The Black Hole Explorer (BHEX) is a proposed mission to launch a sub-millimeter radio telescope into Earth orbit that will take the sharpest images in the history of astronomy and reveal novel horizon-scale features of supermassive black holes. Black Hole Vision is an open-source application, freely available on the iOS App Store, that produces lensed images which highlight the key features expected to appear in the black hole images BHEX will capture. The app combines video feeds from the front- and rear-facing iPhone cameras and uses the black hole lensing equations to synthesize an onscreen image displaying the user's surroundings as if they were gravitationally lensed by a black hole within the cameras' field of view. Here, we describe how light rays are lensed by non-rotating (Schwarzschild) and rotating (Kerr) black holes, and we list the equations needed for computing black-hole-lensed images. We also describe their specific implementation within Black Hole Vision.

A new method for solving quartic equations due to Luo and Lin is investigated both computationally and theoretically. As a result, a completely straightforward elementary method is given for solving Bumby's equation $3X^4-2Y^2=1$, along with a conjecture, which if resolved, would enable a similar proof for a possibly infinite family of similar equations.

We study two closely related families of varieties arising from genus $0$ stable maps to the Lagrangian Grassmannian $\operatorname{LG}(n,2n)$. First, we construct the Kausz--type compactification $\mathcal {TL}_n$ of the space of symmetric matrices and give an explicit description of its birational geometry. Second, we realize $\mathcal {TL}_n$ as a general evaluation fiber in a Kontsevich space, and then exploit this modular interpretation to derive consequences for the birational geometry of the space of pointed conics $\overline{M}_{0,1}(\operatorname{LG}(n,2n),2)$. Analogous compactifications related to orthogonal Grassmannians are also presented.

Narratives in digital spaces are not merely organic phenomena. They are strategically shaped by a range of actors to influence public perception, behavior, and sociopolitical outcomes. This paper offers an actor-oriented expansion of the SAGES Framework, a five-stage model that traces the evolution of narratives from digital inception to real-world impact: Seeding, Amplification, Galvanization, Expansion, and Stickiness. This framework maps how adversarial and constructive actors intervene at each stage to accelerate, redirect, or counter narrative trajectories. Through comparative case studies of the 2021 Myanmar military coup and the 2022 Russia-Ukraine war, we show how narrative manipulation campaigns unfold and how targeted interventions can mitigate their effects. The SAGES framework contributes a practical lens for analyzing influence operations and developing countermeasures in an era of contested information ecosystems.

Charged lepton flavour violating processes are unobservable in the standard model, but they are predicted to be enhanced in several new physics extensions. We present the results of a search for $Υ(2S)$ and $Υ(3S)$ decays to $e^{\pm}μ^{\mp}$ decays. The search was conducted using data samples consisting of 99 million $Υ(2S)$ and 122 million $Υ(3S)$ mesons, collected at center-of-mass energies of 10.02 and 10.36 GeV, respectively, by the BABAR detector at the SLAC PEP-II $e^{+}e^{-}$ collider.

In this work, we present preliminary results from a measurement of the recently proposed observable, $v_0(p_T)$, in lead-lead (PbPb) collisions at $\sqrt{s_{NN}}=2.76 \text{ TeV}$, using public data from the CMS Open Data portal. This observable is directly sensitive to radial flow and characterizes the transverse momentum ($p_T$) dependence of radial flow fluctuations, serving as probe of collective phenomena in the quark-gluon plasma (QGP) formed in heavy-ion collisions. Consistently with the ATLAS results at $\sqrt{s_{NN}}=5.02 \text{ TeV}$, we observe the three key features of collective radial flow: long-range correlations in pseudorapidity ($η$), a centrality-independent shape as a function of $p_T$, and factorization in $p_T$. The results presented in this work are generally compatible, within uncertainties, with the ATLAS measurements at $\sqrt{s_{NN}}=5.02\text{ TeV}$ reported in Phys. Rev. Lett. 136, 032301 (2026).

