Reordering Method Research Articles

Using Meteorological Analogues for Reordering Postprocessed Precipitation Ensembles in Hydrological Forecasting

AbstractMeteorological ensemble forecasts are nowadays widely used as input of hydrological models for probabilistic streamflow forecasting. These forcings are frequently biased and have to be statistically postprocessed, using most of the time univariate techniques that apply independently to individual locations, lead times and weather variables. Postprocessed ensemble forecasts therefore need to be reordered so as to reconstruct suitable multivariate dependence structures. The Schaake shuffle and ensemble copula coupling are the two most popular methods for this purpose. This paper proposes two adaptations of them that make use of meteorological analogues for reconstructing spatiotemporal dependence structures of precipitation forecasts. Performances of the original and adapted techniques are compared through a multistep verification experiment using real forecasts from the European Centre for Medium‐Range Weather Forecasts. This experiment evaluates not only multivariate precipitation forecasts but also the corresponding streamflow forecasts that derive from hydrological modeling. Results show that the relative performances of the different reordering methods vary depending on the verification step. In particular, the standard Schaake shuffle is found to perform poorly when evaluated on streamflow. This emphasizes the crucial role of the precipitation spatiotemporal dependence structure in hydrological ensemble forecasting.

A method for preferential selection of dates in the Schaake shuffle approach to constructing spatiotemporal forecast fields of temperature and precipitation

AbstractHydrological forecasts strongly rely on predictions of precipitation amounts and temperature as meteorological forcings for hydrological models. Ensemble weather predictions provide a number of different scenarios that reflect the uncertainty about these meteorological inputs, but these are often biased and under‐dispersive, and therefore require statistical postprocessing. In addition to correcting the marginal distributions of the two weather variables, postprocessing methods must reconstruct their spatial, temporal, and intervariable dependence in order to generate physically realistic forecast trajectories that can be used as forcings of hydrological streamflow forecast models. For many years, a sample reordering method referred to as “Schaake shuffle” has been used successfully to address this multivariate aspect of forecast distributions by using historical observation trajectories as multivariate “dependence templates.” This paper proposes a variant of the Schaake shuffle, in which the historical dates are selected such that the marginal distributions of the corresponding observation trajectories are similar to the forecast marginal distributions, thus making it more likely that spatial and temporal gradients are preserved during the reordering procedure. This new approach is demonstrated with temperature and precipitation forecasts over four river basins in California, and it is shown to improve upon the standard Schaake shuffle both with respect to verification metrics applied to the forcings, and verification metrics applied to the resulting streamflow predictions.

Optimisation of data locality in energy calculations for large-scale molecular dynamics simulations

The main bottleneck of molecular dynamic simulations is the estimation of nonbonded pairwise interaction, which often employs neighbour search algorithms to find out interacting atom pairs. These methods have some drawbacks in fulfilling data locality principle, which is unable to take full advantage of modern computer architecture. In this article, we developed a new method by introducing a temporary list to reduce the sparsity in data access. This list permits to obtain a compact and sequential data structure which benefits to efficiently fulfil the data locality principle. We tested and compared the performance of the new method with that of the extensively used reordering method. The new method based on linked cell list is shown to increase 13% of computation speed and have better parallelism in comparison with reordering method. The increase in parallel efficiency makes the new method a promising option for large-scale molecular simulations.

Low Power Scan Chain Reordering Method with Limited Routing Congestion for Code-based Test Data Compression

Various test data compression techniques have been developed to reduce the test costs of system–on–a–chips. In this paper, a scan chain reordering algorithm for code–based test data compression techniques is proposed. Scan cells within an acceptable relocation distance are ranked to reduce the number of conflicts in all test patterns and rearranged by a positioning algorithm to minimize the routing overhead. The proposed method is demonstrated on ISCAS ’89 benchmark circuits with their physical layout by using a 180 nm CMOS process library. Significant improvements are observed in compression ratio and test power consumption with minor routing overhead.

