The random walk with hyperbolic probabilities that we are introducing is an example of stochastic diffusion in a one-dimensional heterogeneous media. Although driven by site-dependent one-step transition probabilities, the process retains some of the features of a simple random walk, shows other traits that one would associate with a biased random walk and, at the same time, presents new properties not related to either of them. In particular, we show how the system is not fully ergodic, as not every statistic can be estimated from a single realization of the process. We further give a geometric interpretation for the origin of these irregular transition probabilities

Complex networks are essentially heterogeneous not only in the basic properties of the constituent nodes, such as their degree, but also in the effects that these have on the global dynamical properties of the network. Networks of coupled identical phase oscillators are good examples for analyzing these effects, since an overall synchronized state can be considered a reference state. A small variation of intrinsic node parameters may cause the system to move away from synchronization, and a new phase-locked stationary state can be achieved. We propose a measure of phase dispersion that quantifies the functional response of the system to a given local perturbation. As a particular implementation, we propose a variation of the standard Kuramoto model in which the nodes of a complex network interact with their neighboring nodes, by including a node-dependent frustration parameter. The final stationary phase-locked state now depends on the particular frustration parameter at each node and also on the network topology. We exploit this scenario by introducing individual frustration parameters and measuring what their effect on the whole network is, measured in terms of the phase dispersion, which depends only on the topology of the network and on the choice of the particular node that is perturbed. This enables us to define a characteristic of the node, its functionability, that can be computed analytically in terms of the network topology. Finally, we provide a thorough comparison with other centrality measures.

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications’ perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers’ knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks. © 2020 by the authors.

In the last few years, many investigations have focused on brain activity in general and in populations with different pathologies using non-invasive techniques such as electroencefalography (EEG), positron emission tomography (PET), functional magnetic resonance imaging (fMRI) and magnetic resonance imaging (MRI). However, the use of non-invasive techniques to detect brain signals to evaluate the cognitive activity of people with Down syndrome (DS) has not been sufficiently addressed. The objective of this study is to describe the state-of-the-art in fMRI techniques for recording brain signals in people with DS. Method: A systematic review was performed based on PRISMA recommendations; only nine papers on this topic have been published. Three independent researchers selected all relevant information from each paper. Analyses of information concordance showed a high value of agreement between researchers. Results: Although few relevant works have been published, the use of fMRI in people with DS is becoming an appropriate option to study brain function in this population. Of the nine identified papers, five used task designs, and four used resting-state paradigms. Conclusion: Thus, we emphasize the need to incorporate rigorous cognitive activity procedures in evaluations of the DS population. We suggest several factors (such as head correction movements and paired sample techniques) that must be considered when designing an fMRI study with a task or a resting-state paradigm in a DS population.

Parkinson's disease (PD) is associated with the degeneration of ventral midbrain dopaminergic neurons (vmDAns) and the accumulation of toxic alpha-synuclein. A non-cell-autonomous contribution, in particular of astrocytes, during PD pathogenesis has been suggested by observational studies, but remains to be experimentally tested. Here, we generated induced pluripotent stem cell-derived astrocytes and neurons from familial mutant LRRK2 G2019S PD patients and healthy individuals. Upon co-culture on top of PD astrocytes, control vmDAns displayed morphological signs of neurodegeneration and abnormal, astrocyte-derived alpha-synuclein accumulation. Conversely, control astrocytes partially prevented the appearance of disease-related phenotypes in PD vmDAns. We additionally identified dysfunctional chaperone-mediated autophagy (CMA), impaired macroautophagy, and progressive alpha-synuclein accumulation in PD astrocytes. Finally, chemical enhancement of CMA protected PD astrocytes and vmDAns via the clearance of alpha-synuclein accumulation. Our findings unveil a crucial non-cell-autonomous contribution of astrocytes during PD pathogenesis, and open the path to exploring novel therapeutic strategies aimed at blocking the pathogenic cross talk between neurons and glial cells.

We investigate the effects of resetting mechanisms on random processes that follow the telegrapher's equation instead of the usual diffusion equation. We thus study the consequences of a finite speed of signal propagation, the landmark of telegraphic processes. Likewise diffusion processes where signal propagation is instantaneous, we show that in telegraphic processes, where signal propagation is not instantaneous, random resettings also stabilize the random walk around the resetting position and optimize the mean first-arrival time. We also obtain the exact evolution equations for the probability density of the combined process and study the limiting cases.

