Effects of Nash equilibrium speed changes with the non-sensory motor algorithm on encounters, agent numbers, and payoffs. by Hiroyuki Ichijo (13891153)

“…The agent’s life history in this model is the same as the sensory–motor algorithm model, including movement, feeding, energy gain/loss, reproduction, and death. …”

Integrating AI and OR for investment decision-making in emerging digital lending businesses: a risk-return multi-objective optimization approach by Vajiheh Torkian (21244732)

“…The study proposes a multi-objective decision-making model that leverages data from the Lending Club, the largest P2P marketplace in the United States, to minimize risk and maximize returns. …”

Clustering control:Using data from Experiment 4 exclusively replicates similar clusters. by Claire Lugrin (21156528)

Overview of our deep learning pipeline for analyzing cellular autophagy. by Oriana Presacan (22232246)

Supplementary file 1_Climate change will alter Amazonian bumblebees’ distribution, but effects are species-specific.docx by Patrícia Nunes-Silva (5041430)

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Traditional image analysis of the CELLULAR dataset. by Oriana Presacan (22232246)

Single neurons in the human substantia nigra encode social learning signals by Arianna Davis (21370643)

“…Next, analyze the neural data by identifying putative units using the OSORT offline sorting algorithm (linked in related works and cited in the paper). …”

Code for "Synching with Seasonality: Predicting Roe Deer Parturition Phenology Across its Distributional Range" by Johanna Kauffert (13837630)

“…<p dir="ltr">This code accompanies the manuscript:</p><p dir="ltr"><i>"</i><b>Synching with Seasonality: Predicting Roe Deer Parturition Phenology Across its Distributional Range</b><i>" </i>and holds the code of the algorithms to retrieve the environmental data and to compute the distributional regression models.…”

The embedding of the different classes as generated by the Siamese network. by Eric Yang (243148)

Comparison of key data before and after pruning. by Chunming Wen (17616274)

Mean and root mean square errors of DOA estimate. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Schematic diagram of maritime array arming. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Variation of RMSE with the number of snapshots. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”