nils-herrmann/clean_scirepeval huggingface dataset:
High order numerical method for the nonlinear Helmholtz equation with material discontinuities in one space dimension
Venue: J. Comput. Phys.
Year: 2007

The nonlinear Helmholtz equation (NLH) models the propagation of electromagnetic waves in Kerr media, and describes a range of important phenomena in nonlinear optics and in other areas. In our previous work, we developed a fourth order method for its numerical solution that involved an iterative solver based on freezing the nonlinearity. The method enabled a direct simulation of nonlinear self focusing in the nonparaxial regime, and a quantitative prediction of backscattering. However, our simulations showed that there is a threshold value for the magnitude of the nonlinearity, above which the iterations diverge. In this study, we numerically solve the one dimensional NLH using a Newton type nonlinear solver. Because the Kerr nonlinearity contains absolute values of the field, the NLH has to be recast as a system of two real equations in order to apply Newton's method. Our numerical simulations show that Newton's method converges rapidly and, in contradistinction with the iterations based on freezing the nonlinearity, enables computations for very high levels of nonlinearity. In addition, we introduce a novel compact finite volume fourth order discretization for the NLH with material discontinuities. Our computations corroborate the design fourth order convergence of the method. The one dimensional results of the current paper create a foundation for the analysis of multidimensional problems in the future.
NAM: Non Adversarial Unsupervised Domain Mapping
Venue: ECCV
Year: 2018

Several methods were recently proposed for the task of translating images between domains without prior knowledge in the form of correspondences. The existing methods apply adversarial learning to ensure that the distribution of the mapped source domain is indistinguishable from the target domain, which suffers from known stability issues. In addition, most methods rely heavily on "cycle" relationships between the domains, which enforce a one to one mapping. In this work, we introduce an alternative method: Non Adversarial Mapping (NAM) which separates the task of target domain generative modeling from the cross domain mapping task. NAM relies on a pre trained generative model of the target domain, and aligns each source image with an image synthesized from the target domain, while jointly optimizing the domain mapping function. It has several key advantages: higher quality and resolution image translations, simpler and more stable training and reusable target models. Extensive experiments are presented validating the advantages of our method.
Underground coal mine gas monitoring emergency preparation
Year: 2017

An emergency situation in an underground coal mine site is an unexpected and often dangerous event that can quickly escalate if immediate action is not undertaken. Emergency situations often occur at the worst possible time and have the potential for multiple loss of life, loss of equipment, loss of coal assets and major damage to a company's reputation. In an underground coal mine, an emergency situation involving a coal heating or small fire can quickly deteriorate into a large fire or worse a gas explosion. As the emergency situation unfolds, various activities will need to be undertaken that are not part of the normal work practices, for example gas sampling from a bore hole. The equipment required for bore hole sampling, may not be available onsite and the onsite personnel may not be familiar with its use. The aim of this paper is to provide a number of examples, from previous mine incidents to highlight some opportunities as to the emergency planning and preparation that can be implemented to improve the efficiency of the mines emergency response.
A Gradient heatmap based Table Structure Recognition
Venue: ICMLC
Year: 2021

Most methods to recognize the structure of a table are to use the object detection approach to directly locate each cell in the table or to segment the table line based on the fully convolutional network (FCN) The problem of the former is that it is laborious to recognize the distorted table, while the problem of the latter is that the sample imbalance makes it difficult to train the model. In this paper, a gradient heatmap based table structure recognition method is proposed, by exploring the gradient heatmaps of the vertical lines and horizontal lines in the table. Specifically, the pixels of the vertical lines of the table are obtained according to the gradient heatmap, then the pixels of the horizontal lines are obtained using the same method, and finally the table structure is restored by using the connected domain search method. Compared with the Single Shot MultiBox Detector (SSD) and Faster RCNN that directly detects cells, our Average Precision (AP) value reached up to 99.5% which is much higher than the above models. Additionally, we demonstrate that the AP values of the proposed models are reduced almost negligibly when the IoU threshold increased from 0.5 to 0.75, while the AP value of the fast RCNN and SSD model decreased significantly.
A Cross sectional Survey on the Preference of Patients with Rheumatoid Arthritis for Route of Administration of Disease Modifying Anti Rheumatic Drugs: Oral Target Specific Versus Parenteral Biologic.
Venue: The Israel Medical Association journal IMAJ
Year: 2020

