Complexity Measures Research Articles

Complexity synchronization in living matter: a mini review

Fractal time series have been argued to be ubiquitous in human physiology and some of the implications of that ubiquity are quite remarkable. One consequence of the omnipresent fractality is complexity synchronization (CS) observed in the interactions among simultaneously recorded physiologic time series discussed herein. This new kind of synchronization has been revealed in the interaction triad of organ-networks (ONs) consisting of the mutually interacting time series generated by the brain (electroencephalograms, EEGs), heart (electrocardiograms, ECGs), and lungs (Respiration). The scaled time series from each member of the triad look nothing like one another and yet they bear a deeply recorded synchronization invisible to the naked eye. The theory of scaling statistics is used to explain the source of the CS observed in the information exchange among these multifractal time series. The multifractal dimension (MFD) of each time series is a measure of the time-dependent complexity of that time series, and it is the matching of the MFD time series that provides the synchronization referred to as CS. The CS is one manifestation of the hypothesis given by a “Law of Multifractal Dimension Synchronization” (LMFDS) which is supported by data. Therefore, the review aspects of this paper are chosen to make the extended range of the LMFDS hypothesis sufficiently reasonable to warrant further empirical testing.

Efficiency of neural-network state representations of one-dimensional quantum spin systems

Neural-network state representations of quantum many-body systems are attracting great attention and more rigorous quantitative analysis of their expressibility and complexity is warranted. Our analysis of the restricted Boltzmann machine (RBM) state representation of one-dimensional (1D) quantum spin systems provides new insight into their computational complexity. We define a class of long-range-fast-decay (LRFD) RBM states with quantifiable upper bounds on truncation errors and provide numerical evidence for a large class of 1D quantum systems that may be approximated by LRFD RBMs of at most polynomial complexities. These results lead us to conjecture that the ground states of a wide range of quantum systems may be exactly represented by LRFD RBMs or a variant of them, even in cases where other state representations become less efficient. At last, we provide the relations between multiple typical state manifolds. Our work proposes a paradigm for doing complexity analysis for generic long-range RBMs which naturally yields a further classification of this manifold. This paradigm and our characterization of their nonlocal structures may pave the way for understanding the natural measure of complexity for quantum many-body states described by RBMs and are generalizable for higher-dimensional systems and deep neural-network quantum states. Published by the American Physical Society 2024

Task repetition versus task rehearsal: Understanding effects of task-readiness factors and elemental genres on L2 writing task performance

In the literature on task-based language teaching (TBLT), task repetition and task rehearsal have frequently been used interchangeably. However, this distinction, identified as potentially significant (Ellis, 2019), is noteworthy due to the potential impact of participants’ awareness of future repetitions on second language (L2) task performance. Given the lack of empirical exploration regarding task repetition and task rehearsal distinction in the context of L2 writing, this study investigates how task rehearsal and task repetition affect the narrative and argumentative writing performances of 120 students of English as a second language (ESL) from a U.S. university. They were randomly assigned to four groups. Groups 1 and 3 wrote the narrative text initially, repeated the same task, and then proceeded to write the argumentative text, which was also repeated. The intervals between these performances were one week apart. However, Group 3 knew in advance that they were going to repeat the task, whereas Group 1 lacked this knowledge. Conversely, Groups 2 and 4 began by writing the argumentative text, repeated that task, and then proceeded to complete the narrative text, which was also repeated within the same time intervals. Group 4 had forewarning of their future performance, while Group 2 did not. We analysed the essays using the complexity, accuracy, lexis, and fluency (CALF) measures. The results indicated that time significantly influenced almost all CALF measures in narrative and argumentative writing, irrespective of the forewarning condition. Additionally, task readiness and essay order had a significant impact on most CALF measures, particularly in narrative writing. Specifically, task readiness positively affected the majority of CALF measures in narratives, whereas essay order exhibited sporadic influence.

