nils-herrmann/clean_scirepeval huggingface dataset:
CO2, the greenhouse effect and global warming: from the pioneering work of Arrhenius and Callendar to today's Earth System Models.
Venue: Endeavour

Climate warming during the course of the twenty first century is projected to be between 1.0 and 3.7degC depending on future greenhouse gas emissions, based on the ensemble mean results of state of the art Earth System Models (ESMs) Just how reliable are these projections, given the complexity of the climate system? The early history of climate research provides insight into the understanding and science needed to answer this question. We examine the mathematical quantifications of planetary energy budget developed by Svante Arrhenius (1859 1927) and Guy Stewart Callendar (1898 1964) and construct an empirical approximation of the latter, which we show to be successful at retrospectively predicting global warming over the course of the twentieth century. This approximation is then used to calculate warming in response to increasing atmospheric greenhouse gases during the twenty first century, projecting a temperature increase at the lower bound of results generated by an ensemble of ESMs (as presented in the latest assessment by the Intergovernmental Panel on Climate Change) This result can be interpreted as follows. The climate system is conceptually complex but has at its heart the physical laws of radiative transfer. This basic, or "core" physics is relatively straightforward to compute mathematically, as exemplified by Callendar's calculations, leading to quantitatively robust projections of baseline warming. The ESMs include not only the physical core but also climate feedbacks that introduce uncertainty into the projections in terms of magnitude, but not sign: positive (amplification of warming) As such, the projections of end of century global warming by ESMs are fundamentally trustworthy: quantitatively robust baseline warming based on the well understood physics of radiative transfer, with extra warming due to climate feedbacks. These projections thus provide a compelling case that global climate will continue to undergo significant warming in response to ongoing emissions of CO2 and other greenhouse gases to the atmosphere.
REVISITING THE SCATTERING GREENHOUSE EFFECT OF CO2 ICE CLOUDS

Carbon dioxide ice clouds are thought to play an important role for cold terrestrial planets with thick CO2 dominated atmospheres. Various previous studies showed that a scattering greenhouse effect by carbon dioxide ice clouds could result in a massive warming of the planetary surface. However, all of these studies only employed simplified two stream radiative transfer schemes to describe the anisotropic scattering. Using accurate radiative transfer models with a general discrete ordinate method, this study revisits this important effect and shows that the positive climatic impact of carbon dioxide clouds was strongly overestimated in the past. The revised scattering greenhouse effect can have important implications for the early Mars, but also for planets like the early Earth or the position of the outer boundary of the habitable zone.
A new opinion of the greenhouse effect

The article analyzes the correspondence between the actually occurring physical and chemical processes and the concept of the greenhouse effect. The absorption of the solar radiation by the gases existing in the Earth's atmosphere has been examined. It was demonstrated that, despite the absorption of radiation from the Earth's surface in the middle and long wave infrared (IR) regions, there is a strong absorption of the overtones and combined frequencies of water vapor in the solar radiation (visible and near IR regions) i.e. the transmittance bandwidth of 'the glass greenhouse' Thus, the Earth atmosphere does not really function as a greenhouse, and the terms 'greenhouse effect' and 'greenhouse gases' lost their original meaning and have remained symbolical.
The super greenhouse effect in a changing climate

AbstractIn all outputs of the 1% yr 1 increase in CO2 climate model experiments archived under the World Climate Research Programme's (WCRP) phase 5 of the Coupled Model Intercomparison Project (CMIP5) regions exist in the low latitudes where both the clear sky and all sky OLR decrease with surface warming. These are identified as regions of positive longwave feedback and are regions of a super greenhouse effect (SGE) These SGE regions are identified from feedback analysis of the 4 x CO2 abrupt experiments of CMIP5, and despite their existence, there is little agreement across models as to the magnitude of the effect. The general effects of clouds on the SGE are to amplify the clear sky SGE, but there is also poor agreement on the magnitude of the amplification that varies by an order of magnitude across models. Sensitivity analyses indicate that localized SGE regions are spatially aligned with a large moistening of the upper troposphere. The reduction in clear sky OLR arises from a reduction in emissio.
Optimization based locomotion planning, estimation, and control design for the atlas humanoid robot
Venue: Auton. Robots

