1,515 research outputs found
Superstition, family planning, and human development
Are wanted and unwanted children treated equally by their parents? To address this question, the authors rely on the observation that, according to Vietnamese astrology, dates of birth are believed to be determinants of success, luck, character, and good match between individuals. They then examine fertility decisions made in Vietnam between 1976 and 1996. The authors find that birth cohorts in auspicious years are significantly larger than in other years. Children born in auspicious years moreover do better both in health and education. While parental characteristics seem to affect fertility choices and human development simultaneously, their analysis suggests that family planning is one key mechanism leading to the observed differences in outcomes: in a society in which superstition is widespread, children born in auspicious years are more likely to have been planned by their parents, thus benefiting from more favorable financial, psychological, or emotional conditions for better human development.Health Monitoring&Evaluation,Youth and Governance,Adolescent Health,Population Policies,Gender and Social Development
Does 'About the whole world' imply the sins of the whole world in 1 John 2,2?
In 1 John 2,2 the phrases (2b) peri ton amartion emon, (2c) ou peri ton emeteron de monon, (2d) alla kai peri olou tou kosmou, demand careful interpretation. The construction ou monon alla kai, explains the sequence of 2b and 2c, following the peri-clause in 2a. However, this does not explain theologically to what peri olou tou kosmou in 2d refers. This essay seeks, in some measure, to remedy this syntactical conundrum by proposing a contextual reading of 2a as parallel with 2d
Development of surface roughness model in turning process of 3X13 steel using TiAlN coated carbide insert
Surface roughness that is one of the most important parameters is used to evaluate the quality of a machining process. Improving the accuracy of the surface roughness model will contribute to ensure an accurate assessment of the machining quality. This study aims to improve the accuracy of the surface roughness model in a machnining process. In this study, Johnson and Box-Cox transformations were successfully applied to improve the accuracy of surface roughness model when turning 3X13 steel using TiAlN insert. Four input parameters that were used in experimental process were cutting velocity, feed rate, depth of cut, and insert-nose radius. The experimental matrix was designed using Central Composite Design (CCD) with 29 experiments. By analyzing the experimental data, the influence of input parameters on surface roughness was investigated. A quadratic model was built to explain the relationship of surface roughness and the input parameters. Box-Cox and Johnson transformations were applied to develop two new models of surface roughness. The accuracy of three surface roughness models showed that the surface roughness model using Johnson transformation had the highest accuracy. The second one model of surface roughness is the model using Box-Cox transformation. And surface roughness model without transformation had the smallest accuracy. Using the Johnson transformation, the determination coefficient of surface roughness model increased from 80.43 % to 84.09 %, and mean absolute error reduced from 19.94 % to 16.64 %. Johnson and Box-Cox transformations could be applied to improve the acuaracy of the surface roughness prediction in turning process of 3X13 steel and can be extended with other materials and other machining processe
Policy Poisoning in Batch Learning for Linear Quadratic Control Systems via State Manipulation
In this work, we study policy poisoning through state manipulation, also
known as sensor spoofing, and focus specifically on the case of an agent
forming a control policy through batch learning in a linear-quadratic (LQ)
system. In this scenario, an attacker aims to trick the learner into
implementing a targeted malicious policy by manipulating the batch data before
the agent begins its learning process. An attack model is crafted to carry out
the poisoning strategically, with the goal of modifying the batch data as
little as possible to avoid detection by the learner. We establish an
optimization framework to guide the design of such policy poisoning attacks.
The presence of bi-linear constraints in the optimization problem requires the
design of a computationally efficient algorithm to obtain a solution.
Therefore, we develop an iterative scheme based on the Alternating Direction
Method of Multipliers (ADMM) which is able to return solutions that are
approximately optimal. Several case studies are used to demonstrate the
effectiveness of the algorithm in carrying out the sensor-based attack on the
batch-learning agent in LQ control systems.Comment: First appeared at CISS 202
Optimization of network traffic anomaly detection using machine learning
In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyber-attack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms
Quantum-based Distributed Algorithms for Edge Node Placement and Workload Allocation
Edge computing is a promising technology that offers a superior user
experience and enables various innovative Internet of Things applications. In
this paper, we present a mixed-integer linear programming (MILP) model for
optimal edge server placement and workload allocation, which is known to be
NP-hard. To this end, we explore the possibility of addressing this
computationally challenging problem using quantum computing. However, existing
quantum solvers are limited to solving unconstrained binary programming
problems. To overcome this obstacle, we propose a hybrid quantum-classical
solution that decomposes the original problem into a quadratic unconstrained
binary optimization (QUBO) problem and a linear program (LP) subproblem. The
QUBO problem can be solved by a quantum solver, while the LP subproblem can be
solved using traditional LP solvers. Our numerical experiments demonstrate the
practicality of leveraging quantum supremacy to solve complex optimization
problems in edge computing
Genome-wide analysis points to roles for extracellular matrix remodeling, the visual cycle, and neuronal development in myopia
Myopia, or nearsightedness, is the most common eye disorder, resulting
primarily from excess elongation of the eye. The etiology of myopia, although
known to be complex, is poorly understood. Here we report the largest ever
genome-wide association study (43,360 participants) on myopia in Europeans. We
performed a survival analysis on age of myopia onset and identified 19
significant associations (p < 5e-8), two of which are replications of earlier
associations with refractive error. These 19 associations in total explain 2.7%
of the variance in myopia age of onset, and point towards a number of different
mechanisms behind the development of myopia. One association is in the gene
PRSS56, which has previously been linked to abnormally small eyes; one is in a
gene that forms part of the extracellular matrix (LAMA2); two are in or near
genes involved in the regeneration of 11-cis-retinal (RGR and RDH5); two are
near genes known to be involved in the growth and guidance of retinal ganglion
cells (ZIC2, SFRP1); and five are in or near genes involved in neuronal
signaling or development. These novel findings point towards multiple genetic
factors involved in the development of myopia and suggest that complex
interactions between extracellular matrix remodeling, neuronal development, and
visual signals from the retina may underlie the development of myopia in
humans
Character Time-series Matching For Robust License Plate Recognition
Automatic License Plate Recognition (ALPR) is becoming a popular study area
and is applied in many fields such as transportation or smart city. However,
there are still several limitations when applying many current methods to
practical problems due to the variation in real-world situations such as light
changes, unclear License Plate (LP) characters, and image quality. Almost
recent ALPR algorithms process on a single frame, which reduces accuracy in
case of worse image quality. This paper presents methods to improve license
plate recognition accuracy by tracking the license plate in multiple frames.
First, the Adaptive License Plate Rotation algorithm is applied to correctly
align the detected license plate. Second, we propose a method called Character
Time-series Matching to recognize license plate characters from many
consequence frames. The proposed method archives high performance in the
UFPR-ALPR dataset which is \boldmath accuracy in real-time on RTX A5000
GPU card. We also deploy the algorithm for the Vietnamese ALPR system. The
accuracy for license plate detection and character recognition are 0.881 and
0.979 @.5 respectively. The source code is available at
https://github.com/chequanghuy/Character-Time-series-Matching.gi
- …