We study the defocusing nonlinear Schrödinger equation on noncompact metric graphs under general self-adjoint vertex conditions ensuring the existence of a negative eigenvalue of the Hamiltonian operator. First, we focus on the existence of energy ground states with prescribed mass. We show that existence and stability always hold for small masses and fail for large masses in the $L^2$-subcritical regime. For $δ$-type vertex conditions, we provide more precise results: ground states exist for all masses in the $L^2$-critical and supercritical cases, while in the subcritical case, for one vertex graphs, there exists a sharp mass threshold such that ground states exist below it and do not exist above it. Moreover, we show that the ground state bifurcates from the vanishing solution at the bottom of the Hamiltonian spectrum. Finally, we present multiplicity results for stationary solutions, both in the fixed-frequency and fixed-mass settings.

Daily or weekly cone-beam computed tomography (CBCT) is employed in image-guided radiotherapy (IGRT) for precise patient alignment. However, its clinical utility in quantitative tasks is hindered by severe artifacts and inaccurate Hounsfeld unit (HU). It is essential to enhance CBCT image quality to a level comparable with that of conventional CT scans. This study proposed a conditional flow matching model that gradually transforms a sample from normal distribution to the corresponding CT sample conditioned on the input CBCT image. The proposed model was trained using CBCT and deformed planning CT (dpCT) image pairs in a supervised learning scheme. The feasibility of the conditional flow matching model was verified using studies of brain, head-and-neck (HN), and lung patients. The quantitative performance was evaluated using three metrics, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). The proposed flow matching model was also compared to other flow matching and diffusion-based generative models for sCT generation. The proposed flow matching model effectively reduced multiple types of artifacts on CBCT images in all the studies. In the study of brain patient, the MAE, PSNR, and NCC of the sCT were improved to 26.02 HU, 32.35 dB, and 0.99, respectively, from 40.63 HU, 27.87 dB, and 0.98 on the CBCT images. In the study of HN patient, the metrics were improved to 33.17 HU, 28.68 dB, 0.98 from 38.99 HU, 27.00 dB, 0.98. In the lung patient study, the metrics were 25.09 HU, 32.81 dB, 0.99 and 32.90 HU, 30.48 dB, 0.98 for sCT and CBCT, respectively. The proposed conditional flow matching model effectively synthesizes high-quality CT-like images from CBCT, achieving accurate HU representation and artifact reduction. This enables more reliable organ segmentation and dose calculation in CBCT-guided online ART workflows.

We calculate rovibrational energy levels of H$_2$O using a trapped-ion quantum computer. We first derive the qubit form of Watson's Hamiltonian, including the rovibrational coupling terms. In a second step, we employ a variant of the quantum-selected configuration-interaction method to calculate rovibrational energy levels. A truncated form of the qubit Hamiltonian is used to generate correlated rovibrational wave functions on the quantum computer by time evolution, and a basis set is selected by sampling from the measured probability distribution. The rovibrational energy levels are obtained by constructing a Hamiltonian matrix using the selected basis set, and diagonalizing the matrix using a classical computer. We show that an accuracy of a few cm$^{-1}$ can be achieved for low-lying rovibrational energy levels.

We propose a robust method for location estimation in various matrix manifolds based on the projected Frobenius median, which is closely related to the spatial median. This method applies broadly to matrix manifolds, including Stiefel and Grassmann manifolds, Kendall shape spaces as well as to projective Stiefel manifolds, a type of quotient space of a Stiefel manifold. Our approach involves computation of the Frobenius median in an ambient Euclidean space followed by projection onto the relevant matrix manifold. Our estimation method is computationally attractive, has a unique solution provided the sample data are not colinear in the ambient Euclidean space, has desirable robustness features and has appropriate equivariance properties under natural groups of transformations. We establish asymptotic normality under mild conditions and derive the influence function for matrix manifolds of interest. Simulation studies on the rank-1 complex Grassmann manifold and the projective Stiefel manifold further show the applicability and robustness of our method. We also apply our method to a real-world earthquake moment tensor dataset.

