The interaction between the LMC and SMC (the Clouds) has resulted in prominent tidal features, including an extended bridge of gas and stars connecting the two galaxies. This Bridge has likely formed during the most recent interaction between the Clouds, about 150-250 Myr ago. While some young stars observed in the Bridge have formed in-situ from the tidally stripped gas, stellar populations may also have been drawn out of the SMC during the tidal interaction. We aim to identify a clean sample of likely Bridge stars in the region between the LMC and SMC using Gaia DR3 astrometric and photometric data combined with machine-learning techniques. We use the dimensionality-reduction algorithm UMAP to construct a training sample of young stars in the outskirts of the SMC and LMC. A neural network trained on this sample is then applied to Gaia sources in the inter-Cloud region to classify the stars and identify candidate Bridge members. We present and characterise a new sample of young candidate Bridge stars, selected from Gaia DR3. We investigate its spatial distribution, kinematic properties and colour-magnitude diagram and validate it using existing Bridge samples. The young stellar Bridge aligns well with HI gas, clusters, and cepheid samples, apart from a small offset near the LMC outer disc. We measure a Bridge length of ~15 kpc and the stars are travelling from the SMC to the LMC at a median tangential velocity of ~114 km/s. This implies a crossing time of ~125 Myr, which is within the timeframe of the last interaction of the Clouds and therefore supports tidal stripping as a possible formation scenario of the Bridge.

Graphical functions have emerged as a powerful framework for evaluating multi-loop Feynman integrals in perturbative quantum field theory. Defined as massless three-point position-space integrals, they reveal rich analytic structures and have enabled major advances, including the highest-loop results currently known in several quantum field theories. Their role extends to conformal field theory, and recent algorithmic developments now allow many graphical functions to be computed automatically. This review, based on graduate-level lectures held by O.S. in 2025/26 at the University of Hamburg, introduces the central ideas behind graphical functions, covering periods, Feynman residues, and the treatment of regular and singular cases in both integer and non-integer dimensions. It also discusses connections to momentum space and self-duality, and provides guidance for further study, offering a coherent entry point into a topic not addressed in standard textbooks.

This letter presents a shifted passivity analysis of the single-machine infinite-bus system in the stationary ($αβ$) reference frame. We study the attractivity of a periodic synchronous steady state with constant rotor frequency and formulate shifted passivity with respect to this motion. A port-Hamiltonian representation of the machine dynamics is used to construct a local shifted passivity condition from the error Hamiltonian and a correction term adapted to the synchronous steady state. For the infinite-bus interconnection, the resulting dissipation inequality leads to a sufficient stability condition expressed in terms of field excitation magnitude, damping, inertia, and steady-state current. This condition implies local asymptotic stability of the synchronous steady state and yields a sublevel-set estimate of its region of attraction under an additional small-inertia condition. A distinctive feature of the analysis is that it preserves the periodic structure of the rotor angle and provides a compact passivity-based stability certificate for the stationary-frame model.

We investigate the effect of inter-disk coupling on the magnetoplasmon dispersion in a square lattice of two-dimensional electron system (2DES) disks etched from a GaAs quantum well. Using magneto-optical terahertz (THz) spectroscopy, we track the evolution of the collective modes as disk lattice period is systematically reduced, thereby increasing the coupling strength. At large distances, the system exhibits magnetoplasma modes corresponding to individual excitations in disks. As the inter-disk distance decreases, we observe a modification to magnetoplasma dispersion.

