nils-herrmann/clean_scirepeval huggingface dataset:
Research on road extraction of remote sensing image based on convolutional neural network
Venue: EURASIP J. Image Video Process.
Year: 2019

Road is an important kind of basic geographic information. Road information extraction plays an important role in traffic management, urban planning, automatic vehicle navigation, and emergency management. With the development of remote sensing technology, the quality of high resolution satellite images is improved and more easily obtained, which makes it possible to use remote sensing images to locate roads accurately. Therefore, it is an urgent problem to extract road information from remote sensing images. To solve this problem, a road extraction method based on convolutional neural network is proposed in this paper. Firstly, convolutional neural network is used to classify the high resolution remote sensing images into two classes, which can distinguish the road from the non road and extract the road information initially. Secondly, the convolutional neural network is optimized and improved from the training algorithm. Finally, because of the influence of natural scene factors such as house and tree shadow, the non road noise still exists in the road results extracted by the optimized convolutional neural network method. Therefore, this paper uses wavelet packet method to filter these non road noises, so as to accurately present the road information in remote sensing images. The simulation results show that the road information of remote sensing image can be preliminarily distinguished by convolutional neural network; the road information can be distinguished effectively by optimizing convolutional neural network; and the wavelet packet method can effectively remove noise interference. Therefore, the proposed road extraction method based on convolutional neural network has good road information extraction effect.
Experimental study and constitutive modeling on viscoelastic plastic mechanical properties of ETFE foils subjected to uniaxial monotonic tension at various strain rates
Year: 2020

Apart from the well known excellent basic properties, the mechanical behaviors of ETFE (ethylene tetrafluoroethylene) foils are more fundamental for the refined design and analysis of ETFE membrane structures. This paper investigates the nonlinear mechanical properties of ETFE foils at varying strain rates (0.01 to 10 min 1) through a combination of experiments and modeling. First, the uniaxial tensile tests at nine loading strain rates were completed on ETFE foil dumbbell specimens. The analysis of experimental results shows that ETFE foils exhibit the rate sensitive and viscoelastic plastic behaviors with two distinct yield points. Next, to simulate the nonlinearity of ETFE foils at the former deformation stage, the stress strain constitutive equations at certain strain rates are established based on the generalized Maxwell model comprising one basic spring element and several basic Maxwell models. After identifying the model parameters, it is observed that the proposed model can accurately describe the viscoelastic plastic mechanical behaviors of ETFE foils since good agreement is gained from the comparison between experimental results and model predictions. Finally, through establishing the relationship functions between model parameters and strain rates, the continuum constitutive model can be extended to predict the rate dependent viscoelastic plastic behaviors of ETFE foils. The experimental observations and constitutive model concerning the rate dependent mechanical properties of ETFE foils would be helpful for the refined design, optimization and analysis of ETFE membrane structures.
Sibling relationship quality and young children's mental health in Chinese two child families
Venue: Social Behavior and Personality: an international journal
Year: 2019

With enactment of the universal two child policy in China in 2016, the two child family will replace the one child family as the main family form. In this study we examined the contribution of sibling relationship quality in shaping the mental health of Chinese children in a two child family. Parents (N 280) reported the quality of their children's sibling relationship, and their firstborn's problematic and prosocial behavior, and temperament. Parents' marital satisfaction and coparenting were also assessed. The results showed that after we had controlled for the child characteristics of age, gender, temperament, sibling age difference, and sibling gender difference, and for the parental characteristics of age and marital satisfaction, and coparenting, sibling negativity and positivity were both significantly related to child problematic behavior. In addition, sibling positivity was significantly related to child prosocial behavior. The results suggest that parents in a two child family should provide guidance for their children's sibling relationship, and, in particular, increase the children's positive interaction to improve their mental health.
Resilience in Nicaragua: Impact evaluation of climate change adaptation among small scale producers
Year: 2015

This project arose from a concern that the impact of climate change on rural livelihoods in the north west of Nicaragua was becoming more severe, with increased patterns of irregular rainfall and prolonged dry seasons leading to soil degradation, the emergence of new crop diseases, and food crises. The project aimed to improve small producers' understanding of and ability to adapt to climate change, in the department of Chinandega. Project activities were implemented by Oxfam in partnership with the Centro para la Promocion, la Investigacion y el Desarrollo Rural y Social (CIPRES) Project participants almost all of whom were women in producer cooperatives received a programme of support, including training on improved techniques for crop production and soil management, on the selection and storage of seeds, on livestock management, and on protection of the local environment. Project participants were also provided with seeds and tools, as well as metal silos for grain storage, biodigesters, and ecological latrines, to further support experimentation and adaptation in productive activities. In its later stages, the project encouraged direct participants to share their learning with other cooperative members and their neighbours. Climate change adaptation among small scale producers
Responses of fish assemblage structure to large scale weir construction in riverine ecosystems.
Venue: The Science of the total environment
Year: 2019

Worldwide, increasing amounts of dams and weirs have been established in rivers in recent decades, often with drastic effects on their ecosystems. Between late 2009 and 2011, 16 large scale dams were built in the main channels of the four largest rivers in South Korea, eight of these along the main channel of Nakdong River (300 km, 520 km in total) We studied the effect of these constructions on the fish community in the riparian zone based on intensive fish field surveys conducted in the littoral zone during 2007 2017, analysis of fishermen's catch data and molecular analysis of the diet of the keystone species, largemouth bass (Micropterus salmoides) Our results, based on RDA and GLM analyses, showed that environmental characteristics and fish species composition changed significantly with dam construction. Total fish abundance and biomass decreased after the start of the weir construction and fish richness decreased with time. The abundance and biomass of exotic fish decreased during construction but recovered afterwards, leading to higher proportions of exotics. Dominance and Shannon indices did not change significantly from before to after construction, while the Evenness index showed a significant decreasing trend. A comparison of the diet composition of largemouth bass showed decreasing genetic variation after construction. The changes in Functional Feeding Group (FFG) of the benthic communities at the study sites did not coincide with FFG changes in the prey items of largemouth bass, indicating a transition in feeding mode from before to after construction. In conclusion, the endemic and native fish species were most sensitive and showed lower resilience to disturbance by the large scale dam construction than the translocated and exotic species, and the lake like ecosystems after construction markedly improved the competitive capacity of these exotic fish over the native and endemic species in the riparian zone of the river.
An overview of fisheries and sea turtle bycatch along the Atlantic coast of Africa
Year: 2013

Some of the most productive and diverse marine ecosystems found anywhere in the world are located along the Atlantic coast of Africa. Industrial fishing fleets from Africa and foreign nations exploit the commercially valuable fishing resources in coastal exclusive economic zones (EEZs) and on the high seas. Small scale artisanal fisheries operate extensively in coastal areas, catching a wide variety of species for subsistence and local trade. Incidental capture of sea turtles in the world's fisheries poses an urgent challenge to conservation and management efforts, and Atlantic Africa is no exception. The region supports important nesting and foraging grounds for five species of sea turtles loggerheads Caretta caretta (Linnaeus, 1758) green turtles Chelonia mydas (Linnaeus, 1758) leatherbacks Dermochelys coriacea (Vandelli, 1761) hawksbills Eretmochelys imbricata (Linnaeus, 1766) and olive ridleys Lepidochelys olivacea (Eschscholtz, 1829) This study characterizes the predominant fisheries in the region and compiles the available information on sea turtle bycatch in 21 countries between Mauritania and Namibia; intentional take is also described. Despite the active fisheries in the region, detailed information on sea turtle fisheries interactions is sparse for most African countries in the eastern Atlantic, highlighting an urgent need to address this gap.
Intestinal microbiota metabolism of L carnitine, a nutrient in red meat, promotes atherosclerosis
Venue: Nature Medicine
Year: 2013

Intestinal microbiota metabolism of choline and phosphatidylcholine produces trimethylamine (TMA) which is further metabolized to a proatherogenic species, trimethylamine N oxide (TMAO) We demonstrate here that metabolism by intestinal microbiota of dietary l carnitine, a trimethylamine abundant in red meat, also produces TMAO and accelerates atherosclerosis in mice. Omnivorous human subjects produced more TMAO than did vegans or vegetarians following ingestion of l carnitine through a microbiota dependent mechanism. The presence of specific bacterial taxa in human feces was associated with both plasma TMAO concentration and dietary status. Plasma l carnitine levels in subjects undergoing cardiac evaluation (n 2,595) predicted increased risks for both prevalent cardiovascular disease (CVD) and incident major adverse cardiac events (myocardial infarction, stroke or death) but only among subjects with concurrently high TMAO levels. Chronic dietary l carnitine supplementation in mice altered cecal microbial composition, markedly enhanced synthesis of TMA and TMAO, and increased atherosclerosis, but this did not occur if intestinal microbiota was concurrently suppressed. In mice with an intact intestinal microbiota, dietary supplementation with TMAO or either carnitine or choline reduced in vivo reverse cholesterol transport. Intestinal microbiota may thus contribute to the well established link between high levels of red meat consumption and CVD risk.
Cooperation in competition: efficiently representing and reasoning about coalitional games
Year: 2008

Cooperation is a central topic in multiagent systems research. In typical scenarios, agents, with only limited information and capabilities, must cooperate with others to achieve their goals. However, as agents are often only interested in their own welfare, cooperation takes place only if they gain by doing so. How benefits are divided, therefore, plays a crucial role in determining the success of partnerships. In my thesis, I study the problem of payoff division using coalitional game theory. This theory examines how to divide payoffs among agents so as to achieve fairness and stability. My contributions include both computational and conceptual advances of the theory. From a computational standpoint, I investigate how to efficiently find "good" division of payoffs. From a conceptual standpoint, I study how to extend the theory to capture characteristics of realistic multiagent systems. First, I propose two new schemes for representing coalitional games marginal contribution nets and multi attribute representation. I show that these schemes generalize past approaches, and reduce the space required to represent coalitional games by significant amounts. Further, they confer concomitant savings in computation by exploiting the structure exposed by these schemes, I develop algorithms that efficiently find fair and stable payoff divisions in these games. Second, I examine how to model updates and uncertainties in coalitional games. Both concepts are important for modeling multiagent systems in practice. For updates, I study how to efficiently update payoff divisions to maintain fairness or stability as the underlying game evolves. For uncertainties, I introduce Bayesian coalitional games which extend classical coalitional game theory to account for imperfect information, and I generalize the stability criterion of the core to the new setting.
Easy and Efficient Transformer Scalable Inference Solution For large NLP mode
Venue: ArXiv
Year: 2021

The ultra large scale pre training model can effectively improve the effect of a variety of tasks, and it also brings a heavy computational burden to inference. This paper introduces a series of ultra large scale pre training model optimization methods that combine algorithm characteristics and GPU processor hardware characteristics, and on this basis, propose an inference engine Easy and Efficient Transformer (EET) Which has a significant performance improvement over the existing schemes. We firstly introduce a pre padding decoding mechanism that improves token parallelism for generation tasks. Then we design high optimized kernels to remove sequence masks and achieve cost free calculation for padding tokens, as well as support long sequence and long embedding sizes. Thirdly a userfriendly inference system with an easy service pipeline was introduced which greatly reduces the difficulty of engineering deployment with high throughput. Compared to Faster Transformer's implementation for GPT 2 on A100, EET achieves a 1.5 15x state of art speedup varying with context length.EET is available https:/github.com/NetEase FuXi/EET.
Single stranded DNA phages: from early molecular biology tools to recent revolutions in environmental microbiology.
Venue: FEMS microbiology letters
Year: 2016

Single stranded DNA (ssDNA) phages are profoundly different from tailed phages in many aspects including the nature and size of their genome, virion size and morphology, mutation rate, involvement in horizontal gene transfer, infection dynamics and cell lysis mechanisms. Despite the importance of ssDNA phages as molecular biology tools and model systems, the environmental distribution and ecological roles of these phages have been largely unexplored. Viral metagenomics and other culture independent viral diversity studies have recently challenged the perspective of tailed, double stranded DNA (dsDNA) phages, dominance by demonstrating the prevalence of ssDNA phages in diverse habitats. However, the differences between ssDNA and dsDNA phages also substantially limit the efficacy of simultaneously assessing the abundance and diversity of these two phage groups. Here we provide an overview of the major differences between ssDNA and tailed dsDNA phages that may influence their effects on bacterial communities. Furthermore, through the analysis of 181 published metaviromes we demonstrate the environmental distribution of ssDNA phages and present an analysis of the methodological biases that distort their study through metagenomics.
Recent developments in synthetic biology and metabolic engineering in microalgae towards biofuel production
Venue: Biotechnology for Biofuels
Year: 2018

In the wake of the uprising global energy crisis, microalgae have emerged as an alternate feedstock for biofuel production. In addition, microalgae bear immense potential as bio cell factories in terms of producing key chemicals, recombinant proteins, enzymes, lipid, hydrogen and alcohol. Abstraction of such high value products (algal biorefinery approach) facilitates to make microalgae based renewable energy an economically viable option. Synthetic biology is an emerging field that harmoniously blends science and engineering to help design and construct novel biological systems, with an aim to achieve rationally formulated objectives. However, resources and tools used for such nuclear manipulation, construction of synthetic gene network and genome scale reconstruction of microalgae are limited. Herein, we present recent developments in the upcoming field of microalgae employed as a model system for synthetic biology applications and highlight the importance of genome scale reconstruction models and kinetic models, to maximize the metabolic output by understanding the intricacies of algal growth. This review also examines the role played by microalgae as biorefineries, microalgal culture conditions and various operating parameters that need to be optimized to yield biofuel that can be economically competitive with fossil fuels.
Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings
Venue: ArXiv
Year: 2019

