Search

FDR control has been a huge challenge for large-scale metabolome annotation. Although recent research indicated that the target-decoy strategy could be implemented to estimate FDR, it is hard to perform FDR control due to the difficulty of getting a reliable decoy database because of the complex fragmentation mechanism of metabolites and ubiquitous isomers. To tackle this problem, we developed a decoy generation method, which generates forged spectra from the reference target database by preserving the original reference signals to simulate the presence of isomers of metabolites. Benchmarks on GNPS data sets in Passatutto showed that the decoy database generated by our method is closer to the actual FDR than other methods, especially in the low FDR range (0-0.05). Large-scale metabolite annotation on 35 data sets showed that strict FDR reduced the number of annotated metabolites but increased the spectral efficiency, indicating the necessity of quality control. We recommended that the FDR threshold should be set to 0.01 in large-scale metabolite annotation. We implemented decoy generation, database search, and FDR control into a search engine called XY-Meta. It facilitates large-scale metabolome annotation applications.

In most proteome mass spectrometry experiments, more than half of the mass spectra cannot be identified, mainly because of various modifications. The open search strategy allows for a larger precursor tolerance to utilize more spectra, especially those with post-translational modifications; however, thorough quality control based on independent information is lacking. Here, we used the "Suspicious Discovery Rate (SDR)" based on translatome sequencing (RNC-seq) as an independent source to reference the proteome open search results in steady-state cells. We found that the open search strategy increased the spectra utilization with the cost of increased suspicious identifications that lack translation evidence. We further found that restricting the peptide FDR below 0.1% efficiently controlled the suspicious identifications of open search methods and thus enhanced the confidence of the peptide identification with modifications comparable to the level of the traditional narrow window search. We then demonstrated the successful and validated identification of 27 single amino acid variations from the spectra of two cell lines using the open search strategy without a predefined database. These results validated the proper use of open search methods for higher-quality proteome identifications with information on post-translational modifications and single amino acid polymorphisms.

Identifying peptides from the fragmentation spectra is a fundamental step in mass spectrometry (MS) data processing. The significance (discriminability) of every peak varies, providing additional information for potentially enhancing the identification sensitivity and the correct match rate. However this important information was not considered in previous algorithms. Here we presented a novel method based on Peptide Matching Discriminability (PMD), in which the PMD information of every peak reflects the discriminability of candidate peptides. In addition, we developed a novel peptide scoring algorithm Dispec based on PMD, by taking three aspects of discriminability into consideration: PMD, intensity discriminability and m/z error discriminability. Compared with Mascot and Sequest, Dispec identified remarkably more peptides from three experimental datasets with the same confidence at 1% PSM-level FDR. Dispec is also robust and versatile for various datasets obtained on different instruments. The concept of discriminability enhances the peptide identification and thus may contribute largely to the proteome studies. As an open-source program, Dispec is freely available at http://bioinformatics.jnu.edu.cn/software/dispec/.

Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/ .

The most crucial step in data processing from high-throughput sequencing applications is the accurate and sensitive alignment of the sequencing reads to reference genomes or transcriptomes. The accurate detection of insertions and deletions (indels) and errors introduced by the sequencing platform or by misreading of modified nucleotides is essential for the quantitative processing of the RNA-based sequencing (RNA-Seq) datasets and for the identification of genetic variations and modification patterns. We developed a new, fast and accurate algorithm for nucleic acid sequence analysis, FANSe, with adjustable mismatch allowance settings and ability to handle indels to accurately and quantitatively map millions of reads to small or large reference genomes. It is a seed-based algorithm which uses the whole read information for mapping and high sensitivity and low ambiguity are achieved by using short and non-overlapping reads. Furthermore, FANSe uses hotspot score to prioritize the processing of highly possible matches and implements modified Smith-Watermann refinement with reduced scoring matrix to accelerate the calculation without compromising its sensitivity. The FANSe algorithm stably processes datasets from various sequencing platforms, masked or unmasked and small or large genomes. It shows a remarkable coverage of low-abundance mRNAs which is important for quantitative processing of RNA-Seq datasets.

Synonymous codon usage and variations in the level of isoaccepting tRNAs exert a powerful selective force on translation fidelity. We have developed an algorithm to evaluate the relative rate of translation which allows large-scale comparisons of the non-uniform translation rate on the protein biogenesis. Using the complete genomes of Escherichia coli and Bacillus subtilis we show that stretches of codons pairing to minor tRNAs form putative sites to locally attenuate translation; thereby the tendency is to cluster in near proximity whereas long contiguous stretches of slow-translating triplets are avoided. The presence of slow-translating segments positively correlates with the protein length irrespective of the protein abundance. The slow-translating clusters are predominantly located down-stream of the domain boundaries presumably to fine-tune translational accuracy with the folding fidelity of multidomain proteins. Translation attenuation patterns at highly structurally and functionally conserved domains are preserved across the species suggesting a concerted selective pressure on the codon selection and species-specific tRNA abundance in these regions.

Objective. In this study, we investigated the potential material basis of Yupingfeng powder in the prevention and treatment of 2019 novel coronavirus pneumonia (NCP) by applying molecular docking and molecular dynamic simulation technology. Design. The active ingredients and predictive targets of Yupingfeng powder were sourced using the TCMSP, ETCM, and TCMIP traditional Chinese medicine databases. NCP-related targets were then acquired from the DisGeNET and GeneCards databases, and common disease-drug targets were imported into the STRING database, and Cytoscape software was used to generate a protein-protein interaction network following the use of a network topology algorithm to identify key target genes. Gene Ontology (GO) and KEGG pathway enrichment analysis was then performed using the target genes and GOEAST and DAVID online tools. The mechanism of Yupingfeng powder in the prevention and treatment of NCP was analyzed with reference to the relevant literature. AutoDock software was used for molecular docking, the preliminary analysis of binding status, and to identify the best conformation. Desmond software was used to perform molecular dynamic simulations for protein and compound complexes, perform free energy calculations and hydrogen bond analysis, and to further verify the binding mode. Results. Overall, 38 main active components and 218 predictive targets of Yupingfeng powder were identified and 298 disease targets related to NCP were retrieved from disease databases. Yupingfeng powder was found to act predominantly on the TNF, Toll-like receptor, HIF-1, NOD-like receptor, cytokine-receptor interaction, MAPK, T cell receptor, and VEGF signaling pathways. Molecular docking of the three selected key active components with the 3CL-like protease (3CL-Pro) of SARS-CoV-2 showed that they each had a strong binding force and good affinity. Conclusions. Yupingfeng powder primarily acts on multiple active ingredients and potential targets through multiple action channels and signal pathways. Molecular docking and molecular dynamic simulation technology were used to effectively predict and analyze the potential mechanism by which this Chinese medicine can combat NCP. These results provide a reference for developing new modern Chinese medicine preparations against NCP in the future. [ABSTRACT FROM AUTHOR]

The frequency scaling algorithm (FSA) is a popular imaging algorithm for dechirped SAR data. To obtain a large azimuth detection area, the miniature millimeter-wave (mmW) linear-frequency-modulated continuous-wave (LFMCW) surveillance radar requires a wider azimuth beamwidth, which leads to additional range frequency aliasing in FSA. Because of the adoption of the dechirp-on-receive technique, the sampling frequency is much smaller than the range bandwidth during near-field imaging, which further aggravates the aliasing effects. The target cannot be well focused, and it makes the weak targets submerged in the background. To acquire high-quality SAR images, an improved FSA using the local stretch operation is proposed. The aliasing bandwidth properties introduced by the FS operation and the desired objective range cell migration (RCM) factor are used in this proposed local-stretch FSA (LSFSA). The initial RCM factor is adjusted by the stretch operation to eliminate the frequency aliasing to a certain level without increasing the computing load. The LSFSA is suitable for solving the problem of range frequency aliasing in near-field side-looking SAR and high squint SAR with wide azimuth beamwidth. The proposed method is validated using reasonable simulations and convincing experiments. [ABSTRACT FROM AUTHOR]

In recent years, machine learning theory has set off a wave of research in the field of radar signal processing. In this study, a novel algorithm for sea‐surface target detection based on a stacked autoencoder (SAE) is proposed, which has already been applied in the authors' coastal defense radar system. In the proposed algorithm, the sea surface echo data are cut into a large number of two‐dimensional (2‐D) images through a sliding window, mapping the cell under test (CUT) and the 2‐D images one by one and performing classification or detection from the perspective of 2‐D signal processing. Experimental results of simulated data and real radar data show that the proposed algorithm based on the SAE has better target detection performance compared with the traditional cell averaging constant false alarm rate (CA‐CFAR) algorithm. Besides, the proposed algorithm shows certain interference suppression ability in real data processing. As far as it is known, there is no public report on the detection of 2‐D sea surface targets based on the SAE algorithm in coastal defense radar. [ABSTRACT FROM AUTHOR]

Spatially colored noise would lead to performance degradation or even invalidation of multiple input multiple output (MIMO) radar algorithms. For the problem of joint direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic MIMO radar with spatially colored noise, the reason for the failure of the existing algorithms is analyzed. Taking the low-rank property of the noiseless covariance matrix, the sparse feature of the colored noise covariance matrix as well as the multidimensional structure characteristic of the MIMO radar data after matched filtering into consideration, a tensor analysis-based angle estimation is introduced. Firstly, a covariance tensor for the angle estimation is constructed. The colored noise is suppressed via removing the entities that affected by the noise covariance measurement. Then the noiseless covariance tensor is recovered via tensor completion. Thereafter, the factor matrices corresponding to DOD and DOA are achieved via parallel factor (PARAFAC) decomposition. Finally, the DOD and DOA are fitted using least squares algorithm. Simulation results show that the proposed algorithm is not sensitive to the spatially colored noise, and it is free-from the aperture loss. The proposed algorithm performs more accurate estimation performance than the existing matrix and tensor approaches. [ABSTRACT FROM AUTHOR]

A novel two-phase method for two-dimensional (2D) direction-of-arrival (DOA) estimation with L-shaped array based on decoupled atomic norm minimization (DANM) is proposed in this paper. In the first phase, given the sample crosscorrelation matrix, the gridless DANM technique considering the noise and finite snapshots effects is employed to exploit the structure and sparse properties of the crosscorrelation matrix. The resulting DANM-based algorithm not only enables the crosscorrelation matrix reconstruction (CCMR) but also reconstructs the covariance matrix of the L-shaped array. Hence, sequentially, in the second phase, the conventional 2D DOA estimators for the L-shaped array can be adopted for the angle estimation. With appropriate 2D DOA estimators, the resulting proposed algorithms can not only achieve better performance but also detect more source number, compared with conventional crosscorrelation-based DOA estimators. Moreover, the proposed method, termed CCMR-DANM, not only has blind characteristic that it does not require the prior information of source numbers but also is more efficient than the existing CCMR-based counterparts. Numerical simulations demonstrate the effectiveness and outperformance of the proposed method. [ABSTRACT FROM AUTHOR]

A gridless direction-of-arrival (DOA) estimation method to improve the estimation accuracy and resolution in nonuniform noise is proposed in this paper. This algorithm adopts the structure of minimum-redundancy linear array (MRA) and can be composed of two stages. In the first stage, by minimizing the rank of the covariance matrix of the true signal, the covariance matrix that filters out nonuniform noise is obtained, and then a gridless residual energy constraint scheme is designed to reconstruct the signal covariance matrix of the Hermitian Toeplitz structure. Finally, the unknown DOAs can be determined from the recovered covariance matrix, and the number of sources can be acquired as a byproduct. The proposed algorithm can be regarded as a gridless version method based on sparsity. Simulation results indicate that the proposed method has higher estimation accuracy and resolution compared with existing algorithms. [ABSTRACT FROM AUTHOR]

Featured Application: This research is mainly related to the simulation and control of the collaborative robot. It helps to build a more precise simulation model, design the advanced control algorithm, and achieve human-robot collaborative application with higher accuracy and more safely. Robot dynamics and its parameter identification are of great significance to the realization of optimal control and human–machine interaction. The objective of this research is to address the shortcomings of establishing and identifying the self-developed six-degree-of-freedom (6-DoF) collaborative robot dynamics, which leads to a large error in the predicted torque of the proposed robot. A long short-term memory (LSTM) in an optimized recurrent neural network (RNN) is proposed to compensate the dynamic model of the proposed 6-DoF collaborative robot based on the consideration of gravity, Coriolis force, inertial force, and friction force. The analysis and experimental findings provide promising results. The compensated collaborative robot dynamic model based on LSTM in an optimized RNN displays a good prediction on the actual torque, and the root-mean-square (RMS) error between predicted and actual torques are reduced by 61.8% to 78.9% compared to the traditional dynamic model. Results of the experimental applications demonstrate the validity of the proposed systematic error compensation strategy. [ABSTRACT FROM AUTHOR]

Orthogonal frequency division multiplexing which is called OFDM for short is not only a popular modulation technique in communication systems but also a good method to generate radar signals. A joint radar and communication system could be realised by an OFDM system according to some off-the-shelf works. The radar functionality is mainly considered here, which requires the system to equip with the ability to suppress interference. The typical radar signal, frequency-modulated continuous wave, can be viewed as narrowband interference for a large bandwidth OFDM radar with comparably short duration of OFDM symbols. Here, an interference suppression algorithm suitable for any type of narrowband interference is proposed for OFDM radar. The atomic norm minimisation (ANM) method involved in compressed sensing is introduced to obviate the interference. Then, the data with little interference can be reconstructed by reformulating the ANM as a semi-definite program. Meanwhile, the level of noise is quelled effectively in terms of the atomic norm soft-thresholding method and the gridless version of SPICE. Finally, the numerical simulation is performed to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Fog radio access network (Fog-RAN), which pushes caching and computing capabilities to the network edge, is capable of efficiently delivering content to users by using carefully designed caching placement and content replacement algorithms. In this paper, the transmission scheme design and coding parameter optimization will be considered for coded caching in Fog-RAN, where the reliability of content delivery, i.e., content outage probability, is used as the performance metric. The problem will be formulated as a complicated multi-objective probabilistic combinatorial optimization. A novel maximum $b$ -matching approach will then be proposed to obtain the Pareto optimal solution with fairness constraint. Based on the fast message passing approach, a distributed algorithm with a low memory usage of $O(M+N)$ is also proposed, where $M$ is the number of users and $N$ is the number of fog access points (Fog-APs). Although it is usually very difficult to derive the closed-form formulas for the optimal solution, the approximation formulas of the content outage probability will also be obtained as a function of coding parameters. The asymptotic optimal coding parameters can then be obtained by defining and deriving the outage exponent region and diversity-multiplexing region. Simulation results will illustrate the accuracy of the theoretical derivations, and verify the outage performance of the proposed approach. Therefore, this paper not only proposes a practical distributed Fog-AP selection algorithm for coded caching but also provides a systematic way to evaluate and optimize the performance of Fog-RANs. [ABSTRACT FROM AUTHOR]

In this paper, A contourlet domain SAR image de-speckling algorithm via self-snake diffusion and sparse representation theory is presented in order to reduce the influence of the SAR image speckle noise on the large-scale target edge information of the low frequency subband and the texture information of the high frequency subband. For this algorithm, firstly, the contourlet transform is applied to the speckled SAR image, adjusts the directional number of each dimension to represent SAR image in the high dimensional space. Then, the low frequency subband without sparsity is filtered by self-snake diffusion and the filtered coefficient is regarded as the local average estimate of the low-frequency subband in the contourlet domain. Sparse representation optimization model of SAR image is presented for suppressing the speckle noise of the high frequency subbands with sparsity, and solves sparse coefficients of the high frequency subbands by using the improved orthogonal matching pursuit algorithm. Finally, the de-speckled image is reconstructed from all of the filtered subband coefficients by the inverse contourlet transform. This paper simulates three representative experiments and the experimental results demonstrate that the proposed algorithm has a better de-speckling performance with preserving the edge of the SAR image. [ABSTRACT FROM AUTHOR]

Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps. [ABSTRACT FROM PUBLISHER]

Correct and bias-free interpretation of the deep sequencing data is inevitably dependent on the complete mapping of all mappable reads to the reference sequence, especially for quantitative RNA-seq applications. Seed-based algorithms are generally slow but robust, while Burrows-Wheeler Transform (BWT) based algorithms are fast but less robust. To have both advantages, we developed an algorithm FANSe2 with iterative mapping strategy based on the statistics of real-world sequencing error distribution to substantially accelerate the mapping without compromising the accuracy. Its sensitivity and accuracy are higher than the BWT-based algorithms in the tests using both prokaryotic and eukaryotic sequencing datasets. The gene identification results of FANSe2 is experimentally validated, while the previous algorithms have false positives and false negatives. FANSe2 showed remarkably better consistency to the microarray than most other algorithms in terms of gene expression quantifications. We implemented a scalable and almost maintenance-free parallelization method that can utilize the computational power of multiple office computers, a novel feature not present in any other mainstream algorithm. With three normal office computers, we demonstrated that FANSe2 mapped an RNA-seq dataset generated from an entire Illunima HiSeq 2000 flowcell (8 lanes, 608 M reads) to masked human genome within 4.1 hours with higher sensitivity than Bowtie/Bowtie2. FANSe2 thus provides robust accuracy, full indel sensitivity, fast speed, versatile compatibility and economical computational utilization, making it a useful and practical tool for deep sequencing applications. FANSe2 is freely available at http://bioinformatics.jnu.edu.cn/software/fanse2/. [ABSTRACT FROM AUTHOR]

