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Analysis of PDEs

Topological degree for singular $N$-mean field equations

Mohameden Ahmedou, Zhongwei Tang and Heming Wang

We determine the Leray–Schauder degree of the singular Dirichlet $N$-mean field equation on a smooth bounded domain in $\mathbb{R}^N$, $N\geq2$, for every positive nonresonant parameter and singular exponents $-1<\alpha_i\leq0$. The formula depends only on the Euler characteristic of the domain and the weights $(1+\alpha_i)^{N-1}$. We also identify the homotopy type of every sufficiently negative variational sublevel with the corresponding weighted barycenter space. We prove quantization of interior concentration masses and exclude boundary concentration and residual diffuse mass. The degree computation uses common descent fields for nearby parameters, bounds for their trajectories, and a finite-dimensional comparison with the Euler characteristic of negative sublevels. Annular averaging and dilation provide local contractions, which are extended to establish the sublevel homotopy equivalence. The arguments apply to the nonlinear, possibly degenerate $N$-Laplacian with singular weights, without requiring a global Palais–Smale condition or isolation and nondegeneracy of solutions.
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Robotics

Learning-Based Adaptive MPC for Magnetorquer-Only Satellite Attitude Control

Patrick Schwartz and Abhishek Cauligi

Nanosatellites (e.g., CubeSats) have the potential to carry out useful science and exploration activities for outer solar system exploration, but their small size leads to tradeoffs between attitude control and science capabilities. Magnetorquers offer a unique compromise as compact actuators that enable three-axis attitude control, but suffer from a loss of control authority and require full knowledge of the planetary magnetic field. In this work, we present a learning-based adaptive model predictive control (MPC) algorithm that plans rotational trajectories for the satellite while accounting for this loss of control authority. Key to our approach is the inclusion of a learned magnetic field model that is trained in simulation using low-fidelity magnetic field data. Using measurements collected online, our proposed algorithm adapts this learned magnetic field model using a simple last-layer update scheme, allowing the MPC controller to quickly adapt to the true planetary magnetic field model and more effectively plan attitude trajectories. We demonstrate the results of our proposed approach for two use cases, (1) Earth-orbit and (2) Uranus-orbit, and show that our learning-based adaptive MPC algorithm improves controller success for these system simulations by 5% and 61%, respectively.
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Computation and Language

Neural POS Tagging for the Low-Resource Saraiki

Usman Nawaz, Muhammad Junaid Iqbal, Muhammad Asaf et al. (+4 more)

Part-of-speech tagging is a fundamental sequence labeling task and an important component for many downstream natural language processing applications. However, Saraiki, written from right to left in a Perso-Arabic script, remains an under-resourced Indo-Aryan language, despite being spoken as a mother tongue by 28,849,579 people (approximately 28.9 million) according to Pakistan 7th Population and Housing Census 2023. This paper presents a manually annotated Saraiki Universal POS tagging dataset developed from plain-text. We annotated the text with 17 POS categories. We evaluate three pretrained transformer encoders under the same fine-tuning setup: XLM-RoBERTa base, Urdu RoBERTa small, and multilingual DistilBERT. Experimental results over five random seeds show that XLM-RoBERTa base achieves the best performance, with 87.29% +/- 0.21% development accuracy and 88.60% +/- 0.21% test accuracy. Urdu RoBERTa small and multilingual DistilBERT also provide competitive results, suggesting that multilingual and related-script pretrained encoders are effective for Saraiki UPOS tagging. The dataset, evaluation setup, and baseline results introduced in this work provide a benchmark for future research on Saraiki NLP and low-resource sequence labeling.
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All authors Usman Nawaz, Muhammad Junaid Iqbal, Muhammad Asaf, Mariam Munsif Mir, Faisal Rehman, Tahir Alyas, Muhammad Hasnain

Computer Vision

A Valid Watermark Is Not a Valid Identity: Spatial-Spectral Adversarial Face Evasion with Watermark Preservation

Jefferson David Rodriguez Chivata, Davide Ghiani, Simone Maurizio La Cava et al. (+3 more)

Facial image watermarking can support traceability and certification in biometric pipelines, but provenance or manipulation detection alone does not guarantee reliable biometric identity. We introduce a watermark-preserving face evasion attack that targets this gap under a black-box watermarking setting, without access to the watermarking components. The proposed PGD-L_2 attack guides adversarial perturbations with spatial and spectral preservation terms to reduce face-recognition similarity while limiting pixel-level distortion and high-frequency changes that may interfere with hidden watermark cues. Evaluated on controlled facial images with two watermarking schemes, two face-recognition models, and two datasets, the attack achieves evasion rates above 92% at operational thresholds while preserving watermark recovery above 93% for StegaFormer and above 99.7% for FaceSigns, with visual quality above 47dB PSNR at epsilon_2=2.0. These results expose a structural gap in watermark-based facial certification: a face image can remain watermark-valid and visually faithful while becoming biometrically unreliable.
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All authors Jefferson David Rodriguez Chivata, Davide Ghiani, Simone Maurizio La Cava, Marco Micheletto, Giulia Orrù, Gian Luca Marcialis

Machine Learning

TANGO: RNA Topology and Geometry Co-Design

Tianmeng Hu, Biao Luo and Ke Li

Designing RNA sequences that satisfy target tertiary-structure objectives is an important problem in RNA engineering, with broad applications in synthetic biology and therapeutics. Existing 3D RNA design methods have made substantial progress, but they largely treat the target fold as a geometry-only objective. This is incomplete for RNA, whose functional folds are specified not only by tertiary geometry but also by secondary base-pairing topology. We therefore formulate RNA inverse design as a topology-and-geometry co-design problem and propose TANGO, a multi-objective multi-agent reinforcement learning framework for this task. TANGO adopts a staged curriculum: it first learns a topology-aware policy from secondary-structure targets, then transfers this policy to tertiary-structure targets to train a general co-design model, and finally applies frontier-guided fine-tuning to refine target-specific trade-offs among secondary-structure fidelity, tertiary-structure fidelity, and sequence diversity. Experiments show that TANGO generates diverse and novel designs, improving tertiary- and secondary-structure performance by 18.3% and 52.7%, respectively, over the strongest baseline.
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General Relativity and Quantum Cosmology

Search for Compact Binary Coalescences with Sub-solar Mass Components in Data from the Second Part of LIGO-Virgo-KAGRA's Fourth Observing Run

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration: A. G. Abac et al. (+1815 more)

