Selecting the optimal resolution for discretizing high-dimensional data is a central problem in physics and data analysis, particularly in unsupervised settings where the underlying distribution is unknown. The Relevance-Resolution (Res-Rel) framework addresses this issue through an information-theoretic trade-off between descriptive detail and statistical reliability. Here we provide a systematic validation of this approach by comparing its characteristic optima--maximum relevance and the -1 slope (information-theoretic) point--with the discretization that minimizes the Kullback-Leibler divergence from a known or physically motivated ground truth distribution. Across unstructured and structured synthetic datasets, Gaussian clones of MNIST, and molecular dynamics simulations of the alanine dipeptide, we find that as the dimensionality or informative content increases the KL-optimal discretization consistently lies within the Res-Rel optimality region. Furthermore, in high-dimensional regimes the -1 slope criterion closely matches the KL divergence minimum. These results establish the quantitative consistency of unsupervised information-theoretic selection with distribution-based optimality.

The classical Darboux system governing rotation coefficients of three-dimensional metrics of diagonal curvature possesses an equivalent formulation as a sixth-order PDE for a scalar potential (related to the corresponding $τ$-function). We demonstrate that this PDE is Lagrangian and can be viewed as an explicit scalar form of the `generating PDE of the KP hierarchy' as discussed recently in Nijhoff [arXiv:2406.13423] in the Lagrangian multiform approach to the Darboux and KP hierarchies. Scalar Lagrangian formulations for differential-difference and fully discrete versions of the Darboux system are also constructed. In the first three cases (continuous and differential-difference with one and two discrete variables), the corresponding Lagrangians are expressible via elementary functions (logarithms), whereas the fully discrete case requires special functions (dilogarithms). Remarkably, dispersionless limits of the above Lagrangians provide a complete list of 3D second-order integrable Lagrangians of the form $\int f(u_{xy}, u_{xt}, u_{yt})\, dxdydt$.

The dramatic slowdown of dynamics in supercooled liquids approaching the glass transition remains one of the central unresolved problems in condensed matter physics. We review approaches that attribute this slowdown to growing thermodynamic or structural length scales and discuss their difficulties in accounting for recent numerical results. These limitations motivate the present review, which critically examines alternative theories in which the glassy slowdown is instead controlled by localized excitations and their elastic interactions. After reviewing key phenomenology with a focus on the fragility of liquids, dynamical heterogeneities, thermodynamics-dynamics correlation, and the effect of kinetic rules and swap algorithms, we compare elastic descriptions based on homogeneous and local heterogeneous elasticity to excitation-based theories incorporating nonlinear responses. Results are compiled to relate global and local elastic moduli, the Debye-Waller factor, and the density of excitations, leading to a quantitative theory testable in experiments. The thermal evolution of the excitation spectrum provides a parameter-free account of the activation energy, while their elastic interactions quantitatively reproduce dynamical heterogeneities via thermal avalanche processes. Synthesized together, these results lead to a framework where the evolution of the excitation spectrum, rather than the growth of a thermodynamic length scale, governs fragility in simple glass-forming liquids -- yet mean-field concepts of dynamical transitions remain central to describing excitations and building a real-space picture of relaxation.

This paper considers the task of connecting points on a piece of paper by drawing a curve between each pair of them. Under mild assumptions, we prove that many pairwise disjoint curves are unavoidable if either of the following rules is obeyed: any two adjacent curves do not cross, or any two non-adjacent curves cross at most once. Here, two curves are called adjacent if they share an endpoint. On the other hand, we demonstrate how to draw all curves such that any two adjacent curves cross exactly once, any two non-adjacent curves cross at least once and at most twice, and thus no two curves are disjoint. Furthermore, we analyze the emergence of disjoint curves without these mild assumptions, and characterize the plane structures in complete graph drawings guaranteed by each of the rules above.

Let $X$ be a normed space of a finite dimension at least two, and $C\subsetneq X$ a closed convex set with nonempty interior. We are interested in extending Lipschitz quasiconvex functions on $C$ to quasiconvex functions on $X$. We show that, unlike what holds for convex functions, in general one cannot obtain Lipschitz extensions (except for trivial cases). If we require just uniformly continuous or continuous extensions, such extendability properties for $C$ are shown to be characterized by some geometric properties of $C$.

