A Bayesian nonparametric model for hierarchical sequence of images

 Scale features are useful for a great number of applications in computer vision. However, it is difficult to tolerate diversities of features in natural scenes by parametric methods. Empirical studies show that object frequencies and segment sizes follow the power law distributions which are well generated by Pitman-Yor (PY) processes. Based on mid-level segments, we propose a hierarchical sequence of images to obtain scale information stored in a hierarchical structure through the hierarchical Pitman-Yor (HPY) model which is expected to tolerate uncertainty of natural images. We also evaluate our representation by the application of segmentation.

Regularizing topic discovery in emrs with side information by using hierarchical bayesian models

We propose a novel hierarchical Bayesian framework, word-distance-dependent Chinese restaurant franchise (wd-dCRF) for topic discovery from a document corpus regularized by side information in the form of word-to-word relations, with an application on Electronic Medical Records (EMRs). Typically, a EMRs dataset consists of several patients (documents) and each patient contains many diagnosis codes (words). We exploit the side information available in the form of a semantic tree structure among the diagnosis codes for semantically-coherent disease topic discovery. We introduce novel functions to compute word-to-word distances when side information is available in the form of tree structures. We derive an efficient inference method for the wddCRF using MCMC technique. We evaluate on a real world medical dataset consisting of about 1000 patients with PolyVascular disease. Compared with the popular topic analysis tool, hierarchical Dirichlet process (HDP), our model discovers topics which are superior in terms of both qualitative and quantitative measures.

Scalable hierarchical rings based routing to a mobile robot in wireless sensor networks

Location service provides location information of robots to sensors, to enable event reporting. Existing protocols apply partial flooding to trace robots, leading to poor scalability. We propose a novel scalable location service, which applies hierarchical rings to update robot location and guide routing toward it. Each mobile robot creates a set of hierarchical update rings of doubling radii. Whenever the robot leaves its k-th ring, it updates its new location to sensors along its newly defined k-th ring, and re-defines all smaller rings for future decisions. When a sensor needs to route to the mobile robot, it starts searching from its smallest ring and sends location query to the sensors along the ring. If the query fails, the search then extends to the next larger ring, until it intersects an existing update ring, from which the search can be directed towards reported center. The location of destination is updated whenever another more recent ring is intersected. Our scheme guarantees message delivery if robot remains connected to sensors during its move. The theoretical analysis and simulation results demonstrate better scalability than previous protocols for the similar goal. © 2014 IEEE.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Bayesian hierarchical modeling of the temporal dynamics of subjective well-being: A 10 year longitudinal analysis

This study demonstrates, for the first time, how Bayesian hierarchical modeling can be applied to yield novel insights into the long-term temporal dynamics of subjective well-being (SWB). Several models were proposed and examined using Bayesian methods. The models were assessed using a sample of Australian adults (. n=. 1081) who provided annual SWB scores on between 5 and 10 occasions. The best fitting models involved a probit transformation, allowed error variance to vary across participants, and did not include a lag parameter. Including a random linear and quadratic effect resulted in only a small improvement over the intercept only model. Examination of individual-level fits suggested that most participants were stable with a small subset exhibiting patterns of systematic change.

Development of hierarchical porous NiO-YSZ with improved mechanical properties

 The thesis developed an hierarchical porous NiO/YSZ with high mechanical performance using a novel process. This process fabricates initial scaffolds with a controllable porosity by enhancing the surface energy of poly methyl methacrylate (PMMA) for the assembly of NiO-YSZ/PMMA. It maintains the hierarchical porous structure using two-step sintering (TSS) to restrict the growth of nanoparticles, and improves the mechanical properties in combination with a bimodal distribution of NiO/YSZ nano-particles.

