Supervised latent dirichlet allocation models for efficient activity representation

Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of efficiently representing the extracted features for classification to improve the overall performance. We introduce two generative supervised topic models, maximum entropy discrimination LDA (MedLDA) and class- specific simplex LDA (css-LDA), to encode the raw features suitable for discriminative SVM based classification. Unsupervised LDA models disconnect topic discovery from the classification task, hence yield poor results compared to the baseline Bag-of-words framework. On the other hand supervised LDA techniques learn the topic structure by considering the class labels and improve the recognition accuracy significantly. MedLDA maximizes likelihood and within class margins using max-margin techniques and yields a sparse highly discriminative topic structure; while in css-LDA separate class specific topics are learned instead of common set of topics across the entire dataset. In our representation first topics are learned and then each video is represented as a topic proportion vector, i.e. it can be comparable to a histogram of topics. Finally SVM classification is done on the learned topic proportion vector. We demonstrate the efficiency of the above two representation techniques through the experiments carried out in two popular datasets. Experimental results demonstrate significantly improved performance compared to the baseline Bag-of-features framework which uses kmeans to construct histogram of words from the feature vectors.

Racism, Representation and Photography

Representation of Aborigines by Aborigines and non -Aborigines; articles by Andrew Dewdney, Mervyn Biship, Alana Harris, Sandy Edwards, Rea Saunders, Ricky Maynard , Brenda Croft, Ruth Braunstein, Michael Riley, Huw Davies, Penny Taylor, Darlene McKenzie, Kurt Brereton and Eric Michaels, annotated separately.

Representation and authenticity of historic landscapes in Australia and China

Histories of past communities are embedded in landscapes around the world but many are suffering from material change or neglect of their fabric. This study was aimed at discovering and representing the authentic intangible experience of two historic landscapes for conservation purposes. A 2500 year old site in Yangzhou, China and a 2000 year old site on St Helena Island in Moreton Bay were found to be managed under two culturally different regimes of authenticity. This research has contributed to challenging the notion that there is only one way to conserve authenticity in historic landscapes of the Asia Pacific.

Mirror neurons, the representation of word meaning, and the foot of the third left frontal convolution

Previous neuroimaging research has attempted to demonstrate a preferential involvement of the human mirror neuron system (MNS) in the comprehension of effector-related action word (verb) meanings. These studies have assumed that Broca's area (or Brodmann's area 44) is the homologue of a monkey premotor area (F5) containing mouth and hand mirror neurons, and that action word meanings are shared with the mirror system due to a proposed link between speech and gestural communication. In an fMRI experiment, we investigated whether Broca's area shows mirror activity solely for effectors implicated in the MNS. Next, we examined the responses of empirically determined mirror areas during a language perception task comprising effector-specific action words, unrelated words and nonwords. We found overlapping activity for observation and execution of actions with all effectors studied, i.e., including the foot, despite there being no evidence of foot mirror neurons in the monkey or human brain. These "mirror" areas showed equivalent responses for action words, unrelated words and nonwords, with all of these stimuli showing increased responses relative to visual character strings. Our results support alternative explanations attributing mirror activity in Broca's area to covert verbalisation or hierarchical linearisation, and provide no evidence that the MNS makes a preferential contribution to comprehending action word meanings.

Does the microbiome play a causal role in spondyloarthritis?

The purpose of this study is to review the potential causal role of the microbiome in the pathogenesis of spondyloarthritis. The method used for the study is literature review. The microbiome plays a major role in educating the immune response. The microbiome is strongly implicated in inflammatory bowel disease which has clinical and genetic overlap with spondyloarthritis. The microbiome also plays a causal role in bowel and joint disease in HLA B27/human beta 2 microglobulin transgenic rats. The mechanism(s) by which HLA B27 could influence the microbiome is unknown but theories include an immune response gene selectivity, an effect on dendritic cell function, or a mucosal immunodeficiency. Bacteria are strongly implicated in the pathogenesis of spondyloarthritis. Studies to understand how HLA B27 affects bacterial ecosystems should be encouraged.

What is voice? Issues of identity and representation in the framing of reviews

There is a well-founded ethical concern in the present regarding the question Ήow can we include everybody's voice equally in the framing of reviews?' This paper is a response to the complexities that inhere in that question. It is not about Review of Educational Research (RER) as a specific site but about the systems of reasoning that construct the opening question about reviews and that suggest possible answers, including the response: 'What is voice?'

