Miracle-dz/UniFAR-FTUs huggingface dataset:
Response characteristics of a simple neuronal oscillator to a periodic input in noisy environment-an analysis of stochastic phase lockings and stochastic bifurcations -

A van der Pol relaxation (slow-fast) oscillator is considered as a simple model of a neuronal oscillator. When the input to the oscillator is a sinusoidal signal, the oscillator shows various patterns of phase lockings depending on the period and amplitude of the input. Noise effects on the phase lockings and bifurcations in the forced oscillator are investigated. The deterministic (noise-free) one-dimensional mapping is extended to the iteration of the operator defined by a stochastic kernel function. Stochastic phase lockings and bifurcations are analyzed in terms of the density evolution by the operator and its spectral characteristics.
The sensorimotor system, part I: the physiologic basis of functional joint stability.

OBJECTIVE To define the nomenclature and physiologic mechanisms responsible for functional joint stability. DATA SOURCES Information was drawn from an extensive MEDLINE search of the scientific literature conducted in the areas of proprioception, neuromuscular control, and mechanisms of functional joint stability for the years 1970 through 1999. An emphasis was placed on defining pertinent nomenclature based on the original references. DATA SYNTHESIS Afferent proprioceptive input is conveyed to all levels of the central nervous system. They serve fundamental roles in optimal motor control and sensorimotor control over functional joint stability. CONCLUSIONS/APPLICATIONS Sensorimotor control over the dynamic restraints is a complex process that involves components traditionally associated with motor control. Recognizing and understanding the complexities involved will facilitate the continued development and institution of management strategies based on scientific rationales.
Visualizing and Curating Knowledge Graphs over Time and Space

Publicly available knowledge repositories, such as Wikipedia and Freebase, benefit significantly from volunteers, whose contributions ensure that the knowledge keeps expanding and is kept up-to-date and accurate. User interactions are often limited to hypertext, tabular, or graph visualization interfaces. For spatio-temporal information, however, other interaction paradigms may be better-suited. We present an integrated system that combines crowdsourcing, automatic or semi-automatic knowledge harvesting from text, and visual analytics. It enables users to analyze large quantities of structured data and unstructured textual data from a spatio-temporal perspective and gain deep insights that are not easily observed in individual facts.
A Good Beginning Makes a Good Market: The Effect of Different Market Opening Structures on Market Quality

This paper deals with the market structure at the opening of the trading day and its influence on subsequent trading. We compare a single continuous double auction and two complement markets with different call auction designs as opening mechanisms in a unified experimental framework. The call auctions differ with respect to their levels of transparency. We find that a call auction not only improves market efficiency and liquidity at the beginning of the trading day when compared to the stand-alone continuous double auction, but also causes positive spillover effects on subsequent trading. Concerning the design of the opening call auction, we find no significant differences between the transparent and nontransparent specification with respect to opening prices and liquidity. In the course of subsequent continuous trading, however, market quality is slightly higher after a nontransparent call auction.
The Relational Nature of Legislating

JUSTIN H. KIRKLAND: The Relational Nature of Legislating. (Under the direction of Thomas Carsey.) Since the economic revolution of the 1970’s legislative scholars have produced important works examining the relationships between policy preferences and individual choices in a legislature. This focus on understanding individual preferences versus outcomes has led scholars to ignore potential inter-legislator influences on choices. In other words, in the study of relationships between a bill and a legislator scholarship has overlooked the importance of the relationship between one legislator and another legislator. These studies represent an effort to push our theoretical understanding of inter-legislator dynamics forward and build a more comprehensive understanding of how legislators influence, collaborate with, and cooperate with one another. Taken together they provide a unified picture of influence diffusion across a chamber by examining both how relationships between legislators affect outcomes and how institutions affect the formation of legislative relationships. Building on seminal sociological work on the importance of tie strength towards achieving an exogenous goal, I generate a novel theory of influence in a legislature. It will turn out that only the weakest ties between legislators actually produce changes in the probability a legislator will experience success. This is because weak ties between legislators are attempts to generate novel cooperation and support. Strongly tied legislators are strongly tied because of implicit support, thus the observation of the relational tie tells us nothing about their behaviors we could not have learned before a legislative session ever began. Strong ties do not indicate cooperation, they indicate similarity. Weak ties, however, occur between legislators fundamentally different most of
A wake interaction model for the coordinated control of Wind Farms

