On robust face recognition via sparse encoding : the good, the bad, and the ugly

In the field of face recognition, Sparse Representation (SR) has received considerable attention during the past few years. Most of the relevant literature focuses on holistic descriptors in closed-set identification applications. The underlying assumption in SR-based methods is that each class in the gallery has sufficient samples and the query lies on the subspace spanned by the gallery of the same class. Unfortunately, such assumption is easily violated in the more challenging face verification scenario, where an algorithm is required to determine if two faces (where one or both have not been seen before) belong to the same person. In this paper, we first discuss why previous attempts with SR might not be applicable to verification problems. We then propose an alternative approach to face verification via SR. Specifically, we propose to use explicit SR encoding on local image patches rather than the entire face. The obtained sparse signals are pooled via averaging to form multiple region descriptors, which are then concatenated to form an overall face descriptor. Due to the deliberate loss spatial relations within each region (caused by averaging), the resulting descriptor is robust to misalignment & various image deformations. Within the proposed framework, we evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder Neural Network (SANN), and an implicit probabilistic technique based on Gaussian Mixture Models. Thorough experiments on AR, FERET, exYaleB, BANCA and ChokePoint datasets show that the proposed local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods, in both verification and closed-set identification problems. The experiments also show that l1-minimisation based encoding has a considerably higher computational than the other techniques, but leads to higher recognition rates.

Bridging the biology of the ovine TRALI model – human inflammatory cell responses to TRALI inducing supernatants

Background Transfusion-related acute lung injury (TRALI) is a serious and potentially fatal consequence of transfusion. A two-event TRALI model demonstrated date-of-expiry - day (D) 5 platelet (PLT) and D42 packed red blood cell (PRBC) supernatants (SN) induced TRALI in LPS-treated sheep. We have adapted a whole blood transfusion culture model as an investigative bridge between the ovine TRALI model human responses to transfusion. Methods A whole blood transfusion model was adapted to replicate the ovine model - specifically +/- 0.23μg/mL LPS as the first event and 10% SN volume (transfusion) as the second event. Four pooled SN from blood products, previously used in the TRALI ovine model, were investigated: D1-PLT, D5-PLT, D1-PRBC, and D42-PRBC. Fresh human whole blood (recipient) was mixed with combinations of LPS and BP-SN stimuli and incubated in vitro for 6 hrs. Addition of golgi plug enabled measurement of monocyte cytokine production (IL-6, IL-8, IL-10, IL-12, TNF-α, IL-1α, CXCL-5, IP-10, MIP-1α, MCP-1) using multi-colour flow cytometry. Responses for 6 recipients were assessed. Results In the presence of LPS, D42-PRBC-SN significantly increased monocyte IL-6 (P=0.031), IL-8 (P=0.016) and IL-1α (P=0.008) production compared to D1-PRBC-SN. This response to D42-PRBC-SN was LPS-dependent, and was not evident in non-LPSstimulated controls. This response was also specific to D42-PRBC-SN, as similar changes were not evident for the D5-PLT-SN, compared to the D1-PLT-SN, regardless of the presence of LPS. D5-PLT-SN significantly increased IL-12 production (P=0.024) compared to D1-PLT-SN. This response was again LPS-dependent. Conclusions These data demonstrate a novel two-event mechanism of monocyte inflammatory response that was dependent upon both the presence of date-of-expiry blood product SN and LPS. Further, these results demonstrate different cytokines responses induced by date-of-expiry PLT-SN and PRBC-SN. These data are consistent with the evidence from the ovine TRALI model, and enhancing its relevance to transfusion related changes in humans.

Modelling survival data to account for model uncertainty: a single model or model averaging?

This study considered the problem of predicting survival, based on three alternative models: a single Weibull, a mixture of Weibulls and a cure model. Instead of the common procedure of choosing a single “best” model, where “best” is defined in terms of goodness of fit to the data, a Bayesian model averaging (BMA) approach was adopted to account for model uncertainty. This was illustrated using a case study in which the aim was the description of lymphoma cancer survival with covariates given by phenotypes and gene expression. The results of this study indicate that if the sample size is sufficiently large, one of the three models emerge as having highest probability given the data, as indicated by the goodness of fit measure; the Bayesian information criterion (BIC). However, when the sample size was reduced, no single model was revealed as “best”, suggesting that a BMA approach would be appropriate. Although a BMA approach can compromise on goodness of fit to the data (when compared to the true model), it can provide robust predictions and facilitate more detailed investigation of the relationships between gene expression and patient survival. Keywords: Bayesian modelling; Bayesian model averaging; Cure model; Markov Chain Monte Carlo; Mixture model; Survival analysis; Weibull distribution

Bayesian inference of traffic volumes based on Bluetooth data

The study of the relationship between macroscopic traffic parameters, such as flow, speed and travel time, is essential to the understanding of the behaviour of freeway and arterial roads. However, the temporal dynamics of these parameters are difficult to model, especially for arterial roads, where the process of traffic change is driven by a variety of variables. The introduction of the Bluetooth technology into the transportation area has proven exceptionally useful for monitoring vehicular traffic, as it allows reliable estimation of travel times and traffic demands. In this work, we propose an approach based on Bayesian networks for analyzing and predicting the complex dynamics of flow or volume, based on travel time observations from Bluetooth sensors. The spatio-temporal relationship between volume and travel time is captured through a first-order transition model, and a univariate Gaussian sensor model. The two models are trained and tested on travel time and volume data, from an arterial link, collected over a period of six days. To reduce the computational costs of the inference tasks, volume is converted into a discrete variable. The discretization process is carried out through a Self-Organizing Map. Preliminary results show that a simple Bayesian network can effectively estimate and predict the complex temporal dynamics of arterial volumes from the travel time data. Not only is the model well suited to produce posterior distributions over single past, current and future states; but it also allows computing the estimations of joint distributions, over sequences of states. Furthermore, the Bayesian network can achieve excellent prediction, even when the stream of travel time observation is partially incomplete.

Revolute joint approach to model joint mechanism in water-filled road safety barriers

Portable water-filled road barriers (PWFB) are roadside structures placed on temporary construction zones to separate work site from traffic. Recent changes in governing standards require PWFB to adhere to strict compliance in terms of lateral displacement and vehicle redirectionality. Actual PWFB test can be very costly, thus researchers resort to Finite Element Analysis (FEA) in the initial designs phase. There has been many research conducted on concrete barriers and flexible steel barriers using FEA, however not many was done pertaining to PWFB. This research probes a new technique to model joints in PWFB. Two methods to model the joining mechanism are presented and discussed in relation to its practicality and accuracy. Moreover, the study of the physical gap and mass of the barrier was investigated. Outcome from this research will benefit PWFB research and allow road barrier designers better knowledge in developing the next generation of road safety structures.

Heterogeneity on the technical efficiency in Japanese airports

This paper evaluates the production activities of Japanese airports by using a finite mixture model that allows controlling for unobserved heterogeneity. In doing so, a stochastic frontier latent class model, which allows the existence of different technologies, is adopted to estimate production frontiers. This procedure not only enables the identification of different groups of Japanese airports but also permits the analysis of their production efficiency. The main result is that there are two groups of Japanese airports, both following completely different "technologies" to obtain passengers and cargo, suggesting that business strategies need to be adapted to the characteristics of the airports. Some managerial implications are developed.

Role Ambiguity and conflicts: A study of company secretaries and two-tier boards in the Netherlands

Recent research suggests that company secretaries are increasingly involved in governance responsibilities in addition to traditional administrative tasks. Little is known in the literature, however, about company secretaries' changing governance role, and their daily challenges in liaising with boards and other stakeholders. In addition, few studies have been able to gain access to learn firsthand how company secretaries operate. This exploratory study fills this void by gaining access to the opinions of about one hundred company secretaries in the Netherlands who operate in the two-tier board system. Our findings indicate that company secretaries significantly influence an organisation's governance framework, while they face a number of practical challenges with directors, employees and management in fulfilling their diverse roles and responsibilities.

Complete-linkage clustering for voice activity detection in audio and visual speech

We propose a novel technique for conducting robust voice activity detection (VAD) in high-noise recordings. We use Gaussian mixture modeling (GMM) to train two generic models; speech and non-speech. We then score smaller segments of a given (unseen) recording against each of these GMMs to obtain two respective likelihood scores for each segment. These scores are used to compute a dissimilarity measure between pairs of segments and to carry out complete-linkage clustering of the segments into speech and non-speech clusters. We compare the accuracy of our method against state-of-the-art and standardised VAD techniques to demonstrate an absolute improvement of 15% in half-total error rate (HTER) over the best performing baseline system and across the QUT-NOISE-TIMIT database. We then apply our approach to the Audio-Visual Database of American English (AVDBAE) to demonstrate the performance of our algorithm in using visual, audio-visual or a proposed fusion of these features.

Multiple sulfur isotope analyses support a magmatic model for the volcanogenic massive sulfide deposits of the Teutonic Bore volcanic complex, Yilgarn Craton, Western Australia

We report sensitive high mass resolution ion microprobe, stable isotopes (SHRIMP SI) multiple sulfur isotope analyses (32S, 33S, 34S) to constrain the sources of sulfur in three Archean VMS deposits—Teutonic Bore, Bentley, and Jaguar—from the Teutonic Bore volcanic complex of the Yilgarn Craton, Western Australia, together with sedimentary pyrites from associated black shales and interpillow pyrites. The pyrites from VMS mineralization are dominated by mantle sulfur but include a small amount of slightly negative mass-independent fractionation (MIF) anomalies, whereas sulfur from the pyrites in the sedimentary rocks has pronounced positive MIF, with ∆33S values that lie between 0.19 and 6.20‰ (with one outlier at −1.62‰). The wall rocks to the mineralization include sedimentary rocks that have contributed no detectable positive MIF sulfur to the VMS deposits, which is difficult to reconcile with the leaching model for the formation of these deposits. The sulfur isotope data are best explained by mixing between sulfur derived from a magmatic-hydrothermal fluid and seawater sulfur as represented by the interpillow pyrites. The massive sulfide lens pyrites have a weighted mean ∆33S value of −0.27 ± 0.05‰ (MSWD = 1.6) nearly identical with −0.31 ± 0.08‰ (MSWD = 2.4) for pyrites from the stringer zone, which requires mixing to have occurred below the sea floor. We employed a two-component mixing model to estimate the contribution of seawater sulfur to the total sulfur budget of the two Teutonic Bore volcanic complex VMS deposits. The results are 15 to 18% for both Teutonic Bore and Bentley, much higher than the 3% obtained by Jamieson et al. (2013) for the giant Kidd Creek deposit. Similar calculations, carried out for other Neoarchean VMS deposits give value between 2% and 30%, which are similar to modern hydrothermal VMS deposits. We suggest that multiple sulfur isotope analyses may be used to predict the size of Archean VMS deposits and to provide a vector to ore deposit but further studies are needed to test these suggestions.

Mathematical modelling of gas production and compositional shift of a CSG (coal seam gas) field: Local model development

In this work we discuss the development of a mathematical model to predict the shift in gas composition observed over time from a producing CSG (coal seam gas) well, and investigate the effect that physical properties of the coal seam have on gas production. A detailed (local) one-dimensional, two-scale mathematical model of a coal seam has been developed. The model describes the competitive adsorption and desorption of three gas species (CH4, CO2 and N2) within a microscopic, porous coal matrix structure. The (diffusive) flux of these gases between the coal matrices (microscale) and a cleat network (macroscale) is accounted for in the model. The cleat network is modelled as a one-dimensional, volume averaged, porous domain that extends radially from a central well. Diffusive and advective transport of the gases occurs within the cleat network, which also contains liquid water that can be advectively transported. The water and gas phases are assumed to be immiscible. The driving force for the advection in the gas and liquid phases is taken to be a pressure gradient with capillarity also accounted for. In addition, the relative permeabilities of the water and gas phases are considered as functions of the degree of water saturation.

