1,702 research outputs found
Biclustering of expression microarray data using Affinity Propagation
No full textBiclustering, namely simultaneous clustering of genes and samples, represents a challenging and important research line in the expression microarray data analysis. In this paper, we investigate the use of Affinity Propagation, a popular clustering method, to perform biclustering. Specifically, we cast Affinity Propagation into the Couple Two Way Clustering scheme, which allows to use a clustering technique to perform biclustering. We extend the CTWC approach, adapting it to Affinity Propagation, by introducing a stability criterion and by devising an approach to automatically assemble couples of stable clusters into biclusters. Empirical results, obtained in a synthetic benchmark for biclustering, show that our approach is extremely competitive with respect to the state of the art, achieving an accuracy of 91% in the worst case performance and 100% accuracy for all tested noise levels in the best case
Biclustering of time series data using factor graphs
No full textBiclustering regards the simultaneous clustering of both rows and columns of a given data matrix. A specific applica- tion scenario for biclustering techniques concerns the anal- ysis of gene expression time-series data, wherein columns dataset are temporally related. In this context, bicluster- ing solutions should involve subset of genes sharing ‘simi- lar’ behaviours among consecutive experimental conditions. Due to the intrinsic spatial constraint required by time-series dataset, current Factor Graph (FG) based approaches can- not be applied. In this paper we introduce Time-Series constraints forcing biclustering solution to have contiguous columns. We optimize the model by using the Max-Sum algorithm, whose message update rules have been derived exploiting The Higher Order Potentials (THOP). The pro- posed method has been assessed on a real world dataset and the retrieved biclusters show that it can provide accurate and biologically relevant solutions
Biclustering gene expressions using factor graphs and the max-sum algorithm
No full textBiclustering is an intrinsically challenging andhighly complex problem, particularly studied in thebiology field, where the goal is to simultaneouslycluster genes and samples of an expression data matrix.In this paper we present a novel approach togene expression biclustering by providing a binaryFactor Graph formulation to such problem. In moredetail, we reformulate biclustering as a sequentialsearch for single biclusters and use an efficient optimizationprocedure based on the Max Sum algorithm.Such approach, drastically alleviates thescaling issues of previous approaches for biclusteringbased on Factor Graphs obtaining significantlymore accurate results on synthetic datasets. A furtheranalysis on two real-world datasets confirmsthe potentials of the proposed methodology whencompared to alternative state of the art methods
A binary Factor Graph model for biclustering
No full textBiclustering, which can be defined as the simultaneous clustering of rows and columns in a data matrix, has received increasing attention in recent years, particularly in the field of Bioinformatics (e.g. for the analysis of microarray data). This paper proposes a novel biclustering approach, which extends the Affinity Propagation [Frey 07] clustering algorithm to the biclustering case. In particular, we propose a new exemplar based model, encoded as a binary factor graph, which allows to cluster rows and columns simultaneously. Moreover, we propose a linear formulation of such model to solve the optimization problem using Linear Programming techniques. The proposed approach has been tested by using a well known synthetic microarray benchmark, with encouraging results
A biclustering approach based on factor graphs and the max-sum algorithm
No full textBiclustering represents an intrinsically complex problem, where the aim is to perform a simultaneous row- and column-clustering of a given data matrix. Some recent approaches model this problem using factor graphs, so to exploit their ability to open the door to efficient optimization approaches for well designed function decompositions. However, while such models provide promising results, they do not scale to data matrices of reasonable size. In this paper, we take a step towards addressing this issue, by proposing a novel approach to biclustering based on factor graphs, which yields high quality solutions and scales more favorably than previous methods. Specifically, we cast biclustering as the sequential search for a single bicluster, and propose a binary and compact factor graph that can be solved efficiently using the max-sum algorithm. The proposed approach has been tested and compared with state-of-the-art methods on four datasets (two synthetic and two real world data), providing encouraging results with respect both to previous approaches based on factor graphs and to other state-of-the-art method
TUTELA DEL LAVORO E LIBERTA' D'IMPRESA NEI PROCESSI DI ESTERNALIZZAZIONE
No full textL’elaborato analizza le conseguenze lavoristiche della successione fra imprenditori, muovendo da una ricognizione delle varie tipologie di esternalizzazione con le relative esigenze e principali criticità.
L’indagine si concentra in primo luogo sul trasferimento d’azienda, esaminando la normativa e la giurisprudenza europee per passare poi alla disciplina di diritto interno, alle procedure sindacali e a uno specifico focus sul trasferimento delle aziende in crisi.
