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All of arXiv

Wednesday 16 September

Audio and Speech Processing

Inverse Problems in Musical Instrument Modeling: A Structured Taxonomy and Review

Xinmeng Luan and Gary Scavone

Inverse problems arise in a wide range of applications in musical acoustics, including physics-based sound synthesis, musical instrument modeling, design, and optimization. However, these problems are inherently challenging due to their ill-conditioned nature and strong sensitivity to measurement noise. This paper presents a structured taxonomy and systematic review of inverse problems in musical instrument modeling, providing a unified framework for their classification and analysis. We categorize inverse problems in musical instrument modeling into ten distinct tasks: mechanical parameter estimation; geometric parameter estimation; loss estimation; boundary condition estimation; modal parameter estimation; excitation and articulatory parameter estimation; sound matching or model parameter fitting; instrument design and optimization; field reconstruction, characterization and separation; physical model identification and discovery.
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Solar and Stellar Astrophysics

Extragalactic Multi-Band Exploration of Red Supergiants (EMBERS): Pipeline, Source Classification, and Bolometric Properties of 101,219 Red Supergiants from James Webb Space Telescope Imaging

Aswin Suresh, Charles D. Kilpatrick, Wen-fai Fong et al. (+6 more)

The evolution of red supergiants (RSGs), especially in their final stages before exploding as hydrogen-rich Type II supernovae (SNe), remains poorly understood, owing to small samples of well-characterized stars in the Local Group. We present the largest population study of RSGs to date using $\sim10^5$ stars with bolometric parameters inferred from archival James Webb Space Telescope/Near-Infrared Camera (NIRCam) imaging of 15 galaxies within 20 Mpc. We also present jwst123, a newly-developed general-purpose NIRCam reduction and photometry pipeline, and its application to our set of 15 galaxies. We build a simulation-based parameter inference framework to rapidly fit multi-band photometry of $\sim1.5$ million luminous stars in milliseconds per source. Using clustering in the effective temperature ($T_{\rm eff}$)-luminosity ($\log L/L_{\odot}$)-dust optical depth ($\tau_V$) phase space, we introduce a novel method to select RSGs with high completeness and purity. Leveraging the scale of our catalog, we find that the dustiest RSGs prefer metal-rich hosts, and RSG luminosities correlate with host specific star formation rate, suggesting that environment shapes RSG evolution. While most selected RSGs agree with theoretical stellar evolution tracks, we identify a new population of 1,669 RSGs that are heavily dust-enshrouded ($\tau_V>4$), overluminous ($\log(L/L_{\odot})>5.55$), or anomalously cool ($T_{\rm eff}<3000\,{\rm K}$), including analogs of known SN progenitors. Our catalog provides a population-scale baseline for understanding massive stellar evolution and connecting field RSGs to dusty progenitors of Type II SNe.
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All authors Aswin Suresh, Charles D. Kilpatrick, Wen-fai Fong, Maria R. Drout, Daichi Hiramatsu, Luca Izzo, Wynn V. Jacobson-Galán, Armin Rest, Samaporn Tinyanont

Optimization and Control

Computing the convex envelope of bivariate piecewise linear-quadratic functions in linear time

Tanmaya Karmarkar and Yves Lucet

We compute the convex envelope of (nonconvex) bivariate piecewise linear-quadratic (PLQ) functions (bivariate quadratic functions defined on a polyhedral subdivision). Our algorithm is composed of the following steps: (1) compute the convex envelope of each quadratic piece obtaining piecewise rational functions (quadratic divided by linear function) defined over a polyhedral subdivision; (2) compute the (Legendre-Fenchel) conjugate of each resulting piece to obtain piecewise quadratic functions defined over a parabolic subdivision; (3) compute the maximum of all those functions to obtain the conjugate of the original PLQ function as a piecewise quadratic function defined on a parabolic subdivision; (4) compute the conjugate of each resulting piece; and finally (5) compute the maximum over all those functions to obtain the convex envelope (biconjugate) as rational functions (quadratic divided by linear function) defined over a polyhedral subdivision. Our contribution includes a practical algorithm running in linear time, and proving that the convex envelope is a piecewise rational function.
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Systems and Control

Resilient Design and Optimal Operation of Battery Energy Storage Systems for Behind-the-Meter Data Center Microgrids

Hamed Haggi and Chinmay Morankar

This paper proposes a high-fidelity hyperscale data center load modeling framework that disaggregates facility demand into IT and non-IT components and represents load behavior at second- and minute-level resolutions. Building upon this foundation, a comprehensive multi-phase optimization framework is developed for the planning and resilient design of BESS for BTM data center microgrids. The framework deploys BESS on the load side to act as a dynamic buffer between stochastic demand and generation assets. By smoothing the load spikes and excursions before they are observed by gas turbines, the load-side BESS reduces operational stress and limits conditions that can contribute to SSTI issues, shaft fatigue, system trips, equipment damage, and forced outages. Simultaneously, generation-side BESS resources are optimally sized to provide spinning reserves and enhance system resilience. The proposed methodology co-optimizes BESS with solar, wind, natural gas fuel cells, simple-cycle gas turbines, and combined-cycle gas turbines while incorporating operational, reliability, resiliency, and technology-specific constraints. Simulation results demonstrate the proposed methodology's ability to identify cost-effective, resilient, and operationally feasible designs that satisfy the demanding reliability requirements and energy market challenges of next-generation hyperscale data centers while providing planning insights for investors, developers, and system operators.
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Algebraic Geometry

The Drinfeld associator and $A_\infty$ functors

Nikita Markarian

We compare two mixed Hodge structures attached to a unipotent variation $\mathbb V$ of mixed Hodge–Tate structures on $X=\mathbb P^1\setminus{0,1,\infty}$: the cohomology of its extension by $*$ at $0$ and by $!$ at $\infty$, and the vanishing cycles at $1$. The semi-holonomy isomorphism constructed in [arXiv:2604.26357] identifies these two structures on their underlying rational structures, compatibly with their weight filtrations, but need not respect their Hodge filtrations. We compute the resulting Hodge defect. Its universal noncommutative series is given by a specific component of the inverse Drinfeld associator multiplied by an explicit local monodromy factor. We conclude with a formal $A_\infty$ interpretation of the coefficients of the Hodge defect in terms of weight-framed Tate extensions.
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Robotics

ViLoMan: Learning Visual-Proprioceptive Whole-Body Loco-Manipulation Skills for Humanoid Robots

Zejie Tian, Ruibing Hou, Bingpeng Ma et al. (+2 more)

Humanoid loco-manipulation requires adaptive whole-body coordination to seamlessly integrate locomotion and physical interaction. Despite recent advances, learning autonomous loco-manipulation remains challenging due to the scarcity of diverse, physically executable robot-object interaction data and the difficulty of learning unified whole-body control directly from onboard observations. We present ViLoMan, a scalable framework for autonomous humanoid loco-manipulation. ViLoMan first transforms partial kinematic demonstrations of human-object interactions into complete, physically executable robot trajectories. It then leverages these trajectories within a teacher-student distillation framework to learn a unified policy that maps egocentric depth observations and proprioceptive measurements directly to joint-level whole-body actions. During deployment, the policy requires neither reference motions nor intermediate commands. We evaluate ViLoMan on door-closing tasks across diverse door configurations and robot initial conditions in both simulation and the real world. Experimental results demonstrate that a single policy enables a Unitree G1 humanoid to complete the full task using only onboard depth sensing and proprioception, while generalizing robustly across task variations and transferring effectively from simulation to reality. Project page: viloman-anonymous.pages.dev.
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All authors Zejie Tian, Ruibing Hou, Bingpeng Ma, Börje F. Karlsson, Shiguang Shan

Solar and Stellar Astrophysics

A Grid of Contribution Functions to Help the Interpretation of Time-resolved Observations of Brown Dwarfs

Myrla Phillippe, Theodora Karalidi, Elena Manjavacas et al. (+4 more)

We present a grid of thermal emission contribution functions and their corresponding atmospheric spectra model for cloudy and cloud-free brown dwarfs, spanning effective temperatures $T_{\rm eff} \in [500, 2000]$ K, surface gravities $\log g = [3.5, 5.5]$ [cgs], sedimentation efficiencies $f_{\rm sed} = 1, 2, 3, 4$ and 8, and eddy diffusion coefficients $\log K_{\rm zz} = [6, 11]$. We use temperature-pressure profiles from the Sonora Diamondback models in conjunction with the picaso radiative transfer code and the cloud modeling code virga, to explore how different atmospheric parameters influence the vertical distribution of emitted radiation for atmospheres in chemical equilibrium. We confirm that cloud opacity significantly impacts atmospheric emission depth in prominent molecular features such as CH$_4$, H$_2$O, CO, NH$_3$, CO$_2$, and atomic features Na and K. The atmospheric emission depth varies most notably in the $J$-band, and at mid-resolution spectra in the Na and K doublets. Our analysis highlights pressure-wavelength differences between cloudy and cloud-free atmospheres for characterizing L, L/T transition, and T-type brown dwarfs and demonstrates how contribution functions can guide targeted spectroscopic investigations. To facilitate observational planning, we provide an accessible database and web-based application that enable the community to compare observational data with our model grid of spectra and contribution functions with a wavelength range of 0.5 - 30 $\mu$m.
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All authors Myrla Phillippe, Theodora Karalidi, Elena Manjavacas, Natalia Oliveros-Gomez, Daniel Apai, Jonathan Fernandez, Kieran M. Manjrawala

Materials Science

Electron $g$-value comparison of paramagnetic muonium and hydrogen centers in rutile TiO$_2$

T. U. Ito, W. Higemoto and A. Koda

Muonium (Mu) is widely used as a model for isolated hydrogen in condensed matter, but whether its electronic state is truly equivalent to that of hydrogen remains to be tested with an orbital-sensitive probe. Here we use double electron-muon resonance to determine the electron $g$ values of the paramagnetic Mu center in rutile TiO$_2$ for magnetic fields along [100], [110], and [001] directions. The obtained $g$ values closely follow those of the H-related Ti$^{3+}$ paramagnetic center in both magnitude and anisotropy. This agreement tests the Mu–H correspondence at the level of the Ti $3d$ electronic state and supports a common localized Ti$^{3+}$ electronic core for the two centers.
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Machine Learning

Personalized Federated Hierarchical Gaussian Processes for Privacy-Preserving Modeling of Heterogeneous Distributed Systems

Xianjian Xie and Hao Yan

We present Personalized Federated Hierarchical Gaussian Processes (pFedHGP) for probabilistic regression and classification when data are distributed across heterogeneous clients. Each client's latent function decomposes into (i) a shared global component, (ii) a client-specific deviation that shares the global kernel structure, and (iii) a flexible local residual. Sparse inducing-variable approximations and federated variational inference keep raw data local while the server synchronizes only low-dimensional statistics for the shared component. Full predictive distributions support uncertainty-aware decisions. In application studies, pFedHGP attains perfect fault classification in press tonnage monitoring using 13.77% of labeled cycles and recovers geographic zones in federated air-quality modeling without centralizing station-level time series. An Instantaneous Linear Mixing Model viewpoint links the hierarchy to multi-output Gaussian processes for correlated sensors.
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Robotics

Dynamic-LIVO: A Dynamic-Aware LiDAR-Inertial-Visual Odometry System Using Spatio-Temporal Normals

Zhixin Zhang, Samuel Ahiwe, Matthew Hale et al. (+2 more)

This paper proposes Dynamic-LIVO, a dynamic-aware LiDAR-Inertial-Visual Odometry (LIVO) system for robust state estimation and static colored mapping in dynamic environments. Dynamic-LIVO employs Spatio-Temporal (S-T) normal analysis to identify dynamic LiDAR points and propagates the resulting classification to both LiDAR-inertial and visual-inertial updates, preventing dynamic LiDAR measurements and their associated visual observations from affecting state estimation and mapping. However, S-T normal estimation can be unreliable in newly observed and spatially sparse regions due to insufficient spatio-temporal observations. To address this issue, we introduce a time-delayed S-T normal estimation strategy that defers the classification of insufficiently constrained points and re-evaluates them as additional observations become available. This strategy improves dynamic classification reliability while preserving valid static points for map construction. Extensive experiments on public and self-collected datasets with diverse sensor configurations demonstrate that Dynamic-LIVO improves localization accuracy and produces cleaner static colored maps in challenging dynamic environments. The source code and self-collected dataset will be publicly released upon acceptance.
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All authors Zhixin Zhang, Samuel Ahiwe, Matthew Hale, Liang Zhao, Pawel Ladosz

Numerical Analysis

Deep Learning for Singular PDEs: A Weighted Neural Network Approach

Badr Oulgiht

We propose a deep learning framework for the numerical approximation of weakly singular semilinear elliptic equations subject to homogeneous Dirichlet boundary conditions and positivity constraints. These problems are challenging because the nonlinear source term becomes unbounded as the positive solution approaches zero near the boundary. The proposed approach combines three components. First, the Dirichlet boundary conditions are embedded directly into the neural representation through a hard-constraint formulation. Second, a Softplus transformation preserves positivity in the interior of the computational domain, ensuring that the singular nonlinear term remains well-defined during training. Third, a singularity-aware weighted residual loss emphasizes regions where the predicted solution becomes small and the singular behavior is more pronounced. The method is evaluated in the weak singularity regime through numerical reference solutions and controlled verification tests. Comparisons with the standard residual formulation are used to assess approximation accuracy and convergence behavior. The numerical experiments illustrate the potential of combining hard boundary enforcement, positivity preservation, and singularity-aware residual weighting for weakly singular elliptic problems.
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Computation and Language

A frontend-backend architecture for tool calls in full-duplex speech models

Ke Hu, Slyne Deng, Chen Chen et al. (+10 more)

Full-duplex speech-to-speech (S2S) models provide natural, low-latency conversational interaction and would benefit from the ability to use external tools and complete voice-agent tasks. We propose a frontend-backend architecture where a duplex speech-to-text frontend learns to emit a delegation token and forwards streaming ASR transcripts to a text-based backend LLM for tool calls. Tool-call results from the backend are injected back into the frontend through a lightweight prefill-and-repeat mechanism and then synthesized using streaming TTS to the user. Our approach largely preserves regular duplex turn-taking, interruption handling, and low-latency interaction as it requires minimal modifications to the frontend model. In a single-turn tool-call evaluation, our system achieves 92-97% tool-call recall, competitive tool-call prediction performance, and 81.2% accuracy in rejecting irrelevant calls. When equipped with a larger backend (e.g., Qwen3-235B-A22B), our system achieves competitive results on Full-Duplex-Bench-V3 compared to open and closed source models, and significantly outperforms GPT-realtime-mini and Qwen3-Omni-30B-A3B-Instruct on EVA-Bench. These results demonstrate that backend delegation is an effective and modular approach for combining natural duplex speech interaction with strong agentic tool-call capabilities.
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All authors Ke Hu, Slyne Deng, Chen Chen, Elena Rastorgueva, Edresson Casanova, Punit Kumar, Dharmendra Choudhary, Nikhil Srihari, Ameya Sunil Mahabaleshwarkar, Viet Anh Trinh, Slim Essid, Oluwatobi Olabiyi, Zhehuai Chen

Optimization and Control

NomadBBO: A direct-search framework for constrained mixed-variable blackbox optimization in Python

Edward Hallé-Hannan and Christophe Tribes

Mixed-variable blackbox optimization arises in simulation-based engineering and machine learning, where objective and constraint functions are expensive to evaluate and variables may be continuous, integer, binary, or categorical. Several software libraries are available for derivative-free optimization, but few combine ease of use, computational efficiency, flexibility, and theoretical convergence guarantees. This work presents NomadBBO, a user-friendly Python library for inequality-constrained mixed-variable blackbox optimization built around Nomad. Its core optimization framework is CatADS, a new direct-search method that extends Adaptive Direct Search (ADS) to mixed-variable problems. CatADS combines the mechanisms of ADS for quantitative variables with neighborhoods for handling categorical variables. These neighborhoods use Gaussian process-based or empirical Wasserstein distances derived from available data. Inequality constraints are handled through the progressive barrier. The mixed-variable and surrogate-based mechanisms are implemented in Python and connected to the efficient C++ backend of Nomad via Cython. This integration allows Nomad to handle categorical variables. It also enables the use of external libraries and hybrid optimization strategies, including Gaussian process models from SMT 2.0 and Bayesian optimization. Numerical experiments compare NomadBBO with other solvers on constrained and unconstrained mixed-variable problems from the Cat-Suite benchmark collection. The beta release is available at https://test.pypi.org/project/NomadBBO/
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Applied Physics

