Axions and axion-like particles (ALPs) are protected by Peccei-Quinn (PQ) symmetries that quantum gravity is expected to break. Modeling quantum gravity by Planck-suppressed PQ-breaking operators with unsuppressed Wilson coefficients and random phases, we quantify the fine-tuning required for an acceptable strong CP phase or a given ALP mass. For the QCD axion to account for the observed dark matter abundance at $f_a \simeq 10^{11}\,\text{GeV}$, PQ-breaking operators must be absent up to mass dimension $D \gtrsim 12$. We show that the residual strong CP phase could be measurable in upcoming neutron electric dipole moment searches. For ALPs, we map the mass-decay constant plane by the degree of UV protection required, and find that parts of the parameter space targeted by future laboratory experiments are already fine-tuned at the part-per-million level or worse, or equivalently, require PQ quality to be protected up to dimension $D\gg 10$. We argue that quality, not mass alone, is the central naturalness question for the axion program.

Let $G = (V,E)$ be an edge-colored graph, and let $\delta^c(G) = \min_{v \in V} \{ d^{c}(v) \}$ where $d^c(v)$ is the number of colors on edges incident to a vertex $v$. We show that for a sufficiently large $n$ if $G$ is an edge-colored triangle-free graph of order $n$ that satisfies $\delta^c(G)\geq (n+7)/5$, then $G$ contains a rainbow cycle of length four, which improves a bound of Ding et al. and is best possible. In addition, we show that given $k$, there is $n_0$ such that for $n\geq n_0$, if $G$ is an edge-colored triangle-free graph with $\delta^c(G)> n/5+3$, then $G$ contains a rainbow cycle of length $4k$.

Determining whether one concurrent operation completed before another began is a fundamental prerequisite for reasoning about the correctness of concurrent systems. We formalize this challenge as the Causal Observability Problem (COP): assign timestamps to the observable boundary events of a concurrent execution, invocations and responses, that faithfully reflect real-time operation order. A solution is complete if it never misses a genuine precedence, and sound if it never reports a spurious one. We prove that a strongly consistent solution, one that is simultaneously complete and sound, is unachievable at the observable boundary. We then show that the placement of instrumentation events relative to operation boundaries deterministically governs what a monitor can guarantee: internal placement yields completeness, external placement yields soundness, and neither achieves both. This dichotomy holds independently of the underlying timestamp mechanism. We instantiate this framework with three non-blocking implementations of a Causal Monitor object: FAInc (centralized atomic counter), Striped (decentralized counter), and Collect (iterative register snapshot). FAInc and Striped are linearizable; Collect is only quiescently consistent. Despite this internal consistency gap, we prove that all three provide identical COP guarantees: placement alone determines observable behavior. We validate these claims empirically on a 64-core NUMA architecture, showing that Striped matches Collect in throughput while preserving linearizability, resolving the cache-contention bottleneck of FAInc at high thread counts.

Ridge-regularized Hotelling-type (RHT) change-point tests depend on a ridge parameter $\lambda$, but the power-optimal value is determined by the unknown covariance structure and the unknown mean shift. We avoid selecting a single ridge value by computing fixed-ridge p-values on a finite deterministic grid and aggregating them with the Cauchy combination rule. Under the standard random-matrix conditions for fixed-ridge RHT statistics, we establish finite-grid joint weak convergence of the ridge processes. This leads to fixed-level validity under joint-limit calibration and small-tail validity for the analytic Cauchy p-value. Monte Carlo experiments show that deterministic-grid Cauchy aggregation has stable size behavior and achieves power close to the best stable fixed ridge choice across a range of covariance and signal configurations.

We present target speaker tagging (TST), a task that integrates speaker diarization, verification, and identification into a unified workflow for multi-speaker conversations. Given long recordings and pre-enrolled speakers, TST detects and labels speech segments of known speakers while rejecting unknown ones. Despite its practical importance, research has been limited by the absence of suitable evaluation resources. To address this, we introduce TST-Bench, a large-scale synthetic benchmark with over 150 enrolled speakers, 300 sessions of 20-60 minutes, and reference annotations with global speaker labels. We define an evaluation protocol encompassing diarization and full-pipeline scenarios. Experiments on both real and synthetic data show that TST poses challenges not captured by conventional benchmarks, and that dedicated system design yields significant gains over naive integration of existing solutions. The benchmark dataset and evaluation protocols are publicly released.

