Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. by Francisco Páscoa dos Santos (16510676)

“…<p>A) Resulting cluster inertia from applying the k-means algorithm described in the methods to empirical averaged functional connectivity from healthy subjects, with different numbers of clusters. …”

Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Flowchart of the denoising convolutional neural network metal artifact reduction hybrid reconstruction (DnCNN-MARHR) algorithm. by Tsutomu Gomi (7382183)

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Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density. by Hassan Mehboob (8960273)

“…<p>A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density.…”

Texture analysis results. by Tsutomu Gomi (7382183)

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Spectra of the Sonial Vision Safire II tube at 70 and 140 kV potentials. by Tsutomu Gomi (7382183)

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Fig 4 - by Xutao Liu (13006965)

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Fig 9 - by Xutao Liu (13006965)

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Fig 10 - by Xutao Liu (13006965)

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Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

“…We developed a mechanism which converts a given medical image to a spectral space which have a base set composed of special functions. …”

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

“…We investigate the shape of a discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not required to achieve results similar to those obtained using differentiable programming approaches such as deep learning. …”

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

“…We investigate the shape of a discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not required to achieve results similar to those obtained using differentiable programming approaches such as deep learning. …”

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

“…We investigate the shape of a discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not required to achieve results similar to those obtained using differentiable programming approaches such as deep learning. …”

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

“…We investigate the shape of a discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not required to achieve results similar to those obtained using differentiable programming approaches such as deep learning. …”

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. …”

ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space by Koushik Naskar (7510592)

“…In order to overcome such shortcoming, we develop a generalized algorithm, “ADT” to generate the nonadiabatic equations through symbolic manipulation and to construct highly accurate diabatic surfaces for molecular processes involving excited electronic states. …”

Constructing the dynamical law. by Robert Marsland (8616483)

Definitions of global parameters used for constructing random ecosystems. by Robert Marsland (8616483)