Transport measurements of hybrid nanowires often rely on the observation of a zero-bias conductance peak as a hallmark of Majorana bound states (MBSs). However, such signatures can also be produced by trivial zero-energy Andreev bound states (ABSs) or by quasi-Majorana bound states (QMBSs), complicating their unambiguous identification. Here we propose microwave absorption visibility, extracted from parity-dependent cavity-nanowire susceptibility measurements, as a complementary probe of MBSs nonlocality. We study a Rashba spin-orbit nanowire consisting of a proximitized superconducting segment and an uncovered quantum-dot region, capacitively coupled to a single-mode microwave cavity. We show that true MBSs yield finite visibility only when both MBSs are simultaneously coupled to the cavity, reflecting their intrinsic nonlocality. In contrast, ABSs and QMBSs exhibit visibility extrema even when the cavity couples only locally to part of the nanowire. We further demonstrate that this distinction persists in the presence of Gaussian disorder, which may otherwise generate trivial subgap states. Motivated by recent experiments, we also analyze ``poor man's" Majoranas in double-quantum-dot setups, where analytical results confirm the same nonlocal visibility criterion. Finally, we discuss a cavity-driven scheme for initializing the electronic system in a given parity state. Our results establish cavity-based visibility as a robust and versatile probe of MBSs, providing a clear route to distinguish them from trivial zero-energy states in hybrid superconducting platforms.

The recent advent of a new class of magnetic material named as {\it altermagnet} (AM), characterized by a combination of momentum-dependent spin splitting with zero net magnetization, has opened up promising prospects for spintronic applications. We theoretically explore how the altermagnetic spin splitting affects the thermoelectric quasiparticle current in AM-based superconducting heterostructures. Our setup comprises of a bilayer system where a $d$-wave AM is proximity coupled to an ordinary $s$-wave superconductor (SC). We calculate the thermoelectric current carried by the quasiparticles applying a finite thermal bias across the junction. The behavior of the thermoelectric current with the system's base temperature and chemical potential is very similar to that in traditional SC heterostructures. Remarkably, the dissipative thermoelectric current found in the AM junction is spin split and thus generates finite spin polarization in the AM-based junction, which can approach $100\%$ spin polarization in the strong altermagnetic phase. We further investigate the thermoelectric current in AM-based Josephson junction (JJ) and illustrate how to achieve almost perfect diode effect in this AM-based JJ characterized by its efficiency $\sim 100\%$ with its sign decided by the strength of the AM, enhancing the potential for spin-caloritronics applications.

A pivotal task for quantum computing is to speed up solving problems that are both classically intractable and practically valuable. Among these, combinatorial optimization problems have attracted tremendous attention due to their broad applicability and natural fitness to Ising Hamiltonians. Here we propose a quantum sampling strategy, based on which we design an algorithm for accelerating solving the ground states of Ising model, a class of NP-hard problems in combinatorial optimization. The algorithm employs a hybrid quantum-classical workflow, with a shallow-circuit quantum sampling subroutine dedicated to navigating the energy landscape. Using up to 104 superconducting qubits, we demonstrate that this algorithm outputs favorable solutions against even a highly-optimized classical simulated annealing (SA) algorithm. Furthermore, we illustrate the path toward quantum speedup based on the time-to-solution metric against SA running on a single-core CPU with just 100 qubits. Our results indicate a promising alternative to classical heuristics for combinatorial optimization, a paradigm where quantum advantage might become possible on near-term superconducting quantum processors with thousands of qubits and without the assistance of error correction.

