A penalty-based genetic algorithm for the migration cost-aware virtual machine placement problem in cloud data centers

In the past few years, the virtual machine (VM) placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used in today's cloud data centers as they do not consider the migration cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the migration cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm (GA) for the VM placement problem that considers the migration cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.

Induction of a Th1 immune response and suppression of IgE via immunotherapy with a recombinant hybrid molecule encapsulated in liposome-protamine-DNA nanoparticles in a model of experimental allergy

Liposome-protamine-DNA nanoparticles (LPD) are safe, effective, and non-toxic adjuvants that induce Th1-like immune responses. We hypothesized that encapsulation of allergens into liposomes could be an appropriate option for immunotherapy. The present study evaluated the immunotherapeutic potential of a recombinant hybrid molecule (rHM) encapsulated in LPD nanoparticles in a murine model of Chenopodium album allergy. BALB/c mice were sensitized with the allergen in alum, and the immunotherapy procedure was performed by subcutaneous injections of LPD-rHM, rHM, or empty LPD at weekly intervals. Sensitized mice developed a Th2-biased immune response characterized by strong specific IgG1 and IgE production, IL-4, and the transcription factor GATA3 in spleen cell cultures. Treatment with the LPD-rHM resulted in a reduction in IgE and a marked increase in IgG2a. The LPD-rHM induced allergen-specific responses with relatively high interferon-gamma production, as well as expression of the transcription factor T-bet in stimulated splenocytes. In addition, lymphoproliferative responses were higher in the LPD-rHM-treated mice than in the other groups. Removal of the nanoparticles from the rHM resulted in a decrease in the allergen's immunogenicity. These results indicate that the rHM complexed with LPD nanoparticles has a marked suppressive effect on the allergic response and caused a shift toward a Th1 pathway.

Two-scale computational modelling of water flow in unsaturated soils containing irregular-shaped inclusions

The focus of this paper is two-dimensional computational modelling of water flow in unsaturated soils consisting of weakly conductive disconnected inclusions embedded in a highly conductive connected matrix. When the inclusions are small, a two-scale Richards’ equation-based model has been proposed in the literature taking the form of an equation with effective parameters governing the macroscopic flow coupled with a microscopic equation, defined at each point in the macroscopic domain, governing the flow in the inclusions. This paper is devoted to a number of advances in the numerical implementation of this model. Namely, by treating the micro-scale as a two-dimensional problem, our solution approach based on a control volume finite element method can be applied to irregular inclusion geometries, and, if necessary, modified to account for additional phenomena (e.g. imposing the macroscopic gradient on the micro-scale via a linear approximation of the macroscopic variable along the microscopic boundary). This is achieved with the help of an exponential integrator for advancing the solution in time. This time integration method completely avoids generation of the Jacobian matrix of the system and hence eases the computation when solving the two-scale model in a completely coupled manner. Numerical simulations are presented for a two-dimensional infiltration problem.

Carbon nanodot decorated graphitic carbon nitride: New insights into the enhanced photocatalytic water splitting from ab initio studies

Interfacing carbon nanodots (C-dots) with graphitic carbon nitride (g-C3N4) produces a metal-free system that has recently demonstrated significant enhancement of photo-catalytic performance for water splitting into hydrogen [Science, 2015, 347, 970–974]. However, the underlying photo-catalytic mechanism is not fully established. Herein, we have carried out density functional theory (DFT) calculations to study the interactions between g-C3N4 and trigonal/hexagonal shaped C-dots. We find that hybrid C-dots/g-C3N4 can form a type-II van der Waals heterojunction, leading to significant reduction of band gap. The C-dot decorated g-C3N4 enhances the separation of photogenerated electron and hole pairs and the composite's visible light response. Interestingly, the band alignment of C-dots and g-C3N4 calculated by the hybrid functional method indicates that C-dots act as a spectral sensitizer in hybrid C-dots/g-C3N4 for water splitting. Our results offer new theoretical insights into this metal-free photocatalyst for water splitting.

From vision to actions: Towards adaptive and autonomous humanoid robots

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions.

