178 research outputs found
Beyond dispute: EEOC v. Sears and the politics of gender, class, and affirmative action, 1968-1986
No full textIn 1973 the federal government began investigating Sears, Roebuck & Co. for discrimination against women employees because, among other things, its commissioned sales force was predominantly male. At trial, Sears argued that women were not interested in commissioned positions because they were too demanding. The decision, which found Sears not liable for discrimination, sparked a great deal of debate among feminists and in the media over the expert witness testimony of two women's historians. Employing oral histories, organizational records, court documents, and media accounts I use the case as a lens through which to view broader historical issues regarding women and work, social class, and national political changes during the 1970s and 1980s. I give a detailed social history of the case, focusing on the players and events that affected the outcome, and a legal-political history of the time period as reflected through developments in the case.
This dissertation recovers cross-class organizing at the beginning of a case known only for its divisiveness. It examines dynamics within second-wave feminism, including the implications of a shift in focus to the Equal Rights Amendment, the role of the equality/difference dilemma, and whether the loss in court to Sears was merely a defeat. The company's corporate personality foreshadowed the lengths to which it would go to fight the EEOC. I also reveal a significant amount of resistance within the first Reagan administration to changes in civil rights policy and show that the case continued with strong support despite ambivalence on the part of the government. The long litigation process ensured a case that looked very different from its beginnings. The feminist debate surrounding the trial highlights the end of a much longer story and distracts attention from critical issues. I argue for de-centering this feminist dispute, and remembering the case instead for what it can tell us about debates over affirmative action, attempts by women activists alternatively to work within and challenge national policy, the limits of the law and second-wave feminism for improving the lives of working women, and the reasons why the workplace revolution for women stalled and remains so today.Ph.D.Includes bibliographical references (p. 394-403)by Emily Beth Zuckerma
Jonathan Ned Katz Author Event: The Daring Life and Dangerous Times of Eve Adam
No full text“The Daring Life and Dangerous Times of Eve Adams,” interview with author, Jonathan Ned Katz, moderated by Emily Weiner (WWU) and organized by Congregation Beth Israel
Detection and localization of aerosol releases from sparse sensor measurements
No full textThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 111-114).In this thesis we focus on addressing two aspects pertinent to biological release detection. The first is that of detecting and localizing an aerosolized particle release using a sparse array of sensors. The problem is challenging for several reasons. It is often the case that sensors are costly and consequently only a sparse deployment is possible. Additionally, while dynamic models can be formulated in many environmental conditions, the underlying model parameters may not be precisely known. The combination of these two issues impacts the effectiveness of inference approaches. We restrict ourselves to propagation models consisting of diffusion plus transport according to a Gaussian puff model. We derive optimal inference algorithms utilizing sparse sensor measurements, provided the model parameterization is known precisely. The primary assumptions are that the mean wind field is deterministically known and that the Gaussian puff model is valid. Under these assumptions, we characterize the change in performance of detection, time-to-detection and localization as a function of the number of sensors. We then examine some performance impacts when the underlying dynamical model deviates from the assumed model. In addition to detecting an abrupt change in particles in an environment, it is also important to be able to classify the releases as not all contaminants are of interest. For this reason, the second aspect of addressed is feature extraction, a stage where sensor measurements are reduced to a set of pertinent features that can be used as an input to the classifier.(cont.) Shift invariance of the feature set is critical and thus the Dual Tree Complex Wavelet Transform (DT CWT) is proposed as the wavelet feature domain.by Emily Beth Fox.M.Eng
Bayesian Nonparametric Methods for Learning Markov Switching Processes
No full textIn this article, we explored a Bayesian nonparametric approach to learning Markov switching processes. This framework requires one to make fewer assumptions about the underlying dynamics, and thereby allows the data to drive the complexity of the inferred model. We began by examining a Bayesian nonparametric HMM, the sticky HDPHMM, that uses a hierarchical DP prior to regularize an unbounded mode space. We then considered extensions to Markov switching processes with richer, conditionally linear dynamics, including the HDP-AR-HMM and HDP-SLDS. We concluded by considering methods for transferring knowledge among multiple related time series. We argued that a featural representation is more appropriate than a rigid global clustering, as it encourages sharing of behaviors among objects while still allowing sequence-specific variability. In this context, the beta process provides an appealing alternative to the DP
