Mr. Jonathan P. Berger: Gentle Conflations

Sentimentality is a critical aspect of human existence because it is human-natural, agendered, and provides ground for gentle conflation of the domestic sphere and the roles within it. As an artist, I am able to utilize sentimentality to open possibilities and welcome, instead of molest, viewers into contemplation with the assumed norms of domesticity. With its origins founded in the Age of Enlightenment, sentimentality was a praiseworthy endeavor, one based on intelligence and contemplation. I define sentimentality as the emotional intellect’s way of encoding or decoding the soft emotions surrounding and within objects, people, times or ideas. Soft emotions are those emotions that when positive warm us and when negative nibble away at us. Because of its foundation in our innate emotional intelligence, sentimentality is a human-natural and agendered phenomenon. I posit that sentimentality can be strategically used to induce gentle conflation between world-representations, especially those located within the domestic. Essentially, world- representations are bundles of facts that are true in some world, be it fictional or non-fictional. Because of their quietness, soft emotions are able to linger mysteriously around and between their source world-representations, blurring their distinctions. Within my artistic practice I contemplate concepts of labor, love and the fine line between loneliness and solitude found within the domestic sphere by utilizing sentimentality as a tool of gentle conflation

Low Cost Direction Finding with the Electronically Steerable Parasitic Array Radiator (ESPAR) Antenna

Faculty of Engineering and the Built Environment; School of Electrical and Information System; MSC DissertationIn this paper, the Electronically Steerable Parasitic Array Radiator (ESPAR) antenna, developed by the Advanced Telecommunications Research Institute (ATR) in Japan was analyzed to determine its feasibility as a low cost direction finding (DF) system. Simulations of the antenna were performed in SuperNEC and Matlab was used to determine the direction of arrival (DOA) using the Reactance Domain multiple signal classification (MUSIC) algorithm. Results show the ideal configuration has 6 parasitic elements with a diameter of 0.5 . Up to 5 periodic, uncorrelated signals spread 360° in azimuth and above 45° elevation produce sharp peaks in the MUSIC spectra. Azimuth separations of only 2° at 40 dB are resolvable while signals arriving with 25% full power are still detectable. For the DOA to be resolved the radiation pattern should be asymmetrical and hence the reactance set should have a range of unequal values. Comparative results show that the 6 element ESPAR offers excellent overall performance despite the reduction in cost and is comparable in performance to the 6 element uniform linear array

Taking responsibilities seriously: The role of the state in preventing transmission of HIV from mother to child

No Abstract

Heavy bottom squark mass in the light gluino and light bottom squark scenario

Restrictive upper bounds on the heavy bottom squark mass when the gluino and one bottom squark are both light are based on the predicted reduction of $R_b$ (the fraction of $Z$ hadronic decays to $b \bar b$ pairs) in such a scenario. These bounds are found to be relaxed by the process $Z \to b\bar{\tilde b}{\tilde g}/{\bar b}{\tilde b}{\tilde g}$, which may partially compensate for the reduction of $R_b$. The relaxation of bounds on the top squark and the scale-dependence of the strong coupling constant are also discussed.Comment: 9 pages, LaTeX, 2 figures, to be submitted to Phys. Lett. B, more discussions adde

Self-Supervised Representation Learning for Vocal Music Context

In music and speech, meaning is derived at multiple levels of context. Affect, for example, can be inferred both by a short sound token and by sonic patterns over a longer temporal window such as an entire recording. In this paper we focus on inferring meaning from this dichotomy of contexts. We show how contextual representations of short sung vocal lines can be implicitly learned from fundamental frequency ($F_0$) and thus be used as a meaningful feature space for downstream Music Information Retrieval (MIR) tasks. We propose three self-supervised deep learning paradigms which leverage pseudotask learning of these two levels of context to produce latent representation spaces. We evaluate the usefulness of these representations by embedding unseen vocal contours into each space and conducting downstream classification tasks. Our results show that contextual representation can enhance downstream classification by as much as 15 % as compared to using traditional statistical contour features.Comment: Working on more updated versio