Miracle-dz/UniFAR-FTUs huggingface dataset:
Observer Based Chaotic Message Transmission

We consider observer based synchronization of continuous-time chaotic systems. We present two message transmission schemes for such systems. The first one is based on chaotic masking and modulation, and the second one is based on only chaotic modulation. We show that in these schemes, the message may be recovered under certain conditions. We show that the proposed schemes are robust with respect to noise and parameter mismatch. We also present some simulation results.
Adaptive Time Window Size to Track Concept Drift

This paper proposes an approach to track concept drift in order to improve the classifier performance. This approach uses an adaptive time window size in order to detect a drift according to its dynamics (slow/moderate/fast). The goal is to update the classifier using sufficient number of patterns related to environment changes. Since the classifier may misclassify drifted patterns with its old parameters, an expert is asked to provide the true class label for these patterns. This approach is used to detect at early stage a leak in the steam generator of nuclear power generators Prototype Fast Reactors.
Exploring Departmental Leadership: How Department Chairs Can Be Transformative Leaders

This article explores the role of departmental leadership in the context of transforming departmental climate and culture to be supportive work and family balance. The issue of work and family is ideal for framing discussions of leadership and cultural change because efforts to improve the family-friendliness of careers tend to focus on the creation of family-friendly policies, but leadership is needed to transform the workplace culture before employees feel safe using the policies. Discussions of leadership, including critically oriented and transformative qualities of leadership, support the claim that department chairs can be transformative leaders – leading their departments to become supportive environments for faculty with caregiving responsibilities.
Applying Wireless Camera to Emergency Traffic Control

Traffic management becomes very important in our society, especially trailer traffic light with camera is used in the emergency traffic management situations. The paper discusses different compression method, and proposes a corresponding multi-layer coding for the video over wireless system. By comparing the experiment results, we explore the solutions as how to build a complete emergency traffic control system.
Impact of the mapping strategy on the performance of APP decoded space-time block codes

In this paper, we propose a "turbo" coding scheme consisting of the serially concatenation of a channel code and OSTBC with iterative processing at the receiver. We analyze the impact of different mapping strategies on the information transfer of the space-time detector with EXIT-charts and derive some criteria for the optimum mapping strategy.
MODMatcher: Multi-Omics Data Matcher for Integrative Genomic Analysis

Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. Different types of genetic and genomic data are inter-connected by cis-regulations. On that basis, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in multiple types of molecular data, which can be used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Applied to a large lung genomic study, MODMatcher increased statistically significant genetic associations and genomic correlations by more than two-fold. In a simulation study, MODMatcher provided more robust results by using three types of omics data than two types of omics data. We further demonstrate that MODMatcher can be broadly applied to large genomic data sets containing multiple types of omics data, such as The Cancer Genome Atlas (TCGA) data sets.
Modelling in health care finance

This unique book provides a solid understanding of the basics of modelling and assists health care professionals in grasping its uses in the policy-making process. A valuable guidebook for health system and health insurance managers alike, this volume offers the quantitative and analytic tools needed for sound resource allocation and financial governance of health systems. It creates synergies and bridges gaps between quantitative health economics, health financing and actuarial science while presenting methods for improving the efficiency, and lowering the costs, of current health systems. A joint technical publication of the ILO and the International Social Security Association (ISSA)
Policy Research Working Paper 5931 Innovative and Absorptive Capacity of International Knowledge An Empirical Analysis of Productivity Sources in Latin American Countries

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. This paper examines two sources of global knowledge spillovers: foreign direct investments and trade. Empirical evidence demonstrates that foreign direct investment and trade can contribute to overall domestic productivity growth only when the technology gap between domestic and foreign firms is not too large and when a sufficient absorptive capacity is available in domestic firms. The paper proposes the terms research and development and labor quality to capture the innovative and absorptive capacity of the country. The spillover effects in productivity are analyzed using a stochastic frontier approach. This productivity (in terms of total This paper is a product of the Finance & Private Sector, Latin America and Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at factor productivity) is decomposed using a generalized Malmquist output oriented index, in order to evaluate the specific effect in technical change, technical efficiency change, and scale efficiency change. Using country-level data for 16 Latin American countries for 1996–2006, the empirical analysis shows positive productivity spillovers from foreign direct investment and trade only when the country has absorptive capacity in terms of research and development. Foreign direct investment and trade spillovers are found to be positive and significant for scale efficiency change and total productivity factor change. 1 The authors are thankful to Esperanza Lasagabaster and two anonymous referees for very useful comments.
CHEMICAL RAIL TRANSPORT : THE BENEFITS OF RELIABILITY

Transit time and service reliability are increasingly important for rail shippers and carriers alike. This is particularly true for chemical firms, which tend to be large rail users. Yet the chemical industry lags behind other industries in overall supply chain performance. This research addresses how chemical firms could improve their supply chain performance by managing railcar assets more effectively. Factors influencing transit time and variability are identified: the difference between hopper cars and tank cars; the service reliability of multi-carrier versus single-carrier shipments; and the impact of distance on transit time and variability. Lastly, the discussion focuses on the impact of unreliable rail transit on raw material inventory requirements at customer plants. Analysis suggests that even minor improvements in railcar transit time and variability can substantially reduce customers' needs for holding safety stock, which would significantly reduce shippers' railcar and product inventory. Managerial implications for rail carriers and chemical shippers are provided.
Grupo de Economía Europea European Economy Group

This paper argues that multinational companies can impact positively on the survival of plants in the host country through technology spillovers. We study this effect empirically by estimating a Cox proportional hazard model on plant level data for Irish manufacturing. Our results show that, ceteris paribus, the presence of multinationals has a life enhancing effect on indigenous plants in high tech sectors only. JEL Classification: F23, L60
Adoption of E-learning Systems in Organization in China

The success of implementing an e-learning system in organizations relies on its effective adoption by users. This research investigates factors influencing the adoption of e-learning systems in organizations. The acceptance of e-learning systems is proposed to be subject to the influence of organization culture, top management support, organization facilitation infrastructure, training and education, perceived usefulness, perceived ease of use, and subjective norms. A research framework of e-learning systems adoption is designed and the rationale of the proposed hypotheses is discussed. An e- learning System Adoption Questionnaire Instrument is developed to test the proposed hypotheses empirically in China. This research is expected to contribute to the theoretical development of e-learning adoption theories and to provide insights for the design and management intervention of e-learning systems in organizations.
NP Animacy Identification for Anaphora Resolution

In anaphora resolution for English, animacy identification can play an integral role in the application of agreement restrictions between pronouns and candidates, and as a result, can improve the accuracy of anaphora resolution systems. In this paper, two methods for animacy identification are proposed and evaluated using intrinsic and extrinsic measures. The first method is a rule-based one which uses information about the unique beginners in WordNet to classify NPs on the basis of their animacy. The second method relies on a machine learning algorithm which exploits a WordNet enriched with animacy information for each sense. The effect of word sense disambiguation on the two methods is also assessed. The intrinsic evaluation reveals that the machine learning method reaches human levels of performance. The extrinsic evaluation demonstrates that animacy identification can be beneficial in anaphora resolution, especially in the cases where animate entities are identified with high precision.
Let's Get Personal Personalised Television Listings on the Web

The future success of the Web as an information resource relies on new technologies to provide a more targeted content delivery system. At the moment users are responsible for locating and selecting content. In the future user modelling and information filtering techniques will partially relieve the user of this burden as new personalised content delivery services come online. PTV is one such service, dedicated to providing users with personalised TV guides. In this paper we describe and evaluate the PTV system, paying particular attention to the techniques used for selecting content that matches the personal preferences of individual users.
Driving story generation with learnable character models

