Pub Date : 2024-10-11DOI: 10.1016/j.physa.2024.130159
Qing Zhu , Jianhua Che , Shan Liu
Because there is a discrepancy between how individual investors and investment institutions choose Bitcoin and its new derivatives and Exchange-Traded Funds (ETFs), this paper used Bitcoin and ProShares Bitcoin Strategy ETF (BITO) data and a mixed variational mode decomposition and bidirectional gated cycle unit model to examine the interconnections between Bitcoin and its new derivative ETFs, from which actionable recommendations were developed. As well as conducting financial simulation trading using Bitcoin and BITO, the study expanded to examine other major ETFs. It was found that: (1) Bitcoin data could be employed to forecast and describe BITO; (2) under +0 trading, Bitcoin was more volatile, profitable, and risky than BITO; and (3) under +1 trading, Bitcoin was less volatile, profitable, and risky than BITO; however, the +1 trading was found to have higher volatility, profits, and risk than +0 trading. This study, therefore, builds a bridge from theory to practice for the prediction and description of new ETFs. Different from previous studies, this study explored the relationships between Bitcoin and BITO using Artificial Intelligence and quantitative financial simulations, which extends the practical and theoretical understanding of the Bitcoin market.
{"title":"Comparative analysis of profits from Bitcoin and its derivatives using artificial intelligence for hedge","authors":"Qing Zhu , Jianhua Che , Shan Liu","doi":"10.1016/j.physa.2024.130159","DOIUrl":"10.1016/j.physa.2024.130159","url":null,"abstract":"<div><div>Because there is a discrepancy between how individual investors and investment institutions choose Bitcoin and its new derivatives and Exchange-Traded Funds (ETFs), this paper used Bitcoin and ProShares Bitcoin Strategy ETF (BITO) data and a mixed variational mode decomposition and bidirectional gated cycle unit model to examine the interconnections between Bitcoin and its new derivative ETFs, from which actionable recommendations were developed. As well as conducting financial simulation trading using Bitcoin and BITO, the study expanded to examine other major ETFs. It was found that: (1) Bitcoin data could be employed to forecast and describe BITO; (2) under <span><math><mi>T</mi></math></span>+0 trading, Bitcoin was more volatile, profitable, and risky than BITO; and (3) under <span><math><mi>T</mi></math></span>+1 trading, Bitcoin was less volatile, profitable, and risky than BITO; however, the <span><math><mi>T</mi></math></span>+1 trading was found to have higher volatility, profits, and risk than <span><math><mi>T</mi></math></span>+0 trading. This study, therefore, builds a bridge from theory to practice for the prediction and description of new ETFs. Different from previous studies, this study explored the relationships between Bitcoin and BITO using Artificial Intelligence and quantitative financial simulations, which extends the practical and theoretical understanding of the Bitcoin market.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130159"},"PeriodicalIF":2.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.physa.2024.130161
Werner Kristjanpoller , Ramzi Nekhili , Elie Bouri
This paper examines the impact of the introduction of Ethereum futures contracts on the market efficiency of major cryptocurrency (Bitcoin, Ethereum, Ripple, Litecoin, and Dogecoin) spot prices. Using a multifractality-based approach and daily data from September 4, 2017 to February 16, 2024, the main results show a slight improvement in market efficiency. Specifically, the degree of multifractality persistence decreases, implying reduced market inefficiencies in major cryptocurrencies. The temporal linear correlation effect and thick tail effect are less pronounced post-launch. The asymmetry of the generalized Hurst exponent increases after the launch of Ethereum futures, with a higher persistence under the downward trend of cryptocurrencies noted. This downward trend emerges after the launch of Ethereum futures, coinciding with the final stage of the COVID-19 pandemic. Additional analysis shows that fat tails and temporal linear correlations are the main sources of multifractality. The results highlight the influence of introducing financial derivatives into the relatively new and volatile cryptocurrency area, which should concern traders, hedgers, investors, and regulators.