We study the reconfiguration of plane spanning trees on point sets in the plane in convex position, where a reconfiguration step (flip) replaces one edge with another, yielding again a plane spanning tree. The flip distance between two trees is then the minimum number of flips needed to transform one tree into the other. We study structural properties of shortest flip sequences. The folklore happy edge conjecture suggests that any edge shared by both the initial and target tree is never flipped in a shortest flip sequence. The more recent parking edge conjecture, which would have implied the happy edge conjecture, states that there exist shortest flip sequences which use only edges of the start and target tree, and edges in the convex hull of the point set. Finally, another conjecture that is implicit in the literature is the reparking conjecture which states that no edge is flipped more than twice. Essentially all recent flip algorithms respect these three conjectures and the properties they imply. We study cases in which the latter two conjectures hold and disprove them for the general setting. (Shortened abstract due to arXiv restrictions.)

Multi-robot motion planning is a hard problem. We investigate restricted variants of the problem where square robots are allowed to slide over an arbitrary curve to a new position only a constant number of times each. We show that the problem remains NP-hard in most cases, except when the squares have unit size and when the problem is unlabeled, i.e., the location of each square in the target configuration is left unspecified.

Rhombohedral-stacked transition metal dichalcogenides (TMDs) break inversion symmetry between adjacent layers, giving rise to an intrinsic out-of-plane ferroelectric polarization.Controlling the formation of this stacking polytype is therefore essential for harnessing ferroelectric effects in two-dimensional materials. In this work, we demonstrate the epitaxial growth of rhombohedral bilayer tungsten diselenide (3R-WSe2) on a cubic W(110) single crystal by molecular beam epitaxy. We show that selenium passivation of the substrate is key to enable a quasi van der Waals epitaxy effectively suppressing strong interfacial bonding and promoting the growth of quasi free standing bilayer films. The 3R stacking order is confirmed through a combination of Raman spectroscopy and high-resolution angle-resolved photoemission spectroscopy (ARPES), supported by density functional theory (DFT) calculations. ARPES and DFT reveal an indirect-gap electronic structure with the valence-band maximum at the Gamma point, as well as a pronounced spin orbit driven splitting of 520 +- 20 meV at the K point. Analysis of the measured dispersions yields hole effective masses of 0.46 +- 0.04 me and 0.75 +- 0.06 me for the upper and lower valence bands at K point, respectively. These results establish a robust route for synthesizing quasi free standing 3R-WSe2 and provide a platform for exploring the electronic, optical, and ferroelectric functionalities that emerge from inversion symmetry breaking in layered TMDs. Our findings further highlight the potential of cubic substrates for deterministic fabrication of rhombohedral TMD heterostructures and ferroelectric devices at the nanoscale.

This paper focuses on optimization problems constrained by Parametric Variational Inequalities (PVI) defined on a moving set. Unlike most existing works on mathematical programs with equilibrium constraints, the equilibrium constraints have parameters not only in the function but also in the related set. We show that the solution function of the PVI is Lipschitz continuous with respect to the upper-level decision variables and the solution set of the optimization problem is nonempty and bounded. Moreover, we prove that the metric regularity of the constraints holds automatically, which allow us to characterize stationary points without any additional assumptions. A Smoothing Implicit Gradient Algorithm (SIGA) is proposed based on the smoothing approximation of the PVI. We prove the convergence of SIGA to a stationary point of the optimization problem and numerically validate the efficiency of SIGA by portfolio management problems with real data.

