Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining PCA and LFA for Surface Reconstruction from a Sparse Set of Control Points

This paper presents a novel method for 3D surface reconstruction based on a sparse set of 3D control points. For object classes such as human heads, prior information about the class is used in order to constrain the results. A common strategy to represent object classes for a reconstruction application is to build holistic models, such as PCA models. Using holistic models involves a trade-off between reconstruction of the measured points and plausibility of the result. We introduce a novel object representation that provides local adaptation of the surface, able to fit 3D control points exactly without affecting areas of the surface distant from the control points. The method is based on an interpolation scheme, opposed to approximation schemes generally used for surface reconstruction. Our interpolation method reduces the Euclidean distance between a reconstruction and its ground truth while preserving its smoothness and increasing its perceptual quality
The Design of Wireless Sensor Network Gateway based on ZigBee and GPRS

This paper proposes a design of wireless sensor network gateways based on ARM to solve the poor performance of the network, and low efficiency of communication, which is caused by the poor computing capacity and limited bandwidth in wireless sensor network (WSN). The paper introduces the composition and characteristics of the gateways, and the design of their hardware and software. In this design, the data is transmitted by the network nodes to the gateways with the help of ZigBee near field communication (NFC) technology after its collection, and then the gateways transmit the data to the monitoring center through GPRS. It makes high performance of long distance transmission and the holding of gateways online come true.
Hybrid Crowdsensing: A Novel Paradigm to Combine the Strengths of Opportunistic and Participatory Crowdsensing

Crowdsensing systems can be either participatory or opportunistic, depending on whether the user intentionally contributes data, or she simply acts as the bearer of a sensing device from which data is transparently collected. In this paper, we propose hybrid crowdsensing, a social media-based paradigm which aims at combining the strengths of both participatory and opportunistic crowdsensing. With hybrid crowdsensing, possibly relevant data is collected via an opportunistic approach. Then, users that spontaneously contributed are directly contacted and asked to provide additional information following a participatory approach. To demonstrate its feasibility and usefulness, we experimented the proposed paradigm for involving Twitter users in an emergency relief scenario. For each of the two real-world experiments we analyze the answer ratio to our questions, their time distribution, and responders' willingness to collaborate. Results support the adoption of hybrid crowdsensing, especially in those practical scenarios where users are emotionally involved.
Melt index modeling with support vector machines, partial least squares, and artificial neural networks

This article presents the application of three black-box modeling methods to two industrial polymerization processes to predict the melt index, which is considered an important quality variable determining product specifications. The modeling methods covered in this study are support vector machines (SVMs; known as state-of-the-art modeling methods), partial least squares (PLS), and artificial neural networks (ANNs); the processes are styrene–acrylonitrile (SAN) and polypropylene (PP) polymerizations currently operated for commercial purposes in Korea. Brief outlines of the modeling procedure are presented for each method, followed by the procedures for training and validating the models. The SVM models yield the best prediction performances for both the SAN and PP polymerization processes. However, the ANN models fail to accurately predict the melt index when sufficient data are not available for model training in the PP polymerization process. The PLS models are not effective either when applied to the SAN polymerization process, for which the melt index has strong nonlinear functionality with the process variables. The good prediction performance that the SVM models show despite the insufficient data or strong process nonlinearity suggests that SVMs can be effectively used as alternative to PLS or ANNs for modeling the melt indices in other polymerization processes as well. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 95: 967–974, 2005
The Contention-Friendly Tree

This paper proposes a new lock-based concurrent binary tree using a methodology for writing concurrent data structures. This methodology limits the high contention induced by today’s multicore environments to come up with efficient alternatives to the most widely used search structures. Data structures are generally constrained to guarantee a big-oh step complexity even in the presence of concurrency. By contrast our methodology guarantees the big-oh complexity only in the absence of contention and limits the contention when concurrency appears. The key concept lies in dividing update operations within an eager abstract access that returns rapidly for efficiency reason and a lazy structural adaptation that may be postponed to diminish contention. Our evaluation clearly shows that our lock-based tree is up to 2.2× faster than the most recent lock-based tree algorithm we are aware of.
A Performance Evaluation Methodology for Parallel Simulation Protocols

Most experimental studies of the performance of parallel simulation protocols use speedup or number of events processed per unit time as the performance metric. Although helpful in evaluating the usefulness of parallel simulation for a given simulation model, these metrics tell us little about the efficiency of the simulation protocol used. In this paper, we describe an Ideal Simulation Protocol (ISP), based on the concept of critical path, which experimentally computes the best possible execution time for a simulation model on a given parallel architecture. Since ISP computes the bound by actually executing the model on the given parallel architecture, it is much more realistic than that computed by a uniprocessor critical path analysis. The paper illustrates, using parameterized synthetic benchmarks, how an ISP-based performance evaluation can lead to much better insights into the performance of parallel simulation protocols than what would be gained from speedup graphs alone.
Next-Point Prediction Metrics for Perceived Spatial Errors

