nils-herrmann/clean_scirepeval huggingface dataset:
A Robust Pansharpening Algorithm Based on Convolutional Sparse Coding for Spatial Enhancement
Venue: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2019

Pansharpening (PS) is a prominent remote sensing image fusion technique. It yields high resolution multispectral (HRMS) images, which are imperative for the applications, such as recognition and detection. The PS methods based on conventional sparse representation induce blurring effects and are unable to preserve the essential spatial details in the fused outcome. In this article, to overcome these drawbacks, a robust fusion scheme is proposed based on convolutional sparse coding (CSC) The source images are decomposed into its constituent texture and cartoon components. The sparse coefficient maps are acquired from texture components by adapting CSC. Texture components are fused using activity level measurement, whereas averaging mechanism is used to fuse the cartoon components. The HRMS image is reconstructed by combining the fused components in proportion to the gradient information. Impact of number of filters on quality metrics estimation is analyzed. Comprehensive experiments are performed on the images acquired from distinct sensors. The proposed method is evaluated in terms of visual analysis and the quantitative metrics with reduced scale and full scale experiments. Extensive evaluations manifest the capability of the proposed method of maintaining the balanced tradeoff and retaining the desired spatial and spectral details.
Objective Measures of Emotion Related to Brand Attitude: A New Way to Quantify Emotion Related Aspects Relevant to Marketing
Venue: PloS one
Year: 2011

With this study we wanted to test the hypothesis that individual like and dislike as occurring in relation to brand attitude can be objectively assessed. First, individuals rated common brands with respect to subjective preference. Then, they volunteered in an experiment during which their most liked and disliked brand names were visually presented while three different objective measures were taken. Participant's eye blinks as responses to acoustic startle probes were registered with electromyography (EMG) (i) and their skin conductance (ii) and their heart rate (iii) were recorded. We found significantly reduced eye blink amplitudes related to liked brand names compared to disliked brand names. This finding suggests that visual perception of liked brand names elicits higher degrees of pleasantness, more positive emotion and approach oriented motivation than visual perception of disliked brand names. Also, skin conductance and heart rate were both reduced in case of liked versus disliked brand names. We conclude that all our physiological measures highlight emotion related differences depending on the like and dislike toward individual brands. We suggest that objective measures should be used more frequently to quantify emotion related aspects of brand attitude. In particular, there might be potential interest to introduce startle reflex modulation to measure emotion related impact during product development, product design and various further fields relevant to marketing. Our findings are discussed in relation to the idea that self reported measures are most often cognitively polluted.
Dynamic response of piping systems subjected to underwater explosion
Year: 2012

Pipeline is one of the weaknesses in anti impacting capability of ships,which makes the investigation significant for improving the life of warships.This paper models the compressor exhaust pipelines by FEM.It analyzes the response of the model to the impact loads due to the underwater explosion.The response characteristics of the air pipelines subjected to the underwater explosion is concluded from the analyses of piping stress,strain,acceleration and displacement.It can provide references for the investigation of anti impacting capability of warship piping systems.
A Detailed Study of the Distributed Rough Set Based Locality Sensitive Hashing Feature Selection Technique
Venue: Fundam. Informaticae
Year: 2021