We report on a gravitational wave search for compact binary coalescences with secondary mass between 0.2 M and 1 M and primary mass between 0.2 M and 10 M, restricting the component mass ratio to lie between 0.1 and 1. The analysis uses data collected by the LIGO and Virgo detectors between 2024 April 10 15:00:00 and 2025 January 28 17:00:00, the second part of the fourth observing run of the LIGO-Virgo-KAGRA Collaboration. Results from two out of three search pipelines show outlier candidates, but not at a sufficient level of significance to provide evidence for detection. We report the combined sensitivity of the current searches over the first and second parts of the observing run to the target sub-solar mass (SSM) black hole (BH) population, as well as to neutron star (NS) binaries with one or more sub-solar component. We place upper limits on the merger rate of SSM BHs, ranging from $50 {\rm Gpc}^{-3} {\rm yr}^{-1}$ to~$5000 {\rm Gpc}^{-3} {\rm yr}^{-1}$ (90% confidence) for different component mass combinations. We use the merger rate limits to constrain two illustrative dark matter (DM) scenarios that can form SSM compact objects: primordial BHs, and dark BHs forming in a dissipative DM model. Our search limits the fraction of DM in primordial BHs, assuming a monochromatic mass distribution, to be $\leq$ (0.018-0.032) at 1 M, and $\leq$ (0.10-0.18) at 0.35 M for early-forming binaries; or to be less than 1 at masses above (0.62-0.74) M for late-forming binaries. For the dissipative model, we constrain the fraction of DM for a range of minimum dark black hole masses below $1\,M$ to be as low as (1.0-1.1) x $10^{-5}$; we also exclude minimum masses above (0.62-0.74) M, if dark BHs constitute all of the DM. For binary NSs that include sub-solar mass components, assuming a simple, fixed population model we find a merger rate upper limit of $249 {\rm Gpc}^{-3} {\rm yr}^{-1}$.
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All authors The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration: A. G. Abac, A. Abe, I. Abouelfettouh, F. Acernese, K. Ackley, A. Adam, S. Adhicary, D. Adhikari, R. X. Adhikari, V. K. Adkins, S. Afroz, A. Agapito, D. Agarwal, M. Agathos, N. Aggarwal, S. Aggarwal, O. D. Aguiar, I.-L. Ahrend, L. Aiello, A. Ain, P. Ajith, T. Akutsu, L. Albers, W. Ali, S. Al-Kershi, C. Allan, C. Allene, A. Allocca, S. Al-Shammari, J. A. Alvarez, S. Alvarez-Lopez, W. Amar, O. Amarasinghe, A. Amato, F. Amicucci, C. Amra, A. B. Anand, C. Anand, A. Ananyeva, S. B. Anderson, W. G. Anderson, M. Andia, M. Ando, F. Andrade-Oliveira, M. Andrés-Carcasona, J. L. Andrey, T. Andrić, J. Anglin, J. Anna, J. M. Antelis, S. Antier, T. Aoki, M. Aoumi, E. Z. Appavuravther, E. A. Appelt, S. Appert, S. K. Apple, K. Arai, A. Araya, M. C. Araya, M. Arca Sedda, F. Arciprete, J. S. Areeda, N. Aritomi, F. Armato, S. Armstrong, N. Arnaud, M. Arogeti, S. M. Aronson, G. Ashton, Y. Aso, L. Asprea, M. Assiduo, S. Assis de Souza Melo, S. M. Aston, P. Astone, P. S. Aswathi, F. Attadio, F. Aubin, K. AultONeal, G. Avallone, N. Avdeev, E. A. Avila, S. Babak, C. Badger, S. Bae, S. Bagnasco, S. Baimukhametova, L. Baiotti, T. Baka, K. A. Baker, T. Baker, G. Balbi, G. Baldi, N. Baldicchi, M. Ball, G. Ballardin, M. Ballelli, S. W. Ballmer, S. Banagiri, B. Banerjee, D. Bankar, T. M. Baptiste, P. Baral, M. Baratti, J. C. Barayoga, K. Baric, B. C. Barish, D. Barker, D. Barman, N. Barman, F. Barone, B. Barr, M. Barrios, L. Barsotti, M. Barsuglia, D. Barta, M. A. Barton, I. Bartos, A. Basalaev, R. Bassiri, A. Basti, M. Bawaj, J. C. Bayley, A. C. Baylor, P. A. Baynard II, M. Bazzan, V. M. Bedakihale, F. Beirnaert, M. Bejger, A. S. Bell, C. Bellani, D. S. Bellie, D. Beltran-Martinez, E. Benedetti, W. Benoit, I. Bentara, M. Ben Yaala, S. Bera, F. Bergamin, B. K. Berger, M. Beroiz, C. P. L. Berry, I. Berry, D. Bersanetti, T. Bertheas, A. Bertolini, J. Betzwieser, D. Beveridge, N. Bevins, J. Bezerra-Sobrinho, R. Bhandare, R. Bhatt, A. Bhattacharjee, D. Bhattacharjee, S. Bhattacharyya, S. Bhaumik, V. Biancalana, F. Bianchi, I. A. Bilenko, M. Bilicki, G. Billingsley, A. Binetti, S. Bini, S. Biot, O. Birnholtz, S. Biscoveanu, A. Bisht, M. Bitossi, M.-A. Bizouard, S. Blaber, J. Black, J. K. Blackburn, L. A. Blagg, C. D. Blair, D. G. Blair, M. Bloch, N. Bode, N. Boettner, P. Bogdan, G. Boileau, M. Boldrini, G. N. Bolingbroke, L. D. Bonavena, V. A. Bonhomme, E. Bonilla, M. S. Bonilla, A. Bonino, R. Bonnand, A. Borchers, N. Borghi, V. Boschi, S. Bose, V. Bossilkov, Y. Bothra, A. Boudon, T. D. Boybeyi, M. Boyle, A. Bozzi, C. Bradaschia, M. J. Brady, P. R. Brady, A. Branch, M. Branchesi, T. Briant, A. Brillet, M. Brinkmann, P. Brockill, E. Brockmueller, A. F. Brooks, D. D. Brown, M. L. Brozzetti, S. Brunett, G. Bruno, R. Bruntz, J. Bryant, Y. Bu, F. Bucci, A. Buchicchio, A. Buggiani, O. Bulashenko, T. Bulik, H. J. Bulten, A. Buonanno, K. Burtnyk, R. Buscicchio, N. Busdon, D. Buskulic, R. L. Byer, R. Cabrita, V. A. Cáceres-Barbosa, L. Cadonati, G. Cagnoli, C. Cahillane, A. Calafat, J. Calderón Bustillo, J. D. Callaghan, T. A. Callister, E. Calloni, S. R. Callos, K. Cannon, V. Cantory, H. Cao, L. A. Capistran, E. Capocasa, G. Capoccia, E. Capote, C. Capuano, G. Capurri, F. Carbognani, K. J. Cardona-Martínez, M. Carlassara, M. Carpinelli, G. Carrillo, G. Carullo, A. Casallas-Lagos, J. Casanueva Diaz, C. Casentini, S. Caudill, M. Cavaglià, R. Cavalieri, A. Ceja, G. Cella, P. Cerdá-Durán, N. Chabbra, W. Chaibi, A. Chakraborty, P. Chakraborty, S. Chakraborty, S. Chalathadka Subrahmanya, C. Chan, J. C. L. Chan, M. Chan, C.-Y. Chang, K. Chang, S. Chao, P. Charlton, E. Chassande-Mottin, C. Chatterjee, Debarati Chatterjee, Deep Chatterjee, M. Chaturvedi, S. Chaty, A. Chen, A. H.-Y. Chen, D. Chen, H. Chen, H. Y. Chen, S. Chen, Yanbei Chen, Yiwen Chen, G. Cheng, H. P. Cheng, P. Chessa, T. Cheunchitra, H. T. Cheung, S. Y. Cheung, F. Chiadini, G. Chiarini, A. Chiba, A. Chincarini, D. Chintala, A. Chiummo, A. Chopra, C. Chou, S. Choudhary, N. Christensen, Y. K. Chu, S. S. Y. Chua, G. Ciani, P. Ciecielag, M. Cieślar, M. Cifaldi, B. Cirok, F. Clara, J. A. Clark, T. A. Clarke, A. Claveus, M. R. Claypool, S. Clesse, F. Cleva, S. M. Clyne, E. Coccia, E. Codazzo, P.-F. Cohadon, D. E. Cohen, E. Colangeli, O. Cole, M. Colleoni, C. G. Collette, J. Collins, S. Colloms, A. Colombo, G. Compère, C. M. Compton, G. Connolly, L. Conti, T. R. Corbitt, I. Cordero-Carrión, S. Corezzi, N. J. Cornish, A. Corsi, S. Cortese, L. A. Corubolo, L. Cotnoir, R. Cottingham, J. A. Cotturone, M. W. Coughlin, P. Couvares, R. Coyne, A. Cozzumbo, J. D. E. Creighton, T. D. Creighton, S. Crook, R. Crouch, J. Csizmazia, K. Csukás, T. J. Cullen, A. Cumming, E. Cuoco, M. Cusinato, R. R. Cuzinatto, L. V. da Conceição, T. Dal Canton, S. Dall'Osso, S. Dal Pra, G. Dálya, Y. Dang, B. D'Angelo, S. Danilishin, O. Danner, S. D'Antonio, K. Danzmann, K. E. Darroch, L. P. Dartez, R. Das, S. Das, A. Dasgupta, V. Dattilo, A. Daumas, I. Dave, A. Davenport, T. F. Davies, D. Davis, M. C. Davis, P. Davis, E. J. Daw, M. Dax, J. De Bolle, E. deBruin, M. Deenadayalan, J. Degallaix, U. Deka, M. De Laurentis, C. J. Delgado Mendez, F. De Lillo, S. Della Torre, W. Del Pozzo, O. M. del Rio, A. Demagny, F. De Marco, G. Demasi, F. De Matteis, C. de Melo, N. Demos, T. Dent, A. Depasse, N. DePergola, R. De Pietri, R. De Rosa, C. De Rossi, E. K. Derrick, M. Desai, D. DeSantis, S. Deshmukh, V. Deshmukh, R. De Simone, S. Determan, S. Dhage, A. Dhani, R. Dhatri, R. Dhurkunde, R. Diab, C. Diaz, M. C. Díaz, F. Diaz Guerra, M. Di Cesare, M. A. Dicorato, T. Dietrich, C. Di Fronzo, M. Di Giovanni, D. Diksha, J. Ding, S. Di Pace, I. Di Palma, D. Di Piero, F. Di Renzo, Divyajyoti, A. Dmitriev, J. P. Docherty, Z. Doctor, N. Doerksen, E. Dohmen, A. Doke, A. Domiciano De Souza, L. D'Onofrio, F. Donovan, K. L. Dooley, S. Doravari, O. Dorosh, F. Dosopoulou, M. Drago, J. C. Driggers, M. Dubois, R. S. Dumbreck, U. Dupletsa, D. D'Urso, P. Dutta Roy, H. Duval, S. Dwivedi, S. E. Dwyer, C. Eassa, M. Eberhardt, M. Ebersold, M. Ebiri, G. Eddolls, A. Effler, J. Eichholz, H. Einsle, M. Eisenmann, M. Emma, K. Endo, R. Enficiaud, V. Ernst, L. Errico, R. Espinosa, M. Esposito, R. C. Essick, H. Estellés, T. Etzel, M. Evans, T. Evstafyeva, J. M. Ezquiaga, F. Fabrizi, V. Fafone, S. Fairhurst, X. Fan, A. M. Farah, B. Farr, W. M. Farr, M. Favata, M. Fays, M. Fazio, J. Feicht, M. M. Fejer, J.