Assessing the Impact of Distance Functions on K-Nearest Neighbours Imputation of Biomedical Datasets

2020 ◽  
pp. 486-496
Author(s):  
Miriam S. Santos ◽  
Pedro H. Abreu ◽  
Szymon Wilk ◽  
João Santos
Keyword(s):  
Distance Functions ◽  
Nearest Neighbours ◽  
The Impact

scholarly journals Coherent spin qubit transport in silicon

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
J. Yoneda ◽  
W. Huang ◽  
M. Feng ◽  
C. H. Yang ◽  
K. W. Chan ◽  
...  
Keyword(s):  
Fault Tolerant ◽  
High Fidelity ◽  
Nearest Neighbours ◽  
Quantum Processor ◽  
Spin Qubit ◽  
State Tomography ◽  
On Chip ◽  
The Cost ◽  
Silicon Based ◽  
The Impact

AbstractA fault-tolerant quantum processor may be configured using stationary qubits interacting only with their nearest neighbours, but at the cost of significant overheads in physical qubits per logical qubit. Such overheads could be reduced by coherently transporting qubits across the chip, allowing connectivity beyond immediate neighbours. Here we demonstrate high-fidelity coherent transport of an electron spin qubit between quantum dots in isotopically-enriched silicon. We observe qubit precession in the inter-site tunnelling regime and assess the impact of qubit transport using Ramsey interferometry and quantum state tomography techniques. We report a polarization transfer fidelity of 99.97% and an average coherent transfer fidelity of 99.4%. Our results provide key elements for high-fidelity, on-chip quantum information distribution, as long envisaged, reinforcing the scaling prospects of silicon-based spin qubits.

scholarly journals Does China’s Pollution Levy Standards Reform Promote Green Growth?

2019 ◽  
Vol 11 (21) ◽  
pp. 6186 ◽  
Author(s):  
Tu ◽  
Zhou ◽  
Zhang
Keyword(s):  
Inhibitory Effect ◽  
Distance Functions ◽  
Productivity Index ◽  
Environmental Taxes ◽  
Data Envelopment ◽  
Green Growth ◽  
Large Cities ◽  
Economic Output ◽  
The Impact ◽  
Standards Reform

Estimating the impact of environmental taxes on economic output is of great theoretical value for promoting green growth in China. Using a dataset of 232 cities from 2004 to 2014, this paper investigates the effect of pollution levy standards reform (PSR) on green total factor productivity (GTFP). We employ directional distance functions (DDF) computed by data envelopment analysis (DEA) to derive GTFP based on the Malmquist–Luenberger (ML) productivity index. Then, we investigate the impacts of PSR on China’s GTFP using Difference-in-Differences (DID) estimation. The results reveal that PSR has an inhibitory effect on GTFP, via the mechanism of technological change. Furthermore, PSR has heterogeneous impacts on different city types. The results indicate that PSR statistically significantly reduces GTFP in key environmental protection cities (KEPCs), large cities, and eastern cities, but that it has less impact on non-KEPCs, small/medium cities, megacities, and cities in central areas.

scholarly journals Reducing the Discard of MBT Test Cases

2020 ◽  
Vol 8 ◽  
pp. 4-1 - 4:15
Author(s):  
Thomaz Diniz ◽  
Everton L G Alves ◽  
Anderson G F Silva ◽  
Wilkerson L Andrade
Keyword(s):  
Machine Learning ◽  
Distance Functions ◽  
Test Case ◽  
Test Cases ◽  
Use Case ◽  
Industrial Data ◽  
Model Based Testing ◽  
Industrial Projects ◽  
The Impact ◽  
Test Suites

Model-Based Testing (MBT) is used for generating test suites from system models. However, as software evolves, its models tend to be updated, which may lead to obsolete test cases that are often discarded. Test case discard can be very costly since essential data, such as execution history, are lost. In this paper, we investigate the use of distance functions and machine learning to help to reduce the discard of MBT tests. First, we assess the problem of managing MBT suites in the context of agile industrial projects. Then, we propose two strategies to cope with this problem: (i) a pure distance function-based. An empirical study using industrial data and ten different distance functions showed that distance functions could be effective for identifying low impact edits that lead to test cases that can be updated with little effort. We also found the optimal configuration for each function. Moreover, we showed that, by using this strategy, one could reduce the discard of test cases by 9.53%; (ii) a strategy that combines machine learning with distance values. This strategy can classify the impact of edits in use case documents with accuracy above 80%; it was able to reduce the discard of test cases by 10.4% and to identify test cases that should, in fact, be discarded.

Strategic alliances’ effects over hospital efficiency and capacity utilization in México

2020 ◽  
Vol 33 (1) ◽  
pp. 128-146 ◽  
Author(s):  
Víctor Giménez ◽  
Diego Prior ◽  
Jorge R. Keith
Keyword(s):  
Strategic Alliances ◽  
Management Practices ◽  
Capacity Utilization ◽  
Distance Functions ◽  
Control Group ◽  
Private Hospitals ◽  
Content Type ◽  
Health Organization ◽  
Installed Capacity ◽  
The Impact

PurposeThis paper aims to investigate the efficiency implications of belonging to a strategic hospital alliance (SHA) and measuring the effects over capacity utilization of such agreements in a Mexican healthcare context.Design/methodology/approachData Envelopment Analysis (DEA) is the nonparametric methodology used, which supports both objectives. Technological gaps ratios are calculated by using DEA-metafrontier approach to compare efficiency between SHA members and a hospital’s control group. Also, hospital capacity utilization ratios are used as the maximum rate of output possible from fixed inputs in a frontier setting using directional distance functions. Data were collected from an alliance called Consorcio Mexicano de Hospitales in México, which has 29 general private hospitals and a group of 47 hospitals with same characteristics from a database made by the Instituto Nacional de Estadística y Geografía for year 2014.FindingsThe results indicate that efficiency is better at hospitals that belong to an alliance; it also shows an improvement of installed capacity management for hospital alliances in México.Originality/valueThe results can be useful for both private health organization managers and regulators themselves to adopt management practices that may end up having a favorable impact on cost and prices containment. Additionally, there are no previous studies neither in Mexico nor in Latin America that analyze the impact of strategic hospitality alliances on the efficiency and utilization of the capacity of private hospitals.

scholarly journals Parameter tuning in KNN for software defect prediction: an empirical analysis

2019 ◽  
Vol 7 (4) ◽  
pp. 121-126 ◽  
Author(s):  
Modinat Abolore Mabayoje ◽  
Abdullateef Olwagbemiga Balogun ◽  
Hajarah Afor Jibril ◽  
Jelili Olaniyi Atoyebi ◽  
Hammed Adeleye Mojeed ◽  
...  
Keyword(s):  
Distance Function ◽  
Predictive Performance ◽  
Parameter Tuning ◽  
Distance Functions ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
K Nearest Neighbor ◽  
K Value ◽  
Software Defect ◽  
The Impact

Software Defect Prediction (SDP) provides insights that can help software teams to allocate their limited resources in developing software systems. It predicts likely defective modules and helps avoid pitfalls that are associated with such modules. However, these insights may be inaccurate and unreliable if parameters of SDP models are not taken into consideration. In this study, the effect of parameter tuning on the k nearest neighbor (k-NN) in SDP was investigated. More specifically, the impact of varying and selecting optimal k value, the influence of distance weighting and the impact of distance functions on k-NN. An experiment was designed to investigate this problem in SDP over 6 software defect datasets. The experimental results revealed that k value should be greater than 1 (default) as the average RMSE values of k-NN when k>1(0.2727) is less than when k=1(default) (0.3296). In addition, the predictive performance of k-NN with distance weighing improved by 8.82% and 1.7% based on AUC and accuracy respectively. In terms of the distance function, kNN models based on Dilca distance function performed better than the Euclidean distance function (default distance function). Hence, we conclude that parameter tuning has a positive effect on the predictive performance of k-NN in SDP.

scholarly journals Predicting Attack Surface Effects on Attack Vectors in an Open Congested Network Transmission Session by Machine Learning

2021 ◽  
Vol 17 (11) ◽  
pp. 47
Author(s):  
Nahla Aljojo
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Transmission Rate ◽  
Network Congestion ◽  
Network Attack ◽  
Security Issues ◽  
Nearest Neighbours ◽  
Network Transmission ◽  
Attack Surface ◽  
The Impact

<p>This paper examined the impact of a network attack on a congested transmission session. The research is motivated by the fact that the previous research community has neglected to evaluate security issues related to network congestion environments, and has instead concentrated on resolving congestion issues only. At any point in time, attackers can take advantage of the congestion problem, exploit the attack surface, and inject attack vectors. In order to circumvent this issue, a machine learning algorithm is trained to correlate attack vectors from the attack surface in a network congestion signals environment with the value of decisions over time in order to maximise expected attack vectors from the attack surface. Experimental scenario that dwell on transmission rate overwhelming transmission session, resulting in a standing queue was used. The experiment produced a dataset in which a TCP transmission through bursting transmission were capture. The data was acquired using a variety of experimental scenarios. Nave Bayes, and K-Nearest Neighbours prediction analyses demonstrate strong prediction performance. As a result, this study re-establishes the association between attack surface and vectors with network attack prediction.    </p>

scholarly journals Can Local Geographically Restricted Measurements Be Used to Recover Missing Geo-Spatial Data?

