Published: 2026-04-29 16:32:57
Authors: Dimitris Dimakopoulos, Shay B. Cohen, Ioannis Konstas
Categories: cs.CL, cs.LG
Abstract:
Large language models (LLMs) acquire most of their factual knowledge during the pre-training stage, through next token prediction. Subsequent stages of post-training often introduce new facts outwith the parametric knowledge, giving rise to hallucinations. While it has been demonstrated that supervised fine-tuning (SFT) on new knowledge may exacerbate the problem, the underlying mechanisms are still poorly understood. We conduct a controlled fine-tuning experiment, focusing on closed-book QA, and find latent directions that causally contribute to hallucinations. Specifically, we fine-tune Llama 3.1 8B, Gemma 2 9B and Mistral 7B v03 on seven distinct single QA datasets, controlling for the percentage of new knowledge and number of training epochs. By measuring performance on the test set, we validate that incrementally introducing new knowledge increases hallucinations, with the effect being more pronounced with prolonged training. We leverage pre-trained sparse autoencoders (SAEs) to analyze residual stream activations across various checkpoints for each model and propose Monotonic Relationship Feature Identification (MoRFI) for capturing causally relevant latents. MoRFI filters SAE features that respond monotonically to controlled fine-tuning data mixtures of a target property. Our findings show that exposure to unknown facts disrupts the model's ability to retrieve stored knowledge along a set of directions in the residual stream. Our pipeline reliably discovers them across distinct models, recovering knowledge through single-latent interventions.
Published: 2026-04-29 16:01:22
Authors: Mahya Ramezani, Holger Voos
Categories: cs.RO, cs.AI, cs.LG
Abstract:
This paper presents a hierarchical decision-making framework for unmanned aerial vehicle (UAV) missions motivated by search-and-rescue (SAR) scenarios under limited simulation training. The framework combines a fixed rule-based high-level advisor with an online goal-conditioned low-level reinforcement learning (RL) controller. To stress-test early adaptation, we also consider a strict no-pretraining deployment regime. The high-level advisor is defined offline from a structured task specification and compiled into deterministic rules. It provides interpretable mission- and safety-aware guidance through recommended actions, avoided actions, and regime-dependent arbitration weights. The low-level controller learns online from task-defined dense rewards and reuses experience through a mode-aware prioritized replay mechanism augmented with rule-derived metadata. We evaluate the framework on two tasks: battery-aware multi-goal delivery and moving-target delivery in obstacle-rich environments. Across both tasks, the proposed method improves early safety and sample efficiency primarily by reducing collision terminations, while preserving the ability to adapt online to scenario-specific dynamics.
Published: 2026-04-29 15:52:44
Authors: Haoyuan Xu, Wei Miao, Geert Dhaene, Jad Beyhum
Categories: econ.EM, stat.ME
Abstract:
The maximum likelihood estimator in nonlinear panel data models with interactive fixed effects is biased. Several bias correction methods, such as analytical and jackknife approaches, have been proposed to enable valid inference. This paper shows that the parametric bootstrap also enables valid inference in such models. In particular, we show that the parametric bootstrap replicates the asymptotic distribution of the maximum likelihood estimator. Therefore, it yields asymptotically unbiased estimates and confidence sets with asymptotically correct coverage. We also propose a transformation-based bootstrap confidence interval that delivers improved finite-sample performance. Simulation results support the theoretical findings. Finally, we apply the proposed method to examine technological and product market spillover effects on firms' innovation behavior.
Published: 2026-04-29 15:48:46
Authors: Yiqi Liu, Noelle Crawford, Michael Wang, Jilong Xue, Jian Huang
Categories: cs.AR, cs.DC
Abstract:
To overcome the well-known memory bottleneck of AI chips, 3D stacked architectures that employ advanced packaging technology with high-density through-silicon vias (TSVs) pins have proven to be a promising solution. The 3D-stacked AI chip enables ultra-high memory bandwidth between compute and memory by stacking numerous DRAM banks atop many AI cores in a distributed manner. However, it is not easy to explore the efficiency of the 3D-stacked AI chip, due to its unique distributed nature. And we need to carefully consider multiple intertwined factors that range from upper-level computing paradigm to machine learning (ML) compiler optimizations, and to the underlying hardware architecture.
In this paper, we develop Voxel, a fast and compiler-aware end-to-end simulation framework to facilitate exploring the efficiency of 3D-stacked AI chips for large language model (LLM) inference. Voxel enables the software/hardware co-exploration by employing a programming interface that allows ML compilers to customize the model execution plans. After validating the results of Voxel with an emulator on real silicon, we thoroughly examine the impact and correlation of different aspects of 3D-stacked AI chips, including state-of-the-art compute paradigms, tile-to-core mapping, tensor-to-bank mapping, NoC topologies and link bandwidth, DRAM bank bandwidth, per-core SRAM capacity, and energy/thermal constraints. Our findings disclose that the end-to-end efficiency of a 3D stacked AI chip not only is determined by the cooperative function of these factors, but also significantly depends on the mappings from tiles to AI core and DRAM banks. We report our findings throughout the paper, with the expectation that they will shed light on the development of the 3D-stacked AI chip ecosystem. We will open source Voxel and our study results for public research.
Published: 2026-04-29 15:47:30
Authors: Yihan Zhang
Categories: math.PR, cs.IT, math.ST, stat.ML
Abstract:
We prove that any random variable $X$ whose moment generating function is point-wise upper bounded by that of $ G \sim \mathcal{N}(0,1) $ must be dominated by $ G/\mathbb{E}[|G|] $ in convex order, meaning $ \mathbb{E}[f(X)] \le \mathbb{E}[f(G/\mathbb{E}[|G|])] $ for all convex $f$. Equality is attained by taking $ X \sim \mathrm{Unif}(\{-1,1\}) $ and $ f(x) = |x| $.
Published: 2026-04-29 15:46:46
Authors: Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang
Categories: cs.LG
Abstract:
This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic function solution. We motivate the approach, compare it to existing work, and prove a bound on its generalization error. The quality of our solutions is evaluated on a synthetic problem and three UCI ML repository datasets. In most cases, we outperform manifold regularization of support vector machines, which is a state-of-the-art approach to semi-supervised max-margin learning.
Published: 2026-04-29 15:44:21
Authors: Jeremy Diamond, Vincenzo Stoico
Categories: cs.SE, cs.PF
Abstract:
The Running Average Power Limit (RAPL) interface is widely used to estimate software energy consumption via CPU and DRAM counters, but tool design differences and high-frequency polling can introduce measurement overhead, namely, extra time and energy consumed by the tool itself.This paper quantifies the impact of RAPL-based tools on high-frequency (1 kHz) energy monitoring and investigates mitigation strategies. We conduct two controlled experiments: the first evaluates seven tools, including a user-space application and a kernel module developed by the authors, against a no-tool baseline, using six NAS Benchmark functions to quantify overhead. The second experiment isolates and times key functions for polling Model-Specific Registers (MSRs) (rdmsr and sys/proc_read) to estimate their execution latencies and identify potential slowdowns. The results show that existing user-space tools can introduce substantial time overhead at 1 kHz, whereas our tools significantly reduce system call overhead and inline math overhead. The time overhead of existing tools ranges from 0.25% to 46.75%. Our solutions maintain time overhead levels close to the baseline. We also find that system calls are slower than rdmsr, which in turn is slower than traditionally long-running instructions like cpuid. These findings indicate that RAPL-based energy measurement can be substantially improved by simplifying tool design and employing lower-level instructions to access RAPL values. Our findings provide guidance for practitioners on how to develop high-frequency energy profiling tools, show possible situations that can skew energy values, and demonstrate that access to RAPL values can be faster using specific techniques.
Published: 2026-04-29 15:42:59
Authors: Changhun Oh, Michał Oszmaniec, Oliver Reardon-Smith, Zoltán Zimborás
Categories: quant-ph
Abstract:
Establishing the precise computational boundary between classically tractable fermionic systems and those capable of genuine quantum advantage is a central challenge in quantum simulation. While injecting non-Gaussian ``magic" inputs into free-fermion circuits is widely expected to generate intractable complexity, we identify a physically motivated intermediate regime. Supported by rigorous bounds and numerical evidence, we show that for a class of paired non-Gaussian fermionic states, essential quantum simulation primitives -- transition amplitudes, overlaps, and arbitrary-weight number correlators -- can be efficiently approximated to additive error under free-fermionic dynamics. This tractability stems from an algebraic reduction that compresses exponentially large multiparticle interference into a single coefficient of a multivariate Pfaffian polynomial. Because these classical estimators match the intrinsic $O(1/\sqrt{K})$ statistical uncertainty of quantum hardware utilizing $K$ measurement shots, they constitute a practical benchmark. Building on this foundation, we construct an additive-error estimator for high-weight Wilson observables in the noninteracting quench of recent trapped-ion experiments, providing a rigorous classical benchmark. Extending this to quantum chemistry, we demonstrate that core overlap-based subroutines for antisymmetrized products of strongly orthogonal geminals admit exact Pfaffian reductions. Ultimately, these results sharpen the boundary of quantum advantage, establishing that the paired-electron scaffold is effectively dequantized and clarifying exactly where quantum resources are indispensable.
Published: 2026-04-29 15:41:11
Authors: Zhaoyuan Cai, Xinglin Zhang
Categories: cs.LG
Abstract:
Federated Unlearning (FU) is an emerging paradigm in Federated Learning (FL) that enables participating clients to fully remove their contributions from a trained global model, driven by data protection regulations that mandate the right to be forgotten. However, existing FU methods mostly rely on synchronous coordination. This requirement forces the entire federation to halt and wait for stragglers to complete erasure, creating significant delays due to device heterogeneity. Furthermore, these methods often face the problem that the influence of erased data is merely suppressed temporarily and resurfaces during subsequent training, rather than being genuinely removed. To overcome these limitations, this paper proposes Asynchronous Federated Unlearning with Invariance Calibration (AFU-IC), a novel framework for medical imaging that decouples the erasure process from the global training workflow. This enables the target client to perform unlearning asynchronously without interrupting global training. Meanwhile, a server-side invariance calibration mechanism prevents the model from relearning the erased data. Extensive experiments on three medical benchmarks demonstrate that AFU-IC achieves unlearning efficacy and model fidelity comparable to gold-standard retraining while significantly reducing wall-clock latency compared to synchronous baselines. AFU-IC ensures efficient, compliant and reliable FL in cross-silo medical environments.
