Customized design of hearing aids using statistical shape learning

3D shape modeling is a crucial component of rapid prototyping systems that customize shapes of implants and prosthetic devices to a patient’s anatomy. In this paper, we present a solution to the problem of customized 3D shape modeling using a statistical shape analysis framework. We design a novel method to learn the relationship between two classes of shapes, which are related by certain operations or transformation. The two associated shape classes are represented in a lower dimensional manifold, and the reduced set of parameters obtained in this subspace is utilized in an estimation, which is exemplified by a multivariate regression in this paper.We demonstrate our method with a felicitous application to estimation of customized hearing aid devices

Memory formation in matter

Memory formation in matter is a theme of broad intellectual relevance; it sits at the interdisciplinary crossroads of physics, biology, chemistry, and computer science. Memory connotes the ability to encode, access, and erase signatures of past history in the state of a system. Once the system has completely relaxed to thermal equilibrium, it is no longer able to recall aspects of its evolution. Memory of initial conditions or previous training protocols will be lost. Thus many forms of memory are intrinsically tied to far-from-equilibrium behavior and to transient response to a perturbation. This general behavior arises in diverse contexts in condensed matter physics and materials: phase change memory, shape memory, echoes, memory effects in glasses, return-point memory in disordered magnets, as well as related contexts in computer science. Yet, as opposed to the situation in biology, there is currently no common categorization and description of the memory behavior that appears to be prevalent throughout condensed-matter systems. Here we focus on material memories. We will describe the basic phenomenology of a few of the known behaviors that can be understood as constituting a memory. We hope that this will be a guide towards developing the unifying conceptual underpinnings for a broad understanding of memory effects that appear in materials

Viscous to Inertial Crossover in Liquid Drop Coalescence

Using an electrical method and high-speed imaging we probe drop coalescence down to 10 ns after the drops touch. By varying the liquid viscosity over two decades, we conclude that at sufficiently low approach velocity where deformation is not present, the drops coalesce with an unexpectedly late crossover time between a regime dominated by viscous and one dominated by inertial effects. We argue that the late crossover, not accounted for in the theory, can be explained by an appropriate choice of length-scales present in the flow geometry.Comment: 4 pages, 4 figure

Multiple transient memories in sheared suspensions: robustness, structure, and routes to plasticity

Multiple transient memories, originally discovered in charge-density-wave conductors, are a remarkable and initially counterintuitive example of how a system can store information about its driving. In this class of memories, a system can learn multiple driving inputs, nearly all of which are eventually forgotten despite their continual input. If sufficient noise is present, the system regains plasticity so that it can continue to learn new memories indefinitely. Recently, Keim & Nagel showed how multiple transient memories could be generalized to a generic driven disordered system with noise, giving as an example simulations of a simple model of a sheared non-Brownian suspension. Here, we further explore simulation models of suspensions under cyclic shear, focussing on three main themes: robustness, structure, and overdriving. We show that multiple transient memories are a robust feature independent of many details of the model. The steady-state spatial distribution of the particles is sensitive to the driving algorithm; nonetheless, the memory formation is independent of such a change in particle correlations. Finally, we demonstrate that overdriving provides another means for controlling memory formation and retention

Multiple transient memories in experiments on sheared non-Brownian suspensions

A system with multiple transient memories can remember a set of inputs but subsequently forgets almost all of them, even as they are continually applied. If noise is added, the system can store all memories indefinitely. The phenomenon has recently been predicted for cyclically sheared non-Brownian suspensions. Here we present experiments on such suspensions, finding behavior consistent with multiple transient memories and showing how memories can be stabilized by noise.Comment: 5 pages, 4 figure

Low-Temperature Features of Nano-Particle Dynamics

In view of better characterizing possible quantum effects in the dynamics of nanometric particles, we measure the effect on the relaxation of a slight heating cycle. The effect of the field amplitude is studied; its magnitude is chosen in order to induce the relaxation of large particles (~7nm), even at very low temperatures (100mK). Below 1K, the results significantly depart from a simple thermal dynamics scenario.Comment: 1 tex file, 4 PostScript figure

The inexorable resistance of inertia determines the initial regime of drop coalescence

Drop coalescence is central to diverse processes involving dispersions of drops in industrial, engineering and scientific realms. During coalescence, two drops first touch and then merge as the liquid neck connecting them grows from initially microscopic scales to a size comparable to the drop diameters. The curvature of the interface is infinite at the point where the drops first make contact, and the flows that ensue as the two drops coalesce are intimately coupled to this singularity in the dynamics. Conventionally, this process has been thought to have just two dynamical regimes: a viscous and an inertial regime with a crossover region between them. We use experiments and simulations to reveal that a third regime, one that describes the initial dynamics of coalescence for all drop viscosities, has been missed. An argument based on force balance allows the construction of a new coalescence phase diagram

Unconventional antiferromagnetic correlations of the doped Haldane gap system Y2_2BaNi1−x_{1-x}Znx_xO5_5

We make a new proposal to describe the very low temperature susceptibility of the doped Haldane gap compound Y$_2$BaNi$_{1-x}$Zn$_x$O$_5$. We propose a new mean field model relevant for this compound. The ground state of this mean field model is unconventional because antiferromagnetism coexists with random dimers. We present new susceptibility experiments at very low temperature. We obtain a Curie-Weiss susceptibility $\chi(T) \sim C / (\Theta+T)$ as expected for antiferromagnetic correlations but we do not obtain a direct signature of antiferromagnetic long range order. We explain how to obtain the ``impurity'' susceptibility $\chi_{imp}(T)$ by subtracting the Haldane gap contribution to the total susceptibility. In the temperature range [1 K, 300 K] the experimental data are well fitted by $T \chi_{imp}(T) = C_{imp} (1 + T_{imp}/T )^{-\gamma}$. In the temperature range [100 mK, 1 K] the experimental data are well fitted by $T \chi_{imp}(T) = A \ln{(T/T_c)}$, where $T_c$ increases with $x$. This fit suggests the existence of a finite N\'eel temperature which is however too small to be probed directly in our experiments. We also obtain a maximum in the temperature dependence of the ac-susceptibility $\chi'(T)$ which suggests the existence of antiferromagnetic correlations at very low temperature.Comment: 19 pages, 17 figures, revised version (minor modifications