Subjectively interpreted shape dimensions as privileged and orthogonal axes in mental shape space

The shape of an object is fundamental in object recognition but it is still an open issue to what extent shape differences are perceived analytically (i.e., by the dimensional structure of the shapes) or holistically (i.e., by the overall similarity of the shapes). The dimensional structure of a stimulus is available in a primary stage of processing for separable dimensions, although it can also be derived cognitively from a perceived stimulus consisting of integral dimensions. Contrary to most experimental paradigms, the present study asked participants explicitly to analyze shapes according to two dimensions. The dimensions of interest were aspect ratio and medial axis curvature, and a new procedure was used to measure the participants' interpretation of both dimensions (Part I, Experiment 1). The subjectively interpreted shape dimensions showed specific characteristics supporting the conclusion that they also constitute perceptual dimensions with objective behavioral characteristics (Part II): (1) the dimensions did not correlate in overall similarity measures (Experiment 2), (2) they were more separable in a speeded categorization task (Experiment 3), and (3) they were invariant across different complex 2-D shapes (Experiment 4). The implications of these findings for shape-based object processing are discussed

Fast vocabulary acquisition in an NMF-based self-learning vocal user interface

AbstractIn command-and-control applications, a vocal user interface (VUI) is useful for handsfree control of various devices, especially for people with a physical disability. The spoken utterances are usually restricted to a predefined list of phrases or to a restricted grammar, and the acoustic models work well for normal speech. While some state-of-the-art methods allow for user adaptation of the predefined acoustic models and lexicons, we pursue a fully adaptive VUI by learning both vocabulary and acoustics directly from interaction examples. A learning curve usually has a steep rise in the beginning and an asymptotic ceiling at the end. To limit tutoring time and to guarantee good performance in the long run, the word learning rate of the VUI should be fast and the learning curve should level off at a high accuracy. In order to deal with these performance indicators, we propose a multi-level VUI architecture and we investigate the effectiveness of alternative processing schemes. In the low-level layer, we explore the use of MIDA features (Mutual Information Discrimination Analysis) against conventional MFCC features. In the mid-level layer, we enhance the acoustic representation by means of phone posteriorgrams and clustering procedures. In the high-level layer, we use the NMF (Non-negative Matrix Factorization) procedure which has been demonstrated to be an effective approach for word learning. We evaluate and discuss the performance and the feasibility of our approach in a realistic experimental setting of the VUI-user learning context

Evaluation of the persistence and gene expression of an anti-Chlamydophila psittaci DNA vaccine in turkey muscle

BACKGROUND: DNA vaccination has been shown to elicit specific cellular and humoral immune responses to many different agents in a broad variety of species. However, looking at a commercial use, the duration of the immune response against the vaccine is critical. Therefore the persistence of the DNA vaccine, as well as its expression, should be investigated. We conducted these investigations on a DNA vaccine against Chlamydophila psittaci, a Gram-negative intracellular bacterium which causes respiratory disease in turkeys and humans. Previous studies showed that the DNA vaccine confers partial protection against C. psittaci infection in turkeys. Turkeys were injected intramuscularly with the DNA vaccine : a eukaryotic expression vector (pcDNA1::MOMP) expressing the major outer membrane protein (MOMP) of an avian C. psittaci serovar D strain. Over a period of 11 weeks, cellular uptake of the DNA vaccine was examined by PCR, transcription of the insert by reverse transcript-PCR (RT-PCR) and mRNA translation by immunofluorescence staining of muscle biopsies. RESULTS: The results indicate that the DNA vaccine persists in turkey muscle for at least 10 weeks. Moreover, during this period of time MOMP was continuously expressed, as evidenced by the immunofluorescence staining and RT-PCR. CONCLUSION: Since C. psittaci infections occur at the age of 3 to 6 and 8 to 12 weeks, a vaccine persistence of 10 weeks seems adequate. Therefore, further research should concentrate on improving the elicited immune response, more specifically the cell-mediated immune response, rather than prolonging the lifespan of the plasmid

