diffusion models

Beyond Mobility Flows: A Network Perspective on Academic Exchange and National Innovation Performance

International academic exchange is widely regarded as an important mechanism of knowledge transfer and international collaboration, yet relatively little is known about how countries’ structural positions within academic mobility networks relate to national innovation performance. This study examines the relationship between Erasmus academic mobility and innovation using a multidimensional quantitative framework integrating Social Network Analysis (SNA), econometric modelling, and inferential statistical analysis. Innovation performance is operationalised through the European Innovation Scoreboard (EIS), while Erasmus mobility data from 2008–2013 are analysed at both country and institutional levels. The empirical design combines regression analyses of mobility characteristics with two complementary network models: a classical model based on conventional centrality measures and an extended model incorporating weighted, influence-based, and temporal network metrics. The results indicate that overall mobility volume is only weakly associated with innovation performance, whereas mobility directed towards highly innovative countries exhibits substantially stronger associations. Network analyses further demonstrate that countries occupying more influential structural positions within Erasmus mobility networks consistently achieve higher innovation performance across multiple EIS dimensions. The comparison of the two analytical models shows that the extended multidimensional network representation captures complementary aspects of knowledge diffusion that remain only partially visible through conventional centrality measures. The study contributes conceptually by framing academic mobility as a knowledge network and methodologically by integrating econometric and network-based approaches within a unified analytical framework for examining international academic exchange and innovation.

Candidate Intermediary Node Deployment Under the Linear Threshold Model: A Branch-and-Benders-Cut Approach

This paper studies candidate intermediary node deployment for influence diffusion under the linear threshold model (LTM). Given fixed diffusion sources, target nodes, and a budget, the decision maker selects candidate intermediary nodes to maximize the expected total weight of activated targets. Once deployed, a candidate node enables its associated potential arcs whose other endpoints belong to the effective network. Using the LTM live-arc representation, we establish distributional equivalence between sampling on the potential graph and then restricting each scenario to the deployed induced network, and sampling directly on the deployed network. This leads to a finite-scenario sample-average approximation (SAA) mixed-integer formulation based on canonical live paths; the resulting deployment objective is monotone and supermodular but is generally not submodular, so the classical greedy-approximation guarantee for monotone submodular maximization does not apply in general. Since the compact SAA formulation contains many scenario–target variables and covering constraints, solving the formulation directly can be computationally demanding. We therefore propose a scenario-decomposed branch-and-Benders-cut algorithm that solves the finite-scenario SAA model to optimality. Each scenario subproblem is separable by target and has a closed-form dual optimum, so Benders cuts are separated by scanning required-node sets rather than solving linear programs inside callbacks. On five real networks and 225 SAA instances, the algorithm solves all instances within one hour, averaging 27.46 s; the compact SAA formulation solves 172 instances, with an average capped time of 1444.11 s.

Several Structural Properties and Characterisations of Affine Gould–Hopper-Based Appell Polynomials

In this paper, we introduce and systematically study a new two-parameter family of affine (q,η)-Gould–Hopper-based Appell polynomials. These polynomials arise by fusing the affine (q,η)-exponential Gould–Hopper kernel with an invertible Appell multiplier. A central contribution is the affine quasi-monomial construction: an explicit raising operator obtained from a logarithmic-difference quotient of the combined kernel, together with the lowering operator and the resulting q-commutation structure. An explicit double-sum series representation and an affine diffusion equation of order j connecting differences in the two variables, and the full quasi-monomial framework identifying the operator pair (Pq,η+,Pq,η−) are developed. Additional results include a governing difference equation, a converse characterisation, a determinantal representation, an affine addition formula, and an order recursion. Further, the Bernoulli and Euler sub-families are obtained as invertible specialisations, while the Genocchi family is treated separately as a derived noninvertible family through its exact relation with the Euler family. The corresponding structural results are stated with these hypotheses made explicit. Surface plots, numerical value tables, and a numerical illustration of real zeros accompany the theoretical development. The diffusion relation provides a discrete affine analogue of a higher-order evolution equation; no claim of a fully developed physical model is made in the present work.

Experimental and numerical investigation on the actual flowability and diffusion morphologies of cement-based grouting materials on quantified 3D rough surfaces

Title: Multi-Scale Topology Mapping via Graph Embedding and Diffusion Models

This paper introduces a novel method for generating detailed topological maps of complex systems, leveraging graph embedding and diffusion models. Traditional topological analysis often struggles with capturing fine-grained details, and this approach aims to overcome this limitation by iteratively refining a graph representation using diffusion models. We formulate the mapping process as a Markov chain, incorporating graph embedding to define the initial topology and diffusion models to refine the resulting map. The resulting maps provide a significant advancement in visualizing and analyzing complex systems, offering a pathway for improved understanding and potentially aiding in scientific discovery.

Title: Multi-Scale Topology Mapping via Graph Embedding and Diffusion Models

This paper introduces a novel method for generating detailed topological maps of complex systems, leveraging graph embedding and diffusion models. Traditional topological analysis often struggles with capturing fine-grained details, and this approach aims to overcome this limitation by iteratively refining a graph representation using diffusion models. We formulate the mapping process as a Markov chain, incorporating graph embedding to define the initial topology and diffusion models to refine the resulting map. The resulting maps provide a significant advancement in visualizing and analyzing complex systems, offering a pathway for improved understanding and potentially aiding in scientific discovery.

