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2022 ◽  
Vol 41 (2) ◽  
pp. 1-16
Author(s):  
Yuichi Nagata ◽  
Shinji Imahori

Escher tiling is well known as a tiling that consists of one or a few recognizable figures, such as animals. The Escherization problem involves finding the most similar shape to a given goal figure that can tile the plane. However, it is easy to imagine that there is no similar tile shape for complex goal shapes. This article devises a method for finding a satisfactory tile shape in such a situation. To obtain a satisfactory tile shape, the tile shape is generated by deforming the goal shape to a considerable extent while retaining the characteristics of the original shape. To achieve this, both goal and tile shapes are represented as triangular meshes to consider not only the contours but also the internal similarity of the shapes. To measure the naturalness of the deformation, energy functions based on traditional as-rigid-as-possible shape modeling are incorporated into a recently developed framework of the exhaustive search of the templates for the Escherization problem. The developed algorithms find satisfactory tile shapes with natural deformations for fairly complex goal shapes.


2022 ◽  
Author(s):  
Krisma Asmoro ◽  
I Nyoman Apraz Ramatryana ◽  
Soo Young Shin

Reconfigurable intelligent surface (RIS) as a supportive technology for aiding downlink non-orthogonal multiple access (NOMA) can enhance the bit error rate (BER) performance. In this paper, a novel BER-aware reflecting elements allocation (REA) on an RIS is proposed to maintain the BER order among paired RIS-NOMA users. The RIS REA is useful for minimizing the average user BER, ompared with a system that allocates the same number of elements to all users. Additionally, the Ricean fading is considered instead of Rayleigh fading as it is more practical and general. Furthermore,an REA optimization objective function for equalizing the user BER is proposed. In order to solve the problem, a modified exhaustive search is proposed to reduce complexity. The distribution of the objective function is observed first; subsequently, the exhaustive search range is determined. Both the analytical and simulation results show that the proposed algorithm can minimize the average user BER.


2022 ◽  
Author(s):  
Krisma Asmoro ◽  
I Nyoman Apraz Ramatryana ◽  
Soo Young Shin

Reconfigurable intelligent surface (RIS) as a supportive technology for aiding downlink non-orthogonal multiple access (NOMA) can enhance the bit error rate (BER) performance. In this paper, a novel BER-aware reflecting elements allocation (REA) on an RIS is proposed to maintain the BER order among paired RIS-NOMA users. The RIS REA is useful for minimizing the average user BER, ompared with a system that allocates the same number of elements to all users. Additionally, the Ricean fading is considered instead of Rayleigh fading as it is more practical and general. Furthermore,an REA optimization objective function for equalizing the user BER is proposed. In order to solve the problem, a modified exhaustive search is proposed to reduce complexity. The distribution of the objective function is observed first; subsequently, the exhaustive search range is determined. Both the analytical and simulation results show that the proposed algorithm can minimize the average user BER.


ICGA Journal ◽  
2021 ◽  
pp. 1-23
Author(s):  
Connor Gregor ◽  
Daniel Ashlock ◽  
Allan R. Willms

In this study, the group of finite cyclic lamplighter states is reinterpreted as the novel lamplighter puzzle. The rules of the puzzle are outlined and related back to properties of the lamplighter group with specific interest placed upon the discussion of which puzzle instances are solvable. The paper shows that, through the use of algebra, many puzzle instances can be identified as solvable without the use of an exhaustive search algorithm. Solvability depends upon the creation of irregular generating sets for subgroups of the finite cyclic lamplighter group and the cosets formed by these subgroups. Further possible generalizations of the lamplighter puzzle are also discussed in closing.


Retos ◽  
2021 ◽  
Vol 44 ◽  
pp. 464-476
Author(s):  
Cristián Andrés Mateluna Núñez ◽  
Juan Pablo Zavala-Crichton ◽  
Matías Monsalves-Álvarez ◽  
Jorge Olivares-Arancibia ◽  
Rodrigo Yáñez-Sepúlveda

  La capacidad de generar máxima potencia neuromuscular es el factor más importante y determinante en el rendimiento atlético. Debido a esto, el entrenamiento con movimientos de Halterofilia (EMH) y sus derivados es uno de los métodos más usados, ya que la evidencia muestra que genera adaptaciones de fuerza-potencia superiores comparadas con el entrenamiento de fuerza tradicional, de salto y de kettlebells. Objetivo: Identificar los efectos del EMH en la capacidad de salto, esprint y cambio de dirección (COD) en población deportista. Método: Se realizó una búsqueda exhaustiva en diferentes bases de datos, como PUBMED, Sportdiscus (EBSCO), Scopus y Web of Science (WOS) bajo modelo PRISMA. Los trabajos revisados fueron experimentales con y sin grupo de control, entre los años 2000 y 2020. Resultados: El EMH produce mejoras significativas en las capacidades de salto, de esprint y de COD en población deportista. Conclusión: El EMH genera mejoras significativas en el rendimiento de salto, carreras y cambio de dirección bajo distintos protocolos. Existe evidencia que sustenta la aplicación de EMH, recomendando sus derivados centrados en el segundo tirón y aquellos que utilicen el ciclo de estiramiento-acortamiento en sus variantes colgantes. Abstract: The ability to generate maximum power is the most important and determining neuromuscular function in sports performance. Therefore, weightlifting training (WT) and its derivatives is one of the most widely used methods, generating superior strength-power adaptations compared to traditional strength training, jumping and kettlebell training. Objective: To identify the effects of WT on the ability to jump, sprint and change of direction (COD) in athletes. Method: An exhaustive search was carried out in different databases, such as PUBMED, Sportdiscus (EBSCO), Scopus and Web of Science (WOS) under the PRISMA model. The reviewed papers were experimental with and without a control group, between the years 2000 and 2020. Results: The WT produces significant improvements in jump, sprint and in change of direction capacities in the sport population. Conclusion: WT generates significant improvements in jumping, running and change of direction performance under different protocols. There is evidence supporting the use of WT, suggesting its derivatives focused on the second pull and those that use the stretch-shortening cycle in their hanging variants.


Author(s):  
Mengli He ◽  
Yue Li ◽  
Xiaofei Wang ◽  
Zelong Liu

AbstractTo meet the demands of massive connections in the Internet-of-vehicle communications, non-orthogonal multiple access (NOMA) is utilized in the local wireless networks. In NOMA technique, various optimization methods have been proposed to provide optimal resource allocation, but they are limited by computational complexity. Recently, the deep reinforcement learning network is utilized for resource optimization in NOMA system, where a uniform sampled experience replay algorithm is used to reduce the correlation between samples. However, the uniform sampling ignores the importance of sample. To this point, this paper proposes a joint prioritized DQN user grouping and DDPG power allocation algorithm to maximize the system sum rate. At the user grouping stage, a prioritized sampling method based on TD-error (temporal-difference error) is proposed. At the power allocation stage, to deal with the problem that DQN cannot process continuous tasks and needs to quantify power into discrete form, a DDPG network is utilized. Simulation results show that the proposed algorithm with prioritized sampling can increase the learning rate and perform a more stable training process. Compared with the previous DQN algorithm, the proposed method improves the sum rate of the system by 2% and reaches 94% and 93% of the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively. Although the sum rate is improved by only 2%, the computational complexity is reduced by 43% and 64% compared to the exhaustive search algorithm and the optimal iterative power optimization algorithm, respectively.


2021 ◽  
Author(s):  
Mengli He ◽  
Yue Li ◽  
Xiaofei Wang ◽  
Zelong Liu

Abstract To meet the demands of massive connections in the Internet-of-vehicle (IoV) communications, non-orthogonal multiple access (NOMA) is utilized in the local wireless networks. In NOMA technique, power multiplexing and successive interference cancellation techniques are utilized at the transmitter and the receiver respectively to increase system capacity, and user grouping and power allocation are two key issues to ensure the performance enhancement. Various optimization methods have been proposed to provide optimal resource allocation, but they are limited by computational complexity. Recently, the deep reinforcement learning (DRL) network is utilized to solve the resource allocation problem. In a DRL network, an experience replay algorithm is used to reduce the correlation between samples. However, the uniform sampling ignores the importance of sample. Different from conventional methods, this paper proposes a joint prioritized DQN user grouping and DDPG power allocation algorithm to maximize the sum rate of the NOMA system. At the user grouping stage, a prioritized sampling method based on TD-error (temporal-difference error) is proposed to solve the problem of random sampling, where TD-error is used to represent the priority of sample, and the DQN takes samples according to their priorities. In addition, sum tree is used to store the priority to speed up the searching process. At the power allocation stage, to deal with the problem that DQN cannot process continuous tasks and needs to quantify power into discrete form, a DDPG network is utilized to complete power allocation tasks for each user. Simulation results show that the proposed algorithm with prioritized sampling can increase the learning rate and perform a more stable training process. Compared with the previous DQN algorithm, the proposed method improves the sum rate of the system by 2% and reaches 94% and 93% of the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively. While the computational complexity is reduced by 43% and 64% compared with the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively.


2021 ◽  
Author(s):  
◽  
Mukhlis Matti

<p>This thesis explores and evaluates MAXCCLUS, a bioinformatics clustering algorithm, which was designed to be used to cluster genes from microarray experimental data. MAXCCLUS does the clustering of genes depending on the textual data that describe the genes. MAXCCLUS attempts to create clusters of which it selects only the statistically significant clusters by running a significance test. It then attempts to generalise these clusters by using a simple greedy generalisation algorithm. We explore the behaviour of MAXCCLUS by running several clustering experiments that investigate various modifications to MAXCCLUS and its data. The thesis shows (a) that using the simple generalisation algorithm of MAXCCLUS gives better result than using an exhaustive search algorithm for generalisation, (b) the significance test that MAXCCLUS uses needs to be modified to take into consideration the dependency of some genes on other genes functionally, (c) it is advantageous to delete the non domain-relevant textual data that describe the genes but disadvantageous to add more textual data to describe the genes, and (d) that MAXCCLUS behaves poorly when it attempts to cluster genes that have adjacent categories instead of having two distinct categories only.</p>


2021 ◽  
Author(s):  
◽  
Mukhlis Matti

<p>This thesis explores and evaluates MAXCCLUS, a bioinformatics clustering algorithm, which was designed to be used to cluster genes from microarray experimental data. MAXCCLUS does the clustering of genes depending on the textual data that describe the genes. MAXCCLUS attempts to create clusters of which it selects only the statistically significant clusters by running a significance test. It then attempts to generalise these clusters by using a simple greedy generalisation algorithm. We explore the behaviour of MAXCCLUS by running several clustering experiments that investigate various modifications to MAXCCLUS and its data. The thesis shows (a) that using the simple generalisation algorithm of MAXCCLUS gives better result than using an exhaustive search algorithm for generalisation, (b) the significance test that MAXCCLUS uses needs to be modified to take into consideration the dependency of some genes on other genes functionally, (c) it is advantageous to delete the non domain-relevant textual data that describe the genes but disadvantageous to add more textual data to describe the genes, and (d) that MAXCCLUS behaves poorly when it attempts to cluster genes that have adjacent categories instead of having two distinct categories only.</p>


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