{"title":"Ethereum futures and the efficiency of cryptocurrency spot markets","authors":"Werner Kristjanpoller , Ramzi Nekhili , Elie Bouri","doi":"10.1016/j.physa.2024.130161","DOIUrl":"10.1016/j.physa.2024.130161","url":null,"abstract":"<div><div>This paper examines the impact of the introduction of Ethereum futures contracts on the market efficiency of major cryptocurrency (Bitcoin, Ethereum, Ripple, Litecoin, and Dogecoin) spot prices. Using a multifractality-based approach and daily data from September 4, 2017 to February 16, 2024, the main results show a slight improvement in market efficiency. Specifically, the degree of multifractality persistence decreases, implying reduced market inefficiencies in major cryptocurrencies. The temporal linear correlation effect and thick tail effect are less pronounced post-launch. The asymmetry of the generalized Hurst exponent increases after the launch of Ethereum futures, with a higher persistence under the downward trend of cryptocurrencies noted. This downward trend emerges after the launch of Ethereum futures, coinciding with the final stage of the COVID-19 pandemic. Additional analysis shows that fat tails and temporal linear correlations are the main sources of multifractality. The results highlight the influence of introducing financial derivatives into the relatively new and volatile cryptocurrency area, which should concern traders, hedgers, investors, and regulators.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130161"},"PeriodicalIF":2.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.physa.2024.130165
Vinita , Chandra Kumar , R.P. Yadav , B.K. Singh
Mono- and multi-fractal geometry have been used to explore the surface characteristics of scanning electron microscopy (SEM) micrographs of the SnS films with thicknesses of 100 nm (SnS1) to 600 nm (SnS4), respectively. For this investigation, the SnS thin films have been grown on fluorine-doped tin oxide (FTO)-coated glass substrate through the thermal evaporation route, and surface morphologies are captured by SEM. Two-dimensional multi-fractal detrended fluctuation analysis (MFDFA) based on the partition function is used to examine whether the surfaces have a multi-fractal nature or not. The partition function is applied to extract the generalized Hurst exponent from the segment size. It has been found that surfaces with higher surface roughness induce substantial nonlinearity and a wider width of the multi-fractal spectrum. The multi-fractal spectrum acquired from the analysis of the geometry and shape of the singularity spectrum is used to quantify the irregularity and complexity of surfaces. Minkowski functionals (MFs) parameters such as volume, boundary, and connectivity were measured for each thin film. Moreover, we tried to correlate the electrical conductivity with the mono- and multi-fractal parameters such as fractal dimension (Df), singularity strength function (Δα), singularity spectrum Δf(α), and it is observed that the conductivity of a thin film decreases with decreasing fractal dimension. The minimum (maximum) resistivity (conductivity) was observed for the surface having a larger fractal dimension. The present investigation suggests that such SnS surfaces, having minimal resistivity and maximum conductivity on the roughest surface, indicate enhanced light trapping capacity and can be utilized as active layers for advanced optoelectronics devices.
{"title":"Impact of surface-roughness and fractality on electrical conductivity of SnS thin films","authors":"Vinita , Chandra Kumar , R.P. Yadav , B.K. Singh","doi":"10.1016/j.physa.2024.130165","DOIUrl":"10.1016/j.physa.2024.130165","url":null,"abstract":"<div><div>Mono- and multi-fractal geometry have been used to explore the surface characteristics of scanning electron microscopy (SEM) micrographs of the SnS films with thicknesses of 100 nm (SnS1) to 600 nm (SnS4), respectively. For this investigation, the SnS thin films have been grown on fluorine-doped tin oxide (FTO)-coated glass substrate through the thermal evaporation route, and surface morphologies are captured by SEM. Two-dimensional multi-fractal detrended fluctuation analysis (MFDFA) based on the partition function is used to examine whether the surfaces have a multi-fractal nature or not. The partition function is applied to extract the generalized Hurst exponent from the segment size. It has been found that surfaces with higher surface roughness induce substantial nonlinearity and a wider width of the multi-fractal spectrum. The multi-fractal spectrum acquired from the analysis of the geometry and shape of the singularity spectrum is used to quantify the irregularity and complexity of surfaces. Minkowski functionals (MFs) parameters such as volume, boundary, and connectivity were measured for each thin film. Moreover, we tried to correlate the electrical conductivity with the mono- and multi-fractal parameters such as fractal dimension (D<sub>f</sub>), singularity strength function (Δα), singularity spectrum Δf(α), and it is observed that the conductivity of a thin film decreases with decreasing fractal dimension. The minimum (maximum) resistivity (conductivity) was observed for the surface having a larger fractal dimension. The present investigation suggests that such SnS surfaces, having minimal resistivity and maximum conductivity on the roughest surface, indicate enhanced light trapping capacity and can be utilized as active layers for advanced optoelectronics devices.