A fast feature vector approach for revealing simplex and equi-correlation data patterns in reorderable matrices

Reorderable matrices may be used as support for tabular displays such as heatmaps. Matrix reordering algorithms provide an initial permutation of these matrices, which should help to reveal hidden patterns in the dataset in the visual structure. Some of these algorithms directly permute the data matrix, instead of its row- and column-proximity matrices. We present a data matrix reordering method ( feature vector-based sort – FVS), which reorders a data matrix aiming to reveal simplex and equi-correlation patterns. Our approach extracts feature vectors from a data matrix and uses them to calculate row and column permutations of the data matrix. We used FVS for reordering data matrices of distinct real-world scenarios, in which it revealed those patterns. Our experiments with synthetic matrices revealed that FVS is faster than other known matrix-reordering algorithms and produces results of approximately the same quality (in terms of stress function) when these patterns are hidden in the data matrix. We also present some real-world datasets reordered by our algorithm and discuss the patterns that it uncovers.

Combining parametric low‐dimensional ensemble postprocessing with reordering methods

State‐of‐the‐art weather forecasts usually rely on ensemble prediction systems, accounting for the different sources of uncertainty. As ensembles are typically uncalibrated, they should get statistically postprocessed. Several multivariate ensemble postprocessing techniques that additionally consider spatial, inter‐variable and/or temporal dependences have been developed. These can be roughly divided into two groups. The first group comprises parametric, mainly low‐dimensional, approaches that are tailored to specific settings. The second group involves non‐parametric reordering methods that impose a specific dependence template on univariately postprocessed forecasts and are suitable in any dimension. In this article, these different strategies are combined, with the aim of exploiting the benefits of both concepts. Specifically, a high‐dimensional postprocessing problem is divided into multiple low‐dimensional instances, each of which is postprocessed via a suitable multivariate parametric method. From each postprocessed low‐dimensional distribution, a sample is drawn, which is then reordered according to the corresponding multidimensional rank structure of an appropriately chosen dependence template. In this context, different ranking concepts for multivariate settings are discussed. Finally, all reordered samples are aggregated to obtain the overall postprocessed ensemble. The new approach is applied to ensemble forecasts for temperature and wind speed at several locations from the European Centre for Medium‐Range Weather Forecasts, using a recent bivariate ensemble model output statistics postprocessing technique and a reordering based on the raw ensemble forecasts similar to the ensemble copula coupling method. It shows good predictive skill and outperforms reference ensembles.

Transistor and pin reordering for leakage reduction in CMOS circuits

Leakage power is currently a critical problem in nanometer-scale CMOS circuit technology. In this paper, a novel reordering method for reducing the overall leakage currents is proposed for CMOS logic gates, including CMOS complex gates. This new method takes into account the subthreshold leakage current (ISUB) and gate leakage current (IG) and includes the often-ignored reverse gate tunneling current (IRG). Additionally, this method considers the interaction between leakage components based on the stacking/non-stacking effect case and different W/L ratios of an on-/off-transistor block in a stack. Thus, unlike existing approaches, the proposed method can generate the best configuration for leakage reduction even in CMOS complex gates, and can be used in combination with other leakage reduction techniques to achieve further improvement.

