Accurate modeling of the absorption of tens-of-MeV $ν_e$ on $^{40}$Ar is needed to enable measurements of astrophysical neutrinos using large liquid argon time projection chamber (LArTPC) detectors, such as those planned for the Deep Underground Neutrino Experiment (DUNE). We revisit the MARLEY neutrino interaction model used in present estimates of DUNE sensitivity to supernova and solar neutrino signals. Multiple theoretical refinements are pursued, especially in the unbound continuum region of nuclear excitation energy. Inclusive charged-current neutrino-argon cross sections are calculated using a hybrid strategy. Nuclear transitions to unbound states are treated using a Hartree-Fock Continuum Random Phase Approximation (HF-CRPA) model, including forbidden contributions. Allowed transitions to low-lying discrete levels are also included using indirect measurements and approximate corrections for the momentum transfer dependence. Exclusive predictions are obtained by coupling these calculations with a statistical nuclear de-excitation model. The impact on observables of interest for DUNE and similar experiments is examined in terms of both total and differential cross sections. Our refined calculations predict a lower allowed portion of the cross section relative to the prior MARLEY model. At neutrino energies appreciably below 100 MeV, the inclusion of forbidden transitions does not fully compensate for the loss of allowed strength. For a representative neutrino burst from a galactic core-collapse supernova, our results suggest that MARLEY 1.2.0 overestimates the event yield in a DUNE-like detector by approximately 20%. However, because this overestimation is more severe at backwards angles, use of the charged-current $ν_e$-$^{40}$Ar reaction for supernova pointing may be more feasible than previously expected.

We investigate quantum transport in an off-diagonal Aubry--André--Harper chain. The periodic hopping modulation generates effective internal boundaries that strongly influence the transmission characteristics. We show that edge, in-band bulk, and band-edge bulk states can be clearly distinguished through their transport signatures. In particular, bulk states near the band edges exhibit behavior similar to edge states, with weak dependence on system size, whereas in-band bulk states display pronounced size-dependent oscillations. We further demonstrate that the chain--electrode coupling strength controls the broadening of transmission resonances and drives a crossover from tunneling-dominated to nearly ballistic transport. In addition, dephasing introduces distinct sensitivity across different state classes, depending on their degree of spatial localization. These results highlight the key role of internal boundaries and quantum coherence in governing transport in modulated one-dimensional systems.

We present GLoop, a Fortran90 computational framework that allows one to compute by Monte Carlo a certain class of higher-loop integrals in terms of lower-loop building blocks. This is based on a recently introduced method that enables the numerical computation of integrals defined by i epsilon deformations acting on single pole singularities without the need for an explicit analytic contour deformation. We provide detailed, worked-out examples and routines to show how our strategy works. These can be used as a starting point for the reader to develop her/his own calculations.

Monitoring a 118 km subsea cable using Distributed acoustic, state-of-polarization, and Brillouin sensing captured storm-induced strain up to $\approx 0.003 με$ (dynamic) and $\approx 180 με$ (static), demonstrating consistent yet distinct modal responses to environmental loading.

We study the inverse eigenvector centrality problem on connected undirected graphs, namely, whether a given positive vector can be realized by assigning suitable edge weights. We provide a complete characterization in terms of stable sets and their neighborhoods, showing that the undirected case requires nontrivial global constraints absent in the directed setting.

The Euclidean bounce for vacuum decay enjoys an $O(4)$ symmetry that is lost in the presence of impurities than can catalyze the decay. We present a formulation for the calculation of the tunneling decay action, that is explicitly positive definite, for impurities whose effects are spherically symmetric so that the bounce symmetry is reduced to $O(3)$. The action constructed can be regarded as a generalization of the tunneling potential method, which implicitly assumed $O(4)$ symmetry. We show that the action obtained reduces to the tunneling potential for $O(4)$-symmetric cases and provide analytic examples with $O(3)$ symmetry and arbitrary wall thickness.