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3507
Author(s):  
Hrvoje Kalinić ◽  
Zvonimir Bilokapić ◽  
Frano Matić
Keyword(s):  
Spatial Data ◽  
Sensor Placement ◽  
Machine Learning Techniques ◽  
Location Selection ◽  
Weather Forecasts ◽  
Learning Techniques ◽  
Nearest Neighbours ◽  
Average Angular Error ◽  
Increase In Accuracy ◽  
The Impact

The experiments conducted on the wind data provided by the European Centre for Medium-range Weather Forecasts show that 1% of the data is sufficient to reconstruct the other 99% with an average amplitude error of less than 0.5 m/s and an average angular error of less than 5 degrees. In a nutshell, our method provides an approach where a portion of the data is used as a proxy to estimate the measurements over the entire domain based only on a few measurements. In our study, we compare several machine learning techniques, namely: linear regression, K-nearest neighbours, decision trees and a neural network, and investigate the impact of sensor placement on the quality of the reconstruction. While methods provide comparable results the results show that sensor placement plays an important role. Thus, we propose that intelligent location selection for sensor placement can be done using k-means, and show that this indeed leads to increase in accuracy as compared to random sensor placement.

Formation of Lunar Craters and Bright Rays as a Result of Meteorite Impacts

1962 ◽  
Vol 14 ◽  
pp. 415-418
Author(s):  
K. P. Stanyukovich ◽  
V. A. Bronshten
Keyword(s):  
Explosive Charge ◽  
The Moon ◽  
Meteorite Impacts ◽  
The Earth ◽  
Lunar Craters ◽  
The Impact

The phenomena accompanying the impact of large meteorites on the surface of the Moon or of the Earth can be examined on the basis of the theory of explosive phenomena if we assume that, instead of an exploding meteorite moving inside the rock, we have an explosive charge (equivalent in energy), situated at a certain distance under the surface.

The Geosciences Applied to Lunar Exploration

1962 ◽  
Vol 14 ◽  
pp. 169-257 ◽  
Author(s):  
J. Green
Keyword(s):  
Lunar Surface ◽  
Probable Mechanism ◽  
Point Of View ◽  
Lunar Exploration ◽  
Geological Model ◽  
Apply Geophysics ◽  
Surface Features ◽  
The Impact

The term geo-sciences has been used here to include the disciplines geology, geophysics and geochemistry. However, in order to apply geophysics and geochemistry effectively one must begin with a geological model. Therefore, the science of geology should be used as the basis for lunar exploration. From an astronomical point of view, a lunar terrain heavily impacted with meteors appears the more reasonable; although from a geological standpoint, volcanism seems the more probable mechanism. A surface liberally marked with volcanic features has been advocated by such geologists as Bülow, Dana, Suess, von Wolff, Shaler, Spurr, and Kuno. In this paper, both the impact and volcanic hypotheses are considered in the application of the geo-sciences to manned lunar exploration. However, more emphasis is placed on the volcanic, or more correctly the defluidization, hypothesis to account for lunar surface features.

Asteroid and Comet Impact Speeds upon Mars: Significance for Panspermia and the Supply of Organics

1997 ◽  
Vol 161 ◽  
pp. 197-201 ◽  
Author(s):  
Duncan Steel
Keyword(s):  
Probability Distributions ◽  
Regions Of Interest ◽  
Significant Fraction ◽  
Organic Chemicals ◽  
Impact Speed ◽  
The Mean ◽  
The Impact

AbstractWhilst lithopanspermia depends upon massive impacts occurring at a speed above some limit, the intact delivery of organic chemicals or other volatiles to a planet requires the impact speed to be below some other limit such that a significant fraction of that material escapes destruction. Thus the two opposite ends of the impact speed distributions are the regions of interest in the bioastronomical context, whereas much modelling work on impacts delivers, or makes use of, only the mean speed. Here the probability distributions of impact speeds upon Mars are calculated for (i) the orbital distribution of known asteroids; and (ii) the expected distribution of near-parabolic cometary orbits. It is found that cometary impacts are far more likely to eject rocks from Mars (over 99 percent of the cometary impacts are at speeds above 20 km/sec, but at most 5 percent of the asteroidal impacts); paradoxically, the objects impacting at speeds low enough to make organic/volatile survival possible (the asteroids) are those which are depleted in such species.

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