Fast radio bursts (FRBs) are bright (Jansky-level) and short-duration ($\sim 1$ ms) flashes of extragalactic origin. Observations of single events have now been complemented by large-area surveys, delivering FRB catalogues and enabling the first population studies. The Northern Cross (NC) radio interferometer is one of the instruments performing observations of FRBs. In this work, we present the Italian Northern Cross Atlas of Radio Transients ({\tt INCART}), a public platform for the distribution of data products from the NC. {\tt INCART} makes available to the community the FRBs observed by the NC through manageable frequency-time series datasets and catalogues with best-fit physical parameters. The design of {\tt INCART} guarantees the possibility of scientific re-analysis of the FRB properties, in view also of future releases of the processing pipeline. Furthermore, {\tt INCART} focuses on long-term storage optimisation, which is a key aspect of state-of-the-art instrumentation. Public access to the FRB data from the NC maximises the legacy value of the collection, facilitates the synergy with other publicly-available catalogues, and fosters research group collaborations.

We study Lusin-measurable functions with values in locally convex spaces. In particular, the behavior of pointwise limits of sequences of Lusin-measurable functions and exhibit pathological phenomena arising in the nonmetrizable setting. Moreover, we establish approximation and density results for $L^p$-spaces constructed with this notion of measurability, including the density of simple functions in Hausdorff locally convex spaces and convergence results obtained through dyadic approximations.

The scope of this text is to study a process that induces another proof of the Spectral Embedding Theorem: that any densely defined symmetric operator can be extended by a multiplication operator through an embedding of the Hilbert space into an $L_2$ space. Furthermore, that process is meant to be used for specific operators, where natural spectral embeddings or equivalences may be found. That process has previously been considered in arXiv:2411.06281 and in arXiv:2511.18189, where it has been introduced through nonstandard techniques. Our contribution aims to be the reformulation of the theory through classical analysis arguments, without the use of nonstandard techniques nor ultraproducts.

Large-scale offshore Wind Farms (WFs) are considered key assets towards realizing a sustainable power system. These systems are often configured as offshore AC islands and their integration largely depends on the High-Voltage-Direct-Current (HVDC) technology. This topology, while it enables cost-effective transmission over large offshore distances, may lead to operational challenges. Specifically, the operation of offshore AC islands during faults and the grid code requirement fulfillment are identified as a major challenges for their large-scale deployment. To address this pressing issue, a comprehensive coordination control strategy for the different participating converters in multi-infeed AC offshore islands during Fault Ride Through (FRT) operation is presented in this work. The proposed strategy introduces advanced control functions in the FRT schemes of both the HVDC and WF converters, such as zero active and reactive power injection during faults, as well as post-fault active power droop control coordination to tackle power imbalances. The proposed FRT coordination strategy is validated through both extensive simulations in PSCAD/EMTDC, as well as with Power Hardware-in-the-Loop (PHIL) experimental results, considering both AC and DC faults.

Given a matrix $A$, a matrix nearness problem seeks an $X$ that most closely approximates $A$ in the sense of minimizing $\lVert A - X\rVert$ under a variety of constraints on $X$. A generalized matrix nearness problem seeks the same but with three given matrices $A,B,C$ and $\lVert A - BXC\rVert$ in place of $\lVert A - X\rVert$. We extend previous studies of the latter problem in three directions: incorporating an affine term, replacing matrix product by Kronecker product in various manners, and generalizing Frobenius norm to any orthogonally invariant norm. We will solve several of these in closed form. For the rest, we develop an iterative algorithm that works for any Schatten norm, proving that it converges to a global minimizer regardless of the initial point. In addition, the algorithm relies purely on numerical linear algebra, and notably does not compute any explicit gradients or subgradients. Along the way, we will also show that there is no Mirsky-type theorem for rank constrained generalized matrix nearness problems.

This work presents calculations of thermal dilepton emission and polarization observables. It features a comprehensive framework which comprises virtual photon spectral functions complete at next-to-leading-order in the strong coupling and iEBE-MUSIC hydrodynamic simulations. The polarization of thermal lepton pairs is shown to be sensitive to in-medium properties of the quark-gluon plasma. We consider Pb+Pb collisions performed in conditions specific to the LHC and examine the magnitude and behaviour of the polarization as measured in different frames, the effects of the pre-equilibrium gluon abundance, and we derive a one-to-one mapping between dielectron and dimuon polarization.

