For prime Fano threefolds $X$ of genus $g=12$, $10$ or $9$, and for totally disjoint pairs of lines $Z_1$, $Z_2$ in $X$, we establish links from the blowups of $X$ along $Z_1$ and $Z_2$. If $g=12$, then the links end with the blowups of Fano threefolds of type 2.21 along bi-cubic curves; if $g=10$, then the links end with the blowups of the projectivization of the tangent bundle of the projective plane along genus $2$ bi-quintic curves with a mild condition; if $g=9$, then the links end with conic bundles over the product of two projective lines with the discriminant loci of bidegree $(3,3)$. When $g=12$ or $g=10$, we also establish the converses of the above links. Moreover, we especially focus on the links when $g=12$ and the links are $\mathbb{G}_m$-equivariant.

In his influential work, Thurston introduced a norm on the second homology group of compact orientable 3-manifolds M, which by duality also determines a dual norm on the second cohomology group. A natural question, initiated by Thurston, is whether integral points on the boundary of the dual norm ball have a geometric interpretation. Thurston showed that the Euler class of the oriented tangent plane field to any taut foliation of M lies in the dual unit ball, and conjectured that, conversely, any integral point on the boundary of the dual unit ball is realised as the Euler class of a taut foliation. In this chapter, we discuss how several geometric, topological, and dynamical structures on a 3-manifold give rise to integral points in the dual unit ball of the Thurston norm, and what is known about Thurston's Euler class one conjecture in these contexts. These structures are taut foliations, tight contact structures, pseudo-Anosov flows, quasigeodesic flows, and circular orders on the fundamental group.

We investigate how to combine a collection of quantum binary models into a multinomial classifier. We employ a hybrid approach, adopting strategies like one-vs-one, one-vs-rest and a binary decision tree. We benchmark each method, by emphasizing their computational overhead and their impact on the quantum advantage. By comparison against a classical binary model (generalized using the same approach), we show that the decision tree represents a cost-effective solution, achieving similar accuracies to other methods with an overhead at most logarithmic in the total number of classes.

In this paper, we use a version of the BF formulation of two-dimensional dilaton gravity that allows to define a gauge theory of the two-dimensional Poincaré or Maxwell algebras and several of their higher-spin generalisations, both of finite and infinite dimension. The spectrum of the two-dimensional higher-spin gravity with vanishing cosmological constant based on the extended, infinite-dimensional higher-spin algebra is shown to contain an infinite collection of scalar degrees of freedom with a continuum of ever increasing mass, corresponding to the twisted-(co)adjoint representation. We comment on an approach to include backreaction of the scalar fields on the gravity sector at the level of formal equations of motion, thereby providing a first example of a fully interacting higher-spin gravity theory with vanishing cosmological constant in two dimensions.

Larmor frequency shifts in white matter (WM) vary with fiber orientation due to anisotropic microstructure. Since clinical voxels are significantly larger than these microscopic frequency variations, the measured signal represents a bulk average of local shifts. Accurate estimation of magnetic susceptibility therefore requires accounting for these underlying frequency distributions that exist below the imaging resolution. We evaluated whether Microstructure-informed Quantitative Susceptibility Mapping (μQSM) can predict orientation-dependent sub-voxel frequency shifts from orientationally dispersed hollow cylinders and spherical inclusions. Diffusion-weighted and multi-gradient-echo images were acquired from ex vivo pig optic nerves at multiple orientations relative to the main magnetic field using a 3T Siemens Connectom scanner. We also analyzed de-ironed optic nerves to try and separate the effects of myelin and iron on susceptibility. The estimated sub-voxel frequency shifts closely matched μQSM predictions, consistent with mesoscopic field perturbations generated by uniformly magnetized axons. De-ironing had minimal effect on the frequency shifts, indicating negligible iron contribution. μQSM accurately reproduces the orientation dependence of Larmor frequency shifts in optic nerve WM, providing new insight into their microstructural origin and supporting improved estimation of tissue magnetic susceptibility in Quantitative Susceptibility Mapping.

