The Status of Multi-Dimensional Core-Collapse Supernova Models

Models of neutrino-driven core-collapse supernova explosions have matured considerably in recent years. Explosions of low-mass progenitors can routinely be simulated in 1D, 2D, and 3D. Nucleosynthesis calculations indicate that these supernovae could be contributors of some lighter neutron-rich elements beyond iron. The explosion mechanism of more massive stars remains under investigation, although first 3D models of neutrino-driven explosions employing multi-group neutrino transport have become available. Together with earlier 2D models and more simplified 3D simulations, these have elucidated the interplay between neutrino heating and hydrodynamic instabilities in the post-shock region that is essential for shock revival. However, some physical ingredients may still need to be added/improved before simulations can robustly explain supernova explosions over a wide range of progenitors. Solutions recently suggested in the literature include uncertainties in the neutrino rates, rotation, and seed perturbations from convective shell burning. We review the implications of 3D simulations of shell burning in supernova progenitors for the ‘perturbations-aided neutrino-driven mechanism,’ whose efficacy is illustrated by the first successful multi-group neutrino hydrodynamics simulation of an 18 solar mass progenitor with 3D initial conditions. We conclude with speculations about the impact of 3D effects on the structure of massive stars through convective boundary mixing.

Exploring the mobile structural assessment tool: Concept maps for learning website: Concept Maps for Learning Website

In this paper, we describe how the pathfinder algorithm converts relatedness ratings of concept pairs to concept maps; we also present how this algorithm has been used to develop the Concept Maps for Learning website (www.conceptmapsforlearning.com) based on the principles of effective formative assessment. The pathfinder networks, one of the network representation tools, claim to help more students memorize and recall the relations between concepts than spatial representation tools (such as Multi- Dimensional Scaling). Therefore, the pathfinder networks have been used in various studies on knowledge structures, including identifying students’ misconceptions. To accomplish this, each student’s knowledge map and the expert knowledge map are compared via the pathfinder software, and the differences between these maps are highlighted. After misconceptions are identified, the pathfinder software fails to provide any feedback on these misconceptions. To overcome this weakness, we have been developing a mobile-based concept mapping tool providing visual, textual and remedial feedback (ex. videos, website links and applets) on the concept relations. This information is then placed on the expert concept map, but not on the student’s concept map. Additionally, students are asked to note what they understand from given feedback, and given the opportunity to revise their knowledge maps after receiving various types of feedback.

Doing Relationships and Sexuality Education with Young People in State Care

Background: Existing literature indicates that young people in state carehave particular sexual health needs that include addressing their social andemotional well-being, yet little has been published as to how thesecomponents of sex education are actually delivered by service-providers.Objective: To analyse the processes involved in delivering relationship andsexuality education to young people in state care from the perspectives ofa sample of service-providers with a role in sexual health care delivery.Design: Qualitative methodological strategy.Setting: Service-delivery sites at urban and rural locations in Ireland.Method: Twenty-two service-providers were interviewed in depth, and datawere analysed using a qualitative analytical strategy resembling modifiedanalytical induction.Findings: Participants proffered their perceptions and examples of theirpractices of sex education in relation to the following themes: (1)acknowledging the multi-dimensional nature of sexual health in the case ofyoung people in care; (2) personal and emotional development educationto address poor self-esteem, emotional disconnectedness and an inabilityto recognise and express emotions; (3) social skills’ education as part of arepertoire of competencies needed to negotiate relationships and safer sex;(4) the application of positive social skills embedded in everyday socialsituations; and (5) factual sexuality education.Conclusion: Insights into service providers’ perceptions of the multidimensionalnature of the sexual health needs of young people in statecare, and the ways in which these service-providers justified their practicemake visible the complex character of sex education and the degree of skillrequired to deliver it to those in state care.

Feature Study on a Programmable Network Traffic Classifier

Monitoring and tracking of IP traffic flows are essential for network services (i.e. packet forwarding). Packet header lookup is the main part of flow identification by determining the predefined matching action for each incoming flow. In this paper, an improved header lookup and flow rule update solution is investigated. A detailed study of several well-known lookup algorithms reveals that searching individual packet header field and combining the results achieve high lookup speed and flexibility. The proposed hybrid lookup architecture is comprised of various lookup algorithms, which are selected based on the user applications and system requirements.

Towards a Resilience Metric Framework for Cyber-Physical Systems

Resilience is widely accepted as a desirable system property for cyber-physical systems. However, there are no metrics that can be used to measure the resilience of cyber-physical systems (CPS) while the multi-dimensional nature of performance in these systems is considered. In this work, we present first results towards a resilience metric framework. The key contributions of this framework are threefold: First, it allows to evaluate resilience with respect to different performance indicators that are of interest. Second, complexities that are relevant to the performance indicators of interest, can be intentionally abstracted. Third and final, it supports the identification of reasons for good or bad resilience to improve system design.

Three-dimensional simulations of gravitationally confined detonations compared to observations of SN 1991T

The gravitationally confined detonation (GCD) model has been proposed as a possible explosion mechanism for Type Ia supernovae in the single-degenerate evolution channel. It starts with ignition of a deflagration in a single off-centre bubble in a near-Chandrasekhar-mass white dwarf. Driven by buoyancy, the deflagration flame rises in a narrow cone towards the surface. For the most part, the main component of the flow of the expanding ashes remains radial, but upon reaching the outer, low-pressure layers of the white dwarf, an additional lateral component develops. This causes the deflagration ashes to converge again at the opposite side, where the compression heats fuel and a detonation may be launched. We first performed five three-dimensional hydrodynamic simulations of the deflagration phase in 1.4 M⊙ carbon/oxygen white dwarfs at intermediate-resolution (2563computational zones). We confirm that the closer the initial deflagration is ignited to the centre, the slower the buoyant rise and the longer the deflagration ashes takes to break out and close in on the opposite pole to collide. To test the GCD explosion model, we then performed a high-resolution (5123 computational zones) simulation for a model with an ignition spot offset near the upper limit of what is still justifiable, 200 km. This high-resolution simulation met our deliberately optimistic detonation criteria, and we initiated a detonation. The detonation burned through the white dwarf and led to its complete disruption. For this model, we determined detailed nucleosynthetic yields by post-processing 106 tracer particles with a 384 nuclide reaction network, and we present multi-band light curves and time-dependent optical spectra. We find that our synthetic observables show a prominent viewing-angle sensitivity in ultraviolet and blue wavelength bands, which contradicts observed SNe Ia. The strong dependence on the viewing angle is caused by the asymmetric distribution of the deflagration ashes in the outer ejecta layers. Finally, we compared our model to SN 1991T. The overall flux level of the model is slightly too low, and the model predicts pre-maximum light spectral features due to Ca, S, and Si that are too strong. Furthermore, the model chemical abundance stratification qualitatively disagrees with recent abundance tomography results in two key areas: our model lacks low-velocity stable Fe and instead has copious amounts of high-velocity 56Ni and stable Fe. We therefore do not find good agreement of the model with SN 1991T.

Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance.