Introduction and MotivationContext of inconsistencies in federated modelsChallenge of identifying inconsistenciesFrame this problem as a graph reasoning problem    - graphs are generic   - similarities between graphs allow one to identify equivalences and inconsistencies.What are other applications of inexact graph reasoning problems? -- provides a broader motivationRelated work- on inconsistency management- on inexact reasoning with graphsApproach- development of an algorithm for inexact reasoning with graphs- characterization of algorithm performance

INTRODUCTION - Current practices for developing Materials Data Analytics Toolsets are highly localized within a few individual groups resulting in major inefficiency (unnecessary duplication of codes, inadequate verification and validation of multiple instantiations of code, not engaging the right talent for the right task, etc.) - Community development and sharing of code repositories has been successful in certain science communities. Advantages of this approach include increased e-teaming and e-collaborations, vastly improved code hygiene, promotion of open science, rapid verification and validation, and a dramatic increase in overall productivity. All of these are key ingredients for realizing a major bump (scale-up) in innovation in sciences and technology - Community development of a materials data analytics toolset can significantly change the landscape of materials innovation through the emergence and adoption of the cross-disciplinary field of Materials Informatics; help realize the vision outlined in MGI and ICME. This is probably the only practical way to get materials scientists and computer scientists to establish meaningful, mutually beneficial, and highly productive collaborations. - Materialas - The Materials Project is currently powered by pyMatgen - Integrated tools in python from first principles (DFT and MD) for thermodynamic and electrical quantities with applications in functional materials. - Open Quantum Materials Database (OQMD) - Open source data repository for phase diagrams and electronic ground states computing using DFT. OQMD also offers visualization tools and a Python API. - OpenKIM for atomic potentials - Citrine ? All of the above are essentilaly data/information repositories. Maybe we can break this down as Data/Information Repositories, Code Repositories, and e-collaboration platforms. Perhaps not talk too much about the first and last - but just mention some names and provide some links and focus more of your review and paper on the second one? ##ICME - PRedictive Integrated Structural Materials Science (PRISMS) http://www.prisms-center.org/#/home - ICME tool and datastorage for the Metals community focusing on microstructure evolution and mechanical properties. Presence on github https://github.com/prisms-center. C++ and Python, first public release in Aug/Sept of 2015. - Questeck ? - I do not think they do anything in the "open" arena - In terms of code repositories - I think there are also codes dedicated to classes of problems. Would LAMMPS fall into this category? How about SPARRKS and MOOSE. DREAM-3D claims to be a code repository as well. In Europe there in DAMASK from Max Planck in Dusseldorf. Data Storage - NIST Data Gateway http://srdata.nist.gov/gateway/gateway?dblist=0 - Over 100 free and paid querirbale on-line materials databases. Some examples of data are atomic structure, thermodynamics, kinetics, fundamental physical constants, x-ray spectroscopy, and more. - NIST D Space - - NIST Materials Data Curation Systems (MDCS) - General nline databased for materials data - Computational Materials Data Network (CMD Network) http://www.asminternational.org/web/cmdnetwork/home - Founded by ASM International, CMD Network is a data focus project that facilitates data warehousing, sharing and collaborations. - Granta ? - MatWeb http://www.matweb.com/ - Database containing materials properties for over 100,000 materials. - Materials Atlas (link not working) - Database storing 3D results from experiments and simulations. Structure Tools - Dream3D - Software tool that does synthetic microstructures generation, image processing and mesh creation for finite element. - PyMKS aims to seed and nurture an emergent user group in the materials data analytics for establishing homogenization and localization linkages by leveraging open source scientific and machine learning packages in Python. The approach used to develop PyMKS as well as several examples are presented. This paper is a call to others interested in participating in this open science activity.

INTRODUCTION - Current practices for developing Materials Data Analytics Tool-Sets are highly localized within a few individual groups resulting in major inefficiency (unnecessary duplication of codes, inadequate verification and validation of multiple instantiations of code, not engaging the right talent for the right task, etc.) - Community development and sharing of code repositories has been successful in certain science communities. Advantages of this approach include increased e-teaming and e-collaborations, vastly improved code hygiene, promotion of open science, rapid verification and validation, and a dramatic increase in overall productivity. - Community development of a materials data analytics tool-set can significantly change the landscape of the emerging cross-disciplinary field of Materials Informatics and address the critical needs outlined in MGI and ICME. This is probably the only practical way to get materials scientists and computer scientists to establish meaningful, mutually beneficial, and highly productive collaborations. Current efforts in materials science are: - PyMatgen http://pymatgen.org/ - Other projects http://materials-informatics-lab.github.io/material-hammers/ - PyMKS aims to seed and nurture an emergent user group in the materials data analytics for establishing homogenization and localization linkages by leveraging open source scientific and machine learning packages in Python. The approach used to develop PyMKS as well as several examples are presented. This paper is a call to others interested in participating in this open science activity.

This paper presents a novel method of digitizing notes and/or diagrams that are drawn on a sheet of paper. Most modern phones contain magnetometers that output the strength of the surrounding magnetic field in the x, y, and z direction. If a magnet is brought closer to the device (and the magnetometer), the values from the magnetometer will be altered. By determining the change in the altered magnetic field, we can determine the position of the magnet. With the position, we can determine the location of the magnet relative to the phone. We have created a magnet based pen (MagPen) and built an android application that allows users to write notes on a sheet of paper while their mobile phone automatically digitizes. Users will also be able to perform certain actions using the button on the pen and select various pen attributes using the MagPen. AUTHOR KEYWORDS Pen input, magnetometer, mobile devices, notes ACM CLASSIFICATION KEYWORDS H.5.2 [Information interfaces and presentation]: User Interfaces: Input Devices and Strategies.

This paper presents an alternative method of interacting with mobile devices. Most modern phones contain magnetometers that output the strength of the surrounding magnetic field in the x, y, and z direction. If a magnet is brought closer to the device (and the magnetometer), the values from the magnetometer will be altered. By determining the change in the altered magnetic field, we can determine the position of the magnet. With the position, we can determine the location of the magnet relative to the screen and detect gestures above the screen in 3D space. We will be building a photo browser & drawing application that will contain implementation for magnet based interactions. Users can flip through, select, and annotate/draw on photos using external magnets. AUTHOR KEYWORDS Gesture-based interaction, magnetometer, mobile devices ACM CLASSIFICATION KEYWORDS H.5.2 [Information interfaces and presentation]: User Interfaces: Input Devices and Strategies.

Material science is experiencing pressure to deliver new and improvement materials to the marketplace in half the time and at half the cost.[ref2] To reach this goal, a hurdle that will need to be overcome is the increasing dissonance between the strength of material science in generating massive stores of empirical and simulated physical information and its weakness to utilize the data at scale. Materials science is challenged by the 3 V’s of Big Data as the Volumes of the unstructured datasets are reaching terabyte sizes, as high performance computing is increasing the Velocity which information is generated, and with the Variety of information being generated by conduits in the field from both experiment and simulation. The current subjective, undocumented, and ad-hoc data analysis tools cannot be scaled to suit these needs. New data science techniques will need to be explored to offer objective statistical representations of materials science information.[ref] Microstructure Informatics is an emerging framework that provides a growing suite of data science tools to extract bi-directional structure-property/processing relationships for most classes of materials science information. Microstructure Informatics uses digital signal processing and advanced statistics to properly encode material information into useful forms for machine learning, data structures, and algorithms that extract added value from the information. In microstructure informatics, the material structure, or microstructure, is considered to be the independent of the dependent property or processing response. Spatial statistics (i.e. Pair Correlations, N-Point statistics) are commonly used statistical utilities as it provides an objective statistical description of the material structure. The following successful case studies have been illustrated to shown the effective of spatial statistics in (1) determining objective microstructure comparisons in heat treated α-β Titanium [ref], (2) building regression models for the homogenized structure-property connections between the internal structure of fuel cell materials and their diffusivity [ref], and (3) determining the variance of properties associated with individual microstructures [ref]. This paper discusses fast algorithms to encode material structure information using parameterized basis function and spatial correlation functions. The concept of the microstructure function is employed to parameterize the material information and produce a digital microstructure signal; encoding can be performed on most classes of experimental and simulated material structure information.[ref] The digital signals are convolved using embarrassingly parallel Fast Fourier methods to compute the spatial statistics of the digitized microstructure. The following properties of the spatial correlation functions make them a worthy candidate for an objective material structure descriptor: several widely used statistical metrics are embedded in the correlations such as volume fraction [ref] and specific surface area [ref]; for binary images, they contain information about the original material structure information within a translation [ref]; and they can describe most types of materials science information in raw signal is processed appropriately. N case studies will be presented to illustrate the generality of the technique when applied to things. This paper will begin with a discussion on classifications of materials science information and their proposed conversions to a digital signal using the microstructure function. Once the raw material structure information is digitized, they will form the foundations that allow spatial statistics to be computed using fast, scalable FFT algorithms. N case studies will be shown to express the diversity and general applicability of the spatial correlation functions in structure-structure comparison.