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The water quality monitoring of lotic and lentic ecosystems allows for informing the possible use in human activities and the consumption of the vital liquid. This work measures the biochemical parameters in Coyuca Lagoon and Tecpan River, localized in Guerrero, Mexico. A comparative statistical analysis of six physicochemical factors in lentic and lotic ecosystems was carried out, finding individual pH values slightly higher for the lagoon ecosystem and lower for the river. For electrical conductivity, we find river sites with parameters lower than 500 μS/cm ideal for human use and consumption. On the contrary, in sites of the lagoon system, the conductivity was higher. As for the total hardness of the river, the values are within the Mexican standard; however, for the lagoon ecosystem, the water has a higher amount of calcium and magnesium salts and is not recommended for human consumption. For chlorides, the lagoon system exceeds the limits of regulations for human consumption; otherwise, it happens with the lotic system. The values of total alkalinity and total dissolved solids are higher for the lentic system than for the lotic one. Finally, the machine learning method shows the importance of measuring other parameters to determine the water quality, especially the salinity and calcium hardness. [ABSTRACT FROM AUTHOR]

A qualitative and comparative analysis is performed regarding the possible consequences of the introduction in the school curriculum of 10-12 years old children, of Robotics and Applied Mathematics in Movement Transmission (AMMT) courses, respectively. The research has been done at the Gymnazial School Number 195, from Bucharest, during two scholar years (2017-2019). Each class had 36 students and the two hours Robotics / AMMT courses had a weekly frequency. Direct observation, interviews of the children and parents, together with the class and individual journals, where children noted their opinions during the two years, are analyzed and interpreted. The results show better scholar performances of the children following Robotics courses, in respect to the ones following AMMT ones and a greater attraction of the children in Robotics. Further, a quantitative evaluation regarding the improvement of mathematical performances of children is presented, given a very similar level of the two classes at the beginning of the research in respect to their average Maths scores. The analysis involved several statistical parameters, like the average scores obtained at Math tests, their variances, the evolution of the children with the lowest performances and the number of the maximum scores in each class. All of them suggest the Robotics course efficiency in increasing mathematical performances level of young children is superior to the one of the AMMT course, recommending the introduction of such courses in the school curriculum. [ABSTRACT FROM AUTHOR]

Traditionally, social scientists perceived causality as regularity. As a consequence, qualitative comparative case study research was regarded as unsuitable for drawing causal inferences since a few cases cannot establish regularity. The dominant perception of causality has changed, however. Nowadays, social scientists define and identify causality through the counterfactual effect of a treatment. This brings causal inference in qualitative comparative research back on the agenda since comparative case studies can identify counterfactual treatment effects. We argue that the validity of causal inferences from the comparative study of cases depends on the employed case-selection algorithm. We employ Monte Carlo techniques to demonstrate that different case-selection rules strongly differ in their ex ante reliability for making valid causal inferences and identify the most and the least reliable case selection rules. [ABSTRACT FROM AUTHOR]

The determination of the weights of decision makers (DMs) is an important problem in multi-attribute group decision making. Many approaches have been presented to determine DMs’ weights. However, the computed weight vectors of DMs are usually assumed to be constant in existing studies, and this may cause irrationalities in the decision results. Therefore, this article proposes a novel method to determine DMs’ weights based on variable weights theory in which the evaluation information is described as intuitionistic fuzzy sets (IFSs). First, DMs provide their assessment with IFSs, and the intuitionistic fuzzy weighted averaging (IFWA) operator is applied to obtain weighted decision matrix based on the prior given DMs’ and attributes’ weights. Second, the DMs’ weights are obtained based on variable weights theory, and an alternative decision can be computed. Finally, the converted value of the achieved IFS of each alternative is calculated, and the best appropriate alternative is acquired. Two illustrative examples and the comparisons with exsiting approaches are also used to reflect the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Background: Poor early childhood development (ECD) in low- and middle-income countries is a major concern. There are calls to universalise access to ECD interventions through integrating them into existing government services but little evidence on the medium- or long-term effects of such scalable models. We previously showed that a psychosocial stimulation (PS) intervention integrated into a cash transfer programme improved Colombian children's cognition, receptive language, and home stimulation. In this follow-up study, we assessed the medium-term impacts of the intervention, 2 years after it ended, on children's cognition, language, school readiness, executive function, and behaviour.Methods and Findings: Study participants were 1,419 children aged 12-24 months at baseline from beneficiary households of the cash transfer programme, living in 96 Colombian towns. The original cluster randomised controlled trial (2009-2011) randomly allocated the towns to control (N = 24, n = 349), PS (N = 24, n = 357), multiple micronutrient (MN) supplementation (N = 24, n = 354), and combined PS and MN (N = 24, n = 359). Interventions lasted 18 months. In this study (26 September 2013 to 11 January 2014), we assessed impacts on cognition, language, school readiness, executive function, and behaviour 2 years after intervention, at ages 4.5-5.5 years. Testers, but not participants, were blinded to treatment allocation. Analysis was on an intent-to-treat basis. We reassessed 88.5% of the children in the original study (n = 1,256). Factor analysis of test scores yielded 2 factors: cognitive (cognition, language, school readiness, executive function) and behavioural. We found no effect of the interventions after 2 years on the cognitive factor (PS: -0.031 SD, 95% CI -0.229-0.167; MN: -0.042 SD, 95% CI -0.249-0.164; PS and MN: -0.111 SD, 95% CI -0.311-0.089), the behavioural factor (PS: 0.013 SD, 95% CI -0.172-0.198; MN: 0.071 SD, 95% CI -0.115-0.258; PS and MN: 0.062 SD, 95% CI -0.115-0.239), or home stimulation. Study limitations include that behavioural development was measured through maternal report and that very small effects may have been missed, despite the large sample size.Conclusions: We found no evidence that a scalable PS intervention benefited children's development 2 years after it ended. It is possible that the initial effects on child development were too small to be sustained or that the lack of continued impact on home stimulation contributed to fade out. Both are likely related to compromises in implementation when going to scale and suggest one should not extrapolate from medium-term effects of small efficacy trials to scalable interventions. Understanding the salient differences between small efficacy trials and scaled-up versions will be key to making ECD interventions effective tools for policymakers.Trial Registration: ISRCTN18991160. [ABSTRACT FROM AUTHOR]

The intravoxel incoherent motion (IVIM) model for diffusion-weighted imaging (DWI) MRI data bears much promise as a tool for visualizing tumours and monitoring treatment response. To improve the currently poor precision of IVIM, several fit algorithms have been suggested. In this work, we compared the performance of two Bayesian IVIM fit algorithms and four other IVIM fit algorithms for pancreatic cancer imaging. DWI data were acquired in 14 pancreatic cancer patients during two MRI examinations. Three different measures of performance of the fitting algorithms were assessed: (i) uniqueness of fit parameters (Spearman’s rho); (ii) precision (within-subject coefficient of variation, wCV); and (iii) contrast between tumour and normal-appearing pancreatic tissue. For the diffusivity D and perfusion fraction f, a Bayesian fit (IVIM-Bayesian-lin) offered the best trade-off between tumour contrast and precision. With the exception for IVIM-Bayesian-lin, all algorithms resulted in a very poor precision of the pseudo-diffusion coefficient D* with a wCV of more than 50%. The pseudo-diffusion coefficient D* of the Bayesian approaches were, however, significantly correlated with D and f. Therefore, the added value of fitting D* was considered limited in pancreatic cancer patients. The easier implemented least squares fit with fixed D* (IVIM-fixed) performed similar to IVIM-Bayesian-lin for f and D. In conclusion, the best performing IVIM fit algorithm was IVM-Bayesian-lin, but an easier to implement least squares fit with fixed D* performs similarly in pancreatic cancer patients. [ABSTRACT FROM AUTHOR]

Best investment in the road infrastructure or the network design is perceived as a fundamental and benchmark problem in transportation. Given a set of candidate road projects with associated costs, finding the best subset with respect to a limited budget is known as a bilevel Discrete Network Design Problem (DNDP) of NP-hard computationally complexity. We engage with the complexity with a hybrid exact-heuristic methodology based on a two-stage relaxation as follows: (i) the bilevel feature is relaxed to a single-level problem by taking the network performance function of the upper level into the user equilibrium traffic assignment problem (UE-TAP) in the lower level as a constraint. It results in a mixed-integer nonlinear programming (MINLP) problem which is then solved using the Outer Approximation (OA) algorithm (ii) we further relax the multi-commodity UE-TAP to a single-commodity MILP problem, that is, the multiple OD pairs are aggregated to a single OD pair. This methodology has two main advantages: (i) the method is proven to be highly efficient to solve the DNDP for a large-sized network of Winnipeg, Canada. The results suggest that within a limited number of iterations (as termination criterion), global optimum solutions are quickly reached in most of the cases; otherwise, good solutions (close to global optimum solutions) are found in early iterations. Comparative analysis of the networks of Gao and Sioux-Falls shows that for such a non-exact method the global optimum solutions are found in fewer iterations than those found in some analytically exact algorithms in the literature. (ii) Integration of the objective function among the constraints provides a commensurate capability to tackle the multi-objective (or multi-criteria) DNDP as well. [ABSTRACT FROM AUTHOR]

Music genre classification is a challenging research concept, for which open questions remain regarding classification approach, music piece representation, distances between/within genres, and so on. In this paper an investigation on the classification of generated music pieces is performed, based on the idea that grouping close related known pieces in different sets –or clusters– and then generating in an automatic way a new song which is somehow “inspired” in each set, the new song would be more likely to be classified as belonging to the set which inspired it, based on the same distance used to separate the clusters. Different music pieces representations and distances among pieces are used; obtained results are promising, and indicate the appropriateness of the used approach even in a such a subjective area as music genre classification is. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is a promising imaging technique for bedside monitoring of lung function. It is easily applicable, cheap and requires no ionizing radiation, but clinical interpretation of EIT-images is still not standardized. One of the reasons for this is the ill-posed nature of EIT, allowing a range of possible images to be produced–rather than a single explicit solution. Thus, to further advance the EIT technology for clinical application, thorough examinations of EIT-image reconstruction settings–i.e., mathematical parameters and addition of a priori (e.g., anatomical) information–is essential. In the present work, regional ventilation distribution profiles derived from different EIT finite-element reconstruction models and settings (for GREIT and Gauss Newton) were compared to regional aeration profiles assessed by the gold-standard of 4-dimensional computed tomography (4DCT) by calculating the root mean squared error (RMSE). Specifically, non-individualized reconstruction models (based on circular and averaged thoracic contours) and individualized reconstruction models (based on true thoracic contours) were compared. Our results suggest that GREIT with noise figure of 0.15 and non-uniform background works best for the assessment of regional ventilation distribution by EIT, as verified versus 4DCT. Furthermore, the RMSE of anteroposterior ventilation profiles decreased from 2.53±0.62% to 1.67±0.49% while correlation increased from 0.77 to 0.89 after embedding anatomical information into the reconstruction models. In conclusion, the present work reveals that anatomically enhanced EIT-image reconstruction is superior to non-individualized reconstruction models, but further investigations in humans, so as to standardize reconstruction settings, is warranted. [ABSTRACT FROM AUTHOR]

Reduction of sedentary time and an increase in physical activity offer potential to improve public health. However, quantifying physical activity behaviour under real world conditions is a major challenge and no standard of good practice is available. Our aim was to compare the results of physical activity and sedentary behaviour obtained with a self-reported instrument (Global Physical Activity Questionnaire (GPAQ)) and a wearable sensor (SenseWear) in a repeated measures study design. Healthy adults (41 in Antwerp, 41 in Barcelona and 40 in London) wore the SenseWear armband for seven consecutive days and completed the GPAQ on the final day. This was repeated three times. We used the Wilcoxon signed rank sum test, Spearman correlation coefficients, mixed effects regression models and Bland-Altman plots to study agreement between both methods. Mixed models were used to assess the effect of personal characteristics on the absolute and relative difference between estimates obtained with the GPAQ and SenseWear. Moderate to vigorous energy expenditure and duration derived from the GPAQ were significantly lower (p<0.05) compared to the SenseWear, yet these variables showed significant correlations ranging from 0.45 to 0.64. Estimates of vigorous-intensity physical activity in particular showed high similarity (r>0.59). Results for sedentary behaviour did not differ, yet were poorly correlated (r<0.25). The differences between all variables were reproducible across repeated measurements. In addition, we observed a relationship between these differences and BMI, body fat and physical activity domain. Due to the lack of a standardized protocol, results from different studies measuring physical activity and sedentary behaviour are difficult to compare. Therefore, we suggested an easy-to-implement approach for future studies adding the GPAQ to the wearable of choice as a basis for comparisons. [ABSTRACT FROM AUTHOR]

Purpose: To compare the quality of four OCT-angiography(OCT-A) modules. Method: The retina of nineteen healthy volunteers were scanned with four OCT-devices (Topcon DRI-OCT Triton Swept-source OCT, Optovue RTVue-XR, a prototype Spectralis OCT2, Heidelberg-Engineering and Zeiss Cirrus 5000-HD-OCT). The device-software generated en-face OCT-A images of the superficial (SCP) and deep capillary plexuses (DCP) were evaluated and scored by 3 independent retinal imaging experts. The SCP vessel density was assessed using Angiotool-software. After the inter-grader reliability assessment, a consensus grading was performed and the modules were ranked based on their scoring. Results: There was no significant difference in the vessel density among the modules (Zeiss 48.7±4%, Optovue 47.9±3%, Topcon 48.3±2%, Heidelberg 46.5±4%, p = 0.2). The numbers of discernible vessel-bifurcations differed significantly on each module (Zeiss 2±0.9 bifurcations, Optovue 2.5±1.2, Topcon 1.3±0.7 and Heidelberg 0.5±0.6, p≤0.001). The ranking of each module differed depending on the evaluated parameter. In the overall ranking, the Zeiss module was superior and in 90% better than the median (Bonferroni corrected p-value = 0.04). Optovue was better than the median in 60%, Topcon in 40% and Heidelberg module in 10%, however these differences were not statistically significant. Conclusion: Each of the four evaluated OCT-A modules had particular strengths, which differentiated it from their competitors. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study of three numerical schemes such as Linear, Quadratic and Quadratic–Linear scheme for the fractional integro-differential equations defined in terms of the Caputo fractional derivatives. The error estimates of the respective approximations are also established. Numerical tests of the discussed schemes show that all schemes work well, and when the number of terms approximating the solution is increased, the desired solution is achieved. The accuracy of the numerical schemes with respect to the step size h is analysed and illustrated through various tables. Finally, comparative performances of the schemes are discussed. [ABSTRACT FROM AUTHOR]

An effective evaluation of transportation network efficiency could offer guidance for the optimal control of urban traffic. Based on the introduction and related mathematical analysis of three quantitative evaluation methods for transportation network efficiency, this paper compares the information measured by them, including network structure, traffic demand, travel choice behavior and other factors which affect network efficiency. Accordingly, the applicability of various evaluation methods is discussed. Through analyzing different transportation network examples it is obtained that Q-H method could reflect the influence of network structure, traffic demand and user route choice behavior on transportation network efficiency well. In addition, the transportation network efficiency measured by this method and Braess’s Paradox can be explained with each other, which indicates a better evaluation of the real operation condition of transportation network. Through the analysis of the network efficiency calculated by Q-H method, it can also be drawn that a specific appropriate demand is existed to a given transportation network. Meanwhile, under the fixed demand, both the critical network structure that guarantees the stability and the basic operation of the network and a specific network structure contributing to the largest value of the transportation network efficiency can be identified. [ABSTRACT FROM AUTHOR]

The development of GPS tags for tracking wildlife has revolutionised the study of home ranges, habitat use and behaviour. Concomitantly, there have been rapid developments in methods for estimating habitat use from GPS data. In combination, these changes can cause challenges in choosing the best methods for estimating home ranges. In primatology, this issue has received little attention, as there have been few GPS collar-based studies to date. However, as advancing technology is making collaring studies more feasible, there is a need for the analysis to advance alongside the technology. Here, using a high quality GPS collaring data set from 10 proboscis monkeys (Nasalis larvatus), we aimed to: 1) compare home range estimates from the most commonly used method in primatology, the grid-cell method, with three recent methods designed for large and/or temporally correlated GPS data sets; 2) evaluate how well these methods identify known physical barriers (e.g. rivers); and 3) test the robustness of the different methods to data containing either less frequent or random losses of GPS fixes. Biased random bridges had the best overall performance, combining a high level of agreement between the raw data and estimated utilisation distribution with a relatively low sensitivity to reduced fixed frequency or loss of data. It estimated the home range of proboscis monkeys to be 24–165 ha (mean 80.89 ha). The grid-cell method and approaches based on local convex hulls had some advantages including simplicity and excellent barrier identification, respectively, but lower overall performance. With the most suitable model, or combination of models, it is possible to understand more fully the patterns, causes, and potential consequences that disturbances could have on an animal, and accordingly be used to assist in the management and restoration of degraded landscapes. [ABSTRACT FROM AUTHOR]

We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user’s graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score. [ABSTRACT FROM AUTHOR]

Color image compression is a commonly used process to represent image data as few bits as possible, which removes redundancy in the data while maintaining an appropriate level of quality for the user. Color image compression algorithms based on quaternion are very common in recent years. In this paper, we propose a color image compression scheme, based on the real SVD, named real compression scheme. First, we form a new real rectangular matrix C according to the red, green and blue components of the original color image and perform the real SVD for C. Then we select several largest singular values and the corresponding vectors in the left and right unitary matrices to compress the color image. We compare the real compression scheme with quaternion compression scheme by performing quaternion SVD using the real structure-preserving algorithm. We compare the two schemes in terms of operation amount, assignment number, operation speed, PSNR and CR. The experimental results show that with the same numbers of selected singular values, the real compression scheme offers higher CR, much less operation time, but a little bit smaller PSNR than the quaternion compression scheme. When these two schemes have the same CR, the real compression scheme shows more prominent advantages both on the operation time and PSNR. [ABSTRACT FROM AUTHOR]

We investigate at the subscale of the neighborhoods of a highly populated city the incidence of property crimes in terms of both the resident and the floating population. Our results show that a relevant allometric relation could only be observed between property crimes and floating population. More precisely, the evidence of a superlinear behavior indicates that a disproportional number of property crimes occurs in regions where an increased flow of people takes place in the city. For comparison, we also found that the number of crimes of peace disturbance only correlates well, and in a superlinear fashion too, with the resident population. Our study raises the interesting possibility that the superlinearity observed in previous studies [Bettencourt et al., Proc. Natl. Acad. Sci. USA 104, 7301 (2007) and Melo et al., Sci. Rep. 4, 6239 (2014)] for homicides versus population at the city scale could have its origin in the fact that the floating population, and not the resident one, should be taken as the relevant variable determining the intrinsic microdynamical behavior of the system. [ABSTRACT FROM AUTHOR]

In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error. Finding the best fitting dictionary remains a very difficult task, leaving the question still open. A well-established heuristic method for tackling this problem is an iterative alternating scheme, adopted for instance in the well-known K-SVD algorithm. Essentially, it consists in repeating two stages; the former promotes sparse coding of the training set and the latter adapts the dictionary to reduce the error. In this paper we present R-SVD, a new method that, while maintaining the alternating scheme, adopts the Orthogonal Procrustes analysis to update the dictionary atoms suitably arranged into groups. Comparative experiments on synthetic data prove the effectiveness of R-SVD with respect to well known dictionary learning algorithms such as K-SVD, ILS-DLA and the online method OSDL. Moreover, experiments on natural data such as ECG compression, EEG sparse representation, and image modeling confirm R-SVD’s robustness and wide applicability. [ABSTRACT FROM AUTHOR]

Background: The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. Methods and finding: We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755–0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691–0.783) and 0.742 (0.698–0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. Conclusions: According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction. [ABSTRACT FROM AUTHOR]

How much does a fumble affect the probability of winning an American football game? How balanced should your offense be in order to increase the probability of winning by 10%? These are questions for which the coaching staff of National Football League teams have a clear qualitative answer. Turnovers are costly; turn the ball over several times and you will certainly lose. Nevertheless, what does “several” mean? How “certain” is certainly? In this study, we collected play-by-play data from the past 7 NFL seasons, i.e., 2009–2015, and we build a descriptive model for the probability of winning a game. Despite the fact that our model incorporates simple box score statistics, such as total offensive yards, number of turnovers etc., its overall cross-validation accuracy is 84%. Furthermore, we combine this descriptive model with a statistical bootstrap module to build (short for Football Prediction Matchup) for predicting future match-ups. The contribution of is pertinent to its simplicity and transparency, which however does not sacrifice the system’s performance. In particular, our evaluations indicate that our prediction engine performs on par with the current state-of-the-art systems (e.g., ESPN’s FPI and Microsoft’s Cortana). The latter are typically proprietary but based on their components described publicly they are significantly more complicated than . Moreover, their proprietary nature does not allow for a head-to-head comparison in terms of the core elements of the systems but it should be evident that the features incorporated in are able to capture a large percentage of the observed variance in NFL games. [ABSTRACT FROM AUTHOR]

Various systems have been proposed to support biological image analysis, with the intent of decreasing false annotations and reducing the heavy burden on biologists. These systems generally comprise a feature extraction method and a classification method. Task-oriented methods for feature extraction leverage characteristic images for each problem, and they are very effective at improving the classification accuracy. However, it is difficult to utilize such feature extraction methods for versatile task in practice, because few biologists specialize in Computer Vision and/or Pattern Recognition to design the task-oriented methods. Thus, in order to improve the usability of these supporting systems, it will be useful to develop a method that can automatically transform the image features of general propose into the effective form toward the task of their interest. In this paper, we propose a semi-supervised feature transformation method, which is formulated as a natural coupling of principal component analysis (PCA) and linear discriminant analysis (LDA) in the framework of graph-embedding. Compared with other feature transformation methods, our method showed favorable classification performance in biological image analysis. [ABSTRACT FROM AUTHOR]

The reliability of quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) models is often difficult to assess due to the problems of accessing the tools and data used to build the models. We present here BioPPSy, which aims to fill this gap by providing an easy-to-use open-source software platform. We demonstrate the program capabilities by calculating three key properties used in drug discovery, aqueous solubility, Caco-2 cell permeability and blood-brain barrier permeability. A comparison is made with a number of previously reported methods, taken from the literature, for each property. The software, including source code, current models and databases, is available from . [ABSTRACT FROM AUTHOR]

This paper proposes the nonnegative adaptive lasso method for variable selection both in the classical fixed p setting (OLS initial estimator) and the ultra-high dimensional setting (root-n-consistent initial estimator). This method is an extension of the adaptive lasso with nonnegative constraint on the coefficients. It is shown to have asymptotic unbiasedness, asymptotic normality and variable selection consistency and its mean squared error decays fast too. Comparing with other procedures, nonnegative adaptive lasso satisfies oracle properties and can select the true variables under fewer assumptions. To get the solution of the nonnegative adaptive lasso, we extend the multiplicative approach for computing. This algorithm is valid for the general framework where the number of regression parameters p is allowed to very large. Simulations are performed to illustrate above results. The constrained index tracking problem in the stock market without short sales is studied in the empirical part. A two-stage method, nonnegative adaptive lasso + nonnegative LS, is applied in the financial modeling. The tracking results indicate that nonnegative adaptive lasso and the two-stage method can both get small tracking error and is successful in assets selection. [ABSTRACT FROM AUTHOR]

Much research has investigated the relationship between the Approximate Number System (ANS) and mathematical achievement, with continued debate surrounding the existence of such a link. The use of different stimulus displays may account for discrepancies in the findings. Indeed, closer scrutiny of the literature suggests that studies supporting a link between ANS acuity and mathematical achievement in adults have mostly measured the ANS using spatially intermixed displays (e.g. of blue and yellow dots), whereas those failing to replicate a link have primarily used spatially separated dot displays. The current study directly compared ANS acuity when using intermixed or separate dots, investigating how such methodological variation mediated the relationship between ANS acuity and mathematical achievement. ANS acuity was poorer and less reliable when measured with intermixed displays, with performance during both conditions related to inhibitory control. Crucially, mathematical achievement was significantly related to ANS accuracy difference (accuracy on congruent trials minus accuracy on incongruent trials) when measured with intermixed displays, but not with separate displays. The findings indicate that methodological variation affects ANS acuity outcomes, as well as the apparent relationship between the ANS and mathematical achievement. Moreover, the current study highlights the problem of low reliabilities of ANS measures. Further research is required to construct ANS measures with improved reliability, and to understand which processes may be responsible for the increased likelihood of finding a correlation between the ANS and mathematical achievement when using intermixed displays. [ABSTRACT FROM AUTHOR]

Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks. [ABSTRACT FROM AUTHOR]

The pneumotachometer is currently the most accepted device to measure tidal breathing, however, it requires the use of a mouthpiece and thus alteration of spontaneous ventilation is implied. Respiratory inductive plethysmography (RIP), which includes two belts, one thoracic and one abdominal, is able to determine spontaneous tidal breathing without the use of a facemask or mouthpiece, however, there are a number of as yet unresolved issues. In this study we aimed to describe and validate a new RIP method, relying on a combination of thoracic RIP and nasal pressure signals taking into account that exercise-induced body movements can easily contaminate RIP thoracic signals by generating tissue motion artifacts. A custom-made time domain algorithm that relies on the elimination of low amplitude artifacts was applied to the raw thoracic RIP signal. Determining this tidal ventilation allowed comparisons between the RIP signal and simultaneously-recorded airflow signals from a calibrated pneumotachometer (PT). We assessed 206 comparisons from 30 volunteers who were asked to breathe spontaneously at rest and during walking on the spot. Comparisons between RIP signals processed by our algorithm and PT showed highly significant correlations for tidal volume (Vt), inspiratory (Ti) and expiratory times (Te). Moreover, bias calculated using the Bland and Altman method were reasonably low for Vt and Ti (0.04 L and 0.02 s, respectively), and acceptable for Te (<0.1 s) and the intercept from regression relationships (0.01 L, 0.06 s, 0.17 s respectively). The Ti/Ttot and Vt/Ti ratios obtained with the two methods were also statistically correlated. We conclude that our methodology (filtering by our algorithm and calibrating with our calibration procedure) for thoracic RIP renders this technique sufficiently accurate to evaluate tidal ventilation variation at rest and during mild to moderate physical activity. [ABSTRACT FROM AUTHOR]

The growing capacity to process and store animal tracks has spurred the development of new methods to segment animal trajectories into elementary units of movement. Key challenges for movement trajectory segmentation are to (i) minimize the need of supervision, (ii) reduce computational costs, (iii) minimize the need of prior assumptions (e.g. simple parametrizations), and (iv) capture biologically meaningful semantics, useful across a broad range of species. We introduce the Expectation-Maximization binary Clustering (EMbC), a general purpose, unsupervised approach to multivariate data clustering. The EMbC is a variant of the Expectation-Maximization Clustering (EMC), a clustering algorithm based on the maximum likelihood estimation of a Gaussian mixture model. This is an iterative algorithm with a closed form step solution and hence a reasonable computational cost. The method looks for a good compromise between statistical soundness and ease and generality of use (by minimizing prior assumptions and favouring the semantic interpretation of the final clustering). Here we focus on the suitability of the EMbC algorithm for behavioural annotation of movement data. We show and discuss the EMbC outputs in both simulated trajectories and empirical movement trajectories including different species and different tracking methodologies. We use synthetic trajectories to assess the performance of EMbC compared to classic EMC and Hidden Markov Models. Empirical trajectories allow us to explore the robustness of the EMbC to data loss and data inaccuracies, and assess the relationship between EMbC output and expert label assignments. Additionally, we suggest a smoothing procedure to account for temporal correlations among labels, and a proper visualization of the output for movement trajectories. Our algorithm is available as an R-package with a set of complementary functions to ease the analysis. [ABSTRACT FROM AUTHOR]

Duplicate address detection (DAD) is an important component of the address resolution protocol (ARP) and the neighbor discovery protocol (NDP). DAD determines whether an IP address is in conflict with other nodes. In traditional DAD, the target address to be detected is broadcast through the network, which provides convenience for malicious nodes to attack. A malicious node can send a spoofing reply to prevent the address configuration of a normal node, and thus, a denial-of-service attack is launched. This study proposes a hash method to hide the target address in DAD, which prevents an attack node from launching destination attacks. If the address of a normal node is identical to the detection address, then its hash value should be the same as the “Hash_64” field in the neighboring solicitation message. Consequently, DAD can be successfully completed. This process is called DAD-h. Simulation results indicate that address configuration using DAD-h has a considerably higher success rate when under attack compared with traditional DAD. Comparative analysis shows that DAD-h does not require third-party devices and considerable computing resources; it also provides a lightweight security resolution. [ABSTRACT FROM AUTHOR]

The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS) which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN). We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours), the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition. [ABSTRACT FROM AUTHOR]

In industrial computed tomography (CT), the mismatch between the X-ray energy and the effective thickness makes it difficult to ensure the integrity of projection data using the traditional scanning model, because of the limitations of the object’s complex structure. So, we have developed a CT imaging method that is based on a spherical trajectory. Considering an unrestrained trajectory for iterative reconstruction, an iterative algorithm can be used to realise the CT reconstruction of a spherical trajectory for complete projection data only. Also, an inclined circle trajectory is used as an example of a spherical trajectory to illustrate the accuracy and feasibility of this new scanning method. The simulation results indicate that the new method produces superior results for a larger cone-beam angle, a limited angle and tabular objects compared with traditional circle trajectory scanning. [ABSTRACT FROM AUTHOR]

Modeling stochastic behavior of chemical reaction networks is an important endeavor in many aspects of chemistry and systems biology. The chemical master equation (CME) and the Gillespie algorithm (GA) are the two most fundamental approaches to such modeling; however, each of them has its own limitations: the GA may require long computing times, while the CME may demand unrealistic memory storage capacity. We propose a method that combines the CME and the GA that allows one to simulate stochastically a part of a reaction network. First, a reaction network is divided into two parts. The first part is simulated via the GA, while the solution of the CME for the second part is fed into the GA in order to update its propensities. The advantage of this method is that it avoids the need to solve the CME or stochastically simulate the entire network, which makes it highly efficient. One of its drawbacks, however, is that most of the information about the second part of the network is lost in the process. Therefore, this method is most useful when only partial information about a reaction network is needed. We tested this method against the GA on two systems of interest in biology - the gene switch and the Griffith model of a genetic oscillator—and have shown it to be highly accurate. Comparing this method to four different stochastic algorithms revealed it to be at least an order of magnitude faster than the fastest among them. [ABSTRACT FROM AUTHOR]

Purpose: We reviewed the survival time for patients with primary brain tumors undergoing treatment with stereotactic radiation methods at the Masaryk Memorial Cancer Institute Brno. We also identified risk factors and characteristics, and described their influence on survival time. Methods: In summarizing survival data, there are two functions of principal interest, namely, the survival function and the hazard function. In practice, both of them can depend on some characteristics. We focused on nonparametric methods, propose a method based on kernel smoothing, and compared our estimates with the results of the Cox regression model. The hazard function is conditional to age and gross tumor volume and visualized as a color-coded surface. A multivariate Cox model was also designed. Results: There were 88 patients with primary brain cancer, treated with stereotactic radiation. The median survival of our patient cohort was 47.8 months. The estimate of the hazard function has two peaks (about 10 months and about 40 months). The survival time of patients was significantly different for various diagnoses (p≪0.001), KI (p = 0.047) and stereotactic methods (p = 0.033). Patients with a greater GTV had higher risk of death. The suitable threshold for GTV is 20 cm3. Younger patients with a survival time of about 50 months had a higher risk of death. In the multivariate Cox regression model, the selected variables were age, GTV, sex, diagnosis, KI, location, and some of their interactions. Conclusion: Kernel methods give us the possibility to evaluate continuous risk variables and based on the results offer risk-prone patients a different treatment, and can be useful for verifying assumptions of the Cox model or for finding thresholds of continuous variables. [ABSTRACT FROM AUTHOR]

Individual acts of cooperation give rise to dynamic social networks. Traditionally, models for cooperation in structured populations are based on a separation of individual strategies and of population structure. Individuals adopt a strategy—typically cooperation or defection, which determines their behaviour toward their neighbours as defined by an interaction network. Here, we report a behavioural experiment that amalgamates strategies and structure to empirically investigate the dynamics of social networks. The action of paying a cost c to provide a benefit b is represented as a directed link point from the donor to the recipient. Participants can add and/or remove links to up to two recipients in each round. First, we show that dense networks emerge, where individuals are characterized by fairness: they receive to the same extent they provide. More specifically, we investigate how participants use information about the generosity and payoff of others to update their links. It turns out that aversion to payoff inequity was the most consistent update rule: adding links to individuals that are worse off and removing links to individuals that are better off. We then investigate the effect of direct reciprocation, showing that the possibility of direct reciprocation does not increase cooperation as compared to the treatment where participants are totally unaware of who is providing benefits to them. [ABSTRACT FROM AUTHOR]

For high-dimensional and massive data sets, traditional centralized gene expression programming (GEP) or improved algorithms lead to increased run-time and decreased prediction accuracy. To solve this problem, this paper proposes a new improved algorithm called distributed function mining for gene expression programming based on fast reduction (DFMGEP-FR). In DFMGEP-FR, fast attribution reduction in binary search algorithms (FAR-BSA) is proposed to quickly find the optimal attribution set, and the function consistency replacement algorithm is given to solve integration of the local function model. Thorough comparative experiments for DFMGEP-FR, centralized GEP and the parallel gene expression programming algorithm based on simulated annealing (parallel GEPSA) are included in this paper. For the waveform, mushroom, connect-4 and musk datasets, the comparative results show that the average time-consumption of DFMGEP-FR drops by 89.09%%, 88.85%, 85.79% and 93.06%, respectively, in contrast to centralized GEP and by 12.5%, 8.42%, 9.62% and 13.75%, respectively, compared with parallel GEPSA. Six well-studied UCI test data sets demonstrate the efficiency and capability of our proposed DFMGEP-FR algorithm for distributed function mining. [ABSTRACT FROM AUTHOR]

To assess the functional capacities of microbial communities, including those inhabiting the human body, shotgun metagenomic reads are often aligned to a database of known genes. Such homology-based annotation practices critically rely on the assumption that short reads can map to orthologous genes of similar function. This assumption, however, and the various factors that impact short read annotation, have not been systematically evaluated. To address this challenge, we generated an extremely large database of simulated reads (totaling 15.9 Gb), spanning over 500,000 microbial genes and 170 curated genomes and including, for many genomes, every possible read of a given length. We annotated each read using common metagenomic protocols, fully characterizing the effect of read length, sequencing error, phylogeny, database coverage, and mapping parameters. We additionally rigorously quantified gene-, genome-, and protocol-specific annotation biases. Overall, our findings provide a first comprehensive evaluation of the capabilities and limitations of functional metagenomic annotation, providing crucial goal-specific best-practice guidelines to inform future metagenomic research. [ABSTRACT FROM AUTHOR]

This paper presents a high payload watermarking scheme for High Efficiency Video Coding (HEVC). HEVC is an emerging video compression standard that provides better compression performance as compared to its predecessor, i.e. H.264/AVC. Considering that HEVC may will be used in a variety of applications in the future, the proposed algorithm has a high potential of utilization in applications involving broadcast and hiding of metadata. The watermark is embedded into the Quantized Transform Coefficients (QTCs) during the encoding process. Later, during the decoding process, the embedded message can be detected and extracted completely. The experimental results show that the proposed algorithm does not significantly affect the video quality, nor does it escalate the bitrate. [ABSTRACT FROM AUTHOR]

With the introduction of omics-technologies such as transcriptomics and proteomics, numerous methods for the reliable identification of significantly regulated features (genes, proteins, etc.) have been developed. Experimental practice requires these tests to successfully deal with conditions such as small numbers of replicates, missing values, non-normally distributed expression levels, and non-identical distributions of features. With the MeanRank test we aimed at developing a test that performs robustly under these conditions, while favorably scaling with the number of replicates. The test proposed here is a global one-sample location test, which is based on the mean ranks across replicates, and internally estimates and controls the false discovery rate. Furthermore, missing data is accounted for without the need of imputation. In extensive simulations comparing MeanRank to other frequently used methods, we found that it performs well with small and large numbers of replicates, feature dependent variance between replicates, and variable regulation across features on simulation data and a recent two-color microarray spike-in dataset. The tests were then used to identify significant changes in the phosphoproteomes of cancer cells induced by the kinase inhibitors erlotinib and 3-MB-PP1 in two independently published mass spectrometry-based studies. MeanRank outperformed the other global rank-based methods applied in this study. Compared to the popular Significance Analysis of Microarrays and Linear Models for Microarray methods, MeanRank performed similar or better. Furthermore, MeanRank exhibits more consistent behavior regarding the degree of regulation and is robust against the choice of preprocessing methods. MeanRank does not require any imputation of missing values, is easy to understand, and yields results that are easy to interpret. The software implementing the algorithm is freely available for academic and commercial use. [ABSTRACT FROM AUTHOR]

Recent tools for aligning short DNA reads have been designed to optimize the trade-off between correctness and speed. This paper introduces a method for assigning a set of short DNA reads to a reference genome, under Local Rank Distance (LRD). The rank-based aligner proposed in this work aims to improve correctness over speed. However, some indexing strategies to speed up the aligner are also investigated. The LRD aligner is improved in terms of speed by storing -mer positions in a hash table for each read. Another improvement, that produces an approximate LRD aligner, is to consider only the positions in the reference that are likely to represent a good positional match of the read. The proposed aligner is evaluated and compared to other state of the art alignment tools in several experiments. A set of experiments are conducted to determine the precision and the recall of the proposed aligner, in the presence of contaminated reads. In another set of experiments, the proposed aligner is used to find the order, the family, or the species of a new (or unknown) organism, given only a set of short Next-Generation Sequencing DNA reads. The empirical results show that the aligner proposed in this work is highly accurate from a biological point of view. Compared to the other evaluated tools, the LRD aligner has the important advantage of being very accurate even for a very low base coverage. Thus, the LRD aligner can be considered as a good alternative to standard alignment tools, especially when the accuracy of the aligner is of high importance. Source code and UNIX binaries of the aligner are freely available for future development and use at http://lrd.herokuapp.com/aligners. The software is implemented in C++ and Java, being supported on UNIX and MS Windows. [ABSTRACT FROM AUTHOR]

In the context of genetics and breeding research on multiple phenotypic traits, reconstructing the directional or causal structure between phenotypic traits is a prerequisite for quantifying the effects of genetic interventions on the traits. Current approaches mainly exploit the genetic effects at quantitative trait loci (QTLs) to learn about causal relationships among phenotypic traits. A requirement for using these approaches is that at least one unique QTL has been identified for each trait studied. However, in practice, especially for molecular phenotypes such as metabolites, this prerequisite is often not met due to limited sample sizes, high noise levels and small QTL effects. Here, we present a novel heuristic search algorithm called the QTL+phenotype supervised orientation (QPSO) algorithm to infer causal directions for edges in undirected phenotype networks. The two main advantages of this algorithm are: first, it does not require QTLs for each and every trait; second, it takes into account associated phenotypic interactions in addition to detected QTLs when orienting undirected edges between traits. We evaluate and compare the performance of QPSO with another state-of-the-art approach, the QTL-directed dependency graph (QDG) algorithm. Simulation results show that our method has broader applicability and leads to more accurate overall orientations. We also illustrate our method with a real-life example involving 24 metabolites and a few major QTLs measured on an association panel of 93 tomato cultivars. Matlab source code implementing the proposed algorithm is freely available upon request. [ABSTRACT FROM AUTHOR]

For reliability testing an Energy Expansion Tree (EET) and a companion Energy Function Model (EFM) are proposed and described in this paper. Different from conventional approaches, the EET provides a more comprehensive and objective way to systematically identify external energy factors affecting reliability. The EFM introduces energy loss into a traditional Function Model to identify internal energy sources affecting reliability. The combination creates a sound way to enumerate the energies to which a system may be exposed during its lifetime. We input these energies into planning an accelerated life test, a Multi Environment Over Stress Test. The test objective is to discover weak links and interactions among the system and the energies to which it is exposed, and design them out. As an example, the methods are applied to the pipe in subsea pipeline. However, they can be widely used in other civil engineering industries as well. The proposed method is compared with current methods. [ABSTRACT FROM AUTHOR]

In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two “traditional”, rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments. [ABSTRACT FROM AUTHOR]

The automatic detection of bilateral symmetry is a challenging task in computer vision and pattern recognition. This paper presents an approach for the detection of bilateral symmetry in digital single object images. Our method relies on the extraction of Scale Invariant Feature Transform (SIFT) based feature points, which serves as the basis for the ascertainment of the centroid of the object; the latter being the origin under the Cartesian coordinate system to be converted to the polar coordinate system in order to facilitate the selection symmetric coordinate pairs. This is followed by comparing the gradient magnitude and orientation of the corresponding points to evaluate the amount of symmetry exhibited by each pair of points. The experimental results show that our approach draw the symmetry line accurately, provided that the observed centroid point is true. [ABSTRACT FROM AUTHOR]

This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML) is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). Using the BoVW model with partition learning, the mean average precision (mAP) of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel fractal coding method with the block classification scheme based on a shared domain block pool. In our method, the domain block pool is called dictionary and is constructed from fractal Julia sets. The image is encoded by searching the best matching domain block with the same BTC (Block Truncation Coding) value in the dictionary. The experimental results show that the scheme is competent both in encoding speed and in reconstruction quality. Particularly for large images, the proposed method can avoid excessive growth of the computational complexity compared with the traditional fractal coding algorithm. [ABSTRACT FROM AUTHOR]