Quantum-Inspired Support Vector Machine

Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyzes data for both classification and regression, whose usual algorithm complexity scales polynomially with the dimension of data space and the number of data points. To tackle the big data challenge, a quantum SVM algorithm was proposed, which is claimed to achieve exponential speedup for least squares SVM (LS-SVM). Here, inspired by the quantum SVM algorithm, we present a quantum-inspired classical algorithm for LS-SVM. In our approach, a improved fast sampling technique, namely indirect sampling, is proposed for sampling the kernel matrix and classifying. We first consider the LS-SVM with a linear kernel, and then discuss the generalization of our method to non-linear kernels. Theoretical analysis shows our algorithm can make classification with arbitrary success probability in logarithmic runtime of both the dimension of data space and the number of data points for low rank, low condition number and high dimensional data matrix, matching the runtime of the quantum SVM

Reducing velocity error and its consequences by an iterative feedback immersed boundary method

The immersed boundary method (IBM) has attracted growing interest in the computational fluid dynamics (CFD) research community due to its simplicity in dealing with moving boundaries in fluid-structure interaction (FSI) systems. We present a study on streamline penetration, velocity error and consequences of a FSI solver based on an iterative feedback IBM. In the FSI, the fluid flows are solved by the lattice Boltzmann method; the solid structure deformation is solved by the finite difference method, and an iterative feedback IBM is used to realize the interaction between fluid and structure. The iteration can improve the no-slip and no-penetration boundary conditions at the fluid-solid interface. Four benchmark cases are simulated to study the reduced velocity error and its consequences: a uniform flow over a flapping foil, flow-induced vibration of a flexible plate attached behind a stationary cylinder in a channel, flow through a two-dimensional asymmetric stenosis and a one-sided collapsible channel. Results show that the iterative IBM can suppress the boundary-slip error and spurious flow penetration on the solid wall. While the iterative IBM does not have significant effect on the force production and structure deformation for external flows, it significantly improves the prediction of the force distribution and structure deformation for internal flows. The increased computational cost incurred by the iteration can be largely reduced by increasing the feedback coefficient. This study will provide a better understanding of the feedback IBM and a better option for the CFD community

Non-receptor tyrosine kinase Src is required for ischemia-stimulated neuronal cell proliferation via Raf/ERK/CREB activation in the dentate gyrus

<p>Abstract</p> <p>Background</p> <p>Neurogenesis in the adult mammalian hippocampus may contribute to repairing the brain after injury. However, Molecular mechanisms that regulate neuronal cell proliferation in the dentate gyrus (DG) following ischemic stroke insult are poorly understood. This study was designed to investigate the potential regulatory capacity of non-receptor tyrosine kinase Src on ischemia-stimulated cell proliferation in the adult DG and its underlying mechanism.</p> <p>Results</p> <p>Src kinase activated continuously in the DG 24 h and 72 h after transient global ischemia, while SU6656, the Src kinase inhibitor significantly decreased the number of bromodeoxyuridine (BrdU) labeling-positive cells of rats 7 days after cerebral ischemia in the DG, as well as down-regulated Raf phosphorylation at Tyr(340/341) site, and its down-stream signaling molecules ERK and CREB expression followed by 24 h and 72 h of reperfusion, suggesting a role of Src kinase as an enhancer on neuronal cell proliferation in the DG via modifying the Raf/ERK/CREB cascade. This hypothesis is supported by further findings that U0126, the ERK inhibitor, induced a reduction of adult hippocampal progenitor cells in DG after cerebral ischemia and down-regulated phospho-ERK and phospho-CREB expression, but no effect was detected on the activities of Src and Raf.</p> <p>Conclusion</p> <p>Src kinase increase numbers of newborn neuronal cells in the DG via the activation of Raf/ERK/CREB signaling cascade after cerebral ischemia.</p

Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation

Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field.Comment: Please feel free to email He-Liang Huang with any comments, questions, suggestions or concern

The impact of renal function on the prognostic value of N-terminal pro–B-type natriuretic peptide in patients with coronary artery disease

Background: The impact of renal function on the prognostic value of N-terminal pro–B-type natriureticpeptide (NT-proBNP) remains unclear in coronary artery disease (CAD). This study sought toinvestigate the value of using NT-proBNP level to predict prognoses of CAD patients with differentestimated glomerular filtration rates (eGFRs).Methods: A retrospective analysis was conducted from a single registered database. 2087 consecutivepatients with CAD confirmed by coronary angiography were enrolled. The primary endpoint was allcausemortality.Results: The mean follow-up time was 26.4 ± 11.9 months and death events occurred in 197 cases.The NT-proBNP levels increased with the deterioration of renal function, as well as the optimal cutoffvalues based on eGFR stratification to predict endpoint outcome (179.4 pg/mL, 1443.0 pg/mL,3478.0 pg/mL, for eGFR ≥ 90, 60–90 and &lt; 60 mL/min/1.73 m2, respectively). Compared with theroutine cut-off value or overall optimal one, stratified optimal ones had superior predictive ability forendpoint in each eGFR group (all with the highest Youden’s J statistics). And the prognostic value becameweaker as eGFR level decreased (eGFR ≥ 90 vs. 60–90 vs. &lt; 60 mL/min/1.73 m2, odds ratio [OR]7.7; 95% confidence interval [CI] 1.7–33.9 vs. OR 4.8; 95% CI 2.7–8.5 vs. OR 3.0; 95% CI 1.5–6.2).Conclusions: This study demonstrated that NT-proBNP exhibits different predictive values for prognosisfor CAD patients with different levels of renal function. Among the assessed values, the NT-proBNPcut-off value determined using renal function improve the accuracy of the prognosis prediction of CAD.Moreover, lower eGFR is associated with a higher NT-proBNP cut-off value for prognostic prediction

