Video 1_TDE-3: an improved prior for optical flow computation in spiking neural networks.mp4 حسب Matthew Yedutenko (5142461)

"…Proposed in the literature bioinspired neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and processors with spiking neural networks to provide real-time and energy-efficient motion detection through extracting temporal correlations between two points in space. However, on the algorithmic level, this design leads to a loss of direction-selectivity of individual TDEs in textured environments. …"

Comparative Cellular and Spatial Transcriptomic Analysis of Paucibacillary and Multibacillary pulmonary TB Lesions of B6.Sst1S Mice. حسب Shivraj M. Yabaji (22379255)

Early Parkinson’s disease identification via hybrid feature selection from multi-feature subsets and optimized CatBoost with SMOTE حسب Subhashree Mohapatra (17387852)

"…The analysis was conducted on a PD dataset derived from speech recording signals. To address the data imbalance, the synthetic minority oversampling technique (SMOTE) is applied as a pre-processing step to improve the robustness and reliability of the model. …"

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

"…Experimental Methodology Framework Local Processing Pipeline Architecture Data Flow: Storage I/O → Memory Buffer → CPU/GPU Processing → Cache Coherency → Storage I/O ├── Input Vector: mmap() system call for zero-copy file access ├── Processing Engine: OpenMP parallelization with NUMA-aware thread affinity ├── Memory Management: Custom allocator with hugepage backing └── Output Vector: Direct I/O bypassing kernel page cache Cloud Processing Pipeline Architecture Data Flow: Local Storage → Network Stack → TLS Tunnel → CDN Edge → Origin Server → Processing Grid → Response Pipeline ├── Upload Phase: TCP window scaling with congestion control algorithms ├── Network Layer: Application-layer protocol with adaptive bitrate streaming ├── Server-side Processing: Containerized microservices on Kubernetes orchestration ├── Load Balancing: Consistent hashing with geographic affinity routing └── Download Phase: HTTP/2 multiplexing with server push optimization Dataset Schema and Semantic Structure Primary Data Vectors Field Data Type Semantic Meaning Measurement Unit test_type Categorical Processing paradigm identifier {local_processing, cloud_processing} photo_count Integer Cardinality of input asset vector Count avg_file_size_mb Float64 Mean per-asset storage footprint Mebibytes (2^20 bytes) total_volume_gb Float64 Aggregate data corpus size Gigabytes (10^9 bytes) processing_time_sec Integer Wall-clock execution duration Seconds (SI base unit) cpu_usage_watts Float64 Thermal design power consumption Watts (Joules/second) ram_usage_mb Integer Peak resident set size Mebibytes network_upload_mb Float64 Egress bandwidth utilization Mebibytes energy_consumption_kwh Float64 Cumulative energy expenditure Kilowatt-hours co2_equivalent_g Float64 Carbon footprint estimation Grams CO₂e test_date ISO8601 Temporal execution marker RFC 3339 format hardware_config String Node topology identifier Alphanumeric encoding Statistical Distribution Characteristics The dataset exhibits non-parametric distribution patterns with significant heteroscedasticity across computational load vectors. …"

Methodological flowchart outlining the study’s workflow. حسب Ezekiel Ahn (6697256)

PySilsub—a toolbox for silent substitution حسب Joel Martin (11864048)

In vivo N2 athero data.xlsx حسب Jessica Davis-Knowlton (11472595)

"…For ORO images, color thresholding within red hued pixels allowed for selection of ORO positive area. The smooth muscle actin and Mac2 sum projections were thresholded using the Otsu algorithm.…"

Presentation_1_Feasibility of Dialysate Bolus-Based Absolute Blood Volume Estimation in Maintenance Hemodialysis Patients.pdf حسب Simon Krenn (9874167)

Network Intrusion Detection Datasets حسب Ogobuchi Daniel Okey (15854591)

"…The primary focus of this project is to develop an effective Intrusion Detection System (IDS) using the aforementioned algorithm. …"

Image_10_Detecting COVID-19-Related Fake News Using Feature Extraction.PNG حسب Suleman Khan (3909964)

Image_8_Detecting COVID-19-Related Fake News Using Feature Extraction.PNG حسب Suleman Khan (3909964)

Image_9_Detecting COVID-19-Related Fake News Using Feature Extraction.PNG حسب Suleman Khan (3909964)

Image_4_Detecting COVID-19-Related Fake News Using Feature Extraction.PNG حسب Suleman Khan (3909964)

Image_5_Detecting COVID-19-Related Fake News Using Feature Extraction.PNG حسب Suleman Khan (3909964)

Image_1_Detecting COVID-19-Related Fake News Using Feature Extraction.PNG حسب Suleman Khan (3909964)

Image_13_Detecting COVID-19-Related Fake News Using Feature Extraction.PNG حسب Suleman Khan (3909964)

Image_12_Detecting COVID-19-Related Fake News Using Feature Extraction.PNG حسب Suleman Khan (3909964)

DataSheet_1_Identification of cuproptosis-related lncRNA prognostic signature for osteosarcoma.docx حسب Binfeng Liu (9282914)

Proposed method approach. حسب Muhammad Usman Tariq (11022141)

"…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …"

Models’ performance without optimization. حسب Muhammad Usman Tariq (11022141)

"…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …"