1,013 research outputs found

    Playing for Data: Ground Truth from Computer Games

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    Recent progress in computer vision has been driven by high-capacity models trained on large datasets. Unfortunately, creating large datasets with pixel-level labels has been extremely costly due to the amount of human effort required. In this paper, we present an approach to rapidly creating pixel-accurate semantic label maps for images extracted from modern computer games. Although the source code and the internal operation of commercial games are inaccessible, we show that associations between image patches can be reconstructed from the communication between the game and the graphics hardware. This enables rapid propagation of semantic labels within and across images synthesized by the game, with no access to the source code or the content. We validate the presented approach by producing dense pixel-level semantic annotations for 25 thousand images synthesized by a photorealistic open-world computer game. Experiments on semantic segmentation datasets show that using the acquired data to supplement real-world images significantly increases accuracy and that the acquired data enables reducing the amount of hand-labeled real-world data: models trained with game data and just 1/3 of the CamVid training set outperform models trained on the complete CamVid training set.Comment: Accepted to the 14th European Conference on Computer Vision (ECCV 2016

    Unusual pegmatoid crystallisations in a nephelinite plug, near Round Lagoon, eastern Central Plateau, Tasmania

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    Pegmatoids in a Late Oligocene olivine nephelinite plug near Round Lagoon form a complex low-pressure fractionation suite. The host nephelinite contains meta-peridotite and meta-wehrlite mantle xenoliths and its composition (Mg# 0.63) may reflect both mantle and then limited fractionation. The pegmatoids range from ultramafic through mafic to feldspathic assemblages in a progressive, but discontinuous, fractionation sequence ~ olivine ~ sodalite ijolite, nepheline syenite ~ alkali syenite). Within this sequence, olivine and clinopyroxene compositions decrease in content, while clinopyroxene becomes increasingly Na- and Fe-rich to produce late stage aegirine-augite and aegirine. Nepheline is prominent in the sequence and crystallised over a wide temperature range from 10000 to <500°C. The presence of sodalite suggests volatile Cl-rich fluxing. Mg-rich spinel crystallised in assemblages, distinct from Fe- and Ti-rich oxides of the magnetite-ulvospinel series in later assemblages. The Round Lagoon low-pressure pegmatoids developed by fractionation in a narrow, vertical feeder rather than in broad lava ponds such as those noted in nephelinite flows at Inverell, New South Wales, and at La Madera, Argentina

    Subcutaneous rupture of the Achilles tendon and ipsilateral fracture of the medial malleolus

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    BACKGROUND: Although ankle fractures and an Achilles tendon rupture are relatively frequent in isolation, their association in the same injury is uncommon. CASE PRESENTATION: A 38 year old male tree surgeon fell six meters from a tree, sustaining a subcutaneous rupture of the Achilles tendon and an ipsilateral closed fracture of the medial malleolus. The injuries were diagnosed following clinical examination and imaging. CONCLUSION: This injury combination is infrequent, and management of the Achilles tendon rupture should take into account the necessity not to secondarily displace the fracture of the medial malleollus

    A Comparative Study of Defeasible Argumentation and Non-monotonic Fuzzy Reasoning for Elderly Survival Prediction Using Biomarkers

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    Computational argumentation has been gaining momentum as a solid theoretical research discipline for inference under uncertainty with incomplete and contradicting knowledge. However, its practical counterpart is underdeveloped, with a lack of studies focused on the investigation of its impact in real-world settings and with real knowledge. In this study, computational argumentation is compared against non-monotonic fuzzy reasoning and evaluated in the domain of biological markers for the prediction of mortality in an elderly population. Different non-monotonic argument-based models and fuzzy reasoning models have been designed using an extensive knowledge base gathered from an expert in the field. An analysis of the true positive and false positive rate of the inferences of such models has been performed. Findings indicate a superior inferential capacity of the designed argument-based models