Intercurrent events, such as treatment switching, rescue medication, dropout, or truncation by death, frequently complicate intention-to-treat analyses in randomized clinical trials. Existing causal inference frameworks typically target hypothetical or principal stratum estimands (e.g., survivor average causal effects), which rely on unverifiable assumptions and can be sensitive to unmeasured confounders or positivity violations. We propose a novel approach that mitigates this sensitivity by using only information measured prior to the intercurrent event. Our key idea is to compare treated and untreated individuals, matched on baseline covariates, at the most recent time point before either experiences an intercurrent event. We call these contrasts Pairwise Last Observation Time (PLOT) estimands. PLOT estimands are identified in randomized trials without structural assumptions, even under severe positivity violations. Although PLOT-based tests may theoretically be susceptible to residual selection bias, we show this bias vanishes under standard conditions and remains negligible in extensive simulations. We develop asymptotically efficient, model-free tests and treatment effect estimators using data-adaptive nuisance parameter estimation. We evaluate performance via simulation and apply the method to re-analyze the DEVOTE trial, affected by truncation by death. PLOT offers a robust, data-driven alternative for evaluating treatment efficacy in the presence of complex intercurrent events.

We present the preparation and measurement of the radioactive isotope $\rm ^{37}Ar$, which was produced using thermal neutrons from a reactor, as a calibration source for liquid xenon time projection chambers. $\rm ^{37}Ar$ is a low-energy calibration source with a half-life of 35.01 days, making it suitable for calibration in the low-energy region of liquid xenon dark-matter experiments. Radioactive isotope $\rm ^{37}Ar$ was produced by irradiating $\rm ^{36}Ar$ with thermal neutrons. It was subsequently measured in a gaseous xenon time projection chamber (GXe TPC) to validate its radioactivity. Our results demonstrate that $\rm ^{37}Ar$ is an effective and viable calibration source that offers precise calibration capabilities in the low-energy domain of xenon-based detectors.

Scalar leptoquarks (sLQs) appear in a wide range of ultraviolet-motivated extensions of the Standard Model and provide a natural link between the quark and lepton sectors. In this work, we investigate the discovery potential of an sLQ doublet $\widetilde{R}_2(\mathbf{3},\mathbf{2},1/6)$ that couples to light quarks and right-handed neutrinos (RHNs) at the proposed muon collider. We analyze both indirect probes arising from $t$-channel sLQ exchange that affects the high-$p_T$ behavior of dijet spectra and direct searches exploiting pair and single production of the sLQs, incorporating the full interplay of kinematic thresholds and decay topologies. We find that indirect probes at muon colliders deliver remarkably robust sensitivity to the sLQ-quark-muon coupling over a broad mass range. Assuming a sub-$\mathcal{O}(1)$ Yukawa coupling, we achieve a $5σ$ sensitivity up to sLQ masses $\sim 4.0$ TeV ($7.0$ TeV) at $\sqrt{s}=5$ (10) TeV center-of-mass energy with $\mathcal{L}=3~\mathrm{ab}^{-1}$ ($10~\mathrm{ab}^{-1}$) integrated luminosity. Direct production channels provide complementary reach: pair production dominates below threshold, while single production, driven by the sLQ-quark-muon/RHN interaction, decisively extends the mass reach well into the multi-TeV regime. We demonstrate that with $\mathcal{O}(1)$ Yukawa couplings, the single production channel can probe sLQ masses up to $3.0$ TeV ($6.0$ TeV) for $\sqrt{s}=5$ TeV ($10$ TeV). Together, these channels enable a unified exploration of parameter space far beyond the projected capabilities of the HL-LHC, including regions where conventional charged-lepton signatures are subdominant.

We describe in detail a mathematical framework in which statistical ensembles of hybrid classical-quantum systems can be properly described. We show how a maximum entropy principle can be applied to derive the microcanonical ensemble of hybrid systems. We investigate its properties, and in particular how the microcanonical ensemble and its marginal classical and quantum ensembles can be defined for arbitrarily small range of energies for the whole system. We show how, in this situations, the ensembles are well defined for a continuum of energy values, unlike the purely quantum microcanonical ensemble, thus proving that hybrid systems translate properties of classical systems to the quantum realm. We also analyze the relation with the hybrid canonical ensemble by considering the microcanonical ensemble of a compound system composed of a hybrid subsystem weakly coupled to a reservoir and computing the marginal ensemble of the hybrid subsystem. Lastly, we apply the theory to the statistics of a toy model, which gives some insight on the different properties presented along the article.

We present a realistic implementation of a quantum engine powered by a phaseonium gas of coherently prepared three-level atoms -- where quantum coherence acts as a thermodynamic resource. Using a collision model framework for phaseonium-cavity interactions and cavity optomechanics, we construct a full engine cycle based on two non-thermal reservoirs, each characterized by coherence-induced effective temperatures. This configuration enhances the efficiency of a simple optomechanical engine operating beyond standard thermal paradigms. We further address scalability by coupling a second cavity in cascade configuration, where the same phaseonium gas drives both cycles. Our results demonstrate the operational viability of coherence-driven quantum engines and their potential for future thermodynamic applications.

We study the quantum invariants of projective varieties over the number fields. Namely, explicit formulas for a functor $\mathscr{Q}$ on such varieties are proved. The case of abelian varieties with complex multiplication is treated in detail.

We study how dark matter environments influence nonlinear gravitational memory from intermediate-mass-ratio binaries. Incorporating environmental effects from the dark matter gravitational potential, dynamical friction, and accretion, we compute the leading-order nonlinear memory for both bound and unbound orbits under dark matter minispikes and Navarro-Frenk-White haloes. For quasi-circular inspirals in a minispike, we additionally include an empirical prescription for the time-dependent evolution of the dark matter profile, which gradually evolves along the inspiral and captures the cumulative environmental response. We find that dark matter can modify the time evolution and mode content of the memory relative to the vacuum case, with the cumulative effect depending sensitively on the density profile and on how the environment accelerates the inspiral. We use these waveforms to assess signal-to-noise ratios and mismatches in representative space-based detector configurations, highlighting where memory-driven differences may be large enough to warrant targeted parameter-estimation studies. Our results emphasize that astrophysical environments can leave a hereditary imprint on gravitational memory and provide a framework for connecting memory observables with dark matter dynamics.

Optimization-based solvers play a central role in a wide range of signal processing and communication tasks. However, their applicability in latency-sensitive systems is limited by the sequential nature of iterative methods and the high computational cost per iteration. While deep unfolding has emerged as a powerful paradigm for converting iterative algorithms into learned models that operate with a fixed number of iterations, it does not inherently address the cost of each iteration. In this paper, we introduce a learned optimization framework that jointly tackles iteration count and per-iteration complexity. Our approach is based on unfolding a fixed number of optimization steps, replacing selected iterations with low-complexity approximated computations, and learning extended hyperparameters from data to compensate for the introduced approximations. We demonstrate the effectiveness of our method on two representative problems: (i) hybrid beamforming; and (ii) robust principal component analysis. These fundamental case studies show that our learned approximated optimizers can achieve state-of-the-art performance while reducing computational complexity by over three orders of magnitude. Our results highlight the potential of our approach to enable rapid, interpretable, and efficient decision-making in real-time systems.

Two-dimensional (2D) layered magnetic materials (LMMs) are a newly emerging class of van der Waals materials, opening new opportunities to study magneto-excitonic coupling. The air-stable, structurally and optically anisotropic A-type antiferromagnetic chromium sulfur bromide (CrSBr) is one of the most prominent examples of such LMMs. We investigate photoluminescence (PL) and PL excitation of mono- to tri-layers CrSBr and find that it exhibits a unique duplexity, supporting both Frenkel- and Wannier-Mott-like excitons. Our magneto-optical experiments reveal a similar excitonic response from the mono- and trilayer systems and a completely different signature in the bilayer flake. This shows a different origin of the low-lying excitonic species (A, A', and B) in the band structure. We confirm the robustness of the magneto-excitonic coupling in few-layer CrSBr. Our work enables a more comprehensive exploration of the dual excitonic behavior in 2D materials.

