Identification of acoustic emission wave modes for accurate source location in plate-like structures

Acoustic emission (AE) technique is a popular tool used for structural health monitoring of civil, mechanical and aerospace structures. It is a non-destructive method based on rapid release of energy within a material by crack initiation or growth in the form of stress waves. Recording of these waves by means of sensors and subsequent analysis of the recorded signals convey information about the nature of the source. Ability to locate the source of stress waves is an important advantage of AE technique; but as AE waves travel in various modes and may undergo mode conversions, understanding of the modes (‘modal analysis’) is often necessary in order to determine source location accurately. This paper presents results of experiments aimed at finding locations of artificial AE sources on a thin plate and identifying wave modes in the recorded signal waveforms. Different source locating techniques will be investigated and importance of wave mode identification will be explored.

Carbon pools and fluxes along an environmental gradient in northern Arizona

Carbon pools and fluxes were quantified along an environmental gradient in northern Arizona. Data are presented on vegetation, litter, and soil C pools and soil CO2 fluxes from ecosystems ranging from shrub-steppe through woodlands to coniferous forest and the ecotones in between. Carbon pool sizes and fluxes in these semiarid ecosystems vary with temperature and precipitation and are strongly influenced by canopy cover. Ecosystem respiration is approximately 50 percent greater in the more mesic, forest environment than in the dry shrub-steppe environment. Soil respiration rates within a site vary seasonally with temperature but appear to be constrained by low soil moisture during dry summer months, when approximately 75% of total annual soil respiration occurs. Total annual amount of CO2 respired across all sites is positively correlated with annual precipitation and negatively correlated with temperature. Results suggest that changes in the amount and periodicity of precipitation will have a greater effect on C pools and fluxes than will changes in temperature :in the semiarid Southwestern United States.

Implications of patterns of carbon pools and fluxes across a semiarid environmental gradient

Landscape scale environmental gradients present variable spatial patterns and ecological processes caused by climate, topography and soil characteristics and, as such, offer candidate sites to study environmental change. Data are presented on the spatial pattern of dominant species, biomass, and carbon pools and the temporal pattern of fluxes across a transitional zone shifting from Great Basin Desert scrub, up through pinyon-juniper woodlands and into ponderosa pine forest and the ecotones between each vegetation type. The mean annual temperature (MAT) difference across the gradient is approximately 3 degrees C from bottom to top (MAT 8.5-5.5) and annual precipitation averages from 320 to 530 mm/yr, respectively. The stems of the dominant woody vegetation approach a random spatial pattern across the entire gradient, while the canopy cover shows a clustered pattern. The size of the clusters increases with elevation according to available soil moisture which in turn affects available nutrient resources. The total density of woody species declines with increasing soil moisture along the gl-adient, but total biomass increases. Belowground carbon and nutrient pools change from a heterogenous to a homogenous distribution on either side of the woodlands. Although temperature controls the: seasonal patterns of carbon efflux from the soils, soil moisture appears to be the primary driving variable, but response differs underneath the different dominant species, Similarly, decomposition of dominant litter occurs faster-at the cooler and more moist sites, but differs within sites due to litter quality of the different species. The spatial pattern of these communities provides information on the direction of future changes, The ecological processes that we documented are not statistically different in the ecotones as compared to the: adjoining communities, but are different at sites above the woodland than those below the woodland. We speculate that an increase in MAT will have a major impact on C pools and C sequestering and release processes in these semiarid landscapes. However, the impact will be primarily related to moisture availability rather than direct effects of an increase in temperature. (C) 1998 Elsevier Science B.V.

Differentially Expressed Protein Profile of Human Dental Pulp Cells in the Early Process of Odontoblast-like Differentiation In Vitro

Dental pulp cells (DPCs) are capable of differentiating into odontoblasts that secrete reparative dentin after pulp injury. The molecular mechanisms governing reparative dentinogenesis are yet to be fully understood. Here we investigated the differential protein profile of human DPCs undergoing odontogenic induction for 7 days. Using two-dimensional differential gel electrophoresis coupled with matrix-assisted laser adsorption ionization time of flight mass spectrometry, 2 3 protein spots related to the early odontogenic differentiation were identified. These proteins included cytoskeleton proteins, nuclear proteins, cell membrane-bound molecules, proteins involved in matrix synthesis, and metabolic enzymes. The expression of four identified proteins, which were heteronuclear ribonuclear proteins C, annexin VI, collagen type VI, and matrilin-2, was confirmed by Western blot and real-time realtime polymerase chain reaction analyses. This study generated a proteome reference map during odontoblast- like differentiation of human DPCs, which will be valuable to better understand the underlying molecular mechanisms in odontoblast-like differentiation.

‘Universities are not the safe places we would like to think they are, but they are getting safer’: Indigenous women academics in higher education

This paper outlines some of the experiences of Indigenous women academics in higher education. The author offers these experiences, not to position Indigenous women academics as victims, but to expose the problematic nature of racism, systemic marginalisation, white race privilege and radicalised subjectivity played out within Australian higher education institutions. By utilising the experiences and examples she seeks to bring the theoretical into the everyday world of being Indigenous within academe. In analysing these examples, the author reveals the relationships between oppression, white race privilege, institutional privilege and the epistemology that maintains them. She argues that, in moving from a position of being silent to speaking about what she has witnessed and experienced, she is able to move from the position of object to subject and gain a form of liberated voice (hooks 1989: 9) for herself and other Indigenous women. She seeks to challenge the practices within universities that continue to subjugate Indigenous women academics.