Classical discriminant analysis (DA) is based on the mean and empirical covariance matrix of each class, both of which are sensitive to outliers in the data. In the past the focus was on casewise outliers, that is, datapoints that lie far away. But nowadays there is increasing interest in cellwise outliers, that are unexpected entries in the data matrix. Removing an entire case because it has one or a few outlying cells would lose much information. Cellwise robust methods aim to detect the outlying cells and to preserve the information in the other cells. We propose a DA method that is trained by estimating the location and covariance of each class by cellwise and casewise robust estimators, that can also handle NA's. The main novelty of our approach is in the prediction on test data, that may contain outlying cells and NA's themselves. The new robust discriminant function is derived from a novel statistical model by penalized maximum likelihood. We focus on quadratic DA, but also cover the setting of linear DA. The new cellQDA and cellLDA methods perform well in simulation. The approach is illustrated on real data, and the results are interpreted with the help of graphical displays.

The 14 Herculis system hosts two super-Jupiters on eccentric, significantly misaligned orbits. This orbital architecture represents a dynamical puzzle that demands explanation. In this work, we reproduce the system's dynamical history and current architecture using a large suite of N-body simulations of planet-planet scattering. Our results demonstrate that planet-planet scattering is able to reproduce 14 Her's peculiar orbital architecture, but only if additional massive bodies were initially present in the system that were subsequently ejected. The mass of any such ejected planet can in turn constrain the system's initial configuration. We also analyze the present-day secular evolution of the system and conclude that while there are most likely nontrivial eccentricity and inclination oscillations currently occurring, the magnitudes of these oscillations are not strong enough to allow tidal forces to meaningfully alter the system's architecture. Finally, we discuss how forthcoming observations from future Gaia data releases and the Roman mission may situate 14 Her's dynamical history within a broader, population-level framework.

We study the Dirac equation in the Reissner-Nordström geometry in horizon-penetrating coordinates up to the Cauchy horizon. A mass decomposition theorem is proved, which gives a covariant representation of the spacetime inner product that naturally involves the fermionic signature operator and the fermionic flux operator. We compute their spectra and show that both are bounded symmetric operators on the solution space $\mathcal{H}_m$ of the massive Dirac equation. The corresponding fermionic projector state is constructed and shown to satisfy the Hadamard condition. Lastly, we give some physical interpretations of the fermionic flux operator.

The manipulation of single spins through spin-polarized tunneling opens new routes for quantum control at the atomic scale. We present a theoretical framework describing spin-transfer, spin torques and spin resonance in molecular quantum dots weakly coupled to magnetic electrodes. By deriving a Lindblad master equation from microscopic tunneling processes, we capture both coherent exchange interactions and dissipative spin torque effects within a unified approach. We analyze how charge transport through localized orbitals influences spin dynamics and show that modulating the tunneling rates in time can induce electron spin resonance. This framework is further extended to coupled spin systems, revealing how spin coherence and entanglement respond to local spin torques and highlighting sources of transport-driven decoherence. Our results provide a general model to interpret spin-resolved tunneling experiments and extend classical spin torque concepts into the quantum regime.

In this work, we present a compact surrogate circuit for electro-quasi-static (EQS) head modeling. A three-shell geometry (brain, skull, scalp) is considered, and each layer is modeled through radial and tangential pathways, implemented as RC branches. Frequency-dependent tissue conductivity and permittivity are mapped into dispersive resistive and capacitive elements. The model is validated against a semi-analytical spherical-harmonics reference solution over multiple geometrical configurations and operating frequencies, demonstrating good agreement. Neglecting dispersion and capacitive pathways can lead to an overestimation of scalp potentials over the considered frequency range, highlighting the need for dispersive RC circuit modeling.

