nils-herrmann/clean_scirepeval huggingface dataset:
Sieve BLP: A Semi Nonparametric Model of Demand for Differentiated Products
Year: 2021

We develop a semi nonparametric approach to identify and estimate the demand for differentiated products. The proposed method adopts a random coefficients discrete choice logit model (i.e. mixed logit model) in which the distribution of random coefficients is nonparametrically specified. Our method minimizes misspecification error in this distribution to which routinely used parametric approach is subject. In addition, it overcomes the practical challenge of dimensionality in the number of products that remains the main hurdle in the nonparametric estimation of demand functions. We propose a sieve estimation procedure (referred to as sieve BLP) that remains simple to implement. Extensive Monte Carlo simulations show its robust finite sample performance under various data generating processes. We use our method to investigate the welfare implications of a sugar tax in the ready to eat cereal industry in the US. This application underscores the usefulness of sieve BLP due to its ability to allow for flexibly specified individual heterogeneity in demand, especially when the researcher aims to quantify the distributional effects of a policy change.
Data Collection and Network Architecture Analysis in Internet of Vehicles Based on NB IoT
Venue: 2018 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS)
Year: 2018

Existing traffic data collection methods was expounded, advantages and disadvantages of each methods was compared and analyzed. Classification of IoT communication technology was introduced. Narrow Band Internet of Things(NB IoT) as one of the Low Power Wide Area Network(LPWAN) with mess device access, low cost module, low power consumption, wide coverage, time delay was not sensitive and other business characteristics. The car networking data acquisition scheme based on NB IoT transformation was put forward, the network architecture was deployed and analyzed. Finally, applications of Internet of Vehicles(IoV) was put, as well as the prospects in the future.
Current harmonic suppression for permanent magnet synchronous motors
Venue: 2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) 2019 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)
Year: 2019

This paper deals with problem of current harmonic suppression in permanent magnet synchronous motors (PMSM) First, the current harmonic model is considered in details in the dq coordinate frame. Then the current harmonic observer design is proposed to estimate online all harmonic components. Based on the harmonic observer, current harmonics are suppressed to obtain accurate currents. Finally, we present some simulations and experiments to validate the algorithm performance.
Closure of schools during an influenza pandemic
Venue: The Lancet Infectious Diseases
Year: 2009

In response to WHO raising the influenza pandemic alert level from phase five to phase six, health officials around the world are carefully reviewing pandemic mitigation protocols. School closure (also called class dismissal in North America) is a non pharmaceutical intervention that is commonly suggested for mitigating influenza pandemics. Health officials taking the decision to close schools must weigh the potential health benefits of reducing transmission and thus case numbers against high economic and social costs, difficult ethical issues, and the possible disruption of key services such as health care. Also, if schools are expected to close as a deliberate policy option, or just because of high levels of staff absenteeism, it is important to plan to mitigate the negative features of closure. In this context, there is still debate about if, when, and how school closure policy should be used. In this Review, we take a multidisciplinary and holistic perspective and review the multiple aspects of school closure as a public health policy. Implications for the mitigation of the swine origin influenza A H1N1 pandemic are also discussed.
Global recycling services for short and long term risk reduction
Year: 2013

New schemes are being developed by AREVA in order to provide global solutions for safe and non proliferating management of used fuels, thereby significantly contributing to overall risks reduction and sustainable nuclear development. Utilities are thereby provided with a service through which they will be able to send their used fuels and only get returned vitrified and compacted waste, the only waste remaining after reprocessing. This waste is stable, standard and has demonstrated capability for very long term interim storage. They are provided as well with associated facilities and all necessary services for storage in a demonstrated safely manner. Recycled fuels, in particular MOX, would be used either in existing LWRs or in a very limited number of full MOX reactors (like the EPR reactor) located in selected countries, that will recycle MOX so as to downgrade the isotopic quality of the Pu inventories in a significant manner. Reprocessed uranium also can be recycled. These schemes, on top of offering demonstrated operational advantages and a responsible approach, result into optimized economics for all shareholders of the scheme, as part of reactor financing (under Opex or Capex form) will be secured thanks to the value of the recycled flows. It also increasesmore fuel cost predictability as recycled fuel is not subject to market fluctuations as much and allows, in a limited span of time, for clear risk mitigation. (authors) less
Efficient Purchase Pattern Clustering Based on SOM for Recommender System in u Commerce
Year: 2014

