Studio e applicazione di tecniche di riduzione di dimensionalita a modelli geometrici di percezione visiva

La tesi affronta il tema della neuromatematica della visione, in particolare l’integrazione di modelli geometrici di percezione visiva con tecniche di riduzione di dimensionalità. Dall’inizio del secolo scorso, la corrente ideologica della Gestalt iniziò a definire delle regole secondo le quali stimoli visivi distinti tra loro possono essere percepiti come un’unica unità percettiva, come ad esempio i principi di prossimità, somiglianza o buona continuazione. Nel tentativo di quantificare ciò che gli psicologi avevano definito in maniera qualitativa, Field, Hayes e Hess hanno descritto, attraverso esperimenti psicofisiologici, dei campi di associazione per stimoli orientati, che definiscono quali caratteristiche due segmenti dovrebbero avere per poter essere associati allo stesso gruppo percettivo. Grazie alle moderne tecniche di neuroimaging che consentono una mappatura funzionale dettagliata della corteccia visiva, è possibile giustificare su basi neurofisiologiche questi fenomeni percettivi. Ad esempio è stato osservato come neuroni sensibili ad una determinata orientazione siano preferenzialmente connessi con neuroni aventi selettività in posizione e orientazione coerenti con le regole di prossimità e buona continuazione. Partendo dal modello di campi di associazione nello spazio R^2xS^1 introdotto da Citti e Sarti, che introduce una giustificazione del completamento percettivo sulla base della funzionalità della corteccia visiva primaria (V1), è stato possibile modellare la connettività cellulare risolvendo un sistema di equazioni differenziali stocastiche. In questo modo si sono ottenute delle densità di probabilità che sono state interpretate come probabilità di connessione tra cellule semplici in V1. A queste densità di probabilità è possibile collegare direttamente il concetto di affinità tra stimoli visivi, e proprio sulla costruzione di determinate matrici di affinità si sono basati diversi metodi di riduzione di dimensionalità. La fenomenologia del grouping visivo descritta poco sopra è, di fatto, il risultato di un procedimento di riduzione di dimensionalità. I risultati ottenuti da questa analisi e gli esempi applicativi sviluppati si sono rivelati utili per comprendere più nel dettaglio la possibilità di poter riprodurre, attraverso l’analisi spettrale di matrici di affinità calcolate utilizzando i modelli geometrici di Citti-Sarti, il fenomeno percettivo di grouping nello spazio R^2xS^1.

Clustering composite SaaS components in cloud computing using a grouping genetic algorithm

Recently, Software as a Service (SaaS) in Cloud computing, has become more and more significant among software users and providers. To offer a SaaS with flexible functions at a low cost, SaaS providers have focused on the decomposition of the SaaS functionalities, or known as composite SaaS. This approach has introduced new challenges in SaaS resource management in data centres. One of the challenges is managing the resources allocated to the composite SaaS. Due to the dynamic environment of a Cloud data centre, resources that have been initially allocated to SaaS components may be overloaded or wasted. As such, reconfiguration for the components’ placement is triggered to maintain the performance of the composite SaaS. However, existing approaches often ignore the communication or dependencies between SaaS components in their implementation. In a composite SaaS, it is important to include these elements, as they will directly affect the performance of the SaaS. This paper will propose a Grouping Genetic Algorithm (GGA) for multiple composite SaaS application component clustering in Cloud computing that will address this gap. To the best of our knowledge, this is the first attempt to handle multiple composite SaaS reconfiguration placement in a dynamic Cloud environment. The experimental results demonstrate the feasibility and the scalability of the GGA.

