We study positional properties in the context of game-based reactive synthesis. Our motivation stems from having a usable specification logic, for which tractable synthesis is guaranteed. We demonstrate that every $ω$-regular positional property (with respect to state- or edge-labelled game graphs), is expressible in linear-time temporal logic. Additionally, we provide some necessary and sufficient conditions for when an $ω$-regular property is positional, and identify well-behaved subclasses of $ω$-regular positional properties. Using varieties of languages, we prove that no class of $ω$-regular positional properties can simultaneously contain a prefix-independent property and be closed under Boolean operations. We conclude by discussing the implications on alternating-time temporal logic, where we isolate a few different fragments with tractable model checking, and compare the associated expressivity of such fragments.

We present PHANGS-Hα, a narrow-band imaging survey that maps Hα emission over a sample of 65 nearby massive star-forming galaxies. The data were obtained using the MPG-ESO 2.2-meter telescope at La Silla and the du Pont 2.5-meter telescope at Las Campanas Observatory, in the framework of the multi-wavelength cloud-scale (50-100 pc) resolution mapping of molecular gas and star formation conducted by the Physics at High Angular resolution in Nearby GalaxieS (PHANGS) collaboration. PHANGS-Hα complements the already published PHANGS-ALMA, PHANGS-MUSE, PHANGS-HST, and PHANGS-JWST surveys, providing an anchor point for the photometric and astrometric calibration of these datasets, as well as samples of H ii regions, and star formation rate maps for the bulk of the PHANGS sample. We present observations, data processing, and calibration of the PHANGS-Hα dataset, as well as the procedures used to derive emission-line fluxes from narrow-band imaging. A subset of galaxies with available spectroscopic Ha mapping from the PHANGS-MUSE survey allows for a detailed comparison with the narrow-band photometry presented here. This informs a series of best practices for the processing of narrow-band Hα imaging that we apply to the full dataset.

In this work, a comprehensive first-principles investigation of the structural, hydrogen storage potential, electronic, elastic, mechanical, thermophysical, superconducting, and optical properties of Mg2TmH6 (Tm = Rh, Pd, Ir, Pt) hydrides is presented. Obtained results demonstrate that Mg2TmH6 hydrides combine favorable hydrogen storage, mechanical robustness, superconductivity, and multifunctional optical properties, making them promising candidates for energy storage, superconducting and advanced optoelectronic applications.

Making new methods for quantum problems often relies on using basic operations in linear algebra. Often these routines are hidden behind well-known libraries that have been optimized over decades. Attempting to improve on those basic routines would be highly time-consuming. We aim in this article to review those basic routines and provide a knowledge foundation for how to perform basic operations on a computer that would be inaccessible with pen and paper. Elementary details on the solutions to linear algebra problems and computational complexity are reviewed. The focus is on solving eigenvalue problems for quantum systems, but the discussion is generic to many other applications. Common matrix forms relevant to quantum systems and their solution strategies are covered. The discussion extends to computational numerical methods for which the most efficient functions exist in freely available libraries. These include eigenvalue, Schur, QR, LU, LDL, Cholesky, and singular value decompositions. The algorithms for obtaining some of these decompositions are discussed, with focus being placed on those used in modern libraries.

The joint training of speech enhancement and speaker embedding networks for speaker recognition is widely adopted under noisy acoustic environments. While effective, this paradigm often fails to leverage the generalization and robustness benefits inherent in large-scale speech enhancement pre-training. Moreover, maintaining the speaker information in the denoised speech is not an explicit objective of the speech enhancement process. To address these limitations, we proposed a scalable \textbf{U}Net-based \textbf{F}usion framework (UF-EMA) that considers the noisy and enhanced speech as a multi-channel input, thereby enabling the speaker encoder to exploit speaker information effectively. In addition, an \textbf{E}xponential \textbf{M}oving \textbf{A}verage strategy is applied to a speaker encoder pre-trained on clean speech to mitigate overfitting and facilitate a smooth transition from clean to noisy conditions. Experimental results on multiple noise-contaminated test sets showcase the superiority of the proposed approach.

