nils-herrmann/clean_scirepeval huggingface dataset:
A Highly Precise Demodulation Method for Fiber Fabry-Perot Cavity Through Spectrum Reconstruction

A highly precise demodulation method based on spectrum sampling and reconstruction for optical fiber extrinsic Fabry–Perot interferometer (EFPI) sensor is proposed and experimentally evaluated. A tunable laser and an optical power meter were used to sample the response of the EFPI sensor discretely. The full spectral response of the EFPI sensor is obtained through a nonlinear least squares fitting between the measured data and the theoretical model. An EFPI sensor was experimentally tested to verify the performance of the proposed demodulation method. The demodulation fluctuation range of the cavity length was within 0.84 nm, during 50 continuous measurements. The demodulation resolution of 4.59 nm was achieved for the change of cavity length around  $112~\mu \text{m}$  in a strain sensing experiment. Furthermore, the maximum measurement fluctuation of the cavity length just increased from 0.84 to 1.27 nm, when the number of sampling points decreased to one-third by increasing the sampling step from 0.4 to 1.2 nm. This demodulation method may be applied to a wide range of interference spectrum demodulation applications for the high precision and simplicity.
Compensation of the beam dynamics effects caused by the extraction lambertson septum of the higs booster

As part of the high intensity gamma-ray source (HIGS) upgrade, the booster synchrotron has been built and commissioned in 2006. It ramps the electron beam between 0.24 and 1.2 GeV for top-off injection into the Duke storage ring. The booster has vertical injection/extraction which uses symmetrical schemes with a bumped orbit. The injection/extraction kickers and corresponding septum magnets are located in the opposite straight sections of the booster ring separated by about 1/4 of the vertical betatron wave. Due to the non-ideal properties of the magnetic material, the magnetic field leaks out into the stored beam chamber, which results in orbit distortion, tune and chromaticity shifts, and change of coupling. The dynamics impacts due to the leaked septum fields have been successfully compensated to achieve good injection efficiency.
Heteroscedastic max-min distance analysis

Many discriminant analysis methods such as LDA and HLDA actually maximize the average pairwise distances between classes, which often causes the class separation problem. Max-min distance analysis (MMDA) addresses this problem by maximizing the minimum pairwise distance in the latent subspace, but it is developed under the homoscedastic assumption. This paper proposes Heteroscedastic MMDA (HMMDA) methods that explore the discriminative information in the difference of intra-class scatters for dimensionality reduction. WHMMDA maximizes the minimal pairwise Chenoff distance in the whitened space. OHMMDA incorporates this objective and the minimization of class compactness into a trace quotient formulation and imposes an orthogonal constraint to the final transformation, which can be solved by a bisection search algorithm. Two variants of OHMMDA are further proposed to encode the margin information. Experiments on several UCI Machine Learning datasets and the Yale Face database demonstrate the effectiveness of the proposed HMMDA methods.
Notice of Retraction“People-oriented” incentives for organizational knowledge management

Competitiveness of the enterprises depends on the valuable and irreplaceable knowledge and resources inside the organization. The ability of acquiring, sharing and innovating knowledge effective can promote the development of the organization. But in the process of implementing knowledge management will meet some obstacles, one reason is that the traditional incentive mechanism does not adapt to the present development of knowledge management. Based on the organizational behavior management and the psychology, combined with the characteristics of Chinese organizations, this essay proposed the “people-oriented” incentive mechanism and the corresponding measures.
Determinants of Customer Purchase Intention in Electronic Service

Compared to traditional service, the characteristics of the customer behavior in electronic service are personalized demand, convenient consumed circumstance and perceptual consumer behavior. Therefore, customer behavior is an important factor to facilitate online electronic service. The purpose of this study is to explore the key success factors affecting customer purchase intention of electronic service through the behavioral perspectives of customers. Based on the theory of technology acceptance model (TAM) and self service technology (SST), the study proposes a theoretical model for the empirical examination of the customer intention for purchasing electronic services. A comprehensive survey of online customers having e-shopping experiences is undertaken. Then this model is tested by means of the statistical analysis method of structure equation model (SEM). The empirical results indicated that perceived usefulness and perceived assurance have a significant impact on purchase in e-service. Discussion and implication are presented in the end.
A Distributed cooperation strategy in cognitive radio networks