In the context of big data, granular computing has recently been implemented by some mathematical tools, especially Rough Set Theory (RST) As a key topic of rough set theory, feature selection has been investigated to adapt the related granular concepts of RST to deal with large amounts of data, leading to the development of the distributed RST version. However, despite of its scalability, the distributed RST version faces a key challenge tied to the partitioning of the feature search space in the distributed environment while guaranteeing data dependency. Therefore, in this manuscript, we propose a new distributed RST version based on Locality Sensitive Hashing (LSH) named LSH dRST, for big data feature selection. LSH dRST uses LSH to match similar features into the same bucket and maps the generated buckets into partitions to enable the splitting of the universe in a more efficient way. More precisely, in this paper, we perform a detailed analysis of the performance of LSH dRST by comparing it to the standard distributed RST version, which is based on a random partitioning of the universe. We demonstrate that our LSH dRST is scalable when dealing with large amounts of data. We also demonstrate Address for correspondence: INRIA Nancy Grand Est 615 Rue du Jardin Botanique, 54600 Villers les Nancy, France *This work is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska Curie grant agreement No 702527. 2 Z. Chelly Dagdia, C. Zarges LSH RST for an Efficient Big Data Pre processing that LSH dRST ensures the partitioning of the high dimensional feature search space in a more reliable way; hence better preserving data dependency in the distributed environment and ensuring a lower computational cost.
Acrylamide and 5 hydroxymethylfurfural (HMF) A review on metabolism, toxicity, occurrence in food and mitigation strategies
Year: 2011

Neo formed contaminants (NFCs) are compounds forming during heating or preservation processes and exhibiting possible harmful effects to humans. Among the several NFCs described in literature, Acrylamide and 5 hydroxymethylfurfural (HMF) have attracted the attention of the scientific community in recent years. Both acrylamide and HMF are considered as probably or potentially carcinogenic to humans or might be metabolized by humans to potentially carcinogenic compounds. Acrylamide and HMF are mainly formed through Maillard Reaction and can be regarded as the most important heat induced contaminants occurring in bread and bakery products. Acrylamide is carcinogen in rodent and some recent epidemiological studies have highlighted the association between dietary acrylamide and an increased risk of some types of cancer. HMF has been recently shown to be converted in vivo to 5 sulfoxymethylfurfural (SMF) which is a genotoxic compound. Dietary intake of HMF is in the order of mg/kg far above that of other food toxicants. In this paper, the latest available data on acrylamide and HMF have been reviewed focusing on available mitigation strategies, metabolism, dietary exposure, and toxicity. The results from the epidemiological studies about acrylamide and cancer risk and their relevance have been discussed, the major gaps of knowledge have been identified and the perspective of ongoing and future research was established.
Social Network Analysis and the Study of Sociality in Bats
Year: 2013

Many bat species are known for being gregarious, forming mixed or single sex social groups commonly referred to as colonies. The number of studies investigating sociality in bats is rapidly increasing, with studies ranging from basic descriptions of the number of males and females within social groups to studies using social network analysis. Studies of sociality in bats are taking increasingly diverse approaches to data collection, analysis and interpretation, leaving researchers with an array of perspectives on how to conduct future research. These perspectives are difficult to synthesize, but an integrated understanding of pioneering works in this field should help researchers build upon what is already known about sociality in bats and formulate new hypotheses. Herein we provide a review of methodologies used to measure social interactions, relationships, and structure in bats. We review assumptions, sources of bias, strengths, and limitations of these methods. We emphasize that while all of the reviewed methods are well suited for assessing social interactions and relationships, each method will impact analyses of social structure and should be considered carefully. We encourage further use of social network analysis as a framework for conceptualizing, designing, and analyzing studies of bat sociality. We do not advocate any single network analysis methodology, as network analysis is continually evolving and no one technique is well suited for all research questions. Instead, we recommend several specific network measures we believe are appropriate for different types of research questions and datasets and discuss the strengths and limitations of popular analyses.
Data visualization for assessing the biofuel commercialization potential within the business intelligence framework.
Year: 2018

