Data Management Systems (DMS): Complex data types study. Volume 1: Appendices A-B. Volume 2: Appendices C1-C5. Volume 3: Appendices D1-D3 and E

Two categories were chosen for study: the issue of using a preprocessor on Ada code of Application Programs which would interface with the Run-Time Object Data Base Standard Services (RODB STSV), the intent was to catch and correct any mis-registration errors of the program coder between the user declared Objects, their types, their addresses, and the corresponding RODB definitions; and RODB STSV Performance Issues and Identification of Problems with the planned methods for accessing Primitive Object Attributes, this included the study of an alternate storage scheme to the 'store objects by attribute' scheme in the current design of the RODB. The study resulted in essentially three separate documents, an interpretation of the system requirements, an assessment of the preliminary design, and a detailing of the components of a detailed design

Interpreting Adversarially Trained Convolutional Neural Networks

We attempt to interpret how adversarially trained convolutional neural networks (AT-CNNs) recognize objects. We design systematic approaches to interpret AT-CNNs in both qualitative and quantitative ways and compare them with normally trained models. Surprisingly, we find that adversarial training alleviates the texture bias of standard CNNs when trained on object recognition tasks, and helps CNNs learn a more shape-biased representation. We validate our hypothesis from two aspects. First, we compare the salience maps of AT-CNNs and standard CNNs on clean images and images under different transformations. The comparison could visually show that the prediction of the two types of CNNs is sensitive to dramatically different types of features. Second, to achieve quantitative verification, we construct additional test datasets that destroy either textures or shapes, such as style-transferred version of clean data, saturated images and patch-shuffled ones, and then evaluate the classification accuracy of AT-CNNs and normal CNNs on these datasets. Our findings shed some light on why AT-CNNs are more robust than those normally trained ones and contribute to a better understanding of adversarial training over CNNs from an interpretation perspective.Comment: To apper in ICML1

Quantum Darwinism and non-Markovian dissipative dynamics from quantum phases of the spin-1/2 XX model

Quantum Darwinism explains the emergence of a classical description of objects in terms of the creation of many redundant registers in an environment containing their classical information. This amplification phenomenon, where only classical information reaches the macroscopic observer and through which different observers can agree on the objective existence of such object, has been revived lately for several types of situations, successfully explaining classicality. We explore quantum Darwinism in the setting of an environment made of two level systems which are initially prepared in the ground state of the XX model, which exhibits different phases; we find that the different phases have different ability to redundantly acquire classical information about the system, being the "ferromagnetic phase" the only one able to complete quantum Darwinism. At the same time we relate this ability to how non-Markovian the system dynamics is, based on the interpretation that non-Markovian dynamics is associated to back flow of information from environment to system, thus spoiling the information transfer needed for Darwinism. Finally, we explore mixing of bath registers by allowing a small interaction among them, finding that this spoils the stored information as previously found in the literature

Inner Classes and Virtual Types

This paper studies the interplay between inner classes and virtual types. The combination of these two concepts can be observed in object-oriented languages like Beta or Scala. This study is based on a calculus of classes and objects composed of a very limited number of constructs. For example the calculus has neither methods nor class constructors. Instead it has a more general concept of abstract inheritance which lets a class extend an arbitrary object. Thanks to an interpretation of terms as types the calculus also unifies type fields and term fields. The main contribution of this work is to show that typing virtual types in the presence of inner classes requires some kind of alias analysis and to formalize this mechanism with a simple calculus

