Comparison of compression rates of DDQR and Geco3 using simulated data. by Yujun Wang (1947196)

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Evaluation metrics graphs for different loss functions. by Huiying Zhang (200681)

Proportional survival plot of asymptotic, linear, logistic, and pseudo-gamma functional forms against growth time. by Ryu B. Lippmann (14797597)

Analysis of AW6 automated Interpretation algorithm. by Lauren Littell (13281602)

<b>The Application of the Optimized Genetic Algorithm in SerDes Circuit Design</b> by Xuan Wei (18564826)

“…The low-frequency gain, bandwidth, and slew rate target circuit optimization design is completed. …”

Comparison between simulations for all algorithms and for all cases (1, 2 and 3). by Rutinaldo Aguiar Nascimento (14259594)

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Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

Cleavage time as a function of scaled fiber diameter. by Roukayatou R. Ouedraogo (20460980)

Regression function between network training, verification and prediction output values. by Bo Yu (241082)

Subjects: “…corresponding learning rate…”

Estimated HR trace comparison for two subjects of the ISPC dataset, obtained by DFDF (with MA cancellation only), FSM-DFDF (with MA cancellation and FSM framework), and FSM-SGPS (t... by Heewon Chung (2881286)

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(a-c) Sampling event times with an intensity function . by Gerrit Großmann (9585147)

The model learning rate and loss of the TabNet model across187 epochs. by Aastha Vatsyayan (9981916)

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

In this example the MPC algorithm perfectly tracks the noisy signal , which results in many false spikes. by Nicholas A. Rondoni (21573261)

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Risk Groups Immune Infiltration Analysis by ssGSEA Algorithm. by Yongling Wang (13261656)

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …”

Integration of three machine learning algorithms to characterize lipid metabolism-associated hub genes in sepsis and atrial fibrillation. by Changze Ou (22806374)

Post scan feature estimates and confidence ratings. by Sam Hall-McMaster (10343795)

Data fitting and inference of the time-dependent parameters by TDINN algorithm for the local outbreaks in Xi’an, Guangzhou, and Yangzhou. by Mengqi He (5753525)

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