The performance of nanoscale magnetic devices is often limited by the presence of thermal fluctuations, whereas in micro- and nanofluidic applications the same fluctuations may be used to spread reactants or drugs. Here, we demonstrate the controlled motion and the enhancement of diffusion of magnetic nanoparticles that are manipulated and driven across a series of Bloch walls within an epitaxially grown ferrite garnet film. We use a rotating magnetic field to generate a traveling wave potential that unidirectionally transports the nanoparticles at a frequency tunable speed. Strikingly, we find an enhancement of diffusion along the propulsion direction and a frequency-dependent diffusion coefficient that can be precisely controlled by varying the system parameters. To explain the reported phenomena, we develop a theoretical approach that shows a fair agreement with the experimental data enabling an exact analytical expression for the enhanced diffusivity above the magnetically modulated periodic landscape. Our technique to control thermal fluctuations of driven magnetic nanoparticles represents a versatile and powerful way to programmably transport magnetic colloidal matter in a fluid, opening the doors to different fluidic applications based on exploiting magnetic domain wall ratchets.

Development, regeneration and cancer involve drastic transitions in tissue morphology. In analogy with the behaviour of inert fluids, some of these transitions have been interpreted as wetting transitions. The validity and scope of this analogy are unclear, however, because the active cellular forces that drive tissue wetting have been neither measured nor theoretically accounted for. Here we show that the transition between two-dimensional epithelial monolayers and three-dimensional spheroidal aggregates can be understood as an active wetting transition whose physics differs fundamentally from that of passive wetting phenomena. By combining an active polar fluid model with measurements of physical forces as a function of tissue size, contractility, cell-cell and cell-substrate adhesion, and substrate stiffness, we show that the wetting transition results from the competition between traction forces and contractile intercellular stresses. This competition defines a new intrinsic length scale that gives rise to a critical size for the wetting transition in tissues, a striking feature that has no counterpart in classical wetting. Finally, we show that active shape fluctuations are dynamically amplified during tissue dewetting. Overall, we conclude that tissue spreading constitutes a prominent example of active wetting-a novel physical scenario that may explain morphological transitions during tissue morphogenesis and tumour progression.

A systematic study of acoustic emission avalanches in coal and charcoal samples under slow uniaxial compression is presented. The samples exhibit a range of organic composition in terms of chemical elements as well as different degrees of heterogeneity in the microstructure. The experimental analysis focuses on the energies E of the individual acoustic emission events as well as on the time correlations between successive events. The studied samples can be classified into three groups. The more homogeneous samples (group I) with pores in the micro and nanoscales, with signatures of hardening effects in the stress-strain curves, exhibit the cleanest critical power-law behavior for the energy distributions g(E)dE similar to E(similar to epsilon)dE with a critical exponent epsilon = 1.4. The more heterogeneous samples with voids, macropores, and granular microstructures (group III), show signatures of weakening effects and a larger effective exponent close to the value epsilon = 1.66, but in some cases truncated by exponential damping factors. The rest of the samples (group II) exhibit a mixed crossover behavior still compatible with an effective exponent epsilon = 1.4 but clearly truncated by exponential factors. These results suggest the existence of two possible universality classes in the failure of porous materials under compression: one for homogeneous samples and another for highly heterogeneous samples. Concerning time correlations between avalanches, all samples exhibit very similar waiting time distributions although some differences for the Omori aftershock distributions cannot be discarded.

Diversity, understood as the variety of different elements or configurations that an extensive system has, is a crucial property that allows maintaining the system's functionality in a changing environment, where failures, random events or malicious attacks are often unavoidable. Despite the relevance of preserving diversity in the context of ecology, biology, transport, finances, etc., the elements or configurations that more contribute to the diversity are often unknown, and thus, they can not be protected against failures or environmental crises. This is due to the fact that there is no generic framework that allows identifying which elements or configurations have crucial roles in preserving the diversity of the system. Existing methods treat the level of heterogeneity of a system as a measure of its diversity, being unsuitable when systems are composed of a large number of elements with different attributes and types of interactions. Besides, with limited resources, one needs to find the best preservation policy, i.e., one needs to solve an optimization problem. Here we aim to bridge this gap by developing a metric between labeled graphs to compute the diversity of the system, which allows identifying the most relevant components, based on their contribution to a global diversity value. The proposed framework is suitable for large multiplex structures, which are constituted by a set of elements represented as nodes, which have different types of interactions, represented as layers. The proposed method allows us to find, in a genetic network (HIV-1), the elements with the highest diversity values, while in a European airline network, we systematically identify the companies that maximize (and those that less compromise) the variety of options for routes connecting different airports.

Albeit epidemic models have evolved into powerful predictive tools for the spread of diseases and opinions, most assume memoryless agents and independent transmission channels. We develop an infection mechanism that is endowed with memory of past exposures and simultaneously incorporates the joint effect of multiple infectious sources. Analytic equations and simulations of the susceptible-infected-susceptible model in unstructured substrates reveal the emergence of an additional phase that separates the usual healthy and endemic ones. This intermediate phase shows fundamentally distinct characteristics, and the system exhibits either excitability or an exotic variant of bistability. Moreover, the transition to endemicity presents hybrid aspects. These features are the product of an intricate balance between two memory modes and indicate that non-Markovian effects significantly alter the properties of spreading processes.

Self-propulsion of magneto-elastic composite microswimmers is demonstrated under a uniaxial field at both the air-water and the water-substrate interfaces. The microswimmers are made of elastically linked magnetically hard Co-Ni-P and soft Co ferromagnets, fabricated using standard photolithography and electrodeposition. Swimming speed and direction are dependent on the field frequency and amplitude, reaching a maximum of 95.1 mu m/s on the substrate surface. Fastest motion occurs at low frequencies via a spinning (air-water interface) or tumbling (water-substrate interface) mode that induces transient inertial motion. Higher frequencies result in low Reynolds number propagation at both interfaces via a rocking mode. Therefore, the same microswimmer can be operated as either a high or a low Reynolds number swimmer. Swimmer pairs agglomerate to form a faster superstructure that propels via spinning and rocking modes analogous to those seen in isolated swimmers. Microswimmer propulsion is driven by a combination of dipolar interactions between the Co and Co-Ni-P magnets and rotational torque due to the applied field, combined with elastic deformation and hydrodynamic interactions between different parts of the swimmer, in agreement with previous models.

In this article, we combine experiments and theory to investigate the transport properties of anisotropic hematite colloidal rotors that dynam ically assemble into translating clusters upon application of a rotating magnetic field. The applied field exerts a torque to the particles forcing rotation close to a surface and thus a net translational motion at a frequency tunable speed. When approaching, pairs of particles are observed to assemble into stable three-dimensional clusters that perform a periodic leap-frog type dynamics and propel at a faster speed. We analyze the cluster formation and its lifetime and investigate the role of particle shape in the propulsion speed and stability. We show that the dynamics of the system results from a delicate balance between magnetic dipolar interactions and hydrodynamics, and we introduce a theoretical model that qualitatively explains the observed phenomena.

Brassinosteroids (BRs) are steroid hormones that are essential for plant growth and development. These hormones control the division, elongation and differentiation of various cell types throughout the entire plant life cycle. Our current understanding of the BR signaling pathway has mostly been obtained from studies using Arabidopsis thaliana as a model. In this context, the membrane steroid receptor BRI1 (BRASSINOSTEROID INSENSITIVE 1) binds directly to the BR ligand, triggering a signal cascade in the cytoplasm that leads to the transcription of BR-responsive genes that drive cellular growth. However, recent studies of the primary root have revealed distinct BR signaling pathways in different cell types and have highlighted cell-specific roles for BR signaling in controlling adaptation to stress. In this Review, we summarize our current knowledge of the spatiotemporal control of BR action in plant growth and development, focusing on BR functions in primary root development and growth, in stem cell selfrenewal and death, and in plant adaption to environmental

In this paper we develop a methodology, based on Mutual Information and Transfer of Entropy, that allows to identify, quantify and map on a network the synchronization and anticipation relationships between financial traders. We apply this methodology to a dataset containing 410,612 real buy and sell operations, made by 566 non-professional investors from a private investment firm on 8 different assets from the Spanish IBEX market during a period of time from 2000 to 2008. These networks present a peculiar topology significantly different from the random networks. We seek alternative features based on human behavior that might explain part of those 12,158 synchronization links and 1031 anticipation links. Thus, we detect that daily synchronization with price (present in 64.90% of investors) and the one-day delay with respect to price (present in 4.38% of investors) play a significant role in the network structure. We find that individuals reaction to daily price changes explains around 20% of the links in the Synchronization Network, and has significant effects on the Anticipation Network. Finally, we show how using these networks we substantially improve the prediction accuracy when Random Forest models are used to nowcast and predict the activity of individual investors.

