nils-herrmann/clean_scirepeval huggingface dataset:
Comparison between manual and automated analysis for the quantification of carotid wall by using sonography. A validation study with CT.
Venue: European journal of radiology
Year: 2012

The purpose of this paper was to compare manual and automated analysis for the quantification of carotid wall obtained with sonography by using the computed tomography as validation technique.  21 consecutive patients underwent MDCTA and ultrasound analysis of carotid arteries (mean age 68 years; age range 59 81 years) The intima media thickness (IMT) of the 42 carotids was measured with novel and dedicated automated software analysis (called AtheroEdge(tm) Biomedical Technologies, Denver, CO, USA) and by four observers that manually calculated the IMT. The carotid artery wall thickness (CAWT) was also quantified in the CT datasets. Bland Altman statistics was employed to measure the agreement between methods. A Student's t test was used to test the differences between the IMT values of AtheroEdge(tm) The study obtained the IRB approval.  The correlation between automated AtheroEdge(tm) measurements and those of the human experts were equal to 95.5% 73.5% 88.9% and 81.7% The IMT coefficient of variation of the human experts was equal to 11.9% By using a Student's t test, the differences between the IMT values of AtheroEdge(tm) and those of the human experts were not found statistically significant (p value=0.02) On comparing AtheroEdge(tm) (using Ultrasound) with CAWT (using CT) the results suggested a very good concordance of 84.96% CONCLUSIONS Data of this preliminary study indicate that automated software AtheroEdge(tm) can analyze with precision the IMT of carotid arteries and that the concordance with CT is optimal.
PENGARUH PEMBERIAN UMPAN BALIK LATIHAN OSCE TERHADAP KETERAMPILAN KLINIS MAHASISWA FAKULTAS KEDOKTERAN UMSU PADA ANGKATAN 2016
Year: 2020

Pendahuluan: Keterampilan klinis merupakan hal yang penting untuk dikuasai bagi seorang dokter. Keterampilan klinis yang dimaksud meliputi keterampilan dalam melakukan penegakan diagnosis melalui anamnesis, pemeriksaan fisik, dan pemeriksaan penunjang serta keterampilan dalam melakukan prosedur penatalaksanaan masalah kesehatan pasien.  Examination (OSCE) merupakan salah satu metode evaluasi keterampilan klinis mahasiwa fakultas kedokteran. Proses pembelajaran keterampilan klinis dasar Fakultas Kedokteran Universitas Muhammadiyah Sumatera Utara mencakup role play dan learning using mannequins keterampilan klinis sesuai dengan blok yang berlangsung serta latihan OSCE. Dalam pelaksanaannya, setelah dilakukan latihan OSCE, mahasiswa akan mendapatkan umpan balik atau evaluasi kembali. Manfaat dilakukannya umpan balik latihan OSCE ini agar mahasiswa dapat mengetahui apakah yang mereka lakukan selama latihan OSCE sudah sempurna atau masih terdapat kesalahan dan kekurangan. Diharapkan dengan dilakukannya umpan balik latihan OSCE, mahasiswa dapat mencari tahu lebih lanjut apa kesalahan dan kekurangan mereka sehingga mahasiswa dapat berbenah diri dan memberikan penampilan yang lebih baik di OSCE yang sesungguhnya.  Mengetahui pengaruh pemberian umpan balik latihan OSCE terhadap keterampilan klinis mahasiswa Fakultas Kedokteran Universitas Muhammadiyah Sumatera Utara. Metode: Penelitian ini menggunakan metode penelitian eksperimental dengan rancangan one group pretest post test Hasil: Berdasarkan hasil uji T berpasangan didapatkan nilai nilai p value 0,000 (p<0,05) secara statistik menunjukkan bahwa terdapat perbedaan yang signifikan antara keterampilan klinis pretest dan post test Kesimpulan: umpan balik latihan OSCE berpengaruh terhadap keterampilan klinis mahasiswa.  Umpan Balik, Keterampilan Klinis,
Reply to Lenz et al. Quantifying the smallest microplastics is the challenge for a comprehensive view of their environmental impacts
Venue: Proceedings of the National Academy of Sciences
Year: 2016

