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Contemporary biomedical research is performed by increasingly large teams. Consequently, an increasingly large number of individuals are being listed as authors in the bylines, which complicates the proper attribution of credit and responsibility to individual authors. Typically, more importance is given to the first and last authors, while it is assumed that the others (the middle authors) have made smaller contributions. However, this may not properly reflect the actual division of labor because some authors other than the first and last may have made major contributions. In practice, research teams may differentiate the main contributors from the rest by using partial alphabetical authorship (i.e., by listing middle authors alphabetically, while maintaining a contribution-based order for more substantial contributions). In this paper, we use partial alphabetical authorship to divide the authors of all biomedical articles in the Web of Science published over the 1980–2015 period in three groups: primary authors, middle authors, and supervisory authors. We operationalize the concept of middle author as those who are listed in alphabetical order in the middle of an authors’ list. Primary and supervisory authors are those listed before and after the alphabetical sequence, respectively. We show that alphabetical ordering of middle authors is frequent in biomedical research, and that the prevalence of this practice is positively correlated with the number of authors in the bylines. We also find that, for articles with 7 or more authors, the average proportion of primary, middle and supervisory authors is independent of the team size, more than half of the authors being middle authors. This suggests that growth in authors lists are not due to an increase in secondary contributions (or middle authors) but, rather, in equivalent increases of all types of roles and contributions (including many primary authors and many supervisory authors). Nevertheless, we show that the relative contribution of alphabetically ordered middle authors to the overall production of knowledge in the biomedical field has greatly increased over the last 35 years. [ABSTRACT FROM AUTHOR]

In the scientific literature, spin refers to reporting practices that distort the interpretation of results and mislead readers so that results are viewed in a more favourable light. The presence of spin in biomedical research can negatively impact the development of further studies, clinical practice, and health policies. This systematic review aims to explore the nature and prevalence of spin in the biomedical literature. We searched MEDLINE, PreMEDLINE, Embase, Scopus, and hand searched reference lists for all reports that included the measurement of spin in the biomedical literature for at least 1 outcome. Two independent coders extracted data on the characteristics of reports and their included studies and all spin-related outcomes. Results were grouped inductively into themes by spin-related outcome and are presented as a narrative synthesis. We used meta-analyses to analyse the association of spin with industry sponsorship of research. We included 35 reports, which investigated spin in clinical trials, observational studies, diagnostic accuracy studies, systematic reviews, and meta-analyses. The nature of spin varied according to study design. The highest (but also greatest) variability in the prevalence of spin was present in trials. Some of the common practices used to spin results included detracting from statistically nonsignificant results and inappropriately using causal language. Source of funding was hypothesised by a few authors to be a factor associated with spin; however, results were inconclusive, possibly due to the heterogeneity of the included papers. Further research is needed to assess the impact of spin on readers’ decision-making. Editors and peer reviewers should be familiar with the prevalence and manifestations of spin in their area of research in order to ensure accurate interpretation and dissemination of research. [ABSTRACT FROM AUTHOR]

This paper presents the use of the Aimsun microsimulation program to simulate vehicle violating behaviors and observe their impact on road traffic crash risk. Plugins for violations of speeding, slow driving, and abrupt stopping were developed using Aimsun’s API and SDK module. A safety analysis plugin for investigating probability of rear-end collisions was developed, and a method for analyzing collision risk is proposed. A Fuzzy C-mean Clustering algorithm was developed to identify high risk states in different road segments over time. Results of a simulation experiment based on the G15 Expressway in Shanghai showed that abrupt stopping had the greatest impact on increasing collision risk, and the impact of violations increased with traffic volume. The methodology allows for the evaluation and monitoring of risks, alerting of road hazards, and identification of hotspots, and could be applied to the operations of existing facilities or planning of future ones. [ABSTRACT FROM AUTHOR]

Staff education is considered key to quality of early childhood education and care (ECEC) programs. However, findings about associations between staff education and children’s outcomes have been inconsistent. We conducted a systematic review and meta-analysis of associations between ECEC staff education and child outcomes. Searches of Medline, PsycINFO, and ERIC, websites of large datasets and reference sections of all retrieved articles were conducted. Eligible studies provided a statistical link between staff education and child outcomes for preschool-aged children in ECEC programs. Titles, abstracts and paper reviews as well as all data extraction were conducted by two independent raters. Of the 823 studies reviewed for eligibility, 39 met our inclusion criteria. Research in this area is observational in nature and subject to the inherent biases of that research design. Results from our systematic review were hampered by heterogeneity in how staff education was defined, variability in whose education was measured and the child outcomes that were assessed. However, overall the qualitative summary indicates that associations between staff education and childhood outcomes are non-existent to very borderline positive. In our meta-analysis of more homogeneous studies we identified certain positive, albeit very weak, associations between staff education and children’s language outcomes (specifically, vocabulary and letter word identification) and no significant association with a mathematics outcome (WJ Applied Problems). Thus, our findings suggest that within the range of education levels found in the existing literature, education is not a key driver of child outcomes. However, since we only explored levels of education that were reported in the literature, our findings cannot be used to argue for lowering education standards in ECEC settings. [ABSTRACT FROM AUTHOR]