Identifying peptides from the fragmentation spectra is a fundamental step in mass spectrometry (MS) data processing. The significance (discriminability) of every peak varies, providing additional information for potentially enhancing the identification sensitivity and the correct match rate. However this important information was not considered in previous algorithms. Here we presented a novel method based on Peptide Matching Discriminability (PMD), in which the PMD information of every peak reflects the discriminability of candidate peptides. In addition, we developed a novel peptide scoring algorithm Dispec based on PMD, by taking three aspects of discriminability into consideration: PMD, intensity discriminability and m/z error discriminability. Compared with Mascot and Sequest, Dispec identified remarkably more peptides from three experimental datasets with the same confidence at 1% PSM-level FDR. Dispec is also robust and versatile for various datasets obtained on different instruments. The concept of discriminability enhances the peptide identification and thus may contribute largely to the proteome studies. As an open-source program, Dispec is freely available at http://bioinformatics.jnu.edu.cn/software/dispec/. [ABSTRACT FROM AUTHOR]

An algorithm of terrain hazard assessment for soft landing based on information fusion is presented. Firstly, two types of terrain features are extracted from the gray images of landing navigation camera and the elevation data of Lidar respectively. Then, fuzzy variables and membership functions on terrain features are presented, and local terrain hazard assessments are realized by fuzzy inference. Finally, the global terrain hazard is assessed based on the decision-class fusion of weighted means, and the safe landing sites are obviously showed. The simulation experiments using 3D digital terrain image have validated the proposed algorithm effectively. [ABSTRACT FROM AUTHOR]

Abstract: The main problem of particle filter (PF) in nonlinear state estimation is the particle degeneracy. Resampling operation solves degeneracy to some extent, but it results in the problem of sample impoverishment. Variance reduction technique is proposed to deal with the degeneration phenomenon in this paper, which reduces the variance of the particle weights by selecting an exponential fading factor, and this factor can be chosen adaptively and iteratively in terms of the effective particle number. A theorem is presented to show that this idea is feasible, and the procedure of this new adaptive particle filtering (APF) algorithm is presented. Then, the principle of parameter choice and the limitation of APF are discussed. Finally, a numerical example illustrates that the proposed APF has a higher estimation precision than particle filter – sampling importance resampling (PF-SIR), genetic particle filter (GPF), and particle swarm optimization particle filter (PSOPF), while the computation load of APF is mild. [Copyright &y& Elsevier]

An ellipse-fitting algorithm was developed to separate touching grain kernels in images. The algorithm randomly tracks the edge of touching kernels to find the sample points for fitted ellipses. The fitted ellipses were generated by a direct least-squares ellipse-fitting method. Then, clustering was used to identify the best representative ellipse for each kernel of the touching instance. With representative ellipses, touching grain kernel images were separated by morphology transform. Typical touching kernel patterns of four grain types, namely barley, Canada Western Amber Durum (CWAD) wheat, Canada Western Red Spring (CWRS) wheat, and oats obtained from composite samples from several growing locations across the western Canadian prairies were used to test this algorithm. The accuracies of separation were: 92·4% (barley), 96·1% (CWAD), 94·8% (oats), and 97·3% (CWRS wheat). The kernels used for the touching grains images were separated physically to create non-touching instances and another set of images of kernels were acquired. Morphological features were extracted from images of physically separated kernels and compared with features extracted from software-separated kernels. To decide if the difference between two kinds of features was significant, the large sample Z test of hypotheses was employed. Except for Fourier descriptor 1 of barley and CWAD, software separation did not change the values of morphological features more than the tolerance limits of the measurement system. To assess the classification capability after software separation, the morphological features extracted from physically separated kernels were used as training and basic testing data sets, and the features from software-separated kernels were used as production testing data sets. A back-propagation neural network was employed for grain type classification with morphological features as inputs. Compared to 97·1% physically separated grain kernels being correctly classified, the mean classification accuracy for all the software-separated grain types was 96·6%. The morphological features of software-separated kernels were not distorted during software separation and can be successfully used in grain type classification. [Copyright &y& Elsevier]

