Optimal fast single pulse readout of qubits

The computer simulations of the process of single pulse readout from the flux-biased phase qubit is performed in the frame of one-dimensional Schroedinger equation. It has been demonstrated that the readout error can be minimized by choosing the optimal pulse duration and the depth of a potential well, leading to the fidelity of 0.94 for 2ns and 0.965 for 12ns sinusoidal pulses.Comment: 4 pages, 6 figure

Emotions and a Prior Knowledge Representation in Artificial General Intelligence

In this paper a prior knowledge representation for Artificial General Intelligence is proposed based on fuzzy rules using linguistic variables. These linguistic variables may be produced by neural network. Rules may be used for generation of basic emotions – positive and negative, which influence on planning and execution of behavior. The representation of Three Laws of Robotics as such prior knowledge is suggested as highest level of motivation in AGI

Association of Jets with the Signal Vertex

A method for the association of jets with the signal vertex is presented. The technique is shown to reduce the instrumental background rate from jets and overlapping particles originating from additional minimum bias vertices. As a benchmark, the performance of the algorithm is evaluated using the qq->qqH search channel where the vertex assignment parameters are optimized for separating qqH and ttbar events. The efficiency of a central jet veto is shown to increase for the same level of background rejection. The whole study is conducted on Monte Carlo generated events that were passed through the CMS full detector-level simulation in low luminosity conditions (L = 2x10^33 cm^-2 s^-1). It is shown that the method should give similar results also for high luminosity conditions (L = 10^34 cm^-2 s^-1)

A Novel Hybrid Neural Network for Data Clustering

Abstract. Clustering plays an indispensable role for data analysis. Many clustering algorithms have been developed. However, most of them suffer either poor performance of unsupervised learning or lacking of mechanisms to utilize some prior knowledge about data (semi-supervised learning) for improving clustering result. In an effort to archive the ability of semisupervised clustering and better unsupervised clustering performance, we develop a hybrid neural network model (HNN). It is the sequential combination of Multi-Layer Perceptron (MLP) and Adaptive Resonance Theory-2 (ART2). It inherits two distinct advantages of stability and plasticity from ART2. Meanwhile, by combining the merits of MLP, it not only improves the performance for unsupervised clustering, but also supports for semi-supervised clustering if partial knowledge about data is available. Experiment results show that our model can be used both for unsupervised clustering and semisupervised clustering with promising performance

Principal nonlinear dynamical modes of climate variability

We are grateful to Michael Ghil and Dmitri Kondrashov for fruitful discussions. The study was supported by Government of Russian Federation (agreement #14.Z50.31.0033 with the Institute of Applied Physics of RAS).Peer reviewedPublisher PD

Bayesian Data Analysis for Revealing Causes of the Middle Pleistocene Transition

Currently, causes of the middle Pleistocene transition (MPT) – the onset of large-amplitude glacial variability with 100 kyr time scale instead of regular 41 kyr cycles before – are a challenging puzzle in Paleoclimatology. Here we show how a Bayesian data analysis based on machine learning approaches can help to reveal the main mechanisms underlying the Pleistocene variability, which most likely explain proxy records and can be used for testing existing theories. We construct a Bayesian data-driven model from benthic δ18O records (LR04 stack) accounting for the main factors which may potentially impact climate of the Pleistocene: internal climate dynamics, gradual trends, variations of insolation, and millennial variability. In contrast to some theories, we uncover that under long-term trends in climate, the strong glacial cycles have appeared due to internal nonlinear oscillations induced by millennial noise. We find that while the orbital Milankovitch forcing does not matter for the MPT onset, the obliquity oscillation phase-locks the climate cycles through the meridional gradient of insolation

Модель осредненной молекулярной вязкости для турбулентных течений неньютоновских жидкостей

A novel turbulence model for flows of viscoplastic fluid is presented. It is based on the Reynolds-Averaged approach. A closed model for the averaged viscosity that takes into account its nonlinear dependence on the fluctuating rate of deformation tensor is proposed. Test calculations were performed for power-law fluid and Herschel–Bulkley fluid flows in a straight round pipe. Numerical data obtained with the use of the proposed model are compared with the results of direct numerical simulations. The proposed model adequately describes the reduction in the turbulent transport of momentum with decreasing power-law index and with increasing yield stress of the fluidВ статье представлена модель турбулентности для вязкопластических жидкостей. С использованием процедуры осреднения по Рейнольдсу разработана модель осредненной молекулярной вязкости для неньютоновских сред, учитывающая нелинейную зависимость от флуктуирующего тензора скоростей деформации. В качестве базовой модели турбулентности использована двух- параметрическая дифференциальная модель турбулентности. Тестовые расчеты выполнены для течений степенной жидкости и жидкости Гершеля–Балкли в прямой круглой трубе. Получен- ные расчетные данные сопоставлялись с результатами прямого численного моделирования. Предложенная модель позволяет правильно описать снижение турбулентного переноса импульса с уменьшением степени среды и с увеличением предельного напряжени