Miracle-dz/UniFAR-FTUs huggingface dataset:
Updating the evidence for the effect of radiofrequency catheter ablation on left atrial volume and function in patients with atrial fibrillation: a meta-analysis

Summary Objectives To systematically review the effects of radiofrequency catheter ablation (RFCA) on left atrial (LA) size, volumes and function in patients with atrial fibrillation (AF). Methods We searched MEDLINE, EMBASE, ScienceDirect, Highwire, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and the reference lists of retrieved reports in July 2012. Setting China Participants Twenty-six studies (enrolling 1821 patients) were included in the final analysis. Main outcome measures Changes of LA size or volumes and/or function in patients with AF after RFCA. Results Compared to pre-ablation values, there were significant decreases in LA diameter and LA volumes at post-ablation follow-up. However, compared to pre-ablation values, there were no significant differences in LA ejection fraction/LA active emptying fraction and LA strain at post-ablation follow-up. Decreases in LA diameter and LA volumes remained significant in those without AF recurrence but not in those with AF recurrence. LA ejection fraction/LA active emptying fraction did not decrease in patients without AF recurrence, whereas they decreased in patients with AF recurrence. As for LA strain, it seems that LA strain increases in patients without AF recurrence, with less fibrosis and with more LA volumes decrease, but the differences were not significant. Conclusions Successful RFCA in patients with AF significantly decreases LA size and volumes and does not seem to adversely affect LA function.
An Analytical Model of Vehicle-Based Automated Material Handling Systems in Semiconductor Fabs

This research explores analytical models useful in the design of vehicle-based automated material handling systems (AMHS) to support semiconductor manufacturing. The objective is to correctly estimate the throughput and move request delay. This analysis proposes a computationally effective analytical approach to multi-vehicle AMHS performance modeling for a simple closed loop. A probabilistic model is developed, based on a detailed description of AMHS operations, and the system is analyzed as an extended Markov chain. The model tracks the operations of one vehicle on the closed-loop considering the possibility of vehicle-blocking. This analysis provides the essential parameters such as the blocking probabilities in order to estimate the performance measures. A numerical example is analyzed and simulated using Automod to demonstrate and validate the queuing model
Prostate Tissue Classification Methods and Critiques

The work in this paper examines and criticizes different methods used for prostate tissue classification using trans-rectal ultra-sound (TRUS) images. The suspicious regions are first identified by an accurate region of interest (ROI) identification algorithm. The identified ROIs' are then analyzed using statistical based features as well as spectral based features. For the statistical based features each ROI is treated as an image and different statistical features are constructed from the ROI image. The statistical feature set is constructed from the grey level difference vector (GLDV), as well as the grey level dependence matrix (GLDM). While for the spectral based features, all of the ROIs' pixels are aligned to form a ROI I-D signal. Different spectral features are then constructed from the I-D ROI signals. The spectral feature set is constructed using geometrical features extracted from the estimated power spectrum density (PSD) as well as the estimation of signal parameters via rotational invariance technique (ESPRIT) features. A classifier based feature selection algorithm using ants colony optimization (ACO), a recently proposed optimization technique is adopted and used to select an optimal subset from each of the above extracted features. The obtained accuracy ranges from 72.2% to 93.75% using a Support Vector Machine classifier.
Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network

Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM) and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83%) and a three-degree grading area (89.02%, 86.63%, and 90.75%), density (92.68%, 91.05%, and 93.94%) and location (89.28%, 82.70%, and 93.08%). Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model.
Query Optimization and Execution in a Parallel Analytics DBMS

Over the past 15 years, data warehousing and OLAP technologies have matured to the point whereby they have become a cornerstone for the decision making process in organizations of all sizes. With the underlying databases growing enormously in size, parallel DBM systems have become a popular target platform. Perhaps the most ``obvious'' approach to scalable warehousing is to combine a small collection of conventional relational DBMSs into a loosely connected parallel DBMS. Such systems, however, benefit little, if at all, from advances in OLAP indexing, storage, compression, modeling, or query optimization. In the current paper, we discuss a parallel analytics server that has been designed from the ground up as a high performance OLAP query engine. Moreover, its indexing and query processing model directly exploits an OLAP-specific algebra that enables performance optimizations beyond the reach of simple relational DBMS clusters. Taken together, the server provides class-leading query performance with the scalability of shared nothing databases and, perhaps most importantly, achieves this balance with a modest physical architecture.
E6-associated transcription patterns in human papilloma virus 16-positive cervical tissues

The change in transcription pattern induced by post-transcriptional RNA splicing is an important mechanism in the regulation of the early gene expression of human papilloma virus (HPV). The present study was conducted to establish a method to specifically amplify HPV-16 E6-associated transcripts. The E6-related transcripts from 63 HPV-16-positive cervical tumor tissue samples were amplified, consisting of eight cases of low-risk intraepithelial lesions, 38 cases of high-risk intraepithelial lesions and 17 cases of cervical cancer (CxCa). The appropriate amplified segments were recovered following agarose gel electrophoresis, and subjected to further sequencing and sequence alignment analysis. Six groups of E6 transcription patterns were identified from HPV-16-positive cervical tumor tissue, including five newly-discovered transcripts. Different HPV-16 E6-associated transcription patterns were detected during the development of CxCa. Over the course of the progression of the low-grade squamous intraepithelial lesions to CxCa, the specific HPV-16 E6-associated transcription patterns and the dominant transcripts were all different. As indicated by this study, the transcription pattern of the E6 early gene of HPV-16 was closely associated with the stages of cervical carcinogenesis, and may also be involved in the development of CxCa.
Examining Additivity and Weak Baselines

We present a study of which baseline to use when testing a new retrieval technique. In contrast to past work, we show that measuring a statistically significant improvement over a weak baseline is not a good predictor of whether a similar improvement will be measured on a strong baseline. Sometimes strong baselines are made worse when a new technique is applied. We investigate whether conducting comparisons against a range of weaker baselines can increase confidence that an observed effect will also show improvements on a stronger baseline. Our results indicate that this is not the case -- at best, testing against a range of baselines means that an experimenter can be more confident that the new technique is unlikely to significantly harm a strong baseline. Examining recent past work, we present evidence that the information retrieval (IR) community continues to test against weak baselines. This is unfortunate as, in light of our experiments, we conclude that the only way to be confident that a new technique is a contribution is to compare it against nothing less than the state of the art.
Towards a Taxonomy and Theory of the Interdependence between Learning Regimes and Sectorial Patterns of Innovation and Competition : an Empirical Analysis of an Elaborated Pavitt Taxonomy applying Danish Data

The aim of the author’s thesis, of which this paper is a part, is to develop some building blocks for an evolutionary based theory of the firms’ organisation and appropriation of learning and knowledge. A main proposition of the thesis is the existence of aninterdependence between the firm’s organisation and appropriation of knowledge and learning and the environment in which the firm acts. This interdependence points towards a dynamic structure conduct performance framework, where the environment is presumed to give certain ‘rules of the game’ to which the firm must reply. The aim of this paper is to empirically analyse and discuss a proposed taxonomy of innovation and competition, to test if it is possible to disaggregate the economy into distinct sectors or environments that are different from one another. The taxonomy is based on the well-known Pavitt taxonomy (Pavitt, 1984), but has been developed to include parts of the service sector. In the analysis of the taxonomy we apply two data set namely the CIS 1 data and the DISKO 1 data to find as many stylised facts of the sectors in the taxonomy as possible. While we claim to present an empirically sound taxonomy, we also acknowledge that the analysis points towards some general trends, which hold across the sectors.
Practical sensor management for an energy-limited detection system

Real-time detection of intermittent events requires continual monitoring and processing of sensor data. A battery-powered device that supports multiple sensing modalities and processing algorithms has the potential to save energy by using expensive sensors and algorithms only when the event of interest is most likely to occur. To develop a policy for sensing and processing management, we adopt maximum sequential information gain as an objective criterion for such energy-limited systems, which can be solved via dynamic programming. For binary hypothesis testing with two sensing options, the optimal management policy is a simple two-threshold test on the posterior belief. Detection of bird presence/absence in a wildlife monitoring application shows up to a 37% reduction in error rate over standard constant-duty-cycle sensing.
On Training Deep Networks for Satellite Image Super-Resolution

The capabilities of super-resolution (SR) reconstruction (i.e., techniques for enhancing image spatial resolution) have been boosted recently by the use of deep convolutional neural networks. For SR, they are learned using huge training sets composed of original images, each of which is coupled with a low-resolution counterpart. In this paper, we explore how the SR performance depends on the procedure employed to obtain the training data. Up to date, this has not been given much attention—commonly, bicubic downsampling is used. Our extensive experimental study indicates that the training data characteristics have a large impact on the reconstruction accuracy, and the widely-adopted approach is not the most effective for dealing with satellite images. Overall, we argue that developing better training data preparation routines may be pivotal in making SR suitable for real-world applications.
Isogeometric Residual Minimization Method (iGRM) with Direction Splitting Preconditoner for Stationary Advection-Diffusion Problems

In this paper, we propose the Isogeometric Residual Minimization (iGRM) with direction splitting. The method mixes the benefits resulting from isogeometric analysis, residual minimization, and alternating direction solver. Namely, we utilize tensor product B-spline basis functions and alternating direction methods. We apply a stabilized mixed method based on residual minimization. We propose a preconditioned conjugate gradients method with a linear computational cost resulting from a Kronecker product structure of the system of linear equations. We test our method on two-dimensional simulations of advection-diffusion problems, including the problem with the manufactured solution, the Eriksson-Johnson problem, and a rotating flow problem. We compare our method to the Discontinuous Petrov-Galerkin and the Streamline Upwind Petrov-Galerkin (SUPG) stabilization methods. The resulting method is not restricted to a Kronecker product structure of the diffusion or advection data.
Optimal Adaptive Control for Weakly Coupled Nonlinear Systems: A Neuro-Inspired Approach :

SUMMARY This paper proposes a new approximate dynamic programming algorithm to solve the inﬁnite-horizon optimal control problem for weakly coupled nonlinear systems. The algorithm is implemented as a three-critics/four-actors approximators structure, where the critic approximators are used to learn the optimal costs, while the actor approximators are used to learn the optimal control policies. Simultaneous continuous-time adaptation of both critic and actor approximators is implemented, a method commonly known as synchronous policy iteration. The adaptive control nature of the algorithm requires a persistence of excitation condition to be a priori guaranteed, but this can be relaxed by using previously stored data concurrently with current data in the update of the critic approximators. A robustifying control term is added to the controller to eliminate the effects of the residual errors, leading to asymptotic stability of the equilibrium point of the closed-loop system. Simulation results show the effectiveness of the proposed approach for a fourth-order dynamical example. Copyright c (cid:13) 2015 John Wiley & Sons, Ltd. Received .
Image based flow visualization for curved surfaces

A new method for the synthesis of dense, vector-field aligned textures on curved surfaces is presented, called IBFVS. The method is based on image based flow visualization (IBFV). In IBFV two-dimensional animated textures are produced by defining each frame of a flow animation as a blend between a warped version of the previous image and a number of filtered white noise images. We produce flow aligned texture on arbitrary three-dimensional triangular meshes in the same spirit as the original method: texture is generated directly in image space. We show that IBFVS is efficient and effective. High performance (typically fifty frames or more per second) is achieved by exploiting graphics hardware. Also, IBFVS can easily be implemented and a variety of effects can be achieved. Applications are flow visualization and surface rendering. Specifically, we show how to visualize the wind field on the earth and how to render a dirty bronze bunny.
Fusion Techniques for Reliable Information: A Survey

Information fused by multi-sensors is an important factor for obtaining reliable contextual information in smart spaces which use the pervasive and ubiquitous computing techniques. Adaptive fusion improves robust operational system performances then makes a reliable decision by reducing uncertain information. However, these fusion techniques suffer from problems regarding the accuracy of estimation or inference. No commonly accepted approaches exist currently. In this survey, we introduce the advantages and disadvantages of fusion techniques which can be used in specific applications. Second, we categorize well-known models, algorithms, systems, and applications depending on the proposed approaches. Finally, we discuss the related issues for fusion techniques within the smart spaces then suggest research directions for improving the decision-making in uncertain situation.
On Metamotivation: Consumers’ Knowledge about the Role of Construal Level in Enhancing Task Performance

Self-regulation research typically focuses on the modulation of thoughts, feelings, and behavior to achieve desired ends. We propose that understanding the regulation of the underlying motivational orientations that drive these reactions is a critical yet underappreciated research question. We review research on metamotivation—people’s understanding and goal-directed regulation of their motivational states. A central metamotivational challenge is identifying the type and amount of motivation that best promotes goal outcomes in a task, and then finding the means to instantiate this state. To illustrate these principles, we review research in the context of construal level theory. This work suggests that people recognize high-level and low-level construal as motivational orientations that benefit performance on distinct tasks and may strategically select the construal level induction that best promotes performance. We then discuss the implications of this work (and the metamotivational approach more generally) for consumer behavior research.
Inbreeding and Outbreeding Depression in Natural Populations of Chamaecrista fasciculata (Fabaceae)

Abstract: The deleterious consequences of inbreeding have been well documented. There are, however, few empirical studies that have examined the consequences of restoring heterozygosity and hence the fitness of inbred populations by conducting interpopulation crosses and measuring the performance of later‐generation hybrids under field conditions. We conducted interpopulation crosses of 100 m to 2000 km, which spans the range of Chamaecrista fasciculata ( Fabaceae) in eastern North America. We then contrasted the performance of the F1 and later‐segregating F3 hybrids with the parental generation. We found almost universal F1 superiority over the parents. The F3 hybrids suffered a loss of fitness compared to the F1 hybrids. The drop off in fitness of the F3 reflects both the loss of heterozygosity and the disruption of coadapted gene complexes. The F3 performance, however, was still often equal to that of the parents, suggesting that heterosis can outweigh the loss of coadaptation except for the longest‐distance crosses. In a subset of environments, the F3 performance of long‐distance (≥1000 km) interpopulation crosses was less than that of both parents and indicated true outbreeding depression. For C. fasciculata, it appears that crossing populations of up to intermediate distances of hundreds of kilometers has a short‐term beneficial effect on progeny performance through F1, and that longer‐term effects are not necessarily disruptive of fitness, at least relative to parental performance. The degree of F1 heterosis and F3 outbreeding depression varied between site and year, however, indicating an important role for the environment in the expression of these effects.
Carbon balance of the taiga forest within Alaska: present and future

Forest biomass, rates of production, and carbon dynamics are a function of climate, plant species present, and the structure of the soil organic and mineral layers. Inventory data from the U.S. Forest Service (USFS) Inventory Analysis Unit was used to develop estimates of the land area represented by the major overstory species at various age-classes. The CENTURY model was then used to develop an estimate of carbon dynamics throughout the age sequence of forest development for the major ecosystem types. The estimated boreal forest area in Alaska, based on USFS inventory data is 17 244 098 ha. The total aboveground biomass within the Alaska boreal forest was estimated to be 815 330 000 Mg. The CENTURY model estimated maximum net ecosystem production (NEP) at 137, 88, 152, 99, and 65 g·m2·year1 for quaking aspen (Populus tremuloides Michx.), paper birch (Betula papyrifera Marsh.), balsam poplar (Populus balsamifera L.), white spruce (Picea glauca (Moench) Voss), and black spruce (Picea mariana (Mill.) BSP.
Impulsivity Subtypes and Maladaptive Road Performance among Drivers in Germany and Switzerland

Excessive speed and speeding substantially compromise road safety in Germany and Switzerland. Approximately one third of all fatal accidents are caused by maladjusted speed. Recent studies attribute a special importance to the impulsivity construct in the context of maladaptive road behavior. Thus, the effects of impulsivity on risky driving behaviors (speeding violations) were examined in a Swiss-German sample of N = 361 car drivers (both on speed affine drivers and putative ordinary drivers). The participants filled in a questionnaire battery consisting of an impulsiveness scale as well as traffic-related attitudes and cognitive appraisal tendencies on the one hand and indicators for maladaptive behaviors at and beyond traffic domain on the other hand. The directions of the observed correlations between the scales were as expected, with impulsivity correlating negatively with age (young drivers scored higher) but not at all with gender or driving experience. To find out more about the functionality of impulsivity, specific personality profiles were carried out via cluster analysis. Three different control types were empirically found (impulsivity subtype, reduced compliance subtype, vulnerability subtype), while high impulsive drivers scored high in impulsivity, low on compliance, high on affective responsiveness and described themselves as affordance-prone. The impulsive type additionally shows more speeding offences stored in the driving license file, overrides speed limits for more than 15 km/h more frequently and even shows deviancy beyond traffic domain. The results are discussed in the light of the impulse control system and conclusions are drawn regarding assessment of driving aptitude and interventions. The theoretical framework including a hierarchical structured model of deviance was confirmed empirically.
A fast and robust traffic sign recognition method using ring of RIBP histograms based feature

Fast and robust traffic sign recognition is very important but difficult for the safety driving assist systems. This study addresses the fast and robust traffic sign recognition to enhance safety driving. We first adopt the typical Hough transform methods to implement coarse-grained locating of the candidate regions (shapes of rectangle, triangle and circle, etc.) of the traffic signs; and then propose a ring of RIBP (Rotation Invariant Binary Pattern) histograms based feature in Gaussian space to reduce the traffic sign detection time and achieve the robustness on traffic sign detection in terms of scale, rotation, and illumination; Finally, the learning based techniques are used to reduce the feature dimension and implement the classification, which greatly reduce the processing time of traffic sign recognition. Experiments on the GTSRB dataset show that this work achieves 98.62% recognition accuracy and average 0.005 second per image recognition time, which exhibit the comparable recognition accuracy and higher recognition speed comparing to the state-of-the-art works.