We develop the theory of difference algebraic groups in the case where we have finitely many pairwise commuting difference operators. We show that the defining ideal of a difference algebraic group is finitely generated as a difference ideal, and this result allows us to prove the existence of a dimension polynomial for any partial difference algebraic group.

We investigate the influence of realistic sheared grazing flow on acoustic propagation in three-dimensional rectangular ducts. We show that conclusions reached in the literature about the effects of sheared grazing flow on acoustic propagation in lined ducts are dependent on the flow profiles used in those studies, and that significantly different conclusions are reached once a realistic flow profile is used. We particularly focus on small ducts typical of most experimental impedance eduction facilities, for which velocity gradients are relevant in a significant fraction of the duct cross-section. We assess the effect of simplifying the velocity distribution in the cross-section to either a one-dimensional (2D spanwise-infinite duct) or uniform flow profile. Three flow profiles are considered, namely (i) the tensorised hyperbolic tangent, (ii) the law of the wall, and (iii) one obtained from a RANS simulation. These flow profiles are used as input in numerical simulations, based on the solution of the 3D Pridmore-Brown equation, to perform in silico impedance eduction experiments. Results show that realistic flow profiles can be well approximated for acoustic wave propagation in ducts by uniform or 1D flow profiles, provided the bulk Mach number is correctly accounted for, which contrasts with previous findings based on more simplistic flow profiles. The key conclusion of this work is that, if viscous effects are negligible and acoustic impedance is a good representation of a lined wall with grazing flow, then the simplification to a uniform flow is a reasonable approximation and traditional eduction methods are sufficiently accurate.

We present a data-driven framework to model the stochastic evolution of volume-price distribution from the New York Stock Exchange (NYSE) equities. The empirical distributions are sampled every 10 minutes over 976 trading days, and fitted to different models, namely Gamma, Inverse Gamma, Weibull, and Log-Normal distributions. Each of these models is parameterized by a shape parameter, $phi$, and a scale parameter, $θ$, which are detrended from their daily average behavior. The time series of the detrended parameters is analyzed using adaptive binning and regression-based extraction of the Kramers-Moyal (KM) coefficients, up to their sixth order, enabling to classification of its intrinsic dynamics. We show that (i) $ϕ$ is well described as a pure diffusion with a linear mean regression for the Gamma, Inverse Gamma, and Weibull models, while $θ$ shows dominant jump-diffusion dynamics, with an elevated fourth- and sixth-order moment contributions; (ii) the log-normal model shows however the opposite: $θ$ is predominantly diffusive, with $ϕ$ showing weak jump signatures; (iii) global moment inversion yields jump rates and amplitudes that account for a large share of total variance for $θ$, confirming that rare discontinuities dominate volatility.

We investigate a model for a mosquito-borne epidemic in which human hosts may adopt protective behaviour against vector bites in response to information on both past and current disease prevalence. Assuming that mosquitoes can also feed on non-competent hosts (i.e.\ hosts that do not contribute to disease transmission), we first revisit existing results and show that behaviour-driven protection may either decrease or increase the basic reproduction number, depending on the interaction between behavioural response, host composition, and transmission parameters. Assuming that opinion dynamics evolves on a much faster time scale than disease transmission, we then apply Geometric Singular Perturbation Theory to effectively reduce the original two-group model to a model for a homogeneous host population. The reduced system enables a detailed investigation of the impact of information-induced behavioural changes on the transient dynamics of the epidemic, including scenarios in which protective measures lead to outbreaks with low attack rates. Our analysis shows that behavioural responses may either facilitate epidemic control or prolong disease persistence, potentially generating recurrent damped epidemic waves. Numerical simulations are provided to illustrate and support the analytical findings.

Formal verification of cryptographic protocols typically relies on symbolic models that abstract away compiled code and microarchitectural side channels, leaving a gap between verified specifications and deployed executables. We present a toolchain that extracts protocol-relevant models from real binaries and analyzes them under explicit leakage contracts for constant-time and Spectre-PHT-style speculative observations. Starting from a selected binary region, we lift machine code to an intermediate representation, instrument it with leakage contracts, symbolically execute it to obtain event/observation traces, and translate these traces into Sapic for analysis with Tamarin, ProVerif, and DeepSec. As case studies, we extract models of WhatsApp Desktop's session-management and double-ratchet components from its binary and analyze forward secrecy and post-compromise security under a state-cloning compromise. For side-channel analysis, we study the Basic Access Control (BAC) protocol used in e-passports and WhatsApp's session establishment. Under our observation models, we identify an instruction-cache side channel in WhatsApp Desktop enabling social-graph inference, and we reproduce known unlinkability issues in BAC under microarchitectural observations.

The Discrete Non Linear Schrödinger (DNLS) model, due to the existence of two conserved quantities, displays an equilibrium transition between a homogeneous phase at positive absolute temperature and a localized phase at negative absolute temperature. Here, we provide a mean-field theory of DNLS through a suitable approximation of the grandcanonical partition function which makes it factorizable and can be used to describe the equilibrium state at positive temperatures as well as the metastable state at negative temperatures. By comparing our mean-field results with numerically exact ones, we show that this approximation is good-to-excellent in the whole grandcanonical phase diagram. Explicit approximate expressions for equilibrium observables are provided in the high-temperature limit. Our theory represents a clear advancement over the model that neglects the interaction between sites.

Josephson junctions are essential devices in superconducting electronics and quantum computing hardware. Here we predict electrical control of the supercurrent in composite superconductor-insulator-ferroelectric-insulator-superconductor (S-I-FE-I-S) Josephson junctions. Inversion symmetry broken by unequal dielectric barrier thicknesses and/or potentials converts ferroelectric polarization reversal into a substantial change of the critical current. With a WKB tunneling model we obtain non-volatile switching of the critical current with on-off efficiency up to 0.9 for physically realistic parameters. This can be achieved by optimizing the thicknesses and potential barriers of the insulating layers, as well as the thickness and dielectric constant of the ferroelectric layer. We also derive a compact linear expression for the critical current valid for small polarizations. Our results identify ferroelectric Josephson junctions as electrically programmable superconducting current switches for cryogenic memory and logic applications.

We demonstrate that a machine learning framework based on kernel ridge regression can encode and predict the self-energy of one-dimensional Hubbard models using only mean-field features such as static and dynamic Hartree-Fock quantities and first-order GW calculations. This approach is applicable across a wide range of on-site Coulomb interaction strengths $U/t$, ranging from weakly interacting systems ($U/t \ll 1$) to strong correlations ($U/t > 8$). The predicted self-energy is transformed via Dyson's equation and analytic continuation to obtain the real-frequency Green's function, which allows access to the spectral function and density of states. This method can be used for nearest-neighbor interactions $t$ and long-range hopping terms $t'$, $t''$, and $t'''$.

Since the launch of JWST, observations of exoplanetary atmospheres have seen a revolution in data quality. Given that atmospheric parameter inferences depend heavily on the underlying data, a re-evaluation of current methodologies is warranted to assess the reliability of these results. We investigate the impact of variations in input spectra on atmospheric retrievals for the hot Jupiter WASP-39 b using JWST transit data. Specifically, we analyse the reliability of parameter estimations from random perturbations of the underlying spectrum and their sensitivity to three transmission spectra derived from the same observational data. Using the NIRSpec PRISM observation from a single transit of WASP-39 b, we perform retrievals with the TauREx framework. As a baseline, we use a spectrum derived with the Eureka! data reduction pipeline. To evaluate retrieval reliability, we analyse posterior distributions under deviations from this spectrum. We simulate random noise by performing retrievals on scattered instances of this spectrum and compare them with retrievals based on existing spectra reduced from the same raw observation. Our analysis identifies three types of posterior distributions: (1) Stable, Gaussian distributions for species constrained across the entire spectrum (e.g., H2O, CO2); (2) Uniform posteriors with upper bounds for weakly constrained species (e.g., CO, CH4); and (3) Unstable, heavy-tailed posteriors for species constrained by minor spectrum features (e.g., SO2, C2H2). We find that other parameters, such as the planetary radius and p-T profile, are stable under spectral perturbations. Posterior distributions differ for retrievals on independently reduced transmission spectra from the same raw data, complicating interpretation, particularly for skewed distributions. Based on this, we advocate for careful assessment and selection of credible interval sizes to reflect this.

We consider 2D fermions on a plane with a perpendicular magnetic field, described by Landau levels. It is wellknown that, semiclassically, restriction to the lowest Landau levels (LLL) implies two constraints on a 4D phase space, that transforms the 2D coordinate space (x,y) into a 2D phase space, thanks to the non-zero Dirac bracket between x and y. A naive application of Dirac's prescription of quantizing LLL in terms of L2 functions of x (or of y) fails because the wavefunctions are functions of x and y. We are able, however, to construct a 1D QM, sitting differently inside the 2D QM, which describes the LLL physics. The construction includes an exact 1D-2D correspondence between the fermion density ρ(x,y) and the Wigner distribution of the 1D QM. In a suitable large N limit, (a) the Wigner distribution is upper bounded by 1, since a phase space cell can have at most one fermion (Pauli exclusion principle) and (b) the 1D-2D correspondence becomes an identity transformation. (a) and (b) imply an upper bound for the fermion density ρ(x,y). We also explore the entanglement entropy (EE) of subregions of the 2D noncommutative space. It behaves differently from conventional 2D systems as well as conventional 1D systems, falling somewhere between the two. The main new feature of the EE, directly attributable to the noncommutative space, is the absence of a logarithmic dependence on the size of the entangling region, even though there is a Fermi surface. In this paper, instead of working directly with the Landau problem, we consider a more general problem, of 2D fermions in a rotating harmonic trap, which reduces to the Landau problem in a special limit. Among other consequences of the emergent 1D physics, we find that post-quench dynamics of the (generalized) LLL system is computed more simply in 1D terms, which is described by well-developed methods of 2D phase space hydrodynamics.

We develop a systematic framework for computing symmetry-resolved entanglement entropies (SREE) in charged quantum systems based on an improved heat kernel approach. Although the conventional Sommerfeld formula proves effective for neutral systems, it encounters limitations when gauge fields or chemical potentials are introduced due to incomplete residue prescriptions and violations of asymptotic boundary conditions. By reconstructing the analytic structure of the heat kernel using a phase factor, we derive a globally convergent expansion that reconciles discrete residue summations with continuous spectral decompositions. We further apply this framework to Gaussian continuous multi-scale entanglement renormalization ansatz (cMERA) states and show that the entanglement entropy (EE) can be expressed in terms of the cMERA flow functions. In particular, we obtain a symmetry-resolved entanglement entropy flow equation in the presence of a chemical potential. This formulation extends naturally to arbitrary spacetime dimensions and recovers established results for neutral systems in the mu -> 0 limit. We validate our framework through two settings: (1) exact agreement with (1+1)-dimensional conformal field theory (CFT) predictions using twist-operator techniques, and (2) consistency with holographic entropy calculations on S1 x H^(d-1) geometries. Our results both unify the treatment of charged and neutral entanglement entropy and extend this treatment to real-space renormalization frameworks, providing a robust tool for probing symmetry-resolved entanglement in conformal field theories, their holographic duals, and cMERA representations.

Quantum resource theories use distillation protocols to convert less resourceful states into fully resourceful ones. However, these protocols often also generate an additional, unused output-referred to as a residual. We propose a framework for the quantum residual management, in which states discarded after a resource distillation protocol are repurposed as inputs for subsequent quantum information tasks. This approach extends conventional quantum resource theories by incorporating secondary resource extraction from residual states, thereby enhancing overall resource utility. As a concrete example, we investigate the distillation of private randomness from the residual states remaining after quantum key distribution (QKD). More specifically, we quantitatively show that after performing a well-known coherent Devetak-Winter protocol, one can locally extract private randomness from its residual. We further consider the Gottesman-Lo QKD protocol and provide the achievable rate of private randomness from the discarded states that are left after its performance. We also provide a formal framework that highlights a general principle for improving quantum resource utilization across sequential information processing tasks.

Unlike classical correlations, entanglement cannot be freely shared among multiple parties. This unique feature of quantum systems is known as the monogamy of entanglement. While it holds for all multipartite pure states, its converse -- weak entanglement between two parties enforces strong entanglement with a third party -- occurs only under specific conditions. In particular, Hayashi and Chen [Phys. Rev. A \textbf{84}, 012325 (2011)] demonstrated a qualitative version of the converse monogamy of entanglement (CMoE) for tripartite pure states by employing a hierarchy of bipartite entanglement defined through the relations among various separability criteria, and Singh and Datta [IEEE Trans. Inf. Theory \textbf{69}, 6564 (2023)] later extended this notion of the CMoE from the viewpoint of distillability under one-way or two-way classical communication. In this work, we extend their results to the CMoE with broader conditions, and furthermore show that our extensions are maximal with respect to the hierarchies they considered.

