Privacy Data Decomposition and Discretization Method for SaaS Services

In cloud computing, user functional requirements are satisfied through service composition. However, due to the process of interaction and sharing among SaaS services, user privacy data tends to be illegally disclosed to the service participants. In this paper, we propose a privacy data decomposition and discretization method for SaaS services. First, according to logic between the data, we classify the privacy data into discrete privacy data and continuous privacy data. Next, in order to protect the user privacy information, continuous data chains are decomposed into discrete data chain, and discrete data chains are prevented from being synthesized into continuous data chains. Finally, we propose a protection framework for privacy data and demonstrate its correctness and feasibility with experiments

Scattering of 7^{7}Be and 8^{8}B and the astrophysical S17_{17} factor

Measurements of scattering of $^{7}$Be at 87 MeV on a melamine (C$_{3}$N$_{6}$H$_{6}$) target and of $^{8}$B at 95 MeV on C were performed. For $^{7}$Be the angular range was extended over previous measurements and monitoring of the intensity of the radioactive beam was improved. The measurements allowed us to check and improve the optical model potentials used in the incoming and outgoing channels for the analysis of existing data on the proton transfer reaction $^{14}$N($^{7}$Be,$^{8}$B)$^{13}$C. The resultslead to an updated determination of the asymptotic normalization coefficient for the virtual decay $^{8}$B $\to$ $^{7}$Be + $p$. We find a slightly larger value, $C_{tot}^{2}(^{8}B)=0.466\pm 0.047$ fm$^{-1}$, for the melamine target. This implies an astrophysical factor, $S_{17}(0)=18.0\pm 1.8$ eV$\cdot$b, for the solar neutrino generating reaction $^{7}$Be($p$,$\gamma$)$^{8}$B.Comment: 7 pages, 4 figure

Machine learning method for 12^{12}C event classification and reconstruction in the active target time-projection chamber

Active target time projection chambers are important tools in low energy radioactive ion beams or gamma rays related researches. In this work, we present the application of machine learning methods to the analysis of data obtained from an active target time projection chamber. Specifically, we investigate the effectiveness of Visual Geometry Group (VGG) and the Residual neural Network (ResNet) models for event classification and reconstruction in decays from the excited $2^+_2$ state in $^{12}$C Hoyle rotation band. The results show that machine learning methods are effective in identifying $^{12}$C events from the background noise, with ResNet-34 achieving an impressive precision of 0.99 on simulation data, and the best performing event reconstruction model ResNet-18 providing an energy resolution of $\sigma_E<77$ keV and an angular reconstruction deviation of $\sigma_{\theta}<0.1$ rad. The promising results suggest that the ResNet model trained on Monte Carlo samples could be used for future classifying and predicting experimental data in active target time projection chambers related experiments.Comment: 9 pages, 10 figures, 9 table

Synaptic Plasticity, a Prominent Contributor to the Anxiety in Fragile X Syndrome

Fragile X syndrome (FXS) is an inheritable neuropsychological disease caused by expansion of the CGG trinucleotide repeat affecting the fmr1 gene on X chromosome, resulting in silence of the fmr1 gene and failed expression of FMRP. Patients with FXS suffer from cognitive impairment, sensory integration deficits, learning disability, anxiety, autistic traits, and so forth. Specifically, the morbidity of anxiety in FXS individuals remains high from childhood to adulthood. By and large, it is common that the change of brain plasticity plays a key role in the progression of disease. But for now, most studies excessively emphasized the one-sided factor on the change of synaptic plasticity participating in the generation of anxiety during the development of FXS. Here we proposed an integrated concept to acquire better recognition about the details of this process