Long-range quantum entanglement is essential for building large-scale quantum networks and unconditionally secure cryptographic systems based on quantum key distribution (QKD). While photonic integrated circuits offer a highly scalable platform, the fragility of phase coherence between spatial modes has prevented the distribution of path-encoded entanglement over long distances. Here, we report chip-to-chip distribution of path-encoded entangled states over 80 km between fully integrated silicon photonic transmitter and receiver chips. Telecom-band entangled photon pairs are generated via spontaneous four-wave mixing in on-chip spiral waveguides and distributed between chips over a dual-core, actively stabilized fiber link. Upon distribution, we measure a Bell state fidelity of $85.7 \pm 0.2 \%$. Implementing the BBM92 protocol with the same source, we obtain a secure key rate of 2.03 bit/s in the infinite-key regime. These results establish silicon photonic chips as a viable platform for long-distance path-encoded entanglement-based quantum key distribution, paving the way toward scalable, device-independent quantum networks.

We demonstrate optical squeezing below the shot-noise level generated through the interaction of an optical cavity field with two center-of-mass modes of a levitated nanoparticle, simultaneously cooled to occupation numbers well below unity. By analyzing the quadrature fluctuations of the cavity output through heterodyne detection, we resolve the full spectral covariance matrix of the optical field and map regions of sub-shot-noise squeezing as a function of detection phase and frequency. Operating in the resolved sideband and strong coupling regime where mechanical modes hybridize with the optical mode, we observe consistent squeezing in the band 70-95 kHz with a lowest variance of 0.98 (2$\%$ below vacuum fluctuations). We thus demonstrate optical squeezing mediated by multiple mechanical oscillators in their quantum ground state, bridging mechanical quantum control with non-classical light and establishing levitated optomechanics as a platform for multimode quantum interactions.

The impact of the sub - nucleon fluctuations on the Deeply Virtual Compton Scattering (DVCS) process at the Electron - Ion Collider (EIC) is investigated considering proton and nuclear targets. Assuming the hot - spot model, we estimate the energy dependence of the coherent and incoherent cross - sections for different values of the photon virtuality and atomic number. Predictions for the $t$ - distributions are also presented. We demonstrate that the sub - nucleon fluctuations in the proton, as described by the hot - spot model, implies a turn - over in the energy dependence of the incoherent cross - section, with the position of the maximum being dependent of the photon virtuality. Our results indicate that the ratio between the coherent and incoherent cross - sections increases with energy, atomic numbers and for smaller values of $Q^2$. Moreover, we predict a maximum in the $t$ - distribution of the nuclear incoherent cross - section at a fixed center - of - mass energy, which is dependent on the atomic number and $Q^2$.

Hybrid quantum--classical workflows often execute large ensembles of circuits that differ syntactically but implement identical operations, leading to substantial redundant computation. To address this, we introduce the Quantum Circuit Cache, a content-addressable system that detects semantic equivalence and reuses previously computed results across executions, backends, and workflow stages. Our approach combines ZX-calculus reduction with isomorphism-invariant Weisfeiler--Leman graph hashing to generate deterministic circuit identifiers, enabling constant-time lookup in distributed caches supporting both lightweight LMDB and scalable Redis deployments. The system integrates transparently into hybrid HPC workflows and remains backend-agnostic across CPU, GPU, and QPU environments. We evaluate the system on MareNostrum 5 with two representative workloads: distributed wire cutting and Differential Evolution-based QAOA optimization. For wire cutting, caching eliminates up to 91.98% of redundant subcircuit simulations, yielding speedups up to 7.0 times on a single node and maintaining advantages at scale, with Redis-based caching achieving up to 1.6 times speedups under high parallelism. Validation on a 35-qubit superconducting QPU confirms these benefits, achieving an 11.2 times speedup on real hardware. In distributed QAOA optimization, equivalence-aware caching avoids up to 27.6% of circuit evaluations and consistently reduces execution cost without altering the optimization algorithm. In both cases, reuse grows with concurrency and circuit structure, highlighting redundancy as a major systems bottleneck and demonstrating the effectiveness of our Quantum Circuit Cache.

We introduce equivariant localization as a method for computing the action of probe branes in supergravity backgrounds. We apply it to supersymmetric probe D3-branes in type IIB supersymmetric spacetimes obtained by uplifting the Kerr-Newman-AdS$_5$ black hole on a toric Sasaki-Einstein space. Depending on the cycles they wrap, such branes represent non-perturbative corrections to or defect operator insertions in the superconformal index of a large family of four-dimensional $\mathcal{N}=1$ quiver superconformal field theories. The resulting action reduces to equivariant integrals and can be evaluated entirely from toric data.

As flow estimators, multi-particle correlators, particularly the higher-order ones, are generally regarded as effective tools for suppressing non-flow contributions. In this work, however, using two well-known toy models that simulate non-flow effects, we demonstrate that multi-particle correlators can, especially in small systems, yield estimates that deviate even further from the underlying flow harmonics than those obtained from other conventional approaches. The two toy models considered here are designed to mimic non-flow effects arising from particle decay and global momentum conservation, such that the {\it apparent} harmonic coefficients become significantly different from the {\it input} values. We provide an analytic explanation for the observed behavior of flow estimates based on multi-particle correlators. Specifically, in the toy model mimicking particle decay, we elucidate the oscillations observed in $v_2\{2\}$ and $v_2\{4\}$. For the other toy model simulating momentum conservation, we show that multi-particle cumulants introduce a deformation in the collective flow that is unique to multi-particle correlators. Additionally, we compare these results with those obtained using the maximum-likelihood estimation method, a recently proposed flow estimator that serves as a viable alternative to traditional techniques.

Using high-resolution data from the Chashan Broadband Solar radio spectrometer at meter wavelengths (CBSm) of the Chinese Meridian Project-Phase II (CMP-II), Li et al. (2025) identified a novel fine spectral structure of solar radio bursts, termed periodic beaded stripes, and proposed a generation mechanism. Here we report additional events and develop a quantitative method to determine the physical conditions in the emission region. Periodic stripes tend to occur in the post-phase of flares and are associated with complex magnetic configurations. They repeat on sub-second timescales and show $\sim$0.1 s bead-like modulations, often accompanied by low-frequency absorptions. Modeling the chained stripes with linear kinetic theory of the double plasma resonance (DPR) instability constrains the source-region magnetic field to 0.2-1.7 G and the plasma density to (1-7) $\times 10^8$ cm $^{-3}$. The former follows the drift of individual stripes, and the latter tracks the overall trend. This study summarizes the key properties of periodic beaded stripes and establishes a quantitative DPR-based framework for coronal diagnostics.

Multi-messenger astronomy requires real-time systems capable of rapidly responding to external alerts and sharing significant detections with partner observatories. KM3NeT, a deep-sea Cherenkov neutrino telescope in the Mediterranean Sea, is actively contributing to these efforts through a dedicated real-time analysis framework. It comprises two detectors - ARCA, optimised for TeV-PeV neutrinos, and ORCA, for GeV-TeV neutrinos - both also sensitive to MeV neutrinos from core-collapse supernovae, providing a wide field of view and an almost continuous duty cycle. The framework performs low-latency event reconstruction and classification, follows up external alerts from the multi-messenger community, monitors for core-collapse supernova neutrino bursts, and autonomously identifies and distribute cosmic neutrino alerts. Now in advanced commissioning, the KM3NeT real-time alert system represents a major step toward rapid, coordinated multi-messenger observations.

The implementation of the \ac{isac} feature in \ac{6g} networks is most likely to be based on the framework of \ac{ofdm}. Input distribution design, or constellation design, is a crucial technique in \ac{ofdm}-\ac{isac} systems enabling a favorable balance between communication rate and sensing performance. In this treatise, we propose a computationally efficient input distribution design approach for \ac{ofdm}-\ac{isac} under frequency-selective channels, following the theoretical framework of capacity distortion. We highlight that under practical sensing constraints, the optimal strategy is to treat the kurtosis of constellations as a resource, and allocate it appropriately over subcarriers.

The Italian quantum eye (IQUEYE) is a fast photon counter based on the single photon avalanche diode detectors and capable of preserving a ~0.5 ns/h accuracy photon time of arrival. IQUEYE was originally developed for intensity interferometry experiments, but now its scientific scope has been extended towards ultra fast astronomy, including optical pulsars, millisecond pulsars and the enigmatic fast radio bursts. IQUEYE's capabilities are mainly restricted by the number of photons detected, a quantity that scales with the collector size of an optical telescope. Through the visitor instrument program at Gemini South (Cerro Pachón, Chile) we brought IQUEYE to the 8.1-m dish, reaching an order magnitude sensitivity increased from previous operations. At Gemini South we installed IQUEYE to observe giant pulse emitters, millisecond pulsars, and transitional millisecond pulsars for over 40 hours in the span of a week.

