nils-herrmann/clean_scirepeval huggingface dataset:
What are the consequences of deployment to Iraq and Afghanistan on the mental health of the UK armed forces? A cohort study
Venue: The Lancet
Year: 2010

Concerns have been raised about the psychological effect of continued combat exposure and of repeated deployments. We examined the consequences of deployment to Iraq and Afghanistan on the mental health of UK armed forces from 2003 to 2009, the effect of multiple deployments, and time since return from deployment.  We reassessed the prevalence of probable mental disorders in participants of our previous study (2003 05) We also studied two new randomly chosen samples: those with recent deployment to Afghanistan, and those who had joined the UK armed forces since April, 2003, to ensure that the final sample continued to be representative of the UK armed forces. Between November, 2007, and September, 2009, participants completed a questionnaire about their deployment experiences and health outcomes.  (56% participants completed the study questionnaire (8278 regulars, 1712 reservists) The prevalence of probable post traumatic stress disorder was 4.0% (95% CI 3.5 4.5; n=376) 19.7% (18.7 20.6; n=1908) for symptoms of common mental disorders, and 13.0% (12.2 13.8; n=1323) for alcohol misuse. Deployment to Iraq or Afghanistan was significantly associated with alcohol misuse for regulars (odds ratio 1.22, 95% CI 1.02 1.46) and with probable post traumatic stress disorder for reservists (2.83, 1.23 6.51) Regular personnel in combat roles were more likely than were those in support roles to report probable post traumatic stress disorder (1.87, 1.26 2.78) There was no association with number of deployments for any outcome. There was some evidence for a small increase in the reporting of probable post traumatic stress disorder with time since return from deployment in regulars (1.13, 1.03 1.24) INTERPRETATION Symptoms of common mental disorders and alcohol misuse remain the most frequently reported mental disorders in UK armed forces personnel, whereas the prevalence of probable post traumatic stress disorder was low. These findings show the importance of continued health surveillance of UK military personnel.
A preliminary apparatus and teaching structure for passive tactile training of stenography
Venue: 2019 IEEE World Haptics Conference (WHC)
Year: 2019

Passive tactile learning (PTL) is a method of training motor skills using repeated tactile cues applied to the body even while the user is focused on unrelated tasks.Prior inquiry into PTL has determined that it may be used to teach a variety of input methods, including those that require simultaneous actions (chords) and spatial tasks that give individual fingers multiple responsibilities. No research has combined these techniques to construct a training system that teaches a spatial, chorded input method. Computer stenography is one such input method, and the need for improved training techniques for computer stenography is apparent. Many aspiring stenographers quickly lose motivation due to the difficulty of the input method and the scarcity of effective learning resources. Passive tactile training may be a useful training mechanism to combat the learning crisis in the stenography field. In a study with 12 participants, we demonstrate that PTL may be a useful aide to learning computer stenography. Despite the complexity of the input method, results suggest that PTL may be effective in teaching users fundamental building blocks of stenography that can be applied broadly to the task of mastering the input method.
Discovery and Subtyping of Neo Epitope Specific T Cell Responses for Cancer Immunotherapy: Addressing the Mutanome.
Venue: Methods in molecular biology
Year: 2017

Cancer accumulates 10s to 1000s of genomic mutations of which a fraction is immunogenic and may serve as an Achilles' heel of tumor cells. Mutation specific T cells can recognize these antigens and destroy malignant cells. Strategies to immunotherapeutically address individual tumor mutations employing peptide or mRNA based vaccines are now actively investigated in mice and humans. An important step of determining the therapeutic potential of a mutanome vaccine is the detection of mutation reactive T cell responses. In this chapter we provide protocols to identify and subtype mutation specific T cells in mice based on IFN g ELISpot and flow cytometry.
A review on primary synthesis and secondary treatment of aluminium matrix composites
Year: 2020

In this paper, the primary synthesis and secondary treatment of Aluminium matrix composites (AMCs) has been reviewed. The renewed quest for component materials with high strength to weight ratio, unusual and superlative combination of properties for applications in automotive, aerospace, marine and warfare armoury manufacturing industries has increased the versatility potential of aluminium alloy based composites. Several categories (synthetic and agro based ceramics) of reinforcement materials for aluminium composite are discussed. The manufacturing/fabrication techniques which could be solid phase (powder metallurgy and rapid prototyping or 3 D printing method) or liquid phase (casting and pressure infiltration) methods are discussed in this review work. Secondary treatment such as heat treatment, forging and other thermomechanical treatments which improves the properties of as synthesized composites are also discussed. A review synopsis of recent studies provides opportunity for concise but a more robust understanding of potential benefits and detrimental effects associated with the use of various primary synthesis routes and secondary treatment for manufacturing of ceramic reinforced AMCs. Despite the laudable research efforts that have been made towards development and enhancement of the properties of AMCs, this review work revealed that literature is very sparse on synergetic adoption of multi synthesis route and multi approach secondary treatment for producing AMCs. Sequel to the aforementioned unexplored research concept, some lacunae are identified and suggested for further elaborations and study.
The Heeger Bergen Pyramid Based Texture Synthesis Algorithm
Venue: Image Process. Line
Year: 2014

This contribution deals with the Heeger Bergen pyramid based texture analysis/synthesis algorithm. It brings a detailed explanation of the original algorithm tested on many characteristic examples. Our analysis reproduces the original results, but also brings a minor improvement concerning non periodic textures. Inspired by visual perception theories, Heeger and Bergen proposed to characterize a texture by its first order statistics of both its color and its responses to multiscale and multi orientation filters, namely the steerable pyramid. The Heeger Bergen algorithm consists in the following procedure: starting from a white noise image, histogram matchings are performed to the noise alternatively in both the image domain and steerable pyramid domain, so that the corresponding histograms match the ones of the input texture.
Natural biodegradable polymers based nano formulations for drug delivery: A review
Venue: International journal of pharmaceutics
Year: 2019

No caption available.  Nanomedicines are now considered as the new generation medication in the current era mainly because of their features related to nano size. The efficacy of many drugs in their micro/macro formulations is shown to have poor bioavailability and pharmacokinetics after oral administration. To overcome this predicament, use of natural/synthetic biodegradable polymeric nanoparticles (NPs) have gained prominence in the field of nanomedicine for targeted drug delivery to improve biocompatibility, bioavailability, safety, enhanced permeability, better retention time and lower toxicity. For drug delivery, it is essential to have biodegradable nanoparticle formulations for safe and efficient transport and release of drug at the intended site. Moreover, depending on the target organ, a suitable biodegradable polymer can be selected as the drug carrier for target specific as well as for sustained drug delivery. The aim of this review is to present the current status and scope of natural biodegradable polymers as well as some emerging polymers with special characteristics as suitable carriers for drug delivery applications. The most widely preferred preparation methods are discussed along with their characterization using different analytical techniques. Further, the review highlights significant features of methods developed using natural polymers for drug entrapment and release studies.
mTOR Signaling in Growth Control and Disease
Venue: Cell
Year: 2012

The mechanistic target of rapamycin (mTOR) signaling pathway senses and integrates a variety of environmental cues to regulate organismal growth and homeostasis. The pathway regulates many major cellular processes and is implicated in an increasing number of pathological conditions, including cancer, obesity, type 2 diabetes, and neurodegeneration. Here, we review recent advances in our understanding of the mTOR pathway and its role in health, disease, and aging. We further discuss pharmacological approaches to treat human pathologies linked to mTOR deregulation.
Global labor flow network reveals the hierarchical organization and dynamics of geo industrial clusters
Venue: Nature Communications
Year: 2019

Groups of firms often achieve a competitive advantage through the formation of geo industrial clusters. Although many exemplary clusters are the subjects of case studies, systematic approaches to identify and analyze the hierarchical structure of geo industrial clusters at the global scale are scarce. In this work, we use LinkedIn's employment history data from more than 500 million users over 25 years to construct a labor flow network of over 4 million firms across the world, from which we reveal hierarchical structure by applying network community detection. We show that the resulting geo industrial clusters exhibit a stronger association between the influx of educated workers and financial performance, compared to traditional aggregation units. Furthermore, our analysis of the skills of educated workers reveals richer insights into the relationship between the labor flow of educated workers and productivity growth. We argue that geo industrial clusters defined by labor flow provide useful insights into the growth of the economy. There is a lack of systematic approaches to identify and analyze the hierarchical structure of geo industrial clusters at the global scale. Here the authors use LinkedIn's employment history data to construct a global labor flow network from which they find that the resulting geo industrial clusters exhibit a stronger association between the influx of educated workers and financial performance compared to existing aggregation units.
Microwave bio sensor based on symmetrical split ring resonator with spurline filters for therapeutic goods detection
Venue: PloS one
Year: 2017

A novel symmetrical split ring resonator (SSRR) based microwave sensor with spurline filters for detecting and characterizing the properties of solid materials has been developed. Due to the weak perturbation in the interaction of material under test (MUT) and planar microwave sensor, spurline filters were embedded to the SSRR microwave sensor which effectively enhanced Q factor with suppressing the undesired harmonic frequency. The spurline filter structures force the presented sensor to resonate at a fundamental frequency of 2.2 GHz with the capabilities of suppressing rejected harmonic frequency and miniaturization in circuit size. A wide bandwidth rejection is achieved by using double spurlines filters with high Q factor achievement (up to 652.94) compared to single spurline filter. The new SSRR sensor with spurline filters displayed desired properties such as high sensitivity, accuracy, and performance with a 1.3% typical percentage error in the measurement results. Furthermore, the sensor has been successfully applied for detecting and characterizing solid materials (such as Roger 5880, Roger 4350, and FR4) and evidently demonstrated that it can suppress the harmonic frequency effectively. This novel design with harmonic suppression is useful for various applications such as food industry (meat, fruit, vegetables) biological medicine (derived from proteins and other substances produced by the body) and Therapeutic goods (antiseptics, vitamins, anti psychotics, and other medicines)
GUESS: projecting machine learning scores to well calibrated probability estimates for clinical decision making
Venue: Bioinform.
Year: 2019

Clinical decision support systems (CDSS) have been applied in numerous fields, ranging from cancer survival towards drug resistance prediction. Nevertheless, CDSS typically have a caveat: many of them are perceived as black boxes by non experts and, unfortunately, the obtained scores cannot usually be interpreted as class probability estimates. In probability focused medical applications, it is not sufficient to perform well with regards to discrimination and, consequently, various calibration methods have been developed to enable probabilistic interpretation. The aims of the current study were 1) to develop a tool for fast and comparative analysis of different calibration methods, 2) to demonstrate their limitations for the use on clinical data, and 3) to introduce our novel method GUESS.  We compared the performances of two different state of the art calibration methods, namely histogram binning and Bayesian Binning in Quantiles (BBQ) as well as our novel method GUESS on both, simulated and real world datasets. GUESS demonstrated calibration performance comparable to the state of the art methods and always retained accurate class discrimination. GUESS showed superior calibration performance in small datasets and therefore may be an optimal calibration method for typical clinical datasets. Moreover, we provide a framework (CalibratR) for R, which can be used to identify the most suitable calibration method for novel datasets in a timely and efficient manner. Using calibrated probability estimates instead of original classifier scores will contribute to the acceptance and dissemination of machine learning based classification models in cost sensitive applications, such as clinical research. Availability GUESS as part of CalibratR can be downloaded at CRAN. Supplementary information Supplementary data are available at Bioinformatics online.
Beyond the neutron drip line: The unbound oxygen isotopes O 25 and O 26
Year: 2013

The very neutron rich oxygen isotopes O 25 and O 26 are investigated experimentally and theoretically. The unbound states are populated in an experiment performed at the R3B LAND setup at GSI via proton knockout reactions from F 26 and F 27 at relativistic energies around 442 and 414 MeV/nucleon, respectively. From the kinematically complete measurement of the decay into O 24 plus one or two neutrons, the O 25 ground state energy and width are determined, and upper limits for the O 26 ground state energy and lifetime are extracted. In addition, the results provide indications for an excited state in O 26 at around 4 MeV. The experimental findings are compared to theoretical shell model calculations based on chiral two and three nucleon (3N) forces, including for the first time residual 3N forces, which are shown to be amplified as valence neutrons are added.
Fine grained sport action recognition with Twin spatio temporal convolutional neural networks
Venue: Multimedia Tools and Applications
Year: 2020

Human action recognition in video is one of the key problems in visual data interpretation. Despite intensive research, the recognition of actions with low inter class variability remains a challenge. This paper presents a new Twin Spatio Temporal Convolutional Neural Network (TSTCNN) for this purpose. When applied to table tennis, it is possible to detect and recognize 20 table tennis strokes. The model has been trained on a specific dataset, so called TTStroke 21 recorded in natural conditions at the Faculty of Sports of the University of Bordeaux. Our model takes as inputs an RGB image sequence and its computed Optical Flow. The proposed Twin architecture is a two stream network both comprising 3 spatio temporal convolutional layers, followed by a fully connected layer where data are fused. Our method reaches an accuracy of 91.4% against 43.1% for our baseline, a Two Stream Inflated 3D ConvNet (I3D)
Graph Policy Network for Transferable Active Learning on Graphs
Venue: NeurIPS
Year: 2020