In order to understand solar atmospheric heating it is important to test heating models against spatially resolved data from solar active regions. Here we model a small active region, AR~12760 observed on 2020 April 28, with the GX Simulator package by fitting the EUV intensities in wavebands observed by Solar Dynamics Observatory's Atmospheric Imaging Assembly. We assume the temporally and spatially averaged heating rate along a loop has a power-law dependence on loop length, $L$ and average magnetic field strength along the loop, $B_{avg}$. We find that the best fit heating model for the 211~Å band is $<\!\!Q\!\!>\approx 7\times 10^{-3} (B_{avg}/{100 \mbox{G}})^{1.5}(L/{10^9\mbox{cm}})^{-1}$ erg cm$^{-3}$ s$^{-1}$ but that there is a range of parameters that give qualitatively reasonable fits, which we conclude is due to a correlation between $B_{avg}$ and $L$. In addition, we find that the models of the bands including cooler emission (131 and 171~Å) greatly underestimate the extent of the emission in the legs of the longer loops at the peripheries of the active region that are the strongest contributors of the emission in those bands. We conclude that this is because the modeling assumes that all transition region emission is confined to the loop foot points, but in reality the upper transition region of longer loops extends significantly farther into the loop. It is important to consider this aspect of the transition region in future efforts to model EUV emission.

We propose boundary terms for the action of the NS sector of supergravity on $M_3 \times S^3 \times T^4$ spacetimes -- where $M_3$ is an AdS$_3$ or a linear dilaton background -- that render the Brown-York stress tensor and the on-shell action finite. For AdS$_3$ backgrounds, we show that the on-shell action yields a free energy with chemical potentials determined by the $B$-field. TsT (T-duality + shift + T-duality) transformations of these backgrounds generate classes of linear dilaton backgrounds distinguished by their boundary conditions. Among them, there is one class of backgrounds that reproduces features of the single-trace $T\bar T$ deformation. These backgrounds have additional chemical potentials that can be turned off by large gauge transformations of the $B$-field. We show that the Brown-York stress tensor of these backgrounds reproduces the energies and the trace flow equation of $T\bar T$-deformed CFTs. We also show that the on-shell action matches the $T\bar T$ partition function and discuss the interpretation of these results.

Quantum geometry appears as a key factor in understanding the optical properties of quantum materials, with the anticipation on diverging or quantized responses near the Dirac and Weyl points. Here we investigate linear and nonlinear optical responses -- optical conductivity and injection current -- in a magnetic Rashba semiconductor in the mid-infrared region, with varying the Fermi energy across the Dirac point. We reveal that the linear optical conductivity reflects quantum metric, which remains finite irrespective of the diminishing joint density-of-states at lower photon energy. It is also confirmed that the magnetic injection current enhances depending on the energy of the Fermi level relative to the Dirac point. These optical spectra are nicely reproduced by our theoretical calculations with geometrical effects taken into account.

Integrated Sensing and Communication (ISAC) systems face stringent hardware constraints, particularly regarding the high Peak-to-Average Power Ratio (PAPR) of standard OFDM, which necessitates power amplifier (PA) back-off and reduces sensing range. This paper investigates Frequency Modulated OFDM (FM-OFDM) as a constant-envelope solution capable of operating in the PA saturation region, thereby maximizing output power without the non-linear distortion penalties typical of conventional waveforms. We derive a comprehensive analytical framework for FM-OFDM in doubly dispersive channels, explicitly quantifying the inter-carrier interference (ICI) dynamics and effective channel gains in the discriminator domain. To address the unique phase structure of the waveform, we propose a tailored sensing receiver architecture utilizing slow time phase differencing for robust velocity estimation. Unlike prior works, we evaluate performance under a strictly normalized bandwidth constraint (B99), ensuring a fair comparison against CP-OFDM and Constant-Envelope OFDM (CE-OFDM). Simulation results demonstrate that FM-OFDM maintains superior detection accuracy and low BER even under fully saturated PA conditions and high Doppler shifts, validating its suitability for hardware-constrained ISAC transceivers.

Precision measurements of observables in neutron $β$-decay are used to test the Standard Model description of the weak interaction and search for evidence of new physics. The Nab experiment at the Fundamental Neutron Physics Beamline at the Spallation Neutron Source was constructed to measure correlations in neutron decay by utilizing an asymmetric spectrometer and novel detection system to accurately reconstruct the proton momentum and electron energy for each $β$-decay. This work describes the detection of neutron $β$-decay products in the Nab spectrometer and presents the first full Dalitz plot representation of the phase space of neutron $β$-decay for all electrons >100 keV. In addition, new constraints are placed on a possible excited neutron state, hypothesized to explain the disagreement between the appearance and disappearance neutron lifetime techniques.

