Optimal and Efficient Learning In Classification

We study a natural extension of classical empirical risk minimization, where the hypothesis space is a random subspace of a given space. In particular, we consider possibly data dependent subspaces spanned by a random subset of the data, recovering as a special case Nyström approaches for kernel methods. Considering random subspaces naturally leads to computational savings, but the question is whether the corresponding learning accuracy is degraded. These statistical-computational tradeoffs have been recently explored for the least squares loss and self-concordant loss functions, such as the logistic loss. Here, we work to extend these results to convex Lipschitz loss functions, that might not be smooth, such as the hinge loss used in support vector machines. This unified analysis requires developing new proofs, that use different technical tools to establish fast rates. Our main results show the existence of different settings, depending on how hard the learning problem is, for which computational efficiency can be improved with no loss in performance. The analysis is also specialized to smooth loss functions. In the final part of the paper we convert our surrogates risk bounds into classification error bounds and compare the choice of hinge loss with respect to square loss

Learning Experiences of Nursing Students in Online RN-BSN Programs in the United States

The complex profession of nursing requires the practitioner to be knowledgeable, skilled, and autonomous. It is estimated in the USA 36.8 percent of nurses hold degrees at the baccalaureate level and above. Evidence indicating baccalaureate-degreed nurses are better prepared to meet the demands of this complex profession has led to policymakers and practice leaders touting the importance of this degree. RNs are seeking the BSN degree in increasing numbers. However, due to family, work, and personal time constraints, traditional means of education may not be a viable option for many, and distance learning provides an alternative for students who might not be able to pursue degrees. The number of RN-BSN online education programs has increased significantly over the last decade. There is a great deal of research regarding the efficacy of these programs but little research exists regarding the learning experiences RN-BSN students have in these programs. The intent of this research study was to examine the structure of the learning experiences of RN-BSN students participating in online education programs. To accomplish this goal, data were obtained from 11 interviews with RN-BSN online education students about their perceptions and assumptions before beginning their coursework, and their perceptions and actual experiences, as well as, perceived challenges after experiencing the programs

Online Learning, Physics and Algorithms

In recent years, we have witnessed an increasing cross-fertilization between the fields of computer science, statistics, optimization and the statistical physics of learning. The area of machine learning is at the interface of these subjects. We start with an analysis in the statistical physics of learning, where we analyze some properties of the loss landscape of simple models of neural networks using the computer science formalism of Constraint Satisfaction Problems. Some of the techniques we employ are probabilistic, but others have their root in the studies of disorder systems in the statistical physics literature. After that, we focus mainly on online prediction problems, which were initially investigated in statistics but are now very active areas of research also in computer science and optimization, where they are studied in the adversarial case through the lens of (online) convex optimization. We are particularly interested in the cooperative setting, where we show that cooperation improves learning. More specifically, we give efficient algorithms and unify previous works under a simplified and more general framework

Stability, Flying Qualities and Parameter Estimation of a Twin-Engine CS23/FAR23 Certified Light Aircraft

This paper presents some results of the flight test campaign conducted on the Tecnam P2006T aircraft, on the occasion of its certification process. This twin-engine propeller airplane is certified in the category CS23/FAR23. Many preliminary flight tests on a prototype of this light aircraft were aimed at optimizing performances and flight qualities. These experiences led to the application of two winglets to the original wing. The final configuration was extensively tested for the purpose of CS23 certification achievement. At the same time the airplane model, through a dedicated set of flight maneuvers, has been characterized by means of parameter estimation studies. The longitudinal and lateral-directional response mode were assessed and quantified. All the aircraft stability derivatives have been estimated from the acquired flight data using the well-known Maximum Likelihood Method (MLM). Some estimated stability derivatives have been also compared with the corresponding values extracted from leveled flight tests and from wind-tunnel tests performed on a scaled model of the aircraft

Moduli spaces of gauge theories in 3 dimensions.