The kinetic isotope effect (KIE) is the conventional probe for quantum tunneling, yet its composite nature conflates tunneling with zero-point energy and classical kinetics. Here, we introduce the tunneling phase diagram, a machine-learning framework that decouples true tunneling strength by decoding the nonlinear relationship between KIE and the tunneling factor (\k{appa}). With exceptional fidelity (R^2 > 0.98, RMSE = 0.21), this framework reveals an anomalous high KIE-low \k{appa} spanning 300-600 K, thereby defining a paradigm for the quantitative assessment of quantum tunneling.

We provide the first example of a finitely presented, and the first example of a simple, group of non-uniform exponential growth. The example is given by Thompson's group V.

In many important statistical analyses, the number of covariates $p$ often exceeds the data size $n$, a regime commonly referred to as high-dimensional. While considerable progress has been made in high-dimensional regression under the assumption of error-free covariates, real-world data frequently involve noisy or corrupted measurements. When left unaddressed, measurement errors can silently distort the analysis and mislead the conclusions. This paper reviews and evaluates some advisable statistical inference methods for high-dimensional regression in the presence of mismeasured covariates. We discuss four penalized regression methods -- ridge, lasso, Dantzig selector, and Elastic-net -- alongside their measurement-error-corrected variants, and conduct a comparative study under linear additive and uncorrelated measurement error models. Through simulation studies and a real application to high-dimensional medical genetic data, we illustrate the methods studied, show that the choice of correction procedure is problem-specific, and provide practical recommendations to help practitioners navigate this methodological landscape.

Large language models (LLMs) are increasingly used in statistical research and applications. However,they are also notorious for unreliable or biased information. Here, we explore whether LLMs can be used to improve the precision of randomized controlled trials (RCTs) in a safe and rigorous way. Following similar work on leveraging observational data, we incorporate LLM predictions into an RCT analysis. While incorporating external predictions to improve precision is not new, the value of using LLM predictions in this manner is an open question. We develop a pipeline for best leveraging LLM predictions in this context and apply it to three different case studies. We find that these predictions can safely improve precision, particularly when the RCT lacks predictive covariates or contains covariates, such as text data, that are well-suited to LLMs.

Geo-distributed OLTP databases are widely deployed across cloud regions, yet current evaluation practices do not cover the challenges of this aspect. Existing benchmarks assume stable network conditions; they lack explicit settings for data and client locality, and they largely ignore data transfer costs across regions. In addition, most evaluations rely on a limited set of geo-distribution patterns. In this paper, we propose Gaia, a comprehensive evaluation framework that addresses these gaps. We use Gaia to perform a comprehensive evaluation of existing geo-distributed OLTP systems. We deploy them across multiple cloud regions, using different geo-distribution patterns and variable cross-region network conditions. Among other interesting findings, our framework reveals that: i) most systems are sensitive to network instabilities, ii) network costs dominate cloud deployment expenses iii) multi-region fault-tolerance mechanisms incur measurable critical-path overhead that is often overlooked in prior evaluations. We argue that for the design of future geo-distributed databases, we must rethink the trade-offs between performance, fault-tolerance, and cost.

We present a deep photonic neural network architecture based on ultrafast binary optical modulation from a digital micro-mirror device (DMD), optical scattering in random medium, high-speed photodetection with a CMOS sensor, and time-multiplexed deep layer structure. Operating at Gigabit-per-second (Gb/s) processing rates, our system based on the reservoir computing (RC) framework achieves state-of-the-art performance across various multimedia tasks, including video, image and speech recognition. We show that the careful optimization of key physical intra- and inter-layer hyper-parameters can significantly enhance the deep photonic RC system ability to extract relevant temporal and spatial features via balancing memory retention and dynamical response of individual layers. This approach paves the way for highly scalable hierarchical photonic reservoir computing systems for high-throughput real-time multimedia signal processing.

