Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral methods for solving nonlinear Schrödinger equations with singular defect terms

Singular source terms play an important role in various nonlinear PDEs such as the nonlinear Schrödinger equation, the sine--Gordon equation, and the forced Korteweg--de Vries equation [1, 5, 6]. It is well known that the presence of such singular source terms yields the critical phenomenon of the solution to those PDEs [3, 7, 9]. e singular source term in the equation mimics the local defect. e defect is highly localized and is represented mathematically as a Dirac delta function that can be understood in the distribution sense. High-order approximation, such as the spectral approximation, of the solution with the singular source term is challenging. Moreover, if the singular source term is sensitive to the associated parameter values due to its locality and those values are stochastic, the highorder approximation needs a special treatment particularly to reduce the computational complexity. In this talk, we will explain several developments that deal with the PDEs perturbed by the singular source terms including the consistent method and the generalized polynomial chaos (gPC) method. e consistent method is based on the Schwartz duality of the delta distribution and uses the consistent derivative operator to the numerical scheme. e consistent method provides the cancellation of higher-order error terms and improves the convergence order [4, 8]. We also use the gPCmethod [10] for the nonlinear Schrödinger equation under the assumption that the singular source term is parameterized by the random variables. For the solution in the random space, we adopt the orthogonal polynomials associated with the distribution that construct the random space. We, then, seek a numerical solution in the polynomial space by the Galerkin procedure. Using the gPC method, we show that we can find the critical values efficiently and determine the statistical quantities with high accuracy [2].
A Savvy Robot Standup Comic: Online Learning through Audience Tracking

In this paper, we propose Robot Theater as novel framework to develop and evaluate the interaction capabilities of embodied machines. By using onlinelearning algorithms to match the machine‟s actions to dynamic target environments, we hope to develop an extensible system of social intelligence. Specifically, we describe an early performance robot that caters its joke selection, animation level, and interactivity to a particular audience based on real-time audio-visual tracking. Learning from human signals, the robot will generate performance sequences on the fly.
DE TRAVAIL 2007-024 Access to Primary Schools and House Values : Testing the Spatial Stationarity of Hedonic Prices

Since the mid 1970s, the impact of nearby schools on property values has been a major theme in the North-American literature, with most studies focusing on the influence of school quality. To a lesser extent, the impact proximity to school exerts on house prices has also been investigated together with the way the urban environment and distances to school affect student modal choices. Using the hedonic approach, this paper aims at assessing the price effect ensuing from proximity of, and accessibility to, the nearest primary school based on a sample of 8,285 single-family houses sold over the 1993-1996 period in Quebec City, Canada. While home-to-school walking distances are computed for properties located within 1.6 kilometre (one mile) – a distance beyond which a school bus service is provided -, car travel times measured on the 1994 GIS-operated topological street network are used for houses located further away from school. Main property features as well as neighbourhood and household descriptors are used as control variables in the model, with four urbanization patterns being distinguished on the grounds of residential densities. A major objective of this study is to test the spatial stationarity of hedonic prices with respect to primary schools. In order to do so, the OLS method and the geographically weighted regression (GWR) method are compared. Each method is assessed in regard to its ability to account for, and deal with, the presence of spatial autocorrelation in the residuals while bringing out spatial drifts in household preferences.
Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records

Unstructured information in electronic health records provide an invaluable resource for medical research. To protect the confidentiality of patients and to conform to privacy regulations, de-identification methods automatically remove personally identifying information from these medical records. However, due to the unavailability of labeled data, most existing research is constrained to English medical text and little is known about the generalizability of de-identification methods across languages and domains. In this study, we construct a varied dataset consisting of the medical records of 1260 patients by sampling data from 9 institutes and three domains of Dutch healthcare. We test the generalizability of three de-identification methods across languages and domains. Our experiments show that an existing rule-based method specifically developed for the Dutch language fails to generalize to this new data. Furthermore, a state-of-the-art neural architecture performs strongly across languages and domains, even with limited training data. Compared to feature-based and rule-based methods the neural method requires significantly less configuration effort and domain-knowledge. We make all code and pre-trained de-identification models available to the research community, allowing practitioners to apply them to their datasets and to enable future benchmarks.
The Macroeconomic Effects of Fiscal Policy Shock in Ethiopia: Evidence from a Bayesian VAR Approach

