Cognition and Consciousness Entwined.

We argue that cognition (information processing) and internal phenomenological sensations, including emotions, are intimately related and are not separable. We aver that phenomenological sensations are dynamical "modes" of firing behaviour that (i) exist over time and over large parts of the cortex's neuron-to-neuron network and (ii) are consequences of the network-of-networks architecture, coupling the individual neuronal dynamics and the necessary time delay incurred by neuron-to-neuron transmission: if you possess those system properties, then you will have the dynamical modes and, thus, the phenomenological sensations. These modes are consequences of incoming external stimuli and are competitive within the system, suppressing and locking-out one another. On the other hand, the presence of any such mode acts as a preconditioner for the immediate (dynamic) cognitive processing of information. Thus, internal phenomenological sensations, including emotions, reduce the immediate decision set (of feasible interpretations) and hence the cognitive load. For organisms with such a mental inner life, there would clearly be a large cognitive evolutionary advantage, resulting in the well-known "thinking fast, thinking slow" phenomena. We call this the entwinement hypothesis: how latent conscious phenomena arise from the dynamics of the cognitive processing load, and how these precondition the cognitive tasks immediately following. We discuss how internal dynamical modes, which are candidates for emotions down to single qualia, can be observed by reverse engineering large sets of simulations of system's stimulated responses, either using vast supercomputers (with full 10B neuronal network analyses) or else using laptops to do the same for appropriately generalised Kuramoto models (networks of k-dimensional clocks, each representing the 10,000 neurons within a single neural column). We explain why such simplifications are appropriate. We also discuss the consequent cognitive advantages for information-processing systems exhibiting internal sensations and the exciting implications for next-generation (non-binary) computation and for AI.

Organization and evolution of the UK far-right network on Telegram.

The instant messaging platform Telegram has become popular among the far-right movements in the US and UK in recent years. These groups use public Telegram channels and group chats to disseminate hate speech, disinformation, and conspiracy theories. Recent works revealed that the far-right Telegram network structure is decentralized and formed of several communities divided mostly along ideological and national lines. Here, we investigated the UK far-right network on Telegram and are interested in understanding the different roles of different channels and their influence relations. We apply a community detection method, based on the clustering of a flow of random walkers, that allows us to uncover the organization of the Telegram network in communities with different roles. We find three types of communities: (1) upstream communities contain mostly group chats that comment on content from channels in the rest of the network; (2) core communities contain broadcast channels tightly connected to each other and can be seen as forming echo chambers; (3) downstream communities contain popular channels that are highly referenced by other channels. We find that the network is composed of two main sub-networks: one containing mainly channels related to the English-speaking far-right movements and one with channels in Russian. We analyze the dynamics of the different communities and the most shared external links in the different types of communities over a period going from 2015 to 2020. We find that different types of communities have different dynamics and share links to different types of websites. We finish by discussing several directions for further work.

Oxia Planum: The Landing Site for the ExoMars "Rosalind Franklin" Rover Mission: Geological Context and Prelanding Interpretation.

The European Space Agency (ESA) and Roscosmos ExoMars mission will launch the "Rosalind Franklin" rover in 2022 for a landing on Mars in 2023.The goals of the mission are to search for signs of past and present life on Mars, investigate the water/geochemical environment as a function of depth in the shallow subsurface, and characterize the surface environment. To meet these scientific objectives while minimizing the risk for landing, a 5-year-long landing site selection process was conducted by ESA, during which eight candidate sites were down selected to one: Oxia Planum. Oxia Planum is a 200 km-wide low-relief terrain characterized by hydrous clay-bearing bedrock units located at the southwest margin of Arabia Terra. This region exhibits Noachian-aged terrains. We show in this study that the selected landing site has recorded at least two distinct aqueous environments, both of which occurred during the Noachian: (1) a first phase that led to the deposition and alteration of ∼100 m of layered clay-rich deposits and (2) a second phase of a fluviodeltaic system that postdates the widespread clay-rich layered unit. Rounded isolated buttes that overlie the clay-bearing unit may also be related to aqueous processes. Our study also details the formation of an unaltered mafic-rich dark resistant unit likely of Amazonian age that caps the other units and possibly originated from volcanism. Oxia Planum shows evidence for intense erosion from morphology (inverted features) and crater statistics. Due to these erosional processes, two types of Noachian sedimentary rocks are currently exposed. We also expect rocks at the surface to have been exposed to cosmic bombardment only recently, minimizing organic matter damage.

Longitudinal ridges imparted by high-speed granular flow mechanisms in martian landslides.

