Beauty in Imperfection: Post-hyperreal Cosmetic Containers

An unhealthy reliance on vision alone, fed by pervasive, doctored, hyperreal imagery in the mass media, suppresses a more balanced use of other senses, reinforcing superficial beauty standards. Trapped by an uncritical preference for the visually “perfect” and harmonious, people increasingly seek to remove physical attributes they consider “imperfect,” without first considering how these “imperfections” benefit and distinguish them as unique individuals. This thesis addresses superficial beauty standards by shifting focus from singularly visual experience to a more nuanced sensory aesthetic that also considers haptic qualities. Through a combination of research writing and targeted making, my work examines society’s understanding of flaws and imperfections by strategically embedding natural qualities of texture and randomness—blemishes—into ceramics, a medium treated as analogous to human skin. The resulting tools and objects, designed to support a healthy, ritualized daily skincare routine, examine beauty through the lens of wabi-sabi—the philosophy of things imperfect, impermanent, and incomplete

Deterministic Online Classification: Non-iteratively Reweighted Recursive Least-Squares for Binary Class Rebalancing

Deterministic solutions are becoming more critical for interpretability. Weighted Least-Squares (WLS) has been widely used as a deterministic batch solution with a specific weight design. In the online settings of WLS, exact reweighting is necessary to converge to its batch settings. In order to comply with its necessity, the iteratively reweighted least-squares algorithm is mainly utilized with a linearly growing time complexity which is not attractive for online learning. Due to the high and growing computational costs, an efficient online formulation of reweighted least-squares is desired. We introduce a new deterministic online classification algorithm of WLS with a constant time complexity for binary class rebalancing. We demonstrate that our proposed online formulation exactly converges to its batch formulation and outperforms existing state-of-the-art stochastic online binary classification algorithms in real-world data sets empirically

Automated crack control analysis for concrete pavement construction

The focus of this research is on the control of random cracking in concrete paving by using sawcut notch locations in the early stages of construction. This is a major concern in concrete pavement construction. This research also addresses a probabilistic approach to determine the optimum time and depth of sawcutting for newly constructed portland cement concrete pavements. Variability in climate conditions and material characteristics during the hardening process affects the potential of cracking at any sawcut depth. Several factors affecting the probability of crack initiation are material strength parameters, method and quality of curing, slab/subbase stiffness, the amount and depth of steel reinforcement, friction between the slab and the subbase, and concrete shrinkage. Other factors relevant to concrete mixture characteristics such as cement content and type of coarse aggregate affect development of early aged stresses caused by shrinkage and thermally induced contraction. A probabilistic analysis of the factors that affect crack control using sawcut notches is presented in relation to different weather conditions (concrete placement temperature) at the time of construction, and concrete mixture characteristics such as fly ash replacement (FA) and cement factor (CF). Both of these significantly affect sawcut timing and depth requirement. The determination of crack initiation is based on fracture mechanics. Estimation of the time of cracking is based on predicted tensile strength and stress in the concrete at the bottom of the sawcut notch to assess the feasibility of crack control in the early stages of construction