With the rapid advancement of computer technologies enabling fast calculations of complex structures, numerical methods have become a central tool in engineering sciences, while physical models have increasingly receded into the background. Nevertheless, owing to their clarity and comprehensibility, these former engineering tools remain of great value and their use can still be highly relevant today. At the example of the scale model of the Lillebælt Bridge -- developed by the Copenhagen engineers Christen Ostenfeld and Wriborg Jønson and given for research purposes to the Bauhaus-Universität Weimar -- this paper illustrates how physical models can still serve as useful instruments in research and teaching. By applying operational modal analysis, the natural frequencies and damping ratios of the bridge model are experimentally determined, which in turn can serve as reference data for the calibration and validation of numerical models.

One of the most important applications of topological recursion concerns spectral curves for which the functions $(x,y)$ defining the spectral curve are allowed to have logarithmic singularities. This occurs for instance for Seiberg-Witten curves and mirror curves computing Gromov--Witten invariants of toric Calabi--Yau threefolds. A recently introduced extension of topological recursion, the so-called logarithmic topological recursion, exhibits the correct behavior under certain limits of those spectral curves. In this article, we derive the dilaton equations in the setting of logarithmic topological recursion, as well as variational formulas, and provide a definition of the free energies in situations where standard topological recursion was known to fail. We present examples in which the new definition of the free energies \textit{directly} (without any computation) reproduces the full perturbative part of the Nekrasov--Shatashvili partition function of 4d $\mathcal{N}=2$ pure supersymmetric gauge theory, as well as the all-genus free energies of mirror curves of strip geometries, including in particular the topological vertex and the resolved conifold.

Platinum (Pt) is widely used for carrier-lifetime control in silicon power devices, yet the microscopic nonradiative recombination mechanism of the substitutional platinum ($\text{Pt}_\text{Si}$) dopant remains debated. Using first-principles calculations combined with nonradiative multiphonon theory, we systematically investigate the electronic structures and carrier capture dynamics of $\text{Pt}_\text{Si}$. Our results show that both the donor ($+/0$) and acceptor ($0/-$) levels of $\text{Pt}_\text{Si}$ exhibit large capture cross sections for electron and hole carriers, thereby making $\text{Pt}_\text{Si}$ an effective recombination center. Notably, the calculated capture cross sections are sensitive to the symmetry-equivalent defect configurations with different Jahn-Teller distortions. By accounting for two different $D_{2d}$ configurations of neutral $\text{Pt}_\text{Si}$ during transitions properly, our calculated carrier capture cross sections align well with experimental values. This work provides a microscopic picture of the carrier capture processes induced by $\text{Pt}_\text{Si}$ and emphasizes the importance of symmetry-equivalent configurations in defect-assisted nonradiative recombination.

The security of quantum key distribution (QKD) systems relies on the physical integrity of their components. While laser-damage attacks (LDAs) using high-power continuous-wave (cw) lasers have been well studied, the threat posed by pulsed lasers at alternative wavelengths remains underestimated. Here, we experimentally investigated the stability of four types of fiber-optic attenuators under exposure to sub-picosecond pulses at 1061 nm with average power reaching 1 W. Mechanical variable attenuators with blocking elements and fixed air-gap attenuators show resistance to this attack. MEMS-based variable attenuators exhibit increased attenuation or irreversible damage that causes a permanent reduction in attenuation of approximately 3.8 dB. For fixed attenuators with an absorption element, we demonstrate that initial pulsed irradiation significantly lowers the optical damage threshold of the components compared to direct cw attacks. The attenuation reduction achieved is up to 7 dB at a 1 W cw laser at 1550 nm. These results highlight the possibility of establishing a hidden side-channel for eavesdropping attacks and underscore the insufficiency of existing countermeasures against sophisticated LDA scenarios.

Minimizing the size of finite automata is a fundamental problem in theoretical computer science. Beyond standard minimization, further reductions can be achieved by decomposing an automaton into smaller components whose languages combine via intersection or union to recover the original language. However, in general, no polynomial-time algorithm is known for computing such decompositions. In this paper, we focus on automata that recognize ideals, that is, languages at level 1/2 in the Straubing-Thérien hierarchy. Equivalently, these languages are expressible as a finite union of languages of the form $Σ^*a_1Σ^*\dotsΣ^*a_nΣ^*$ where $Σ$ is an alphabet and $a_i$ are letters of $Σ$. We show that the two problems of deciding whether such a language can be decomposed into an intersection or a union of smaller automata are decidable in NL. Moreover, we provide a polynomial-time algorithm that computes a decomposition into an intersection, if one exists, while ensuring that the resulting components also recognize ideal languages.