-N. Feldhusen, E. Fenyvesi, A. Feo, J. Fernandes, T. Fernandes, G. Fernández Rodríguez, D. Fernando, S. Ferraiuolo, T. A. Ferreira, M. Ferrer-Martinez, F. Fidecaro, P. Figura, I. Fiori, M. Fishbach, R. P. Fisher, S. K. Fitzgerald, V. Fiumara, R. Flaminio, B. Flanagan, S. M. Fleischer, L. S. Fleming, F. Flocco, E. Floden, H. Fong, J. A. Font, F. Fontinele-Nunes, C. Foo, B. Fornal, P. W. F. Forsyth, A. Fragkos, N. Franchini, A. Franco-Ordovas, F. Frappez, F. Frasconi, J. P. Freed, Z. Frei, A. Freise, O. Freitas, R. Frey, W. Frischhertz, P. Fritschel, V. V. Frolov, M. Fuentes-Garcia, R. Fujii, T. Fujimori, Y. Fujiwara, P. Fulda, M. Fyffe, B. U. Gadre, J. R. Gair, S. Galaudage, V. Galdi, M. Galimberti, A. Gamboa, S. Gamoji, A. Ganguly, B. Garaventa, P. García Abia, J. García-Bellido, C. García-Quirós, J. W. Gardner, S. Garg, J. Gargiulo, X. Garrido, A. Garron, F. Garufi, P. A. Garver, C. Gasbarra, F. Gautier, V. Gayathri, T. Gayer, G. Gemme, A. Gennai, V. Gennari, J. George, R. George, O. Gerberding, L. Gergely, A. Ghinassi, Archisman Ghosh, Sayantan Ghosh, Shaon Ghosh, Shrobana Ghosh, Suprovo Ghosh, Tathagata Ghosh, J. A. Giaime, K. D. Giardina, D. R. Gibson, C. Gier, F. Gittins, J. Glanzer, F. Glotin, E. Glowacki, J. Godfrey, R. V. Godley, O. Godwin, A. S. Goettel, E. Goetz, J. Golomb, S. Gomez Lopez, G. González, P. Goodarzi, S. R. Goode, A. Goodwin-Jones, M. Gosselin, S. M. Goss-Grubbs, C. Gostiaux, R. Gouaty, D. W. Gould, D. Goupilliere, K. Govorkova, A. Grado, V. Graham, A. E. Granados, M. Granata, V. Granata, S. Gras, P. Grassia, C. Gray, R. Gray, G. Greco, A. C. Green, L. Green, S. R. Green, A. M. Gretarsson, E. M. Gretarsson, D. Griffith, C. Grimaud, H. Grote, S. Grunewald, A. G. Guerrero, G. M. Guidi, T. Guidry, H. K. Gulati, F. Gulminelli, H. Guo, W. Guo, Y. Guo, A. Gupta, I. Gupta, N. C. Gupta, S. K. Gupta, V. Gupta, N. Gupte, N. Guttman, F. Guzman, M. Haberland, S. Haino, E. D. Hall, E. Z. Hamilton, G. Hammond, W.-B. Han, M. Haney, J. Hanks, C. Hanna, M. D. Hannam, O. A. Hannuksela, H. Hansen, J. Hanson, R. Harada, A. R. Hardison, S. Harikumar, K. Haris, I. Harley-Trochimczyk, J. Harms, G. M. Harry, I. W. Harry, M. T. Hartman, B. Haskell, C.-J. Haster, K. Haughian, H. Hayakawa, K. Hayama, J. Hedberg, A. Heffernan, D. Hegde, M. C. Heintze, J. Heinzel, H. Heitmann, F. Hellman, A. F. Helmling-Cornell, G. Hemming, O. Henderson-Sapir, M. Hendry, I. S. Heng, M. H. Hennig, C. Henshaw, A. Heranval, M. Heurs, A. L. Hewitt, J. Heynen, J. Heyns, S. Hido, S. Hild, M. Hill, S. Hill, Y. Himemoto, C. Hirose, D. Hofman, N. A. Holland, K. Holley-Bockelmann, I. J. Hollows, D. E. Holz, L. Honet, K. M. Hoops, M. E. Hoque, D. J. Horton-Bailey, J. Hough, S. Hourihane, N. T. Howard, E. J. Howell, C. G. Hoy, P. Hsi, H.-Y. Hsieh, C. Hsiung, S.-H. Hsu, W.-F. Hsu, H. Y. Huang, Y. Huang, A. D. Huddart, B. Hughey, D. C. Y. Hui, S. Husa, L. Iampieri, G. A. Iandolo, M. Ianni, Y. Ichinose, K. Ide, R. Iden, A. Ierardi, S. Ikeda, H. Imafuku, K. Imai, G. Inguglia, Y. Inoue, P. Iosif, J. Irwin, K. Ishida, R. Ishikawa, T. Ishikawa, H. Ishino, M. Isi, K. S. Isleif, Y. Itoh, S. Iwaguchi, M. M. Iwaya, B. R. Iyer, C. Jacquet, T. Jacquot, S. J. Jadhav, S. P. Jadhav, K. Jain, A. L. James, K. Jani, S. Jani, J. Janquart, N. N. Janthalur, S. Jaraba, P. Jaranowski, R. Jaume, W. Javed, M. Jensen, W. Jia, J. Jiang, H.-B. Jin, S.-J. Jin, G. R. Johns, N. A. Johnson, M. C. Johnston, R. Johnston, N. Johny, D. H. Jones, D. I. Jones, R. Jones, P. Joshi, S. K. Joshi, G. Joubert, J. Ju, L. Ju, I. L. Juarez-Reyes, K. Jung, H. B. Kabagoz, B. Kacskovics, T. Kajita, I. Kaku, V. Kalogera, M. Kalomenopoulos, M. Kamiizumi, N. Kanda, S. Kandhasamy, G. Kang, J. B. Kanner, S. J. Kapadia, D. P. Kapasi, A. Karia, A. S. Karia, R. Kashyap, M. Kasprzack, H. Kato, T. Kato, E. Katsavounidis, W. Katzman, R. Kaushik, K. Kawabe, S. Kawamura, D. Keitel, S. A. Kemper, L. J. Kemperman, J. Kennington, R. Kesharwani, J. S. Key, R. Khadela, S. S. Khadkikar, F. Y. Khalili, C. Khamar, F. Khan, M. Khursheed, N. M. Khusid, W. Kiendrebeogo, C. Kim, G. Kim, J. C. Kim, K. Kim, M. H. Kim, S. Kim, Y.-M. Kim, C. Kimball, K. Kimes, M. Kinnear, J. S. Kissel, S. Klimenko, A. M. Knee, N. Knust, K. Kobayashi, S. M. Koehlenbeck, K. Kohri, K. Kokeyama, S. Koley, P. Kolitsidou, A. E. Koloniari, K. Komori, K. Kompanets, A. K. H. Kong, A. Kontos, K. Kopczuk, L. M. Koponen, M. Korobko, X. Kou, N. Kouvatsos, T. Koyama, D. B. Kozak, E. Kraja, S. L. Kranzhoff, V. Kringel, N. V. Krishnendu, S. Kroker, A. Królak, K. Kruska, J. Kubisz, K. Kubota, G. Kuehn, D. Kukla, A. Kulur Ramamohan, Achal Kumar, Anil Kumar, Dhruv Kumar, Praveen Kumar, Prayush Kumar, Rahul Kumar, Rakesh Kumar, Ravi Kumar, Sumit Kumar, J. Kume, K. Kuns, N. Kuntimaddi, S. Kuroyanagi, K. Kwak, K. Kwan, S. Kwon, G. Lacaille, D. Laghi, A. H. Laity, N. Lajili, A. Lakhal, E. Lalande, M. Lalleman, S. Lalvani, M. Landry, R. N. Lang, A. Lange, J. A. Lange, R. Langgin, B. Lantz, I. La Rosa, O. Laske, P. D. Lasky, L. Lavezzi, J. Lawrence, M. Laxen, A. Lazzarini, C. Lazzaro, P. Leaci, L. Leali, Y. K. Lecoeuche, H. W. Lee, J. Lee, K. Lee, R.-K. Lee, R. Lee, Sungho Lee, Sunjae Lee, W. Lee, Y. Lee, F. Legger, I. N. Legred, J. Lehmann, L. Lehner, M. Le Jean, A. Lemaître, R. Lemrani Alaoui, M. Lenti, M. Leonardi, M. Lequime, M. Lesovsky, N. Letendre, M. Lethuillier, Y. Levin, S. Lexmond, K. Leyde, A. K. Y. Li, K. L. Li, T. G. F. Li, X. Li, Y. Li, Z. Li, Q. Liang, C-Y. Lin, E. T. Lin, F. Lin, L. C.-C. Lin, Y.-C. Lin, C. Lindsay, S. D. Linker, A. Liu, F. Liu, G. C. Liu, Jian Liu, S. Liu, F. Llamas Villarreal, J. Llobera-Querol, R. K. L. Lo, J.-P. Locquet, S. C. G. Loggins, L. T. London, A. Longo, M. Lopez Portilla, A. Lorenzo-Medina, V. Loriette, M. Lormand, M. Lorusso, G. Losurdo, T. P. Lott IV, J. D. Lough, H. A. Loughlin, C. O. Lousto, N. K. Y. Low, N. Lu, H. Lück, O. Lukina, D. Lumaca, A. P. Lundgren, L. Lunghini, A. W. Lussier, L.-T. Ma, X. Ma, M. Ma'arif, S. MacBride, K. Machida, D. M. Macleod, I. A. O. MacMillan, A. Macquet, S. S. Madekar, S. Maenaut, S. S. Magare, R. M. Magee, E. Maggio, M. Magnozzi, P. Mahapatra, M. Mahesh, S. Majhi, E. Majorana, C. N. Makarem, E. Makelele, N. Malagon, D. Malakar, J. A. Malaquias-Reis, U. Mali, S. Maliakal, A. Malik, L. Mallick, A.-K. Malz, N. Man, M. Mancarella, V. Mandic, V. Mangano, Z. Mangi, B. Mannix, G. L. Mansell, M. Manske, M. Mantovani, M. Mapelli, S. Marchetti, F. Marion, J. Mark, A. S. Markosyan, J. Markus, E. Maros, S. Marsat, F. Martelli, I. W. Martin, R. M. Martin, B. B. Martinez, M. Martinez, V. Martinez, A. Martini, Juan Carlos Martins, Julio C. Martins, D. V. Martynov, E. J. Marx, L. Massaro, A. Masserot, M. Masso-Reid, T. Masters, S. Mastrogiovanni, G. Mastropasqua, M. Matiushechkina, A. Matte-Landry, L. Maurin, N. Mavalvala, N. Maxwell, A. McCann, G. McCarrol, R. McCarthy, D. E. McClelland, S. McCormick, L. McCuller, L. I. McDermott, C. McElhenny, G. I. McGhee, K. B. M. McGowan, J. McIver, A. McLeod, I. McMahon, T. McRae, R. McTeague, K. McWhirter, D. Meacher, B. N. Meagher, R. Mechum, L. G. Medeiros, R. M. Mehta, A. Melatos, C. S. Menoni, R. A. Mercer, L. Mereni, K. Merfeld, E. L. Merilh, J. R. Mérou, C. Messick, M. Meyer-Conde, F. Meylahn, H. Miao, C. Michel, Y. Michimura, H. Middleton, D. P. Mihaylov, S. J. Miller, M. Millhouse, E. Milotti, V. Milotti, E. Minakaki, Y. Minenkov, Ll. M. Mir, L. Mirasola, C.-A. Miritescu, A. Mishra, C. Mishra, T. Mishra, A. Mitchell, J. G. Mitchell, O. Mitchem, S. Mitra, V. P. Mitrofanov, K. Mitsuhashi, R. Mittleman, O. Miyakawa, S. Miyoki, G. Mo, L. Mobilia, S. R. P. Mohapatra, M. Molina-Ruiz, M. Mondin, M. Montani, G. Montefusco, C. J. Moore, D. Moraru, A. More, S. More, C. Moreno, E. A. Moreno, G. Moreno, A. Moreso Serra, C. Morgan, S. Morisaki, S. Moriwaki, Y. Moriwaki, G. Morras, A. Moscatello, M. Mould, B. Mours, C. M. Mow-Lowry, L. Muccillo, F. Muciaccia, Arunava Mukherjee, D. Mukherjee, Samanwaya Mukherjee, Soma Mukherjee, Subroto Mukherjee, Suvodip Mukherjee, N. Mukund, A. Mullavey, C. L. Mungioli, Y. Murakami, M. Murakoshi, P. G. Murray, D. Nabari, S. Nadji, A. Nagar, N. Nagarajan, K. Nakagaki, A. Nakamura, K. Nakamura, H. Nakano, M. Nakano, D. Nanadoumgar-Lacroze, D. Nandi, V. Napolano, S. U. Naqvi, P. Narayan, A. Nardecchia, I. Nardecchia, T. Narikawa, H. Narola, L. Naticchioni, R. K. Nayak, J. Neeson, L. Negri, A. Nela, C. Nelle, A. Nelson, T. J. N. Nelson, A. Nemmani, A. Neunzert, M. Newell, S. Ng, T. C. K. Ng, L.