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All of arXiv

Thursday 17 September

Functional Analysis

Quantum expanders and dimension-free commutator bounds

Tuan Tran

We prove that every traceless real or complex matrix $A$ can be written as $A=BC-CB$, with factors of the same size and over the same field satisfying $\|B\|\,\|C\|\le K\|A\|$, where $K$ is an absolute constant. The proof combines an approximate-rank dichotomy with stable commutator representations obtained from quantum expansion.
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Computer Vision

Needles in a Raystack: Ultra-Sparse LiDAR Occupancy Detection for Bat Tracks

Nico Klar, Pankaj Rana, Nizam Gifary et al. (+2 more)

Monitoring flying animals is important for understanding and protecting biodiversity, but nocturnal species such as bats are difficult to observe in the field. Using LiDAR, bat movements at night result in ultra-sparse 3D spatio-temporal data in which standard reconstruction losses tend to predict only background and miss real flight paths. We study this problem as voxel-wise occupancy detection in sensor-centric LiDAR raystacks. A lightweight 3D U-Net is proposed that preserves temporal resolution, uses skip connections for spatial detail, and combines weighted binary cross-entropy with Dice loss to handle the strong class imbalance. In real LiDAR recordings of bats over open fields, cross-checked with acoustic monitoring, a reconstruction-based 3D convolutional autoencoder baseline fails to recover foreground trajectories. In contrast, the proposed U-Net recovers sparse foreground occupancy in diagnostic experiments and produces coherent occupancy patterns along bat flight trajectories, providing a practical basis for validation-scale experiments, later clustering of flight tracks, and future integration of bat activity information into biodiversity-aware turbine curtailment strategies.
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All authors Nico Klar, Pankaj Rana, Nizam Gifary, Jakob Traub, Aamir Ahmad

Differential Geometry

Special Lagrangian cones in Deep Learning

Tejas Kotwal and Govind Menon

We introduce a matrix generalization of the cone of Harvey and Lawson and prove that it is an exact special Lagrangian manifold. We further show that it belongs to a family of exact special Lagrangian manifolds that foliate the balanced manifold arising in deep learning.

Mesoscale and Nanoscale Physics

Plasmons in twisted bilayer graphene across dispersive and flat bands

Antonio Palamara, Michele Pisarra and Antonello Sindona

The dynamical dielectric response of twisted bilayer graphene is explored in large-angle dispersive-band and small-angle quasi-flat-band regimes using time-dependent density-functional theory within the random-phase approximation. At the reference bilayer-graphene interlayer distance, weak coupling in the largest-angle structures preserves Dirac dispersions and the intrinsic $\pi$ plasmon. Electron doping activates a two-dimensional Dirac plasmon with energies obeying approximate geometric twist-angle scaling, while acoustic-like branches remain embedded in the single-particle continuum. The prohibitively large first-magic-angle supercell is represented by a tractable cell with its interlayer separation reduced to the angle-dependent magic distance, where four quasi-flat bands emerge around the Fermi level. Their partial occupation produces a dispersive low-energy plasmon-like excitation without a clear dielectric zero at resonance. A distinct interband plasmon is instead identified, supported by transitions involving the quasi-flat manifold and neighboring high-density-of-states regions. Band-energy rescaling places its characteristic energy in the mid-infrared range of interband collective excitations measured near the magic angle.
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Applied Physics

Energy Harvesting for Self-Powered Microsystems: A Critical Review of Materials, Power Management, and System Integration

Lyuye Lin, Feng Zhifu, Ermanno Miele et al. (+12 more)

The relentless proliferation of the Internet of Things (IoT), wearable bioelectronics, and cyber-physical infrastructure has rendered the conventional electrochemical battery the single most prohibitive bottleneck to long-term, maintenance-free autonomous microsystems. Energy harvesting, i.e. the conversion of ambient mechanical, thermal, and radiative energy into usable electrical power, has consequently emerged as a transformative paradigm to realize perpetual, battery-independent operation. This review critically synthesizes the most significant advances in energy harvesting technologies over the recent period, with a focus on triboelectric nanogenerators (TENGs), piezoelectric and pyroelectric transducers, indoor photovoltaics, radio-frequency (RF) rectennas, and their multi-source hybrid integrations. We highlight paradigm-shifting breakthroughs, including liquid-solid TENGs that eliminate mechanical wear, nonlinear piezoelectric oscillators that broaden operational bandwidth by over 300%, machine-learning-accelerated material discovery for high charge-density dielectrics, and multiband and broadband RF harvesting enabled by metamaterial architectures. Crucially, we move beyond conventional materials-centric narratives to critically interrogate the "unseen" system-level bottlenecks: ultra-low-voltage cold-start power management integrated circuits (PMICs), the impedance-matching challenges of hybrid energy sources, and the persistent degradation of micro-supercapacitors and thin-film batteries under realistic field conditions.
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All authors Lyuye Lin, Feng Zhifu, Ermanno Miele, Giuseppe Cantarella, Elena Degoli, Diego Angeli, Lethy Krishnan Jagadamma, Feng Gao, Michele Magno, Ivano Eligio Castelli, Tommaso Ongarello, Chunbo Liu, Roman Krahne, Denis Garoli, Remo Proietti Zaccaria

Machine Learning

QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization

Yujie Li, Zezhi Shao, Chengqing Yu et al. (+7 more)

