nils-herrmann/clean_scirepeval huggingface dataset:
Full Field Reconstruction of Structural Deformations and Loads from Measured Strain Data on a Wing Using the Inverse Finite Element Method

A study was undertaken to investigate the measurement of wing deformation and internal loads using measured strain data. Future aerospace vehicle research depends on the ability to accurately measure the deformation and internal loads during ground testing and in flight. The approach uses the inverse Finite Element Method (iFEM) The iFEM is a robust, computationally efficient method that is well suited for real time measurement of real time structural deformation and loads. The method has been validated in previous work, but has yet to be applied to a large scale test article. This work is in preparation for an upcoming loads test of a half span test wing in the Flight Loads Laboratory at the National Aeronautics and Space Administration Armstrong Flight Research Center (Edwards, California) The method has been implemented into an efficient MATLAB (The MathWorks, Inc. Natick, Massachusetts) code for testing different sensor configurations. This report discusses formulation and implementation along with the preliminary results from a representative aerospace structure. The end goal is to investigate the modeling and sensor placement approach so that the best practices can be applied to future aerospace projects.
Evolutionary Conservation and Divergence of Gene Coexpression Networks in Gossypium (Cotton) Seeds
Venue: Genome biology and evolution

The cotton genus (Gossypium) provides a superior system for the study of diversification, genome evolution, polyploidization, and human mediated selection. To gain insight into phenotypic diversification in cotton seeds, we conducted coexpression network analysis of developing seeds from diploid and allopolyploid cotton species and explored network properties. Key network modules and functional associations were identified related to seed oil content and seed weight. We compared species specific networks to reveal topological changes, including rewired edges and differentially coexpressed genes, associated with speciation, polyploidy, and cotton domestication. Network comparisons among species indicate that topologies are altered in addition to gene expression profiles, indicating that changes in transcriptomic coexpression relationships play a role in the developmental architecture of cotton seed development. The global network topology of allopolyploids, especially for domesticated G. hirsutum, resembles the network of the A genome diploid more than that of the D genome parent, despite its D like phenotype in oil content. Expression modifications associated with allopolyploidy include coexpression level dominance and transgressive expression, suggesting that the transcriptomic architecture in polyploids is to some extent a modular combination of that of its progenitor genomes. Among allopolyploids, intermodular relationships are more preserved between two different wild allopolyploid species than they are between wild and domesticated forms of a cultivated cotton, and regulatory connections of oil synthesis related pathways are denser and more closely clustered in domesticated vs. wild G. hirsutum. These results demonstrate substantial modification of genic coexpression under domestication. Our work demonstrates how network inference informs our understanding of the transcriptomic architecture of phenotypic variation associated with temporal scales ranging from thousands (domestication) to millions (speciation) of years, and by polyploidy.
Data Center Energy Consumption Modeling: A Survey
Venue: IEEE Communications Surveys Tutorials

Data centers are critical, energy hungry infrastructures that run large scale Internet based services. Energy consumption models are pivotal in designing and optimizing energy efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state of the art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware centric and software centric power models. Under hardware centric approaches we start from the digital circuit level and move on to describe higher level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state of the art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.
Heavy work investment, personality and organizational climate

The purpose of this paper is to assess the relative importance of personality and organizational climate for two forms of heavy work investment; workaholism, a "bad" and work engagement, which represents a "good" kind of heavy work investment. More specifically, it is hypothesized that workaholism is positively related to neuroticism (H1) and that work engagement is negatively related to neuroticism and positively to the remaining Big Five personality traits (H2) In addition it is hypothesized that workaholism is positively related to an overwork climate (H3) whereas work engagement is positively related to an employee growth climate (H4) Design/methodology/approach An online survey was conducted among a sample of the Dutch workforce (n=1,973) and the research model was tested using structural equation modeling.  It appeared that, in accordance to H1 and H2, particularly neuroticism is related to workaholism, while all personality traits are related to work engagement (predominant.
Locally Stationary Functional Time Series

The literature on time series of functional data has focused on processes of which the probabilistic law is either constant over time or constant up to its second order structure. Especially for long stretches of data it is desirable to be able to weaken this assumption. This paper introduces a framework that will enable meaningful statistical inference of functional data of which the dynamics change over time. We put forward the concept of local stationarity in the functional setting and establish a class of processes that have a functional time varying spectral representation. Subsequently, we derive conditions that allow for fundamental results from nonstationary multivariate time series to carry over to the function space. In particular, time varying functional ARMA processes are investigated and shown to be functional locally stationary according to the proposed definition. As a side result, we establish a Cram\'er representation for an important class of weakly stationary functional processes. Important in our context is the notion of a time varying spectral density operator of which the properties are studied and uniqueness is derived. Finally, we provide a consistent nonparametric estimator of this operator and show it is asymptotically Gaussian using a weaker tightness criterion than what is usually deemed necessary.
Sieve Bootstrap for Functional Time Series

