nils-herrmann/clean_scirepeval huggingface dataset:
Similarities and differences among half-marathon runners according to their performance level

This study aimed to identify the similarities and differences among half-marathon runners in relation to their performance level. Forty-eight male runners were classified into 4 groups according to their performance level in a half-marathon (min): Group 1 (n = 11, < 70 min), Group 2 (n = 13, < 80 min), Group 3 (n = 13, < 90 min), Group 4 (n = 11, < 105 min). In two separate sessions, training-related, anthropometric, physiological, foot strike pattern and spatio-temporal variables were recorded. Significant differences (p<0.05) between groups (ES = 0.55-3.16) and correlations with performance were obtained (r = 0.34-0.92) in training-related (experience and running distance per week), anthropometric (mass, body mass index and sum of 6 skinfolds), physiological (VO2max, RCT and running economy), foot strike pattern and spatio-temporal variables (contact time, step rate and length). At standardized submaximal speeds (11, 13 and 15 km·h-1), no significant differences between groups were observed in step rate and length, neither in contact time when foot strike pattern was taken into account. In conclusion, apart from training-related, anthropometric and physiological variables, foot strike pattern and step length were the only biomechanical variables sensitive to half-marathon performance, which are essential to achieve high running speeds. However, when foot strike pattern and running speeds were controlled (submaximal test), the spatio-temporal variables were similar. This indicates that foot strike pattern and running speed are responsible for spatio-temporal differences among runners of different performance level.
Identifying incipient dementia individuals using machine learning and amyloid imaging

Identifying individuals destined to develop Alzheimer's dementia within time frames acceptable for clinical trials constitutes an important challenge to design studies to test emerging disease-modifying therapies. Although amyloid-β protein is the core pathologic feature of Alzheimer's disease, biomarkers of neuronal degeneration are the only ones believed to provide satisfactory predictions of clinical progression within short time frames. Here, we propose a machine learning-based probabilistic method designed to assess the progression to dementia within 24 months, based on the regional information from a single amyloid positron emission tomography scan. Importantly, the proposed method was designed to overcome the inherent adverse imbalance proportions between stable and progressive mild cognitive impairment individuals within a short observation period. The novel algorithm obtained an accuracy of 84% and an under-receiver operating characteristic curve of 0.91, outperforming the existing algorithms using the same biomarker measures and previous studies using multiple biomarker modalities. With its high accuracy, this algorithm has immediate applications for population enrichment in clinical trials designed to test disease-modifying therapies aiming to mitigate the progression to Alzheimer's disease dementia.
Adaptive Cardinality Estimation

In this paper we address cardinality estimation problem which is an important subproblem in query optimization. Query optimization is a part of every relational DBMS responsible for finding the best way of the execution for the given query. These ways are called plans. The execution time of different plans may differ by several orders, so query optimizer has a great influence on the whole DBMS performance. We consider cost-based query optimization approach as the most popular one. It was observed that costbased optimization quality depends much on cardinality estimation quality. Cardinality of the plan node is the number of tuples returned by it. In the paper we propose a novel cardinality estimation approach with the use of machine learning methods. The main point of the approach is using query execution statistics of the previously executed queries to improve cardinality estimations. We called this approach adaptive cardinality estimation to reflect this point. The approach is general, flexible, and easy to implement. The experimental evaluation shows that this approach significantly increases the quality of cardinality estimation, and therefore increases the DBMS performance for some queries by several times or even by several dozens of times.
Structural patterns of information cascades and their implications for dynamics and semantics

Information cascades are ubiquitous in both physical society and online social media, taking on large variations in structures, dynamics and semantics. Although the dynamics and semantics of information cascades have been studied, the structural patterns and their correlations with dynamics and semantics are largely unknown. Here we explore a large-scale dataset including 432 million information cascades with explicit records of spreading traces, spreading behaviors, information content as well as user profiles. We find that the structural complexity of information cascades is far beyond the previous conjectures. We first propose a ten-dimensional metric to quantify the structural characteristics of information cascades, reflecting cascade size, silhouette, direction and activity aspects. We find that bimodal law governs majority of the metrics, information flows in cascades have four directions, and the selfloop number and average activity of cascades follows power law. We then analyze the high-order structural patterns of information cascades. Finally, we evaluate to what extent the structural features of information cascades can explain its dynamic patterns and semantics, and finally uncover some notable implications of structural patterns in information cascades. Our discoveries also provide a foundation for the microscopic mechanisms for information spreading, potentially leading to implications for cascade prediction and outlier detection.
Convolutional Neural Networks on 3D Surfaces Using Parallel Frames

We extend Convolutional Neural Networks (CNNs) on flat and regular domains (e.g. 2D images) to curved 2D manifolds embedded in 3D Euclidean space that are discretized as irregular surface meshes and widely used to represent geometric data in Computer Vision and Graphics. We define surface convolution on tangent spaces of a surface domain, where the convolution has two desirable properties: 1) the distortion of surface domain signals is locally minimal when being projected to the tangent space, and 2) the translation equi-variance property holds locally, by aligning tangent spaces for neighboring points with the canonical torsion-free parallel transport that preserves tangent space metric. To implement such a convolution, we rely on a parallel N -direction frame field on the surface that minimizes the field variation and therefore is as compatible as possible to and approximates the parallel transport. On the tangent spaces equipped with parallel frames, the computation of surface convolution becomes standard routine. The tangential frames have N rotational symmetry that must be disambiguated, which we resolve by duplicating the surface domain to construct its covering space induced by the parallel frames and grouping the feature maps into N sets accordingly; each surface convolution is computed on the N branches of the cover space with their respective feature maps while the kernel weights are shared. To handle the irregular data points of a discretized surface mesh while being able to share trainable kernel weights, we make the convolution semi-discrete, i.e. the convolution kernels are smooth polynomial functions, and their convolution with discrete surface data points becomes discrete sampling and weighted summation. In addition, pooling and unpooling operations for surface CNNs on a mesh are computed along the mesh hierarchy built through simplification. The presented surface-based CNNs allow us to do effective deep learning on surface meshes using network structures very similar to those for flat and regular domains. In particular, we show that for various tasks, including classification, segmentation and non-rigid registration, surface CNNs using only raw input signals achieve superior performances than other neural network models using sophisticated pre-computed input features, and enable a simple non-rigid human-body registration procedure by regressing to restpose positions directly.
Using automatically labelled examples to classify rhetorical relations: an assessment

Being able to identify which rhetorical relations (e.g., contrast or explanation) hold between spans of text is important for many natural language processing applications. Using machine learning to obtain a classifier which can distinguish between different relations typically depends on the availability of manually labelled training data, which is very time-consuming to create. However, rhetorical relations are sometimes lexically marked, i.e., signalled by discourse markers (e.g., because, but, consequently etc.), and it has been suggested (Marcu and Echihabi, 2002) that the presence of these cues in some examples can be exploited to label them automatically with the corresponding relation. The discourse markers are then removed and the automatically labelled data are used to train a classifier to determine relations even when no discourse marker is present (based on other linguistic cues such as word co-occurrences). In this paper, we investigate empirically how feasible this approach is. In particular, we test whether automatically labelled, lexically marked examples are really suitable training material for classifiers that are then applied to unmarked examples. Our results suggest that training on this type of data may not be such a good strategy, as models trained in this way do not seem to generalise very well to unmarked data. Furthermore, we found some evidence that this behaviour is largely independent of the classifiers used and seems to lie in the data itself (e.g., marked and unmarked examples may be too dissimilar linguistically and removing unambiguous markers in the automatic labelling process may lead to a meaning shift in the examples).
FUZZY MULTI-CRITERIA DECISION MAKING MODEL FOR RISK ASSESSMENT

Decision making and risk assessment are becoming a challenging task in oil and gas due to the risk related to the uncertainty and imprecision. This paper proposed a model for the risk assessment based on multi-criteria decision making (MCDM) method by integrating Fuzzy-set theory. In this model, decision makers (experts) provide their preference of risk assessment information in four categories; people, environment, asset, and reputation. A fuzzy set theory is used to evaluate likelihood, consequence and total risk level associated with each category. A case study is presented to demonstrate the proposed model. The results indicate that the proposed Fuzzy MCDM method has the potential to be used by decision makers in evaluating the risk based on multiple inputs and criteria.
A Theory and Methodology of Inductive Learning

A theory of inductive learning is presented that characterizes it as a heuristic search through a space of symbolic descriptions, generated by an application of certain inference rules to the initial observational statements (the teacher-provided examples of some concepts, or facts about a class of objects or a phenomenon). The inference rules include generalization rules, which perform generalizing transformations on descriptions, and conventional truth-preserving deductive rules (specialization and reformulation rules). The application of the inference rules to descriptions is constrained by problem background knowledge, and guided by criteria evaluating the 'quality' of generated inductive assertions. Based on this theory, a general methodology for learning structural descriptions from examples, called STAR, is described and illustrated by a problem from the area of conceptual data analysis. " . . . scientific knowledge through demonstration 1 is impossible unless a man knows the primary immediate premises . . . . " " . . . we must get to know the primary premises by induction; for the method by which even senseperception implants the universal is induc t ive . . . "
Semi-supervised distance metric learning for Collaborative Image Retrieval

Learning a good distance metric plays a vital role in many multimedia retrieval and data mining tasks. For example, a typical content-based image retrieval (CBIR) system often relies on an effective distance metric to measure similarity between any two images. Conventional CBIR systems simply adopting Euclidean distance metric often fail to return satisfactory results mainly due to the well-known semantic gap challenge. In this article, we present a novel framework of Semi-Supervised Distance Metric Learning for learning effective distance metrics by exploring the historical relevance feedback log data of a CBIR system and utilizing unlabeled data when log data are limited and noisy. We formally formulate the learning problem into a convex optimization task and then present a new technique, named as “Laplacian Regularized Metric Learning” (LRML). Two efficient algorithms are then proposed to solve the LRML task. Further, we apply the proposed technique to two applications. One direct application is for Collaborative Image Retrieval (CIR), which aims to explore the CBIR log data for improving the retrieval performance of CBIR systems. The other application is for Collaborative Image Clustering (CIC), which aims to explore the CBIR log data for enhancing the clustering performance of image pattern clustering tasks. We conduct extensive evaluation to compare the proposed LRML method with a number of competing methods, including 2 standard metrics, 3 unsupervised metrics, and 4 supervised metrics with side information. Encouraging results validate the effectiveness of the proposed technique.
The effects of presentation method and simulation fidelity on psychomotor education in a bimanual metrology training simulation

In this study, we empirically evaluated the effects of presentation method and simulation fidelity on task performance and psychomotor skills acquisition in an immersive bimanual simulation towards precision metrology education. In a 2 × 2 experiment design, we investigated a large-screen immersive display (LSID) with a head-mounted display (HMD), and the presence versus absence of gravity. Advantages of the HMD include interacting with the simulation in a more natural manner as compared to using a large-screen immersive display due to the similarities between the interactions afforded in the virtual compared to the real-world task. Suspending the laws of physics may have an effect on usability and in turn could affect learning outcomes. Our dependent variables consisted of a pre and post cognition questionnaire, quantitative performance measures, perceived workload and system usefulness, and a psychomotor assessment to measure to what extent transfer of learning took place from the virtual to the real world. Results indicate that the HMD condition was preferable to the immersive display in several metrics while the no-gravity condition resulted in users adopting strategies that were not advantageous for task performance.
Strength training for plantar fasciitis and the intrinsic foot musculature: A systematic review.

The aim was to critically evaluate the literature investigating strength training interventions in the treatment of plantar fasciitis and improving intrinsic foot musculature strength. A search of PubMed, CINHAL, Web of Science, SPORTSDiscus, EBSCO Academic Search Complete and PEDRO using the search terms plantar fasciitis, strength, strengthening, resistance training, intrinsic flexor foot, resistance training. Seven articles met the eligibility criteria. Methodological quality was assessed using the modified Downs and Black checklist. All articles showed moderate to high quality, however external validity was low. A comparison of the interventions highlights significant differences in strength training approaches to treating plantar fasciitis and improving intrinsic strength. It was not possible to identify the extent to which strengthening interventions for intrinsic musculature may benefit symptomatic or at risk populations to plantar fasciitis. There is limited external validity that foot exercises, toe flexion against resistance and minimalist running shoes may contribute to improved intrinsic foot musculature function. Despite no plantar fascia thickness changes being observed through high-load plantar fascia resistance training there are indications that it may aid in a reduction of pain and improvements in function. Further research should use standardised outcome measures to assess intrinsic foot musculature strength and plantar fasciitis symptoms.
Micro-grid autonomous operation during and subsequent to islanding process

This paper investigates (i) preplanned switching events and (ii) fault events that lead to islanding of a distribution subsystem and formation of a micro-grid. The micro-grid includes two distributed generation (DG) units. One unit is a conventional rotating synchronous machine and the other is interfaced through a power electronic converter. The interface converter of the latter unit is equipped with independent real and reactive power control to minimize islanding transients and maintain both angle stability and voltage quality within the micro-grid. The studies are performed based on a digital computer simulation approach using the PSCAD/EMTDC software package. The studies show that an appropriate control strategy for the power electronically interfaced DG unit can ensure stability of the micro-grid and maintain voltage quality at designated buses, even during islanding transients. This paper concludes that presence of an electronically-interfaced DG unit makes the concept of micro-grid a technically viable option for further investigations.
Achievable Rates for Tomlinson-Harashima Precoding