Conversational multimodal understanding aims to infer the meaning or label of the current utterance from its preceding dialogue context together with textual, acoustic, and visual signals. Existing methods mainly strengthen contextual modeling through enhanced encoding, fusion, or propagation, but rarely abstract the context-utterance dependency into an explicit cue and incorporate it into later multimodal reasoning. To address this issue, we propose CUCI-Net for conversational multimodal understanding. CUCI-Net fully preserves the structural distinction between context and utterance during encoding, effectively abstracts their dependency into an interpretation cue by combining local modality evidence with global contextual evidence, and seamlessly integrates the resulting cue into the final multimodal interaction stage for context-conditioned prediction. Extensive experiments on mainstream benchmark datasets fully demonstrate the effectiveness of the proposed method.

Recent advances in combustion science have led to the generation of large volumes of data from high-fidelity simulations, detailed chemical-kinetic calculations and engine-relevant measurements and create new opportunities for data-driven modelling across interacting physical and chemical scales. Among these approaches, artificial intelligence has emerged as a promising framework for constructing surrogate models that reduce computational costs, deliver substantial speed-up and support prediction in complex reacting systems. This review provides a state-of-the-art assessment of AI-powered surrogate modelling for multiscale combustion, spanning chemical kinetics, mechanism reduction, turbulent flames, combustors, engines, and emissions prediction. Supervised, unsupervised, and hybrid or physics-guided learning approaches are examined and compared in terms of predictive accuracy, physical consistency, computational efficiency, and generalizability across conditions and scales. The review further discusses key challenges, including limited transferability across fuels and operating regimes, extrapolation errors, inconsistency in datasets and benchmarks, and the difficulty of building robust and trustworthy models for practical combustion workflows. Future opportunities are identified in the development of more reliable, scalable, and physically grounded surrogate frameworks for next-generation combustion research.

In a previous paper, we introduce and study formal manifolds, which generalize smooth manifolds. In this paper, we establish the basic theory of formal Lie groups, which are group objects in the category of formal manifolds. In particular, extending the classical formal Lie theory theorem, we prove that the category of formal Lie groups is equivalent to the category of Lie pairs.

Scalable optical quantum technologies require interference between large numbers of indistinguishable single-photons emitted by independent sources. Semiconductor quantum dots are known to be excellent on-demand sources of single-photons. They show record efficiency when inserted into optical cavities to control their spontaneous emission and generate trains of near identical photons over microsecond timescales. However, generating perfectly identical photons from distant cavity-based sources has remained a long-standing challenge. It requires precise matching of the emission wavelengths and emission dynamics, while simultaneously minimizing spectral noise across all time scales for distant emitters in uncorrelated environments. Here, we report on the nanofabrication of a large number of quantum dot-cavity sources with ultra-low spectral noise and wavelength dispersion. The high source efficiency and the use of two tuning mechanisms enable precise optimization of the spectral overlap between distant sources. With this approach, we demonstrate a two-photon indistinguishability of $88\pm1$ % between photons emitted from two distant sources. Remarkably, this value reaches the upper bound set by the intrinsic indistinguishability of photons emitted successively by each source. These results represent a key milestone for scaling photon-based quantum technologies.

In this paper, we resolve an open question in the field of optimization algorithms for training parametrized quantum circuits: Does the popular Rotosolve algorithm converge? Until now, interpolation-based coordinate descent methods such as Rotosolve have mostly been treated as heuristics, lacking any formal convergence guarantees. We rigorously analyze Rotosolve, and show that it converges to $\varepsilon$-stationary points if the optimization landscape is non-convex and smooth; and to $\varepsilon$-suboptimal points if the objective function additionally obeys the Polyak-Lojasiewicz (PL) condition. Further, we derive explicit worst-case rates of convergence in the finite quantum measurement regime. These rates are contrasted against those from a similar coordinate-based method: Randomized Coordinate Descent (RCD). Although in the worst case their rates are, prima facie, equivalent, we present arguments for a more nuanced comparison between the two. We highlight that Rotosolve is hyperparameter-free, and implicitly uses first and second derivatives in its updates. Finally, we supplement our theoretical findings with numerical experiments from Quantum Machine Learning; and compare the performance of Rotosolve against RCD, Stochastic Gradient Descent, Simultaneous Perturbation Stochastic Approximation, and Randomized Stochastic Gradient Free methods.