This paper presents five studies on the development and validation of a scale of intellectual humility. This scale captures cognitive, affective, behavioral, and motivational components of the construct that have been identified by various philosophers in their conceptual analyses of intellectual humility. We find that intellectual humility has four core dimensions: Open-mindedness (versus Arrogance), Intellectual Modesty (versus Vanity), Corrigibility (versus Fragility), and Engagement (versus Boredom). These dimensions display adequate self-informant agreement, and adequate convergent, divergent, and discriminant validity. In particular, Open-mindedness adds predictive power beyond the Big Six for an objective behavioral measure of intellectual humility, and Intellectual Modesty is uniquely related to Narcissism. We find that a similar factor structure emerges in Germanophone participants, giving initial evidence for the model’s cross-cultural generalizability. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV) grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP) by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance. [ABSTRACT FROM AUTHOR]

In this paper, we aim to examine the relationship between traffic flow and potential conflict risks by using crash surrogate metrics. It has been widely recognized that one traffic flow corresponds to two distinct traffic states with different speeds and densities. In view of this, instead of simply aggregating traffic conditions with the same traffic volume, we represent potential conflict risks at a traffic flow fundamental diagram. Two crash surrogate metrics, namely, Aggregated Crash Index and Time to Collision, are used in this study to represent the potential conflict risks with respect to different traffic conditions. Furthermore, Beijing North Ring III and Next Generation SIMulation Interstate 80 datasets are utilized to carry out case studies. By using the proposed procedure, both datasets generate similar trends, which demonstrate the applicability of the proposed methodology and the transferability of our conclusions. [ABSTRACT FROM AUTHOR]

Due to its environmentally sustainable and energy-saving characteristics, railway transportation nowadays plays a fundamental role in delivering passengers and goods. Emerged in the area of transportation planning, the crew (workforce) sizing problem and the crew scheduling problem have been attached great importance by the railway industry and the scientific community. In this paper, we aim to solve the two problems by proposing a novel two-stage optimization approach in the context of the electric multiple units (EMU) depot shunting driver assignment problem. Given a predefined depot shunting schedule, the first stage of the approach focuses on determining an optimal size of shunting drivers. While the second stage is formulated as a bi-objective optimization model, in which we comprehensively consider the objectives of minimizing the total walking distance and maximizing the workload balance. Then we combine the normalized normal constraint method with a modified Pareto filter algorithm to obtain Pareto solutions for the bi-objective optimization problem. Furthermore, we conduct a series of numerical experiments to demonstrate the proposed approach. Based on the computational results, the regression analysis yield a driver size predictor and the sensitivity analysis give some interesting insights that are useful for decision makers. [ABSTRACT FROM AUTHOR]

Arrogant behavior is as old as human nature. Nonetheless, the factors that cause people to be perceived as arrogant have received very little research attention. In this paper, we focused on a typical manifestation of arrogance: dismissive behavior. In particular, we explored the conditions under which a person who dismissed advice would be perceived as arrogant. We examined two factors: the advisee’s competence, and the manner in which he or she dismissed the advice. The effect of the advisee’s competence was tested by manipulating two competence cues: relative expertise, and the outcome of the advice dismissal (i.e., whether the advisee was right or wrong). In six studies (N = 1304), participants made arrogance judgments about protagonists who dismissed the advice of another person while the advisees’ relative expertise (compared to the advisor), their eventual correctness, and the manner of their dismissal were manipulated in between-participant designs. Across various types of decisions and advisee-advisor relationships, the results show that less expert, less correct, and ruder advisees are perceived as more arrogant. We also find that outcome trumps expertise, and manner trumps both expertise and outcomes. In two additional studies (N = 101), we examined people’s naïve theories about the relative importance of the aforementioned arrogance cues. These studies showed that people overestimate the role of expertise information as compared to the role of interpersonal manner and outcomes. Thus, our results suggest that people may commit arrogant faux pas because they erroneously expect that their expertise will justify their dismissive behavior. [ABSTRACT FROM AUTHOR]

Introduction: This research paper aims to assess factors reported by parents associated with the successful transition of children with complex additional support requirements that have undergone a transition between school environments from 8 European Union member states. Methods: Quantitative data were collected from 306 parents within education systems from 8 EU member states (Bulgaria, Cyprus, Greece, Ireland, the Netherlands, Romania, Spain and the UK). The data were derived from an online questionnaire and consisted of 41 questions. Information was collected on: parental involvement in their child’s transition, child involvement in transition, child autonomy, school ethos, professionals’ involvement in transition and integrated working, such as, joint assessment, cooperation and coordination between agencies. Survey questions that were designed on a Likert-scale were included in the Principal Components Analysis (PCA), additional survey questions, along with the results from the PCA, were used to build a logistic regression model. Results: Four principal components were identified accounting for 48.86% of the variability in the data. Principal component 1 (PC1), ‘child inclusive ethos,’ contains 16.17% of the variation. Principal component 2 (PC2), which represents child autonomy and involvement, is responsible for 8.52% of the total variation. Principal component 3 (PC3) contains questions relating to parental involvement and contributed to 12.26% of the overall variation. Principal component 4 (PC4), which involves transition planning and coordination, contributed to 11.91% of the overall variation. Finally, the principal components were included in a logistic regression to evaluate the relationship between inclusion and a successful transition, as well as whether other factors that may have influenced transition. All four principal components were significantly associated with a successful transition, with PC1 being having the most effect (OR: 4.04, CI: 2.43–7.18, p<0.0001). Discussion: To support a child with complex additional support requirements through transition from special school to mainstream, governments and professionals need to ensure children with additional support requirements and their parents are at the centre of all decisions that affect them. It is important that professionals recognise the educational, psychological, social and cultural contexts of a child with additional support requirements and their families which will provide a holistic approach and remove barriers for learning. [ABSTRACT FROM AUTHOR]

Considering that metro network expansion brings us with more alternative routes, it is attractive to integrate the impacts of routes set and the interdependency among alternative routes on route choice probability into route choice modeling. Therefore, the formulation, estimation and application of a constrained multinomial probit (CMNP) route choice model in the metro network are carried out in this paper. The utility function is formulated as three components: the compensatory component is a function of influencing factors; the non-compensatory component measures the impacts of routes set on utility; following a multivariate normal distribution, the covariance of error component is structured into three parts, representing the correlation among routes, the transfer variance of route, and the unobserved variance respectively. Considering multidimensional integrals of the multivariate normal probability density function, the CMNP model is rewritten as Hierarchical Bayes formula and M-H sampling algorithm based Monte Carlo Markov Chain approach is constructed to estimate all parameters. Based on Guangzhou Metro data, reliable estimation results are gained. Furthermore, the proposed CMNP model also shows a good forecasting performance for the route choice probabilities calculation and a good application performance for transfer flow volume prediction. [ABSTRACT FROM AUTHOR]

Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders. However, assessment of gait speed in coordination with delivery of comprehensive care is usually constrained to clinical environments and is often limited due to mounting demands on the availability of trained clinical staff. These limitations in assessment design could give rise to poor ecological validity and limited ability to tailor interventions to individual patients. Recent advances in wearable sensor technologies have fostered the development of new methods for monitoring parameters that characterize mobility impairment, such as gait speed, outside the clinic, and therefore address many of the limitations associated with clinical assessments. However, these methods are often validated using normal gait patterns; and extending their utility to subjects with gait impairments continues to be a challenge. In this paper, we present a machine learning method for estimating gait speed using a configurable array of skin-mounted, conformal accelerometers. We establish the accuracy of this technique on treadmill walking data from subjects with normal gait patterns and subjects with multiple sclerosis-induced gait impairments. For subjects with normal gait, the best performing model systematically overestimates speed by only 0.01 m/s, detects changes in speed to within less than 1%, and achieves a root-mean-square-error of 0.12 m/s. Extending these models trained on normal gait to subjects with gait impairments yields only minor changes in model performance. For example, for subjects with gait impairments, the best performing model systematically overestimates speed by 0.01 m/s, quantifies changes in speed to within 1%, and achieves a root-mean-square-error of 0.14 m/s. Additional analyses demonstrate that there is no correlation between gait speed estimation error and impairment severity, and that the estimated speeds maintain the clinical significance of ground truth speed in this population. These results support the use of wearable accelerometer arrays for estimating walking speed in normal subjects and their extension to MS patient cohorts with gait impairment. [ABSTRACT FROM AUTHOR]

A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning in VisNet. [ABSTRACT FROM AUTHOR]

Dempster-Shafer evidence theory has been extensively used in many information fusion systems since it was proposed by Dempster and extended by Shafer. Many scholars have been conducted on conflict management of Dempster-Shafer evidence theory in past decades. However, how to determine a potent parameter to measure evidence conflict, when the given environment is in an open world, namely the frame of discernment is incomplete, is still an open issue. In this paper, a new method which combines generalized conflict coefficient, generalized evidence distance, and generalized interval correlation coefficient based on ordered weighted averaging (OWA) operator, to measure the conflict of evidence is presented. Through ordered weighted average of these three parameters, the combinatorial coefficient can still measure the conflict effectively when one or two parameters are not valid. Several numerical examples demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Background: Moderate correlation exists between the imaging quantification of brain white matter lesions and cognitive performance in people with multiple sclerosis (MS). This may reflect the greater importance of other features, including subvisible pathology, or methodological limitations of the primary literature. Objectives: To summarise the cognitive clinico-radiological paradox and explore the potential methodological factors that could influence the assessment of this relationship. Methods: Systematic review and meta-analysis of primary research relating cognitive function to white matter lesion burden. Results: Fifty papers met eligibility criteria for review, and meta-analysis of overall results was possible in thirty-two (2050 participants). Aggregate correlation between cognition and T2 lesion burden was r = -0.30 (95% confidence interval: -0.34, -0.26). Wide methodological variability was seen, particularly related to key factors in the cognitive data capture and image analysis techniques. Conclusions: Resolving the persistent clinico-radiological paradox will likely require simultaneous evaluation of multiple components of the complex pathology using optimum measurement techniques for both cognitive and MRI feature quantification. We recommend a consensus initiative to support common standards for image analysis in MS, enabling benchmarking while also supporting ongoing innovation. [ABSTRACT FROM AUTHOR]

People reliably encode information more effectively when it is related in some way to the self—a phenomenon known as the self-reference effect. This effect has been recognized in psychological research for almost 40 years, and its scope as a tool for investigating the self-concept is still expanding. The self-reference effect has been used within a broad range of psychological research, from cultural to neuroscientific, cognitive to clinical. Traditionally, the self-reference effect has been investigated in a laboratory context, which limits its applicability in non-laboratory samples. This paper introduces an online version of the self-referential encoding paradigm that yields reliable effects in an easy-to-administer procedure. Across four studies (total N = 658), this new online tool reliably replicated the traditional self-reference effect: in all studies self-referentially encoded words were recalled significantly more than semantically encoded words (d = 0.63). Moreover, the effect sizes obtained with this online tool are similar to those obtained in laboratory samples, and are robust to experimental variations in encoding time (Studies 1 and 2) and recall procedure (Studies 3 and 4), and persist independent of primacy and recency effects (all studies). [ABSTRACT FROM AUTHOR]

In this paper we study the interval scoring rule as a mechanism to elicit subjective beliefs under varying degrees of uncertainty. In our experiment, subjects forecast the termination time of a time series to be generated from a given but unknown stochastic process. Subjects gradually learn more about the underlying process over time and hence the true distribution over termination times. We conduct two treatments, one with a high and one with a low volatility process. We find that elicited intervals are better when subjects are facing a low volatility process. In this treatment, participants learn to position their intervals almost optimally over the course of the experiment. This is in contrast with the high volatility treatment, where subjects, over the course of the experiment, learn to optimize the location of their intervals but fail to provide the optimal length. [ABSTRACT FROM AUTHOR]

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the ‘mean-shift’ algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters’ centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network’s state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series. [ABSTRACT FROM AUTHOR]

Music emotion recognition (MER) field rapidly expanded in the last decade. Many new methods and new audio features are developed to improve the performance of MER algorithms. However, it is very difficult to compare the performance of the new methods because of the data representation diversity and scarcity of publicly available data. In this paper, we address these problems by creating a data set and a benchmark for MER. The data set that we release, a MediaEval Database for Emotional Analysis in Music (DEAM), is the largest available data set of dynamic annotations (valence and arousal annotations for 1,802 songs and song excerpts licensed under Creative Commons with 2Hz time resolution). Using DEAM, we organized the ‘Emotion in Music’ task at MediaEval Multimedia Evaluation Campaign from 2013 to 2015. The benchmark attracted, in total, 21 active teams to participate in the challenge. We analyze the results of the benchmark: the winning algorithms and feature-sets. We also describe the design of the benchmark, the evaluation procedures and the data cleaning and transformations that we suggest. The results from the benchmark suggest that the recurrent neural network based approaches combined with large feature-sets work best for dynamic MER. [ABSTRACT FROM AUTHOR]

One of the most accepted findings across psychology is that people are unrealistically optimistic in their judgments of comparative risk concerning future life events—they judge negative events as less likely to happen to themselves than to the average person. Harris and Hahn (2011), however, demonstrated how unbiased (non-optimistic) responses can result in data patterns commonly interpreted as indicative of optimism due to statistical artifacts. In the current paper, we report the results of 5 studies that control for these statistical confounds and observe no evidence for residual unrealistic optimism, even observing a ‘severity effect’ whereby severe outcomes were overestimated relative to neutral ones (Studies 3 & 4). We conclude that there is no evidence supporting an optimism interpretation of previous results using the prevalent comparison method. [ABSTRACT FROM AUTHOR]

A typical rule that has been used for the endorsement of new medications by the Food and Drug Administration is to have two trials, each convincing on its own, demonstrating effectiveness. “Convincing” may be subjectively interpreted, but the use of p-values and the focus on statistical significance (in particular with p < .05 being coined significant) is pervasive in clinical research. Therefore, in this paper, we calculate with simulations what it means to have exactly two trials, each with p < .05, in terms of the actual strength of evidence quantified by Bayes factors. Our results show that different cases where two trials have a p-value below .05 have wildly differing Bayes factors. Bayes factors of at least 20 in favor of the alternative hypothesis are not necessarily achieved and they fail to be reached in a large proportion of cases, in particular when the true effect size is small (0.2 standard deviations) or zero. In a non-trivial number of cases, evidence actually points to the null hypothesis, in particular when the true effect size is zero, when the number of trials is large, and when the number of participants in both groups is low. We recommend use of Bayes factors as a routine tool to assess endorsement of new medications, because Bayes factors consistently quantify strength of evidence. Use of p-values may lead to paradoxical and spurious decision-making regarding the use of new medications. [ABSTRACT FROM AUTHOR]

We have empirically assessed the distribution of published effect sizes and estimated power by analyzing 26,841 statistical records from 3,801 cognitive neuroscience and psychology papers published recently. The reported median effect size was D = 0.93 (interquartile range: 0.64–1.46) for nominally statistically significant results and D = 0.24 (0.11–0.42) for nonsignificant results. Median power to detect small, medium, and large effects was 0.12, 0.44, and 0.73, reflecting no improvement through the past half-century. This is so because sample sizes have remained small. Assuming similar true effect sizes in both disciplines, power was lower in cognitive neuroscience than in psychology. Journal impact factors negatively correlated with power. Assuming a realistic range of prior probabilities for null hypotheses, false report probability is likely to exceed 50% for the whole literature. In light of our findings, the recently reported low replication success in psychology is realistic, and worse performance may be expected for cognitive neuroscience. [ABSTRACT FROM AUTHOR]

In heterogeneous networks (HetNets), the large-scale deployment of small base stations (BSs) together with traditional macro BSs is an economical and efficient solution that is employed to address the exponential growth in mobile data traffic. In dense HetNets, network switching, i.e., handovers, plays a critical role in connecting a mobile terminal (MT) to the best of all accessible networks. In the existing literature, a handover decision is made using various handover metrics such as the signal-to-noise ratio, data rate, and movement speed. However, there are few studies on handovers that focus on energy efficiency in HetNets. In this paper, we propose a handover strategy that helps to minimize energy consumption at BSs in HetNets without compromising the quality of service (QoS) of each MT. The proposed handover strategy aims to capture the effect of the stochastic behavior of handover parameters and the expected energy consumption due to handover execution when making a handover decision. To identify the validity of the proposed handover strategy, we formulate a handover problem as a constrained Markov decision process (CMDP), by which the effects of the stochastic behaviors of handover parameters and consequential handover energy consumption can be accurately reflected when making a handover decision. In the CMDP, the aim is to minimize the energy consumption to service an MT over the lifetime of its connection, and the constraint is to guarantee the QoS requirements of the MT given in terms of the transmission delay and call-dropping probability. We find an optimal policy for the CMDP using a combination of the Lagrangian method and value iteration. Simulation results verify the validity of the proposed handover strategy. [ABSTRACT FROM AUTHOR]

Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent’s environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned. [ABSTRACT FROM AUTHOR]

This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods—random forest and gradient boosting and/or multinomial logistic regression—as implemented in the packages , , and . The results of 10–fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License. [ABSTRACT FROM AUTHOR]

Neuroimaging studies commonly associate dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex with conscious perception. However, such studies only investigate correlation, rather than causation. In addition, many studies conflate objective performance with subjective awareness. In an influential recent paper, Rounis and colleagues addressed these issues by showing that continuous theta burst transcranial magnetic stimulation (cTBS) applied to the DLPFC impaired metacognitive (subjective) awareness for a perceptual task, while objective performance was kept constant. We attempted to replicate this finding, with minor modifications, including an active cTBS control site. Using a between-subjects design for both DLPFC and posterior parietal cortices, we found no evidence of a cTBS-induced metacognitive impairment. In a second experiment, we devised a highly rigorous within-subjects cTBS design for DLPFC, but again failed to find any evidence of metacognitive impairment. One crucial difference between our results and the Rounis study is our strict exclusion of data deemed unsuitable for a signal detection theory analysis. Indeed, when we included this unstable data, a significant, though invalid, metacognitive impairment was found. These results cast doubt on previous findings relating metacognitive awareness to DLPFC, and inform the current debate concerning whether or not prefrontal regions are preferentially implicated in conscious perception. [ABSTRACT FROM AUTHOR]

Bacterial cells can be characterized in terms of their cell properties using flow cytometry. Flow cytometry is able to deliver multiparametric measurements of up to 50,000 cells per second. However, there has not yet been a thorough survey concerning the identification of the population to which bacterial single cells belong based on flow cytometry data. This paper not only aims to assess the quality of flow cytometry data when measuring bacterial populations, but also suggests an alternative approach for analyzing synthetic microbial communities. We created so-called in silico communities, which allow us to explore the possibilities of bacterial flow cytometry data using supervised machine learning techniques. We can identify single cells with an accuracy >90% for more than half of the communities consisting out of two bacterial populations. In order to assess to what extent an in silico community is representative for its synthetic counterpart, we created so-called abundance gradients, a combination of synthetic (i.e., in vitro) communities containing two bacterial populations in varying abundances. By showing that we are able to retrieve an abundance gradient using a combination of in silico communities and supervised machine learning techniques, we argue that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of synthetic communities and bacterial flow cytometry data in general. [ABSTRACT FROM AUTHOR]

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers. [ABSTRACT FROM AUTHOR]

In line with the dimensional theory of emotional space, we developed affective norms for words rated in terms of valence, arousal and dominance in a group of older adults to complete the adaptation of the Affective Norms for English Words (ANEW) for Italian and to aid research on aging. Here, as in the original Italian ANEW database, participants evaluated valence, arousal, and dominance by means of the Self-Assessment Manikin (SAM) in a paper-and-pencil procedure. We observed high split-half reliabilities within the older sample and high correlations with the affective ratings of previous research, especially for valence, suggesting that there is large agreement among older adults within and across-languages. More importantly, we found high correlations between younger and older adults, showing that our data are generalizable across different ages. However, despite this across-ages accord, we obtained age-related differences on three affective dimensions for a great number of words. In particular, older adults rated as more arousing and more unpleasant a number of words that younger adults rated as moderately unpleasant and arousing in our previous affective norms. Moreover, older participants rated negative stimuli as more arousing and positive stimuli as less arousing than younger participants, thus leading to a less-curved distribution of ratings in the valence by arousal space. We also found more extreme ratings for older adults for the relationship between dominance and arousal: older adults gave lower dominance and higher arousal ratings for words rated by younger adults with middle dominance and arousal values. Together, these results suggest that our affective norms are reliable and can be confidently used to select words matched for the affective dimensions of valence, arousal and dominance across younger and older participants for future research in aging. [ABSTRACT FROM AUTHOR]

Among the scientific challenges posed by complex diseases with a strong genetic component, two stand out. One is unveiling the role of rare and common genetic variants; the other is the design of classification models to improve clinical diagnosis and predictive models for prognosis and personalized therapies. In this paper, we present a data fusion framework merging gene, domain, pathway and protein-protein interaction data related to a next generation sequencing epilepsy gene panel. Our method allows integrating association information from multiple genomic sources and aims at highlighting the set of common and rare variants that are capable to trigger the occurrence of a complex disease. When compared to other approaches, our method shows better performances in classifying patients affected by epilepsy. [ABSTRACT FROM AUTHOR]

Peer-reviewed publications focusing on climate change are growing exponentially with the consequence that the uptake and influence of individual papers varies greatly. Here, we derive metrics of narrativity from psychology and literary theory, and use these metrics to test the hypothesis that more narrative climate change writing is more likely to be influential, using citation frequency as a proxy for influence. From a sample of 732 scientific abstracts drawn from the climate change literature, we find that articles with more narrative abstracts are cited more often. This effect is closely associated with journal identity: higher-impact journals tend to feature more narrative articles, and these articles tend to be cited more often. These results suggest that writing in a more narrative style increases the uptake and influence of articles in climate literature, and perhaps in scientific literature more broadly. [ABSTRACT FROM AUTHOR]

DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at . [ABSTRACT FROM AUTHOR]

This paper presents a robust satisficing decision-making method for Unmanned Aerial Vehicles (UAVs) executing complex missions in an uncertain environment. Motivated by the info-gap decision theory, we formulate this problem as a novel robust satisficing optimization problem, of which the objective is to maximize the robustness while satisfying some desired mission requirements. Specifically, a new info-gap based Markov Decision Process (IMDP) is constructed to abstract the uncertain UAV system and specify the complex mission requirements with the Linear Temporal Logic (LTL). A robust satisficing policy is obtained to maximize the robustness to the uncertain IMDP while ensuring a desired probability of satisfying the LTL specifications. To this end, we propose a two-stage robust satisficing solution strategy which consists of the construction of a product IMDP and the generation of a robust satisficing policy. In the first stage, a product IMDP is constructed by combining the IMDP with an automaton representing the LTL specifications. In the second, an algorithm based on robust dynamic programming is proposed to generate a robust satisficing policy, while an associated robustness evaluation algorithm is presented to evaluate the robustness. Finally, through Monte Carlo simulation, the effectiveness of our algorithms is demonstrated on an UAV search mission under severe uncertainty so that the resulting policy can maximize the robustness while reaching the desired performance level. Furthermore, by comparing the proposed method with other robust decision-making methods, it can be concluded that our policy can tolerate higher uncertainty so that the desired performance level can be guaranteed, which indicates that the proposed method is much more effective in real applications. [ABSTRACT FROM AUTHOR]

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications. [ABSTRACT FROM AUTHOR]

It is well known that a network structure plays an important role in addressing a collective behavior. In this paper we study a network of firms and corporations for addressing metastable features in an Ising based model. In our model we observe that if in a recession the government imposes a demand shock to stimulate the network, metastable features shape its response. Actually we find that there exists a minimum bound where any demand shock with a size below it is unable to trigger the market out of recession. We then investigate the impact of network characteristics on this minimum bound. We surprisingly observe that in a Watts-Strogatz network, although the minimum bound depends on the average of the degrees, when translated into the language of economics, such a bound is independent of the average degrees. This bound is about 0.44ΔGDP, where ΔGDP is the gap of GDP between recession and expansion. We examine our suggestions for the cases of the United States and the European Union in the recent recession, and compare them with the imposed stimulations. While the stimulation in the US has been above our threshold, in the EU it has been far below our threshold. Beside providing a minimum bound for a successful stimulation, our study on the metastable features suggests that in the time of crisis there is a “golden time passage” in which the minimum bound for successful stimulation can be much lower. Hence, our study strongly suggests stimulations to arise within this time passage. [ABSTRACT FROM AUTHOR]

The remaining useful life (RUL) prediction of Lithium-ion batteries is closely related to the capacity degeneration trajectories. Due to the self-charging and the capacity regeneration, the trajectories have the property of multimodality. Traditional prediction models such as the support vector machines (SVM) or the Gaussian Process regression (GPR) cannot accurately characterize this multimodality. This paper proposes a novel RUL prediction method based on the Gaussian Process Mixture (GPM). It can process multimodality by fitting different segments of trajectories with different GPR models separately, such that the tiny differences among these segments can be revealed. The method is demonstrated to be effective for prediction by the excellent predictive result of the experiments on the two commercial and chargeable Type 1850 Lithium-ion batteries, provided by NASA. The performance comparison among the models illustrates that the GPM is more accurate than the SVM and the GPR. In addition, GPM can yield the predictive confidence interval, which makes the prediction more reliable than that of traditional models. [ABSTRACT FROM AUTHOR]

To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO). [ABSTRACT FROM AUTHOR]

It has recently been suggested that movement variability directly increases the speed of motor learning. Here we use computational modeling of motor adaptation to show that variability can have a broad range of effects on learning, both negative and positive. Experimentally, we also find contributing and decelerating effects. Lastly, through a meta-analysis of published papers, we verify that across a wide range of experiments, movement variability has no statistical relation with learning rate. While motor learning is a complex process that can be modeled, further research is needed to understand the relative importance of the involved factors. [ABSTRACT FROM AUTHOR]

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. [ABSTRACT FROM AUTHOR]

The validity of mixed treatment comparisons (MTCs), also called network meta-analysis, relies on whether it is reasonable to accept the underlying assumptions on similarity, homogeneity, and consistency. The aim of this paper is to propose a practicable approach to addressing the underlying assumptions of MTCs. Using data from clinical studies of antidepressants included in a health technology assessment (HTA), we present a stepwise approach to dealing with challenges related to checking the above assumptions and to judging the robustness of the results of an MTC. At each step, studies that were dissimilar or contributed to substantial heterogeneity or inconsistency were excluded from the primary analysis. In a comparison of the MTC estimates from the consistent network with the MTC estimates from the homogeneous network including inconsistencies, few were affected by notable changes; that is, a change in effect size (factor 2), direction of effect or statistical significance. Considering the small proportion of studies excluded from the network due to inconsistency, as well as the number of notable changes, the MTC results were deemed sufficiently robust. In the absence of standard methods, our approach to checking assumptions in MTCs may inform other researchers in need of practical options, particularly in HTA. [ABSTRACT FROM AUTHOR]

Previous researches support that graphs are relevant decision aids to tasks related to the interpretation of numerical information. Moreover, literature shows that different types of graphical information can help or harm the accuracy on decision making of accountants and financial analysts. We conducted a 4×2 mixed-design experiment to examine the effects of numerical information disclosure on financial analysts’ accuracy, and investigated the role of overconfidence in decision making. Results show that compared to text, column graph enhanced accuracy on decision making, followed by line graphs. No difference was found between table and textual disclosure. Overconfidence harmed accuracy, and both genders behaved overconfidently. Additionally, the type of disclosure (text, table, line graph and column graph) did not affect the overconfidence of individuals, providing evidence that overconfidence is a personal trait. This study makes three contributions. First, it provides evidence from a larger sample size (295) of financial analysts instead of a smaller sample size of students that graphs are relevant decision aids to tasks related to the interpretation of numerical information. Second, it uses the text as a baseline comparison to test how different ways of information disclosure (line and column graphs, and tables) can enhance understandability of information. Third, it brings an internal factor to this process: overconfidence, a personal trait that harms the decision-making process of individuals. At the end of this paper several research paths are highlighted to further study the effect of internal factors (personal traits) on financial analysts’ accuracy on decision making regarding numerical information presented in a graphical form. In addition, we offer suggestions concerning some practical implications for professional accountants, auditors, financial analysts and standard setters. [ABSTRACT FROM AUTHOR]

Traffic state estimation from the floating car system is a challenging problem. The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, many methods have been proposed to estimate the traffic state for the uncovered links. However, these methods cannot provide traffic state of the entire road networks. In this paper, the traffic state estimation is transformed to solve a missing data imputation problem, and the tensor completion framework is proposed to estimate missing traffic state. A tensor is constructed to model traffic state in which observed entries are directly derived from floating car system and unobserved traffic states are modeled as missing entries of constructed tensor. The constructed traffic state tensor can represent spatial and temporal correlations of traffic data and encode the multi-way properties of traffic state. The advantage of the proposed approach is that it can fully mine and utilize the multi-dimensional inherent correlations of traffic state. We tested the proposed approach on a well calibrated simulation network. Experimental results demonstrated that the proposed approach yield reliable traffic state estimation from very sparse floating car data, particularly when dealing with the floating car penetration rate is below 1%. [ABSTRACT FROM AUTHOR]