ZnTe is arguably the most widely used nonlinear crystal for the generation and detection of THz radiation, used in conjunction with sub-bandgap optical excitation by femtosecond lasers operating near 800 nm. The THz dielectric function of ZnTe is the key parameter defining the efficiency and bandwidth of THz generation and detection. Here, we demonstrate that the THz dielectric function of ZnTe undergoes substantial transient modification at 800 nm sub-bandgap excitation under conditions typical for THz generation. These modifications arise from significant free-carrier generation via two-photon absorption of the 800 nm pump, accompanied by the pump-driven activation of the THz-active phonon modes. Using optical pump-THz probe spectroscopy, we characterized the THz dielectric function of ZnTe under 800 nm excitation as a function of pump fluence and pump-probe delay. Analysis of the experimental data within the Drude-Lorentz model provided the generated free carrier density and momentum scattering time, and oscillator strength of the pump activated THz phonon modes, revealing their transient evolution in dependence on the excitation conditions.

ESA's Hera space mission is on its way to the mission target, the binary asteroid (65803) Didymos. HyperScout-H, one of the instruments onboard Hera, is a hyperspectral imager operating in the visible and near-infrared regions between 0.65 and 0.95 microns. HyperScout-H will enable a detailed assessment of the composition of both objects, Didymos and its satellite Dimorphos, the characterization of space weathering effects, and the possible presence of exogenous material on their surfaces. To monitor instrument functionality, calibration exposures are acquired regularly. This article describes the in-flight calibrations carried out for HyperScout-H during the commissioning and cruise phases. Bias and dark exposures, as well as stellar field observations, were acquired several times after launch. We update the calibration data and monitor instrument performance in the space environment. In five images, the surface of Mars fills the entire field of view, enabling cross-validation of HyperScout-H results with those reported by other Mars missions. The calibration data indicate that the bias pattern is stable, the dark current remains negligible for short exposures, and the detector response is highly linear. We quantify the field-of-view alignment and geometric distortion, and evaluate the point spread function based on the stellar field observations. Stellar observations and Mars swing-by data provide updated radiometric calibration constants, suggesting that in-flight conditions have slightly modified the detector's spectral response. In-flight calibrations are essential to ensure data quality and reliability. The results obtained for HyperScout-H demonstrate that the instrument can achieve its scientific goals in observations of the Didymos-Dimorphos system.

Large Language Model (LLM)-based Automated Program Repair (APR) has shown strong potential on textual benchmarks, yet struggles in multimodal scenarios where bugs are reported with GUI screenshots. Existing methods typically convert images into plain text, which discards critical spatial relationships and causes a severe disconnect between visual observations and code components, leading localization to degrade into imprecise keyword matching. To bridge this gap, we propose GALA (Graph Alignment for Localization in APR), a framework that shifts multimodal APR from implicit semantic guessing to explicit structural reasoning. GALA operates in four stages: it first constructs an Image UI Graph to capture visual elements and their structural relationships; then performs file-level alignment by cross-referencing this UI graph with repository-level structures (e.g., file references) to locate candidate files; next conducts function-level alignment by reasoning over fine-grained code dependencies (e.g., call graphs) to precisely ground visual elements to corresponding code components; and finally performs patch generation within the grounded code context based on the aligned files and functions. By systematically enforcing both semantic and relational consistency across modalities, GALA establishes a highly accurate visual-to-code mapping. Evaluations on the SWE-bench Multimodal benchmark demonstrate that GALA achieves state-of-the-art performance, highlighting the effectiveness of hierarchical structural alignment.

In this paper, we study a general class of inhomogeneous kinetic models that unifies fundamental models in both the statistical physics of particles and of waves, namely the kinetic Boltzmann equations and the kinetic wave equations, in both classical (non-relativistic), relativistic and quantum settings. We formulate this unified equation into the GENERIC (General Equation for Non-Equilibrium Reversible-Irreversible Coupling) framework. We then derive the grazing (small-angle) limit in two-body interaction systems, which leads to Landau-type equations. Finally, we show that these limiting systems can also be formulated as GENERIC systems.

Understanding the transport properties of cuprate superconductors is one of the central challenges in the physics of strongly correlated electrons. The most common approach is to define and solve a low-energy lattice model, but it is still unclear what the minimal model is to capture all relevant mechanisms and provide quantitative predictions. The main uncertainty concerns the choice of the orbital degrees of freedom to be included in the model, as well as the definition of the effective coupling. In this paper, we study the two most commonly considered models, namely the single-orbital Hubbard model and the three-orbital Emery model. We investigate and compare their spectral and transport properties, and find that the two models present a similar, but not the same, physical picture. We identify several strong quantitative differences which might allow one to discriminate between the two models by comparing theory with experiments. We compare our results for several physical quantities with 7 different experiments on 3 different La$_2$CuO$_4$-based cuprates, and in general find excellent agreement. The dc resistivity and the effective mass results suggest that the coupling constant in the effective Hubbard model is larger than expected. We find several more properties that are sensitive to the precise value of the coupling constant, including the critical doping for the Lifshitz transition, and the local spectral weight in the vicinity of the Fermi level; the latter provides a promising way to estimate the effective coupling constant in future photoemission experiments.