Ubiquitous time series data across diverse domains enables critical applications in areas such as transportation systems and power grids. Recently, training foundation models on massive datasets to achieve accurate zero-shot forecasting has emerged as a major research focus. However, current studies predominantly prioritize architectural innovations while insufficiently addressing data diversity, often relying on simple data sampling strategies that fail to manage complex data distributions effectively, leading to inefficient use of training data and suboptimal performance. To address this, we propose QUALS, a large-scale time series corpus equilibrium framework. QUALS significantly enhances data efficiency, i.e., enabling existing models to achieve superior performance using only a small fraction of the original training data. Specifically, QUALS operates through two core mechanisms. First, a pattern quantization framework systematically decodes heterogeneous patterns from mixed corpora via vector quantization and uniform binning. Second, a learnability synchronization framework calibrates sampling weights for heterogeneous patterns, bridging the optimization gap between simple and complex motifs to maximize overall training efficiency. Extensive benchmarks demonstrate that pre-training on QUALS consistently achieves superior zero-shot performance, even under substantially reduced training budgets.
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All authors Yujie Li, Zezhi Shao, Chengqing Yu, Yisong Fu, Weijie Zhu, Yifan Du, Jilin Hu, Bin Yang, Yongjun Xu, Fei Wang

Earth and Planetary Astrophysics

Thermal Evolution of Lava Planets Across System Ages: Predictions for Hell of a Survey

Mariana Sastre, Tim Lichtenberg, Lisa Dang et al. (+22 more)

Ultra-short-period (USP) rocky exoplanets can have dayside temperatures high enough to maintain permanent magma oceans, sitting at the intersection of interior geophysics and atmospheric chemistry. Coupled feedbacks between the molten surface and outgassed atmosphere can sustain or enhance a volatile envelope, while stellar interactions can erode it. Understanding which outcome prevails, and its observable imprint, requires a multi-target approach across planets at different stages of thermal evolution. We present predictions for the five targets of JWST Cycle 4 program 8864: TOI-1807 b, TOI-2260 b, TOI-431 b, TOI-6255 b, and TOI-2431 b. Using the PROTEUS coupled interior-atmosphere framework, we construct a simulation grid and classify outcomes into six categories, defined by the final interior melt state and by whether the planet retains a detectable atmosphere thick enough to redistribute heat to the nightside. For targets retaining a non-negligible volatile envelope, our models predict higher partial pressures for most species when the surface is molten, except for S$_{2}$, whose enhancement in the solid regime suggests it may serve as a tracer of interior melt state. Despite some targets showing outcomes across multiple scenarios, most tend toward a bare-rock end-member, with global melt fraction $\leq$ 20% and atmospheric retention sensitive to escape efficiency. Our analysis reveals a minimum escape efficiency threshold below which volatile envelopes survive under energy-limited escape, constraining the conditions required for atmosphere survival on irradiated rocky planets. These predictions will guide interpretation of MIRI-LRS phase curve observations and identify which targets and features best discriminate between competing geophysical states.
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All authors Mariana Sastre, Tim Lichtenberg, Lisa Dang, Anjali Piette, Haiyang S. Wang, Mercedes López-Morales, Thomas Wilson, Charles-Édouard Boukaré, Mahesh Herath, Nicolas Cowan, Md Abdullah Al Zaman, Madyson G. Barber, Casey Brinkman-Traverse, Nicholas Connors, Ian Crossfield, Lina D'Aoust, Oliver Herbort, Leoni Janssen, Mathilde Kervazo, Owen Lammert, Yamila Miguel, Raymond Pierrehumbert, Allona Vazan, Joost P. Wardenier, Sebastian Zieba

High Energy Physics (Experiment)

Observation of double $s\bar{s}$ production in $e^+e^-$ collision at $\sqrt{s} = 3.08~\textrm{GeV}$

BESIII Collaboration: M. Ablikim, M. N. Achasov, P. Adlarson et al. (+779 more)