Climate change necessitates rapid expansion of renewable energy, with wind energy offering a scalable and low-impact solution. However, accurate prediction of wind loads and power generation remains challenging due to uncertainties in wind shear and turbulence stresses under atmospheric boundary layer (ABL) conditions. High-fidelity Large Eddy Simulations (LES) are typically used to reduce these uncertainties but are computationally expensive and impractical for large-scale or real-time applications. This work addresses this limitation using generative AI, specifically Conditional Denoising Diffusion Probabilistic Models, to reconstruct high-resolution turbulent flow fields from coarse inputs. A high-fidelity dataset is generated using a parallel high-order finite-difference solver across varying geostrophic wind speeds, surface roughness conditions aligned with IEC wind classes, and multiple grid resolutions. The diffusion model is trained for super-resolution across different scale factors and evaluated under interpolation and extrapolation scenarios. Results show accurate recovery of fine-scale turbulent structures, Reynolds stresses, and statistical properties in interpolation cases, indicating strong physical consistency within the training domain. However, extrapolation to higher wind speeds leads to increased noise and overprediction of turbulent stresses, highlighting limitations in generalization. Overall, the study demonstrates that physics-informed generative models can significantly reduce computational cost while maintaining acceptable accuracy, enabling faster and more reliable turbulent inflow characterization for wind energy applications.

We will generalize the concept of aggregation function for mathematical structures as a certain function between quantales. In fact, these functions turn to be exactly the lax morphism of quantales. This provides a global framework for the study of aggregation functions. As a consequence of our theory, we are able to deduce several known results about the aggregation of metrics and fuzzy metrics.

The electron ionization predicted by the Migdal effect in dark matter-nucleus scattering enhances experimental sensitivity to sub-GeV dark matter. In this work, we demonstrate that lower-energy electron excitations provide a novel and promising pathway, enabling the detection of even lighter dark matter particles previously considered inaccessible for direct searches. Direct detection experiments employing a superfluid $^4$He target can exploit this channel by observing electronic excitations via UV-photon emission. We calculate the resulting event rates and find that electron excitations induced by the Migdal effect make it possible to probe dark matter-nucleus scattering for dark matter masses as small as a few MeV.

We present the SPHEREx Ultracool Dwarf spectral Atlas (SUDA), a homogeneous sample of 1675 ultracool dwarfs with continuous 0.75--5 $μ$m spectroscopy from SPHEREx QR2. Using the SAND and ATMO2020++ atmospheric model grids, we derive atmospheric parameters and calculate bolometric luminosities ($L_{\rm bol}$). We combine the inferred $T_{\mathrm{eff}}$ and radii with evolutionary tracks to estimate masses and ages for the full sample. Evolutionary surface gravities ($\log g$) are also reported for sources with parallaxes. In the 1700--2500~K range, the atmospheric $\log g$ from spectral fitting are systematically lower than the evolutionary $\log g$, with a median offset of about 1.1~dex, likely reflecting residual degeneracy between $\log g$ and metallicity in low resolution SPHEREx spectra. We also construct an empirical spectral atlas by grouping the spectra in the adopted parameter space, using the $T_{\mathrm{eff}}$ and the evolutionary $\log g$. The resulting atlas contains 52 parameter bins and spans $T_{\mathrm{eff}}\simeq 700$--$3000$~K. Molecular indices reveal a coherent atmospheric sequence across the sample. H$_2$O and CH$_4$ strengthen toward lower $T_{\mathrm{eff}}$, whereas CO and CO$_2$ rise below $\sim$1500~K and turn over near $\sim$1000~K. Comparison with model sequences shows that the CO$_2$ index is a useful empirical metallicity tracer at $T_{\mathrm{eff}}\sim 800$--1300~K. Together, these results establish SUDA as a reference sample for linking observed 0.75--5~$μ$m spectral morphology to atmospheric and evolutionary trends in ultracool dwarfs.

