Quality Of Partitions Research Articles

The ODYSSEY approach to early simulation-based equivalence checking at ESL level using automatically generated executable transaction-level model

Design technology is expected to rise to electronic system-level (ESL). This necessitates new techniques and tools for synthesizing ESL designs and for verifying them before and after ESL synthesis. A promising verification strategy for future very complex designs is to initially verify the design at the highest level of abstraction, and then check the equivalence of the lower level automatically generated models against that initial golden model. We present one such approach to simulation-based functional verification implemented in our ESL design methodology called ODYSSEY. Our ESL synthesis tool generates a transaction-level model (TLM) at TLM level 2 (i.e., design with partial timing) that corresponds to the input ESL design (which is at TLM level 3; i.e., sole functionality without timing). Both the ESL design and its generated TLM model can be simulated on a host machine with corresponding input stimuli to establish their functional equivalence. The TLM is in SystemC, and hence executable, and also models both hardware and software components in C++ to achieve higher simulation speed. We introduce an implementation of a TLM level 2 model that is tailored to our ESL design methodology and apply our approach to a number of benchmarks to evaluate the TLM simulation performance. Experimental results show that the approach suits early validation of the ESL synthesis process since its simulation performance is more than 4 orders of magnitude higher than simulations at lower levels and it is generated early in the design cycle. Also the co-simulation overhead – compared to simulating the original ESL design in C++ – depends on the partitioning quality in terms of communication to computation ratio.

Identification of upwelling areas on sea surface temperature images using fuzzy clustering

Sixteen sea surface temperature (SST) images obtained over the coastal ocean of Portugal during the period September 1992–September 2003 were used aiming to identify automatically the areas covered by upwelling waters. Suitable high resolution colour scales were applied to the SST images in order to enhance the thermal patterns and easily identify the waters with a coastal upwelling origin. The automatic identification of the areas covered by upwelling waters was developed by the authors in a previous work, through the application of fuzzy clustering and validation indexes, and here is explored as an oceanographic application to the Portuguese coastal upwelling. The fuzzy c-means (FCM) algorithm showed to be able to find partitions that closely defined the upwelling areas and the visualization of the fuzzy c-partitions was achieved through the application of a colour scale. The Xie-Beni validation index was used to select the c-partition that best represented the stage of the upwelling event and showed an agreement with the oceanographic interpretation in 10 of the 14 SST segmented images used in this work. Two SST images without upwelling were also used in order to check the response of the algorithm to the absence of the phenomena. The computation of the matching rate between a c-partition and the two areas split by the hand-contoured upwelling boundary also allowed the evaluation of how closely the obtained segmentation reproduced the shape of the areas covered by upwelling waters. This method successfully identified the upwelling boundary regions in 10 of the 14 SST images. The values obtained for the matching rate were higher than 0.77, thus indicating the good quality of the fuzzy partitions. The segmented images with 3 or 4 clusters were the most suitable ones to reproduce the areas covered by upwelling waters, but it was also shown that, for some cases, the upwelling areas could be reasonably well reproduced by the FCM 2-partition images. While in the latter, the area covered with upwelling waters was coincident with the first cluster, in the former, the segmented image showed two clusters within the upwelling area: the first cluster coincided with the area occupied by the most recently upwelled waters near the coast, while the second cluster was coincident with the area occupied by the “older” upwelling waters with its extensions offshore, the so-called cold filaments. The FCM algorithm revealed to be a promising technique in the automatic identification of upwelling areas on SST images.

Top-Down Parameter-Free Clustering of High-Dimensional Categorical Data

A parameter-free, fully-automatic approach to clustering high-dimensional categorical data is proposed. The technique is based on a two-phase iterative procedure, which attempts to improve the overall quality of the whole partition. In the first phase, cluster assignments are given, and a new cluster is added to the partition by identifying and splitting a low-quality cluster. In the second phase, the number of clusters is fixed, and an attempt to optimize cluster assignments is done. On the basis of such features, the algorithm attempts to improve the overall quality of the whole partition and finds clusters in the data, whose number is naturally established on the basis of the inherent features of the underlying data set rather than being previously specified. Furthermore, the approach is parametric to the notion of cluster quality: Here, a cluster is defined as a set of tuples exhibiting a sort of homogeneity. We show how a suitable notion of cluster homogeneity can be defined in the context of high-dimensional categorical data, from which an effective instance of the proposed clustering scheme immediately follows. Experiments on both synthetic and real data prove that the devised algorithm scales linearly and achieves nearly optimal results in terms of compactness and separation.

