Mining high utility itemsets in massive transactional datasets

Mining High Utility Itemsets from a transaction database is to find itemsets that have utility beyond an user-specified threshold. Existing High Utility Itemsets mining algorithms suffer from many problems when being applied to massive transactional datasets. One major problem is the high memory dependency: the gigantic data structure built is assumed to fit in the computer main memory. This paper proposes a new disk-based High Utility Itemsets mining algorithm, which achieves its efficiency by applying three new ideas. First, transactional data is converted into a new database layout called Transactional Array that prevents multiple scanning of the database during the mining phase. Second, for each frequent item, a relatively small independent tree is built for summarizing co-occurrences. Finally, a simple and non-recursive mining process reduces the memory requirements as minimum candidacy generation and counting is needed. We have tested our algorithm on several very large transactional databases and the results show that our algorithm works efficiently

Unrolling of Graph Total Variation for Image Denoising

While deep learning have enabled effective solutions in image denoising, in general their implementations overly rely on training data and require tuning of a large parameter set. In this thesis, a hybrid design that combines graph signal filtering with feature learning is proposed. It utilizes interpretable analytical low-pass graph filters and employs 80\% fewer parameters than a state-of-the-art DL denoising scheme called DnCNN. Specifically, to construct a graph for graph spectral filtering, a CNN is used to learn features per pixel, then feature distances are computed to establish edge weights. Given a constructed graph, a convex optimization problem for denoising using a graph total variation prior is formulated. Its solution is interpreted in an iterative procedure as a graph low-pass filter with an analytical frequency response. For fast implementation, this response is realized by Lanczos approximation. This method outperformed DnCNN by up to 3dB in PSNR in statistical mistmatch case

Sex differences in the outcome of very low birth weight premature infants born in a regional Australian Neonatal Intensive Care Unit

Background: Advancements in neonatal care have improved survival for premature and very low birth weight (VLBW) infants. Despite this, differences have been reported when comparing males and females. While the previously described concept of the "male disadvantage" asserts that there is a higher risk of mortality and morbidity for male infants, many studies have also found no sex differences in outcomes. Aim: The objective of this study is to determine if the sex of VLBW premature infants is associated with survival and neurodevelopmental outcome in a regional Australian Neonatal Intensive Care Unit (NICU). Methods: A retrospective cohort study was conducted for infants born at < 37 weeks gestation with VLBW (< 1,500 g) admitted to The Townsville Hospital NICU between 2010 and 2015. Comparisons for survival and neurodevelopment between males and females were made with Chi-square, Fisher's exact test and the Independent t-test. Multivariate logistic regression analysis was performed for the outcomes of death before NICU discharge and developmental delay assessed by the Bayley Scales of Infant and Toddler Development, the 3rd Edition. Results: Data were collected for 430 infants. Fifty-three infants died before NICU discharge, with no sex difference in survival. Follow-up assessment was completed for 84 infants from the original cohort and demonstrated no sex differences in neurodevelopmental outcome. Male infants had a significantly higher prevalence of chronic lung disease (p = 0.009). Neither the logistic regression model for death by NICU discharge nor for neurodevelopmental delay identified sex as a significant predictor of outcome. Conclusions: Male and female VLBW premature infants did not differ in survival or neurodevelopmental outcome at this center

Estimation of Above-Ground Mangrove Biomass Using Landsat-8 Data- Derived Vegetation Indices: A Case Study in Quang Ninh Province, Vietnam