Radon is one of the most important sources of background in rare event search experiments, such as those searching for Dark Matter and neutrinos, due to its unavoidable production from natural uranium. In low-background experiments, radon emanation from detector materials and components accounts for a major portion of contamination. To investigate this, a radon detection system was developed at the Carleton nOble Liquid Detector Laboratory (COLD Lab). The setup consists of a stainless steel emanation chamber, a low-background ZnS(Ag) cell, and an assembly for radon transfer and collection. This setup was used to study radon emanation from materials under vacuum conditions. Additionally, a charcoal trap made of activated charcoal and equipped with a flow meter was constructed to study radon levels in nitrogen gas and the residual radon in the gas filter used in the DEAP-3600 processing system. The radon concentration in the glove box, where critical DEAP-3600 internal detector components were completed, was also calculated based on these measurements. Now calibrated and in-use, the COLD lab radon emanation counter is an essential diagnostic tool for reducing backgrounds in future rate-event search experiments.

We present a unified analysis of the $Z_c(3900)$ and $Z_{cs}(3985)$ states considering both experimental and lattice data. The study simultaneously includes the processes $e^+e^- \rightarrow J/ψπ^+π^-, J/ψK^+ K^-, D^0 D^{\ast-} π^+, (D^{\ast 0} D_s^{-}+D^0 D_s^{\ast -}) K^+$, together with finite-volume energy levels from recent lattice QCD simulations. Open-charm meson loops with triangle singularities, the $J/ψπ(J/ψ\bar{K})$-$\bar{D}D^*(\bar{D}D^*_s)$ coupled-channel interactions, and the $ππ$-$K\bar K$ final-state interaction are all taken into account. We find that pole contributions associated with the $Z_c(3900)$ and $Z_{cs}(3985)$ are indispensable for describing the data. The successful joint description of the experimental and lattice data supports the interpretation that the $Z_c(3900)$ and $Z_{cs}(3985)$ are SU(3) flavor partners within the same octet multiplet and indicates that both are resonance states. The extracted pole masses and half-widths of the $Z_c(3900)$ and the $Z_{cs}(3985)$ are $(3879.6 \pm 4.8)$ MeV and $(32.2 \pm 4.7)$ MeV, and $(3976.9 \pm 5.1)$ MeV and $(28.8 \pm 5.9)$ MeV, respectively. The ratios of the $Z_c(Z_{cs})$ couplings to the $D\bar D^*(D_s\bar{D}^\ast+D\bar{D}_s^\ast)$ and $J/ψπ(J/ψK)$ channels are also determined. A compositeness analysis indicates that, although the $D\bar D^* (D_s\bar{D}^\ast+D\bar{D}_s^\ast)$ component in the $Z_c(3900) (Z_{cs}(3985))$ state is sizable, additional components are still needed to form these exotic states.

In this paper, we propose a novel Physics-Informed Neural Network (PINN) framework based on the Cordès condition for solving both linear and fully nonlinear partial differential equations (PDEs) in non-divergence form, together with their applications. By incorporating the operator structure into the loss function, the proposed method improves the conditioning of the associated optimization problem, thereby enhancing training stability and solution accuracy. The framework is further extended to include Hamilton-Jacobi-Bellman and Monge-Ampère equations, with applications to optimal transport. Numerical experiments demonstrate the effectiveness and robustness of the method, as well as its capability to address high-dimensional problems, highlighting the promise of learning-based approaches for tackling challenging PDEs. Owing to its generality and simplicity, the proposed method is expected to be of broad interest to the scientific and engineering communities.