X-ray Thomson scattering (XRTS) has emerged as a widely used diagnostics for extreme states of matter in a great variety of situations, and over a broad range of parameters. The standard approach for the interpretation of XRTS measurements is given by the forward modeling approach, where the electronic dynamic structure factor $S_{ee}(\mathbf{q},ω)$ is computed from a suitable theoretical model, convolved with the combined source-and-instrument function, and then matched with the experimental observation, treating a-priori unknown parameters such as the mass density, temperature and ionization state as free fit parameters. Very recently, it has been suggested that this inherent model dependence can be avoided by analyzing XRTS spectra in terms of the imaginary-time correlation function (ITCF) $F_{ee}(\mathbf{q},τ)$ [Dornheim \textit{et al.}, \textit{Nature Commun.}~\textbf{13}, 7911 (2022)], giving one model-free access to the temperature, normalization, Rayleigh weight, as well as a number of other properties. Here, we present a comprehensive review article on these developments, including accessible discussions of the method's theoretical background in terms of Feynman's imaginary-time path integral picture of statistical mechanics as well as its remaining limitations, in particular with respect to the source-and-instrument function of the experimental set-up. In addition, we discuss new chances for the further development of this framework by utilizing emerging capabilities for high-repetition XRTS experiments with meV resolution over spectral ranges of tens of eV at state-of-the-art x-ray free electron laser (XFEL) facilities such as the European XFEL in Germany.

Rank aggregation seeks a representative permutation for a collection of rankings and plays a central role in areas such as social choice, information retrieval, and computational biology. Two fundamental aggregation tasks are the center and median problems, which minimize the maximum and the total distance to the input permutations, respectively. While these problems are well understood under Kendall's tau and related distances, their parameterized complexity under the Ulam metric, an edit-distance-based metric on permutations, has remained largely unexplored. In this work, we initiate a systematic study of the parameterized complexity of rank aggregation under the Ulam metric. We consider both the center and median problems, as well as their generalizations to the $k$-center and $k$-median clustering settings, parameterized by the number of centers $k$ and the distance budget $d$ (corresponding to the maximum distance for center variants and the total distance for median variants). Both problems are known to be NP-hard already for $k=1$. We show that the Ulam $k$-center problem remains NP-hard when $d=1$, but is fixed-parameter tractable when parameterized by $k + d$. Our algorithm is based on a novel local-search framework tailored to the non-local nature of Ulam distances. We complement this by proving that no polynomial kernel exists for the $k+d$ parameterization unless NP $\subseteq$ coNP/poly. For the Ulam $k$-median problem parameterized by the total distance $d$, we establish W[1]-hardness and provide an XP algorithm. We also provide a polynomial kernel for the parameter $k + d$, which in turn yields a fixed-parameter tractable algorithm.

Cross-domain recommendation (CDR) has demonstrated to be an effective solution for alleviating the user cold-start issue. By leveraging rich user-item interactions available in a richly informative source domain, CDR could improve the recommendation performance for cold-start users in the target domain. Previous CDR approaches mostly adhere the Embedding and Mapping (EMCDR) paradigm, which learns a user-shared mapping function to transfer users' preference from the source domain to the target domain, neglecting users' personalized preference. Recent CDR approaches further leverage the meta-learning paradigm, considering the CDR task for each user independently and learning user-specific mapping functions for each user. However, they mostly learn representations for each user individually, which ignores the common preference between different users, neglecting valuable information for CDR. In addition, all these approaches usually summarize the user's preference into an overall representation, which can hardly capture the user's multi-interest preference. To this end, we propose a personalized multi-interest modeling framework for CDR to cold-start users, termed as NF-NPCDR. Specifically, we propose a personalized preference encoder that enhances the neural process (NP) with the normalizing flow (NF) to convert the Gaussian (unimodal) distribution to a multimodal distribution, providing a novel way to capture the user's personalized multi-interest preference. Then, we propose a common preference encoder with a preference pool to capture the common preference between different users. Furthermore, we introduce a stochastic adaptive decoder to incorporate both the personalized and common preference for cold-start users, adaptively modulating both preference for better recommendation.

We study enumeration problems for multi-operator monomials generated from one indeterminate by an associative multiplication together with finitely many unary operators. We consider four regimes, according to whether multiplication is commutative and whether the unary operators commute. In the case where the unary operators do not commute, we obtain explicit multigraded generating functions and coefficient formulas, including a multinomial refinement of the Narayana numbers, together with interpretations in terms of rooted trees, restricted lattice paths, and binary trees. When the unary operators commute, we derive canonical representatives and effective recurrences, with corresponding monotonicity conditions in the combinatorial models. When multiplication is commutative, the sequence decomposition is replaced by a multiset decomposition, leading to exp--log generating functions and Euler-transform recurrences. In special cases, the resulting sequences recover classical families including the Catalan numbers, the small Schröder numbers, and rooted-tree numbers.

