nils-herrmann/clean_scirepeval huggingface dataset:
Cooperation among competitors: A comparison of cost sharing mechanisms

In this paper, we investigate the consequences of using outcome based versus ex ante based cost sharing mechanisms in terms of competing firms' profitability and total welfare. We consider two firms making a joint expenditure, which can positively affect firms' demand and/or unit operating costs, while competing in the final market by setting either price or quantity. We compare two outcome based cost sharing mechanisms, i.e. Quantity Proportional (QP) and Total Margin proportional (TM) with the more competitive Fixed Share (FS) mechanism where cost sharing is set up on an ex ante basis. We show that outcome based mechanisms, and even a fully collusive behavior induced by the optimal cost sharing mechanism, might actually enhance total welfare as compared with the more competitive FS mechanism. We also find that, although the FS mechanism is never more preferable than the TM mechanism, it can lead to higher profits than the QP mechanism when competition is mild. These results can support firms cooperating with competitors in the choice of the cost sharing mechanism as well as provide important implications to policy makers.
A Comparative Study of the Performance of HMM, DNN, and RNN based Speech Synthesis Systems Trained on Very Large Speaker Dependent Corpora
Venue: SSW

This study investigates the impact of the amount of training data on the performance of parametric speech synthesis systems. A Japanese corpus with 100 hours' audio recordings of a male voice and another corpus with 50 hours' recordings of a female voice were utilized to train systems based on hidden Markov model (HMM) feed forward neural network and recurrent neural network (RNN) The results show that the improvement on the accuracy of the predicted spectral features gradually diminishes as the amount of training data increases. However, different from the "diminishing returns" in the spectral stream, the accuracy of the predicted F0 trajectory by the HMM and RNN systems tends to consistently benefit from the increasing amount of training data.
Model Integration for HMM and DNN Based Speech Synthesis Using Product of Experts Framework
Venue: INTERSPEECH

In this paper, we propose a model integration method for hidden Markov model (HMM) and deep neural network (DNN) based acoustic models using a product of experts (PoE) framework in statistical parametric speech synthesis. In speech parameter generation, DNN predicts a mean vector of the probability density function of speech parameters frame by frame while keeping its covariance matrix constant over all frames. On the other hand, HMM predicts the covariance matrix as well as the mean vector but they are fixed within the same HMM state, i.e. they can actually vary state by state. To make it possible to predict a better probability density function by leveraging advantages of individual models, the proposed method integrates DNN and HMM as PoE, generating a new probability density function satisfying conditions of both DNN and HMM. Furthermore, we propose a joint optimization method of DNN and HMM within the PoE framework by effectively using additional latent variables. We conducted objective and subjective evaluations, demonstrating that the proposed method significantly outperforms the DNN based speech synthesis as well as the HMM based speech synthesis.
Convolutional Sparse Coding for High Dynamic Range Imaging
Venue: Comput. Graph. Forum

Current HDR acquisition techniques are based on either (i) fusing multibracketed, low dynamic range (LDR) images, (ii) modifying existing hardware and capturing different exposures simultaneously with multiple sensors, or (iii) reconstructing a single image with spatially varying pixel exposures. In this paper, we propose a novel algorithm to recover high quality HDRI images from a single, coded exposure. The proposed reconstruction method builds on recently introduced ideas of convolutional sparse coding (CSC) this paper demonstrates how to make CSC practical for HDR imaging. We demonstrate that the proposed algorithm achieves higher quality reconstructions than alternative methods, we evaluate optical coding schemes, analyze algorithmic parameters, and build a prototype coded HDR camera that demonstrates the utility of convolutional sparse HDRI coding with a custom hardware platform.
A simplex social spider algorithm for solving integer programming and minimax problems
Venue: Memetic Comput.

In this paper, we propose a new hybrid social spider algorithm with simplex Nelder Mead method in order to solve integer programming and minimax problems. We call the proposed algorithm a Simplex Social Spider optimization (SSSO) algorithm. In the the proposed SSSO algorithm, we combine the social spider algorithm with its powerful capability of performing exploration, exploitation, and the Nelder Mead method in order to refine the best obtained solution from the standard social spider algorithm. In order to investigate the general performance of the proposed SSSO algorithm, we test it on 7 integer programming problems and 10 minimax problems and compare against 10 algorithms for solving integer programming problems and 9 algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time.
A robot exploration strategy based on Q learning network