Guidelines recommend initiation of parenteral biologic or oral target specific disease modifying anti rheumatic drugs (bDMARDs/tsDMARDs) in rheumatoid arthritis (RA) patients who do not adequately respond to conventional DMARDs.  To compare the preferred route of administration of bDMARDs or tsDMARDs in RA patients who were previously treated with at least one type.  A cross sectional survey was conducted of consecutive RA patients previously prescribed bDMARDs or tsDMARDs. We analyzed the factors associated with patients' preferred route of administration.  The cohort included 95 patients, mostly female (72.6% seropositive (81.05% mean age 63.4 11.9 years. The oral route was preferred by 39 patients (41% and 56 (59% preferred the parenteral route. Most patients (65.9% preferred to continue with their current route (P 0.001) Switching from a current route was less common with patients who were currently using the oral route (13.3% vs. 38.2% P 0.04) Many patients (53.8% who preferred the oral route had never experienced it before, while this was rare (3.6% regarding the parenteral route (P 0.0001) Employment status was associated with preference of the subcutaneous route over the intravenous route of bDMARDs (P 0.01) Of the 21 patients who had previously experienced both parenteral and oral treatment, 16 (76.2% preferred the oral route.  RA patients preferred to continue treatment with an administration route they have already experienced. However, when choosing an unexperienced route, significantly more patients preferred the oral route. Our results strengthen the understanding of patient preferences, which could improve drug adherence, compliance, and disease outcome.
Public goods and government action
Year: 2015

It is widely agreed that one of the core functions of government is to supply public goods that markets either fail to provide or cannot provide efficiently. I will suggest that arguments for government provision of public goods require fundamental moral judgments in addition to the usual economic considerations about the relative efficacy of markets and governments in supplying them. While philosophers and policymakers owe a debt of gratitude to economists for developing the theory of public goods, the link between public goods and public policy cannot be forged without moral reflection on the proper function and scope of government power.
A New Hybrid Deep Learning Model based Recommender System using Artificial Neural Network and Hidden Markov Model
Venue: FNC/MobiSPC
Year: 2020

Recommendation systems based on Deep Learning have recently led to significant progress in different application domains. Most models are influenced by hyperparameter optimization or tuning, the stability of training, and architecture configuration. Hence, in the present study, we introduced a Deep Learning model, which is named RHMM, for recommender systems, by using the Hidden Markov Model and Artificial Neural Networks. The model selection technique is applied to optimize the bias variance tradeoff of the expected prediction. The model aggregation technique is used to improve the robustness and accuracy of training. Experiment results showed that our Deep Leaning model led to significant improvement over benchmarking models.
Scale space approximated convolutional neural networks for retinal vessel segmentation
Venue: Comput. Methods Programs Biomed.
Year: 2019

Retinal fundus images are widely used to diagnose retinal diseases and can potentially be used for early diagnosis and prevention of chronic vascular diseases and diabetes. While various automatic retinal vessel segmentation methods using deep learning have been proposed, they are mostly based on common CNN structures developed for other tasks such as classification.  We present a novel and simple multi scale convolutional neural network (CNN) structure for retinal vessel segmentation. We first provide a theoretical analysis of existing multi scale structures based on signal processing. In previous structures, multi scale representations are achieved through downsampling by subsampling and decimation. By incorporating scale space theory, we propose a simple yet effective multi scale structure for CNNs using upsampling, which we term scale space approximated CNN (SSANet) Based on further analysis of the effects of the SSA structure within a CNN, we also incorporate residual blocks, resulting in a multi scale CNN that outperforms current state of the art methods.  Quantitative evaluations are presented as the area under curve (AUC) of the receiver operating characteristic (ROC) curve and the precision recall curve, as well as accuracy, for four publicly available datasets, namely DRIVE, STARE, CHASE_DB1, and HRF. For the CHASE_DB1 set, the SSANet achieves state of the art AUC value of 0.9916 for the ROC curve. An ablative analysis is presented to analyze the contribution of different components of the SSANet to the performance improvement.  The proposed retinal SSANet achieves state of the art or comparable accuracy across publicly available datasets, especially improving segmentation for thin vessels, vessel junctions, and central vessel reflexes.
Rapidly Dried Antimicrobial Spray for Foot Deodorant
Year: 2012

This paper aimed to develop a physiologically activated dried antimicrobial spray for foot deodorant and select the suitable components such as film forming agent, solvent, antimicrobial agent and flavoring agents. The evaporation rates, antimicrobial activity, spray pattern, viscosity and cooling effect were evaluated. The developed formulation exhibited the high evaporating rate, high antimicrobial activity, appropriate spray pattern, slightly viscous solution with cooling effect after spraying onto skin. The developed formulation exhibited the potential application as the rapidly dried antimicrobial spray for foot deodorant.
PENGARUH PENERAPAN MODEL PEMBELAJARAN CONTEXTUAL TEACHING AND LEARNING DAN QUANTUM TEACHING TERHADAP MOTIVASI BERPRESTASI DAN HASIL BELAJAR IPS SISWA KELAS VII SMP NEGERI 1 KEDIRI
Year: 2018

