Published: 2026-05-29 17:59:50
Authors: Jiazheng Xing, Hangjie Yuan, Lingling Cai, Xinyu Liu, Yujie Wei, Fei Du, Hai Ci, Tao Feng, Jiasheng Tang, Weihua Chen, Fan Wang, Yong Liu
Categories: cs.CV, cs.AI
Abstract:
Connector-based video unified models have demonstrated strong capability in instruction-grounded video synthesis, but integrating a large high-fidelity generator into the unified training loop is computationally prohibitive, limiting achievable visual quality. We therefore propose Lumos-Nexus, a training-efficient unified video generation framework that facilitates the development of strong reasoning-driven generation capabilities while significantly enhancing visual fidelity. Lumos-Nexus adopts a two-stage design: 1) During training, only a lightweight generator is aligned with the understanding block to learn to take in reasoning-driven semantic control. 2) During inference, we introduce Unified Progressive Frequency Bridging (UPFB) to progressively hand off generation to a high-capacity pretrained generator in the shared latent space, enabling coarse-to-fine refinement and producing high-fidelity videos without compromising reasoning quality. To fill the gap in reasoning-driven video generation benchmarks, we introduce VR-Bench, which assesses a model's capability to translate inferred intent into coherent and semantically aligned video content. Extensive experiments demonstrate that Lumos-Nexus achieves substantial gains in visual realism and temporal coherence on VBench, while exhibiting strong reasoning-based generative performance on VR-Bench. Code and models are available at https://jiazheng-xing.github.io/nexus-lumos-home/.
Published: 2026-05-29 17:56:48
Authors: Vincenzo Bruno, Corinna Kollath, Roberta Citro, Ameneh Sheikhan
Categories: cond-mat.quant-gas, quant-ph
Abstract:
Floquet engineering offers powerful tools to manipulate quantum states by periodically driving physical parameters. In this work, we investigate the quantum transport through two periodically driven impurities in a mesoscopic one-dimensional channel. By mapping the time-dependent Hamiltonian into an effective multichannel scattering problem, we unveil a rich landscape of transport phenomena arising from the interplay between Fabry-Perot cavity modes and Fano interference. We demonstrate that the inter-impurity distance acts as a critical control parameter, allowing for the formation of Bound States in the Continuum (BICs). Furthermore, we identify Quasi-BICs, extremely narrow resonances with finite lifetimes, that can be dynamically tuned by the drive amplitude. We show that these states enable a robust coherent trapping mechanism, allowing the system to switch from perfect transparency or reflection to strong localization with giant Wigner time delays. Our results suggest possible applications for tunable delay lines and quantum memories, with feasible experimental realizations in the context of cold atoms.
Published: 2026-05-29 17:56:36
Authors: Nurjahan Sultana, Moi Hoon Yap, Xinqi Fan, Wenqi Lu
Categories: cs.CV
Abstract:
Models for AI-based skin cancer screening suffer a severe performance drop when shifting from expert dermoscopic (source) images to consumer-grade clinical (target) images, hindering real-world deployment. Existing domain adaptation methods often ignore crucial semantic invariants, such as clinical concepts. While new foundation models like MONET can provide this semantic information as dense, probabilistic scores, this metadata is unavailable at test time, creating a deployment paradox for practical image-only screening tools. We address this gap by proposing CoFiDA-M, a privileged information framework that learns from concepts at training time but deploys as an image-only model. Our method trains a teacher network that uses MONET concept probabilities to guide a FiLM modulator, transforming visual features into a semantically ``edited" feature space. A lightweight, image-only student is then trained to reproduce this edited representation, not just the teacher's final predictions. This distillation ``bakes" the clinical reasoning into the student's weights. On a challenging multi-dataset benchmark, our image-only student significantly outperforms state-of-the-art approaches, especially in melanoma recall. Our work provides a practical and generalizable framework for leveraging noisy, probabilistic metadata as privileged information, demonstrating strong cross-dataset robustness and potential for real-world deployment beyond dermatology. Implementation code is available at: https://github.com/mmu-dermatology-research/CoFiDA.git
Published: 2026-05-29 17:48:30
Authors: Utsav Dutta, Gerardo Pastrana, Sina Khoshfetrat Pakazad, Henrik Ohlsson
Categories: cs.LG
Abstract:
Transformer-based architectures have advanced sequence modeling in language and vision, yet general-purpose representation learning for heterogeneous multivariate time series remains underexplored. We introduce CHARM (Channel-Aware Representation Model), which incorporates channel-level textual descriptions into a Transformer encoder equivariant to channel order. CHARM is trained with a Joint Embedding Predictive Architecture (JEPA) and a novel loss promoting informative, temporally stable embeddings; latent-space prediction encourages robustness to sensor noise while description-aware gating provides interpretability through learned inter-channel relationships. Across anomaly detection, classification, and short- and long-term forecasting, the learned embeddings achieve strong performance using only a linear probe. Performance is driven primarily by the JEPA objective and conditioning architecture, with text descriptions serving as channel identifiers for cross-dataset generalization.
Published: 2026-05-29 17:47:15
Authors: Prin Sawasdipol, Chinadanai Bubpatate, Daris Samart
Categories: hep-ph, nucl-th
Abstract:
We propose an RG-improved effective-field-theory framework for the deeply bound dibaryon $d^*(2380)$, a $ΔΔ$ bound state in the $(J,I)=(3,0)$ ${}^7S_3$ channel. Its binding momentum $γ\simeq 320$ MeV gives $γ/m_π\simeq 2.3$, indicating the need to re-organize the short-range dynamics beyond a formal pionless EFT. We match the large-$N_c$-constrained pionless contact potential to a meson-exchange-saturated contact interaction in which the $σ,ρ,ω$ dynamics are integrated out at the hadronic scale $m_V$, yielding the controlled expansion parameter $γ/m_V\simeq 0.42$. Normalizing the contact coupling to the deuteron and substituting the phenomenological CD-Bonn couplings gives $B_{ΔΔ}\simeq 96$ MeV. The $\simeq 14\%$ discrepancy from $B_{\rm exp}=84$ MeV is of the natural size of $\mathcal{O}(1/N_c^2)\simeq 11\%$ corrections to the $NN$ potential, confirming compatibility with a controlled EFT expansion organized around the finite-range hadronic scale. As a result, the observed $d^*(2380)$ pole emerges from the virtual state to bound state by using the EFT re-organization in this work.
Published: 2026-05-29 17:45:26
Authors: Aziz Al-Najjar, Marzieh Amini, James R. Green, Felix Kwamena
Categories: cs.CV
Abstract:
Most learning-based camera-LiDAR calibration methods treat each camera-LiDAR pair independently, ignoring the rigid geometric coupling in multi-camera platforms. As a result, per-camera estimates may be individually accurate yet inconsistent at the system level. We present a two-stage framework for joint multi-camera LiDAR extrinsic calibration that combines learned pairwise matching with geometric refinement. First, CMRNext is applied independently to each camera to produce initial extrinsic estimates and dense 2D-3D correspondences. These predictions are then jointly refined through a multi-frame bundle adjustment with reprojection, per-camera prior, and relative-pose prior terms. This approach converts pairwise predictions into a globally consistent multi-camera calibration. Experiments on KITTI (in-domain for CMRNext) and Walkley (out-of-domain) datasets show improved per-camera accuracy and inter-camera consistency. On KITTI, the method achieves 0.89 cm translation error and 0.038 rotation error. On Walkley, it reduces translation error from 108.6 cm to 3.1 cm, highlighting the benefit of explicit multi-camera coupling when single-camera predictions are less reliable.