This paper proposes an efficient purchase pattern clustering method based on SOM(Self Organizing Map) for Personal Ontology Recommender System in u Commerce under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, it is necessary for us to keep clustering the user's information to join the user's score based on RFM factors using SOM network and the analysis of RFM to be able to reflect the attributes of the user in order to reflect frequently changing trends of purchase pattern by emphasizing the important users and items, and to improve better performance of recommendation. The proposed makes the task of an efficient purchase pattern clustering based on SOM for preprocessing so as to be possible to recommend by the loyalty of RFM factors as considering user's propensity. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.
a Synuclein and Parkinsonism: Updates and Future Perspectives
Venue: Current Neurology and Neuroscience Reports
Year: 2017

Mutations in the SNCA gene, which encodes the a synuclein protein, were the first discovered genetic causes of familial parkinsonism with Lewy pathology. To date, six different SNCA missense mutations as well as multiplications are known to cause parkinsonism. For this review, we performed a literature search to identify all published cases of SNCA related parkinsonism to provide an updated summary of the clinical and neuropathological features of parkinsonism due to SNCA mutations. Familial parkinsonism associated with SNCA is rare, but a synuclein aggregation is a core feature of sporadic parkinsonism, including Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. Research into a synuclein and parkinsonism has impacted how we define the pathology and understand the pathogenesis of Parkinson's disease and related neurodegenerative disorders. We briefly discuss some of the lessons we have learned from research into the physiological role of a synuclein and its pathological links to neurodegeneration and parkinsonism.
Stock Price Prediction Using a Multivariate Multistep LSTM: A Sentiment and Public Engagement Analysis Model
Venue: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)
Year: 2021

The impact of many factors on stock price has made the prediction of the stock market a problematic and highly complicated task to achieve. IoT analytics has enabled predictive analysis concerning the stock market, with internet search trends, reactions to current events, Twitter data, and historical stock returns as input data. Although inconsistencies remain as to which data sources are deemed most adequate, data preprocessing techniques have successfully overcome data integrity issues and unstructured data formats in specific applications. Additionally, advancements in computational power and machine learning technologies have led to the ability to handle tremendous amounts of information, accompanied by the growth of interest in this specific domain. In this paper, a Multivariate Multistep Output Long Short Term Memory (MMLSTM) model is proposed to provide a one week prediction on the stock close value for the technology company, "Apple Inc." with the stock name "AAPL" A large variety of data sources enabled by IoT platforms have been employed to model the impact of public sentiment and engagement on the closing price of this particular stock by looking at Google Search Trends, e News headlines, and Tweets involving AAPL and its products. The proposed MMLSTM has improved the Mean Square Error (MSE) of up to 65% compared to ARIMA and Random Forest models. In addition, the proposed MMLSTM has outperformed most of the LSTM models introduced in the literature.
Sindrome da dor abdominal funcional: dor abdominal constante ou frequentemente recorrente
Year: 2012

Presidente, Rome Foundation e Codiretor, UNC Center for Functional Gastrointestinal and Motility Disorders, Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill, Chapel Hill, New California, USA.Correspondencia: Ami D. Sperber, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva 84105,
Design of Power and Area Efficient Lower Part OR Approximate Multiplier
Venue: TENCON 2018 2018 IEEE Region 10 Conference
Year: 2018

Approximate computing has been paid attention as a promising technique to decrease power and area for error tolerant applications by simplifying the internal operations with sacrificing their accuracy. In this paper, a new power and area efficient approximate multiplier is proposed using OR based compressor with no carry propagation for lower bit positions and a carry propagation compressor with inexact half adders and full adders for upper bit positions. The proposal is effective to reduce the critical path delay with almost the same precision with previous methods. Firstly, an inexact half adder and an inexact full adder are proposed and a construction method of 4x4 multiplier is shown. Then, a construction method of 8x8 multiplier is proposed using OR based compressor, approximate 4x4 multipliers and an accurate 4x4 multiplier. The proposed construction method can also be applied to 16x16 multiplier. The accuracy loss of proposed multipliers is evaluated using MATLAB simulation and that of the proposed 8x8 multiplier is low as 0.20% the effect of which is shown to be negligible by applying to discrete cosine transform (DCT) inverse DCT and convolutional neural networks for image classification. The proposed 8x8 multiplier reduces power and area by 50.78% and 53.19% respectively, compared with the accurate Wallace tree multiplier when evaluated using SMIC 40nm process.
The Development of an Augmented Reality Framework for Constructing Circuit Learning Aids
Venue: ICHL
Year: 2014

The potential of Augmented Reality (AR) as an effective approach to support learning has been discovered in numerous research studies and practical cases today. With its power to enhance the expressiveness of the real world and its multiple platform implementation choices, AR brings new visions in the organization of virtual and physical resources, thus providing a powerful hybrid learning environment. While there exits popular open source and commercial SDKs for AR application development, a way for more agile and domain specific implementation is required by non professional developers as teachers. In this work, an AR framework was developed to support AR circuit construction. Core design issues such as object modeling, algorithms for circuit recognition and calculation were discussed, and the work flow was also illustrated by implementing a circuit demo with the framework.
Self Propelled Rods: Insights and Perspectives for Active Matter
Year: 2019

