Bellman Error Based Feature Generation using Random Projections on Sparse Spaces

We address the problem of automatic generation of features for value function approximation. Bellman Error Basis Functions (BEBFs) have been shown to improve the error of policy evaluation with function approximation, with a convergence rate similar to that of value iteration. We propose a simple, fast and robust algorithm based on random projections to generate BEBFs for sparse feature spaces. We provide a finite sample analysis of the proposed method, and prove that projections logarithmic in the dimension of the original space are enough to guarantee contraction in the error. Empirical results demonstrate the strength of this method

Optical wavefront phase-tilt measurement using Si-photonic waveguide grating couplers

Silicon photonic wavefront phase-tilt sensors for wavefront monitoring using surface coupling grating arrays are demonstrated. The first design employs the intrinsic angle dependence of the grating coupling efficiency to determine local wavefront tilt, with a measured sensitivity of 7 dB/degree. A second design connects four gratings in an interferometric waveguide circuit to determine incident wavefront phase variation across the sensor area. In this device, one fringe spacing corresponds to approximately 2 degree wavefront tilt change. These sensor elements can sample a wavefront incident on the chip surface without the use of bulk optic elements, fiber arrays, or imaging arrays. Both sensor elements are less than 60 um across, and can be combined into larger arrays to monitor wavefront tilt and distortion across an image or pupil plane in adaptive optics systems for free space optical communications, astronomy and beam pointing applications

Global design optimization in photonics: from high performance to fabrication robustness

Modern photonic devices are characterized by a large number of parameters and the need for an “holistic” optimization of their behavior taking into account multiple figures of merit, noteworthy tolerance to fabrication uncertainty. We present here a set of tools based on dimensionality reduction capable of handling such multi-parameter, multi-objectives design problems

Machine-assisted design and stochastic analysis in integrated photonics

Integrated photonic devices are steadily making their way into many application fields including modern optical communication networks and advanced sensors. On the other hand, the design of photonic devices and circuits mostly remains a time-consuming process largely based on the designer experience. This limits the size and complexity of the parameter space that can be handled. Moreover, addressing the effect of manufacturing variability remains a fundamental challenge since small fabrication errors can have a significant impact on light propagation, especially in high-index-contrast platforms such as silicon-on-insulator. The analysis of this variability with conventional approaches (e.g. Monte Carlo) can become prohibitive due to the large number of required simulations. Recent advances in machine-assisted design methods are opening the possibility to vastly expand the number of design parameters, exploring novel functionalities and non-intuitive geometries. In this invited talk we discuss the use of machine learning methods for the design of integrated photonic devices. We show the existence of a large number of possible designs that are all equivalent with respect to a given primary design objective but with distinct properties in other performance criteria. We use pattern recognition to reveal their relationship and to reduce the dimensionality of the large design space by properly defining new design variables. Likewise, we show how efficient stochastic techniques allow a quick assessment of the performance robustness and the expected fabrication yield for each tentative device. We focus in particular on stochastic spectral methods that have been regarded as a promising alternative to the classical Monte Carlo method, achieving a considerable reduction of the simulation time. Together, the reduction in the design space dimensionality and efficient stochastic techniques allow for the integration of the fabrication tolerance considerations into the design process

Smart on-chip Fourier-transform spectrometers harnessing machine learning algorithms

Miniaturized silicon photonics spectrometers have great potential for mass market applications like medicine and hazard detection. However, the performance of state-of-the-art silicon spectrometers is limited by fabrication imperfections and temperature variations. In this work, we present a fundamentally new strategy that combines machine learning algorithms and on-chip spatial heterodyne Fourier-transform spectroscopy to identify specific absorption features operated under a wide range of temperatures in the presence of fabrication imperfections. We experimentally show differentiation of four different input spectra with unknown temperature variations as large as 10 °C. This is about 100x increase in operational range, compared to state-of-the-art retrieval techniques

Dimensionality reduction and optimization for the inverse design of photonic integrated devices

The widespread use of metamaterials and non-trivial geometries has radically changed the way photonic integrated devices are developed, opening new design possibility and allowing for unprecedented performance. Yet, these devices are often described by a large number of interrelated parameters which cannot be handled manually, requiring innovative design approaches for their effective optimization. In this invited talk, we will discuss the potentiality offered by the combination of machine learning dimensionality reduction and multi-objective optimization for the design of high performance photonic integrated device

Expression of ZIC family genes in meningiomas and other brain tumors

<p>Abstract</p> <p>Background</p> <p>Zic zinc finger proteins are present in the developing rodent meninges and are required for cell proliferation and differentiation of meningeal progenitors. Although human <it>ZIC </it>genes are known to be molecular markers for medulloblastomas, their expression in meningioma has not been addressed to date.</p> <p>Methods</p> <p>We examined the mRNA and protein expression of human <it>ZIC1</it>, <it>ZIC2</it>, <it>ZIC3</it>, <it>ZIC4 </it>and <it>ZIC5 </it>genes in meningiomas in comparison to other brain tumors, using RT-PCR, analysis of published microarray data, and immunostaining.</p> <p>Results</p> <p><it>ZIC1</it>, <it>ZIC2 </it>and <it>ZIC5 </it>transcript levels in meningiomas were higher than those in whole brain or normal dura mater, whereas all five <it>ZIC </it>genes were abundantly expressed in medulloblastomas. The expression level of <it>ZIC1 </it>in public microarray data was greater in meningiomas classified as World Health Organization Grade II (atypical) than those classified as Grade I (benign). Immunoscreening using anti-ZIC antibodies revealed that 23 out of 23 meningioma cases were ZIC1/2/3/5-immunopositive. By comparison, nuclear staining by the anti-ZIC4 antibody was not observed in any meningioma case, but was strongly detected in all four medulloblastomas. ZIC-positive meningiomas included meningothelial, fibrous, transitional, and psammomatous histological subtypes. In normal meninges, ZIC-like immunoreactivities were detected in vimentin-expressing arachnoid cells both in human and mouse.</p> <p>Conclusions</p> <p>ZIC1, ZIC2, and ZIC5 are novel molecular markers for meningiomas whereas <it>ZIC4 </it>expression is highly selective for medulloblastomas. The pattern of <it>ZIC </it>expression in both of these tumor types may reflect the properties of the tissues from which the tumors are derived.</p

Subwavelength metamaterial devices with optimization and machine learning

Subwavelength metamaterials allow to synthesize tailored optical properties which enabled the demonstration of photonic devices with unprecedented performance and scale of integration. Yet, the development of metamaterial-based devices often involves a large number of interrelated parameters and figures of merit whose manual design can be impractical or lead to suboptimal solutions. In this invited talk, we will discuss the potentiality offered by multi-objective optimization and machine learning for the design of high-performance photonic devices based on metamaterials. We will present both integrated devices for on-chip photonic systems as well as recent advances in the development of devices for free-space applications and optical beam control