Hierarchical analysis of avian re-nesting behavior: mean, across-individual, and intra-individual responses

© 2015, Springer-Verlag Berlin Heidelberg. Anti-predator behavior is a key aspect of life history evolution, usually studied at the population (mean), or across-individual levels. However individuals can also differ in their intra-individual (residual) variation, but to our knowledge, this has only been studied once before in free-living animals. Here we studied the distances moved and changes in nest height and concealment between successive nesting attempts of marked pairs of grey fantails (Rhipidura albiscapa) in relation to nest fate, across the breeding season. We predicted that females (gender that decides where the nest is placed) should on average show adaptive behavioral responses to the experience of prior predation risk such that after an unsuccessful nesting attempt, replacement nests should be further away, higher from the ground, and more concealed compared with replacement nests after successful nesting attempts. We found that, on average, females moved greater distances to re-nest after unsuccessful nesting attempts (abandoned or depredated) in contrast to after a successful attempt, suggesting that re-nesting decisions are sensitive to risk. We found no consistent across-individual differences in distances moved, heights, or concealment. However, females differed by 53-fold (or more) in their intra-individual variability (i.e., predictability) with respect to distances moved and changes in nest height between nesting attempts, indicating that either some systematic variation went unexplained and/or females have inherently different predictability. Ignoring these individual differences in residual variance in our models obscured the effect of nest fate on re-nesting decisions that were evident at the mean level.

Hierarchical dirichlet process for tracking complex topical structure evolution and its application to autism research literature

In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension. In contrast to previous work our model does not impose a prior on the rate at which documents are added to the corpus nor does it adopt the Markovian assumption which overly restricts the type of changes that the model can capture. Our key technical contribution is a framework based on (i) discretization of time into epochs, (ii) epoch-wise topic discovery using a hierarchical Dirichlet process-based model, and (iii) a temporal similarity graph which allows for the modelling of complex topic changes: emergence and disappearance, evolution, splitting and merging. The power of the proposed framework is demonstrated on the medical literature corpus concerned with the autism spectrum disorder (ASD) - an increasingly important research subject of significant social and healthcare importance. In addition to the collected ASD literature corpus which we made freely available, our contributions also include two free online tools we built as aids to ASD researchers. These can be used for semantically meaningful navigation and searching, as well as knowledge discovery from this large and rapidly growing corpus of literature.

Hierarchical Bayesian nonparametric models for knowledge discovery from electronic medical records

Electronic Medical Record (EMR) has established itself as a valuable resource for large scale analysis of health data. A hospital EMR dataset typically consists of medical records of hospitalized patients. A medical record contains diagnostic information (diagnosis codes), procedures performed (procedure codes) and admission details. Traditional topic models, such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet process (HDP), can be employed to discover disease topics from EMR data by treating patients as documents and diagnosis codes as words. This topic modeling helps to understand the constitution of patient diseases and offers a tool for better planning of treatment. In this paper, we propose a novel and flexible hierarchical Bayesian nonparametric model, the word distance dependent Chinese restaurant franchise (wddCRF), which incorporates word-to-word distances to discover semantically-coherent disease topics. We are motivated by the fact that diagnosis codes are connected in the form of ICD-10 tree structure which presents semantic relationships between codes. We exploit a decay function to incorporate distances between words at the bottom level of wddCRF. Efficient inference is derived for the wddCRF by using MCMC technique. Furthermore, since procedure codes are often correlated with diagnosis codes, we develop the correspondence wddCRF (Corr-wddCRF) to explore conditional relationships of procedure codes for a given disease pattern. Efficient collapsed Gibbs sampling is derived for the Corr-wddCRF. We evaluate the proposed models on two real-world medical datasets - PolyVascular disease and Acute Myocardial Infarction disease. We demonstrate that the Corr-wddCRF model discovers more coherent topics than the Corr-HDP. We also use disease topic proportions as new features and show that using features from the Corr-wddCRF outperforms the baselines on 14-days readmission prediction. Beside these, the prediction for procedure codes based on the Corr-wddCRF also shows considerable accuracy.