A wide range of experimental systems including gliding, swarming and swimming bacteria, in vitro motility assays as well as shaken granular media are commonly described as self propelled rods. Large ensembles of those entities display a large variety of self organized, collective phenomena, including formation of moving polar clusters, polar and nematic dynamic bands, mobility induced phase separation, topological defects and mesoscale turbulence, among others. Here, we give a brief survey of experimental observations and review the theoretical description of self propelled rods. Our focus is on the emergent pattern formation of ensembles of dry self propelled rods governed by short ranged, contact mediated interactions and their wet counterparts that are also subject to long ranged hydrodynamic flows. Altogether, self propelled rods provide an overarching theme covering many aspects of active matter containing well explored limiting cases. Their collective behavior not only bridges the well studied regimes of polar self propelled particles and active nematics, and includes active phase separation, but also reveals a rich variety of new patterns.
5 a day fruit and vegetable food product labels: reduced fruit and vegetable consumption following an exaggerated compared to a modest label
Venue: BMC Public Health
Year: 2018

BackgroundFood product labels based on the WHO 5 a day fruit and vegetable (FV) message are becoming increasingly common, but these labels may impact negatively on complementary or subsequent FV consumption. This study aimed to investigate the impact of a '3 of your 5 a day' versus a '1 of your 5 a day' smoothie product label on subsequent FV consumption.MethodsUsing an acute experimental design, 194 participants (90 males, 104 females) were randomised to consume a smoothie labelled as either '3 of your 5 a day' (N 97) or '1 of your 5 a day' (N 97) in full, following a usual breakfast. Subsequent FV consumption was measured for the rest of the day using 24 h recall. Usual FV consumption was also assessed via 24 h recall for the day before the study.ResultsRegression analyses revealed a significantly lower subsequent FV consumption following smoothies displaying the '3 of your 5 a day' label compared to the '1 of your 5 a day' label (Beta 0.15, p 0.04) Secondary analyses revealed these effects to be driven mainly by changes to consumption in usual high FV consumers, in females and in vegetable as opposed to fruit consumption.ConclusionsThese findings demonstrate a role for label information in food intake, and the potential negative impacts of an exaggerated food product label on healthy food consumption and healthy dietary profiles.
Predicting Occupancy Distributions of Walking Humans With Convolutional Neural Networks
Venue: IEEE Robotics and Automation Letters
Year: 2018

As robots are increasingly entering human environments, many subtleties of socially compliant navigation are still unsolved. To behave in a socially compliant way, robots need to have an understanding of the natural motion paths of humans in the shared environment. Humans intuitively follow social norms, which allows them to navigate smoothly even in crowded environments. For example, when humans enter a previously unseen building, they are still able to infer from their surroundings where humans would typically walk and use this information to obviate interference. In this letter, we propose an approach to learn such a predictive method. A robot could use this information to find nondisturbing waiting positions, avoid crowded areas, or clean heavily frequented areas more often. We propose the use of convolutional neural networks to predict average occupancy maps of walking humans even in environments where no human trajectory data are available. In experiments, we show that our method transfers from simulation to real world data and performs better than several baseline methods. We demonstrate the applicability on a real robot to find good waiting positions near narrow passages as well as a planner, which avoids areas where human interference is likely.
Practically Feasible Closed Loop Digital Predistortion for VCSEL MMF Based Radio over Fiber links
Year: 2020

The article demonstrates a novel Digital Predistortion (DPD) architecture for Mobile Front Haul links for the advanced Long Term Evolution (LTE) and upcoming 5G networks. Precisely, the use of a feedback approximation method has been proposed and experimentally demonstrated herein that simplifies the complexities in realizing the practical DPD technique for Multi Mode VCSELs and Multi Mode Fibers based Radio over Fiber systems. As a figure of merit, linearization efficiency is provided in terms of Adjacent Channel Power Ratio, Normalized Mean Square Error and Error Vector Magnitude referring to a complete LTE frame occupying 10 MHz with 256 QAM modulation format.
A Weakly Supervised Framework for COVID 19 Classification and Lesion Localization From Chest CT
Venue: IEEE Transactions on Medical Imaging
Year: 2020