In all the processes of Wind Energy (WE) utilization, the Wind Power (WP) assessment is critical stage for all the Wind Farms (WFs). This paper is focused on the WE systems in Luxembourg. It describes the overview of the wind resources in all the WFs and presents an Unified Cooperation Wake Model (UCWM) and Coordination and Optimization Control (CnOC) for WFs. Based on WP assessment of WFs, the statistical method is used to model the distribution of wind speed and Wind Direction (WD). Some simulation figures about the wind rose andWeibull distribution demonstrate the detailed description and assessment of WP. These assessments are expected to enhance the effectiveness of WP exploitation and utilization in WFs of Luxembourg.
DATA FITTING WITH RULE-BASED REGRESSION

In the classical regression theory we try to build one functional model to fit a set of data. In noisy and complex domains this methodology can be highly unreliable and/or demand too complex functional models. Piecewise regression models provide means to overcome these difficulties. Some existing approaches to piecewise regression are based on regression trees. However, rules are known to be more powerful descriptive languages than trees. This paper describes the rule learning system R 2 . This system learns a set of regression rules from a classical machine learning data set. Regression rules are IF-THEN rules that have regression models in the conclusion. The conditional part of these rules determines the domain of applicability of the respective model. We believe that by adopting a rule-based formalism, R 2 will out-perform regression trees. The initial set of experiments that we have conducted in artificial data sets show that R 2 compares reasonably to other machine learning systems.
Multi-dimensional composition by objective in aspect-oriented requirements analysis

This paper focuses on composition of requirements artefacts in Aspect-Oriented Requirements Engineering (AORE). Our goal is to equip the Aspect-Oriented Requirements Analysis (AORA) approach [1, 2] with an enhanced composition mechanism. The AORA approach consists in the identification, modularization and composition of crosscutting concerns. But the AORA composition [3] operates at a coarser granularity level and its reduced number of operators results in limited composition expressiveness. The core of the work presented in this paper is the description of a composition language and approach that enables a multi-dimensional composition of artefacts based on Objectives, therefore stating the purpose or the goal of the composition. This approach provides a composition process with a well-defined syntax and semantics, as well as a tool support integrated to the AORA original tool [3, 4].
Optimization Strategy of Top-Down Join Enumeration on Modern Multi-Core CPUs

Most contemporary database systems query optimizers exploit System-R’s bottom-up dynamic programming method (DP) to find the optimal query execution plan (QEP) without evaluating redundant sub-plans. The distinguished exceptions are Volcano/Cascades using transforms to generate new plans according to a top-down approach. As recent research has revealed, bottom-up dynamic programming can improve performance with respect to the shape of the join graph and parallelism. However top-down join enumeration dynamic programming method can derive upper bounds for the costs of the plans it generates which is not available to typical bottom-up DP method. In this paper, we propose a comprehensive and practical framework for parallelizing top-down dynamic programming query optimization with complex non-inner join in the multi-core processor architecture, referred as PTDhyp. We have implemented such a search strategy and experimental results show that can improve optimization time effective compared to known existing algorithms.
The ICT predicament of new ICT-enabled service

The advancement of information and communication technologies (ICT) has triggered many ICT-enabled services. Regarding this service, the complementary ICT system involves with customers' devices, industry-wide ICT development and nation-wide ICT infrastructure, which are difficult for any individual organization to control. The ICT predicament is the phenomenon that the complementary ICT system is inferior in delivering the promised service quality of new ICT-enabled service. With the ICT predicament, companies face the decision-making dilemma in launching the new service or postponing the launch. This study proposes a process to resolve the decision-making dilemma regarding the ICT predicament.
Accurate Assessment of Thermal Field and Ampacity of Underground Power Cables

A newer approach to cable thermal field and ampacity computations using a proposed concept of perturbed finite-element analysis is formulated. This approach involves the use of derived sensitivity coefficients associated with various cable parameters of interest and using such sensitivity coefficients to achieve optimal cable performance. The technique is applied to both design phase and operational aspects of power cable buried in a complex media of soils, heat sources and sinks as well as variable boundary conditions
S-IAMB Algorithm for Markov Blanket Discovery

Using Markov Blanket for feature selection is one of important methods in machine learning. This paper analyses IAMB algorithm for discovering the Markov Blanket of a target variable from training data. Based on its characters and deficiency we introduce an improve algorithm of IAMB: SIAMB.The experimental results show that the S-IAMB algorithm performs better than IAMB by finding Markov Blanket of variables of ALARM dataset and by testing the performance with the classification tasks.
The impact of anaemia and kidney function in congestive heart failure and preserved systolic function.