A search for supersymmetric electroweakinos is performed using events with a low-momentum (soft) isolated track and large missing transverse momentum, targeting nearly mass-degenerate higgsino-like charginos and neutralinos. For mass splittings of 0.3$-$1 GeV, the chargino decays to the lightest neutralino and a low-momentum pion, which can produce a soft, potentially displaced track. A parameterized neural network separates signal from background using kinematic and impact parameter information. The analysis uses 138 fb$^{-1}$ of proton$-$proton collision data at a center-of-mass energy of 13 TeV recorded with the CMS detector. No significant excess above the standard model expectation is observed. At 95% confidence level, the considered higgsino model is excluded for mass splittings in the range 0.28$-$1.15 GeV and for chargino masses up to 185 GeV, setting stringent constraints on natural supersymmetry scenarios.

Surface hopping (SH) methods are typically employed to simulate ultrafast nonadiabatic processes, but long timescales often remain beyond their reach. To address this, accelerated SH scheme mitigate this limitation by scaling the driving forces of such process, either nonadiabatic couplings (NACs) in case of internal conversion or spin-orbit couplings (SOCs) for intersystem crossing. However, obtaining the actual time constant requires extrapolation from several ensembles of trajectories with different scaling factors. This introduces a significant computational demand, often restricting the number of trajectories per ensemble and, therefore, reducing the statistical confidence in the resulting time constant. In this work, we investigate the accelerated scheme using silaethylene (CH$_2$SiH$_2$) as a case study, evaluating various population fitting methods and extrapolation techniques. We trained machine learning models for potential energy surfaces (PESs) and NACs, and extended our rotate-predict-rotate approach to fit SOCs. These models demonstrate high performance, yielding populations within the confidence interval of the reference MR-CISD/SA-CASSCF(2,2) data; however, the extrapolation itself is highly sensitive to the fitted time constants, leading to discrepancies in the final time constant. Finally, we showcase and discuss how ML models can enhance the reliability of an accelerated SH scheme.

The minimum-enstrophy theory of Bretherton and Haidvogel postulates that two-dimensional turbulent systems evolve to a state that minimises enstrophy at a fixed energy level. We extend this to the rotating spherical quasi-geostrophic setting, accounting for bottom topography and the fully nonlinear Coriolis effect, resulting in latitude-dependent effects not present in planar approximations. We prove existence and nonlinear stability of minimum-enstrophy solutions and describe analytically asymptotic regimes for certain rates of rotation, topography scales, and energy values. We compute the minimum-enstrophy solutions by a structure-preserving method for the quasi-geostrophic equations on the sphere. We apply the method to a range of parameter values, including those describing Jupiter's atmosphere. The results reveal a distinct latitude dependence of the flow, with a tendency for topographical trapping near the poles and zonal flow near the equator, depending on the chosen parameters. The predicted nonlinear stability is confirmed numerically by integrating perturbed solutions using a structure-preserving time discretisation.

Moiré heterostructures provide a powerful framework for tailoring electronic band structures via controlled long-range periodic superlattice potentials. Beyond widely studied moiré-tailored flat bands, folded band structures can host emergent Dirac states, which have recently attracted considerable interest. Direct momentum-resolved observation of gapless moiré-Dirac quasiparticles, however, is challenging and has so far remained elusive. By performing angle-resolved photoemission spectroscopy measurements on an epitaxial surface-moiré structure, we here provide direct spectroscopic evidence of moiré-dressed Dirac states with topological character. Driven by the one-dimensional superlattice potential, electrons propagate anisotropically with a weak but massless Dirac dispersion along the confinement direction. The observed band crossings belong to topological nodal lines pinned to the mini-Brillouin zone boundaries. As such, they are enforced and robustly protected by the non-symmorphic symmetry of the superlattice. Finally, we demonstrate that the topological excitations can be almost continuously controlled by tuning the moiré lattice periodicity, directly unveiling moiré heterostructures as a promising platform for creating and controlling topological moiré-Dirac states.

Generating realistic synthetic citation, patent, or component dependency networks is essential for benchmarking community detection, graph visualisation, and network data mining algorithms. We present the first systematic comparison of generators of directed graphs that are nearly acyclic and have a ground-truth community structure. We evaluate 12 methods across 7 real citation networks and 26 metrics. We propose the practice of reversing directions of edges in static generators to break cycles and induce a citation-like flow, which significantly improves the performance of a degree-corrected Stochastic Block Model. Our novel methodological approach to evaluating community detection benchmarks distinguishes between endogenous and exogenous mesoscopic similarities, with the latter proving more important. This distinction reveals that high-parameter models suffer from overfitting by memorising planted community statistics which lead to their failing to produce realistic networks. Finally, we introduce the Citation Seeder (CS) algorithm, an iterative generator grounded in the Price-Pareto model of citation networks, with interpretable parameters and O(N+E) runtime. CS achieves competitive results against the best-performing baselines while using up to four orders of magnitude fewer parameters and providing a clean framework for explaining and predicting a network's future growth.

Formal models for concurrent and distributed systems describe machines; the people who operate them are either ignored or treated as external environment. Yet key distributed systems -- notably grassroots platforms -- include people operating their personal machines (smartphones), and their faithful description must include the states of both people and machines and how they jointly effect system behaviour. Here, we propose volitional multiagent atomic transactions -- executed atomically by machines and guarded by their people's volitions -- as a novel mathematical foundation for specifying systems consisting of people operating machines. Each agent's state consists of a volitional state and machine state; a transaction is enabled when the machine precondition holds and the guarding persons are willing. For example, befriending two people is guarded by both; unfriending, by either; voluntary swap of coins and bonds is guarded by both parties, while a payment is guarded by the payer. We develop the mathematical machinery to express safety and liveness of platforms specified in this framework, and provide example specifications of two grassroots platforms: social networks, and coins and bonds. These specifications are then used by AI to derive working implementations. % We employ here a novel and simpler definition of `grassroots' that better captures the informal notion -- multiple instances can form and operate independently, yet may coalesce -- and show that the platforms specified here, as well as those hitherto proven grassroots under the original definition, are grassroots under the new definition.

In this paper, we develop a groupoid approach to the equivariant coarse Baum--Connes conjecture. For a bounded geometry metric space $X$ equipped with a proper, free, and isometric action of a countable discrete group $Γ$, we introduce the equivariant coarse groupoid $G(X, Γ)$. We prove that the groupoid Baum--Connes conjecture for $G(X, Γ)$ with coefficients in $\ell^{\infty}(X,\mathcal{K})^Γ$ is equivalent to the equivariant coarse Baum--Connes conjecture for $(X, Γ)$ using a localization algebra description of equivariant $KK^\mathcal{G}$-theory for étale groupoids. As applications of this framework, we prove that if the space $X$ admits a coarse embedding into Hilbert space (which is not required to be $Γ$-equivariant), then the equivariant coarse Novikov conjecture holds for $(X, Γ)$, i.e., the assembly map $μ_{X,Γ}$ is an injection. We also obtain a new proof of the equivariant coarse Baum--Connes conjecture if $X$ admits an equivariant coarse embedding into Hilbert space.

We present a comprehensive study of the pulsating primary component of the long-period binary system RY Lep. The spectral energy distribution and the absence of detectable lines indicate that the companion is likely a white dwarf. Atmospheric parameters and chemical abundances were determined from a high-resolution spectrum obtained with the the Southern African Large Telescope (SALT). The spectroscopic analysis reveals an underabundance of iron-group elements and an enhancement of neutron-capture elements, including barium and europium, with an overall metallicity of [m/H]$\approx -0.4$. In the next step, we performed the first Fourier analysis of long-term photometric data from ASAS, SuperWASP, and TESS. In the TESS observations, we identify several additional frequencies not present in the ground-based data. The dominant frequency at 4.4415 d$^{-1}$ is identified as a radial mode, most likely the first radial overtone. Finally, seismic modeling of RY Lep was carried out by fitting the dominant mode together with the secondary frequency at 6.5991 d$^{-1}$, considering two possible identification for the latter: the third radial overtone or a dipole mode. Bayesian inference based on Monte Carlo simulations yields a stellar mass of $\sim 2.0 $M$_\odot$ and an age of $\sim 730$ Myr. All viable seismic models place RY Lep in the hydrogen shell-burning evolutionary phase, with a metallicity consistent with the spectroscopic determination.

