Density propagation based adaptive multi-density clustering algorithm

This research was supported by the Science & Technology Development Foundation of Jilin Province (Grants Nos. 20160101259JC, 20180201045GX), the National Natural Science Foundation of China (Grants No. 61772227) and the Natural Science Foundation of Xinjiang Province (Grants No. 2015211C127). This resarch is also supported by the Engineering and Physical Sciences Research Council (EPSRC) funded project on New Industrial Systems: Manufacturing Immortality (EP/R020957/1).Peer reviewedPublisher PD

Clustering Single-cell RNA-sequencing Data based on Matching Clusters Structures

Single-cell sequencing technology can generate RNA-sequencing data at the single cell level, and one important single-cell RNA-sequencing data analysis method is to identify their cell types without supervised information. Clustering is an unsupervised approach that can help find new insights into biology especially for exploring the biological functions of specific cell type. However, it is challenging for traditional clustering methods to obtain high-quality cell type recognition results. In this research, we propose a novel Clustering method based on Matching Clusters Structures (MCSC) for identifying cell types among single-cell RNA-sequencing data. Firstly, MCSC obtains two different groups of clustering results from the same K-means algorithm because its initial centroids are randomly selected. Then, for one group, MCSC uses shared nearest neighbour information to calculate a label transition matrix, which denotes label transition probability between any two initial clusters. Each initial cluster may be reassigned if merging results after label transition satisfy a consensus function that maximizes structural matching degree of two different groups of clustering results. In essence, the MCSC may be interpreted as a label training process. We evaluate the proposed MCSC with five commonly used datasets and compare MCSC with several classical and state-of-the-art algorithms. The experimental results show that MCSC outperform other algorithms

LatinPSO : An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models

This research is supported by the National Natural Science Foundation of China (Grants Nos.61772227, 61572227), the Science & Technology Development Foundation of Jilin Province (Grants No. 20180201045GX), the Science Foundation of Education Department of Guangdong Province (Grants Nos. 2017KQNCX251, 2018XJCQSQ026) and the Social Science Foundation of Education Department of Jilin Province (Grants No. JJKH20181315SK). WP was supported by the 2015 Scottish Crucible award funded by Royal Society of Edinburgh.Peer reviewedPostprin

A systematic density-based clustering method using anchor points

National Research Foundation (NRF) Singapore under its AI Singapore Programme; Singapore Ministry of Health under its National Innovation Challenge on Active and Confident Agein

A Linear Fitting Density Peaks Clustering Algorithm for Image Segmentation

Clustering by fast search and finding of density peaks algorithm (DPC) is a recently developed method and can obtain promising results. However, DPC needs users to determine the number of clusters in advance, thus the clustering results are unstable and deeply influenced by the number of clusters. To address this issue, we proposed a novel algorithm, namely LDPC (Linear fitting Density Peaks Clustering algorithm). LDPC uses a novel linear fitting method to choose cluster centres automatically. In the experiments, we use public datasets to access the effectiveness of LDPC. Especially, we applied LDPC to image segmentation tasks. The experimental results show that LDPC can obtain competitive results compared with other clustering algorithms

Controlling electron motion with attosecond precision by shaped femtosecond intense laser pulse

We propose the scheme of temporal double-slit interferometer to precisely measure the electric field of shaped intense femtosecond laser pulse directly, and apply it to control the electron tunneling wave packets in attosecond precision. By manipulating the spectra phase of the input femtosecond pulse in frequency domain, one single pulse is split into two sub-pulses whose waveform can be precisely controlled by adjusting the spectra phase. When the shaped pulse interacts with atoms, the two sub-pulses are analogous to the Young's double-slit in time domain. The interference pattern in the photoelectron momentum distribution can be used to precisely retrieve the peak electric field and the time delay between two sub-pulses. Based on the precise characterization of the shaped pulse, we demonstrate that the sub-cycle dynamics of electron can be controlled with attosecond precision. The above scheme is proved to be feasible by both quantum-trajectory Monte Carlo simulations and numerical solutions of three-dimensional time-dependent Schr\"{o}dinger equation.Comment: 10 pages,4 figure

