Classification of CT brain images based on deep learning networks

While Computerised Tomography (CT) may have been the first imag-ing tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimers disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great ex-tent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early di-agnosis of Alzheimers disease. Towards this end, three categories of CT images (N=285) are clustered into three groups, which are AD, Lesion (e.g. tumour) and Normal ageing. In addition, considering the character-istics of this collection with larger thickness along the direction of depth (z) (∼3-5mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification ac-curacy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, Lesion and Normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6+-1:10, 86.3+-1:04, 85.2+-1:60, 83.1+-0:35 for 2D CNN, 2D SIFT, 2DKAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information rooted in both 2D slices and 3D blocks of CT images and an elaborated hand-crated approach of 3D KAZE

A new approach to estimation of non-isotropic scale factors for correction of MR distortion.

Purpose: When performing image-guided neurosurgery, MR images are widely applied for the planning of surgical path. However, a MR image sometimes suffers geometry distortion, limiting the surgical outcome. Correction of geometry distortions are thus performed prior to the surgical operation, which is normally in the reference of CT images. Usually distortions can be system inherent, e.g., field inhomogeneity, or patient induced, such as wearing implantable devices, and are detected using the fiducial markers from a head frame. By registration of the markers located from both MR and CT images, it is expected the distorted or transformed parameters from MR images can be found. As such, most existing approaches apply the work developed by Arun et al to locate translate and rotate matrixes using least-squares technique, which however does not take scale transformation into account and has since been extended to include an isotropic scaling. In our study, it is found that the scale factors are not the same along 3 axial directions of a MR image, i.e, with nonisotropic scale, necessitating the need to find scale matrix as well as the other transformation matrixes

Effect of uniaxial strain on the structural and magnetic phase transitions in BaFe2_2As2_2

We report neutron scattering experiments probing the influence of uniaxial strain on both the magnetic and structural order parameters in the parent iron pnictide compound, BaFe$_2$As$_2$. Our data show that modest strain fields along the in-plane orthorhombic b-axis can affect significant changes in phase behavior simultaneous to the removal of structural twinning effects. As a result, we demonstrate in BaFe$_2$As$_2$ samples detwinned via uniaxial strain that the in-plane C$_4$ symmetry is broken by \textit{both} the structural lattice distortion \textit{and} long-range spin ordering at temperatures far above the nominal (strain-free), phase transition temperatures. Surprising changes in the magnetic order parameter of this system under relatively small strain fields also suggest the inherent presence of magnetic domains fluctuating above the strain-free ordering temperature in this material.Comment: 4 pages, 3 figure

Overexpression AtNHX1 confers salt-tolerance of transgenic tall fescue

Saline soil is a serious problem worldwide, and it is necessary to improve the salt tolerance of plants so as to avoid the progressive deterioration of saline soil. Here we report that over-expression of AtNHX1 improves salt tolerance in transgenic tall fescue. The AtNHX1 gene driven with CaMV35S promoter wasconstructed into the plant expression vector pGreen0229, and introduced into the embryonic calli of hypocotyls of tall fescue (Festuca arundinacea ) by particle bombardment. Regenerated plantlets were obtained by screening of herbicide (PPT, 2 mg/L), and the putative transformants were assayed by PCRand western blot analysis. 29 transgenic plants were obtained. The results indicated that the exogenous genes had been integrated into the genomes of transgenic plants, and AtNHX1 is expressed in the plants. There was remarkable salt tolerance in transgenic plants compared to control plant

A general equilibrium analysis on the impacts of regional and sectoral emission allowance allocation at carbon trading market

It is critical to adapt to climate change and reduce the overall carbon emissions. China announced its Nationally Determined Contributions (NDC) at the Paris climate conference in 2015. The carbon cap-and trade scheme, which plays a key role in carbon emissions abatement, is an effective policy for China to achieve its NDC. This study focuses on the allocation of regional and sectoral initial carbon emission allowances in Shanghai. An impact evaluation on the macro-economy, carbon trading markets and participating sectors for the year 2030 was conducted by applying a computable general equilibrium (CGE) model. The results show that the carbon cap-and-trade scheme would cause a 3.4% GDP loss and an 8.9% welfare loss in 2030. The carbon price would be 161.2 USD/t and 147.2 USD/t under the two representative scenarios. The allocation of initial allowances would have a significant impact on both carbon market scale and sectoral trading behaviors. The power generation sector and the petrol oil sector would undertake the greatest output loss, while the metal smelting sector would become the main seller. Furthermore, the initial allowances allocation under a certain abatement target would hardly affect sectoral production but remarkably affect trade behaviors at the carbon trading markets

An Edge-Cloud Collaboration Framework for Generative AI Service Provision with Synergetic Big Cloud Model and Small Edge Models

Generative artificial intelligence (GenAI) offers various services to users through content creation, which is believed to be one of the most important components in future networks. However, training and deploying big artificial intelligence models (BAIMs) introduces substantial computational and communication overhead. This poses a critical challenge to centralized approaches, due to the need of high-performance computing infrastructure and the reliability, secrecy and timeliness issues in long-distance access of cloud services. Therefore, there is an urging need to decentralize the services, partly moving them from the cloud to the edge and establishing native GenAI services to enable private, timely, and personalized experiences. In this paper, we propose a brand-new bottom-up BAIM architecture with synergetic big cloud model and small edge models, and design a distributed training framework and a task-oriented deployment scheme for efficient provision of native GenAI services. The proposed framework can facilitate collaborative intelligence, enhance adaptability, gather edge knowledge and alleviate edge-cloud burden. The effectiveness of the proposed framework is demonstrated through an image generation use case. Finally, we outline fundamental research directions to fully exploit the collaborative potential of edge and cloud for native GenAI and BAIM applications

Breakdown effect of periodic perturbations to the robustness of topological phase\\ in a gyromagnetic photonic crystal

In the known field of topological photonics, what remains less so is the breakdown effect of topological phases deteriorated by perturbation. In this paper, we investigate the variance on topological invariants for a periodic Kekul\'e medium perturbed in unit cells, which was a gyromagnetic photonic crystal holding topological phases induced by \emph{synchronized rotation} of unit cells. Two parameters for geometric and material perturbation are respectively benchmarked to characterise the topological degradation. Our calculation demonstrates that such a periodic perturbation easily destructs the topological phase, and thus calls for further checkups on robustness under such unit-cell-perturbation in realization.Comment: 9 pages, 6 figures, 1 table, re-submitted to Phys. Lett.