Pub Date : 2023-11-01Epub Date: 2023-08-29DOI: 10.3114/fuse.2023.12.08
N Mao, Y Y Xu, Y X Zhang, H Zhou, X B Huang, C L Hou, L Fan
Helvella is a widespread, frequently encountered fungal group appearing in forests, but the species diversity and molecular phylogeny of Helvella in China remains incompletely understood. In this work, we performed comprehensive phylogenetic analyses using multilocus sequence data. Six datasets were employed, including a five-locus concatenated dataset (ITS, nrLSU, tef1-α, rpb2, hsp), a two-locus concatenated dataset (ITS, nrLSU), and four single-locus datasets (ITS) that were divided based on the four different phylogenetic clades of Helvella recognized in this study. A total of I 946 sequences were used, of which 713 were newly generated, including 170 sequences of ITS, 174 sequences of nrLSU, 131 sequences of tef1-α, 107 sequences of rpb2 and 131 sequences of hsp. The phylogeny based on the five-locus concatenated dataset revealed that Helvellas. str. is monophyletic and four phylogenetic clades are clearly recognized, i.e., Acetabulum clade, Crispa clade, Elastica clade, and Lacunosa clade. A total of 24 lineages or subclades were recognized, II of which were new, the remaining 13 corresponding with previous studies. Chinese Helvella species are distributed in 22 lineages across four clades. Phylogenetic analyses based on the two-locus concatenated dataset and four single-locus datasets confirmed the presence of at least 93 phylogenetic species in China. Among them, 58 are identified as known species, including a species with a newly designated lectotype and epitype, 18 are newly described in this paper, and the remaining 17 taxa are putatively new to science but remain unnamed due to the paucity or absence of ascomatal materials. In addition, the Helvella species previously recorded in China are discussed. A list of 76 confirmed species, including newly proposed species, is provided. The occurrence of H. crispa and H. elastica are not confirmed although both are commonly recorded in China. Citation: Mao N, Xu YY, Zhang YX, Zhou H, Huang XB, Hou CL, Fan L (2023). Phylogeny and species diversity of the genus Helvella with emphasis on eighteen new species from China. Fungal Systematics and Evolution12: 111-152. doi: 10.3114/fuse.2023.12.08.
{"title":"Phylogeny and species diversity of the genus <i>Helvella</i> with emphasis on eighteen new species from China.","authors":"N Mao, Y Y Xu, Y X Zhang, H Zhou, X B Huang, C L Hou, L Fan","doi":"10.3114/fuse.2023.12.08","DOIUrl":"10.3114/fuse.2023.12.08","url":null,"abstract":"<p><p><b></b> <i>Helvella</i> is a widespread, frequently encountered fungal group appearing in forests, but the species diversity and molecular phylogeny of <i>Helvella</i> in China remains incompletely understood. In this work, we performed comprehensive phylogenetic analyses using multilocus sequence data. Six datasets were employed, including a five-locus concatenated dataset (ITS, nrLSU, <i>tef1-α, rpb2, hsp)</i>, a two-locus concatenated dataset (ITS, nrLSU), and four single-locus datasets (ITS) that were divided based on the four different phylogenetic clades of <i>Helvella</i> recognized in this study. A total of I 946 sequences were used, of which 713 were newly generated, including 170 sequences of ITS, 174 sequences of nrLSU, 131 sequences of <i>tef1-α</i>, 107 sequences of <i>rpb2</i> and 131 sequences of <i>hsp.</i> The phylogeny based on the five-locus concatenated dataset revealed that <i>Helvellas. str</i>. is monophyletic and four phylogenetic clades are clearly recognized, <i>i.e., Acetabulum</i> clade, <i>Crispa</i> clade, <i>Elastica</i> clade, and <i>Lacunosa</i> clade. A total of 24 lineages or subclades were recognized, II of which were new, the remaining 13 corresponding with previous studies. Chinese <i>Helvella</i> species are distributed in 22 lineages across four clades. Phylogenetic analyses based on the two-locus concatenated dataset and four single-locus datasets confirmed the presence of at least 93 phylogenetic species in China. Among them, 58 are identified as known species, including a species with a newly designated lectotype and epitype, 18 are newly described in this paper, and the remaining 17 taxa are putatively new to science but remain unnamed due to the paucity or absence of ascomatal materials. In addition, the <i>Helvella</i> species previously recorded in China are discussed. A list of 76 confirmed species, including newly proposed species, is provided. The occurrence of <i>H. crispa</i> and <i>H. elastica</i> are not confirmed although both are commonly recorded in China. <b>Citation:</b> Mao N, Xu YY, Zhang YX, Zhou H, Huang XB, Hou CL, Fan L (2023). Phylogeny and species diversity of the genus <i>Helvella</i> with emphasis on eighteen new species from China. <i>Fungal Systematics and Evolution</i> <b>12</b>: 111-152. doi: 10.3114/fuse.2023.12.08.