Advanced Microspheres as Injectable Cell Carriers for Tissue Engineering.

Biodegradable polymer microspheres have emerged as injectable cell carriers for the regeneration and repair of irregularly-shaped tissue defects. The physical structure and chemical composition of the microsphere are critical to its function and performance. However, it is challenging to manipulate the physical structure of microspheres at various length scales and introduce desirable chemistry on the microspheres for bioconjugation at the same time. In this thesis, the author develops a series of versatile techniques, including polymer self-assembly and novel emulsification methods, to simultaneously control the physical and chemical structure of spheres. Firstly, the author investigates the self-assembly of star-shaped polymers at both the nano- and micro-meter scales, and develops a versatile method to fabricate microspheres with simultaneous control over the nano- and micro-meter scale features. Secondly, the author summarizes a more generalized emulsification technique to produce nano- and micro-structured spheres from various types of polymers. Based on the discovered principles of microsphere assembly, the author builds a functional nanofibrous hollow microsphere platform, which can conjugate biomolecules and guide stem cell dierentiation for cartilage and bone tissue enigneering. Last but not the least, the author describes the use of the unique nanofibrous spongy micro-spheres for human dental pulp stem cell delivery and dental pulp regeneration. These new microcarriers also show great potential for other applications in tissue regeneration and biomolecule deliveries.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111559/1/zzp_1.pd

Learning Social Relation Traits from Face Images

Social relation defines the association, e.g, warm, friendliness, and dominance, between two or more people. Motivated by psychological studies, we investigate if such fine-grained and high-level relation traits can be characterised and quantified from face images in the wild. To address this challenging problem we propose a deep model that learns a rich face representation to capture gender, expression, head pose, and age-related attributes, and then performs pairwise-face reasoning for relation prediction. To learn from heterogeneous attribute sources, we formulate a new network architecture with a bridging layer to leverage the inherent correspondences among these datasets. It can also cope with missing target attribute labels. Extensive experiments show that our approach is effective for fine-grained social relation learning in images and videos.Comment: To appear in International Conference on Computer Vision (ICCV) 201

From Nanofibrous Hollow Microspheres to Nanofibrous Hollow Discs and Nanofibrous Shells

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/115931/1/marc201500342.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/115931/2/marc201500342-sup-0001-S1.pd

Learning Baseline Values for Shapley Values

This paper aims to formulate the problem of estimating the optimal baseline values for the Shapley value in game theory. The Shapley value measures the attribution of each input variable of a complex model, which is computed as the marginal benefit from the presence of this variable w.r.t.its absence under different contexts. To this end, people usually set the input variable to its baseline value to represent the absence of this variable (i.e.the no-signal state of this variable). Previous studies usually determine the baseline values in an empirical manner, which hurts the trustworthiness of the Shapley value. In this paper, we revisit the feature representation of a deep model from the perspective of game theory, and define the multi-variate interaction patterns of input variables to define the no-signal state of an input variable. Based on the multi-variate interaction, we learn the optimal baseline value of each input variable. Experimental results have demonstrated the effectiveness of our method