Structured Dropout for Weak Label and Multi-Instance Learning and Its Application to Score-Informed Source Separation

Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, however, can be expensive and thus there is increasing interest in weak labels which only provide coarse information, with uncertainty regarding time, location or value. Using such labels often leads to considerable challenges for the learning process. Current methods for weak-label training often employ standard supervised approaches that additionally reassign or prune labels during the learning process. The information gain, however, is often limited as only the importance of labels where the network already yields reasonable results is boosted. We propose treating weak-label training as an unsupervised problem and use the labels to guide the representation learning to induce structure. To this end, we propose two autoencoder extensions: class activity penalties and structured dropout. We demonstrate the capabilities of our approach in the context of score-informed source separation of music

Polarization of electric field noise near metallic surfaces

Electric field noise in proximity to metallic surfaces is a poorly understood phenomenon that appears in different areas of physics. Trapped ion quantum information processors are particular susceptible to this noise, leading to motional decoherence which ultimately limits the fidelity of quantum operations. On the other hand they present an ideal tool to study this effect, opening new possibilities in surface science. In this work we analyze and measure the polarization of the noise field in a micro-fabricated ion trap for various noise sources. We find that technical noise sources and noise emanating directly from the surface give rise to different degrees of polarization which allows us to differentiate between the two noise sources. Based on this, we demonstrate a method to infer the magnitude of surface noise in the presence of technical noise

Slow waves in locally resonant metamaterials line defect waveguides

In the past decades, many efforts have been devoted to the temporal manipulation of waves, especially focusing on slowing down their propagation. In electromagnetism, from microwave to optics, as well as in acoustics or for elastic waves, slow wave propagation indeed largely benefits both applied and fundamental physics. It is for instance essential in analog signal computing through the design of components such as delay lines and buffers, and it is one of the prerequisite for increased wave/matter interactions. Despite the interest of a broad community, researches have mostly been conducted in optics along with the development of wavelength scaled structured composite media, that appear promising candidates for compact slow light components. Yet their minimum structural scale prevents them from being transposed to lower frequencies where wavelengths range from sub-millimeter to meters. In this article, we propose to overcome this limitation thanks to the deep sub-wavelength scale of locally resonant metamaterials. In our approach, implemented here in the microwave regime, we show that introducing coupled resonant defects in such composite media allows the creation of deep sub-wavelength waveguides. We experimentally demonstrate that waves, while propagating in such waveguides, exhibit largely reduced group velocities. We qualitatively explain the mechanism underlying this slow wave propagation and first experimentally demonstrate, then numerically verify, how it can be taken advantage of to tune the velocity, achieving group indices ng as high as 227 over relatively large bandwidths. We conclude by highlighting the three beneficial consequences of our line defect slow wave waveguides in locally resonant metamaterials: the deep sub-wavelength scale, the very large group indices and the fact that slow wave propagation does not occur at the expense of drastic bandwidth reductions

Fast, scalable, Bayesian spike identification for multi-electrode arrays

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human intervention is minimized and streamlined via a graphical interface. We illustrate our method on data from a mammalian retina preparation and document its performance on simulated data consisting of spikes added to experimentally measured background noise. The algorithm is highly accurate

Transient LTRE analysis reveals the demographic and trait-mediated processes that buffer population growth.

Temporal variation in environmental conditions affects population growth directly via its impact on vital rates, and indirectly through induced variation in demographic structure and phenotypic trait distributions. We currently know very little about how these processes jointly mediate population responses to their environment. To address this gap, we develop a general transient life table response experiment (LTRE) which partitions the contributions to population growth arising from variation in (1) survival and reproduction, (2) demographic structure, (3) trait values and (4) climatic drivers. We apply the LTRE to a population of yellow-bellied marmots (Marmota flaviventer) to demonstrate the impact of demographic and trait-mediated processes. Our analysis provides a new perspective on demographic buffering, which may be a more subtle phenomena than is currently assumed. The new LTRE framework presents opportunities to improve our understanding of how trait variation influences population dynamics and adaptation in stochastic environments