Published: 2026-05-29 17:36:29
Authors: Elena Yanakieva, Annette Bieniusa, Stefania Dumbrava
Categories: cs.DC, cs.DB, cs.LO, cs.PL
Abstract:
Distributed applications increasingly support local-first collaboration over shared data, allowing multiple users to perform updates concurrently without global coordination. Such collaboration requires careful design to capture the intended semantics of the concurrent interactions.
We introduce a declarative framework for specifying and reasoning about the semantics of conflict-free replicated data types (CRDTs) and CRDT-based applications in Datalog. The framework models CRDT semantics as executable logic programs over operation contexts, making concurrency explicit and compositional, and thus amenable to automated analysis. As one application, we use property-based testing to compare implementations. To the best of our knowledge, this is the first work to systematically use Datalog as a foundation for prototyping and analyzing complex CRDTs and their compositions.
We evaluate our methodology using a collaborative graph data editing case study and report experimentation results assessing correctness validation and scalability with an increasing number of operations and replicas.
Published: 2026-05-29 17:31:20
Authors: Giovanni Della Casa, Fabrizio Fiore, Giuseppe Dilillo, Simonetta Puccetti, Andrea Vacchi
Categories: astro-ph.HE
Abstract:
Gamma-ray bursts temporal profile can be extremely variable, going from a single pulse of a few seconds duration to multiple superimposed pulses occurring over tens or even hundreds of seconds. The variability displayed in the lightcurve of each gamma-ray burst can be the result of the activity taking place in the central engine that generates these violent phenomena, as well as due to magnetic reconnection activities at larger distances. The objective of this work is to find the shortest variability hidden in the lightcurves of the GRBs, with particular focus for the ones with measured redshift, on timescales as short as few milliseconds. This variability will then be related to physical characteristics of the central engine, and evidences of its relation with the spectral parameters of the burst, such as the isotropic energy and peak energy, will be presented. This research is even more relevant in view of the future generation of satellites with improved timing resolution, that will allow us to explore the possible variability in the microsecond region.
Published: 2026-05-29 17:29:35
Authors: Qing Wang, Jacob Devasier, Chengkai Li
Categories: cs.CL, cs.AI
Abstract:
We present the first systematic study of masked diffusion language models (MDLMs) for graph-to-text generation. We analyze MDLM generation trajectories -- the order in which tokens are unmasked during iterative decoding -- and find that, unlike autoregressive LLMs which generate text linearly, MDLMs naturally prioritize entities first, followed by relational and function words, with structural tokens resolved last. We further identify a previously undocumented failure mode of supervised fine-tuning: SFT disrupts this strategy by prematurely anchoring structural sentence-ending tokens early in the decoding trajectory, effectively fixing the output length which can lead to omitted or hallucinated information. To address this, we propose lambda-scaled structural decoding, a training-free inference-time modification that downweights structural token confidence and recovers +9.4 BLEU-4. Finally, we introduce Graph-LLaDA, which integrates a Graph Transformer encoder into LLaDA's decoding process to explicitly incorporate relational graph structure. Cross-dataset evaluation on LAGRANGE reveals that previous baselines overfit to dataset-specific patterns, while LLM- and MDLM-based approaches generalize significantly better.
Published: 2026-05-29 17:29:28
Authors: Benedetta Muscato, Beiduo Chen, Gizem Gezici, Barbara Plank, Fosca Giannotti
Categories: cs.CL
Abstract:
Human disagreement is ubiquitous and well-known in labeling. However, variation in explanations, captured through token-level human rationales, remains far less explored. At the same time, it is unclear how to best evaluate human labels and rationales -- or even how to best aggregate rationales beyond majority vote -- in light of this variation. Yet, rationales may provide additional insights into the richness of human reasoning, that may differ in style, values and interpretations -- especially in subjective NLP tasks like hate speech detection. In this work, we unify diverse models, training strategies, loss functions, and existing evaluation metrics under a single protocol by systematically re-implementing them across different label and rationale representation spaces. Classification metrics are organized around two key properties -- predictive and distributional -- while explainability metrics through three complementary dimensions: plausibility, faithfulness, and complexity. In this unified supervision framework, we evaluate model behavior across classification and explainability metrics, as well as metric sensitivity to the choice of label (hard and soft) and rationale representation space (hard, intermediate and soft). Results show that both hard and soft metrics favor softer representations, highlighting their effectiveness in capturing variation and the need to rethink evaluation in subjective NLP.
Published: 2026-05-29 17:28:58
Authors: Kian Kenyon-Dean, Alina Selega, Ihab Bendidi, Jordan M. Sorokin, Luca Bertinetto, David Errington, Hayley Donnella, Oren Kraus
Categories: cs.LG
Abstract:
RNA sequencing produces rich and diverse datasets of gene expression, offering compelling insights into cellular state and function that have many applications in drug discovery. Modeling such data is challenging due to inherent technical noise and experimental batch effects, as evidenced by many existing transcriptomic foundation models (FMs) underperforming relative to linear baselines. Such results raise the question of whether deep representation learning provides a distinct advantage over the direct use of raw transcript counts. Our work explores this by developing a new self-supervised model, TxFM, with a focus on inductive representation learning evaluations. TxFM employs a masked autoencoding approach tailored to diverse RNA-seq count data, and our ablation study empirically identifies crucial architecture configurations required for strong transfer performance. Additionally, we curate a public training corpus, DiverseRNA-1.4M, and find that TxFM trained on this curated dataset yields high-fidelity gene representations that outperform FMs trained on atlas-scale corpora over 100x larger. Overall, our results indicate that inductive self-supervised learning is a viable modeling approach for transcriptomics representation, provided a careful synthesis of model architecture and training data curation.
Published: 2026-05-29 17:19:53
Authors: Jinseok Kim
Categories: cs.DL, cs.IR, cs.SI
Abstract:
Researchers analyze coauthorship networks, but author name ambiguity in their network data remains a significant challenge as it can change the number of vertices, distorting network properties. Although many scholars use straightforward heuristics for author name disambiguation using author's forename initials, these techniques can skew our understanding of network properties by merging or splitting vertices, raising concerns about the reliability and validity of these methods. This study investigates how different levels of vertex merging and splitting errors that are induced by name ambiguity impact network measures, using three large coauthorship networks with highly accurate algorithmic author name disambiguation. As a counterfactual scenario, two initial-based disambiguation methods widely used in coauthorship network research were applied to these datasets. Nine coauthorship network metrics were computed while varying randomly the numbers of merged or split vertices. Results show that initial-based disambiguation generates coauthorship networks with specific network properties underestimated, leading to the discovery of coauthorship networks that are smaller and more closely connected than they genuinely are. In contrast, other network metric values increase, making authors appear more collaborative and embedded within less fragmented research communities than they are. The study emphasizes the importance of careful disambiguation of vertex names in analyzing coauthorship networks for rigorous and valid findings.
