The criterion, measured. This version adds the experiment the programme lacked: Pollard's criterion applied directly to a real decay curve, without fitting, and it withdraws a way of reading every fitted exponent in the corpus — including our own. 72 pages, 27 sections in 7 parts, 8 appendices. Eloy René Becerra Daly What is measured 20 non-adjacent qubits of one superconducting device — one session, one fixed layout, 39 circuits, 958,452 shots. The criterion answers differently in two channels of the same qubit. Energy relaxation admits a positive rate density (non-negative least squares over 200 free rates, χ²/dof = 0.99, 19/20 replicates compatible); pure dephasing does not (χ²/dof = 12.43, 17/20 refuted). The cause needs no model: the echo curve rises, in 10 of 220 consecutive segments above 3σ and six above 5σ, peaking at z = +11.01, across 7 of 20 qubits, while the T1 control gives 0 of 220 and backflow exactly zero in all twenty. Three independent confirmations: the Breuer–Laine–Piilo witness on the same counts, the residual against a one-rate Lindblad twin (+10.6σ), and frev = 0.674 failing to correlate with that witness (ρ = −0.107). What is withdrawn Reading a fitted β without reporting its goodness of fit. At σ/C ≈ 0.011 the two-parameter Kohlrausch form is rejected in both dephasing arms (χ²/dof = 11.49 and 1598), so its exponent licenses nothing in either direction. A non-rejection at low statistical power is not evidence of adequacy. Every β-dependent quantity of the new run. The pre-registered positive control fired: the T1 arm returned β = 0.8664 ± 0.0285 where a single rate must give 1. Its premise proved false — T1 is not a single rate on real hardware — but a premise falsified after the fact does not annul the clause. The distance to the pole and the branch assignment are withdrawn for that run. The universal claim that no platform violates β < 1, which survived into the conclusion of v2.0.0 after being corrected elsewhere. What is falsified in the previous version §20 of v2.0.0 states that revivals are slowdowns and not reversals, and that no local operation can revert the decoherence arrow. Both are falsified by measurement: the echo rises with the control at zero, and a single X pulse — a local unitary — reverts 67 % of the free-induction dephasing. The backflow bound of §3, which admits recoherence and bounds it, is the section the data support; it receives its first experimental test and is not refuted. What is unchanged The exact identities, the no-go theorem, the transferable criteria and the dark-matter conjecture stand as in v2.0.0, with that version's errata intact. The decisive test the programme identifies — β = (1 + αdiff)/2 on a single sample, refutable against existing NMR and diffusion literature without new experiments — is still not performed here. Scope, stated because it is easy to overstate One device, one session, twenty qubits, and a single-qubit observable blind to entanglement by construction. The measurement establishes that the criterion is applicable to hardware and that it separates two channels of the same qubit. It establishes nothing about superconducting qubits in general, and the corrected pre-registration for a second device is published rather than attempted. Reproducibility Every number of the new section is regenerated from the raw counts by a single extractor that writes them with the SHA-256 of the counts file, and a verification mode fails if any has moved. The figures are produced by a script that refuses to write if its own numbers disagree with that file. The register documents 44 inconsistencies, of which 20 remain open, each with the observation that would close it; one was closed by returning to hardware and one was falsified there.
elb8-dev· Zenodo (CERN European Organi...· 0 citations
Abstract The diffusion equation and its time-fractional counterpart can be obtained via the diffusion limit of continuous time random walks with exponential and heavy-tailed waiting time distributions. The space-dependent variable-order time-fractional diffusion equation is a generalization of the time-fractional diffusion equation with a fractional exponent that varies over space, modelling systems with spatial heterogeneity. However, there has been limited work on defining a global diffusion limit and an underlying random walk for this macroscopic governing equation, which is needed to make meaningful interpretations of the parameters for applications. Here, we introduce continuous time and discrete time random walk models that limit to the variable-order fractional diffusion equation via a global diffusion limit and space- and time-continuum limits. From this, we show how the master equation of the discrete time random walk can be used to provide a numerical method for solving the variable-order fractional diffusion equation. The results in this work provide underlying random walks and an improved understanding of the diffusion limit for the variable-order fractional diffusion equation, which is critical for the development, calibration and validation of models for diffusion in spatially inhomogeneous media with traps and obstacles.