This paper investigated the macroeconomic effects of fiscal policy shocks in Ethiopia using a Bayesian Vector Auto Regression model. We examined the dynamic responses of output, inflation, interest rate and exchange rate to fiscal policy shocks employing quarterly data from 2000/01Q1 to 2015/16Q4. The empirical evidence suggests that government spending shock had a positive impact on output and inflation but effect was too small. Initially the interest rate responded negatively to government spending shocks and was positive with small effect and the nominal exchange rate showed deterioration. In addition, government revenue shocks had positive effect real GDP and exchange rate and then they responded negatively. The inflation response to the net tax was medium and negative whereas its effect on interest rate was positive, and persistent. Furthermore, positive shocks to recurrent expenditure had a persistent positive impact on real output. Recurrent expenditure appeared to be responsible for inflationary pressure. Interest rate picked up slightly as a result of recurrent spending shocks in the short run. The response of exchange rate recurrent expenditure was small and remained negative. In contrast, capital expenditure was found to have an insignificant effect on output. The reasons could the administrative lag and contractual bottleneck that are sometimes involved executing capital projects and that appeared to be responsible for inflationary pressure. In the short term, the interest rate responded negatively and the estimated impact on exchange rate was insignificant. Following indirect tax revenue shocks rise in output and inflation was very persistent. Regarding the effects of indirect taxes on interest rate and exchange rate our results show a clear and negative impact whereas direct taxes were found to affect output and inflation very little and were insignificant. Initially, interest and exchange rates responded positively to direct shock later interest rate and exchange rate become insignificant and negative respectively. The results support the idea of a ‘crowding-in’ effect and when we take into account the feedback from government debt, the results suggest that the effects fiscal shocks on the majority of macro variables is too small except for the real GDP for government revenue shock. Therefore, empirical evidence shows that it important to consider government debt dynamics in the model. Key words : Ethiopia, Fiscal policy shock, public debt, Bayesian VAR JEL Code: E44, E62, C12, C32
Resubmission of SWJ # 956-2167 , Ripple Down Rules for Question Answering

Recent years have witnessed a new trend of building ontology-based question answering systems. These systems use semantic web information to produce more precise answers to users’ queries. However, these systems are mostly designed for English. In this paper, we introduce an ontology-based question answering system named KbQAS which, to the best of our knowledge, is the first one made for Vietnamese. KbQAS employs our question analysis approach that systematically constructs a knowledge base of grammar rules to convert each input question into an intermediate representation element. KbQAS then takes the intermediate representation element with respect to a target ontology and applies concept-matching techniques to return an answer. On a wide range of Vietnamese questions, experimental results show that the performance of KbQAS is promising with accuracies of 84.1% and 82.4% for analyzing input questions and retrieving output answers, respectively. Furthermore, our question analysis approach can easily be applied to new domains and new languages, thus saving time and human effort.
C OLLABORATIVE T RAINING OF B ALANCED R ANDOM F ORESTS FOR O PEN S ET D OMAIN A DAPTATION

In this paper, we introduce a collaborative training algorithm of balanced random forests for domain adaptation tasks which can avoid the overfitting problem. In real scenarios, most domain adaptation algorithms face the challenges from noisy, insufficient training data. Moreover in open set categorization, unknown or misaligned source and target categories adds difficulty. In such cases, conventional methods suffer from overfitting and fail to successfully transfer the knowledge of the source to the target domain. To address these issues, the following two techniques are proposed. First, we introduce the optimized decision tree construction method, in which the data at each node are split into equal sizes while maximizing the information gain. Compared to the conventional random forests, it generates larger and more balanced decision trees due to the even-split constraint, which contributes to enhanced discrimination power and reduced overfitting. Second, to tackle the domain misalignment problem, we propose the domain alignment loss which penalizes uneven splits of the source and target domain data. By collaboratively optimizing the information gain of the labeled source data as well as the entropy of unlabeled target data distributions, the proposed CoBRF algorithm achieves significantly better performance than the state-of-the-art methods. The proposed algorithm is extensively evaluated in various experimental setups in challenging domain adaptation tasks with noisy and small training data as well as open set domain adaptation problems, for two backbone networks of AlexNet and ResNet-50.
Why Is It Difficult to Detect Outbreaks in Twitter?

