Miracle-dz/UniFAR-FTUs huggingface dataset:
Sports Participation in Youth as a Predictor of Physical Activity: A 5-Year Longitudinal Study.

BACKGROUND The contribution of sports related factors to predicting long-term physical activity (PA) are unclear. The purpose of this study is to examine tracking of PA during key transition periods in youth and to determine the longitudinal associations between sports club participation and PA. METHODS Participants (n = 873, baseline age 10 to 18 years) completed self-report surveys in 2009 and 2014 that included the PACE+ PA tool and sports club participation questions. Spearman correlations assessed PA tracking. ANCOVA analyses examined predictors (sports participation at baseline) of PA (follow-up), adjusting for (a) age and sex; and (b) age, sex, and baseline PA. RESULTS Tracking of PA was weak-to-moderate (ρ = .16 to .47). Greater sports participation frequency at baseline significantly predicted PA at follow-up (P < .01). Involvement in club sports at an elite level had a medium-to-large effect on PA levels 5 years later [d = . 75 adjusting for (a); d = . 60 adjusting for (b)]. CONCLUSION PA should be promoted in youth as tracking coefficients suggest it can, to an extent, continue into later life. The standard achieved in sport has a role in predicting later PA. PA promotion strategies should include frequent, high quality opportunities for sports participation.
Combining Car-to-Infrastructure Communication and Multi-Agent Reinforcement Learning in Route Choice

Route choice is an important stage in transport planning and modeling. Most of the existing approaches do not consider that road users can nowadays consult new technologies to plan their routes. In this paper, we combine multi-agent reinforcement learning (MARL) and car-to-infrastructure communication (C2I) to deal with route choice. The agents (road users) and the infrastructure interact with each other to exchange traffic information about the road network. The agents send the travel cost of the edges they crossed to the infrastructure. The infrastructure uses these costs to compute shortest paths, which are transmitted to the agents when requested. The agents use such received shortest path to update their knowledge base. The obtained results are compared against a classical MARL approach that does not use C2I communication. Experimental results show that our approach overcomes the compared method in terms of average travel cost.
Energy Savings Achievable in Connection Preserving Energy Saving Algorithms

Energy saving is an important design consideration in wireless sensor networks. In this paper, we analyze the energy savings that can be achieved in a sensor network where each sensor is capable of reducing its transmission power from a maximum power pm, compared with that in a sensor network where each sensor can only transmit at a constant power level pm. To achieve a fair comparison, we assume sensors in both types of sensor networks are connected to the same set of neighbors, i.e. no connection is lost as a result of a sensor reducing its transmission power. We further assume that sensors are distributed in a given area following a Poisson distribution with known node density and the radio propagation is described by a log-normal model. Ignoring boundary effect, we establish analytically the probability for a sensor to achieve an energy saving of at least h dB. We also obtain the expected percentage of energy savings which can be substantial. The research reported in the paper helps to answer questions such as whether the energy savings achieved by using a sensor with a variable-transmission-power (and the consequent extension of its lifetime) justify the additional cost involved in manufacturing it.
Helium Star/Black Hole Mergers: A New Gamma-Ray Burst Model

We present a model for gamma-ray bursts (GRBs) in which a stellar mass black hole acquires a massive accretion disk by merging with the helium core of its red giant companion. The black hole enters the helium core after it, or its neutron star progenitor, first experiences a common envelope phase that carries it inward through the hydrogen envelope. Accretion of the last several solar masses of helium occurs on a timescale of roughly a minute and provides a neutrino luminosity of approximately 1051-1052 ergs s−1. Neutrino annihilation, 0.01%-0.1% efficient, along the rotational axis then gives a baryon-loaded fireball of electron-positron pairs and radiation (about 1050 ergs total) whose beaming and relativistic interaction with the circumstellar material makes the GRB (see, e.g., Rees & Mészáros). The useful energy can be greatly increased if energy can be extracted from the rotational energy of the black hole by magnetic interaction with the disk. Such events should occur at a rate comparable to that of merging neutron stars and black hole neutron star pairs and may be responsible for long complex GRBs but not short hard ones.
Probabilistic Combination of Classifier and Cluster Ensembles for Non-transductive Learning

Unsupervised models can provide supplementary soft constraints to help classify new target data under the assumption that similar objects in the target set are more likely to share the same class label. Such models can also help detect possible differences between training and target distributions, which is useful in applications where concept drift may take place. This paper describes a Bayesian framework that takes as input class labels from existing classifiers (designed based on labeled data from the source domain), as well as cluster labels from a cluster ensemble operating solely on the target data to be classified, and yields a consensus labeling of the target data. This framework is particularly useful when the statistics of the target data drift or change from those of the training data. We also show that the proposed framework is privacy-aware and allows performing distributed learning when data/models have sharing restrictions. Experiments show that our framework can yield superior results to those provided by applying classifier ensembles only.
Invertebrate Metacommunity Structure and Dynamics in an Andean Glacial Stream Network Facing Climate Change