Published: 2026-04-29 15:36:41
Authors: Joss Armstrong
Categories: cs.GT, cs.IT
Abstract:
Category-based coordination mechanisms allocate resources by mapping a declared service category to a fixed resource profile, without observing individual demand types. We establish three results for this class of mechanisms. First, the relative welfare gap Delta satisfies a tight two-sided bound in terms of the aggregate within-category allocation variance epsilon: (alpha/2W*)epsilon <= Delta <= (beta/2W*)epsilon. Second, the expected misreporting gain is bounded by the same epsilon without assumptions on agent strategy; demand-derived categories minimise both welfare loss and misreporting incentive simultaneously. Third, aggregate outcome metrics strictly dominate per-agent metrics for service-level detection under a homogeneity condition, for all parameter values, with a finite-sample power gap of O(1/m). At any fixed K, the demand-derived category label is the sufficient statistic for coordination: collecting per-agent data beyond the category label adds noise to the detection problem without reducing the welfare gap. However, welfare and detection impose structurally opposed demands on K: welfare improves with finer categories, detection worsens. The designer faces a feasibility band [Kmin, Kmax] and must choose K within it as a value judgement. We claim that any protocol achieving welfare gap Delta <= epsilon* and missed-detection rate <= beta* requires at least Hlb(epsilon*, beta*) bits of category entropy. We illustrate the mechanism on a synthetic population of 50,000 demand vectors and five weeks of production performance-management data from four anonymised operator networks (28,249 cells).
Published: 2026-04-29 15:35:01
Authors: Bochao Liu, Zhipeng Qian, Yang Zhao, Xinyuan Jiang, Zihan Liang, Yufei Ma, Junpeng Zhuang, Ben Chen, Shuo Yang, Hongen Wan, Yao Wu, Chenyi Lei, Xiao Liang
Categories: cs.AI, cs.MA
Abstract:
Operating and maintaining (O&M) large-scale online engine systems (search, recommendation, advertising) demands substantial human effort for release monitoring, alert response, and root cause analysis. While LLM-based agents are a natural fit for these tasks, the deployment bottleneck is not reasoning capability but orchestration: selecting, for each operational event, the relevant data (metrics, logs, change events) and the applicable operational knowledge (handbook rules and practitioner experience). Feeding all signals indiscriminately causes dilution and hallucination, while manually curating the event-to-(data, knowledge) mapping is intractable under dozens of daily releases. We present Bian Que, an agentic framework with three contributions: (i) a \emph{unified operational paradigm} abstracting day-to-day O&M into three canonical patterns: release interception, proactive inspection, and alert root cause analysis; (ii) \emph{Flexible Skill Arrangement}, where each Skill specifies which data and knowledge to retrieve for a given business-module context and can be automatically generated and updated by LLMs or iteratively refined through natural-language instructions from on-call engineers; (iii) a \emph{unified self-evolving mechanism} in which one correction signal drives two parallel pathways, case-memory-to-knowledge distillation and targeted Skill refinement. Deployed on the e-commerce search engine of KuaiShou, the major short-video platform in China, Bian Que reduces alert volume by 75%, achieves 80% root-cause analysis accuracy, and cuts mean time to resolution by over 50%. Our framework achieves 99.0% pass rate on offline evaluations. Our code is available at https://github.com/benchen4395/BianQue_Assistant.
Published: 2026-04-29 15:30:21
Authors: Moumita Patra
Categories: cond-mat.mes-hall
Abstract:
We investigate quantum transport in an off-diagonal Aubry--André--Harper chain. The periodic hopping modulation generates effective internal boundaries that strongly influence the transmission characteristics. We show that edge, in-band bulk, and band-edge bulk states can be clearly distinguished through their transport signatures. In particular, bulk states near the band edges exhibit behavior similar to edge states, with weak dependence on system size, whereas in-band bulk states display pronounced size-dependent oscillations. We further demonstrate that the chain--electrode coupling strength controls the broadening of transmission resonances and drives a crossover from tunneling-dominated to nearly ballistic transport. In addition, dephasing introduces distinct sensitivity across different state classes, depending on their degree of spatial localization. These results highlight the key role of internal boundaries and quantum coherence in governing transport in modulated one-dimensional systems.
Published: 2026-04-29 15:30:06
Authors: Shuzhao Xie, Junchen Ge, Weixiang Zhang, Jiahang Liu, Chen Tang, Yunpeng Bai, Shijia Ge, Jingyan Jiang, Yuzhi Huang, Fengnian Yang, Cong Zhang, Xiaoyi Fan, Zhi Wang
Categories: cs.CV, cs.GR, cs.MM
Abstract:
3D Gaussian Splatting (3DGS) achieves high-quality novel view synthesis with real-time rendering, but its storage cost remains prohibitive for practical deployment. Existing post-training compression methods still rely on many coupled hyperparameters across pruning, transformation, quantization, and entropy coding, making it difficult to control the final compressed size and fully exploit the rate-distortion trade-off. We propose MesonGS++, a size-aware post-training codec for 3D Gaussian compression. On the codec side, MesonGS++ combines joint importance-based pruning, octree geometry coding, attribute transformation, selective vector quantization for higher-degree spherical harmonics, and group-wise mixed-precision quantization with entropy coding. On the configuration side, it treats the reserve ratio and bit-width allocation as the dominant rate-distortion knobs and jointly optimizes them under a target storage budget via discrete sampling and 0--1 integer linear programming. We further propose a linear size estimator and a CUDA parallel quantization operator to accelerate the hyperparameter searching process. Extensive experiments show that MesonGS++ achieves over 34$\times$ compression while preserving rendering fidelity, outperforming state-of-the-art post-training methods and accurately meeting target size budgets. Remarkably, without any training, MesonGS++ can even surpass the PSNR of vanilla 3DGS at a 20$\times$ compression rate on the Stump scene. Our code is available at https://github.com/mmlab-sigs/mesongs_plus
Published: 2026-04-29 15:29:59
Authors: Roberto Pittau
Categories: hep-ph
Abstract:
We present GLoop, a Fortran90 computational framework that allows one to compute by Monte Carlo a certain class of higher-loop integrals in terms of lower-loop building blocks. This is based on a recently introduced method that enables the numerical computation of integrals defined by i epsilon deformations acting on single pole singularities without the need for an explicit analytic contour deformation. We provide detailed, worked-out examples and routines to show how our strategy works. These can be used as a starting point for the reader to develop her/his own calculations.
Published: 2026-04-29 15:17:21
Authors: Chong Ye, Wei-Liang Qian, Yue Cui, Dan Wen, Yutao Xing, Rui-Hong Yue, Takeshi Kodama
Categories: nucl-th
Abstract:
As flow estimators, multi-particle correlators, particularly the higher-order ones, are generally regarded as effective tools for suppressing non-flow contributions. In this work, however, using two well-known toy models that simulate non-flow effects, we demonstrate that multi-particle correlators can, especially in small systems, yield estimates that deviate even further from the underlying flow harmonics than those obtained from other conventional approaches. The two toy models considered here are designed to mimic non-flow effects arising from particle decay and global momentum conservation, such that the {\it apparent} harmonic coefficients become significantly different from the {\it input} values. We provide an analytic explanation for the observed behavior of flow estimates based on multi-particle correlators. Specifically, in the toy model mimicking particle decay, we elucidate the oscillations observed in $v_2\{2\}$ and $v_2\{4\}$. For the other toy model simulating momentum conservation, we show that multi-particle cumulants introduce a deformation in the collective flow that is unique to multi-particle correlators. Additionally, we compare these results with those obtained using the maximum-likelihood estimation method, a recently proposed flow estimator that serves as a viable alternative to traditional techniques.
