Exploring Cultural Traffic

Review of:Peter Beilharz, Thinking the Antipodes: Australian Essays,Monash University Publishing, Melbourne, 2015ISBN 9781922235558 RRP AU$39.95 (pb

Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition

When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is error-prone and can impact target model accuracy. To identify the extent of such impact, we perform and briefly present a differential analysis against three DNNs widely used for image recognition (MobileNetV2, ResNet101, and InceptionV3) converted across four well-known deep learning frameworks (PyTorch, Keras, TensorFlow (TF), and TFLite), which revealed numerous model crashes and output label discrepancies of up to 72%. To mitigate such errors, we present a novel approach towards fault localization and repair of buggy deep learning framework conversions, focusing on pre-trained image recognition models. Our technique consists of four stages of analysis: 1) conversion tools, 2) model parameters, 3) model hyperparameters, and 4) graph representation. In addition, we propose various strategies towards fault repair of the faults detected. We implement our technique on top of the Apache TVM deep learning compiler, and we test it by conducting a preliminary fault localization analysis for the conversion of InceptionV3 from TF to TFLite. Our approach detected a fault in a common DNN converter tool, which introduced precision errors in weights, reducing model accuracy. After our fault localization, we repaired the issue, reducing our conversion error to zero

DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal mapping on hardware accelerators during model deployment, which may lead to timing uncertainty and erroneous behavior. Mapping on hardware accelerators is done using multiple software components like deep learning frameworks, compilers, and device libraries, that we refer to as the computational environment. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment, as the impact of parameters like deep learning frameworks, compiler optimizations, and hardware devices on model performance and correctness is not yet well understood. In this paper we present a differential testing framework, DeltaNN, that allows us to assess the impact of different computational environment parameters on the performance of image recognition models during deployment, post training. DeltaNN generates different implementations of a given image recognition model for variations in environment parameters, namely, deep learning frameworks, compiler optimizations and hardware devices and analyzes differences in model performance as a result. Using DeltaNN, we conduct an empirical study of robustness analysis of three popular image recognition models using the ImageNet dataset. We report the impact in terms of misclassifications and inference time differences across different settings. In total, we observed up to 72% output label differences across deep learning frameworks, and up to 81% unexpected performance degradation in terms of inference time, when applying compiler optimizations

Practitioners’ multi-disciplinary perspectives of soccer talent according to phase of development and playing position

The study aimed to establish the perceived importance that academy soccer practitioners placed on technical/tactical, physical, psycho-social player attributes during player selection and explore whether perceptions change according to Elite Player Performance Plan phase. Seventy academy practitioners working within Elite Player Performance Plan programs (Category 1: n = 29; Category 2: n = 13 and Category 3: n = 28) completed an online survey. Psychological factors were rated significantly (p ≤ 0.01) higher than sociological, technical/tactical, and physical factors, with recruitment staff specifically valuing psychological factors significantly (p ≤ 0.01) more than medical staff. Youth Development phase practitioners valued sociological factors significantly (p < 0.05) more than in the Foundation phase, which was also true for physical factors. Practitioners indicated significant positional differences for most physical and technical/tactical attributes. There was no playing position effect for relative age effect or maturity. Between playing position variance of outfield players for most technical and physical attributes increased according to advancing Elite Player Performance Plan phase. Attitudes to holistic talent identification criteria likely change according to practitioner role. Therefore, this study provides evidence to suggest that Elite Player Performance Plan practitioners place less perceived importance on enhanced maturity status and relative age of players but does indicate an enhancing and significant positional preference for physical and technical/tactical attributes. Suggesting that practitioners are less likely to (de)select players based on transient, maturity-related attributes and instead place greater emphasis on specialist physical/technical position-specific attributes as players navigate the Elite Player Performance Plan pathway towards professional status

Altered protein dynamics of disease-associated lamin A mutants

BACKGROUND: Recent interest in the function of the nuclear lamina has been provoked by the discovery of lamin A/C mutations in the laminopathy diseases. However, it is not understood why mutations in lamin A give such a range of tissue-specific phenotypes. Part of the problem in rationalising genotype-phenotype correlations in the laminopathies is our lack of understanding of the function of normal and mutant lamin A. To investigate this we have used photobleaching in human cells to analyse the dynamics of wild-type and mutant lamin A protein at the nuclear periphery. RESULTS: We have found that a large proportion of wild-type lamin A at the nuclear periphery is immobile, but that there is some slow movement of lamin A within the nuclear lamina. The mobility of an R482W mutant lamin A was indistinguishable from wild-type, but increased mobility of L85R and L530P mutant proteins within the nuclear lamina was found. However, the N195K mutant shows the most enhanced protein mobility, both within the nucleoplasm and within the lamina. CONCLUSION: The slow kinetics of lamin A movement is compatible with its incorporation into a stable polymer that only exchanges subunits very slowly. All of the myopathy-associated lamin A mutants that we have studied show increased protein movement compared with wild-type. In contrast, the dynamic behaviour of the lipodystrophy-associated lamin A mutant was indistinguishable from wild-type. This supports the hypothesis that the underlying defect in lamin A function is quite distinct in the laminopathies that affect striated muscle, compared to the diseases that affect adipose tissue. Our data are consistent with an alteration in the stability of the lamin A molecules within the higher-order polymer at the nuclear lamina in myopathies