Published: 2026-05-29 17:17:43
Authors: M. A. Bezuglov, B. A. Kniehl, A. I. Onishchenko, O. L. Veretin
Categories: math-ph
Abstract:
We present a general framework for the high-precision numerical evaluation of multivariate hypergeometric functions defined as solutions of holonomic systems of partial differential equations. Our approach adapts and extends methods originally developed for multi-loop Feynman integrals to the setting of hypergeometric functions of many variables. In particular, we construct Pfaffian systems for arbitrary multivariate hypergeometric functions by applying the Laporta reduction algorithm to suitable systems of differential relations. Next, we construct a numerical scheme based on the Frobenius method, which allows us to compute local power-series solutions with controlled precision and to transport them along prescribed paths in the space of variables. A central part of the paper is devoted to a systematic analysis of multivaluedness and branch structure: we show how the Frobenius method can be used to access different Riemann sheets in a controlled way and to track changes of the solution under analytic continuation around singular loci.
Published: 2026-05-29 17:12:34
Authors: Parthsarthi Rawat
Categories: cs.CV
Abstract:
We present our approach to the FIFA Skeletal Tracking Challenge 2026, which requires estimating 3D world-space poses of soccer players from broadcast video. Our method finetunes SMPLest-X (ViT-H, 687 M parameters) via a stratified clip split, multi-task depth supervision, and broadcast augmentation, paired with a RAFT dense optical flow camera tracker, foot-plane anchoring, and two-pass temporal smoothing. Against the FIFA baseline score of 1.053 on the validation set, SMART achieves 0.647, a 38.6% improvement; on the held-out test set, SMART scores 0.593 (Global MPJPE: 0.324 m, Local MPJPE: 0.054 m).
Published: 2026-05-29 17:01:16
Authors: Gyula O. H. Katona, Yaping Mao
Categories: math.CO
Abstract:
We introduce weak and strong poset Ramsey-Turán numbers for $t$-chains in host poset families, focusing on the Boolean lattice family $\mathcal{B}=\{B_n:n\ge 1\}$. For any poset $P$, we show $\operatorname{RT}(\mathcal{B};n,P,l,t)\le \operatorname{RT}^{\sharp}(\mathcal{B};n,P,l,t)$, with equality when $P$ is a chain. In particular, for $t=1$, $\operatorname{RT}(\mathcal{B};n,C_k,l)=\operatorname{RT}^{\sharp}(\mathcal{B};n,C_k,l)=(k-1)(l-1)$. We also give universal upper bounds for both versions. For fixed $k,l,t$ with $\min\{l-1,k-1\}\ge 1$, we prove $\operatorname{RT}^{\sharp}(\mathcal{B};n,A_k,l,t)=Θ(n^t)$. More generally, for every non-chain poset $P$, the strong number is $Θ(n^t)$ for fixed $l,t$. Finally, if $h(P)=r>t$ and $l(n)=\lfloor M_n^β\rfloor$ with $0<β\le α<1$, then both weak and strong versions admit lower bounds of order $Ω\!\left(2^{βn}n^{-β/2}\right)$.
Published: 2026-05-29 16:55:19
Authors: Ashok Choudhary, Chris Varghese, Leo Y. Li-Han, Frank G. Lee, Ellen L. Larson, Elizabeth B. Habermann, Cornelius A. Thiels, Hojjat Salehinejad
Categories: cs.CV, cs.LG, q-bio.QM
Abstract:
Postoperative pancreatic fistula (POPF) is a serious complication after pancreatic resection, increasing morbidity, hospital stay, and healthcare costs. We present an automatic, end-to-end deep learning pipeline-from pancreatic segmentation to classification-for preoperative POPF risk estimation and stratification using preoperative CT scans. A data set with auto-segmented pancreas volumes and surgical outcomes was used to evaluate multiple architectures, including a custom lightweight 3D CNN baseline (CNN3D), R(2+1)D ResNet-18, and ResNet-MC3-18 models. Evaluation across multiple 3D architectures demonstrated promising predictive performance. This approach offers a clinically valuable tool and a methodological benchmark for pancreas-specific CT classification, supporting improved preoperative decision-making in pancreatic surgery.
Published: 2026-05-29 16:43:07
Authors: Zhaoqing Li, Haoning Xu, Jingran Su, Yaofang Liu, Zhefan Rao, Huimeng Wang, Jiajun Deng, Tianzi Wang, Zengrui Jin, Rui Liu, Haoxuan Che, Xunying Liu
Categories: eess.AS, cs.SD
Abstract:
We present UNISON, a latent diffusion framework that unifies speech generation, sound generation, and audio editing within a single model. A single model handles text-to-audio, text-to-speech, zero-shot speaker cloning, mixed speech-and-sound generation, scene-level audio editing, speech-in-scene editing, and timed temporal composition, all of which share a single set of weights. Our architecture features two core designs: (1) Layer-wise deep LLM fusion, which injects hidden states from uniformly sampled layers of a frozen MLLM into corresponding MM-DiT blocks via learned projections, providing depth-matched semantic conditioning that improves instruction following over single-layer baselines; and (2) a unified multi-task architecture where task identity is encoded solely by a channel-wise mask and source audio is provided through VAE-encoded channel concatenation. Training is stabilized by an online GPU-side multi-task data synthesis pipeline with task-homogeneous batching and a two-stage curriculum. With 621M--732M trainable parameters, UNISON achieves results competitive with or exceeding task-specialist models across evaluated domains, while being roughly $4\times$ smaller than comparable unified systems.
Published: 2026-05-29 16:39:02
Authors: Alessandro Abate, Daniel Contro, Mirco Giacobbe, Agustín Martínez-Suñé, Diptarko Roy
Categories: cs.LG, cs.LO
Abstract:
Certification methods for stochastic systems provide sufficient proof rules, based on real-valued supermartingale certificates, to determine the almost-sure satisfaction of $ω$-regular properties (and therefore of linear temporal logic) over general state spaces, encompassing both countably infinite and continuous state spaces. Conversely, reinforcement learning (RL) methods for $ω$-regular tasks have received considerable attention, but they typically lack formal guarantees that the learned policy satisfies the specification, except possibly for finite state and action spaces. We bridge these two lines of research by establishing a novel theoretical connection: under an appropriate reward, the value function associated to a policy that almost surely satisfies an $ω$-regular property encodes a Streett supermartingale certificate for that specification. Our results, validated experimentally on finite Markov decision processes, hold for finite, countably infinite, and continuous state spaces, suggesting a principled route to certificate synthesis via RL.