Cardiopulmonary resuscitation (CPR) is a critical life-saving procedure, and effective training benefits from self-directed practice beyond instructor-led sessions. In this paper, we propose a closed-loop CPR training glove that integrates a high-resolution tactile sensing array and vibrotactile actuators for self-directed practice. The tactile sensing array measures distributed pressures across the palm and dorsum to enable real-time estimation of compression rate, force, and hand pose. Based on these estimations, the glove delivers immediate haptic feedback to guide the user for proper CPR, reducing reliance on external audio-visual displays. We quantified the tactile sensor performance by measuring wide-range sensitivity (~0.85 over 0-600 N), computing hysteresis (56.04%), testing stability (11.05% drift over 300 cycles), and estimating global signal-to-noise ratio (18.90 +/- 2.41 dB at 600 N). Our closed-loop pipeline provides continuous modeling and feedback of key performance metrics essential for high-quality CPR. Our lightweight statistical models achieves >92% accuracy for force estimation and hand pose classification within sub-millisecond inference time. Our user study (N=8) showed that haptic feedback reduced visual distraction compared to audio-visual cues, though simplified patterns were required for reliable perception under dynamic load. These results highlight the feasibility of the proposed system and offer design insights for future haptic CPR self-training system.

We present CrossCheck, a system that validates inputs to the Software-Defined Networking (SDN) controller in a Wide Area Network (WAN). By detecting incorrect inputs - often stemming from bugs in the SDN control infrastructure - CrossCheck alerts operators before they trigger network outages. Our analysis at a large-scale WAN operator identifies invalid inputs as a leading cause of major outages, and we show how CrossCheck would have prevented those incidents. We deployed CrossCheck as a shadow validation system for four weeks in a production WAN, during which it accurately detected the single incident of invalid inputs that occurred while sustaining a 0% false positive rate under normal operation, hence imposing little additional burden on operators. In addition, we show through simulation that CrossCheck reliably detects a wide range of invalid inputs (e.g., detecting demand perturbations as small as 5% with 100% accuracy) and maintains a near-zero false positive rate for realistic levels of noisy, missing, or buggy telemetry data (e.g., sustaining zero false positives with up to 30% of corrupted telemetry data).

In 2019, Gohr pioneered the application of deep neural networks to differential cryptanalysis, developing DNN-based neural distinguisher classifiers to analyze the SPECK lightweight block cipher. Unlike traditional differential analysis, which relies on Boolean operations on 0-1 sequences, neural distinguishers extract continuous features, introducing 32-bit multiplications operations that increase complexity and potential redundancy. This study proposes a lightweight neural distinguisher based on quantization-aware training. Leveraging learnable step-size quantization, the model's weights are quantized to 1.58 bits, enabling the replacement of all convolutional multiplication operations with Boolean logic. Additionally, the ReLU activation function is reimplemented as a comparison-based indicator function. This transforms the original 32-bit multiplication-dependent architecture into a lightweight structure composed solely of Boolean operations, additions, and indicator functions. Experimental results confirm significant computational complexity reduction. Owing to a high proportion of zero-valued weights, the total operations amount to just 13.9% of Gohr's model. Critically, the most costly 32-bit multiplications are eliminated, with classification accuracy dropping by only 2.87%. When applied exclusively to the initial convolutional layer, the 128 1-by-1 convolutions are replaced with 4 Boolean operations on 16-bit sequences, incurring a negligible 0.3% accuracy loss.

Let $(M,g,J,ω)$ be an almost Hermitian manifold. Given an automorphism $ψ\in \mathrm{Aut}(TM)$, the existing structure can be twisted to obtain a new almost Hermitian manifold $(M,g^ψ,J^ψ,ω^ψ)$. In the current paper, we study these $ψ$-twisted almost Hermitian structures with particular emphasis on questions regarding the integrability of $J^ψ$ and the Riemannian geometry of $g^ψ$. By studying the latter, we identity a certain class of $\mathrm{Aut}(TM)$ with nice transformation properties. We call these automorphisms $g$-\textit{Codazzi maps} because of their close relationship with Codazzi tensors. The aforementioned results are ultimately applied to the standard nearly Kähler structure on the $6$-sphere where we prove a nonintegrability result for the class of $g$-Codazzi maps.

