A. Mozeika

F. Aguirre Lopez

 M. Sheikh

 F. Antenucci

T. Coolen

Exact methods supersede approximations used in high-dimensional linear regression to find correlations in statistical physics problems.

Some exact results for the statistical physics problem of high-dimensional linear regression

Exact linear regression

R. Farr

Rapid temperature cycling from one extreme to another affects the rate at which the mean particle size in solid or liquid solutions changes. 

Coarsening without interfacial energy

Microstructural coarsening

T. Fink

Parallels between the perfect and abundant numbers and their recursive analogs point to deeper structure in the recursive divisor function.

Recursively abundant and recursively perfect numbers

Ample & pristine numbers

P. Barucca

M. Bardoscia

F. Caccioli

M. D’Errico

G. Visentin

G. Caldarelli

S. Battiston

Consistent valuation of interbank claims within an interconnected financial system can be found with a recursive update of banks' equities.

Network valuation in financial systems

B. Li

D. Saad

The ability of deep neural networks to generalize can be unraveled using path integral methods to compute their typical Boolean functions.

The space of functions computed by deep layered machines

Deep layered machines

M. Sheikh

F. Aguirre-Lopez

F. Antenucci

Statistical methods that normally fail for very high-dimensional data can be rescued via mathematical tools from statistical physics.

Replica analysis of overfitting in generalized linear models

A fix for overfitting

S. E. Ahnert

The structure of network motifs determines fundamental properties of their dynamical state space.

Form and function in gene regulatory networks

M. Reeves

S. Levin

A. Levina

Insights from biology, physics and business shed light on the nature and costs of complexity and how to manage it in business organizations.

Taming complexity

Recursively divisible numbers are a new kind of number that are highly divisible, whose quotients are highly divisible, and so on.

Recursively divisible numbers

We optimize Bayesian data clustering by mapping the problem to the statistical physics of a gas and calculating the lowest entropy state.

Replica analysis of Bayesian data clustering

Replica clustering

I. Teimouri

A theoretical model of recursive innovation suggests that new technologies are recursively built up from new combinations of existing ones.

The mathematical structure of innovation

F. Vanni

A mathematical model captures the temporal and steady state behaviour of  networks whose two sets of nodes either generate or destroy links.

Degree-correlations in a bursting dynamic network model

Bursting dynamic networks

A. Scala

A. Facchini

U. Perna

R. Basosi

Portfolio analysis and geographical allocation of renewable sources: A stochastic approach

F. Liu

Y. Zhang

O. Dahlsten

F. Wang

Machine learning techniques enhance the efficiency of energy harvesters by implementing reversible energy-conserving operations.

Intelligently chosen interventions have potential to outperform the diode bridge in power conditioning

Energy harvesting with AI

J. Balogh

B. Bollobas

B. Narayanan

Two constructions for an intersecting family of r-sets.

Transference for the Erdős-Ko-Rado theorem

S. Talaganis

The number of particles in a higher derivative theory of gravity relates to its effective mass scale, which signals the theory’s viability.

Scale of non-locality for a system of n particles

Scale of non-locality

Y. Mao

The thirteen canonical knot classes and the corresponding most aesthetic knots.

Tie knots, random walks and topology

A phase transition creates the geometry of the continuum from discrete space, but it needs disorder if it is to have the right metric.

Phase transition creates the geometry of the continuum from discrete space

Geometry of discrete space

A. Zegarac

F. Caravelli

A simple solvable model of memristive networks suggests a correspondence between the asymptotic states of memristors and the Ising model.

Memristive networks: from graph theory to statistical physics

Memristive networks

G. Cimini

T. Squartini

F. Saracco

D. Garlaschelli

A. Gabrielli

Statistical physics harnesses links between maximum entropy and information theory to capture null model and real-world network features. 

The statistical physics of real-world networks

Physics of networks

The distribution of product complexity helps explain why some technology sectors tend to exhibit faster innovation rates than others.

How much can we influence the rate of innovation?

The rate of innovation

N. Halpern

A. Garner

V. Vedral

One-shot analogs of fluctuation-theorem results help unify these two approaches for small-scale, nonequilibrium statistical physics.

Maximum one-shot dissipated work from Rényi divergences

One-shot statistic

Network users who have access to the network’s most informative node, as quantified by a novel index, the InfoRank, have a competitive edge.

Tackling information asymmetry in networks: a new entropy-based ranking index

Information asymmetry

D. Branford

An explicit recipe for defining the Hamiltonian in general probabilistic theories, which have the potential to generalise quantum theory.

On defining the Hamiltonian beyond quantum theory

A Hamiltonian recipe

Z. Vukmanovic

M. Holness

E. Griffiths

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.

Reconstructing grain-shape statistics from electron back-scatter diffraction microscopy

Grain shape inference

T. Kobayashi

Network models of financial systemic risk: a review

S. Sebastio

M. Amoretti

A. Lafuente

A novel approach to volunteer clouds outperforms traditional distributed task scheduling algorithms in the presence of intensive workloads.

A holistic approach for collaborative workload execution in volunteer clouds

Volunteer clouds

M. Straka

Bipartite networks model the structures of ecological and economic real-world systems, enabling hypothesis testing  and crisis forecasting.

From ecology to finance (and back?): a review on entropy-based null models for the analysis of bipartite networks

From ecology to finance

B. Bollobás

J. Lee

S. Letzter

Hamming balls, subgraphs of the hypercube, maximise the graph’s largest eigenvalue exactly when the dimension of the cube is large enough.

Eigenvalues of subgraphs of the cube

Hypercube eigenvalues

I. Bordino

M. Cristelli

A. Ukkonen

I. Weber

Graphical illustration of the analysis presented in this paper.

Web search queries can predict stock market volumes

P. Auconi

M. Scazzocchio

J. McNamara

L. Franchi

Using networks to understand medical data: the case of class III malocclusions

V. Zlatić

Edge multiplicity—the number of triangles attached to edges—is a powerful analytic tool to understand and generalize network properties.

Networks with arbitrary edge multiplicities

Clustering inverted

G. D'Agostino

Robustness and assortativity for diffusion-like processes in scale-free networks

Robust and assortative

S. Ahnert

Optimal scales in weighted networks

A. Chessa

F. Pammolli

M. Puliga

New mathematical tools can help infer financial networks from partial data to understand the propagation of distress through the network.

Reconstructing a credit network

C. Georg

R. May

J. Stiglitz

Network-based metrics to assess systemic risk and the importance of financial institutions can help tame the financial derivatives market.

Complex derivatives

T. Roukny

H. Bersini

H. Pirotte

Frontier of large cascades evolution of the e-MID market in the period between January 1999 and January 2011.

Default cascades in complex networks: topology and systemic risk

G. Ranco

D. Aleksovski

M. Grčar

I. Mozetič

Distribution of sentiment polarity for the 260 detected Twitter peaks

The effects of Twitter sentiment on stock price returns

Y. Eom

J. Smailović

Daily tweet volume for each party around elections

Twitter-based analysis of the dynamics of collective attention to political parties

CDS spreads over time for the selected institutions

Credit default swaps networks and systemic risk

M. Borassi

Analysis of the hyperbolicity of real-world networks distinguishes between those which are aristocratic and those which are democratic.

Hyperbolicity measures democracy in real-world networks

Democracy in networks

J. Perotti

C. Tessone

A new tool derived from information theory quantitatively identifies trees, hierarchies and community structures within complex networks.

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks.

Communities in networks

G. Bormetti

F. Lillo

M. Treccani

Complementary of the cumulative distribution function of the number of clicks a news receives for the ten assets.

Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics