Asymptotic weighted-periodicity of the impulsive parabolic equation with time delay

The main aim of this paper is to investigate the effects of the impulse and time delay on a type of parabolic equations. In view of the characteristics of the equation, a particular iteration scheme is adopted. The results show that Under certain conditions on the coefficients of the equation and the impulse, the solution oscillates in a particular manner-called "asymptotic weighted-periodicity".

Formalizing Triggers: A Learning Model for Finite Spaces

In a recent seminal paper, Gibson and Wexler (1993) take important steps to formalizing the notion of language learning in a (finite) space whose grammars are characterized by a finite number of parameters. They introduce the Triggering Learning Algorithm (TLA) and show that even in finite space convergence may be a problem due to local maxima. In this paper we explicitly formalize learning in finite parameter space as a Markov structure whose states are parameter settings. We show that this captures the dynamics of TLA completely and allows us to explicitly compute the rates of convergence for TLA and other variants of TLA e.g. random walk. Also included in the paper are a corrected version of GW's central convergence proof, a list of "problem states" in addition to local maxima, and batch and PAC-style learning bounds for the model.

Modifying weighted fuzzy subsethood-based rule models with fuzzy quantifiers

K. Rasmani and Q. Shen. Modifying weighted fuzzy subsethood-based rule models with fuzzy quantifiers. Proceedings of the 13th International Conference on Fuzzy Systems, pages 1679-1684, 2004

From Limen to Lumen: computing students in liminal spaces.From Limen to Lumen: computing students in liminal spaces

Eckerdal, A., McCartney, R., Mostr?m, J. E., Sanders, K., Thomas, L., and Zander, C. 2007. From Limen to Lumen: computing students in liminal spaces. In Proceedings of the Third international Workshop on Computing Education Research (Atlanta, Georgia, USA, September 15 - 16, 2007). ICER '07. ACM, New York, NY, 123-132.

Learning Euclidean Embeddings for Indexing and Classification

BoostMap is a recently proposed method for efficient approximate nearest neighbor retrieval in arbitrary non-Euclidean spaces with computationally expensive and possibly non-metric distance measures. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. The key idea is formulating embedding construction as a machine learning task, where AdaBoost is used to combine simple, 1D embeddings into a multidimensional embedding that preserves a large amount of the proximity structure of the original space. This paper demonstrates that, using the machine learning formulation of BoostMap, we can optimize embeddings for indexing and classification, in ways that are not possible with existing alternatives for constructive embeddings, and without additional costs in retrieval time. First, we show how to construct embeddings that are query-sensitive, in the sense that they yield a different distance measure for different queries, so as to improve nearest neighbor retrieval accuracy for each query. Second, we show how to optimize embeddings for nearest neighbor classification tasks, by tuning them to approximate a parameter space distance measure, instead of the original feature-based distance measure.

Weighted and two-stage least squares estimation of semiparametric truncated regression models

This paper provides a root-n consistent, asymptotically normal weighted least squares estimator of the coefficients in a truncated regression model. The distribution of the errors is unknown and permits general forms of unknown heteroskedasticity. Also provided is an instrumental variables based two-stage least squares estimator for this model, which can be used when some regressors are endogenous, mismeasured, or otherwise correlated with the errors. A simulation study indicates that the new estimators perform well in finite samples. Our limiting distribution theory includes a new asymptotic trimming result addressing the boundary bias in first-stage density estimation without knowledge of the support boundary. © 2007 Cambridge University Press.

Bayesian variable selection in structured high-dimensional covariate spaces with applications in genomics

We consider the problem of variable selection in regression modeling in high-dimensional spaces where there is known structure among the covariates. This is an unconventional variable selection problem for two reasons: (1) The dimension of the covariate space is comparable, and often much larger, than the number of subjects in the study, and (2) the covariate space is highly structured, and in some cases it is desirable to incorporate this structural information in to the model building process. We approach this problem through the Bayesian variable selection framework, where we assume that the covariates lie on an undirected graph and formulate an Ising prior on the model space for incorporating structural information. Certain computational and statistical problems arise that are unique to such high-dimensional, structured settings, the most interesting being the phenomenon of phase transitions. We propose theoretical and computational schemes to mitigate these problems. We illustrate our methods on two different graph structures: the linear chain and the regular graph of degree k. Finally, we use our methods to study a specific application in genomics: the modeling of transcription factor binding sites in DNA sequences. © 2010 American Statistical Association.

Long-distance quantum communication just around the corner?

info:eu-repo/semantics/published

Minimizing total weighted completion time in a proportionate flow shop

We study the special case of the m machine flow shop problem in which the processing time of each operation of job j is equal to pj; this variant of the flow shop problem is known as the proportionate flow shop problem. We show that for any number of machines and for any regular performance criterion we can restrict our search for an optimal schedule to permutation schedules. Moreover, we show that the problem of minimizing total weighted completion time is solvable in O(n2) time. © 1998 John Wiley & Sons, Ltd.

