9,079 research outputs found
Issues in the Scalability of Gate-level Morphogenetic Evolvable Hardware
Traditional approaches to evolvable hardware (EHW), in which the field programmable gate array (FPGA) configuration is directly encoded, have not scaled well with increasing circuit and FPGA complexity. To overcome this there have been moves towards encoding a growth process, known as morphogenesis. Using a morphogenetic approach, has shown success in scaling gate-level EHW for a signal routing problem, however, when faced with a evolving a one-bit full adder, unforseen difficulties were encountered. In this paper, we provide a measurement of EHW problem difficulty that takes into account the salient features of the problem, and when combined with a measure of feedback from the fitness function, we are able to estimate whether or not a given EHW problem is likely to be able to be solved successfully by our morphogenetic approach. Using these measurements we are also able to give an indication of the scalability of morphogenesis when applied to EHW
Constraining renormalon effects in lattice determination of heavy quark mass
The Borel summation technique of infrared renormalons is applied to the
lattice determination of heavy quark mass. With Borel summation a physical
heavy quark pole mass and binding energy of a heavy-light meson can be defined
in a rigorous and calculable manner. A notable feature of the Borel summation,
compared to the usual perturbative cancellation of IR renormalons, is an
automatic scale separation. The two approaches of handling renormalon
divergence are compared in the B-meson as well as in an (imaginary) heavy-light
meson with a mass much larger than the inverse of the lattice spacing.Comment: References and NNLO analysis added. Version to appear in Phys Rev
The Deterrence Effect of Prison: Dynamic Theory and Evidence
Using administrative, longitudinal data on felony arrests in Florida, we exploit the discontinuous increase in the punitiveness of criminal sanctions at 18 to estimate the deterrence effect of incarceration. Our analysis suggests a 2 percent decline in the log-odds of offending at 18, with standard errors ruling out declines of 11 percent or more. We interpret these magnitudes using a stochastic dynamic extension of Becker’s (1968) model of criminal behavior. Calibrating the model to match key empirical moments, we conclude that deterrence elasticities with respect to sentence lengths are no more negative than -0.13 for young offenders.Prison, crime, deterrence, incarceration
Crime, Punishment, and Myopia
Economic theory predicts that increasing the severity of punishments will deter criminal behavior by raising the expected price of committing crime. This implicit price can be substantially raised by making prison sentences longer, but only if offenders' discount rates are relatively low. We use a large sample of felony arrests to measure the deterrence effect of criminal sanctions. We exploit the fact that young offenders are legally treated as adults--and face longer lengths of incarceration--the day they turn 18. Sufficiently patient individuals should therefore significantly lower their offending rates immediately upon turning 18. The small behavioral responses that we estimate suggest that potential offenders are extremely impatient, myopic, or both.
Learning Matchable Image Transformations for Long-term Metric Visual Localization
Long-term metric self-localization is an essential capability of autonomous
mobile robots, but remains challenging for vision-based systems due to
appearance changes caused by lighting, weather, or seasonal variations. While
experience-based mapping has proven to be an effective technique for bridging
the `appearance gap,' the number of experiences required for reliable metric
localization over days or months can be very large, and methods for reducing
the necessary number of experiences are needed for this approach to scale.
Taking inspiration from color constancy theory, we learn a nonlinear
RGB-to-grayscale mapping that explicitly maximizes the number of inlier feature
matches for images captured under different lighting and weather conditions,
and use it as a pre-processing step in a conventional single-experience
localization pipeline to improve its robustness to appearance change. We train
this mapping by approximating the target non-differentiable localization
pipeline with a deep neural network, and find that incorporating a learned
low-dimensional context feature can further improve cross-appearance feature
matching. Using synthetic and real-world datasets, we demonstrate substantial
improvements in localization performance across day-night cycles, enabling
continuous metric localization over a 30-hour period using a single mapping
experience, and allowing experience-based localization to scale to long
deployments with dramatically reduced data requirements.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the
IEEE International Conference on Robotics and Automation (ICRA'20), Paris,
France, May 31-June 4, 202
Senior Recital, Justin Lee, piano
The presentation of this senior recital will fulfill in part the requirements for the Bachelor of Music degree in Performance. Justin Lee studies piano with Dr. Yin Zheng
Signaling pathways in osteogenesis and osteoclastogenesis: Lessons from cranial sutures and applications to regenerative medicine.
One of the simplest models for examining the interplay between bone formation and resorption is the junction between the cranial bones. Although only roughly a quarter of patients diagnosed with craniosynostosis have been linked to known genetic disturbances, the molecular mechanisms elucidated from these studies have provided basic knowledge of bone homeostasis. This work has translated to methods and advances in bone tissue engineering. In this review, we examine the current knowledge of cranial suture biology derived from human craniosynostosis syndromes and discuss its application to regenerative medicine
Linear magnetoresistance in metals: guiding center diffusion in a smooth random potential
We predict that guiding center (GC) diffusion yields a linear and
non-saturating (transverse) magnetoresistance in 3D metals. Our theory is
semi-classical and applies in the regime where the transport time is much
greater than the cyclotron period, and for weak disorder potentials which are
slowly varying on a length scale much greater than the cyclotron radius. Under
these conditions, orbits with small momenta along magnetic field are
squeezed and dominate the transverse conductivity. When disorder potentials are
stronger than the Debye frequency, linear magnetoresistance is predicted to
survive up to room temperature and beyond. We argue that magnetoresistance from
GC diffusion explains the recently observed giant linear magnetoresistance in
3D Dirac materials
A Convergent Iterative Solution of the Quantum Double-well Potential
We present a new convergent iterative solution for the two lowest quantum
wave functions and of the Hamiltonian with a quartic
double well potential in one dimension. By starting from a trial function,
which is by itself the exact lowest even or odd eigenstate of a different
Hamiltonian with a modified potential , we construct the Green's
function for the modified potential. The true wave functions, or
, then satisfies a linear inhomogeneous integral equation, in which
the inhomogeneous term is the trial function, and the kernel is the product of
the Green's function times the sum of , the potential difference, and
the corresponding energy shift. By iterating this equation we obtain successive
approximations to the true wave function; furthermore, the approximate energy
shift is also adjusted at each iteration so that the approximate wave function
is well behaved everywhere. We are able to prove that this iterative procedure
converges for both the energy and the wave function at all .Comment: 76 pages, Latex, no figure, 1 tabl
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