Holographic coherent X-ray imaging enables nanoscale imaging of biological cells and tissues, rendering both phase and absorption contrast, i.e. real and imaginary parts of the refractive index. Unlike the standard model, which assumes a perfectly coherent incident beam, we consider partial coherence characterized by a known covariance operator. In addition, we assume time-resolved intensity measurements, granting access not only to expected intensities but also to their correlations. We investigate the information content of these correlations and analytically demonstrate that, under a symmetry-breaking condition on the sample and the illumination area, both phase and absorption contrast can be uniquely recovered in both the full and the linearized models. A key challenge in numerical reconstruction is the substantial increase in data dimensionality caused by computing intensity correlations during preprocessing. We propose a novel approach that leverages a low-rank assumption on the incident beam covariance operator, bypassing explicit correlation computation while still exploiting its full information. Numerical experiments demonstrate its feasibility, yielding accurate simultaneous reconstructions of phase and absorption contrast.

We study black hole (BH) formation from the nonlinear growth and collapse of primordial perturbations during the matter-dominated era. Modelling cold dark matter (CDM) as pressureless dust, we describe the collapse in a fully nonlinear relativistic framework using the Lemaître-Tolman-Bondi (LTB) and quasi-spherical Szekeres solutions as exact perturbations of a spatially-flat Friedmann-Lemaître-Robertson-Walker (FLRW) $Λ$CDM background. At first order in relativistic scalar perturbation theory, the growing mode of any relevant quantity can be expressed in terms of the conserved gauge-invariant curvature perturbation $\mathcal{R}_c$, which acts as a potential for the 3-curvature of hypersurfaces orthogonal to the matter 4-velocity. We use this result to express the active gravitational mass and curvature functions of the LTB and Szekeres models in terms of the initial values of $\mathcal{R}_c$ and its spatial derivatives. From these initial curvature data we derive: (i) the turn-around, collapse, and apparent-horizon formation times, and (ii) the regularity conditions required for BH formation. We show that sinusoidal and Gaussian profiles do not provide viable BH-forming channels, whereas broad compensated curvature peaks, naturally predicted by peak theory, do. We then estimate the formation times of $10^{3}-10^{6}~\mathrm{M}_\odot$ massive BH seeds produced by the direct collapse of primordial CDM curvature peaks, finding full BH formation at redshifts $z>5$, with core collapse beginning at $10 \lesssim z \lesssim 16$. Finally, we characterize the local dynamics and singularity type of the collapse (point-like, cigar-like, or pancake-like) directly from the initial comoving curvature data, clarifying the role of the initial shear in selecting the collapse end-state.

We construct a quantum-circuit framework for finite-temperature molecular dynamics in the canonical ensemble (NVT) with a Langevin thermostat, connecting canonical state preparation to subsequent physical-property readouts. The classical nuclear phase-space distribution is encoded as a Koopman--von Neumann (KvN) wave function, and canonical state preparation is formulated as Langevin-type Fokker--Planck relaxation. The Hamiltonian Liouville flow, momentum friction, and momentum diffusion are decomposed into separate circuit blocks. The friction block is represented by a symmetrized momentum-space dilation, whereas the diffusion block is implemented as a cosine filter realized by probabilistic imaginary-time evolution (PITE). We analytically quantify the leading-order temperature bias caused by replacing the Gaussian diffusion kernel with this PITE-realized cosine filter. This analysis yields an internal-temperature correction that targets the desired physical equilibrium distribution. As a proof-of-concept demonstration connecting quantum chemistry to KvN nuclear dynamics, we study the H$_2$ molecule. Numerical simulations show relaxation from a nonequilibrium phase-space distribution to a canonical KvN state. From this canonical state, we demonstrate two complementary readouts: a dynamical quantum-phase-estimation readout of the vibrational density of states associated with the H--H stretch coordinate and a static canonical evaluation of the transition-state-theory (TST) rate constant. This work demonstrates, in a minimal molecular system, a circuit-level protocol that connects Langevin canonical state preparation to physical-property calculations, providing a concrete step toward quantum--classical hybrid molecular dynamics on quantum computers.