In this paper, we study how to improve the usage of wireless spectrum for secondary users (SUs) in cognitive radio networks (CRNs) by cooperative transmissions. There are two types of cooperation in CRNs, namely, vertical cooperation, which takes place between primary users (PUs) and SUs, and horizontal cooperation, which occurs among SUs. For vertical cooperation, PUs can lease their frequency bandwidth for a fraction of time to SUs in exchange for better transmission quality through cooperative transmission from the SUs. For horizontal cooperation, some SUs will help other SUs to increase their transmission throughput through cooperative transmissions without interfering with their neighboring PUs. In fact, both vertical cooperation and horizontal cooperation can improve the system throughput. Hence, if both types of cooperation can be supported in a CRN, the system throughput can be improved effectively. However, SUs cannot perform both types of cooperation simultaneously. Therefore, determining the kind of cooperation for an SU is an important issue on the system throughput improvement. We solve this problem via a greedy distributed solution in which the decision made by each SU depends only on the local information. We show by simulations that our proposed method outperforms traditional vertical cooperation schemes or horizontal cooperation schemes in terms of better resource utilization and higher system throughput.
Executable System-of-Systems architecting based on DoDAF meta-model

Methods used to gain executable architecture are all based on transforming view models to executable models, which can not take the advantage of “data-centric” approach adopted by DoDAF2.0. A novel executable architecting approach transforming DoDAF Meta-model (DM2) to executable model directly is proposed, which can overcame the limitation of traditional approaches that information consistency and data completeness may be damaged. An example to analyze Command and Control (C2) capability in Air Defense System-of-Systems (ADSoS) is provided in detail to illustrate the process to transform DM2 to Coloured Petri Net (CPN). Besides, the input and output data is also given in detail along with the process illustration.
Context-aware clustering

Most existing methods of semi-supervised clustering introduce supervision from outside, e.g., manually label some data samples or introduce constraints into clustering results. This paper studies an interesting problem: can the supervision come from inside, i.e., the unsupervised training data themselves? If the data samples are not independent, we can capture the contextual information reflecting the dependency among the data samples, and use it as supervision to improve the clustering. This is called context-aware clustering. The investigation is substantialized on two scenarios of (1) clustering primitive visual features (e.g., SIFT features) with help of spatial contexts, and (2) clustering dasia0psila-dasia9psila hand written digits with help of contextual patterns among different types of features. Our context-aware clustering can be well formulated in a closed-form, where the contextual information serves as a regularization term to balance the data fidelity in original feature space and the influences of contextual patterns. A nested-EM algorithm is proposed to obtain an efficient solution, which proves to converge. By exploring the dependent structure of the data samples, this method is completely unsupervised, as no outside supervision is introduced.
Efficient Online Local Metric Adaptation via Negative Samples for Person Re-identification

Many existing person re-identification (PRID) methods typically attempt to train a faithful global metric offline to cover the enormous visual appearance variations, so as to directly use it online on various probes for identity match- ing. However, their need for a huge set of positive training pairs is very demanding in practice. In contrast to these methods, this paper advocates a different paradigm: part of the learning can be performed online but with nominal costs, so as to achieve online metric adaptation for different input probes. A major challenge here is that no positive training pairs are available for the probe anymore. By only exploiting easily-available negative samples, we propose a novel solution to achieve local metric adaptation effectively and efficiently. For each probe at the test time, it learns a strictly positive semi-definite dedicated local metric. Comparing to offline global metric learning, its com- putational cost is negligible. The insight of this new method is that the local hard negative samples can actually provide tight constraints to fine tune the metric locally. This new local metric adaptation method is generally applicable, as it can be used on top of any global metric to enhance its performance. In addition, this paper gives in-depth the- oretical analysis and justification of the new method. We prove that our new method guarantees the reduction of the classification error asymptotically, and prove that it actually learns the optimal local metric to best approximate the asymptotic case by a finite number of training data. Extensive experiments and comparative studies on almost all major benchmarks (VIPeR, QMUL GRID, CUHK Campus, CUHK03 and Market-1501) have confirmed the effectiveness and superiority of our method.
Brain dynamics of mathematical problem solving

The purpose of this study is to examine brain activities of participants solving mental math problems. The research investigated how problem difficulty affected the subjects' responses and electroencephalogram (EEG) in different brain regions. In general, it was found that solution latencies (SL) to the math problems increased with difficulty. The EEG results showed that across subjects, the right-central beta, left-parietal theta, left-occipital theta and alpha, right-parietal alpha and beta, medial-frontal beta and medial central theta power decreased as task difficulty increased. This study further explored the effects of problem-solving performance on the EEG. Slow solvers exhibited greater frontal theta activities in the right hemisphere, whereas an inverse pattern of hemispheric asymmetry was found in fast solvers. Furthermore, analyses of spatio-temporal brain dynamics during problem solving show progressively stronger alpha- and beta-power suppression and theta-power augmentation as subjects were reaching a solution. These findings provide a better understanding of cortical activities mediating math-based problem solving and knowledge acquisition that can ultimately benefit math learning and education.
The apparel higher education in China: Reform and development

