Tensor reduction for user profiling in personalized recommender systems

User profiling is the process of constructing user models which represent personal characteristics and preferences of customers. User profiles play a central role in many recommender systems. Recommender systems recommend items to users based on user profiles, in which the items can be any objects which the users are interested in, such as documents, web pages, books, movies, etc. In recent years, multidimensional data are getting more and more attention for creating better recommender systems from both academia and industry. Additional metadata provides algorithms with more details for better understanding the interactions between users and items. However, most of the existing user/item profiling techniques for multidimensional data analyze data through splitting the multidimensional relations, which causes information loss of the multidimensionality. In this paper, we propose a user profiling approach using a tensor reduction algorithm, which we will show is based on a Tucker2 model. The proposed profiling approach incorporates latent interactions between all dimensions into user profiles, which significantly benefits the quality of neighborhood formation. We further propose to integrate the profiling approach into neighborhoodbased collaborative filtering recommender algorithms. Experimental results show significant improvements in terms of recommendation accuracy.

Latent class analysis reveals distinct subgroups of patients based on symptom occurrence and demographic and clinical characteristics

Context Cancer patients experience a broad range of physical and psychological symptoms as a result of their disease and its treatment. On average, these patients report ten unrelieved and co-occurring symptoms. Objectives To determine if subgroups of oncology outpatients receiving active treatment (n=582) could be identified based on their distinct experience with thirteen commonly occurring symptoms; to determine whether these subgroups differed on select demographic, and clinical characteristics; and to determine if these subgroups differed on quality of life (QOL) outcomes. Methods Demographic, clinical, and symptom data from one Australian and two U.S. studies were combined. Latent class analysis (LCA) was used to identify patient subgroups with distinct symptom experiences based on self-report data on symptom occurrence using the Memorial Symptom Assessment Scale (MSAS). Results Four distinct latent classes were identified (i.e., All Low (28.0%), Moderate Physical and Lower Psych (26.3%), Moderate Physical and Higher Psych (25.4%), All High (20.3%)). Age, gender, education, cancer diagnosis, and presence of metastatic disease differentiated among the latent classes. Patients in the All High class had the worst QOL scores. Conclusion Findings from this study confirm the large amount of interindividual variability in the symptom experience of oncology patients. The identification of demographic and clinical characteristics that place patients are risk for a higher symptom burden can be used to guide more aggressive and individualized symptom management interventions.

Influence of traffic characteristics on polycyclic aromatic hydrocarbon build-up on urban road surfaces

Traffic is one of the prominent sources of polycyclic aromatic hydrocarbons (PAHs) and road surfaces are the most critical platform for stormwater pollution. Build-up of pollutants on road surfaces was the focus of this research study. The study found that PAHs build-up on road surfaces primarily originate from traffic activities, specifically gasoline powered vehicles. Other sources such as diesel vehicles, industrial oil combustion and incineration were also found to contribute to the PAH build-up. Additionally, the study explored the linkages between concentrations of PAHs and traffic characteristics such as traffic volume, vehicle mix and traffic flow. While traffic congestion was found to be positively correlated with 6- ring and 5- ring PAHs in road build-up, it was negatively correlated with 3-ring and 4 ring PAHs. The absence of positive correlation between 3-ring and 4-ring PAHs and traffic parameters is attributed to the propensity of these relatively volatile PAHs to undergo re-suspension and evaporation. The outcomes of this study are expected to contribute effective transport and land use planning for the prevention of PAH pollution in the urban environment.

On the use of optical flow for scene change detection and description

We propose the use of optical flow information as a method for detecting and describing changes in the environment, from the perspective of a mobile camera. We analyze the characteristics of the optical flow signal and demonstrate how robust flow vectors can be generated and used for the detection of depth discontinuities and appearance changes at key locations. To successfully achieve this task, a full discussion on camera positioning, distortion compensation, noise filtering, and parameter estimation is presented. We then extract statistical attributes from the flow signal to describe the location of the scene changes. We also employ clustering and dominant shape of vectors to increase the descriptiveness. Once a database of nodes (where a node is a detected scene change) and their corresponding flow features is created, matching can be performed whenever nodes are encountered, such that topological localization can be achieved. We retrieve the most likely node according to the Mahalanobis and Chi-square distances between the current frame and the database. The results illustrate the applicability of the technique for detecting and describing scene changes in diverse lighting conditions, considering indoor and outdoor environments and different robot platforms.