Published: 2026-04-29 15:14:41
Authors: LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, M. Akthar, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis, L. An, L. Anderlini, M. Andersson, P. Andreola, M. Andreotti, S. Andres Estrada, A. Anelli, D. Ao, C. Arata, F. Archilli, Z. Areg, M. Argenton, S. Arguedas Cuendis, L. Arnone, A. Artamonov, M. Artuso, E. Aslanides, R. Ataíde Da Silva, M. Atzeni, B. Audurier, J. A. Authier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J. J. Back, P. Baladron Rodriguez, V. Balagura, A. Balboni, W. Baldini, Z. Baldwin, L. Balzani, H. Bao, J. Baptista de Souza Leite, C. Barbero Pretel, M. Barbetti, I. R. Barbosa, R. J. Barlow, M. Barnyakov, S. Barsuk, W. Barter, J. Bartz, S. Bashir, B. Batsukh, P. B. Battista, A. Bay, A. Beck, M. Becker, F. Bedeschi, I. B. Bediaga, N. A. Behling, S. Belin, A. Bellavista, K. Belous, I. Belov, I. Belyaev, G. Benane, G. Bencivenni, E. Ben-Haim, A. Berezhnoy, R. Bernet, S. Bernet Andres, A. Bertolin, F. Betti, J. Bex, O. Bezshyyko, S. Bhattacharya, M. S. Bieker, N. V. Biesuz, A. Biolchini, M. Birch, F. C. R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J. E. Blank, S. Blusk, V. Bocharnikov, J. A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, A. Boldyrev, C. Bolognani, R. Bolzonella, R. B. Bonacci, N. Bondar, A. Bordelius, F. Borgato, S. Borghi, M. Borsato, J. T. Borsuk, E. Bottalico, S. A. Bouchiba, M. Bovill, T. J. V. Bowcock, A. Boyer, C. Bozzi, J. D. Brandenburg, A. Brea Rodriguez, N. Breer, J. Brodzicka, J. Brown, D. Brundu, E. Buchanan, M. Burgos Marcos, A. T. Burke, C. Burr, C. Buti, J. S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, Y. Cai, A. Caillet, R. Calabrese, S. Calderon Ramirez, L. Calefice, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J. I. Cambon Bouzas, P. Campana, A. F. Campoverde Quezada, Y. Cao, S. Capelli, M. Caporale, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, A. Casais Vidal, R. Caspary, G. Casse, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, I. Celestino, S. Cesare, A. J. Chadwick, I. Chahrour, H. Chang, M. Charles, Ph. Charpentier, E. Chatzianagnostou, R. Cheaib, M. Chefdeville, C. Chen, J. Chen, S. Chen, Z. Chen, A. Chen Hu, M. Cherif, A. Chernov, S. Chernyshenko, X. Chiotopoulos, V. Chobanova, M. Chrzaszcz, A. Chubykin, V. Chulikov, P. Ciambrone, X. Cid Vidal, G. Ciezarek, P. Cifra, P. E. L. Clarke, M. Clemencic, H. V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, J. Cogan, E. Cogneras, L. Cojocariu, S. Collaviti, P. Collins, T. Colombo, M. Colonna, A. Comerma-Montells, L. Congedo, J. Connaughton, A. Contu, N. Cooke, G. Cordova, C. Coronel, I. Corredoira, A. Correia, G. Corti, J. Cottee Meldrum, B. Couturier, D. C. Craik, M. Cruz Torres, M. Cubero Campos, E. Curras Rivera, R. Currie, C. L. Da Silva, S. Dadabaev, X. Dai, E. Dall'Occo, J. Dalseno, C. D'Ambrosio, J. Daniel, G. Darze, A. Davidson, J. E. Davies, O. De Aguiar Francisco, C. De Angelis, F. De Benedetti, J. de Boer, K. De Bruyn, S. De Capua, M. De Cian, U. De Freitas Carneiro Da Graca, E. De Lucia, J. M. De Miranda, L. De Paula, M. De Serio, P. De Simone, F. De Vellis, J. A. de Vries, F. Debernardis, D. Decamp, S. Dekkers, L. Del Buono, B. Delaney, J. Deng, V. Denysenko, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, I. Diachkov, S. Didenko, S. Ding, Y. Ding, L. Dittmann, V. Dobishuk, A. D. Docheva, A. Doheny, C. Dong, A. M. Donohoe, F. Dordei, A. C. dos Reis, A. D. Dowling, L. Dreyfus, W. Duan, P. Duda, L. Dufour, V. Duk, P. Durante, M. M. Duras, J. M. Durham, O. D. Durmus, A. Dziurda, A. Dzyuba, S. Easo, E. Eckstein, U. Egede, A. Egorychev, V. Egorychev, S. Eisenhardt, E. Ejopu, L. Eklund, M. Elashri, D. Elizondo Blanco, J. Ellbracht, S. Ely, A. Ene, J. Eschle, T. Evans, F. Fabiano, S. Faghih, L. N. Falcao, B. Fang, R. Fantechi, L. Fantini, M. Faria, K. Farmer, F. Fassin, D. Fazzini, L. Felkowski, C. Feng, M. Feng, A. Fernandez Casani, M. Fernandez Gomez, A. D. Fernez, F. Ferrari, F. Ferreira Rodrigues, M. Ferrillo, M. Ferro-Luzzi, S. Filippov, R. A. Fini, M. Fiorini, M. Firlej, K. L. Fischer, D. S. Fitzgerald, C. Fitzpatrick, T. Fiutowski, F. Fleuret, A. Fomin, M. Fontana, L. A. Foreman, R. Forty, D. Foulds-Holt, V. Franco Lima, M. Franco Sevilla, M. Frank, E. Franzoso, G. Frau, C. Frei, D. A. Friday, J. Fu, Q. Führing, T. Fulghesu, G. Galati, M. D. Galati, A. Gallas Torreira, D. Galli, S. Gambetta, M. Gandelman, P. Gandini, B. Ganie, H. Gao, R. Gao, T. Q. Gao, Y. Gao, Y. Gao, Y. Gao, L. M. Garcia Martin, P. Garcia Moreno, J. García Pardiñas, P. Gardner, L. Garrido, C. Gaspar, A. Gavrikov, L. L. Gerken, E. Gersabeck, M. Gersabeck, T. Gershon, S. Ghizzo, Z. Ghorbanimoghaddam, F. I. Giasemis, V. Gibson, H. K. Giemza, A. L. Gilman, M. Giovannetti, A. Gioventù, L. Girardey, M. A. Giza, F. C. Glaser, V. V. Gligorov, C. Göbel, L. Golinka-Bezshyyko, E. Golobardes, D. Golubkov, A. Golutvin, S. Gomez Fernandez, W. Gomulka, F. Goncalves Abrantes, I. Gonçales Vaz, M. Goncerz, G. Gong, J. A. Gooding, I. V. Gorelov, C. Gotti, E. Govorkova, J. P. Grabowski, L. A. Granado Cardoso, E. Graugés, E. Graverini, L. Grazette, G. Graziani, A. T. Grecu, N. A. Grieser, L. Grillo, S. Gromov, C. Gu, M. Guarise, L. Guerry, A. -K. Guseinov, E. Gushchin, Y. Guz, T. Gys, K. Habermann, T. Hadavizadeh, C. Hadjivasiliou, G. Haefeli, C. Haen, S. Haken, G. Hallett, P. M. Hamilton, J. Hammerich, Q. Han, X. Han, S. Hansmann-Menzemer, L. Hao, N. Harnew, T. J. Harris, M. Hartmann, S. Hashmi, J. He, N. Heatley, A. Hedes, F. Hemmer, C. Henderson, R. Henderson, R. D. L. Henderson, A. M. Hennequin, K. Hennessy, L. Henry, J. Herd, P. Herrero Gascon, J. Heuel, A. Heyn, A. Hicheur, G. Hijano Mendizabal, J. Horswill, R. Hou, Y. Hou, D. C. Houston, N. Howarth, W. Hu, X. Hu, W. Hulsbergen, R. J. Hunter, M. Hushchyn, D. Hutchcroft, M. Idzik, D. Ilin, P. Ilten, A. Iniukhin, A. Iohner, A. Ishteev, K. Ivshin, H. Jage, S. J. Jaimes Elles, S. Jakobsen, T. Jakoubek, E. Jans, B. K. Jashal, A. Jawahery, C. Jayaweera, V. Jevtic, Z. Jia, E. Jiang, X. Jiang, Y. Jiang, Y. J. Jiang, E. Jimenez Moya, N. Jindal, M. John, A. John Rubesh Rajan, D. Johnson, C. R. Jones, S. Joshi, B. Jost, J. Juan Castella, N. Jurik, I. Juszczak, K. Kalecinska, D. Kaminaris, S. Kandybei, M. Kane, Y. Kang, C. Kar, M. Karacson, A. Kauniskangas, J. W. Kautz, M. K. Kazanecki, F. Keizer, M. Kenzie, T. Ketel, B. Khanji, A. Kharisova, S. Kholodenko, G. Khreich, T. Kirn, V. S. Kirsebom, S. Klaver, N. Kleijne, A. Kleimenova, D. K. Klekots, K. Klimaszewski, M. R. Kmiec, T. Knospe, R. Kolb, S. Koliiev, L. Kolk, A. Konoplyannikov, P. Kopciewicz, P. Koppenburg, A. Korchin, M. Korolev, I. Kostiuk, O. Kot, S. Kotriakhova, E. Kowalczyk, A. Kozachuk, P. Kravchenko, L. Kravchuk, O. Kravcov, M. Kreps, P. Krokovny, W. Krupa, W. Krzemien, O. Kshyvanskyi, S. Kubis, M. Kucharczyk, V. Kudryavtsev, E. Kulikova, A. Kupsc, V. Kushnir, B. Kutsenko, J. Kvapil, I. Kyryllin, D. Lacarrere, P. Laguarta Gonzalez, A. Lai, A. Lampis, D. Lancierini, C. Landesa Gomez, J. J. Lane, G. Lanfranchi, C. Langenbruch, J. Langer, T. Latham, F. Lazzari, C. Lazzeroni, R. Le Gac, H. Lee, R. Lefèvre, A. Leflat, S. Legotin, M. Lehuraux, E. Lemos Cid, O. Leroy, T. Lesiak, E. D. Lesser, B. Leverington, A. Li, C. Li, C. Li, H. Li, J. Li, K. Li, L. Li, M. Li, P. Li, P. -R. Li, Q. Li, T. Li, T. Li, Y. Li, Y. Li, Y. Li, Z. Lian, Q. Liang, X. Liang, Z. Liang, S. Libralon, A. Lightbody, C. Lin, T. Lin, R. Lindner, H. Linton, R. Litvinov, D. Liu, F. L. Liu, G. Liu, K. Liu, S. Liu, W. Liu, Y. Liu, Y. Liu, Y. L. Liu, G. Loachamin Ordonez, I. Lobo, A. Lobo Salvia, A. Loi, T. Long, F. C. L. Lopes, J. H. Lopes, A. Lopez Huertas, C. Lopez Iribarnegaray, S. López Soliño, Q. Lu, C. Lucarelli, D. Lucchesi, M. Lucio Martinez, Y. Luo, A. Lupato, E. Luppi, K. Lynch, S. Lyu, X. -R. Lyu, G. M. Ma, H. Ma, S. Maccolini, F. Machefert, F. Maciuc, B. Mack, I. Mackay, L. M. Mackey, L. R. Madhan