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Experiments that exploit non-classical properties of light promise to provide unique information about many-body systems. The limited availability of non-classical light sources, however, makes their implementation challenging. A method to calculate the quantum-optical response of a material from signals measured by using coherent-light excitation might provide an alternative route.

The authors demonstrate optical nonlinear effect based on light-heat-liquid interaction between the geometry of liquid surface and a photonic waveguide mode. They use the liquid as an optical memory to perform nanoscale reservoir computing.

Here the authors identify TNIP1 as a risk factor for a fatal neurodegenerative disorder and discover specific genetic loci associated with the three main subtypes of this disorder. The findings highlight distinct disease mechanisms, emphasizing the roles of immunity and the notch signaling pathway.

Memristors are passive electrical components that can act like simple memories. Here, the authors use an array of hafnium oxide memristors to create a type of artificial neural network, known as a Hopfield network, that is capable of retrieving data from partial information

Cui et al. report analogue photoelectronic reservoir computing with IGZO synaptic transistors as the reservoir and TaOX-based memristor array as the output layer. A receptive field inspired encoding scheme is implemented to simplify the feature extraction for dynamic vision processing.

Characterizing quantum states is vital for quantum information or metrology tasks, but it remains challenging. Here, by a combination of weak and strong measurements, the authors directly measure the probability amplitudes of a pure state in the orbital angular momentum basis with dimensionality of 27.

Cai et al. report gate-injection-mode floating-gate memories based on MoS2 channel. The coplanar device design and bi-pulse state programming enable 8-bit conductance states at 3 V. Image convolutions are implemented and 38,592 convolutional kernel parameters are projected on a 9 × 2 device array.

The morphology and mesostructure of the prismatic layer of a growing mollusc shell is observed by means of high-resolution synchrotron-based tomography and is shown to be fully predicted by classical theories of normal grain growth.

Combined theoretical and experimental efforts provide systematic insights into the origin of self-switchable conductivity in layered semiconductor ferroelectrics. These mechanistic findings may enable advanced logic-in-memory devices.

Observation-based modelling suggests that recent acceleration of Earth’s north magnetic pole towards Siberia can be linked to elongation of a lobe of negative magnetic flux at the core–mantle boundary beneath Canada.

FinFETs are an evolution of metal-oxide-semiconductor field effect transistors (MOSFETs) featuring a semiconducting channel vertically wrapped by conformal gate electrodes. Here, the authors use a two-dimensional semiconductor to push the FinFET width to sub-nm whilst achieving a 107 ON/OFF ratio.

Gunawan et al. propose an Extensible Immunofluorescence (ExIF) strategy that integrates distinct 4-plex image panels from routine fluorescence microscopy into multiplexed datasets enabling quantitative interrogation of complex, multimolecular single-cell processes.

Absorption lineshape of H₂ is coherently controlled by using intense near-infrared laser pulses. Depending on the time delay between the near-infrared and extreme ultraviolet pulses, the profiles display a Lorentzian or an asymmetric Fano lineshape.

The authors present characterisations of highly-structured visual receptive fields of any phase symmetry, implemented in an event-based spiking neuromorphic processor. They specifically demonstrate emerging properties of recurrent inhibitory circuits that reduce the drain of hardware resources.

Observations of the young supernova remnant SNR 0509-67.5 in the Large Magellanic Cloud reveal concentric shells of ionized calcium and sulfur that resemble hydrodynamical simulations of the double detonation of a sub-Chandrasekhar-mass white dwarf.

Our understanding of material instabilities in soft solids remains elusive mainly due to the mathematical challenges in capturing localised phenomena within nonlinear elastic materials. Ciarletta develops an analytical theory to describe the nucleation threshold of creases in agreement with experiments.

Multi-client demonstrations of blind quantum computation are still missing, due to their high resource overhead. Here, the authors fill this gap, by proposing a more scalable solution based on a recently introduced linear quantum network structure with high modularity, and demonstrating it in the two-client case.

Designing reliable and energy-efficient memristor-based artificial neural networks remains a challenge. Here, the authors demonstrate a technology-agnostic approach, committee machines, which increases the inference accuracy of memristive neural networks that suffer from device variability, faulty devices, random telegraph noise and line resistance.

Skyrmions and anti-skyrmions are magnetic textures that have garnered much interest due to their stability. Here, Jena et al demonstrate the existence of fractional spin textures at the edges of Heusler alloy sample, which can have continuous variable topological charges.

High voltages/light intensities are typically needed to mimic human visual adaptability. Here, the authors present an image sensor array with low operation voltage that mimics synaptic functions with ultraweak light stimulation and performs image processing tasks accurately.

Measurements on a chiral magnet show that non-symmorphic symmetries enforce topological crossings exactly at the Fermi level in certain materials; these crossings can be controlled by an applied magnetic field.

Machine learning tools allow to extract dynamical systems from data, however this problem remains challenging for networks and systems of interacting agents. The authors introduce an approach to learn a predictive model for the dynamics of coupled agents in the form of partial differential equations using emergent spatial embeddings.

Simulating ultrafast quantum dissipation in molecular excited states is a strongly demanding computational task. Here, the authors combine tensor network simulation, entanglement renormalisation and machine learning to simulate linear vibronic models, and test the method by analysing singlet fission dynamics.

Energetic tradeoffs help determine where individual traits confer a competitive advantage. Here, the authors grow ten Eucalyptus species at four common gardens along a rainfall gradient and show that 50 traits mostly vary as predicted, and that species in their native ranges generally outperform others in height growth.

The study develops a machine learning approach for predicting bone regeneration in an additively manufactured bioceramic scaffold, which is correlated with an in vivo sheep model, exhibiting effectiveness for solving such a multiscale modeling problem.

An agent-based model suggests that bacteria use direct-contact systems for inhibiting competitors when the attacking strain is outnumbered, and long-range diffusion systems when the attacker is common. These predictions are supported by competition experiments with Pseudomonas aeruginosa, which also show that strains can use both types of system in conjunction.

Controlled particle acceleration in plasmas requires precise measurements of the excited wakefield. Here the authors report and demonstrate a high-resolution method to measure the effective longitudinal electric field of a beam-driven plasma-wakefield accelerator.