Biconservative submanifolds arise as a natural relaxation of the biharmonic condition and play an important role in the submanifold theory. In this paper, we study non-CMC biconservative surfaces with parallel normalized mean curvature vector field (PNMC surfaces) in the four-dimensional hyperbolic space $\mathbb{H}^4$, for which we consider the hyperboloid model. We provide a local extrinsic description of such surfaces, showing that they are generated by a directrix curve lying in a totally geodesic hypersurface $\mathbb{H}^3$ of $\mathbb{H}^4$, through a certain normal flow. This extrinsic classification of non-CMC, PNMC biconservative surfaces in $\mathbb{H}^4$ splits naturally into three cases according to the type of a certain vector field, which can be non-zero null, spacelike or timelike. Together with the previous results, the classification of non-CMC, PNMC surfaces in four-dimensional space forms is now completed, from intrinsic and extrinsic point of view.

A well-known result of Burr, Erdős and Spencer [Transactions of the American Mathematical Society, 1975] determines the $2$-colour Ramsey number for any sufficiently large collection of vertex-disjoint copies of a fixed graph $H$ without isolated vertices. In this short note we prove a random version of this result, thereby generalising the random Ramsey theorem of Rödl and Ruciński [Journal of the American Mathematical Society, 1995].

Efficiently recovering dynamic correlation in strongly correlated systems without incurring prohibitive computational costs remains a central challenge in quantum chemistry. In this Perspective, we review and benchmark methods capable of recovering dynamic correlation for multireference wave functions exclusively from low-order reduced density matrices and densities. These approaches require at most the two-electron reduced density matrix of the reference wave function and fall into two categories: density functional theory (DFT)-based methods and purely ab initio multireference adiabatic connection (AC) methods. The former include MC-srDFT, which recovers dynamic correlation through a short-range exchange-correlation functional depending on the charge and spin densities, as well as MC-PDFT and MC-srPDFT, which employ translated functionals that additionally depend on the on-top pair density. Within the post-CASSCF framework, we perform a direct, head-to-head benchmark of these approaches under identical computational settings (including active spaces and basis sets) against challenging multireference problems, including singlet-triplet gaps in organic biradicals, excitation energies, and spin-state splittings in iron complexes. Among the DFT-based methods, MC-srPDFT emerges as the most accurate, underscoring the benefit of incorporating the on-top pair density. However, all considered DFT-based methods fail to provide reliable spin-state energetics for transition-metal complexes. Conversely, linearized AC0 rivals or outperforms more computationally expensive second-order perturbation theory approaches across all benchmark sets. We discuss these findings in the context of alternative formulations and existing literature, highlighting critical limitations and identifying promising directions for the future development of scalable multireference methods.

Scientific workflows are pipelines of interdependent tasks. They are increasingly executed on shared Kubernetes clusters via workflow engines such as Nextflow. Their energy consumption matters for both cost and sustainability. It is necessary to examine and optimize workflow tasks individually, because they can be very heterogeneous. However, estimating task-level energy on clusters is difficult: Intel RAPL counters report only node-level energy, access to counters and host process information is typically restricted, and concurrent workloads introduce resource contention and measurement noise. We present Nf-PEAK, a containerized method to attribute CPU-package and DRAM energy to individual processes and Nextflow tasks. Nf-PEAK (i) identifies workflow pods, (ii) maps pods to host processes via cgroup metadata, (iii) samples RAPL and per-process performance counters, and (iv) applies a non-linear energy-credit model before aggregating results at task level. On a Kubernetes cluster, we evaluate three nf-core workflows under controlled co-located CPU load. Nf-PEAK reaches an average Mean Absolute Percentage Error of 6.6% in isolated runs and 10.9% when an unrelated workload saturates 8 of 32 hardware threads per node, and remains stable across 2, 3, 4, and 8 nodes. Compared to the state-of-the-art Kubernetes tool Kepler, Nf-PEAK yields lower error on average, particularly under co-located load.