We report the observation of significant double-$s\bar{s}$ production in the $e^+e^-$ continuum, based on the measurement of prompt $\phi$ mesons produced in association with hadrons containing an $s$ quark or an $s\bar{s}$ pair. In an analysis of $e^+e^-$ collision data collected by the BESIII experiment at $\sqrt{s}=3.08~\textrm{GeV}$, the ratio $\sigma(e^+e^- \to \phi\ s\bar{s}+\textrm{anything}) / \sigma(e^+e^-\rightarrow\phi+\textrm{anything})$ is determined to be $(40.4\pm1.7_{\rm stat.}\pm1.5_{\rm syst.})\%$ by detecting and measuring $e^+e^-\to\phi + X(s\bar{s})$, where $X(s\bar{s})$ denotes an $\eta$ meson, an $\eta^{\prime}$ meson, or one of the strange-meson pairs $K^+K^-$, $K^+K^{*-}$, $K^-K^{*+}$, $K^0\bar{K}^{0}$, and $K^0\bar{K}^{*0}+\textrm{c.c.}$. The level of double-$s\bar{s}$ production is in line with the double-$c\bar{c}$ production reported by the Belle and \babar\ collaborations, for which theoretical calculations predict lower rates. The experimental measurement of double $s\bar{s}$ production at BESIII can shed light on the understanding of quark hadronization and QCD.
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All authors BESIII Collaboration: M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, C. S. Akondi, R. Aliberti, A. Amoroso, Q. An, Y. H. An, M. S. Anderson, Y. Bai, O. Bakina, H. R. Bao, X. L. Bao, M. Barbagiovanni, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, D. Cabiati, H. Cai, M. H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, T. T. Chang, G. R. Che, Y. Z. Che, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. M. Chen, T. Chen, W. Chen, X. R. Chen, X. T. Chen, X. Y. Chen, Y. B. Chen, Y. Q. Chen, Z. K. Chen, J. Cheng, L. N. Cheng, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. Cottee-Meldrum, H. L. Dai, J. P. Dai, X. C. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, Z. Y. Deng, A. Denig, I. Denisenko, M. Destefanis, F. De Mori, E. Di Fiore, X. X. Ding, Y. Ding, Y. X. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, Z. J. Dong, M. C. Du, S. X. Du, Shaoxu Du, X. L. Du, Y. Q. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, G. F. Fan, J. J. Fan, K. X. Fan, Y. H. Fan, J. Fang, Jin Fang, S. S. Fang, W. X. Fang, Y. Q. Fang, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Q. X. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, Xu Gao, Y. Gao, Y. N. Gao, Y. Y. Gao, Yunong Gao, Z. Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, A. Gilman, K. Goetzen, J. Gollub, J. B. Gong, J. D. Gong, L. Gong, W. X. Gong, W. Gradl, M. Greco, M. D. Gu, M. H. Gu, C. Y. Guan, A. Q. Guo, H. Guo, J. N. Guo, L. B. Guo, M. J. Guo, R. P. Guo, X. Guo, Y. P. Guo, Z. Guo, A. Guskov, J. Gutierrez, J. Y. Han, T. T. Han, X. Han, F. Hanisch, K. D. Hao, X. Q. Hao, F. A. Harris, C. Z. He, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, Y. X. Hu, Z. M. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, N. Hüsken, N. in der Wiesche, J. Jackson, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, L. K. Jia, X. Q. Jia, D. Jiang, S. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, L. C. L. Jin, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, X. L. Kang, X. S. Kang, B. C. Ke, V. Khachatryan, A. Khoukaz, O. B. Kolcu, B. Kopf, L. Kröger, L. Krümmel, Y. Y. Kuang, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, Q. Lan, W. N. Lan, T. T. Lei, M. Lellmann, T. Lenz, C. Li, C. H. Li, C. K. Li, Chunkai Li, Cong Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. L. Li, H. N. Li, H. P. Li, Hui Li, J. N. Li, J. S. Li, J. W. Li, K. Li, K. L. Li, L. J. Li, L. K. Li, Lei Li, M. H. Li, M. R. Li, M. T. Li, P. L. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, S. Li, S. X. Li, S. Y. Li, Shanshan Li, T. Li, T. Y. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. K. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. Li, Y. H. Li, Y. B. Li, Y. C. Li, Y. G. Li, Y. P. Li, Z. H. Li, Z. J. Li, Z. L. Li, Z. X. Li, Z. Y. Li, Zaiyi Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, Z. Z. Liang, G. R. Liao, L. B. Liao, M. H. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, C. X. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. J. Liu, K. Liu, K. Y. Liu, Ke Liu, Kun Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. T. Liu, X. Liu, X. K. Liu, X. L. Liu, X. P. Liu, X. T. Liu, X. Y. Liu, Y. Liu, Y. B. Liu, Yi Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, Z. X. Liu, Z. Y. Liu, X. C. Lou, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. H. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, J. S. Luo, M. X. Luo, T. Luo, X. L. Luo, Z. Y. Lv, X. R. Lyu, Y. F. Lyu, Y. H. Lyu, C. L. Ma, F. C. Ma, H. L. Ma, Heng Ma, J. L. Ma, L. L. Ma, L. R. Ma, Q. M. Ma, R. Q. Ma, R. Y. Ma, T. Ma, X. T. Ma, X. Y. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Maity, S. Malde, Q. A. Malik, L. M. Mansur, Y. J. Mao, Z. P. Mao, S. Marcello, A. Marshall, F. M. Melendi, Y. H. Meng, Z. X. Meng, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, A. F. Mohammad, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, H. Neuwirth, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, C. Normand, S. L. Olsen, Q. Ouyang, I. V. Ovtin, S. Pacetti, Y. Pan, C. Y. Pang, A. Pathak, Y. P. Pei, M. Pelizaeus, G. L. Peng, H. P. Peng, X. J. Peng, Y. Y. Peng, K. Peters, K. Petridis, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, L. Pöpping, F. Z. Qi, H. R. Qi, L. Y. Qian, S. Qian, W. B. Qian, C. F. Qiao, J. H. Qiao, J. J. Qin, J. L. Qin, L. Q. Qin, L. Y. Qin, P. B. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, J. Rademacker, K. Ravindran, C. F. Redmer, A. Rivetti, M. Rolo, G. Rong, S. S. Rong, F. Rosini, Ch. Rosner, M. Q. Ruan, W. R. Ruangyoo, N. Salone, A. Sarantsev, Y. Schelhaas, M. Schernau, K. Schoenning, M. Scodeggio, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, Ch. Y. Shi, H. Shi, J. L. Shi, J. Y. Shi, M. H. Shi, S. Shi, S. Y. Shi, X. Shi, X. D. Shi, H. L. Song, J. J. Song, M. H. Song, T. Z. Song, W. M. Song, Y. X. Song, Zirong Song, S. Sosio, S. Spataro, S. Stansilaus, F. Stieler, M. Stolte, S. S Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, R. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. C. Sun, Y. H. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, H. Tabaharizato, N. T. Tagsinsit, C. J. Tang, G. Y. Tang, J. Tang, J. J. Tang, L. F. Tang, Y. A. Tang, Z. H. Tang, L. Y. Tao, M. Tat, J. X. Teng, J. Y. Tian, W. H. Tian, Y. Tian, Z. F. Tian, K. Yu. Todyshev, I. Uman, E. van der Smagt, B. Wang, Bin Wang, Bo Wang, C. Wang, Chao Wang, Cong Wang, D. Y. Wang, F. K. Wang, H. J. Wang, H. R. Wang, J. Wang, J. H. Wang, J. J. Wang, J. P. Wang, K. Wang, L. L. Wang, L. W. Wang, M. Wang, Mi Wang, N. Y. Wang, P. Wang, S. Wang, Shun Wang, T. Wang, W. Wang, W. P. Wang, X. F. Wang, X. L. Wang, X. N. Wang, Xin Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. J. Wang, Y. L. Wang, Y. N. Wang, Yanning Wang, Yaqian Wang, Yi Wang, Yuan Wang, Z. Wang, Z. L. Wang, Z. Q. Wang, Z. Y. Wang, Zhi Wang, Ziyi Wang, D. Wei, D. H. Wei, D. J. Wei, H. R. Wei, F. Weidner, H. R. Wen, S. P. Wen, U. Wiedner, G. Wilkinson, J. F. Wu, L. H. Wu, L. J. Wu, Lianjie Wu, S. G. Wu, S. M. Wu, X. W. Wu, Z. Wu, H. L. Xia, L. Xia, B. H. Xiang, D. Xiao, G. Y. Xiao, H. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, K. J. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, D. B. Xiong, G. F. Xu, H. Y. Xu, Q. J. Xu, Q. N. Xu, T. D. Xu, X. P. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, W. H. Yan, W. P. Yan, X. Q. Yan, Y. Y. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, L. Y. Yang, R. J. Yang, X. Y. Yang, Y. Yang, Y. G. Yang, Y. H. Yang, Y. M. Yang, Y. Q. Yang, Y. Z. Yang, Youhua Yang, Z. Y. Yang, W. J. Yao, Z. P. Yao, M. Ye, M. H. Ye, Z. J. Ye, K. Yi, Junhao Yin, Qiqin Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, L. W. Yu, T. Yu, X. D. Yu, Y. C. Yu, Yongchao Yu, C. Z. Yuan, H. Yuan, J. Yuan, Jie Yuan, L. Yuan, M. K. Yuan, S. H. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, Ying Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. J. Zeng, Yujie Zeng, Y. C. Zhai, Y. H. Zhan, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, Gengyuan Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. L. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, Han Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jin Zhang, Jiyuan Zhang, L. M. Zhang, Lei Zhang, N. Zhang, P. Zhang, Q. Zhang, Q. Y. Zhang, Q. Z. Zhang, R. Y. Zhang, S. H. Zhang, S. N. Zhang, Shulei Zhang, X. M. Zhang, X. Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. P. Zhang, Yao Zhang, Yu Zhang, Yu Zhang, Z. Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. X. Zhang, Z. Y. Zhang, Z. Z. Zhang, Zh. Zh. Zhang, Zhaoke Zhang, Zhilong Zhang, Ziyang Zhang, Ziyu Zhang, G. Zhao, J.-P. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. L. Zhao, Y. P. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, W. Q. Zheng, X. R. Zheng, Y. H. Zheng, B. Zhong, C. Zhong, X. Zhong, H. Zhou, J. Q. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. X. Zhou, Y. Z. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. X. Zhu, Lin Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, W. J. Zhu, W. Z. Zhu, Y. C. Zhu, Z. A. Zhu, X. Y. Zhuang, M. Zhuge, J. H. Zou, J. Zu

