Contact Distribution Encodes Frictional Strength

The static friction coefficient, $\mu$, is a central quantity in modeling mechanical phenomena. However, experiments show that it is highly variable, even for a single interface under carefully controlled experimental conditions. Traditionally, this inconsistency is attributed to fluctuations in the real area of contact between samples, $A_R$. In this work, we perform a variety of experimental protocols on three pairs of solid blocks while imaging the contact interface and measuring $\mu$. Using linear regression and images of the interface taken prior to tangential loading, we predict the static friction coefficient. Our model strongly outperforms two benchmarks, the Bowden and Tabor picture ($\mu \propto A_R$) and prediction using experimental variables, indicating that a large portion of the observed variance in the initialization of slip is encoded in the contact plane. We perform the same analysis using only sub-sections of the interface, and find that regions as small as $1\%$ of the interface can still can beat both benchmarks. However, bigger sub-sections of the interface, even when comprised of many small regions with bad individual predictive power, outperform the best small regions alone, suggesting that the interfacial state is not dependent on any single point, but is rather distributed across the contact ensemble.Comment: 5 pages 4 figure

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

A study in human attention to guide computational action recognition

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 93-95).Computer vision researchers have a lot to learn from the human visual system. We, as humans, are usually unaware of how enormously difficult it is to watch a scene and summarize its most important events in words. We only begin to appreciate this truth when we attempt to build a system that performs comparably. In this thesis, I study two features of human visual apparatus: Attention and Peripheral Vision. I then use these to propose heuristics for computational approaches to action recognition. I think that building a system modeled after human vision, with the nonuniform distribution of resolution and processing power, can greatly increase the performance of the computer systems that target action recognition. In this study: (i) I develop and construct tools that allow me to study human vision and its role in action recognition, (ii) I perform four distinct experiments to gain insight into the role of attention and peripheral vision in this task, (iii) I propose computational heuristics, as well as mechanisms, that I believe will increase the efficiency, and recognition power of artificial vision systems. The tools I have developed can be applied to a variety of studies, including those performed on online crowd-sourcing markets (e.g. Amazon's Mechanical Turk). With my human experiments, I demonstrate that there is consistency of visual behavior among multiple subjects when they are asked to report the occurrence of a verb. Further, I demonstrate that while peripheral vision may play a small direct role in action recognition, it is a key component of attentional allocation, whereby it becomes fundamental to action recognition. Moreover, I propose heuristics based on these experiments, that can be informative to the artificial systems. In particular, I argue that the proper medium for action recognition are videos, not still images, and the basic driver of attention should be movement. Finally, I outline a computational mechanism that incorporates these heuristics into an implementable scheme.by Sam Sinai.M. Eng