One-class classifiers based on entropic spanning graphs

One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic spanning graphs. Our approach takes into account the possibility to process also non-numeric data by means of an embedding procedure. The spanning graph is learned on the embedded input data and the outcoming partition of vertices defines the classifier. The final partition is derived by exploiting a criterion based on mutual information minimization. Here, we compute the mutual information by using a convenient formulation provided in terms of the $\alpha$-Jensen difference. Once training is completed, in order to associate a confidence level with the classifier decision, a graph-based fuzzy model is constructed. The fuzzification process is based only on topological information of the vertices of the entropic spanning graph. As such, the proposed one-class classifier is suitable also for data characterized by complex geometric structures. We provide experiments on well-known benchmarks containing both feature vectors and labeled graphs. In addition, we apply the method to the protein solubility recognition problem by considering several representations for the input samples. Experimental results demonstrate the effectiveness and versatility of the proposed method with respect to other state-of-the-art approaches.Comment: Extended and revised version of the paper "One-Class Classification Through Mutual Information Minimization" presented at the 2016 IEEE IJCNN, Vancouver, Canad

Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere

Among the various architectures of Recurrent Neural Networks, Echo State Networks (ESNs) emerged due to their simplified and inexpensive training procedure. These networks are known to be sensitive to the setting of hyper-parameters, which critically affect their behaviour. Results show that their performance is usually maximized in a narrow region of hyper-parameter space called edge of chaos. Finding such a region requires searching in hyper-parameter space in a sensible way: hyper-parameter configurations marginally outside such a region might yield networks exhibiting fully developed chaos, hence producing unreliable computations. The performance gain due to optimizing hyper-parameters can be studied by considering the memory--nonlinearity trade-off, i.e., the fact that increasing the nonlinear behavior of the network degrades its ability to remember past inputs, and vice-versa. In this paper, we propose a model of ESNs that eliminates critical dependence on hyper-parameters, resulting in networks that provably cannot enter a chaotic regime and, at the same time, denotes nonlinear behaviour in phase space characterised by a large memory of past inputs, comparable to the one of linear networks. Our contribution is supported by experiments corroborating our theoretical findings, showing that the proposed model displays dynamics that are rich-enough to approximate many common nonlinear systems used for benchmarking

Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings

Mapping complex input data into suitable lower dimensional manifolds is a common procedure in machine learning. This step is beneficial mainly for two reasons: (1) it reduces the data dimensionality and (2) it provides a new data representation possibly characterised by convenient geometric properties. Euclidean spaces are by far the most widely used embedding spaces, thanks to their well-understood structure and large availability of consolidated inference methods. However, recent research demonstrated that many types of complex data (e.g., those represented as graphs) are actually better described by non-Euclidean geometries. Here, we investigate how embedding graphs on constant-curvature manifolds (hyper-spherical and hyperbolic manifolds) impacts on the ability to detect changes in sequences of attributed graphs. The proposed methodology consists in embedding graphs into a geometric space and perform change detection there by means of conventional methods for numerical streams. The curvature of the space is a parameter that we learn to reproduce the geometry of the original application-dependent graph space. Preliminary experimental results show the potential capability of representing graphs by means of curved manifold, in particular for change and anomaly detection problems.Comment: To be published in IEEE IJCNN 201

Magnetism and unusual Cu valency in quadruple perovskites

We study a selection of Cu-containing magnetic quadruple perovskites (CaCu$_{3}$Ti$_{4}$O$_{12}$, LaCu$_{3}$Fe$_{4}$O$_{12}$, and YCu$_{3}$Co$_{4}$O$_{12}$) by ab initio calculations, and show that Cu is in an effective divalent Cu(II)-like state or a trivalent Cu(III) state depending on the choice of octahedral cation. Based on the electronic structure, we also discuss the role of Mott and Zhang-Rice physics in this materials class.Comment: 5 pages, 4 figure