We formulate the Green--Kubo transport coefficients of classical molecular dynamics as a readout problem for quantum algorithms using the Koopman--von Neumann (KvN) representation. Both NVE and Nosé--Hoover-type NVT dynamics are derived as unitary evolutions on Hilbert spaces associated with the corresponding classical phase spaces. Numerical benchmarks on finite grids show that the discretization error in the correlation function decreases as a power law in the number of grid points $N_z$. Equivalently, with $N_z=2^{n_z}$, the error decreases exponentially in the register size $n_z$, so a target accuracy $ε$ requires $n_z=\mathcal{O}(\log(1/ε))$ qubits. To read out a transport coefficient, we input a flux-excited state to quantum phase estimation (QPE). The probability $P_0$ of measuring the QPE ancilla register in the all-zero state corresponds to a Bartlett-windowed Green--Kubo integral. With maximum-likelihood amplitude estimation, the statistical estimation of $P_0$ defined by this QPE oracle improves from the $N_{\rm queries}^{-1/2}$ scaling of direct shot sampling to scaling close to $N_{\rm queries}^{-1}$. Our circuit-resource analysis shows that one step of the NVE propagator can be built with $\mathcal{O}(n^2)$ CX gates, where $n=n_x+n_p$ is the total number of position and momentum qubits. For the NVT propagator, the centered-difference Pauli-decomposition implementation of the Nosé--Hoover friction term scales as $\mathcal{O}(n_ξn_p\,2^{n_p})$, where $n_p$ and $n_ξ$ are the numbers of momentum and thermostat qubits, respectively. The proposed framework is a concrete step toward translating the principles of quantum algorithms into the transport-coefficient calculations required in practical molecular simulation.

Ground-state preparation is a fundamental task in quantum simulation, because the overlap of the prepared state with the true ground state significantly affects the overall cost of subsequent quantum algorithms. We propose a three-stage framework in which a matrix product state (MPS) of an $N$-site system obtained by the density-matrix renormalization group (DMRG) is loaded onto an $N$-qubit quantum register through an optimization-free matrix product disentangler (MPD) encoding circuit, and the residual error is then reduced by probabilistic imaginary-time evolution (PITE). We demonstrate that the central-bond Schmidt rank of intermediate states during MPS encoding grows logistically with the number of layers. Its inflection point $L^{*}$ marks the boundary of the efficient encoding regime. Beyond this point, the gain in fidelity slows rapidly, and the number of additional MPD layers required to reach a target infidelity $\varepsilon$ empirically scales as $\mathcal{O}(N^5\log(N/\varepsilon))$. To avoid this encoding-only tail, we stop the encoder at $L^{*}$ and suppress the remaining excited-state components by PITE, with the linear PITE schedule fixed deterministically from the ground-state energy, the effective gap, and the reference overlap estimated by DMRG. Numerical experiments on the spin-$1/2$ staggered-field Heisenberg chain show that the framework avoids very deep encoding circuits and substantially suppresses the post-selection overhead intrinsic to PITE. Combining classical preprocessing by DMRG, optimization-free MPS encoding, and deterministically scheduled PITE, the present framework offers a practical hybrid route to ground-state preparation in quantum simulation.

We introduce Cosmo-PINN, a Physics-Informed Neural Network for reconstruction of the cosmological theory. In this work we demonstrate the application of the Cosmo-PINN in the reconstruction of the dark energy equation of state parameter $w_{DE}\left( z\right) $ directly from late-time cosmological observations. This framework overcomes the main limitation shared by Gaussian Process and Artificial Neural Network reconstruction approaches, where the recovered solution is driven by the data and it is not necessarily true that it is physically consistent, by embedding the cosmological constraints directly into the loss function as hard constraints, ensuring that the reconstructed quantities satisfy the physical laws at every point during the training. For the training of the network, we employed background data, and specifically the Baryon Acoustic Oscillation from DESI DR2, the Cosmic Chronometers and three different Supernova compilations, while we simultaneously introduce the cosmological parameters $H_{0},~Ω_{m0}$ and $r_{\mathrm{drag}}$ as trained parameters. The reconstruction shows that the trained $w_{DE}\left( z\right) $ crosses the phantom divide within the redshift range $z=0.27-0.42$ in agreement with the value obtained by the Chevallier-Polarski-Linder model. In the quintessence scenario, for large redshifts the dark energy $Ω_{DE}\left( z\right) $ provides a pressureless nonzero contribution to the cosmological fluid suggesting a unified scenario. Finally, we demonstrate the significance of imposing the physical constraints within the loss function by comparing the Cosmo-PINN reconstruction against a purely data-driven neural network with the same architecture.

