Published: 2026-07-22 17:26:08
Authors: Sebastian Lorca Godoy, Ciera McFarland, Michael Val, Antonio Alvarez Valdivia, Nathaniel Hanson, Margaret McGuinness
Categories: cs.RO
Abstract:
Soft robot exteroception is increasingly being explored for a variety of field applications. In this work, we present a sound-based system for localizing disaster victims in confined and unstructured environments, based on a distributed acoustic sensing architecture embedded along the body of a soft everting vine robot. We propose a dynamic Steered Response Power with Phase Transform framework that supports both far-field direction-of-arrival estimation and near-field three-dimensional source localization as the robot approaches the sound source. To better understand the design and control space related to localizing sound using a soft, shape-morphing robot body, we conduct experiments measuring the accuracy of these methods for a five-microphone array attached to the robot body using three placements relative to the outer membrane of the robot (inside the pressurized body, inside the inner tail, and outside the outer wall) and in four robot configurations (linear, double linear, circular, and sinusoidal). We measure the change in accuracy as the signal-to-noise ratio, the direction of approach, and the distance of the sound source from the center of the array change. Finally, we demonstrate a vine robot growing into an arbitrary shape while carrying microphones along its outer wall, and show that a sound source located with the array's near field can be localized with high accuracy after only three microphones have everted from the robot body. These results highlight the potential of distributed acoustic sensing for reliable victim localization using soft growing robots.
Published: 2026-07-22 17:05:42
Authors: Rostyslav Sipakov
Categories: quant-ph, cs.LG
Abstract:
Quantum-kernel methods encode a dataset's geometry in a Gram matrix, so learning claims on hardware kernels assume the intended geometry survives execution. We measure that survival for one frozen four-qubit ZZ feature-map kernel on $N=24$ real indoor air-quality windows, reconstructed on ibm_fez (1024 shots per circuit) under baseline, dynamical decoupling alone, and gate twirling alone, each a single non-interleaved job. Every configuration returned a complete, finite, positive-semidefinite Gram matrix and preserved the centered statevector geometry to a substantial but incomplete descriptive degree (full-matrix centered kernel alignment, CKA, 0.933-0.989). Gate twirling was most faithful on every reported geometry axis, with the only jackknife-resolved improvement over baseline (persisted Spearman, mean absolute error, and full-matrix CKA diagnostics); dynamical decoupling alone was not separated from baseline at the frozen-window scale. Residual hardware distortion, not finite sampling, dominates the discrepancy. Yet fidelity and label alignment were reversed: the most faithful configuration had the lowest centered kernel-target alignment, which sits at or below label-permutation references for statevector and hardware alike. We read the small hardware uplift as a normalization property of the non-affine distortion, not captured signal. These are descriptive results for single jobs on one backend, not causal mitigation-efficacy estimates; no quantum-advantage, hardware-classifier-superiority, or forecasting claim is made. Implementation fidelity and task relevance are distinct axes; hardware quantum machine-learning studies should report both.
Published: 2026-07-22 16:53:16
Authors: Michele Liberatore, Massimo Riccaboni
Categories: econ.GN
Abstract:
We study how licensing affects the allocation of innovation in pharmaceutical R&D. We develop a model in which projects differ in both quality and innovation regime, distinguishing between incremental and novel innovations. Information precision is higher for incremental projects and lower for novel ones, generating different equilibrium dynamics in the market for technology. The model predicts that licensing sustains positive selection and competitive return equalization for incremental innovation, while novel projects may exhibit weaker screening consistent with lemons-type frictions. Using product-level data and Double Machine Learning methods, we test these predictions across success probabilities and monetary returns. We find that licensing increases success probability overall, but return equalization holds primarily for incremental projects. For novel innovation, licensing does not exhibit the same equilibrium adjustment, suggesting residual market imperfections. Instrumenting for licensing using exogenous pipeline shocks confirms this pattern causally: the competitive risk-return trade-off is preserved for incremental 'rushed' licenses, but it breaks down for novel ones. Our results reconcile evidence on both competitive efficiency and information frictions in markets for technologies, showing that market performance depends systematically on the type of innovation being transacted.
Published: 2026-07-22 16:50:42
Authors: Xiaoming Sun, Chengu Wang
Categories: math.MG
Abstract:
We construct non-lattice sphere packings in dimensions $39$ and $43$, improving records that had stood since the Laminated lattices construction by Conway and Sloane (1982). Both packings come from the antipode construction applied to cross-sections of extremal even unimodular $48$-dimensional lattices. The $43$-dimensional packing also raises the best known kissing number. The $39$-dimensional packing is built on the very cross-section that Conway and Sloane used in 1982; only the ten-point antipode cluster placed over it is new. Machine-checkable certificates for all claims, together with the search and verification code, are publicly available.
Published: 2026-07-22 16:45:03
Authors: Xinzhao Li, Charles Power, Pengyu Ren, Jongun Won, Likai Pei, Yuting Hu, Jinjun Xiong, Alptekin Vardar, Ningyuan Cao, Xiaobo Sharon Hu, Thomas Kämpfe, Kai Ni, Ruiyang Qin
Categories: cs.ET, cs.AR
Abstract:
Cross-modal retrieval on edge devices benefits from probabilistic embeddings that capture semantic uncertainty, but deploying them on compute-in-memory (CiM) hardware remains an open problem. The core difficulty is a sampling gap: probabilistic methods such as PCME rely on Monte Carlo sampling and nonlinear distance evaluation at inference, which are fundamentally incompatible with CiM crossbar arrays that support only deterministic, single-step matrix-vector multiplication. Few existing probabilistic retrieval methods can be executed on a conventional crossbar. To bridge this gap, we propose PolySim, a framework that reformulates probabilistic retrieval into a fully deterministic pipeline. PolySim approximates each Gaussian embedding dimension using low-order polynomial bases and computes similarity via a learnable order-bilinear kernel, eliminating stochastic sampling while preserving distributional information. In experiments on six benchmarks spanning video, image, and audio retrieval, PolySim improves R@1 over deterministic baselines by up to 10.3\% and matches or exceeds PCME, while reducing inference to a single crossbar-compatible matrix-vector multiplication. CrossSim evaluation under realistic device non-idealities confirms robust deployment on conventional crossbar arrays. To the best of our knowledge, PolySim is the first method to enable probabilistic cross-modal retrieval on CiM hardware.
Published: 2026-07-22 16:41:21
Authors: Ziqian Xiang, Rongcheng Chen, Zhangmin Chen, Qian Chen, Diwash Ghimire, Jiaqi Hui, Junting Huang, Junjie Jiang, Daijin Li, Haojing Lai, Kai Luo, Rui Li, Yilin Liao, Jianglai Liu, Yue Meng, Yazhen Shi, Duo Teng, Linwei Tao, Qi Wang, Changsheng Ye, Guolei Zhu, Ping Zhang, Tao Zhang
Categories: physics.ins-det, hep-ex
Abstract:
Precise source positioning is essential for detector calibration in large liquid scintillator detectors such as JUNO, particularly in regions where purely mechanical control is insufficient. An ultrasonic positioning system has been developed to reconstruct the three-dimensional coordinates of a calibration source without interfering with photon collection or contaminating the liquid scintillator. The method combines a sound-speed modeling based on dedicated laboratory measurements and in-detector temperature profiles, waveform-based arrival-time reconstruction, and an in-situ calibration of the effective receiver geometry using central-axis deployments. With six active receivers, central-axis positioning yields a mean error of 1.23 cm relative to the known deployment reference. For off-axis operation in the Cable Loop System calibration plane, a detector-realistic simulation that includes timing resolution, sound-speed variation, and receiver-coordinate smearing predicts a positioning uncertainty of 2.40 cm. These results demonstrate that ultrasonic positioning can provide centimetre-level source accuracy for large liquid scintillator detectors and can support off-axis calibration in JUNO-like experiments.
Published: 2026-07-22 16:37:05
Authors: Alejandro Gonzalez-Garcia, Wei Wang, Wei Xiao, Wilm Decre, Jan Swevers, Carlo Ratti, Daniela Rus
Categories: cs.RO
Abstract:
Aquatic self-reconfigurable robots must assemble into desired shapes while ensuring safe interactions among multiple agents. This paper proposes a hybrid framework that combines distributed Model Predictive Control (MPC) with Control Barrier Functions (CBFs) for multi-agent shape formation and reconfiguration. Given a desired shape and target assignment, a distributed MPC scheme, solved via the Alternating Direction Method of Multipliers (ADMM), computes coordinated trajectories through local optimization and information exchange. To ensure safety in real time, distributed CBF-based filters are applied to enforce inter-agent collision avoidance. The proposed approach leverages the predictive capabilities of MPC to mitigate local minima, while CBFs provide formal safety guarantees despite the nonconvexity of the underlying optimization problem. Simulation results with up to 25 agents and experimental validation with four physical robots demonstrate the effectiveness and scalability of the framework.
Published: 2026-07-22 16:37:02
Authors: Aditi Gupta, Yossi Gandelsman
Categories: cs.CV, cs.CL
Abstract:
We find that vision-language models are sensitive to a specific semantically irrelevant change: the order in which the image and question are presented. Across three models and three benchmarks, image first prompting consistently outperforms question-first prompting, revealing a repeatable modality order failure. We use this gap to design an order-consistent test-time training method. Our method substantially closes the modality-order gap across all evaluated settings. Surprisingly, it also yields consistent improvements in the stronger image-first branch over the baseline, hence bootstrapping both orderings toward mutual consistency. Activation patching localizes the ordering failure to a narrow mid-network region where representations diverge sharply between prompt orders. We find that the test-time training method repairs this misalignment across layers. Together, our results identify modality-order sensitivity as a circuit-level failure in VLMs and demonstrate that simple, asymmetric test-time adaptation can effectively mitigate it and even improve performance over the baseline.
Published: 2026-07-22 16:32:12
Authors: Abdullah Al Imran, Meilin Yu
Categories: physics.flu-dyn, physics.comp-ph
Abstract:
This work couples a high-order flux reconstruction/correction procedure via reconstruction (FR/CPR) solver with a rotating actuator-line model (ALM) to simulate rotating-blade aerodynamics on fixed Cartesian grids. Blade loading is represented by volumetric body force source terms projected through an isotropic Gaussian kernel in a blade-attached frame, eliminating the need to resolve blade geometry. Vertical-axis wind turbines (VAWTs) serve as the demonstration configuration, with a modified Boeing-Vertol dynamic stall model incorporated to capture unsteady lift and drag. A mesh-resolution criterion for the Gaussian projection kernel on reasonably coarse meshes is derived. It shows that cost-effective coarse meshes can operate in a mesh-controlled regime with negligible induction feedback, motivating a Double Multiple Streamtube (DMST) correction to recover the physical inflow. Simulations are carried out over a range of tip-speed ratios at a chord-based Reynolds number of Re_c ~ 3.6 x 10^5. The framework is validated against experimental near-wake measurements and previously reported LES-ALM results, and the mean wake profile shows good agreement. The predicted power-coefficient curve matches high-fidelity three-dimensional LES-ALM data to within 6% around the optimal VAWT operation conditions. The framework also captures the regime-dependent influence of dynamic stall, azimuthal blade loading, lift hysteresis, and characteristic wake structures. These results demonstrate that the FR/CPR-ALM framework provides an accurate and computationally efficient geometry-free approach for VAWT analysis, making it well suited for parametric studies and large-scale wind energy applications.
Published: 2026-07-22 16:14:38
Authors: Hae Min Kim, Stacy Stanislaw
Categories: cs.DL, cs.AI
Abstract:
This study empirically analyzed generative AI as an emerging discovery pathway to academic library resources. Utilizing web analytics from August 2023 to October 2025, the research identifies a significant increase in AI-mediated traffic, particularly following the integration of linked citation features. Referral analysis identified ChatGPT, Perplexity, and Gemini as the primary platforms driving this traffic. A substantial portion of users reached the institutional repository, primarily accessing electronic theses and dissertations. This pattern suggests that AI retrieval mechanisms effectively surface resources with structured metadata and stable permalinks that are Open Access and freely available. The results illustrate how AI ecosystems currently expose library resources and underscore the need for continued analysis and a strategic response to the evolving AI landscape.
Published: 2026-07-22 16:08:54
Authors: Kshema Shaju, Thomas Laepple, Peter Zaspel
Categories: physics.comp-ph
Abstract:
Reconstructing past surface temperature from shallow ice borehole temperature profiles requires solving an ill-posed inverse problem while quantifying uncertainties arising from measurements and prior assumptions. Bayesian formulations enable probabilistic reconstruction of surface temperature histories and uncertainty quantification. Existing reversible jump-Markov chain Monte Carlo approach based on adaptive piecewise-linear surface temperature models can, however, be computationally demanding. Here, we introduce a kernel-based surface temperature model that enables the use of a parallel ensemble Markov chain Monte Carlo sampler for efficient exploration of the solution space and quantification of the posterior. Using synthetic experiments, we investigate the effects of kernel configuration, measurement uncertainty, measurement density, and temporal smearing on reconstruction performance. We find that reconstruction quality is largely insensitive to the number of kernels once the kernel basis is sufficiently dense. Reducing measurement uncertainty substantially improves reconstructions, whereas increasing the number of borehole temperature measurements provides only marginal benefit. Finally, we evaluate the method using realistic surrogate climate histories that combine long-term temperature changes with stochastic climate variability. The kernel-based surface temperature model cannot represent short-term variability and therefore cannot fully explain the realistic measurements, highlighting the need to account for this approximation uncertainty. The likelihood is adapted to include the approximation uncertainty of the surface temperature model, yielding robust reconstructions with reliable posterior uncertainties. Overall, our results demonstrate that kernel-based Bayesian inversion provides an efficient framework for shallow ice borehole based climate reconstructions.
Published: 2026-07-22 15:59:38
Authors: D. R. Junior, H. Reinhardt
Categories: hep-th
Abstract:
We resolve Gauss' law in the maximal Abelian gauge supplemented by the Coulomb gauge for the Abelian gauge field and derive the gauge-fixed Hamiltonian of QCD.
Published: 2026-07-22 15:55:24
Authors: Sina Tootoonian, Andreas T. Schaefer
Categories: q-bio.NC
Abstract:
Recent works have highlighted the use of dual nostril 'stereo' olfaction by a variety of animals. In this work we perform "back of the envelope" calculations to determine when stereo olfaction is useful compared to single nostril 'mono' olfaction. We find that stereo olfaction is advantageous when there are large relative changes in the odour concentration, and when the spatial length scales of correlations in air are large, such as in the boundary layer near surfaces. In other words, stereo olfaction is useful when animals are searching surfaces for olfactory edges, such as when tracking odour trails.
Published: 2026-07-22 15:40:53
Authors: Scott Hudson
Categories: math.GR
Abstract:
For a finite group acting on a finite set, a statistic called relational complexity can be calculated for the action. This notion was defined by Gregory Cherlin and motivated by considerations in model theory. Another related statistic is the height of the action, which provides an upper bound for relational complexity. In this paper, both concepts are defined and some basic results proved. The main focus later on is examining the primitive actions of $ PSL_{2} (q) $ and $ PGL_{2} (q) $ and computing both the height and relational complexity for each one.