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130165"},"PeriodicalIF":2.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Significant variations of delays among connecting neurons cause an inevitable disadvantage of asynchronous brain dynamics compared to synchronous deep learning. However, this study demonstrates that this disadvantage can be converted into a computational advantage using a network with a single output and multiple delays between successive layers, thereby generating a polynomial time-series outputs with . The proposed role of delay in brain dynamics (RoDiB) model, is capable of learning increasing number of classified labels using a fixed architecture, and overcomes the inflexibility of the brain to update the learning architecture using additional neurons and connections. Moreover, the achievable accuracies of the RoDiB system are comparable with those of its counterpart tunable single delay architectures with outputs. Further, the accuracies are significantly enhanced when the number of output labels exceeds its fully connected input size. The results are mainly obtained using simulations of VGG-6 on CIFAR datasets and also include multiple label inputs. However, currently only a small fraction of the abundant number of RoDiB outputs is utilized, thereby suggesting its potential for advanced computational power yet to be discovered.
与同步深度学习相比，连接神经元之间延迟的显著变化导致异步大脑动力学不可避免地存在劣势。然而，本研究证明，利用具有单个输出和连续层之间 M 个多重延迟的网络，可以将这一劣势转化为计算优势，从而生成具有 M 个多项式时间序列输出的网络。此外，RoDiB 系统可达到的准确度可与具有 M 个输出的可调单延迟架构相媲美。此外，当输出标签的数量超过完全连接的输入规模时，系统的准确度会显著提高。这些结果主要是通过模拟 CIFAR 数据集上的 VGG-6 获得的，也包括多标签输入。不过，目前只利用了 RoDiB 大量输出中的一小部分，这表明其高级计算能力的潜力还有待发掘。
{"title":"Role of delay in brain dynamics","authors":"Yuval Meir , Ofek Tevet , Yarden Tzach , Shiri Hodassman , Ido Kanter","doi":"10.1016/j.physa.2024.130166","DOIUrl":"10.1016/j.physa.2024.130166","url":null,"abstract":"<div><div>Significant variations of delays among connecting neurons cause an inevitable disadvantage of asynchronous brain dynamics compared to synchronous deep learning. However, this study demonstrates that this disadvantage can be converted into a computational advantage using a network with a single output and <span><math><mi>M</mi></math></span> multiple delays between successive layers, thereby generating a polynomial time-series outputs with <span><math><mi>M</mi></math></span>. The proposed role of delay in brain dynamics (RoDiB) model, is capable of learning increasing number of classified labels using a fixed architecture, and overcomes the inflexibility of the brain to update the learning architecture using additional neurons and connections. Moreover, the achievable accuracies of the RoDiB system are comparable with those of its counterpart tunable single delay architectures with <span><math><mi>M</mi></math></span> outputs. Further, the accuracies are significantly enhanced when the number of output labels exceeds its fully connected input size. The results are mainly obtained using simulations of VGG-6 on CIFAR datasets and also include multiple label inputs. However, currently only a small fraction of the abundant number of RoDiB outputs is utilized, thereby suggesting its potential for advanced computational power yet to be discovered.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130166"},"PeriodicalIF":2.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.physa.2024.130140
David Alaminos , M. Belén Salas-Compás , Manuel Á. Fernández-Gámez
In recent years, Bitcoin has garnered attention as a digital currency, prompting increasing debate regarding its effects on traditional financial markets, particularly the US dollar. This study investigates the relationship between Bitcoin and the US dollar, especially in the contexts of speculative attacks, where investors attempt to devalue a currency, and short squeezes, where rapid price rises force short sellers to quickly buy back assets to avoid further losses. The study employs a novel hybrid model combining an autoregressive moving average, Generalized Autoregressive Conditional Heteroskedasticity, and Wavelet Neural Networks techniques with neural networks approaches. The results suggest that significant trading activity in Bitcoin/US dollar, particularly during speculative attacks and short squeezes, can substantially impact the US dollar/EUR market, increasing price volatility as traders adjust their strategies. These adjustments, along with risk management strategies, drive higher trading volumes and further volatility. Our findings demonstrate that our novel hybrid model combined with Quantum Recurrent Neural Networks provides the most accurate predictions, offering valuable insights to inform trading strategies in both Bitcoin/US dollar and US dollar/EUR markets. This study has important implications for policymakers and market participants, emphasising the need to understand the relationship between Bitcoin and the US dollar for financial stability and effective policy formulation. It also highlights the necessity of advanced modeling techniques to accurately predict cryptocurrency market behavior.
{"title":"Can Bitcoin trigger speculative pressures on the US Dollar? A novel ARIMA-EGARCH-Wavelet Neural Networks","authors":"David Alaminos , M. Belén Salas-Compás , Manuel Á. Fernández-Gámez","doi":"10.1016/j.physa.2024.130140","DOIUrl":"10.1016/j.physa.2024.130140","url":null,"abstract":"<div><div>In recent years, Bitcoin has garnered attention as a digital currency, prompting increasing debate regarding its effects on traditional financial markets, particularly the US dollar. This study investigates the relationship between Bitcoin and the US dollar, especially in the contexts of speculative attacks, where investors attempt to devalue a currency, and short squeezes, where rapid price rises force short sellers to quickly buy back assets to avoid further losses. The study employs a novel hybrid model combining an autoregressive moving average, Generalized Autoregressive Conditional Heteroskedasticity, and Wavelet Neural Networks techniques with neural networks approaches. The results suggest that significant trading activity in Bitcoin/US dollar, particularly during speculative attacks and short squeezes, can substantially impact the US dollar/EUR market, increasing price volatility as traders adjust their strategies. These adjustments, along with risk management strategies, drive higher trading volumes and further volatility. Our findings demonstrate that our novel hybrid model combined with Quantum Recurrent Neural Networks provides the most accurate predictions, offering valuable insights to inform trading strategies in both Bitcoin/US dollar and US dollar/EUR markets. This study has important implications for policymakers and market participants, emphasising the need to understand the relationship between Bitcoin and the US dollar for financial stability and effective policy formulation. It also highlights the necessity of advanced modeling techniques to accurately predict cryptocurrency market behavior.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130140"},"PeriodicalIF":2.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.physa.2024.130153
Oscar Bohórquez
A battery is a device that stores energy in the form of work for later use by other devices. In this work, we study the realization of a quantum battery in a double quantum dot in series, charged by two electrodes at different chemical potentials and optimized by a Markovian quantum feedback protocol. Using the concept of ergotropy as a figure of merit, we first establish a simple expression for the maximum ergotropy in a two-level system, and then find the parameters under which Markovian feedback can achieve this optimal ergotropy. We find that quantum coherence can be used as an energy storage resource, and we study the influence of interactions with a phonon environment to mitigate the discharge process with the environment by fine-tuning the system parameters.
