Papers

Here we present a comprehensive derivation of the analytical expression for the spatiotemporal acoustic pressure generated by photoacoustic sources with spherically symmetric initial pressure distributions. Starting from the fundamental photoacoustic wave equation, we derive a unified analytical solution applicable to arbitrary spherically symmetric initial distributions. Specific expressions are provided for several common distributions including uniform spherical sources, Gaussian distributions, exponential distributions, and power-law distributions. Far-field approximations are also discussed. The derived expressions provide valuable tools for photoacoustic imaging system design and signal analysis. We provide codes for ultrafast forward simulation using the general analytical spherically symmetric model, the implementation is available in the GitHub repository: \href{https://github.com/JaegerCQ/SlingBAG_Ultra}.

The environmental impact of electricity consumption is commonly assessed through its carbon footprint (CF), while water-related impacts are often overlooked despite the strong interdependence between energy and water systems. This is particularly relevant for electricity-intensive activities such as data center (DC) operations, where both carbon emissions and water use occur largely off-site through electricity consumption. In this work, we present Wattnet, an open-source tool that jointly assesses the CF and water footprint (WF) of electricity consumption across Europe with high temporal resolution. Wattnet implements an electricity flow-tracing methodology that accounts for local generation mixes, as well as for cross-border electricity imports and exports at a 15-minute resolution. Operational and life-cycle impact factors are used to quantify and compare local (generation-based) and global (consumption-based) footprints for multiple European regions during 2024. Wattnet includes a 72-h forecasting module to facilitate informed energy-aware decision-making. The results demonstrate that neglecting electricity flows and temporal variability can lead to significant misestimations of both CF and WF, particularly in countries with high levels of electricity trade or hydropower dependence. Furthermore, the joint analysis reveals trade-offs between decarbonisation and water use, highlighting the prominent role of reservoir-based hydropower in increasing WF even in low-carbon systems. Wattnet facilitates informed energy-aware decision-making, while also enhancing transparency regarding the environmental impacts of electricity consumption for end users and policymakers.

We study stem-terminally differentiated (TD) lineages in small niches where demographic noise from discrete division and death events is non-negligible. Starting from a mechanistic five-channel, density-dependent CTMC (symmetric self-renewal, symmetric differentiation, asymmetric division, dedifferentiation, TD death), we derive its mean-field limit and a functional CLT, obtaining a chemical Langevin diffusion whose explicit state-dependent covariance exactly matches the CTMC's aggregated channel-wise infinitesimal covariances. Within this diffusion approximation we remove the dedifferentiation flux and obtain a sharp dichotomy: in subcritical regimes the stem coordinate becomes extinct asymptotically almost surely, whereas in supercritical regimes polynomial moments diverge exponentially. This identifies, at the diffusion level, a structural failure mode of strictly hierarchical lineages under demographic fluctuations and clarifies how a cyclic return flux can rescue homeostasis. For interpretation we also derive an exact totals ODE backbone from a damage-structured transport model and obtain two steady-state constraints (ratio and equalization laws) linking compartment ratios to turnover and balancing dedifferentiation against fate bias. Numerical experiments corroborate the $\Omega^{-1/2}$ fluctuation scaling, illustrate the pathology, and contrast theorem-regime global convergence with threshold (Allee-type) behaviour outside the theorem hypotheses.

The emergence of artificial intelligence (AI) accelerators like NVIDIA Tensor Cores offers new opportunities to speed up tensor-heavy scientific computations. However, applying them to quantum chemistry is challenging due to strict accuracy demands and irregular data patterns. We propose an adaptive precision algorithm to accelerate the density fitting (DF) method with Gaussian basis sets on AI accelerators using 8-bit integer (INT8) arithmetics. Implemented in the GPU-accelerated PySCF package, the algorithm is tested on more than twenty molecular systems with different NVIDIA GPUs. Compared to the standard FP64 code, our algorithm is up to 204% faster on a RTX 4090 gaming GPU and up to 364% faster on a RTX 6000 Ada workstation GPU without compromising the converged energy. This work demonstrates a practical approach to use AI hardware for reliable quantum chemistry simulations.