A prototype expert system for interpretation of remote sensing image data

Automated image interpretation systems of remotely sensed images are of great help in the present scenario of growing applications. In this paper, we have critically studied visual interpretation processes for urban land cover and land use information. It is observed that the core activity of interpretation can be described as plausible combinations of pieces of evidential information from various sources such as images, collateral data, experiential knowledge and pragmatics. Interpretation keys for the interpretation of standard false colour composites are considered to be tone/colour, pattern, texture, size, shape, association, relief and season. These interpretation keys encompass the spectral, spatial and temporal knowledge required for image interpretation. Our focus is on a knowledge-based approach for interpretation of standard false colour composites (fcc). Basic information required for a knowledge-based approach is of four types viz., spectral, spatial, temporal and heuristic. Generic classes and subclasses of image objects are identified for the land use/land cover theme. Logical image objects are conceptualised as region/area, line and point objects. An object-oriented approach for the representation of spectral and spatial knowledge has been adopted. Heuristic information is stored in rules. The Dempster-Shafer theory of evidence is used to combine evidence from various interpretation keys for identification of generic class and subclass of a logical image object. Analysis of some Indian Remote Sensing Satellite images has been done using various basic probability assignments in combination with learning. Explanation facility is provided by tracing the rules fired in the sequence

A feature extraction software tool for agricultural object-based image analysis

A software application for automatic descriptive feature extraction from image-objects, FETEX 2.0, is presented and described in this paper. The input data include a multispectral high resolution digital image and a vector file in shapefile format containing the polygons or objects, usually extracted from a geospatial database. The design of the available descriptive features or attributes has been mainly focused on the description of agricultural parcels, providing a variety of information: spectral information from the different image bands; textural descriptors of the distribution of the intensity values based on the grey level co-occurrence matrix, the wavelet transform and a factor of edgeness; structural features describing the spatial arrangement of the elements inside the objects, based on the semivariogram curve and the Hough transform; and several descriptors of the object shape. The output file is a table that can be produced in four alternative formats, containing a vector of features for every object processed. This table of numeric values describing the objects from different points of view can be externally used as input data for any classification software. Additionally, several types of graphs and images describing the feature extraction procedure are produced, useful for interpretation and understanding the process. A test of the processing times is included, as well as an application of the program in a real parcel-based classification problem, providing some results and analyzing the applicability, the future improvement of the methodologies, and the use of additional types of data sets. This software is intended to be a dynamic tool, integrating further data and feature extraction algorithms for the progressive improvement of land use/land cover database classification and agricultural database updating processes. © 2011 Elsevier B.V.The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion and the FEDER in the framework of the Project CGL2009-14220 and CGL2010-19591/BTE, the Spanish Institut Geografico Nacional (IGN), Institut Cartografico Valenciano (ICV), Institut Murciano de Investigacion y Desarrollo Agrario y Alimentario (IMIDA) and Banco de Terras de Galicia (Bantegal).Ruiz Fernández, LÁ.; Recio Recio, JA.; Fernández-Sarría, A.; Hermosilla, T. (2011). A feature extraction software tool for agricultural object-based image analysis. Computers and Electronics in Agriculture. 76(2):284-296. https://doi.org/10.1016/j.compag.2011.02.007S28429676

Extraction Landscape Elements from Remote Sensing Data

V této práci je popsán postup pro automatickou detekci krajinných prvků z dat pořízených bezkontaktními dálkovými metodami. Tato interpretace dat byla provedena v softwaru eCognition Developer prostřednictvím procesu klasifikace. Pro klasifikaci byla využita matoda obektově orientované analýzy, která dělí data takovým způsobem, že přiřazuje informaci o příslušnosti k nějaké třídě, například krajinnému typu, skupinám pixelů - objektům. Klasifikace byla provedena se současným využitím produktů dvou různých mapovacích technik - ortofot pořízených z leteckého snímkování a normalizovaného digitálního modelu povrchu, který byl určen pomocí LiDARU. Bylo identifikováno a klasikováno pět typů krajinných prvků.In this thesis, an approach to automatically derive information about land cover from the remotely sensed data is presented. The data interpretation was done with classification process and performed in software eCognition Developer. The Object-based image analysis, which assignes the classes - for example land cover types, to clusters of pixels (=objects), was used. For the classification, products of two different data sources were combined - the orthophotos generated from aerial imagery and Normalized Digital surface model derived from LiDAR data. Five types of landscape elements were identified and classified.

Homotopy theoretic models of identity types

This paper presents a novel connection between homotopical algebra and mathematical logic. It is shown that a form of intensional type theory is valid in any Quillen model category, generalizing the Hofmann-Streicher groupoid model of Martin-Loef type theory.Comment: 11 page