Does mortality vary between Asian subgroups in New Zealand: an application of hierarchical Bayesian modelling

The aim of this paper was to see whether all-cause and cause-specific mortality rates vary between Asian ethnic subgroups, and whether overseas born Asian subgroup mortality rate ratios varied by nativity and duration of residence. We used hierarchical Bayesian methods to allow for sparse data in the analysis of linked census-mortality data for 25-75 year old New Zealanders. We found directly standardised posterior all-cause and cardiovascular mortality rates were highest for the Indian ethnic group, significantly so when compared with those of Chinese ethnicity. In contrast, cancer mortality rates were lowest for ethnic Indians. Asian overseas born subgroups have about 70% of the mortality rate of their New Zealand born Asian counterparts, a result that showed little variation by Asian subgroup or cause of death. Within the overseas born population, all-cause mortality rates for migrants living 0-9 years in New Zealand were about 60% of the mortality rate of those living more than 25 years in New Zealand regardless of ethnicity. The corresponding figure for cardiovascular mortality rates was 50%. However, while Chinese cancer mortality rates increased with duration of residence, Indian and Other Asian cancer mortality rates did not. Future research on the mechanisms of worsening of health with increased time spent in the host country is required to improve the understanding of the process, and would assist the policy-makers and health planners.

The role of fear of self and responsibility in obsessional doubt processes: a bayesian hierarchical model

The literature concerning obsessive-compulsive disorder (OCD) indicates that obsessions frequently imply negative evaluative beliefs regarding the self. The construct of the feared self has been used to describe the set of harmful attributes an individual worries they may possess. This study aimed to partially replicate previous research that demonstrated a relationship between feared-self beliefs and obsessional doubt in OCD-relevant contexts. The relationship between perceptions of personal responsibility and associated levels of doubt was also examined. Nonclinical participants (N = 221; 155 female; Mage = 26.4, SD = 9.2) were presented with vignettes related to checking and non OCD-relevant themes, which quantified doubt through the presentation of alternating reality-based (i.e., sensory) and possibility-based information. Of the total sample, 112 participants were randomly allocated to a personally relevant condition (in which the action implied in the vignettes was completed by the reader), and 109 were allocated to a second, other-relevant, condition (in which the action implied in the vignettes was completed by a proximal other). The results provided support for reasoning processes implicated in OCD, suggesting that feared-self beliefs may partially contribute to heightened levels of doubt in response to possibility vs. reality-based information in OCD-relevant contexts. Personal relevance contributed to greater baseline levels of doubt, but not to greater responses to the reality- and possibility-based statements accompanying the OCD-relevant vignette. Implications for theory and future research are discussed.

Saliency detection using hierarchical manifold learning

Saliency detection is critical to many applications in computer vision by eliminating redundant backgrounds. The saliency detection approaches can be divided into two categories, i.e., top-down and bottom-up. Among them, bottom-up models have attracted more attention due to their simple mechanisms. However, many existing bottom-up models are not robust to crowded backgrounds because of missing salient regions within feedforward frameworks which is often not effective for complex scenes. We tackle these problems by modifying and extending a bottom-up saliency detection model through three phases, (1) constructing a hierarchical sequence of images from the perspective of entropy, (2) estimated mid-level cues are used as feedback information, (3) subsequently generating saliency maps by global context and local uniqueness in a graph-based framework. We also compare the proposed bottom-up model with state-of-the-art approaches on two benchmark datasets to evaluate its saliency detection performance. The experimental results demonstrate that the proposed bottom-up saliency detection approach is not only robust to both cluttered and clean scenes, but also able to obtain objects with different scales.

Condition assessment of timber utility poles based on a hierarchical data fusion model

This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyzes stress wave data from multisensor and multiexcitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition, and decision fusion algorithms. The researchers validate the proposed technique using guided wave tests of a sample of in situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT), and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimize information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the Dempster–Shafer (D-S) evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles, demonstrating the effectiveness of the new method.

Control and performance in IS projects: a meta-analysis of hierarchical and market-based control relationships

Literature on IS project control distinguishes between hierarchical and market-based control relationships. Prior studies typically investigate one of these two forms of control relationships in isolation. Hence, little is known about the differences between hierarchical and market-based control relationships. Responding to this gap, we analyze how the effects of control modes on IS project performance differ in hierarchical compared with market-based control relationships. Specifically, we conduct a metaanalysis to compare the effects of control modes on IS project performance reported in research on hierarchical and market-based control relationships. The results suggest that the effects of behavior and self-control on performance differ between these two forms of control relationships. Based on our results, we derive implications for complementary and substitutive effects between control modes, and for interrelations among hierarchical and market-based control relationships.