Clustering algorithm based on minimal path loss ratio for vehicular communication

Emerging vehicular comfort applications pose a host of completely new set of requirements such as maintaining end-to-end connectivity, packet routing, and reliable communication for internet access while on the move. One of the biggest challenges is to provide good quality of service (QoS) such as low packet delay while coping with the fast topological changes. In this paper, we propose a clustering algorithm based on minimal path loss ratio (MPLR) which should help in spectrum efficiency and reduce data congestion in the network. The vehicular nodes which experience minimal path loss are selected as the cluster heads. The performance of the MPLR clustering algorithm is calculated by rate of change of cluster heads, average number of clusters and average cluster size. Vehicular traffic models derived from the Traffic Wales data are fed as input to the motorway simulator. A mathematical analysis for the rate of change of cluster head is derived which validates the MPLR algorithm and is compared with the simulated results. The mathematical and simulated results are in good agreement indicating the stability of the algorithm and the accuracy of the simulator. The MPLR system is also compared with V2R system with MPLR system performing better. © 2013 IEEE.

IPOs, clustering, indirect learning and filing independently

IPO underpricing has been attributed to valuation uncertainty, which can be at least partially resolved by the indirect learning associated with IPO clustering [Benveniste, L.M., Ljungqvist, A., Wilhelm, W.J., Yu, X.Y., 2003. Evidence of information spillovers in the production of investment banking services. Journal of Finance 58, 577–608]. We examine why firms might choose not to issue their IPOs contemporaneously with clusters of similar firms, forgoing opportunities to learn from their peers. We find that the willingness to file an IPO without the benefit of indirect learning from peer firm IPOs is directly related to insiders’ needs for portfolio diversification and the firm’s need to raise capital.

Reliability of vegetation community information derived using decorana ordination and fuzzy c-means clustering

Descriptions of vegetation communities are often based on vague semantic terms describing species presence and dominance. For this reason, some researchers advocate the use of fuzzy sets in the statistical classification of plant species data into communities. In this study, spatially referenced vegetation abundance values collected from Greek phrygana were analysed by ordination (DECORANA), and classified on the resulting axes using fuzzy c-means to yield a point data-set representing local memberships in characteristic plant communities. The fuzzy clusters matched vegetation communities noted in the field, which tended to grade into one another, rather than occupying discrete patches. The fuzzy set representation of the community exploited the strengths of detrended correspondence analysis while retaining richer information than a TWINSPAN classification of the same data. Thus, in the absence of phytosociological benchmarks, meaningful and manageable habitat information could be derived from complex, multivariate species data. We also analysed the influence of the reliability of different surveyors' field observations by multiple sampling at a selected sample location. We show that the impact of surveyor error was more severe in the Boolean than the fuzzy classification. © 2007 Springer.

The multiple pheromone Ant clustering algorithm

Ant Colony Optimisation algorithms mimic the way ants use pheromones for marking paths to important locations. Pheromone traces are followed and reinforced by other ants, but also evaporate over time. As a consequence, optimal paths attract more pheromone, whilst the less useful paths fade away. In the Multiple Pheromone Ant Clustering Algorithm (MPACA), ants detect features of objects represented as nodes within graph space. Each node has one or more ants assigned to each feature. Ants attempt to locate nodes with matching feature values, depositing pheromone traces on the way. This use of multiple pheromone values is a key innovation. Ants record other ant encounters, keeping a record of the features and colony membership of ants. The recorded values determine when ants should combine their features to look for conjunctions and whether they should merge into colonies. This ability to detect and deposit pheromone representative of feature combinations, and the resulting colony formation, renders the algorithm a powerful clustering tool. The MPACA operates as follows: (i) initially each node has ants assigned to each feature; (ii) ants roam the graph space searching for nodes with matching features; (iii) when departing matching nodes, ants deposit pheromones to inform other ants that the path goes to a node with the associated feature values; (iv) ant feature encounters are counted each time an ant arrives at a node; (v) if the feature encounters exceed a threshold value, feature combination occurs; (vi) a similar mechanism is used for colony merging. The model varies from traditional ACO in that: (i) a modified pheromone-driven movement mechanism is used; (ii) ants learn feature combinations and deposit multiple pheromone scents accordingly; (iii) ants merge into colonies, the basis of cluster formation. The MPACA is evaluated over synthetic and real-world datasets and its performance compares favourably with alternative approaches.

Constrained nonparametric estimation of input distance function

This paper proposes a constrained nonparametric method of estimating an input distance function. A regression function is estimated via kernel methods without functional form assumptions. To guarantee that the estimated input distance function satisfies its properties, monotonicity constraints are imposed on the regression surface via the constraint weighted bootstrapping method borrowed from statistics literature. The first, second, and cross partial analytical derivatives of the estimated input distance function are derived, and thus the elasticities measuring input substitutability can be computed from them. The method is then applied to a cross-section of 3,249 Norwegian timber producers.

Multi-level genetic algorithm for the resource-constrained re-entrant scheduling problem in the flow shop

The re-entrant flow shop scheduling problem (RFSP) is regarded as a NP-hard problem and attracted the attention of both researchers and industry. Current approach attempts to minimize the makespan of RFSP without considering the interdependency between the resource constraints and the re-entrant probability. This paper proposed Multi-level genetic algorithm (GA) by including the co-related re-entrant possibility and production mode in multi-level chromosome encoding. Repair operator is incorporated in the Multi-level genetic algorithm so as to revise the infeasible solution by resolving the resource conflict. With the objective of minimizing the makespan, Multi-level genetic algorithm (GA) is proposed and ANOVA is used to fine tune the parameter setting of GA. The experiment shows that the proposed approach is more effective to find the near-optimal schedule than the simulated annealing algorithm for both small-size problem and large-size problem. © 2013 Published by Elsevier Ltd.

Improving distributed representation of word sense via WordNet Gloss composition and context clustering

In recent years, there has been an increas-ing interest in learning a distributed rep-resentation of word sense. Traditional context clustering based models usually require careful tuning of model parame-ters, and typically perform worse on infre-quent word senses. This paper presents a novel approach which addresses these lim-itations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned represen-tations outperform the publicly available embeddings on 2 out of 4 metrics in the word similarity task, and 6 out of 13 sub tasks in the analogical reasoning task.

A gloss composition and context clustering based distributed word sense representation model

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

Predictors of household hardship in a resource constrained environment:the case of sub-Saharan Africa

Using data from the 2004 wave of the Afrobarometer survey, this study examines correlates of household hardship in three countries of sub-Saharan Africa: Tanzania, Zambia, and Zimbabwe. Findings provide partial support for the hypothesized relationship. Specifically, poverty reduction initiatives and informal assistance are associated with reduced hardship while civic engagement is related to an increase in household hardship. We also note that certain demographic characteristics are linked to hardship. Policy and practice implications are suggested. © The Author(s) 2011.

Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

In this paper, we focus on the design of bivariate EDAs for discrete optimization problems and propose a new approach named HSMIEC. While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, we employ the Selfish gene theory (SG) in this approach, as well as a Mutual Information and Entropy based Cluster (MIEC) model is also set to optimize the probability distribution of the virtual population. This model uses a hybrid sampling method by considering both the clustering accuracy and clustering diversity and an incremental learning and resample scheme is also set to optimize the parameters of the correlations of the variables. Compared with several benchmark problems, our experimental results demonstrate that HSMIEC often performs better than some other EDAs, such as BMDA, COMIT, MIMIC and ECGA. © 2009 Elsevier B.V. All rights reserved.

Weakly-constrained codes for suppression of patterning effects in digital communications

We propose weakly-constrained stream and block codes with tunable pattern-dependent statistics and demonstrate that the block code capacity at large block sizes is close to the the prediction obtained from a simple Markov model published earlier. We demonstrate the feasibility of the code by presenting original encoding and decoding algorithms with a complexity log-linear in the block size and with modest table memory requirements. We also show that when such codes are used for mitigation of patterning effects in optical fibre communications, a gain of about 0.5dB is possible under realistic conditions, at the expense of small redundancy 10%). © 2006 IEEE.

