Miracle-dz/UniFAR-FTUs huggingface dataset:
International Journal of Advanced Robotic Systems Position-singularity Analysis of a Class of the 3/6-gough-stewart Manipulators Based on Singularity-equivalent-mechanism Regular Paper

This paper addresses the problem of identifying the property of the singularity loci of a class of 3/6‐ Gough‐Stewart manipulators for general orientations in which the moving platform is an equilateral triangle and the base is a semiregular hexagon. After constructing the Jacobian matrix of this class of 3/6‐Gough‐Stewart manipulators according to the screw theory, a cubic polynomial expression in the moving platform position parameters that represents the position‐singularity locus of the manipulator in a three‐dimensional space is derived. Graphical representations of the position‐ singularity locus for different orientations are given so as to demonstrate the results. Based on the singularity kinematics principle, a novel method referred to as 'singularity‐equivalent‐mechanism' is proposed, by which the complicated singularity analysis of the parallel manipulator is transformed into a simpler direct position analysis of the planar singularity‐equivalent‐mechanism. The property of the position‐singularity locus of this class of parallel manipulators for general orientations in the principal‐section, where the moving platform lies, is identified. It shows that the position‐singularity loci of this class of 3/6‐Gough‐Stewart manipulators for general orientations in parallel principal‐sections are all quadratic expressions, including a parabola, four pairs of intersecting lines and infinite hyperbolas. Finally, the properties of the position‐singularity loci of this class of 3/6‐Gough‐Stewart parallel manipulators in a three‐ dimensional space for all orientations are presented. 1. Introduction The past few decades, parallel manipulator systems have become one of the main focuses of attention in research in robotics. This popularity has been motivated by the fact that parallel manipulators possess certain specific advantages over serial manipulators, namely higher rigidity and load‐carrying capacity, better dynamic performance and a simple inverse position kinematics, etc. Among them, the best‐known parallel manipulator is
Optimization of Resilient Networks with Column Generation

An optimal cost-reducing capacity-dimensioning is crucial for network operators when designing communication networks. Failure-free working as well as backup routes have to be installed in order to guarantee Quality of Service and availability requirements. With traditional network planning techniques using linear programming with the flow-approach [1] or the path-approach [2] optimal configurations can be calculated for small-size networks only. In this paper we present a network planning approach based on linear programming with cut-and-price for the optimization of path protection and path restoration. With this approach paths and path-variables are generated during the optimization process. The memory consumption and equation complexity can be reduced while optimality of solutions can still be guaranteed. The new optimization approach is capable of calculating optimal configurations for medium and large size networks and reduces calculation times and memory consumption.
The HCI Paradigm of HyperPrinting

Today, printing and reverse printing (scanning, OCR, logical labeling etc.) technologies have become quite mature and thus allow for an easy transition of documents between physical and electronic world. However, there is no technology today which supports the lossless interpretation of paper-based user interaction with direct effects upon the electronic representation of that document. The HyperPrinting environment tries to fill in this gap and thus accounts for the personal favors of a majority of office workers: Not only managers and knowledge workers prefer to read longer documents, articles or news from paper in contrast to a computer monitor or handheld computer. With the help of HyperPrinting, users can annotate, send notes or initiate tasks and it thus offers a completely new paradigm in the usage and treatment of paper documents. As a side-effect, the use of HyperPrinting builds up a document repository which is not only searchable by full text but also by meta-information, which in turn is depending on the selected user scenario.
Identification and Characterization of a Glyoxalase I Gene in a Rapeseed Cultivar with Seed Thermotolerance

Glyoxalase I (GLYI) is a ubiquitous enzyme in all organisms that catalyzes the conversion of the potent cytotoxin methylglyoxal to S-D-lactoylglutathione. Although many reports suggest the importance of GLYI in the plant response to stress, its function in seeds requires further study. Here, we identified a heat-induced GLYI from Brassica napus seeds, BnGLYI, using a comparative proteomics approach. Two-dimensional gel analyses revealed that BnGLYI protein expression upon heat treatment was significantly elevated in thermotolerant seeds but was diminished in heat-sensitive seeds. The BnGLYI-2 and BnGLYI-3 genes from the heat-sensitive and thermotolerant cultivars, respectively, were characterized, and analyzed. Only two amino acid residue variations were found between the amino acid sequences of the two genes. Moreover, overexpressing BnGLYI-3 in yeast cells enhanced tolerance to heat and cold stress and significantly increased GLYI activity compared to overexpressing BnGLYI-2. In addition, BnGLYI-3 transformants showed enhanced superoxide dismutase activities under heat and cold treatment, whereas these activities were diminished for BnGLYI-2 transformants. Taken together, these results indicate that overexpression of the BnGLYI-3 gene imparts thermotolerance and cold tolerance in yeast and that the variations in BnGLYI-3 may play an important role in the responses to temperature stresses.
A Comparison of Privacy and Security Knowledge and Privacy Concern as Influencing Factors for Mobile Protection Behavior