A bootstrap procedure for functional time series is proposed which exploits a general vector autoregressive representation of the time series of Fourier coefficients appearing in the Karhunen Lo\`eve expansion of the functional process. A double sieve type bootstrap method is developed which avoids the estimation of process operators and generates functional pseudo time series that appropriately mimic the dependence structure of the functional time series at hand. The method uses a finite set of functional principal components to capture the essential driving parts of the infinite dimensional process and a finite order vector autoregressive process to imitate the temporal dependence structure of the corresponding vector time series of Fourier coefficients. By allowing the number of functional principal components as well as the autoregressive order used to increase to infinity (at some appropriate rate) as the sample size increases, a basic bootstrap central limit theorem is established which shows validity of the bootstrap procedure proposed for functional finite Fourier transforms. Some numerical examples illustrate the good finite sample performance of the new bootstrap method proposed.
A Simple Test for White Noise in Functional Time Series

We propose a new procedure for white noise testing of a functional time series. Our approach is based on an explicit representation of the L2aEUR distance between the spectral density operator and its best (L2aEUR )approximation by a spectral density operator corresponding to a white noise process. The estimation of this distance can be easily accomplished by sums of periodogram kernels, and it is shown that an appropriately standardized version of the estimator is asymptotically normal distributed under the null hypothesis (of functional white noise) and under the alternative. As a consequence, we obtain a very simple test (using the quantiles of the normal distribution) for the hypothesis of a white noise functional process. In particular, the test does not require either the estimation of a longaEUR run variance (including a fourth order cumulant) or resampling procedures to calculate critical values. Moreover, in contrast to all other methods proposed in the literature, our approach also allows testing for aEUR~relevantaEUR(tm) deviations from white noise and constructing confidence intervals for a measure that measures the discrepancy of the underlying process from a functional white noise process.
Face reconstruction from image sequences for forensic face comparison
Venue: IET Biom.

The authors explore the possibilities of a dense model free three dimensional (3D) face reconstruction method, based on image sequences from a single camera, to improve the current state of forensic face comparison. They propose a new model free 3D reconstruction method for faces, based on the Lambertian reflectance model to estimate the albedo and to refine the 3D shape of the face. This method avoids any form of bias towards face models and is therefore suitable in a forensic face comparison process. The proposed method can reconstruct frontal albedo images, from multiple non frontal images. Also a dense 3D shape model of the face is reconstructed, which can be used to generate faces under pose. In the authors' experiments, the proposed method is able to improve the face recognition scores in more than 90% of the cases. Using the likelihood ratio framework, they show for the same experiment that for data initially unsuitable for forensic use, the reconstructions become meaningful in a forensic context in more than 60% of the cases.
Arabic Teachers' Perception of an Integrated Approach for Teaching Arabic as a Foreign Language in Colleges and Universities in the United States.

ii SIGNATURE PAGE iv DEDICATION v ACKNOWLEDGMENTS vi LIST OF TABLES xi LIST OF FIGURES xii CHAPTER I THE RESEARCH PROBLEM 1 Statement of the Problem 1 Background and Need 3 Arabic Diglossia 3 Teaching of Arabic as a Foreign Language (TAFL) in the United States 3 Raising Students' Arabic Communicative Competence 4 Purpose of the Study 5 Research Questions 5 Conceptual Framework 6 Communicative Competence Theory 7 Borg Language Teacher Cognition Model 9 Delimitations 11 Limitations 11 Significance of the Study 12 Definitions of Terms 13  CHAPTER II REVIEW OF THE LITERATURE 18 Restatement of the Problem 18 Outline of the Literature Review 18 Background 18 The Origin of Arabic Diglossia 18 Acquired Arabic Language Versus Learned Arabic Language 21 Continuum Arabic 23 Arabic Diglossic Code Switching 24 Linguistic Ideology 25 Formation of Linguistic Ideologies 25 Standardization and Language Ideologies 27 Theological Language and Arab Nationalism 31 Teaching of Arabic as Foreign Language 34 The Study of Arabic in the United States, When and Why 34 MSA Only Approach as a Common Practice 41
Underwater Depth Estimation and Image Restoration Based on Single Images
Venue: IEEE Computer Graphics and Applications

