Evaluating the Evaluators: Which UDA validation methods are most effective? Can they be improved?

This paper compares and ranks 8 UDA validation methods. Validators estimate model accuracy, which makes them an essential component of any UDA train-test pipeline. We rank these validators to indicate which of them are most useful for the purpose of selecting optimal model checkpoints and hyperparameters. To the best of our knowledge, this large-scale benchmark study is the first of its kind in the UDA field. In addition, we propose three new validators that outperform all the existing checkpoint-based validators that we were able to find in the existing literature. Code is available at https://www.github.com/KevinMusgrave/powerful-benchmarker.Comment: This paper was previously titled Benchmarking Validation Methods for Unsupervised Domain Adaptation. This version contains new experiments, analysis, and figure

Cell-type specific potent Wnt signaling blockade by bispecific antibody.

Cell signaling pathways are often shared between normal and diseased cells. How to achieve cell type-specific, potent inhibition of signaling pathways is a major challenge with implications for therapeutic development. Using the Wnt/β-catenin signaling pathway as a model system, we report here a novel and generally applicable method to achieve cell type-selective signaling blockade. We constructed a bispecific antibody targeting the Wnt co-receptor LRP6 (the effector antigen) and a cell type-associated antigen (the guide antigen) that provides the targeting specificity. We found that the bispecific antibody inhibits Wnt-induced reporter activities with over one hundred-fold enhancement in potency, and in a cell type-selective manner. Potency enhancement is dependent on the expression level of the guide antigen on the target cell surface and the apparent affinity of the anti-guide antibody. Both internalizing and non-internalizing guide antigens can be used, with internalizing bispecific antibody being able to block signaling by all ligands binding to the target receptor due to its removal from the cell surface. It is thus feasible to develop bispecific-based therapeutic strategies that potently and selectively inhibit signaling pathways in a cell type-selective manner, creating opportunity for therapeutic targeting

Paclitaxel Drug Elution from a Biodegradable Stent

Recently, drug-eluting stents have become a common treatment for coronary heart disease. These stents are usually loaded with a drug that prevents restenosis. Unfortunately, there are risks associated with the placement of these metallic structures in the body. Stent thrombosis is one such problem, and can lead to restenosis despite the presence of drug. Advances in biomaterials have led to the development of biodegradable stents, which can reduce the risks associated with stents. However, since it is a relatively new technology, it is not known to what degree the biodegradability affects the drug releasing properties of the stent. We hope to characterize these effects and to determine if the biodegradability reduces the efficacy of the stent when compared to normal non-degradable stents. To accomplish this, we modeled a stent that diminished in size over time using COMSOL Multiphysics, and monitored the drug concentration in the nearby tissue. We established that our model was a viable predictor of actual stent behavior by comparing our simulated results with previous studies. We were then able to determine the optimal initial loading stent concentration of our modeled drug, paclitaxel, to ensure therapeutic levels in the tissue. Lastly, we found that drug concentrations in the tissue were not substantially different between the degradable and non-degradable models. This affirms the effectiveness of using biodegradable stents, showing them as a viable alternative to traditional metal stents

Arkose: A Prototype Mechanism and Tool for Collaborative Information Generation and Distillation.

The goals of this thesis have been to gain a better understanding of collaborative knowledge sharing and distilling and to build a prototype collaborative system that supports flexible knowledge generation and distillation. To reach these goals, I have conducted two user studies and built two systems. The first system, Arkose 1.0, is a prototype collaborative distillation system for a discussion space, which provides a set of augmentative tools to facilitate the filtering, structuring, and organizing of discussion information. Arkose 1.0 supports editors to distill a discussion space incrementally and collaboratively, and allows a gradual increase in the order and reusability of the information space. The study of an online question-answering community, Naver Knowledge-iN, investigates users’ knowledge sharing behaviors in a large online question-answering community. Through the analyses of a large quantity of question/answer pairs and 26 user interviews, the study analyzes the characteristics of knowledge generation and user participation behavior and gains insights into their motivations, roles, usage and expertise. It reveals that the limiting nature of the reply interfaces of Knowledge-iN leads to the accumulation of simple and easy questions and answers. This tendency is encouraged by the point system that rewards users who answer many questions quickly. Arkose2 is designed and implemented based on the lessons and insights gained in building Arkose 1.0 and examining Naver Knowledge-iN. Arkose2 provides a host of additional interaction mechanisms and supportive tools over Arkose 1.0 that assists users to flexibly generate knowledge and distill and organize it better. Finally, the evaluation of Arkose2 reveals a number of insights, issues, and lessons about users’ distillation activities of discussion spaces and features of Arkose2. These together provide valuable lessons and insights for the architecture and features of the next generation collective intelligent system.Ph.D.InformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78880/1/ksnam_1.pd

Electroluminescence from Strained Ge membranes and Implications for an Efficient Si-Compatible Laser

We demonstrate room-temperature electroluminescence (EL) from light-emitting diodes (LED) on highly strained germanium (Ge) membranes. An external stressor technique was employed to introduce a 0.76% bi-axial tensile strain in the active region of a vertical PN junction. Electrical measurements show an on-off ratio increase of one order of magnitude in membrane LEDs compared to bulk. The EL spectrum from the 0.76% strained Ge LED shows a 100nm redshift of the center wavelength because of the strain-induced direct band gap reduction. Finally, using tight-binding and FDTD simulations, we discuss the implications for highly efficient Ge lasers.Comment: 4 Pages, 5 figure