Ultra-high-energy cosmic-ray (UHECR) observatories require unbiased direction reconstruction to enable multi-messenger astronomy with sparse, nanosecond-scale radio pulses. Explicit likelihood methods often rely on simplified models, which may bias results and understate uncertainties. We introduce a simulation-based inference pipeline that couples a physics-informed graph neural network (GNN) to a normalizing-flow posterior within the Learning the Universe Implicit Likelihood Inference framework. Each event is seeded by an analytic plane-wavefront fit; the GNN refines this estimate by learning spatiotemporal correlations among antenna signals, and its frozen embedding conditions an eight-block autoregressive flow that returns the full Bayesian posterior. Trained on about $8,000$ realistic UHECR air-shower simulations generated with the ZHAireS code, the posteriors are temperature-calibrated to meet empirical coverage targets. We demonstrate a sub-degree median angular resolution on test UHECR events, and find that the nominal 68% highest-posterior-density contours capture $71\% \pm 2\%$ of true arrival directions, indicating a mildly conservative uncertainty calibration. This approach provides physically interpretable reconstructions, well-calibrated uncertainties, and rapid inference, making it ideally suited for upcoming experiments targeting highly inclined events, such as GRAND, AugerPrime Radio, and BEACON.

Time of arrival refers to the time a particle takes after emission to impinge upon a suitably idealized detector surface. Within quantum theory, no generally accepted solution exists so far for the corresponding probability distribution of arrival times. In this work we derive a general solution for a single body without spin impacting on a so called ideal detector in the absence of any other forces or obstacles. A solution of the so called screen problem for this case is also given. After discussing the shortcomings of the so called "absorbing boundary condition", which is arguably the natural approach within quantum mechanics, we construct the ideal detector model via mathematical probability theory. This detector model assures that the probability flux through the detector surface is always positive, so that the corresponding distributions can be derived via an approach originally suggested by Daumer, Dürr, Goldstein, and Zanghì. The resulting dynamical model is based on an adaption of the Madelung equations and is, strictly speaking, not compatible with quantum mechanics. Still, it is well-described within geometric quantum theory. Geometric quantum theory is a novel adaption of quantum mechanics, which makes the latter consistent with mathematical probability theory. Implications to the general theory of measurement and avenues for future research are also provided. Future mathematical work should focus on finding an appropriate distributional formulation of the evolution equations and studying the well-posedness of the corresponding Cauchy problem.

Optical whispering gallery mode microcavity acoustic sensors have emerged significant potential in high-sensitivity acoustic signal detection, but the narrow dynamic range limits the application prospect. To address the challenge, we propose and experimentally demonstrate a high-sensitivity acoustic sensor in optical whispering gallery mode microcavity. The proposed sensor integrates an extended Mach-Zehnder polarization interferometer with postselection, extending the dynamic range to the free spectral range. The sensing regions can be categorized into phase-drastic and phase-enhanced regions, both of which yield an optimal acoustic response that surpasses traditional transmission method. Experiment results demonstrate improvements of 57.87 dB in detection sensitivity and 26 times in minimal detectable acoustic pressure over the transmission method. Moreover, the application of coherent state and heterodyne detection can enhance response amplitude, further improving the detection sensitivity. Given the wide application range for acoustic dispersion and dissipation response, as well as the performance advantages of high sensitivity and wide dynamic range, the proposed WGM sensor offers a promising solution for acoustic sensing. Potentially, the structural design may be adaptable to other application scenarios involving time-varying signals through optical microcavity sensing.