Quantum Algebra

On the Mext groups of sVec_R and sVec_H

Sean Sanford

We compute the groups of minimal nondegenerate extensions of the real symmetric fusion categories $\mathrm{sVec}_{\mathbb R}$ and $\mathrm{sVec}_{\mathbb H}$. We find that they are both isomorphic to the Klein-four group $(\mathbb{Z}/2\mathbb{Z})^2$. Along the way, we classify all nondegenerately braided fusion categories over $\mathbb R$ that have Frobenius-Perron dimension 4, and we determine exactly which of these categories is a Drinfeld center. We end the paper by discussing a homotopy-theoretic conjecture that organizes all these structures.
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Artificial Intelligence

MTVA-Bench: Evaluating the Language Model Inside Cascaded Voice Agents

Pritish Mishra, Ishaan Kumar, Akshat Mandoli et al. (+1 more)

Generally, most voice agents are cascaded systems, i.e., an ASR model transcribes the caller's audio, a language model reads the transcript and decides what to say and which backend tools to call, and a TTS model speaks the reply. Nearly all of the decision making happens in the language model, but existing evaluations measure it either too broadly or too narrowly. End-to-end voice benchmarks score the full pipeline, so recognition errors and model errors mix into a single number. LLM benchmarks isolate the model but they do not evaluate what makes real phone calls hard, such as transcription issues, caller's voice being split across messages and the requirement that replies follow the language and script specified. We introduce the Multi-Turn Voice Agent Benchmark (MTVA-Bench), which evaluates the language model on the same conditions it faces inside a cascaded system. The caller is played by an LLM following a set of rubrics and tool calls are answered by a mock backend which responds to the arguments the model actually sent. The benchmark contains 49 agents working across 490 reviewed scenarios and supports 7 languages. Scoring is a combination of deterministic checks on tool calls with two LLM judges, one that scores scenario specific rules and one that grades conversation quality without access to the task. Both judges must cite specific messages from the transcript. Task and conversation scores are weighted equally, since a call can complete its task and still go badly for the caller. In a seven-model study, six of the models select the correct tool within 6.4 points of one another, but their overall scores span 24.4 points. Most of the gap comes from argument values, action ordering, rule compliance, and what the model says around its tool calls.
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All authors Pritish Mishra, Ishaan Kumar, Akshat Mandoli, Sudarshan Kamath

Computer Vision

Ischemic Stroke Segmentation and Net Water Uptake Quantification on Multicenter Non-Contrast CT Using Supervised Target-Domain Adaptation