Deobfuscating binary code remains a fundamental challenge in reverse engineering, as obfuscation is widely used to hinder analysis and conceal program logic. Although large language models (LLMs) have shown promise in recovering semantics from obfuscated binaries, a systematic evaluation of their effectiveness is still lacking. In this work, we present BinDeObfBench, the first comprehensive benchmark for assessing LLM-based binary deobfuscation across diverse transformations spanning pre-compilation, compile-time, and post-compilation stages. Our evaluation shows that deobfuscation performance depends more on reasoning capability and domain expertise than on model scale, and that task-specific supervised fine-tuning consistently outperforms broad domain pre-training. Reasoning models can maintain robustness under severe obfuscation, generalize across different instruction set architectures (ISAs) and optimization levels. In-context learning benefits standard models but yields limited gains for reasoning models. Overall, our study highlights the importance of task-specific fine-tuning and reasoning-driven strategies, and positions BinDeObfBench as a basis for future work in binary deobfuscation.

Generative AI tools often answer questions using source documents, e.g., through retrieval augmented generation. Current groundedness and hallucination evaluations largely frame the relationship between an answer and its sources as binary (the answer is either supported or unsupported). However, this obscures both the syntactic moves (e.g., direct quotation vs. paraphrase) and the interpretive moves (e.g., induction vs. deduction) performed when models reformulate evidence into an answer. This limits both benchmarking and user-facing provenance interfaces. We propose the development of a reader-centred taxonomy of grounding as a set of support relations between generated statements and source documents. We explain how this might be synthesised from prior research in linguistics and philosophy of language, and evaluated through a benchmark and human annotation protocol. Such a framework would enable interfaces that communicate not just whether a claim is grounded, but how.

The electrocatalytic activity of oxophilic Ag nanoparticles, combined with small amounts of Pd and Au, was investigated for ethanol oxidation reactions (EOR) and glycerol oxidation reactions (GOR) in alkaline media. The EOR and GOR results revealed competitive current densities and less positive onset potentials for the AgPd/C and AgPdAu/C electrocatalysts, both containing 5 wt% Pd, compared to the commercial Pd/C catalyst, which has a significantly higher loading of the costly noble metal (20 wt%). In situ FTIR analyses during EOR confirmed that ethanol is initially adsorbed as acetylated species, which are subsequently oxidized to acetate ions, the main stable product in alkaline medium. However, the incorporation of Pd and Au into the Ag matrix did not significantly alter the reaction mechanism. During GOR, the in situ FTIR studies demonstrated that catalyst composition influences the oxidation pathways: Pd-rich surfaces favor oxalate formation, while a significant presence of Ag promotes deeper oxidation (up to carbonate), with the AgPdAu ternary catalyst exhibiting intermediate behavior. One key benefit is the lower susceptibility of Ag to irreversible adsorption of reaction byproducts, which enhances electrocatalyst durability. Thus, surface segregation of Ag at high potentials can modify the catalytic surface reactivity, affecting both stability and efficiency.

We study finite-horizon optimal switching with discrete intervention dates on a general filtration, allowing continuous-time observations between decision dates, and develop a deep-learning-based dual framework with computable upper bounds. We first derive a dual representation for multiple switching by introducing a family of martingale penalties. The minimal penalty is characterized by the Doob martingales of the continuation values, which yields a fully computable upper bound. We then extend DeepMartingale from optimal stopping to optimal switching and establish convergence under both the upper-bound loss and an $L^2$-surrogate loss. We also provide an expressivity analysis: under the stated structural assumptions, for any target accuracy $\varepsilon>0$, there exist neural networks of size at most $c d^{q}\varepsilon^{-r}$ whose induced dual upper bound approximates the true value within $\varepsilon$, where $c$, $q$, and $r$ are independent of $d$ and $\varepsilon$. Hence, the dual solver avoids the curse of dimensionality under the stated structural assumptions. For numerical assessment, we additionally implement a deep policy-based approach to produce feasible lower bounds and empirical upper--lower gaps. Numerical experiments on Brownian and Brownian--Poisson models demonstrate small upper--lower gaps and favorable performance in high dimensions. The learned dual martingale also yields a practical delta-hedging strategy.

Despite a growing desire among consumers to shop responsibly, translating this intention into behaviour remains challenging. Previous work has identified that information seeking (or lack thereof) is a contributing factor to this intention-behaviour gap.In this paper, we hypothesize that searching can bridge this gap - helping consumers to make purchasing decisions that are better aligned with their values. We conducted a task-based study with 308 participants, asking them to search for information on one of eight ethical aspects regarding a product they were actively shopping for. Our findings show that actively searching for such information led to an overall increase in the importance participants' assigned to ethical aspects.However, it was the recognition and understanding of ethical considerations, rather than ethical intentions or search activity, that drove shifts towards more responsible purchasing decisions. Participants who acknowledged and filled knowledge gaps in their decision making showed significant behaviour change, including increased searching and a stronger desire to alter their future shopping habits. We conclude that responsible consumption can be considered a partial information problem, where awareness of one's own knowledge limitations may be the catalyst needed for meaningful consumer behaviour change.

Over extended systems of finite type arithmetic, we utilize a formal representation of the outer measure to define a translation which allows for the systematic formalization of probabilistic statements. As a main result, this translation gives rise to novel probabilistic logical metatheorems in the style of proof mining, guaranteeing the extractability of computable bounds from (non-effective) proofs of probabilistic existence statements. We further show how the set-theoretically false principle of uniform boundedness due to Kohlenbach can be used to replicate logically strong continuity properties of probability measures in the context of these bound extraction theorems in a tame way, i.e. without affecting the computational complexity of the resulting bounds in question, all the while guaranteeing the validity of those bounds even over finitely additive probability spaces. This in particular provides a formal perspective on the elimination of the principle of $σ$-additivity during bound extraction, as previously only observed ad hoc in the practice of proof mining. In that context, we for the first time provide a proof-theoretic treatment of higher-type uniform boundedness principles and related contra-collection principles via Kohlenbach's monotone variant of Gödel's functional interpretation, which is of independent interest. All together, these new metatheorems provide a systematic proof-theoretic approach towards extracting various types of quantitative information for probabilistic theorems considered in the literature, justifying a range of recent applications to probability theory and stochastic optimization. This paper represents a major logical contribution to a recent advance of bringing the methods of proof mining to bear on probability theory, significantly extending previous work by the first and third author [Forum Math. Sigma, 13, e187 (2025)] in that direction.

We present $ϕ-$DeepONet, a physics-informed neural operator designed to learn mappings between function spaces that may contain discontinuities or exhibit non-smooth behavior. Classical neural operators are based on the universal approximation theorem which assumes that both the operator and the functions it acts on are continuous. However, many scientific and engineering problems involve naturally discontinuous input fields as well as strong and weak discontinuities in the output fields caused by material interfaces. In $ϕ$-DeepONet, discontinuities in the input are handled using multiple branch networks, while discontinuities in the output are learned through a nonlinear latent embedding of the interface. This embedding is constructed from a {\it one-hot} representation of the domain decomposition that is combined with the spatial coordinates in a modified trunk network. The outputs of the branch and trunk networks are then combined through a dot product to produce the final solution, which is trained using a physics- and interface-informed loss function. We evaluate $ϕ$-DeepONet on several one- and two-dimensional benchmark problems and demonstrate that it delivers accurate and stable predictions even in the presence of strong interface-driven discontinuities.

Understanding the dynamics of current-driven skyrmion is essential for their practical applications. In this study, we apply an AC current pulse (a) in x-- direction, and (b) in both x-- and y-- directions through the free layer of a Co/Pt thin film and investigate the motion of the skyrmion. We show that the skyrmion follows the sinusoidal current pulse and behaves like a forced oscillator in the range of current amplitude 1 $\times$ 10$^{11}$ A/m$^2$ to 1 $\times$ 10$^{12}$ A/m$^2$ and frequency 5 $\times$ 10$^{8}$ Hz to 1 $\times$ 10$^{10}$ Hz. For current pulse of (A$_1$sin$ω_1$t, A$_2$sin($ω_2$t+$ϕ$), 0), the skyrmion forms Lissajous figures in the x-y plane, same as observed in classical mechanics. The results are compared at T = 0 K and T $>$ 0 K to analyze the effect of temperature. As the skyrmion Hall angle ($θ_{SkH}$) and stochastic thermal fluctuation ($\textbf{F}^{Th}$) are functions of temperature, the skyrmion starts deviating from its path at T = 0 K with increasing temperature and eventually generates somewhat deformed Lissajous figures from ideal.

A fundamental algorithmic problem in computational biology is to find all subgraphs of a given type (superbubbles, snarls, and ultrabubbles) in a directed or bidirected input graph. These correspond to regions of genetic variation and are useful in analyzing collections of genomes. We present the first linear-time algorithms for identifying all snarls and all ultrabubbles, resolving problems open since 2018. The algorithm for snarls relies on a new linear-size representation of all snarls with respect to the number of vertices in the graph. We employ the well-known SPQR-tree decomposition, which encodes all 2-separators of a biconnected graph. After several dynamic-programming-style traversals of this tree, we maintain key properties (such as acyclicity) that allow us to decide whether a given 2-separator defines a subgraph to be reported. A crucial ingredient for linear-time complexity is that acyclicity of linearly many subgraphs can be tested simultaneously via the problem of computing all arcs in a directed graph whose removal renders it acyclic (so-called feedback arcs). As such, we prove a fundamental result for bidirected graphs, that may be of independent interest: all feedback arcs can be computed in linear time for tipless bidirected graphs, while in general this is at least as hard as matrix multiplication, assuming the k-Clique Conjecture. Our results form a unified framework that also yields a completely different linear-time algorithm for finding all superbubbles. Although some of the results are technically involved, the underlying ideas are conceptually simple, and may extend to other bubble-like subgraphs. More broadly, our work contributes to the theoretical foundations of computational biology and advances a growing line of research that uses SPQR-tree decompositions as a general tool for designing efficient algorithms, beyond their traditional role in graph drawing.

We define a Brauer group for differential graded algebras over differential graded graded-commutative or commutative base rings. Based on previous work we give an explicit classification of dg-fields, and compute the so-defined Brauer group in each case explicitly.

Large-scale nonsmooth optimization problems arise in many real-world applications, but obtaining exact function and subgradient values for these problems may be computationally expensive or even infeasible. In many practical settings, only inexact information is available due to measurement or modeling errors, privacy-preserving computations, or stochastic approximations, making inexact optimization methods particularly relevant. In this paper, we propose a novel inexact limited memory bundle method for large-scale nonsmooth nonconvex optimization. The method tolerates noise in both function values and subgradients. We prove the global convergence of the proposed method to an approximate stationary point. Numerical experiments with different levels of noise in function and/or subgradient values show that the method performs well with both exact and noisy data. In particular, the results demonstrate competitiveness in large-scale nonsmooth optimization and highlight the suitability of the method for applications where noise is unavoidable, such as differential privacy in machine learning.

For a finite group $G$, we compute the algebraic $K$-theory of the category of equivariant sheaves on a locally compact Hausdorff $G$-space, generalizing a result of Efimov, and determine the equivariant $E$-theory of the $C^*$-algebra of continuous functions. These invariants admit natural descriptions in terms of a new equivariant cohomology theory, which we call Bredon sheaf cohomology. This theory recovers classical Bredon cohomology for $G$-CW complexes and ordinary sheaf cohomology when $G$ is trivial. We establish its basic structural properties and prove a strong uniqueness theorem: any functor from the category of locally compact Hausdorff $G$-spaces to a dualizable stable category satisfying equivariant open descent and cofiltered compact codescent is equivalent to Bredon sheaf cohomology, generalizing a result of Clausen.

