Efficient Modeling of Spatial Extremes over Large Geographical Domains by Arnab Hazra (11470537)

“…However, existing models proposed in the spatial extremes literature often assume that extremal dependence persists across the entire domain. …”

Data Sheet 1_Human-AI collaboration is not very collaborative yet: a taxonomy of interaction patterns in AI-assisted decision making from a systematic review.pdf by Catalina Gomez (7057646)

“…<p>Leveraging Artificial Intelligence (AI) in decision support systems has disproportionately focused on technological advancements, often overlooking the alignment between algorithmic outputs and human expectations. …”

Table 1_Exploring the role of TikTok for intersectionality marginalized groups: the case of Muslim female content creators in Germany.docx by Fatima El Sayed (20146977)

“…They shape the platform’s functionalities through creative use, while TikTok’s algorithm and virality logic drive creators to blend entertainment with personal content. …”

Table 3_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.xlsx by Tianyue Sun (15831659)

“…</p>Methods<p>We analyzed 46,374 epithelial cells from 17 CRC patients treated with PD-1 blockade to develop an amino acid (AA) metabolism score using the AUCell algorithm. This score was applied to a separate single-cell RNA sequencing (scRNA-seq) dataset from 23 CRC patients to investigate cell-cell interactions and functions of tumor-infiltrating immune cells, revealing distinct immune TME landscapes shaped by tumor metabolism. …”

Table 4_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.xlsx by Tianyue Sun (15831659)

“…</p>Methods<p>We analyzed 46,374 epithelial cells from 17 CRC patients treated with PD-1 blockade to develop an amino acid (AA) metabolism score using the AUCell algorithm. This score was applied to a separate single-cell RNA sequencing (scRNA-seq) dataset from 23 CRC patients to investigate cell-cell interactions and functions of tumor-infiltrating immune cells, revealing distinct immune TME landscapes shaped by tumor metabolism. …”

Table 1_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.xlsx by Tianyue Sun (15831659)

“…</p>Methods<p>We analyzed 46,374 epithelial cells from 17 CRC patients treated with PD-1 blockade to develop an amino acid (AA) metabolism score using the AUCell algorithm. This score was applied to a separate single-cell RNA sequencing (scRNA-seq) dataset from 23 CRC patients to investigate cell-cell interactions and functions of tumor-infiltrating immune cells, revealing distinct immune TME landscapes shaped by tumor metabolism. …”

Table 2_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.xlsx by Tianyue Sun (15831659)

“…</p>Methods<p>We analyzed 46,374 epithelial cells from 17 CRC patients treated with PD-1 blockade to develop an amino acid (AA) metabolism score using the AUCell algorithm. This score was applied to a separate single-cell RNA sequencing (scRNA-seq) dataset from 23 CRC patients to investigate cell-cell interactions and functions of tumor-infiltrating immune cells, revealing distinct immune TME landscapes shaped by tumor metabolism. …”

Image 3_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.jpeg by Tianyue Sun (15831659)

“…</p>Methods<p>We analyzed 46,374 epithelial cells from 17 CRC patients treated with PD-1 blockade to develop an amino acid (AA) metabolism score using the AUCell algorithm. This score was applied to a separate single-cell RNA sequencing (scRNA-seq) dataset from 23 CRC patients to investigate cell-cell interactions and functions of tumor-infiltrating immune cells, revealing distinct immune TME landscapes shaped by tumor metabolism. …”

Image 1_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.jpeg by Tianyue Sun (15831659)

“…</p>Methods<p>We analyzed 46,374 epithelial cells from 17 CRC patients treated with PD-1 blockade to develop an amino acid (AA) metabolism score using the AUCell algorithm. This score was applied to a separate single-cell RNA sequencing (scRNA-seq) dataset from 23 CRC patients to investigate cell-cell interactions and functions of tumor-infiltrating immune cells, revealing distinct immune TME landscapes shaped by tumor metabolism. …”

Image 4_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.jpeg by Tianyue Sun (15831659)

“…</p>Methods<p>We analyzed 46,374 epithelial cells from 17 CRC patients treated with PD-1 blockade to develop an amino acid (AA) metabolism score using the AUCell algorithm. This score was applied to a separate single-cell RNA sequencing (scRNA-seq) dataset from 23 CRC patients to investigate cell-cell interactions and functions of tumor-infiltrating immune cells, revealing distinct immune TME landscapes shaped by tumor metabolism. …”

Image 5_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.jpeg by Tianyue Sun (15831659)

“…</p>Methods<p>We analyzed 46,374 epithelial cells from 17 CRC patients treated with PD-1 blockade to develop an amino acid (AA) metabolism score using the AUCell algorithm. This score was applied to a separate single-cell RNA sequencing (scRNA-seq) dataset from 23 CRC patients to investigate cell-cell interactions and functions of tumor-infiltrating immune cells, revealing distinct immune TME landscapes shaped by tumor metabolism. …”

Image 2_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.jpeg by Tianyue Sun (15831659)

“…</p>Methods<p>We analyzed 46,374 epithelial cells from 17 CRC patients treated with PD-1 blockade to develop an amino acid (AA) metabolism score using the AUCell algorithm. This score was applied to a separate single-cell RNA sequencing (scRNA-seq) dataset from 23 CRC patients to investigate cell-cell interactions and functions of tumor-infiltrating immune cells, revealing distinct immune TME landscapes shaped by tumor metabolism. …”

Image 6_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.jpeg by Tianyue Sun (15831659)

“…</p>Methods<p>We analyzed 46,374 epithelial cells from 17 CRC patients treated with PD-1 blockade to develop an amino acid (AA) metabolism score using the AUCell algorithm. This score was applied to a separate single-cell RNA sequencing (scRNA-seq) dataset from 23 CRC patients to investigate cell-cell interactions and functions of tumor-infiltrating immune cells, revealing distinct immune TME landscapes shaped by tumor metabolism. …”

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). …”