Si- and C-Induced Modifications in Vapor-Solid-Grown InGaAs Nanowires: From Crystal Structure to Carrier Dynamics

Hamidreza Esmaielpour, Leopold Rothmayer, Thomas Trinkl et al. (+5 more)

Determining the impact of dopants in ternary III-V nanowires (NWs) is crucial for their applications in next-generation optoelectronic and photonic devices; however, the interplay between dopant species, crystal structure, and carrier dynamics in vapor-solid-grown InGaAs NWs remains underexplored. In this study, the influence of silicon (Si) and carbon (C) doping on the structural, morphological, and optical properties of catalyst-free, selective-area grown InGaAs NWs fabricated by molecular beam epitaxy is investigated. Scanning electron microscopy demonstrates that C-doping increases the NW aspect ratio by over 100% compared to undoped NWs, while a concomitant decrease in their planar defect density is observed, indicating enhanced crystal quality with C-doping. Despite their superior crystal quality, the C-doped NWs present a diminished photoluminescence intensity, due to point defects and increased surface recombination velocity. This is further supported by time-resolved photoluminescence spectroscopy, revealing accelerated non-radiative recombination in the C-doped NWs. Lastly, hot-carrier effects in these nanostructures are studied, and their signatures remain observable, albeit with reduced intensity in both Si- and C-doped NWs. This comprehensive study establishes clear correlations between doping parameters and the resulting material characteristics and carrier dynamics, offering valuable insights into the fundamental mechanisms governing the VS-grown InGaAs NWs and their implications in optoelectronic devices.
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All authors Hamidreza Esmaielpour, Leopold Rothmayer, Thomas Trinkl, Laura Niermann, Tore Niermann, Michael Lehmann, Jonathan J. Finley, Gregor Koblmüller

Fluid Dynamics

Fredholm Backstepping with Heymann Pre-Feedback for Linearized Navier–Stokes 2D Channel

Miroslav Krstic

We stabilize the linearized 2-D Poiseuille channel flow at arbitrary Reynolds numbers, and assign the decay of every wavenumber to any user-prescribed rate. The actuation is as in Vazquez–Krstic (IEEE TAC, 2007), VK'07 below: the two velocity components at only one wall. This is the first Fredholm backstepping design for a Navier–Stokes system. In VK'07 the wall-normal input renders the plant spatially causal, after which the tangential input closes a Volterra backstepping loop. Here the normal input carries a finite-rank feedback of arbitrarily small gain, whose sole role, in the sense of Heymann's lemma (TAC, 1968), is to give the plant simple spectrum and make every mode controllable from the tangential velocity. The tangential input then applies a stabilizing feedback, obtained through an invertible Fredholm transform to a Stokes target PDE, shifted left by an arbitrary decay. No simplicity of eigenvalues of the Orr–Sommerfeld operator is assumed and the exclusions of plant coefficient values that have accompanied Fredholm backstepping are removed. We state the comparison with the Volterra design without embellishment: the Fredholm architecture is powerful, but its gain is spectral where VK'07 is closed-form, the simpler of the two, and one from which the Fredholm approach takes its model, its actuation, and its target. What is demonstrated here is the reach of the Fredholm method on its most demanding plant to date. The feedback is tested at Reynolds number $30000$, with the dominant part of the closed-loop spectrum assigned so that it amounts to slowing the flow about twentyfold.
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Robotics

SemSafe-3DGS: Semantic Risk-Aware Active Navigation in Uncertain 3D Gaussian Splatting Maps

Amirhossein Mollaei Khass, Athanasios Cosse and Nader Motee

Autonomous robots operating in partially observed environments must navigate safely while acquiring observations that improve future planning. Existing safety formulations generally reason primarily about geometry. Consequently, geometrically similar scene elements may induce comparable control responses despite having different semantic consequences. We present a semantic risk aware safe-active perception framework for navigation in attributed 3D Gaussian maps. Semantic attributes modulate an Average Value-at-Risk collision clearance model through class dependent risk weights, allowing safety-critical Gaussian primitives to receive greater influence in the composite barrier. The resulting weighted clearances are aggregated into a control barrier function, while a trajectory-relevant active perception barrier promotes observations that reduce geometric map uncertainty along the robot's anticipated motion. Both objectives are integrated in a unified CBF-QP that enforces semantic risk-aware collision avoidance as a hard constraint while relaxing information acquisition when it conflicts with safety or task progress. Experiments demonstrate efficient safety constraint, improved navigation through active perception, semantic dependent trajectory adaptation, and real-robot execution under Ackermann dynamics.
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Quantum Algebra

Skein theory and deformations

Noah Snyder and Benjamin Spencer

We use skein theoretic `recognition' theorems to classify deformations of certain pivotal or ribbon monoidal categories. We first show that a slight generalization of Kuperberg's characterization of quantum $G_2$ shows that any infinitesimal deformation of quantum $G_2$ as a pivotal category arises by varying $q$. This result applies both at generic $q$, and for the category of tilting modules at $q$ a root of unity (provided we exclude a few small roots of unity). Second, we prove a new Kuperberg-like characterization of Deligne's $S_t$ and use it to show that any infinitesimal deformation of $S_t$ (excluding $t=0$) as a ribbon category comes from varying $t$.
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Robotics

A Morphing Aerial Robot With Thruster-Integrated Flexible Continuum Links for Shape Adaptive Aerial Manipulation

Eri Sawada, Kazuki Sugihara, Ayano Miyamichi et al. (+2 more)

In recent years, aerial manipulation has attracted increasing attention as a key to expand the application of aerial robots. In this work, we focus on two major research directions for achieving versatile aerial manipulation: (i) acquiring high environmental adaptability using soft manipulators, and (ii) expanding the feasible wrench space by distributing thrusters along the manipulator. However, no aerial robot has simultaneously satisfied these two requirements. Therefore, in this paper, we propose a morphing rotor-distributed aerial robot with flexible continuum links that achieves both high shape adaptability and an expanded wrench space. The flexible continuum links function as soft manipulators, passively conforming to the shape of the environment, while the thrusters distributed along the continuum links expand the feasible thrust wrench space and enable the end-effector to exert large interaction forces. To realize the proposed robot, it is essential to suppress vibrations of the lightweight continuum links. Thus, we develop a composite leaf-spring structure that provides both high torsional and vertical stiffness, and vibration-suppressing control methods. Using these implementations, we demonstrate stable flight and a variety of aerial manipulation tasks. To the best of our knowledge, this is the first work to realize aerial manipulations using flexible links with an integrated thruster.
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All authors Eri Sawada, Kazuki Sugihara, Ayano Miyamichi, Kunio Kojima, Kei Okada

Instrumentation and Methods for Astrophysics

Gravitas: A Browser-Based Astrophysics Laboratory for Prediction, Measurement, and Discovery

Carl Ziegler

Astronomy students are often asked to reason about systems that cannot be handled in a laboratory, evolve over inaccessible timescales, or reveal themselves only through indirect measurements. Gravitas is a free, browser-based astrophysics environment designed to turn those limitations into opportunities for experimentation. Its version 1.0.0 release combines a configurable gravitational sandbox with 59 ready-to-run scenarios and 22 guided investigations comprising 636 steps and roughly 15-19 hours of structured work. Students can build and alter systems, change reference frames, measure distances, angles, periods, energies, forces, and orbital elements, and observe simulated systems through transit, radial-velocity, astrometric, rotation-curve, stellar-evolution, and gravitational-wave displays. Guided activities use a predict-test-measure-revise-explain cycle, retain the student's evidence, and export a report for submission. Instructor guides, answer keys, short classroom routes, lecture and embed modes, reproducible share links, Spanish localization, keyboard-accessible navigation, and offline operation lower the practical barrier to adopting the simulation in introductory astronomy and physics courses. A public dashboard reports 243 scientific checks across 25 areas and names the sources and tolerances behind them. This article describes the learning experiences available in Gravitas, illustrates several ways it can be used from a brief demonstration to a full laboratory exercise, and states the scientific scope and limitations of the models. The aim is not to replace quantitative calculation or observation, but to give students a place where they can make a prediction, change one thing, measure what follows, and build an explanation from their own evidence.
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Combinatorics

Posets of Hyper $(b,t)$-ary Partitions are Distributive Lattices

Elsa Frankel

Posets of integer partitions, ordered by refinement, were criticized for unruly structural behavior by both Birkhoff and Ziegler. However, recent work of Propp, McConville, and Sagan demonstrated that such posets of hyperbinary partitions are distributive, with posets of join irreducible elements isomorphic to the well-studied class of fence posets. We provide a generalization of this result for hyper $(b,t)$-ary partitions, where all parts are powers of some positive integer $b$, and multiplicities are restricted to $t\ge b$. Then, we show that posets of hyper $(b,t)$-ary partitions are indeed also distributive lattices.
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Cryptography and Security

AUDITPLAN: Commit, Then Answer for Auditable Safety Alignment

Sai Sri Pushpa Jampani, Kshitij Mishra and Asif Ekbal

Safety tuning pipelines judge only the final answer, which makes it difficult to distinguish robust refusal from two undesirable shortcuts: blanket refusal on benign requests and polished but unfaithful safety rationales that do not actually constrain the answer. We propose AUDITPLAN, a single-model plan-then-answer approach where the model first emits a compact structured safety plan and then answers conditioned on it. The plan records a threat label, intended action, and explicit constraints, enabling machine-checkable auditing while remaining hidden from users at deployment. We train this behavior with supervised fine-tuning followed by reinforcement learning with FAITHGATE, a reward-gating objective that grants answer reward only when the safety plan is correct. This discourages safe-looking but unfaithful behavior and promotes tighter plan-answer coupling. Across Qwen backbones, AUDITPLAN improves both robustness and auditability: on Qwen2.5-3B-Instruct, FAITHGATE reduces ASR from 24.0% to 11.6%, LSR from 1.0% to 0.36%, and over-refusal from 11.0% to 2.0%, outperforming answer-only RL, free-form explanation, and weighted-sum structured rewards. Similar trends hold for Qwen2.5-1.5B-Instruct. Larger-model confirmation runs on Qwen-3-4B-Instruct and Qwen2.5-7B-Instruct preserve the same trend suggesting that explicit internal commitments can make safety alignment more faithful, robust, and auditable.
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Audio and Speech Processing

PersianVox: A Prosody-Aware Approach for Speech Dataset Generation from In-the-Wild Data

Saeedreza Zouashkiani, Soheil Khalesi, Saman Soleimani Roudi et al. (+2 more)