-A. T. Nguyen, T. T. Nguyen, L. Nguyen Quynh, A. B. Nielsen, Y. Nishino, A. Nishizawa, S. Nissanke, W. Niu, F. Nocera, J. Noller, M. Norman, M. Norris, C. North, J. Novak, G. Nurbek, L. K. Nuttall, K. Obayashi, J. Oberling, C. E. Ochoa, C. O'Connor, J. O'Dell, E. Oelker, M. Oertel, G. Oganesyan, J. J. Oh, T. O'Hanlon, M. Ohashi, F. Ohme, Y. Okabe, I. Oke, R. Oliveira, R. Omer, N. O'Neill, M. Onishi, K. Oohara, P. Ophardt, R. J. Oram, B. O'Reilly, R. O'Shaughnessy, S. Oshino, J. Ostrovska, A. Osumi, I. Ota, G. Othman, M. Otsuka, D. J. Ottaway, A. Ouzriat, H. Overmier, B. J. Owen, A. E. Pace, M. A. Page, A. Pai, S. Pal, M. A. Palaia, M. Pálfi, C. Palomba, H. Pan, J. Pan, K.-C. Pan, P. K. Panda, Shiksha Pandey, Swadha Pandey, P. T. H. Pang, F. Pannarale, B. C. Pant, F. H. Panther, M. Panzeri, F. Paoletti, A. Paoli, A. Paolone, A. Papadopoulos, E. E. Papalexakis, L. Papalini, G. Papigkiotis, A. Paquis, A. Parisi, B.-J. Park, Jihwan Park, Junegyu Park, W. Parker, G. Pascale, D. Pascucci, A. Pasqualetti, L. Passenger, D. Passuello, O. Patane, A. V. Patel, L. Pathak, A. Patra, B. Patricelli, B. G. Patterson, K. Paul, S. Paul, E. Payne, T. Pearce, M. Pedraza, A. Pele, F. E. Peña Arellano, X. Peng, Y. Peng, S. Penn, A. Perreca, J. Perret, D. Pesios, S. Petracca, C. Petrillo, H. P. Pfeiffer, H. Pham, K. A. Pham, K. S. Phukon, H. Phurailatpam, L. Piccari, O. J. Piccinni, M. Pichot, A. Pied, M. Piendibene, F. Piergiovanni, L. Pierini, G. Pierra, V. Pierro, M. Pillas, B. Pillon, L. Pinard, I. M. Pinto, M. Pinto, B. J. Piotrzkowski, M. Pirello, A. Pisarski, M. D. Pitkin, E. Placidi, M. L. Planas, C. Plunkett, R. Poggiani, E. Polini, M. Polo, J. Pomper, L. Pompili, J. Poon, E. Porcelli, A. S. Porter, E. K. Porter, C. Posnansky, J. Powell, G. S. Prabhu, M. Pracchia, A. K. Prajapati, K. Prasai, R. Prasanna, P. Prasia, G. Pratten, G. Principe, G. A. Prodi, P. Prosperi, P. Prosposito, A. Puecher, J. Pullin, M. Punturo, P. Puppo, M. Pürrer, H. Qi, M. Qiao, J. Qin, G. Quéméner, V. Quetschke, P. J. Quinonez, R. Rading, I. Rainho, S. Raja, C. Rajan, B. Rajbhandari, M. R. Raj Sah, K. E. Ramirez, F. A. Ramis Vidal, M. Ramos Arevalo, A. Ramos-Buades, S. Ranjan, M. Ranjbar, K. Ransom, P. Rapagnani, B. Ratto, A. Ravichandran, A. Ray, V. Raymond, M. Razzano, J. Read, J. Redepenning, J. Regan, T. Regimbau, T. Reichardt, S. Reid, C. Reissel, D. H. Reitze, A. I. Renzini, B. Revenu, A. Revilla-Peña, F. Ricci, M. Ricci, A. Ricciardone, J. Rice, J. W. Richardson, M. L. Richardson, K. Riles, H. K. Riley, A. Riminucci, F. Robinet, M. Robinson, A. Rocchi, J. Rodriguez, R. Rodriguez Lopez, L. Rolland, J. G. Rollins, A. E. Romano, R. Romano, A. Romero-Rodríguez, I. M. Romero-Shaw, J. H. Romie, S. Ronchini, T. J. Roocke, T. J. Rosauer, C. A. Rose, D. Rosińska, M. P. Ross, M. Rossello-Sastre, S. Rowan, K. Rowlands, S. K. Roy, S. Roy, T. RoyChowdhury, D. Rozza, P. Ruggi, G. H. Ruiz, E. Ruiz Morales, K. Ruiz-Rocha, V. Russ, S. M. S, S. Sachdev, T. Sadecki, F. Safai Tehrani, P. Saffarieh, S. Safi-Harb, S. Saha, T. Sainrat, S. Sajith Menon, K. Sakai, Y. Sakai, M. Sakellariadou, S. Sakon, F. Salces-Carcoba, L. Salconi, M. Saleem, F. Salemi, M. Sallé, M. Salomé, S. U. Salunkhe, S. Salvador, A. Salvarese, A. Samajdar, P. M. Samir, A. Sanchez, E. J. Sanchez, J. Sanchez, D. Sanchez-Cid, N. Sanchis-Gual, J. R. Sanders, E. M. Sänger, F. Santoliquido, E. Sapkin, F. Sarandrea, T. R. Saravanan, P. Sarkar, A. Sasli, P. Sassi, B. Sassolas, O. Sauter, R. L. Savage, T. Savicheva, T. Sawada, H. L. Sawant, D. Schaetzl, M. Scheel, A. Schiebelbein, M. G. Schiworski, K. Schluterman, P. Schmidt, R. Schnabel, M. Schneewind, R. M. S. Schofield, M. Schoor, K. Schouteden, B. W. Schulte, M. Schulz, B. F. Schutz, E. Schwartz, M. Scialpi, J. Scott, S. M. Scott, R. M. Sedas, T. C. Seetharamu, M. Seglar-Arroyo, Y. Sekiguchi, D. Sellers, N. Sembo, E. G. Seo, J. W. Seo, G. Seong, V. Sequino, M. Serra, C. K. Sethi, A. Sevrin, T. Shaffer, U. S. Shah, M. A. Shaikh, L. Shao, J. Sharkey, A. Sharma, A. K. Sharma, Preeti Sharma, Priyanka Sharma, Sushant Sharma-Chaudhary, P. Shawhan, T. Shen, Z.-H. Shi, K. Shimode, H. Shinkai, S. Shirke, D. H. Shoemaker, D. M. Shoemaker, R. W. Short, S. ShyamSundar, H. Siegel, V. Sierra, D. Sigg, L. Silenzi, P. J. S. Silva, L. Silvestri, M. Simmonds, L. P. Singer, A. Singh, D. Singh, M. K. Singh, N. Singh, S. Singh, M. R. Sinha, P. Sinha, A. M. Sintes, V. Skliris, B. J. J. Slagmolen, T. J. Slaven-Blair, J. Smetana, D. A. Smith, J. R. Smith, J. Smith, L. Smith, W. J. Smith, S. Soares de Albuquerque Filho, M. Soares-Santos, K. Somiya, I. Song, Q. Song, S. Soni, V. Sordini, F. Sorrentino, H. Sotani, N. E. Sovitzky, F. Spada, V. Spagnuolo, A. P. Spencer, M. Spera, P. Spinicelli, A. K. Srivastava, F. Stachurski, V. V. Stanford, A. Stanton, D. A. Steer, N. Steinle, J. Steinlechner, S. Steinlechner, C. Stephens, N. Stergioulas, M. StPierre, J. Stremiz, M. D. Strong, A. Strunk, M. Suchenek, S. Sudhagar, R. Sugimoto, L. Suleiman, K. D. Sullivan, J. Sun, L. Sun, S. Sunil, J. Suresh, P. J. Sutton, K. Suzuki, M. Suzuki, A. Svizzeretto, B. L. Swinkels, A. Syx, M. J. Szczepańczyk, M. Tacca, M. Tagliazucchi, H. Tagoshi, S. C. Tait, H. Takaba, K. Takada, H. Takahashi, R. Takahashi, A. Takamori, S. Takano, H. Takeda, I. Takimoto Schmiegelow, C. Talbot, M. Tamaki, N. Tamanini, D. Tanabe, K. Tanaka, S. J. Tanaka, S. Tanioka, D. B. Tanner, W. Tanner, L. Tao, R. D. Tapia, E. N. Tapia San Martín, A. Taruya, J. D. Tasson, J. G. Tau, A. Tejera, J. G. Temple, Y. Teng, H. Themann, A. Theodoropoulos, M. P. Thirugnanasambandam, L. M. Thomas, M. Thomas, P. Thomas, J. E. Thompson, S. R. Thondapu, E. Thrane, J. Tissino, A. Tiwari, Pawan Tiwari, Praveer Tiwari, S. Tiwari, V. Tiwari, M. R. Todd, E. Tofani, M. Toffano, A. M. Toivonen, K. Toland, T. Tomaru, V. Tommasini, H. Tong, C. I. Torrie, I. Tosta e Melo, E. Tournefier, A. Trapananti, R. Travaglini, F. Travasso, G. Traylor, L. Traylor, M. Trevor, M. C. Tringali, A. Tripathee, G. Troian, A. Trovato, L. Trozzo, R. J. Trudeau, T. Tsang, S. Tsuchida, K. Tsuji, L. Tsukada, A. Tuci, M. Turconi, C. Turski, H. Ubach, A. S. Ubhi, N. Uchikata, T. Uchiyama, R. P. Udall, T. Uehara, V. Undheim, V. Upadhyaya, L. E. Uronen, T. Ushiba, M. Vacatello, H. Vahlbruch, G. Vajente, J. Valencia, M. Valentini, E. Vallejo-Pagès, S. A. Vallejo-Peña, S. Vallero, M. van Dael, E. Van den Bossche, J. F. J. van den Brand, C. Van Den Broeck, M. van der Kolk, M. van der Sluys, A. Van de Walle, J. van Dongen, K. Vandra, M. VanDyke, H. van Haevermaet, J. V. van Heijningen, P. Van Hove, J. Vanier, J. Vanosky, N. van Remortel, M. Vardaro, A. F. Vargas, V. Varma, A. Vecchio, G. Vedovato, J. Veitch, P. J. Veitch, S. Venikoudis, P. Verdier, M. Vereecken, D. Verkindt, B. Verma, S. Verma, Y. Verma, S. M. Vermeulen, F. Vetrano, A. Veutro, A. Viceré, S. Vidyant, A. D. Viets, A. Vijaykumar, A. Vilkha, N. Villanueva Espinosa, E. T. Vincent, J.-Y. Vinet, S. Viret, S. Vitale, A. Vives, L. Vizmeg, B. Vizzone, H. Vocca, D. Voigt, E. R. G. von Reis, J. S. A. von Wrangel, W. E. Vossius, L. Vujeva, S. P. Vyatchanin, J. Wack, L. E. Wade, M. Wade, K. J. Wagner, L. Wallace, R.-Z. Wan, H. Wang, P. Wang, W. H. Wang, Y. F. Wang, Z. Wang, R. L. Ward, J. Warner, M. Was, T. Washimi, N. Y. Washington, D. Watarai, B. Weaver, S. A. Webster, N. L. Weickhardt, M. Weinert, A. J. Weinstein, R. Weiss, L. Wen, K. Wette, C. Wheeler, J. T. Whelan, B. F. Whiting, E. G. Wickens, D. Wilken, B. M. Williams, D. Williams, M. J. Williams, N. S. Williams, J. L. Willis, B. Willke, M. Wils, L. Wimmer, C. W. Winborn, A. Wingfield, J. Winterflood, C. C. Wipf, G. Woan, N. E. Wolfe, H. T. Wong, I. C. F. Wong, T. Wouters, J. L. Wright, M. Wright, B. Wu, C. Wu, D. S. Wu, H. Wu, K. Wu, T. Wu, Z. Wu, E. Wuchner, D. M. Wysocki, V. A. Xu, Y. Xu, N. Yadav, H. Yamamoto, K. Yamamoto, T. S. Yamamoto, T. Yamamoto, R. Yamazaki, T. Yan, H. Yang, K. Z. Yang, Y. Yang, Z. Yarbrough, J. Yébana Carrilero, A. B. Yelikar, X. Yin, J. Yokoyama, T. Yokozawa, M. Yoshihara, S. Yuan, H. Yuzurihara, M. Zanatta, M. Zanolin, M. Zeeshan, T. Zelenova, J.-P. Zendri, M. Zeoli, M. Zerrad, M. Zevin, H. Zhang, J. Zhang, L. Zhang, N. Zhang, R. Zhang, T. Zhang, C. Zhao, J. Zhao, Yue Zhao, Yuhang Zhao, L.-M. Zheng, Y. Zheng, L. Zhizhong, H. Zhong, H. Zhou, H. O. Zhu, X.-J. Zhu, Z.-H. Zhu, Z. Zhu, D. Z. Zieba, A. B. Zimmerman, L. Zimmermann, M. E. Zucker

