Discovering Restricted Regular Expressions with Interleaving

Discovering a concise schema from given XML documents is an important problem in XML applications. In this paper, we focus on the problem of learning an unordered schema from a given set of XML examples, which is actually a problem of learning a restricted regular expression with interleaving using positive example strings. Schemas with interleaving could present meaningful knowledge that cannot be disclosed by previous inference techniques. Moreover, inference of the minimal schema with interleaving is challenging. The problem of finding a minimal schema with interleaving is shown to be NP-hard. Therefore, we develop an approximation algorithm and a heuristic solution to tackle the problem using techniques different from known inference algorithms. We do experiments on real-world data sets to demonstrate the effectiveness of our approaches. Our heuristic algorithm is shown to produce results that are very close to optimal.Comment: 12 page

Perceived Blur in Naturally Contoured Images Depends on Phase

Perceived blur is an important measure of image quality and clinical visual function. The magnitude of image blur varies across space and time under natural viewing conditions owing to changes in pupil size and accommodation. Blur is frequently studied in the laboratory with a variety of digital filters, without comparing how the choice of filter affects blur perception. We examine the perception of image blur in synthetic images composed of contours whose orientation and curvature spatial properties matched those of natural images but whose blur could be directly controlled. The images were blurred by manipulating the slope of the amplitude spectrum, Gaussian low-pass filtering or filtering with a Sinc function, which, unlike slope or Gaussian filtering, introduces periodic phase reversals similar to those in optically blurred images. For slope-filtered images, blur discrimination thresholds for over-sharpened images were extremely high and perceived blur could not be matched with either Gaussian or Sinc filtered images, suggesting that directly manipulating image slope does not simulate the perception of blur. For Gaussian- and Sinc-blurred images, blur discrimination thresholds were dipper-shaped and were well-fit with a simple variance discrimination model and with a contrast detection threshold model, but the latter required different contrast sensitivity functions for different types of blur. Blur matches between Gaussian- and Sinc-blurred images were used to test several models of blur perception and were in good agreement with models based on luminance slope, but not with spatial frequency based models. Collectively, these results show that the relative phases of image components, in addition to their relative amplitudes, determines perceived blur

Subjective Logic and Arguing with Evidence

Peer reviewedPreprin

Can Neuromorphic Computer Vision Inform Vision Science? Disparity Estimation as a Case Study

The primate visual system efficiently and effectively solves a multitude of tasks from orientation detection to motion detection. The Computer Vision community is therefore beginning to implement algorithms that mimic the processing hierarchies present in the primate visual system in the hope of achieving flexible and robust artificial vision systems. Here, we reappropriate the neuroscience “borrowed” by the Computer Vision community and ask whether neuromorphic computer vision solutions may give us insight into the functioning of the primate visual system. Specifically, we implement a neuromorphic algorithm for disparity estimation and compare its performance against that of human observers. The algorithm greatly outperforms human subjects when tuned with parameters to compete with non-neural approaches to disparity estimation on benchmarking stereo image datasets. Conversely, when the algorithm is implemented with biologically plausible receptive field sizes, spatial selectivity, phase tuning, and neural noise, its performance is directly relatable to that of human observers. The receptive field size and the number of spatial scales sensibly determine the range of spatial frequencies in which the algorithm successfully operates. The algorithm’s phase tuning and neural noise in turn determine the algorithm’s peak disparity sensitivity. When included, retino-cortical mapping strongly degrades disparity estimation in the model’s periphery, further closening human and algorithm performance. Hence, a neuromorphic computer vision algorithm can be reappropriated to model human behavior, and can provide interesting insights into which aspects of human visual perception have been or are yet to be explained by vision science

Scientific Twitter: The flow of Paleontological Communication Across a Topic Network