Christopher N. Angstmann, Daniel Han, B. I. Henry et al.· Proceedings of the Royal Soc...· 0 citations
Proteins can diffuse micrometers in seconds, yet bacterial cells are able to maintain stable protein gradients. The best studied bacterial protein gradient is the Min system of Escherichia coli. In rod-shaped bacteria the MinCD proteins prevent formation of minicells by inhibiting FtsZ polymerization close to the cell poles. In E. coli these proteins oscillate between cell poles within a minute, resulting in an increased MinCD concentration at the poles. This oscillation is caused by the interaction between MinD and the protein MinE, which form an ATP-driven reaction-diffusion system, whereby the ATPase MinD cycles between a monomeric cytosolic and a dimeric membrane attached states. Bacillus subtilis also has MinCD, but lacks MinE. In this case MinCD form a static gradient that requires the transmembrane protein MinJ, located at cell poles and cell division sites. A recent reaction-diffusion model was successful in recreating the MinD gradient in B. subtilis, assuming that MinD cycles between cytosol and membrane, like in E. coli. Here we show that the monomeric and dimeric states of B. subtilis MinD have comparable membrane affinities, that MinD interacts with MinJ as a dimer, and that MinJ is not required for membrane localization of MinD. Based on these new findings we tested different models, using kinetic Monte Carlo simulations, and found that a difference in diffusion rate between the monomer and dimer, rather than a difference in membrane affinity, is important for B. subtilis MinCD gradient formation.
Laura C. Bohórquez, Henrik Strahl, Davide Marenduzzo et al.· eLife· 0 citations
The article examines the impact of artificial intelligence technologies on the transformation of marketing strategies of transnational companies in international business. The relevance of the study stems from the rapid expansion of the global artificial intelligence market and the fact that marketing and sales have consistently remained among the business functions with the highest intensity of AI adoption worldwide. For transnational companies, which coordinate marketing activities across dozens of national markets with different languages, consumer cultures and regulatory regimes, artificial intelligence directly affects the classical dilemma of international marketing between standardisation and adaptation: the technology promises to deliver globally consistent brand communication that is simultaneously tailored to local audiences at a scale and speed unattainable for traditional marketing organisations. The article discusses the main directions in which artificial intelligence penetrates the international marketing activity of transnational companies, including hyper-personalisation of customer experience based on machine learning, generative production of advertising and communication content, restructuring of organisational models of marketing departments, and localisation of global campaigns for national markets. Particular attention is paid to the experience of leading transnational corporations in the consumer goods, digital services and e-commerce sectors, as well as to the organisational conditions under which investments in artificial intelligence translate into measurable economic outcomes. The study also addresses the risks accompanying the diffusion of the technology, including changes in consumer attitudes towards AI-generated advertising, reputational threats, and the new regulatory environment created by the European Union Artificial Intelligence Act for companies operating in the European market. The consequences of the uneven global distribution of AI infrastructure, investment and competencies for the competitive positions of companies from developed and developing economies in international markets are considered as a separate dimension of the problem within the field of international economic relations.
М. С. Гожик· State and regions Series Eco...· 0 citations
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Results for counterfactual image generation for the CRC-VAL-HE-7K dataset. A, Tissue-type classifier-guided counterfactuals of a dysplastic tile, generated by shifting its feature vector toward the predicted healthy colon mucosa class. Changes reflect what the model requires to flip the prediction, with increasing manipulation amplitude shown above each image. B, The t-SNE projection of the feature extractor’s latent space (training set), colored by class: adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), healthy colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium/dysplastic tissue (TUM). The features of dysplastic tile in A, initially near the boundary between TUM (aqua) and NORM (gray), shift toward the healthy cluster (pink points in the zoomed-in plot; gray area—arbitrary healthy mucosa zone) as manipulation amplitude increases. C, Morphologic feature prevalence in original and counterfactual image pairs. Horizontal bars show the percentage of image pairs in which at least one of four raters (three board-certified pathologists and one pathology trainee) identified each morphologic feature as present in the original (dark gray) or counterfactual (light gray) image. Right, Mean pairwise inter-rater agreement (Cohen κ) per feature, computed across all pairs and both directions combined. D, Counterfactuals of a healthy mucosa tile shifted toward dysplastic epithelium. Below, Pixel-level difference maps (darker regions indicate greater changes), showing the most change in gland regions, and SSIM values, which quantify the similarity between the original and manipulated tiles. Bottom, Segmented and classified nuclei: pink (epithelial), orange (connective tissue), blue (plasma), deep purple (lymphocytes), aqua (neutrophils), and green (eosinophils). E, Tile-level differences in cell type fractions between (left) real NORM tiles (n = 741) and synthetic (counterfactual images generated from TUM tiles, n = 1,233; full statistical details are provided in Supplementary Table S4). Right, Tile-level differences in cell-type fractions between real TUM tiles (n = 1,233) and synthetic (counterfactual images generated from NORM tiles, n = 741).
