nils-herrmann/clean_scirepeval huggingface dataset:
Automatic extrinsic calibration for lidar stereo vehicle sensor setups
Venue: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2017

Sensor setups consisting of a combination of 3D range scanner lasers and stereo vision systems are becoming a popular choice for on board perception systems in vehicles; however, the combined use of both sources of information implies a tedious calibration process. We present a method for extrinsic calibration of lidar stereo camera pairs without user intervention. Our calibration approach is aimed to cope with the constraints commonly found in automotive setups, such as low resolution and specific sensor poses. To demonstrate the performance of our method, we also introduce a novel approach for the quantitative assessment of the calibration results, based on a simulation environment. Tests using real devices have been conducted as well, proving the usability of the system and the improvement over the existing approaches. Code is available at http:/wiki.ros.org/velo2cam_calibration.
Keypoints into the Future: Self Supervised Correspondence in Model Based Reinforcement Learning
Venue: ArXiv
Year: 2020

Predictive models have been at the core of many robotic systems, from quadrotors to walking robots. However, it has been challenging to develop and apply such models to practical robotic manipulation due to high dimensional sensory observations such as images. Previous approaches to learning models in the context of robotic manipulation have either learned whole image dynamics or used autoencoders to learn dynamics in a low dimensional latent state. In this work, we introduce model based prediction with self supervised visual correspondence learning, and show that not only is this indeed possible, but demonstrate that these types of predictive models show compelling performance improvements over alternative methods for vision based RL with autoencoder type vision training. Through simulation experiments, we demonstrate that our models provide better generalization precision, particularly in 3D scenes, scenes involving occlusion, and in category generalization. Additionally, we validate that our method effectively transfers to the real world through hardware experiments. Videos and supplementary materials available at this https URL
Disease Specific Induced Pluripotent Stem Cells
Venue: Cell
Year: 2008

Tissue culture of immortal cell strains from diseased patients is an invaluable resource for medical research but is largely limited to tumor cell lines or transformed derivatives of native tissues. Here we describe the generation of induced pluripotent stem (iPS) cells from patients with a variety of genetic diseases with either Mendelian or complex inheritance; these diseases include adenosine deaminase deficiency related severe combined immunodeficiency (ADA SCID) Shwachman Bodian Diamond syndrome (SBDS) Gaucher disease (GD) type III, Duchenne (DMD) and Becker muscular dystrophy (BMD) Parkinson disease (PD) Huntington disease (HD) juvenile onset, type 1 diabetes mellitus (JDM) Down syndrome (DS)/trisomy 21, and the carrier state of Lesch Nyhan syndrome. Such disease specific stem cells offer an unprecedented opportunity to recapitulate both normal and pathologic human tissue formation in vitro, thereby enabling disease investigation and drug development.
Analysis and Treatment of Low Ammonia Pressure in 330 MW Boiler Denitrification System
Year: 2012

Reasons of frequently occurred low ammonia pressure at the inlet of evaporator during the operation of 330MW Boiler SCR denitrification system in Guohua Huizhou power plant,are analyzed,which reduced the efficiency of denitrification.Measures to solve the problem by the operation manipulation are proposed,which resumed the ammonia pressure at the inlet of denitrification evaporator to its design value.Control measures has been made and executed based on the analysis of impurity's chemical component and occurrence reason in the evaporator,which guaranteed the stablilty operation of denitrification system.
Using QSPM and WASPAS methods for determining outsourcing strategies
Year: 2014

The aim of the current study is to select the best strategies for outsourcing development, applying decision making tools that enable to make reasoned decision. To evaluate outsourcing strategies, a new model of Comprehensive Framework for Strategy Formulation and Selection is presented. Development of outsourcing of healthcare services in Tehran is analysed in the paper. Strengths, Weaknesses, Opportunities, and Threats analysis is used to evaluate the internal and external factors affecting the outsourcing of healthcare services in health deputy of the Tehran University of Medical Sciences in different aspects and to develop several possible outsourcing strategies. Quantitative Strategic Planning Matrix is applied for quantitative evaluation of strategies. It is proposed to validate the approach by applying Multiple Criteria Decision Making methods. Robust and accurate Weighted Aggregated Sum Product Assessment method is selected and applied for quantitative evaluation of strategies. Based on the consistency of the results of the both approaches, the five best strategies to develop healthcare services outsourcing are offered.
Road Traffic State Identification and Prediction Based on Mobile Phone Signaling
Year: 2015

A method of road traffic state identification and prediction based on mobile phone signaling was proposed, which is basically able to meet the demands of intelligent transportation construction on wide area, comprehensive, real time and dynamic data. It is a reliable way to acquire real time traffic information which is capable of providing traffic condition query and statistical warning analysis. In view of its advantages of less investment and lower maintenance cost, it is suitable to be quickly popularized and applied in second and third tier cities on a large scale.
Energy efficiency in sleep mode for 5G femtocells
Venue: 2017 Wireless Days
Year: 2017