On the relationship between complexity of primary occupation and L2 varietal behavior in adult migrants in Austria

Abstract This article explores for the first time the relationship between occupational complexity (operationalized using the complexity measures indicated in the Dictionary of occupational titles) and adult migrants’ second language (L2) sociolinguistic repertoires in the Austro-Bavarian naturalistic context. We analyze the data of 36 adult migrant L2 German speakers who participated in a virtual reality experiment involving interactions with a dialect-speaking and standard German-speaking interlocutor, the goal being to capture participants’ interpersonal varietal behavior, that is, their addressee-relational, differential use of standard German, Austro-Bavarian dialect, and mixture varieties. Bayesian multinomial mixed-effects models reveal that participants with occupations requiring more handling and physical precision work are predicted to employ dialect varieties more often, but exclusively in interaction with the standard German-speaking interlocutor. A person-centered visual-quantitative analysis additionally facilitates insights concerning which migrants deviate most notably from group-level patterns, and how these deviations may relate to the complexity of their primary occupation. More generally, this study paves new ground with respect to how we can operationally define and capture the complexity of occupational status, and moreover sets out a new direction for research questions investigating how career-related differences impact on (L2) language variation and use.

Multi-dimension-precision chaotic encryption mechanism for Internet of Things

The massive amount of information processed in the Internet of Things(IoT) environment requires effective encryption schemes to ensure secure transmission. However, traditional encryption methods such as Elliptic Curve Cryptography (ECC) or Data Encryption Standard (DES) are not suitable for IoT environments where large amounts of data need to be quickly encrypted. In this paper, a multi-precision encryption mechanism based on multi-dimensional chaotic system is proposed. Firstly, the proposed scheme uses floating-point, 32-bit, 24-bit, and 16-bit data accuracy respectively, which can effectively reduce the complexity through fixed-point calculation. Secondly, aiming at the problem that traditional chaotic dynamic analysis methods such as the Lyapunov index cannot be applied, an analysis scheme based on complexity measure is proposed. Finally, a free dimension encryption mechanism based on Logistic, Tent, and PWLCM is proposed. By adding random disturbance to a one-dimensional chaotic map, the weak randomness of the single chaotic sequence is overcome effectively. Finally, the security analysis of the encryption scheme is carried out, and the result proves that the scheme can effectively resist the common attack means. In the 3D chaotic system, the encryption effect of 24-bit data accuracy is close to floating point accuracy, which is suitable for the IoT environment with large data volume, high encryption efficiency, and vulnerability.

Socialist Sex Education and Its Transnational Entanglements: Monika Krause and the Effort to “Teach Tenderness” to the People

AbstractThis article traces the transnational circulation of socialist reforms in the field of sex education through the work of Monika Krause, a citizen of the German Democratic Republic (GDR) who migrated to Cuba and became the “Cuban Queen of Condoms.” For Krause, the overarching goal of sex education was to “teach tenderness” to the people. The socialist state’s mission to prepare the population for love, marriage, partnership, and family in Cuba and the GDR involved using complex measures. This paper describes these, contextualizes them in transnational debates, and explains some of the internal reasoning behind their institutionalization. It also explains why looking at state-level efforts to “teach tenderness to the people” matters for a transnational history of sex education.

Multimodality quantitative ultrasound envelope statistics imaging based support vector machines for characterizing tissue scatterer distribution patterns: Methods and application in detecting microwave-induced thermal lesions