Hydrogen flames exhibit multiple intrinsic instabilities. The low molar masses of H and H2 lead to significant Soret diffusion near the flame front; however, its influence on hydrogen flame instabilities remains to be quantified. This study investigates the effect of Soret diffusion on instability evolution dynamics via one-dimensional counterflow analysis and two-dimensional, high-fidelity direct numerical simulations covering both the linear growth regime and the fully developed nonlinear regime over a wide range of equivalence ratios (phi). In the linear regime, Soret diffusion increases the perturbation growth rate at phi < 1.7, especially under lean conditions, but reduces the growth rate at phi > 1.7. A similar sensitivity reversal is observed in the Markstein length near the peak equivalence ratio of unstretched laminar flame speed. In the nonlinear regime, Soret diffusion accelerates the formation of small-scale wrinkles in lean hydrogen flames and reduces the characteristic size of large-scale finger structure by one-third. An interesting observation is that, although Soret diffusion promotes preferential diffusion and increases the local flame displacement speed, the global fuel consumption rate decreases due to a reduction in the overall flame surface area. In addition, curvature-based flame segment analysis reveals a synergistic effect between Soret diffusion and Fickian diffusion that enhances/reduces the local equivalence ratio in positively/negatively curved regions of the flame front. The probability distributions of the Karlovitz number and the density-weighted displacement speed are also analyzed; results suggest that, for lean hydrogen flames, Soret diffusion broadens the distributions for both parameters, particularly on the positive side. These findings promise to advance the fundamental understanding of hydrogen flame dynamics under complex differential transport.

A key task in embedded vision is visual odometry (VO), which estimates camera motion from visual sensors, and it is a core component in many embedded power-constrained systems, from autonomous robots to augmented and virtual reality wearable devices. The newest class of VO systems combines deep learning models with bio-inspired event-based cameras, which are robust to motion blur and lighting conditions. However, state-of-the-art (SoA) event-based VO algorithms require significant memory and computation. For example, the leading approach DEVO requires 733 MB of memory and 155 billion multiply-accumulate (MAC) operations per frame. We present TinyDEVO, an event-based VO deep learning model designed for resource-constrained microcontroller units (MCUs). We deploy TinyDEVO on an ultra-low-power (ULP) 9-core RISC-V-based MCU, achieving a throughput of approximately 1.2 frames per second with an average power consumption of only 86 mW. Thanks to our neural network architectural optimizations and hyperparameter tuning, TinyDEVO reduces the memory footprint by 11.5x (to 63.8 MB) and the number of operations per frame by 29.7x (to 5.2 billion MACs per frame) compared to DEVO, while maintaining an average trajectory error of 27 cm, i.e., only 19 cm higher than DEVO, on three state-of-the-art datasets. Our work demonstrates, for the first time, the feasibility of an event-based VO pipeline on ultra-low-power devices.

We investigate the Jahn-Teller structural phase transition in LaMnO$_3$ at $T_{JT} \simeq 750$ K using molecular dynamics simulations based on machine-learning force fields trained on ab initio data. Analysis of the site-site correlation function of the distortions reveals that the transition is driven by the ordering of the $Q_2$ Jahn-Teller distortion of the MnO$_6$ octahedra, which acts as the order parameter and establishes the order-disorder nature of the transition. Dynamical local distortions are found to persist above $T_{JT}$. Our results reproduce the experimental temperature dependence of both structural and phonon properties and highlight the presence of anharmonic effects at finite temperature. More broadly, the combined use of machine-learning molecular dynamics and velocity autocorrelation function analysis provides a robust framework for uncovering the microscopic mechanisms of structural phase transitions in correlated materials. In particular, this approach enables a clear distinction between order-disorder transitions and alternative mechanisms, such as displacive behavior, through the temperature evolution of vibrational properties.

We introduce the concept of self-guided imaging (SGI) as a linear analogue of self-guided quantum tomography (SGQT). We show that SGI is mathematically equivalent to single-pixel imaging (SPI). Taking inspiration from orthogonalised ghost imaging, a recent advance in SPI, we introduce orthogonalised SGQT. This requires no additional experimental overhead and leads to faster and more accurate final convergence, as we demonstrate numerically (fidelity $95.2\% \rightarrow 99.17\%$) and experimentally (fidelity $92.1\% \rightarrow 95.3\%$). This work suggests that further routines from SPI and SGQT can be interchanged to optimise measurements and convergence.