Linus Britt, Maximilian Nielsen, Susan Klapproth et al. (+5 more)

Objectives: Quantitative assessment of infarct hypodensity on non-contrast computed tomography (NCCT), including net water uptake (NWU), requires manual or semi-manual lesion delineation, often guided by CT perfusion or diffusion-weighted MRI, limiting clinical applicability. Automated segmentation on NCCT could enable efficient biomarker extraction such as NWU but remains challenging across heterogeneous multicenter data. This study aimed to develop and externally test a domain-aware deep learning framework for ischemic stroke segmentation on NCCT and assess its suitability for NWU quantification. Materials & Methods: In this retrospective multicenter study of 801 patients from four datasets, an nnU-Net-based model was trained on NCCT scans from the University Medical Center Hamburg-Eppendorf and the Acute Ischemic Stroke Dataset. To adapt to new domains, the model was fine-tuned on target-domain subsets from Boston (n=11) and ISLES (n=75), with evaluation on held-out cases not used for fine-tuning. Automated segmentations and NWU values were compared with expert references. Results: For lesions $\geq$ 30 mL, median Dice was 0.68 (Boston) and 0.56 (ISLES). Including smaller lesions, which predominated in ISLES, median Dice was 0.54 (interquartile range [IQR] 0.30-0.70) for acute lesion segmentation (Boston dataset) and 0.20 (IQR 0.03-0.41) for NCCT lesion segmentations when compared to post-treatment infarct (primary target of the ISLES challenge). Automated NWU mean absolute error was 1.37 percentage points (SD 1.61, Boston). Conclusion: Target-domain adaptation supported NCCT-only infarct segmentation across heterogeneous external cohorts, although performance varied across domains. The approach enabled low-error NWU quantification from baseline NCCT without advanced imaging, supporting further prospective clinical evaluation.
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All authors Linus Britt, Maximilian Nielsen, Susan Klapproth, Andre Kemmling, Michael H. Lev, Gabriel Broocks, Rene Werner, Thilo Sentker

Computer Vision

Task-Oriented Semantic Feature Transmission for Multi-Task Satellite Remote Sensing over Low-SNR Channels

Shuoyuan Sun, Hongyu Wang, Mugen Peng et al. (+1 more)

Conventional satellite remote sensing transmission follows a reconstruct-then-infer paradigm that optimizes pixel-level fidelity, creating an objective mismatch with downstream tasks such as classification and detection, especially at low SNR. This paper investigates a task-oriented framework that bypasses image reconstruction and directly transmits semantic features extracted by a multitask-pretrained backbone. A lightweight channel adaptation module (CAM) compresses feature dimensionality for bandwidth reduction, and a feature restorer recovers task-relevant structure after channel corruption. With the backbone frozen, the CAM and task-specific downstream heads are jointly optimized with task and feature-level supervision under random-SNR training. Under the adopted AWGN setting, experiments on scene classification and object detection show consistent gains over reconstruction-oriented JSCC baselines across different SNR conditions, with the largest improvements in the low-SNR regime.
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All authors Shuoyuan Sun, Hongyu Wang, Mugen Peng, Wenjia Xu

Quantum Physics

An exact Jordan signature in cumulative-count first-passage statistics

Lachlan Bridges

For cumulative-count first-passage problems, the Laplace transform of the time $\mathcal T_N$ of the $N$th count is naturally expressed through powers of a one-count kernel. A Jordan defect of that kernel does not, however, automatically survive the complete sum over terminal phases. We give an exact three-state Markov-renewal example in which it does. At $s=1$, the one-count kernel has spectrum $\{1/2,1/8,1/8\}$, with a one-dimensional eigenspace at $1/8$, and for initial phase $1$, \[ \mathbb E_1[e^{-\mathcal T_N}] =\frac56\,2^{-N}+\frac{N+1}{6}\,8^{-N}. \] Thus the ordinary, unconditioned cumulative-count transform contains the explicit Jordan contribution $N/(6\,8^N)$ at every threshold $N\ge1$. For the same fixed stochastic process, the two eigenvalues meeting at $1/8$ unfold as \[ \lambda_\pm(s)=\frac18\pm\frac1{96}\sqrt{s-1} -\frac{131}{2304}(s-1)+O((s-1)^{3/2}), \] and the characteristic discriminant has a simple zero at $s=1$. We place the example beside two structures that suppress such a marginal signature: fixed post-count reset, which gives scalar renewal, and a common total holding rate, for which terminal summation scalarizes the matrix power. The model also admits a monitored-Lindblad realization, but the mechanism is entirely classical. The result provides a small exact benchmark for how generalized spectral modes can remain visible in finite-threshold first-passage statistics.
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Computer Vision

Bridging Modalities on the Cortex: Surface-based MRI to PET Translation with a Diffusion Bridge

Yitong Li, Alexandra Samoylova, Fabian Bongratz et al. (+4 more)

