Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, ...robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers. Recently, quantum machine learning algorithms Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411 were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.
Alloy design based on single-principal-element systems has approached its limit for performance enhancements. A substantial increase in strength up to gigapascal levels typically causes the premature ...failure of materials with reduced ductility. Here, we report a strategy to break this trade-off by controllably introducing high-density ductile multicomponent intermetallic nanoparticles (MCINPs) in complex alloy systems. Distinct from the intermetallic-induced embrittlement under conventional wisdom, such MCINP-strengthened alloys exhibit superior strengths of 1.5 gigapascals and ductility as high as 50% in tension at ambient temperature. The plastic instability, a major concern for high-strength materials, can be completely eliminated by generating a distinctive multistage work-hardening behavior, resulting from pronounced dislocation activities and deformation-induced microbands. This MCINP strategy offers a paradigm to develop next-generation materials for structural applications.
Summary Objective Glucocorticoids (GCs) have been widely used in the management of osteoarthritis (OA) and rheumatoid arthritis (RA). Nevertheless, there has been some concern about their ability of ...increasing reactive oxygen species (ROS) in the cartilage. Forkhead-box class O (FOXO) transcription factors have been proved to have a protective role in chondrocytes through regulation of autophagy and defending oxidative stress. The objective of this study was to investigate the role of FOXO3 in Dex-induce up-regulation of ROS. Design Healthy cartilages debris from six patients were used for chondrocytes culture. After the treatment of dexamethasone (Dex), the ROS levels, autophagic flux, the expression of FOXO3 in chondrocytes were measured. RNA interference technique was also used to determine the role of FOXO3 in Dex-induced autophagy. The metabolism of the extra-cellular matrix was also investigated. The results Dex increased intracellular ROS level, the expression of Akt, FOXO3 as well as autophagy flux in human chondrocytes. The expression of aggrecanases also increased after the treatment of Dex. Catalase, the ROS scavenger, suppressed Dex-induced up-regulation of autophagy flux and expression of aggrecanases and Akt. MK-2206 and LY294002, the PI3K/Akt inhibitors, repressed Dex-induced up-regulation of FOXO3. Silencing FOXO3 resulted in down-regulation of Dex-induced autophagy. Moreover, knockdown of FOXO3 increased Dex-induced apoptosis as well as ROS levels in chondrocytes. In addition, up-regulation of autophagy by Rapamycin resulted in decreasing ROS level in chondrocytes. Conclusion Dex could advance the degenerative process in cartilage. Autophagy was induced in response to Dex-induced up-regulation of ROS via ROS/Akt/FOXO3 signal pathway.
Large-scale, highly integrated and low-power-consuming hardware is becoming progressively more important for realizing optical neural networks (ONNs) capable of advanced optical computing. ...Traditional experimental implementations need N
units such as Mach-Zehnder interferometers (MZIs) for an input dimension N to realize typical computing operations (convolutions and matrix multiplication), resulting in limited scalability and consuming excessive power. Here, we propose the integrated diffractive optical network for implementing parallel Fourier transforms, convolution operations and application-specific optical computing using two ultracompact diffractive cells (Fourier transform operation) and only N MZIs. The footprint and energy consumption scales linearly with the input data dimension, instead of the quadratic scaling in the traditional ONN framework. A ~10-fold reduction in both footprint and energy consumption, as well as equal high accuracy with previous MZI-based ONNs was experimentally achieved for computations performed on the MNIST and Fashion-MNIST datasets. The integrated diffractive optical network (IDNN) chip demonstrates a promising avenue towards scalable and low-power-consumption optical computational chips for optical-artificial-intelligence.
Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued ...representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.
We report the observation of new properties of primary cosmic rays He, C, and O measured in the rigidity (momentum/charge) range 2 GV to 3 TV with 90×10^{6} helium, 8.4×10^{6} carbon, and 7.0×10^{6} ...oxygen nuclei collected by the Alpha Magnetic Spectrometer (AMS) during the first five years of operation. Above 60 GV, these three spectra have identical rigidity dependence. They all deviate from a single power law above 200 GV and harden in an identical way.