Accurate and rapid diagnosis of COVID 19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning based model for automatic COVID 19 diagnosis on chest CT is helpful to counter the outbreak of SARS CoV 2. A weakly supervised deep learning framework was developed using 3D CT volumes for COVID 19 classification and lesion localization. For each patient, the lung region was segmented using a pre trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID 19 infectious; the COVID 19 lesions are localized by combining the activation regions in the classification network and the unsupervised connected components. 499 CT volumes were used for training and 131 CT volumes were used for testing. Our algorithm obtained 0.959 ROC AUC and 0.976 PR AUC. When using a probability threshold of 0.5 to classify COVID positive and COVID negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient's CT volume using a dedicated GPU. Our weakly supervised deep learning model can accurately predict the COVID 19 infectious probability and discover lesion regions in chest CT without the need for annotating the lesions for training. The easily trained and high performance deep learning algorithm provides a fast way to identify COVID 19 patients, which is beneficial to control the outbreak of SARS CoV 2. The developed deep learning software is available at https:/github.com/sydney0zq/covid 19 detection.
Rational, biologically based treatment of EGFR mutant non small cell lung cancer
Venue: Nature Reviews Cancer
Year: 2010

Epidermal growth factor receptor (EGFR) mutant non small cell lung cancer (NSCLC) was first recognized in 2004 as a distinct, clinically relevant molecular subset of lung cancer. The disease has been the subject of intensive research at both the basic scientific and clinical levels, becoming a paradigm for how to understand and treat oncogene driven carcinomas. Although patients with EGFR mutant tumours have increased sensitivity to tyrosine kinase inhibitors (TKIs) primary and acquired resistance to these agents remains a major clinical problem. This Review summarizes recent developments aimed at treating and ultimately curing the disease.
The structure and mechanism of the bacterial type II secretion system
Venue: Molecular microbiology
Year: 2020

The type II secretion system (T2SS) is a multi protein complex used by many bacteria to move substrates across their cell membrane. Substrates released into the environment serve as local and long range effectors that promote nutrient acquisition, biofilm formation, and pathogenicity. In both animals and plants, the T2SS is increasingly recognized as a key driver of virulence. The T2SS spans the bacterial cell envelope and extrudes substrates through an outer membrane secretin channel using a pseudopilus. An inner membrane assembly platform and a cytoplasmic motor controls pseudopilus assembly. This microreview focuses on the structure and mechanism of the T2SS. Advances in cryo electron microscopy are enabling increasingly elaborate sub complexes to be resolved. However, key questions remain regarding the mechanism of pseudopilus extension and retraction, and how this is coupled with the choreography of the substrate moving through the secretion system. The T2SS is part of an ancient type IV filament superfamily that may have been present within the last universal common ancestor (LUCA) Overall, mechanistic principles that underlie T2SS function have implication for other closely related systems such as the type IV and tight adherence pilus systems.
Elder Mistreatment: Abuse, Neglect, and Exploitation in an Aging America
Year: 2013

Since the late 1970s when Congressman Claude Pepper held widely publicized hearings on the mistreatment of the elderly, policy makers and practitioners have sought ways to protect older Americans from physical, psychological, and financial abuse. Yet, during the last 20 years fewer than 50 articles have addressed the shameful problem that abusersa "and sometimes the abused themselvesa "want to conceal. Elder Mistreatment in an Aging America takes a giant step toward broadening our understanding of the mistreatment of the elderly and recommends specific research and funding strategies that can be used to deepen it. The book includes a discussion of the conceptual, methodological, and logistical issues needed to create a solid research base as well as the ethical concerns that must be considered when working with older subjects. It also looks at problems in determination of a reporta (TM)s reliability and the role of physicians, EMTs, and others who are among the first to recognize situations of mistreatment. Elder Mistreatment in an Aging America will be of interest to anyone concerned about the elderly and ways to intervene when abuse is suspected, including family members, caregivers, and advocates for the elderly. It will also be of interest to researchers, research sponsors, and policy makers who need to know how to advance our knowledge of this problem.
La historia entre la sociologia y la filosofia de la ciencia: sociologia del conocimiento y 'nueva' historia de la ciencia en la historiografia de la psicologia latinoamericana a traves de la obra de Alberto Ramon Vilanova (1942 2003)
Year: 2018

Con el objetivo de caracterizar la relacion entre la investigacion historica en psicologia en Latinoamerica y otras meta ciencias, se analiza la recepcion de la 'nueva historiografia de la ciencia' y de la sociologia del conocimiento en un caso ejemplar de relevancia regional: la obra edita de Alberto Vilanova. Se llevo a cabo un estudio descriptivo, expost facto de tipo historiografico, con fase cuantitativa y cualitativa. Se relevo y sometio a un analisis de contenido a cada una de las fuentes que componen el conjunto de trabajos publicados por Vilanova (n 64) No se detectaron extensivas citas a sociologos o epistemologos de la psicologia en el corpus documental, aunque si transversalmente una conceptualizacion de la ciencia pasada y contemporanea como empresa y como cometido social.