DFL: A Data Flow Language

Many novel computer architectures like array and multiprocessors which achieve high performance through the use of concurrency exploit variations of the von Neumann model of computation. The effective utilization of the machines makes special demands on programmers and their programming languages, such as the structuring of data into vectors or the partitioning of programs into concurrent processes. In comparison, the data flow model of computation demands only that the principle of structured programming be followed. A data flow program, often represented as a data flow graph, is a program that expresses a computation by indicating the data dependencies among operators. A data flow computer is a machine designed to take advantage of concurrency in data flow graphs by executing data independent operations in parallel. In this paper, we discuss the design of a high level language (DFL: Data Flow Language) suitable for data flow computers. Some sample procedures in DFL are presented. The implementation aspects have not been discussed in detail since there are no new problems encountered. The language DFL embodies the concepts of functional programming, but in appearance closely resembles Pascal. The language is a better vehicle than the data flow graph for expressing a parallel algorithm. The compiler has been implemented on a DEC 1090 system in Pascal.

A heterogeneous computing approach to simulation of the Heston Stochastic Volatility Model

Stochastic volatility models are of fundamental importance to the pricing of derivatives. One of the most commonly used models of stochastic volatility is the Heston Model in which the price and volatility of an asset evolve as a pair of coupled stochastic differential equations. The computation of asset prices and volatilities involves the simulation of many sample trajectories with conditioning. The problem is treated using the method of particle filtering. While the simulation of a shower of particles is computationally expensive, each particle behaves independently making such simulations ideal for massively parallel heterogeneous computing platforms. In this paper, we present our portable Opencl implementation of the Heston model and discuss its performance and efficiency characteristics on a range of architectures including Intel cpus, Nvidia gpus, and Intel Many-Integrated-Core (mic) accelerators.

Knowledge unlatched : A global library consortium model for funding Open Access scholarly books

Specialist scholarly books, including monographs, allow researchers to present their work, pose questions and to test and extend areas of theory through long-form writing. In spite of the fact that research communities all over the world value monographs and depend heavily on them as a requirement of tenure and promotion in many disciplines, sales of this kind of book are in free fall, with some estimates suggesting declines of as much as 90% over twenty years (Willinsky 2006). Cashstrapped monograph publishers have found themselves caught in a negative cycle of increasing prices and falling sales, with few resources left to support experimentation, business model innovation or engagement with digital technology and Open Access (OA). This chapter considers an important attempt to tackle failing markets for scholarly monographs, and to enable the wider adoption of OA licenses for book-length works: the 2012 – 2014 Knowledge Unlatched pilot. Knowledge Unlatched is a bold attempt to reconfigure the market for specialist scholarly books: moving it beyond the sale of ‘content’ towards a model that supports the services valued by scholarly and wider communities in the context of digital possibility. Its success has powerful implications for the way we understand copyright’s role in the creative industries, and the potential for established institutions and infrastructure to support the open and networked dynamics of a digital age.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Language independent automated segmentation of speech using Bach scale filter-banks

This correspondence describes a method for automated segmentation of speech. The method proposed in this paper uses a specially designed filter-bank called Bach filter-bank which makes use of 'music' related perception criteria. The speech signal is treated as continuously time varying signal as against a short time stationary model. A comparative study has been made of the performances using Mel, Bark and Bach scale filter banks. The preliminary results show up to 80 % matches within 20 ms of the manually segmented data, without any information of the content of the text and without any language dependence. The Bach filters are seen to marginally outperform the other filters.

A denotational semantics for the generalized ER model and a simple ER algebra

The recent spurt of research activities in Entity-Relationship Approach to databases calls for a close scrutiny of the semantics of the underlying Entity-Relationship models, data manipulation languages, data definition languages, etc. For reasons well known, it is very desirable and sometimes imperative to give formal description of the semantics. In this paper, we consider a specific ER model, the generalized Entity-Relationship model (without attributes on relationships) and give denotational semantics for the model as well as a simple ER algebra based on the model. Our formalism is based on the Vienna Development Method—the meta language (VDM). We also discuss the salient features of the given semantics in detail and suggest directions for further work.

