Miracle-dz/UniFAR-FTUs huggingface dataset:
High Intensity Training Improves Health and Physical Function in Middle Aged Adults

High intensity training (HIT) is effective at improving health; however, it is unknown whether HIT also improves physical function. This study aimed to determine whether HIT improves metabolic health and physical function in untrained middle aged individuals. Fourteen (three male and eleven female) untrained individuals were recruited (control group n = 6: age 42 ± 8 y, weight 64 ± 10 kg, BMI 24 ± 2 kg·m−2 or HIT group n = 8: age 43 ± 8 y, weight 80 ± 8 kg, BMI 29 ± 5 kg·m−2). Training was performed twice weekly, consisting of 10 × 6-second sprints with a one minute recovery between each sprint. Metabolic health (oral glucose tolerance test), aerobic capacity (incremental time to exhaustion on a cycle ergometer) and physical function (get up and go test, sit to stand test and loaded 50 m walk) were determined before and after training. Following eight weeks of HIT there was a significant improvement in aerobic capacity (8% increase in VO2 peak; p < 0.001), physical function (11%–27% respectively; p < 0.05) and a reduction in blood glucose area under the curve (6% reduction; p < 0.05). This study demonstrates for the first time the potential of HIT as a training intervention to improve skeletal muscle function and glucose clearance as we age.
RAMP

RAMP (Reduce And Map Provenance) is an extension to Hadoop that supports provenance capture and tracing for workflows of MapReduce jobs. RAMP uses a wrapper-based approach, requiring little if any user intervention in most cases, while retaining Hadoop's parallel execution and fault tolerance. We demonstrate RAMP on a real-world MapReduce workflow generated from a Pig script that performs sentiment analysis over Twitter data. We show how RAMP's automatic provenance capture and tracing capabilities provide a convenient and efficient means of drilling-down and verifying output elements.
Validación y adaptación al español de la escala del entorno de práctica enfermera del Nursing Work Index.

Objetivo: determinar la validez y fiabilidad de una version adaptada culturalmente al entorno espanol de la escala de entorno de practica enfermera del Nursing Work Index para su uso en Atencion Primaria y hospitalaria. Material y metodo: estudio descriptivo, multicentrico, transversal, de adaptacion transcultural y validacion. Se llevo a cabo un proceso de traduccion-retrotraduccion siguiendo la metodologia por la Comision Internacional de Tests. Fueron seleccionadas 291 enfermeras del Servei de Salut de les Illes Balears pertenecientes a 5 hospitales, 1 centro socio sanitario y 18 centros de Atencion Primaria. Para el analisis de los datos se realizo un analisis descriptivo, pruebas de normalidad, analisis de fiabilidad mediante coeficiente Alfa de Cronbach y analisis factorial exploratorio y confirmatorio. Resultados: la fiabilidad global mostro un alfa de Cronbach de 0,906. El analisis factorial exploratorio replicaba correctamente la estructura pentafactorial (participacion de la enfermera en asuntos del centro; fundamento enfermero de la calidad de los cuidados; capacidad, liderazgo y apoyo a las enfermeras por parte de los gestores enfermeros; dimension de la plantilla y adecuacion de los recursos humanos; y relaciones entre medicos y enfermeras) original salvo para un factor. El analisis factorial confirmatorio mostro un buen ajuste tras la eliminacion de un item. Conclusiones: la disponibilidad de instrumentos validos y fiables que permitan evaluar factores del entorno de practica enfermera en las organizaciones, resulta de una utilidad enorme en la gestion de cuidados. Los resultados de este estudio ponen de manifiesto que la version adaptada del PES-NWI con 30 items es un instrumento valido para su uso en el contexto espanol.
Depth Estimation from Image Structure

In the absence of cues for absolute depth measurements as binocular disparity, motion, or defocus, the absolute distance between the observer and a scene cannot be measured. The interpretation of shading, edges, and junctions may provide a 3D model of the scene but it will not provide information about the actual "scale" of the space. One possible source of information for absolute depth estimation is the image size of known objects. However, object recognition, under unconstrained conditions, remains difficult and unreliable for current computational approaches. We propose a source of information for absolute depth estimation based on the whole scene structure that does not rely on specific objects. We demonstrate that, by recognizing the properties of the structures present in the image, we can infer the scale of the scene and, therefore, its absolute mean depth. We illustrate the interest in computing the mean depth of the scene with application to scene recognition and object detection.
Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model's parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.
Application Feedback in Guiding a Deep-Layered Perception Model