Touch screens have a delay between user input and corresponding visual interface feedback, called input 'latency' (or 'lag'). Visual latency is more noticeable during continuous input actions like dragging, so methods to display feedback based on the most likely path for the next few input points have been described in research papers and patents. Designing these 'next-point prediction' methods is challenging, and there have been no standard metrics to compare different approaches. We introduce metrics to quantify the probability of 7 spatial error 'side-effects' caused by next-point prediction methods. Types of side-effects are derived using a thematic analysis of comments gathered in a 12 participants study covering drawing, dragging, and panning tasks using 5 state-of-the-art next-point predictors. Using experiment logs of actual and predicted input points, we develop quantitative metrics that correlate positively with the frequency of perceived side-effects. These metrics enable practitioners to compare next-point predictors using only input logs.
The Complexity of Biomechanics Causing Primary Blast-Induced Traumatic Brain Injury: A Review of Potential Mechanisms

Primary blast-induced traumatic brain injury (bTBI) is a prevalent battlefield injury in recent conflicts, yet biomechanical mechanisms of bTBI remain unclear. Elucidating specific biomechanical mechanisms is essential to developing animal models for testing candidate therapies and for improving protective equipment. Three hypothetical mechanisms of primary bTBI have received the most attention. Because translational and rotational head accelerations are primary contributors to TBI from non-penetrating blunt force head trauma, the acceleration hypothesis suggests that blast-induced head accelerations may cause bTBI. The hypothesis of direct cranial transmission suggests that a pressure transient traverses the skull into the brain and directly injures brain tissue. The thoracic hypothesis of bTBI suggests that some combination of a pressure transient reaching the brain via the thorax and a vagally mediated reflex result in bTBI. These three mechanisms may not be mutually exclusive, and quantifying exposure thresholds (for blasts of a given duration) is essential for determining which mechanisms may be contributing for a level of blast exposure. Progress has been hindered by experimental designs, which do not effectively expose animal models to a single mechanism and by over-reliance on poorly validated computational models. The path forward should be predictive validation of computational models by quantitative confirmation with blast experiments in animal models, human cadavers, and biofidelic human surrogates over a range of relevant blast magnitudes and durations coupled with experimental designs, which isolate a single injury mechanism.
An Improved Method for Deriving Word Meaning from Lexical Co-Occurrence

The lexical semantic system is an important component of human language and cognitive processing. One approach to modeling semantic knowledge makes use of hand-constructed networks or trees of interconnected word senses (Miller, Beckwith, Fellbaum, Gross, & Miller, 1990; Jarmasz & Szpakowicz, 2003). An alternative approach seeks to model word meanings as high-dimensional vectors, which are derived from the cooccurrence of words in unlabeled text corpora (Landauer & Dumais, 1997; Burgess & Lund, 1997a). This paper introduces a new vector-space method for deriving word-meanings from large corpora that was inspired by the HAL and LSA models, but which achieves better and more consistent results in predicting human similarity judgments. We explain the new model, known as COALS, and how it relates to prior methods, and then evaluate the various models on a range of tasks, including a novel set of semantic similarity ratings involving both semantically and morphologically related terms.
Railway Passenger Volume Forecast Based on IPSO-BP Neural Network

This paper improves the basic Particle Swarm Optimization(PSO) algorithm with adaptive interior and acceleration coefficients which is called IPSO, and use the IPSO algorithm to optimize authority value and threshold value of BP nerve network. Thus IPSO-BP neural network algorithm model has been established and applied into the railway passenger volume forecast. The result shows that this model has significant advantages inspect of fast convergence speed, good generalization ability and not easy to yield minimal local results. In generally, this model exhibits good representation and strong prediction ability, and is a helpful tool in the future railway passenger volume prediction.
Tuning of Type-2 Fuzzy Systems by Simulated A nnealing to Predict Time Series

In this paper, a combination of interval type-2 fuzzy system (IT2FS) models and simulated annealing are used to predict the Mackey-Glass time series by searching for the best configuration of the IT2FS. Simulated annealing is used to optimise the parameters of the antecedent and the consequent parts of the rules for a Mamdani model. Simulated annealing is combined with a method to reduce the computations associated with it using an adaptive step size. The results of the proposed methods are compared to results of a type-1 fuzzy system. Index Terms—Type-2-Fuzzy-Systems, Simulated-Annealing, Time-Series-Forecasting.
The Impact of Nudging in the Meteorological Model for Retrospective Air Quality Simulations. Part I: Evaluation against National Observation Networks

