nils-herrmann/clean_scirepeval huggingface dataset:
Chinese College English Teachers' Ability to Develop Students' Informationized Learning in the Era of Big Data: Status and Suggestions
Year: 2018

College English teachers should vigorously promote the integration of the latest information technology and curriculum teaching. The application of modern information technology to college English teaching has not only modernized, diversified and facilitated the teaching methods, but also changed the teaching concepts, teaching contents and teaching methods. The ultimate goal of integration is to promote the development of students' informationized learning ability. Therefore, the purpose of this paper is to investigate college English teachers' development of students' informationized learning ability in the era of big data, and explore a motivation system that motivates teachers to develop students' informationized learning ability. A questionnaire survey was conducted in three universities with different degrees of informationization. It is found that the universities do not make enough investments to accommodate the future development needs of smart education; the teachers show weak ability to use the software, do not master the use of software and fail to implement the design, development and the nation has not issued institutional policies and offered enough time and opportunities for teachers' training. It is advised that the development of teachers' informationized teaching ability is to strike a balance between promoting the development of teachers' teaching ability and enhancing the development of students' learning ability; China should make dynamic adjustments to the assessment and monitoring system of teachers' informationized teaching ability and China should keep pace with the times in terms of investment in education teaching cause, and gradually improve its policy supports and incentives.
Attributing and Detecting Fake Images Generated by Known GANs
Venue: 2020 IEEE Security and Privacy Workshops (SPW)
Year: 2020

The quality of GAN generated fake images has improved significantly, and recent GAN approaches, such as StyleGAN, achieve near indistinguishability from real images for the naked eye. As a result, adversaries are attracted to using GAN generated fake images for disinformation campaigns and fraud on social networks. However, training an image generation network to produce realistic looking samples remains a time consuming and difficult problem, so adversaries are more likely to use published GAN models to generate fake images. In this paper, we analyze the frequency domain to attribute and detect fake images generated by a known GAN model. We derive a similarity metric on the frequency domain and develop a new approach for GAN image attribution. We conduct experiments on four trained GAN models and two real image datasets. Our results show high attribution accuracy against real images and those from other GAN models. We further analyze our method under evasion attempts and find the frequency based approach is comparatively robust. In this paper, we analyze the frequency domain to attribute and detect fake images generated by a known GAN model. We derive a similarity metric on the frequency domain and develop a new approach for GAN image attribution. We conduct experiments on four trained GAN models and two real image datasets. Our results show high attribution accuracy against real images and those from other GAN models. We further analyze our method under evasion attempts and find the frequency based approach is comparatively robust.
DSNet: an efficient CNN for road scene segmentation
Venue: APSIPA Transactions on Signal and Information Processing
Year: 2020

Road scene understanding is a critical component in an autonomous driving system. Although the deep learning based road scene segmentation can achieve very high accuracy, its complexity is also very high for developing real time applications. It is challenging to design a neural net with high accuracy and low computational complexity. To address this issue, we investigate the advantages and disadvantages of several popular convolutional neural network (CNN) architectures in terms of speed, storage, and segmentation accuracy. We start from the fully convolutional network with VGG, and then we study ResNet and DenseNet. Through detailed experiments, we pick up the favorable components from the existing architectures and at the end, we construct a light weight network architecture based on the DenseNet. Our proposed network, called DSNet, demonstrates a real time testing (inferencing) ability (on the popular GPU platform) and it maintains an accuracy comparable with most previous systems. We test our system on several datasets including the challenging Cityscapes dataset (resolution of 1024 x 512) with an Mean Intersection over Union (mIoU) of about 69.1% and runtime of 0.0147 s/image on a single GTX 1080Ti. We also design a more accurate model but at the price of a slower speed, which has an mIoU of about 72.6% on the CamVid dataset.
Breaking the Current Density Threshold in Spin orbit torque Magnetic Random Access Memory
Year: 2018

Spin orbit torque magnetic random access memory (SOT MRAM) is a promising technology for the next generation of data storage devices. The main bottleneck of this technology is the high reversal current density threshold. This outstanding problem of SOT MRAM is now solved by using a current density of constant magnitude and varying flow direction that reduces the reversal current density threshold by a factor of more than the Gilbert damping coefficient. The Euler Lagrange equation for the fastest magnetization reversal path and the optimal current pulse are derived for an arbitrary magnetic cell. The theoretical limit of minimal reversal current density and current density for a GHz switching rate of the new reversal strategy for CoFeB/Ta SOT MRAMs are respectively of the order of $10^5$ A/cm$^2$ and $10^6$ A/cm$^2$ far below $10^7$ A/cm$^2$ and $10^8$ A/cm$^2$ in the conventional strategy. Furthermore, no external magnetic field is needed for a deterministic reversal in the new strategy.
The implicit price of urban public parks and greenways: a spatial contextual approach
Year: 2015

This paper examines the value residents place on public parks in a mid sized urban area. The analysis makes a direct contribution to the literature by examining the extent to which spatial and neighbourhood context is related to the house price premium or discount associated with public recreational opportunities, natural resource areas and urban greenways. The analysis shows that the value of public parks and greenways varies across space, neighbourhood context and park type. Community area fixed effects are included to bolster the findings. The findings indicate that park and greenway investment should be planned and managed contextually in urban areas. Park planners can use these findings to inform public policy debates over park investment and, perhaps, support efforts focused on comprehensive neighbourhood planning.
Carotid Endarterectomy at Punjab Institute of Cardiology, Lahore: mid term postoperative results
Year: 2016

This study was designed to review postoperative results of the carotid endarterectomy (CABG/CEA) performed at the Punjab Institute of Cardiology, Lahore.  Cconsecutive 10 patients with coexisting severe ischemic cardiac and carotid artery disease were operated on during 1 year period ending December 2003 at Punjab Institute of Cardiology, Lahore.  The mean age of the study population was 60.20A+ 7.56 years, among theses 9 were male and 1 female patient. Four (40% patients with carotid artery stenosis were asymptomatic. Previous stroke occurred in 2 (20% patients: two (20% patients presented with a history of blackouts and two (20% with a transient ischemic attack. The mid term results with regard to 30 day postoperative mortality, stroke and major postoperative complications were significantly low in terms of post operative infection as only one (10% patient had chest infection and there was no in hospital mortality.  Combined interventions of CE and CABG can be performed with an acceptable morbidity and mortality when severe carotid stenosis is associated with advanced, symptomatic ischemic heart disease. The introduction of routine preoperative carotid duplex scanning resulted in higher diagnostic rate for asymptomatic carotid artery disease among the patients scheduled for combined CABG/CE procedure.
The nursing of cerebral angiography in patients with acute cerebral ischemia infarction
Year: 2011

To summarize the nursing experience of cerebral angiography in elderly patients with acute cerebral ischemia infarction. To review the preoperative preparation,intraoperative cooperation and postoperative observation in 432 patients who had cerebral angiography.  Appropriate pre ,intra and post operative nursing was observed and the therapeutic effect was satisfactory. Full preoperative preparation,close intraoperative observation and targeted postoperative nursing intervention could improve the patients' tolerance, and decrease the incidence rate of complications at the same time.
Foreground detection via robust low rank matrix factorization including spatial constraint with Iterative reweighted regression
Venue: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)
Year: 2012

Foreground detection is the first step in video surveillance system to detect moving objects. Robust Principal Components Analysis (RPCA) shows a nice framework to separate moving objects from the background. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. In this paper, we propose to use a low rank matrix factorization with IRLS scheme (Iteratively reweighted least squares) and to address in the minimization process the spatial connexity of the pixels. Experimental results on the Wallflower and I2R datasets show the pertinence of the proposed approach.
A survey of semiconductor supply chain models Part II: demand planning, inventory management, and capacity planning
Venue: Int. J. Prod. Res.
Year: 2018

Part I of this three part series described semiconductor supply chains from the decision making and functional perspectives, using this as a framework to review the industrial engineering and operations research literature on the problems arising in these supply chains. Part I then reviewed the literature on Strategic Network Design, supply chain coordination, sustainability and simulation based decision support. This paper, Part II, reviews the areas of Demand Planning, Inventory Management and Capacity Planning in semiconductor supply chains. Part III concludes the series by discussing models to support Master Planning, Production Planning and Demand Fulfilment in this industry.
Hierarchical Multi Task Word Embedding Learning for Synonym Prediction
Venue: KDD
Year: 2019

Automatic synonym recognition is of great importance for entity centric text mining and interpretation. Due to the high language use variability in real life, manual construction of semantic resources to cover all synonyms is prohibitively expensive and may also result in limited coverage. Although there are public knowledge bases, they only have limited coverage for languages other than English. In this paper, we focus on medical domain and propose an automatic way to accelerate the process of medical synonymy resource development for Chinese, including both formal entities from healthcare professionals and noisy descriptions from end users. Motivated by the success of distributed word representations, we design a multi task model with hierarchical task relationship to learn more representative entity/term embeddings and apply them to synonym prediction. In our model, we extend the classical skip gram word embedding model by introducing an auxiliary task "neighboring word semantic type prediction' and hierarchically organize them based on the task complexity. Meanwhile, we incorporate existing medical term term synonymous knowledge into our word embedding learning framework. We demonstrate that the embeddings trained from our proposed multi task model yield significant improvement for entity semantic relatedness evaluation, neighboring word semantic type prediction and synonym prediction compared with baselines. Furthermore, we create a large medical text corpus in Chinese that includes annotations for entities, descriptions and synonymous pairs for future research in this direction.
Prevalence of probable mental disorders and help seeking behaviors among veteran and non veteran community college students.
Venue: General hospital psychiatry
Year: 2016

Millions of disadvantaged youth and returning veterans are enrolled in community colleges. Our objective was to determine the prevalence of mental disorders and help seeking behaviors among community college students.  Veterans (n=211) and non veterans (n=554) were recruited from 11 community colleges and administered screeners for depression (PHQ 9) generalized anxiety (GAD 7) posttraumatic stress disorder (PC PTSD) non lethal self injury, suicide ideation and suicide intent. The survey also asked about the perceived need for, barriers to and utilization of services. Regression analysis was used to compare prevalence between non veterans and veterans adjusting for non modifiable factors (age, gender and race/ethnicity)  A large proportion of student veterans and non veterans screened positive and unadjusted bivariate comparisons indicated that student veterans had a significantly higher prevalence of positive depression screens (33.1% versus 19.5% P<.01) positive PTSD screens (25.7% versus 12.6% P<.01) and suicide ideation (19.2% versus 10.6% P=.01) Adjusting for age, gender and race/ethnicity, veterans were significantly more likely than non veterans to screen positive for depression (OR=2.10, P=.01) and suicide ideation (OR=2.31, P=.03) Student veterans had significantly higher odds of perceiving a need for treatment than non veterans (OR=1.93, P=.02) but were more likely to perceive stigma (beta=0.28, P=.02) Despite greater need among veterans, there were no significant differences between veterans and non veterans in use of psychotropic medications, although veterans were more likely to receive psychotherapy (OR=2.35, P=.046) CONCLUSIONS Findings highlight the substantial gap between the prevalence of probable mental health disorders and treatment seeking among community college students. Interventions are needed to link community college students to services, especially for student veterans.
Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree
Year: 2018

In this study, four different heuristic regression methods including Kriging, response surface method (RSM) multivariate adaptive regression (MARS) and M5 model tree (M5Tree) have been investigated for accurate estimating of solar radiation with different input data. Monthly solar radiation (SR) from Adana and Antakya stations, which are located in Eastern Mediterranean Region of Turkey is estimated based on the input data of maximum temperature (Tmax) minimum temperature (Tmin) sunshine hours (Hs) wind speed (Ws) and relative humidity (RH) In Adana station, the best MARS model provided slightly better accuracy than the Kriging, RSM and M5Tree while the Kriging was found to be the better than the MARS, RSM and M5Tree in Antakya station. The predictions of M5Tree model are shown inaccurate results for both maximum errors and minimum agreement compared to another models. The effect of periodicity input is examined to obtain the accurate predictions of solar radiation for these stations based on the four heuristic based modeling Kriging, MARS, RSM, M5Tree approaches. Periodicity input data improved the root mean square errors of the best MARS, RSM, M5Tree and Kriging models as 34% 37% 46% and 39% for Adana station and by 51% 47% 38% and 49% for Antakya station, respectively. The periodic Kriging models performed superior to the periodic MARS, RSM and M5Tree models.
A nanofiber based filter composed of electrospun PA 66/TiO2 nanofibers for removal of cement dust
Venue: International Journal of Environmental Analytical Chemistry
Year: 2019