Published: 2026-05-29 16:36:20
Authors: Maksuda Bilkis Baby, Khushika Shah, Naiyue Liang, Lei Zhang
Categories: cs.SE, cs.AI, cs.CR
Abstract:
Credential leakage in public source code repositories poses a critical security threat, with over 23.8 million secrets exposed in 2024 alone. Existing detection tools suffer from high false-positive rates because rigid pattern matching and binary classification schemes fail to distinguish genuine credentials from placeholder or weak credentials. We propose a three-class classification framework that explicitly models placeholder or weak credentials as a distinct class, leveraging CodeBERT-based semantic understanding combined with character-level pattern recognition. We evaluate our approach on a newly constructed dataset of 9,426 samples spanning 10 programming languages. Our model achieves a Matthews Correlation Coefficient of 0.86 and a macro F1-score of 0.90, achieving 93% recall and 89% precision for genuine credential leaks while reducing high severity alerts by 33.0% (from 373 to 250) without sacrificing security coverage. Compared to prior character-level approaches, our method improves placeholder or weak credential detection from 54% to 81% F1-score while maintaining strong cross language generalization, with 9 of 10 languages achieving F1 above 0.80 under leave-one-language-out evaluation.
Published: 2026-05-29 16:30:45
Authors: Danish Ali, Li Xiaojian, Sundas Iqbal, Farrukh Zaidi
Categories: cs.CL
Abstract:
Multilingual orthopedic decision support remains challenging in low-resource healthcare settings, where clinical narratives contain specialized terminology, mixed scripts, incomplete evidence, label imbalance and language-dependent documentation patterns. This article presents a reliability-oriented framework for classifying free-text orthopedic notes in English, Hindi and Punjabi. We compare task-aligned multilingual transformer encoders, a task-fine-tuned DistilBERT baseline, zero-shot instruction-tuned large language models (LLMs) and a domain-adaptive encoder, IndicBERT-HPA. IndicBERT-HPA augments IndicBERT with language-aware orthopedic adapter heads to support clinically relevant multilingual representation learning. Evaluation extends beyond aggregate accuracy to per-class performance, ROC-AUC, AUPRC, expected calibration error, cross-language stability and robustness under controlled balanced and natural-prevalence distributions. The evaluated zero-shot LLMs remain substantially less effective than task-adapted encoders for closed-set classification, with language-dependent instability. Under natural clinical prevalence, IndicBERT-HPA achieves the strongest overall performance, reaching an averaged Macro-F1 of 0.8792, Macro-AUROC of 0.894 and AUPRC of 0.902. We further implement a deterministic selective-verification layer combining confidence gating, evidence-consistency checking and language-risk screening. On a randomly selected held-out 5,000-record subset, it achieves 84.4% selective accuracy and 0.76 selective Macro-F1 at 72.3% coverage, compared with 71.5% accuracy and 0.65 Macro-F1 for accept-all prediction. These results support reliability-oriented multilingual clinical decision support with explicit deferral.
Published: 2026-05-29 16:25:31
Authors: Dylan Steiner, Gustavo Arango-Argoty, Gerald Sun, Etai Jacob
Categories: cs.LG, stat.ML
Abstract:
Multimodal models in oncology can produce accurate predictions, but accurate prediction does not reveal whether the model has learned biology that is shared across modalities, biology confined to one modality, or spurious correlations that reflect confounders rather than genuine biology. We introduce DECAT, a model-agnostic post-hoc evaluation framework that classifies multimodal representations into four diagnostic scenarios for a given task and modality, using five null-referenced metrics and a rule-based decision procedure. The framework operates on learned representations, requires no knowledge of which specific confounder is present, and returns indeterminate when the evidence is insufficient. We validate DECAT on synthetic data across four multimodal model classes (over 2,500 trained representations) and on real data from 8,979 TCGA patients, evaluating both multimodal embeddings and five pretrained pathology foundation models. Entangled models (e.g., CLIP) achieve near-perfect shared biology detection but falsely claim shared biology in the majority of cases where it is absent on real foundation model embeddings. This false claim rate increases with confound strength so that larger cohorts and stronger representations produce more confident but still incorrect diagnoses. Applied to both multimodal TCGA embeddings and five pathology foundation models without paired RNA, DECAT detects confounding invisible to AUROC without requiring the confounder labels, as confirmed by post-hoc stratification.
Published: 2026-05-29 16:23:35
Authors: Nathan Dalaklis, Yan Mary He
Categories: math.DS
Abstract:
We study the Hausdorff dimension spectrum of asymptotic approximation rates of complex Diophantine approximation and that of the asymptotic average excursion time of cusp excursions on the Bianchi orbifold $\mathbb{H}^3/\operatorname{PSL}(2,\mathbb {Z}[i])$ via a unified approach using the Hurwitz map. In particular, we construct a conformal graph directed system (CGDS) for the Hurwitz map and show that the Lyapunov exponent of the Hurwitz CGDS simultaneously captures the asymptotic approximation rate and the the asymptotic average excursion time. Applying the multifractal analysis of Lyapunov exponents for this system, we obtain a formula and real-analyticity for the Hausdorff dimension spectrum functions.
Published: 2026-05-29 16:20:59
Authors: Daniel Peñaherrera, Rishal Aggarwal, David Ryan Koes
Categories: cs.LG, q-bio.BM
Abstract:
A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generative modeling have been proposed to address the limitations of conventional sampling techniques by eliminating the computational cost of simulation. A promising direction is iteratively finetuning diffusion models along a temperature ladder whereby training data is generated via importance sampling during inference-time annealing. Unfortunately, these methods require computing a divergence over the score field to estimate importance weights, rendering them intractable for larger systems. Here we present scalable inference-time annealing (SITA), which retrains flow-based models to generate samples at progressively lower temperatures using an energy-based model to facilitate fast surrogate likelihoods. We demonstrate state-of-the-art performance on both Alanine Dipeptide and Alanine Tripeptide while avoiding costly divergence terms. Our code is available at: https://github.com/countrsignal/sita.git
Published: 2026-05-29 16:08:49
Authors: Vincent Wang-Maścianica, Nikhil Khatri
Categories: cs.LG, math.CT
Abstract:
Architecture diagrams are ubiquitous in deep learning, but they are usually only representational: the tensor-program identities they suggest are still proved by prose and tensor-axis manipulation. We introduce a formal graphical calculus for the structural fragment of tensor programming underlying einops, making such diagrams proof-enabling. Our calculus represents tensor axes as nested graded tubes around a base type. The tube boundary recovers the undirected tensor-network view of axes, while the directed interior retains the operational reading of computation graphs. The key rewrite is grade-naturality: sliding spectacles over tubes. Standard equivariance proofs become short diagrammatic derivations. We additionally demonstrate how our rewrite system may be applied to convert attention masks into pre-processing operations, recovering efficient implementations of sparse attention blocks.
Published: 2026-05-29 16:08:28
Authors: Valérie Castin, Kimia Nadjahi, Pierre Ablin, Gabriel Peyré
Categories: cs.LG
Abstract:
Low-Rank Adaptation (LoRA) is the most widely adopted method for fine-tuning large language models. Notably, LoRA is inherently overparameterized: multiple pairs of low-rank factors can yield the same adapted weight matrix. We show--both theoretically and empirically--that these pairs exhibit significantly different condition numbers. As a result, converging to different loss minimizers directly impacts the convergence rate of LoRA. Building on this observation, we introduce Balanced Low-Rank Adaptation (BaLoRA), a variant of LoRA that projects iterates onto a balanced manifold. This manifold improves the conditioning of the loss landscape while preserving the adapted matrix. The projection step is computationally lightweight and integrates seamlessly into existing fine-tuning pipelines. Empirically, BaLoRA converges faster than standard LoRA and achieves superior performance across a range of fine-tuning tasks.
Published: 2026-05-29 16:06:12
Authors: Zetian Yan, Xingyu Zhu
Categories: math.DG, math.MG
Abstract:
We confirm a compactness conjecture of M. Li. If a complete Riemannian manifold has nonnegative Ricci curvature and uniformly convex boundary in the sense that the second fundamental form satisfies $h\ge1$. Then we prove it is compact, and consequently has finite fundamental group. The proof uses monotone quantities constructed via positive proper harmonic functions with Neumann condition.
Published: 2026-05-29 16:02:31
Authors: Artur Sidorenko, Vasilii Kolokoltsov
Categories: math.PR, math.NA
Abstract:
We investigate uniform weak convergence rates for probabilistic numerical methods applied to backward time-fractional diffusion equations whose dynamics are driven by diffusions with possibly unbounded coefficients, such as the Geometric Brownian Motion. The fractional structure is represented through a random time-change by the inverse of a stable subordinator. To approximate the underlying fractional dynamics, we combine discrete Markov chain schemes for the diffusion component with heavy-tailed random walk approximations of the time change.
Our analysis builds on Feller semigroup techniques and a high-order sensitivity framework for diffusion semigroups based on the Kunita stochastic flows and tensor fields. We derive uniform bounds for all orders of sensitivities, establish a quasi-contraction property for the associated semigroup, and transfer these estimates to the fractional setting via the convolution representation with the inverse subordinator. As a result, under killing conditions which dominate at least the base-space semigroup growth, we obtain weak convergence rates for the combined continuous-time-random-walk scheme to the time-fractional diffusion, with a logarithmic regime before the discount dominates the stronger smooth-space growth.
Published: 2026-05-29 16:01:25
Authors: Máté Gedeon, Piroska Zsófia Barta, Péter Mihajlik, Katalin Mády
Categories: cs.CL, cs.AI, cs.SD, eess.AS
Abstract:
Conversational automatic speech recognition in Hungarian is constrained by the limited amount of publicly available dialogue-style training data. The BEA-Dialogue corpus addresses this need, but its strictly speaker-disjoint train/dev/eval split reduces the usable material to only 85 hours. In this paper, we introduce BEA-Dialogue+, an expanded version of the corpus that relaxes the split criterion for experimenters and dialogue partners while preserving complete separation of the primary speakers. This results in 200 hours of transcribed natural conversations and enables a controlled study of the trade-off between additional training data and speaker overlap across the splits. We evaluate several Whisper- and FastConformer-based models on both corpus versions, including Serialized Output Training (SOT)-based fine-tuning for dialogue transcription. Our results show that the larger corpus is more challenging for models without fine-tuning, whereas SOT-based adaptation yields consistent improvements in WER, CER, cpWER, and cpCER. Overall, BEA-Dialogue+ provides a substantially larger yet still demanding benchmark for Hungarian dialogue ASR, and a practical resource for training and evaluating dialogue transcription systems.
Published: 2026-05-29 15:50:13
Authors: Arpan Sharma, Bhargava Jogi, Ken Roberts, Muralikrishna Molli, S. R. Valluri
Categories: math-ph
Abstract:
The underdamped harmonic oscillator is analyzed through the complex mapping $ζ= e^{-i\varphi}we^{-w}$ with $w = βt + iΩt$, which transforms the dynamics into a logarithmic spiral. Within this framework, the displacement extrema correspond to crossings of the imaginary axis by $ζ(t)$, yielding the explicit times $t_n = (θ- \varphi - π/2 + nπ)/Ω$, where $θ= \arctan(Ω/β)$. The Lambert $W$ function provides closed-form solutions $t = -β^{-1}W_k(-βA/ω_0)$ for the times at which the spiral radius attains a given threshold $A$, covering both the rising and decaying branches. The quality factor $Q = ω_0/(2β) = \tfrac{1}{2}\secθ$ is directly encoded in the ray angle $θ$ of the $(u,v)$-plane. Key geometric invariants are derived: the winding number $N_\varepsilon \approx (Q/π)\ln(2Q/\varepsilon)$ for large $Q$, the enclosed area $A = ω_0^2Ω/(8β^3) \approx Q^3$ in the lightly damped limit, and the energy decay $E(t) = E_0 e^{-ω_0 t/Q}$. Three methods for determining $Q$ from experimental data are compared: logarithmic decrement, ray-angle measurement, and spiral turn counting. The turn-counting method proves particularly robust for high-$Q$ systems, where successive amplitude peaks differ by tiny fractions. The framework unifies classical damped oscillations with complex analysis and special functions.
Published: 2026-05-29 15:40:58
Authors: Wenna Lai, Haoran Xie, Guandong Xu, Qing Li, S. Joe Qin
Categories: cs.CL, cs.AI
Abstract:
Aspect Sentiment Triplet Extraction (ASTE) aims to identify aspect terms, opinion terms, and sentiment polarities as structured triplets, providing essential inputs for downstream information system applications such as opinion mining, explainable recommendations, and review summarization. Prior work mainly focuses on end-to-end extraction, while post hoc verification of extracted triplets remains comparatively underexplored. This gap limits the reliability of ASTE systems, since predicted triplets may be locally plausible while being globally invalid. Moreover, candidate invalidity is multi-faceted and candidate usability is inherently graded, motivating a fine-grained verification mechanism that can filter or re-rank outputs from diverse extractors. In this paper, we propose FiVeD, a framework for Fine-grained Verification with Diagnostic reasoning supervision. Specifically, the verifier is trained with multiple complementary objectives, including validity classification and quality score estimation as primary tasks, with error type classification and rationale generation as auxiliary tasks. We define hierarchical error categories and construct plausible incorrect triplets under semantic and syntactic constraints, and leverage an off-the-shelf LLM with task-specific rubrics to produce quality scores and diagnostic rationales. During inference, the resulting quality scores are used to filter candidate outputs, supporting adjustable precision-recall tradeoffs. Experiments across multiple ASTE baselines demonstrate that FiVeD consistently improves extraction performance by up to 3.53 F1 points as a plug-and-play verification module.
Published: 2026-05-29 15:40:07
Authors: Naoki Chihara, Tatsushi Oka, Yasuko Matsubara, Yasushi Sakurai, Shota Yasui
Categories: stat.ME, cs.LG, econ.EM, math.ST
Abstract:
We present a regression-adjustment framework designed for the estimation of longitudinal treatment effects in randomized experiments under static regimes. While regression-adjustment methods are useful for variance reduction in randomized experiments by using pre-treatment covariates, they usually focus only on average effects, from which we cannot obtain valuable insights into when the effects appear and how long they continue. To address this issue, we consider intermediate outcomes and evolving post-treatment covariates over time, and we represent such dynamic trajectories using transition kernels. Furthermore, we establish the asymptotic normality and the semiparametric efficiency bound for our estimator, enabling more powerful statistical inference. Simulation studies and empirical analysis using A/B test data from a streaming platform in Japan show the practical advantages of our method.
