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All of arXiv

Saturday 19 September

Mathematical Physics

Six-wave scattering in Hamiltonian Dysthe equations: Euler matching and integrability obstructions

Alex J. Sutherland and Solomon C. Yim

We compute the effective six-wave interaction in spatial and temporal Hamiltonian Dysthe equations and compare it with the complete degree-six coefficient of the deep-water Euler Hamiltonian. On every compact subset $K$ of a generic quadratic-dispersion resonance patch, exact resonances persist under a small cubic correction to the dispersion, and \[ W_6\bigl(\Phi_{\lambda,\varepsilon}(z)\bigr) =\varepsilon\frac{4BD}{aL(z)}+O_K(\varepsilon^2) \] uniformly in the resonance point $z$ and the deformation parameter $\lambda$. Here $L$ is the spectral diameter and $B,D$ are the local cubic and mean-flow coefficients. Thus $BD\ne0$ gives nonzero scattering on relatively open resonance sets. At the Craig–Guyenne–Sulem temporal coefficients, the corresponding exact Euler resonances satisfy \[ \frac{\mu}{4}W_6^{\rm E}\bigl(1+\mu\Psi_\mu(z)\bigr) =-\frac{8}{L(z)}+O_K(\mu), \] where $\mu$ is the relative bandwidth. The leading term comes from nine contractions of quartic vertices; every other connected degree-six contribution is uniformly bounded. We classify the coefficient values for which the generic six-wave coefficient vanishes identically; in the temporal case, this also gives cancellation on every non-permutation four-wave resonance. Outside these sets, the Zakharov–Schulman condition restricts every regular $C^3$ quadratic leading symbol to $\operatorname{span}\{1,k,\omega(k)\}$, the span of the symbols of wave action, momentum, and linear energy. Consequently the physical Dysthe models cannot support an inverse-scattering hierarchy with infinitely many linearly independent regular $C^3$ quadratic leading symbols. The cancellation sets contain known nonlinear Schrödinger, mixed Chen–Lee–Liu, Calogero–Moser derivative nonlinear Schrödinger, and Hirota representatives.
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Dynamical Systems

Sofic systems and extreme points of self-affine limit sets

Stefano Silvestri and Rodrigo A. Pérez

Given any iterated function system (IFS) on the plane whose affine transformations share the same contracting matrix $T$, we characterize the set of extreme points of the attractor $A$ (along the convex hull $\operatorname{conv}(A)$) in terms of a directed graph on the edges of $\operatorname{conv}(A)$. This graph corresponds to a sofic system on the coding space associated to the IFS. When $T$ is proper orthogonal or purely real, we give conditions for $\operatorname{conv}(A)$ to be a finite polygon and, under additional symmetry of the transformations, we give conditions for $A$ itself to coincide with $\operatorname{conv}(A)$.
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Cryptography and Security

Exploiting Software-level Abstractions To Support Practical Hardware Trojan Attacks

Athanasios Moschos, Kevin Valakuzhy, Georgios Kokolakis et al. (+2 more)

Hardware trojan (HT) attacks against CPUs typically assume threat scenarios where an attacker targeting a system with a trojanized CPU is able to execute arbitrary code (i.e. machine-level instructions) to reliably interact with the implanted trojan. On end-user devices (i.e., mobiles, laptops), achieving arbitrary code execution in practice requires software exploits tailored to each specific target. Such strong adversarial premises reduce the generality of existing threat models casting doubt on CPU trojan attacks as a pragmatic threat vector. To push the envelope on HT attacks against client devices, we introduce the SURF class of CPU-trojans that can be activated without arbitrary code execution. Our key insight is that integer operations expressed in a high-level language can be mapped to microarchitectural side-effects distinguishable by a SURF trigger circuit. This observation unlocks HT activation via runtime engines, constrained environments executing untrusted high-level code. We demonstrate a SURF trojan inside a RISC-V processor and exploit JavaScript-level memory indexing operations inside Google's V8 engine to perform a code injection attack. Importantly, we show that SURF trojans remain effective across multiple JavaScript engine versions, enabling long-term compromise of endpoint devices. To facilitate research, we opensource SURF's design and supporting software.
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All authors Athanasios Moschos, Kevin Valakuzhy, Georgios Kokolakis, Fabian Monrose, Angelos D. Keromytis

Differential Geometry

Free maps in critical dimension on $\mathbb{T}^m$

Roberto De Leo

The critical dimension for free maps on m-manifolds is $q_m=m(m+3)/2$. We show that $Free^\infty(\mathbb{T}^m,\mathbb{R}^{q_m})\neq\emptyset$ for every $m\ge 1$. The main tool is a product construction: given free maps in critical dimension on $\mathbb{T}^a$ and on $\mathbb{T}^b$, together with a cross-free map on $\mathbb{T}^a\times \mathbb{T}^b$, whose mixed Hessian is invertible everywhere, we obtain a free map in critical dimension on $\mathbb{T}^{a+b}$, the osculating matrix of the product being block-triangular. Cross-free maps are additive in each argument, and in the torus setting none exists when one factor is one-dimensional. We construct explicit cross-free maps on $\mathbb{T}^2\times \mathbb{T}^2$ and on $\mathbb{T}^3\times\mathbb{T}^4$ using quaternionic multiplication. Combined with the low-dimensional cases $m\leq 5$, constructed explicitly by the author in a previous publication, these yield the result by induction.
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Quantum Physics

