Using the quantum probability ranking principle to rank interdependent documents

A known limitation of the Probability Ranking Principle (PRP) is that it does not cater for dependence between documents. Recently, the Quantum Probability Ranking Principle (QPRP) has been proposed, which implicitly captures dependencies between documents through “quantum interference”. This paper explores whether this new ranking principle leads to improved performance for subtopic retrieval, where novelty and diversity is required. In a thorough empirical investigation, models based on the PRP, as well as other recently proposed ranking strategies for subtopic retrieval (i.e. Maximal Marginal Relevance (MMR) and Portfolio Theory(PT)), are compared against the QPRP. On the given task, it is shown that the QPRP outperforms these other ranking strategies. And unlike MMR and PT, one of the main advantages of the QPRP is that no parameter estimation/tuning is required; making the QPRP both simple and effective. This research demonstrates that the application of quantum theory to problems within information retrieval can lead to significant improvements.

Social tagging, guppy effect and the role of interference : a quantum-inspired model for tags combination

Social tagging systems are shown to evidence a well known cognitive heuristic, the guppy effect, which arises from the combination of different concepts. We present some empirical evidence of this effect, drawn from a popular social tagging Web service. The guppy effect is then described using a quantum inspired formalism that has been already successfully applied to model conjunction fallacy and probability judgement errors. Key to the formalism is the concept of interference, which is able to capture and quantify the strength of the guppy effect.

Multi-party computation with conversion of secret sharing

Classical results in unconditionally secure multi-party computation (MPC) protocols with a passive adversary indicate that every n-variate function can be computed by n participants, such that no set of size t < n/2 participants learns any additional information other than what they could derive from their private inputs and the output of the protocol. We study unconditionally secure MPC protocols in the presence of a passive adversary in the trusted setup (‘semi-ideal’) model, in which the participants are supplied with some auxiliary information (which is random and independent from the participant inputs) ahead of the protocol execution (such information can be purchased as a “commodity” well before a run of the protocol). We present a new MPC protocol in the trusted setup model, which allows the adversary to corrupt an arbitrary number t < n of participants. Our protocol makes use of a novel subprotocol for converting an additive secret sharing over a field to a multiplicative secret sharing, and can be used to securely evaluate any n-variate polynomial G over a field F, with inputs restricted to non-zero elements of F. The communication complexity of our protocol is O(ℓ · n 2) field elements, where ℓ is the number of non-linear monomials in G. Previous protocols in the trusted setup model require communication proportional to the number of multiplications in an arithmetic circuit for G; thus, our protocol may offer savings over previous protocols for functions with a small number of monomials but a large number of multiplications.

The path to stoichiometric composition of III–V binary quantum dots through plasma/ion-assisted self-assembly

Semiconductor III-V quantum dots (QDs) are particularly enticing components for the integration of optically promising III-V materials with the silicon technology prevalent in the microelectronics industry. However, defects due to deviations from a stoichiometric composition [group III: group V = 1] may lead to impaired device performance. This paper investigates the initial stages of formation of InSb and GaAs QDs on Si(1 0 0) through hybrid numerical simulations. Three situations are considered: a neutral gas environment (NG), and two ionized gas environments, namely a localized ion source (LIS) and a background plasma (BP) case. It is shown that when the growth is conducted in an ionized gas environment, a stoichiometric composition may be obtained earlier in the QD as compared to a NG. Moreover, the stoichiometrization time, tst, is shorter for the BP case compared to the LIS scenario. A discussion of the effect of ion/plasma-based tools as well as a range of process conditions on the final island size distribution is also included. Our results suggest a way to obtain a deterministic level of control over nanostructure properties (in particular, elemental composition and size) during the initial stages of growth which is a crucial step towards achieving highly tailored QDs suitable for implementation in advanced technological devices.