A fully polynomial approximation scheme for the single machine weighted total tardiness problem with a common due date

We develop a fully polynomial-time approximation scheme (FPTAS) for minimizing the weighted total tardiness on a single machine, provided that all due dates are equal. The FPTAS is obtained by converting an especially designed pseudopolynomial dynamic programming algorithm.

Probabilistic approach in weighted Markov branching processes

This note provides a new probabilistic approach in discussing the weighted Markov branching process (WMBP) which is a natural generalisation of the ordinary Markov branching process. Using this approach, some important characteristics regarding the hitting times of such processes can be easily obtained. In particular, the closed forms for the mean extinction time and conditional mean extinction time are presented. The explosion behaviour of the process is investigated and the mean explosion time is derived. The mean global holding time and the mean total survival time are also obtained. The close link between these newly developed processes and the well-known compound Poisson processes is investigated. It is revealed that any weighted Markov branching process (WMBP) is a random time change of a compound Poisson process.

Two simple constant ratio approximation algorithms for minimizing the total weighted completion time on a single machine with a fixed non-availability interval

In this note, we consider the scheduling problem of minimizing the sum of the weighted completion times on a single machine with one non-availability interval on the machine under the non-resumable scenario. Together with a recent 2-approximation algorithm designed by Kacem [I. Kacem, Approximation algorithm for the weighted flow-time minimization on a single machine with a fixed non-availability interval, Computers & Industrial Engineering 54 (2008) 401–410], this paper is the first successful attempt to develop a constant ratio approximation algorithm for this problem. We present two approaches to designing such an algorithm. Our best algorithm guarantees a worst-case performance ratio of 2+ε. © 2008 Elsevier B.V. All rights reserved.

Spaces of observation and obscurity: cinematic prisons of light and dark

In The Eye of Power, Foucault delineated the key concerns surrounding hospital architecture in the latter half of the eighteenth century as being the ‘visibility of bodies, individuals and things'. As such, the ‘new form of hospital' that came to be developed ‘was at once the effect and support of a new type of gaze'. This was a gaze that was not simply concerned with ways of minimising overcrowding or cross-contamination. Rather, this was a surveillance intended to produce knowledge about the pathological bodies contained within the hospital walls. This would then allow for their appropriate classification. Foucault went on to describe how these principles came to be applied to the architecture of prisons. This was exemplified for him in the distinct shape of Bentham's panopticon. This circular design, which has subsequently become an often misused synonym for a contemporary culture of surveillance, was premised on a binary of the seen and the not-seen. An individual observer could stand at the central point of the circle and observe the cells (and their occupants) on the perimeter whilst themselves remaining unseen. The panopticon in its purest form was never constructed, yet it conveys the significance of the production of knowledge through observation that became central to institutional design at this time and modern thought more broadly. What is curious though is that whilst the aim of those late eighteenth century buildings was to produce wellventilated spaces suffused with light, this provoked an interest in its opposite. The gothic movement in literature that was developing in parallel conversely took a ‘fantasy world of stone walls, darkness, hideouts and dungeons…' as its landscape (Vidler, 1992: 162). Curiously, despite these modern developments in prison design, the façade took on these characteristics. The gothic imagination came to describe that unseen world that lay behind the outer wall. This is what Evans refers to as an architectural ‘hoax'. The façade was taken to represent the world within the prison walls and it was the façade that came to inform the popular imagination about what occurred behind it. The rational, modern principles ordering the prison became conflated with the meanings projected by and onto the façade. This confusion of meanings have then been repeated and reenforced in the subsequent representations of the prison. This is of paramount importance since it is the cinematic and televisual representation of the prison, as I argue here and elsewhere, that maintain this erroneous set of meanings, this ‘hoax'.

Safe spaces and sense of identity: views and experiences of lesbian, gay and bisexual young people

Empirical data on the life experiences of contemporary school-age lesbian, gay and bisexual (LGB) young people in Britain remains somewhat sparse. This paper reports the preliminary findings of a study conducted at a recently-initiated LGB youth Summer School. To further an appreciation of issues of concern to today's LGB teenagers, in-depth interviews were conducted with 10 Summer School participants (five female and five male, aged 15-18 years). The aim was to elicit their views and experiences relating to their need for support such as that offered by the Summer School. Themes drawn from participants' interviews are presented. Key issues included: being positioned as different by their majority heterosexual peers; feelings of isolation and loneliness in their peer groups and families; difficulties in finding others like themselves for companionship; and the importance of meeting more LGB people of their own age.