The Neganov-Trofimov-Luke (NTL) effect is used by experiments based on cryogenic detectors to boost the sensitivity of light-sensitive devices down to a few optical photons. In this work we introduce a silicon light-detector technology that implements NTL amplification at millikelvin temperatures using transparent indium-tin-oxide (ITO) electrodes. The ITO electrodes enable an electric field perpendicular to the wafer surface, mitigating surface charge recombination, and thanks to their optical properties, simultaneously serve as an anti-reflective coating. By combining these two functions in a single element, the fabrication process is simplified, yielding more robust and cost-effective devices. We report on the production and characterization of the first batch of these detectors. We performed a room-temperature characterization of the ITO electrodes, verifying the structural and optical characteristics of the deposited electrodes. We then operated 2 of these devices as cryogenic calorimeters at millikelvin temperatures. Finally, we develop a consistent analytical model for the NTL gain for both ionizing particles and optical photons, successfully describing the gain dependence on the NTL bias and explicitly accounting for the partial electrode coverage of the device surface.

We construct derivations on the triplet $W$-algebras $\mathcal{W}_{p_+,p_-}$ by refining the Frobenius homomorphisms of Tsuchiya-Wood and show that the property of the Adamović-Milas derivation for $\mathcal{W}_{2,p}$ extends to our derivations. As an application, we show that the $\mathfrak{sl}_2$-symmetry of $\mathcal{W}_{p_+,p_-}$ arises naturally from our construction. We further show that our method applies to the triplet $W$-superalgebra $\mathcal{SW}(m)$ and that the full automorphism group ${\rm Aut}(\mathcal{SW}(m))$ is $PSL_2(\mathbb{C})\times \mathbb{Z}_2$.

Designing Doppler-resilient unimodular discrete phase-coded waveforms (DPWs) with low delay-Doppler sidelobes is critical for multiple-input multiple-output (MIMO) radar. Existing block coordinate descent (BCD) methods suffer from high computational cost for designing long sequences or large waveform sets. Meanwhile, learning-based alternatives such as the soft-quantization network (SQN) only address correlation optimization in the delay domain, without considering ambiguity function (AF) optimization in the joint delay-Doppler domain. To address these issues, this paper proposes a novel Doppler-resilient DPW design framework, termed SQNGD, for joint transmit-receive optimization that simultaneously optimizes the auto-AF, cross-AF (CAF), and signal-to-noise ratio loss (SNRL) under unimodular constraints. To solve the multi-objective optimization problem (MOOP), a joint transmit-receive design and an alternating optimization strategy are developed. The transmit waveforms are optimized via soft-quantization-based differentiable parameterization, while the receive filters are updated by gradient descent (GD) with an energy constraint and SNRL penalty. An FFT-accelerated evaluation of the AF and CAF is further incorporated, reducing the optimization time by 1.9x - 11x compared with the state-of-the-art (SOTA) majorization-minimization-coordinate descent (MMCD) method. Numerical results show that SQNGD achieves a peak sidelobe level (PSL) of approximately -43 dB over the Doppler range [-0.5,0.5] and -31 dB over [-600,600], respectively, outperforming MMCD by 5.85 dB and 3.45 dB, while maintaining the same SNRL of 0.5 dB.

Domain wall annihilation can leave a distinctive imprint on the induced gravitational wave spectrum. During annihilation, most of the domain wall energy transforms into the scalar field responsible for the initial $\mathbb{Z}_2$ symmetry breaking that created the walls, along with any coupled species. If the produced scalar is sufficiently long-lived, its delayed decay drives an early matter-dominated phase following domain wall annihilation, significantly amplifying induced gravitational waves from primordial perturbations. The subsequent transition to radiation domination dilutes the domain wall contribution through entropy injection while preserving the enhanced induced signal. This creates a gravitational wave spectrum with two distinct peaks detectable across complementary frequency bands. We explore the observable parameter space and demonstrate how multi-band detection can probe early universe symmetry breaking.