Secondary electron (SE) imaging techniques, such as scanning electron microscopy and helium ion microscopy (HIM), use electrons emitted by a sample in response to a focused beam of charged particles incident at a grid of raster scan positions. Spot size -- the diameter of the incident beam's spatial profile -- is one of the limiting factors for resolution, along with various sources of noise in the SE signal. The effect of the beam spatial profile is commonly understood as convolutional. We show that under a simple and plausible physical abstraction for the beam, though convolution describes the mean of the SE counts, the full distribution of SE counts is a mixture. We demonstrate that this more detailed modeling can enable resolution improvements over conventional estimators through a stylized application inspired by semiconductor inspection: localizing the edge in a two-valued sample. We derive Fisher information about edge location in conventional and time-resolved measurements (TRM) and also derive the maximum likelihood estimate (MLE) from the latter. Empirically, the MLE computed from TRM is approximately efficient except at very low beam diameter, so Fisher information comparisons are predictive of performance and can be used to optimize the beam diameter relative to the raster scan spacing. Monte Carlo simulations provide an example of the MLE giving a 5-fold reduction in root mean-squared error (RMSE) of edge localization as compared to conventional interpolation-based estimation. The RMSE is substantially below both the beam diameter and the raster scan spacing and thus sub-pixel localization is demonstrated. Applied to three real HIM datasets, the average RMSE reduction factor is 5.4.

We develop the theory of the discrete periodic Pitman transform, first introduced by Corwin, Gu, and the fifth author. We prove that the discrete periodic Pitman transform satisfies the same braid relations that are satisfied for the full-line Pitman transform shown by Biane, Bougerol, and O'Connell. This defines a group action of the infinite symmetric group on sequences of vectors in $\mathbb R^{\mathbb Z_N}$. We prove that, for polymers in a periodic environment, single-path and multi-path partition functions are preserved under the action of this transform on the weights in the polymer model. Combined with a new inhomogeneous Burke property for the periodic Pitman transform, we prove a multi-path invariance result for the periodic inverse-gamma polymer under permutations of the column parameters. In the limit to the full-line case, we obtain a multi-path extension of a recent invariance result of Bates, Emrah, Martin, Seppäläinen, and the fifth author, in both positive and zero-temperature.

Photonic media modulated periodically in time, termed photonic time crystals (PTCs), have attracted considerable attention for their ability to open momentum bandgaps hosting amplifying modes. These momentum gaps, however, generally appear only at the system's parametric resonance condition which constrain many features derived from amplification to a narrow frequency band. Moreover, they are accompanied by exceptional points (EPs) and may drive the system into an instability, which render their analysis more intricate. Here, we show that a careful consideration of dispersion and absorption can overcome these issues. By investigating the dissipated power of a point-dipole embedded in a dispersive and absorptive PTC, we unveil that temporal modulation enables the conversion of dipole emission into dipole absorption within a broadband frequency window free of EPs. We demonstrate that this effect is general, emerging in both the stable and unstable regimes, and occurs from weak modulation strength to low modulations frequencies that could be achieved for various material platforms.

Direct numerical simulations of a Couette Poiseuille flow were performed near the transition to turbulence to investigate the nonlinear relationship between streak waviness and rolls. This relationship is a key step in Waleffe's model for a self sustaining process (SSP). Simulations were conducted for Reynolds numbers ranging from 500 to 940, and a range of initial perturbation amplitudes was used. In these simulations, the streaks, rolls, and streak waviness initially grow. The optimal time for this growth closely matches the linear transient growth period for small perturbations, but is much shorter when the initial perturbations are large and highly nonlinear. For higher Reynolds numbers and large initial perturbations, the velocity field reaches a turbulent steady state, while in the remaining cases the flow relaminarizes. The main result is that the waviness of the streaks is a quadratic function of the rolls, provided that the roll amplitude is sufficiently large.

Effectively interpreting strategic interactions among multiple agents requires us to infer each agent's objective from limited information. Existing inverse game-theoretic approaches frame this challenge in terms of a "level-1" inference problem, in which we take the perspective of a third-party observer and assume that individual agents share complete knowledge of one another's objectives. However, this assumption breaks down in decentralized, real-world scenarios like urban driving and bargaining, in which agents may act based on conflicting views of one another's objectives. We demonstrate the necessity of inferring agents' different estimates of each other's objectives through empirical examples, and by theoretically characterizing the prediction error of level-1 inference on fictitious gameplay data from linear-quadratic games. To address this fundamental issue, we propose a framework for level-2 inference to address the question: "What does each agent believe about other agents' objectives?" We prove that the level-2 inference problem is non-convex even in benign settings like linear-quadratic games, and we develop an efficient gradient-based approach for identifying local solutions. Experiments on a synthetic urban driving example show that our approach uncovers nuanced misalignments that level-1 methods miss.