A dosimetric retrospective planning study comparing volumetric arc therapy (VMAT) and stereotactic body radiotherapy (SBRT) treatment plans for non-small cell lung cancer (NSCLC)

Purpose A retrospective planning study comparing volumetric arc therapy (VMAT) and stereotactic body radiotherapy (SBRT) treatment plans for non-small cell lung cancer (NSCLC). Methods and materials Five randomly selected early stage lung cancer patients were included in the study. For each patient, four plans were created: the SBRT plan and three VMAT plans using different optimisation methodologies. A total of 20 different plans were evaluated. The dose parameters of dose conformity results and the target dose constraints results were compared for these plans. Results The mean planning target volume (PTV) for all the plans (SBRT and VMAT) was 18·3 cm3, with a range from 15·6 to 20·1 cm3. The maximum dose tolerance to 1 cc of all the plans was within 140% (84 Gy) of the prescribed dose, and 95% of the PTV of all the plans received 100% of the prescribed dose (60 Gy). In all the plans, 99% of the PTV received a dose >90% of the prescribed dose, and the mean dose in all the plans ranged from 67 to 72 Gy. The planning target dose conformity for the SBRT and the VMAT (0°, 15° collimator single arc plans and dual arc) plans showed the tightness of the prescription isodose conformity to the target. Conclusions SBRT and VMAT are radiotherapy approaches that increase doses to small tumour targets without increasing doses to the organs at risk. Although VMAT offers an alternative to SBRT for NSCLC and the potential advantage of VMAT is the reduced treatment times over SBRT, the statistical results show that there was no significant difference between the SBRT and VMAT optimised plans in terms of dose conformity and organ-at-risk sparing.

An analysis of TLS handshake proxying

Content delivery networks (CDNs) are an essential component of modern website infrastructures: edge servers located closer to users cache content, increasing robustness and capacity while decreasing latency. However, this situation becomes complicated for HTTPS content that is to be delivered using the Transport Layer Security (TLS) protocol: the edge server must be able to carry out TLS handshakes for the cached domain. Most commercial CDNs require that the domain owner give their certificate's private key to the CDN's edge server or abandon caching of HTTPS content entirely. We examine the security and performance of a recently commercialized delegation technique in which the domain owner retains possession of their private key and splits the TLS state machine geographically with the edge server using a private key proxy service. This allows the domain owner to limit the amount of trust given to the edge server while maintaining the benefits of CDN caching. On the performance front, we find that latency is slightly worse compared to the insecure approach, but still significantly better than the domain owner serving the content directly. On the security front, we enumerate the security goals for TLS handshake proxying and identify a subtle difference between the security of RSA key transport and signed-Diffie--Hellman in TLS handshake proxying; we also discuss timing side channel resistance of the key server and the effect of TLS session resumption.

Mechanical performances of track-shaped rebar stiffened concrete-filled steel tubular (SCFRT) stub columns under axial compression

This paper presents a combined experimental, numerical, and theoretical study on the mechanical behaviors of track-shaped concrete-filled steel tubular (SCFRT) stub columns stiffened by rebars under compressive load. A total of 18 track-shaped concrete-filled steel tubular specimens including 12 specimens stiffened by rebars and 6 non-stiffened counterparts are tested, with consideration of parameters including flakiness ratio, concrete strength, and stiffeners. Failure pattern, bearing capacity, and ductility are all analyzed and discussed based on the experimental results. The numerical simulation by finite element (FE) software ABAQUS is also conducted. Based on both experimental and numerical results, theoretical formula to predict the load-bearing capacity of SCFRT stub columns subjected to axial compression loading is established according to the superposition principle of ultimate load-bearing capacity with rational simplification. The proposed theoretical method provides accurate predictions on the load bearing capacity by comparing with experimental results from 18 groups of specimens.