BACKGROUND The importance of anaemia in chronic heart failure was highlighted recently by different cohort studies. The aim of this study was to assess the prevalence of anaemia and its relationship to renal function, left ventricular function and symptoms of heart failure. METHODS We surveyed cases of patients admitted to the Department of Cardiology during 22 consecutive months. Laboratory measurements, blood pressure and echocardiographic parameters were obtained with standardized methods. RESULTS Out of a total number of 2941 patients, 238 patients (8.1%) had haemoglobin values <11 g/dl. There was a positive association of anaemia with the symptoms of heart failure with a lowering of the median haemoglobin from 14.2 g/dl [New York Heart Association (NYHA) I] to 12.9 g/dl (NYHA IV, P<0.001). Interestingly, anaemia was not associated with left ventricular function or any left ventricular parameters. Symptoms of heart failure, however, were associated with kidney function. The estimated glomerular filtration rate (GFR) was 82 ml/min at NYHA I and 59 ml/min at NYHA IV, P<0.05. There was an association between impaired renal function and haemoglobin values. Haemoglobin was 14.2 g/dl in the group with normal renal function and 11.1 g/dl in the group with a GFR <25 ml/min (P<0.001). Even in patients with normal renal function (878 patients, GFR >85 ml/min), we still found an association of anaemia with the symptoms of heart failure. Haemoglobin was 14.5 g/dl at NYHA I and 13.4 g/dl at NYHA IV, P<0.0001. CONCLUSION Anaemia is found in 8.1% of patients admitted to cardiology service. Anaemia was clearly associated with symptoms of congestive heart failure even in patients with normal renal function. Anaemia was not associated with left ventricular function.
Functional brain organization for visual search in ASD

Abstract Although previous studies have shown that individuals with autism spectrum disorder (ASD) excel at visual search, underlying neural mechanisms remain unknown. This study investigated the neurofunctional correlates of visual search in children with ASD and matched typically developing (TD) children, using an event-related functional magnetic resonance imaging design. We used a visual search paradigm, manipulating search difficulty by varying set size (6, 12, or 24 items), distractor composition (heterogeneous or homogeneous) and target presence to identify brain regions associated with efficient and inefficient search. While the ASD group did not evidence accelerated response time (RT) compared with the TD group, they did demonstrate increased search efficiency, as measured by RT by set size slopes. Activation patterns also showed differences between ASD group, which recruited a network including frontal, parietal, and occipital cortices, and the TD group, which showed less extensive activation mostly limited to occipito-temporal regions. Direct comparisons (for both homogeneous and heterogeneous search conditions) revealed greater activation in occipital and frontoparietal regions in ASD than in TD participants. These results suggest that search efficiency in ASD may be related to enhanced discrimination (reflected in occipital activation) and increased top-down modulation of visual attention (associated with frontoparietal activation). (JINS, 2008, 14, 990–1003.)
An Exploration of Professional Knowledge Needed for Reading and Responding to Student Writing

This study explored the knowledge elementary teachers need for one core practice: reading and responding to students’ writing. Forty-five preservice teachers read and responded to an elementary student’s narrative writing sample. Using teacher noticing as a framework, we first decomposed the practice into five components indicative of differences in teachers’ attention to writing features, reasoning about those features, and suggested responses. We used multiple correspondence analyses to investigate potential underlying relationships among components and developed cases to highlight one underlying relationship that was found. The findings indicate reading and responding draws upon teachers’ pedagogical content knowledge. More specifically, it draws on both knowledge of content and students and knowledge of content and teaching.
Self-Esteem and Mastery in Maintenance Cardiac Rehabilitation

Background: CVD is the second leading cause of death in Canada. Mastery and selfesteem are psychosocial factors, suggested to be emerging risk factors for CVD. Purpose: The purpose of the study was two-fold; first to establish whether mastery and self-esteem predicted adherence to maintenance CR; and second, whether mastery and self-esteem improved after a 6-month maintenance CR program. Methods: Data were collected at the Brock University Heart Institute. The study involved a sample of 98 participants. At intake to the program and 6-month follow-up, participants completed a questionnaire battery which included the Rosenberg Self-Esteem Scale and the Pearlin-Schooler Mastery Scale. Results: Mastery and self-esteem scores did not alter the likelihood of adherence to the CR program. Mastery and self-esteem did significantly improve after 6-months of CR amongst participants with the lowest exercise capacity. Conclusion: Maintenance CR does improve mastery and self-esteem amongst those with diminished exercise capacity.
An Ordinal Regression Model with SVD Hebbian Learning for Collaborative Recommendation

The Internet is disseminating more and more information as it continues to grow. This large amount of information, however, can cause an information overload problem for users. Recommender systems to help predict user preferences for new information can ease users' mental loads. The model-based collaborative filtering (CF) approach and its variants for recommender systems have recently received considerable attention. Nonetheless, two issues should be carefully considered in practical applications. First, the data reliability of the rating matrix can affect the prediction performance. Second, most current models view the measurement scale of output classes as nominal instead of ordinal ratings. This study proposes a model-based CF approach that deals with both issues. Specifically, this approach uses the Hebbian learning rule to facilitate singular value decomposition in reducing noisy and redundant data, and employs support vector ordinal regression to build up the models. The results of the experiments conducted in this study show that the proposed approach outperforms other methods, especially under data of mild data sparsity and large-scale conditions. The feasibility of the proposed approach is justified accordingly.
Category Estimation of Complaint Reports about City Park

