Winner-take-all selection in a neural system with delayed feedback

We consider the effects of temporal delay in a neural feedback system with excitation and inhibition. The topology of our model system reflects the anatomy of the avian isthmic circuitry, a feedback structure found in all classes of vertebrates. We show that the system is capable of performing a `winner-take-all' selection rule for certain combinations of excitatory and inhibitory feedback. In particular, we show that when the time delays are sufficiently large a system with local inhibition and global excitation can function as a `winner-take-all' network and exhibit oscillatory dynamics. We demonstrate how the origin of the oscillations can be attributed to the finite delays through a linear stability analysis.Comment: 8 pages, 6 figure

Bayesian Surprise in Indoor Environments

This paper proposes a novel method to identify unexpected structures in 2D floor plans using the concept of Bayesian Surprise. Taking into account that a person's expectation is an important aspect of the perception of space, we exploit the theory of Bayesian Surprise to robustly model expectation and thus surprise in the context of building structures. We use Isovist Analysis, which is a popular space syntax technique, to turn qualitative object attributes into quantitative environmental information. Since isovists are location-specific patterns of visibility, a sequence of isovists describes the spatial perception during a movement along multiple points in space. We then use Bayesian Surprise in a feature space consisting of these isovist readings. To demonstrate the suitability of our approach, we take "snapshots" of an agent's local environment to provide a short list of images that characterize a traversed trajectory through a 2D indoor environment. Those fingerprints represent surprising regions of a tour, characterize the traversed map and enable indoor LBS to focus more on important regions. Given this idea, we propose to use "surprise" as a new dimension of context in indoor location-based services (LBS). Agents of LBS, such as mobile robots or non-player characters in computer games, may use the context surprise to focus more on important regions of a map for a better use or understanding of the floor plan.Comment: 10 pages, 16 figure

A visual programming model to implement coarse-grained DSP applications on parallel and heterogeneous clusters

International audienceThe digital signal processing (DSP) applications are one of the biggest consumers of computing. They process a big data volume which is represented with a high accuracy. They use complex algorithms, and must satisfy a time constraints in most of cases. In the other hand, it's necessary today to use parallel and heterogeneous architectures in order to speedup the processing, where the best examples are the su-percomputers "Tianhe-2" and "Titan" from the top500 ranking. These architectures could contain several connected nodes, where each node includes a number of generalist processor (multi-core) and a number of accelerators (many-core) to finally allows several levels of parallelism. However, for DSP programmers, it's still complicated to exploit all these parallelism levels to reach good performance for their applications. They have to design their implementation to take advantage of all heteroge-neous computing units, taking into account the architecture specifici-ties of each of them: communication model, memory management, data management, jobs scheduling and synchronization . . . etc. In the present work, we characterize DSP applications, and based on their distinctive-ness, we propose a high level visual programming model and an execution model in order to drop down their implementations and in the same time make desirable performances

Contextual cropping and scaling of TV productions

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-011-0804-3. Copyright @ Springer Science+Business Media, LLC 2011.In this paper, an application is presented which automatically adapts SDTV (Standard Definition Television) sports productions to smaller displays through intelligent cropping and scaling. It crops regions of interest of sports productions based on a smart combination of production metadata and systematic video analysis methods. This approach allows a context-based composition of cropped images. It provides a differentiation between the original SD version of the production and the processed one adapted to the requirements for mobile TV. The system has been comprehensively evaluated by comparing the outcome of the proposed method with manually and statically cropped versions, as well as with non-cropped versions. Envisaged is the integration of the tool in post-production and live workflows

Self-Control in Cyberspace: Applying Dual Systems Theory to a Review of Digital Self-Control Tools

Many people struggle to control their use of digital devices. However, our understanding of the design mechanisms that support user self-control remains limited. In this paper, we make two contributions to HCI research in this space: first, we analyse 367 apps and browser extensions from the Google Play, Chrome Web, and Apple App stores to identify common core design features and intervention strategies afforded by current tools for digital self-control. Second, we adapt and apply an integrative dual systems model of self-regulation as a framework for organising and evaluating the design features found. Our analysis aims to help the design of better tools in two ways: (i) by identifying how, through a well-established model of self-regulation, current tools overlap and differ in how they support self-control; and (ii) by using the model to reveal underexplored cognitive mechanisms that could aid the design of new tools.Comment: 11.5 pages (excl. references), 6 figures, 1 tabl

Visual saliency and semantic incongruency influence eye movements when inspecting pictures

Models of low-level saliency predict that when we first look at a photograph our first few eye movements should be made towards visually conspicuous objects. Two experiments investigated this prediction by recording eye fixations while viewers inspected pictures of room interiors that contained objects with known saliency characteristics. Highly salient objects did attract fixations earlier than less conspicuous objects, but only in a task requiring general encoding of the whole picture. When participants were required to detect the presence of a small target, then the visual saliency of nontarget objects did not influence fixations. These results support modifications of the model that take the cognitive override of saliency into account by allowing task demands to reduce the saliency weights of task-irrelevant objects. The pictures sometimes contained incongruent objects that were taken from other rooms. These objects were used to test the hypothesis that previous reports of the early fixation of congruent objects have not been consistent because the effect depends upon the visual conspicuity of the incongruent object. There was an effect of incongruency in both experiments, with earlier fixation of objects that violated the gist of the scene, but the effect was only apparent for inconspicuous objects, which argues against the hypothesis

Information dynamics: patterns of expectation and surprise in the perception of music

This is a postprint of an article submitted for consideration in Connection Science © 2009 [copyright Taylor & Francis]; Connection Science is available online at:http://www.tandfonline.com/openurl?genre=article&issn=0954-0091&volume=21&issue=2-3&spage=8

Biased Competition in Visual Processing Hierarchies: A Learning Approach Using Multiple Cues

In this contribution, we present a large-scale hierarchical system for object detection fusing bottom-up (signal-driven) processing results with top-down (model or task-driven) attentional modulation. Specifically, we focus on the question of how the autonomous learning of invariant models can be embedded into a performing system and how such models can be used to define object-specific attentional modulation signals. Our system implements bi-directional data flow in a processing hierarchy. The bottom-up data flow proceeds from a preprocessing level to the hypothesis level where object hypotheses created by exhaustive object detection algorithms are represented in a roughly retinotopic way. A competitive selection mechanism is used to determine the most confident hypotheses, which are used on the system level to train multimodal models that link object identity to invariant hypothesis properties. The top-down data flow originates at the system level, where the trained multimodal models are used to obtain space- and feature-based attentional modulation signals, providing biases for the competitive selection process at the hypothesis level. This results in object-specific hypothesis facilitation/suppression in certain image regions which we show to be applicable to different object detection mechanisms. In order to demonstrate the benefits of this approach, we apply the system to the detection of cars in a variety of challenging traffic videos. Evaluating our approach on a publicly available dataset containing approximately 3,500 annotated video images from more than 1 h of driving, we can show strong increases in performance and generalization when compared to object detection in isolation. Furthermore, we compare our results to a late hypothesis rejection approach, showing that early coupling of top-down and bottom-up information is a favorable approach especially when processing resources are constrained