Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of leisure activity on the incidence of Alzheimer’s Disease

Objective: To determine whether leisure activities modify the risk for incident dementia. Background: Although high educational and occupational attainments have been associated with reduced risk of incident dementia, the relation between leisure activities and dementia risk has not been adequately investigated. Methods: A total of 1,772 nondemented individuals aged 65 years or older, living in northern Manhattan, New York, were identified and followed longitudinally in a community-based cohort incidence study. Subjects’ leisure activities at baseline were assessed, annual examinations with the same standardized neurologic and neuropsychological measures were performed for up to 7 years (mean 2.9 years), and incident dementia was assessed as the main outcome measure. Cox proportional hazards models, adjusting for age, ethnic group, education, and occupation, were used to estimate the relative risk (RR) of incident dementia associated with high leisure activities. Results: Of the 1,772 subjects, 207 became demented. The risk of dementia was decreased in subjects with high leisure activities (RR, 0.62; 95% CI 0.46 to 0.83). The association of high leisure with decreased RR of incident dementia was present even when baseline cognitive performance, health limitations interfering with desired leisure activities, cerebrovascular disease, and depression were considered. Conclusions: The data suggest that engagement in leisure activities may reduce the risk of incident dementia, possibly by providing a reserve that delays the onset of clinical manifestations of the disease.
Inter-Operator Feedback in Data Stream Management Systems via Punctuation

High-volume, high-speed data streams may overwhelm the capabilities of stream processing systems; techniques such as data prioritization, avoidance of unnecessary processing and on-demand result production may be necessary to reduce processing requirements. However, the dynamic nature of data streams, in terms of both rate and content, makes the application of such techniques challenging. Such techniques have been addressed in the context of static and centralized query optimization; however, they have not been fully addressed for data stream management systems. In this work, we present a comprehensive framework that supports prioritization, avoidance of unnecessary work, and on-demand result production over distributed, unreliable, bursty, disordered data sources, typical of many data streams. We propose a form of inter-operator feedback, which flows against the stream direction, to communicate the information needed to enable execution of these techniques. This feedback leverages punctuations to describe the subsets of interest. We identify potential sources of feedback information, characterize new types of punctuation to support feedback, and describe the roles of producers, exploiters, and relayers of feedback that query operators may implement. We present initial experimental observations using the NiagaraST data-stream system.
Local Three-dimensional Simulations of Magnetorotational Instability in Radiation-dominated Accretion Disks

We examine the small-scale dynamics of black hole accretion disks in which radiation pressure exceeds gas pressure. Local patches of disk are modeled by numerically integrating the equations of radiation MHD in the flux-limited diffusion approximation. The shearing-box approximation is used, and the vertical component of gravity is neglected. Magnetorotational instability (MRI) leads to turbulence in which accretion stresses are due primarily to magnetic torques. When radiation is locked to gas over the length and timescales of fluctuations in the turbulence, the accretion stress, density contrast, and dissipation differ little from those in the corresponding calculations, with radiation replaced by extra gas pressure. However, when radiation diffuses each orbit a distance that is comparable to the rms vertical wavelength of the MRI, radiation pressure is less effective in resisting squeezing. Large density fluctuations occur, and radiation damping of compressive motions converts P dV work into photon energy. The accretion stress in calculations having a net vertical magnetic field is found to be independent of opacity over the range explored and approximately proportional to the square of the net field. In calculations with zero net magnetic flux, the accretion stress depends on the portion of the total pressure that is effective in resisting compression. The stress is lower when radiation diffuses rapidly with respect to the gas. We show that radiation-supported Shakura-Sunyaev disks accreting via internal magnetic stresses are likely to have radiation marginally coupled to turbulent gas motions in their interiors.
The Effect of Resource Aggregation at Different Scales: Optimal Foraging Behavior of Cotesia rubecula

Resources can be aggregated both within and between patches. In this article, we examine how aggregation at these different scales influences the behavior and performance of foragers. We developed an optimal foraging model of the foraging behavior of the parasitoid wasp Cotesia rubecula parasitizing the larvae of the cabbage butterfly Pieris rapae. The optimal behavior was found using stochastic dynamic programming. The most interesting and novel result is that the effect of resource aggregation within and between patches depends on the degree of aggregation both within and between patches as well as on the local host density in the occupied patch, but lifetime reproductive success depends only on aggregation within patches. Our findings have profound implications for the way in which we measure heterogeneity at different scales and model the response of organisms to spatial heterogeneity.
CORRELATION OF BLOOD GLUCOSE AND LIVER GLYCOGEN PROFILE OF JAPANESE QUAIL AT DIFFERENT STAGES OF GROWTH

Although considerable informations are available on the carbohydrate metabolism of mammals, that pertaining to avian species is scanty. According to Ganong (1991) liver functions as a sort of glucostat maintaining a constant circulating blood glucose level. The present study was undertaken to assess the normal concentration of blood glucose, liver glycogen content and their correlation to different stages of growth in order to find out the carbohydrate status of the bird and the role of liver in carbohydrate metabolism of birds in general and that of Japanese quail in particular.
Guiding human motions in physical human-robot interaction through COM motion control of a dance teaching robot

This paper presents an approach for robot leading in physical human-robot interaction during dance training and support with a mobile humanoid robot. In this highly coupled physical interaction, we convey the direction of motion by varying the center of mass (COM) elevation. This method increases the human partner's dependency on the robot for stability, allowing the robot to guide the interaction. To achieve a human-like leading, we based the robot control on an impedance behavior while following a desired trajectory. In experimental tests, the COM control improved the understanding of motion direction from 70% to 90%. Moreover, it reduced the interaction forces by 10N (on average) during the guidance. We conclude that the COM control enhances the synchronization of the moving human-robot couple, and encourages humanlike behavior of the humanoid robot. Consequently, the human partner can better understand the humanoid robot, especially when the robot takes the leading role.
A Probabilistic Method for Analyzing Japanese Anaphora Integrating Zero Pronoun Detection and Resolution

This paper proposes a method to analyze Japanese anaphora, in which zero pronouns (omitted obligatory cases) are used to refer to preceding entities (antecedents). Unlike the case of general coreference resolution, zero pronouns have to be detected prior to resolution because they are not expressed in discourse. Our method integrates two probability parameters to perform zero pronoun detection and resolution in a single framework. The first parameter quantifies the degree to which a given case is a zero pronoun. The second parameter quantifies the degree to which a given entity is the antecedent for a detected zero pronoun. To compute these parameters efficiently, we use corpora with/without annotations of anaphoric relations. We show the effectiveness of our method by way of experiments.
Discrepancy, chaining and subgaussian processes

The research leading to the results presented here has received funding from the European Research Council under the European Community’s Seventh Frame-work Programme (FP7/2007-2013)/ERC Grant Agreement [203134], from the Israel Science Foundation Grant 666/06 and from the Australian Research Council Grant DP0986563.
VLSI implementation of type-2 fuzzy inference processor

The main concept of type-2 fuzzy logic is that "words mean different things to different people"; thus, there are uncertainties associated with words. Recently, a lot of mathematical theorems have been developed for type-2 fuzzy logic. However, to the best of our knowledge, there has been no hardware implementation. In this paper, we propose the first hardware architecture to support the type-2 fuzzy inference execution. Moreover, we also use a 0.35/spl mu/m cell library to implement the proposed architecture. Implementation data show that the inference speed reaches 3.125 MFLIPS with 2 inputs, 1output and 64 rules.
Program analysis as constraint solving

A constraint-based approach to invariant generation in programs translates a program into constraints that are solved using off-the-shelf constraint solvers to yield desired program invariants. In this paper we show how the constraint-based approach can be used to model a wide spectrum of program analyses in an expressive domain containing disjunctions and conjunctions of linear inequalities. In particular, we show how to model the problem of context-sensitive interprocedural program verification. We also present the first constraint-based approach to weakest precondition and strongest postcondition inference. The constraints we generate are boolean combinations of quadratic inequalities over integer variables. We reduce these constraints to SAT formulae using bitvector modeling and use off-the-shelf SAT solvers to solve them. Furthermore, we present interesting applications of the above analyses, namely bounds analysis and generation of most-general counter-examples for both safety and termination properties. We also present encouraging preliminary experimental results demonstrating the feasibility of our technique on a variety of challenging examples.
Quantification of Blood Flow in the Carotid Arteries: Comparison of Doppler Ultrasound and Three Different Phase-Contrast Magnetic Resonance Imaging Sequences

Seitz J, Strotzer M, Wild T, et al. Quantification of blood flow in the carotid arteries: Comparison of Doppler ultrasound and three different phase-contrast magnetic resonance imaging sequences. Invest Radiol 2001;36:642-647. rationale and objectives. To compare blood flow velocities in the carotid arteries measured with three different magnetic resonance (MR) phase-contrast imaging techniques and with percutaneous Doppler ultrasound. methods.Fourteen healthy male volunteers with a mean age of 33 ± 3.8 years were studied. Ultrasound and MR phase velocity mapping of both common carotid arteries (n = 28) was performed within 5 hours. A two-dimensional fast low-angle shot sequence with retrospective cardiac gating, a sequence with prospective cardiac triggering, and a breath-hold sequence with prospective cardiac triggering were used. Resistance indexes and pulsatility indexes were calculated for all modalities. results.The comparison of flow velocities obtained with ultrasound and the different MR techniques led to a moderate correlation of the retrospective gated and prospective triggered MR techniques (eg, r = 0.73 for maximum systolic velocity). The worst correlation was found between the breath-hold technique and retrospective cardiac gating (eg, r = 0.004 for pulsatility index). There was a weak correlation of all three MR sequences compared with ultrasound (r = 0.19–0.60) conclusions.A moderate correlation was found between velocities and indexes measured with the prospective cardiac-triggered phase-contrast MR technique and the retrospective cardiac-gated phase-contrast MR technique. A weak correlation was found between the three different MR techniques and ultrasound, as well as between the breath-hold prospective cardiac-triggered MR sequence and both of the other MR sequences. The influence of temporal and spatial resolution on MR phase-contrast velocity mapping was confirmed.
Microwave Involved Synthesis of Graphene/Polyaniline Nanocomposite with Superior Electrochemical Performance

A microwave irradiation involved process was applied to fabrication of graphene/polyaniline nanocomposite via in-situ polymerization of aniline monomers on graphene sheets. Structure and morphology of composites were characterized through scanning electron microscopy, Raman spectra, X-ray diffraction, Fourier transform infrared spectroscopy and X-ray photoelectron spectra. Electrochemical performances for energy storage applications were examined by cyclic voltammetry, electrochemical impedance spectroscopy and galvanostatic charge/discharge measurements. Owing to the homogeneous coating of polyanilline on the large surface of graphene, graphene/polyaniline composite-based electrode exhibits remarkably enhanced capacitive behavior with a specific capacitance of 429 F/g at 0.2 A g-1, a good cyclic stability and an excellent conducting behavior, which are much superior to those of individual components of composites. The improved electrochemical behavior of the composite resulting from the irradiation of microwave suggests the promising potentials for supercapacitors.
Design of high torque and high speed leg module for high power humanoid

The high power ability of humanoid is desired for application of nursing or running or jumping motions. Achievement of the actuator of light and powerful equivalent to humans is required. In this paper, we propose a method to extract inherent performance from motors by an active temperature control. The method safely improves the output of motors. The active temperature control is achieved by combining the estimation of an internal temperature of the motor with the forced cooling by liquid. We also developed high power motor drivers for the proposed method. An experiment of a high power joint test bench is shown. In this paper, we show a high power prototype biped robot for application of nursing, running or jumping motions. High power actuator system and robust internal body network are developed for high power robot. Basic demonstration experiments of high power motion are shown.
Demand based crop recommender system for farmers

About half of the population of India depends on agriculture for its livelihood, but its contribution towards the GDP of India is only 14 per cent. One possible reason for this is the lack of adequate crop planning by farmers. There is no system in place to advice farmers what crops to grow. In this paper we present an attempt to predict crop yield and price that a farmer can obtain from his land, by analysing patterns in past data. We make use of a sliding window non-linear regression technique to predict based on different factors affecting agricultural production such as rainfall, temperature, market prices, area of land and past yield of a crop. The analysis is done for several districts of the state of Tamilnadu, India. Our system intends to suggest the best crop choices for a farmer to adapt to the demand of the prevailing social crisis facing many farmers today.
Gastroenteritis Attributable to 16 Enteropathogens in Children Attending Day Care: Significant Effects of Rotavirus, Norovirus, Astrovirus, Cryptosporidium and Giardia

Background: Children attending day care experience substantial gastrointestinal morbidity due to circulating seasonal enteropathogens in the day-care environment. The lack of a distinct clinical presentation of gastroenteritis (GE) in these children, in combination with the high diversity of enteropathogenic agents, complicates the assessment of the individual contributions of enteropathogens that may cause GE. We aimed to estimate the proportion of day-care attendees experiencing GE that could be attributed to a range of enteropathogens circulating in day care in the Netherlands in 2010–2013. Methods: Using time-series data from a national laboratory-based and syndrome-based surveillance system in Dutch day-care centers and generalized estimating equation analysis, we modelled the variation in prevalence of 16 enteropathogens of bacterial (8), viral (5) and parasitic origin (3) circulating in day care to the variation of GE incidence among children attending day care. Results: Rotavirus, norovirus, astrovirus, Giardia and Cryptosporidium were significantly associated with GE morbidity among day-care attendees in our time-series analysis. Together, these enteropathogens accounted for 39% of the GE morbidity: 11% by rotavirus, 10% by norovirus, 8% by Giardia, 7% by astrovirus and 3% by Cryptosporidium. Conclusions: We demonstrate that circulating viruses and parasites, rather than bacteria, contribute to seasonal GE experienced by children in day care.
On the spectral geometry of spaces with cone-like singularities.

