Image-based relighting of indoor rooms creates an immersive virtual understanding of the space, which is useful for interior design, virtual staging, and real estate. Relighting indoor rooms from a single image is especially challenging due to complex illumination interactions between multiple lights and cluttered objects featuring a large variety in geometrical and material complexity. Recently, generative models have been successfully applied to imagebased relighting conditioned on a target image or a latent code, albeit without detailed local lighting control. In this paper, we introduce ScribbleLight, a generative model that supports local fine-grained control of lighting effects through scribbles that describe changes in lighting. Our key technical novelty is an Albedo-conditioned Stable Image Diffusion model that preserves the intrinsic color and texture of the original image after relighting and an encoder-decoder-based ControlNet architecture that enables geometry-preserving lighting effects with normal map and scribble annotations. We demonstrate ScribbleLight’s ability to create different lighting effects (e.g., turning lights on/off, adding highlights, cast shadows, or indirect lighting from unseen lights) from sparse scribble annotations.

This work in progress research paper describes part of a project that aims to understand what qualifications BME industry employers prefer in the hiring process. An inductive content analysis of 95 job postings relevant to upper-level or soon-to-be graduated biomedical engineering (BME) bachelor's degree students was performed to explore patterns in responsibilities and qualifications within these postings. Results from this work indicate a high emphasis on employees' ability to apply technical documentation and communication skills in the job setting. Codes related to job qualifications indicate industry employers prefer employees with general professional skills that allow them to work in a team setting and solve problems. Additionally, we found that employers are often open to hiring BME majors in addition to multiple other engineering and life sciences majors for the positions listed in the postings in this targeted BME job search. In future work, the results of this content analysis will be used to inform the development of a set of archetypal BME student resumes that will be used in semi-structured interviews with BME industry hiring managers. The interviews seek to examine the alignment between what job postings indicate as important, what hiring managers articulate as important in hiring decisions, and what hiring managers view in resumes as representing those qualifications. The overarching goal of this work is to understand the landscape of BME industry hiring practices and increase alignment in the goals of industry, students, and educators. Results have the potential to inform how students engage with BME education, how they represent their experiences in application materials, and how educators structure BME curriculum.

Existing drug discovery processes follow a reductionist model of "one-drug-one-gene-one-disease," which is inadequate to tackle complex diseases involving multiple malfunctioned genes. The availability of big omics data offers opportunities to transform drug discovery process into a new paradigm of systems pharmacology that focuses on designing drugs to target molecular interaction networks instead of a single gene. Here, we develop a reliable multi-rank, multi-layered recommender system, ANTENNA, to mine large-scale chemical genomics and disease association data for prediction of novel drug-gene-disease associations. ANTENNA integrates a novel tri-factorization based dual-regularized weighted and imputed One Class Collaborative Filtering (OCCF) algorithm, tREMAP, with a statistical framework based on Random Walk with Restart and assess the reliability of specific predictions. In the benchmark, tREMAP clearly outperforms the single-rank OCCF. We apply ANTENNA to a real-world problem: repurposing old drugs for new clinical indications without effective treatments. We discover that FDA-approved drug diazoxide can inhibit multiple kinase genes responsible for many diseases including cancer and kill triple negative breast cancer (TNBC) cells efficiently [Formula: see text]. TNBC is a deadly disease without effective targeted therapies. Our finding demonstrates the power of big data analytics in drug discovery and developing a targeted therapy for TNBC.

Electronic energy disorder associated within amorphous and polycrystaline molecular organic thin film structures strongly affects the macroscopic observable behavior of organic field effect transistors (OFET) and poses practical challenges to implementing OFET circuits. It has been convenient to ignore the detailed physical processes associated with this disorder and model OFET behavior as equivalent to silicon FETs, but such simplifications limit our ability to develop integrated circuit technology as they fail to predict the true integrated OFET behavior. This talk will highlight the evolution of the organic electronic circuit technology and the challenges that lay ahead, emphasizing the need for physically accurate models of device behavior as the cornerstone of any future circuit advancements.