In the present work, a novel nanofiber based filter has been presented which is comprised of composite nanofibers of polyamide 66 and TiO2 nanoparticles (PA 66/TiO2) for trapping cement dust. The produced nanofibers under optimum conditions were characterised by Fourier transform infrared spectroscopy (FT IR) scanning electron microscopy (SEM) transmission electron microscopy (TEM) and X ray diffraction (XRD) The effect of some important electrospinning process parameters on the obtained electrospun nanofibers including solution concentration, flow rate, voltage, tip to collector distance and the presence of salt was investigated. Studies showed that the solution concentration of 15 wt. of PA 66 resulted in thinner and smother nanofibers than the other solution concentrations and also the average diameter of the electrospun nanofibers was decreased with increasing 1 wt. salt to the polymer solution. The produced nanofiber based filter can be applied for capturing cement dust with a good filtration efficiency of 97% for 3 500 nm particles and a quality factor in the range of 0.069 0.034 Pa 1
Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning
Year: 2021

Spatially resolved transcriptomics provides a new way to define spatial contexts and understand biological functions in complex diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs as gene expression or RNA velocity, RESEPT learns a three dimensional embedding with a spatial retained graph neural network from the spatial transcriptomics. The embedding is then visualized by mapping as color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of sixteen 10x Genomics Visium spatial transcriptomics datasets on the human cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in house AD samples, RESEPT can localize cortex layers and cell types based on a pre defined region or cell type specific genes and furthermore provide critical insights into the identification of amyloid beta plaques in Alzheimer's disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor enriched, non tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.
Exchange Traded Funds in Europe
Year: 2019

Exchange Traded Funds in Europe provides a single point of reference on a diverse set of regional ETF markets, illuminating the roles ETFs can play in risk mitigation and speculation. Combining empirical data with models and case studies, the authors use diffusion models and panel/country specific regressions as well as graphical and descriptive analyses to show how ETFs are more than conventional, passive investments. With new insights on how ETFs can improve market efficiency and how investors can benefit when using them as investment tools, this book reveals the complexity of the world's second largest ETF market and the ways that ETFs are transforming it.
Microstructure modelling for metallic additive manufacturing: a review
Venue: Virtual and Physical Prototyping
Year: 2019

The microstructure of metals depends on the additive manufacturing (AM) process and the process parameters. However, experimentation on different process parameters for different materials is costly and time consuming. To overcome these challenges, numerical simulations provide insights that allow prediction of microstructure formed under different process parameters. Microstructure modelling requires coupling of macro scale thermal model or experimentally measured temperature profiles with either a meso scale or micro scale microstructure model. In this review, the commonly used AM techniques for metals are introduced, followed by a discussion on the microstructure of parts fabricated using these processes. This review then presents the latest models used in simulating different microstructural aspects of metal AM parts. In addition, the models were compared and the potential and challenges in microstructure modelling were discussed.
Evaluation of coagulation function by rotation thromboelastometry in critically ill patients with severe COVID 19 pneumonia
Venue: Journal of Thrombosis and Thrombolysis
Year: 2020

Critically ill patients with COVID 19 pneumonia suffered both high thrombotic and bleeding risk. The effect of SARS CoV 2 on coagulation and fibrinolysis is not well known. We conducted a retrospective study of critically ill patients admitted to an intensive care unit (ICU) a cause of severe COVID 19 pneumonia and we evaluated coagulation function using rotational thromboelastometry (ROTEM) on day of admission (T0) and 5 (T5) and 10 (T10) days after admission to ICU. Coagulation standard parameters were also evaluated. Forty patients were enrolled into the study. The ICU and the hospital mortality were 10% and 12.5% respectively. On ICU admission, prothrombin time was slightly reduced and it increased significantly at T10 (T0 65.1 9.8 vs T10 85.7 1.5, p 0.002) while activated partial thromboplastin time and fibrinogen values were higher at T0 than T10 (32.2 2.9 vs 27.2 2.1, p 0.017 and 895.1 110 vs 332.5 50, p 0.002, respectively) moreover, whole blood thromboelastometry profiles were consistent with hypercoagulability characterized by an acceleration of the propagation phase of blood clot formation [i.e. CFT below the lower limit in INTEM 16/40 patients (40% and EXTEM 20/40 patients (50% and significant higher clot strength [MCF above the upper limit in INTEM 20/40 patients (50% in EXTEM 28/40 patients (70% and in FIBTEM 29/40 patients (72.5% however, this hypercoagulable state persists in the first five days, but it decreases ten day after, without returning to normal values. No sign of secondary hyperfibrinolysis or sepsis induced coagulopathy (SIC) were found during the study period. In six patients (15% a deep vein thrombosis and in 2 patients (5% a thromboembolic event, were found; 12 patients (30% had a catheter related thrombosis. ROTEM analysis confirms that patients with severe COVID 19 pneumonia had a hypercoagulation state that persisted over time.