This study situates the literary works of Amy Lowell, Djuna Barnes, H.D., and Gertrude Stein in a genealogy of American modernist war writing by women that disrupts and revises patriarchal war narrative. These authors take ownership of war and war-related trauma as subjects for women writers. Combining the theories of Dominick LaCapra, Judith Butler, Elaine Scarry, and Elizabeth Grosz with close readings of primary texts, I offer feminist analyses that account for trauma and real-world materiality in literary representations of female embodiment in wartime. This framework enables an interdisciplinary discussion that focuses on representations of war and trauma in conjunction with identity politics.I examine Lowell's poetry collection Men, Women and Ghosts (1916), Barnes's novel Nightwood (1936), H.D.'s poem Trilogy (1944-1946), and Stein's novel Mrs. Reynolds (1952). The chapters highlight the progressively feminist and personal ownership of war and trauma embedded in the texts. Lowell and Barnes begin the work of deconstructing gendered binary constructions and inserting women into war narrative, and H.D. and Stein continue this trajectory through cultivation of more pronounced depictions of women and their bodies in war narrative.The strategies are distinct and specific to each author, but there are common characteristics in their literary responses to World War I and World War II. Each author protests war: war is destructive for Lowell, perverse for Barnes, traumatic for H.D., and disruptive for Stein. Additionally, each author renders female bodies as sites of contested identity and as markers of presence in war narrative. The female bodies portrayed are often traumatized and marked by the ravages of war: bodily injury and psychological and emotional distress. H.D. and Stein envision strategies for resolving (if only partially) trauma, but Lowell and Barnes do not.This project recovers alternative war narratives by important American modernist women writers, expands the definition and canon of war literature, contributes new scholarship on works by the selected authors, and constructs an original critical framework. The ramifications of this study are an increased awareness of who was writing about war and the shape that responses to it took in avant-garde literature of the early twentieth century. / Department of English

Ce mémoire examine les poétiques de trois poètes très différentes, mais dont les œuvres peuvent être qualifiées d'indéterminées et de radicales : Emily Dickinson (1830-1886), Gertrude Stein (1874-1946) et Caroline Bergvall (née en 1962). Dickinson et Stein sont anglo-américaines, tandis que Bergvall est d’origine franco-norvégienne, bien qu'elle choisisse d’écrire en anglais. Toutes les trois rompent la structure syntaxique conventionnelle de l’anglais par leurs poétiques, ce qui comporte des implications esthétiques et politiques. Dans ce qui suit, j’analyse l’indétermination de leurs poétiques à partir de la notion, décrite par Lyn Hejinian, de la description comme appréhension qui présente l’écriture comme un mode de connaissance plutôt qu'un moyen d’enregistrer ce que le poète sait déjà. La temporalité de cette activité épistémologique est donc celle du présent de l’écriture, elle lui est concomitante. J'affirme que c'est cette temporalité qui, en ouvrant l’écriture aux événements imprévus, aux vicissitudes, aux hésitations, aux erreurs et torsions de l’affect, cause l'indétermination de la poésie. Dans le premier chapitre, j'envisage l'appréhension chez Gertrude Stein à travers son engagement, tout au long de sa carrière, envers « le présent continu » de l’écriture. Le deuxième chapitre porte sur le sens angoissé de l’appréhension dans la poésie de Dickinson, où le malaise, en empêchant ou en refoulant une pensée, suspend la connaissance. Le langage, sollicité par une expérience qu'il ne peut lui-même exprimer, donne forme à l'indétermination. Un dernier chapitre considère l’indétermination linguistique du texte et de l’exposition Say Parsley, dans lesquels Bergvall met en scène l’appréhension du langage : une appréhension qui survient plutôt chez le lecteur ou spectateur que chez la poète. / This thesis investigates the poetics of three very different female poets, whose works nevertheless are characterized as both indeterminate and radical: Emily Dickinson (1830-1886), Gertrude Stein (1874-1946), and Caroline Bergvall (b. 1962). Dickinson and Stein are Anglo-American, while Bergvall is of French-Norwegian descent yet writes in English, but all three fracture the conventional syntactic structures of the English language in their poetics. This move bears both aesthetic and political implications. In this thesis, I read the indeterminacies of their poetics through Lyn Hejinian’s notion of description as apprehension, which figures writing as a mode of knowing rather than a means of recording something the poet already knows. The temporality of epistemology in their work is thus the present tense of writing; thinking is concomitant with it. Following Hejinian, I contend that it is this temporality that, in making writing open to the vicissitudes, hesitations, reprisals, unexpected events, errors, and the torsions of affect, perturbs determination. The first chapter explores apprehension in Gertrude Stein’s work through her career-long commitment to the present tense of writing: perception occurs concurrently with composition. The second chapter, on Dickinson, hinges on the anxious dimension of apprehension, in which unease, in thwarting or repressing a thought, suspends its understanding. Indeterminacy figures as language claimed by an experience it can’t itself claim. Finally, the last chapter considers the linguistic indeterminacies of Say Parsley, where Bergvall stages the apprehension of language itself in using indeterminacy as a poetic strategy to determinate ends, placing the possibilities, uncertainties and responsibilities of apprehension onto the reader or spectator.

