Forced-choice conjoint designs have become a staple method in the experimentalist's toolkit. However, the forced-choice outcome is neither always consistent with the types of choices individuals make in real political contexts, nor is it statistically efficient. In this paper, we formalize how ranked outcomes can be integrated into the conjoint framework. We provide a proof that rank-expanded estimators are equivalent to conventional AMCE, a theoretical account of how additional profiles increase the efficiency of conjoint designs, and design-based tests for the transitivity and independence of irrelevant alternatives assumptions that underpin the expansion. Across two pre-registered survey experiments--the first comparing forced-choice and ranked-choice designs across candidate and policy domains, and the second varying the number of ranked profiles--we find that ranked-choice conjoints yield substantively similar but more precise AMCE estimates, shrinking standard errors by 12-13% with one additional profile and up to 55% with six profiles per vignette. Based on efficiency--validity trade-offs, we recommend K = 4 profiles for most applications. We provide an accompanying open-source R package, cjrank, that implements rank expansion, AMCE estimation, efficiency diagnostics, and the assumption tests described in this paper.

Hydrodynamic electron transport, namely, the electric behaviors in solid materials at the macroscopic level are similar to the fluid hydrodynamics when the momentum-conserving electron-electron scattering plays the leading role, has got much attention in the past ten years. However, most of previous studies mainly focus on the electric properties. In this work, the thermal behaviors of hydrodynamic electron transport in a homogeneous 2D Corbino disk geometry is studied by the electron Boltzmann transport equation (eBTE) coupled with the Poisson equation under the magnetic field perpendicular to disk plane. Results show that in the electron hydrodynamic regime, the heat flux deflection phenomenon appears under the radial electric field or temperature gradient, namely, the heat flux no longer flows only along the radial direction and there is heat flux in the tangential direction of the radius. Heat flux deflection phenomenon is suppressed by momentum-relaxing scattering process and promoted by momentum-conserving scattering process. When an electric potential gradient or temperature gradient in the same direction is applied separately, the direction of heat flux is reversed in the electron hydrodynamic regime.

Weighted extropy has recently emerged as a flexible information measure for quantifying uncertainty, with particular relevance to order statistics. In this paper, we introduce and study a weighted cumulative analogue of extropy, extending the framework of weighted cumulative residual and cumulative past entropies to extreme order statistics. Specifically, we define the general weighted cumulative residual extropy (GWCREx) for the smallest order statistic and the general weighted cumulative past extropy (GWCPEx) for the largest order statistic, along with their dynamic versions. We show that these weighted measures and their dynamic counterparts uniquely characterize the underlying distribution. Moreover, we establish new characterization results for two widely used reliability models: the generalized Pareto distribution and the power distribution. The proposed framework provides a unified information-theoretic tool for analysing extreme lifetimes in reliability engineering and survival analysis.

The ways people remember and recall places reveal an invisible aspect of cultural heritage (CH), reflecting how individuals and communities relate to these places. Heritage is communal, emerging through collaboratively constructed narratives rather than individual records. To probe how people may share collective memories, we designed an immersive two-person workflow for collaboratively co-designing 3D artifacts and environments in virtual heritage locations, using Generative AI (GenAI) to instantiate these intangible memories. Observations of the co-creation process revealed that participants merged prompts and model placements when negotiating different perspectives. They used spatial operations to compose scenes, and also to express personal and embodied experiences of CH. When GenAI failed to meet their needs, participants engaged in creative appropriation, re-purposing unsatisfactory generated objects as sources of design inspiration to further shared narratives. While GenAI may have a homogenizing effect on CH expression, this work shows how people may overcome limitations in immersive collaborative workflows.

Let $\text{E}/\text{F}$ be a quadratic extension of non-Archimedean local fields with odd residual characteristic. In this paper, we give equivalent conditions for a simple supercuspidal representation $π$ of $\text{GL}(n, \text{E})$ to be distinguished by $\text{GL}(n, \text{F})$ in terms of its defining maximal simple type and twisted gamma factors. Furthermore, we prove that the collection of twisted gamma factors evaluated at $\frac{1}{2}$ between $π$ and all unitary, tamely ramified quasi-characters of $\text{E}^{\times}$ that are trivial on $\text{F}^{\times}$ is sufficient to determine whether $π$ is distinguished by $\text{GL}(n, \text{F})$.

