In this paper, we study the prototypical model of liquid-liquid phase separation, the Cahn-Hilliard functional, in a highly irregular setting. Specifically, we analyze potentials with low regularity vanishing on space-dependent wells. Under remarkably weak hypotheses, we establish a robust compactness result. Strengthening the regularity of the wells and of the growth of the potential close to the wells only slightly, we completely characterize the asymptotic behavior of the associated family of functionals through a $Γ$-convergence analysis. As a notable technical result, we prove the existence of geodesics for a degenerate metric and establish a uniform bound on their Euclidean length.

In this paper, we explicitly determine the local $2$-adic component of a non-selfdual automorphic representation $Π$ of $\mathrm{GL}_3$ constructed by van Geemen and Top. We prove that $Π_2$ is a parabolically induced representation of $\mathrm{GL}_3(\mathbb{Q}_2)$ given by $Π_2 = \mathrm{Ind}_P^{\mathrm{GL}_3(\mathbb{Q}_2)}(π\boxtimes χ)$, where $P$ is the standard parabolic subgroup of $\mathrm{GL}_3$ with Levi subgroup $\mathrm{GL}_2 \times \mathrm{GL}_1$, $χ$ is an unramified character of $\mathbb{Q}_2^\times$ satisfying $χ(2) = -2\sqrt{-1}$, and $π$ is a supercuspidal representation of $\mathrm{GL}_2(\mathbb{Q}_2)$. Furthermore, we describe $π$ explicitly as a compactly induced representation $π= \mathrm{c-Ind}_{J_α}^{\mathrm{GL}_2(\mathbb{Q}_2)} Λ$ and determine the representation $Λ$ explicitly. The proof relies on explicit computations of Hecke eigenvalues using computer calculations. The automorphic representation $Π$ is realized in the cuspidal cohomology of the congruence subgroup $Γ_0(128) \subset \mathrm{SL}_3(\mathbb{Z})$. By computing the Hecke eigenvalues of an associated Hecke eigenvector, we are able to uniquely identify the local structure of $Π_2$. As an application, we obtain an explicit description of the $2$-adic local component of the Galois representation $ρ_{\mathrm{vGT},\ell}$ associated with $Π$.

Continuing our previous study of Deeply Virtual Meson Production (DVMP) at twist-3 accuracy, we derive compact expressions for all helicity amplitudes. We perform a phenomenological analysis of the helicity-amplitude ratio $\mathcal{A}^{11}/\mathcal{A}^{00}$ and of the spin-density matrix element $r_{00}^{04}$ within the Color Glass Condensate framework. Small-$x$ evolution is incorporated by numerically solving the running-coupling-and-collinearly-improved Balitsky-Kovchegov and Balitsky-Fadin-Kuraev-Lipatov equations with the McLerran-Venugopalan model as the initial condition. By capturing a relevant subset of next-to-leading order corrections, we provide the most theoretically accurate description of these observables to date. Our results are compared to HERA data, and predictions are presented for electron-lead collisions at the future Electron-Ion Collider. We discuss the impact of non-linear effects at low photon virtuality and the role of genuine higher-twist contributions associated with light vector meson distribution amplitudes, corresponding to higher-Fock-state components of the projectile.

We study linear codes over Gaussian integers equipped with the Mannheim distance. We develop Mannheim-metric analogues of several classical bounds. We derive an explicit formula for the volume of Mannheim balls, which yields a sphere packing bound and constraints on the parameters of two-error-correcting perfect codes. We prove several other useful bounds, and exhibit families of codes meeting these bounds for some parameters, thereby showing that these bounds are tight. We also discuss self-dual codes over Gaussian integers and obtain upper bounds on their minimum Mannheim distance for certain parameter regions using a Mannheim version of the Macwilliams-type identity. Finally, we present decoding algorithms for codes over Gaussian integer residue rings. We give examples showing that certain errors which are not correctable under the Hamming metric become correctable under the Mannheim metric.

