Direct numerical simulations of chemically reacting turbulent flows

This dissertation considers, in three parts, problems concerning mixing and chemical reactions in homogeneous turbulent flows. Direct numerical simulations of finite-rate chemical reactions occurring in moderate Reynolds number turbulence were used to provide data that are difficult to measure experimentally. In the first two parts, models for two phenomena contributing to unknown terms in statistical treatments of reacting flows are considered. In Part I models that are used in the solution of probability density function (pdf) equations are examined, whereas in Part II models used in moment methods for the mean reaction rate are evaluated. In Part III, results of the simulations were used to examine the structure of the reaction zone. (Abstract shortened with permission of author.

DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning

Data augmentation techniques, such as simple image transformations and combinations, are highly effective at improving the generalization of computer vision models, especially when training data is limited. However, such techniques are fundamentally incompatible with differentially private learning approaches, due to the latter's built-in assumption that each training image's contribution to the learned model is bounded. In this paper, we investigate why naive applications of multi-sample data augmentation techniques, such as mixup, fail to achieve good performance and propose two novel data augmentation techniques specifically designed for the constraints of differentially private learning. Our first technique, DP-Mix_Self, achieves SoTA classification performance across a range of datasets and settings by performing mixup on self-augmented data. Our second technique, DP-Mix_Diff, further improves performance by incorporating synthetic data from a pre-trained diffusion model into the mixup process. We open-source the code at https://github.com/wenxuan-Bao/DP-Mix.Comment: 17 pages, 2 figures, to be published in Neural Information Processing Systems 202

Entre escuchar y sonar: explorando los límites de los instrumentos aumentados

This paper explores some of the changed relationships between body and environment that occur when instruments are augmented by electronic or digital circuits. Taking Gregory Bateson’s theorisation of the schizophrenic body (1973) as its starting point, the paper explores situations in which the relationship between the performer, body, and instrument takes on increasingly separate communicational modes, in which the body and its meanings come to resemble the ‘unlabelled metaphor’ of the schizophrenic. A series of instrument/personas are brought before us, representing both the ‘norm’ of acoustic instrumental performances and the extreme limits of instrumental identity, offering critical insight into the space that augmented instruments occupy and transform.  In considering some of these changes, and in reaching towards their extremities, attention is paid to the friction or awkwardness that accompanies the metamorphosis. In the same way that the ability of a language to ‘point’ is fraught with inconsistencies and potentials for misunderstanding, so the transformation in instrumental identities does not happen in a smooth and transparent way. However, the changes do create the potential for new sensibilities and forms of critical and ethical awareness.Este artículo explora algunos de los cambios en las relaciones entre cuerpo y ambiente que ocurren cuando los instrumentos son aumentados por circuitos electrónicos o digitales. Tomando como punto de partida las teorías de Gregory Bateson sobre el cuerpo esquizofrénico (1973), este texto pone en escena una serie de situaciones donde la relación entre ejecutor, cuerpo e instrumento toma formas de comunicación cada vez más separadas en las cuales el cuerpo y sus significados pueden parecer una ‘metáfora no etiquetada’ de esquizofrenia. Para recorrer este proceso, una serie de instrumentos/personas se nos presentan como representantes de la ‘norma’ en la ejecución de los instrumentos acústicos y los límites extremos de identidad instrumental, y ofrecen una perspectiva crítica en el espacio que los instrumentos aumentados ocupan y transforman. Explorando algunos de estos cambios y alcanzando sus extremos, se hace hincapié en la fricción o incomodidad que acompaña esta metamorfosis. Así como el lenguaje tiene la habilidad de ‘indicar’ y el posicionamiento de una ‘auto-icona’ en su interior se revela un proceso complejo cargado de inconsistencias y potenciales malentendidos, la transformación de las identidades instrumentales no ocurre de una manera transparente y fluida, sino que deja vestigios importantes de estados previos latentes en los nuevos

Local Differential Privacy Is Equivalent to Contraction of EγE_\gamma-Divergence

We investigate the local differential privacy (LDP) guarantees of a randomized privacy mechanism via its contraction properties. We first show that LDP constraints can be equivalently cast in terms of the contraction coefficient of the $E_\gamma$-divergence. We then use this equivalent formula to express LDP guarantees of privacy mechanisms in terms of contraction coefficients of arbitrary $f$-divergences. When combined with standard estimation-theoretic tools (such as Le Cam's and Fano's converse methods), this result allows us to study the trade-off between privacy and utility in several testing and minimax and Bayesian estimation problems