In hierarchical organizations, authenticating data from multiple users can be complex and resource-intensive. Hierarchical Identity-Based Signature with Designated Aggregator (HIBS-DA) provides an efficient solution by allowing users at different levels to generate signatures that can be combined into a single, compact signature. We first introduce the HIBS-DA framework and present the {\em{first}} lattice-based construction of HIBS-DA. Our scheme allows users at different hierarchical levels to generate individual signatures that can be aggregated into a single, compact signature, reducing communication and verification costs. The proposed construction is secure, correct, and resistant to forgery, making it suitable for large-scale environments such as universities, corporations, and government agencies.

Near-term variational classifiers incur substantial error and latency from two-qubit gates, yet practitioners often assume that additional entangling depth is the default route to higher accuracy. This work studies Optimal Quantum Measurement Decoding (OQMD): optimizing how quantum outcomes are mapped to classical labels by training a readout layer before measurement, jointly with the variational circuit, without adding CNOTs. Experiments use trainable triple single-qubit rotations as one concrete, hardware-native realization of OQMD; other single-qubit parametrizations fit the same classical outer loop. On the Iris benchmark with a 30-point stratified test split, the best observed 0-CNOT configuration with OQMD reaches 83.33\% accuracy, with a 96\% at 9 CNOTs, exceeding the best 18-CNOT controls (56.67\%) and the best 18-CNOT configuration with OQMD (66.67\%) under a common protocol. A six-point CNOT-depth series from 0 to 18 (fixed optimizer, iteration budget, random-seed count, and ZXZ readout) shows that the highest raw scores need not occur at the largest template, so aggregate complexity is not summarized by CNOT count alone. Because run-level accuracies are discrete and non-Gaussian, we emphasize best-observed scores and, where a global comparison of pooled runs is required, Mann--Whitney $U$ tests rather than parametric tests on means. Across architectures, OQMD shows statistically consistent but magnitude-dependent gains: large peak lifts on minimal circuits coexist with a small pooled mean shift on complex 18-CNOT runs ($p\approx 0.03$) that is not ``universal'' in the sense of uniformly large practical effects.%

We consider the problem of estimating the gradient ascent line of a density originating at a given point. Going beyond mere consistency, we establish a weak convergence result for a plugin estimator based on a kernel density estimator of the density. We then leverage that result to construct a confidence region for the gradient ascent line, including by bootstrap.

The tail index parameter of heavy-tailed probability models plays a key role in characterizing the tail decay of the underlying distribution function and is often involved in extrapolation procedures for various extreme value analysis questions. In this paper we revisit the question of tail index estimation and combine the ideas of bias-correction and empirical likelihood estimation to propose an estimator that offers an attractive alternative to some of the existing estimators. We develop an asymptotic theory for the proposed estimator and conduct simulation studies to demonstrate its performance in finite sample situations. The method is also applied to a data example for illustration.

In this paper, we investigate the cocompact inverse \(\sigma_k\) curvature flow in Minkowski space. We prove the longtime existence and convergence of this flow. As a consequence, quermassintegral inequalities are established.

We prove that if $M$ is a closed Riemannian surface of diameter $d$ and area $v$ with sectional curvature in the $[-1,1]$ interval, then a closed geodesic net of length $l$ has at most $f(l,d,v)$ branch points, where $f(l,d,v)=(400\tilde{l})^{(180\tilde{l})^4}$ for $\tilde{l}= \max\{l, \frac{\exp(d)}{\min\{1, \frac{v}{4}\}}\}$ This answers a question posed by S. Becker-Kahn. We also prove that for each geodesic net in the Euclidean plane with at most n unbalanced (boundary) vertices such that all its unbalanced vertices have degree 1, the number of balanced vertices of degree $\ge 3$ (=branch points) does not exceed $(25n)^{2n^2}$. This answers a question posed in [GM] and [NP].

Precise timing synchronization is essential for distributed quantum networking, enabling entanglement distribution, quantum teleportation, and entanglement swapping across remote nodes. Existing synchronization architectures rely on dedicated timing-distribution infrastructure, most notably White Rabbit networks, which constrain topology, scalability, and deployment in free-space and satellite environments. Here we demonstrate link-free synchronization of quantum network nodes using independently operating miniature rubidium atomic clocks and computational post-processing. We validate the approach on a deployed metropolitan-scale telecom fiber network spanning three geographically separated nodes. Following drift correction, atomic-clock-based synchronization achieves timing performance approaching that of a White Rabbit benchmark and remains stable over continuous 8-hour operation. As a stringent test of quantum-network functionality, we observe Hong-Ou-Mandel interference across spatially separated nodes with visibility exceeding 70%, statistically equivalent to that obtained using dedicated White Rabbit timing links. To the best of our knowledge, this represents the first observation of quantum interference across a deployed metropolitan-scale telecom fiber network synchronized entirely without dedicated timing-transfer infrastructure. These results establish atomic-clock-based synchronization as a scalable, topology-independent alternative to conventional timing-distribution architectures and a practical pathway toward terrestrial, airborne, and space-based quantum networks where dedicated timing links are unavailable.

In this paper, we show that under a suitable set of symmetry arguments and asymptotic conditions, the uniqueness of the Kerr solution can be proven. None of the conditions considered herein explicitly assume the validity of the Einstein equations. Hence, we are able to construct a theory agnostic uniqueness theorem for the Kerr spacetime. This result has implications for theories of quantum gravity or modified theories of gravity which wish to excise singular behaviour from their corresponding spacetimes. Furthermore, this work is complimentary to Penrose's singularity theorem. While the spacetime considered herein is not as general as that assumed by Penrose, we show that singularities can be unavoidable even when the validity of the Einstein equations is not presupposed.

How a two-dimensional solid melts remains unsettled after 60 years of study, as theory, model systems, simulations, and atomic-resolution experiments continue to suggest conflicting scenarios. The same transition can appear continuous or abrupt depending on how it is observed, where this ambiguity is especially acute in confined water. Here we study bilayer water under nanoconfinement and ask not only where its phase boundaries lie, but how the system responds to the two fields that drive them: temperature and lateral pressure. Using Lee-Yang zeros together with enhanced sampling, we find that some phase boundaries are field-selective: the two responses can differ either in continuity itself, or in how strongly they are rounded in finite systems. This distinction changes the two-step melting picture. The solid--hexatic transition is field-selective first-order, with the density channel remaining unusually rounded, whereas the hexatic--liquid transition becomes a conventional first-order transition once larger cells reveal a hidden bimodal enthalpy distribution. This framework organizes the apparent disagreement among confined-water simulations, hard-disk models and AgI experiments by identifying which thermodynamic channel each probe sees.

We consider a bound of degrees of curves by their genera in the log pair $(\mathbb{P}^{n}\times C, D)$, where $C$ is a smooth complex projective curve and $D$ is a very general smooth divisor on $\mathbb{P}^{n}\times C$. Our study is motivated by a function field analogue of the Lang-Vojta conjecture.

Real-temperature topological magnetic dynamics in functional materials is governed by coupled lattice and spin evolution, yet remains inaccessible to predictive simulation at device-relevant scales. As a flagship example, thermally driven helix-to-skyrmion transformation in FeGe requires atomistic resolution, explicit lattice motion, and micrometer-scale domains to resolve device-scale topological texture formation. We combine a spin-constrained density-functional-theory-trained neuro-evolution potential with a structure-preserving spin-lattice integrator within one machine-learned framework. Architecture-specific optimizations, kernel fusion, SVE2 vectorization, and NUMA-aware data layout deliver a seven orders-of-magnitude speedup over prior spin-aware methods. Deployed on LineShine exascale supercomputer, the full application scales to 12.45 million CPU cores with 89.7% weak-scaling efficiency, enabling simulations of 1.34 trillion atoms and an equal number of spins while reaching 48.5 PFLOPS in double precision. The simulations directly resolve real-temperature skyrmion nucleation and reorganization at previously inaccessible scales, establishing a new regime for predictive simulation of coupled spin-lattice topological magnetic dynamics.

Temporal online analytical processing (OLAP) analyzes past states of data whose values change over time. Such histories are naturally stored as interval histories, in which each row records the period during which a value remained valid. Because temporal analyses typically arrive in infrequent, intermittent bursts, serverless execution that launches functions only at query time offers a cost advantage over always-on clusters. Splitting a computation that a single process performs as a whole across independent serverless functions, however, breaks correctness in two ways. A function may not receive the rows that determine the state of its time range, and naively summing partial results yields incorrect answers for duration-weighted and cumulative-threshold queries. Existing SQL engines and serverless analytics do not address both problems together. This paper presents Vivace, a serverless system for exact temporal OLAP over interval histories. Vivace resolves the two problems in separate stages. Before any query arrives, a pre-query layout step partitions the interval history, replicating boundary-crossing intervals so each function computes its range completely from a single file. At query time, a merge step combines partial results under operator-specific rules. Associative aggregates merge intermediate values, and ranking re-orders candidates within each time range. We prove that this partitioned execution matches single-process computation up to canonical form. Evaluated on AWS Lambda with real-world datasets, Vivace reduces latency and monetary cost by up to 82% and 84%, respectively, against an equivalent SQL baseline that queries the history directly, demonstrating robust generality and efficiency.

Accurate joint tracking of mobile users, surrounding scatterers, and dynamic channels is a critical task for sixth-generation (6G) wireless systems, essential for both ensuring high-quality communications and empowering advanced selsing applications such as autonomous driving and immersive extended reality. While extremely large-scale multiple-input multiple-output (XL-MIMO) inherently offers strong support for this task through its high spatial resolution and spectral efficiency, its massive scale of antenna arrays, coupled with near-field propagation characteristics, makes joint trajectory and channel tracking time-consuming and hardware-intensive. To address these challenges, we rethink the problem from a vision-based signal perspective. Specifically, we design a subarray-based partially connected hybrid beamforming (PC-HBF) architecture with a tailored time-multiplexed (TM) mechanism. This effectively compensates for the aperture loss caused by limited radio frequency (RF) chains, generating high-fidelity Cartesian-domain signal images that inherently capture near-field spatial features. Based on this visual representation, we propose an improved CenterNet to perform accurate one-shot path localization, circumventing the path-iterative search required by conventional compressed-sensing-based methods. Building upon this to further improve the accuracy and exploit temporal correlation, a local small-scale orthogonal matching pursuit (OMP) refiner and a lightweight cascaded OMP tracker are developed. Finally, a Hungarian-based trajectory association module is incorporated to maintain track continuity and provide trajectory-level information for environment monitoring. Simulation results show that the proposed framework consistently outperforms representative baselines in position and channel tracking accuracy, especially under low-SNR and limited-hardware conditions.

Large Language Model (LLM) coding agents have achieved strong results on software engineering tasks, yet repository exploration remains a major bottleneck: locating relevant code consumes substantial token budget and pollutes the agent's context with irrelevant snippets. In most agents, the same model explores the repository and solves the task, leaving exploratory reads and searches in the solver's history. We present FastContext, a dedicated exploration subagent that separates repository exploration from solving. Invoked on demand, FastContext issues parallel tool calls and returns concise file paths and line ranges as focused context. FastContext is powered by specialized exploration models spanning 4B--30B parameters. We bootstrap them from strong reference-model trajectories and refine them with task-grounded rewards for broad first-turn search, multi-turn evidence gathering, and precise citation generation. Across SWE-bench Multilingual, SWE-bench Pro, and SWE-QA, integrating FastContext into Mini-SWE-Agent improves end-to-end resolution rates up to 5.5\% while reducing coding-agent token consumption up to 60\%, with marginal overhead. These results show that repository exploration can be separated from solving and handled effectively by specialized models. Code and data: this https URL

We study near-symmetric resonant stratified media to show how a tiny broken spatial symmetry can effectively be probed by structured light with or without orbital angular momentum. This is achieved by examining both the in-plane and out of plane Goos-Hänchen and Imbert Fedorov shifts, respectively, in the reflected light, magnified by resonant enhancement and weak value amplification. We show that non-reciprocity in reflection for illumination from opposite ends can result in different shifts, even to the extent of shifts with opposite signs for tiny imbalance resulting from the broken symmetry. We believe that our results can lead to new type of extra-sensitive sensors for any agent (eg. refractive index, displacement, etc.) that can break the symmetry.