Deterministic synthesis and processing of frequency-bin qubits in a macroscopically coherent quantum memory

S.A.Moiseev

Quantum information processing requires efficient storage and manipulation of photonic states. In this work, we harness pre-created macroscopic spin coherence as an additional degree of freedom to control a cavity-assisted quantum memory. We derive equations of motion for such a memory that predict efficient emission and absorption of two-color fields under a generalized nonlocal spectral impedance-matching condition, which reduces to the well-known condition in the single-mode case. These equations reveal a new operating mechanism of cavity-assisted quantum memory wherein the controlled spin coherence couples the emitted spectral modes. As a result, our memory deterministically synthesizes arbitrary frequency-bin photonic qubits using simple radio-frequency rotations, while the temporal reversibility of the protocol provides perfect transformation of the qubit states. Our findings establish a unified platform bridging quantum storage with elementary signal processing for quantum networks based on spectral encoding of photonic qubits.
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Machine Learning (Statistics)

Conformal Robustness in Prediction-Driven Decision-Making

Lingjie Zhao, Hansheng Jiang and Wei Qi

Modern prediction-driven decision systems often rely on black-box predictors, but a point forecast alone does not provide the uncertainty scale required for robust downstream decision-making. We build a score-calibrated robustness framework that converts any fixed point predictor into a decision-relevant uncertainty representation through distribution-free conformal calibration. We use the conformal score, rather than a particular uncertainty set, as the primitive unit of robustness. The same score determines coverage-calibrated uncertainty sets for reliability-based robust optimization and normalizes target violations in a target-oriented formulation, Conformal Robust Satisficing. This formulation induces a conformal fragility measure that quantifies how rapidly performance deteriorates as the realized parameter departs from the forecast on the conformal score scale. For objective-uncertainty problems under standard convexity and duality conditions, we show that the reliability-based and target-oriented formulations parameterize the same score-calibrated robust decision frontier. This equivalence yields a data-driven mapping between reliability levels and acceptable targets and characterizes the marginal cost of robustness. Synthetic experiments validate the theoretical guarantees and illustrate the reliability-target correspondence. A real-data online-grocery case study demonstrates how the interface combines deep-learning demand forecasts with tractable inventory optimization, thereby improving reliability and reducing operational costs. Overall, our work shows that conformal scores endow fixed black-box predictors with an interpretable uncertainty scale for downstream decision-making while enabling reliability guarantees, acceptable-target selection, and fragility analysis within a unified framework.
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Computer Vision

UltraTex: Unleashing 2K Multi-View Diffusion for 3D Texturing

Yibo Zhang, Ze Yuan, Nan Cao et al. (+4 more)

High-quality texture generation is essential for creating realistic and production-ready 3D assets. Recent multi-view diffusion methods have shown promising results for image-guided 3D texturing, but they are typically constrained to low operating resolutions such as 512 or 768, making it difficult to preserve high-frequency details from high-resolution reference images. Scaling this paradigm to 2048 resolution is computationally prohibitive, as the unified multi-view sequence exceeds 212K tokens and incurs excessive memory and latency. In this paper, we present UltraTex, an efficient end-to-end framework for high-resolution multi-view diffusion-based 3D texturing. Our key observation is that object-centric multi-view renderings contain two major sources of redundancy: background-induced sequence redundancy and sparse token interactions within the foreground. To address them, we introduce Background Token Dropping, which removes background tokens before the DiT backbone, and Block-Sparse Attention, which reduces attention computation over the retained foreground sequence. To enable efficient foreground-only inference while avoiding reconstruction artifacts, we further design Foreground-Aware VAE Decoding to ensure the quality of the final high-resolution views. To satisfy the demanding data requirements of 2K-resolution multi-view diffusion training, we construct G-buffer TexVerse, a large-scale, ultra-high-resolution multi-view rendering dataset covering over 268,000 3D assets. Extensive experiments show that UltraTex generates visually faithful textures with rich fine-grained details, while substantially improving efficiency, achieving $20.6\times$–$91.1\times$ training speedup and $22.3\times$–$74.6\times$ end-to-end inference speedup over the baseline on common samples in our dataset. Code and data is at https://yiboz2001.github.io/UltraTex.
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All authors Yibo Zhang, Ze Yuan, Nan Cao, Li Zhang, Yan-Pei Cao, Yuan-Chen Guo, Rui Ma

Soft Condensed Matter

An active soft glassy rheology model

Raffaele Mendozza, Tobias Müller and Peter Sollich

Biological materials such as the cytoskeleton and confluent cell monolayers are active, dense systems continuously subjected to internal stresses and strains, making their rheological characterization essential. While activity in soft matter can be modeled across multiple length scales, its mechanical consequences remain strongly model dependent and no unified theoretical framework has yet emerged. Here, we study the rheology of dense active amorphous materials using the Soft Glassy Rheology (SGR) model, incorporating activity at the mesoscopic scale of local elements as a stochastic strain rate that is persistent on some timescale $\tau_p$. We show that activity opens a long-time relaxation channel, driving a crossover from SGR-like power-law rheology to Maxwell-like behavior at the lowest frequencies. Combining analytical arguments in limiting regimes with numerical simulations, we characterize the resulting fluidisation time scale and its dependence on the activity parameters, which shows strong analogies with effective temperatures introduced elsewhere that similarly encode activity-induced fluidisation. Our active SGR model provides a minimal mesoscopic route to understanding how driving by activity modifies the rheology of dense amorphous materials.
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Functional Analysis