Published: 2026-07-22 15:25:05
Authors: Soroosh Tayebi Arasteh, Sebastian Ziegelmayer, Mahshad Lotfinia, Lisa Adams, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn
Categories: cs.CV, cs.AI, cs.CL, cs.LG
Abstract:
Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure. Whether this convergence is real, what produces it, and whether it is clinically usable are untested, and the similarity measures behind such claims are fragile. We present a controlled dissection across 18 image and 7 text encoders, all open-weight and run locally, spanning 7M to 27B parameters and five imaging modalities, including 650,982 chest radiographs from six datasets. To isolate cause, we train encoders that vary only the objective under fixed data, architecture, and scale, and reproduce the effect in a synthetic model. Convergence is modest but above a random floor, driven by the self-supervised objective, not clinical supervision: matched self-supervised encoders aligned most (40.4% on chest radiography), with label-supervised (21.1%) and image-text (3.3%) far lower, and did not grow with size (Spearman 0.302, p=0.223) or capability. It is within-modality, does not reach clinical language, and does not reproduce how radiologists judge case similarity. Yet a linear classifier transfers across encoders and to five held-out hospitals, retaining about 85% of within-encoder performance. Convergence in medical imaging is therefore set by the pretraining objective, not inherited from scale or clinical supervision. Interoperability is accordingly something to design for through that objective, and to validate where the shared geometry is weakest, across patient subgroups and against clinical judgment.
Published: 2026-07-22 15:24:13
Authors: Jefferson Baudin
Categories: math.AG
Abstract:
We solve certain questions related to the geometry and singularities of quasi-$F$-split varieties with trivial canonical bundle. First, we prove that regular quasi-$F^{\infty}$-split varieties are not geometrically uniruled (this generalizes and significantly simplifies the earlier results of Patakfalvi and Zdanowicz) and have geometrically canonical singularities. Second, we show that there exist quasi-$F$-split surfaces with trivial canonical bundle which are not quasi-$F^{\infty}$-split, answering negatively a question raised by Kawakami, Takamatsu, Tanaka, Witaszek, Yobuko and Yoshikawa. Third, we show that normal quasi-$F$-split varieties with trivial canonical bundle are geometrically normal (this extends a result of Kawakami, Takamatsu and Yoshikawa), and finally we prove that quasi-$F^e$-pure normal varieties $X$ such that $mp^eK_X$ is Cartier for $m$ coprime to $p$ are log canonical, under a resolution of singularities hypothesis.
Published: 2026-07-22 15:22:14
Authors: Andrei Chetvergov, Stepan Ukolov, Timofei Sivoraksha, Alexander Evseev, Mikhail Solovev, Valeriia Kuschenko, Maria Chistyakova, Sergey Bolovtsov
Categories: cs.CL
Abstract:
Large language models are increasingly evaluated through the values they endorse, but such evaluations presuppose that models can identify the value expressed in a concrete situation. We study this prerequisite as controlled top-1 recognition over Schwartz's ten basic values. Our evaluation set contains 1,000 Russian situational texts, balanced across the ten values and independently labeled by two human annotators per item. We evaluate 21 instruction-tuned LLM runs under a fixed ranked-response protocol; 20 runs with reliable outputs form the semantic panel. Pooled Acc@1 is 0.683 and Acc@3 is 0.892, showing that models often locate the correct motivational region while ranking close alternatives unstably. Adjacent values account for 50.9% of semantic errors, compared with 24.4% under a checkpoint-specific null. Eight directed confusions recur across checkpoints and human-confirmed subsets. Several are strongly asymmetric, including Universalism to Benevolence, Tradition to Conformity, and Security to Power, whereas Stimulation-Hedonism forms a bidirectional boundary. Their severity is checkpoint-specific and can bias higher-order value profiles. The results motivate value-recognition evaluation that combines exact accuracy, ranked recovery, and directed error analysis.
Published: 2026-07-22 15:20:06
Authors: Lixiang Chen, Bobo Hua, Yongtang Shi
Categories: math.CO, math.SP
Abstract:
Let $G=(V,E)$ be a finite connected graph with boundary $B$. We prove that for a generic positive edge weight function $w \in \mathbb{R}^{|E|}$, the Steklov eigenvalues of $(G,B,w)$ are simple and every Steklov eigenfunction does not vanish on the boundary. More precisely, the exceptional weights are contained in a zero set of a non-identically zero polynomial and hence form a set of Lebesgue measure zero and Hausdorff dimension at most $|E|-1$. Our results provide a discrete extension of the genericity theorem for the Steklov problem on compact manifolds.
Published: 2026-07-22 15:19:23
Authors: Ahmad Pouramini, Mahsa Afsharzadeh
Categories: cs.CL, cs.AI
Abstract:
Large-scale pretrained language models such as T5 and BERT have demonstrated strong capabilities for generating structured knowledge. However, their performance depends on how closely the prompting strategy matches the objectives used during pretraining. We introduce the Maskability Index (MI), a quantitative metric that estimates whether a knowledge relation is better suited to masked-style prompting or prefix-style prompting in few-shot generation. MI is computed from differences in DepthRank scores between masked and unmasked templates, providing a principled measure of objective-template alignment. We evaluate MI on a diverse set of relations from the ATOMIC2020 knowledge base completion benchmark and show that it is positively correlated with downstream generation performance. These results indicate that MI can help select appropriate prompting templates and adaptation strategies for extracting relational knowledge from pretrained language models, especially in low-resource settings.
Published: 2026-07-22 15:15:50
Authors: Nolan Smyth, Laurence Perreault-Levasseur, Yashar Hezaveh
Categories: astro-ph.IM, astro-ph.EP, astro-ph.GA
Abstract:
We present a unified framework for gravitational microlensing event detection and parameter inference. Traditional pipelines use deterministic hard cuts on photometric statistics, systematically missing low-magnification events in the finite-source regime. We instead frame detection as Bayesian model comparison using Evidence Networks, which learn calibrated Bayes factors from binary-labeled simulations, and combine this with Neural Posterior Estimation (NPE) for amortized parameter inference. Both share a transformer encoder that handles irregularly-sampled time series without imputation. On simulated Roman Space Telescope data, our Evidence Network achieves $99.9\%$ detection efficiency with a false-positive rate below $6\times10^{-4}$ on simulated data with augmentation and noise, but no astrophysical confounders. Gains are most dramatic in the extreme finite-source regime ($ρ\gtrsim 5$), where detection rates reach ${\sim}95\%$ versus ${\sim}65\%$ for hard cuts, precisely the short-duration free-floating planet events most constraining for formation scenarios. Our NPE provides calibrated posteriors, working towards real-time analysis at survey scale.
Published: 2026-07-22 15:15:28
Authors: Matteo Castiglioni, Anna Lunghi, Alberto Marchesi
Categories: cs.LG
Abstract:
We study regret minimization for learning CDF-related objectives of the form \[ g(x)\cdot\mathbb{P}_{X\sim\mathcal{D}}(X\le x), \] over $[0,1]^2$, where $g$ is a known Lipschitz function and $\mathcal{D}$ is an unknown distribution. At each round $t$, the learner selects a point $x_t$ and observes the binary feedback $\mathbb{I}(X_t\le x_t)$, where $X_t\sim\mathcal{D}$. We design an algorithm achieving regret $\widetilde{\mathcal{O}}(T^{7/10})$, improving over the previous best-known bound of $\widetilde{\mathcal{O}}(T^{3/4})$ and showing that the curse of dimensionality can be at least partially lifted for this class of objectives, though a gap remains with the $Ω(T^{2/3})$ lower bound. As an application, our techniques yield the same $\widetilde{\mathcal{O}}(T^{7/10})$ regret bound for profit maximization in repeated bilateral trade with fixed prices.