Cortical hypometabolism measured by Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a highly sensitive biomarker for dementia diagnosis. However, high costs, radiation exposure, and limited accessibility constrain its clinical utility. While cross-modal synthesis from Magnetic Resonance Imaging (MRI) offers a promising alternative, existing volumetric generation methods do not explicitly account for the highly folded cortical geometry, where disease-related patterns predominantly reside. To address this, we introduce a novel surface-based diffusion bridge framework DB-SUiT for MRI-to-PET translation that operates natively on the cortical manifold. A conditional Spherical U-shaped vision Transformer (SUiT) is specifically designed to model the intricate cross-modal relationships while preserving surface topology. It combines spherical convolutional encoders for multi-scale surface feature extraction with bottleneck Transformers to capture long-range spatial dependencies, while incorporating demographic and subcortical conditions to refine the synthesis. Evaluated on two datasets, including subjects with different dementia types, DB-SUiT demonstrates high-fidelity synthesis that substantially outperforms other baselines. In automated dementia classification, synthesized PET surfaces improve performance over MRI by 14.2% and PET volumes by 11.3%, approaching the performance of real PET surfaces. In a blinded reader study, synthetic PET achieved 85.5% diagnostic accuracy, compared with 75.8% for MRI and 95.2% for real PET. This further demonstrates cross-cohort and cross-pathology generalization, as the model was evaluated without retraining on an external cohort that included a dementia subtype not represented during training. Our code is available at https://github.com/ai-med/DB-SUiT.
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All authors Yitong Li, Alexandra Samoylova, Fabian Bongratz, Timo Grimmer, Dennis M. Hedderich, Igor Yakushev, Christian Wachinger

Statistical Mechanics

Landau diamagnetism and the de Haas-van Alphen effect from a single geometric construction

Sung-Hoon Lee

Landau diamagnetism is usually derived from the grand canonical potential, a calculation that yields the correct susceptibility but little physical insight, while the rule that extremal cross sections govern the de Haas-van Alphen (dHvA) oscillations is commonly asserted rather than exhibited. We present an elementary zero-temperature construction in which the occupied states of a free-electron gas are grouped, interval by interval along the field direction, into the Landau levels onto which they condense. Within each interval the field-induced energy cost reduces to a transfer of states between two congruent triangles, and center-of-mass arithmetic yields the Landau susceptibility. The construction fails only in a narrow neighborhood of an extremal cross section of the Fermi surface; there the contribution oscillates with the dHvA period, one oscillation for each Landau level that passes through the extremal cross section. Direct zero-temperature state counting locates the oscillation: the residual is concentrated in the few intervals nearest the extremal cross section, the summed contribution of the distant ones being negligible on the scale of the peak oscillation amplitude.
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Analysis of PDEs

Sharp regularity results for eikonal equations in Banach spaces

Trí Minh Lê and Sebastián Tapia-García

Viscosity solutions of eikonal equations need not enjoy regularity beyond Lipschitz continuity. In contrast, differentiable solutions exhibit stronger structural properties. In the Euclidean setting, building on observations of Caffarelli and Crandall (Comm. Partial Differential Equations $\textbf{35}$ (2010), 391–414), one finds that differentiable solutions of the eikonal equation $\| Df \| \equiv 1 \,\, \text{in } \mathcal{U} \subset \mathbb{R}^d$ are of class $C^{1, 1}_{\mathrm{loc}}(\mathcal U)$. Moreover, if $\mathcal{U} = \mathbb{R}^d$, then $f$ must be affine. This manuscript revisits these phenomena in the setting of (finite– and infinite–dimensional) Banach spaces. Our results show how the geometry of the underlying Banach space determines the regularity and affineness of Gateaux–differentiable solutions of eikonal equations. As a geometric application in finite dimensions, we characterize differentiable functions whose gradient ranges are constrained to boundaries of convex bodies.
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Dynamical Systems

Extended dynamic mode decomposition with Fourier dictionaries: Error bounds and fast implementation

Felix Bartel, Sandra Ritter, Manuel Schaller et al. (+1 more)

The Koopman operator has gained considerable attention in dynamical systems due to its capability to provide a linear viewpoint for nonlinear systems using data-driven methods such as extended dynamic mode decomposition (EDMD). In this work, we propose an EDMD-variant with a Fourier dictionary on the $d$-dimensional torus, where the data are sampled on an equispaced tensor grid. We show that the EDMD regression problem admits a unique closed-form solution and coincides with trigonometric interpolation of the Koopman image. This identification has two consequences. First, using Koopman invariance of Sobolev spaces, we transfer approximation-theoretic results for trigonometric interpolation to derive $L^p$-error bounds, $2 \le p \le \infty$, on approximations of the Koopman operator. In particular, the established bounds are of optimal order with explicit constants. Second, the EDMD matrix never has to be assembled, since its action reduces to (nonequispaced) fast Fourier transforms such that the EDMD-approximation can be evaluated matrix-free with quasi-linear cost in the dictionary size. We illustrate the results for the Kuramoto model of coupled oscillators and for the chaotic ABC flow with dictionaries of up to $10^7$ modes.
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All authors Felix Bartel, Sandra Ritter, Manuel Schaller, Karl Worthmann

Human-Computer Interaction

Designing Against Deskilling: Metacognitive Feedback Reduces Cognitive Offloading to LLM Assistants

Sebastian Maier, Kai Schwabe, Manuel Schneider et al. (+1 more)

Cognitive offloading to AI can reduce opportunities to practice skills, creating risks of deskilling. However, it remains unclear how to prevent deskilling without restricting access to AI. Here, we design two interventions to reduce offloading decisions: (1) metacognitive feedback that makes the implications of offloading for users explicit, and (2) an effort-based reward that incentivizes less extensive LLM assistance. We test both in a preregistered online experiment ($N = 704$) with a 2$\times$2 design and a no-AI control. The task was to practice fraction arithmetic with an LLM-based assistant that provided solutions only on explicit request, followed by an unaided test. Metacognitive feedback reduced answer offloading (OR $= 0.47$) and improved test performance (OR $= 1.51$). We found no evidence that the reward affected either outcome. Our results identify metacognitive feedback as a promising design choice to reduce cognitive offloading.
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All authors Sebastian Maier, Kai Schwabe, Manuel Schneider, Stefan Feuerriegel

Statistical Mechanics

Higher-Order Interactions in Complex Systems: Mechanisms, Behaviour, Representation and Reducibility