Soft Condensed Matter

A Sideways Crack-Path Instability Amplified by Slow Loading

G. Pallares, O. Ramos and L. Vanel

Crack-path instabilities in solids are typically associated with high propagation velocities. In contrast, we show that nanoparticle-filled elastomers exhibit a sideways crack-path instability that is amplified as the pulling velocity decreases and nearly vanishes at high pulling velocities. The sideways deviation amplitude decreases logarithmically with increasing pulling velocity and scales as the square root of the filler fraction. Large deviations develop at low mean crack velocities and are associated with effective fracture energies up to two orders of magnitude lower. These findings are consistent with subcritical rupture kinetics and suggest that the deviation amplitude is limited by the elasto-adhesive length.
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Machine Learning

Resolution Allocation with Learnable Resizing and Spectral Filter Parameterization

Wei Wu and Eric C. Larson

Learnable downsampling and spectral-domain filter parameterizations have each made convolutional networks more flexible, but their interaction has not been systematically examined. We bridge this gap by examining convolutional kernels learned in the spatial and spectral domains, together with learned downsampling ratios under a cropped resizing implementation. We find that spectral and spatial kernels respond differently to learned resolution allocation, with larger gains for the spectral kernel. We observe this consistently on three datasets (EuroSAT, CIFAR-10 and STL-10) and at two model sizes (differences in accuracy gain of +0.013 to +0.028 on held-out test sets). Part of this difference reflects the greater gain of the spectral kernel from retaining more spatial area, but the difference remains positive when total retained area is matched. This pattern is observed across datasets and model sizes, although its magnitude and dependence on the complexity budget vary with model size and the resizing implementation.
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Number Theory

An upper bound for valuations of elliptic nets at primes of bad reduction

Hang Lok Edison Au-Yeung

Let $E/K$ be an elliptic curve over a field defined by the short Weierstrass equation and let $\mathbf{P}=(P_1, P_2) \in E(K)^2\setminus \{(O, O), (\pm P, \pm P)\}$ be a pair of non-torsion points, where $O$ denotes the point at infinity. We give a uniform upper bound for the quantity $\min(\nu(\Phi_{(v_1, v_2)}(\mathbf{P})), 2\nu(\Psi_{(v_1, v_2)}(\mathbf{P})))$, where $\nu$ is a normalised discrete valuation associated to primes where $E$ has bad reduction and $\Psi^2_{(v_1, v_2)}(\mathbf{P})$, $\Phi_{(v_1, v_2)}(\mathbf{P})$ are rational functions for describing $\mathbb{Z}$–linear combination of points $[v_1]P_1+[v_2]P_2$ on $E$, higher–dimensional analogues of division polynomials $\psi^2_n, \phi_n$ known as net polynomials. They are also elliptic nets, which are $n$–dimensional arrays with values in $K$ satisfying the same nonlinear recurrence relation that division polynomials do as well. Hence, they are useful in studying counting problems on elliptic curves.
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Computation and Language

CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing

Zewen Jiang, Yahan Li, Chenlu Guo et al. (+3 more)

Large language models (LLMs) have demonstrated multilingual capabilities across diverse language generation and understanding tasks. Yet effective code-switching requires more than multilingual proficiency: models must decide when, where, and why to switch languages in ways that fit the surrounding context. This context-sensitive ability remains insufficiently evaluated. We introduce CSWBench, a benchmark for evaluating context-sensitive code-switching in creative writing through two complementary settings: Focused Narrative and Extended Narrative. We further propose CSWEval, a theory-informed framework with two complementary levels: event-level evaluation of Licensing, Contextual Appropriateness, and Functional Gain, and whole-text evaluation of task-specific quality—overall literary and narrative quality in Focused Narrative and quality emphasizing multilingual organization in Extended Narrative. Evaluating twelve LLMs, we find that models exhibit meaningful but uneven code-switching competence across narrative contexts. Model differences are particularly pronounced in multilingual literary integration, while multilingual-focused dimensions show stronger difficulty effects overall than the general narrative dimensions; narratively salient switches are associated with stronger contextual and functional realization; and event-level CS quality is moderately associated with whole-text performance.
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All authors Zewen Jiang, Yahan Li, Chenlu Guo, Diana Inkpen, Yuan Wu, Yi Chang

Biological Physics

Memory-resolved prediction of ligand residence times on the minutes timescale

Hossein Batebi, Lucas Tepper and Roland R. Netz

AI-enabled computational drug discovery is advancing rapidly in predicting molecular structures, binding poses and affinities, but ligand dissociation kinetics remain harder to access. Bound-state residence time, important for biological response and kinetic selectivity, is not fixed by equilibrium affinity but depends on the dissociation free-energy landscape and dissipative friction effects. Here, we show that restrained umbrella-sampling trajectories can be used to calculate residence times on the minutes timescale. Time-resolved coordinate fluctuations yield friction profiles that incorporate non-Markovian effects on dissociation kinetics. Combining them with the free-energy profile, we predict a residence time of $\tau_{\rm res}$ = 275 s for the adenosine A$_{2A}$ receptor with ZM241385 (comparable to the experimental value of 125 s) and reproduce the experimental ranking of wild type and two receptor mutants. For HSP90 with compound 18 and RNase S with the S15 peptide, we predict $\tau_{\rm res}$ = 2,169 s and 2,259 s (experimental values are 833 s and 2,439 s). Neglecting memory effects on residence kinetics overestimates $\tau_{\rm res}$ by factors of 2.6 to 29.
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Optimization and Control

Benign Degeneracy for First-Order Methods in Exact Boolean Moment-SOS Relaxations

Shucheng Kang

Pseudo-Boolean optimization – minimizing a polynomial over $\{\pm 1\}^n$ – includes MAX-CUT, binary quadratic optimization, and Ising models. The moment-sum-of-squares (Moment-SOS) hierarchy provides certified semidefinite relaxations that are often empirically exact at low order. We ask: once a relaxation is exact, how difficult is the resulting semidefinite program (SDP) to solve with first-order methods? Exactness does not imply regularity. At every order $d \ge 2$, a unique rank-one moment optimum is primal degenerate; moreover, if one optimal SOS certificate is strictly complementary, then the certificate set is positive-dimensional and contains singular boundary points. Thus first-order methods may converge to singular endpoints, around which pointwise analyses that assume regularity at the limit cannot establish a local linear convergence rate. We show that, for exact Boolean Moment-SOS relaxations in their canonical quotient form, this degeneracy is nevertheless benign. We develop an abstract mechanism, which we call residual-field rigidity, that propagates regularity from the relative interior of the optimal set to its singular boundary. For Boolean Moment-SOS, unique rank-one exactness and the existence of one strictly complementary certificate suffice to trigger this mechanism, yielding a uniform error bound around the entire optimal set. Consequently, exact ADMM, exact sGS-ADMM, and fixed-step PDHG converge eventually linearly from every initialization, even when their limiting certificate is singular. Finally, outside a lower-dimensional semialgebraic exceptional set of Boolean objectives, these hypotheses hold simultaneously at every uniquely rank-one exact order.
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Signal Processing

FlowSep-RF: Rectified Flow Matching for Single-Channel Radio-Frequency Signal Separation

Mohamed Kamel and Tamer Nadeem

Single-channel radio-frequency (RF) signal separation is important for interference mitigation when a signal of interest overlaps with a co-channel interferer in time and frequency. Classical matched filtering and LMMSE receivers are limited under structured non-Gaussian interference, while diffusion and score-based methods often require iterative sampling or optimization. We propose FlowSep-RF, a rectified flow matching framework that learns a clean-endpoint transport from the received mixture to the signal of interest. The model combines time-domain IQ samples with frequency-domain features and recovers the separated waveform using one endpoint velocity evaluation. We evaluate FlowSep-RF on QPSK mixtures with OFDM-BPSK, OFDM-QPSK, and RFChallenge CommSignal2 interference against classical, discriminative, and generative baselines. FlowSep-RF achieves the lowest average BER and MSE among evaluated baselines across all three settings with one-step inference.
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Superconductivity

Transport regimes in tunnel ferromagnetic Josephson junctions

R. Satariano, A. A. Golubov, E. Raja et al. (+13 more)

We present a systematic study of the temperature-dependent transport properties of Nb- and Al-based superconductor–insulator–thin superconductor–ferromagnet–superconductor (SIsFS) Josephson junctions. By varying the superconducting material and the thickness of the intermediate superconducting layer, we identify three distinct transport regimes: a tunnel-dominated SIS-like regime, a temperature-driven crossover, and a complex weak-link regime. A unified Usadel-based description connects these regimes through the self-consistent pair potential of the intermediate s-layer. While for Al-based devices an SIS-like transport regime is observed, a self-consistent calculation of the s/F/S proximity electrode, combined with perturbative tunnel coupling to the outer electrode, accounts for Nb devices with different thickness of the superconducting interlayer. The calculation places the 10-nm-interlayer sample in a proximity-supported, strongly suppressed s state, close to the SInFS limit in which the interlayer is effectively driven normal (n). A reduced nonlinear long-junction description captures the same limiting physics. The evolution of I_cR_N(T) thereby provides a direct experimental signature of the three transport regimes. Beyond the fundamental interest, the results establish practical design guidelines: Nb junctions with sufficiently thick s interlayers are well suited for high-speed cryogenic memory elements and tunable superconducting parametric amplifiers, while Al junctions are natural candidates for coherent superconducting quantum circuits
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All authors R. Satariano, A. A. Golubov, E. Raja, R. Ferraiuolo, F. Calloni, H. G. Ahmad, A. Vettoliere, N. Poccia, L. Parlato, G. Ausanio, C. Granata, G. P. Pepe, M. Yu. Kupriyanov, F. Tafuri, D. Montemurro, D. Massarotti

Combinatorics

A new generalization of parking functions: $\rho$-Naples parking functions

Luca Ferrari and Francesco Verciani

We introduce a new model of parking functions, called $\rho$-Naples parking functions, in which each car has a certain number of spots to check preceding its preferred one before looking for available spots by moving forward. This notion, where each car has its own number of previous spots to check, generalizes that of Naples parking function, where this number is the same for all cars. We start by showing that, for a given sequence of preferences for each car, a successful parking strategy (that is, an assignment of how many backward spots each car is allowed to check) remains successful if we increase the number of backwards spots to check for some of the cars. This means that the set of successful strategies for a given parking preference is an up-set in the lattice of parking strategies, which raises the intriguing question of characterizing such up-sets. Though we are not able to completely solve this problem, we provide a characterization of those successful strategies which minimize the total number of allowed backward steps for a given parking preference, and we determine a lower bound for the number of cars which are allowed to move backwards (also showing that it can be attained). To conclude, we analyze the specific situation in which each car can take at most one backward step, and we completely characterize the set of successful parking preferences in this case.
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Numerical Analysis

Wavelet compression for sampling deep Gaussian processes

Oskar Broukal, Michael Feischl and Conor Osborne

Deep Gaussian processes (DGPs) use iterative sampling to create non-stationary random fields. This is notoriously expensive because the covariance at each non-initial layer depends on the realised field from the layer below, so it changes with every sample. Moreover, standard fast sampling techniques like circulant embedding or matrix compression fail since the underlying kernel usually lacks the necessary regularity and structure. We develop and analyse a wavelet compression method for convolution Matérn DGPs. At each layer the covariance matrix is compressed using wavelets and a Lanczos algorithm computes the random sample. For specific parameter ranges involving the smoothness of the Matérn kernel as well as the smoothness of the wavelets, we show that the accumulative compression and sampling errors through all layers decay exponentially with increasing truncation parameter. We obtain a practical DGP sampling algorithm by choosing a truncation that gives log-linear covariance storage and matrix-vector costs, enabling DGP sampling at nearly linear computational cost.
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Differential Geometry