Biomass burning aerosols (BBAs) from Southern Africa seasonally overlie the semi-permanent South-East Atlantic (SEA) stratocumulus deck, impacting the region's energy budget through complex aerosol-cloud-radiation-meteorology interactions. Climate model intercomparison initiatives, like the Aerosol Comparisons between Observations and Models (AeroCom), have highlighted the large inter-model variability for BBA radiative effects, especially over the SEA, due to parameterization of emission modeling and smoke properties. Observational constraints are needed to reduce these uncertainties, but correlative observational studies are typically affected by confounding meteorological influences. We propose a physically informed statistical approach, based on causal graphs applied to satellite observations, to disentangle BBA influences on shortwave radiation over the SEA and identify the main sources of statistical biases plaguing observational studies. We find that, during the fire season, BBAs cause a regional shortwave cooling of -2.5 W m$^{-2}$, which can be decomposed into equal contributions from three physical pathways: aerosol-radiation interactions (ARI), adjustments to ARI, and aerosol-cloud interactions (ACI). We also perform ablation experiments with graph variants to investigate the main sources of confounding - like large-scale winds, humidity-biased retrievals or spatial aggregation of data - and show that they result in biased radiative effect estimates (between -50 $\%$ and +15 $\%$). Once free of such biases, our derived causal estimates of smoke radiative effects can be used as observational constraints to improve climate models.

We investigate the task of local marking for locally implementable unitary operations. In this setting, multipartite quantum unitary channels, chosen randomly from a known set, are distributed among spatially separated parties without revealing their identities. The objective is to correctly identify (mark) the applied process using only local operations supplemented with classical communication (LOCC). While local distinguishability implies local marking, local marking does not guarantee either local or even global distinguishability of a set of unitaries. Thus the task of marking is not equivalent to the task of discrimination. We demonstrate a stronger manifestation of nonlocality without entanglement by constructing a set of globally distinguishable tripartite product unitaries that cannot be locally marked. In contrast to state marking, we find that marking a subset of product unitaries does not imply the ability to mark a larger subset. Finally, we explore the hierarchy of probes-entangled and product-in the context of local marking with respect to the standard discrimination scenario.

The dense environment of our Galactic Center (GC) offers a unique laboratory for probing ultralight dark matter (ULDM). We explore the prospect of detecting a scalar ULDM field through its effects on the orbital dynamics of S-stars around the supermassive black hole in the GC, Sgr A$^*$. We consider both linear and quadratic couplings between the real scalar field $ϕ$ and Standard Model particles, and analyze two representative ULDM structures: the scalar gravitational atom and the spherical soliton. We find that quadratic coupling induces a non-oscillatory perturbation, leading to a long-term secular orbital evolution. We use the observed periastron precession rate of S2 star to put stringent constraints on the total ULDM mass in the GC and the quadratic coupling constant. For the gravitational atom $|211\rangle$ state, we constrain the mass ratio of ULDM to Sgr A$^*$ to $β\lesssim 10^{-3}$ at $m \sim 10^{-18}$ eV, and for the spherical soliton which extends to $\sim 0.2\,$pc, the mass ratio is limited to $β\lesssim 1$ at $m \sim 3\times10^{-20}$ eV. Notably, the resulting limits on the quadratic coupling constant surpass current bounds in the mass range $10^{-20} \,\text{eV} \lesssim m \lesssim 10^{-18}$ eV.

X-ray spectra of accretion-powered X-ray pulsars can often be described using a power-law continuum with a high-energy cutoff, which might be further modified by additional spectral components. The Be X-ray binary system 4U 0115+63 is well known for having one of the highest numbers of detected harmonics of its cyclotron resonant scattering features (CRSFs), a pronounced spectral component known as the ''10 keV feature,'' and quasiperiodic oscillations (QPOs) with a period of about 500 s during outbursts. The changes in count rate by a factor of two during the approximately 500 s QPOs allow us to probe the variation in the spectral components with flux. We study the ''10 keV feature'' in emission, aiming to disentangle it from the broadband continuum and CRSFs and investigate its origin. We focus on the flux-dependent behavior of the CRSF and its harmonics, and particularly the contribution of the ''10 keV feature,'' as seen in the flux-resolved analysis of two NuSTAR observations of the 2015 outburst. Comparing the flux-resolved spectra of a given observation with the respective total dataset revealed a distinct change in overall spectral shape at the position of the ''10 keV feature'' but no comparable deviation at the energies of the harmonic CRSFs. The change associated with the ''10 keV feature'' does not seem to involve its centroid energy, which remains constant within a given observation. We find indications for an anticorrelation between the continuum flux and the ratio of the ''10 keV feature'' flux to the continuum flux within each observation. The analysis strengthens previous claims that the ''10 keV feature'' shows some independence from the remaining features. This result supports the interpretation that the ''10 keV feature'' has a different formation mechanism than the continuum emission, although its origin lies within the same physical environment.