</p>","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"10 1","pages":"111-152"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10964050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78899145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vladimir Korkhov, Ivan Gankevich, Anton Gavrikov, Maria Mingazova, Ivan Petriakov, Dmitrii Tereshchenko, Artem Shatalin, Vitaly Slobodskoy
Bottlenecks and imbalance in parallel programs can significantly affect performance of parallel execution. Finding these bottlenecks is a key issue in performance analysis of MPI programs especially on a large scale. One of the ways to discover bottlenecks is to analyze the critical path of the parallel program: the longest execution path in the program activity graph. There are a number of methods of finding the critical path; however, most of them suffer a performance drop when scaled. In this paper, we analyze several methods of critical path finding based on classical Dijkstra and Delta-stepping algorithms along with the proposed algorithm based on topological sorting. Corresponding algorithms for each approach are presented including additional enhancements for increasing performance. The implementation of the algorithms and resulting performance for several benchmark applications (NAS Parallel Benchmarks, CP2K, OpenFOAM, LAMMPS, and MiniFE) are analyzed and discussed.
{"title":"Finding Bottlenecks in Message Passing Interface Programs by Scalable Critical Path Analysis","authors":"Vladimir Korkhov, Ivan Gankevich, Anton Gavrikov, Maria Mingazova, Ivan Petriakov, Dmitrii Tereshchenko, Artem Shatalin, Vitaly Slobodskoy","doi":"10.3390/a16110505","DOIUrl":"https://doi.org/10.3390/a16110505","url":null,"abstract":"Bottlenecks and imbalance in parallel programs can significantly affect performance of parallel execution. Finding these bottlenecks is a key issue in performance analysis of MPI programs especially on a large scale. One of the ways to discover bottlenecks is to analyze the critical path of the parallel program: the longest execution path in the program activity graph. There are a number of methods of finding the critical path; however, most of them suffer a performance drop when scaled. In this paper, we analyze several methods of critical path finding based on classical Dijkstra and Delta-stepping algorithms along with the proposed algorithm based on topological sorting. Corresponding algorithms for each approach are presented including additional enhancements for increasing performance. The implementation of the algorithms and resulting performance for several benchmark applications (NAS Parallel Benchmarks, CP2K, OpenFOAM, LAMMPS, and MiniFE) are analyzed and discussed.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135869745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advancements in technology have increased the potential for demand-responsive feeder transit services to enhance mobility in areas with limited public transit access. For long rail headways, feeder bus network algorithms are straightforward, as the maximum feeder service cycle time is determined by rail headway, and bus–train matching is unnecessary. However, for short rail headways, the algorithm must address both passenger–feeder-bus and feeder-bus–train matching. This study presents a simulated annealing (SA) algorithm for flexible feeder bus routing, accommodating short headway trunk lines and multiple bus relocations for various stations and trains. A 5 min headway rail trunk line example was utilized to test the algorithm. The algorithm effectively managed bus relocations when optimal routes were infeasible at specific stations. Additionally, the algorithm minimized total costs, accounting for vehicle operating expenses and passenger in-vehicle travel time costs, while considering multiple vehicle relocations.