A Computational Model of Visual Anisotropy

Visual anisotropy has been demonstrated in multiple tasks where performance differs between vertical, horizontal, and oblique orientations of the stimuli. We explain some principles of visual anisotropy by anisotropic smoothing, which is based on a variation on Koenderink's approach in [1]. We tested the theory by presenting Gaussian elongated luminance profiles and measuring the perceived orientations by means of an adjustment task. Our framework is based on the smoothing of the image with elliptical Gaussian kernels and it correctly predicted an illusory orientation bias towards the vertical axis. We discuss the scope of the theory in the context of other anisotropies in perception

Enhanced Traffic Management Procedures of Connected and Autonomous Vehicles in Transition Areas

In light of the increasing trend towards vehicle connectivity and automation, there will be areas and situations on the roads where high automation can be granted, and others where it is not allowed or not possible. These are termed ‘Transition Areas’. Without proper traffic management, such areas may lead to vehicles issuing take-over requests (TORs), which in turn can trigger transitions of control (ToCs), or even minimum-risk manoeuvres (MRMs). In this respect, the TransAID Horizon 2020 project develops and demonstrates traffic management procedures and protocols to enable smooth coexistence of automated, connected, andconventional vehicles, with the goal of avoiding ToCs and MRMs, or at least postponing/accommodating them. Our simulations confirmed that proper traffic management, taking the traffic mix into account, can prevent drops in traffic efficiency, which in turn leads to a more performant, safer, and cleaner traffic system, when taking the capabilities of connected and autonomous vehicles into account

TransAID Deliverable 6.2/2 - Assessment of Traffic Management Procedures in Transition Areas

This Deliverable 6.2 of the TransAID project presents and evaluates the simulation results obtained for the scenarios considered during the project's first and second iterations. To this end, driver- and AV-models designed in WP3, traffic management procedures developed in WP4, and V2X communication protocols and models from WP5 were implemented within the iTETRIS simulation framework. Previous main results from Deliverable 4.2, where baseline and traffic management measures without V2X communication were compared, have been confirmed. While not all TransAID scenarios' traffic KPIs were affected, the realistic simulation of V2X communication has shown a discernible impact on some of them, which makes it an indispensable modelling aspect for a realistic performance evaluation of V2X traffic scenarios. Flaws of the first iteration's traffic management algorithms concerning wireless V2X communication and the accompanying possibility of packet loss were identified and have been addressed during the project's second iteration. Finally, lessons learned while working on these simulation results and assessments have additionally been described in the form of recommendations for the real-world prototype to be developed in WP7. We conclude that all results obtained for all scenarios when employing ideal communication confirmed the statistical trends of the results from the original TM scenarios as reported in Deliverable 4.2 where no V2X communication was considered. Furthermore, the performance evaluation of the considered scenarios and parameter combinations has shown the following, which held true in both the first and second iterations: (1) The realistic simulation of V2X communication has an impact on traffic scenarios, which makes them indispensable for a realistic performance evaluation of V2X traffic scenarios. (2) Traffic management algorithms need to account for sporadic packet loss of various message types in some way. (3) Although important, the realistic modelling and simulation of V2X communication also induces a significant computational overhead. Thus, from a general perspective, a trade-off between computation time and degree of realism should be considered