We consider the question of quantitative stability of minimisers for a well-known variational problem for which the infimum of the energy is not achieved in the classical sense, namely for the Dirichlet energy of degree $1$ maps from closed surfaces $(Σ,g_Σ)$ of positive genus into the unit sphere $S^2\subset \mathbb{R}^3$. For this variational problem it is natural to view configurations which consist of a constant map from the given domain and an infinitely concentrated rotation as generalised minimisers and to hence ask whether the distance of almost minimisers $v:Σ\to S^2$ to this set of infinitely concentrated minimisers can be controlled in terms of the energy defect $δ_v=E(v)-\inf E=E(v)-4π$. In this paper we develop a new dynamic approach that allows us to change the topology of the domain in a well controlled manner and to deform almost minimising maps from surfaces of general genus into harmonic maps from the sphere in a way that yields sharp quantitative estimates on all key features that characterise the distance to the set of infinitely concentrated minimisers, i.e. the scale of concentration, the $H^1$-distance to the nearest bubble on the concentration region and the $H^1$-distance to the nearest constant away from the concentration point.

We investigate the impact of isovector and isoscalar proton-neutron pairing correlations on the ground-state properties of even-even N=Z nuclei with mass numbers between A=16 and A=120. Nuclear mean fields are generated using the quark-meson coupling (QMC) energy density functional, while pairing correlations are treated within the quartet condensation model (QCM). Ground-state energies are obtained from axially deformed, self-consistent QMC+QCM calculations employing a zero-range pairing interaction with a density-dependent term derived consistently within the QMC framework. We show that proton-neutron pairing provides a significant contribution to the binding energies of N=Z nuclei, leading to improved agreement with experimental data.

Importance sampling based on quasi-probability decomposition is the backbone of many widely used techniques, such as error mitigation, circuit knitting, and, more generally, virtual quantum resource distillation, as it allows one to simulate operations that are not accessible in a given setting. However, this class of protocols faces a fundamental problem -- it only allows to estimate expectation values. Here, we provide a general framework that lifts any quasi-probability-based protocol from expectation value estimation to a weak simulator, realizing sampling from the desired distribution only using a restricted class of quantum resources. Our method runs with the sampling cost proportional to the negativity of the quasi-probability, in stark contrast to the naive estimation-based approach that requires a large number of samples even in the case of small negativity. We show that our method requires significantly fewer samples in a number of relevant scenarios, such as error mitigation, entanglement distillation and magic state distillation. Our framework realizes the weak simulation of quantum resources without actually distilling the state, introducing a new notion of quantum resource distillation.

We present the development of ProtoPol - a medium resolution echelle spectro-polarimeter for the PRL 1.2m and 2.5m telescopes at Mt Abu observatory, India. In this second and final part of the paper series, we report on the development of a dedicated data reduction pipeline of ProtoPol along with several characterization, performance evaluation, and scientific observations to quantify the performance of the instrument. ProtoPol provides a spectral resolution in the range of $\sim$0.4 - 0.75 angstrom across various orders in the visible wavelength range of 4000-9600 angstrom. On PRL 2.5m telescope, an SNR of 10 is achieved for $m_V\sim13.2$ source in 1 hour of integration time, and its throughput is estimated to be $\sim$6\% including all the contributing factors such as atmospheric transmission, telescope reflectivity, instrument's optics, CCD efficiency etc. ProtoPol achieved a linear polarization accuracy $δP \approx 0.1-0.2\%$ in 2 hours of integration time for a source with $m_V\approx8$. The instrumental polarization is determined to be around $0.1\%$. We also present the first science results with ProtoPol to demonstrate the capabilities of the instrument. A sample of Herbig Ae/Be stars, classical Herbig stars, Symbiotic stars, and AGB/post-AGB stars were observed over the period of one and half years for their spectro-polarimetry measurements covering various physical mechanisms such as intrinsic line polarization in Herbig and classical Be stars, Raman scattered features in Symbiotic stars, as well as continuum polarization in AGB/post-AGB stars to verify the polarization performance of the instrument.

