31,288 research outputs found
A detection theory account of change detection
Previous studies have suggested that visual short-term memory (VSTM) has a storage limit of approximately four items. However, the type of high-threshold (HT) model used to derive this estimate is based on a number of assumptions that have been criticized in other experimental paradigms (e.g., visual search). Here we report findings from nine experiments in which VSTM for color, spatial frequency, and orientation was modeled using a signal detection theory (SDT) approach. In Experiments 1-6, two arrays composed of multiple stimulus elements were presented for 100 ms with a 1500 ms ISI. Observers were asked to report in a yes/no fashion whether there was any difference between the first and second arrays, and to rate their confidence in their response on a 1-4 scale. In Experiments 1-3, only one stimulus element difference could occur (T = 1) while set size was varied. In Experiments 4-6, set size was fixed while the number of stimuli that might change was varied (T = 1, 2, 3, and 4). Three general models were tested against the receiver operating characteristics generated by the six experiments. In addition to the HT model, two SDT models were tried: one assuming summation of signals prior to a decision, the other using a max rule. In Experiments 7-9, observers were asked to directly report the relevant feature attribute of a stimulus presented 1500 ms previously, from an array of varying set size. Overall, the results suggest that observers encode stimuli independently and in parallel, and that performance is limited by internal noise, which is a function of set size
Learning Multiple Visual Tasks while Discovering their Structure
Multi-task learning is a natural approach for computer vision applications
that require the simultaneous solution of several distinct but related
problems, e.g. object detection, classification, tracking of multiple agents,
or denoising, to name a few. The key idea is that exploring task relatedness
(structure) can lead to improved performances.
In this paper, we propose and study a novel sparse, non-parametric approach
exploiting the theory of Reproducing Kernel Hilbert Spaces for vector-valued
functions. We develop a suitable regularization framework which can be
formulated as a convex optimization problem, and is provably solvable using an
alternating minimization approach. Empirical tests show that the proposed
method compares favorably to state of the art techniques and further allows to
recover interpretable structures, a problem of interest in its own right.Comment: 19 pages, 3 figures, 3 table
Towards Automated Performance Bug Identification in Python
Context: Software performance is a critical non-functional requirement,
appearing in many fields such as mission critical applications, financial, and
real time systems. In this work we focused on early detection of performance
bugs; our software under study was a real time system used in the
advertisement/marketing domain.
Goal: Find a simple and easy to implement solution, predicting performance
bugs.
Method: We built several models using four machine learning methods, commonly
used for defect prediction: C4.5 Decision Trees, Na\"{\i}ve Bayes, Bayesian
Networks, and Logistic Regression.
Results: Our empirical results show that a C4.5 model, using lines of code
changed, file's age and size as explanatory variables, can be used to predict
performance bugs (recall=0.73, accuracy=0.85, and precision=0.96). We show that
reducing the number of changes delivered on a commit, can decrease the chance
of performance bug injection.
Conclusions: We believe that our approach can help practitioners to eliminate
performance bugs early in the development cycle. Our results are also of
interest to theoreticians, establishing a link between functional bugs and
(non-functional) performance bugs, and explicitly showing that attributes used
for prediction of functional bugs can be used for prediction of performance
bugs
Historical forest biomass dynamics modelled with Landsat spectral trajectories
Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin
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