J. Bao

The algebra of a toric quiver gauge theory recovers the Bethe ansatz, revealing the relation between gauge theories and integrable systems.

A note on quiver Yangians and ℛ-matrices

Gauge theory and integrability

T. Fink

Y. He

The eigenvalues of the mortality equation fall into two classes—the flower and the stem—but only the stem eigenvalues control the dynamics.

Flowers of immortality

A. Zahabi

Certain states in quantum field theories are described by the geometry and algebra of melting crystals via properties of partition functions.

Crystal melting, BPS quivers and plethystics

Algebra of melting crystals

The structural and functional building blocks of gene regulatory networks correspond, which tell us how genetic computation is organised.

Regulatory motifs: structural and functional building blocks of genetic computation

Structure of genetic computation

J. Ipiña

A neural network learns to classify different types of spacetime in general relativity according to their algebraic Petrov classification.

Machine-learning the classification of spacetimes

AI classifies space-time

E. Hirst

J. Hofscheier

A .Kasprzyk

S. Majumder

Neural networks find efficient ways to compute the Hilbert series, an important counting function in algebraic geometry and gauge theory.

Hilbert series, machine learning, and applications to physics

Machine learning Hilbert series

A. Ashmore

R. Deen

B. A. Ovrut

Neural networks find numerical solutions to Hermitian Yang-Mills equations, a difficult system of PDEs crucial to mathematics and physics.

Machine learning line bundle connections

Line bundle connections

D. S. Berman

Unsupervised machine-learning of the Hodge numbers of Calabi-Yau hypersurfaces detects new patterns with an unexpected linear dependence.

Machine learning Calabi-Yau hypersurfaces

Calabi-Yau anomalies 

The Mahler measure is shown to be at the intersection between number theory, algebraic geometry, combinatorics, and quantum field theory.

Mahler measure for a quiver symphony

Mahler measure for quivers

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

Recursively divisible numbers

K. Lee

T. Oliver

Machine-learning methods can distinguish between Sato-Tate groups, promoting a data-driven approach for problems involving Euler factors.

Machine-learning the Sato–Tate conjecture

Learning the Sato–Tate conjecture

Machine-learning is a powerful tool for sifting through the landscape of possible Universes that could derive from Calabi-Yau manifolds.

Universes as big data

F. Aguirre López

A. Coolen

The generation of large graphs with a controllable number of short loops paves the way for building more realistic random networks.

Transitions in loopy random graphs with fixed degrees and arbitrary degree distributions

Transitions in loopy graphs

P. Villegas

T. Gili

G. Caldarelli

Scale-invariant plant clusters explain the ability for a diverse range of plant species to coexist in ecosystems such as Barra Colorado.

Emergent spatial patterns of coexistence in species-rich plant communities

Coexistence in diverse ecosystems

S. Lal

M. Zaid Zaz

The few-shot machine learning technique reduces the vast geometric landscape of string theory vacua to a tiny cluster of representatives.

The World in a Grain of Sand: Condensing the String Vacuum Degeneracy

Condensing the String Landscape

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. Marsden

N. McCartan

J. Hadley

S. Tuck

L. Brown

A. J. Haire

Ch. L. Moss

S. Green

M. Van Hemelrijck

A. Santaolalla

E. Isaac

G. Brembilla

D. Kopcke

F. Giganti

H. Sidhu

S. Punwani

M. Emberton

C. M. Moore

An ongoing study tests the feasibility of using MRI scans to screen men for prostate cancer in place of unreliable antigen blood tests.

Update from the ReIMAGINE Prostate Cancer Screening Study NCT04063566: Inviting Men for Prostate Cancer Screening Using Magnetic Resonance Imaging

Cancer screening with MRI

Y. Liao

D. Ebler

F. Liu

O. Dahlsten

The notion of quantum superposition speeds up the training process for binary neural networks and ensures that their parameters are optimal.

Quantum speed-up in global optimization of binary neural nets

Quick quantum neural nets

A. Akil

L. Modesto

A solution to the information paradox uses standard quantum field theory to show that black holes can evaporate in a predictable way.

Conditional entanglement transfer via black holes: restoring predictability

Going, going, gone

Ch. J. Hurry

A. Mozeika

A. Annibale

A delicate balance between white blood cell protein expression and the molecules on the surface of tumour cells determines cancer prognoses.

Modelling the interplay between the CD4+/CD8+ T-cell ratio and the expression of MHC-I in tumours

Tumour infiltration

F. Caravelli

F. Sheldon

F. L. Traversa

Circuits of memristors, resistors with memory, can exhibit instabilities which allow classical tunnelling through potential energy barriers.

Global minimization via classical tunneling assisted by collective force field formation

Breaking classical barriers

O. Foda

J. Read

Y. Xiao

F. Yagi

Groethendieck's “children’s drawings”, a type of bipartite graph, link number theory, geometry, and the physics of conformal field theory.

Dessins d’enfants, Seiberg-Witten curves and conformal blocks

QFT and kids’ drawings

P. Barucca

T. Mahmood

L. Silvestri

Fire sales of common asset holdings can whip through a channel of contagion between banks, insurance companies and investments funds.

Common asset holdings and systemic vulnerability across multiple types of financial institution

Channels of contagion

S. M. Krause

H. Štefančić

V. Zlatić

Networks where risky banks are mostly exposed to other risky banks have higher levels of systemic risk than those with stable bank interactions.

Controlling systemic risk: Network structures that minimize it and node properties to calculate it

Risky bank interactions

S. A. Jawad

L. Baú

T. Alaguthurai

I. del Molino del Barrio

A. G. Laing

T. S. Hayday

L. Monin

M. Muneoz-Ruiz

L. McDonald

I. F. Quijorna

D. McKenzie

R. Davis

A. Lorenc

J. Nuo En Chan

S. Ryan

E. Bugallo-Blanco

R. Yorke

S. Kamdar

M. Fish

I. Zlatareva

P. Vantourout

A. Jennings

S. Gee

K. Doores

K. Bailey

S. Hazell

J. De Naurois

Ch. Moss

B. Russell

A. A. Khan

M. Rowley

R. Benjamin

D. Enting

D. Alrifai

Y. Wu

Y. Zhou

Cancer patients who contract and recover from Coronavirus-2 exhibit long-term immune system weaknesses, depending on the type of cancer.

Acute immune signatures and their legacies in severe acute respiratory syndrome coronavirus-2 infected cancer patients

Cancer and coronavirus

S. Talaganis

I. Teimouri

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

The mortality equation governs the dynamics of an evolving population with a given maximum age, offering a theory for programmed ageing.

Mortality equation offers a theory for programmed aging

I want to be forever young

The fraction of logics that are biologically permitted can be bounded and shown to be tiny, which makes inferring them from experiments easier.

On the number of biologically valid logics

Biological logics are restricted

R. Hannam

The bipartite nature of regulatory networks means gene-gene logics are composed, which severely restricts which ones can show up in life.

In life, there are few rules

M. Bardoscia

S. Battiston

F. Caccioli

G. Cimini

D. Garlaschelli

F. Saracco

T. Squartini

Statistical physics contributes to new models and metrics for the study of financial network structure, dynamics, stability and instability.

The physics of financial networks

Physics of financial networks

M.Serafinoa

A. Maritan

A. Rinaldo

S.Suweis

J. R. Banavar

Naturally occurring networks have an underlying scale-free structure that is often clouded by finite-size effects in the sample data.

True scale-free networks hidden by finite size effects

True scale-free networks