A survey on the Asplund property for spaces $C(X)$ of continuous functions

Marian Fabian, Jerzy Kakol and Arkady Leiderman

The Asplund property plays an important role in Banach space theory due to its connections with differentiability properties of continuous convex functions, optimization problems, and the weak topology of Banach spaces. Motivated by the variety of nonequivalent definitions proposed in the literature for locally convex spaces, in this survey we provide a unified framework for studying the Asplund property beyond the Banach space setting. The main object of our study is the locally convex space of continuous functions $C_k(X)$ endowed with the compact-open topology, where $X$ is an arbitrary Tychonoff space. We completely characterize the Asplund property for $C_k(X)$ in terms of topological properties of the underlying space $X$. As an essential step, we revisit the proof of several classical results, including the Namioka–Phelps theorem. Our approach is independent of differentiability techniques and relies solely on topological methods. The exposition is self-contained, and all major results are provided with complete proofs, making the paper accessible to both specialists and newcomers.
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Other Statistics

See You at the Posterior Line: Learning Bayesian Modeling Through a Car Racing Game

Federica Zoe Ricci and Mine Dogucu

We present an interactive classroom activity designed to address a central challenge in teaching introductory Bayesian statistics: how to formalize subjective knowledge and available information into prior distributions and then update them with empirical data. Role-playing as data analysts for a racing team, students evaluate candidate tires by converting qualitative engineering reports into prior distributions, collecting primary data via a virtual racing game, and using a Beta-Binomial model to inform team strategy. This discovery-based exercise allows small groups to observe directly how different prior choices and sample data jointly shape posterior inference. Student feedback ($n=32$) highlights high enjoyment, engagement and improved conceptual clarity. Open-access materials to implement the activity are provided, alongside recommendations for adapting it to other teaching contexts.
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Applied Physics

Magnetic Particle Magnetometer Based on Langevin Response of Superparamagnetic Nanoparticles for Remote Magnetic Field Sensing

M. Jurj, C. McDonough, C. Bastajian et al. (+1 more)

We demonstrate a novel magnetometer that utilizes the Langevin nonlinear magnetization of superparamagnetic nanoparticles as the sensing medium. The magnetic sensor exploits the even-order harmonics of the magnetization response, particularly the second harmonic, which is proportional to the local magnetic field component. By remotely detecting the magnetization response, the magnetometer enables non-invasive, localized, and distributed magnetic field sensing without requiring wired connections, with a small sensing volume, which can be used for in situ biomedical sensing, geomagnetic monitoring, smart diagnostics, and distributed industrial sensing. In the linear operating regime, the magnetometer provides a measurement range of $\pm$ 0.2 mT and a high sensitivity of 10 pT/$\sqrt{Hz}$, while an extended operating mode allows measurements over a range exceeding $\pm$ 0.5 mT. The sensor offers a dynamic bandwidth from dc up to a few kilohertz, determined by the excitation frequency.
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All authors M. Jurj, C. McDonough, C. Bastajian, A. Tonyushkin

Audio and Speech Processing

Signal-Informed Temporal Routing for Vinyl Defect Regime Detection

Yi-Hung Kan and Homayoon Beigi

Vinyl restoration systems must distinguish isolated clicks, short bursts, dense crackle, and overlapping damage before selecting a repair operation. We present a lightweight two-stage detector in which signal-informed sparse, burst, and dense experts produce complementary defect evidence, and a temporal backend converts that evidence into stable repair regimes. The backend factors the five-way decision hierarchically, applies a validation-only mixed regime gate, and decodes with validation-selected transition penalties that outperform a maximum-likelihood transition matrix on the same emissions. On a source-separated synthetic benchmark of 597 non-overlapping 15 s excerpts drawn from 21 recordings, the held-out system reaches 0.730 pooled five-class macro F1 (95% CI 0.694-0.761), an improvement of 0.043 over a flat frame-level router (paired bootstrap p=0.001). Clean and dense frames are highly reliable, while sparse, burst, and mixed frames remain far more ambiguous.
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Machine Learning (Statistics)

Toscani-Fourier Distance on Probability Measures: Wasserstein Control, Topological Equivalence on Model Classes, and Duality

Mehrdad Mohammadi

Comparing probability measures in machine learning trades transport geometry against computational cost: Wasserstein distances encode the geometry of $\mathbb R^d$ but require solving a transport problem, while kernel discrepancies are cheap to evaluate yet depend delicately on their test class. We study the Toscani–Fourier family $\mathrm T_{s,p}$, the weighted $L^p$ norm of the difference of two characteristic functions, as a continuous Fourier-side discrepancy on $\mathbb R^d$. For $1\le p<\infty$ we show that $d/p<s<1+d/p$ is exactly the window in which $\mathrm T_{s,p}$ is finite on $\mathcal P_p(\mathbb R^d)$, both endpoints already failing for a pair of Dirac measures, and we establish the metric, embedding, and compactness structure of the resulting space, which we prove to be complete. Duality identifies $\mathrm T_{s,p}$ as an integral probability metric over a homogeneous Fourier–Lebesgue ball, with an explicit extremizer when $1<p<\infty$. We prove the global bound $\mathrm T_{s,p}\lesssim W_p^{\,s-d/p}$, whose exponent is sharp, show that no global converse of any form can hold, and recover topological equivalence with $W_p$ on bounded-support and uniform-tail classes, together with explicit reverse moduli on bounded-support classes that improve the imported energy-kernel exponent at $p=2$. There, $\mathrm T_{s,2}$ is a constant multiple of the classical energy distance, which yields an exact finite-sample identity for the mean of the empirical discrepancy; the numerical experiments are otherwise diagnostic.
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Information Retrieval

From Prompt to Recommendation: A Fitted Stage Model of Brand Visibility in AI Search

Benjamin Tannenbaum

We analyze 34,960 unbranded prompt-engine observations from 75 anonymized Aiso projects, covering 2,854 distinct monitored prompts and repeated GPT and Gemini runs from June-September 2026. When neither the target brand nor its own domain appears in the observable live retrieval path, target mention rates are 2.8% for GPT and 3.8% for Gemini. With an own-domain citation but no branded fan-out, they rise to 49.0% and 58.4%. When both own-domain exposure and a branded fan-out occur, mention rates reach 91.4% and 100%. The relationship persists within the same project, prompt, and engine across repeated runs: among prompt cells that vary in own-domain exposure while holding branded fan-out absent, exposure is associated with a mean mention-rate increase of 40.2 percentage points on GPT and 49.0 points on Gemini. Prior visibility is independently persistent. A previous non-mention plus no current own-domain exposure yields next-run mention rates of 1.6% and 1.9%; previous mention plus current exposure yields 80.5% and 83.7%. We fit a chronological diagnostic model using prior-run history and contemporaneous retrieval indicators: $ \operatorname{logit}P(M_t=1)=\alpha_e+\beta_e\operatorname{logit}(\widetilde P_{t-1})+\gamma_e E_t+\delta_e F_t+\theta_e^\top X. $ On the latest 30% holdout, the full model achieves AUC 0.963 on GPT and 0.942 on Gemini, compared with 0.937/0.917 for prior history alone and 0.880/0.840 for live signals alone. A manually curated prompt sensitivity gives nearly identical AUCs (0.960 and 0.943). A separate 199-prompt page-corpus validation finds that prompt-page match predicts Gemini exposure (AUC 0.641) more clearly than GPT exposure (0.545), placing relevance upstream of a larger engine-mediated exposure effect. The equation is predictive and observational, not a causal description of proprietary engine internals.
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Computer Vision

An Eternal Irradiance Camera

Jeremy Klotz and Shree K. Nayar

A conventional camera uses millions of pixels to measure radiance from all directions within its field of view. We present an omnidirectional irradiance camera that measures the irradiance function—the illumination incident upon every point on a sphere. The irradiance function varies smoothly over the sphere and hence is bandlimited. We have analyzed this function in the frequency domain and have shown that it is well approximated by a weighted sum of the first seven degrees of spherical harmonics. As a result, the irradiance function can be accurately reconstructed from a small number of samples. This implies that an irradiance camera does not need millions of detectors (pixels)—just a handful of measurements suffice. This brings two major benefits. First, the camera consumes such little energy that it can be completely powered by the light falling on its detectors. Second, it does not capture the visual details needed to identify an individual, and hence privacy is preserved. We have built a prototype irradiance camera, called FluxCam, using 49 detectors arranged on the surface of a sphere. In a well-lit indoor environment, FluxCam can read out and wirelessly transmit its measurements at 30 frames per second using energy harvested from the light falling on it (i.e., without a battery, cable, or external power supply). We show how FluxCam can be used as an optical gyroscope for computing rotation, to monitor a workspace, as an untethered light probe for diffuse relighting, and as an omnidirectional pyranometer for estimating sky conditions and determining the best orientation of a solar panel.
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Instrumentation and Detectors

Precise measurement of the thermal quenching factor of alpha particles in Li2MoO4 cryogenic calorimeters

M. Girola, R. Serino, M. Biassoni et al. (+7 more)

We present direct measurements of the thermal quenching factor (QF)for alpha-particles in Li2MoO4 scintillating crystals, conducted at base temperatures of 13 mK and 19 mK. Using gamma-calibration peaks from a 232Th source and alpha-lines from a surface 224Ra source, the QF_alpha was determined to range from 1.07 to 1.09 which is consistent with previous studies. A slight energy-dependent trend in QF_alpha was observed.
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All authors M. Girola, R. Serino, M. Biassoni, C. Brofferio, M. Faverzani, E. Ferri, A. Gianvecchio, L. Gironi, I. Nutini, S. Pozzi

Earth and Planetary Astrophysics

Marginalized planet-to-star radius ratio posteriors for low-SNR transiting planets via dimensionality reduction