{"title":"Dynamic Demand-Responsive Feeder Bus Network Design for a Short Headway Trunk Line","authors":"Amirreza Nickkar, Young-Jae Lee","doi":"10.3390/a16110506","DOIUrl":"https://doi.org/10.3390/a16110506","url":null,"abstract":"Recent advancements in technology have increased the potential for demand-responsive feeder transit services to enhance mobility in areas with limited public transit access. For long rail headways, feeder bus network algorithms are straightforward, as the maximum feeder service cycle time is determined by rail headway, and bus–train matching is unnecessary. However, for short rail headways, the algorithm must address both passenger–feeder-bus and feeder-bus–train matching. This study presents a simulated annealing (SA) algorithm for flexible feeder bus routing, accommodating short headway trunk lines and multiple bus relocations for various stations and trains. A 5 min headway rail trunk line example was utilized to test the algorithm. The algorithm effectively managed bus relocations when optimal routes were infeasible at specific stations. Additionally, the algorithm minimized total costs, accounting for vehicle operating expenses and passenger in-vehicle travel time costs, while considering multiple vehicle relocations.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"2007 26","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, where at least two objective functions are in conflict with one another. When solving real-world problems, the incorporation of human decision-makers (DMs)’ preferences or expert knowledge into the optimization process and thereby restricting the search to a specific region of the Pareto-optimal Front (POF) may result in more preferred or suitable solutions. This study proposes approaches that enable DMs to influence the search process with their preferences by reformulating the optimization problems as constrained problems. The subsequent constrained problems are solved using various constraint handling approaches, such as the penalization of infeasible solutions and the restriction of the search to the feasible region of the search space. The proposed constraint handling approaches are compared by incorporating the approaches into a differential evolution (DE) algorithm and measuring the algorithm’s performance using both standard performance measures for dynamic multi-objective optimization (DMOO), as well as newly proposed measures for constrained DMOPs. The new measures indicate how well an algorithm was able to find solutions in the objective space that best reflect the DM’s preferences and the Pareto-optimality goal of dynamic multi-objective optimization algorithms (DMOAs). The results indicate that the constraint handling approaches are effective in finding Pareto-optimal solutions that satisfy the preference constraints of a DM.
{"title":"Decision-Maker’s Preference-Driven Dynamic Multi-Objective Optimization","authors":"Adekunle Rotimi Adekoya, Mardé Helbig","doi":"10.3390/a16110504","DOIUrl":"https://doi.org/10.3390/a16110504","url":null,"abstract":"Dynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, where at least two objective functions are in conflict with one another. When solving real-world problems, the incorporation of human decision-makers (DMs)’ preferences or expert knowledge into the optimization process and thereby restricting the search to a specific region of the Pareto-optimal Front (POF) may result in more preferred or suitable solutions. This study proposes approaches that enable DMs to influence the search process with their preferences by reformulating the optimization problems as constrained problems. The subsequent constrained problems are solved using various constraint handling approaches, such as the penalization of infeasible solutions and the restriction of the search to the feasible region of the search space. The proposed constraint handling approaches are compared by incorporating the approaches into a differential evolution (DE) algorithm and measuring the algorithm’s performance using both standard performance measures for dynamic multi-objective optimization (DMOO), as well as newly proposed measures for constrained DMOPs. The new measures indicate how well an algorithm was able to find solutions in the objective space that best reflect the DM’s preferences and the Pareto-optimality goal of dynamic multi-objective optimization algorithms (DMOAs). The results indicate that the constraint handling approaches are effective in finding Pareto-optimal solutions that satisfy the preference constraints of a DM.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"2023 7-8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexey Kozin, Anton Gerasimov, Maxim Bakaev, Anton Pashkov, Olga Razumnikova
Brain–computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) are inexpensive and do not require user training. However, the highly personalized reaction to visual stimulation is an obstacle to the wider application of this technique, as it can be ineffective, tiring, or even harmful at certain frequencies. In our experimental study, we proposed a new approach to the selection of optimal frequencies of photostimulation. By using a custom photostimulation device, we covered a frequency range from 5 to 25 Hz with 1 Hz increments, recording the subjects’ brainwave activity (EEG) and analyzing the signal-to-noise ratio (SNR) changes at the corresponding frequencies. The proposed set of SNR-based coefficients and the discomfort index, determined by the ratio of theta and beta rhythms in the EEG signal, enables the automation of obtaining the recommended stimulation frequencies for use in SSVEP-based BCIs.