An $n$-tuple $D=(d(1),\dots,d(n))$ is a \emph{feasible degree sequence} if there is a graph on $\{1,\dots,n\}$ such that $i$ has degree $d(i)$. Any such graph will have $m=\sum_{i=1}^n d(i)/2$ edges. Letting $G(D)$ be a graph chosen uniformly from those with the given degree sequence, we upper-bound the probability that $G(D)$ is disconnected based on the number of vertices of degree $d$ for small $d$, and develop a powerful tool for proving such bounds. If there are any vertices of degree zero the probability $G$ is disconnected is $1$, so we assume there are no such vertices. Our results then imply that if there are $o(\sqrt{m})$ vertices of degree $1$ and $o(m)$ vertices of degree 2 then with high probability $G$ is connected, while if there are no vertices of degree 1 or 2 then the probability $G$ is disconnected is $O(\frac{n^4}{m^6})$.

In this paper, we establish Liouville-type theorems for parabolic differential inequalities with $(p,q)-$Laplacian operator on Riemannian manifolds. By a test function argument, we establish nonexistence results under suitable weighted volume growth assumptions involving potential. In particular, we can obtain nonexistence results for a wider class of parabolic inequalities.

The work is devoted to the development and computational implementation of the homogenization method for modeling unsteady flows of a viscous incompressible fluid in periodic porous media taking into account memory effects. At the macrolevel, the flow is described by an integro-differential Darcy law with a tensor memory kernel determined by solving unsteady problems on the periodicity cell. The developed approach to computational homogenization is based on finding the steady-state and unsteady components of the conductivity tensor from solving auxiliary boundary value and spectral problems on the periodicity cell. The nonlocal macroscopic problem is transformed into a local system of differential equations by approximating the memory kernel as a sum of exponentials. Issues of spatial finite element approximation are discussed, and stable two-level schemes in time are constructed. The results of applying the developed computational homogenization technology for unsteady filtration problems in porous media to a two-dimensional test problem are presented.

This workshop addresses this gap by bringing together researchers and practitioners from AI, HCI, and the learning sciences to explore how interactive systems can better support learning. We focus on the design and evaluation of human-AI collaborative learning interfaces that are technically robust, human-centered, and pedagogically grounded. By fostering interdisciplinary dialogue, the workshop aims to identify shared challenges, design principles, and research directions for next-generation learning technologies.

This study investigates the lithium conversion behavior of a hydrogen storage material based on vanadium oxide added magnesium hydride. To understand the chemical interaction between vanadium oxide and magnesium hydride, detailed X ray diffraction and X ray photoelectron spectroscopy analyses were performed on ball milled composites with varying compositions. The results confirm the formation of a combined magnesium vanadium oxide with a rock salt structure, indicating strong chemical interaction between the components. It is further shown that the presence of a small amount of this oxide additive significantly influences the lithium reaction with magnesium hydride, leading to a high initial discharge capacity and limited recharge capacity in lithium ion coin cells. Post use analyses confirm the presence of magnesium hydride, suggesting that volume expansion is not responsible for the observed irreversibility. Electrochemical impedance spectroscopy using differential function of relaxation times indicates that electrolyte degradation is not a major issue. Instead, slow charge transfer processes are identified as the limiting factor, and these are sensitive to the composition of the additive. These findings highlight that improving electrode and electrolyte compatibility is essential for enhancing performance in this system.

The generalized Langevin equation (GLE) constitutes a fundamental model for describing nonequilibrium dynamics with memory effects. To overcome the numerical challenges arising from superquadratically growing potentials and degenerate noise, we propose and analyze a structure-preserving splitting averaged vector field (AVF) method for a quasi-Markovian GLE. The core advantage of this method lies in its ability to simultaneously preserve the exponential integrability, Malliavin differentiability, and ergodicity of the underlying continuous system. Notably, by leveraging exponential integrability, Malliavin differentiability, and uniform non-degeneracy of the numerical solution, we obtain the existence and smoothness of its probability density function, which converges to that of the exact solution with first-order accuracy. Furthermore, by validating the Lyapunov condition and the minorization condition using a localized technique, we establish the geometric ergodicity of the numerical solution. Finally, numerical experiments are conducted to confirm the theoretical results.

We find the ground-state energy of the Ising model using the Cascaded Variational Quantum Eigensolver (CVQE) algorithm with the Guided-Sampling Ansatz (GSA) using up to 63 qubits on a quantum computer. We study a heavy-hex lattice to match the qubit architecture, allowing us to perform calculations in the quantum utility regime. We study both a homogeneous and random-coupling model. We locate the boundary of acceptable quantum errors as a function of the number of qubits and coupling strength. An entropic analysis is performed giving insights into the quantum computing performance. A subspace analysis is performed that suggests that the Ising model is especially suited for near-term quantum computing.

The Density Functional Theory plus Hubbard $U$ (DFT+$U$) technique is one of the most widely used tools by condensed matter physicists and solid state chemists for the simulation of transition-metal and lanthanide bearing crystals, and increasingly of much more diverse chemistries. Although often synonymous with the corrective functionals of Dudarev et al. and Liechtenstein et al., there exists a wide variety of DFT+$U$-type functionals ready to be utilized, and no doubt yet to be developed. Since the earliest days, the gap in the DFT+$U$ single-particle eigenspectrum has been associated with the fundamental band gap, and the method has typically found more success for spectra than for total-energy derived properties. There has been some doubt, however, as to the conceptual validity of this association. Here, extending findings from recent years regarding local and semi-local functionals, we prove that the DFT+$U$ eigenspectrum gap is indeed valid, in the sense that it matches its own fundamental gap calculated using total-energy differences. This is true for pristine periodic systems with converged $k$-point sampling but not, however, for defective ones or isolated systems. We show that bandgap validity for solids holds in the presence of pseudopotentials and PAW potentials, when using hybrid functionals, and in DFT+$U$(+$J$) irrespective of the level of subspace projection onto the band-edge states. We survey every DFT+$U$-type functional known to have been published to date, within a unified notation. We verify analytically under which conditions the eigenvalue gap equals its fundamental gap for each functional, and analyze its effect on total energies and gaps for the hydrogen lattice in the Mott-Hubbard limit.

We propose a general method to fully characterize a classical stochastic noise process causing qubit dephasing through repetitive Ramsey interferometry measurements (RIMs) on the qubit. Compared to filter-function-based spectroscopy, our method does not require complicated dynamical decoupling pulses and can directly detect arbitrary-order correlation functions of such noise processes. We show that each RIM with a short evolution time and suitably chosen control pulses can perform a direct sampling of the noise field and the $n$-point correlations of the RIM outcomes are proportional to the $n$-point correlation functions of the noise processes. Then we numerically demonstrate this method for characterizing two typical examples of classical noises, including the Ornstein-Uhlenbeck processes producing Gaussian noises and an ensemble of TLFs producing non-Gaussian noises. Our method is independent of qubit lifetime and robust against qubit decoherence and measurement errors, thus offering a universal and efficient protocol for qubit noise spectroscopy across diverse platforms.

Mean-motion resonances are expected to frequently arise at the inner edges of protoplanetary disks, where planet-disk interactions facilitate large-scale orbital convergence. Under certain conditions, however, the same dissipative forces that promote resonant capture can drive resonant librations overstable, ultimately breaking commensurabilities. Here we examine the onset of overstability near disk torque reversals and show that it can be subdued when the transition is sufficiently sharp. Adopting the dissipative circular restricted three-body problem as a paradigm, we present a WKB-style analysis that reduces the resonant dynamics to a damped, driven harmonic oscillator. Within this framework, we obtain an effective frictional term that is proportional to the local migration-rate gradient, parameterized by a dimensionless coefficient $β$ that encodes the steepness of the local torque reversal. Our analytical theory predicts that overstability is quenched once $β\gtrsim τ_a/τ_e$, where $τ_a$ and $τ_e$ denote the characteristic disk-driven evolution timescales of semi-major axis and eccentricity. We verify and refine our analytic results with direct $N$-body integrations. Simple estimates based on conventional type-I scalings suggest that the competition between overstability and its mitigation at disk inner edges is a borderline outcome that is sensitive to the detailed structure of planet-disk interactions.

