In a series of groundbreaking studies Balachander Krishnamurthy, Craig Wills, and Konstantin Naryshkin have demonstrated that information leakage is a pervasive problem (1, 2, 3).
However, as we have demonstrated, using information from visible spectrum images is an efficient way to help extract reliable and sensitive facial temperature profile from thermal images using advanced machine learning algorithms.
We have demonstrated that information extracted by simply monitoring users' location and activity (through accelerometer) can serve as an implicit indicator of several factors of interest such as their working and exercising schedule as well as their daily social interactions.
Previous research (Demian and Balatsoukas 2012) and our own studies of construction projects (Tory et al. 2008)) have demonstrated that information seeking and retrieval from large, shared construction project archives is a very difficult and time-consuming process.
Earlier studies have demonstrated that information such as weight, elasticity, length.can be accurately retrieved from kinematic displays of physical phenomena (free fall, pendulum motion, weight lifting.[69] [71]) proving that observers are sensitive to more general laws of physics.
The efficacy and tolerability of the switch from allopurinol to febuxostat in patients with moderate CKD in this study have demonstrated useful information for clinical practice in renal clinic.
Furthermore, investigations have demonstrated how such information can be meaningfully correlated with observations and predictions at different "omic" information levels, e.g. genomic variation [ 6], gene expression [ 7, 8] and standard functional annotations [ 9].
Recent studies, however, have demonstrated that important information about hydraulic fractures can be ascertained from flowback data and simulation studies are now being designed to model flowback along with the online production.
Using this simulated data, we have demonstrated how an information-theoretic approach to x-ray system design and analysis provides insight into system performance.
Results obtained have demonstrated that essential information can be achieved by carrying out backward uncertainty propagation analysis.