This paper studies the gathering problem for a set of $N \ge 2$ autonomous mobile robots operating in the Euclidean plane under the distributed Look-Compute-Move model. We consider oblivious robots executing under the adversarial defected view model, in which an activated robot may observe only a restricted subset of robots due to adversarial visibility faults. Consequently, the information obtained during each Look phase may be incomplete and dynamically altered. The objective is to guarantee deterministic finite-time gathering at a location not known a priori despite such sensing restrictions. We present two distributed algorithms under distinct scheduling assumptions. In the fully synchronous (FSYNC) model, we prove finite-time gathering in the adversarial (4, 2) defected view setting, resolving a previously open case without requiring additional capabilities or coordinate agreement. In the asynchronous (ASYNC) model, we establish finite-time gathering under the general adversarial (N, K) defected view model, where an activated robot observes at most K of the other $N - 1$ robots for any $1 \le K < N - 1$. Both results hold under non-rigid motion. The proposed algorithm for the ASYNC model assumes agreement in the direction and orientation of one coordinate axis.

High-resolution chemical imaging within deep tissues and intact spheroids remains a grand challenge. Here, we introduce mid-wave infrared photothermal (MWIP) microscopy operating in the underexplored 2000-2500 nm spectral window for submicron-resolution molecular and metabolic imaging in intact tumor spheroids and deep tissues. A dark-field photothermal detection scheme significantly suppresses water background and enhances contrast. By accessing strong carbon-hydrogen combination absorptions, a detection limit of 0.12% for dimethyl sulfoxide is achieved, comparable to stimulated Raman scattering microscopy. Depth-resolved imaging of endogenous biomolecules up to 500 micrometers in excised mouse skin and brain tissues is demonstrated. MWIP further enables depth-resolved tracking of transdermal drug transport via carbon-deuterium overtone absorption. Using deuterium metabolic probes, fatty-acid metabolism is imaged at 200 micrometers deep within intact tumor spheroids through carbon-deuterium overtone and combination bands. Collectively, MWIP offers a platform for functional imaging of 3D biological systems in their native environments.

Nearly all previous binary black hole searches in LIGO--Virgo--KAGRA (LVK) gravitational wave data have assumed that the component spins are aligned with the orbital angular momentum, thereby neglecting spin-precession effects in the waveform, which can lead to potentially missing interesting signals. Precessing searches are challenging, because the extra degrees of freedom due to misaligned spins lead to: $(i)$ a much larger number of templates compared to the aligned-spin configurations, $(ii)$ an increased rate of background triggers. To address this, we develop novel precessing signal template banks using mode-by-mode filtering and marginalization methods. We use the precession harmonic decomposition from Fairhurst et al. (2019) and filter each precessing harmonic separately with the data. We then marginalize over the SNRs from different harmonics in our detection statistic. We also use machine learning methods to improve our search efficiency: $(i)$ we use singular value decomposition together with random forest regressor to reduce redundancy in the dominant precessing-harmonic templates; $(ii)$ we use normalizing flows to generate optimal prior samples for harmonic SNRs for the marginalized statistic. We show that marginalizing (instead of maximizing) over the harmonic mode SNRs increases the search sensitive volume by $\sim 10\%$. Results from searching in LVK data using this framework will be reported in a companion paper.

We examine unitary and nonunitary representations of the Heisenberg-Weyl Lie algebra $\mathfrak{hw}_n$, with particular emphasis on tensor products of unitary representations and on indecomposable nonunitary representations. In the unitary setting, the irreducible representations with nontrivial central character are the Schrödinger representations, as classified by the Stone-von Neumann theorem. Although tensor products of these representations are considered in the literature, we give a detailed Lie-algebraic analysis and construct explicit unitary intertwining operators, including the case where the central characters sum to zero. In the nonunitary setting, we consider a natural realization of $\mathfrak{hw}_n$ as a subalgebra of the real symplectic Lie algebra $\mathfrak{sp}_{2n+2}(\mathbb R)$ and prove that every finite-dimensional complex irreducible representation of $\mathfrak{sp}_{2n+2}(\mathbb{R})$ remains indecomposable upon restriction to $\mathfrak{hw}_n$. This yields a large natural family of finite-dimensional, nonunitary indecomposable representations of $\mathfrak{hw}_n$.