In underwater environments, the scattering and absorption phenomena affect the propagation of light, degrading the quality of captured images. In this work, the authors present a method based on a physical model of light propagation that takes into account the most significant effects to image degradation: absorption, scattering, and backscattering. The proposed method uses statistical priors to restore the visual quality of the images acquired in typical underwater scenarios.
Forecasting Volatility and Value at Risk of Pakistan Stock Market with Markov Regime Switching GARCH Models

This paper compares and evaluates various generalized autoregressive conditional heteroscedastic (GARCH) models in terms of their ability to forecast volatility and risk of Karachi Stock Exchange (KSE) Linear and nonlinear GARCH and Markov Regime Switching GARCH (MRS GARCH) models with normal and fat tails errors are employed to predict 1 day to 1 month ahead forecast of volatility and Value at Risk (VaR) The MRS GARCH model is estimated with two regimes, representing periods of low and high volatility of stock returns. The MRS GARCH model for short horizon and EGARCH model for long horizon are found to predict the volatility and risk of KSE better than the competing models.
An Empirical Study of Automatic Chinese Word Segmentation for Spoken Language Understanding and Named Entity Recognition
Venue: NAACL

Word segmentation is usually recognized as the first step for many Chinese natural language processing tasks, yet its impact on these subsequent tasks is relatively under studied. For example, how to solve the mismatch problem when applying an existing word segmenter to new data? Does a better word segmenter yield a better subsequent NLP task performance? In this work, we conduct an initial attempt to answer these questions on two related subsequent tasks: semantic slot filling in spoken language understanding and named entity recognition. We propose three techniques to solve the mismatch problem: using word segmentation outputs as additional features, adaptation with partial learning and taking advantage of n best word segmentation list. Experimental results demonstrate the effectiveness of these techniques for both tasks and we achieve an error reduction of about 11% for spoken language understanding and 24% for named entity recognition over the baseline systems.
Effects of zinc addition to a copper contaminated vineyard soil on sorption of Zn by soil and plant physiological responses.
Venue: Ecotoxicology and environmental safety

The occurrence of high levels of Cu in vineyard soils is often the result of intensive use of fungicides for the preventive control of foliar diseases and can cause toxicity to plants. Nowadays many grape growers in Southern Brazil have replaced Cu based with Zn based products. The aim of the study was to evaluate whether the increase in Zn concentration in a soil with high Cu contents can interfere with the dynamics of these elements, and if this increase in Zn may cause toxicity to maize (Zea mays L. Soil samples were collected in two areas, one in a vineyard with more than 30 years of cultivation and high concentration of Cu and the other on a natural grassland area adjacent to the vineyard. Different doses of Cu and Zn were added to the soil, and the adsorption isotherms were built following the Langmuir's model. In a second experiment, the vineyard soil was spiked with different Zn concentrations (0, 30, 60, 90, 180, and 270mg Zn kg( 1) in 3kg pots where maize was grown in a greenhouse for 35 days. When Cu and Zn were added together, there was a reduction in the quantities adsorbed, especially for Zn. Zn addition decreased the total plant dry matter and specific leaf mass. Furthermore, with the increase in the activity of catalase, an activation of the antioxidant system was observed. However, the system was not sufficiently effective to reverse the stress levels imposed on soil, especially in plants grown in the highest doses of Zn. At doses higher than 90Znmgkg( 1) in the Cu contaminated vineyard soil, maize plants were no longer able to activate the protection mechanism and suffered from metal stress, resulting in suppressed dry matter yields due to impaired functioning of the photosynthetic apparatus and changes in the enzymatic activity of plants. Replacement of Cu by Zn based fungicides to avoid Cu toxicity has resulted in soil vineyards contaminated with these metals and damaging of plant photosynthetic apparatus and enzyme activity.
Distributed Cognition and Process Management Enabling Individualized Translational Research: The NIH Undiagnosed Diseases Program Experience
Venue: Front. Med.

The National Institutes of Health Undiagnosed Diseases Program (NIH UDP) applies translational research systematically to diagnose patients with undiagnosed diseases. The challenge is to implement an information system enabling scalable translational research. The authors hypothesized that similar complex problems are resolvable through process management and the distributed cognition of communities. The team, therefore, built the NIH UDP integrated collaboration system (UDPICS) to form virtual collaborative multidisciplinary research networks or communities. UDPICS supports these communities through integrated process management, ontology based phenotyping, biospecimen management, cloud based genomic analysis, and an electronic laboratory notebook. UDPICS provided a mechanism for efficient, transparent, and scalable translational research and thereby addressed many of the complex and diverse research and logistical problems of the NIH UDP. Full definition of the strengths and deficiencies of UDPICS will require formal qualitative and quantitative usability and process improvement measurement.
Phonological Processing in Primary Progressive Aphasia
Venue: Journal of Cognitive Neuroscience

