Miracle-dz/UniFAR-FTUs huggingface dataset:
Four essays in housing economics

This essay consists of four self-contained essays in housing economics.Essay I: A dynamic partial equilibrium asset model is used to study the capitalisation effects in themarket for owner-occupied housing that resulted from the Swedish 1991 tax reform. Simulations ofanticipated as well as unanticipated implementations of the tax reform suggest that the 1991 tax reformcaused a substantial decline in real housing prices and housing investments. Further, we find that thepost reform tax system is far less sensitive to inflation compared to the pre-reform tax system.However, the post-reform tax system is still a nominal tax system and therefore inflation will still causelarge and arbitrary tax subsidies to owner-occupiers.Essay II: Housing tenure choice is studied in a two-period model with stochastic rents and houseprices. We show that a risk-averse household chooses the tenure mode with lowest variance in the costof housing. The tenure mode chosen depends on the variance in the prices of apartment buildings, thevariance in the prices of owner-occupied housing and the correlation between these two prices. Whenthe correlation is positive and above a critical level, a risk-averse household prefers ownership as aninsurance against future uncertain housing costs.Essay III: Housing tenure choice is investigated in a numerical two-period model where. both rents andprices on owner-occupied housing are stochastic. With a positive correlation between prices onapartment buildings and prices on private homes, owner occupation serves as an insurance against thefuture uncertainty in housing costs. Because of this insurance mechanism, increased uncertainty in thecost of housing has an ambiguous effect on saving. In addition, non-durable consumption may either bean increasing or a decreasing function of unrealised capital gains from owner-occupied housing.Essay IV: Pooled and longitudinal data for the years 1989 and 1992 were used to study the housingdecisions of young Swedish adults before and after the 1991 Swedish tax reform. Although thehousehold formation and tenure choice decisions of young adults were found to be simultaneouslydetermined, neglecting the cross equation correlation between these two decisions had no major impacton the estimated coefficients. Demographic factors were found to significantly affect both thehousehold formation and tenure choice decisions. Economic factors were also found to significantlyaffect young adults' choice of tenure mode. This is, however, not the case in the household formationdecision. Young adults' decision whether to form a household was found to be rather insensitive toeconomic factors. Furthermore, the impact of the relative cost of owned versus rented housing wasfound to be significantly lower after the Swedish 1991 tax reform. A possible explanation to accountfor this phenomenon is the smaller variation in relative cost of owned versus rented housing betweenhouseholds after the 1991 Swedish tax reform. Finally, neglecting household specific heterogeneity inthe tenure choice estimation causes a downward bias in the parameter estimates.
Using Machine Learning Techniques to Analyze and Recognize Complex Patterns of Student E-Discussions

Acknowledgements I would like to express my gratitude to my supervisor, Dr. Bruce McLaren, for his guidance in my work on this thesis, his numerous comments and his great reviews that definitely significantly improved the quality of this work. Special thanks to all researchers working on the Argunaut project, particularly to the pedagogical experts, Rakheli Hever and Maarten De-Laat, who provided me with helpful suggestions and annotated data for the experiments; it was my pleasure to cooperate with all of them! Last but not least, great thanks to my family and my friends in Saarbrücken and Prague, for their support and tolerance especially in the final phase of the work. Intelligence (Deutsches Forschungszetrum für Künstliche Inteligenz) and as part of ARGUNAUT project that is partially funded by the EC under the 6th Framework Program (IST-2005-027728). I hereby declare that I wrote the thesis by myself and listed all used sources. I agree with making the thesis publicly available. Abstract Název práce: Analýza a rozeznávání komplexních interakcí v e-diskusích studentů pomocí technik strojového učení Katedra: Katedra teoretické informatiky a matematické logiky Abstrakt: Visuální diskuse je formou debaty, ve které se příspěvky zapisují do grafických tvarů a propojují se s ostatními na základě jejich vztahů. Pro moderování několika současně probíhajících diskusí je důležité nasměrovat učitele na zajímavé skupiny příspěvků. Pro tento účel jsme navrhli algoritmus, který používá isomorfismus grafů společně s textovou analýzou a klasifikátory strojového učení pro vyhledávání těchto interakcí na základě poskytnutého příkladu. Vhodnost našeho přístupu jsme experimentálně ověřili na reálných diskusích se slibnými počátečními výsledky. Abstract: Visual e-discussion is a form of debate in that contributions are written into graphical shapes and linked to one another according to their relationship. In order to moderate several simultaneous e-discussions effectively, it is important to point teachers to interesting clusters of contributions. We designed an algorithm that uses inexact graph matching along with text analysis and machine learning classifiers for searching for such patterns based on a provided example. The method was evaluated on a dataset of real discussion and demonstrated promising initial results.
Grammatical Concept Representation for Randomised Optimisation Algorithms in Relational Learning

