The technology of Incremental Sheet Forming - a brief review of the history

This paper describes the history of Incremental Sheet Forming (ISF) focusing on technological developments. These developments are in general protected by patents, so the paper can also be regarded as an overview of ISF patents in addition to a description of the early history. That history starts with the early work by Mason in 1978 and continues up to the present day. An extensive list of patents including Japanese patents is provided.\ud \ud The overall conclusion is that ISF has received the attention of the world, in particular of the automotive industry, and that most proposed or suspected applications focus on the flexibility offered by the process. Only one patent has been found that is explicitly related to the enhancement of formability. Furthermore, most patents refer to TPIF (Two-Point Incremental Forming) as a process.\ud \ud Besides simply presenting a historical overview the paper can act as an inspiration for the researcher, and present a rough idea of the patentability of new developments

Enhancing Sensitivity Classification with Semantic Features using Word Embeddings

Government documents must be reviewed to identify any sensitive information they may contain, before they can be released to the public. However, traditional paper-based sensitivity review processes are not practical for reviewing born-digital documents. Therefore, there is a timely need for automatic sensitivity classification techniques, to assist the digital sensitivity review process. However, sensitivity is typically a product of the relations between combinations of terms, such as who said what about whom, therefore, automatic sensitivity classification is a difficult task. Vector representations of terms, such as word embeddings, have been shown to be effective at encoding latent term features that preserve semantic relations between terms, which can also be beneficial to sensitivity classification. In this work, we present a thorough evaluation of the effectiveness of semantic word embedding features, along with term and grammatical features, for sensitivity classification. On a test collection of government documents containing real sensitivities, we show that extending text classification with semantic features and additional term n-grams results in significant improvements in classification effectiveness, correctly classifying 9.99% more sensitive documents compared to the text classification baseline

Black hole and de Sitter solutions in a covariant renormalizable field theory of gravity

It is shown that Schwarzschild black hole and de Sitter solutions exist as exact solutions of a recently proposed relativistic covariant formulation of (power-counting) renormalizable gravity with a fluid. The formulation without a fluid is also presented here. The stability of the solutions is studied and their corresponding entropies are computed, by using the covariant Wald method. The area law is shown to hold both for the Schwarzschild and for the de Sitter solutions found, confirming that, for the $\beta=1$ case, one is dealing with a minimal modification of GR.Comment: 7 paages, latex fil

Evolving rules for document classification

We describe a novel method for using Genetic Programming to create compact classification rules based on combinations of N-Grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that because the induced rules are meaningful to a human analyst they may have a number of other uses beyond classification and provide a basis for text mining applications

Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition

Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. When using CAD, there is often a choice for the ordering placed on the variables. This can be important, with some problems infeasible with one variable ordering but easy with another. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we use machine learning (specifically a support vector machine) to select between heuristics for choosing a variable ordering, outperforming each of the separate heuristics.Comment: 16 page

Role of defects in the electronic properties of amorphous/crystalline Si interface

The mechanism determining the band alignment of the amorphous/crystalline Si heterostructures is addressed with direct atomistic simulations of the interface performed using a hierarchical combination of various computational schemes ranging from classical model-potential molecular dynamics to ab-initio methods. We found that in coordination defect-free samples the band alignment is almost vanishing and independent on interface details. In defect-rich samples, instead, the band alignment is sizeably different with respect to the defect-free case, but, remarkably, almost independent on the concentration of defects. We rationalize these findings within the theory of semiconductor interfaces.Comment: 4 pages in two-column format, 2 postscript figures include

An efficient k.p method for calculation of total energy and electronic density of states

An efficient method for calculating the electronic structure in large systems with a fully converged BZ sampling is presented. The method is based on a k.p-like approximation developed in the framework of the density functional perturbation theory. The reliability and efficiency of the method are demostrated in test calculations on Ar and Si supercells

Solutions for f(R) gravity coupled with electromagnetic field

In the presence of external, linear / nonlinear electromagnetic fields we integrate f(R) \sim R+2{\alpha}\surd(R+const.) gravity equations. In contrast to their Einsteinian cousins the obtained black holes are non-asymptotically flat with a deficit angle. In proper limits we obtain from our general solution the global monopole solution in f(R) gravity. The scale symmetry breaking term adopted as the nonlinear electromagnetic source adjusts the sign of the mass of the resulting black hole to be physical.Comment: 7 pages no figure, final version for publication in European Physical Journal

Conserved Quantities in f(R)f(R) Gravity via Noether Symmetry

This paper is devoted to investigate $f(R)$ gravity using Noether symmetry approach. For this purpose, we consider Friedmann Robertson-Walker (FRW) universe and spherically symmetric spacetimes. The Noether symmetry generators are evaluated for some specific choice of $f(R)$ models in the presence of gauge term. Further, we calculate the corresponding conserved quantities in each case. Moreover, the importance and stability criteria of these models are discussed.Comment: 14 pages, accepted for publication in Chin. Phys. Let