Preface

In recent years the involvement of Information Technology in business, governments, and education has increased dramatically. More and more research works have been conducted in different areas of Information Technology such as Artificial Intelligence, Database Managements, Algorithms, Web Technologies, Computer Graphics, Networks, etc. In recognizing the importance and major advances, Information Technology has been chosen to be the theme of this special issue of the Information Science Journal

Asymptotically optimal sequential anomaly identification with ordering sampling rules

The problem of sequential anomaly detection and identification is considered in the presence of a sampling constraint. Specifically, multiple data streams are generated by distinct sources and the goal is to quickly identify those that exhibit ``anomalous'' behavior, when it is not possible to sample every source at each time instant. Thus, in addition to a stopping rule, which determines when to stop sampling, and a decision rule, which indicates which sources to identify as anomalous upon stopping, one needs to specify a sampling rule that determines which sources to sample at each time instant. The focus of this work is on ordering sampling rules, which sample the data sources, among those currently estimated as anomalous (resp. non-anomalous), for which the corresponding local test statistics have the smallest (resp. largest) values. It is shown that with an appropriate design, which is specified explicitly, an ordering sampling rule leads to the optimal expected time for stopping, among all policies that satisfy the same sampling and error constraints, to a first-order asymptotic approximation as the false positive and false negative error rates under control both go to zero. This is the first asymptotic optimality result for ordering sampling rules when multiple sources can be sampled per time instant. Moreover, this is established under a general setup where the number of anomalies is not required to be a priori known. A novel proof technique is introduced, which unifies different versions of the problem regarding the homogeneity of the sources and prior information on the number of anomalies

Orienteering in Knowledge Spaces: The Hyperbolic Geometry of Wikipedia Mathematics

In this paper we show how the coupling of the notion of a network with directions with the adaptation of the four-point probe from materials testing gives rise to a natural geometry on such networks. This four-point probe geometry shares many of the properties of hyperbolic geometry wherein the network directions take the place of the sphere at infinity, enabling a navigation of the network in terms of pairs of directions: the geodesic through a pair of points is oriented from one direction to another direction, the pair of which are uniquely determined. We illustrate this in the interesting example of the pages of Wikipedia devoted to Mathematics, or “The MathWiki.” The applicability of these ideas extends beyond Wikipedia to provide a natural framework for visual search and to prescribe a natural mode of navigation for any kind of “knowledge space” in which higher order concepts aggregate various instances of information. Other examples would include genre or author organization of cultural objects such as books, movies, documents or even merchandise in an online store

Generating random AR(p) and MA(q) Toeplitz correlation matrices

AbstractMethods are proposed for generating random (p+1)×(p+1) Toeplitz correlation matrices that are consistent with a causal AR(p) Gaussian time series model. The main idea is to first specify distributions for the partial autocorrelations that are algebraically independent and take values in (−1,1), and then map to the Toeplitz matrix. Similarly, starting with pseudo-partial autocorrelations, methods are proposed for generating (q+1)×(q+1) Toeplitz correlation matrices that are consistent with an invertible MA(q) Gaussian time series model. The density can be uniform or non-uniform over the space of autocorrelations up to lag p or q, or over the space of autoregressive or moving average coefficients, by making appropriate choices for the densities of the (pseudo)-partial autocorrelations. Important intermediate steps are the derivations of the Jacobians of the mappings between the (pseudo)-partial autocorrelations, autocorrelations and autoregressive/moving average coefficients. The random generating methods are useful for models with a structured Toeplitz matrix as a parameter

Electric and magnetic aspects of gravitational theories

This thesis deals with the construction of conserved charges for asymptotically flat spacetimes at spatial infinity in four spacetime dimensions in a hopefully pedagogical way. As a first motivation of this work, it highlights the difficulties one encounters when trying to understand the gravitational duality, present at the linearized level, in the full non-linear Einstein's theory or even just in an asymptotic regime of it. In the first part, we restrict the discussion to the Noetherian surface charges, called "electric charges", and study the existence of a larger phase space, than previously known in the literature, where the awkward parity boundary conditions, firstly imposed by T. Regge and C. Teitelboim, are relaxed. In the absence of these parity conditions, we show how the Einstein-Hilbert action is a correct variational principle when it is supplemented by an anomalous counter-term and construct conserved and finite charges associated to the larger asymptotic symmetry group. The second and third parts focus on the magnetic information obtained through gravitational duality. As this duality is only known at the linearized level, asymptotic linearity is implicitly assumed at spatial infinity. In the second part, gravitational duality for the linearized gravity theory is reviewed and ten dual Poincar\'e charges, or topological "magnetic" charges, are constructed \`a la Abbott-Deser. The last part explains how the NUT charge N, gravitational dual of the mass M and present in the BPS bound of the supersymmetric charged Taub-NUT black hole, copes with the N=2 superalgebra. This is achieved through a complexification of the Witten-Nester 2-form.Comment: 220 pages, 3 figure