Parallel multilevel algorithms for hypergraph partitioning

In this paper, we present parallel multilevel algorithms for the hypergraph partitioning problem. In particular, we describe for parallel coarsening, parallel greedy k-way refinement and parallel multi-phase refinement. Using an asymptotic theoretical performance model, we derive the isoefficiency function for our algorithms and hence show that they are technically scalable when the maximum vertex and hyperedge degrees are small. We conduct experiments on hypergraphs from six different application domains to investigate the empirical scalability of our algorithms both in terms of runtime and partition quality. Our findings confirm that the quality of partition produced by our algorithms is stable as the number of processors is increased while being competitive with those produced by a state-of-the-art serial multilevel partitioning tool. We also validate our theoretical performance model through an isoefficiency study. Finally, we evaluate the impact of introducing parallel multi-phase refinement into our parallel multilevel algorithm in terms of the trade off between improved partition quality and higher runtime cost.

A dominance-based rough set approach to Kansei Engineering in product development

Keen competitions in the global market have led product development to a more knowledge-intensive activity than ever, which requires not only tremendous expert knowledge but also effective analysis of design information. Kansei Engineering as a customer-oriented methodology for product development, often has to analyse imprecise design information inherent with nonlinearity and uncertainty. This paper proposes a systematic approach to Kansei Engineering based on the dominance-based rough set theory. Two novel concepts known as category score and partition quality have been developed and incorporated into the proposed approach. The new approach proposed is able to identify and analyse two types of inconsistencies caused by indiscernibility relations and dominance principles respectively. The result of an illustrative case study shows that the proposed approach can effectively extract Kansei knowledge from imprecise design information, and it can be easily integrated into an expert system for customer-oriented product development.

Information technology of variational automatic classification of objects and pattern recognition

A software information technology which employs a variational algorithm for automatic classification of objects with the use of a new partition quality functional based on uniformity measure is described. An algorithm for estimation using feature weights and representativeness of classes is also implemented.

Comparison between two coevolutionary feature weighting algorithms in clustering

Feature weighting is an aspect of increasing importance in clustering because data are becoming more and more complex nowadays. In this paper, we propose two new feature weighting methods based on coevolutive algorithms. The first one is inspired by the Lamarck theory (inheritance of acquired characteristics) and uses the distance-based cost function defined in the LKM algorithm as fitness function. The second method uses a fitness function based on a new partitioning quality measure. It does not need a distance-based measure. We compare classical hill-climbing optimization with these new genetic algorithms on three data sets from UCI. Results show that the proposed methods are better than the hill-climbing based algorithms. We also present a process of hyperspectral remotely sensed image classification. The experiments, corroborated by geographers, highlight the benefits of using coevolutionary feature weighting methods to improve knowledge discovery process.

On the evaluation of the Bunch search-based software modularization algorithm

The first part of this paper describes an automatic reverse engineering process to infer subsystem abstractions that are useful for a variety of software maintenance activities. This process is based on clustering the graph representing the modules and module-level dependencies found in the source code into abstract structures not in the source code called subsystems. The clustering process uses evolutionary algorithms to search through the enormous set of possible graph partitions, and is guided by a fitness function designed to measure the quality of individual graph partitions. The second part of this paper focuses on evaluating the results produced by our clustering technique. Our previous research has shown through both qualitative and quantitative studies that our clustering technique produces good results quickly and consistently. In this part of the paper we study the underlying structure of the search space of several open source systems. We also report on some interesting findings our analysis uncovered by comparing random graphs to graphs representing real software systems.