This study aims to determine differences in achievement motivation and IPS learning outcomes between students who follow the learning with contextual teaching and learning learning model and quantum teaching in grade VII students of Junior High School State 1 Kediri in separate or simultaneous. The population in this study are all students of class VII of Junior High School State 1 Kediri spread in fourteen equivalent classes. Taken by random two classes as sample of research. Data were collected by questionnaire and IPS learning result test and analyzed by anava with F and manova test with F Wilks' Lambda test. The result of the analysis shows that: (1) there is difference of achievement motivation among students who follow the learning with Contextual Teaching and Learning model and Quantum Teaching in grade VII students of Junior High School State 1 Kediri with F count 52,613 (p 0,000 <0,05) 2) there is difference of learning result of IPS student between follow learning with learning model Contextual Teaching and Learning and Quantum Teaching at student of class VII of Junior High School State 1Kediri with F count 30,384 (p 0,000 <0,05) and (3) there is difference achievement motivation and learning outcomes of IPS students between those who followed the learning with Contextual Teaching and Learning model and Quantum Vol. 6 No. 1 Juni 2018 Ekuitas Jurnal Pendidikan Ekonomi Sari Astiti 24 dari 50 Teaching in grade VII students of Junior High School State 1 Kediri with FWilks' Lambda 48,307 (p 0,000 <0.05) Thus it can be concluded that the learning model Contextual Teaching and Learning and Quantum Teaching have a significant effect on achievement motivation and student learning outcomes of grade VII SMP Negeri 1 Kediri.
Binary Classification for Failure Risk Assessment.
Venue: Methods in molecular biology
Year: 2021

Survival analysis is tremendously powerful, and is a popular methodology for analyzing time to event models in bioinformatics. Furthermore, several of its extensions can simultaneously perform variable selection in conjunction with model estimation. While this flexibility is extremely desirable, under certain scenarios, binary class variable selection and classification methods might provide more reliable risk estimates. Synthetic simulations and real data case studies suggest that when (1) randomly censored points comprise only a small portion of data, (2) biological markers are weak, (3) it is desired to compute risk across predetermined time intervals, and (4) the assumptions of the competing time to event models are violated, binary class models tend to perform superior. In practice, it might be prudent to test both model families to guarantee adequate analysis. Here we describe the pipeline of binary class feature selection and classification for time to event risk assessment.
Miniemulsion polymerization and the structure of polymer and hybrid nanoparticles.
Venue: Angewandte Chemie
Year: 2009

The miniemulsion process allows the formation of complex structured polymeric nanoparticles and the encapsulation of a solid or liquid, an inorganic or organic, or a hydrophobic or hydrophilic material into a polymer shell. Many different materials, ranging from organic and inorganic pigments, magnetite, or other solid nanoparticles, to hydrophobic and hydrophilic liquids, such as fragrances, drugs, or photoinitators, can be encapsulated. Functionalization of the nanoparticles can also be easily obtained. Compared to polymerization processes in organic solvents, polymerization to obtain polymeric nanoparticles can be performed in environmentally friendly solvents, usually water.
Optimized spectral reconstruction based on adaptive training set selection.
Venue: Optics express
Year: 2017

This paper proposes an improved reflectance reconstruction method by adaptively selecting training samples. Modified Principal Component Analysis estimation was proposed by orthogonal regression considering the system noise; deriving the optimum number of training samples by BP Adaboost neural network; and grouping the representative samples together by hierarchical cluster analysis from a large database of samples. Finally, the training samples were selected by colorimetric subspace tracking. Experimental results indicated that the proposed method significantly outperforms the traditional methods in terms of both spectral and colorimetric accuracy, and our reflectance modeling is a reasonable and convenient tool to generate adaptive training sets.
Fully Collusion Resistant Black Box Traitor Revocable Broadcast Encryption with Short Private Keys
Venue: ICALP
Year: 2007