The field of paleontology, which is based principally on observations of the natural world, includes an active community that is engaged across multiple social media platforms, consisting of museums, academic researchers, amateur fossil collectors, paleontological artists, and commercial fossil dealers. As such, it represents an ideal environment for examining the people, interactions, and flow of scientific information. Using interactions involving the four most popular Twitter hashtags for paleontology, this embedded mixed methods study defined the members of this social world and investigated how they influenced and controlled the flow of information, as well as how their expression of scientific practice was related to their identity. Results provide further evidence for the diversity of people and practice involved in this domain of science and indicate that the magnitude and breadth of the public’s impact may be larger than previously projected. Certain types of messages were shown to be effective for different segments of the community, but news posts, essentially media outlet stories, were ineffective for generating any form of engagement. This study adds to our understanding of the important scientific contribution being made by members of the public as they interact with professional scientists and educators as peers in an open social media platform that supports a diverse and active community

Near-optimal combination of disparity across a log-polar scaled visual field

The human visual system is foveated: we can see fine spatial details in central vision, whereas resolution is poor in our peripheral visual field, and this loss of resolution follows an approximately logarithmic decrease. Additionally, our brain organizes visual input in polar coordinates. Therefore, the image projection occurring between retina and primary visual cortex can be mathematically described by the log-polar transform. Here, we test and model how this space-variant visual processing affects how we process binocular disparity, a key component of human depth perception. We observe that the fovea preferentially processes disparities at fine spatial scales, whereas the visual periphery is tuned for coarse spatial scales, in line with the naturally occurring distributions of depths and disparities in the real-world. We further show that the visual system integrates disparity information across the visual field, in a near-optimal fashion. We develop a foveated, log-polar model that mimics the processing of depth information in primary visual cortex and that can process disparity directly in the cortical domain representation. This model takes real images as input and recreates the observed topography of human disparity sensitivity. Our findings support the notion that our foveated, binocular visual system has been moulded by the statistics of our visual environment

Social Media Interaction as Informal Science Learning: a Comparison of Message Design in Two Niches

Social media provides science learners opportunities to interact with content-specific messages. However, most science-specific social media content is designed to disseminate information instead of encouraging dialog. In this novel, ex post facto exploratory study of a science social media community, we sought to understand the relationships among community member interaction, design elements of messages, and post type on two digital niches (i.e., Facebook and Twitter). Framed by the theory of symbolic interactionism, we conducted a content analysis of 1370 messages that were systematically created by an informal science learning project and found that usage frequency of messaging elements varied by niche; interaction within each niche differed, varying by messaging element; and differential interaction was found to be associated with post types within Facebook only. This study suggests a pathway for developing and examining social media as an educational component of informal science learning

Evaluation of the Tobii EyeX Eye tracking controller and Matlab toolkit for research

The Tobii Eyex Controller is a new low-cost binocular eye tracker marketed for integration in gaming and consumer applications. The manufacturers claim that the system was conceived for natural eye gaze interaction, does not require continuous recalibration, and allows moderate head movements. The Controller is provided with a SDK to foster the development of new eye tracking applications. We review the characteristics of the device for its possible use in scientific research. We develop and evaluate an open source Matlab Toolkit that can be employed to interface with the EyeX device for gaze recording in behavioral experiments. The Toolkit provides calibration procedures tailored to both binocular and monocular experiments, as well as procedures to evaluate other eye tracking devices. The observed performance of the EyeX (i.e. accuracy < 0.6°, precision < 0.25°, latency < 50 ms and sampling frequency ≈55 Hz), is sufficient for some classes of research application. The device can be successfully employed to measure fixation parameters, saccadic, smooth pursuit and vergence eye movements. However, the relatively low sampling rate and moderate precision limit the suitability of the EyeX for monitoring micro-saccadic eye movements or for real-time gaze-contingent stimulus control. For these applications, research grade, high-cost eye tracking technology may still be necessary. Therefore, despite its limitations with respect to high-end devices, the EyeX has the potential to further the dissemination of eye tracking technology to a broad audience, and could be a valuable asset in consumer and gaming applications as well as a subset of basic and clinical research settings