A lot of actions have recently been taken to support Government 2.0 movement. As the number of the actions increase, many people submit greater number of complaint reports by phone or mobile devices, and make sure the situation reported with each other. According to the actions, the delay in taking action of the government side becomes more clearly identified due to overloads of the government side to deal with the activities. To remedy the above situations, it increases of importance to develop an efficient approach to deal with the complaint reports. Automatic classification of the complaint reports, or estimation and extraction of demanding sentences from the reports are contributory to the approach. In this paper, we propose a method of automatically estimating categories of the complaint reports as a first step. We conducted experiments of estimating categories of the complaint reports. The experiment results showed the following findings: (1) Feature selection is a key to improve the F-score of estimating categories of complaint reports. The percentage of the words strongly effective for the category estimation is about 3.9% of the entire distinct words. (2) Proposed Mutual-Information-based methods outperform the F-score of a conventional Random-Forest-based method. (3) The F-score performance of estimating a category depends on the ambiguity level of the category. In particular, the F-score of estimating categories of a complaint report assigned multiple categories is 1.5 times worse than that of a complaint report assigned single category.
Combining content-based and context-based methods for Persian web page classification

As the Internet includes millions of web pages for each and every search query, a fast retrieving of the desired and related information from the Web becomes very challenging subject. Automatic classification of web pages into relevant categories is an important and effective way to deal with the difficulty of retrieving information from the Internet. There are many automatic classification methods and algorithms that have been propose for contentbased or context-based features of web pages. In this paper we analyze these features and try to exploit a combination of features to improve categorization accuracy of Persian web page classification. We conduct various experiments on a dataset consisting of 352 pages belonging to Persian Wekipedia, using content-based and context-based web page features. Our experiments demonstrate the usefulness of combining these features.
Knowledge convergence among pre-service mathematics teachers through online reciprocal peer feedback

This research focused on pre-service mathematics teachers’ sharing of knowledge through reciprocal peer feedback. In this study, pre-service teachers were divided into groups of five and engaged in an online reciprocal peer feedback activity. Specifically, after creating an individual concept map indicating high school students’ possible solutions to an algebra problem, pre-service teachers shared their individual maps with team members and engaged in online discussion, commenting on the concept maps of other group members and responding to peers’ feedback. Similarities in team members’ knowledge representations before and after this peer feedback activity were compared in order to analyze their knowledge convergence. It was found that a team member’s knowledge was more likely to match that of other team members after the online reciprocal peer feedback activity. Qualitative analysis was also conducted in order to explore the possible influence of a team’s interaction process on members’ knowledge convergence. It was also found that, after engaging in this peer feedback process, pre-service teachers demonstrated greater improvement in their convergence of concepts relating to problem-solving strategies than in the concepts representing problem context and domains. https://doi.org/10.34105/j.kmel. 2017.09. 001
Pid Controller Design for Multiple Time Delays System

This paper presents an approach of stabilization and control of time invariant linear system of an arbitrary order that include several time delays. In this work, the stability is ensured by PI, PD and PID controller. The method is analytical and needs the knowledge of transfer function parameters of the plant. It permits to find stability region by the determination of p K , i K and d K gains.
Extrinsic calibration of a push-broom lidar and camera using 3-D multi-planar association

In this paper we propose a method of extrinsically calibrating two of the most commonly used sensors in autonomous robotic systems: a 2-D lidar sensor and camera. Using a tri-planar calibration target, our method extracts rich point and planar feature information from the measurements of both sensors. We approach the problem of estimating the extrinsic paramaters using optimisation, and by minimising the direct 3-D point-plane correspondences between these features, are able to accurately estimate the parameters. Our method is shown to be visually accurate and robust.
Adaptive Mesh Refinement Using Wave-Propagation Algorithms for Hyperbolic Systems

An adaptive mesh refinement algorithm developed for the Euler equations of gas dynamics has been extended to employ high-resolution wave-propagation algorithms in a more general framework. This allows its use on a variety of new problems, including hyperbolic equations not in conservation form, problems with source terms or capacity functions, and logically rectangular curvilinear grids. This framework requires a modified approach to maintaining consistency and conservation at grid interfaces, which is described in detail. The algorithm is implemented in the AMRCLAW package, which is freely available.