Dynamic Magneto-Conductance Fluctuations and Oscillations in Mesoscopic Wires and Rings

Using a finite-frequency recursive Green's function technique, we calculate the dynamic magneto-conductance fluctuations and oscillations in disordered mesoscopic normal metal systems, incorporating inter-particle Coulomb interactions within a self-consistent potential method. In a disordered metal wire, we observe ergodic behavior in the dynamic conductance fluctuations. At low $\omega$, the real part of the conductance fluctuations is essentially given by the dc universal conductance fluctuations while the imaginary part increases linearly from zero, but for $\omega$ greater than the Thouless energy and temperature, the fluctuations decrease as $\omega^{-1/2}$. Similar frequency-dependent behavior is found for the Aharonov-Bohm oscillations in a metal ring. However, the Al'tshuler-Aronov-Spivak oscillations, which predominate at high temperatures or in rings with many channels, are strongly suppressed at high frequencies, leading to interesting crossover effects in the $\omega$-dependence of the magneto-conductance oscillations.Comment: 4 pages, REVTeX 3.0, 5 figures(ps file available upon request), #phd0

A Network Based Theory Of Foreign Market Entry Mode And Post-Entry Performance

Foreign market entry through equity investment has been extensively studied and various theoretical lenses have been used. Most previous research also focuses attention on either the entry mode selection decision or the topic of post-entry performance, but rarely both. We build on existing research by developing a model of foreign market entry and post-entry performance that uses network theory and organizational ecology to provide a fuller explanation of this complex and critical multinational enterprise strategic behaviour. Four pairs of total eight propositions were developed and justified based on extent literature and sound logical reasoning. By focusing on both entry mode choice and the post-entry performance implications of these choices, we cover both sides of the logic of profit as a function of both costs and revenues. Finally, potential managerial implications are discussed at the end

Terahertz electron-hole recollisions in GaAs/AlGaAs quantum wells: robustness to scattering by optical phonons and thermal fluctuations

Electron-hole recollisions are induced by resonantly injecting excitons with a near-IR laser at frequency $f_{\text{NIR}}$ into quantum wells driven by a ~10 kV/cm field oscillating at $f_{\text{THz}} = 0.57$ THz. At $T=12$ K, up to 18 sidebands are observed at frequencies $f_{\text{sideband}}=f_{\text{NIR}}+2n f_{\text{THz}}$, with $-8 \le 2n \le 28$. Electrons and holes recollide with total kinetic energies up to 57 meV, well above the $E_{\text{LO}} = 36$ meV threshold for longitudinal optical (LO) phonon emission. Sidebands with order up to $2n=22$ persist up to room temperature. A simple model shows that LO phonon scattering suppresses but does not eliminate sidebands associated with kinetic energies above $E_{\text{LO}}$.Comment: 5 pages, 4 figure

A Cost-based Optimizer for Gradient Descent Optimization

As the use of machine learning (ML) permeates into diverse application domains, there is an urgent need to support a declarative framework for ML. Ideally, a user will specify an ML task in a high-level and easy-to-use language and the framework will invoke the appropriate algorithms and system configurations to execute it. An important observation towards designing such a framework is that many ML tasks can be expressed as mathematical optimization problems, which take a specific form. Furthermore, these optimization problems can be efficiently solved using variations of the gradient descent (GD) algorithm. Thus, to decouple a user specification of an ML task from its execution, a key component is a GD optimizer. We propose a cost-based GD optimizer that selects the best GD plan for a given ML task. To build our optimizer, we introduce a set of abstract operators for expressing GD algorithms and propose a novel approach to estimate the number of iterations a GD algorithm requires to converge. Extensive experiments on real and synthetic datasets show that our optimizer not only chooses the best GD plan but also allows for optimizations that achieve orders of magnitude performance speed-up.Comment: Accepted at SIGMOD 201

Determination of Stark parameters by cross-calibration in a multi-element laser-induced plasma

We illustrate a Stark broadening analysis of the electron density N(e) and temperature T(e) in a laser-induced plasma (LIP), using a model free of assumptions regarding local thermodynamic equilibrium (LTE). The method relies on Stark parameters determined also without assuming LTE, which are often unknown and unavailable in the literature. Here, we demonstrate that the necessary values can be obtained in situ by cross-calibration between the spectral lines of different charge states, and even different elements, given determinations of N(e) and T(e) based on appropriate parameters for at least one observed transition. This approach enables essentially free choice between species on which to base the analysis, extending the range over which these properties can be measured and giving improved access to low-density plasmas out of LTE. Because of the availability of suitable tabulated values for several charge states of both Si and C, the example of a SiC LIP is taken to illustrate the consistency and accuracy of the procedure. The cross-calibrated Stark parameters are at least as reliable as values obtained by other means, offering a straightforward route to extending the literature in this area