The rapid growth of LLMs demands high-throughput, memory-capacity-intensive inference on resource-constrained edge devices, where single-batch decoding remains fundamentally memory-bound. Existing out-of-core GPU-based and SSD-like accelerators are limited by DRAM-bound weight movement and inefficient storage access granularity. We present NVLLM, a 3D NAND-centric inference architecture that offloads feed-forward network (FFN) computation into the Flash while executing attention on lightweight CMOS logic with external DRAM. Through wafer-to-wafer stacking, NVLLM tightly integrates multi-plane 3D NAND with compute pipelines, error correction code (ECC) units, and buffers, enabling page-level FFN weight access without DRAM traversal. All GEMM/GEMV operations are decomposed into dot-product primitives executed by out-of-order PE lanes, operating directly on raw NAND reads with integrated ECC. Attention weights remain in DRAM, and a KV-cache-aware scheduler sustains throughput as the context length grows. Evaluated on OPT and LLaMA models with up to 30B parameters, NVLLM achieves a 16.7$\times$--37.9$\times$ speedup over A800-based out-of-core inference and up to 4.7$\times$ speedup over SSD-like designs, with only 2.7\% CMOS area overhead.

We consider a class of gauge glass models with Gaussian disorder on the Nishimori line, including the Ising spin glass, the $XY$ gauge glass, the $Z_q$ gauge glass, and the gauge-invariant Potts model. We prove that the first and second Griffiths inequalities hold for these models on arbitrary lattice structures. As a consequence, both the pressure and the correlation functions are monotonically increasing with respect to the inverse temperature along the Nishimori line. Furthermore, we establish an analogue of the Gibbs--Bogoliubov inequality for this class of models. This result implies that, on the Nishimori line, the approximate quenched free energy obtained via the replica method with a replica-symmetric mean-field approximation is always greater than the true quenched free energy. Our results provide a broad generalization of previous results established for the Ising spin glass with Gaussian disorder on the Nishimori line.

Our research is closely related to ontological studies in mathematics. It provides crucial insights into the nature of decisions and strategies characterized by Markov moments. In a stopping game, a holistic decision-maker would evaluate comprehensive information by assessing the probabilities of various outcomes and their associated payoffs. This involves understanding the current state, historical data, and potential future scenarios. Such a decision-maker must also consider strategic interactions by anticipating and accounting for the strategies of other players. They must be flexible in adapting their strategy as the game evolves and able to integrate uncertainty by incorporating risk preferences and tolerances. They would perform scenario analysis to evaluate the impact of different stopping times under varying conditions. The goal of this modeling and its implementation in psychological practice is to introduce a novel method for assessing the state of players, leveraging deviations from rational strategies as diagnostic indicators of their psychological and decision-making profiles. The details of other models will be subject to contributed papers. The article presents the theoretical basis for combining various factors when modeling decision-making processes. The original title is "Rationality, Deviation, and Diagnosis: A Holistic Approach to Stopping Games" and will be used when it is possible to describe and interpret the results of the experiments we write about in the last section of the paper.

Symmetry is an implicit objective in structural form-finding that often reconciles efficiency and aesthetics. This paper identifies the symmetry of polyhedral diagrams in three-dimensional graphic statics (3DGS) as point groups and formulates them as constraints, enabling the optimization and manipulation of polyhedral diagrams that preserve such symmetry. 3DGS has been an efficient and effective tool for the form-finding of funicular structures. However, when modifying complex diagrams for design exploration or optimization, one can easily break the symmetry of the reciprocal design input, rendering the result undesirable for practical use. To address this problem, this paper investigates symmetry transformations and introduces point groups, an abstract algebra tool commonly used in crystallography to represent the symmetry and equivalence between a network of atoms (points with labels). It then discusses the hierarchy of symmetry in the geometry types of a polyhedral diagram, and proposes the constraint of symmetry through edge lengths. Based on the crystal symmetry search algorithm by spglib and pymatgen, a fast fingerprinting algorithm is developed to identify the point group of a polyhedral diagram and sort equivalent edges into sets. Finally, the paper shows that the necessary and sufficient condition for preserving the point group symmetry is that each set of edges has the same length. This constraint is compatible with the algebraic formulation of 3DGS and effectively preserves symmetry while reducing the dimension of the solution space. The method is implemented in the PolyFrame 2 plug-in for Rhino and Grasshopper.