Graph neural networks (GNNs) have been attracting increasing popularity due to their simplicity and effectiveness in a variety of fields. However, a large number of labeled data is generally required to train these networks, which could be very expensive to obtain in some domains. In this paper, we study active learning for GNNs, i.e. how to efficiently label the nodes on a graph to reduce the annotation cost of training GNNs. We formulate the problem as a sequential decision process on graphs and train a GNN based policy network with reinforcement learning to learn the optimal query strategy. By jointly training on several source graphs with full labels, we learn a transferable active learning policy which can directly generalize to unlabeled target graphs. Experimental results on multiple datasets from different domains prove the effectiveness of the learned policy in promoting active learning performance in both settings of transferring between graphs in the same domain and across different domains.
Energy stability and convergence of SAV block centered finite difference method for gradient flows
Venue: Math. Comput.
Year: 2019

We present in this paper construction and analysis of a block centered finite difference method for the spatial discretization of the scalar auxiliary variable Crank Nicolson scheme (SAV/CN BCFD) for gradient flows, and show rigorously that scheme is second order in both time and space in various discrete norms. When equipped with an adaptive time strategy, the SAV/CN BCFD scheme is accurate and extremely efficient. Numerical experiments on typical Allen Cahn and Cahn Hilliard equations are presented to verify our theoretical results and to show the robustness and accuracy of the SAV/CN BCFD scheme.
Study on Recommend Model of Online Shopping for Music and Dance majors under the Background of Big Data
Year: 2018

With the rapid development of Internet technology and e commerce, online shopping has become the main mode of consumption nowadays. Music and dance majors have become the main group of online shopping because of their particularity. Based on the theory of big data and Bayesian networks, this paper collects data of college students' online shopping apparel by means of questionnaire survey and builds the style recommendation model and the clothing's main color recommendation model of online shopping.
Raman lasers for trace gas detection
Venue: Advanced High Power Lasers and Applications
Year: 2019

The emission of commercial solid state lasers is shifted by Raman lasers to selected wavelengths suitable for trace gas detection. High power Raman lasers based on Ba(NO3)2, diamond and silicon detect CO2, O3 and H2O. Raman lasers base on the physical effect of stimulated Raman scattering. Two new Raman crystals (Spodumene and LuAlO3) are investigated and their SRS spectra is shown.
Identifying sustainable warehouse management system indicators and proposing new weighting method
Year: 2020

The performance of a sustainable warehouse management system is a multidimensional concept based on the triple bottom line approach. It has been a challenge to identify the key performance indicators for a sustainable warehouse management system and to develop a model for evaluating the direct and indirect indicators. In order to overcome this challenge, this paper describes a method to identify and weight indicators that assess sustainability in a sustainable warehouse management system using structural equation modeling. A comprehensive literature review has been conducted and a questionnaire survey involving experts in the field has been undertaken. A list of 33 key performance indicators for a sustainable warehouse management system has been proposed, and this can be used by policymakers to appraise the sustainability performance of a sustainable warehouse management system. The proposed robust model can weight indicators and evaluate the total effect of each indicator in incorporating sustainability in a sustainable warehouse management system. The developed method could also be applied to weighting indicators for other industries.
The cognitive structure of movements in classical dance
Year: 2009

This study was designed to elicit differences in the mental representations of two basic movements from classical ballet, the Pirouette en dehors and the Pas assemble, stored in long term memory of dancers of different skill levels.  The movements were demonstrated and explained verbally to professional ballet dancers, amateur dancers, and non dancers. Subsequently, participants were assigned to a hierarchical sorting (splitting) task in which Basic Action Concepts (BACs) of the movements had to be sorted according to their functional relevance in movement execution. The task was presented as verbal labels on a computer screen. The responses were subjected to the application of a new analytical method, called SDA M, which includes a hierarchical cluster analysis. The method enabled eliciting cognitive structures of the movements in the participants' long term memory, and thus enabled comparing these cognitive structures in subjects of different skill level.  Participants of different skill level showed movement specific differences in their mental representation structures in long term memory. A similar structure was noted in advanced amateurs and professionals for the Pirouette en dehors, which referred to the functional phases of the movement, and less functional representations were noted in beginners and novices. For the Pas assemble, the experts' representation structure was different from that of amateurs and novices, pointing toward differences in movement execution. It is concluded that movement representations of this kind in long term memory might provide the basis for motor control in skilled ballet movements in the form of suitably organized perceptual cognitive reference structures.  The results point toward a unique mental representation as a function of skill level and movement nature. Individual and group results obtained with the applied method can be implemented to support (mental) training methods in classical dance practice.