This paper proposes a novel grammar-based framework of concept representation for randomized search in Relational Learning (RL), namely for Inductive Logic Programming. The utilization of grammars guarantees that the search operations produce syntactically correct concepts and that the background knowledge encoded in the grammar can be used both for directing the search and for restricting the space of possible concepts to relevant candidate concepts (semantically valid concepts). Not only that it enables handling and incorporating the domain knowledge in a declarative fashion, but grammars also make the new approach transparent, flexible, less problem-specific and allow it to be easily used by almost any randomized algorithm within RL. Initial test results suggest that the grammar-based algorithm has strong potential for RL tasks.
Exploring Inter-Module Relationships in Evolving Software Systems

Many of the existing approaches to reverse architecting - the reverse engineering of the architecture of software systems - are based on software exploration tools which provide interactive ways of exploring the system. These tools start with high-level views of the system and refine them with drill-down techniques applied on the high-level entities such as modules and packages, leaving aside valuable information contained in the dependencies between them. In this article we argue that the visualization of inter-module relations bears great potential for supporting the understanding of large evolving software systems. We present two concrete examples of such visualizations. The first, the semantic dependency matrix is a technique for displaying details about a dependency between two modules which groups together classes with similar behavior. The second, the edge evolution film-strip presents the evolution of an inter-module relation through multiple versions of the system. Based on our experience with the edge evolution film strip, we propose a pattern language for inter-module relationships. We exemplify both the visualizations and the pattern language with examples from two large open source software systems
Influence of exogenous application of silicon on physiological response of salt-stressed maize (Zea mays L.)

The influence of silicon (Si, 3 mM), sodium chloride (NaCl, 135 mM), and Si, 3 mM + NaCl, 135 mM supply on chlorophyll content, photosynthetic activity (CO2 -fixation), the concentration of malondialdehyde (MDA) and H2O2, activities of superoxide dismutase (SOD), catalase (CAT) enzymes, free proline and protein contents were studied in maize seedlings leaves after two month of treatments. The results indicated that silicon partially offset the negative impacts and increased tolerance of maize to NaCl stress by enhancing SOD and CAT activities, chlorophyll content and photosynthetic activity. Salt stress although decreased SOD, CAT activities and total soluble protein content, addition of silicon (3 mM) to the nutrient solution enhanced SOD and CAT activities and total protein. In contrast, salt stress considerably increased H2O2, free proline level and MDA concentration and Si addition significantly reduced H2O2, free proline level and MDA concentration in stressed maize leaves. Enhanced activities of SOD and CAT by Si addition may protect the plant tissues from salt induced oxidative damage, thus mitigating salt toxicity and improving the maize growth. These results suggest that the scavenging system forms the primary defense line in protecting oxidative damage under salt stress in crop plants.
Learning to Recognize Human Activities from Soft Labeled Data