Tetragonal states from epitaxial strain on metal films

The tetragonal states produced by isotropic pseudomorphic epitaxial strain in the (001) plane on a tetragonal phase of a crystal are calculated for V, Ti, Rb, Li, K, Sr from first-principles electronic theory. It is shown that each metal has two tetragonal phases corresponding to minima of the total energy with respect to tetragonal deformations, hence are equilibrium phases, and that the equilibrium phases are separated by a region of inherent instability. The equilibrium phase for any strained tetragonal state can thus be uniquely identified. Lattice constants and relative energies of the two phases and the saddle point between them are tabulated, as well as the tetragonal elastic constants of each phase.Comment: 6 pages, 4 figures, appeared in Phys. Rev. B 57, 1971 (1998). Other related publications can be found at http://www.rz-berlin.mpg.de/th/paper.htm

Intelligence for embedded systems: A methodological approach

Addressing current issues of which any engineer or computer scientist should be aware, this monograph is a response to the need to adopt a new computational paradigm as the methodological basis for designing pervasive embedded systems with sensor capabilities. The requirements of this paradigm are to control complexity, to limit cost and energy consumption, and to provide adaptation and cognition abilities allowing the embedded system to interact proactively with the real world. The quest for such intelligence requires the formalization of a new generation of intelligent systems able to exploit advances in digital architectures and in sensing technologies. The book sheds light on the theory behind intelligence for embedded systems with specific focus on: ·        robustness (the robustness of a computational flow and its evaluation); ·        intelligence (how to mimic the adaptation and cognition abilities of the human brain), ·        the capacity to learn in non-stationary and evolving environments by detecting changes and reacting accordingly; and ·        a new paradigm that, by accepting results that are correct in probability, allows the complexity of the embedded application the be kept under control. Theories, concepts and methods are provided to motivate researchers in this exciting and timely interdisciplinary area. Applications such as porting a neural network from a high-precision platform to a digital embedded system and evaluating its robustness level are described. Examples show how the methodology introduced can be adopted in the case of cyber-physical systems to manage the interaction between embedded devices and physical world.. Researchers and graduate students in computer science and various engineering-related disciplines will find the methods and approaches propounded in Intelligence for Embedded Systems of great interest. The book will also be an important resource for practitioners working on embedded systems and applications

Change Point Methods on a Sequence of Graphs

Given a finite sequence of graphs, e.g., coming from technological, biological, and social networks, the paper proposes a methodology to identify possible changes in stationarity in the stochastic process generating the graphs. In order to cover a large class of applications, we consider the general family of attributed graphs where both topology (number of vertexes and edge configuration) and related attributes are allowed to change also in the stationary case. Novel Change Point Methods (CPMs) are proposed, that (i) map graphs into a vector domain; (ii) apply a suitable statistical test in the vector space; (iii) detect the change --if any-- according to a confidence level and provide an estimate for its time occurrence. Two specific multivariate CPMs have been designed: one that detects shifts in the distribution mean, the other addressing generic changes affecting the distribution. We ground our proposal with theoretical results showing how to relate the inference attained in the numerical vector space to the graph domain, and vice versa. We also show how to extend the methodology for handling multiple change points in the same sequence. Finally, the proposed CPMs have been validated on real data sets coming from epileptic-seizure detection problems and on labeled data sets for graph classification. Results show the effectiveness of what proposed in relevant application scenarios

Histopatología de hojas de tomate inoculadas con Xanthomonas campestris pv vesicatoria

Se estudió la infección de Xanthomonas campestris pv vesicatoria en hojas de tomate por medio de microscopía óptica y electrónica de barrido. Se determinó que la bacteria penetra en el hospedante por los estomas y se localiza y multiplica en las cámaras subestomáticas que sirven como sitios de supervivencia. Adicionalmente, también penetraría por las bases de los tricomas deteriorados. Luego, las bacterias colonizan los espacios intercelulares del parénquima esponjoso y, al alcanzar las cámaras subestomáticas (no invadidas previamente), se multiplican abundantemente y son expulsadas, aglutinadas en un sustancia de naturaleza mucosa, dando origen a nuevas fuentes de inóculo. Tras la inoculación, se notó una mayor concentración de células bacterianas en la cara inferior de las hojas. Los estomas y las cámaras subestomáticas fueron el nicho ecológico preferido por el patógeno para la posterior colonización del hospedante