Broadcast encryption schemes enable senders to efficiently broadcast ciphertexts to a large set of receivers in a way that only nonrevoked receivers can decrypt them. Black box traitor revocable broadcast encryption schemes are broadcast encryption schemes that enable a tracer, who is given a pirate decoder, to identify traitors by black box accessing the given pirated decoder and to revoke traitors so identified. In this paper, we propose a fully collusion resistant black box traitor revocable broadcast encryption scheme in which the size of each private key is constant, the size of the public key is proportional to the number of receivers, and the sizes of ciphertexts are sub linear with respect to the number of receivers. The encryption procedure in our scheme requires only a public key. The tracing procedure in it requires only a public key and black box access to a resettable pirate decoder. The security of our scheme is proved in the generic bilinear group model if the subgroup decision assumption holds.
Facial expression recognition algorithm based on parameter adaptive initialization of CNN and LSTM
Venue: The Visual Computer
Year: 2019

In view of the high dimensionality, nonrigidity, multiscale variation and the influence of illumination and angle on facial expressions, it is quite difficult to obtain facial expression images or videos using computers and analyze facial morphology and changes to accurately obtain the emotional changes of the subjects. Existing facial expression recognition algorithms have the following problems in the application process: the existing shallow feature extraction model has lost a lot of effective feature information and low recognition accuracy. The facial expression recognition method based on deep learning has problems such as overfitting, gradient explosion and parameter initialization. Therefore, this paper develops a facial expression recognition algorithm based on the deep learning method. An adaptive model parameter initialization based on the multilayer maxout network linear activation function is proposed to initialize the convolutional neural network (CNN) and the long short term memory network (LSTM) method. It can effectively overcome the gradient disappearance and gradient explosion problems in the deep learning model training process. At the same time, the convolutional neural network with an LSTM memory unit is used to extract the related information from the image sequence, and the facial expression judgment is based on a single frame image and historical related information. However, the top level structure of the CNN model is a fully connected feedforward neural network, which undertakes the task of expression classification. Therefore, the SVM classification method replaces the top level classifier to further improve the expression classification accuracy. Experiments show that the facial expression recognition method proposed in this paper not only accurately identifies various expressions but also has good adaptive ability. This is because the method achieves the adaptive initialization of the parameters of the deep learning model construction process and also analyzes the relevance of the expression database expression, thereby improving the accuracy of expression recognition.
Mobile Laser Scanned Point Clouds for Road Object Detection and Extraction: A Review
Venue: Remote. Sens.
Year: 2018

The mobile laser scanning (MLS) technique has attracted considerable attention for providing high density, high accuracy, unstructured, three dimensional (3D) geo referenced point cloud coverage of the road environment. Recently, there has been an increasing number of applications of MLS in the detection and extraction of urban objects. This paper presents a systematic review of existing MLS related literature. This paper consists of three parts. Part 1 presents a brief overview of the state of the art commercial MLS systems. Part 2 provides a detailed analysis of on road and off road information inventory methods, including the detection and extraction of on road objects (e.g. road surface, road markings, driving lines, and road crack) and off road objects (e.g. pole like objects and power lines) Part 3 presents a refined integrated analysis of challenges and future trends. Our review shows that MLS technology is well proven in urban object detection and extraction, since the improvement of hardware and software accelerate the efficiency and accuracy of data collection and processing. When compared to other review papers focusing on MLS applications, we review the state of the art road object detection and extraction methods using MLS data and discuss their performance and applicability. The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS related applications, high definition maps, and other highly accurate localization services.
Taurine is Involved in Energy Metabolism in Muscles, Adipose Tissue, and the Liver
Venue: Molecular nutrition food research
Year: 2019

Energy metabolism is a basic and general process, by which the body acquires and uses energy to maintain normal function, and taurine plays a vital role in energy metabolism. Taurine deficiency may cause a weak energy metabolism and energy metabolism dysfunction. Taurine biosynthetic ability is limited, and its supplementation in the diet can strengthen energy metabolism in muscle performance, cardiac function, liver activity, and adipose tissue. Combining taurine with other drugs may have a superior effect in energy metabolism. In many metabolic disorders, taurine, or the combination of taurine with other drugs, also functions as a repair treatment for damaged tissues, and acts as a promoter for the balance of energy metabolism. The present study discusses the potential roles of taurine in energy metabolism.
Characterizing Rock Art Pigments
Year: 2019

The materials used to make rock art contain important evidence about the cultural practices of the people who created it: their technologies, movements, and social interactions. The number of studies of archaeological pigments in the recent literature demonstrates how fruitful such enquiries can be. In this chapter, the authors discuss the physicochemical characterization of rock art pigments, outline the history of research in this area, differentiate key concepts and terminology, and describe principal methods. They conclude with illustrative case studies from France, South Africa, and Australia to demonstrate the kinds of archaeological information that can be preserved in rock art pigments.