In most stabilizer-based quantum computing schemes, so-called magic states are a necessary resource for implementing non-transversal quantum gates. With the resource theory of magic, it is possible to analyze and quantify the generation of the non-stabilizer states. The relative entropy is a measure used in various resource theories. For single qubits, we characterize magic states and their closest stabilizer states by applying analytical results known from the relative entropy of entanglement and show that the magic states and their closest stabilizer states are arranged symmetrically around the states at the centers of the faces of the stabilizer octahedron. For tensor products of single-qubit states, we prove analytically that the relative entropy of magic is nonadditive in almost all cases.

We present the results of an observation of the broad-line radio galaxy 3C 111 with the X-Ray Imaging and Spectroscopy Mission (XRISM). The unprecedentedly high spectral resolution of XRISM/Resolve revealed that the Fe K$β$ emission line is significantly weaker than expected from the Fe K$α$ line. This feature may be explained by a blueshifted absorption line from an ionized wind overlapping the Fe K$β$ energy. The inferred outflow velocity is 4600 km s$^{-1}$ or 17200 km s$^{-1}$, depending on whether the absorption feature is identified as Fe XXVI or Fe XXV, with the current data unable to distinguish between the two interpretations. Based on spectral modeling, the kinetic power of the wind is estimated to lie in the range 10$^{41}$-10$^{44}$ erg s$^{-1}$, although this estimate is subject to large uncertainties primarily due to the poorly constrained location of the absorber. The inferred wind power is smaller than the jet power of 3C 111 ($\sim 3\times 10^{44}$ erg s$^{-1}$), and is broadly consistent with theoretical expectations that the jet power exceeds that of disk winds.

Experiments performed on quantum systems often measure multitime correlation functions. When quantum systems are weakly coupled to their environment, the time evolution of such correlation functions can be reduced to that of the reduced density matrix by the quantum regression theorem (QRT). While no QRT is available for general non-Markovian open quantum systems, we show that for time-independent Hamiltonians and finite memory times $τ_c$, an exact factorization rule exists that relates higher-order multitime correlations to products of lower-order correlations. Consequently, all information needed to reconstruct $n$-time correlations is contained in a temporal volume of $\mathcal{O}(τ_c^n)$. On the example of quantum dots coupled to phonons, we demonstrate that this factorization makes numerical calculations of multitime correlations extremely efficient and even enables semianalytical solutions in systems where the standard QRT breaks down.

Communication performance and channel estimation accuracy in MIMO systems are known to be limited by hardware impairments. Specifically, the presence of phase impairments, such as phase noise, makes real-time coherent transmission a challenging task. While phase impairment compensation is typically performed at the receiver, practical methods for enabling coherent transmission at the transmitter side remain underexplored. Established methods for OTA calibration of MIMO systems face several limitations such as assumptions of phase stationarity and accurate channel knowledge. In this work, a real-time local phase calibration method is experimentally compared with OTA calibration on a fully digital array of USRP X310 software-defined radios. Using RMS cycle-to-cycle jitter as a metric, it is shown that for low and high synchronization signal bandwidths, both approaches effectively eliminate phase drift and whiten the phase noise. Local calibration achieves higher phase stability and is channel-independent, whereas OTA calibration requires no additional hardware but is sensitive to multipath effects and channel-induced impairments. Practical deployment trade-offs are discussed based on the measurement results.

We study aggregation-fragmentation processes in which pairs of clusters can aggregate, and each cluster can break into two fragments. If the rates of aggregation and fragmentation do not depend on the masses, detailed balance does not hold, but nonequilibrium steady states can still be deduced from an exact solution for the Laplace transform. For models in which aggregation rates remain constant but fragmentation rates scale as $(\text{mass})^β$, detailed balance holds only when $β=1$. Away from this solvable case, we employ asymptotic techniques and show that when $β\geq 0$, the steady states share similarities with those from the mass-independent ($β=0$) model. An instantaneous shattering transition with continuous mass loss occurs when $β<0$.