Two axes re-ordering methods in parallel coordinates plots

Visualization and interaction of multidimensional data are challenges in visual data analytics, which requires optimized solutions to integrate the display, exploration and analytical reasoning of data into one visual pipeline for human-centered data analysis and interpretation. Even though it is considered to be one of the most popular techniques for visualization and analysis of multidimensional data, parallel coordinate visualization is also suffered from the visual clutter problem as well as the computational complexity problem, same as other visualization methods in which visual clutter occurs where the volume of data needs to be visualized to be increasing. One straightforward way to address these problems is to change the ordering of axis to reach the minimal number of visual clutters. However, the optimization of the ordering of axes is actually a NP-complete problem. In this paper, two axes re-ordering methods are proposed in parallel coordinates visualization: (1) a contribution-based method and (2) a similarity-based method.The contribution-based re-ordering method is mainly based on the singular value decomposition (SVD) algorithm. It can not only provide users with the mathmetical theory for the selection of the first remarkable axis, but also help with visualizing detailed structure of the data according to the contribution of each data dimension. This approach reduces the computational complexity greatly in comparison with other re-ordering methods. A similarity-based re-ordering method is based on the combination of nonlinear correlation coefficient (NCC) and SVD algorithms. By using this approach, axes are re-ordered in line with the degree of similarities among them. It is much more rational, exact and systemic than other re-ordering methods, including those based on Pearson’s correlation coefficient (PCC). Meanwhile, the paper also proposes a measurement of contribution rate of each dimension to reveal the property hidden in the dataset. At last, the rationale and effectiveness of these approaches are demonstrated through case studies. For example, the patterns of Smurf and Neptune attacks hidden in KDD 1999 dataset are visualized in parallel coordinates using contribution-based re-ordering method; NCC re-ordering method can enlarge the mean crossing angles and reduce the amount of polylines between the neighboring axes.

Preordering using a Target-Language Parser via Cross-Language Syntactic Projection for Statistical Machine Translation

When translating between languages with widely different word orders, word reordering can present a major challenge. Although some word reordering methods do not employ source-language syntactic structures, such structures are inherently useful for word reordering. However, high-quality syntactic parsers are not available for many languages. We propose a preordering method using a target-language syntactic parser to process source-language syntactic structures without a source-language syntactic parser. To train our preordering model based on ITG, we produced syntactic constituent structures for source-language training sentences by (1) parsing target-language training sentences, (2) projecting constituent structures of the target-language sentences to the corresponding source-language sentences, (3) selecting parallel sentences with highly synchronized parallel structures, (4) producing probabilistic models for parsing using the projected partial structures and the Pitman-Yor process, and (5) parsing to produce full binary syntactic structures maximally synchronized with the corresponding target-language syntactic structures, using the constraints of the projected partial structures and the probabilistic models. Our ITG-based preordering model is trained using the produced binary syntactic structures and word alignments. The proposed method facilitates the learning of ITG by producing highly synchronized parallel syntactic structures based on cross-language syntactic projection and sentence selection. The preordering model jointly parses input sentences and identifies their reordered structures. Experiments with Japanese--English and Chinese--English patent translation indicate that our method outperforms existing methods, including string-to-tree syntax-based SMT, a preordering method that does not require a parser, and a preordering method that uses a source-language dependency parser.

New results on stabilization of networked control systems with packet disordering

In this paper, the stabilization problem is studied for a class of networked control systems (NCSs) with delays, packet disordering and packet dropouts. A new packet reordering method is presented to deal with packet disordering and to choose the newest control input. A relationship between the reordered packet over two consecutive sampling intervals is given for the NCS with both time delays and packet dropouts. A sufficient condition for the NCS to be exponentially stable is presented by using the average dwell-time method. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.

Salt-and-Pepper Noise Removal Based on Image Patch Reordering

A new method based on image patch reordering for removing salt-and-pepper noise from corrupted images is presented. Firstly, the problem of salt-and-pepper noise removal can be turned into the problem of image in-painting. Then, we can use the image patch reordering method to recover the missing pixels and fulfill the salt-and-pepper noise removal. Experimental results demonstrate that the proposed method obtain much better performance in terms of both qualitative and quantitative assessment. Especially, the proposed method provides the improvement in the performance of noise suppression and detail preservation.

Dynamic Network Visualization withExtended Massive Sequence Views.

Networks are present in many fields such as finance, sociology, and transportation. Often these networks are dynamic: they have a structural as well as a temporal aspect. In addition to relations occurring over time, node information is frequently present such as hierarchical structure or time-series data. We present a technique that extends the Massive Sequence View ( msv) for the analysis of temporal and structural aspects of dynamic networks. Using features in the data as well as Gestalt principles in the visualization such as closure, proximity, and similarity, we developed node reordering strategies for the msv to make these features stand out that optionally take the hierarchical node structure into account. This enables users to find temporal properties such as trends, counter trends, periodicity, temporal shifts, and anomalies in the network as well as structural properties such as communities and stars. We introduce the circular msv that further reduces visual clutter. In addition, the (circular) msv is extended to also convey time-series data associated with the nodes. This enables users to analyze complex correlations between edge occurrence and node attribute changes. We show the effectiveness of the reordering methods on both synthetic and a rich real-world dynamic network data set.