Advanced mass spectrometry-based multi-omics technologies for exploring the pathogenesis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the primary hepatic malignancies and is the third most common cause of cancer related death worldwide. Although a wealth of knowledge has been gained concerning the initiation and progression of HCC over the last half century, efforts to improve our understanding of its pathogenesis at a molecular level are still greatly needed, to enable clinicians to enhance the standards of the current diagnosis and treatment of HCC. In the post-genome era, advanced mass spectrometry driven multi-omics technologies (e.g., profiling of DNA damage adducts, RNA modification profiling, proteomics, and metabolomics) stand at the interface between chemistry and biology, and have yielded valuable outcomes from the study of a diversity of complicated diseases. Particularly, these technologies are being broadly used to dissect various biological aspects of HCC with the purpose of biomarker discovery, interrogating pathogenesis as well as for therapeutic discovery. This proof of knowledge-based critical review aims at exploring the selected applications of those defined omics technologies in the HCC niche with an emphasis on translational applications driven by advanced mass spectrometry, toward the specific clinical use for HCC patients. This approach will enable the biomedical community, through both basic research and the clinical sciences, to enhance the applicability of mass spectrometry-based omics technologies in dissecting the pathogenesis of HCC and could lead to novel therapeutic discoveries for HCC.

Tailoring the composition of self-assembled Si1−xCx quantum dots : simulation of plasma/ion-related controls

Precise control of composition and internal structure is essential for a variety of novel technological applications which require highly tailored binary quantum dots (QDs) with predictable optoelectronic and mechanical properties. The delicate balancing act between incoming flux and substrate temperature required for the growth of compositionally graded (Si1-xC x; x varies throughout the internal structure), core-multishell (discrete shells of Si and C or combinations thereof) and selected composition (x set) QDs on low-temperature plasma/ion-flux-exposed Si(100) surfaces is investigated via a hybrid numerical simulation. Incident Si and C ions lead to localized substrate heating and a reduction in surface diffusion activation energy. It is shown that by incorporating ions in the influx, a steady-state composition is reached more quickly (for selected composition QDs) and the composition gradient of a Si1-xCx QD may be fine tuned; additionally (with other deposition conditions remaining the same), larger QDs are obtained on average. It is suggested that ionizing a portion of the influx is another way to control the average size of the QDs, and ultimately, their internal structure. Advantages that can be gained by utilizing plasma/ion-related controls to facilitate the growth of highly tailored, compositionally controlled quantum dots are discussed as well.

Robust clustering of multi-type relational data via a heterogeneous manifold ensemble

High-Order Co-Clustering (HOCC) methods have attracted high attention in recent years because of their ability to cluster multiple types of objects simultaneously using all available information. During the clustering process, HOCC methods exploit object co-occurrence information, i.e., inter-type relationships amongst different types of objects as well as object affinity information, i.e., intra-type relationships amongst the same types of objects. However, it is difficult to learn accurate intra-type relationships in the presence of noise and outliers. Existing HOCC methods consider the p nearest neighbours based on Euclidean distance for the intra-type relationships, which leads to incomplete and inaccurate intra-type relationships. In this paper, we propose a novel HOCC method that incorporates multiple subspace learning with a heterogeneous manifold ensemble to learn complete and accurate intra-type relationships. Multiple subspace learning reconstructs the similarity between any pair of objects that belong to the same subspace. The heterogeneous manifold ensemble is created based on two-types of intra-type relationships learnt using p-nearest-neighbour graph and multiple subspaces learning. Moreover, in order to make sure the robustness of clustering process, we introduce a sparse error matrix into matrix decomposition and develop a novel iterative algorithm. Empirical experiments show that the proposed method achieves improved results over the state-of-art HOCC methods for FScore and NMI.

Job satisfaction of preventive medicine workers in northern Vietnam : a multi-method approach

In the health care industry, Job Satisfaction (JS) is linked with work performance, psychological well-being and employee turnover. Although research into JS among health professionals has a long history worldwide, there has been very little analysis in Vietnam. No study has addressed JS of preventive medicine workers in Vietnam, and there is no reliable and valid instrument in Vietnamese language and context for evaluation of JS in this group. This project was conducted to fill these gaps. The findings contribute evidence regarding factors that influence JS in this sector of the health industry that should be applied to personnel management policies and practices in Vietnam.