Language universals and linguistic complexity : Three case studies in core argument marking

In this dissertation I study language complexity from a typological perspective. Since the structuralist era, it has been assumed that local complexity differences in languages are balanced out in cross-linguistic comparisons and that complexity is not affected by the geopolitical or sociocultural aspects of the speech community. However, these assumptions have seldom been studied systematically from a typological point of view. My objective is to define complexity so that it is possible to compare it across languages and to approach its variation with the methods of quantitative typology. My main empirical research questions are: i) does language complexity vary in any systematic way in local domains, and ii) can language complexity be affected by the geographical or social environment? These questions are studied in three articles, whose findings are summarized in the introduction to the dissertation. In order to enable cross-language comparison, I measure complexity as the description length of the regularities in an entity; I separate it from difficulty, focus on local instead of global complexity, and break it up into different types. This approach helps avoid the problems that plagued earlier metrics of language complexity. My approach to grammar is functional-typological in nature, and the theoretical framework is basic linguistic theory. I delimit the empirical research functionally to the marking of core arguments (the basic participants in the sentence). I assess the distributions of complexity in this domain with multifactorial statistical methods and use different sampling strategies, implementing, for instance, the Greenbergian view of universals as diachronic laws of type preference. My data come from large and balanced samples (up to approximately 850 languages), drawn mainly from reference grammars. The results suggest that various significant trends occur in the marking of core arguments in regard to complexity and that complexity in this domain correlates with population size. These results provide evidence that linguistic patterns interact among themselves in terms of complexity, that language structure adapts to the social environment, and that there may be cognitive mechanisms that limit complexity locally. My approach to complexity and language universals can therefore be successfully applied to empirical data and may serve as a model for further research in these areas.

LZW Based Distance Measures for Spoken Language Identification

We present a new approach to spoken language modeling for language identification (LID) using the Lempel-Ziv-Welch (LZW) algorithm. The LZW technique is applicable to any kind of tokenization of the speech signal. Because of the efficiency of LZW algorithm to obtain variable length symbol strings in the training data, the LZW codebook captures the essentials of a language effectively. We develop two new deterministic measures for LID based on the LZW algorithm namely: (i) Compression ratio score (LZW-CR) and (ii) weighted discriminant score (LZW-WDS). To assess these measures, we consider error-free tokenization of speech as well as artificially induced noise in the tokenization. It is shown that for a 6 language LID task of OGI-TS database with clean tokenization, the new model (LZW-WDS) performs slightly better than the conventional bigram model. For noisy tokenization, which is the more realistic case, LZW-WDS significantly outperforms the bigram technique

A mixture model approach for formant tracking and the robustness of student's-t distribution

We address the problem of robust formant tracking in continuous speech in the presence of additive noise. We propose a new approach based on mixture modeling of the formant contours. Our approach consists of two main steps: (i) Computation of a pyknogram based on multiband amplitude-modulation/frequency-modulation (AM/FM) decomposition of the input speech; and (ii) Statistical modeling of the pyknogram using mixture models. We experiment with both Gaussian mixture model (GMM) and Student's-t mixture model (tMM) and show that the latter is robust with respect to handling outliers in the pyknogram data, parameter selection, accuracy, and smoothness of the estimated formant contours. Experimental results on simulated data as well as noisy speech data show that the proposed tMM-based approach is also robust to additive noise. We present performance comparisons with a recently developed adaptive filterbank technique proposed in the literature and the classical Burg's spectral estimator technique, which show that the proposed technique is more robust to noise.

Translation Induction on Indian Language Corpora Using Translingual Themes from Other Languages

Identifying translations from comparable corpora is a well-known problem with several applications, e.g. dictionary creation in resource-scarce languages. Scarcity of high quality corpora, especially in Indian languages, makes this problem hard, e.g. state-of-the-art techniques achieve a mean reciprocal rank (MRR) of 0.66 for English-Italian, and a mere 0.187 for Telugu-Kannada. There exist comparable corpora in many Indian languages with other ``auxiliary'' languages. We observe that translations have many topically related words in common in the auxiliary language. To model this, we define the notion of a translingual theme, a set of topically related words from auxiliary language corpora, and present a probabilistic framework for translation induction. Extensive experiments on 35 comparable corpora using English and French as auxiliary languages show that this approach can yield dramatic improvements in performance (e.g. MRR improves by 124% to 0.419 for Telugu-Kannada). A user study on WikiTSu, a system for cross-lingual Wikipedia title suggestion that uses our approach, shows a 20% improvement in the quality of titles suggested.