Non-invasive electrophysiology lacks methods that accurately reconstruct whole-brain spatiotemporal dynamics while incorporating individual cortical geometry, leaving current electroencephalography and magnetoencephalography source imaging limited by simplistic or biologically implausible priors. Here, we show that embedding participant-specific Geometric Basis Functions (GBFs), eigenmodes derived from each individual's cortical surface, provides a powerful anatomic constraint that resolves the inverse problem and improves reconstruction fidelity. The method reconstructs neural sources as linear combinations of geometric basis functions, thereby aligning source estimates with the geometric organization of neural dynamics. We validate GBF across the Meta-Source Benchmark, task-evoked data, resting-state networks, intracranial stimulation, and epilepsy data. The results demonstrate that GBF yields high localization accuracy and captures fast spatiotemporal dynamics consistent with anatomical pathways. These findings suggest that both spontaneous and evoked whole-brain activity can be described by hundreds of geometric modes, providing a compact yet accurate representation of neural sources. By linking cortical geometry to electrophysiological dynamics, GBF offers a versatile source imaging tool for both scientific and clinical applications.

The HyGAL Stratospheric Observatory for Infrared Astronomy (SOFIA) legacy program aims at characterizing the interstellar medium in the Milky Way using hydrides, [C II], and [O I] absorption lines with the 2.7 m SOFIA telescope toward twenty-five submillimeter-bright Galactic star-forming regions. As part of HyGAL, we investigated correlations among the known H$_2$ tracers -- CH and OH from SOFIA observations, and HCO$^+$ and CCH from ancillary absorption line data from ground-based telescopes. We also examined the abundance variation of neutral atomic oxygen, [O I], observed in absorption. CH, OH, HCO$^+$, and CCH all exhibit strong mutual correlations. OH in particular shows tight correlations with HCO$^+$ and CCH, reflecting their linked chemical and physical pathways. Column density ratios among these H$_2$ tracers are consistent with previous measurements in local diffuse clouds and remain uniform across Galactic environments and velocity intervals. The gas phase oxygen abundance relative to total hydrogen, $\langle X$(O)$\rangle=N$(O)/$N$(H$_{\rm total}$), is $(3.09\pm0.64)\times10^{-4}$, slightly below the elemental solar value but consistent with the previous observations measuring gas-phase abundances. We also find that $N$(HI) decreases toward the regions where the molecular fraction exceeds $f_{H_2}^N \sim 0.5$, marking the onset of the molecular phase. While the atomic oxygen abundance remains roughly constant, the abundances of OH, HCO$^+$, and CCH increase with the molecular fraction. Gas traced by the HCO$^+$ absorption corresponds to higher molecular fractions than that traced by HI and hydride ions, highlighting density variations in the diffuse-to-translucent ISM along different lines of sight.

This paper addresses the problem of identifying time interval separators in temporal networks. We introduce d-MinIntSep, a new variant of the temporal separator problem, which models failures as time intervals assigned to vertices and aims to block all temporal paths between a source and a target that can be completed within a given deadline d. We prove that the d-MinIntSep problem is NP-hard and hard to approximate within a logarithmic function of the size of the vertex set, assuming P is not equal to NP, and we propose an Integer Linear Programming (ILP) formulation to compute minimum interval separators. This latter method is evaluated on synthetic and real-world temporal networks derived from transportation datasets. The experimental results show that the running time is strongly influenced by the temporal dimension, the imposed deadline, and the density of temporal paths.

Magnomechanical systems provide a promising route for exploring coherent hybrid magnon-phonon interactions and hybrid information processing, but their realization has so far been limited by weak magnon-phonon coupling in conventional bulk platforms. We show that a suspended membrane of a two-dimensional van der Waals ferromagnet with in-plane magnetization and out-of-plane mechanical oscillations exhibits large magnomechanical coupling dominated by magnetoelastic interactions. The parametric single magnon-phonon coupling rate scales linearly with pre-strain and can reach hundreds of Hertz to low kiloHertz in suspended membranes of van der Waals ferromagnets such as CrGeTe_3 under experimentally realistic conditions. This rate exceeds typical values reported for YIG spheres by more than three orders of magnitude. Our results demonstrate that suspended membranes of van der Waals magnets provide a robust and highly tunable platform for magnomechanics.