Active turbulence describes a flow regime that is erratic, and yet endowed with a characteristic length scale(1). It arises in animate soft-matter systems as diverse as bacterial baths(2), cell tissues(3) and reconstituted cytoskeletal preparations(4.) However, the way that these turbulent dynamics emerge in active systems has so far evaded experimental scrutiny. Here, we unveil a direct route to active nematic turbulence by demonstrating that, for radially aligned unconfined textures, the characteristic length scale emerges at the early stages of the instability. We resolve two-dimensional distortions of a microtubule-based extensile systems(5) in space and time, and show that they can be characterized in terms of a growth rate that exhibits quadratic dependence on a dominant wavenumber. This wavelength selection mechanism is justified on the basis of a continuum model for an active nematic including viscous coupling to the adjacent fluid phase. Our findings are in line with the classical pattern-formation studies in non-active systems(6), bettering our understanding of the principles of active self-organization, and providing potential perspectives for the control of biological fluids.

Artificial ice systems have been designed to replicate paradigmatic phenomena observed in frustrated spin systems. Here, we present a detailed theoretical analysis based on Monte Carlo simulations of the low-energy phases in an artificial colloidal ice system, a recently introduced ice system where an ensemble of repulsive colloids are two-dimensionally confined by gravity to a lattice of double wells at a one-to-one filling. Triggered by recent results obtained by Brownian dynamics simulations [A. Libal et al., Phys. Rev. Lett. 120, 027204 (2018)], we analyze the energetics and the phase transitions that occur in the honeycomb geometry (realizing the analog of a spin-ice system on a kagome lattice) when decreasing the temperature. When the particles are restricted to occupy the two minima of the potential well, we recover the same phase diagram as the dipolar spin-ice system, with a long-range-ordered chiral ground state. In contrast, when considering the particle motion and their relaxation within the traps, we observe ferromagnetic ordering at low temperature. This observation highlights the fundamental role played by the continuous motion of colloids in artificial ice systems.

Greco-Roman culture classified a great variety of gems. Authors such as
Theophrastus, Plutarch and Pliny the Elder dealt with the subject. To now which gems were most highly valued in ancient Rome, it is essential to consult book 37 of Pliny the Elder. Book 37 of Pliny’s Natural History is one of the few accounts on precious stones, gems and amber that collects information from various sources of antiquity, which in many cases have survived only thanks to Pliny’s transcription. He catalogued the most prestigious gems, and discussed their origin, their exploitation techniques, their properties and their etymology. This corpus collects a total of 240 different variants of gems, of which, in 93 cases, its place of origin is known. In order to know to what extent the words of Pliny reflect the reality of the Roman market, we have analyzed as examples ten catalogs of modern collections of gems from various places and compared them with Pliny’s comments. This analysis confirms the fact that the urban Roman elites valued precious stones extracted from the territories beyond the Roman Empire, especially those of the East. The ten catalogues contain more than 4000 different gems and glasses. It compares the information in Pliny’s book on gems with ten current catalogs of various museums, adding more than 4000 analyzed copies. Both of these sources similar results and therefore confirms the interest of the Romans for these productions.

We analyze how future costs must be balanced against present costs. This is traditionally done using an exponential function with a constant discount rate. The choice of discount rate can dramatically e_ect the question on what is the value of the future. This is specially critical for environmental problems such as global warming, and it has generated a controversy as to the urgency for immediate action (Stern, 2006; Nordhaus, 2007a,b). We briey review the issue for the nonspecialist and take into account the randomness of the economic evolution by studying the discount function of three widely used processes for the dynamics of interest rates: Ornstein-Uhlenbeck, Feller and log-normal.We also outline our previous empirical survey on 9 stable countries (countries that have not su_ered periods of destabilizing ination) over time spans ranging up to more than 300 years (Farmer et. al. 2014). We estimate the parameters of one of the models studied (the Ornstein-Uhlenbeck process) and obtain the long-run discount rate for all these countries. The long-run discount obtained supports the low discounting rate proposed by Stem (2006) over higher rates that have been advocated by others (Nordhaus, 2007a,b).

Ancient regional routes were vital for interactions between settlements and deeply influenced the development of past societies and their "complexification". At the same time, since any transportation infrastructure needs some level of inter-settlement cooperation to be established, they can also be regarded as an epiphenomenon of social interactions at the regional scale. Here, we propose to analyze ancient pathway networks to understand the organization of cities and villages located in a certain territory, attempting to clarify whether such organization existed and if so, how it functioned. To address such a question, we chose a quantitative approach. Adopting network science as a general framework, by means of formal models, we try to identify how the collective effort that produced the terrestrial infrastructure was directed and organized. We selected a paradigmatic case study: Iron Age southern Etruria, a very well-studied context, with detailed archaeological information about settlement patterns and an established tradition of studies on terrestrial transportation routes, perfectly suitable for testing new techniques. The results of the modelling suggest that a balanced coordinated decision-making process was shaping, the route network in Etruria, a scenario which correlates well with the picture elaborated by different scholars using a more traditional technique.

We introduce a model for the randomization of complex networks with geometric structure. The geometric randomization (GR) model assumes a homogeneous distribution of the nodes in a hidden similarity space and uses rewirings of the links to find configurations that maximize a connection probability akin to that of the popularity-similarity geometric network models. The rewiring preserves exactly the original degree sequence, thus preventing fluctuations in the degree cutoff. The GR model is manifestly simple as it relies upon a single free parameter controlling the clustering of the rewired network, and it does not require the explicit estimation of hidden degree variables. We demonstrate the applicability of GR by implementing it as a null model for the analysis of community structure. As a result, we find that geometric and topological communities detected in real networks are consistent, while topological communities are also detected in randomized counterparts as an effect of structural constraints.

The process of crystallization is difficult to observe for transported, out-of-equilibrium systems, as the continuous energy injection increases activity and competes with ordering. In emerging fields such as microfluidics and active matter, the formation of long-range order is often frustrated by the presence of hydrodynamics. Here we show that a population of colloidal rollers assembled by magnetic fields into large-scale propelling carpets can form perfect crystalline materials upon suitable balance between magnetism and hydrodynamics. We demonstrate a field-tunable annealing protocol based on a controlled colloidal flow above the carpet that enables complete crystallization after a few seconds of propulsion. The structural transition from a disordered to a crystalline carpet phase is captured via spatial and temporal correlation functions. Our findings unveil a novel pathway to magnetically anneal clusters of propelling particles, bridging driven systems with crystallization and freezing in material science.

Due to the combined effect of anisotropic interactions and activity, Janus swimmers are capable to self-assemble in a wide variety of structures, many more than their equilibrium counterpart. This might lead to the development of novel active materials capable of performing tasks without any central control. Their potential application in designing such materials endows trying to understand the fundamental mechanism in which these swimmers self-assemble. In the present work, we study a quasi-two-dimensional semidilute suspensions of two classes of amphiphilic spherical swimmers whose direction of motion can be tuned: either swimmers propelling in the direction of the hydrophobic patch or swimmers propelling in the opposite direction (toward the hydrophilic side). In both systems we have systematically tuned swimmers' hydrophobic strength and signature and observed that the anisotropic interactions, characterized by the angular attractive potential and its interaction range, in competition with the active stress, pointing toward or against the attractive patch gives rise to a rich aggregation phenomenology.

Living tissues undergo wetting transitions: On a surface, they can either form a dropletlike cell aggregate or spread as a monolayer of migrating cells. Tissue wetting depends not only on the chemical but also on the mechanical properties of the substrate. Here, we study the role of substrate stiffness in tissue spreading, which we describe by means of an active polar fluid model. Taking into account that cells exert larger active traction forces on stiffer substrates, we predict a tissue wetting transition at a critical substrate stiffness that decreases with tissue size. On substrates with a stiffness gradient, we find that the tissue spreads faster on the stiffer side. Furthermore, we show that the tissue can wet the substrate on the stiffer side while dewetting from the softer side. We also show that, by means of viscous forces transmitted across the tissue, the stiffer-side interface can transiently drag the softer-side interface toward increasing stiffness, against its spreading tendency. These two effects result in directed tissue migration up the stiffness gradient. This phenomenon-tissue durotaxis-can thus emerge both from dewetting on the soft side and from hydrodynamic interactions between the tissue interfaces. Overall, our work unveils mechanisms whereby substrate stiffness impacts the collective migration and the active wetting properties of living tissues, which are relevant in development, regeneration, and cancer.

Recently, we characterized the complete phase transition diagram in the phi-Pe parameter space for two models of active brownian particles in two dimensions. These models are composed of hard disks and dumbbells, respectively, the former being isotropic and the latter anisotropic. Here, we want to outline all the most significant features of these two paradigmatic models and compare them.

Remarkably, the phase diagrams of the two models are affected differently by the introduction of activity. Disks present a two-stage melting scenario from Pe=0 to about Pe=3, with a first order phase transition between liquid and hexatic and a Berezinskii-Kosterlitz-Thouless transition between hexatic and solid. At higher activities, the three phases are still observed, but the transition between liquid and hexatic becomes a BKT transitions without a distinguishable coexistence region. Dumbbells, instead, present a macroscopic coexistence between hexatically ordered regions and disordered ones, over a finite interval of packing fractions, for all activities, included Pe=0, without any observable discontinuity in the behavior upon increasing Pe.