Antiferromagnets (AFMs) hold significant potential for spintronic devices owing to their insensitivity to external magnetic fields and the absence of stray fields. Beyond these inherent advantages, an AFM can manipulate the magnetic dynamics of a ferromagnet (FM) layer in AFM/FM bilayers, whereas the mechanism of such manipulation remains controversial. Here, we investigate the magnetic dynamics of AFM/FM Mn2Au/CoFeB bilayers via Ferromagnetic Resonance (FMR). It is found that the Néel temperature of 2-nm-thick Mn2Au is as low as ~40 K, in sharp contrast to that of bulk Mn2Au, which exceeds 1000 K. In the Mn2Au(2 nm)/CoFeB(4 nm) bilayer, the magnetic damping $α$ of the CoFeB layer increases from 0.013 to 0.047 as temperature decreases from 160 K to 10 K, accompanied by a synchronous increase in the exchange coupling field H_rot. Such an increase in $α$ is attributed to the enhanced spin angular momentum transfer from CoFeB to Mn2Au, mediated through AFM-FM exchange coupling between Mn2Au and CoFeB, which is enhanced by the Mn2Au antiferromagnetic ordering as the temperature decreases. Our study provides deeper insights into AFM/FM dynamics and spintronic storage technology.

Nanopore sequencing accuracy is inherently limited by the quality of data from individual molecular translocation events, requiring advances beyond traditional sequencing-by-synthesis methods. We introduce an oxidized pyramidal sub-nm pore (OPSP) integrated in a threeterminal sensing platform, where the sub-nm silicon pore functions as an electrode for detecting displacement currents across an oxide barrier, induced by counter-ion migration within the electric double layer. This platform achieves sub-1-nm-scale spatial resolution and a signal-tonoise ratio (SNR) up to 15 for biopolymer sequencing, enabling direct identification of individual bases in single-stranded DNA and single amino acids in peptides, with raw-read accuracies exceeding 98.5% and 95.5%, respectively, without consensus-based computational correction. The OPSP demonstrates high acid tolerance, reusability in varied chemical environments, and operational stability for over six months. This work establishes OPSP as a durable, high-accuracy platform for single-mer resolution sequencing, defining a reliable and robust paradigm for next-generation sequencing technologies.

We develop a general method for constructing the many-body Hamiltonian of pairwise interactions describing homonuclear mixtures of atoms occupying states with different total angular momenta or other quantum numbers. The advantage of the irreducible spherical tensor operator formalism is demonstrated: these operators give the Hamiltonian an explicit physical structure, account for all scattering channels, and include multipolar exchange interactions. The latter correspond to the exchange of both angular-momentum projections and the total angular momentum. Particular realizations of the general Hamiltonian, widely used in the physics of ultracold gases, are also analyzed. The resulting Hamiltonian provides a universal framework for investigating a broad range of quantum many-body phenomena in bosonic and fermionic atomic gases.

Symbolic regression with genetic programming (GPSR) may suffer from overfitting and structural bloat, especially when noise is present. In this paper we evaluate description length (DL) and fractional Bayes factor (FBF) criteria as principled, data-efficient alternatives to heuristics for selecting compact expressions that generalise well. We implement DL using a Fisher-information-based parameter encoding and compare it to AIC and BIC across multiple datasets, including noisy synthetic benchmarks and real-world regression problems. We study three search/selection strategies: (i) multi-objective search for accuracy and program length followed by DL/FBF selection; (ii) multi-objective search using DL directly as an objective; and (iii) single-objective optimisation with DL/FBF as the fitness. Across datasets we find that DL/FBF post-selection improves test performance compared to AIC/BIC baseline and that BIC in combination with the same function complexity penalty from DL/FBF produces similar results. In contrast, using DL/FBF directly as a fitness function in single-objective GPSR frequently induces premature convergence to overly simple models. We conclude with practical guidance for using DL/FBF as robust model-selection tools in genetic programming workflows.

Let $k$ be a positive integer and let $X_k$ be the cubic hypersurface defined by the equation $x^3-(y_1^2+\cdots+y_{4k}^2)z=0$. In this paper, we give an asymptotic formula for the counting function of semi-integral points on $X_k$. We also prove that this asymptotic formula agrees with Manin's conjecture for $\mathcal{M}$-points \cite[Conjecture~1.4]{Moe26a} on the $a$-invariant and the $b$-invariant.

We report the generation of an anomalous linearly dispersing, spin-polarized band in Bi-based topological insulator (TI) thin films, induced by soft Ar-ion bombardment followed by annealing. This extra band -- which we call the anomalous linearly dispersing state (ALS) -- is superimposed on the regular band structure including the topological surface state (TSS), spans an unusually large energetic range of up to ${\sim}\,\SI{650}{\milli\electronvolt}$ at the $Γ$-point, and appears near the Fermi energy. Spin-resolved measurements indicate spin-momentum locking with a helicity \emph{opposite} to that of the regular TSS. The Fermi velocity of the ALS, $v_\mathrm{F} = (5.1\pm 0.4)\times 10^{5}\,\frac m s$, is indistinguishable from that of the regular TSS, $(5.3\pm 0.5)\times 10^{5}\,\frac m s$. The observation is reproducible across samples of varying thickness and was confirmed at two independent synchrotron radiation facilities. We discuss different mechanisms for the physical origin of the observed ALS including sputtering-induced TSS relocation, bi-layer formation by,e.g., chalcogen removal, and high-index surface relocation.

Piezocatalysis, based on the piezoelectric properties of catalysts, breaks down the barrier between mechanical energy and chemical energy. It describes the use of charges induced by piezoelectricity to assist typical chemical processes while harvesting various forms of mechanical green energy. The performance of piezocatalysis is predominantly governed by the piezoelectric properties of materials. The main aim of this work is to evaluate and analyze the potential of potassium doped sodium bismuth titanate Na0.5-xKxBi0.5TiO3 abbreviated as NKxBT (0, 0.15, 0.20, and 0.25), as a piezocatalyst in the degradation of the organic dye Rhodamine B RhB under ultrasonic vibration. The synthesis of NKxBT nanopowders was conducted using the sol-gel autocombustion method. Coupled structural analysis reveals the presence of an intermediate Morphotropic Phase Boundary (MPB, where two phases coexist) in the optimal NK15BT composition. The piezocatalytic degradation results showed a total piezo-degradation in only 90 min and a rate constant 8 times higher than the undoped NK0BT. The enhanced piezocatalytic activity results from a synergistic effect of MPB presence, reduced particle size, optimal bandgap and high lattice strain. The NK15BT sample also demonstrated good reusability and good mineralization.

Machine learning has transformed the field of atomistic simulations by enabling the development of interatomic potentials that are computationally efficient and highly accurate. These advances have opened the door to modeling molecular vibrations and predicting infrared spectra with near ab-initio accuracy at a fraction of the computational cost. Among these approaches, message-passing neural networks (MPNNs) have emerged as a particularly powerful class of models for representing complex atomic interactions. In this study, we benchmark five MPNN architectures, SchNet, FieldSchNet, SO3Net, PaiNN, and MACE, for predicting infrared spectra of small organic molecules. SchNet and FieldSchNet are invariant models, while SO3Net, PaiNN, and MACE are equivariant, explicitly accounting for rotational symmetries in molecular representations. We evaluate their performance in terms of computational efficiency, accuracy, and robustness. All models accurately predict properties, such as energies, forces, and dipole moments, required for infrared spectra calculations. They also capture harmonic frequencies and infrared spectra derived from molecular dynamics with high fidelity for molecules in the training set. However, SchNet and FieldSchNet show limited transferability to unseen systems, while SO3Net, PaiNN, and MACE generalize more effectively. In terms of computational efficiency, SchNet is the most efficient and FieldSchNet enables field-dependent response modeling but with higher cost. PaiNN achieves the best balance between accuracy and efficiency, MACE provides the highest spectral accuracy and transferability, and SO3Net performs between PaiNN and MACE.