Modern power systems are increasingly under stress as aging assets approach retirement and load growth outpaces new generation construction. The severity of this challenge varies by region: in the EU, the transmission grid can partially compensate for local generation shortfalls, while in the US, generation tends to be more localized, making retirements harder to offset. Retirement of generation has consequences for system reserves, fuel supply chain, and public health. We present an high-performance computing (HPC) framework for rapidly assessing the grid importance of individual generating units and ranking them by primary fuel type, operating cost, or grid impact. Historically, such studies were computationally intensive and therefore conducted infrequently. This work demonstrates that such assessments can be completed in minutes, enabling planners to evaluate a much broader range of generation portfolio scenarios than was previously possible.

Mechanical computation, in which logic functions are realized through deformation rather than electronics, has been demonstrated in systems such as origami, kirigami, and mechanical metamaterials. In these systems, logic states and functions are typically determined by geometry and material properties, making it sensitive to deformation and imperfections. Here we introduce a mechanical computing architecture in which logic is defined by topology rather than geometry. The circuit is realized as a knitted textile formed from a single continuous yarn, where information is encoded in the topology of stitches and processed through controlled unraveling. By discretizing the textile into a lattice of interacting cells, we construct topological propagation rules that implement universal logic operations, including NOT, AND, and OR gates, as well as a half-adder. Experiments demonstrate that the logical output is robust against geometric deformation, while mechanical factors affect only if the computation can be executed. These results establish topology-defined computation as a model for information processing in textiles and other reconfigurable physical systems.

Coding agents powered by large language models have demonstrated strong performance on software engineering tasks. Yet most agents consume repositories almost entirely as text, which differs from how human developers use visual structure such as folder hierarchies and dependency relationships to orient themselves in large codebases. With multimodal large language models (MLLMs), it is an open question whether agents can effectively benefit from visual representations of repositories. This paper presents the first systematic empirical study of visual repository representations for LLM-based agents on repository-level issue resolution. We evaluate four recent multimodal models. Our results show that a strictly vision-only setup degrades accuracy and increases token cost, because agents lack sufficient symbolic detail and compensate with repeated visual queries. In contrast, integrating visual graphs of repository structure as a supplementary modality alongside standard text interfaces helps agents understand structure more efficiently: input token consumption decreases by up to 26% while issue-resolution accuracy is maintained or improved. Visualization is most useful during fault localization and when the agent autonomously controls exploration depth. These findings point to a practical hybrid text-and-vision design for next-generation coding agents.

This note studies the spreading speed of the predator component in a class of prey--predator reaction--diffusion systems with spatiotemporal heterogeneity depending on a moving variable. The main difficulty is that the full system lacks a direct comparison principle. We establish a pointwise estimate showing that the prey density is close to its carrying capacity wherever the predator density is sufficiently small. This allows us to compare solutions of the system with those of scalar Fisher--KPP equations in shifting environments. Consequently, we obtain an explicit spreading speed of the predator. In particular, for nonmonotone shifting environments, locking and nonlocal pulling phenomena may occur.

We develop an estimation and inference framework for granular instrumental variables (GIVs) in models with latent aggregate shocks. Our key insight is that valid GIVs are characterized by the orthogonal complement of the factor-loading space. This characterization yields a feasible procedure for constructing GIVs when factor loadings are unknown and does not require a large cross-sectional dimension. We provide practical procedures for inference and specification testing, and apply the framework to estimate the aggregate equity market multiplier. Our empirical results reveal substantial heterogeneity in equity demand elasticities across investor sectors and may provide nuanced support for the inelastic-markets hypothesis.

