An adaptive dynamical low-rank tensor approximation scheme for fast circuit simulation

Tensors, as higher order generalization of matrices, have received growing attention due to their readiness in representing multidimensional data intrinsic to numerous engineering problems. This paper develops an efficient and accurate dynamical update algorithm for the low-rank mode factors. By means of tangent space projection onto the low-rank tensor manifold, the repeated computation of a full tensor Tucker decomposition is replaced with a much simpler solution of nonlinear differential equations governing the tensor mode factors. A worked-out numerical example demonstrates the excellent efficiency and scalability of the proposed dynamical approximation scheme.postprin

Highly Improved Staggered Quarks on the Lattice, with Applications to Charm Physics

We use perturbative Symanzik improvement to create a new staggered-quark action (HISQ) that has greatly reduced one-loop taste-exchange errors, no tree-level order a^2 errors, and no tree-level order (am)^4 errors to leading order in the quark's velocity v/c. We demonstrate with simulations that the resulting action has taste-exchange interactions that are at least 3--4 times smaller than the widely used ASQTAD action. We show how to estimate errors due to taste exchange by comparing ASQTAD and HISQ simulations, and demonstrate with simulations that such errors are no more than 1% when HISQ is used for light quarks at lattice spacings of 1/10 fm or less. The suppression of (am)^4 errors also makes HISQ the most accurate discretization currently available for simulating c quarks. We demonstrate this in a new analysis of the psi-eta_c mass splitting using the HISQ action on lattices where a m_c=0.43 and 0.66, with full-QCD gluon configurations (from MILC). We obtain a result of~111(5) MeV which compares well with experiment. We discuss applications of this formalism to D physics and present our first high-precision results for D_s mesons.Comment: 21 pages, 8 figures, 5 table

A dynamic multi-objective evolutionary algorithm based on polynomial regression and adaptive clustering

In this paper, a dynamic multi-objective evolutionary algorithm is proposed based on polynomial regression and adaptive clustering, called DMOEA-PRAC. As the Pareto-optimal solutions and fronts of dynamic multi-objective optimization problems (DMOPs) may dynamically change in the optimization process, two corresponding change response strategies are presented for the decision space and objective space, respectively. In the decision space, the potentially useful information contained in all historical populations is obtained by the proposed predictor based on polynomial regression, which extracts the linear or nonlinear relationship in the historical change. This predictor can generate good initial population for the new environment. In the objective space, in order to quickly adapt to the new environment, an adaptive reference vector regulator is designed in this paper based on K-means clustering for the complex changes of Pareto-optimal fronts, in which the adjusted reference vectors can effectively guide the evolution. Finally, DMOEA-PRAC is compared with some recently proposed dynamic multi-objective evolutionary algorithms and the experimental results verify the effectiveness of DMOEA-PRAC in dealing with a variety of DMOPs

Robust Integrated Data and Energy Transfer Aided by Intelligent Reflecting Surfaces: Successive Target Migration Optimization Towards Energy Sustainability

Intelligent reflecting surfaces (IRSs) can actively adjust the wireless environment. However, accurate channel estimation on IRS-aided communication systems is difficult to obtain. Therefore, we study a robust beamforming design for an IRS-aided integrated data and energy transfer (IDET) with imperfect channel state information (CSI). Against the uncertain channel estimation error, we robustly design the transmit beamformers of the transmitter and the passive reflecting beamformer of the IRS to minimize the transmit power by satisfying both the wireless data transfer (WDT) and wireless energy transfer (WET) requirements for realising energy-sustainability in 6G. A successive target migration optimization (STMO) algorithm is proposed to obtain a robust design. The transmit covariance matrices are optimized by relaxing rank-one constraints, when a passive reflecting beamformer is given. Then, the target to minimize the transmit power is migrated to maximize the QoS requirements of energy users due to the fixed transmit power. A local optimal reflecting beamformer is obtained for improving the attainable WET performance, when the transmit covariance matrices are given. Finally, we prove that the rank-one transmit beamformers can always be found, which have the same WET and WDT performance as the transmit covariance matrices. The numerical results demonstrate the advantage of our design

Efficient mining of pan-correlation patterns from time course data

© Springer International Publishing AG 2016. There are different types of correlation patterns between the variables of a time course data set, such as positive correlations, negative correlations, time-lagged correlations, and those correlations containing small interrupted gaps. Usually, these correlations are maintained only on a subset of time points rather than on the whole span of the time points which are traditionally required for correlation definition. As these types of patterns underline different trends of data movement, mining all of them is an important step to gain a broad insight into the dependencies of the variables. In this work, we prove that these diverse types of correlation patterns can be all represented by a generalized form of positive correlation patterns. We also prove a correspondence between positive correlation patterns and sequential patterns. We then present an efficient single-scan algorithm for mining all of these types of correlations. This “pan-correlation” mining algorithm is evaluated on synthetic time course data sets, as well as on yeast cell cycle gene expression data sets. The results indicate that: (i) our mining algorithm has linear time increment in terms of increasing number of variables; (ii) negative correlation patterns are abundant in real-world data sets; and (iii) correlation patterns with time lags and gaps are also abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely

Discovering pan-correlation patterns from time course data sets by efficient mining algorithms

© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely

Thoracoscopic repair of congenital diaphragmatic hernia: two centres’ experience with 60 patients

© 2014, Springer-Verlag Berlin Heidelberg. Methods: All patients who underwent thoracoscopic repair of congenital diaphragmatic hernia between 2010 and 2013 at the two tertiary referral centres were identified. Medical records were retrospectively reviewed. Data including patients’ demographics, peri-operative outcomes, length of hospitalisation and post-operative complications were extracted and analysed. Introduction: Congenital diaphragmatic hernia is a potentially life-threatening neonatal condition which required surgical intervention. With the advances in endosurgical instruments and techniques, thoracoscopic approach is gaining popularity as a standard procedure in the treatment of this condition. In this study, we reviewed our two centres’ experience with thoracoscopic repair of congenital diaphragmatic hernia in recent years. Conclusion: Thoracoscopic repair of congenital diaphragmatic hernia can be performed safely in specialised centres. The post-operative recovery and cosmesis are excellent. Diaphragmatic hernia with large defect remains a challenge for surgeons. Results: 60 patients were identified over the study period, with 46 males and 14 females. 48 patients received operation within the first 7 days of life. There were seven patients with delayed presentation and were operated after 1 month old. The average body weight was 3.03 kg. Left-sided hernia was more prevalent (n = 50). The mean operative time was 88.5 min (range 31–194 min). No conversion to open thoracotomy or laparotomy was required in any of the patients. All patients except one were intubated and paralysed in neonatal intensive care units for at least 3 days after operation. Average hospital stay was 14.6 days. There was no mortality in this series. There were five recurrences, one being the patient without post-operative paralysis, and the others with deficient posterior muscle rim. No musculoskeletal deformity was noted on follow-up examination.postprin