The condensation of electron quartets, known as charge-4e superconductivity (SC), represents a novel quantum state of matter beyond the standard paradigm of Cooper pairing. However, concrete microscopic models realizing this phase in two dimensions remain a central challenge. Here, we introduce a non-engineered and sign-problem-free model, unambiguously demonstrating the emergence of a robust and high-temperature charge-4e SC phase using unbiased quantum Monte Carlo simulations. At zero temperature, the phase diagram reveals that charge-4e SC is the primary ground state in the strong-coupling regime. At finite temperature in the absence of charge-2e SC, we identify charge-4e SC through a Berezinskii-Kosterlitz-Thouless transition, marked by a universal jump in the superfluid stiffness consistent with a condensate of charge 4e. Remarkably, the transition temperature Tc increases nearly linearly with interaction strength, providing a robust mechanism for high-Tc quartet superconductivity. Furthermore, spectral analysis reveals a prominent pseudogap above Tc arising from strong phase fluctuations. Our results establish a canonical and numerically exact model system for charge-4e superconductivity, offering crucial guidance for its realization in experimental platforms such as moiré materials and ultracold atomic systems.

Lifshitz invariant is a symmetry-allowed term in the Ginzburg-Landau free energy of an ordered phase, involving the order parameters and a single spatial derivative, which serves as a source of unusual optical responses. Here we introduce a ``type II" Lifshitz invariant for superconductors, which changes its sign under the particle-hole transformation and can be distinguished from the ordinary particle-hole even ``type I" Lifshitz invariant. We show that the type II Lifshitz invariant appears only in superconductors that break time-reversal symmetry and allows the Higgs mode to be visible in the optical conductivity spectrum. We provide a classification of all pairs of irreducible corepresentations of order parameters in the magnetic point groups that admit a type II Lifshitz invariant. We also numerically calculate the optical conductivity for various models of time-reversal symmetry broken multiband superconductors, finding agreement with the group-theoretical analysis. Our results establish a universal class of time-reversal symmetry broken superconductors hosting an optically active Higgs mode.

We improve previous results on dispersive decay for 1D Klein- Gordon equation. We develop a novel approach, which allows us to establish the decay in more strong norms and weaken the assumption on the potential.

We report a hardware validation of the DAGI (Directed Acyclic Graph Information) framework on IBM Quantum hardware using a small, controlled experiment whose ideal output distribution is constrained to a low-dimensional modular manifold (a "ridge"). For two $n$-bit registers $(u,v)$ with $n=4$ (modulus 16), each key instance $k$ induces an ideal relation $v \equiv k \cdot u \pmod{16}$, producing a visually distinct ridge in the joint $(u, v)$ distribution. Executed on ibm\_torino in a single Sampler V2 job (8 keys, 1024 shots/key, $N=8192$ total shots), the ridge persists under hardware noise with ridge-hit probability $p_{hit} = 0.1830$ (uniform baseline $1/16$), corresponding to a ridge contrast of $2.93\times$ (95\% bootstrap CI [2.80, 3.06]). Key recovery exceeds chance: per-shot accuracy 0.1689 (chance 0.125, 95\% Wilson CI [0.1610, 0.1772]), and per-group dictionary recovery 0.375 (chance 0.125). To test the central DAGI hypothesis -- that recoverable key information is predominantly high-order/synergistic rather than visible in low-order marginals -- we compute a Möbius-based information decomposition of $I(K;D_S)$ over detector-bit subsets $S$ via a Möbius inversion pipeline and evaluate targeted positive synergy $CPS_K$ at order $k_{max}=3$. We observe $CPS_K(k=3) = 0.08788$ with significance under label-shuffle permutation tests (accuracy $p=0.001996$, $CPS_K$ $p=0.004975$). Uniformity diagnostics show near-uniform single-bit marginals while correlation concentrates in specific low-order pairs, and a bootstrap reliability sweep confirms order-3 targeted synergy remains statistically reliable at the full 1024-shot target budget. These results support the claim that DAGI detects and quantifies nontrivial, hardware-resilient, higher-order information structure associated with a known global algebraic constraint.

We map the three-dimensional structure and large-scale kinematics of the young stellar populations in the G352 giant molecular cloud (GMC) complex. In radio and infrared images, G352 appears as long filament extending ~$3^{\circ}$ (~150 pc) parallel to the Galactic midplane. It connects the NGC 6357 and NGC 6334 giant H II regions and the GM1-24 compact H II region. We identify 1727 stellar members of G352 via matching large catalogs of Chandra X-ray point sources and Spitzer mid-infrared excess sources to the Gaia DR3 astrometric catalog. Our catalog of 11,470 X-ray point sources ranks among the three largest contiguous X-ray survey datasets ever assembled for a massive star-forming complex. We revise the mean heliocentric distance of G352 to $1670\pm 80$ pc, with the median parallaxes of seven constituent groups exhibiting a trend toward increasing distance with decreasing Galactic longitude. We identify two foreground stellar groups superimposed on NGC 6357 that may belong to the Sag OB4 association. The three massive clusters in NGC 6357 exhibit peculiar velocities that trail Galactic circular motion by ${\sim}8$ km/s, while the stars associated with NGC 6334 are more consistent with a circular orbit. GM1-24 has a distinct proper motion and smaller parallax compared to NGC 6334. The steep pitch angle of the GMC filament into the sky appears inconsistent with a spiral arm. The various stellar groups are not gravitationally bound to each other, making G352 a proto-OB association.

Searches at high object masses probe both resonant production of new particles and nonresonant distortions of Standard Model spectra. This contribution follows the material presented in the Moriond Electroweak 2026 talk and summarizes recent CMS results in this regime: the Run~2 combination of heavy vector boson searches, the Run~3 search for $W^\prime \to \ell ν$, the Run~2 dijet angular analysis, and searches for pair-produced dijet resonances in inclusive and $b$-tagged final states. No significant deviation from the SM expectation is observed, and the new results extend the sensitivity of CMS to multi-TeV scales in several benchmark scenarios.

Hybrid Quantum Neural Networks (HQNNs) combine classical learning with parameterized quantum circuits, but their practical performance is often limited by (i) the noise of Noisy Intermediate-Scale Quantum (NISQ) devices and (ii) the large, discrete design space of quantum circuit architectures. Moreover, HQNNs are commonly trained using a fixed circuit and a single backend, even though deployment frequently targets heterogeneous backends where compilation and execution characteristics may differ. To address these challenges, we propose GAT-QNN, a genetic algorithm (GA)-based framework that trains a macroCircuit (search space) by iteratively sampling microCircuits (subcircuits), training them, and reintegrating their learned parameters into the macroCircuit. After training, we run an independent GA-driven inference stage that evaluates candidate microCircuits using the trained macroCircuit weights and selects top-performing architectures for deployment. This two-stage approach enables backend-aware microCircuit selection without retraining each candidate architecture and can also reduce computational resources (gate count) by deploying smaller microCircuits derived from the macroCircuit. We validate the approach on MNIST classification (four classes) and report consistent 22-23% test accuracy gains for GA-driven inference across multiple backends.

Plasmoid-driven magnetic reconnection in elongated current sheets is suspected to be an ubiquitous phenomenon in space and astrophysical plasmas, but the mechanisms driving its onset and dynamics are still debated. Deciphering the physical mechanisms dominating the destabilization and fragmentation of the current sheet, as well as its evolution, would have a wide impact into our understanding of the induced plasma turbulence and particle acceleration. Here, by coupling 3D hybrid simulations with laser-driven experiments that involve counterflowing high-energy-density magnetized plasmas with a long aspect ratio of their contact layer, we show that electron pressure anisotropy is the driving factor of the growth rate of the tearing instability, and will sustain the reconnection process even without classical resistivity. Dissipative mechanisms, such as resistivity and isotropization, are further found to stabilize the sheet to varying degrees, thus modifying plasmoid formation. By identifying the roles of pressure anisotropy, dissipation, and large-scale geometry, our work lays the groundwork for the evaluation of plasmoid-driven reconnection impact on the dynamics of laboratory and astrophysical plasmas.