Under the ongoing climate change, understanding the mechanisms structuring the spatial distribution of aquatic species in glacial stream networks is of critical importance to predict the response of aquatic biodiversity in the face of glacier melting. In this study, we propose to use metacommunity theory as a conceptual framework to better understand how river network structure influences the spatial organization of aquatic communities in glacierized catchments. At 51 stream sites in an Andean glacierized catchment (Ecuador), we sampled benthic macroinvertebrates, measured physico-chemical and food resource conditions, and calculated geographical, altitudinal and glaciality distances among all sites. Using partial redundancy analysis, we partitioned community variation to evaluate the relative strength of environmental conditions (e.g., glaciality, food resource) vs. spatial processes (e.g., overland, watercourse, and downstream directional dispersal) in organizing the aquatic metacommunity. Results revealed that both environmental and spatial variables significantly explained community variation among sites. Among all environmental variables, the glacial influence component best explained community variation. Overland spatial variables based on geographical and altitudinal distances significantly affected community variation. Watercourse spatial variables based on glaciality distances had a unique significant effect on community variation. Within alpine catchment, glacial meltwater affects macroinvertebrate metacommunity structure in many ways. Indeed, the harsh environmental conditions characterizing glacial influence not only constitute the primary environmental filter but also, limit water-borne macroinvertebrate dispersal. Therefore, glacier runoff acts as an aquatic dispersal barrier, isolating species in headwater streams, and preventing non-adapted species to colonize throughout the entire stream network. Under a scenario of glacier runoff decrease, we expect a reduction in both environmental filtering and dispersal limitation, inducing a taxonomic homogenization of the aquatic fauna in glacierized catchments as well as the extinction of specialized species in headwater groundwater and glacier-fed streams, and consequently an irreversible reduction in regional diversity.
Cortical control of human motoneuron firing during isometric contraction.

We recorded whole scalp magnetoencephalographic (MEG) signals simultaneously with the surface electromyogram from upper and lower limb muscles of six healthy right-handed adults during voluntary isometric contraction. The 15- to 33-Hz MEG signals, originating from the anterior bank of the central sulcus, i.e., the primary motor cortex, were coherent with motor unit firing in all subjects and for all muscles. The coherent cortical rhythms originated in the hand motor area for upper limb muscles (1st dorsal interosseus, extensor indicis proprius, and biceps brachii) and close to the foot area for lower limb muscles (flexor hallucis brevis). The sites of origin corresponding to different upper limb muscles did not differ significantly. The cortical signals preceded motor unit firing by 12-53 ms. The lags were shortest for the biceps brachii and increased systematically with increasing corticomuscular distance. We suggest that the motor cortex drives the spinal motoneuronal pool during sustained contractions, with the observed cortical rhythmic activity influencing the timing of efferent commands. The cortical rhythms could be related to motor binding, but the rhythmic output may also serve to optimize motor cortex output during isometric contractions.
Text Mining to Decipher Free-Response Consumer Complaints

Objective: This study applies text mining to extract clusters of vehicle problems and associated trends from free-response data in the National Highway Traffic Safety Administration’s vehicle owner’s complaint database. Background: As the automotive industry adopts new technologies, it is important to systematically assess the effect of these changes on traffic safety. Driving simulators, naturalistic driving data, and crash databases all contribute to a better understanding of how drivers respond to changing vehicle technology, but other approaches, such as automated analysis of incident reports, are needed. Method: Free-response data from incidents representing two severity levels (fatal incidents and incidents involving injury) were analyzed using a text mining approach: latent semantic analysis (LSA). LSA and hierarchical clustering identified clusters of complaints for each severity level, which were compared and analyzed across time. Results: Cluster analysis identified eight clusters of fatal incidents and six clusters of incidents involving injury. Comparisons showed that although the airbag clusters across the two severity levels have the same most frequent terms, the circumstances around the incidents differ. The time trends show clear increases in complaints surrounding the Ford/Firestone tire recall and the Toyota unintended acceleration recall. Increases in complaints may be partially driven by these recall announcements and the associated media attention. Conclusion: Text mining can reveal useful information from free-response databases that would otherwise be prohibitively time-consuming and difficult to summarize manually. Application: Text mining can extend human analysis capabilities for large free-response databases to support earlier detection of problems and more timely safety interventions.
The Innate Immune Response in HIV/AIDS Septic Shock Patients: A Comparative Study