{"title":"Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces","authors":"Alexey Kozin, Anton Gerasimov, Maxim Bakaev, Anton Pashkov, Olga Razumnikova","doi":"10.3390/a16110502","DOIUrl":"https://doi.org/10.3390/a16110502","url":null,"abstract":"Brain–computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) are inexpensive and do not require user training. However, the highly personalized reaction to visual stimulation is an obstacle to the wider application of this technique, as it can be ineffective, tiring, or even harmful at certain frequencies. In our experimental study, we proposed a new approach to the selection of optimal frequencies of photostimulation. By using a custom photostimulation device, we covered a frequency range from 5 to 25 Hz with 1 Hz increments, recording the subjects’ brainwave activity (EEG) and analyzing the signal-to-noise ratio (SNR) changes at the corresponding frequencies. The proposed set of SNR-based coefficients and the discomfort index, determined by the ratio of theta and beta rhythms in the EEG signal, enables the automation of obtaining the recommended stimulation frequencies for use in SSVEP-based BCIs.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"312 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136158114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md. Ashikur Rahman, Lway Faisal Abdulrazak, Md. Mamun Ali, Imran Mahmud, Kawsar Ahmed, Francis M. Bui
Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. From a clinical perspective, the most significant approach to mitigating the effects of diabetes is early-stage control and management, with the aim of a potential cure. However, lack of awareness and expensive clinical tests are the primary reasons why clinical diagnosis and preventive measures are neglected in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, this study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes, due to the fact that clinical features are not always accessible to all people from lower-income countries. To find the best fit of the supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. This study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for developing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.
{"title":"Machine Learning-Based Approach for Predicting Diabetes Employing Socio-Demographic Characteristics","authors":"Md. Ashikur Rahman, Lway Faisal Abdulrazak, Md. Mamun Ali, Imran Mahmud, Kawsar Ahmed, Francis M. Bui","doi":"10.3390/a16110503","DOIUrl":"https://doi.org/10.3390/a16110503","url":null,"abstract":"Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. From a clinical perspective, the most significant approach to mitigating the effects of diabetes is early-stage control and management, with the aim of a potential cure. However, lack of awareness and expensive clinical tests are the primary reasons why clinical diagnosis and preventive measures are neglected in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, this study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes, due to the fact that clinical features are not always accessible to all people from lower-income countries. To find the best fit of the supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. This study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for developing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"59 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136136169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carmelo Scribano, Danilo Pezzi, Giorgia Franchini, Marco Prato
With the recent advancements in the field of diffusion generative models, it has been shown that defining the generative process in the latent space of a powerful pretrained autoencoder can offer substantial advantages. This approach, by abstracting away imperceptible image details and introducing substantial spatial compression, renders the learning of the generative process more manageable while significantly reducing computational and memory demands. In this work, we propose to replace autoencoder coding with a model-based coding scheme based on traditional lossy image compression techniques; this choice not only further diminishes computational expenses but also allows us to probe the boundaries of latent-space image generation. Our objectives culminate in the proposal of a valuable approximation for training continuous diffusion models within a discrete space, accompanied by enhancements to the generative model for categorical values. Beyond the good results obtained for the problem at hand, we believe that the proposed work holds promise for enhancing the adaptability of generative diffusion models across diverse data types beyond the realm of imagery.