A penalty-based grouping genetic algorithm for multiple composite SaaS components clustering in Cloud

Software as a Service (SaaS) in Cloud is getting more and more significant among software users and providers recently. A SaaS that is delivered as composite application has many benefits including reduced delivery costs, flexible offers of the SaaS functions and decreased subscription cost for users. However, this approach has introduced a new problem in managing the resources allocated to the composite SaaS. The resource allocation that has been done at the initial stage may be overloaded or wasted due to the dynamic environment of a Cloud. A typical data center resource management usually triggers a placement reconfiguration for the SaaS in order to maintain its performance as well as to minimize the resource used. Existing approaches for this problem often ignore the underlying dependencies between SaaS components. In addition, the reconfiguration also has to comply with SaaS constraints in terms of its resource requirements, placement requirement as well as its SLA. To tackle the problem, this paper proposes a penalty-based Grouping Genetic Algorithm for multiple composite SaaS components clustering in Cloud. The main objective is to minimize the resource used by the SaaS by clustering its component without violating any constraint. Experimental results demonstrate the feasibility and the scalability of the proposed algorithm.

Grouping people in social networks using a weighted multi-constraints clustering method

Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.

Valutazione sperimentale della rigenerazione cartilaginea articolare dopo stimolazione biofisica con campi elettromagnetici pulsati in associazione a trattamenti chirurgici

Diverse tecniche di ingegneria tessutale sono state sviluppate per promuovere la riparazione delle lesioni della cartilagine articolare. Nonostante i buoni risultati clinici a breve termine, il tessuto rigenerato fallisce nel tempo poiché non possiede le caratteristiche meccaniche e funzionali della cartilagine articolare nativa. La stimolazione con campi elettromagnetici pulsati (CEMP) rappresenta un approccio terapeutico innovativo. I CEMP aumentano l’attività anabolica dei condrociti con conseguente incremento della sintesi della matrice, e limitano l’effetto catabolico delle citochine pro-infiammatorie riducendo la degradazione della cartilagine nel microambiente articolare. I CEMP agiscono mediante l’up-regolazione dei recettori adenosinici A2A potenziando il loro affetto anti-infiammatorio. Lo scopo di questo studio è stato quello di valutare l’effetto della stimolazione con CEMP sulla guarigione di difetti osteocondrali in un modello sperimentale nel coniglio. Un difetto osteocondrale del diametro di 4mm è stato eseguito nel condilo femorale mediale di entrambe le ginocchia di 20 conigli. A destra la lesione è stata lasciata a guarigione spontanea mentre a sinistra e stata trattata mediante inserimento di scaffold collagenico o trapianto di cellule mesenchimali midollari sul medesimo scaffold precedentemente prelevate dalla cresta iliaca. In base al trattamento eseguito 10 animali sono stati stimolati con CEMP 4 ore/die per 40 giorni mentre altri 10 hanno ricevuto stimolatori placebo. Dopo il sacrificio a 40 giorni, sono state eseguite analisi istologiche mediante un punteggio di O’Driscoll modificato. Confrontando le lesioni lasciate a guarigione spontanea, la stimolazione con CEMP ha migliorato significativamente il punteggio (p=0.021). Lo stesso risultato si è osservato nel confronto tra lesioni trattate mediante trapianto di cellule mesenchimali midollari (p=0.032). Nessuna differenza è stata osservata tra animali stimolati e placebo quando la lesione è stata trattata con il solo scaffold (p=0.413). La stimolazione con CEMP è risultata efficace nel promuovere la guarigione di difetti osteocartilaginei in associazione a tecniche chirurgiche di ingegneria tessutale.

Diffusion maps in the Subriemannian motion group and perceptual grouping

Il presente lavoro è motivato dal problema della constituzione di unità percettive a livello della corteccia visiva primaria V1. Si studia dettagliatamente il modello geometrico di Citti-Sarti con particolare attenzione alla modellazione di fenomeni di associazione visiva. Viene studiato nel dettaglio un modello di connettività. Il contributo originale risiede nell'adattamento del metodo delle diffusion maps, recentemente introdotto da Coifman e Lafon, alla geometria subriemanniana della corteccia visiva. Vengono utilizzati strumenti di teoria del potenziale, teoria spettrale, analisi armonica in gruppi di Lie per l'approssimazione delle autofunzioni dell'operatore del calore sul gruppo dei moti rigidi del piano. Le autofunzioni sono utilizzate per l'estrazione di unità percettive nello stimolo visivo. Sono presentate prove sperimentali e originali delle capacità performanti del metodo.

