Miracle-dz/UniFAR-FTUs huggingface dataset:
The automaticity of complex motor skill learning as a function of attentional focus

The present experiment was designed to test the predictions of the constrained-action hypothesis. This hypothesis proposes that when performers utilize an internal focus of attention (focus on their movements) they may actually constrain or interfere with automatic control processes that would normally regulate the movement, whereas an external focus of attention (focus on the movement effect) allows the motor system to more naturally self-organize. To test this hypothesis, a dynamic balance task (stabilometer) was used with participants instructed to adopt either an internal or external focus of attention. Consistent with earlier experiments, the external focus group produced generally smaller balance errors than did the internal focus group and responded at a higher frequency indicating higher confluence between voluntary and reflexive mechanisms. In addition, probe reaction times (RTs) were taken as a measure of the attention demands required under the two attentional focus conditions. Consistent with the hypothesis, the external focus participants demonstrated lower probe RTs than did the internal focus participants, indicating a higher degree of automaticity and less conscious interference in the control processes associated with the balance task.
A Hybrid Intelligent Algorithm for Fuzzy Dynamic Inventory Problem

In this paper, a fuzzy inventory problem with multiple commodities is casted into a dynamic pro- gramming model with continuous state space and decision space. In order to solve the dynamic programming model, genetic algorithms are used to get samples of the optimal cost functions, and then neural networks are trained to approximate the optimal cost function on a randomly generated sample set, which may bypass "the curse of dimensionality". A hybrid intelligent algorithm is thus produced to get the optimal cost functions functions that represented by neural networks. Lastly, a numerical example is given for illustrating purpose
Comparison of quantitative perfusion imaging using arterial spin labeling at 1.5 and 4.0 Tesla

High‐field arterial spin labeling (ASL) perfusion MRI is appealing because it provides not only increased signal‐to‐noise ratio (SNR), but also advantages in terms of labeling due to the increased relaxation time T1 of labeled blood. In the present study, we provide a theoretical framework for the dependence of the ASL signal on the static field strength, followed by experimental validation in which a multislice pulsed ASL (PASL) technique was carried out at 4T and compared with PASL and continuous ASL (CASL) techniques at 1.5T, both in the resting state and during motor activation. The resting‐state data showed an SNR ratio of 2.3:1.4:1 in the gray matter and a contrast‐to‐noise ratio (CNR) of 2.7:1.1:1 between the gray and white matter for the difference perfusion images acquired using 4T PASL, 1.5T CASL, and 1.5T PASL, respectively. However, the functional data acquired using 4T PASL did not show significantly improved sensitivity to motor cortex activation compared with the 1.5T functional data, with reduced fractional perfusion signal change and increased intersubject variability. Possible reasons for these experimental results, including susceptibility effects and physiological noise, are discussed. Magn Reson Med 48:242–254, 2002. © 2002 Wiley‐Liss, Inc.
Uncommitted Morphological Merging of Watershed Segments

Over the past decades, watershed segmentation has gained much popularity due to its attractive properties: closer to what a human observer would decide, efficient implementation and elegant combination of edge- and region-based segmentation. Nevertheless, it is often associated with the problem of over-segmentation owing to its high sensitivity to variation in intensity. Various merging algorithms have been proposed to tackle this problem, but most of them are either user-dependent, knowledge-based, or computationally expensive. This paper presents a novel merging algorithm, which is automatic, uncommitted and yet computationally cheap. The algorithm is proposed based on a careful analysis of the roots of the spurious segments and on a morphological processing of the root
TRANSITS AND OCCULTATIONS OF AN EARTH-SIZED PLANET IN AN 8.5 hr ORBIT

We report the discovery of an Earth-sized planet (1.16 ± 0.19 R⊕) in an 8.5 hr orbit around a late G-type star (KIC 8435766, Kepler-xx). The object was identified in a search for short-period planets in the Kepler database and confirmed to be a transiting planet (as opposed to an eclipsing stellar system) through the absence of ellipsoidal light variations or substantial radial-velocity variations. The unusually short orbital period and the relative brightness of the host star (mKep = 11.5) enable robust detections of the changing illumination of the visible hemisphere of the planet, as well as the occultations of the planet by the star. We interpret these signals as representing a combination of reflected and reprocessed light, with the highest planet dayside temperature in the range of 2300 K–3100 K. Follow-up spectroscopy combined with finer sampling photometric observations will further pin down the system parameters and may even yield the mass of the planet.
Correlation of health-related quality of life after liver transplant with the Model for End-Stage Liver Disease score.

OBJECTIVE To determine whether a correlation exists between the Model for End-Stage Liver Disease (MELD) score and health-related quality of life (HRQOL) after liver transplant (LT). DESIGN Prospective cohort. SETTING University hospital. PATIENTS Adult LT recipients (N = 209). MAIN OUTCOME MEASURES Postoperative HRQOL over a 1-year period after LT as measured via multiple regression-based path analysis testing the effects of the MELD score, preoperative variables, and postoperative variables on scores on the physical component summary and mental component summary scales of the 36-Item Short Form Health Survey and on composite physical and mental HRQOL scores derived from multiple scales. RESULTS The MELD score (beta = .16), cholestatic cirrhosis (beta = .12), autoimmune/metabolic disease (beta = .18), neoplasm (beta = .23), time after LT (beta = .16), and the Karnofsky score (beta = .49) had significant effects on the physical component summary scale score. Autoimmune/metabolic disease (beta = .16) and the Karnofsky score (beta = .25) had significant effects on the mental component summary scale score. The MELD score (beta = .15), high school education (beta = .15), college education (beta = .17), autoimmune/metabolic disease (beta = .15), neoplasm (beta = .23), time after LT (beta = .11), and the Karnofsky score (beta = .51) had significant effects on the composite physical HRQOL score. Autoimmune/metabolic disease (beta = .23), neoplasm (beta = .15), and the Karnofsky score (beta = .42) had significant effects on the composite mental HRQOL score. CONCLUSIONS An increasing MELD score, when computed without any diagnosis-based exception points, was associated with improved physical HRQOL in the first year after LT. The MELD score did not affect mental HRQOL.
Characterizing strong normalization in a language with control operators

We investigate some fundamental properties of the reduction relation in the untyped term calculus derived from Curien and Herbelin's λμμ. The original λμμ has a system of simple types, based on sequent calculus, embodying a Curry-Howard correspondence with classical logic; the significance of the untyped calculus of raw terms is that it is a Turing-complete language for computation with explicit representation of control as well as code. We define a type assignment system for the raw terms satisfying: a term is typable if and only if it is strongly normalizing. The intrinsic symmetry in the λμμ calculus leads to an essential use of both intersection and union types; in contrast to other union-types systems in the literature, our system enjoys the Subject Reduction property.
Bayesian hierarchical model for the prediction of football results

The problem of modelling football data has become increasingly popular in the last few years and many different models have been proposed with the aim of estimating the characteristics that bring a team to lose or win a game, or to predict the score of a particular match. We propose a Bayesian hierarchical model to fulfil both these aims and test its predictive strength based on data about the Italian Serie A 1991–1992 championship. To overcome the issue of overshrinkage produced by the Bayesian hierarchical model, we specify a more complex mixture model that results in a better fit to the observed data. We test its performance using an example of the Italian Serie A 2007–2008 championship.
Investigation of laser pulsing parameters’ importance in Er:YAG laser skin ablation: a theoretical study conducted via newly developed thermo-mechanical ablation model

Abstract Objective: Importance of laser pulsing parameters and tissue’s mechanical properties in the Er:YAG laser skin-tissue ablation is not adequately understood. The goal here was to develop a computational model that incorporates skin tissue’s mechanical properties to investigate the influence of Er:YAG laser pulsing parameters on tissue ablation and coagulation. Methods: Tissue’s mechanical properties were incorporated by modeling ablation as a tissue water vaporization occurring under elevated pressures that depend on tissue’s stress–strain relationships. Tissue deformation was assumed unidirectional; therefore, a one-dimensional model was utilized. Analytical solution and experimental results were used to verify and validate the model. Then, influence of pulse duration (10 µs–2 ms) and fluence (0–30 J cm−2) on coagulation depth and ablation efficiency was explored. Results: Verification and validation results suggested that the model is acceptably accurate. Minimal effect of pulse duration on coagulation depth was predicted at sub-ablative conditions. At those conditions, coagulation depth increased asymptotically to ∼90 µm with increasing pulse fluence. At ablative conditions, coagulation depth decreased asymptotically to 22–28 µm with increasing pulse irradiance. Ablation efficiency plateaued at high pulse fluences and long pulse durations. Mechanical properties were important as about 50% increase in coagulation depth and 25% decrease in ablation efficiency were predicted when considering the high strain-rate loading effect in comparison with quasi-static loading. Conclusions: Proper tuning of Er:YAG laser pulsing parameters can substantially improve its therapeutic outcomes. The effect of these parameters varies and depends on whether the laser-tissue conditions are ablative or sub-ablative.
Numerical Simulation of the Effect of Crack on the Tensile Mechanical Properties of Graphene

Molecular dynamics (MD) method with Tersoff bond-order interatomic potential function is applied to study (1) the impact of the crack-length on the tensile mechanical properties of graphene, and (2) how the crack affects the temperature correlation of graphene. The results show that the increase of crack length significantly reduces the tensile strength and tensile strain, and have an effect on Young's modulus. Meanwhile, the sensitivity of the tensile strength and tensile rate on temperature will be weakened by crack.
The investigation of fatty acid composition of breast milk and its relationship with dietary fatty acid intake in 5 regions of China

Abstract To investigate the relationship between dietary fatty acid (FA) intake and the lactate FA levels in Chinese women. A total of 2007 samples from 5 regions of China were collected, including 431 in Shandong, 402 in Changchun, 419 in Chongqing, 398 in Guangzhou, and 357 women in Hohhot. All participants were mothers of healthy full-term newborns, collecting the foremilk from day 1 to day 7 and the mature breast milk on day 42 after delivery. The FA composition of the breast milk was analyzed by high resolution capillary gas chromatography. The total FA content of mature breast milk in Shandong was the highest (34.95 g/L), and that was the lowest in Guangzhou (29.72 g/L). The saturated FA content of breast milk was increased from 1 to 7 days after delivery and decreased in mature breast milk. The saturated FA content of breast milk in Hohhot was the highest (37.43%), but that was the lowest in Changchun (32.80%). Maternal dietary FA composition was positively correlated with saturated FAs (SFA), polyunsaturated FAs (PUFA), and docosahexaenoic acid (DHA) (P < .001), and negatively with monounsaturated FAs (MUFA) (P > .05) in breast milk. The FA content of breast milk is different in different regions of China, which was affected by maternal dietary FA composition. Lactating women in China should consume more foods rich in polyunsaturated FAs to ensure the growth of infant.
The Systems Integration Technical Risk Assessment Model based on a Bayesian Belief Networks

This paper presents an approach for modelling Systems Integration Technical Risks (SITR) assessment using Bayesian Belief Networks (BBN). SITR represent significant part of project risks associated with development of large software intensive systems in Defence and Civilian Industry. We propose conceptual modelling framework to address the problem of SITR assessment at early stages of a system life cycle. This framework includes a set of BBN models, representing the risk contributing factors, and complementing Parametric Models (PM), used for providing input data to the BBN models. We present rationale and modelling objectives, describes details of BBN models, including their topology and point out the use of idioms. In conclusion we summarise BBN model benefits and constraints for SITR assessment, and provide suggestions for further research direction for model improvement.
PHYSICAL CONSISTENCY OF THE HYSTERETIC BOUC-WEN MODEL

Abstract This paper deals with the determination of the conditions for which the hysteretic Bouc-Wen model is consistent with the physical phenomenon it is supposed to represent. In particular, we derive the conditions on the model parameters under which the model is stable, asymptotically dissipative and verifies the hysteretic property. These conditions are in the form of algebraic inequalities to be verified by the Bouc-Wen model parameters.
Management of Patient Care in Hemodialysis While Focusing on Cardiovascular Disease Events and the Atypical Role of Hyper- and/or Hypotension: A Systematic Review

Background. Hemodialysis related hemodynamic instability is a major but an underestimated issue. Moreover, cardiovascular events are the leading cause of morbidity and mortality associated with blood pressure in hemodialysis patients. However, there have been many controversies regarding the role and management of hyper- and/or hypotension during hemodialysis that needs to be addressed. Objective. To critically review the available published data on the atypical role of hyper- and/or hypotension in cardiovascular associated morbidity and mortality in patients on hemodialysis and to understand the discrepancies in this context. Methods. A comprehensive search of literature employing electronic as well as manual sources and screening 2783 papers published between Jan 1980 and Oct 2015 was conducted to collect, identify, and analyze relevant information through peer-reviewed research articles, systematic reviews, and other published works. The cardiovascular events, including accelerated atherosclerotic cardiovascular disease (ASCVD), stroke, heart failure, myocardial infarction, myocardial ischemia, and stress induced myocardial dysfunction, leading to death were considered relevant. Results. A total of 23 published articles met the inclusion criteria and were included for in-depth review and analysis to finalize a comprehensive systematic review article. All the studies showed a significant association between the blood pressure and cardiovascular disease events in hemodialysis patients. Conclusions. Both intradialytic hypertension/hypotension episodes are major risk factors for cardiovascular mortality with a high percentage of probable causality; however, clinicians are faced with a dilemma on how to evaluate blood pressure and treat this condition.
Graphene-Coated Poly(ethylene terephthalate) Nonwoven Hollow Tube for Continuous and Highly Effective Oil Collection from the Water Surface

Graphene (GE) has attracted significant attention on account of its unique structure and superior performance, arousing a new research field for materials science. Herein, a novel GE-coated poly(ethylene terephthalate) nonwoven (PGNW) hollow tube (PGNW-T) was fabricated for continuous and highly effective oil collection from the water surface. The PGNW was prepared via a dip-spray coating method, which possessed superhydrophobicity–superoleophilicity and could absorb a variety of oils or organic solvents with the absorption capacity (Q) value of 18–34 times its own weight. Then, PGNW-T was obtained through winding the PGNW on the surface of a porous polypropylene hollow tube. As-prepared PGNW-T was competent for dynamic oil collection with high flux (18 799.94 L/m2 h), outstanding separation efficiency (97.14%), and excellent recyclability (>96% after 10 cycles) from the oil/water mixture. In particular, a miniature device based on as-prepared PGNW-T was developed for continuous thin oil film collection, which could dynamically “catch up” floated oils or organic solvents from the water surface. Finally, our strategy is extremely facile to scale up, showing its huge potential application in practical oil-spill remediation.
Linear Convergence on Positively Homogeneous Functions of a Comparison Based Step-Size Adaptive Randomized Search: the (1+1) ES with Generalized One-fifth Success Rule

In the context of unconstraint numerical optimization, this paper investigates the global linear convergence of a simple probabilistic derivative-free optimization algorithm (DFO). The algorithm samples a candidate solution from a standard multivariate normal distribution scaled by a step-size and centered in the current solution. This solution is accepted if it has a better objective function value than the current one. Crucial to the algorithm is the adaptation of the step-size that is done in order to maintain a certain probability of success. The algorithm, already proposed in the 60's, is a generalization of the well-known Rechenberg's $(1+1)$ Evolution Strategy (ES) with one-fifth success rule which was also proposed by Devroye under the name compound random search or by Schumer and Steiglitz under the name step-size adaptive random search. In addition to be derivative-free, the algorithm is function-value-free: it exploits the objective function only through comparisons. It belongs to the class of comparison-based step-size adaptive randomized search (CB-SARS). For the convergence analysis, we follow the methodology developed in a companion paper for investigating linear convergence of CB-SARS: by exploiting invariance properties of the algorithm, we turn the study of global linear convergence on scaling-invariant functions into the study of the stability of an underlying normalized Markov chain (MC). We hence prove global linear convergence by studying the stability (irreducibility, recurrence, positivity, geometric ergodicity) of the normalized MC associated to the $(1+1)$-ES. More precisely, we prove that starting from any initial solution and any step-size, linear convergence with probability one and in expectation occurs. Our proof holds on unimodal functions that are the composite of strictly increasing functions by positively homogeneous functions with degree $\alpha$ (assumed also to be continuously differentiable). This function class includes composite of norm functions but also non-quasi convex functions. Because of the composition by a strictly increasing function, it includes non continuous functions. We find that a sufficient condition for global linear convergence is the step-size increase on linear functions, a condition typically satisfied for standard parameter choices. While introduced more than 40 years ago, we provide here the first proof of global linear convergence for the $(1+1)$-ES with generalized one-fifth success rule and the first proof of linear convergence for a CB-SARS on such a class of functions that includes non-quasi convex and non-continuous functions. Our proof also holds on functions where linear convergence of some CB-SARS was previously proven, namely convex-quadratic functions (including the well-know sphere function).
Conversion Law of Infrared Luminosity to Star-Formation Rate for Galaxies

We construct a new algorithm for estimating the star formation rate (SFR) of galaxies from their infrared (IR) luminosity by developing the theory of the IR emission from a dusty H II region. The derived formula is ${\rm SFR}/(\MO {\rm yr^{-1}})=\{3.3\times 10^{-10}(1-\eta)/ (0.4-0.2f+0.6 \epsilon)\}(L{\rm ^{obs}_{IR}}/\LO)$, where $f$ is the fraction of ionizing photons absorbed by hydrogen, $\epsilon$ is the efficiency of dust absorption for nonionizing photons from OB stars, and $\eta$ is the cirrus fraction of observed IR luminosity. The previous conversion formulae of SFR from the IR luminosity is applicable to only the case where the observed IR luminosity is nearly equal to the bolometric luminosity (starburst galaxies etc.), except for some empirical formulae. On the other hand, our theoretical SFR is applicable to galaxies even with a moderate star formation activity. That is, our simple and convenient formula is significantly useful for estimating the SFR of various morphologies and types of galaxies --- from early elliptical to late spiral and irregular galaxies, or from active starburst to quiescent galaxies --- as far as they have neither an abnormal dust-to-gas ratio nor an evident active galactic nucleus.
Multi-objective multi-task learning

This dissertation presents multi-objective multi-task learning, a new learning framework. Given a fixed sequence of tasks, the learned hypothesis space must minimize multiple objectives. Since these objectives are often in conflict, we cannot find a single best solution, so we analyze a set of solutions. We first propose and analyze a new learning principle, empirically efficient learning. From a sample complexity perspective, following this principle is not much worse than the single-objective multi-task learning case. In the context of empirically efficient learning, algorithms for the new learning frameworks are proposed and evaluated. First, we pose regularization as a multi-objective problem, in which training error must balance the complexity of the hypothesis space. Second, we consider multiple data-dependent loss functions, in which the error rate in one class must balance the error rate in the other class. Finally, we assume that tasks share a clustering structure in which the average loss in one cluster must balance the loss in another cluster. The algorithms are evaluated on synthetic and real datasets. The results motivate the application of multi-objective optimization, indicating that the objectives are in conflict. By controlling the relative performance of the algorithms to generate a tradeoff surface, we can effectively explore the multi-objective nature of the learning problem.