Francisco J. Pérez-Reche

Higher-order interactions couple three or more elements in ways that cannot be decomposed into pairwise contributions. In diverse complex systems they produce striking collective behaviours such as explosive synchronization, discontinuous transitions and altered stability. Neither such behaviour nor a description written in many-body terms establishes a many-body mechanism, since both depend on how the system is represented. This review distinguishes three questions: what mechanism governs the system, what behaviour does it produce, and how is either represented? Representation covers both which variables are included and how relations among them are encoded. We examine direct many-body mechanisms, nonlinear but pairwise mechanisms that mimic them, and indirect routes through shared environments or eliminated variables, together with temporal aggregation, an artefact of observation rather than a mechanism. The same behaviour can arise from different mechanisms, and a given higher-order mechanism has no universal behavioural consequence. We then formulate reducibility, the question of whether a higher-order model can be replaced by a lower-order one, as a comparison between a source model and an admissible class of lower-order models, relative to a target and a tolerance. The target may be a microscopic rule, an observable or a qualitative feature. Information-theoretic and inferential methods are reviewed as complementary tools for detecting higher-order dependence, testing lower-order alternatives and reconstructing interaction structure. However, observational data alone generally cannot establish the mechanism. We close by identifying open problems in linking mechanisms to measurable signatures, coarse-graining, identifiability and causality, and in extending reductions beyond mean field. (Abridged)
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Optics

Characterization of spatially inhomogeneous chirp in ultrashort multielectron beams via femtosecond hole burning

Yuichi Tachibana and Yuya Morimoto

Direct observation of the temporal-energy structure of pulsed electron beams is crucial for beam-driven light generation and time-resolved microscopic imaging. In this study, we characterized the spatially non-uniform time-energy structure in multi-electron ultrashort pulses exhibiting significant space-charge effects. By combining spectral hole burning originating from the photon-induced near-field electron microscopy (PINEM) effect with angle-resolved energy analysis, we observed time-energy correlations at each beam angle. Applying this method to 37-keV pulses containing up to 17 electrons per pulse, we determined energy spread and chirp rate, which vary by up to 100% and 40%, respectively, across the entire beam. These findings highlight the importance of characterizing multi-electron pulses with spatial or angular resolution and suggest the utility of spatially modulated light for the shaping of high-flux pulsed electron beams.
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Logic

Existence of bases implies the axiom of choice, a foundation-free proof

Gabriel Fernandes, Renan Maneli Mezabarba and Vinicius de Oliveira Rodrigues

We prove that, in Zermelo–Fraenkel set theory with the axiom of Foundation removed, the statement that every vector space has a basis implies the Axiom of Choice ($\mathsf{AC}$), concluding that the classical equivalence between $\mathsf{AC}$ and the existence of bases does not require regularity. This result extends to set theory with atoms.
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Computer Vision

Cross-Modal Attention Acts as a Frequency Filter: Why Verbose Prompts Improve Robustness in Vision-Language Models

Farooq Ahmad Wani, Maria Sofia Bucarelli, Mujtaba Hussain Mirza et al. (+5 more)

Vision-language models (VLMs) are fragile under image corruption. We find that the wording of the question affects VLMs in two opposite ways. Verbose questions make VLMs substantially more robust—e.g., rephrasing "Is there a cat?" into "Please look carefully and answer: is there a cat?". Conversely, VLMs become more fragile under corruption when the question is semantically complex or finer-grained, e.g., "what colour is the cup left of the chair?" instead of "is there a cup?". Both effects stem from question-conditioned cross-modal attention, which induces a spectral filter over image patches: verbose questions broaden its frequency support, while fine-grained questions concentrate it onto fewer visual scales. The model's answer drifts most when this filter and the corruption sit on the same spatial frequencies. We test the filter view on Qwen3-VL and LLaVA-OneVision across GQA and CLEVR; verbose paraphrasing reduces drift variance by 70–81% on the 8B models. The practical recipe—pad the prompt—further yields measurable gains in accuracy, even under image corruption.
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All authors Farooq Ahmad Wani, Maria Sofia Bucarelli, Mujtaba Hussain Mirza, Oleksandr Pryymak, Aryo Pradipta Gema, Iacopo Masi, Pasquale Minervini, Fabrizio Silvestri

Machine Learning

Fast-varying Natural Frequencies and Damping Ratio Identification for Linear Time-Varying System

Melisa Bozaci and Alice Cicirello

This work proposes a physics-enhanced machine learning approach for the system identification of Linear Time-Varying (LTV) systems under time-varying operating conditions in terms of fast-varying natural frequencies and damping ratios by combining a long short-term memory network with an Extended Kalman Filter (EKF). The proposed approach uses vibration data (displacement and velocity measurements), domain knowledge of modal damping ratios, and a physics-based model that can yield an approximate natural frequencies time-dependency model. The approach is validated using synthetic data generated from a finite element model of a 2-blade offshore wind turbine under realistic environmental and operating conditions. This system displays fast time-varying frequencies due to operating conditions, whose identification is particularly challenging because of the wind and wave loading. The robustness of the proposed approach is assessed under assumed incorrect system information (e.g. damping ratio). The proposed approach is evaluated across different environmental and operating conditions to show its applicability to different operating regimes. The results show the approach can accurately identify the selected fast-varying natural frequency, 1st Fore-Aft (FA-1) mode, with a maximum root mean square error of 0.0012 Hz. The results demonstrate that the model trained on EKF estimates depends on accurate damping values, whereas the model trained on physics-based data exhibits robustness to incorrect damping assumptions. The approach is extended to damping ratio identification for the selected mode by estimating the root mean square error between models trained on EKF estimates and physics-based data. The results show that the approach can yield a good approximation of the FA-1 mode damping ratio using grid search, offering an improvement over covariance-driven stochastic subspace identification.
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Quantum Physics

