Loss functions between PCA-DNN model and PCA-LSTM model. by Cihun-Siyong Alex Gong (11874066)

ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space by Koushik Naskar (7510592)

“…For the numerical case, user can directly provide <i>ab initio</i> data (adiabatic PESs and NACTs) as input files to this software or can generate those input files through in-built python codes interfacing MOLPRO followed by ADT calculation. …”

Image 6_MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets.tiff by Jared Lichtarge (20548571)

“…For the first time, we applied the PCA-GLASSO algorithm (i.e., MetaboLINK) to metabolomics data derived from Nuclear Magnetic Resonance (NMR) spectroscopy performed on neural cells at various developmental stages, from human embryonic stem cells to neurons.…”

Image 5_MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets.tiff by Jared Lichtarge (20548571)

“…For the first time, we applied the PCA-GLASSO algorithm (i.e., MetaboLINK) to metabolomics data derived from Nuclear Magnetic Resonance (NMR) spectroscopy performed on neural cells at various developmental stages, from human embryonic stem cells to neurons.…”

Image 3_MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets.tiff by Jared Lichtarge (20548571)

“…For the first time, we applied the PCA-GLASSO algorithm (i.e., MetaboLINK) to metabolomics data derived from Nuclear Magnetic Resonance (NMR) spectroscopy performed on neural cells at various developmental stages, from human embryonic stem cells to neurons.…”

Image 4_MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets.tiff by Jared Lichtarge (20548571)

“…For the first time, we applied the PCA-GLASSO algorithm (i.e., MetaboLINK) to metabolomics data derived from Nuclear Magnetic Resonance (NMR) spectroscopy performed on neural cells at various developmental stages, from human embryonic stem cells to neurons.…”

Image 2_MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets.tiff by Jared Lichtarge (20548571)

“…For the first time, we applied the PCA-GLASSO algorithm (i.e., MetaboLINK) to metabolomics data derived from Nuclear Magnetic Resonance (NMR) spectroscopy performed on neural cells at various developmental stages, from human embryonic stem cells to neurons.…”

Performance of PCA and Joint Autoencoder (JAE) denoising algorithms. by Ege Altan (11768156)

Testing function diagram of QIMPN-PCA-DC. by Jun Zhao (59250)

Workflow of the BOMS algorithm. by Ocima Kamboj (21533199)

Constructing the dynamical law. by Robert Marsland (8616483)

Infrared Spectroscopy Coupled with Machine Learning Algorithms to Investigate Vascular Dysfunction in Ovariectomy: An Animal Model Study by Tháfanys S. Travezani (20609398)

S1 File - by Yuh-Chin T. Huang (17867207)

Subjects:

S1 Dataset - by Yuh-Chin T. Huang (17867207)

Subjects:

Algorithm membership function. by Mohamed Raef Smaoui (9865830)

“…<p>(Top) Input Membership Function. The algorithm classifies glucose input into 4 sets: low, medium, high, and ex_high. …”

The loss function of network structures, LPC-DNN, LPC-PCA-DNN, LPC-LSTM, LPC-PCA-LSTM, MFCC-DNN, MFCC-PCA-DNN, MFCC-LSTM and MFCC-PCA-LSTM, as the epoch increases. by Cihun-Siyong Alex Gong (11874066)

Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic by NELSON L. DIAS (8078003)

“…With these practical rules, simple summation algorithms provide sufficiently robust results for the calculation of the density function and its definite integrals. …”

<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

(<i>Left</i>) Reconstruction and performance of the message-passing algorithm for binary patterns. by Sebastian Goldt (14522594)

“…We plot whether reconstruction of the patterns better than a random guess is easy (blue) or impossible (white) using the message-passing algorithm as a function of the constant threshold <i>τ</i> and the variance <i>ν</i> of the Gaussian noise appearing in the connectivity structure (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e013" target="_blank">3</a>). …”