Numerous mitigation methods exist for quantum noise suppression, making it challenging to identify the optimum approach for a specific application; especially as ongoing advances in hardware tuning and error correction are expected to reduce logical error rates. In order to facilitate the future-proof application-dependent comparison of mitigation methods, we develop a set of quantitative metrics that account for continual improvements in logical gate quality. We use these metrics to define qualitative criteria (e.g. scalability, efficiency, and robustness to characterised imperfections in the mitigation implementation), which we combine into application-specific certifications. We then provide a taxonomy of linear mitigation methods, characterising them by their features and requirements. Finally, we use our framework to produce and evaluate a mitigation strategy. A mitigation strategy is a collections of mitigation methods and compilation procedures designed to mitigate all relevant errors for a given piece of characterised hardware. Our example mitigation strategy is targeted at mitigating the outputs of hardware suffering from stochastic noise and/or rotational errors. We find the most significant determinant of efficient mitigation is accurate and precise characterisation.

We introduce a model-independent framework to reinterpret Belle II results using only public data, analytically reconstructing the mapping between true and reconstructed kinematic variables within the statistically dominant Inclusive Tagging Analysis. This enables rare-decay measurements to probe light invisible particles -- such as the QCD axion or axion-like particles, collectively denoted $a$ -- without relying on internal simulations. Applying the method to $B^+ \! \to \! K^+ ν\barν$ yields the strongest bound on the branching fraction for $B^+ \! \to \! K^+ a$, improving existing limits by about a factor of nine and constraining the axion's fundamental flavour-changing coupling to $b$ and $s$ quarks. The approach establishes $B^+ \! \to \! K^+ ν\barν$ as a dual probe -- simultaneously testing short-distance new physics and light invisible states, the two probes working independently to an excellent approximation -- and provides a general strategy for model-independent reinterpretation of collider data.

Inter symbol interference (ISI), which occurs in a wide variety of channels, is a result of time dispersion. It can be mitigated by equalization, which results in noise coloring. Inspired by the development of Approximate Independence in statistical physics, for such colored noise we propose a decoder called Ordered Reliability Bits Guessing Random Additive Noise Decoding (ORBGRAND-AI) that operates without the need for turbo equalization or interleaving. By foregoing interleaving, ORBGRAND-AI can deliver the same, or lower, block error rate (BLER) for the same amount of energy per information bit in an ISI channel as a state-of-the-art soft input decoder, such as Cyclic Redundancy Check Assisted-Successive Cancellation List (CA-SCL) decoding, with an interleaver. To assess the decoding performance of ORBGRAND-AI, we consider delay tap models and their associated colored noise. In particular, we examine a two-tap dicode ISI channel as well as an ISI channel derived from data from RFView, a physics-informed modeling and simulation tool. We investigate the dicode and RFView channel under a variety of imperfect channel state information assumptions and show that a second order autoregressive model adequately represents the RFView channel effect.

A $3$-uniform hypergraph (or $3$-graph) $H=(V,E)$ is $(d,μ,1)$-\emph{dense} if for any subsets $X,Y,Z\subseteq V$, the number of triples $(x,y,z)\in X\times Y\times Z$ such that $\{x,y,z\}$ is an edge of $H$ is at least $d|X||Y||Z|-μ|V|^3$. The \emph{$k$-star} $S_k$ is the $3$-graph with a center vertex and $k$ distinct leaf vertices, whose edge set consists of all triples containing the center and two distinct leaves. Restricting to $dot$-dense $3$-graphs, determining the \emph{$1$-uniform Turán density} $π_1(S_k)$ of $S_k$ for $k\ge 4$ was proposed by Schacht in ICM 2022. In particular, Reiher, Rödl and Schacht gave a palette construction showing that $π_1(S_k)\ge \frac{k^2-5k+7}{(k-1)^2}$ for $k\ge 3$, and also proved that $π_1(S_3)=1/4$. Lamaison and Wu later showed that this palette construction is optimal for $k\ge 48$. In this paper, we improve the results of Lamaison and Wu by proving that \[ π_1(S_k)=\frac{k^2-5k+7}{(k-1)^2} \qquad\text{for all } k\ge 9. \]