Abstract It is common practice to use Newtonian relaxation, or nudging, throughout meteorological model simulations to create “dynamic analyses” that provide the characterization of the meteorological conditions for retrospective air quality model simulations. Given the impact that meteorological conditions have on air quality simulations, it has been assumed that the resultant air quality simulations would be more skillful by using dynamic analyses rather than meteorological forecasts to characterize the meteorological conditions, and that the statistical trends in the meteorological model fields are also reflected in the air quality model. This article, which is the first of two parts, demonstrates the impact of nudging in the meteorological model on retrospective air quality model simulations. Here, meteorological simulations are generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis appr.
The Utility of Rural and Underserved Designations in Geospatial Assessments of Distance Traveled to Healthcare Services: Implications for Public Health Research and Practice

Health disparities research in rural populations is based on several common taxonomies identified by geography and population density. However, little is known about the implications of different rurality definitions on public health outcomes. To help illuminate the meaning of different rural designations often used in research, service delivery, or policy reports, this study will (1) review the different definitions of rurality and their purposes; (2) identify the overlap of various rural designations in an eight-county Brazos Valley region in Central Texas; (3) describe participant characteristic profiles based on distances traveled to obtain healthcare services; and (4) examine common profile characteristics associated with each designation. Data were analyzed from a random sample from 1,958 Texas adults participating in a community assessment. K-means cluster analysis was used to identify natural groupings of individuals based on distance traveled to obtain three healthcare services: medical care, dental care, and prescription medication pick-up. Significant variation in cluster representation and resident characteristics was observed by rural designation. Given widely used taxonomies for designating areas as rural (or provider shortage) in health-related research, this study highlights differences that could influence research results and subsequent program and policy development based on rural designation.
Infectious Greed: Restoring Confidence in America's Companies

In Infectious Greed, two leading financial experts offer a powerful new explanation of why the corporate scandals happened-and propose market-driven reforms that don't just “patch” the system but fix it for generations to come. Discover how the system came to provide massive incentives for malfeasance by CEOs, boards, auditors, analysts, and investment houses-and learn how those “bad” incentives can be replaced by even more powerful incentives for integrity.
The Absorption Signature of Six Mg II-selected Systems over 0.5 ≤ z ≤ 0.9

We present the results of photoionization modeling of six Mg II-selected absorption systems, at redshift 0.5 ≤ z ≤ 0.9, along lines of sight toward three quasars: PG 1241+176, PG 1248+401, and PG 1317+274. These are part of a larger sample of ~20 Mg II absorbers at intermediate redshift, which facilitates a survey of the properties of, and processes active in, the multiple phases of gas, both in and outside of galaxies. We present new high-resolution ultraviolet spectra from the Space Telescope Imaging Spectrograph (STIS) on the Hubble Space Telescope (HST), as well as high-resolution optical spectra obtained with Keck HIRES. Together, these spectra allow simultaneous study of a variety of ionization states of different chemical elements, with resolution of the kinematics providing strong constraints on multiphase properties. The six systems presented here include several that are likely to arise from lines of sight through disk and halo structures of spiral galaxies. For these we find that the high-ionization gas, traced by C IV, is consistent with coronal structures that resemble those of the Milky Way, along with high-velocity clouds at 100-200 km s-1. Another system has relatively weak C IV, relative to the low-ionization absorption, which leads to a discussion of the circumstances in which a corona might be weak or absent in a galaxy, i.e., an early-type morphology or low star formation rate. Finally, we present systems that are examples of single-cloud and multiple-cloud weak Mg II absorbers, which are as a class not likely to be within ~50 kpc of luminous galaxies. Based on the physical conditions inferred from models, we discuss the nature of these types of systems, which may trace outer regions of galaxies, dwarf galaxy satellites, or metal-rich regions of the intergalactic medium.
Estimating Rational Expectations Models

Most dynamic models in economics assume that agents form expectations rationally. An equilibrium of a dynamic model can typically be described by a probability distribution over sequences of data. The rational expectations assumption says that every agent’s subjective belief about the data is a conditional of this equilibrium probability distribution, where the conditioning is on the agent’s information set. Expectations are thus consistent with outcomes generated by the model. They are also optimal, in the sense that they correctly use all information available to the agent.
“Yes-Men in Tournaments

We study a rank-order tournament in which employees acquire and use private information for an investment decision. In this environment, competition can turn employees into yes men who make investment decisions that excessively agree with preconceived notions. The specter of yes-man behavior may drive the tournament incentive intensity and the employees' information-collection effort either to zero or above the first-best efficient levels. We also show that yes-man problems are alleviated by a stronger correlation between the employees' sources of uncertainty and by the use of individual compensation contracts rather than a tournament.
The Impact of Firm Size on Dividend Behaviour: A Study With Reference to Corporate Firms across Industries in India