Vigoureux suggests replacing the usual linear superposition rule of quantum mechanics with a M"obius-type "composition law" $\oplus$, motivated by (i) bounded-domain composition laws in special relativity, (ii) familiar transfer-matrix formulas in multilayer optics, and (iii) an analogy with the inclusion-exclusion rule for classical probabilities. In this note we explain why the proposal does not work as a modification of quantum theory. For two components, the new rule differs from the ordinary sum only by an overall scalar factor, so after normalization it represents the same ray and cannot change any physical prediction. For three or more components, if one extends the two-term prescription in the natural recursive way, the result becomes bracket/order dependent and can even change the ray, so a "state" is no longer uniquely determined by a given preparation. We also clarify why the inclusion-exclusion argument and the optics analogy do not support a foundational change to Hilbert-space linearity.

Lithium-ion batteries (LIBs) are the leading technology used in consumer electronics, electric vehicles, and grid-level electrochemical energy storage applications. The ever-increasing use of LIBs has highlighted a gap in understanding of their behavior throughout their life cycle. Current monitoring systems rely on electrical and sometimes temperature measurements to assess the internal state which limits information about complex electrochemical processes. In response, ultrasonic testing (UT) has shown promise for non-invasive assessment due to its ease of use and sensitivity to mechanical changes which are correlated with electrochemical changes within the battery. We summarize the research in UT methods applied to LIBs throughout their life cycle. We also discuss physics-based and data-driven modeling approaches used to interpret ultrasonic signals in the context of LIBs, with an emphasis on the existing challenge of establishing rigorous links between electrochemical behavior and elastic and poroelastic wave physics to gain insight regarding physical changes in the LIB that can be directly measured using UT. Finally, we discuss the challenges of implementing UT across the LIB life cycle and identify opportunities for further research. This review aims to provide helpful guidance to researchers and practitioners of UT in the growing field of UT for electrochemical battery systems.

The impossibility of a transposable 12 semitone tuning of the octave arises from the mathematical fact that $2 \times 2^{7/12} \neq 3$ i.e., the second harmonic of the fifth can not exactly match the third harmonic of the fundamental. This in turn, stems from the whole number harmonic structure of western music, and the subsequent fundamental character of the octave interval as multiples of 2 in frequency, a property inherited by our music system from the physics of instruments with vibrating elements being to a good approximation one dimensional. In the current era of electronic music, one can relax the above assumptions to construct an analogous music system where all the structural properties of the standard music system are preserved, but where harmonics are not whole number multiples of the fundamental frequency, and the octave is no longer a factor of 2 in frequency. This now allows to construct a transposable 12 semitone music system where the second harmonic of the fifth exactly matches the third harmonic of the fundamental. The enhanced harmonic qualities of this system recover to a good approximation the musical qualities of Just Intonation, whilst retaining by construction all the versatility and modulating ability of 12TET.

High-resolution imaging of magnetic nanostructures is essential for understanding fundamental spin phenomena and designing advanced devices. Recent developments in three-dimensional (3D) nanomagnetism have highlighted the growing need for imaging techniques that can capture magnetic structures across curved or tilted surfaces with high sensitivity. Though widely used as a powerful technique for imaging magnetization states, conventional magneto-optical Kerr effect (MOKE) microscopy faces inherent limitations in measuring non-planar systems due to its shallow depth of field (DOF). Here, we present an extended depth of field MOKE imaging approach, combining through-focus scanning with image-stitching-based reconstruction, to obtain sharp and well-resolved magnetic domain images across non-planar sample geometries. The method is validated on both perpendicularly and in-plane magnetized films tilted on purpose for this study, enabling quantitative analysis of domain morphology and detection sensitivity. This laboratory-accessible technique provides a fast and versatile route for 3D magnetic imaging, complementing large-scale X-ray-based methods and offering a practical tool for investigating non-planar magnetic samples with tilted or curved 3D geometries.

The physical origin of 'anomalous' resistivity in magnetic reconnection remains one of the longest-standing problems in space plasma physics. While the empirical Bohm diffusion scaling ($\eta \propto T/B$) is widely invoked to explain fast reconnection rates, it lacks a rigorous derivation from first principles. Here, we derive this scaling by modeling the ensemble of electron gyro-axes in a magnetized plasma as an overdamped spintronic condensate governed by the Landau-Lifshitz-Gilbert equation. We demonstrate that the breakdown of the "frozen-in" condition is rigorously identified as an Adler-Ohmic bifurcation: a topological phase transition where electron gyro-axes lose synchronization with the mean magnetic field. Unlike stochastic turbulence models, this framework predicts a coherent, explosive onset of resistivity that naturally saturates at the Bohm limit. We support this thesis with renormalization group theory and a novel analysis of Magnetospheric Multiscale mission data, which reveals an explosive phase space confinement consistent with collective phase slippage rather than chaotic scattering. These results suggest that Bohm resistivity is a universal topological property of magnetized matter at the critical point of reconnection.

The rapid deployment of drones poses significant challenges for airspace management, security, and surveillance. Current detection and classification technologies, including cameras, LiDAR, and conventional radar systems, often struggle to reliably identify and differentiate drones, especially those of similar models, under diverse environmental conditions and at extended ranges. Moreover, low radar cross sections and clutter further complicate accurate drone identification. To address these limitations, we propose a novel drone classification method based on artificial micro-Doppler signatures encoded by resonant electromagnetic stickers attached to drone blades. These tags generate distinctive, configuration-specific radar returns, enabling robust identification. We develop a tailored convolutional neural network (CNN) capable of processing raw radar signals, achieving high classification accuracy. Extensive experiments were conducted both in anechoic chambers with 43 tag configurations and outdoors under realistic flight trajectories and noise conditions. Dimensionality reduction techniques, including Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP), provided insight into code separability and robustness. Our results demonstrate reliable drone classification performance at signal-to-noise ratios as low as 7 dB, indicating the feasibility of long-range detection with advanced surveillance radar systems. Preliminary range estimations indicate potential operational distances of several kilometers, suitable for critical applications such as airport airspace monitoring. The integration of electromagnetic tagging with machine learning enables scalable and efficient drone identification, paving the way for enhanced aerial traffic management and security in increasingly congested airspaces.

Self-imaging represents a core hallmark of paraxial wave evolution; yet, across its many realizations and generalizations over the past two centuries, the uniformity of recurrence planes along the propagation axis has been considered fundamental. Here we reformulate the general phenomenon of self-imaging within the natural framework of canonical phase-space geometry, revealing a hidden canonical coordinate in which all exact self-imaging is indeed uniform, but which need not correspond to the physical propagation axis. This leads to a general law of self-imaging, in which the spacing of the physical recurrence planes can be prescribed through the choice of initial transverse phase structure. Using a single programmable spatial light modulator, we demonstrate the construction of Talbot carpets characterized by recurrence spacings that accelerate and decelerate along the propagation axis, as well as those that follow polynomial, exponential, and sinusoidal axial trajectories. These results reveal a hidden geometric freedom in paraxial wave propagation: exact self-imaging is rigid in canonical coordinates, but freely programmable in physical space, allowing for qualitatively new forms of optical recurrence.

Plasma diagnostics have a shortage of fast and sensitive calorimetric sensors that can track substrate temperature during plasma-assisted microfabrication. In this work, energy fluxes from argon and oxygen radiofrequency (RF) glow discharges have been probed using a novel nanocalorimeter sensor. The probe consists of an ultrathin SiNx membrane (100 nm) with a lithographically defined Pt micro-strip (100 nm) that serves as a calibrated resistance thermometer. The sensor temperature can increase from room temperature to several hundred degrees within a second upon exposure to RF plasma, depending on the experiment's geometry and plasma parameters. Such sensitivity and response time are due to the pre-designed reduced heat capacity of the sensor and significantly reduced thermal conductance of the cooling channels. These features enable the sensitive detection of low-energy plasma fluxes on surfaces and their rapid discrimination, as in the case of ion and electron fluxes, by biasing the sensor at negative or positive potentials. These biased nanocalorimeter energy readings have been compared with ion and electron kinetic energy dissipations assessed using a Langmuir probe and retarding field energy analyzer (RFEA). Finally, the robustness of the plasma nanocalorimeter is discussed in terms of its baseline drifts, degradation, and longevity.

