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We report on the influence a preferential magnetic vortices motion has on the magnitude of the inverse ac Josephson effect (the appearance of dc current Shapiro steps) and the coherent operation of asymmetrical parallel arrays of YBaCuO Josephson junctions (JJ) irradiated with microwave (MW) radiation in the presence of an applied magnetic field B. The preferential direction of motion of the Josephson vortices is due to the asymmetry-induced ratchet effect and has a dramatic impact: for a particular positive dc bias current I when the flux-flow is robust multiple pronounced Shapiro-steps are observed consistent with a coherent operation of the array. This suggests an efficient emission/detection of MW in related applications. In contrast, when we reverse the direction of I, the flux-flow is reduced and the Shapiro-steps are strongly suppressed due to a highly incoherent operation that suggests an inefficient emission/detection of MW. Remarkably, by changing B slightly, the situation is reversed: Shapiro steps are now suppressed for a positive I, while well pronounced for a reverse current -I. Our results suggest that a preferential vortex-flow has a very significant impact on the coherent MW operation of superconducting devices consisting of either multiple JJs or a single long JJ asymmetrically biased. This is particular relevant in the case of flux-flow oscillators for sub-terahertz integrated-receivers, flux-driven Josephson (travelling-wave) parametric amplifiers, or on-chip superconducting MW generators which usually operate at bias currents in the Shapiro step region., Comment: 15 pages, 5 figures that includes a figure in the Supplementary material

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries., Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, USA

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms., Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference on Neural Networks (IJCNN 2017)

The significant computational costs of deploying neural networks in large-scale or resource constrained environments, such as data centers and mobile devices, has spurred interest in model compression, which can achieve a reduction in both arithmetic operations and storage memory. Several techniques have been proposed for reducing or compressing the parameters for feed-forward and convolutional neural networks, but less is understood about the effect of parameter compression on recurrent neural networks (RNN). In particular, the extent to which the recurrent parameters can be compressed and the impact on short-term memory performance, is not well understood. In this paper, we study the effect of complexity reduction, through singular value decomposition rank reduction, on RNN and minimal gated recurrent unit (MGRU) networks for several tasks. We show that considerable rank reduction is possible when compressing recurrent weights, even without fine tuning. Furthermore, we propose a perturbation model for the effect of general perturbations, such as a compression, on the recurrent parameters of RNNs. The model is tested against a noiseless memorization experiment that elucidates the short-term memory performance. In this way, we demonstrate that the effect of compression of recurrent parameters is dependent on the degree of temporal coherence present in the data and task. This work can guide on-the-fly RNN compression for novel environments or tasks, and provides insight for applying RNN compression in low-power devices, such as hearing aids., Comment: Accepted to IJCNN 2017. Final camera ready paper

The emergence of coherent quantum feedback control (CQFC) as a new paradigm for precise manipulation of dynamics of complex quantum systems has led to the development of efficient theoretical modeling and simulation tools and opened avenues for new practical implementations. This work explores the applicability of the integrated silicon photonics platform for implementing scalable CQFC networks. If proven successful, on-chip implementations of these networks would provide scalable and efficient nanophotonic components for autonomous quantum information processing devices and ultra-low-power optical processing systems at telecommunications wavelengths. We analyze the strengths of the silicon photonics platform for CQFC applications and identify the key challenges to both the theoretical formalism and experimental implementations. In particular, we determine specific extensions to the theoretical CQFC framework (which was originally developed with bulk-optics implementations in mind), required to make it fully applicable to modeling of linear and nonlinear integrated optics networks. We also report the results of a preliminary experiment that studied the performance of an in situ controllable silicon nanophotonic network of two coupled cavities and analyze the properties of this device using the CQFC formalism., Comment: 13 pages, 4 figures. Comments welcome

