Estimates of for each problem instance. by Jeffrey Keithley (14626551)

Approximation factors for each problem instance. by Jeffrey Keithley (14626551)

Efficient Sampling From the Watson Distribution in Arbitrary Dimensions by Lukas Sablica (19933752)

“…Both algorithms are available in the R package watson on CRAN. …”

Supplementary file 1_Optimizing quantum convolutional neural network architectures for arbitrary data dimension.pdf by Changwon Lee (20812727)

“…The network architecture is most natural when the number of input qubits is a power of two, as this number is reduced by a factor of two in each pooling layer. The number of input qubits determines the dimensions (i.e., the number of features) of the input data that can be processed, restricting the applicability of QCNN algorithms to real-world data. …”

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

“…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”

Distribution of (A) temporal difference set (DistT) and (B) spatial distance set (DistS) values by sample number, for the lightning data subset (Fig 3(B)) identified by the CC3D al... by Manxing Shi (22367181)

The instance of MaxCasesAverted and MaxPeaksReduced is a graph <i>G</i> constructed from the given instance of Max k-SI. by Jeffrey Keithley (14626551)

Statistical properties of high-flash-rate (≥500 lightning strokes) grid clusters identified in 2013 within the study region: (A) Spatial distribution of cluster centroids; (B, C) P... by Manxing Shi (22367181)

Sample lightning stroke data. by Manxing Shi (22367181)

“…Then, the cylinder-based scan clustering algorithm with adaptive parameters (CSCAP) is applied to each subset for fine-scale identification of lightning clusters. …”

Similar to Fig 7, but for the year 2014. by Manxing Shi (22367181)

“…Then, the cylinder-based scan clustering algorithm with adaptive parameters (CSCAP) is applied to each subset for fine-scale identification of lightning clusters. …”

Similar to Fig 7, but for the year 2015. by Manxing Shi (22367181)

“…Then, the cylinder-based scan clustering algorithm with adaptive parameters (CSCAP) is applied to each subset for fine-scale identification of lightning clusters. …”

Cluster distance and size for AC STORM data. by Lingxia Qiao (19996756)

Gillespie algorithm simulation parameters. by Nicholas H. Vitale (20469289)

“…Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. The implications of the model on the design of biomolecular fluorescence detection systems are explored in three relevant numerical examples. …”

Simulation specifications for figure. by Nicholas H. Vitale (20469289)

“…Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. The implications of the model on the design of biomolecular fluorescence detection systems are explored in three relevant numerical examples. …”

Expected behavior system of ODEs. by Nicholas H. Vitale (20469289)

“…Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. The implications of the model on the design of biomolecular fluorescence detection systems are explored in three relevant numerical examples. …”

Example fluorophores. by Nicholas H. Vitale (20469289)

“…Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. The implications of the model on the design of biomolecular fluorescence detection systems are explored in three relevant numerical examples. …”

Identifying the groups becomes harder when the degradation chain is long, especially for groups catalyzing upstream reactions. by Yuanchen Zhao (12905580)

“…The recovery quality of a group is defined as the Jaccard Similarity between the true functional group and its closest match in the algorithm output. Here, the recovery qualities of each of the true functional groups (groups 1, 2, and 3) are shown as a function of <i>k</i>, the number of groups requested from the algorithm. …”

Recursive-Expansive Dynamics Frameworks Formalization with Negation Isolation and Inverse Zero Operators by Del Bel, Julian (21403013)

Data Sheet 1_Calculating 3D rugosity maps for complex habitat scans.pdf by Kindrat Beregovyi (20762342)

“…This paper presents novel algorithms that extend traditional rugosity metrics to generate multi-scale rugosity maps for complex 3D models. …”

Data Sheet 2_Calculating 3D rugosity maps for complex habitat scans.zip by Kindrat Beregovyi (20762342)

“…This paper presents novel algorithms that extend traditional rugosity metrics to generate multi-scale rugosity maps for complex 3D models. …”