{"title":"A double quantum dot quantum battery controlled with a Markovian feedback","authors":"Oscar Bohórquez","doi":"10.1016/j.physa.2024.130153","DOIUrl":"10.1016/j.physa.2024.130153","url":null,"abstract":"<div><div>A battery is a device that stores energy in the form of work for later use by other devices. In this work, we study the realization of a quantum battery in a double quantum dot in series, charged by two electrodes at different chemical potentials and optimized by a Markovian quantum feedback protocol. Using the concept of ergotropy as a figure of merit, we first establish a simple expression for the maximum ergotropy in a two-level system, and then find the parameters under which Markovian feedback can achieve this optimal ergotropy. We find that quantum coherence can be used as an energy storage resource, and we study the influence of interactions with a phonon environment to mitigate the discharge process with the environment by fine-tuning the system parameters.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"655 ","pages":"Article 130153"},"PeriodicalIF":2.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.physa.2024.130157
Yangjian He , Libi Fu , Qiyi Chen , Yu Zhang , Chenxin Shen , Yongqian Shi , Shuchao Cao
With the development of urbanization and the growth of population, there is a growing demand for safety in public building facilities. As one of the essential building components of urban architecture, bottlenecks have a significant impact on the evacuation efficiency of crowds. Furthermore, the heterogeneity of crowds also contributes to the complexity of crowd movement through bottlenecks, while aggravating the magnitude of congestion induced by bottlenecks. The objective of this paper is to explore the movement characteristics of heterogeneous crowds passing through a corridor with a bottleneck by conducting a controlled experiment. There were three variables in this experiment, namely the individual categories (i.e., able-bodied individuals, simulated individuals on crutches and simulated wheelchair users), bottleneck width (i.e., 1.2, 1.6 and 2.0 m) and proportion of simulated disabilities in crowds (i.e., 0 %, 5 % and 10 %). Then offset angle, passing efficiency, fundamental diagram, etc., were analyzed. In trials involving simulated individuals on crutches, a higher detouring degree is observed compared to trials involving simulated wheelchair users or mixed groups of two types of simulated disabilities. There is an increase in flow rate induced by increasing the bottleneck width and decreasing the proportion of simulated disabilities. The passing efficiency at the upstream of the bottleneck in all tests is primarily influenced by the bottleneck width, while by the type and proportion of simulated disabilities at the downstream or inside the bottleneck. The findings are intended to complement the dynamic theory of heterogeneous crowds at building bottlenecks, while providing a reference for congestion control of crowds at bottlenecks.
{"title":"The effect of building bottlenecks on crowd dynamics involving individuals with simulated disabilities","authors":"Yangjian He , Libi Fu , Qiyi Chen , Yu Zhang , Chenxin Shen , Yongqian Shi , Shuchao Cao","doi":"10.1016/j.physa.2024.130157","DOIUrl":"10.1016/j.physa.2024.130157","url":null,"abstract":"<div><div>With the development of urbanization and the growth of population, there is a growing demand for safety in public building facilities. As one of the essential building components of urban architecture, bottlenecks have a significant impact on the evacuation efficiency of crowds. Furthermore, the heterogeneity of crowds also contributes to the complexity of crowd movement through bottlenecks, while aggravating the magnitude of congestion induced by bottlenecks. The objective of this paper is to explore the movement characteristics of heterogeneous crowds passing through a corridor with a bottleneck by conducting a controlled experiment. There were three variables in this experiment, namely the individual categories (i.e., able-bodied individuals, simulated individuals on crutches and simulated wheelchair users), bottleneck width (i.e., 1.2, 1.6 and 2.0 m) and proportion of simulated disabilities in crowds (i.e., 0 %, 5 % and 10 %). Then offset angle, passing efficiency, fundamental diagram, etc., were analyzed. In trials involving simulated individuals on crutches, a higher detouring degree is observed compared to trials involving simulated wheelchair users or mixed groups of two types of simulated disabilities. There is an increase in flow rate induced by increasing the bottleneck width and decreasing the proportion of simulated disabilities. The passing efficiency at the upstream of the bottleneck in all tests is primarily influenced by the bottleneck width, while by the type and proportion of simulated disabilities at the downstream or inside the bottleneck. The findings are intended to complement the dynamic theory of heterogeneous crowds at building bottlenecks, while providing a reference for congestion control of crowds at bottlenecks.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130157"},"PeriodicalIF":2.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1016/j.physa.2024.130124
Dima Mrad , Sara Najem , Pablo Padilla , Francis Knights
Complex networks and statistical physics have been proposed as powerful frameworks and tools in the analysis of the properties of complex systems and in particular musical pieces. They can reveal variations in musical features such as harmony, melody, rhythm as well as the composer’s style. The empirical study of a wide range of digitized scores of Western classical music and their corresponding networks brought to light quantitative evidence for changes in harmonic complexity. We complement the common topological analysis of these networks with musicological or music-theoretical considerations. We illustrate this by studying J. S. Bach’s sonatas and partitas for solo violin by constructing duration-weighted transition matrices between notes, or melody networks, as well as harmony networks, which are transition matrices between the chords, or equivalently synchronously played notes. We further propose statistical physics measures that were first introduced in the study of socio-economic networks: the partition function and communicability and provide evidence for their significance. Our findings and observations include: the detection of three main communities centered around the tonic, the dominant, and submediant in most of the pieces; the association of the nodes with the highest betweenness centrality, the lowest clustering coefficient and highest in and out degrees respectively with the tonic and the dominant; the high similarity between pieces which share the same key or when the key of one is the dominant of the other; finally, the association of the highest partition function, the shortest average path length, and the highest communicability with the Fugues.