Reordering of hybrid unstructured grids for an implicit Navier-Stokes solver based on OpenMP parallelization

Grid reordering is an efficient way to obtain better implicit convergence speed in viscous flow simulation based on unstructured grids. When performing parallel computation on the shared memory machines, the convergence performance for solving high-Reynolds number flow with LU-SGS implicit scheme is ruined by the interface between different sub-domains divided by OpenMP parallelization. In order to improve the compatibility between OpenMP parallel environment and the implicit LU-SGS time-stepping scheme, a grid reordering method for unstructured hybrid grids is proposed. In this method, the structured-grid cells in the viscous layer near-body surface are reordered along the normal direction (like columns) and the unstructured part is reordered layer by layer according to the neighboring relations. To investigate the performance of the current implementation, turbulent flows around the RAE2822 airfoil, the NHLP-2D L1T2 multi-element airfoil configuration, the DLR-F6 wing–body–nacelle–pylon configuration and an aerospace plane has been simulated on unstructured hybrid grids. The numerical results show that the grid reordering method is an efficient and practical strategy for improving the convergence rate and the overall efficiency in the parallel computation with unstructured flow solver.

PQR sort: using PQR trees for binary matrix reorganization

AbstractBackgroundReorganization of rows and columns of a matrix does not modify data but may ease or impair visual analysis of data similarities in this structure, according to Gestalt spatial proximity laws. However, there are a factorial number of permutations of rows and columns. Matrix reordering algorithms, such as 2D sort and Sugiyama-based reordering, permute matrix rows and columns in order to highlight hidden patterns.MethodsWe present PQR sort, a matrix reordering algorithm based on a recent data structure called PQR tree, and compare it with the previous ones in terms of time complexity and quality of reordering, according to predefined evaluation criteria.ResultsWe found that PQR sort is an interesting method for minimizing minimal span loss functions based on Jaccard or simple matching coefficients, specially for a given pattern called Rectnoise with a noise ratio of 0.01 or 0.02 and a matrix size of 100 × 100 or 1,000 × 1,000.ConclusionWe concluded that “PQR sort” is a valid alternative method for matrix reordering, which may also be extended for other visual structures.

Post-Ordering by Parsing with ITG for Japanese-English Statistical Machine Translation

Word reordering is a difficult task for translation between languages with widely different word orders, such as Japanese and English. A previously proposed post-ordering method for Japanese-to-English translation first translates a Japanese sentence into a sequence of English words in a word order similar to that of Japanese, then reorders the sequence into an English word order. We employed this post-ordering framework and improved upon its reordering method. The existing post-ordering method reorders the sequence of English words via SMT, whereas our method reorders the sequence by (1) parsing the sequence using ITG to obtain syntactic structures which are similar to Japanese syntactic structures, and (2) transferring the obtained syntactic structures into English syntactic structures according to the ITG. The experiments using Japanese-to-English patent translation demonstrated the effectiveness of our method and showed that both the RIBES and BLEU scores were improved over compared methods.

Syntax-Based Post-Ordering for Efficient Japanese-to-English Translation

This article proposes a novel reordering method for efficient two-step Japanese-to-English statistical machine translation (SMT) that isolates reordering from SMT and solves it after lexical translation. This reordering problem, called post-ordering , is solved as an SMT problem from Head-Final English (HFE) to English. HFE is syntax-based reordered English that is very successfully used for reordering with English-to-Japanese SMT. The proposed method incorporates its advantage into the reverse direction, Japanese-to-English, and solves the post-ordering problem by accurate syntax-based SMT with target language syntax. Two-step SMT with the proposed post-ordering empirically reduces the decoding time of the accurate but slow syntax-based SMT by its good approximation using intermediate HFE. The proposed method improves the decoding speed of syntax-based SMT decoding by about six times with comparable translation accuracy in Japanese-to-English patent translation experiments.