Application of K-tree to document clustering

Digital collections are growing exponentially in size as the information age takes a firm grip on all aspects of society. As a result Information Retrieval (IR) has become an increasingly important area of research. It promises to provide new and more effective ways for users to find information relevant to their search intentions. Document clustering is one of the many tools in the IR toolbox and is far from being perfected. It groups documents that share common features. This grouping allows a user to quickly identify relevant information. If these groups are misleading then valuable information can accidentally be ignored. There- fore, the study and analysis of the quality of document clustering is important. With more and more digital information available, the performance of these algorithms is also of interest. An algorithm with a time complexity of O(n2) can quickly become impractical when clustering a corpus containing millions of documents. Therefore, the investigation of algorithms and data structures to perform clustering in an efficient manner is vital to its success as an IR tool. Document classification is another tool frequently used in the IR field. It predicts categories of new documents based on an existing database of (doc- ument, category) pairs. Support Vector Machines (SVM) have been found to be effective when classifying text documents. As the algorithms for classifica- tion are both efficient and of high quality, the largest gains can be made from improvements to representation. Document representations are vital for both clustering and classification. Representations exploit the content and structure of documents. Dimensionality reduction can improve the effectiveness of existing representations in terms of quality and run-time performance. Research into these areas is another way to improve the efficiency and quality of clustering and classification results. Evaluating document clustering is a difficult task. Intrinsic measures of quality such as distortion only indicate how well an algorithm minimised a sim- ilarity function in a particular vector space. Intrinsic comparisons are inherently limited by the given representation and are not comparable between different representations. Extrinsic measures of quality compare a clustering solution to a “ground truth” solution. This allows comparison between different approaches. As the “ground truth” is created by humans it can suffer from the fact that not every human interprets a topic in the same manner. Whether a document belongs to a particular topic or not can be subjective.

Document clustering algorithms, representations and evaluation for information retrieval

This thesis presents new methods for classification and thematic grouping of billions of web pages, at scales previously not achievable. This process is also known as document clustering, where similar documents are automatically associated with clusters that represent various distinct topic. These automatically discovered topics are in turn used to improve search engine performance by only searching the topics that are deemed relevant to particular user queries.

Knowledge-based clustering

In the knowledge-based clustering approaches reported in the literature, explicit know ledge, typically in the form of a set of concepts, is used in computing similarity or conceptual cohesiveness between objects and in grouping them. We propose a knowledge-based clustering approach in which the domain knowledge is also used in the pattern representation phase of clustering. We argue that such a knowledge-based pattern representation scheme reduces the complexity of similarity computation and grouping phases. We present a knowledge-based clustering algorithm for grouping hooks in a library.

Modified particle swarm optimization for pattern clustering

Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning and data mining. Clustering is grouping of a data set or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait according to some defined distance measure. In this paper we present the genetically improved version of particle swarm optimization algorithm which is a population based heuristic search technique derived from the analysis of the particle swarm intelligence and the concepts of genetic algorithms (GA). The algorithm combines the concepts of PSO such as velocity and position update rules together with the concepts of GA such as selection, crossover and mutation. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The performance of our method is better than k-means and PSO algorithm.

Support vector clustering-based direct coherency identification of generators in a multi-machine power system

This study investigates the application of support vector clustering (SVC) for the direct identification of coherent synchronous generators in large interconnected multi-machine power systems. The clustering is based on coherency measure, which indicates the degree of coherency between any pair of generators. The proposed SVC algorithm processes the coherency measure matrix that is formulated using the generator rotor measurements to cluster the coherent generators. The proposed approach is demonstrated on IEEE 10 generator 39-bus system and an equivalent 35 generators, 246-bus system of practical Indian southern grid. The effect of number of data samples and fault locations are also examined for determining the accuracy of the proposed approach. An extended comparison with other clustering techniques is also included, to show the effectiveness of the proposed approach in grouping the data into coherent groups of generators. This effectiveness of the coherent clusters obtained with the proposed approach is compared in terms of a set of clustering validity indicators and in terms of statistical assessment that is based on the coherency degree of a generator pair.