This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments. To make challenging locomotion tasks tractable, we describe several novel applications of convex, mixed integer, and sparse nonlinear optimization to problems ranging from footstep placement to whole body planning and control. We also present a state estimator formulation that, when combined with our walking controller, permits highly precise execution of extended walking plans over non flat terrain. We describe our complete system integration and experiments carried out on Atlas, a full size hydraulic humanoid robot built by Boston Dynamics, Inc.
Design of a Momentum Based Control Framework and Application to the Humanoid Robot Atlas
Venue: Int. J. Humanoid Robotics

This paper presents a momentum based control framework for floating base robots and its application to the humanoid robot "Atlas" At the heart of the control framework lies a quadratic program that reconciles motion tasks expressed as constraints on the joint acceleration vector with the limitations due to unilateral ground contact and force limited grasping. We elaborate on necessary adaptations required to move from simulation to real hardware and present results for walking across rough terrain, basic manipulation, and multi contact balancing on sloped surfaces (the latter in simulation only) The presented control framework was used to secure second place in both the DARPA Robotics Challenge Trials in December 2013 and the Finals in June 2015.
Walking on partial footholds including line contacts with the humanoid robot atlas
Venue: 2016 IEEE RAS 16th International Conference on Humanoid Robots (Humanoids)

We present a method for humanoid robot walking on partial footholds such as small stepping stones and rocks with sharp surfaces. Our algorithm does not rely on prior knowledge of the foothold, but information about an expected foothold can be used to improve the stepping performance. After a step is taken, the robot explores the new contact surface by attempting to shift the center of pressure around the foot. The available foothold is inferred by the way in which the foot rotates about contact edges and/or by the achieved center of pressure locations on the foot during exploration. This estimated contact area is then used by a whole body momentum based control algorithm. To walk and balance on partial footholds, we combine fast, dynamic stepping with the use of upper body angular momentum to regain balance. We applied this method to the Atlas humanoid designed by Boston Dynamics to walk over small contact surfaces, such as line and point contacts. We present experimental results and discuss performance limitations.
Incarcerating Youth With Mental Health Problems

Mental health problems among youth in the juvenile justice system are of particular concern given their high prevalence rate. The current study applies attribution theory and focal concerns to examine how mental health problems influence the judicial decision to commit youth to confinement. Furthermore, the study examines whether the effect of mental health problems is conditioned by race and ethnicity, hypothesizing that minorities with mental health problems will be treated more severely than Whites with mental health problems. Using administrative court records from Maricopa County, AZ (n 5,501) findings reveal that mental health problems increased the likelihood of confinement, and this effect was moderated by race. Implications for theory and policy are discussed.
Origami Robot: A Self Folding Paper Robot With an Electrothermal Actuator Created by Printing
Venue: IEEE/ASME Transactions on Mechatronics

A piece of paper has many useful characteristics; it is affordable, lightweight, thin, strong, and highly absorbent. These features allow inexpensive and flexible devices to be fabricated easily and rapidly. We have proposed a new field, "paper mechatronics," which merges printed robotics and paper electronics, and to realize electronic and mechanical systems by printing. Herein, we develop a method to print an actuator and a structure on a sheet of paper. A trilayer electrothermal actuator is printed to activate a printed robot. The paper self folds along the printed pattern to form the three dimensional (3 D) structure of the robot body. We also investigate important factors necessary to develop a printed robot. Experiments, including finite element analysis (FEA) confirm our bimetal modeling assumption for the printed actuator and improve the locomotive ability. The key factors in self folding are paper thickness and humidity. Our findings can improve the reliability of printed robot designs. A self folding A7 sized paper robot demonstrates locomotion at 10 mm per step.
Adaptive Neural Network Control of an Uncertain Robot With Full State Constraints
Venue: IEEE Transactions on Cybernetics