Stories are a ubiquitous part of the human experience and an essential component of human intelligence. Stories serve countless functions, working in tandem with our language facilities to enable communication, problem solving, imagination, and more. Stories are eminently cross disciplinary, fueling how people think about everything from chemical reactions and novels to social encounters and politics. If we’re to develop a comprehensive computational system mirroring human intelligence, the system must be able to both understand stories and go beyond that, using stories to empower how it thinks about the world. On a high level, a key component of story understanding is story generation. In human thought generation enables us to imagine, solve problems and make decisions. Therefore, to take computational story understanding to the next level, this research makes a number of contributions centered on story generation. In particular, this includes the development a novel story generation system that is capable of creating new and interesting stories by learning from a human written corpus of stories. The design of robust learnable character models facilitated this development. The character models are capable of handling characters that span a number of genres including romantic comedies, fairy tales, warfare, and Shakespearean tragedies. The work also includes methods for automatic learning of characters traits in both an unsupervised way using topic modeling and a semi-supervised way using a new method of alignment-based trait learning. Through my research these algorithms were tested using the Genesis system for story understanding. The results from the research illustrate how the robust methods are able to handle a variety of situations such as internally conflicted characters, characterization spanning genres, and conflicting character goals. Critically, the research demonstrates how story generation is important to story understanding through an example of using generation to better understand an existing story. Dissertation Supervisor: Patrick H. Winston Professor, Electrical Engineering and Computer Science Dissertation Committee: Boris Katz Principal Research Scientist, MIT Computer Science and Artificial Intelligence Laboratory Nick Montfort Professor, Comparative Media Studies
How Closely do Top Income Shares Track Other Measures of Inequality?

In recent years, researchers have used taxation statistics to estimate the share of total income held by the richest groups, such as the top 10% or the top 1%. Compiling a standardised top income shares dataset for 13 developed countries, I find that there is a strong and significant relationship between top income shares and broader inequality measures, such as the Gini coefficient. This suggests that panel data on top income shares may be a useful substitute for other measures of inequality over periods when alternative income distribution measures are of low quality, or unavailable.
Portfolio Optimisation with Jumps: Illustration with a Pension Accumulation Scheme

In this paper, we address portfolio optimisation when stock prices follow general Levy processes in the context of a pension accumulation scheme. The optimal portfolio weights are obtained in quasi-closed form and the optimal consumption in closed form. To solve the optimisation problem, we show how to switch back and forth between the stochastic differential and standard exponentials of the Levy processes. We apply this procedure to both the Variance Gamma process and a Levy process whose arrival rate of jumps exponentially decreases with size. We show through a numerical example that when jumps, and therefore asymmetry and leptokurtosis, are suitably taken into account, then the optimal portfolio share of the risky asset is around half that obtained in the Gaussian framework.
End-User Feature Labeling via Locally Weighted Logistic Regression

Applications that adapt to a particular end user often make inaccurate predictions during the early stages when training data is limited. Although an end user can improve the learning algorithm by labeling more training data, this process is time consuming and too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on Locally Weighted Logistic Regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. In our user study, the first allowing ordinary end users to freely choose features to label directly from text documents, our algorithm was more effective than others at leveraging end users’ feature labels to improve the learning algorithm. Our results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms effectively.
Mathematical Programming Manuscript No

Consider the problem of routing the electrical connections among two large terminal sets in circuit layout. A realistic model for this problem is given by the vertex-disjoint packing of two Steiner trees (2VPST), which is known to be NP-complete. This work presents an investigation on the 2VPST polyhedra. The main idea is to start from facet-deening inequalities for a vertex-weighted Steiner tree polyhedra. Some of these inequalities are proven to also deene facets for the packing polyhedra, while others are lifted to derive new important families of inequalities, including proven facets. Separation algorithms are provided. Branch-and-cut implementation issues are also discussed, including some new practical techniques to improve the performance of the algorithm. The resulting code is capable of solving problems on grid graphs with up to 10000 vertices and 5000 terminals in a few minutes.
Hypobaric Intermittent Hypoxia Attenuates Hypoxia-induced Depressor Response