An account of 2-factors in graphs and their history is presented. We give a direct graph-theoretic proof of the 2-Factor Theorem and a new variant of it, and also a new complete characterisation of the maximal graphs without 2-factors. This is based on the important works of Tibor Gallai on 1-factors and of Hans-Boris Belck on k-factors, both published in 1950 and independently containing the theory of alternating chains. We also present an easy proof that a $(2k+1)$-regular graph with at most $2k$ leaves has a 2-factor, and we describe all connected $(2k+1)$-regular graphs with exactly $2k+1$ leaves without a 2-factor. This generalises Julius Petersen's famous theorem, that any 3-regular graph with at most two leaves has a 1-factor, and it generalises the extremal graphs Sylvester discovered for that theorem.

Decentralized Autonomous Organizations (DAOs) aim to enable participatory governance, but in practice face challenges of voter apathy, concentration of voting power, and misaligned delegation. Existing delegation mechanisms often reinforce visibility biases, where a small set of highly ranked delegates accumulate disproportionate influence regardless of their alignment with the broader community. In this paper, we conduct an empirical study of delegation in DAO governance off-chain discussions from 14 DAO forums. We develop a methodology to link forum participants to on-chain addresses, extract governance interests using large language models, and compare these interests against delegates' historical behavior. Our analysis reveals that delegations are frequently misaligned with token holders' expressed priorities and that current ranking-based interfaces exacerbate power concentration. We argue that incorporating interest alignment into delegation processes could mitigate these imbalances and improve the representativeness of DAO decision-making. To support future research, we will release our dataset and code in a public repository.

Existing AI agents typically execute multi-step tasks autonomously and only allow user confirmation at the end. During execution, users have little control, making the confirm-at-end approach brittle: a single error can cascade and force a complete restart. Confirming every step avoids such failures, but imposes tedious overhead. Balancing excessive interruptions against costly rollbacks remains an open challenge. We address this problem by modeling confirmation as a minimum time scheduling problem. We conducted a formative study with eight participants, which revealed a recurring Confirmation-Diagnosis-Correction-Redo (CDCR) pattern in how users monitor errors. Based on this pattern, we developed a decision-theoretic model to determine time-efficient confirmation point placement. We then evaluated our approach using a within-subjects study where 48 participants monitored AI agents and repaired their mistakes while executing tasks. Results show that 81 percent of participants preferred our intermediate confirmation approach over the confirm-at-end approach used by existing systems, and task completion time was reduced by 13.54 percent.

Simulating the kinetic Langevin dynamics is a popular approach for sampling from distributions, where only their unnormalized densities are available. Various discretizations of the kinetic Langevin dynamics have been considered, where the resulting algorithm is collectively referred to as the kinetic Langevin Monte Carlo (KLMC) or underdamped Langevin Monte Carlo. Specifically, the stochastic exponential Euler discretization, or exponential integrator for short, has previously been studied under strongly log-concave and log-Lipschitz smooth potentials via the synchronous Wasserstein coupling strategy. Existing analyses, however, impose restrictions on the parameters that do not explain the behavior of KLMC under various choices of parameters. In particular, all known results fail to hold in the overdamped regime, suggesting that the exponential integrator degenerates in the overdamped limit. In this work, we revisit the synchronous Wasserstein coupling analysis of KLMC with the exponential integrator. Our refined analysis results in Wasserstein contractions and bounds on the asymptotic bias that hold under weaker restrictions on the parameters, which assert that the exponential integrator is capable of stably simulating the kinetic Langevin dynamics in the overdamped regime, as long as proper time acceleration is applied.

We study the geometry and topology of real analytic maps $\mathbb{C}^n \to \mathbb{C}^k$, where $n > k$, regarded as mixed maps, defined below. Firstly, we give two natural families of mixed isolated complete intersection singularities, called mixed ICIS, which are interesting on their own. We consider the notion of (partial) non-degeneracy for mixed maps; we prove that these define mixed ICIS and that, under suitable conditions, admit a local Milnor fibration. Then, building on previous constructions due to Oka, we obtain natural contact structures and adapted open books on a particular class of mixed links. Finally, we look at mixed links that are diffeomorphic to holomorphic ones, and we address the problem of comparing different contact structures.

