Published: 2026-07-09 17:59:58
Authors: Jiangwei Ren, Xingyu Jiang, Zijie Song, Wei Xu, Hongkai Lin, Dingkang Liang, Xiang Bai
Categories: cs.CV
Abstract:
Estimating 3D geometry in underwater environments presents unique challenges due to light attenuation, scattering, and the absence of large-scale, high-quality 3D annotations. Pioneering methods rely on massive dense annotations that are impractical in underwater settings. In this paper, we propose Wat3R, a cross-domain semi-supervised learning framework designed to adapt feed-forward 3D reconstruction models from air to underwater scenes. Uniquely, our method eliminates the need for any annotated underwater data following a teacher-student architecture, that learns robust geometry representations merely on abundant unlabeled real underwater video footage. We also design a cross-view consistency loss that leverages geometric cues from other views to compensate for the information degradation in the current view caused by water attenuation and scattering. Furthermore, considering the lack of comprehensive evaluation benchmarks, we construct Water3D, a diverse dataset covering various water bodies and underwater scenarios, designed for geometric task evaluation. Experimental results demonstrate that Wat3R outperforms current state-of-the-art methods in underwater multi-view depth estimation and point cloud reconstruction. The dataset and code are available at https://github.com/LSXI7/Wat3R .
Published: 2026-07-09 17:59:27
Authors: Raul Conchello Vendrell, Carlos Díaz López, Ish Dhand, Kshitij Kapoor, Davide Laureti, Marcello Massaro, Pranjal Nayak, Ivan Ogloblin, Martin B. Plenio, Shreya Prasanna Kumar, Matteo Santandrea, Varun Seshadri, Antal Száva, Trevor Vincent, Raphael Weber
Categories: quant-ph
Abstract:
Hardware teams building fault-tolerant quantum computers (FTQCs) must decide which imperfections to suppress, and that decision requires the logical performance of the architecture under the device's actual noise. Hardware noise often departs from the stochastic Pauli models used by scalable stabilizer simulators: superconducting transmons leak out of the computational subspace, neutral atoms scatter through intermediate states, trapped ions heat as their motional modes absorb phonons, and miscalibrated controls over-rotate coherently. We present Plaquette, a theoretical framework and software suite that computes the logical performance of fault-tolerant architectures directly from the physics of such imperfections. In Plaquette, a hardware error model is specified once, as Kraus operators, Hamiltonian-Lindblad dynamics, or an experimentally reconstructed quantum channel, and is compiled automatically into the exact or approximate representation required by each of four sampler classes: stabilizer sampling for Pauli noise, the new XPauli sampler for leakage and environment sectors, near-Clifford samplers for coherent errors, and full-state simulation for exact reference calculations. We validate the XPauli and near-Clifford samplers against full-state simulation, which they can match within statistical uncertainty while Pauli twirling can fall short depending on the error model. We demonstrate the framework on three error models: leakage in superconducting qubits, intermediate-state scattering in neutral atoms, and heating in trapped ions. The size of the discrepancy between Plaquette and Clifford-only simulations varies with platform and noise process, so reliable thresholds, error budgets, and overhead estimates require the most accurate simulation available. Plaquette provides a direct path from the open-system physics of a device to the logical performance of the FTQC built on it.
Published: 2026-07-09 17:56:23
Authors: Kaito Kobayashi
Categories: quant-ph, cond-mat.str-el
Abstract:
Programmable quantum simulators are beginning to access correlators of increasing complexity, ranging from four-point out-of-time-ordered correlators to even higher-order many-body correlators. The theoretical framework for interpreting such data, however, remains comparatively underdeveloped. Although a variety of higher-order correlators can be constructed straightforwardly, their physical meaning is often difficult to infer. A further complication is that different correlators are generally not independent: some may be mutually redundant, while others may encode genuinely distinct information. These features make it necessary to analyze correlators not as isolated quantities, but as a structured family. In this work, we develop a geometric framework for the collective analysis of higher-order correlator families. By representing correlators as inner products between operator words, we recast each family as a geometry in operator space. The key idea is to introduce conditioning subspaces that separate this geometry into reducible information, already explained by a chosen resolved sector, and irreducible information, encoded in the residual correlator geometry. Focusing on the latter component, we define irreducible volume profiles that quantify how broadly the unexplained part of a correlator family spreads over independent geometric directions. This perspective leads to several complementary forms of conditioning. Canonical conditioning optimally explains a correlator family. Targeted conditioning fixes the resolved sector to isolate a chosen physical feature. Krylov and cross conditioning extend the framework from a single correlator family to comparisons among correlator geometries. Our framework reveals irreducible structures hidden at the level of individual correlator values and establishes correlator geometry as a higher-level description of quantum many-body dynamics.
Published: 2026-07-09 17:47:37
Authors: Peng Yang, Kilar Zhang
Categories: hep-th, astro-ph.HE, gr-qc, hep-ph, math-ph
Abstract:
We investigate the angular eigenvalue problem of the extreme charged C-metric. In the extreme limit ($Q \to M$), the governing differential equation degenerates from a Fuchsian equation with five regular singular points into a Confluent Extended Heun Equation. To evaluate the angular spectrum analytically, we formulate a decoupling limit within the dual four-dimensional $\mathcal{N}=2$, $\mathrm{SU(2)}\times \mathrm{SU(2)}$ linear quiver gauge theory. Within this framework, we derive the parameter dictionary and renormalized Matone relations, which absorb the macroscopic residue shifts induced by the singularity fusion. Based on the regular boundary conditions of the angular equation, we utilize the instanton counting method to establish an algebraic quantization condition, yielding angular eigenvalues consistent with numerical results.
Published: 2026-07-09 17:33:48
Authors: K. S. Sruthy, Chandrachur Chakraborty, Sudip Bhattacharyya
Categories: astro-ph.HE, gr-qc
Abstract:
We derive a steady-state warped-disk equation in the full Kerr spacetime to study the tilt dynamics of a thin, viscous accretion disk around a spinning collapsed object. The formulation, based on Pringle's framework, remains valid for all values of the Kerr parameter $a$, thereby encompassing both Kerr black holes (BHs; $0 < a \le 1$) and Kerr naked singularities ($a > 1$). By incorporating the exact Keplerian and Lense-Thirring precession frequencies, we analytically obtain the radial tilt profiles of the disk without invoking slow-spin or weak-field approximations. Numerical solutions of the resulting equations, obtained under realistic boundary conditions, reveal significant deviations from slow-spin approximations, particularly in the inner disk where the relativistic effects dominate. In the diffusive regime, we find that for Kerr naked singularities the tilt profile exhibits distinct inner hump(s) near the radius where the specific angular momentum vanishes -- a feature absent for Kerr BHs. Consideration of a tilt in the inner disk could significantly influence the interpretations from observed X-ray spectral, timing, and polarization features, which are crucial to probe the strong gravity regime and to infer the spin of the central object. While such a distinct hump feature alone does not uniquely distinguish Kerr BHs from Kerr naked singularities, their interpretation in conjunction with constraints on the disk regime may provide a potential observational handle on the nature of the accreting collapsed object.
Published: 2026-07-09 17:31:44
Authors: Joshua Kasiri, Aaron Smith, Kevin Lorinc, Olof Nebrin, Kazutaka Kimura
Categories: astro-ph.GA
Abstract:
Monte Carlo radiative transfer (MCRT) is widely used to model Lyman-alpha (Lya) resonant-line transport, but convergence is difficult to assess in optically thick media where photons undergo many scatterings before escape. This is especially important for internal quantities such as radiative acceleration and the force multiplier, which depend on momentum deposition throughout the gas rather than only on emergent spectra. We study the convergence of Lya MCRT momentum-transfer estimators in static spherical clouds. We first establish diffusion-limit benchmarks for radial acceleration profiles and integrated force multipliers, then develop a moment-based framework for diagnosing convergence from the photon-packet contribution distribution. This framework separates three distinct questions: whether the estimator converges to the correct mean, how large its finite-sampling uncertainty is, and whether the estimated uncertainty is itself stable. We apply this hierarchy to the direct event-based scattering estimator, a gradient-of-energy-density estimator, and a divergence-of-radiation-pressure estimator. Zeroth-order convergence is assessed with profile comparisons, integrated force-multiplier bias, and finite-group relative error. First-order convergence is quantified with fractional error, the photon number required to reach a target precision, and the corresponding runtime requirement. Second-order convergence is tested with the coefficient of variation of variance, which measures the reliability of the variance estimate used in the first-order diagnostics. Core-skipping prescriptions, source geometry, estimator construction, and spatial resolution enter this hierarchy in different ways. Our results provide a practical convergence framework for internal Lya MCRT force calculations and show why statistical precision, computational cost, and physical accuracy must be evaluated separately.