Background Hypobaric intermittent hypoxia (HIH) produces many favorable effects in the cardiovascular system such as anti-hypertensive effect. In this study, we showed that HIH significantly attenuated a depressor response induced by acute hypoxia. Methodology/Principal Findings Sprague-Dawley rats received HIH in a hypobaric chamber simulating an altitude of 5000 m. The artery blood pressure (ABP), heart rate (HR) and renal sympathetic nerve activity (RSNA) were recorded in anesthetized control rats and rats received HIH. The baseline ABP, HR and RSNA were not different between HIH and control rats. Acute hypoxia-induced decrease in ABP was significantly attenuated in HIH rat compared with control rats. However, acute hypoxia-induced increases in HR and RSNA were greater in HIH rat than in control rats. After removal of bilateral ascending depressor nerves, acute hypoxia-induced depressor and sympathoexcitatory responses were comparable in control and HIH rats. Furthermore, acute hypoxia-induced depressor and sympathoexcitatory responses did not differ between control and HIH groups after blocking ATP-dependent K+ channels by glibenclamide. The baroreflex function evaluated by intravenous injection of phenylephrine and sodium nitroprusside was markedly augmented in HIH rats compared with control rats. The pressor and sympathoexcitatory responses evoked by intravenous injection of cyanide potassium were also significantly greater in HIH rats than in control rats. Conclusions/Significance Our findings suggest that HIH suppresses acute hypoxia-induced depressor response through enhancement of baroreflex and chemoreflex function, which involves activation of ATP-dependent K+ channels. This study provides new information and underlying mechanism on the beneficiary effect of HIH on maintaining cardiovascular homeostasis.
First steps towards autonomous recognition of Monterey Bay ’ s most common midwater organisms : Mining the ROV video database on behalf of the Automated Visual Event Detection ( AVED ) system

The development of remotely operated vehicles (ROVs) has revolutionized the world of marine science by allowing quantitative video transects (QVTs) to be recorded underwater. This non-invasive technique allows previously unavailable information to be obtained concerning organism diversity, distribution, and abundance. Unfortunately, processing of these QVTs at the Monterey Bay Aquarium Research Institute (MBARI) is a lengthy and tedious process that prevents rapid data analysis. The development of a neuromorphic computer vision system is currently in progress to solve this problem of the data processing bottleneck. The proposed computer vision system combines a saliency-based attentional module and a recognition module, both designed after the human visual system. The “saliency” model mimics the largely unconscious “bottomup” search mechanism of primates, responding specifically to visual cues in the surrounding environment. The recognition module involves learning of specific targets and requires “top-down” biasing based on learned traits. The attentional module at MBARI can already determine areas of potential interest in the marine environment. However, in order to begin training the computer system for the recognition module, a test database of representative organisms must first be established. The extensive MBARI database was mined to reveal the relative abundance and frequency of annotations of all organisms in the Monterey Bay. Based on the rankings, representative video clips were compiled of the top 25 most common mid-water organisms as a preliminary test database for the recognition module.
Effects of Climatic Factors and Ecosystem Responses on the Inter-Annual Variability of Evapotranspiration in a Coniferous Plantation in Subtropical China

Because evapotranspiration (ET) is the second largest component of the water cycle and a critical process in terrestrial ecosystems, understanding the inter-annual variability of ET is important in the context of global climate change. Eight years of continuous eddy covariance measurements (2003–2010) in a subtropical coniferous plantation were used to investigate the impacts of climatic factors and ecosystem responses on the inter-annual variability of ET. The mean and standard deviation of annual ET for 2003–2010 were 786.9 and 103.4 mm (with a coefficient of variation of 13.1%), respectively. The inter-annual variability of ET was largely created in three periods: March, May–June, and October, which are the transition periods between seasons. A set of look-up table approaches were used to separate the sources of inter-annual variability of ET. The annual ETs were calculated by assuming that (a) both the climate and ecosystem responses among years are variable (Vcli-eco), (b) the climate is variable but the ecosystem responses are constant (Vcli), and (c) the climate is constant but ecosystem responses are variable (Veco). The ETs that were calculated under the above assumptions suggested that the inter-annual variability of ET was dominated by ecosystem responses and that there was a negative interaction between the effects of climate and ecosystem responses. These results suggested that for long-term predictions of water and energy balance in global climate change projections, the ecosystem responses must be taken into account to better constrain the uncertainties associated with estimation.
Nonlinear Model Predictive Control for Omnidirectional Robot Motion Planning and Tracking With Avoidance of Moving Obstacles

This paper presents a nonlinear model predictive control algorithm for online motion planning and tracking of an omnidirectional autonomous robot. The formalism is based on a Hamiltonian minimization to optimize a control path as evaluated by a cost function. This minimization is constrained by a nonlinear plant model, which confines the solution space to those paths which are physically feasible. The cost function penalizes tracking error, control amplitude, and the presence in a potential field cast by moving obstacles and Boards. An experiment is presented demonstrating the successful navigation of a field of stationary obstacles. Simulations are presented demonstrating that the algorithm enables the robot to react dynamically to moving obstacles.