Published: 2026-07-22 15:05:41
Authors: Andrea Brugnoli, Philipp L. Kinon, Francesco Sanfedino, Peter Betsch, Olivier A. Bauchau
Categories: math.NA, physics.comp-ph
Abstract:
The Reissner-Simo and Hodges models are two equivalent continuous descriptions of finite-strain beam dynamics. The Reissner-Simo formulation uses displacements and rotations, while the Hodges formulation is intrinsic and avoids both variables. Although equivalent in theory, the two approaches behave differently after discretization and offer distinct numerical advantages. In this work, we develop a structure-preserving discretization of the intrinsic formulation. Because the intrinsic equations involve linear differential operators, both kinematic and dynamic boundary conditions can be imposed naturally using mixed finite elements. The resulting formulation also enables multibody systems to be assembled without algebraic constraints, avoiding the stiff differential-algebraic equations typically introduced by kinematic constraints. We demonstrate the approach on different examples, also showing that closed kinematic loops can be modeled without algebraic constraints. The resulting interconnected systems retain a port-Hamiltonian structure,with all nonlinearities confined to the interconnection operator. This structure allows exact energy preservation when combined with implicit midpoint time integration. Furthermore the scheme appear to require less Newton iterations compared to existing energy preserving scheme.
Published: 2026-07-22 14:57:42
Authors: Yankai Zheng, Yuhe Liu, Yuxin Ma, Tianci Xue, Jiayuan Tian, Yu Fu, Yuxuan Hu, Jianing Wang, Zichun Xiao, Junya Mu, Shaohui Ma
Categories: cs.LG
Abstract:
Rehabilitation scoring systems are most useful when their outputs can be reviewed and interpreted within clinical workflows. This study presents PhaseAware, a compact framework for continuous rehabilitation quality assessment that combines a temporal backbone with phase- and body-group descriptors through a backbone-conditioned gated residual pathway. The model was evaluated on the UI-PRMD deep-squat protocol and further tested on the KIMORE squatting subset. On UI-PRMD, PhaseAware achieved an RMSE of 0.0230, corresponding to an 88.9% reduction relative to the accepted baseline. It also maintained favorable performance on KIMORE, suggesting that the phase-aware design transfers across related squatting protocols. In addition to score prediction, PhaseAware generates structured review cues based on phase- and body-level sensitivity, highlighting the movement stages and body regions most relevant to each prediction. The architecture employs a backbone-conditioned gated residual mechanism to stabilize feature representation, supporting use in resource-constrained settings. These cues are intended to support clinician review, boundary-case monitoring, and human-in-the-loop triage rather than autonomous decision-making. Overall, PhaseAware offers a practical and interpretable approach to rehabilitation scoring that may help integrate automated assessment into information systems while preserving clinician oversight.
Published: 2026-07-22 14:56:56
Authors: Pablo Guillermo Carmona Rufo, Anupam Mazumdar, Carlos Sabín
Categories: quant-ph
Abstract:
We study the dynamics of quantum entanglement between two harmonically trapped electrons interacting via the electromagnetic force. Starting from two-mode Gaussian states at thermal equilibrium, we make use of the covariance matrix formalism in order to compute the logarithmic negativity of the evolved state as a quantitative measure of entanglement. We analyze two initial configurations: thermal single-mode and two-mode squeezed states, and describe the time evolution of entanglement in the system for different values of squeezing and temperature, while identifying the parameter regimes accessible to current and near-future single-electron trap experiments.
Published: 2026-07-22 14:40:00
Authors: Zhicheng Chen
Categories: math.LO
Abstract:
Fundamental logic (Holliday 2023) is a non-classical logic based only on the introduction and elimination rules for conjunction, disjunction, and negation in a Fitch-style natural deduction system, while a preconditional (Holliday 2025) is a binary operation on a bounded lattice satisfying five natural axioms and subsuming Heyting implication, the Sasaki hook on ortholattices, and Lewis-Stalnaker-style conditionals satisfying flattening. We combine the two by giving a consequence-relation presentation $\mathsf{K}$ whose algebras are exactly Holliday's bounded lattices with a preconditional, and then studying two natural extensions, $\mathsf{T}$ and $\mathsf{F}$, the latter being fundamental propositional logic with a preconditional. For $\mathsf{T}$ and $\mathsf{F}$, we prove strong completeness with respect to a purely relational semantics, using a canonical model whose points are pairs of theories, and establish the finite model property and hence decidability. Finally, following Holliday and Massas (2026), we adapt their GMT- and Goldblatt-style embeddings to fundamental logic with a preconditional. The resulting translations are full and faithful into ortho-$\mathsf{S4}$ and intuitionistic $\mathsf{KTB}$, respectively. The new conditional clauses send the preconditional to a boxed Sasaki hook on the former side and to a strict intuitionistic conditional on the latter whose classical $\mathsf{KTB}$ reading is equivalent to the Goldblatt translation of the Sasaki hook. The frame constructions follow the reduct-and-companion pattern of Holliday and Massas; the essential additional ingredient is the semantic transfer calculation for the preconditional.
Published: 2026-07-22 14:30:28
Authors: T. Shaska
Categories: math.AG
Abstract:
Rational points on weighted projective spaces are sparse: a point of $\mathbb{P}^n(\mathbb{Q})$ lifts along the Veronese morphism only when a Kummer condition holds at every prime (arXiv:2509.02319). We ask what this viewpoint says about the Jacobian Conjecture. The map recently announced as a counterexample is equivariant for the grading $\mathrm{wt}(x,y,z)=(1,-1,-2)$, and we show that the sign pattern of such a grading decides everything. If the weights are all positive, the setting of weighted projective spaces, an equivariant Keller map is always an automorphism, so no counterexample can be graded that way. In dimension two the same holds for every sign pattern. The Keller condition itself descends to the quotient, where it says that the Jacobian of the quotient map vanishes to order two along the contracted locus. Over $\mathbb{Q}$ the image of the counterexample is a thin set. But along the line where the group action has stabilizer $μ_2$, the two preimages on the contracted locus are rational exactly when $-a$ is a square, so the Kummer condition of the positive-weight theory reappears on the stacky stratum. We propose a classification of equivariant polynomial maps by the signature of the weights, with weighted projective geometry as the positive case.
Published: 2026-07-22 14:28:28
Authors: Andrés Buxó-Lugo, Aniello De Santo, Morgan Grobol, Ryan J. Hubbard, Cassandra L. Jacobs
Categories: cs.CL
Abstract:
Surprisal Theory is often characterized as a computational-level explanation per (Marr, 1982). We argue in this work that, even though a computational level narrative has been used to support "representation-agnostic research" within computational psycholinguistics, the movement toward black box systems embodied by large language models (LLMs) does not exempt modelers using the surprisal metric from the representational decisions required by computational-level characterizations. In fact, we argue that the uncritical use of LLM-surprisal obfuscates the representational and algorithmic-level commitments of different models. In three analyses, we show that the choice of algorithm and model architecture play significant roles in the computation of language model probabilities. We advise that researchers who wish to test Surprisal Theory re-evaluate the practice of treating large language model probabilities as interchangeable
Published: 2026-07-22 14:25:59
Authors: Yihang Gao, Vincent Y. F. Tan
Categories: stat.ML, cs.LG, math.ST
Abstract:
Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large language models. Since different modules and layers may contribute unequally to downstream adaptation, allocating rank resources under a fixed parameter budget is an important problem for balancing efficiency, expressiveness, and generalization. Existing adaptive rank methods address this problem mainly through carefully designed importance scores constructed from gradient-derived sensitivity and uncertainty measures, without an explicit statistical interpretation. In this paper, we formulate LoRA rank allocation as a statistical hypothesis testing problem and propose StatLoRA, a statistical inference-based rank allocation method. StatLoRA associates each LoRA component with a test statistic and uses estimated p-values to determine which components should be retained or pruned under a prescribed rank budget. The proposed testing procedure is supported by our central limit theory for stochastic optimizer trajectories. In particular, we establish asymptotic normality for a broad class of commonly used optimizers in deep learning, including AdamW, and derive the corresponding asymptotic distributions for the proposed component scores used in hypothesis testing. We evaluate StatLoRA on LoRA fine-tuning of DeBERTaV3-base, BART-Large, and Qwen2.5-7B across natural language understanding, natural language generation, and question answering tasks. Experiments show that StatLoRA achieves comparable or better performance than vanilla LoRA, AdaLoRA, and IGU-LoRA under matched rank budgets. Sensitivity analyses and empirical diagnostics further support the stability of the proposed hypothesis-testing-based allocation rule and provide empirical evidence for the asymptotic theory of component scores.