An activity recognition system is a very important component for assistant robots, but training such a system usually requires a large and correctly labeled dataset. Most of the previous works only allow training data to have a single activity label per segment, which is overly restrictive because the labels are not always certain. It is, therefore, desirable to allow multiple labels for ambiguous segments. In this paper, we introduce the method of soft labeling, which allows annotators to assign multiple, weighted, labels to data segments. This is useful in many situations, e.g. when the labels are uncertain, when part of the labels are missing, or when multiple annotators assign inconsistent labels. We treat the activity recognition task as a sequential labeling problem. Latent variables are embedded to exploit sub-level semantics for better estimation. We propose a novel method for learning model parameters from soft-labeled data in a max-margin framework. The model is evaluated on a challenging dataset (CAD-120), which is captured by a RGBD sensor mounted on the robot. To simulate the uncertainty in data annotation, we randomly change the labels for transition segments. The results show significant improvement over the state-of-the-art approach.
Effect of Bread Containing Resistant Starch on Postprandial Blood Glucose Levels in Humans

We examined the inhibitory effect of a single ingestion of bread containing resistant starch (bread containing about 6 g of resistant starch derived from tapioca per 2 slices) (test food) on the postprandial increase in blood glucose in male and female adults with a fasting blood glucose level between 100 and 140 mg/dl. Bread not containing resistant starch (placebo) was used as the control. The study was conducted in 20 subjects (9 men and 11 women with a mean age of 50.5±7.5 years) using the crossover method, with a single ingestion of either bread containing resistant starch or the placebo. Blood glucose and insulin were measured before ingestion, and at 0.5, 1, 1.5, and 2 h after ingestion. The blood glucose level before ingestion was stratified into a borderline group (blood glucose level ≥ 111 mg/dl) and a normal group (blood glucose level ≤ 110 mg/dl), with the upper limit of the normal range defined as 110 mg/dl. Postprandial increases in both blood glucose and blood insulin were significantly inhibited in subjects in the borderline group who took the test food in comparison with the placebo group (blood glucose: p<0.05 and p<0.01 at 1 and 1.5 h after ingestion respectively; insulin: p<0.05, p<0.01 and p<0.05 at 1, 1.5, and 2 h after ingestion respectively). These results indicate that bread containing resistant starch is useful for prevention of lifestyle-related diseases such as diabetes mellitus, and as a supplementary means of dietetic therapy.
Development of a novel EEG wave controlled security system

The need for good security systems in banking, e-commerce and other applications is ever increasing. The proposed project makes the security system more robust and less prone to attacks. Brain Computer Interface (BCI) is used in order to obtain the password from the user by using their brain waves. This paper describes the current trend in BCI technologies and then the Hardware implementation of the proposed solution has been described. The Electro-Encephalogram (EEG) waves are obtained with the help of active dry electrodes and processed using signal conditioning circuits. These EEG signals are then interpreted to obtain the thought pattern of the user to match them to the stored password in the system. Finally a Transmitter-Receiver model has been used with a lock interface system is used to indicate the opening or closing of the lock.
An energy-efficient routing protocol for ad-hoc cognitive radio networks

This paper proposes a routing protocol enriched with an assigning mechanism and a capacity-aware scheme, enabling for efficient data flow coordination and energy conservation, among networking nodes with heterogeneous radio spectrum availability in ad-hoc cognitive radio networks. The proposed assigning mechanism is able to effectively determine efficient routing paths in a distributed networking architecture, designed based on a simulation scenario, where networking nodes operate over television white spaces (TVWS). The main target of routing in this simulation scenario is to provide optimal, high throughput data transfer, by efficiently selecting the best routing paths. Towards enabling Energy-Efficiency in the proposed research approach, a Backward Traffic Difference (BTD) estimation methodology is exploited. The novelty adopted in the proposed capacity-aware scheme arises by the ability to utilize different assignments of sleep-wake schedules, depending on different incoming traffic that each networking node receives through time. The validity of the proposed energy efficient routing protocol is verified, by conducting experimental simulations and obtaining performance evaluation results. Simulation results validate routing protocol efficiency for minimizing energy consumption, maximizing resources exchange between networking nodes and minimizing routing delays in ad-hoc cognitive radio networks.
Local perceptions of climate variability and change in tropical forests of Papua, Indonesia