Deciphering complex patterns of class-I HLA-peptide cross-reactivity via hierarchical grouping

T-cell responses in humans are initiated by the binding of a peptide antigen to a human leukocyte antigen (HLA) molecule. The peptide-HLA complex then recruits an appropriate T cell, leading to cell-mediated immunity. More than 2000 HLA class-I alleles are known in humans, and they vary only in their peptide-binding grooves. The polymorphism they exhibit enables them to bind a wide range of peptide antigens from diverse sources. HLA molecules and peptides present a complex molecular recognition pattern, as many peptides bind to a given allele and a given peptide can be recognized by many alleles. A powerful grouping scheme that not only provides an insightful classification, but is also capable of dissecting the physicochemical basis of recognition specificity is necessary to address this complexity. We present a hierarchical classification of 2010 class-I alleles by using a systematic divisive clustering method. All-pair distances of alleles were obtained by comparing binding pockets in the structural models. By varying the similarity thresholds, a multilevel classification was obtained, with 7 supergroups, each further subclassifying to yield 72 groups. An independent clustering performed based only on similarities in their epitope pools correlated highly with pocket-based clustering. Physicochemical feature combinations that best explain the basis of clustering are identified. Mutual information calculated for the set of peptide ligands enables identification of binding site residues contributing to peptide specificity. The grouping of HLA molecules achieved here will be useful for rational vaccine design, understanding disease susceptibilities and predicting risk of organ transplants.

Grouping materials and processes for the designer: an application for cluster analysis.

In this paper, an analytical tool - cluster analysis - that is commonly used in biology, archaeology, linguistics and psychology is applied to materials and design. Here we use it to cluster materials and the processes that shape them, using their attributes as indicators of relationship. The attributes that are chosen are important to design and designers. The resulting clusters, and the classifications that can be developed from them, depend on the selected attributes and - to some extent - on the method of clustering. Alternative classifications for design that is focused on the technical or aesthetic attributes of materials and the materials and shapes allowed by processes are explored.

DNS-based Internet Client Clustering and Characterization

This paper proposes a novel protocol which uses the Internet Domain Name System (DNS) to partition Web clients into disjoint sets, each of which is associated with a single DNS server. We define an L-DNS cluster to be a grouping of Web Clients that use the same Local DNS server to resolve Internet host names. We identify such clusters in real-time using data obtained from a Web Server in conjunction with that server's Authoritative DNS―both instrumented with an implementation of our clustering algorithm. Using these clusters, we perform measurements from four distinct Internet locations. Our results show that L-DNS clustering enables a better estimation of proximity of a Web Client to a Web Server than previously proposed techniques. Thus, in a Content Distribution Network, a DNS-based scheme that redirects a request from a web client to one of many servers based on the client's name server coordinates (e.g., hops/latency/loss-rates between the client and servers) would perform better with our algorithm.

Determining feature relevance for the grouping of motor unit action potentials through generative topographic mapping

The study of motor unit action potential (MUAP) activity from electrornyographic signals is an important stage on neurological investigations that aim to understand the state of the neuromuscular system. In this context, the identification and clustering of MUAPs that exhibit common characteristics, and the assessment of which data features are most relevant for the definition of such cluster structure are central issues. In this paper, we propose the application of an unsupervised Feature Relevance Determination (FRD) method to the analysis of experimental MUAPs obtained from healthy human subjects. In contrast to approaches that require the knowledge of a priori information from the data, this FRD method is embedded on a constrained mixture model, known as Generative Topographic Mapping, which simultaneously performs clustering and visualization of MUAPs. The experimental results of the analysis of a data set consisting of MUAPs measured from the surface of the First Dorsal Interosseous, a hand muscle, indicate that the MUAP features corresponding to the hyperpolarization period in the physisiological process of generation of muscle fibre action potentials are consistently estimated as the most relevant and, therefore, as those that should be paid preferential attention for the interpretation of the MUAP groupings.