Rapid progress in silicon photonics has fostered numerous chip-scale sensing, computing, and signal processing technologies. However, many crucial filtering and signal delay operations are difficult to perform with all-optical devices. Unlike photons propagating at luminal speeds, GHz-acoustic phonons with slow velocity allow information to be stored, filtered, and delayed over comparatively smaller length-scales with remarkable fidelity. Hence, controllable and efficient coupling between coherent photons and phonons enables new signal processing technologies that greatly enhance the performance and potential impact of silicon photonics. Here, we demonstrate a novel mechanism for coherent information processing based on traveling-wave photon-phonon transduction, which achieves a phonon emit-and-receive process between distinct nanophotonic waveguides. Using this device physics-which can support 1-20GHz frequencies-we create wavelength-insensitive radio-frequency photonic filters with an unrivaled combination of stopband attenuation, selectivity, linewidth, and power-handling in silicon. More generally, this emit-receive concept is the impetus for numerous powerful new signal processing schemes., Comment: Main text (7 pages and 4 figures). Supplementary information (6 pages and 2 figures)

While nanoscale modal confinement radically enhances a variety of nonlinear light-matter interactions within silicon waveguides, traveling-wave stimulated Brillouin scattering nonlinearities have never been observed in silicon nanophotonics. Through a new class of hybrid photonic-phononic waveguides, we demonstrate tailorable traveling-wave forward stimulated Brillouin scattering in nanophotonic silicon waveguides for the first time, yielding 3000 times stronger forward SBS responses than any previous waveguide system. Simulations reveal that a coherent combination of electrostrictive forces and radiation pressures are responsible for greatly enhanced photon-phonon coupling at nano-scales. Highly tailorable Brillouin nonlinearities are produced by engineering the structure of a membrane-suspended waveguide to yield Brillouin resonances from 1 to 18 GHz through high quality-factor (>1000) phonon modes. Such wideband and tailorable stimulated Brillouin scattering in silicon photonics could enable practical realization of on-chip slow-light devices, RF-photonic filtering and sensing, and ultra-narrow-band laser sources by using standard semiconductor fabrication and CMOS technologies.

The purpose of this dissertation is to study the intersection theory of the moduli spaces of stable maps of degree two from two-pointed, genus zero nodal curves to arbitrary-dimensional projective space. Toward this end, first the Betti numbers of \bar{M}_{0,2}(P^r,2) are computed using Serre polynomials and equivariant Serre polynomials. Then, specializing to the space \bar{M}_{0,2}(P^1,2), generators and relations for the Chow ring are given. Chow rings of simpler spaces are also described, and the method of localization and linear algebra is developed. Both tools are used in finding the relations. It is further demonstrated that no additional relations exist among the generators, so that a presentation for the Chow ring A^*(\bar{M}_{0,2}(P^1,2)) is obtained. As a further check of the presentation, it is applied to give a new computation of the previously known genus zero, degree two, two-pointed gravitational correlators of P^1. Portions of this work also appear in math.AG/0501322 and math.AG/0504575, but the dissertation contains significantly more background and detail for those who may be interested in these. The dissertation is preserved in original form except for spacing changes, elimination of some front and end materials, additions to some references, and correction of typos in Proposition 11., Comment: 100 pages, dissertation

We give a presentation for the Chow ring of the moduli space of degree two stable maps from two-pointed rational curves to P^1. Also, integrals of of all degree four monomials in the hyperplane pullbacks and boundary divisors of this ring are computed using equivariant intersection theory. Finally, the presentation is used to give a new computation of the (previously known) values of the genus zero, degree two, two-pointed gravitational correlators of P^1. This article is a sequel to math.AG/0501322, although the only information truly needed from that article is the Poincare polynomial of the moduli space under consideration., Comment: 29 pages, 1 reference modified and 1 added, geometric explanation for linear relation noted in Section 4.3

We begin a study of the intersection theory of the moduli spaces of degree two stable maps from two-pointed rational curves to arbitrary-dimensional projective space. First we compute the Betti numbers of these spaces using Serre polynomial and equivariant Serre polynomial methods developed by E. Getzler and R. Pandharipande. Then, via the excision sequence, we compute an additive basis for their Chow rings in terms of Chow rings of nonlinear Grassmannians, which have been described by Pandharipande. The ring structure of one of these Chow rings is addressed in a sequel to this paper., Comment: Published in SIGMA (Symmetry, Integrability and Geometry: Methods and Applications) at http://www.emis.de/journals/SIGMA/