Oryna Ivashtenko and Barak Zackay

Occurrence rate estimates of small long-period planets rely on the radii of low signal-to-noise ratio transit candidates whose posteriors should be propagated into the population inference. These posteriors should include the error budget from all latent parameters and account for the effects of the correlated noise. Full Markov chain Monte Carlo fits with Gaussian processes take days of runtime per target, and provide information on latent parameters that will be marginalized over anyway. We present a method that computes the marginalized posterior of the planet-to-star radius ratio while sampling only a lower-dimensional effective parameter space. The 11-dimensional problem reduces to a 4-dimensional one, while the posterior retains the uncertainty budget of all the nuisance parameters, including eccentric orbits, stellar parameters, and limb darkening. The method operates on whitened light curves, mitigating biases due to correlated noise. We show that the posteriors are calibrated on simulated systems: the true radii have uniformly distributed p-values. Applied to 419 faint long-period Kepler Objects of Interest, the method yields radius ratios broadly consistent with the KOI table, although their differences exceed those expected from the reported uncertainties.
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Signal Processing

Pre- and Post-Exercise Monitoring of Physiological Skin States in Sportsmen Using 77 GHz FMCW Radar and Wavelet Scattering Transform

Vijith Varma Kotte, Muhammad Mahboob Ur Rahman, Sajid Ahmed et al. (+1 more)

This paper presents a contactless radar sensing framework that classifies physiologically-induced changes in the superficial skin layer following intense fixed-duration physical activity along with water intake restriction. A 77 GHz frequency-modulated continuous wave (FMCW) radar is used to capture high-resolution range profiles from the subject's chest, seated in front of the radar, before and after sports activity. Since the electromagnetic penetration into biological tissue is inherently shallow at millimeter-wave frequencies, the sensing is confined primarily to the stratum corneum and upper epidermis. We demonstrate that exercise-induced thermoregulation, perspiration residue deposition, and transient shifts in superficial tissue hydration produce measurable alterations in quasi-static radar reflectivity. We utilize single-chirp snapshots of duration 102.4 micro sec in order to suppress confounders such as slow-varying cardiopulmonary motion, followed by lightweight feature extraction via the Wavelet Scattering Transform (WST) and classification using light-weight machine learning models suitable for edge nodes. Leave-one-subject-out cross-validation on a dataset of 15 sportsmen measured pre- and post-exercise yields a mean classification accuracy of 91.55% with a 95% confidence interval of [84.27, 98.83]%. Interpretability analysis reveals that low-order scattering coefficients (capturing overall signal energy and amplitude stability) dominate discriminative power, consistent with quasi-static changes in skin surface permittivity. The results establish a proof-of-concept for radar-based, edge deployable classification of post-exercise skin states, opening new avenues for non-contact physiological monitoring in sports science.
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All authors Vijith Varma Kotte, Muhammad Mahboob Ur Rahman, Sajid Ahmed, Tareq Y. Al-Naffouri

Networking

Orientation-Aware Control and Trajectory Design for Aerial RIS-Assisted Wireless Communications

Abdoul Karim A. H. Saliah, Hajar El Hammouti, Daniel Bonilla Licea et al. (+1 more)

Aerial Reconfigurable Intelligent Surfaces (ARISs) can enhance wireless connectivity in obstructed environments by combining reconfigurable metasurfaces with aerial mobility. However, in practical deployments, the Reconfigurable Intelligent Surface (RIS) is rigidly mounted on a multi-rotor Unmanned Aerial Vehicle (UAV), which creates coupled position-orientation dynamics that directly affect communication quality. Since RIS performance is highly sensitive to orientation, point-mass or orientation-invariant models may lead to optimistic performance estimates. This paper proposes an orientation-aware ARIS framework that explicitly accounts for UAV dynamics, actuation limits, and orientation-dependent channels. The proposed approach combines a communications-guided reference trajectory planner with a Nonlinear Model Predictive Control for dynamically feasible trajectory tracking and favorable RIS orientation, while employing lightweight beamforming and RIS phase-shift updates. Simulations show that accounting for orientation improves user throughput by 13.77%, while the proposed joint communication updates improve network throughput by 28% over phase-shift-only optimization and by at least a factor of 2.5 over beamforming-only optimization. These results highlight the importance of orientation-aware modeling and joint communication-control design in ARIS-assisted wireless systems.
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All authors Abdoul Karim A. H. Saliah, Hajar El Hammouti, Daniel Bonilla Licea, Giuseppe Silano

2609.23157 · 19 Sep · Accepted for publication in IEEE Transactions on Vehicular Technology. Copyright (c) 20xx IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org

Abstract PDF $UAV ×2

Sound

EquiSELD: Efficient training of equivariant sound event localization and detection networks