Published: 2026-05-29 15:39:00
Authors: Yaoming Chu, Baiyi Yu, Hartmut Häffner, Markus Heyl, Nathan Goldman, Jianming Cai
Categories: quant-ph
Abstract:
The exponential growth of many-body Hilbert space presents a fundamental barrier to quantum technology, obscuring the search for physically significant states within an astronomically vast landscape. Consequently, resources for quantum-enhanced metrology have been largely confined to the symmetric subspace whose dimensionality scales only polynomially with the particle number-leaving the vast majority of the Hilbert space largely unexplored and poorly understood. Here we challenge this paradigm by demonstrating that metrological advantage can arise as a ubiquitous feature across exponential-dimensional manifolds. By tailoring the first-moment structure of random unitaries, we uncover dense manifolds of engineered random states (ERSs) where Heisenberg-limited scaling emerges as a statistically generic property. This ubiquity endows these resource states with inherent resilience against parameter disorder. We experimentally validate this framework on a trapped-ion processor, achieving a metrological enhancement of $6.98 \pm 0.38$ dB beyond the standard quantum limit. Potential applications extend to diverse platforms, ranging from superconducting circuits and waveguide QED to solid-state spins and polar molecules. Our results establish a powerful paradigm where quantum-enhanced precision can be harvested from the exponential vastness of the Hilbert space.
Published: 2026-05-29 15:38:50
Authors: Yimin Lu, Esther Xiaozhen Fu, Zi-Wen Liu
Categories: quant-ph
Abstract:
Quantum error-correcting codes are a cornerstone of quantum computing, with broad and profound connections to physics and mathematics. In this work, we introduce the notion of intrinsic locality dimension of stabilizer codes that is independent of any background geometry and naturally incorporates flexible architectures and accommodates noninteger values, drawing on mathematical machinery from fractal geometry and geometric measure theory. Important scenarios include topological codes and algebraic codes such as bivariate-bicycle-type codes. We show how the intrinsic dimension serves as a fundamental organizing parameter that unifies code properties. In particular, we prove general limitations on code parameters and compatible fault-tolerant logical gates induced by the intrinsic dimension, generalizing the Bravyi--Poulin--Terhal and Bravyi--König bounds for regular topological codes, respectively. Furthermore, we discuss implications on thermal properties, presenting a conditional no-go result for self-correcting quantum memories in dimension $3-ε$ for any $ε>0$. Our theory lays a versatile and unifying mathematical foundation for studying the fundamental capabilities and geometric implementations of quantum error correction and fault tolerance.
Published: 2026-05-29 15:35:45
Authors: Janez Kos, Jovana Risojević, Samo Ilc
Categories: astro-ph.GA
Abstract:
Open clusters gradually dissolve, and their stars disperse into the Galactic field. Lost stars form tidal tails-elongated streams that trace the cluster orbit ahead of and behind its core. From the shape and orientation of the tidal tails, it is possible to infer the shape of the gravitational potential governing the cluster's motion. The orbits of open clusters, including those in the Solar neighbourhood, are sensitive to the gravitational potential of the inner Galaxy, which is dominated by the Galactic bar. Using n-body simulations of synthetic and real open clusters, we investigate how sensitive the shapes and orientations of tidal tails are to variations of the gravitational potential of the Milky Way. We consider the effects of the bar as well as spiral arms, giant molecular clouds (GMCs) and satellite galaxies. We analyse the stellar distributions within tidal tails using statistical metrics that quantify the differences between tail morphologies. Such non-parametric approach enables us to efficiently explore tidal tails across a large parameter space of gravitational potential models. We find that the Galactic bar-particularly its pattern speed-has a strong influence on the orbits of open clusters and the shape of their tails. Spiral arms have a limited effect, and satellite galaxies do not disturb the tidal tails of nearby open clusters. Perturbations by GMCs affect most clusters, with distortions stronger than those by the bar observed in old and in-plane clusters. We identify nearby open clusters that are most sensitive to the pattern speed of the bar. By observing the tidal tails of a handful of well-selected nearby clusters, we should be able to measure the pattern speed of the bar with a precision in the order of $1\ \mathrm{km\,s^{-1}\,kpc^{-1}}$ independently from length and orientation of the bar. We will present the observability of tidal tails in paper II.
Published: 2026-05-29 15:28:39
Authors: Shuyuan Yang, Grant Boone, Timo Markert, Sebastian Matich, Andreas Theissler, Martin Atzmueller, Zonghe Chua
Categories: cs.RO
Abstract:
Robot-Assisted Minimally Invasive Surgery (RAMIS) enhances surgeon dexterity, with newer platforms leveraging haptic feedback to further improve performance. Such force information has broader potential to inform performance assessment, tactile localization, and surgical autonomy. This motivates the need for accessible approaches to integrating force sensing into RAMIS tools. This work presents a method for integrating a six-axis commercial force sensor into the distal end of a standard cable-driven surgical instrument, enabling end-effector force measurement while preserving the original mechanical functionality of the device. The proposed design emphasizes reproducibility and accessibility for research applications, requiring no specialized manufacturing tools. A transformer neural network integrates force sensor measurements with robot state information to aid estimation of applied forces at the end-effector, compensating for internal cable forces arising from actuation. Our proposed approach achieved normalized errors below 6%, and generalized to unseen conditions better than purely proximal data-driven sensing approaches. High internal cable forces caused sensor saturation and reduced axial force observability, which can degrade performance along the tool's major axis and under higher load conditions. Given current levels of performance, the balance of system integrability and performance enables applications and research into timely topics of haptic feedback, skill assessment, and force-informed autonomy in RAMIS. Videos and code are available at https://enhanced-telerobotics.github.io/shaft force sensing.
Published: 2026-05-29 15:27:26
Authors: Sara Papi, Luisa Bentivogli
Categories: cs.CL, cs.AI, cs.SD
Abstract:
Simultaneous speech-to-text translation (SimulST) generates translations while speech is still unfolding, requiring a streaming policy that decides when to read and when to write. State-of-the-art approaches rely on attention-based encoder-decoder models where cross-attention provides explicit alignment signals. In contrast, Speech Large Language Models (SpeechLLMs) are decoder-only architectures relying solely on self-attention. This raises a central question: whether decoder self-attention contains sufficiently stable alignment signals to guide the streaming policy. Moreover, existing approaches typically rely on training-based adaptations or heuristic wait-$k$ policies and have not been validated in long-form settings. To fill these gaps, we propose Decoder-Only Attention (DOA), a training-free policy that enables long-form simultaneous translation with off-the-shelf SpeechLLMs by deriving a proxy alignment from self-attention. Experiments on Phi4-Multimodal and Qwen3-Omni show that DOA provides an effective alignment signal for supporting streaming decisions, enabling low-latency long-form SimulST with quality close to offline decoding without retraining.
