The Impact of Aligning Business, IT, and Marketing Strategies on Firm Performance

In order to succeed in today's competitive business environment, a firm should have a clear business strategy that is supported by other organizational strategies. While prior studies argue that strategic alignment enhances firm performance, either strategic alignment including multiple factors or strategic orientation of firms has received little attention. This study, drawing on contingency theory and configuration theory, investigates the performance impact of triadic strategic alignment among business, IT, and marketing strategies while simultaneously considers strategic orientation of firms. A research model is tested through SEM and MANOVA using data collected in a questionnaire survey of 242 Yemen managers. The findings indicate that (1) triadic strategic alignment has a positive impact on firm performance and (2) there is an ideal triadic strategic alignment for prospectors and defenders. This research contributes to strategic alignment literature and managers' understanding of how to align business, IT and marketing strategies to improve firm performance

Deep Neural Networks - A Brief History

Introduction to deep neural networks and their history.Comment: 14 pages, 14 figure

Compositional Falsification of Cyber-Physical Systems with Machine Learning Components

Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small adversarial perturbations. This raises the question: can the output from learning components can lead to a failure of the entire CPS? In this work, we address this question by formulating it as a problem of falsifying signal temporal logic (STL) specifications for CPS with ML components. We propose a compositional falsification framework where a temporal logic falsifier and a machine learning analyzer cooperate with the aim of finding falsifying executions of the considered model. The efficacy of the proposed technique is shown on an automatic emergency braking system model with a perception component based on deep neural networks

Design and evaluation of a directional antenna for ocean buoys

A system concept has been developed by Viasat, Inc. and Woods Hole Oceanographic Institution for improving the data telemetry bandwidth available on ocean buoys. This concept utilizes existing communications satellites as data relay stations and mechanically steered antenna arrays to achieve increased data rates and improved power efficiency needed for ocean applications. This report describes an initial feasibility and design study to determine if a mechanically steered antenna array can meet the requirements of open ocean buoy applications. To meet the system requirements, an 18-element microstrip antenna (9-element transmit, 9-element receive) was designed and fabricated under subcontract by Seavey Engineering Associates, Inc. It operates in the 4-6GHz frequency band (C-band) and provides 14 dB of gain. The 1/2 power beamwidth is +-t5° in azimuth and elevation. This antenna design, in conjunction with a simple rotating mount, was used to evaluate the potential of this approach to keep a geostationary satellite in view when mounted on an ocean buoy. The evaluation is based on laboratory measurements using a magnetic compass and a small stepper motor to maintain antenna orientation while the complete assembly was rotated and tilted at speeds similar to what would be expected on an offshore buoy equipped with a stabilizing wind vane. The results are promising, but less than conclusive because of limitations in the experimental test setup. The recent introduction of several commercially available mechanically steered antennas designed for use on small boats may provide a viable alternative to the approach described here with appropriate modification to operate at C-band.Funding was provided by Viasat, Inc., under subcontract No. SC95001 and by a Cecil H. and Ida M. Green Technology Innovation Award

Comparison of Fermi-LAT and CTA in the region between 10-100 GeV

The past decade has seen a dramatic improvement in the quality of data available at both high (HE: 100 MeV to 100 GeV) and very high (VHE: 100 GeV to 100 TeV) gamma-ray energies. With three years of data from the Fermi Large Area Telescope (LAT) and deep pointed observations with arrays of Cherenkov telescope, continuous spectral coverage from 100 MeV to $\sim10$ TeV exists for the first time for the brightest gamma-ray sources. The Fermi-LAT is likely to continue for several years, resulting in significant improvements in high energy sensitivity. On the same timescale, the Cherenkov Telescope Array (CTA) will be constructed providing unprecedented VHE capabilities. The optimisation of CTA must take into account competition and complementarity with Fermi, in particularly in the overlapping energy range 10$-$100 GeV. Here we compare the performance of Fermi-LAT and the current baseline CTA design for steady and transient, point-like and extended sources.Comment: Accepted for Publication in Astroparticle Physic

DeepWalk: Online Learning of Social Representations

We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide $F_1$ scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.Comment: 10 pages, 5 figures, 4 table

Time-Changed Fast Mean-Reverting Stochastic Volatility Models

We introduce a class of randomly time-changed fast mean-reverting stochastic volatility models and, using spectral theory and singular perturbation techniques, we derive an approximation for the prices of European options in this setting. Three examples of random time-changes are provided and the implied volatility surfaces induced by these time-changes are examined as a function of the model parameters. Three key features of our framework are that we are able to incorporate jumps into the price process of the underlying asset, allow for the leverage effect, and accommodate multiple factors of volatility, which operate on different time-scales

Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization

We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivated by total variation techniques, the method involves minimizing an energy functional made up of three terms. The first two terms promote a stepwise continuous classification function with sharp transitions between classes, while preserving symmetry among the class labels. The third term is a data fidelity term, allowing us to incorporate prior information into the model in a semi-supervised framework. The performance of the algorithm on synthetic data, as well as on the COIL and MNIST benchmark datasets, is competitive with state-of-the-art graph-based multiclass segmentation methods.Comment: 16 pages, to appear in Springer's Lecture Notes in Computer Science volume "Pattern Recognition Applications and Methods 2013", part of series on Advances in Intelligent and Soft Computin

Seeing the big PICTURE: A framework for improving the communication of requirements within the Business-IT relationship

The relationship between the business and IT departments in the context of the organisation has been characterised as highly divisive. Contributing problems appear to revolve around the failure to adequately communicate and understand the required information for the alignment of business and IT strategies and infrastructures. This study takes a communication-based view on the concept of alignment, in terms of the relationship between the retail business and IT within a major high street UK bank. A research framework (PICTURE) is used to provide insight into this relationship and guide the analysis of interviews with 29 individuals on mid-high management level for their thematic content. The paper highlights the lessons that can be derived from the study of the BIT relationship and how possible improvements could be made