In this paper, we present an event-based Epidemic Intelligence (EI) system framework leveraging social media data, e.g., Twitter messages (or tweets) for providing public health officials the necessary tools to survey and sift through relevant information, namely, disease outbreak events. There exists three main research challenges in gathering epidemic intelligence from social media streams: 1) dynamic classification to enable message filtering, 2) signal generation producing reliable warnings based on observed term frequency changes in the filtered messages, and 3) providing search and recommendation functionalities to domain experts, for better assessment of the potential outbreak threats associated with the generated signals. We outline possible approaches to solve these important challenges as well as discuss areas where further research is required. The aim of this paper is to provide guidance for similar endeavors, and to give prospective event-based Epidemic Intelligence system builders a more realistic view on the benefits and issues of social media stream analysis.
Stationary and Mobile Target Detection

In this work, we study the target detection and tracking problem in mobile sensor networks, where the performance metrics of interest are probability of detection and tracking coverage, when the target can be stationary or mobi le and its duration is finite. We propose a physical coverage-ba sed mobility model, where the mobile sensor nodes move such that the overlap between the covered areas by different mobile nodes i small. It is shown that for stationary target scenario the proposed mobility model can achieve a desired detection probabilitywith a significantly lower number of mobile nodes especially when the detection requirements are highly stringent. Similarly, when the target is mobile the coverage-based mobility model prod uces a consistently higher detection probability compared to ot her models under investigation.
The Effects of Avocado Fruit Methanolic Extract on Human Gastric Adenocarcinoma Cell Line in Comparison with Peripheral Blood Mononuclear Cells

Objective: Gastric cancer is the second leading cause of cancerrelated deaths in worldwide. Adenocarcinomas comprise the majority of stomach cancers that are usually diagnosed in the late stage with low survival rate and have prompted considerable interest in neo adjuvant therapies to improve longevity. It has been reported that neoplasias in the gastrointestinal system are mostly due to change in dietary habits. The therapeutic effect of herbal materials in suppression of cancer cell proliferation was shown. In this study, we evaluated the efficiency of methanolic extract of avocado fruit on the inhibition of growth of gastric adenocarcinoma cell line, in comparison with normal cells. Material and Methods: The human epithelial gastric cancer cell lines and normal human peripheral blood mononuclear cells were cultured in RPMI supplemented by 10% FCS, penicillin and streptomycin and were treated with increasing concentration of avocado fruit extract. After 48 hours of incubation, cell viability and cell proliferation rate were determined using trypan blue exclusion test and MTT assay. Results: Significant dose dependent growth inhibition of treated stomach cancer cells was observed compared with the untreated control cells. This extract did not inhibit growth of normal human peripheral blood mononuclear cells p<0.05). Conclusion: These data for the first time revealed potential anti tumoral effect of avocado fruit extract on gastric adenocarcinoma cell line. Therefore, postulated avocado is one of phytochemical rich nutrient factor that can be considered as appropriate complementary treatment of refractory gastric cancers.
Evaluation of mineral fiber impact on Hot-Mix Asphalt performance

This study presents characteristics and properties of alkaline mineral fiberreinforced asphalt mixtures. It evaluates the effect of mineral fiber contents (0 %, 0.3 %, 0.5 %, 0.7 % and 0.9 %) and two kinds of asphalt binders (original asphalt AH-90 and 5 % SBS modified asphalt). Marshall test, dynamic stability, moisture susceptibility tests were conducted. The Marshall stability increased initially and then decreased with increasing the mineral fiber contents. The mechanical property (dynamic stability and tensile strength ratio) of fiber-reinforced asphalt mixtures increased with the increase of mineral fiber contents. Fiber contents had great influence on high temperature rutting resistance, while little influence on moisture susceptibility. Based on the performances and the economic factors, an optimum fiber content of 0.5 % was recommended for the alkaline mineral fiber used in this AC-16 mixture. The asphalt mixture modified by 5 % SBS had higher mechanical performances than the mixture modified by 0.5 % mineral fiber, especially for permanent deformation in high temperature. The research results would be of importance for material design of asphalt pavement according to traffic volume and loading distribution.
TPO: TREE SEARCH POLICY OPTIMIZATION

Monte Carlo Tree Search (MCTS) has achieved impressive results on a range of discrete environments, such as Go, Mario and Arcade games, but it has not yet fulfilled its true potential in continuous domains. In this work, we introduce TPO, a tree search based policy optimization method for continuous environments. TPO takes a hybrid approach to policy optimization. Building the MCTS tree in a continuous action space and updating the policy gradient using off-policy MCTS trajectories are non-trivial. To overcome these challenges, we propose limiting tree search branching factor by drawing only a few action samples from the policy distribution and defining a new loss function based on the trajectories’ mean and standard deviations. Our approach led to some non-intuitive findings. MCTS training generally requires a large number of samples and simulations. However, we observed that bootstrapping tree search with a pre-trained policy allows us to achieve high quality results with a low MCTS branching factor and few simulations. Without the proposed policy bootstrapping, continuous MCTS would require a much larger branching factor and simulation count, rendering it prohibitively expensive. In our experiments, we use PPO as our baseline policy optimization algorithm. TPO significantly improves the policy on nearly all the environments. For example, in complex environments such as Humanoid with a 17 dimensional action space, we achieve a 2.5× improvement over the baseline algorithm.
Papillary thyroid cancer in three children.