Robust and clean Majorana zero mode in the vortex core of high-temperature superconductor (Li0.84Fe0.16)OHFeSe

The Majorana fermion, which is its own anti-particle and obeys non-abelian statistics, plays a critical role in topological quantum computing. It can be realized as a bound state at zero energy, called a Majorana zero mode (MZM), in the vortex core of a topological superconductor, or at the ends of a nanowire when both superconductivity and strong spin orbital coupling are present. A MZM can be detected as a zero-bias conductance peak (ZBCP) in tunneling spectroscopy. However, in practice, clean and robust MZMs have not been realized in the vortices of a superconductor, due to contamination from impurity states or other closely-packed Caroli-de Gennes-Matricon (CdGM) states, which hampers further manipulations of Majorana fermions. Here using scanning tunneling spectroscopy, we show that a ZBCP well separated from the other discrete CdGM states exists ubiquitously in the cores of free vortices in the defect free regions of (Li0.84Fe0.16)OHFeSe, which has a superconducting transition temperature of 42 K. Moreover, a Dirac-cone-type surface state is observed by angle-resolved photoemission spectroscopy, and its topological nature is confirmed by band calculations. The observed ZBCP can be naturally attributed to a MZM arising from this chiral topological surface states of a bulk superconductor. (Li0.84Fe0.16)OHFeSe thus provides an ideal platform for studying MZMs and topological quantum computing.Comment: 32 pages, 15 figures (supplementary materials included), accepted by PR

Improving the prediction of overall survival for head and neck cancer patients using image biomarkers in combination with clinical parameters

Purpose: To develop and validate prediction models of overall survival (OS) for head and neck cancer (HNC) patients based on image biomarkers (IBMs) of the primary tumor and positive lymph nodes (Ln) in combination with clinical parameters. Material and methods: The study cohort was composed of 289 nasopharyngeal cancer (NPC) patients from China and 298 HNC patients from the Netherlands. Multivariable Cox-regression analysis was performed to select clinical parameters from the NPC and HNC datasets, and IBMs from the NPC dataset. Final prediction models were based on both IBMs and clinical parameters. Results: Multivariable Cox-regression analysis identified three independent IBMs (tumor Volume density, Run Length Non-uniformity and Ln Major-axis-length). This IBM model showed a concordance (c)-index of 0.72 (95%Cl: 0.65-0.79) for the NPC dataset, which performed reasonably with a c-index of 0.67 (95%Cl: 0.62-0.72) in the external validation HNC dataset. When IBMs were added in clinical models, the c-index of the NPC and HNC datasets improved to 0.75 (95%Cl: 0.68-0.82; p = 0.019) and 0.75 (95%Cl: 0.70-0.81; p <0.001), respectively. Conclusion: The addition of IBMs from the primary tumor and Ln improved the prognostic performance of the models containing clinical factors only. These combined models may improve pre-treatment individualized prediction of OS for HNC patients. (C) 2017 The Authors. Published by Elsevier Ireland Ltd

The Broad Host Range Phage vB_CpeS_BG3P Is Able to Inhibit Clostridium perfringens Growth

Clostridium perfringens is an important pathogen for both humans and animals, causing human foodborne disease and necrotic enteritis in poultry. In the present study, a C. perfringens-specific phage, vB_CpeS_BG3P (designated as BG3P hereafter), was isolated from chicken farm sewage. Both electron microscopy and phylogenetic analysis suggested that phage BG3P is a novel phage belonging to Siphoviridae family. Phage BG3P exhibited a broad host range against different C. perfringens isolates (90.63% of strains were infected). Sequencing of the complete genome revealed a linear double-stranded DNA (43,528 bp) with 28.65% GC content. After sequence analysis, 73 open reading frames (orf s) were predicted, of which only 13 were annotated with known functions. No tRNA and virulence encoding genes were detected. It should be noted that the protein of orf 15 has 97.92% homology to C. perfringens-specific chloramphenicol resistance protein, which has not been reported for any C. perfringens phage. Phylogenetic analysis of the ssDNA binding protein demonstrated that this phage is closely related to C. perfringens phages phiSM101 and phi3626. In considering future use as an antimicrobial agent, some biological characteristics were observed, such as a good pH (3–11) stability and moderate temperature tolerance (<60 C). Moreover, bacteriophage BG3P showed a good antimicrobial effect against C. perfringens liquid cultures. Thus, phage treatment with MOI ≥ 100 completely inhibited bacterial growth compared to untreated cultures. Although phage BG3P shows good lytic efficiency and broad host range in vitro, future development and application may need to consider removal of the chloramphenicol-like resistance gene or exploring its lysin for future antibacterial applications.This work was supported by the National Key Research and Development Program of China (No. 2018YFE0101900) and the Jiangsu Agricultural Science and Technology Foundation (No. cx(21)1004)