Goksenin Yuksel, Marcel van Gerven and Kiki van der Heijden

First-order Ambisonics (FOA) signals exhibit exact O(3) symmetry: The rotation or reflection of the FOA signal modifies the direction of arrival of the sound sources, while preserving the sound sources themselves. Prior attempts to utilize this spatial symmetry of FOA to improve the efficiency and robustness of sound event detection and localization (SELD) systems either learned only an approximation of the symmetry through rotation-based augmentation or relied on computationally expensive methods to integrate equivariance. Furthermore, prior work focused exclusively on SO(3) equivariance , leaving the potential of incorporating O(3) equivariance for SELD tasks unclear. To address these limitations, we developed EquiSELD. This equivariant attention network processes first-order Ambisonics as paired streams of O(3)-invariant scalars and equivariant intensity vectors, producing an invariant activity magnitude and an equivariant DOA with a Multi-ACCDOA readout. To compare the impact of O(3) versus SO(3)-equivariance, we designed a matched SO(3)-only variant. EquiSELD outperforms prior equivariant networks on both simulated scenes with measured RIRs and recordings of real-world sound scenes at a fraction of the training cost. EquiSELD additionally surpasses the performance of non-equivariant SELD networks of a similar size on the simulated real-world sound scenes and achieves competitive performance on the real-world sound scenes.
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Signal Processing

Complementary-Aperture Pulse Sequencing for Fundamental-Band Nonlinearity Parameter Imaging

Esteban Avilés, Michael Oelze, Roberto Lavarello et al. (+1 more)

The depletion method (DM) enables the estimation of the acoustic nonlinearity parameter, B/A, in pulse-echo ultrasound using fundamental-band signals from low- and high-pressure acquisitions, but requires accurate knowledge of the acoustic-pressure scaling factor. This requirement limits experimental translation because of the slightly nonlinear scaling between excitation voltage set on the ultrasound system and pressure at the probe. We propose a complementary-aperture pulse sequence (CAPS) that recovers the linearly scaled low-pressure signal by coherently summing RF acquisitions from interleaved transmit subapertures whereas a full-aperture transmission provides the high-pressure signal maintaining the same excitation voltage configured in the ultrasound system. In silico phantoms of a homogeneous sample and a reference evaluated four aperture partitions of CAPS and compared them with the DM under relative scaling-factor errors from 0.01% to 10%. Using the DM, errors of 0.1% and 1% produced absolute B/A biases of up to 27.3% and 58.3%, respectively. CAPS yielded mean B/A values of 8.9 for a prescribed value of 9, with biases of less than 1.3% across all partitions, matching an ideally calibrated DM. These results demonstrate that CAPS preserves depletion-based B/A estimation without using the pressure scaling factor as a reconstruction input, supporting experimental and in vivo implementation without pressure-ratio calibration.
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All authors Esteban Avilés, Michael Oelze, Roberto Lavarello, Andres Coila

Quantum Physics

Quantum Hashing with QKD States

A.V. Vasiliev and I.G. Zinnatullin

Quantum hashing is a well-known technique. In this paper, we present a new construction of a quantum hash function based on binary error-correcting codes. The construction of the quantum hash strongly resembles the quantum state preparation in QKD protocols (e.g., BB84 protocol) and can be implemented on current QKD-devices.
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Computer Vision

SparkDiffusion: Mitigating the High-Sparsity Trap — A Unified Framework for up to $265\times$ Single-GPU Acceleration of Visual Generation

Yuxi Liu, Haoyu Li, Zekun Zhang et al. (+12 more)

Video diffusion transformers are expensive because attention dominates long spatiotemporal token sequences. We identify the high-sparsity trap: at extreme attention sparsity, step-local training losses keep decreasing while terminal generation quality stagnates or degrades. The trap is one of supervision: the dominant terminal errors originate in the high-noise structure-generation stage, and terminal-aligned training corrects terminal errors that substantially extended step-local training cannot. This yields a simple staging principle: first adapt the sparse architecture into a coarse prior, then correct the terminal distribution. We instantiate the principle as \method, a unified acceleration framework for visual generation that combines a short sparse warm-up, few-step trajectory-mixed distillation, and FP8 quantization with fused kernels. \method sustains $97\%$ attention sparsity with strong visual quality on long-sequence 720P generation across Wan2.1/Wan2.2 backbones and T2V/I2V tasks, and $90\%$ sparsity on Wan2.1-T2V-1.3B-480P. With 3-step CFG-free inference, \method achieves a $265\times$ end-to-end speedup over the 50-step CFG dense baseline for Wan2.1-T2V-14B-720P on a single RTX~5090 ($220\times$ on H100), and denoises a Wan2.1-T2V-1.3B-480P video in $1.3$s.
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All authors Yuxi Liu, Haoyu Li, Zekun Zhang, Tengxu Sun, Yixiang Cai, Jiayong Li, Yifei Xia, Tianle Liu, Baole Ai, Ang Wang, Jiamang Wang, Lin Qu, Kai Zhang, Kun Yuan, Bin Cui

Sound

Bearings: Self-Supervised Soundfield Embeddings from First-Order Ambisonics

Goksenin Yuksel, Marcel van Gerven and Kiki van der Heijden

Recently proposed self-supervised audio encoders learn powerful general-purpose representations of sound scenes, yet they are spatially blind. To supply the missing spatial representation of sound scenes, we introduce Bearings. Bearings is a self-supervised framework that learns soundfield embeddings from unlabeled first-order Ambisonics. We pre-train a masked auto-encoder paired with a decoder conditioned on frozen acoustic embeddings from an off-the-shelf single-channel audio encoder. Our results show that the resulting soundfield embeddings form a reusable stream that can be attached to frozen acoustic encoders with a lightweight trainable fusion head. On sound event localization and detection, concatenating our soundfield embeddings with acoustic representations provides the missing spatial information and enables joint detection and localization, raising the location-dependent F-score from below 4 to 50 on TAU-NIGENS 2021 and 39 on STARSS23. To our knowledge, Bearings is the first self-supervised soundfield encoder whose embeddings plug into frozen acoustic encoders without retraining either model.
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Systems and Control

