nils-herrmann/clean_scirepeval huggingface dataset:
Influence of overexpression of the true and false alternative transcripts of calcium dependent protein kinase CPK9 and CPK3a genes on the growth, stress tolerance, and resveratrol content in Vitis amurensis cell cultures
Venue: Acta Physiologiae Plantarum

Calcium dependent protein kinases (CDPKs or CPKs) are important Ca2+ sensors in plants implicated in plant abiotic and biotic stress responses, e.g. in drought induced stomata closure or pathogen induced stilbene production. We previously found that the VaCPK9 gene of Vitis amurensis was subjected to unusual post transcriptional processing mediated by canonical splice sites leading to production of three mRNA isoforms that lacked important catalytic kinase subdomains. We also detected numerous unusual VaCPK9 and VaCPK3a cDNAs possessing short direct repeated sequences instead of canonical splice sites and suggested that these cDNAs were false alternative transcripts generated by reverse transcriptase template switching in vitro. The present study has been attempted to determine if the full length and spliced CDPKs play a role in the grapevine growth regulation, stilbene biosynthesis, and abiotic stress adaptation. We generated transgenic V. amurensis callus cultures overexpressing different true and false VaCPK9 and VaCPK3a transcripts under control of the CaMV 35S promoter. Overexpression of the full length VaCPK9 and VaCPK3a transcripts slightly increased biomass accumulation but did not affect resistance to abiotic stresses and biosynthesis of resveratrol (3,5,4' trihydroxy trans stilbene) in the calli. Overexpression of the true alternative VaCPK9SF1, VaCPK9SF2, and VaCPK9SF3 transcripts considerably improved growth of the transgenic calli, while overexpression of the false alternative VaCPK3aSF1, VaCPK3aSF2, and VaCPK3aSF3 transcripts did not considerably influence it. The results show that the VaCPK9 and VaCPK3a genes function in growth regulation of V. amurensis but not in stilbene biosynthesis and abiotic stress tolerance. Alternative splicing of VaCPK9 may serve to reinforce the positive effect of VaCPK9 expression on biomass accumulation.
Concentration of measure without independence: a unified approach via the martingale method
Venue: ArXiv

The concentration of measure phenomenon may be summarized as follows: a function of many weakly dependent random variables that is not too sensitive to any of its individual arguments will tend to take values very close to its expectation. This phenomenon is most completely understood when the arguments are mutually independent random variables, and there exist several powerful complementary methods for proving concentration inequalities, such as the martingale method, the entropy method, and the method of transportation inequalities. The setting of dependent arguments is much less well understood. This chapter focuses on the martingale method for deriving concentration inequalities without independence assumptions. In particular, we use the machinery of so called Wasserstein matrices to show that the Azuma Hoeffding concentration inequality for martingales with almost surely bounded differences, when applied in a sufficiently abstract setting, is powerful enough to recover and sharpen several known concentration results for nonproduct measures. Wasserstein matrices provide a natural formalism for capturing the interplay between the metric and the probabilistic structures, which is fundamental to the concentration phenomenon.
Data driven haptic perception for robot assisted dressing
Venue: 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO MAN)

Dressing is an important activity of daily living (ADL) with which many people require assistance due to impairments. Robots have the potential to provide dressing assistance, but physical interactions between clothing and the human body can be complex and difficult to visually observe. We provide evidence that data driven haptic perception can be used to infer relationships between clothing and the human body during robot assisted dressing. We conducted a carefully controlled experiment with 12 human participants during which a robot pulled a hospital gown along the length of each person's forearm 30 times. This representative task resulted in one of the following three outcomes: the hand missed the opening to the sleeve; the hand or forearm became caught on the sleeve; or the full forearm successfully entered the sleeve. We found that hidden Markov models (HMMs) using only forces measured at the robot's end effector classified these outcomes with high accuracy. The HMMs' performance generalized well to participants (98.61% accuracy) and velocities (98.61% accuracy) outside of the training data. They also performed well when we limited the force applied by the robot (95.8% accuracy with a 2N threshold) and could predict the outcome early in the process. Despite the lightweight hospital gown, HMMs that used forces in the direction of gravity substantially outperformed those that did not. The best performing HMMs used forces in the direction of motion and the direction of gravity.
Data Driven Haptic Modeling and Rendering of Viscoelastic and Frictional Responses of Deformable Objects
Venue: IEEE Transactions on Haptics

In this paper, we present an extended data driven haptic rendering method capable of reproducing force responses during pushing and sliding interaction on a large surface area. The main part of the approach is a novel input variable set for the training of an interpolation model, which incorporates the position of a proxy an imaginary contact point on the undeformed surface. This allows us to estimate friction in both sliding and sticking states in a unified framework. Estimating the proxy position is done in real time based on simulation using a sliding yield surface a surface defining a border between the sliding and sticking regions in the external force space. During modeling, the sliding yield surface is first identified via an automated palpation procedure. Then, through manual palpation on a target surface, input data and resultant force data are acquired. The data are used to build a radial basis interpolation model. During rendering, this input output mapping interpolation model is used to estimate force responses in real time in accordance with the interaction input. Physical performance evaluation demonstrates that our approach achieves reasonably high estimation accuracy. A user study also shows plausible perceptual realism under diverse and extensive exploration.
Stimuli Magnitude Adaptive Sample Selection for Data Driven Haptic Modeling
Venue: Entropy

Data driven haptic modeling is an emerging technique where contact dynamics are simulated and interpolated based on a generic input output matching model identified by data sensed from interaction with target physical objects. In data driven modeling, selecting representative samples from a large set of data in a way that they can efficiently and accurately describe the whole dataset has been a long standing problem. This paper presents a new algorithm for the sample selection where the variances of output are observed for selecting representative input output samples in order to ensure the quality of output prediction. The main idea is that representative pairs of input output are chosen so that the ratio of the standard deviation to the mean of the corresponding output group does not exceed an application dependent threshold. This output and standard deviation based sample selection is very effective in applications where the variance or relative error of the output should be kept within a certain threshold. This threshold is used for partitioning the input space using Binary Space Partitioning tree (BSP tree) and k means algorithms. We apply the new approach to data driven haptic modeling scenario where the relative error of the output prediction result should be less than a perceptual threshold. For evaluation, the proposed algorithm is compared to two state of the art sample selection algorithms for regression tasks. Four kinds of haptic related behavior force datasets are tested. The results showed that the proposed algorithm outperformed the others in terms of output approximation quality and computational complexity.
Model based FDIR and Fault Accommodation for a Rendezvous Mission around the Mars Planet: the Mars Sample Return Case

The research work presented in the paper addresses the design of a model based fault diagnosis and fault recovery system for any faults occurring in the actuator and sensor units of the chaser spacecraft of the ESA Mars Sample Return (MSR) mission. Key features of the proposed method are the use of a parity space and covariance based strategy with jointly a H observer for fault diagnosis of sensor faults, a H/H filter for robust fault detection of actuator faults and a bank of unknown input observers jointly used with a dot product of vectors strategy for actuator faults. For fault accommodation, a "retreat" FDIR strategy scheduled by the FDI unit, is retained. The proposed FDIR architecture obeys to a hierarchical one and fits the industrial requirements. Especially, it is compliant with the Aurora avionics architecture. A simulation campaign, based on a nonlinear high fidelity simulator developed by GMV space and Thales Alenia Space industries, is conducted under highly realistic conditions.
Modeling and fault tolerant control of an electro hydraulic actuator

In the modern industry, electro hydraulic actuators (EHAs) have been applied to various applications for precise position pressure/ force control tasks. However, operating EHAs under sensor faults is one of the critical challenges for the control engineers. For its enormous nonlinear characteristics, sensor fault could lead the catastrophic failure to the overall system or even put human life in danger. Thus in this paper, a study on mathematical modeling and fault tolerant control (FTC) of a typical EHA for tracking control under sensor fault conditions has been carried out. In the proposed FTC system, the extended Kalman Bucy unknown input observer (EKBUIO) based robust sensor fault detection and identification (FDI) module estimates the system states and the time domain fault information. Once a fault is detected, the controller feedback is switched from the faulty sensor to the estimated output from the EKBUIO owing to mask the sensor fault swiftly and retains the system stability. Additionally, considering the tracking accuracy of the EHA system, an efficient brain emotional learning based intelligent controller (BELBIC) is suggested as the main control unit. Effectiveness of the proposed FTC architecture has been investigated by experimenting on a test bed using an EHA in sensor failure conditions.
Highly efficient composite electrolyte for natural gas fed fuel cell