Understanding human visual search behavior is a fundamental problem in vision science and computer vision, with direct implications for modeling how observers allocate attention in location-unknown search tasks. In this study, we investigate the relationship between Gabor-based features and gray-level co-occurrence matrix (GLCM) based texture features in modeling early-stage visual search behavior. Two feature-combination pipelines are proposed to integrate Gabor and GLCM features for narrowing the region of possible human fixations. The pipelines are evaluated using simulated digital breast tomosynthesis images. Results show qualitative agreement among fixation candidates predicted by the proposed pipelines and a threshold-based model observer. A strong correlation is observed between GLCM mean and Gabor feature responses, indicating that these features encode related image information despite their different formulations. Eye-tracking data from human observers further suggest consistency between predicted fixation regions and early-stage gaze behavior. These findings highlight the value of combining structural and texture-based features for modeling visual search and support the development of perceptually informed observer models.

Recent works suggest there are periods when the Sun encountered massive interstellar cold clouds which compressed the heliosphere to within Earth's orbit, exposing Earth to interstellar galactic cosmic rays and energetic particles of heliospheric origin. We model 10bE production in Earth's atmosphere during possible interstellar cloud encounters and supernovae. We find that, if the heliosphere is compressed to 0.2 AU (r_TS=0.12 AU), the 10Be production rate is elevated 9x above the background. To be distinguished in marine sediments (iron-manganese crusts) beyond terrestrial variability, this signal must be sustained for >0.01 Myr (>0.5 Myr). A 0.7 AU (r_TS=0.4 AU) compression increases the 10Be production rate 3x above the background, which may be detectable if the event lasts >1 Myr. We find that the 10Be peak observed by Koll et al. (2025) 10 Ma is too prolonged to be produced by a supernova, but could be attributed to an interstellar cloud crossing.

This study advances task-based image quality assessment by developing an anthropomorphic thresholded visual-search model observer. The model is an ideal observer for thresholded data inspired by the human visual system, allowing selective processing of high-salience features to improve discrimination performance. By filtering out irrelevant variability, the model enhances diagnostic accuracy and computational efficiency. The observer employs a two-stage framework: candidate selection and decision-making. Using thresholded data during candidate selection refines regions of interest, while stage-specific feature processing optimizes performance. Simulations were conducted to evaluate the effects of thresholding on feature maps, candidate localization, and multi-feature scenarios. Results demonstrate that thresholding improves observer performance by excluding low-salience features, particularly in noisy environments. Intermediate thresholds often outperform no thresholding, indicating that retaining only relevant features is more effective than keeping all features. Additionally, the model demonstrates effective training with fewer images while maintaining alignment with human performance. These findings suggest that the proposed novel framework can predict human visual search performance in clinically realistic tasks and provide solutions for model observer training with limited resources. Our novel approach has applications in other areas where human visual search and detection tasks are modeled such as in computer vision, machine learning, defense and security image analysis.

The transmission eigenchannels of a disordered medium range from unity transmission and high internal energy density to vanishing transmission and low internal energy density. Although the transmission matrix underlies central ideas in mesoscopic electronic transport, it cannot be measured directly in electronic systems. For classical waves, however, the measurable matrix gives access to eigenchannels that control transmission and energy density within the medium. Coherence underlies transmission-eigenvalue scaling, the bimodal distribution in diffusive samples, and Anderson localization. What has remained unresolved is how interference among modal contributions is organized within individual eigenchannels throughout the sample. Here, microwave transmission-matrix measurements resolve the complex contribution of each incident mode to every transmitted mode of each eigenchannel. In simulations, we construct a flux matrix that generalizes the transmission matrix to every depth and further separate each internal modal contribution into forward- and backward-propagating components. For each eigenchannel, the directional flux in every mode throughout the sample factorizes into the squared modal amplitudes of the incident eigenchannel, coupling between waveguide modes, and modal alignment. In high-transmission eigenchannels, the contributions align increasingly constructively with depth, approaching nearly perfect alignment in the highest channel near the localization crossover. In low-transmission eigenchannels, contributions interfere destructively, producing vanishing transmission at a transmission zero. Because the contributions remain appreciable as their coherent sum approaches zero, transmission far below the noise floor of conventional transmission measurements can be determined.

