nils-herrmann/clean_scirepeval huggingface dataset:
Multi Objective Reinforcement Learning using Sets of Pareto Dominating Policies
Year: 2014

Many real world problems involve the optimization of multiple, possibly conicting objectives. Multi objective reinforcement learning (MORL) is a generalization of standard reinforcement learning where the scalar reward signal is extended to multiple feedback signals, in essence, one for each objective. MORL is the process of learning policies that optimize multiple criteria simultaneously. In this paper, we present a novel temporal dierence learning algorithm that integrates the Pareto dominance relation into a reinforcement learning approach. This algorithm is a multi policy algorithm that learns a set of Pareto dominating policies in a single run. We name this algorithm Pareto Q learning and it is applicable in episodic environments with deterministic as well as stochastic transition functions. A crucial aspect of Pareto Q learning is the updating mechanism that bootstraps sets of Q vectors. One of our main contributions in this paper is a mechanism that separates the expected immediate reward vector from the set of expected future discounted reward vectors. This decomposition allows us to update the sets and to exploit the learned policies consistently throughout the state space. To balance exploration and exploitation during learning, we also propose three set evaluation mechanisms. These three mechanisms evaluate the sets of vectors to accommodate for standard action selection strategies, such as greedy. More precisely, these mechanisms use multi objective evaluation principles such as the hypervolume measure, the cardinality indicator and the Pareto dominance relation to select the most promising actions. We experimentally validate the algorithm on multiple environments with two and three objectives and we demonstrate that Pareto Q learning outperforms current state of the art MORL algorithms with respect to the hypervolume of the obtained policies. We note that (1) Pareto Q learning is able to learn the entire Pareto front under the usual assumption that each state action pair is suciently sampled, while (2) not being biased by the shape of the Pareto front. Furthermore, (3) the set evaluation mechanisms provide indicative measures for local action selection and (4) the learned policies can be retrieved throughout the state and action space.
Uplink Fractional Power Control and Downlink Power Allocation for Cell Free Networks
Venue: IEEE Wireless Communications Letters
Year: 2020

This letter proposes respective policies for uplink power control and downlink power allocation in cell free wireless networks. Both policies rely only on large scale quantities and are expressed in closed form, being therefore scalable. The uplink policy, which generalizes the fractional power control employed extensively in cellular networks, features a single parameter; by adjusting this parameter, the SIR distribution experienced by the users can be compressed or expanded, trading average performance for fairness. The downlink policy dualizes the uplink solution, featuring two parameters that again allow effecting a tradeoff between average performance and fairness.
The world systemic network of global elite sociology: the western male monoculture at faculties of the top one hundred sociology departments of the world
Venue: Scientometrics
Year: 2020

In this paper, we analyze the educational paths and networks of core staff members n 3325) of the world's top 100 sociology departments. Results show that a significant overrepresentation of central countries and considerable gender bias can be found throughout sociology departments with strong male dominance in high positions. By using an improved word systemic model for the interpretation of our data, we were able to categorize the main agents and patients in the world system of global elite sociology, and we could also describe those centripetal and centrifugal forces that absorb and reeducate peripheral talent while excluding those without Western reeducation.
Developing a reference model for human robot interaction
Venue: International Journal on Interactive Design and Manufacturing (IJIDeM)
Year: 2019

The use of collaborative robots is on the rise for human centered automation for developing flexible production systems. The deployment of collaborative robots and distribution of tasks between human and robot carries several challenges. One challenge is assessing the right level of human engagement with the fellow robot in the collaborative work while remaining in line with safety limitations and cycle time constraints. This study explores various types and levels of interactions between humans and robots in a manufacturing domain. A synthesizing architecture of human robot collaboration is suggested based on three dimensions of team composition, level of engagement and safety. The architecture describes the collaboration using a 3 dimensional reference scale.
Interface engineering for CVD graphene: current status and progress.
Venue: Small
Year: 2014

In the past decade, graphene and graphene like 2D materials have drawn more and more attention in both academia and industry due to their fascinating properties. As an atomically thin 2D layered material, graphene has extremely high environmental susceptibility, that is, its properties are strongly affected by its surroundings. In this review, the current status and progress in graphene interface engineering are systematically discussed, including the interface between graphene (carbon sources) and an underlying growth substrate (catalyst) the interface between graphene and a supporting layer during a transfer process, as well as the interface between graphene and a modified substrate from the viewpoint of device applications. These key techniques involved in graphene synthesis, transfer, and device substrates can be further applied to other related 2D layered materials such as MoS2 Moreover, by combining 2D crystals in one particular stack, 2D based heterostructures with desired functionalities can be achieved, which opens up a new avenue for the future applications of 2D layered materials.
The rise and rise of meritocracy
Year: 2006