Published: 2026-05-29 15:27:12
Authors: Jonas Gebele, Florian Matthes
Categories: cs.CE
Abstract:
Collateralized prediction markets are contingent-claim markets in which economic uncertainty can disappear before winning claims become redeemable. This paper studies the pricing effect of that delay. When collateral remains locked until oracle settlement, a near-certain dollar is a delayed dollar, so prices embed a maturity-dependent settlement discount in addition to beliefs about outcomes. We recover an implied settlement-discount term structure from persistent near-certain contracts using realized settlement times and summarize it as an annualized settlement wedge (ASW). The recovered wedges are positive, maturity-dependent, and time-varying. Adjusting pricesby these curves reduces the near-certainty horizon gradient by roughly 48-88%, indicating that much of the raw maturity pattern reflects priced settlement frictions rather than forecast error alone. Market architecture changes the wedge: negRisk conversion compresses discounts by recycling part of the position into synthetic collateral, while yield-bearing collateral flattens the term structure by reducing the opportunity cost of lock-up. The results show that pricing quality in prediction markets is endogenous to settlement mechanics, collateral productivity, and capital-recycling design. Prediction-market prices therefore aggregate information through a financial infrastructure whose funding conditions are measurable and economically important.
Published: 2026-05-29 15:20:38
Authors: Nicholas Fry, Eric Dexheimer, Kirill Mazur, Paul H. J. Kelly, Andrew J. Davison
Categories: cs.CV, cs.RO
Abstract:
We present a dense RGB-D SLAM system using differentiable triangles as the 3D map representation. While 3D Gaussian Splatting has emerged as the leading method for novel-view synthesis, triangles remain the standard primitive for traditional rendering hardware, game engines, and downstream tasks requiring explicit geometry such as simulation, collision, and editing. Recent offline methods have demonstrated that an unstructured 'triangle soup' can be optimised into a photorealistic mesh via Delaunay triangulation across a set of posed images. Building upon this insight, we present the first dense SLAM system to employ Triangle Splatting to perform both tracking and mapping through online differentiable rendering of a triangle soup. The map can be converted into a connected mesh on-the-fly via restricted Delaunay triangulation, enabling new online capabilities such as mesh deformation and collision checking. On Replica and TUM-RGBD, our system outperforms baselines on 3D geometry, matches the camera-tracking accuracy, and enables online mesh-based scene editing.
Published: 2026-05-29 15:19:03
Authors: T. V. Mendes, T. Guérin
Categories: cond-mat.stat-mech
Abstract:
In complex media, transport and geometric properties deeply influence the kinetics of random encounters between reactants. Here, we consider the situation where a random walker, moving in a regularly diffusing medium, has to reach and activate a target located inside a compartment characterized by fractal (obstructed) sub-diffusion. We focus on dual-site reactions, which end when two activation events occur within a given time window. Each activation event happens with a finite probability whenever the random walker visits the target. For weakly reactive targets, we demonstrate that the reaction time can be minimized for an optimal compartment size and can even be accelerated when compared to the same system without compartment. Our analytical predictions are validated through simulations of a random walker on a cubic lattice, where some sites inside the compartment are obstructed at the critical percolation threshold. Our theory illustrates the fact that adding a crowded compartment around a target, even if it slows down the motion in its vicinity, can accelerate the kinetics of complex reactions, especially for weakly reactive targets.
Published: 2026-05-29 15:18:49
Authors: Nick Fischer, Mursalin Habib
Categories: cs.DS
Abstract:
We revisit the Binary Closest String problem, which asks, given a set of binary strings $X \subseteq \{0, 1\}^n$, to compute a string minimizing the maximum Hamming distance to $X$. A long line of work has focused on parameterized algorithms with respect to the optimal distance $d$, yielding a sequence of improvements from $O^*(d^d)$ through $O^*(16^d)$, $O^*(9.513^d)$, $O^*(8^d)$, $O^*(6.731^d)$ to the current best-known running time of $O^*(5^d)$ [Chen, Ma, Wang; Algorithmica '16].
We present a faster randomized algorithm running in time $O^*(4^d)$. Our result matches a recent fine-grained lower bound [Abboud, Fischer, Goldenberg, Karthik C.S., Safier; ESA '23], and is therefore conditionally optimal. As an extra benefit, our algorithm is remarkably simple.
Published: 2026-05-29 15:10:52
Authors: Ritwik Yadav, Satyanarayana R. Valluri, Mohamed Zaït
Categories: cs.DB
Abstract:
This paper describes AIM (Automatic Index Manager), a configurable index management system, which identifies impactful secondary indexes for SQL databases to efficiently use available resources such as CPU, I/O and storage. It has been validated on thousands of databases which support production systems. With AIM, the physical design of the database adapts itself to the changes in the workload.We lay out the end to end design of AIM while calling out the guarantees and tradeoffs associated with our design choices. Some of the salient features of AIM include fast convergence even while recommending wide composite indexes, reduced reliance on the query optimizer and a "no regression" guarantee for production workloads. Each index recommendation from AIM is accompanied with a metrics driven explanation, making it easier to verify machine driven changes.AIM is one of the few industrial strength index recommendation engines that is deployed on production databases at a large scale. The experimental results show that AIM is quick in identifying the most effective indexes and the resulting physical design is close to optimal.
Published: 2026-05-29 15:03:44
Authors: Robert A. Krueger, David Staudinger
Categories: math.CO
Abstract:
A randomly perturbed digraph is an $n$-vertex directed graph with all out- and in-degrees linear in $n$, to which a linear number (depending on the degree) of random edges have been randomly added. We show that randomly perturbed digraphs whose edges have been colored uniformly with $n$ colors have a rainbow copy of every orientation of every possible length cycle, simultaneously, with high probability. This is a common generalization of work of Araujo, Balogh, Krueger, Piga, and Treglown in the uncolored setting and Katsamaktsis, Letzter, and Sgueglia for consistently oriented spanning cycles. Our proof uses Montgomery's distributive absorption method.
Published: 2026-05-29 15:03:10
Authors: Samuël Borza, Andrea Pinamonti, Omar Zoghlami
Categories: math.DG, math.MG
Abstract:
We study constant horizontal mean curvature surfaces in the sub-Lorentzian Heisenberg group. We derive the first-variation formula for horizontal area under volume-preserving radial variations and show that smooth isoperimetric candidates have constant horizontal mean curvature away from the characteristic set. We then give a complete classification of smooth boost-symmetric constant mean curvature surfaces: their characteristic sets, causal behaviour, and ambient sub-Lorentzian isometry classes. From this classification, we single out a family of smooth, acausal, boost-symmetric surfaces with nonzero constant mean curvature. Written as a two-sheeted graph over the exterior of a future hyperbola, this family is a natural sub-Lorentzian analogue of the Pansu bubbles and leads us to conjecture that it gives the isoperimetric maximisers in the sub-Lorentzian Heisenberg group.