China's apparel higher education has experienced significant transformations to accommodate the development of society and economy. One purpose of this paper is to review the current changes in apparel higher education through reform and reconstruction during the past two decades in a historical perspective. Another purpose is to identify the new challenges and problems met in terms of globalization, mass higher education, and higher technological revolution. Through analysis, the paper concludes that to produce graduates capable of participating and competing in the emerging global society, China apparel higher education should conduct complicated reform and reconstruction, including further international cooperation and exchanges, further computer application in curriculum, strengthening merchandising, marketing and global resourcing education, curricula updating and diversification, and seamless Linkage of knowledge of different major and specialization.
An Extensive Empirical Study of Automated Evaluation of Multi-Document Summarization

This paper discusses an approach to automated evaluation of multi-document summarization by computing the similarities of automated summaries and human summaries and scoring the automated summaries by their similarities to the human ones. Several schemes are used in our experiment, as well as the effects of stop words and stemming. Our method experimental result is compared to Rouge which is based on n-gram. The test materials for experiments are from DUC 2005 corpus. The results show that our novel scheme produces acceptable results, and may avoid some defects of n-gram.
A Novel Model for Independent RBF Neural Networks Employing Gabor-based Kernel PCA with Fractional Power Polynomial Models for Feature Extraction

A novel model for independent radial basis function (IRBF) neural network employing Gabor-based kernel PCA with fractional power polynomial models for feature extraction is proposed in this paper. In the new model, a bank of Gabor filters is first built to extract Gabor face representations characterized by selected frequency, locality and orientation to cope with various illuminations, facial expression and poses in face recognition. After extracting Gabor face representations for every face sample, a kernel PCA with fractional power polynomial models is chosen to extract high-order statistical features of extracted Gabor face representations. At last, a new IRBF neural network is built to classify these extracted high-order statistical features of Gabor face representations. According to the experiments on the famous CAS-PEAL face database, our proposed approach could outperform PCA, ICA with architecture II (ICA2) and kernel PCA (KPCA) with standing testing sets proposed in the current release disk of the CAS-PEAL face database
A tune measurement system for low current and energy ramping operation of a booster synchrotron

A betatron tune measurement system is one of the most important diagnostic tools for any circular accelerator. For the commissioning of a booster synchrotron newly developed for top-off injection into the Duke storage ring, a versatile tune measurement system employing a network analyzer, a short stripline kicker, a space filter and a photomultiplier tube has been developed to allow tune measurements over a wide range of beam energies from 0.24 to 1.2 GeV, and for low current measurements at a few micro-amperes. The signal from the highly sensitive optical detection system is also used for fast tune measurements with a realtime oscilloscope. This technique is being developed for live tune monitoring during booster energy ramping.
A Current Cycle Feedback Iterative Learning Control Approach for AFM Imaging

In this paper, we proposed a novel current cycle feedback (CCF) iterative learning control (ILC) approach to achieve high-speed imaging on atomic force microscope (AFM). AFM imaging requires precision positioning of the AFM probe relative to the sample in 3-D (x- y-z). It has been demonstrated that, with advanced control techniques such as the inversion-based iterative control (IIC), precision positioning of the AFM probe in the lateral (x- y) scanning can be successfully achieved. Precision positioning of the probe in the vertical z-axis direction, however, is still challenging because the issues such as the sample topography are unknown, in general; the probe-sample interaction is complicated, and the probe-sample position is sensitive to the probe-sample interaction. The main contribution of this paper is the development of the CCF-ILC approach for the AFM z-axis control, which decouples the robustness of the feedback control from the precision tracking of the feedforward control. Particularly, the proposed CCF-ILC controller design utilizes the recently developed robust inversion technique to minimize the model uncertainty effect on the feedforward control and to remove the causality constraints in other CCF-ILC approaches. It is shown that the iterative law converges and attains a bounded tracking error upon noise and disturbances. The proposed method is illustrated through experimental implementation, and the experimental results show an increase of eight times faster imaging speed for contact-mode imaging.
LAMP, A Lyrics and Audio MandoPop Dataset for Music Mood Estimation: Dataset Compilation, System Construction, and Testing