Antitrust authorities frequently rely on structural divestitures to address competitive concerns raised by mergers. Using census-level establishment data and proprietary transaction records from the U.S. grocery sector, we provide systematic evidence on the long-run effects of such remedies. Divested stores experience an average 31 percent decline in employment over five years, driven by elevated exit rates and persistent contraction among surviving establishments. Sales similarly decline. Transaction-level evidence indicates that divested assets are systematically weaker and are often transferred to lower-capability buyers. These findings suggest that structural remedies may be less effective when the implementation of divestitures allows merging parties substantial discretion over the assets and buyers involved.

The classical simulation of universal quantum circuits is crucial both fundamentally and practically for quantum computation. We propose SyQMA, a simulator with several convenient features, particularly suited for quantum error correction (QEC). SyQMA simulates universal quantum circuits with incoherent Pauli noise and computes exact expectation values and measurement probabilities as symbolic functions of circuit parameters: rotation angles, measurement outcomes, and noise rates. This simulator can sample measurement outcomes, enabling the simulation of dynamic quantum programs where circuit composition depends on prior measurement outputs. For QEC, it performs circuit-level maximum-likelihood decoding, provides exact symbolic expressions for logical error rates, and verifies the fault distance of fault-tolerant (FT) stabiliser and magic state preparation protocols. These features are enabled by an intuitive extension of stabiliser simulators, where each non-Clifford Pauli rotation and incoherent Pauli channel is compactly represented via auxiliary qubits and a modified trace. Representing the state requires only polynomial memory and time, while computing expectation values and measurement probabilities takes exponential time in the number of non-Clifford rotations and deterministic measurements, but only polynomial memory. The FT preparation of stabiliser and magic states, including the first stage of magic state cultivation, is analysed without approximations. We also exactly convert the disjoint error probabilities of a general multi-qubit Pauli channel to independent ones, a key step for creating and sampling from detector error models. The code is publicly available and open-source.

We prove that there are infinitely many $n$ such that $ω(n+k) \ll \log k$ for all integers $k \ge 2$. This improves on a result of Tao-Teräväinen (2025), who has $O(k)$ in place of $O(\log k)$. As corollaries, we make progress on a number of questions posed by Erdős. The proof is based on a quantitative refinement of the Tao-Teräväinen probabilistic argument, combining a more efficient sieve procedure with stronger exponential concentration-of-measure estimates. Moreover, we formulate a conjecture on integers with many prime factors based on Cramér-type random models. Assuming this conjecture, the main bound is essentially sharp.

Code optimization remains a core objective in software development, yet modern compilers struggle to navigate the enormous optimization spaces. While recent research has looked into employing large language models (LLMs) to optimize source code directly, these techniques can introduce semantic errors and miss fine-grained compiler-level optimization opportunities. We present HintPilot, which bridges LLM-based reasoning with traditional compiler infrastructures via synthesizing compiler hints, annotations that steer compiler behavior. HintPilot employs retrieval-augmented synthesis over compiler documentation and applies profiling-guided iterative refinement to synthesize semantics-preserving and effective hints. Upon PolyBench and HumanEval-CPP benchmarks, HintPilot achieves up to 6.88x geometric mean speedup over -Ofast while preserving program correctness.