X86 Instruction Reordering for Code Compression

Runtime executable code compression is a method which uses standard data compression methods and binary machine code transformations to achieve smaller file size, yet maintaining the ability to execute the compressed file as a regular executable. With a disassembler, an almost perfect instructional and functional level disassembly can be generated. Using the structural information of the compiled machine code each function can be split into so called basic blocks. In this work we show that reordering instructions within basic blocks using data flow constraints can improve code compression without changing the behavior of the code. We use two kinds of data affection (read, write) and 20 data types including registers: 8 basic x86 registers, 11 eflags, and memory data. Due to the complexity of the reordering, some simplification is required. Our solution is to search local optimum of the compression on the function level and then combine the results to get a suboptimal global result. Using the reordering method better results can be achieved, namely the compression size gain for gzip can be as high as 1.24%, for lzma 0.68% on the tested executables.

Hypergraph Partitioning Based Models and Methods for Exploiting Cache Locality in Sparse Matrix-Vector Multiplication

Sparse matrix-vector multiplication (SpMxV) is a kernel operation widely used in iterative linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers. Matrices with irregular sparsity patterns make it difficult to utilize cache locality effectively in SpMxV computations. In this work, we investigate single- and multiple-SpMxV frameworks for exploiting cache locality in SpMxV computations. For the single-SpMxV framework, we propose two cache-size--aware row/column reordering methods based on one-dimensional (1D) and two-dimensional (2D) top-down sparse matrix partitioning. We utilize the column-net hypergraph model for the 1D method and enhance the row-column-net hypergraph model for the 2D method. The primary aim in both of the proposed methods is to maximize the exploitation of temporal locality in accessing input vector entries. The multiple-SpMxV framework depends on splitting a given matrix into a sum of multiple nonzero-disjoint matrices. We propose a cache-size--aware splitting method based on 2D top-down sparse matrix partitioning by utilizing the row-column-net hypergraph model. The aim in this proposed method is to maximize the exploitation of temporal locality in accessing both input- and output-vector entries. We evaluate the validity of our models and methods on a wide range of sparse matrices using both cache-miss simulations and actual runs by using OSKI. Experimental results show that proposed methods and models outperform state-of-the-art schemes.

ECFGM: enriched control flow graph miner for unknown vicious infected code detection

Vicious codes, especially viruses, as a kind of impressive malware have caused many disasters and continue to exploit more vulnerabilities. These codes are injected inside benign programs in order to abuse their hosts and ease their propagation. The offsets of injected virus codes are unknown and their targets usually are latent until they are executed and activated, what in turn makes viruses very hard to detect. In this paper enriched control flow graph miner, ECFGM in short, is presented to detect infected files corrupted by unknown viruses. ECFGM uses enriched control flow graph model to represent the benign and vicious codes. This model has more information than traditional control flow graph (CFG) by utilizing statistical information of dependent assembly instructions and API calls. To the best of our knowledge, the presented approach in this paper, for the first time, can recognize the offset of infected code of unknown viruses in the victim files. The main contributions of this paper are two folds: first, the presented model is able to detect unknown vicious code using ECFG model with reasonable complexity and desirable accuracy. Second, our approach is resistant against metamorphic viruses which utilize dead code insertion, variable renaming and instruction reordering methods.

The Research on Reordering Rule of Chinese-Mongolian Statistical Machine Translation

There are a lot of word order errors that occur in the Chinese-Mongolian statistical machine translation system, so we proposed Chinese sentence reordering method based on the Mongolian word order. In this paper we first introduce the form and the tagset of reordering rules. We mainly introduce the reordering rules related to the verb phrase, prepositional phrase and subject-predicate phrase. Finally, we give out the experimental results. It is proved by experiment that the performance of the Chinese-Mongolian machine translation system can be improved using these reordering rules.