With the ever increasing energy demand, triggered by the continued population growth and accelerated industrial revolution, renewable energy has emerged as the world's fastest growing energy source. Renewable energy's popularity has grown because it is environmentally friendly and abundant in natural environments. Despite its enormous potential as a viable alternative to traditional (fossil fuel based) energy sources, renewable energy has rarely been commercialized and utilized. Its lack of commercialization has something to do with a lack of evidence proving its eco and cost efficiency. With this in mind, this paper aims to assess the eco and cost efficiency of renewable energy such as algae based biofuels using data visualization. It also intends to help increase public awareness and facilitate the commercialization of renewable energy such as biofuels. Through experiments, this paper found that the success of biofuel commercialization hinged on temperature, light intensity, and algae strain. Another important finding is that the low carbon footprint resulting from biofuel consumption may not directly contribute to the immediate revenue growth of a biofuel producing company, but it can foster a long term positive image that will help attract more customers in the future with increased brand recognition. Furthermore, this paper evaluates the effectiveness, the level of user involvement, and the usability of two data visualization tools built upon the dashboard and the balanced scorecard. Based on the case study, this paper demonstrates how effective and useful the tools are in communicating the firm's strategic goals toward sustainability and thus provides easier practical guidelines for renewable energy development decisions.
Effects of intensive short term dynamic psychotherapy on social cognition in major depression
Venue: Journal of mental health
Year: 2018

Social cognition is commonly affected in psychiatric disorders and is a determinant of quality of life. However, there are few studies of treatment.  To investigate the efficacy of intensive short term dynamic psychotherapy on social cognition in major depression.  This study used a parallel group randomized control design to compare pre test and post test social cognition scores between depressed participants receiving ISTDP and those allocated to a wait list control group. Participants were adults (19 40 years of age) who were diagnosed with depression. We recruited 32 individuals, with 16 participants allocated to the ISTDP and control groups, respectively. Both groups were similar in terms of age, sex and educational level.  Multivariate analysis of variance (MANOVA) demonstrated that the intervention was effective in terms of the total score of social cognition: the experimental group had a significant increase in the post test compared to the control group. In addition, the experimental group showed a significant reduction in the negative subjective score compared to the control group as well as an improvement in response to positive neutral and negative states.  Depressed patients receiving ISTDP show a significant improvement in social cognition post treatment compared to a wait list control group.
Artificial neural network forecast of monthly mean daily global solar radiation of selected locations based on time series and month number
Venue: Journal of Cleaner Production
Year: 2019

Prediction and forecast of monthly mean daily global solar energy available at selected locations are carried out using a nonlinear autoregressive, a nonlinear autoregressive (exogenous) and a hybrid time series methods. The methods are implemented with artificial neural networks. The proposed hybrid method is based on a combination of nonlinear autoregressive and structural artificial neural networks where the structural input is the current month number. The aim of the hybridization is to improve prediction accuracy of the nonlinear autoregressive methods and simplify the input layer of the nonlinear autoregressive (exogenous) method. Based on various statistical criteria, the hybrid method is verified to predict and forecast global solar energy with noticeably higher accuracy. For example, while a nonlinear autoregressive predicts the global solar energy availability at Abuja with R2 value of 0.78 and correlation coefficient of 0.89, the hybrid method improves the prediction to 0.96 and 0.98. The hybrid model is verified via one way analysis of variance to consistently perform better than the nonlinear autoregressive method in months ahead forecasting of solar energy. The proposed hybrid method is capable of long term forecast of up to two years ahead within a typical mean percent forecast error of 5.67% therefore, is applicable for designing/planning solar energy integration and simulation of agricultural food security, especially in developing countries. A cases study of power output and sizing of standalone PV options showed that decisions based on forecasted and actual solar energy are the same demonstrating the very useful application of the hybrid method.
SkyNet: A Champion Model for DAC SDC on Low Power Object Detection
Venue: ArXiv
Year: 2019

Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding requirements, such as real time processing, high throughput performance, and high inference accuracy. To overcome these challenges, we propose SkyNet, an extremely lightweight DNN with 12 convolutional (Conv) layers and only 1.82 megabyte (MB) of parameters following a bottom up DNN design approach. SkyNet is demonstrated in the 56th IEEE/ACM Design Automation Conference System Design Contest (DAC SDC) a low power object detection challenge in images captured by unmanned aerial vehicles (UAVs) SkyNet won the first place award for both the GPU and FPGA tracks of the contest: we deliver 0.731 Intersection over Union (IoU) and 67.33 frames per second (FPS) on a TX2 GPU and deliver 0.716 IoU and 25.05 FPS on an Ultra96 FPGA.
Ab initio study of V doping effects on electronic structure and magnetic properties in Co2Fe1 xV xAl
Year: 2012

