SHAP summary plot. by Meng Cao (105914)

“…The optimal model was further assessed for predictor importance utilizing the SHAP method and deployed on a web platform using the Streamlit library.…”

ROC curves for the test set of four models. by Meng Cao (105914)

“…The optimal model was further assessed for predictor importance utilizing the SHAP method and deployed on a web platform using the Streamlit library.…”

Display of the web prediction interface. by Meng Cao (105914)

“…The optimal model was further assessed for predictor importance utilizing the SHAP method and deployed on a web platform using the Streamlit library.…”

ECE6327_SmartGrid by Xingpeng Li (11825663)

Phylogenetic history of genes 5 and 11 in three related genomes. by Geleta Dugassa Barka (9099456)

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…Model evaluation was based on accuracy metrics and qualitative analysis of the confusion matrix.. …”

DataSheet1_Towards Computational Modeling of Human Goal Recognition.pdf by Shify Treger (11973125)

“…Moreover, the proposed algorithm marries rationality-based and plan-library based methods seamlessly.…”

DataSheet1_Towards Computational Modeling of Human Goal Recognition.pdf by Shify Treger (11973125)

“…Moreover, the proposed algorithm marries rationality-based and plan-library based methods seamlessly.…”

primary mouse RT single cell RNA-seq by Mamy ANDRIANTERANAGNA (12913196)

“…The clustering was conducted using the graph-based modularity optimization Louvain algorithm implemented in Seurat v3. …”

primary ATRT single cell RNA-seq by Mamy ANDRIANTERANAGNA (12913196)

“…</p> <p>scRNA-seq data integration was performed using the CCA-based implemented in Seurat version 3. The clustering was conducted using the graph-based modularity optimization Louvain algorithm implemented in Seurat v3. …”

Aluminum alloy industrial materials defect by Ying Han (20349093)

“…</p><h2>Description of the data and file structure</h2><p dir="ltr">This is a project based on the YOLOv8 enhanced algorithm for aluminum defect classification and detection tasks.…”

Otago's Network for Engagement and Research: Mapping Academic Expertise and Connections by Sander Zwanenburg (8552102)

“…<br></div><div><br></div><div>In the next stage of the project, we will develop further the data integration schemes, enhance our algorithm to infer expertise based on this data, and update the interactive visualisation to reflect these inferences. …”

Table 1_Advances in the application of human-machine collaboration in healthcare: insights from China.docx by Wuzhen Wang (20675405)

“…“Human–machine collaboration” is based on an intelligent algorithmic system that utilizes the complementary strengths of humans and machines for data exchange, task allocation, decision making and collaborative work to provide more decision support. …”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …”

LinearSolve.jl: because A\b is not good enough by Christopher Rackauckas (9197216)

“…While with Julia's Base you can use lu(A)\b, qr(A)\b, and svd(A)\b, this idea does not scale to all of the cases that can arise. …”

Code by Baoqiang Chen (21099509)

“…</p><p><br></p><p dir="ltr">This architecture was implemented using the PyTorch library and trained using cross-entropy loss. The model was optimized to classify RNA sequences, achieving robust performance across multiple test sets.…”

Core data by Baoqiang Chen (21099509)

“…</p><p><br></p><p dir="ltr">This architecture was implemented using the PyTorch library and trained using cross-entropy loss. The model was optimized to classify RNA sequences, achieving robust performance across multiple test sets.…”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…</li><li>The dataframe of extracted colour features from all leaf images and lab variables (ecophysiological predictors and variables to be predicted)</li><li>Set of scripts used for image pre-processing, features extraction, data analytsis, visualization and Machine learning algorithms training, using ImageJ, R and Python.…”

Minisymposterium: Muq-Hippylib: A Bayesian Inference Software Framework Integrating Data with Complex Predictive Models under Uncertainty by Ki-Tae Kim (10184066)

“…In this poster, we present an extensible software framework MUQ-hIPPYlib that overcomes this hurdle by providing unprecedented access to state-of-the-art algorithms for Bayesian inverse problems. MUQ provides a spectrum of powerful Bayesian inversion models and algorithms, but expects forward models to come equipped with gradients/Hessians to permit large-scale solution. hIPPYlib implements powerful large-scale gradient/Hessian-based solvers in an environment that can automatically generate needed derivatives, but it lacks full Bayesian capabilities. …”

SI2-SSI: Integrating Data with Complex Predictive Models under Uncertainty: An Extensible Software Framework for Large-Scale Bayesian Inversion by Omar Ghattas (4387300)

“…MUQ provides a spectrum of powerful Bayesian inversion models and algorithms, but expects forward models to come equipped with gradients/Hessians to permit large-scale solution. hIPPYlib implements powerful large-scale gradient/Hessian-based solvers in an environment that can automatically generate needed derivatives, but it lacks full Bayesian capabilities. …”