Published: 2026-07-09 17:26:43
Authors: Emmanouil Kavvousanos, Francky Catthoor, Vassilis Paliouras
Categories: cs.LG, eess.SP
Abstract:
Narrowband interference (NBI) severely degrades orthogonal frequency-division multiplexing (OFDM) systems by corrupting subcarriers and rendering classical soft demodulation ineffective. Conventional compressed-sensing (CS) mitigation exhibits high sequential latency and leaves structured, non-Gaussian residuals that cause log-likelihood ratio (LLR) unreliability, decoder saturation, and severe error floors when employing classical Gaussian demappers. We resolve this pipeline mismatch using a unified deep learning framework for joint NBI cancellation and robust soft demodulation. First, NBI-CNet employs a physics-informed convolutional architecture to estimate NBI parameters and remove multi-tone interference in a single forward pass. Without requiring prior knowledge of the active interferer count, NBI-CNet reduces computational complexity by up to 60% ($N{=}2048, Q{=}64$) compared to the state-of-the-art EOMP-IDS algorithm. Second, LLR-CNet acts as a structural whitener by mapping non-Gaussian post-mitigation residuals onto well-calibrated soft metrics. Simulations demonstrate that this joint framework eliminates the error floors inherent to traditional baselines across dense grids. Under severe interference ($\text{SIR}{=}{-}10$ dB), the pipeline operates within a $0.2$ to $0.5$ dB SNR margin of the optimal iterative baseline at a target block error rate (BLER) of $10^{-4}$. Under mild interference ($\text{SIR}{=}10$ dB) with heavy spectral overlap ($Q{=}12$), where classical greedy algorithms erroneously subtract valid data components and corrupt the payload, NBI-CNet avoids signal-peak confusion to deliver a coding gain exceeding $3$ dB. Finally, the architecture circumvents the $2{\times}10^{-4}$ error floor triggered by interferer-estimation errors, while its scale-invariant design enables robust generalization across arbitrary FFT sizes without retraining.
Published: 2026-07-09 17:25:08
Authors: Albert Mathew, Piyush Jangid, Rebecca Aschwanden, Yves Koppeler, Thomas Zentgraf, Sergey Kruk
Categories: physics.optics
Abstract:
High-harmonic generation (HHG) enables attosecond light pulses and table-top sources of coherent extreme-ultraviolet and soft X-ray radiation. Although HHG has long been associated with gases and plasma, nanostructured solids are emerging as new alternative sources enabling both the enhancement and control of HHG. Here, we experimentally demonstrate and theoretically describe that a single dielectric subwavelength resonator can act as a direction-selective high-harmonic source, enabling control over multiple harmonic orders through the excitation and hybridization of Mie modes. The resonator's geometrical volume is $0.12 λ^3$, and its optical mode volume is $0.03 λ^3$ at its pump wavelength. Structural asymmetry of the resonator along the propagation direction translates into different mode coupling under opposite illumination directions, resulting in pronounced forward-backward asymmetry in the generation of the third, fifth, and seventh harmonics. These results establish bianisotropic subwavelength resonators as a platform for flexible asymmetric generation of high harmonics, expanding the toolbox for controlling strong-field light-matter interactions with Mie-resonant nanophotonics.
Published: 2026-07-09 17:24:27
Authors: Shogo Toma, Atsushi Noguchi, Ken Funo, Hiroyasu Tajima
Categories: quant-ph, cond-mat.stat-mech
Abstract:
Whether a heat engine can approach Carnot efficiency while maintaining finite power is a fundamental question in finite-time thermodynamics. For classical Markovian heat engines with local interactions, the power-efficiency trade-off forbids an asymptotic approach to Carnot efficiency at finite power. In quantum systems, by contrast, degeneracy, symmetry, and collective jumps have been theoretically predicted to enable such an asymptotic attainment by enhancing activity. It has remained open, however, whether this mechanism can be realized in an experimentally implementable heat engine. In this Letter, we propose a superconducting-circuit heat engine that emulates the collective enhancement, thereby enabling an asymptotic approach to Carnot efficiency at finite power. This result demonstrates that, in an implementable model, such an enhanced dissipative mechanism circumvents the power-efficiency trade-off of classical Markovian engines. Our work connects abstract bounds in finite-time thermodynamics to a concrete circuit-QED platform and suggests a route toward quantum-device design based on collectively enhanced dissipative processes.
Published: 2026-07-09 17:18:46
Authors: Monika Mothsara, Suraj Goel, Bohnishikha Ghosh, Vatshal Srivastav, Will McCutcheon, Mehul Malik, Gláucia Murta
Categories: quant-ph
Abstract:
Quantum key distribution (QKD) brings the promise of communication with information-theoretic security but is limited in practice due to its susceptibility to noise, losses, and device imperfections. To address these challenges, we propose a robust high-dimensional (HD) one-sided device-independent QKD (1sDI-QKD) protocol and present a proof-of-principle experimental implementation using photons entangled in the transverse-spatial degree-of-freedom. We develop a systematic security analysis of HD 1sDI-QKD protocols, leveraging quantum steering to certify security, and evaluate achievable secret key rates for different measurement configurations and system dimensions using reverse reconciliation. Our analysis shows that increasing the dimension enhances robustness against both noise and loss. We then demonstrate the key experimental building blocks required for implementing the protocol: (a) a high-quality source of high-dimensional photonic entanglement, and (b) a fully programmable, high-dimensional multi-outcome measurement device operating in up to dimension 11. Using these components, we obtain positive key rates for all investigated dimensions under the fair-sampling assumption, with the highest key rates achieved for dimension d=7. Finally, we discuss the steps required for a practical, loophole-free implementation of 1sDI-QKD in realistic regimes of loss and noise.
Published: 2026-07-09 17:14:20
Authors: Yuyu Chen, Hongbin Li, Lingsheng Meng, Xinyao Qiu, Qingxu Yang
Categories: econ.GN
Abstract:
Generative AI is fast becoming the first place people turn for expert advice. The advice it provides can be directional rather than neutral, shaped in part by the choices of its designers and regulators. When clients consult AI before meeting an expert, they carry this directional advice into a relationship that once rested on the expert's judgment alone. We study its consequences in healthcare through a large-scale preregistered field experiment at a Chinese hospital, where we randomize patients' access to an AI chatbot before their outpatient visit. Examination of the conversation logs shows that the chatbot routinely cautions against the use of medications, especially Traditional Chinese Medicine and antibiotics, while issuing clean recommendations for diagnostic testing, consistent with the liability-driven guardrails encoded in AI training. This directionality propagates into clinical practice. Prescription rates decline among treated patients while diagnostic testing increases, and these effects are more pronounced among physicians who are receptive to patient input and those with more intensive prescribing styles. Beyond shifting healthcare utilization, survey results show that AI access reduces patient compliance and satisfaction, shifting the balance of authority between patients and physicians.
Published: 2026-07-09 17:09:20
Authors: Harrison Rush, Vincent Davis, Simone Antonelli, Vikash Singh, Jesse Shrader, Emanuele Rossi
Categories: cs.LG
Abstract:
We address liquidity placement in the Bitcoin Lightning Network (LN): given a fixed budget, which channels should a node open to maximize its routing capacity? We cast this as a budget-constrained combinatorial optimization problem on graphs, selecting $k$ edge additions that maximize $s$--$t$ max-flow, a theory-grounded measure of routing capacity, and solve it with graph reinforcement learning. Our lightweight agent combines a message-passing policy network with proximal policy optimization (PPO) and action masking, and is trained under a hub-exclusion curriculum: the network's top hubs are removed from training subgraphs, forcing the policy to learn capacity-aware placement rather than hub attachment. In extensive experiments on real Lightning Network snapshots, our method consistently outperforms strong heuristic baselines on the max-flow objective across multiple seeds and unseen graphs. The agent has been deployed in production for peer recommendations, executing 4640 channel-open decisions that cumulatively allocate 267.3 BTC over $16 million across 30 managed nodes.
Published: 2026-07-09 16:47:19
Authors: Seiji Terashima
Categories: hep-th, quant-ph
Abstract:
Bulk reconstruction is a central problem in AdS/CFT, and entanglement wedge reconstruction is its subregion version. We argue that this subregion statement should be separated from the stronger holographic quantum error correction interpretation, in which one region-independent logical bulk operator has code-preserving representatives in several boundary regions. A simple locality argument shows that such a common reconstruction must commute with the code-preserving local algebras in the complementary regions. This is the mechanism realized in HaPPY-type codes: the erased regions are blind to a protected logical algebra. An ordinary finite $N$ holographic CFT does not have such a protected invisible sector for supergravity fields. Its low-energy local observables, in particular, suitably smeared stress tensors, detect the physical support and gravitational dressing of ordinary bulk operators, up to possible center or superselection data. Thus, there is no such holographic quantum error correction and the $N=\infty$ agreement of global and subregion HKLL formulae is a free-theory statement. What remains is entanglement wedge reconstruction without holographic quantum error correction, or subregion complementarity: each boundary region has its own code-preserving low-energy algebra and its own region-adapted bulk interpretation, rather than a shared logical operator.
Published: 2026-07-09 16:42:23
Authors: Indranil Dutta, Taehee Jeong
Categories: cs.CV
Abstract:
Diabetic Retinopathy (DR) is a leading cause of preventable blindness worldwide, requiring automated lesion segmentation using deep learning models for early detection and monitoring. However, DR lesions vary dramatically in size from tiny microaneurysms to large hemorrhages and exudates. This variability creates conflicting demands on the model architecture and input resolution, posing a challenge for effective design. This work investigates the impact of input resolution on different lesion types. Through systematic experimentation with multiple architectures (U-Net, UNet++, Vision Transformers, DeepLabV3+) at $512 \times 512$ and $1024 \times 1024$ resolutions, we identify a critical, counter-intuitive phenomenon where increasing input resolution has opposing effects on different lesion types. We demonstrate that while higher resolution is essential for resolving fine-grained microaneurysms, it can unexpectedly degrade performance on larger hemorrhages. This finding challenges the common assumption that higher resolution is uniformly beneficial. To address this, we propose a novel Multi-Resolution Feature Stem, an input-level pyramid integrated with a UNet++ backbone. This architecture processes multiple scales in parallel, capturing fine-grained details without sacrificing contextual information. This work contributes crucial empirical evidence of this complex, resolution-dependent behavior and a practical, parameter-efficient architecture that successfully resolves this trade-off.
Published: 2026-07-09 16:41:54
Authors: Niccolò Cocciaglia, Luca Biferale, Fabio Bonaccorso, Alessandra S. Lanotte
Categories: physics.flu-dyn
Abstract:
Experimental investigations of forced stably stratified turbulence (SST) have shown that the step-like density profile, made of well-mixed density layers and sharp interfaces alternating along the gravity direction, undergo a slow coarsening dynamics with either decay or merging of interfaces. In this Letter, we focus on the coarsening dynamics phenomenon, by means of Direct Numerical Simulations of forced SST at moderate resolutions, and very long temporal integration. We show that the vertical drift and merging of interfaces is associated to the emergence of spatially-uniform, vertically-sheared helical structures that break the mirror-symmetry of the system. When these are absent, interfaces decay is observed instead. %how the kinetic energy excursions observed at $Fr=0.076$, occurring in parallel to vertical drift of interfaces, are due to the emergence of spatially-uniform, vertically-sheared helical structures that break the mirror-symmetry of the system. This is absent at larger $Fr=0.22$, where interface decay is observed instead. A dynamical correspondence between helicity dissipation rate by buoyancy effects and the vertical buoyancy flux allows to establish a (causal) connection between the chiral structures and the vertical movement of interfaces leading to merging.
Published: 2026-07-09 16:34:55
Authors: Michiel J. van Setten, Tonglin L. Newsom, Christopher Pashartis, Vera van Noort, Rebecca L. Peterson, Geoffrey Pourtois
Categories: cond-mat.mtrl-sci
Abstract:
Ternary and quaternary amorphous oxide semiconductors have many properties that make them promising candidates for use in electronic applications like display, memory, and back end of line logic. However, finding the right material for a given application and optimizing its properties, deposition, and integration, requires a thorough understanding of the physics and chemistry at play. When properly carried out, first principles computations can play a crucial role in enhancing this understanding. In this work, we highlight several pitfalls often observed in research applying these computations, with the Zn-Sn-O system as an example. We show that a proper understanding of the fundamental differences between the physics of the crystalline and amorphous or disordered phases is crucial, as is a proper statistical sampling of structural models. For the Zn-Sn-O system we conclude that from a performance point of view, mobility and initial threshold voltage, it is a promising material class. However, our computed results show that a similar sensitivity to hydrogen induced doping may be present as in IGZO.
Published: 2026-07-09 16:34:15
Authors: Teng-Ruei Chen
Categories: cs.LG
Abstract:
Routing among large language models (LLMs) trades response quality against serving cost, motivated by the reported gap between deployed routers and a per-instance oracle. Recent analysis shows that test-time resampling can recover per-instance selection headroom that no single-commit router captures; however, that guarantee holds only under an idealized oracle equipped with correctness labels and an unconstrained budget, neither of which a deployed system has. To the best of our knowledge, no previous work treats resampling the committed model and rerouting to an alternative model as competing uses of a single per-query cost budget. Therefore, this work formulates budget-aware test-time model selection: given a per-query budget and an imperfect verifier, allocate each unit of budget between resampling and rerouting so that expected correctness is maximized. An online resample-or-reroute (RoR) allocation policy driven by estimated marginal correctness per unit cost is proposed, and its behavior is grounded in the recoverability asymmetry between selection and sampling. Replay experiments on newly regenerated multi-draw correctness tensors from an eleven-model open-weight pool over four benchmarks of differing difficulty show that the proposed RoR policy attains a favorable cost-quality Pareto front relative to single-route, one-commit-router, budget-aware best-of-K, cascade, and random-allocation baselines for the tested pools, with the largest gains on the most heterogeneous benchmark; an ablation further shows the gains are verifier-gated, shrinking as verifier quality degrades, and robustness replays under a provider price vector and a label-free agreement verifier delineate where the conclusions carry over.
Published: 2026-07-09 16:28:49
Authors: Xiaoshuai Song, Liancheng Zhang, Kangzhi Zhao, Yutao Zhu, Zhongyuan Wang, Guanting Dong, Jinghan Yang, Han Li, Kun Gai, Ji-Rong Wen, Zhicheng Dou
Categories: cs.CL, cs.AI, cs.MA
Abstract:
Large language model (LLM)-based web search agents are transforming information seeking from simple factoid question answering into complex, deep-and-wide search and research-oriented tasks. A single ReAct-style agent is constrained by one long trajectory and limited context, making it difficult to handle depth and coverage simultaneously. Existing multi-agent systems improve search coverage through parallel execution and aggregation, but still exhibit clear limitations in recursive depth, collaboration adaptability, and evidence-grounded expansion. We propose WebSwarm, a progressive recursive delegation framework that jointly constructs task decomposition, recursive expansion, and agent collaboration during inference. WebSwarm dynamically instantiates agentic search nodes, each coupling a local objective with a search mode that specifies how the node should organize search and collaboration. Each node can either solve its objective itself or further delegate child nodes; after solving, it returns evidence and results upward, enabling parent nodes to further expand, revise, or aggregate the search process. To guide this process, WebSwarm first probes how task-relevant information is organized on the web to ground subsequent node expansion, and reuses process-level experience across homogeneous sibling nodes. Experiments on BrowseComp-Plus, WideSearch, DeepWideSearch, and GISA show that WebSwarm consistently outperforms single-agent and multi-agent baselines on deep, wide, and interleaved deep-and-wide tasks. Further analyses of ablation, task difficulty, web tool efficiency, and model generalization explain WebSwarm's effectiveness and provide insights for multi-agent search systems.
Published: 2026-07-09 16:24:55
Authors: Maurizio Fagotti
Categories: quant-ph, cond-mat.stat-mech, math-ph
Abstract:
Temperature is one of the central concepts of thermodynamics, yet its meaning away from equilibrium remains elusive. This problem is particularly acute in isolated quantum many-body systems: their states evolve unitarily, need not be close to equilibrium, and can retain energy coherence, a feature with no classical thermodynamic analogue. A non-stationary quantum state contains two kinds of energy fluctuations. One is associated with energy populations and has the usual thermodynamic interpretation; the other arises from coherence between energy sectors and drives time dependence. We propose that temperature, also out of equilibrium, locates the state within the family of regular states compatible with its energy-coherence structure. This leads to a natural definition of temperature for regular nonequilibrium states. The resulting inverse temperature is not generally the derivative of thermodynamic entropy with respect to energy. Indeed the principle of maximum entropy does not extend in its usual form; it is replaced by a principle of minimum discrimination information. We also develop the corresponding theory for subsystems, where temperature cannot in general be inferred from the reduced state alone. Instead, it is determined by the induced local thermodynamic structure, with boundary ambiguities removed in the thermodynamic limit.
Published: 2026-07-09 16:24:29
Authors: Yashi Tiwari, Ujjwal Upadhyay, Shao-Jiang Wang, Vivian Poulin
Categories: astro-ph.CO
Abstract:
The discrepancy between early- and late-Universe determinations of the Hubble constant may point to physics beyond $Λ$CDM or to unaccounted-for systematics. Numerous late-time modifications to the expansion history have been proposed to alleviate this discrepancy, with limited success. Recent works have shown that, when the sound-horizon and supernova calibrations are held fixed, any purely late-time resolution requires a violation of the cosmic distance duality relation (CDDR). Recasting the tension in the $r_d$-$M_B$ plane, we show explicitly that distance duality, together with BAO and uncalibrated supernova data and a fixed sound-horizon calibration, determines $H_0$ independently of the late-time expansion history. We then test the viability of the required CDDR violation by separately constraining reciprocity violation and photon number non-conservation, deriving a new constraint on reciprocity-violating distortions of angular-diameter distances from BAO and cosmic-chronometer data. Combining this result with existing photon-number-conservation constraints, we find that the level of distance-duality violation needed to resolve the tension is strongly disfavoured by current data. We therefore conclude that, for fixed sound-horizon and supernova calibrations, no modification confined to the late-time expansion history -- even one violating distance duality -- can resolve the Hubble tension, pointing instead toward early-Universe physics or unresolved local systematics.
Published: 2026-07-09 16:19:42
Authors: Aleix Bou-Comas, Stefano Carignano, Sergio Cerezo-Roquebrún, Esperanza Lopez, Luca Tagliacozzo
Categories: quant-ph, cond-mat.stat-mech, cond-mat.str-el, hep-th
Abstract:
Conformal field theory provides universal predictions for Loschmidt amplitudes following quenches from product states to critical Hamiltonians. Building on this observation, we develop a route to extracting conformal data from real-time dynamics without preparing critical low-energy states. After analytic continuation, the Loschmidt amplitude is described by a boundary-CFT partition function on a strip, whose transverse transfer matrix encodes both the boundary operator spectrum and the central charge. Local space-time perturbations of the amplitude are governed by equilibrium correlation functions, and therefore provide access to critical exponents. In parallel, generalized temporal entropies exhibit scaling with time analogous to the equilibrium scaling of spatial entanglement entropy. We show that the low-lying boundary spectrum can be reconstructed from the system-size dependence of finite-chain Loschmidt echoes, whose damped oscillations encode differences of boundary scaling dimensions. Finally, we propose a finite-size scaling protocol that can extract these quantities from simulations or experiments on state-of-the-art quantum platforms.
Published: 2026-07-09 16:18:16
Authors: Ali Larian, Qian Lin, Chang Zong Wu, Daniel S. Brown
Categories: cs.LG, cs.AI
Abstract:
As autonomous agents are increasingly deployed across diverse operational contexts, aligning their behavior with human intent demands reward functions that remain robust to such changes rather than overfitting to any single environment. Inverse reinforcement learning (IRL) provides a principled way to infer such objectives from human feedback. However, existing analyses of optimal teaching approaches for IRL focus on single-environment, demonstration-only settings, leaving underexplored how heterogeneous feedback modalities and environment dynamics jointly constrain reward functions that generalize across multiple environments. Because demonstrations in one MDP entangle reward information with that environments specific structure, the resulting rewards frequently fail to generalize when the agent is deployed in a new setting. We first analyze how different feedback modalities constrain rewards, showing that, in the unlimited-data regime, comparisons impose strictly stronger global constraints than other modalities. Beyond this theoretical analysis, we introduce a hierarchical machine teaching algorithm for reward learning that operates across multiple MDPs. The algorithm first greedily selects informative environments that expose complementary reward constraints, then strategically queries low-cost feedback within those environments. Empirically, our method achieves substantially lower regret and stronger generalization to held-out environments than uniform teaching baselines under identical feedback budgets, demonstrating the importance of multi-environment, multi-modal teaching for learning dynamics-robust reward functions.
Published: 2026-07-09 16:16:35
Authors: Saw S. Lin, Jyh-Shing Roger Jang
Categories: cs.CL
Abstract:
Speculative decoding accelerates LLM inference by drafting several tokens and verifying them in parallel. Block-diffusion drafters such as DFlash produce
a draft block in one pass but model only per-position marginals; best-first tree methods such as DDTree expand candidate trees from those marginals. The
released Domino drafter adds a GRU-based causal correction that makes each draft token's distribution path-dependent, a structure DDTree's factorized
formulation cannot represent. We introduce DominoTree, a training-free best-first draft tree scored by Domino's conditional, non-factorized correction
along each root-to-node path, made practical by restricting the per-node correction to a candidate top-M. On Qwen3-4B across eight benchmarks, DominoTree
reaches up to 6.6x speedup over autoregressive decoding and the highest mean accept length of any evaluated method, up to 10.7 tokens per round, at every
temperature we test. DominoTree constructs its tree with a GPU-native, CUDA-graph builder that is bit-identical to a reference Python implementation, so
acceptance is unchanged, while keeping per-round tree construction cheap. With this builder as default, DominoTree wins throughput over the released
Domino decoder at every temperature, 9-10% overall on Qwen3-4B and up to +22% on Alpaca, and over DDTree/CaDDTree at every temperature we test. On Qwen3-
8B, DominoTree keeps the highest accepted length at every temperature and adds a decisive throughput win at T=0, +24% over DDTree; at higher temperature
that edge over DDTree/CaDDTree narrows to a tie and a small loss, while its Overall aggregate wins over DFlash and Domino persist.
Published: 2026-07-09 16:16:17
Authors: Yechan Park
Categories: econ.EM, math.ST, stat.ME
Abstract:
Economic policies rarely affect only their direct targets. To study these spillovers, researchers summarize who else was treated with a simple exposure measure, such as the share of treated neighbors within a radius. But for many settings, economic theory provides little guidance on choosing the functional form (e.g., ring) of that measure or its parameters (e.g., radius). We show that the data can inform both choices. Correctly specified exposure measures imply orthogonality conditions that can be used for both estimation and testing. We establish consistency and asymptotic normality of the resulting estimator under spatial and network dependence in a design-based framework, with all randomness arising from treatment assignment. We then characterize the efficient moment conditions. Applied to two large-scale anti-poverty programs, the framework supports some prior radius estimates but rejects others. In the latter case, the revised radius yields substantively different policy-effect estimates.
Published: 2026-07-09 16:00:50
Authors: Aryeh Lev Zabokritskiy
Categories: math.CO
Abstract:
The Balister--Győri--Schelp (BGS) conjecture asks whether every zero-sum list of $2^{s-1}$ nonzero vectors in $\mathbb{F}_2^s$ is the prescribed-difference profile of a perfect matching. The conjecture remains open in general, whereas the classical Hall hyperplane case is solved when all prescribed differences cross between two affine copies of a hyperplane. We isolate the smallest mixed case beyond Hall: exactly two prescribed differences are internal. Although only two requests have changed type, the complete Hall permutation is replaced by a prescribed-difference bijection between two punctured copies of the hyperplane, with two unknown deleted vertices on each side. We call this the two-hole problem.