We have investigated the electronic and magnetic properties of the doped Heusler alloys Co2Fe1 xV xAl x 0 0.25, 0.5, 0.75, 1) using first principles density functional theory within the generalized gradient approximation (GGA) scheme. The calculated results reveal that with increasing V content the lattice parameter slightly increases. The magnetic moment of the Fe sites increases while the magnetic moment of the Co sites decreases with V doping; the total spin moment of these compounds linearly decreases. We also have performed the electronic structure calculations for Co2Fe1 xV xAl with positional disorder of Co Fe(V) type and Al Fe(V) type. It is found that formation of Al Fe(V) type disorder is more favorable than that of Co Fe(V) type disorder in Co2Fe1 xV xAl alloys. Furthermore, we found that the spin polarization increases with V doping in Co2Fe1 xV xAl with ordered L 2 1 and disordered B2 structures. Detailed calculations show that the stability of the L 2 1 structure will be enhanced with increasing V content. The results are compatible with experiments.
Use of Beta Cyclodextrin to Remove Cholesterol and Increase Astaxanthin Content in Shrimp Oil
Venue: European Journal of Lipid Science and Technology
Year: 2019

Shrimp oil is extracted from shrimp (Litopenaeus vannamei) cephalothorax and subjected to the removal of cholesterol by b cyclodextrin (bCD) Different oil/bCD ratios (1:2, 1:3, and 1:4, w/w) and homogenization times (1, 10, and 20 min) are used. Cholesterol deduction is attained with increasing bCD levels and homogenization time. Astaxanthin content is augmented, while cholesterol concentration is reduced. Nevertheless, oil yield and astaxanthin concentration of treated oil are decreased as bCD levels are increased. To increase the oil yield, the used bCD is further extracted for three times with ethyl acetate at 1:10 (w/v) ratio, in which yield is increased from 44.6% to 64% Cholesterol removal of 95% is obtained, while astaxanthin content is increased. Lipid oxidation is lowered as indicated by the lower TOTOX value, peroxide value, thiobarbituric acid reactive substances, and p anisidine value. However, lipid hydrolysis is slightly increased after treatment. Volatiles, especially aldehydes and alcohols, are decreased after treatment. FTIR spectra confirm the removal of phospholipid, which might be associated with the decreased oil yield after treatment. With the developed process, total fatty acid is increased by 15.6% in which monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) are augmented. bCD could remove cholesterol, increase astaxanthin and fatty acid content.  Shrimp oil has been known to be a rich source of astaxanthin and PUFAs with health benefit. However, it also contains cholesterol, which can be a drawback for consumption as the supplement. The removal of cholesterol, while maintaining PUFA and astaxanthin could pave a way for promoting the intake of shrimp oil. Use of bCD for oil treatment with subsequent extraction of remaining oil in the used bCD could be implemented with ease. Another advantage of the developed process is to increase both fatty acid and astaxanthin contents in the resulting oil. As a consequence, shrimp oil with lowered cholesterol can be directly used as food ingredient and also for neutraceutical purpose.
Using mixed methods to evaluate a mastery approach to teaching mathematics: considerations of quality and equity with regard to individual learning needs
Year: 2019