Mohan, M. J. Madurai, D. Magdalinski, D. Maisuzenko, J. J. Malczewski, S. Malde, L. Malentacca, A. Malinin, T. Maltsev, G. Manca, G. Mancinelli, C. Mancuso, R. Manera Escalero, F. M. Manganella, D. Manuzzi, D. Marangotto, J. F. Marchand, R. Marchevski, U. Marconi, E. Mariani, S. Mariani, C. Marin Benito, J. Marks, A. M. Marshall, L. Martel, G. Martelli, G. Martellotti, L. Martinazzoli, M. Martinelli, D. Martinez Gomez, D. Martinez Santos, F. Martinez Vidal, A. Martorell i Granollers, A. Massafferri, R. Matev, A. Mathad, V. Matiunin, C. Matteuzzi, K. R. Mattioli, A. Mauri, E. Maurice, J. Mauricio, P. Mayencourt, J. Mazorra de Cos, M. Mazurek, M. McCann, N. T. McHugh, A. McNab, R. McNulty, B. Meadows, D. Melnychuk, D. Mendoza Granada, P. Menendez Valdes Perez, F. M. Meng, M. Merk, A. Merli, L. Meyer Garcia, D. Miao, H. Miao, M. Mikhasenko, D. A. Milanes, A. Minotti, E. Minucci, T. Miralles, B. Mitreska, D. S. Mitzel, R. Mocanu, A. Modak, L. Moeser, R. D. Moise, E. F. Molina Cardenas, T. Mombächer, M. Monk, T. Monnard, S. Monteil, A. Morcillo Gomez, G. Morello, M. J. Morello, M. P. Morgenthaler, A. Moro, J. Moron, W. Morren, A. B. Morris, A. G. Morris, R. Mountain, Z. Mu, E. Muhammad, F. Muheim, M. Mulder, K. Müller, F. Muñoz-Rojas, R. Murta, V. Mytrochenko, P. Naik, T. Nakada, R. Nandakumar, T. Nanut, G. Napoletano, I. Nasteva, M. Needham, E. Nekrasova, N. Neri, S. Neubert, N. Neufeld, P. Neustroev, J. Nicolini, D. Nicotra, E. M. Niel, N. Nikitin, L. Nisi, Q. Niu, B. K. Njoki, P. Nogarolli, P. Nogga, C. Normand, J. Novoa Fernandez, G. Nowak, C. Nunez, H. N. Nur, A. Oblakowska-Mucha, V. Obraztsov, T. Oeser, A. Okhotnikov, O. Okhrimenko, R. Oldeman, F. Oliva, E. Olivart Pino, M. Olocco, R. H. O'Neil, J. S. Ordonez Soto, D. Osthues, J. M. Otalora Goicochea, P. Owen, A. Oyanguren, O. Ozcelik, F. Paciolla, A. Padee, K. O. Padeken, B. Pagare, T. Pajero, A. Palano, L. Palini, M. Palutan, C. Pan, X. Pan, S. Panebianco, S. Paniskaki, G. Panshin, L. Paolucci, A. Papanestis, M. Pappagallo, L. L. Pappalardo, C. Pappenheimer, C. Parkes, D. Parmar, G. Passaleva, D. Passaro, A. Pastore, M. Patel, J. Patoc, C. Patrignani, A. Paul, C. J. Pawley, A. Pellegrino, J. Peng, X. Peng, M. Pepe Altarelli, S. Perazzini, D. Pereima, H. Pereira Da Costa, M. Pereira Martinez, A. Pereiro Castro, C. Perez, P. Perret, A. Perrevoort, A. Perro, M. J. Peters, K. Petridis, A. Petrolini, S. Pezzulo, J. P. Pfaller, H. Pham, L. Pica, M. Piccini, L. Piccolo, B. Pietrzyk, G. Pietrzyk, R. N. Pilato, D. Pinci, F. Pisani, M. Pizzichemi, V. M. Placinta, M. Plo Casasus, T. Poeschl, F. Polci, M. Poli Lener, A. Poluektov, N. Polukhina, I. Polyakov, E. Polycarpo, S. Ponce, D. Popov, K. Popp, S. Poslavskii, K. Prasanth, C. Prouve, D. Provenzano, V. Pugatch, A. Puicercus Gomez, G. Punzi, J. R. Pybus, Q. Qian, W. Qian, N. Qin, R. Quagliani, R. I. Rabadan Trejo, R. Racz, J. H. Rademacker, M. Rama, M. Ramírez García, V. Ramos De Oliveira, M. Ramos Pernas, M. S. Rangel, F. Ratnikov, G. Raven, M. Rebollo De Miguel, F. Redi, J. Reich, F. Reiss, Z. Ren, P. K. Resmi, M. Ribalda Galvez, R. Ribatti, G. Ricart, D. Riccardi, S. Ricciardi, K. Richardson, M. Richardson-Slipper, F. Riehn, K. Rinnert, P. Robbe, G. Robertson, E. Rodrigues, A. Rodriguez Alvarez, E. Rodriguez Fernandez, J. A. Rodriguez Lopez, E. Rodriguez Rodriguez, J. Roensch, A. Rogachev, A. Rogovskiy, D. L. Rolf, P. Roloff, V. Romanovskiy, A. Romero Vidal, G. Romolini, F. Ronchetti, T. Rong, M. Rotondo, S. R. Roy, M. S. Rudolph, M. Ruiz Diaz, R. A. Ruiz Fernandez, J. Ruiz Vidal, J. J. Saavedra-Arias, J. J. Saborido Silva, S. E. R. Sacha Emile R., N. Sagidova, D. Sahoo, N. Sahoo, B. Saitta, M. Salomoni, I. Sanderswood, R. Santacesaria, C. Santamarina Rios, M. Santimaria, L. Santoro, E. Santovetti, A. Saputi, D. Saranin, A. Sarnatskiy, G. Sarpis, M. Sarpis, C. Satriano, A. Satta, M. Saur, D. Savrina, H. Sazak, F. Sborzacchi, A. Scarabotto, S. Schael, S. Scherl, M. Schiller, H. Schindler, M. Schmelling, B. Schmidt, N. Schmidt, S. Schmitt, H. Schmitz, O. Schneider, A. Schopper, N. Schulte, M. H. Schune, G. Schwering, B. Sciascia, A. Sciuccati, G. Scriven, I. Segal, S. Sellam, A. Semennikov, T. Senger, M. Senghi Soares, A. Sergi, N. Serra, L. Sestini, A. Seuthe, B. Sevilla Sanjuan, Y. Shang, D. M. Shangase, M. Shapkin, R. S. Sharma, I. Shchemerov, L. Shchutska, T. Shears, L. Shekhtman, J. Shen, Z. Shen, S. Sheng, V. Shevchenko, B. Shi, Q. Shi, W. S. Shi, Y. Shimizu, E. Shmanin, R. Shorkin, J. D. Shupperd, R. Silva Coutinho, G. Simi, S. Simone, M. Singha, N. Skidmore, T. Skwarnicki, M. W. Slater, E. Smith, M. Smith, L. Soares Lavra, M. D. Sokoloff, F. J. P. Soler, A. Solomin, A. Solovev, K. Solovieva, N. S. Sommerfeld, R. Song, Y. Song, Y. Song, Y. S. Song, F. L. Souza De Almeida, B. Souza De Paula, K. M. Sowa, E. Spadaro Norella, E. Spedicato, J. G. Speer, P. Spradlin, F. Stagni, M. Stahl, S. Stahl, S. Stanislaus, M. Stefaniak, O. Steinkamp, D. Strekalina, Y. Su, F. Suljik, J. Sun, J. Sun, L. Sun, D. Sundfeld, W. Sutcliffe, P. Svihra, V. Svintozelskyi, K. Swientek, F. Swystun, A. Szabelski, T. Szumlak, Y. Tan, Y. Tang, Y. T. Tang, M. D. Tat, J. A. Teijeiro Jimenez, A. Terentev, F. Terzuoli, F. Teubert, E. Thomas, D. J. D. Thompson, A. R. Thomson-Strong, H. Tilquin, V. Tisserand, S. T'Jampens, M. Tobin, T. T. Todorov, L. Tomassetti, G. Tonani, X. Tong, T. Tork, L. Toscano, D. Y. Tou, C. Trippl, G. Tuci, N. Tuning, L. H. Uecker, A. Ukleja, D. J. Unverzagt, A. Upadhyay, B. Urbach, A. Usachov, A. Ustyuzhanin, U. Uwer, V. Vagnoni, A. Vaitkevicius, V. Valcarce Cadenas, G. Valenti, N. Valls Canudas, J. van Eldik, H. Van Hecke, E. van Herwijnen, C. B. Van Hulse, R. Van Laak, M. van Veghel, G. Vasquez, R. Vazquez Gomez, P. Vazquez Regueiro, C. Vázquez Sierra, S. Vecchi, J. Velilla Serna, J. J. Velthuis, M. Veltri, A. Venkateswaran, M. Verdoglia, M. Vesterinen, W. Vetens, D. Vico Benet, P. Vidrier Villalba, M. Vieites Diaz, X. Vilasis-Cardona, E. Vilella Figueras, A. Villa, P. Vincent, B. Vivacqua, F. C. Volle, D. vom Bruch, N. Voropaev, K. Vos, C. Vrahas, J. Wagner, J. Walsh, E. J. Walton, G. Wan, A. Wang, B. Wang, C. Wang, G. Wang, H. Wang, J. Wang, J. Wang, J. Wang, J. Wang, M. Wang, N. W. Wang, R. Wang, X. Wang, X. Wang, X. W. Wang, Y. Wang, Y. Wang, Y. H. Wang, Z. Wang, Z. Wang, J. A. Ward, M. Waterlaat, N. K. Watson, D. Websdale, Y. Wei, Z. Weida, J. Wendel, B. D. C. Westhenry, C. White, M. Whitehead, E. Whiter, A. R. Wiederhold, D. Wiedner, M. A. Wiegertjes, C. Wild, G. Wilkinson, M. K. Wilkinson, M. Williams, M. J. Williams, M. R. J. Williams, R. Williams, S. Williams, Z. Williams, F. F. Wilson, M. Winn, W. Wislicki, M. Witek, L. Witola, T. Wolf, E. Wood, G. Wormser, S. A. Wotton, H. Wu, J. Wu, X. Wu, Y. Wu, Z. Wu, K. Wyllie, S. Xian, Z. Xiang, Y. Xie, T. X. Xing, A. Xu, L. Xu, M. Xu, Z. Xu, Z. Xu, Z. Xu, S. Yadav, K. Yang, X. Yang, Y. Yang, Y. Yang, Z. Yang, V. Yeroshenko, H. Yeung, H. Yin, X. Yin, C. Y. Yu, J. Yu, X. Yuan, Y Yuan, J. A. Zamora Saa, M. Zavertyaev, M. Zdybal, F. Zenesini, C. Zeng, M. Zeng, C. Zhang, D. Zhang, J. Zhang, L. Zhang, R. Zhang, S. Zhang, S. L. Zhang, Y. Zhang, Y. Z. Zhang, Z. Zhang, Y. Zhao, A. Zhelezov, S. Z. Zheng, X. Z. Zheng, Y. Zheng, T. Zhou, X. Zhou, Y. Zhou, V. Zhovkovska, L. Z. Zhu, X. Zhu, X. Zhu, Y. Zhu, V. Zhukov, J. Zhuo, D. Zuliani, G. Zunica
Categories: hep-ex
Abstract:
The first evidence for the decay $B^+\toπ^+ e^+ e^-$ is reported using proton-proton collision data recorded by the LHCb experiment at centre-of-mass energies of 7, 8 and 13 TeV, corresponding to an integrated luminosity of 9 fb$^{-1}$. A signal excess with a significance of 3.2$σ$ is observed and the branching fraction is measured to be $\cal{BR}(B^+\toπ^+ e^+ e^-) = (2.4\,{}^{+0.9}_{-0.8} \,{}^{+0.4}_{-0.2}) \times 10^{-8}$, where the first set of uncertainties is statistical and the second is systematic. The result is consistent with the Standard Model expectation.