We develop a new combinatorial method for prescribed-difference matchings, based on counting and the character structure of the binary vector space. Unlike the known Hall-type methods, which construct a matching through a sequence of local algorithmic choices, our approach proves existence through a global noncancellation phenomenon. This loss of algorithmic structure is compensated by a different advantage: the method can retain global boundary information that local exchanges do not control. As a first application, it gives a new proof of the binary Hall theorem, and it then yields a complete solution of the two-hole problem with no multiplicity assumption. We also give direct constructive proofs for symmetric even-multiplicity two-hole and four-hole families. More broadly, the new technique provides a framework for studying further subfamilies of the BGS problem by measuring how far their matching structure departs from the Hall case.
Published: 2026-07-09 16:00:33
Authors: Raúl González-Jiménez
Categories: nucl-th, hep-ex, hep-th, nucl-ex
Abstract:
In these notes, basic concepts and necessary formulas for the modeling of elastic lepton-nucleon and quasielastic lepton-nucleus scattering using relativistic models are discussed. Certain theoretical developments are presented in meticulous detail, particularly those rarely found in papers or standard textbooks. In order to highlight the discrepancies between models and evaluate the impact of various nuclear effects, theoretical predictions are compared with inclusive electron scattering data.
These notes are designed for Master's or PhD students embarking on their research in the field of neutrino-nucleus interactions at intermediate energies (ranging from several hundred MeV to a few GeV). They are intended to serve as a supplemental guide, never as a substitute for traditional textbooks on Scattering Theory and Quantum Field Theory. Currently, these notes are included in the course `Relativistic Quantum Theory: Nuclear Processes', which is integrated into both the Interuniversity Master in Nuclear Physics (https://master.us.es/fisicanuclear/index.php/) and the Erasmus Mundus Joint Master Degree in Nuclear Physics (https://www.emm-nucphys.eu/en), with the University of Seville as one of the partner institutions. Previously, this material was part of the course `Weak Interactions', within the same Master's programs.
Published: 2026-07-09 15:57:42
Authors: Andrea Gambassi
Categories: cond-mat.soft, cond-mat.stat-mech
Abstract:
The effective dynamics of a colloidal particle immersed in a complex medium at equilibrium is usually described in terms of a linear overdamped Langevin equation, possibly with memory. However, numerical simulations and experiments have shown that this linear model fails, suggesting that the effective dynamics of the probe is actually nonlinear. Focusing on the case in which the medium is described by a fluctuating and correlated Gaussian field, linearly coupled to the colloid, we derive this effective dynamics and discuss its various consequences, including those on the stochastic thermodynamics of a driven particle. When the field is generated by the particle itself, with negligible fluctuations, the resulting self-chemotactic dynamics turns out to display anomalous diffusion and run-and-tumble motion in low spatial dimension, which we characterise analytically.
Published: 2026-07-09 15:43:38
Authors: F. De Luca, H. Bourdin, P. Mazzotta, E. Rasia, A. Kozmanyan, W. Cui, M. De Petris, D. de Andres, G. Yepes
Categories: astro-ph.CO
Abstract:
Measurements of thermodynamical quantities in galaxy clusters are differently affected by simplified modelling of radially averaged observables in the X-ray and millimetre bands. This includes assumptions about the cosmological model and the morphology of the cluster intracluster medium (ICM). Within a large sample of clusters extracted from The Three Hundred hydrodynamical simulations, we assess the systematic differences expected from the morphological assumptions between ICM temperatures as inferred from X-ray spectroscopy or joint X-ray and millimetre imaging. We find that these differences show a well-defined statistical behaviour that correlates with the cluster dynamical and morphological indicators. We then investigate how joint inferences of cluster temperature profiles, a priori informed by this statistical behaviour, allow us to constrain cosmological parameters inferred from the apparent cluster sizes. Assuming a flat $Λ$CDM model and priors on $Ω_\mathrm{m}$ and the helium abundance, this method provides us with unbiased estimates of the Hubble constant, $H_0$, characterised with a precision of about $4\%$ and $1.5\%$ for samples of 100 and 1000 clusters, respectively, and ultimately limited by systematic uncertainties of about $0.6$--$0.8\, {\rm km\, s^{-1} Mpc^{-1}}$. This work highlights the potential of joint X-ray and millimetre observations of galaxy cluster samples to place tight constraints on $H_0$.
Published: 2026-07-09 15:40:04
Authors: Seyyed Erfan Fatemi, Wafa Hedhly, Leila Musavian, Nikolaos Thomos
Categories: eess.SP
Abstract:
This paper proposes an adaptive wavelet division multiplexing scheme for wireless systems serving users with heterogeneous mobility profiles over frequency-selective Rayleigh fading channels. By exploiting the multiresolution structure of the discrete wavelet transform (DWT), users are adaptively assigned to different decomposition levels according to their channel dynamics and Doppler conditions. A single-tap minimum mean square error (MMSE) equalizer is applied in the frequency domain, and the system performance is evaluated under realistic time-varying multipath fading environments. Simulation results demonstrate that the proposed adaptive allocation achieves balanced bit error rate (BER) across all user mobility classes while delivering substantial peak-to-average power ratio (PAPR) reductions relative to both conventional orthogonal frequency division multiplexing (OFDM) and orthogonal time-frequency space (OTFS) modulation. The proposed framework is further validated in a four-user heterogeneous-mobility scenario, confirming its scalability and effectiveness to mixed-mobility multi-user scenarios.
Published: 2026-07-09 15:37:57
Authors: Zhiyuan Gao, Yong Yang, Yoshiyuki Kawazoe
Categories: cond-mat.mtrl-sci
Abstract:
Accurate evaluation of Gibbs free energies is essential for constructing pressure-temperature phase diagrams. Conventional methods based on the quasi-harmonic approximation (QHA) require phonon spectra at many volume points and are therefore expensive in general. Here we develop an efficient method based on the interpolation of a few ab initio data points for Gibbs free energy evaluation under volume compression. Phonon spectra are calculated only at selected volumes. An effective Gruneisen parameter derived from the zero-point energy (ZPE) reconstructs the static-ZPE branch, while piecewise mode-resolved Gruneisen slopes reconstruct the finite-temperature vibrational branches on the target volume grids. The method is validated against QHA benchmarks for diamond (C), Al, Si, Ge, rutile TiO2, beta-PtO2, and Ta2O5 polymorphs. For simple benchmark systems (C, Al, Si, Ge, rutile TiO2, and beta-PtO2), the Gibbs free energy mean absolute errors (MAEs) relative to the QHA benchmarks remain below 0.53 meV/atom, with a six-system average of 0.148 meV/atom, while the number of explicit phonon volume points is reduced from about 20-21 to 3 in the lowest-cost implementation. For the more complex Ta2O5 polymorphs, the reconstructed free energies reproduce the main phase-stability topology despite larger phase-dependent errors. With reference to the QHA workflows, the interpolation method in this work achieves speedups of 5.911-9.023 times and remains reliable for moderate compression ranges where phonon frequencies vary smoothly with volume.
Published: 2026-07-09 15:31:31
Authors: Weisheng Zhou, Huaian Diao, Hongyu Liu
Categories: math.AP
Abstract:
This work investigates time-harmonic electromagnetic scattering governed by the Maxwell system in the presence of bounded anisotropic electromagnetic scatterers embedded in an intermediate anisotropic electromagnetic layer. We focus on the localized enhancement of the gradients of the total electric and magnetic fields in small boundary-attached neighborhoods of finitely many prescribed points on the outer interface of the surrounding layer. We show that, through a suitable construction of incident electromagnetic waves, the gradients of both the total electric field and the total magnetic field can be made arbitrarily large in these neighborhoods. Moreover, the localization radius may be chosen according to the prescribed gradient magnitude, thereby describing a localized high-gradient concentration mechanism for electromagnetic fields near anisotropic scatterers.
The main strategy is based on the introduction of auxiliary boundary-attached electromagnetic neighborhoods and the associated electric and magnetic fields, which exhibit strong gradient variation near the prescribed points. Using the approximation property of Maxwell Herglotz wave functions, these auxiliary fields are then approximated by physically admissible incident waves in the neighborhood of the scatterers. Together with the well-posedness and continuous dependence of the anisotropic scattering problem, this implies that the corresponding scattered field can be controlled to be sufficiently weak in the relevant layer region. Consequently, the total field is dominated by the incident field near the prescribed points and inherits its large-gradient behavior.