Introduction In recent years, the incidence of sepsis has increased in critically ill HIV/AIDS patients, and the presence of severe sepsis emerged as a major determinant of outcomes in this population. The inflammatory response and deregulated cytokine production play key roles in the pathophysiology of sepsis; however, these mechanisms have not been fully characterized in HIV/AIDS septic patients. Methods We conducted a prospective cohort study that included HIV/AIDS and non-HIV patients with septic shock. We measured clinical parameters and biomarkers (C-reactive protein and cytokine levels) on the first day of septic shock and compared these parameters between HIV/AIDS and non-HIV patients. Results We included 30 HIV/AIDS septic shock patients and 30 non-HIV septic shock patients. The HIV/AIDS patients presented low CD4 cell counts (72 [7-268] cells/mm3), and 17 (57%) patients were on HAART before hospital admission. Both groups were similar according to the acute severity scores and hospital mortality. The IL-6, IL-10 and G-CSF levels were associated with hospital mortality in the HIV/AIDS septic group; however, the CRP levels and the surrogates of innate immune activation (cytokines) were similar among HIV/AIDS and non-HIV septic patients. Age (odds ratio 1.05, CI 95% 1.02-1.09, p=0.002) and the IL-6 levels (odds ratio 1.00, CI 95% 1.00-1.01, p=0.05) were independent risk factors for hospital mortality. Conclusions IL-6, IL-10 and G-CSF are biomarkers that can be used to predict prognosis and outcomes in HIV/AIDS septic patients. Although HIV/AIDS patients are immunocompromised, an innate immune response can be activated in these patients, which is similar to that in the non-HIV septic population. In addition, age and the IL-6 levels are independent risk factors for hospital mortality irrespective of HIV/AIDS disease.
Diagnosing Data Center Behavior Flow by Flow

Multi-tenant data centers are complex environments, running thousands of applications that compete for the same infrastructure resources and whose behavior is guided by (sometimes) divergent configurations. Small workload changes or simple operator tasks may yield unpredictable results and lead to expensive failures and performance degradation. In this paper, we propose a holistic approach for detecting operational problems in data centers. Our framework, FlowDiff, collects information from all entities involved in the operation of a data center -- applications, operators, and infrastructure -- and continually builds behavioral models for the operation. By comparing current models with pre-computed, known-to-be-stable models, FlowDiff is able to detect many operational problems, ranging from host and network failures to unauthorized access. FlowDiff also identifies common system operations (e.g., VM migration, software upgrades) to validate the behavior changes against planned operator tasks. We show that using passive measurements on control traffic from programmable switches to a centralized controller is sufficient to build strong behavior models; FlowDiff does not require active measurements or expensive server instrumentation. Our experimental results using NEC data center testbed, Amazon EC2, and simulations demonstrate that FlowDiff is effective and robust in detecting anomalous behavior. FlowDiff scales well with the number of applications running in the data center and their traffic volume.
Absence time and user engagement: evaluating ranking functions

In the online industry, user engagement is measured with various engagement metrics used to assess users' depth of engagement with a website. Widely-used metrics include clickthrough rates, page views and dwell time. Relying solely on these metrics can lead to contradictory if not erroneous conclusions regarding user engagement. In this paper, we propose the time between two user visits, or the absence time, to measure user engagement. Our assumption is that if users find a website interesting, engaging or useful, they will return to it sooner -a reflection of their engagement with the site -than if this is not the case. This assumption has the advantage of being simple and intuitive and applicable to a large number of settings. As a case study, we use a community Q&A website, and compare the behaviour of users exposed to six functions used to rank past answers, both in terms of traditional metrics and absence time. We use Survival Analysis to show the relation between absence time and other engagement metrics. We demonstrate that the absence time leads to coherent, interpretable results and helps to better understand other metrics commonly used to evaluate user engagement in search.
Sample Efficient On-Line Learning of Optimal Dialogue Policies with Kalman Temporal Differences

Designing dialog policies for voice-enabled interfaces is a tailoring job that is most often left to natural language processing experts. This job is generally redone for every new dialog task because cross-domain transfer is not possible. For this reason, machine learning methods for dialog policy optimization have been investigated during the last 15 years. Especially, reinforcement learning (RL) is now part of the state of the art in this domain. Standard RL methods require to test more or less random changes in the policy on users to assess them as improvements or degradations. This is called on policy learning. Nevertheless, it can result in system behaviors that are not acceptable by users. Learning algorithms should ideally infer an optimal strategy by observing interactions generated by a non-optimal but acceptable strategy, that is learning off-policy. In this contribution, a sample-efficient, online and off-policy reinforcement learning algorithm is proposed to learn an optimal policy from few hundreds of dialogues generated with a very simple handcrafted policy.
Phase-corrective compressed-beamforming for ultrasound imaging