Based on the analysis by Iwabuchi-Matsuyama-Taniguchi (2019), we first introduce our framework of Besov spaces $\dot B^s_{p, q}$ on the bounded domain $Ω\subset {\mathbb R}^d$ with smooth boundary $\partial Ω$ in terms of the Stokes operator $A=A_2$ with the Neumann boundary condition on $\partialΩ$ in $L^2_σ(Ω)$. Under some geometric assumption on $Ω$, we establish $L^p-L^q$ type estimates of the semi-group $\{e^{-tA}\}_{t \ge 0}$ in $\dot B^s_{p, q}$ and prove a local well-posedness of the Navier-Stokes equations with the initial data in $\dot B^{-1+\frac dp }_{p, q}$ for $d < p < \infty$ and $1 \le q \le \infty$. Since $d < p$, we have $L^{d, \infty} \subset \dot B^{-1+\frac dp }_{p, \infty}$ so that our space for well-posedness is larger than any other previous one in bounded domains.

Quantum Network Tomography (QNT) offers a framework for end-to-end quantum channel characterization by strategically placing monitor nodes within the network. Building upon prior work on single-monitor placement, we study optimal monitor placement and measurement assignments for channel parameter estimation in arbitrary quantum networks. Using an n-node star network as a baseline, we analyze multi-monitor configurations and show that distributing monitors across end nodes can achieve estimation performance comparable to a monitor placed at the hub. Estimation precision is quantified using the Quantum Fisher Information Matrix (QFIM), with channel parameters inferred via Maximum Likelihood Estimation (MLE) and benchmarked against the Quantum Cramer-Rao Bound (QCRB). To generalize, we develop two Integer Linear Program (ILP) formulations: one maximizing estimation accuracy (QF), and another jointly optimizing accuracy and monitoring overhead (QMF). Unlike QF, QMF prevents monitor overloading, enabling scalability and parallelism. We prove optimality for star and analyze applicability to tree-structured quantum networks.

The precoloring problem of a graph involves assigning colors to some vertices beforehand, and the objective is to determine whether it can be extended to a proper k-coloring of the entire graph. In 1958, Grotzsch proved that every triangle-free planar graph can be properly colored by three colors. One of the further generalizations of it is the recent result by Hoang La et al. in (Discrete Mathematics, 345(6) (2022), 112849 ). They proved that any two non-adjacent vertices and a face with a length at most four are precolored, the precolorings can be extended to a 3-coloring of the graph. In the paper, we consider precoloring extension of connected outerplanar graph with at most one or two triangles. Particularly, we show that precoloring of any two or three non-adjacent vertices can be extend to a 3-coloring of the whole graph.

We use molecular beam epitaxy to develop a gate tunable p-n heterojunction that interfaces a canonical Dirac semimetal, Cd$_3$As$_2$, and a ferromagnetic semiconductor, In$_{1-x}$Mn$_x$As, with perpendicular magnetic anisotropy. Measurements of the anomalous Hall effect in top-gated Cd$_3$As$_2$/In$_{1-x}$Mn$_x$As devices show that the ferromagnetic Curie temperature ($T_\mathrm{C}$) can be efficiently tuned using a modest gate voltage of $\sim 10$ V, corresponding to a sensitivity to electric field ($E$) of $ΔT_{\mathrm{C}}/ΔE \sim 10$ K/MV/cm). The voltage tuning of $T_\mathrm{C}$ saturates near the charge neutrality point of Cd$_3$As$_2$ and vanishes at positive gate voltage in appropriately designed heterostructures. This non-monotonic behavior cannot be explained solely by hole-mediated ferromagnetism in the In$_{1-x}$Mn$_x$As alone, suggesting an interaction between the Dirac semimetal and the ferromagnetic semiconductor. Our results identify Cd$_3$As$_2$/In$_{1-x}$Mn$_x$As heterojunctions as a potentially attractive platform for studying emergent phenomena arising from the interplay between broken symmetry, topology, and magnetism in a topological semimetal.

