1,721,038 research outputs found
Classification of Molecular Structures Made Easy
No full textSeveral problems in bioinformatics and cheminformatics concern the classification of molecules. Relevant instances are automatic cancer detection/classification, machine-learning pathologic prediction, automatic predictive toxicology, etc. Molecules may be represented in terms of graphical structures in a natural way: each node in the graph can be used to represent an atom, whilst the edges of the graph represent the atom-atom bonds. Labels (in the form of real-valued vectors) are associated with nodes and edges in order to express physical and chemical properties of the corresponding atoms and bonds, respectively. These structured data are expected to contain more information than a traditional (flat) feature vector, information that may strengthen the classification capabilities of a machine learner. This paper investigates the application of a novel Bayesian/connectionist classifier to this graphical pattern recognition task. The approach is much simpler than state-of-the-art machine learning paradigms for graphical/relational learning. It relies on the idea of describing the graph in terms of a binary relation. The posterior probability of a class given the relation is estimated as a function of probabilistic quantities modeled with a neural network, trained over individual vertex pairs in the graph. The popular and challenging Mutagenesis dataset is considered for the experimental evaluation. Despite its simplicity, the technique turns out to yield the highest recognition accuracies to date on the complete (friendly + unfriendly) dataset, outperforming complex machines (relational and graph neural nets, kernels for graphs, inductive logic programming techniques, etc.). Some preliminary chemical/biological implications are eventually hypothesized in the light of the results obtained
Splash Singularities for a General Oldroyd Model with Finite Weissenberg Number
No full textIn this paper we study a 2D free-boundary Oldroyd-B model which describes the evolution of a viscoelastic fluid. We prove the existence of splash singularities, namely points where the free-boundary remains smooth but self-intersects. This paper extends the previous results obtained for the infinite Weissenberg number by the authors in Di Iorio et al. (Splash singularity for a free boundary incompressible viscoelastic fluid model, 2018. arXiv:1806.11089; Splash singularity for a 2D Oldroyd-B model with nonlinear Piola-Kirchhoff stress, Nonlinear Differ Equ Appl 24:60, 2017) to the more realistic physical case of any finite Weissenberg number. The main difficulty faced in this paper is due to the non-linear balance law of the elastic tensor, which cannot be reduced, as in the case of infinite Weissenberg, to a transport equation for the deformation gradient. Overcoming this difficulty requires a very accurate local existence theorem in terms of dependence on the Weissenberg number. The method in this case is based on the combined use of conformal transformations and lagrangian coordinates, whose formulation must however take into account the general balance law of the elastic tensor and its dependence on the Weissenberg number. The existence of the splash singularities is therefore guaranteed by an adequate choice of initial data, depending also on the elastic tensor, combined with stability estimates
Classification of graphical data made easy
No full textThe classification of graphical patterns (i.e., data that are represented in the form of labeled graphs) is a problem that has been receiving considerable attention by the machine learning community in recent years. Solutions to the problem would be valuable to a number of applications, ranging from bioinformatics and cheminformatics to Web-related tasks, structural pattern recognition for image processing, etc. Several approaches have been proposed so far, e.g. inductive logic programming and kernels for graphs. Connectionist models were introduced too, namely recursive neural nets (RNN) and graph neural nets (GNN). Although their theoretical properties are sound and thoroughly understood, RNNs and GNNs suffer some drawbacks that may limit their application. This paper introduces an alternative connectionist framework for learning discriminant functions over graphical data. The approach is simple and suitable to maximum-a-posteriori classification of broad families of graphs, and overcomes some limitations of RNNs and GNNs. The idea is to describe a graph as an algebraic relation, i.e. as a subset of the Cartesian product. The class-posterior probabilities given the relation are then reduced (under an iid assumption) to products of probabilistic quantities, estimated using a multilayer perceptron. Empirical evidence shows that, in spite of its simplicity, the technique compares favorably with established approaches on several tasks involving different graphical representations of the data. In particular, in the classification of molecules from the Mutagenesis dataset (friendly+unfriendly) the best result to date (93.91%) is obtained
A Simple and Effective Neural Model for the Classification of Structured Patterns