Acknowledgements. Notes on Contributors.  Reviewing Meritocracy: Geoff Dench. Part I: Origin and reception. The Labour Party as crucible: Asa Briggs. Meritocracy in the civil service, 1853 1970: Jon Davis. A tract for the times: Paul Barker. We sat down at the table of privilege and complained about the food: Hilary Land. The chequered career of a cryptic concept: Claire Donovan. Looking back on Meritocracy: Michael Young. Part II: Relevance to modern Britain. A brief profile of the new British establishment: Jim Ogg. Face, race and place: Merit and ethnic minorities: Michelynn Lafleche. Marginalised young men: Yvonne Roberts. The unmaking of the English working class: Ferdinand Mount. Age and inequality: Eric Midwinter. Ship of state in peril: Peregrine Worsthorne. Part III: Analytic value. The moral economy of meritocracy: Irving Louis Horowitz. Japan at the meritocracy frontier: From here, where? Takehiko Kariya and Ronald Dore. Just rewards: Meritocracy fifty years later: Peter Marris. What do we mean by talent? Richard Sennett. Resolving the conflict between family and meritocracy: Belinda Brown. Meritocracy and popular legitimacy: Peter Saunders. Part IV: The future. The new assets agenda: Andrew Gamble and Rajiv Prabhakar. New Labour and the withering away of the working class? Jon Cruddas. A delay on the road to meritocracy: Peter Wilby. Putting social contribution back into merit: Geoff Dench. Ladder of opportunity or engine of inequality? Ruth Lister. The future of meritocracy: David Willetts. Chapter notes. Bibliography. Notes on Contributors. Index.
Affect, cognition, superstition: Novel approaches to examining cancer risk perceptions in cancer prevention and control
Year: 2007

ED08 01 This talk draws from recent behavioral science and decision making research to offer novel solutions to colorectal cancer screening non adherence. I will first discuss research and theory asserting the importance of the perception of being at risk for colorectal cancer as an important prerequisite for screening adherence over and above the presence of epidemiological risk factors, such as family colorectal cancer history. Accordingly, many cancer prevention interventions aim to increase individuals' subjective appreciation of their own cancer risks in order to motivate cancer screening. I will next review recent research arguing that affective (emotional, feeling based) and intuitive cognitive (automatic, non rational) processing is important as individuals' think about their personal cancer risk. However, measurement strategies for cancer risk perceptions have not been revised to encompass this recent research. Assessment of affective and intuitive cognitive processing of risk may shed light on the manner in which cancer risk information is used especially among those who are non adherent to screening after physician recommendation. I will describe our qualitative and quantitative program of research aimed at validating a strategy to measure affective and intuitive cognitive processing of personal cancer risk (Cancer Risk Beliefs Scale) in diverse general population cohorts. I will present our findings regarding the reliability of the Cancer Risk Beliefs Scale, relationships with other key cognitive and behavioral variables, and preliminary findings relating the Cancer Risk Beliefs Scale to adherence with colorectal cancer screening. This program of research is aimed toward enhancing our understanding of diverse strategies for processing cancer risk and prevention messages, and providing the groundwork for new interventions to address screening non adherence for this highly preventable and treatable cancer.
The orbit deployment strategy of OOS system for refueling near earth orbit satellites
Venue: Acta Astronautica
Year: 2019

Extend lifetime of near earth orbit satellites by on orbit refueling has significant economic benefits, while the orbit deployment strategy of the on orbit servicing (OOS) system which implements the refueling task directly affects the efficiency of on orbit refueling mission. Three orbit deployment modes were proposed, including ground deployment, low earth orbit (LEO) deployment and the same orbit deployment. For the client satellites to be refueled on near earth orbit including LEO, medium earth orbit (MEO) and geosynchronous orbit (GEO) the three deployment modes were compared quantitatively based on two evaluation indicators: the refueling response time and the economic benefits. The comparing results showed that for MEO and GEO client satellites, it is appropriate to adopt the same orbit deployment mode, while for LEO client satellites, ground deployment mode is more suitable. As the maximum economic benefits can be earned to refuel GEO client satellites, the orbit deployment scheme of OOS system for refueling GEO client satellites was further studied. A new architecture of OOS system called "1+N" consist of 1 fuel storage station and N refueling vehicles was proposed. The fuel storage station carries a great deal of fuel, running on orbit steadily. The refueling vehicles maneuver on GEO and implement the refueling operations for GEO client satellites. If the vehicles run out of fuel, they arrive at the station to be refueled. The weight and orbital altitude of fuel storage station, the number, weight, orbital altitude and orbital phase of refueling vehicles are the key parameters of the orbit deployment scheme. The mathematical model for optimizing the scheme was constructed, which takes shorten the refueling response time and lower the cost of OOS system as multi objective. The calculation process of the optimization was explained. By analyzing the optimization results, the optimal orbit deployment scheme of OOS system containing 1 fuel storage station and 4 to 6 refueling vehicles was proposed.
An Enhanced PSO Based Clustering Energy Optimization Algorithm for Wireless Sensor Network
Venue: TheScientificWorldJournal
Year: 2016