I describe an extension of a portion of the theory of the Laplace operator on compact riemannian manifolds to certain spaces with singularities. Although this approach can be extended to include quite general spaces, this paper will confine itself to the case of manifolds with cone-like singularities. These singularities are geometrically the simplest possible, but they already serve to illustrate new phenomena that are typical of the more general situation. Moreover, by inductive arguments, the study of simplicial complexes whose simplices have constant curvature and totally geodesic faces (e.g., p.l. manifolds) can in large measure be reduced to the study of cone-like singularities.
Training of Manual Actions Improves Language Understanding of Semantically Related Action Sentences

Conceptual knowledge accessed by language may involve the reactivation of the associated primary sensory-motor processes. Whether these embodied representations are indeed constitutive to conceptual knowledge is hotly debated, particularly since direct evidence that sensory-motor expertise can improve conceptual processing is scarce. In this study, we sought for this crucial piece of evidence, by training naive healthy subjects to perform complex manual actions and by measuring, before and after training, their performance in a semantic language task. Nineteen participants engaged in 3 weeks of motor training. Each participant was trained in three complex manual actions (e.g., origami). Before and after the training period, each subject underwent a series of manual dexterity tests and a semantic language task. The latter consisted of a sentence-picture semantic congruency judgment task, with 6 target congruent sentence-picture pairs (semantically related to the trained manual actions), 6 non-target congruent pairs (semantically unrelated), and 12 filler incongruent pairs. Manual action training induced a significant improvement in all manual dexterity tests, demonstrating the successful acquisition of sensory-motor expertise. In the semantic language task, the reaction times (RTs) to both target and non-target congruent sentence-picture pairs decreased after action training, indicating a more efficient conceptual-semantic processing. Noteworthy, the RTs for target pairs decreased more than those for non-target pairs, as indicated by the 2 × 2 interaction. These results were confirmed when controlling for the potential bias of increased frequency of use of target lexical items during manual training. The results of the present study suggest that sensory-motor expertise gained by training of specific manual actions can lead to an improvement of cognitive-linguistic skills related to the specific conceptual-semantic domain associated to the trained actions.
Adaptive Temporal Radio Maps for Indoor Location Estimation

In this paper, we present a novel method to adapt the temporal radio maps for indoor location estimation by offsetting the variational environmental factors using data mining techniques and reference points. Environmental variations, which cause the signals to change from time to time even at the same location, present a challenging task for indoor location estimation in the IEEE 802.11b infrastructure. In such a dynamic environment, the radio maps obtained in one time period may not be applicable in other time periods. To solve this problem, we apply a regression analysis to learn the temporal predictive relationship between the signal-strength values received by sparsely located reference points and that received by the mobile device. This temporal prediction model can then be used for online localization based on the newly observed signal-strength values at the client side and the reference points. We show that this technique can effectively accommodate the variations of signal-strength values over different time periods without the need to rebuild the radio maps repeatedly. We also show that the location of mobile device can be accurately determined using this technique with lower density in the distribution of the reference points
The Equi-Correlation Network: a New Kernelized-LARS with Automatic Kernel Parameters Tuning

Machine learning heavily relies on the ability to learn/approximate real functions. State variables, the perceptions, internal states, etc, of an agent are often represented as real numbers; grounded on them, the agent has to predict something, or act in some way. In this view, this outcome is a nonlinear function of the inputs. It is thus a very common task to fit a nonlinear function to observations, namely solving a regression problem. Among other approaches, the LARS is very appealing, for its nice theoretical properties, and actual efficiency to compute the whole $l_1$ regularization path of a supervised learning problem, along with the sparsity. In this paper, we consider the kernelized version of the LARS. In this setting, kernel functions generally have some parameters that have to be tuned. In this paper, we propose a new algorithm, the Equi-Correlation Network (ECON), which originality is that while computing the regularization path, ECON automatically tunes kernel hyper-parameters; thus, this opens the way to working with infinitely many kernel functions, from which, the most interesting are selected. Interestingly, our algorithm is still computationaly efficient, and provide state-of-the-art results on standard benchmarks, while lessening the hand-tuning burden.