This paper introduces a reinforcement learning method for exploring a corridor environment with the depth information from an RGB D sensor only. The robot controller achieves obstacle avoidance ability by pre training of feature maps using the depth information. The system is based on the recent Deep Q Network (DQN) framework where a convolution neural network structure was adopted in the Q value estimation of the Q learning method. We separate the DQN into a supervised deep learning structure and a Q learning network. The experiments of a Turtlebot in the Gazebo simulation environment show the robustness to different kinds of corridor environments. All of the experiments use the same pre training deep learning structure. Note that the robot is traveling in environments which are different from the pre training environment. It is the first time that raw sensor information is used to build such an exploring strategy for robotics by reinforcement learning.
A robot exploration strategy based on Q learning network
Venue: 2016 IEEE International Conference on Real time Computing and Robotics (RCAR)

This paper introduces a reinforcement learning method for exploring a corridor environment with the depth information from an RGB D sensor only. The robot controller achieves obstacle avoidance ability by pre training of feature maps using the depth information. The system is based on the recent Deep Q Network (DQN) framework where a convolution neural network structure was adopted in the Q value estimation of the Q learning method. We separate the DQN into a supervised deep learning structure and a Q learning network. The experiments of a Turtlebot in the Gazebo simulation environment show the robustness to different kinds of corridor environments. All of the experiments use the same pre training deep learning structure. Note that the robot is traveling in environments which are different from the pre training environment. It is the first time that raw sensor information is used to build such an exploring strategy for robotics by reinforcement learning.
A Robot Exploration Strategy Based on Q learning Network

This paper introduces a reinforcement learning method for exploring a corridor environment with the depth information from an RGB D sensor only. The robot controller achieves obstacle avoidance ability by pre training of feature maps using the depth information. The system is based on the recent Deep Q Network (DQN) framework where a convolution neural network structure was adopted in the Q value estimation of the Q learning method. We separate the DQN into a supervised deep learning structure and a Q learning network. The experiments of a Turtlebot in the Gazebo simulation environment show the robustness to different kinds of corridor environments. All of the experiments use the same pre training deep learning structure. Note that the robot is traveling in environments which are different from the pre training environment. It is the first time that raw sensor information is used to build such an exploring strategy for robotics by reinforcement learning.
Distributed Adaptive Locomotion Learning in ModRED Modular Self reconfigurable Robot
Venue: DARS

We study the problem of adaptive locomotion learning for modular self reconfigurable robots (MSRs) MSRs are mostly used in unknown and difficult to navigate environments where they can take a completely new shape to accomplish the current task at hand. Therefore it is almost impossible to develop the control sequences for all possible configurations with varying shape and size. The modules have to learn and adapt their locomotion in dynamic time to be more robust in nature. In this paper, we propose a Q learning based locomotion adaptation strategy which balances exploration versus exploitation in a more sophisticated fashion. We have applied our proposed strategy mainly on the ModRED modular robot within the Webots simulator environment. To show the generalizability of our approach, we have also applied it on a Yamor modular robot. Experimental results show that our proposed technique outperforms a random locomotion strategy and it is able to adapt to module failures.
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The effect of sling exercise on sagittal lumbosacral angle and intervertebral disc area of chronic low back pain patients
Venue: Journal of exercise rehabilitation

The purpose of this study was to observe the change of lumbosacral angle and intervertebral disc (IVD) area. The study was conducted on chronic low back pain (CLBP) female patients for 12 weeks by operating sling exercise and general physical therapy. The 57 CLBP were divided into 2 groups which, sling exercise group (SEG, n=34) and general physical therapy group (PTG, n=23) The experiment was conducted three times a week for 12 weeks. The lumbosacral angle, which means the angle between the L1 L2 lumbar was measured by plain radiography. The IVD area, which means the IVD height and volume was measured by magnetic resonance imaging. The pain was measured by visual analogue scale (VAS) As a result, after 12 week exercise, VAS had decreased in all groups. The angle of L3 4 and L4 5 and the height of IVD had increased in SEG. Also, IVD height and volume has more improved in SEG compare the PTG. Therefore, the sling exercise is proper treatment for CLBP patients' recovery because It improve the lumbosacral angle and IVD area.
The change of pain and lumbosacral sagittal alignment after sling exercise therapy for patients with chronic low back pain
Venue: Journal of physical therapy science