Leveraging Lean Production Philosophy for Rationalising a Non-Linear Representation of Construction Specifications

This paper presents an approach, based on Lean production philosophy, for rationalising the processes involved in the production of specification documents for construction projects. Current construction literature erroneously depicts the process for the creation of construction specifications as a linear one. This traditional understanding of the specification process often culminates in process-wastes. On the contrary, the evidence suggests that though generalised, the activities involved in producing specification documents are nonlinear. Drawing on the outcome of participant observation, this paper presents an optimised approach for representing construction specifications. Consequently, the actors typically involved in producing specification documents are identified, the processes suitable for automation are highlighted and the central role of tacit knowledge is integrated into a conceptual template of construction specifications. By applying the transformation, flow, value (TFV) theory of Lean production the paper argues that value creation can be realised by eliminating the wastes associated with the traditional preparation of specification documents with a view to integrating specifications in digital models such as Building Information Models (BIM). Therefore, the paper presents an approach for rationalising the TFV theory as a method for optimising current approaches for generating construction specifications based on a revised specification writing model.

CYP19A1 fine-mapping and Mendelian randomization: Estradiol is causal for endometrial cancer

Candidate gene studies have reported CYP19A1 variants to be associated with endometrial cancer and with estradiol (E2) concentrations. We analyzed 2937 single nucleotide polymorphisms (SNPs) in 6608 endometrial cancer cases and 37 925 controls and report the first genome wide-significant association between endometrial cancer and a CYP19A1 SNP (rs727479 in intron 2, P=4.8x10(-11)). SNP rs727479 was also among those most strongly associated with circulating E2 concentrations in 2767 post-menopausal controls (P=7.4x10(-8)). The observed endometrial cancer odds ratio per rs727479 A-allele (1.15, CI=1.11-1.21) is compatible with that predicted by the observed effect on E2 concentrations (1.09, CI=1.03-1.21), consistent with the hypothesis that endometrial cancer risk is driven by E2. From 28 candidate-causal SNPs, 12 co-located with three putative gene-regulatory elements and their risk alleles associated with higher CYP19A1 expression in bioinformatical analyses. For both phenotypes, the associations with rs727479 were stronger among women with a higher BMI (Pinteraction=0.034 and 0.066 respectively), suggesting a biologically plausible gene-environment interaction.

Multiple instance dictionary learning for activity representation

This paper presents an effective feature representation method in the context of activity recognition. Efficient and effective feature representation plays a crucial role not only in activity recognition, but also in a wide range of applications such as motion analysis, tracking, 3D scene understanding etc. In the context of activity recognition, local features are increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational requirements, their performance is still limited for real world applications due to a lack of contextual information and models not being tailored to specific activities. We propose a new activity representation framework to address the shortcomings of the popular, but simple bag-of-words approach. In our framework, first multiple instance SVM (mi-SVM) is used to identify positive features for each action category and the k-means algorithm is used to generate a codebook. Then locality-constrained linear coding is used to encode the features into the generated codebook, followed by spatio-temporal pyramid pooling to convey the spatio-temporal statistics. Finally, an SVM is used to classify the videos. Experiments carried out on two popular datasets with varying complexity demonstrate significant performance improvement over the base-line bag-of-feature method.

Cointegration and causal relationships between energy consumption and output: Assessing the evidence from Australia

In this paper, we describe our investigation of the cointegration and causal relationships between energy consumption and economic output in Australia over a period of five decades. The framework used in this paper is the single-sector aggregate production function, which is the first comprehensive approach used in an Australian study of this type to include energy, capital and labour as separate inputs of production. The empirical evidence points to a cointegration relationship between energy and output and implies that energy is an important variable in the cointegration space, as are conventional inputs capital and labour. We also find some evidence of bidirectional causality between GDP and energy use. Although the evidence of causality from energy use to GDP was relatively weak when using the thermal aggregate of energy use, once energy consumption was adjusted for energy quality, we found strong evidence of Granger causality from energy use to GDP in Australia over the investigated period. The results are robust, irrespective of the assumptions of linear trends in the cointegration models, and are applicable for different econometric approaches.

Evaluating viewpoint entropy for ribbon representation of protein structure

While many measures of viewpoint goodness have been proposed in computer graphics, none have been evaluated for ribbon representations of protein secondary structure. To fill this gap, we conducted a user study on Amazon’s Mechanical Turk platform, collecting human viewpoint preferences from 65 participants for 4 representative su- perfamilies of protein domains. In particular, we evaluated viewpoint entropy, which was previously shown to be a good predictor for human viewpoint preference of other, mostly non-abstract objects. In a second study, we asked 7 molecular biology experts to find the best viewpoint of the same protein domains and compared their choices with viewpoint entropy. Our results show that viewpoint entropy overall is a significant predictor of human viewpoint preference for ribbon representations of protein secondary structure. However, the accuracy is highly dependent on the complexity of the structure: while most participants agree on good viewpoints for small, non-globular structures with few secondary structure elements, viewpoint preference varies considerably for complex structures. Finally, experts tend to choose viewpoints of both low and high viewpoint entropy to emphasize different aspects of the respective structure.