Published: 2026-07-22 14:16:02
Authors: Nikita Doikov, Anastasia Koloskova
Categories: cs.LG, math.OC
Abstract:
We study machine unlearning: the removal of memorized training data from a trained model. Specifically, we investigate the algorithmic complexity of certified unlearning from an optimization perspective. We formalize the goal of an unlearning algorithm as simultaneously achieving certified unlearning and optimization accuracy. Utilizing the notion of uniformly convex regularizers, we prove new bounds on the distance between initial and unlearned models using a novel substitute for generalization error. Thus we theoretically demonstrate that if the removed data is well-predicted by the unlearned model, the corresponding optimization problem is simple. Furthermore, we develop a new second-order unlearning algorithm with an anisotropic Gaussian mechanism and state-of-the-art global convergence. We prove fast rates for our method in achieving certified unlearning for linear models with quasi-self-concordant losses. As a direct application, our theory covers unlearning for logistic and exponential regressions and shows a provable benefit of utilizing second-order information compared to first-order unlearning methods.
Published: 2026-07-22 14:15:25
Authors: Sebastián Urciuoli
Categories: cs.LO
Abstract:
In this paper we continue assessing the feasibility of the approach to the mechanization of type theory by using classical syntax and Stoughton's multiple substitutions and report some substantial progress. We present formal proofs of confluence for beta-reduction and by using Takahashi's revision of Tait and Martin-Löf's proof, subject reduction for the entire family of the Pure Type Systems and consistency for some impredicative subclass, assuming normalization. As to the proof of confluence, we also develop a theory of alpha-commutative relations which, in our view, entails a clearer presentation and treatment of the problem than in similar developments. Finally, we assess general merits and drawbacks of the approach. The whole development has been machine-checked using Agda.
Published: 2026-07-22 14:14:34
Authors: Dominik Pichler, Mirko Tagliaferri
Categories: cs.LO
Abstract:
Robust classification is commonly understood as the stability of a classifier under small perturbations (often adversarial) of input data. In this paper, we propose a logical framework for robust classification grounded in topological semantics for modal logic. Evaluation points are feature vectors representing machine-readable objects, and formulas express explicit classifications. Robustness is interpreted geometrically as local truth persistence: a classification is robust at a point if it holds throughout some non-empty open neighbourhood of that point. Building on this perspective, we introduce a logical language with a robustness modality interpreted over S4 topological spaces, together with a robustness-sensitive conditional connective. This conditional connective captures global inclusion relations between robust regions and other properties of the classifier: it holds at a point when the neighbourhood witnessing the robustness of one formula is contained in the truth set of another. In this way, robust classifications can be systematically linked to classification conditions. We provide a sound and complete axiomatisation of the resulting logic. Finally, we introduce Minimal Robust Models, a constructive method for generating models from specified robustness constraints, which yields formal tools for analysing, explaining, and structuring robust classification behaviour.
Published: 2026-07-22 14:12:54
Authors: Florence Laurent, Johan Richard, Rémi Giroud, Davor Krajnović, Alexandre Jeanneau, Manuel Abreu, Angela Adamo, Théo Battrel, Didier Boudon, Zhemin Cai, Diane Chapuis-Kerouanton, Christopher Coote, Malak Galal, Joss Guy, Robert J. Harris, Andreas Kelz, Audrey Lanotte, Jon Lawrence, Rémy Le Breton, Helen McGregor, Gloria Mellinand, Jonathan Moller, Manuel Monteiro, Guiliano Parma, Arlette Pécontal, Peter M. Weilbacher
Categories: astro-ph.IM
Abstract:
BlueMUSE is a blue-optimised, medium spectral resolution, panoramic integral field spectrograph under development for the ESO's Very Large Telescope (VLT). The project is now entering preliminary design phase. With an optimised transmission down to 350 nm, spectral resolution of R $\sim$ 3500 on average across the wavelength range, and a large FoV (1 arcmin2), BlueMUSE will open up a new range of galactic and extragalactic science cases facilitated by its specific capabilities. BlueMUSE consists of several subsystems arranged along the light path. A calibration unit reproduces the VLT's optical conditions, while the fore optics reshape the telescope's focal image. The splitting and relay optics divide the field of view into 16 channels, each feeding an integral field unit that contains an image slicer, a spectrograph, and a detector vessel. The image slicer converts the 2D sub-field into a 1D pseudo-slit, which the spectrograph disperses into spectra recorded by a 4k x 4k CCD in each detector vessel. A vacuum and cryogenic system cools the detectors, and the data reduction software processes the raw data into data cubes which are subsequently processed by a data analysis software system. All subsystems are supported by the instrument main structure and enclosed in a thermal housing for stability. The whole instrument is managed by an integrated control system combining electronics and software. This paper summarizes the baseline architecture, interfaces, and functional descriptions of the BlueMUSE instrument at the start of Design Phase. This architecture is derived from the top-level requirements and the experience acquired from MUSE. It presents the global concepts along with their preliminary performance estimates.
Published: 2026-07-22 14:10:32
Authors: Bo Zhang
Categories: physics.flu-dyn
Abstract:
Pressure measurements provide sparse but direct observations of compressible aerodynamic flows, yet how information about hidden aerodynamic parameters is transported through the flow and encoded in these observations remains poorly understood. Here, we investigate information transport and observability in compressible aerodynamics using a differentiable shock-capturing immersed-boundary solver. By propagating gradients through the full unsteady flow solution, an automatic-differentiation-based observability metric is introduced to quantify the sensitivity of sparse pressure measurements to unknown aerodynamic parameters and identify informative sensing locations for inverse learning. The results reveal that aerodynamic information is transported non-uniformly through the flow field, producing localized regions of high observability. Inverse-learning experiments further demonstrate that observability and learnability are related but distinct concepts: although highly observable probes generally facilitate accurate parameter recovery, the highest-observability probe is not consistently the most effective for parameter inference. Furthermore, both the flow regime and the airfoil geometry substantially influence the distribution of observability and the convergence behavior of inverse learning. These findings establish a quantitative framework for understanding how aerodynamic information is encoded in sparse measurements and demonstrate the potential of automatic differentiation for observability analysis, informative sensor selection, and aerodynamic inverse analysis.
Published: 2026-07-22 14:03:38
Authors: Ergun Simsek
Categories: physics.optics
Abstract:
Open-source finite-element frameworks for modal analysis of dielectric ring resonators are presented based on two complementary approaches. The first computes resonant wavelengths for discrete azimuthal mode numbers and obtains the effective index at a target wavelength by interpolation; the second solves directly for the azimuthal mode number at a prescribed wavelength via a quadratic eigenvalue formulation. Both approaches are validated against a commercial electromagnetic mode solver and yield nearly identical effective indices and dispersion curves. The fixed-mode-number approach requires slightly longer computation time but uses half as much memory, making it the preferred choice for memory-limited computers.