Work-stealing is a widely used technique for balancing irregular parallel workloads, and most modern runtime systems adopt lock-free work-stealing deques to reduce contention and improve scalability. However, existing algorithms are designed for general-purpose parallel runtimes and often incur overheads that are unnecessary in specialized settings. In this paper, we present a new lock-free work-stealing queue tailored for a master-worker framework used in the parallelization of a mixed-integer programming optimization solver based on decision diagrams. Our design supports native bulk operations, grows without bounds, and assumes at most one owner and one concurrent stealer, thereby eliminating the need for heavy synchronization. We provide an informal sketch that our queue is linearizable and lock-free under this restricted concurrency model. Benchmarks demonstrate that our implementation achieves constant-latency push performance, remaining stable even as batch size increases, in contrast to existing queues from C++ Taskflow whose latencies grow sharply with batch size. Pop operations perform comparably across all implementations, while our steal operation maintains nearly flat latency across different steal proportions. We also explore an optimized steal variant that reduces latency by up to 3x in practice. Finally, a pseudo workload based on large-graph exploration confirms that all implementations scale linearly. However, we argue that solver workloads with irregular node processing times would further amplify the advantages of our algorithm.

The unsteady separated flow over the three-dimensional Boeing Gaussian Bump is investigated at a Reynolds number based on bump height $Re_H = 2.26\times10^5$ using unsteady wall-pressure measurements and planar particle image velocimetry (PIV). Four major unsteady broadband phenomena spanning more than two decades in frequency are identified: (1) a very-low-frequency (VLF) spanwise motion centered at a Strouhal number of $St_H\sim10^{-3}$ (1 Hz) based on bump height, (2) a low-frequency breathing motion of the separation zone centered at $St_{L_{\rm{sep}}}=0.068$ (13.5 Hz) where $L_{\rm{sep}}$ is the mean separation length, (3) a 20 Hz frequency that appears to be associated with vortex shedding from the lateral shear layers, and (4) a centreline shear-layer vortex shedding at $St_{L_{\rm{sep}}}=0.68-1.01$ (135-200 Hz). Interestingly, while the VLF mode has a characteristic frequency of the same order to that often reported for other rectilinear bodies and hills that exhibit bistable asymmetric wake-switching, it is found that the VLF mode for this geometry exhibits a continuous spanwise meandering motion. Joint symmetric-antisymmetric proper orthogonal decomposition modal statistics from top-down PIV data further show that the spanwise meandering and streamwise stretching of the wake -- likely associated with the breathing motion -- are dynamically coupled, with the separation zone reaching its greatest streamwise extent when in a symmetric state. In this paper, the observed hierarchy of spectral features is comparable with those observed for a wide range of geometries, suggesting connections between geometric lengthscales and the low-frequency dynamics.

Many HPC applications that solve differential equations rely on the Runge-Kutta family of methods for time integration. Among these methods, the fourth-order accurate RK4 scheme is especially popular. This time integration scheme requires applications to evaluate four intermediate stages to take one time step. Depending on the complexity of the problem being solved, the evaluation of these intermediate stages can be computationally expensive. In this paper we develop explicit fourth-order accurate Multistep Runge-Kutta (MSRK) methods. The advantage of such methods is that they re-use data from previous time steps, thus requiring fewer intermediate stage evaluations and potentially speeding up applications. We outline a procedure to obtain and tune the method's coefficients by adjusting their stability regions in an attempt to maximize the size that a time step can take. We validate and evaluate our new methods in the context of Numerical Relativity applications using the EinsteinToolkit. We believe, however, that these methods and results should generalize to other applications using explicit Runge-Kutta methods.

This paper presents a hybrid safety-critical coordination architecture for multi-agent systems operating in dense environments. While control barrier functions (CBFs) provide formal safety guarantees, decentralized implementations typically rely on ego-centric safety filtering and may lead to redundant constraint enforcement and conservative collective behavior. To address this limitation, we introduce a combinatorial coordination layer formulated as a mixed-integer linear program (MILP) that assigns collision-avoidance responsibilities among agents. By explicitly distributing enforcement tasks, redundant reactions are eliminated and computational complexity is reduced. Each agent subsequently solves a reduced local quadratic program enforcing only its assigned constraints.