This paper accompanies Data Release 4 of the JWST Deep Extragalactic Survey (JADES), which presents the full NIRSpec spectroscopy of the survey. We provide spectra of 5190 targets across GOODS-North and GOODS-South (including the Hubble Ultra Deep Field), observed with the low-dispersion (R $\sim$ 30-300) prism and three medium-resolution (R $\sim$ 1000) gratings spanning 0.8 $< λ<$ 5.5 microns; 2654 were also observed with the higher-resolution (R $\sim$ 2700) G395H grating. The tiered survey design obtained more than 20 hr exposures for $\sim$ 700 galaxies in the Deep and Ultra Deep tiers, and shallower observations ($\sim$ 1-3 hr per setting) of $>$ 4400 galaxies in the Medium tiers. Targets were selected from photometric redshifts or colours, with priority given to rest-UV-selected galaxies at $z > 5.7$ and F444W-selected galaxies at $1.5 < z < 5.7$. We describe the full target selection and present spectroscopic redshifts and success rates. In total we obtain robust redshifts for 3297 galaxies, including 396 at $z > 5.7$ and 2545 at $1.5 < z < 5.7$. To facilitate uniform analyses, we define 'gold' sub-samples based on UV- and F444W-selection. Using the parent samples and redshift success rates, we construct rest-UV luminosity functions at $6 \lesssim z \lesssim 9$ from the Medium- and Deep-JWST tiers. Our number densities agree well with previous determinations from both photometric and spectroscopic samples, with modest interloper fractions confirming the reliability of photometric UV-bright galaxy selections at these redshifts.

The rise of distributed energy resources (DERs) is reshaping modern distribution grids, introducing new challenges in attaining voltage stability under dynamic and decentralized operating conditions. This paper presents NEO-Grid, a unified learning-based framework for volt-var optimization (VVO) and volt-var control (VVC) that leverages neural network surrogates for power flow and deep equilibrium models (DEQs) for closed-loop control. Our method replaces traditional linear approximations with piecewise-linear ReLU networks trained to capture the nonlinear relationship between power injections and voltage magnitudes. For control, we model the recursive interaction between voltage and inverter response using DEQs, allowing direct fixed-point computation and efficient training via implicit differentiation. We evaluated NEO-Grid on the IEEE 33-bus system, demonstrating that it significantly improves voltage regulation performance compared to standard linear and heuristic baselines in both optimization and control settings. Our results establish NEO-Grid as a scalable, accurate, and interpretable solution for learning-based voltage regulation in distribution grids.

We uncover a new class of phenomena in gravitational physics, whereby resonances in the complex plane can be excited via tailored time-dependent scattering. We show that specific forms of temporal modulation of an incoming signal can lead to complete absorption for the entire duration of the scattering process. This, then, makes stars and black holes truly black. Such ``virtual absorption'' stores energy with high efficiency, releasing it once the process finishes via relaxation into the characteristic virtual absorption modes -- also known as total transmission modes -- of the object. While such modes are challenging to obtain and four-dimensional black holes have a restricted set of solutions, we also show that higher dimensional black holes have a complex and interesting structure of virtual absorption modes.

We report evidence for high temperature superconductivity in three dimensional networks of boron doped, ultrathin carbon nanotubes (CNTs) grown inside the ~5 Angstrom channels of ZSM-5 zeolite. Confinement stabilizes (2,1) CNTs that are otherwise dynamically unstable, while boron doping shifts the Fermi level toward a van Hove singularity, as supported by ab-initio calculations. The resulting CNT network exhibits multiple, mutually consistent signatures of superconductivity at ambient pressure. DC magnetization and AC susceptibility measurements reveal the onset of a Meissner response between 220 and 250 K, with compacted samples achieving up to 93% of full diamagnetic screening. Electrical transport shows a sharp resistive transition with extrapolated Tc about 239 K and vanishing resistance in optimized samples. Specific heat measurements display a reproducible anomaly at 233 to 236 K that broadens under magnetic field, consistent with strong superconducting fluctuations. Point contact spectroscopy identifies three superconducting gaps, including a leading gap of approximately 30 meV whose temperature dependence follows BCS expectations for Tc of about 224 K, and exhibits particle-hole symmetry and Andreev reflection. Remarkably, applying pressures below 0.1 kbar enhances Tc by nearly 100 K and modulates the room temperature resistance by more than three orders of magnitude, suggesting a pressure driven 1D to 3D crossover in the CNT network. These results identify boron doped ultrathin CNT networks as a promising carbon-based platform for near ambient temperature superconductivity and reveal an unusually large pressure sensitivity with potential technological relevance.

