Heterogeneity in auditory alarm sets makes them easier to learn

The primary objective of the experiments reported here was to demonstrate the effects of opening up the design envelope for auditory alarms on the ability of people to learn the meanings of a set of alarms. Two sets of alarms were tested, one already extant and one newly-designed set for the same set of functions, designed according to a rationale set out by the authors aimed at increasing the heterogeneity of the alarm set and incorporating some well-established principles of alarm design. For both sets of alarms, a similarity-rating experiment was followed by a learning experiment. The results showed that the newly-designed set was judged to be more internally dissimilar, and easier to learn, than the extant set. The design rationale outlined in the paper is useful for design purposes in a variety of practical domains and shows how alarm designers, even at a relatively late stage in the design process, can improve the efficacy of an alarm set.

Scheduling Trains with Priorities : a no-wait blocking parallel-machine job-shop scheduling model

The paper investigates train scheduling problems when prioritised trains and non-prioritised trains are simultaneously traversed in a single-line rail network. In this case, no-wait conditions arise because the prioritised trains such as express passenger trains should traverse continuously without any interruption. In comparison, non-prioritised trains such as freight trains are allowed to enter the next section immediately if possible or to remain in a section until the next section on the routing becomes available, which is thought of as a relaxation of no-wait conditions. With thorough analysis of the structural properties of the No-Wait Blocking Parallel-Machine Job-Shop-Scheduling (NWBPMJSS) problem that is originated in this research, an innovative generic constructive algorithm (called NWBPMJSS_Liu-Kozan) is proposed to construct the feasible train timetable in terms of a given order of trains. In particular, the proposed NWBPMJSS_Liu-Kozan constructive algorithm comprises several recursively-used sub-algorithms (i.e. Best-Starting-Time-Determination Procedure, Blocking-Time-Determination Procedure, Conflict-Checking Procedure, Conflict-Eliminating Procedure, Tune-up Procedure and Fine-tune Procedure) to guarantee feasibility by satisfying the blocking, no-wait, deadlock-free and conflict-free constraints. A two-stage hybrid heuristic algorithm (NWBPMJSS_Liu-Kozan-BIH) is developed by combining the NWBPMJSS_Liu-Kozan constructive algorithm and the Best-Insertion-Heuristic (BIH) algorithm to find the preferable train schedule in an efficient and economical way. Extensive computational experiments show that the proposed methodology is promising because it can be applied as a standard and fundamental toolbox for identifying, analysing, modelling and solving real-world scheduling problems.

Probabilistic matching of image sets for video-based face recognition

We address the problem of face recognition on video by employing the recently proposed probabilistic linear discrimi-nant analysis (PLDA). The PLDA has been shown to be robust against pose and expression in image-based face recognition. In this research, the method is extended and applied to video where image set to image set matching is performed. We investigate two approaches of computing similarities between image sets using the PLDA: the closest pair approach and the holistic sets approach. To better model face appearances in video, we also propose the heteroscedastic version of the PLDA which learns the within-class covariance of each individual separately. Our experi-ments on the VidTIMIT and Honda datasets show that the combination of the heteroscedastic PLDA and the closest pair approach achieves the best performance.

Hessian-based affine adaptation of salient local image features

Affine covariant local image features are a powerful tool for many applications, including matching and calibrating wide baseline images. Local feature extractors that use a saliency map to locate features require adaptation processes in order to extract affine covariant features. The most effective extractors make use of the second moment matrix (SMM) to iteratively estimate the affine shape of local image regions. This paper shows that the Hessian matrix can be used to estimate local affine shape in a similar fashion to the SMM. The Hessian matrix requires significantly less computation effort than the SMM, allowing more efficient affine adaptation. Experimental results indicate that using the Hessian matrix in conjunction with a feature extractor that selects features in regions with high second order gradients delivers equivalent quality correspondences in less than 17% of the processing time, compared to the same extractor using the SMM.

An exploration of affine group laws for elliptic curves

Several forms of elliptic curves are suggested for an efficient implementation of Elliptic Curve Cryptography. However, a complete description of the group law has not appeared in the literature for most popular forms. This paper presents group law in affine coordinates for three forms of elliptic curves. With the existence of the proposed affine group laws, stating the projective group law for each form becomes trivial. This work also describes an automated framework for studying elliptic curve group law, which is applied internally when preparing this work.

