Mining Object Parts from CNNs via Active Question-Answering

Given a convolutional neural network (CNN) that is pre-trained for object classification, this paper proposes to use active question-answering to semanticize neural patterns in conv-layers of the CNN and mine part concepts. For each part concept, we mine neural patterns in the pre-trained CNN, which are related to the target part, and use these patterns to construct an And-Or graph (AOG) to represent a four-layer semantic hierarchy of the part. As an interpretable model, the AOG associates different CNN units with different explicit object parts. We use an active human-computer communication to incrementally grow such an AOG on the pre-trained CNN as follows. We allow the computer to actively identify objects, whose neural patterns cannot be explained by the current AOG. Then, the computer asks human about the unexplained objects, and uses the answers to automatically discover certain CNN patterns corresponding to the missing knowledge. We incrementally grow the AOG to encode new knowledge discovered during the active-learning process. In experiments, our method exhibits high learning efficiency. Our method uses about 1/6-1/3 of the part annotations for training, but achieves similar or better part-localization performance than fast-RCNN methods.Comment: Published in CVPR 201

ImmPort, toward repurposing of open access immunological assay data for translational and clinical research

Immunology researchers are beginning to explore the possibilities of reproducibility, reuse and secondary analyses of immunology data. Open-access datasets are being applied in the validation of the methods used in the original studies, leveraging studies for meta-analysis, or generating new hypotheses. To promote these goals, the ImmPort data repository was created for the broader research community to explore the wide spectrum of clinical and basic research data and associated findings. The ImmPort ecosystem consists of four components–Private Data, Shared Data, Data Analysis, and Resources—for data archiving, dissemination, analyses, and reuse. To date, more than 300 studies have been made freely available through the ImmPort Shared Data portal , which allows research data to be repurposed to accelerate the translation of new insights into discoveries

Lower bounds on photometric redshift errors from Type Ia supernovae templates

Cosmology with Type Ia supernovae heretofore has required extensive spectroscopic follow-up to establish a redshift. Though tolerable at the present discovery rate, the next generation of ground-based all-sky survey instruments will render this approach unsustainable. Photometry-based redshift determination is a viable alternative, but introduces non-negligible errors that ultimately degrade the ability to discriminate between competing cosmologies. We present a strictly template-based photometric redshift estimator and compute redshift reconstruction errors in the presence of photometry and statistical errors. With reasonable assumptions for a cadence and supernovae distribution, these redshift errors are combined with systematic errors and propagated using the Fisher matrix formalism to derive lower bounds on the joint errors in $\Omega_w$ and $\Omega_w'$ relevant to the next generation of ground-based all-sky survey.Comment: 23 pages, 6 figure