Documents and Dependencies: an Exploration of Vector Space Models for Semantic Composition

In most previous research on distributional semantics, Vector Space Models (VSMs) of words are built either from topical information (e.g., documents in which a word is present), or from syntactic/semantic types of words (e.g., dependency parse links of a word in sentences), but not both. In this paper, we explore the utility of combining these two representations to build VSM for the task of semantic composition of adjective-noun phrases. Through extensive experiments on benchmark datasets, we find that even though a type-based VSM is effective for semantic composition, it is often outperformed by a VSM built using a combination of topic- and type-based statistics. We also introduce a new evaluation task wherein we predict the composed vector representation of a phrase from the brain activity of a human subject reading that phrase. We exploit a large syntactically parsed corpus of 16 billion tokens to build our VSMs, with vectors for both phrases and words, and make them publicly available.

The marginal willingness-to-pay for attributes of a hypothetical HIV vaccine

This paper estimates the marginal willingness-to-pay for attributes of a hypothetical HIV vaccine using discrete choice modeling. We use primary data from 326 respondents from Bangkok and Chiang Mai, Thailand, in 2008–2009, selected using purposive, venue-based sampling across two strata. Participants completed a structured questionnaire and full rank discrete choice modeling task administered using computer-assisted personal interviewing. The choice experiment was used to rank eight hypothetical HIV vaccine scenarios, with each scenario comprising seven attributes (including cost) each of which had two levels. The data were analyzed in two alternative specifications: (1) best-worst; and (2) full-rank, using logit likelihood functions estimated with custom routines in Gauss matrix programming language. In the full-rank specification, all vaccine attributes are significant predictors of probability of vaccine choice. The biomedical attributes of the hypothetical HIV vaccine (efficacy, absence of VISP, absence of side effects, and duration of effect) are the most important attributes for HIV vaccine choice. On average respondents are more than twice as likely to accept a vaccine with 99% efficacy, than a vaccine with 50% efficacy. This translates to a willingness to pay US$383 more for a high efficacy vaccine compared with the low efficacy vaccine. Knowledge of the relative importance of determinants of HIV vaccine acceptability is important to ensure the success of future vaccination programs. Future acceptability studies of hypothetical HIV vaccines should use more finely grained biomedical attributes, and could also improve the external validity of results by including more levels of the cost attribute.

Fluid Shear Induces Conformation Change in Human Blood Protein von Willebrand Factor in Solution

Many of the physiological functions of von Willebrand Factor (VWF), including its binding interaction with blood platelets, are regulated by the magnitude of applied fluid/hydrodynamic stress. We applied two complementary strategies to study the effect of fluid forces on the solution structure of VWF. First, small-angle neutron scattering was used to measure protein conformation changes in response to laminar shear rates (G) up to 3000/s. Here, purified VWF was sheared in a quartz Couette cell and protein conformation was measured in real time over length scales from 2-140 nm. Second, changes in VWF structure up to 9600/s were quantified by measuring the binding of a fluorescent probe 1,1'-bis(anilino)-4-,4'-bis(naphtalene)-8,8'-disulfonate (bis-ANS) to hydrophobic pockets exposed in the sheared protein. Small angle neutron scattering studies, coupled with quantitative modeling, showed that VWF undergoes structural changes at G < 3000/s. These changes were most prominent at length scales <10 nm (scattering vector (q) range >0.6/nm). A mathematical model attributes these changes to the rearrangement of domain level features within the globular section of the protein. Studies with bis-ANS demonstrated marked increase in bis-ANS binding at G > 2300/s. Together, the data suggest that local rearrangements at the domain level may precede changes at larger-length scales that accompany exposure of protein hydrophobic pockets. Changes in VWF conformation reported here likely regulate protein function in response to fluid shear.

A Vector Approach for the Analysis and Synthesis of Directional Modulation Transmitters

In order to formalize and extend on previous ad-hoc analysis and synthesis methods a theoretical treatment using vector representations of directional modulation (DM) systems is introduced and used to achieve DM transmitter characteristics. An orthogonal vector approach is proposed which allows the artificial orthogonal noise concept derived from information theory to be brought to bear on DM analysis and synthesis. The orthogonal vector method is validated and discussed via bit error rate (BER) simulations.

Vector representation of directional modulation transmitters

Directional Modulation (DM) is a recently proposed technique for securing wireless communication. In this paper we point out that modulation-directionality is a consequence of varying the beamforming network, either in baseband or in the RF stage, at the information rate In order to formalize and extend on previous analysis and synthesis methods a new theoretical treatment using vector representations of directional modulation (DM) systems is introduced and used to obtain the necessary and sufficient con

Vector bundles on the projective line and finite domination of chain complexes

We present an algebro-geometric approach to a theorem on finite domination of chain complexes over a Laurent polynomial ring. The approach uses extension of chain complexes to sheaves on the projective line, which is governed by a K-theoretical obstruction.

A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.