In this paper, we formulate keyphrase extraction from scholarly articles as a sequence labeling task solved using a BiLSTM CRF, where the words in the input text are represented using deep contextualized embeddings. We evaluate the proposed architecture using both contextualized and fixed word embedding models on three different benchmark datasets (Inspec, SemEval 2010, SemEval 2017) and compare with existing popular unsupervised and supervised techniques. Our results quantify the benefits of (a) using contextualized embeddings (e.g. BERT) over fixed word embeddings (e.g. Glove) (b) using a BiLSTM CRF architecture with contextualized word embeddings over fine tuning the contextualized word embedding model directly, and (c) using genre specific contextualized embeddings (SciBERT) Through error analysis, we also provide some insights into why particular models work better than others. Lastly, we present a case study where we analyze different self attention layers of the two best models (BERT and SciBERT) to better understand the predictions made by each for the task of keyphrase extraction.
Employing Hierarchical Clustering and Reinforcement Learning for Attribute Based Zero Shot Classification
Venue: ADMA
Year: 2017

Zero shot classification (ZSC) is a hot topic of computer vision. Because the training labels are totally different from the testing labels, ZSC cannot be dealt with by classical classifiers. Attribute based classifier is a dominant solution for ZSC. It employs attribute annotations to bridge training labels and testing labels, making it able to realize ZSC. Classical attribute based classifiers treat different attributes equally. However, the attributes contribute to classification unequally. In this paper, a novel attribute based classifier for ZSC named HCRL is proposed. HCRL utilizes hierarchical clustering to obtain a hierarchy from the attribute annotations. Then the attribute annotations are decomposed into hierarchical rules which contain only a few attributes. The discriminative abilities of the rules reflect the significances of attributes to classification, but there are no training samples for evaluating the rules. The discriminative abilities are determined by reinforcement learning during the testing and the most discriminative rules are picked out for classification. Experiments conducted on 2 popular datasets for ZSC show the competitiveness of HCRL.
Dielectric and anti reduction properties of (1 x)BaTiO3 xBi(Zn0.5Y0.5)O2.75 ceramics for BME MLCC application
Venue: Journal of Alloys and Compounds
Year: 2019

In this work, nominal composition (1 x)BaTiO3 xBi(Zn0.5Y0.5)O2.75 [abbreviated as (1 x)BT xBZY, (x 0.05, 0.1, 0.15, 0.2, 0.25) ceramics were prepared by solid state reaction method, and sintered in reducing atmosphere. Effect of introducing BZY on the structure, dielectric and insulating properties of BT was investigated. XRD results demonstrated that the unit cell volume of (1 x)BT xBZY ceramics expanded with the addition of BZY, accompanied with the transformation from tetragonal to pseudo cubic phase when x 0.05. Electrical properties measurement showed that the dielectric temperature stability and insulating properties of (1 x)BT xBZY ceramics were effectively improved by BZY addition, where the optimum performances were achieved at x 0.15, with the dielectric constant of 1362 and dielectric loss of 1.4% and resistivity of 5.5 x 1012 O cm at room temperature, meeting the standard of X8R specification for MLCC application. The feasible mechanism of improved anti reduction properties for (1 x)BT xBZY ceramics was investigated by employing X ray photoelectron spectra (XPS) and thermally stimulated depolarization current (TSDC) testing techniques, where the formation of defect dipoles [2YTi' V O and the deoxidation of Bi ion were revealed and considered to be responsible for the anti reduction behavior.
Generalized Linear Model for Gamma Distributed Variables via Elastic Net Regularization
Year: 2018

The Generalized Linear Model (GLM) for the Gamma distribution (glmGamma) is widely used in modeling continuous, non negative and positive skewed data, such as insurance claims and survival data. However, model selection for GLM depends on AIC/BIC criteria, which is computationally impractical for even a moderate number of variables. In this paper, we develop variable selection for glmGamma using elastic net regularization (glmGammaNet) for which we provide an algorithm and implementation. The glmGammaNet model is more challening than other more common GLMs as the likelihood function has no global quadratic upper bound, and we develop an efficient accelerated proximal gradient algorithm using a local model. We report simulation study results and discuss the choice of regularization parameter. The method is implemented in the R package glmGammaNet.