A Quantum Approach Using Stochastic Simulation and Schrodinger with Multi-Layer Learning for Finding Features from Dataset

2020 ◽  
Vol 17 (6) ◽  
pp. 2514-2518
Author(s):  
Kapil Prashar ◽  
Rajneesh Talwar ◽  
Chander Kant
Keyword(s):  
Quantum Computing ◽  
Mathematical Models ◽  
Stochastic Simulation ◽  
Statistical Models ◽  
State Of The Art ◽  
Research Work ◽  
Current Approach ◽  
Suggested Model ◽  
Quantum Approach ◽  
Multiple State

Quantum computing relies on the quantity of the mechanical phenomenon, such as interference and overlap. It aims to solve issues which are not realistically possible on computers. The research work introduces the new quantum-based model from a provided dataset for forecasting the infection. This technique is beneficial in describing the association among different statistical models. Our study has resulted in highest precision than ever applied technique, which was differentiated and calculated from the defined dataset and results. Such suggested strategies were evaluated and reviewed against multiple state-of-the-art methods to demonstrate efficacy. The qualitative and graphical results are provided for the verification of the current approach. The suggested model is more robust than existing mathematical models due to the findings.

State-of-the-art pulmonary arterial imaging – Part 1

VASA ◽  
2018 ◽  
Vol 47 (5) ◽  
pp. 345-359 ◽  
Author(s):  
Yuki Tanabe ◽  
Luis Landeras ◽  
Abed Ghandour ◽  
Sasan Partovi ◽  
Prabhakar Rajiah
Keyword(s):  
State Of The Art ◽  
Pulmonary Arteries ◽  
Pulmonary Angiography ◽  
Imaging Modalities ◽  
Nuclear Medicine Imaging ◽  
Multiple State ◽  
Pulmonary Arterial ◽  
Magnetic Resonance Imaging Mri ◽  
Acquired Abnormalities ◽  
Arterial Imaging

Abstract. The pulmonary arteries are affected by a variety of congenital and acquired abnormalities. Multiple state-of-the art imaging modalities are available to evaluate these pulmonary arterial abnormalities, including computed tomography (CT), magnetic resonance imaging (MRI), echocardiography, nuclear medicine imaging and catheter pulmonary angiography. In part one of this two-part series on state-of-the art pulmonary arterial imaging, we review these imaging modalities, focusing particularly on CT and MRI. We also review the utility of these imaging modalities in the evaluation of pulmonary thromboembolism.

scholarly journals Quantum Computing for Finance: State-of-the-Art and Future Prospects

2020 ◽  
Vol 1 ◽  
pp. 1-24
Author(s):  
Daniel J. Egger ◽  
Claudio Gambella ◽  
Jakub Marecek ◽  
Scott McFaddin ◽  
Martin Mevissen ◽  
...  
Keyword(s):  
Quantum Computing ◽  
State Of The Art ◽  
Future Prospects

scholarly journals Cash Flow Bullwhip—Literature Review and Research Perspectives

Logistics ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 8
Author(s):  
Hicham Lamzaouek ◽  
Hicham Drissi ◽  
Naima El Haoud
Keyword(s):  
Literature Review ◽  
Customer Service ◽  
Cash Flow ◽  
State Of The Art ◽  
Research Work ◽  
Bullwhip Effect ◽  
Research Perspectives ◽  
Financial Perspective ◽  
The Impact ◽  
Exhaustive List

The bullwhip effect is a pervasive phenomenon in all supply chains causing excessive inventory, delivery delays, deterioration of customer service, and high costs. Some researchers have studied this phenomenon from a financial perspective by shedding light on the phenomenon of cash flow bullwhip (CFB). The objective of this article is to provide the state of the art in relation to research work on CFB. Our ambition is not to make an exhaustive list, but to synthesize the main contributions, to enable us to identify other interesting research perspectives. In this regard, certain lines of research remain insufficiently explored, such as the role that supply chain digitization could play in controlling CFB, the impact of CFB on the profitability of companies, or the impacts of the omnichannel commerce on CFB.

APPLICATIONS OF TWO NON-CENTRAL HYPERGEOMETRIC DISTRIBUTIONS OF BIASED SAMPLING STATISTICAL MODELS

2021 ◽  
Vol 4 (4) ◽  
pp. 415-424
Author(s):  
A. A. Issa ◽  
K. O. Adetunji ◽  
T. Alanamu ◽  
E. J. Adefila ◽  
K. A. Muhammed
Keyword(s):  
Statistical Models ◽  
Research Work ◽  
Hypergeometric Distribution ◽  
Biased Sampling ◽  
Inversion Method ◽  
Urn Model ◽  
Fisher Distribution ◽  
Direct Inversion ◽  
Mean And Variance ◽  
Hypergeometric Distributions

Statistical models of biased sampling of two non-central hypergeometric distributions Wallenius' and Fisher's distribution has been extensively used in the literature, however, not many of the logic of hypergeometric distribution have been investigated by different techniques. This research work examined the procedure of the two non-central hypergeometric distributions and investigates the statistical properties which includes the mean and variance that were obtained. The parameters of the distribution were estimated using the direct inversion method of hyper simulation of biased urn model in the environment of R statistical software, with varying odd ratios (w) and group sizes (mi). It was discovered that the two non - central hypergeometric are approximately equal in mean, variance and coefficient of variation and differ as odds ratios (w) becomes higher and differ from the central hypergeometric distribution with ω = 1. Furthermore, in univariate situation we observed that Fisher distribution at (ω = 0.2, 0.5, 0.7, 0.9) is more consistent than Wallenius distribution, although central hypergeometric is more consistent than any of them. Also, in multinomial situation, it was observed that Fisher distribution is more consistent at (ω = 0.2, 0.5), Wallenius distribution at (ω = 0.7, 0.9) and central hypergeometric at (ω = 0.2)    

Efficient reversible quantum design of sig-magnitude to two's complement converters

2020 ◽  
Vol 20 (9&10) ◽  
pp. 747-765
Author(s):  
F. Orts ◽  
G. Ortega ◽  
E.M. E.M. Garzon
Keyword(s):  
Quantum Computing ◽  
Scientific Community ◽  
Quantum Computer ◽  
Fault Tolerant ◽  
State Of The Art ◽  
The Other ◽  
Quantum Computers ◽  
Binary Number ◽  
Quantum Cost ◽  
The One

Despite the great interest that the scientific community has in quantum computing, the scarcity and high cost of resources prevent to advance in this field. Specifically, qubits are very expensive to build, causing the few available quantum computers are tremendously limited in their number of qubits and delaying their progress. This work presents new reversible circuits that optimize the necessary resources for the conversion of a sign binary number into two's complement of N digits. The benefits of our work are two: on the one hand, the proposed two's complement converters are fault tolerant circuits and also are more efficient in terms of resources (essentially, quantum cost, number of qubits, and T-count) than the described in the literature. On the other hand, valuable information about available converters and, what is more, quantum adders, is summarized in tables for interested researchers. The converters have been measured using robust metrics and have been compared with the state-of-the-art circuits. The code to build them in a real quantum computer is given.

scholarly journals Gaussian Elimination versus Greedy Methods for the Synthesis of Linear Reversible Circuits

2021 ◽  
Vol 2 (3) ◽  
pp. 1-26
Author(s):  
Timothée Goubault De Brugière ◽  
Marc Baboulin ◽  
Benoît Valiron ◽  
Simon Martiel ◽  
Cyril Allouche
Keyword(s):  
Quantum Computing ◽  
State Of The Art ◽  
Gaussian Elimination ◽  
Random Operators ◽  
Lu Factorization ◽  
Large Problem ◽  
Computational Costs ◽  
Elimination Algorithm ◽  
Reversible Circuits ◽  
Greedy Methods

Linear reversible circuits represent a subclass of reversible circuits with many applications in quantum computing. These circuits can be efficiently simulated by classical computers and their size is polynomially bounded by the number of qubits, making them a good candidate to deploy efficient methods to reduce computational costs. We propose a new algorithm for synthesizing any linear reversible operator by using an optimized version of the Gaussian elimination algorithm coupled with a tuned LU factorization. We also improve the scalability of purely greedy methods. Overall, on random operators, our algorithms improve the state-of-the-art methods for specific ranges of problem sizes: The custom Gaussian elimination algorithm provides the best results for large problem sizes (n > 150), while the purely greedy methods provide quasi optimal results when n < 30. On a benchmark of reversible functions, we manage to significantly reduce the CNOT count and the depth of the circuit while keeping other metrics of importance (T-count, T-depth) as low as possible.

scholarly journals REVA as a Well-curated Database for Human Expression-modulating Variants

2021 ◽  
Author(s):  
Yu Wang ◽  
Fang-Yuan Shi ◽  
Yu Liang ◽  
Ge Gao
Keyword(s):  
Large Scale ◽  
Regulatory Mechanism ◽  
State Of The Art ◽  
Scale Analysis ◽  
Computational Tools ◽  
Functional Annotations ◽  
Link Type ◽  
Large Scale Analysis ◽  
Multiple State ◽  
Limited Sensitivity

AbstractMore than 80% of disease- and trait-associated human variants are noncoding. By systematically screening multiple large-scale studies, we compiled REVA, a manually curated database for over 11.8 million experimentally tested noncoding variants with expression-modulating potentials. We provided 2424 functional annotations that could be used to pinpoint plausible regulatory mechanism of these variants. We further benchmarked multiple state-of-the-art computational tools and found their limited sensitivity remains a serious challenge for effective large-scale analysis. REVA provides high-qualify experimentally tested expression-modulating variants with extensive functional annotations, which will be useful for users in the noncoding variants community. REVA is available at http://reva.gao-lab.org.

scholarly journals Densely Supervised Hierarchical Policy-Value Network for Image Paragraph Generation

2019 ◽  
Author(s):  
Siying Wu ◽  
Zheng-Jun Zha ◽  
Zilei Wang ◽  
Houqiang Li ◽  
Feng Wu
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Cross Entropy ◽  
Image Captioning ◽  
Value Network ◽  
Entropy Loss ◽  
Fine Grained ◽  
Performance Improvements ◽  
Single Sentence ◽  
Multiple State

Image paragraph generation aims to describe an image with a paragraph in natural language. Compared to image captioning with a single sentence, paragraph generation provides more expressive and fine-grained description for storytelling. Existing approaches mainly optimize paragraph generator towards minimizing word-wise cross entropy loss, which neglects linguistic hierarchy of paragraph and results in ``sparse" supervision for generator learning. In this paper, we propose a novel Densely Supervised Hierarchical Policy-Value (DHPV) network for effective paragraph generation. We design new hierarchical supervisions consisting of hierarchical rewards and values at both sentence and word levels. The joint exploration of hierarchical rewards and values provides dense supervision cues for learning effective paragraph generator. We propose a new hierarchical policy-value architecture which exploits compositionality at token-to-token and sentence-to-sentence levels simultaneously and can preserve the semantic and syntactic constituent integrity. Extensive experiments on the Stanford image-paragraph benchmark have demonstrated the effectiveness of the proposed DHPV approach with performance improvements over multiple state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document