Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

Local topological scales of hierarchical community organization in structural and functional brain graphs. by Arian Ashourvan (6685232)

datasheet1_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.pdf by Santiago Hernández-Orozco (5070209)

“…We show that this model-driven approach may require less training data and can potentially be more generalizable as it shows greater resilience to random attacks. In an algorithmic space the order of its element is given by its algorithmic probability, which arises naturally from computable processes. …”

datasheet2_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.zip by Santiago Hernández-Orozco (5070209)

“…We show that this model-driven approach may require less training data and can potentially be more generalizable as it shows greater resilience to random attacks. In an algorithmic space the order of its element is given by its algorithmic probability, which arises naturally from computable processes. …”

datasheet1_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.pdf by Santiago Hernández-Orozco (5070209)

“…We show that this model-driven approach may require less training data and can potentially be more generalizable as it shows greater resilience to random attacks. In an algorithmic space the order of its element is given by its algorithmic probability, which arises naturally from computable processes. …”

datasheet2_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.zip by Santiago Hernández-Orozco (5070209)

“…We show that this model-driven approach may require less training data and can potentially be more generalizable as it shows greater resilience to random attacks. In an algorithmic space the order of its element is given by its algorithmic probability, which arises naturally from computable processes. …”

Comparison between different PSO algorithms on function 4–6. by Lai Xu (1828831)

Comparison between different PSO algorithms on function 1–3. by Lai Xu (1828831)

Comparison between different PSO algorithms on function 7–8. by Lai Xu (1828831)

Synthetic Realness: Authenticity as Algorithm (Reality Drift Working Paper Series, 2025) by Reality Drift Working Papers Series 2020-2025 (22445446)

“…<p dir="ltr">This paper explores the concept of synthetic realness: how authenticity is increasingly engineered by algorithms until the line between real and fake becomes functionally irrelevant. …”

Similarity between the allegiance matrices of the multiplex SC-FC graph, the FC graph alone, and the SC graph alone, as a function of the topological scale for the group. by Arian Ashourvan (6685232)

Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation by Gianmarc Grazioli (6752297)

“…The models generated by the AI produced fibril fractions that surpass previously published fibril fractions in 3 of 5 cases, including the most naturally abundant amyloid fibril topology, the <i>1,2 2-ribbon</i>, which features a steric zipper. …”

Exponentially attenuated sinusoidal function. by Hang Zhao (143592)

“…<div><p>Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. …”

RMSE values of different algorithms for different bands in the "Kherson" region. by YueSheng Jiang (19267984)

RMSE values of different algorithms for different bands in the "PY" region. by YueSheng Jiang (19267984)

Final assignment algorithm. by Negin Alisoltani (11949357)

How fries chooses which sequences to keep and which sequences to prune. by Anna U. Lowegard (8051597)

ROI setting diagram for AI calculation and optimization verification results. by Tsutomu Gomi (7382183)

Subjects:

Example of how the Resnet34UNet model functions. by Henrik Mustonen (20378529)

DataSheet1_The evolution of flexibility and function in the Fc domains of IgM, IgY, and IgE.pdf by Rosaleen A. Calvert (10039787)

“…This permits additional flexibility within the Fc region, which has been exploited by nature to modulate antibody effector functions. …”