The objective of this thesis is to study the moduli spaces of pairs of mirror theories in 3 dimensions with N = 4. The original conjecture of 3d mirror symmetry was motivated by the fact that in these pairs of theories the Higgs and Coulomb branches are swapped. After a brief introduction to supersymmetry we will first focus on the Higgs branch. This will be investigated through the Hilbert series and the plethystic program. The methods used for the Higgs branch are very well known in literature, more difficult is the case of the Coulomb branch since it receives quantum corrections. We will explain how it is parametrized in term of monopole operators and having both Higgs and Coulomb branches for theories with different gauge groups we will be able to show how mirror symmetry works in the case of ADE theories. We will show in which cases these Yang- Mills vacua are equivalent to one instanton moduli spaces.ope

Numerical aerodynamic analysis on a trapezoidal wing with high lift devices: a comparison with experimental data

The aerodynamic analysis on the DLR-F11 high lift configuration model has been performed on the supercomputing grid infrastructure SCoPE of the University of Naples ???Federico II???. The model geometry is representative of a wide-body commercial aircraft, which experimental investigations at high Reynolds number have been performed at the European Transonic Wind-tunnel (ETW) for the 2nd AIAA High Lift Prediction Workshop. The commercial CAE package Star-CCM+ has been used to solve the Reynolds-averaged Navier-Stokes equations. Inviscid, viscous incompressible, and compressible analyses have been performed with mesh refinement. The inviscid calculations have been used to assess how far is the eulerian prediction from experimental data. Viscous and compressible calculations have been realized using the Spalart-Allmaras turbulence model at 0.175 Mach number and 15.1 million Reynolds number. Results show that the simple Spalart-Allmaras turbulence model can predict quite accurately the stall and post-stall behaviour, getting the angle of stall and underestimating the maximum lift coefficient by less than 5%. Comparisons among numerical and experimental pressure coefficients at several sections are also shown. Finally, the stall path is described

Fabric and clay activity in soil water retention behaviour

Modelling the water retention behaviour requires proper understanding of all the processes which affect the amount of water stored in the pore network, depending on the soil state and the soil history. Traditionally, in many applications a single water content – suction curve is used. This approach limits the applicability of the retention data to practical cases, especially when fine grain soils are dealt with, when the deformability and activity of the clay fraction significantly affect the interaction with water. On the other side, water retention is being recognised more and more as a fundamental information in the description of the mechanical response of the soil, as it provides the key connection to the partial volumetric strains in a deformation process. With reference to the work performed at the Politecnico di Milano in the last years, a contribution on the understanding and modelling the coupled water retention- mechanical response in deformable soils is presented. The contribution aims to: (i) summarise the mechanisms which contribute to water retention; (ii) point out the role played by an evolving fabric and the fluid properties on water retention; and (iii) provide an overview on some of the consequences of evolving water retention properties on the mechanical behaviour

Some remarks on single- and double-porosity modeling of coupled chemo-hydro-mechanical processes in clays

Active clays are known to possess an aggregated structure, which justifies the use of double-porosity models to reproduce their behavior. Simulation of chemo-mechanical processes requires instead the introduction of a relevant number of coupled mechanical and transport laws. It follows that double porosity models for coupled chemo-hydro-mechanical require a relevant number of parameters, which are twice those needed by single porosity models. The aim of this work is to evaluate the consequences of using single- and double-porosity frameworks to simulate the transient chemo-mechanical processes in active clays, showing how models based on simple microstructural considerations can help in performing simulations which are a reasonable trade-off between simplicity and accuracy. In particular with single porosity models, it might be necessary introducing parameters having a doubtful meaning to describe adsorption-desorption processes. This type of assumption is not required by double porosity models. While for compacted clays these conclusions are corroborated with microstructural observations, the same hold also when reproducing the behavior of an active clay at a remolded condition. In this latter case the delay of swelling with respect to desalinization, typical of remolded conditions, was satisfactorily reproduced only with double porosity models

Complex Shepard Operators and Their Summability

In this paper, we construct the complex Shepard operators to approximate continuous and complex-valued functions on the unit square. We also examine the effects of regular summability methods on the approximation by these operators. Some applications verifying our results are provided. To illustrate the approximation theorems graphically we consider the real or imaginary part of the complex function being approximated and also use the contour lines of the modulus of the function