Deep-layer machine learning architectures continue to emerge as a promising biologically-inspired framework for achieving scalable perception in artificial agents. State inference is a consequence of robust perception, allowing the agent to interpret the environment with which it interacts and map such interpretation to desirable actions. However, in existing deep learning schemes, the perception process is guided purely by spatial regularities in the observations, with no feedback provided from the target application (e.g. classification, control). In this paper, we propose a simple yet powerful feedback mechanism, based on adjusting the sample presentation distribution, which guides the perception model in allocating resources for patterns observed. As a result, a much more focused state inference can be achieved leading to greater accuracy and overall performance. The proposed paradigm is demonstrated on a small-scale yet complex image recognition task, clearly illustrating the advantage of incorporating feedback in a deep-learning based cognitive architecture.
Safety improvement in vehiclar communication systems

In this paper, we propose a transportation safety algorithm based on vehicle to vehicle communication (V2V) and explain its implementation. The proposed algorithm benefit from the information that a host vehicle can get from target vehicle to calculate the time to collision (TTC) and put thresholds for this calculated parameter to make two level collision avoidance, warning and full break according to predefined transportation scenarios. The traffic scenarios of interest in this research is the Rear-end/Forward Collision Avoidance (FCA) and Intersection Collision Avoidance (ICA). The experimental work are implemented and simulated by using the combined tools ‘Prescan’[1] and ‘MATLAB’. The main contribution in this work is to avoid the collision by adapting the vehicle's velocity not by just stopping it. The proposed algorithms are confirmed from simulation results. Contently, these algorithms can be used as a general method to reduce both the time and cost consumed for commercialization of safety services under the umbrella of IEEE 802.11p/wireless access in vehicular environments wireless networks (WAVE).
Deep neural networks for syllable based acoustic modeling in Chinese speech recognition

Recently, the deep neural networks (DNNs) based acoustic modeling methods have been successfully applied to many speech recognition tasks. This paper reports the work about applying DNNs for syllable based acoustic modeling in Chinese automatic speech recognition (ASR). Compared with initial/finals (IFs), syllable can implicitly model the intra-syllable variations in better accuracy. However, the context dependent syllable based modeling set holds too many units, bringing about heavy problems on modeling and decoding implementation. In this paper, a WFST decoding framework is applied. Moreover, the decision tree based state tying and DNNs based models are discussed for the acoustic model training. The experimental results show that compared with the traditional IFs based modeling method, the proposed syllable modeling method using DNNs is more robust for data sparsity problem, which indicates that it has the potential to obtain better performance for Chinese ASR.
A Geographically Temporal Weighted Regression Approach with Travel Distance for House Price Estimation

Previous studies have demonstrated that non-Euclidean distance metrics can improve model fit in the geographically weighted regression (GWR) model. However, the GWR model often considers spatial nonstationarity and does not address variations in local temporal issues. Therefore, this paper explores a geographically temporal weighted regression (GTWR) approach that accounts for both spatial and temporal nonstationarity simultaneously to estimate house prices based on travel time distance metrics. Using house price data collected between 1980 and 2016, the house price response and explanatory variables are then modeled using both the GWR and the GTWR approaches. Comparing the GWR model with Euclidean and travel distance metrics, the GTWR model with travel distance obtains the highest value for the coefficient of determination ( R 2 ) and the lowest values for the Akaike information criterion (AIC). The results show that the GTWR model provides a relatively high goodness of fit and sufficient space-time explanatory power with non-Euclidean distance metrics. The results of this study can be used to formulate more effective policies for real estate management.
Analysis-by-synthesis based switched transform domain split VQ using Gaussian mixture model

Using analysis-by-synthesis (AbS) approach, we develop a soft decision based switched vector quantization (VQ) method for high quality and low complexity coding of wideband speech line spectral frequency (LSF) parameters. For each switching region, a low complexity transform domain split VQ (TrSVQ) is designed. The overall rate-distortion (R/D) performance optimality of new switched quantizer is addressed in the Gaussian mixture model (GMM) based parametric framework. In the AbS approach, the reduction of quantization complexity is achieved through the use of nearest neighbor (NN) TrSVQs and splitting the transform domain vector into higher number of subvectors. Compared to the current LSF quantization methods, the new method is shown to provide competitive or better trade-off between R/D performance and complexity.
The Coronary Dilation Effect of Shen Fu Injection Was Mediated through NO