Localization and place recognition using an ultra-wide band (UWB) radar

This paper presents an approach to mobile robot localization, place recognition and loop closure using a monostatic ultra-wide band (UWB) radar system. The UWB radar is a time-of-flight based range measurement sensor that transmits short pulses and receives reflected waves from objects in the environment. The main idea of the poposed localization method is to treat the received waveform as a signature of place. The resulting echo waveform is very complex and highly depends on the position of the sensor with respect to surrounding objects. On the other hand, the sensor receives similar waveforms from the same positions.Moreover, the directional characteristics of dipole antenna is almost omnidirectional. Therefore, we can localize the sensor position to find similar waveform from waveform database. This paper proposes a place recognitionmethod based on waveform matching, presents a number of experiments that illustrate the high positon estimation accuracy of our UWB radar-based localization system, and shows the resulting loop detection performance in a typical indoor office environment and a forest.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Optimizing preventative maintenance strategies for linear assets

Linear assets are engineering infrastructure, such as pipelines, railway lines, and electricity cables, which span long distances and can be divided into different segments. Optimal management of such assets is critical for asset owners as they normally involve significant capital investment. Currently, Time Based Preventive Maintenance (TBPM) strategies are commonly used in industry to improve the reliability of such assets, as they are easy to implement compared with reliability or risk-based preventive maintenance strategies. Linear assets are normally of large scale and thus their preventive maintenance is costly. Their owners and maintainers are always seeking to optimize their TBPM outcomes in terms of minimizing total expected costs over a long term involving multiple maintenance cycles. These costs include repair costs, preventive maintenance costs, and production losses. A TBPM strategy defines when Preventive Maintenance (PM) starts, how frequently the PM is conducted and which segments of a linear asset are operated on in each PM action. A number of factors such as required minimal mission time, customer satisfaction, human resources, and acceptable risk levels need to be considered when planning such a strategy. However, in current practice, TBPM decisions are often made based on decision makers’ expertise or industrial historical practice, and lack a systematic analysis of the effects of these factors. To address this issue, here we investigate the characteristics of TBPM of linear assets, and develop an effective multiple criteria decision making approach for determining an optimal TBPM strategy. We develop a recursive optimization equation which makes it possible to evaluate the effect of different maintenance options for linear assets, such as the best partitioning of the asset into segments and the maintenance cost per segment.

Temporal characteristics of melanopsin inputs to the human pupil light reflex

Rods, cones and melanopsin containing intrinsically photosensitive retinal ganglion cells (ipRGCs) operate in concert to regulate pupil diameter. The temporal properties of intrinsic ipRGC signalling are distinct to those of rods and cones, including longer latencies and sustained signalling after light offset. We examined whether the melanopsin mediated post-illumination pupil response (PIPR) and pupil constriction were dependent upon the inter-stimulus interval (ISI) between successive light pulses and the temporal frequency of sinusoidal light stimuli. Melanopsin excitation was altered by variation of stimulus wavelength (464 nm and 638 nm lights) and irradiance (11.4 and 15.2 log photons cm(-2) s(-1)). We found that 6s PIPR amplitude was independent of ISI and temporal frequency for all melanopsin excitation levels, indicating complete summation. In contrast to the PIPR, the maximum pupil constriction increased with increasing ISI with high and low melanopsin excitation, but time to minimum diameter was slower with high melanopsin excitation only. This melanopsin response to briefly presented pulses (16 and 100 ms) slows the temporal response of the maximum pupil constriction. We also demonstrate that high melanopsin excitation attenuates the phasic peak-trough pupil amplitude compared to conditions with low melanopsin excitation, indicating an interaction between inner and outer retinal inputs to the pupil light reflex. We infer that outer retina summation is important for rapidly controlling pupil diameter in response to short timescale fluctuations in illumination and may occur at two potential sites, one that is presynaptic to extrinsic photoreceptor input to ipRGCs, or another within the pupil control pathway if ipRGCs have differential temporal tuning to extrinsic and intrinsic signalling.

Real-time assessment of GNSS observation noise with single receivers

Stochastic modelling is critical in GNSS data processing. Currently, GNSS data processing commonly relies on the empirical stochastic model which may not reflect the actual data quality or noise characteristics. This paper examines the real-time GNSS observation noise estimation methods enabling to determine the observation variance from single receiver data stream. The methods involve three steps: forming linear combination, handling the ionosphere and ambiguity bias and variance estimation. Two distinguished ways are applied to overcome the ionosphere and ambiguity biases, known as the time differenced method and polynomial prediction method respectively. The real time variance estimation methods are compared with the zero-baseline and short-baseline methods. The proposed method only requires single receiver observation, thus applicable to both differenced and un-differenced data processing modes. However, the methods may be subject to the normal ionosphere conditions and low autocorrelation GNSS receivers. Experimental results also indicate the proposed method can result on more realistic parameter precision.