The decay $B^0 \to Λ_c^+ \barΛ_c^- K_S^0$ is studied at LHCb for the first time using proton-proton collision data recorded by the LHCb experiment at a center-of-mass energy of $\sqrt{s} = 13$ TeV, corresponding to an integrated luminosity of 5.4 fb$^{-1}$. The branching ratio relative to the decay $B^+ \to Λ_c^+ \barΛ_c^- K^+$ is measured to be $$ \frac{{\cal B}(B^0 \to Λ_c^+ \barΛ_c^- K_S^0)}{{\cal B}(B^+ \to Λ_c^+ \barΛ_c^- K^+)} = 0.53 \pm 0.05 \pm 0.05, $$ where the first uncertainty is statistical and the second is systematic. Evidence is found for contributions from two resonant states, $Ξ_c(2923)^+$ and $Ξ_c(2939)^+$, in the $Λ_c^+ K_S^0$ system. The two states show a significance of $3.9σ$ relative to the nonresonant hypothesis. These two $Ξ_c^+$ states are consistent with being the isospin partners of the states observed in $Λ_c^+ K^-$ system.

Quantum dot based platforms offer a promising route towards realizing the Kitaev chain Hamiltonian hosting Majorana bound states (MBSs). Poor man's MBSs arise in a two-site Kitaev chain when the parameters of the system are fine-tuned to the sweet spot. Based on our previous work [Phys. Rev. B 111, 155410 (2025)], we consider a microscopic model for the Kitaev chain based on quantum dots with proximity effect embedded in a photonic cavity. We find that the photon coupling in the microscopic model yields an effective Hamiltonian where the cavity affects the pairing term. However, we demonstrate that even in this case, it is possible to screen particle interactions and reach the sweet spot condition for the emergence of the poor man's MBSs. In particular, we find that attractive particle interactions can be canceled for the cavity prepared in the zero-photon state, while repulsive ones can be screened with a cavity prepared in the one-photon state. Furthermore, in case of a large number of photons in the cavity, we find that the hopping amplitudes are suppressed resulting in a degenerate spectrum. This motivates the use of quantum light for engineering poor man's MBSs with cavity embedding.

Increasing demand for computational power has led cloud providers to employ multi-NUMA servers and offer multi-NUMA virtual machines to their customers. However, multi-NUMA VMs introduce additional complexity to scheduling algorithms. Beyond merely selecting a host for a VM, the scheduler has to map virtual NUMA topology onto the physical NUMA topology of the server to ensure optimal VM performance and minimize interference with co-located VMs. Under these constraints, maximizing the number of allocated multi-NUMA VMs on a host becomes a combinatorial optimization problem. In this paper, we derive closed-form expressions to compute the maximum number of VMs for a given flavor that can be additionally allocated onto a physical server. We consider nontrivial scenarios of mapping 2- and 4-NUMA symmetric VMs to 4- and 8-NUMA physical topologies. Our results have broad applicability, ranging from real-time dashboards (displaying available cluster capacity per VM flavor) to optimization tools for large-scale cloud resource reorganization.

In nature, estimating the location of a molecule source in turbulent airflow is a central, and yet highly challenging problem for mate search and foraging. Recently, it has also received increasing attention in synthetic molecular communication (SMC), e.g., for leakage detection. One important aspect of source localization is to estimate the distance to the molecule source, e.g., to determine whether it is worth to travel to a potential mating partner or food source, or to decide whether a leak is close enough for inspection. In this study, based on realistic simulations, we show that the diversity induced by molecule mixtures can aid source localization. In particular, when different molecule types in a mixture are subject to atmospheric degradation with different degradation rates, the relative abundance of the different species observed at the receiver enables low-complexity estimation of the source distance. Furthermore, this feature can be combined with already established concentration-based and temporal features of observed molecular signals to further increase estimation accuracy. Thereby, we show that molecule degradation diversity of molecule mixtures can help to realize one of the important envisioned SMC applications, namely source localization, even in turbulent airflow, opening new opportunities for the exploitation of SMC to solve real-world problems.