We address the strong CP problem: why the physical QCD angle theta-bar must be extraordinarily small given the stringent bounds on the neutron electric dipole moment. Peccei-Quinn axion models can relax theta-bar dynamically, but rely on an approximate global symmetry expected to be violated by quantum gravity and face severe astrophysical and cosmological constraints. We propose Discrete theta Projection, an axionless, gauge-protected resolution obtained by gauging a finite cyclic subgroup $Z_N $of the $2π$ shift symmetry of theta. Coupling QCD to a compact, local and gapped topological sector orbifolds the path integral, identifying theta values that differ by $2π/N$ and admitting only instanton sectors whose topological charge lies in $Z_N$. In the large four-volume limit the vacuum energy becomes the lower envelope of the orbifold images, so the theory dynamically selects the branch closest to the CP-symmetric point, enforcing $|\barθ| \le π/N$ without assuming any prior smallness. Because the discrete shift is gauged, continuous renormalization of theta is forbidden; the construction can be formulated via higher-form/two-group structure with integer-quantized couplings fixed by anomaly inflow, ensuring radiative and gravitational stability and satisfying mixed gauge-gravity consistency conditions. The framework predicts a neutron EDM suppressed by $1/N$, no axion signatures, no domain-wall/isocurvature issues, and lattice diagnostics: piecewise-analytic theta dependence with cusps at odd fractions of the reduced period and a global curvature scaling as $1/N^2$. We provide the EFT construction, a nonperturbative proof of vacuum projection, a full anomaly analysis, and UV embeddings (including discrete clockwork chains) that generate large effective N while preserving integrality and consistency throughout.

In this article, we propose an efficient and spectrally accurate numerical method to compute the ground states of three-dimensional (3D) rotating dipolar Bose-Einstein condensates (BEC) under strongly anisotropic trapping potentials.The kernel singularity, convolution non-locality and density anisotropy together complicate the dipolar potential evaluation. The fast rotation mechanism not only induces a complicated energy landscape with many local minima, but also creates a large number of vortices in the condensates. Such factors collectively make the ground state computation challenging in terms of convergence, accuracy and efficiency, especially for 3D anisotropic systems. Coupled with Fourier spectral discretization, we proposed a preconditioned conjugate gradient method (PCG) by integrating the anisotropic truncated kernel method (ATKM) for the dipolar potential evaluation. An adaptive step size control strategy is designed and ATKM allows for a spectral accuracy without introducing any extra anisotropy-dependent memory requirement or computational time. Our algorithm is spectrally accurate, highly efficient and memory-economic. Extensive numerical results are presented to confirm the accuracy and efficiency, together with applications to study impacts of the model parameters on critical rotational frequency, energies and chemical potential. Furthermore, these simulations reveal additional novel ground state patterns, such as bent vortices.

While Aerospace engineering can benefit greatly from collaborative knowledge management, its infrastructure is still fragmented. Bridging this divide is essential to reduce the current practice of redundant work and to address the challenges posed by the rapidly growing volume of aviation data. This study presents an accessible platform, built on Wikibase, to enable collaborative sharing and curation of aerospace engineering knowledge, initially populated with data from a recent systematic literature review. As a solid foundation, the Aerospace.Wikibase provides over 700 terms related to processes, software and data, openly available for future extension. Linking project-specific concepts to persistent, independent infrastructure enables aerospace engineers to collaborate on universal knowledge without risking the appropriation of project information, thereby promoting sustainable solutions to modern challenges while acknowledging the limitations of the industry.

Optimization is ubiquitous in quantum information science and technology, however, the corresponding optimization landscape can encounter false traps, i.e., local but not global optima, likely to prevent used optimizers from finding optimal solutions. Such traps are believed to arise from parameter insufficiency and are expected to disappear when tunable parameters are sufficiently abundant. In this work, we investigate optimization landscapes of quantum optimization problems, and especially obtain that the parameter sufficiency is not enough to ensure the absence of false traps. First, we present a complete framework for analyzing critical features of optimization landscapes, by deriving necessary and sufficient conditions to identify all critical points and to classify them as local maxima, minima, or saddles, under some assumptions. Then, we show that false traps can still emerge on landscapes even with sufficient parameters, implying their appearance cannot be solely attributed to parameter insufficiency. Moreover, a close connection between landscape topology and quantum distinguishability is revealed that the emergence of false traps is linked to the loss of distinguishability among states or operators in the objective function. Finally, implications of our results are noted. Our work not only provides a deeper understanding of the intrinsic complexity of quantum-classical optimization, but also provides practical guidance for solving quantum-classical optimization problems, thus significantly aiding the progress in witnessing quantum advantages of the underlying quantum information processing tasks.