Task aware Dynamic Movement Primitives for failure detection and recovery in contact rich manipulation

Bhavnashri A, Sobia Shafi, Krishnapuram Himavarshini et al. (+1 more)

Assembly remains a challenging robotic manipulation task in presence of tight tolerances and complex contact interactions. While Learning from Demonstration (LfD) frameworks like Dynamic Movement Primitives (DMPs) can effectively encode trajectories from a single demonstration, they are highly sensitive to variations in initial grasp configurations and external contact forces. Such variations often lead to task failures during the contact rich phases. This paper presents a task aware failure detection and recovery framework that integrates DMP based trajectory generation with real time stage classification. Utilizing Quadratic Discriminant Analysis (QDA) trained on multimodal sensor data, the framework segments execution into approach, alignment, and insertion stages for a Peg in Hole (PiH) assembly operation. By using goal relative position data as features, this classification generalizes to unseen goal positions without requiring retraining, matching the inherent generalization capability of DMPs. Anomaly detection is performed online using a Mahalanobis distance metric computed over force features, isolating contact induced failures from nominal trajectory execution. Upon failure detection, a spiral search recovery policy is triggered to actively realign the peg under contact before resuming the learned DMP insertion. The proposed approach is evaluated on an experimental setup achieving 95% stage classification accuracy, and demonstrates reliable failure recovery under lateral misalignments of up to 3 mm using only a single demonstration.
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All authors Bhavnashri A, Sobia Shafi, Krishnapuram Himavarshini, Anuj Tiwari

Strongly Correlated Electrons

Correlation-assisted spin-selective metallicity in strained and bilayer altermagnets

Marnin J. Di Nunzio, Frank Lechermann and Ilya M. Eremin

Altermagnets combine vanishing net magnetization with a momentum-dependent spin-split electronic structure, providing a route to spin-polarized carriers without ferromagnetism. Here, we investigate how electronic correlations, doping, uniaxial strain, and interlayer coupling control altermagnetism within a minimal Hubbard-type model for mono- and bilayer systems. Comparing Hartree–Fock theory with the rotationally invariant slave-boson (RISB) approach, we demonstrate the robustness of altermagnetic order against quasiparticle renormalization and reveal a separation between the onset of magnetic order and the loss of quasiparticle coherence at stronger coupling. In the monolayer, doping produces a pronounced particle–hole asymmetry, while its combination with uniaxial strain generates a fully spin-polarized Fermi surface close to half-filling. In the bilayer, stacking that favors ferroic alignment of the layer altermagnetic order parameters allows their momentum-dependent spin splittings to combine constructively. Weak asymmetric doping additionally induces intra-unit-cell charge order, resulting in fully spin-polarized low-energy carriers. At a bilayer filling of five electrons in four orbitals, we find a continuous paramagnet-to-altermagnet transition followed, at stronger coupling, by an evolution toward a Mott-like regime with strongly suppressed quasiparticle weight. Our results establish correlations, strain, doping, and stacking as complementary means of controlling altermagnetic metals and generating fully spin-polarized Fermi surfaces.
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Atmospheric and Oceanic Physics

ClimTip-GML: A global bias-corrected and downscaled dataset for assessing impacts of climate tipping events

Philipp Hess, Sebastian Bathiany, Lucas Ferreira Correa et al. (+3 more)

Assessing the impacts of future climate scenarios including tipping events of major Earth system components such as the Amazon rainforest (ARF) or the Atlantic meridional overturning circulation (AMOC), requires accurate and high-resolution simulations. Here, we present ClimTip-GML, the first globally bias-corrected and downscaled climate dataset for impact assessment of large-scale tipping scenarios, comprising eight key variables at 0.25{\deg} spatial resolution from three general circulation models (GCMs): CESM1-CAM5, HadGEM3-GC31-MM, and MPI-ESM1-2-HR. The dataset includes 100-year-long climate simulations with preindustrial and historical conditions, as well as scenarios at a +2{\deg}C warming level with and without tipping transitions of the AMOC or ARF. We apply generative machine learning (GML) techniques trained on reanalysis data to bias-correct and downscale the GCMs in a manner that is physically consistent across space, time, and all eight variables. Comprehensive validation shows substantially reduced biases, improved small-scale spatial variability, multivariate correlations, and consistent long-term climate responses to the external forcing and tipping events. The results hence permit substantially improved impact assessments of tipping transitions of the ARF and AMOC, directly informing mitigation and adaptation policies.
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All authors Philipp Hess, Sebastian Bathiany, Lucas Ferreira Correa, Laura C. Jackson, Casey R. Patrizio, Niklas Boers

Astrophysics of Galaxies

Surveying the Universe in 4D: Beating Cosmic Variance with Wide-Field Slitless Spectroscopy from HST, JWST, Euclid, Roman, and Beyond