复杂网络和统计物理学被认为是分析复杂系统，特别是音乐作品属性的强大框架和工具。它们可以揭示和声、旋律、节奏以及作曲家风格等音乐特征的变化。通过对大量西方古典音乐数字化乐谱及其相应网络的实证研究，我们发现了和声复杂性变化的量化证据。我们从音乐学或音乐理论的角度对这些网络的常见拓扑分析进行了补充。我们通过研究巴赫（J. S. Bach）的小提琴独奏奏鸣曲和部分奏鸣曲，构建了音符之间的时长加权转换矩阵，即旋律网络，以及和声网络，即和弦之间的转换矩阵，或等同于同步演奏的音符之间的转换矩阵，来说明这一点。我们进一步提出了首次在社会经济网络研究中引入的统计物理测量方法：分区函数和可传播性，并为其重要性提供了证据。我们的发现和观察结果包括：在大多数乐曲中发现了以调性、主音和副主音为中心的三个主要群落；具有最高间度中心性、最低聚类系数和最高进出度的节点分别与调性和主音相关；具有相同调性的乐曲或其中一个调性是另一个调性的乐曲之间具有高度相似性；最后，具有最高分区函数、最短平均路径长度和最高可传播性的乐曲与赋格相关。
{"title":"A network perspective on J.S Bach’s 6 violin sonatas and partitas, BWV 1001 - 1006","authors":"Dima Mrad , Sara Najem , Pablo Padilla , Francis Knights","doi":"10.1016/j.physa.2024.130124","DOIUrl":"10.1016/j.physa.2024.130124","url":null,"abstract":"<div><div>Complex networks and statistical physics have been proposed as powerful frameworks and tools in the analysis of the properties of complex systems and in particular musical pieces. They can reveal variations in musical features such as harmony, melody, rhythm as well as the composer’s style. The empirical study of a wide range of digitized scores of Western classical music and their corresponding networks brought to light quantitative evidence for changes in harmonic complexity. We complement the common topological analysis of these networks with musicological or music-theoretical considerations. We illustrate this by studying J. S. Bach’s sonatas and partitas for solo violin by constructing duration-weighted transition matrices between notes, or melody networks, as well as harmony networks, which are transition matrices between the chords, or equivalently synchronously played notes. We further propose statistical physics measures that were first introduced in the study of socio-economic networks: the partition function and communicability and provide evidence for their significance. Our findings and observations include: the detection of three main communities centered around the tonic, the dominant, and submediant in most of the pieces; the association of the nodes with the highest betweenness centrality, the lowest clustering coefficient and highest in and out degrees respectively with the tonic and the dominant; the high similarity between pieces which share the same key or when the key of one is the dominant of the other; finally, the association of the highest partition function, the shortest average path length, and the highest communicability with the Fugues.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130124"},"PeriodicalIF":2.8,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1016/j.physa.2024.130149
Nobutoshi Ikeda
Growth is regarded as an important mechanism for explaining the structures of real networks. However, when the increase in the number of nodes is suppressed owing to their lifetime, the growth property alone is not sufficient to explain even fundamental network properties, such as the scale-free property. In this paper, we propose a network model that considers the lifetime of nodes and the excess addition of local internal links as a mechanism that supports network structures. By investigating the model network, we aimed to elucidate the network characteristics supported by local interactions between nodes via their common neighbors even when the rates of node addition and deletion were balanced. We found that the stationary state of the number of nodes is characterized by a scale-free property with the power-law exponent and localization of the peaks at in the distance distributions of neighboring nodes (DDN) as the node degree increases. The specific behavior of the DDN explains the very slow decrease in the clustering strength with compared with the normal behavior and the accelerated growth of the neighborhood graph of each node. Moreover, we showed that some real networks share local structures similar to those of the model network. These findings suggest that the same mechanism as that of the proposed model plays an essential role in supporting the local structures of some real networks.