This study was conducted to quantify the effect of sling exercise therapy in the recovery of lumbosacral sagittal alignment (LSA) and in the control of low back pain.  A total of 102 chronic low back pain patients were divided into two groups, a physical therapy group and a sling exercise group. In both groups, programs were conducted thrice a week for twelve weeks. With respect to LSA, pelvic tilt (PT) sacral slope (SS) and pelvic incidence (PI) were measured with plain radiography. Pain was measured on a visual analogue scale (VAS)  Differences were found in visual analogue scale, delta score of visual analogue scale, pelvic tilt, delta score of pelvic tilt, and delta score of pelvic incidence between sling exercise therapy and physical therapy groups. VAS, pelvic tilt, and pelvic incidence was positively changed after sling exercise. However, only the visual analogue scale was found to be improved after physical therapy.  Sling exercise therapy and physical therapy were effective in reducing pain. However, pelvic tilt and pelvic incidence were positively changed after sling exercise therapy for Lumbosacral Sagittal Alignment, but were unchanged after physical therapy. Therefore, sling exercise therapy is more effective than physical therapy for the recovery of Lumbosacral Sagittal Alignment in patients with chronic low back pain.
Hexagonal structure of phase III of solid hydrogen

A hexagonal structure of solid molecular hydrogen with $P{6}_{1}22$ symmetry is calculated to be more stable below about 200 GPa than the monoclinic $C2/c$ structure identified previously as the best candidate for phase III. We find that the effects of nuclear quantum and thermal vibrations play a central role in the stabilization of $P{6}_{1}22$ The $P{6}_{1}22$ and $C2/c$ structures are very similar and their Raman and infrared data are in good agreement with experiment. However, our calculations show that the hexagonal $P{6}_{1}22$ structure provides better agreement with the available x ray diffraction data than the $C2/c$ structure at pressures below about 200 GPa. We suggest that two phase III like structures may be formed at high pressures: hexagonal $P{6}_{1}22$ below about 200 GPa and monoclinic $C2/c$ at higher pressures.
High pressure Raman study of solid hydrogen up to 300 GPa*

The high pressure behavior of solid hydrogen has been investigated by in situ Raman spectroscopy upon compression to 300 GPa at ambient temperature. The hydrogen vibron frequency begins to decrease after it initially increases with pressure up to 38 GPa. This softening behavior suggests the weakening of the intramolecular bond and the increased intermolecular interactions. Above 237 GPa, the vibron frequency softens very rapidly with pressure at a much higher rate than that of phase III, corresponding to transformation from phase III into phase IV. The phase transition sequence has been confirmed from phase I to phase III and then to phase IV at 208 and 237 GPa, respectively. Previous theoretical calculations lead to the proposal of an energetically favorable monoclinic C2/c structure for phase III and orthorhombic Pbcn structure for phase IV. Up to 304 GPa, solid hydrogen is not yet an alkali metal since the sample is still transparent.
Has Concentration in the Top Accounting Journals Changed Over Time

Prior work has found high levels of concentration in top academic accounting journals relative to finance, management, and marketing. Moreover, concentration has been increasing in accounting while concentration in other disciplines has been decreasing or remaining about the same. We investigate whether recent efforts by the American Accounting Association, among others, have altered these trends. We examine concentration trends in top accounting journals from 1990 to 2014 and compare these trends with those in other major business school disciplines. Ratios of publications to faculty are lowest for accounting across all years, suggesting that accounting has relatively fewer "slots" available for top journal publications than other disciplines. In addition, our results suggest that concentration has decreased in accounting journals in recent years but that concentration has also decreased in other business disciplines. Thus, accounting has retained its position as a discipline where publications.
Cooperation for Publication? An Analysis of Co authorship Patterns in Leading Accounting Journals

This paper examines patterns, antecedents, and effects of (international) cooperation in accounting academia based on 7105 papers published in 15 leading accounting journals. In particular, we investigate the dissemination of different forms of cooperation, identify author characteristics that are related to the propensity of cooperation and analyze whether cooperation is associated with research performance (in terms of research impact and output) We find that scholars from Asian countries tend to be more heavily involved in international cooperation than researchers from most European countries and the USA. A Ph.D. from a leading school, a scholar's previous publication experience and a past appointment as editor or editorial board member are positively associated with the propensity for cooperation, while a researcher's current affiliation has only limited impact. Surprisingly, our findings show that cooperation is not related to a greater research impact as measured by citation numbers per paper. Finally, we find a significant negative relationship between a scholar's share of co authored papers and his or her research output in leading accounting journals as measured by the weighted numbers of papers per author.
An Evaluation of the General vs. Specialist Nature of Top Accounting Journals