Abstract: This paper summarizes the implementation of a physically based algorithm for the retrieval of vegetation green Leaf Area Index (LAI) from Landsat surface reflectance data. The algorithm is based on the canopy spectral invariants theory and provides a computationally efficient way of parameterizing the Bidirectional Reflectance Factor (BRF) as a function of spatial resolution and wavelength. LAI retrievals from the application of this algorithm to aggregated Landsat surface reflectances are consistent with those of MODIS for homogeneous sites represented by different herbaceous and forest cover types. Example results illustrating the physics and performance of the algorithm suggest three key factors that influence the LAI retrieval process: 1) the atmospheric correction procedures to estimate surface reflectances; 2) the proximity of Landsat-observed surface reflectance and corresponding reflectances as characterized by the model simulation; and 3) the quality of the input land cover type in accurately delineating pure vegetated components as opposed to mixed pixels. Accounting for these factors, a pilot implementation of the LAI retrieval algorithm was demonstrated for the state of California utilizing the Global Land Survey (GLS) 2005 Landsat data archive. In a separate exercise, the performance of the LAI algorithm over California was evaluated by using the short-wave infrared band in addition to the red and near-infrared bands. Results show that the algorithm, while ingesting the short-wave infrared band, has the ability to delineate open canopies with understory effects and may provide useful information compared to a more traditional two-band retrieval. Future research will involve implementation of this algorithm at continental scales and a validation exercise will be performed in evaluating the accuracy of the 30-m LAI products at several field sites. [Copyright &y& Elsevier]

It is challenging to recover the large-scale internet traffic data purely from the link-load measurements. With the rapid growth of the problem scale, it will be extremely difficult to sustain the recovery accuracy and the computational cost simultaneously. For the purpose of fast and accurately recovering the traffic data from link-load measurements, we establish a new Sparsity Low-Rank Recovery (SLRR) model based on the intrinsic properties of traffic data and propose an improved Schur Complement Based semi-proximal Alternating Direction Method of Multipliers (SCB-spADMM) to solve the SLRR model. The main contribution lies in the following two aspects. First, we fully exploit the spatial low-rank property and the sparsity of traffic data, which are barely considered in the literature. The model only relates to the traffics in a certain individual time interval. Thus, the scale of the problem is significantly reduced. Second, the global convergence of the proposed ADMM-type algorithm is established and all the intermediate variables' optimums can be calculated analytically in each iteration. According to the numerical results on the classic datasets Abilene and GÉANT, our method achieves the best accuracy with a low computational cost. Moreover, in our newly released large-scale real-life network traffic dataset HOD, our method perfectly reaches the seconds-level feedback, which meets the essential requirement for practical scenarios. [ABSTRACT FROM AUTHOR]

The k shortest paths problem (k -SPP) is fundamentally important to both theoretical and application researches on computational intelligence. Inspired by the natural ripple-spreading phenomenon that occurs on a water surface, this paper proposes a novel ripple-spreading algorithm (RSA). RSA differs from many existing methods which need to reconstruct route networks or to sweep the network for k times, and it can identify the k shortest paths by a single run of ripple relay race in the original route network. Besides the k -SPP in normal route networks, the RSA can also be extended, without losing optimality and effectiveness, to some time-window networks (where various waiting behaviors at nodes are introduced) and dynamical networks (where the network topology and link costs may change over time due to factors such as moving obstacles and spreading disasters). For one-to-all k -SPP, which aims to find all the k shortest paths from a given source to every other node in a network (no matter with or without time windows at nodes, and no matter whether the network topology and link costs can change over time or not), the RSA can still find out all required solutions using only a single run, while the computational complexity is exactly the same as that for the one-to-one k -SPP, i.e., O (k × N L × N A T U) , where N L is the number of links in the network, and N A T U is the average simulated time units for a ripple to travel through a link. The comparative experimental results illustrate the effectiveness and efficiency of the proposed RSA. [ABSTRACT FROM AUTHOR]