Energy efficiency is a major requirement for next generation mobile networks both as an end to reduce operational expenses and to increase the systems' ecological friendliness. Another integral part of 5G networks is the increased density of the deployment of small radius base stations, such as femtocells. Based on the design principle that demands a system to be active and transmitting only when and where it is needed, we evaluate the energy savings harvested when sleep mode techniques are enforced in dense femtocell deployments. We present our novel variations of sleep mode combined with hybrid access strategies and we estimate capacity and energy benefits. Our simulations show significant advantages in performance and energy efficiency.
New insights into the mechanisms of polyphenols beyond antioxidant properties; lessons from the green tea polyphenol, epigallocatechin 3 gallate
Venue: Redox biology
Year: 2014

Green tea is rich in polyphenol flavonoids including catechins. Epigallocatechin 3 gallate (EGCG) is the most abundant and potent green tea catechin. EGCG has been extensively studied for its beneficial health effects as a nutriceutical agent. Based upon its chemical structure, EGCG is often classified as an antioxidant. However, treatment of cells with EGCG results in production of hydrogen peroxide and hydroxyl radicals in the presence of Fe (III) Thus, EGCG functions as a pro oxidant in some cellular contexts. Recent investigations have revealed many other direct actions of EGCG that are independent from anti oxidative mechanisms. In this review, we discuss these novel molecular mechanisms of action for EGCG. In particular, EGCG directly interacts with proteins and phospholipids in the plasma membrane and regulates signal transduction pathways, transcription factors, DNA methylation, mitochondrial function, and autophagy to exert many of its beneficial biological actions.
Pengaruh Size, Return On Assets Dan Financial Leverage Pada Tingkat Underpricing Penawaran Ssham Perdana Di Bursa Efek Indonesia Periode 2011 2016
Year: 2017

Penelitian ini bertujuan untuk mengetahui pengaruh size, return on assets (ROA) dan financial leverage terhadap tingkat underpricing. Data perusahaan diperoleh di Bursa Efek Indonesia (BEI) dengan menggunakan objek penelitian perusahaan yang melakukan initial public offering (IPO) pada periode tahun 2011 sampai dengan 2016. Metode analisis dalam penelitian ini menggunakan metode analisis regresi berganda. Metode penentuan sampel menggunakan purposive sampling yang menghasilkan 70 perusahaan yang melakukan initial public offering (IPO) pada tahun 2011 sampai dengan 2016. Penelitian ini menggunakan data sekunder perusahaan yang meliputi offering price, closing price, total aset, total hutang, total ekuitas dan laba bersih. Hasil penelitian ini menunjukan bahwa variabel financial leverage memiliki pengaruh yang signifikan terhadap tingkat underpricing, sedangkan variabel size dan return on assets (ROA) tidak memiliki pengaruh yang signifikan terhadap tingkat underpricing.
Multivariate Financial Time Series Prediction With Certified Robustness
Venue: IEEE Access
Year: 2020

The futures market's forecasts are significant to investors and policymakers, where the application of deep learning approaches to finance has received a great deal of attention. In this study, we propose a multivariate financial time series forecasting method. Our model addresses the long and short term features, multimodal and non stationarity nature of multivariate time series by incorporating the improved deep neural networks and certified noise injection. Specifically, multimodal variational autoencoder is used to extract deep high level features of multivariate time series, Long and Short Term recurrent neural network is applied for multivariate time series forecasting, and certified noise injection mechanism, inspired by differential privacy, is proposed to improve the robustness and accuracy of prediction. Extensive empirical results on real world agricultural commodity futures price time series and relevant external data demonstrate that our model achieves better performance over that of several state of the art baseline methods.
A Morphogenetic Approach to Self Reconfigurable Modular Robots using a Hybrid Hierarchical Gene Regulatory Network
Venue: ALIFE
Year: 2010

In this paper, we present a morphogenetic approach to selfreconfiguration of a lattice based simulated modular robot, CrossCube, under dynamic environments. A hybrid hierarchical controller inspired by the embryonic development of multi cellular organisms is proposed to form different patterns for modular robots to adapt to environmental changes. The first layer is a rule based controller to generate a number of appropriate target patterns (i.e. configurations) for various environments. The second layer is a gene regulatory network (GRN) based controller to coordinate the modules of CrossCube to transform from its current pattern to the target pattern. This hybrid hierarchical control framework is distributed in the sense that each module makes its own decisions based on its local perception. The global behavior of modular robots emerges from the local interactions with the environment and between the modules. The simulation results demonstrate that the proposed system is efficient and robust in adaptively reconfiguring modular robots to adapt to the changing environment.
Histogram Based Number Plate Recognition with Template Matching for Automatic Granting
Year: 2015