Decreasing menopausal symptoms in women undertaking a web-based multi-modal lifestyle intervention: The Women's Wellness Program

Menopausal transition can be challenging for many women. This study tested the effectiveness of an intervention delivered in different modes in decreasing menopausal symptoms in midlife women. The Women's Wellness Program (WWP) intervention was delivered to 225 Australian women aged between 40 and 65 years through three modes (i.e., on-line independent, face-to-face with nurse consultations, and on-line with virtual nurse consultations). All women in the study were provided with a 12-week Program Book outlining healthy lifestyle behaviors while women in the consultation groups were supported by a registered nurse who provide tailored health education and assisted with individual goal setting for exercise, healthy eating, smoking and alcohol consumption. Pre- and post-intervention data were collected on menopausal symptoms (Greene Climacteric Scale), health related quality of life (SF12), and modifiable lifestyle factors. Linear mixed-effect models showed an average 0.87 and 1.23 point reduction in anxiety (p < 0.01) and depression scores (p < 0.01) over time in all groups. Results also demonstrated reduced vasomotor symptoms (β = −0.19, SE = 0.10, p = 0.04) and sexual dysfunction (β = −0.17, SE = 0.06, p < 0.01) in all participants though women in the face-to-face group generally reported greater reductions than women in the other groups. This lifestyle intervention embedded within a wellness framework has the potential to reduce menopausal symptoms and improve quality of life in midlife women thus potentially enhancing health and well-being in women as they age. Of course, study replication is needed to confirm the intervention effects.

Adaptive compliance control of a multi‐legged stair‐climbing robot based on proprioceptive data

Purpose – The purpose of this paper is to describe an innovative compliance control architecture for hybrid multi‐legged robots. The approach was verified on the hybrid legged‐wheeled robot ASGUARD, which was inspired by quadruped animals. The adaptive compliance controller allows the system to cope with a variety of stairs, very rough terrain, and is also able to move with high velocity on flat ground without changing the control parameters. Design/methodology/approach – The paper shows how this adaptivity results in a versatile controller for hybrid legged‐wheeled robots. For the locomotion control we use an adaptive model of motion pattern generators. The control approach takes into account the proprioceptive information of the torques, which are applied on the legs. The controller itself is embedded on a FPGA‐based, custom designed motor control board. An additional proprioceptive inclination feedback is used to make the same controller more robust in terms of stair‐climbing capabilities. Findings – The robot is well suited for disaster mitigation as well as for urban search and rescue missions, where it is often necessary to place sensors or cameras into dangerous or inaccessible areas to get a better situation awareness for the rescue personnel, before they enter a possibly dangerous area. A rugged, waterproof and dust‐proof corpus and the ability to swim are additional features of the robot. Originality/value – Contrary to existing approaches, a pre‐defined walking pattern for stair‐climbing was not used, but an adaptive approach based only on internal sensor information. In contrast to many other walking pattern based robots, the direct proprioceptive feedback was used in order to modify the internal control loop, thus adapting the compliance of each leg on‐line.

Evolutionary optimization and game strategies for advanced multi-disciplinary design : applications to aeronautics and UAV design

Many complex aeronautical design problems can be formulated with efficient multi-objective evolutionary optimization methods and game strategies. This book describes the role of advanced innovative evolution tools in the solution, or the set of solutions of single or multi disciplinary optimization. These tools use the concept of multi-population, asynchronous parallelization and hierarchical topology which allows different models including precise, intermediate and approximate models with each node belonging to the different hierarchical layer handled by a different Evolutionary Algorithm. The efficiency of evolutionary algorithms for both single and multi-objective optimization problems are significantly improved by the coupling of EAs with games and in particular by a new dynamic methodology named “Hybridized Nash-Pareto games”. Multi objective Optimization techniques and robust design problems taking into account uncertainties are introduced and explained in detail. Several applications dealing with civil aircraft and UAV, UCAV systems are implemented numerically and discussed. Applications of increasing optimization complexity are presented as well as two hands-on test cases problems. These examples focus on aeronautical applications and will be useful to the practitioner in the laboratory or in industrial design environments. The evolutionary methods coupled with games presented in this volume can be applied to other areas including surface and marine transport, structures, biomedical engineering, renewable energy and environmental problems.

Residents’ experiences of privacy and comfort in multi-storey apartment dwellings in subtropical Brisbane

Dwellings in multi-storey apartment buildings (MSAB) are predicted to increase dramatically as a proportion of housing stock in subtropical cities over coming decades. The problem of designing comfortable and healthy high-density residential environments and minimising energy consumption must be addressed urgently in subtropical cities globally. This paper explores private residents’ experiences of privacy and comfort and their perceptions of how well their apartment dwelling modulated the external environment in subtropical conditions through analysis of 636 survey responses and 24 interviews with residents of MSAB in inner urban neighbourhoods of Brisbane, Australia. The findings show that the availability of natural ventilation and outdoor private living spaces play important roles in resident perceptions of liveability in the subtropics where the climate is conducive to year round “outdoor living”. Residents valued choice with regard to climate control methods in their apartments. They overwhelmingly preferred natural ventilation to manage thermal comfort, and turned to the air-conditioner for limited periods, particularly when external conditions were too noisy. These findings provide a unique evidence base for reducing the environmental impact of MSAB and increasing the acceptability of apartment living, through incorporating residential attributes positioned around climate-responsive architecture.

Multi-rotor with suspended load: System dynamics and control toolbox

There is an increasing demand for Unmanned Aerial Systems (UAS) to carry suspended loads as this can provide significant benefits to several applications in agriculture, law enforcement and construction. The load impact on the underlying system dynamics should not be neglected as significant feedback forces may be induced on the vehicle during certain flight manoeuvres. The constant variation in operating point induced by the slung load also causes conventional controllers to demand increased control effort. Much research has focused on standard multi-rotor position and attitude control with and without a slung load. However, predictive control schemes, such as Nonlinear Model Predictive Control (NMPC), have not yet been fully explored. To this end, we present a novel controller for safe and precise operation of multi-rotors with heavy slung load in three dimensions. The paper describes a System Dynamics and Control Simulation Toolbox for use with MATLAB/SIMULINK which includes a detailed simulation of the multi-rotor and slung load as well as a predictive controller to manage the nonlinear dynamics whilst accounting for system constraints. It is demonstrated that the controller simultaneously tracks specified waypoints and actively damps large slung load oscillations. A linear-quadratic regulator (LQR) is derived and control performance is compared. Results show the improved performance of the predictive controller for a larger flight envelope, including aggressive manoeuvres and large slung load displacements. The computational cost remains relatively small, amenable to practical implementations.

A hybrid of modified PSO and local search on a multi-robot search system

Particle swarm optimization (PSO), a new population based algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obstacles. One of these defects is premature convergence. This means that one of the properties of basic PSO is that when particles are spread in a search space, as time increases they tend to converge in a small area. This shortcoming is also evident on a multi-robot search system, particularly when there are big static obstacles in the search space that prevent the robots from finding the target easily; therefore, as time increases, based on this property they converge to a small area that may not contain the target and become entrapped in that area.Another shortcoming is that basic PSO cannot guarantee the global convergence of the algorithm. In other words, initially particles explore different areas, but in some cases they are not good at exploiting promising areas, which will increase the search time.This study proposes a method based on the particle swarm optimization (PSO) technique on a multi-robot system to find a target in a search space containing big static obstacles. This method is not only able to overcome the premature convergence problem but also establishes an efficient balance between exploration and exploitation and guarantees global convergence, reducing the search time by combining with a local search method, such as A-star.To validate the effectiveness and usefulness of algorithms,a simulation environment has been developed for conducting simulation-based experiments in different scenarios and for reporting experimental results. These experimental results have demonstrated that the proposed method is able to overcome the premature convergence problem and guarantee global convergence.

Combining meta- and mega-analytic approaches for multi-site diffusion imaging based genetic studies: From the enigma-DTI working group

Meta-analyses estimate a statistical effect size for a test or an analysis by combining results from multiple studies without necessarily having access to each individual study's raw data. Multi-site meta-analysis is crucial for imaging genetics, as single sites rarely have a sample size large enough to pick up effects of single genetic variants associated with brain measures. However, if raw data can be shared, combining data in a "mega-analysis" is thought to improve power and precision in estimating global effects. As part of an ENIGMA-DTI investigation, we use fractional anisotropy (FA) maps from 5 studies (total N=2, 203 subjects, aged 9-85) to estimate heritability. We combine the studies through meta-and mega-analyses as well as a mixture of the two - combining some cohorts with mega-analysis and meta-analyzing the results with those of the remaining sites. A combination of mega-and meta-approaches may boost power compared to meta-analysis alone.