Studies on impacts of emerging contaminants are challenging, as is the case for studying the smallest sizes <100 um) of microplastics, mainly because there is no clear view of their actual concentration and characteristics in the natural environment (1) Major developments are required to establish standardized procedures for collecting, fractionating, characterizing, and quantifying polymer particles; probably, the best promising method is in a liquid matrix. In our recent article on impacts of microplastics in oysters (2) the microplastic size tested was of 2 and 6 um, the size range preferentially ingested by filter feeders, which is
Antioxidant activity of oak (Quercus) leaves infusions against free radicals and their cardioprotective potential.
Venue: Pakistan journal of biological sciences PJBS
Year: 2010

The aim of present study was to evaluate antioxidant capacity and cardioprotective potential of leaves infusions and partially purified fractions of Quercus sideroxyla and Q. eduardii (red oaks) and Q. resinosa (white oak) Consumption of polyphenol rich beverages derived from plants, such as oak may represent a beneficial diet in terms of cardiovascular protection. Infusions from Oak leaves were obtained and probed for total phenolics by Folin Ciocalteu, DPPH and hydroxyl radicals scavenging by DPPH test and Deoxy D ribose method, the antioxidant capacity was evaluated by FRAP and ORAC tests, inhibitions of Low Density Lipoproteins (LDL) oxidation and Angiotensin Converting Enzyme (ACE) activity were measured. A HPLC analysis was performed by HPLC MS. Bioactive polyphenols such as gallic and ellagic acids, catechin, quercetin and derivatives: naringenin and naringin were detected in Quercus infusions. A distinctive HPLC profile was observed among the red and white oak samples. Q. resinosa infusions have exhibited the highest antioxidant activity in comparison with the other species, although in the inhibition of LDL oxidation no differences were observed. In the inhibition of the ACE, Q. resinosa was more effective (IC50, 18 ppm) than Q. sideroxyla, showing same effect as the control Captopril. From the results it is possible to postulate that not only chelating activity is important in these infusions, especially in Q. resinosa.
Public Irrigation and Well being of Women: A Tale of Two Villages in North east Karnataka, India
Year: 2020

This article explores how public irrigation mediates the well being of women through a study of two villages in north east Karnataka with contrasting natures of access to irrigation. It shows that public irrigation plays an "instrumental role" in the well being of women. Here, the well being of women is defined in terms of four dimensions that include survival, education, health and self awareness. The village that has access to public funded canal irrigation exhibits relatively better performance in terms of well being of women compared to the village with non canal irrigation. These outcomes are explained in terms of access to irrigation and distribution of land. This micro level study shows the need to focus on public expenditure in rural infrastructure projects, including irrigation, to mitigate gender inequities.
Identifying Highly Influential Travellers for Spreading Disease on a Public Transport System
Venue: 2020 IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)
Year: 2020

The recent outbreak of a novel coronavirus and its rapid spread underlines the importance of understanding human mobility. Enclosed spaces, such as public transport vehicles (e.g. buses and trains) offer a suitable environment for infections to spread widely and quickly. Investigating the movement patterns and the physical encounters of individuals on public transit systems is thus critical to understand the drivers of infectious disease outbreaks. For instance, previous work has explored the impact of recurring patterns inherent in human mobility on disease spread, but has not considered other dimensions such as the distance travelled or the number of encounters. Here, we consider multiple mobility dimensions simultaneously to uncover critical information for the design of effective intervention strategies. We use one month of citywide smart card travel data collected in Sydney, Australia to classify bus passengers along three dimensions, namely the degree of exploration, the distance travelled and the number of encounters. Additionally, wes imulate disease spread on the transport network and trace the infection paths. We investigate in detail the transmissions between the classified groups while varying the infection probability and the suspension time of pathogens. Our results show that characterizing individuals along multiple dimensions simultaneously uncovers a complex infection interplay between the different groups of passengers, that would remain hidden when considering only a single dimension. We also identify groups that are more influential than others given specific disease characteristics, which can guide containment and vaccination efforts.
Deep learning based single photon counting compressive imaging via jointly trained subpixel convolution sampling.
Venue: Applied optics
Year: 2020