Automatic feature recognition (AFR) on B-Rep 3D-CAD models is central to CAD/CAM automation, yet most learning-based methods are complex, data-hungry, and evaluate instance grouping and semantic labeling separately. We present FeatureFox, a panoptic AFR pipeline that outputs machining instances with semantic labels: a calibrated binary edge classifier on enriched edge attributes localizes feature boundaries, instances are recovered as connected components in a pruned face-adjacency graph, and a per-instance classifier predicts the machining class from aggregated subgraph attributes. We evaluate on MFInstSeg using Panoptic Quality (PQ), which jointly scores instance separation and semantic correctness. FeatureFox is substantially more sample- and compute-efficient than the deep baseline AAGNet, reaching $\mathrm{PQ}>0.9$ with $\sim250$ training parts versus $\sim5{,}000$ for AAGNet, and training on the full MFInstSeg set takes seconds on a GPU. On the full training set, AAGNet surpasses FeatureFox marginally in PQ, while FeatureFox remains slightly ahead in feature-level recognition and localization accuracy. Finally, leveraging its low data requirement, we train FeatureFox on $270$ manually labeled industrial CAD parts and show qualitative generalization to an unseen real industrial part, indicating practical real-world applicability.

Interval-valued data are one of the most common symbolic data types, which enables the preservation of the underlying variability of the data. The interval mean and covariance matrix can be estimated using the barycenter approach based on the Mallows distance. However, as for conventional data, classical estimates can be significantly affected by anomalous data points, frequently present in real-life datasets. To address this problem, we develop a robust alternative which estimates location and scale by extending the Minimum Covariance Determinant estimator to interval-valued data. The algorithm yields a robust Interval-Mahalanobis distance, which can be used to detect anomalous observations based on adaptive cutoff values. Through extensive simulation studies across various contamination levels, we demonstrate that the interval-valued robust estimator consistently outperforms classical methods in covariance matrix estimation and achieves superior outlier detection accuracy. Finally, the applicability and effectiveness of the proposed method are illustrated through real-world datasets.

Circadian rhythms are endogenous oscillations that regulate various physiological processes and their disruption has been linked to many diseases, making it important to determine how gene-expression rhythms are altered across genotypes, treatments, or environmental exposures. Existing approaches for circadian transcriptomic analysis are often limited to pairwise comparisons or to a single aspect of rhythmic behavior, making them inadequate for comprehensive inference in multi-condition experimental designs. We propose CARhy (Comprehensive Analysis of Rhythmicity), a unified statistical framework for transcriptomic data collected under more than two conditions. Based on first-harmonic Fourier regression, CARhy provides formal tests for the presence of rhythmicity and for differences across conditions in rhythmicity, amplitude, phase, and baseline level. By allowing condition-specific variances and accommodating unbalanced designs, the framework remains reliable under heteroscedastic noise and realistic sampling constraints. Simulations show that CARhy controls type I error and false discovery rates well while achieving higher power than existing approaches in challenging settings. In mouse liver transcriptomic data, CARhy offers an interpretable and practical tool for characterizing how circadian rhythms differ across multiple experimental conditions. CARhy is implemented as an R package and is publicly available at: https://github.com/DrHuang123/Comprehensive-Analyses-of-Circadian-Rhythms-CARhy.git.

We prove a necessary and sufficient condition for a $ C^1 $-hypersurface to have all parallel sets nowhere $ C^1 $-regular. As a corollary, we deduce that for a generic $ C^1 $-regular convex body all interior parallel bodies have nowhere $ C^1 $-regular boundaries.