Since its inception, Bohmian mechanics has been surrounded by a halo of controversy. Originally proposed to bypass the limitations imposed by von Neumann's theorem on the impossibility of hidden-variable models in quantum mechanics, it faced strong opposition from the outset. Over time, however, its use in tackling specific problems across various branches of physics has led to a gradual shift in attitude, turning the early resistance into a more moderate acceptance. A plausible explanation for this change may be that, since the late 1990s and early 2000s, Bohmian mechanics has been taking on a more operational and practical role. The original hidden-variable idea has gradually faded from its framework, giving way to a more pragmatic approach that treats it as a suitable analytical and computational tool. This discussion explores how and why such a shift in perspective has occurred and, therefore, answers questions such as whether Bohmian mechanics should be considered once and for all a legitimate quantum representation (i.e., worth being taught in elementary quantum mechanics courses) or, by extension, whether these ideas can be transferred to and benefit other fields. Here, the Schrödinger equation and several specific numerical examples are re-examined in the light of a less restrictive view than the standard one usually adopted in quantum mechanics.

We propose a general method for studying systems that display low-energy excitations in their low-temperature phase. We argue that in a rectangular right-prism geometry (the 3-dimensional generalization of a strip), with one longitudinal size much larger than the transverse size, correlations decay exponentially (at all temperatures) along the longitudinal direction. Still, the scaling of the correlation length with the transverse size carries crucial information from which the lower critical dimension can be inferred. The method is applied in the particularly demanding context of Ising spin glasses at zero magnetic field. The lower critical dimension and the multifractal spectrum for the correlation functions are computed from large-scale numerical simulations. Several technical novelties (such as the unexpectedly crucial performance of Houdayer's cluster method or the convenience of using open—rather than periodic—boundary conditions) allow us to study three-dimensional prisms with transverse dimensions up to $L=24$ and effectively infinite longitudinal dimensions, down to low temperatures. The value that we find for the exponent controlling the behavior of the correlation length agrees with predictions from the Replica Symmetry Breaking (RSB) theory. We argue that our novel setting holds promise in clarifying which of the two competing theories, RSB or the Droplet Model, more accurately describes three-dimensional spin glasses.

In this paper, we present the potentiality and the application of a Compton source driven by the electron beam of EuPRAXIA, in interaction with a commercially available infrared laser.

A new experimental setup has been installed at the Time-Of-Flight area of the Neutrons For Science facility (NFS) at GANIL/SPIRAL2 for neutron beam monitoring. This setup consists of an array of Position-Sensitive Parallel-Plate Avalanche Counters (PS-PPACs) that detects both fission fragments in coincidence from secondary neutron-induced fission reactions in several ${}^{238}$U targets. The neutron energy is determined on an event-by-event basis using the Time-of-Flight method, and the reaction point within the U targets is reconstructed, enabling the measurement of the neutron beam flux and beam profile. The high transparency of the setup allows it to operate in parallel with other experiments running at NFS, thus providing an in-beam monitor of the neutron intensity. In this work, we report on the characteristics of this new setup, its operating principle, and the first results obtained using the high-intensity white-spectrum neutron beam at NFS. This beam is produced via reactions between a primary 40-MeV deuteron beam, accelerated in the SPIRAL2 LINAC, and a 8 mm-thick rotating beryllium converter target.

A state-of-the-art Resonant Inelastic X-ray Scattering (RIXS) facility, composed of a dedicated beamline and a 2D-RIXS spectrometer, has been constructed and commissioned at BL02U in NanoTerasu, Japan. This paper reports the optical design and optimization of this spectrometer, aiming for an ultrahigh energy resolution of <10 meV in the soft X-ray range. Conventional RIXS spectrometers using monochromatic incident X-rays suffer from a severe trade-off between energy resolution and measurement efficiency, which makes achieving resolutions of <10 meV practically unfeasible. To overcome this limitation, we adopted the $h\nu^2$ concept using dispersive incident X-rays, based on a comparative study. This approach decouples the energy resolution from the incident bandwidth. We estimate that our 2D-RIXS spectrometer improves efficiency by more than a factor of 10 compared to conventional spectrometers. The 2D-RIXS spectrometer has been successfully commissioned, demonstrating that sub-10 meV resolution measurement is achievable with practical throughput. While development continues towards the ultimate design resolution, the successful implementation validates the design strategy and offers a pathway for future high-performance RIXS instrumentation.