    Towards causal benchmarking of bias in face analysis algorithms

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    Measuring algorithmic bias is crucial both to assess algorithmic fairness, and to guide the improvement of algorithms. Current methods to measure algorithmic bias in computer vision, which are based on observational datasets, are inadequate for this task because they conflate algorithmic bias with dataset bias. To address this problem we develop an experimental method for measuring algorithmic bias of face analysis algorithms, which manipulates directly the attributes of interest, e.g., gender and skin tone, in order to reveal causal links between attribute variation and performance change. Our proposed method is based on generating synthetic ``transects'' of matched sample images that are designed to differ along specific attributes while leaving other attributes constant. A crucial aspect of our approach is relying on the perception of human observers, both to guide manipulations, and to measure algorithmic bias. Besides allowing the measurement of algorithmic bias, synthetic transects have other advantages with respect to observational datasets: they sample attributes more evenly allowing for more straightforward bias analysis on minority and intersectional groups, they enable prediction of bias in new scenarios, they greatly reduce ethical and legal challenges, and they are economical and fast to obtain, helping make bias testing affordable and widely available. We validate our method by comparing it to a study that employs the traditional observational method for analyzing bias in gender classification algorithms. The two methods reach different conclusions. While the observational method reports gender and skin color biases, the experimental method reveals biases due to gender, hair length, age, and facial hair

    Differential protein profiling as a potential multi-marker approach for TSE diagnosis

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    Rona Barron - ORCID: 0000-0003-4512-9177 https://orcid.org/0000-0003-4512-9177This "proof of concept" study, examines the use of differential protein expression profiling using surface enhanced laser desorption and ionisationtime of flight mass spectrometry (SELDI-TOF) for the diagnosis of TSE disease. Spectral output from all proteins selectively captured from individual murine brain homogenate samples, are compared as "profiles" in groups of infected and non-infected animals. Differential protein expression between groups is thus highlighted and statistically significant protein "peaks" used to construct a panel of disease specific markers. Studies at both terminal stages of disease and throughout the time course of disease have shown a disease specific protein profile or "disease fingerprint" which could be used to distinguish between groups of TSE infected and uninfected animals at an early time point of disease. Results Our results show many differentially expressed proteins in diseased and control animals, some at early stages of disease. Three proteins identified by SELDI-TOF analysis were verified by immunohistochemistry in brain tissue sections. We demonstrate that by combining the most statistically significant changes in expression, a panel of markers can be constructed that can distinguish between TSE diseased and normal animals. Conclusion Differential protein expression profiling has the potential to be used for the detection of disease in TSE infected animals. Having established that a "training set" of potential markers can be constructed, more work would be required to further test the specificity and sensitivity of the assay in a "testing set". Based on these promising results, further studies are being performed using blood samples from infected sheep to assess the potential use of SELDI-TOF as a pre-mortem blood based diagnostic.https://doi.org/10.1186/1471-2334-9-1889pubpub

    Theory and Validation of Magnetic Resonance Fluid Motion Estimation Using Intensity Flow Data

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    15 p.Background Motion tracking based on spatial-temporal radio-frequency signals from the pixel representation of magnetic resonance (MR) imaging of a non-stationary fluid is able to provide two dimensional vector field maps. This supports the underlying fundamentals of magnetic resonance fluid motion estimation and generates a new methodology for flow measurement that is based on registration of nuclear signals from moving hydrogen nuclei in fluid. However, there is a need to validate the computational aspect of the approach by using velocity flow field data that we will assume as the true reference information or ground truth. Methodology/Principal Findings In this study, we create flow vectors based on an ideal analytical vortex, and generate artificial signal-motion image data to verify our computational approach. The analytical and computed flow fields are compared to provide an error estimate of our methodology. The comparison shows that the fluid motion estimation approach using simulated MR data is accurate and robust enough for flow field mapping. To verify our methodology, we have tested the computational configuration on magnetic resonance images of cardiac blood and proved that the theory of magnetic resonance fluid motion estimation can be applicable practically. Conclusions/Significance The results of this work will allow us to progress further in the investigation of fluid motion prediction based on imaging modalities that do not require velocity encoding. This article describes a novel theory of motion estimation based on magnetic resonating blood, which may be directly applied to cardiac flow imaging.Kelvin Kian Loong Wong, Richard Malcolm Kelso, Stephen Grant Worthley, Prashanthan Sanders, Jagannath Mazumdar, Derek Abbot
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