This paper investigates to which degree privacy and security knowledge and global information privacy concern of a user influence mobile protection behavior. We performed a survey with 154 participants. The results of the survey suggest that both privacy and security knowledge and global information privacy concern are influential for mobile protection behavior. We find that low knowledge and low global information privacy concern can serve as predictors for the non-usage of the evaluated protection methods, whereas high knowledge and high concern can serve as predictors for the usage of the evaluated protection methods.
Modeling and performance analysis of multilevel inverter for single-phase grid connected photovoltaic modules

Power electronic converters always have been circuits of difficult modeling because differential equations that describe them have discontinuities. Although this situation has been improved since the appearance of the bond graph approach, able to jointly describe both continuous and discrete behaviors exhibited by some physical systems, nowadays it is possible to obtain very precise models which help us in the study and design of such circuits. This work gives an overview on single-phase grid converter based on seven level diode clamped multilevel inverter for photovoltaic system with maximum power tracking. In the first part, we develop a bond graph model of the inverter. A bond graph MPPT strategy is developed, the performance of the algorithm is studied. Then, we present the bond graph model of solar cell. Thus, we study a cascade constituted by tow photovoltaic cell panels - five-level NPC VSI - permanent magnet synchronous machine (PMSM) fed by photovoltaic PV energy systems at different illuminations. At the full solar intensity, the maximum power point of current/voltage I/V characteristic of the PV modules is designed to be at the rated conditions of the machines. The steady-state output characteristics, the torque-speed characteristics, of the three DC motors with the two inputs are presented and compared.
Ins and outs of GPCR signaling in primary cilia

Primary cilia are specialized microtubule‐based signaling organelles that convey extracellular signals into a cellular response in most vertebrate cell types. The physiological significance of primary cilia is underscored by the fact that defects in assembly or function of these organelles lead to a range of severe diseases and developmental disorders. In most cell types of the human body, signaling by primary cilia involves different G protein‐coupled receptors (GPCRs), which transmit specific signals to the cell through G proteins to regulate diverse cellular and physiological events. Here, we provide an overview of GPCR signaling in primary cilia, with main focus on the rhodopsin‐like (class A) and the smoothened/frizzled (class F) GPCRs. We describe how such receptors dynamically traffic into and out of the ciliary compartment and how they interact with other classes of ciliary GPCRs, such as class B receptors, to control ciliary function and various physiological and behavioral processes. Finally, we discuss future avenues for developing GPCR‐targeted drug strategies for the treatment of ciliopathies.
Rule extraction from neural networks for medical domains

Neural networks are a powerful classification technique that are capable of discovering complex relationships between inputs and outputs. This powerful classification ability has meant that neural networks have been widely applied to medical domains. However, neural networks suffer from the socalled blackbox problem: they predict accurately but offer no explanation of how the decision has been derived. This lack of explanation has limited their adoption and has been a concern to the medical community. This paper demonstrates ExTree a rule extraction algorithm being applied to neural networks which have been trained on medical datasets. ExTree extracts from the neural network a decision tree which is a graphical and easily understood representation of a decision process.
AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization

We study distributed stochastic convex optimization under the delayed gradient model where the server nodes perform parameter updates, while the worker nodes compute stochastic gradients. We discuss, analyze, and experiment with a setup motivated by the behavior of real-world distributed computation networks, where the machines are differently slow at different time. Therefore, we allow the parameter updates to be sensitive to the actual delays experienced, rather than to worst-case bounds on the maximum delay. This sensitivity leads to larger stepsizes, that can help gain rapid initial convergence without having to wait too long for slower machines, while maintaining the same asymptotic complexity. We obtain encouraging improvements to overall convergence for distributed experiments on real datasets with up to billions of examples and features.
Risk Aversion and Wealth: Evidence from Person-to-Person Lending Portfolios

