
Whitney revisited
A Whitney-type cusp and fold theorem is proved for general algebraic surface projections and for sparse univariate polynomial discriminants.
The London Institute’s papers are the official record of our discoveries. They allow others to build on and apply our work. Each one is the result of many months of research, so we strive to make them clear, inspiring and beautiful, and publish them in leading journals.

A Whitney-type cusp and fold theorem is proved for general algebraic surface projections and for sparse univariate polynomial discriminants.

We introduce CIPro, a new toolkit that automates calculations on Calabi–Yau geometries and helps explore string compactifications and vacua.

Computer vision distinguishes elliptic curves from random data and predicts analytic rank by reading arithmetic patterns in digital images.

A new algebraic method shows beta functions arise when conformal symmetry breaks, reproducing Cardy’s formula without computing correlators.
Strong interactions reshape transport in one-dimensional quantum gases, with unusual temperature effects and anomalous current fluctuations.
The Global Spherical Shell conjecture is proved for a class of complex surfaces with two singular holomorphic foliations, a key open case.
How doping a featureless Mott insulator yields high-temperature superconductivity and explains pressure effects in bilayer nickelates.
A canonical quantum fluid model is solved exactly, revealing universal correlation patterns governed by Gaussian random-matrix ensembles.
Birational methods in algebraic geometry are used to explicitly describe the vacuum structure of the Minimal Supersymmetric Standard Model.
Charge-conjugation, space-parity and time-reversal symmetries are shown to form noncommutative groups, including the order-16 Pauli group.
The Galois group of a typical rational function is described and similar problems solved using the topology of braids and tropical geometry.
This obituary celebrates the life and work of John Keith Stuart McKay, highlighting the mathematical miracles for which he will be remembered.
A new metamaterial composed of oscillators driven by external pumping is used to study the unidirectional drift of stable phase dislocations.
Canonical connections in string compactifications are computed for a broad class of bundles over Calabi-Yau manifolds by a new method.
The principle of maximal transcendentality is proved to all orders for the vacuum energy of a strongly interacting quantum field theory.
The output distribution of a deep-layered machine with random logics exhibits a critical network depth, at which it is maximally biased.
A colour symmetry extension of baryon plus lepton symmetric gapped quantum topological order replaces families of massive sterile neutrinos.
We propose that dark matter consists of topological order, so gapped anyon excitations decay to generate the Standard Model's lepton asymmetry.
Varying the spacetime dimensions fermions occupy shows charge-conjugation C, space-reflection R and time-reversal T symmetries are 8-fold periodic.
Supersymmetric systems are classified via BV formalism, extending localisation and identifying when integrals reduce to critical points.
Simultaneous propagation along multiple paths speeds excitation transfer in long-range lattices, proving an evident quantum advantage.
Extending family symmetry by colour cancels a Standard Model global anomaly, linking three colours to favour three fermion generations.
Weight pruning uncovers critical behaviour in deep neural networks with a sharp transition from functional cooperation to disordered failure.
We give a theory for the output of deep-layered machines and show that, as the network depth increases, it is biased towards simple outputs.
We show that Weyl fermions and anomalous topological order in 4 dimensions can live on the edge of the same 5-dimensional superconductor.
Machine learning finds “champion” codes by predicting and optimising their minimum Hamming distance, a measure of a code’s robustness.
Generalising the recent Kelley–Meka result on sets avoiding arithmetic progressions of length three leads to developments in the theory of the higher energies.
The rich and intricate vacuum geometry of the Minimal Supersymmetric Standard Model—a complex manifold—is characterised for the first time.
Time-optimal control of large quantum systems is computed efficiently by applying boundary conditions to a brachistochrone–Lax framework.
Neural networks classify simple finite groups by generators, unlike earlier methods using Cayley tables, leading to a proven explicit criterion.
A polynomial criterion is obtained for a set to have a small doubling, expressed in terms of the common additive energy of its subsets.
We compute Futaki invariants for gauge theories from D3-branes that probe toric Calabi-Yau singularities arising from reflexive polytopes.
By increasing the effective depth of neural networks, we improve their sequential reasoning abilities in tasks involving cellular automata.
We demonstrate that transformer attention can only discriminate well at shorter context lengths, losing clarity as input length increases.