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Motivated by the classic works of Charles M. Stein, we focus on developing risk-estimation frameworks for denoising problems in both one-and two-dimensions. We assume a standard additive noise model, and formulate the denoising problem as one of estimating the underlying clean signal from noisy measurements by minimizing a risk corresponding to a chosen loss function. Our goal is to incorporate perceptually-motivated loss functions wherever applicable, as in the case of speech enhancement, with the squared error loss being considered for the other scenarios. Since the true risks are observed to depend on the unknown parameter of interest, we circumvent the roadblock by deriving finite-sample un-biased estimators of the corresponding risks based on Stein’s lemma. We establish the link with the multivariate parameter estimation problem addressed by Stein and our denoising problem, and derive estimators of the oracle risks. In all cases, optimum values of the parameters characterizing the denoising algorithm are determined by minimizing the Stein’s unbiased risk estimator (SURE). The key contribution of this thesis is the development of a risk-estimation approach for choosing the two critical parameters affecting the quality of nonparametric regression, namely, the order and bandwidth/smoothing parameters. This is a classic problem in statistics, and certain algorithms relying on derivation of suitable finite-sample risk estimators for minimization have been reported in the literature (note that all these works consider the mean squared error (MSE) objective). We show that a SURE-based formalism is well-suited to the regression parameter selection problem, and that the optimum solution guarantees near-minimum MSE (MMSE) performance. We develop algorithms for both glob-ally and locally choosing the two parameters, the latter referred to as spatially-adaptive regression. We observe that the parameters are so chosen as to tradeoff the squared bias and variance quantities that constitute the MSE. We also indicate the advantages accruing out of incorporating a regularization term in the cost function in addition to the data error term. In the more general case of kernel regression, which uses a weighted least-squares (LS) optimization, we consider the applications of image restoration from very few random measurements, in addition to denoising of uniformly sampled data. We show that local polynomial regression (LPR) becomes a special case of kernel regression, and extend our results for LPR on uniform data to non-uniformly sampled data also. The denoising algorithms are compared with other standard, performant methods available in the literature both in terms of estimation error and computational complexity. A major perspective provided in this thesis is that the problem of optimum parameter choice in nonparametric regression can be viewed as the selection of optimum parameters of a linear, shift-invariant filter. This interpretation is provided by deriving motivation out of the hallmark paper of Savitzky and Golay and Schafer’s recent article in IEEE Signal Processing Magazine. It is worth noting that Savitzky and Golay had shown in their original Analytical Chemistry journal article, that LS fitting of a fixed-order polynomial over a neighborhood of fixed size is equivalent to convolution with an impulse response that is fixed and can be pre-computed. They had provided tables of impulse response coefficients for computing the smoothed function and smoothed derivatives for different orders and neighborhood sizes, the resulting filters being referred to as Savitzky-Golay (S-G) filters. Thus, we provide the new perspective that the regression parameter choice is equivalent to optimizing for the filter impulse response length/3dB bandwidth, which are inversely related. We observe that the MMSE solution is such that the S-G filter chosen is of longer impulse response length (equivalently smaller cutoff frequency) at relatively flat portions of the noisy signal so as to smooth noise, and vice versa at locally fast-varying portions of the signal so as to capture the signal patterns. Also, we provide a generalized S-G filtering viewpoint in the case of kernel regression. Building on the S-G filtering perspective, we turn to the problem of dynamic feature computation in speech recognition. We observe that the methodology employed for computing dynamic features from the trajectories of static features is in fact derivative S-G filtering. With this perspective, we note that the filter coefficients can be pre-computed, and that the whole problem of delta feature computation becomes efficient. Indeed, we observe an advantage by a factor of 104 on making use of S-G filtering over actual LS polynomial fitting and evaluation. Thereafter, we study the properties of first-and second-order derivative S-G filters of certain orders and lengths experimentally. The derivative filters are bandpass due to the combined effects of LPR and derivative computation, which are lowpass and highpass operations, respectively. The first-and second-order S-G derivative filters are also observed to exhibit an approximately constant-Q property. We perform a TIMIT phoneme recognition experiment comparing the recognition accuracies obtained using S-G filters and the conventional approach followed in HTK, where Furui’s regression formula is made use of. The recognition accuracies for both cases are almost identical, with S-G filters of certain bandwidths and orders registering a marginal improvement. The accuracies are also observed to improve with longer filter lengths, for a particular order. In terms of computation latency, we note that S-G filtering achieves delta and delta-delta feature computation in parallel by linear filtering, whereas they need to be obtained sequentially in case of the standard regression formulas used in the literature. Finally, we turn to the problem of speech enhancement where we are interested in de-noising using perceptually-motivated loss functions such as Itakura-Saito (IS). We propose to perform enhancement in the discrete cosine transform domain using risk-minimization. The cost functions considered are non-quadratic, and derivation of the unbiased estimator of the risk corresponding to the IS distortion is achieved using an approximate Taylor-series analysis under high signal-to-noise ratio assumption. The exposition is general since we focus on an additive noise model with the noise density assumed to fall within the exponential class of density functions, which comprises most of the common densities. The denoising function is assumed to be pointwise linear (modified James-Stein (MJS) estimator), and parallels between Wiener filtering and the optimum MJS estimator are discussed.

