EFAB: Batch Production of Functional, Fully-Dense Metal Parts with Micron-Scale Features

EFAB (Electrochemical FABrication) is a new SFF process with the potential to economically fabricate prototypes or mass production quantities of functional, mesoscale-microscale parts and mechanisms. EFAB generates an entire layer simultaneously-versus serially, as with most SFF. Based on electrodeposition, EFAB allows ultra-thin layers (2-10 microns, or even submicron) that minimize stairsteps, and generates a net-shape, fully-dense metal structure that can be homogeneous and isotropic. Minimum feature width is approximately 25 microns, and can be reduced further. EFAB can be used to manufacture micromachines and microelectromechanical systems (MEMS), offering significant advantages over current processes: e.g., true 3-D geometry, IC compatibility, low capital investment, and process automation.Mechanical Engineerin

Tinnitus suppression by electric stimulation of the auditory nerve

Electric stimulation of the auditory nerve via a cochlear implant (CI) has been observed to suppress tinnitus, but parameters of an effective electric stimulus remain unexplored. Here we used CI research processors to systematically vary pulse rate, electrode place, and current amplitude of electric stimuli, and measure their effects on tinnitus loudness and stimulus loudness as a function of stimulus duration. Thirteen tinnitus subjects who used CIs were tested, with nine (70%) being “Responders” who achieved greater than 30% tinnitus loudness reduction in response to at least one stimulation condition and the remaining four (30%) being “Non-Responders” who had less than 30% tinnitus loudness reduction in response to any stimulus condition tested. Despite large individual variability, several interesting observations were made between stimulation parameters, tinnitus characteristics, and tinnitus suppression. If a subject's tinnitus was suppressed by one stimulus, then it was more likely to be suppressed by another stimulus. If the tinnitus contained a “pulsating” component, then it would be more likely suppressed by a given combination of stimulus parameters than tinnitus without these components. There was also a disassociation between the subjects' clinical speech processor and our research processor in terms of their effectiveness in tinnitus suppression. Finally, an interesting dichotomy was observed between loudness adaptation to electric stimuli and their effects on tinnitus loudness, with the Responders exhibiting higher degrees of loudness adaptation than the Non-Responders. Although the mechanisms underlying these observations remain to be resolved, their clinical implications are clear. When using a CI to manage tinnitus, the clinical processor that is optimized for speech perception needs to be customized for optimal tinnitus suppression

Self-Paced Learning: an Implicit Regularization Perspective

Self-paced learning (SPL) mimics the cognitive mechanism of humans and animals that gradually learns from easy to hard samples. One key issue in SPL is to obtain better weighting strategy that is determined by minimizer function. Existing methods usually pursue this by artificially designing the explicit form of SPL regularizer. In this paper, we focus on the minimizer function, and study a group of new regularizer, named self-paced implicit regularizer that is deduced from robust loss function. Based on the convex conjugacy theory, the minimizer function for self-paced implicit regularizer can be directly learned from the latent loss function, while the analytic form of the regularizer can be even known. A general framework (named SPL-IR) for SPL is developed accordingly. We demonstrate that the learning procedure of SPL-IR is associated with latent robust loss functions, thus can provide some theoretical inspirations for its working mechanism. We further analyze the relation between SPL-IR and half-quadratic optimization. Finally, we implement SPL-IR to both supervised and unsupervised tasks, and experimental results corroborate our ideas and demonstrate the correctness and effectiveness of implicit regularizers.Comment: 12 pages, 3 figure