Layered materials that stack different lattice symmetries are rare in nature. Misfit layered chalcogenides, which combine square and hexagonal lattices of rocksalt monochalcogenides and transition-metal dichalcogenides, provide a platform to explore how incommensurability and explicit symmetry breaking impact collective electronic phases. Here we use low-temperature scanning tunneling microscopy/spectroscopy to probe the misfit compounds (MS)$_{1+δ}$TaS$_{2}$ with M = Pb, Sn and track how the misfit interface reshapes the electronic ground state of the embedded 1H-TaS$_{2}$ monolayers. High-resolution STM imaging and Fourier analysis reveal that the charge-density wave (CDW) is incommensurate and fragments into nanometer-sized domains. Strikingly, the CDW exhibits a pronounced and anisotropic response to the uniaxial moiré potential imposed by the misfit layer: its coherence lengths and ordering wavevectors become inequivalent, demonstrating a strong nonlinear coupling between the intrinsic CDW instability and the symmetry-breaking moiré field. First-principles-informed multiscale modeling shows that this reorganization arises from the combined effect of interlayer charge transfer and the spatially anisotropic energy landscape introduced by the misfit interface. In contrast, superconductivity is comparatively insensitive to the moiré, revealing a uniform, single full-gap consistent with s-wave pairing. Our results establish heterosymmetry stacking as a route to engineer correlated states in van der Waals materials.

Evidence for multiple magnetic transitions and unconventional spin exchange interactions in the ilmenite insulator MnTiO3 is provided via neutron scattering. On cooling, while G-type antiferromagnetic (AFM) order sets in first at 63 K with a k1 = (000) characteristic wave vector, a weaker second magnetic transition with k2 = (00 3/2 ) appears near 42 K, giving rise to a noncollinear structure. Intrinsic buckling of the honeycomb lattice along c creates bond anisotropy and a distorted crystal field that can lead to exchange paths that modulate orbital overlap and spin-orbit coupling. The inelastic spectrum is best described by magnetic exchange anisotropy that breaks the local symmetry of the honeycomb, with competing AFM Heisenberg, Dzyaloshinskii-Moriya and alternate intra-planar ferromagnetic (FM) interactions, that may yield a weakly-coupled ladder system.

Recent studies of topologically generic unfoldings of vector fields featuring a "tears of the heart" polycycle with one internal and one external winding separatrix have shown that, in a special one-parameter subfamily where the "heart" is preserved and the "tear" loop if broken, at least four invariants of weak topological classification appear. In this paper, we demonstrate that the metrical perspective yields a different result: for Lebesgue almost all values of the coefficients related to the original vector field, the special one-parameter family generates only two such invariants.

As 6G communications advance, the demand for new services and capabilities, as defined by the international telecommunication union (ITU), is increasing. A crucial aspect of 6G advancement lies in the development of signal waveforms that can meet these demands while maintaining compatibility with existing standards. This paper explores sustainable physical layer waveform options, focusing on a balanced approach that integrates non-orthogonality with orthogonality to achieve both backward compatibility and forward innovation. Specifically, we investigate two key signal formats: single-carrier orthogonal frequency division multiplexing (SC-OFDM) (1D,2D) and single-carrier non-orthogonal frequency shaping (SC-NOFS)(1D,2D). Both can use 1D frequency and 2D time-frequency precoding, offering enhanced frequency and time diversity, simplified processing, and resilience to delay-Doppler effects. SC-NOFS(2D) further introduces advantages such as improved spectral efficiency and reduced latency, making it a strong candidate for future 6G applications. The comparative analysis highlights that SC-NOFS(2D) provides a broader range of capabilities, particularly those requiring high data rate, high mobility, low-latency communication, sustainability, and interoperability, positioning it as a versatile solution for next-generation 6G communication.