ECG beat classification using GreyART network

The grey relational grade is a similarity measure. On the basis of the grey relational grade, an adaptive resonant theory (ART) type network, GreyART, has been developed. When the GreyART is used to classify a dataset with varying amount of data, the measurement between two specific data in the dataset may vary since the measurement is affected by new added data. In this case, the grey relational grade is not a global measure. As the measurement varies, in the GreyART, it is hard to use a fixed vigilance threshold value for determining whether the current input data belong to one of the existing clusters or become the template of a new online-created cluster. A method to solve this problem has been proposed and then applied to develop an electrocardiogram (ECG) beat classifier. The proposed ECG beat classification involves two phases. One is the off-line learning phase. With the proposed performance index, the product of the classification accuracy and the partition quality, an optimal value for the vigilance threshold and the corresponding cluster centres from the learning results can be determined. The other is the online examining phase, which classifies the input ECG beats. In this phase, the vigilance threshold value and the initial cluster centres are the optimal ones obtained in the learning phase. Under these conditions, the GreyART network enables real-time classification of ECG beats. Simulation results show that the proposed network achieves a good accuracy with a good computational efficiency for ECG beat classification problems.

Key issues on sediment quality assessment: A review of South American research with emphasis on Brazil

Conventional approaches to pollution prevention in South American countries have traditionally relied on scientists identifying potentially harmful substances and practices, and advising regulators concerning exposures and effects. Since the 1990s scientific knowledge and skills have contributed to the creation of pollution control legislation. However the extent, to which the legislation is effectively applied, is determined by government policies. To be effective, strategies for environmental protection must be firmly supported by political commitment. Sediment is recognized worldwide as a major repository for persistent contaminants in aquatic ecosystems. Contaminant dynamics promote changes in the overlying water quality, as well as in porewater and can affect the biota even though the overlying water may meet water quality criteria. It is recognized that traditional water quality-based controls are not enough to protect the aquatic environments in which sediment quality assessment is also required. Although chemical analysis of sediment provides information, which may allow for the estimation of potential biological effects, contaminant concentrations are not accurate predictors of biological responses. Sediment Quality Values can only be derived from a database of biological effects verified for different organisms by a variety of methods that show the relationship between effects and contaminants. Given the difficulties in establishing this database, Brazil, like other countries in South America, still does not have Sediment Quality Criteria (SQC) despite the existence of quality criteria for water. Although effluents are often controlled according to normalized standards, high levels of contamination in reservoirs, rivers, estuaries and coastal areas have been found; consequently sediments are receiving greater attention in South America. The principles and techniques globally accepted for sediment toxicity assessment are already in use in many South American countries, in an attempt to solve localized problems of environmental contamination. Most of the countries have chosen local test-species according to required criteria, and have already standardized protocols to establish the relationship between contamination and toxicity. Several integrative approaches for sediment quality evaluation such as; Apparent Effects Threshold (AET), Equilibrium Partitioning (EqP) and Sediment Quality Triad (SQT) are also being applied to hotspots to promote political measures for conservation and remediation.

Partitioning planar graphs with costs and weights

A graph separator is a set of vertices or edges whose removal divides an input graph into components of bounded size. This paper describes new algorithms for computing separators in planar graphs as well as techniques that can be used to speed up the implementation of graph partitioning algorithms and improve the partition quality. In particular, we consider planar graphs with costs and weights on the vertices, where weights are used to estimate the sizes of the partitions and costs are used to estimate the size of the separator. We show that in these graphs one can always find a small cost separator (consisting of vertices or edges) that partitions the graph into components of bounded weight. We describe implementations of the partitioning algorithms and discuss results of our experiments.

Transgenic cotton over-producing spinach sucrose phosphate synthase showed enhanced leaf sucrose synthesis and improved fiber quality under controlled environmental conditions

Prior data indicated that enhanced availability of sucrose, a major product of photosynthesis in source leaves and the carbon source for secondary wall cellulose synthesis in fiber sinks, might improve fiber quality under abiotic stress conditions. To test this hypothesis, a family of transgenic cotton plants (Gossypium hirsutum cv. Coker 312 elite) was produced that over-expressed spinach sucrose-phosphate synthase (SPS) because of its role in regulation of sucrose synthesis in photosynthetic and heterotrophic tissues. A family of 12 independent transgenic lines was characterized in terms of foreign gene insertion, expression of spinach SPS, production of spinach SPS protein, and development of enhanced extractable V (max) SPS activity in leaf and fiber. Lines with the highest V (max) SPS activity were further characterized in terms of carbon partitioning and fiber quality compared to wild-type and transgenic null controls. Leaves of transgenic SPS over-expressing lines showed higher sucrose:starch ratio and partitioning of (14)C to sucrose in preference to starch. In two growth chamber experiments with cool nights, ambient CO(2) concentration, and limited light below the canopy, the transgenic line with the highest SPS activity in leaf and fiber had higher fiber micronaire and maturity ratio associated with greater thickness of the cellulosic secondary wall.

