PS-UNet++ model structure. by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”

Network structure diagram of UNet++. by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”

Sub-pixel convolution upsampling module. by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”

Elementary effects (EE) results for chorionic vessel generation employing the input parameter ranges from Table 1. by Diana C. de Oliveira (9220729)

MODeM 2023 TC NC-A by Anonymized (8222226)

Experimentally measured protection from mutations and expected resulting increase in circuit lifetime. by Antoine L. Decrulle (11540003)

Exponentially attenuated sinusoidal function. by Hang Zhao (143592)

“…The Pareto optimal front was generated using MOCOA with the indicators of spectral kurtosis and KL divergence, by which the optimal intrinsic mode functions were obtained. A deep VMD-attention network based on MOCOA was developed for ECG signal classification. …”

Time-domain and frequency-domain waveform diagrams of AE signals collected from the damaged area. by Li Jiaqing (21212614)

Time-domain and frequency-domain waveform diagrams of AE signals collected from undamaged areas. by Li Jiaqing (21212614)

Machine learning analysis of 10 hub genes related to SCI was performed using eight algorithms applied to the dataset. by Tianbao Feng (21722233)

Representative neurons and their corresponding step functions. by Reem Khalil (10898163)

ClaritySpectra: Raman spectra analysis tool by Aaron Celestian (9395696)

“…</li></ul><h3>PEAK FITTING </h3><ul><li>Automated background subtraction using asymmetric least squares fitting</li><li>A new suggested background feature that lets you preview the background that you like best</li><li>Interactive background fitting lets you further tune the background to perfection</li><li>Four choice of peaks: Gaussian, Lorentzian, Pseudo-Voigt, and the new Asymmetric Voigt functions</li><li>Overlapping view of how well the peaks fit with quality metrics</li><li>No need to define regions, the algorithm is smart enough to what a peak looks like.…”

Word cloud obtained from the descriptions provided by survey participants (N = 22) of their topics of interest related to AN at the contemplation stage. by Diana Ramírez-Cifuentes (20700054)

Fitness comparison on test function. by Kejia Liu (5699651)

“…Its restrictions block GEP from successfully handling high-dimensional along with complex optimization problems. …”

Data_Sheet_1_Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations.CSV by Denise Reimann (7213364)

Data_Sheet_2_Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations.pdf by Denise Reimann (7213364)

500 <i>ϕ</i> vectors learned from ISTA. by Ilias Rentzeperis (10215602)

“…Traditionally, to replicate such biological sparsity, generative models have been using the <i>ℓ</i>₁ norm as a penalty due to its convexity, which makes it amenable to fast and simple algorithmic solvers. In this work, we use biological vision as a test-bed and show that the soft thresholding operation associated to the use of the <i>ℓ</i>₁ norm is highly suboptimal compared to other functions suited to approximating <i>ℓ</i>_<i>p</i> with 0 ≤ <i>p</i> < 1 (including recently proposed continuous exact relaxations), in terms of performance. …”

CIAT genebank workflow suggestion resulting from the proposed methodology. by Diego Felipe Conejo-Rodríguez (18495740)

Conservation and functional variation across TcdB subtypes. by Michael J. Mansfield (9922934)

Figures and supplemental figures for "Optimal sampling of tensor networks targeting wave function’s fast decaying tails" by Marco Ballarin (17780767)

“…<p dir="ltr">Figures and supplemental figures for "Optimal sampling of tensor networks targeting wave function’s fast decaying tails". The supplemental figures are the data from the "Dibona" machine regarding the energy consumption of the algorithm. …”