{"title":"Denoising Diffusion Models on Model-Based Latent Space","authors":"Carmelo Scribano, Danilo Pezzi, Giorgia Franchini, Marco Prato","doi":"10.3390/a16110501","DOIUrl":"https://doi.org/10.3390/a16110501","url":null,"abstract":"With the recent advancements in the field of diffusion generative models, it has been shown that defining the generative process in the latent space of a powerful pretrained autoencoder can offer substantial advantages. This approach, by abstracting away imperceptible image details and introducing substantial spatial compression, renders the learning of the generative process more manageable while significantly reducing computational and memory demands. In this work, we propose to replace autoencoder coding with a model-based coding scheme based on traditional lossy image compression techniques; this choice not only further diminishes computational expenses but also allows us to probe the boundaries of latent-space image generation. Our objectives culminate in the proposal of a valuable approximation for training continuous diffusion models within a discrete space, accompanied by enhancements to the generative model for categorical values. Beyond the good results obtained for the problem at hand, we believe that the proposed work holds promise for enhancing the adaptability of generative diffusion models across diverse data types beyond the realm of imagery.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136231666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Linear probing continues to be one of the best practical hashing algorithms due to its good average performance, efficiency, and simplicity of implementation. However, the worst-case performance of linear probing seems to degrade with high load factors due to a primary-clustering tendency of one collision to cause more nearby collisions. It is known that the maximum cluster size produced by linear probing, and hence the length of the longest probe sequence needed to insert or search for a key in a hash table of size n, is Ω(logn), in probability. In this article, we introduce linear probing hashing schemes that employ two linear probe sequences to find empty cells for the keys. Our results show that two-way linear probing is a promising alternative to linear probing for hash tables. We show that two-way linear probing has an asymptotically almost surely O(loglogn) maximum cluster size when the load factor is constant. Matching lower bounds on the maximum cluster size produced by any two-way linear probing algorithm are obtained as well. Our analysis is based on a novel approach that uses the multiple-choice paradigm and witness trees.
{"title":"Two-Way Linear Probing Revisited","authors":"Ketan Dalal, Luc Devroye, Ebrahim Malalla","doi":"10.3390/a16110500","DOIUrl":"https://doi.org/10.3390/a16110500","url":null,"abstract":"Linear probing continues to be one of the best practical hashing algorithms due to its good average performance, efficiency, and simplicity of implementation. However, the worst-case performance of linear probing seems to degrade with high load factors due to a primary-clustering tendency of one collision to cause more nearby collisions. It is known that the maximum cluster size produced by linear probing, and hence the length of the longest probe sequence needed to insert or search for a key in a hash table of size n, is Ω(logn), in probability. In this article, we introduce linear probing hashing schemes that employ two linear probe sequences to find empty cells for the keys. Our results show that two-way linear probing is a promising alternative to linear probing for hash tables. We show that two-way linear probing has an asymptotically almost surely O(loglogn) maximum cluster size when the load factor is constant. Matching lower bounds on the maximum cluster size produced by any two-way linear probing algorithm are obtained as well. Our analysis is based on a novel approach that uses the multiple-choice paradigm and witness trees.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"48 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136231798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stream ciphers usually rely on highly secure Boolean functions to ensure safe communication within unsafe channels. However, discovering secure Boolean functions is a non-trivial optimization problem that has been addressed by many optimization techniques: in particular by evolutionary algorithms. We investigate in this article the employment of Genetic Programming (GP) for evolving Boolean functions with large non-linearity by examining the search space consisting of Walsh transforms. Especially, we build generic Walsh spectra starting from the evolution of Walsh transform coefficients. Then, by leveraging spectral inversion, we build pseudo-Boolean functions from which we are able to determine the corresponding nearest Boolean functions, whose computation involves filling via a random criterion a certain amount of “uncertain” positions in the final truth table. We show that by using a balancedness-preserving strategy, it is possible to exploit those positions to obtain a function that is as balanced as possible. We perform experiments by comparing different types of symbolic representations for the Walsh transform, and we analyze the percentage of uncertain positions. We systematically review the outcomes of these comparisons to highlight the best type of setting for this problem. We evolve Boolean functions from 6 to 16 bits and compare the GP-based evolution with random search to show that evolving Walsh transforms leads to highly non-linear functions that are balanced as well.