This study aimed to map the status of mangrove forests over the coasts of Hai Ha District and Mong Cai City in Quang Ninh Province by using 2019 Landsat-8 imagery. It then developed the AGB estimation model of mangrove forests based on the AGB estimation-derived plots inventory and vegetation indices-derived from Landsat-8 data. As results, there were five land covers identified, including mangrove forests, other vegetation, wetlands, built-up, and water, with the overall accuracy assessments of 80.0% and Kappa coefficient of 0.74. The total extent of mangrove forests was estimated at 4291.2 ha. The best AGB estimation model that was selected to estimate the AGB and AGC of mangrove forests for the whole coasts of Hai Ha District and Mong Cai City is AGB= 30.38 + 911.95*SAVI (R2=0.924, PValue &lt;0.001). The model validation assessment has confirmed that the selected AGB model can be applied to Hai Ha and Mong Cai coasts with the mean difference between AGB observed and AGB predicted at 16.0 %. This satisfactory AGB model also suggests a good potential for AGB and AGC mapping, which offer the carbon trading market in the study site. As the AGB model selected, the total AGB and AGC of mangrove forests were estimated at about 14,600,000 tons and 6,868,076 tons with a range of from 94.0 - 432.0 tons ha-1, from 44.2 - 203.02 tons ha-1, respectively. It also suggests that the newly-developed AGB model of mangrove forests can be used to estimate AGC stocks and carbon sequestration of mangrove forests for C-PFES in over the coasts of Hai Ha District and Mong Cai City, which is a very importantly financial source for mangrove forest managers, in particular for local mangrove protectors

Nonlinear post-buckling of thin FGM annular spherical shells under mechanical loads and resting on elastic foundations

This paper presents an analytical approach to investigate the nonlinear buckling and post-buckling of thin annular spherical shells made of functionally graded materials (FGM) and subjected to mechanical load and resting on Winkler-Pasternak type elastic foundations. Material properties are graded in the thickness direction according to a simple power law distribution in terms of the volume fractions of constituents. Equilibrium and compatibility equations for annular spherical shells are derived by using the classical thin shell theory in terms of the shell deflection and the stress function. Approximate analytical solutions are assumed to satisfy simply supported boundary conditions and Galerkin method is applied to obtain closed-form of load-deflection paths. An analysis is carried out to show the effects of material and geometrical properties and combination of loads on the stability of the annular spherical shells

MINING TOP-K FREQUENT SEQUENTIAL PATTERN IN ITEM INTERVAL EXTENDED SEQUENCE DATABASE

Abstract. Frequent sequential pattern mining in item interval extended sequence database (iSDB) has been one of interesting task in recent years. Unlike classic frequent sequential pattern mining, the pattern mining in iSDB also consider the item interval between successive items; thus, it may extract more meaningful sequential patterns in real life. Most previous frequent sequential pattern mining in iSDB algorithms needs a minimum support threshold (minsup) to perform the mining. However, it’s not easy for users to provide an appropriate threshold in practice. The too high minsup value will lead to missing valuable patterns, while the too low minsup value may generate too many useless patterns. To address this problem, we propose an algorithm: TopKWFP – Top-k weighted frequent sequential pattern mining in item interval extended sequence database. Our algorithm doesn’t need to provide a fixed minsup value, this minsup value will dynamically raise during the mining proces

Job Satisfaction Amongst Accountants: The Case of Accounting Service Firms in Hanoi

This study is conducted for measuring the job satisfaction amongst accountants in the accounting service firms in Hanoi. The study has also performed some descriptive analysis, Cronbach's Alpha and Independent T-test for evaluating and measuring the Job satisfaction amongst accountants. The results show that the Job satisfaction amongst accountants achieved an average of 3.683/5. The study does not find significant differences on evaluation of the Job satisfaction amongst accountants in terms of gender and age. Keywords: Job satisfaction amongst, accountants, accounting service firms JEL code: M41, O15 DOI: 10.7176/RJFA/10-18-16 Publication date:September 30th 201

On asymptotic periodic solutions of fractional differential equations and applications

In this paper we study the asymptotic behavior of solutions of fractional differential equations of the form $D^{\alpha}_Cu(t)=Au(t)+f(t), u(0)=x, 0<\alpha\le1, ( *)$ where $D^{\alpha}_Cu(t)$ is the derivative of the function $u$ in the Caputo's sense, $A$ is a linear operator in a Banach space \X that may be unbounded and $f$ satisfies the property that $\lim_{t\to \infty} (f(t+1)-f(t))=0$ which we will call asymptotic $1$-periodicity. By using the spectral theory of functions on the half line we derive analogs of Katznelson-Tzafriri and Massera Theorems. Namely, we give sufficient conditions in terms of spectral properties of the operator $A$ for all asymptotic mild solutions of Eq. (*) to be asymptotic $1$-periodic, or there exists an asymptotic mild solution that is asymptotic $1$-periodic.Comment: 13 pages. arXiv admin note: text overlap with arXiv:1910.0860