No full textLearning from structured data (i.e. graphs) is a topic that has recently received the attention of the machine learning community, which proposed connectionist models such as recursive neural nets (RNN) and graph neural nets (GNN). In spite of their sound theoretical properties, RNNs and GNNs suffer some drawbacks that may limit their application. This paper outlines an alternative connectionist framework for learning discriminant functions over structured data. The approach, albeit preliminary, is simple and suitable to maximum-a-posteriori classification of broad families of graphs, and overcomes some limitations of RNNs and GNNs. The idea is to describe a graph as an algebraic relation, i.e. as a subset of the Cartesian product. The class-posterior probabilities given the relation are reduced to products of probabilistic quantities estimated using a multilayer perceptron. Experimental comparisons on tasks that were previously solved via RNNs and GNNs validate the approach
Splash singularity for a free-boundary incompressible viscoelastic fluid model
No full textNumerical computations in viscoelasticity show the failure of many numerical schemes when the Weissenberg number is beyond a critical value Keunings (J Non-Newtonian Fluid Mech 20:209–226, 1986, [6]). The existence of singularities in the continuum model could be the way to explain instability appearing in numerical simulations. We consider here a 2D Oldroyd-B type model at high Weissenberg number, and we show the existence of the so-called splash singularities (namely, points where the free boundary remains smooth but self-intersects). In our case, we assume physically realistic boundary conditions given by the static equilibrium of all the force fields acting at the interface. Our strategy is based on local existence and stability results applied to a family of smooth suitable initial configurations, we show they will evolve into a self-intersecting configuration, and then necessarily there exists a positive time t=t*, where the configuration has a splash singularity. To prove local existence and stability, we first apply a conformal transformation to the 2D domain, in order to separate the contact point with splash, and then we pass into Lagrangian coordinates to fix our domain, inspired by a Thomas Beale’s paper on the initial value problem for the Navier–Stokes equations with a free surface
Unbiased SVM Density Estimation with Application to Graphical Pattern Recognition
Full text linkClassification of structured data (i.e., data that are represented as graphs) is a topic of interest in the machine learning community. This paper presents a different, simple approach to the problem of structured pattern recognition, relying on the description of graphs in terms of algebraic binary relations. Maximum-a- posteriori decision rules over relations require the estimation of class-conditional probability density functions (pdf) defined on graphs. A nonparametric technique for the estimation of the pdfs is introduced, on the basis of a factorization of joint probabilities into individual densities that are modeled, in an unsupervised fashion, via Support Vector Machine (SVM). The SVM training is accomplished applying support vector regression on an unbiased variant of the Parzen Window. The behavior of the estimation algorithm is first demonstrated on a synthetic distribution. Finally, experiments of graph-structured image recognition from the Caltech Benchmark dataset are reported, showing a dramatic improvement over the results (available in the literature) yielded by state-of-the-art connectionist models, for graph processing, namely recursive neural nets and graph neural nets
L’archetipo dannunziano e i suoi aspetti psicologici e religiosi rintracciabili nelle tragedie La figlia di Iorio e La fiaccola sotto il moggio
Full text linkDato che si colloca in un passato lontano, di molto antecedente alla conoscenza dei vari
miti, religioni e credenze popolari, l’archetipo appartiene alla sfera dell’immaginario
collettivo. La presente analisi si prefigge di individuare gli elementi psicologici e
religiosi impliciti nelle due tragedie dannunziane che per certi versi si rispecchiano
l’una nell’altra. La figlia di Iorio e La fiaccola sotto il moggio portano il lettore a riflettere
sulle categorie come la religione, il mito, la superstizione e la società, e fino a quale
punto esse possano prendere il sopravvento sul comportamento di un individuo e
influenzarne l’interazione con la comunità circostante, incidere sulla sua padronanza
comunicativa, sul libero arbitrio e sul destino dei personaggi
Automatic Term Categorization by Extracting Knowledge from the Web
No full textThis paper addresses the problem of categorizing terms or lexical entities into a predefined set of semantic domains exploiting the knowledge available on-line in the Web. The proposed system can be effectively used for the automatic expansion of thesauri, limiting the human effort to the preparation of a small training set of tagged entities. The classification of terms is performed by modeling the contexts in which terms from the same class usually appear. The Web is exploited as a significant repository of contexts that are extracted by querying one or more search engines. In particular, it is shown how the required knowledge can be obtained directly from the snippets returned by the search engines without the overhead of document downloads. Since the Web is continuously updated “World Wide”, this approach allows us to face the problem of open-domain term categorization handling both the geographical and temporal variability of term semantics. The performances attained by different text classifiers are compared, showing that the accuracy results are very good independently of the specific model, thus validating the idea of using term contexts extracted from search engine snippets. Moreover, the experimental results indicate that only very few training examples are needed to reach the best performance (over 90% for the F1 measure)
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