Compressed-sensing (CS) based beamformers have drawn increasing interest in ultrasound community for their potential of providing better image quality with fewer data. In practice, this framework usually requires good signal models for data explanation and the final estimation could be sensitive to model imperfectness on real data. In this paper, we extend our previous work [1] and robustify the compressed beamforming in presence of unknown phase perturbations in acoustic measurements. We explicitly introduce additional phase regularization, at each step of optimization, to correct the discrepancy between the predicted and the measured phases. The effectiveness of this approach is shown on simulated and phantom data with added aberrations.
Sustainable Development in Maramures County

We argue that a new type of adaptive change management is needed in Romania, in which social and territorial cohesion are key elements of governance, in accordance with sustainable development requirements. Based on critical analysis and comparative study, the paper investigates, using several approaches, the possibility to select those elements that enable determining a viable managerial sustainable development “model” for communities in Maramures County, Romania. New trends worldwide, and the multidimensional aspects of sustainable development of communities, were the subject of examination, through a regional polycentric approach, which led to a new model proposed by the authors. The result is a conceptual integrated model which promotes a balance of interaction between systems—economic, human, environmental and technological—in a dynamic, flexible and functional process.
Noise robust features for speech/music discrimination in real-time telecommunication

While many efforts have been made in the audio signal classification field, the noise interruption problem is seldom concerned so far, especially in many telecommunication applications, where a real-time and noise robust approach is needed. This paper addresses this problem by proposing two novel robust features: Average Pitch Density (APD) and Relative Tonal Power Density (RTPD). APD refers to the differences in tone characteristics of music and speech signals, and RTPD especially focuses on the distinct properties of the percussion instruments. The comparison experiments are implemented on two databases. The first one is reorganized from the corpus collected by Scheirer et al [3]. The second one consists of data collected from various recording situations. The novel features are compared with several state-of-the-art features and are found to achieve significant robustness.
Measurement of stable isotopic enrichment and concentration of long-chain fatty acyl-carnitines in tissue by HPLC-MS Published, JLR Papers in Press, November 21, 2005.

We have developed a new method for the simultaneous measurements of stable isotopic tracer enrichments and concentrations of individual long-chain fatty acyl-carnitines in muscle tissue using ion-pairing high-performance liquid chromatography-electrospray ionization quadrupole mass spectrometry in the selected ion monitoring (SIM) mode. Long-chain fatty acyl-carnitines were extracted from frozen muscle tissue samples by acetonitrile/methanol. Baseline separation was achieved by reverse-phase HPLC in the presence of the volatile ion-pairing reagent heptafluorobutyric acid. The SIM capability of a single quadrupole mass analyzer allows further separation of the ions of interest from the sample matrixes, providing very clean total and selected ion chromatograms that can be used to calculate the stable isotopic tracer enrichment and concentration of long-chain fatty acyl-carnitines in a single analysis. The combination of these two separation techniques greatly simplifies the sample preparation procedure and increases the detection sensitivity. Applying this protocol to biological muscle samples proves it to be a very sensitive, accurate, and precise analytical tool.
The Importance of Reading Naturally: Evidence From Combined Recordings of Eye Movements and Electric Brain Potentials.

How important is the ability to freely control eye movements for reading comprehension? And how does the parser make use of this freedom? We investigated these questions using coregistration of eye movements and event-related brain potentials (ERPs) while participants read either freely or in a computer-controlled word-by-word format (also known as RSVP). Word-by-word presentation and natural reading both elicited qualitatively similar ERP effects in response to syntactic and semantic violations (N400 and P600 effects). Comprehension was better in free reading but only in trials in which the eyes regressed to previous material upon encountering the anomaly. A more fine-grained ERP analysis revealed that these regressions were strongly associated with the well-known P600 effect. In trials without regressions, we instead found sustained centro-parietal negativities starting at around 320 ms post-onset; however, these negativities were only found when the violation occurred in sentence-final position. Taken together, these results suggest that the sentence processing system engages in strategic choices: In response to words that don't match built-up expectations, it can either explore alternative interpretations (reflected by regressions, P600 effects, and good comprehension) or pursue a "good-enough" processing strategy that tolerates a deficient interpretation (reflected by progressive saccades, sustained negativities, and relatively poor comprehension).
Analysis of Equation of States for the Suitability at High Pressure: MgO as an Example

A simple theoretical model is developed to study the high pressure behavior of solids and is applied to evaluate the pressure for MgO in case of large compression along with Shanker, Tait, Vinet, and Birch-Murnaghan equation of states (EOSs). These EOSs are also tested for the basic requirements revealed from the fundamental thermodynamics for solids in the limit of extreme compressions, as given by Stacey. It is found that for the high pressure compression behavior of MgO the present model, Tait, Vinet, and Birch-Murnaghan EOSs give the results compatible with the experimental data. It has also been found that in the regime of ultrahigh pressure the present model and Birch-Murnaghan EOS satisfy the Stacey criterion.