nils-herrmann/clean_scirepeval huggingface dataset:
A novel method of identifying damage types in carbon fiber reinforced plastic cross ply laminates based on acoustic emission detection using a fiber optic sensor

We applied a highly sensitive phase shifted fiber optic Bragg grating (PS FBG) sensor with a broad bandwidth to acoustic emission (AE) detection in a composite material. Based on AE detection, this research proposed a novel method to identify damage types in carbon fiber reinforced plastic (CFRP) laminates. Because the PS FBG sensor measured pure dynamic strain, the detected AE signals had great physical reliability, which made elastic wave theory suitable for analysis of the signal. The analysis clarified the different characteristics of Lamb wave modes in three types of AEs caused by a transverse crack, delamination and fiber breakage. Additionally, the ratios of the amplitudes of the S0 mode to the A0 mode and the peak frequencies quantitatively evaluated the mode characteristics in the AE signals. Simultaneously using the two physical parameters, we identified the three types of damage among the AE signals detected in a three point bending test. Furthermore, the finite element method based AE wave propagation simulation agreed well with the identification results. Hence, the proposed identification method with the PS FBG sensor has great physical reliability for evaluating damage in composite laminates.
Investigation of push out delamination using cohesive zone modelling and acoustic emission technique

Push out delamination is a serious concern in the drilling of fibre reinforced composite materials. This damage occurs as the drill reaches the exit side of the material and can reduce the strength and stiffness of the structure. In this paper, a three point bending test is performed on glass/epoxy laminated composites to simulate the push out delamination induced by thrust force during drilling. Cohesive zone modelling and acoustic emission monitoring are utilized to investigate the push out delamination. Initially, double cantilever beam and end notched flexure tests were performed to calibrate the cohesive zone modelling model. Following that, the actual loading condition is simulated using cohesive zone modelling based finite element modelling. Energy of the acoustic emission signals is also used to detect the initiation of the delamination. The results obtained from cohesive zone modelling and acoustic emission showed that the applied methods can be used to understand and predict the initiation of push out delamination and its progression. Finally, it is concluded that the proposed cohesive zone modelling and acoustic emission techniques can be used in the design stage as well as during the drilling process of laminated composite structures to avoid delamination.
Project interdependency management in IT/IS project portfolios from a systems perspective

Capitalizing on inter project interdependencies is widely accepted as being crucial for Information Technology/Information Systems (IT/IS) project portfolio success. However, handling these interdependencies becomes a source of increased management complexity as project portfolios get larger, increasingly diverse, and are much influenced by their environments. This then can lead to less predictable situations regarding the portfolio performance as a whole. Though there are many methods available for optimally selecting projects and cross checking possible synergies and interdependencies among the projects in the portfolio, it remains unclear how one can gain control over the complexity of managing these interdependencies. In this study, we address the problem of handling many project interdependencies that continuously change and develop within a large IT/IS project portfolio. We found that the existing methods for selecting and managing the projects within a portfolio do not focus enough on how interdependencies can disrupt the balance of the whole project portfolio system. To tackle the problem, we propose a systemic approach using the Viable System Model to enhance the way that these interdependencies can be effectively managed in view of the overall portfolio system. We suggest that the Viable System Model, as a supplement to traditional IT/IS project portfolio management approaches, is recursively applied to diagnose and manage the complexity of project interdependencies within IT/IS project portfolios.
SECURITY CHALLENGES IN MOBILE ASSISTED LANGUAGE LEARNING IN THE MILLENNIUM FOR EDUCATION

Distance learning technologies enrich learning opportunities due to many advantages like ubiquity and flexibility. Although the usefulness of such technologies in teaching and learning is clear, their testing part is remained to be discussed due to the security issue. Administrators and teachers need to use more authentic and secure distant testing software in which the scores are guaranteed and the testees keep away from cheating. Static and online authentication systems like "username" and "password" and face detection have empowered educational parties to have more reliable testing outcomes. Mobile devices as the necessity of the new millennium need to use authentication software in their testing. Mobile devices with their multimedia course materials provide learners with many optimistic learning opportunities through collaboration, cooperation, interaction and testing. The unique chances of ubiquity, individualization, informality, and spontaneity make the mobile learning of particular importance not only for digital natives but also for teachers, administrators, developers, instructors, and policy makers. Yielding an economical learning opportunity along with providing authentic contexts for collaborative learning is beneficial for the economy of the country in general and for the meaningful and deep learning of the learners. This paper will discuss how authentication techniques have applied to electronic devices like mobile phones.
Real structure preserving algorithms of Householder based transformations for quaternion matrices
Venue: J. Comput. Appl. Math.

In this paper, we survey three different forms of Householder based transformations for quaternion matrices in the literature, and propose a new form of quaternion Householder based transformation. We propose real structure preserving algorithms of these Householder based transformations, which make the procedure computationally more flexible and efficient. We compare the computation counts and assignment numbers of these algorithms. We also compare the effectiveness of these real structure preserving algorithms applying to the quaternion QRD and the quaternion SVD.All these four real structure preserving algorithms are more efficient, comparing to the algorithms which apply Quaternion Toolbox using quaternion arithmetics, or algorithms which directly performs real Householder transformations on the real representation of a quaternion matrix. Among these four real structure preserving algorithms, the most efficient ones are based on quaternion Householder reflection, and new proposed Householder based transformation.
How Does Multinational Production Affect the Measurement of Competitiveness?

This work assembles a unique bilateral dataset on multinational production in the manufacturing sector, in which value added and factor incomes are broken down by location country and by ultimate owner country. Using this dataset, which covers 44 countries over the years 2004 11, we compute measures of production capabilities in which value added is allocated across countries not according to the location of the activity but according to the nationality of the firm or of the factors involved in production (Baldwin and Kimura 1998) These indicators based on the ownership of production are then compared with standard geography based indicators. This framework is also applied to the analysis of the two modes of supply of foreign markets (exports and FDI) using a common metric based on value added (domestic value added in exports versus value added of foreign affiliates) Overall, the evidence suggests that there are significant differences between geography based and ownership based measures, proving that, in an increasingly integrated global economy, ownership matters for the measurement of competitiveness.
A note on Black Scholes pricing model for theoretical values of stock options

In this paper, we consider some conditions that transform the classical Black Scholes Model for stock options valuation from its partial differential equation (PDE) form to an equivalent ordinary differential equation (ODE) form. In addition, we propose a relatively new semi analytical method for the solution of the transformed Black Scholes model. The obtained solutions via this method can be used to find the theoretical values of the stock options in relation to their fair prices. In considering the reliability and efficiency of the models, we test some cases and the results are in good agreement with the exact solution.
He's Polynomials for Analytical Solutions of theBlack Scholes Pricing Model for Stock OptionValuation

The Black Scholes model is one of the most famous and useful models for option valuation as regards option pricing theory. In this paper, we propose a semianalytical method referred to as He's polynomials for solving the classical Black Scholes pricing model with stock as the underlying asset. The proposed method gives the exact solution of the solved problem in a very simple and quick manner even with less computational work while still maintaining high level of accuracy. Hence, we recommend an extension and adoption of this method for solving problems arising in other areas of financial engineering, finance, and applied sciences
Botanical studies of the leaf, stem and root of Carissa macrocarpa (Apocynaceae) cultivated in Egypt

The present study examines various standardized parameters as morphological and histological characters which could be helpful in authentication of the leaf, stem and root of Carissa macrocarpa. Concerning the microscopical features of Carissa macrocarpa, the plant is characterized microscopically by presence of large calcium oxalate clusters, forked apex fibers as well as pericyclic fibers with balloon like structure.
Linear Feature Encoding for Reinforcement Learning
Venue: NIPS

Feature construction is of vital importance in reinforcement learning, as the quality of a value function or policy is largely determined by the corresponding features. The recent successes of deep reinforcement learning (RL) only increase the importance of understanding feature construction. Typical deep RL approaches use a linear output layer, which means that deep RL can be interpreted as a feature construction/encoding network followed by linear value function approximation. This paper develops and evaluates a theory of linear feature encoding. We extend theoretical results on feature quality for linear value function approximation from the uncontrolled case to the controlled case. We then develop a supervised linear feature encoding method that is motivated by insights from linear value function approximation theory, as well as empirical successes from deep RL. The resulting encoder is a surprisingly effective method for linear value function approximation using raw images as inputs.
Digital and Conventional Microscopy Learning Effects Detected through Eye Tracking and the Use of Interactive Whiteboards