Solid oxide fuel cells (SOFCs) have the ability to operate with different variants of hydro carbon fuel such as biogas, natural gas, methane, ethane, syngas, methanol, ethanol, hydrogen and any other hydrogen rich gas. Utilization of these fuels in SOFC, especially the natural gas, would significantly reduce operating cost and would enhance the viability for commercialization of FC technology. In this paper, the performance of two indigenously manufactured nanocomposite electrolytes; barium and samarium doped ceria (BSDC carbonate) and lanthanum and samarium doped ceria (co precipitation method LSDC carbonate) using natural gas as fuel is discussed. The nanocomposite electrolytes were synthesized using co precipitation and wet chemical methods (here after referred to as nano electrolytes) The structure and morphology of the nano electrolytes were examined by X ray diffraction (XRD) and scanning electron microscopy (SEM) The fuel cell performance (OCV) was tested at temperature (300 600 degC) The ionic conductivity of the nano electrolytes were measured by two probe DC method. The detailed composition analysis of nano electrolytes was performed with the help of Raman Spectroscopy. Electrochemical study has shown an ionic conductivity of 0.16 Scm 1 at 600 degC for BSDC carbonate in hydrogen atmosphere, which is higher than conventional electrolytes SDC and GDC under same conditions. In this article reasonably good ionic conductivity of BSDC carbonate, at 600 degC, has also been achieved in air atmosphere which is comparatively greater than the conventional SDC and GDC electrolytes.
That's not my place: The interacting effects of faultlines, subgroup size, and social competence on social loafing behaviour in work groups

Taking a social relations approach to team diversity, we propose that faultlines hypothetical dividing lines that split a team into relatively homogeneous subgroups impact the team members' propensity to exhibit social loafing behaviour. We further propose that this individual level reaction to the faultline structure of the team can differ among team members, depending on the size of their subgroup and on their individual social competence. We tested the assumptions with a sample of real teams working on an intellective task during which social loafing behaviour was video coded. In partial support of the hypotheses, generalized mixed models revealed that social loafing behaviour was most common for team members who were part of a team with strong faultlines, who belonged to the larger subgroup in their team, and who exhibited low levels of social competence. The results therefore highlight the benefits of taking a multilevel approach to the effects of faultlines.
When Does Virtuality Really "Work" Examining the Role of Work Family and Virtuality in Social Loafing

We sought to clarify the relationship between virtuality and social loafing by exploring two work family moderators family responsibility and dissimilarity in terms of family responsibility and two mediators cohesion and psychological obligation in two studies. We expected that "busy teams" (i.e. comprising similar individuals with many family responsibilities) would exhibit the strongest positive virtuality social loafing relationship, and teams comprising similar individuals with few family responsibilities would experience a weaker virtuality social loafing relationship. We expected that individuals working with dissimilar others would report consistently high levels of social loafing regardless of virtuality. Furthermore, we expected cohesion and psychological obligation to one's teammates would mediate these effects. Similar individuals in teams indeed exhibited different virtuality social loafing relationships in both studies, suggesting that the flexibility provided by virtuality might be more effective in teams comprising similar people with few family responsibilities. Study 2 further revealed that cohesion and obligation may mediate these effects, such that high levels of these mediators were associated with low levels of social loafing in similar teams comprising people with few family responsibilities. We discuss contributions to the virtual work and social loafing literatures, as well as the work family and team literatures. We also suggest several specific actions managers can take on the basis of these findings, including for employees with few versus many family responsibilities.
Investigating the Impact of 'Emphasis Frames' and Social Loafing on Player Motivation and Performance in a Crowdsourcing Game
Venue: CHI

With an increasing reliance on crowdsourcing games as data gathering tools, it is imperative to understand how to motivate and sustain high levels of voluntary contribution. To this end, the present work directly compared the impact of various "emphasis frames," highlighting distinct intrinsic motivational factors, used to describe an online game in which players provide descriptive metadata "tags" for digitized images. An initial study showed that, compared to frames emphasizing personal enjoyment or altruistic motivations, a frame emphasizing a "growing community of players" solicited significantly fewer contributions. A second study tested the hypothesis that this lower level of contribution resulted from social loafing (the tendency to exert less effort in collective tasks in which contributions are anonymous and pooled) Results revealed that, compared to a no frame control condition, a frame emphasizing the preponderance of other players reduced contribution levels and game replay likelihood, whereas a frame emphasizing the scarcity of fellow players increased contribution and replay levels. Various strategies for counteracting social loafing in crowdsourcing contexts are discussed.
Teaching About Social Loafing

Many classroom groups have difficulties with individuals who do not do their fair share of the workload. The experience of "social loafing" is widespread and often not well handled. The Accounting Team exercise was developed to examine this issue. Classroom groups are given the case of six team members of an accounting firm and asked to identify how to deal with a social loafer and another member who was not loafing, but had a performance problem. As an added twist, one group member has been given a slightly different case instructing him/her to be a social loafer in the group discussion. This is revealed in the debrief and discussed, followed by a short lecture/handout on "Addressing Social Loafing." Finally, the group is asked to identify ways they can reduce social loafing in their own classroom group.
Participation in Team Sports Can Eliminate the Effect of Social Loafing
Venue: Perceptual and motor skills

