nils-herrmann/clean_scirepeval huggingface dataset:
Catalysis Hub.org, an open electronic structure database for surface reactions
Venue: Scientific Data
Year: 2019

We present a new open repository for chemical reactions on catalytic surfaces, available at https:/www.catalysis hub.org. The featured database for surface reactions contains more than 100,000 chemisorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the surface geometries and calculational parameters, essential for data reproducibility. By providing direct access via the web interface as well as a Python API, we seek to accelerate the discovery of catalytic materials for sustainable energy applications by enabling researchers to efficiently use the data as a basis for new calculations and model generation.
Foreign currency risk hedging and firm value in China
Year: 2018

The objective of our study is to investigate the association between the use of foreign currency derivatives and corporate value among Chinese firms by examining listed firms' quarterly data from 2000 to 2013. We find that Chinese firms that engage in hedging activities with derivatives to reduce their foreign exchange exposure tend to have higher corporate value. This finding is shown to be consistent through a series of robustness tests. We also find that the incidence of such effects is higher among firms with greater profitability and investment opportunities. The use of foreign currency derivatives exerts a more prominent impact on firm value when the exchange rate depreciates and when the economy is booming. However, the link between derivative use and firm value is weaker during crisis periods. Moreover, an industrial analysis demonstrates that the value enhancing effect varies across industries.
Design and implementation of the online booking system in troops hospitals
Year: 2013

In order to construct our online booking system,B/S structure and JSP and database technology are used to realize the system.By means of authentication system,and combined with the intelligent intrusion detection equipment,to ensure the security of system.The change of medical environment takes convenient for patient.So the realization of online appointment registration system has good application effect,and lay the solid foundation for the digital construction.
Pengaruh Kompetensi Pengguna Dan Dukungan Manajemen Puncak Terhadap Kualitas Sistem Informasi Akuntansi (Survei Pada Cabang Bank BJB Syariah di Kota dan Kabupaten Bandung)
Year: 2018

This study was conducted to determine the effect of user competency and top management support on the quality of accounting information systems at BJB sharia bank branch in Bandung city and regency. Respondents in this research are operational manager, back office, customer service and teller. The sample in this study was obtained from simple random sampling method with a total sample of 8 with 48 respondents. The research method used is empirical study with descriptive and verificative research type. Data analysis technique used is multiple linear regression analysis with the help of program SPSS 24.00 version. The results showed that the user competency at BJB sharia bank branch in Bandung city and regency is not so good, while top management support and quality of accounting information system is good. User competency have a significant effect on accounting information system quality. Top management support significantly influence the quality of accounting information system at BJB sharia bank in Bandung.  Top Management Support,User Competency Quality of accounting information system Abstrak Penelitian ini dilakukan untuk mengetahui pengaruh kompetensi pengguna dan dukungan manajemen puncak terhadap kualitas sistem informasi akuntansi pada cabang Bank BJB Syariah di Kota dan Kabupaten Bandung. Responden dalam penelitian ini adalah manajer operasional, back office, customer service dan teller. Sampel dalam penelitian ini diperoleh dari metode simple random sampling dengan jumlah sampel sebanyak 8 dengan 48 responden. Metode penelitian yang digunakan adalah studi empiris dengan jenis penelitian deskriptif dan verifikatif. Teknik analisis data yang digunakan yaitu analisis regresi linear berganda dengan bantuan program SPSS versi 24.00. Hasil penelitian menunjukkan bahwa kompetensi pengguna pada cabang Bank BJB Syariah di Kota dan Kabupaten Bandung kurang baik, sedangkan dukungan manajemen puncak dan kualitas sistem informasi akuntansi sudah baik. Kompetensi pengguna berpengaruh signifikan terhadap kualitas sistem informasi akuntansi. Dukungan manajemen puncak berpengaruh signifikan terhadap kualitas sistem informasi akuntansi pada cabang Bank BJB Syariah di Kota dan Kabupaten Bandung. Kata kunci Kompetensi Pengguna, Kualitas Sistem Informasi Akuntansi, Dukungan Manajemen Puncak
A deep convolutional neural network based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery
Venue: PloS one
Year: 2017

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast enhanced T1 weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75+ 0.07 in the tumor core and 0.81+ 0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67+ 0.03 and achieved an area under the receiver operating characteristic curve of 0.98+ 0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.
Reducing Network Agnostophobia
Venue: NeurIPS
Year: 2018