Kick, bollocks and scramble: an examination of power and creative decision making in the production process during the golden era of British music videos 1995-2001
No full textIn the golden era of British music video production 1995 – 2001 the responsibilities of a feature film producer were shared by the producer, executive producer, director and video commissioner. The author examines the roles of each, and argues that Pardo’s framework (2010 for evaluating the creative contribution of producers obscures the direct budgetary power of producers in this particular sector. She argues that more empirical research should be conducted on the role of the producer in sectors of the British screen industry other than feature films and television
Izvori informacija u dostupnim EBSCO bazama podataka za istraživanja u visokom školstvu u Srbiji = Academic research in Serbia and available database resources
No full textUniversities in Serbia have access to large amounts of quality information through online full text databases. Specific details regarding the world’s two most comprehensive full text research data-bases, Academic Search Premier and Business Source Premier are provided. The paper examines which databases are strongest in each discipline, and covers issues such as the availability of journals most-cited, full text formats, peer-review status, embargo periods, backfills, and other important facets. Additional information depicts reasons for tremendous increase in the availability of information in the Serbia, and the value that these resources bring to researchers in universities
Nonparametric Bayesian identification of jump systems with sparse dependencies
No full textMany nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such Markov jump linear systems: the switching linear dynamical system (SLDS) and the switching vector autoregressive (S-VAR) process. In this paper, we present a nonparametric Bayesian approach to identifying an unknown number of persistent, smooth dynamical modes by utilizing a hierarchical Dirichlet process prior. We additionally employ automatic relevance determination to infer a sparse set of dynamic dependencies. The utility and flexibility of our models are demonstrated on synthetic data and a set of honey bee dances.United States. Air Force Office of Scientific Research. Multidisciplinary University Research Initiative (Grant FA9550-06-1-0324)United States. Army Research Office (Grant W911NF-06-1- 0076
MFA10 (MFA 2010)
No full textCatalogue of a culminating student exhibition held at the Mildred Lane Kemper Art Museum in 2010. Content includes Foreword / Buzz Spector -- Thinking as making / Robert Gero -- A new set of conversations / Patricia Olynyk -- MFA 2010 graduates. Clyde Ashby / Aaron Bos-Wahl / Andrew Cozzens / John Early / Ryan James Fabel / Joel Fullerton / Mary Beth Hassan / Wenting Hsu / John Nicholas Hutchings/ Dani Kantrowitz / Larry Keaty / Mamie Korpela / Paola Laterza / Mad Mohre / Emily Moorhead / Jonathan Muehlke / Jessa Richardson / Nicolette Ross / Carlie Trosclair / About the Sam Fox School.https://openscholarship.wustl.edu/books/1007/thumbnail.jp
Initial sedimentation processes and the early geological evolution of three maar craters, Hindon Maar Complex, Otago
No full textThe Hindon Maar Complex, located near Hindon, 25 km NW of Dunedin, consists of four volcanic craters infilled by lake sediments and mass flow deposits. The maars form part of the Waipiata volcanic group, which was active from 25–9 Ma. Drilling of Maar 1, Maar 3 and a sediment-filled depression between the inferred locations of Maar 2 and Maar 3 revealed up to 10 m of laminated biogenic lake sediments underlain by siliciclastic mass flow deposits. This project describes the siliciclastic deposits from these drill-cores and from limited outcrops of Maar 2. Physical properties, stratigraphic logs, X-ray fluorescence spectrometry, Also XRD, SEM and optical petrography are used to create a facies analysis and infer the processes of initial sedimentation into the maar craters following the maar-forming eruptions.
A total of 10 facies are identified within the Hindon Maar complex. Facies 1–6 show the progression of crater wall stabilisation from rockfalls which occurred weeks to years after the initial eruption to the formation of organic rich lake-sediment units which would have been deposited hundreds of years post eruption. Facies 1–6 have a high proportion of mica, quartz and schist grains, indicating extensive incorporation of the country rock. Nonconsolidated mottles are found throughout facies 2–5 and are interpreted as the remnants of weathered pyroclastic materials which were sourced from failures in the upper crater walls or tephra ring.
Facies 1 is a laminated carbonaceous lake sediment consisting almost entirely of organic matter. Facies 2 is a nonconsolidated laminated silty clay formed by debris flows. Facies 3 is a consolidated silty fine sand with discrete gravel lenses and 5% mottles formed as a result of mass flows. Facies 4 is a fine gravel breccia comprising up to 35% mottles, which is formed by mass flows originating in the upper crater or tephra ring. Facies 5 is a poorly consolidated fine gravel breccia which formed as a result of turbidity currents. Facies 6 is loose schist and quartz grains of fine gravel to medium pebble size, formed by rockfalls into the early lake.