It really is black and white when you look at it like this : reporting on a study into Australian professors' research and teaching priorities

Despite optimistic claims about the research-teaching nexus, Australian academics still face tension between research and teaching. The teaching and research priorities, beliefs and behaviours of 70 Professorial and Associate Professorial academics in Science, Information Technology and Engineering were examined in this study. The academics from 4 faculties in 3 Australian universities, were asked to rank 16 research activities and 16 matched learning and teaching (L&T) activities from each of three perspectives: job satisfaction, leadership behaviour, and perceptions of professional importance. The findings, which were remarkably consistent across the three universities, were unequivocally in favour of Research. The only L&T activity that was ranked consistently well was “Improving student satisfaction ratings for Teaching”. The data demonstrates that Australian government and university initiatives to raise the status of L&T activity are not impacting significantly on Australia’s future leaders of university learning.

Insulin-like growth factor-i : vitronectin complex-induced changes in gene expression effect breast cell survival and migration

Recent studies have demonstrated that IGF-I associates with VN through IGF-binding proteins (IGFBP) which in turn modulate IGF-stimulated biological functions such as cell proliferation, attachment and migration. Since IGFs play important roles in transformation and progression of breast tumours, we aimed to describe the effects of IGF-I:IGFBP:VN complexes on breast cell function and to dissect mechanisms underlying these responses. In this study we demonstrate that substrate-bound IGF-I:IGFBP:VN complexes are potent stimulators of MCF-7 breast cell survival, which is mediated by a transient activation of ERK/MAPK and sustained activation of PI3-K/AKT pathways. Furthermore, use of pharmacological inhibitors of the MAPK and PI3-K pathways confirms that both pathways are involved in IGF-I:IGFBP:VN complex-mediated increased cell survival. Microarray analysis of cells stimulated to migrate in response to IGF-I:IGFBP:VN complexes identified differential expression of genes with previously reported roles in migration, invasion and survival (Ephrin-B2, Sharp-2, Tissue-factor, Stratifin, PAI-1, IRS-1). These changes were not detected when the IGF-I analogue (\[L24]\[A31]-IGF-I), which fails to bind to the IGF-I receptor, was substituted; confirming the IGF-I-dependent differential expression of genes associated with enhanced cell migration. Taken together, these studies have established that IGF-I:IGFBP:VN complexes enhance breast cell migration and survival, processes central to facilitating metastasis. This study highlights the interdependence of ECM and growth factor interactions in biological functions critical for metastasis and identifies potential novel therapeutic targets directed at preventing breast cancer progression.

‘It’s not like we’re going to jump them’: how transgressing heteronormativity shapes police interactions with LGBT young people

This paper explores how visibly transgressing heteronormativity shapes police interactions with LGBT young people. While research evidences how sexually and gender diverse bodies can be abused in schools, policing is overlooked. Interviews with 35 LGBT young people demonstrate how bodies transgressing heteronormativity (that is, non-heteronormative bodies) mediate their policing experiences in Queensland, Australia. Drawing on Foucault, Butler, and others, the paper suggests police interactions and use of discretion with LGBT young people was informed by non-heteronormative bodies discursively performing queerness in ways read by police. The paper concludes noting tensions produced for youthful LGBT bodies in public spaces.

Infinite-horizon policy-gradient estimation

Gradient-based approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-function methods. In this paper we introduce GPOMDP, a simulation-based algorithm for generating a biased estimate of the gradient of the average reward in Partially Observable Markov Decision Processes (POMDPs) controlled by parameterized stochastic policies. A similar algorithm was proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm's chief advantages are that it requires storage of only twice the number of policy parameters, uses one free parameter β ∈ [0,1) (which has a natural interpretation in terms of bias-variance trade-off), and requires no knowledge of the underlying state. We prove convergence of GPOMDP, and show how the correct choice of the parameter β is related to the mixing time of the controlled POMDP. We briefly describe extensions of GPOMDP to controlled Markov chains, continuous state, observation and control spaces, multiple-agents, higher-order derivatives, and a version for training stochastic policies with internal states. In a companion paper (Baxter, Bartlett, & Weaver, 2001) we show how the gradient estimates generated by GPOMDP can be used in both a traditional stochastic gradient algorithm and a conjugate-gradient procedure to find local optima of the average reward. ©2001 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

Exponentiated gradient algorithms for large-margin structured classification

We consider the problem of structured classification, where the task is to predict a label y from an input x, and y has meaningful internal structure. Our framework includes supervised training of Markov random fields and weighted context-free grammars as special cases. We describe an algorithm that solves the large-margin optimization problem defined in [12], using an exponential-family (Gibbs distribution) representation of structured objects. The algorithm is efficient—even in cases where the number of labels y is exponential in size—provided that certain expectations under Gibbs distributions can be calculated efficiently. The method for structured labels relies on a more general result, specifically the application of exponentiated gradient updates [7, 8] to quadratic programs.

Adaptive online gradient descent

We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.