Successivamente l’autore si sofferma sull’appalto, prendendone in particolare considerazione gli indici di genuinità, i criteri di distinzione dalla somministrazione illecita di manodopera e la tutela delle maestranze in caso di avvicendamento fra imprese.
Da ultimo, la ricerca approfondisce le c.d. “clausole sociali”, sia di prima che di seconda generazione, valutandone la compatibilità con il diritto eurounitario e con la costituzione nonché riflettendo sui possibili rimedi in caso di loro violazione.The author analyzes the labour consequences of the succession between entrepreneurs, starting from a recognition of the various types of outsourcing with the related needs and main critical issues.
The survey focuses primarily on the transfer of businesses, examining European legislation and case-law and then moving on to internal legislation, trade union procedures and a specific focus on the transfer of companies in crisis.
The author then dwells on the contract, taking into account in particular the indications of authenticity, the criteria of distinction from the illicit administration of labour and the protection of workers in the event of turnover between companies.
Finally, the research deepens the "social clauses", both first and second generation, assessing their compatibility with European law and with the constitution and reflecting on possible remedies in case of their violation
Ultra Low Carbon Vehicles: New Parameters for Automotive Design
No full textAs the influence of vehicle emissions on our environment has become better understood, the UK government has recently placed urgent emphasis on the implementation of low carbon
technologies in the automotive industry through: the UK Low Carbon Industrial Strategy. The overall objective is to offer big incentives to consumers and support for the development of infrastructure and engineering solutions. This scheme however does not consider how the development of functional and experiential user value might drive consumer demand, contributing to the adoption of low carbon vehicles (LCVs) in the mass market.
With the emergence of the North East of England as the UK’s first specialised region for the development of ultra-low carbon vehicles (ULCVs), ONE North East, as a development agency for the region's economic and business development, and Northumbria University Ideas-lab have supported a project to facilitate innovation through the collaboration of technology, research and development (R&D) and business. The High Value Low Carbon (HVLC) project aims to envisage
new user value made possible by the integration of low carbon vehicle platforms with new process and network technologies. The HVLC consortium represents vehicle manufacturers and their suppliers as well as technology based companies and through an ongoing process of design concept generation the project offers a hub for innovation led enterprise.
Whilst new technological developments in areas such as power generation, nano materials, hydrogen fuel cells, printed electronics and networked communications will all impact on future automotive design, the mass adoption of low carbon technologies represents a paradigm shift for the motorist. This paper aims to describe how the mapping of new parameters will lead to new transport scenarios that will create the space for new collaborative research on user experiences supported by innovative technologies and related services
Biclustering with a quantum annealer
No full textSeveral problem in Artificial Intelligence and Pattern Recognition are computationally intractable due to their inherent complexity and the exponential size of the solution space. One example of such problems is biclustering, a specific clustering problem where rows and columns of a data-matrix must be clustered simultaneously. Quantum information processing could provide a viable alternative to combat such a complexity. A notable work in this direction is the recent development of the D-Wave computer, whose processor has been designed to the purpose of solving Quadratic Unconstrained Binary Optimization (QUBO) problems. In this paper, we investigate the use of quantum annealing by providing the first QUBO model for biclustering and a theoretical analysis of its properties (correctness and complexity). We empirically evaluated the accuracy of the model on a synthetic data-set and then performed experiments on a D-Wave machine discussing its practical applicability and embedding propertie
Multiple Structure Recovery via Probabilistic Biclustering
No full textMultiple Structure Recovery (MSR) represents an important and challenging problem in the field of Computer Vision and Pattern Recognition. Recent approaches to MSR advocate the use of clustering techniques. In this paper we propose an alternative method which investigates the usage of biclustering in MSR scenario. The main idea behind the use of biclustering approaches to MSR is to isolate subsets of points that behave “coherently” in a subset of models/structures. Specifically, we adopt a recent generative biclustering algorithm and we test the approach on a widely accepted MSR benchmark. The results show that biclustering techniques favorably compares with state-of-the-art clustering methods
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Matteo Maria Boiardo (1441-1494)
No full textMatteo Maria Boiardo – contemporary of Sir Thomas Malory and forerunner of Shakespeare, Spenser, Milton, Cervantes, Tolkien, and C.S. Lewis – is best known as the author of the Orlando Innamorato [Orlando in Love], a poem in ottava rima organically merging Carolingian epic and Arthurian romance. He was also a lyric and pastoral poet, playwright, and translator of classical texts into the Italian vernacular. Matteo was active in Italy during the years of 1463 throughout 1494, the year of his death
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