On robust face recognition via sparse coding: The good, the bad and the ugly

In the field of face recognition, sparse representation (SR) has received considerable attention during the past few years, with a focus on holistic descriptors in closed-set identification applications. The underlying assumption in such SR-based methods is that each class in the gallery has sufficient samples and the query lies on the subspace spanned by the gallery of the same class. Unfortunately, such an assumption is easily violated in the face verification scenario, where the task is to determine if two faces (where one or both have not been seen before) belong to the same person. In this study, the authors propose an alternative approach to SR-based face verification, where SR encoding is performed on local image patches rather than the entire face. The obtained sparse signals are pooled via averaging to form multiple region descriptors, which then form an overall face descriptor. Owing to the deliberate loss of spatial relations within each region (caused by averaging), the resulting descriptor is robust to misalignment and various image deformations. Within the proposed framework, they evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder Neural Network (SANN) and an implicit probabilistic technique based on Gaussian mixture models. Thorough experiments on AR, FERET, exYaleB, BANCA and ChokePoint datasets show that the local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods, on both the traditional closed-set identification task and the more applicable face verification task. The experiments also show that l1-minimisation-based encoding has a considerably higher computational cost when compared with SANN-based and probabilistic encoding, but leads to higher recognition rates.

Exact ground state and kink-like excitations of a two-dimensional Heisenberg antiferromagnet

A rare example of a two-dimensional Heisenberg model with an exact dimerized ground state is presented. This model, which can be regarded as a variation on the kagome' lattice, has several features of interest: it has a highly (but not macroscopically) degenerate ground state; it is closely related to spin chains studied by earlier authors; in particular, it exhibits domain-wall-like "kink" excitations normally associated only with one-dimensional systems. In some limits it decouples into noninteracting chains; unusually, this happens in the limit of strong, rather than weak, interchain coupling. [S0163-1829(99)50338-X].

Flow pattern analysis in a highly stenotic patient-specific carotid bifurcation model using a turbulence model

The aim of this study is to investigate the blood flow pattern in carotid bifurcation with a high degree of luminal stenosis, combining in vivo magnetic resonance imaging (MRI) and computational fluid dynamics (CFD). A newly developed two-equation transitional model was employed to evaluate wall shear stress (WSS) distribution and pressure drop across the stenosis, which are closely related to plaque vulnerability. A patient with an 80% left carotid stenosis was imaged using high resolution MRI, from which a patient-specific geometry was reconstructed and flow boundary conditions were acquired for CFD simulation. A transitional model was implemented to investigate the flow velocity and WSS distribution in the patient-specific model. The peak time-averaged WSS value of approximately 73Pa was predicted by the transitional flow model, and the regions of high WSS occurred at the throat of the stenosis. High oscillatory shear index values up to 0.50 were present in a helical flow pattern from the outer wall of the internal carotid artery immediately after the throat. This study shows the potential suitability of a transitional turbulent flow model in capturing the flow phenomena in severely stenosed carotid arteries using patient-specific MRI data and provides the basis for further investigation of the links between haemodynamic variables and plaque vulnerability. It may be useful in the future for risk assessment of patients with carotid disease.

Two-dimensional phase transitions at electrochemical interfaces

A two-state Ising model has been applied to the two-dimensional condensation of tymine at the mercury-water interface. The model predicts a quadratic dependence of the transition potential on temperature and on the logarithm of the adsorbate concentration. Both predictions have been confirmed experimentally.

Theoretical study of the optimum performance of a two-phase flow CO2 gasdynamic laser

Theoretical optimization studies of the performance of a combustion driven premixed two-phase flow gasdynamic laser are presented. The steady inviscid nonreacting quasi-one-dimensional two-phase flow model including appropriate finite rate vibrational kinetic rates has been used in the analysis. The analysis shows that the effect of the particles on the optimum performance of the two-phase laser is very small. The results are presented in graphical form. Applied Physics Letters is copyrighted by The American Institute of Physics.