Normality and Finite-State Dimension of Liouville Numbers

Liouville numbers were the first class of real numbers which were proven to be transcendental. It is easy to construct non-normal Liouville numbers. Kano (1993) and Bugeaud (2002) have proved, using analytic techniques, that there are normal Liouville numbers. Here, for a given base k ≥ 2, we give a new construction of a Liouville number which is normal to the base k. This construction is combinatorial, and is based on de Bruijn sequences.     

On the applicability of diploid genetic algorithms in dynamic environments

Diploid genetic algorithms (DGAs) promise robustness as against simple genetic algorithms which only work towards optimization. Moreover, these algorithms outperform others in dynamic environments. The work examines the theoretical aspect of the concept by examining the existing literature. The present work takes the example of dynamic TSP to compare greedy approach, genetic algorithms and DGAs. The work also implements a greedy genetic approach for the problem. In the experiments carried out, the three variants of dominance were implemented and 115 runs proved the point that none of them outperforms the other.     

Improving the performance of speaker and language identification tasks using unique characteristics of a class

In classification tasks, the error rate is proportional to the commonality among classes. In conventional GMM-based modeling technique, since the model parameters of a class are estimated without considering other classes in the system, features that are common across various classes may also be captured, along with unique features. This paper proposes to use unique characteristics of a class at the feature-level and at the phoneme-level, separately, to improve the classification accuracy. At the feature-level, the performance of a classifier has been analyzed by capturing the unique features while modeling, and removing common feature vectors during classification. Experiments were conducted on speaker identification task, using speech data of 40 female speakers from NTIMIT corpus, and on a language identification task, using speech data of two languages (English and French) from OGI_MLTS corpus. At the phoneme-level, performance of a classifier has been analyzed by identifying a subset of phonemes, which are unique to a speaker with respect to his/her closely resembling speaker, in the acoustic sense, on a speaker identification task. In both the cases (feature-level and phoneme-level) considerable improvement in classification accuracy is observed over conventional GMM-based classifiers in the above mentioned tasks. Among the three experimental setup, speaker identification task using unique phonemes shows as high as 9.56 % performance improvement over conventional GMM-based classifier.     

Using non-ideal gates to implement universal quantum computing between uncoupled qubits

In many physical systems, when implementing quantum gate operations unavoidable global and relative phases occur as by-products due to the internal structure of the governing Hamiltonian. To correct, additional phase rotation gates are used, which increases the computational overhead. Here, we show how these phase by-products can actually be used to our advantage by using them to implement universal quantum computing between qubits not directly coupled to each other. The gate operations, CNOT, Toffoli, and swap gates, require much less computational overhead than present schemes, and are achieved with fidelity greater than 99%. We then present a linear nearest-neighbor architecture that takes full advantage of the phase by-products, and we show how to implement gates from a universal set efficiently in this layout. In this scheme gate operations are realized by only varying a single control parameter per data qubit, and the ability to tune couplings is not required.     

Trading privacy with incentives in mobile commerce: A game theoretic approach

In mobile commerce, companies provide location based services to mobile users, who report their locations with a certain level of granularity to maintain a degree of anonymity. This level of granularity depends on their perceived risk as well as the incentives they receive in the form of monetary benefits or improved mobile services. This paper formulates a quantitative model in which information theoretic metrics such as entropy, quantify the anonymity level of mobile users. The individual perceived risks of users and the benefits they obtain are defined as functions of their chosen location information granularity. The interaction between the mobile commerce company and its users is investigated using mechanism design techniques as a privacy game. The user best responses and optimal strategies for the company are derived under budgetary constraints on incentives, which are provided to users in order to convince them to share their private information at the desired level of granularity. Information limitations in the system are analyzed to capture more realistic scenarios where the companies do not have access to user utility functions. Iterative distributed algorithm and regression learning methods are investigated to design mechanisms that overcome these limitations. The results obtained are demonstrated with a numerical example and simulations based on real GPS data.     

Geometry based pre-processor for parallel fluid dynamic simulations using a hierarchical basis

The pre-processing stage of finite element analysis of the Navier–Stokes equations is becoming increasingly important as the desire for more general boundary conditions, as well as applications to parallel computers increases. The set up of general boundary conditions and communication structures for parallel computations should be accomplished during the pre-processing phase of the analysis, if possible, to ensure efficient computations for large scale problems in computational fluid dynamics. This paper introduces a general methodology for geometry based boundary condition application and pre-computing of parallel communication tasks. A. K. Karanam was supported by NSF Grant No. 9985340. C. H. Whiting was supported by a grant from NASA LaRC.     

Strategic advantages of interoperability for global manufacturing using CNC technology

In the domain of manufacturing, computer numerically controllers (CNC) technology is a major contributor to the production capacity of the enterprises. The advances in CNC technology coupled with enhancements in computing systems have provided the basis to re-examine the way in which computer-aided systems (CAx) can be used to enable global manufacturing. Interoperability of the various components of the CAx chain is therefore a major prerequisite for manufacturing enterprises for becoming strategically agile and consequently globally competitive. Being interoperable, resources can be utilized interchangeably in a plug-and-produce manner. Over the last 8 years the eminence of a STEP standard for machining entitled STEP-NC (numerical control) has become a well-known vehicle for research to improve the level of information availability at the CNC machine tool. In this paper, the authors introduce the background to the evolution of CNC manufacturing over the last 50 years and the current standards available for programming. A review of the literature in interoperable CNC manufacturing is then provided relating to milling, turn–mill and other NC processes. The major part of the paper provides a strategic view of how interoperability can be implemented across the CAx chain with a range of standards used to regulate the flow of information. Finally, the paper outlines the advantages and major issues for future developments in interoperability, identifying future key requirements and limiting factors.     

The high-conductance state of cortical networks

We studied the dynamics of large networks of spiking neurons with conductance-based (nonlinear) synapses and compared them to networks with current-based (linear) synapses. For systems with sparse and inhibition-dominated recurrent connectivity, weak external inputs induced asynchronous irregular firing at low rates. Membrane potentials fluctuated a few millivolts below threshold, and membrane conductances were increased by a factor 2 to 5 with respect to the resting state. This combination of parameters characterizes the ongoing spiking activity typically recorded in the cortex in vivo. Many aspects of the asynchronous irregular state in conductance-based networks could be sufficiently well characterized with a simple numerical mean field approach. In particular, it correctly predicted an intriguing property of conductance-based networks that does not appear to be shared by current-based models: they exhibit states of low-rate asynchronous irregular activity that persist for some period of time even in the absence of external inputs and without cortical pacemakers. Simulations of larger networks (up to 350,000 neurons) demonstrated that the survival time of self-sustained activity increases exponentially with network size.     

Learning to Locate Informative Features for Visual Identification

Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient “same” versus “different” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.