The effect known as Ringelmann effect states that as group size increases, individual behavior may be less productive. If this decrease in productivity in groups is attributed to a decrement in individual motivation, it is called social loafing. We tested hypotheses that the collectivism associated with participation in team sports would reduce the level of social loafing compared to people who were not involved in team sports. In one experiment, participants (n 72; M age 21.7 years, SD 2.0) had to pull a rope individually and collectively. Groups of two, three, four, and six persons were formed from among individuals with no previous sports experience, and of those who had engaged in individual and team sports. For each team, the sum of individual achievements of the individuals constituting a team was computed. This sum served as the anticipated result (expected value) The expected values were later compared to the actual achievements, i.e. the value achieved by the whole team. The results of the study suggested that previous experience in collective (team) sports eliminated the effect of social loafing.
Yawning Detection Using Embedded Smart Cameras
Venue: IEEE Transactions on Instrumentation and Measurement

Yawning detection has a variety of important applications in a driver fatigue detection, well being assessment of humans, driving behavior monitoring, operator attentiveness detection, and understanding the intentions of a person with a tongue disability. In all of the above applications, an automatic detection of yawning is one important system component. In this paper, we design and implement such automatic system, using computer vision, which runs on a computationally limited embedded smart camera platform to detect yawning. We use a significantly modified implementation of the Viola Jones algorithm for face and mouth detections and, then, use a backprojection theory for measuring both the rate and the amount of the changes in the mouth, in order to detect yawning. As proof of concept, we have also implemented and tested our system on top of an actual smart camera embedded platform, called APEX from CogniVue Corporation. In our design and implementations, we took into consideration the practical aspects that many existing works ignore, such as real time requirements of the system, as well as the limited processing power, memory, and computing capabilities of the embedded platform. Comparisons with existing methods show significant improvements in the correct yawning detection rate obtained by our proposed method.
A New DVFS Algorithm Design for Multi core Processor Chip
Venue: NCCET

With the development of the CMOS process, beyond 3 billion of transistors are integrated on chip. But the increasing power density becomes a serious problem making the performance improvement slow down. Therefore, how to optimize the power consumption of multi core processor is a crisis in processor design. This paper proposes a dual threshold adaptive DVFS algorithm to dynamically control the processor voltage and frequency. Comparing with traditional single threshold algorithm, experimental results show that dual threshold adaptive DVFS can save more power with no obviously performance reduction. The performance of most benchmarks is beyond 90% of the original performance, while the power optimization can be up to 35%
Application aware traffic scheduling for workload offloading in mobile clouds
Venue: IEEE INFOCOM 2016 The 35th Annual IEEE International Conference on Computer Communications

Mobile Cloud Computing (MCC) bridges the gap between limited capabilities of mobile devices and the increasing complexity of mobile applications, by offloading the computational workloads from local devices to the cloud. Current research supports workload offloading through appropriate application partitioning and remote method execution, but generally ignores the impact of wireless network characteristics on such offloading. Wireless data transmissions incurred by remote method execution consume a large amount of additional energy during transmission intervals when the network interface stays in the high power state, and deferring these transmissions increases the response delay of mobile applications. In this paper, we adaptively balance the tradeoff between energy efficiency and responsiveness of mobile applications by developing application aware wireless transmission scheduling algorithms. We take both causality and run time dynamics of application method executions into account when deferring wireless transmissions, so as to minimize the wireless energy cost and satisfy the application delay constraint with respect to the practical system contexts. Systematic evaluations show that our scheme significantly improves the energy efficiency of workload offloading over realistic smartphone applications.
Understanding the Anatomy of Data Driven Business Models towards an Empirical Taxonomy
Venue: ECIS

As a consequence of the increasing digitization, massive amounts of data are created every day. While scholars and practitioners suggest that organizations can use this data to develop new data driven business models, many organizations struggle to systematically develop such models. A fundamental challenge in this regard is presented by the limited research on data driven business models. Accordingly, the goal of this research is to better understand data driven business models by identifying key dimensions that can be used to distinguish them and to develop a taxonomy. As our taxonomy aims to guide future studies in a way that ultimately serves organizations, it is based on dimensions regarded to be most relevant from the practitioners' perspective. To develop this taxonomy, we utilize an established empirical approach based on a combination of multidimensional scaling (MDS) property fitting (ProFit) and qualitative data. Our results reveal that the most important dimensions distinguish data driven business models based on the data source utilized, the target audience, and the technological effort required. Based on these dimensions, our taxonomy distinguishes eight ideal typical categories of data driven business models. By providing an increased understanding regarding the topic, our results form the foundation for subsequent investigations in this new field of research.