In recent years, fuzzing has been widely applied not only to application software but also to system software, including the Linux kernel and firmware, and has become a powerful technique for vulnerability discovery. Among these approaches, Coverage-based grey-box fuzzing, which utilizes runtime code coverage information, has become the dominant methodology. Conventional fuzzing techniques primarily target a single software component and have paid little attention to cooperative execution with other software. However, modern system software architectures commonly consist of firmware and an operating system that operate cooperatively through well-defined interfaces, such as OpenSBI in the RISC-V architecture and OP-TEE in the ARM architecture. In this study, we investigate fuzzing techniques for architectures in which an operating system and firmware operate cooperatively. In particular, we propose a fuzzing method that enables deeper exploration of the system by leveraging the code coverage of each cooperating software component as feedback, compared to conventional Single-target fuzzing. To observe the execution of the operating system and firmware in a unified manner, our method adopts QEMU as a virtualization environment and executes fuzzing by booting the system within a virtual machine. This enables the measurement of code coverage across software boundaries. Furthermore, we implemented the proposed method as a Multi-target Coverage-based Greybox Fuzzer called MTCFuzz and evaluated its effectiveness.

This paper considers an insurer with two collaborating business lines that faces three critical decisions: (1) dividend payout, (2) reinsurance coverage, and (3) capital injection between the lines, in the presence of model uncertainty. The insurer considers the reference model to be an approximation of the true model, and each line has its own robustness preference. The reserve level of each line is modeled using a diffusion process. The objective is to obtain a robust strategy that maximizes the expected weighted sum of discounted dividends until the first ruin time, while incorporating a penalty term for the distortion between the reference and alternative models in the worst-case scenario. We completely solve this problem and obtain the value function and optimal (equilibrium) strategies in closed form. We show that the optimal dividend-capital injection strategy is a barrier strategy. The optimal proportion of risk ceded to the reinsurer and the deviation of the worst-case model from the reference model are decreasing with respect to the aggregate reserve level. Finally, numerical examples are presented to show the impact of the model parameters and ambiguity aversion on the optimal strategies.

Citations are essential for recognizing scientific contributions, yet citation behavior is shaped by more than just relevance or quality. We analyzed approximately 255,000 refereed astronomy articles published between 2000 and 2025 to investigate how journals are cited relative to their publication volume and authorship context. We find that multidisciplinary journals receive disproportionately more citations, up to nine times higher than their share of articles, while field-specific journals are cited less frequently in proportion to their output. Citations to a journal also increase significantly when authors publish within it, a bias particularly pronounced in multidisciplinary journals. Although this effect has declined over the past decade, it remains notable. These patterns likely arise from a combination of topical clustering, institutional/individual publishing habits, and strategic referencing to align with editorial expectations. Our findings reveal persistent structural biases in scientific visibility and suggest that citation-based metrics should be used with greater awareness of the publishing context they reflect. We encourage authors, reviewers, and editors to remain mindful of these dynamics and strive for fairness and inclusivity when selecting references.

Driven Floquet systems can realize topological phases with no static counterparts. These so-called anomalous Floquet topology breaks the bulk-boundary correspondence based on the Chern number. The number of edge modes in each band gap is instead determined by another integer index, a winding number, which is calculated from the time evolution operator of the bulk states within one driving period. While in the non-driven system, Chern markers provide a useful local proxy for the Chern number in the bulk, so far no such local bulk indicator is known for the winding number in Floquet systems. Here we consider two-band models and show that the area enclosed during a Floquet period by an initially localized particle signals the presence of an anomalous phase when it approaches half the unit cell area. In general, we show that at the fine-tuned point of dispersionless dynamics during the micromotion, the enclosed area is quantized and an exact proportionality relation exists between the area and the winding number. Direct detection of anomalous topology in real space could be realized in several quantum simulation platforms, and could be useful for systems with disorder or interactions. Building on the connection between area and winding number, we also show a way to realize arbitrarily high winding numbers.

Various applications of Chern-Simons theory in algebraic topology, in particular knot theory, condensed matter physics and cosmology are reviewed. Special attention is paid to appearances of Chern-Simons actions in the theory of the (integer and fractional) quantum Hall effect. A mechanism related to five-dimensional abelian Chern-Simons theory that may be at the origin of the observed intergalactic magnetic fields in the universe is described.