We study a string-motivated theoretical prior on the quintessential dark energy model with exponential potential, \( V(ϕ) = V_0 e^{-λϕ} \), allowing for non-zero spatial curvature. First, we formulate the corresponding dynamical system and investigate its cosmological evolution numerically, illustrating the phase-space behaviour and the influence of curvature on the background dynamics. In open universes (\( Ω_k > 0 \)), it has been suggested that a curvature-related fixed point may support accelerated expansion even for relatively steep potentials compatible with swampland considerations. Next, we explicitly impose swampland-motivated priors on the slope parameter $λ$, restricting it to values consistent with the de Sitter conjecture that excludes the (curved) $Λ$CDM limit. Furthermore, we restrict our considerations to the range of field excursion that is consistent with the swampland distance conjecture. Our primary interest is the possibility that such theoretically-motivated priors may shift values of cosmological parameters inferred by observational data, compared with the standard analysis based on theory-agnostic priors such as a sufficiently wide flat prior. We examine this possibility using a combination of Planck CMB data, DESI BAO measurements, and recent Type Ia supernova samples, performing a Bayesian inference of the model parameters. Our analysis indicates that the swampland-motivated prior mildly shifts the values of $Ω_k$.

Digital twins (DTs) are promising for wireless deployment, optimization, and data generation, but building a propagation-faithful twin from sparse real measurements remains difficult. This paper proposes a wireless environment digital twin (WEDT) construction paradigm that evolves a reconstructed geometric DT into a propagation-consistent wireless environment representation through calibration of a scene-level electromagnetic (EM) property field. Instead of directly fitting link-specific channel responses, the proposed paradigm first constructs a geometry-prior Bayesian channel map (BCM) to convert sparse position-labeled channel state information (CSI) into dense probabilistic supervision with uncertainty estimates. It then embeds the learnable EM property field into differentiable ray tracing (RT) based channel computation, thereby enabling calibration through an explicit propagation chain. Experiments in both public and real-world scenes show that WEDT achieves accurate channel prediction, generalizes to unseen transceiver topologies, and remains effective across different sampling conditions. WEDT also offers utility for material-related environment sensing, more reliable physical-layer planning, and higher-quality synthetic data generation for wireless AI. These results demonstrate the value of the proposed paradigm for propagation-consistent WEDT construction and related wireless applications.

We address joint active and passive beamforming for uplink RIS-assisted multi-user multi-stream MIMO systems with joint detection. The coupled design of the receive combiner, block-diagonal user precoders, and RIS phase vector is formulated through a third-order composite channel tensor. Exploiting this multilinear structure, we propose a multi-stream tensor alternating optimization method that updates the combiner, user precoders, and RIS coefficients via low-dimensional tensor projections. Simulations show that the proposed method approaches a multi-start alternating-optimization benchmark while reducing computational complexity and improving large-RIS scaling.

While generative retrieval (GR) demonstrates competitive performance on standard retrieval benchmarks, existing approaches directly map queries to document identifiers (docids) without intermediate deliberation, limiting their effectiveness for complex queries that require multi-step reasoning. As a preliminary study on integrating chain-of-thought (CoT) into generative retrieval, we introduce ThinkGR, a unified framework that interleaves CoT with docid generation, enabling iterative thinking and retrieval within a single generative process. To bridge the gap between free-form thought generation and structured retrieval targets, we design (1) a hybrid decoding strategy that dynamically switches between unconstrained thought generation and constrained docid decoding, and (2) a two-phase training approach that first aligns thought-retrieval patterns through supervised fine-tuning, then optimizes thought quality via retrieval-grounded reinforcement learning. Experiments on four multi-hop retrieval benchmarks demonstrate that ThinkGR achieves state-of-the-art performance with an average improvement of +6.86\%. Our work opens new avenues for enhancing generative retrieval with explicit deliberation capabilities, with promising implications for retrieval tasks requiring complex reasoning.

This paper reconstructs the half-century evolution of the scientific school founded by Yuriy P. Kunchenko (1939--2006) as the development of a semiparametric methodology for non-Gaussian estimation. Starting with Kunchenko's 1972/1973 dissertation applying Volterra series to estimate parameters of random processes, the trajectory is followed through 2006--2026. Kunchenko stochastic polynomials are presented as a coherent family of moment-cumulant procedures: the polynomial maximization method (PMM) for parameter estimation, polynomial criteria for hypothesis testing, and decomposition in spaces with a generating element. The paper details the school's structure: a verified genealogy of 15 defended dissertations, collaborations in Poland, Slovakia, and Germany, and the R package EstemPMM. A recent 2026 paper on Volterra-based signal processing is analyzed, showing how Kunchenko's nonlinear formulation reappears in applied radio engineering. We build a formal bridge between finite Volterra models and generalized Kunchenko polynomials, while separating the MMSE/L2 criterion from PMM: the former is a covariance projection for kernel adaptation, whereas PMM is a parameter-dependent moment procedure. PMM efficiency claims are stated conditionally: gains require that moments exist, the centered correlant matrix is nondegenerate, and the variance reduction coefficient is below one. The concluding research program operationalizes the historical reconstruction into testable statistical and signal-processing tasks.

