Continuous symmetry reduction and return maps for high-dimensional flows

We present two continuous symmetry reduction methods for reducing high-dimensional dissipative flows to local return maps. In the Hilbert polynomial basis approach, the equivariant dynamics is rewritten in terms of invariant coordinates. In the method of moving frames (or method of slices) the state space is sliced locally in such a way that each group orbit of symmetry-equivalent points is represented by a single point. In either approach, numerical computations can be performed in the original state-space representation, and the solutions are then projected onto the symmetry-reduced state space. The two methods are illustrated by reduction of the complex Lorenz system, a 5-dimensional dissipative flow with rotational symmetry. While the Hilbert polynomial basis approach appears unfeasible for high-dimensional flows, symmetry reduction by the method of moving frames offers hope.Comment: 32 pages, 7 figure

Reference frames during the acquisition and development of spatial memories

Four experiments investigated the role of reference frames during the acquisition and development of spatial knowledge, when learning occurs incrementally across views. In two experiments, participants learned overlapping spatial layouts. Layout 1 was first studied in isolation, and Layout 2 was later studied in the presence of Layout 1. The Layout 1 learning view was manipulated, whereas the Layout 2 view was held constant. Manipulation of the Layout 1 view influenced the reference frame used to organize Layout 2, indicating that reference frames established during early environmental exposure provided a framework for organizing locations learned later. Further experiments demonstrated that reference frames established after learning served to reorganize an existing spatial memory. These results indicate that existing reference frames can structure the acquisition of new spatial memories and that new reference frames can reorganize existing spatial memories

Spatial Aspects of Metaphors for Information: Implications for Polycentric System Design

This dissertation presents three innovations that suggest an alternative approach to structuring information systems: a multidimensional heuristic workspace, a resonance metaphor for information, and a question-centered approach to structuring information relations. Motivated by the need for space to establish a question-centered learning environment, a heuristic workspace has been designed. Both the question-centered approach to information system design and the workspace have been conceived with the resonance metaphor in mind. This research stemmed from a set of questions aimed at learning how spatial concepts and related factors including geography may play a role in information sharing and public information access. In early stages of this work these concepts and relationships were explored through qualitative analysis of interviews centered on local small group and community users of geospatial data. Evaluation of the interviews led to the conclusion that spatial concepts are pervasive in our language, and they apply equally to phenomena that would be considered physical and geographic as they do to cognitive and social domains. Rather than deriving metaphorically from the physical world to the human, spatial concepts are native to all dimensions of human life. This revised view of the metaphors of space was accompanied by a critical evaluation of the prevailing metaphors for information processes, the conduit and pathway metaphors, which led to the emergence of an alternative, resonance metaphor. Whereas the dominant metaphors emphasized information as object and the movement of objects and people through networks and other limitless information spaces, the resonance metaphor suggests the existence of multiple centers in dynamic proximity relationships. This pointed toward the creation of a space for autonomous problem solving that might be related to other spaces through proximity relationships. It is suggested that a spatial approach involving discrete, discontinuous structures may serve as an alternative to approaches involving movement and transportation. The federation of multiple autonomous problem-solving spaces, toward goals such as establishing communities of questioners, has become an objective of this work. Future work will aim at accomplishing this federation, most likely by means of the IS0 Topic Maps standard or similar semantic networking strategies

LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW

Aspect-Controlled Neural Argument Generation

We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we train a language model for argument generation that can be controlled on a fine-grained level to generate sentence-level arguments for a given topic, stance, and aspect. We define argument aspect detection as a necessary method to allow this fine-granular control and crowdsource a dataset with 5,032 arguments annotated with aspects. Our evaluation shows that our generation model is able to generate high-quality, aspect-specific arguments. Moreover, these arguments can be used to improve the performance of stance detection models via data augmentation and to generate counter-arguments. We publish all datasets and code to fine-tune the language model

Vision, Action, and Make-Perceive

In this paper, I critically assess the enactive account of visual perception recently defended by Alva Noë (2004). I argue inter alia that the enactive account falsely identifies an object’s apparent shape with its 2D perspectival shape; that it mistakenly assimilates visual shape perception and volumetric object recognition; and that it seriously misrepresents the constitutive role of bodily action in visual awareness. I argue further that noticing an object’s perspectival shape involves a hybrid experience combining both perceptual and imaginative elements – an act of what I call ‘make-perceive.

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication