Representation Learning for Shape Decomposition, By Shape Decomposition

The ability to parse 3D objects into their constituent parts is essential for humans to understand and interact with the surrounding world. Imparting this skill in machines is important for various computer graphics, computer vision, and robotics tasks. Machines endowed with this skill can better interact with its surroundings, perform shape editing, texturing, recomposing, tracking, and animation. In this thesis, we ask two questions. First, how can machines decompose 3D shapes into their fundamental parts? Second, does the ability to decompose the 3D shape into these parts help learn useful 3D shape representations? In this thesis, we focus on parsing the shape into compact representations, such as parametric surface patches and Constructive Solid Geometry (CSG) primitives, which are also widely used representations in 3D modeling in computer graphics. Inspired by the advances in neural networks for 3D shape processing, we develop neural network approaches to tackle shape decomposition. First, we present CSGNet, a network architecture to parse shapes into CSG programs, which is trained using combination of supervised and reinforcement learning. Second, we present ParSeNet, a network architecture to decompose a shape into parametric surface patches (B-Spline) and geometric primitives (plane, cone, cylinder and sphere), trained on a large set of CAD models using supervised learning. The training of deep neural network architectures for 3D recognition and generation tasks requires a large amount of labeled datasets. We explore ways to alleviate this problem by relying on shape decomposition methods to guide the learning process. Towards that end, we first study the use of freely available metadata, albeit inconsistent, from shape repositories to learn 3D shape features. Later we show that learning to decompose a 3D shape into geometric primitives also helps in learning shape representations useful for semantic segmentation tasks. Finally, since most 3D shapes encountered in real life are textured, consisting of several fine-grained semantic parts, we propose a method to learn fine-grained representations for textured 3D shapes in a self-supervised manner by incorporating 3D geometric priors

Compressing computer programs

This paper describes a scheme for compressing programs written in a particular programming language—which can be any language that has a formal lexical and syntactic description—in such a way that they can be reproduced exactly. Only syntactically correct programs can be compressed. The scheme is illustrated on the Pascal language, and compression results are given for a corpus of Pascal programs; but it is by no means restricted to Pascal. In fact, we discuss how a "compressor-generator" program can be constructed that creates a compressor automatically from a formal specification of a programming language, in much the same way as a parser generator creates a syntactic parser from a formal language description

Design and implementation data flow analysis of jobs in IBM DataStage for Manta project

Cílem této práce je návrh a implementace funkčního prototypu modulu, provádějícího syntaktickou a sémantickou analýzu úloh v IBM InfoSphere DataStage. Modul se používá pro analýzu datových toků a generaci grafu, který reprezentuje datove toky. Návrh a implementace podporují bezproblémové připojení modulu k projektu Manta. Práce obsahuje důkladnou analýzu nástroje IBM InfoSphere DataStage, návrhovou dokumentaci, implementovaný prototyp modulu a také testy, které zajišťují funkcionalitu modulu.This work aims to design and implement a functional module prototype that performs syntactic and semantic analysis of tasks in IBM InfoSphere DataStage. The module provides data flow analysis and generation of the graph, which represents data flows. Design and implementation support the trouble-free connection of the module to the Manta project. The work contains an in-depth analysis of the IBM InfoSphere DataStage tool, design documentation, implemented the module prototype and tests, which ensures module functionality

Using formal methods to develop WS-BPEL applications

In recent years, WS-BPEL has become a de facto standard language for orchestration of Web Services. However, there are still some well-known difficulties that make programming in WS-BPEL a tricky task. In this paper, we firstly point out major loose points of the WS-BPEL specification by means of many examples, some of which are also exploited to test and compare the behaviour of three of the most known freely available WS-BPEL engines. We show that, as a matter of fact, these engines implement different semantics, which undermines portability of WS-BPEL programs over different platforms. Then we introduce Blite, a prototypical orchestration language equipped with a formal operational semantics, which is closely inspired by, but simpler than, WS-BPEL. Indeed, Blite is designed around some of WS-BPEL distinctive features like partner links, process termination, message correlation, long-running business transactions and compensation handlers. Finally, we present BliteC, a software tool supporting a rapid and easy development of WS-BPEL applications via translation of service orchestrations written in Blite into executable WS-BPEL programs. We illustrate our approach by means of a running example borrowed from the official specification of WS-BPEL

Proceedings of the Third Symposium on Programming Languages and Software Tools : Kääriku, Estonia, August 23-24 1993

http://www.ester.ee/record=b1064507*es

Cognitive Information Processing

Contains research objectives and summary of research on fourteen research projects and reports on four research projects.Joint Services Electronics Program (Contract DAAB07-75-C-1346)National Science Foundation (Grant EPP74-12653)National Science Foundation (Grant ENG74-24344)National Institutes of Health (Grant 2 PO1 GM19428-04)Swiss National Funds for Scientific ResearchM.I.T. Health Sciences Fund (Grant 76-11)National Institutes of Health (Grant F03 GM58698)National Institutes of Health (Biomedical Sciences Support Grant)Associated Press (Grant