Design and Development of an In-Vehicle Human Machine Interface to Improve Fuel Efficiency & Safety

Road transport plays a significant role in various industries and mobility services around the globe and has a vital impact on our daily lives. However it also has serious impacts on both public health and the environment. In-vehicle feedback systems are a relatively new approach to encouraging driver behaviour change for improving fuel efficiency and safety in automotive environments. While many studies claim that the adoption of eco-driving practices, such as eco-driving training programs and in-vehicle feedback to drivers, has the potential to improve fuel efficiency, limited research has integrated safety and eco-driving. Therefore, this research seeks to use human factors related theories and practices to inform the design and evaluation of an in-vehicle Human Machine Interface (HMI) providing real-time driver feedback with the aim of improving both fuel efficiency and safety.

Capturing "organised crime" in Australian Law

This briefing paper presents and foreshadows ongoing PhD research by the first author into how understandings of organised crime in Australia have been shaped, and the extent to which these perceptions have influenced legislative and policing responses. It begins with an historical survey of significant models of organised crime, then reviews current Australian legislative strategies, and goes on to raise questions about the conceptual model that underpins these strategies. The paper concludes with a discussion of the potential policy implications of this research.

A bottom-up integration of vision and actions to create cognitive humanoids

In recent years more and more complex humanoid robots have been developed. On the other hand programming these systems has become more difficult. There is a clear need for such robots to be able to adapt and perform certain tasks autonomously, or even learn by themselves how to act. An important issue to tackle is the closing of the sensorimotor loop. Especially when talking about humanoids the tight integration of perception with actions will allow for improved behaviours, embedding adaptation on the lower-level of the system.

Towards vision-based deep reinforcement learning for robotic motion control

This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only. The capability to autonomously learn robot controllers solely from raw-pixel images and without any prior knowledge of configuration is shown for the first time. We build upon the success of recent deep reinforcement learning and develop a system for learning target reaching with a three-joint robot manipulator using external visual observation. A Deep Q Network (DQN) was demonstrated to perform target reaching after training in simulation. Transferring the network to real hardware and real observation in a naive approach failed, but experiments show that the network works when replacing camera images with synthetic images.

Induction of partial immunity in both males and females is sufficient to protect females against sexual transmission of Chlamydia

Sexually transmitted Chlamydia trachomatis causes infertility, and because almost 90% of infections are asymptomatic, a vaccine is required for its eradication. Mathematical modeling studies have indicated that a vaccine eliciting partial protection (non-sterilizing) may prevent Chlamydia infection transmission, if administered to both sexes before an infection. However, reducing chlamydial inoculum transmitted by males and increasing infection resistance in females through vaccination to elicit sterilizing immunity has yet to be investigated experimentally. Here we show that a partially protective vaccine (chlamydial major outer membrane protein (MOMP) and ISCOMATRIX (IMX) provided sterilizing immunity against sexual transmission between immunized mice. Immunizing male or female mice before an infection reduced chlamydial burden and disease development, but did not prevent infection. However, infection and inflammatory disease responsible for infertility were absent in 100% of immunized female mice challenged intravaginally with ejaculate collected from infected immunized males. In contrast to the sterilizing immunity generated following recovery from a previous chlamydial infection, protective immunity conferred by MOMP/IMX occurred independent of resident memory T cells. Our results demonstrate that vaccination of males or females can further protect the opposing sex, whereas vaccination of both sexes can synergize to elicit sterilizing immunity against Chlamydia sexual transmission.

Professional learning places and spaces: The staffroom as a site of beginning teacher induction and transition

This paper argues that the staffroom is an important professional learning space where beginning teachers interact to understand who they are and the nature of their professional work. The authors highlight the theoretical importance of space and place in the construction and negotiation of beginning teacher subjectivities. To illustrate the staffroom as a particular place where important professional learning could occur the authors use two narratives based on the lived experiences of two beginning teachers, one in a primary context, the other secondary. The authors conclude by calling for greater research attention to the significance of the staffroom and its interaction with teacher subjectivities. At the level of practice we also call for the teaching profession to recognise staffrooms as important sites of professional learning and places that should support induction and mentoring of beginning teachers. Such recognition could enhance the retention, satisfaction, and effectiveness of new and experienced teachers alike.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.