We explore the experimental predictions of the local scale invariant, non-Hermitian pilot-wave (de Broglie-Bohm) formulation of quantum theory introduced in arXiv:2601.03567. We use Weyl's definition of gravitational radius of charge to obtain the fine-structure constant for non-integrable scale effects $\alpha_S$. The minuteness of $\alpha_S$ relative to $\alpha$ ($\alpha_S/\alpha \sim 10^{-21}$) effectively hides the effects in usual quantum experiments. In an Aharonov-Bohm double-slit experiment, the theory predicts that the position probability density depends on which slit the particle trajectory crosses, due to a non-integrable scale induced by the magnetic flux. This experimental prediction can be tested for an electrically neutral, heavy molecule with mass $m \sim 10^{-19} \text{g}$ at a $\sim 10^5 \text{ esu}$ flux regime. We analyse the Weyl-Einstein debate on the second-clock effect using the theory and show that spectral frequencies are history-independent. We thereby resolve Einstein's key objection against local scale invariance, and obtain two further experimental predictions. First, spectral intensities turn out to be history-dependent. Second, energy eigenvalues are modified by tiny imaginary corrections that modify spectral linewidths. We argue that the trajectory dependence of the probabilities renders our theory empirically distinguishable from other quantum formulations that do not use pilot-wave trajectories, or their mathematical equivalents, to derive experimental predictions.

Analog coprocessors are intensively developing nowadays with the aim to optimize energy computations of neural networks. In this work we focus on the possibility of using detection of collective oscillations in optical systems for computational purposes. We show that in a system of coupled resonators, collective oscillations can be used to implement matrix-vector multiplication. The matrix is formed by the coupling constants between the resonators, and the input vector is formed by the initial occupancies of the involved modes. The frequency of the collective oscillations is growing with the number of the involved modes, similarly to Rabi oscillations. The time needed for their detection, i.e., averaging, decreases with an increase in the input vector dimension. We discuss the limitations imposed on parallel computation in the system by restriction of the allowed optical frequency band.

We propose a topology-optimization framework for optimizing finite structures of arbitrary shape by combining density-based methods with level-set approaches. We first optimize regular polygonal structures to suppress the photonic density of states and find that the best performing polygon is consistent with a tiling of space with hexagonal unit cells. We next show that introducing cavities into hexagonal structures further suppresses the photonic density of states, particularly when the cavity is also hexagonal. Such a result would find application in the design of fiber-optic cables. We then describe an approach for optimizing arbitrary x-simple or y-simple designs that can recover finite supercells of a hexagonal unit cell. Our approach can therefore discover the symmetry of photonic-crystal primitive unit cells that significantly suppress the photonic density of states for a given set of material parameters within a single optimization.

Categorizing events using discriminant observables is central to many high-energy physics analyses. Yet, bin boundaries are often chosen manually. A simple, popular choice in multi-classification tasks is to assign events according to the largest per-class score ("argmax") and to apply equidistant binning to the resulting one-dimensional discriminants. We propose a binning optimization for signal significance directly in multi-dimensional discriminants. We use a Gaussian Mixture Model (GMM) to define flexible regions in the score space, which can be interpreted either as bins or as analysis categories. While this GMM-based strategy is applicable in both one and multiple dimensions, we also study a direct bin-boundary optimization in one dimension as a simpler alternative for binary discriminants. On this binning model, we study two optimization strategies: a differentiable and a Bayesian optimization approach. We study two toy setups: a binary classification and a three-class problem with two signals and backgrounds. In the one-dimensional case, both approaches achieve similar gains in signal sensitivity compared to equidistant binning for a given number of bins, while in the multi-dimensional case the differentiable approach performs best. We show that the GMM-based optimization can outperform argmax classification even after optimized binning is applied to the one-dimensional projections. We further study the performance of our methods on the FAIR Universe $H\rightarrow\tau\tau$ dataset, where the GMM-based optimization gives the highest signal significance. Both methods are released as lightweight Python plugins intended for straightforward integration into existing analyses.

Motile microorganisms navigate environments that vary in space and time by coupling sensory inputs to internal physiological states and active motion. In photosynthetic microorganisms, such coupling can be mediated by phototaxis, yet how temporal sensing combines with spatial variations of the environment to shape population organisation remains poorly understood. To probe this coupling, we expose populations of the phototactic microalga Chlamydomonas reinhardtii to structured light landscapes. Under ring-shaped illumination, cells robustly accumulate at the center of the illuminated region, rather than at the light-intensity maximum. Single-cell tracking shows that cells steer relative to local intensity gradients and undergo a non-trivial phototactic response, where, on average, cells outside the ring perform positive phototaxis while cells inside perform negative phototaxis. Population experiments, numerical simulations and analytical modelling show that this trapping cannot be reproduced by an instantaneous switching rule, but requires a finite phototactic memory acting on the transition between positive and negative phototaxis. This internal timescale couples to the geometry of the light field and to the persistence of active motion, enabling controllable spatial localisation without physical confinement. Our results generically show how memory in a single-cell sensory response can control population-level organisation in a living active system.