We present results from a mixed method evaluation of a mastery oriented, Singapore based mathematics textbook series and teaching approach in Year 1 classrooms in England (Hall, Lindorff, Sammons, 2016) reanalysed with a focus on individual learning needs not a predefined focus, but one that emerged during fieldwork/analysis. The present objectives are: 1.To illustrate the potential of mixed methods to investigate of emergent findings beyond a priori aims; 2.To explore substantive findings regarding equity and quality of teaching/learning with a mastery orientation for diverse learning needs. This study was framed by Bloom's mastery learning theory combined with a teacher effectiveness perspective. We used a mixed methods Cluster Randomised Controlled Trial (mmCRCT) design with a delayed treatment group. Data sources included pupil assessments and attitude questionnaires, lesson observations, and teacher interviews (all conducted in the first, second and third terms of 2015 16) Findings suggest implications for policy and practice based on a reconceptualization of "ability" and differentiation for mastery oriented teaching and learning. Further, the study illustrates how mixed methods evaluation can facilitate understandings of educational interventions' effects and implementation processes relevant to equity and quality issues, extending to emergent findings beyond predetermined aims. Hall, J. Lindorff, A. Sammons, P. (2016) Evaluation of the Impact and Implementation of Inspire Maths in Year 1 Classrooms in England.  University of Oxford.
Comparative milk proteome analysis of Kashmiri and Jersey cattle identifies differential expression of key proteins involved in immune system regulation and milk quality
Venue: BMC Genomics
Year: 2020

Exploration of the bioactive components of bovine milk has gained global interest due to their potential applications in human nutrition and health promotion. Despite advances in proteomics profiling, limited studies have been carried out to fully characterize the bovine milk proteome. This study explored the milk proteome of Jersey and Kashmiri cattle at day 90 of lactation using high resolution mass spectrometry based quantitative proteomics nano scale LC MS/Q TOF technique. Data are available via ProteomeXchange with identifier PXD017412.  Proteins from whey were fractionated by precipitation into high and low abundant proteins. A total of 81 high abundant and 99 low abundant proteins were significantly differentially expressed between Kashmiri and Jersey cattle, clearly differentiating the two breeds at the proteome level. Among the top differentiating proteins, the Kashmiri cattle milk proteome was characterised by increased concentrations of immune related proteins (apelin, acid glycoprotein, CD14 antigen) neonatal developmental protein (probetacellulin) xenobiotic metabolising enzyme (flavin monooxygenase 3 (FMO3) GLYCAM1 and HSP90AA1 (chaperone) while the Jersey milk proteome presented higher concentrations of enzyme modulators (SERPINA1, RAC1, serine peptidase inhibitor) and hydrolases (LTF, LPL, CYM, PNLIPRP2) Pathway analysis in Kashmiri cattle revealed enrichment of key pathways involved in the regulation of mammary gland development like Wnt signalling pathway, EGF receptor signalling pathway and FGF signalling pathway while a pathway (T cell activation pathway) associated with immune system regulation was significantly enriched in Jersey cattle. Most importantly, the high abundant FMO3 enzyme with an observed 17 fold higher expression in Kashmiri cattle milk seems to be a characteristic feature of the breed. The presence of this (FMO3) bioactive peptide/enzyme in Kashmiri cattle could be economically advantageous for milk products from Kashmiri cattle.  In conclusion, this is the first study to provide insights not only into the milk proteome differences between Kashmiri and Jersey cattle but also provides potential directions for application of specific milk proteins from Kashmiri cattle in special milk preparations like infant formula.
Imperfect risk adjustment, risk preferences, and sorting in competitive health insurance markets.
Venue: Journal of health economics
Year: 2017

I develop a model of insurer price setting and consumer welfare under risk adjustment, a policy commonly used to combat inefficient sorting due to adverse selection in health insurance markets. I use the model to illustrate graphically that risk adjustment causes health plan prices to be based on costs not predicted by the risk adjustment model "residual costs" rather than total costs, either weakening or exacerbating selection problems depending on the correlation between demand and costs predicted by the risk adjustment model. I then use a structural model to estimate the welfare consequences of risk adjustment, finding a welfare gain of over $600 per person year.