Drawing QSers' mind : a cognitively-informed critical metaphor analysis tracing the cultural model of the Quantified Self

With the spread of digital surveillance technologies from the domains of military and medical to those of personal and everyday, we have seen invention of novel metaphors to conceptualise our daily practices as well as our selves in relation to such emerging technologies as Big Data, which aggregate, crunch and sort our personal information collected from the sensors and cameras embedded ubiquitously now in our living environment, known as an ‘infosphere.’ They sort our selves in a new way, thus altering our self-concept and informing a new, data-driven self culture. The epitome of this trend is the Quantified Self (QS) movement. The participants, known as QSers and who are prosumers, seek ‘self knowledge through numbers’ generated by commercial self-monitoring devices, such as Fitbit and Mi Band. They put their bodily activities under self-surveillance for becoming the experts of self-management and self-optimisation. The global popularisation of QS culture has three implications for our human condition. First, it creates a sham utopia. The platform economy brings into being a precariat, who struggle daily for security and success. In response, the QS gadget companies advertise to a white, middle-class clientele that they can offer them both. Second, it promotes neoliberal reflexive practices and discourse of selfhood. QS culture is historically rooted in the American success culture, which prizes individual success made through self-reliance and continuous self-reinvention. This culture foregrounds personal agency in influencing individuals’ living conditions and life chances, while discounting social structural factors. Third, it makes privacy, hence self-reinvention, problematic. When it comes to the issue of ownership of QSers’ self-data, it is ambiguous to whom they belong and whether the QSers can still enjoy ‘the right to forget’ once the data are uploaded to the cloud. Sociologists have studied the QS culture and its relations to neoliberalism, but they have not tackled the QSers’ subjective experience, particularly their own discourse and mind, in a systematic manner. Meanwhile, although cognitive linguists have had the tools to probe QSers’ discourse, mind and culture, or the cognitive schemas and structures that influence QSers’ beliefs and behaviours, they have not done so, either. Therefore, my thesis contributes to the QS research by cross-fertilising, or transgressing the boundaries of, the disciplines, adding to it another dimension of cognitively-informed critical metaphor analysis of QSers’ mind. I have applied critical discourse analysis for both literature review and empirical analysis. For the empirical chapters, I have systematically mapped out the relations between a QSer’s use of conceptual metaphors in a blog post and the underlying cognitive schemas, which constitute a cultural model of Quantified Self for a sample consisting of a small corpus (52,177 words in total). I used the methods of MIP and SMA to identify the linguistic, conceptual and systematic metaphors in a prototypical blog post, sampled from my proprietary corpus of 40 unique QSers’ blog texts. Based on the identifications, I further traced three metaphor trajectories, or the blogger’s thought patterns, that involved the self, QS tools and data. I found that 1) the blogger thought his HEALTH CONDITIONS WERE OBJECTS that could be managed and controlled with hard work and help from self-monitoring devices, thus giving him a sense of self-made success and being in control. 2) He thought the QS TOOLS WERE PEOPLE, who were productive, capable, intelligent and friendly. This reflects the infosphere’s structural influence on people’s cognition, which decentres the humans and places them on par with other informational agents. 3) He conceived that his DATA WERE VALUABLE RESOURCES, whose ownership was unclear. Meanwhile, alternative metaphors that were relegated to the background by the QS culture were revived and discussed along these trajectories. Altogether, they have demonstrated the framing effects of QS metaphors, i.e. the metaphors can both enable and constrain a QSer’s conceptualisation of self in connection with data and self-control

Chernoff Information in Community Detection

In network inference applications, it is desirable to detect community structure, i.e., cluster vertices into potential blocks. Beyond adjacency matrices, many real-world networks also involve vertex covariates that may carry information about underlying block structure. Since accurate inference on random networks depends on exploiting all available signal, we need scalable algorithms that can incorporate both network connectivity data and additional insight from vertex covariates. In addition, it can be prohibitively expensive to observe the entire graph in many real applications, especially for large graphs. Thus it becomes essential to identify vertices that have the most impact on block structure and only check whether there are edges between them given a limited budget. To assess the effects of vertex covariates on block recovery, we consider two model-based spectral algorithms. The first algorithm uses only the adjacency matrix, and directly estimates the block assignments. The second algorithm incorporates both the adjacency matrix and the vertex covariates into the estimation of block assignments. We employ Chernoff information to analytically compare the algorithms’ performance and derive the information-theoretic Chernoff ratio for certain models of interest. Analytic results and simulations suggest that the second algorithm is often preferred: one can better estimate the induced block assignments by first estimating the effect of vertex covariates. In addition, real data experiments also indicate that the second algorithm has the advantage of revealing underlying block structure while considering observed vertex heterogeneity in real applications. Moreover, we propose a dynamic network sampling scheme to optimize block recovery for stochastic blockmodel in the case where it is prohibitively expensive to observe the entire graph. Theoretically, we provide justification of our proposed Chernoff-optimal dynamic sampling scheme via the Chernoff information. Practically, we evaluate the performance of our method on several real datasets from different domains. Both theoretically and practically results suggest that our method can identify vertices that have the most impact on block structure so that one can only check whether there are edges between them to save significant resources but still recover the block structure

