Freeform User Interfaces for Graphical Computing

報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専

Digital interaction: where are we going?

In the framework of the AVI 2018 Conference, the interuniversity center ECONA has organized a thematic workshop on "Digital Interaction: where are we going?". Six contributions from the ECONA members investigate different perspectives around this thematic

Screen-based musical instruments as semiotic machines

The ixi software project started in 2000 with the intention to explore new interactive patterns and virtual interfaces in computer music software. The aim of this paper is not to describe these programs, as they have been described elsewhere, but rather explicate the theoretical background that underlies the design of these screen-based instruments. After an analysis of the similarities and differences in the design of acoustic and screen-based instruments, the paper describes how the creation of an interface is essentially the creation of a semiotic system that affects and influences the musician and the composer. Finally the terminology of this semiotics is explained as an interaction model

Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti

Machine learning techniques are presented for automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia cathedral in Kyiv (Ukraine). A new image dataset of these carved Glagolitic and Cyrillic letters (CGCL) was assembled and pre-processed for recognition and prediction by machine learning methods. The dataset consists of more than 4000 images for 34 types of letters. The explanatory data analysis of CGCL and notMNIST datasets shown that the carved letters can hardly be differentiated by dimensionality reduction methods, for example, by t-distributed stochastic neighbor embedding (tSNE) due to the worse letter representation by stone carving in comparison to hand writing. The multinomial logistic regression (MLR) and a 2D convolutional neural network (CNN) models were applied. The MLR model demonstrated the area under curve (AUC) values for receiver operating characteristic (ROC) are not lower than 0.92 and 0.60 for notMNIST and CGCL, respectively. The CNN model gave AUC values close to 0.99 for both notMNIST and CGCL (despite the much smaller size and quality of CGCL in comparison to notMNIST) under condition of the high lossy data augmentation. CGCL dataset was published to be available for the data science community as an open source resource.Comment: 11 pages, 9 figures, accepted for 25th International Conference on Neural Information Processing (ICONIP 2018), 14-16 December, 2018 (Siem Reap, Cambodia

Sketching as a solid modeling tool

Journal ArticleThis paper describes 'Quick-sketch', a 2-d and 3d modeling tool for pen based computers. Users of this system define a model by simple pen strokes drawn directly on the screen of a pen-based PC. Lines, circles, arcs, or B-spline curves are automatically distinguished and interpreted from these strokes. The system also automatically determines relations, such as right angles, tangencies, symmetry, and parallelism, from the sketch input. These relationships are then used to clean up the drawing by making the approximate relationships exact. Constraints are established to maintain the relationships during further editing. A constraint maintenance system, which is based on gestural manipulation and soft constraints, is employed in this system. Several techniques for sketch based definitions of 3d objects are provided as well, including extrusion, surface of revolution, ruled surfaces and sweep. Features can be sketched on the surface of a 3d object, using the same 2d and 3d techniques. This way, objects of medium complexity can be sketched in seconds. The system can be viewed as a front-end to more sophisticated modeling, rendering or animation environments, serving as a hand sketching tool in the preliminary design phase

PennyLane: Automatic differentiation of hybrid quantum-classical computations

PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantum-classical computations. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for Strawberry Fields, Rigetti Forest, Qiskit, Cirq, and ProjectQ, allowing PennyLane optimizations to be run on publicly accessible quantum devices provided by Rigetti and IBM Q. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, and autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.Comment: Code available at https://github.com/XanaduAI/pennylane/ . Significant contributions to the code (new features, new plugins, etc.) will be recognized by the opportunity to be a co-author on this pape