Large Language Models (LLMs) are increasingly deployed in resume screening pipelines. Although explicit PII (e.g., names) is commonly redacted, resumes typically retain subtle sociocultural markers (languages, co-curricular activities, volunteering, hobbies) that can act as demographic proxies. We introduce a generalisable stress-test framework for hiring fairness, instantiated in the Singapore context: 100 neutral job-aligned resumes are augmented into 4100 variants spanning four ethnicities and two genders, differing only in job-irrelevant markers. We evaluate 18 LLMs in two realistic settings: (i) Direct Comparison (1v1) and (ii) Score & Shortlist (top-scoring rate), each with and without rationale prompting. Even without explicit identifiers, models recover demographic attributes with high F1 and exhibit systematic disparities, with models favouring markers associated with Chinese and Caucasian males. Ablations show language markers suffice for ethnicity inference, whereas gender relies on hobbies and activities. Furthermore, prompting for explanations tends to amplify bias. Our findings suggest that seemingly innocuous markers surviving anonymisation can materially skew automated hiring outcomes.

The Quantum Approximate Optimization Algorithm (QAOA) is expected to offer advantages over classical approaches when solving combinatorial optimization problems in the Noisy Intermediate-Scale Quantum (NISQ) era. In its standard formulation, however, QAOA is not suited for constrained problems. One way to incorporate certain types of constraints is to restrict the mixing operator to the feasible subspace; however, this substantially increases circuit size, thereby reducing noise robustness. In this work, we refine an existing hypercube mixer method for enforcing hard constraints in QAOA. We present a modification that generates circuits with fewer gates for a broad class of constrained problems defined by linear functions. Furthermore, we calculate an analytical upper bound on the number of binary variables for which this reduction might not apply. Additionally, we present numerical experimental results demonstrating that the proposed approach improves robustness to noise. In summary, the method proposed in this paper allows for more accurate QAOA performance in noisy settings, bringing us closer to practical, real-world NISQ-era applications.

These are course notes for the 'Introduction to holography' Master level course at University of Cologne. The goal of the course is to give a pedogogical introduction to holography. Holography is a popular approach to quantum gravity, in which a theory of gravity can be described by a lower-dimensional boundary theory that itself has no gravity. The most concrete known example of a holographic model is the AdS/CFT correspondence, where the gravitational theory has a negative cosmological constant (the universe is asymptotically Anti-de Sitter) and the boundary theory is a conformal field theory. Symmetry plays a very important role in this duality. We therefore start the course with a review of Poincaré symmetry in quantum field theory, before moving on in the second chapter to conformal symmetry in conformally invariant quantum field theories or CFT's. Then we move to the basics of AdS physics in chapters 3 and 4, which will already reveal hints to the existence of a duality with CFT. After gathering the basic ingredients (CFT and AdS), in the second half of the course we are ready to formulate the AdS/CFT correspondence (chapter 5), including finite temperature AdS/CFT (chapter 6), which involves black holes and their thermodynamics in the gravitational theory (chapter 7). We end the course with an introduction to entanglement in AdS/CFT and the origin of statements that 'gravity emerges from entanglement' in holography.

Limited-angle computed tomography (LACT) reconstruction is an inverse problem with severe ill-posedness arising from missing projection angles, and it is difficult to restore high-precision images without sufficient prior knowledge. In recent years, machine learning methods represented by diffusion models have demonstrated high image generation capabilities. However, accurate restoration of three-dimensional structures of organs and vessels and preservation of contrast remain challenges, and the impact of differences in diverse clinical imaging conditions such as field of view (FOV) and projection angle range on reconstruction accuracy has not been sufficiently investigated. In this study, we propose a multi-volume latent diffusion model that uses three-dimensional latent representations obtained from multiple effective fields of view as guidance for LACT reconstruction in clinical practical problems. The proposed method achieves fast and stable inference by introducing consistency models into latent space, and enables high-precision preservation of organ boundary information and internal structures under different FOV conditions through a Multi-volume encoder that acquires latent variables from different scales of the global region and central region. The evaluation experiments demonstrated that the proposed method achieved high-precision synthetic CT image generation compared to existing methods. Under the limited-angle condition of 60 degrees, MAE of 10.12 HU and SSIM of 0.9677 were achieved, and under the extreme limited-angle condition of 30 degrees, MAE of 16.69 HU and SSIM of 0.9393 were achieved. Furthermore, stable reconstruction performance was demonstrated even for unknown projection angle conditions not included during training, confirming the applicability to diverse imaging conditions in clinical practice.