Individuals with primary progressive aphasia (PPA) show selective breakdown in regions within the proposed dorsal (articulatory phonological) and ventral (lexical semantic) pathways involved in language processing. Phonological STM impairment, which has been attributed to selective damage to dorsal pathway structures, is considered to be a distinctive feature of the logopenic variant of PPA. By contrast, phonological abilities are considered to be relatively spared in the semantic variant and are largely unexplored in the nonfluent/agrammatic variant. Comprehensive assessment of phonological ability in the three variants of PPA has not been undertaken. We investigated phonological processing skills in a group of participants with PPA as well as healthy controls, with the goal of identifying whether patterns of performance support the dorsal versus ventral functional anatomical framework and to discern whether phonological ability differs among PPA subtypes. We also explored the neural bases of phonological performance using voxel based morphometry. Phonological performance was impaired in patients with damage to dorsal pathway structures (nonfluent/agrammatic and logopenic variants) with logopenic participants demonstrating particular difficulty on tasks involving nonwords. Binary logistic regression revealed that select phonological tasks predicted diagnostic group membership in the less fluent variants of PPA with a high degree of accuracy, particularly in conjunction with a motor speech measure. Brain behavior correlations indicated a significant association between the integrity of gray matter in frontal and temporoparietal regions of the left hemisphere and phonological skill. Findings confirm the critical role of dorsal stream structures in phonological processing and demonstrate unique patterns of impaired phonological processing in logopenic and nonfluent/agrammatic variants of PPA.
The Role of Inhibitory Control in Second Language Phonological Processing

This study investigated the role of inhibition in second language (L2) learners' phonological processing. Participants were Spanish learners of L2 English and American learners of L2 Spanish. We measured inhibition through a retrieval induced inhibition task. Accuracy of phonological representations (perception and production) was assessed through a speeded ABX categorization task and a delayed sentence repetition task. We used a measure of L2 vocabulary size to tease out L2 proficiency effects. Higher inhibitory control was related to lower error rate in segmental perception. Inhibition was also related to consonant but not to vowel production accuracy. These results suggest a potential role for inhibition in L2 phonological acquisition, with inhibition enhancing the processing of phonologically relevant acoustic information in the L2 input, which in turn might lead to more accurate L2 phonological representations.
Multi Objective Virtual Machine Allocation in Cloud Data Centers
Venue: 2016 5th IEEE International Conference on Cloud Networking (Cloudnet)

In this paper, we propose a Virtual Machine (VM) allocator for Cloud Computing Data Center (DC) We allocate a set of VMs on servers that are interconnected through a three tier fat tree network topology. VMs require four different resources: CPU, memory, disk, and bi directional network bandwidth forcommunications directed to and coming from the external gateway. Our goal is not to overload computing devices (i.e. allocating more resource than servers' availability) while reducing servers and switches power consumption, in the current proposal, power consumption of each device follows a load proportional trend. The allocation problem is combinatorial and non convex, and it is a variant of the multi objective bin packing problem which is NP Hard. For these reasons, we solve the problem using a particular kind of heuristics called Multi Objective Genetic Algorithm (MOGA) and inspired by the natural process of evolution, MOGA is quite often able to effectively approximate complex problems, such us the one considered. We perform a comparison with a simplified and single objective formulation of the problem that is solved using CPLEX, while solutions are evaluated using specific quality indicators. The results show how the presented approach solves the allocation problem: MOGA retrieves good quality solutions in less than ten seconds allocating thousands of Vms and obtaining the sameresults as CPLEX.
Energy Efficient Dynamic Resource Scheduling for Cloud Data Center

Massive scale cloud data centers host many applications and comprise of millions of servers thus consuming more power than ever. In order to efficiently manage the power usage of these data centers, Green computing offers schemes like load balancing across physical machines, live migration of virtual machines and Sever Consolidation which aims at minimizing the number of Active Physical Machines (APM) Server consolidation is a result of Virtual Machine (VM) scheduling which involves VM selection, VM placement and VM placement re optimization. In this paper, we present a VM placement optimization technique used in green cloud, particularly based on the classical problem of Bin Packing. Bin packing is inspired by the NP Hard knapsack problem and reduces the total number of Active Physical Machines (APM) Further these placements are optimized using Rank based VM scheduling algorithm. The proposed approach subsequently reduces the energy consumption and provides improved server consolidation.