Ultrasound envelope statistics imaging, including ultrasound Nakagami imaging, homodyned-K imaging, and information entropy imaging, is an important group of quantitative ultrasound techniques for characterizing tissue scatterer distribution patterns, such as scatterer concentrations and arrangements. In this study, we proposed a machine learning approach to integrate the strength of multimodality quantitative ultrasound envelope statistics imaging techniques and applied it to detecting microwave ablation induced thermal lesions in porcine liver ex vivo. The quantitative ultrasound parameters included were homodyned-K α which is a scatterer clustering parameter related to the effective scatterer number per resolution cell, Nakagami m which is a shape parameter of the envelope probability density function, and Shannon entropy which is a measure of signal uncertainty or complexity. Specifically, the homodyned-K log10(α), Nakagami-m, and horizontally normalized Shannon entropy parameters were combined as input features to train a support vector machine (SVM) model to classify thermal lesions with higher scatterer concentrations from normal tissues with lower scatterer concentrations. Through heterogeneous phantom simulations based on Field II, the proposed SVM model showed a classification accuracy above 0.90; the area accuracy and Dice score of higher-scatterer-concentration zone identification exceeded 83% and 0.86, respectively, with the Hausdorff distance <26. Microwave ablation experiments of porcine liver ex vivo at 60–80 W, 1–3 min showed that the SVM model achieved a classification accuracy of 0.85; compared with single log10(α),m, or hNSE parametric imaging, the SVM model achieved the highest area accuracy (89.1%) and Dice score (0.77) as well as the smallest Hausdorff distance (46.38) of coagulation zone identification. We concluded that the proposed multimodality quantitative ultrasound envelope statistics imaging based SVM approach can enhance the capability to characterize tissue scatterer distribution patterns and has the potential to detect the thermal lesions induced by microwave ablation.

Unintended Consequences of Technostress Mitigation: An Employee Perspective on the Effectiveness of Mitigation Measures

The continuous use of IT, even beyond regular office hours, is considered a cause of technostress, which impairs the health and performance of employees. To mitigate technostress, European countries have established the right to disconnect, and many organizations are struggling to identify and implement other effective measures. Based on a qualitative study (N=30) with 23 IT workers, five managers, and two CIOs, this study identifies eight technological, social, and cultural measures to mitigate common techno-stressors. By focusing on the employees' perspective, the results reveal the extent to which the measures actually work, showing that well-intended countermeasures, such as email restrictions, might have unintended negative and even harmful side effects. Our analysis shows that mitigation measures seldom work in isolation and without spillover effects. We conclude that although technostress mitigation is complex and mitigation measures adopted in isolation can fail and sometimes cause additional harm, employees still appreciate the effort.

Exploring the associations among task complexity, task motivation, task engagement, and linguistic complexity in L2 writing

AbstractThe interactions among cognitive, affective, and linguistic factors have received scant attention in task‐based writing research. To address this gap, this study aims to examine the relationships among task complexity, task motivation, task engagement, and syntactic complexity in second language (L2) writing. One hundred L2 learners enrolled in an English‐as‐a‐second‐language (ESL) writing course at a university in the United States performed simple and complex versions of an argumentative writing task at a 1‐week interval. After each task performance, participants completed questionnaires examining their task motivation. Task engagement was measured through time on task and length of production. The essays were analyzed using specific syntactic complexity measures. The results showed that the participants dedicated more time to the complex task and displayed higher motivation levels in two orientations (identified and intention). Furthermore, they produced fewer words when faced with a complex task. Additionally, task complexity influenced only one dimension of syntactic complexity. Finally, time on task predicted two dimensions of syntactic complexity (mean length of T‐unit and mean number of complex nominals per clause). These results emphasize the need to consider cognitive, affective, and linguistic factors in task‐based writing research. By doing so, educators can develop writing tasks that effectively engage ESL learners, enhance their motivation, and promote their linguistic growth.

River Erosion Impact on Transport Infrastructure in "Gilort River Nature2000 site", Romania

River erosion is a complex process due to multiple factors, such as climate changes, discharge quantity, the type of transported sediments, and variations in the hydrological regime. This paper aims to analyse river erosion's impact on transport elements' stability (especially on bridges). To do so, a Geomorphic Change Detection analysis was used, which involves calculating the altitude differences between two successive digital models, as a basis for calculating surfaces and volumes of sediments displaced through the erosion process. This analysis was doubled by a series of cross-sections in the proximity of bridges to observe the current river bed configuration and identify the active processes. The variation of these processes is directly dependent on human interventions undertaken to reduce the erosion process and to protect the infrastructure elements. The bridges located in the studied area are in different stages of damage by lateral and depth erosion processes, depending on their intensity and the human interventions made to protect these bridges. The least affected are the bridges where complex measures have been taken (construction of bed sills) or the dynamic processes have a low intensity.

Comparison of two statistical measures of complexity applied to ecological bipartite networks

Networks are a convenient way to represent many interactions among different entities as they provide an efficient and clear methodology to evaluate and organize relevant data. While there are many features for characterizing networks, a quantity seems rather elusive: Complexity. The quantification of the complexity of networks is nowadays a fundamental problem. Here, we present a novel tool for identifying the complexity of ecological networks. We compare the behavior of two relevant indices of complexity: K-complexity and Single Value Decomposition (SVD) entropy. For that, we use real data and two types of null models. Both null models consist of randomized networks built by swapping a controlled number of links of the original ones. We analyze 23 plant–pollinator and 19 host-parasite networks as case studies. Our results show that (a) it is necessary to calculate, not only the original network K-complexity and SVD entropy but also to calculate the corresponding indices of the randomized networks (b) the density and degree distribution are essential in the characterization of a network and the randomized networks are a suitable tool to detect the network complexity, and (c) plant–pollinator networks are more complex than host-parasite networks. We found that, for the first null model, K-complexity and SVD did not change with link swapping in both pollinator-plant and host-parasite networks. For the second null model, K-complexity for pollinator-plant networks generally decreased with an increasing number of links swapped (i.e. negative slope), showing that plant–pollinator networks lose complexity with increasing link swapping. In contrast, there was a positive slope between K-complexity and link swapping for host-parasite networks, showing that these networks are less complex than plant–pollinator networks. For both types of networks, in general, the slope between K-complexity and the number of links swapped became more positive with network density. Overall, SVD entropy was less responsive to link swapping. Our analyses show that although SVD entropy has been widely used to characterize network complexity, K-complexity is a more reliable tool. Additionally, they show that degree distribution and density are important drivers of network complexity and should be accounted for in future studies.

Experimentally elevated corticosterone increases song output and complexity in common mynas.

Vocalization is an important communication tool that can reflect many aspects of an individual's internal and external condition. This is especially true for birds. Previous research has shown that bird calls and songs change in response to a variety of potential stressors, although the extent and direction of the changes depend on the nature of the stressor and the environment. Circulating glucocorticoids, such as corticosterone, often increase in response to stressors and mediate some of the observed changes via alterations of the individual's physiological state. Acute elevations of corticosterone often occur as a physiological response to short-term stressors; however, the effects of this elevation on adult vocalizations have not been well documented. Here, we experimentally elevated corticosterone at two different levels using a noninvasive method and examined the effects on the vocal communication of male and female adult common mynas (Acridotheres tristis). Corticosterone elevation temporarily increased song output and some measures of song complexity, while call output decreased. These effects were dosage dependent (higher corticosterone levels had a stronger effect), most evident 40 min after ingestion, and some vocal changes were sex-specific. Future studies should investigate whether the changes in vocal performance due to elevated glucocorticoids have consequences for the birds' behavior, reproductive success, and survival.

An attentional and working memory theory of hallucination vulnerability in frontotemporal dementia

Abstract The rate and prevalence of hallucinations in behavioural variant frontotemporal dementia is well established. The mechanisms for underlying vulnerability however are the least well described in FTD compared with other neuropsychiatric conditions, despite the presence of these features significantly complicating the diagnostic process. As such, this present study aimed to provide a detailed characterization of the neural, cognitive and behavioural profile associated with a predisposition to hallucinatory experiences in behavioural variant frontotemporal dementia. In total, 153 patients with behavioural variant frontotemporal dementia were recruited sequentially for this study. A group of patients with well characterized hallucinations and good-quality volumetric MRI scans (n = 23) were genetically and demographically matched to a group without hallucinations (n = 23) and a healthy control cohort (n = 23). All patients were assessed at their initial visit by means of a detailed clinical interview, a comprehensive battery of neuropsychological tests and MRI. Data were analysed according to three levels: (i) the relationship between neural structures, cognition, behaviour and hallucinations in behavioural variant frontotemporal dementia; (ii) the impact of the C9orf72 expansion; and (iii) hallucination subtype on expression of hallucinations. Basic and complex attentional (including divided attention and working memory) and visual function measures differed between groups (all P &amp;lt; 0.001) with hallucinators demonstrating poorer performance, along with evidence of structural changes centred on the prefrontal cortex, caudate and cerebellum (corrected for False Discovery Rate at P &amp;lt; 0.05 with a cluster threshold of 100 contiguous voxels). Attentional processes were also implicated in C9orf72 carriers with hallucinations with structural changes selectively involving the thalamus. Patients with visual hallucinations in isolation showed a similar pattern with emphasis on cerebellar atrophy. Our findings provided novel insights that attentional and visual function subsystems and related distributed brain structures are implicated in the generation of hallucinations in behavioural variant frontotemporal dementia, that dissociate across C9orf72, sporadic behavioural variant frontotemporal dementia and for the visual subtype of hallucinations. This loading on attentional and working memory measures is in line with current mechanistic models of hallucinations that frequently suggest a failure of integration of cognitive and perceptual processes. We therefore propose a novel cognitive and neural model for hallucination predisposition in behavioural variant frontotemporal dementia that aligns with a transdiagnostic model for hallucinations across neurodegeneration and psychiatry.

Biomedical text readability after hypernym substitution with fine-tuned large language models.

The advent of patient access to complex medical information online has highlighted the need for simplification of biomedical text to improve patient understanding and engagement in taking ownership of their health. However, comprehension of biomedical text remains a difficult task due to the need for domain-specific expertise. We aimed to study the simplification of biomedical text via large language models (LLMs) commonly used for general natural language processing tasks involve text comprehension, summarization, generation, and prediction of new text from prompts. Specifically, we finetuned three variants of large language models to perform substitutions of complex words and word phrases in biomedical text with a related hypernym. The output of the text substitution process using LLMs was evaluated by comparing the pre- and post-substitution texts using four readability metrics and two measures of sentence complexity. A sample of 1,000 biomedical definitions in the National Library of Medicine's Unified Medical Language System (UMLS) was processed with three LLM approaches, and each showed an improvement in readability and sentence complexity after hypernym substitution. Readability scores were translated from a pre-processed collegiate reading level to a post-processed US high-school level. Comparison between the three LLMs showed that the GPT-J-6b approach had the best improvement in measures of sentence complexity. This study demonstrates the merit of hypernym substitution to improve readability of complex biomedical text for the public and highlights the use case for fine-tuning open-access large language models for biomedical natural language processing.

Strategy for Weed Control in Potato Fields of the Ganja-Dashkesan Region of Azerbaijan

The impact of integrated weed control measures is assessed. One of the factors that worsens the productivity of a potato plant, and its quality indicators is weed infestation. Weeds choke plants. The research was carried out in the Samukh district of the Ganja-Dashkesan economic region. It was established that the degree of weeding of alternating and continuous potato crops was significantly influenced by complex measures. the use of herbicides through proper agrotechnical processing of the growing season in a potato field cultivated in alternating and continuous crops. This has a positive effect on the productivity of the potato plant, its quality indicators, and creates the basis for the environmental safety of products. Data are provided on various methods of treating plantings against weeds.

The Species Composition and Prevalence of Thorny-Headed Worms (Acanthocephala: Echinorhynchidae, Neoechinorhynchidae and Pomphorhynchidae) in Trouts, Complex Measures to Control Them in Fish Farms in the Territory of Azerbaijan Republic

During period 2015-2020 spreading of acanthocephaliasis in the rainbow and brown trout in the fish breeding farms of Azerbaijan Republic was studied. Totally, from 668 fish examined specimens 430 (64.37%) were infected. Correlation of data on the extensiveness and intensity of invasion by thorny-headed worms (Acanthocephala) in trout farms is ventured to make an assumption about unfavorable epidemiological situation in trout breeding farms of the Republic of Azerbaijan. For the purpose of control acanthocephaliasis in trout, a number of drugs were used. The most effective drug was Tetramizole-20%, which was adopted in the trout farms.