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO Based Clustering Energy Optimization (EPSO CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.
Effects of Distance Measure Choice on K Nearest Neighbor Classifier Performance: A Review
Venue: Big Data
Year: 2019

The K nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested examples and the training examples. This raises a major question about which distance measures to be used for the KNN classifier among a large number of distance and similarity measures available? This review attempts to answer this question through evaluating the performance (measured by accuracy, precision, and recall) of the KNN using a large number of distance measures, tested on a number of real world data sets, with and without adding different levels of noise. The experimental results show that the performance of KNN classifier depends significantly on the distance used, and the results showed large gaps between the performances of different distances. We found that a recently proposed nonconvex distance performed the best when applied on most data sets comparing with the other tested distances. In addition, the performance of the KNN with this top performing distance degraded only ~20% while the noise level reaches 90% this is true for most of the distances used as well. This means that the KNN classifier using any of the top 10 distances tolerates noise to a certain degree. Moreover, the results show that some distances are less affected by the added noise comparing with other distances.
Introduction to Partial Differential Equations for Scientists and Engineers Using Mathematica
Year: 2013

Fourier Series The Fourier Series of a Periodic Function Convergence of Fourier Series Integration and Differentiation of Fourier Series Fourier Sine and Fourier Cosine Series Mathematica Projects Integral Transforms The Fourier Transform and Elementary Properties  Expansion Singular Sturm Liouville Problem: Legendre's Equation Singular Sturm Liouville Problem: Bessel's Equation Partial Differential Equations Basic Concepts and Definitions Formulation of Initial and Boundary Problems Classification of Partial Differential Equations Some Important Classical Linear Partial Differential Equations The Principle of Superposition First Order Partial Differential Equations Linear Equations with Constant Coefficients Linear Equations with Variable Coefficients First Order Non Linear Equations Cauchy's Method of Characteristics Mathematica Projects Hyperbolic Partial Differential Equations The Vibrating String and Derivation of the Wave Equation Separation of Variables for the Homogeneous Wave Equation D'Alambert's Solution of the Wave Equation Inhomogeneous Wave Equations Solution of the Wave Equation by Integral Transforms Two Dimensional Wave Equation: Vibrating Membrane The Wave Equation in Polar and Spherical Coordinates Numerical Solutions of the Wave Equation Mathematica Projects Parabolic Partial Differential Equations Heat Flow and Derivation of the Heat Equation Separation of Variables for the One Dimensional Heat Equation Inhomogeneous Heat Equations Solution of the Heat Equation by Integral Transforms Two Dimensional Heat Equation The Heat Equation in Polar and Spherical Coordinates Numerical Solutions of the Heat Equation Mathematica Projects Elliptic Partial Differential Equations The Laplace and Poisson Equations Separation of Variables for the Laplace Equation The Laplace Equation in Polar and Spherical Coordinates Poisson Integral Formula Numerical Solutions of the Laplace
The mCME Project: A Randomized Controlled Trial of an SMS Based Continuing Medical Education Intervention for Improving Medical Knowledge among Vietnamese Community Based Physicians' Assistants
Venue: PloS one
Year: 2016