We estimate risk aversion from the actual financial decisions of 2,168 investors in Lending Club (LC), a person-to-person lending platform. We develop a methodology that allows us to estimate risk aversion parameters from each portfolio choice. Since the same individual makes repeated investments, we are able to construct a panel of risk aversion parameters that we use to disentangle heterogeneity in attitudes towards risk from the elasticity of investor-specific risk aversion to changes in wealth. In the cross section, we find that wealthier investors are more risk averse. Using changes in house prices as a source of variation, we find that investors become more risk averse after a negative wealth shock. These preferences consistently extrapolate to other investor decisions within LC.
Novel Bovine Papillomavirus Type Discovered by Rolling-Circle Amplification Coupled with Next-Generation Sequencing

Currently, fifteen bovine papillomavirus (BPV) types have been identified and classified into four genera: Deltapapillomavirus, Epsilonpapillomavirus, Dyoxipapillomavirus, and Xipapillomavirus. Here, the complete genome sequence of a new BPV type (BPV 04AC14) recovered from a papillomatous lesion is reported. The genome is 7,282 bp in length and exhibits the classic genetic organization and motifs of the members of Papillomaviridae. Maximum likelihood phylogenetic analyses revealed that BPV 04AC14 clusters with members of the Xipapillomavirus genus. The nucleotide sequence of the L1 capsid protein of the novel BPV is closely related to its counterpart, BPV3, with which it shares 79% similarity. These findings suggest that this virus is a new BPV type of the Xipapillomavirus genus.
Metamodel-Based Shape Optimization of Connecting Rod Considering Fatigue Life

To optimize a connecting rod satisfying fatigue life, metamodel-based design optimization is proposed. To approximately predict both volume and fatigue life of connecting rod, kriging metamodel is constructed based on maximin eigenvalue sampling. Fatigue analysis is accomplished for the calculation of fatigue life. The results of metamodel-based design optimization are compared with those of classical optimization. The advantages of metamodel-based optimization are discussed.
Source code reuse evaluation by using real/potential copy and paste

Developers often reuse existing software by copy and paste. Source code reuse improves productivity and software quality. On the other hand, source code reuse requires several professional skills to developers. In source code reuse, developers must locate reusable code fragments, and judge whether such reusable code is adequate to copy and paste into the source file under development. This paper presents extraction and analysis methods for developers' source code reuse behavior (copy and paste). Our method extracts developers' actual source code reuse (real copy and paste). Then, by using a code clone detection tool, the method extracts code fragments for (potential reuse). Our study of real and potential copy and paste provides a quantitative assessment for source code reuse by developers.
F-KDB: An K-D-B Tree Implementation over Flash Memory

Flash memory has been widely used in the storage system, mainly due to its superiority such as smaller size, lower power consumption, faster read performance and no mechanical delay. Though the traditional multi-dimensional applications with spatial data management, such as GIS, CAD/CAM, data mining, could directly run over flash memory under the support of FTL (Flash Translation Layer), the performance of this approach is very poor. Because of the inherent limitation of flash memory, such as the erase-before-write, endurance characteristics, the fine-grained updates in the spatial database management system will introduce a significant number of out-place updates over flash memory. As a result, the performance of multi-dimensional applications over flash memory deteriorates rapidly and the reliability is damaged. The traditional multi-dimensional access method of choice in many practical database management systems is the K-D-B tree. In this paper, a new K-D-B tree implementation over flash memory, called F-KDB, is proposed to efficiently handle the large number of fine-grained updates. F-KDB is a log-structured implementation over flash memory storage system. A logging buffer and a node translation table in main memory are proposed to reduce the frequent updates over the flash memory. A series of experiments is conducted to demonstrate the efficiency of F-KDB over flash memory and the system performance is improved significantly.
On the online minimization of completion time in an energy harvesting system

This paper considers a single-transmitter energy harvesting system and looks into the problem of completion time minimization from the worst-case point of view. In offline study, an optimal algorithm [1] is given to yield the minimum completion time of transmission. However, for online algorithms, the randomness of future energy arrivals adds to the difficulty of scheduling, thus the offline minimum completion time cannot always be reached. This leads to the question "What is the deterministic performance bound of online algorithms compared to the offline optimum". By a game-theoretic method, this paper shows that with an infinite-sized battery, there exist several algorithms that guarantee a completion time no more than the twice of its offline counterpart for all possible energy arrivals, and more importantly that the ratio of two cannot be further reduced. This property is of great significance especially when reliability is valued in the system.
Modified affinity propagation clustering