Published: 2026-07-09 15:28:48
Authors: Simone Vaccaro, Maria Isabel Carnerero, Claudia M. Raiteri, Massimo Brescia, Ylenia Maruccia, Natale De Bonis, Giuseppe Riccio, Stefano Cavuoti
Categories: astro-ph.GA, astro-ph.IM
Abstract:
Active galactic nuclei (AGNs), including blazars, exhibit distinctive variability in their optical light curves, making them ideal for classification studies. This work uses data from the latest GAIA and Pan-STARRS data releases to analyze these patterns. The goal of this work is to classify AGNs into two categories: "blazars" and "non-blazars'' using only optical light curves. This strategy differs from most existing works, as it relies exclusively on optical variability without employing any other multiwavelength information. We processed optical light curves from GAIA and Pan-STARRS using the FATS library to extract standard time-series features. We computed additional features with custom algorithms based on literature methods. A Light Gradient-Boosting Machine (LightGBM) model was trained to classify AGNs into blazars and non-blazars based on these features. We then used this knowledge base to carry out a self-learning experiment with AGN candidates of an unknown nature. The LightGBM model achieved an accuracy of $86\%$, with precision, recall, and F1 score above $80-85\%$ for classifying blazars and non-blazar AGNs using optical data. The application of a BoostBoruta algorithm for feature selection reduced the feature space from 70 to 13. while maintaining comparable performance. A self-training classifier yielded similar results $85\%$, confirming the robustness of the model and the reliability of pseudo-labeling for unknown objects.
Published: 2026-07-09 15:23:45
Authors: Anna Taylor, Michele Panariello, Massimiliano Todisco, Chiara Galdi, Nicholas Evans, Driss Matrouf
Categories: eess.AS, cs.SD
Abstract:
As the accuracy of speech deepfake detection improves with the use of self-supervised representations such as wav2vec 2.0 and HuBERT, understanding why the speech is classified as bona fide or deepfake remains an open challenge. In pursuit of more trustworthy and interpretable artificial intelligence, we introduce a phoneme-level analysis framework that connects model predictions to measurable phonetic units. Our post-hoc explainability method is generally applicable to a variety of speech deepfake detection systems based on convolutional neural networks since it leverages Gradient-weighted Class Activation Mapping in conjunction with speech recognition to generate saliency maps aligned with phonemes and pauses. This pipeline reveals statistically significant attack- and speaker-dependent phonetic cues associated with spoofed speech in terms that humans can understand. Experiments using ASVspoof 5 show comparable detection performance to similar architectures while providing linguistic interpretations across speakers and spoofing conditions.
Published: 2026-07-09 15:20:26
Authors: Peter Bradshaw, Alexandr Kostochka, Zimu Xiang
Categories: math.CO
Abstract:
The vertex arboricity $\mathrm{va}(G)$ of a multigraph $G$ is the minimum number $k$ for which $V(G)$ can be partitioned into $k$ subsets, each of which induces an acyclic subgraph of $G$. By definition, if $\mathrm{va}(G)= k$, then the chromatic number, $χ(G)$, satisfies $k\leq χ(G)\leq 2k$. Fundamental results by Borodin from 1976 and Bollobás and Manvel from 1979 imply an analog of Gallai's lower bound on the number of edges in a $(2k-1)$-critical graph. We consider a slight generalization of vertex arboricity in the setting of DP-coloring. Using this framework, we derive lower bounds on the number of edges in graphs critical for vertex arboricity and for list arboricity that are better than Gallai's bound, along with similar bounds in our DP-setting.
Published: 2026-07-09 15:15:25
Authors: Bich Van Nguyen, Ngoc Anh Khong
Categories: cs.LG, math.SP
Abstract:
Extreme Learning Machine (ELM) computes output weights analytically using the Moore-Penrose pseudoinverse. Although this leads to fast training, its numerical stability depends strongly on the conditioning of the hidden layer matrix. This paper studies pseudoinverse-based ELM from a spectral perspective. We show that the smallest singular value governs perturbation amplification in the output weights, while the condition number provides a quantitative measure of hidden-layer instability. We compare SVD-based pseudoinverse computation with iterative hyperpower methods and discuss width-dependent conditioning through a random feature interpretation. Experiments on synthetic matrices and ELM benchmarks show that SVD-based methods remain the most reliable under ill conditioning, while iterative methods are more sensitive to spectral properties. The results suggest that ELM stability is fundamentally governed by the singular value structure of the hidden layer matrix.
Published: 2026-07-09 15:07:58
Authors: Sören Bartels, Lucas Bouck, Christian Palus
Categories: math.NA
Abstract:
In this paper, we study the approximation of gradient flows of harmonic maps, which serve as model problems for applications in micromagnetics, liquid crystals, and nonlinear plate bending. Harmonic maps are vector fields that are critical points of the Dirichlet energy subject to the constraint that the vector field be unit length pointwise. Most existing time-stepping schemes for gradient flows deal with the constraint by linearizing the unit length constraint at every step, which involves solving for the solution increment in the tangent space of the constraint. These schemes lead to robust control over the violation of the constraint, but require solving degenerate saddle point systems at every step that may be difficult to precondition. In this paper, we propose a scheme that first computes the unconstrained increment and then projects this increment pointwise onto the tangent space. With an additional stabilization, this scheme is energy stable under mild step size restrictions and provides robust control of the unit length constraint violation. Our new scheme only requires the solution of decoupled symmetric positive definite systems at every step, which translates to a large increase in computational efficiency. We also propose a computable a posteriori criterion and a variable time-stepping procedure that guarantee the stability of the scheme. We conclude with computational examples demonstrating the efficacy of the scheme, and present a computational extension of the scheme to nonlinear plate bending.
Published: 2026-07-09 15:07:28
Authors: Tian Li, Thomas E. Collett, Coleman M. Krawczyk, Wolfgang J. R. Enzi, Aymeric Galan
Categories: astro-ph.GA, astro-ph.CO
Abstract:
We use high-resolution JWST/NIRCam imaging and measured time delays to model the quadruply imaged quasar WFI2033--4723 with a composite stellar plus dark-matter mass model. We first construct an elliptical power-law baseline model and recover Fermat-potential differences (fpd) consistent with previous HST-based and JWST-based analyses, providing a reference scale for composite modelling. We then replace the total mass profile with a physically motivated decomposition in which the stellar mass follows a multi-Gaussian expansion of the lens light, with a free radial mass-to-light gradient, and the dark matter is described by a generalized Navarro--Frenk--White (gNFW) halo. Using two external cosmological priors, Planck+DESI and Pantheon+SH0ES, the measured time delays constrain the mass-sheet-transformation freedom that would otherwise damage the stellar--dark-matter decomposition. In both cosmological cases, the stellar normalization lies between the expectations for Chabrier and Salpeter initial mass functions, while the radial mass-to-light gradient is not strongly required by the data (mildly positive). The dark matter halo has an inner slope $γ_{\rm in}\simeq1.3$, steeper than a standard NFW cusp, and the main astrophysical conclusions are insensitive to the adopted cosmological prior. This work shows that composite time-delay lens modelling can effectively separate baryons from dark matter. As a qualitative check, we reverse the logic and use our composite lens model without kinematic information to infer the cosmology instead. However, the strong degeneracy between $H_0$ and the halo scale radius $R_s$ prevents a robust standalone constraint.
Published: 2026-07-09 14:55:02
Authors: Jiahao Wang, Kaizhan Lin, Kaixi Zhang, Jinbo Han, Xingda Wei, Sijie Shen, Chenguang Fang, Wenyuan Yu, Rong Chen, Haibo Chen
Categories: cs.DC, cs.AI
Abstract:
LLM scheduling is critical to serving, yet it remains unclear how well existing designs fit agentic serving--with LLM requests issued by agents instead of humans. This shifts the workload in two ways: (1) agents act only on complete responses, making the cluster's tokens per second (TPS) the primary goal and relaxing--not eliminating--per-token latency requirements; and (2) requests share much of their KV\$-reuse exceeds 80% of request tokens in a production trace from BAILIAN, versus 54-62% in chat.