Agnostophobia, the fear of the unknown, can be experienced by deep learning engineers while applying their networks to real world applications. Unfortunately, network behavior is not well defined for inputs far from a networks training set. In an uncontrolled environment, networks face many instances that are not of interest to them and have to be rejected in order to avoid a false positive. This problem has previously been tackled by researchers by either a) thresholding softmax, which by construction cannot return "none of the known classes" or b) using an additional background or garbage class. In this paper, we show that both of these approaches help, but are generally insufficient when previously unseen classes are encountered. We also introduce a new evaluation metric that focuses on comparing the performance of multiple approaches in scenarios where such unseen classes or unknowns are encountered. Our major contributions are simple yet effective Entropic Open Set and Objectosphere losses that train networks using negative samples from some classes. These novel losses are designed to maximize entropy for unknown inputs while increasing separation in deep feature space by modifying magnitudes of known and unknown samples. Experiments on networks trained to classify classes from MNIST and CIFAR 10 show that our novel loss functions are significantly better at dealing with unknown inputs from datasets such as Devanagari, NotMNIST, CIFAR 100 and SVHN.
RNA guided genome editing in plants using a CRISPR Cas system.
Venue: Molecular plant
Year: 2013

Precise and straightforward methods to edit the plant genome are much needed for functional genomics and crop improvement. Recently, RNA guided genome editing using bacterial Type II cluster regularly interspaced short palindromic repeats (CRISPR) associated nuclease (Cas) is emerging as an efficient tool for genome editing in microbial and animal systems. Here, we report the genome editing and targeted gene mutation in plants via the CRISPR Cas9 system. Three guide RNAs (gRNAs) with a 20 22 nt seed region were designed to pair with distinct rice genomic sites which are followed by the protospacer adjacent motif (PAM) The engineered gRNAs were shown to direct the Cas9 nuclease for precise cleavage at the desired sites and introduce mutation (insertion or deletion) by error prone non homologous end joining DNA repairing. By analyzing the RNA guided genome editing events, the mutation efficiency at these target sites was estimated to be 3 8% In addition, the off target effect of an engineered gRNA Cas9 was found on an imperfectly paired genomic site, but it had lower genome editing efficiency than the perfectly matched site. Further analysis suggests that mismatch position between gRNA seed and target DNA is an important determinant of the gRNA Cas9 targeting specificity, and specific gRNAs could be designed to target more than 90% of rice genes. Our results demonstrate that the CRISPR Cas system can be exploited as a powerful tool for gene targeting and precise genome editing in plants.
Why are Some Healthcare Chaplains Registered Professionals and Some are Not? A Survey of Healthcare Chaplains in Scotland
Year: 2020

The professional status of UK healthcare chaplains remains partial, with voluntary accreditation effective in achieving around 50% registration. This study set out to elicit reasons for this by surveying healthcare chaplains working in Scotland. An online survey was created to gather demographic details and chaplains' opinions on the importance of five key elements of professional status: A body of knowledge that underpins practice; A code of professional ethics; An occupational organization controlling the profession; Substantial intellectual and practical training; and Provision of a specialized skill or service. Most respondents (38/43) agreed that chaplains should belong to a professional body in order to maintain standards, ensure accountability and formalize professional development. They said that registration reinforced their professional status, added credibility and a clear governance structure to protect the public. However, a minority felt disconnected from the professionalization agenda. This paper discusses the reasons for this. Further UK and international studies into the professional status of chaplains are planned.
The short term impact of director trading in UK closed end funds
Year: 2015

Most closed end funds are transparent entities that hold securities that are actively traded in liquid markets. In such a setting, the argument that director transactions mitigate information asymmetry has very limited applicability. Our results provide support for the theory of Barber and Odean [2008. "All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors." Review of Financial Studies 21: 785 818] retail investor decision making is influenced by attention grabbing events. Director purchases are one such attention grabbing event and are associated with significant positive price returns the magnitudes of which are linked to the size of the purchase, the size of the fund, and the investment mandate. Trading volumes increase at the time of the purchase but most of the initial price responses and trading volumes dissipate over the following 15 days.
Measuring banking efficiency in Vietnam: parametric and non parametric methods
Venue: Banks and Bank Systems
Year: 2019

The article aims to evaluate the business efficiency of commercial banks in Vietnam using both parametric and non parametric approaches. In this study, the Stochastic Frontier Analysis (SFA) which belongs to a parametric method, and Data Envelopment Analysis (DEA) a non parametric approach, are applied to a sample of 30 joint stock commercial banks in Vietnam in the period of 2011 2015. Applying Tobit regression model, the impact of bank size, bank age, and the ownership feature on the efficiency of bank service industry in Vietnam is also investigated. The analysis results show that in general, the Vietnamese banking efficiency is improving during the selected period regardless of techniques used. However, there is small level of similarity in efficiency rankings identified from the SFA and DEA models. In terms of efficiency determinants, the results show that all three variables of size, age, and state ownership have a positive impact on bank efficiency.
ALS: A Disease of Motor Neurons and Their Nonneuronal Neighbors
Venue: Neuron
Year: 2006