Measurement incompatibility and bipartite quantum steering are known to display a strong connection: a set of measurements is incompatible if and only if it can lead to bipartite steering. Despite such a close link between these concepts in bipartite scenarios, little is known in the multipartite setting, where notions of genuine multipartite correlations play major roles. In this work we prove that, as in the bipartite case, incompatibility is also necessary and sufficient for genuine multipartite steering in any multipartite scenario with a single uncharacterised party. Interestingly, genuine multipartite steering can be extracted from any set of incompatible measurements using states which are not SLOCC equivalent, such as GHZ and W states. In contrast, we prove that this result does not hold in scenarios with more than one uncharacterised party, by presenting a set of incompatible measurements that can never lead to genuine multipartite steering in these cases. In order to obtain our main results, we introduce methods tailored for multipartite correlations, paving the way to understanding the role of measurement incompatibility beyond bipartite scenarios.

We develop a theoretical framework for quantum field description of neutrino evolution in a medium, with a focus on quantum decoherence induced by neutrino scattering on fermions. By deriving a generalized Lindblad master equation that accounts for neutrino momentum-changing transitions, we go beyond the standard treatment that assumes fixed neutrino momentum. Our model explicitly connects decoherence parameters to scattering cross sections, offering a bridge between theoretical predictions and experimental constraints on neutrino quantum decoherence. We apply the developed formalism to several scenarios: 1) neutrino scattering on electrons, 2) neutrino scattering on protons and neutrons through non-standard interactions (NSI), 3) neutrino scattering on dark matter fermions. The obtained results demonstrate that decoherence effects can significantly alter neutrino oscillation patterns and provide a new probe for physics beyond the Standard Model.

Wall-Sun-Sun primes (shortly WSS primes) are defined as those primes $p$ such that the period of the Fibonacci recurrence is the same modulo $p$ and modulo $p^2.$ This concept has been generalized recently to certain second order recurrences whose characteristic polynomials admit as a zero the principal unit of $\mathbb{Q}(\sqrt{d}),$ for some integer $d>0.$ Primes of the latter type we call $WSS(d).$ They correspond to the case when $\mathbb{Q}(\sqrt{d})$ is not $p$-rational. For such a prime $p$ we study the weight distributions of the cyclic codes over $\mathbb{F}_p$ and $\mathbb{Z}_{p^2}$ whose check polynomial is the reciprocal of the said characteristic polynomial. Some of these codes are MDS (reducible case) or NMDS (irreducible case).

Resonance fluorescence from a coherently driven two-level emitter is a minimal quantum optical field that combines phase coherence with single-photon-level nonlinearity. Here we show that it can be engineered, using only passive linear interferometry, into energy-time entanglement. By injecting resonance fluorescence from a single quantum dot into an asymmetric Mach--Zehnder interferometer operated near destructive interference of the single-photon component, we generate an output field whose coincidence statistics are dominated by the simultaneous two-photon contribution |2> and the temporally separated photon-pair contribution |11>. In a Franson geometry, these two sectors are resolved on the coincidence-delay axis, and both exhibit high-visibility nonlocal interference fringes and violate the Clauser--Horne--Shimony--Holt Bell inequality. Our results reveal a general route for engineering entanglement from resonance fluorescence using passive optics.

This note starts with a recapitulation of what people call the ``Measurement Problem'' of Quantum Mechanics (QM). The dissipative nature of the quantum-mechanical time-evolution of averages of states over large ensembles of identical isolated systems consisting of matter interacting with the radiation field is discussed and shown to correspond to a stochastic time-evolution of states of individual systems. The importance of dissipation for the successful completion of measurements is highlighted. To conclude, a solution of the ``Measurement Problem'' is sketched in an idealized model of a double-slit experiment.