Epigenetic modelling

The field of epigenetics looks at changes in the chromosomal structure that affect gene expression without altering DNA sequence. A large-scale modelling project to better understand these mechanisms is gaining momentum. Early advances in genetics led to the all-genetic paradigm: phenotype (an organism's characteristics/behaviour) is determined by genotype (its genetic make-up). This was later amended and expressed by the well-known formula P = G + E, encompassing the notion that the visible characteristics of a living organism (the phenotype, P) is a combination of hereditary genetic factors (the genotype, G) and environmental factors (E). However, this method fails to explain why in diseases such as schizophrenia we still observe differences between identical twins. Furthermore, the identification of environmental factors (such as smoking and air quality for lung cancer) is relatively rare. The formula also fails to explain cell differentiation from a single fertilized cell. In the wake of early work by Waddington, more recent results have emphasized that the expression of the genotype can be altered without any change in the DNA sequence. This phenomenon has been tagged as epigenetics. To form the chromosome, DNA strands roll over nucleosomes, which are a cluster of nine proteins (histones), as detailed in Figure 1. Epigenetic mechanisms involve inherited alterations in these two structures, eg through attachment of a functional group to the amino acids (methyl, acetyl and phosphate). These 'stable alterations' arise during development and cell proliferation and persist through cell division. While information within the genetic material is not changed, instructions for its assembly and interpretation may be. Modelling this new paradigm, P = G + E + EpiG, is the object of our study.

Folk conceptions of humanness: Beliefs about distinctive and core human characteristics in Australia, Italy, and China

The present research explores cultural understandings of what it means to be human. We used open-ended responses to examine whether the most culturally salient aspects of humanness are captured by two theoretical dimensions: human uniqueness (HU) and human nature (HN). Australians, Italians, and Chinese (N = 315) showed differences in the characteristics considered human and in the emphasis placed on HU and HN. These findings contribute to developing cross-cultural folk psychological models of humanness.

Characterisation of the cascode gate drive of power MOSFETs in clamped inductive switching applications

This thesis proposes a novel gate drive circuit to improve the switching performance of MOSFET power switches in power electronic converters. The proposed topology exploits the cascode configuration, allowing the minimisation of switching losses in the presence of practical circuit constraints, which enables efficiency and power density improvements. Switching characteristics of the new topology are investigated and key mechanisms that control the switching process are identified. Unique analysis tools and techniques are also developed to demonstrate the application of the cascode gate drive circuit for switching performance optimisation.

The implicit structure of positive characteristics

The implicit structure of positive character traits was examined in two studies of 190 and 100 undergraduates. Participants judged the pairwise covariation or semantic similarity of 42 positive characteristics using a sorting or a rating task. Characteristics were drawn from a new classification of strengths and virtues, the Five-Factor Model, and a taxonomy of values. Participants showed consistent patterns of perceived association among the characteristics across the study conditions. Multidimensional scaling yielded three consistent dimensions underlying these judgments (“warmth vs. self-control,” “vivacity vs. decency,” and “wisdom vs. power”). Cluster analyses yielded six consistent groupings—“self-control,” “love,” “wisdom,” “drive,” “vivacity,” and “collaboration”—that corresponded only moderately to the virtue classification. All three taxonomies were systematically related to this implicit structure, but none captured it satisfactorily on its own. Revisions to positive psychology’s classification of strengths are proposed.

Impact of real-time traffic characteristics on freeway crash occurrence: Systematic review and meta-analysis

The development of methods for real-time crash prediction as a function of current or recent traffic and roadway conditions is gaining increasing attention in the literature. Numerous studies have modeled the relationships between traffic characteristics and crash occurrence, and significant progress has been made. Given the accumulated evidence on this topic and the lack of an articulate summary of research status, challenges, and opportunities, there is an urgent need to scientifically review these studies and to synthesize the existing state-of-the-art knowledge. This paper addresses this need by undertaking a systematic literature review to identify current knowledge, challenges, and opportunities, and then conducts a meta-analysis of existing studies to provide a summary impact of traffic characteristics on crash occurrence. Sensitivity analyses were conducted to assess quality, publication bias, and outlier bias of the various studies; and the time intervals used to measure traffic characteristics were also considered. As a result of this comprehensive and systematic review, issues in study designs, traffic and crash data, and model development and validation are discussed. Outcomes of this study are intended to provide researchers focused on real-time crash prediction with greater insight into the modeling of this important but extremely challenging safety issue.

Spatio-temporal characteristics of locomotion and functional outcomes of individuals with transfemoral amputation fitted with OPRA fixation

The conventional method of attachment of prosthesis involves a socket. A new method relying on osseointegrated fixation has emerged in the last decades. It has significant prosthetic benefits. Only a few studies demonstrated the biomechanical benefits. The ultimate aim of this study was to characterise the functional outcome of individuals with lower limb amputation fitted with osseointegrated fixation, which can be assess through temporal and spatial gait characteristics. The specific objective of this study was to present the key temporal and spatial gait characteristics of individuals with transfemoral amputation (TFA).