In this paper, in the first instance the attitude dynamics of the Earth is modelled based on physical principles so as to correctly predict the Chandler wobble and its features such as its period. To this end not only the steady state and dynamic gravity gradient torques were included, but also the effects of centrifugal acceleration on the deformable viscoelastic model of the planet. After validating the Chandler wobble response, the paper seeks to go beyond and predict the attitude response of the Earth not just over a few years but over much longer term. This requires modelling of the energy balance effects of the entire planet as a whole and not just the redistribution of mass and inertia but also the changes in the inertial properties. To this end simpler physical model of the energy balance process was included to demonstrate the sustained changes in the length of day response of the Earth over a long term. The over aim of the paper is to bring into focus the key physical processes responsible for the changes to the attitude dynamics of the Earth over a reasonable longer time frame. The overall responses of the attitude dynamics over, the short and medium terms are presented.

We provide a new formulation of Time-Dependent Density Functional Theory (TDDFT) based on the geometric structure of the set of states constrained to have a fixed density. Orbital-free TDDFT is formulated using a hydrodynamics equation involving a new density-to-current functional map. In the corresponding Kohn–Sham equation, the density is reproduced using a non-local operator. Finally, we present numerical simulations for one-dimensional soft-Coulomb systems.

Time-dependent density-functional theory (TDDFT) is a central tool for studying the dynamical electronic structure of molecules and solids, yet aspects of its mathematical foundations remain insufficiently understood. In this work, we revisit the foundations of TDDFT within a finite-dimensional setting by developing a general geometric framework for Schrödinger dynamics subject to prescribed expectation values of selected observables. We show that multiple natural definitions of such constrained dynamics arise from the underlying geometry of the state manifold. The conventional TDDFT formulation emerges from demanding stationarity of the action functional, while an alternative, purely geometric construction leads to a distinct form of constrained Schrödinger evolution that has not been previously explored. This alternative dynamics may provide a more mathematically robust route to TDDFT and may suggest new strategies for constructing nonadiabatic approximations. Applying the theory to interacting fermions on finite lattices, we derive novel Kohn–Sham schemes in which the density constraint is enforced via an imaginary potential or, equivalently, a nonlocal Hermitian operator. Numerical illustrations for the Hubbard dimer demonstrate the behavior of these new approaches.

Integrated optical transceivers, utilizing wavelength-division-multiplexing, offer a path forward for implementation of compact, high-bandwidth and energy-efficient interconnects for future data centers. Here we report the demonstration of a monolithically integrated optical receiver in 45nm CMOS, where efficient multi-layer optical demultiplexing with capacitive tuning, energy efficient electronics and wideband inverse designed grating couplers enable implementation of a 32-channel receiver chip based on wavelength-division multiplexing. The chip operates at an aggregate data-rate of 1.024 Tb/s with all channels operating simultaneously at a data-rate of 32 Gb/s/channel achieving a record energy efficiency of 71 fJ/b, including the power consumption of both the electronic circuitry and the tuning and control of photonic devices, and a record bandwidth density of 4 Tb/s/mm2. The receiver achieves a bit-error-rate below 1E-12 without requiring equalization, error correction or digital processing. Inverse-designed broadband grating couplers provide efficient, low-loss optical coupling into the chip. An on-chip demultiplexer, composed of Mach-Zehnder interferometers (MZIs) and ring resonators, offers a large channel-to-channel isolation sufficient for error-free operation. Capacitive phase shifters embedded within the ring resonators of the demultiplexer are used for wavelength alignment at a zero static power consumption. MZIs and ring-resonators are periodically selected and autonomously locked to the wavelength of the corresponding optical carrier. The implemented monolithic receiver offers a scalable, energy-efficient and reliable solution for the beyond Tb/s optical interconnects.