Decoding evidence for feature triplets on test tasks. by Sam Hall-McMaster (10343795)

Comparison of cue-locked and feedback-locked decoding. by Sam Hall-McMaster (10343795)

Associative memory paradigm used to train the feature decoders. by Sam Hall-McMaster (10343795)

Choices on individual test tasks were not explained by model-free perseveration. by Sam Hall-McMaster (10343795)

Subgroup analysis. by Sam Hall-McMaster (10343795)

Learning dynamics on training tasks. by Sam Hall-McMaster (10343795)

Choice behavior on individual test tasks. by Sam Hall-McMaster (10343795)

Data and code resources. by Sam Hall-McMaster (10343795)

Validation tests of feature decoding during the associative memory paradigm. by Sam Hall-McMaster (10343795)

Comparison of city decoders trained on session one and session two data. by Sam Hall-McMaster (10343795)

Decoding evidence for policies on each individual test task. by Sam Hall-McMaster (10343795)

Validation of the neural decoding pipeline. by Sam Hall-McMaster (10343795)

Effects of Nash equilibrium attack and defense behaviors with the sensory–motor algorithm on encounters, agent numbers, and payoffs. by Hiroyuki Ichijo (13891153)

Attack and defense behaviors with the sensory–motor algorithm between a single predator and a single prey. by Hiroyuki Ichijo (13891153)

Variants of weighted methods. by Limin Ma (556873)

“…At this point, considering the weights as the probabilities of the contributions of different attributes to the model can enhance the interpretation ability of the algorithm. Specifically, we add an entropy regularization term to the objective function of the problem model and then use the Lagrange multiplier method to solve the weights. …”

Attack and defense behaviors between a single predator and a single prey using the sensory–motor algorithm. by Hiroyuki Ichijo (13891153)

GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

“…Using occurrence data and environmental variable, we employ the Maximum Entropy (MaxEnt) algorithm within the species distribution modeling (SDM) framework to estimate occurrence probability at a spatial resolution of 1/12° (~10 km). …”

Accelerated MPC Algorithms and Augmented System Design for Legged Robots by John Ndegwa Nganga (21088838)

“…The approach is powered by a newly introduced algorithm, named the modified Recursive Newton Euler Algorithm~(mRNEA), which is inspired by classical RNEA, and computes the dynamics information pre-multiplied with a fixed vector. …”

Generated blended data using mouse brain cells and human embryonic heart cells, labeled as <i>Mix</i>[<i>a</i>,<i>b</i>,<i>c</i>], indicates the proportion of mouse brain cells fou... by Nuray Sogunmez Erdogan (21533872)

Example of a typical workflow to construct a network using NeKo. by Marco Ruscone (11747264)