Published: 2026-05-29 15:00:20
Authors: Vinícius P. Bacurau, Camilo Salvador, Guilherme C. Stumpfa, Angelo F. Andreolli, Caroline B. Stocoa, Eric M. Mazzer, Lewis Owen, Yifan Cao, Rodrigo Freitas, Daniel Miracle, Francisco G. Coury
Categories: cond-mat.mtrl-sci
Abstract:
This study provides experimental evidence of chemical short-range order (CSRO) in the equiatomic CoCrNi alloy, identified through neutron diffraction. The phenomenon manifests as a distinct diffuse peak at Q = 1.85 A-1, the intensity increases under thermodynamically favorable conditions for CSRO development such as prolonged aging (100 h and 240 h) at 748 K or shorter aging (24 h) at slightly higher temperature (798 K). The degree of ordering was measured by integrating the diffuse scattering intensity, revealing that the gas-atomized sample, i.e. the sample with the least amount of CSRO, still displays approximately 70% of the CSRO level observed in the sample subsequently aged for 240 h at 748 K, i.e. the sample with the highest amount of CSRO produced in this study. Predictive atomistic simulations reproduced both the presence and position of the diffuse peak, while two-dimensional fast Fourier transform (FT-2D) analyses indicated that reflections at (1 1/2 0) within the <001> zone axis originate from some structural projections associated with like D022, Pt2Mo and D1a motifs. Complementary small-angle neutron scattering (SANS) measurements identified Ni-rich, disk-shaped domains with radii of approximately 11 A and thicknesses of about 1 A, consistent with nanoscale CSRO characteristic length scale. These findings demonstrate that CSRO is an intrinsic and energetically favorable feature of the CoCrNi system, remaining stable even under rapid solidification and further enhanced by low-temperature aging. Combined use of neutron diffraction and atomistic modeling provides a framework for probing local ordering phenomena in multi-principal element alloys (MPEAs).
Published: 2026-05-29 14:58:50
Authors: Cecilia Manente, Marco Alfò, Silvia D'Angelo
Categories: stat.ME, stat.AP
Abstract:
Healthcare mobility -- patients seeking treatment outside their territory of residence -- represents a major source of inequality and financial imbalance in decentralised health systems. In Italy, persistent north-south asymmetries in patient flows among Local Health Authorities (ASLs) have reinforced existing disparities within the National Health Service; yet the structural organisation and temporal dynamics of these flows remain poorly understood at the sub-regional level. We propose a Bayesian dynamic latent space model for directed weighted networks with a hurdle negative binomial likelihood, and apply it to administrative discharge records on mobility for hip replacement procedures among 109 Italian ASLs over 2018-2024. The model jointly addresses excess zeros, overdispersion and network dependence, while capturing directional heterogeneity through multiplicative sender and receiver effects and controlling for differences in territorial size via an appropriate exposure term. Applied to Italian mobility data, the model reveals the evolving geometry of the healthcare system, quantifies the disruption induced by the COVID-19 pandemic, and uncovers structural asymmetries in outward propensity and ASLs attractiveness. The framework provides a flexible tool for the statistical analysis of dynamic healthcare mobility networks with direct relevance to the monitoring and evaluation of territorial healthcare provision.
Published: 2026-05-29 14:55:19
Authors: Dimitris Michail Gerontogiannis, Magnus Goffeng
Categories: math.OA, math.DS, math.SP
Abstract:
We study spectral invariants arising in the noncommutative geometry of topological Markov chains and Cuntz--Krieger algebras. Their noncommutative geometry is described by spectral triples built from log-Laplacians, which are known to have non-trivial index theory and exotic quantum symmetries. We prove statistical eigenvalue asymptotics, local heat trace asymptotics, local Weyl laws, and an analogue of Connes' trace theorem. In all cases the local asymptotics are governed by the Kubo--Martin--Schwinger state of the gauge action on the Cuntz--Krieger algebra.
Published: 2026-05-29 14:50:08
Authors: Krishnapriya Vishnubhotla, Soumya Vajjala, Akriti Vij, Isar Nejadgholi
Categories: cs.CL
Abstract:
We evaluate the consistency of automated judges in conducting a multi-dimensional safety evaluation in a reference-free setup. Our results indicate that Large Language Models are unreliable judges in identifying safety issues related to machine-generated advice in regulated domains such as finance, although they are more reliable at identifying more overt forms of unsafe/harmful content such as violence. The degree of inconsistency in a model's judgments can vary significantly by the chosen safety criteria and can be impacted by the language of the content and its linguistic style as well. Finally, there is high disagreement among different judges for the same output, across domains, safety criteria, and languages. These findings provide new insights on the practice of using LLMs as evaluators and offer several recommendations for practitioners on how to use automated judges in practical scenarios.
Published: 2026-05-29 14:44:59
Authors: Hannah Schieber, Dominik Frischmann, Victor Schaack, Angela P. Schoellig, Daniel Roth
Categories: cs.RO, cs.CV, cs.GR
Abstract:
Autonomous robots in unknown indoor environments require both reliable collision avoidance and object-level understanding. Classical representations such as TSDF support safe planning but lack semantics, while photorealistic methods like Gaussian Splatting (GS) provide rich appearance yet suffer from soft geometry, limiting precise obstacle avoidance. We present LiftNav, a hybrid navigation framework built on GSFusion's TSDF+GS dual map, augmented with a real-time pipeline of YOLO-based detection, TSDF-based 3D lifting, and B-spline trajectory optimization. This design enables flexible semantic navigation without dense 3D embeddings. We further introduce a hinge-loss-based collision penalty that improves trajectory smoothness and safety. We evaluate our approach in a simulation using the Replica dataset. Compared against a state-of-the-art radiance field baseline we show a 100% feasibility rate and shorter trajectories.
Published: 2026-05-29 14:44:51
Authors: Hong-Bin Chen, Jiaming Xia
Categories: math.PR, cond-mat.stat-mech, math-ph
Abstract:
We study the full-plane two-state fuzzy $4$-Potts model, obtained by assigning independent balanced $\{\pm1\}$ spins to the open clusters of a critical $q=4$ random-cluster configuration. This model corresponds exactly to the single-spin projection of the isotropic Ashkin-Teller model at its Potts point. We prove that, after proper normalization, all even multi-point spin correlation functions converge to explicit conformally covariant Coulomb-gas type neutral charge sums. As a consequence, we prove convergence in law of the rescaled magnetization field and identify the moments of the limiting field. The proof combines the Baxter-Kelland-Wu coupling, convergence of the six-vertex height function to the Gaussian free field, and a charge-completion mechanism: an enlarged discrete sum over charge assignments with total charge in $4\mathbb Z$ produces a combinatorial cancellation of connection patterns, while only the neutral charge sector survives in the scaling limit.
Published: 2026-05-29 14:42:00
Authors: Juan Bory-Reyes, Edil D. Molina-Fernandez, José M. Sigarreta-Almira
Categories: math.CV, math.DS, math.PR
Abstract:
In the context of hyperbolic numbers we define the concept of negation of finite hyperbolicvalued probability distributions that is based on the partial order induced by the idempotent structure of hyperbolic numbers. Then, a hyperbolic majorization and general hyperbolic negators are introduced. For a broad class of generated negators, we prove that the original distribution majorizes its negation. This comparison yields that entropy increase for the strong hyperbolic Shannon entropy and the hyperbolic Gini-Simpson entropy, and it implies component-wise uniformization of the iterated negation. Finally, we analyze involutive property of hyperbolic negators and prove that are structurally distinct from the generated negators responsible for the entropy increase. These results show that hyperbolic probabilistic negation is not merely a component-wise copy of the real case, but a theory governed by the interaction between idempotent decomposition, partial order, and entropy measure.