Facies 7 is a silty clay which is geochemically and mineralogically different to all other facies. Facies 7 exhibits convoluted bedding and is an example of a slump deposit occurring locally in Maar 3. Due to its significant differences to all other facies, it is assumed to have had a different parent material.
Facies 8–10 are found in the area of a major gravity anomaly associated with Maar 3. These deposits are composed of silt and clay sized particles and have elevated Fe. They have high magnetic susceptibility and density and are interpreted as weathered pyroclastic material of the tephra ring.
The infilling of Maar 1 occurred initially as a series of coarse-grained mass flows into the crater (Facies 6 and Facies 5). Once the crater wall began to stabilise and the occurrence of rockfalls decreased, Facies 4 was deposited, resulting from high crater wall collapse. This was followed by furthered, small cater wall collapse of Facies 3. Facies 2 was later deposited once crater wall failures halted, and resulted from the erosion, rather than the collapse, of the crater wall. Once the crater wall was fully vegetated, Facies 1 began to accumulate, forming thick organic rich deposits, indicating the maar lake was stratified and had an anoxic bottom layer.
The infilling of Maar 3 followed the same pattern. However, there is a significant lateral offset between some units of the same facies, which may indicate more complex processes, such as faulting. The exact history of Maar 3 cannot be determined with the information acquired
Bayesian nonparametric learning of complex dynamical phenomena
No full textThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 257-270).The complexity of many dynamical phenomena precludes the use of linear models for which exact analytic techniques are available. However, inference on standard nonlinear models quickly becomes intractable. In some cases, Markov switching processes, with switches between a set of simpler models, are employed to describe the observed dynamics. Such models typically rely on pre-specifying the number of Markov modes. In this thesis, we instead take a Bayesian nonparametric approach in defining a prior on the model parameters that allows for flexibility in the complexity of the learned model and for development of efficient inference algorithms. We start by considering dynamical phenomena that can be well-modeled as a hidden discrete Markov process, but in which there is uncertainty about the cardinality of the state space. The standard finite state hidden Markov model (HMM) has been widely applied in speech recognition, digital communications, and bioinformatics, amongst other fields. Through the use of the hierarchical Dirichlet process (HDP), one can examine an HMM with an unbounded number of possible states. We revisit this HDPHMM and develop a generalization of the model, the sticky HDP-HMM, that allows more robust learning of smoothly varying state dynamics through a learned bias towards self-transitions. We show that this sticky HDP-HMM not only better segments data according to the underlying state sequence, but also improves the predictive performance of the learned model. Additionally, the sticky HDP-HMM enables learning more complex, multimodal emission distributions.(cont.) We demonstrate the utility of the sticky HDP-HMM on the NIST speaker diarization database, segmenting audio files into speaker labels while simultaneously identifying the number of speakers present. Although the HDP-HMM and its sticky extension are very flexible time series models, they make a strong Markovian assumption that observations are conditionally independent given the discrete HMM state. This assumption is often insufficient for capturing the temporal dependencies of the observations in real data. To address this issue, we develop extensions of the sticky HDP-HMM for learning two classes of switching dynamical processes: the switching linear dynamical system (SLDS) and the switching vector autoregressive (SVAR) process. These conditionally linear dynamical models can describe a wide range of complex dynamical phenomena from the stochastic volatility of financial time series to the dance of honey bees, two examples we use to show the power and flexibility of our Bayesian nonparametric approach. For all of the presented models, we develop efficient Gibbs sampling algorithms employing a truncated approximation to the HDP that allows incorporation of dynamic programming techniques, greatly improving mixing rates. In many applications, one would like to discover and model dynamical behaviors which are shared among several related time series. By jointly modeling such sequences, we may more robustly estimate representative dynamic models, and also uncover interesting relationships among activities.(cont.) In the latter part of this thesis, we consider a Bayesian nonparametric approach to this problem by harnessing the beta process to allow each time series to have infinitely many potential behaviors, while encouraging sharing of behaviors amongst the time series. For this model, we develop an efficient and exact Markov chain Monte Carlo (MCMC) inference algorithm. In particular, we exploit the finite dynamical system induced by a fixed set of behaviors to efficiently compute acceptance probabilities, and reversible jump birth and death proposals to explore new behaviors. We present results on unsupervised segmentation of data from the CMU motion capture database.by Emily B. Fox.Ph.D
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