Space-based GNSS reflectometry (GNSS-R) can detect terrestrial radio frequency interference (RFI) through elevated noise power in delay-Doppler map forbidden zones. This study evaluates how constellation size affects detection performance using Level 1 delay-Doppler observations from seven CYGNSS spacecraft collected over three months from the NASA this http URL archive. Four metrics are analysed: detection latency, spatial coverage, spatial coherence, and persistence monitoring reliability. Results show that the full seven-satellite constellation reduces median detection latency by a factor of 4.7 compared with a single satellite and increases interception probability for a 5-minute emission from 2\% to 11.5\%. Median footprint revisit time improves from 5.8 hours to under 2.0 hours. Spatial coherence analysis indicates that a single satellite leaves up to 72\% of source structure unresolved. Persistence monitoring confirms interference onset 39 days earlier than single-satellite deployment. The largest gains occur between one and three satellites, establishing three satellites as the minimum effective constellation size.

Electrical control of magnetism is crucial for next-generation spintronics. While recent advances have demonstrated ferroelectric switching in two-dimensional magnets, a general design strategy spanning different dimensionalities remains elusive. Here, we develop a group-theoretical framework for achieving ferroelectric control of spin splitting in collinear antiferromagnets, including altermagnets and compensated ferrimagnets. By systematically classifying switching operators through symmetry analysis, we identify a universal pathway for the simultaneous reversal of electric polarization and nonrelativistic spin this http URL validate this approach in three representative systems: quasi-one-dimensional $(6,14)$ Zigzag graphene nanoribbons, two-dimensional~\ch{Nb3I8}, and three-dimensional altermagnetic~\ch{MnSe2}. Our work establishes a versatile design paradigm for magnetoelectric devices and expands the functional landscape of low-power spintronic materials beyond the low-dimensional limit.

This paper presents a longitudinal, observational case study of how student GenAI adoption shifted across three cohorts (Fall 2022, 2023, and 2025) of the same graduate-level HCI prototyping course, using computational analysis of 203 GitHub repositories with student activity and 23,065 student commits. Building on a prior qualitative study of the 2023 cohort, we distinguish two levels of AI accountability trace: disclosure (naming that an AI tool was used) and attribution (crediting a specific artifact or task to an AI tool). We find that tool disclosure grew from 0% to 66% of repositories across the three cohorts, while explicit contribution attribution remains a minority practice, and the gap between the two reveals where accountability is missing even among students who disclose. By 2025, AI is infrastructure embedded in course templates and student-built devices: students increasingly name the tools they used, but rarely specify what those tools contributed. We argue that disclosure-based frameworks are insufficient for the vibe-coding era. The failure is not that students conceal AI use; it is that a norm built for episodic, identifiable acts cannot capture continuous, ambient co-creation. We offer this case study as grounding for the workshop's conversation about what genuine co-thinking accountability looks like.

In this work, we propose a Nonlinear Pluggable Optic (NLPO) transceiver that combines the low latency and low power consumption of Linear Pluggable Optics (LPO) with the range and robustness of digital signal processing (DSP)-based transceivers. The proposed NLPO uses an analog photonic Next-Generation Reservoir Computing (NGRC) architecture, constructed on a photonic integrated circuit (PIC), to compensate for electrical-domain distortions as well as optical-channel impairments from chromatic dispersion and Kerr nonlinearity. Focusing on a simulated 50 GBd PAM-4 link, we find that the NGRC-based NLPO not only extends the range of LPO, but actually outperforms DSP-based solutions as well. Our simulations reveal two key advantages compared to DSP-based Intensity Modulation/Direct Detection (IM/DD) links: (1) the NGRC can take advantage of the optical phase information without requiring a local oscillator and (2) the NGRC can optically sample the transmitted data well above the symbol rate without requiring high-bandwidth electronics. This work showcases the potential for photonic NGRCs to outperform state-of-the-art digital solutions in real-world applications and opens a path to low-latency, lower-power IM/DD links at ranges of 10s of km.

We study a variety of nano-scale superconducting devices containing more than one weak link. Even in devices with multiple weak links the IB symmetry is usually obeyed, which means that if we reverse both the bias current direction and the magnetic field direction at the same time, the superconducting response, namely the critical current, remains unchanged. We also provide a detailed analysis of the situations in which such symmetry is violated.