Based on Extreme Gradient Boosting (XGBoost) framework optimized via Bayesian hyperparameter tuning, we investigated the α-decay energy and half-life of superheavy nuclei. By incorporating key nuclear structural features-including mass number, proton-to-neutron ratio, magic number proximity, and angular momentum transfer-the optimized model captures essential physical mechanisms governing $α$-decay. On the test set, the model achieves significantly lower mean absolute error (MAE) and root mean square error (RMSE) compared to empirical models such as Royer and Budaca, particularly in the low-energy region. SHapley Additive exPlanations (SHAP) analysis confirms these mechanisms are dominated by decay energy, angular momentum barriers, and shell effects. This work establishes a physically consistent, data-driven tool for nuclear property prediction and offers valuable insights into $α$-decay processes from a machine learning perspective.

Long-term stability of superconducting microwave resonators is essential for scalable quantum technologies; however, surface and interface degradation continue to limit device stability. Here, we demonstrate exceptional stability in microstrip resonators fabricated from epitaxial tantalum and aluminum films, protected by in situ deposited Al$_2$O$_3$ under ultra-high vacuum. These resonators initially exhibit internal quality factors (Qi) exceeding one million and maintain high performance with minimal degradation after up to fourteen months of air exposure. In contrast, devices relying on native surface oxides show substantial declines in Qi over time, indicating increased microwave losses. X-ray photoelectron spectroscopy reveals that the in situ Al$_2$O$_3$ effectively suppresses interfacial oxidation and preserves the chemical integrity of the underlying superconducting films, whereas native oxides permit progressive oxidation, leading to device degradation. These findings establish a robust, scalable passivation strategy that addresses a longstanding materials challenge in the development of superconducting quantum circuits.

Let ${\bf G}$ be a connected reductive algebraic group defined over the finite field $\mathbb{F}_q$ with $q$ elements. We propose some conjectures concerning the simple quotients of $M\otimes N$, where $M,N$ are objects in the representation category $\mathscr{X}({\bf G})$ introduced by the author in a previous work to study the complex representations of ${\bf G}$. We provide several pieces of evidence for these conjectures. In particular, we show that these conjectures are valid for ${\bf G}=SL_2(\bar{\mathbb{F}}_q)$.

In this work, we consider, in a general setting, multiparameter multidimensional Markov processes that are time-changed by an independent additive subordinator. By extending Phillips theorem, we show that the resulting process is a Feller evolution and we characterize its generator. We further derive its pseudo-differential representation and show that its symbol admits a Lévy-Khintchine representation. In the specific case of multiparameter Ornstein-Uhlenbeck processes, we obtain explicit expression of the symbol, along with the associated characteristic Lévy triplet. As an application, we consider a factor-based specification for the Ornstein-Uhlenbeck process subordinated by a Sato process. The constructive nature of this process is inspired by applications in finance.

Deep learning has advanced efficient chemical process simulations on the surfaces, accelerating high-throughput materials screening and rational design in heterogeneous catalysis, energy storage and conversion, and gas separation. However, the accuracy of the deep learning model generally depends on the quality of the training data. Unfortunately, precise experimental data in surface chemistry, such as adsorption energies, are scarce, while accurate quantum chemistry simulations remain computationally prohibitive for large-scale studies. Herein, we present a deep learning model of DOS Transformer for Adsorption (DOTA) for efficient surface chemistry simulations with chemical accuracy. It enables the alignment of experimental data and multi-fidelity quantum chemistry calculation data by capturing latent orbital interaction patterns based on the map between local density of states (LDOS) and adsorption energy. This minimizes the reliance on scarce high-precision training data in surface chemistry to accomplish efficient prediction of adsorption energies rivaling the high-precision experimental data, resolving the long-standing challenge of "CO puzzle". It provides a robust framework for efficient materials screening, effectively bridging the gap between computational and experimental data.