Amyotrophic lateral sclerosis is a late onset progressive neurodegenerative disease affecting motor neurons. The etiology of most ALS cases remains unknown, but 2% of instances are due to mutations in Cu/Zn superoxide dismutase (SOD1) Since sporadic and familial ALS affects the same neurons with similar pathology, it is hoped that therapies effective in mutant SOD1 models will translate to sporadic ALS. Mutant SOD1 induces non cell autonomous motor neuron killing by an unknown gain of toxicity. Selective vulnerability of motor neurons likely arises from a combination of several mechanisms, including protein misfolding, mitochondrial dysfunction, oxidative damage, defective axonal transport, excitotoxicity, insufficient growth factor signaling, and inflammation. Damage within motor neurons is enhanced by damage incurred by nonneuronal neighboring cells, via an inflammatory response that accelerates disease progression. These findings validate therapeutic approaches aimed at nonneuronal cells.
UWB NLOS identification with feature combination selection based on genetic algorithm
Venue: 2019 IEEE International Conference on Consumer Electronics (ICCE)
Year: 2019

Non line of sight (NLOS) identification is very important for accurate localization based on ultra wide band (UWB) system. One of the most widely used approach for NLOS detection is based on machine learning algorithms with features extracted from the channel impulse response (CIR) Features, such as kurtosis, mean excess delay, root mean delay, energy and rise time are discussed in a lot of papers. Other features, like signal to noise ratio, form factor and crest factor etc. are barely discussed but they are also very useful parameters for NLOS detection. In this paper 18 useful features are discussed in total. The support vector machine (SVM) is used for the identification of the NLOS condition. Since the identification accuracy does not always improve with an increase in the number of used features, in this paper the best feature combination is selected based on genetic algorithm. By reducing the used features, not only the accuracy improves, but also the computation complexity is reduced. The experimental results show that, the RMS delay, maximal amplitude, received signal energy, distance between MS and BS, peak to start of the received pulses time delay are the optimal combination leading to best accuracy.
IV Spanish Consensus Conference on Helicobacter pylori infection treatment.
Venue: Gastroenterologia y hepatologia
Year: 2016

Helicobacter pylori approximately infect 50% of Spanish population and causes chronic gastritis, peptic ulcer and gastric cancer. Until now, three consensus meetings on H.pylori infection had been performed in Spain (the last in 2012) The changes in the treatment schemes, and the increasing available evidence, have justified organizing the IVSpanish Consensus Conference (March 2016) focused on the treatment of this infection. Nineteen experts participated, who performed a systematic review of the scientific evidence and developed a series of recommendation that were subjected to an anonymous Delphi process of iterative voting. Scientific evidence and the strength of the recommendation were classified using GRADE guidelines. As starting point, this consensus increased the minimum acceptable efficacy of recommended treatments that should reach, or preferably surpass, the 90% cure rate when prescribed empirically. Therefore, only quadruple therapies (with or without bismuth) and generally lasting 14 days, are recommended both for first and second line treatments. Non bismuth quadruple concomitant regimen, including a proton pump inhibitor, clarithromycin, amoxicillin and metronidazole, is recommended as first line. In the present consensus, other first line alternatives and rescue treatments are also reviewed and recommended.
Organizational Behavior and Human Decision Processes
Year: 2018

The dehumanization of other people is an unfortunately common occurrence that drives discrimination and conflict. We examined when and why the self can also be dehumanized. Across six studies, we found a reciprocal relationship between self dehumanization and the prioritization of money. Participants who prioritized money (vs. control participants) attributed less humanness to themselves (Studies 1 4) and in turn, chose to socially distance themselves from a coworker (Study 4) Participants led to self humanize (vs. control participants) were less likely to prioritize money over other goals (Studies 5A 6) The human nature dimension of humanness, which refers to attributes that separate humans from inanimate objects, was more sensitive to money prioritization than was the human uniqueness dimension, which refers to attributes that separate humans from nonhuman animals. Alternative explanations based on affect and self esteem were ruled out. These results suggest that the prioritization of money is at odds with our perceptions of human nature.
Pedestrian detection based on improved LeNet 5 convolutional neural network
Year: 2019