Published: 2026-04-29 15:13:23
Authors: Shuixin Fang, Shupeng Wang, Zhen Wu, Hui Zhang, Tao Zhou
Categories: math.NA
Abstract:
This paper develops a deep policy iteration method for high-dimensional finite-horizon mean-field games. We reformulate the game as a regenerative problem with deterministic cycles, which allows policy evaluation (PE), policy improvement (PI), and population measure estimation to be carried out cycle by cycle. Within this formulation, we approximate the population measure by a particle system and update it using a one-step random mapping induced by the Euler-Maruyama discretization of the state dynamics. This update transports a mini-batch of particles from one cycle to the next, avoiding sequential trajectory simulation over the entire time horizon at each iteration. The PE and PI subproblems are formulated through the relation between consecutive cycles, with adversarial training used for evaluation and averaged optimization used for improvement. The resulting method is efficient and scalable in high dimensions, as it avoids the direct solution of the coupled Hamilton-Jacobi-Bellman and Fokker-Planck system, the full simulation of trajectories to estimate the population measure, the explicit computation of conditional expectations in policy evaluation, and pointwise optimization in policy improvement. Numerical experiments demonstrate that the proposed method effectively handles dimensions up to 10,000.
Published: 2026-04-29 15:12:18
Authors: Raj Kumar Ranabhat, Tayler D Ross, Tony Jiao, Jeremie Larouche, Joel Finkelstein, Michael Hardisty
Categories: cs.CV
Abstract:
Surgical training involves didactic teaching, mentor-led learning, surgical skills laboratories, and direct exposure to surgery; however, increasing clinical pressures have limited operating room (OR) exposure. This work leverages virtual reality (VR) to provide a safe and immersive training environment. Existing VR training is often based on standardized scenarios not tailored to individual clinical cases. This study addresses this limitation using artificial intelligence (AI) based computer vision methods to generate patient-specific simulations from computed tomography (CT) and magnetic resonance imaging (MRI). This study focuses on patient-specific spinal decompression simulation for spinal stenosis in a virtual operating room. The objectives were (1) automatic creation of 3D anatomical models and (2) VR simulation of spinal decompression procedures including laminectomy, disc resection, and foraminotomy. Model construction required multimodal fusion (registration) of CT and MRI and segmentation of relevant structures. Segmentation was evaluated using the Dice Similarity Coefficient (DSC), and registration accuracy using Target Registration Error (TRE). Qualitative feedback was obtained from surgeons and trainees. High-fidelity patient-specific 3D models were generated efficiently (approximately 2.5 minutes per case, N = 15). Segmentation accuracy was high, with a DSC of 0.95 (+/- 0.03) for vertebral bone and 0.895 (+/- 0.02) for soft tissue structures. Registration accuracy showed a mean TRE of 1.73 (+/- 0.42) mm. Semi-structured interviews indicated improved spatial understanding, increased procedural confidence, and strong perceived educational value. This platform significantly reduced the time and costs of patient-specific modelling, thereby facilitating pre-operative planning, post-procedural assessments, and comprehensive surgical simulation.
Published: 2026-04-29 15:05:37
Authors: Zuzheng Kuang, Honghao Chang, Boqiang Liang, Haoqian Wang, Lijun He, Fan Li, Haixia Bi
Categories: cs.CV, cs.AI
Abstract:
Open-vocabulary change detection aims to identify semantic changes in bi-temporal remote sensing images without predefined categories. Recent methods combine foundation models such as SAM, DINO and CLIP, but typically process each timestamp independently or interact only at the final comparison stage. Such paradigms suffer from insufficient temporal coupling during semantic reasoning, which limits their ability to distinguish genuine semantic changes from non-semantic appearance discrepancies. In addition, patch-dominant inference on high-resolution images often weakens global semantic continuity and produces fragmented change regions. To address these issues, we propose MemOVCD, a training-free open-vocabulary change detection framework based on cross-temporal memory reasoning and global-local adaptive rectification. Specifically, we reformulate bi-temporal change detection as a two-frame tracking problem and introduce weighted bidirectional propagation to aggregate semantic evidence from both temporal directions. To stabilize memory propagation across large temporal gaps, we construct histogram-aligned transition frames to smooth abrupt appearance changes. Moreover, a global-local adaptive rectification strategy adaptively fuses local and global-view predictions, improving spatial consistency while preserving fine-grained details. Experiments on five benchmarks demonstrate that MemOVCD achieves favorable performance on two change detection tasks, validating its effectiveness and generalization under diverse open-vocabulary settings.
Published: 2026-04-29 15:02:38
Authors: Bertram Fuchs, Altay Kacan, Aaron Haag, Oliver Lohse
Categories: cs.CE
Abstract:
Automatic feature recognition (AFR) on B-Rep 3D-CAD models is central to CAD/CAM automation, yet most learning-based methods are complex, data-hungry, and evaluate instance grouping and semantic labeling separately. We present FeatureFox, a panoptic AFR pipeline that outputs machining instances with semantic labels: a calibrated binary edge classifier on enriched edge attributes localizes feature boundaries, instances are recovered as connected components in a pruned face-adjacency graph, and a per-instance classifier predicts the machining class from aggregated subgraph attributes. We evaluate on MFInstSeg using Panoptic Quality (PQ), which jointly scores instance separation and semantic correctness. FeatureFox is substantially more sample- and compute-efficient than the deep baseline AAGNet, reaching $\mathrm{PQ}>0.9$ with $\sim250$ training parts versus $\sim5{,}000$ for AAGNet, and training on the full MFInstSeg set takes seconds on a GPU. On the full training set, AAGNet surpasses FeatureFox marginally in PQ, while FeatureFox remains slightly ahead in feature-level recognition and localization accuracy. Finally, leveraging its low data requirement, we train FeatureFox on $270$ manually labeled industrial CAD parts and show qualitative generalization to an unseen real industrial part, indicating practical real-world applicability.
Published: 2026-04-29 14:55:53
Authors: Tianqi Gao, Chengkai Huang, Zihan Wang, Cao Liu, Ke Zeng, Lina Yao
Categories: cs.IR
Abstract:
Large language models (LLMs) have recently been adopted for recommendation by framing user preference modeling as a language generation problem. However, existing latent reasoning approaches typically represent user intent with a single latent vector, which struggles to capture the inherently multi-faceted nature of user preferences. We propose Factorized Latent Reasoning (FLR), a novel framework for LLM-based sequential recommendation that decomposes latent reasoning into multiple disentangled preference factors. FLR introduces a lightweight multi-factor attention module that iteratively refines a latent thought representation, where each factor attends to distinct aspects of the user's interaction history. To encourage diversity and specialization, we design orthogonality, attention diversity, and sparsity regularization objectives, and dynamically aggregate factor contributions for the final prediction. We further integrate FLR with an efficient reinforcement learning strategy based on group-relative policy optimization, enabling stable alignment directly in the latent reasoning space. Experiments on multiple benchmarks show that FLR consistently outperforms strong baselines while improving robustness and interpretability.
Published: 2026-04-29 14:50:15
Authors: Yifan Jing
Categories: math.LO, math.CO
Abstract:
We study stability theory in Hilbert spaces quantitatively. We prove that the inner product on the unit ball is $(k,ε)$-stable for all $k\ge \exp(π/ε)$, and it is not $(k,ε)$-stable for $k\le \exp(\log 2/ε)$, showing that the growth is necessarily exponential in $1/ε$.
We then analyze how stability scales under nonlinear connectives applied to the inner product. In particular, for power-type predicates $f(x,y)=\langle x,y\rangle_+^β$ with $β<1$ we obtain upper and lower bounds of the form $\exp(Cε^{-1/β})$, and for $β>1$ and integer powers $\langle x,y\rangle^d$ we retain the bilinear scale $\exp(C/ε)$.
Published: 2026-04-29 14:48:59
Authors: Chuanwei Dai, Yunbiao Zhao, Xiao Ouyang, Huaqing Huang, Yulan Liang, Jiaqi Bai, Yingjie Song, Jianhan Sun, Yiqun Duan, Wenjun Ma, Senlin Huang, Shufeng Wang, Jianming Xue, Xiaoping Ouyang
Categories: cond-mat.mes-hall
Abstract:
Ultrafast scintillators are indispensable for precise timing in high-energy physics and medical diagnostics. Fundamentally constrained by the trade-off between emission rate and light yield, conventional scintillators remain kinetically trapped in the sub-nanosecond regime, failing to break 50-picosecond limit. Here, we demonstrate a strategy to bypass this limitation by harnessing the coherent radiative acceleration in weakly confined CsPbCl3 perovskite nanocrystals to generate an ultrafast photon burst. This effect originates from the giant oscillator strength, which we unlock by suppressing exciton-phonon scattering at mild cryogenic temperatures. Consequently, our scintillator achieves an unprecedented dominant lifetime of 13.11 ps alongside a high light yield of 21,851 ph/MeV. The resulting prompt photon emission rate more than 100 times higher than that of state-of-the-art ultrafast scintillators. We validate this breakthrough in realistic detection scenarios, achieving a coincidence time resolution of 30.8 ps and accurately resolving 13.5 ps electron bunches and 16.6 ps single-shot gamma-ray pulses. Our findings establish a robust coherent framework for next-generation ultrafast scintillators, pushing extreme radiation diagnostics into the picosecond frontier.