Published: 2026-07-22 13:58:57
Authors: Emanuele Cavalleri, Miad Alavinezhad, Dario Malchiodi, Marco Mesiti
Categories: q-bio.QM, cs.DB, cs.LG
Abstract:
The rapid growth of biomedical knowledge has made the validation of automatically generated biological annotations a major bottleneck in biomedical curation. While computational methods can rapidly produce large numbers of candidate annotations, determining which are biologically valid still requires costly expert review. Prioritizing these candidates before manual curation has therefore become a fundamental challenge. Machine learning techniques can support this process by exploiting biomedical knowledge graphs (bioKGs), which capture biological entities and their functional associations. In this work, we propose a framework that leverages bioKGs to estimate the plausibility of candidate annotations and guide expert curation. Starting from knowledge graph embeddings, we train relation-specific binary classifiers using a community-based negative sampling strategy to obtain reliable confidence estimates. We then introduce a family of plausibility measures that combine classifier confidence, classifier reliability, and the semantic context provided by alternative relationships involving the same pair of biological entities. Unlike conventional confidence estimation, the proposed approach explicitly accounts for multiple biologically meaningful relations that may coexist between the same entities. Experimental results on five large bioKGs demonstrate that the proposed negative sampling strategy consistently improves classifier robustness, increasing balanced accuracy by an average of 5.8%. Moreover, the plausibility measures outperform classifier confidence alone, enabling more effective prioritization of candidate annotations for expert review. Overall, our results show that the use of bioKGs improves the efficiency of AI-assisted biomedical curation while preserving expert control over the final annotation assessment.
Published: 2026-07-22 13:52:49
Authors: G. Garreau, T. Birbacher, L. Desdoigts, L. D. Feinberg, A. M. Glauser, J. T. Hansen, M. Ireland, J. Pino, E. Spalding, A. K. Taras, S. P. Quanz
Categories: astro-ph.IM
Abstract:
Nulling interferometry is one of the most promising techniques that is envisioned for the imaging and characterization of exoplanets in the mid-infrared for ground-based and space-based observatories. On the ground, the upcoming Asgard/NOTT visitor instrument for the Very Large Telescope Interferometer (VLTI) is expected to be the first nuller to observe young giant exoplanets. The Large Interferometer For Exoplanets (LIFE) project aims at implementing long-baseline nulling interferometry in space to image and characterize Earth-like exoplanets. LIFE requires to reach deep ($<10^{-5}$) null depths over a large bandwidth in the mid-infrared (MIR: 4-18.5$\,μ$m) with a high throughput ($>15\,\%$). These requirements are necessary to detect and characterize the thermal emission of Earth-like exoplanets. To achieve deep null depths, a spatial filter is necessary to wash away the wavefront aberrations that would otherwise be a limiting factor for the contrast. However, efficient spatial filtering with high throughput ($>95\,\%$) is challenging to achieve over such a large bandwidth. In this study, we explore the possibility of broadband spatial filtering using two step-index fibers previously studied for the Darwin mission proposal: Te-As-Se chalcogenide (TAS) and silver halide (AgBr) fibers. Using $\partial$Lux, we also simulate the performance of phase-induced amplitude apodization (PIAA) with aspherical mirrors to achromatically apodize the pupil plane of a beam and improve its coupling efficiency in both fibers. The results show that a broadband geometric coupling efficiency of $>95\,\%$ can be achieved, with a manufacturing precision of $<100\,$nm for the PIAA mirrors. An achromatic apodization of the beams for LIFE is therefore compatible with a number of spectral channels of $\geq2$, defined by the number of spatial filters used.
Published: 2026-07-22 13:52:00
Authors: Feng-Yu Wang
Categories: math.PR
Abstract:
To derive dimension-free convergence rates of empirical measures for Markov processes on a Banach space, we adopt the sliced Wasserstein distance (SW distance) induced by a probability measure with full support on the unit ball of the dual space. This distance is topologically stronger than the convergence in finite-dimensional distributions, and is topologically equivalent to the Wasserstein distance when the Banach space is finite-dimensional. Under this distance, we derive dimension-free convergence rates for the empirical measures of ergodic Markov processes on $\BB$, which can be sharp as illustrated by concrete examples.
The study provides an efficient way to simulate infinite-dimensional distributions using sample trajectories of Markov processes, so that the $``$curse of dimensionality" appearing to the classical Wasserstein distance is avoided. The main results apply to a broad class of infinite-dimensional models, and are illustrated by partially dissipative SPDEs in the end of the paper.
Published: 2026-07-22 13:51:56
Authors: Ralf Raumanns, Theresa Elstner, Louis Ferger-Andrews, Louise M. Carlsen, Martin Potthast, Gerard Schouten, Josien P. W. Pluim, Veronika Cheplygina
Categories: cs.CY
Abstract:
Machine learning courses often use pre-labeled datasets, hiding the subjectivity of human annotation. This creates students with an overly trusting view of AI data and models, undervaluing interpretive diversity. We investigated whether manual data annotation tasks teach students about subjective labeling.
Study Design: An annotation activity was implemented at two universities: Fontys (Netherlands) and IT University Copenhagen (Denmark). Students annotated skin lesion images for hair coverage on a 3-point scale. Surveys were collected from 43 participants measuring their understanding of annotation ambiguity, data quality, bias, fairness, implementation barriers, and pedagogical effectiveness.
Key Findings: Self-reported familiarity with course content increased substantially across all concepts. Most students recognised that personal interpretation affects annotations. Students rated the activity as more effective than traditional lectures for understanding bias. Participants were motivated to learn more.
Main Drawbacks: Emotional unease from viewing medical images was the primary issue. Many students still requested clearer guidelines to reduce disagreement, suggesting they hadn't internalised that disagreement from different perspectives is a learning feature, not a bug.
Recommendations for Future Iterations: Ensure sufficient interpretive ambiguity in materials. Reduce repetitive annotation workload. Mitigate emotional unease from sensitive content. Explicitly frame disagreement as a learning opportunity rather than a problem to solve.
Manual data annotations effectively teach students that human judgment shapes model behavior and that disagreement reflects domain complexity, not just noise.
Published: 2026-07-22 13:50:47
Authors: Math Dicker
Categories: math.HO, math.NT
Abstract:
In Proposition II of his manuscript, Galois writes the well-known remark: Il y a quelque chose à compléter dans cette démonstration. Je n'ai pas le temps. Although Galois did not complete the proof, it is possible to reconstruct the essential content of Proposition II and to supply the missing arguments. As usual, V denotes the linear form ax1+bx2+cx3+..., where x1,x2,x3,... are distinct roots of a separable polynomial. Let L be the corresponding splitting field over a ground field of characteristic zero. On the one hand, Proposition II concerns the factorization of the minimal polynomial g(x) of V over an intermediate field M contained in L; on the other hand, it concerns the factorization of the same polynomial into irreducible factors whose coefficients belong to the intermediate fields conjugate to M. It is precisely this latter aspect that constitutes the central theme of Proposition II. The notions of 'groupe de permutations' and 'groupe de substitutions' are of fundamental importance in the Mémoire. We provide a characterization of these notions in modern terminology. Associated with every 'groupe de permutations' is a polynomial whose coefficients are invariant under the corresponding 'groupe de substitutions'. Moreover, a 'groupe de permutations' determines a partition of the Galois group, making it possible to factor the minimal polynomial g(x) into factors whose coefficients belong to the intermediate fields corresponding to the associated 'groupes de substitutions'. Finally, we prove that the substitutions of the Galois group are field automorphisms of the splitting field L. This establishes the connection between Galois' original formulation and the modern formulation of Galois theory.