We demonstrate that the Lawrence–Krammer representation arises as a q-deformation of the symmetric square of the Burau representation.
Quantum many-body systems share patterns of dynamics that are exactly described by tridiagonal matrices based on continuous Hahn polynomials.
Producing the first examples of breathing solitons in one-dimensional non-reciprocal media allows their propagation dynamics to be analysed.
Fractional conductivity between the nuclear and electromagnetic higher symmetries reveals four global Lie gauge groups of the Standard Model.
We construct the unitary representation of an infinite-dimensional general linear group acting on a space and establish its irreducibility.
We use the quantum brachistochrone method to design an optimal control strategy for the fastest quantum state transfer in long qubit chains.
We show reinforcement learning can be used to check whether a certain class of quantum field theory has a finite spectrum of stable particles.
Using Newton polynomials from reflexive polygons, we find that the Mahler measure and dessin d’enfants are in one-to-one correspondence.
A new approach to the large distance asymptotic of the finite-temperature deformation is discussed for a sine-kernel Fredholm determinant.
With IBM Quantum, we braid non-abelian Fibonacci anyons in string-net condensates to realise fault-tolerant universal quantum computation.
A general approach to proving the irreducibility of representations of infinite-dimensional groups within the frame of Ismagilov's conjecture.
We introduce an AI-based framework for finding solutions to the Yang-Baxter equation and discover hundreds of new integrable Hamiltonians.
Sterile neutrinos are replaced by topological order as dark matter candidates to counterbalance the Standard Model’s gravitational anomalies.
The abilities and power of a type of transformer model with memory are greatly improved by learning several key tasks at once during training.
3-manifolds represented as isomorphism signatures of their triangulations and associated Pachner graphs are analysed with machine learning.
We use simulated annealing to efficiently construct all brane tilings that encode supersymmetric gauge theories and discover a new one.
We achieve maximal-fidelity state transfer in the fastest possible time for a 3-qubit chain by applying the quantum variational method.
Continuous quivers enable exact Wilson loop calculation, reveal an emergent dimension, and raise tantalising questions on dual strings.
The quadratic complexity of attention in transformers is tackled by combining token-based memory and segment-level recurrence, using RMT.
We derive dynamical equations for networks with memristors and the Lyapunov functions of purely memristive circuits to study their stability.
Gravitational anomalies causing baryon and lepton number violation in the Standard Model are resolved using new fermionic topological orders.
A family of transformer-based DNA language models can interpret genomic sequences, opening new possibilities for complex biological research.
Using Inception, a convolutional neural network, we predict certain divisibility invariants of Calabi-Yau manifolds with up to 90% accuracy.
We study the geometry of finite dimensional space as the dimension grows to infinity with an accent on the height of the parallelotope.
Additive combinatorics sheds light on the distribution of the set of squares in the prime field, revealing a new upper bound for the number of gaps.
We give a solution of the linearisation problem in the Cremona group of rank two over an algebraically closed field of characteristic zero.
We demonstrate that the Standard Model's baryon minus lepton symmetry defect can become categorical by absorbing the gravitational anomaly.
Charge conjugation C, space reflection R, and time-reversal T operators are regularised in a quantum many-body Hilbert space on a discrete lattice.
The dynamics of the Kauffman network can be expressed as a product of the dynamics of its disjoint loops, revealing a new algebraic structure.
Modelling the behaviour of two interacting bosonic particles in a chiral, dimerized optical lattice shows the pair form a vortex bound state.
Machine learning generates desirable triangulations of geometric objects that are required for Calabi-Yau compactification in string theory.
Using the free energy principle to derive multiple theories of associative learning allows us to combine them into a single, unifying framework.
Classical Kerr amplitudes for rotating black holes are derived using insights from recent work in massive higher-spin quantum field theory.
Two approaches that provide local formulae for Compton amplitudes of higher-spin massive objects in the quantum regime and classical limit.
Based on computer simulations, we argue developmental plasticity accelerates evolution and drives organisms towards ever-greater complexity.
The training algorithm for digital neural networks is adapted and implemented entirely on an experimental chip inspired by brain physiology.
The bipartite nature of regulatory networks means gene-gene logics are composed, which severely restricts which ones can show up in life.
Reviewing progress in the field of AI-assisted discovery for maths and theoretical physics reveals a triumvirate of different approaches.
Certain properties of the bivariate cubic equations used to prove Fermat’s last theorem exhibit flocking patterns, machine learning reveals.
Inconsistencies between two approaches to deriving beta functions in two-dimensional sigma models are resolved by adding heavy superpartners.
Genetic symbolic regression methods reveal the relationship between amoebae from tropical geometry and the Mahler measure from number theory.
Insights from number theory suggest a new way to solve the critical Kauffman model, giving new bounds on the number and length of attractors.
Modelling the final state of a mobile impurity particle immersed in a one-dimensional quantum fluid after the abrupt application of a force.
An AI algorithm of few-shot learning finds that the vast string landscape could be reduced by only seeing a tiny fraction to predict the rest.
A uniform approach to a class of varieties is described that includes important types of objects from geometry, optimisation and physics.
The structural and functional building blocks of gene regulatory networks correspond, which tell us how genetic computation is organised.
The tools used to study polynomial equations with indeterminate coefficients are extended to some important cases with interrelated ones.
Coxeter transformations for root diagrams of simply-laced Lie groups are exhaustively computed then machine learned to very high accuracy.
We link the statistical properties of one-dimensional systems of free fermions initialised in states of either half- or alternating-occupancy.
The first exact solution for the vacuum state of an asymptotically free QFT in a general external field found for the Principal Chiral Model.
A new non-linear mechanical metamaterial can sustain topological solitons, robust solitary waves that could have exciting applications.
Topological quantities for the Calabi-Yau link construction of G2 manifolds are computed and machine learnt with high performance scores.
A new proof of the Chern–Gauss–Bonnet theorem is derived using supersymmetry and BV localisation, reducing geometry to critical points.
Three new closed-form expressions give the number of recursive divisors and ordered factorisations, which were until now hard to compute.
A transformation for spin and charge degrees of freedom in one-dimensional lattice systems allows direct access to the dynamical correlations.
The additive dimension of a set, which is the size of a maximal dissociated subset, is closely connected to the rapid growth of higher sumsets.
Surprisingly, the number of attractors in the critical Kauffman model with connectivity one grows exponentially with the size of the network.
Explicit computation of injection and ejection impurity’s Green’s function reveals a generalisation of the Kubo-Martin-Schwinger relation.
Genetic algorithms, which solve optimisation problems in a natural selection-inspired way, reveal previously unconstructed Calabi-Yau manifolds.
By approximating the basis of eigenfunctions, we computationally determine the harmonic modes of bundle-valued Laplacians on Calabi-Yau manifolds.
How gravitational waves are absorbed by a black hole is understood, for the first time, through effective on-shell scattering amplitudes.
Expanding the known multiplicative properties of large difference sets yields a new, quantitative proof on the structure of product sets.
Effective field theories for Kerr black holes, showing the 3-point Kerr amplitudes are uniquely predicted using higher-spin gauge symmetry.
A new open-source platform is specifically tailored for developing complex dialogue systems, like generative conversational AI assistants.
Recursively divisible numbers are a new kind of number that are highly divisible, whose quotients are highly divisible, and so on, recursively.
The beta function for a class of sigma models is not found to be geometric, but rather has an elegant form in the context of algebraic data.
A cyclic group with small difference set has a nonzero element for which the second largest number of representations is twice the average.
Investigating cluster algebras through the lens of modern data science reveals an elegant symmetry in the quiver exchange graph embedding.
Large language models like ChatGPT can generate human-like text but businesses that overestimate their abilities risk misusing the technology.
The spin-spin correlation function of the Hubbard model reveals that finite temperature spin transport in one spatial dimension is diffusive.
The recursive divisor function has a simple Dirichlet series that relates it to the divisor function and other standard arithmetic functions.
Parallels between the perfect and abundant numbers and their recursive analogs point to deeper structure in the recursive divisor function.
Geometric properties, including delta invariants, are computed for singular points defined by polynomials with indeterminate coefficients.
Models trained on a Russian topical dataset, of knowledge-grounded human-human conversation, are capable of real-world tasks across languages.