The combination of single pixel imaging and single photon counting technology can achieve ultrahigh sensitivity photon counting imaging. However, its applications in high resolution and real time scenarios are limited by the long sampling and reconstruction time. Deep learning based compressive sensing provides an effective solution due to its ability to achieve fast and high quality reconstruction. This paper proposes a sampling and reconstruction integrated neural network for single photon counting compressive imaging. To effectively remove the blocking artefact, a subpixel convolutional layer is jointly trained with a deep reconstruction network to imitate compressed sampling. By modifying the forward and backward propagation of the network, the first layer is trained into a binary matrix, which can be applied to the imaging system. An improved deep reconstruction network based on the traditional Inception network is proposed, and the experimental results show that its reconstruction quality is better than existing deep learning based compressive sensing reconstruction algorithms.
Adaptive Control of a Cable Actuated Parallel Manipulator Mounted on a Platform with Differential Wheels under Payload Uncertainty
Year: 2018

Cable actuated parallel Manipulators (CPMs) are widely employed for object handling applications. In order to displace the carried object along the ground to an unlimited distance, the CPMs can be mounted on wheeled mobile robots (WMRs) The derivation of the dynamic equations of motion for this integrated system is presented using Lagrange method. Since in load carrying task, the inertia of the moving load is the main source of uncertainty, an adaptive control approach is considered for the CPM, whereas the WMR uses a feedback linearized sliding mode approach. In order to maintain the end effector of the CPM in within its relative workspace in the WMR frame, the convergence rate in the two controllers should be similar. Decentralization of the control law can be accomplished if the inertia of the CPM motors are negligible compared with the other inertias of the system. This assumption is shown to be applicable if an introduced index is small enough to have noticeable effect on the tracking error.
A new crossover technique for Cartesian genetic programming
Venue: GECCO '07
Year: 2007

Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random sub branches of the parents' trees are swapped to create the offspring. Later Miller and Thomson introduced Cartesian Genetic Programming, which uses directed graphs as a representation to replace the tree structures originally introduced by Koza. Cartesian Genetic Programming has been shown to perform better than the traditional Genetic Programming; but it does not use crossover to create offspring, it is implemented using mutation only. In this paper a new crossover method in Genetic Programming is introduced. The new technique is based on an adaptation of the Cartesian Genetic Programming representation and is tested on two simple regression problems. It is shown that by implementing the new crossover technique, convergence is faster than that of using mutation only in the Cartesian Genetic Programming method.
Evolution of the Gut Microbiome in Early Childhood: A Cross Sectional Study of Chinese Children
Venue: Frontiers in Microbiology
Year: 2020