Affinity propagation clustering is an efficient clustering technique that does not require prior knowledge of the number of clusters. However, it sets the input preferences without considering data set distribution and competition in the former iteration is ignored when updating messages passing between data points. This paper presents a modified affinity propagation algorithm. Firstly, preference for each data point to serve as an exemplar is computed self-adaptively based on data set distribution; then encouragement and chastisement mechanism is introduced for updating message of availability. Experimental results on standard data sets and synthetic data sets demonstrate feasibility and effectiveness of the proposed algorithm.
Importance of phenotypic information in ADHD diagnosis

Attention Deficit Hyperactivity Disorder (ADHD) is one of the widely researched neuro-developmental disorder. This paper highlights the importance of phenotypic information in the diagnosis of ADHD, in addition to Magnetic Resonance Image (MRI) based features. In this study, features from amygdala region of the brain is extracted using region of interest based feature extraction technique. These features are used in combination with phenotypic information for effective discrimination of ADHD from typically developing controls. Further, to improve the effectiveness of the detection system, SBMLR based feature selection technique is used to select the most discriminative features. Performance of the proposed ADHD diagnosis is evaluated on a benchmark ADHD-200 consortium database. The performance evaluation on two state-of-the-art classification techniques clearly highlight the importance of phenotypic information in the detection of ADHD.
Analytical Valuation of Contingent Claims by Stochastic Interacting Systems for Stock Market

In the present paper, by applying the theory of stochastic processes and interacting particle systems and models, including stopping time theory and stochastic voter model, we model a financial stock price model that contains two types of investors, and we use this financial model to describe the behavior and fluctuations of a stock price process in a stock market. In the financial model, besides the professional investors, we also consider the general investors or nonprofessional investors, where the stopping time and the voter model are applied to model and study the statistical properties of investment of the nonprofessional investors. By using the stochastic methods of statistical analysis, we show that the probability distribution of the normalized random price process for this financial model converges to the corresponding distribution of the Black-Scholes model. Further, we discuss the valuation and hedging of European contingent claims for this price process model.
Bibliographic Analysis with the Citation Network Topic Model

Bibliographic analysis considers author’s research areas, the citation network and paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents using a non-parametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. We propose a novel and ecient inference algorithm for the model to explore subsets of research publications from CiteSeer X . Our model demonstrates improved performance in both model tting and a clustering task compared to several baselines.
On the Combined Use of HLDDs and EFSMs for Functional ATPG ∗

This paper describes a functional test pattern generator wh ich exploits two different paradigms: high-level decision dia grams (HLDDs) and extended finite state machines (EFSMs). HLDDs and EFSMs are deterministically explored by using propagat ion, justification, learning and backjumping. The integration o f such strategies allows the ATPG to more efficiently analyze the st ate space of the design under verification and to generate very ef fective test sequences.
A perceptual distortion metric for digital color images

This paper presents a comprehensive distortion metric for digital color images. It is based on a normalization model of the human visual system that incorporates color perception. The model is shown to accurately fit psychophysical contrast sensitivity data as well as intra- and inter-channel contrast masking data from several different psychophysical experiments. The output of the metric is compared with subjective data for natural images.
Parental Struggles with Obtaining Services for Children with Special Needs

PARENTAL STRUGGLES WITH OBTAINING SERVICES FOR CHILDREN WITH SPECIAL NEEDS by Regina Lynn Carr May 2011 This thesis is an in-depth review of the literature addressing parental struggles, attitudes, and perceptions of barriers in obtaining services for young children with special needs. Researchers, policy makers, and educators need to be aware of how families identify and report their experiences with obtaining services for their young children. Service providers for young children and families with special needs must understand the perspective of the family in order to better serve the children and families. Implementations for future research, practice, and policy changes are discussed.
Real-time music generation for a virtual environment

We describe how we are adapting musical techniques used in films to build a computer program to generate atmospheric music suited to an educational virtual environment. The generator produces music to convey fear using suspense and surprise. The paper motivates the search for a mapping between these emotions and musical structure and outlines how the music generator is implemented. It also explains how we intend to evaluate the effects the music has on the users’ subjective sense of presence or “being there”.