In this article, according to the real time and accuracy requirements of advanced vehicle assisted driving in pedestrian detection, an improved LeNet 5 convolutional neural network is proposed. Firstly, the structure of LeNet 5 network model is analyzed, and the structure and parameters of the network are improved and optimized on the basis of this network to get a new LeNet network model, and then it is used to detect pedestrians. Finally, the miss rate of the improved LeNet convolutional neural network is found to be 25% by contrast and analysis. The experiment proves that this method is better than SA Fast R CNN and classical LeNet 5 CNN algorithm.
Integrating genetic gain and gap analysis to predict improvements in crop productivity
Year: 2020

A Crop Growth Model (CGM) is used to demonstrate a biophysical framework for predicting grain yield outcomes for Genotype by Environment by Management (GxExM) scenarios. This required development of a CGM to encode contributions of genetic and environmental determinants of biophysical processes that influence key resource (radiation, water, nutrients) use and yield productivity within the context of the target agricultural system. Prediction of water driven yield productivity of maize for a wide range of GxExM scenarios in the U.S. corn belt is used as a case study to demonstrate applications of the framework. Three experimental evaluations are conducted to test predictions of GxExM yield expectations derived from the framework: (1) A maize hybrid genetic gain study, (2) A maize yield potential study, and (3) A maize drought study. Examples of convergence between key GxExM predictions from the CGM and the results of the empirical studies are demonstrated. Potential applications of the prediction framework for design of integrated crop improvement strategies are discussed. The prediction framework opens new opportunities for rapid design and testing of novel crop improvement strategies based on an integrated understanding of GxExM interactions. Importantly the CGM ensures that the yield predictions for the GxExM scenarios are grounded in the biophysical properties and limits of predictability for the crop system. The identification and delivery of novel pathways to improved crop productivity can be accelerated through use of the proposed framework to design crop improvement strategies that integrate genetic gains from breeding and crop management strategies that reduce yield gaps.
VNet: A Versatile Network for Efficient Real Time Semantic Segmentation
Venue: 2019 IEEE 37th International Conference on Computer Design (ICCD)
Year: 2019

Many recent excellent methods for efficient real time semantic segmentation are of low precision and heavily rely on multiple GPUs for training. In this paper, we rethink the critical factors affecting the accuracy of efficient segmentation models. The previous works usually reduce the input resolution prior to training the parameters of models by cropping or resizing the images. On the contrary, our empirical study shows that the reduced images lose the important content information and details, which are vital to the high precision. However, the previous methods are unable to train the original high resolution images due to the memory limited GPUs. To tackle this problem, we propose a novel versatile network (VNet) which employs reversible mechanism and asymmetric convolution to achieve highly efficient and extremely low memory consumption in backward propagation. In particular, we keep all the detailed spatial information of the input images without cropping or resizing to pursue decent prediction accuracy. It is worth noting that VNet can train multiple 1024x2048 high resolution images on only one standard GPU card. Under the same conditions, our model achieves a new state of the art result on Cityscapes datasets. Specifically, it can process the 1024x2048 high resolution inputs at a rate of 37.4 and 15.5 frames per second (fps) on a standard GPU and an edge device, respectively, with only 0.16 million parameters.
Ferumoxytol enhanced MRI in the peripheral vasculature.
Venue: Clinical radiology
Year: 2019

Ferumoxytol is a promising non gadolinium based contrast agent with numerous varied magnetic resonance imaging applications. Previous reviews of vascular applications have focused primarily on cardiac and aortic applications. After considering safety concerns and technical issues, the objective of this paper is to explore peripheral applications for ferumoxytol enhanced magnetic resonance angiography (MRA) and venography (MRV) in the upper and lower extremities. Separate searches for each of the following keywords were performed in pubmed: "ferumoxytol," "ultrasmall superparamagnetic iron oxide," and "USPIO." All studies pertaining to MRA or MRV in humans are included in this review. Case based examples of various peripheral applications are used to supplement a relatively scant literature in this space. Ferumoxytol's unique properties including high T1 relaxivity and prolonged intravascular half life make it the optimal vascular imaging contrast agent on the market and one whose vast potential has only begun to be tapped.
3D Morphable Models as Spatial Transformer Networks
Venue: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
Year: 2017

In this paper, we show how a 3D Morphable Model (i.e. a statistical model of the 3D shape of a class of objects such as faces) can be used to spatially transform input data as a module (a 3DMM STN) within a convolutional neural network. This is an extension of the original spatial transformer network in that we are able to interpret and normalise 3D pose changes and self occlusions. The trained localisation part of the network is independently useful since it learns to fit a 3D morphable model to a single image. We show that the localiser can be trained using only simple geometric loss functions on a relatively small dataset yet is able to perform robust normalisation on highly uncontrolled images including occlusion, self occlusion and large pose changes.