Temporal development of the human gut microbiome from infancy to childhood is driven by a variety of factors. We surveyed the fecal microbiome of 729 Chinese children aged 0 36 months, aiming to identify the age specific patterns of microbiota succession, and evaluate the impact of birth mode, gender, geographical location, and gastrointestinal tract symptoms on the shaping of the gut microbiome. We demonstrated that phylogenetic diversity of the gut microbiome increased gradually over time, which was accompanied by an increase in Bacteroidetes and a reduction in Proteobacteria species. Analysis of community wide phenotypes revealed a succession from aerobic bacteria and anaerobic bacteria to facultative anaerobes, and from Gram negative to Gram positive species during gut microbiota development in early childhood. The metabolic functions of the gut microbiome shifted tremendously alongside early physiological development, including an increase in alanine, aspartate, and glutamate metabolism, and a reduction in glutathione, fatty acid, and tyrosine metabolism. During the first year of life, the Bacteroidetes phylum was less abundant in children born by casarean section compared with those delivered vaginally. The Enterococcaceae family, a group of facultative anaerobic microorganisms with pathogenic potential, was predominant in preterm infants. No measurable effect of maternal antibiotic exposure on gut microbiota development was found in the first 3 years of life. The relative abundances of Coriobacteriaceae and Streptococcaceae families, and Megasphaera genus were found to be higher in girls than in boys. Among the three first tier Chinese cities, children born and fed in Beijing had a higher abundance of Enterococcaceae and Lachnospiraceae families, and Shenzhen children had a higher abundance of Fusobacteriaceae. The families Alcaligenaceae, Bacteroidaceae, and Porphyromonadaceae were more abundant in children with constipation, whereas the relative abundance of the Clostridium genus was higher in those with diarrhea.
Blockholders Diversity and Company Value*
Year: 2016

According to my new and extensive data on all US public companies, the majority of them have multiple blockholders (large shareholders) These blockholders could differ along several characteristics even within one company. Diversity between blockholders within a firm could have a positive and synergistic impact on its value. Alternatively, conflicting objectives and interests may cause diversity to adversely impact company operations. To investigate the resulting impact of blockholders diversity on company value, I construct diversity measures reflecting their heterogeneity in identity, portfolio size and investment horizon. Using shocks from blockholder acquisitions of financial firms and unexpected increases in payouts they receive from other positions to identify the causality channel, I find that block diversity has a strong negative influence on company value and operations. This result is robust to a variety of specifications and to exclusion of different groups of blockholders. Additionally, simulated placebo tests reject alternative explanations related to other observed and unobserved characteristics of block ownership.
Parts of Speech Tagging in NLP: Runtime Optimization with Quantum Formulation and ZX Calculus
Venue: ArXiv
Year: 2020

This paper proposes an optimized formulation of the parts of speech tagging in Natural Language Processing with a quantum computing approach and further demonstrates the quantum gate level runnable optimization with ZX calculus, keeping the implementation target in the context of Noisy Intermediate Scale Quantum Systems (NISQ) Our quantum formulation exhibits quadratic speed up over the classical counterpart and further demonstrates the implementable optimization with the help of ZX calculus postulates.
Patient Similarity Analysis with Longitudinal Health Data
Venue: ArXiv
Year: 2020

Healthcare professionals have long envisioned using the enormous processing powers of computers to discover new facts and medical knowledge locked inside electronic health records. These vast medical archives contain time resolved information about medical visits, tests and procedures, as well as outcomes, which together form individual patient journeys. By assessing the similarities among these journeys, it is possible to uncover clusters of common disease trajectories with shared health outcomes. The assignment of patient journeys to specific clusters may in turn serve as the basis for personalized outcome prediction and treatment selection. This procedure is a non trivial computational problem, as it requires the comparison of patient data with multi dimensional and multi modal features that are captured at different times and resolutions. In this review, we provide a comprehensive overview of the tools and methods that are used in patient similarity analysis with longitudinal data and discuss its potential for improving clinical decision making.
Transcriptomic response to divergent selection for flowering time in maize reveals convergence and key players of the underlying gene regulatory network
Year: 2019