Motivated by the classic works of Charles M. Stein, we focus on developing risk-estimation frameworks for denoising problems in both one-and two-dimensions. We assume a standard additive noise model, and formulate the denoising problem as one of estimating the underlying clean signal from noisy measurements by minimizing a risk corresponding to a chosen loss function. Our goal is to incorporate perceptually-motivated loss functions wherever applicable, as in the case of speech enhancement, with the squared error loss being considered for the other scenarios. Since the true risks are observed to depend on the unknown parameter of interest, we circumvent the roadblock by deriving finite-sample un-biased estimators of the corresponding risks based on Stein’s lemma. We establish the link with the multivariate parameter estimation problem addressed by Stein and our denoising problem, and derive estimators of the oracle risks. In all cases, optimum values of the parameters characterizing the denoising algorithm are determined by minimizing the Stein’s unbiased risk estimator (SURE). The key contribution of this thesis is the development of a risk-estimation approach for choosing the two critical parameters affecting the quality of nonparametric regression, namely, the order and bandwidth/smoothing parameters. This is a classic problem in statistics, and certain algorithms relying on derivation of suitable finite-sample risk estimators for minimization have been reported in the literature (note that all these works consider the mean squared error (MSE) objective). We show that a SURE-based formalism is well-suited to the regression parameter selection problem, and that the optimum solution guarantees near-minimum MSE (MMSE) performance. We develop algorithms for both glob-ally and locally choosing the two parameters, the latter referred to as spatially-adaptive regression. We observe that the parameters are so chosen as to tradeoff the squared bias and variance quantities that constitute the MSE. We also indicate the advantages accruing out of incorporating a regularization term in the cost function in addition to the data error term. In the more general case of kernel regression, which uses a weighted least-squares (LS) optimization, we consider the applications of image restoration from very few random measurements, in addition to denoising of uniformly sampled data. We show that local polynomial regression (LPR) becomes a special case of kernel regression, and extend our results for LPR on uniform data to non-uniformly sampled data also. The denoising algorithms are compared with other standard, performant methods available in the literature both in terms of estimation error and computational complexity. A major perspective provided in this thesis is that the problem of optimum parameter choice in nonparametric regression can be viewed as the selection of optimum parameters of a linear, shift-invariant filter. This interpretation is provided by deriving motivation out of the hallmark paper of Savitzky and Golay and Schafer’s recent article in IEEE Signal Processing Magazine. It is worth noting that Savitzky and Golay had shown in their original Analytical Chemistry journal article, that LS fitting of a fixed-order polynomial over a neighborhood of fixed size is equivalent to convolution with an impulse response that is fixed and can be pre-computed. They had provided tables of impulse response coefficients for computing the smoothed function and smoothed derivatives for different orders and neighborhood sizes, the resulting filters being referred to as Savitzky-Golay (S-G) filters. Thus, we provide the new perspective that the regression parameter choice is equivalent to optimizing for the filter impulse response length/3dB bandwidth, which are inversely related. We observe that the MMSE solution is such that the S-G filter chosen is of longer impulse response length (equivalently smaller cutoff frequency) at relatively flat portions of the noisy signal so as to smooth noise, and vice versa at locally fast-varying portions of the signal so as to capture the signal patterns. Also, we provide a generalized S-G filtering viewpoint in the case of kernel regression. Building on the S-G filtering perspective, we turn to the problem of dynamic feature computation in speech recognition. We observe that the methodology employed for computing dynamic features from the trajectories of static features is in fact derivative S-G filtering. With this perspective, we note that the filter coefficients can be pre-computed, and that the whole problem of delta feature computation becomes efficient. Indeed, we observe an advantage by a factor of 104 on making use of S-G filtering over actual LS polynomial fitting and evaluation. Thereafter, we study the properties of first-and second-order derivative S-G filters of certain orders and lengths experimentally. The derivative filters are bandpass due to the combined effects of LPR and derivative computation, which are lowpass and highpass operations, respectively. The first-and second-order S-G derivative filters are also observed to exhibit an approximately constant-Q property. We perform a TIMIT phoneme recognition experiment comparing the recognition accuracies obtained using S-G filters and the conventional approach followed in HTK, where Furui’s regression formula is made use of. The recognition accuracies for both cases are almost identical, with S-G filters of certain bandwidths and orders registering a marginal improvement. The accuracies are also observed to improve with longer filter lengths, for a particular order. In terms of computation latency, we note that S-G filtering achieves delta and delta-delta feature computation in parallel by linear filtering, whereas they need to be obtained sequentially in case of the standard regression formulas used in the literature. Finally, we turn to the problem of speech enhancement where we are interested in de-noising using perceptually-motivated loss functions such as Itakura-Saito (IS). We propose to perform enhancement in the discrete cosine transform domain using risk-minimization. The cost functions considered are non-quadratic, and derivation of the unbiased estimator of the risk corresponding to the IS distortion is achieved using an approximate Taylor-series analysis under high signal-to-noise ratio assumption. The exposition is general since we focus on an additive noise model with the noise density assumed to fall within the exponential class of density functions, which comprises most of the common densities. The denoising function is assumed to be pointwise linear (modified James-Stein (MJS) estimator), and parallels between Wiener filtering and the optimum MJS estimator are discussed.