People everywhere experience changes and events that impact their lives. Knowing how they perceive, react, and adapt to climatic changes and events is helpful in developing strategies to support adaptation to climate change. Mamberamo in Papua, Indonesia, is a sparsely populated watershed of 7.8 million hectares possessing rich tropical forests. Our study compares scientific and traditional ecological knowledge (TEK) on climate, and analyzes how local people in Mamberamo perceive and react to climatic variations. We compared meteorological data for the region with local views gathered through focus group discussions and interviews in six villages. We explored the local significance of seasonality, climate variability, and climate change. Mamberamo is subject to strikingly low levels of climatic variation; nonetheless local people highlighted certain problematic climate-related events such as floods and droughts. As our results illustrate, the implications vary markedly among villages. People currently consider climate variation to have little impact on their livelihoods when contrasted with other factors, e.g., logging, mining, infrastructure development, and political decentralization. Nonetheless, increased salinity of water supplies, crop loss due to floods, and reduced hunting success are concerns in specific villages. To gain local engagement, adaptation strategies should initially focus on factors that local people already judge important. Based on our results we demonstrate that TEK, and an assessment of local needs and concerns, provide practical insights for the development and promotion of locally relevant adaptation strategies. These insights offer a foundation for further engagement.
Patients with ROS1 rearrangement‐positive non‐small‐cell lung cancer benefit from pemetrexed‐based chemotherapy

ROS1 gene‐rearrangement in non‐small‐cell lung cancer (NSCLC) patients has recently been identified as a driver gene and benefited from crizotinib treatment. However, no data are available for ROS1‐positive NSCLC about chemotherapeutic options and prognostic data. We investigated pemetrexed‐based treatment efficacy in ROS1 translocation NSCLC patients and determined the expression of thymidylate synthetase (TS) to provide a rationale for the efficacy results. We determined the ROS1 status of 1750 patients with lung adenocarcinoma. Patients' clinical and therapeutic profiles were assessed. In positive cases, thymidylate synthetase (TS) mRNA level was performed by RT‐PCR. For comparison, we evaluated the TS mRNA status and pemetrexed‐based treatment efficacy from 170 NSCLC patients with anaplastic lymphoma kinase (ALK) translocation (n = 46), EGFR mutation (n = 50), KRAS mutation (n = 32), and wild‐type of EGFR/ALK/ROS1/KRAS (n = 42). Thirty‐four ROS1 translocation patients were identified at two institutions. Among the 34 patients, 12 with advanced stage or recurrence were treated with pemetrexed‐based first‐line chemotherapy. The median progression‐free survivals of pemetrexed‐based first‐line chemotherapy in ROS1 translocation, ALK translocation, EGFR mutation, KRAS mutation, and EGFR/ALK/ROS1/KRAS wild‐type patients were 6.8, 6.7, 5.2, 4.2, and 4.5 months, respectively (P = 0.003). The TS mRNA level was lower in patients with ROS1‐positive than ROS1‐negative patients (264 ± 469 × 10−4 vs. 469 ± 615 × 10−4, P = 0.03), but similar with ALK‐positive patients (264 ± 469 × 10−4 vs. 317 ± 524 × 10−4, P = 0.64). Patients diagnosed with ROS1 translocation lung adenocarcinoma may benefit from pemetrexed‐based chemotherapy. TS mRNA level enables the selection of therapeutic options for ROS1 translocation patients.
The estimation of powerful language models from small and large corpora

