nils-herrmann/clean_scirepeval huggingface dataset:
Expertise Differences in the Comprehension of Visualizations: a Meta-Analysis of Eye-Tracking

This meta-analysis integrates 296 effect sizes reported in eye-tracking research on expertise differences in the comprehension of visualizations. Three theories were evaluated: Ericsson and Kintsch’s ( 1995) theory of long-term working memory, Haider and Frensch’s ( 1999) information-reduction hypothesis, and the holistic model of image perception of Kundel et al. ( 2007). Eye movement and performance data were cumulated from 819 experts, 187 intermediates, and 893 novices. In support of the evaluated theories, experts, when compared with non-experts, had shorter fixation durations, more fixations on task-relevant areas, and fewer fixations on task-redundant areas; experts also had longer saccades and shorter times to first fixate relevant information, owing to superiority in parafoveal processing and selective attention allocation. Eye movements, reaction time, and performance accuracy were moderated by characteristics of visualization (dynamics, realism, dimensionality, modality, and text annotation), task (complexity, time-on-task, and task control), and domain (sports, medicine, transportation, other). These findings are discussed in terms of their implications for theories of visual expertise in professional domains and their significance for the design of learning environments.
ESTIMATES FOR THE FINITE ELEMENT APPROXIMATION OF A SEMILINEAR ELLIPTIC CONTROL PROBLEM WITH STATE CONSTRAINTS AND FINITE DIMENSIONAL CONTROL SPACE

The finite element approximation of optimal control problems for semilinear elliptic partial differential equation is considered, where the control belongs to a finite-dimensional set and state constraints are given in finitely many points of the domain. Under the standard linear independency condition on the active gradients and a strong second-order sufficient optimality condition, optimal error estimates are derived for locally optimal controls.  Revised July 16, 2009. Published online December 16, 2009.
Vitamin C as an antioxidant: evaluation of its role in disease prevention.

Vitamin C in humans must be ingested for survival. Vitamin C is an electron donor, and this property accounts for all its known functions. As an electron donor, vitamin C is a potent water-soluble antioxidant in humans. Antioxidant effects of vitamin C have been demonstrated in many experiments in vitro. Human diseases such as atherosclerosis and cancer might occur in part from oxidant damage to tissues. Oxidation of lipids, proteins and DNA results in specific oxidation products that can be measured in the laboratory. While these biomarkers of oxidation have been measured in humans, such assays have not yet been validated or standardized, and the relationship of oxidant markers to human disease conditions is not clear. Epidemiological studies show that diets high in fruits and vegetables are associated with lower risk of cardiovascular disease, stroke and cancer, and with increased longevity. Whether these protective effects are directly attributable to vitamin C is not known. Intervention studies with vitamin C have shown no change in markers of oxidation or clinical benefit. Dose concentration studies of vitamin C in healthy people showed a sigmoidal relationship between oral dose and plasma and tissue vitamin C concentrations. Hence, optimal dosing is critical to intervention studies using vitamin C. Ideally, future studies of antioxidant actions of vitamin C should target selected patient groups. These groups should be known to have increased oxidative damage as assessed by a reliable biomarker or should have high morbidity and mortality due to diseases thought to be caused or exacerbated by oxidant damage.
Health-Promoting Food Ingredients and Functional Food Processing

One of the greatest challenges food research is facing in this century lies in maintaining sustainable food production and at the same time delivering high quality food products with an added functionality to prevent life-style related diseases such as, cancer, obesity, diabetes, heart disease, stroke. Functional foods that contain bioactive components may provide desirable health benefits beyond basic nutrition and play important roles in the prevention of life-style related diseases. Polyphenols and carotenoids are plant secondary metabolites which are well recognized as natural antioxidants linked to the reduction of the development and progression of life-style related diseases. This chapter focuses on healthpromoting food ingredients (polyphenols and carotenoids), food structure and functionality, and bioavailability of these bioactive ingredients, with examples on their commercial applications, namely on functional foods. Thereafter, in order to support successful development of health-promoting food ingredients, this chapter contributes to an understanding of the relationship between food structures, ingredient functionality, in relation to the breakdown of food structures in the gastrointestinal tract and its impact on the bioavailability of bioactive ingredients. The overview on food processing techniques and the processing of functional foods given here will elaborate novel delivery systems for functional food ingredients and their applications in food. Finally, this chapter concludes with microencapsulation techniques and examples of encapsulation of polyphenols and carotenoids; the physical structure of microencapsulated food ingredients and their impacts on food sensorial properties; yielding an outline on the controlled release of encapsulated bioactive compounds in food products.
A Compositional and Dynamic Model for Face Aging

In this paper, we present a compositional and dynamic model for face aging. The compositional model represents faces in each age group by a hierarchical And-or graph, in which And nodes decompose a face into parts to describe details (e.g., hair, wrinkles, etc.) crucial for age perception and Or nodes represent large diversity of faces by alternative selections. Then a face instance is a transverse of the And-or graph-parse graph. Face aging is modeled as a Markov process on the parse graph representation. We learn the parameters of the dynamic model from a large annotated face data set and the stochasticity of face aging is modeled in the dynamics explicitly. Based on this model, we propose a face aging simulation and prediction algorithm. Inversely, an automatic age estimation algorithm is also developed under this representation. We study two criteria to evaluate the aging results using human perception experiments: (1) the accuracy of simulation: whether the aged faces are perceived of the intended age group, and (2) preservation of identity: whether the aged faces are perceived as the same person. Quantitative statistical analysis validates the performance of our aging model and age estimation algorithm.
Attacks and Defenses in the Data Plane of Networks

Security issues in computer networks have focused on attacks on end systems and the control plane. An entirely new class of emerging network attacks aims at the data plane of the network. Data plane forwarding in network routers has traditionally been implemented with custom-logic hardware, but recent router designs increasingly use software-programmable network processors for packet forwarding. These general-purpose processing devices exhibit software vulnerabilities and are susceptible to attacks. We demonstrate-to our knowledge the first-practical attack that exploits a vulnerability in packet processing software to launch a devastating denial-of-service attack from within the network infrastructure. This attack uses only a single attack packet to consume the full link bandwidth of the router's outgoing link. We also present a hardware-based defense mechanism that can detect situations where malicious packets try to change the operation of the network processor. Using a hardware monitor, our NetFPGA-based prototype system checks every instruction executed by the network processor and can detect deviations from correct processing within four clock cycles. A recovery system can restore the network processor to a safe state within six cycles. This high-speed detection and recovery system can ensure that network processors can be protected effectively and efficiently from this new class of attacks.
Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction

Semantic segmentation is the task of labeling every pixel in an image with a predefined object category. It has numerous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical diagnosis. This problem has traditionally been solved with probabilistic models known as conditional random fields (CRFs) due to their ability to model the relationships between the pixels being predicted. However, deep neural networks (DNNs) recently have been shown to excel at a wide range of computer vision problems due to their ability to automatically learn rich feature representations from data, as opposed to traditional handcrafted features. The idea of combining CRFs and DNNs have achieved state-of-the-art results in a number of domains. We review the literature on combining the modeling power of CRFs with the representation-learning ability of DNNs, ranging from early work that combines these two techniques as independent stages of a common pipeline to recent approaches that embed inference of probabilistic models directly in the neural network itself. Finally, we summarize future research directions.
Performance Prediction for Graph Queries

Query performance prediction has shown benefits to query optimization and resource allocation for relational databases. Emerging applications are leading to search scenarios where workloads with heterogeneous, structure-less analytical queries are processed over large-scale graph and network data. This calls for effective models to predict the performance of graph analytical queries, which are often more involved than their relational counterparts.
 In this paper, we study and evaluate predictive techniques for graph query performance prediction. We make several contributions. (1) We propose a general learning framework that makes use of practical and computationally efficient statistics from query scenarios and employs regression models. (2) We instantiate the framework with two routinely issued query classes, namely, reachability and graph pattern matching, that exhibit different query complexity. We develop modeling and learning algorithms for both query classes. (3) We show that our prediction models readily apply to resource-bounded querying, by providing a learning-based workload optimization strategy. Given a query workload and a time bound, the models select queries to be processed with a maximized query profit and a total cost within the bound. Using real-world graphs, we experimentally demonstrate the efficacy of our framework in terms of accuracy and the effectiveness of workload optimization.
Posterior fixation of thoracolumbar burst fracture: short-segment pedicle fixation versus long-segment instrumentation.

The treatment of thoracolumbar burst fracture is a controversial issue. Short-segment (SS) pedicle fixation has become a popular treatment option. However, there are several studies regarding the high rate of failure. The aim of this prospective study was to compare SS versus long-segment (LS) instrumentation.



For this purpose, 18 consecutive patients were assigned to two groups. Group 1 included nine patients treated by SS pedicle fixation, whereas group 2 included nine patients treated by LS instrumentation. SS instrumentation was pedicle fixation one level above and below the fractured vertebra. LS instrumentation was hook fixation (claw hooks attached to second upper vertebra and infralaminar hooks attached to first upper vertebra) above and pedicle fixation (pedicle screws attached to first and second lower vertebrae) below the fractured vertebra.



As a result, measurements of local kyphosis, sagittal index, and anterior vertebral height compression showed that the LS group had a better outcome at final follow-up (P < 0.05). Also, the SS group had a 55% failure rate, whereas the LS group had prolonged operative time and increased blood loss. However, there was no difference between the two groups according to Low Back Outcome Score.



In conclusion, radiographic parameters demonstrated that LS instrumentation is a more effective management of thoracolumbar burst fractures. Nevertheless, clinical outcome was the same between the two groups. However, our conclusions were based on posterior-only surgery. Anterior column support would negate the need for LS fixation. Also, SS would have been more successful if two above and two below pedicle screws were used.
A Pan-Cancer Proteogenomic Atlas of PI3K/AKT/mTOR Pathway Alterations.

Molecular alterations involving the PI3K/AKT/mTOR pathway (including mutation, copy number, protein, or RNA) were examined across 11,219 human cancers representing 32 major types. Within specific mutated genes, frequency, mutation hotspot residues, in silico predictions, and functional assays were all informative in distinguishing the subset of genetic variants more likely to have functional relevance. Multiple oncogenic pathways including PI3K/AKT/mTOR converged on similar sets of downstream transcriptional targets. In addition to mutation, structural variations and partial copy losses involving PTEN and STK11 showed evidence for having functional relevance. A substantial fraction of cancers showed high mTOR pathway activity without an associated canonical genetic or genomic alteration, including cancers harboring IDH1 or VHL mutations, suggesting multiple mechanisms for pathway activation.
Improving Gloss Vector Semantic Relatedness Measure by Integrating Pointwise Mutual Information: Optimizing Second-Order Co-occurrence Vectors Computed from Biomedical Corpus and UMLS

Methods of semantic relatedness are essential for wide range of tasks such as information retrieval and text mining. This paper, concerned with these automated methods, attempts to improve Gloss Vector semantic relatedness measure for more reliable estimation of relatedness between two input concepts. Generally, this measure by considering frequency cut-off for big rams tries to remove low and high frequency words which usually do not end up being significant features. However, this naive cutting approach can lead to loss of valuable information. By employing point wise mutual information (PMI) as a measure of association between features, we will try to enforce the foregoing elimination step in a statistical fashion. Applying both approaches to the biomedical domain, using MEDLINE as corpus, MeSH as thesaurus, and available reference standard of 311 concept pairs manually rated for semantic relatedness, we will show that PMI for removing insignificant features is more effective approach than frequency cut-off.
A band pass microwave filter with planar spoof surface plasmon polaritons structure

In the present paper a band pass microwave filter with spoof surface plasmon polaritions (SPPs) structure is designed and simulated using FDTD method. The filter is composed of four sections, where in the third section, parallel and symmetrically arranged corrugated metallic strip is the spoof SPPs transmission line, in which, periodically arranged rectangular slot is designed to fulfill the spoof SPPs effect. In the fourth section, the spoof SPPs lines are coupled with each other to realize the transmission zero in the up stop band range. The transmission and reflection properties of the spoof SPPs filter in microwave frequency region are elaborately investigated by FDTD method. Through adjusting the geometrical dimensions of the plasmonic coupling structure as well as the rectangular slots structure, the bandwidth and suppression characteristics of the filter can be precisely controlled and the filter has great resistant ability to space electromagnetic interference.
Evaluating a Semantic Network Automatically Constructed from Lexical Co-occurrence on a Word Sense Disambiguation Task

We describe the extension and objective evaluation of a network of semantically related noun senses (or concepts) that has been automatically acquired by analyzing lexical cooccurrence in Wikipedia. The acquisition process makes no use of the metadata or links that have been manually built into the encyclopedia, and nouns in the network are automatically disambiguated to their corresponding noun senses without supervision. For this task, we use the noun sense inventory of WordNet 3.0. Thus, this work can be conceived of as augmenting the WordNet noun ontology with unweighted, undirected relatedto edges between synsets. Our network contains 208,832 such edges. We evaluate our network’s performance on a word sense disambiguation (WSD) task and show: a) the network is competitive with WordNet when used as a stand-alone knowledge source for two WSD algorithms; b) combining our network with WordNet achieves disambiguation results that exceed the performance of either resource individually; and c) our network outperforms a similar resource that has been automatically derived from semantic annotations in the Wikipedia corpus.
The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients

This research aimed at the case of customers’ default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification credible or not credible clients. Because the real probability of default is unknown, this study presented the novel ‘‘Sorting Smoothing Method” to estimate the real probability of default. With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y = A + BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural network is the only one that can accurately estimate the real probability of default.
Demand Based Hierarchical QoS Using Storage Resource Pools

The high degree of storage consolidation in modern virtualized datacenters requires flexible and efficient ways to allocate IO resources among virtual machines (VMs). Existing IO resource management techniques have two main deficiencies: (1) they are restricted in their ability to allocate resources across multiple hosts sharing a storage device, and (2) they do not permit the administrator to set allocations for a group of VMs that are providing a single service or belong to the same application. In this paper we present the design and implementation of a novel software system called Storage Resource Pools (SRP). SRP supports the logical grouping of related VMs into hierarchical pools. SRP allows reservations, limits and proportional shares, at both the VM and pool levels. Spare resources are allocated to VMs in the same pool in preference to other VMs. The VMs may be distributed across multiple physical hosts without consideration of their logical groupings. We have implemented a prototype of storage resource pools in the VMware ESX hypervisor. Our results demonstrate that SRP provides hierarchical performance isolation and sharing among groups of VMs running across multiple hosts, while maintaining high utilization of the storage device.
Influence of Age, Maturity, and Body Size on the Spatiotemporal Determinants of Maximal Sprint Speed in Boys.

Meyers, RW, Oliver, JL, Hughes, MG, Lloyd, RS, and Cronin, JB. Influence of age, maturity, and body size on the spatiotemporal determinants of maximal sprint speed in boys.  2017-The aim of this study was to investigate the influence of age, maturity, and body size on the spatiotemporal determinants of maximal sprint speed in boys. Three-hundred and seventy-five boys (age: 13.0 ± 1.3 years) completed a 30-m sprint test, during which maximal speed, step length, step frequency, contact time, and flight time were recorded using an optical measurement system. Body mass, height, leg length, and a maturity offset represented somatic variables. Step frequency accounted for the highest proportion of variance in speed (∼58%) in the pre-peak height velocity (pre-PHV) group, whereas step length explained the majority of the variance in speed (∼54%) in the post-PHV group. In the pre-PHV group, mass was negatively related to speed, step length, step frequency, and contact time; however, measures of stature had a positive influence on speed and step length yet a negative influence on step frequency. Speed and step length were also negatively influence by mass in the post-PHV group, whereas leg length continued to positively influence step length. The results highlighted that pre-PHV boys may be deemed step frequency reliant, whereas those post-PHV boys may be marginally step length reliant. Furthermore, the negative influence of body mass, both pre-PHV and post-PHV, suggests that training to optimize sprint performance in youth should include methods such as plyometric and strength training, where a high neuromuscular focus and the development force production relative to body weight are key foci.
A Survey on Social Media Anomaly Detection

Social media anomaly detection is of critical importance to prevent malicious activities such as bullying, terrorist attack planning, and fraud information dissemination. With the recent popularity of social media, new types of anomalous behaviors arise, causing concerns from various parties. While a large amount of work have been dedicated to traditional anomaly detection problems, we observe a surge of research interests in the new realm of social media anomaly detection. In this paper, we present a survey on existing approaches to address this problem. We focus on the new type of anomalous phenomena in the social media and review the recent developed techniques to detect those special types of anomalies. We provide a general overview of the problem domain, common formulations, existing methodologies and potential directions. With this work, we hope to call out the attention from the research community on this challenging problem and open up new directions that we can contribute in the future.