A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment

A fuzzy extension of the Rand index [Rand, W.M., 1971. Objective criteria for the evaluation of clustering methods. J. Amer. Statist. Assoc. 846–850] is introduced in this paper. The Rand index is a traditional criterion for assessment and comparison of different results provided by classifiers and clustering algorithms. It is able to measure the quality of different hard partitions of a data set from a classification perspective, including partitions with different numbers of classes or clusters. The original Rand index is extended here by making it able to evaluate a fuzzy partition of a data set – provided by a fuzzy clustering algorithm or a classifier with fuzzy-like outputs – against a reference hard partition that encodes the actual (known) data classes. A theoretical formulation based on formal concepts from the fuzzy set theory is derived and used as a basis for the mathematical interpretation of the Fuzzy Rand Index proposed. The fuzzy counterparts of other (five) related indexes, namely, the Adjusted Rand Index of Hubert and Arabie, the Jaccard coefficient, the Minkowski measure, the Fowlkes–Mallows Index, and the Γ statistics, are also derived from this formulation.

Silencing leaf sorbitol synthesis alters long-distance partitioning and apple fruit quality.

Sorbitol and sucrose are major products of photosynthesis distributed in apple trees (Malus domestica Borkh. cv. "Greensleeves") that affect quality in fruit. Transgenic apple plants were silenced or up-regulated for sorbitol-6-phosphate dehydrogenase by using the CaMV35S promoter to define the role of sorbitol distribution in fruit development. Transgenic plants with suppressed sorbitol-6-phosphate dehydrogenase compensated by accumulating sucrose and starch in leaves, and morning and midday net carbon assimilation rates were significantly lower. The sorbitol to sucrose ratio in leaves was reduced by approximately 90% and in phloem exudates by approximately 75%. The fruit accumulated more glucose and less fructose, starch, and malic acid, with no overall differences in weight and firmness. Sorbitol dehydrogenase activity was reduced in silenced fruit, but activities of neutral invertase, vacuolar invertase, cell wall-bound invertase, fructose kinase, and hexokinase were unaffected. Analyses of transcript levels and activity of enzymes involved in carbohydrate metabolism throughout fruit development revealed significant differences in pathways related to sorbitol transport and breakdown. Together, these results suggest that sorbitol distribution plays a key role in fruit carbon metabolism and affects quality attributes such as sugar-acid balance and starch accumulation.

On the automatic modularization of software systems using the Bunch tool

Since modern software systems are large and complex, appropriate abstractions of their structure are needed to make them more understandable and, thus, easier to maintain. Software clustering techniques are useful to support the creation of these abstractions by producing architectural-level views of a system's structure directly from its source code. This paper examines the Bunch clustering system which, unlike other software clustering tools, uses search techniques to perform clustering. Bunch produces a subsystem decomposition by partitioning a graph of the entities (e.g., classes) and relations (e.g., function calls) in the source code. Bunch uses a fitness function to evaluate the quality of graph partitions and uses search algorithms to find a satisfactory solution. This paper presents a case study to demonstrate how Bunch can be used to create views of the structure of significant software systems. This paper also outlines research to evaluate the software clustering results produced by Bunch.

Isoperimetric Partitioning: A New Algorithm for Graph Partitioning

We present a new algorithm for graph partitioning based on optimization of the combinatorial isoperimetric constant. It is shown empirically that this algorithm is competitive with other global partitioning algorithms in terms of partition quality. The isoperimetric algorithm is easy to parallelize, does not require coordinate information, and handles nonplanar graphs, weighted graphs, and families of graphs which are known to cause problems for other methods. Compared to spectral partitioning, the isoperimetric algorithm is faster and more stable. An exact circuit analogy to the algorithm is also developed with a natural random walks interpretation. The isoperimetric algorithm for graph partitioning is implemented in our publicly available Graph Analysis Toolbox [L. Grady, Ph.D. thesis, Boston University, Boston, MA, 2004], [L. Grady and E. L. Schwartz, Technical report TR-03-021, Boston University, Boston, MA, 2003] for MATLAB obtainable at http://eslab.bu.edu/software/graphanalysis/. This toolbox was used to generate all of the results compiled in the tables of this paper.

