A challenging problem in the study of complex systems is that of resolving, without prior information, the emergent, mesoscopic organization determined by groups of units whose dynamical activity is more strongly correlated internally than with the rest of the system. The existing techniques to filter correlations are not explicitly oriented towards identifying such modules and can suffer from an unavoidable information loss. A promising alternative is that of employing community detection techniques developed in network theory. Unfortunately, this approach has focused predominantly on replacing network data with correlation matrices, a procedure that we show to be intrinsically biased because of its inconsistency with the null hypotheses underlying the existing algorithms. Here, we introduce, via a consistent redefinition of null models based on random matrix theory, the appropriate correlation-based counterparts of the most popular community detection techniques. Our methods can filter out both unit-specific noise and system-wide dependencies, and the resulting communities are internally correlated and mutually anticorrelated. We also implement multiresolution and multifrequency approaches revealing hierarchically nested subcommunities with “hard” cores and “soft” peripheries. We apply our techniques to several financial time series and identify mesoscopic groups of stocks which are irreducible to a standard, sectorial taxonomy; detect “soft stocks” that alternate between communities; and discuss implications for portfolio optimization and risk management.

The time variation of contacts in a networked system may fundamentally alter the properties of spreading processes and affect the condition for large-scale propagation, as encoded in the epidemic threshold. Despite the great interest in the problem for the physics, applied mathematics, computer science, and epidemiology communities, a full theoretical understanding is still missing and currently limited to the cases where the time-scale separation holds between spreading and network dynamics or to specific temporal network models. We consider a Markov chain description of the susceptible-infectious-susceptible process on an arbitrary temporal network. By adopting a multilayer perspective, we develop a general analytical derivation of the epidemic threshold in terms of the spectral radius of a matrix that encodes both network structure and disease dynamics. The accuracy of the approach is confirmed on a set of temporal models and empirical networks and against numerical results. In addition, we explore how the threshold changes when varying the overall time of observation of the temporal network, so as to provide insights on the optimal time window for data collection of empirical temporal networked systems. Our framework is of both fundamental and practical interest, as it offers novel understanding of the interplay between temporal networks and spreading dynamics.

The effort to understand network systems in increasing detail has resulted in a diversity of methods designed to extract their large-scale structure from data. Unfortunately, many of these methods yield diverging descriptions of the same network, making both the comparison and understanding of their results a difficult challenge. A possible solution to this outstanding issue is to shift the focus away from ad hoc methods and move towards more principled approaches based on statistical inference of generative models. As a result, we face instead the more well-defined task of selecting between competing generative processes, which can be done under a unified probabilistic framework. Here, we consider the comparison between a variety of generative models including features such as degree correction, where nodes with arbitrary degrees can belong to the same group, and community overlap, where nodes are allowed to belong to more than one group. Because such model variants possess an increasing number of parameters, they become prone to overfitting. In this work, we present a method of model selection based on the minimum description length criterion and posterior odds ratios that is capable of fully accounting for the increased degrees of freedom of the larger models and selects the best one according to the statistical evidence available in the data. In applying this method to many empirical unweighted networks from different fields, we observe that community overlap is very often not supported by statistical evidence and is selected as a better model only for a minority of them. On the other hand, we find that degree correction tends to be almost universally favored by the available data, implying that intrinsic node proprieties (as opposed to group properties) are often an essential ingredient of network formation.

To comprehend interconnected systems across the social and natural sciences, researchers have developed many powerful methods to identify functional modules. For example, with interaction data aggregated into a single network layer, flow-based methods have proven useful for identifying modular dynamics in weighted and directed networks that capture constraints on flow processes. However, many interconnected systems consist of agents or components that exhibit multiple layers of interactions, possibly from several different processes. Inevitably, representing this intricate network of networks as a single aggregated network leads to information loss and may obscure the actual organization. Here, we propose a method based on a compression of network flows that can identify modular flows both within and across layers in nonaggregated multilayer networks. Our numerical experiments on synthetic multilayer networks, with some layers originating from the same interaction process, show that the analysis fails in aggregated networks or when treating the layers separately, whereas the multilayer method can accurately identify modules across layers that originate from the same interaction process. We capitalize on our findings and reveal the community structure of two multilayer collaboration networks with topics as layers: scientists affiliated with the Pierre Auger Observatory and scientists publishing works on networks on the arXiv. Compared to conventional aggregated methods, the multilayer method uncovers connected topics and reveals smaller modules with more overlap that better capture the actual organization.

Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent searching, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state of the art in topic modeling. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results that are not accurate in inferring the most suitable model parameters. Adapting approaches from community detection in networks, we propose a new algorithm that displays high reproducibility and high accuracy and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure.

The two single-pass, externally seeded free-electron lasers (FELs) of the FERMI user facility are designed around Apple-II-type undulators that can operate at arbitrary polarization in the vacuum ultraviolet-to-soft x-ray spectral range. Furthermore, within each FEL tuning range, any output wavelength and polarization can be set in less than a minute of routine operations. We report the first demonstration of the full output polarization capabilities of FERMI FEL-1 in a campaign of experiments where the wavelength and nominal polarization are set to a series of representative values, and the polarization of the emitted intense pulses is thoroughly characterized by three independent instruments and methods, expressly developed for the task. The measured radiation polarization is consistently $>90\%$ and is not significantly spoiled by the transport optics; differing, relative transport losses for horizontal and vertical polarization become more prominent at longer wavelengths and lead to a non-negligible ellipticity for an originally circularly polarized state. The results from the different polarimeter setups validate each other, allow a cross-calibration of the instruments, and constitute a benchmark for user experiments.

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.

Many real-world networks contain a statistically surprising number of certain subgraphs, called network motifs. In the prevalent approach to motif analysis, network motifs are detected by comparing subgraph frequencies in the original network with a statistical null model. In this paper, we propose an alternative approach to motif analysis where network motifs are defined to be connectivity patterns that occur in a subgraph cover that represents the network using minimal total information. A subgraph cover is defined to be a set of subgraphs such that every edge of the graph is contained in at least one of the subgraphs in the cover. Some recently introduced random graph models that can incorporate significant densities of motifs have natural formulations in terms of subgraph covers, and the presented approach can be used to match networks with such models. To prove the practical value of our approach, we also present a heuristic for the resulting NP hard optimization problem and give results for several real-world networks.

The study of human mobility is both of fundamental importance and of great potential value. For example, it can be leveraged to facilitate efficient city planning and improve prevention strategies when faced with epidemics. The newfound wealth of rich sources of data—including banknote flows, mobile phone records, and transportation data—has led to an explosion of attempts to characterize modern human mobility. Unfortunately, the dearth of comparable historical data makes it much more difficult to study human mobility patterns from the past. In this paper, we present an analysis of long-term human migration, which is important for processes such as urbanization and the spread of ideas. We demonstrate that the data record from Korean family books (called “jokbo”) can be used to estimate migration patterns via marriages from the past 750 years. We apply two generative models of long-term human mobility to quantify the relevance of geographical information to human marriage records in the data, and we find that the wide variety in the geographical distributions of the clans poses interesting challenges for the direct application of these models. Using the different geographical distributions of clans, we quantify the “ergodicity” of clans in terms of how widely and uniformly they have spread across Korea, and we compare these results to those obtained using surname data from the Czech Republic. To examine population flow in more detail, we also construct and examine a population-flow network between regions. Based on the correlation between ergodicity and migration in Korea, we identify two different types of migration patterns: diffusive and convective. We expect the analysis of diffusive versus convective effects in population flows to be widely applicable to the study of mobility and migration patterns across different cultures.