This correspondence examines Tomlinson–Harashima precoding (THP) on discrete-time channels having intersymbol interference and additive white Gaussian noise. An exact expression for the maximum achievable information rate of zero-forcing (ZF) THP is derived as a function of the channel impulse response, the input power constraint, and the additive white Gaussian noise variance. Information rate bounds are provided for the minimum mean-square error (MMSE) THP. The performance of ZF-THP and MMSE-THP relative to each other and to channel capacity is explored in general and for some example channels. The importance of symbol rate to ZF-THP performance is demonstrated.
An analytical least-squares solution to the line scan LIDAR-camera extrinsic calibration problem

In this paper, we present an elegant solution to the 2D LIDAR-camera extrinsic calibration problem. Specifically, we develop a simple method for establishing correspondences between a line-scan (2D) LIDAR and a camera using a small calibration target that only contains a straight line. Moreover, we formulate the nonlinear least-squares problem for finding the unknown 6 degree-of-freedom (dof) transformation between the two sensors, and solve it analytically to determine its global minimum. Additionally, we examine the conditions under which the unknown transformation becomes unobservable, which can be used for avoiding ill-conditioned configurations. Finally, we present extensive simulation and experimental results for assessing the performance of the proposed algorithm as compared to alternative analytical approaches.
What Happens Next? Event Prediction Using a Compositional Neural Network Model

We address the problem of automatically acquiring knowledge of event sequences from text, with the aim of providing a predictive model for use in narrative generation systems. We present a neural network model that simultaneously learns embeddings for words describing events, a function to compose the embeddings into a representation of the event, and a coherence function to predict the strength of association between two events. We introduce a new development of the narrative cloze evaluation task, better suited to a setting where rich information about events is available. We compare models that learn vector-space representations of the events denoted by verbs in chains centering on a single protagonist. We find that recent work on learning vector-space embeddings to capture word meaning can be effectively applied to this task, including simple incorporation of a verb’s arguments in the representation by vector addition. These representations provide a good initialization for learning the richer, compositional model of events with a neural network, vastly outperforming a number of baselines and competitive alternatives.
Interspecies Chimerism with Mammalian Pluripotent Stem Cells

Interspecies blastocyst complementation enables organ-specific enrichment of xenogenic pluripotent stem cell (PSC) derivatives. Here, we establish a versatile blastocyst complementation platform based on CRISPR-Cas9-mediated zygote genome editing and show enrichment of rat PSC-derivatives in several tissues of gene-edited organogenesis-disabled mice. Besides gaining insights into species evolution, embryogenesis, and human disease, interspecies blastocyst complementation might allow human organ generation in animals whose organ size, anatomy, and physiology are closer to humans. To date, however, whether human PSCs (hPSCs) can contribute to chimera formation in non-rodent species remains unknown. We systematically evaluate the chimeric competency of several types of hPSCs using a more diversified clade of mammals, the ungulates. We find that naïve hPSCs robustly engraft in both pig and cattle pre-implantation blastocysts but show limited contribution to post-implantation pig embryos. Instead, an intermediate hPSC type exhibits higher degree of chimerism and is able to generate differentiated progenies in post-implantation pig embryos.
An Overview of 3D Printing Technologies for Soft Materials and Potential Opportunities for Lipid-based Drug Delivery Systems

Three-dimensional printing (3DP) is a rapidly growing additive manufacturing process and it is predicted that the technology will transform the production of goods across numerous fields. In the pharmaceutical sector, 3DP has been used to develop complex dosage forms of different sizes and structures, dose variations, dose combinations and release characteristics, not possible to produce using traditional manufacturing methods. However, the technology has mainly been focused on polymer-based systems and currently, limited information is available about the potential opportunities for the 3DP of soft materials such as lipids. This review paper emphasises the most commonly used 3DP technologies for soft materials such as inkjet printing, binder jetting, selective laser sintering (SLS), stereolithography (SLA), fused deposition modeling (FDM) and semi-solid extrusion, with the current status of these technologies for soft materials in biological, food and pharmaceutical applications. The advantages of 3DP, particularly in the pharmaceutical field, are highlighted and an insight is provided about the current studies for lipid-based drug delivery systems evaluating the potential of 3DP to fabricate innovative products. Additionally, the challenges of the 3DP technologies associated with technical processing, regulatory and material issues of lipids are discussed in detail. The future utility of 3DP for printing soft materials, particularly for lipid-based drug delivery systems, offers great advantages and the technology will potentially support patient compliance and drug effectiveness via a personalised medicine approach.