Automatic Vehicle number plate recognition (AVNR) is a very popular and highly demanding in transportation industry, law and enforcement such as automating fines for traffic rule violations, automating parking lots, entry and exit in highways, quickly identifying granted vehicles. Most of the AVNRs use image processing techniques such as image enhancement, restoration, segmentations, block based character recognition, optical character recognition and template matching. This paper presents the design and implementation of such a system to General Sir John Kotelawala Defence University (KDU) to capture and recode vehicle logs. People face difficulties at the gate premises due to the existing manual system. To collect the data, qualitative research techniques such as interviews and observations were used. Once an image captured, median filters were used to remove noises. Sharpen filters used to detect edges. Camera calibration was used to correct the perspective view. Lines were detected using Hough transformation and, rectangles were identified. Histogram processing is used to identify the number plates. Template matching with different letters, numbers, and special character were used to recognize its content. Once a license plate is found, its figures are recognized, displayed on the user interface and checked in the database for grants. Moreover arrival or departure times are being recorded. MATLAB was used to develop the prototype together with MySQL. Two cameras with 720x480 resolution and an i5 laptop with 4GB RAM is used to test the system. Accuracy of the system was over 84% Accuracy was differing under different muddy conditions. As a quantitative research technique data, such as light conditions, distance from vehicle to camera have collected and analysed. Findings show that some of the factors like non uniform vehicle number plates, language specific characters such as "Sri" and dash, are challenging in AVNR. Post research questioners have proven that the system is very useful and time saving methods for KDU and also its reliability.  Processing, Camera Calibration, Histogram
[Animal models of fulminant hepatic failure]
Venue: Nutricion hospitalaria
Year: 2007

Fulminant hepatic failure (FHF) is a very serious clinical sindrome that, in spite of the important therapeutical advances that have taken place in the last years by means of bioartifical hepatic support devices and hepatic transplantation, is still associated to a high mortality. Knowledge and treatment of the FHF have been limited by the lack of satisfactory animal models. Among the attempts to develop a suitable model are surgical models, such as hepatectomy and total and/or partial devascularization, or the use of chemical substances with hepatic toxicity, such as acetaminophen, azoximethane, galactosamine or thioacetamide, among others. However, most of these models do not adequatly reflect the pattern of the human disease and all of them present important limitations. Although viral hepatitis is one of the most frequent causes of FHF, the use of viral agents to develop animal models has been little and unfortunate. Our group has recently developed a viral animal model of FHF by means of the inoculation of rabbits with the virus of the rabbit hemorrhagic disease. This model displays biochemical, and histological characteristics, and clinical signs that ressemble those in human FHF. In the present article, the most widely used animal models of FHF, together with their main advantages and disadvantages, are presented.
Hierarchical geometry modelling using the immersed boundary method
Venue: Computer Methods in Applied Mechanics and Engineering
Year: 2019

The Immersed Boundary Method has been used to simulate a range of fundamental flows and turbulence. Such studies have demonstrated the method's promising applicability for engineering analysis. However, to the authors' knowledge, flows in coupled components or in scenarios with coupled physics, such as rotor stator interaction, fan intake interaction, aeroelastics, aeroacoustics, etc. are still rarely investigated using high fidelity methods. Due to its high computational costs, the complexity of geometry meshing process and the requirement for moving boundaries limit the investigation of flows in such environments. Previous research suggests that high fidelity simulations with an acceptable geometry modelling strategy may tackle these issues and provide useful insights. There exists a hierarchy of geometry modelling methods which includes the conventional Directly Mesh Resolving (DMR) method, the Immersed Boundary Method (IBM) and the IBM with Smeared Geometry (IBMsg, or eIBMg) The present research proposes an alternative to these approaches in the form of the Euler IBM with local force (eIBMl) by imposing a distribution function generated from blade configuration. Compared to the eIBMg, this method can include more realistic flow physics within each blade passage without smearing its geometry. This method is applied to the study of fan intake interaction focusing on the transport of inlet distortion through blade passages and pressure wave propagation.
PeRView: A Framework for Personalized Review Selection Using Micro Reviews
Venue: ArXiv
Year: 2018

