BLACK HOLES

The intersection of computer vision and fashion e-commerce has witnessed remarkable progress with the advent of deep learning technologies. This comprehensive survey examines recent advances in fashion and clothing detection systems, synthesizing findings from five major research directions: video-to-shop retrieval, clothing classification using YOLO architectures, ensemble-based recommendation systems, fashion attribute detection, and outfit compatibility prediction. We analyze benchmark datasets including DeepFashion, DeepFashion2, Fashion-MNIST, and emerging Roboflow collections, while evaluating the performance of state-of-the-art architectures including YOLOv5, Faster R-CNN, EfficientNet, MobileNet, and transformer-based models. Our analysis reveals that single-stage detectors achieve superior inference speeds (up to 45 FPS) while maintaining competitive accuracy, ensemble methods improve classification robustness by 8-12%, and multi-task learning frameworks enhance retrieval prec...

From Stargazing Dreams to Code Stargazing:) Ever since childhood, I have been passionate about the mysteries of the universe . Over time, that passion grew into something more technical — not just looking at the stars, but wanting to understand their signals. This summer, through Google Summer of Code 2025 (GSoC) , I had the opportunity to contribute to Stingray.jl , a Julia library for time series analysis in astronomy. What follows is both a story of my personal journey and an exploration of the science and code I worked on. The Beginning – Switching Languages and First Steps When the organization was announced, I immediately started exploring Stingray (the Python version). I wanted to understand the structure, so I made my first PRs there. Soon, I understood the Stingray a bit, then I started working on  Stingray.jl , the Julia port of Stingray initiated by Aman Pandey in 2022. That meant learning Julia from scratch! Thankfully, Julia was similar to Python in many ways, so I...

  Exploring Light Curve Plotting in Stingray.jl: Recipes and Examples: In my continued work with Stingray.jl , so hello everyone, let's learns about plotting my favorite topic:) Light curves are essential in high-energy astrophysics, as they represent the brightness of an astronomical object as a function of time. Precise visualization and filtering of these curves help astronomers perform accurate timing analysis, detect variability, and identify astrophysical phenomena. This post demonstrates how to generate and customize light curve plots using Stingray.jl , leveraging real NICER datasets. Dataset and Setup: For this demonstration, I used a dataset from NICER-HESRAC-CL.EVT . The first step is to load the event data into an EventList object: julia using Plots events = readevents("ni1200120104_0mpu7_cl.evt", load_gti=true, sort=true) Output: pgsql Found GTI data: 16 intervals GTI time range: 1.3253976595089495e8 to 1.3261337476368216e8 EventList with 212...