Artificial selection experiments are designed to investigate phenotypic evolution of complex traits and its genetic basis. Here we focused on flowering time, a trait of key importance for plant adaptation and life cycle shifts. We undertook divergent selection experiments from two maize inbred lines. After 13 generations of selection, we obtained a time lag of roughly two weeks between Early and Late populations. We used this material to characterize the genome wide transcriptomic response to selection in the shoot apical meristem before, during and after floral transition in field conditions during two consecutive years. We validated the reliability of performing RNA sequencing in uncontrolled conditions. We found that roughly half of maize genes were expressed in the shoot apical meristem, 59.3% of which were differentially expressed. We detected a majority of genes with differential expression between inbreds and across meristem status, and retrieved a subset of 2,451 genes involved in the response to selection. Among these, we found a significant enrichment for genes with known function in maize flowering time. Furthermore, they were more often shared between inbreds than expected by chance, suggesting convergence of gene expression. We discuss new insights into the expression pattern of key players of the underlying gene regulatory network including the Zea mays genes CENTRORADIALIS (ZCN8) RELATED TO AP2.7 (RAP2.7) MADS4 (ZMM4) KNOTTED1 (KN1) GIBBERELLIN2 OXIDASE1 (GA2ox1) as well as alternative scenarios for genetic convergence.
Answer Supervised Question Reformulation for Enhancing Conversational Machine Comprehension
Venue: MRQA@EMNLP
Year: 2019

In conversational machine comprehension, it has become one of the research hotspots integrating conversational history information through question reformulation for obtaining better answers. However, the existing question reformulation models are trained only using supervised question labels annotated by annotators without considering any feedback information from answers. In this paper, we propose a novel Answer Supervised Question Reformulation (ASQR) model for enhancing conversational machine comprehension with reinforcement learning technology. ASQR utilizes a pointer copy based question reformulation model as an agent, takes an action to predict the next word, and observes a reward for the whole sentence state after generating the end of sequence token. The experimental results on QuAC dataset prove that our ASQR model is more effective in conversational machine comprehension. Moreover, pretraining is essential in reinforcement learning models, so we provide a high quality annotated dataset for question reformulation by sampling a part of QuAC dataset.
DAN: Deep Attention Neural Network for News Recommendation
Venue: AAAI
Year: 2019

With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem. Many existing recommendation methods that regard the recommendation procedure as the static process, have achieved better recommendation performance. However, they usually fail with the dynamic diversity of news and user's interests, or ignore the importance of sequential information of user's clicking selection. In this paper, taking full advantages of convolution neural network (CNN) recurrent neural network (RNN) and attention mechanism, we propose a deep attention neural network DAN for news recommendation. Our DAN model presents to use attention based parallel CNN for aggregating user's interest features and attention based RNN for capturing richer hidden sequential features of user's clicks, and combines these features for new recommendation. We conduct experiment on real world news data sets, and the experimental results demonstrate the superiority and effectiveness of our proposed DAN model.
Prospect theory for enhanced cyber physical security of drone delivery systems: A network interdiction game
Venue: 2017 IEEE International Conference on Communications (ICC)
Year: 2017

The use of unmanned aerial vehicles (UAVs) as delivery systems of online goods is rapidly becoming a global norm, as corroborated by Amazon's "Prime Air" and Google's "Project Wing" projects. However, the real world deployment of such drone delivery systems faces many cyber physical security challenges. In this paper, a novel mathematical framework for analyzing and enhancing the security of drone delivery systems is introduced. In this regard, a zero sum network interdiction game is formulated between a vendor, operating a drone delivery system, and a malicious attacker. In this game, the vendor seeks to find the optimal path that its UAV should follow, to deliver a purchase from the vendor's warehouse to a customer location, to minimize the delivery time. Meanwhile, an attacker seeks to choose an optimal location to interdict the potential paths of the UAVs, so as to inflict cyber or physical damage to it, thus, maximizing its delivery time. First, the Nash equilibrium point of this game is characterized. Then, to capture the subjective behavior of both the vendor and attacker, new notions from prospect theory are incorporated into the game. These notions allow capturing the vendor's and attacker's i) subjective perception of attack success probabilities, and ii) their disparate subjective valuations of the achieved delivery times relative to a certain target delivery time. Simulation results have shown that the subjective decision making of the vendor and attacker leads to adopting risky path selection strategies which inflict delays to the delivery, thus, yielding unexpected delivery times which surpass the target delivery time set by the vendor.