The presence of longitudinal ridges documented in long runout landslides across our solar system is commonly associated with the existence of a basal layer of ice. However, their development, the link between their occurrence and the emplacement mechanisms of long runout landslides, and the necessity of a basal ice layer remain poorly understood. Here, we analyse the morphometry of longitudinal ridges of a martian landslide and show that the wavelength of the ridges is 2-3 times the average thickness of the landslide deposit, a unique scaling relationship previously reported in ice-free rapid granular flow experiments. We recognize en-echelon features that we interpret as kinematic indicators, congruent with experimentally-measured transverse velocity gradient. We suggest that longitudinal ridges should not be considered as unequivocal evidence for presence of ice, rather as inevitable features of rapid granular sliding material, that originate from a mechanical instability once a kinematic threshold is surpassed.

A Diverse Array of Fluvial Depositional Systems in Arabia Terra: Evidence for mid-Noachian to Early Hesperian Rivers on Mars.

Branching to sinuous ridges systems, hundreds of kilometers in length and comprising layered strata, are present across much of Arabia Terra, Mars. These ridges are interpreted as depositional fluvial channels, now preserved as inverted topography. Here we use high-resolution image and topographic data sets to investigate the morphology of these depositional systems and show key examples of their relationships to associated fluvial landforms. The inverted channel systems likely comprise indurated conglomerate, sandstone, and mudstone bodies, which form a multistory channel stratigraphy. The channel systems intersect local basins and indurated sedimentary mounds, which we interpret as paleolake deposits. Some inverted channels are located within erosional valley networks, which have regional and local catchments. Inverted channels are typically found in downslope sections of valley networks, sometimes at the margins of basins, and numerous different transition morphologies are observed. These relationships indicate a complex history of erosion and deposition, possibly controlled by changes in water or sediment flux, or base-level variation. Other inverted channel systems have no clear preserved catchment, likely lost due to regional resurfacing of upland areas. Sediment may have been transported through Arabia Terra toward the dichotomy and stored in local and regional-scale basins. Regional stratigraphic relations suggest these systems were active between the mid-Noachian and early Hesperian. The morphology of these systems is supportive of an early Mars climate, which was characterized by prolonged precipitation and runoff.

High Modularity Creates Scaling Laws.

Scaling laws have been observed in many natural and engineered systems. Their existence can give useful information about the growth or decay of one quantitative feature in terms of another. For example, in the field of city analytics, it is has been fruitful to compare some urban attribute, such as energy usage or wealth creation, with population size. In this work, we use network science and dynamical systems perspectives to explain that the observed scaling laws, and power laws in particular, arise naturally when some feature of a complex system is measured in terms of the system size. Our analysis is based on two key assumptions that may be posed in graph theoretical terms. We assume (a) that the large interconnection network has a well-defined set of communities and (b) that the attribute under study satisfies a natural continuity-type property. We conclude that precise mechanistic laws are not required in order to explain power law effects in complex systems-very generic network-based rules can reproduce the behaviors observed in practice. We illustrate our results using Twitter interaction between accounts geolocated to the city of Bristol, UK.

On human consciousness: A mathematical perspective.

We consider the implications of the mathematical modeling and analysis of large modular neuron-to-neuron dynamical networks. We explain how the dynamical behavior of relatively small-scale strongly connected networks leads naturally to nonbinary information processing and thus to multiple hypothesis decision-making, even at the very lowest level of the brain's architecture. In turn we build on these ideas to address some aspects of the hard problem of consciousness. These include how feelings might arise within an architecture with a foundational decision-making and classification layer of unit processors. We discuss how a proposed "dual hierarchy model," made up from both externally perceived, physical elements of increasing complexity, and internally experienced, mental elements (which we argue are equivalent to feelings), may support aspects of a learning and evolving consciousness. We introduce the idea that a human brain ought to be able to reconjure subjective mental feelings at will, and thus these feelings cannot depend on internal chatter or internal instability-driven activity (patterns). An immediate consequence of this model, grounded in dynamical systems and nonbinary information processing, is that finite human brains must always be learning and forgetting and that any possible subjective internal feeling that might be fully idealized with a countable infinity of facets could never be learned completely a priori by zombies or automata. It may be experienced more and more fully by an evolving human brain (yet never in totality, not even in a lifetime). We argue that, within our model, the mental elements and thus internal modes (feelings) play a role akin to latent variables in processing and decision-making, and thus confer an evolutionary "fast-thinking" advantage.

Two-Species Migration and Clustering in Two-Dimensional Domains.

We extend two-species models of individual aggregation or clustering to two-dimensional spatial domains, allowing for more realistic movement of the populations compared with one spatial dimension. We assume that the domain is bounded and that there is no flux into or out of the domain. The motion of the species is along fitness gradients which allow the species to seek out a resource. In the case of competition, species which exploit the resource alone will disperse while avoiding one another. In the case where one of the species is a predator or generalist predator which exploits the other species, that species will tend to move toward the prey species, while the prey will tend to avoid the predator. We focus on three primary types of interspecies interactions: competition, generalist predator-prey, and predator-prey. We discuss the existence and stability of uniform steady states. While transient behaviors including clustering and colony formation occur, our stability results and numerical evidence lead us to believe that the long-time behavior of these models is dominated by spatially homogeneous steady states when the spatial domain is convex. Motivated by this, we investigate heterogeneous resources and hazards and demonstrate how the advective dispersal of species in these environments leads to asymptotic steady states that retain spatial aggregation or clustering in regions of resource abundance and away from hazards or regions or resource scarcity.

An overview of city analytics.

We introduce the 14 articles in the Royal Society Open Science themed issue on City Analytics. To provide a high-level, strategic, overview, we summarize the topics addressed and the analytical tools deployed. We then give a more detailed account of the individual contributions. Our overall aims are (i) to highlight exciting advances in this emerging, interdisciplinary field, (ii) to encourage further activity and (iii) to emphasize the variety of new, public-domain, datasets that are available to researchers.

A roadmap for the computation of persistent homology.

Persistent homology (PH) is a method used in topological data analysis (TDA) to study qualitative features of data that persist across multiple scales. It is robust to perturbations of input data, independent of dimensions and coordinates, and provides a compact representation of the qualitative features of the input. The computation of PH is an open area with numerous important and fascinating challenges. The field of PH computation is evolving rapidly, and new algorithms and software implementations are being updated and released at a rapid pace. The purposes of our article are to (1) introduce theory and computational methods for PH to a broad range of computational scientists and (2) provide benchmarks of state-of-the-art implementations for the computation of PH. We give a friendly introduction to PH, navigate the pipeline for the computation of PH with an eye towards applications, and use a range of synthetic and real-world data sets to evaluate currently available open-source implementations for the computation of PH. Based on our benchmarking, we indicate which algorithms and implementations are best suited to different types of data sets. In an accompanying tutorial, we provide guidelines for the computation of PH. We make publicly available all scripts that we wrote for the tutorial, and we make available the processed version of the data sets used in the benchmarking. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1140/epjds/s13688-017-0109-5) contains supplementary material.

The effect of rock particles and D2O replacement on the flow behaviour of ice.

Ice-rock mixtures are found in a range of natural terrestrial and planetary environments. To understand how flow processes occur in these environments, laboratory-derived properties can be extrapolated to natural conditions through flow laws. Here, deformation experiments have been carried out on polycrystalline samples of pure ice, ice-rock and D2O-ice-rock mixtures at temperatures of 263, 253 and 233 K, confining pressure of 0 and 48 MPa, rock fraction of 0-50 vol.% and strain-rates of 5 × 10-7 to 5 × 10-5 s-1 Both the presence of rock particles and replacement of H2O by D2O increase bulk strength. Calculated flow law parameters for ice and H2O-ice-rock are similar to literature values at equivalent conditions, except for the value of the rock fraction exponent, here found to be 1. D2O samples are 1.8 times stronger than H2O samples, probably due to the higher mass of deuterons when compared with protons. A gradual transition between dislocation creep and grain-size-sensitive deformation at the lowest strain-rates in ice and ice-rock samples is suggested. These results demonstrate that flow laws can be found to describe ice-rock behaviour, and should be used in modelling of natural processes, but that further work is required to constrain parameters and mechanisms for the observed strength enhancement.This article is part of the themed issue 'Microdynamics of ice'.

Beyond privacy and exposure: ethical issues within citizen-facing analytics.

We discuss the governing forces for analytics, especially concerning citizens' behaviours and their transactions, that depend on which of three spheres of operation an institution is in (corporate, public sector/government and academic). We argue that aspirations and missions also differ by sphere even as digital spaces have drawn these spheres ever closer together. We propose that citizens' expectations and implicit permissions for any exploitation of their data require the perception of a fair balance of benefits, which should be transparent (accessible to citizens) and justifiable. We point out that within the corporate sphere most analytics does not concern identity, targeted marketing nor any direct interference with individual citizens; but instead it supports strategic decision-making, where the data are effectively anonymous. With the three spheres we discuss the nature of models deployed in analytics, including 'black-box' modelling uncheckable by a human mind, and the need to track the provenance and workings or models. We also examine the recent evolution of personal data, where some behaviours, or tokens, identifying individuals (unique and yet non-random) are partially and jointly owned by other individuals that are themselves connected. We consider the ability of heavily and lightly regulated sectors to increase access or to stifle innovation. We also call for clear and inclusive definitions of 'data science and analytics', avoiding the narrow claims of those in technical sub-sectors or sub-themes. Finally, we examine some examples of unethical and abusive practices. We argue for an ethical responsibility to be placed upon professional data scientists to avoid abuses in the future.This article is part of the themed issue 'The ethical impact of data science'.

A dynamical systems view of network centrality.

To gain insights about dynamic networks, the dominant paradigm is to study discrete snapshots, or timeslices, as the interactions evolve. Here, we develop and test a new mathematical framework where network evolution is handled over continuous time, giving an elegant dynamical systems representation for the important concept of node centrality. The resulting system allows us to track the relative influence of each individual. This new setting is natural in many digital applications, offering both conceptual and computational advantages. The novel differential equations approach is convenient for modelling and analysis of network evolution and gives rise to an interesting application of the matrix logarithm function. From a computational perspective, it avoids the awkward up-front compromises between accuracy, efficiency and redundancy required in the prevalent discrete-time setting. Instead, we can rely on state-of-the-art ODE software, where discretization takes place adaptively in response to the prevailing system dynamics. The new centrality system generalizes the widely used Katz measure, and allows us to identify and track, at any resolution, the most influential nodes in terms of broadcasting and receiving information through time-dependent links. In addition to the classical static network notion of attenuation across edges, the new ODE also allows for attenuation over time, as information becomes stale. This allows 'running measures' to be computed, so that networks can be monitored in real time over arbitrarily long intervals. With regard to computational efficiency, we explain why it is cheaper to track good receivers of information than good broadcasters. An important consequence is that the overall broadcast activity in the network can also be monitored efficiently. We use two synthetic examples to validate the relevance of the new measures. We then illustrate the ideas on a large-scale voice call network, where key features are discovered that are not evident from snapshots or aggregates.

Communicability across evolving networks.

Many natural and technological applications generate time-ordered sequences of networks, defined over a fixed set of nodes; for example, time-stamped information about "who phoned who" or "who came into contact with who" arise naturally in studies of communication and the spread of disease. Concepts and algorithms for static networks do not immediately carry through to this dynamic setting. For example, suppose A and B interact in the morning, and then B and C interact in the afternoon. Information, or disease, may then pass from A to C, but not vice versa. This subtlety is lost if we simply summarize using the daily aggregate network given by the chain A-B-C. However, using a natural definition of a walk on an evolving network, we show that classic centrality measures from the static setting can be extended in a computationally convenient manner. In particular, communicability indices can be computed to summarize the ability of each node to broadcast and receive information. The computations involve basic operations in linear algebra, and the asymmetry caused by time's arrow is captured naturally through the noncommutativity of matrix-matrix multiplication. Illustrative examples are given for both synthetic and real-world communication data sets. We also discuss the use of the new centrality measures for real-time monitoring and prediction.

Review of uses of network and graph theory concepts within proteomics.

The size and nature of data collected on gene and protein interactions has led to a rapid growth of interest in graph theory and modern techniques for describing, characterizing and comparing networks. Simultaneously, this is a field of growth within mathematics and theoretical physics, where the global properties, and emergent behavior of networks, as a function of the local properties has long been studied. In this review, a number of approaches for exploiting modern network theory to help describe and analyze different data sets and problems associated with proteomic data are considered. This review aims to help biologists find their way towards useful ideas and references, yet may also help scientists from a mathematics and physics background to understand where they may apply their expertise.

Modeling proteome networks with range-dependent graphs.

In this paper we consider the problem of characterizing and modeling large-scale protein-protein association networks using a class of range-dependent graphs which possess appropriate small world properties. These graphs may be employed in representing given association network using a maximum likelihood approach. This in turn annotates every observed association with its 'range', representing the tendency for such an association to be transitive. The application of a very rapidly developing field of graph theory to the emerging field of proetemics is novel and allows for a many-to-many relationship between individual proteins and groupings of proteins, which in turn may correspond to distinct functional behavior.

Range-dependent random graphs and their application to modeling large small-world Proteome datasets.

In this paper we consider the problem of characterizing and modeling large-scale networks using classes of range-dependent graphs which possess appropriate small-world properties. The application we have in mind is to bioinformatics, where methods of rapid protein identification mean that such proteome datasets, listing various observed protein-protein associations, will become more and more prevalent. We introduce a class of range-dependent graphs, governed by a power law relating intervertex range to edge probability, which are amenable to analysis, and for which macroscopic graph parameters are given by explicit forms. We show how these may be employed in representing a given network using a maximum likelihood approach. This in turn annotates every given edge with its range, representing the tendency for such an association to be transitive. We apply this technique to published proteome data, and demonstrate that known protein associations are thus identified.