Structure and bioinformatic analysis of the four different promoters. by Ali Hosseini Rad S. M. (9167497)

SpatialKNifeY analysis landscape. by Shunsuke A. Sakai (13789939)

An overview of the Multi-CD method. by Ji Hyun Bak (2096773)

DataSheet1_An adaptive time stepping stiffness confinement method for solving reactor dynamics equations.docx by Dan Wang (34472)

“…With a pre-set error tolerance, the ATS algorithm is exempted from the empirical selection of the time-step size in transient simulations. …”

Statistical Analysis of Submicron X‑ray Tomography Data on Polymer Imbibition into Arrays of Cylindrical Nanopores by Fernando Vazquez Luna (9521558)

“…Frozen transient imbibition states in arrays of straight cylindrical pores 400 nm in diameter were imaged by phase-contrast X-ray computed tomography with single-pore resolution. A semiautomatic algorithm yielding brightness profiles along all pores identified within the probed sample volume is described. …”

Composition of MIX (new) set. by Youzhi Zhang (728859)

“…This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protein hotspot residues. …”

The final data sets used in this work. by Youzhi Zhang (728859)

“…This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protein hotspot residues. …”

One-dimensional convolution diagram. by Youzhi Zhang (728859)

“…This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protein hotspot residues. …”

Composition of MIX set. by Youzhi Zhang (728859)

“…This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protein hotspot residues. …”

S5 File - by Youzhi Zhang (728859)

“…This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protein hotspot residues. …”

Illustration of the Embed-1dCNN model. by Youzhi Zhang (728859)

“…This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protein hotspot residues. …”

General Chemically Intuitive Atom- and Bond-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction by Miguel Nouman (21557202)

General Chemically Intuitive Atom- and Bond-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction by Miguel Nouman (21557202)

General Chemically Intuitive Atom- and Bond-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction by Miguel Nouman (21557202)

Outcome scenarios sets. by Lukas D. Sauer (19588975)

“…In a comparison study using simulations and numerical calculations, we are planning to investigate the use of utility functions for quantifying the compromise between power and type-I error inflation and the use of numerical optimization algorithms for optimizing these functions. …”

Run time of 1000 iterations in a pilot study. by Lukas D. Sauer (19588975)

“…In a comparison study using simulations and numerical calculations, we are planning to investigate the use of utility functions for quantifying the compromise between power and type-I error inflation and the use of numerical optimization algorithms for optimizing these functions. …”

INSTA (IN situ Sequencing and Transcriptomics Annotation) is a pipeline for tuning and validating spot detection methods using crowdsourced annotations. by Jenny M. Vo-Phamhi (11250803)

Swin-cryoEM model structure. by Kun Fang (619812)

“…This paper combines the characteristics of the MixUp hybrid enhancement algorithm, enhances the image feature information in the pre-processing stage, builds a feature perception network based on the channel self-attention mechanism in the forward network of the Swin Transformer model network, achieving adaptive adjustment of self-attention mechanism between different single particles, increasing the network’s tolerance to noise, Incorporating PReLU activation function to enhance information exchange between pixel blocks of different single particles, and combining the Cross-Entropy function with the softmax function to construct a classification network based on Swin Transformer suitable for cryo-electron micrograph single particle detection model (Swin-cryoEM), achieving mixed detection of multiple types of single particles. …”

Comparative experiment results. by Kun Fang (619812)

“…This paper combines the characteristics of the MixUp hybrid enhancement algorithm, enhances the image feature information in the pre-processing stage, builds a feature perception network based on the channel self-attention mechanism in the forward network of the Swin Transformer model network, achieving adaptive adjustment of self-attention mechanism between different single particles, increasing the network’s tolerance to noise, Incorporating PReLU activation function to enhance information exchange between pixel blocks of different single particles, and combining the Cross-Entropy function with the softmax function to construct a classification network based on Swin Transformer suitable for cryo-electron micrograph single particle detection model (Swin-cryoEM), achieving mixed detection of multiple types of single particles. …”

Comparison of model training results. by Kun Fang (619812)

“…This paper combines the characteristics of the MixUp hybrid enhancement algorithm, enhances the image feature information in the pre-processing stage, builds a feature perception network based on the channel self-attention mechanism in the forward network of the Swin Transformer model network, achieving adaptive adjustment of self-attention mechanism between different single particles, increasing the network’s tolerance to noise, Incorporating PReLU activation function to enhance information exchange between pixel blocks of different single particles, and combining the Cross-Entropy function with the softmax function to construct a classification network based on Swin Transformer suitable for cryo-electron micrograph single particle detection model (Swin-cryoEM), achieving mixed detection of multiple types of single particles. …”