Learning the meaningful use of the microscope is an essential requirement in school curricula. Modern science and medicine is hardly conceivable without the inclusion of microscopy. The number of didactic studies in this area, however, is negligible. Real microscopy is rarely used to gain knowledge in higher school years. Could the understanding of cell concept and the skills to interpret histological structures be complemented and supported by digital microscopy? The current study investigates this problem in two methodological ways. First, learning effects of modules with interactive whiteboards (IWB) are compared with original microscopy (2012: n=70; 2013: 25+14; 2013/14: n=21+27; 2014: n=40) using the example of human biology with pre service student teachers. The instruments were questionnaires (short scale of intrinsic motivation, flow, and knowledge test) and interviews. Second, eye movements of 12th grade students were measured during histological image processing of retina and nervous tissue in the student laboratory of neuroscience in Tuebingen (High Quality Tower mounted eye tracking) (2013: n=11+26; 2014: n=28) We measured that fixation in the area of interest (AoI) corresponded with the level of prior knowledge. The estimation of instructional aids (e.g. schematic representation) while viewing demanding histological images also depends on prior knowledge. The same applies to the IWB. The use of the whiteboard is valued because of the possibility to make visible connections and put these in contexts. The use of the whiteboard does not, however, surpass the motivation and flow of real microscopy in problem oriented learning processes. The IWB is a good supplement but is not a substitute for the microscope.
Foxo3 circular RNA retards cell cycle progression via forming ternary complexes with p21 and CDK2
Venue: Nucleic acids research

Most RNAs generated by the human genome have no protein coding ability and are termed non coding RNAs. Among these include circular RNAs, which include exonic circular RNAs (circRNA) mainly found in the cytoplasm, and intronic RNAs (ciRNA) predominantly detected in the nucleus. The biological functions of circular RNAs remain largely unknown, although ciRNAs have been reported to promote gene transcription, while circRNAs may function as microRNA sponges. We demonstrate that the circular RNA circ Foxo3 was highly expressed in non cancer cells and were associated with cell cycle progression. Silencing endogenous circ Foxo3 promoted cell proliferation. Ectopic expression of circ Foxo3 repressed cell cycle progression by binding to the cell cycle proteins cyclin dependent kinase 2 (also known as cell division protein kinase 2 or CDK2) and cyclin dependent kinase inhibitor 1 (or p21) resulting in the formation of a ternary complex. Normally, CDK2 interacts with cyclin A and cyclin E to facilitate cell cycle entry, while p21works to inhibit these interactions and arrest cell cycle progression. The formation of this circ Foxo3 p21 CDK2 ternary complex arrested the function of CDK2 and blocked cell cycle progression.
Instance Aware Semantic Segmentation via Multi task Network Cascades
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Semantic segmentation research has recently witnessed rapid progress, but many leading methods are unable to identify object instances. In this paper, we present Multitask Network Cascades for instance aware semantic segmentation. Our model consists of three networks, respectively differentiating instances, estimating masks, and categorizing objects. These networks form a cascaded structure, and are designed to share their convolutional features. We develop an algorithm for the nontrivial end to end training of this causal, cascaded structure. Our solution is a clean, single step training framework and can be generalized to cascades that have more stages. We demonstrate state of the art instance aware semantic segmentation accuracy on PASCAL VOC. Meanwhile, our method takes only 360ms testing an image using VGG 16, which is two orders of magnitude faster than previous systems for this challenging problem. As a by product, our method also achieves compelling object detection results which surpass the competitive Fast/Faster R CNN systems. The method described in this paper is the foundation of our submissions to the MS COCO 2015 segmentation competition, where we won the 1st place.
Semantic Segmentation via Multi task, Multi domain Learning
Venue: S+SSPR

We present an approach that leverages multiple datasets possibly annotated using different classes to improve the semantic segmentation accuracy on each individual dataset. We propose a new selective loss function that can be integrated into deep networks to exploit training data coming from multiple datasets with possibly different tasks (e.g. different label sets) We show how the gradient reversal approach for domain adaptation can be used in this setup. Thorought experiments on semantic segmentation applications show the relevance of our approach.
DesktopGlove: A multi finger force feedback interface separating degrees of freedom between hands
Venue: 2016 IEEE Symposium on 3D User Interfaces (3DUI)

In virtual environments, interacting directly with our hands and fingers greatly contributes to immersion, especially when force feedback is provided for simulating the touch of virtual objects. Yet, common haptic interfaces are unfit for multi finger manipulation and only costly and cumbersome grounded exoskeletons do provide all the efforts expected from object manipulation. To make multi finger haptic interaction more accessible, we propose to combine two affordable haptic interfaces into a bimanual setup named DesktopGlove. With this approach, each hand is in charge of different components of object manipulation: one commands the global motion of a virtual hand while the other controls its fingers for grasping. In addition, each hand is subjected to forces that relate to its own degrees of freedom so that users perceive a variety of haptic effects through both of them. Our results show that (1) users are able to integrate the separated degrees of freedom of DesktopGlove to efficiently control a virtual hand in a posing task, (2) DesktopGlove shows overall better performance than a traditional data glove and is preferred by users, and (3) users considered the separated haptic feedback realistic and accurate for manipulating objects in virtual environments.
Building Dual Domain Representations for Compression Artifacts Reduction
Venue: ECCV