Advancement of zero-shot text-to-speech synthesis is currently hindered for low-resource languages by the scarcity of large-scale, high-fidelity speech datasets. Traditional alignment-based methods require rare verbatim transcripts, while standard in-the-wild pipelines often rely on single-model automatic speech recognition and silence-based segmentation, leading to transcription errors and truncated prosody. To address these challenges for the Persian language, this paper introduces PersianVox, a fully automated pipeline designed to generate high-quality speech corpora from unlabeled web data. Our approach integrates a novel prosody-aware segmentation strategy that utilizes acoustic turn-detection to preserve linguistic completeness and optimize utterance duration for long-context modeling. Furthermore, we employ a dual-model agreement mechanism, leveraging two distinct model architectures to filter unreliable transcriptions without ground truth. This pipeline yields a 2,400-hour multi-speaker dataset, the largest open-source speech resource available for Persian to date. Additionally, we provide the first comparative benchmark of speech quality assessment methods for Persian, releasing a human-annotated subset to facilitate future research.
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All authors Saeedreza Zouashkiani, Soheil Khalesi, Saman Soleimani Roudi, Sajjad Amini, Shahrokh Ghaemmaghami

Image and Video Processing

NIMARC-MRI: Abdominal HASTE Dataset and a Baseline U-Net Exposing the Synthetic-to-Real Gap in Low-Resource Motion Correction

Abdulrazaq A. Zubair, Nafiu Musa Muhammad, Simeon Krah et al. (+7 more)

Respiratory motion degrades abdominal T2 HASTE MRI in low- and middle-income countries where vendor motion-correction licenses are unavailable and failed scans are deleted during routine PACS cleanup, precluding supervised training. To address this, we introduce NIMARC-MRI, the first public abdominal MRI dataset from West Africa, comprising 139 clean HASTE, 25 native motion-degraded HASTE, and 79 paired HASTE-TSE TRIGGER acquisitions from a Nigerian centre operating 1.5 T Siemens scanner without integrated motion-correction licenses. A 2D U-Net with 7.7 million parameters was trained on 3D-consistent synthetic respiratory motion necessitated by local infrastructure reality and validated via a three-tier strategy: held-out synthetic data, blinded radiologist Likert scoring on native real motion, and cross-sequence TRIGGER generalisation. On synthetic test data the model achieved SSIM 0.863 +/- 0.042 and PSNR 30.06 +/- 1.80 dB. On native real motion, however, blinded radiologist and radiographer scores showed no significant improvement (mean Likert 4.06 +/- 0.55 original versus 4.04 +/- 0.61 corrected, P = 0.914), with 28% of cases rated worse after correction. Cross-sequence TRIGGER evaluation showed modest SSIM improvement (0.404 +/- 0.073 versus 0.334 +/- 0.055, P < 0.001) without radiologist-perceived gain. These findings demonstrate that conservative synthetic motion fails to capture clinical motion severity, exposing a reproducible synthetic-to-real gap. NIMARC-MRI is released on Zenodo (https://doi.org) under a controlled data-use agreement requiring citation. Sequence metadata and a sample subset are publicly accessible to facilitate discovery, while patient-level data remain restricted to approved collaborators. The aim is to establish a reproducible baseline for motion correction in resource-constrained settings.
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All authors Abdulrazaq A. Zubair, Nafiu Musa Muhammad, Simeon Krah, Ummasalma Usman Ibrahim, Yusuf Tijjani Abdumumin, Ismail Ismail Tijjani, Ummulkhairi Ibrahim, Alyasaa Anas, Mubaraq Yakubu, Abbas Rabiu Muhammad

Mathematical Physics

The spectral $\zeta$-function of Sturm–Liouville operators with $N$ generalized point potentials

Guglielmo Fucci and Jonathan Stanfill

This work analyzes the spectral zeta function associated with regular and singular Sturm–Liouville operators endowed with $N$ generalized point potentials. The point interactions are characterized as a particular class of self-adjoint extensions of Sturm–Liouville operators defined on a chain of $N+1$ adjacent intervals. Each interaction is described by $SL(2,\R)$ matching conditions relating generalized boundary values across neighboring intervals. We construct the spectral zeta function associated with Sturm–Liouville operators endowed with $N$ generalized point potentials in terms of a contour integral involving an appropriate characteristic function. The process of analytic continuation of the spectral zeta function to a neighborhood of the origin is illustrated by means of specific examples where the $\zeta$-regularized functional determinant is also explicitly computed.
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Superconductivity

Encapsulation epitaxy of air-stable monolayer superconducting films for quantum circuits and qubits

Xudong Zheng, Sameia Zaman, Kenan Zhang et al. (+22 more)