Published: 2026-04-29 14:39:49
Authors: Paul Zheng, Yao Zhu, Xiaopeng Yuan, Yulin Hu, Anke Schmeink
Categories: cs.IT, eess.SP, math.OC
Abstract:
In the Internet-of-Things (IoT) era, efficient functionality integration is essential to address the growing demands of communication, computation, and sensing. Signal-level integrated sensing, computing, and communication (Sig-ISCC) is envisioned, where a single waveform simultaneously supports sensing, computing and communication via over-the-air computation (AirComp). Meanwhile, federated learning (FL) is widely regarded as a promising distributed machine learning framework that enables network intelligence in a privacy-preserving and secure manner, and exhibits strong synergy with AirComp, which alleviates the communication bottleneck of FL. In this paper, we study uplink Sig-ISCC design for AirComp-FL with joint target detection. We formulate the joint power and receive-scaling control problem, where edge devices' transmitted signals should serve both sensing and AirComp purposes. The goal is to minimize the AirComp aggregation distortion subject to a joint target-detection requirement. Although the resulting problem is non-convex in the original variables, we show that it admits an equivalent convex reformulation after a suitable variable transformation. By exploiting analytical optimality properties, we develop a robust, optimal, and polynomial-time-complexity algorithm that efficiently achieves the optimal transmit powers and receive scaling factor. Simulation results validate the optimality and numerical robustness of the proposed algorithm and show its superior FL performance compared to baseline methods.
Published: 2026-04-29 14:38:05
Authors: P. RubÍ Pantaleón-Mondragón
Categories: math.AC
Abstract:
We analyze the weight diagram associated with foliations on the complex projective plane through the Hilbert-Mumford criterion in Geometric Invariant Theory, focusing in particular on invariants such as the algebraic multiplicity and the existence of invariant curves.
Published: 2026-04-29 14:25:30
Authors: Yuye Wu, Hong-Bo Jin
Categories: gr-qc
Abstract:
Near-degenerate resonance poles arise widely in open-wave systems, including gravitational-wave ringdowns, when two neighboring modes have almost the same complex frequency. On a finite observation window, the physical waveform is then dominated by a common carrier with a slowly varying interference envelope, so that attempting to treat the signal as a sum of two independently resolved damped sinusoids can become unstable in practice. Mathematically, an exact coalescence of poles leads to a double pole and a Jordan-chain (associated-vector) time dependence with a $t\,e^{-iωt}$ factor; here we show that the same carrier-plus-first-jet structure emerges more generally from a near-degenerate neighboring-pole \emph{pair} on a finite window, without requiring a true pole merger. We show that the local response is more naturally organized as a centered two-pole block about the shared carrier and, in the time domain, as a carrier-plus-first-jet waveform. This centered organization leads to a finite-window two-scale hierarchy: $κ$ controls when the leading correction to the carrier must be included, while $η^2$ controls the remaining error once that correction is retained. Toy two-pole numerics verify this scaling, and Kerr quasinormal modes provide a representative gravitational realization of the same finite-window centered organization.
Published: 2026-04-29 14:25:07
Authors: Xiejin Li, Fenghui Zhang, Yinghe Zhao, Cheng Li, Zhanwen Han, Yunkun Han, Xiaoyu Kang
Categories: astro-ph.GA
Abstract:
The radial profile of binary fraction may vary with environment and is of significant importance for studying the formation mechanisms of binary stars and their dynamical evolution within globular clusters (GCs) and galaxies. However, existing studies remain limited to the Milky Way and its neighboring galaxies. Leveraging the method proposed by Zhang et al. for estimating the variation of binary fraction from integrated spectral features, we analyze a sample of 513 elliptical galaxies drawn from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey to measure their radial binary fraction profiles. Our results show that after accounting for the effect induced by radial variations in the stellar population (SP), the median SP-subtracted binary fraction, $r_{\rm b,sub}^{\rm med}$, becomes approximately flat. For nearly all elliptical galaxies in our sample, the variation in binary fraction relative to the galaxy center at $1R_e$ is less than 5%. No clear correlation is found between the binary fraction gradient and the gradients of SP properties. Moreover, we also compare differences between ultraviolet (UV) upturn and non-UV upturn galaxies. The overall binary fraction profiles and SP properties of the non-UV upturn galaxies in our sample are comparable to those of the UV upturn galaxies. This similarity may arise from the presence of residual star formation (RSF) in the non-UV upturn systems.
Published: 2026-04-29 14:24:37
Authors: Diego Alba Alonso, Javier Sánchez González
Categories: math.AC
Abstract:
The purpose of this article is to study certain binary relations of endomorphisms over infinite dimensional vector spaces defined by GD1 and 1GD generalized inverses. In order to do so, these generalized inverses are studied over arbitrary vector spaces (namely, infinite dimensional ones) using finite potent endomorphisms. We characterize them in terms of the AST decomposition of a finite potent endomorphism and we obtain algorithms for their respective computation. This theory is then used to characterize the GD1 and 1GD binary relations for finite potent endomorphisms in terms of the AST decomposition and to prove that they define partial orders in the set of finite potent endomorphisms, thus, completing the theory of these generalized inverses for matrices.
Published: 2026-04-29 14:24:17
Authors: Konstantinos Bampouras, Karl-Mikael Perfekt
Categories: math.FA, math.CA, math.CV
Abstract:
We prove that if a small Hankel operator on the product Hardy space belongs to some Schatten class $S^p$, $p < \infty$, then it has a symbol in product BMO. In other words, the conclusion of Nehari's theorem holds under the hypothesis that the operator belongs to a Schatten class.
Published: 2026-04-29 14:23:10
Authors: Diana Méndez-Avalos, Théo Louzada Meireles, Morgan W. Mitchell, Aleksandra Sierant
Categories: physics.atom-ph, physics.optics, quant-ph
Abstract:
We use a $^{87}\text{Rb}$ atomic vapor, suitable for an optically-pumped magnetometer (OPM) in Earth-field conditions, to study the noise properties of three strategies for generating pulsed optical pumping. We compare a frequency-modulated (FM) laser, amplitude modulation (AM) via an acousto-optic modulator (AOM), and amplitude modulation via a semiconductor optical amplifier (SOA). Pumping the ensemble to operate as a Bell-Bloom OPM, and with an equal degree of spin polarization, the three methods give nearly identical sensitivity, showing that the SOA, despite being an active device, can introduce negligible additional noise. Pumping the ensemble to operate as a free-induction-decay OPM, we observe longer unpumped coherence times with the SOA-AM method than with the FM method. Finally, using the higher power available from the SOA, we demonstrate an environment-limited sensitivity of $80\text{fT}/\sqrt{\text{Hz}}$ at $600\text{Hz}$ and 200fT$200\text{fT}/\sqrt{\text{Hz}}$ at $4\text{kHz}$, one to two orders of magnitude beyond what was achievable with the other pumping methods.
Published: 2026-04-29 14:19:39
Authors: Hung P. Hoang, Naoto Ohsaka, Rin Saito, Yuma Tamura
Categories: cs.DS
Abstract:
In the Independent Set Reconfiguration problem under the Token Addition/Removal rule, given a graph $G$ and two independent sets $I$ and $J$ of $G$, we want to transform $I$ into $J$ by adding and removing vertices, such that all the sets throughout the process are independent sets. Its approximate version called MaxMin Independent Set Reconfiguration aims to maximise the minimum size of the independent sets in the process above. We study the (in)approximability of this problem for general graphs as well as restricted graph classes. Firstly, on general graphs, we obtain a polynomial-time $(n / \log n)$-factor approximation algorithm, complementing the $\mathsf{PSPACE}$-hardness of $n^{Ω(1)}$-factor approximation due to Hirahara and Ohsaka [STOC 2024, ICALP 2024] and the $\mathsf{NP}$-hardness of $n^{1-\varepsilon}$-factor approximation due to Ito, Demaine, Harvey, Papadimitriou, Sideri, Uehara, and Uno [TCS 2011]. Secondly, we present a polynomial-time approximation algorithm for degenerate graphs as well as $\mathsf{FPT}$-approximation schemes for bounded-treewidth graphs and $H$-minor-free graphs. Lastly, we extend the above inapproximability results to bounded-degree graphs, graphs of bandwidth $n^{\frac{1}{2}+Θ(1)}$, and bipartite graphs.
Published: 2026-04-29 14:19:37
Authors: Konstantinos Kourliouros, Iacopo P. Longo, Martin Rasmussen
Categories: math.DS
Abstract:
In this paper we propose a finite-dimensional and deterministic approach to the study of invariant sets of certain nonautonomous differential inclusions naturally arising in the context of random and control dynamical systems, as well as in systems modeling the dynamical propagation of uncertainty. In particular, to any such differential inclusion, we associate a finite-dimensional and deterministic system of nonautonomous ordinary differential equations, which we call the boundary system, due to its following characteristic property: invariant sets of the differential inclusion lift in a unique way to backward invariant unit normal cones of the associated boundary system, and these are even invariant if the boundary is smooth. We further illustrate the strength of this approach in the study of minimal attractors of nonautonomous linear differential inclusions. Under the natural assumption of exponential stability for the unperturbed problem, we establish existence and uniqueness of a minimal attractor for the differential inclusion with fiberwise strictly convex, closed, and continuously differentiable boundaries. We also show that the unit normal bundle is in fact the pullback attractor for the skew-product flow associated to the boundary system which extends to the global attractor when the underlying admits a compact base.
