The Automatic Quasi-Clique Merger algorithm is a new algorithm adapted from early work published under the name QCM (introduced by Ou and Zhang (2007)). The AQCM algorithm performs hierarchical clustering in any data set for which there is an associated similarity measure quantifying the similarity of any data i and data j. Importantly, the method exhibits two valuable performance properties: (1) the ability to automatically return either a larger or smaller number of clusters depending on the inherent properties of the data rather than on a parameter. (2) the ability to return a very large number of relatively small clusters automatically when such clusters are reasonably well defined in a data set. In this work we present the general idea of a quasi-clique agglomerative approach, provide the full details of the mathematical steps of the AQCM algorithm, and explain some of the motivation behind the new methodology. The main achievement of the new methodology is that the agglomerative process now unfolds adaptively according to the inherent structure unique to a given data set, and this happens without the time-costly parameter adjustment that drove the previous QCM algorithm. For this reason we call the new algorithm automatic. We provide a demonstration of the algorithm's performance at the task of community detection in a social media network of 22,900 nodes.
We show that inner composition alignment networks derived for financial time-series data, studied in response to worldwide lockdown imposed in response to COVID-19 situation, show distinct patterns before, during and after lockdown phase. It is observed that significant couplings between companies as captured by inner composition alignment between time series, reduced considerably across the globe during lockdown and post-lockdown recovery period. The study of global community structure of the networks show that factions of companies emerge during recovery phase, with strong coupling within the members of the faction group, a trend which was absent before lockdown period. The study of strongly connected components of the networks further show that market has fragmented in response to COVID-19 situation. We find that most central firms as characterized by in-degree, out-degree and betweenness centralities belong to Chinese and Japanese economies, indicating a role played by these organizations in financial information propagation across the globe. We further observe that recovery phase of the lockdown period is strongly influenced by financial sector, which is one of the main result of this study. It is also observed that two different group of companies, which may not be co-moving, emerge across economies during COVID-19. We further notice that many companies in US and European economy tend to shield themselves from local influences.
A theory of coalescence of signal receptor clusters in mast cells is developed in close connection with experiments. It is based on general considerations involving a feedback procedure and a time-dependent capture as part of a reaction-diffusion process. Characteristic features of observations that need to be explained are indicated and it is shown why calculations available in the literature are not satisfactory. While the latter involves static centers at which the reaction part of the phenomenon occurs, by its very nature, coalescence involves dynamically evolving centers. This is so because the process continuously modifies the size of the cluster aggregate which then proceeds to capture more material. We develop a procedure that consists of first solving a static reaction-diffusion problem and then imbuing the center with changing size. The consequence is a dependence of the size of the signal receptor cluster aggregate on time. A preliminary comparison with experiment is shown to reveal a sharp difference between theory and data. The observation indicates that the reaction occurs slowly at first and then picks up rapidly as time proceeds. Parameter modification to fit the observations cannot solve the problem. We use this observation to build into the theory an accumulation rate that is itself dependent on time. A memory representation and its physical basis are explained. The consequence is a theory that can be fit to observations successfully.
It is unclear how building occupants use information about the social and built environment when choosing an egress route during emergency evacuation. Conflicting tendencies have been previously reported: to follow the crowd, to avoid congestion, and to avoid unknown egress routes alone. We hypothesize that these tendencies depend on an interaction between social influence and the affordances (opportunities for egress) of the built environment. In three virtual reality (VR) experiments (each N = 15), we investigated how social influence interacts with the affordances of available exits to determine exit choice. Participants were immersed in a crowd of virtual humans walking to the left or right exit and were asked to walk to one of the exits. Experiment 1 tested the role of social influence by manipulating both the proportion of the crowd walking toward one exit point (Crowd Proportion of 0 to 100%, in 10% increments) and the absolute number of virtual humans going to the exit point (Crowd Size of 10 or 20). Experiment 2 tested the role of affordances by introducing two visible exit doors (1 m width) in a closed room, and following the same protocol. Experiment 3 tested larger exit doors (2 m width) that afford rapid egress for more people. In the small crowd, participants were increasingly likely to follow the majority as its proportion increased. In the large crowd, however, participants tended to avoid the more crowded exit if the doors were narrow (Experiment 2), but not if the doors were wide (Experiment 3). Participants tended to follow a 100% majority in all experiments, thereby avoiding going to an exit alone. We propose that the dynamics of exit choice can be understood in terms of competition between alternative egress routes: the attraction of an exit increases with the proportion of the crowd moving toward it, becoming dominant at 100%, but decreases with the absolute number in the crowd moving toward it, relative to the exit's affordance for egress. Crown Copyright (C) 2021 Published by Elsevier B.V. All rights reserved.
We explore global stock markets' connections during the financial crises or risks since 1995 with emphasis on the situation under COVID-19. We choose 40 countries/regions and take one index from each of them, and then compute the correlation coefficients and distances between each pair of the indices with a sliding window. We construct the complexes and carry out topological data analysis mainly through persistence landscapes and their Lp -norms, which exhibit the complexes' daily changes. We establish a critical dates' detection system based on the persistence landscapes. Topological features of the complex networks are shown on the critical dates and dates before them. All the results show clearly that the connections became even closer among the markets when COVID-19 spread worldwide than those of any other risk. The robustness and effectiveness of these methods provide guidance for the analysis of financial crises in the future.
This paper examines how COVID-19 pandemic has affected volatility persistence in the G7's stock markets. Based on daily data we divided the whole sample into two sub-samples according to its breakpoints and found that they occurred right after the declaration of COVID-19 pandemic by the World Health Organization - WHO (2020). This approach allows us to assess the main differences between these two distinct phases. Thus, while the first sub-period is relatively calm, the second one, which coincides with the pandemic outbreak, shows higher levels of volatility. Considering this, we rely on GARCH-type models to assess the degree of persistence of volatility and to evaluate how it has evolved across sub-samples. Our results show that the FIGARCH(1,d,1) is the best model to describe the data and that the degree of persistence is very different from the first to the second sub-sample. Thus, while the pre-pandemic period exhibits lower levels of persistence it has greatly increased with the COVID-19 outbreak. In particular, S&P 500 and FTSE/MIB became the most persistent indices in contrast to NIKKEI 225 and FTSE 100, which were amongst the least persistent.
This paper uses new and recently introduced methodologies to study the similarity in the dynamics and behaviours of cryptocurrencies and equities surrounding the COVID-19 pandemic. We study two collections; 45 cryptocurrencies and 72 equities, both independently and in conjunction. First, we examine the evolution of cryptocurrency and equity market dynamics, with a particular focus on their change during the COVID-19 pandemic. We demonstrate markedly more similar dynamics during times of crisis. Next, we apply recently introduced methods to contrast trajectories, erratic behaviours, and extreme values among the two multivariate time series. Finally, we introduce a new framework for determining the persistence of market anomalies over time. Surprisingly, we find that although cryptocurrencies exhibit stronger collective dynamics and correlation in all market conditions, equities behave more similarly in their trajectories and extremes, and show greater persistence in anomalies over time.
Active Matter models commonly consider particles with overdamped dynamics subject to a force (speed) with constant modulus and random direction. Some models include also random noise in particle displacement (Wiener process) resulting in a diffusive motion at short time scales. On the other hand, Ornstein-Uhlenbeck processes consider Langevin dynamics for the particle velocity and predict a motion that is not diffusive at short time scales. However, experiments show that migrating cells may present a varying speed as well as a short-time diffusive behavior. While Ornstein-Uhlenbeck processes can describe the varying speed, Active Mater models can explain the short-time diffusive behavior. Isotropic models cannot explain both: short-time diffusion renders instantaneous velocity ill-defined, hence impeding dynamical equations that consider velocity time-derivatives. On the other hand, both models apply for migrating biological cells and must, in some limit, yield the same observable predictions. Here we propose and analytically solve an Anisotropic Ornstein-Uhlenbeck process that considers polarized particles, with a Langevin dynamics for the particle movement in the polarization direction while following a Wiener process for displacement in the orthogonal direction. Our characterization provides a theoretically robust way to compare movement in dimensionless simulations to movement in dimensionful experiments, besides proposing a procedure to deal with inevitable finite precision effects in experiments or simulations.
We analyze China's rural urban migration and endogenous social network structures using agent-based modeling. The agents from census micro data are located in their rural origin with an empirical-estimated prior propensity to move. The population-scale social network is a hybrid one, combining observed family ties and locations of the origin with a parameter space calibrated from census, survey and aggregate data and sampled using a stepwise Latin Hypercube Sampling method. At monthly intervals, some agents migrate and these migratory acts change the social network by turning within-nonmigrant connections to between-migrant-nonmigrant connections, turning local connections to nonlocal connections, and adding among-migrant connections. In turn, the changing social network structure updates migratory propensities of those well-connected nonmigrants who become more likely to move. These two processes iterate over time. Using a core periphery method developed from the k-core decomposition method, we identify and quantify the network structural changes and map these changes with the migration acceleration patterns. We conclude that network structural changes are essential for explaining migration acceleration observed in China during the 1995-2000 period. Published by Elsevier B.V.
We explain the structural origin of the jamming transition in jammed matter as the sudden appearance of k-cores at precise coordination numbers which are related not to the isostatic point, but to the sudden emergence of the 3- and 4-cores as given by k-core percolation theory. At the transition, the k-core variables freeze and the k-core dominates the appearance of rigidity. Surprisingly, the 3-D simulation results can be explained with the result of mean-field k-core percolation in the Erdos-Renyi network. That is, the finite-dimensional transition seems to be explained by the infinite-dimensional k-core, implying that the structure of the jammed pack is compatible with a fully random network.
Many complex biological processes such as cell differentiation, cell proliferation, and cell motility are governed by cell signaling. This mode of intercellular communication is of paramount importance for tissue function and ultimately for entire organism. In intercellular communication cells secrete signaling molecules such as cyto/chemokines which diffuse through the surrounding medium and eventually bind to receptors on other cells whereby the signal transduction is completed. An accurate estimation of the effective communication distances and the time scale on which signaling takes place are important for the interpretation of cell and organ physiology and ultimately in the effective and efficient chemotactically driven tissue engineering. The present study uses a solitary cell model incorporating degradation of secreted molecules to estimate the effective communication distances and the time scale on which signaling takes place. We demonstrate through our model that in presence of degradation the effective communication distances are significantly reduced.
Recent studies of brain activities show that cross-frequency coupling (CFC) play an important role in memory and learning. Many measures have been proposed to investigate the CFC phenomenon, including the correlation between the amplitude envelopes of two brain waves at different frequencies - cross-frequency amplitude-amplitude coupling (AAC). In this short communication, we describe how nonstationary, nonlinear oscillatory signals may produce spurious cross-frequency AAC. Utilizing the empirical mode decomposition, we also propose a new method for assessment of AAC that can potentially reduce the effects of nonlinearity and nonstatonarity and, thus, help to avoid the detection of artificial AACs. We compare the performances of this new method and the traditional Fourier-based AAC method. We also discuss the strategies to identify potential spurious AACs.
The function of DNA in cells depends on its interactions with protein molecules, which recognize and act on base sequence patterns along the double helix. These notes aim to introduce basic polymer physics of DNA molecules, biophysics of protein–DNA interactions and their study in single-DNA experiments, and some aspects of large-scale chromosome structure. Mechanisms for control of chromosome topology will also be discussed.
Aging associated brain decline often result in some kind of dementia. Even when this is a complex brain disorder a physical model can be used in order to describe its general behavior. A probabilistic model for the development of dementia is obtained and fitted to some experimental data obtained from the Alzheimer's Disease Neuroimaging Initiative. It is explained how dementia appears as a consequence of aging and why it is irreversible.
The objective of this study was to investigate the effects of local heating on complexity of skin blood flow oscillations (BFO) under prolonged surface pressure in rats. Eleven Sprague-Dawley rats were studied: 7 rats underwent surface pressure with local heating (Δt = 10 °C) and 4 rats underwent pressure without heating. A pressure of 700 mmHg was applied to the right trochanter area of rats for 3 h. Skin blood flow was measured using laser Doppler flowmetry. The loading period was divided into nonoverlapping 30 min epochs. For each epoch, multifractal detrended fluctuation analysis (MDFA) was utilized to compute DFA coefficients and complexity of endothelia related metabolic, neurogenic, and myogenic frequencies of BFO. The results showed that under surface pressure, local heating led to a significant decrease in DFA coefficients of myogenic frequency during the initial epoch of loading period, a sustained decrease in complexity of myogenic frequency, and a significantly higher degree of complexity of metabolic frequency during the later phase of loading period. Surrogate tests showed that the reduction in complexity of myogenic frequency was associated with a loss of nonlinearity whereas increased complexity of metabolic frequency was associated with enhanced nonlinearity. Our results indicate that increased metabolic activity and decreased myogenic response due to local heating manifest themselves not only in magnitudes of metabolic and myogenic frequencies but also in their structural complexity. This study demonstrates the feasibility of using complexity analysis of BFO to monitor the ischemic status of weight-bearing skin and risk of pressure ulcers.