Published: 2026-07-22 13:44:22
Authors: William Lehn-Schiøler, Mads Sverker Nilsson, Nicki Skafte Detlefsen
Categories: cs.CV, eess.IV
Abstract:
We present a two-stage vision system that detects EEG cap electrodes in a live webcam stream and validates their anatomical placement in real time. A single-class YOLO detector localises electrodes; a geometric stage assigns each detection to a named 10-20 role from facial landmarks. Evaluating under subject-disjoint leave-one-subject-out (LOSO) cross-validation across five subjects wearing the clinically-validated Small/Medium/Large caps, the detector attains mAP@.5 = 0.94 +/- 0.07 across five held-out folds (0.96 pooled). A dedicated leave-one-cap-out axis, holding out every frame of a cap regardless of subject, leaves Medium and Large mAP@.5 within 0.01 of LOSO (0.97, 0.97) while Small drops to 0.72 +/- 0.28, a gap confounded with subject familiarity rather than cap style. Geometric augmentation (rotation, perspective, mixup) improves in-plane-roll robustness and temporal-electrode recall at no inference cost, and a landmark-driven head crop extends the usable distance range, lifting mAP@.5 from 0.23 to 0.45 at 0.6 x apparent scale. A compact mobile-candidate backbone (YOLOv10n) keeps the detector at real-time throughput (19 FPS) on a commodity CPU at 640 px.
Published: 2026-07-22 13:34:17
Authors: El Hassane Ettifouri, Ayoub Belfatmi, Mahaman Sanoussi Yahaya Alassan, Walid Dahhane
Categories: cs.AI
Abstract:
Quantized small autoregressive reasoning models can enter long, repetitive, or unproductive trajectories, yet inference-time compute is usually allocated without observing how a trajectory develops. Building on an earlier token-level e-CUSUM controller, we develop MGT-B (Monitoring-Guided Test-time Backtracking), a revised external controller that maps overlapping windows of pre-sampling uncertainty and degeneration features to position-conditional empirical tail probabilities, accumulates mixture betting factors with a CUSUM-shaped reset, and responds to an alarm by estimating a rollback point, restoring token and key-value-cache state, and performing constrained re-decoding. To audit whether the effect persists on problem identities first observed after the manual choice of log threshold h = 10, we retrospectively exclude 260 IDs present in pre-threshold artifacts and retain the chronologically first post-threshold pair for each remaining ID, yielding a 240-pair chronology-audit set. On this set, accuracy changes from 82/240 to 88/240 (+2.50 percentage points; 13 corrections, 7 regressions; exact McNemar p = 0.2632; paired bootstrap 95% interval [-1.25, +6.25]). A broader 467-pair historical-coverage set of seed-matched pairs changes accuracy from 146/467 to 167/467 (+4.50 points; McNemar p = 0.000753), but includes 200 seed-1 IDs available before or during threshold selection and is reported only as an exploratory estimate. All 316 no-alarm outputs in the 467-pair set are identical to vanilla, while the 151 alarmed trajectories contain 29 corrections and 8 regressions. Neither analysis is confirmatory, and the empirical factors are not established as a valid e-process or e-detector. The results support a selective monitoring-and-repair mechanism for the studied MATH-500 setting, rather than a general or theoretically certified reasoning improvement.
Published: 2026-07-22 13:26:39
Authors: Mohd Kamran, Gabriella De Lucia, Marta Spinelli, Lizhi Xie, Fabio Fontanot, Michaela Hirschmann
Categories: astro-ph.CO, astro-ph.GA
Abstract:
The redshifted 21-cm line of neutral hydrogen (HI) is a powerful tracer of large-scale structure, and post-reionization HI intensity mapping is emerging as a competitive cosmological probe whose interpretation requires a description of how HI populates galaxies and dark matter halos. We characterize the HI--halo mass relation, its redshift evolution, and its intrinsic scatter, identifying its secondary dependences. We use the updated GAlaxy Evolution and Assembly (GAEA) semi-analytic model, applied to the Millennium-I and Millennium-II simulations, to predict the HI mass function (HIMF) and the HI--halo mass relation from the present day to redshift $z\simeq5$. At $z=0$, the model reproduces the observed HIMF and its decomposition by host-halo mass. The median HI--halo mass relation rises with halo mass, peaks near $10^{11.7}\,M_\odot$, declines as central galaxies are quenched by feedback from active galactic nuclei, and rises again where satellites dominate, approaching a single power law at high redshift. We show that the substantial scatter, of about 0.5 dex, is not random but is governed by halo assembly: at fixed mass, higher-spin, later-forming, and less-concentrated halos are systematically HI-richer, with spin together with either concentration or formation time accounting for part of this scatter and leaving an intrinsic dispersion of about 0.3 dex. We encode the median relation, these secondary trends, and the intrinsic scatter in a compact, physically motivated prescription expressed entirely in terms of quantities available in dark-matter halo catalogs. This prescription reproduces the full scatter and enables the construction of large-volume 21-cm mock catalogs for interpreting ongoing intensity-mapping measurements with SKA precursor facilities, such as MeerKAT, and for preparing for forthcoming surveys with the SKA.
Published: 2026-07-22 13:23:08
Authors: Shijie Zhong, Jiangfeng Fu
Categories: stat.ML, cs.LG
Abstract:
We introduce the Directional Kernel Mean Difference (DKMD), a signed statistic for univariate distribution comparison that preserves the direction of distributional shifts. Unlike the squared Maximum Mean Discrepancy (MMD), which discards directional information by squaring the RKHS distance, DKMD integrates the difference of kernel mean embeddings against a fixed odd weighting function. This construction yields three structural properties: antisymmetry, immunity to symmetric distributional differences, and directional monotonicity under stochastic dominance. We derive a data-driven Riemann estimator that ensures asymptotic consistency with the continuous formulation, strictly preserving the theoretical guarantees of the signed statistic in empirical evaluations. To overcome the quadratic computational cost of kernel methods, we develop an $O(N \log N)$ prefix--suffix scanning algorithm that exploits the total order of the real line while requiring only $O(N)$ memory. Experiments on synthetic benchmarks demonstrate that DKMD correctly isolates directional shifts from symmetric perturbations, remains robust to heavy-tailed outliers that can flip the sign of the mean difference, and scales to millions of samples in seconds.
Published: 2026-07-22 13:20:26
Authors: Xin Li, Siyuan Duan, Shang Wang, Zhimin Mao, Bingliang Hu, Geng Zhang
Categories: cs.CV
Abstract:
Global visual localization of unmanned aerial vehicles (UAVs) using remote-sensing reference maps has attracted increasing attention. However, acquisition-time and imaging-platform differences between UAV and reference imagery induce substantial cross-domain appearance and viewpoint shifts, challenging robust six-degree-of-freedom (6-DoF) pose estimation. We address these shifts by sampling UAV-viewpoint reference views from Google 3D Tiles across locations, altitudes, and orientations. A two-stage cross-domain fine-tuning recipe adapts SALAD using pose-near positives and geographically distant hard negatives, while local geometric consistency re-ranks the Top-K candidates. We further propose Retrieval-In-Matching (RIM), which freezes the adapted DINOv2-B retriever and distils a local-descriptor decoder that reuses its token field alongside a shallow VGG19 detail stream. One query-side DINOv2-B forward thus serves both SALAD retrieval and local description, eliminating a second foundation-model backbone while preserving retrieval descriptors by construction. We evaluate RIM zero-shot on the reconstructed EPFL Urbanscape and self-collected Chang'an Park datasets, both geographically disjoint from the training data. RIM outperforms ten recent retrieval baseline families. At 25/50 m under the full 3D distance metric, it improves Recall@1 over SALAD by 8.55/13.77 percentage points on EPFL and 4.45/8.94 points on Park. At Top-K=5, the complete measured localization query, including retrieval, candidate matching, and robust geometric verification, takes 67.9 ms end-to-end: 1.8 times faster than the strongest separate sparse-matching baseline and over 40 times faster than RoMa, while achieving comparable re-ranking accuracy. These results establish an efficient and deployable pipeline for UAV global visual localization in GNSS-challenged environments.