A new way to estimate indices via representation theory reveals links to the sum-product phenomena and Zaremba’s conjecture in number theory.
A new connection between continued fractions and the Bourgain–Gamburd machine reveals a girth-free variant of this widely-celebrated theorem.
AI can predict invariants of low genus arithmetic curves, including those key to the Birch-Swinnerton-Dyer conjecture—a millennium prize problem.
A complexity-science approach to digital twins of cities views them as self-organising phenomena, instead of machines or logistic systems.
Machine-learning 2-dimensional amoeba in algebraic geometry and string theory is able to recover the complicated conditions from so-called lopsidedness.
The distribution of partial sums of a Steinhaus random multiplicative function, of polynomials in a given form, converges to the standard complex Gaussian.
We study the geometry of generic spatial curves with a symmetry in order to understand the Galois group of a family of sparse polynomials.
Bursting cells can introduce noise in transcription factor screens, but modelling this process allows us to discern true counts from false.
Cluster variables in Grassmannian cluster algebras can be classified with HPC by applying the tableaux method up to a fixed number of columns.
Using methods related to the Bourgain–Gamburd machine refines the previous bound on Zaremba’s conjecture in the theory of continued fractions.
Applying diffusion-based graph operators to complex networks identifies the proper spatiotemporal scales by overcoming small-world effects.
Balancing memory from linear components with nonlinearities from memristors optimises the computational capacity of electronic reservoirs.
The eigenvalues of the mortality equation fall into two classes—the flower and the stem—but only the stem eigenvalues control the dynamics.
A neural network learns to classify different types of spacetime in general relativity according to their algebraic Petrov classification.
The algebra of a toric quiver gauge theory recovers the Bethe ansatz, revealing the relation between gauge theories and integrable systems.
Editorial of the last set of lectures given by the founder, McKay, of Moonshine Conjectures, the proof of which got Borcherds the Fields Medal.
Certain states in quantum field theories are described by the geometry and algebra of melting crystals via properties of partition functions.
Mahler measure from number theory is used for the first time in physics, yielding “Mahler flow” which extrapolates different phases in QFT.
Neural networks find efficient ways to compute the Hilbert series, an important counting function in algebraic geometry and gauge theory.
Unsupervised machine-learning of the Hodge numbers of Calabi-Yau hypersurfaces detects new patterns with an unexpected linear dependence.
Neural networks find numerical solutions to Hermitian Yang-Mills equations, a difficult system of PDEs crucial to mathematics and physics.
Machine-learning methods can distinguish between Sato-Tate groups, promoting a data-driven approach for problems involving Euler factors.
We find a physical interpretation, in terms of combinatorial topological string theory, of a classic result in finite group theory theory.
Circuits of memristors, resistors with memory, can exhibit instabilities which allow classical tunnelling through potential energy barriers.
Scale-invariant plant clusters explain the ability for a diverse range of plant species to coexist in ecosystems such as Barra Colorado.
A solution to the information paradox uses standard quantum field theory to show that black holes can evaporate in a predictable way.
A delicate balance between white blood cell protein expression and the molecules on the surface of tumour cells determines cancer prognoses.
Statistical physics contributes to new models and metrics for the study of financial network structure, dynamics, stability and instability.
The notion of quantum superposition speeds up the training process for binary neural networks and ensures that their parameters are optimal.
The number of particles in a higher derivative theory of gravity relates to its effective mass scale, which signals the theory’s viability.
The mortality equation governs the dynamics of an evolving population with a given maximum age, offering a theory for programmed ageing.
Groethendieck's “children’s drawings”, a type of bipartite graph, link number theory, geometry, and the physics of conformal field theory.
Exact methods supersede approximations used in high-dimensional linear regression to find correlations in statistical physics problems.
Networks where risky banks are mostly exposed to other risky banks have higher levels of systemic risk than those with stable bank interactions.
Bounds for additive energies of modular roots can be generalised and improved with tools from additive combinatorics and algebraic number theory.
Machine-learning is a powerful tool for sifting through the landscape of possible Universes that could derive from Calabi-Yau manifolds.
Cancer patients who contract and recover from Coronavirus-2 exhibit long-term immune system weaknesses, depending on the type of cancer.
Fire sales of common asset holdings can whip through a channel of contagion between banks, insurance companies and investments funds.
The underlying scale invariance properties of naturally occurring networks are often clouded by finite-size effects due to the sample data.
Quantum tunnelling only occurs if either the Wigner function is negative, or the tunnelling rate operator has a negative Wigner function.
The ability of deep neural networks to generalize can be unraveled using path integral methods to compute their typical Boolean functions.
Statistical methods that normally fail for very high-dimensional data can be rescued via mathematical tools from statistical physics.
Consistent valuation of interbank claims within an interconnected financial system can be found with a recursive update of banks' equities.
The generation of large graphs with a controllable number of short loops paves the way for building more realistic random networks.
Insights from biology, physics and business shed light on the nature and costs of complexity and how to manage it in business organizations.
We optimize Bayesian data clustering by mapping the problem to the statistical physics of a gas and calculating the lowest entropy state.
A theoretical model of recursive innovation suggests that new technologies are recursively built up from new combinations of existing ones.
A mathematical model captures the temporal and steady state behaviour of networks whose two sets of nodes either generate or destroy links.
A phase transition creates the geometry of the continuum from discrete space, but it needs disorder if it is to have the right metric.
Machine learning techniques enhance the efficiency of energy harvesters by implementing reversible energy-conserving operations.
Modern portfolio theory inspires a strategy for allocating renewable energy sources which minimises the impact of production fluctuations.
The distribution of product complexity helps explain why some technology sectors tend to exhibit faster innovation rates than other sectors.
A simple solvable model of memristive networks suggests a correspondence between the asymptotic states of memristors and the Ising model.
Statistical physics harnesses links between maximum entropy and information theory to capture null model and real-world network features.
An explicit recipe for defining the Hamiltonian in general probabilistic theories, which have the potential to generalise quantum theory.
The distributions of size and shape of a material’s grains can be constructed from a 2D slice of the material and electron diffraction data.
Exact solutions for the dynamics of interacting memristors predict whether they relax to higher or lower resistance states given random initialisations.
Network users who have access to the network’s most informative node, as quantified by a novel index, the InfoRank, have a competitive edge.
One-shot analogs of fluctuation-theorem results help unify these two approaches for small-scale, nonequilibrium statistical physics.
Hamming balls, subgraphs of the hypercube, maximise the graph’s largest eigenvalue exactly when the dimension of the cube is large enough.
A novel approach to volunteer clouds outperforms traditional distributed task scheduling algorithms in the presence of intensive workloads.
Bipartite networks model the structures of ecological and economic real-world systems, enabling hypothesis testing and crisis forecasting.
Forecast errors for simple experience curve models facilitate more reliable estimates for the costs of technology deployment.
An iterative version of a method to identify hierarchies and rankings of nodes in directed networks can partly overcome its resolution limit.
An explicit analytical solution reproduces the main features of random graph ensembles with many short cycles under strict degree constraints.
The large-scale structure of the interbank network changes drastically in times of crisis due to the effect of measures from central banks.
The usefulness of components and the complexity of products inform the best strategy for innovation at different stages of the process.
In systems of innovation, the relative usefulness of different components changes as the number of components we possess increases.
The structure of two-dimensional borane, a new semi-metallic single-layered material, has two Dirac cones that meet right at the Fermi energy.
Complex networks model the links between financial institutions and how these channels can transition from diversifying to propagating risk.
Bayesian networks describe the evolution of orthodontic features on patients receiving treatment versus no treatment for malocclusion.
We generalise neural networks into a quantum framework, demonstrating the possibility of quantum auto-encoders and teleportation.
Statistical mechanics concepts reconstruct connections between financial institutions and the stock market, despite limited data disclosure.
A new algorithm unveils complicated structures in the bipartite mapping between countries and products of the international trade network.
Spectroscopy experiments show that energy shifts due to photon emission from individual molecules satisfy a fundamental quantum relation.
When people operate in echo chambers, they focus on information adhering to their system of beliefs. Debunking them is harder than it seems.
Moment-based methods provide a simple way to describe a population of spherical particles and extract 3d information from 2d measurements.
The spectral density of graph ensembles provides an exact solution to the graph partitioning problem and helps detect community structure.