This paper studies the tracking control problem for an uncertain {n} link robot with full state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) control for the robotic system with full state constraints is designed. In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances. The Moore Penrose inverse term is employed in order to prevent the violation of the full state constraints. A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed loop system. The control performance of the closed loop system is guaranteed by appropriately choosing the design parameters. Simulation studies are performed to illustrate the effectiveness of the proposed control.
The action research cycle reloaded: Conducting action research across buyer supplier relationships

Buyer supplier relationships in purchasing and supply chain management practice are instrumental and often "messy" Indeed, the buyer and the supplier generally interact while pursuing their own interest, which are often subject to change over time. The action research method can help address the complexity of buyer supplier relationships, generating important theoretical insights and relevant managerial implications. First, action research helps the researcher to better understand the problem by integrating diverse perspectives. Second, it allows the researcher to influence the buyer supplier relationship directly, providing mutually beneficial solutions. This study proposes action research as a suitable interactive method that could complement other methodologies in the field of purchasing and supply chain management, as well as in other fields. An expanded action research framework the "action research cycle reloaded" is proposed and the role of the action researcher in the buyer supplier context is discussed. The framework is applied to study the design and implementation of a supplier performance measurement system in the banking industry.
Can the Biomedical Research Cycle be a Model for Political Science?
Venue: Perspectives on Politics

In sciences such as biomedicine, researchers and journal editors are well aware that progress in answering difficult questions generally requires movement through a research cycle: Research on a topic or problem progresses from pure description, through correlational analyses and natural experiments, to phased randomized controlled trials (RCTs) In biomedical research all of these research activities are valued and find publication outlets in major journals. In political science, however, a growing emphasis on valid causal inference has led to the suppression of work early in the research cycle. The result of a potentially myopic emphasis on just one aspect of the cycle reduces incentives for discovery of new types of political phenomena, and more careful, efficient, transparent, and ethical research practices. Political science should recognize the significance of the research cycle and develop distinct criteria to evaluate work at each of its stages.
Establishing Interventions via a Theory Driven Single Case Design Research Cycle

Recent studies have suggested single case design (SCD) intervention research is subject to publication bias, wherein studies are more likely to be published if they possess large or statistically significant effects and use rigorous experimental methods. The nature of SCD and the purposes for which it might be used could suggest that large effects and rigorous methods should not always be expected. The purpose of the current paper is to propose and describe a theory driven cycle of SCD intervention research. The proposed SCD specific cycle serves several purposes including (a) defining the purposes for which SCD research might be adopted, (b) specifying the types of evidence to be collected in establishing an intervention for applied use, and (c) illustrating the phases of SCD based intervention research (i.e. development, efficacy, effectiveness, contextualization, and implementation) The proposed model is intended to serve as an intermediary between theory and research, facilitating the consideration of theory driven expectations when selecting particular SCDs (e.g. reversal, multiple baseline) and interpreting SCD data. Next steps and future directions for research are included.
Channel capacity improvement for cooperative MIMO wireless sensor networks via adaptive MIMO SVD
Venue: 2016 HONET ICT

Over the past decade, wireless sensor networks (WSNs) have gained more research attention for their potential applications in, e.g. healthcare, defence and environmental monitoring. However, limited channel capacity and battery life of sensors are the main design challenges in WSNs. The use of cooperative multiple input multiple output (MIMO) technology is one of the most promising techniques that can enhance the channel capacity and reduce transmission energy in WSNs. In this paper, we propose an adaptive MIMO singular value decomposition (SVD) method in order to enhance the channel capacity of WSNs, in the case of imperfect channel state information (CSI) The SVD is conditioned to provide efficient space time precoding/decoding with the use of either least square (LS) or minimum mean square error (MMSE) filtering. Channel capacity is improved yet further by way of optimized power allocation, via a water filling strategy. The bit error rate (BER) and channel capacity performances of the encoding methods are evaluated for Rayleigh fading channels. Our simulation results show that the adaptive MIMO SVD technique works best with MMSE channel estimation for cooperative MIMO in WSNs.
Optimizing distributed actor systems for dynamic interactive services
Venue: EuroSys

Distributed actor systems are widely used for developing interactive scalable cloud services, such as social networks and on line games. By modeling an application as a dynamic set of lightweight communicating "actors" developers can easily build complex distributed applications, while the underlying runtime system deals with low level complexities of a distributed environment. We present ActOp a data driven, application independent runtime mechanism for optimizing end to end service latency of actor based distributed applications. ActOp targets the two dominant factors affecting latency: the overhead of remote inter actor communications across servers, and the intra server queuing delay. ActOp automatically identifies frequently communicating actors and migrates them to the same server transparently to the running application. The migration decisions are driven by a novel scalable distributed graph partitioning algorithm which does not rely on a single server to store the whole communication graph, thereby enabling efficient actor placement even for applications with rapidly changing graphs (e.g. chat services) Further, each server autonomously reduces the queuing delay by learning an internal queuing model and configuring threads according to instantaneous request rate and application demands. We prototype ActOp by integrating it with Orleans a popular open source actor system [4, 13] Experiments with realistic workloads show latency improvements of up to 75% for the 99th percentile, up to 63% for the mean, with up to 2x increase in peak system throughput.
Situating the default mode network along a principal gradient of macroscale cortical organization
Venue: Proceedings of the National Academy of Sciences

We describe an overarching organization of large scale connectivity that situates the default mode network at the opposite end of a spectrum from primary sensory and motor regions. This topography, based on the differentiation of connectivity patterns, is also embedded in the spatial distance along the cortical surface between these respective systems. In addition, this connectivity gradient accounts for the respective positions of canonical networks and captures a functional spectrum from perception and action to more abstract cognitive functions. These results suggest that the default mode network consists of regions at the top of a representational hierarchy that describe the current cognitive landscape in the most abstract terms. Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default mode network (DMN) These DMN regions exhibit the greatest geodesic distance along the cortical surface and are precisely equidistant from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.
Quantum Image Steganography and Steganalysis Based On LSQu Blocks Image Information Concealing Algorithm

Quantum steganography can solve some problems that are considered inefficient in image information concealing. It researches on Quantum image information concealing to have been widely exploited in recent years. Quantum image information concealing can be categorized into quantum image digital blocking, quantum image stereography, anonymity and other branches. Least significant bit (LSB) information concealing plays vital roles in the classical world because many image information concealing algorithms are designed based on it. Firstly, based on the novel enhanced quantum representation (NEQR) image uniform blocks clustering around the concrete the least significant Qu block (LSQB) information concealing algorithm for quantum image steganography is presented. Secondly, a clustering algorithm is proposed to optimize the concealment of important data. Finally, we used Con Steg algorithm to conceal the clustered image blocks. Information concealing located on the Fourier domain of an image can achieve the security of image information, thus we further discuss the Fourier domain LSQu block information concealing algorithm for quantum image based on Quantum Fourier Transforms. In our algorithms, the corresponding unitary Transformations are designed to realize the aim of concealing the secret information to the least significant Qu block representing color of the quantum cover image. Finally, the procedures of extracting the secret information are illustrated. Quantum image LSQu block image information concealing algorithm can be applied in many fields according to different needs.
The pair wise LSB matching steganography with a discrete quantum behaved Gravitational Search Algorithm
Venue: J. Intell. Fuzzy Syst.

This paper presents a discrete quantum behaved gravitational search algorithm (DQGSA) which is used to provide a novel steganographic algorithm based on the spatial domain: Least Significant Bits (LSB) This approach improves the LSB matching method proposed by Mielikaien. Whereas Mielikaien's method uses a binary function to reduce the number of changed pixel values, in suggested method a three score system is proposed to assess the performance of different orders for LSB matching. Afterward, the proposed DQGSA is employed to search for an optimal solution among all the permutation orders. By employing the proposed method, the distortion of the stegano image could be reduced while the probability of detection is decreased. The experimental results show that the proposed method achieves better performance than Mielikainen's pair wise LSB matching method in less distortion (means higher visual quality) and more preservation against steganalysis.