Enhanced perceptual strip-block embedding and diffusion model in vision transformer based fault diagnosis method of rolling bearing with few-shot

Addressing Request For Explicit Falsification - Admissibility Targets 4-2-1 & Inverse Admissibility Program

Addressing requests to stress-test pre-geometric model and find a viable path to spatial dimension, here is the full open falsification record covering Runs 1–27. While early tests (Runs 1–16) disproved that smooth 3D geometry emerges from simple diffusion, Runs 18–27 demonstrate that higher-order relational structure is built through ordered boundary closure. Across 10 new test series, local capacity limits repeatedly created a stable, self-repairing dimension-3 simplicial plateau. This report details the exact failure limits, null controls, and mathematical bounds for external review."

Title: Quantum-Enhanced Diffusion for Pattern Synthesis

Quantum-enhanced diffusion modeling represents a paradigm shift in pattern generation, offering the potential for significantly faster and more precise synthesis of complex, realistic patterns compared to classical methods. This research investigates the feasibility of implementing a novel quantum algorithm designed to accelerate the generation and refinement of patterns through the exploitation of quantum phenomena. We explore the core mechanism by leveraging quantum algorithms to enhance the iterative refinement process inherent in diffusion modeling, ultimately addressing the computational bottleneck hindering the creation of intricate designs. The paper details the proposed quantum algorithm, its potential benefits, and preliminary results demonstrating improved pattern quality and speed. The core claim centers on a quantum-accelerated diffusion process that surpasses existing techniques.

A Non-Deterministic Computation Model Based on Cellular Automata

This paper proposes a novel computational model based on cellular automata (CA) that incorporates non-deterministic elements to better represent and simulate complex systems exhibiting inherently stochastic behavior. Traditional CA models operate under deterministic rules, limiting their applicability to systems with predictable outcomes. This work introduces a framework where each cell's state is influenced by its neighbors and a set of non-deterministic rules, mimicking the random interactions and probabilistic transitions prevalent in biological and physical systems. The model utilizes a grid-based structure, with each cell representing an entity within the system and its state determined by a combination of local interactions and random events. The core of the model lies in the definition of non-deterministic rules that govern state transitions, allowing for multiple possible outcomes for a given cell state and its neighborhood. This approach offers a flexible and powerful tool for modeling phenomena such as pattern formation, reaction-diffusion processes, and emergent behavior in biological systems, as well as complex physical dynamics. The model's design allows for the exploration of the impact of non-deterministic factors on system dynamics, providing a new perspective on modeling complex systems.

Ultrasonic-Assisted Deep Eutectic Solvent Extraction of Quercetin from Onion Peel: Experimental Optimization and Mechanistic Insights

Abstract This study developed an ultrasound-assisted deep eutectic solvent (DES) platform for the green recovery of quercetin from onion peels. It elucidated the extraction mechanism through process optimization and molecular simulations. Among the screened systems, betaine-malonic acid (Bet-MLN) exhibited the strongest synergistic extraction capability. Single-factor tests and Box–Behnken design optimized the conditions at extraction temperature 50 °C, extraction time 40 min, DES water content 34.11%, liquid–solid ratio 26.98:1, and ultrasonic power 376.26 W, yielding 21.83 mg/g quercetin, 30.27 mg RE/g flavonoids, and 77.33 mg GAE/g polyphenols, consistent with model predictions. LC-MS identified quercetin diglucoside, monoglucoside, and aglycone, with monoglucoside predominating. C18 reversed-phase solid-phase extraction enabled stepwise purification and enrichment and markedly enhanced DPPH and ABTS scavenging. Quantum chemistry and molecular dynamics simulations confirmed that hydrogen bonding, electrostatic complementarity, van der Waals synergy, and diffusion collectively drove flavonoid/polyphenol desorption, solvation, and migration, thereby supporting the valorization of agricultural by-products.

Norms, Tools, and the Say/Do Gap: Seventeen Months of Autonomous Frontier-Model Agents in the AI Village

The AI Village is a public experiment in which up to ~30 frontier language-model agents from seven developers act autonomously for eight hours every weekday, each with its own computer, shared chat, persistent self-written memory and weekly human-set goals. Using its recently released event log (350,537 events, 2.3 million deduplicated computer-use turns, ~26,000 session summaries; April 2025–September 2026; 42 agent identities after one opt-out) we present the first longitudinal empirical study of this population. Study 1 traces an unrequested verification norm—posting receipts, hashes and "verified" markers for claimed artefacts—from one agent's spontaneous choice in October 2025 to population-wide use (2–5% of messages before, 28–31% at peak). Its strict form is episodic and task-triggered; a human counter-nudge and 1,574 automated anti-idling nudges left it intact, and a placebo-controlled event study finds no idling reduction beyond matched no-nudge windows. Newcomers arriving during diffusion over-adopted; those arriving after the plateau under-adopted, consistent with memory-mediated persistence. Study 2 documents a GUI-to-shell shift (shell share of turns 0.2% → 48%) that ratchets after a coding goal and persists within agents, while newcomers arrive already high—pointing to model generation and scaffolding rather than imitation. Study 3 audits end-of-session narratives against logged actions: agents under-report effort (median claimed 25 turns vs 40 actual), but among 1,566 concrete action claims coded with a public codebook (90 double-rated cases, κ = 0.68) all but one hand-read mismatch is a measurement or scope error, not a fabricated action. A dated live case shows an accusation and a sincere denial both falsified by the git record. Oversight of long-running agent populations should rest on telemetry and artefacts, not self-report. All code and coding sheets are public.