Forward layer-wise learning of convolutional neural networks through separation index maximizing

This paper proposes a forward layer-wise learning algorithm for CNNs in classification problems. The algorithm utilizes the Separation Index (SI) as a supervised complexity measure to evaluate and train each layer in a forward manner. The proposed method explains that gradually increasing the SI through layers reduces the input data’s uncertainties and disturbances, achieving a better feature space representation. Hence, by approximating the SI with a variant of local triplet loss at each layer, a gradient-based learning algorithm is suggested to maximize it. Inspired by the NGRAD (Neural Gradient Representation by Activity Differences) hypothesis, the proposed algorithm operates in a forward manner without explicit error information from the last layer. The algorithm’s performance is evaluated on image classification tasks using VGG16, VGG19, AlexNet, and LeNet architectures with CIFAR-10, CIFAR-100, Raabin-WBC, and Fashion-MNIST datasets. Additionally, the experiments are applied to text classification tasks using the DBPedia and AG’s News datasets. The results demonstrate that the proposed layer-wise learning algorithm outperforms state-of-the-art methods in accuracy and time complexity.

EEG complexity measures for detecting mind wandering during video-based learning

This study explores the efficacy of various EEG complexity measures in detecting mind wandering during video-based learning. Employing a modified probe-caught method, we recorded EEG data from participants engaged in viewing educational videos and subsequently focused on the discrimination between mind wandering (MW) and non-MW states. We systematically investigated various EEG complexity metrics, including metrics that reflect a system’s regularity like multiscale permutation entropy (MPE), and metrics that reflect a system’s dimensionality like detrended fluctuation analysis (DFA). We also compare these features to traditional band power (BP) features. Data augmentation methods and feature selection were applied to optimize detection accuracy. Results show BP features excelled (mean area under the receiver operating characteristic curve (AUC) 0.646) in datasets without eye-movement artifacts, while MPE showed similar performance (mean AUC 0.639) without requiring removal of eye-movement artifacts. Combining all kinds of features improved decoding performance to 0.66 mean AUC. Our findings demonstrate the potential of these complexity metrics in EEG analysis for mind wandering detection, highlighting their practical implications in educational contexts.

Data complexity measures for classification of a multi-concept dataset

Data complexity measures for classification of a multi-concept dataset

Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

We analyzed the possibility of detecting and predicting ventricular fibrillation (VF), a medical emergency that may put people’s lives at risk, as the medical prognosis depends on the time in which medical personnel intervene. Therefore, besides immediate detection of VF, the possibility of predicting VF 40 or even 50 min in advance was analyzed. For testing the proposed algorithm, we used ECG signals taken from the MIT-BIH database, namely, Malignant Ventricular Ectopy Database, Sudden Cardiac Death Holter Database and Normal Sinus Rhythm Database. The presented method is based on features extracted from the ECG signals in the time domain, frequency domain and complexity measures. For VF detection, the possibility of identifying the VF episode in the first 3 s after its occurrence was tested. For this, the first 3 s immediately after the appearance of VF were cut out and the features were computed on these sections. For VF prediction, 3 min of the ECG signal clipped 40 or 50 min before VF onset was used. Then, on these pieces of ECG signal, the specific features were calculated for 1 s segments. For the normal signal situation, 3 min was randomly selected from the database with normal ECGs. For the classification or detection stage, both an MLP-type neural network and the classifiers from the Machine Learning toolbox of the MATLAB® environment were used. The results obtained for both detection and classification are over 94% in both cases. The novelty of our results compared to those previously obtained is the time interval with which the possibility of prediction was analyzed, namely, 50 min in advance of the VF installation date. This means that the patient will be informed that it is possible to suffer a VF and has time to take the necessary measures to overcome a possible medical emergency.