Published: 2026-07-22 12:58:02
Authors: Heping Wang, Zhongqin Wang, J. Andrew Zhang
Categories: eess.SP
Abstract:
Practical WiFi sensing must handle clock-asynchronous links, cross-domain variation, and post-deployment updating under the limited compute budget of access point (AP), router, and embedded Internet-of-Things platforms such as ESP-class devices. Reservoir computing (RC) is attractive in this setting because its temporal encoder can remain fixed while only a lightweight readout needs to be optimized and updated. To address these deployment challenges under tight compute budgets, we present ReWiS, a WiFi-sensing-oriented reservoir framework that transforms channel state information (CSI) into structured micro-Doppler streams with common, antenna-specific, and differential motion cues, encodes them with a graph-coupled reservoir, and adapts to a new domain after deployment by freezing the reservoir and fine-tuning only a compact readout with a few labeled target samples. On a large-scale WiFi sensing benchmark, ReWiS achieves 89.2% in-domain macro-F1 and 82.0% mean cross-domain macro-F1 with only a 0.72M trainable readout, improves to 88.5\% after lightweight post-deployment adaptation, and remains competitive with recent deep baselines evaluated under the same protocol, which achieve 87.5%-89.2% mean cross-domain macro-F1, while requiring lower optimization cost and lower CPU latency. These results indicate that ReWiS provides a practical reservoir-based design for deployable WiFi sensing, with further potential for low-power hardware realization.
Published: 2026-07-22 12:51:43
Authors: Medet Nursultanov, Grigori Rozenblum
Categories: math.SP
Abstract:
We study spectral estimates for polyharmonic Schrödinger operators $-Δ^l-μ$ in the subcritical regime $2l<\mathbf{N}$. The measure potential $μ$ is assumed to satisfy a capacitary smallness condition which guarantees that the corresponding operator is semibounded and self-adjoint. With such a measure $μ$ we associate an Otelbaev function, which reflects both the local concentration and the spatial distribution of the potential. In terms of this function, we obtain two-sided estimates for the distribution function of the negative eigenvalues, and derive a sufficient condition and a necessary condition for the discreteness of the negative spectrum. As an application, we establish two-sided estimates of Lieb-Thirring-type, improving the classical ones.
Published: 2026-07-22 12:47:13
Authors: C. Lardo, M. Salaris, N. Bastian, C. Charbonnel, E. Dalessandro, E. Dondoglio, M. Gieles, M. G. H. Krause, F. Martins, S. Monty
Categories: astro-ph.GA, astro-ph.SR
Abstract:
Chromosome maps (ChMs) are two-dimensional diagrams of UV/optical pseudocolours widely used to diagnose the multiple stellar populations (MPs) phenomenon in globular clusters. Their raw morphology is affected by the metallicity-dependent response of the photometric filters, preventing unbiased comparisons across clusters of different metallicities. We identify a cross-cluster ChM framework that accounts for this dependence, enabling an unbiased investigation of the physical drivers of MP diversity. We analyse ChMs for 23 Galactic globulars and devise a technique to correct the raw maps for the clusters' different metallicities. On the resulting "universal" ChM we define a new photometric enrichment index $S_{\rm ChM,z}$, validated against APOGEE spectroscopy. We compare this index with cluster masses, structural parameters, orbital quantities, and accretion-origin classifications. $S_{\rm ChM,z}$ correlates with the multivariate chemical abundance ranges of the enriched population and with the aluminium spread. Across the sample it increases with initial mass but correlates most strongly with a family of orbital-confinement quantities ($z_{\max}$, vertical action, apocentre, Galactocentric radius, orbital energy). The corrected ChMs provide a chemically meaningful, population-level measure of the enriched sequence. $S_{\rm ChM,z}$ does not trace a single abundance ratio but captures the cluster-to-cluster amplitude of the combined light-element variations, with particular sensitivity to the high-temperature Mg-Al/O component of proton-capture processing. Its dependence on both cluster potential depth and orbital confinement suggests that 2P chemical diversity is shaped by internal enrichment physics together with an environmental imprint, whether inherited at formation, modified by early evolution, or filtered by subsequent orbital survival.
Published: 2026-07-22 12:22:45
Authors: Farnaz Faramarzi Lighvan, Lynn Houthuys
Categories: cs.LG
Abstract:
AI-based recruitment systems that rely on machine learning models trained on historical CV data, risk perpetuating and amplifying social biases. A key challenge arises in unstructured CV text, where pre-trained language model embeddings may infer sensitive attributes such as gender even after explicit indicators are removed. In this paper, we evaluate nine pre-trained embedding models on the synthetic FairCVdb dataset, analyzing the informativeness of their embeddings for applicant scoring and their susceptibility to gender leakage, on both original and gender-scrubbed biographies. We further use a multi-task adversarial learning framework with gradient reversal to predict applicant suitability while suppressing gender information from learned representations. Finally, we use a multi-objective Pareto-front-based model selection to balance predictive utility and fairness. Our experimental results show that explicit gender scrubbing substantially reduces but does not eliminate gender leakage, while adversarial learning improves fairness mainly on original biographies and acts as a complementary strategy rather than a substitute for text-level debiasing.
Published: 2026-07-22 12:14:05
Authors: Chase T. Gabbard, Mario Ibrahim, Joseph Quinton, Eli Silver, Jesse Belden, Daniel M. Harris
Categories: physics.flu-dyn
Abstract:
When a sphere crosses an air-water interface it can entrain a significant volume of air, a process relevant to numerous naval, industrial, and environmental settings. While air entrainment through sphere impact onto quiescent baths has been extensively studied, real-world interfaces are inherently unsteady, and the influence of surface waves is less understood. In this Letter, we systematically investigate the effect of interfacial geometry on the air entrained by impacting hydrophobic spheres onto an axisymmetric wavefield. By analyzing the resulting cavity across a wide parameter space, including wave phase, driving amplitude, and frequency, we reveal that local interface deformation dramatically alters air entrainment. This effect is driven by a geometric modulation of the splash curtain, which shifts the transition between cavity closure modes. We demonstrate that the influence of the waves is fully described by the local wave slope at the radius of the sphere, which alongside the Weber number We and Bond number Bo, establishes a foundational parametric framework for predicting air entrainment and cavity metrics across highly dynamic, real-world surfaces like the open ocean.
Published: 2026-07-22 12:02:49
Authors: Markus J. Buehler
Categories: cs.AI, cond-mat.mes-hall, cond-mat.mtrl-sci, cs.CL
Abstract:
Large language models can answer scientific questions, yet a correct output does not reveal whether the model represents or uses the governing physics. Here we show that materials science mechanism information in the open-weight google/gemma-4-E4B-it model has three experimentally separable forms: concepts are readable in individual hidden states, constitutive orientation is carried by controlled transformations between states, and selected internal representations causally control engineering answers. We combine matched direct and Jacobian vocabulary readouts, option-free state geometry, a 60-law counterfactual benchmark and causal interventions. In 50 held-out materials descriptions, three independently fitted Jacobian lenses reproduced concept ranks, and target-free word sets from both readouts enabled blinded identification of 9 of 10 mechanism families. A separate 72-prompt benchmark produced mechanism-specific hidden-state neighborhoods, but an exact graph audit showed that this apparent physical organization was equally explained by numerical comparison. We therefore compared otherwise identical prompts in which only the direction of the physical input was reversed, asking whether the resulting hidden-state movement followed the supplied constitutive law. These state transformations ordered direct, physically neutral and inverse laws across 60 frozen relations and correctly oriented 39 of 40 directional laws, whereas lexical controls were near chance. Bidirectional interventions shifted answer probabilities toward or away from the physically appropriate outcome across all 12 matched cases, while counterfactual state patches transferred opposing decision signals across mechanisms and answer formats. Physical relationships were therefore more visible in controlled state changes than in absolute states alone.