We develop a hypergraph container method for the Boolean Satisfiability Problem (SAT) via the newly developed container results [Campos and Samotij (2024)]. This provides an explicit connection between the extent of spread of clauses and the efficiency of container-based algorithms. Informally, the more evenly the clauses are distributed, the stronger the shrinking effect of the containers, which leads to faster algorithms for SAT. To quantify the extent of spread, we use a weighted point of view, in which a clause of size $s$ receives weight $p^s$ for some $0<p\le 1$.In this way, we introduce the notion of $(λ,p)_k$-structure for SAT formulas, where $λ$ is the spread parameter and $k$ is the maximum size of clauses. By the almost-independence property of containers, we prove that for formulas with $(λ,p)_k$-structures, one can distinguish between ``unsatisfiable formulas'' and ``formulas satisfying at least a $(1-δ)$-fraction of clauses'' in sub-exponential time. This shows that sufficiently spread formulas are not worst-case instances for Gap-ETH. Moreover, we show that the speedup is directly controlled by the spread parameter $λ$, yielding faster exact algorithms for SAT formulas containing a $(λ,p)_k$-structure. This result extends previous work [Zamir (STOC 2023)] to the non-uniform case.

We present a novel lackadaisical alternating quantum walk (LAQW) algorithm whose circuit depth scales as $\mathcal{O}(n^2+nt)$ for a $n\times n$ lattice over $t$ time steps. We show that this is a significant depth reduction compared to the existing controlled alternating quantum walk (CAQW) model, which has a circuit depth that scales as $\mathcal{O}(n^2t)$ (Li et al., 2017, arXiv:1707.07389). This makes the implementation of the LAQW viable for Noisy Intermediate-scale Quantum (NISQ) devices. We then showcase the applicability of the LAQW algorithm by proposing a chaos-based symmetric-key generation scheme. Our approach uses the LAQW as a quantum entropy source from which reproducible random bitstring sequences are generated using the underlying probability distribution and subsequent post-processing methods. We provide a comprehensive evaluation of the LAQW algorithm and demonstrate the reproducibility of 128-bit keys under simulated quantum noise provided by IBM's FakeTorino backend. A direct comparison with the CAQW model, which has been used in image encryption and hash function schemes (Li et al., 2017, arXiv:1707.07389; Abd EL-Latif et al., 2020, ScienceDirect; Abd El-Latif, Abd El-Atty, and Venegas-Andraca, 2020, ScienceDirect), highlights the potential and usefulness of the LAQW model in cryptographic applications.

The certification of quantum systems is essential for emerging quantum technologies, particularly in quantum communication, networks, and distributed computing, where maintaining a common reference frame across distant nodes poses significant challenges. Reference frame independent approaches, such as randomized measurement schemes, offer a promising route by reducing experimental demands while granting access to basis-independent quantities, including entanglement. However, the efficiency of such schemes in measuring such local invariants has remained unclear. In this work, we determine the minimal number of measurement settings required to access all two-qubit invariants. We further demonstrate that entanglement certification necessarily involves the most demanding invariants, establishing it as a maximally difficult task. Our results reveal a fundamental hierarchy among invariants, with direct implications for experimental feasibility and theoretical understanding of quantum certification. Finally, we extend our analysis beyond bipartite systems by applying it to the Kempe invariant in three-qubit systems, improving known measurement protocols and providing a first step toward uncovering similar hierarchies in higher dimensions.

The nonlinear saturation of toroidal Alfven eigenmode (TAE) due to thermal plasma nonlinearities is investigated using gyrokinetic particle-in-cell simulations and theoretical analysis. In the single toroidal mode number simulations with zonal fields filtered out, we find that the saturation level of TAE is governed by thermal plasma nonlinearities for gamma_L/omega_n > 0.47%, which has weak dependence on the linear drive gamma_L, i.e., "stiffness" in saturation level. We find that the frequency of TAE decreases as the amplitude of it increases, which is induced by the phase-space zonal structure (PSZS) of thermal plasmas universally existed in particle-in-cell simulations. The saturation of TAE can be finally reached when the mode merges into the continuum. Following this process, the separation of neighboring poloidal harmonics and mode transition to energetic particle modes can be observed. In simulations with zonal fields, zonal fields can essentially counteract the effects of PSZS of thermal plasmas, leading to roughly a factor of 2 enhancement of the TAE saturation level compared to the single toroidal mode number simulation, implying the necessity of including zonal modes in evaluating the saturation level of TAE.