Automated sky surveys frequently report sparse-in-time multiband photometric observations of asteroids passing through their fields of view. Photometric data are currently available for tens of thousands of asteroids, and new data collection is ongoing. We aim to describe and characterize the performance of a statistical test for identifying asteroids that display surface color heterogeneity based on sparse-in-time multiband photometric survey data. Using simulated photometry for a set of synthetic asteroids with predetermined physical properties, we estimated the sensitivity of the statistical test for surface color heterogeneity to errors in assumed model properties using a Monte Carlo approach. We evaluated the detection and false positive rates as a function of the number of observations, measurement noise, error in assumed period, pole orientation, shape, and phase function. We examined the required accuracy in various parameters of the photometric model needed to obtain reliable results to evaluate the feasibility of applying the test to realistic datasets. Regional-scale surface color heterogeneity can be detected by examining differences in the shape of an asteroid's light curve as a function of viewing geometry across multiple bandpasses. Differences in light curve shapes as a function of wavelength are highlighted in this work through comparison of the observed photometric measurements to the predictions of a well-fitting, uniformly colored photometric model. Statistically significant deviations from the prediction of the uniformly colored model are taken as evidence of surface color heterogeneity. The performance of this test depends on the accuracy of model assumptions, with the detection rate being most sensitive to errors in the assumed rotational period, while the false positive rate is most sensitive to errors in the assumed band-dependent phase functions.

Highly pathogenic avian influenza A(H5N1) has expanded geographically and ecologically, affecting wild birds, mammalian wildlife, domestic animals, and humans. Wildlife surveillance provides critical early warning for One Health preparedness, yet national-scale analyses integrating host ecology, spatial patterns, seasonality, viral lineage, and risk factors remain limited. This study analysed Canadian wildlife HPAI A(H5N1) surveillance records from 2022 to 2026 to characterise spatiotemporal dynamics and identify factors associated with detection counts. A retrospective analysis of 2,657 detections across 13 provinces and territories was conducted using descriptive epidemiology, spatial clustering methods, and Negative Binomial mixed models. Detections were predominantly avian, with waterfowl and raptors as the major host groups, while mammals accounted for a smaller but epidemiologically important proportion. Detection burden was highest in 2022, with increased activity in autumn and spring. Ontario, Alberta, and British Columbia were identified as major hotspots, with evidence of local clustering in parts of the Prairie region. Reassortant Eurasian-North American lineages dominated detections and were strongly associated with higher detection counts. Modelling results identified year, season, and lineage as key predictors. These findings support risk-based One Health surveillance prioritising high-burden regions, migration-associated periods, key avian host groups, reassortant viral lineages, and continued monitoring of mammalian wildlife.

Generative Artificial Intelligence (GenAI) is rapidly reshaping software development, with growing emphasis on accelerating productivity and optimizing performance. However, excessive focus on such dimensions risks overlooking the critical implications for developer well-being. GenAI tools can amplify cognitive load, introduce new forms of oversight labor, and escalate expectations around output and pace, contributing to stress, burnout, and diminished work-life balance. The GenAI movement is also transforming professional norms, altering career entry points, demanding continuous adaptation, and deepening inequalities in access and support. This position paper calls for a reorientation of the GenAI research agenda in software development and proposes a theoretical framework to move beyond narrow performance metrics toward investigations that also center on human experience, social context, and sustainable productivity.

