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

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 $B$ 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

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

A Convergent Iterative Solution of the Quantum Double-well Potential

We present a new convergent iterative solution for the two lowest quantum wave functions $\psi_{ev}$ and $\psi_{od}$ of the Hamiltonian with a quartic double well potential $V$ 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 $V+\delta V$, we construct the Green's function for the modified potential. The true wave functions, $\psi_{ev}$ or $\psi_{od}$, 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 $\delta V$, 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 $x$.Comment: 76 pages, Latex, no figure, 1 tabl