Usability of small crisis data sets in the absence of big data

Big data is big news in almost every sector including crisis communication. However, not everyone has access to big data and even if we have access to big data, we often do not have necessary tools to analyze and cross reference such a large data set. Therefore this paper looks at patterns in small data sets that we have ability to collect with our current tools to understand if we can find actionable information from what we already have. We have analyzed 164390 tweets collected during 2011 earthquake to find out what type of location specific information people mention in their tweet and when do they talk about that. Based on our analysis we find that even a small data set that has far less data than a big data set can be useful to find priority disaster specific areas quickly.

Blocking layer optimisation of poly(3-hexylthiopene)based solid state dye sensitized solar cells

The optimisation study of the fabrication of a compact TiO2 blocking layer (via Spray Pyrolysis Deposition) for poly (3-hexylthiopene) (P3HT) for Solid State Dye Sensitized Solar Cells (SDSCs) is reported. We used a novel spray TiO2 precursor solution composition obtained by adding acetylacetone to a conventional formulation (Diisopropoxytitanium bis (acetylacetonate) in ethanol). By Scanning Electron Microscopy a TiO2 layer with compact morphology and thickness of around 100 nmis shown. Through a Tafel plot analysis an enhancement of the device diode-like behaviour induced by the acetylacetone blocking layer respect to the conventional one is observed. Significantly, the device fabricatedwith the acetylacetone blocking layer shows an overall increment of the cell performance with respect to the cellwith the conventional one (DJsc/Jsc = +13.8%, DFF/FF = +39.7%, DPCE/PCE = +55.6%). A conversion efficiency optimumis found for 15 successive spray cycles where the diode-like behaviour of the acetylacetone blocking layer is more effective. Over three batches of cells (fabricated with P3HT and dye D35) an average conversion efficiency value of 3.9% (under a class A sun simulator with 1 sun A.M. 1.5 illumination conditions) was measured. From the best cell we fabricated a conversion efficiency value of 4.5% was extracted. This represents a significant increment with respect to previously reported values for P3HT/dye D35 based SDSCs.

Who’s blocking the Chinese Internet? The rise of cybercultures and the generational conflicts in China

As a decentralised communication technology, the Internet has offered much autonomy and unprecedented communication freedom to the Chinese public. Yet the Chinese government has imposed different forms of censorship over cyberspace. However, the Hong Kong erotic photo scandal reshuffles the traditional understanding of censorship in China as it points to a different territory. The paper takes the Hong Kong erotic photo scandal in 2008 as a case study and aims to examine the social and generational conflicts hidden in China. When thousands of photos containing sexually explicit images of Hong Kong celebrities were released on the Internet, gossip, controversies and eroticism fuelled the public discussion and threatened traditional values in China. The Internet provides an alternative space for the young Chinese who have been excluded from mainstream social discourse to engage in public debates. This, however, creates concerns, fear and even anger among the older generations in China, because they can no longer control, monitor and educate their children in the way that their predecessors have done for centuries. The photo scandal illustrates the internal social conflicts and distrust between generations in China and the generational conflict has a far-reaching political ramification as it creates a new concept of censorship.

The efficiency of corpus-based distributional models for literature-based discovery on large data sets

This paper evaluates the efficiency of a number of popular corpus-based distributional models in performing discovery on very large document sets, including online collections. Literature-based discovery is the process of identifying previously unknown connections from text, often published literature, that could lead to the development of new techniques or technologies. Literature-based discovery has attracted growing research interest ever since Swanson's serendipitous discovery of the therapeutic effects of fish oil on Raynaud's disease in 1986. The successful application of distributional models in automating the identification of indirect associations underpinning literature-based discovery has been heavily demonstrated in the medical domain. However, we wish to investigate the computational complexity of distributional models for literature-based discovery on much larger document collections, as they may provide computationally tractable solutions to tasks including, predicting future disruptive innovations. In this paper we perform a computational complexity analysis on four successful corpus-based distributional models to evaluate their fit for such tasks. Our results indicate that corpus-based distributional models that store their representations in fixed dimensions provide superior efficiency on literature-based discovery tasks.