Two-dimensional (2D) superconductors are an emerging platform for strongly correlated physics and quantum information science. Their reduced dimensionality, atomically flat interfaces, and high crystallinity are attractive for realizing compact lumped-element devices in superconducting circuits. However, synthesizing large-area, monolayer 2D superconductors remains challenging because of their susceptibility to oxidation. Here, we report an "encapsulation epitaxy" mechanism that enables the growth of large-area, air-stable, monolayer superconducting NbSe2 films and explore their use in superconducting quantum circuits. A 2D encapsulation layer, such as graphene or hexagonal boron nitride (hBN), pre-deposited on a 3D substrate (e.g., SiO2 or Si3N4), serves both as a template for epitaxial growth of monolayer NbSe2 (1L-NbSe2) underneath it and as a protective cover. This approach produces uniform, large-area (>1-inch) 1L-NbSe2 with greatly enhanced ambient stability, enabling device fabrication in air. The resulting 1L-graphene/NbSe2 heterostructures exhibit robust superconductivity (Tc ~ 1 K) and enhanced charge density wave order (TCDW ~ 177 K), indicative of high material quality. We further integrate 1L-NbSe2 into superconducting circuits using oxidation-free transfer and superconducting edge-contact techniques. The 1L-NbSe2 exhibits a measured kinetic inductance LK ~ 0.7 nH/square, making it suitable for quantum circuits requiring high-kinetic-inductance elements. Encapsulation epitaxy thus provides a route to air-stable 2D superconductors and van der Waals heterostructures, with potential for wafer-scale, monolithic fabrication of superconducting quantum circuitry.
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All authors Xudong Zheng, Sameia Zaman, Kenan Zhang, Connor A Occhialini, Haowei Xu, Zhien Wang, Fangyuan Liu, Luiz Gustavo Pimenta Martins, Sejoon Lim, Tianyi Zhang, Tilo H. Yang, Jiangtao Wang, Yunyue Zhu, Zachariah Hennighausen, Sein Park, Steven Vitale, Kevin Tibbetts, Stephen Margiotta, Phillip Kim, Cong Su, Ju Li, Riccardo Comin, William D. Oliver, Joel Î-j. Wang, Jing Kong

Systems and Control

Optimal Control and Closed-Loop Stability of Droplet Transport in a Microchannel

Rajneesh Anand and Mayuresh V. Kothare

Understanding the efficient transport of fluid droplets in confined geometries has been a domain of interest for industrial applications in recent times. In the present work, we focus on designing control strategies that optimally steer droplet motion. Here, we apply optimal control framework to the droplet transport problem in a microchannel based on lubrication theory that minimizes viscous dissipation. Two complementary modeling routes are adopted: a reduced-order ordinary differential equation (ODE) model optimized via Pontryagin's Maximum Principle, and a full nonlinear partial differential equation (PDE) model optimized using a Covariance Matrix Adaptation-Evolutionary Strategy. By parameterizing target displacement, droplet size, and capillary number, we uncover two distinct optimal transport regimes: a "translate-relax" strategy for short distances and a "compact-translate-relax" strategy for longer targets. In continuum mechanics, the competition between surface forces and cumulative viscous dissipation decides the optimal transport strategies. We further show that the displacement range over which the reduced-order controller transfers to the continuum model is governed by capillary stiffness. We finally address closed-loop stability by adapting a control Lyapunov function (CLF) framework to the reduced-order dynamics. We demonstrate that the terminal cost which penalizes deviation from the target, serves as a cost-compatible CLF on the physical domain, and prove that the CLF-compatible feedback exponentially stabilizes the target state.
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Game Theory

Asymptotic Max-Min Fair Allocation with Random Utilities

Noam Glazner and Amir Leshem

We investigate the asymptotic behavior of max-min fair allocations for indivisible goods under i.i.d. random utilities. For $N$ agents and $K$ goods with utilities ${\mU_{i,j}}$ drawn independently from a common distribution $F$, we derive asymptotic characterizations of the max-min value in the balanced case $K=N$ (and in $K=LN$ extensions) via distributional quantiles. We then study the efficiency impact of max-min fairness by comparing the resulting total welfare with the optimal sum welfare. For distributions with sufficiently light tails, we prove that the relative efficiency loss converges to zero as the market grows, implying that max-min fairness incurs negligible welfare loss in large random instances for a broad class of distributions.
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Human-Computer Interaction

"I Know Where to Look," But Does the LLM? Charting the Gaps Between Clinical Expert Needs and Unstructured Data Abstraction Tools

Venkatesh Sivaraman, Rigney Turnham, George Bonano et al. (+16 more)

Clinical data abstraction, the process of distilling structured information from patient records, plays a key role in advancing knowledge about diseases such as cancer. Information extraction (IE) with large language models (LLMs) could accelerate this process, but it is unclear whether current frameworks effectively support clinical researchers without AI expertise. To address this, we co-designed an interactive LLM-based abstraction system called Libretto with seven cancer research teams, then evaluated the system's ability to help them answer real-world research questions. We found that while clinicians knew where and how to annotate complex concepts in patient notes, in twelve of fourteen tasks they faced barriers to replicating those intuitions with LLMs. Contextual note reliability judgments, difficulties in steering vibe-coded prompts, and inflexible evaluation strategies necessitated fundamental changes to the IE workflow. Our results highlight open problems for HCI research to bridge the gaps between AI data work tools and clinical users' needs.
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All authors Venkatesh Sivaraman, Rigney Turnham, George Bonano, Nevin Aresh, Renumathy Dhanasekaran, Margaret Guo, Sindhu Kubendran, Olivia Lin, Jonathan D Louie, Kristan Olazo, Jeanne Shen, Harish Vasudevan, Jeanette Wong, Emily Alsentzer, Jason A Fries, Anobel Odisho, John Gordan, Jean Feng, Julian C Hong

Quantum Physics

Observing and evading quantum back-action on a kilogram-scale oscillator

Begüm Kabagöz, Eric Oelker, Dhruva Ganapathy et al. (+203 more)