We report on the observation of new properties of secondary cosmic rays Li, Be, and B measured in the rigidity (momentum per unit charge) range 1.9 GV to 3.3 TV with a total of 5.4×10^{6} nuclei ...collected by AMS during the first five years of operation aboard the International Space Station. The Li and B fluxes have an identical rigidity dependence above 7 GV and all three fluxes have an identical rigidity dependence above 30 GV with the Li/Be flux ratio of 2.0±0.1. The three fluxes deviate from a single power law above 200 GV in an identical way. This behavior of secondary cosmic rays has also been observed in the AMS measurement of primary cosmic rays He, C, and O but the rigidity dependences of primary cosmic rays and of secondary cosmic rays are distinctly different. In particular, above 200 GV, the secondary cosmic rays harden more than the primary cosmic rays.
Aims
Vibrio alginolyticus was frequently isolated from diseased farmed fish in the coaster waters of Hainan Island over the past two decades. In this study, we attempted to identify candidates of ...virulent strain‐specific DNA regions for this pathogen.
Methods and Results
Suppression subtractive hybridization (SSH) and PCR were successively performed between the typical virulent strain and avirulent strain of V. alginolyticus, in which they shared 99·54% homology of 16S rDNAs. Out of 2873 subtracted clones, nine clones were finally indicated to harbour virulent strain‐specific DNA fragments. The receivable functions of the major fragments in the nine clones were believed to encode methyl‐accepting chemotaxis protein (n = 1), type VI secretion system‐associated FHA domain protein TagH (n = 1), diguanylate cyclase (n = 1), AraC family transcriptional regulator (n = 1), ABC‐type uncharacterized transport system permease component (n = 1) and hypothetical proteins (n = 4). Two hypothetical proteins contain several disordered regions.
Conclusions
Some specific DNA regions existed in the virulent strain of V. alginolyticus, and the SSH assay could be a highly sensitive method for identifying virulent regions in pathogens.
Significance and Impact of the Study
This report is the first to describe the identification of virulent strain‐specific DNA regions in the V. alginolyticus genome, which is helpful in developing virulent strain‐specific rapid detection methods and is a pivotal precondition for clarifying the molecular virulence mechanism of V. alginolyticus.
Summary
Long‐term prognosis varies widely among patients with hepatitis B virus (HBV)‐related liver cirrhosis. Our study aimed to investigate the applicability of albumin‐bilirubin (ALBI), Child‐Pugh ...and model for end‐stage liver disease (MELD) scores to the long‐term prognosis prediction of HBV‐related cirrhosis. Patients diagnosed with HBV‐associated cirrhosis from the First Affiliated Hospital of Wenzhou Medical University between January 2010 and December 2015 were enrolled in this study. The patients were followed up every 3 months. The prognostic performance of ALBI in long‐term outcome prediction for HBV‐related cirrhosis was compared with Child‐Pugh and MELD scores using time‐dependent receiver operating characteristic curve (tdROC) and decision curve analysis. A total of 806 patients were included in our study with 275 (34.1%) deceased during the follow‐up. Multivariate Cox regression analysis showed that ALBI grade was an independent predictor associated with mortality. The tdROC analysis showed that ALBI score (0.787, 0.830 and 0.833) was superior to MELD (0.693, P=.003; 0.717, P<.001; 0.744, P<.001) and Child‐Pugh score (0.641, P<.001; 0.649, P<.001; 0.657, P<.001) for predicting 1‐year, 2‐year and 3‐year mortality. Additionally, decision curves also got the similar results. In addition, patients with lower ALBI score had a longer life expectancy, even among patients within the same Child‐Pugh class. Thus, ALBI score was effective in predicting the long‐term prognosis for patients with HBV‐related cirrhosis and more accurate than Child‐Pugh and MELD scores.
Solving linear systems of equations is ubiquitous in all areas of science and engineering. With rapidly growing data sets, such a task can be intractable for classical computers, as the best known ...classical algorithms require a time proportional to the number of variables N. A recently proposed quantum algorithm shows that quantum computers could solve linear systems in a time scale of order log(N), giving an exponential speedup over classical computers. Here we realize the simplest instance of this algorithm, solving 2×2 linear equations for various input vectors on a quantum computer. We use four quantum bits and four controlled logic gates to implement every subroutine required, demonstrating the working principle of this algorithm.
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