Published: 2026-04-29 14:19:21
Authors: Yan Wang, Shao-qi Lin, Rui-qi Shen, Fang-liang Chen, Fang-liang Chen, Fang-liang Chen, Yong-nan Sun, Qi-ping Su, Chui-ping Yang
Categories: quant-ph
Abstract:
The memory effects in open quantum systems can induce information backflow and revive quantum correlations, thereby providing a powerful way to protect and recover useful quantum resources in realistic noisy environments. However, such dynamics remains experimentally unexplored in multipartite quantum steering. Here we observe different non-Markovian evolution of tripartite quantum steering using Greenberger-Horne-Zeilinger-type mixed states, covering both death and revival processes. In particular, we experimentally demonstrate the more intricate asymmetric steering structure of tripartite quantum steering through different bipartitions, which do not arise in bipartite systems. Our results provide foundational insights into the hierarchical and directional structures in multipartite quantum steering, and highlight its potential as a useful resource for asymmetric quantum information processing.
Published: 2026-04-29 14:15:40
Authors: Sayantan Banerjee
Categories: math.ST
Abstract:
In many analyses the object reported at the end is not fixed in advance, but is chosen after a preliminary search over variables, subgroups, transformations, models or contrasts. Classical selective-inference methods are most effective when this search can be written as an explicit selection event. This note treats the less structured case in which the selection rule is a black box and inference is required for the target indexed by the selected object. We show that, for any fixed-target confidence procedure, selected-target noncoverage is bounded by the nominal fixed-target noncoverage plus the average total variation distance between the marginal law of the inferential data and its conditional law given the selected object. A mutual-information bound follows immediately. The result recovers sample splitting as the zero-leakage case and gives explicit guarantees for noisy screening through a Gaussian information bound. Thus the inferential cost of black-box selection is quantified by the information that the selected object carries about the inferential sample.
Published: 2026-04-29 14:02:49
Authors: Michael Mitterlindner, Maximilian Graber, Regina Kratzer, Markus Reichhartinger, Stefan Radl
Categories: physics.flu-dyn
Abstract:
Detailed Computational Fluid Dynamics (CFD) simulations are too computationally expensive for the real-time control and design optimization of multiphase flow reactors. To address these limitations, we introduce CLARA, a software toolbox that automates the generation of Compartment Models (CM) via the unsupervised clustering of CFD data. Unlike previous studies, our toolbox enables the modelling of multiphase phenomena and interphase mass transfer within each compartment. CLARA employs unsupervised clustering algorithms, graph reassignment, and optimization routines to ensure mass conservation and spatial connectivity across all compartments. Verification studies utilizing analytical benchmarks and reactive multiphase CFD simulations demonstrate that the CMs produced by CLARA accurately reproduce reactor performance and spatial species distributions. The significantly reduced computational demand of CMs compared to full CFD models enables the optimal control of multiphase reactors and facilitates their rational design and optimization.
Published: 2026-04-29 13:56:27
Authors: Jungkai Chen, Yongnam Lee, Phin-Sing Soo
Categories: math.AG
Abstract:
Motivated by DeVleming's work on moduli of surfaces in $\mathbb{P}^3$ and Chen-Hu-Jiang's work on moduli of threefolds with volume $2$ and geometric genus $4$, we study the deformation of pairs of $\mathbb{P}^3$ and hypersurfaces using the classification of $\mathbb{Q}$-Gorenstein degenerations of $\mathbb{P}^3$ with canonical singularities. We prove that if a degenerating threefold has canonical singularities, then the moduli space is smooth at the corresponding pair. Consequently, we find some boundary divisors of the moduli of smooth hypersurfaces. Finally, using the double cover method, we derive some information on the moduli space of threefolds $X$ with canonical singularities with the same volume and geometric genus as a double cover of $\mathbb{P}^3$ branched over a hypersurface.
Published: 2026-04-29 13:56:11
Authors: Xue Qin, Simin Luan, John See, Cong Yang, Zhijun Li
Categories: cs.RO, cs.AI
Abstract:
Skill libraries in deployed robotic systems are continually updated through fine-tuning, fresh demonstrations, or domain adaptation, yet existing typed-composition methods (BLADE, SymSkill, Generative Skill Chaining) treat the library as frozen at test time and do not analyze how composition outcomes change when a skill is replaced. We introduce a paired-sampling cross-version swap protocol on robosuite manipulation tasks to characterize this dimension of compositional skill learning. On a dual-arm peg-in-hole task we discover a dominant-skill effect: one ECM achieves 86.7% atomic success rate while every other ECM is at or below 26.7%, and whether this dominant ECM enters a composition shifts the success rate by up to +50pp. We characterize the boundary on a simpler pick task where all atomic policies saturate at 100% and the effect is undefined. Across three tasks we further find that off-policy behavioral distance metrics fail to identify the dominant ECM, ruling out the natural cheap predictor. We propose an atomic-quality probe and a Hybrid Selector combining per-skill probes (zero per-decision cost) with selective composition revalidation (full cost), and characterize its Pareto frontier on 144 skill-update decisions. On T6 the atomic-only probe sits 23pp below full revalidation (64.6% vs 87.5% oracle match) at zero per-decision cost; a Hybrid Selector with m=10 closes most of that gap to ~12pp at 46% of full-revalidation cost. On the cross-task average over 144 events, atomic-only is within 3pp of full revalidation under a mixed-oracle caveat. The atomic-quality probe is, to our knowledge, the first principled, deployment-ready primitive for skill-update governance in compositional robot policies.
Published: 2026-04-29 13:50:36
Authors: Jaeyong Lee, Heeju Kang, Ahra Cho, Baek Eunkyung
Categories: cs.HC
Abstract:
With the rapid spread of generative AI services, the token has gained value not only as a technical unit of language processing but also as an economic currency for accessing AI services. Major AI model providers have adopted token-based billing as their default service model, requiring users to purchase platform-bound, fixed token usage rights. However, the fixedness of these usage rights is grounded in the billing-policy decisions of service providers rather than in any technical necessity. This study defines the Transferability of token usage rights as a design property that allows users to flexibly reallocate purchased data resources free from the constraints of time, account, and service. Drawing on the Design Space Analysis framework of MacLean et al. (1991), we identify five design axes (Target, Direction, Unit, Control, Reversibility) and five concrete Transferability types (carry-over, co-management, transfer, conversion, and trade) by analyzing the billing policies and terms of service of four major LLM services (ChatGPT, Claude, Gemini, Grok). Our analysis reframes the token from a purely economic-technical primitive into a core element of user-centered system design that expands user choice and autonomy.
Published: 2026-04-29 13:50:04
Authors: Kayla Fallon, Reshma Peremadathil-Pradeep, Christopher E. A. Barker, Zoey Tumbleson, Emily Darwin, Andrea Meo, Eloi Haltz, Benjamin A. Brereton, Trevor Almeida, Colin Kirkbride, Sara Villa, Sophie A. Morley, Mario Carpentieri, Riccardo Tomasello, Hans J. Hug, Christopher H. Marrows, Stephen McVitie
Categories: cond-mat.mes-hall, cond-mat.mtrl-sci
Abstract:
Magnetic skyrmions are topologically protected spin textures that can act as reconfigurable nanoscale information carriers. In synthetic antiferromagnets (SAFs), interlayer exchange coupling offers an additional control parameter beyond the interfacial Dzyaloshinskii-Moriya interaction (DMI) and magnetic anisotropy. Here, we engineer a SAF composed of two chemically distinct ferromagnets (CoB and CoFeB), in which the external magnetic field and interlayer exchange act asymmetrically on the sublattices. The competition of these effects, acting as a resultant effective-field, gives rise to two distinct skyrmion families in different field regimes. In large fields, conventional-polarity skyrmions nucleate, with core antiparallel to the external field, whereas in smaller fields an inverse-polarity skyrmion state emerges as the effective-field reverses sign and almost saturates the CoFeB layers. Return-point memory measurements confirm independent nucleation pathways for the two families. Using element-resolved x-ray magnetometry, correlative magnetic force and Lorentz transmission electron microscopies, and parameter-matched micromagnetic modelling, we show that all textures reside only in the CoFeB layers, which experience a Ruderman-Kittel-Kasuya-Yosida (RKKY) exchange field originating from the CoB layers. This effective-field method provides a robust route to programmable three-dimensional spin textures with controlled polarity in selected layers of a multilayer with potential for applications in skyrmion-based computing and spin-logic architectures.
Published: 2026-04-29 13:47:22
Authors: Lara Gauder, Pablo Riera, Andrea Slachevsky, Gonzalo Forno, Adolfo M. García, Luciana Ferrer
Categories: cs.SD, cs.AI, cs.DB
Abstract:
We introduce a toolkit for uncovering spurious correlations between recording characteristics and target class in speech datasets. Spurious correlations may arise due to heterogeneous recording conditions, a common scenario for health-related datasets. When present both in the training and test data, these correlations result in an overestimation of the system performance -- a dangerous situation, specially in high-stakes application where systems are required to satisfy minimum performance requirements. Our toolkit implements a diagnostic method based on the detection of the target class using only the non-speech regions in the audio. Better than chance performance at this task indicates that information about the target class can be extracted from the non-speech regions, flagging the presence of spurious correlations. The toolkit is publicly available for research use.
Published: 2026-04-29 13:42:25
Authors: Domenico Cotroneo, Giuseppe De Rosa, Cristina Improta, Benedetta Gaia Varriale
Categories: cs.SE
Abstract:
Understanding how software defects manifest and evolve in production environments is critical for improving reliability. While previous research has largely focused on pre-release defects, the nature of residual faults, i.e., those escaping testing and surfacing post-release, remains poorly understood. This paper presents a large-scale characterization of pre- and post-release defects across C/C++ and Java systems, encompassing over 14k defects mined from open-source projects. We employ a broad suite of software metrics to capture diverse code attributes such as complexity, size, structure, and development history.