Memristive networks preserve memory and have the ability to learn according to analysis of the network’s internal memory dynamics.
A new equality which depends on the maximum entropy describes the worst-case amount of work done by finite-dimensional quantum systems.
Firms can harness the shifting importance of component building blocks to build better products and services and hence increase their chances of sustained success.
Processes believed to stabilize financial markets can drive them towards instability by creating cyclical structures that amplify distress.
Exact equations of motion provide an analytical description of the evolution and relaxation properties of complex memristive circuits.
Inference from single snapshots of temporal networks can misleadingly group communities if the links between snapshots are correlated.
Compact heat exchangers can be designed to run at low power if the exchange is concentrated in a crumpled surface fed by a fractal network.
Non-linear models of distress propagation in financial networks characterise key regimes where shocks are either amplified or suppressed.
Targeted immunisation policies limit distress propagation and prevent system-wide crises in financial networks according to sandpile models.
An extension of the Kelly criterion maximises the growth rate of multiplicative stochastic processes when limited resources are available.
Increasing the complexity of the network of contracts between financial institutions decreases the accuracy of estimating systemic risk.
The structural properties of a network motif predict its functional versatility and relate to gene regulatory networks.
Coupled distribution grids are more vulnerable to a cascading systemic failure but they have larger safe regions within their networks.
An adaptive network of oscillators in fragmented and incoherent states can re-organise itself into connected and synchronized states.
The community matrix of a complex ecosystem captures the population dynamics of interacting species and transitions to unstable abundances.
Percolation theory shows that the formation of giant clusters of neurons relies on a few parameters that could be measured experimentally.
The principal eigenvalue of small neutral networks determines their robustness, and is bounded by the logarithm of the number of vertices.
In an infinitely bouncing Universe, the scalar field driving the cosmological expansion and contraction carries information between phases.
A formulation of Moore’s law estimates the probability that a given technology will outperform another at a certain point in the future.
With inspiration from Maxwell’s classic thought experiment, it is possible to extract macroscopic work from microscopic measurements of photons.
A subset of bootstrap percolation models, which stabilise systems of cells on infinite lattices, exhibit non-trivial phase transitions.
News sentiment analysis and web browsing data are unilluminating alone, but inspected together, predict fluctuations in stock prices.
A new tool derived from information theory quantitatively identifies trees, hierarchies and community structures within complex networks.
When the number of tweets about an event peaks, the sentiment of those tweets correlates strongly with abnormal stock market returns.
Analysis of the hyperbolicity of real-world networks distinguishes between those which are aristocratic and those which are democratic.
Properties of protein interaction networks test the reliability of data and hint at the underlying mechanism with which proteins recruit each other.
A random analogue of the Erdős-Ko-Rado theorem sheds light on its stability in an area of parameter space which has not yet been explored.
Tweet volume is a good indicator of political parties' success in elections when considered over an optimal time window so as to minimise noise.
Single-shot information theory inspires a new formulation of statistical mechanics which measures the optimal guaranteed work of a system.
A dynamical microscopic theory of instability for financial networks reformulates the DebtRank algorithm in terms of basic accounting principles.
Exact equations for the thermodynamic quantities of lattices made of d-dimensional hypercubes are obtainable with the Bethe-Peierls approach.
The stable structures of calcium and magnesium carbonate at high pressures are crucial for understanding the Earth's deep carbon cycle.
The speed of a financial crisis outbreak sets the maximum delay before intervention by central authorities is no longer effective.
The Yule-Simon distribution describes the diffusion of knowledge and ideas in a social network which in turn influences economic growth.
A local model of preferential attachment with short-term memory generates scale-free networks, which can be readily computed by memristors.
Dynamical systems theory predicts the growth potential of countries with heterogeneous patterns of evolution where regression methods fail.
A simple formula gives the maximum time for an n x n grid to become entirely infected having undergone a bootstrap percolation process.
The analysis of real networks which contain many short loops requires novel methods, because they break the assumptions of tree-like models.
Less developed countries have to learn simple capabilities in order to start a stable industrialization and development process.
A fast and simple way to measure how polydisperse spheres crowd around each other, termed the packing fraction, agrees well with rheological data.
Time series data from networks of credit default swaps display no early warnings of financial crises without additional macroeconomic indicators.
Explicit formulae for the Shannon entropies of random graph ensembles provide measures to compare and reproduce their topological features.
When networks come under attack, a repairable architecture is superior to, and globally distinct from, an architecture that is robust.
A review of the achievements concerning typical bipartite entanglement for random quantum states involving a large number of particles.
Generating random structures in the vicinity of a material’s defect predicts the low and high energy atomic structure at the grain boundary.
The critical probability for bootstrap percolation, a process which mimics the spread of an infection in a graph, is bounded for Galton-Watson trees.
The likelihood of stock prices bouncing on specific values increases due to memory effects in the time series data of the price dynamics.
The interplay between redundancies and smart reconfiguration protocols can improve the resilience of networked infrastructures to failures.
Fractal structures need very little mass to support a load; but for current designs, this makes them vulnerable to manufacturing errors.
The optimal architecture of a financial system is only dependent on its topology when the market is illiquid, and no topology is always superior.
Lognormal distributions (and mixtures of same) are a useful model for the size distribution in emulsions and sediments.
The immune system must simultaneously recall multiple defense strategies because many antigens can attack the host at the same time.
A new non-monetary metric captures diversification, a dominant effect on the globalised market, and the effective complexity of products.
Coupled non-linear maps extract information about the competitiveness of countries to the complexity of their products from trade data.
A new concept, graph temperature, enables the prediction of distinct topological properties of real-world networks simultaneously.
Information theory fixes weighted networks’ degeneracy issues with a generalisation of binary graphs and an optimal scale of link intensities.
Associative networks with different loads model the ability of the immune system to respond simultaneously to multiple distinct antigen invasions.
The most efficient load-bearing fractals are designed as big structures under gentle loads, a common situation in aerospace applications.
Complex networks detect the driver institutions of an interbank market and ascertain that intervention policies should be time-scale dependent.
New mathematical tools can help infer financial networks from partial data to understand the propagation of distress through the network.
Network-based metrics to assess systemic risk and the importance of financial institutions can help tame the financial derivatives market.
Information about 10% of the links in a complex network is sufficient to reconstruct its main features and resilience with the fitness model.
A statistical procedure identifies dominant edges within weighted networks to determine whether a network has reached its steady state.
The transition from solid to hollow beams changes the scaling of stability versus loading analogously to increasing the hierarchical order by one.
A systematic way to vary the power-law scaling relations between loading parameters and volume of material aids the hierarchical design process.
Network theory finds unexpected interactions between the number of products a country produces and the number of countries producing each product.
A quantitative assessment of the non-monetary advantage of diversification represents a country’s hidden potential for development and growth.
Network analysis of diagnostic data identifies combinations of the key factors which cause Class III malocclusion and how they evolve over time.
Analysis of web search queries about a given stock, from the seemingly uncoordinated activity of many users, can anticipate the trading peak.
Unbiased randomisation processes generate sophisticated synthetic networks for modelling and testing the properties of real-world networks.
Spectral analysis shows that disassortative networks exhibit a higher epidemiological threshold and are therefore easier to immunize.
Edge multiplicity—the number of triangles attached to edges—is a powerful analytic tool to understand and generalize network properties.
Methods from tailored random graph theory reveal the relation between true biological networks and the often-biased samples taken from them.
Analysis of the linear elastic behaviour of plant cell dispersions improves our understanding of how to stabilise and texturise food products.
A transfer operator formalism solves the macroscopic dynamics of disordered Ising chain systems which are relevant for ageing phenomena.
A Monte Carlo model simulates the microstructural evolution of metallic and ceramic powders during the consolidation process liquid-phase sintering.
New mathematical tools quantify the topological structure of large directed networks which describe how genes interact within a cell.
The information needed to self-assemble a structure quantifies its modularity and explains the prevalence of certain structures over others.
Techniques from random sphere packing predict the dimension of the Apollonian gasket, a fractal made up of non-overlapping hyperspheres.
Of the 256 elementary cellular automata, 28 of them exhibit random behavior over time, but spatio-temporal currents still lurk underneath.
In single elimination competition the best indicator of success is a player's wealth: the accumulated wealth of all defeated players.