Geometric $C^1$-stability of geodesic spheres under volume-preserving mean curvature flow

Esther Cabezas-Rivas and Carlo Sinestrari

We prove stability of geodesic spheres under volume-preserving mean curvature flow in the simply connected space forms under genuinely first-order geometric assumptions. The initial hypersurface is required to lie in a sufficiently thin annulus around a geodesic sphere and to have outward normal making a small angle with the radial direction. No convexity, mean convexity, initial curvature bound, or Hölder control of the normal is assumed. The main ingredient is a quantitative Alexandrov reflection argument that converts these initial assumptions into a strong reflection property preserved by the flow. The resulting radial and angular control allows us to derive estimates for the mean curvature and the full second fundamental form, and hence long-time existence, eventual convexity, and smooth exponential convergence to a geodesic sphere. In hyperbolic space, the eventual convexity can be strengthened to horospherical convexity. The reflection threshold also yields a quantitative estimate for the displacement of the limiting centre. For Lipschitz initial hypersurfaces, we develop a separate positive-time curvature estimate that is uniform along smooth approximations. This produces an instantaneously smooth evolution with the same eventual convexity and smooth exponential convergence.
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Machine Learning

FUSE: Fairness-aware Update Screening against Byzantine Exploitation in $q$-Fair Federated Learning

Tiantong Sun, Jinlin Liu, Xingru Wei et al. (+4 more)

Client-level fair federated learning, in the form of $q$-fair federated learning ($q$-FFL), agnostic federated learning, or tilted empirical risk minimization, has emerged as an attractive solution to equalize utility across heterogeneous clients. This paradigm grants high-loss clients greater relative influence, while a central server aggregates their loss-weighted updates into a single shared model. Yet, the Byzantine robustness of $q$-fair federated learning has been largely neglected, leaving the server unable to tell legitimate fairness-induced amplification from malicious update scaling. To address this critical gap, we formalize the problem of Byzantine-robust $q$-fair federated learning under an update-only interface, in which the server observes nothing but the fairness-scaled client updates. Our analysis under shared class-conditional label skew shows that fairness scaling preserves the nonnegative class-mixture structure of honest updates, while replacing their unit-sum constraint by a client-dependent radial scale. Moving beyond magnitude-based screening, we show that this warped geometry supplies two complementary tests, one radial and one structural, that a single norm cannot provide. We then introduce FUSE, a structure-aware robust aggregator that uses a small trusted labeled dataset for coefficient recovery, scale and cone-consistency screening, and post-screening step-size control. Across four benchmarks and seven Byzantine attacks, FUSE improves seed-wise worst-attack accuracy over the strongest baseline on each benchmark by 3.9–30.1 percentage points, while largely preserving the influence of the high-loss honest clients that fairness is meant to protect.
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All authors Tiantong Sun, Jinlin Liu, Xingru Wei, Zequan Fang, Wei Chen, Qiang Yang, Xiaojin Zhang

Computation and Language

Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages

Stephen Fernandes and Narayan Choudhary

Every multilingual speech pipeline begins with a language decision, an error there propagates silently through recognition, translation and routing. In India it spans 22 scheduled languages across four language families, heard over short, noisy, narrowband and code-mixed audio. Foundational speech models increasingly present language identification (LID) as an inherent latent, generalized capability. Autoregressive decoders in speech models emit a special language ID token, and modern LLMs with audio capabilities can name the language when prompted. We test this assumption on identical clips and find that whisper and similar speech models has tokens for only 15 of the 23 languages, on one second inputs Gemini 3.5 Flash reaches 0.52 macro-F1 and GPT-audio 0.14. We present Anuvani LID, a dedicated 115M parameter LID conformer model trained on 54,643 hours of Indian speech spanning 22 Indian languages and Indian accented English. Along with designed and controlled studies on 102 condition benchmark with speaker and corpus held out tests. Across 37 conditions, it reaches 0.914 mean macro-F1, 5.2 points above Gemini 3.5 Flash and ahead of 14 other systems. It is 24 points ahead of Gemini at one second, scores 0.897 on all 23 languages where no other system exceeds 0.713, and is calibrated to an ECE of 0.008. Reliable Indian LID still requires a robust, dedicated and calibrated model.
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Solar and Stellar Astrophysics

HD 120678: a classical Be star with a tilting heartbeat disk

Dietrich Baade, Jonathan Labadie-Bartz and Thomas Rivinius

Classical Oe/Be/Ae stars possess Keplerian circum-equatorial decretion disks which produce not just the eponymous emission lines but also the bewildering diversity of photometric variations on timescales above a few days. The variability is due to varying amounts and geometric distributions of the circumstellar gas. The ASAS-3 V-band light curve of HD 120678 between March 2005 and November 2008 resembled an inverse P Cygni profile, which may have been a one-off event. The peak-to-valley amplitude reached ~0.9 mag. Before and after this photometric event, the Balmer emission lines and their variability matched those of classical Be stars with an intermediate to low inclination angle. At the sharp minimum of the light curve, Gamen et al. (2012) observed shell-type profiles implying an edge-on (high-inclination) view. For the historical activity phases of gamma Cas and 59 Cyg, such a Be => Be-shell => Be sequence has been successfully interpreted as rotations of the disk plane. HD 120678 and a few other Be stars add a not previously recognized phenomenon to the plethora of Be-star variabilities, namely heartbeat light curves. Analogously to the distortion of heartbeat stars, the variability is due to heartbeat disks gravitationally tilted by an additional star as modeled by Marr et al. (2022) for Pleione. While the typical amplitudes of heartbeat stars are only a few 0.01 mag, heartbeat disks can explain the ten times larger amplitudes observed in Be stars.
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Machine Learning

Harness Policy Distillation: Aligning Agent Policies with Runtime Semantics

Junze Zhu, Weihao Chen, Zhiyang Zhou et al. (+2 more)

Deploying a general-purpose model as an agent means running it inside a product-specific runtime harness that decides which actions are executable, how state advances, and when the loop may terminate. Ordinary trajectory supervision shows which action was taken but leaves the runtime semantics behind that action implicit. We introduce Harness Policy Distillation (HPD), which makes these runtime control semantics an explicit training target alongside actions to align the harness policy. HPD replays reference trajectories through the harness to recover the runtime semantics that are implicit in ordinary trajectory data, and represents them as control documents at each model-call boundary. These documents provide privileged information for a teacher to generate actions, while harness verification ensures that the resulting targets are executable under the runtime contract. We jointly train a shared model on the runtime semantics recovered from control-state privileged information and native history, thereby internalizing these semantics into the model policy. At deployment, the trained model uses the original harness interface without requiring the control document or any additional supplement. On TRAJECT-Bench with the Pi harness, Qwen3-8B trained with HPD reaches 22.98% autonomous task success and 49.23% runtime compliance, against 3.09% and 19.90% for the unadapted model. Harness-verified teacher actions substantially improve the native deployment policy, while joint control-state supervision provides further gains in task completion and runtime compliance. HPD turns executable runtime semantics into dense supervision for learning harness-compatible actions at each decision boundary.
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All authors Junze Zhu, Weihao Chen, Zhiyang Zhou, Zhen Wu, Xinyu Dai

Astrophysics of Galaxies

Constrain the Size of Broad Line Region in NGC 1068 Using Sub-millimeter Excess

Qi Guo, Junzhi Wang, Lingrui Lin et al. (+3 more)

Constraining the size of broad-line region (BLR) close to the accretion disk in active galactic nuclei (AGNs) is key to understand the central engine and evolution of AGN. We propose a new method to constrain the BLR size, based on millimeter to sub-millimeter spectral energy distribution (SED), from which a sub-millimeter excess may be found. Such excess can be used to determine the solid angle of the free-free emission region from BLR, as a region with high electron density and optically thick at sub-millimeter wavelength for free-free continuum. Millimeter to sub-millimeter SED analysis toward the central source S1 in a nearby type II AGN NGC 1068 was done, which gave an upper limit on the solid angle of BLR region as(4.94 +/- 0.61) *10^-6 arcsec^2, corresponding to a radius no more than 0.085 pc. Such a constraint on the BLR size is important for estimating the masses of central supermassive black holes in AGNs.
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All authors Qi Guo, Junzhi Wang, Lingrui Lin, Yuqiang Li, Zhi-yu Zhang, Fengyao Zhu

Combinatorics

Nonexistence of Strongly Regular Graphs via Gegenbauer Positivity and SRG Counting

Zhen-Qi Liao, Jiaxin Wang, Wei-Hsuan Yu et al. (+1 more)

This paper develops a natural extension of the multipoint spherical semidefinite method of Liao and Yu~\cite{LY}, studying the interaction between semidefinite programming and strongly regular graph counting. Starting from the positive semidefinite kernels furnished by Gegenbauer polynomials, we express weighted kernel sums and counting identities in the same labelled induced-subgraph counts. Eliminating the counting equations gives a semidefinite feasibility problem. Numerical dual solutions guide the construction of rational certificates, whose contradiction identities are verified exactly. Our aim is a systematic method that treats many parameter sets through the same construction. For least eigenvalue $-3$, it gives new counting certificates for $(351,140,73,44)$, $(550,162,75,36)$, $(703,182,81,35)$ and $(1344,221,88,26)$, previously excluded by Liao and Yu~\cite[Theorem~1]{LY}, and for six additional parameter sets: \[ \begin{array}{@{}lll@{}} (375,102,45,21), & (405,132,63,33), & (441,88,35,13),\\ (476,133,60,28), & (540,147,66,30), & (575,112,45,16). \end{array} \] Seven certificates use Gegenbauer positivity and counting information alone at the displayed orders. The cases $v=375,441,575$ additionally use proved short-vector restrictions, unique vertex extensions, and integer common-neighbour inequalities. The proofs end in displayed inequalities with incompatible signs. Their justification is given by the dual identities and the proved positivity and counting relations; accompanying code automates the longer rational arithmetic.
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All authors Zhen-Qi Liao, Jiaxin Wang, Wei-Hsuan Yu, Jianfeng Cai