This paper investigates a smart spectrum-sharing framework for reconfigurable intelligent surface (RIS)-aided local high-quality wireless networks (LHQWNs) within a mobile network operator (MNO) ecosystem. Although RISs are often considered potentially harmful due to interference, this work shows that properly controlled RISs can enhance the quality of service (QoS). The proposed system enables temporary spectrum access for multiple vertical service providers (VSPs) by dynamically allocating radio resources according to traffic demand. The spectrum is divided into dedicated subchannels assigned to individual VSPs and reusable subchannels shared among multiple VSPs, while RIS is employed to improve propagation conditions. We formulate a multi-VSP utility maximization problem that jointly optimizes subchannel assignment, transmit power, and RIS phase configuration while accounting for spectrum access costs, RIS leasing costs, and QoS constraints. The resulting mixed-integer non-linear program (MINLP) is intractable using conventional optimization methods. To address this challenge, the problem is modeled as a Markov decision process (MDP) and solved using deep reinforcement learning (DRL). Specifically, deep deterministic policy gradient (DDPG) and soft actor-critic (SAC) algorithms are developed and compared. Simulation results show that SAC outperforms DDPG in convergence speed, stability, and achievable utility, reaching up to 96% of the exhaustive search benchmark and demonstrating the potential of RIS to improve overall utility in multi-VSP scenarios.

The design of efficient electrolysis devices for pure metal production requires accurate data on the properties of the melts used in the process. This work focuses on two key systems for calcium production: the molten Ca-Cu alloy and the CaCl$_2$-KCl electrolyte. High-temperature experiments are often expensive and time-consuming; however, we demonstrate that molecular dynamics (MD) simulations driven by machine-learned Moment Tensor Potentials (MTPs), trained on highly accurate density functional theory data, offer an effective and accurate alternative. Our MTP-driven MD simulations accurately reproduce the structural, thermodynamic, and transport properties across a range of temperatures and compositions relevant to electrolysis systems. We report calculated densities, radial distribution functions, heat capacities, thermal conductivities, ionic conductivities (for the electrolyte), viscosities, and diffusion coefficients, with deviations from experimental data within 20%. The strong agreement between calculations and experiments validates the proposed approach, establishing a robust framework for the computational exploration and optimization of liquid systems in metallurgical applications.

The introduction of Su-Schrieffer-Heeger model has led to a major breakthrough in the area of one-dimensional topological insulators, even though this model was primarily formulated on an organic polymer called $trans$-polyacetylene in order to explain its anomalous conductivity. In this study, a group of five tight-binding models has been introduced which are formulated on another organic polymer called polyacene, where exotic topological behavior has been observed. Topological properties of the most common geometric isomers known as $cis$-polyacene, and $trans$-polyacene have been investigated along with three additional modified polyacene structures. Although their geometric structures differ by mirror symmetry, tight-binding band structures of $cis$-polyacene and $trans$-polyacene are found the same, where again their topological characters are found totally opposite. The $trans$-polyacene is nontrivial as it exhibits topological phase with nonzero winding number, while the $cis$-polyacene is topologically trivial, although both the structures adhere to the same set of symmetries required for the topological character. However, $cis$-polyacene possesses additional mirror symmetry in the real space. Three modified structures of polyacene have been considered in order to induce the nontrivial topology, where exotic topological behavior is noted in two of them.

Between 2.4 and 0.6 Gy ago, our planet underwent several episodes of global glaciations, including the Snowball Earth case that ended 635 My ago. Causes of this last Snowball event presumably included a decreased greenhouse gas concentration and high continental albedo, both associated with the passage of the super-continent Rodinia at equatorial latitudes. When large continental masses are in equatorial regions, silicate weathering is enhanced, leading to decreased atmospheric CO2 concentration, while the bare continental masses, which at the time hosted no vegetation, enhanced reflection of solar radiation. Since then, no other Snowball episodes were recorded. Here we numerically explore the climatic dynamics of a rocky planet for different values of solar output, continental configuration (current and Rodinia-like), CO2 concentration and continental albedo, simulating the effects of land vegetation. We found that for the solar input typical of 600-700 My ago (95% of the current value), the presence of bare continents with albedo 0.35 (granite) in the position estimated for Rodinia was sufficient to trigger a Snowball state for CO2 concentrations up to at least 1000 ppm. When bare continents are located in modern positions, Snowball could be triggered only for values of CO2 concentration below 400 ppm. At current solar input values, Snowball states appear only at or below 100 ppm. Thus, we found: a lower solar output is an essential component of the transition to Snowball; the presence of land vegetation is crucial and reduces the probability of entering a Snowball state; a low CO2 concentration was not needed for triggering a Snowball in bare Rodinia-like conditions and reduced solar output; current solar luminosity does not allow Snowball states, even for equatorial continents, unless continental albedo is that of granite and CO2 concentration is 100 ppm or less. [Abridged]

