nils-herrmann/clean_scirepeval huggingface dataset:
Monocular vision-based obstacle detection/avoidance for unmanned aerial vehicles

Robust real-time obstacle detection/avoidance is a challenging problem especially for micro and small aerial vehicles due to the limited number of the on-board sensors due to the battery constraint and low payload. Usually lightweight sensors such as CMOS camera are the best choice comparing with laser or radar sensors. For real-time applications, most studies focus on using stereo cameras to reconstruct a 3D model of the obstacles or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the state of the approaching obstacles using single camera is proposed. During the flight, this method is able to detect the changes of the size area of the obstacles. First, the method detects the feature points of the obstacles, and then extracts the obstacles that has probability of getting close. In addition, by comparing the changes in the area ratios of the obstacle in the image sequence, the method can decide if it is obstacle or not. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV can take the action of avoidance.
Accelerated genetic drift on chromosome X during the human dispersal out of Africa

Comparisons of chromosome X and the autosomes can illuminate differences in the histories of males and females as well as shed light on the forces of natural selection. We compared the patterns of variation in these parts of the genome using two datasets that we assembled for this study that are both genomic in scale. Three independent analyses show that around the time of the dispersal of modern humans out of Africa, chromosome X experienced much more genetic drift than is expected from the pattern on the autosomes. This is not predicted by known episodes of demographic history, and we found no similar patterns associated with the dispersals into East Asia and Europe. We conclude that a sex-biased process that reduced the female effective population size, or an episode of natural selection unusually affecting chromosome X, was associated with the founding of non-African populations.
An Adaptive Query Execution System for Data Integration

Query processing in data integration occurs over network-bound, autonomous data sources. This requires extensions to traditional optimization and execution techniques for three reasons: there is an absence of quality statistics about the data, data transfer rates are unpredictable and bursty, and slow or unavailable data sources can often be replaced by overlapping or mirrored sources. This paper presents the Tukwila data integration system, designed to support adaptivity at its core using a two-pronged approach. Interleaved planning and execution with partial optimization allows Tukwila to quickly recover from decisions based on inaccurate estimates. During execution, Tukwila uses adaptive query operators such as the double pipelined hash join, which produces answers quickly, and the dynamic collector, which robustly and efficiently computes unions across overlapping data sources. We demonstrate that the Tukwila architecture extends previous innovations in adaptive execution (such as query scrambling, mid-execution re-optimization, and choose nodes), and we present experimental evidence that our techniques result in behavior desirable for a data integration system.
An Efficient Outpatient Scheduling Approach

Outpatient scheduling is considered as a complex problem. Efficient solutions to this problem are required by many health care facilities. This paper proposes an efficient approach to outpatient scheduling by specifying a bidding method and converting it to a group role assignment problem. The proposed approach is validated by conducting simulations and experiments with randomly generated patient requests for available time slots. The major contribution of this paper is an efficient outpatient scheduling approach making automatic outpatient scheduling practical. The exciting result is due to the consideration of outpatient scheduling as a collaborative activity and the creation of a qualification matrix in order to apply the group role assignment algorithm.
Restrictive dermopathy—a lethal congenital laminopathy. Case report and review of the literature

Restrictive dermopathy (RD) is a rare, fatal, and genetically heterogeneous laminopathy with a predominant autosomal recessive heredity pattern. The phenotype can be caused by mutations in either LMNA (primary laminopathy) or ZMPSTE24 (secondary laminopathy) genes but mostly by homozygous or compound heterozygous ZMPSTE24 mutations. Clinicopathologic findings are unique, allowing a specific diagnosis in most cases. We describe a premature newborn girl of non-consanguineous parents who presented a rigid, translucent and tightly adherent skin, dysmorphic facies, multiple joint contractures and radiological abnormalities. The overall clinical, radiological, histological, and ultrastructural features were typical of restrictive dermopathy. Molecular genetic analysis revealed a homozygous ZMPSTE24 mutation (c.1085_1086insT). Parents and sister were heterozygous asymptomatic carriers. We conclude that RD is a relatively easy and consistent clinical and pathological diagnosis. Despite recent advances in our understanding of RD, the pathogenetic mechanisms of the disease are not entirely clarified. Recognition of RD and molecular genetic diagnosis are important to define the prognosis of an affected child and for recommending genetic counseling to affected families. However, the outcome for a live born patient in the neonatal period is always fatal.
Very Early Nimodipine Use in Stroke (VENUS): a randomized, double-blind, placebo-controlled trial.

The Very Early Nimodipine Use in Stroke (VENUS) trial was designed to test the hypothesis that early treatment with nimodipine has a positive effect on survival and functional outcome after stroke. This was suggested in a previous meta-analysis on the use of nimodipine in stroke. However, in a recent Cochrane review we were unable to reproduce these positive results. This led to the early termination of VENUS after an interim analysis.



In this randomized, double-blind, placebo-controlled trial, treatment was started by general practitioners or neurologists within 6 hours after stroke onset (oral nimodipine 30 mg QID or identical placebo, for 10 days). Main analyses included comparisons of the primary end point (poor outcome, defined as death or dependency after 3 months) and secondary end points (neurological status and blood pressure 24 hours after inclusion, mortality after 10 days, and adverse events) between treatment groups. Subgroup analyses (on final diagnosis and based on the per-protocol data set) were performed.



At trial termination, after inclusion of 454 patients (225 nimodipine, 229 placebo), no effect of nimodipine was found. After 3 months of follow-up, 32% (n=71) of patients in the nimodipine group had a poor outcome compared with 27% (n=62) in the placebo group (relative risk, 1.2; 95% CI, 0.9 to 1.6). A treatment effect was not found for secondary outcomes and in the subgroup analyses.



The results of VENUS do not support the hypothesis of a beneficial effect of early nimodipine in stroke patients.
Running head : DEVELOPMENT OF SHORT PYD SCALES Creation of Short and Very Short Measures of the Five Cs of Positive Youth Development

As developmental scientists seek to index the strengths of adolescents and adopt the positive youth development (PYD) perspective, psychometrically sound measurement tools will be needed to assess adolescents’ positive attributes. Using a series of exploratory factor analyses and CFA models, this research creates short and very short versions of the scale used to measure the Five Cs of PYD in the 4-H Study of Positive Youth Development. We created separate forms for earlier versus later adolescence and ensured that items displayed sufficient conceptual overlap across forms to support tests of factorial invariance. We discuss implications for further scale development and advocate for the use of these convenient tools, especially in research and applications pertinent to the Five Cs model of PYD. DOI: https://doi.org/10.1111/jora.12039 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-108444 Accepted Version Originally published at: Geldhof, G J; Bowers, Edmond P; Boyd, Michelle J; Mueller, Megan K; Napolitano, Christopher M; Schmid, Kristina L; Lerner, Jacqueline V; Lerner, Richard M (2013). Creation of Short and Very Short Measures of the Five Cs of Positive Youth Development. Journal of Research on Adolescence, 24(1):163176.  https://doi.org/10.1111/jora.12039 Running head: DEVELOPMENT OF SHORT PYD SCALES Creation of Short and Very Short Measures of the Five Cs of Positive Youth Development G. John Geldhof, Edmond P. Bowers, Michelle J. Boyd, Megan Kiely Mueller, Christopher M. Napolitano, Kristina L. Schmid Jacqueline V. Lerner, Richard M. Lerner DEVELOPMENT OF SHORT PYD SCALES 2
Multivariate Time Series Classification with WEASEL+MUSE

Multivariate time series (MTS) arise when multiple interconnected sensors record data over time. Dealing with this high-dimensional data is challenging for every classifier for at least two reasons: First, an MTS is not only characterized by individual feature values, but also by the interplay of features in different dimensions. Second, the high dimensionality typically adds large amounts of irrelevant data and noise. We present our novel MTS classifier WEASEL+MUSE which addresses both challenges. WEASEL+MUSE builds a multivariate feature vector, first using a sliding-window approach applied to each dimension of the MTS, then extracting discrete features per window and dimension. The feature vector is subsequently fed through feature selection, removing non-discriminative features, and analysed by a machine learning classifier. The novelty of WEASEL+MUSE lies in its specific way of extracting and filtering multivariate features from MTS by encoding context information into each feature. Still, the resulting feature set is small, yet very discriminative and useful for MTS classification. Based on a benchmark of 20 MTS datasets, we found that WEASEL+MUSE is among the most accurate state-of-the-art classifiers.
On classification of distorted images with deep convolutional neural networks

Image blur and image noise are common distortions during image acquisition. In this paper, we systematically study the effect of image distortions on the deep neural network (DNN) image classifiers. First, we examine the DNN classifier performance under four types of distortions. Second, we propose two approaches to alleviate the effect of image distortion: re-training and fine-tuning with noisy images. Our results suggest that, under certain conditions, fine-tuning with noisy images can alleviate much effect due to distorted inputs, and is more practical than re-training.
One-shot learning of object categories

Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be. We explore a Bayesian implementation of this idea. Object categories are represented by probabilistic models. Prior knowledge is represented as a probability density function on the parameters of these models. The posterior model for an object category is obtained by updating the prior in the light of one or more observations. We test a simple implementation of our algorithm on a database of 101 diverse object categories. We compare category models learned by an implementation of our Bayesian approach to models learned from by maximum likelihood (ML) and maximum a posteriori (MAP) methods. We find that on a database of more than 100 categories, the Bayesian approach produces informative models when the number of training examples is too small for other methods to operate successfully.
Crushed cartilage grafts for concealing irregularities in rhinoplasty.

To assess the clinical outcome of crushed cartilage grafts used to conceal contour irregularities in rhinoplasty.



We reviewed the medical records of 462 patients in whom crushed autogenous cartilage grafts were used, selected from a total of 669 patients in whom rhinoplasty procedures were performed at our institution between June 1, 1999, and June 1, 2006. The grafts were used as slightly, moderately, significantly, or severely crushed.



Eight hundred nine cartilage grafts (41 slightly crushed grafts [5%], 650 moderately crushed grafts [80%], and 118 significantly crushed grafts [15%]) were used in 462 patients. Resorption occurred in 11 of the 462 patients (2.4%). All of the resorbed grafts (6 moderately crushed grafts and 5 significantly crushed grafts) had been placed in the dorsal area. The resorption rate of those grafts was lower in the moderately crushed cartilage grafts (6 of 284 grafts [2.1%]) than in the significantly crushed grafts (5 of 38 grafts [13.1%]). There was no resorption of slightly crushed grafts.



The degree of crushing applied is important for long-term clinical outcome of autogenous crushed cartilage grafts. Slight or moderate crushing of cartilage creates an outstanding graft material for concealing irregularities and provides both excellent long-term clinical outcome and predictable esthetic results.
Clusters, Trends, and Outliers: How Immersive Technologies Can Facilitate the Collaborative Analysis of Multidimensional Data

Immersive technologies such as augmented reality devices are opening up a new design space for the visual analysis of data. This paper studies the potential of an augmented reality environment for the purpose of collaborative analysis of multidimensional, abstract data. We present ART, a collaborative analysis tool to visualize multidimensional data in augmented reality using an interactive, 3D parallel coordinates visualization. The visualization is anchored to a touch-sensitive tabletop, benefiting from well-established interaction techniques. The results of group-based, expert walkthroughs show that ART can facilitate immersion in the data, a fluid analysis process, and collaboration. Based on the results, we provide a set of guidelines and discuss future research areas to foster the development of immersive technologies as tools for the collaborative analysis of multidimensional data.
Rex: replication at the speed of multi-core

Standard state-machine replication involves consensus on a sequence of totally ordered requests through, for example, the Paxos protocol. Such a sequential execution model is becoming outdated on prevalent multi-core servers. Highly concurrent executions on multi-core architectures introduce non-determinism related to thread scheduling and lock contentions, and fundamentally break the assumption in state-machine replication. This tension between concurrency and consistency is not inherent because the total-ordering of requests is merely a simplifying convenience that is unnecessary for consistency. Concurrent executions of the application can be decoupled with a sequence of consensus decisions through consensus on partial-order traces, rather than on totally ordered requests, that capture the non-deterministic decisions in one replica execution and to be replayed with the same decisions on others. The result is a new multi-core friendly replicated state-machine framework that achieves strong consistency while preserving parallelism in multi-thread applications. On 12-core machines with hyper-threading, evaluations on typical applications show that we can scale with the number of cores, achieving up to 16 times the throughput of standard replicated state machines.
Prediction of Stock Market Index Movement by Ten Data Mining Techniques

Ability to predict direction of stock/index price accurately is crucial for market dealers or investors to maximize their profits. Data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, in stead of a single method, traders need to use various forecasting techniques to gain multiple signals and more information about the future of the markets. In this paper, ten different techniques of data mining are discussed and applied to predict price movement of Hang Seng index of Hong Kong stock market. The approaches include Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA), K-nearest neighbor classification, Naïve Bayes based on kernel estimation, Logit model, Tree based classification, neural network, Bayesian classification with Gaussian process, Support vector machine (SVM) and Least squares support vector machine (LS-SVM). Experimental results show that the SVM and LS-SVM generate superior predictive performances among the other models. Specifically, SVM is better than LS-SVM for in-sample prediction but LS-SVM is, in turn, better than the SVM for the out-of-sample forecasts in term of hit rate and error rate criteria.
Continuous Path Smoothing for Car-Like Robots Using B-Spline Curves

A practical approach for generating motion paths with continuous steering for car-like mobile robots is presented here. This paper addresses two key issues in robot motion planning; path continuity and maximum curvature constraint for nonholonomic robots. The advantage of this new method is that it allows robots to account for their constraints in an efficient manner that facilitates real-time planning. Bspline curves are leveraged for their robustness and practical synthesis to model the vehicle’s path. Comparative navigational-based analyses are presented to selected appropriate curve and nominate its parameters. Path continuity is achieved by utilizing a single path, to represent the trajectory, with no limitations on path, or orientation. The path parameters are formulated with respect to the robot’s constraints. Maximum curvature is satisfied locally, in every segment using a smoothing algorithm, if needed. It is M. Elbanhawi ( ) · M. Simic · R. N. Jazar School of Aerospace, Mechanical, and Manufacturing Engineering (SAMME), RMIT University.Bundoora East Campus, Corner of Plenty Road, McKimmies Road, Bundoora VIC 3083, Melbourne, Australia e-mail: mohamed.elbenhawi@rmit.edu.au M. Simic e-mail: milan.simic@rmit.edu.au R. N. Jazar e-mail: reza.jazar@rmit.edu.au demonstrated that any local modifications of single sections have minimal effect on the entire path. Rigorous simulations are presented, to highlight the benefits of the proposed method, in comparison to existing approaches with regards to continuity, curvature control, path length and resulting acceleration. Experimental results validate that our approach mimics human steering with high accuracy. Accordingly, efficiently formulated continuous paths ultimately contribute towards passenger comfort improvement. Using presented approach, autonomous vehicles generate and follow paths that humans are accustomed to, with minimum disturbances.
Imaging single synaptic vesicles undergoing repeated fusion events: kissing, running, and kissing again

At synapses of the mammalian central nervous system, release of neurotransmitter occurs at rates transiently as high as 100 Hz, putting extreme demands on nerve terminals with only tens of functional vesicles at their disposal. Thus, the presynaptic vesicle cycle is particularly critical to maintain neurotransmission. To understand vesicle cycling at the most fundamental level, we studied single vesicles undergoing exo/endocytosis and tracked the fate of newly retrieved vesicles. This was accomplished by minimally stimulating boutons in the presence of the membrane-fluorescent styryl dye FM1-43, then selecting for terminals that contained only one dye-filled vesicle. We then observed the kinetics of dye release during single action potential stimulation. We found that most vesicles lost only a portion of their total dye during a single fusion event, but were able to fuse again soon thereafter. We interpret this as direct evidence of "kiss-and-run" followed by rapid reuse. Other interpretations such as "partial loading" and "endosomal splitting" were largely excluded on the basis of multiple lines of evidence. Our data placed an upper bound of <1.4 s on the lifetime of the kiss-and-run fusion event, based on the assumption that aqueous departitioning is rate limiting. The repeated use of individual vesicles held over a range of stimulus frequencies up to 30 Hz and was associated with neurotransmitter release. A small percentage of fusion events did release a whole vesicle's worth of dye in one action potential, consistent with a classical picture of exocytosis as fusion followed by complete collapse or at least very slow retrieval.
An Online One Class Support Vector Machine-Based Person-Specific Fall Detection System for Monitoring an Elderly Individual in a Room Environment

In this paper, we propose a novel computer vision-based fall detection system for monitoring an elderly person in a home care, assistive living application. Initially, a single camera covering the full view of the room environment is used for the video recording of an elderly person's daily activities for a certain time period. The recorded video is then manually segmented into short video clips containing normal postures, which are used to compose the normal dataset. We use the codebook background subtraction technique to extract the human body silhouettes from the video clips in the normal dataset and information from ellipse fitting and shape description, together with position information, is used to provide features to describe the extracted posture silhouettes. The features are collected and an online one class support vector machine (OCSVM) method is applied to find the region in feature space to distinguish normal daily postures and abnormal postures such as falls. The resultant OCSVM model can also be updated by using the online scheme to adapt to new emerging normal postures and certain rules are added to reduce false alarm rate and thereby improve fall detection performance. From the comprehensive experimental evaluations on datasets for 12 people, we confirm that our proposed person-specific fall detection system can achieve excellent fall detection performance with 100% fall detection rate and only 3% false detection rate with the optimally tuned parameters. This work is a semiunsupervised fall detection system from a system perspective because although an unsupervised-type algorithm (OCSVM) is applied, human intervention is needed for segmenting and selecting of video clips containing normal postures. As such, our research represents a step toward a complete unsupervised fall detection system.