The Marulan data sets: multi-sensor perception in a natural environment with challenging conditions

In this paper we present large, accurately calibrated and time-synchronized data sets, gathered outdoors in controlled and variable environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. These include four 2D laser scanners, a radar scanner, a color camera and an infrared camera. It provides a full description of the system used for data collection and the types of environments and conditions in which these data sets have been gathered, which include the presence of airborne dust, smoke and rain.

Synthesis of novel inhibitors blocking Wnt signaling downstream of β-Catenin

Large scale screening of libraries consisting of natural and small molecules led to the identification of many small molecule inhibitors repressing Wnt/β-Catenin signaling. However, targeted synthesis of novel Wnt pathway inhibitors has been rarely described. We developed a modular and expedient way to create the aromatic ring system with an aliphatic ring in between. Our synthesis opens up the possibility, in principle, to substitute all positions at the ring system with any desired substituent. Here, we tested five different haloquinone analogs carrying methoxy- and hydroxy-groups at different positions. Bona fide Wnt activity assays in cell culture and in Xenopus embryos revealed that two of these compounds act as potent inhibitors of aberrant activated Wnt/β-Catenin signaling.

Improved image set classification via joint sparse approximated nearest subspaces

Existing multi-model approaches for image set classification extract local models by clustering each image set individually only once, with fixed clusters used for matching with other image sets. However, this may result in the two closest clusters to represent different characteristics of an object, due to different undesirable environmental conditions (such as variations in illumination and pose). To address this problem, we propose to constrain the clustering of each query image set by forcing the clusters to have resemblance to the clusters in the gallery image sets. We first define a Frobenius norm distance between subspaces over Grassmann manifolds based on reconstruction error. We then extract local linear subspaces from a gallery image set via sparse representation. For each local linear subspace, we adaptively construct the corresponding closest subspace from the samples of a probe image set by joint sparse representation. We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold. Experiments on Honda, ETH-80 and Cambridge-Gesture datasets show that the proposed method consistently outperforms several other recent techniques, such as Affine Hull based Image Set Distance (AHISD), Sparse Approximated Nearest Points (SANP) and Manifold Discriminant Analysis (MDA).

Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets

Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated sampling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1–28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model diversity for thorough exploration of the model space. This algorithm was applied to a validating example to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission examples of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.

Characterisation of the half-field beam penumbra for a variety of blocking set-ups

Measurements of half-field beam penumbra were taken using EBT2 film for a variety of blocking techniques. It was shown that minimizing the SSD reduces the penumbra as the effects of beam divergence are diminished. The addition of a lead block directly on the surface provides optimal results with a 10-90% penumbra of 0.53 ± 0.02 cm. To resolve the uncertainties encountered in film measurements, future Monte Carlo measurements of halffield penumbras are to be conducted.

Intravascular device administration sets: Replacement after standard versus prolonged use in hospitalised patients--a study protocol for a randomised controlled trial (The RSVP Trial)

Introduction Vascular access devices (VADs), such as peripheral or central venous catheters, are vital across all medical and surgical specialties. To allow therapy or haemodynamic monitoring, VADs frequently require administration sets (AS) composed of infusion tubing, fluid containers, pressure-monitoring transducers and/or burettes. While VADs are replaced only when necessary, AS are routinely replaced every 3–4 days in the belief that this reduces infectious complications. Strong evidence supports AS use up to 4 days, but there is less evidence for AS use beyond 4 days. AS replacement twice weekly increases hospital costs and workload. Methods and analysis This is a pragmatic, multicentre, randomised controlled trial (RCT) of equivalence design comparing AS replacement at 4 (control) versus 7 (experimental) days. Randomisation is stratified by site and device, centrally allocated and concealed until enrolment. 6554 adult/paediatric patients with a central venous catheter, peripherally inserted central catheter or peripheral arterial catheter will be enrolled over 4 years. The primary outcome is VAD-related bloodstream infection (BSI) and secondary outcomes are VAD colonisation, AS colonisation, all-cause BSI, all-cause mortality, number of AS per patient, VAD time in situ and costs. Relative incidence rates of VAD-BSI per 100 devices and hazard rates per 1000 device days (95% CIs) will summarise the impact of 7-day relative to 4-day AS use and test equivalence. Kaplan-Meier survival curves (with log rank Mantel-Cox test) will compare VAD-BSI over time. Appropriate parametric or non-parametric techniques will be used to compare secondary end points. p Values of <0.05 will be considered significant.