Drawing QSers' mind : a cognitively-informed critical metaphor analysis tracing the cultural model of the Quantified Self

With the spread of digital surveillance technologies from the domains of military and medical to those of personal and everyday, we have seen invention of novel metaphors to conceptualise our daily practices as well as our selves in relation to such emerging technologies as Big Data, which aggregate, crunch and sort our personal information collected from the sensors and cameras embedded ubiquitously now in our living environment, known as an ‘infosphere.’ They sort our selves in a new way, thus altering our self-concept and informing a new, data-driven self culture. The epitome of this trend is the Quantified Self (QS) movement. The participants, known as QSers and who are prosumers, seek ‘self knowledge through numbers’ generated by commercial self-monitoring devices, such as Fitbit and Mi Band. They put their bodily activities under self-surveillance for becoming the experts of self-management and self-optimisation. The global popularisation of QS culture has three implications for our human condition. First, it creates a sham utopia. The platform economy brings into being a precariat, who struggle daily for security and success. In response, the QS gadget companies advertise to a white, middle-class clientele that they can offer them both. Second, it promotes neoliberal reflexive practices and discourse of selfhood. QS culture is historically rooted in the American success culture, which prizes individual success made through self-reliance and continuous self-reinvention. This culture foregrounds personal agency in influencing individuals’ living conditions and life chances, while discounting social structural factors. Third, it makes privacy, hence self-reinvention, problematic. When it comes to the issue of ownership of QSers’ self-data, it is ambiguous to whom they belong and whether the QSers can still enjoy ‘the right to forget’ once the data are uploaded to the cloud. Sociologists have studied the QS culture and its relations to neoliberalism, but they have not tackled the QSers’ subjective experience, particularly their own discourse and mind, in a systematic manner. Meanwhile, although cognitive linguists have had the tools to probe QSers’ discourse, mind and culture, or the cognitive schemas and structures that influence QSers’ beliefs and behaviours, they have not done so, either. Therefore, my thesis contributes to the QS research by cross-fertilising, or transgressing the boundaries of, the disciplines, adding to it another dimension of cognitively-informed critical metaphor analysis of QSers’ mind. I have applied critical discourse analysis for both literature review and empirical analysis. For the empirical chapters, I have systematically mapped out the relations between a QSer’s use of conceptual metaphors in a blog post and the underlying cognitive schemas, which constitute a cultural model of Quantified Self for a sample consisting of a small corpus (52,177 words in total). I used the methods of MIP and SMA to identify the linguistic, conceptual and systematic metaphors in a prototypical blog post, sampled from my proprietary corpus of 40 unique QSers’ blog texts. Based on the identifications, I further traced three metaphor trajectories, or the blogger’s thought patterns, that involved the self, QS tools and data. I found that 1) the blogger thought his HEALTH CONDITIONS WERE OBJECTS that could be managed and controlled with hard work and help from self-monitoring devices, thus giving him a sense of self-made success and being in control. 2) He thought the QS TOOLS WERE PEOPLE, who were productive, capable, intelligent and friendly. This reflects the infosphere’s structural influence on people’s cognition, which decentres the humans and places them on par with other informational agents. 3) He conceived that his DATA WERE VALUABLE RESOURCES, whose ownership was unclear. Meanwhile, alternative metaphors that were relegated to the background by the QS culture were revived and discussed along these trajectories. Altogether, they have demonstrated the framing effects of QS metaphors, i.e. the metaphors can both enable and constrain a QSer’s conceptualisation of self in connection with data and self-control

Ancestry-informative markers for African Americans based on the Affymetrix Pan-African genotyping array