INTRODUCTION Thyroid cancers (TC) are rare diseases in the pediatric population and represents 0.5-3% of all malignant tumors in children. Differenti-ated thyroid cancer (DTC) is a major TC in children. Every patient under 18 years of age diagnosed with a thyroid nodule should under-go a detailed medical examination. The screening test for children with an increased risk for DTC is ultrasound examination of the neck. Both ultrasound and clinical images of a tissue lesion are more important than its size. It should also be emphasized that autoimmune thyroiditis (AIT), a comorbid condition for TC, is increasingly often diagnosed in young patients. Because of the rare incidence of this kind of cancer, we present 3 case studies of patients with papillary thyroid carcinoma, hospitalized in the Department of Pediatrics, En-docrinology, Diabetology, Metabolic Diseases and Cardiology of Developmental Age at Pomeranian Medical University (PMU) in Szczecin.
Automatically Learning Patterns for Self-Admitted Technical Debt Removal

Technical Debt (TD) expresses the need for improvements in a software system, e.g., to its source code or architecture. In certain circumstances, developers “self-admit” technical debt (SATD) in their source code comments. Previous studies investigate when SATD is admitted, and what changes developers perform to remove it. Building on these studies, we present a first step towards the automated recommendation of SATD removal strategies. By leveraging a curated dataset of SATD removal patterns, we build a multi-level classifier capable of recommending six SATD removal strategies, e.g., changing API calls, conditionals, method signatures, exception handling, return statements, or telling that a more complex change is needed. SARDELE (SAtd Removal using DEep LEarning) combines a convolutional neural network trained on embeddings extracted from the SATD comments with a recurrent neural network trained on embeddings extracted from the SATD-affected source code. Our evaluation reveals that SARDELE is able to predict the type of change to be applied with an average precision of ~55%, recall of ~ 57%, and AUC of 0.73, reaching up to 73% precision, 63% recall, and 0.74 AUC for certain categories such as changes to method calls. Overall, results suggest that SATD removal follows recurrent patterns and indicate the feasibility of supporting developers in this task with automated recommenders.
A Study on High School Students’ Hopelessness Level Related to School Type, Taking Art Education, Sex and Class level (grade)

In this study, the aim is to determine hopelessness level of teenagers according to high school types and to research the difference in the hopelessness levels of students in high school fine arts and sports education departments in which art education is given and students in other high school types. This research is a descriptive one and it is conducted on 933 students whose ages vary between 15 and 19. Hopelessness level of students is measured with Beck Hopelessness Scale. In analyzing the data, Kruskal Wallis H Test is used in order to test the hopelessness level according to school type and class level (grade) while Mann Whitney U Test is used in order to test in which groups significant differences exist according to sex and school type. There wasn’t found to be a significant difference between hopelessness levels according to school type. In the test that was conducted according to sex, it was determined that there was a significant difference in favor of male students. It was also found that difference between hopelessness levels according to class levels (grade) was significant and this difference was between 2 nd and 3 rd grades and 3 rd and 4 th
Contract-based Incentive Mechanism Design for Renewable Energy Trading Markets

Renewable Energy Trading Markets Oanh Tran Thi Kim, Sun Moo Kang, and Choong Seon Hong* Department of Computer Science and Engineering, Kyung Hee University {ttkoanh, etxkang, cshong}@khu.ac. kr Abstract With the projected benefits, including a decrease not only in greenhouse gas emissions but also energy costs, renewable energy sources (RES) are increasingly deployed day by day. As a result, research on renewable energy has attracted extensive attention. However, most of the studies on RES focuses on RES’s harvested energy supply. With the popularity of RESs, harvested energy from RESs is a highly competitive product. It thus needs to have effective incentive mechanisms for renewable energy trading markets in such a way that economic benefits related to renewable energy trading are guaranteed. In this study, we proposed an incentive mechanism design for renewable energy trading markets based on contract theory framework. The simulation results illustrate that our proposed approach can not only maximize the profit of the local energy aggregator (LEA) but also outperforms compared to take-or-leave-it contract approach in term of LEA’s total profit. Key word: Energy trading, renewable energy sources (RES), contract theory.