We provide a theoretical framework to understand how widely used measures of choice difficulty relate. In a binary-option Bayesian expected-utility framework, we show that three measures of difficulty, (i) understanding (ex-ante value), (ii) choice randomness, and (iii) confidence that the chosen option is ex post correct, are, in general, unrelated, and that this result extends to other potential measures like attenuation. We provide intuitive sufficient conditions which align the orders, using both restrictions on Blackwell experiments that capture well known classes (such as logit) and restrictions on payoffs and demonstrate that in psychophysical tasks that pay only for correctness, confidence coincides with understanding. We show willingness-to-accept to switch, when measured in utils, is equivalent to understanding. Our results suggest caution in interpreting measures of choice difficulty as well as the degree of portability between economics and psychophysics experiments

Time-of-arrival (TOA)-based localization in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments is challenging because conventional Euclidean range models do not capture diffraction-dominated propagation. We show that the diffraction path-length model smoothly transitions between LOS and diffraction-dominated NLOS conditions, eliminating the need for explicit path classification. Although this model provides a unified geometric description of mixed LOS/NLOS propagation, the resulting 3D maximum-likelihood problem is nonconvex, and a direct Gauss--Newton estimator based on this model can converge to suboptimal local minima. This motivates the development of a class of structure-exploiting estimators. For known target height, the model induces a virtual-anchor representation of the reduced 2D problem, enabling estimators that exhibit a clear complexity--performance tradeoff: surrogate formulations provide structure and computational efficiency, while a semidefinite-relaxation formulation more faithfully preserves the original likelihood at higher cost. Building on this same structure, we develop 3D sample--polish--select estimators that reduce the global search to one dimension, solve the associated fixed-height 2D subproblems, and then apply local nonlinear refinement in 3D. The proposed estimators achieve near-Cramér--Rao lower bound (CRLB) performance with substantially lower complexity than multistart Gauss--Newton, while also being far more robust to initialization than a direct single-start Gauss--Newton estimator.

This paper investigates the asymptotic behavior of high-order vector rogue wave (RW) solutions of the coupled nonlinear Schrödinger (CNLS) equation in the presence of multiple large internal parameters. We report several new high-order RW patterns in the CNLS system, including double-sector, double-heart, and mixed sector-heart configurations. The main novelty is that each RW pattern contains two distinct regions in which two different fundamental first-order RWs coexist simultaneously, potentially appearing as bright (eye-shaped) versus four-petaled or dark (anti-eye-shaped) forms. These two regions are respectively associated with the simple root structures of two different Adler--Moser polynomials: each region consists of well-separated first-order RWs in one-to-one correspondence with the simple roots of the associated polynomial. In addition, by tuning certain free parameters, the two regions of the RW pattern can be shifted to arbitrary locations in the $ (x,t) $-plane. This flexibility, together with the rich simple-root structures of Adler--Moser polynomials, enables the systematic generation of a much broader family of structured RW patterns in the CNLS equation.

In this work, we study the codes over the integers with locality constraints. We introduce a weighted notion of locality over $\mathbb{Z}/q_1\mathbb{Z} \times \cdots \times \mathbb{Z}/q_n\mathbb{Z}$ and derive a Singleton-like bound for locally recoverable codes. We also propose some code constructions with locality, including integer analogs of Tamo--Barg codes.

We propose the first examples of scale-separated vacua with extended supersymmetry. They arise as circle compactifications of four-dimensional vacua of massive type IIA supergravity with scale separation, upon introducing additional fluxes and sources. We provide both the ten-dimensional solutions and the three-dimensional effective descriptions in terms of Kähler potential and superpotential. The conformal dimensions of the putative dual two-dimensional field theory appear not to be integers. The superpotential for the additional fluxes of one of our models was guessed by ChatGPT and, to the best of our knowledge, it does not appear in existing literature. Should these vacua be solutions of string theory, they would allow to address the open problem of scale separation from the vantage point of extended supersymmetry.

We study stability theory in Hilbert spaces quantitatively. We prove that the inner product on the unit ball is $(k,ε)$-stable for all $k\ge \exp(π/ε)$, and it is not $(k,ε)$-stable for $k\le \exp(\log 2/ε)$, showing that the growth is necessarily exponential in $1/ε$. We then analyze how stability scales under nonlinear connectives applied to the inner product. In particular, for power-type predicates $f(x,y)=\langle x,y\rangle_+^β$ with $β<1$ we obtain upper and lower bounds of the form $\exp(Cε^{-1/β})$, and for $β>1$ and integer powers $\langle x,y\rangle^d$ we retain the bilinear scale $\exp(C/ε)$.