The 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2020)

 Ensuring food security has become a challenge in Sub-Saharan Africa (SSA) due to combined effects of climate change, high population growth, and relying on rainfed farming. Governments are establishing shared irrigation infrastructure for smallholder farmers as part of the solutions for food security. However, the irrigated farms often failed to achieve the expected crop yield. This is partly due to lack of water management system in the irrigation infrastructure. In this work, IoT-based irrigation management system is proposed after investigating problems of irrigated farmlands in three SSA countries, Ethiopia, Kenya, and South Africa as case studies. Resource-efficient IoT architecture is developed that monitors soil, microclimate and water parameters and performs appropriate irrigation management. Indigenous farming and expert knowledge, regional weather information, crop and soil specific characteristics are also provided to the system for informed-decision making and efficient operation of the irrigation management system. In SSA, broadband connectivity and cloud services are either unavailable or expensive. To tackle these limitations, data processing, network management and irrigation decisions and communication to the farmers are carried out locally, without the involvement of any back-end servers. Furthermore, the use of green energy sources and resource-aware intelligent data analysis algorithm is studied. The intelligent data analysis helps to discover new knowledge that support further development of agricultural expert knowledge. The proposed IoT-based irrigation management system is expected to contribute towards long term and sustainable high crop yield with minimum resource consumption and impact to the biodiversity around the case farmlands.</p

Sparse Model Identification and Learning for Ultra-high-dimensional Additive Partially Linear Models

The additive partially linear model (APLM) combines the flexibility of nonparametric regression with the parsimony of regression models, and has been widely used as a popular tool in multivariate nonparametric regression to alleviate the "curse of dimensionality". A natural question raised in practice is the choice of structure in the nonparametric part, that is, whether the continuous covariates enter into the model in linear or nonparametric form. In this paper, we present a comprehensive framework for simultaneous sparse model identification and learning for ultra-high-dimensional APLMs where both the linear and nonparametric components are possibly larger than the sample size. We propose a fast and efficient two-stage procedure. In the first stage, we decompose the nonparametric functions into a linear part and a nonlinear part. The nonlinear functions are approximated by constant spline bases, and a triple penalization procedure is proposed to select nonzero components using adaptive group LASSO. In the second stage, we refit data with selected covariates using higher order polynomial splines, and apply spline-backfitted local-linear smoothing to obtain asymptotic normality for the estimators. The procedure is shown to be consistent for model structure identification. It can identify zero, linear, and nonlinear components correctly and efficiently. Inference can be made on both linear coefficients and nonparametric functions. We conduct simulation studies to evaluate the performance of the method and apply the proposed method to a dataset on the Shoot Apical Meristem (SAM) of maize genotypes for illustration

Mathematics, cognition, and you!

In what follows I argue for an epistemic bridge principle that allows us to move from real mathematics to ideal mathematics (and back again) without losing anything that is characteristic of either methodological class. Mathematics is a collection of actions performed in the pursuit of mathematical understanding. The actions are the processes of proving claims that come in such forms as lemmas, theorems, or conjectures. These proofs can be accomplished through a variety of means including (though not limited to) logical deduction, geometric intuition, diagramming, or computer assistance. What is common to each of these is the characteristic of being convincing to a sound mathematical mind. Even though what in particular makes each of these methods of proof convincing differs, that they are convincing is enough to usher forth mathematical understanding. The chapters of this dissertation explore (i) a new naturalistic metaphysics for under- standing “where mathematics comes from”, (ii) recent psychological findings in the the nature of mathematical reasoning, (iii) the concatenation of two historical forms of reasoning (real and ideal), (iv) the possibility of an epistemic bridge between real and ideal methods, and (v) the implication of this new bridge principle for the long-standing concern that Go ̈del’s Incompleteness Theorems shake the foundation of modern mathematics