增长被认为是解释真实网络结构的重要机制。然而，当节点数量的增长因节点寿命而受到抑制时，仅靠增长特性甚至不足以解释基本的网络特性，如无标度特性。在本文中，我们提出了一种网络模型，将节点的生命周期和局部内部链接的过度增加作为支持网络结构的机制。通过对模型网络的研究，我们旨在阐明即使在节点增减率平衡的情况下，节点之间通过其共同邻居进行的局部互动所支持的网络特性。我们发现，随着节点度 k 的增加，节点数的静止状态具有无标度特性，即幂律指数 γ≃1，并且相邻节点距离分布（DDN）的峰值定位在 l=2 处。DDN 的特殊行为解释了聚类强度 C(k) 随 k 下降的速度与正常行为 C(k)∼k-1 相比非常缓慢，以及每个节点的邻域图加速增长的原因。此外，我们还发现一些真实网络的局部结构与模型网络相似。这些发现表明，与模型相同的机制在支持某些真实网络的局部结构方面发挥了重要作用。
{"title":"Elucidation of characteristics of networks where every node has its own lifetime","authors":"Nobutoshi Ikeda","doi":"10.1016/j.physa.2024.130149","DOIUrl":"10.1016/j.physa.2024.130149","url":null,"abstract":"<div><div>Growth is regarded as an important mechanism for explaining the structures of real networks. However, when the increase in the number of nodes is suppressed owing to their lifetime, the growth property alone is not sufficient to explain even fundamental network properties, such as the scale-free property. In this paper, we propose a network model that considers the lifetime of nodes and the excess addition of local internal links as a mechanism that supports network structures. By investigating the model network, we aimed to elucidate the network characteristics supported by local interactions between nodes via their common neighbors even when the rates of node addition and deletion were balanced. We found that the stationary state of the number of nodes is characterized by a scale-free property with the power-law exponent <span><math><mrow><mi>γ</mi><mo>≃</mo><mn>1</mn></mrow></math></span> and localization of the peaks at <span><math><mrow><mi>l</mi><mo>=</mo><mn>2</mn></mrow></math></span> in the distance distributions of neighboring nodes (DDN) as the node degree <span><math><mi>k</mi></math></span> increases. The specific behavior of the DDN explains the very slow decrease in the clustering strength <span><math><mrow><mi>C</mi><mrow><mo>(</mo><mi>k</mi><mo>)</mo></mrow></mrow></math></span> with <span><math><mi>k</mi></math></span> compared with the normal behavior <span><math><mrow><mi>C</mi><mrow><mo>(</mo><mi>k</mi><mo>)</mo></mrow><mo>∼</mo><msup><mrow><mi>k</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span> and the accelerated growth of the neighborhood graph of each node. Moreover, we showed that some real networks share local structures similar to those of the model network. These findings suggest that the same mechanism as that of the proposed model plays an essential role in supporting the local structures of some real networks.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130149"},"PeriodicalIF":2.8,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1016/j.physa.2024.130151
Samuele De Bartolo
Taylor’s law is a well-known power law (TPL) for analysing the scaling behaviour of many fluctuating physical phenomena in nature. The scaling exponent of this law forms the basis of the aggregation process to which a precise probability density function corresponds. In some phenomena, TPL behaviour with periodic components of the aggregates has been observed for small partitions, especially for physical processes characterised by values of where fluctuation-related aggregation processes are supported by Poissonian distributions. We intend to show that for values of very close to unity it is possible to find a trend, in the double logarithmic scale, of the TPL that there are ‘periodic patterns’ (components) between variance and mean. This behaviour is found in other binomial-type distributions, of which the Poissonian is a particular case, with mappings characterised by a variance close to 1.
泰勒定律是著名的幂律（TPL），用于分析自然界中许多波动物理现象的缩放行为。该定律的缩放指数 b 构成了精确概率密度函数所对应的聚集过程的基础。在某些现象中，我们观察到小分区的聚集体具有周期性成分的 TPL 行为，特别是对于 b=1 值的物理过程，其中与波动相关的聚集过程得到泊松分布的支持。我们打算证明，当 b 值非常接近统一时，有可能在 TPL 的双对数尺度中发现一种趋势，即在方差和均值之间存在 "周期模式"（成分）。这种行为在其他二项分布中也能发现，泊松分布是其中的一种特殊情况，其映射的特点是方差接近 1。
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