비만아 부모의 돌봄 경험: q 방법론

https://bura.brunel.ac.uk/bitstream/2438/14221/1/FullText.pdf

A survey of deep neural network architectures and their applications

Simulating quantum mechanics is known to be a difficult computational problem, especially when dealing with large systems However, this difficulty may be overcome by using some controllable quantum system to study another less controllable or accessible quantum system, ie, quantum simulation Quantum simulation promises to have applications in the study of many problems in, eg, condensed-matter physics, high-energy physics, atomic physics, quantum chemistry and cosmology Quantum simulation could be implemented using quantum computers, but also with simpler, analog devices that would require less control, and therefore, would be easier to construct A number of quantum systems such as neutral atoms, ions, polar molecules, electrons in semiconductors, superconducting circuits, nuclear spins and photons have been proposed as quantum simulators This review outlines the main theoretical and experimental aspects of quantum simulation and emphasizes some of the challenges and promises of this fast-growing field

Quantum Simulation

Many quantum algorithms have daunting resource requirements when compared to what is available today. To address this discrepancy, a quantum-classical hybrid optimization scheme known as 'the quantum variational eigensolver' was developed (Peruzzo et al 2014 Nat. Commun. 5 4213) with the philosophy that even minimal quantum resources could be made useful when used in conjunction with classical routines. In this work we extend the general theory of this algorithm and suggest algorithmic improvements for practical implementations. Specifically, we develop a variational adiabatic ansatz and explore unitary coupled cluster where we establish a connection from second order unitary coupled cluster to universal gate sets through a relaxation of exponential operator splitting. We introduce the concept of quantum variational error suppression that allows some errors to be suppressed naturally in this algorithm on a pre-threshold quantum device. Additionally, we analyze truncation and correlated sampling in Hamiltonian averaging as ways to reduce the cost of this procedure. Finally, we show how the use of modern derivative free optimization techniques can offer dramatic computational savings of up to three orders of magnitude over previously used optimization techniques.

/pdf/the-theory-of-variational-hybrid-quantum-classical-42rnrhdbbg.pdf

The theory of variational hybrid quantum-classical algorithms

State-of-the-art in artificial neural network applications: A survey

Controllability of probabilistic Boolean control networks based on transition probability matrices

Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

/pdf/machine-learning-with-applications-in-breast-cancer-c5vnw8oadi.pdf

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

We theoretically analyzed and experimentally demonstrated a novel 4- to 40-GHz frequency quadrupler for radio-over- fiber systems. By using an optical carrier suppression modulation scheme in two cascaded intensity modulators, four-fold microwave or millimeter wave signals are optically generated without the need for an optical and electrical notch filter to remove the residual carrier components. Moreover, the limitation of high drive voltage is greatly reduced through tandem intensity modulation with pi/2 phase shift between two driving signals.

A Photonic Microwave Frequency Quadrupler Using Two Cascaded Intensity Modulators With Repetitious Optical Carrier Suppression

Unidirectional chaotic synchronization between two remote injection-locked semiconductor lasers to achieve chaotic communications is investigated numerically. Different from the direct chaotic masking methods, the chaotic carrier wave is generated from different chaotic states in transience instead of a fixed chaotic state in static to prevent it from being reproduced through a reconstructed embedding phase space. The testing digital and sinusoidal message signals in the gigahertz range can be easily recovered without the use of any electronic or optical filter to filter out the synchronization error. The robustness of synchronization is examined by using the intrinsic white noise of the transmitter and the receiver as the perturbation. The effects of parameter mismatches on the quality and robustness of synchronization are analyzed in detail. The results show that different internal parameters have very different tolerances for parameter mismatch. A short discussion on the phase sensitivity of synchronization is also given.

Hongwei Chen

Papers

Controllability of probabilistic Boolean control networks based on transition probability matrices

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

A Photonic Microwave Frequency Quadrupler Using Two Cascaded Intensity Modulators With Repetitious Optical Carrier Suppression

Open-loop chaotic synchronization of injection-locked semiconductor lasers with gigahertz range modulation

Function perturbations on singular Boolean networks