No description available.

No description available.

Probabilistic inference, especially in the Bayesian framework, is a foundation for quantifying uncertainties in geophysical inversion applications. However, due to the presence of high-dimensional datasets and the large-scale nature of geophysical inverse problems, the applicability and scalability of probabilistic inference face significant challenges for such applications. This thesis is dedicated to improving the probabilistic inference algorithms' scalability and demonstrating their applicability for large-scale geophysical inversion applications. In this thesis, I delve into three leading applied approaches in computing the Bayesian posterior distribution in geophysical inversion applications: Laplace's approximation, Markov chain Monte Carlo (MCMC), and variational Bayesian inference. The first approach, Laplace's approximation, is the simplest form of approximation for intractable Bayesian posteriors. However, its accuracy relies on the estimation of the posterior covariance matrix. I study the visualization of the misfit landscape in low-dimensional subspace and the low-rank approximations of the covariance for full waveform inversion (FWI). I demonstrate that a non-optimal Hessian's eigenvalues truncation for the low-rank approximation will affect the approximation accuracy of the standard deviation, leading to a biased statistical conclusion. Furthermore, I also demonstrate the propagation of uncertainties within the Bayesian physics-informed neural networks for hypocenter localization applications through this approach. For the MCMC approach, I develop approximate Langevin MCMC algorithms that provide fast sampling at efficient computational costs for large-scale Bayesian FWI; however, this inflates the variance due to asymptotic bias. To account for this asymptotic bias and assess their sample quality, I introduce the kernelized Stein discrepancy (KSD) as a diagnostic tool. When larger computational resources are available, exact MCMC algorithms (i.e., with a Metropolis-Hastings criterion) should be favored for an accurate posterior distribution statistical analysis. For the variational Bayesian inference, I propose a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence loss with a deep denoiser through a Plug-and-Play method. I also developed Plug-and-Play Stein Variational Gradient Descent (PnP-SVGD), a novel algorithm to sample the regularized posterior distribution. The PnP-SVGD demonstrates its ability to produce high-resolution, trustworthy samples representative of the subsurface structures for a post-stack seismic inversion application.

本論文は19世紀のデンマーク社会にディアコニッセとディアコニアの働きを広めるために尽力したハラルド・スタインのディアコニア思想に光を当てる。スタインは、産業革命や社会主義の台頭により激変する社会において、キリスト教会は人々の身体的、霊的救いに力を尽くすべきだと考え、キリスト教の愛の業すなわちディアコニアの働きを教会に根付かせようとした。スタインのディアコニアの働きの理想と実践はのちのデンマークの社会民主主義の政権にも継承された。 / This study sheds light on the ideas of Harald Stein, who did his utmost to spread the work of deaconesses and diakonia in 19th century Denmark. In a society rapidly changing under the influence of industrialization and socialism, Stein thought the Christian Church ought to aim at saving people physically and spiritually, and he sought to plant the Christian Works of Love, i.e., the work of diakonia, in the Church. The ideals and practices of Stein's work of diakonia were later inherited by the Danish Social Democratic governments. / 博士(神学) / Doctor of Theology / 同志社大学 / Doshisha University

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