The authors consider the estimation of powerful statistical language models using a technique that scales from very small to very large amounts of domain-dependent data. They begin with improved modeling of the grammar statistics, based on a combination of the backing-off technique and zero-frequency techniques. These are extended to be more amenable to the particular system considered here. The resulting technique is greatly simplified, more robust, and gives improved recognition performance over either of the previous techniques. The authors also consider the problem of robustness of a model based on a small training corpus by grouping words into obvious semantic classes. This significantly improves the robustness of the resulting statistical grammar. A technique that allows the estimation of a high-order model on modest computation resources is also presented. This makes it possible to run a 4-gram statistical model of a 50-million word corpus on a workstation of only modest capability and cost. Finally, the authors discuss results from applying a 2-gram statistical language model integrated in the HMM (hidden Markov model) search, obtaining a list of the N-best recognition results, and rescoring this list with a higher-order statistical model. <<ETX>>
Role of Brain-Derived Neurotrophic Factor in Beneficial Effects of Repetitive Transcranial Magnetic Stimulation for Upper Limb Hemiparesis after Stroke

Background Repetitive transcranial magnetic stimulation (rTMS) can improve upper limb hemiparesis after stroke but the mechanism underlying its efficacy remains elusive. rTMS seems to alter brain-derived neurotrophic factor (BDNF) and such effect is influenced by BDNF gene polymorphism. Objectives To investigate the molecular effects of rTMS on serum levels of BDNF, its precursor proBDNF and matrix metalloproteinase-9 (MMP-9) in poststroke patients with upper limb hemiparesis. Methods Poststroke patients with upper limb hemiparesis were studied. Sixty-two patients underwent rehabilitation plus rTMS combination therapy and 33 patients underwent rehabilitation monotherapy without rTMS for 14 days at our hospital. One Hz rTMS was applied over the motor representation of the first dorsal interosseous muscle on the non-lesional hemisphere. Fugl-Meyer Assessment and Wolf Motor Function (WMFT) were used to evaluate motor function on the affected upper limb before and after intervention. Blood samples were collected for analysis of BDNF polymorphism and measurement of BDNF, proBDNF and MMP-9 levels. Results Two-week combination therapy increased BDNF and MMP-9 serum levels, but not serum proBDNF. Serum BDNF and MMP-9 levels did not correlate with motor function improvement, though baseline serum proBDNF levels correlated negatively and significantly with improvement in WMFT (ρ = -0.422, p = 0.002). The outcome of rTMS therapy was not altered by BDNF gene polymorphism. Conclusions The combination therapy of rehabilitation plus low-frequency rTMS seems to improve motor function in the affected limb, by activating BDNF processing. BDNF and its precursor proBDNF could be potentially suitable biomarkers for poststroke motor recovery.
Fractal analysis: an objective method for identifying atypical nuclei in dysplastic lesions of the cervix uteri.

OBJECTIVES Fractal geometry is a tool used to characterize irregularly shaped and complex figures. It can be used not only to generate biological structures (e.g., the human renal artery tree), but also to derive parameters such as the fractal dimension in order to quantify the shapes of structures. As such, it allows user-independent evaluation and does not rely on the experience level of the examiner. METHODS We applied a box-counting algorithm to determine the fractal dimension of atypical nuclei in dysplastic cervical epithelium. An automatic algorithm was used to determine the fractal dimension of nuclei in order to prevent errors from manual segmentation. Four groups of patients (CIN 1-3 and control) with 10 subjects each were examined. In total, the fractal dimensions of 1200 nuclei were calculated. RESULTS We found that the fractal dimensions of the nuclei increased as the degree of dysplasia increased. There were significant differences between control and atypical nuclei found by an analysis of variance. Atypical nuclei associated with CIN 1, CIN 2, and CIN 3 also differed significantly among these groups. CONCLUSION We conclude that the fractal dimension is a valuable tool for detecting irregularities in atypical nuclei of the cervix uteri and thus allows objective nuclear grading.
The Role of Thermodynamics in Model Reduction when using Invariant Grids

In the present work, we develop in detail the process leading to reduction of models in chemical kinetics when using the Method of Invariant Grids (MIG). To this end, reduced models (invariant grids) are obtained by refining initial approximations of slow invariant manifolds, and used for integrating smaller and less stiff systems of equations capable to recover the detailed description with high accuracy. Moreover, we clarify the role played by thermodynamics in model reduction, and carry out a comparison between detailed and reduced solutions for a model hydrogen oxidation reaction.