Music mood estimation (MME) is an emerging subfield in music information retrieval research. Whereas most MME research focuses on audio analysis, exploring the significance of lyrics in predicting song emotion has been receiving more attention in recent years. One major impediment to MME research is the lack of clearly-labeled and publicly-available datasets of separately annotated lyrics and audio. In the first section of this paper, we describe the creation of the LAMP dataset, containing 492 mandarin pop songs with separate mood annotations for lyrics text and audio music. Our second contribution is to demonstrate with statistical analysis on the LAMP dataset how lyrics and audio contribute individually to a song’s overall mood. Our analysis suggests that lyrics can serve as a valid measure for music mood estimation, especially in song valence, and provide supplementary mood information to audio. Thirdly, we propose the Sentiment Score Approach for extracting affective words from lyrics text and show that it is the most effective individual method for improving MME accuracy while reducing the number of features. Lastly, we combine our best lyrical feature configuration with audio features in an MME system for estimating song valence. This configuration outperforms audio-features-only by 16.517% and lyrical-features-only by 1.5%, suggesting strongly that lyrical features can be an important source of supplementary information for audio-music features when predicting song valence.
A 3.5–6.8-GHz Wide-Bandwidth DTC-Assisted Fractional-N All-Digital PLL With a MASH $\Delta \Sigma $ -TDC for Low In-Band Phase Noise

This paper proposes a digital-to-time converter (DTC)-assisted fractional-N wide-bandwidth all-digital phase-locked loop (ADPLL) with a fine-resolution time-to-digital converter (TDC). The TDC employs a two-channel time-interleaved time-domain register with an implicit adder/subtractor realizing an error-feedback topology. Such an error-feedback unit of a first-order <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula>-TDC can be cascaded as a multi-stage noise shaping configuration to achieve higher-order noise-shaping and, thereby, low in-band phase noise (PN) of the ADPLL. A digitally controlled oscillator with a transformer and a pair of cross-coupled NMOS amplifiers exploits magnetic and capacitive coupling to achieve nearly an octave frequency coverage, i.e., 1.73–3.38 GHz (after a <inline-formula> <tex-math notation="LaTeX">$\div $ </tex-math></inline-formula>2 division). Fabricated in 40-nm CMOS, the ADPLL achieves better than −110-dBc/Hz in-band PN and occupies an active area of 0.5 mm<sup>2</sup>. With a 50-MHz reference clock, a 2-GHz output RF clock, and a loop bandwidth of 800 kHz, this prototype achieves 420-fs<inline-formula> <tex-math notation="LaTeX">$_{{\mathrm{rms}}}$ </tex-math></inline-formula> jitter, integrated from 1-kHz to 30-MHz offset, while drawing 10.7 mW.
Manipulation of plasmonic resonances in graphene coated dielectric cylinders

Graphene sheets can support surface plasmon as the Dirac electrons oscillate collectively with electromagnetic waves. Compared with the surface plasmon in conventional metal (e.g., Ag and Au), graphene plasmonic owns many remarkable merits especially in Terahertz and far infrared frequencies, such as deep sub-wavelength, low loss, and high tunability. For graphene coated dielectric nano-scatters, localized surface plasmon (LSP)exist and can be excited under specific conditions. The LSPs are associated with the Mie resonance modes, leading to extraordinary large scattering and absorption cross section. In this work, we study systematically the optical scattering properties for graphene coated dielectric cylinders. It is found that the LSP can be manipulated by geometrical parameters and external electric gating. Generally, the resonance frequencies for different resonance modes are not the same. However, under proper design, we show that different resonance modes (e.g., dipole mode, quadruple mode etc.) can be excited at the same frequency. Thus, the scattering and absorption by graphene coated dielectric cylinders can indeed overcome the single channel limit. Our finding may open up new avenues in applications for the graphene-based THz optoelectronic devices.
An experimental iterative learning strategy for a biped performing semi-active walking

In this paper, an iterative learning strategy is proposed for a biped which performs semi-active walking in the way that during the single support phase of walking, only the joints in the support leg are actuated and those in the swing leg are unactuated. This strategy is intended to provide further experimental tuning on the actuated joint trajectories computed by a model-based optimization procedure. The strategy demands the biped to swing its leg repeatedly, and during each swinging the hip trajectory is iteratively modified by a learning law to minimize foot scuffing of the swing leg and yet keep the associated foot clearance to within a small limit for reducing power consumption. Experiments show that the learning strategy leads to a convergent hip trajectory after 11 iterations. Besides, when the trajectory is adopted for actual walking, the biped demonstrates a good balance between power efficiency and robustness to ground conditions.
The VUV/UV OK-5 Duke storage ring FEL with variable polarization

We discuss the design and expected performance of the OK-5 FEL, which will replace in 2002 the OK-4 FEL operating now at the Duke storage ring. The OK-5 FEL is the first distributed optical klystron with variable polarization. The design of wigglers and matching sections is flexible for providing optimal conditions for variety of operation modes. In this paper we present calculations of the main OK-5 FEL parameters such as its tuning range, gain and lasing power. Based on our calculations we present the predictions for the OK-5 gamma-ray source. We conclude with description of the current status of the OK-5 FEL and plans for its installation.