Electrostatic Stabilization of Near-Surface Quantum Sensors via Dielectric Interface Engineering

Atharva Paranjape, Kathrin Küster, Olga Shevstova et al. (+6 more)

Control of charge-state stability in near-surface quantum defects is critical for nanoscale sensing, yet remains particularly challenging under ultra-high vacuum (UHV), where surface-induced band bending destabilizes the metrologically relevant charge-state. Here, we present a robust and reproducible approach for stabilizing shallowly implanted (< 10 nm deep) nitrogen-vacancy (NV) centers in near-UHV conditions (P = $3 \times 10^{-9}$ mbar) based on dielectric interface engineering. Through measurements on individually addressable NV centers, we demonstrate that a TiO2 coating on the diamond suppresses surface-induced electrostatic fields, yielding a 79% increase n NV- population and a 45% enhancement in NV-spin resonance contrast at room temperature. Coherent control measurements further reveal suppressed charge-state conversion dynamics. These results establish dielectric screening as a powerful and reproducible strategy to engineer charge transition energetics of NV centers in scanning-probe-compatible geometries under extreme conditions.
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All authors Atharva Paranjape, Kathrin Küster, Olga Shevstova, Lisa Ebo, Toni Hache, Klaus Kern, Rainer Stöhr, Jörg Wrachtrup, Aparajita Singha

Accelerator Physics

Reconstruction of Beam Transverse Parameters in the Fermilab Side-Coupled Linac Using a Normalized Coordinate Framework

E. V. Chen, J-P. Carneiro, R. V. Sharankova et al. (+1 more)

Quadrupole scans are a commonly used tool for beam second moment reconstruction. Limitations in the strength of the magnets and layout of the beamline elements frequently preclude simple quadrupole-drift-detector scans from collecting sufficient data for reconstruction. Using a normalized coordinate framework, we characterize the prerequisites for a robust simple quadrupole scan and expand these prerequisites to reconstruction from more complex optics. The beam second moments are investigated at two locations in the Fermilab Side-Coupled Linac under simple and complex optics, using this framework to maximize information gained from wire scanner profile measurements.
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All authors E. V. Chen, J-P. Carneiro, R. V. Sharankova, A. V. Shemyakin

High Energy Physics (Experiment)

A heterogeneous and vectorized sequence for the HL-LHC full tracking reconstruction of the CMS experiment

Emmanouil Vourliotis

This contribution presents the new baseline strategy for the Phase-2 tracking of the CMS experiment for online event reconstruction, and for the main iteration of offline tracking. This tracking sequence takes advantage of the combination of cutting-edge tracking algorithms that are either optimized for parallel execution on GPUs (Patatrack and LST), or vectorized for efficient CPU performance (mkFit). Such a combined approach offers an effective solution to deal with the unprecedented computational challenges caused by the large number of simultaneous collisions per bunch crossing expected at the High-Luminosity Large Hadron Collider (HL-LHC). The proposed combination not only reduces the computational resource requirements but also enhances the physics reach by incorporating displaced tracking and increasingly leveraging machine learning techniques.
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Analysis of PDEs

On an inverse boundary problem in elastodynamics

Lu Chen, Yan Jiang, Hongyu Liu et al. (+1 more)

We study the simultaneous identification of the Lamé parameters $\lambda$ and $\mu$ in three-dimensional static isotropic elasticity. For smooth coefficients, we establish uniqueness from the full Dirichlet-to-Neumann map when $\mu$ is axisymmetric, admits a multiplicative or additive separation on a product domain, or is quasianalytic in one fixed direction. These results require neither smallness of $\nabla\mu$ nor real analyticity of the coefficients, and impose no corresponding structural condition on $\lambda$. In the axisymmetric and separated cases, partial boundary data suffice to determine $\mu$.
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All authors Lu Chen, Yan Jiang, Hongyu Liu, Longyue Tao

Quantum Physics

Private communication from Pauli channels with no privacy

Uthirakalyani G, Pritam Halder and David Elkouss

A channel capacity quantifies the communication capability of a noisy physical process. In contrast to communication channels in the classical world, quantum theory makes this capability contextual. We show that two Pauli two-qubit channels, each with zero private classical capacity, can be used to transmit private information when used together. One is a two-qubit Pauli channel whose environment can reconstruct the receiver output up to matrix transposition; the other is an antidegradable channel. We obtain a similar result when the second channel is the 50% qubit erasure channel. A simple binary code built from rank-three mixtures of Bell states activates private communication. The main ingredient in our construction is a transpose-antidegradable channel that is not antidegradable.
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Optics

Frequency Attraction and Repulsion in Lasers with Complex Mutual Coupling

Amit Pando, Tomer Hacohen, Eran Bernstein et al. (+3 more)

We experimentally investigate laser synchronization under complex mutual coupling, featuring both dissipative and dispersive components. Using a reconfigurable digital degenerate cavity laser with precise control over system parameters, we show that complex coupling can induce both frequency attraction and repulsion, in contrast to common models of coupled oscillators. Furthermore, we show that these frequency shifts can be exploited to significantly enhance global synchronization in multi-laser arrays. Comparison with numerical and theoretical results indicate that amplitude dynamics of complex coupled lasers play an important role in the observed phenomena. Our results establish a versatile platform for exploring non-Hermitian physics and topological states in tailored laser networks.
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All authors Amit Pando, Tomer Hacohen, Eran Bernstein, Victor Shelukin, Asher Friesem, Nir Davidson