We develop a dynamic generalized conditional gradient method (DGCG) for dynamic inverse problems with optimal transport regularization. We consider the fra
Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, t
It is possible to import the trained network to Python environment as well. Design validation, fabrication, and measurement Chips are fabricated using a 90-nm BiCMOS process. Relevant parts of the chip are shown in Figs. 6 and 7. Circuit details for the mm-Wave amplifier can be found in ...
Light gradient boosting model lncRNA: Long non-coding RNAs LR: Logistic regression LSTM: Long short-term memory MCC: Multiclass classification miRNA: MicroRNA ML: Machine learning mRNA: Messenger RNA NB: Naïve Bayes ORF: Open reading frame RF: Random forest ROC: Receiver oper...
python create_squad_dataset.py Train model The training script train_llama_squad.py is heavily based on one provided by younesbelkada. You may need to adjust the per_device_train_batch_size in order to fit the model in your GPU memory. You should also set the gradient_accumulation_steps ...
The ‘Matplotlib Basemap Toolkit’ (a dedicated library of Python to generate 2D maps from coordinates of the location) has been employed to illustrate the chosen buoys' positions. The longitude range provided for the input data spans from 58 degrees West to 178 degrees West, while the latitude...
A python-based deep-learning library Keras [127] has been used for ANN analysis. Guided by our cut-based analysis we have chosen the input variables that yield large signal-background separation. The 1.4 BP1 BP3 1.2 BP5 1 0.8 0.6 0.4 0.2 0 0 0.002 0.004 0.006 0.008 False Negative Rate...
The systems of differential equations were solved using the solve_ivp code provided by the SciPy open-source Python-based ecosystem. The integration method used was Radau that is an implicit Runge–Kutta method of the Radau IIa family of order 5 with a relative and absolute tolerances of 10...
Classification trees are one of the most common models in interpretable machine learning. Although such models are usually built with greedy strategies, in
A python-based deep-learning library Keras [127] has been used for ANN analysis. Guided by our cut-based analysis we have chosen the input variables that yield large signal-background separation. The relevant observables and their definitions are listed in Table 3. We have used these ...