Quantum Physics

Gaussian and Qubit Semigroup Dynamics beyond GKSL

Nadine Diesel, Charlotte Bäcker and Walter T. Strunz

A well established approach to describe open quantum system dynamics is the celebrated Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) master equation. In this article we aim to shed light on semigroup dynamics that are not of this form. Such dynamics may appear in real physical systems describing asymptotic long-time dynamics. Thus, they correspond to semigroup dynamics applicable to a restricted set of initial states only. As a way of motivating our studies, first we review known results about the single-mode Gaussian Quantum Brownian motion channel and corresponding master equations. Next we discuss a conceptually simpler family of qubit semigroup generators and identify parameter regions of completely positive, merely positive, and non-positive dynamics. We then focus on the latter case to which we will refer to as restricted-positive, as the semigroup generator may well be physically meaningful when applied to a restricted set of admissible initial states.
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Group Theory

The finite basis problem and subvariety intervals for max-plus matrix semirings

Jun Jiao and Mengya Yue

Let $S=(\N,\max,+)$ be the max-plus algebra of the nonnegative integers, and let $\overline S$ be obtained from $S$ by adjoining $-\infty$ as a semiring zero. We first prove that the varieties generated by the full matrix semirings $\Mat_n(S)$ and by the upper triangular matrix semirings $\UT_n(\overline S)$ form strictly ascending chains. Every interval between distinct members of either chain has cardinality $2^{\aleph_0}$ and contains a chain and an antichain of that cardinality. The proof uses finite syntactic semirings associated with prime-cycle languages and an independent family of identities. We then construct finite commutative ai-semirings from languages of monomials closed under divisibility and prove their bounded-variable property by cancellation, explicit integer evaluations and independence in a free abelian group. A loop-block identity for upper triangular matrices and dimension-independent cubic-padding identities for full matrices show that every variety in each of the above intervals has infinite axiomatic rank. In fact, every variety between $\V(S)$ and the variety generated by all full matrix dimensions has infinite axiomatic rank. These varieties are therefore nonfinitely based, also relative to the ai-semiring axioms. Finally, for every fixed full matrix dimension, we prove equational equivalence over all six positive or nonnegative integer, rational and real coefficient domains. The original convex-ideal countermodels and their full-matrix transfer proof are recalled separately for comparison; they are not used in the algebraic proofs. Throughout, the signature consists of two binary operations and contains no constant symbols.
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Machine Learning

Long-Horizon RL Needs Muon

Kai Ruan, Jinghao Lin, Tao Chen et al. (+3 more)

Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions. Learning from sparse outcome rewards requires weighting trajectories of different lengths and updating the policy while rollouts are still being collected. We examine a single-rollout asynchronous recipe combining Muon, token-averaged loss, truncated importance sampling, and auxiliary length and tool-error penalties. Single-rollout sampling allows asynchronous learning without waiting for multiple trajectories for the same issue. We study optimizer choices and recipe components for repository-level issue resolution across two model families. The evaluated training runs show higher training success with the full recipe than with the baselines.
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All authors Kai Ruan, Jinghao Lin, Tao Chen, Ziqi Zhou, Qianshan Wei, Zihe Huang

Robotics

Safe Reinforcement Learning with Receding-Horizon Safety Critic and Differentiable Action Shields

Siwei Ju, Jan Peters and Oleg Arenz

Despite the Markov assumption, safety failures in robot reinforcement learning rarely occur instantaneously; they typically emerge over several steps as the policy enters unfamiliar states under disturbances and domain shift. We address this limitation by learning a receding-horizon safety critic that predicts whether a state will remain non-terminating over the next receding horizon, enabling anticipatory detection of impending locomotion failures. The critic is trained from rollout-derived horizon labels with a composite objective that combines horizon classification, control-barrier-function-inspired temporal consistency, and smoothness regularization, and we aggregate an ensemble of critics with a conservative Conditional Value at Risk estimate to account for uncertainty. Building on the learned critic, we derive a closed-form, differentiable CBF-style safety filter that minimally modifies the policy action to improve predicted safety, and integrate it into PPO with an anticipation loss to reduce policy-filter mismatch during training. In simulated quadruped locomotion, our method improves robustness under action noise, external perturbations, and terrain shifts, reducing catastrophic failures while maintaining competitive returns.
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Computer Vision

GLoD-LVSM: Learning Geometry-Grounded Continuous-LoD Scene Representations for Large View Synthesis Models

Weikai Lin, Nicholas Milef, Carl S Marshall et al. (+3 more)

Large view synthesis models (LVSMs) reconstruct scenes from sparse views and render novel views quickly, but they incur unnecessary storage and rendering costs due to token redundancy. We present GLoD-LVSM, a compact, geometry-grounded scene representation for LVSMs with continuous levels of detail. First, instead of learning a fixed set of scene anchor initializations, GLoD-LVSM learns a continuous field that initializes a flexible number of anchors to match the desired budget. A condenser then aggregates scene information into these anchors, with both the field and condenser trained end-to-end. Second, the condenser infers reference-token and anchor geometry to guide compression through geometry-grounded attention, improving rendering quality and enabling frustum culling during decoding. Compared with existing LVSM scene-compression baselines, GLoD-LVSM achieves the best trade-off between rendering quality and scene token count. Compared with the uncompressed base model, GLoD-LVSM reduces the scene-token count by 33.4% and accelerates rendering by $1.23\times$ with no quality loss. Under a relaxed quality-loss budget, the reduction increases to 85.5%, with $3.00\times$ rendering speedup. Further analysis reveals that view-synthesis supervision yields emergent scene depth and self-organized anchors that align with surfaces and adapt their density to local texture complexity. Beyond rendering, we further show that our scene representation also supports scene-semantic understanding, highlighting its potential for broader downstream tasks.
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All authors Weikai Lin, Nicholas Milef, Carl S Marshall, Grant Kaijuin Yang, Todd Keeler, Yuhao Zhu

Computation and Language

Repairing Speech Access After Text Updates with Frozen Affine Fields

Ming-Hao Hsu, Yuxiang Wang, Yingda Shen et al. (+1 more)

Speech-capable language models inherit most of their knowledge from text-rich language backbones, and continued model maintenance is likewise dominated by text updates. Knowledge acquired through a text update can nevertheless remain difficult to access when the same query is spoken. We formulate this mismatch as post-update speech access repair, where the knowledge-bearing checkpoint stays frozen and a detachable operator repairs speech-only access in one model pass. We introduce Affine Fields for Interface Repair across Modalities (AFFIRM), which first learns and freezes a base correction for speech hidden states, then adds query-specific corrections with zero mean over the operator-training queries. Paired speech and text states provide the coordinates that condition these corrections on each query. On balanced synthetic-speech PopQA, paired comparisons do not establish a clear advantage for AFFIRM over global SALSA steering or low-rank ReFT interventions under the same frozen single-state constraint. On recorded SLUE-SQA-5 questions with text passages, a full-scale PopQA-fitted AFFIRM operator transfers without refitting and improves ROUGE-L by 0.0941 over its paired No Repair baseline. Low-rank ReFT interventions also transfer positively, whereas the matched SALSA variant falls 0.0707 below its paired baseline. These results show that text acquisition and speech access can diverge after an update and should be verified and repaired separately.
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All authors Ming-Hao Hsu, Yuxiang Wang, Yingda Shen, Zhizheng Wu

Machine Learning

Adversarial Reinforcement Learning for Robust Planetary Landing

Miguel Leiva-Vélez, Nicolas Gaston-Rozado, Andrea Scorsoglio et al. (+2 more)

Deep reinforcement learning (DRL) policies for planetary landing and other aerospace guidance, navigation, and control tasks are commonly trained under randomized disturbances to improve robustness. However, typical stochastic perturbation sampling assumes independent variations, whereas real mission disturbances often exhibit spatial and temporal correlation and cannot be reproduced through independent random draws. This work investigates adversarial reinforcement learning to discover challenging off-nominal conditions. A secondary agent generates physically constrained perturbations that degrade the performance of a landing controller, forming an asymmetric sequential zero-sum game. Training proceeds in three stages: baseline controller learning, robustness enhancement with stochastic perturbations, and an iterative adversarial phase in which the controller and disturbance generator are alternately optimized. Preliminary experiments in a three-degree-of-freedom landing environment show that adversarial disturbances reveal vulnerabilities missed by conventional randomization and provide early indications that, with a more extensive training campaign, the approach can yield improved robustness and stronger generalization under previously unseen perturbation regimes.
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All authors Miguel Leiva-Vélez, Nicolas Gaston-Rozado, Andrea Scorsoglio, Hodei Urrutxua, Víctor Rodríguez-Fernández