Continuous quantum displacement measurements are fundamentally limited by a trade-off between readout imprecision and measurement back-action, constrained by the Heisenberg uncertainty principle. In the Laser Interferometric Gravitational-Wave Observatory (LIGO), these two quantum noise components dominate much of the observation band, making it an excellent testbed. We induce a sub-Hz-linewidth optomechanical mode by trapping the differential motion of the 40-kg mirrors in a band where radiation-pressure back-action dominates the motion. Engineering the quantum state entering the dark port creates correlations between imprecision and back-action that partially cancel their contributions, reducing observed motion near resonance by ~47%. A framework resolving the imprecision, back-action, and correlation terms identifies the origin of this suppression. These results demonstrate quantum back-action evasion and quantum reservoir engineering in a macroscopic optomechanical system.
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All authors Begüm Kabagöz, Eric Oelker, Dhruva Ganapathy, Nergis Mavalvala, Vivishek Sudhir, Vladimir Bossilkov, Joseph Betzweiser, Valery V. Frolov, Anamaria Effler, Adam Mullavey, Lisa Barsotti, Evan D. Hall, Peter Fritschel, R. Abbott, I. Abouelfettouh, R. X. Adhikari, A. Ananyeva, S. Appert, S. K. Apple, K. Arai, N. Aritomi, S. M. Aston, M. Ball, S. W. Ballmer, D. Barker, B. K. Berger, D. Bhattacharjee, G. Billingsley, S. Biscans, C. D. Blair, N. Bode, E. Bonilla, A. Branch, A. F. Brooks, D. D. Brown, J. Bryant, C. Cahillane, A. Calafat, S. R. Callos, H. Cao, E. Capote, F. Clara, J. Collins, C. M. Compton, G. Connolly, R. Cottingham, D. C. Coyne, R. Crouch, J. Csizmazia, A. Cumming, L. P. Dartez, D. Davis, N. Demos, E. Dohmen, K. L. Dooley, J. C. Driggers, S. E. Dwyer, A. Ejlli, T. Etzel, M. Evans, J. Feicht, R. Frey, W. Frischhertz, M. Fuentes-Garcia, P. Fulda, M. Fyffe, B. Gateley, T. Gayer, J. A. Giaime, K. D. Giardina, J. Glanzer, E. Goetz, R. Goetz, A. W. Goodwin-Jones, S. Gras, C. Gray, D. Griffith, H. Grote, T. Guidry, J. Gurs, J. Hanks, J. Hanson, M. C. Heintze, A. F. Helmling-Cornell, N. A. Holland, D. Hoyland, H. Y. Huang, Y. Inoue, A. L. James, A. Jamies, R. Jaume, A. Jennings, W. Jia, D. H. Jones, S. Karat, S. Karki, M. Kasprzack, K. Kawabe, N. Kijbunchoo, P. J. King, J. S. Kissel, K. Komori, A. Kontos, R. Kumar, K. Kuns, M. Landry, B. Lantz, M. Laxen, K. Lee, M. Lesovsky, F. Llamas Villarreal, M. Lormand, H. A. Loughlin, R. Macas, M. MacInnis, C. N. Makarem, B. Mannix, G. L. Mansell, R. M. Martin, K. Mason, F. Matichard, N. Maxwell, G. McCarrol, R. McCarthy, D. E. McClelland, S. McCormick, T. McRae, F. Mera, E. L. Merilh, J. R. Mérou, F. Meylahn, R. Mittleman, D. Moraru, G. Moreno, M. Nakano, T. J. N. Nelson, A. Neunzert, J. Notte, J. Oberling, T. O'Hanlon, R. Oram, C. Osthelder, D. J. Ottaway, H. Overmier, W. Parker, O. Patane, A. Pele, H. Pham, M. Pirello, J. Pullin, V. Quetschke, K. E. Ramirez, K. Ransom, J. Reyes, J. W. Richardson, M. Robinson, J. G. Rollins, C. L. Romel, J. H. Romie, M. P. Ross, B. I. Rotimi, K. Ryan, T. Sadecki, A. Sanchez, E. J. Sanchez, L. E. Sanchez, R. L. Savage, D. Schaetzl, M. G. Schiworski, R. Schnabel, R. M. S. Schofield, E. Schwartz, D. Sellers, T. Shaffer, R. W. Short, D. Sigg, B. J. J. Slagmolen, C. Soike, S. Soni, V. Srivastava, L. Sun, D. B. Tanner, M. Thomas, P. Thomas, K. A. Thorne, M. R. Todd, C. I. Torrie, G. Traylor, A. S. Ubhi, G. Vajente, J. Vanosky, A. Vecchio, P. J. Veitch, A. M. Vibhute, E. R. G. von Reis, J. Warner, B. Weaver, R. Weiss, C. Whittle, B. Willke, C. C. Wipf, J. L. Wright, V. A. Xu, H. Yamamoto, L. Zhang, M. E. Zucker

Optimization and Control

Persistent mixed worldviews in the theory of chosen preferences

Ziran Liu

Bernheim, Braghieri, Martínez-Marquina, and Zuckerman (2021, American Economic Review) develop a theory of chosen preferences and conjecture convergence to pure worldviews at low mindset flexibility. We show that the (original) conjecture fails: with arbitrarily many worldviews, a common-value action sustains mixed worldviews in every stationary equilibrium from a relatively open set of initial worldviews, even when all pure worldviews are absorbing. However, finite linear programs characterize a sufficient persistence condition. Conversely, mixed accumulation points require shared valuation maxima. If each implementable action has a unique maximizing evaluator, every existing stationary equilibrium converges to purity for all interior flexibility and discount parameters. Experienced utility converges without uniqueness. In existing equilibria, small payoff perturbations preserve compromise over fixed horizons with high probability. Strictly tailored worldviews yield existence and a one-step equilibrium characterization at sufficiently low mindset flexibility; weak implementation can preclude stationary pure-strategy existence. The persistence and convergence results extend to stationary randomization.
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