Dynamic capabilities to match multiple product generations and market rhythm

Purpose : The purpose of this paper is to provide greater insights to managers seeking to time properly the launches of innovative new products (NPs) across multiple generations. This paper aims to address the rhythm matching problem by developing a typology and a conceptual framework of the interaction between a firm’s technological readiness to launch NPs and a market’s receptivity in influencing a firm’s long-term performance. Design/methodology/approach –:Based on the new product development (NPD) and diffusion of innovation literatures, the paper develops a model explicitly to address the rhythm matching problem by highlighting the interaction between a firm’s technological readiness to launch new products and a market’s receptivity in influencing a firm’s long-term performance. The logic of this model may be described as follows: long-term performance is a function of matching: products to customer needs, marketingmix dynamics to customer segments and buying behavior dynamics, and logistics, supply chain management, and inventory to market dynamics and financial efficiency; uncertainty in: knowledge of needs, market segments and their dynamics, and market dynamics is all a function of time, as is financial efficiency. Therefore, a firm’s long-term performance is a function of these matches over time. Findings : Deriving from the proposed model and typology, it was found that in independent rhythm windows, the management focus is on a single generation and each successive generation can be planned independently. In market- imposed windows, firms aim at adapting their own NP readiness rhythm to the market receptivity rhythm. In firm-imposed windows, firms have the initiative to drive the market receptivity rhythm. In dynamically resultant windows, everything is more complicated because firms’ NP readiness rhythm and market receptivity rhythm influence each other. Originality/value : The model and typology developed in this paper are a breakthrough result of synthesizing various traditions of NPD and diffusion of innovation research. It is believed that the paper provides a rich conceptual framework drawing together extant research on the development and introduction of new products. The framework is intended both to explicitly inform managers of the importance of rhythm matching as well as to the factors that influence such matching. It is also intended to provide a lens with which further research can be directed to increase the efficiency and effectiveness of resource utilization in NPD and the long- term success of the firms

A framework for innovative service design

Drawing on research from design science, marketing and service science, our paper provides an integrated framework for evaluating and directing innovative service design. The main goal of our review is to highlight the strengths of existing frameworks and to suggest how they can be enhanced in combination with design science principles. Based on our review, we propose a new framework for the design of innovative services that integrates several key paradigmatic approaches and identifies fundamental open research questions. Our approach is unique as it combines three service disciplines, namely services marketing, service science, and design science, and provides a new framework that describes step by step the procedure that needs to be taken and the conditions that need to be met for developing innovative services. We believe that providing such a framework is a valuable addition to the literature

Machine-Learning-Aided Dynamic Reconfiguration in Optical DC/HPC Networks (Invited)

The high bandwidth and low latency requirements of modern computing applications with their dynamic and nonuniform traffic patterns impose severe challenges to current data center (DC) and high performance computing (HPC) networks. Therefore, we present a dynamic network reconfiguration mechanism that could satisfy the time-varying applications' demands in an optical DC/HPC network. We propose a direct and an indirect topology extraction methods based on a machine learning-Aided traffic prediction approach under multi-Application scenario. The traffic prediction for topology extraction and bandwidth reconfiguration (PredicTER) method could lead to frequent topology and bandwidth reconfiguration. In contrast, the indirect approach, namely traffic prediction with clustering for topology extraction and bandwidth reconfiguration (PrediCLUSTER), utilizes an unsupervised learning-based clustering model to first associate the predicted traffic to one of possible traffic clusters, and then extracts a common topology for the cluster. This restricts the reconfigured topology set to the number of traffic clusters. Our simulation results show that the time-Average of mean packet latencies (and total dropped packets) over 60 seconds of timevarying traffic under the PredicTER, PrediCLUSTER and a static topology are 37.7μs,41.2μs, and 50.2μs (and 37,967, 12,305, and 36,836), respectively. Overall, the PredicTER (and PrediCLUSTER) method(s) can improve the end-To-end packet latency by 24.9% (and 17.8%), and the packet loss rate by-3.1% (and 66.6%), as compared to the static flat Hyper-X-like topology

Collaborative learning in multi-domain optical networks

This paper presents a collaborative learning framework for multi-domain optical networks to enable cognitive end-to-end networking while guaranteeing the autonomy of each administrative domain