Ghost-free non-local gravity is investigated with regards to its late-time dynamics. Viable solutions in this model are confronted with the observational data including the Pantheon+ catalogue of Type Ia supernovae, the Dark Energy Spectroscopic Instrument, the measurements of baryon acoustic oscillations and the Hubble parameter estimations $H(z)$. The ghost-free non-local gravity is found to be successful in these tests in comparison to the $Λ$CDM model and can be also comparable with the generalized exponential $F(R)$ gravity scenario. However the model encounters difficulties when the data from the above observations and the cosmic microwave background radiation data are combined. In tests with the whole set of Pantheon+, DESI, $H(z)$ and CMB data, the generalized exponential $F(R)$ model is essentially more successful. This success is related with the dynamical behavior of its effective dark energy equation of state evolving from a phantom to a quintessence phase during the late-time epoch, whereas the ghost-free non-local model demonstrates only a quintessence behavior. Hence the ghost-free non-local gravity scenario is successful only when the Pantheon+, DESI and $H(z)$ data are considered. The generalized exponential $F(R)$ model satisfies the viability conditions and in tests with all observational data including CMB surpasses the $Λ$CDM model in $χ^2$ statistics and also with information criteria.

We prove uniform diameter estimates, volume non-collapsing estimates and Gromov-Hausdorff convergence for the normalized Chern-Ricci flow on smooth complex minimal surfaces of general type, starting from an arbitrary Hermitian metric. This removes the local Kahler assumption near the null locus used in our previous work and confirms the Tosatti-Weinkove conjecture in complex dimension two. The main analytic ingredients are a surface torsion estimate, a uniform total variation bound for Delta |G|, a Green-weighted L^2 estimate for the torsion, and a linear iteration of real Poisson equations, which together give the required Green function estimates.

We study the existence of large or boundary blow-up solutions to semilinear equations involving the Finsler p-Laplacian on bounded domains with sufficiently smooth boundaries. We establish a Keller-Osserman-type condition that ensures the existence of such solutions, and show that this condition retains the same integrability as that of the p-Laplacian. We examine the influence of the anisotropic norm underlying the Finsler p-Laplacian on the boundary behaviour of the solution, then derive asymptotic estimates for large solutions near the boundary of the domain. Using these boundary asymptotics, we prove uniqueness results for power type nonlinearities.

We report the demonstration of a bulk, intrinsically phase-stable source of polarization- and time-energy-entangled photon pairs at 810nm and 1550nm, directly coupled into single-mode optical fibers. This highly non-degenerate wavelength combination is well suited for hybrid quantum communication networks, enabling low-loss transmission in optical fibers at 1550nm while maintaining efficient free-space propagation and detection at 810nm. The source is based on spontaneous parametric down-conversion in a periodically poled lithium niobate crystal embedded in a polarization Sagnac interferometer, providing inherent stability and dual-degree-of-freedom entanglement. We measure a spectral brightness of B = 4800 pair/s/mW/GHz, with fiber coupling efficiencies exceeding 0.48 at both wavelengths. The entanglement quality is characterized by high-visibility two-photon interference, yielding net visibilities of 0.995 in the polarization basis and 0.991 in the energy-time basis. These performances demonstrate a compact and robust entanglement source compatible with hybrid fiber/free-space quantum key distribution architectures, and suitable for future ground-to-satellite quantum communication links.

Neutrino signal from a population of Seyfert galaxies has been detected by IceCube neutrino telescope in the muon neutrino channel that has sensitivity mostly to the Northern Hemisphere sources. This detection can be verified by KM3NeT telescope that has sensitivity also in the Southern Hemisphere. We define a catalog of Seyfert galaxies that are expected to be detectable with KM3NeT, assuming that the neutrino luminosity scales with the intrinsic hard X-ray luminosity of the sources. We find that four sources: NGC 1068, NGC 4151, NGC 4945 and Circinus galaxy, are detectable by KM3NeT, if their spectra follow either NGC 1068 or NGC 4151 spectral template based on IceCube data. We discuss uncertainties of the neutrino flux estimate, considering the Compton-thick nature of three of the four detectable sources: NGC 1068, NGC 4945 and Circinus. The limited catalog of the four sources can be used in KM3NeT source search to reduce the trial factor of analysis aimed at independent verification of the neutrino signal from Seyfert galaxies.