Kerr black hole (BH) superradiance can form gravitational atoms and produce characteristic gravitational-wave signals, providing a probe of ultralight bosons and dark matter. In realistic systems, accretion-disk gravity can shift energy levels and mix states, modifying the effective superradiant growth. We model the disk as a weak external perturbation via a multipole expansion and derive an effective three-level Hamiltonian for the $n=2$ subspace $\{\ket{211},\ket{210},\ket{21-1}\}$ in the weak-coupling regime. The leading disk effect is the quadrupolar ($\ell_d=2$) tidal field, whose symmetries fix the selection rules: axisymmetry gives only diagonal shifts, equatorial nonaxisymmetry activates $Δm=\pm2$ mixing ($\ket{211}\leftrightarrow\ket{21-1}$), and breaking equatorial reflection opens $Δm=\pm1$ couplings involving $\ket{210}$. As illustrations, a transient equatorial $m=2$ spiral wave drives the resulting two-level system and can suppress or quench superradiance by populating a decaying mode, while a quasi-static warp produces full three-level mixing and can generate narrow ``growth gaps'' near accidental near-degeneracies, with the same static reshuffling also allowing enhancement when weight shifts toward the growing mode. These findings demonstrate that accretion disk perturbations are a crucial environmental factor in determining the dynamics of BH superradiance and the evolution of boson clouds, thereby providing a more reliable theoretical basis for assessing the detectability of ultralight bosons in realistic astrophysical settings.

As the dimensionality of modern learned representations increases to thousands of dimensions, the state-of-the-art Approximate Nearest Neighbor (ANN) indices exhibit severe limitations. Graph-based methods (e.g., HNSW) suffer from prohibitive memory consumption and routing degradation, while recent randomized quantization and learned rotation approaches (e.g., RaBitQ, OPQ) impose significant preprocessing overheads. We introduce CRISP, a novel framework designed for ANN search in very-high-dimensional spaces. Unlike rigid pipelines that apply expensive orthogonal rotations indiscriminately, CRISP employs a lightweight, correlation- aware adaptive strategy that redistributes variance only when necessary, effectively reducing the preprocessing complexity. We couple this adaptive mechanism with a cache-coherent Compressed Sparse Row (CSR) index structure. Furthermore, CRISP incorporates a multi-stage dual-mode query engine: a Guaranteed Mode that preserves rigorous theoretical lower bounds on recall, and an Optimized Mode that leverages rank-based weighted scoring and early termination to reduce query latency. Extensive evaluation on datasets of very high dimensionality (up to 4096) demonstrates that CRISP achieves state-of-the-art query throughput, low construction costs, and peak memory efficiency.

The geometric contribution to the superfluid density has been found to be of great importance in the inner crust of neutron stars. In this work we clarify how this contribution arises in the context of a band theory for neutrons. Specifically, we derive the dependence of the superfluid density on the magnitude of the pairing gap when the system has many bands cutting the Fermi energy, as it is the case for the neutrons in the inner crust. Also, in the perturbation theory framework, we find that it is essential to account for the corrections to the (Bogoliubov) quasi-particle states in order to get the geometric contribution. Accounting only for the corrections to the (Hartree-Fock) single-particle states leads to the conventional contribution only.

Unidimensional (1D) Gaussian-modulated continuous-variable quantum key distribution protocols have been proposed as a way to simplify implementation and reduce costs through single-quadrature modulation, requiring only one modulator while maintaining compatibility with standard optical infrastructure. Here, we determine security bounds for 1D discrete-modulated protocol under the Gaussian extremality assumption by extending the method of Ghorai et al. [Phys. Rev. X 9, 021059 (2019)]. We establish the appropriate symmetry arguments to extend the method to the 1D discrete-modulated case, define the physicality zone in which the protocol is allowed to operate, and prove security against collective attacks in the asymptotic regime via semidefinite programming. Our analysis for uniformly distributed coherent states reveals a fundamental limitation: the Gaussian extremality assumption systematically overestimates Eve's information with increasing constellation size, yielding bounds so conservative that secure key extraction becomes impossible for constellations larger than four states, even under ideal conditions. This overestimation worsens with excess noise and restricts viable modulation amplitudes to impractically small values. Unlike two-dimensional (2D) protocols, where Gaussian extremality improves with constellation size, 1D protocols lack the growing phase-space isotropy required for the approximation to remain tight as the constellation grows. Our results expose these limitations and highlight the necessity of alternative methods or optimized non-uniform constellation designs for this class of protocols.