The method of [formula omitted]-grams in large-scale clustering of DNA texts

This paper is devoted to the techniques of clustering of texts based on the comparison of vocabularies of N-grams. In contrast to the regular N-grams approach, the proposed N-grams method is based on calculation of imperfect occurrences of N-grams in a text up to a number of mismatched strings. We demonstrated that such an approach essentially improves the resolving capacity of the N-grams method for DNA texts. Additionally, we discuss a mutual usage scheme of different clustering technique types to verify the partition quality.

Distributed CTL model checking

As model checking becomes increasingly used in industry, there is a big need for efficient new methods to deal with the large real-size designs. The author presents a novel method for improving the performance of model checking using parallelisation techniques. The model checking is performed on a distributed-memory environment consisting of a network of machines. The important two keys to focus on are the memory balance and communication reduction. A new algorithm for partitioning the large state space modelling industrial designs with hundreds of millions of states and transitions is proposed. The state space is supposed to be represented by a weighted Kripke structure (this is an extension of the Kripke structure where weights are associated with the states and with the transitions). This algorithm partitions the weighted Kripke structure by performing a combination of abstraction-partition-refinement on this structure. The CTL model checking algorithm is distributed on processes located on different network machines. Each one owns a partition and executes the algorithm on it. The algorithm for CTL model checking is designed to reduce the communication overhead between the processes. The experimental results on large real designs show that this method improves the quality of partitions, the communication overhead and then the overall performance of the model checking.

Adaptive data parallel computing on workstation clusters

Many important parallel applications are data parallel, and may be efficiently implemented on a workstation cluster by allocating each workstation a contiguous partition of the data domain. Implementation on non-dedicated clusters, however, is complicated by the possibility of changes in workstation availability. For example, a personal workstation may be reclaimed by its primary user for interactive use. In such situations, a node must be removed from the collection of workstations forming the “virtual parallel machine” allocated to the application, and data redistributed accordingly. Conversely, workstations may become available to join the virtual parallel machine. This paper identifies fundamental characteristics of efficient policies for data redistribution following addition/removal of workstations from the cluster. The following conclusions are obtained based on mathematical analysis and simulations: (a) allocating data to a new node from the center of the data domain substantially reduces data migration costs compared to allocation from the edge; (b) addition in groups is beneficial compared to repeated single additions; and (c) even a large number of incremental adjustments of the data domain partitions, owing to successive additions/removals of nodes, do not appear to substantially degrade partition quality compared to that obtained by partitioning from scratch. We believe that these observations can be fruitfully incorporated in the design of workstation cluster support systems for data parallel computing.

Identifying spatial relationships in neural processing using a multiple classification approach

The application of statistical classification methods to in vivo functional neuroimaging data makes it possible to explore spatial patterns in task-related changes in neural processing. Cluster analysis is one group of descriptive statistical procedures that can assist in identifying classes of brain regions that exhibit similar task-related functionality. In practice, a limitation of cluster analysis is that the performances of clustering algorithms rely on unknown characteristics of the data, making it difficult to determine which procedure best suits a particular analysis. We present a multiple classification approach that incorporates numerous algorithms, evaluates the associated classifications, and either selects a plausible partition relative to the others considered or pools the results from the numerous methods. The multiple classification approach utilizes a new performance criterion, called the relative information (RI) measure, to evaluate the quality of the candidate partitions and as the basis for producing a composite classification image. Employing multiple classifications, rather than a single algorithm, our methodology increases the chance of detecting the functional relationships within the data and, therefore, produces more reliable results. We apply our methodology to a PET study to explore spatial relationships in measured brain function associated with increasing blood alcohol concentration levels, and we perform a simulation study to evaluate the performance of RI.