In the contemporary era, social media has its influence on people in making decisions. The proliferation of online reviews with diversified and verbose content often causes problems inaccurate decision making. Since online reviews have an impact on people of all walks of life while taking decisions, choosing appropriate reviews based on the podsolization consisting is very important since it relies on using such micro reviews consistency to evaluate the review set section. Micro reviews are very concise and directly talk about product or service instead of having unnecessary verbose content. Thus, micro reviews can help in choosing reviews based on their personalized consistency that is related to directly or indirectly to the main profile of the reviews. Personalized reviews selection that is highly relevant with high personalized coverage in terms of matching with micro reviews is the main problem that is considered in this paper. Furthermore, personalization with user preferences while making review selection is also considered based on the personalized users' profile. Towards this end, we proposed a framework known as PeRView for personalized review selection using micro reviews based on the proposed evaluation metric approach which considering two main factors (personalized matching score and subset size) Personalized Review Selection Algorithm (PRSA) is proposed which makes use of multiple similarity measures merged to have highly efficient personalized reviews matching function for selection. The experimental results based on using reviews dataset which is collected from YELP.COM while micro reviews dataset is obtained from Foursqure.COM. show that the personalized reviews selection is a very empirical case of study.
Teaching Virtually: Strategies and Challenges in the 21st Century Online Classroom
Venue: Int. J. Online Pedagog. Course Des.
Year: 2014

Enrollment in K 12 online learning continues to grow at an exponential rate throughout the United States. With this increase, one of the key factors for ensuring quality educational experiences for students is the knowledge, skills, and dispositions of their teachers. To address issues of online pedagogy, this study reports practices from a group of K 12 online teachers from a cyber charter school located in the desert southwest of the United States. Results show that online teachers use web based programs, internal communication systems, and/or a combination of these types of tools to provide clear instruction, while also encouraging student participation, engaging students, and clearly communicating learning goals. Challenges found with online teaching include dealing with a heavy workload, garnering parental support, coping with high student teacher ratios, and learning "the job." This article calls for additional support for virtual teachers, including coursework and field experiences within teacher education programs that address the needs of K 12 online educators.
Click Models for Web Search
Venue: Click Models for Web Search
Year: 2015

With the rapid growth of web search in recent years the problem of modeling its users has started to attract more and more attention of the information retrieval community. This has several motivations. By building a model of user behavior we are essentially developing a better understanding of a user, which ultimately helps us to deliver a better search experience. A model of user behavior can also be used as a predictive device for non observed items such as document relevance, which makes it useful for improving search result ranking. Finally, in many situations experimenting with real users is just infeasible and hence user simulations based on accurate models play an essential role in understanding the implications of algorithmic changes to search engine results or presentation changes to the search engine result page. In this survey we summarize advances in modeling user click behavior on a web search engine result page. We present simple click models as well as more complex models aimed at.
Indian Instrument Identification from Polyphonic Audio using KNN Classifier
Venue: 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)
Year: 2019

Classical Indian Music is considered as a very contrast and unique area of music across the globe. It has its own different melodies made up from Indian musical instruments which consists a wide range of variety in it. This paper basically deals with the Indian based musical instruments and presents an actual study on feature analysis for Indian musical instrument identification using machine learning techniques and to select and evaluate features extracted from a number of various feature scheme. All effort is employed to improve the different parts of the recognition system and our main work is linked to the features of the musical Instruments and the classification scheme includes K Nearest Neighbor (KNN) The obtained results using 7 Indian based musical instruments exhibit the good discrimination capabilities of the method.
Common Factors Underlying the Five Facets of Mindfulness and Proposed Mechanisms: a Psychometric Study Among Meditators and Non meditators
Year: 2020

This study investigated whether common factors underlie the established mindfulness facets, as assessed by the Five Facet Mindfulness Questionnaire (FFMQ) and some of the mechanisms, which have been previously proposed to explain the beneficial effects of mindfulness on mental health.  Multigroup exploratory structural equation models (ESEM) were fitted to samples of non meditators and meditators (total N 3265) to (1) identify the number of factors that underlie the facets and mechanisms of mindfulness, (2) establish measurement invariance, and (3) conduct path analyses to determine the associations of extracted factors with psychological symptoms.  Five measurement invariant common factors were found to underlie the mechanisms and facets of mindfulness. The FFMQ facets loaded distinctly, but none of them highest, on these common factors. The common factors represented different ways of focusing, dealing with distress, and relating towards one's own thoughts, feelings, emotions, and body sensations. Three of the common factors appeared to specifically reflect meditation experience. The FFMQ facets accounted for less variance of depression, anxiety, somatization, and stress scores than marker scales of the five common factors, all of which derived from the proposed mechanisms.  The common factors appear to be elements of the supporting mechanisms and psychological faculties of mindfulness. Their existence may explain the mutual interrelations between mechanisms and self reported mindfulness but also suggests that self reported mindfulness may not be factorially distinct from its assumed mechanisms. Longitudinal studies as well as behavioral data are needed to probe the generalizability and causality of these psychometric results.