DNA-coated particles are promising as building blocks for functional and finite-sized assemblies because they can be programmed with orthogonal interactions owing to the sequence-specific hybridization of DNA strands. To fully exploit this programmability, it is important to develop particles with coatings that incorporate multiple distinct DNA sequences in tunable ratios and to understand how the coating composition influences self-assembly. Here, we compared two strategies to graft multiple DNA sequences in tunable and well-defined ratios on micron-sized colloidal particles. We found that a method based on click chemistry yielded mixed coatings with large batch-to-batch variation in the composition, while a method based on isothermal DNA polymerization produced coatings of predictable composition with a precision of a few percent, but requires reaction rate measurements for each new sequence in the coating. Our self-assembly experiments showed that, even with precise control over coating composition, equilibrium co-assembly of multiple types of DNA-coated particles is limited by the number of interactions that are reversible within the same narrow temperature window. This finding highlights the need to explicitly incorporate sequential assembly pathways into structure design, with coating composition dictating the order of binding events, Together, our results show how systematic tuning of interaction strength and sequential assembly through multispecific DNA coatings is a prerequisite for the experimental realization of finite-sized and dynamic structures that have so far remained largely theoretical.

Magnetic skyrmions are emerging as promising candidates for next-generation information technologies, while the realization of scalable skyrmion lattices with tailored configurations is essential for advancing fundamental skyrmion physics and developing future applications. Here we achieved the controllable generation and regulation of a large-area, highly oriented skyrmion track array (STA) in ferromagnetic Fe3GaTe2 using a vector magnetic field manipulation technique. The orientation and ordering of STA, along with the types and density of skyrmions, are precisely controlled by modulating parameters during the manipulation. The critical roles of in-plane magnetic fields and Dzyaloshinskii-Moriya interaction in STA generation is further confirmed by micromagnetic simulation. Our findings develop a strategy for engineering large-area and highly-oriented skyrmion configurations, offering a new pathway for the future application of next-generation spintronic and information technologies.

The Wilson line defect half-indices for 3d $\mathcal{N}=2$ gauge theories with boundary confining phases admit a formulation in terms of the Askey-Wilson type moments. In the dual Landau-Ginzburg description the dual line operators can be realized as vortex line defects which induce singular behavior of chiral multiplets associated with the minimal monopole operators, together with additional one-dimensional degrees of freedom. By incorporating such a singular structure as an effective spin shift into the index computation, we obtain exact closed-form expressions for the line defect half-indices which are Askey-Wilson type moments.

Ultrasound imaging tasks such as calibration, inverse parameter estimation, and acquisition design require models that are physically grounded, efficient, and differentiable with respect to meaningful material and system parameters. While full-wave solvers offer high fidelity, they are often too expensive for iterative optimization, and existing ray-based methods have mostly been limited to forward simulation. In this work, we present a fully differentiable end-to-end ultrasound simulation framework based on full-path Monte Carlo ray tracing. Building on UltraRay, the method propagates gradients from image-space losses back through acoustic transport, beamforming, and post-processing, enabling gradient-based optimization over scene and acquisition parameters. The framework combines differentiable ray transport in Mitsuba 3/Dr.Jit with a custom differentiable bridge through the ultrasound image-formation pipeline. Forward examples reproduce expected geometric image features and capture more complex anatomical structures. In inverse problems, the method recovers known parameters in a simulated-reference setting and identifies effective parameters that improve agreement between simulated and experimental B-mode images in a simulation-to-real setting. Finite-difference comparisons further support the consistency of the computed gradients. Overall, this work provides a practical foundation for differentiable, physics-based ultrasound simulation and optimization.

We combine the Glauber--Lachs formula from quantum optics and the two-component picture for pion production to analyze data on two- and three-pion Bose--Einstein correlation at 7 TeV from the LHCb Collaboration. For the pion exchange function $E_{\rm 2B}$, we chose a dipole form and an inverse one-and-a-half pole form. The extensions are computed in the configuration space of 4-dimensional Euclidean space ($ξ=\sqrt{|\bm r_1-\bm r_2|^2+(t_1-t_2)^2}$).