facts can be mined from superlative tables in Wikipedia, whose rows are ranked according to some statistics, to provide a large volume of reliable and interesting content. We employ a template-based approach to semi-automatically generate natural language statements as fun facts. We show how to bootstrap and streamline the process for faster and cheaper task completion. However, the content contained in these tables is dynamic. Therefore, we address the problem of automatically maintaining the pairing of templates to tables as the tables are updated over time. Fun facts produced by our work is now part of Google's production search results.

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

The launch of the new Google News in 2018 (https://www.blog.google/products/news/new-google-news-ai-meets-human-intelligence/.) introduced the Frequently asked questions feature to structurally summarize the news story in its full coverage page. While news summarization has been a research topic for decades, this new feature is poised to usher in a new line of news summarization techniques. There are two fundamental approaches: mining the questions from data associated with the news story and learning the questions from the content of the story directly. This paper provides the first study, to the best of our knowledge, of a learning based approach to generate a structured summary of news articles with question and answer pairs to capture salient and interesting aspects of the news story. Specifically, this learning-based approach reads a news article, predicts its attention map (i.e., important snippets in the article), and generates multiple natural language questions corresponding to each snippet. Furthermore, we describe a mining-based approach as the mechanism to generate weak supervision data for training the learning based approach. We evaluate our approach on the existing SQuAD dataset (https://rajpurkar.github.io/SQuAD-explorer/.) and a large dataset with 91K news articles we constructed. We show that our proposed system can achieve an AUC of 0.734 for document attention map prediction, a BLEU-4 score of 12.46 for natural question generation and a BLEU-4 score of 24.4 for question summarization, beating state-of-art baselines.

Nanobubble water (NBW) contains 106–108 gas bubbles per milliliter with diameter

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.

This paper investigates the effects of interface layer on thermal transport behavior of LZ/YSZ coupled system at various temperatures. Thermal transport behavior of LZ/YSZ coupled system was studied at atomic level. Trajectories of atoms near the interface layer in both LZ and YSZ unit were simulated at various temperatures between 1273 and 1473 K. Spatial distribution of atoms near the interface layer was illustrated at elevated temperatures. Radial distribution function of Y3+ ions was used to analyze changes in this spatial distribution, as it was affected by thermal transport inside the coupled system. Distances of typical atomic pairs close to the interface layer were used to describe the vibrational intensity in coupled system. Simulation results show that thermal transport behavior was influenced by the interface layer in LZ/YSZ systems with different LZ:YSZ ratio. The vibrational intensity of atoms and distance between atoms reflected heat flow inside the system from an atomic perspective.

The mutual effects of heat transfer and ions distribution on YSZ with porous structures have been investigated in this paper. Various of porous YSZ microstructures were simulated to elucidate the relationships of ions spacial distribution and heat transfer from atomic perspective. Typical ion movement and the phonon vibrational density of state were investigated in various porous systems. The simulation results show that thermal transport behavior bonds close to the ions distribution in porous systems. The ions distribution nearby pores in the system have a predictable reduction effects on thermal transport behavior, which could lead to a decrease in thermal conductivity.

Optimal trajectories for two platforms with similar dynamics are calculated using a game theoretic formulation. Each platform makes noisy observations of the kinematic state of the other. The objective of each is to maximise observable information about the other while minimising the information the other is able to acquire about it. That is to say, each platform maximises the mutual information between the expected future measurement of the opposing platform and the current likelihood of the state whilst minimising the estimated mutual information between potential measurements of itself by the other and its actual state. The multi-objective optimisation problem for each platform is converted to a single optimisation using the Pareto parameter to weigh the relative importance of the two information measures. The relationship between the two Pareto parameters, and different initial track initialisations is investigated. Remarkably this complex coupled system of two platforms exhibits, for suitably chosen values of the Pareto parameters, interesting cyclical behaviours that are worthy of further exploration.

Multipath propagation makes the use of received signal strength (RSS) unreliable as a signal propagation model for localization of a radio source based on RSS data. An approach to mitigation of this problem is the use of a Hidden Markov model (HMM) to represent the relationship between RSS and the radio source location by incorporating an environment prior and RSS source dynamics. The HMM structure forces a geometric form for the distribution for the sojourn time. This, combined with missing data problems, reduces confidence in location estimation. It is found, in this paper, that Hidden semi-Markov Models (HsMMs), with a more flexible sojourn time distribution are more able to represent source dynamics while retaining the advantages of HMMs for environmental constraints and improving resilience to missing measurements. The performance of the proposed HsMM algorithm is compared with a standard HMM via experiments and simulations involving indoor radio source localization using RSS data. Simulations use a ray tracing software-based simulator and the experiments for transmitter localization with RSS data are collected by software defined radios.

Minimum Quantity Lubrication (MQL) is increasingly applied in the grinding process of the hard-to-cut material (HCM) as an eco-friendly and efficient technology of lubricating and cooling. The grinding characteristics of the HCM directly affect their reliability and serviceability and so, evaluating the grinding characteristics during the MQL is very vital. In this work, the MQL with different weight concentrations of graphene-enhanced plant-oil-based cutting fluids (POBCFs) (0.05 wt%, 0.1 wt%, 0.15 wt%, and 0.2 wt%) was adopted in the grinding of the TC4 alloy, where the graphene nanoparticle (GPNP) was added to the POBCF for preparing the graphene-enhanced POBCF for improving the grinding characteristics of the TC4 alloy. The experimental evaluation of the grinding characteristics of the TC4 alloy was carried out through the grinding experiments during the six working conditions (dry, pure MQL (PMQL), and four graphene MQL (GMQL)), and the optimal working condition was the GMQL (0.1 wt%). The influence mechanisms of the GPNP on the grinding characteristics with respect to the grinding force, grinding temperature, specific grinding energy, and surface integrity were revealed. In summary, the oil films generated during the grinding area exhibit excellent cooling and lubricating performances and the appropriate amount of GPNPs can strengthen the oil film performances in both cooling and lubricating to improve the grinding characteristics. The research results show that the GPNP exhibits a remarkable impact in improving the grinding characteristics and provide the experiment basis for applying the GPNP to the MQL grinding.

In this work, we study the case of tracking a moving sensor using the radio interferometric positioning system measurements. The system consists of four wireless network nodes and it measures the sum of signal phase differences between two receivers while the other two are transmitting sine waves at close frequencies. With the locations of three sensors known, we can estimate the fourth sensor moving trajectory from a sequence of phase measurements using a nonlinear filter. Two nonlinear filters, a natural gradient Kalman filter and a extended Kalman filter, are investigated and implemented for this moving sensor tracking problem. The performances of them are compared in a computer simulated tracking experiment.