Despite a growing ecosystem of tools supporting Systematic Literature Reviews (SLRs), integrating them into user-friendly workflows remains challenging. The Streamlined Workflow for Automating Machine-Actionable Systematic Literature Reviews (SWARM-SLR) unified the tool annotation and provided a cohesive yet modular workflow, but faced scalability and usability issues. We introduce the SWARM-SLR AIssistant, a unified framework that combines the SWARM-SLR's structured methodology with an agent-based assistant that integrates research tools in a modular interface. The first SWARM-SLR stage is integrated, enabling conversational, LLM-guided support and persistent data storage. To address the tool assessment bottleneck, we propose a centralized tool registry that allows developers to annotate and register tools autonomously using a shared metadata schema. Preliminary evaluation shows improved usability, but challenges remain in balancing efficiency, accessibility, and transparency. Further development is needed to realize scalable SLR automation.

Employing amorphous superconductors, such as Type-II molybdenum silicide (MoSi), instead of crystalline materials significantly simplifies the material deposition and scalable nanoscale prototyping, beneficial for quantum electronic and photonic device fabrication. In this work, deposition of amorphous superconductive MoSi thin films on flat and nanowire (NW) substrates was demonstrated via pulsed direct-current magnetron co-sputtering from molybdenum and silicon targets in an argon atmosphere. MoSi films were deposited on oxidized silicon wafers and Ga2O3 NWs with 6 nm Al2O3 insulating shell, grown around the NWs using atomic layer deposition, and studied using scanning and transmission electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. Four-point Cr/Au electrical contacts were defined on the thin films and on individual Ga2O3-Al2O3-MoSi core-shell NWs using lithography for low-temperature electrical measurements. By controlling the sputtering power of the targets and thus adjusting the molybdenum-to-silicon ratio in the MoSi films, their properties were optimized to achieve critical temperature Tc of 7.25 K. Such superconducting shell NWs could provide new avenues for fundamental studies and interfacing with other materials for quantum device applications.

By analogy with the Wiener measure on the Euclidean plane that is invariant under the group of rotations and quasi-invariant under the group of diffeomorphisms, we construct the path integrals measure that is invariant under the Lorentz group and quasi-invariant under the group of diffeomorphisms. The correspondence between the paths in the future cone of the Minkowskian plane and the paths in the coverings of the Euclidean plane is established.

Estimating entropy production in continuous systems that can only be observed with a limited resolution remains an open problem in stochastic thermodynamics. Extant estimators based on the measurement of waiting-time distributions require either the detection of Markovian events, which uniquely determine the state of the system, or assume a discrete underlying dynamics. We present a novel estimator that relies solely on the detection of a single particle leaving or entering regions, or crossing manifolds, in continuous space. This estimator is based on the frequency and the duration of transitions between such events. We derive this bound by introducing two kinds of discretization of space. Finally, we compare our novel bound to the TUR using simulations of a Brownian vortex and discuss its relation to other lower bounds to entropy production.

Let $Ω$ be a Cartan domain and $K = \sum_{\underline s}a_{\underline s}K_{\underline s}$ be a $\mathbb K$-invariant kernel on $Ω$. In this article, we first obtain a necessary condition on $K$ to have the complete Nevanlinna-Pick property in terms of the sequence $\{a_{\underline s}\}_{\underline s}$ with the assumption that each $a_{\underline s}$ is non-zero and $K$ is non-vanishing. This generalizes the well-known Kaluza's Lemma in the context of $\mathbb K$-invariant kernels. The notion of the characteristic function of the classical Sz.-Nagy--Foias Theory is extended to a commuting tuple of $\frac{1}{K}$-contraction where $K$ is an irreducible $\mathbb K$-invariant kernel. An explicit construction of the characteristic function of a $\frac{1}{K}$-contraction is provided. A characterization of a $\mathbb K$-invariant kernel with the complete Nevanlinna-Pick property is obtained via the existence of characteristic functions associated with $\frac{1}{K}$-contractions.

