An International Comparison of Health Care Expenditure Determinants

In this paper, we estimate a health care demand function for 18 OECD countries for the period 1972-1995. We consider a demand side approach where health expenditure depend on per capita GDP and the relative price of health care. We use panel data unit root and stationarity tests to characterize our data. Then, we test cointegration between our variables with Kao[16] panel data cointegration tests. As we accept cointegration, we compare different estimators (OLS, FMOLS, DOLS). Results give conflicting evidence for the value of health expenditure income elasticity. The least biased estimator gives a value that exceeds unity.Cointegration, Health Expenditure, OECD, Panel test

DISTORTION-BASED HEURISTIC METHOD FOR SENSITIVE ASSOCIATION RULE HIDING

In the past few years, privacy issues in data mining have received considerable attention in the data mining literature. However, the problem of data security cannot simply be solved by restricting data collection or against unauthorized access, it should be dealt with by providing solutions that  not only protect sensitive information, but also not affect to the accuracy of the results in data mining and not violate the sensitive knowledge related with individual privacy or competitive advantage in businesses. Sensitive association rule hiding is an important issue in privacy preserving data mining. The aim of association rule hiding is to minimize the side effects on the sanitized database, which means to reduce the number of missing non-sensitive rules and the number of generated ghost rules. Current methods for hiding sensitive rules cause side effects and data loss. In this paper, we introduce a new distortion-based method to hide sensitive rules. This method proposes the determination of critical transactions based on the number of non-sensitive maximal frequent itemsets that contain at least one item to the consequent of the sensitive rule, they can be directly affected by the modified transactions. Using this set, the number of non-sensitive itemsets that need to be considered is reduced dramatically. We compute the smallest number of transactions for modification in advance to minimize the damage to the database. Comparative experimental results on real datasets showed that the proposed method can achieve better results than other methods with fewer side effects and data loss

Improving Efficiency of Incremental Mining by Trie Structure and Pre-Large Itemsets

Incremental data mining has been discussed widely in recent years, as it has many practical applications, and various incremental mining algorithms have been proposed. Hong et al. proposed an efficient incremental mining algorithm for handling newly inserted transactions by using the concept of pre-large itemsets. The algorithm aimed to reduce the need to rescan the original database and also cut maintenance costs. Recently, Lin et al. proposed the Pre-FUFP algorithm to handle new transactions more efficiently, and make it easier to update the FP-tree. However, frequent itemsets must be mined from the FP-growth algorithm. In this paper, we propose a Pre-FUT algorithm (Fast-Update algorithm using the Trie data structure and the concept of pre-large itemsets), which not only builds and updates the trie structure when new transactions are inserted, but also mines all the frequent itemsets easily from the tree. Experimental results show the good performance of the proposed algorithm

Unsupervised naming of speakers in broadcast TV: using written names, pronounced names or both ?

International audiencePersons identification in video from TV broadcast is a valuable tool for indexing them. However, the use of biometric mod- els is not a very sustainable option without a priori knowledge of people present in the videos. The pronounced names (PN) or written names (WN) on the screen can provide hypotheses names for speakers. We propose an experimental comparison of the potential of these two modalities (names pronounced or written) to extract the true names of the speakers. The names pronounced offer many instances of citation but transcription and named-entity detection errors halved the potential of this modality. On the contrary, the written names detection benefits of the video quality improvement and is nowadays rather robust and efficient to name speakers. Oracle experiments presented for the mapping between written names and speakers also show the complementarity of both PN and WN modalities

HUPSMT: AN EFFICIENT ALGORITHM FOR MINING HIGH UTILITY-PROBABILITY SEQUENCES IN UNCERTAIN DATABASES WITH MULTIPLE MINIMUM UTILITY THRESHOLDS

The problem of high utility sequence mining (HUSM) in quantitative se-quence databases (QSDBs) is more general than that of frequent sequence mining in se-quence databases. An important limitation of HUSM is that a user-predened minimum tility threshold is used commonly to decide if a sequence is high utility. However, this is not convincing in many real-life applications as sequences may have diferent importance. Another limitation of HUSM is that data in QSDBs are assumed to be precise. But in the real world, collected data such as by sensor maybe uncertain. Thus, this paper proposes a framework for mining high utility-probability sequences (HUPSs) in uncertain QSDBs (UQS-DBs) with multiple minimum utility thresholds using a minimum utility. Two new width and depth pruning strategies are also introduced to early eliminate low utility or low probability sequences as well as their extensions, and to reduce sets of candidate items for extensions during the mining process. Based on these strategies, a novel ecient algorithm named HUPSMT is designed for discovering HUPSs. Finally, an experimental study conducted in both real-life and synthetic UQSDBs shows the performance of HUPSMT in terms of time and memory consumption

Can ChatGPT pass the Vietnamese National High School Graduation Examination?

This research article highlights the potential of AI-powered chatbots in education and presents the results of using ChatGPT, a large language model, to complete the Vietnamese National High School Graduation Examination (VNHSGE). The study dataset included 30 essays in the literature test case and 1,700 multiple-choice questions designed for other subjects. The results showed that ChatGPT was able to pass the examination with an average score of 6-7, demonstrating the technology's potential to revolutionize the educational landscape. The analysis of ChatGPT performance revealed its proficiency in a range of subjects, including mathematics, English, physics, chemistry, biology, history, geography, civic education, and literature, which suggests its potential to provide effective support for learners. However, further research is needed to assess ChatGPT performance on more complex exam questions and its potential to support learners in different contexts. As technology continues to evolve and improve, we can expect to see the use of AI tools like ChatGPT become increasingly common in educational settings, ultimately enhancing the educational experience for both students and educators.Comment: 9 pages, 13 figures, 4 table

TGFβR-SMAD3 signaling induces resistance to PARP inhibitors in the bone marrow microenvironment

Synthetic lethality triggered by PARP inhibitor (PARPi) yields promising therapeutic results. Unfortunately, tumor cells acquire PARPi resistance, which is usually associated with the restoration of homologous recombination, loss of PARP1 expression, and/or loss of DNA double-strand break (DSB) end resection regulation. Here, we identify a constitutive mechanism of resistance to PARPi. We report that the bone marrow microenvironment (BMM) facilitates DSB repair activity in leukemia cells to protect them against PARPi-mediated synthetic lethality. This effect depends on the hypoxia-induced overexpression of transforming growth factor beta receptor (TGFβR) kinase on malignant cells, which is activated by bone marrow stromal cells-derived transforming growth factor beta 1 (TGF-β1). Genetic and/or pharmacological targeting of the TGF-β1-TGFβR kinase axis results in the restoration of the sensitivity of malignant cells to PARPi in BMM and prolongs the survival of leukemia-bearing mice. Our finding may lead to the therapeutic application of the TGFβR inhibitor in patients receiving PARPis