Field-Trial of Machine Learning-Assisted Quantum Key Distribution (QKD) Networking with SDN

We demonstrated, for the first time, a machine-learning method to assist the coexistence between quantum and classical communication channels. Software-defined networking was used to successfully enable the key generation and transmission over a city and campus network

Generalization of hysteresis modeling to anisotropic materials

An extension to the model of hysteresis has been presented earlier which included the effect of anisotropy in the modeling of the anhysteretic magnetization curves of uniaxially anisotropic single crystalline materials. Further exploration of this extension shown here considers different kinds of crystal anisotropy in materials. Theory considers that the differential susceptibility at any given field is determined by the displacement of the prevailing magnetization from the anhysteretic magnetization. Thus, it has been shown that the effect of anisotropy on magnetic hysteresis in materials can be incorporated into the model of hysteresis through the anisotropic anhysteretic. This extension is likely to be particularly useful in the case of hard magnetic materials which exhibit high anisotropy

Far-infrared optical properties of the pyrochlore spin ice compound Dy2Ti2O4

Near normal incident far-infrared reflectivity spectra of [111] dysprosium titanate (Dy2Ti2O4) single crystal have been measured at different temperatures. Seven phonon modes (eight at low temperature) are identified at frequency below 1000 cm-1. Optical conductivity spectra are obtained by fitting all the reflectivity spectra with the factorized form of the dielectric function. Both the Born effective charges and the static optical primitivity are found to increase with decreasing temperature. Moreover, phonon linewidth narrowering and phonon modes shift with decreasing temperature are also observed, which may result from enhanced charge localization. The redshift of several low frequency modes is attributed to the spin-phonon coupling. All observed optical properties can be explained within the framework of nearest neighbor ferromagnetic(FM) spin ice model

Auto-tracking system for human lumbar motion analysis

Previous lumbar motion analyses suggest the usefulness of quantitatively characterizing spine motion. However, the application of such measurements is still limited by the lack of user-friendly automatic spine motion analysis systems. This paper describes an automatic analysis system to measure lumbar spine disorders that consists of a spine motion guidance device, an X-ray imaging modality to acquire digitized video fluoroscopy (DVF) sequences and an automated tracking module with a graphical user interface (GUI). DVF sequences of the lumbar spine are recorded during flexion-extension under a guidance device. The automatic tracking software utilizing a particle filter locates the vertebra-of-interest in every frame of the sequence, and the tracking result is displayed on the GUI. Kinematic parameters are also extracted from the tracking results for motion analysis. We observed that, in a bone model test, the maximum fiducial error was 3.7%, and the maximum repeatability error in translation and rotation was 1.2% and 2.6%, respectively. In our simulated DVF sequence study, the automatic tracking was not successful when the noise intensity was greater than 0.50. In a noisy situation, the maximal difference was 1.3 mm in translation and 1° in the rotation angle. The errors were calculated in translation (fiducial error: 2.4%, repeatability error: 0.5%) and in the rotation angle (fiducial error: 1.0%, repeatability error: 0.7%). However, the automatic tracking software could successfully track simulated sequences contaminated by noise at a density ≤ 0.5 with very high accuracy, providing good reliability and robustness. A clinical trial with 10 healthy subjects and 2 lumbar spondylolisthesis patients were enrolled in this study. The measurement with auto-tacking of DVF provided some information not seen in the conventional X-ray. The results proposed the potential use of the proposed system for clinical applications. © 2011 - IOS Press and the authors. All rights reserved.postprin

Modeling ϵ\epsilon Eridani and asteroseismic tests of element diffusion

Taking into account the helium and metal diffusion, we explore the possible evolutionary status and perform seismic analysis of MOST target: the star $\epsilon$ Eridani. We adopt the different input parameters to construct the models by fitting the available observational constraints: e.g., $T_{eff}$, $L$, $R$, $[Fe/H]$. From computation, we obtain the average large spacings of $\epsilon$ Eridani about $194\pm 1 \mu$Hz. The age of the diffused models has been found to be about 1 Gyr, which is younger than one determined previously by models without diffusion. We found that the effect of pure helium diffusion on the internal structure of the young low-mass star is slight, but the metal diffusion influence is obvious. The metal diffusion leads the models to have much higher temperature in the radiation interior, correspondingly the higher sound speed in the interior of the model, thereby the larger frequency and spacings.Comment: 16 pages, 4 figures, accepted for publication in ChjA

Impact of Social Network and Business Model on Innovation Diffusion of Electric Vehicles in China

The diffusion of electric vehicles (EVs) involves not only the technological development but also the construction of complex social networks. This paper uses the theory of network control to analyze the influence of network forms on EV diffusion in China, especially focusing on the building of EV business models (BMs) and the resulting effects and control on the diffusion of EVs. The Bass model is adopted to forecast the diffusion process of EVs and genetic algorithm is used to estimate the parameters based on the diffusion data of Hybrid Electric Vehicle (HEV) in the United States and Japan. Two different social network forms and BMs are selected, that is, battery leasing model and vehicle purchasing model, to analyze how different network forms may influence the innovation coefficient and imitation coefficient in the Bass model, which will in turn result in different diffusion results. Thereby, we can find the appropriate network forms and BMs for EVs which is suitable to the local market conditions

Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201

Macronutrient composition of three cucurbit species cultivated for seed consumption in Côte d’Ivoire

Dry seeds from three indigenous cucurbits [Citrullus lanatus var. citroides (Thumb.) Matsum. & Nakai.,Cucumeropsis mannii Naudin, and Cucumis melo var. agrestis L.] largely cultivated in Côte d’Ivoire andconsumed as sauce thickeners were analyzed for their proximate composition and compared to a locallandrace of peanut (Arachis hypogae L.). The protein contents were 29.23±1.74, 36±2.17, 29.55±2.09, and24.79±0.44% for C. lanatus, C. mannii, C. melo, and A. hypogaea, respectively. The highest estimates offat content was observed with C. lanatus (56.67±4.90%) followed in decreased order by the peanut(48.17±1.60%), C. mannii (45.89±4.73%), and C. melo (42.67±3.43%). The carbohydrate content for C.lanatus was 9.87±3.52% and C. mannii and C. melo had 13.86±3.64 and 23.18±4.80%, respectively. C.melo was then the highest in carbohydrate content whereas A. hypogaea has the lowest value(6.39±2.66%). The crude fibre contents for C. lanatus, C. mannii, and C. melo averaged 2.87±1.07,2.30±0.85, and 2.94±0.75%, respectively. The three cucurbit species were markedly low in fibre value,compared to the analyzed peanut (17.14±3.82%). As expected on the basis of several published data,ash content of seeds from indigenous cucurbits was generally low: 1.33±0.52% (C. lanatus), 2.50±1.38%(C. mannii), and 1.67±0.82% (C. melo)