A Local Density Shape Context Algorithm for Point Pattern Matching in Three Dimensional Space

Three dimensional space point pattern matching technology shows significant usage in many scientific fields. It is a great challenge to match pairwise with rigid transformation in three dimensional space. In this paper, we propose an effect of Local Density Shape Context algorithm (LDSC). In LDSC, the point local density is firstly used for cutting down the negative impacting on extracting the feature descriptor. And the optimization of pairwise matching is firstly used in LDSC for improving the effectiveness. To demonstrate the performance of LDSC, we conduct experiments on synthetic datasets and real world datasets. The experimental results indicate that LDSC outperforms the three compared classical methods in most cases. LDSC is robust to outliers and noise

Microstructure and Mechanical Properties of Spray-Formed H13 Steel Tooling

This paper presents results on the microstructure and hardness of spray-formed H13 (Fe-0.40C-5.00Cr-1.10V-1.30Mo (wt%)) tooling. There is very low porosity in both as-spray formed samples and aged samples. The microstructure in the as-spray-formed sample is characterized by primary carbides, acicular lower bainite, and a small amount of martensite and of retained austenite. Spray formed and aged tooling H13 has higher hardness values than those of H13 in conventional tooling. The experimental results of microstructures and hardness are rationalized on the basis of numerical analysis of cooling during processing of spray-formed tooling

Precise Measurements of Branching Fractions for Ds+D_s^+ Meson Decays to Two Pseudoscalar Mesons

We measure the branching fractions for seven $D_{s}^{+}$ two-body decays to pseudo-scalar mesons, by analyzing data collected at $\sqrt{s}=4.178\sim4.226$ GeV with the BESIII detector at the BEPCII collider. The branching fractions are determined to be $\mathcal{B}(D_s^+\to K^+\eta^{\prime})=(2.68\pm0.17\pm0.17\pm0.08)\times10^{-3}$, $\mathcal{B}(D_s^+\to\eta^{\prime}\pi^+)=(37.8\pm0.4\pm2.1\pm1.2)\times10^{-3}$, $\mathcal{B}(D_s^+\to K^+\eta)=(1.62\pm0.10\pm0.03\pm0.05)\times10^{-3}$, $\mathcal{B}(D_s^+\to\eta\pi^+)=(17.41\pm0.18\pm0.27\pm0.54)\times10^{-3}$, $\mathcal{B}(D_s^+\to K^+K_S^0)=(15.02\pm0.10\pm0.27\pm0.47)\times10^{-3}$, $\mathcal{B}(D_s^+\to K_S^0\pi^+)=(1.109\pm0.034\pm0.023\pm0.035)\times10^{-3}$, $\mathcal{B}(D_s^+\to K^+\pi^0)=(0.748\pm0.049\pm0.018\pm0.023)\times10^{-3}$, where the first uncertainties are statistical, the second are systematic, and the third are from external input branching fraction of the normalization mode $D_s^+\to K^+K^-\pi^+$. Precision of our measurements is significantly improved compared with that of the current world average values

Degassing Behavior of Nanostructured Al and Its Composites

The synthesis of bulk ultrafine-grained (UFG) and nanostructured Al via cryomilling can frequently require a degassing step prior to consolidation, partly due to the large surface area of the as-milled powders. The objective of this study is to investigate the effects associated with cryomilling with stearic acid additions (as a process-control agent) on the degassing behavior of Al powders. This objective was accomplished by completing select experiments with Al-7.5Mg, Al-6.4 wt pct Al85Ni10La5, and Al-14.3 wt pct B4C. The interaction between Al and stearic acid was determined using thermal analysis combined with Fourier transform infrared spectroscopy (FTIR). The degassing experiments were carried out under high vacuum (10−4 to ~10−6 torr) in a range from room temperature to 400 °C, with the pressure of the released gases monitored using a digital vacuum gage. The results showed that the liberation of chemisorbed water was suppressed in cryomilled Al powders and both the chemisorbed water and stearic acid were primarily released in the form of hydrogen. It was also demonstrated that under certain conditions, a nanostructure (grain size ~100 nm) can be retained following the hot vacuum degassing of cryomilled Al