A Systematic Review of Strong Gravitational Lens Modeling Software

Despite expanding research activity in gravitational lens modeling, there is no particular software which is considered a standard. Much of the gravitational lens modeling software is written by individual investigators for their own use. Some gravitational lens modeling software is freely available for download but is widely variable with regard to ease of use and quality of documentation. This review of 13 software packages was undertaken to provide a single source of information. Gravitational lens models are classified as parametric models or non-parametric models, and can be further divided into research and educational software. Software used in research includes the GRAVLENS package (with both gravlens and lensmodel), Lenstool, LensPerfect, glafic, PixeLens, SimpLens, Lensview, and GRALE. In this review, GravLensHD, G-Lens, Gravitational Lensing, lens and MOWGLI are categorized as educational programs that are useful for demonstrating various aspects of lensing. Each of the 13 software packages is reviewed with regard to software features (installation, documentation, files provided, etc.) and lensing features (type of model, input data, output data, etc.) as well as a brief review of studies where they have been used. Recent studies have demonstrated the utility of strong gravitational lensing data for mass mapping, and suggest increased use of these techniques in the future. Coupled with the advent of greatly improved imaging, new approaches to modeling of strong gravitational lens systems are needed. This is the first systematic review of strong gravitational lens modeling software, providing investigators with a starting point for future software development to further advance gravitational lens modeling research

Supervised learning on graphs of spatio-temporal similarity in satellite image sequences

High resolution satellite image sequences are multidimensional signals composed of spatio-temporal patterns associated to numerous and various phenomena. Bayesian methods have been previously proposed in (Heas and Datcu, 2005) to code the information contained in satellite image sequences in a graph representation using Bayesian methods. Based on such a representation, this paper further presents a supervised learning methodology of semantics associated to spatio-temporal patterns occurring in satellite image sequences. It enables the recognition and the probabilistic retrieval of similar events. Indeed, graphs are attached to statistical models for spatio-temporal processes, which at their turn describe physical changes in the observed scene. Therefore, we adjust a parametric model evaluating similarity types between graph patterns in order to represent user-specific semantics attached to spatio-temporal phenomena. The learning step is performed by the incremental definition of similarity types via user-provided spatio-temporal pattern examples attached to positive or/and negative semantics. From these examples, probabilities are inferred using a Bayesian network and a Dirichlet model. This enables to links user interest to a specific similarity model between graph patterns. According to the current state of learning, semantic posterior probabilities are updated for all possible graph patterns so that similar spatio-temporal phenomena can be recognized and retrieved from the image sequence. Few experiments performed on a multi-spectral SPOT image sequence illustrate the proposed spatio-temporal recognition method

Factors Influencing Progressive Failure Analysis Predictions for Laminated Composite Structure

Progressive failure material modeling methods used for structural analysis including failure initiation and material degradation are presented. Different failure initiation criteria and material degradation models are described that define progressive failure formulations. These progressive failure formulations are implemented in a user-defined material model for use with a nonlinear finite element analysis tool. The failure initiation criteria include the maximum stress criteria, maximum strain criteria, the Tsai-Wu failure polynomial, and the Hashin criteria. The material degradation model is based on the ply-discounting approach where the local material constitutive coefficients are degraded. Applications and extensions of the progressive failure analysis material model address two-dimensional plate and shell finite elements and three-dimensional solid finite elements. Implementation details are described in the present paper. Parametric studies for laminated composite structures are discussed to illustrate the features of the progressive failure modeling methods that have been implemented and to demonstrate their influence on progressive failure analysis predictions

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Parametric Macromodels of Differential Drivers with Pre-Emphasis

This paper discusses the extraction of behavioral models of differential drivers with pre-emphasis for the assessment of signal integrity and electromagnetic compatibility effects in multigigabit data transmission systems. A suitable model structure is derived and the procedure for its estimation from port transient waveforms is illustrated. The proposed methodology is an extension of the macromodeling based on parametric relations applied to plain differential drivers. The obtained models preserve the accuracy and efficiency strengths of behavioral parametric macromodels for conventional devices. A realistic application example involving a high-speed communication path and a 3.125 Gb/s commercial driver model with pre-emphasis is presente

A continues multi-material toolpath planning for tissue scaffolds with hollowed features

This paper presents a new multi-material based toolpath planning methodology for porous tissue scaffolds with multiple hollowed features. Ruled surface with hollowed features generated in our earlier work is used to develop toolpath planning. Ruling lines are reoriented to enable continuous and uniform size multi-material printing through them in two steps. Firstly, all ruling lines are matched and connected to eliminate start and stops during printing. Then, regions with high number of ruling lines are relaxed using a relaxation technique to eliminate over deposition. A novel layer-by-layer deposition process is progressed in two consecutive layers: The first layer with hollow shape based zigzag pattern and the next layer with spiral pattern deposition. Heterogeneous material properties are mapped based on the parametric distances from the hollow features

A Survey of Languages for Specifying Dynamics: A Knowledge Engineering Perspective

A number of formal specification languages for knowledge-based systems has been developed. Characteristics for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects. They have to provide the means to specify a complex and large amount of knowledge and they have to provide the means to specify the dynamic reasoning behavior of a knowledge-based system. We focus on the second aspect. For this purpose, we survey existing approaches for specifying dynamic behavior in related areas of research. In fact, we have taken approaches for the specification of information systems (Language for Conceptual Modeling and TROLL), approaches for the specification of database updates and logic programming (Transaction Logic and Dynamic Database Logic) and the generic specification framework of abstract state machine

The Effect of Incorporating End-User Customization into Additive Manufacturing Designs

In the realm of additive manufacturing there is an increasing trend among makers to create designs that allow for end-users to alter them prior to printing an artifact. Online design repositories have tools that facilitate the creation of such artifacts. There are currently no rules for how to create a good customizable design or a way to measure the degree of customization within a design. This work defines three types of customizations found in additive manufacturing and presents three metrics to measure the degree of customization within designs based on the three types of customization. The goal of this work is to ultimately provide a consistent basis for which a customizable design can be evaluated in order to assist makers in the creation of new customizable designs that can better serve end-user. The types of customization were defined by doing a search of Thingiverse’s online data base of customizable designs and evaluating commonalities between designs. The three types of customization defined by this work are surface, structure, and personal customization. The associated metrics are used to quantify the adjustability of a set of online designs which are then plot against the daily use rate and each other on separate graphs. The use rate data used in this study is naturally biased towards hobbyists due to where the designs used to create the data resides. A preliminary analysis is done on the metrics to evaluate their correlation with design use rate as well as the dependency of the metrics in relation to each other. The trends between the metrics are examined for an idea of how best to provide customizable designs. This work provides a basis for measuring the degree of customization within additive manufacturing design and provides an initial framework for evaluating the usability of designs based on the measured degree of customization relative to the three types of defined customizations