Representation based classification methods, such as Sparse Representation Classification (SRC) and Linear Regression Classification (LRC) have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances) in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP) features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbial features presents new challenges, and validated computational tools for learning tasks are lacking. Moreover, classification rules have scarcely been validated in independent studies, posing questions about the generality and generalization of disease-predictive models across cohorts. In this paper, we comprehensively assess approaches to metagenomics-based prediction tasks and for quantitative assessment of the strength of potential microbiome-phenotype associations. We develop a computational framework for prediction tasks using quantitative microbiome profiles, including species-level relative abundances and presence of strain-specific markers. A comprehensive meta-analysis, with particular emphasis on generalization across cohorts, was performed in a collection of 2424 publicly available metagenomic samples from eight large-scale studies. Cross-validation revealed good disease-prediction capabilities, which were in general improved by feature selection and use of strain-specific markers instead of species-level taxonomic abundance. In cross-study analysis, models transferred between studies were in some cases less accurate than models tested by within-study cross-validation. Interestingly, the addition of healthy (control) samples from other studies to training sets improved disease prediction capabilities. Some microbial species (most notably Streptococcus anginosus) seem to characterize general dysbiotic states of the microbiome rather than connections with a specific disease. Our results in modelling features of the “healthy” microbiome can be considered a first step toward defining general microbial dysbiosis. The software framework, microbiome profiles, and metadata for thousands of samples are publicly available at . [ABSTRACT FROM AUTHOR]

Bioimpedance spectroscopy (BIS) measurement errors may be caused by parasitic stray capacitance, impedance mismatch, cross-talking or their very likely combination. An accurate detection and identification is of extreme importance for further analysis because in some cases and for some applications, certain measurement artifacts can be corrected, minimized or even avoided. In this paper we present a robust method to detect the presence of measurement artifacts and identify what kind of measurement error is present in BIS measurements. The method is based on supervised machine learning and uses a novel set of generalist features for measurement characterization in different immittance planes. Experimental validation has been carried out using a database of complex spectra BIS measurements obtained from different BIS applications and containing six different types of errors, as well as error-free measurements. The method obtained a low classification error (0.33%) and has shown good generalization. Since both the features and the classification schema are relatively simple, the implementation of this pre-processing task in the current hardware of bioimpedance spectrometers is possible. [ABSTRACT FROM AUTHOR]

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making. [ABSTRACT FROM AUTHOR]

This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results. [ABSTRACT FROM AUTHOR]

The high-dose, alcohol-induced influences on risk perception and loss aversion depend on sex. On the other hand, low-dose alcohol has less effect on risky behavior. However, the effect of low-dose alcohol on subjective valuation of gain or loss and also the effect of placebo (expectancy of alcohol) on risk perception have not been fully investigated. We investigated the effects of low-dose alcohol (0.02 g/100 ml blood alcohol concentration) and placebo effects on subjective risk perception and subjective valuation of uncertain gain and loss in females and males. Participants in the control group and the placebo group were served alcohol-free, wine-flavored beverage and participants of alcohol group were served wine (14% alcohol). The placebo group was not informed that the drink was not alcohol but the control group was informed. Then paper–pencil tasks for subjective risk perception and valuation of gain or loss were performed 45 min after drinking the beverage. The participants were asked to draw the line on a 180 mm scale for each question. The placebo effects as well as the low-dose alcohol effects were observed in subjective valuations of gain or loss. Except for effect of beverages, a gender difference was also observed for subjective likelihood. The females estimated a low-probability loss as more likely and estimated a high-probability gain as less likely than did the males. From the Stevens’ law fitting analysis, the placebo, not alcohol, significantly induced the psychophysical effect of the subjective valuation of gain or loss. These results indicate that the psychological effects of expectancy of alcohol (placebo) could be a major factor in changing the subjective valuation of gain or loss over the pharmacological effects of a small amount of alcohol (like a glass of wine). Furthermore, these results also indicate that gender differences should be taken into account when investigating pharmacological or psychological effect on decision-making. [ABSTRACT FROM AUTHOR]

Background: Piwi-interacting RNA (piRNA) is the largest class of small non-coding RNA molecules. The transposon-derived piRNA prediction can enrich the research contents of small ncRNAs as well as help to further understand generation mechanism of gamete. Methods: In this paper, we attempt to differentiate transposon-derived piRNAs from non-piRNAs based on their sequential and physicochemical features by using machine learning methods. We explore six sequence-derived features, i.e. spectrum profile, mismatch profile, subsequence profile, position-specific scoring matrix, pseudo dinucleotide composition and local structure-sequence triplet elements, and systematically evaluate their performances for transposon-derived piRNA prediction. Finally, we consider two approaches: direct combination and ensemble learning to integrate useful features and achieve high-accuracy prediction models. Results: We construct three datasets, covering three species: Human, Mouse and Drosophila, and evaluate the performances of prediction models by 10-fold cross validation. In the computational experiments, direct combination models achieve AUC of 0.917, 0.922 and 0.992 on Human, Mouse and Drosophila, respectively; ensemble learning models achieve AUC of 0.922, 0.926 and 0.994 on the three datasets. Conclusions: Compared with other state-of-the-art methods, our methods can lead to better performances. In conclusion, the proposed methods are promising for the transposon-derived piRNA prediction. The source codes and datasets are available in . [ABSTRACT FROM AUTHOR]

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs). [ABSTRACT FROM AUTHOR]