Consider, on the space of marked groups, the map $\mathrm{Res}_{\mathcal{C}}$ which associates to a marked group its greatest residually-$\mathcal{C}$ quotient, for different sets $\mathcal{C}$ of groups. Except for trivial cases, this map is discontinuous. We use the Weihrauch lattice to quantify how discontinuous it is. We show that equational noetherianity of $\mathcal{C}$ and whether the set of residually-$\mathcal{C}$ groups is a quasivariety both can be characterized in terms of the position of $\mathrm{Res}_{\mathcal{C}}$ within the Weihrauch lattice. We give exact classifications of $\mathrm{Res}_{\mathcal{C}}$, for $\mathcal{C}$ one of: the set of finite groups, of nilpotent groups, of $k$-nilpotent groups, $k\ge1$, of finitely presentable groups, of LEF groups, of torsion free groups.

Ternary transition-metal nitrides with layered crystal structures host anisotropic bonding and reduced dimensionality that may enable unconventional electronic and magnetic behavior. Yet, synthesis of such nitride thin films remains challenging because reactive sputtering often favors metastable rocksalt-derived structures. We report the composition-dependent synthesis, structure, and properties of layered FeMN2 (M = W, Mo) thin films with triangular Fe sublattices, prepared by reactive sputtering and post-deposition NH3 annealing. Using synchrotron grazing-incidence wide-angle X-ray scattering (GIWAXS) and X-ray absorption spectroscopy (XAS), we investigate the evolution of phase formation, crystallographic texture, and local Fe coordination across composition. Both layered phase form over broad composition ranges. FeWN2 maintains high phase purity across compositions, consistent with cation substitution, whereas FeMoN2 only exhibits good phase purity at Fe-poor compositions. Azimuthal GIWAXS analysis shows that Fe-rich films in both systems exhibit out-of-plane fiber texture, which progressively evolves toward predominantly in-plane orientation near stoichiometry in FeWN2, while FeMoN2 develops a more randomly oriented polycrystalline microstructure. Electrical measurements reveal relatively low and composition-insensitive resistivity in FeWN2 (~1 mΩ.cm), whereas FeMoN2 exhibits a pronounced resistivity maximum near nominal stoichiometry. Preliminary room-temperature magnetization measurements on FeWN2 further reveal weak ferromagnetic-like behavior in Fe-poor films, while stoichiometric compositions remain predominantly paramagnetic. These results demonstrate fundamentally different structural accommodation mechanisms in FeWN2 and FeMoN2, and highlight the strong coupling between composition, microstructure, and electronic/magnetic properties in layered nitride thin films.

Latent position models (LPMs) are a large and popular class of models for random graphs. However, fitting Bayesian LPMs is computationally challenging - computing the likelihood even once takes time that is quadratic in the number of vertices $|V|$ of the observed graph $G = (V,E)$. Many previous papers have introduced approximate MCMC algorithms to speed this up, with the most similar to ours, Rastelli et al (2024), presenting an algorithm that has amortized running time that can be reduced almost to $O(|E|)$ and good empirical performance on reasonable inference problems. The present paper offers two algorithms for solving the same problem: a ``fast" algorithm with running time of the same almost-$O(|E|)$ order as astelli et al and much stronger accuracy guarantees, and a ``faster" algorithm with an improved running time of almost $O(|V|)$, and accuracy guarantees that are slightly improved compared to Rastelli et al (but not sufficient for all tasks). The main improvements come from the introduction of a simple auxiliary data structure that can be cheaply updated during an MCMC run; we suspect that the same ``cheap sketch" may be useful for other MCMC algorithms.