Suppression of patterning effects in digital communications by an adaptive constrained coding

We quantify the error statistics and patterning effects in a 5x 40 Gbit/s WDM RZ-DBPSK SMF/DCF fibre link using hybrid Raman/EDFA amplification. We propose an adaptive constrained coding for the suppression of errors due to patterning effects. It is established, that this coding technique can greatly reduce the bit error rate (BER) value even for large BER (BER > 101). The proposed approach can be used in the combination with the forward error correction schemes (FEC) to correct the errors even when real channel BER is outside the FEC workspace.

Clustering of tau-immunoreactive pathology in chronic traumatic encephalopathy

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disorder which may result from repetitive brain injury. A variety of tau-immunoreactive pathologies are present, including neurofibrillary tangles (NFT), neuropil threads (NT), dot-like grains (DLG), astrocytic tangles (AT), and occasional neuritic plaques (NP). In tauopathies, cellular inclusions in the cortex are clustered within specific laminae, the clusters being regularly distributed parallel to the pia mater. To determine whether a similar spatial pattern is present in CTE, clustering of the tau-immunoreactive pathology was studied in the cortex, hippocampus, and dentate gyrus in 11 cases of CTE and 7 cases of Alzheimer’s disease neuropathologic change (ADNC) without CTE. In CTE: (1) all aspects of tau-immunoreactive pathology were clustered and the clusters were frequently regularly distributed parallel to the tissue boundary, (2) clustering was similar in two CTE cases with minimal co-pathology compared with cases with associated ADNC or TDP-43 proteinopathy, (3) in a proportion of cortical gyri, estimated cluster size was similar to that of cell columns of the cortico-cortical pathways, and (4) clusters of the tau-immunoreactive pathology were infrequently spatially correlated with blood vessels. The NFT and NP in ADNC without CTE were less frequently randomly or uniformly distributed and more frequently in defined clusters than in CTE. Hence, the spatial pattern of the tau-immunoreactive pathology observed in CTE is typical of the tauopathies but with some distinct differences compared to ADNC alone. The spread of pathogenic tau along anatomical pathways could be a factor in the pathogenesis of the disease.

What to do when K-means clustering fails:a simple yet principled alternative algorithm

The K-means algorithm is one of the most popular clustering algorithms in current use as it is relatively fast yet simple to understand and deploy in practice. Nevertheless, its use entails certain restrictive assumptions about the data, the negative consequences of which are not always immediately apparent, as we demonstrate. While more flexible algorithms have been developed, their widespread use has been hindered by their computational and technical complexity. Motivated by these considerations, we present a flexible alternative to K-means that relaxes most of the assumptions, whilst remaining almost as fast and simple. This novel algorithm which we call MAP-DP (maximum a-posteriori Dirichlet process mixtures), is statistically rigorous as it is based on nonparametric Bayesian Dirichlet process mixture modeling. This approach allows us to overcome most of the limitations imposed by K-means. The number of clusters K is estimated from the data instead of being fixed a-priori as in K-means. In addition, while K-means is restricted to continuous data, the MAP-DP framework can be applied to many kinds of data, for example, binary, count or ordinal data. Also, it can efficiently separate outliers from the data. This additional flexibility does not incur a significant computational overhead compared to K-means with MAP-DP convergence typically achieved in the order of seconds for many practical problems. Finally, in contrast to K-means, since the algorithm is based on an underlying statistical model, the MAP-DP framework can deal with missing data and enables model testing such as cross validation in a principled way. We demonstrate the simplicity and effectiveness of this algorithm on the health informatics problem of clinical sub-typing in a cluster of diseases known as parkinsonism.

Weakly-constrained codes for suppression of patterning effects in digital communications

We propose weakly-constrained stream and block codes with tunable pattern-dependent statistics and demonstrate that the block code capacity at large block sizes is close to the the prediction obtained from a simple Markov model published earlier. We demonstrate the feasibility of the code by presenting original encoding and decoding algorithms with a complexity log-linear in the block size and with modest table memory requirements. We also show that when such codes are used for mitigation of patterning effects in optical fibre communications, a gain of about 0.5dB is possible under realistic conditions, at the expense of small redundancy (≈10%). © 2010 IEEE