{"title":"Discovering Non-Linear Boolean Functions by Evolving Walsh Transforms with Genetic Programming","authors":"Luigi Rovito, Andrea De Lorenzo, Luca Manzoni","doi":"10.3390/a16110499","DOIUrl":"https://doi.org/10.3390/a16110499","url":null,"abstract":"Stream ciphers usually rely on highly secure Boolean functions to ensure safe communication within unsafe channels. However, discovering secure Boolean functions is a non-trivial optimization problem that has been addressed by many optimization techniques: in particular by evolutionary algorithms. We investigate in this article the employment of Genetic Programming (GP) for evolving Boolean functions with large non-linearity by examining the search space consisting of Walsh transforms. Especially, we build generic Walsh spectra starting from the evolution of Walsh transform coefficients. Then, by leveraging spectral inversion, we build pseudo-Boolean functions from which we are able to determine the corresponding nearest Boolean functions, whose computation involves filling via a random criterion a certain amount of “uncertain” positions in the final truth table. We show that by using a balancedness-preserving strategy, it is possible to exploit those positions to obtain a function that is as balanced as possible. We perform experiments by comparing different types of symbolic representations for the Walsh transform, and we analyze the percentage of uncertain positions. We systematically review the outcomes of these comparisons to highlight the best type of setting for this problem. We evolve Boolean functions from 6 to 16 bits and compare the GP-based evolution with random search to show that evolving Walsh transforms leads to highly non-linear functions that are balanced as well.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the mathematical discipline of computational geometry (CG), practical algorithms for resolving geometric input and output issues are designed, analyzed, and put into practice. It is sometimes used to refer to pattern recognition and to define the solid modeling methods for manipulating curves and surfaces. CG is a rich field encompassing theories to solve complex optimization problems, such as path planning for mobile robot systems and extension to distributed multi-robot systems. This brief review discusses the fundamentals of CG and its application in solving well-known automated path-planning problems in single- and multi-robot systems. We also discuss three winning algorithms from the CG-SHOP (Computational Geometry: Solving Hard Optimization Problems) 2021 competition to evidence the practicality of CG in multi-robotic systems. We also mention some open problems at the intersection of CG and robotics. This review provides insights into the potential use of CG in robotics and future research directions at their intersection.
{"title":"On the Intersection of Computational Geometry Algorithms with Mobile Robot Path Planning","authors":"Ehsan Latif, Ramviyas Parasuraman","doi":"10.3390/a16110498","DOIUrl":"https://doi.org/10.3390/a16110498","url":null,"abstract":"In the mathematical discipline of computational geometry (CG), practical algorithms for resolving geometric input and output issues are designed, analyzed, and put into practice. It is sometimes used to refer to pattern recognition and to define the solid modeling methods for manipulating curves and surfaces. CG is a rich field encompassing theories to solve complex optimization problems, such as path planning for mobile robot systems and extension to distributed multi-robot systems. This brief review discusses the fundamentals of CG and its application in solving well-known automated path-planning problems in single- and multi-robot systems. We also discuss three winning algorithms from the CG-SHOP (Computational Geometry: Solving Hard Optimization Problems) 2021 competition to evidence the practicality of CG in multi-robotic systems. We also mention some open problems at the intersection of CG and robotics. This review provides insights into the potential use of CG in robotics and future research directions at their intersection.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"15 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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