This paper addresses two interrelated problems of the nonlinear filtering mechanism and fast attitude filtering with the matrix Fisher distribution (MFD) on the special orthogonal group. By analyzing the distribution evolution along Bayes' rule, we reveal two essential properties that enhance the performance of Bayesian attitude filters with MFDs, particularly in challenging conditions. Benefiting from the new understanding of the filtering mechanism associated with MFDs, two closed-form filters with MFDs are then proposed. These filters avoid the burdensome computations in previous MFD-based filters by introducing linearized error systems with right-invariant errors but retaining the two advantageous properties. The proposed filter with right-invariant error is proven to be almost globally asymptotically stable for any trajectory on $SO(3)$ leveraging its closed-form iteration and global uncertainty representation with MFDs. Moreover, we further prove the local exponential stability of the filter for single-axis rotations to reveal the effect of the two properties on the convergence rate. These stability results support the performance of the proposed filter with large initial error from a theoretical viewpoint, which to our knowledge, is not achieved by existing directional statistics-based filters. Numerical simulations demonstrate that proposed filters are as accurate as recent MFD-based Bayesian filters in challenging circumstances but consume far less computation time (about 1/5 to 1/100 of previous MFD-based attitude filters).

The angular distributions of Drell-Yan lepton pairs provide sensitive probes of the underlying dynamics of quantum chromodynamics (QCD) effects in vector-boson production. This paper presents for the first time the measurement of the full set of angular coefficients together with the differential cross-section as a function of the transverse momentum of the $W$ boson, in the full phase space of the decay leptons. The measurements are performed separately for the $W^-$ and $W^+$ channels. The analysis uses proton-proton collision data recorded by the ATLAS experiment at the Large Hadron Collider in 2017 and 2018, during special low-luminosity runs with a reduced number of interactions per bunch crossings (pile-up). The data correspond to an integrated luminosity of 338 pb$^{-1}$ at a centre-of-mass energy of $\sqrt{s}=$ 13 TeV. The low pile-up environment provides excellent experimental conditions for high-precision measurements of $W$-boson production. All results agree with theoretical predictions incorporating finite-order QCD corrections up to order $α_S^2$.

Spin ensembles are central to quantum science, from frequency standards and fundamental physics searches to magnetic resonance spectroscopy and quantum sensing. Their performance is ultimately constrained by spin projection noise, yet solid-state implementations have so far been limited by much larger photon shot noise. Here we demonstrate a direct, quantum non-demolition readout of a mesoscopic ensemble of nitrogen-vacancy (NV) centers in diamond that surpasses the photon shot-noise limit and approaches the intrinsic spin projection noise. By stabilizing the $^{14}$N nuclear spin bath at high magnetic fields and employing repetitive nuclear-assisted spin readout, we achieve a noise reduction of 3.8 dB below the thermal projection noise level. This enables direct access to the intrinsic fluctuations of the spin ensemble, allowing us to directly observe the signatures of correlated spin states. Our results establish projection noise-limited readout as a practical tool for solid-state quantum sensors, opening pathways to quantum-enhanced metrology, direct detection of many-body correlations, and the implementation of spin squeezing in mesoscopic solid-state ensembles.

Crafting neural-network interatomic potentials (NNIPs) remains a complex task, demanding specialized expertise in both machine learning and electronic-structure calculations. Here, we introduce AiiDA-TrainsPot, an automated, open-source, and user-friendly workflow that streamlines the creation of accurate NNIPs by orchestrating density-functional-theory calculations, data augmentation strategies, and classical molecular dynamics. Our active-learning strategy leverages on-the-fly calibration of committee disagreement against ab initio reference errors to ensure reliable uncertainty estimates. We use electronic-structure descriptors and dimensionality reduction to analyze the efficiency of this calibrated criterion, and show that it minimizes both false positives and false negatives when deciding what to compute from first principles. AiiDA-TrainsPot has a modular design that supports multiple NNIP backends, enabling both the training of NNIPs from scratch and the fine-tuning of foundation models. We demonstrate its capabilities through automated training campaigns targeting pristine and defective carbon allotropes, including amorphous carbon, as well as structural phase transitions in monolayer $\mathrm{W_xMo_{1-x}Te_2}$ alloys.