Objectives Shen Fu Injection (SF), which consisted of Red ginseng extraction injection (RG) and prepared aconite extraction injection (RA), is a traditional Chinese medicine mainly used for various cardiac diseases. This study is to analyse SF's effects on cardiac performance and coronary circulation. And the coronary dilating effect and mechanism of the above three injections were also explored. Methods Mature male guinea pigs were used as our animal model. We employed two types of perfusion methods (constant pressure and constant flow) in vitro, using Langendorff heart preparations to observe the cardiac function and coronary response to SF (1/200). The coronary dilation effects of the above three injections (1/800, 1/400 and 1/200) were recorded at basal coronary resting tone and when coronary vessels were pre-contracted with a thromboxane A2 analogue (U46619), in the presence or the absence of the inhibitor of nitric oxide synthesis (L-NAME, 10−4 M), the blocker of Ca2+-activated potassium channel(TEA, 10−3 M), or the blocker of adenosine triphosphate (ATP)-sensitive potassium channel (glybenclamide) (10−5 M). Results When perfused with constant pressure, SF significantly increased coronary flow, left ventricular developed pressure (LVDP) and the rate-pressure product (RPP). When perfused with constant flow, SF produced a significant reduction in the coronary perfusion pressure (CPP), LVDP and RPP. The coronary vasodilatation response of the above three injections can be reduced by L-NAME but was unaffected by TEA or glybenclamide when coronary vessels were pre-contracted with U46619 but not at resting tone. SF, RG and RA can all up-regulate eNOS expression in the human umbilical vein cells (EA.hy926). Conclusion We demonstrated that SF does not contribute to the inotropic change of myocardium whose improvement is due to alternation of coronary flow. The coronary dilation effect of SF was mediated through RG and RA, via promoting NO release.
Adaptive Swarm Formation Control for Hybrid Ground and Aerial Assets

The use of Unmanned Aerial Vehicles (UAVs) with Unmanned Ground Vehicles (UGVs) allows for cooperation, coordination, and tight or loose collaboration related to multiple missions. UAVs can provide a global perspective of the surrounding environment, obstacles, and possible threats, broadcasting goals, sub-goals and alterations to the overall mission of the swarm. Further, the deployment of UAVs creates a 3-D sensor network increasing communication capabilities allowing for more complete information about the environment. UAV-UGV coordination has obvious applicability in military applications due to the line of sight issue. Air vehicles can detect items of interest long before UGVs. Related literature in the area refers to general frameworks and simulation results only. In (Chaimowicz and Kumar 2004; Chaimowicz and Kumar 2004), UGV swarms are coordinated and directed by “shepherd” UAVs. A hierarchy is formed between the UAV and the UGVs. UAVs are responsible for grouping and merging swarms as well as controlling swarm distributions and motion. In (Sukhatme, Montgomery et al. 2001), an architecture is proposed for coordinating an autonomous helicopter and a group of UGVs using decentralized controllers. In (Tanner 2007), an approach is proposed to coordinate groups of ground and aerial vehicles for the purpose of locating a moving target in a given area. This is done by combining decentralized flocking algorithms with navigation functions. Other instances utilizing coordination between air and ground vehicles can be seen in (Elfes, Bergerman et al. 1999; Lacroix, Jung et al. 2001; Stentz, Kelly et al. 2002). In this work, the problem of controlling and coordinating heterogeneous unmanned systems required to move as a group is addressed. A strategy is proposed to coordinate groups of Unmaned Ground Vehicles with one or more Unmanned Aerial Vehicles (UAVs). UAVs can be utilized in one of two ways: (1) as alpha robots to guide and oversee the UGVs; and (2) as beta robots to surround the UGVs and adapt accordingly. In the first approach, the UAV guides a swarm of UGVs controlling their overall formation. In the second approach, the 17
An efficient approach for generating frequent patterns without candidate generation

Research on biological science is an emerging and critical task to solve crucial problems, especially in prediction of diseases, drug discovery, etc. Our approach aims at extracting the hidden and the most dominating amino acids among the infected protein sequence which causes some infections in human. We handle this problem by predicting patterns without using candidate generation and apply strong association rules over the predicted patterns. The dominating amino acids have been identified using support and confidence measure threshold. We found that association rules can reveal biologically relevant associations between different gene and gene expressions, protein and protein sequences. We have applied this technique over the chromaffin tumor found in the adrenal glands of a human kidney and found the results to be positive.
Short paper: location privacy: user behavior in the field

Current smartphone platforms provide ways for users to control access to information about their location. For instance, on the iPhone, when an application requests access to location information, the operating system asks the user whether to grant location access to this application. In this paper, we study how users are using these controls. Do iPhone users allow applications to access their location? Do their decisions differ from application to application? Can we predict how a user will respond for a particular application, given their past responses for other applications? We gather data from iPhone users that sheds new light on these questions. Our results indicate that there are different classes of users: some deny all applications access to their location, some allow all applications access to their location, and some selectively permit a fraction of their applications to access their location. We also find that apps can be separated into different classes by what fraction of users trust the app with their location data. Finally, we investigate using machine learning techniques to predict users' location-sharing decisions; we find that we are sometimes able to predict the user's actual choice, though there is considerable room for improvement. If it is possible to improve the accuracy rate further, this information could be used to relieve users of the cognitive burden of individually assigning location permissions for each application, allowing users to focus their attention on more critical matters.
Applying LDPC Codes to the GPRS