Intersections are critical areas for road safety and traffic efficiency, accounting for a significant portion of vehicle crashes and fatalities. While connected and autonomous vehicle (CAV) technologies offer a promising solution for autonomous intersection management, many existing proposals either rely on computationally heavy centralized controllers or overlook the practical impairments of real-world communication networks. This paper introduces seamless mobility of vehicles over intersections (Moveover), a novel algorithm comprising a vehicle-to-network (V2N) communication protocol designed to let vehicles cross autonomous intersections without stopping. Moveover delegates trajectory and speed profile selection to individual vehicles, allowing each CAV to optimize them according to its unique kinematic characteristics. Simultaneously, a local intersection controller prevents collisions through deterministic conflict zone reservations. The algorithm is rigorously evaluated under both ideal and non-ideal networking conditions, specifically modeling 4G and 5G communication delays, across multiple layouts including single-lane, multi-lane, and roundabouts. Furthermore, we test Moveover on a real urban map with multiple intersections. Simulation results demonstrate that Moveover significantly outperforms baseline strategies, offering substantial improvements in travel times and reduced pollutant emissions.

We train machine learning algorithms to infer the entanglement structure of disordered long-range interacting quantum spin chains by learning from the strong disorder renormalisation group (SDRG) method. The system consists of $S=1/2$-quantum spins coupled by antiferromagnetic power-law interactions with decay exponent $α$ at random positions on a one-dimensional chain. Using SDRG as a physics-informed teacher, we compare a Random Forest classifier as a classical baseline with a graph neural network (GNN) that operates directly on the interaction graph and learns a bond-ranking rule mirroring the SDRG decimation policy. The GNN achieves a disorder-averaged pairing accuracy close to one and reproduces the entanglement entropy $S(\ell)$ in excellent quantitative agreement with SDRG across all subsystem sizes and interaction exponents. RG flow heat maps confirm that the GNN learns the sequential decimation hierarchy rather than merely fitting final-state observables. Finite-temperature entanglement properties are incorporated via the SDRGX framework through a two-stage strategy, using the zero-temperature GNN to generate the RG flow and sampling thermal occupations from the canonical ensemble, yielding results in agreement with both numerical SDRGX and analytical predictions without retraining.

The purpose of this paper is to estimate the limiting variance of asymptotically stationary Gaussian processes observed at high frequency, using the second moment estimator (SME). We study rates of convergence of the central limit theorem for the SME in terms of the total variation, Kolmogorov and Wasserstein distances, using some novel techniques and sharp estimates for cumulants. We apply our approach to provide Berry-Esseen bounds in Kolmogorov and Wasserstein distances for estimators of the drift parameters of Gaussian Ornstein-Uhlenbeck processes. Moreover, we prove that most of our estimates are strictly sharper than the ones obtained in the existing literature.

The development of high-speed storage devices such as NVMe SSDs has shifted the primary I/O bottleneck from hardware to software. Modern database systems also rely on kernel-based I/O paths, where frequent system call invocations and kernel-user space transitions lead to relatively large overheads and performance degradation. This issue is particularly pronounced in Log-Structured Merge-tree (LSM-tree)-based NoSQL databases. We identified that, in particular, the background compaction process generates a large number of read system calls, causing significant overhead. To address this problem, we propose RESYSTANCE, which leverages eBPF and io_uring to free compaction from system calls and unlock hidden performance potential. RESYSTANCE improves disk I/O efficiency during read operations via io uring and significantly reduces software stack overhead by handling compaction directly inside the kernel through eBPF. Moreover, RESYSTANCE minimizes user-kernel transitions by offloading key I/O routines into the kernel without modifying the LSM-tree structure or compaction algorithm. RESYSTANCE was extensively evaluated using db_bench, YCSB, and OLTP workloads. Compared to baseline RocksDB, it reduced the average number of system call invocations during compaction by 99% and shortened compaction time by 50%. Consequently, in write-intensive workloads, RESYSTANCE improved throughput by up to 75% and reduced the p99 latency by 40%.