Andreea Petric, Gaël Noirot, Stacey Alberts et al. (+71 more)

We summarize strategies, lessons learned, and future directions from the Space Telescope Science Institute workshop Surveying the Universe in 4D: Beating Cosmic Variance with Wide-Field Slitless Spectroscopy from HST, JWST, Euclid, Roman, and Beyond, held August 24–28, 2026. The workshop examined scientific results, observational and data analysis challenges, extraction tools, and future opportunities. Discussions highlighted (1) the transformative potential of WFSS for the study of transient phenomena, galaxy evolution –both spatially-resolved and within the broader context of the cosmic web–, and rare populations and (2) the synergies among Euclid and Roman surveys, Rubin-LSST monitoring, JWST WFSS, and high-resolution integral-field observations. Participants identified advances in forward modeling and physics-informed machine learning as essential for addressing spectral overlap, crowded fields, and upcoming, very large data volumes. Realizing WFSS's full potential will require community-wide infrastructure, science-ready data products, accessible cloud-based analysis tools, and robust benchmarking of reduction pipelines. Crucially, participants called for systemic changes to properly recognize early-career researchers who invest significant efforts in pipeline, code, and calibration developments that enable WFSS science, and stressed that progress requires collaborative, multidisciplinary practices that optimize the participation and benefits of the next generation.
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All authors Andreea Petric, Gaël Noirot, Stacey Alberts, Russell Ryan, Louis-Gregory Strolger, Vicente Estrada-Carpenter, Mainak Singha, Adrian E. Bayer, Olivier Gilbert, Farhanul Hasan, Mason Huberty, Bingcheng Jin, Xiangyu Jin, Anika Kumar, Kasra Mokhtarpour, Preethi Nair, Eleni Tsiakaliari, Vivian U, Brittany N. Vanderhoof, Arshia Akhtarkavan, Natalia Alvarez-Ibanez, Roman Fernandez Aranda, Tri L. Astraatmadja, Clive Binu, Claire Bolda, Westley Brown, Guillermo Romero Cruz, Mohamed H. Abdullah, Jeremy Favaro, Mason Footh, Helen Fraser, Yuming Fu, Annie Giman, Nicolas Gomez, Massimo Griggio, Peter Gwartney, Aryana Haghjoo, Fabian Hervas-Peters, Jiamu Huang, Sandra Jaison, Jeyhan S. Kartaltepe, Sarah Kendrew, Ivan Kramarenko, Sahana Kumar, Zhaoran Liu, Arianna S. Long, Sangeeta Malhotra, Faezeh Manesh, Jasleen Matharu, Vihang Mehta, Edward M. Molter, Ivelina Momcheva, Zhiwei Pan, Yuxuan Pang, Pallavi Patil, Rachel Plesha, Marc Rafelski, Mitchell Revalski, Jyotika Roychowdhury, Gregory Rudnick, Swetha Sankar, Maya Seagraves, Lu Shen, Sabnam Shrestha, Manuchehr Taghizadeh-Popp, Jo Taylor, Wei Leong Tee, Scott Tompkins, Feige Wang, Xin Wang, Isak Wold, Jerry J.-Y. Zhang, Qianqiao Zhou, Zihao Zuo

Probability

A Gaussian Chain Rule Proof of the Banach Space Hanson Wright Bound

Witold Bednorz, Rafal Martynek and Rafal Meller

For a finite family of real matrices, we bound the Gaussian width of the union of its image ellipsoids by its maximal Hilbert Schmidt norm, its maximal fixed input Gaussian width, and the square root of the product of its operator radius and decoupled Gaussian chaos supremum.

Machine Learning

Ask for Any Appliance: A Prompt-Programmable Foundation Model for Non-Intrusive Load Monitoring

Xudong Wang, Jiacheng Cui, Junyu Xue et al. (+2 more)

Non-intrusive load monitoring (NILM) estimates appliance-level consumption from a whole-home meter, but appliance-specific models and fixed output inventories make coverage costly to extend. We present FM4NILM (Foundation Model for NILM), a single prompt-programmable model that estimates a requested appliance's power trajectory from aggregate measurements, a natural-language description, and optional activation exemplars. A lightweight cadence-aware transformer is pretrained by masked reconstruction on 645k sequences from seven public corpora spanning 1-60 s sampling intervals, then aligned with appliance requests using observation-masked losses for partially labeled households. A Bernoulli-lognormal decoder separates activity detection from conditional power estimation. On held-out households and time periods from REDD, UK-DALE, and REFIT, one frozen text-prompted model serves twelve appliance-corpus requests, achieving 0.556 event F1, 0.625 AUPRC, and the lowest active-window MAE (251.8 W) among seven appliance-specific baselines. Streaming score aggregation raises event F1 to 0.582 with a 60 s aggregation delay. In a separate category-held-out evaluation, adding ten activation exemplars raises microwave AUPRC from 0.132 to 0.214 without parameter updates. Input-intervention ablations probe the model's dependence on appliance requests and aggregate measurements. These results demonstrate competitive disaggregation with one shared model and support extending appliance coverage through prompts and examples rather than additional specialist networks.
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All authors Xudong Wang, Jiacheng Cui, Junyu Xue, Tongxin Li, Guoming Tang