Genetic admixture has been utilized as a tool for identifying loci associated with complex traits and diseases in recently admixed populations such as African Americans. In particular, admixture mapping is an efficient approach to identifying genetic basis for those complex diseases with substantial racial or ethnic disparities. Though current advances in admixture mapping algorithms may utilize the entire panel of SNPs, providing ancestry-informative markers (AIMs) that can differentiate parental populations and estimate ancestry proportions in an admixed population may particularly benefit admixture mapping in studies of limited samples, help identify unsuitable individuals (e.g., through genotyping the most informative ancestry markers) before starting large genome-wide association studies (GWAS), or guide larger scale targeted deep re-sequencing for determining specific disease-causing variants. Defining panels of AIMs based on commercial, high-throughput genotyping platforms will facilitate the utilization of these platforms for simultaneous admixture mapping of complex traits and diseases, in addition to conventional GWAS. Here, we describe AIMs detected based on the Shannon Information Content (SIC) or Fst for African Americans with genome-wide coverage that were selected from ∼2.3 million single nucleotide polymorphisms (SNPs) covered by the Affymetrix Axiom Pan-African array, a newly developed genotyping platform optimized for individuals of African ancestry

A General Traffic Flow Prediction Approach Based on Spatial-Temporal Graph Attention

Funding Information: This work was supported in part by the Science and Technology Project of Hunan Provincial Communications Department, China, under Grant 2018037, and in part by the National Nature Science Foundation of China under Grant 61674054. Publisher Copyright: © 2013 IEEE.Accurate and reliable traffic flow prediction is critical to the safe and stable deployment of intelligent transportation systems. However, it is very challenging due to the complex spatial and temporal dependence of traffic flows. Most existing works require the information of the traffic network structure and human intervention to model the spatial-temporal association of traffic data, resulting in low generality of the model and unsatisfactory prediction performance. In this paper, we propose a general spatial-temporal graph attention based dynamic graph convolutional network (GAGCN) model to predict traffic flow. GAGCN uses the graph attention networks to extract the spatial associations among nodes hidden in the traffic feature data automatically which can be dynamically adjusted over time. And then the graph convolution network is adjusted based on the spatial associations to extract the spatial features of the road network. Notably, the information of road network structure and human intervention are not required in GAGCN. The forecasting accuracy and the generality are evaluated with two real-world traffic datasets. Experimental results indicate that our GAGCN surpasses the state-of-the-art baselines on one of the two datasets.Peer reviewe

Deep Learning is Provably Robust to Symmetric Label Noise

Deep neural networks (DNNs) are capable of perfectly fitting the training data, including memorizing noisy data. It is commonly believed that memorization hurts generalization. Therefore, many recent works propose mitigation strategies to avoid noisy data or correct memorization. In this work, we step back and ask the question: Can deep learning be robust against massive label noise without any mitigation? We provide an affirmative answer for the case of symmetric label noise: We find that certain DNNs, including under-parameterized and over-parameterized models, can tolerate massive symmetric label noise up to the information-theoretic threshold. By appealing to classical statistical theory and universal consistency of DNNs, we prove that for multiclass classification, $L_1$-consistent DNN classifiers trained under symmetric label noise can achieve Bayes optimality asymptotically if the label noise probability is less than $\frac{K-1}{K}$, where $K \ge 2$ is the number of classes. Our results show that for symmetric label noise, no mitigation is necessary for $L_1$-consistent estimators. We conjecture that for general label noise, mitigation strategies that make use of the noisy data will outperform those that ignore the noisy data

Physical properties of noncentrosymmetric superconductor Ru7_7B3_3

Transition metal boride Ru$_7$B$_3$ was found to be a noncentrosymmetric superconductor with $T_{C}$ equal to 3.3 K. Superconducting and normal state properties of Ru$_7$B$_3$ were determined by a self-consistent analysis through resistivity($\rho_{xx}$ and $\rho_{xy}$), specific heat, lower critical field measurement and electronic band structure calculation. It is found that Ru$_7$B$_3$ belongs to an s-wave dominated single band superconductor with energy gap 0.5 meV and could be categorized into type II superconductor with weak electron-phonon coupling. Unusual 'kink' feature is clearly observed in field-broadening resistivity curves, suggesting the possible mixture of spin triplet induced by the lattice without inversion symmetry.Comment: 11 pages, 16 figures. submitted to Phys. Rev.