Laura Žigutytė, Tim Lenz, Tianyu Han et al.· 0 citations
Existing diffusion-based enhancement methods provide strong generative capability for low-light image enhancement (LLIE), yet they either rely on paired supervision or lack reliable scene constraints in zero-shot settings, often leading to structural inconsistency and color drift. Motivated by conventional Retinex models, which offer physically interpretable priors that can serve as reliable scene constraints yet struggle with mixed degradations in real-world scenarios, we propose DARD, a zero-shot Degradation-Aware Retinex-guided Diffusion framework for LLIE. DARD first extracts image-specific physical priors from the degraded input through a test-time degradation-aware Retinex decomposition, thereby providing reliable structural guidance for zero-shot restoration. It then injects these priors into reverse diffusion through a timestep-adaptive frequency fusion strategy to balance structural anchoring and detail generation. Finally, a guided reverse refinement process with physical consistency and Contrastive Language-Image Pre-training (CLIP)-based semantic guidance is introduced to suppress structural artifacts and semantic drift during sampling. Extensive experiments show that DARD achieves strong distortion and perceptual performance and consistently outperforms existing zero-shot baselines across multiple real-world low-light benchmarks. To further validate the practical utility of our method for downstream applications, we evaluated its impact on semantic segmentation. Experiments demonstrate that images enhanced by DARD achieve a 28.10% relative improvement in mIoU over AGLLDiff.
Wenjie Cai, Yuezhe Yang, Jian-Yang Xia et al.· 0 citations
Purpose: To develop and evaluate a diffusion-based reconstruction framework for highly accelerated 2D real-time (RT) cine cardiovascular magnetic resonance imaging (CMR). Methods: We trained an unconditional patch-based diffusion model and incorporated it into a reconstruction framework, termed CineDiff, using diffusion posterior sampling for data consistency. CineDiff was evaluated in four settings: (i) 30 retrospectively undersampled breath-held cine at 1.5T and 3T from healthy participants across multiple acceleration rates, (ii) 15 prospectively undersampled free-breathing RT cine at 1.5T and 3T from patients indicated for clinical CMR, and (iii) 10 prospectively undersampled mid-field (0.55T) free-breathing scans, including five from healthy subjects and five from porcine models. For retrospective undersampling, reconstruction quality was assessed using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), learned perceptual image patch similarity (LPIPS), and deep image structure and texture similarity (DISTS). For prospective undersampling, image quality was evaluated by blinded expert scoring on a 5-point Likert scale. Results: In retrospectively undersampled breath-held cine data, CineDiff achieved higher PSNR and SSIM and lower LPIPS and DISTS than the comparison methods across all evaluated acceleration rates. In prospectively undersampled free-breathing RT cine data, CineDiff received higher expert image-quality scores. Qualitatively, CineDiff reduced block-like artifacts and preserved finer anatomical detail compared with traditional compressed sensing and a variational network method, termed CineVN. Conclusion: CineDiff enabled high-quality reconstruction of highly accelerated 2D RT cine CMR. The method also demonstrated robustness to out-of-distribution data, including mid-field and porcine acquisitions.
Xuan Lei, Philip Schniter, Juliet Varghese et al.· 0 citations
This paper presents a multi-branch fusion framework for detecting and characterising the propagation of health misinformation in online social networks (OSNs). Grounded in the Elaboration Likelihood Model (ELM) and the Theory of Planned Behaviour (TPB), the model fuses transformer-based semantics with rhetorical cues, stance representations, and psychologically motivated proxies in a unified multi-task architecture. In addition to binary classification, we introduce the Cognitive Propagation Score (CPS), an interpretable post-hoc auxiliary score computed from psychologically motivated, text-derived cues capturing argument complexity, emotional intensity, and content-derived virality potential, to support diffusion-risk reasoning when engagement ground truth is incomplete or unavailable. Experiments on three benchmark datasets, Constraint, COVID--19\_FNIR, and Monkeypox, show strong classification performance, achieving ROC--AUC up to 0.9999 on COVID--19\_FNIR, while propagation-oriented ranking achieves near-perfect agreement when engagement-derived supervision is available (Monkeypox, Spearman's $\rho = 0.9952$) and similarly high ranking alignment under proxy-based supervision on COVID--19\_FNIR ($\rho = 0.9954$). Compared with representative literature baselines, the fusion model improves detection on Constraint and COVID--19\_FNIR, while Monkeypox remains more challenging, reflecting domain- and signal-specific differences. Ablation analysis further indicates that psychological and rhetorical branches provide complementary gains beyond semantic embeddings. Overall, the framework bridges cognitive theory and neural modelling to improve transparency and to support scalable misinformation monitoring, with future work required to validate CPS against human-centred diffusion judgements.
Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun Ajao· 0 citations
Differentially private (DP) synthesis has been extensively studied for tabular and image data separately, yet many real-world datasets contain images paired with multivariate tabular records. Synthesizing such data is particularly challenging under DP, as the two modalities favor different private learning mechanisms while their dependence must also be preserved. To address this challenge, we propose DP-TabImage, a modality-specialized framework for private paired synthesis. DP-TabImage instantiates the factorization $p(x,y)=p_T(y)p_I(x\;|\;y)$ using a private Probabilistic Graphical Model for the multivariate table distribution and a table-conditioned diffusion model trained with DP-SGD for the conditional image distribution. To facilitate conditional learning under clipped and noisy gradients, we further pretrain the model on private table-image prototypes, pairing privately constructed attribute-conditioned images with tabular vectors derived from the already private tabular model at no additional privacy cost. Experiments on three real-world datasets show that DP-TabImage achieves a strong balance among tabular fidelity, image fidelity, and cross-modal alignment. Our analysis further reveals that visual warm-up primarily improves marginal image fidelity, whereas aligned table-image warm-up is critical for improving cross-modal correspondence. Our source code is available in the GitHub repository, https://github.com/KaiChen9909/TabImage_Syn.
Kai Chen, Josephine Lamp, S. Jha et al.· 0 citations
Satellites are emerging as promising platforms to extend generative \emph{artificial intelligence} (AI) services to remote areas lacking terrestrial infrastructure. However, deploying space generative AI is fundamentally constrained by the limited, time-varying onboard energy supplied by solar \emph{energy harvesting} (EH). This paper presents a framework for solar-powered space generative AI in which a satellite receives a user prompt, executes a diffusion-based image-generation model, and downlinks the compressed result within a strict time window. We identify the fundamental \emph{computation--communication} (C$^2$) trade-offs governed by the shared harvested-energy budgets. Specifically, increasing the number of generation steps improves intrinsic image quality but depletes energy and time available for downlink transmission, whereas prioritizing communication guarantees reliable delivery but sacrifices semantic quality. To balance these trade-offs and maximize \emph{end-to-end} (E2E) generative performance, we exploit the predictable solar-EH dynamics induced by deterministic orbital motion and develop a joint C$^2$ resource-optimization framework using a tractable two-step approach. First, we characterize the maximum downlink throughput for a fixed generation depth under continuous solar EH. This establishes a separation principle that decouples waiting-time selection from optimal transmit-power control. Next, we formulate a joint C$^2$ utility-maximization problem and derive a closed-form, low-complexity step-selection policy in the dominant constant-power regime. Extensive experiments under realistic orbital dynamics demonstrate that the proposed policy dynamically balances generation quality and transmission reliability. This yields significant E2E performance gains over static computation- and communication-centric baselines across diverse solar-EH states.
Jierui Zhang, Jianhao Huang, Zhanwei Wang et al.· 0 citations
Retrieval-Augmented Generation (RAG) mitigates large language models (LLMs) hallucinations, yet conventional dense retrieval struggles with the complex reasoning paths of multi-hop question answering (QA). Graph-based RAG captures multi-step relationships but suffers from severe semantic drift and high online latency due to noisy global graph traversals. Thus, we propose ISO-RAG (ISOperimetric Retrieval-Augmented Generation), a geometry-aware RAG framework. By projecting the underlying knowledge graph into a hyperbolic Poincare ball to precompute node-wise isoperimetric profiles, ISO-RAG prunes spurious edges during retrieval, restricting the search space to a strictly localized subgraph. This topological purification regulates Personalized PageRank (PPR) diffusion driving the retrieval process, ensuring exact and low-latency convergence. Experiments on multi-hop QA benchmarks demonstrate that ISO-RAG outperforms state-of-the-art baselines by average absolute gains of 10.0% in retrieval recall and 4.3% in downstream exact match, achieving a superior accuracy-efficiency trade-off by fundamentally eliminating the latency bottleneck of global traversals. Our source code is available at https://github.com/ZaiizaiZHANG/ISO-RAG.
Si-Yuan Zhang, Hanchen Wang, Dong Wen et al.· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