We examine the two-dimensional spin-orbit-coupled bosons in the presence of an anisotropic dipolar interaction in square lattices. The spin-orbit coupling leads to finite-momentum superfluid and supersolid states, while the nearest-neighbour interaction induces crystalline characteristics in the quantum phases of soft-core bosons. We employ site-decoupled Gutzwiller ansatz and mean-field decoupling theory to obtain the phase diagrams and investigate the effects of the tilt of magnetic dipoles with respect to the polarization axis. Our study reveals the intriguing quantum phase transition of checkerboard finite-momentum phase-twisted and phase-stripe states into their stripe counterparts at a magic tilt angle, at which the off-site interaction along one of the directions becomes zero. At smaller tilt angles, the checkerboard charge-density-wave phase intervened by two compressible finite-momentum phases, and at strong spin-orbit coupling strengths, the phase-twisted supersolid and superfluid phases emerge. At larger tilt angles, a transition between the striped order of phase-twisted states and phase-stripe states occurs. The inclusion of off-site inter-component correlation leads to density-correlated phases, lattice-induced supersolid, and ferromagnetic quantum phases. Our study highlights novel finite-momentum crystal phases of spin-orbit-coupled dipolar bosons and provides a parameter space to observe them in quantum gas experiments.

We investigate completed interlacing of zeros for pairs of polynomial sequences that fail to interlace by exactly two points. Using a general mixed recurrence relation, we identify a quadratic polynomial whose zeros serve as the two extra points required to achieve complete interlacing. We determine the precise positions of these two extra points relative to the zeros of the higher-degree polynomial, thereby establishing full interlacing results. The theory is applied to several classical families of orthogonal polynomials. In the Jacobi case, we improve earlier results by giving explicit extra points that complete the interlacing of $P_n^{(α,β)}$ and $P_{n+1}^{(α+1,β+1)}$. Second, we address an open question regarding the interlacing of zeros for Meixner-Pollaczek polynomials of consecutive degree with parameter increased by one. Finally, we establish new interlacing results for Pseudo-Jacobi polynomials.

The time-dependent deformation of concrete, particularly creep, remains a key challenge for reliable and material-efficient design. Experimental results show that tailored preloading, short-term loads exceeding the subsequent sustained load, can reduce both the magnitude and variability of creep strains which may be associated with beneficial microstructural changes. Building on these insights, this article employs Gaussian Process Regression (GPR) to calibrate analytical creep models, incorporating the effects of preloading intensity, timing, and concrete age into conventional predictions. The study pursues three main objectives: (i) calibrating a creep model using GPR based on experimental data, (ii) evaluating the impact of training data selection and preparation, and (iii) analysing model performance depending on the available experimental duration. The results demonstrate that GPR can improve model accuracy, quantify uncertainties, and support optimal test planning, while also enhancing understanding of preloading effects and contributing to more reliable and sustainable concrete creep predictions.

In dense neutrino gas, pairing correlations between neutrinos and antineutrinos with opposite momenta can be nonzero in generalized neutrino quantum kinetic equations at the mean-field level. In this Letter, we investigate for the first time the condition under which collective neutrino-antineutrino ($ν\barν$) pairing instabilities can occur, using simplified toy models consisting of discretized $ν\barν$ pairs in a homogeneous neutrino gas. We find that, in ansiotropic systems, $ν\barν$ pairing instabilities generally emerge when the phase space distribution of the excessive pair-occupation number, defined as the sum of the neutrino and antineutrino occupation numbers of a pair minus 1, changes signs. The associated instability growth rate is set by the forward scattering potential and is comparable to that of collective fast neutrino flavor instabilities. The instabilities can result in pair conversions of $ν\barν$ occupation numbers between different momentum modes. Our results motivate further studies to assess the relevance of $ν\barν$ pairing effects in realistic astrophysical and cosmological environments.