Results show that post-release defects are concentrated in older, frequently modified, and high-churn components, typically requiring longer and more complex fixes than pre-release ones. These findings highlight that residual defects arise more from evolutionary and process dynamics than code structure alone, suggesting that reliability engineering should prioritize targeted testing in mature and complex code regions.
Published: 2026-04-29 13:38:03
Authors: Théophile Dupré, Romain Couderc, Miguel Moleron, Axel Coulon, Rémy Bruno, Arnaud Laborie
Categories: cs.SD, math.OC
Abstract:
Conventional wavelet-domain methods for room impulse response denoising rely on thresholding detail coefficients, which is unsuited for low frequencies. In this work, we introduce a wavelet-based post-processing algorithm that extends denoising to approximation coefficients by means of sparse dictionary learning with a time-varying error tolerance. The proposed method leverages an exponential decay envelope model to adapt reconstruction accuracy according to the local signal-to-noise ratio. This approach significantly improves low-frequency denoising of synthetic and measured room impulse responses compared to the baseline method, leading to more accurate estimation of acoustic parameters such as decay time.
Published: 2026-04-29 13:29:17
Authors: Longjun Xiang, Hao Jin, Jian Wang
Categories: cond-mat.mtrl-sci
Abstract:
The intrinsic anomalous Hall effect (IAHE) provides a powerful transport fingerprint of quantum magnets, with its linear and second-order responses distinguishing ferromagnets and $\mathcal{P}\mathcal{T}$-symmetric antiferromagnets, respectively. Altermagnets, as an emergent class of quantum magnets, have recently been shown to host a third-order extrinsic anomalous Hall effect, raising a question of whether an \textit{intrinsic} counterpart can serve as a diagnostic of altermagnetic order. Based on spin-group symmetry analysis, we demonstrate that the third-order IAHE is generically allowed in the ten spin Laue groups relevant to altermagnets when spin-orbit coupling (SOC) is taken into account. By combining these symmetry constraints with the anomalous velocity induced by the second-order Berry curvature, we uncover a resonant third-order IAHE arising near the altermagnetic band crossings at generic momenta in both the Lieb-lattice altermagnet and the experimentally realized altermagnet V$_2$Se$_2$O. Notably, we identify the Berry curvature quadrupole, encoded in the second-order Berry curvature and activated by finite SOC, as the microscopic quantum geometric origin of this resonance. Our results establish the third-order IAHE as an intrinsic quantum geometric transport fingerprint of altermagnets and extend the hierarchy of IAHE across collinear quantum magnets.
Published: 2026-04-29 13:24:53
Authors: Yang An, Wen Ting Zhang, Yi He
Categories: math.GR
Abstract:
Let $\mathcal{OP}_n$ be the monoid of all orientation-preserving full transformations on $X_n=\{1,\dots, n\}$ with the natural order. For $α\in \mathcal{OP}_n$, let $F(α)=\{y\in X_n: yα=y\}$ and $F(n,m)=|\{α:|F(α)|=m\}|$. Umar posed the question about the number $F(n,m)$ of elements of $\mathcal{OP}_n$ with $m$ fixed points. In this paper, we show that the number $F(n,m)$ of $\mathcal{OP}_n$ is $\binom{2n}{n-m}$ for $2\leqslant m\leqslant n$ and get the expectation and probability distribution of the cardinality of fixed-point set $F(α)$ for $α\in\mathcal{OP}_n$.
Published: 2026-04-29 13:23:25
Authors: Hirak Kansara, Leo Guo, Wei Tan
Categories: physics.app-ph
Abstract:
The rise of machine learning and additive manufacturing has enabled the design of architected materials with tailored properties that surpass those of natural materials. Inverse design offers a data-efficient alternative to trial-and-error methods, yet most existing approaches depend on either large datasets or scarce high-fidelity data from simulations and experiments. These requirements pose a particular challenge for architected materials with nonlinear mechanical responses, where capturing complex deformation modes requires expensive evaluations. To address this, a Multi-Fidelity Bayesian Optimisation (MFBO) framework for the inverse design of cellular composites that directly targets their full nonlinear response is introduced. By integrating information from multiple fidelity sources and scalarising the response using a similarity score, the framework enables efficient exploration of the design space while reducing reliance on costly evaluations. As a proof of concept, the method is applied to spinodoid cellular composites using finite element models, validated with compression tests on short carbon-fibre reinforced PET-G composites. Four target responses were considered, with three multi-fidelity strategies benchmarked against a standard single-fidelity approach. Across all cases, MFBO achieved higher similarity scores and consistently recovered the targeted responses, outperforming the single-fidelity baseline under the same evaluation budget, while also successfully recovering all targeted responses. These results demonstrate the effectiveness of MFBO for inverse design of stochastic architected materials, where high-quality data is scarce but lower-cost proxies exist. By efficiently navigating complex design spaces, MFBO enables the creation of cellular composites with precisely tailored nonlinear mechanical behaviour.
Published: 2026-04-29 13:13:32
Authors: Matías I. Caruso, Javier Fernández, Cora Tori, Marcela Zuccalli
Categories: math.DG
Abstract:
In this note we survey some of our results on the Lagrangian reduction of discrete-time mechanical systems (DMSs). It is intended as an introduction to the general ideas that we used in the reduction of DMSs with nonholonomic constraints, DMSs with external forcing, as well as a theory of reduction by stages for such systems. This line of work was inspired by the paper and the monograph written by H. Cendra, J. Marsden and T. Ratiu in 2001.
Published: 2026-04-29 13:05:09
Authors: Zheng-Chuan Wang
Categories: quant-ph
Abstract:
There exists a geometric phase for a quantum state during the adiabatic evolution of the system. If the adiabatic procedure happens between the system and the environment interacting with it similar to Born-Oppenheimer (BO) approximation, we can introduce a temperature into the environment, which can be regarded as in an equilibrium state. Then a temperature-dependent geometric phase can be obtained for the system, which originates from the Abelian gauge potential induced by the BO approximation. This gauge potential contributes to the effective potential of the system, which is temperature dependent, too. Finally, we demonstrate them using an example of H_2^+ ion system.
Published: 2026-04-29 12:57:22
Authors: Jianmin Guo, Yao Du, Yizhen Yu, Yong Zou, Xingang Wang
Categories: nlin.CD, cs.LG
Abstract:
We propose a dual-channel reservoir-computing scheme for inferring the dynamics of two distinct chaotic systems with a single machine. By augmenting a standard reservoir with a system-label channel and a parameter-control channel, the machine can be trained from time series collected from a few sampled states of the two systems. We show that the trained machine not only predicts the short-time evolution of the sampled states, but also reproduces the long-term statistical properties of unseen states, thereby enabling reconstruction of the bifurcation diagrams of both systems from partial observations. The effectiveness of the scheme is demonstrated for the Lorenz and Rössler systems in numerical simulations and for the Chua and Rossler circuits in experiments. Functional-network analysis further shows that the two target systems are encoded by distinct dynamical patterns in the reservoir. These results extend multifunctional and parameter-aware reservoir computing, and provide a route to data-driven inference of multiple nonlinear systems using a single machine.
Published: 2026-04-29 12:53:58
Authors: Tanveer Ahmad
Categories: quant-ph
Abstract:
We study a projection-type gradient flow for equality-constrained maximisation of a smooth bilinear control objective on $\mathcal{H}=L^2(0,T;\mathbb{R})$, eliminating Lagrange multipliers through an $(M{+}1)\times(M{+}1)$ moving Gram matrix $Γ(s)_{\ell\ell'}=\int_0^T S(t)\,c_\ell(s,t)\,c_{\ell'}(s,t)\,\mathrm{d}t$. The flow generates monotonic ascent in continuous time but becomes unstable on discretisation; existing implementations rely on heuristic step-size safeguards lacking rigorous justification. We close this gap by replacing $Γ$ with $Γ_{\varepsilon}:=Γ+\varepsilon^{2}I$ and prove: (i) an exact spectral identity giving $κ(Γ_{\varepsilon})=(σ_{\max}^{2}+\varepsilon^{2})/(σ_{\min}^{2}+\varepsilon^{2})$; (ii) objective monotonicity $\mathrm{d}J/\mathrm{d}s\ge 0$ for all $\varepsilon\ge 0$; (iii) constraint drift $|h_{m}-C_{m}|=\mathcal{O}(\varepsilon^{2})$ with a computable prefactor; (iv) convergence of the regularised trajectory to the unregularised one in $L^{2}(0,T)$ at rate $\mathcal{O}(\varepsilon^{2})$ under uniform invertibility of $Γ$; and (v) a discrete CFL criterion $Δs\,G\,\|Γ_{\varepsilon}^{-1}\|\leα<2$ guaranteeing objective monotonicity of the forward-Euler scheme up to $\mathcal{O}(Δs^{2})$ local truncation error. The theory is validated on a three-level bilinear benchmark for all-optical Bell-state preparation, where $κ(Γ)\in[10^{9},10^{11}]$, the predicted $\varepsilon^{2}$ rate is confirmed over eight decades, and moderate regularisation eliminates step rejections and reduces constraint drift by more than an order of magnitude at unchanged final fidelity.
Published: 2026-04-29 12:53:25
Authors: Sergio Iserte, Rafael Mayo, Enrique S. Quintana-Ortí, Antonio J. Peña
Categories: cs.DC
Abstract:
Process malleability has proved to have a highly positive impact on the resource utilization and global productivity in data centers compared with the conventional static resource allocation policy. However, the non-negligible additional development effort this solution imposes has constrained its adoption by the scientific programming community. In this work, we present DMRlib, a library designed to offer the global advantages of process malleability while providing a minimalist MPI-like syntax. The library includes a series of predefined communication patterns that greatly ease the development of malleable applications. In addition, we deploy several scenarios to demonstrate the positive impact of process malleability featuring different scalability patterns. Concretely, we study two job submission modes (rigid and moldable) in order to identify the best-case scenarios for malleability using metrics such as resource allocation rate, completed jobs per second, and energy consumption. The experiments prove that our elastic approach may improve global throughput by a factor higher than 3x compared to the traditional workloads of non-malleable jobs.