Current modeling of infectious diseases allows for the study of complex and realistic scenarios that go from the population to the individual level of description. However, most epidemic models assume that the spreading process takes place on a single level (be it a single population, a metapopulation system, or a network of contacts). In particular, interdependent contagion phenomena can be addressed only if we go beyond the scheme-one pathogen-one network. In this paper, we propose a framework that allows us to describe the spreading dynamics of two concurrent diseases. Specifically, we characterize analytically the epidemic thresholds of the two diseases for different scenarios and compute the temporal evolution characterizing the unfolding dynamics. Results show that there are regions of the parameter space in which the onset of a disease’s outbreak is conditioned to the prevalence levels of the other disease. Moreover, we show, for the susceptible-infected-susceptible scheme, that under certain circumstances, finite and not vanishing epidemic thresholds are found even at the limit for scale-free networks. For the susceptible-infected-removed scenario, the phenomenology is richer and additional interdependencies show up. We also find that the secondary thresholds for the susceptible-infected-susceptible and susceptible-infected-removed models are different, which results directly from the interaction between both diseases. Our work thus solves an important problem and paves the way toward a more comprehensive description of the dynamics of interacting diseases.

Online social networks (OSNs) enable researchers to study the social universe at a previously unattainable scale. The worldwide impact and the necessity to sustain the rapid growth of OSNs emphasize the importance of unraveling the laws governing their evolution. Empirical results show that, unlike many real-world growing networked systems, OSNs follow an intricate path that includes a dynamical percolation transition. In light of these results, we present a quantitative two-parameter model that reproduces the entire topological evolution of a quasi-isolated OSN with unprecedented precision from the birth of the network. This allows us to precisely gauge the fundamental macroscopic and microscopic mechanisms involved. Our findings suggest that the coupling between the real preexisting underlying social structure, a viral spreading mechanism, and mass media influence govern the evolution of OSNs. The empirical validation of our model, on a macroscopic scale, reveals that virality is 4–5 times stronger than mass media influence and, on a microscopic scale, individuals have a higher subscription probability if invited by weaker social contacts, in agreement with the “strength of weak ties” paradigm.

We introduce and theoretically investigate here a novel analytical method that we have called focal molography, in which molecular interactions are made visible through scattering of coherent light by a coherent pattern of molecules. The scattered light quantifies the presence of molecules at molecular interaction sites. It is separated from noncoherent background scatter by a combination of local dark-field illumination, interference enhancement, and spatial filtering. The latter is achieved by holographic focusing of the wave field generated by the coherently assembled molecules onto an Airy disk and by subtraction of the noncoherent irradiance in the focal plane outside the disk from the irradiance in the disk. This new microscopic method allows distinct detection of low-refractive-index contrast in the nanoenvironment of biomolecules from which information on the interaction of the coherently assembled molecules with molecules in a liquid or gaseous sample may be deduced. The noncoherent surroundings of the coherently assembled molecules consist of freely diffusing solvent and solute molecules. The surroundings, as well as changes in temperature, do not contribute to the coherent signal in the diffraction focus. Interference lithography or high-resolution-imaging lithography can be used to synthesize the coherent pattern of molecules on a monolithic substrate. The coherent pattern of molecules constitutes a synthetic phase hologram that creates a diffraction-limited light wave. We suggest the term “mologram” for the coherent assembly of functional nanostructures and the term “focal molography” for label-free or labeled analysis of molecular interactions through the measurement of the properties of light in the focus of the mologram. We derive analytical formulas that express the detection signal and the sensitivity of focal molography on the surface of a high-refractive-index thin-film optical waveguide in terms of known parameters. We discuss the implementation of a readout system for molograms on a thin-film optical waveguide by adapting a confocal laser-scanning microscope to a bifocal laser-scanning microscope.

Inspired by the recent demonstration that ${\mathrm{Ca}}_2\mathrm{N}$ is a two-dimensional electride in which electrons near the Fermi level float between ionic layers, we have conducted a systematic search for 2D electrides by an approach combining crystal structure databases and ab initio electronic structure calculations. Starting from a set of working hypotheses, we have identified six 2D metallic electrides, of which one is nonmagnetic and five are nearly ferromagnetic ferrimagnets with a distinct spin texture. The latter five materials are the first magnetic electrides reported at ambient pressure.

We provide a unified thermodynamic formalism describing information transfers in autonomous as well as nonautonomous systems described by stochastic thermodynamics. We demonstrate how information is continuously generated in an auxiliary system and then transferred to a relevant system that can utilize it to fuel otherwise impossible processes. Indeed, while the joint system satisfies the second law, the entropy balance for the relevant system is modified by an information term related to the mutual information rate between the two systems. We show that many important results previously derived for nonautonomous Maxwell demons can be recovered from our formalism and use a cycle decomposition to analyze the continuous information flow in autonomous systems operating at a steady state. A model system is used to illustrate our findings.

Can quantum mechanics help us build intelligent learning agents? A defining signature of intelligent behavior is the capacity to learn from experience. However, a major bottleneck for agents to learn in real-life situations is the size and complexity of the corresponding task environment. Even in a moderately realistic environment, it may simply take too long to rationally respond to a given situation. If the environment is impatient, allowing only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all. Here, we show that quantum physics can help and provide a quadratic speedup for active learning as a genuine problem of artificial intelligence. This result will be particularly relevant for applications involving complex task environments.

By using random matrix models, we uncover a connection between the low-energy sector of four-dimensional QCD at finite volume and the Heisenberg XX model in a 1D spin chain. This connection allows us to relate crucial properties of QCD with physically meaningful properties of the spin chain, establishing a dictionary between both worlds. For the spin chain, we predict a third-order phase transition and a Tracy-Widom law in the transition region. We also comment on possible numerical implications of the connection as well as on possible experimental implementations.

We experimentally investigate the Dicke phase transition in chaotic optical resonators realized with two-dimensional photonics crystals. This setup circumvents the constraints of the system originally investigated by Dicke and allows a detailed study of the various properties of the superradiant transition. Our experimental results, analytical prediction, and numerical modeling based on random-matrix theory demonstrate that the probability density $P(\mathrm{\Gamma })$ of the resonance widths provides a new criterion to test the occurrence of the Dicke transition.

Leukemia epitomizes the class of highly complex diseases that new technologies aim to tackle by using large sets of single-cell-level information. Achieving such a goal depends critically not only on experimental techniques but also on approaches to interpret the data. A most pressing issue is to identify the salient quantitative features of the disease from the resulting massive amounts of information. Here, I show that the entropies of cell-population distributions on specific multidimensional molecular and morphological landscapes provide a set of measures for the precise characterization of normal and pathological states, such as those corresponding to healthy individuals and acute myeloid leukemia (AML) patients. I provide a systematic procedure to identify the specific landscapes and illustrate how, applied to cell samples from peripheral blood and bone marrow aspirates, this characterization accurately diagnoses AML from just flow cytometry data. The methodology can generally be applied to other types of cell populations and establishes a straightforward link between the traditional statistical thermodynamics methodology and biomedical applications.

Spatial tailoring of the material constitutive properties is a well-known strategy to mold the local flow of given observables in different physical domains. Coordinate-transformation-based methods (e.g., transformation optics) offer a powerful and systematic approach to design anisotropic, spatially inhomogeneous artificial materials (metamaterials) capable of precisely manipulating wave-based (electromagnetic, acoustic, elastic) as well as diffusion-based (heat) phenomena in a desired fashion. However, as versatile as these approaches have been, most designs have thus far been limited to serving single-target functionalities in a given physical domain. Here, we present a step towards a “transformation multiphysics” framework that allows independent and simultaneous manipulation of multiple physical phenomena. As a proof of principle of this new scheme, we design and synthesize (in terms of realistic material constituents) a metamaterial shell that simultaneously behaves as a thermal concentrator and an electrical “invisibility cloak.” Our numerical results open up intriguing possibilities in the largely unexplored phase space of multifunctional metadevices, with a wide variety of potential applications to electrical, magnetic, acoustic, and thermal scenarios.