We develop a semi-static framework for the variance-optimal hedging of multi-asset derivatives exposed to correlation and covariance risk. The approach combines continuous-time dynamic trading in the underlying assets with a static portfolio of auxiliary contingent claims. Using a multivariate Galtchouk--Kunita--Watanabe decomposition, we show that the resulting global mean-variance problem decouples naturally into an inner continuous-time projection onto the space spanned by the underlying assets and an outer finite-dimensional quadratic optimization over the static hedging instruments. To systematically select suitable auxiliary claims, we leverage multidimensional functional spanning theory, establishing that otherwise unhedgeable cross-gamma exposures can be structurally mitigated through static strips of vanilla, product, and spread options. As a central application, we derive explicit semi-static replication formulas for covariance swaps and geometric dispersion trades. Our framework accommodates a broad class of asset dynamics, including quadratic and stochastic Volterra covariance models, as well as affine stochastic covariance models with jumps, yielding tractable semi-closed-form solutions via Fourier transform techniques. Extensive numerical experiments demonstrate that incorporating optimally weighted static strips of cross-asset instruments substantially reduces the mean-squared hedging error relative to purely dynamic benchmark strategies across various model classes.

In this paper we combine the non-linear filtering capabilities of particle filters with the transdimensional inference of the reversible-jump Markov chain Monte Carlo method for a data assimilation methodology over dynamic problems with variable dimensionality. By using transdimensional MCMC steps for the rejuvenation of the particle filter, the algorithm could change the number of state space parameters on the fly and can be applied for transdimensional data assimilation purposes. Classic inversion methodologies use pre-defined models, and only changes the individual parameter values during interpretation. This is often not feasible when the optimal model parametrization is not known a priori or when the model resolution needs to change with time. The proposed transdimensional particle filter algorithm, combines the advantages of particle filters and the transdimensional MCMC methods, and provides an easily implementable data assimilation algorithm that could tackle such problems. The methodology could also improve the computational efficiency of particle filters as it could inherently optimize the model complexity in a data-driven way. We demonstrate the capabilities of the enhanced algorithm on two simple model examples.

The confinement of electronic wavefunctions in momentum space can give rise to flat electronic bands, where the quenching of kinetic energy enhances the density of states and amplifies interaction effects. Such conditions are fertile ground for emergent quantum phases, as spin, charge and lattice degrees of freedom become strongly entangled. In these regimes, subtle competitions between intertwined order parameters often dictate the macroscopic ground state, producing complex and sometimes unexpected collective behavior. Here we show that the altermagnet CrSb provides a realization of this scenario, and uncover short-range charge-order fluctuations at the M point of the Brillouin zone, q*=(1/2 0), persisting above the Neel temperature (TN). Remarkably, these fluctuations collapse upon entering the magnetically ordered phase, revealing a direct and robust competition between charge and spin order. At TN, the phonon dispersion at q* develops a pronounced Kohn-like anomaly, signaling strong electron-phonon coupling in the vicinity of the magnetic transition. Below TN, exchange striction dramatically renormalizes the associated soft phonon mode by approximately ~6 meV, the largest spin-phonon coupling ever reported. First-principles calculations attribute this behavior to a strong coupling between nearly dispersionless electronic states and a phonon branch that appears unstable at the harmonic level only when no magnetic order is considered, revealing the large sensitivity of the lattice to magnetic symmetry breaking. The competition between charge and spin order parameters, amplified by flat-band physics, drives the observed phonon anomaly and its abrupt reconstruction at TN. With its chemically simple structure and symmetry-protected altermagnetic state, CrSb emerges as a model platform to explore how flat electronic bands mediate giant spin-phonon coupling and competing broken symmetries.