This paper first contributes a systematic study of request scheduling for agents on two real-world traces. We find that to increase KV\$ reuse, existing schedulers overly prioritize routing requests to instances caching their KV\$, overloading a few while leaving the rest idle, capping TPS. We thus present two key insights: (1) load balance need not sacrifice all KV\$ reuse, thanks to the global-tier KV\$ store and (2) by utilizing the workload's intra-session locality, balancing a small fraction of requests--the first request in each agent session--suffices to balance the cluster without sacrificing most KV\$ reuse on local instances.
SMETRIC realizes these insights with balanced session-centric scheduling: it routes each session's first request purely for load balance and its follow-up requests in a cache-aware manner, preserving load balance and local reuse while keeping demand on the global tier low. Using the session turn information as the scheduling metric is deliberate: it is derived efficiently and accurately from the user inputs alone, so the scheduler stays clean and stateless. SMETRIC improves cluster TPS by 10-16% under prefill-decode colocation with a global store and prefill TPS by 2-34% under disaggregation over state-of-the-art schedulers, also with a better per-token latency.
Published: 2026-07-09 14:54:08
Authors: Eric Jullo, Christophe Boghossian, Luderic Chapel, Felipe Urcelay, Christopher Storfer, Xiaosheng Huang, Raphael Gavazzi, Jens-Kristian Krogager, Aleksandar Cikota
Categories: astro-ph.CO
Abstract:
Large imaging surveys in cosmology are detecting orders of magnitude more lens systems than known so far. This unprecedented dataset will lead to robust constraints on cosmology and galaxy evolution models. However, a preliminary careful characterization of the lens and source samples are mandatory. In this work, we report on a VLT/XShooter observation program of 67 lens systems to characterize their spectroscopic redshift distribution. These systems were previously detected on the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys by Huang et al. 2021 and Storfer et al. 2022 with deep residual neural network. We manage to measure redshifts for 58 lenses and 57 sources. We also identify 2 sources with indication of outflow in the shape of the emission lines and 7 sources with rotating disks in $[OII]$ or $Hα$. We find no particular bias associated to the redshift measurement operation, meaning that our measured source redshift distribution is likely representative of the true one and can be used to calibrate analyses in large imaging surveys.
Published: 2026-07-09 14:48:41
Authors: Guillermo Arregui, Sander Jæger Linde, Magnus Vejby Nielsen, Bingrui Lu, Nikolaj B. Hougs, Babak Vosoughi Lahijani, Søren Stobbe
Categories: physics.optics, physics.app-ph
Abstract:
While initially deployed for optical interconnects, silicon photonics is increasingly being explored as a hardware platform for programmable optical systems, including linear optical processors, neuromorphic photonic networks, quantum photonic circuits and multiplexed sensor arrays. Common to most existing implementations is that light is controlled with electronics, and even basic demonstrations wherein light directly controls light remain limited. Here we demonstrate a broadband all-optical silicon photonic phase shifter based on an optomechanically mediated light-light interaction arising from the gradient optical force. Our device concept relies on slot-mode waveguides suspended by subwavelength gratings, which provide mechanical support while preserving optical confinement. We demonstrate all-optical phase shifting using a guided pump beam co-propagating with the signal beam, with only 60 $μ$W required to achieve a $π$ phase shift in a 178.6 $μ$m-long device. In addition, we measure the required pump power across a wide parameter space and find quantitative agreement with a lumped force-equilibrium model. Since the actuation relies on an all-optical geometric deformation rather than on material-index tuning, the approach avoids local electrical connections to the active element, carries no Kramers-Kronig absorption penalty, and is naturally compatible with cryogenic quantum photonic platforms.
Published: 2026-07-09 14:44:40
Authors: Chao Deng, Motoharu Kitatani, Guiwen Jiang, Siqi Guo, Niklas Witt, Ao Zhang, Wenfeng Wu, Mi Jiang, Karsten Held, Liang Si
Categories: cond-mat.supr-con, cond-mat.mtrl-sci, cond-mat.str-el
Abstract:
Despite enormous expenditures in the research field, the electron-doped side of nickelate superconductors remains uncharted territory. Substituting the trivalent rare-earth cations by a tetravalent one hitherto failed. Here, we demonstrate by first-principles calculations a disorder-free route to electron dope Ruddlesden-Popper nickelates. When intercalating wide-band-gap insulating layers such as La$X$O$_3$ ($X$=Al, Ga, Sc) into La$_2$NiO$_4$, the extra (LaO)$^+$ layers act as electron donors, releasing carriers into the Ni-3$d$ orbitals. This electron doping puts La$_2$NiO$_4$:La$_2$AlO$_4$ naturally in the optimal region for $d_{x^2-y^2}$-wave superconductivity with T$_c$ exceeding 50 K. The same concept also allows us to electron dope La$_3$Ni$_2$O$_7$, the superconductor in the limelight.
Published: 2026-07-09 14:42:15
Authors: Pierre Dantas, Lucas Cordeiro, Waldir Junior
Categories: cs.PL, cs.AR, eess.SY
Abstract:
OpenPLC, Arduino OPTA, CONTROLLINO, and Industrial Shields M-Duino bring IEC 61131-3 to low-cost microcontrollers used in real automation and industrial control system (ICS) security research. Existing open-source verifiers for IEC 61131-3, including ESBMC-PLC, prove safety over an abstract scan-cycle model with idealized unbounded integers. The board artifact runs on a resource-constrained microcontroller unit (MCU) with 16-bit words (8-bit AVR Arduinos), and sensors are read via a finite-resolution analog-to-digital converter (ADC). We show this deployment gap makes naive width-aware verification unsound: across 123 real programs, checking 16-bit overflow without a hardware input model yields 44% false alarms (54/123) and finds no genuine defects, because it explores sensor values no ADC can produce. Since the gap lies where computation meets the physical process - a bounded sensor reading scaled by finite-width arithmetic into an actuation command - an overflow can silently suppress a safety action, such as a high-level alarm. An unbounded input model fabricates alarms that no environment can trigger. We present hardware-faithful verification for IEC 61131-3 on open hardware: a declarative hardware abstraction layer (HAL) descriptor (width, ADC/PWM resolution, I/O binding) and a sound lowering that interprets arithmetic at target width and constrains inputs to hardware-realizable ranges. We instantiate it for Arduino as ArduinoTool, deriving HAL parameters from official cores and realizing the input-range model in the ESBMC Ladder Diagram (LD) frontend. On the 123-program corpus, the HAL annotator eliminates all 54 false alarms while preserving robustness proofs, and a controlled corpus demonstrates the rare width-dependent defects it detects with realizable witnesses.
Published: 2026-07-09 14:41:33
Authors: Harrison Grodin, Ethan Chu, Runming Li, Jan Hoffmann, Robert Harper
Categories: cs.PL, cs.DS
Abstract:
Amortized analysis can be framed from the physicist's view, amenable to manual verification in dependent type theory using potential functions, and the banker's view, amenable to automated inference in substructural type theory using type-level credit annotations. In this work, we synthesize these perspectives in Calf, a dependent type theory cost verification. From the physicist's view, we present a fracture and gluing theorem that renders every type as containing a fusion of an abstraction function and a potential function. By construction, every program between two such types must preserve abstraction, to facilitate modularity of behavior, and conserve potential, to facilitate modularity of cost. Incorporating the banker's view, we synthetically construct type operators for credits and debits. We then define Giralf, a graded substructural dependent type theory for programming with credits and debits, which is semantically interpreted as a sub-language of Calf. Finally, we adapt an inference algorithm to transform a limited class of Calf programs into Giralf counterparts, automating the cost analysis of common algorithms in Calf.
Published: 2026-07-09 14:32:22
Authors: Yun Li, Benedek Valkó, Jiaming Xu
Categories: math.PR, math-ph
Abstract:
We show that the lower edge of the appropriately scaled size $n$ Laguerre beta-ensemble with parameter $a=a_n$ converges to the $\operatorname{Airy}_β$ process as $n\to \infty$ when $a_n\to \infty$ and $\tfrac{a_n}{n}\to 0$. This completes the picture of the possible edge scaling limits of the Laguerre beta-ensemble with a fixed $β>0$. When $a_n\gg (\log \log n)^3$ our proof establishes operator level convergence of the inverse of the scaled Dumitriu-Edelman tridiagonal matrix to the inverse of the stochastic Airy operator. Our methods allow us to prove similar operator level limits for the known soft edge scaling limits of the Laguerre and Gaussian beta-ensembles. For $a_n\le (\log n)^{1/2}$ we give a different argument that relies on coupling and a result of Dumaz-Li-Valko for the transition between the hard and soft edge limits of the Laguerre beta-ensemble.
Published: 2026-07-09 14:31:05
Authors: Zongyou Yang, Yinghan Hou, Xiaokun Yang
Categories: cs.CL, cs.AI
Abstract:
An LLM-as-judge score can move even when the candidate responses stay fixed, simply because the evaluator has changed. We treat this evaluator-replacement ambiguity as a measurement-validity problem. Across four judgment datasets, we compare two upgrade paths available in practice: scaling Qwen3 dense judges from 1.7B to 32B parameters and moving across MiniMax M2-M2.7 released APIs. The main pattern is that judge upgrades are not interchangeable: only Qwen3 1.7B to 4B gives a robust adjacent gain, while MiniMax adjacent releases do not. Stronger judges reduce but do not remove position and verbosity bias. Repeated-sample juries add little when errors are correlated. Structured debate can move decisions substantially, but without parser and fallback logs those shifts cannot be attributed to deliberation. We argue that LLM-as-judge reports should include dataset slices, bias probes, error-dependence estimates, and protocol audit trails.
Published: 2026-07-09 14:29:54
Authors: Anne Kerachni, Thomas Lavigne, Stéphane Urcun, Mireia Alenya, Oscar Camara, Julien Lefèvre François Rousseau
Categories: physics.bio-ph
Abstract:
The human cerebral cortex, initially smooth, progressively folds during fetal brain development in utero, giving rise to cortical convolutions. Atypical cortical folding patterns can be associated with neurodevelopmental and neurological disorders. To better understand these conditions, it is crucial to first examine the factors governing healthy cortical folding. Computational modeling provides a powerful way for this purpose and has already helped understanding the influence of key biomechanical parameters on the folding pattern. However, most existing models use simplified geometries, limiting calibration and validation with fetal and neonatal brain Magnetic Resonance Imaging (MRI) and neglecting the influence of initial geometry on fold development. On the other hand, simulations on realistic brain geometries introduce additional challenges, including collision handling, fold characterization, and additional computational cost. Furthermore, model parameters are often difficult to interpret, complicating comparison, clinical translation, and calibration. Finally, computational models of cortical folding also remain rarely accessible. In this work, we introduce a novel computational model of cortical folding, developed using the open-source code FEniCS to simulate folding on a whole-brain geometry generated from fetal MRI data. We also propose a modular, interpretable, and scalable simulation framework built around this computational model and openly available to the community. It uses fetal MRI data to generate realistic input brain meshes and estimate key biomechanical parameters such as cortical growth rate. The framework also integrates a spectral metric for cortical surface analysis to optimize folding pattern predictions from an healthy fetal MRI dataset.
Published: 2026-07-09 14:25:31
Authors: Carlos Garcia-Hernandez, Aymane Abdali, Guangyu Wu, Mingxue Wang, Fei Shen, Zhaoyu Pang, Yanbin Zhang
Categories: cs.IR
Abstract:
Diagnosing production incidents in large-scale microservice systems is time-critical for Site Reliability Engineers (SREs). A single 30-minute incident window in our deployment can generate over two million log lines--approximately 1.2 billion characters, far exceeding standard LLM context windows--making direct LLM-based Root Cause Analysis (RCA) infeasible. Existing approaches leave gaps: template-based parsers lack semantic anomaly reasoning, deep-learning detectors emit black-box binary signals, and LLM pipelines suffer context overflow and domain hallucination on raw telemetry.
We present Log-Insight, an automated incident-diagnosis system deployed in production at Huawei. The core design principle automates the SRE's manual triage workflow: symbolic stages replicate the structured investigation a skilled SRE would perform--sampling, schema understanding, pattern clustering, and statistical anomaly ranking. This hands the LLM a compact, pre-ranked evidence dossier to synthesise into a hypothesis report. Our six-stage pipeline reduces millions of raw events by 1,000-7,000x while preserving statistically significant failure signals.
Evaluated on 11 historical production incidents (110 runs, SRE-validated ground truth), Log-Insight achieves MRR = 0.790, returning the correct root cause within the top-3 hypotheses in over 90% of runs in under a minute of latency. We report systematic failure modes, active mitigations, and open research directions. The Forensic Evidence section--listing exact log templates and skew statistics--was consistently identified by operators as a key adoption factor, shifting the system's perceived role from opaque oracle to investigative assistant.
Published: 2026-07-09 14:21:34
Authors: Matteo Spanio, Antonio Rodà
Categories: cs.SD, cs.PF
Abstract:
Semantic audio applications increasingly require controllable generation on commodity and embedded hardware rather than through framework-heavy datacenter stacks. We present \textit{aria}, a dependency-free native runtime that runs the complete text-to-music pipeline of Stable Audio~3 (SA3) on ordinary GPUs, CPU-only machines, and a Raspberry~Pi~5, with no Python or deep-learning framework underneath. Our main contribution is a study of quantization: running the model at lower numerical precision to fit tight memory budgets, saving memory in place rather than adding to it. Because the runtime owns every internal tensor, it also exposes activation steering, a low-cost way to steer what the model generates. We judge the quality cost with three independent measures of the output (prompt adherence, overall audio quality, taste preservation), each compared against the ordinary variation between random seeds. Eight-bit precision shows no measurable quality loss on any measure while sharply cutting memory, and it is the fastest mode on the GPU; four-bit adds a small, bounded cost but shrinks the footprint enough to run the $1.2$-billion-parameter model on an $8$\,GB Pi. Against the official implementation, aria matches or exceeds generation speed and starts about seven times faster. A case study of the steering interface generates music carrying taste associations (\emph{sonic seasoning}), with genuine but bounded control for a subset of attributes. These results make a compact, quantized runtime with built-in control a practical basis for on-device semantic audio in Internet-of-Sounds settings. The \textit{aria} runtime is released at https://github.com/matteospanio/aria.
Published: 2026-07-09 14:06:19
Authors: Yanpeng Su, Norman Franchi, Maximilian Lübke
Categories: eess.SP
Abstract:
This paper presents a Cramér-Rao lower bound (CRLB)-based performance bound analysis of cooperative multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) networks. We first show the CRLB transformation of the signal-level parameters to the state parameters (position and velocity) in cooperative ISAC networks. Unlike existing studies that primarily ignored coupling between position and velocity in the Fisher information matrix (FIM), we derive the full FIM and the corresponding exact CRLB. Particularly, the results of multi-monostatic sensing, multi-bistatic sensing, and their hybrid are discussed. Addressing the complexity and tractability, we simplify the FIM and CRLB by excluding the coupling terms between the position and velocity, and provide a criterion for determining whether the simplification is valid. The simplified CRLB benefits from low computational complexity and provides a tractable and reliable performance metric for optimization problems such as resource allocation and beamforming. Finally, the position and velocity CRLBs and the simplification-induced error are examined in the simulation. The results demonstrate that the simplified CRLB can be applied in general cases. Based on the simulation results, the impact of resource and geometric parameters on position and velocity error bounds, and the validity of the simplified CRLBs is explained through the corresponding CRLB expressions.
Published: 2026-07-09 13:59:08
Authors: Bendegúz Váradi, Zoltán Kmetty
Categories: cs.CL
Abstract:
We present a Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencoder (SAE) for extracting cross-seed universal features from independently trained BERT models. Cross-seed feature universality is a fundamental challenge in mechanistic interpretability: because dictionary learning is non-convex, independently trained networks learn misaligned feature spaces, so apparently identical features may differ by random initialization. We address this by computing an orthogonal Procrustes rotation between seeds' activation spaces before joint SAE training, combining Top-K sparsity, end-to-end downstream optimization, and an auxiliary dead-feature revival loss based on previous SAE literature. Evaluating on five independent seed pairs (ten BERT models) across three benchmark datasets (SST-2, Stanford Politeness, TweetEval Emotion), our full pipeline produces more universal features (Pearson r $\geq$ 0.70 across seeds) than post-hoc alignment baselines on all three datasets. A minimal qualitative analysis confirms that high-universality features encode interpretable sociolinguistic patterns.