For GPRS the well-known convolutional code is currently used as a Forward Error Correction (FEC) mechanism, and the Viterbi algorithm for decoding. With the recent introduction of Multimedia Broadcast Multicast Services (MBMS), new applications that require more bandwidth and reliability are foreseen. It is known that the current GPRS system does not perform well enough without further modifications, therefore, more efficient information distribution schemes need to be considered. To meet these increased requirements, especially for MBMS, we study the introduction of carefully designed LDPC codes at the physical layer. To exploit the full benefit of the superior performance of LDPC codes for longer streams in MBMS, a modification in the GPRS protocol stack is proposed where larger RLC/MAC blocks are allowed, and its impact on the existing GPRS protocol stack is minimized.
Calculation of magnet losses in concentrated-winding permanent magnet synchronous machines using a Computationally Efficient - Finite Element method

The proposed hybrid method combines Computationally Efficient-Finite Element Analysis (CE-FEA) with a new analytical formulation for eddy-current losses in the permanent magnets (PMs) of sine-wave current regulated brushless synchronous motors. The CE-FEA only employs a reduced set of magnetostatic solutions yielding substantial reductions in the computational time as compared with conventional FEA. The 3D end effects and the effect of PWM switching harmonics are incorporated in the analytical calculations. The algorithms are applied to two fractional-slot concentrated-winding interior permanent magnet motors with different circumferential and axial PM block segmentation arrangements. The method is validated against 2D and 3D Time Stepping (TS) FEA.
An Improved Algorithm for Constructing kth-Order Voronoi Diagrams

The kth-order Voronoi diagram of a finite set of sites in the Euclidean plane E<sup>2</sup> subdivides E<sup>2</sup> into maximal regions such that all points within a given region have the same k nearest sites. Two versions of an algorithm are developed for constructing the kth-order Voronoi diagram of a set of n sites in O(n<sup>2</sup> log n + k(n - k) log<sup>2</sup> n) time, O(k(n - k)) storage, and in O(n<sup>2</sup> + k(n - k) log<sup>2</sup> n) time, O(n<sup>2</sup>) storage, respectively.
Properties of IA in a serotonergic neuron of the dorsal raphe nucleus

Voltage clamp data were analyzed in order to characterize the properties of the fast potassium transient current IA for a serotonergic neuron of the rat dorsal raphe nucleus (DRN). We obtain maximal conductance, time constants of activation and inactivation, and the steady state activation and inactivation functions m1 and h1, as Boltzmann curves, dened by half-activation potentials and slope factors. We employ a novel method to accurately obtain the activation function and compare the results with those obtained by other methods. The form of IA is estimated as g(V Vrev)m 4 h, with g = 20:5 nS. For activation, the half-activation potential is Va = 52:5 mV with slope factor ka = 16:5 mV, whereas for inactivation the corresponding quantities are -91.5 mV and -9.3 mV. We discuss the results in terms of the corresponding properties of IA in other cell types and their possible relevance to pacemaking activity in 5-HT cells of the DRN.
Rape Law Fundamentals

Modern American rape law is the product of historical contingencies, compromises, legislative inattention, successful reforms, and backlash. It is neither a puzzle to be solved nor a coherent system of rules and values. Perhaps the clearest lesson to draw from our criminal laws regarding sex is that there is no logic, reason, or consistency among them. As a result of its checkered past, myths and misunderstandings about rape law abound. Jed Rubenfeld’s recent article, The Riddle of Rape-by-Deception and the Myth of Sexual Autonomy, exemplifies the confusion many courts, scholars, and members of the public have about modern rape law. Rubenfeld’s specific proposal to base rape statutes on a right to self-possession, because it is derived from mistaken premises about rape law, would likely make legal over 90% of rapes in America. By replacing the non-consent element of modern rape statutes with a narrow force requirement, Rubenfeld’s recommendation essentially decriminalizes non-stranger rape and rape by a victim’s intoxication.In this Article, I examine the missteps Rubenfeld makes to explain why he ends up supporting such a disastrous conclusion. For example, Rubenfeld sees the need for his right to self-possession theory because he believes that autonomy is the sole basis that scholars offer for the foundation of rape law. However, rape is also properly supported as an independent offense by the nature and severity of harm caused, gender dynamics involved, and terror inflicted on the general population by widespread sexual violence. He also uses specious analogies and idiosyncratic conceptions of autonomy to establish the critical components of his argument.Despite the faults of his specific claims, Rubenfeld points rape scholars in a worthwhile direction. Instead of seeking diminishing returns with statutory tinkering, there is much to be gained by focusing on the foundations of rape law. By better integrating the fundamental values of rape law (autonomy, gender, harm, and terror), the major problems of high rape prevalence, law enforcement failure, and political hostility to rape victims that plague America can be better addressed.