We study finite-momentum superconductivity in a two-dimensional $d$-wave altermagnetic superconductor using a non-perturbative Monte Carlo approach beyond mean-field theory. We show that altermagnetism stabilizes a pair density wave (PDW) state without external magnetic fields and enables its survival at finite temperatures with robust phase coherence. Our results establish altermagnetism as a promising route to realizing thermally stable PDW superconductivity and identify clear thermodynamic and spectroscopic signatures.

Motivated by a one-cycle cosmological scenario where the big bang marks the egress from a Euclidean metric signature regime, we investigate the possibility of black holes (BH) hosting the mirroring entryways. Analogous to the inflationary stage following the exit end, the entry portals must be enveloped by de Sitter cores inside BHs, in order to satisfy regularity conditions at the metric signature transition boundary. We examine the interior structure of BHs that could be consistent with such a physical picture, and conclude that the presence of a spacelike shell of non-inflationary matter is likely required.

We present a high-flux source of cold strontium atoms based on a two-dimensional magneto-optical trap (2D MOT) and a Zeeman slower. We use the source to load a 3D MOT in a separate science chamber, observing a loading rate of $4 \times 10^{10}$ atoms/s -- to our knowledge, the highest reported loading flux for strontium. To characterise the vacuum pressure in the science chamber, we load the atoms into a magnetic trap and measure a lifetime of between 8 and 24 seconds, depending on oven temperature. Finally, we characterise the atom flux and velocity distributions from the oven and from the 2D MOT source, finding reasonable agreement with models in the free molecular flow regime. Our results show it is possible to readily produce a cold strontium flux at comparable levels to alkali species, at oven temperatures compatible with long-term operation, and at vacuum pressures suitable for state-of-the-art quantum experiments. We make our design available at no cost, to benefit researchers in the quantum community.

Deep learning (DL) has been widely studied for assisting applications of modern wireless communications. One of the applications is automatic modulation classification (AMC). However, DL models are found to be vulnerable to adversarial machine learning (AML) threats. One of the most persistent and stealthy threats is the backdoor (Trojan) attack. Nevertheless, most studied threats originate from other AI domains, such as computer vision (CV). Therefore, in this paper, a physical backdoor attack targeting the wireless signal before transmission is studied. The adversary is considered to be using explainable AI (XAI) to guide the placement of the trigger in the most vulnerable parts of the signal. Then, a class prototype combined with principal components is used to generate the trigger. The studied threat was found to be efficient in breaching multiple DL-based AMC models. The attack achieves high success rates for a wide range of SNR values and a small poisoning ratio.

Multiphase flows frequently occur naturally and in manufactured devices. Controlling such phenomena is extremely challenging due to the strongly non-linear dynamics, rapid phase transitions, and the limited spatial and temporal resolution of available sensors, which can lead to significant inaccuracies in predicting and managing these flows. In most cases, numerical models are the only way to access high spatial and temporal resolution data to an extent that allows for fine control. While embedding numerical models in control algorithms could enable fine control of multiphase processes, the significant computational burden currently limits their practical application. This work proposes a surrogate-assisted model predictive control (MPC) framework for regulating multiphase processes using learned operators. A Fourier Neural Operator (FNO) is trained to forecast the spatiotemporal evolution of a phase-indicator field (the volume fraction) over a finite horizon from a short history of recent states and a candidate actuation signal. The neural operator surrogate is then iteratively called during the optimisation process to identify the optimal control variable. To illustrate the approach, we solve an optimal control problem (OCP) on a two-phase Eulerian bubble column. Here, the controller tracks piecewise-constant liquid level setpoints by adjusting the gas flow rate introduced into the system. The results we obtained indicate that field-level forecasting with FNOs are well suited for closed-loop optimization since they have relatively low evaluation cost. The latter provide a practical route toward MPC for fast multiphase unit operations and a foundation for future extensions to partial observability and physics-informed operator learning.