We study instantaneous, mixing-driven, bimolecular equilibrium reactions in a system where transport is governed by a multidimensional space fractional dispersion equation. The superdiffusive, nonlocal nature of the system causes the location and magnitude of reactions that take place to change significantly from a classical Fickian diffusion model. In particular, regions where reaction rates would be zero for the Fickian case become regions where the maximum reaction rate occurs when anomalous dispersion operates. We also study a global metric of mixing in the system, the scalar dissipation rate and compute its asymptotic scaling rates analytically. The scalar dissipation rate scales asymptotically as t−(d+α)/α, where d is the number of spatial dimensions and α is the fractional derivative exponent.
A variational method for the classical Liouville equation is introduced that facilitates the development of theories for non-equilibrium classical systems. The method is based on the introduction of a complex-valued auxiliary quantity Ψ that is related to the classical position-momentum probability density ρ via ρ=Ψ∗Ψ. A functional of Ψ is developed whose extrema imply that ρ satisfies the Liouville equation. Multiscale methods are used to develop trial functions to be optimized by the variational principle. The present variational principle with multiscale trial functions can capture both the microscopic and the coarse-grained descriptions, thereby yielding theories that account for the two way exchange of information across multiple scales in space and time. Equations of the Smoluchowski form for the coarse-grained state probability density are obtained. Constraints on the initial state of the N-particle probability density for which the aforementioned equation is closed and conserves probability are presented. The methodology has applicability to a wide range of systems including macromolecular assemblies, ionic liquids, and nanoparticles.
Recurrent international financial crises inflict significant damage to societies and stress the need for mechanisms or strategies to control risk and tamper market uncertainties. Unfortunately, the complex network of market interactions often confounds rational approaches to optimize financial risks. Here we show that investors can overcome this complexity and globally minimize risk in portfolio models for any given expected return, provided the margin requirement remains below a critical, empirically measurable value. In practice, for markets with centrally regulated margin requirements, a rational stabilization strategy would be keeping margins small enough. This result follows from ground states of the random field spin glass Ising model that can be calculated exactly through convex optimization when relative spin coupling is limited by the norm of the network's Laplacian matrix. In that regime, this novel approach is robust to noise in empirical data and may be also broadly relevant to complex networks with frustrated interactions that are studied throughout scientific fields.
We investigate how various coarse-graining (signal quantization) methods affect the scaling properties of long-range power-law correlated and anti-correlated signals, quantified by the detrended fluctuation analysis. Specifically, for coarse-graining in the magnitude of a signal, we consider (i) the Floor, (ii) the Symmetry and (iii) the Centro-Symmetry coarse-graining methods. We find that for anti-correlated signals coarse-graining in the magnitude leads to a crossover to random behavior at large scales, and that with increasing the width of the coarse-graining partition interval Δ, this crossover moves to intermediate and small scales. In contrast, the scaling of positively correlated signals is less affected by the coarse-graining, with no observable changes when Δ < 1, while for Δ > 1 a crossover appears at small scales and moves to intermediate and large scales with increasing Δ. For very rough coarse-graining (Δ > 3) based on the Floor and Symmetry methods, the position of the crossover stabilizes, in contrast to the Centro-Symmetry method where the crossover continuously moves across scales and leads to a random behavior at all scales; thus indicating a much stronger effect of the Centro-Symmetry compared to the Floor and the Symmetry method. For coarse-graining in time, where data points are averaged in non-overlapping time windows, we find that the scaling for both anti-correlated and positively correlated signals is practically preserved. The results of our simulations are useful for the correct interpretation of the correlation and scaling properties of symbolic sequences.
There is an abundance of literature on complex networks describing a variety of relationships among units in social, biological, and technological systems. Such networks, consisting of interconnected nodes, are often self-organized, naturally emerging without any overarching designs on topological structure yet enabling efficient interactions among nodes. Here we show that the number of nodes and the density of connections in such self-organized networks exhibit a power law relationship. We examined the size and connection density of 46 self-organizing networks of various biological, social, and technological origins, and found that the size-density relationship follows a fractal relationship spanning over 6 orders of magnitude. This finding indicates that there is an optimal connection density in self-organized networks following fractal scaling regardless of their sizes.