The Self-taught Speech Interface

With advances in technology, human-machine interfaces have become commonplace. Their design requires a great deal of engineering efforts to make them functional and accessible. One of these engineering efforts is the embedding of voice control. This improves the accessibility for people with a physical disability. In common speech-enabled command-and-control applications, the spoken commands are restricted to a predefined list of phrases and grammars. These conventions work well as long as the system does not have to stray too far from the conditions considered by the designer or from the characteristics of the training material. Speech technology would benefit from training during usage; learning the specific vocalizations and the emerging expressions of the user. Designing a vocal user interface (VUI) model from this developmental perspective would widen accessibilitynbsp;caternbsp;users with non-standard or dysarthric speech. The research in this dissertation is aimed at the development of a self-taught VUI that learns speech commands from the user while it is operational. Tonbsp;end, we adopt and introduce different procedures in order to build a VUI-model that learnsnbsp;a few learning examples. A learning example consists of two sources of information: the spoken command and the demonstration of the commanded action.nbsp;sources of information are converted to fixed-length utterance-based vectors. The followed approach links the acoustic patterns that are embedded in the spoken utterances to the concepts that jointly define the meaning of the utterance. The method represents the data by its recurrent acoustic and semantic patterns and the incidence of these patterns in the data. Since these patterns are embedded in the data, the representation of the data has a significant influence over the performance of thenbsp;model. A thorough analysis ofnbsp;representations resorting to speaker-dependent and speaker-independent data resources, is made. Attention is also given to the representation of thenbsp;action. The representation of the commanded action consists of an incidence vector representing the semantic content of the demanded action. Users are non-experts innbsp;a VUI, therefore, errors such as uttering an incomplete command or pushing a wrong button,nbsp;emerge. We demonstrate robustness against these kinds of errors. Another issue pertaining to semantics is the correlation between relevant concepts in a spoken utterance. This dependency is an additional source of information. We exploit this information and compare different semantic structures pertaining to these semantic dependencies.nbsp;nbsp; With the focus on the learning process rather than on the resulting model, we develop procedures for incremental and adaptive learning. By exploiting a semi-Bayesian procedure called maximum a posteriori (MAP) estimation, the VUI model can be made to learn incrementally, one utterance at a time. Incremental learning procedures are developed at the level of the basic acoustic atoms and at the level of the word models. They are compared with their batch learning variants and yield comparable accuracy. The implementation of a forgetting factor makesnbsp;models adaptive to changes in the speech of the user. The learning curves are an assessment of the quality of learning in function of the amount of training data. We analyse the learning curves for all these developments by numerous experiments in realistic learning scenarios implemented on computer. By this, we acquire a sense of the systemnbsp;performance in a real-world training environment. Thenbsp;of the VUI in its operational context and the training of the VUI by the user are the twonbsp;important key aspects that inspired the conception, the developments and the research questions in this study.Ons B., ''The self-taught speech interface'', Proefschrift voorgedragen tot het behalen van het doctoraat in de ingenieurswetenschappen, KU Leuven, May 2015, Leuven, Belgium.status: publishe

A developmental difference in shape processing and word-shape associations in 4 and 6.5 year olds

In distinguishing individual shapes (defined by their contours), older children (6.5 years of age on average) performed better than younger children (4 years of age on average), and, although the task did not involve any categorization or generalization, the error pattern was qualitatively affected by shape differences that are generally common distinctions between objects belonging to different categories. The influence of these shape differences was also observed for unfamiliar shapes, demonstrating that the influence of categorization experience was not modulated by the retrieval of shape features from known categories but rather related to a different perception of shape by age. The results suggest a direct influence of categorization experience on more abstract shape processing. When children were distinguishing shapes, new words were paired with the target shapes, and in 2 additional tasks, the acquired name-shape associations were tested. The younger age group was able to remember more words correctly.status: publishe

Development of differential sensitivity for shape changes resulting from linear and nonlinear planar transformations

A shape bias for extending names to objects that look visually similar has been commonly accepted but it is hard to define which kind of shape dissimilarities are diagnostic for the identity of an object. Here, we present a transformational approach to describe shape differences that can incorporate many significant shape features. We introduce two kinds of transformations: one kind concerns linear transformations of the image plane (affine transformations), generally limiting shape variations within the borders of basic-level categories; the other kind concerns nonlinear continuous transformations of the image plane (topological transformations), allowing all kinds of shape variation crossing and not crossing the borders of basic-level categories. We administered stimulus pairs differing in these shape transformations to children of 3 years to 7 years old in a delayed match-to-sample task. With increasing age, especially between 5 years and 6 years, children became more sensitive to the topological deformations that are relevant for between-category distinctions, indicating that acquired categorical knowledge in early years induces perceptual learning of the relevant generic shape differences between categories.status: publishe