C. A. Metzler, A. Mousavi, R. Heckel, and R. G. Baraniuk, “Unsupervised Learning with Stein’s Unbiased Risk Estimator,” https://arxiv.org/abs/1805.10531, June 2018.
Appearing today on NSF's Science360 News: Taking a closer look with a lens-free fluorescent microscope
Rice University engineers are building a flat microscope, called FlatScope (TM), and developing software that can decode and trigger neurons on the surface of the brain. The goal as part of a new government initiative is to provide an alternate path for sight and sound to be delivered directly to the brain. The project is part of a $65 million effort announced this week by the federal Defense Advanced Research Projects Agency (DARPA) to develop a high-resolution neural interface. Among many long-term goals, the Neural Engineering System Design (NESD) program hopes to compensate for a person's loss of vision or hearing by delivering digital information directly to parts of the brain that can process it.
OpenStax’s market share in the college textbook market continues to grow rapidly. According to the latest Babson Survey Research Group Report on Open Educational Resources (OER), 16.5% of faculty who recently chose a new textbook for a large-enrollment introductory-level course adopted a textbook from OpenStax. This is up 50% over 2016’s adoption rate of 10.8%. The survey finds that OpenStax textbooks are being adopted for large-enrollment introductory courses at roughly the same rate as commercial textbooks.
J. K. Adams, V. Boominathan, B. W. Avants, D. G. Vercosa, F. Ye, R. G. Baraniuk, J. T. Robinson, A. Veeraraghavan, “Single-Frame 3D Fluorescence Microscopy with Ultraminiature Lensless FlatScope,” Science Advances, Vol. 3, No. 12, 8 December 2017.
Abstract: Modern biology increasingly relies on fluorescence microscopy, which is driving demand for smaller, lighter, and cheaper microscopes. However, traditional microscope architectures suffer from a fundamental trade-off: As lenses become smaller, they must either collect less light or image a smaller field of view. To break this fundamental trade-off between device size and performance, we present a new concept for three-dimensional (3D) fluorescence imaging that replaces lenses with an optimized amplitude mask placed a few hundred micrometers above the sensor and an efficient algorithm that can convert a single frame of captured sensor data into high-resolution 3D images. The result is FlatScope: perhaps the world’s tiniest and lightest microscope. FlatScope is a lensless microscope that is scarcely larger than an image sensor (roughly 0.2 g in weight and less than 1 mm thick) and yet able to produce micrometer-resolution, high–frame rate, 3D fluorescence movies covering a total volume of several cubic millimeters. The ability of FlatScope to reconstruct full 3D images from a single frame of captured sensor data allows us to image 3D volumes roughly 40,000 times faster than a laser scanning confocal microscope while providing comparable resolution. We envision that this new flat fluorescence microscopy paradigm will lead to implantable endoscopes that minimize tissue damage, arrays of imagers that cover large areas, and bendable, flexible microscopes that conform to complex topographies.
Fig. 1. (A) Traditional microscopes capture the scene through an objective and tube lens (~20 to 460 mm), resulting in a quality image directly on the imaging sensor. (B) FlatScope captures the scene through an amplitude mask and spacer (~0.2 mm) and computationally reconstructs the image. Scale bars, 100 μm (inset, 50 μm). (C) Comparison of form factor and resolution for traditional lensed research microscopes, GRIN lens microscope, and FlatScope. FlatScope achieves high-resolution imaging while maintaining a large ratio of FOV relative to the cross-sectional area of the device (see Materials and Methods for elaboration). Microscope objectives are Olympus MPlanFL N (1.25×/2.5×/5×, NA = 0.04/0.08/0.15), Nikon Apochromat (1×/2×/4×, NA = 0.04/0.1/0.2), and Zeiss Fluar (2.5×/5×, NA = 0.12/0.25). (D) FlatScope prototype (shown without absorptive filter). Scale bars, 100 μm.
Mark Perry of the American Enterprise Institute posits that the long-term trend of rapidly increasing textbook prices might have finally been disrupted by market competition, partly by new textbook alternatives like OpenStax.
His bottom line: “I think we can expect continued disruption in the college textbook market and a continued downward trend in college textbook prices as competitive forces and more alternatives continue to erode the power of traditional textbook publishers. The trend has been broken, and college students can expect lower and lower textbook prices in the future.”
Read the entire blog entry
Patients who have to undergo a magnetic resonance imaging (MRI) scan may be spared the ordeal of having to lie still in the scanner for up to 45 minutes, thanks to new compressive sensing technology developed in the groups of Rice ECE faculty Richard Baraniuk and Kevin Kelly. The patented technology was recently licensed from Rice by Siemens Healthineers.
Magnetic resonance imaging (MRI) scanners equipped with compressive sensing operate much more quickly than current scanners. Siemens Healthineers has applied the technology to help solve an important clinical problem: how to reduce long scan times while maintaining high diagnostic quality. The result is the first clinical application of compressive sensing for cardiovascular imaging; it was approved for clinical use in February 2017 by the Food and Drug Administration. Thanks to compressive sensing, scans of the beating heart can be completed in as few as 25 seconds while the patient breathes freely. In contrast, in an MRI scanner equipped with conventional acceleration techniques, patients must lie still for six minutes or more and hold their breath as many as seven to 12 times throughout a cardiovascular-related procedure.
A. Mousavi, G. Dasarathy, R. G. Baraniuk, “DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks,” arXiv:1707.03386, July 2017.
We develop a novel computational sensing framework for sensing and recovering structured signals called DeepCodec. When trained on a set of representative signals, our framework learns to take undersampled measurements and recover signals from them using a deep convolutional neural network. In other words, it learns a transformation from the original signals to a near-optimal number of undersampled measurements and the inverse transformation from measurements to signals. This is in contrast to conventional compressive sensing (CS) systems that use random linear measurements and convex optimization or iterative algorithms for signal recovery. We compare our new framework with ℓ1-minimization from the phase transition point of view and demonstrate that it outperforms ℓ1-minimization in the regions of phase transition plot where ℓ1-minimization cannot recover the exact solution. In addition, we experimentally demonstrate how learning measurements enhances recovery performance, speeds up training, and reduces the number of parameters to learn.
DeepCodec learns a transformation from signals x to measurement vectors y and an approximate inverse transformation from measurement vectors y to signals x using a deep convolutional network that consists of convolutional and sub-pixel convolution layers.
Recovery comparison of DeepCodec vs. LASSO (with optimal regularization parameter).
Rice University-based nonprofit OpenStax, which is already changing the economics of higher education by providing free textbooks to more than 1 million college students per year, today launched a low-cost, personalized learning system called OpenStax Tutor Beta that analyzes how students learn to offer them individualized homework and tutoring. In development for three years, the system will be available this fall for three courses: college physics, biology and sociology. While students study using OpenStax Tutor, it learns how they learn — what they struggle with, what helps them most — and it uses that information to offer just-in-time remediation and enrichment. The system provides personalized assessment and spaced practice, helping students focus their studying efforts on their weak areas and remember what they learned earlier in the course.
I am actively recruiting to fill open postdoc positions for research in machine learning theory and methods, computational sensing/imaging, and sparse signal processing. The Rice DSP and Machine Learning groups offer an energizing environment for research; 25 recent postdocs and PhD students have been placed in top faculty positions. Apply here!