Understanding the electronic coupling and energy flow across layered two-dimensional heterostructures (HSs) is crucial to the exploitation of carrier and phonon transports as well as thermal management in next-generation optoelectronic devices. By using reflection ultrafast electron diffraction, we directly examine photoinduced out-of-plane structural dynamics of supported MoS2/WS2 bilayer HSs and their individual monolayers. Experimental evidence reveals the launch of ultrafast carrier-coupled intralayer atomic motions due to interlayer charge transfer across the van der Waals (vdW) heterojunctions that is absent for individual monolayers. Such a notable carrier-lattice correlation is in addition to the electronic coupling manifested in the enhanced optical absorption for HSs. Also, different pathways of energy flow as a result of carrier-phonon coupling and phonon scattering are reported with the corresponding characteristic times. On longer timescales, relaxation of thermalized atomic motions can be sufficiently described by a thermal transport model. A higher thermal boundary conductance (TBC) across MoS2/WS2 HSs is obtained compared to those at the monolayer-substrate interfaces; however, the similar TBC values suggest comparable couplings of phonons across vdW contacts. These results further shed light on the optical, phonon, and interfacial thermal properties of vertically-stacked vdW HSs.

Disorder-induced localization of electrons and electron-electron interaction are among the most fundamental problems in condensed matter physics. In two-dimensional electron systems, extensive studies have led to the emergence of a scaling picture, characterized by a set of universal critical exponents that govern the transitions between the integer quantum Hall plateaus. From the temperature dependence of the plateau-to-plateau transitions, experiments primarily report k ~ 0.42, implying a dynamic exponent z = 1, consistent with a theoretical picture where electrons have a long-range (1/r) interaction. Theory also predicts that z = 2 for short-range electron interaction, but an experimental verification has remained elusive. Here, we directly probe the influence of Coulomb interaction on these transitions using a bilayer electron system confined to a GaAs double quantum well device. The two layers are in close proximity, with an interlayer distance approximately equal to the magnetic length at the relevant magnetic fields. By tuning the electron density in the top layer, we access both insulating and metallic phases of the electrons in this layer as a function of magnetic field, allowing in-situ control of the unscreened and screened interaction strengths in the bottom layer as it goes through its plateau-to-plateau transitions. In the unscreened case, we measure k ~ 0.42 consistent with the widely reported value. More importantly, when screening is introduced, k is reduced to ~ 0.22, implying z = 2. Our results provide direct experimental evidence for the role of electron-electron interaction in determining critical behavior in the quantum Hall regime, and demonstrate screening as a powerful tuning parameter for engineering quantum criticality.

Noncollinear (NC) magnetism and spin-orbit coupling (SOC) are indispensable for predictive ab initio materials simulations with pronounced relativistic effects and magnetic frustration, yet they significantly increase the cost of cubic-scaling density functional theory (DFT) by introducing complex 2-component wavefunctions per electron and consequently much larger eigenproblems. We present a GPU-centric high-performance framework for NC-SOC DFT that combines: (i) algorithmic advances for solving finite-element (FE) discretized DFT equations; (ii) residual-based Chebyshev filtered subspace iteration (R-ChFSI), tolerant to inexact matrix-vector products, for the resulting sparse generalized eigenproblem; (iii) a matrix-free strategy for accelerating FE Poisson solver; (iv) R-ChFSI-enabled mixed-precision computation with block floating-point compressed MPI communication at compression ratios over 4x, preserving double-precision robustness while reducing compute and data movement costs; and (v) a communication efficient band-partitioning algorithm to improve scalability. Numerical results demonstrate improved time-to-solution and excellent scaling on exascale architectures, enabling fully relativistic pseudopotential DFT simulations of up to 100,000 electrons.

Surface electromyography (sEMG) enables continuous hand pose estimation on wearable devices, but models trained on multi-user corpora degrade on unseen individuals due to inter-user variability in anatomy and electrode placement. We propose REACT, a lightweight conditioning framework that personalizes a frozen pretrained EMG-to-pose backbone at inference time using only a handful of calibration recordings. REACT learns a compact user embedding from calibration data and applies Feature-wise Linear Modulation (FiLM) to adapt the shared encoder's feature space, requiring no gradient updates at deployment. On the large-scale EMG2POSE benchmark, REACT improves over the state-of-the-art baseline across all three generalization splits in both regression and tracking modes, reducing angular error by up to 3.9% with minimal parameter overhead and under 45 seconds of per-user calibration.