We propose a highly accurate approach to remove artifacts of JPEG compressed images. Our approach jointly learns a very deep convolutional network in both DCT and pixel domains. The dual domain representation can make full use of DCT domain prior knowledge of JPEG compression, which is usually lacking in traditional network based approaches. At the same time, it can also benefit from the prowess and the efficiency of the deep feed forward architecture, in comparison to capacity limited sparse coding based approaches. Two simple strategies, i.e. Adam and residual learning, are adopted to train the very deep network and later proved to be a success. Extensive experiments demonstrate the large improvements of our approach over the state of the arts.
Improved modified Reynolds equation for thin film gas lubrication from an extended slip velocity boundary condition

An extended slip velocity boundary condition is derived from the regularized 13 moment equations firstly. Different from the existing slip velocity boundary condition, the slip coefficients of the extended one are not fixed, which will change with the wall accommodation coefficient and the Knudsen number of the gas flow. Using the extended slip velocity condition, an improved modified Reynolds equation for thin film gas lubrication is established. From solving the improved modified Reynolds equation, the pressure distribution of the slider gas bearing is obtained and has a better agreement with that from the direct simulation Monte Carlo method under different pitch angles and wall velocities. It is found that the improved modified Reynolds equation can predict a more accurate pressure distribution of the slider gas bearing than the Fukui and Kaneko's lubrication model from the linearized Boltzmann equation in the near transition regime.
Improved lattice Boltzmann modeling of binary flow based on the conservative Allen Cahn equation.
Venue: Physical review. E

The primary and key task of binary fluid flow modeling is to track the interface with good accuracy, which is usually challenging due to the sharp interface limit and numerical dispersion. This article concentrates on further development of the conservative Allen Cahn equation (ACE) [Geier et al. Phys. Rev. E 91, 063309 ( under the framework of the lattice Boltzmann method (LBM) with incorporation of the incompressible hydrodynamic equations [Liang et al. Phys. Rev. E 89, 053320 ( Utilizing a modified equilibrium distribution function and an additional source term, this model is capable of correctly recovering the conservative ACE through the Chapman Enskog analysis. We also simulate four phase tracking benchmark cases, including one three dimensional case; all show good accuracy as well as low numerical dispersion. By coupling the incompressible hydrodynamic equations, we also simulate layered Poiseuille flow and the Rayleigh Taylor instability, illustrating satisfying performance in dealing with complex flow problems, e.g. high viscosity ratio, high density ratio, and high Reynolds number situations. The present work provides a reliable and efficient solution for binary flow modeling.
Reduced order modeling of fluid structure interaction and vortex induced vibration systems using an extension of Jourdain's principle

A first principles variational approach is proposed for reduced order modeling of fluid structure interaction (FSI) systems, specifically vortex induced vibration (VIV) FSI has to be taken into account in the design and analysis of many engineering applications, yet a comprehensive theoretical development where analytical equations are derived from first principles is nonexistent. An approach where Jourdain's principle is modified and extended for FSI is used to derive reduced order models from an extended variational formulation where assumptions are explicitly stated. Two VIV models are considered: an elastically supported, inverted pendulum and a translating cylinder, both immersed in a flow and allowed to move transversely to the flow direction. Their reduced order models are obtained in the form of (i) a single governing equation and (ii) two general coupled equations as well as the coupled lift oscillator model. Comparisons are made with three existing models. Based on our theoretical results, and especially the reduced order model, we conclude that the first principles development herein is a viable framework for the modeling of complex fluid structure interaction problems such as vortex induced oscillations.
Cognitive Control Network Contributions to Memory Guided Visual Attention.
Venue: Cerebral cortex

Visual attentional capacity is severely limited, but humans excel in familiar visual contexts, in part because long term memories guide efficient deployment of attention. To investigate the neural substrates that support memory guided visual attention, we performed a set of functional MRI experiments that contrast long term, memory guided visuospatial attention with stimulus guided visuospatial attention in a change detection task. Whereas the dorsal attention network was activated for both forms of attention, the cognitive control network(CCN) was preferentially activated during memory guided attention. Three posterior nodes in the CCN, posterior precuneus, posterior callosal sulcus/mid cingulate, and lateral intraparietal sulcus exhibited the greatest specificity for memory guided attention. These 3 regions exhibit functional connectivity at rest, and we propose that they form a subnetwork within the broader CCN. Based on the task activation patterns, we conclude that the nodes of this subnetwork are preferentially recruited for long term memory guidance of visuospatial attention.