A method to correlate the uninterpreted tandem mass spectra of peptides produced under low energy (lo-50 eV) collision conditions with amino acid sequences in the Genpept database has been developed. In this method the protein database is searched to identify linear amino acid sequences within a

for physical arrays, though at a loss of spectral efficiency due to half-duplex operation and possibly at the cost of additional receive hardware. Applicable to any wireless setting, including cellular or ad hoc networks—wherever space constraints preclude the use of physical arrays—the performance

attractive loss and utility functions that can be tailored to the problem at hand. This article reviews and develops the theory of proper scoring rules on general probability spaces, and proposes and discusses examples thereof. Proper scoring rules derive from convex functions and relate to information

show up to a 38 % reduction in wrong-path instructions with a negligible performance loss (   ¢¡¤ £). Best of all, even in programs with a high branch prediction accuracy, performance does not noticeably degrade. Our analysis indicates that there is little risk in implementing this method in existing

explore methods of reducing energy consumption that do not lead to performance loss (i.e., wasted energy), and explore methods to reduce delay by exploiting instruction level parallelism. We found that careful design reduced the energy dissipation by almost 25%. Pipelining can give approximately a 2x

Electron energy-loss spectroscopy (EELS) is an analytical technique that measures the change in kinetic energy of electrons after they have interacted with a specimen. When carried out in a modern transmission electron microscope, EELS is capable of giving structural and chemical information about

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Ion energy loss at maximum stopping power in a laser-generated plasma Defended on December, 2nd 2013 in front of the committee composed of

, and similar problems. These approaches are essential for emerging applications such as sensor networks where resources such as bandwidth and energy are limited. This paper is the first description of the software architecture that supports named data and in-network processing in an operational, multi

of support vectors, with negligible loss in classification accuracy on various tasks. This approximation also applies to 1 − χ2 and other kernels of similar form. We also introduce novel features based on a multi-level histograms of oriented edge energy and present experiments on various detection datasets