Network awareness and application adaptability

In this paper, we argue that pervasive applications need to be aware of and adaptive to network conditions. We begin with an overview of three software projects in which we are currently involved, and highlight network awareness and application adaptability as a common thread among them. We argue that these features stem from the fundamental architectural principles of the Internet. We generalize our experience and elaborate on the principles for developing network awareness and adaptable applications.     

Density-driven generalized regression neural networks (DD-GRNN) for function approximation.

This paper proposes a new nonparametric regression method, based on the combination of generalized regression neural networks (GRNNs), density-dependent multiple kernel bandwidths, and regularization. The presented model is generic and substitutes the very large number of bandwidths with a much smaller number of trainable weights that control the regression model. It depends on sets of extracted data density features which reflect the density properties and distribution irregularities of the training data sets. We provide an efficient initialization scheme and a second-order algorithm to train the model, as well as an overfitting control mechanism based on Bayesian regularization. Numerical results show that the proposed network manages to reduce significantly the computational demands of having individual bandwidths, while at the same time, provides competitive function approximation accuracy in relation to existing methods.     

Preventing Location-Based Identity Inference in Anonymous Spatial Queries

The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of location-based services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to location-based services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial queries. Extensive experimental studies suggest that the proposed techniques are applicable to real-life scenarios with numerous mobile users.     

Contextualization as an independent abstraction mechanism for conceptual modeling

The notion of context appears in computer science, as well as in several other disciplines, in various forms. In this paper, we present a general framework for representing the notion of context in information modeling. First, we define a context as a set of objects, within which each object has a set of names and possibly a reference: the reference of the object is another context which “hides” detailed information about the object. Then, we introduce the possibility of structuring the contents of a context through the traditional abstraction mechanisms, i.e., classification, generalization, and attribution. We show that, depending on the application, our notion of context can be used as an independent abstraction mechanism, either in an alternative or a complementary capacity with respect to the traditional abstraction mechanisms. We also study the interactions between contextualization and the traditional abstraction mechanisms, as well as the constraints that govern such interactions. Finally, we present a theory for contextualized information bases. The theory includes a set of validity constraints, a model theory, as well as a set of sound and complete inference rules. We show that our core theory can be easily extended to support embedding of particular information models in our contextualization framework.     

Dynamic pricing for multiple class deterministic demand fulfillment

We consider how a firm should allocate inventory to multiple customer classes that differ based on the price they pay and their willingness to incur delay in fulfillment of their demand. The problem is set in a deterministic demand, economic-order-quantity-like environment with holding, backorder, lost demand and setup costs. The firm either fulfills demand or offers a price discount to induce the demand to wait for fulfillment from the next reorder. We determine the optimal policy and discuss how changes in various parameters affect profitability, customer service, and operational measures such as order frequency and base stock levels. We compare the results to a policy that only rations inventory without dynamic discounting and to a policy that only provides discounts. Through the comparison, we observe that dynamic pricing can be seen as a combination of a pricing mechanism which determines demand and an allocation mechanism that differentiates between customer classes, serving each ones needs. We show that if lower-value customers are distinguished by accepting reduced service, it is possible that both high and low-value customer classes see better levels of service under the optimal policy than under a discounting only policy. In addition we demonstrate the applicability of the results to a stochastic version of the problem.     

Selective radiation coatings. Preparation and high temperature stability

Selective radiation coatings suitable for operation at high temperatures with focused sunlight are described. Coatings of CuO and Co₃O₄ on polished nickel, silver, and platinum have high absorptivity for solar radiation and low emissivity in the infrared when heated. They are stable at at high temperatures.     

Improved sensitivity and characterization of high-speed ion chromatography of inorganic anions

In this work, a reversed-phase monolithic column was permanently coated with didodecyldimethylammonium bromide (DDAB) to perform ultrafast separations of iodate, chloride, nitrite, bromide, nitrate, phosphate, and sulfate in as little as 30 s. Separations were performed using 6 mM o-cyanophenol (pH 7.0) at flow rates up to 10 ml/min and suppressed conductivity detection. Detection limits in the parts-per-billion range were observed for all anions studied (e.g., ranging from 30 ppb for phosphate to 4 ppb for sulfate). The reproducibility was 0.7 and 0.4% RSD for retention time and peak area, respectively. Coated columns were stable for up to 12 h of continuous use at 5 mL/min (i.e., 1-min separations).     

Competition, Learning and Investment in New Technology

Efforts to describe the evolution of production costs must simultaneously include the impact of investment in new technologies as well as experience-based learning. In this paper we formulate a dynamic model of technological investments, which includes the impacts of learning curve effects, investment levels in new technologies with uncertain timing, and competitive responses. Our results highlight the interaction of investment and output rate decisions in monopolistic and duopolistic situations, and illuminate the impact of the planning horizon length on such decisions. The model results elaborate upon how the attractiveness of new technologies is related to the firm's ability to learn using its existing technology, how competition can increase or decrease the market size for new technologies, and how investment levels are driven in opposite directions by considering longer horizons on one hand and the competitive response on the other.