Community health workers (CHWs) provide critical services to underserved populations in low and middle income countries, but maintaining CHW's clinical knowledge through formal continuing medical education (CME) activities is challenging and rarely occurs. We tested whether a Short Message Service (SMS) based mobile CME (mCME) intervention could improve medical knowledge among a cadre of Vietnamese CHWs (Community Based Physician's Assistants CBPAs) who are the leading providers of primary medical care for rural underserved populations.  The mCME Project was a three arm randomized controlled trial. Group 1 served as controls while Groups 2 and 3 experienced two models of the mCME intervention. Group 2 (passive model) participants received a daily SMS bullet point, and were required to reply to the text to acknowledge receipt; Group 3 (interactive model) participants received an SMS in multiple choice question format addressing the same thematic area as Group 2, entering an answer (A, B, C or D) in their response. The server provided feedback immediately informing the participant whether the answer was correct. Effectiveness was based on standardized examination scores measured at baseline and endline (six months later) Secondary outcomes included job satisfaction and self efficacy.  CBPAs were enrolled, randomized, and tested at baseline, with 592 returning at endline (93.7% Baseline scores were similar across all three groups. Over the next six months, participation of Groups 2 and 3 remained high; they responded to >75% of messages. Group 3 participants answered 43% of the daily SMS questions correctly, but their performance did not improve over time. At endline, the CBPAs reported high satisfaction with the mCME intervention, and deemed the SMS messages highly relevant. However, endline exam scores did not increase over baseline, and did not differ between the three groups. Job satisfaction and self efficacy scores also did not improve. Average times spent on self study per week did not increase, and the kinds of knowledge resources used by the CBPAs did not differ between the three groups; textbooks, while widely available, were seldom used.  The SMS based mCME intervention, while feasible and acceptable, did not result in increased medical knowledge. We hypothesize that this was because the intervention failed to stimulate lateral learning. For an intervention of this kind to be effective, it will be essential to find more effective ways to couple SMS as a stimulus to promote increased self study behaviors.
Design and implementation of modern greenhouse remote monitoring system based on the Android system
Venue: 2015 34th Chinese Control Conference (CCC)
Year: 2015

According to the demand of modern greenhouse information management, a greenhouse remote monitoring system was developed based on Android. This system consists of local monitoring module, server and Android client. The local monitoring module accomplishes data acquisition and device control through CAN bus, and interacts with Android client via the MySQL database. Media streaming server is developed using Real Time Streaming Protocol (RTSP) to achieve the greenhouse remote video monitoring. Android client with user friendly interfaces can realize real time data display, historical data query, parameter setting, device control, video monitoring etc. Using this system, people can realize real time and long distance scientific management of greenhouse. And the labor intensity can be reduced, what is more, the level of greenhouse automation monitoring can be improved.
Magnetocaloric effect in Ni Mn X (X Ga, In, Sn, Sb) Heusler alloys
Year: 2011

A review is given of experimental and theoretical works concerning the investigation of magnetic and structural phase transitions and of the magnetocaloric effect in Ni Mn X (X Ga, In, Sn, Sb) Heusler alloys possessing unique properties, such as the existence of coupled magnetostructural and metamagneto structural phase transitions, giant magnetocaloric effect, shape memory effect in the ferromagnetic state, giant magnetodeformation and magnetoresistance, exchange anisotropy. A conclusion is made that the Heusler alloys, because of their unique properties, are promising for the application in various engineering devices, including technology of magnetic refrigeration.
Debris Disks: Structure, Composition, and Variability
Year: 2018

Debris disks are tenuous, dust dominated disks commonly observed around stars over a wide range of ages. Those around main sequence stars are analogous to the Solar System's Kuiper Belt and Zodiacal light. The dust in debris disks is believed to be continuously regenerated, originating primarily with collisions of planetesimals. Observations of debris disks provide insight into the evolution of planetary systems; the composition of dust, comets, and planetesimals outside the Solar System; as well as placing constraints on the orbital architecture and potentially the masses of exoplanets that are not otherwise detectable. This review highlights recent advances in multiwavelength, high resolution scattered light and thermal imaging that have revealed a complex and intricate diversity of structures in debris disks, and discusses how modeling methods are evolving with the breadth and depth of the available observations. Two rapidly advancing subfields highlighted in this review include observations of atomic and molecular gas around main sequence stars, and variations in emission from debris disks on very short (days to years) timescales, providing evidence of non steady state collisional evolution particularly in young debris disks.
A Novel Approach to Address Process Plant Layout based on a Bacterial Genetic Optimization Algorithm
Year: 2019

The process plant layout is a determining factor for the efficient and secure operation of it. In this sense, define the best position of each process unit contributes to reducing transport costs and the explosion and fire risk. This issue has been addressed in the past by different methods like expert criteria, simulation, and optimization. In this contribution, a hybrid bacterial genetic optimization algorithm for process plant layout is introduced, which solves a mathematical model that minimize interconnection cost and land cost. In addition, the model takes into consideration facilities dimensions, minimal distances among facilities, and minimum distance up to the property boundary. The performance evaluation was carried out with a case of study of literature, and the results expose the advantages of this proposal in performance and time.