Academic research has a role in advancing and enlightening society in broad areas of study. Many forces interact to influence the directions, topics, and methodologies used in research. In this paper, we explore and discuss the relationships between the top general interest and specialist accounting journals. We test whether top journals (e.g. "Top 3" or "Top 6" relative to a set of high quality but specialist journals, (1) are perceived to be general in what they will consider publishing, (2) have historically published diverse sets of articles, (3) have editorial board members with diverse sets of interests and skills, and (4) publish the most highly cited articles by topic area and methodology. The results suggest that some of the top journals are not as general as their mission statements suggest and that they do not publish the most highly cited articles in some topic areas and methodologies. These results may help institutions consider whether "counting" only the top journals is an appropriate measure for judging faculty scholarship. The results suggest that counting articles in the traditional top journals alone will be problematic if institutions want to promote research in a broad range of accounting topics and issues in the profession.
"The moustache sits down first" on the acquisition of metonymy*
Venue: Journal of Child Language

This study investigates preschoolers' ability to understand and produce novel metonyms. We gave forty seven children (aged 2;9 5;9) and twenty seven adults one comprehension task and two elicitation tasks. The first elicitation task investigated their ability to use metonyms as referential shorthands, and the second their willingness to name animates metonymically on the basis of a salient property. Although children were outperformed by adults, even three year olds could understand and produce metonyms in certain circumstances. Our results suggest that young children may find it easier to produce a metonym than a more elaborate referential description in certain contexts, and that metonymy may serve as a useful strategy in referring to entities that lack a conventional label. However, metonymy comprehension appeared to decrease with age, with older children tending to choose literal interpretations of some metonyms. This could be a result of growing metalinguistic awareness, which leads children to overemphasize literal meanings.
The investigation of physical conditions of boron uptake region in proton boron fusion therapy (PBFT)

We conducted a quantitative study to identify the effectiveness of proton boron fusion therapy (PBFT) Four simulation scenarios were designed to investigate the escalation in total dose with the proton boron reaction using a Monte Carlo n particle extended (MCNPX 2.6.0) simulation. The peak integrated dose was obtained for three different physical conditions (i.e. boron uptake region (BUR) thickness, BUR location, and boron concentration) with differing proton beam energy (60 90 MeV) We found that the peak integrated dose was increased by up to 96.62% compared to the pristine proton Bragg peak. For the synergetic effect to take place with 60 70 MeV proton beam, the BUR had to be at least 0.3 cm thick while spanning the Bragg peak. Similarly to the thickness, the BUR location needed to be within 0.3 cm from the Bragg peak when the thickness was maintained at 0.9 cm. An effective proton boron reaction required the boron concentration to be equal to or greater than 14.4 mg/g. These results demonstrate the.
Evaluating the effects of stress and fatigue on police officer response and recall:a challenge for research, training, practice and policy

Armed police officers frequently respond to evolving, dynamic, and potentially dangerous incidents. Given the challenging and often controversial nature of this response context, understanding the impact of officer stress and fatigue on performance in incidents involving use of force is important for trainers, investigators, legal professionals, and policy makers. The psychological literature on human performance is broadly informative with respect to the potential effects of stress and fatigue on response performance (e.g. shooting accuracy) and the reliability of accounts provided by officers. Unsurprisingly, stress and fatigue typically impair performance, although further research is needed to (i) delineate the precise nature of the effects of stress and fatigue on response performance and memory, and (ii) explore relevant contextual and boundary conditions. This article considers what current research can contribute to training and practice in use of force contexts, and outlines key methodological challenges for researchers and consumers of research in this field.
Microservices and Their Design Trade Offs: A Self Adaptive Roadmap
Venue: 2016 IEEE International Conference on Services Computing (SCC)

Migrating to microservices (microservitization) enables optimising the autonomy, replaceability, decentralised governance and traceability of software architectures. Despite the hype for microservitization the state of the art still lacks consensus on the definition of microservices, their properties and their modelling techniques. This paper summarises views of microservices from informal literature to reflect on the foundational context of this paradigm shift. A strong foundational context can advance our understanding of microservitization and help guide software architects in addressing its design problems. One such design problem is finalising the optimal level of granularity of a microservice architecture. Related design trade offs include: balancing the size and number of microservices in an architecture and balancing the nonfunctional requirement satisfaction levels of the individual microservices as well as their satisfaction for the overall system. We propose how self adaptivity can assist in addressing these design trade offs and discuss some of the challenges such a selfadaptive solution. We use a hypothetical online movie streaming system to motivate these design trade offs. A solution roadmap is presented in terms of the phases of a feedback control loop.