The objective of this paper is to examine the association between the Corporate Leverage (CL) and the Dividend Policy (DP) of firms across industries in India in respect of Size of Corporate Firms. The investigation is conducted on a panel sample of 73 firms across industries [Cement, Chemical and Fertilizer, it, Oil and Gas, Pharmaceutical, Shipping, and Textiles], which listed their shares in National Stock Exchange (NSE) in India for the period 1996–2007. The impacts of Capital Structure (CS) variables (leverage) on DP measures – dividend payout (Net dividend paid/net income) in the presence of some basic fundamental variables are considered to be the determinants of DP, using the Multiple Regression Technique (OLS method). The results of the cross-sectional ols Model for the selected sample firms under various sectors show that there is a significant effect of selected independent variables. Therefore, this study proves that the DP of Small Size, Medium Size, Large Size, and Overall Corporate Firms across industries in India is dependent on the level of debt in CS.
Thailand's Monetary Policy since the 1997 Crisis

This paper discusses Thailand’s monetary policy frameworks after the 1997 Asian fi nancial crisis with a focus on the present-day infl ation targeting regime. Aspects ranging from institutional arrangements, policy objectives, policy targets, policy instruments to policy decision making processes are examined. The paper also presents a brief evaluation of the country’s infl ation targeting regime up to the end of 2007.
Hemodynamic Flow Modeling Through an Abdominal Aorta Aneurysm Using Data Mining Tools

Geometrical changes of blood vessels, called aneurysm, occur often in humans with possible catastrophic outcome. Then, the blood flow is enormously affected, as well as the blood hemodynamic interaction forces acting on the arterial wall. These forces are the cause of the wall rupture. A mechanical quantity characteristic for the blood-wall interaction is the wall shear stress, which also has direct physiological effects on the endothelial cell behavior. Therefore, it is very important to have an insight into the blood flow and shear stress distribution when an aneurysm is developed in order to help correlating the mechanical conditions with the pathogenesis of pathological changes on the blood vessels. This insight can further help in improving the prevention of cardiovascular diseases evolution. Computational fluid dynamics (CFD) has been used in general as a tool to generate results for the mechanical conditions within blood vessels with and without aneurysms. However, aneurysms are very patient specific and reliable results from CFD analyses can be obtained by a cumbersome and time-consuming process of the computational model generation followed by huge computations. In order to make the CFD analyses efficient and suitable for future everyday clinical practice, we have here employed data mining (DM) techniques. The focus was to combine the CFD and DM methods for the estimation of the wall shear stresses in an abdominal aorta aneurysm (AAA) underprescribed geometrical changes. Additionally, computing on the grid infrastructure was performed to improve efficiency, since thousands of CFD runs were needed for creating machine learning data. We used several DM techniques and found that our DM models provide good prediction of the shear stress at the AAA in comparison with full CFD model results on real patient data.
EXPLOITING TEMPORAL VARIABILITY TO UNDERSTAND TREE RECRUITMENT RESPONSE TO CLIMATE CHANGE

Predicting vegetation shifts under climate change is a challenging endeavor, given the complex interactions between biotic and abiotic variables that influence demographic rates. To determine how current trends and variation in climate change affect seedling establishment, we analyzed demographic responses to spatiotemporal variation to temperature and soil moisture in the southern Appalachian Mountains. We monitored seedling establishment for 10 years in five plots located along an elevational gradient of five dominant tree species: Acer rubrum, Betula spp. , Liriodendron tulipifera, Nyssa sylvatica, and Quercus rubra. A hierarchical Bayes model allowed us to incorporate different sources of information, observation errors, and the inherent variability of the establishment process. From our analysis, spring temperatures and heterogeneity in soil moisture emerge as key drivers, and they act through nonlinear population demographic processes. We found that all species benefited from warmer springs, in particular the species found on dry slopes, N. sylvatica, and those dominant at higher elevations, Betula spp. and Q. rubra. This last species also benefited from dry environments. Conversely, L. tulipifera, which is abundant on mesic sites, experienced highest establishment rates at high moisture. The mechanisms behind these results may differ among species. Higher temperatures are apparently more important for some, while dry conditions and reduced pathogenic attacks on their seeds and new seedlings have a large impact for others. Our results suggest that only communities found at higher elevations are in danger of regional extinction when their habitats disappear given the current climatic trends. We conclude that the recruitment dynamics of the communities where these species are dominant could be affected by minor changes in climate in ways that cannot be predicted using only climate envelopes, which use different variables and miss the nonlinearities.