16,536 research outputs found
CLPGUI: a generic graphical user interface for constraint logic programming over finite domains
CLPGUI is a graphical user interface for visualizing and interacting with
constraint logic programs over finite domains. In CLPGUI, the user can control
the execution of a CLP program through several views of constraints, of finite
domain variables and of the search tree. CLPGUI is intended to be used both for
teaching purposes, and for debugging and improving complex programs of
realworld scale. It is based on a client-server architecture for connecting the
CLP process to a Java-based GUI process. Communication by message passing
provides an open architecture which facilitates the reuse of graphical
components and the porting to different constraint programming systems.
Arbitrary constraints and goals can be posted incrementally from the GUI. We
propose several dynamic 2D and 3D visualizations of the search tree and of the
evolution of finite domain variables. We argue that the 3D representation of
search trees proposed in this paper provides the most appropriate visualization
of large search trees. We describe the current implementation of the
annotations and of the interactive execution model in GNU-Prolog, and report
some evaluation results.Comment: 16 pages; Alexandre Tessier, editor; WLPE 2002,
http://xxx.lanl.gov/abs/cs.SE/020705
BRIAN (Brain image analysis): A toolkit for the analysis of multimodal brain datasets
The analysis of cognitive processes in man usually involves multiple examination modalities which map different aspects of the brain. Among these procedures, at least one modality yielding anatomical information (i.e. MRI*) besidesone or more functional modalities (fMRI, PET, SPECT, EEG, MEG) are involved.Because these different examination methods yield complimentary informationabout the anatomical, metabolical and neurophysiological state of the brain, acombined data evaluation is highly desirable and will lead to results not achievable within one examination domain.Such studies are of importance in research (cognitive neuroscience) and withan emphasis on pathological processes in clinical disciplines like neurology,neurosurgery and psychiatry.We have developed a program package for the handling of image datasets(MRI, PET, SPECT, CCT) and signal datasets (EEG, MEG) which allows a combined analysis of these data sources in a fourdimensional coordinate space (x, y,z, and time)
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns
visual concepts, words, and semantic parsing of sentences without explicit
supervision on any of them; instead, our model learns by simply looking at
images and reading paired questions and answers. Our model builds an
object-based scene representation and translates sentences into executable,
symbolic programs. To bridge the learning of two modules, we use a
neuro-symbolic reasoning module that executes these programs on the latent
scene representation. Analogical to human concept learning, the perception
module learns visual concepts based on the language description of the object
being referred to. Meanwhile, the learned visual concepts facilitate learning
new words and parsing new sentences. We use curriculum learning to guide the
searching over the large compositional space of images and language. Extensive
experiments demonstrate the accuracy and efficiency of our model on learning
visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes,
compositions, language concepts, scenes and questions, and even new program
domains. It also empowers applications including visual question answering and
bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu
Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition
This paper deals with the rotation synchronization problem, which arises in
global registration of 3D point-sets and in structure from motion. The problem
is formulated in an unprecedented way as a "low-rank and sparse" matrix
decomposition that handles both outliers and missing data. A minimization
strategy, dubbed R-GoDec, is also proposed and evaluated experimentally against
state-of-the-art algorithms on simulated and real data. The results show that
R-GoDec is the fastest among the robust algorithms.Comment: The material contained in this paper is part of a manuscript
submitted to CVI
Mining structured Petri nets for the visualization of process behavior
Visualization is essential for understanding the models obtained by process mining. Clear and efficient visual representations make the embedded information more accessible and analyzable. This work presents a novel approach for generating process models with structural properties that induce visually friendly layouts. Rather than generating a single model that captures all behaviors, a set of Petri net models is delivered, each one covering a subset of traces of the log. The models are mined by extracting slices of labelled transition systems with specific properties from the complete state space produced by the process logs. In most cases, few Petri nets are sufficient to cover a significant part of the behavior produced by the log.Peer ReviewedPostprint (author's final draft
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